Refactor - ArrayManager overview issue

jorisvandenbossche commented 3 years ago

Related to the discussion in https://github.com/pandas-dev/pandas/issues/10556, and following up on the mailing list discussion "A case for a simplified (non-consolidating) BlockManager with 1D blocks" (archive).

Initial proof of concept for a non-consolidating "ArrayManager" (storing the columns of a DataFrame as a list of 1D arrays instead of blocks) is merged in https://github.com/pandas-dev/pandas/pull/36010.

This issue is meant to track the required follow-up work items to get this to a more feature-complete implementation.

Functionality: get all tests passing
- There are big chunks of tests failing because some larger sets of functionality is not yet implemented or relying on BlockManager internals (this last aspect is also covered in https://github.com/pandas-dev/pandas/issues/34669). Those bigger topics are:
- [x] quantile / describe related (ArrayManager.quantile is not yet implemented) -> https://github.com/pandas-dev/pandas/pull/40189
- [x] equals related (ArrayManager.equals is not yet implemented) -> #39721
- [x] groupby related tests (there are still a few parts of groupby that directly uses the blocks) -> #39885, #40050
- [x] concat related (internals/concat.py only deals with the simple case when no reindexing is needed for ArrayManager at the moment, the full functionality (similarly to what concatenate_block_managers / the JoinUnits now cover) still needs to be implemented) -> https://github.com/pandas-dev/pandas/pull/39612
- [ ] indexing related (some of the ArrayManager methods like setitem, iset, insert are not yet fully implementated for all corner cases + get indexing tests passing)
- [ ] IO related:
  - [x] JSON (https://github.com/pandas-dev/pandas/issues/27164, fixed in #41809
  - [ ] pytables code still relies on block internals
- In addition, the ArrayManager currently also uses an "apply_with_block" fallback for things that are right now implemented directly on the Block classes. Long term, all those cases should also be refactored so that the core functionality of the specific function can be shared between ArrayManager and BlockManager, without directly relying on the Block classes.
- [ ] Some examples of this: replace, where, interpolate, shift, diff, downcast, putmask, ... (those could all be refactored one at a time).
- There will also be tests that either are 1) BlockManager specific (using block internals to test) or 2) testing behaviour specific to DataFrames with BlockManager (we will probably want to change some aspects about eg copy/view, setitem-like ops, etc. Those changes all need to discussed separately of course, but might also require some skipped tests initially). Such tests can be skipped with eg @td.skip_array_manager_invalid_test.
Design questions:
- [ ] What to do with Series, which now is a SingleBlockManager inheriting from BlockManager (should we also have a "SingleArrayManager"?) -> #40152
- [ ] ... (probably more items will come up) ...
Performance
- Currently, I didn't yet look at performance (I only ran a few of the ASV benchmarks, see top post of https://github.com/pandas-dev/pandas/pull/36010). I also think that we should first focus on getting a larger part of the functionality working (which will also make it easier to run benchmarks), but afterwards we will need to identify the different areas where performance improvements are needed.

jorisvandenbossche commented 3 years ago

One design question that was still left open is what to do with Series, for which we currently have a SingleBlockManager. The two obvious options I can think of:

Do something similar and also have a "SingleArrayManager" class
Directly store the array on the Series without involving a manager object

A while ago I thought the second option could be an attractive simplification (because in the end, a Series "just" consists of an array and an index, so why needing a manager?). But that was probably a bit naive ;) The Manager still does quite some things, and moreover, doing a SingleArrayManager keeps the changes more limited (we can still see later if getting rid of Single(Block/Array)Manager is an option we want to explore, independent from the BlockManager vs ArrayManager debate) and for implementing certain features consistently between Series and DataFrame, having both with an underlying manager is probably useful.

Now, for the actual SingleArrayManager, some thoughts:

For SingleBlockManager, that's actually a subclass of BlockManager. But many methods of the BlockManager are not written to work with SingleBlockManager, which means you have quite some methods on the SingleBlockManager that are never used / would error if used. Which I don't find a very nice design pattern. An alternative for SingleArrayManager could be to not subclass ArrayManager itself (but only a base Manager class). We could of course still have a mixin to share those parts that can be shared.
I was wondering if we could actually "just" reuse the ArrayManager for Series as well, in which case it would store a single array (list of arrays with length 1). So the underlying storage would be the same as for a DataFrame of 1 column. But, I suppose that storing the name of the Series as a len-1 Index probably gives quite some overhead (and in addition we already have many places that explicitly does "name resolution" to determine the name of the resulting series, so that might give bigger changes)

I am currently testing out the approach of a separate SingleArrayManager class to see what is needed to implement it fully.

cc @jreback @jbrockmendel @TomAugspurger

jorisvandenbossche commented 3 years ago

Posting here some results from parts of the benchmark suite. I always ran the benchmark on a commit where I changed the default to ArrayManager (HEAD) vs the previous commit with the normal default of BlockManager (HEAD~1). So using the diff formatting to get some color: green is slower and red is faster for the ArrayManager. The "am-benchmarks" branch I am using is currently master + the 2 perf-related PRs I opened today + the commit to change the default.

First batch of results are benchmarks related to reshaping / take / reindex:

join_merge

$ asv continuous -f 1.01 -b join_merge HEAD~1 HEAD

```diff before after ratio [78aa7f40] [ea965b27]

+ 35.0±3ms 78.9±0.3ms 2.26 join_merge.ConcatDataFrames.time_c_ordered(0, False) + 35.0±3ms 78.7±0.2ms 2.25 join_merge.ConcatDataFrames.time_c_ordered(0, True) + 39.5±5ms 78.5±0.4ms 1.99 join_merge.ConcatDataFrames.time_f_ordered(0, True) + 39.9±4ms 78.8±0.2ms 1.98 join_merge.ConcatDataFrames.time_f_ordered(0, False) - 238±0.2ms 233±0.2ms 0.98 join_merge.MergeAsof.time_by_object('nearest', None) - 239±0.3ms 234±0.2ms 0.98 join_merge.MergeAsof.time_by_object('nearest', 5) - 171±0.1ms 166±0.2ms 0.98 join_merge.MergeAsof.time_by_object('forward', 5) - 170±0.2ms 166±0.2ms 0.97 join_merge.MergeAsof.time_by_object('forward', None) - 227±5ms 218±0.6ms 0.96 join_merge.MergeAsof.time_by_int('nearest', 5) - 226±5ms 218±0.6ms 0.96 join_merge.MergeAsof.time_by_int('nearest', None) - 659±2ms 630±1ms 0.96 join_merge.MergeCategoricals.time_merge_object - 162±4ms 154±0.3ms 0.95 join_merge.MergeAsof.time_by_int('forward', 5) - 249±2μs 237±3μs 0.95 join_merge.Concat.time_concat_empty_left(0) - 83.8±0.5ms 79.4±0.1ms 0.95 join_merge.MergeAsof.time_by_object('backward', None) - 84.1±0.5ms 79.6±0.3ms 0.95 join_merge.MergeAsof.time_by_object('backward', 5) - 15.4±0.3ms 14.6±0.5ms 0.95 join_merge.Join.time_join_dataframe_index_shuffle_key_bigger_sort(True) - 75.2±1ms 71.1±0.9ms 0.95 join_merge.MergeAsof.time_by_int('backward', None) - 163±4ms 154±0.1ms 0.95 join_merge.MergeAsof.time_by_int('forward', None) - 76.1±1ms 71.4±0.6ms 0.94 join_merge.MergeAsof.time_by_int('backward', 5) - 5.25±0.04ms 4.91±0.05ms 0.94 join_merge.Merge.time_merge_dataframe_integer_2key(False) - 24.0±0.3ms 22.3±0.3ms 0.93 join_merge.Join.time_join_dataframe_index_multi(True) - 78.5±0.7ms 71.5±0.7ms 0.91 join_merge.Concat.time_concat_series(1) - 363±1μs 321±3μs 0.89 join_merge.Concat.time_concat_mixed_ndims(0) - 587±1μs 509±3μs 0.87 join_merge.Concat.time_concat_mixed_ndims(1) - 260±0.4ms 223±0.6ms 0.86 join_merge.MergeCategoricals.time_merge_cat - 84.6±0.5ms 71.2±0.3ms 0.84 join_merge.MergeOrdered.time_merge_ordered - 1.22±0.03s 1.02±0s 0.84 join_merge.JoinIndex.time_left_outer_join_index - 795±2ms 661±8ms 0.83 join_merge.I8Merge.time_i8merge('right') - 743±2ms 607±8ms 0.82 join_merge.I8Merge.time_i8merge('left') - 157±2μs 127±2μs 0.81 join_merge.Concat.time_concat_empty_left(1) - 724±9ms 587±9ms 0.81 join_merge.I8Merge.time_i8merge('inner') - 725±1ms 588±8ms 0.81 join_merge.I8Merge.time_i8merge('outer') - 156±2μs 126±2μs 0.81 join_merge.Concat.time_concat_empty_right(1) - 2.36±0.01ms 1.86±0.01ms 0.79 join_merge.Merge.time_merge_dataframe_integer_key(True) - 19.6±0.2ms 15.1±0.3ms 0.77 join_merge.Join.time_join_dataframe_index_multi(False) - 19.1±0.06ms 14.7±0.08ms 0.77 join_merge.MergeAsof.time_on_int('nearest', 5) - 2.00±0.01ms 1.53±0.01ms 0.77 join_merge.Merge.time_merge_dataframe_integer_key(False) - 18.5±0.03ms 14.1±0.1ms 0.76 join_merge.MergeAsof.time_on_int32('nearest', None) - 18.8±0.05ms 14.3±0.04ms 0.76 join_merge.MergeAsof.time_on_uint64('nearest', 5) - 18.9±0.05ms 14.4±0.07ms 0.76 join_merge.MergeAsof.time_on_int32('nearest', 5) - 348±4μs 264±3μs 0.76 join_merge.Append.time_append_homogenous - 18.6±0.06ms 14.1±0.02ms 0.76 join_merge.MergeAsof.time_on_int('nearest', None) - 18.3±0.2ms 13.8±0.04ms 0.75 join_merge.MergeAsof.time_on_uint64('nearest', None) - 15.4±0.08ms 10.9±0.04ms 0.71 join_merge.MergeAsof.time_on_int('forward', 5) - 15.2±0.04ms 10.6±0.09ms 0.70 join_merge.MergeAsof.time_on_int32('forward', 5) - 14.9±0.04ms 10.4±0.03ms 0.70 join_merge.MergeAsof.time_on_int32('forward', None) - 15.0±0.06ms 10.5±0.05ms 0.70 join_merge.MergeAsof.time_on_int('forward', None) - 14.9±0.1ms 10.4±0.04ms 0.70 join_merge.MergeAsof.time_on_uint64('forward', 5) - 14.6±0.06ms 10.2±0.1ms 0.70 join_merge.MergeAsof.time_on_uint64('forward', None) - 14.5±0.03ms 10.1±0.06ms 0.69 join_merge.MergeAsof.time_on_int32('backward', 5) - 14.4±0.08ms 9.90±0.04ms 0.69 join_merge.MergeAsof.time_on_int32('backward', None) - 5.12±0.02ms 3.52±0.01ms 0.69 join_merge.Join.time_join_dataframes_cross(True) - 14.0±0.2ms 9.55±0.08ms 0.68 join_merge.MergeAsof.time_on_int('backward', 5) - 13.7±0.05ms 9.26±0.05ms 0.68 join_merge.MergeAsof.time_on_uint64('backward', 5) - 13.8±0.09ms 9.24±0.04ms 0.67 join_merge.MergeAsof.time_on_int('backward', None) - 4.90±0.03ms 3.27±0.03ms 0.67 join_merge.Join.time_join_dataframes_cross(False) - 13.4±0.08ms 8.91±0.03ms 0.67 join_merge.MergeAsof.time_on_uint64('backward', None) - 12.9±0.1ms 8.55±0.02ms 0.66 join_merge.Join.time_join_dataframe_index_shuffle_key_bigger_sort(False) - 13.2±0.1ms 8.66±0.04ms 0.66 join_merge.Join.time_join_dataframe_index_single_key_bigger(False) - 12.4±0.3ms 7.73±0.03ms 0.62 join_merge.Join.time_join_dataframe_index_single_key_small(False) - 857±20μs 513±9μs 0.60 join_merge.Append.time_append_mixed - 11.6±0.04ms 6.82±0.03ms 0.59 join_merge.Concat.time_concat_small_frames(1) - 17.7±0.1ms 10.1±0.02ms 0.57 join_merge.Concat.time_concat_small_frames(0) - 789±5ms 413±1ms 0.52 join_merge.Merge.time_merge_dataframes_cross(False) - 789±2ms 413±0.7ms 0.52 join_merge.Merge.time_merge_dataframes_cross(True) - 50.4±0.9ms 255±0.8μs 0.01 join_merge.ConcatDataFrames.time_f_ordered(1, False) - 49.4±3ms 215±1μs 0.00 join_merge.ConcatDataFrames.time_f_ordered(1, True) - 77.3±40ms 256±1μs 0.00 join_merge.ConcatDataFrames.time_c_ordered(1, False) - 76.8±30ms 214±2μs 0.00 join_merge.ConcatDataFrames.time_c_ordered(1, True) SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY. PERFORMANCE DECREASED. ```

All benchmarks are on par or faster using ArrayManager, except for the concat( , axis=0) cases. Many of the join/merge/merge_asof cases show some speedup.
Some of the concat(.., axis=1) benchmarks give a huge difference (factor "0.00" for after), but this is largely explained by copy (the BlockManager based method copies in this case of no reindexing, while the current ArrayManager version doesn't do that (yet)). If I add a copy, it becomes a more modest speedup of 2-3x (instead of >100x).
The concat(.., axis=0) case that shows a 2x slowdown with ArrayManager is the expected case, I think. That specific benchmark is using a DataFrame with a single float dtype with 200 columns (single block) and concatting this 20 times, so concatenating 20 2D arrays is always going to be faster as concatenating 200 times 20 1D arrays (the benchmark case has 200 columns, and does pd.concat([df] * 20, axis=0)). From profiling the operation, almost all time is spent in the actual numpy concatenation routine (for both cases), so I don't expect there is much room for improvement here.

reindex

$ asv continuous -f 1.01 -b reindex HEAD~1 HEAD

```diff before after ratio [78aa7f40] [ea965b27]

+ 16.4±0.6ms 95.1±40ms 5.81 frame_methods.Reindex.time_reindex_both_axes + 41.0±0.3ms 103±0.5ms 2.52 frame_methods.Reindex.time_reindex_axis1_missing + 7.04±0.2ms 16.7±0.3ms 2.37 frame_methods.Reindex.time_reindex_upcast + 3.70±0.05ms 6.86±0.03ms 1.85 frame_methods.Reindex.time_reindex_axis0 + 789±6μs 1.13±0.01ms 1.43 reindex.ReindexMethod.time_reindex_method('pad', ) + 935±9μs 1.28±0.01ms 1.37 reindex.ReindexMethod.time_reindex_method('pad', ) + 733±6μs 977±20μs 1.33 reindex.ReindexMethod.time_reindex_method('backfill', ) + 741±30μs 985±30μs 1.33 reindex.LevelAlign.time_align_level + 770±40μs 999±20μs 1.30 reindex.LevelAlign.time_reindex_level + 251±2μs 324±20μs 1.29 reindex.Fillna.time_reindexed('pad') + 879±5μs 1.10±0.01ms 1.26 reindex.ReindexMethod.time_reindex_method('backfill', ) + 303±2μs 377±3μs 1.24 reindex.Reindex.time_reindex_dates + 198±2μs 226±3μs 1.14 reindex.Fillna.time_float_32('pad') + 288±4μs 319±6μs 1.11 reindex.Fillna.time_reindexed('backfill') - 2.51±0.04ms 2.44±0.01ms 0.97 reindex.DropDuplicates.time_frame_drop_dups_bool(False) - 412±4μs 368±2μs 0.89 reindex.DropDuplicates.time_series_drop_dups_string(False) - 356±2μs 309±0.9μs 0.87 reindex.DropDuplicates.time_series_drop_dups_int(False) - 5.91±0.06ms 5.12±0.07ms 0.86 reindex.DropDuplicates.time_frame_drop_dups_int(True) - 264±1μs 219±0.4μs 0.83 reindex.DropDuplicates.time_series_drop_dups_string(True) - 245±2μs 200±0.9μs 0.82 reindex.DropDuplicates.time_series_drop_dups_int(True) - 525±4μs 242±2μs 0.46 reindex.Reindex.time_reindex_columns - 28.6±0.07ms 640±10μs 0.02 frame_methods.Reindex.time_reindex_axis1 ```

Reindexing the columns can be a lot faster with ArrayManager (the two last cases with the biggest speedup), or slower when the reindexing introduces many "null" columns (the two first cases with the biggest slowdown). Personally, I don't worry too much about this case of reindexing with many non-existing columns.
Another benchmark with a slowdown is reindexing the rows (frame_methods.Reindex.time_reindex_axis0) with a slowdown factor of 1.8. But since this is reindexing a wide (1000 columns) and homogenously dtyped (1 block) DataFrame, I think this is a "worst case", and so an acceptable slowdown IMO.

reshape

$ asv continuous -f 1.01 -b reshape HEAD~1 HEAD

```diff before after ratio [c9628cbd] [707df345]

+ 93.8±2ms 1.10±0.02s 11.78 reshape.Unstack.time_without_last_row('int') + 43.2±0.1ms 205±10ms 4.75 reshape.Unstack.time_full_product('int') + 4.13±0.03ms 9.47±0.06ms 2.30 reshape.SimpleReshape.time_stack + 2.10±0.02ms 2.70±0.02ms 1.29 reshape.SimpleReshape.time_unstack - 268±0.5μs 263±1μs 0.98 reshape.Explode.time_explode(100, 10) - 309±2ms 297±1ms 0.96 reshape.WideToLong.time_wide_to_long_big - 26.5±0.4ms 24.7±0.1ms 0.93 reshape.Crosstab.time_crosstab - 87.4±0.3ms 78.9±0.9ms 0.90 reshape.Crosstab.time_crosstab_normalize_margins - 25.8±0.4ms 23.1±0.2ms 0.89 reshape.Crosstab.time_crosstab_values - 3.24±0.02ms 2.44±0.01ms 0.75 reshape.Melt.time_melt_dataframe - 353±0.9ms 193±2ms 0.55 reshape.Unstack.time_without_last_row('category') - 349±0.7ms 64.3±2ms 0.18 reshape.Unstack.time_full_product('category') ```

Many of the cases didn't show a difference for reshape.py (eg includes cases like pd.cut, so that's expected), and for unstack it shows a bit varying results: some cases are faster, some are slower (it depends on the number of columns, different types, whether the BlockManager version could take the fast implementation or not, etc).
The 2 cases with the biggest slowdown (reshape.Unstack with int dtype) creates a DataFrame with a shape of 100 rows × 100000 columns (so a very wide DataFrame, and thus a slowdown is expected / acceptable IMO). For the "full product" case (no missing values get introduced), the ArrayManager is "only" 5 times slower compared to reshaping the single block. When missing values get introduced, the difference gets bigger, but that is potentially something that can be further improved. reshape.SimpleReshape.time_unstack is similar, but there the slowdown is much smaller (x1.29), because it produces a less wide dataframe (100 rows × 400 columns) and uses floats (in which case we don't need to check for upcasting if nulls get introduced).
The stack case can be probably be optimized, as currently that goes through a conversion of the DataFrame to a numpy arrays (which is more expensive for non-single Block ArrayManager).

jbrockmendel commented 3 years ago

Are there any consistent benchmark results that dont make sense to you? (xref #40066)

If I add a copy, it becomes a more modest speedup of 2-3x (instead of >100x).

can you mark the ones that are significantly affected by this?

Big picture, can you give an update on what is left to implement (e.g. im guessing json and pytables)

jorisvandenbossche commented 3 years ago

Are there any consistent benchmark results that dont make sense to you?

I didn't see anything particularly strange, up to now. (in the next batch about reductions, I noticed some strange speed-ups, that probably point to an issue in the BlockManager implementation, but comment about that in detail in the next post, probably tomorrow)

If I add a copy, it becomes a more modest speedup of 2-3x (instead of >100x).

can you mark the ones that are significantly affected by this?

Only the onces doing a simple concat(.., axis=1), so the 4ConcatDataFramesbenchmarks (the 4 that show the biggest speedup, so at the bottom of the list). Other benchmarks likemerge` that also have a speedup don't have that issue, since those already did a reindexing (and thus a copy) anyway. But note that, if we decide to go with copy-on-write, we don't need to add a copy to preserve behaviour.

Big picture, can you give an update on what is left to implement (e.g. im guessing json and pytables)

Yes, with the several indexing related PRs I opened today, we are running most of the tests. And the biggest chunks of skipped tests are related to JSON and PyTables IO. Related to IO, there is also Parquet (but that's depending on downstream libraries) and pickling. Next to that, there are still various smaller corner cases to fix (the TODO(ArrayManager) skipped tests), but in the grand scheme of things, the ones that are left are relatively minor, I think. I also need to get back to concat(.., axis=0) with reindexing (#39612), and there are still some usages of apply_with_block that ideally would be cleaned up (although that doesn't prevent AraryManager being usable). For performance related issues, I mainly need to get back to the element-wise ops I started with a while ago (#39772, and related PRs).

jorisvandenbossche commented 3 years ago

Some more benchmark results:

groupby

$ asv continuous -f 1.01 -b groupby HEAD~1 HEAD

```diff before after ratio [c9628cbd] [707df345]

+ 3.83±0.02ms 61.6±0.2ms 16.07 groupby.GroupManyLabels.time_sum(1000) + 8.23±0.05ms 21.4±0.06ms 2.60 groupby.Apply.time_scalar_function_single_col(4) + 23.9±0.1ms 59.6±0.2ms 2.49 groupby.Apply.time_scalar_function_multi_col(4) + 51.8±0.4ms 58.4±0.5ms 1.13 groupby.GroupByCythonAgg.time_frame_agg('float64', 'max') + 267±1μs 300±2μs 1.12 groupby.GroupByMethods.time_dtype_as_field('datetime', 'head', 'direct') + 266±2μs 299±2μs 1.12 groupby.GroupByMethods.time_dtype_as_field('datetime', 'head', 'transformation') + 278±0.8μs 313±0.9μs 1.12 groupby.GroupByMethods.time_dtype_as_field('datetime', 'tail', 'transformation') + 279±1μs 313±2μs 1.12 groupby.GroupByMethods.time_dtype_as_field('datetime', 'tail', 'direct') + 6.54±0.1ms 7.24±0.09ms 1.11 groupby.Apply.time_scalar_function_single_col(5) + 66.9±0.6ms 70.1±1ms 1.05 groupby.GroupByCythonAgg.time_frame_agg('float64', 'var') - 273±1μs 271±2μs 0.99 groupby.GroupByMethods.time_dtype_as_field('float', 'tail', 'direct') - 28.0±0.07ms 27.7±0.2ms 0.99 groupby.AggEngine.time_series_cython(False) - 114±0.2ms 112±0.7ms 0.98 groupby.GroupByMethods.time_dtype_as_group('datetime', 'unique', 'direct') - 517±2μs 509±0.7μs 0.98 groupby.GroupByMethods.time_dtype_as_field('object', 'bfill', 'transformation') - 447±4μs 439±1μs 0.98 groupby.GroupByMethods.time_dtype_as_field('int', 'median', 'direct') - 363±2μs 356±1μs 0.98 groupby.GroupByMethods.time_dtype_as_group('int', 'ffill', 'direct') - 505±3μs 495±2μs 0.98 groupby.GroupByMethods.time_dtype_as_field('int', 'nunique', 'transformation') - 363±3μs 356±0.9μs 0.98 groupby.GroupByMethods.time_dtype_as_group('int', 'ffill', 'transformation') - 860±4μs 844±0.9μs 0.98 groupby.GroupByMethods.time_dtype_as_field('object', 'value_counts', 'transformation') - 519±1μs 509±2μs 0.98 groupby.GroupByMethods.time_dtype_as_field('object', 'ffill', 'transformation') - 477±2μs 467±1μs 0.98 groupby.GroupByMethods.time_dtype_as_field('object', 'nunique', 'direct') - 333±2μs 326±0.7μs 0.98 groupby.GroupByMethods.time_dtype_as_field('float', 'bfill', 'transformation') - 356±1μs 348±1μs 0.98 groupby.GroupByMethods.time_dtype_as_group('float', 'bfill', 'direct') - 545±2μs 533±0.3μs 0.98 groupby.GroupByMethods.time_dtype_as_field('float', 'quantile', 'transformation') - 461±2μs 451±1μs 0.98 groupby.GroupByMethods.time_dtype_as_field('float', 'nunique', 'transformation') - 6.59±0.02ms 6.44±0.02ms 0.98 groupby.Transform.time_transform_multi_key3 - 333±2μs 325±0.7μs 0.98 groupby.GroupByMethods.time_dtype_as_field('float', 'ffill', 'direct') - 478±1μs 466±0.7μs 0.98 groupby.GroupByMethods.time_dtype_as_field('object', 'nunique', 'transformation') - 561±5μs 548±1μs 0.98 groupby.GroupByMethods.time_dtype_as_field('int', 'quantile', 'direct') - 584±3μs 570±1μs 0.98 groupby.GroupByMethods.time_dtype_as_field('float', 'rank', 'direct') - 378±3μs 369±0.9μs 0.98 groupby.GroupByMethods.time_dtype_as_field('object', 'first', 'direct') - 569±3μs 555±1μs 0.98 groupby.GroupByMethods.time_dtype_as_group('float', 'quantile', 'direct') - 358±1μs 349±0.5μs 0.98 groupby.GroupByMethods.time_dtype_as_group('float', 'ffill', 'transformation') - 525±2μs 512±0.7μs 0.97 groupby.GroupByMethods.time_dtype_as_group('int', 'nunique', 'direct') - 449±3μs 438±0.6μs 0.97 groupby.GroupByMethods.time_dtype_as_group('int', 'median', 'direct') - 440±6μs 428±2μs 0.97 groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'transformation') - 367±1μs 357±2μs 0.97 groupby.GroupByMethods.time_dtype_as_group('object', 'nunique', 'direct') - 531±1μs 517±2μs 0.97 groupby.GroupByMethods.time_dtype_as_field('datetime', 'quantile', 'direct') - 436±3μs 423±0.9μs 0.97 groupby.GroupByMethods.time_dtype_as_group('float', 'mean', 'transformation') - 429±3μs 416±1μs 0.97 groupby.GroupByMethods.time_dtype_as_field('int', 'sum', 'transformation') - 527±4μs 512±0.7μs 0.97 groupby.GroupByMethods.time_dtype_as_group('int', 'nunique', 'transformation') - 224±3μs 218±0.6μs 0.97 groupby.GroupByMethods.time_dtype_as_group('object', 'ffill', 'direct') - 1.22±0.01ms 1.19±0ms 0.97 groupby.GroupByMethods.time_dtype_as_field('float', 'pct_change', 'direct') - 256±2μs 248±2μs 0.97 groupby.GroupByMethods.time_dtype_as_field('float', 'min', 'direct') - 294±0.7μs 285±1μs 0.97 groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'transformation') - 977±2μs 948±1μs 0.97 groupby.GroupByMethods.time_dtype_as_group('datetime', 'value_counts', 'transformation') - 30.3±0.1ms 29.4±0.2ms 0.97 groupby.AggEngine.time_dataframe_cython(False) - 405±4μs 393±2μs 0.97 groupby.GroupByMethods.time_dtype_as_field('int', 'mean', 'transformation') - 402±3μs 390±0.4μs 0.97 groupby.GroupByMethods.time_dtype_as_group('float', 'cumprod', 'transformation') - 443±3μs 429±2μs 0.97 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'transformation') - 429±4μs 416±0.7μs 0.97 groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'transformation') - 454±2μs 440±1μs 0.97 groupby.GroupByMethods.time_dtype_as_group('float', 'median', 'direct') - 229±2μs 221±0.9μs 0.97 groupby.GroupByMethods.time_dtype_as_group('datetime', 'head', 'transformation') - 285±3μs 275±1μs 0.97 groupby.GroupByMethods.time_dtype_as_group('int', 'last', 'direct') - 370±3μs 358±1μs 0.97 groupby.GroupByMethods.time_dtype_as_group('object', 'nunique', 'transformation') - 253±3μs 245±2μs 0.97 groupby.GroupByMethods.time_dtype_as_field('int', 'cummax', 'direct') - 366±0.8μs 353±0.8μs 0.97 groupby.GroupByMethods.time_dtype_as_field('object', 'last', 'direct') - 296±3μs 286±0.8μs 0.97 groupby.GroupByMethods.time_dtype_as_group('int', 'min', 'transformation') - 365±0.9μs 353±0.5μs 0.97 groupby.GroupByMethods.time_dtype_as_field('object', 'last', 'transformation') - 296±1μs 286±2μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'direct') - 564±2μs 544±1μs 0.96 groupby.GroupByMethods.time_dtype_as_group('datetime', 'quantile', 'direct') - 316±3μs 304±2μs 0.96 groupby.GroupByMethods.time_dtype_as_group('int', 'var', 'transformation') - 415±3μs 400±0.9μs 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'prod', 'direct') - 986±10μs 950±3μs 0.96 groupby.GroupByMethods.time_dtype_as_group('datetime', 'value_counts', 'direct') - 441±6μs 424±2μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'mean', 'direct') - 296±4μs 285±1μs 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'min', 'transformation') - 297±3μs 286±0.9μs 0.96 groupby.GroupByMethods.time_dtype_as_field('object', 'shift', 'direct') - 298±2μs 287±1μs 0.96 groupby.GroupByMethods.time_dtype_as_field('object', 'shift', 'transformation') - 294±2μs 283±1μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'last', 'transformation') - 293±2μs 281±2μs 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'var', 'transformation') - 296±3μs 285±2μs 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'min', 'direct') - 293±3μs 282±0.7μs 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'max', 'transformation') - 224±0.9μs 215±1μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'transformation') - 226±3μs 217±1μs 0.96 groupby.GroupByMethods.time_dtype_as_group('object', 'bfill', 'transformation') - 271±3μs 260±2μs 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'cumcount', 'direct') - 253±2μs 243±1μs 0.96 groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'direct') - 295±1μs 284±1μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'direct') - 297±3μs 286±2μs 0.96 groupby.GroupByMethods.time_dtype_as_group('int', 'min', 'direct') - 270±2μs 259±1μs 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'cumcount', 'transformation') - 286±4μs 275±2μs 0.96 groupby.GroupByMethods.time_dtype_as_group('int', 'last', 'transformation') - 272±3μs 261±0.8μs 0.96 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'direct') - 226±3μs 217±0.7μs 0.96 groupby.GroupByMethods.time_dtype_as_group('object', 'bfill', 'direct') - 4.29±0.01ms 4.12±0.01ms 0.96 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'std') - 299±2μs 287±1μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'first', 'direct') - 270±1μs 260±0.9μs 0.96 groupby.GroupByMethods.time_dtype_as_field('datetime', 'cumcount', 'direct') - 246±1μs 236±1μs 0.96 groupby.GroupByMethods.time_dtype_as_field('float', 'last', 'direct') - 224±0.8μs 215±0.6μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'direct') - 4.32±0.02ms 4.15±0.03ms 0.96 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'skew') - 259±2μs 249±2μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'direct') - 567±4μs 544±3μs 0.96 groupby.GroupByMethods.time_dtype_as_group('datetime', 'quantile', 'transformation') - 257±1μs 247±0.6μs 0.96 groupby.GroupByMethods.time_dtype_as_field('object', 'count', 'transformation') - 202±1μs 194±1μs 0.96 groupby.GroupByMethods.time_dtype_as_group('int', 'std', 'direct') - 270±1μs 259±0.9μs 0.96 groupby.GroupByMethods.time_dtype_as_field('datetime', 'cumcount', 'transformation') - 298±2μs 285±2μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'max', 'direct') - 271±2μs 259±0.8μs 0.96 groupby.GroupByMethods.time_dtype_as_field('object', 'cumcount', 'direct') - 243±2μs 233±1μs 0.96 groupby.GroupByMethods.time_dtype_as_field('float', 'prod', 'transformation') - 282±3μs 270±2μs 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'last', 'direct') - 277±1μs 265±2μs 0.96 groupby.GroupByMethods.time_dtype_as_field('float', 'median', 'direct') - 8.42±0.03ms 8.06±0.01ms 0.96 groupby.CountMultiInt.time_multi_int_nunique - 163±0.8μs 156±0.7μs 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'direct') - 4.49±0.01ms 4.30±0.02ms 0.96 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'count') - 219±0.9μs 210±2μs 0.96 groupby.GroupByMethods.time_dtype_as_field('float', 'cumsum', 'transformation') - 238±3μs 227±2μs 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'shift', 'transformation') - 590±0.6μs 565±0.7μs 0.96 groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'transformation') - 241±2μs 230±0.5μs 0.96 groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'direct') - 215±1μs 205±0.8μs 0.96 groupby.GroupByMethods.time_dtype_as_field('float', 'shift', 'transformation') - 248±1μs 237±2μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'direct') - 4.30±0.02ms 4.12±0.02ms 0.96 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'skew') - 493±2μs 471±1μs 0.96 groupby.GroupByMethods.time_dtype_as_group('datetime', 'nunique', 'transformation') - 251±3μs 240±0.9μs 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'cummin', 'direct') - 450±3μs 430±3μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'direct') - 259±3μs 247±2μs 0.96 groupby.GroupByMethods.time_dtype_as_field('float', 'max', 'transformation') - 271±1μs 259±1μs 0.96 groupby.GroupByMethods.time_dtype_as_field('float', 'cumcount', 'direct') - 4.27±0.02ms 4.08±0.03ms 0.96 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'sum') - 237±2μs 226±1μs 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'shift', 'direct') - 212±0.5μs 202±1μs 0.96 groupby.GroupByMethods.time_dtype_as_field('float', 'cumprod', 'direct') - 328±1μs 313±1μs 0.96 groupby.GroupByMethods.time_dtype_as_field('datetime', 'min', 'direct') - 246±3μs 235±1μs 0.96 groupby.GroupByMethods.time_dtype_as_field('float', 'last', 'transformation') - 275±1μs 262±1μs 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'cumcount', 'direct') - 592±2μs 565±1μs 0.95 groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'direct') - 247±2μs 236±0.9μs 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'transformation') - 297±2μs 284±0.5μs 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'transformation') - 4.23±0.01ms 4.04±0.02ms 0.95 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'min') - 4.27±0.04ms 4.07±0.01ms 0.95 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'sum') - 164±0.5μs 156±1μs 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'direct') - 469±3μs 448±2μs 0.95 groupby.GroupByMethods.time_dtype_as_group('object', 'unique', 'direct') - 272±1μs 260±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('object', 'cumcount', 'transformation') - 256±2μs 245±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'first', 'direct') - 4.33±0.02ms 4.13±0.02ms 0.95 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'median') - 164±0.8μs 156±0.8μs 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'direct') - 4.29±0.01ms 4.09±0.04ms 0.95 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'mean') - 299±2μs 285±1μs 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'max', 'transformation') - 223±0.5μs 212±0.9μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'var', 'direct') - 215±0.6μs 205±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'shift', 'direct') - 495±1μs 472±0.7μs 0.95 groupby.GroupByMethods.time_dtype_as_group('datetime', 'nunique', 'direct') - 666±2μs 634±2μs 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'sem', 'direct') - 672±2μs 641±3μs 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'sem', 'direct') - 250±1μs 238±0.3μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'sum', 'transformation') - 245±0.6μs 234±0.7μs 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'transformation') - 670±2μs 638±2μs 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'sem', 'transformation') - 245±2μs 234±0.8μs 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'direct') - 250±1μs 238±2μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'sum', 'direct') - 3.17±0.02ms 3.02±0.03ms 0.95 groupby.CountMultiInt.time_multi_int_count - 219±1μs 208±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'cumsum', 'direct') - 163±2μs 156±0.8μs 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'transformation') - 4.25±0.02ms 4.05±0.02ms 0.95 rolling.ExpandingMethods.time_expanding_groupby('Series', 'int', 'max') - 244±1μs 232±2μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'prod', 'direct') - 673±2μs 640±0.9μs 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'sem', 'transformation') - 246±1μs 234±1μs 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'cumcount', 'direct') - 162±0.6μs 154±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('int', 'all', 'transformation') - 329±0.7μs 313±2μs 0.95 groupby.GroupByMethods.time_dtype_as_field('datetime', 'first', 'direct') - 162±1μs 154±2μs 0.95 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'direct') - 329±1μs 313±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('datetime', 'first', 'transformation') - 246±0.6μs 234±2μs 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'cumcount', 'transformation') - 166±0.6μs 157±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'all', 'direct') - 260±2μs 247±2μs 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'transformation') - 4.24±0.02ms 4.03±0.03ms 0.95 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'max') - 211±0.8μs 200±0.4μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'cumprod', 'transformation') - 226±0.6μs 214±0.7μs 0.95 groupby.GroupByMethods.time_dtype_as_group('datetime', 'bfill', 'transformation') - 317±2μs 301±2μs 0.95 groupby.GroupByMethods.time_dtype_as_field('datetime', 'max', 'transformation') - 235±2μs 223±0.6μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'mean', 'direct') - 162±3μs 153±0.6μs 0.95 groupby.GroupByMethods.time_dtype_as_field('int', 'any', 'transformation') - 196±2μs 186±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('int', 'std', 'direct') - 192±0.4μs 182±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'std', 'transformation') - 166±0.7μs 157±2μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'any', 'transformation') - 198±0.5μs 188±0.8μs 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'std', 'direct') - 4.24±0.02ms 4.02±0.02ms 0.95 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'int', 'min') - 318±1μs 301±0.8μs 0.95 groupby.GroupByMethods.time_dtype_as_field('datetime', 'max', 'direct') - 127±3μs 120±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'count', 'direct') - 328±2μs 310±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('datetime', 'min', 'transformation') - 228±1μs 215±1μs 0.95 groupby.GroupByMethods.time_dtype_as_group('object', 'cumcount', 'direct') - 239±0.8μs 226±1μs 0.95 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cumcount', 'transformation') - 205±1μs 193±0.7μs 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'std', 'transformation') - 314±2μs 297±0.9μs 0.94 groupby.GroupByMethods.time_dtype_as_field('datetime', 'last', 'transformation') - 217±1μs 205±0.5μs 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'cummax', 'transformation') - 842±3μs 796±5μs 0.94 groupby.Datelike.time_sum('date_range') - 218±1μs 206±0.6μs 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'cummin', 'transformation') - 218±2μs 206±0.6μs 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'cummin', 'direct') - 226±0.4μs 213±0.4μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'ffill', 'transformation') - 295±2μs 279±2μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'first', 'direct') - 126±3μs 119±2μs 0.94 groupby.GroupByMethods.time_dtype_as_group('object', 'count', 'direct') - 286±2μs 270±1μs 0.94 groupby.GroupByMethods.time_dtype_as_field('datetime', 'cummin', 'direct') - 227±2μs 215±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('object', 'cumcount', 'transformation') - 226±0.7μs 214±0.3μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'ffill', 'direct') - 239±1μs 226±0.9μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cumcount', 'direct') - 225±1μs 213±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'bfill', 'direct') - 5.70±0.2ms 5.38±0.03ms 0.94 rolling.GroupbyEWM.time_groupby_method('cov') - 295±1μs 278±2μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'min', 'direct') - 162±0.5μs 153±0.9μs 0.94 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'transformation') - 198±1μs 187±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('float', 'std', 'transformation') - 241±0.9μs 227±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'transformation') - 193±0.8μs 182±0.8μs 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'std', 'direct') - 659±2μs 621±1μs 0.94 groupby.GroupByMethods.time_dtype_as_field('int', 'sem', 'direct') - 168±1μs 158±0.6μs 0.94 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'direct') - 198±1μs 186±1μs 0.94 groupby.GroupByMethods.time_dtype_as_field('int', 'std', 'transformation') - 287±3μs 270±1μs 0.94 groupby.GroupByMethods.time_dtype_as_field('datetime', 'cummin', 'transformation') - 219±0.8μs 206±0.2μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'shift', 'direct') - 167±0.5μs 158±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'transformation') - 161±1μs 151±2μs 0.94 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'direct') - 2.88±0.01ms 2.71±0.02ms 0.94 rolling.GroupbyEWMEngine.time_groupby_mean('cython') - 131±2μs 123±1μs 0.94 groupby.GroupByMethods.time_dtype_as_field('int', 'size', 'transformation') - 161±1μs 152±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'transformation') - 41.2±0.7ms 38.7±0.6ms 0.94 groupby.Nth.time_series_nth_any('datetime') - 40.8±0.3ms 38.3±0.5ms 0.94 groupby.Nth.time_series_nth_all('datetime') - 315±1μs 296±1μs 0.94 groupby.GroupByMethods.time_dtype_as_field('datetime', 'last', 'direct') - 131±1μs 123±2μs 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'size', 'transformation') - 165±0.6μs 155±0.7μs 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'any', 'direct') - 218±0.8μs 205±0.8μs 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'cummax', 'direct') - 296±2μs 278±3μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'min', 'transformation') - 292±3μs 274±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'last', 'transformation') - 166±0.8μs 156±0.9μs 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'all', 'transformation') - 660±4μs 619±3μs 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'sem', 'transformation') - 168±0.9μs 158±0.5μs 0.94 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'direct') - 24.0±0.07ms 22.5±0.2ms 0.94 groupby.AggFunctions.time_different_python_functions_multicol - 131±2μs 123±1μs 0.94 groupby.GroupByMethods.time_dtype_as_field('object', 'size', 'direct') - 135±1μs 127±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('float', 'size', 'transformation') - 666±10μs 625±2μs 0.94 groupby.GroupByMethods.time_dtype_as_field('int', 'sem', 'transformation') - 263±2μs 247±1μs 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'max', 'direct') - 168±1μs 157±0.7μs 0.94 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'transformation') - 295±1μs 276±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'max', 'direct') - 297±4μs 278±2μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'first', 'transformation') - 292±2μs 273±0.9μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'last', 'direct') - 161±1μs 151±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'direct') - 135±2μs 127±2μs 0.94 groupby.GroupByMethods.time_dtype_as_group('float', 'size', 'direct') - 131±2μs 123±1μs 0.94 groupby.GroupByMethods.time_dtype_as_field('object', 'size', 'transformation') - 132±2μs 124±1μs 0.94 groupby.GroupByMethods.time_dtype_as_field('datetime', 'size', 'direct') - 813±3μs 761±2μs 0.94 groupby.Datelike.time_sum('date_range_tz') - 131±2μs 123±1μs 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'size', 'direct') - 129±3μs 120±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('int', 'count', 'direct') - 296±3μs 277±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'max', 'transformation') - 130±2μs 122±0.7μs 0.94 groupby.GroupByMethods.time_dtype_as_group('float', 'count', 'transformation') - 132±2μs 123±1μs 0.93 groupby.GroupByMethods.time_dtype_as_field('int', 'size', 'direct') - 132±2μs 123±1μs 0.93 groupby.GroupByMethods.time_dtype_as_group('object', 'size', 'direct') - 4.38±0.01ms 4.09±0.04ms 0.93 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'sum') - 134±2μs 125±2μs 0.93 groupby.GroupByMethods.time_dtype_as_group('int', 'size', 'transformation') - 2.89±0.03ms 2.69±0.01ms 0.93 rolling.GroupbyEWM.time_groupby_method('var') - 243±2μs 226±1μs 0.93 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'direct') - 128±3μs 119±2μs 0.93 groupby.GroupByMethods.time_dtype_as_field('int', 'count', 'direct') - 4.47±0.01ms 4.15±0.02ms 0.93 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'median') - 132±2μs 122±2μs 0.93 groupby.GroupByMethods.time_dtype_as_group('object', 'size', 'transformation') - 1.35±0.01ms 1.25±0.01ms 0.93 groupby.RankWithTies.time_rank_ties('float32', 'max') - 4.40±0.03ms 4.09±0.03ms 0.93 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'min') - 4.39±0.01ms 4.07±0.02ms 0.93 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'max') - 1.35±0.01ms 1.26±0.01ms 0.93 groupby.RankWithTies.time_rank_ties('float32', 'dense') - 4.42±0.01ms 4.09±0.02ms 0.92 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'mean') - 167±0.5μs 155±0.6μs 0.92 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'transformation') - 4.48±0.06ms 4.14±0.03ms 0.92 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'skew') - 4.47±0.03ms 4.13±0.01ms 0.92 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'skew') - 168±2μs 156±1μs 0.92 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'direct') - 4.38±0.01ms 4.05±0.02ms 0.92 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'max') - 1.34±0.01ms 1.23±0.01ms 0.92 groupby.RankWithTies.time_rank_ties('float64', 'dense') - 114±1ms 106±0.2ms 0.92 groupby.GroupByMethods.time_dtype_as_field('datetime', 'unique', 'transformation') - 115±1ms 106±0.3ms 0.92 groupby.GroupByMethods.time_dtype_as_field('datetime', 'unique', 'direct') - 167±0.7μs 154±0.9μs 0.92 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'direct') - 4.48±0.03ms 4.13±0.01ms 0.92 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'std') - 4.41±0.01ms 4.06±0.03ms 0.92 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'sum') - 4.48±0.02ms 4.13±0.02ms 0.92 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'median') - 277±2ms 255±2ms 0.92 groupby.GroupByCythonAgg.time_frame_agg('float64', 'median') - 1.34±0ms 1.23±0ms 0.92 groupby.RankWithTies.time_rank_ties('float64', 'max') - 1.39±0.01ms 1.28±0.01ms 0.92 groupby.RankWithTies.time_rank_ties('int64', 'first') - 4.38±0.03ms 4.02±0.01ms 0.92 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'min') - 1.36±0ms 1.25±0ms 0.92 groupby.RankWithTies.time_rank_ties('float32', 'min') - 1.36±0.01ms 1.24±0ms 0.92 groupby.RankWithTies.time_rank_ties('float32', 'first') - 169±0.7μs 155±2μs 0.92 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'transformation') - 1.34±0.01ms 1.23±0ms 0.92 groupby.RankWithTies.time_rank_ties('float64', 'average') - 1.34±0.01ms 1.23±0.01ms 0.92 groupby.RankWithTies.time_rank_ties('float64', 'min') - 1.38±0ms 1.26±0.01ms 0.92 groupby.RankWithTies.time_rank_ties('int64', 'min') - 1.36±0.01ms 1.25±0ms 0.92 groupby.RankWithTies.time_rank_ties('float32', 'average') - 1.39±0.01ms 1.27±0ms 0.92 groupby.RankWithTies.time_rank_ties('int64', 'dense') - 1.38±0.01ms 1.26±0ms 0.92 groupby.RankWithTies.time_rank_ties('int64', 'average') - 6.15±0.2ms 5.61±0.03ms 0.91 rolling.GroupbyEWM.time_groupby_method('corr') - 1.39±0.02ms 1.26±0ms 0.91 groupby.RankWithTies.time_rank_ties('int64', 'max') - 33.4±0.03ms 30.4±0.08ms 0.91 rolling.Pairwise.time_groupby(10, 'cov', False) - 131±2μs 120±1μs 0.91 groupby.GroupByMethods.time_dtype_as_group('datetime', 'count', 'transformation') - 4.73±0.01ms 4.29±0.01ms 0.91 rolling.ExpandingMethods.time_expanding_groupby('Series', 'float', 'count') - 138±2μs 125±1μs 0.91 groupby.GroupByMethods.time_dtype_as_group('datetime', 'size', 'transformation') - 33.5±0.2ms 30.4±0.1ms 0.91 rolling.Pairwise.time_groupby(1000, 'cov', False) - 4.75±0.01ms 4.30±0.03ms 0.91 rolling.ExpandingMethods.time_expanding_groupby('DataFrame', 'float', 'count') - 33.9±0.2ms 30.7±0.09ms 0.91 rolling.Pairwise.time_groupby(10, 'corr', False) - 33.9±0.2ms 30.6±0.1ms 0.90 rolling.Pairwise.time_groupby(1000, 'corr', False) - 534±2μs 482±2μs 0.90 groupby.GroupByMethods.time_dtype_as_field('datetime', 'rank', 'direct') - 31.7±0.3ms 28.6±0.1ms 0.90 rolling.Pairwise.time_groupby(None, 'cov', False) - 535±3μs 483±2μs 0.90 groupby.GroupByMethods.time_dtype_as_field('datetime', 'rank', 'transformation') - 138±2μs 125±1μs 0.90 groupby.GroupByMethods.time_dtype_as_group('datetime', 'size', 'direct') - 32.2±0.1ms 28.9±0.07ms 0.90 rolling.Pairwise.time_groupby(None, 'corr', False) - 1.78±0.01s 1.60±0s 0.90 groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'transformation') - 1.12±0ms 1.01±0.01ms 0.90 groupby.SumMultiLevel.time_groupby_sum_multiindex - 1.21±0.01s 1.09±0s 0.90 groupby.GroupByMethods.time_dtype_as_field('int', 'describe', 'direct') - 2.78±0.01s 2.48±0.01s 0.89 groupby.GroupByMethods.time_dtype_as_group('float', 'describe', 'transformation') - 1.79±0.01s 1.60±0s 0.89 groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'direct') - 1.47±0.01ms 1.31±0.01ms 0.89 groupby.RankWithTies.time_rank_ties('datetime64', 'dense') - 135±3μs 120±2μs 0.89 groupby.GroupByMethods.time_dtype_as_group('datetime', 'count', 'direct') - 1.23±0.01s 1.10±0s 0.89 groupby.GroupByMethods.time_dtype_as_field('float', 'describe', 'direct') - 1.21±0.01s 1.08±0s 0.89 groupby.GroupByMethods.time_dtype_as_field('int', 'describe', 'transformation') - 461±3μs 411±1μs 0.89 groupby.GroupByMethods.time_dtype_as_field('datetime', 'ffill', 'transformation') - 2.78±0.01s 2.48±0.01s 0.89 groupby.GroupByMethods.time_dtype_as_group('float', 'describe', 'direct') - 4.53±0.02ms 4.03±0.03ms 0.89 groupby.CountMultiDtype.time_multi_count - 461±2μs 409±1μs 0.89 groupby.GroupByMethods.time_dtype_as_field('datetime', 'bfill', 'direct') - 1.24±0.01s 1.10±0.01s 0.89 groupby.GroupByMethods.time_dtype_as_field('float', 'describe', 'transformation') - 1.47±0.01ms 1.31±0.02ms 0.89 groupby.RankWithTies.time_rank_ties('datetime64', 'first') - 1.47±0.01ms 1.30±0ms 0.89 groupby.RankWithTies.time_rank_ties('datetime64', 'average') - 1.47±0.01ms 1.30±0.01ms 0.88 groupby.RankWithTies.time_rank_ties('datetime64', 'min') - 462±0.8μs 409±0.9μs 0.88 groupby.GroupByMethods.time_dtype_as_field('datetime', 'bfill', 'transformation') - 463±0.9μs 409±0.9μs 0.88 groupby.GroupByMethods.time_dtype_as_field('datetime', 'ffill', 'direct') - 1.46±0.01ms 1.29±0.01ms 0.88 groupby.RankWithTies.time_rank_ties('datetime64', 'max') - 1.90±0.01ms 1.62±0.03ms 0.86 groupby.TransformNaN.time_first - 267±2μs 228±1μs 0.85 groupby.GroupByMethods.time_dtype_as_field('datetime', 'any', 'direct') - 269±2μs 229±2μs 0.85 groupby.GroupByMethods.time_dtype_as_field('datetime', 'all', 'direct') - 269±3μs 228±0.9μs 0.85 groupby.GroupByMethods.time_dtype_as_field('datetime', 'all', 'transformation') - 267±2μs 227±0.9μs 0.85 groupby.GroupByMethods.time_dtype_as_field('datetime', 'any', 'transformation') - 2.41±0.01ms 2.00±0.02ms 0.83 groupby.FillNA.time_df_ffill - 418±3ms 346±2ms 0.83 groupby.GroupByMethods.time_dtype_as_field('int', 'mad', 'transformation') - 418±3ms 346±3ms 0.83 groupby.GroupByMethods.time_dtype_as_field('int', 'mad', 'direct') - 616±3ms 510±4ms 0.83 groupby.GroupByMethods.time_dtype_as_group('int', 'mad', 'transformation') - 962±2ms 797±7ms 0.83 groupby.GroupByMethods.time_dtype_as_group('float', 'mad', 'transformation') - 414±2ms 342±3ms 0.83 groupby.GroupByMethods.time_dtype_as_field('float', 'mad', 'direct') - 1.33±0.02ms 1.10±0ms 0.83 groupby.SumBools.time_groupby_sum_booleans - 964±5ms 796±7ms 0.83 groupby.GroupByMethods.time_dtype_as_group('float', 'mad', 'direct') - 619±2ms 510±5ms 0.82 groupby.GroupByMethods.time_dtype_as_group('int', 'mad', 'direct') - 417±2ms 342±4ms 0.82 groupby.GroupByMethods.time_dtype_as_field('float', 'mad', 'transformation') - 2.43±0.01ms 1.99±0.01ms 0.82 groupby.FillNA.time_df_bfill - 309±0.9μs 253±0.9μs 0.82 groupby.GroupByMethods.time_dtype_as_field('datetime', 'shift', 'transformation') - 310±1μs 253±1μs 0.81 groupby.GroupByMethods.time_dtype_as_field('datetime', 'shift', 'direct') - 96.1±0.8ms 77.7±0.8ms 0.81 groupby.TransformEngine.time_dataframe_cython(False) - 96.6±1ms 77.4±1ms 0.80 groupby.TransformEngine.time_dataframe_cython(True) - 579±2μs 460±5μs 0.80 groupby.GroupManyLabels.time_sum(1) - 195±0.7μs 150±1μs 0.77 groupby.GroupByMethods.time_dtype_as_field('datetime', 'count', 'transformation') - 195±2μs 150±0.4μs 0.77 groupby.GroupByMethods.time_dtype_as_field('datetime', 'count', 'direct') - 6.89±0.9ms 4.86±0.06ms 0.70 groupby.Transform.time_transform_ufunc_max - 66.6±0.3ms 46.8±0.2ms 0.70 groupby.Apply.time_copy_function_multi_col(5) - 959±6μs 674±2μs 0.70 groupby.GroupByMethods.time_dtype_as_field('datetime', 'value_counts', 'transformation') - 961±7μs 673±3μs 0.70 groupby.GroupByMethods.time_dtype_as_field('datetime', 'value_counts', 'direct') - 546±5μs 326±0.5μs 0.60 groupby.GroupByMethods.time_dtype_as_field('datetime', 'nunique', 'transformation') - 545±6μs 326±1μs 0.60 groupby.GroupByMethods.time_dtype_as_field('datetime', 'nunique', 'direct') - 608±0.4ms 172±0.8ms 0.28 groupby.Apply.time_copy_overhead_single_col(4) - 1.86±0.01s 455±2ms 0.24 groupby.Apply.time_copy_function_multi_col(4) ```

A few things are significantly slower:

The benchmarks that are affected by not using the "fast_apply" from libreduction (groupby.Apply.time_scalar_function_single_col(4) and groupby.Apply.time_scalar_function_multi_col(4)) are 2-3x slower. This would be fixed by https://github.com/pandas-dev/pandas/pull/40171 (supporting ArrayManager in libreduction for dataframes), but so that's pending a decision on getting rid of libreduction alltogether or not.
The groupby.GroupManyLabels.time_sum(1000) case is 16x slower. This is a case of a small but wide dataframe (shape of 1000x1000), and it's per-column overhead of checking dtypes etc in BaseGrouper._cython_operation. https://github.com/pandas-dev/pandas/pull/40317 already improves this a little bit, but there is certainly still room to further cut down this overhead by using more specialized dtype checks.

