[FIXED] Fillna in multiple columns in place in Python Pandas

Issue

I have a pandas dataFrame of mixed types, some are strings and some are numbers. I would like to replace the NAN values in string columns by '.', and the NAN values in float columns by 0.

Consider this small fictitious example:

df = pd.DataFrame({'Name':['Jack','Sue',pd.np.nan,'Bob','Alice','John'],
    'A': [1, 2.1, pd.np.nan, 4.7, 5.6, 6.8],
    'B': [.25, pd.np.nan, pd.np.nan, 4, 12.2, 14.4],
    'City':['Seattle','SF','LA','OC',pd.np.nan,pd.np.nan]})

Now, I can do it in 3 lines:

df['Name'].fillna('.',inplace=True)
df['City'].fillna('.',inplace=True)
df.fillna(0,inplace=True)

Since this is a small dataframe, 3 lines is probably ok. In my real example (which I cannot share here due to data confidentiality reasons), I have many more string columns and numeric columns. SO I end up writing many lines just for fillna. Is there a concise way of doing this?

Solution

You could use apply for your columns with checking dtype whether it's numeric or not by checking dtype.kind:

res = df.apply(lambda x: x.fillna(0) if x.dtype.kind in 'biufc' else x.fillna('.'))

print(res)
     A      B     City   Name
0  1.0   0.25  Seattle   Jack
1  2.1   0.00       SF    Sue
2  0.0   0.00       LA      .
3  4.7   4.00       OC    Bob
4  5.6  12.20        .  Alice
5  6.8  14.40        .   John

Answered By - Anton Protopopov