Here, we'll look at how to use the DataFrame's isnull() and sum() methods to count NaN or missing entries in a Pandas DataFrame.
Dataframe.isnull() method:
To count the number of missing values in each column of a pandas DataFrame, we can use the isnull() method to create a boolean mask indicating which values are missing.
Dataframe.sum() method:
The sum() method is used to count the number of True values in each column.
Let us first load the libraries needed.
 pandas
 numpy

Make a pandas dataframe first.

Total the NaN values in each column of the DataFrame.
This shows that column Name has 2 missing value, column Age has 3 missing values, column Place has 2 and column College has 3 missing values. 
Total the NaNs in the DataFrame.