What's faster:

groupby.Apply.time_copy_overhead_single_col(4) is considerably faster (4x), but that might actually an (existing) bug in inconsistency between libreduction fast_apply and the fallback python apply. As with AM (taking the python fallback), it seems to infer it's doing a transform (same indexed result) and thus not reordering the result / not prepending the index with the groupby keys. Will open an issue about this -> https://github.com/pandas-dev/pandas/issues/40446
Some of the GroupByMethods benchmarks are slightly faster with ArrayManager, which from a quick look seems to stem from a smaller overhead in _get_numeric_data, but I think this overhead for BlockManager is also only visible because of the relatively small size of the data.

jorisvandenbossche commented 3 years ago

Reductions (stat_ops)

$ asv continuous -f 1.01 -b stat_ops HEAD~1 HEAD

```diff before after ratio [c9628cbd] [707df345]

+ 11.9±0.05ms 425±5ms 35.75 stat_ops.Correlation.time_corrwith_rows('pearson') + 1.78±0.03ms 6.63±0.1ms 3.72 stat_ops.FrameOps.time_op('mean', 'float', 1) + 1.76±0.03ms 6.41±0.06ms 3.64 stat_ops.FrameOps.time_op('sum', 'float', 1) + 6.51±0.3ms 17.9±0.2ms 2.74 stat_ops.Correlation.time_corrwith_cols('pearson') + 6.28±0.2ms 16.9±0.4ms 2.68 stat_ops.FrameOps.time_op('mad', 'float', 1) + 3.35±0.06ms 6.07±0.3ms 1.81 stat_ops.FrameMultiIndexOps.time_op(0, 'var') + 3.46±0.07ms 5.97±0.08ms 1.72 stat_ops.FrameMultiIndexOps.time_op(1, 'var') + 3.09±0.05ms 5.23±0.09ms 1.69 stat_ops.FrameMultiIndexOps.time_op(0, 'sum') + 3.12±0.07ms 5.18±0.1ms 1.66 stat_ops.FrameMultiIndexOps.time_op(0, 'mean') + 3.16±0.04ms 5.12±0.04ms 1.62 stat_ops.FrameMultiIndexOps.time_op(1, 'sum') + 3.19±0.1ms 5.13±0.09ms 1.61 stat_ops.FrameMultiIndexOps.time_op(1, 'mean') + 1.82±0.03ms 2.89±0.02ms 1.59 stat_ops.FrameOps.time_op('sum', 'int', 1) + 4.20±0.08ms 6.44±0.1ms 1.54 stat_ops.FrameOps.time_op('prod', 'float', 1) + 2.06±0.04ms 3.16±0.02ms 1.53 stat_ops.FrameOps.time_op('prod', 'int', 1) + 639±5ms 927±7ms 1.45 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'skew') + 6.41±0.3ms 8.93±0.1ms 1.39 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'var') + 75.5±1ms 104±2ms 1.37 stat_ops.FrameMultiIndexOps.time_op(1, 'skew') + 3.17±0.07ms 4.30±0.05ms 1.36 stat_ops.FrameOps.time_op('mean', 'int', 1) + 6.10±0.3ms 8.09±0.07ms 1.32 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'mean') + 1.48±0.01s 1.92±0.02s 1.30 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'mad') + 2.76±0.05ms 3.54±0.05ms 1.28 stat_ops.FrameMultiIndexOps.time_op(1, 'prod') + 2.88±0.05ms 3.63±0.02ms 1.26 stat_ops.FrameMultiIndexOps.time_op(0, 'prod') + 168±3ms 205±3ms 1.22 stat_ops.FrameMultiIndexOps.time_op(1, 'mad') + 12.0±0.8ms 14.6±0.2ms 1.21 stat_ops.FrameOps.time_op('mad', 'int', 1) + 519±6μs 613±10μs 1.18 stat_ops.Correlation.time_corr('pearson') + 14.9±0.09ms 17.5±0.1ms 1.18 stat_ops.Correlation.time_corr_wide_nans('pearson') + 1.57±0.02ms 1.79±0.02ms 1.15 stat_ops.FrameOps.time_op('mean', 'float', 0) + 6.95±0.2ms 7.95±0.04ms 1.14 stat_ops.FrameOps.time_op('median', 'float', 0) + 12.4±0.05ms 12.7±0.07ms 1.02 stat_ops.Rank.time_average_old('DataFrame', False) - 6.57±0.04ms 6.23±0.06ms 0.95 stat_ops.FrameMultiIndexOps.time_op(0, 'std') - 1.25±0.03ms 1.18±0.01ms 0.94 stat_ops.FrameOps.time_op('sum', 'Int64', 0) - 6.72±0.3ms 6.23±0.1ms 0.93 stat_ops.FrameMultiIndexOps.time_op(1, 'std') - 39.2±1ms 36.3±0.2ms 0.93 stat_ops.FrameOps.time_op('prod', 'Int64', 1) - 13.4±0.5ms 12.3±0.05ms 0.92 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'sem') - 41.8±0.7ms 38.3±0.7ms 0.92 stat_ops.FrameOps.time_op('mean', 'Int64', 1) - 10.2±0.2ms 9.30±0.06ms 0.91 stat_ops.FrameMultiIndexOps.time_op(0, 'sem') - 69.0±1ms 55.1±3ms 0.80 stat_ops.SeriesMultiIndexOps.time_op(1, 'mad') - 2.39±0.2ms 1.86±0.04ms 0.78 stat_ops.FrameOps.time_op('prod', 'float', 0) - 613±6ms 477±6ms 0.78 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'mad') - 13.2±0.5ms 9.90±0.1ms 0.75 stat_ops.SeriesMultiIndexOps.time_op(0, 'mad') - 1.56±0.03ms 1.15±0.01ms 0.74 stat_ops.FrameOps.time_op('sum', 'float', 0) - 6.60±1ms 3.95±0.05ms 0.60 stat_ops.FrameOps.time_op('std', 'float', 0) - 6.59±1ms 3.81±0.04ms 0.58 stat_ops.FrameOps.time_op('var', 'float', 0) - 9.73±0.3ms 5.63±0.07ms 0.58 stat_ops.FrameOps.time_op('mad', 'int', 0) - 9.11±0.7ms 4.85±0.4ms 0.53 stat_ops.FrameOps.time_op('skew', 'float', 0) - 9.03±0.6ms 4.64±0.06ms 0.51 stat_ops.FrameOps.time_op('kurt', 'float', 0) - 2.92±0.01ms 1.34±0.01ms 0.46 stat_ops.SeriesOps.time_op('mad', 'float') - 2.19±0.02ms 962±40μs 0.44 stat_ops.FrameOps.time_op('sum', 'int', 0) - 2.80±0.04ms 1.22±0.02ms 0.44 stat_ops.SeriesOps.time_op('mad', 'int') - 2.42±0.02ms 1.05±0.01ms 0.43 stat_ops.FrameOps.time_op('prod', 'int', 0) - 14.9±0.5ms 6.13±0.04ms 0.41 stat_ops.FrameOps.time_op('sem', 'int', 0) - 5.90±0.02ms 2.36±0.04ms 0.40 stat_ops.FrameOps.time_op('std', 'int', 0) - 5.96±0.1ms 2.28±0.02ms 0.38 stat_ops.FrameOps.time_op('var', 'int', 0) - 2.84±0.02ms 1.06±0.01ms 0.37 stat_ops.FrameOps.time_op('mean', 'int', 0) - 11.2±0.2ms 3.48±0.1ms 0.31 stat_ops.FrameOps.time_op('skew', 'int', 0) - 11.1±0.1ms 3.11±0.07ms 0.28 stat_ops.FrameOps.time_op('kurt', 'int', 0) ```

DataFrame.corrwith(..., method="pearson", axis=1) is a lot slower:
- for "pearson" we have custom code that operates on the datafame itself (a couple of arithmetic operations) -> those have currently a higher overhead with ArrayManager
- this overhead is especially visible with axis=1, because then we transpose the dataframe, and get a (15, 500) shape
But in general, this is a tiny but wide dataframe, so where the fixed overhead is significant. If I increase the size of the dataset, both have more or less the same performance.
Reductions with axis=1 (so taking eg the mean of each row, instead of each column) are slower, as can be expected (since this needs to go through a transpose / conversion to numpy array).
The FrameMultiIndexOps are 1.2-1.8x slower. This are basically groupby benchmarks, and thus showing that the built-in groupby aggregations are slightly slower when done column-wise (but so in the future we might be able to optimize this, if we can go all-in on 1D arrays in the cython code, xref #39861)
The FrameOps with float dtypes are a bit faster with ArrayManager. From a quick look at the profiles, it seems that the handling of min_count is more efficient for 1D arrays (which have scalars as resut) as for 2D arrays (probably something that can be optimized).
The FrameOps with int dtypes are quite a bit faster with ArrayManager, but that seems to be caused by a suboptimal memory layout for the BlockManager case (so that being slower as it could be), so that might be an artefact of the setup code of the benchmark case.

jorisvandenbossche commented 3 years ago

Element-wise ops (arithmetic)

$ asv continuous -f 1.01 -b arithmetic HEAD~1 HEAD

```diff before after ratio [cc1802b8] [42274f9e]

+ 450±4μs 5.59±0.08ms 12.42 arithmetic.Ops2.time_frame_series_dot + 1.30±0.01ms 8.33±0.03ms 6.42 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.30±0.02ms 8.31±0.05ms 6.40 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.31±0.01ms 8.34±0.04ms 6.39 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.31±0.01ms 8.35±0.03ms 6.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.31±0.01ms 8.33±0.05ms 6.36 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.39±0.01ms 8.84±0.06ms 6.35 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.40±0.01ms 8.84±0.08ms 6.32 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.32±0.01ms 8.30±0.07ms 6.31 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 45.1±0.06ms 277±1ms 6.14 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000, 10000)) + 1.48±0.01ms 8.99±0.04ms 6.07 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.48±0ms 8.91±0.08ms 6.04 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.39±0.01ms 8.37±0.06ms 6.03 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.48±0.01ms 8.93±0.05ms 6.02 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.40±0ms 8.31±0.06ms 5.95 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.47±0.01ms 8.52±0.05ms 5.77 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.47±0.01ms 8.50±0.03ms 5.77 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.47±0.01ms 8.45±0.07ms 5.74 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.61±0.01ms 9.13±0.05ms 5.67 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 6.14±0.03ms 33.8±0.3ms 5.50 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000, 10000)) + 1.73±0.02ms 9.27±0.08ms 5.35 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.61±0.01ms 8.60±0.06ms 5.34 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.74±0.02ms 9.26±0.06ms 5.33 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.73±0.01ms 9.21±0.05ms 5.32 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.74±0.02ms 9.24±0.02ms 5.31 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.74±0.03ms 9.22±0.08ms 5.29 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.74±0.02ms 9.18±0.05ms 5.29 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.81±0.02ms 9.36±0.06ms 5.18 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.81±0.01ms 9.35±0.04ms 5.17 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.75±0.01ms 8.87±0.06ms 5.08 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.84±0.02ms 9.31±0.05ms 5.07 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.85±0.01ms 9.36±0.05ms 5.05 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.75±0.01ms 8.85±0.02ms 5.05 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.91±0.01ms 9.49±0.06ms 4.98 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.79±0.02ms 8.91±0.03ms 4.98 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.91±0.01ms 9.48±0.04ms 4.97 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.79±0.02ms 8.87±0.06ms 4.95 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.92±0.02ms 9.45±0.05ms 4.93 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 18.2±0.04ms 88.8±0.3ms 4.88 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000, 10000)) + 1.93±0.02ms 9.43±0.07ms 4.88 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.94±0.01ms 9.42±0.07ms 4.86 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 10.1±0.04ms 49.3±0.7ms 4.86 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000, 10000)) + 1.95±0.01ms 9.46±0.03ms 4.85 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.86±0.01ms 8.94±0.03ms 4.79 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.87±0.02ms 8.92±0.04ms 4.76 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.89±0.02ms 9.00±0.05ms 4.76 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.88±0.01ms 8.95±0.03ms 4.76 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.02±0.02ms 9.58±0.05ms 4.75 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.91±0.01ms 9.01±0.05ms 4.73 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.91±0.01ms 8.95±0.04ms 4.69 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.06±0.01ms 9.61±0.04ms 4.66 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.01±0.01ms 9.26±0.05ms 4.60 arithmetic.Ops.time_frame_comparison(True, 'default') + 2.00±0.02ms 9.05±0.04ms 4.52 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.04±0.02ms 9.13±0.02ms 4.47 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 8.83±5ms 36.3±0.1ms 4.11 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000, 10000)) + 2.41±0.02ms 9.89±0.04ms 4.11 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.36±0.01ms 9.65±0.04ms 4.09 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.37±0.02ms 9.67±0.06ms 4.09 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.35±0.01ms 9.59±0.05ms 4.09 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.43±0.01ms 9.93±0.08ms 4.08 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.35±0.01ms 9.58±0.09ms 4.08 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.01ms 9.60±0.05ms 4.08 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.37±0.01ms 9.62±0.07ms 4.05 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.43±0.01ms 9.72±0.06ms 4.00 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.44±0.01ms 9.71±0.05ms 3.99 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.38±0.01ms 9.45±0.05ms 3.98 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.44±0.01ms 9.71±0.03ms 3.97 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.38±0.01ms 9.43±0.05ms 3.96 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.39±0.01ms 9.44±0.06ms 3.96 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.38±0.01ms 9.42±0.02ms 3.95 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.37±0.01ms 9.07±0.05ms 3.82 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.39±0.01ms 9.12±0.1ms 3.82 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.36±0.01ms 9.01±0.09ms 3.82 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.36±0.02ms 9.02±0.06ms 3.82 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.42±0.02ms 9.22±0.04ms 3.82 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.38±0.02ms 9.07±0.1ms 3.81 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.42±0.02ms 9.21±0.04ms 3.80 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.54±0.02ms 9.66±0.08ms 3.80 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 13.7±1ms 52.2±0.3ms 3.80 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000, 10000)) + 2.37±0.01ms 9.00±0.04ms 3.80 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.38±0.01ms 9.01±0.05ms 3.79 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.37±0.02ms 8.97±0.06ms 3.79 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.37±0.01ms 8.99±0.06ms 3.79 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.82±0.08ms 10.6±0.09ms 3.77 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.43±0.01ms 9.14±0.07ms 3.76 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.89±0.08ms 10.8±0.06ms 3.75 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.77±0.06ms 9.90±0.06ms 3.57 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 3.18±0.1ms 10.7±0.2ms 3.35 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 3.20±0.02ms 10.1±0.05ms 3.16 arithmetic.Ops.time_frame_add(True, 'default') + 3.21±0.02ms 10.1±0.06ms 3.15 arithmetic.Ops.time_frame_mult(True, 'default') + 2.10±0.04ms 6.27±0.03ms 2.98 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('truediv') + 1.37±0.02ms 3.49±0.02ms 2.55 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('lt') + 1.36±0.02ms 3.45±0.07ms 2.53 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('gt') + 1.38±0.01ms 3.48±0.03ms 2.52 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('le') + 1.37±0.02ms 3.42±0.06ms 2.49 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('eq') + 1.37±0.01ms 3.42±0.03ms 2.49 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('ne') + 1.65±0.02ms 4.06±0.07ms 2.47 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('mul') + 1.38±0.01ms 3.41±0.03ms 2.47 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('ge') + 1.63±0.01ms 3.93±0.09ms 2.42 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('sub') + 1.64±0.06ms 3.92±0.05ms 2.39 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('add') + 32.9±0.2ms 78.1±0.4ms 2.37 arithmetic.Ops.time_frame_multi_and(True, 'default') + 6.08±0.07ms 13.5±0.1ms 2.21 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (100000, 100)) + 2.99±0.08ms 5.99±0.04ms 2.00 arithmetic.Ops.time_frame_comparison(True, 1) + 3.29±0.01ms 6.19±0.06ms 1.88 arithmetic.Ops.time_frame_add(True, 1) + 3.30±0.07ms 6.18±0.04ms 1.88 arithmetic.Ops.time_frame_mult(True, 1) + 10.0±0.03ms 18.5±0.09ms 1.85 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (10000, 1000)) + 48.0±0.6ms 83.2±0.2ms 1.74 arithmetic.Ops2.time_frame_float_floor_by_zero + 4.62±0.2ms 8.00±0.1ms 1.73 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('lt') + 4.57±0.04ms 7.88±0.1ms 1.73 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('le') + 4.67±0.03ms 7.93±0.04ms 1.70 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('gt') + 4.62±0.3ms 7.83±0.05ms 1.70 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('ge') + 4.69±0.2ms 7.91±0.07ms 1.69 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('eq') + 10.1±0.05ms 17.1±0.07ms 1.69 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (100000, 100)) + 4.75±0.04ms 7.82±0.03ms 1.65 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('ne') + 39.3±0.8ms 64.4±0.3ms 1.64 arithmetic.Ops.time_frame_multi_and(True, 1) + 27.5±0.4ms 43.2±0.4ms 1.57 arithmetic.Ops2.time_frame_dot + 20.7±0.03ms 31.9±0.2ms 1.54 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 20.7±0.03ms 31.1±0.2ms 1.50 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 45.1±0.09ms 64.1±0.2ms 1.42 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (10000, 1000)) + 6.06±0.04ms 8.56±0.04ms 1.41 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (10000, 1000)) + 37.4±0.4ms 51.8±0.2ms 1.39 arithmetic.Ops.time_frame_multi_and(False, 1) + 37.3±0.3ms 51.5±0.2ms 1.38 arithmetic.Ops.time_frame_multi_and(False, 'default') + 41.5±0.3ms 56.9±0.6ms 1.37 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 41.3±0.07ms 56.1±0.2ms 1.36 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 41.3±0.08ms 55.9±0.3ms 1.35 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 41.9±0.09ms 56.4±0.4ms 1.35 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.02±0.05ms 2.38±0.01ms 1.18 arithmetic.Ops.time_frame_comparison(False, 'default') + 2.07±0.08ms 2.39±0.01ms 1.16 arithmetic.Ops.time_frame_comparison(False, 1) + 52.5±0.4ms 60.4±0.2ms 1.15 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('floordiv') + 32.0±0.3ms 36.5±0.04ms 1.14 arithmetic.Ops2.time_frame_float_mod + 18.1±0.1ms 20.0±0.05ms 1.10 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (10000, 1000)) + 6.02±0.06ms 6.49±0.07ms 1.08 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000000, 10)) + 33.1±0.09ms 34.8±0.04ms 1.05 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 32.4±0.06ms 33.9±0.1ms 1.05 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 27.3±0.05ms 28.5±0.08ms 1.04 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 27.2±0.1ms 28.4±0.03ms 1.04 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 27.0±0.1ms 27.8±0.05ms 1.03 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 29.2±0.09ms 30.0±0.1ms 1.03 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 27.2±0.03ms 27.9±0.09ms 1.03 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 44.6±0.05ms 45.3±0.1ms 1.02 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000000, 10)) - 1.40±0s 1.37±0s 0.98 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.77±0.01ms 1.73±0ms 0.98 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.43±0ms 1.40±0ms 0.98 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.58±0ms 1.54±0ms 0.98 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.38±0ms 1.34±0ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.02±0.01ms 987±5μs 0.97 arithmetic.NumericInferOps.time_divide() - 1.54±0ms 1.50±0ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.47±0ms 1.43±0ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.46±0.01ms 1.42±0ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.41±0ms 1.37±0.01ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 4.30±0.02ms 4.17±0.02ms 0.97 arithmetic.DateInferOps.time_timedelta_plus_datetime - 1.31±0ms 1.27±0ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.34±0.01ms 1.30±0ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.46±0ms 1.41±0ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 938±6ms 908±8ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.38±0.01ms 1.33±0ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.27±0ms 1.23±0ms 0.96 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 478±10μs 458±2μs 0.96 arithmetic.NumericInferOps.time_multiply() - 186±2μs 176±2μs 0.94 arithmetic.NumericInferOps.time_subtract() - 480±10μs 453±3μs 0.94 arithmetic.NumericInferOps.time_add() - 290±2μs 271±2μs 0.93 arithmetic.NumericInferOps.time_add() - 52.7±0.1ms 49.1±0.09ms 0.93 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) - 295±3μs 274±2μs 0.93 arithmetic.NumericInferOps.time_add() - 186±2μs 173±3μs 0.93 arithmetic.NumericInferOps.time_subtract() - 53.3±0.2ms 49.2±0.1ms 0.92 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) - 57.8±0.2ms 52.9±0.2ms 0.92 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) - 52.9±0.2ms 48.4±0.2ms 0.92 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) - 55.4±0.4ms 50.6±0.3ms 0.91 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) - 55.9±0.4ms 50.8±0.1ms 0.91 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) - 55.6±0.2ms 50.3±0.08ms 0.90 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) - 52.4±0.2ms 47.2±0.08ms 0.90 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) - 30.6±0.2ms 27.3±0.2ms 0.89 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) - 78.5±1ms 69.7±4ms 0.89 arithmetic.BinaryOpsMultiIndex.time_binary_op_multiindex('div') - 45.1±0.04ms 40.0±0.1ms 0.89 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (100000, 100)) - 106±0.6ms 89.9±0.1ms 0.84 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('pow') - 59.1±0.4ms 46.3±0.4ms 0.78 arithmetic.Ops2.time_frame_float_div - 252±1μs 194±0.6μs 0.77 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 21.8±0.4ms 16.5±0.2ms 0.76 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) - 18.1±0.08ms 12.5±0.04ms 0.69 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (100000, 100)) - 57.9±0.4ms 29.4±0.3ms 0.51 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('floordiv') - 79.6±1ms 29.8±0.2ms 0.37 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('pow') - 55.9±0.1ms 5.97±0.03ms 0.11 arithmetic.Ops2.time_frame_int_div_by_zero - 55.9±0.3ms 4.85±0.02ms 0.09 arithmetic.Ops2.time_frame_float_div_by_zero ```

The above is with a branch that combines several WIP changes (https://github.com/pandas-dev/pandas/compare/master...jorisvandenbossche:ops-refactor-combined?expand=1, for some there is already an open PR, eg #40445, #40444, #40396, #39772, for others I still need to open a PR but are dependent on other changes).

In general the element-wise ops are probably that set of operations that can see the biggest impact of performing column-by-column instead of on a single block.
A few first notes:

dot() is slower, but since that's basically a 2D array operation, that's to be expected.
It seems that specifically the comparison ops are slower column-wise when using numexpr (eg many of the IntFrameWithScalar.time_frame_op_with_scalar benchmarks in the output above with lt/gt/eq/ne/... ops). That's something I need to investigate a bit more in detail, because this slowdown doesn't seem to come from per-column overhead, but actually numexpr being slower.
The majority of the FrameWithFrameWide benchmarks actually don't show up in the above output (so meaning that they didn't give a significant difference), and the ones that do show up with the biggest slowdown are the cases with shape (1000, 10000), so wide dataframes. For wide dataframes, some slowdown will be inevitable (question is of course how much we find acceptable).

UPDATE 2021-04-01: Using latest master + https://github.com/pandas-dev/pandas/pull/40482

$ asv continuous -f 1.01 -b arithmetic HEAD~1 HEAD

```diff before after ratio [fd958262] [89c230c8]

+ 6.59±0.08ms 98.1±0.9ms 14.88 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000, 10000)) + 10.7±0.1ms 128±9ms 11.96 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000, 10000)) + 472±3μs 5.63±0.6ms 11.92 arithmetic.Ops2.time_frame_series_dot + 14.8±0.4ms 148±3ms 9.99 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000, 10000)) + 45.5±0.06ms 384±6ms 8.44 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000, 10000)) + 1.25±0.01ms 10.5±1ms 8.41 arithmetic.Ops2.time_frame_float_div_by_zero + 13.7±0.03ms 99.9±2ms 7.27 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000, 10000)) + 1.93±0.02ms 12.3±0.8ms 6.41 arithmetic.Ops2.time_frame_int_div_by_zero + 1.76±0.06ms 11.0±0.2ms 6.25 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('eq') + 1.78±0.05ms 11.1±0.1ms 6.21 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('gt') + 1.77±0.06ms 10.9±0.03ms 6.18 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('ne') + 1.80±0.06ms 11.0±0.1ms 6.12 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('lt') + 1.80±0.06ms 11.0±0.04ms 6.11 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('ge') + 1.80±0.07ms 10.9±0.05ms 6.06 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('le') + 24.8±0.03ms 114±0.2ms 4.61 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('pow') + 2.04±0.02ms 9.31±0.09ms 4.57 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('sub') + 2.06±0.03ms 9.38±0.03ms 4.56 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('add') + 2.26±0.04ms 9.51±0.04ms 4.21 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('mul') + 3.15±0.01ms 12.8±0.08ms 4.06 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('truediv') + 21.9±7ms 82.7±2ms 3.78 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000, 10000)) + 23.4±3ms 66.4±7ms 2.84 arithmetic.Ops.time_frame_multi_and(True, 'default') + 10.7±0.09ms 29.7±2ms 2.77 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (10000, 1000)) + 44.6±0.3ms 116±9ms 2.59 arithmetic.Ops2.time_frame_float_floor_by_zero + 6.49±0.06ms 16.0±0.09ms 2.46 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (10000, 1000)) + 13.7±0.07ms 31.1±2ms 2.27 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (10000, 1000)) + 29.6±3ms 66.7±7ms 2.25 arithmetic.Ops.time_frame_multi_and(False, 1) + 29.9±3ms 65.8±6ms 2.20 arithmetic.Ops.time_frame_multi_and(False, 'default') + 1.98±0.03ms 4.24±0.03ms 2.14 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.99±0.02ms 4.24±0.03ms 2.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.97±0.03ms 4.10±0.03ms 2.08 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.98±0.04ms 4.08±0.02ms 2.06 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 32.5±3ms 66.3±6ms 2.04 arithmetic.Ops.time_frame_multi_and(True, 1) + 2.09±0.03ms 4.23±0.03ms 2.02 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.36±0.01ms 4.76±0.03ms 2.01 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.40±0.01ms 2.81±0.02ms 2.01 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.41±0.01ms 2.83±0.02ms 2.01 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.40±0.02ms 2.80±0.01ms 2.01 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.41±0.01ms 2.82±0.02ms 2.01 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.38±0.01ms 4.77±0.02ms 2.00 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.13±0.03ms 4.24±0.03ms 1.99 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.12±0.01ms 4.23±0.02ms 1.99 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.38±0.01ms 4.72±0.02ms 1.98 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.42±0.01ms 2.81±0.02ms 1.97 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.43±0.01ms 2.81±0.02ms 1.97 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.36±0.02ms 4.66±0.02ms 1.97 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.38±0.01ms 4.65±0.03ms 1.95 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.11±0.01ms 4.10±0.03ms 1.95 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.40±0.01ms 2.72±0.01ms 1.94 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.10±0.03ms 4.08±0.02ms 1.94 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.40±0.02ms 2.70±0.01ms 1.93 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.11±0.02ms 4.08±0.02ms 1.93 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.40±0.02ms 2.71±0.01ms 1.93 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.39±0.02ms 4.61±0.03ms 1.93 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.41±0.01ms 2.70±0.03ms 1.92 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.51±0.02ms 2.82±0.02ms 1.86 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.28±0.03ms 4.22±0.03ms 1.85 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.55±0.01ms 2.84±0.01ms 1.83 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.54±0.01ms 2.82±0.02ms 1.83 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 45.4±0.04ms 83.1±0.5ms 1.83 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (10000, 1000)) + 15.5±0.7ms 28.2±0.2ms 1.82 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (10000, 1000)) + 2.26±0.03ms 4.08±0.03ms 1.81 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 3.03±0.02ms 5.43±0.03ms 1.79 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.52±0.03ms 2.71±0.01ms 1.78 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.54±0.01ms 2.73±0.02ms 1.77 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.53±0.02ms 2.70±0.01ms 1.77 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 3.01±0.03ms 5.31±0.01ms 1.76 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 46.5±0.9ms 80.1±0.5ms 1.72 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('floordiv') + 24.0±0.06ms 41.1±0.2ms 1.71 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 24.1±0.1ms 41.0±0.2ms 1.70 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 3.25±0.08ms 5.48±0.02ms 1.69 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.01±0.04ms 3.39±0.02ms 1.68 arithmetic.Ops.time_frame_comparison(True, 'default') + 31.7±0.2ms 53.3±4ms 1.68 arithmetic.Ops2.time_frame_dot + 10.6±0.8ms 17.9±0.1ms 1.68 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('eq') + 10.6±0.8ms 17.8±0.3ms 1.68 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('gt') + 10.6±0.7ms 17.7±0.1ms 1.66 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('ge') + 10.7±0.8ms 17.8±0.2ms 1.66 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('ne') + 10.7±0.8ms 17.7±0.09ms 1.66 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('lt') + 10.7±0.9ms 17.6±0.3ms 1.65 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('le') + 1.73±0.01ms 2.84±0.01ms 1.64 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.87±0.01ms 2.98±0.02ms 1.60 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.88±0.02ms 2.99±0.02ms 1.59 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.88±0.01ms 2.97±0.02ms 1.58 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.89±0.02ms 2.99±0.01ms 1.58 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.74±0.01ms 2.72±0.01ms 1.56 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 10.6±0.07ms 16.4±0.1ms 1.54 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (100000, 100)) + 1.89±0.03ms 2.89±0.03ms 1.52 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.89±0.03ms 2.86±0.03ms 1.52 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.39±0.01ms 3.60±0.02ms 1.51 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.19±0.03ms 3.31±0.02ms 1.51 arithmetic.Ops.time_frame_comparison(False, 'default') + 2.21±0.05ms 3.30±0.01ms 1.49 arithmetic.Ops.time_frame_comparison(False, 1) + 3.61±0.07ms 5.37±0.02ms 1.48 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.45±0.02ms 3.60±0.03ms 1.47 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.98±0.03ms 2.89±0.02ms 1.46 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.35±0.01ms 3.42±0.03ms 1.45 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.44±0.02ms 3.55±0.03ms 1.45 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.94±0.01ms 2.79±0.02ms 1.44 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.95±0.03ms 2.79±0.02ms 1.43 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.02±0.02ms 2.89±0.03ms 1.43 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.40±0.01ms 3.43±0.01ms 1.43 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.40±0.01ms 3.42±0.02ms 1.43 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.39±0.01ms 3.40±0.03ms 1.42 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.39±0.01ms 3.41±0.03ms 1.42 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.01ms 3.36±0.02ms 1.42 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.40±0.01ms 3.40±0.02ms 1.42 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.42±0.02ms 3.44±0.02ms 1.42 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 3.00±0.03ms 4.25±0.4ms 1.42 arithmetic.Ops.time_frame_mult(False, 'default') + 2.37±0.01ms 3.35±0.02ms 1.41 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.39±0.01ms 3.37±0.02ms 1.41 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 10.6±0.1ms 14.9±0.08ms 1.41 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000000, 10)) + 2.37±0.02ms 3.34±0.05ms 1.41 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.37±0.02ms 3.33±0.01ms 1.40 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 45.4±0.06ms 63.7±4ms 1.40 arithmetic.Ops2.time_frame_float_div + 2.38±0.02ms 3.31±0.02ms 1.39 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.36±0.03ms 3.28±0.02ms 1.39 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.11±0.01ms 2.92±0.03ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.01ms 3.26±0.02ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.09±0.02ms 2.88±0.02ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.01ms 3.25±0.03ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.38±0.01ms 3.28±0.02ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.03±0.03ms 2.80±0.03ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.02±0.02ms 2.79±0.02ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 11.1±1ms 15.2±0.1ms 1.38 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('truediv') + 10.9±1ms 14.9±0.2ms 1.37 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('mul') + 2.37±0.02ms 3.23±0.03ms 1.37 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 3.02±0.04ms 4.12±0.3ms 1.37 arithmetic.Ops.time_frame_mult(False, 1) + 10.9±1ms 14.8±0.2ms 1.36 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('add') + 2.13±0.03ms 2.89±0.02ms 1.36 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 10.9±1ms 14.8±0.3ms 1.35 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('sub') + 2.06±0.02ms 2.77±0.02ms 1.35 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 48.4±0.06ms 65.0±0.05ms 1.34 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 48.4±0.05ms 65.1±0.03ms 1.34 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 48.4±0.09ms 64.9±0.05ms 1.34 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.20±0.02ms 2.89±0.02ms 1.31 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.72±0.04ms 3.52±0.02ms 1.29 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 3.05±0.06ms 3.94±0.01ms 1.29 arithmetic.Ops.time_frame_add(False, 1) + 38.9±0.1ms 49.9±3ms 1.28 arithmetic.Ops2.time_frame_float_mod + 2.19±0.02ms 2.79±0.02ms 1.27 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 34.2±0.2ms 42.4±3ms 1.24 arithmetic.Ops2.time_frame_int_mod + 35.6±0.8ms 41.4±0.3ms 1.16 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('pow') + 45.4±0.02ms 49.4±0.4ms 1.09 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (100000, 100)) + 40.1±0.02ms 42.7±0.07ms 1.06 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 32.9±0.03ms 34.8±0.04ms 1.06 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 39.2±0.01ms 41.5±0.1ms 1.06 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 32.8±0.05ms 34.5±0.04ms 1.05 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 32.6±0.03ms 34.2±0.03ms 1.05 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 32.6±0.02ms 34.1±0.04ms 1.04 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 32.8±0.01ms 34.2±0.03ms 1.04 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 39.9±1ms 41.3±0.4ms 1.03 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('floordiv') - 3.08±0.01ms 3.03±0ms 0.99 arithmetic.IndexArithmetic.time_divide('int') - 2.31±0.01ms 2.27±0.01ms 0.98 arithmetic.IndexArithmetic.time_subtract('int') - 2.32±0.01ms 2.26±0.01ms 0.98 arithmetic.IndexArithmetic.time_add('int') - 1.17±0.01ms 1.14±0ms 0.98 arithmetic.NumericInferOps.time_divide() - 1.63±0.1ms 1.57±0ms 0.97 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 308±2μs 294±2μs 0.95 arithmetic.NumericInferOps.time_add() - 14.9±0.6ms 14.2±0.01ms 0.95 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000000, 10)) - 308±2μs 292±1μs 0.95 arithmetic.NumericInferOps.time_multiply() - 310±2μs 294±3μs 0.95 arithmetic.NumericInferOps.time_subtract() - 510±20μs 483±2μs 0.95 arithmetic.NumericInferOps.time_divide() - 510±10μs 484±3μs 0.95 arithmetic.NumericInferOps.time_add() - 509±10μs 483±3μs 0.95 arithmetic.NumericInferOps.time_add() - 308±3μs 292±3μs 0.95 arithmetic.NumericInferOps.time_subtract() - 4.15±0.07ms 3.92±0.01ms 0.95 arithmetic.NumericInferOps.time_modulo() - 199±2μs 188±1μs 0.95 arithmetic.NumericInferOps.time_subtract() - 203±2μs 191±1μs 0.94 arithmetic.NumericInferOps.time_add() - 251±1μs 236±0.9μs 0.94 arithmetic.OffsetArrayArithmetic.time_add_series_offset() - 1.27±0.1ms 1.19±0ms 0.94 arithmetic.NumericInferOps.time_divide() - 213±2μs 199±1μs 0.93 arithmetic.NumericInferOps.time_multiply() - 202±3μs 188±0.6μs 0.93 arithmetic.NumericInferOps.time_subtract() - 513±20μs 474±2μs 0.92 arithmetic.NumericInferOps.time_subtract() - 529±20μs 487±2μs 0.92 arithmetic.NumericInferOps.time_multiply() - 523±20μs 477±4μs 0.91 arithmetic.NumericInferOps.time_subtract() - 6.97±0.4ms 5.82±0.03ms 0.84 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000000, 10)) - 68.3±0.1ms 46.8±0.1ms 0.69 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) - 62.7±0.1ms 42.6±0.02ms 0.68 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) - 65.4±0.2ms 44.1±0.07ms 0.67 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) - 65.6±0.2ms 44.2±0.1ms 0.67 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) - 65.3±0.2ms 44.0±0.07ms 0.67 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) - 61.7±0.1ms 41.3±0.1ms 0.67 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) - 62.1±0.07ms 41.5±0.03ms 0.67 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) - 62.1±0.04ms 41.4±0.04ms 0.67 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) - 35.3±0.1ms 14.1±0.03ms 0.40 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) - 24.4±0.1ms 3.35±0.02ms 0.14 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) - 48.8±0.05ms 3.27±0.02ms 0.07 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) ```

Two notes about a few benchmarks that are (unexpectedly) a lot faster with ArrayManager:

The IntFrameWithScalar benchmark is more than 10x faster for the pow operations for certain cases. I could reproduce this outside of ASV, and turns out this is due to numexpr being slower, and because of the size of the dataframe, the column-wise op (ArrayManager) doesn't use numexpr, while block-wise (BlockManager) uses numexpr. In an environment without numexpr, both were more or less the same.
The IntFrameWithScalar benchmark with the floordiv operation is faster with ArrayManager. This turns out because when using numexpr, we incorrectly end up falling back using _masked_arith_op (which is slower), because the operation is not supported by numexpr.

jorisvandenbossche commented 2 years ago

Over the last weeks I have been updating the status of this project (and fixing some regressions), and rerunning the benchmarks. This (long) post gives an overview of the current ASV benchmarks with the ArrayManager.

Technical notes: I always ran the benchmark on a commit where I changed the default to ArrayManager (HEAD) vs the previous commit with the normal default of BlockManager (HEAD~1) -> asv continuous -f 1.0 HEAD~1 HEAD. So using the diff formatting to get some color: green is slower and red is faster for the ArrayManager.
The "am-benchmarks" branch I am using is master (of yesterday morning) + a few open close-to-mergeable (all-green) ArrayManager related PRs (#41104 (fillna) , #44736 (interpolate), #44791 (eval)) + the commit to change the default.

I am going to split the results here by topic / file (each time with a small discussion, repeating some stuff from above), but the results of the full run are also included at the bottom.

ToNumpy

$ asv continuous -f 1.01 -b ToNumpy HEAD~1 HEAD

```diff before after ratio [f8052655] [5815ddc3]

+ 3.18±0.04μs 11.5±0.1ms 3632.89 frame_methods.ToNumpy.time_values_wide + 3.59±0.07μs 11.5±0.07ms 3193.82 frame_methods.ToNumpy.time_to_numpy_wide + 3.18±0.04μs 263±1μs 82.80 frame_methods.ToNumpy.time_values_tall + 3.64±0.07μs 265±0.6μs 72.79 frame_methods.ToNumpy.time_to_numpy_tall + 3.70±0.2ms 20.0±0.2ms 5.40 frame_methods.ToNumpy.time_to_numpy_mixed_wide + 3.74±0.3ms 19.9±0.2ms 5.33 frame_methods.ToNumpy.time_values_mixed_wide ```

All those benchmarks perform much slower for ArrayManager, as is expected. For the slowest case (dataframe with 10,000 columns), we are comparing creating a new ndarray from 10,000 1D arrays vs just returning the single existing array that is backing this DataFrame. Creating a new array is always going to be slower, but even for an dataframe with 10,000 columns, I would say it's still fast (11ms)
The case of a tall + mixed DataFrame (time_values_mixed_tall) doesn't show up here, so for this case there is no significant difference (in this case a new array needs to be created for both ArrayManager or BlockManager).

reshape

$ asv continuous -f 1.01 -b reshape HEAD~1 HEAD

```diff before after ratio [f8052655] [5815ddc3]

+ 210±1μs 80.0±0.6ms 381.47 reshape.ReshapeExtensionDtype.time_transpose('datetime64[ns, US/Pacific]') + 44.5±0.09ms 184±0.5ms 4.14 reshape.Unstack.time_full_product('int') + 1.79±0.03ms 7.32±0.03ms 4.09 reshape.ReshapeExtensionDtype.time_unstack_fast('datetime64[ns, US/Pacific]') + 3.59±0.03ms 11.2±0.04ms 3.10 reshape.SimpleReshape.time_stack + 97.8±2ms 216±0.4ms 2.21 reshape.Unstack.time_without_last_row('int') + 5.82±0.07ms 9.65±0.04ms 1.66 reshape.ReshapeExtensionDtype.time_stack('datetime64[ns, US/Pacific]') + 40.2±0.4ms 51.2±0.3ms 1.27 reshape.ReshapeExtensionDtype.time_transpose('Period[s]') + 1.98±0.02ms 2.44±0.02ms 1.23 reshape.SimpleReshape.time_unstack + 80.5±0.8ms 88.7±0.7ms 1.10 reshape.PivotTable.time_pivot_table_margins - 281±1ms 275±0.9ms 0.98 reshape.WideToLong.time_wide_to_long_big - 21.0±0.1ms 19.0±0.06ms 0.91 reshape.Crosstab.time_crosstab - 2.58±0.02ms 2.34±0.03ms 0.90 reshape.ReshapeExtensionDtype.time_unstack_slow('Period[s]') - 24.5±0.3ms 20.9±0.1ms 0.86 reshape.Unstack.time_full_product('category') - 2.86±0.01ms 2.20±0.02ms 0.77 reshape.Melt.time_melt_dataframe - 111±2ms 23.4±0.06ms 0.21 reshape.Unstack.time_without_last_row('category') ```

The big slowdown is in transposing a (10, 1000) shaped DataFrame with all datetimetz columns. Most of the time here is spent in boxing/unboxing Timestamp scalars (because for datetimetz we use object dtype array). I think it would be relatively straightforward to have a specialized method that avoids going through scalars but uses a native numpy dtype for the transpose operation. In general, transpose is an operation that would of course always do worse when using a columnar layout in an otherwise single-block case.
The case with the biggest slowdown for unstack (reshape.Unstack with int dtype) creates a DataFrame with a shape of 100 rows × 100000 columns (so a very wide DataFrame, and thus a slowdown is expected / acceptable IMO). For the "full product" case (no missing values get introduced), the ArrayManager is "only" 5 times slower compared to reshaping the single block. Many of the reshape benchmarks also don't show up above, meaning there is no significant difference in performance for those.
(the speedup in reshape.Unstack.time_without_last_row('category') can be ignored, as that has been optimized for BlockManager on master since then (https://github.com/pandas-dev/pandas/pull/44758))

reindex

$ asv continuous -f 1.01 -b reindex HEAD~1 HEAD

```diff before after ratio [f8052655] [5815ddc3]

+ 41.1±0.09ms 99.5±0.4ms 2.42 frame_methods.Reindex.time_reindex_axis1_missing + 3.52±0.07ms 6.93±0.02ms 1.97 frame_methods.Reindex.time_reindex_axis0 + 6.49±0.2ms 9.98±0.03ms 1.54 frame_methods.Reindex.time_reindex_upcast + 734±40μs 1.03±0.09ms 1.40 reindex.LevelAlign.time_align_level + 742±30μs 1.00±0.09ms 1.35 reindex.LevelAlign.time_reindex_level + 1.20±0ms 1.60±0.01ms 1.34 reindex.ReindexMethod.time_reindex_method('pad', ) + 1.28±0.02ms 1.69±0.02ms 1.33 reindex.ReindexMethod.time_reindex_method('pad', ) + 1.36±0.02ms 1.71±0.01ms 1.25 reindex.ReindexMethod.time_reindex_method('backfill', ) + 1.32±0.02ms 1.61±0.03ms 1.23 reindex.ReindexMethod.time_reindex_method('backfill', ) + 7.66±0.2ms 9.10±0.02ms 1.19 frame_methods.Reindex.time_reindex_both_axes - 516±4μs 259±0.8μs 0.50 reindex.Reindex.time_reindex_columns - 30.4±0.06ms 13.3±0.1ms 0.44 frame_methods.Reindex.time_reindex_axis1 ```

Reindexing the columns can be faster with ArrayManager (the two last cases with the biggest speedup), or slower when the reindexing introduces many "null" columns (the first case with the biggest slowdown). Personally, I don't worry too much about this case of reindexing with many non-existing columns.
Another benchmark with a slowdown is reindexing the rows (frame_methods.Reindex.time_reindex_axis0) with a slowdown factor of ~2. But since this is reindexing a wide (1000 columns) and homogenously dtyped (1 block) DataFrame, I think this is a "worst case", and so an acceptable slowdown IMO.

other frame_methods (leaving out ToNumpy and reindex from above)

$ asv continuous -f 1.01 -b frame_methods HEAD~1 HEAD

```diff before after ratio [f8052655] [5815ddc3]

+ 508±4μs 99.7±0.5ms 196.07 frame_methods.Quantile.time_frame_quantile(1) + 48.7±0.3μs 3.34±0.2ms 68.49 frame_methods.XS.time_frame_xs(0) + 58.5±0.3μs 1.38±0.01ms 23.52 frame_methods.Dtypes.time_frame_dtypes + 6.93±0.04ms 124±0.9ms 17.81 frame_methods.MaskBool.time_frame_mask_floats + 977±5μs 14.3±0.04ms 14.64 frame_methods.GetDtypeCounts.time_frame_get_dtype_counts + 662±8μs 5.31±0.06ms 8.03 frame_methods.Isnull.time_isnull_floats_no_null + 664±6μs 5.28±0.05ms 7.96 frame_methods.Isnull.time_isnull + 34.3±1ms 202±2ms 5.90 frame_methods.MaskBool.time_frame_mask_bools + 21.5±0.2ms 97.3±0.5ms 4.52 frame_methods.Dropna.time_dropna('any', 1) + 2.39±0.1ms 10.4±0.2ms 4.36 frame_methods.Equals.time_frame_float_equal + 25.8±0.05ms 98.1±0.7ms 3.81 frame_methods.Dropna.time_dropna_axis_mixed_dtypes('any', 1) + 1.85±0.01ms 6.05±0.3ms 3.26 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'datetime64[ns]') + 57.2±0.4ms 184±2ms 3.22 frame_methods.Count.time_count_level_multi(1) + 1.85±0.01ms 5.76±0.3ms 3.12 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'timedelta64[ns]') + 61.6±0.4ms 188±0.6ms 3.06 frame_methods.Count.time_count_level_multi(0) + 9.34±0.05ms 25.4±0.3ms 2.72 frame_methods.Shift.time_shift(0) + 72.6±0.5ms 196±10ms 2.70 frame_methods.Count.time_count_level_mixed_dtypes_multi(0) + 43.0±0.2ms 114±0.5ms 2.65 frame_methods.Iteration.time_iteritems_indexing + 3.79±0.1ms 9.77±0.2ms 2.58 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'timedelta64[ns]') + 42.8±0.4ms 110±0.7ms 2.58 frame_methods.Dropna.time_dropna_axis_mixed_dtypes('all', 1) + 25.3±0.4ms 65.3±0.3ms 2.58 frame_methods.Dropna.time_dropna('any', 0) + 3.78±0.1ms 9.67±0.01ms 2.56 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'datetime64[ns]') + 3.75±0.1ms 9.19±0.2ms 2.45 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'timedelta64[ns]') + 45.0±0.3ms 110±0.6ms 2.44 frame_methods.Dropna.time_dropna('all', 1) + 3.75±0.1ms 9.06±0.05ms 2.42 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'datetime64[ns]') + 28.5±0.4ms 67.5±0.2ms 2.36 frame_methods.Dropna.time_dropna_axis_mixed_dtypes('any', 0) + 85.0±0.3ms 195±10ms 2.29 frame_methods.Count.time_count_level_mixed_dtypes_multi(1) + 3.06±0.2ms 6.94±0.06ms 2.27 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'timedelta64[ns]') + 3.08±0.2ms 6.94±0.1ms 2.25 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'datetime64[ns]') + 12.0±0.1ms 24.8±0.7ms 2.07 frame_methods.Iteration.time_items + 41.5±0.5ms 81.9±0.4ms 1.97 frame_methods.Dropna.time_dropna('all', 0) + 516±3μs 1.00±0.03ms 1.94 frame_methods.Iteration.time_itertuples_start + 365±1μs 686±20μs 1.88 frame_methods.Iteration.time_itertuples_raw_start + 366±2μs 686±10μs 1.87 frame_methods.Iteration.time_itertuples_raw_read_first + 523±4μs 976±30μs 1.86 frame_methods.Iteration.time_itertuples_read_first + 51.8±0.2ms 85.9±0.5ms 1.66 frame_methods.Dropna.time_dropna_axis_mixed_dtypes('all', 0) + 727±9μs 1.19±0.04ms 1.64 frame_methods.Iteration.time_items_cached + 3.25±0.02μs 5.28±0.5μs 1.62 frame_methods.XS.time_frame_xs(1) + 353±6μs 572±5μs 1.62 frame_methods.Quantile.time_frame_quantile(0) + 7.73±0.6ms 11.1±0.1ms 1.44 frame_methods.Equals.time_frame_float_unequal + 49.3±3ms 68.6±0.6ms 1.39 frame_methods.Equals.time_frame_object_unequal + 32.0±0.3μs 44.1±0.4μs 1.38 frame_methods.GetNumericData.time_frame_get_numeric_data + 69.6±0.2ms 92.9±0.4ms 1.34 frame_methods.Repr.time_frame_repr_wide + 37.0±0.05ms 48.7±0.3ms 1.32 frame_methods.Equals.time_frame_nonunique_equal + 37.2±0.05ms 49.0±0.1ms 1.32 frame_methods.Equals.time_frame_nonunique_unequal + 4.74±0.05ms 6.09±0.03ms 1.29 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'float32') + 5.37±0.06ms 6.81±0.04ms 1.27 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'float64') + 208±1ms 262±2ms 1.26 frame_methods.GetDtypeCounts.time_info + 248±3ms 311±1ms 1.25 frame_methods.Iteration.time_iterrows + 328M 409M 1.25 frame_methods.Iteration.peakmem_itertuples_raw + 328M 409M 1.25 frame_methods.Iteration.peakmem_itertuples_raw_read_first + 328M 409M 1.25 frame_methods.Iteration.peakmem_itertuples_raw_start + 328M 409M 1.25 frame_methods.Iteration.peakmem_itertuples + 328M 409M 1.25 frame_methods.Iteration.peakmem_itertuples_start + 43.2±0.2ms 51.0±0.2ms 1.18 frame_methods.Isnull.time_isnull_obj + 4.68±0.03ms 5.49±0.02ms 1.17 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'float32') + 4.80±0.02μs 5.50±0.08μs 1.15 frame_methods.ToDict.time_to_dict_ints('series') + 1.70±0.01ms 1.93±0.04ms 1.14 frame_methods.Interpolate.time_interpolate_some_good(None) + 4.82±0.02μs 5.47±0.03μs 1.14 frame_methods.ToDict.time_to_dict_datetimelike('series') + 3.91±0.04ms 4.44±0.05ms 1.13 frame_methods.Rank.time_rank('uint') + 3.93±0.03ms 4.43±0.03ms 1.13 frame_methods.Rank.time_rank('int') + 4.27±0.02ms 4.78±0.04ms 1.12 frame_methods.Lookup.time_frame_fancy_lookup + 17.1±0.2ms 19.0±0.09ms 1.11 frame_methods.ToDict.time_to_dict_ints('index') + 157±2ms 172±6ms 1.09 frame_methods.ToDict.time_to_dict_datetimelike('list') + 167±2ms 181±7ms 1.09 frame_methods.ToDict.time_to_dict_datetimelike('index') + 173±1ms 188±7ms 1.08 frame_methods.ToDict.time_to_dict_datetimelike('split') + 3.69±0.02ms 3.93±0.04ms 1.07 frame_methods.Interpolate.time_interpolate_some_good('infer') + 89.6±0.8ms 95.1±0.2ms 1.06 frame_methods.Interpolate.time_interpolate(None) + 10.5±0.05ms 10.9±0.02ms 1.04 frame_methods.Rank.time_rank('float') + 6.49±0.03ms 6.72±0.1ms 1.03 frame_methods.Repr.time_html_repr_trunc_mi + 731±0.6ms 743±1ms 1.02 frame_methods.Iteration.time_itertuples_raw_tuples + 56.2±0.2ms 56.9±0.3ms 1.01 frame_methods.ToHTML.time_to_html_mixed - 107±0.3ms 103±0.6ms 0.96 frame_methods.Apply.time_apply_axis_1 - 4.73±0.09ms 4.13±0.07ms 0.87 frame_methods.Apply.time_apply_pass_thru - 460±1μs 371±0.9μs 0.81 frame_methods.Shift.time_shift(1) - 5.95±0.05ms 4.70±0.03ms 0.79 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'Float64') - 7.40±0.05ms 5.84±0.03ms 0.79 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'float64') - 6.52±0.04ms 5.10±0.04ms 0.78 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'Int64') - 5.90±0.03ms 4.59±0.02ms 0.78 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'Int64') - 6.64±0.03ms 5.14±0.04ms 0.77 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'Float64') - 5.99±0.07ms 4.60±0.05ms 0.77 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'Float64') - 6.00±0.04ms 4.60±0.06ms 0.77 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'Int64') - 9.56±0.08ms 7.24±0.04ms 0.76 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'datetime64[ns, tz]') - 7.22±0.08ms 5.46±0.03ms 0.76 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'float32') - 5.84±0.2ms 4.38±0.3ms 0.75 frame_methods.NSort.time_nlargest_one_column('all') - 5.18±0.2ms 3.87±0.2ms 0.75 frame_methods.NSort.time_nlargest_one_column('last') - 5.88±0.2ms 4.36±0.3ms 0.74 frame_methods.NSort.time_nlargest_one_column('first') - 8.19±0.05ms 5.92±0.1ms 0.72 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'datetime64[ns, tz]') - 7.87±0.08ms 5.68±0.04ms 0.72 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'datetime64[ns, tz]') - 7.42±0.1ms 5.26±0.01ms 0.71 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'float64') - 7.14±0.08ms 4.79±0.02ms 0.67 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'float32') - 4.58±0.01ms 3.04±0.02ms 0.66 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'Float64') - 4.59±0.01ms 3.04±0.02ms 0.66 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'Int64') - 632±5ms 401±1ms 0.64 frame_methods.Nunique.time_frame_nunique - 309±3ms 180±2ms 0.58 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'object') - 314±2ms 180±1ms 0.57 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'object') - 525±4ms 238±2ms 0.45 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'object') - 526±8ms 226±1ms 0.43 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'object') - 51.1±0.2ms 20.2±0.05ms 0.40 frame_methods.Rename.time_rename_both_axes - 51.1±0.1ms 20.1±0.2ms 0.39 frame_methods.Rename.time_dict_rename_both_axes - 49.3±0.1ms 18.3±0.07ms 0.37 frame_methods.Rename.time_rename_axis0 - 47.1±0.3ms 16.4±0.2ms 0.35 frame_methods.Rename.time_rename_single - 44.8±0.2ms 14.2±0.05ms 0.32 frame_methods.Rename.time_rename_axis1 ```

The quantile with axis=1 is much slower (similar as reductions over the rows instead of the columns, in general, see below in the "Reductions" section). In this case it will probably be more efficient to calculate the quantiles on the 2D array instead of column-by-column (since we already transpose the DataFrame in case of axis=1). This is also what happens for the other row-wise reductions, which only show a 1-5x slowdown
XS.time_frame_xs(0) selects a row as a Series from a homogeneously dtyped DataFrame with 10,000 columns. So constructing the array for the Series instead of selecting from the 2D block array is of course more expensive.
There are some slowdowns in isnull related benchmarks: 1) those are using square DataFrames with shape of (1000, 1000) (take a 10000x100 instead, and there is hardly a difference anymore), and 2) some overhead can easily be avoided (see eg https://github.com/jorisvandenbossche/pandas/commit/2921441359b9ae09ad040e201f9d262ad630414b, which uses isna_array instead of isna in the ArrayManager method, and optimizes some dtype checks)
The MaskBool benchmarks still go through BlockManager.apply, causing some overhead (this is a TODO in ArrayManager).

join_merge

$ asv continuous -f 1.01 -b join_merge HEAD~1 HEAD

```diff before after ratio [f8052655] [5815ddc3]

