Pandas DataFrame count() Method – Be on the Right Side of Change

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.

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 count()

The count() method provides the count of all non-NaN values in a DataFrame/Series.

The syntax for this method is as follows:

DataFrame.count()

Parameters	Description
`axis`	If zero (0) or index is selected, apply the function to each column. Default is `None`. If one (1) is selected, apply the function to each row.
`level`	A string specifies the level name.
`numeric_only`	This parameter can be a float, integer, or Boolean value. By default, `False`.

For this example, the Human Resources Dept. of Rivers Clothing wants to determine the cost of benefit coverage based on the marital status of their staff. The issue here is some data contains the NaN value.

df_staff = pd.DataFrame({'EID':    [100, 101, 102, 103],
                         'Name':   ['Micah', 'Alycia', 'Philip', 'Josiah'],
                         'Status': ['M', 'S', np.nan, np.nan]})

result = df_staff.count()
print(result)

Line [1] creates a DataFrame from a Dictionary of Lists and saves it to df_staff.
Line [2] uses the count() method to determine how many non-NaN values exist. The output saves to the result variable.
Line [3] outputs the result to the terminal.

Output

EID	4
Name	4
Status	2
dtype: int64

More Pandas DataFrame Methods

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

👈 df.corrwith()

df.cov() 👉

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