Replace None with NaN in pandas dataframe

I have table x:

        website
0 	http://www.google.com/
1 	http://www.yahoo.com
2 	None

I want to replace python None with pandas NaN. I tried:

x.replace(to_replace=None, value=np.nan)

But I got:

TypeError: 'regex' must be a string or a compiled regular expression or a list or dict of strings or regular expressions, you passed a 'bool'

How should I go about it?

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)

Here's another option:

df.replace(to_replace=[None], value=np.nan, inplace=True)

The following line replaces None with NaN:

df['column'].replace('None', np.nan, inplace=True)

If you use df.replace([None], np.nan, inplace=True), this changed all datetime objects with missing data to object dtypes. So now you may have broken queries unless you change them back to datetime which can be taxing depending on the size of your data.

If you want to use this method, you can first identify the object dtype fields in your df and then replace the None:

obj_columns = list(df.select_dtypes(include=['object']).columns.values)
df[obj_columns] = df[obj_columns].replace([None], np.nan)

DataFrame['Col_name'].replace("None", np.nan, inplace=True)

Its an old question but here is a solution for multiple columns:

values = {'col_A': 0, 'col_B': 0, 'col_C': 0, 'col_D': 0}
df.fillna(value=values, inplace=True)

For more options, check the docs:

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html