+ 35.9±3ms 81.5±1ms 2.27 join_merge.ConcatDataFrames.time_c_ordered(0, True) + 36.3±3ms 82.1±2ms 2.26 join_merge.ConcatDataFrames.time_c_ordered(0, False) + 40.7±6ms 80.5±2ms 1.98 join_merge.ConcatDataFrames.time_f_ordered(0, True) + 41.8±5ms 81.3±1ms 1.95 join_merge.ConcatDataFrames.time_f_ordered(0, False) + 50.6±0.6ms 88.6±2ms 1.75 join_merge.ConcatDataFrames.time_f_ordered(1, False) + 51.0±4ms 87.6±2ms 1.72 join_merge.ConcatDataFrames.time_f_ordered(1, True) + 253±0.7ms 404±0.6ms 1.60 join_merge.Merge.time_merge_dataframes_cross(True) + 254±2ms 403±0.5ms 1.59 join_merge.Merge.time_merge_dataframes_cross(False) + 8.17±0.04ms 12.5±0.03ms 1.53 join_merge.Concat.time_concat_small_frames(1) + 399±3ms 587±0.9ms 1.47 join_merge.MergeCategoricals.time_merge_object + 9.07±0.4ms 12.1±0.3ms 1.33 join_merge.Join.time_join_dataframe_index_single_key_small(True) + 11.3±0.2ms 14.9±0.2ms 1.32 join_merge.Join.time_join_dataframe_index_single_key_bigger(True) + 11.3±0.2ms 14.8±0.4ms 1.32 join_merge.Join.time_join_dataframe_index_shuffle_key_bigger_sort(True) + 9.33±0.03ms 11.2±0.1ms 1.20 join_merge.Concat.time_concat_small_frames(0) + 36.5±0.6ms 40.0±0.5ms 1.09 join_merge.Merge.time_merge_2intkey(True) - 253±0.4μs 244±2μs 0.97 join_merge.Concat.time_concat_empty_right(0) - 218±1μs 209±1μs 0.96 join_merge.Concat.time_concat_empty_right(1) - 252±1μs 242±2μs 0.96 join_merge.Concat.time_concat_empty_left(0) - 3.19±0.02ms 3.04±0.03ms 0.95 join_merge.Join.time_join_dataframes_cross(False) - 208±0.6ms 198±0.6ms 0.95 join_merge.MergeCategoricals.time_merge_cat - 19.9±0.06ms 18.7±0.05ms 0.94 join_merge.MergeAsof.time_on_int32('nearest', 5) - 19.2±0.05ms 18.0±0.05ms 0.94 join_merge.MergeAsof.time_on_int('nearest', 5) - 18.0±0.03ms 16.9±0.06ms 0.94 join_merge.MergeAsof.time_on_uint64('nearest', 5) - 19.2±0.09ms 17.9±0.03ms 0.94 join_merge.MergeAsof.time_on_int32('nearest', None) - 18.7±0.09ms 17.5±0.05ms 0.94 join_merge.MergeAsof.time_on_int('nearest', None) - 17.3±0.09ms 16.2±0.08ms 0.93 join_merge.MergeAsof.time_on_uint64('nearest', None) - 15.3±0.08ms 14.2±0.03ms 0.93 join_merge.MergeAsof.time_on_int('forward', 5) - 72.7±0.2ms 67.2±0.3ms 0.92 join_merge.MergeOrdered.time_merge_ordered - 15.2±0.01ms 14.0±0.08ms 0.92 join_merge.MergeAsof.time_on_int('forward', None) - 16.4±0.1ms 15.1±0.09ms 0.92 join_merge.MergeAsof.time_on_int32('forward', 5) - 14.5±0.05ms 13.3±0.08ms 0.91 join_merge.MergeAsof.time_on_uint64('forward', 5) - 13.7±0.04ms 12.5±0.06ms 0.91 join_merge.MergeAsof.time_on_int('backward', 5) - 15.7±0.07ms 14.3±0.05ms 0.91 join_merge.MergeAsof.time_on_int32('backward', 5) - 13.5±0.02ms 12.3±0.09ms 0.91 join_merge.MergeAsof.time_on_int('backward', None) - 14.2±0.04ms 12.9±0.09ms 0.91 join_merge.MergeAsof.time_on_uint64('forward', None) - 15.3±0.09ms 13.9±0.03ms 0.91 join_merge.MergeAsof.time_on_int32('backward', None) - 2.14±0.01ms 1.94±0.01ms 0.91 join_merge.Merge.time_merge_dataframe_integer_key(True) - 13.5±0.05ms 12.2±0.03ms 0.91 join_merge.MergeAsof.time_on_uint64('backward', 5) - 13.2±0.05ms 11.9±0.05ms 0.90 join_merge.MergeAsof.time_on_uint64('backward', None) - 594±4μs 526±5μs 0.89 join_merge.Append.time_append_mixed - 1.82±0.02ms 1.61±0ms 0.88 join_merge.Merge.time_merge_dataframe_integer_key(False) - 86.5±0.5ms 71.2±0.3ms 0.82 join_merge.Concat.time_concat_series(1) - 818±2ms 672±10ms 0.82 join_merge.I8Merge.time_i8merge('inner') - 929±8ms 761±4ms 0.82 join_merge.I8Merge.time_i8merge('right') - 819±1ms 671±8ms 0.82 join_merge.I8Merge.time_i8merge('outer') - 854±9ms 684±8ms 0.80 join_merge.I8Merge.time_i8merge('left') ```

Many of the benchmarks are quite similar (not shown, a bit faster or a bit slower), mostly except for the concat( , axis=0) cases.
The concat(.., axis=0) case that shows a 2x slowdown with ArrayManager is the expected case, I think. That specific benchmark is using a DataFrame with a single float dtype with 200 columns (single block) and concatting this 20 times, so concatenating 20 2D arrays is always going to be faster as concatenating 200 times 20 1D arrays (the benchmark case has 200 columns, and does pd.concat([df] * 20, axis=0)). From profiling the operation, almost all time is spent in the actual numpy concatenation routine (for both cases), so I don't expect there is much room for improvement here.

groupby

$ asv continuous -f 1.01 -b groupby HEAD~1 HEAD

```diff before after ratio [f8052655] [5815ddc3]

+ 4.01±0.06ms 54.9±0.5ms 13.71 groupby.GroupManyLabels.time_sum(1000) + 112±0.7μs 382±2μs 3.41 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumsum', 'direct', 5) + 113±0.5μs 384±3μs 3.39 groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'direct', 5) + 115±0.5μs 387±2μs 3.37 groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'direct', 5) + 344±4μs 1.01±0.02ms 2.93 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumprod', 'direct', 5) + 349±3μs 1.02±0.01ms 2.91 groupby.GroupByMethods.time_dtype_as_group('int', 'cumprod', 'direct', 5) + 351±3μs 1.02±0.01ms 2.90 groupby.GroupByMethods.time_dtype_as_group('float', 'cumprod', 'direct', 5) + 127±1μs 354±0.9μs 2.79 groupby.GroupByMethods.time_dtype_as_group('uint', 'cummax', 'direct', 5) + 126±0.6μs 350±0.9μs 2.78 groupby.GroupByMethods.time_dtype_as_group('uint', 'cummin', 'direct', 5) + 120±1μs 333±1μs 2.77 groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'direct', 5) + 129±0.8μs 356±1μs 2.75 groupby.GroupByMethods.time_dtype_as_group('int', 'cummax', 'direct', 5) + 119±0.3μs 325±1μs 2.72 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'direct', 5) + 129±0.7μs 349±0.8μs 2.71 groupby.GroupByMethods.time_dtype_as_group('int', 'cummin', 'direct', 5) + 121±2μs 326±0.7μs 2.70 groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'direct', 5) + 236±2μs 628±9μs 2.66 groupby.GroupByMethods.time_dtype_as_group('uint', 'var', 'direct', 5) + 240±2μs 627±4μs 2.61 groupby.GroupByMethods.time_dtype_as_group('uint', 'mean', 'direct', 5) + 240±1μs 623±2μs 2.60 groupby.GroupByMethods.time_dtype_as_group('int', 'var', 'direct', 5) + 244±1μs 627±3μs 2.58 groupby.GroupByMethods.time_dtype_as_group('int', 'mean', 'direct', 5) + 240±2μs 608±5μs 2.54 groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'direct', 5) + 263±2μs 660±3μs 2.51 groupby.GroupByMethods.time_dtype_as_group('float', 'mean', 'direct', 5) + 113±0.8μs 275±3μs 2.42 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'direct', 5) + 112±0.9μs 270±2μs 2.40 groupby.GroupByMethods.time_dtype_as_group('uint', 'any', 'direct', 5) + 108±0.4μs 258±0.4μs 2.39 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'direct', 5) + 114±0.8μs 269±2μs 2.37 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'direct', 5) + 114±0.3μs 267±0.6μs 2.35 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'direct', 5) + 115±0.7μs 269±1μs 2.34 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'direct', 5) + 182±0.9μs 426±3μs 2.34 groupby.GroupByMethods.time_dtype_as_group('uint', 'std', 'direct', 5) + 116±1μs 271±2μs 2.34 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'direct', 5) + 115±2μs 267±2μs 2.33 groupby.GroupByMethods.time_dtype_as_group('uint', 'all', 'direct', 5) + 111±1μs 257±2μs 2.33 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'direct', 5) + 316±1μs 731±4μs 2.31 groupby.GroupByMethods.time_dtype_as_group('float', 'median', 'direct', 5) + 116±1μs 268±0.9μs 2.30 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'direct', 5) + 186±2μs 426±3μs 2.28 groupby.GroupByMethods.time_dtype_as_group('int', 'std', 'direct', 5) + 185±0.8μs 412±4μs 2.23 groupby.GroupByMethods.time_dtype_as_group('float', 'std', 'direct', 5) + 355±1μs 789±9μs 2.23 groupby.GroupByMethods.time_dtype_as_group('uint', 'median', 'direct', 5) + 359±2μs 785±5μs 2.19 groupby.GroupByMethods.time_dtype_as_group('int', 'median', 'direct', 5) + 747±7μs 1.56±0.01ms 2.09 groupby.GroupByMethods.time_dtype_as_group('uint', 'prod', 'direct', 5) + 746±8μs 1.55±0.01ms 2.08 groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'direct', 5) + 799±4μs 1.64±0.02ms 2.06 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'direct', 5) + 766±4μs 1.57±0.02ms 2.05 groupby.GroupByMethods.time_dtype_as_group('uint', 'sum', 'direct', 5) + 773±4μs 1.58±0.01ms 2.04 groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'direct', 5) + 33.3±0.6ms 67.4±0.9ms 2.02 groupby.GroupByCythonAgg.time_frame_agg('float64', 'prod') + 825±5μs 1.64±0.02ms 1.99 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'direct', 5) + 13.4±3ms 24.8±0.08ms 1.84 groupby.Cumulative.time_frame_transform('int64', 'cumsum') + 36.4±0.6ms 67.1±0.8ms 1.84 groupby.GroupByCythonAgg.time_frame_agg('float64', 'first') + 45.2±0.6ms 82.2±1ms 1.82 groupby.GroupByCythonAgg.time_frame_agg('float64', 'sum') + 44.9±0.7ms 81.5±0.8ms 1.82 groupby.GroupByCythonAgg.time_frame_agg('float64', 'mean') + 44.4±0.7ms 79.1±2ms 1.78 groupby.GroupByCythonAgg.time_frame_agg('float64', 'var') + 524±2μs 918±3μs 1.75 groupby.GroupByMethods.time_dtype_as_group('uint', 'min', 'direct', 5) + 537±1μs 940±20μs 1.75 groupby.GroupByMethods.time_dtype_as_group('datetime', 'max', 'direct', 5) + 518±3μs 904±30μs 1.74 groupby.GroupByMethods.time_dtype_as_group('datetime', 'first', 'direct', 5) + 116±0.4μs 203±1μs 1.74 groupby.GroupByMethods.time_dtype_as_group('int', 'count', 'direct', 5) + 456±2μs 792±6μs 1.74 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'direct', 5) + 515±9μs 891±70μs 1.73 groupby.GroupByMethods.time_dtype_as_group('datetime', 'last', 'direct', 5) + 534±2μs 921±20μs 1.72 groupby.GroupByMethods.time_dtype_as_group('datetime', 'min', 'direct', 5) + 532±1μs 915±4μs 1.72 groupby.GroupByMethods.time_dtype_as_group('uint', 'max', 'direct', 5) + 523±0.9μs 900±4μs 1.72 groupby.GroupByMethods.time_dtype_as_group('int', 'first', 'direct', 5) + 537±2μs 924±7μs 1.72 groupby.GroupByMethods.time_dtype_as_group('int', 'max', 'direct', 5) + 526±9μs 900±8μs 1.71 groupby.GroupByMethods.time_dtype_as_group('uint', 'first', 'direct', 5) + 544±3μs 931±6μs 1.71 groupby.GroupByMethods.time_dtype_as_group('float', 'max', 'direct', 5) + 538±0.8μs 915±4μs 1.70 groupby.GroupByMethods.time_dtype_as_group('int', 'min', 'direct', 5) + 457±2μs 777±5μs 1.70 groupby.GroupByMethods.time_dtype_as_group('object', 'first', 'direct', 5) + 539±4μs 915±7μs 1.70 groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'direct', 5) + 86.8±1ms 147±1ms 1.70 groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'direct', 5) + 516±2μs 874±7μs 1.69 groupby.GroupByMethods.time_dtype_as_group('uint', 'last', 'direct', 5) + 533±2μs 897±9μs 1.68 groupby.GroupByMethods.time_dtype_as_group('float', 'last', 'direct', 5) + 56.8±0.3ms 95.7±0.5ms 1.68 groupby.GroupByMethods.time_dtype_as_group('uint', 'skew', 'direct', 5) + 88.2±1ms 148±1ms 1.68 groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'transformation', 5) + 536±4μs 901±5μs 1.68 groupby.GroupByMethods.time_dtype_as_group('float', 'first', 'direct', 5) + 56.6±0.3ms 95.0±1ms 1.68 groupby.GroupByMethods.time_dtype_as_group('int', 'skew', 'direct', 5) + 519±4μs 866±9μs 1.67 groupby.GroupByMethods.time_dtype_as_group('int', 'last', 'direct', 5) + 58.1±0.5ms 96.7±0.4ms 1.67 groupby.GroupByMethods.time_dtype_as_group('uint', 'skew', 'transformation', 5) + 58.0±0.3ms 96.6±0.3ms 1.66 groupby.GroupByMethods.time_dtype_as_group('int', 'skew', 'transformation', 5) + 50.1±2ms 81.9±1ms 1.64 groupby.GroupByCythonAgg.time_frame_agg('float64', 'any') + 51.0±2ms 81.9±1ms 1.61 groupby.GroupByCythonAgg.time_frame_agg('float64', 'all') + 40.4±0.5ms 64.5±0.7ms 1.60 groupby.GroupByCythonAgg.time_frame_agg('float64', 'last') + 132±1μs 207±1μs 1.56 groupby.GroupByMethods.time_dtype_as_group('datetime', 'count', 'direct', 5) + 131±0.6μs 203±2μs 1.56 groupby.GroupByMethods.time_dtype_as_group('uint', 'count', 'direct', 5) + 136±1μs 209±1μs 1.54 groupby.GroupByMethods.time_dtype_as_group('float', 'count', 'direct', 5) + 128±0.6μs 197±0.9μs 1.53 groupby.GroupByMethods.time_dtype_as_group('object', 'count', 'direct', 5) + 47.3±0.7ms 71.0±0.6ms 1.50 groupby.GroupByCythonAgg.time_frame_agg('float64', 'min') + 47.5±0.7ms 71.1±1ms 1.50 groupby.GroupByCythonAgg.time_frame_agg('float64', 'max') + 15.9±3ms 22.9±0.05ms 1.44 groupby.Cumulative.time_frame_transform('int64', 'cummin') + 14.2±2ms 20.6±0.04ms 1.44 groupby.Cumulative.time_frame_transform_many_nulls('int64', 'cumsum') + 14.4±0.9ms 20.6±0.1ms 1.43 groupby.Cumulative.time_frame_transform_many_nulls('float64', 'cumsum') + 16.0±2ms 22.8±0.06ms 1.43 groupby.Cumulative.time_frame_transform_many_nulls('int64', 'cummin') + 16.1±0.9ms 22.9±0.3ms 1.42 groupby.Cumulative.time_frame_transform_many_nulls('float64', 'cummin') + 176±2ms 250±1ms 1.42 groupby.GroupByMethods.time_dtype_as_group('float', 'mad', 'direct', 5) + 17.4±0.9ms 24.7±0.1ms 1.42 groupby.Cumulative.time_frame_transform('float64', 'cummin') + 16.8±3ms 23.8±0.08ms 1.42 groupby.Cumulative.time_frame_transform('int64', 'cummax') + 177±2ms 251±2ms 1.41 groupby.GroupByMethods.time_dtype_as_group('float', 'mad', 'transformation', 5) + 114±1ms 161±0.8ms 1.41 groupby.GroupByMethods.time_dtype_as_group('uint', 'mad', 'direct', 5) + 16.3±3ms 22.9±0.04ms 1.40 groupby.Cumulative.time_frame_transform_many_nulls('int64', 'cummax') + 116±1ms 162±0.9ms 1.40 groupby.GroupByMethods.time_dtype_as_group('uint', 'mad', 'transformation', 5) + 114±1ms 160±1ms 1.40 groupby.GroupByMethods.time_dtype_as_group('int', 'mad', 'direct', 5) + 116±1ms 163±0.7ms 1.40 groupby.GroupByMethods.time_dtype_as_group('int', 'mad', 'transformation', 5) + 16.6±0.9ms 23.0±0.04ms 1.39 groupby.Cumulative.time_frame_transform_many_nulls('float64', 'cummax') + 15.9±0.8ms 21.9±0.06ms 1.38 groupby.Cumulative.time_frame_transform('float64', 'cumsum') + 18.8±0.7ms 25.2±0.07ms 1.34 groupby.Cumulative.time_frame_transform('float64', 'cummax') + 848±9ms 1.13±0.03s 1.33 groupby.String.time_str_func('string[python]', 'any') + 848±10ms 1.13±0.03s 1.33 groupby.String.time_str_func('string[python]', 'all') + 1.83±0.01ms 2.38±0.02ms 1.30 groupby.GroupByMethods.time_dtype_as_group('float', 'cumprod', 'transformation', 5) + 1.74±0.01ms 2.27±0.01ms 1.30 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumprod', 'transformation', 5) + 2.31±0.02ms 2.99±0.03ms 1.29 groupby.GroupByMethods.time_dtype_as_group('uint', 'sum', 'transformation', 5) + 1.77±0.01ms 2.28±0.01ms 1.29 groupby.GroupByMethods.time_dtype_as_group('int', 'cumprod', 'transformation', 5) + 2.29±0.02ms 2.95±0.03ms 1.28 groupby.GroupByMethods.time_dtype_as_group('uint', 'prod', 'transformation', 5) + 1.01±0.01s 1.29±0.03s 1.28 groupby.String.time_str_func('string[python]', 'first') + 1.02±0.01s 1.31±0.02s 1.28 groupby.String.time_str_func('string[python]', 'last') + 606±2μs 775±7μs 1.28 groupby.Datelike.time_sum('date_range') + 22.2±0.2ms 28.4±0.1ms 1.28 groupby.Cumulative.time_frame_transform('Int64', 'cummax') + 22.0±0.05ms 28.1±0.09ms 1.28 groupby.Cumulative.time_frame_transform('Int64', 'cummin') + 2.33±0.01ms 2.94±0.02ms 1.26 groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'transformation', 5) + 2.46±0.02ms 3.10±0.03ms 1.26 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'transformation', 5) + 2.35±0.02ms 2.95±0.02ms 1.26 groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'transformation', 5) + 643±3μs 803±4μs 1.25 groupby.Datelike.time_sum('date_range_tz') + 1.97±0.02ms 2.46±0.02ms 1.25 groupby.GroupByMethods.time_dtype_as_group('int', 'sem', 'direct', 5) + 2.48±0.03ms 3.10±0.02ms 1.25 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'transformation', 5) + 1.97±0.04ms 2.45±0.03ms 1.25 groupby.GroupByMethods.time_dtype_as_group('uint', 'sem', 'direct', 5) + 2.04±0.02ms 2.49±0.01ms 1.22 groupby.GroupByMethods.time_dtype_as_group('float', 'sem', 'direct', 5) + 1.15±0ms 1.41±0ms 1.22 groupby.GroupByMethods.time_dtype_as_group('object', 'rank', 'direct', 5) + 1.72±0.03ms 2.04±0.1ms 1.18 groupby.GroupByMethods.time_dtype_as_group('datetime', 'quantile', 'direct', 5) + 1.65±0ms 1.92±0.01ms 1.17 groupby.GroupByMethods.time_dtype_as_group('int', 'quantile', 'direct', 5) + 1.92±0.01ms 2.23±0.02ms 1.16 groupby.GroupByMethods.time_dtype_as_group('uint', 'median', 'transformation', 5) + 459±2μs 531±2μs 1.16 groupby.GroupManyLabels.time_sum(1) + 1.83±0.01ms 2.11±0.02ms 1.15 groupby.GroupByMethods.time_dtype_as_group('int', 'var', 'transformation', 5) + 1.94±0.02ms 2.23±0.02ms 1.15 groupby.GroupByMethods.time_dtype_as_group('int', 'median', 'transformation', 5) + 1.99±0.01ms 2.29±0.03ms 1.15 groupby.GroupByMethods.time_dtype_as_group('float', 'median', 'transformation', 5) + 1.75±0ms 2.01±0.01ms 1.15 groupby.GroupByMethods.time_dtype_as_group('float', 'rank', 'direct', 5) + 1.67±0.01ms 1.91±0.01ms 1.15 groupby.GroupByMethods.time_dtype_as_group('uint', 'quantile', 'direct', 5) + 11.6±0.08ms 13.3±0.2ms 1.14 groupby.Apply.time_scalar_function_multi_col(5) + 1.74±0.01ms 1.99±0ms 1.14 groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'direct', 5) + 1.78±0.01ms 2.04±0.01ms 1.14 groupby.GroupByMethods.time_dtype_as_group('uint', 'rank', 'direct', 5) + 1.83±0.01ms 2.09±0.02ms 1.14 groupby.GroupByMethods.time_dtype_as_group('uint', 'mean', 'transformation', 5) + 1.79±0ms 2.04±0.01ms 1.14 groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'direct', 5) + 2.07±0.01ms 2.35±0.03ms 1.14 groupby.GroupByMethods.time_dtype_as_group('uint', 'max', 'transformation', 5) + 1.69±0ms 1.92±0ms 1.14 groupby.GroupByMethods.time_dtype_as_group('float', 'quantile', 'direct', 5) + 2.05±0.02ms 2.32±0ms 1.13 groupby.GroupByMethods.time_dtype_as_group('uint', 'min', 'transformation', 5) + 1.94±0.01ms 2.19±0.01ms 1.13 groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'transformation', 5) + 28.2±0.5ms 31.9±0.2ms 1.13 groupby.Nth.time_groupby_nth_all('datetime') + 2.03±0.01ms 2.30±0.01ms 1.13 groupby.GroupByMethods.time_dtype_as_group('uint', 'first', 'transformation', 5) + 1.96±0.01ms 2.22±0.01ms 1.13 groupby.GroupByMethods.time_dtype_as_group('float', 'mean', 'transformation', 5) + 89.3±0.6μs 101±0.9μs 1.13 groupby.GroupByMethods.time_dtype_as_field('float', 'shift', 'direct', 1) + 100±0.7μs 113±0.7μs 1.13 groupby.GroupByMethods.time_dtype_as_field('float', 'shift', 'direct', 5) + 2.07±0.02ms 2.33±0.01ms 1.13 groupby.GroupByMethods.time_dtype_as_group('int', 'min', 'transformation', 5) + 17.7±0.3ms 19.9±0.5ms 1.13 groupby.Cumulative.time_frame_transform_many_nulls('Int64', 'cummax') + 1.07±0s 1.21±0.02s 1.13 groupby.String.time_str_func('str', 'first') + 2.06±0.03ms 2.32±0.01ms 1.12 groupby.GroupByMethods.time_dtype_as_group('int', 'max', 'transformation', 5) + 2.15±0.01ms 2.42±0.02ms 1.12 groupby.GroupByMethods.time_dtype_as_group('float', 'last', 'transformation', 5) + 1.09±0s 1.23±0.02s 1.12 groupby.String.time_str_func('str', 'last') + 17.6±0.02ms 19.8±0.1ms 1.12 groupby.Cumulative.time_frame_transform_many_nulls('Int64', 'cummin') + 20.9±0.4ms 23.4±0.7ms 1.12 groupby.Cumulative.time_frame_transform_many_nulls('Float64', 'cummin') + 3.41±0.02ms 3.82±0.01ms 1.12 groupby.GroupByMethods.time_dtype_as_group('int', 'sem', 'transformation', 5) + 2.15±0.01ms 2.41±0.04ms 1.12 groupby.GroupByMethods.time_dtype_as_group('float', 'first', 'transformation', 5) + 1.46±0.01ms 1.64±0ms 1.12 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumsum', 'transformation', 5) + 2.16±0.01ms 2.42±0.01ms 1.12 groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'transformation', 5) + 1.33±0.02ms 1.49±0.03ms 1.12 groupby.Datelike.time_sum('period_range') + 2.18±0.02ms 2.43±0.01ms 1.12 groupby.GroupByMethods.time_dtype_as_group('float', 'max', 'transformation', 5) + 2.04±0.02ms 2.28±0.03ms 1.12 groupby.GroupByMethods.time_dtype_as_group('uint', 'last', 'transformation', 5) + 102±0.3μs 114±1μs 1.12 groupby.GroupByMethods.time_dtype_as_field('object', 'shift', 'direct', 1) + 1.89±0.01ms 2.12±0.01ms 1.12 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'transformation', 5) + 11.4±0.03ms 12.7±0.2ms 1.12 groupby.Float32.time_sum + 3.42±0.06ms 3.81±0.01ms 1.11 groupby.GroupByMethods.time_dtype_as_group('uint', 'sem', 'transformation', 5) + 1.88±0.02ms 2.09±0.02ms 1.11 groupby.GroupByMethods.time_dtype_as_group('int', 'mean', 'transformation', 5) + 112±0.4μs 124±0.7μs 1.11 groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'direct', 5) + 1.57±0.01ms 1.74±0.01ms 1.11 groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'transformation', 5) + 21.8±0.04ms 24.2±0.1ms 1.11 groupby.Cumulative.time_frame_transform('Float64', 'cummax') + 1.48±0.02ms 1.64±0.01ms 1.11 groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'transformation', 5) + 20.7±0.3ms 22.9±0.08ms 1.11 groupby.Cumulative.time_frame_transform_many_nulls('Float64', 'cummax') + 1.91±0.01ms 2.11±0.02ms 1.10 groupby.GroupByMethods.time_dtype_as_group('object', 'first', 'transformation', 5) + 115±1μs 127±0.6μs 1.10 groupby.GroupByMethods.time_dtype_as_field('object', 'shift', 'direct', 5) + 1.47±0.01ms 1.62±0.01ms 1.10 groupby.GroupByMethods.time_dtype_as_group('uint', 'cummax', 'transformation', 5) + 2.05±0.02ms 2.26±0.02ms 1.10 groupby.GroupByMethods.time_dtype_as_group('int', 'last', 'transformation', 5) + 1.47±0.01ms 1.61±0.01ms 1.10 groupby.GroupByMethods.time_dtype_as_group('int', 'cummin', 'transformation', 5) + 22.4±0.1ms 24.6±0.06ms 1.10 groupby.Cumulative.time_frame_transform('Float64', 'cummin') + 91.6±0.9μs 100±1μs 1.09 groupby.GroupByMethods.time_dtype_as_field('object', 'head', 'direct', 5) + 891±4μs 972±6μs 1.09 groupby.SumMultiLevel.time_groupby_sum_multiindex + 1.56±0.01ms 1.69±0ms 1.08 groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'transformation', 5) + 1.76±0.01ms 1.90±0.01ms 1.08 groupby.GroupByMethods.time_dtype_as_group('uint', 'std', 'transformation', 5) + 74.9±0.6μs 81.1±0.1μs 1.08 groupby.GroupByMethods.time_dtype_as_group('float', 'head', 'direct', 1) + 74.0±0.4μs 80.1±0.5μs 1.08 groupby.GroupByMethods.time_dtype_as_group('datetime', 'head', 'direct', 1) + 82.4±0.7μs 89.1±0.6μs 1.08 groupby.GroupByMethods.time_dtype_as_field('object', 'head', 'direct', 1) + 1.46±0.01ms 1.58±0.01ms 1.08 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'transformation', 5) + 83.7±0.4μs 90.5±0.8μs 1.08 groupby.GroupByMethods.time_dtype_as_field('int', 'head', 'direct', 5) + 109±0.3μs 118±0.5μs 1.08 groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'direct', 5) + 6.23±0.1ms 6.73±0.03ms 1.08 groupby.Apply.time_scalar_function_single_col(5) + 74.6±0.8μs 80.6±0.3μs 1.08 groupby.GroupByMethods.time_dtype_as_group('uint', 'head', 'direct', 1) + 3.68±0.02ms 3.98±0.03ms 1.08 groupby.GroupByMethods.time_dtype_as_group('float', 'sem', 'transformation', 5) + 67.7±0.7μs 73.1±0.6μs 1.08 groupby.GroupByMethods.time_dtype_as_group('object', 'head', 'direct', 1) + 1.58±0.02ms 1.70±0.02ms 1.08 groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'transformation', 5) + 109±0.8μs 118±0.5μs 1.08 groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'direct', 5) + 74.3±0.7μs 79.9±0.2μs 1.08 groupby.GroupByMethods.time_dtype_as_field('float', 'head', 'direct', 1) + 146±2μs 157±0.8μs 1.08 groupby.GroupByMethods.time_dtype_as_field('int', 'tail', 'direct', 1) + 83.6±0.3μs 89.7±0.8μs 1.07 groupby.GroupByMethods.time_dtype_as_field('float', 'head', 'direct', 5) + 74.9±0.1μs 80.4±0.4μs 1.07 groupby.GroupByMethods.time_dtype_as_field('int', 'head', 'direct', 1) + 107±0.8μs 115±0.8μs 1.07 groupby.GroupByMethods.time_dtype_as_group('datetime', 'shift', 'direct', 5) + 2.35±0.01ms 2.51±0.01ms 1.07 groupby.GroupByMethods.time_dtype_as_group('object', 'rank', 'transformation', 5) + 148±2μs 158±0.7μs 1.07 groupby.GroupByMethods.time_dtype_as_field('uint', 'tail', 'direct', 1) + 144±2μs 154±0.8μs 1.07 groupby.GroupByMethods.time_dtype_as_group('uint', 'tail', 'direct', 1) + 74.9±0.4μs 80.0±0.3μs 1.07 groupby.GroupByMethods.time_dtype_as_field('uint', 'head', 'direct', 1) + 1.78±0.01ms 1.90±0.01ms 1.07 groupby.GroupByMethods.time_dtype_as_group('int', 'std', 'transformation', 5) + 35.9±0.2ms 38.2±0.5ms 1.06 groupby.Nth.time_groupby_nth_all('object') + 28.5±0.08ms 30.3±0.07ms 1.06 rolling.Pairwise.time_groupby(({}, 'expanding'), 'cov', False) + 134±1μs 142±1μs 1.06 groupby.GroupByMethods.time_dtype_as_group('datetime', 'tail', 'direct', 1) + 76.1±0.8μs 80.8±0.2μs 1.06 groupby.GroupByMethods.time_dtype_as_group('int', 'head', 'direct', 1) + 30.1±0.06ms 31.9±0.2ms 1.06 rolling.Pairwise.time_groupby(({'window': 1000}, 'rolling'), 'cov', False) + 41.3±0.3ms 43.8±0.4ms 1.06 groupby.Nth.time_frame_nth_any('object') + 30.0±0.05ms 31.7±0.05ms 1.06 rolling.Pairwise.time_groupby(({'window': 10}, 'rolling'), 'cov', False) + 3.11±0.01ms 3.29±0.02ms 1.06 groupby.GroupByMethods.time_dtype_as_group('uint', 'quantile', 'transformation', 5) + 135±1μs 142±2μs 1.05 groupby.GroupByMethods.time_dtype_as_group('float', 'tail', 'direct', 1) + 260±1μs 273±2μs 1.05 groupby.GroupByMethods.time_dtype_as_group('object', 'bfill', 'direct', 5) + 30.6±0.1ms 32.2±0.1ms 1.05 rolling.Pairwise.time_groupby(({'window': 10}, 'rolling'), 'corr', False) + 1.89±0.01ms 1.99±0.01ms 1.05 groupby.GroupByMethods.time_dtype_as_group('float', 'std', 'transformation', 5) + 157±1μs 165±0.6μs 1.05 groupby.GroupByMethods.time_dtype_as_field('object', 'tail', 'direct', 1) + 262±1μs 275±3μs 1.05 groupby.GroupByMethods.time_dtype_as_group('datetime', 'bfill', 'direct', 5) + 167±1μs 175±2μs 1.05 groupby.GroupByMethods.time_dtype_as_field('datetime', 'shift', 'direct', 5) + 166±0.8μs 174±1μs 1.05 groupby.GroupByMethods.time_dtype_as_field('float', 'tail', 'direct', 5) + 176±2μs 185±0.7μs 1.05 groupby.GroupByMethods.time_dtype_as_field('object', 'tail', 'direct', 5) + 116±0.7μs 121±0.3μs 1.05 groupby.GroupByMethods.time_dtype_as_field('datetime', 'head', 'direct', 5) + 20.3±0.07ms 21.3±0.05ms 1.05 groupby.AggFunctions.time_different_python_functions_multicol + 3.04±0.01ms 3.18±0ms 1.04 groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'transformation', 5) + 3.23±0.02ms 3.37±0.01ms 1.04 groupby.GroupByMethods.time_dtype_as_group('float', 'quantile', 'transformation', 5) + 120±0.9μs 125±0.6μs 1.04 groupby.GroupByMethods.time_dtype_as_group('uint', 'shift', 'direct', 5) + 33.6±0.2ms 35.0±0.8ms 1.04 groupby.Nth.time_series_nth_all('float32') + 3.05±0.02ms 3.18±0.01ms 1.04 groupby.GroupByMethods.time_dtype_as_group('uint', 'rank', 'transformation', 5) + 1.56±0.01ms 1.63±0.01ms 1.04 groupby.GroupByMethods.time_dtype_as_group('uint', 'pct_change', 'direct', 5) + 261±0.9μs 272±3μs 1.04 groupby.GroupByMethods.time_dtype_as_group('datetime', 'ffill', 'direct', 5) + 1.57±0.01ms 1.63±0.01ms 1.04 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'transformation', 5) + 38.8±0.06ms 40.2±0.1ms 1.04 groupby.Nth.time_series_nth_any('object') + 188±0.7μs 194±3μs 1.03 groupby.GroupByMethods.time_dtype_as_field('uint', 'first', 'direct', 5) + 540±3μs 558±0.8μs 1.03 groupby.GroupByMethods.time_dtype_as_field('float', 'shift', 'transformation', 1) + 227±0.7ms 234±0.5ms 1.03 groupby.AggFunctions.time_different_python_functions_singlecol + 1.68±0.01ms 1.72±0.02ms 1.03 groupby.GroupByMethods.time_dtype_as_group('uint', 'any', 'transformation', 5) + 1.63±0ms 1.67±0.02ms 1.03 groupby.GroupByMethods.time_dtype_as_group('float', 'pct_change', 'direct', 5) + 189±0.5μs 195±0.6μs 1.03 groupby.GroupByMethods.time_dtype_as_field('uint', 'max', 'direct', 5) + 389±0.9μs 398±2μs 1.02 groupby.GroupByMethods.time_dtype_as_group('float', 'bfill', 'direct', 5) + 2.01±0ms 2.06±0.01ms 1.02 groupby.GroupByMethods.time_dtype_as_field('object', 'rank', 'transformation', 1) + 192±0.3μs 197±0.5μs 1.02 groupby.GroupByMethods.time_dtype_as_field('int', 'min', 'direct', 5) + 198±0.3ms 200±0.7ms 1.01 groupby.Cumulative.time_frame_transform('Float64', 'cumsum') - 719±2μs 712±0.9μs 0.99 groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'transformation', 1) - 320±0.7μs 316±0.6μs 0.99 groupby.GroupByMethods.time_dtype_as_field('object', 'bfill', 'direct', 1) - 632±2μs 622±1μs 0.98 groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'transformation', 1) - 392±1μs 385±1μs 0.98 groupby.GroupByMethods.time_dtype_as_field('datetime', 'rank', 'direct', 5) - 1.37±0.01ms 1.35±0.01ms 0.98 groupby.RankWithTies.time_rank_ties('float32', 'dense') - 227±0.8μs 223±0.3μs 0.98 groupby.GroupByMethods.time_dtype_as_group('int', 'ffill', 'direct', 1) - 208±1μs 204±0.3μs 0.98 groupby.GroupByMethods.time_dtype_as_field('float', 'bfill', 'direct', 1) - 1.08±0.01ms 1.06±0.01ms 0.98 groupby.GroupByMethods.time_dtype_as_group('int', 'pct_change', 'direct', 1) - 51.9±0.2ms 50.8±0.5ms 0.98 groupby.GroupByMethods.time_dtype_as_field('uint', 'describe', 'direct', 5) - 1.02±0.01ms 1.00±0ms 0.98 groupby.GroupByMethods.time_dtype_as_field('uint', 'pct_change', 'direct', 1) - 221±0.6μs 217±0.5μs 0.98 groupby.GroupByMethods.time_dtype_as_group('float', 'bfill', 'direct', 1) - 495±2μs 485±2μs 0.98 groupby.GroupByMethods.time_dtype_as_field('float', 'sem', 'direct', 5) - 227±0.7μs 222±1μs 0.98 groupby.GroupByMethods.time_dtype_as_field('int', 'ffill', 'direct', 1) - 256±0.5μs 251±1μs 0.98 groupby.GroupByMethods.time_dtype_as_field('int', 'bfill', 'direct', 5) - 426±3μs 417±1μs 0.98 groupby.GroupByMethods.time_dtype_as_group('int', 'sem', 'direct', 1) - 414±3μs 405±0.8μs 0.98 groupby.GroupByMethods.time_dtype_as_field('float', 'sem', 'direct', 1) - 2.68±0.03ms 2.62±0ms 0.98 groupby.GroupByMethods.time_dtype_as_field('uint', 'pct_change', 'direct', 5) - 418±2μs 408±0.7μs 0.98 groupby.GroupByMethods.time_dtype_as_field('uint', 'sem', 'direct', 1) - 1.10±0ms 1.07±0ms 0.98 groupby.FillNA.time_srs_ffill - 262±1μs 256±0.5μs 0.98 groupby.GroupByMethods.time_dtype_as_field('uint', 'bfill', 'direct', 5) - 1.38±0.01ms 1.34±0ms 0.98 groupby.RankWithTies.time_rank_ties('float32', 'average') - 234±2μs 228±0.7μs 0.97 groupby.GroupByMethods.time_dtype_as_field('uint', 'bfill', 'direct', 1) - 255±0.9μs 248±1μs 0.97 groupby.GroupByMethods.time_dtype_as_field('int', 'ffill', 'direct', 5) - 258±0.9μs 252±2μs 0.97 groupby.GroupByMethods.time_dtype_as_group('int', 'tail', 'direct', 5) - 428±3μs 417±0.7μs 0.97 groupby.GroupByMethods.time_dtype_as_group('float', 'sem', 'direct', 1) - 231±3μs 224±0.7μs 0.97 groupby.GroupByMethods.time_dtype_as_group('int', 'bfill', 'direct', 1) - 21.1±0.1ms 20.5±0.06ms 0.97 groupby.GroupByMethods.time_dtype_as_field('float', 'mad', 'direct', 1) - 14.5±0.04ms 14.0±0.07ms 0.97 groupby.AggFunctions.time_different_numpy_functions - 5.29±0.03ms 5.11±0.03ms 0.97 groupby.GroupByMethods.time_dtype_as_group('float', 'nunique', 'transformation', 5) - 46.4±0.4ms 44.9±0.3ms 0.97 groupby.GroupByMethods.time_dtype_as_group('float', 'describe', 'direct', 1) - 96.7±0.3μs 93.4±0.2μs 0.97 groupby.GroupByMethods.time_dtype_as_group('object', 'ffill', 'direct', 1) - 267±0.7μs 257±2μs 0.96 groupby.GroupByMethods.time_dtype_as_group('datetime', 'tail', 'direct', 5) - 98.1±0.5μs 94.5±0.4μs 0.96 groupby.GroupByMethods.time_dtype_as_group('object', 'bfill', 'direct', 1) - 21.1±0.2ms 20.3±0.04ms 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'describe', 'direct', 1) - 270±2μs 259±4μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'tail', 'direct', 5) - 36.2±0.09ms 34.8±0.08ms 0.96 groupby.GroupByMethods.time_dtype_as_group('uint', 'describe', 'direct', 1) - 1.53±0.01ms 1.47±0.01ms 0.96 rolling.GroupbyEWM.time_groupby_method('var') - 1.42±0ms 1.36±0.01ms 0.96 groupby.RankWithTies.time_rank_ties('datetime64', 'min') - 1.40±0.04ms 1.34±0.01ms 0.96 groupby.RankWithTies.time_rank_ties('float64', 'dense') - 36.1±0.2ms 34.6±0.1ms 0.96 groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'direct', 1) - 1.42±0ms 1.36±0.01ms 0.96 groupby.RankWithTies.time_rank_ties('datetime64', 'max') - 1.79±0.01ms 1.71±0.01ms 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'bfill', 'transformation', 5) - 1.41±0.01ms 1.35±0.01ms 0.96 groupby.RankWithTies.time_rank_ties('datetime64', 'dense') - 1.78±0.01ms 1.71±0.01ms 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'ffill', 'transformation', 5) - 1.89±0.02ms 1.81±0.01ms 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'cumcount', 'transformation', 5) - 176±1μs 168±0.9μs 0.96 groupby.GroupByMethods.time_dtype_as_group('uint', 'head', 'direct', 5) - 5.09±0.04ms 4.87±0.02ms 0.96 groupby.GroupByMethods.time_dtype_as_group('datetime', 'nunique', 'transformation', 5) - 222±2ms 212±0.8ms 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'direct', 5) - 1.43±0.01ms 1.36±0.01ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'shift', 'transformation', 5) - 13.6±0.08ms 13.0±0.03ms 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'skew', 'transformation', 1) - 294±2ms 280±1ms 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'describe', 'direct', 5) - 10.9±0.06ms 10.4±0.03ms 0.95 groupby.GroupByMethods.time_dtype_as_field('uint', 'skew', 'transformation', 5) - 9.45±0.04ms 9.02±0.04ms 0.95 groupby.GroupByMethods.time_dtype_as_field('int', 'skew', 'direct', 5) - 509±2μs 485±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('uint', 'sem', 'direct', 5) - 9.33±0.07ms 8.89±0.05ms 0.95 groupby.GroupByMethods.time_dtype_as_field('int', 'skew', 'transformation', 1) - 1.45±0ms 1.38±0.01ms 0.95 groupby.GroupByMethods.time_dtype_as_group('object', 'ffill', 'transformation', 5) - 20.4±0.2ms 19.4±0.08ms 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'direct', 1) - 13.3±0.1ms 12.6±0.06ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'skew', 'direct', 1) - 1.55±0.02ms 1.47±0.01ms 0.95 rolling.GroupbyEWMEngine.time_groupby_mean('cython') - 5.26±0.03ms 5.01±0.05ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'nunique', 'transformation', 5) - 8.83±0.08ms 8.40±0.05ms 0.95 groupby.GroupByMethods.time_dtype_as_field('int', 'skew', 'direct', 1) - 14.7±0.2ms 14.0±0.03ms 0.95 groupby.AggFunctions.time_different_str_functions - 223±1ms 212±1ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'describe', 'direct', 5) - 13.7±0.1ms 13.0±0.05ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'skew', 'transformation', 1) - 4.31±0.03ms 4.09±0.01ms 0.95 groupby.GroupByMethods.time_dtype_as_group('object', 'nunique', 'transformation', 5) - 1.70±0.01ms 1.61±0.01ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'ffill', 'transformation', 5) - 16.1±0.3ms 15.2±0.07ms 0.95 groupby.Nth.time_frame_nth('float64') - 9.70±0.1ms 9.19±0.04ms 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'skew', 'transformation', 1) - 1.45±0.01ms 1.38±0.01ms 0.95 groupby.GroupByMethods.time_dtype_as_group('object', 'bfill', 'transformation', 5) - 1.13±0.02ms 1.07±0.01ms 0.95 groupby.FillNA.time_df_ffill - 13.3±0.06ms 12.6±0.08ms 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'skew', 'direct', 1) - 1.71±0.01ms 1.61±0.01ms 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'ffill', 'transformation', 5) - 8.92±0.1ms 8.42±0.04ms 0.94 groupby.GroupByMethods.time_dtype_as_field('uint', 'skew', 'direct', 1) - 9.81±0.09ms 9.27±0.02ms 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'skew', 'direct', 5) - 5.30±0.01ms 5.00±0ms 0.94 groupby.GroupByMethods.time_dtype_as_group('int', 'nunique', 'transformation', 5) - 9.41±0.1ms 8.88±0.03ms 0.94 groupby.GroupByMethods.time_dtype_as_field('uint', 'skew', 'transformation', 1) - 9.58±0.09ms 9.03±0.01ms 0.94 groupby.GroupByMethods.time_dtype_as_field('uint', 'skew', 'direct', 5) - 1.81±0.01ms 1.70±0.01ms 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cumcount', 'transformation', 5) - 31.7±0.1ms 29.9±0.1ms 0.94 groupby.TransformEngine.time_series_cython(True) - 1.68±0.01ms 1.58±0ms 0.94 groupby.GroupByMethods.time_dtype_as_group('object', 'cumcount', 'transformation', 5) - 1.71±0.02ms 1.62±0.01ms 0.94 groupby.GroupByMethods.time_dtype_as_group('uint', 'bfill', 'transformation', 5) - 21.0±0.3ms 19.8±0.1ms 0.94 groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'transformation', 1) - 9.25±0.09ms 8.72±0.1ms 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'skew', 'direct', 1) - 1.57±0.01ms 1.48±0.01ms 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'bfill', 'transformation', 5) - 1.56±0.01ms 1.47±0.01ms 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'ffill', 'transformation', 5) - 31.7±0.3ms 29.8±0.2ms 0.94 groupby.TransformEngine.time_series_cython(False) - 1.53±0.02ms 1.43±0.01ms 0.94 groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'transformation', 5) - 210±1μs 197±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('object', 'tail', 'direct', 5) - 1.16±0.02ms 1.08±0.01ms 0.94 groupby.FillNA.time_df_bfill - 289±2ms 269±3ms 0.93 groupby.GroupByCythonAgg.time_frame_agg('float64', 'median') - 33.0±0.2ms 30.6±0.2ms 0.93 groupby.TransformEngine.time_dataframe_cython(True) - 128±1μs 119±0.7μs 0.93 groupby.GroupByMethods.time_dtype_as_field('uint', 'shift', 'direct', 5) - 1.31±0.01ms 1.22±0.01ms 0.93 groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'transformation', 5) - 1.86±0.04ms 1.73±0.01ms 0.93 groupby.GroupByMethods.time_dtype_as_group('int', 'cumcount', 'transformation', 5) - 1.47±0.01ms 1.36±0.01ms 0.93 groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'transformation', 5) - 115±1μs 107±1μs 0.93 groupby.GroupByMethods.time_dtype_as_field('uint', 'shift', 'direct', 1) - 123±0.9μs 114±0.5μs 0.93 groupby.GroupByMethods.time_dtype_as_field('int', 'shift', 'direct', 5) - 1.04±0.01ms 960±6μs 0.92 groupby.GroupByMethods.time_dtype_as_group('object', 'cumcount', 'direct', 5) - 1.07±0.01ms 994±5μs 0.92 groupby.GroupByMethods.time_dtype_as_group('float', 'cumcount', 'direct', 5) - 1.06±0.02ms 980±4μs 0.92 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cumcount', 'direct', 5) - 108±0.7μs 100.0±0.4μs 0.92 groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'direct', 1) - 69.6±0.6ms 64.2±0.3ms 0.92 groupby.GroupByMethods.time_dtype_as_field('datetime', 'unique', 'direct', 1) - 1.10±0.01ms 1.01±0.01ms 0.92 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumcount', 'direct', 5) - 106±0.5μs 97.4±0.7μs 0.92 groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'direct', 1) - 38.6±0.2ms 35.5±0.4ms 0.92 groupby.GroupByMethods.time_dtype_as_field('uint', 'unique', 'direct', 1) - 110±0.6μs 101±0.8μs 0.92 groupby.GroupByMethods.time_dtype_as_field('int', 'shift', 'direct', 1) - 696±9μs 638±5μs 0.92 groupby.Shift.time_fill_value - 1.42±0.01ms 1.30±0.01ms 0.92 groupby.GroupByMethods.time_dtype_as_group('datetime', 'shift', 'transformation', 5) - 111±2μs 102±0.7μs 0.92 groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'direct', 1) - 38.7±0.3ms 35.4±0.1ms 0.92 groupby.GroupByMethods.time_dtype_as_field('int', 'unique', 'direct', 1) - 56.7±0.3ms 51.9±0.7ms 0.92 groupby.GroupByMethods.time_dtype_as_group('uint', 'unique', 'direct', 1) - 89.5±0.5ms 81.7±0.6ms 0.91 groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'direct', 1) - 138±1μs 126±1μs 0.91 groupby.GroupByMethods.time_dtype_as_group('object', 'head', 'direct', 5) - 93.1±0.4ms 84.9±0.4ms 0.91 groupby.GroupByMethods.time_dtype_as_group('datetime', 'unique', 'direct', 1) - 42.0±0.3ms 38.0±0.2ms 0.90 groupby.GroupByMethods.time_dtype_as_field('object', 'unique', 'direct', 1) - 57.4±0.7ms 51.8±0.1ms 0.90 groupby.GroupByMethods.time_dtype_as_group('int', 'unique', 'direct', 1) - 39.7±0.2ms 35.6±0.2ms 0.90 groupby.GroupByMethods.time_dtype_as_field('float', 'unique', 'direct', 1) - 43.0±0.6ms 37.9±0.2ms 0.88 groupby.Sample.time_sample - 50.9±0.2ms 44.8±0.5ms 0.88 groupby.Apply.time_scalar_function_multi_col(4) - 17.8±0.1ms 15.6±0.1ms 0.88 groupby.Apply.time_scalar_function_single_col(4) - 281±4ms 241±0.9ms 0.86 groupby.MultiColumn.time_lambda_sum - 150±2ms 127±1ms 0.85 groupby.MultiColumn.time_col_select_lambda_sum - 158±0.9ms 90.4±0.7ms 0.57 groupby.Apply.time_copy_overhead_single_col(4) - 409±2ms 230±0.9ms 0.56 groupby.Apply.time_copy_function_multi_col(4) ```

The groupby.GroupManyLabels.time_sum(1000) case is ~13x slower. This is a case of a small but wide dataframe (shape of 1000x1000), and it's per-column overhead of checking dtypes etc in BaseGrouper._cython_operation and _call_cython_op. https://github.com/pandas-dev/pandas/pull/44738 shows that this overhead can be reduced a bit. Also worth noting that if you go from (1000, 1000) to (10,000, 100), the overhead reduces to a 3x slowdown (on current master), and with another 10x more rows, to a 2x slowdown.
I can't reproduce the differences in GroupByMethods.time_dtype_as_group in an interactive python session with %timeit. For example the groupby cumsum() with 5 columns would show a 3x slowdown, but interactively this is within 10% of each other. I didn't yet figure out why we see those slowdowns (for some cases, speedups for others) through ASV. (while looking into this, I also noticed that the actual group_cumsum cython algo is only 1-2% of the overall time (for both ArrayManager and BlockManager), so those GroupByMethods benchmarks, due to the size of the dataframe / number of groups, is mostly benchmarking the factorize step of groupby, which is the same for each of the groupby methods)
Note that there can be in general some room for small speed-up if we simplify our groupby algos for 1D arrays, see the discussion in https://github.com/pandas-dev/pandas/pull/39861

Reductions (stat_ops)

$ asv continuous -f 1.01 -b stat_ops HEAD~1 HEAD

```diff before after ratio [f8052655] [5815ddc3]

