Pandas – replacing column values

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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

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