'value trying to be set on copy' warning-CodePudding

I'm trying to do a simple replacement of values in multiple columns of a dataframe with the .loc function, using this this code

df.loc[:,'Q014':'Q032'] = 
df.loc[:,'Q014':'Q032'].replace({'a':1,'b':0})

I don't understand why I get this warning

A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead

I've read the documentation but I cannot see what the problem is. Someone please help me to understand. Cheers

CodePudding user response：

The following article explains clearly. Please refer https://www.analyticsvidhya.com/blog/2021/11/3-ways-to-deal-with-settingwithcopywarning-in-pandas/

If your code is working as intended, you could turn off the warning by pd.options.mode.chained_assignment = None.

Edit: I am unable to leave comments hence posting in the answer section.

CodePudding user response：

It's not possible to answer your question with this single line because your code can't trigger SettingWithCopyWarning as it. It happens probably earlier in your code.

cols = [f'Q{i:03}' for i in range(1, 50)]
vals = np.random.choice(['a', 'b'], (10, len(cols)))
df = pd.DataFrame(vals, columns=cols)

df.loc[:,'Q014':'Q032'] = df.loc[:,'Q014':'Q032'].replace({'a':1,'b':0})

Output:

>>> df.loc[:, 'Q014':'Q032']
  Q014 Q015 Q016 Q017 Q018 Q019 Q020 Q021 Q022 Q023 Q024 Q025 Q026 Q027 Q028 Q029 Q030 Q031 Q032
0    1    0    0    0    1    0    1    1    1    0    0    0    1    0    1    0    0    1    0
1    1    0    1    0    1    0    0    0    0    0    0    0    0    1    0    0    0    1    1
2    1    1    1    0    1    1    0    0    0    1    0    0    0    0    0    0    0    1    0
3    1    0    1    0    0    1    0    0    1    0    1    0    1    0    1    1    1    1    0
4    0    1    0    0    0    0    0    0    1    0    1    1    0    1    1    0    0    0    0
5    0    1    0    1    1    1    0    0    0    1    1    1    0    1    1    1    1    1    1
6    0    0    0    0    0    0    0    1    1    1    0    0    0    1    0    1    1    0    1
7    1    1    0    0    0    1    1    1    0    0    0    1    1    1    1    1    0    1    1
8    1    0    0    0    1    0    1    1    1    0    0    1    1    0    1    1    1    1    0
9    0    1    1    0    0    0    0    1    1    0    1    1    1    1    1    1    1    0    0