splice, concat, join, hstack and vstack

[librasteve]

Dan::Polars

• now implemented hstack and vstack
• next is join

Dan

• has splice (row-wise & column-wise)
• has concat

this issue to weigh the benefit of implementing as splice & concat for Dan::Polars

and thus to mask Dan splice & concat

Series

• Dan has ...s.concat: t;
• Pandas has ... concat
• Polars has ... pub fn append(&mut self, other: &Series);

these are both "in place"

DataFrame

Dan has ...

dfa.concat: dfc, join => 'inner';
#`[
      letter  number
 0    a       1
 1    b       2
 0⋅1  c       3
 1⋅1  d       4
#]

• concat is the method call for join operations
• splice is used for append, hstack, vstack

Polars has...

• concat as an alternate interface for hstack, vstack

Options

1. take the Polars approach, deprecate splice & concat, replace with append, hstack, vstack, join
2. take the Dan approach, implement splice & concat as wrapper

Conclusion

In the light of implementation, it appears the best common solution is to take a 3rd path that replaces this method zoo with .concat and .join. This is detailed below....

[librasteve]

These tables compare the API methods:

Table 1: Combining functions for DataFrames (Pandas and Polars)

Function	Description	Pandas	Polars	Dan
vstack	Stack vertically	pd.concat([df1, df2], axis=0)	pl.vstack([df1, df2]) or pl.concat([df1, df2])	df1.concat(df2)
hstack	Stack horizontally	pd.concat([df1, df2], axis=1)	pl.hstack([df1, df2]) or pl.concat([df1, df2], how="horizontal")	df1.concat(df2, :axis(1))
concat	Concatenate along an axis	pd.concat([df1, df2], axis=0/1)	pl.concat([df1, df2], axis=0/1)	df1.concat(df2, axis=>0/1)
join	Join on a column	df1.join(df2, on="col") or pd.merge(df1, df2, how="inner", on="col")	df1.join(df2, how="inner", on="col")	df1.join(df2, how=>'inner', on=>'col')

Table 2: Combining functions for Series (Pandas and Polars)

Function	Description	Pandas	Polars	Dan
concat	Append one Series to another	n/a	n/a	series1.concat( series2 )
append	Append one Series to another	series1.append(series2)	n/a	series1.concat( series2 )
join	Join Series on index	series1.join(series2, how='inner')	pl.join([series1, series2], on='index_column')	n/a

Sources:

• https://pandas.pydata.org/docs/user_guide/merging.html#
• https://pola-rs.github.io/polars-book/user-guide/transformations/joins/#cross-join
• https://pola-rs.github.io/polars-book/user-guide/transformations/concatenation/

[librasteve]

The solution is:

Table 1: Combining functions for DataFrames

Function	Description	Dan
concat	Concatenate along an axis	df1.concat(df2, axis=>0/1)
join	Join on a column	df1.join(df2, how=>'inner', on=>'col')

Table 2: Combining functions for Series

Function	Description	Dan
concat	Append one Series to another	series1.concat( series2 )

[librasteve]

Notes:

1. use concat in place of hstack, vstack a. concat – diagonal is not provided b. concat - multiple is not provided
2. merge (Python) becomes join a. join - right is not provided (you need to swap arguments) b. join - [semi, asof] are not (yet) provided
3. Dan and Dan::Pandas concat to be refactored out to concat and join
4. Dan splice to be replaced with some combination of 'concat', ‘with_columns’ and ‘drop’
5. Dan set ignore-index as default 1 (?)