df.loc causes a SettingWithCopyWarning warning message

Question

The following line of my code causes a warning :

import pandas as pd

s = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))
s.loc[-1] = [5,np.nan,np.nan,6]
grouped = s.groupby(['A'])
for key_m, group_m in grouped:
    group_m.loc[-1] = [10,np.nan,np.nan,10]

C:\Anaconda3\lib\site-packages\ipykernel\__main__.py:10: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

According to the documentation this is the recommended way of doing, so what is happening ?

Thanks for your help.

Aperture

You need to show the complete code from the creation of the df to this line in order for us to assist – Touchstone

Answer 1

The documentation is slightly confusing.

Your dataframe is a copy of another dataframe. You can verify this by running bool(df.is_copy) You are getting the warning because you are trying to assign to this copy.

The warning/documentation is telling you how you should have constructed df in the first place. Not how you should assign to it now that it is a copy.

df = some_other_df[cols]

will make df a copy of some_other_df. The warning suggests doing this instead

df = some_other_df.loc[:, [cols]]

---

Now that it is done, if you choose to ignore this warning, you could

df = df.copy()

or

df.is_copy = None

Tigre

is_copy is deprecated. Is there a new technique to use that is "future-approved"? – Aramen

df = df.copy() should work just fine. I personally avoid editing dataframes in place. Each transformation produces a new copy and I overwrite the old one when desired. – Tigre