Conditional Replace Pandas

Question

I have a DataFrame, and I want to replace the values in a particular column that exceed a value with zero. I had thought this was a way of achieving this:

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Answer 1

The reason your original dataframe does not update is because chained indexing may cause you to modify a copy rather than a view of your dataframe. The docs give this advice:

When setting values in a pandas object, care must be taken to avoid what is called chained indexing.

You have a few alternatives:-

loc + Boolean indexing

loc may be used for setting values and supports Boolean masks:

df.loc[df['my_channel'] > 20000, 'my_channel'] = 0

mask + Boolean indexing

You can assign to your series:

df['my_channel'] = df['my_channel'].mask(df['my_channel'] > 20000, 0)

Or you can update your series in place:

df['my_channel'].mask(df['my_channel'] > 20000, 0, inplace=True)

np.where + Boolean indexing

You can use NumPy by assigning your original series when your condition is not satisfied; however, the first two solutions are cleaner since they explicitly change only specified values.

df['my_channel'] = np.where(df['my_channel'] > 20000, 0, df['my_channel'])