Closed bgatessucks closed 1 year ago
this is very much like #13446 . Since pandas doesn't know that an uncertainity
is numeric it cannot deal with it, similar to Decimal
.
Without a custom dtype, or special support baked into object
dtypes, this is not supported.
If someone wanted to contribute this functionaility then that would be great. Conceptually this is very easy, but there are lots of implementation details.
@jreback Do I understand correctly that there is nothing that the uncertainties module can do to solve this issue?
I have no idea if u want t dig in and see would be great
A useful first step would be to see if you can reproduce the issue with numpy
alone (not using pandas).
@shoyer No issue with numpy
alone:
import pandas as pd
import numpy as np
from uncertainties import unumpy
value = np.arange(12).reshape(3,4)
err = 0.01 * np.arange(12).reshape(3,4) + 0.005
data = unumpy.uarray(value, err)
df = pd.DataFrame(data, index=['r1', 'r2', 'r3'], columns=['c1', 'c2', 'c3', 'c4'])
print (df.apply(lambda x: x.sum() / x.size).values), "\n"
print (data.mean(axis=0)), "\n"
print (df.T.apply(lambda x: x.sum() / x.size).values), "\n"
print (data.mean(axis=1))
@bgatessucks what is the type/dtype of unumpy.uarray
? Is it a numpy array with dtype=object
?
@shoyer
type(data)
is <type 'numpy.ndarray'>
.
And data.dtype
?
I just wanted to be sure that you're not using subclassing or something else like that.
In any case, I think this is probably a pandas bug (but would need someone to work through/figure out). We should have a fallback implementation of mean
(like NumPy's mean) that works on object arrays.
@shoyer Sorry I had missed that:
data.dtype
is object
.
For what it's worth, the same example as above works with a DataFrame initialized with a numpy array of dtype='object'
containing floats.
import pandas as pd
import numpy as np
from IPython.display import display
data = np.arange(12).reshape(3,4).astype('object')
df = pd.DataFrame(data, index=['r1', 'r2', 'r3'],
columns=['c1', 'c2', 'c3', 'c4'], dtype='object')
display(df.sum(axis=0))
display(df.sum(axis=1))
display(df.mean(axis=0))
display(df.mean(axis=1))
so I guess that pandas is able to correctly infer in this case that an array of dtype="object"
contains numbers (floats
) unlike with the array containing ufloat
elements from the uncertainties package.
Seen from the outside, it looks like in both cases Pandas decrees that the result of mean()
should be of type float64
: in @rth's example above the NumPy array actually contains integers, that are converted to float64
(which is doable); in the case of uncertainties.UFloat
numbers with uncertainty, forcing the result to float64
is mostly meaningless (as this would get rid of the uncertainty) and mean()
does not produce the expected result.
In contrast, as the original post shows, Pandas is more open on the data type of sum()
, which is, correctly, object
, for uncertainties.UFloat
objects.
I think that it is desirable that since Pandas is able to sum()
, it be able to get the mean()
too (since the mean is not much more than a sum).
Is there any news on this subject? Same problem here, with pandas version 1.0.1.
I have the same issue with pandas version 1.0.3
Was this removed from the Someday milestone because it's more definitive than that now? I've just done a bunch of work to make uncertainties
work with Pint
and Pint-Pandas
, and am seeing that some work needs to be done in Pandas
as well. Just taking the temperature on how open that door might be.
https://github.com/hgrecco/pint/pull/1615 https://github.com/hgrecco/pint-pandas/pull/140
Was this removed from the Someday milestone because it's more definitive than that now
We stopped using the "Someday" label entirely.
I'm getting the same behavior on main as in the OP. Looks like the data is an object-type np.ndarray. As jreback said in 2016, this would need some special handling (probably in core.nanops). A PR would be welcome.
Something like pint-pandas would probably be a better user experience than an object-dtype.
@topper-123 this might be closed by your reduce_wrap PR?
Sorry for the slow reply, I had a big project before going on a family vacation (which will last until the end of this week). but yes, #52788 will allow extension arrays like pint-pandas to use _reduce_wrap
to control the dtype of reduction results.
Related to #6898.
I find it very convenient to use a DataFrame of
ufloat
from theuncertainties
package. Each entry consists of (value, error) and could represent the result of Monte Carlo simulations or an experiment.At present taking sums along both axes gives the expected result, but taking the mean does not.
Examples: