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i am using the answer from Pandas SettingWithCopyWarning in my script but it returned me 'SettingWithCopyWarning', may i know how should i fix it?

attemped1: dff['changed'] = dff.col1.ne(dff.col1.shift(1)) attemped2: dff.loc[:, 'changed] = dff.col1.ne(dff.col1.shift(1))

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Your code is correct, problem is in some line above.

I guess you filter your DataFrame and solution is add copy:

dff = df[df['col2'] == 1].copy()
dff['changed'] = dff.col1.ne(dff.col1.shift(1))

If you modify values in dff later you will find that the modifications do not propagate back to the original data (df), and that Pandas does warning.

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is it true that without copy() will modify original dataframe? i don't see the effect in the test case below? df = pd.DataFrame({'col1':['A', 'B', 'C'], 'col2':[111,222,333], 'col3':['aaa', 'bbb', 'aaa']}) dff = df[df.col3 == 'aaa'] dff.loc[0, 'col3'] = 'zzz'
@user466130 - Give me a sec.
@user466130 - No, there is no modification in original df. It is warning, because possible chaining indexing. Also check this for explanation with sample data.
@user466130 - Also there are another way for turn off this warning, pd.options.mode.chained_assignment = None

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