+ 4.30±0.03ms 187±2ms 43.44 stat_ops.Correlation.time_corrwith_rows('pearson') + 616±9μs 3.18±0.02ms 5.16 stat_ops.FrameOps.time_op('prod', 'int', 1) + 590±4μs 2.92±0.01ms 4.94 stat_ops.FrameOps.time_op('sum', 'int', 1) + 1.67±0.01ms 6.25±0.02ms 3.74 stat_ops.FrameOps.time_op('sum', 'float', 1) + 3.91±0.01ms 9.27±0.07ms 2.37 stat_ops.Correlation.time_corrwith_cols('pearson') + 975±4μs 2.06±0.01ms 2.11 stat_ops.FrameOps.time_op('mean', 'int', 1) + 1.09±0.01ms 2.17±0.02ms 1.98 stat_ops.FrameOps.time_op('mean', 'float', 1) + 9.77±0.03ms 17.6±0.07ms 1.81 stat_ops.FrameOps.time_op('kurt', 'int', 1) + 11.4±0.09ms 19.8±0.04ms 1.73 stat_ops.FrameOps.time_op('skew', 'int', 1) + 3.86±0.05ms 6.63±0.08ms 1.72 stat_ops.FrameOps.time_op('mad', 'int', 1) + 1.43±0.01ms 2.46±0.01ms 1.71 stat_ops.FrameOps.time_op('var', 'int', 1) + 1.57±0.01ms 2.61±0.02ms 1.67 stat_ops.FrameOps.time_op('var', 'float', 1) + 1.71±0.01ms 2.73±0.02ms 1.60 stat_ops.FrameOps.time_op('std', 'int', 1) + 4.65±0.06ms 7.36±0.04ms 1.58 stat_ops.FrameOps.time_op('sem', 'float', 1) + 4.05±0.03ms 6.28±0ms 1.55 stat_ops.FrameOps.time_op('prod', 'float', 1) + 1.93±0.01ms 2.97±0.02ms 1.54 stat_ops.FrameOps.time_op('std', 'float', 1) + 5.56±0.02ms 8.43±0.02ms 1.52 stat_ops.FrameMultiIndexOps.time_op(0, 'std') + 5.68±0.06ms 8.57±0.04ms 1.51 stat_ops.FrameMultiIndexOps.time_op(0, 'var') + 5.76±0.02ms 8.48±0.05ms 1.47 stat_ops.FrameMultiIndexOps.time_op(1, 'var') + 518±1ms 761±4ms 1.47 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'skew') + 5.71±0.06ms 8.37±0.08ms 1.47 stat_ops.FrameMultiIndexOps.time_op(1, 'std') + 4.45±0.2ms 6.53±0.1ms 1.46 stat_ops.FrameOps.time_op('mad', 'float', 1) + 240±0.4μs 333±2μs 1.39 stat_ops.Correlation.time_corr('pearson') + 5.66±0.03ms 7.69±0.04ms 1.36 stat_ops.FrameMultiIndexOps.time_op(0, 'mean') + 5.70±0.01ms 7.67±0.01ms 1.35 stat_ops.FrameMultiIndexOps.time_op(0, 'sum') + 66.7±0.5ms 88.5±1ms 1.33 stat_ops.FrameMultiIndexOps.time_op(1, 'skew') + 5.83±0.03ms 7.70±0.06ms 1.32 stat_ops.FrameMultiIndexOps.time_op(1, 'sum') + 5.81±0.03ms 7.62±0.03ms 1.31 stat_ops.FrameMultiIndexOps.time_op(1, 'mean') + 3.71±0.03ms 4.79±0.06ms 1.29 stat_ops.FrameOps.time_op('sem', 'int', 1) + 3.82±0.01ms 4.81±0.02ms 1.26 stat_ops.FrameOps.time_op('median', 'int', 1) + 9.02±0.1ms 11.3±0.03ms 1.26 stat_ops.FrameMultiIndexOps.time_op(1, 'sem') + 9.19±0.04ms 11.5±0.03ms 1.25 stat_ops.FrameMultiIndexOps.time_op(0, 'sem') + 4.59±0.02ms 5.65±0.02ms 1.23 stat_ops.FrameOps.time_op('median', 'float', 1) + 978±8ms 1.20±0s 1.23 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'mad') + 4.10±0.01ms 4.96±0.1ms 1.21 stat_ops.Correlation.time_corr_wide('pearson') + 9.87±0.3ms 11.9±0.3ms 1.21 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'std') + 10.0±0.2ms 12.1±0.3ms 1.21 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'var') + 5.18±0.04ms 6.21±0.04ms 1.20 stat_ops.FrameMultiIndexOps.time_op(0, 'prod') + 112±0.8ms 133±0.7ms 1.19 stat_ops.FrameMultiIndexOps.time_op(1, 'mad') + 5.17±0.03ms 6.15±0.03ms 1.19 stat_ops.FrameMultiIndexOps.time_op(1, 'prod') + 3.64±0.04ms 4.27±0.02ms 1.17 stat_ops.FrameOps.time_op('mad', 'int', 0) + 13.2±0.04ms 15.2±0.2ms 1.14 stat_ops.Rank.time_rank('DataFrame', True) + 15.8±0.01ms 18.0±0.06ms 1.14 stat_ops.Correlation.time_corr_wide_nans('pearson') + 13.1±0.05ms 14.8±0.2ms 1.13 stat_ops.Rank.time_rank('DataFrame', False) + 13.3±0.03ms 15.1±0.2ms 1.13 stat_ops.Rank.time_average_old('DataFrame', False) + 13.5±0.03ms 15.2±0.2ms 1.12 stat_ops.Rank.time_average_old('DataFrame', True) + 753±10μs 841±10μs 1.12 stat_ops.FrameOps.time_op('sum', 'int', 0) + 973±6μs 1.08±0ms 1.11 stat_ops.FrameOps.time_op('mean', 'int', 0) + 983±4μs 1.08±0ms 1.10 stat_ops.FrameOps.time_op('mean', 'float', 0) + 1.15±0ms 1.27±0.01ms 1.10 stat_ops.Correlation.time_corr('spearman') + 14.0±0.2ms 15.1±0.04ms 1.08 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'sem') + 2.91±0.01ms 3.13±0.01ms 1.07 stat_ops.FrameOps.time_op('skew', 'int', 0) + 4.01±0.01ms 4.30±0.01ms 1.07 stat_ops.FrameOps.time_op('kurt', 'float', 0) + 22.0±0.09ms 23.6±0.3ms 1.07 stat_ops.FrameMultiIndexOps.time_op(0, 'mad') + 4.17±0.01ms 4.45±0.01ms 1.07 stat_ops.FrameOps.time_op('skew', 'float', 0) + 2.78±0.02ms 2.96±0.01ms 1.06 stat_ops.FrameOps.time_op('kurt', 'int', 0) + 1.64±0ms 1.73±0.01ms 1.06 stat_ops.FrameOps.time_op('var', 'int', 0) + 1.65±0.01ms 1.73±0.01ms 1.05 stat_ops.FrameOps.time_op('var', 'float', 0) + 1.68±0ms 1.74±0.01ms 1.04 stat_ops.FrameOps.time_op('prod', 'float', 0) + 3.50±0.02ms 3.60±0.02ms 1.03 stat_ops.FrameOps.time_op('sem', 'int', 0) + 3.84±0.02ms 3.93±0.02ms 1.02 stat_ops.FrameOps.time_op('sem', 'float', 0) - 22.0±0.2ms 21.3±0.09ms 0.97 stat_ops.SeriesMultiIndexOps.time_op(1, 'skew') - 150±1ms 145±0.9ms 0.97 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'kurt') - 1.29±0.01ms 1.24±0ms 0.97 stat_ops.FrameOps.time_op('prod', 'Int64', 0) - 1.13±0ms 1.09±0.01ms 0.96 stat_ops.FrameOps.time_op('sum', 'Int64', 0) - 575±2ms 548±4ms 0.95 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'kurt') - 148±0.7ms 141±0.7ms 0.95 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'skew') - 36.4±1ms 34.7±0.05ms 0.95 stat_ops.FrameOps.time_op('sum', 'Int64', 1) - 39.1±1ms 37.2±0.2ms 0.95 stat_ops.FrameOps.time_op('mean', 'Int64', 1) - 1.24±0.01ms 1.18±0.01ms 0.95 stat_ops.FrameOps.time_op('mean', 'Int64', 0) - 20.2±0.1ms 19.1±0.1ms 0.94 stat_ops.FrameMultiIndexOps.time_op(0, 'skew') - 59.4±1ms 55.5±0.2ms 0.93 stat_ops.FrameOps.time_op('skew', 'Int64', 1) - 77.9±0.4ms 72.4±0.5ms 0.93 stat_ops.FrameMultiIndexOps.time_op(1, 'kurt') - 25.8±0.05ms 23.6±0.05ms 0.92 stat_ops.FrameMultiIndexOps.time_op(0, 'kurt') - 39.3±1ms 35.5±0.06ms 0.90 stat_ops.FrameOps.time_op('prod', 'Int64', 1) ```

DataFrame.corrwith(..., method="pearson", axis=1) is a lot slower:
- for "pearson" we have custom code that operates on the datafame itself (a couple of arithmetic operations) -> those have currently a higher overhead with ArrayManager
- this overhead is especially visible with axis=1, because then we transpose the dataframe, and get a (15, 500) shape
But in general, this is a tiny but wide dataframe, so where the fixed overhead is significant. If I increase the size of the dataset, both have more or less the same performance.
Reductions with axis=1 (so taking eg the mean of each row, instead of each column) are slower, as can be expected (since this needs to go through a transpose / conversion to numpy array).
For the rest, column-wise reductions are more or less the same for ArrayManager vs BlockManager on those benchmarks (some a bit faster, some a bit slower)

Element-wise ops (arithmetic)

$ asv continuous -f 1.01 -b arithmetic HEAD~1 HEAD

```diff before after ratio [f8052655] [5815ddc3]

+ 6.10±0.04ms 87.1±2ms 14.28 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000, 10000)) + 9.92±0.05ms 106±0.5ms 10.67 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000, 10000)) + 416±5μs 4.32±0.4ms 10.37 arithmetic.Ops2.time_frame_series_dot + 12.9±0.03ms 119±0.6ms 9.16 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000, 10000)) + 1.15±0.03ms 9.37±0.09ms 8.15 arithmetic.Ops2.time_frame_float_div_by_zero + 35.6±0.07ms 285±3ms 8.01 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000, 10000)) + 1.52±0.04ms 11.7±0.08ms 7.73 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('eq') + 1.52±0.04ms 11.6±0.04ms 7.60 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('ne') + 1.54±0.05ms 11.7±0.1ms 7.58 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('gt') + 1.54±0.04ms 11.6±0.05ms 7.51 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('le') + 1.56±0.05ms 11.7±0.1ms 7.46 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('ge') + 1.56±0.06ms 11.6±0.05ms 7.43 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('lt') + 1.82±0.01ms 10.5±0.07ms 5.78 arithmetic.Ops2.time_frame_int_div_by_zero + 13.6±0.06ms 77.6±0.8ms 5.72 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000, 10000)) + 1.79±0.01ms 8.77±0.04ms 4.91 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('add') + 1.83±0.03ms 8.81±0.03ms 4.83 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('sub') + 20.5±0.08ms 96.0±0.4ms 4.68 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('pow') + 2.82±0.02ms 13.2±0.07ms 4.67 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('truediv') + 1.99±0.02ms 8.93±0.03ms 4.49 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('mul') + 19.4±7ms 60.6±0.3ms 3.12 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000, 10000)) + 5.54±0.5ms 15.7±0.1ms 2.84 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('truediv') + 9.77±0.07ms 27.0±0.4ms 2.76 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (10000, 1000)) + 5.42±0.5ms 14.2±0.1ms 2.61 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('sub') + 5.45±0.5ms 14.1±0.08ms 2.59 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('mul') + 5.86±0.09ms 15.0±0.3ms 2.56 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (10000, 1000)) + 30.4±0.08ms 76.8±0.9ms 2.53 arithmetic.Ops2.time_frame_float_floor_by_zero + 9.50±1ms 22.3±1ms 2.35 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('eq') + 9.52±1ms 22.2±2ms 2.34 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('ne') + 9.49±1ms 22.1±2ms 2.33 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('le') + 9.50±1ms 22.0±2ms 2.32 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('lt') + 9.55±1ms 22.1±2ms 2.32 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('ge') + 9.56±1ms 22.0±2ms 2.30 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('gt') + 1.79±0.01ms 4.11±0.2ms 2.30 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.81±0ms 4.13±0.2ms 2.28 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.78±0.03ms 4.00±0.2ms 2.25 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.28±0.01ms 2.85±0.2ms 2.23 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.28±0.02ms 2.84±0.3ms 2.23 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.81±0.02ms 3.99±0.2ms 2.21 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 6.41±1ms 14.1±0.1ms 2.20 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('add') + 1.90±0.01ms 4.15±0.1ms 2.18 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.90±0.04ms 4.13±0.2ms 2.18 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.93±0.03ms 4.14±0.2ms 2.15 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 21.6±3ms 46.1±0.5ms 2.14 arithmetic.Ops.time_frame_multi_and(True, 'default') + 1.28±0.01ms 2.74±0.2ms 2.14 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.88±0.01ms 3.99±0.2ms 2.12 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.27±0.01ms 2.69±0.2ms 2.12 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.88±0.02ms 3.97±0.2ms 2.11 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.89±0.02ms 3.96±0.2ms 2.09 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.43±0.02ms 5.04±0.09ms 2.08 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.26±0.02ms 2.60±0.1ms 2.06 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.42±0.01ms 4.96±0.5ms 2.05 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.02±0.02ms 4.14±0.2ms 2.05 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.81±0.01ms 5.73±0.1ms 2.04 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.42±0.01ms 4.91±0.06ms 2.02 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.42±0ms 4.85±0.06ms 2.00 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.02±0.02ms 4.03±0.2ms 1.99 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.36±0.01ms 2.70±0.08ms 1.99 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 13.5±0.04ms 26.8±0.2ms 1.98 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (10000, 1000)) + 1.37±0.01ms 2.70±0.08ms 1.98 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.92±0.07ms 5.78±0.07ms 1.98 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.27±0.01ms 2.51±0.02ms 1.97 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.78±0.02ms 5.45±0.07ms 1.96 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.42±0.01ms 4.73±0.07ms 1.95 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.42±0.01ms 4.71±0.2ms 1.95 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.35±0.01ms 2.58±0.1ms 1.91 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.35±0ms 2.57±0.2ms 1.89 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 33.4±0.7ms 62.6±0.8ms 1.88 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('floordiv') + 12.7±0.01ms 23.8±0.09ms 1.87 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (10000, 1000)) + 1.45±0ms 2.69±0.09ms 1.86 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.45±0ms 2.68±0.1ms 1.85 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.46±0.01ms 2.70±0.07ms 1.85 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.43±0.01ms 2.60±0.1ms 1.81 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.44±0.02ms 2.57±0.1ms 1.79 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 35.7±0.09ms 63.3±0.4ms 1.77 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (10000, 1000)) + 1.44±0.01ms 2.56±0.1ms 1.77 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.96±0.03ms 3.44±0.04ms 1.75 arithmetic.Ops.time_frame_comparison(True, 'default') + 26.2±2ms 45.6±0.6ms 1.74 arithmetic.Ops.time_frame_multi_and(False, 'default') + 26.5±2ms 46.0±0.2ms 1.74 arithmetic.Ops.time_frame_multi_and(False, 1) + 1.57±0.01ms 2.72±0.06ms 1.73 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 20.9±0.1ms 35.8±0.5ms 1.71 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 20.9±0.03ms 35.7±0.2ms 1.71 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 3.27±0.06ms 5.49±0.1ms 1.68 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.03±0.04ms 3.39±0.04ms 1.67 arithmetic.Ops.time_frame_comparison(False, 'default') + 2.03±0.02ms 3.39±0.03ms 1.67 arithmetic.Ops.time_frame_comparison(False, 1) + 1.70±0.01ms 2.83±0.07ms 1.67 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.71±0.01ms 2.85±0.1ms 1.66 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.71±0.01ms 2.83±0.1ms 1.66 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 28.2±2ms 46.2±0.5ms 1.64 arithmetic.Ops.time_frame_multi_and(True, 1) + 1.56±0ms 2.55±0.1ms 1.63 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.72±0.01ms 2.80±0.09ms 1.63 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 11.1±0.1ms 18.0±3ms 1.62 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (100000, 100)) + 1.71±0.02ms 2.75±0.1ms 1.61 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.71±0.01ms 2.73±0.1ms 1.59 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.70±0.01ms 2.66±0.1ms 1.56 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.70±0.01ms 2.63±0.1ms 1.55 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.79±0.02ms 2.75±0.07ms 1.54 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.45±0.02ms 3.73±0.07ms 1.53 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.81±0.01ms 2.75±0.06ms 1.52 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.01ms 3.59±0.05ms 1.52 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.43±0.02ms 3.65±0.06ms 1.50 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 27.6±0.6ms 40.6±0.05ms 1.47 arithmetic.Ops2.time_frame_dot + 9.86±0.07ms 14.5±0.3ms 1.47 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000000, 10)) + 2.36±0.02ms 3.46±0.03ms 1.47 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.01ms 3.45±0.06ms 1.47 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.90±0.02ms 2.78±0.08ms 1.46 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.02ms 3.45±0.03ms 1.46 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.36±0.01ms 3.44±0.02ms 1.46 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.34±0.01ms 3.41±0.04ms 1.46 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.01ms 3.44±0.06ms 1.45 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.42±0.01ms 3.51±0.07ms 1.45 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.90±0.01ms 2.76±0.08ms 1.45 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.88±0.02ms 2.73±0.07ms 1.45 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.85±0.01ms 2.66±0.1ms 1.43 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.85±0.01ms 2.63±0.1ms 1.43 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.85±0.02ms 2.61±0.1ms 1.41 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 26.2±1ms 37.0±0.4ms 1.41 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('pow') + 2.36±0.01ms 3.32±0.05ms 1.40 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.37±0.01ms 3.32±0.03ms 1.40 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.37±0.01ms 3.32±0.04ms 1.40 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.37±0.02ms 3.30±0.04ms 1.40 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.60±0.03ms 3.62±0.04ms 1.39 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.36±0.01ms 3.28±0.06ms 1.39 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.38±0.02ms 3.29±0.03ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.37±0.01ms 3.26±0.04ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.00±0.02ms 2.76±0.09ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.01ms 3.25±0.04ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.38±0.01ms 3.26±0.03ms 1.37 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.38±0.01ms 3.25±0.04ms 1.37 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.97±0.02ms 2.64±0.1ms 1.34 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 41.8±0.03ms 56.0±0.7ms 1.34 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 41.6±0.05ms 55.7±0.3ms 1.34 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 41.7±0.02ms 55.7±0.3ms 1.34 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.91±0.02ms 3.88±0.05ms 1.33 arithmetic.Ops.time_frame_add(False, 'default') + 2.37±0.02ms 3.14±0.05ms 1.32 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.92±0.2ms 3.85±0.03ms 1.32 arithmetic.Ops.time_frame_mult(False, 'default') + 37.3±0.4ms 49.0±0.1ms 1.31 arithmetic.Ops2.time_frame_float_div + 2.37±0.02ms 3.10±0.04ms 1.31 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.95±0.03ms 3.84±0.05ms 1.30 arithmetic.Ops.time_frame_mult(False, 1) + 2.98±0.1ms 3.87±0.03ms 1.30 arithmetic.Ops.time_frame_add(False, 1) + 10.8±0.06ms 13.6±0.3ms 1.26 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 32.3±0.2ms 39.2±0.08ms 1.21 arithmetic.Ops2.time_frame_float_mod + 3.31±0.02ms 3.98±0.03ms 1.20 arithmetic.Ops.time_frame_add(True, 1) + 2.96±0.03ms 3.48±0.03ms 1.18 arithmetic.Ops.time_frame_comparison(True, 1) + 30.4±0.02ms 34.5±0.3ms 1.14 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 30.3±0.1ms 34.5±0.3ms 1.14 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 33.2±0.03ms 37.7±0.2ms 1.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 33.1±0.06ms 37.4±0.3ms 1.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 33.3±0.05ms 37.6±0.1ms 1.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 33.3±0.1ms 37.5±0.1ms 1.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 32.2±0.05ms 36.3±0.1ms 1.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 33.4±0.1ms 37.5±0.3ms 1.12 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 29.1±0.2ms 32.6±0.2ms 1.12 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 32.4±0.07ms 36.1±0.3ms 1.12 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 29.1±0.05ms 32.4±0.2ms 1.11 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 30.3±0.04ms 33.7±0.2ms 1.11 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 28.9±0.6ms 32.1±0.2ms 1.11 arithmetic.Ops2.time_frame_int_mod + 28.2±0.07ms 31.2±0.1ms 1.11 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 28.1±0.02ms 31.1±0.2ms 1.11 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 28.3±0.07ms 31.2±0.3ms 1.10 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 12.7±0.05ms 13.7±0.09ms 1.08 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (100000, 100)) + 35.6±0.03ms 38.2±0.07ms 1.07 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (100000, 100)) - 35.5±0.04ms 35.1±0.05ms 0.99 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000000, 10)) - 463±20μs 426±4μs 0.92 arithmetic.NumericInferOps.time_multiply() - 469±20μs 421±5μs 0.90 arithmetic.NumericInferOps.time_add() - 9.40±0.5ms 8.28±0.06ms 0.88 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000000, 10)) - 42.2±0.07ms 3.40±0.03ms 0.08 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) ```

In general the element-wise ops are probably that set of operations that can see the biggest impact of performing column-by-column instead of on a single block:

dot() is slower, but since that's basically a 2D array operation, that's to be expected.
The biggest slowdown is seen in the FrameWithFrameWide class. However, many of the cases actually don't show up in the above output (so meaning that they didn't give a significant difference), and the ones that do show up with the biggest slowdown are the cases with shape (1000, 10000), so very wide dataframes. For wide dataframes, some slowdown will be inevitable (question is of course how much we find acceptable). Similarly, the next set of benchmarks that show the biggest slowdown is for the MixedFrameWithSeriesAxis class, which is using a DataFrame of shape (1000, 1000), i.e. also a wide DataFrame. When changing the shape to (10,000, 100) the difference already reduces from a 7x slowdown to 2x, for the slowest case.
For the wide dataframes, the main issue is the per-column overhead (checking dtypes, checking wich array op should be used, ...). I think there is quite some room for improvement to reduce this per-column overhead for those operations to specifically improve the wide dataframe case. Some examples:
- Currently, the functions in array_ops.py involve a lot of array type checking (should_extension_dispatch), dtype checking, validating of the right operand, etc that can be optimized or moved upwards.
  For example, in the FrameWithFrameWide case of summing two dataframes (df + df), the actual summing (the numpy operation) takes only around 20% of the overall time, while should_extension_dispatch and dispatch_fill_zeros together already take more time than that (both functions called in the arithmetic_op array op function, where should_extension_dispatch is mainly slow because of using ABC check, and dispatch_fill_zeros is actually a no-op for addition, so should never be called)
- In the step before the array op (_dispatch_frame_op), we could avoiding broadcasting Series to DataFrame as explored in https://github.com/pandas-dev/pandas/pull/40482, which improves the time_frame_op_with_series_axis0/1 cases (eg frame + series) quite a bit.
One note about the IntFrameWithScalar.time_frame_op_with_scalar: those have such a shape of DataFrame, that the BlockManager version is using numexpr, and the ArrayManager version not (because the minimum number per elements has to be reached here per column, and not per block). It seems that the cases that are ~2x slower for ArrayManager, are mostly comparison operations, and apparently numexpr is quite a bit faster for those ops compared to numpy, and so the difference in those benchmarks is mostly showing the effect of numexpr. It might be worth investigating if we should reduce the minimum of elements specifically for those operations, but also worth noting that when using a larger dataframe, also the ArrayManager case will start using numexpr.

The remainder

$ asv continuous -f 1.01 HEAD~1 HEAD with the above entries removed

```diff before after ratio [f8052655] [5815ddc3]

