Replacing Empty Strings With Nan In Pandas

I have a pandas dataframe (that was created by importing a csv file). I want to replace blank values with NaN. Some of these blank values are empty and some contain a (variable num

Solution 1:

Indicate it has to start with blank and end with blanks with ^ and $ :

df.replace(r'^\s*$', np.nan, regex=True, inplace = True)

Solution 2:

If you are reading a csv file and want to convert all empty strings to nanwhile reading the file itself then you can use the option

skipinitialspace=True

Example code

pd.read_csv('Sample.csv', skipinitialspace=True)

This will remove any white spaces that appear after the delimiters, Thus making all the empty strings as nan

From the documentation http://pandas.pydata.org/pandas-docs/stable/io.html

Note: This option will remove preceding white spaces even from valid data, if for any reason you want to retain the preceding white space then this option is not a good choice.