pandas concat ignore_index doesn't work

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

import pandas as pddf1 = 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   D20   A0  B0  D0  A4  C4  D41   A1  B1  D1  A5  C5  D52   A2  B2  D2  A6  C6  D63   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.

The ignore_index option is working in your example, you just need to know that it is ignoring the axis of concatenation which in your case is the columns. (Perhaps a better name would be ignore_labels.) If you want the concatenation to ignore the index labels, then your axis variable has to be set to 0 (the default).

Agree with the comments, always best to post expected output.

Is this what you are seeking?

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 = df1.transpose().reset_index(drop=True).transpose()df2 = df2.transpose().reset_index(drop=True).transpose()dfs = [df1,df2]df = pd.concat( dfs,axis=0,ignore_index=True)print df    0   1   20  A0  B0  D01  A1  B1  D12  A2  B2  D23  A3  B3  D34  A4  C4  D45  A5  C5  D56  A6  C6  D67  A7  C7  D7