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...
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Updated on: 01-Oct-2021

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