Pandas is one of those packages and makes importing and analyzing data much easier. While making a Data Frame from a Pandas CSV file, many blank columns are imported as null values into the DataFrame 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.
Pandas DataFrame isnull() Method
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
In the following example, The 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.
Python3
# importing pandas packageimport pandas as pd# making data frame from csv filedata = pd.read_csv("employees.csv")# creating bool series True for NaN valuesbool_series = pd.isnull(data["Team"])# filtering data# displaying data only with team = NaNdata[bool_series] |
Output:
As shown in the output image, only the rows having Team=NULL are displayed.
Pandas DataFrame notnull() Method
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
In the following example, the Gender column is checked for NULL values and a boolean series is returned by the notnull() method which stores True for every NON-NULL value and False for a null value.
Python3
# importing pandas packageimport pandas as pd# making data frame from csv filedata = pd.read_csv("employees.csv")# creating bool series False for NaN valuesbool_series = pd.notnull(data["Gender"])# displayed data only with team = NaNdata[bool_series] |
Output:
As shown in the output image, only the rows having some value in Gender are displayed.
