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?

Answer 1

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 

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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