Pandas - replacing column values

Question

I know there are a number of topics on this question  but none of the methods worked for me so I m posting about my specific situation  I have a dataframe that looks like this   data   pd DataFrame   1 0   0 1   1 0   0 1    columns   sex    split    data  sex   replace 0   Female   data  sex   replace 1   Male   data   What I want to do is replace all 0 s in the sex column with  Female   and all 1 s with  Male   but the values within the dataframe don t seem to change when I use the code above  Am I using replace   incorrectly  Or is there a better way to do conditional replacement of values

User · Accepted Answer

Yes  you are using it incorrectly  Series replace   is not inplace operation by default  it returns the replaced dataframe series  you need to assign it back to your dataFrame Series for its effect to occur  Or if you need to do it inplace  you need to specify the inplace keyword argument as True Example -  data  sex   replace 0   Female  inplace True  data  sex   replace 1   Male  inplace True    Also  you can combine the above into a single replace function call by using list for both to replace argument as well as value argument   Example -  data  sex   replace  0 1    Female   Male   inplace True    Example Demo -  In  10   data   pd DataFrame   1 0   0 1   1 0   0 1    columns   sex    split     In  11   data  sex   replace  0 1    Female   Male   inplace True   In  12   data Out 12         sex  split 0    Male      0 1  Female      1 2    Male      0 3  Female      1     You can also use a dictionary  Example -  In  15   data   pd DataFrame   1 0   0 1   1 0   0 1    columns   sex    split     In  16   data  sex   replace  0  Female  1  Male   inplace True   In  17   data Out 17         sex  split 0    Male      0 1  Female      1 2    Male      0 3  Female      1

User · Answer

You can also try using apply with get method of dictionary  seems to be little faster than replace   data  sex     data  sex   apply  1  Male   0  Female   get    Testing with timeit     timeit data  sex   replace  0 1    Female   Male   inplace True    Result   The slowest run took 5 83 times longer than the fastest  This could mean that an intermediate result is being cached  1000 loops  best of 3  510   s per loop   Using apply     timeit data  sex     data  sex   apply  1  Male   0  Female   get    Result   The slowest run took 5 92 times longer than the fastest  This could mean that an intermediate result is being cached  1000 loops  best of 3  331   s per loop   Note  apply with dictionary should be used if all the possible values of the columns in the dataframe are defined in the dictionary else  it will have empty for those not defined in dictionary

User · Answer

Can try this too  Create a dictionary of replacement values  import pandas as pd data   pd DataFrame   1 0   0 1   1 0   0 1    columns   quot sex quot    quot split quot      replace dict   0  Female  1  Male   print replace dict    Use the map function for replacing values data  sex   data  sex   map replace dict   Output after replacing

[python] Pandas - replacing column values

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