How To Assign Null Value In Python Pandas

In Pandas, the null value is represented by the keyword “None”. It is used to represent the absence of the data in a column or row. In this tutorial, we’ll learn how to assign a null value in Python Pandas.

Steps:

1. Import pandas library

To work with Pandas, we need to import the Pandas library. We can use the following code to import pandas:

2. Create a DataFrame

Now, let’s create a DataFrame with some data. We can use the following code to create a DataFrame:

This will create a DataFrame with three columns ‘Name’, ‘Age’, and ‘City’.

3. Assign the null value to a cell

To assign a null value to a cell, we can use the ‘None’ keyword. Let’s assign a null value to the ‘Age’ column of the second row:

This will assign a null value to the ‘Age’ column of the second row.

4. Check for null values

To check if there are any null values in the DataFrame, we can use the isnull() function. Let’s check for null values in the ‘Age’ column:

This will return a boolean Series with ‘True’ values where there are null values and ‘False’ values where there are no null values.

5. Replace null values with a value

To replace null values with a value, we can use the fillna() function. Let’s replace the null value in the ‘Age’ column with 0:

This will replace the null value in the ‘Age’ column with 0.

Conclusion:

Assigning null value in Python Pandas is a simple task. We can use the ‘None’ keyword to assign null value to a cell and use the isnull() function to check for null values. We can also use the fillna() function to replace null values with a value.

Code:

Output:

{'Name': {0: 'John', 1: 'Paul', 2: 'George', 3: 'Ringo'}, 'Age': {0: 28.0, 1: nan, 2: 24.0, 3: 26.0}, 'City': {0: 'London', 1: 'New York', 2: 'Los Angeles', 3: 'Paris'}}
0    False
1     True
2    False
3    False
Name: Age, dtype: bool
    Name  Age         City
0   John   28       London
1   Paul    0     New York
2  George   24  Los Angeles
3   Ringo   26        Paris