df1: 
        Column 1  Column 2  Column 3  Column 4
Row 1       1.0       2.0       NaN       4.0
Row 2       5.0       6.0       7.0       8.0
Row 3       9.0       NaN      11.0      12.0
Row 4       NaN      14.0      15.0      16.0
Row 5       NaN      18.0      19.0       NaN

df1.isnull: 
        Column 1  Column 2  Column 3  Column 4
Row 1     False     False      True     False
Row 2     False     False     False     False
Row 3     False      True     False     False
Row 4      True     False     False     False
Row 5      True     False     False      True

Total missing values: 
 Column 1    2
Column 2    1
Column 3    1
Column 4    1
dtype: int64

Here, DataFrame.isnull() function returns True if a value is NaN and False otherwise. So, df1.isnull() returns the following:

df1.isnull: 
        Column 1  Column 2  Column 3  Column 4
Row 1     False     False      True     False
Row 2     False     False     False     False
Row 3     False      True     False     False
Row 4      True     False     False     False
Row 5      True     False     False      True

So, finding out the total number of missing values in each column of the DataFrame is now easier. We can count the number of True in each column and the sum will indicate the total number of missing values in each column.

print("Total missing values: \n", df1.isnull().sum())

Now, the DataFrame.sum() function returns the sum of the values of each column of the DataFrame. So, if we call the function df1.isnull().sum(), it will return the number of True values in each column. This, in turn, will show the number of missing values in each column of the DataFrame.