How to replace all blank/empty cells in a pandas dataframe with NaNs

As has already been mentioned, the best way to accomplish this is to use df.replace().

1. >>> import pandas as pd 
2. >>> import numpy as np 
3. >>> df = pd.DataFrame({'A':[1, 2, '', 4, ''], 'B':[6, 7, '', '', 10]}) 
4. >>> df 
5. A B 
6. 0 1 6 
7. 1 2 7 
8. 2  
9. 3 4  
10. 4 10 
11.  
12. >>> df.replace('', np.nan) 
13. A B 
14. 0 1.0 6.0 
15. 1 2.0 7.0 
16. 2 NaN NaN 
17. 3 4.0 NaN 
18. 4 NaN 10.0 

You can either use this to create a new DataFrame or use inplace = True to apply it to your current DataFrame object.

This also works with columns. If, for example, you only wanted to replace all of the blanks in column A while leaving the blanks in column B, then you could use df.A.replace().

1. >>> df.A.replace('', np.nan, inplace = True) 
2. >>> df 
3. A B 
4. 0 1.0 6 
5. 1 2.0 7 
6. 2 NaN  
7. 3 4.0  
8. 4 NaN 10