+ 6.71±1ms 75.1±4ms 11.20 eval.Eval.time_add('numexpr', 'all') + 6.74±1ms 72.8±5ms 10.79 eval.Eval.time_mult('numexpr', 'all') + 11.3±0.2ms 96.3±2ms 8.49 eval.Eval.time_chained_cmp('numexpr', 'all') + 9.06±0.6ms 73.5±5ms 8.11 eval.Eval.time_mult('numexpr', 1) + 9.01±0.6ms 73.1±5ms 8.11 eval.Eval.time_add('numexpr', 1) + 414±20μs 2.72±0.2ms 6.56 rolling.TableMethod.time_apply('table') + 14.6±0.7ms 65.9±1ms 4.52 eval.Eval.time_and('numexpr', 'all') + 25.3±0.2ms 111±0.7ms 4.38 eval.Eval.time_chained_cmp('numexpr', 1) + 15.9±0.1μs 45.2±0.4μs 2.85 dtypes.SelectDtypes.time_select_dtype_string_include() + 16.1±0.1μs 45.7±0.2μs 2.84 dtypes.SelectDtypes.time_select_dtype_int_include() + 15.9±0.2μs 45.1±0.4μs 2.84 dtypes.SelectDtypes.time_select_dtype_float_include() + 16.0±0.1μs 45.4±0.4μs 2.84 dtypes.SelectDtypes.time_select_dtype_bool_include() + 16.0±0.02μs 45.4±0.4μs 2.83 dtypes.SelectDtypes.time_select_dtype_string_include() + 16.0±0.2μs 45.2±0.4μs 2.82 dtypes.SelectDtypes.time_select_dtype_bool_include() + 11.0±0.7ms 29.5±0.5ms 2.69 io.hdf.HDFStoreDataFrame.time_read_store_table_wide + 15.7±0.09μs 41.5±0.2μs 2.65 dtypes.SelectDtypes.time_select_dtype_int_exclude() + 15.8±0.1μs 41.6±0.4μs 2.64 dtypes.SelectDtypes.time_select_dtype_float_exclude() + 33.1±0.07ms 84.4±0.8ms 2.55 eval.Eval.time_and('numexpr', 1) + 23.1±0.09μs 52.4±0.4μs 2.26 dtypes.SelectDtypes.time_select_dtype_string_include() + 23.1±0.08μs 52.3±0.3μs 2.26 dtypes.SelectDtypes.time_select_dtype_float_include() + 23.4±0.2μs 52.4±0.2μs 2.24 dtypes.SelectDtypes.time_select_dtype_int_include() + 23.5±0.2μs 52.5±0.4μs 2.24 dtypes.SelectDtypes.time_select_dtype_string_include() + 23.5±0.1μs 52.5±0.3μs 2.23 dtypes.SelectDtypes.time_select_dtype_float_include() + 23.5±0.09μs 52.5±0.1μs 2.23 dtypes.SelectDtypes.time_select_dtype_int_include() + 23.6±0.07μs 52.6±0.2μs 2.23 dtypes.SelectDtypes.time_select_dtype_bool_include() + 26.3±0.1μs 56.3±0.4μs 2.14 dtypes.SelectDtypes.time_select_dtype_string_include('Int8') + 26.5±0.1μs 56.0±0.3μs 2.11 dtypes.SelectDtypes.time_select_dtype_float_include('Int8') + 26.6±0.1μs 55.9±0.2μs 2.10 dtypes.SelectDtypes.time_select_dtype_int_include('Int8') + 26.7±0.1μs 56.1±0.4μs 2.10 dtypes.SelectDtypes.time_select_dtype_bool_include('Int8') + 23.3±0.2μs 48.7±0.1μs 2.09 dtypes.SelectDtypes.time_select_dtype_bool_exclude() + 27.3±0.3μs 57.1±0.3μs 2.09 dtypes.SelectDtypes.time_select_dtype_float_include('Int16') + 27.2±0.2μs 56.8±0.3μs 2.09 dtypes.SelectDtypes.time_select_dtype_string_include('Int16') + 27.3±0.2μs 56.7±0.3μs 2.08 dtypes.SelectDtypes.time_select_dtype_bool_include('Int16') + 27.4±0.3μs 56.8±0.2μs 2.07 dtypes.SelectDtypes.time_select_dtype_int_include('Int16') + 27.9±0.1μs 57.5±0.3μs 2.06 dtypes.SelectDtypes.time_select_dtype_int_include('Int32') + 28.0±0.1μs 57.6±0.2μs 2.06 dtypes.SelectDtypes.time_select_dtype_float_include('Int32') + 28.0±0.2μs 57.6±0.1μs 2.05 dtypes.SelectDtypes.time_select_dtype_string_include('Int32') + 28.1±0.3μs 57.5±0.3μs 2.05 dtypes.SelectDtypes.time_select_dtype_bool_include('Int32') + 28.8±0.4μs 58.7±0.5μs 2.04 dtypes.SelectDtypes.time_select_dtype_string_include('Int64') + 28.9±0.4μs 58.8±0.4μs 2.03 dtypes.SelectDtypes.time_select_dtype_float_include('Int64') + 28.8±0.4μs 58.5±0.3μs 2.03 dtypes.SelectDtypes.time_select_dtype_bool_include('Int64') + 29.2±0.2μs 59.3±0.5μs 2.03 dtypes.SelectDtypes.time_select_dtype_int_include('UInt8') + 29.7±0.1μs 60.0±0.6μs 2.02 dtypes.SelectDtypes.time_select_dtype_int_include('UInt16') + 29.5±0.1μs 59.3±0.6μs 2.01 dtypes.SelectDtypes.time_select_dtype_bool_include('UInt8') + 29.9±0.1μs 60.0±0.3μs 2.01 dtypes.SelectDtypes.time_select_dtype_string_include('UInt16') + 29.5±0.2μs 59.3±0.3μs 2.01 dtypes.SelectDtypes.time_select_dtype_float_include('UInt8') + 29.6±0.2μs 59.1±0.2μs 2.00 dtypes.SelectDtypes.time_select_dtype_string_include('UInt8') + 29.9±0.2μs 59.8±0.2μs 2.00 dtypes.SelectDtypes.time_select_dtype_bool_include('UInt16') + 29.9±0.1μs 59.7±0.3μs 2.00 dtypes.SelectDtypes.time_select_dtype_float_include('UInt16') + 30.4±0.2μs 60.5±0.3μs 1.99 dtypes.SelectDtypes.time_select_dtype_bool_include('UInt32') + 30.5±0.1μs 60.6±0.2μs 1.99 dtypes.SelectDtypes.time_select_dtype_string_include('UInt32') + 30.7±0.2μs 60.7±0.3μs 1.98 dtypes.SelectDtypes.time_select_dtype_int_include('UInt32') + 31.0±0.08μs 61.2±0.3μs 1.97 dtypes.SelectDtypes.time_select_dtype_int_include('UInt64') + 31.1±0.1μs 61.3±0.3μs 1.97 dtypes.SelectDtypes.time_select_dtype_string_include('UInt64') + 30.7±0.1μs 60.5±0.3μs 1.97 dtypes.SelectDtypes.time_select_dtype_float_include('UInt32') + 31.3±0.09μs 61.4±0.2μs 1.96 dtypes.SelectDtypes.time_select_dtype_float_include('UInt64') + 31.4±0.2μs 61.6±0.5μs 1.96 dtypes.SelectDtypes.time_select_dtype_bool_include('UInt64') + 4.56±0.05ms 8.86±3ms 1.94 gil.ParallelRolling.time_rolling('min') + 28.5±0.3μs 54.8±0.3μs 1.92 dtypes.SelectDtypes.time_select_dtype_int_exclude('Int64') + 212±0.7ns 389±4ns 1.84 attrs_caching.DataFrameAttributes.time_get_index + 6.39±0.08ms 11.6±0.03ms 1.81 timeseries.AsOf.time_asof_nan('DataFrame') + 6.60±0.06ms 11.8±0.02ms 1.78 timeseries.AsOf.time_asof('DataFrame') + 40.1±0.3μs 71.4±2μs 1.78 dtypes.SelectDtypes.time_select_dtype_int_include('int8') + 40.4±0.2μs 71.6±2μs 1.77 dtypes.SelectDtypes.time_select_dtype_int_include('int16') + 39.8±0.2μs 70.1±0.2μs 1.76 dtypes.SelectDtypes.time_select_dtype_string_include('m8[ns]') + 39.9±0.02μs 70.1±0.6μs 1.76 dtypes.SelectDtypes.time_select_dtype_string_include('M8[ns]') + 40.0±0.2μs 70.1±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('m8[ns]') + 40.0±0.3μs 70.2±0.5μs 1.75 dtypes.SelectDtypes.time_select_dtype_float_include('M8[ns]') + 40.1±0.08μs 70.3±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_float_include('uint16') + 39.9±0.08μs 69.9±0.1μs 1.75 dtypes.SelectDtypes.time_select_dtype_bool_include('m8[ns]') + 40.1±0.2μs 70.3±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_string_include('uint16') + 40.0±0.2μs 70.1±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_float_include('uint64') + 39.9±0.1μs 70.0±0.2μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('M8[ns]') + 40.1±0.1μs 70.2±0.9μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('float64') + 39.8±0.2μs 69.7±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_string_include('int8') + 40.1±0.4μs 70.1±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_float_include('m8[ns]') + 40.2±0.1μs 70.2±0.6μs 1.75 dtypes.SelectDtypes.time_select_dtype_string_include('complex64') + 40.0±0.2μs 69.8±0.09μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('float32') + 39.9±0.3μs 69.7±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('bool') + 40.6±0.1μs 70.8±0.1μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('timedelta64[ns]') + 40.1±0.1μs 70.0±0.5μs 1.75 dtypes.SelectDtypes.time_select_dtype_bool_include('M8[ns]') + 40.3±0.2μs 70.3±0.5μs 1.75 dtypes.SelectDtypes.time_select_dtype_float_include('uint32') + 40.1±0.2μs 70.1±0.2μs 1.75 dtypes.SelectDtypes.time_select_dtype_string_include('int32') + 40.0±0.1μs 69.9±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('int32') + 40.2±0.2μs 70.1±0.4μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('uint16') + 40.0±0.2μs 69.9±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_string_include('complex128') + 40.2±0.06μs 70.1±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_string_include('int64') + 40.0±0.2μs 69.8±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_bool_include('complex64') + 40.1±0.3μs 69.9±0.6μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('uint8') + 40.0±0.1μs 69.8±0.3μs 1.74 dtypes.SelectDtypes.time_select_dtype_int_include('complex128') + 40.1±0.5μs 70.0±0.3μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('uint8') + 40.1±0.1μs 69.9±0.2μs 1.74 dtypes.SelectDtypes.time_select_dtype_bool_include('int64') + 40.2±0.1μs 70.1±0.1μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('float32') + 40.1±0.1μs 69.9±0.2μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('int64') + 40.2±0.2μs 70.0±0.2μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('complex64') + 40.2±0.3μs 70.2±0.5μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('int32') + 40.7±0.2μs 70.9±0.1μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('timedelta64[ns]') + 40.0±0.09μs 69.7±0.5μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('float32') + 39.9±0.1μs 69.5±0.4μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('bool') + 40.2±0.2μs 69.9±0.5μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('float64') + 40.1±0.06μs 69.8±0.2μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('complex128') + 40.7±0.2μs 70.8±0.2μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('timedelta64[ns]') + 40.4±0.3μs 70.2±0.6μs 1.74 dtypes.SelectDtypes.time_select_dtype_bool_include('float32') + 40.2±0.2μs 69.8±0.7μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('uint64') + 40.2±0.1μs 69.8±0.3μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('int8') + 40.1±0.1μs 69.7±0.6μs 1.74 dtypes.SelectDtypes.time_select_dtype_bool_include('float64') + 40.3±0.2μs 69.9±0.5μs 1.74 dtypes.SelectDtypes.time_select_dtype_int_include('uint32') + 40.2±0.1μs 69.8±0.5μs 1.74 dtypes.SelectDtypes.time_select_dtype_bool_include('uint16') + 39.9±0.07μs 69.3±0.5μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('bool') + 40.3±0.4μs 70.0±0.4μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('int16') + 40.8±0.2μs 70.7±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('timedelta64[ns]') + 40.1±0.2μs 69.6±0.4μs 1.73 dtypes.SelectDtypes.time_select_dtype_int_include('complex64') + 40.2±0.2μs 69.8±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_int_include('uint64') + 40.3±0.5μs 69.9±0.5μs 1.73 dtypes.SelectDtypes.time_select_dtype_string_include('uint32') + 40.4±0.3μs 70.0±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_float_include('int16') + 40.2±0.1μs 69.7±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('int32') + 40.2±0.4μs 69.7±0.4μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('uint64') + 40.3±0.5μs 69.8±0.5μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('int16') + 41.1±0.2μs 71.1±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_int_include('datetime64[ns]') + 41.0±0.3μs 70.9±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_string_include('datetime64[ns]') + 40.3±0.5μs 69.7±0.4μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('int8') + 40.4±0.1μs 69.9±0.5μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('complex128') + 40.4±0.3μs 69.8±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('uint32') + 41.1±0.3μs 71.0±0.2μs 1.72 dtypes.SelectDtypes.time_select_dtype_float_include('datetime64[ns]') + 40.5±0.4μs 69.7±0.4μs 1.72 dtypes.SelectDtypes.time_select_dtype_int_include('uint8') + 40.6±0.3μs 69.9±0.5μs 1.72 dtypes.SelectDtypes.time_select_dtype_bool_include('uint8') + 41.4±0.2μs 71.0±0.4μs 1.72 dtypes.SelectDtypes.time_select_dtype_bool_include('datetime64[ns]') + 47.2±0.7μs 80.5±0.6μs 1.71 replace.ReplaceList.time_replace_list(True) + 12.9±0.03ms 21.4±3ms 1.65 indexing.ChainIndexing.time_chained_indexing(None) + 40.1±0.2μs 66.0±0.4μs 1.65 dtypes.SelectDtypes.time_select_dtype_int_exclude('int64') + 40.3±0.4μs 66.3±0.5μs 1.65 dtypes.SelectDtypes.time_select_dtype_float_exclude('float64') + 545±2ms 894±8ms 1.64 io.csv.ToCSVDatetimeBig.time_frame(100000) + 40.2±0.3μs 65.9±0.3μs 1.64 dtypes.SelectDtypes.time_select_dtype_bool_exclude('bool') + 47.2±0.8ms 77.2±0.4ms 1.64 replace.Convert.time_replace('DataFrame', 'Timedelta') + 1.25±0.02ms 2.04±0.03ms 1.64 timeseries.ResampleDataFrame.time_method('mean') + 47.9±0.7ms 77.1±0.4ms 1.61 replace.Convert.time_replace('DataFrame', 'Timestamp') + 6.11±0.04ms 9.83±0.03ms 1.61 io.csv.ToCSVDatetimeBig.time_frame(1000) + 55.4±0.4ms 88.9±0.2ms 1.61 io.csv.ToCSVDatetimeBig.time_frame(10000) + 8.93±0.3ms 14.3±0.1ms 1.60 eval.Eval.time_chained_cmp('python', 'all') + 1.54±0.03ms 2.31±0.02ms 1.50 timeseries.AsOf.time_asof_nan_single('DataFrame') + 11.6±0.05ms 17.3±0.1ms 1.50 eval.Eval.time_and('python', 'all') + 17.1±0.2ms 25.5±0.3ms 1.49 indexing.ChainIndexing.time_chained_indexing('warn') + 1.04±0.01ms 1.55±0.3ms 1.49 series_methods.NanOps.time_func('sum', 1000000, 'int8') + 13.1±0.06ms 18.2±0.08ms 1.40 io.hdf.HDFStoreDataFrame.time_query_store_table_wide + 3.39±0ms 4.67±0.06ms 1.38 timeseries.ResampleSeries.time_resample('datetime', '5min', 'ohlc') + 116±1μs 159±3μs 1.37 indexing.IndexSingleRow.time_iloc_row(True) + 1.57±0μs 2.13±0.03μs 1.36 indexing.GetItemSingleColumn.time_frame_getitem_single_column_label + 3.39±0.03ms 4.59±0.03ms 1.36 timeseries.ResampleSeries.time_resample('period', '5min', 'ohlc') + 1.47±0ms 1.99±0.3ms 1.35 series_methods.NanOps.time_func('prod', 1000000, 'int8') + 1.78±0.01ms 2.39±0.03ms 1.34 timeseries.AsOf.time_asof_single('DataFrame') + 125±2μs 163±4μs 1.30 indexing.IndexSingleRow.time_loc_row(True) + 115±1μs 149±4μs 1.30 indexing.IndexSingleRow.time_iloc_row(False) + 1.77±0.01μs 2.30±0.03μs 1.30 indexing.GetItemSingleColumn.time_frame_getitem_single_column_int + 143±0.9μs 185±2μs 1.29 indexing.DataFrameStringIndexing.time_boolean_rows + 148±1μs 191±2μs 1.29 indexing.DataFrameStringIndexing.time_boolean_rows_boolean + 124±2μs 159±2μs 1.28 indexing.IndexSingleRow.time_loc_row(False) + 190±1μs 240±0.7μs 1.26 indexing.NumericSeriesIndexing.time_loc_slice(, 'nonunique_monotonic_inc') + 193±3μs 240±3μs 1.25 hash_functions.NumericSeriesIndexing.time_loc_slice(, 1000000) + 110±0.9μs 137±0.4μs 1.24 hash_functions.NumericSeriesIndexing.time_loc_slice(, 500000) + 21.5±0.2μs 26.5±0.2μs 1.24 indexing.NumericSeriesIndexing.time_iloc_slice(, 'nonunique_monotonic_inc') + 1.68±0ms 2.07±0.01ms 1.23 timeseries.ResampleDataFrame.time_method('max') + 21.2±0.2μs 26.2±0.3μs 1.23 indexing.NumericSeriesIndexing.time_iloc_slice(, 'nonunique_monotonic_inc') + 44.3±0.1μs 54.4±0.3μs 1.23 dtypes.SelectDtypes.time_select_dtype_float_exclude() + 21.4±0.07μs 26.3±0.3μs 1.23 indexing.NumericSeriesIndexing.time_iloc_slice(, 'unique_monotonic_inc') + 181±1μs 223±2μs 1.23 indexing.DataFrameStringIndexing.time_boolean_rows_object + 44.2±0.2μs 54.3±0.4μs 1.23 dtypes.SelectDtypes.time_select_dtype_string_exclude() + 44.1±0.2μs 54.1±0.3μs 1.23 dtypes.SelectDtypes.time_select_dtype_bool_exclude() + 21.3±0.2μs 26.0±0.1μs 1.22 indexing.NumericSeriesIndexing.time_iloc_slice(, 'nonunique_monotonic_inc') + 21.3±0.2μs 25.9±0.2μs 1.21 indexing.NumericSeriesIndexing.time_iloc_slice(, 'unique_monotonic_inc') + 45.1±0.4μs 54.6±0.4μs 1.21 dtypes.SelectDtypes.time_select_dtype_bool_exclude() + 45.2±0.6μs 54.7±0.4μs 1.21 dtypes.SelectDtypes.time_select_dtype_int_exclude() + 37.0±0.5μs 44.8±0.4μs 1.21 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'nonunique_monotonic_inc') + 37.1±0.4μs 44.9±0.2μs 1.21 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'unique_monotonic_inc') + 37.0±0.2μs 44.8±0.3μs 1.21 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'non_monotonic') + 21.4±0.2μs 25.8±0.2μs 1.20 indexing.NumericSeriesIndexing.time_iloc_slice(, 'unique_monotonic_inc') + 45.4±0.7μs 54.4±0.5μs 1.20 dtypes.SelectDtypes.time_select_dtype_string_exclude() + 1.66±0.01ms 1.99±0.01ms 1.20 timeseries.ResampleDataFrame.time_method('min') + 26.6±0.3μs 31.8±0.1μs 1.20 indexing.NumericSeriesIndexing.time_getitem_slice(, 'nonunique_monotonic_inc') + 47.5±0.5μs 56.8±0.1μs 1.20 hash_functions.NumericSeriesIndexing.time_loc_slice(, 100000) + 35.8±0.2μs 42.8±0.09μs 1.20 hash_functions.NumericSeriesIndexing.time_loc_slice(, 10000) + 49.9±0.1μs 59.6±0.1μs 1.19 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('string', 'unique_monotonic_inc') + 47.4±0.5μs 56.6±0.9μs 1.19 hash_functions.NumericSeriesIndexing.time_loc_slice(, 100000) + 2.14±0.03μs 2.55±0.02μs 1.19 attrs_caching.DataFrameAttributes.time_set_index + 552±5ns 658±9ns 1.19 attrs_caching.SeriesArrayAttribute.time_array('datetime64tz') + 555±10ns 661±8ns 1.19 attrs_caching.SeriesArrayAttribute.time_array('category') + 41.5±0.1μs 49.4±0.2μs 1.19 indexing.NumericSeriesIndexing.time_loc_slice(, 'unique_monotonic_inc') + 61.6±0.3ms 73.2±1ms 1.19 io.style.Render.time_tooltips_render(24, 120) + 32.9±0.3μs 39.0±0.2μs 1.18 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('period', 'non_monotonic') + 43.9±0.1μs 52.0±0.2μs 1.18 indexing.NumericSeriesIndexing.time_getitem_slice(, 'unique_monotonic_inc') + 210±2μs 249±1μs 1.18 indexing.NumericSeriesIndexing.time_getitem_slice(, 'nonunique_monotonic_inc') + 26.8±0.2μs 31.7±0.3μs 1.18 indexing.NumericSeriesIndexing.time_getitem_slice(, 'unique_monotonic_inc') + 36.5±0.4μs 43.1±0.2μs 1.18 hash_functions.NumericSeriesIndexing.time_loc_slice(, 10000) + 26.8±0.1μs 31.6±0.2μs 1.18 indexing.NumericSeriesIndexing.time_getitem_slice(, 'nonunique_monotonic_inc') + 26.7±0.1μs 31.5±0.2μs 1.18 indexing.NumericSeriesIndexing.time_getitem_slice(, 'unique_monotonic_inc') + 33.0±0.8μs 38.8±0.07μs 1.18 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('period', 'nonunique_monotonic_inc') + 4.58±0.03μs 5.38±0.05μs 1.18 indexing.DataFrameStringIndexing.time_getitem_scalar + 38.5±0.2μs 45.2±0.5μs 1.17 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('datetime', 'unique_monotonic_inc') + 87.8±0.5ms 103±0.8ms 1.17 io.style.Render.time_tooltips_render(36, 120) + 7.19±0.05μs 8.40±0.07μs 1.17 indexing.DataFrameStringIndexing.time_loc + 49.8±0.3μs 58.1±0.5μs 1.17 indexing.NumericSeriesIndexing.time_loc_slice(, 'unique_monotonic_inc') + 39.2±0.6μs 45.5±0.8μs 1.16 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('datetime', 'non_monotonic') + 61.4±0.2μs 71.1±0.1μs 1.16 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('string', 'nonunique_monotonic_inc') + 33.3±0.5μs 38.5±0.3μs 1.16 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('period', 'unique_monotonic_inc') + 39.1±0.3μs 45.2±0.6μs 1.16 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('datetime', 'nonunique_monotonic_inc') + 43.3±0.2μs 50.0±0.08μs 1.15 indexing.NumericSeriesIndexing.time_loc_slice(, 'unique_monotonic_inc') + 53.5±0.2μs 61.8±0.2μs 1.15 dtypes.SelectDtypes.time_select_dtype_bool_exclude() + 57.6±0.4μs 66.4±0.6μs 1.15 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int8') + 37.2±0.4μs 42.9±0.6μs 1.15 timeseries.SortIndex.time_get_slice(False) + 60.6±0.2μs 69.8±0.3μs 1.15 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt16') + 27.8±0.1ms 32.0±0.2ms 1.15 io.hdf.HDF.time_write_hdf('fixed') + 1.72±0.1μs 1.97±0.3μs 1.15 index_cached_properties.IndexCache.time_values('PeriodIndex') + 60.5±0.2μs 69.4±0.3μs 1.15 dtypes.SelectDtypes.time_select_dtype_string_exclude('UInt16') + 61.5±1μs 70.5±0.4μs 1.15 dtypes.SelectDtypes.time_select_dtype_int_exclude('UInt32') + 36.6±0.08ms 42.0±0.6ms 1.15 io.style.Render.time_tooltips_render(12, 120) + 60.1±0.3μs 68.9±0.2μs 1.15 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt8') + 53.7±0.3μs 61.5±0.3μs 1.15 dtypes.SelectDtypes.time_select_dtype_string_exclude() + 57.0±0.3μs 65.2±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('Int8') + 57.8±0.1μs 66.1±0.1μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int16') + 53.6±0.3μs 61.3±0.4μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude() + 53.7±0.6μs 61.5±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_int_exclude() + 59.6±0.3μs 68.1±0.5μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude('Int64') + 15.6±0.05ms 17.8±0.2ms 1.14 io.csv.ReadCSVThousands.time_thousands(',', None, 'c') + 58.5±0.5μs 66.9±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude('Int32') + 61.4±0.2μs 70.2±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude('UInt32') + 58.1±0.2μs 66.4±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('Int16') + 60.1±0.7μs 68.6±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_int_exclude('UInt8') + 61.4±0.4μs 70.1±0.3μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('UInt32') + 53.9±0.8μs 61.5±0.3μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude() + 60.2±0.4μs 68.7±0.5μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude('UInt8') + 60.2±0.2μs 68.6±0.4μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('UInt8') + 57.3±0.3μs 65.2±0.3μs 1.14 dtypes.SelectDtypes.time_select_dtype_int_exclude('Int8') + 61.6±0.2μs 70.2±0.5μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt32') + 61.0±0.2μs 69.4±0.4μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('UInt16') + 59.7±0.2μs 67.9±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int64') + 58.0±0.1μs 66.1±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude('Int16') + 58.6±0.2μs 66.7±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int32') + 53.8±0.2μs 61.2±0.4μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude() + 57.4±0.5μs 65.3±0.3μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude('Int8') + 62.1±0.2μs 70.6±0.3μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('UInt64') + 62.3±0.3μs 70.8±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_int_exclude('UInt64') + 59.8±0.09μs 67.9±0.4μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('Int64') + 25.5±0.09ms 28.9±0.1ms 1.14 rolling.Apply.time_rolling('Series', 3, 'float', , False) + 15.5±0.02ms 17.6±0.1ms 1.14 io.csv.ReadCSVThousands.time_thousands('|', None, 'c') + 85.4±0.3ms 96.9±2ms 1.14 io.json.ToJSON.time_to_json('split', 'df') + 62.1±0.5μs 70.5±0.3μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt64') + 44.0±0.5μs 49.9±0.2μs 1.13 dtypes.SelectDtypes.time_select_dtype_int_include() + 25.5±0.09ms 29.0±0.2ms 1.13 rolling.Apply.time_rolling('Series', 3, 'int', , False) + 62.0±0.4μs 70.3±0.3μs 1.13 dtypes.SelectDtypes.time_select_dtype_string_exclude('UInt64') + 25.7±0.3ms 29.0±0.2ms 1.13 rolling.Apply.time_rolling('DataFrame', 3, 'int', , False) + 25.6±0.2ms 28.9±0.4ms 1.13 rolling.Apply.time_rolling('DataFrame', 3, 'float', , False) + 44.5±0.2μs 50.2±0.1μs 1.13 dtypes.SelectDtypes.time_select_dtype_float_include() + 78.4±0.3ms 88.4±2ms 1.13 io.json.ToJSONWide.time_to_json('values', 'df') + 70.8±0.1μs 79.8±0.4μs 1.13 dtypes.SelectDtypes.time_select_dtype_int_exclude('m8[ns]') + 71.2±0.3μs 80.2±1μs 1.13 dtypes.SelectDtypes.time_select_dtype_float_exclude('int16') + 88.9±2ms 100±0.9ms 1.13 io.json.ToJSON.time_to_json('split', 'df_date_idx') + 59.3±0.9μs 66.6±0.2μs 1.12 dtypes.SelectDtypes.time_select_dtype_bool_exclude('Int32') + 77.3±0.5ms 86.7±1ms 1.12 io.json.ToJSON.time_to_json('values', 'df_date_idx') + 59.5±0.4μs 66.8±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_int_exclude('Int32') + 52.6±0.2μs 59.0±0.6μs 1.12 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(, 10000) + 496±9ns 557±8ns 1.12 index_object.IntervalIndexMethod.time_is_unique(100000) + 70.9±0.1μs 79.5±0.9μs 1.12 dtypes.SelectDtypes.time_select_dtype_bool_exclude('int64') + 71.0±0.3μs 79.5±0.5μs 1.12 dtypes.SelectDtypes.time_select_dtype_int_exclude('float32') + 70.9±0.3μs 79.3±0.5μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('m8[ns]') + 23.7±0.3ms 26.5±0.1ms 1.12 io.style.Render.time_tooltips_render(36, 12) + 70.9±0.4μs 79.3±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_bool_exclude('m8[ns]') + 70.9±0.3μs 79.3±0.8μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('uint64') + 76.9±0.7ms 86.0±0.5ms 1.12 io.json.ToJSON.time_to_json('values', 'df') + 70.8±0.3μs 79.1±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('M8[ns]') + 3.07±0ms 3.43±0.1ms 1.12 indexing.InsertColumns.time_assign_list_of_columns_concat + 70.8±0.3μs 79.1±0.7μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('uint16') + 70.9±0.3μs 79.3±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_float_exclude('m8[ns]') + 71.3±0.3μs 79.6±0.4μs 1.12 dtypes.SelectDtypes.time_select_dtype_int_exclude('M8[ns]') + 70.9±0.2μs 79.3±0.8μs 1.12 dtypes.SelectDtypes.time_select_dtype_bool_exclude('int8') + 71.1±0.3μs 79.5±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('int64') + 55.2±0.2μs 61.6±0.3μs 1.12 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('period', 'nonunique_monotonic_inc') + 70.8±0.2μs 79.1±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_int_exclude('bool') + 80.1±2ms 89.5±2ms 1.12 io.json.ToJSONWide.time_to_json('values', 'df_date_idx') + 70.8±0.2μs 79.0±0.5μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('int8') + 157±20ms 176±7ms 1.12 gil.ParallelGroupbyMethods.time_loop(8, 'count') + 70.9±0.3μs 79.1±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_bool_exclude('int32') + 70.7±0.3μs 78.9±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_float_exclude('int8') + 71.2±0.3μs 79.4±0.9μs 1.12 dtypes.SelectDtypes.time_select_dtype_float_exclude('float32') + 70.9±0.4μs 79.1±0.5μs 1.12 dtypes.SelectDtypes.time_select_dtype_float_exclude('bool') + 71.1±0.4μs 79.3±0.4μs 1.12 dtypes.SelectDtypes.time_select_dtype_float_exclude('int64') + 70.7±0.3μs 78.8±0.4μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('bool') + 70.9±0.2μs 79.0±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('uint8') + 71.4±0.5μs 79.6±0.7μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('M8[ns]') + 71.2±0.3μs 79.3±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('float32') + 70.9±0.4μs 79.0±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('uint16') + 70.9±0.2μs 79.0±0.7μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint64') + 56.5±0.8μs 62.9±0.2μs 1.11 indexing.DataFrameNumericIndexing.time_loc + 70.9±0.4μs 79.0±0.2μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('uint8') + 70.9±0.3μs 78.9±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('complex128') + 71.2±0.4μs 79.2±0.5μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint16') + 71.1±0.5μs 79.1±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('uint32') + 72.7±0.7μs 81.0±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('datetime64[ns]') + 71.2±0.4μs 79.3±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('complex128') + 71.0±0.2μs 78.9±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint8') + 51.7±0.6μs 57.5±0.2μs 1.11 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(, 10000) + 46.8±3ms 52.0±0.1ms 1.11 io.hdf.HDFStoreDataFrame.time_write_store_table_wide + 71.4±0.3μs 79.4±0.5μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('float32') + 71.1±0.3μs 79.0±0.2μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('int8') + 71.1±0.5μs 79.0±0.2μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('uint32') + 72.6±0.5μs 80.6±0.5μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('datetime64[ns]') + 71.4±0.3μs 79.3±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('int16') + 71.3±0.5μs 79.2±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('int32') + 71.3±0.4μs 79.2±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('M8[ns]') + 71.2±0.3μs 79.1±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('int32') + 71.0±0.5μs 78.8±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('uint8') + 71.2±0.2μs 79.0±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('uint64') + 71.0±0.2μs 78.8±0.6μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('int16') + 71.3±0.5μs 79.1±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint32') + 71.2±0.3μs 78.9±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('int16') + 71.2±0.4μs 79.0±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('complex128') + 71.5±0.3μs 79.3±0.7μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('complex64') + 87.4±3ms 97.0±2ms 1.11 io.json.ToJSONWide.time_to_json('records', 'df') + 71.4±0.3μs 79.2±0.5μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('uint32') + 71.5±0.6μs 79.2±0.6μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('float64') + 78.2±8ms 86.6±3ms 1.11 gil.ParallelGroupbyMethods.time_loop(4, 'count') + 71.1±0.2μs 78.8±0.5μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('int32') + 71.5±0.3μs 79.2±0.6μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('uint64') + 72.7±0.6μs 80.5±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('datetime64[ns]') + 71.3±0.5μs 78.9±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('complex128') + 72.3±0.3μs 80.0±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('timedelta64[ns]') + 51.5±0.3μs 56.9±0.4μs 1.11 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(, 10000) + 71.3±0.5μs 78.7±0.4μs 1.10 dtypes.SelectDtypes.time_select_dtype_bool_exclude('float64') + 57.2±0.1μs 63.2±0.7μs 1.10 hash_functions.NumericSeriesIndexing.time_loc_slice(, 10000) + 71.2±0.3μs 78.6±0.2μs 1.10 dtypes.SelectDtypes.time_select_dtype_int_exclude('complex64') + 71.4±0.5μs 78.9±0.4μs 1.10 dtypes.SelectDtypes.time_select_dtype_bool_exclude('complex64') + 49.2±0.7μs 54.3±0.3μs 1.10 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('period', 'non_monotonic') + 71.5±0.6μs 78.9±0.4μs 1.10 dtypes.SelectDtypes.time_select_dtype_string_exclude('complex64') + 71.4±0.5μs 78.6±0.4μs 1.10 dtypes.SelectDtypes.time_select_dtype_bool_exclude('uint16') + 136±0.5ms 150±1ms 1.10 io.stata.StataMissing.time_read_stata('tc') + 73.0±0.5μs 80.4±0.3μs 1.10 dtypes.SelectDtypes.time_select_dtype_float_exclude('datetime64[ns]') + 72.6±0.7μs 79.9±0.4μs 1.10 dtypes.SelectDtypes.time_select_dtype_string_exclude('timedelta64[ns]') + 77.8±0.3μs 85.5±0.2μs 1.10 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'non_monotonic') + 87.2±0.4ms 95.8±0.8ms 1.10 io.json.ToJSON.time_to_json('records', 'df') + 71.8±0.8μs 78.9±0.5μs 1.10 dtypes.SelectDtypes.time_select_dtype_int_exclude('float64') + 55.1±0.5μs 60.5±0.8μs 1.10 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('period', 'unique_monotonic_inc') + 136±0.6ms 149±1ms 1.10 io.stata.StataMissing.time_read_stata('tw') + 92.0±0.6ms 101±0.6ms 1.10 io.json.ToJSONWide.time_to_json('split', 'df_date_idx') + 32.7±0.2ms 35.8±0.03ms 1.10 rolling.Apply.time_rolling('Series', 300, 'float', , False) + 110±0.5ms 121±3ms 1.09 io.json.ToJSONWide.time_to_json('index', 'df_date_idx') + 65.3±0.5μs 71.3±0.5μs 1.09 indexing.DataFrameNumericIndexing.time_iloc + 131±1ms 142±2ms 1.09 io.stata.StataMissing.time_read_stata('td') + 133±1ms 144±0.9ms 1.09 io.stata.StataMissing.time_read_stata('tm') + 85.4±0.6μs 92.7±0.2μs 1.09 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'nonunique_monotonic_inc') + 32.8±0.08ms 35.6±0.1ms 1.09 rolling.Apply.time_rolling('Series', 300, 'int', , False) + 88.1±0.8ms 95.5±1ms 1.08 io.json.ToJSONWide.time_to_json('split', 'df') + 5.09±0.04μs 5.52±0.1μs 1.08 indexing.NonNumericSeriesIndexing.time_getitem_scalar('period', 'non_monotonic') + 104±0.5ms 113±0.6ms 1.08 io.json.ToJSON.time_to_json('index', 'df') + 103±2ms 112±1ms 1.08 io.json.ToJSONWide.time_to_json('index', 'df') + 13.5±0.05ms 14.6±0.03ms 1.08 io.csv.ReadCSVSkipRows.time_skipprows(10000, 'c') + 14.5±0.04ms 15.6±0.07ms 1.08 io.style.Render.time_tooltips_render(12, 12) + 1.12±0s 1.21±0.02s 1.08 strings.Dummies.time_get_dummies('string[pyarrow]') + 134±0.6ms 145±1ms 1.08 io.stata.StataMissing.time_read_stata('tq') + 3.20±0.01ms 3.45±0.02ms 1.08 series_methods.NanOps.time_func('std', 1000000, 'int8') + 84.0±0.7μs 90.7±0.4μs 1.08 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'unique_monotonic_inc') + 53.1±0.3μs 57.3±0.1μs 1.08 dtypes.SelectDtypes.time_select_dtype_bool_include() + 256±0.9ns 276±6ns 1.08 timeseries.DatetimeIndex.time_is_dates_only('dst') + 128±2μs 137±3μs 1.08 indexing.IntervalIndexing.time_getitem_list + 33.1±0.3ms 35.6±0.1ms 1.08 rolling.Apply.time_rolling('DataFrame', 300, 'float', , False) + 1.10±0.01s 1.18±0s 1.08 strings.Dummies.time_get_dummies('str') + 137±0.9ms 147±2ms 1.07 io.json.ToJSONLines.time_floats_with_int_idex_lines + 4.43±0.05μs 4.75±0.05μs 1.07 indexing.NonNumericSeriesIndexing.time_getitem_scalar('string', 'non_monotonic') + 89.5±0.6ms 96.0±0.9ms 1.07 io.json.ToJSON.time_to_json('records', 'df_date_idx') + 177±20ms 190±5ms 1.07 gil.ParallelGroupbyMethods.time_loop(8, 'mean') + 33.0±0.2ms 35.4±0.07ms 1.07 rolling.Apply.time_rolling('DataFrame', 300, 'int', , False) + 145±0.6μs 155±2μs 1.07 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(, 100000) + 80.9±8ms 86.7±1ms 1.07 gil.ParallelGroupbyMethods.time_loop(4, 'last') + 134±1ms 144±2ms 1.07 io.stata.StataMissing.time_read_stata('th') + 2.53±0.07μs 2.71±0.05μs 1.07 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(0, 'end', 'year', 'QS', 12) + 59.1±0.2μs 63.2±0.2μs 1.07 dtypes.SelectDtypes.time_select_dtype_int_include('Int64') + 1.11±0.01s 1.19±0s 1.07 strings.Dummies.time_get_dummies('string[python]') + 2.53±0.03μs 2.70±0.05μs 1.07 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'quarter', None, 5) + 6.91±0.2μs 7.36±0.07μs 1.07 tslibs.timestamp.TimestampProperties.time_is_quarter_start(None, 'B') + 70.6±0.3μs 75.2±0.7μs 1.07 dtypes.SelectDtypes.time_select_dtype_float_include('float64') + 88.2±9ms 93.9±1ms 1.06 gil.ParallelGroupbyMethods.time_loop(4, 'sum') + 26.1±0.1ms 27.8±0.05ms 1.06 io.hdf.HDF.time_read_hdf('fixed') + 3.21±0.01ms 3.41±0.03ms 1.06 series_methods.NanOps.time_func('var', 1000000, 'int8') + 6.11±0.02μs 6.49±0.05μs 1.06 indexing.NonNumericSeriesIndexing.time_getitem_scalar('string', 'unique_monotonic_inc') + 11.2±0.1ms 11.9±0.09ms 1.06 io.style.Render.time_apply_render(36, 12) + 16.0±0.2μs 16.9±0.2μs 1.06 indexing.NonNumericSeriesIndexing.time_getitem_scalar('datetime', 'nonunique_monotonic_inc') + 2.51±0.02μs 2.66±0.01μs 1.06 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'month', None, 12) + 137±2ms 145±0.5ms 1.06 io.json.ToJSONLines.time_floats_with_dt_index_lines + 7.18±0.01ms 7.61±0.1ms 1.06 series_methods.NanOps.time_func('sem', 1000000, 'int8') + 170±20ms 180±5ms 1.06 gil.ParallelGroupbyMethods.time_loop(8, 'min') + 81.8±9ms 86.6±0.7ms 1.06 gil.ParallelGroupbyMethods.time_loop(4, 'prod') + 147±1μs 156±3μs 1.06 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(, 100000) + 70.3±0.2μs 74.4±0.5μs 1.06 dtypes.SelectDtypes.time_select_dtype_bool_include('bool') + 70.3±0.2μs 74.3±0.3μs 1.06 dtypes.SelectDtypes.time_select_dtype_int_include('int64') + 2.51±0.02μs 2.65±0.02μs 1.06 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'month', None, 3) + 2.91±0.04μs 3.07±0.02μs 1.05 series_methods.SeriesGetattr.time_series_datetimeindex_repr + 2.55±0.02μs 2.69±0.07μs 1.05 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(0, 'end', 'quarter', 'QS', 5) + 5.20±0.09μs 5.47±0.02μs 1.05 indexing.NumericSeriesIndexing.time_getitem_scalar(, 'unique_monotonic_inc') + 254±2ns 268±1ns 1.05 timeseries.DatetimeIndex.time_is_dates_only('tz_local') + 8.62±0.05ms 9.07±0.1ms 1.05 io.style.Render.time_format_render(36, 12) + 2.56±0.03μs 2.69±0.01μs 1.05 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'start', 'month', 'QS', 5) + 288±3ns 303±1ns 1.05 timeseries.DatetimeIndex.time_is_dates_only('tz_naive') + 28.8±0.1ms 30.2±0.2ms 1.05 io.style.Render.time_apply_render(12, 120) + 1.36±0μs 1.43±0.02μs 1.05 attrs_caching.SeriesArrayAttribute.time_extract_array_numpy('object') + 2.64±0.03μs 2.77±0.04μs 1.05 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'quarter', 'QS', 5) + 254±0.7ns 265±2ns 1.05 timeseries.DatetimeIndex.time_is_dates_only('tz_aware') + 1.34±0.01μs 1.41±0.02μs 1.05 attrs_caching.SeriesArrayAttribute.time_extract_array('datetime64') + 5.35±0.03μs 5.59±0.01μs 1.04 tslibs.offsets.OffestDatetimeArithmetic.time_apply() + 2.55±0.02μs 2.65±0.04μs 1.04 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'month', None, 5) + 4.50±0.03ms 4.68±0.03ms 1.04 io.csv.ReadCSVParseSpecialDate.time_read_special_date('mdY', 'c') + 45.0±0.2μs 46.8±0.2μs 1.04 indexing.NumericSeriesIndexing.time_iloc_list_like(, 'nonunique_monotonic_inc') + 9.10±0.06ms 9.46±0.05ms 1.04 inference.ToDatetimeISO8601.time_iso8601_infer_zero_tz_fromat + 5.57±0.02μs 5.79±0.07μs 1.04 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64() + 13.2±0.3μs 13.7±0.06μs 1.04 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64() + 2.63±0.04μs 2.73±0.02μs 1.04 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'month', 'QS', 12) + 1.36±0.01μs 1.41±0.01μs 1.04 attrs_caching.SeriesArrayAttribute.time_extract_array('category') + 6.02±0.2μs 6.24±0.1μs 1.04 index_cached_properties.IndexCache.time_engine('DatetimeIndex') + 24.8±0.1ms 25.7±0.07ms 1.04 gil.ParallelGroupbyMethods.time_parallel(2, 'last') + 11.2±0.08ms 11.6±0.05ms 1.04 algorithms.Hashing.time_series_string + 315±0.5ms 326±5ms 1.03 io.json.ReadJSONLines.time_read_json_lines('datetime') + 83.0±0.2ms 85.9±0.6ms 1.03 io.hdf.HDF.time_write_hdf('table') + 259±0.2ms 268±6ms 1.03 io.json.ReadJSON.time_read_json('records', 'int') + 270±2μs 280±1μs 1.03 hash_functions.NumericSeriesIndexing.time_loc_slice(, 100000) + 1.36±0.01μs 1.41±0.01μs 1.03 attrs_caching.SeriesArrayAttribute.time_extract_array_numpy('datetime64tz') + 23.3±0.1μs 24.1±0.1μs 1.03 tslibs.offsets.OffestDatetimeArithmetic.time_apply() + 1.30±0.01μs 1.34±0μs 1.03 tslibs.offsets.OnOffset.time_on_offset() + 1.05±0.03μs 1.09±0.04μs 1.03 index_cached_properties.IndexCache.time_is_monotonic_decreasing('Int64Index') + 26.7±0.2ms 27.5±0.07ms 1.03 gil.ParallelGroupbyMethods.time_parallel(2, 'sum') + 5.43±0.04μs 5.58±0.03μs 1.03 tslibs.offsets.OffestDatetimeArithmetic.time_add_10() + 1.46±0.01μs 1.50±0.01μs 1.03 tslibs.timestamp.TimestampConstruction.time_from_datetime_aware + 153±1μs 157±0.7μs 1.03 period.Algorithms.time_drop_duplicates('series') + 1.19±0.02ms 1.22±0.02ms 1.03 index_cached_properties.IndexCache.time_engine('MultiIndex') + 431±1ns 442±2ns 1.03 indexing_engines.NumericEngineIndexing.time_get_loc_near_middle((, ), 'non_monotonic', True, 2000000) + 5.23±0.02ms 5.36±0.01ms 1.02 indexing.DataFrameNumericIndexing.time_bool_indexer + 146±0.5μs 149±0.6μs 1.02 tslibs.period.PeriodProperties.time_property('M', 'end_time') + 402±0.6ns 411±1ns 1.02 indexing_engines.NumericEngineIndexing.time_get_loc((, ), 'monotonic_decr', True, 100000) + 7.01±0.1ms 7.15±0.1ms 1.02 index_cached_properties.IndexCache.time_is_all_dates('CategoricalIndex') + 101±0.1ms 103±0.4ms 1.02 rolling.Apply.time_rolling('Series', 3, 'int', , False) + 5.94±0.01ms 6.06±0.01ms 1.02 io.hdf.HDFStoreDataFrame.time_read_store_table + 71.6±0.3ms 73.0±0.3ms 1.02 rolling.Apply.time_rolling('DataFrame', 300, 'float', , False) + 4.62±0.02ms 4.71±0.06ms 1.02 ctors.SeriesConstructors.time_series_constructor(, False, 'float') + 414±2ns 422±2ns 1.02 indexing_engines.NumericEngineIndexing.time_get_loc_near_middle((, ), 'non_monotonic', True, 100000) + 414±1ns 422±2ns 1.02 indexing_engines.NumericEngineIndexing.time_get_loc_near_middle((, ), 'monotonic_decr', True, 100000) + 90.6±0.2μs 92.3±0.5μs 1.02 tslibs.timestamp.TimestampOps.time_floor(datetime.timezone.utc) + 73.0±0.5ms 74.3±0.1ms 1.02 rolling.Apply.time_rolling('DataFrame', 300, 'int', at 0x7ff1b2310940>, False) + 4.62±0.03ms 4.70±0.05ms 1.02 ctors.SeriesConstructors.time_series_constructor(, False, 'int') + 79.5±0.3μs 80.9±0.7μs 1.02 tslibs.timestamp.TimestampOps.time_floor(None) + 71.8±0.2ms 72.9±0.1ms 1.02 rolling.Apply.time_rolling('Series', 300, 'float', , False) + 68.5±0.7μs 69.6±0.7μs 1.02 ctors.SeriesConstructors.time_series_constructor(, True, 'float') + 73.1±0.2ms 74.2±0.2ms 1.02 rolling.Apply.time_rolling('Series', 300, 'int', at 0x7ff1b2310940>, False) + 4.22±0.03ms 4.28±0.06ms 1.01 ctors.SeriesConstructors.time_series_constructor(, True, 'float') + 73.2±0.2ms 74.2±0.2ms 1.01 rolling.Apply.time_rolling('Series', 300, 'float', at 0x7ff1b2310940>, False) + 804±9μs 815±9μs 1.01 ctors.SeriesConstructors.time_series_constructor(, True, 'int') + 103±0.3ms 105±0.04ms 1.01 rolling.Apply.time_rolling('Series', 3, 'float', at 0x7ff1b2310940>, False) - 128±1ms 127±0.8ms 0.99 index_cached_properties.IndexCache.time_engine('IntervalIndex') - 535±2μs 529±2μs 0.99 indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_decr') - 5.39±0.01ms 5.33±0ms 0.99 rolling.Methods.time_method('Series', ('rolling', {'window': 1000}), 'float', 'count') - 219±7μs 217±7μs 0.99 index_cached_properties.IndexCache.time_is_monotonic('UInt64Index') - 55.2±0.09ms 54.6±0.05ms 0.99 strings.Cat.time_cat(3, None, '-', 0.0) - 1.68±0.02ms 1.66±0.01ms 0.99 index_cached_properties.IndexCache.time_is_all_dates('RangeIndex') - 2.32±0.04ms 2.29±0.02ms 0.99 index_cached_properties.IndexCache.time_is_all_dates('IntervalIndex') - 48.4±0.1ms 47.8±0.1ms 0.99 algos.isin.IsInLongSeriesLookUpDominates.time_isin('float64', 5, 'monotone_misses') - 129±1ms 127±0.7ms 0.99 index_cached_properties.IndexCache.time_is_monotonic_increasing('IntervalIndex') - 840±2ns 828±5ns 0.99 dtypes.Dtypes.time_pandas_dtype() - 4.12±0.2ms 4.06±0.03ms 0.98 index_cached_properties.IndexCache.time_is_unique('MultiIndex') - 33.1±0.1ms 32.5±0.2ms 0.98 algos.isin.IsInLongSeriesLookUpDominates.time_isin('float32', 5, 'random_misses') - 33.0±0.2ms 32.5±0.1ms 0.98 rolling.Rank.time_rank('Series', 10, 'float', True, False, 'min') - 49.5±0.1μs 48.7±0.2μs 0.98 series_methods.NanOps.time_func('sum', 1000, 'float64') - 889±4μs 874±7μs 0.98 frame_ctor.FromRecords.time_frame_from_records_generator(1000) - 217±0.6ns 213±3ns 0.98 libs.ScalarListLike.time_is_scalar(array('123', dtype=', ), 'monotonic_decr', True, 100000) - 3.61±0.01ms 3.53±0.02ms 0.98 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'midpoint') - 844±4ns 824±2ns 0.98 dtypes.Dtypes.time_pandas_dtype() - 34.2±0.2ms 33.4±0.2ms 0.98 rolling.Methods.time_method('Series', ('rolling', {'window': 10}), 'int', 'median') - 20.1±0.2μs 19.7±0.1μs 0.98 series_methods.NanOps.time_func('argmax', 1000, 'int8') - 660±4ns 644±2ns 0.98 tslibs.timestamp.TimestampOps.time_to_pydatetime(tzfile('/usr/share/zoneinfo/Asia/Tokyo')) - 29.4±0.1μs 28.7±0.2μs 0.98 series_methods.NanOps.time_func('prod', 1000, 'int32') - 506±1ns 494±2ns 0.98 tslibs.timestamp.TimestampProperties.time_dayofweek(, None) - 54.9±0.1ms 53.6±0.2ms 0.98 io.csv.ReadCSVSkipRows.time_skipprows(10000, 'python') - 3.80±0.01ms 3.70±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'int', 'min') - 33.5±0.08ms 32.6±0.1ms 0.97 algos.isin.IsInLongSeriesLookUpDominates.time_isin('float32', 5, 'monotone_misses') - 4.28±0.01ms 4.17±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'kurt') - 3.81±0.02ms 3.71±0.02ms 0.97 libs.InferDtype.time_infer_dtype('py-object') - 2.89±0ms 2.81±0.01ms 0.97 indexing.IntervalIndexing.time_getitem_scalar - 1.74±0.01ms 1.70±0ms 0.97 rolling.Pairwise.time_pairwise(({'window': 1000}, 'rolling'), 'corr', False) - 4.28±0.02ms 4.17±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'kurt') - 217±0.5ns 211±3ns 0.97 libs.ScalarListLike.time_is_scalar((1, 2, 3)) - 2.80±0.01ms 2.72±0ms 0.97 series_methods.NSort.time_nlargest('all') - 175±1ms 170±2ms 0.97 strings.Extract.time_extract_single_group('string[pyarrow]', True) - 4.05±0.02ms 3.94±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'int', 'min') - 3.66±0.01ms 3.56±0.02ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'nearest') - 4.09±0.01ms 3.98±0.03ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'int', 'max') - 7.00±0.08μs 6.80±0.08μs 0.97 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2000, tzfile('/usr/share/zoneinfo/Asia/Tokyo')) - 21.2±0.1ms 20.6±0.2ms 0.97 io.sql.WriteSQLDtypes.time_to_sql_dataframe_column('sqlite', 'float_with_nan') - 4.08±0.01ms 3.96±0.02ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'max') - 3.76±0.01ms 3.64±0ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'int', 'max') - 5.53±0.03ms 5.37±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'count') - 10.7±0.06μs 10.4±0.03μs 0.97 timedelta.TimedeltaIndexing.time_series_loc - 1.28±0.01ms 1.24±0.01ms 0.97 rolling.Pairwise.time_pairwise(({'window': 10}, 'rolling'), 'cov', False) - 3.69±0.01ms 3.58±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'skew') - 3.66±0.01ms 3.55±0.01ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'linear') - 3.62±0.02ms 3.51±0.01ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'nearest') - 4.20±0.01ms 4.08±0.02ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'int', 'max') - 3.97±0.01ms 3.85±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'min') - 3.69±0.02ms 3.58±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'min') - 3.56±0.01ms 3.45±0.02ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'std') - 4.15±0.03ms 4.02±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'int', 'min') - 144±1ms 140±0.5ms 0.97 plotting.FramePlotting.time_frame_plot('hist') - 634±4μs 615±0.5μs 0.97 indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'nonunique_monotonic_inc') - 4.05±0.01ms 3.93±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'int', 'min') - 3.66±0.02ms 3.55±0ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'midpoint') - 3.67±0.01ms 3.55±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'max') - 3.69±0.02ms 3.57±0.01ms 0.97 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'higher') - 2.66±0.02ms 2.58±0.02ms 0.97 io.csv.ReadCSVParseDates.time_multiple_date('python') - 2.62±0.01ms 2.54±0.01ms 0.97 series_methods.NSort.time_nlargest('last') - 3.74±0.03ms 3.62±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'max') - 3.63±0.02ms 3.51±0.02ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'linear') - 4.02±0.01ms 3.89±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'max') - 3.86±0.01ms 3.74±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'int', 'max') - 5.48±0.02ms 5.30±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'int', 'count') - 19.9±0.1ms 19.2±0.1ms 0.97 io.sql.WriteSQLDtypes.time_to_sql_dataframe_column('sqlite', 'int') - 3.70±0.01ms 3.58±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'skew') - 3.99±0.03ms 3.86±0.01ms 0.97 io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(';', '.', None) - 4.63±0.02ms 4.48±0.02ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'kurt') - 3.74±0.02ms 3.61±0.01ms 0.97 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'midpoint') - 9.14±0.1μs 8.84±0.02μs 0.97 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(100, tzfile('/usr/share/zoneinfo/Asia/Tokyo')) - 3.69±0.01ms 3.56±0.01ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'higher') - 3.36±0.02ms 3.24±0.01ms 0.97 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'min') - 3.57±0.01ms 3.45±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'std') - 4.54±0.02ms 4.38±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'float', 'kurt') - 3.99±0.02ms 3.85±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'float', 'max') - 3.64±0.02ms 3.51±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'min') - 3.13±0.01ms 3.02±0.02ms 0.97 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'max') - 4.03±0ms 3.89±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'min') - 28.2±0.1ms 27.2±0.3ms 0.96 rolling.Groupby.time_method('sum', ('rolling', {'window': '30s', 'on': 'C'})) - 9.80±0.08ms 9.45±0.04ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'sem') - 2.32±0.02ms 2.24±0.02ms 0.96 io.csv.ReadCSVParseDates.time_multiple_date('c') - 4.04±0.03ms 3.89±0.01ms 0.96 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'skew') - 3.34±0.01ms 3.23±0.01ms 0.96 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'max') - 3.30±0.01ms 3.19±0ms 0.96 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'min') - 2.83±0.01ms 2.73±0.01ms 0.96 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'skew') - 2.43±0.01ms 2.34±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'linear') - 3.97±0.01ms 3.83±0ms 0.96 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'float', 'skew') - 9.89±0.06ms 9.53±0.06ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'int', 'sem') - 3.41±0.2μs 3.29±0.2μs 0.96 index_cached_properties.IndexCache.time_values('TimedeltaIndex') - 4.65±0.01ms 4.48±0.01ms 0.96 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'kurt') - 3.52±0.07ms 3.39±0.02ms 0.96 index_cached_properties.IndexCache.time_is_unique('Float64Index') - 1.41±0.01ms 1.36±0.01ms 0.96 rolling.Apply.time_rolling('DataFrame', 3, 'int', , True) - 4.42±0.03ms 4.26±0.01ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'int', 'kurt') - 2.57±0.01ms 2.48±0.04ms 0.96 series_methods.NSort.time_nsmallest('all') - 5.59±0.07ms 5.38±0.01ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'count') - 4.03±0.02ms 3.88±0.01ms 0.96 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'skew') - 6.28±0.02ms 6.05±0.02ms 0.96 index_object.SetOperations.time_operation('int', 'symmetric_difference') - 12.4±0.03μs 11.9±0.04μs 0.96 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(1, tzlocal()) - 2.42±0.02μs 2.33±0.01μs 0.96 indexing_engines.NumericEngineIndexing.time_get_loc_near_middle((, ), 'monotonic_decr', True, 2000000) - 22.5±0.3μs 21.6±0.1μs 0.96 series_methods.All.time_all(1000, 'fast', 'bool') - 3.48±0.02ms 3.34±0.02ms 0.96 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'float', 'std') - 3.75±0.02ms 3.60±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'linear') - 9.87±0.1ms 9.48±0.04ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'sem') - 3.43±0.02ms 3.30±0.01ms 0.96 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'max') - 35.5±0.5μs 34.1±0.3μs 0.96 series_methods.NanOps.time_func('argmax', 1000, 'float64') - 30.2±0.5μs 28.9±0.1μs 0.96 series_methods.NanOps.time_func('prod', 1000, 'int8') - 3.71±0.03ms 3.56±0.02ms 0.96 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'midpoint') - 2.30±0.01ms 2.21±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'lower') - 77.8±0.4ms 74.6±0.6ms 0.96 io.csv.ReadCSVSkipRows.time_skipprows(None, 'python') - 2.44±0ms 2.33±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'nearest') - 3.33±0.01ms 3.20±0.01ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'int', 'std') - 2.46±0.01ms 2.36±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'lower') - 2.47±0.01ms 2.36±0ms 0.96 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'linear') - 3.09±0.01ms 2.96±0.03ms 0.96 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'min') - 87.9±0.3ms 84.1±0.7ms 0.96 strings.Methods.time_partition('str') - 3.26±0.02ms 3.12±0.02ms 0.96 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'kurt') - 3.70±0.2μs 3.54±0.2μs 0.96 index_cached_properties.IndexCache.time_values('UInt64Index') - 2.43±0.01ms 2.32±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'midpoint') - 2.18±0.01ms 2.08±0.01ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'mean') - 79.8±0.9μs 76.2±0.6μs 0.96 indexing.DataFrameNumericIndexing.time_iloc_dups - 2.47±0.01ms 2.36±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'higher') - 1.05±0.05μs 1.01±0.03μs 0.95 index_cached_properties.IndexCache.time_is_monotonic_decreasing('RangeIndex') - 3.80±0.2μs 3.62±0.2μs 0.95 index_cached_properties.IndexCache.time_inferred_type('IntervalIndex') - 2.46±0.01ms 2.35±0ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'nearest') - 2.44±0.02ms 2.33±0ms 0.95 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'higher') - 170±0.7ms 162±0.5ms 0.95 io.stata.Stata.time_write_stata('tw') - 2.33±0.01ms 2.22±0ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'higher') - 2.76±0.05μs 2.63±0.01μs 0.95 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 1, None) - 2.34±0.06μs 2.23±0.08μs 0.95 index_cached_properties.IndexCache.time_shape('DatetimeIndex') - 2.34±0.01ms 2.23±0ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'midpoint') - 2.33±0.02ms 2.22±0ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'linear') - 7.43±0.4μs 7.08±0.3μs 0.95 index_cached_properties.IndexCache.time_inferred_type('Float64Index') - 2.15±0.01ms 2.05±0.01ms 0.95 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'mean') - 35.9±0.3ms 34.2±0.7ms 0.95 io.sql.SQL.time_read_sql_query('sqlalchemy') - 602±10μs 573±20μs 0.95 index_cached_properties.IndexCache.time_is_monotonic('MultiIndex') - 2.34±0ms 2.22±0.01ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'nearest') - 3.23±0.01ms 3.07±0.01ms 0.95 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'std') - 2.39±0.02ms 2.27±0.01ms 0.95 series_methods.NSort.time_nsmallest('first') - 207M 197M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('columns', 'df_int_floats') - 2.26±0ms 2.15±0.01ms 0.95 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 10}, 'std'), 'float') - 10.9±0.5μs 10.4±0.3μs 0.95 index_cached_properties.IndexCache.time_engine('UInt64Index') - 3.25±0.02ms 3.09±0.01ms 0.95 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'std') - 2.44±0ms 2.32±0.04ms 0.95 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 1000}, 'std'), 'int') - 2.16±0.04μs 2.05±0.01μs 0.95 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(100, datetime.timezone.utc) - 2.33±0.01ms 2.22±0.01ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'lower') - 208M 198M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('columns', 'df_int_float_str') - 203M 193M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('columns', 'df_td_int_ts') - 11.1±0.4μs 10.5±0.3μs 0.95 index_cached_properties.IndexCache.time_engine('TimedeltaIndex') - 2.50±0.01ms 2.37±0.01ms 0.95 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'mean') - 22.6±0.3μs 21.4±0.2μs 0.95 series_methods.Any.time_any(1000, 'slow', 'bool') - 2.51±0ms 2.38±0.01ms 0.95 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'mean') - 2.44±0.01ms 2.32±0.01ms 0.95 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'int', 'sum') - 611±2μs 580±6μs 0.95 indexing.NonNumericSeriesIndexing.time_getitem_list_like('string', 'nonunique_monotonic_inc') - 2.65±0.02ms 2.51±0.02ms 0.95 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'int', 'mean') - 2.30±0.01ms 2.18±0.01ms 0.95 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'int', 'mean') - 197M 187M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('records', 'df_int_floats') - 197M 187M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('index', 'df_int_floats') - 3.62±0.2μs 3.43±0.2μs 0.95 index_cached_properties.IndexCache.time_values('Float64Index') - 2.98±0.01ms 2.82±0.01ms 0.95 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'kurt') - 2.97±0.01ms 2.81±0ms 0.95 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'kurt') - 3.39±0.03ms 3.21±0.01ms 0.95 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'kurt') - 2.32±0.04ms 2.19±0.01ms 0.95 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'higher') - 198M 188M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('records', 'df_int_float_str') - 198M 188M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('index', 'df_int_float_str') - 193M 183M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('records', 'df_td_int_ts') - 193M 183M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('index', 'df_td_int_ts') - 603±10μs 571±20μs 0.95 index_cached_properties.IndexCache.time_is_monotonic_increasing('MultiIndex') - 15.9±0.3ms 15.1±0.1ms 0.95 io.csv.ReadCSVParseSpecialDate.time_read_special_date('hm', 'python') - 3.26±0.03ms 3.09±0.02ms 0.95 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'min') - 2.33±0.04ms 2.20±0.02ms 0.95 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'linear') - 35.0±1μs 33.1±0.2μs 0.95 tslibs.timestamp.TimestampOps.time_replace_tz() - 2.64±0.02ms 2.50±0.01ms 0.95 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'int', 'mean') - 3.27±0.03μs 3.10±0.05μs 0.95 index_object.Indexing.time_get_loc_sorted('Int') - 2.15±0.01ms 2.03±0.01ms 0.94 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'std') - 6.57±0.04μs 6.20±0.2μs 0.94 tslibs.timestamp.TimestampOps.time_replace_None() - 2.04±0.05μs 1.92±0.01μs 0.94 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1, datetime.timezone.utc) - 2.15±0.01ms 2.03±0.01ms 0.94 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'max') - 2.46±0.02ms 2.33±0.01ms 0.94 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'int', 'sum') - 2.32±0.01ms 2.19±0.01ms 0.94 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'sum') - 1.69±0.08μs 1.60±0.1μs 0.94 index_cached_properties.IndexCache.time_inferred_type('PeriodIndex') - 181M 170M 0.94 io.json.ToJSONWide.peakmem_to_json_wide('split', 'df_td_int_ts') - 2.99±0.02ms 2.82±0.01ms 0.94 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'skew') - 181M 170M 0.94 io.json.ToJSONWide.peakmem_to_json_wide('values', 'df_td_int_ts') - 3.26±0.01μs 3.07±0.04μs 0.94 index_object.Indexing.time_get_loc('Int') - 2.34±0ms 2.21±0.01ms 0.94 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'float', 'sum') - 3.14±0.1ms 2.96±0.03ms 0.94 index_cached_properties.IndexCache.time_is_unique('DatetimeIndex') - 1.83±0.01ms 1.72±0.01ms 0.94 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'mean') - 76.6±0.4μs 72.1±0.4μs 0.94 series_methods.ToFrame.time_to_frame('Int64', None) - 2.38±0.08μs 2.24±0.01μs 0.94 indexing_engines.NumericEngineIndexing.time_get_loc_near_middle((, ), 'monotonic_incr', False, 100000) - 2.33±0.01ms 2.19±0.01ms 0.94 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'sum') - 1.98±0.01ms 1.86±0.01ms 0.94 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'min') - 76.6±0.1μs 72.0±0.2μs 0.94 series_methods.ToFrame.time_to_frame('int64', None) - 77.6±0.8μs 73.0±0.1μs 0.94 series_methods.ToFrame.time_to_frame('category', None) - 1.99±0.04ms 1.87±0.01ms 0.94 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'sum') - 2.33±0.03μs 2.19±0.05μs 0.94 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1, datetime.timezone.utc) - 2.26±0.01ms 2.12±0.01ms 0.94 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 1000}, 'std'), 'float') - 852±50ns 799±20ns 0.94 index_cached_properties.IndexCache.time_shape('Int64Index') - 10.9±0.06ms 10.2±0.02ms 0.94 io.csv.ReadCSVConcatDatetimeBadDateValue.time_read_csv('0') - 2.05±0.01ms 1.92±0.01ms 0.94 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'std') - 3.17±0.2μs 2.97±0.2μs 0.94 index_cached_properties.IndexCache.time_values('CategoricalIndex') - 4.32±0.2μs 4.05±0.2μs 0.94 index_cached_properties.IndexCache.time_shape('IntervalIndex') - 841±40ns 788±40ns 0.94 index_cached_properties.IndexCache.time_shape('RangeIndex') - 6.40±0.08μs 6.00±0.1μs 0.94 tslibs.timestamp.TimestampOps.time_replace_None(tzfile('/usr/share/zoneinfo/Asia/Tokyo')) - 2.44±0ms 2.28±0.02ms 0.94 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 10}, 'std'), 'int') - 165±0.9ms 154±0.3ms 0.94 strings.Split.time_split('str', True) - 2.15±0.03ms 2.01±0ms 0.93 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'int', 'sum') - 77.7±0.9μs 72.6±0.1μs 0.93 series_methods.ToFrame.time_to_frame('category', 'foo') - 1.80±0.2μs 1.68±0.07μs 0.93 index_cached_properties.IndexCache.time_values('DatetimeIndex') - 3.39±0.2μs 3.17±0.2μs 0.93 index_cached_properties.IndexCache.time_values('IntervalIndex') - 2.01±0.01ms 1.87±0.01ms 0.93 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'sum') - 1.96±0.02ms 1.83±0ms 0.93 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'mean') - 1.29±0.01ms 1.20±0.01ms 0.93 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 10}, 'mean'), 'int') - 5.68±0.05ms 5.30±0.04ms 0.93 indexing.MultiIndexing.time_index_slice - 2.55±0.03μs 2.38±0.02μs 0.93 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1, None) - 212M 198M 0.93 io.json.ToJSONWide.peakmem_to_json('columns', 'df_int_floats') - 3.77±0.2μs 3.52±0.2μs 0.93 index_cached_properties.IndexCache.time_inferred_type('CategoricalIndex') - 207M 193M 0.93 io.json.ToJSONWide.peakmem_to_json('columns', 'df_date_idx') - 206M 192M 0.93 io.json.ToJSONWide.peakmem_to_json_wide('columns', 'df_date_idx') - 213M 199M 0.93 io.json.ToJSONWide.peakmem_to_json('columns', 'df_int_float_str') - 210M 196M 0.93 io.json.ToJSONWide.peakmem_to_json('columns', 'df_td_int_ts') - 208M 193M 0.93 io.json.ToJSONWide.peakmem_to_json('index', 'df_int_floats') - 204M 190M 0.93 io.json.ToJSONWide.peakmem_to_json('records', 'df_int_floats') - 208M 194M 0.93 io.json.ToJSONWide.peakmem_to_json('index', 'df_int_float_str') - 1.96±0.03ms 1.82±0.01ms 0.93 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'mean') - 199M 185M 0.93 io.json.ToJSONWide.peakmem_to_json('columns', 'df') - 199M 185M 0.93 io.json.ToJSONWide.peakmem_to_json('index', 'df_date_idx') - 204M 189M 0.93 io.json.ToJSONWide.peakmem_to_json('index', 'df_td_int_ts') - 198M 184M 0.93 io.json.ToJSONWide.peakmem_to_json_wide('index', 'df') - 198M 184M 0.93 io.json.ToJSONWide.peakmem_to_json_wide('records', 'df') - 198M 184M 0.93 io.json.ToJSONWide.peakmem_to_json_wide('records', 'df_date_idx') - 198M 184M 0.93 io.json.ToJSONWide.peakmem_to_json_wide('index', 'df_date_idx') - 1.69±0.01ms 1.57±0.01ms 0.93 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'sum') - 197M 183M 0.93 io.json.ToJSONWide.peakmem_to_json('index', 'df') - 196M 182M 0.93 io.json.ToJSONWide.peakmem_to_json_wide('columns', 'df') - 200M 186M 0.93 io.json.ToJSONWide.peakmem_to_json('records', 'df_td_int_ts') - 1.61±0.01ms 1.50±0.01ms 0.93 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'mean') - 195M 181M 0.93 io.json.ToJSONWide.peakmem_to_json('records', 'df') - 195M 181M 0.93 io.json.ToJSONWide.peakmem_to_json('records', 'df_date_idx') - 205M 190M 0.93 io.json.ToJSONWide.peakmem_to_json('records', 'df_int_float_str') - 1.65±0.01ms 1.53±0ms 0.93 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'sum') - 194M 180M 0.93 io.json.ToJSONWide.peakmem_to_json('split', 'df_date_idx') - 4.44±0.2μs 4.11±0.1μs 0.93 index_cached_properties.IndexCache.time_engine('PeriodIndex') - 196M 182M 0.93 io.json.ToJSONWide.peakmem_to_json('split', 'df_int_floats') - 1.29±0.01ms 1.19±0.01ms 0.93 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 1000}, 'mean'), 'int') - 1.65±0.01ms 1.52±0.01ms 0.93 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'sum') - 188M 174M 0.93 io.json.ToJSONWide.peakmem_to_json('split', 'df') - 187M 173M 0.93 io.json.ToJSONWide.peakmem_to_json('values', 'df') - 197M 182M 0.93 io.json.ToJSONWide.peakmem_to_json('split', 'df_int_float_str') - 1.55±0.01ms 1.44±0.01ms 0.93 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'sum') - 187M 173M 0.93 io.json.ToJSONWide.peakmem_to_json('values', 'df_date_idx') - 187M 173M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('split', 'df') - 187M 173M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('values', 'df') - 187M 173M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('split', 'df_date_idx') - 189M 175M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('split', 'df_int_floats') - 187M 173M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('values', 'df_date_idx') - 189M 175M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('values', 'df_int_floats') - 207M 192M 0.92 io.json.ToJSON.peakmem_to_json('columns', 'df_int_float_str') - 190M 176M 0.92 io.json.ToJSONWide.peakmem_to_json('split', 'df_td_int_ts') - 189M 175M 0.92 io.json.ToJSONWide.peakmem_to_json('values', 'df_int_floats') - 206M 191M 0.92 io.json.ToJSON.peakmem_to_json('columns', 'df_int_floats') - 1.74±0.01ms 1.61±0.01ms 0.92 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'mean') - 203M 188M 0.92 io.json.ToJSON.peakmem_to_json('columns', 'df_date_idx') - 204M 188M 0.92 io.json.ToJSON.peakmem_to_json('columns', 'df_td_int_ts') - 187M 173M 0.92 io.json.ToJSONWide.peakmem_to_json('values', 'df_td_int_ts') - 190M 176M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('split', 'df_int_float_str') - 190M 175M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('values', 'df_int_float_str') - 4.05±0.2μs 3.74±0.2μs 0.92 index_cached_properties.IndexCache.time_shape('Float64Index') - 2.16±0.03ms 1.99±0.01ms 0.92 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'int', 'sum') - 3.63±0.1μs 3.35±0.1μs 0.92 index_cached_properties.IndexCache.time_inferred_type('MultiIndex') - 202M 186M 0.92 io.json.ToJSON.peakmem_to_json('index', 'df_int_float_str') - 202M 186M 0.92 io.json.ToJSON.peakmem_to_json('index', 'df_int_floats') - 7.97±0.3μs 7.36±0.3μs 0.92 index_cached_properties.IndexCache.time_inferred_type('UInt64Index') - 808±40ns 745±4ns 0.92 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 12000) - 199M 183M 0.92 io.json.ToJSON.peakmem_to_json('records', 'df_int_float_str') - 190M 175M 0.92 io.json.ToJSONWide.peakmem_to_json('values', 'df_int_float_str') - 198M 183M 0.92 io.json.ToJSON.peakmem_to_json('records', 'df_int_floats') - 195M 180M 0.92 io.json.ToJSON.peakmem_to_json('columns', 'df') - 197M 181M 0.92 io.json.ToJSON.peakmem_to_json('index', 'df_td_int_ts') - 195M 179M 0.92 io.json.ToJSON.peakmem_to_json('index', 'df_date_idx') - 193M 178M 0.92 io.json.ToJSON.peakmem_to_json('index', 'df') - 193M 178M 0.92 io.json.ToJSON.peakmem_to_json('records', 'df_td_int_ts') - 190M 175M 0.92 io.json.ToJSON.peakmem_to_json('split', 'df_date_idx') - 191M 176M 0.92 io.json.ToJSON.peakmem_to_json('records', 'df') - 191M 176M 0.92 io.json.ToJSON.peakmem_to_json('records', 'df_date_idx') - 190M 175M 0.92 io.json.ToJSON.peakmem_to_json('split', 'df_int_floats') - 1.17±0ms 1.08±0ms 0.92 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 1000}, 'mean'), 'float') - 191M 175M 0.92 io.json.ToJSON.peakmem_to_json('split', 'df_int_float_str') - 3.98±0.2μs 3.66±0.1μs 0.92 index_cached_properties.IndexCache.time_shape('CategoricalIndex') - 184M 169M 0.92 io.json.ToJSON.peakmem_to_json('split', 'df') - 183M 168M 0.92 io.json.ToJSON.peakmem_to_json('values', 'df') - 183M 168M 0.92 io.json.ToJSON.peakmem_to_json('values', 'df_date_idx') - 1.18±0ms 1.08±0ms 0.92 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 10}, 'mean'), 'float') - 122±0.7ms 112±0.6ms 0.92 strings.Split.time_rsplit('str', True) - 184M 169M 0.92 io.json.ToJSON.peakmem_to_json('values', 'df_int_float_str') - 183M 168M 0.92 io.json.ToJSON.peakmem_to_json('values', 'df_int_floats') - 183M 168M 0.91 io.json.ToJSON.peakmem_to_json('split', 'df_td_int_ts') - 180M 165M 0.91 io.json.ToJSON.peakmem_to_json('values', 'df_td_int_ts') - 1.81±0.03ms 1.65±0.01ms 0.91 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'sum') - 834±40ns 761±5ns 0.91 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 12000) - 4.17±0.2μs 3.80±0.2μs 0.91 index_cached_properties.IndexCache.time_shape('UInt64Index') - 70.6±0.7μs 64.4±0.6μs 0.91 timeseries.SortIndex.time_sort_index(True) - 36.2±0.3ms 33.0±0.1ms 0.91 rolling.GroupbyLargeGroups.time_rolling_multiindex_creation - 29.1±0.5ms 26.6±0.2ms 0.91 io.sql.SQL.time_read_sql_query('sqlite') - 79.2±0.5μs 72.1±0.3μs 0.91 series_methods.ToFrame.time_to_frame('datetime64[ns]', None) - 900±80ns 819±40ns 0.91 index_cached_properties.IndexCache.time_is_monotonic('RangeIndex') - 835±40ns 759±3ns 0.91 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 12000) - 79.2±0.3μs 71.9±0.3μs 0.91 series_methods.ToFrame.time_to_frame('datetime64[ns]', 'foo') - 4.19±0.2μs 3.80±0.2μs 0.91 index_cached_properties.IndexCache.time_shape('TimedeltaIndex') - 525±4μs 476±5μs 0.91 categoricals.Concat.time_concat_overlapping_index - 88.1±1ms 79.7±3ms 0.90 frame_ctor.FromRecords.time_frame_from_records_generator(None) - 801±40ns 724±4ns 0.90 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 12000) - 16.3±0.2ms 14.7±0.3ms 0.90 eval.Query.time_query_datetime_index - 352±1μs 317±2μs 0.90 algos.isin.IsIn.time_isin_mismatched_dtype('object') - 4.18±0.03ms 3.77±0.02ms 0.90 io.sas.SAS.time_read_sas('xport') - 15.6±0.3ms 14.0±0.1ms 0.90 eval.Query.time_query_datetime_column - 757±9μs 681±4μs 0.90 period.Indexing.time_align - 2.26±0.09ms 2.03±0.07ms 0.90 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'count') - 1.83±0ms 1.65±0.01ms 0.90 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'sum') - 420±30ns 378±4ns 0.90 dtypes.DtypesInvalid.time_pandas_dtype_invalid('array-string') - 993±2μs 893±7μs 0.90 libs.InferDtype.time_infer_dtype('np-object') - 3.72±0.02ms 3.33±0.02ms 0.90 index_cached_properties.IndexCache.time_is_unique('IntervalIndex') - 706±4μs 632±4μs 0.89 frame_ctor.FromDicts.time_dict_of_categoricals - 1.72±0.1μs 1.54±0.07μs 0.89 index_cached_properties.IndexCache.time_inferred_type('DatetimeIndex') - 1.91±0.02ms 1.70±0.01ms 0.89 io.csv.ReadCSVParseDates.time_baseline('c') - 235±2ms 208±1ms 0.89 io.stata.StataMissing.time_write_stata('tw') - 228±2ms 201±1ms 0.88 io.stata.StataMissing.time_write_stata('th') - 230±6ms 203±1ms 0.88 io.stata.StataMissing.time_write_stata('tq') - 218±3ms 192±4ms 0.88 io.stata.StataMissing.time_write_stata('td') - 3.44±0.02ms 3.01±0.02ms 0.87 index_cached_properties.IndexCache.time_is_unique('UInt64Index') - 3.44±0.02ms 3.01±0.02ms 0.87 index_cached_properties.IndexCache.time_is_unique('PeriodIndex') - 3.70±0.2μs 3.22±0.2μs 0.87 index_cached_properties.IndexCache.time_inferred_type('TimedeltaIndex') - 37.5±0.1μs 32.5±0.4μs 0.87 frame_ctor.FromSeries.time_mi_series - 216±6ms 187±2ms 0.87 io.stata.StataMissing.time_write_stata('tc') - 390±3μs 326±3μs 0.84 timeseries.ResetIndex.time_reset_datetimeindex(None) - 395±2μs 329±3μs 0.83 timeseries.ResetIndex.time_reset_datetimeindex('US/Eastern') - 9.26±0.09ms 7.65±0.7ms 0.83 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'float', 'sem') - 9.26±0.1ms 7.60±0.7ms 0.82 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'sem') - 22.4±0.09μs 18.3±0.1μs 0.82 frame_ctor.FromNDArray.time_frame_from_ndarray - 19.5±0.07ms 15.8±0.03ms 0.81 strings.Cat.time_cat(0, ',', None, 0.15) - 6.90±0.05ms 5.51±0.6ms 0.80 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'sem') - 16.4±2ms 12.7±0.07ms 0.77 eval.Eval.time_mult('python', 'all') - 3.47±0.1ms 2.58±0.01ms 0.75 frame_ctor.FromArrays.time_frame_from_arrays_float - 17.5±3ms 12.6±0.08ms 0.72 eval.Eval.time_add('python', 'all') - 4.36±0.03ms 2.99±0.01ms 0.69 frame_ctor.FromArrays.time_frame_from_arrays_int - 4.47±0.01ms 3.04±0.01ms 0.68 frame_ctor.FromArrays.time_frame_from_arrays_sparse - 27.4±0.4ms 17.1±0.09ms 0.62 indexing.InsertColumns.time_insert_middle - 17.1±0.07ms 10.5±0.1ms 0.62 indexing.InsertColumns.time_insert - 17.0±0.07ms 9.28±0.04ms 0.55 indexing.InsertColumns.time_assign_list_like_with_setitem - 17.6±0.1ms 9.38±0.02ms 0.53 indexing.InsertColumns.time_assign_with_setitem - 169±3μs 76.8±2μs 0.45 indexing.AssignTimeseriesIndex.time_frame_assign_timeseries_index - 81.0±0.5μs 28.2±0.4μs 0.35 period.DataFramePeriodColumn.time_setitem_period_column ```

The pd.eval benchmarks using the numexpr engine show 5 to 10x slowdown. This seems to be due to the fact that our numexpr engine works on the full dataframe as one array (so effectively calling df.values for something like pd.eval("df1 + df2")). It is this df.values call that causes the slowdown, and we will probably need to refactor the numexpr engine to also be able to work column-by-column when dealing with dataframes.
The rolling.TableMethod.time_apply('table') shows a 6x slowdown, but uses a DataFrame of shape (10, 1000), so a very wide dataframe. When changing this to (1000, 10), there is no difference between both.
The SelectDtypes.time_select_dtype_... benchmarks show a ~2x slowdown, because now the dtype of every column instead of every block needs to be checked (i.e. more dtype checks). But for a DataFrame of 50 columns, we are still speaking of times around 50µs, so not something to worry about I think.

