Python - How to Count the NaN Occurrences in a Column in Pandas Dataframe?

To count the NaN occurrences in a column, use the isna(). Use the sum() to add the values and find the count.

At first, let us import the required libraries with their respective aliases −

import pandas as pd import numpy as np

Create a DataFrame. We have set the NaN values using the Numpy np.inf in “Units_Sold” column −

dataFrame = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],"Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],"Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000],"Units_Sold": [ 100, np.NaN, 150, np.NaN, 200, np.NaN] })

Count NaN values from column "Units_Sold" −

dataFrame["Units_Sold"].isna().sum() 

Example

Following is the code −

import pandas as pd import numpy as np # creating dataframe dataFrame = pd.DataFrame({"Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'],"Cubic_Capacity": [2000, 1800, 1500, 2500, 2200, 3000],"Reg_Price": [7000, 1500, 5000, 8000, 9000, 6000],"Units_Sold": [ 100, np.NaN, 150, np.NaN, 200, np.NaN] }) print("Dataframe...\n",dataFrame) # count NaN values from column "Units_Sol" count = dataFrame["Units_Sold"].isna().sum() print("\nCount of NaN values in column Units_Sold...\n",count)

Output

This will produce the following output −

Dataframe...         Car   Cubic_Capacity   Reg_Price Units_Sold 0       BMW             2000        7000 100.0 1     Lexus             1800        1500 NaN 2     Tesla             1500        5000 150.0 3   Mustang             2500        8000 NaN 4  Mercedes             2200        9000 200.0 5    Jaguar             3000        6000 NaN Count of NaN values in column Units_Sold... 3