Pandas DataFrame - Replace NULL String with Blank and NULL Numeric with 0

Question

I am working on a large dataset with many columns of different types. There are a mix of numeric values and strings with some NULL values. I need to change the NULL Value to

Answer 1

Use DataFrame.select_dtypes for numeric columns, filter by subset and replace values to 0, then repalce all another columns to empty string:

print (df)
   0     1    2    3  4     5    6       7   8      9
0  1  John  2.0  Doe  3  Mike  4.0  Orange   5  Stuff
1  9   NaN  NaN  NaN  8   NaN  NaN   Lemon  12    NaN

print (df.dtypes)
0      int64
1     object
2    float64
3     object
4      int64
5     object
6    float64
7     object
8      int64
9     object
dtype: object

c = df.select_dtypes(np.number).columns
df[c] = df[c].fillna(0)
df = df.fillna("")
print (df)
   0     1    2    3  4     5    6       7   8      9
0  1  John  2.0  Doe  3  Mike  4.0  Orange   5  Stuff
1  9        0.0       8        0.0   Lemon  12       

Another solution is create dictionary for replace:

num_cols = df.select_dtypes(np.number).columns
d1 = dict.fromkeys(num_cols, 0)
d2 = dict.fromkeys(df.columns.difference(num_cols), "")

d  = {**d1,  **d2}
print (d)
{0: 0, 2: 0, 4: 0, 6: 0, 8: 0, 1: '', 3: '', 5: '', 7: '', 9: ''}

df = df.fillna(d)
print (df)
   0     1    2    3  4     5    6       7   8      9
0  1  John  2.0  Doe  3  Mike  4.0  Orange   5  Stuff
1  9        0.0       8        0.0   Lemon  12       

Answer 2

You could try this to substitute a different value for each different column (A to C are numeric, while D is a string):

import pandas as pd
import numpy as np

df_pd = pd.DataFrame([[np.nan, 2, np.nan, '0'],
        [3, 4, np.nan, '1'],
        [np.nan, np.nan, np.nan, '5'],
        [np.nan, 3, np.nan, np.nan]],
        columns=list('ABCD'))

df_pd.fillna(value={'A':0.0,'B':0.0,'C':0.0,'D':''})