Pandas DataFrame notna() and notnull() Method

Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

• The Pandas library enables access to/from a DataFrame.
• The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

To install these libraries, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

$ pip install numpy

Hit the <Enter> key on the keyboard to start the installation process.

If the installations were successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required libraries.

• How to Install Pandas on PyCharm
• How to Install NumPy on PyCharm

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd
import numpy as np 

DataFrame notna() & notnull()

The DataFrame notna() and notnull() methods return Boolean (True/False) values. These values returned are in the same shape as the DataFrame/Series passed. If any empty values are of the following type, they will resolve to False.

• None
• NaN
• NaT
• NA

All other values that are not of the above type (valid data) will resolve to True.

The syntax for these methods is as follows:

DataFrame.notna()
DataFrame.notnull()

Parameters: These methods contain no parameters.

For this example, three (3) temperatures over three (3) days for Anchorage, Alaska, save to a DataFrame. Unfortunately, some temperatures did not accurately record.

The code below returns a new DataFrame containing True values in the same position as the missing temperatures and False in the remainder.

df_temps = pd.DataFrame({'Day-1':  [np.nan, 11, 12], 
                   'Day-2':  [13, 14, pd.NaT],
                   'Day-3':  [None, 15, 16]},
                   index=['Morning', 'Noon', 'Evening'])
print(df_temps)

result = df_temps.notna()
print(result)

• Line [1] creates a dictionary of lists and saves it to df_temps.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] uses notna() to set the empty values (np.nan, pd.NaT, None) to False and the remainder (valid values) to True. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output

original df_temps

result

df_temps = pd.DataFrame({'Day-1':  [np.nan, 11, 12], 
                   'Day-2':  [13, 14, pd.NaT],
                   'Day-3':  [None, 15, 16]},
                   index=['Morning', 'Noon', 'Evening'])
print(df_temps)

result = df_temps.notnull()
print(result)

• Line [1] creates a dictionary of lists and saves it to df_temps.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] uses notnull() to set the empty values (np.nan, pd.NaT, None) to False and the remainder (valid values) to True. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output

original df_temps

result

💡 Note: The notnull() method is an alias of the notna() method. The output from both examples is identical.

More Pandas DataFrame Methods

Feel free to learn more about the previous and next pandas DataFrame methods (alphabetically) here:

Also, check out the full cheat sheet overview of all Pandas DataFrame methods.