I have a series (month_addded) in my DataFrame like this:
9.0
12.0
12.0
nan
1.0
I want all the floats to be ints, and the NaN's to stay as they are. I did this:
for i in df['month_added']:
if i > 0:
i=int(i)
But it did nothing.
NaN
is float typed, so Pandas would always downcast your column to float as long as you have NaN
. You can use Nullable Integer , available from Pandas 0.24.0:
df['month_added'] = df['month_added'].astype('Int64')
If that's not possible, you can force Object
type (not recommended):
df['month_added'] = pd.Series([int(x) if x > 0 else x for x in df.month_added], dtype='O')
Or since your data is positive and NaN
, you can mask NaN
with 0
:
df['month_added'] = df['month_added'].fillna(0).astype(int)
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