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Pandas is a powerful and widely used Python library for data manipulation and analysis. One of the essential tasks in data analysis is handling missing values. The notnull() function in Pandas is a handy tool that helps you identify non-null (i.e., not missing) values in your DataFrame or Series. In this tutorial, we will delve deep into the notnull() function, understanding its syntax, use cases, and providing multiple examples to illustrate its functionality.

Table of Contents

  1. Introduction to the notnull() Function
  2. Syntax of the notnull() Function
  3. Examples of Using notnull()
  • Example 1: Checking Non-null Values in a DataFrame
  • Example 2: Filtering Rows Based on Non-null Values
  1. Handling Null Values in Pandas
  2. Conclusion

1. Introduction to the notnull() Function

The notnull() function in Pandas is a boolean function that helps us determine whether each element in a DataFrame or Series is not a null value. Null values, often denoted as NaN (Not a Number), signify missing or undefined data points. It’s crucial to identify and handle these null values appropriately to ensure accurate data analysis and processing.

The notnull() function returns a boolean mask with the same shape as the input object, where each element of the mask is True if the corresponding element in the input is not null and False otherwise.

2. Syntax of the notnull() Function

The syntax of the notnull() function is straightforward:


Here, obj represents the DataFrame or Series that you want to check for null values.

3. Examples of Using notnull()

Let’s explore the usage of the notnull() function through two comprehensive examples.

Example 1: Checking Non-null Values in a DataFrame

Suppose we have a dataset containing information about students, including their names, ages, and test scores. The dataset might contain missing values in the test score column, which we need to identify using the notnull() function.

import pandas as pd

# Sample data
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
    'Age': [21, 20, 22, 19, 20],
    'Test_Score': [89, None, 75, 92, None]

df = pd.DataFrame(data)

# Checking for non-null values using notnull()
score_not_null = df['Test_Score'].notnull()



0     True
1    False
2     True
3     True
4    False
Name: Test_Score, dtype: bool

In this example, the notnull() function is applied to the ‘Test_Score’ column of the DataFrame df. The resulting boolean mask indicates that the first, third, and fourth rows have non-null test scores, while the second and fifth rows have null test scores.

Example 2: Filtering Rows Based on Non-null Values

Continuing from the previous example, we can use the boolean mask created by notnull() to filter out rows with null test scores and create a new DataFrame containing only valid test scores.

valid_scores_df = df[score_not_null]



      Name  Age  Test_Score
0    Alice   21        89.0
2  Charlie   22        75.0
3    David   19        92.0

In this case, the valid_scores_df DataFrame contains only rows with non-null test scores, effectively filtering out the rows with missing values.

4. Handling Null Values in Pandas

The notnull() function is just one tool in Pandas for handling null values. It’s often used in conjunction with other functions like isnull() to comprehensively manage missing data.

  • isnull(): Returns a boolean mask indicating null values.
  • fillna(): Fills null values with specified values or strategies.
  • dropna(): Drops rows or columns containing null values.

When working with real-world datasets, you’ll often need to apply a combination of these functions to effectively clean and preprocess your data.

5. Conclusion

The notnull() function in Pandas is a valuable tool for identifying non-null values in DataFrames and Series. By using this function, you can make informed decisions about how to handle missing data in your analysis. Through the examples provided in this tutorial, you’ve seen how to apply the notnull() function to filter rows based on non-null values, aiding you in data preprocessing and cleaning tasks.

Remember that effectively managing missing data is a critical step in the data analysis pipeline, and the notnull() function is a fundamental building block in achieving this goal. As you continue to work with Pandas and handle various datasets, a strong understanding of functions like notnull() will empower you to conduct more accurate and insightful data analyses.

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