Replacing column values in a pandas DataFrame

ghz 3months ago ⋅ 37 views

I'm trying to replace the values in one column of a dataframe. The column ('female') only contains the values 'female' and 'male'.

I have tried the following:

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

But receive the exact same copy of the previous results.

I would ideally like to get some output which resembles the following loop element-wise.

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

I've looked through the gotchas documentation (http://pandas.pydata.org/pandas-docs/stable/gotchas.html) but cannot figure out why nothing happens.

Any help will be appreciated.

Answers

In pandas, you cannot directly assign values to specific elements in a column in the way you are attempting. Instead, you should use the replace() method, or better yet, use map() or apply() to perform element-wise operations. Here's how you can achieve what you want.

1. Using map() (Recommended for this case)

The map() function is ideal for replacing values based on a mapping dictionary.

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

This will map each value of the 'female' column to '1' if it's 'female', and to '0' if it's 'male'.

2. Using replace()

Alternatively, you can use the replace() function, which allows you to specify values to replace directly.

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

This will achieve the same result by replacing 'female' with '1' and 'male' with '0'.

3. Using apply() (if you want more flexibility)

If you need a more complex condition, apply() allows you to use a function on each element of the column.

w['female'] = w['female'].apply(lambda x: '1' if x == 'female' else '0')

This method is slightly more flexible because you can define more complex logic inside the lambda function if needed.

Why Your Original Code Didn't Work

Your original attempt used incorrect syntax for accessing and modifying values in pandas. Specifically, this line:

w['female']['female']='1'

is not valid, as it attempts to use indexing on the values directly rather than performing a conditional check and update on the column.

By using map(), replace(), or apply(), you'll efficiently update the values of the 'female' column in the way you're aiming to.

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