Python | Pandas isnull() and notnull()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
While making a Data Frame from a csv file, many blank columns are imported as null value into the Data Frame which later creates problems while operating that data frame. Pandas isnull() and notnull() methods are used to check and manage NULL values in a data frame.

Dataframe.isnull()

Syntax: Pandas.isnull(“DataFrame Name”) or DataFrame.isnull()
Parameters: Object to check null values for
Return Type: Dataframe of Boolean values which are True for NaN values 

To download the CSV file used, Click Here.
Example #1: Using isnull() 
In the following example, Team column is checked for NULL values and a boolean series is returned by the isnull() method which stores True for ever NaN value and False for a Not null value. 

Python

# importing pandas package
import pandas as pd

# making data frame from csv file
data = pd.read_csv("employees.csv")

# creating bool series True for NaN values
bool_series = pd.isnull(data["Team"])

# filtering data
# displaying data only with team = NaN
data[bool_series]

Output:
As shown in output image, only the rows having Team=NULL are displayed. 

Dataframe.notnull()

Syntax: Pandas.notnull(“DataFrame Name”) or DataFrame.notnull()
Parameters: Object to check null values for
Return Type: Dataframe of Boolean values which are False for NaN values 

Example #1: Using notnull() 
In the following example, Gender column is checked for NULL values and a boolean series is returned by the notnull() method which stores True for ever NON-NULL value and False for a null value.

Python

# importing pandas package
import pandas as pd

# making data frame from csv file
data = pd.read_csv("employees.csv")

# creating bool series False for NaN values
bool_series = pd.notnull(data["Gender"])

# displayed data only with team = NaN
data[bool_series]

Output:
As shown in output image, only the rows having some value in Gender are displayed.