Next, we are going to remove NaN values from the field.

We can do this as follows:

dfs = dfs[dfs['date'].notna()]

Next, it is good to save the preprocessed file into a separate CSV file in case we need it again. We can save the dataframe into a separate CSV file as follows:

dfs.to_csv('gmail.csv')

Great! Having done that, let's do some descriptive statistics.