How to replace all non-NaN entries of a dataframe with 1 and all NaN with 0

Question

I have a dataframe with 71 columns and 30597 rows. I want to replace all non-nan entries with 1 and the nan values with 0.

Initially I tried for-loop on each value of the dataframe which was taking too much time.

Then I used data_new=data.subtract(data) which was meant to subtract all the values of the dataframe to itself so that I can make all the non-null values 0. But an error occurred as the dataframe had multiple string entries.

Answer 1

You can take the return value of df.notnull(), which is False where the DataFrame contains NaN and True otherwise and cast it to integer, giving you 0 where the DataFrame is NaN and 1 otherwise:

newdf = df.notnull().astype('int') 

If you really want to write into your original DataFrame, this will work:

df.loc[~df.isnull()] = 1  # not nan df.loc[df.isnull()] = 0   # nan