jorisvandenbossche commented 2 years ago

All results combined

$ asv continuous -f 1.01 HEAD~1 HEAD

```diff before after ratio [f8052655] [5815ddc3]

+ 3.18±0.04μs 11.5±0.1ms 3632.89 frame_methods.ToNumpy.time_values_wide + 3.59±0.07μs 11.5±0.07ms 3193.82 frame_methods.ToNumpy.time_to_numpy_wide + 210±1μs 80.0±0.6ms 381.47 reshape.ReshapeExtensionDtype.time_transpose('datetime64[ns, US/Pacific]') + 508±4μs 99.7±0.5ms 196.07 frame_methods.Quantile.time_frame_quantile(1) + 3.18±0.04μs 263±1μs 82.80 frame_methods.ToNumpy.time_values_tall + 3.64±0.07μs 265±0.6μs 72.79 frame_methods.ToNumpy.time_to_numpy_tall + 48.7±0.3μs 3.34±0.2ms 68.49 frame_methods.XS.time_frame_xs(0) + 4.30±0.03ms 187±2ms 43.44 stat_ops.Correlation.time_corrwith_rows('pearson') + 58.5±0.3μs 1.38±0.01ms 23.52 frame_methods.Dtypes.time_frame_dtypes + 6.93±0.04ms 124±0.9ms 17.81 frame_methods.MaskBool.time_frame_mask_floats + 977±5μs 14.3±0.04ms 14.64 frame_methods.GetDtypeCounts.time_frame_get_dtype_counts + 6.10±0.04ms 87.1±2ms 14.28 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000, 10000)) + 4.01±0.06ms 54.9±0.5ms 13.71 groupby.GroupManyLabels.time_sum(1000) + 6.71±1ms 75.1±4ms 11.20 eval.Eval.time_add('numexpr', 'all') + 6.74±1ms 72.8±5ms 10.79 eval.Eval.time_mult('numexpr', 'all') + 9.92±0.05ms 106±0.5ms 10.67 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000, 10000)) + 416±5μs 4.32±0.4ms 10.37 arithmetic.Ops2.time_frame_series_dot + 12.9±0.03ms 119±0.6ms 9.16 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000, 10000)) + 11.3±0.2ms 96.3±2ms 8.49 eval.Eval.time_chained_cmp('numexpr', 'all') + 1.15±0.03ms 9.37±0.09ms 8.15 arithmetic.Ops2.time_frame_float_div_by_zero + 9.06±0.6ms 73.5±5ms 8.11 eval.Eval.time_mult('numexpr', 1) + 9.01±0.6ms 73.1±5ms 8.11 eval.Eval.time_add('numexpr', 1) + 662±8μs 5.31±0.06ms 8.03 frame_methods.Isnull.time_isnull_floats_no_null + 35.6±0.07ms 285±3ms 8.01 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000, 10000)) + 664±6μs 5.28±0.05ms 7.96 frame_methods.Isnull.time_isnull + 1.52±0.04ms 11.7±0.08ms 7.73 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('eq') + 1.52±0.04ms 11.6±0.04ms 7.60 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('ne') + 1.54±0.05ms 11.7±0.1ms 7.58 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('gt') + 1.54±0.04ms 11.6±0.05ms 7.51 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('le') + 1.56±0.05ms 11.7±0.1ms 7.46 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('ge') + 1.56±0.06ms 11.6±0.05ms 7.43 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('lt') + 414±20μs 2.72±0.2ms 6.56 rolling.TableMethod.time_apply('table') + 34.3±1ms 202±2ms 5.90 frame_methods.MaskBool.time_frame_mask_bools + 1.82±0.01ms 10.5±0.07ms 5.78 arithmetic.Ops2.time_frame_int_div_by_zero + 13.6±0.06ms 77.6±0.8ms 5.72 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000, 10000)) + 3.70±0.2ms 20.0±0.2ms 5.40 frame_methods.ToNumpy.time_to_numpy_mixed_wide + 3.74±0.3ms 19.9±0.2ms 5.33 frame_methods.ToNumpy.time_values_mixed_wide + 616±9μs 3.18±0.02ms 5.16 stat_ops.FrameOps.time_op('prod', 'int', 1) + 590±4μs 2.92±0.01ms 4.94 stat_ops.FrameOps.time_op('sum', 'int', 1) + 1.79±0.01ms 8.77±0.04ms 4.91 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('add') + 1.83±0.03ms 8.81±0.03ms 4.83 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('sub') + 20.5±0.08ms 96.0±0.4ms 4.68 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('pow') + 2.82±0.02ms 13.2±0.07ms 4.67 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('truediv') + 14.6±0.7ms 65.9±1ms 4.52 eval.Eval.time_and('numexpr', 'all') + 21.5±0.2ms 97.3±0.5ms 4.52 frame_methods.Dropna.time_dropna('any', 1) + 1.99±0.02ms 8.93±0.03ms 4.49 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('mul') + 25.3±0.2ms 111±0.7ms 4.38 eval.Eval.time_chained_cmp('numexpr', 1) + 2.39±0.1ms 10.4±0.2ms 4.36 frame_methods.Equals.time_frame_float_equal + 44.5±0.09ms 184±0.5ms 4.14 reshape.Unstack.time_full_product('int') + 1.79±0.03ms 7.32±0.03ms 4.09 reshape.ReshapeExtensionDtype.time_unstack_fast('datetime64[ns, US/Pacific]') + 25.8±0.05ms 98.1±0.7ms 3.81 frame_methods.Dropna.time_dropna_axis_mixed_dtypes('any', 1) + 1.67±0.01ms 6.25±0.02ms 3.74 stat_ops.FrameOps.time_op('sum', 'float', 1) + 112±0.7μs 382±2μs 3.41 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumsum', 'direct', 5) + 113±0.5μs 384±3μs 3.39 groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'direct', 5) + 115±0.5μs 387±2μs 3.37 groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'direct', 5) + 1.85±0.01ms 6.05±0.3ms 3.26 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'datetime64[ns]') + 57.2±0.4ms 184±2ms 3.22 frame_methods.Count.time_count_level_multi(1) + 1.85±0.01ms 5.76±0.3ms 3.12 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'timedelta64[ns]') + 19.4±7ms 60.6±0.3ms 3.12 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000, 10000)) + 3.59±0.03ms 11.2±0.04ms 3.10 reshape.SimpleReshape.time_stack + 61.6±0.4ms 188±0.6ms 3.06 frame_methods.Count.time_count_level_multi(0) + 344±4μs 1.01±0.02ms 2.93 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumprod', 'direct', 5) + 349±3μs 1.02±0.01ms 2.91 groupby.GroupByMethods.time_dtype_as_group('int', 'cumprod', 'direct', 5) + 351±3μs 1.02±0.01ms 2.90 groupby.GroupByMethods.time_dtype_as_group('float', 'cumprod', 'direct', 5) + 15.9±0.1μs 45.2±0.4μs 2.85 dtypes.SelectDtypes.time_select_dtype_string_include() + 16.1±0.1μs 45.7±0.2μs 2.84 dtypes.SelectDtypes.time_select_dtype_int_include() + 15.9±0.2μs 45.1±0.4μs 2.84 dtypes.SelectDtypes.time_select_dtype_float_include() + 5.54±0.5ms 15.7±0.1ms 2.84 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('truediv') + 16.0±0.1μs 45.4±0.4μs 2.84 dtypes.SelectDtypes.time_select_dtype_bool_include() + 16.0±0.02μs 45.4±0.4μs 2.83 dtypes.SelectDtypes.time_select_dtype_string_include() + 16.0±0.2μs 45.2±0.4μs 2.82 dtypes.SelectDtypes.time_select_dtype_bool_include() + 127±1μs 354±0.9μs 2.79 groupby.GroupByMethods.time_dtype_as_group('uint', 'cummax', 'direct', 5) + 126±0.6μs 350±0.9μs 2.78 groupby.GroupByMethods.time_dtype_as_group('uint', 'cummin', 'direct', 5) + 120±1μs 333±1μs 2.77 groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'direct', 5) + 9.77±0.07ms 27.0±0.4ms 2.76 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (10000, 1000)) + 129±0.8μs 356±1μs 2.75 groupby.GroupByMethods.time_dtype_as_group('int', 'cummax', 'direct', 5) + 119±0.3μs 325±1μs 2.72 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'direct', 5) + 9.34±0.05ms 25.4±0.3ms 2.72 frame_methods.Shift.time_shift(0) + 129±0.7μs 349±0.8μs 2.71 groupby.GroupByMethods.time_dtype_as_group('int', 'cummin', 'direct', 5) + 121±2μs 326±0.7μs 2.70 groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'direct', 5) + 72.6±0.5ms 196±10ms 2.70 frame_methods.Count.time_count_level_mixed_dtypes_multi(0) + 11.0±0.7ms 29.5±0.5ms 2.69 io.hdf.HDFStoreDataFrame.time_read_store_table_wide + 236±2μs 628±9μs 2.66 groupby.GroupByMethods.time_dtype_as_group('uint', 'var', 'direct', 5) + 15.7±0.09μs 41.5±0.2μs 2.65 dtypes.SelectDtypes.time_select_dtype_int_exclude() + 43.0±0.2ms 114±0.5ms 2.65 frame_methods.Iteration.time_iteritems_indexing + 15.8±0.1μs 41.6±0.4μs 2.64 dtypes.SelectDtypes.time_select_dtype_float_exclude() + 240±2μs 627±4μs 2.61 groupby.GroupByMethods.time_dtype_as_group('uint', 'mean', 'direct', 5) + 5.42±0.5ms 14.2±0.1ms 2.61 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('sub') + 240±1μs 623±2μs 2.60 groupby.GroupByMethods.time_dtype_as_group('int', 'var', 'direct', 5) + 5.45±0.5ms 14.1±0.08ms 2.59 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('mul') + 3.79±0.1ms 9.77±0.2ms 2.58 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'timedelta64[ns]') + 42.8±0.4ms 110±0.7ms 2.58 frame_methods.Dropna.time_dropna_axis_mixed_dtypes('all', 1) + 25.3±0.4ms 65.3±0.3ms 2.58 frame_methods.Dropna.time_dropna('any', 0) + 244±1μs 627±3μs 2.58 groupby.GroupByMethods.time_dtype_as_group('int', 'mean', 'direct', 5) + 5.86±0.09ms 15.0±0.3ms 2.56 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (10000, 1000)) + 3.78±0.1ms 9.67±0.01ms 2.56 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'datetime64[ns]') + 33.1±0.07ms 84.4±0.8ms 2.55 eval.Eval.time_and('numexpr', 1) + 240±2μs 608±5μs 2.54 groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'direct', 5) + 30.4±0.08ms 76.8±0.9ms 2.53 arithmetic.Ops2.time_frame_float_floor_by_zero + 263±2μs 660±3μs 2.51 groupby.GroupByMethods.time_dtype_as_group('float', 'mean', 'direct', 5) + 3.75±0.1ms 9.19±0.2ms 2.45 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'timedelta64[ns]') + 45.0±0.3ms 110±0.6ms 2.44 frame_methods.Dropna.time_dropna('all', 1) + 113±0.8μs 275±3μs 2.42 groupby.GroupByMethods.time_dtype_as_group('float', 'any', 'direct', 5) + 41.1±0.09ms 99.5±0.4ms 2.42 frame_methods.Reindex.time_reindex_axis1_missing + 3.75±0.1ms 9.06±0.05ms 2.42 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'datetime64[ns]') + 112±0.9μs 270±2μs 2.40 groupby.GroupByMethods.time_dtype_as_group('uint', 'any', 'direct', 5) + 108±0.4μs 258±0.4μs 2.39 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'direct', 5) + 3.91±0.01ms 9.27±0.07ms 2.37 stat_ops.Correlation.time_corrwith_cols('pearson') + 114±0.8μs 269±2μs 2.37 groupby.GroupByMethods.time_dtype_as_group('int', 'any', 'direct', 5) + 28.5±0.4ms 67.5±0.2ms 2.36 frame_methods.Dropna.time_dropna_axis_mixed_dtypes('any', 0) + 114±0.3μs 267±0.6μs 2.35 groupby.GroupByMethods.time_dtype_as_group('datetime', 'any', 'direct', 5) + 9.50±1ms 22.3±1ms 2.35 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('eq') + 115±0.7μs 269±1μs 2.34 groupby.GroupByMethods.time_dtype_as_group('datetime', 'all', 'direct', 5) + 182±0.9μs 426±3μs 2.34 groupby.GroupByMethods.time_dtype_as_group('uint', 'std', 'direct', 5) + 9.52±1ms 22.2±2ms 2.34 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('ne') + 116±1μs 271±2μs 2.34 groupby.GroupByMethods.time_dtype_as_group('float', 'all', 'direct', 5) + 115±2μs 267±2μs 2.33 groupby.GroupByMethods.time_dtype_as_group('uint', 'all', 'direct', 5) + 9.49±1ms 22.1±2ms 2.33 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('le') + 111±1μs 257±2μs 2.33 groupby.GroupByMethods.time_dtype_as_group('object', 'all', 'direct', 5) + 9.50±1ms 22.0±2ms 2.32 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('lt') + 9.55±1ms 22.1±2ms 2.32 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('ge') + 316±1μs 731±4μs 2.31 groupby.GroupByMethods.time_dtype_as_group('float', 'median', 'direct', 5) + 9.56±1ms 22.0±2ms 2.30 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('gt') + 116±1μs 268±0.9μs 2.30 groupby.GroupByMethods.time_dtype_as_group('int', 'all', 'direct', 5) + 1.79±0.01ms 4.11±0.2ms 2.30 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 85.0±0.3ms 195±10ms 2.29 frame_methods.Count.time_count_level_mixed_dtypes_multi(1) + 186±2μs 426±3μs 2.28 groupby.GroupByMethods.time_dtype_as_group('int', 'std', 'direct', 5) + 1.81±0ms 4.13±0.2ms 2.28 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 35.9±3ms 81.5±1ms 2.27 join_merge.ConcatDataFrames.time_c_ordered(0, True) + 3.06±0.2ms 6.94±0.06ms 2.27 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'timedelta64[ns]') + 23.1±0.09μs 52.4±0.4μs 2.26 dtypes.SelectDtypes.time_select_dtype_string_include() + 36.3±3ms 82.1±2ms 2.26 join_merge.ConcatDataFrames.time_c_ordered(0, False) + 23.1±0.08μs 52.3±0.3μs 2.26 dtypes.SelectDtypes.time_select_dtype_float_include() + 3.08±0.2ms 6.94±0.1ms 2.25 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'datetime64[ns]') + 1.78±0.03ms 4.00±0.2ms 2.25 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 23.4±0.2μs 52.4±0.2μs 2.24 dtypes.SelectDtypes.time_select_dtype_int_include() + 23.5±0.2μs 52.5±0.4μs 2.24 dtypes.SelectDtypes.time_select_dtype_string_include() + 23.5±0.1μs 52.5±0.3μs 2.23 dtypes.SelectDtypes.time_select_dtype_float_include() + 23.5±0.09μs 52.5±0.1μs 2.23 dtypes.SelectDtypes.time_select_dtype_int_include() + 1.28±0.01ms 2.85±0.2ms 2.23 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 23.6±0.07μs 52.6±0.2μs 2.23 dtypes.SelectDtypes.time_select_dtype_bool_include() + 1.28±0.02ms 2.84±0.3ms 2.23 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 185±0.8μs 412±4μs 2.23 groupby.GroupByMethods.time_dtype_as_group('float', 'std', 'direct', 5) + 355±1μs 789±9μs 2.23 groupby.GroupByMethods.time_dtype_as_group('uint', 'median', 'direct', 5) + 1.81±0.02ms 3.99±0.2ms 2.21 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 97.8±2ms 216±0.4ms 2.21 reshape.Unstack.time_without_last_row('int') + 6.41±1ms 14.1±0.1ms 2.20 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('add') + 359±2μs 785±5μs 2.19 groupby.GroupByMethods.time_dtype_as_group('int', 'median', 'direct', 5) + 1.90±0.01ms 4.15±0.1ms 2.18 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.90±0.04ms 4.13±0.2ms 2.18 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.93±0.03ms 4.14±0.2ms 2.15 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 21.6±3ms 46.1±0.5ms 2.14 arithmetic.Ops.time_frame_multi_and(True, 'default') + 1.28±0.01ms 2.74±0.2ms 2.14 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 26.3±0.1μs 56.3±0.4μs 2.14 dtypes.SelectDtypes.time_select_dtype_string_include('Int8') + 1.88±0.01ms 3.99±0.2ms 2.12 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.27±0.01ms 2.69±0.2ms 2.12 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 26.5±0.1μs 56.0±0.3μs 2.11 dtypes.SelectDtypes.time_select_dtype_float_include('Int8') + 975±4μs 2.06±0.01ms 2.11 stat_ops.FrameOps.time_op('mean', 'int', 1) + 1.88±0.02ms 3.97±0.2ms 2.11 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 26.6±0.1μs 55.9±0.2μs 2.10 dtypes.SelectDtypes.time_select_dtype_int_include('Int8') + 26.7±0.1μs 56.1±0.4μs 2.10 dtypes.SelectDtypes.time_select_dtype_bool_include('Int8') + 747±7μs 1.56±0.01ms 2.09 groupby.GroupByMethods.time_dtype_as_group('uint', 'prod', 'direct', 5) + 23.3±0.2μs 48.7±0.1μs 2.09 dtypes.SelectDtypes.time_select_dtype_bool_exclude() + 1.89±0.02ms 3.96±0.2ms 2.09 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 27.3±0.3μs 57.1±0.3μs 2.09 dtypes.SelectDtypes.time_select_dtype_float_include('Int16') + 27.2±0.2μs 56.8±0.3μs 2.09 dtypes.SelectDtypes.time_select_dtype_string_include('Int16') + 2.43±0.02ms 5.04±0.09ms 2.08 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 27.3±0.2μs 56.7±0.3μs 2.08 dtypes.SelectDtypes.time_select_dtype_bool_include('Int16') + 746±8μs 1.55±0.01ms 2.08 groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'direct', 5) + 27.4±0.3μs 56.8±0.2μs 2.07 dtypes.SelectDtypes.time_select_dtype_int_include('Int16') + 12.0±0.1ms 24.8±0.7ms 2.07 frame_methods.Iteration.time_items + 1.26±0.02ms 2.60±0.1ms 2.06 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 27.9±0.1μs 57.5±0.3μs 2.06 dtypes.SelectDtypes.time_select_dtype_int_include('Int32') + 799±4μs 1.64±0.02ms 2.06 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'direct', 5) + 28.0±0.1μs 57.6±0.2μs 2.06 dtypes.SelectDtypes.time_select_dtype_float_include('Int32') + 766±4μs 1.57±0.02ms 2.05 groupby.GroupByMethods.time_dtype_as_group('uint', 'sum', 'direct', 5) + 2.42±0.01ms 4.96±0.5ms 2.05 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 28.0±0.2μs 57.6±0.1μs 2.05 dtypes.SelectDtypes.time_select_dtype_string_include('Int32') + 2.02±0.02ms 4.14±0.2ms 2.05 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 28.1±0.3μs 57.5±0.3μs 2.05 dtypes.SelectDtypes.time_select_dtype_bool_include('Int32') + 773±4μs 1.58±0.01ms 2.04 groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'direct', 5) + 2.81±0.01ms 5.73±0.1ms 2.04 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 28.8±0.4μs 58.7±0.5μs 2.04 dtypes.SelectDtypes.time_select_dtype_string_include('Int64') + 28.9±0.4μs 58.8±0.4μs 2.03 dtypes.SelectDtypes.time_select_dtype_float_include('Int64') + 28.8±0.4μs 58.5±0.3μs 2.03 dtypes.SelectDtypes.time_select_dtype_bool_include('Int64') + 29.2±0.2μs 59.3±0.5μs 2.03 dtypes.SelectDtypes.time_select_dtype_int_include('UInt8') + 2.42±0.01ms 4.91±0.06ms 2.02 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 33.3±0.6ms 67.4±0.9ms 2.02 groupby.GroupByCythonAgg.time_frame_agg('float64', 'prod') + 29.7±0.1μs 60.0±0.6μs 2.02 dtypes.SelectDtypes.time_select_dtype_int_include('UInt16') + 29.5±0.1μs 59.3±0.6μs 2.01 dtypes.SelectDtypes.time_select_dtype_bool_include('UInt8') + 29.9±0.1μs 60.0±0.3μs 2.01 dtypes.SelectDtypes.time_select_dtype_string_include('UInt16') + 29.5±0.2μs 59.3±0.3μs 2.01 dtypes.SelectDtypes.time_select_dtype_float_include('UInt8') + 2.42±0ms 4.85±0.06ms 2.00 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 29.6±0.2μs 59.1±0.2μs 2.00 dtypes.SelectDtypes.time_select_dtype_string_include('UInt8') + 29.9±0.2μs 59.8±0.2μs 2.00 dtypes.SelectDtypes.time_select_dtype_bool_include('UInt16') + 29.9±0.1μs 59.7±0.3μs 2.00 dtypes.SelectDtypes.time_select_dtype_float_include('UInt16') + 2.02±0.02ms 4.03±0.2ms 1.99 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.36±0.01ms 2.70±0.08ms 1.99 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 825±5μs 1.64±0.02ms 1.99 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'direct', 5) + 30.4±0.2μs 60.5±0.3μs 1.99 dtypes.SelectDtypes.time_select_dtype_bool_include('UInt32') + 30.5±0.1μs 60.6±0.2μs 1.99 dtypes.SelectDtypes.time_select_dtype_string_include('UInt32') + 13.5±0.04ms 26.8±0.2ms 1.98 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (10000, 1000)) + 1.09±0.01ms 2.17±0.02ms 1.98 stat_ops.FrameOps.time_op('mean', 'float', 1) + 30.7±0.2μs 60.7±0.3μs 1.98 dtypes.SelectDtypes.time_select_dtype_int_include('UInt32') + 1.37±0.01ms 2.70±0.08ms 1.98 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.92±0.07ms 5.78±0.07ms 1.98 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 40.7±6ms 80.5±2ms 1.98 join_merge.ConcatDataFrames.time_f_ordered(0, True) + 31.0±0.08μs 61.2±0.3μs 1.97 dtypes.SelectDtypes.time_select_dtype_int_include('UInt64') + 1.27±0.01ms 2.51±0.02ms 1.97 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 41.5±0.5ms 81.9±0.4ms 1.97 frame_methods.Dropna.time_dropna('all', 0) + 31.1±0.1μs 61.3±0.3μs 1.97 dtypes.SelectDtypes.time_select_dtype_string_include('UInt64') + 3.52±0.07ms 6.93±0.02ms 1.97 frame_methods.Reindex.time_reindex_axis0 + 30.7±0.1μs 60.5±0.3μs 1.97 dtypes.SelectDtypes.time_select_dtype_float_include('UInt32') + 31.3±0.09μs 61.4±0.2μs 1.96 dtypes.SelectDtypes.time_select_dtype_float_include('UInt64') + 31.4±0.2μs 61.6±0.5μs 1.96 dtypes.SelectDtypes.time_select_dtype_bool_include('UInt64') + 2.78±0.02ms 5.45±0.07ms 1.96 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 2.42±0.01ms 4.73±0.07ms 1.95 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 41.8±5ms 81.3±1ms 1.95 join_merge.ConcatDataFrames.time_f_ordered(0, False) + 2.42±0.01ms 4.71±0.2ms 1.95 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 516±3μs 1.00±0.03ms 1.94 frame_methods.Iteration.time_itertuples_start + 4.56±0.05ms 8.86±3ms 1.94 gil.ParallelRolling.time_rolling('min') + 28.5±0.3μs 54.8±0.3μs 1.92 dtypes.SelectDtypes.time_select_dtype_int_exclude('Int64') + 1.35±0.01ms 2.58±0.1ms 1.91 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.35±0ms 2.57±0.2ms 1.89 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 365±1μs 686±20μs 1.88 frame_methods.Iteration.time_itertuples_raw_start + 33.4±0.7ms 62.6±0.8ms 1.88 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis0('floordiv') + 366±2μs 686±10μs 1.87 frame_methods.Iteration.time_itertuples_raw_read_first + 12.7±0.01ms 23.8±0.09ms 1.87 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (10000, 1000)) + 523±4μs 976±30μs 1.86 frame_methods.Iteration.time_itertuples_read_first + 1.45±0ms 2.69±0.09ms 1.86 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.45±0ms 2.68±0.1ms 1.85 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.46±0.01ms 2.70±0.07ms 1.85 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 13.4±3ms 24.8±0.08ms 1.84 groupby.Cumulative.time_frame_transform('int64', 'cumsum') + 36.4±0.6ms 67.1±0.8ms 1.84 groupby.GroupByCythonAgg.time_frame_agg('float64', 'first') + 212±0.7ns 389±4ns 1.84 attrs_caching.DataFrameAttributes.time_get_index + 45.2±0.6ms 82.2±1ms 1.82 groupby.GroupByCythonAgg.time_frame_agg('float64', 'sum') + 44.9±0.7ms 81.5±0.8ms 1.82 groupby.GroupByCythonAgg.time_frame_agg('float64', 'mean') + 1.43±0.01ms 2.60±0.1ms 1.81 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 6.39±0.08ms 11.6±0.03ms 1.81 timeseries.AsOf.time_asof_nan('DataFrame') + 9.77±0.03ms 17.6±0.07ms 1.81 stat_ops.FrameOps.time_op('kurt', 'int', 1) + 1.44±0.02ms 2.57±0.1ms 1.79 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 6.60±0.06ms 11.8±0.02ms 1.78 timeseries.AsOf.time_asof('DataFrame') + 44.4±0.7ms 79.1±2ms 1.78 groupby.GroupByCythonAgg.time_frame_agg('float64', 'var') + 40.1±0.3μs 71.4±2μs 1.78 dtypes.SelectDtypes.time_select_dtype_int_include('int8') + 35.7±0.09ms 63.3±0.4ms 1.77 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (10000, 1000)) + 40.4±0.2μs 71.6±2μs 1.77 dtypes.SelectDtypes.time_select_dtype_int_include('int16') + 1.44±0.01ms 2.56±0.1ms 1.77 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 39.8±0.2μs 70.1±0.2μs 1.76 dtypes.SelectDtypes.time_select_dtype_string_include('m8[ns]') + 39.9±0.02μs 70.1±0.6μs 1.76 dtypes.SelectDtypes.time_select_dtype_string_include('M8[ns]') + 40.0±0.2μs 70.1±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('m8[ns]') + 40.0±0.3μs 70.2±0.5μs 1.75 dtypes.SelectDtypes.time_select_dtype_float_include('M8[ns]') + 40.1±0.08μs 70.3±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_float_include('uint16') + 39.9±0.08μs 69.9±0.1μs 1.75 dtypes.SelectDtypes.time_select_dtype_bool_include('m8[ns]') + 40.1±0.2μs 70.3±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_string_include('uint16') + 40.0±0.2μs 70.1±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_float_include('uint64') + 39.9±0.1μs 70.0±0.2μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('M8[ns]') + 524±2μs 918±3μs 1.75 groupby.GroupByMethods.time_dtype_as_group('uint', 'min', 'direct', 5) + 40.1±0.1μs 70.2±0.9μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('float64') + 1.96±0.03ms 3.44±0.04ms 1.75 arithmetic.Ops.time_frame_comparison(True, 'default') + 50.6±0.6ms 88.6±2ms 1.75 join_merge.ConcatDataFrames.time_f_ordered(1, False) + 39.8±0.2μs 69.7±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_string_include('int8') + 40.1±0.4μs 70.1±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_float_include('m8[ns]') + 537±1μs 940±20μs 1.75 groupby.GroupByMethods.time_dtype_as_group('datetime', 'max', 'direct', 5) + 40.2±0.1μs 70.2±0.6μs 1.75 dtypes.SelectDtypes.time_select_dtype_string_include('complex64') + 40.0±0.2μs 69.8±0.09μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('float32') + 39.9±0.3μs 69.7±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('bool') + 40.6±0.1μs 70.8±0.1μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('timedelta64[ns]') + 40.1±0.1μs 70.0±0.5μs 1.75 dtypes.SelectDtypes.time_select_dtype_bool_include('M8[ns]') + 40.3±0.2μs 70.3±0.5μs 1.75 dtypes.SelectDtypes.time_select_dtype_float_include('uint32') + 40.1±0.2μs 70.1±0.2μs 1.75 dtypes.SelectDtypes.time_select_dtype_string_include('int32') + 40.0±0.1μs 69.9±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('int32') + 40.2±0.2μs 70.1±0.4μs 1.75 dtypes.SelectDtypes.time_select_dtype_int_include('uint16') + 40.0±0.2μs 69.9±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_string_include('complex128') + 40.2±0.06μs 70.1±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_string_include('int64') + 40.0±0.2μs 69.8±0.3μs 1.75 dtypes.SelectDtypes.time_select_dtype_bool_include('complex64') + 40.1±0.3μs 69.9±0.6μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('uint8') + 40.0±0.1μs 69.8±0.3μs 1.74 dtypes.SelectDtypes.time_select_dtype_int_include('complex128') + 40.1±0.5μs 70.0±0.3μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('uint8') + 40.1±0.1μs 69.9±0.2μs 1.74 dtypes.SelectDtypes.time_select_dtype_bool_include('int64') + 518±3μs 904±30μs 1.74 groupby.GroupByMethods.time_dtype_as_group('datetime', 'first', 'direct', 5) + 40.2±0.1μs 70.1±0.1μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('float32') + 40.1±0.1μs 69.9±0.2μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('int64') + 40.2±0.2μs 70.0±0.2μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('complex64') + 40.2±0.3μs 70.2±0.5μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('int32') + 40.7±0.2μs 70.9±0.1μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('timedelta64[ns]') + 40.0±0.09μs 69.7±0.5μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('float32') + 39.9±0.1μs 69.5±0.4μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('bool') + 40.2±0.2μs 69.9±0.5μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('float64') + 26.2±2ms 45.6±0.6ms 1.74 arithmetic.Ops.time_frame_multi_and(False, 'default') + 116±0.4μs 203±1μs 1.74 groupby.GroupByMethods.time_dtype_as_group('int', 'count', 'direct', 5) + 40.1±0.06μs 69.8±0.2μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('complex128') + 40.7±0.2μs 70.8±0.2μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('timedelta64[ns]') + 40.4±0.3μs 70.2±0.6μs 1.74 dtypes.SelectDtypes.time_select_dtype_bool_include('float32') + 40.2±0.2μs 69.8±0.7μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('uint64') + 40.2±0.1μs 69.8±0.3μs 1.74 dtypes.SelectDtypes.time_select_dtype_float_include('int8') + 40.1±0.1μs 69.7±0.6μs 1.74 dtypes.SelectDtypes.time_select_dtype_bool_include('float64') + 40.3±0.2μs 69.9±0.5μs 1.74 dtypes.SelectDtypes.time_select_dtype_int_include('uint32') + 456±2μs 792±6μs 1.74 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'direct', 5) + 40.2±0.1μs 69.8±0.5μs 1.74 dtypes.SelectDtypes.time_select_dtype_bool_include('uint16') + 26.5±2ms 46.0±0.2ms 1.74 arithmetic.Ops.time_frame_multi_and(False, 1) + 39.9±0.07μs 69.3±0.5μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('bool') + 40.3±0.4μs 70.0±0.4μs 1.74 dtypes.SelectDtypes.time_select_dtype_string_include('int16') + 40.8±0.2μs 70.7±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('timedelta64[ns]') + 40.1±0.2μs 69.6±0.4μs 1.73 dtypes.SelectDtypes.time_select_dtype_int_include('complex64') + 40.2±0.2μs 69.8±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_int_include('uint64') + 40.3±0.5μs 69.9±0.5μs 1.73 dtypes.SelectDtypes.time_select_dtype_string_include('uint32') + 40.4±0.3μs 70.0±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_float_include('int16') + 40.2±0.1μs 69.7±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('int32') + 40.2±0.4μs 69.7±0.4μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('uint64') + 40.3±0.5μs 69.8±0.5μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('int16') + 41.1±0.2μs 71.1±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_int_include('datetime64[ns]') + 41.0±0.3μs 70.9±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_string_include('datetime64[ns]') + 11.4±0.09ms 19.8±0.04ms 1.73 stat_ops.FrameOps.time_op('skew', 'int', 1) + 40.3±0.5μs 69.7±0.4μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('int8') + 40.4±0.1μs 69.9±0.5μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('complex128') + 515±9μs 891±70μs 1.73 groupby.GroupByMethods.time_dtype_as_group('datetime', 'last', 'direct', 5) + 40.4±0.3μs 69.8±0.3μs 1.73 dtypes.SelectDtypes.time_select_dtype_bool_include('uint32') + 1.57±0.01ms 2.72±0.06ms 1.73 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 41.1±0.3μs 71.0±0.2μs 1.72 dtypes.SelectDtypes.time_select_dtype_float_include('datetime64[ns]') + 534±2μs 921±20μs 1.72 groupby.GroupByMethods.time_dtype_as_group('datetime', 'min', 'direct', 5) + 40.5±0.4μs 69.7±0.4μs 1.72 dtypes.SelectDtypes.time_select_dtype_int_include('uint8') + 40.6±0.3μs 69.9±0.5μs 1.72 dtypes.SelectDtypes.time_select_dtype_bool_include('uint8') + 532±1μs 915±4μs 1.72 groupby.GroupByMethods.time_dtype_as_group('uint', 'max', 'direct', 5) + 523±0.9μs 900±4μs 1.72 groupby.GroupByMethods.time_dtype_as_group('int', 'first', 'direct', 5) + 537±2μs 924±7μs 1.72 groupby.GroupByMethods.time_dtype_as_group('int', 'max', 'direct', 5) + 3.86±0.05ms 6.63±0.08ms 1.72 stat_ops.FrameOps.time_op('mad', 'int', 1) + 41.4±0.2μs 71.0±0.4μs 1.72 dtypes.SelectDtypes.time_select_dtype_bool_include('datetime64[ns]') + 51.0±4ms 87.6±2ms 1.72 join_merge.ConcatDataFrames.time_f_ordered(1, True) + 1.43±0.01ms 2.46±0.01ms 1.71 stat_ops.FrameOps.time_op('var', 'int', 1) + 526±9μs 900±8μs 1.71 groupby.GroupByMethods.time_dtype_as_group('uint', 'first', 'direct', 5) + 20.9±0.1ms 35.8±0.5ms 1.71 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 544±3μs 931±6μs 1.71 groupby.GroupByMethods.time_dtype_as_group('float', 'max', 'direct', 5) + 20.9±0.03ms 35.7±0.2ms 1.71 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 47.2±0.7μs 80.5±0.6μs 1.71 replace.ReplaceList.time_replace_list(True) + 538±0.8μs 915±4μs 1.70 groupby.GroupByMethods.time_dtype_as_group('int', 'min', 'direct', 5) + 457±2μs 777±5μs 1.70 groupby.GroupByMethods.time_dtype_as_group('object', 'first', 'direct', 5) + 539±4μs 915±7μs 1.70 groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'direct', 5) + 86.8±1ms 147±1ms 1.70 groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'direct', 5) + 516±2μs 874±7μs 1.69 groupby.GroupByMethods.time_dtype_as_group('uint', 'last', 'direct', 5) + 533±2μs 897±9μs 1.68 groupby.GroupByMethods.time_dtype_as_group('float', 'last', 'direct', 5) + 56.8±0.3ms 95.7±0.5ms 1.68 groupby.GroupByMethods.time_dtype_as_group('uint', 'skew', 'direct', 5) + 88.2±1ms 148±1ms 1.68 groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'transformation', 5) + 3.27±0.06ms 5.49±0.1ms 1.68 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 536±4μs 901±5μs 1.68 groupby.GroupByMethods.time_dtype_as_group('float', 'first', 'direct', 5) + 56.6±0.3ms 95.0±1ms 1.68 groupby.GroupByMethods.time_dtype_as_group('int', 'skew', 'direct', 5) + 2.03±0.04ms 3.39±0.04ms 1.67 arithmetic.Ops.time_frame_comparison(False, 'default') + 519±4μs 866±9μs 1.67 groupby.GroupByMethods.time_dtype_as_group('int', 'last', 'direct', 5) + 2.03±0.02ms 3.39±0.03ms 1.67 arithmetic.Ops.time_frame_comparison(False, 1) + 1.70±0.01ms 2.83±0.07ms 1.67 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.57±0.01ms 2.61±0.02ms 1.67 stat_ops.FrameOps.time_op('var', 'float', 1) + 58.1±0.5ms 96.7±0.4ms 1.67 groupby.GroupByMethods.time_dtype_as_group('uint', 'skew', 'transformation', 5) + 58.0±0.3ms 96.6±0.3ms 1.66 groupby.GroupByMethods.time_dtype_as_group('int', 'skew', 'transformation', 5) + 1.71±0.01ms 2.85±0.1ms 1.66 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.71±0.01ms 2.83±0.1ms 1.66 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 51.8±0.2ms 85.9±0.5ms 1.66 frame_methods.Dropna.time_dropna_axis_mixed_dtypes('all', 0) + 5.82±0.07ms 9.65±0.04ms 1.66 reshape.ReshapeExtensionDtype.time_stack('datetime64[ns, US/Pacific]') + 12.9±0.03ms 21.4±3ms 1.65 indexing.ChainIndexing.time_chained_indexing(None) + 40.1±0.2μs 66.0±0.4μs 1.65 dtypes.SelectDtypes.time_select_dtype_int_exclude('int64') + 40.3±0.4μs 66.3±0.5μs 1.65 dtypes.SelectDtypes.time_select_dtype_float_exclude('float64') + 545±2ms 894±8ms 1.64 io.csv.ToCSVDatetimeBig.time_frame(100000) + 40.2±0.3μs 65.9±0.3μs 1.64 dtypes.SelectDtypes.time_select_dtype_bool_exclude('bool') + 28.2±2ms 46.2±0.5ms 1.64 arithmetic.Ops.time_frame_multi_and(True, 1) + 50.1±2ms 81.9±1ms 1.64 groupby.GroupByCythonAgg.time_frame_agg('float64', 'any') + 47.2±0.8ms 77.2±0.4ms 1.64 replace.Convert.time_replace('DataFrame', 'Timedelta') + 727±9μs 1.19±0.04ms 1.64 frame_methods.Iteration.time_items_cached + 1.25±0.02ms 2.04±0.03ms 1.64 timeseries.ResampleDataFrame.time_method('mean') + 1.56±0ms 2.55±0.1ms 1.63 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.72±0.01ms 2.80±0.09ms 1.63 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 3.25±0.02μs 5.28±0.5μs 1.62 frame_methods.XS.time_frame_xs(1) + 11.1±0.1ms 18.0±3ms 1.62 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (100000, 100)) + 353±6μs 572±5μs 1.62 frame_methods.Quantile.time_frame_quantile(0) + 47.9±0.7ms 77.1±0.4ms 1.61 replace.Convert.time_replace('DataFrame', 'Timestamp') + 6.11±0.04ms 9.83±0.03ms 1.61 io.csv.ToCSVDatetimeBig.time_frame(1000) + 51.0±2ms 81.9±1ms 1.61 groupby.GroupByCythonAgg.time_frame_agg('float64', 'all') + 1.71±0.02ms 2.75±0.1ms 1.61 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 55.4±0.4ms 88.9±0.2ms 1.61 io.csv.ToCSVDatetimeBig.time_frame(10000) + 8.93±0.3ms 14.3±0.1ms 1.60 eval.Eval.time_chained_cmp('python', 'all') + 1.71±0.01ms 2.73±0.02ms 1.60 stat_ops.FrameOps.time_op('std', 'int', 1) + 40.4±0.5ms 64.5±0.7ms 1.60 groupby.GroupByCythonAgg.time_frame_agg('float64', 'last') + 253±0.7ms 404±0.6ms 1.60 join_merge.Merge.time_merge_dataframes_cross(True) + 1.71±0.01ms 2.73±0.1ms 1.59 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 254±2ms 403±0.5ms 1.59 join_merge.Merge.time_merge_dataframes_cross(False) + 4.65±0.06ms 7.36±0.04ms 1.58 stat_ops.FrameOps.time_op('sem', 'float', 1) + 1.70±0.01ms 2.66±0.1ms 1.56 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 132±1μs 207±1μs 1.56 groupby.GroupByMethods.time_dtype_as_group('datetime', 'count', 'direct', 5) + 131±0.6μs 203±2μs 1.56 groupby.GroupByMethods.time_dtype_as_group('uint', 'count', 'direct', 5) + 4.05±0.03ms 6.28±0ms 1.55 stat_ops.FrameOps.time_op('prod', 'float', 1) + 1.70±0.01ms 2.63±0.1ms 1.55 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.93±0.01ms 2.97±0.02ms 1.54 stat_ops.FrameOps.time_op('std', 'float', 1) + 136±1μs 209±1μs 1.54 groupby.GroupByMethods.time_dtype_as_group('float', 'count', 'direct', 5) + 6.49±0.2ms 9.98±0.03ms 1.54 frame_methods.Reindex.time_reindex_upcast + 1.79±0.02ms 2.75±0.07ms 1.54 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 128±0.6μs 197±0.9μs 1.53 groupby.GroupByMethods.time_dtype_as_group('object', 'count', 'direct', 5) + 8.17±0.04ms 12.5±0.03ms 1.53 join_merge.Concat.time_concat_small_frames(1) + 2.45±0.02ms 3.73±0.07ms 1.53 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.81±0.01ms 2.75±0.06ms 1.52 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.01ms 3.59±0.05ms 1.52 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 5.56±0.02ms 8.43±0.02ms 1.52 stat_ops.FrameMultiIndexOps.time_op(0, 'std') + 5.68±0.06ms 8.57±0.04ms 1.51 stat_ops.FrameMultiIndexOps.time_op(0, 'var') + 2.43±0.02ms 3.65±0.06ms 1.50 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 1.54±0.03ms 2.31±0.02ms 1.50 timeseries.AsOf.time_asof_nan_single('DataFrame') + 47.3±0.7ms 71.0±0.6ms 1.50 groupby.GroupByCythonAgg.time_frame_agg('float64', 'min') + 11.6±0.05ms 17.3±0.1ms 1.50 eval.Eval.time_and('python', 'all') + 47.5±0.7ms 71.1±1ms 1.50 groupby.GroupByCythonAgg.time_frame_agg('float64', 'max') + 17.1±0.2ms 25.5±0.3ms 1.49 indexing.ChainIndexing.time_chained_indexing('warn') + 1.04±0.01ms 1.55±0.3ms 1.49 series_methods.NanOps.time_func('sum', 1000000, 'int8') + 27.6±0.6ms 40.6±0.05ms 1.47 arithmetic.Ops2.time_frame_dot + 399±3ms 587±0.9ms 1.47 join_merge.MergeCategoricals.time_merge_object + 5.76±0.02ms 8.48±0.05ms 1.47 stat_ops.FrameMultiIndexOps.time_op(1, 'var') + 9.86±0.07ms 14.5±0.3ms 1.47 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000000, 10)) + 518±1ms 761±4ms 1.47 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'skew') + 5.71±0.06ms 8.37±0.08ms 1.47 stat_ops.FrameMultiIndexOps.time_op(1, 'std') + 2.36±0.02ms 3.46±0.03ms 1.47 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.01ms 3.45±0.06ms 1.47 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 4.45±0.2ms 6.53±0.1ms 1.46 stat_ops.FrameOps.time_op('mad', 'float', 1) + 1.90±0.02ms 2.78±0.08ms 1.46 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.02ms 3.45±0.03ms 1.46 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.36±0.01ms 3.44±0.02ms 1.46 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.34±0.01ms 3.41±0.04ms 1.46 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.36±0.01ms 3.44±0.06ms 1.45 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.42±0.01ms 3.51±0.07ms 1.45 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.90±0.01ms 2.76±0.08ms 1.45 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 1.88±0.02ms 2.73±0.07ms 1.45 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 15.9±3ms 22.9±0.05ms 1.44 groupby.Cumulative.time_frame_transform('int64', 'cummin') + 14.2±2ms 20.6±0.04ms 1.44 groupby.Cumulative.time_frame_transform_many_nulls('int64', 'cumsum') + 7.73±0.6ms 11.1±0.1ms 1.44 frame_methods.Equals.time_frame_float_unequal + 1.85±0.01ms 2.66±0.1ms 1.43 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 14.4±0.9ms 20.6±0.1ms 1.43 groupby.Cumulative.time_frame_transform_many_nulls('float64', 'cumsum') + 16.0±2ms 22.8±0.06ms 1.43 groupby.Cumulative.time_frame_transform_many_nulls('int64', 'cummin') + 1.85±0.01ms 2.63±0.1ms 1.43 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 16.1±0.9ms 22.9±0.3ms 1.42 groupby.Cumulative.time_frame_transform_many_nulls('float64', 'cummin') + 176±2ms 250±1ms 1.42 groupby.GroupByMethods.time_dtype_as_group('float', 'mad', 'direct', 5) + 17.4±0.9ms 24.7±0.1ms 1.42 groupby.Cumulative.time_frame_transform('float64', 'cummin') + 16.8±3ms 23.8±0.08ms 1.42 groupby.Cumulative.time_frame_transform('int64', 'cummax') + 1.85±0.02ms 2.61±0.1ms 1.41 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 177±2ms 251±2ms 1.41 groupby.GroupByMethods.time_dtype_as_group('float', 'mad', 'transformation', 5) + 114±1ms 161±0.8ms 1.41 groupby.GroupByMethods.time_dtype_as_group('uint', 'mad', 'direct', 5) + 26.2±1ms 37.0±0.4ms 1.41 arithmetic.MixedFrameWithSeriesAxis.time_frame_op_with_series_axis1('pow') + 2.36±0.01ms 3.32±0.05ms 1.40 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.37±0.01ms 3.32±0.03ms 1.40 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 16.3±3ms 22.9±0.04ms 1.40 groupby.Cumulative.time_frame_transform_many_nulls('int64', 'cummax') + 2.37±0.01ms 3.32±0.04ms 1.40 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 116±1ms 162±0.9ms 1.40 groupby.GroupByMethods.time_dtype_as_group('uint', 'mad', 'transformation', 5) + 114±1ms 160±1ms 1.40 groupby.GroupByMethods.time_dtype_as_group('int', 'mad', 'direct', 5) + 116±1ms 163±0.7ms 1.40 groupby.GroupByMethods.time_dtype_as_group('int', 'mad', 'transformation', 5) + 734±40μs 1.03±0.09ms 1.40 reindex.LevelAlign.time_align_level + 2.37±0.02ms 3.30±0.04ms 1.40 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 13.1±0.06ms 18.2±0.08ms 1.40 io.hdf.HDFStoreDataFrame.time_query_store_table_wide + 2.60±0.03ms 3.62±0.04ms 1.39 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 49.3±3ms 68.6±0.6ms 1.39 frame_methods.Equals.time_frame_object_unequal + 2.36±0.01ms 3.28±0.06ms 1.39 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 16.6±0.9ms 23.0±0.04ms 1.39 groupby.Cumulative.time_frame_transform_many_nulls('float64', 'cummax') + 240±0.4μs 333±2μs 1.39 stat_ops.Correlation.time_corr('pearson') + 2.38±0.02ms 3.29±0.03ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 15.9±0.8ms 21.9±0.06ms 1.38 groupby.Cumulative.time_frame_transform('float64', 'cumsum') + 32.0±0.3μs 44.1±0.4μs 1.38 frame_methods.GetNumericData.time_frame_get_numeric_data + 2.37±0.01ms 3.26±0.04ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 2.00±0.02ms 2.76±0.09ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 3.39±0ms 4.67±0.06ms 1.38 timeseries.ResampleSeries.time_resample('datetime', '5min', 'ohlc') + 2.36±0.01ms 3.25±0.04ms 1.38 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 116±1μs 159±3μs 1.37 indexing.IndexSingleRow.time_iloc_row(True) + 2.38±0.01ms 3.26±0.03ms 1.37 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 2.38±0.01ms 3.25±0.04ms 1.37 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 1.57±0μs 2.13±0.03μs 1.36 indexing.GetItemSingleColumn.time_frame_getitem_single_column_label + 5.66±0.03ms 7.69±0.04ms 1.36 stat_ops.FrameMultiIndexOps.time_op(0, 'mean') + 3.39±0.03ms 4.59±0.03ms 1.36 timeseries.ResampleSeries.time_resample('period', '5min', 'ohlc') + 742±30μs 1.00±0.09ms 1.35 reindex.LevelAlign.time_reindex_level + 1.47±0ms 1.99±0.3ms 1.35 series_methods.NanOps.time_func('prod', 1000000, 'int8') + 5.70±0.01ms 7.67±0.01ms 1.35 stat_ops.FrameMultiIndexOps.time_op(0, 'sum') + 1.20±0ms 1.60±0.01ms 1.34 reindex.ReindexMethod.time_reindex_method('pad', ) + 1.97±0.02ms 2.64±0.1ms 1.34 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 18.8±0.7ms 25.2±0.07ms 1.34 groupby.Cumulative.time_frame_transform('float64', 'cummax') + 1.78±0.01ms 2.39±0.03ms 1.34 timeseries.AsOf.time_asof_single('DataFrame') + 41.8±0.03ms 56.0±0.7ms 1.34 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 41.6±0.05ms 55.7±0.3ms 1.34 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 41.7±0.02ms 55.7±0.3ms 1.34 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 69.6±0.2ms 92.9±0.4ms 1.34 frame_methods.Repr.time_frame_repr_wide + 2.91±0.02ms 3.88±0.05ms 1.33 arithmetic.Ops.time_frame_add(False, 'default') + 9.07±0.4ms 12.1±0.3ms 1.33 join_merge.Join.time_join_dataframe_index_single_key_small(True) + 848±9ms 1.13±0.03s 1.33 groupby.String.time_str_func('string[python]', 'any') + 66.7±0.5ms 88.5±1ms 1.33 stat_ops.FrameMultiIndexOps.time_op(1, 'skew') + 848±10ms 1.13±0.03s 1.33 groupby.String.time_str_func('string[python]', 'all') + 1.28±0.02ms 1.69±0.02ms 1.33 reindex.ReindexMethod.time_reindex_method('pad', ) + 2.37±0.02ms 3.14±0.05ms 1.32 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 5.83±0.03ms 7.70±0.06ms 1.32 stat_ops.FrameMultiIndexOps.time_op(1, 'sum') + 11.3±0.2ms 14.9±0.2ms 1.32 join_merge.Join.time_join_dataframe_index_single_key_bigger(True) + 2.92±0.2ms 3.85±0.03ms 1.32 arithmetic.Ops.time_frame_mult(False, 'default') + 37.0±0.05ms 48.7±0.3ms 1.32 frame_methods.Equals.time_frame_nonunique_equal + 11.3±0.2ms 14.8±0.4ms 1.32 join_merge.Join.time_join_dataframe_index_shuffle_key_bigger_sort(True) + 37.2±0.05ms 49.0±0.1ms 1.32 frame_methods.Equals.time_frame_nonunique_unequal + 37.3±0.4ms 49.0±0.1ms 1.31 arithmetic.Ops2.time_frame_float_div + 5.81±0.03ms 7.62±0.03ms 1.31 stat_ops.FrameMultiIndexOps.time_op(1, 'mean') + 2.37±0.02ms 3.10±0.04ms 1.31 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 1.83±0.01ms 2.38±0.02ms 1.30 groupby.GroupByMethods.time_dtype_as_group('float', 'cumprod', 'transformation', 5) + 125±2μs 163±4μs 1.30 indexing.IndexSingleRow.time_loc_row(True) + 2.95±0.03ms 3.84±0.05ms 1.30 arithmetic.Ops.time_frame_mult(False, 1) + 1.74±0.01ms 2.27±0.01ms 1.30 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumprod', 'transformation', 5) + 115±1μs 149±4μs 1.30 indexing.IndexSingleRow.time_iloc_row(False) + 1.77±0.01μs 2.30±0.03μs 1.30 indexing.GetItemSingleColumn.time_frame_getitem_single_column_int + 2.98±0.1ms 3.87±0.03ms 1.30 arithmetic.Ops.time_frame_add(False, 1) + 2.31±0.02ms 2.99±0.03ms 1.29 groupby.GroupByMethods.time_dtype_as_group('uint', 'sum', 'transformation', 5) + 1.77±0.01ms 2.28±0.01ms 1.29 groupby.GroupByMethods.time_dtype_as_group('int', 'cumprod', 'transformation', 5) + 143±0.9μs 185±2μs 1.29 indexing.DataFrameStringIndexing.time_boolean_rows + 3.71±0.03ms 4.79±0.06ms 1.29 stat_ops.FrameOps.time_op('sem', 'int', 1) + 148±1μs 191±2μs 1.29 indexing.DataFrameStringIndexing.time_boolean_rows_boolean + 4.74±0.05ms 6.09±0.03ms 1.29 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'float32') + 2.29±0.02ms 2.95±0.03ms 1.28 groupby.GroupByMethods.time_dtype_as_group('uint', 'prod', 'transformation', 5) + 1.01±0.01s 1.29±0.03s 1.28 groupby.String.time_str_func('string[python]', 'first') + 1.02±0.01s 1.31±0.02s 1.28 groupby.String.time_str_func('string[python]', 'last') + 124±2μs 159±2μs 1.28 indexing.IndexSingleRow.time_loc_row(False) + 606±2μs 775±7μs 1.28 groupby.Datelike.time_sum('date_range') + 22.2±0.2ms 28.4±0.1ms 1.28 groupby.Cumulative.time_frame_transform('Int64', 'cummax') + 22.0±0.05ms 28.1±0.09ms 1.28 groupby.Cumulative.time_frame_transform('Int64', 'cummin') + 40.2±0.4ms 51.2±0.3ms 1.27 reshape.ReshapeExtensionDtype.time_transpose('Period[s]') + 5.37±0.06ms 6.81±0.04ms 1.27 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'float64') + 208±1ms 262±2ms 1.26 frame_methods.GetDtypeCounts.time_info + 2.33±0.01ms 2.94±0.02ms 1.26 groupby.GroupByMethods.time_dtype_as_group('int', 'prod', 'transformation', 5) + 190±1μs 240±0.7μs 1.26 indexing.NumericSeriesIndexing.time_loc_slice(, 'nonunique_monotonic_inc') + 3.82±0.01ms 4.81±0.02ms 1.26 stat_ops.FrameOps.time_op('median', 'int', 1) + 10.8±0.06ms 13.6±0.3ms 1.26 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 9.02±0.1ms 11.3±0.03ms 1.26 stat_ops.FrameMultiIndexOps.time_op(1, 'sem') + 2.46±0.02ms 3.10±0.03ms 1.26 groupby.GroupByMethods.time_dtype_as_group('float', 'prod', 'transformation', 5) + 2.35±0.02ms 2.95±0.02ms 1.26 groupby.GroupByMethods.time_dtype_as_group('int', 'sum', 'transformation', 5) + 248±3ms 311±1ms 1.25 frame_methods.Iteration.time_iterrows + 1.36±0.02ms 1.71±0.01ms 1.25 reindex.ReindexMethod.time_reindex_method('backfill', ) + 9.19±0.04ms 11.5±0.03ms 1.25 stat_ops.FrameMultiIndexOps.time_op(0, 'sem') + 643±3μs 803±4μs 1.25 groupby.Datelike.time_sum('date_range_tz') + 193±3μs 240±3μs 1.25 hash_functions.NumericSeriesIndexing.time_loc_slice(, 1000000) + 1.97±0.02ms 2.46±0.02ms 1.25 groupby.GroupByMethods.time_dtype_as_group('int', 'sem', 'direct', 5) + 328M 409M 1.25 frame_methods.Iteration.peakmem_itertuples_raw + 328M 409M 1.25 frame_methods.Iteration.peakmem_itertuples_raw_read_first + 328M 409M 1.25 frame_methods.Iteration.peakmem_itertuples_raw_start + 328M 409M 1.25 frame_methods.Iteration.peakmem_itertuples + 328M 409M 1.25 frame_methods.Iteration.peakmem_itertuples_start + 2.48±0.03ms 3.10±0.02ms 1.25 groupby.GroupByMethods.time_dtype_as_group('float', 'sum', 'transformation', 5) + 1.97±0.04ms 2.45±0.03ms 1.25 groupby.GroupByMethods.time_dtype_as_group('uint', 'sem', 'direct', 5) + 110±0.9μs 137±0.4μs 1.24 hash_functions.NumericSeriesIndexing.time_loc_slice(, 500000) + 21.5±0.2μs 26.5±0.2μs 1.24 indexing.NumericSeriesIndexing.time_iloc_slice(, 'nonunique_monotonic_inc') + 1.68±0ms 2.07±0.01ms 1.23 timeseries.ResampleDataFrame.time_method('max') + 21.2±0.2μs 26.2±0.3μs 1.23 indexing.NumericSeriesIndexing.time_iloc_slice(, 'nonunique_monotonic_inc') + 1.98±0.02ms 2.44±0.02ms 1.23 reshape.SimpleReshape.time_unstack + 4.59±0.02ms 5.65±0.02ms 1.23 stat_ops.FrameOps.time_op('median', 'float', 1) + 978±8ms 1.20±0s 1.23 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'mad') + 44.3±0.1μs 54.4±0.3μs 1.23 dtypes.SelectDtypes.time_select_dtype_float_exclude() + 21.4±0.07μs 26.3±0.3μs 1.23 indexing.NumericSeriesIndexing.time_iloc_slice(, 'unique_monotonic_inc') + 181±1μs 223±2μs 1.23 indexing.DataFrameStringIndexing.time_boolean_rows_object + 44.2±0.2μs 54.3±0.4μs 1.23 dtypes.SelectDtypes.time_select_dtype_string_exclude() + 44.1±0.2μs 54.1±0.3μs 1.23 dtypes.SelectDtypes.time_select_dtype_bool_exclude() + 1.32±0.02ms 1.61±0.03ms 1.23 reindex.ReindexMethod.time_reindex_method('backfill', ) + 21.3±0.2μs 26.0±0.1μs 1.22 indexing.NumericSeriesIndexing.time_iloc_slice(, 'nonunique_monotonic_inc') + 2.04±0.02ms 2.49±0.01ms 1.22 