Pandas DataFrame: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame

Question

I know there are tons of posts about this warning, but I couldn't find a solution to my situation. Here's my code:

df.loc[:, 'my_col'] = df.loc[:, 'my_col'].astype(int)
#df.loc[:, 'my_col'] = df.loc[:, 'my_col'].astype(int).copy()
#df.loc[:, 'my_col'] = df['my_col'].astype(int)

It produces the warning:

SettingWithCopyWarning: 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

Even though I changed the code as suggested, I still get this warning? All I need to do is to convert the data type of one column.

**Remark: ** Originally the column is of type float having one decimal (example: 4711.0). Therefore I change it to integer (4711) and then to string ('4711') - just to remove the decimal.

Appreciate your help!

Update: The warning was a side effect on a filtering of the original data that was done just before. I was missing the DataFrame.copy(). Using the copy instead, solved the problem!

df = df[df['my_col'].notnull()].copy()
df.loc[:, 'my_col'] = df['my_col'].astype(int).astype(str)
#df['my_col'] = df['my_col'].astype(int).astype(str) # works too!

Answer 1

I think need copy and omit loc for select columns:

df = df[df['my_col'].notnull()].copy()
df['my_col'] = df['my_col'].astype(int).astype(str)

Explanation :

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

Answer 2

another way is to disable chained assignments, which works on your code without the need to create a copy :

# disable chained assignments
pd.options.mode.chained_assignment = None 

Answer 3

If you need to change the data type of a single column, it's easier to address that column directly:

df['my_col'] = df['my_col'].astype(int)

Or using .assign :

df = df.assign(my_col=lambda d: d['my_col'].astype(int))

The .assign is useful if you only need the conversion once, and don't want to alter your df outside of that scope.