How to Replace Values in a Specific Column of a Pandas DataFrame Using Conditional Statements or the \'map\' Function?

Replacing Values in a Pandas DataFrame's Specific Column

In this scenario, one seeks to replace values within a specific DataFrame column named 'female' that holds only 'female' and 'male' values. While attempting the following:

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

the results remain unchanged. The desired outcome is achieved through the provided loop implementation:

if w['female'] =='female':
    w['female'] = '1';
else:
    w['female'] = '0';

However, an alternative and efficient approach is to employ the 'map' function:

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

This solution effectively assigns numerical values (1 for 'female' and 0 for 'male') instead of strings containing numbers. The 'female' column within the DataFrame is subsequently populated with these converted values.

In contrast, the initial approach didn't succeed as the double labeling ['female'] doesn't search for rows where values are 'female.' Instead, it targets rows with an index of 'female,' which might not exist within the DataFrame.

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