groupby.GroupByMethods.time_dtype_as_group('float', 'sem', 'direct', 5) + 1.15±0ms 1.41±0ms 1.22 groupby.GroupByMethods.time_dtype_as_group('object', 'rank', 'direct', 5) + 32.3±0.2ms 39.2±0.08ms 1.21 arithmetic.Ops2.time_frame_float_mod + 21.3±0.2μs 25.9±0.2μs 1.21 indexing.NumericSeriesIndexing.time_iloc_slice(, 'unique_monotonic_inc') + 45.1±0.4μs 54.6±0.4μs 1.21 dtypes.SelectDtypes.time_select_dtype_bool_exclude() + 4.10±0.01ms 4.96±0.1ms 1.21 stat_ops.Correlation.time_corr_wide('pearson') + 45.2±0.6μs 54.7±0.4μs 1.21 dtypes.SelectDtypes.time_select_dtype_int_exclude() + 37.0±0.5μs 44.8±0.4μs 1.21 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'nonunique_monotonic_inc') + 9.87±0.3ms 11.9±0.3ms 1.21 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'std') + 37.1±0.4μs 44.9±0.2μs 1.21 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'unique_monotonic_inc') + 37.0±0.2μs 44.8±0.3μs 1.21 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('string', 'non_monotonic') + 10.0±0.2ms 12.1±0.3ms 1.21 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'var') + 3.31±0.02ms 3.98±0.03ms 1.20 arithmetic.Ops.time_frame_add(True, 1) + 9.33±0.03ms 11.2±0.1ms 1.20 join_merge.Concat.time_concat_small_frames(0) + 21.4±0.2μs 25.8±0.2μs 1.20 indexing.NumericSeriesIndexing.time_iloc_slice(, 'unique_monotonic_inc') + 45.4±0.7μs 54.4±0.5μs 1.20 dtypes.SelectDtypes.time_select_dtype_string_exclude() + 5.18±0.04ms 6.21±0.04ms 1.20 stat_ops.FrameMultiIndexOps.time_op(0, 'prod') + 1.66±0.01ms 1.99±0.01ms 1.20 timeseries.ResampleDataFrame.time_method('min') + 26.6±0.3μs 31.8±0.1μs 1.20 indexing.NumericSeriesIndexing.time_getitem_slice(, 'nonunique_monotonic_inc') + 47.5±0.5μs 56.8±0.1μs 1.20 hash_functions.NumericSeriesIndexing.time_loc_slice(, 100000) + 35.8±0.2μs 42.8±0.09μs 1.20 hash_functions.NumericSeriesIndexing.time_loc_slice(, 10000) + 49.9±0.1μs 59.6±0.1μs 1.19 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('string', 'unique_monotonic_inc') + 47.4±0.5μs 56.6±0.9μs 1.19 hash_functions.NumericSeriesIndexing.time_loc_slice(, 100000) + 7.66±0.2ms 9.10±0.02ms 1.19 frame_methods.Reindex.time_reindex_both_axes + 2.14±0.03μs 2.55±0.02μs 1.19 attrs_caching.DataFrameAttributes.time_set_index + 552±5ns 658±9ns 1.19 attrs_caching.SeriesArrayAttribute.time_array('datetime64tz') + 555±10ns 661±8ns 1.19 attrs_caching.SeriesArrayAttribute.time_array('category') + 112±0.8ms 133±0.7ms 1.19 stat_ops.FrameMultiIndexOps.time_op(1, 'mad') + 5.17±0.03ms 6.15±0.03ms 1.19 stat_ops.FrameMultiIndexOps.time_op(1, 'prod') + 41.5±0.1μs 49.4±0.2μs 1.19 indexing.NumericSeriesIndexing.time_loc_slice(, 'unique_monotonic_inc') + 61.6±0.3ms 73.2±1ms 1.19 io.style.Render.time_tooltips_render(24, 120) + 32.9±0.3μs 39.0±0.2μs 1.18 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('period', 'non_monotonic') + 1.72±0.03ms 2.04±0.1ms 1.18 groupby.GroupByMethods.time_dtype_as_group('datetime', 'quantile', 'direct', 5) + 43.9±0.1μs 52.0±0.2μs 1.18 indexing.NumericSeriesIndexing.time_getitem_slice(, 'unique_monotonic_inc') + 210±2μs 249±1μs 1.18 indexing.NumericSeriesIndexing.time_getitem_slice(, 'nonunique_monotonic_inc') + 26.8±0.2μs 31.7±0.3μs 1.18 indexing.NumericSeriesIndexing.time_getitem_slice(, 'unique_monotonic_inc') + 36.5±0.4μs 43.1±0.2μs 1.18 hash_functions.NumericSeriesIndexing.time_loc_slice(, 10000) + 26.8±0.1μs 31.6±0.2μs 1.18 indexing.NumericSeriesIndexing.time_getitem_slice(, 'nonunique_monotonic_inc') + 43.2±0.2ms 51.0±0.2ms 1.18 frame_methods.Isnull.time_isnull_obj + 26.7±0.1μs 31.5±0.2μs 1.18 indexing.NumericSeriesIndexing.time_getitem_slice(, 'unique_monotonic_inc') + 2.96±0.03ms 3.48±0.03ms 1.18 arithmetic.Ops.time_frame_comparison(True, 1) + 33.0±0.8μs 38.8±0.07μs 1.18 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('period', 'nonunique_monotonic_inc') + 4.58±0.03μs 5.38±0.05μs 1.18 indexing.DataFrameStringIndexing.time_getitem_scalar + 38.5±0.2μs 45.2±0.5μs 1.17 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('datetime', 'unique_monotonic_inc') + 3.64±0.04ms 4.27±0.02ms 1.17 stat_ops.FrameOps.time_op('mad', 'int', 0) + 87.8±0.5ms 103±0.8ms 1.17 io.style.Render.time_tooltips_render(36, 120) + 4.68±0.03ms 5.49±0.02ms 1.17 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'float32') + 7.19±0.05μs 8.40±0.07μs 1.17 indexing.DataFrameStringIndexing.time_loc + 49.8±0.3μs 58.1±0.5μs 1.17 indexing.NumericSeriesIndexing.time_loc_slice(, 'unique_monotonic_inc') + 1.65±0ms 1.92±0.01ms 1.17 groupby.GroupByMethods.time_dtype_as_group('int', 'quantile', 'direct', 5) + 1.92±0.01ms 2.23±0.02ms 1.16 groupby.GroupByMethods.time_dtype_as_group('uint', 'median', 'transformation', 5) + 39.2±0.6μs 45.5±0.8μs 1.16 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('datetime', 'non_monotonic') + 61.4±0.2μs 71.1±0.1μs 1.16 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('string', 'nonunique_monotonic_inc') + 33.3±0.5μs 38.5±0.3μs 1.16 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('period', 'unique_monotonic_inc') + 39.1±0.3μs 45.2±0.6μs 1.16 indexing.NonNumericSeriesIndexing.time_getitem_pos_slice('datetime', 'nonunique_monotonic_inc') + 459±2μs 531±2μs 1.16 groupby.GroupManyLabels.time_sum(1) + 43.3±0.2μs 50.0±0.08μs 1.15 indexing.NumericSeriesIndexing.time_loc_slice(, 'unique_monotonic_inc') + 53.5±0.2μs 61.8±0.2μs 1.15 dtypes.SelectDtypes.time_select_dtype_bool_exclude() + 57.6±0.4μs 66.4±0.6μs 1.15 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int8') + 37.2±0.4μs 42.9±0.6μs 1.15 timeseries.SortIndex.time_get_slice(False) + 60.6±0.2μs 69.8±0.3μs 1.15 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt16') + 27.8±0.1ms 32.0±0.2ms 1.15 io.hdf.HDF.time_write_hdf('fixed') + 1.83±0.01ms 2.11±0.02ms 1.15 groupby.GroupByMethods.time_dtype_as_group('int', 'var', 'transformation', 5) + 1.94±0.02ms 2.23±0.02ms 1.15 groupby.GroupByMethods.time_dtype_as_group('int', 'median', 'transformation', 5) + 1.99±0.01ms 2.29±0.03ms 1.15 groupby.GroupByMethods.time_dtype_as_group('float', 'median', 'transformation', 5) + 1.75±0ms 2.01±0.01ms 1.15 groupby.GroupByMethods.time_dtype_as_group('float', 'rank', 'direct', 5) + 4.80±0.02μs 5.50±0.08μs 1.15 frame_methods.ToDict.time_to_dict_ints('series') + 1.72±0.1μs 1.97±0.3μs 1.15 index_cached_properties.IndexCache.time_values('PeriodIndex') + 60.5±0.2μs 69.4±0.3μs 1.15 dtypes.SelectDtypes.time_select_dtype_string_exclude('UInt16') + 61.5±1μs 70.5±0.4μs 1.15 dtypes.SelectDtypes.time_select_dtype_int_exclude('UInt32') + 36.6±0.08ms 42.0±0.6ms 1.15 io.style.Render.time_tooltips_render(12, 120) + 1.67±0.01ms 1.91±0.01ms 1.15 groupby.GroupByMethods.time_dtype_as_group('uint', 'quantile', 'direct', 5) + 60.1±0.3μs 68.9±0.2μs 1.15 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt8') + 53.7±0.3μs 61.5±0.3μs 1.15 dtypes.SelectDtypes.time_select_dtype_string_exclude() + 11.6±0.08ms 13.3±0.2ms 1.14 groupby.Apply.time_scalar_function_multi_col(5) + 13.2±0.04ms 15.2±0.2ms 1.14 stat_ops.Rank.time_rank('DataFrame', True) + 57.0±0.3μs 65.2±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('Int8') + 1.74±0.01ms 1.99±0ms 1.14 groupby.GroupByMethods.time_dtype_as_group('datetime', 'rank', 'direct', 5) + 57.8±0.1μs 66.1±0.1μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int16') + 1.78±0.01ms 2.04±0.01ms 1.14 groupby.GroupByMethods.time_dtype_as_group('uint', 'rank', 'direct', 5) + 53.6±0.3μs 61.3±0.4μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude() + 53.7±0.6μs 61.5±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_int_exclude() + 59.6±0.3μs 68.1±0.5μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude('Int64') + 15.6±0.05ms 17.8±0.2ms 1.14 io.csv.ReadCSVThousands.time_thousands(',', None, 'c') + 58.5±0.5μs 66.9±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude('Int32') + 61.4±0.2μs 70.2±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude('UInt32') + 58.1±0.2μs 66.4±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('Int16') + 60.1±0.7μs 68.6±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_int_exclude('UInt8') + 1.83±0.01ms 2.09±0.02ms 1.14 groupby.GroupByMethods.time_dtype_as_group('uint', 'mean', 'transformation', 5) + 61.4±0.4μs 70.1±0.3μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('UInt32') + 53.9±0.8μs 61.5±0.3μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude() + 60.2±0.4μs 68.7±0.5μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude('UInt8') + 60.2±0.2μs 68.6±0.4μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('UInt8') + 1.79±0ms 2.04±0.01ms 1.14 groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'direct', 5) + 57.3±0.3μs 65.2±0.3μs 1.14 dtypes.SelectDtypes.time_select_dtype_int_exclude('Int8') + 61.6±0.2μs 70.2±0.5μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt32') + 61.0±0.2μs 69.4±0.4μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('UInt16') + 59.7±0.2μs 67.9±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int64') + 58.0±0.1μs 66.1±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude('Int16') + 58.6±0.2μs 66.7±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude('Int32') + 53.8±0.2μs 61.2±0.4μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude() + 57.4±0.5μs 65.3±0.3μs 1.14 dtypes.SelectDtypes.time_select_dtype_string_exclude('Int8') + 1.70±0.01ms 1.93±0.04ms 1.14 frame_methods.Interpolate.time_interpolate_some_good(None) + 15.8±0.01ms 18.0±0.06ms 1.14 stat_ops.Correlation.time_corr_wide_nans('pearson') + 2.07±0.01ms 2.35±0.03ms 1.14 groupby.GroupByMethods.time_dtype_as_group('uint', 'max', 'transformation', 5) + 1.69±0ms 1.92±0ms 1.14 groupby.GroupByMethods.time_dtype_as_group('float', 'quantile', 'direct', 5) + 30.4±0.02ms 34.5±0.3ms 1.14 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 62.1±0.2μs 70.6±0.3μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('UInt64') + 62.3±0.3μs 70.8±0.2μs 1.14 dtypes.SelectDtypes.time_select_dtype_int_exclude('UInt64') + 30.3±0.1ms 34.5±0.3ms 1.14 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 59.8±0.09μs 67.9±0.4μs 1.14 dtypes.SelectDtypes.time_select_dtype_bool_exclude('Int64') + 25.5±0.09ms 28.9±0.1ms 1.14 rolling.Apply.time_rolling('Series', 3, 'float', , False) + 15.5±0.02ms 17.6±0.1ms 1.14 io.csv.ReadCSVThousands.time_thousands('|', None, 'c') + 85.4±0.3ms 96.9±2ms 1.14 io.json.ToJSON.time_to_json('split', 'df') + 62.1±0.5μs 70.5±0.3μs 1.14 dtypes.SelectDtypes.time_select_dtype_float_exclude('UInt64') + 4.82±0.02μs 5.47±0.03μs 1.14 frame_methods.ToDict.time_to_dict_datetimelike('series') + 2.05±0.02ms 2.32±0ms 1.13 groupby.GroupByMethods.time_dtype_as_group('uint', 'min', 'transformation', 5) + 44.0±0.5μs 49.9±0.2μs 1.13 dtypes.SelectDtypes.time_select_dtype_int_include() + 25.5±0.09ms 29.0±0.2ms 1.13 rolling.Apply.time_rolling('Series', 3, 'int', , False) + 62.0±0.4μs 70.3±0.3μs 1.13 dtypes.SelectDtypes.time_select_dtype_string_exclude('UInt64') + 3.91±0.04ms 4.44±0.05ms 1.13 frame_methods.Rank.time_rank('uint') + 33.2±0.03ms 37.7±0.2ms 1.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 13.1±0.05ms 14.8±0.2ms 1.13 stat_ops.Rank.time_rank('DataFrame', False) + 33.1±0.06ms 37.4±0.3ms 1.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 25.7±0.3ms 29.0±0.2ms 1.13 rolling.Apply.time_rolling('DataFrame', 3, 'int', , False) + 1.94±0.01ms 2.19±0.01ms 1.13 groupby.GroupByMethods.time_dtype_as_group('float', 'var', 'transformation', 5) + 13.3±0.03ms 15.1±0.2ms 1.13 stat_ops.Rank.time_average_old('DataFrame', False) + 25.6±0.2ms 28.9±0.4ms 1.13 rolling.Apply.time_rolling('DataFrame', 3, 'float', , False) + 28.2±0.5ms 31.9±0.2ms 1.13 groupby.Nth.time_groupby_nth_all('datetime') + 33.3±0.05ms 37.6±0.1ms 1.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 2.03±0.01ms 2.30±0.01ms 1.13 groupby.GroupByMethods.time_dtype_as_group('uint', 'first', 'transformation', 5) + 1.96±0.01ms 2.22±0.01ms 1.13 groupby.GroupByMethods.time_dtype_as_group('float', 'mean', 'transformation', 5) + 44.5±0.2μs 50.2±0.1μs 1.13 dtypes.SelectDtypes.time_select_dtype_float_include() + 89.3±0.6μs 101±0.9μs 1.13 groupby.GroupByMethods.time_dtype_as_field('float', 'shift', 'direct', 1) + 78.4±0.3ms 88.4±2ms 1.13 io.json.ToJSONWide.time_to_json('values', 'df') + 100±0.7μs 113±0.7μs 1.13 groupby.GroupByMethods.time_dtype_as_field('float', 'shift', 'direct', 5) + 70.8±0.1μs 79.8±0.4μs 1.13 dtypes.SelectDtypes.time_select_dtype_int_exclude('m8[ns]') + 71.2±0.3μs 80.2±1μs 1.13 dtypes.SelectDtypes.time_select_dtype_float_exclude('int16') + 3.93±0.03ms 4.43±0.03ms 1.13 frame_methods.Rank.time_rank('int') + 2.07±0.02ms 2.33±0.01ms 1.13 groupby.GroupByMethods.time_dtype_as_group('int', 'min', 'transformation', 5) + 33.3±0.1ms 37.5±0.1ms 1.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 88.9±2ms 100±0.9ms 1.13 io.json.ToJSON.time_to_json('split', 'df_date_idx') + 17.7±0.3ms 19.9±0.5ms 1.13 groupby.Cumulative.time_frame_transform_many_nulls('Int64', 'cummax') + 1.07±0s 1.21±0.02s 1.13 groupby.String.time_str_func('str', 'first') + 32.2±0.05ms 36.3±0.1ms 1.13 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 13.5±0.03ms 15.2±0.2ms 1.12 stat_ops.Rank.time_average_old('DataFrame', True) + 2.06±0.03ms 2.32±0.01ms 1.12 groupby.GroupByMethods.time_dtype_as_group('int', 'max', 'transformation', 5) + 2.15±0.01ms 2.42±0.02ms 1.12 groupby.GroupByMethods.time_dtype_as_group('float', 'last', 'transformation', 5) + 1.09±0s 1.23±0.02s 1.12 groupby.String.time_str_func('str', 'last') + 33.4±0.1ms 37.5±0.3ms 1.12 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 59.3±0.9μs 66.6±0.2μs 1.12 dtypes.SelectDtypes.time_select_dtype_bool_exclude('Int32') + 17.6±0.02ms 19.8±0.1ms 1.12 groupby.Cumulative.time_frame_transform_many_nulls('Int64', 'cummin') + 77.3±0.5ms 86.7±1ms 1.12 io.json.ToJSON.time_to_json('values', 'df_date_idx') + 59.5±0.4μs 66.8±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_int_exclude('Int32') + 52.6±0.2μs 59.0±0.6μs 1.12 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(, 10000) + 29.1±0.2ms 32.6±0.2ms 1.12 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 496±9ns 557±8ns 1.12 index_object.IntervalIndexMethod.time_is_unique(100000) + 20.9±0.4ms 23.4±0.7ms 1.12 groupby.Cumulative.time_frame_transform_many_nulls('Float64', 'cummin') + 3.41±0.02ms 3.82±0.01ms 1.12 groupby.GroupByMethods.time_dtype_as_group('int', 'sem', 'transformation', 5) + 70.9±0.1μs 79.5±0.9μs 1.12 dtypes.SelectDtypes.time_select_dtype_bool_exclude('int64') + 2.15±0.01ms 2.41±0.04ms 1.12 groupby.GroupByMethods.time_dtype_as_group('float', 'first', 'transformation', 5) + 1.46±0.01ms 1.64±0ms 1.12 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumsum', 'transformation', 5) + 2.16±0.01ms 2.42±0.01ms 1.12 groupby.GroupByMethods.time_dtype_as_group('float', 'min', 'transformation', 5) + 71.0±0.3μs 79.5±0.5μs 1.12 dtypes.SelectDtypes.time_select_dtype_int_exclude('float32') + 1.33±0.02ms 1.49±0.03ms 1.12 groupby.Datelike.time_sum('period_range') + 70.9±0.3μs 79.3±0.5μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('m8[ns]') + 2.18±0.02ms 2.43±0.01ms 1.12 groupby.GroupByMethods.time_dtype_as_group('float', 'max', 'transformation', 5) + 23.7±0.3ms 26.5±0.1ms 1.12 io.style.Render.time_tooltips_render(36, 12) + 70.9±0.4μs 79.3±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_bool_exclude('m8[ns]') + 4.27±0.02ms 4.78±0.04ms 1.12 frame_methods.Lookup.time_frame_fancy_lookup + 70.9±0.3μs 79.3±0.8μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('uint64') + 76.9±0.7ms 86.0±0.5ms 1.12 io.json.ToJSON.time_to_json('values', 'df') + 2.04±0.02ms 2.28±0.03ms 1.12 groupby.GroupByMethods.time_dtype_as_group('uint', 'last', 'transformation', 5) + 102±0.3μs 114±1μs 1.12 groupby.GroupByMethods.time_dtype_as_field('object', 'shift', 'direct', 1) + 70.8±0.3μs 79.1±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('M8[ns]') + 3.07±0ms 3.43±0.1ms 1.12 indexing.InsertColumns.time_assign_list_of_columns_concat + 1.89±0.01ms 2.12±0.01ms 1.12 groupby.GroupByMethods.time_dtype_as_group('object', 'last', 'transformation', 5) + 753±10μs 841±10μs 1.12 stat_ops.FrameOps.time_op('sum', 'int', 0) + 70.8±0.3μs 79.1±0.7μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('uint16') + 70.9±0.3μs 79.3±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_float_exclude('m8[ns]') + 71.3±0.3μs 79.6±0.4μs 1.12 dtypes.SelectDtypes.time_select_dtype_int_exclude('M8[ns]') + 70.9±0.2μs 79.3±0.8μs 1.12 dtypes.SelectDtypes.time_select_dtype_bool_exclude('int8') + 71.1±0.3μs 79.5±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('int64') + 55.2±0.2μs 61.6±0.3μs 1.12 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('period', 'nonunique_monotonic_inc') + 70.8±0.2μs 79.1±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_int_exclude('bool') + 32.4±0.07ms 36.1±0.3ms 1.12 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 80.1±2ms 89.5±2ms 1.12 io.json.ToJSONWide.time_to_json('values', 'df_date_idx') + 70.8±0.2μs 79.0±0.5μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('int8') + 11.4±0.03ms 12.7±0.2ms 1.12 groupby.Float32.time_sum + 157±20ms 176±7ms 1.12 gil.ParallelGroupbyMethods.time_loop(8, 'count') + 70.9±0.3μs 79.1±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_bool_exclude('int32') + 70.7±0.3μs 78.9±0.3μs 1.12 dtypes.SelectDtypes.time_select_dtype_float_exclude('int8') + 71.2±0.3μs 79.4±0.9μs 1.12 dtypes.SelectDtypes.time_select_dtype_float_exclude('float32') + 70.9±0.4μs 79.1±0.5μs 1.12 dtypes.SelectDtypes.time_select_dtype_float_exclude('bool') + 71.1±0.4μs 79.3±0.4μs 1.12 dtypes.SelectDtypes.time_select_dtype_float_exclude('int64') + 70.7±0.3μs 78.8±0.4μs 1.12 dtypes.SelectDtypes.time_select_dtype_string_exclude('bool') + 29.1±0.05ms 32.4±0.2ms 1.11 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 70.9±0.2μs 79.0±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('uint8') + 71.4±0.5μs 79.6±0.7μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('M8[ns]') + 71.2±0.3μs 79.3±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('float32') + 70.9±0.4μs 79.0±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('uint16') + 3.42±0.06ms 3.81±0.01ms 1.11 groupby.GroupByMethods.time_dtype_as_group('uint', 'sem', 'transformation', 5) + 70.9±0.2μs 79.0±0.7μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint64') + 56.5±0.8μs 62.9±0.2μs 1.11 indexing.DataFrameNumericIndexing.time_loc + 70.9±0.4μs 79.0±0.2μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('uint8') + 70.9±0.3μs 78.9±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('complex128') + 71.2±0.4μs 79.2±0.5μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint16') + 71.1±0.5μs 79.1±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('uint32') + 72.7±0.7μs 81.0±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('datetime64[ns]') + 71.2±0.4μs 79.3±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('complex128') + 30.3±0.04ms 33.7±0.2ms 1.11 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) + 71.0±0.2μs 78.9±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint8') + 51.7±0.6μs 57.5±0.2μs 1.11 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(, 10000) + 46.8±3ms 52.0±0.1ms 1.11 io.hdf.HDFStoreDataFrame.time_write_store_table_wide + 71.4±0.3μs 79.4±0.5μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('float32') + 1.88±0.02ms 2.09±0.02ms 1.11 groupby.GroupByMethods.time_dtype_as_group('int', 'mean', 'transformation', 5) + 71.1±0.3μs 79.0±0.2μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('int8') + 17.1±0.2ms 19.0±0.09ms 1.11 frame_methods.ToDict.time_to_dict_ints('index') + 28.9±0.6ms 32.1±0.2ms 1.11 arithmetic.Ops2.time_frame_int_mod + 71.1±0.5μs 79.0±0.2μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('uint32') + 72.6±0.5μs 80.6±0.5μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('datetime64[ns]') + 71.4±0.3μs 79.3±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('int16') + 71.3±0.5μs 79.2±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('int32') + 71.3±0.4μs 79.2±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('M8[ns]') + 71.2±0.3μs 79.1±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('int32') + 112±0.4μs 124±0.7μs 1.11 groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'direct', 5) + 71.0±0.5μs 78.8±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('uint8') + 71.2±0.2μs 79.0±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('uint64') + 1.57±0.01ms 1.74±0.01ms 1.11 groupby.GroupByMethods.time_dtype_as_group('float', 'cumsum', 'transformation', 5) + 71.0±0.2μs 78.8±0.6μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('int16') + 21.8±0.04ms 24.2±0.1ms 1.11 groupby.Cumulative.time_frame_transform('Float64', 'cummax') + 71.3±0.5μs 79.1±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('uint32') + 71.2±0.3μs 78.9±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_int_exclude('int16') + 71.2±0.4μs 79.0±0.4μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('complex128') + 71.5±0.3μs 79.3±0.7μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('complex64') + 1.48±0.02ms 1.64±0.01ms 1.11 groupby.GroupByMethods.time_dtype_as_group('int', 'cumsum', 'transformation', 5) + 28.2±0.07ms 31.2±0.1ms 1.11 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 4, ) + 87.4±3ms 97.0±2ms 1.11 io.json.ToJSONWide.time_to_json('records', 'df') + 71.4±0.3μs 79.2±0.5μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('uint32') + 28.1±0.02ms 31.1±0.2ms 1.11 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 5.0, ) + 20.7±0.3ms 22.9±0.08ms 1.11 groupby.Cumulative.time_frame_transform_many_nulls('Float64', 'cummax') + 71.5±0.6μs 79.2±0.6μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('float64') + 78.2±8ms 86.6±3ms 1.11 gil.ParallelGroupbyMethods.time_loop(4, 'count') + 71.1±0.2μs 78.8±0.5μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('int32') + 71.5±0.3μs 79.2±0.6μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('uint64') + 72.7±0.6μs 80.5±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_bool_exclude('datetime64[ns]') + 71.3±0.5μs 78.9±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_string_exclude('complex128') + 973±6μs 1.08±0ms 1.11 stat_ops.FrameOps.time_op('mean', 'int', 0) + 72.3±0.3μs 80.0±0.3μs 1.11 dtypes.SelectDtypes.time_select_dtype_float_exclude('timedelta64[ns]') + 51.5±0.3μs 56.9±0.4μs 1.11 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(, 10000) + 71.3±0.5μs 78.7±0.4μs 1.10 dtypes.SelectDtypes.time_select_dtype_bool_exclude('float64') + 57.2±0.1μs 63.2±0.7μs 1.10 hash_functions.NumericSeriesIndexing.time_loc_slice(, 10000) + 71.2±0.3μs 78.6±0.2μs 1.10 dtypes.SelectDtypes.time_select_dtype_int_exclude('complex64') + 28.3±0.07ms 31.2±0.3ms 1.10 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 3.0, ) + 71.4±0.5μs 78.9±0.4μs 1.10 dtypes.SelectDtypes.time_select_dtype_bool_exclude('complex64') + 1.91±0.01ms 2.11±0.02ms 1.10 groupby.GroupByMethods.time_dtype_as_group('object', 'first', 'transformation', 5) + 49.2±0.7μs 54.3±0.3μs 1.10 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('period', 'non_monotonic') + 115±1μs 127±0.6μs 1.10 groupby.GroupByMethods.time_dtype_as_field('object', 'shift', 'direct', 5) + 71.5±0.6μs 78.9±0.4μs 1.10 dtypes.SelectDtypes.time_select_dtype_string_exclude('complex64') + 983±4μs 1.08±0ms 1.10 stat_ops.FrameOps.time_op('mean', 'float', 0) + 1.47±0.01ms 1.62±0.01ms 1.10 groupby.GroupByMethods.time_dtype_as_group('uint', 'cummax', 'transformation', 5) + 80.5±0.8ms 88.7±0.7ms 1.10 reshape.PivotTable.time_pivot_table_margins + 71.4±0.5μs 78.6±0.4μs 1.10 dtypes.SelectDtypes.time_select_dtype_bool_exclude('uint16') + 2.05±0.02ms 2.26±0.02ms 1.10 groupby.GroupByMethods.time_dtype_as_group('int', 'last', 'transformation', 5) + 136±0.5ms 150±1ms 1.10 io.stata.StataMissing.time_read_stata('tc') + 73.0±0.5μs 80.4±0.3μs 1.10 dtypes.SelectDtypes.time_select_dtype_float_exclude('datetime64[ns]') + 72.6±0.7μs 79.9±0.4μs 1.10 dtypes.SelectDtypes.time_select_dtype_string_exclude('timedelta64[ns]') + 1.15±0ms 1.27±0.01ms 1.10 stat_ops.Correlation.time_corr('spearman') + 77.8±0.3μs 85.5±0.2μs 1.10 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'non_monotonic') + 87.2±0.4ms 95.8±0.8ms 1.10 io.json.ToJSON.time_to_json('records', 'df') + 71.8±0.8μs 78.9±0.5μs 1.10 dtypes.SelectDtypes.time_select_dtype_int_exclude('float64') + 1.47±0.01ms 1.61±0.01ms 1.10 groupby.GroupByMethods.time_dtype_as_group('int', 'cummin', 'transformation', 5) + 55.1±0.5μs 60.5±0.8μs 1.10 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('period', 'unique_monotonic_inc') + 136±0.6ms 149±1ms 1.10 io.stata.StataMissing.time_read_stata('tw') + 22.4±0.1ms 24.6±0.06ms 1.10 groupby.Cumulative.time_frame_transform('Float64', 'cummin') + 92.0±0.6ms 101±0.6ms 1.10 io.json.ToJSONWide.time_to_json('split', 'df_date_idx') + 32.7±0.2ms 35.8±0.03ms 1.10 rolling.Apply.time_rolling('Series', 300, 'float', , False) + 110±0.5ms 121±3ms 1.09 io.json.ToJSONWide.time_to_json('index', 'df_date_idx') + 36.5±0.6ms 40.0±0.5ms 1.09 join_merge.Merge.time_merge_2intkey(True) + 91.6±0.9μs 100±1μs 1.09 groupby.GroupByMethods.time_dtype_as_field('object', 'head', 'direct', 5) + 65.3±0.5μs 71.3±0.5μs 1.09 indexing.DataFrameNumericIndexing.time_iloc + 891±4μs 972±6μs 1.09 groupby.SumMultiLevel.time_groupby_sum_multiindex + 157±2ms 172±6ms 1.09 frame_methods.ToDict.time_to_dict_datetimelike('list') + 131±1ms 142±2ms 1.09 io.stata.StataMissing.time_read_stata('td') + 167±2ms 181±7ms 1.09 frame_methods.ToDict.time_to_dict_datetimelike('index') + 133±1ms 144±0.9ms 1.09 io.stata.StataMissing.time_read_stata('tm') + 85.4±0.6μs 92.7±0.2μs 1.09 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'nonunique_monotonic_inc') + 32.8±0.08ms 35.6±0.1ms 1.09 rolling.Apply.time_rolling('Series', 300, 'int', , False) + 1.56±0.01ms 1.69±0ms 1.08 groupby.GroupByMethods.time_dtype_as_group('float', 'cummin', 'transformation', 5) + 88.1±0.8ms 95.5±1ms 1.08 io.json.ToJSONWide.time_to_json('split', 'df') + 5.09±0.04μs 5.52±0.1μs 1.08 indexing.NonNumericSeriesIndexing.time_getitem_scalar('period', 'non_monotonic') + 173±1ms 188±7ms 1.08 frame_methods.ToDict.time_to_dict_datetimelike('split') + 1.76±0.01ms 1.90±0.01ms 1.08 groupby.GroupByMethods.time_dtype_as_group('uint', 'std', 'transformation', 5) + 104±0.5ms 113±0.6ms 1.08 io.json.ToJSON.time_to_json('index', 'df') + 74.9±0.6μs 81.1±0.1μs 1.08 groupby.GroupByMethods.time_dtype_as_group('float', 'head', 'direct', 1) + 74.0±0.4μs 80.1±0.5μs 1.08 groupby.GroupByMethods.time_dtype_as_group('datetime', 'head', 'direct', 1) + 103±2ms 112±1ms 1.08 io.json.ToJSONWide.time_to_json('index', 'df') + 82.4±0.7μs 89.1±0.6μs 1.08 groupby.GroupByMethods.time_dtype_as_field('object', 'head', 'direct', 1) + 1.46±0.01ms 1.58±0.01ms 1.08 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cummin', 'transformation', 5) + 83.7±0.4μs 90.5±0.8μs 1.08 groupby.GroupByMethods.time_dtype_as_field('int', 'head', 'direct', 5) + 13.5±0.05ms 14.6±0.03ms 1.08 io.csv.ReadCSVSkipRows.time_skipprows(10000, 'c') + 14.5±0.04ms 15.6±0.07ms 1.08 io.style.Render.time_tooltips_render(12, 12) + 109±0.3μs 118±0.5μs 1.08 groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'direct', 5) + 1.12±0s 1.21±0.02s 1.08 strings.Dummies.time_get_dummies('string[pyarrow]') + 134±0.6ms 145±1ms 1.08 io.stata.StataMissing.time_read_stata('tq') + 3.20±0.01ms 3.45±0.02ms 1.08 series_methods.NanOps.time_func('std', 1000000, 'int8') + 6.23±0.1ms 6.73±0.03ms 1.08 groupby.Apply.time_scalar_function_single_col(5) + 74.6±0.8μs 80.6±0.3μs 1.08 groupby.GroupByMethods.time_dtype_as_group('uint', 'head', 'direct', 1) + 3.68±0.02ms 3.98±0.03ms 1.08 groupby.GroupByMethods.time_dtype_as_group('float', 'sem', 'transformation', 5) + 67.7±0.7μs 73.1±0.6μs 1.08 groupby.GroupByMethods.time_dtype_as_group('object', 'head', 'direct', 1) + 84.0±0.7μs 90.7±0.4μs 1.08 indexing.NonNumericSeriesIndexing.time_getitem_label_slice('datetime', 'unique_monotonic_inc') + 12.7±0.05ms 13.7±0.09ms 1.08 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (100000, 100)) + 1.58±0.02ms 1.70±0.02ms 1.08 groupby.GroupByMethods.time_dtype_as_group('float', 'cummax', 'transformation', 5) + 14.0±0.2ms 15.1±0.04ms 1.08 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'sem') + 53.1±0.3μs 57.3±0.1μs 1.08 dtypes.SelectDtypes.time_select_dtype_bool_include() + 109±0.8μs 118±0.5μs 1.08 groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'direct', 5) + 256±0.9ns 276±6ns 1.08 timeseries.DatetimeIndex.time_is_dates_only('dst') + 128±2μs 137±3μs 1.08 indexing.IntervalIndexing.time_getitem_list + 33.1±0.3ms 35.6±0.1ms 1.08 rolling.Apply.time_rolling('DataFrame', 300, 'float', , False) + 74.3±0.7μs 79.9±0.2μs 1.08 groupby.GroupByMethods.time_dtype_as_field('float', 'head', 'direct', 1) + 146±2μs 157±0.8μs 1.08 groupby.GroupByMethods.time_dtype_as_field('int', 'tail', 'direct', 1) + 1.10±0.01s 1.18±0s 1.08 strings.Dummies.time_get_dummies('str') + 137±0.9ms 147±2ms 1.07 io.json.ToJSONLines.time_floats_with_int_idex_lines + 2.91±0.01ms 3.13±0.01ms 1.07 stat_ops.FrameOps.time_op('skew', 'int', 0) + 4.43±0.05μs 4.75±0.05μs 1.07 indexing.NonNumericSeriesIndexing.time_getitem_scalar('string', 'non_monotonic') + 83.6±0.3μs 89.7±0.8μs 1.07 groupby.GroupByMethods.time_dtype_as_field('float', 'head', 'direct', 5) + 89.5±0.6ms 96.0±0.9ms 1.07 io.json.ToJSON.time_to_json('records', 'df_date_idx') + 177±20ms 190±5ms 1.07 gil.ParallelGroupbyMethods.time_loop(8, 'mean') + 74.9±0.1μs 80.4±0.4μs 1.07 groupby.GroupByMethods.time_dtype_as_field('int', 'head', 'direct', 1) + 35.6±0.03ms 38.2±0.07ms 1.07 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (100000, 100)) + 33.0±0.2ms 35.4±0.07ms 1.07 rolling.Apply.time_rolling('DataFrame', 300, 'int', , False) + 4.01±0.01ms 4.30±0.01ms 1.07 stat_ops.FrameOps.time_op('kurt', 'float', 0) + 145±0.6μs 155±2μs 1.07 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(, 100000) + 80.9±8ms 86.7±1ms 1.07 gil.ParallelGroupbyMethods.time_loop(4, 'last') + 107±0.8μs 115±0.8μs 1.07 groupby.GroupByMethods.time_dtype_as_group('datetime', 'shift', 'direct', 5) + 134±1ms 144±2ms 1.07 io.stata.StataMissing.time_read_stata('th') + 22.0±0.09ms 23.6±0.3ms 1.07 stat_ops.FrameMultiIndexOps.time_op(0, 'mad') + 2.53±0.07μs 2.71±0.05μs 1.07 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(0, 'end', 'year', 'QS', 12) + 59.1±0.2μs 63.2±0.2μs 1.07 dtypes.SelectDtypes.time_select_dtype_int_include('Int64') + 2.35±0.01ms 2.51±0.01ms 1.07 groupby.GroupByMethods.time_dtype_as_group('object', 'rank', 'transformation', 5) + 148±2μs 158±0.7μs 1.07 groupby.GroupByMethods.time_dtype_as_field('uint', 'tail', 'direct', 1) + 144±2μs 154±0.8μs 1.07 groupby.GroupByMethods.time_dtype_as_group('uint', 'tail', 'direct', 1) + 1.11±0.01s 1.19±0s 1.07 strings.Dummies.time_get_dummies('string[python]') + 74.9±0.4μs 80.0±0.3μs 1.07 groupby.GroupByMethods.time_dtype_as_field('uint', 'head', 'direct', 1) + 4.17±0.01ms 4.45±0.01ms 1.07 stat_ops.FrameOps.time_op('skew', 'float', 0) + 2.53±0.03μs 2.70±0.05μs 1.07 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'quarter', None, 5) + 1.78±0.01ms 1.90±0.01ms 1.07 groupby.GroupByMethods.time_dtype_as_group('int', 'std', 'transformation', 5) + 3.69±0.02ms 3.93±0.04ms 1.07 frame_methods.Interpolate.time_interpolate_some_good('infer') + 6.91±0.2μs 7.36±0.07μs 1.07 tslibs.timestamp.TimestampProperties.time_is_quarter_start(None, 'B') + 70.6±0.3μs 75.2±0.7μs 1.07 dtypes.SelectDtypes.time_select_dtype_float_include('float64') + 88.2±9ms 93.9±1ms 1.06 gil.ParallelGroupbyMethods.time_loop(4, 'sum') + 35.9±0.2ms 38.2±0.5ms 1.06 groupby.Nth.time_groupby_nth_all('object') + 26.1±0.1ms 27.8±0.05ms 1.06 io.hdf.HDF.time_read_hdf('fixed') + 3.21±0.01ms 3.41±0.03ms 1.06 series_methods.NanOps.time_func('var', 1000000, 'int8') + 28.5±0.08ms 30.3±0.07ms 1.06 rolling.Pairwise.time_groupby(({}, 'expanding'), 'cov', False) + 2.78±0.02ms 2.96±0.01ms 1.06 stat_ops.FrameOps.time_op('kurt', 'int', 0) + 6.11±0.02μs 6.49±0.05μs 1.06 indexing.NonNumericSeriesIndexing.time_getitem_scalar('string', 'unique_monotonic_inc') + 89.6±0.8ms 95.1±0.2ms 1.06 frame_methods.Interpolate.time_interpolate(None) + 11.2±0.1ms 11.9±0.09ms 1.06 io.style.Render.time_apply_render(36, 12) + 16.0±0.2μs 16.9±0.2μs 1.06 indexing.NonNumericSeriesIndexing.time_getitem_scalar('datetime', 'nonunique_monotonic_inc') + 134±1μs 142±1μs 1.06 groupby.GroupByMethods.time_dtype_as_group('datetime', 'tail', 'direct', 1) + 2.51±0.02μs 2.66±0.01μs 1.06 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'month', None, 12) + 76.1±0.8μs 80.8±0.2μs 1.06 groupby.GroupByMethods.time_dtype_as_group('int', 'head', 'direct', 1) + 137±2ms 145±0.5ms 1.06 io.json.ToJSONLines.time_floats_with_dt_index_lines + 7.18±0.01ms 7.61±0.1ms 1.06 series_methods.NanOps.time_func('sem', 1000000, 'int8') + 170±20ms 180±5ms 1.06 gil.ParallelGroupbyMethods.time_loop(8, 'min') + 30.1±0.06ms 31.9±0.2ms 1.06 rolling.Pairwise.time_groupby(({'window': 1000}, 'rolling'), 'cov', False) + 41.3±0.3ms 43.8±0.4ms 1.06 groupby.Nth.time_frame_nth_any('object') + 81.8±9ms 86.6±0.7ms 1.06 gil.ParallelGroupbyMethods.time_loop(4, 'prod') + 147±1μs 156±3μs 1.06 hash_functions.NumericSeriesIndexingShuffled.time_loc_slice(, 100000) + 70.3±0.2μs 74.4±0.5μs 1.06 dtypes.SelectDtypes.time_select_dtype_bool_include('bool') + 1.64±0ms 1.73±0.01ms 1.06 stat_ops.FrameOps.time_op('var', 'int', 0) + 30.0±0.05ms 31.7±0.05ms 1.06 rolling.Pairwise.time_groupby(({'window': 10}, 'rolling'), 'cov', False) + 70.3±0.2μs 74.3±0.3μs 1.06 dtypes.SelectDtypes.time_select_dtype_int_include('int64') + 2.51±0.02μs 2.65±0.02μs 1.06 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'month', None, 3) + 3.11±0.01ms 3.29±0.02ms 1.06 groupby.GroupByMethods.time_dtype_as_group('uint', 'quantile', 'transformation', 5) + 2.91±0.04μs 3.07±0.02μs 1.05 series_methods.SeriesGetattr.time_series_datetimeindex_repr + 135±1μs 142±2μs 1.05 groupby.GroupByMethods.time_dtype_as_group('float', 'tail', 'direct', 1) + 260±1μs 273±2μs 1.05 groupby.GroupByMethods.time_dtype_as_group('object', 'bfill', 'direct', 5) + 30.6±0.1ms 32.2±0.1ms 1.05 rolling.Pairwise.time_groupby(({'window': 10}, 'rolling'), 'corr', False) + 1.89±0.01ms 1.99±0.01ms 1.05 groupby.GroupByMethods.time_dtype_as_group('float', 'std', 'transformation', 5) + 2.55±0.02μs 2.69±0.07μs 1.05 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(0, 'end', 'quarter', 'QS', 5) + 157±1μs 165±0.6μs 1.05 groupby.GroupByMethods.time_dtype_as_field('object', 'tail', 'direct', 1) + 5.20±0.09μs 5.47±0.02μs 1.05 indexing.NumericSeriesIndexing.time_getitem_scalar(, 'unique_monotonic_inc') + 254±2ns 268±1ns 1.05 timeseries.DatetimeIndex.time_is_dates_only('tz_local') + 8.62±0.05ms 9.07±0.1ms 1.05 io.style.Render.time_format_render(36, 12) + 1.65±0.01ms 1.73±0.01ms 1.05 stat_ops.FrameOps.time_op('var', 'float', 0) + 2.56±0.03μs 2.69±0.01μs 1.05 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'start', 'month', 'QS', 5) + 262±1μs 275±3μs 1.05 groupby.GroupByMethods.time_dtype_as_group('datetime', 'bfill', 'direct', 5) + 288±3ns 303±1ns 1.05 timeseries.DatetimeIndex.time_is_dates_only('tz_naive') + 167±1μs 175±2μs 1.05 groupby.GroupByMethods.time_dtype_as_field('datetime', 'shift', 'direct', 5) + 28.8±0.1ms 30.2±0.2ms 1.05 io.style.Render.time_apply_render(12, 120) + 166±0.8μs 174±1μs 1.05 groupby.GroupByMethods.time_dtype_as_field('float', 'tail', 'direct', 5) + 1.36±0μs 1.43±0.02μs 1.05 attrs_caching.SeriesArrayAttribute.time_extract_array_numpy('object') + 176±2μs 185±0.7μs 1.05 groupby.GroupByMethods.time_dtype_as_field('object', 'tail', 'direct', 5) + 2.64±0.03μs 2.77±0.04μs 1.05 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'quarter', 'QS', 5) + 116±0.7μs 121±0.3μs 1.05 groupby.GroupByMethods.time_dtype_as_field('datetime', 'head', 'direct', 5) + 20.3±0.07ms 21.3±0.05ms 1.05 groupby.AggFunctions.time_different_python_functions_multicol + 254±0.7ns 265±2ns 1.05 timeseries.DatetimeIndex.time_is_dates_only('tz_aware') + 1.34±0.01μs 1.41±0.02μs 1.05 attrs_caching.SeriesArrayAttribute.time_extract_array('datetime64') + 5.35±0.03μs 5.59±0.01μs 1.04 tslibs.offsets.OffestDatetimeArithmetic.time_apply() + 3.04±0.01ms 3.18±0ms 1.04 groupby.GroupByMethods.time_dtype_as_group('int', 'rank', 'transformation', 5) + 3.23±0.02ms 3.37±0.01ms 1.04 groupby.GroupByMethods.time_dtype_as_group('float', 'quantile', 'transformation', 5) + 2.55±0.02μs 2.65±0.04μs 1.04 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'month', None, 5) + 120±0.9μs 125±0.6μs 1.04 groupby.GroupByMethods.time_dtype_as_group('uint', 'shift', 'direct', 5) + 33.6±0.2ms 35.0±0.8ms 1.04 groupby.Nth.time_series_nth_all('float32') + 3.05±0.02ms 3.18±0.01ms 1.04 groupby.GroupByMethods.time_dtype_as_group('uint', 'rank', 'transformation', 5) + 10.5±0.05ms 10.9±0.02ms 1.04 frame_methods.Rank.time_rank('float') + 1.56±0.01ms 1.63±0.01ms 1.04 groupby.GroupByMethods.time_dtype_as_group('uint', 'pct_change', 'direct', 5) + 4.50±0.03ms 4.68±0.03ms 1.04 io.csv.ReadCSVParseSpecialDate.time_read_special_date('mdY', 'c') + 45.0±0.2μs 46.8±0.2μs 1.04 indexing.NumericSeriesIndexing.time_iloc_list_like(, 'nonunique_monotonic_inc') + 9.10±0.06ms 9.46±0.05ms 1.04 inference.ToDatetimeISO8601.time_iso8601_infer_zero_tz_fromat + 5.57±0.02μs 5.79±0.07μs 1.04 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64() + 261±0.9μs 272±3μs 1.04 groupby.GroupByMethods.time_dtype_as_group('datetime', 'ffill', 'direct', 5) + 13.2±0.3μs 13.7±0.06μs 1.04 tslibs.offsets.OffestDatetimeArithmetic.time_apply_np_dt64() + 2.63±0.04μs 2.73±0.02μs 1.04 tslibs.fields.TimeGetStartEndField.time_get_start_end_field(1, 'end', 'month', 'QS', 12) + 1.57±0.01ms 1.63±0.01ms 1.04 groupby.GroupByMethods.time_dtype_as_group('object', 'any', 'transformation', 5) + 38.8±0.06ms 40.2±0.1ms 1.04 groupby.Nth.time_series_nth_any('object') + 1.36±0.01μs 1.41±0.01μs 1.04 attrs_caching.SeriesArrayAttribute.time_extract_array('category') + 1.68±0ms 1.74±0.01ms 1.04 stat_ops.FrameOps.time_op('prod', 'float', 0) + 6.02±0.2μs 6.24±0.1μs 1.04 index_cached_properties.IndexCache.time_engine('DatetimeIndex') + 24.8±0.1ms 25.7±0.07ms 1.04 gil.ParallelGroupbyMethods.time_parallel(2, 'last') + 11.2±0.08ms 11.6±0.05ms 1.04 algorithms.Hashing.time_series_string + 315±0.5ms 326±5ms 1.03 io.json.ReadJSONLines.time_read_json_lines('datetime') + 83.0±0.2ms 85.9±0.6ms 1.03 io.hdf.HDF.time_write_hdf('table') + 6.49±0.03ms 6.72±0.1ms 1.03 frame_methods.Repr.time_html_repr_trunc_mi + 259±0.2ms 268±6ms 1.03 io.json.ReadJSON.time_read_json('records', 'int') + 270±2μs 280±1μs 1.03 hash_functions.NumericSeriesIndexing.time_loc_slice(, 100000) + 1.36±0.01μs 1.41±0.01μs 1.03 attrs_caching.SeriesArrayAttribute.time_extract_array_numpy('datetime64tz') + 188±0.7μs 194±3μs 1.03 groupby.GroupByMethods.time_dtype_as_field('uint', 'first', 'direct', 5) + 540±3μs 558±0.8μs 1.03 groupby.GroupByMethods.time_dtype_as_field('float', 'shift', 'transformation', 1) + 23.3±0.1μs 24.1±0.1μs 1.03 tslibs.offsets.OffestDatetimeArithmetic.time_apply() + 1.30±0.01μs 1.34±0μs 1.03 tslibs.offsets.OnOffset.time_on_offset() + 1.05±0.03μs 1.09±0.04μs 1.03 index_cached_properties.IndexCache.time_is_monotonic_decreasing('Int64Index') + 227±0.7ms 234±0.5ms 1.03 groupby.AggFunctions.time_different_python_functions_singlecol + 26.7±0.2ms 27.5±0.07ms 1.03 gil.ParallelGroupbyMethods.time_parallel(2, 'sum') + 1.68±0.01ms 1.72±0.02ms 1.03 groupby.GroupByMethods.time_dtype_as_group('uint', 'any', 'transformation', 5) + 1.63±0ms 1.67±0.02ms 1.03 groupby.GroupByMethods.time_dtype_as_group('float', 'pct_change', 'direct', 5) + 189±0.5μs 195±0.6μs 1.03 groupby.GroupByMethods.time_dtype_as_field('uint', 'max', 'direct', 5) + 5.43±0.04μs 5.58±0.03μs 1.03 tslibs.offsets.OffestDatetimeArithmetic.time_add_10() + 3.50±0.02ms 3.60±0.02ms 1.03 stat_ops.FrameOps.time_op('sem', 'int', 0) + 1.46±0.01μs 1.50±0.01μs 1.03 tslibs.timestamp.TimestampConstruction.time_from_datetime_aware + 153±1μs 157±0.7μs 1.03 period.Algorithms.time_drop_duplicates('series') + 1.19±0.02ms 1.22±0.02ms 1.03 index_cached_properties.IndexCache.time_engine('MultiIndex') + 431±1ns 442±2ns 1.03 indexing_engines.NumericEngineIndexing.time_get_loc_near_middle((, ), 'non_monotonic', True, 2000000) + 5.23±0.02ms 5.36±0.01ms 1.02 indexing.DataFrameNumericIndexing.time_bool_indexer + 389±0.9μs 398±2μs 1.02 groupby.GroupByMethods.time_dtype_as_group('float', 'bfill', 'direct', 5) + 146±0.5μs 149±0.6μs 1.02 tslibs.period.PeriodProperties.time_property('M', 'end_time') + 2.01±0ms 2.06±0.01ms 1.02 groupby.GroupByMethods.time_dtype_as_field('object', 'rank', 'transformation', 1) + 192±0.3μs 197±0.5μs 1.02 groupby.GroupByMethods.time_dtype_as_field('int', 'min', 'direct', 5) + 3.84±0.02ms 3.93±0.02ms 1.02 stat_ops.FrameOps.time_op('sem', 'float', 0) + 402±0.6ns 411±1ns 1.02 indexing_engines.NumericEngineIndexing.time_get_loc((, ), 'monotonic_decr', True, 100000) + 7.01±0.1ms 7.15±0.1ms 1.02 index_cached_properties.IndexCache.time_is_all_dates('CategoricalIndex') + 101±0.1ms 103±0.4ms 1.02 rolling.Apply.time_rolling('Series', 3, 'int', , False) + 5.94±0.01ms 6.06±0.01ms 1.02 io.hdf.HDFStoreDataFrame.time_read_store_table + 71.6±0.3ms 73.0±0.3ms 1.02 rolling.Apply.time_rolling('DataFrame', 300, 'float', , False) + 4.62±0.02ms 4.71±0.06ms 1.02 ctors.SeriesConstructors.time_series_constructor(, False, 'float') + 414±2ns 422±2ns 1.02 indexing_engines.NumericEngineIndexing.time_get_loc_near_middle((, ), 'non_monotonic', True, 100000) + 414±1ns 422±2ns 1.02 indexing_engines.NumericEngineIndexing.time_get_loc_near_middle((, ), 'monotonic_decr', True, 100000) + 90.6±0.2μs 92.3±0.5μs 1.02 tslibs.timestamp.TimestampOps.time_floor(datetime.timezone.utc) + 73.0±0.5ms 74.3±0.1ms 1.02 rolling.Apply.time_rolling('DataFrame', 300, 'int', at 0x7ff1b2310940>, False) + 4.62±0.03ms 4.70±0.05ms 1.02 ctors.SeriesConstructors.time_series_constructor(, False, 'int') + 79.5±0.3μs 80.9±0.7μs 1.02 tslibs.timestamp.TimestampOps.time_floor(None) + 71.8±0.2ms 72.9±0.1ms 1.02 rolling.Apply.time_rolling('Series', 300, 'float', , False) + 731±0.6ms 743±1ms 1.02 frame_methods.Iteration.time_itertuples_raw_tuples + 68.5±0.7μs 69.6±0.7μs 1.02 ctors.SeriesConstructors.time_series_constructor(, True, 'float') + 73.1±0.2ms 74.2±0.2ms 1.02 rolling.Apply.time_rolling('Series', 300, 'int', at 0x7ff1b2310940>, False) + 4.22±0.03ms 4.28±0.06ms 1.01 ctors.SeriesConstructors.time_series_constructor(, True, 'float') + 73.2±0.2ms 74.2±0.2ms 1.01 rolling.Apply.time_rolling('Series', 300, 'float', at 0x7ff1b2310940>, False) + 804±9μs 815±9μs 1.01 ctors.SeriesConstructors.time_series_constructor(, True, 'int') + 56.2±0.2ms 56.9±0.3ms 1.01 frame_methods.ToHTML.time_to_html_mixed + 198±0.3ms 200±0.7ms 1.01 groupby.Cumulative.time_frame_transform('Float64', 'cumsum') + 103±0.3ms 105±0.04ms 1.01 rolling.Apply.time_rolling('Series', 3, 'float', at 0x7ff1b2310940>, False) - 719±2μs 712±0.9μs 0.99 groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'transformation', 1) - 128±1ms 127±0.8ms 0.99 index_cached_properties.IndexCache.time_engine('IntervalIndex') - 535±2μs 529±2μs 0.99 indexing.CategoricalIndexIndexing.time_get_indexer_list('monotonic_decr') - 5.39±0.01ms 5.33±0ms 0.99 rolling.Methods.time_method('Series', ('rolling', {'window': 1000}), 'float', 'count') - 219±7μs 217±7μs 0.99 index_cached_properties.IndexCache.time_is_monotonic('UInt64Index') - 55.2±0.09ms 54.6±0.05ms 0.99 strings.Cat.time_cat(3, None, '-', 0.0) - 35.5±0.04ms 35.1±0.05ms 0.99 arithmetic.FrameWithFrameWide.time_op_different_blocks(, (1000000, 10)) - 1.68±0.02ms 1.66±0.01ms 0.99 index_cached_properties.IndexCache.time_is_all_dates('RangeIndex') - 2.32±0.04ms 2.29±0.02ms 0.99 index_cached_properties.IndexCache.time_is_all_dates('IntervalIndex') - 48.4±0.1ms 47.8±0.1ms 0.99 algos.isin.IsInLongSeriesLookUpDominates.time_isin('float64', 5, 'monotone_misses') - 129±1ms 127±0.7ms 0.99 index_cached_properties.IndexCache.time_is_monotonic_increasing('IntervalIndex') - 840±2ns 828±5ns 0.99 dtypes.Dtypes.time_pandas_dtype() - 320±0.7μs 316±0.6μs 0.99 groupby.GroupByMethods.time_dtype_as_field('object', 'bfill', 'direct', 1) - 4.12±0.2ms 4.06±0.03ms 0.98 index_cached_properties.IndexCache.time_is_unique('MultiIndex') - 632±2μs 622±1μs 0.98 groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'transformation', 1) - 33.1±0.1ms 32.5±0.2ms 0.98 algos.isin.IsInLongSeriesLookUpDominates.time_isin('float32', 5, 'random_misses') - 33.0±0.2ms 32.5±0.1ms 0.98 rolling.Rank.time_rank('Series', 10, 'float', True, False, 'min') - 49.5±0.1μs 48.7±0.2μs 0.98 series_methods.NanOps.time_func('sum', 1000, 'float64') - 392±1μs 385±1μs 0.98 groupby.GroupByMethods.time_dtype_as_field('datetime', 'rank', 'direct', 5) - 889±4μs 874±7μs 0.98 frame_ctor.FromRecords.time_frame_from_records_generator(1000) - 217±0.6ns 213±3ns 0.98 libs.ScalarListLike.time_is_scalar(array('123', dtype=', ), 'monotonic_decr', True, 100000) - 3.61±0.01ms 3.53±0.02ms 0.98 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'midpoint') - 414±3μs 405±0.8μs 0.98 groupby.GroupByMethods.time_dtype_as_field('float', 'sem', 'direct', 1) - 844±4ns 824±2ns 0.98 dtypes.Dtypes.time_pandas_dtype() - 34.2±0.2ms 33.4±0.2ms 0.98 rolling.Methods.time_method('Series', ('rolling', {'window': 10}), 'int', 'median') - 20.1±0.2μs 19.7±0.1μs 0.98 series_methods.NanOps.time_func('argmax', 1000, 'int8') - 660±4ns 644±2ns 0.98 tslibs.timestamp.TimestampOps.time_to_pydatetime(tzfile('/usr/share/zoneinfo/Asia/Tokyo')) - 2.68±0.03ms 2.62±0ms 0.98 groupby.GroupByMethods.time_dtype_as_field('uint', 'pct_change', 'direct', 5) - 29.4±0.1μs 28.7±0.2μs 0.98 series_methods.NanOps.time_func('prod', 1000, 'int32') - 418±2μs 408±0.7μs 0.98 groupby.GroupByMethods.time_dtype_as_field('uint', 'sem', 'direct', 1) - 1.10±0ms 1.07±0ms 0.98 groupby.FillNA.time_srs_ffill - 262±1μs 256±0.5μs 0.98 groupby.GroupByMethods.time_dtype_as_field('uint', 'bfill', 'direct', 5) - 506±1ns 494±2ns 0.98 tslibs.timestamp.TimestampProperties.time_dayofweek(, None) - 54.9±0.1ms 53.6±0.2ms 0.98 io.csv.ReadCSVSkipRows.time_skipprows(10000, 'python') - 1.38±0.01ms 1.34±0ms 0.98 groupby.RankWithTies.time_rank_ties('float32', 'average') - 3.80±0.01ms 3.70±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'int', 'min') - 33.5±0.08ms 32.6±0.1ms 0.97 algos.isin.IsInLongSeriesLookUpDominates.time_isin('float32', 5, 'monotone_misses') - 4.28±0.01ms 4.17±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'kurt') - 234±2μs 228±0.7μs 0.97 groupby.GroupByMethods.time_dtype_as_field('uint', 'bfill', 'direct', 1) - 3.81±0.02ms 3.71±0.02ms 0.97 libs.InferDtype.time_infer_dtype('py-object') - 2.89±0ms 2.81±0.01ms 0.97 indexing.IntervalIndexing.time_getitem_scalar - 255±0.9μs 248±1μs 0.97 groupby.GroupByMethods.time_dtype_as_field('int', 'ffill', 'direct', 5) - 1.74±0.01ms 1.70±0ms 0.97 rolling.Pairwise.time_pairwise(({'window': 1000}, 'rolling'), 'corr', False) - 258±0.9μs 252±2μs 0.97 groupby.GroupByMethods.time_dtype_as_group('int', 'tail', 'direct', 5) - 428±3μs 417±0.7μs 0.97 groupby.GroupByMethods.time_dtype_as_group('float', 'sem', 'direct', 1) - 4.28±0.02ms 4.17±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'kurt') - 217±0.5ns 211±3ns 0.97 libs.ScalarListLike.time_is_scalar((1, 2, 3)) - 231±3μs 224±0.7μs 0.97 groupby.GroupByMethods.time_dtype_as_group('int', 'bfill', 'direct', 1) - 2.80±0.01ms 2.72±0ms 0.97 