How to Add a NaN Value in Python Dataframe
To add a NaN value to a DataFrame in Python, you can use the Numpy library.
The following example shows how to add a NaN value to a DataFrame.
Use np.nan to Add NaN Value to DataFrame
We can use np.nan to add a NaN value to a DataFrame.
Suppose we have the following DataFrame:
# Import libraries
import pandas as pd
import numpy as np
# Create dataframe
office_stuff = pd.DataFrame({
'Date': ['01-03-2023', '01-03-2023', '01-03-2023', '01-03-2023', '02-03-2023', '02-03-2023'],
'Product_Code': ['A-101', 'A-102', 'A-103', 'B-101', 'B-102', 'B-104'],
'Product_Name': ['Laptop', 'Mobile', 'Printer', 'Keyboard', 'Scanner', 'Mouse'],
'Price': [4500, 550, 250, 50, 350, 50],
'Status': [1, 1, 1, 0, 1, 1]
})
# Add a NaN value in the Price column
office_stuff.at[3, 'Price'] = np.nan
# Show updated dataframe
print(office_stuff)
Output: 👇️
Date Product_Code Product_Name Price Status
0 01-03-2023 A-101 Laptop 4500.0 1
1 01-03-2023 A-102 Mobile 550.0 1
2 01-03-2023 A-103 Printer 250.0 1
3 01-03-2023 B-101 Keyboard NaN 0
4 02-03-2023 B-102 Scanner 350.0 1
5 02-03-2023 B-104 Mouse 50.0 1
In this example, we add a NaN value to the Price column at position 3.