Replacing column values in a pandas DataFrame

Solution 1:

If I understand right, you want something like this:

w['female'] = w['female'].map({'female': 1, 'male': 0})

(Here I convert the values to numbers instead of strings containing numbers. You can convert them to "1" and "0", if you really want, but I'm not sure why you'd want that.)

The reason your code doesn't work is because using ['female'] on a column (the second 'female' in your w['female']['female']) doesn't mean "select rows where the value is 'female'". It means to select rows where the index is 'female', of which there may not be any in your DataFrame.

Solution 2:

You can edit a subset of a dataframe by using loc:

df.loc[<row selection>, <column selection>]

In this case:

w.loc[w.female != 'female', 'female'] = 0
w.loc[w.female == 'female', 'female'] = 1

Solution 3:

w.female.replace(to_replace=dict(female=1, male=0), inplace=True)

See pandas.DataFrame.replace() docs.

Solution 4:

Slight variation:

w.female.replace(['male', 'female'], [1, 0], inplace=True)

Solution 5:

This should also work:

w.female[w.female == 'female'] = 1 
w.female[w.female == 'male']   = 0