series_methods.NSort.time_nlargest('all') - 21.1±0.1ms 20.5±0.06ms 0.97 groupby.GroupByMethods.time_dtype_as_field('float', 'mad', 'direct', 1) - 175±1ms 170±2ms 0.97 strings.Extract.time_extract_single_group('string[pyarrow]', True) - 4.05±0.02ms 3.94±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'int', 'min') - 3.66±0.01ms 3.56±0.02ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'nearest') - 4.09±0.01ms 3.98±0.03ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'int', 'max') - 7.00±0.08μs 6.80±0.08μs 0.97 tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(0, 2000, tzfile('/usr/share/zoneinfo/Asia/Tokyo')) - 21.2±0.1ms 20.6±0.2ms 0.97 io.sql.WriteSQLDtypes.time_to_sql_dataframe_column('sqlite', 'float_with_nan') - 4.08±0.01ms 3.96±0.02ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'max') - 3.76±0.01ms 3.64±0ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'int', 'max') - 5.53±0.03ms 5.37±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'count') - 10.7±0.06μs 10.4±0.03μs 0.97 timedelta.TimedeltaIndexing.time_series_loc - 1.28±0.01ms 1.24±0.01ms 0.97 rolling.Pairwise.time_pairwise(({'window': 10}, 'rolling'), 'cov', False) - 3.69±0.01ms 3.58±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'skew') - 3.66±0.01ms 3.55±0.01ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'linear') - 3.62±0.02ms 3.51±0.01ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'nearest') - 4.20±0.01ms 4.08±0.02ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'int', 'max') - 3.97±0.01ms 3.85±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'min') - 3.69±0.02ms 3.58±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'min') - 3.56±0.01ms 3.45±0.02ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'std') - 22.0±0.2ms 21.3±0.09ms 0.97 stat_ops.SeriesMultiIndexOps.time_op(1, 'skew') - 4.15±0.03ms 4.02±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'int', 'min') - 144±1ms 140±0.5ms 0.97 plotting.FramePlotting.time_frame_plot('hist') - 634±4μs 615±0.5μs 0.97 indexing.NonNumericSeriesIndexing.time_getitem_list_like('datetime', 'nonunique_monotonic_inc') - 4.05±0.01ms 3.93±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'int', 'min') - 3.66±0.02ms 3.55±0ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'midpoint') - 3.67±0.01ms 3.55±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'max') - 3.69±0.02ms 3.57±0.01ms 0.97 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'higher') - 14.5±0.04ms 14.0±0.07ms 0.97 groupby.AggFunctions.time_different_numpy_functions - 2.66±0.02ms 2.58±0.02ms 0.97 io.csv.ReadCSVParseDates.time_multiple_date('python') - 2.62±0.01ms 2.54±0.01ms 0.97 series_methods.NSort.time_nlargest('last') - 3.74±0.03ms 3.62±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'max') - 3.63±0.02ms 3.51±0.02ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 0, 'linear') - 4.02±0.01ms 3.89±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'max') - 3.86±0.01ms 3.74±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'int', 'max') - 5.48±0.02ms 5.30±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'int', 'count') - 19.9±0.1ms 19.2±0.1ms 0.97 io.sql.WriteSQLDtypes.time_to_sql_dataframe_column('sqlite', 'int') - 3.70±0.01ms 3.58±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'skew') - 3.99±0.03ms 3.86±0.01ms 0.97 io.csv.ReadCSVFloatPrecision.time_read_csv_python_engine(';', '.', None) - 4.63±0.02ms 4.48±0.02ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'kurt') - 3.74±0.02ms 3.61±0.01ms 0.97 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'midpoint') - 9.14±0.1μs 8.84±0.02μs 0.97 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(100, tzfile('/usr/share/zoneinfo/Asia/Tokyo')) - 5.29±0.03ms 5.11±0.03ms 0.97 groupby.GroupByMethods.time_dtype_as_group('float', 'nunique', 'transformation', 5) - 46.4±0.4ms 44.9±0.3ms 0.97 groupby.GroupByMethods.time_dtype_as_group('float', 'describe', 'direct', 1) - 3.69±0.01ms 3.56±0.01ms 0.97 rolling.Quantile.time_quantile('DataFrame', 1000, 'float', 1, 'higher') - 96.7±0.3μs 93.4±0.2μs 0.97 groupby.GroupByMethods.time_dtype_as_group('object', 'ffill', 'direct', 1) - 3.36±0.02ms 3.24±0.01ms 0.97 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'min') - 3.57±0.01ms 3.45±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'std') - 4.54±0.02ms 4.38±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'float', 'kurt') - 3.99±0.02ms 3.85±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'float', 'max') - 3.64±0.02ms 3.51±0.01ms 0.97 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'min') - 3.13±0.01ms 3.02±0.02ms 0.97 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'max') - 150±1ms 145±0.9ms 0.97 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'kurt') - 253±0.4μs 244±2μs 0.97 join_merge.Concat.time_concat_empty_right(0) - 4.03±0ms 3.89±0.01ms 0.97 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'min') - 1.29±0.01ms 1.24±0ms 0.97 stat_ops.FrameOps.time_op('prod', 'Int64', 0) - 28.2±0.1ms 27.2±0.3ms 0.96 rolling.Groupby.time_method('sum', ('rolling', {'window': '30s', 'on': 'C'})) - 9.80±0.08ms 9.45±0.04ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'sem') - 2.32±0.02ms 2.24±0.02ms 0.96 io.csv.ReadCSVParseDates.time_multiple_date('c') - 4.04±0.03ms 3.89±0.01ms 0.96 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'skew') - 267±0.7μs 257±2μs 0.96 groupby.GroupByMethods.time_dtype_as_group('datetime', 'tail', 'direct', 5) - 3.34±0.01ms 3.23±0.01ms 0.96 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'max') - 3.30±0.01ms 3.19±0ms 0.96 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'min') - 2.83±0.01ms 2.73±0.01ms 0.96 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'skew') - 2.43±0.01ms 2.34±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'linear') - 3.97±0.01ms 3.83±0ms 0.96 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'float', 'skew') - 9.89±0.06ms 9.53±0.06ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'int', 'sem') - 3.41±0.2μs 3.29±0.2μs 0.96 index_cached_properties.IndexCache.time_values('TimedeltaIndex') - 4.65±0.01ms 4.48±0.01ms 0.96 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'kurt') - 1.13±0ms 1.09±0.01ms 0.96 stat_ops.FrameOps.time_op('sum', 'Int64', 0) - 3.52±0.07ms 3.39±0.02ms 0.96 index_cached_properties.IndexCache.time_is_unique('Float64Index') - 1.41±0.01ms 1.36±0.01ms 0.96 rolling.Apply.time_rolling('DataFrame', 3, 'int', , True) - 4.42±0.03ms 4.26±0.01ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'int', 'kurt') - 98.1±0.5μs 94.5±0.4μs 0.96 groupby.GroupByMethods.time_dtype_as_group('object', 'bfill', 'direct', 1) - 2.57±0.01ms 2.48±0.04ms 0.96 series_methods.NSort.time_nsmallest('all') - 21.1±0.2ms 20.3±0.04ms 0.96 groupby.GroupByMethods.time_dtype_as_field('int', 'describe', 'direct', 1) - 5.59±0.07ms 5.38±0.01ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'count') - 4.03±0.02ms 3.88±0.01ms 0.96 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'skew') - 6.28±0.02ms 6.05±0.02ms 0.96 index_object.SetOperations.time_operation('int', 'symmetric_difference') - 12.4±0.03μs 11.9±0.04μs 0.96 tslibs.tz_convert.TimeTZConvert.time_tz_localize_to_utc(1, tzlocal()) - 2.42±0.02μs 2.33±0.01μs 0.96 indexing_engines.NumericEngineIndexing.time_get_loc_near_middle((, ), 'monotonic_decr', True, 2000000) - 270±2μs 259±4μs 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'tail', 'direct', 5) - 218±1μs 209±1μs 0.96 join_merge.Concat.time_concat_empty_right(1) - 22.5±0.3μs 21.6±0.1μs 0.96 series_methods.All.time_all(1000, 'fast', 'bool') - 3.48±0.02ms 3.34±0.02ms 0.96 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'float', 'std') - 3.75±0.02ms 3.60±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 1, 'linear') - 9.87±0.1ms 9.48±0.04ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'sem') - 3.43±0.02ms 3.30±0.01ms 0.96 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'max') - 36.2±0.09ms 34.8±0.08ms 0.96 groupby.GroupByMethods.time_dtype_as_group('uint', 'describe', 'direct', 1) - 35.5±0.5μs 34.1±0.3μs 0.96 series_methods.NanOps.time_func('argmax', 1000, 'float64') - 30.2±0.5μs 28.9±0.1μs 0.96 series_methods.NanOps.time_func('prod', 1000, 'int8') - 1.53±0.01ms 1.47±0.01ms 0.96 rolling.GroupbyEWM.time_groupby_method('var') - 3.71±0.03ms 3.56±0.02ms 0.96 rolling.Quantile.time_quantile('DataFrame', 10, 'float', 0, 'midpoint') - 1.42±0ms 1.36±0.01ms 0.96 groupby.RankWithTies.time_rank_ties('datetime64', 'min') - 2.30±0.01ms 2.21±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'lower') - 1.40±0.04ms 1.34±0.01ms 0.96 groupby.RankWithTies.time_rank_ties('float64', 'dense') - 252±1μs 242±2μs 0.96 join_merge.Concat.time_concat_empty_left(0) - 77.8±0.4ms 74.6±0.6ms 0.96 io.csv.ReadCSVSkipRows.time_skipprows(None, 'python') - 2.44±0ms 2.33±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'nearest') - 36.1±0.2ms 34.6±0.1ms 0.96 groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'direct', 1) - 3.33±0.01ms 3.20±0.01ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'int', 'std') - 1.42±0ms 1.36±0.01ms 0.96 groupby.RankWithTies.time_rank_ties('datetime64', 'max') - 2.46±0.01ms 2.36±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'lower') - 1.79±0.01ms 1.71±0.01ms 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'bfill', 'transformation', 5) - 1.41±0.01ms 1.35±0.01ms 0.96 groupby.RankWithTies.time_rank_ties('datetime64', 'dense') - 1.78±0.01ms 1.71±0.01ms 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'ffill', 'transformation', 5) - 2.47±0.01ms 2.36±0ms 0.96 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'linear') - 107±0.3ms 103±0.6ms 0.96 frame_methods.Apply.time_apply_axis_1 - 3.09±0.01ms 2.96±0.03ms 0.96 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'min') - 1.89±0.02ms 1.81±0.01ms 0.96 groupby.GroupByMethods.time_dtype_as_group('float', 'cumcount', 'transformation', 5) - 87.9±0.3ms 84.1±0.7ms 0.96 strings.Methods.time_partition('str') - 176±1μs 168±0.9μs 0.96 groupby.GroupByMethods.time_dtype_as_group('uint', 'head', 'direct', 5) - 3.26±0.02ms 3.12±0.02ms 0.96 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'kurt') - 3.70±0.2μs 3.54±0.2μs 0.96 index_cached_properties.IndexCache.time_values('UInt64Index') - 5.09±0.04ms 4.87±0.02ms 0.96 groupby.GroupByMethods.time_dtype_as_group('datetime', 'nunique', 'transformation', 5) - 2.43±0.01ms 2.32±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'midpoint') - 2.18±0.01ms 2.08±0.01ms 0.96 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'mean') - 79.8±0.9μs 76.2±0.6μs 0.96 indexing.DataFrameNumericIndexing.time_iloc_dups - 2.47±0.01ms 2.36±0.01ms 0.96 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'higher') - 1.05±0.05μs 1.01±0.03μs 0.95 index_cached_properties.IndexCache.time_is_monotonic_decreasing('RangeIndex') - 222±2ms 212±0.8ms 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'describe', 'direct', 5) - 1.43±0.01ms 1.36±0.01ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'shift', 'transformation', 5) - 13.6±0.08ms 13.0±0.03ms 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'skew', 'transformation', 1) - 294±2ms 280±1ms 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'describe', 'direct', 5) - 3.19±0.02ms 3.04±0.03ms 0.95 join_merge.Join.time_join_dataframes_cross(False) - 3.80±0.2μs 3.62±0.2μs 0.95 index_cached_properties.IndexCache.time_inferred_type('IntervalIndex') - 10.9±0.06ms 10.4±0.03ms 0.95 groupby.GroupByMethods.time_dtype_as_field('uint', 'skew', 'transformation', 5) - 2.46±0.01ms 2.35±0ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 1, 'nearest') - 9.45±0.04ms 9.02±0.04ms 0.95 groupby.GroupByMethods.time_dtype_as_field('int', 'skew', 'direct', 5) - 509±2μs 485±1μs 0.95 groupby.GroupByMethods.time_dtype_as_field('uint', 'sem', 'direct', 5) - 2.44±0.02ms 2.33±0ms 0.95 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 1, 'higher') - 170±0.7ms 162±0.5ms 0.95 io.stata.Stata.time_write_stata('tw') - 2.33±0.01ms 2.22±0ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'higher') - 575±2ms 548±4ms 0.95 stat_ops.FrameMultiIndexOps.time_op([0, 1], 'kurt') - 148±0.7ms 141±0.7ms 0.95 stat_ops.SeriesMultiIndexOps.time_op([0, 1], 'skew') - 2.76±0.05μs 2.63±0.01μs 0.95 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('datetime', 1, None) - 2.34±0.06μs 2.23±0.08μs 0.95 index_cached_properties.IndexCache.time_shape('DatetimeIndex') - 36.4±1ms 34.7±0.05ms 0.95 stat_ops.FrameOps.time_op('sum', 'Int64', 1) - 9.33±0.07ms 8.89±0.05ms 0.95 groupby.GroupByMethods.time_dtype_as_field('int', 'skew', 'transformation', 1) - 2.34±0.01ms 2.23±0ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'midpoint') - 1.45±0ms 1.38±0.01ms 0.95 groupby.GroupByMethods.time_dtype_as_group('object', 'ffill', 'transformation', 5) - 2.33±0.02ms 2.22±0ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'linear') - 20.4±0.2ms 19.4±0.08ms 0.95 groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'direct', 1) - 13.3±0.1ms 12.6±0.06ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'skew', 'direct', 1) - 39.1±1ms 37.2±0.2ms 0.95 stat_ops.FrameOps.time_op('mean', 'Int64', 1) - 7.43±0.4μs 7.08±0.3μs 0.95 index_cached_properties.IndexCache.time_inferred_type('Float64Index') - 2.15±0.01ms 2.05±0.01ms 0.95 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'mean') - 35.9±0.3ms 34.2±0.7ms 0.95 io.sql.SQL.time_read_sql_query('sqlalchemy') - 1.55±0.02ms 1.47±0.01ms 0.95 rolling.GroupbyEWMEngine.time_groupby_mean('cython') - 5.26±0.03ms 5.01±0.05ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'nunique', 'transformation', 5) - 602±10μs 573±20μs 0.95 index_cached_properties.IndexCache.time_is_monotonic('MultiIndex') - 2.34±0ms 2.22±0.01ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'nearest') - 8.83±0.08ms 8.40±0.05ms 0.95 groupby.GroupByMethods.time_dtype_as_field('int', 'skew', 'direct', 1) - 1.24±0.01ms 1.18±0.01ms 0.95 stat_ops.FrameOps.time_op('mean', 'Int64', 0) - 14.7±0.2ms 14.0±0.03ms 0.95 groupby.AggFunctions.time_different_str_functions - 3.23±0.01ms 3.07±0.01ms 0.95 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'std') - 2.39±0.02ms 2.27±0.01ms 0.95 series_methods.NSort.time_nsmallest('first') - 207M 197M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('columns', 'df_int_floats') - 2.26±0ms 2.15±0.01ms 0.95 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 10}, 'std'), 'float') - 10.9±0.5μs 10.4±0.3μs 0.95 index_cached_properties.IndexCache.time_engine('UInt64Index') - 223±1ms 212±1ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'describe', 'direct', 5) - 3.25±0.02ms 3.09±0.01ms 0.95 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'std') - 2.44±0ms 2.32±0.04ms 0.95 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 1000}, 'std'), 'int') - 13.7±0.1ms 13.0±0.05ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'skew', 'transformation', 1) - 2.16±0.04μs 2.05±0.01μs 0.95 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(100, datetime.timezone.utc) - 2.33±0.01ms 2.22±0.01ms 0.95 rolling.Quantile.time_quantile('DataFrame', 1000, 'int', 0, 'lower') - 208M 198M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('columns', 'df_int_float_str') - 203M 193M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('columns', 'df_td_int_ts') - 11.1±0.4μs 10.5±0.3μs 0.95 index_cached_properties.IndexCache.time_engine('TimedeltaIndex') - 208±0.6ms 198±0.6ms 0.95 join_merge.MergeCategoricals.time_merge_cat - 2.50±0.01ms 2.37±0.01ms 0.95 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'mean') - 22.6±0.3μs 21.4±0.2μs 0.95 series_methods.Any.time_any(1000, 'slow', 'bool') - 2.51±0ms 2.38±0.01ms 0.95 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'mean') - 2.44±0.01ms 2.32±0.01ms 0.95 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'int', 'sum') - 4.31±0.03ms 4.09±0.01ms 0.95 groupby.GroupByMethods.time_dtype_as_group('object', 'nunique', 'transformation', 5) - 1.70±0.01ms 1.61±0.01ms 0.95 groupby.GroupByMethods.time_dtype_as_group('uint', 'ffill', 'transformation', 5) - 611±2μs 580±6μs 0.95 indexing.NonNumericSeriesIndexing.time_getitem_list_like('string', 'nonunique_monotonic_inc') - 2.65±0.02ms 2.51±0.02ms 0.95 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'int', 'mean') - 2.30±0.01ms 2.18±0.01ms 0.95 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'int', 'mean') - 197M 187M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('records', 'df_int_floats') - 16.1±0.3ms 15.2±0.07ms 0.95 groupby.Nth.time_frame_nth('float64') - 197M 187M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('index', 'df_int_floats') - 9.70±0.1ms 9.19±0.04ms 0.95 groupby.GroupByMethods.time_dtype_as_field('float', 'skew', 'transformation', 1) - 3.62±0.2μs 3.43±0.2μs 0.95 index_cached_properties.IndexCache.time_values('Float64Index') - 2.98±0.01ms 2.82±0.01ms 0.95 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'kurt') - 2.97±0.01ms 2.81±0ms 0.95 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'kurt') - 3.39±0.03ms 3.21±0.01ms 0.95 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'kurt') - 1.45±0.01ms 1.38±0.01ms 0.95 groupby.GroupByMethods.time_dtype_as_group('object', 'bfill', 'transformation', 5) - 2.32±0.04ms 2.19±0.01ms 0.95 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'higher') - 1.13±0.02ms 1.07±0.01ms 0.95 groupby.FillNA.time_df_ffill - 13.3±0.06ms 12.6±0.08ms 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'skew', 'direct', 1) - 198M 188M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('records', 'df_int_float_str') - 198M 188M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('index', 'df_int_float_str') - 193M 183M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('records', 'df_td_int_ts') - 193M 183M 0.95 io.json.ToJSONWide.peakmem_to_json_wide('index', 'df_td_int_ts') - 603±10μs 571±20μs 0.95 index_cached_properties.IndexCache.time_is_monotonic_increasing('MultiIndex') - 15.9±0.3ms 15.1±0.1ms 0.95 io.csv.ReadCSVParseSpecialDate.time_read_special_date('hm', 'python') - 3.26±0.03ms 3.09±0.02ms 0.95 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'min') - 2.33±0.04ms 2.20±0.02ms 0.95 rolling.Quantile.time_quantile('DataFrame', 10, 'int', 0, 'linear') - 35.0±1μs 33.1±0.2μs 0.95 tslibs.timestamp.TimestampOps.time_replace_tz() - 2.64±0.02ms 2.50±0.01ms 0.95 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'int', 'mean') - 3.27±0.03μs 3.10±0.05μs 0.95 index_object.Indexing.time_get_loc_sorted('Int') - 1.71±0.01ms 1.61±0.01ms 0.95 groupby.GroupByMethods.time_dtype_as_group('int', 'ffill', 'transformation', 5) - 2.15±0.01ms 2.03±0.01ms 0.94 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'std') - 8.92±0.1ms 8.42±0.04ms 0.94 groupby.GroupByMethods.time_dtype_as_field('uint', 'skew', 'direct', 1) - 6.57±0.04μs 6.20±0.2μs 0.94 tslibs.timestamp.TimestampOps.time_replace_None() - 9.81±0.09ms 9.27±0.02ms 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'skew', 'direct', 5) - 2.04±0.05μs 1.92±0.01μs 0.94 tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1, datetime.timezone.utc) - 2.15±0.01ms 2.03±0.01ms 0.94 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'max') - 2.46±0.02ms 2.33±0.01ms 0.94 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'int', 'sum') - 2.32±0.01ms 2.19±0.01ms 0.94 rolling.VariableWindowMethods.time_method('DataFrame', '1h', 'float', 'sum') - 5.30±0.01ms 5.00±0ms 0.94 groupby.GroupByMethods.time_dtype_as_group('int', 'nunique', 'transformation', 5) - 1.69±0.08μs 1.60±0.1μs 0.94 index_cached_properties.IndexCache.time_inferred_type('PeriodIndex') - 9.41±0.1ms 8.88±0.03ms 0.94 groupby.GroupByMethods.time_dtype_as_field('uint', 'skew', 'transformation', 1) - 181M 170M 0.94 io.json.ToJSONWide.peakmem_to_json_wide('split', 'df_td_int_ts') - 2.99±0.02ms 2.82±0.01ms 0.94 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'skew') - 181M 170M 0.94 io.json.ToJSONWide.peakmem_to_json_wide('values', 'df_td_int_ts') - 9.58±0.09ms 9.03±0.01ms 0.94 groupby.GroupByMethods.time_dtype_as_field('uint', 'skew', 'direct', 5) - 1.81±0.01ms 1.70±0.01ms 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cumcount', 'transformation', 5) - 31.7±0.1ms 29.9±0.1ms 0.94 groupby.TransformEngine.time_series_cython(True) - 1.68±0.01ms 1.58±0ms 0.94 groupby.GroupByMethods.time_dtype_as_group('object', 'cumcount', 'transformation', 5) - 3.26±0.01μs 3.07±0.04μs 0.94 index_object.Indexing.time_get_loc('Int') - 2.34±0ms 2.21±0.01ms 0.94 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'float', 'sum') - 20.2±0.1ms 19.1±0.1ms 0.94 stat_ops.FrameMultiIndexOps.time_op(0, 'skew') - 1.71±0.02ms 1.62±0.01ms 0.94 groupby.GroupByMethods.time_dtype_as_group('uint', 'bfill', 'transformation', 5) - 3.14±0.1ms 2.96±0.03ms 0.94 index_cached_properties.IndexCache.time_is_unique('DatetimeIndex') - 21.0±0.3ms 19.8±0.1ms 0.94 groupby.GroupByMethods.time_dtype_as_group('float', 'skew', 'transformation', 1) - 9.25±0.09ms 8.72±0.1ms 0.94 groupby.GroupByMethods.time_dtype_as_field('float', 'skew', 'direct', 1) - 1.57±0.01ms 1.48±0.01ms 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'bfill', 'transformation', 5) - 1.83±0.01ms 1.72±0.01ms 0.94 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'mean') - 76.6±0.4μs 72.1±0.4μs 0.94 series_methods.ToFrame.time_to_frame('Int64', None) - 2.38±0.08μs 2.24±0.01μs 0.94 indexing_engines.NumericEngineIndexing.time_get_loc_near_middle((, ), 'monotonic_incr', False, 100000) - 2.33±0.01ms 2.19±0.01ms 0.94 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'sum') - 1.98±0.01ms 1.86±0.01ms 0.94 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'min') - 76.6±0.1μs 72.0±0.2μs 0.94 series_methods.ToFrame.time_to_frame('int64', None) - 77.6±0.8μs 73.0±0.1μs 0.94 series_methods.ToFrame.time_to_frame('category', None) - 1.99±0.04ms 1.87±0.01ms 0.94 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'float', 'sum') - 19.9±0.06ms 18.7±0.05ms 0.94 join_merge.MergeAsof.time_on_int32('nearest', 5) - 1.56±0.01ms 1.47±0.01ms 0.94 groupby.GroupByMethods.time_dtype_as_group('datetime', 'ffill', 'transformation', 5) - 2.33±0.03μs 2.19±0.05μs 0.94 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1, datetime.timezone.utc) - 19.2±0.05ms 18.0±0.05ms 0.94 join_merge.MergeAsof.time_on_int('nearest', 5) - 2.26±0.01ms 2.12±0.01ms 0.94 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 1000}, 'std'), 'float') - 31.7±0.3ms 29.8±0.2ms 0.94 groupby.TransformEngine.time_series_cython(False) - 1.53±0.02ms 1.43±0.01ms 0.94 groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'transformation', 5) - 852±50ns 799±20ns 0.94 index_cached_properties.IndexCache.time_shape('Int64Index') - 10.9±0.06ms 10.2±0.02ms 0.94 io.csv.ReadCSVConcatDatetimeBadDateValue.time_read_csv('0') - 2.05±0.01ms 1.92±0.01ms 0.94 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'std') - 3.17±0.2μs 2.97±0.2μs 0.94 index_cached_properties.IndexCache.time_values('CategoricalIndex') - 4.32±0.2μs 4.05±0.2μs 0.94 index_cached_properties.IndexCache.time_shape('IntervalIndex') - 18.0±0.03ms 16.9±0.06ms 0.94 join_merge.MergeAsof.time_on_uint64('nearest', 5) - 841±40ns 788±40ns 0.94 index_cached_properties.IndexCache.time_shape('RangeIndex') - 6.40±0.08μs 6.00±0.1μs 0.94 tslibs.timestamp.TimestampOps.time_replace_None(tzfile('/usr/share/zoneinfo/Asia/Tokyo')) - 210±1μs 197±1μs 0.94 groupby.GroupByMethods.time_dtype_as_group('object', 'tail', 'direct', 5) - 19.2±0.09ms 17.9±0.03ms 0.94 join_merge.MergeAsof.time_on_int32('nearest', None) - 2.44±0ms 2.28±0.02ms 0.94 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 10}, 'std'), 'int') - 18.7±0.09ms 17.5±0.05ms 0.94 join_merge.MergeAsof.time_on_int('nearest', None) - 165±0.9ms 154±0.3ms 0.94 strings.Split.time_split('str', True) - 1.16±0.02ms 1.08±0.01ms 0.94 groupby.FillNA.time_df_bfill - 17.3±0.09ms 16.2±0.08ms 0.93 join_merge.MergeAsof.time_on_uint64('nearest', None) - 2.15±0.03ms 2.01±0ms 0.93 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'int', 'sum') - 77.7±0.9μs 72.6±0.1μs 0.93 series_methods.ToFrame.time_to_frame('category', 'foo') - 1.80±0.2μs 1.68±0.07μs 0.93 index_cached_properties.IndexCache.time_values('DatetimeIndex') - 59.4±1ms 55.5±0.2ms 0.93 stat_ops.FrameOps.time_op('skew', 'Int64', 1) - 3.39±0.2μs 3.17±0.2μs 0.93 index_cached_properties.IndexCache.time_values('IntervalIndex') - 2.01±0.01ms 1.87±0.01ms 0.93 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 10}), 'float', 'sum') - 1.96±0.02ms 1.83±0ms 0.93 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'mean') - 1.29±0.01ms 1.20±0.01ms 0.93 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 10}, 'mean'), 'int') - 5.68±0.05ms 5.30±0.04ms 0.93 indexing.MultiIndexing.time_index_slice - 2.55±0.03μs 2.38±0.02μs 0.93 tslibs.tslib.TimeIntsToPydatetime.time_ints_to_pydatetime('time', 1, None) - 212M 198M 0.93 io.json.ToJSONWide.peakmem_to_json('columns', 'df_int_floats') - 3.77±0.2μs 3.52±0.2μs 0.93 index_cached_properties.IndexCache.time_inferred_type('CategoricalIndex') - 207M 193M 0.93 io.json.ToJSONWide.peakmem_to_json('columns', 'df_date_idx') - 206M 192M 0.93 io.json.ToJSONWide.peakmem_to_json_wide('columns', 'df_date_idx') - 213M 199M 0.93 io.json.ToJSONWide.peakmem_to_json('columns', 'df_int_float_str') - 210M 196M 0.93 io.json.ToJSONWide.peakmem_to_json('columns', 'df_td_int_ts') - 208M 193M 0.93 io.json.ToJSONWide.peakmem_to_json('index', 'df_int_floats') - 289±2ms 269±3ms 0.93 groupby.GroupByCythonAgg.time_frame_agg('float64', 'median') - 204M 190M 0.93 io.json.ToJSONWide.peakmem_to_json('records', 'df_int_floats') - 208M 194M 0.93 io.json.ToJSONWide.peakmem_to_json('index', 'df_int_float_str') - 1.96±0.03ms 1.82±0.01ms 0.93 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'mean') - 199M 185M 0.93 io.json.ToJSONWide.peakmem_to_json('columns', 'df') - 77.9±0.4ms 72.4±0.5ms 0.93 stat_ops.FrameMultiIndexOps.time_op(1, 'kurt') - 199M 185M 0.93 io.json.ToJSONWide.peakmem_to_json('index', 'df_date_idx') - 204M 189M 0.93 io.json.ToJSONWide.peakmem_to_json('index', 'df_td_int_ts') - 198M 184M 0.93 io.json.ToJSONWide.peakmem_to_json_wide('index', 'df') - 198M 184M 0.93 io.json.ToJSONWide.peakmem_to_json_wide('records', 'df') - 198M 184M 0.93 io.json.ToJSONWide.peakmem_to_json_wide('records', 'df_date_idx') - 198M 184M 0.93 io.json.ToJSONWide.peakmem_to_json_wide('index', 'df_date_idx') - 1.69±0.01ms 1.57±0.01ms 0.93 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'sum') - 197M 183M 0.93 io.json.ToJSONWide.peakmem_to_json('index', 'df') - 196M 182M 0.93 io.json.ToJSONWide.peakmem_to_json_wide('columns', 'df') - 200M 186M 0.93 io.json.ToJSONWide.peakmem_to_json('records', 'df_td_int_ts') - 1.61±0.01ms 1.50±0.01ms 0.93 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'mean') - 33.0±0.2ms 30.6±0.2ms 0.93 groupby.TransformEngine.time_dataframe_cython(True) - 195M 181M 0.93 io.json.ToJSONWide.peakmem_to_json('records', 'df') - 195M 181M 0.93 io.json.ToJSONWide.peakmem_to_json('records', 'df_date_idx') - 205M 190M 0.93 io.json.ToJSONWide.peakmem_to_json('records', 'df_int_float_str') - 1.65±0.01ms 1.53±0ms 0.93 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'float', 'sum') - 194M 180M 0.93 io.json.ToJSONWide.peakmem_to_json('split', 'df_date_idx') - 128±1μs 119±0.7μs 0.93 groupby.GroupByMethods.time_dtype_as_field('uint', 'shift', 'direct', 5) - 4.44±0.2μs 4.11±0.1μs 0.93 index_cached_properties.IndexCache.time_engine('PeriodIndex') - 1.31±0.01ms 1.22±0.01ms 0.93 groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'transformation', 5) - 1.86±0.04ms 1.73±0.01ms 0.93 groupby.GroupByMethods.time_dtype_as_group('int', 'cumcount', 'transformation', 5) - 196M 182M 0.93 io.json.ToJSONWide.peakmem_to_json('split', 'df_int_floats') - 1.29±0.01ms 1.19±0.01ms 0.93 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 1000}, 'mean'), 'int') - 1.47±0.01ms 1.36±0.01ms 0.93 groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'transformation', 5) - 115±1μs 107±1μs 0.93 groupby.GroupByMethods.time_dtype_as_field('uint', 'shift', 'direct', 1) - 1.65±0.01ms 1.52±0.01ms 0.93 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'float', 'sum') - 15.3±0.08ms 14.2±0.03ms 0.93 join_merge.MergeAsof.time_on_int('forward', 5) - 188M 174M 0.93 io.json.ToJSONWide.peakmem_to_json('split', 'df') - 187M 173M 0.93 io.json.ToJSONWide.peakmem_to_json('values', 'df') - 197M 182M 0.93 io.json.ToJSONWide.peakmem_to_json('split', 'df_int_float_str') - 1.55±0.01ms 1.44±0.01ms 0.93 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'sum') - 123±0.9μs 114±0.5μs 0.93 groupby.GroupByMethods.time_dtype_as_field('int', 'shift', 'direct', 5) - 187M 173M 0.93 io.json.ToJSONWide.peakmem_to_json('values', 'df_date_idx') - 187M 173M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('split', 'df') - 187M 173M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('values', 'df') - 187M 173M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('split', 'df_date_idx') - 189M 175M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('split', 'df_int_floats') - 187M 173M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('values', 'df_date_idx') - 189M 175M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('values', 'df_int_floats') - 1.04±0.01ms 960±6μs 0.92 groupby.GroupByMethods.time_dtype_as_group('object', 'cumcount', 'direct', 5) - 1.07±0.01ms 994±5μs 0.92 groupby.GroupByMethods.time_dtype_as_group('float', 'cumcount', 'direct', 5) - 72.7±0.2ms 67.2±0.3ms 0.92 join_merge.MergeOrdered.time_merge_ordered - 207M 192M 0.92 io.json.ToJSON.peakmem_to_json('columns', 'df_int_float_str') - 190M 176M 0.92 io.json.ToJSONWide.peakmem_to_json('split', 'df_td_int_ts') - 189M 175M 0.92 io.json.ToJSONWide.peakmem_to_json('values', 'df_int_floats') - 206M 191M 0.92 io.json.ToJSON.peakmem_to_json('columns', 'df_int_floats') - 1.74±0.01ms 1.61±0.01ms 0.92 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'int', 'mean') - 203M 188M 0.92 io.json.ToJSON.peakmem_to_json('columns', 'df_date_idx') - 204M 188M 0.92 io.json.ToJSON.peakmem_to_json('columns', 'df_td_int_ts') - 187M 173M 0.92 io.json.ToJSONWide.peakmem_to_json('values', 'df_td_int_ts') - 190M 176M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('split', 'df_int_float_str') - 190M 175M 0.92 io.json.ToJSONWide.peakmem_to_json_wide('values', 'df_int_float_str') - 4.05±0.2μs 3.74±0.2μs 0.92 index_cached_properties.IndexCache.time_shape('Float64Index') - 2.16±0.03ms 1.99±0.01ms 0.92 rolling.Methods.time_method('DataFrame', ('rolling', {'window': 1000}), 'int', 'sum') - 3.63±0.1μs 3.35±0.1μs 0.92 index_cached_properties.IndexCache.time_inferred_type('MultiIndex') - 202M 186M 0.92 io.json.ToJSON.peakmem_to_json('index', 'df_int_float_str') - 202M 186M 0.92 io.json.ToJSON.peakmem_to_json('index', 'df_int_floats') - 7.97±0.3μs 7.36±0.3μs 0.92 index_cached_properties.IndexCache.time_inferred_type('UInt64Index') - 1.06±0.02ms 980±4μs 0.92 groupby.GroupByMethods.time_dtype_as_group('datetime', 'cumcount', 'direct', 5) - 108±0.7μs 100.0±0.4μs 0.92 groupby.GroupByMethods.time_dtype_as_group('float', 'shift', 'direct', 1) - 808±40ns 745±4ns 0.92 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(100, 12000) - 199M 183M 0.92 io.json.ToJSON.peakmem_to_json('records', 'df_int_float_str') - 69.6±0.6ms 64.2±0.3ms 0.92 groupby.GroupByMethods.time_dtype_as_field('datetime', 'unique', 'direct', 1) - 190M 175M 0.92 io.json.ToJSONWide.peakmem_to_json('values', 'df_int_float_str') - 198M 183M 0.92 io.json.ToJSON.peakmem_to_json('records', 'df_int_floats') - 1.10±0.01ms 1.01±0.01ms 0.92 groupby.GroupByMethods.time_dtype_as_group('uint', 'cumcount', 'direct', 5) - 106±0.5μs 97.4±0.7μs 0.92 groupby.GroupByMethods.time_dtype_as_group('object', 'shift', 'direct', 1) - 38.6±0.2ms 35.5±0.4ms 0.92 groupby.GroupByMethods.time_dtype_as_field('uint', 'unique', 'direct', 1) - 195M 180M 0.92 io.json.ToJSON.peakmem_to_json('columns', 'df') - 197M 181M 0.92 io.json.ToJSON.peakmem_to_json('index', 'df_td_int_ts') - 195M 179M 0.92 io.json.ToJSON.peakmem_to_json('index', 'df_date_idx') - 463±20μs 426±4μs 0.92 arithmetic.NumericInferOps.time_multiply() - 193M 178M 0.92 io.json.ToJSON.peakmem_to_json('index', 'df') - 110±0.6μs 101±0.8μs 0.92 groupby.GroupByMethods.time_dtype_as_field('int', 'shift', 'direct', 1) - 193M 178M 0.92 io.json.ToJSON.peakmem_to_json('records', 'df_td_int_ts') - 15.2±0.01ms 14.0±0.08ms 0.92 join_merge.MergeAsof.time_on_int('forward', None) - 190M 175M 0.92 io.json.ToJSON.peakmem_to_json('split', 'df_date_idx') - 191M 176M 0.92 io.json.ToJSON.peakmem_to_json('records', 'df') - 191M 176M 0.92 io.json.ToJSON.peakmem_to_json('records', 'df_date_idx') - 190M 175M 0.92 io.json.ToJSON.peakmem_to_json('split', 'df_int_floats') - 1.17±0ms 1.08±0ms 0.92 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 1000}, 'mean'), 'float') - 191M 175M 0.92 io.json.ToJSON.peakmem_to_json('split', 'df_int_float_str') - 3.98±0.2μs 3.66±0.1μs 0.92 index_cached_properties.IndexCache.time_shape('CategoricalIndex') - 16.4±0.1ms 15.1±0.09ms 0.92 join_merge.MergeAsof.time_on_int32('forward', 5) - 696±9μs 638±5μs 0.92 groupby.Shift.time_fill_value - 1.42±0.01ms 1.30±0.01ms 0.92 groupby.GroupByMethods.time_dtype_as_group('datetime', 'shift', 'transformation', 5) - 184M 169M 0.92 io.json.ToJSON.peakmem_to_json('split', 'df') - 111±2μs 102±0.7μs 0.92 groupby.GroupByMethods.time_dtype_as_group('int', 'shift', 'direct', 1) - 183M 168M 0.92 io.json.ToJSON.peakmem_to_json('values', 'df') - 183M 168M 0.92 io.json.ToJSON.peakmem_to_json('values', 'df_date_idx') - 25.8±0.05ms 23.6±0.05ms 0.92 stat_ops.FrameMultiIndexOps.time_op(0, 'kurt') - 38.7±0.3ms 35.4±0.1ms 0.92 groupby.GroupByMethods.time_dtype_as_field('int', 'unique', 'direct', 1) - 1.18±0ms 1.08±0ms 0.92 rolling.EWMMethods.time_ewm('DataFrame', ({'halflife': 10}, 'mean'), 'float') - 122±0.7ms 112±0.6ms 0.92 strings.Split.time_rsplit('str', True) - 184M 169M 0.92 io.json.ToJSON.peakmem_to_json('values', 'df_int_float_str') - 183M 168M 0.92 io.json.ToJSON.peakmem_to_json('values', 'df_int_floats') - 56.7±0.3ms 51.9±0.7ms 0.92 groupby.GroupByMethods.time_dtype_as_group('uint', 'unique', 'direct', 1) - 183M 168M 0.91 io.json.ToJSON.peakmem_to_json('split', 'df_td_int_ts') - 180M 165M 0.91 io.json.ToJSON.peakmem_to_json('values', 'df_td_int_ts') - 14.5±0.05ms 13.3±0.08ms 0.91 join_merge.MergeAsof.time_on_uint64('forward', 5) - 1.81±0.03ms 1.65±0.01ms 0.91 rolling.ForwardWindowMethods.time_rolling('DataFrame', 10, 'int', 'sum') - 13.7±0.04ms 12.5±0.06ms 0.91 join_merge.MergeAsof.time_on_int('backward', 5) - 89.5±0.5ms 81.7±0.6ms 0.91 groupby.GroupByMethods.time_dtype_as_group('float', 'unique', 'direct', 1) - 834±40ns 761±5ns 0.91 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1000000, 12000) - 4.17±0.2μs 3.80±0.2μs 0.91 index_cached_properties.IndexCache.time_shape('UInt64Index') - 138±1μs 126±1μs 0.91 groupby.GroupByMethods.time_dtype_as_group('object', 'head', 'direct', 5) - 70.6±0.7μs 64.4±0.6μs 0.91 timeseries.SortIndex.time_sort_index(True) - 93.1±0.4ms 84.9±0.4ms 0.91 groupby.GroupByMethods.time_dtype_as_group('datetime', 'unique', 'direct', 1) - 15.7±0.07ms 14.3±0.05ms 0.91 join_merge.MergeAsof.time_on_int32('backward', 5) - 36.2±0.3ms 33.0±0.1ms 0.91 rolling.GroupbyLargeGroups.time_rolling_multiindex_creation - 29.1±0.5ms 26.6±0.2ms 0.91 io.sql.SQL.time_read_sql_query('sqlite') - 13.5±0.02ms 12.3±0.09ms 0.91 join_merge.MergeAsof.time_on_int('backward', None) - 14.2±0.04ms 12.9±0.09ms 0.91 join_merge.MergeAsof.time_on_uint64('forward', None) - 79.2±0.5μs 72.1±0.3μs 0.91 series_methods.ToFrame.time_to_frame('datetime64[ns]', None) - 15.3±0.09ms 13.9±0.03ms 0.91 join_merge.MergeAsof.time_on_int32('backward', None) - 900±80ns 819±40ns 0.91 index_cached_properties.IndexCache.time_is_monotonic('RangeIndex') - 835±40ns 759±3ns 0.91 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(10000, 12000) - 21.0±0.1ms 19.0±0.06ms 0.91 reshape.Crosstab.time_crosstab - 79.2±0.3μs 71.9±0.3μs 0.91 series_methods.ToFrame.time_to_frame('datetime64[ns]', 'foo') - 4.19±0.2μs 3.80±0.2μs 0.91 index_cached_properties.IndexCache.time_shape('TimedeltaIndex') - 2.14±0.01ms 1.94±0.01ms 0.91 join_merge.Merge.time_merge_dataframe_integer_key(True) - 525±4μs 476±5μs 0.91 categoricals.Concat.time_concat_overlapping_index - 13.5±0.05ms 12.2±0.03ms 0.91 join_merge.MergeAsof.time_on_uint64('backward', 5) - 88.1±1ms 79.7±3ms 0.90 frame_ctor.FromRecords.time_frame_from_records_generator(None) - 801±40ns 724±4ns 0.90 tslibs.period.TimePeriodArrToDT64Arr.time_periodarray_to_dt64arr(1, 12000) - 2.58±0.02ms 2.34±0.03ms 0.90 reshape.ReshapeExtensionDtype.time_unstack_slow('Period[s]') - 42.0±0.3ms 38.0±0.2ms 0.90 groupby.GroupByMethods.time_dtype_as_field('object', 'unique', 'direct', 1) - 39.3±1ms 35.5±0.06ms 0.90 stat_ops.FrameOps.time_op('prod', 'Int64', 1) - 16.3±0.2ms 14.7±0.3ms 0.90 eval.Query.time_query_datetime_index - 352±1μs 317±2μs 0.90 algos.isin.IsIn.time_isin_mismatched_dtype('object') - 57.4±0.7ms 51.8±0.1ms 0.90 groupby.GroupByMethods.time_dtype_as_group('int', 'unique', 'direct', 1) - 4.18±0.03ms 3.77±0.02ms 0.90 io.sas.SAS.time_read_sas('xport') - 15.6±0.3ms 14.0±0.1ms 0.90 eval.Query.time_query_datetime_column - 757±9μs 681±4μs 0.90 period.Indexing.time_align - 2.26±0.09ms 2.03±0.07ms 0.90 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'count') - 13.2±0.05ms 11.9±0.05ms 0.90 join_merge.MergeAsof.time_on_uint64('backward', None) - 1.83±0ms 1.65±0.01ms 0.90 rolling.ForwardWindowMethods.time_rolling('DataFrame', 1000, 'int', 'sum') - 420±30ns 378±4ns 0.90 dtypes.DtypesInvalid.time_pandas_dtype_invalid('array-string') - 993±2μs 893±7μs 0.90 libs.InferDtype.time_infer_dtype('np-object') - 39.7±0.2ms 35.6±0.2ms 0.90 groupby.GroupByMethods.time_dtype_as_field('float', 'unique', 'direct', 1) - 469±20μs 421±5μs 0.90 arithmetic.NumericInferOps.time_add() - 3.72±0.02ms 3.33±0.02ms 0.90 index_cached_properties.IndexCache.time_is_unique('IntervalIndex') - 706±4μs 632±4μs 0.89 frame_ctor.FromDicts.time_dict_of_categoricals - 1.72±0.1μs 1.54±0.07μs 0.89 index_cached_properties.IndexCache.time_inferred_type('DatetimeIndex') - 1.91±0.02ms 1.70±0.01ms 0.89 io.csv.ReadCSVParseDates.time_baseline('c') - 235±2ms 208±1ms 0.89 io.stata.StataMissing.time_write_stata('tw') - 594±4μs 526±5μs 0.89 join_merge.Append.time_append_mixed - 1.82±0.02ms 1.61±0ms 0.88 join_merge.Merge.time_merge_dataframe_integer_key(False) - 43.0±0.6ms 37.9±0.2ms 0.88 groupby.Sample.time_sample - 9.40±0.5ms 8.28±0.06ms 0.88 arithmetic.FrameWithFrameWide.time_op_same_blocks(, (1000000, 10)) - 50.9±0.2ms 44.8±0.5ms 0.88 groupby.Apply.time_scalar_function_multi_col(4) - 228±2ms 201±1ms 0.88 io.stata.StataMissing.time_write_stata('th') - 230±6ms 203±1ms 0.88 io.stata.StataMissing.time_write_stata('tq') - 218±3ms 192±4ms 0.88 io.stata.StataMissing.time_write_stata('td') - 17.8±0.1ms 15.6±0.1ms 0.88 groupby.Apply.time_scalar_function_single_col(4) - 3.44±0.02ms 3.01±0.02ms 0.87 index_cached_properties.IndexCache.time_is_unique('UInt64Index') - 3.44±0.02ms 3.01±0.02ms 0.87 index_cached_properties.IndexCache.time_is_unique('PeriodIndex') - 4.73±0.09ms 4.13±0.07ms 0.87 frame_methods.Apply.time_apply_pass_thru - 3.70±0.2μs 3.22±0.2μs 0.87 index_cached_properties.IndexCache.time_inferred_type('TimedeltaIndex') - 37.5±0.1μs 32.5±0.4μs 0.87 frame_ctor.FromSeries.time_mi_series - 216±6ms 187±2ms 0.87 io.stata.StataMissing.time_write_stata('tc') - 281±4ms 241±0.9ms 0.86 groupby.MultiColumn.time_lambda_sum - 24.5±0.3ms 20.9±0.1ms 0.86 reshape.Unstack.time_full_product('category') - 150±2ms 127±1ms 0.85 groupby.MultiColumn.time_col_select_lambda_sum - 390±3μs 326±3μs 0.84 timeseries.ResetIndex.time_reset_datetimeindex(None) - 395±2μs 329±3μs 0.83 timeseries.ResetIndex.time_reset_datetimeindex('US/Eastern') - 9.26±0.09ms 7.65±0.7ms 0.83 rolling.VariableWindowMethods.time_method('DataFrame', '1d', 'float', 'sem') - 86.5±0.5ms 71.2±0.3ms 0.82 join_merge.Concat.time_concat_series(1) - 818±2ms 672±10ms 0.82 join_merge.I8Merge.time_i8merge('inner') - 9.26±0.1ms 7.60±0.7ms 0.82 rolling.VariableWindowMethods.time_method('DataFrame', '50s', 'float', 'sem') - 929±8ms 761±4ms 0.82 join_merge.I8Merge.time_i8merge('right') - 819±1ms 671±8ms 0.82 join_merge.I8Merge.time_i8merge('outer') - 22.4±0.09μs 18.3±0.1μs 0.82 frame_ctor.FromNDArray.time_frame_from_ndarray - 19.5±0.07ms 15.8±0.03ms 0.81 strings.Cat.time_cat(0, ',', None, 0.15) - 460±1μs 371±0.9μs 0.81 frame_methods.Shift.time_shift(1) - 854±9ms 684±8ms 0.80 join_merge.I8Merge.time_i8merge('left') - 6.90±0.05ms 5.51±0.6ms 0.80 rolling.Methods.time_method('DataFrame', ('expanding', {}), 'float', 'sem') - 5.95±0.05ms 4.70±0.03ms 0.79 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'Float64') - 7.40±0.05ms 5.84±0.03ms 0.79 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'float64') - 6.52±0.04ms 5.10±0.04ms 0.78 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'Int64') - 5.90±0.03ms 4.59±0.02ms 0.78 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'Int64') - 6.64±0.03ms 5.14±0.04ms 0.77 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'Float64') - 16.4±2ms 12.7±0.07ms 0.77 eval.Eval.time_mult('python', 'all') - 5.99±0.07ms 4.60±0.05ms 0.77 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'Float64') - 2.86±0.01ms 2.20±0.02ms 0.77 reshape.Melt.time_melt_dataframe - 6.00±0.04ms 4.60±0.06ms 0.77 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'Int64') - 9.56±0.08ms 7.24±0.04ms 0.76 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'datetime64[ns, tz]') - 7.22±0.08ms 5.46±0.03ms 0.76 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'float32') - 5.84±0.2ms 4.38±0.3ms 0.75 frame_methods.NSort.time_nlargest_one_column('all') - 3.47±0.1ms 2.58±0.01ms 0.75 frame_ctor.FromArrays.time_frame_from_arrays_float - 5.18±0.2ms 3.87±0.2ms 0.75 frame_methods.NSort.time_nlargest_one_column('last') - 5.88±0.2ms 4.36±0.3ms 0.74 frame_methods.NSort.time_nlargest_one_column('first') - 8.19±0.05ms 5.92±0.1ms 0.72 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'datetime64[ns, tz]') - 7.87±0.08ms 5.68±0.04ms 0.72 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'datetime64[ns, tz]') - 17.5±3ms 12.6±0.08ms 0.72 eval.Eval.time_add('python', 'all') - 7.42±0.1ms 5.26±0.01ms 0.71 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'float64') - 4.36±0.03ms 2.99±0.01ms 0.69 frame_ctor.FromArrays.time_frame_from_arrays_int - 4.47±0.01ms 3.04±0.01ms 0.68 frame_ctor.FromArrays.time_frame_from_arrays_sparse - 7.14±0.08ms 4.79±0.02ms 0.67 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'float32') - 4.58±0.01ms 3.04±0.02ms 0.66 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'Float64') - 4.59±0.01ms 3.04±0.02ms 0.66 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'Int64') - 632±5ms 401±1ms 0.64 frame_methods.Nunique.time_frame_nunique - 27.4±0.4ms 17.1±0.09ms 0.62 indexing.InsertColumns.time_insert_middle - 17.1±0.07ms 10.5±0.1ms 0.62 indexing.InsertColumns.time_insert - 309±3ms 180±2ms 0.58 frame_methods.Fillna.time_frame_fillna(False, 'pad', 'object') - 314±2ms 180±1ms 0.57 frame_methods.Fillna.time_frame_fillna(False, 'bfill', 'object') - 158±0.9ms 90.4±0.7ms 0.57 groupby.Apply.time_copy_overhead_single_col(4) - 409±2ms 230±0.9ms 0.56 groupby.Apply.time_copy_function_multi_col(4) - 17.0±0.07ms 9.28±0.04ms 0.55 indexing.InsertColumns.time_assign_list_like_with_setitem - 17.6±0.1ms 9.38±0.02ms 0.53 indexing.InsertColumns.time_assign_with_setitem - 516±4μs 259±0.8μs 0.50 reindex.Reindex.time_reindex_columns - 169±3μs 76.8±2μs 0.45 indexing.AssignTimeseriesIndex.time_frame_assign_timeseries_index - 525±4ms 238±2ms 0.45 frame_methods.Fillna.time_frame_fillna(True, 'bfill', 'object') - 30.4±0.06ms 13.3±0.1ms 0.44 frame_methods.Reindex.time_reindex_axis1 - 526±8ms 226±1ms 0.43 frame_methods.Fillna.time_frame_fillna(True, 'pad', 'object') - 51.1±0.2ms 20.2±0.05ms 0.40 frame_methods.Rename.time_rename_both_axes - 51.1±0.1ms 20.1±0.2ms 0.39 frame_methods.Rename.time_dict_rename_both_axes - 49.3±0.1ms 18.3±0.07ms 0.37 frame_methods.Rename.time_rename_axis0 - 47.1±0.3ms 16.4±0.2ms 0.35 frame_methods.Rename.time_rename_single - 81.0±0.5μs 28.2±0.4μs 0.35 period.DataFramePeriodColumn.time_setitem_period_column - 44.8±0.2ms 14.2±0.05ms 0.32 frame_methods.Rename.time_rename_axis1 - 111±2ms 23.4±0.06ms 0.21 reshape.Unstack.time_without_last_row('category') - 42.2±0.07ms 3.40±0.03ms 0.08 arithmetic.IntFrameWithScalar.time_frame_op_with_scalar(, 2, ) SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY. PERFORMANCE DECREASED. ```

jbrockmendel commented 2 years ago

Thanks for putting this together @jorisvandenbossche. The discussion seemed quite fair to me, and the only real points of disagreement are entirely subjective (e.g. "X is acceptable IMO").

A few other places where I've found ArrayManager performs exceptionally well (in ways that I don't think that BlockManager can meaningfully optimize):

DataFrame.insert
DataFrame._from_arrays
DataFrame(a_dict_that_can_consolidate)

Assorted Notes:

IntFrameWithScalar.time_frame_op_with_scalar
- I tend to get more extreme results for this on my linux desktop than on my mac laptop. Don't know what to make of this.
groupby.Apply.time_copy_function_multi_col(4) and groupby.Apply.time_copy_overhead_single_col(4) are each a little less than 2x faster for AM than BM locally. My tentative analysis of this is that it is discarding alignment information (all_indexes_same) that can improve perf (likely for both AM and BM, but mainly BM). xref https://github.com/pandas-dev/pandas/issues/43155#issuecomment-913831505
Many of the places where AM performs well are due to contiguity. I'm experimenting with
- a) making DataFrame.copy preserve contiguity (so far seems to make a big improvement in frame_methods.Rename but a slowdown in arithmetic.FrameWithFrameWide.time_op_different_blocks) and
- b) making consolidation retain F-contiguity (so far seems like ive implemented it wrong)

corleyma commented 1 year ago

@jorisvandenbossche @jbrockmendel This might be a weird place to ask about this, but given the various roadmap/design docs that mentioned the BlockManager refactor as part of the goals for pandas 2.0, and the wide release of pandas 2.0, is there any one place with a summary of where the project is at with respect to the original goals of moving to a 1d block manager?

I am wondering what folks can expect if using Arrow-backed dtypes in pandas 2.0 as it relates to block manager behavior; the changelog for pandas 2.0 is very large and detailed but I couldn't quite find anything that spelled this out at the high level. E.g., I am curious to know the set of operations that are copy-free (no consolidation) when using pandas 2.0 with Arrow-backed dtypes.

jbrockmendel commented 1 year ago

Unless you actively opt in to ArrayManager usage (which hasn't seen much discussion in the last year and change), you won't be affected.

E.g., I am curious to know the set of operations that are copy-free (no consolidation)

automatic consolidation has been removed, so you won't be getting any copies on that front. For more copy-free-ness, I suggest trying out pd.set_option("mode.copy_on_write", True)

rhshadrach commented 1 year ago

The ArrayManager is now deprecated. Closing.

pandas-dev / pandas

Refactor - ArrayManager overview issue #39146