Handling float nan columns that need to be formatted without decimal places

Question

What is the best method to convert a dataframe column (object) containing nan and floats 900.0 to nan and 900?

Are you asking how to convert the column to integer or do you just want to print it as an integer. Read [this post](https://stackoverflow.com/questions/11548005/numpy-or-pandas-keeping-array-type-as-integer-while-having-a-nan-value). — pault, Jul 31 '18 at 16:03

score 0 · Answer 1 · answered Jul 31 '18 at 18:29

IIUC: Input:

    df = pd.DataFrame(['1.0','5.0',np.nan,'9.0', '100.0', np.nan, '500.0'], columns=['a'])

       a
0    1.0
1    5.0
2    NaN
3    9.0
4  100.0
5    NaN
6  500.0

Let's try this:

pd.to_numeric(df.a).fillna(0).astype(int).astype(str).mask(df.a.isnull())

Output:

0      1
1      5
2    NaN
3      9
4    100
5    NaN
6    500
Name: a, dtype: object

Handling float nan columns that need to be formatted without decimal places

1 Answers1