skrub.DataOp.skb.concat#

DataOp.skb.concat(others, axis=0)[source]#

Concatenate arrays or dataframes vertically or horizontally.

Parameters:

otherslist of arrays or dataframes: The arrays or dataframes to stack with self. Must be of the same type as self: do not mix arrays with dataframes.
axis{0, 1}, default 0: The axis to concatenate along. 0: stack vertically (rows) 1: stack horizontally (columns)

Returns:

array or dataframe: The combined arrays or dataframes.

Examples

>>> import pandas as pd
>>> import skrub
>>> a = skrub.var('a', pd.DataFrame({'a1': [0], 'a2': [1]}))
>>> b = skrub.var('b', pd.DataFrame({'b1': [2], 'b2': [3]}))
>>> c = skrub.var('c', pd.DataFrame({'c1': [4], 'c2': [5]}))
>>> d = skrub.var('d', pd.DataFrame({'c1': [6], 'c2': [7]}))
>>> e = skrub.var('e', pd.DataFrame({'c1': [8], 'c2': [9]}))
>>> a
<Var 'a'>
Result:
―――――――
   a1  a2
0   0   1
>>> a.skb.concat([b, c], axis=1)
<Concat: 3 tables>
Result:
―――――――
   a1  a2  b1  b2  c1  c2
0   0   1   2   3   4   5

>>> c.skb.concat([d, e], axis=0)
<Concat: 3 tables>
Result:
―――――――
   c1  c2
0   4   5
1   6   7
2   8   9

Note that even if we want to concatenate a single dataframe we must still put it in a list:

>>> a.skb.concat([b], axis=1)
<Concat: 2 tables>
Result:
―――――――
   a1  a2  b1  b2
0   0   1   2   3