pandas concat ignore_index doesn't work

python pandas append concat

118,516

Solution 1

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.

Solution 2

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).

Solution 3

In case you want to retain the index of the left data frame, set the index of df2 to be df1 using set_index:

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

Solution 4

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   2
0  A0  B0  D0
1  A1  B1  D1
2  A2  B2  D2
3  A3  B3  D3
4  A4  C4  D4
5  A5  C5  D5
6  A6  C6  D6
7  A7  C7  D7

Solution 5

You can use numpy's concatenate to achieve the result.

cols = df1.columns.to_list() + df2.columns.to_list()
dfs = [df1,df2]
df = np.concatenate(dfs, axis=1)  
df = pd.DataFrame(df, columns=cols)

Out[1]: 
    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

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118,516

muon

−1 e

Updated on October 22, 2021

Comments

muon over 2 years

I am trying to column-bind dataframes 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!

Alex Riley over 8 years

Does pd.concat([df1, df2], axis=0, ignore_index=True) produce what you want? If not, can you specify your expected output?
muon over 8 years

no, it binds the rows . I want to bind the columns (append). I tried append, that doesn't seem to work either.
cel over 8 years

@ajcr, have you compared the output of pd.concat([df1, df2], axis=1, ignore_index=True) and pd.concat([df1, df2], axis=1)? Shouldn't the first intuitively emulate a cbind?
Alex Riley over 8 years

I think ignore_index only ignores the labels on the axis you're joining on, so it still does an outer join on the index labels. I agree the names of function arguments aren't the most intuitive here.
muon over 8 years

yes, i realized that from @Alex answer ... but i have the same results even with ignore_index=False

muon over 8 years

Oh that works ... Thanks! Funny thing is I was using same method to bind dataframes inside a function and that was working fine! but one outside function wasn't
muon over 8 years

Thanks! that was helpful (can't upvote yet, low rep)
cel over 8 years

@mau, I have updated my answer and now use pd.reset_index(). I think this is a cleaner way.
muon over 8 years

I happened to try that out myself, could have saved myself few hours if i had seen this earlier :). Thanks... df = pd.concat( [df1.reset_index(drop=True), df2.reset_index(drop=True)], axis=1)
Hugo Santos Silva almost 4 years

Indeed, this is a useful explanation that is missing in the docs.