df = df.replace({np.nan: None})

Note: For pandas versions <1.4, this changes the dtype of all affected columns to object.
To avoid that, use this syntax instead:

df = df.replace(np.nan, None)

Note 2: If you don't want to import numpy, np.nan can be replaced with native float('nan'):

df = df.replace({float('nan'): None})

Credit goes to this guy here on this Github issue, Killian Huyghe's comment and Matt's answer.

You can use DataFrame.fillna or Series.fillna which will replace the Python object None, not the string 'None'.

import pandas as pd
import numpy as np

For dataframe:

df = df.fillna(value=np.nan)

For column or series:

df.mycol.fillna(value=np.nan, inplace=True)

df = df.fillna('')

This will fill na's (e.g. NaN's) with ''.

inplace is possible but should be avoided as it makes a copy internally anyway, and it will be deprecated:

df.fillna('', inplace=True)

To fill only a single column:

df.column1 = df.column1.fillna('')

One can use df['column1'] instead of df.column1.

DataFrame.fillna() or Series.fillna() will do this for you.

Example:

In [7]: df
Out[7]: 
          0         1
0       NaN       NaN
1 -0.494375  0.570994
2       NaN       NaN
3  1.876360 -0.229738
4       NaN       NaN

In [8]: df.fillna(0)
Out[8]: 
          0         1
0  0.000000  0.000000
1 -0.494375  0.570994
2  0.000000  0.000000
3  1.876360 -0.229738
4  0.000000  0.000000

To fill the NaNs in only one column, select just that column.

In [12]: df[1] = df[1].fillna(0)

In [13]: df
Out[13]: 
          0         1
0       NaN  0.000000
1 -0.494375  0.570994
2       NaN  0.000000
3  1.876360 -0.229738
4       NaN  0.000000

Or you can use the built in column-specific functionality:

df = df.fillna({1: 0})