pandas concat columns ignore_index doesn't work

concatenation, pandas, python

Solution

If I understood you correctly, this is what you would like to do.

import pandas as pd

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
                    'B': ['B0', 'B1', 'B2', 'B3'],
                    'D': ['D0', 'D1', 'D2', 'D3']},
                   index=[0, 2, 3, 4])

df2 = pd.DataFrame({'A1': ['A4', 'A5', 'A6', 'A7'],
                    'C': ['C4', 'C5', 'C6', 'C7'],
                    'D2': ['D4', 'D5', 'D6', 'D7']},
                   index=[4, 5, 6 , 7])


df1.reset_index(drop=True, inplace=True)
df2.reset_index(drop=True, inplace=True)

df = pd.concat([df1, df2], axis=1)

Which gives:

    A   B   D   A1  C   D2
0   A0  B0  D0  A4  C4  D4
1   A1  B1  D1  A5  C5  D5
2   A2  B2  D2  A6  C6  D6
3   A3  B3  D3  A7  C7  D7

Actually, I would have expected that `df = pd.concat(dfs, axis=1, ignore_index=True)` gives the same result.

This is the excellent explanation from jreback:

`ignore_index=True` ‘ignores’, meaning doesn’t align on the joining axis. it simply pastes them together in the order that they are passed, then reassigns a range for the actual index (e.g. `range(len(index))`) so the difference between joining on non-overlapping indexes (assume `axis=1` in the example), is that with `ignore_index=False` (the default), you get the concat of the indexes, and with `ignore_index=True` you get a range.

Problem

I am trying to column-bind dataframes (like R's `cbind()` does) and having issue with pandas `concat`, as `ignore_index=True` doesn't seem to work: ``` df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3'], 'D': ['D0', 'D1', 'D2', 'D3']}, index=[0, 2, 3, 4]) df2 = pd.DataFrame({'A1': ['A4', 'A5', 'A6', 'A7'], 'C': ['C4', 'C5', 'C6', 'C7'], 'D2': ['D4', 'D5', 'D6', 'D7']}, index=[5, 6, 7, 3]) df1 # A B D # 0 A0 B0 D0 # 2 A1 B1 D1 # 3 A2 B2 D2 # 4 A3 B3 D3 df2 # A1 C D2 # 5 A4 C4 D4 # 6 A5 C5 D5 # 7 A6 C6 D6 # 3 A7 C7 D7 dfs = [df1, df2] df = pd.concat(dfs, axis=1, ignore_index=True) print df ``` and the result is ``` 0 1 2 3 4 5 0 A0 B0 D0 NaN NaN NaN 2 A1 B1 D1 NaN NaN NaN 3 A2 B2 D2 A7 C7 D7 4 A3 B3 D3 NaN NaN NaN 5 NaN NaN NaN A4 C4 D4 6 NaN NaN NaN A5 C5 D5 7 NaN NaN NaN A6 C6 D6 ``` Even if I reset index using ``` df1.reset_index() df2.reset_index() ``` and then try ``` pd.concat([df1, df2], axis=1) ``` it still produces the same result! The expected result is a 6x4 dataframe where the contents of columns A,B,D, A1,C,D2 are horizontally concatenated.

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