Pandas isnull() vs. isna() – Explained with Examples

Data manipulation is a crucial aspect of data analysis and machine learning tasks. Pandas, a popular Python library, provides a wide array of tools for data manipulation and analysis. Two important methods for identifying missing values in a DataFrame or Series are isnull() and isna(). In this tutorial, we will delve into the differences between these two methods and provide comprehensive examples to demonstrate their usage.

Table of Contents

1. Introduction
2. Understanding isnull() and isna()
3. Differences Between isnull() and isna()
4. Examples

• Example 1: Handling Missing Values in a DataFrame
• Example 2: Counting Missing Values in a Series

1. Best Practices
2. Conclusion

1. Introduction

Missing data is a common issue in real-world datasets. It can arise due to various reasons, such as data collection errors, incomplete surveys, or other issues. Identifying and handling missing data is crucial for accurate analysis and modeling. Pandas, a powerful Python library, offers various methods to work with missing data, and two of the most commonly used methods are isnull() and isna().

2. Understanding isnull() and isna()

Both isnull() and isna() are methods provided by Pandas to check for missing values in a DataFrame or Series. These methods return a DataFrame or Series of Boolean values, where True indicates a missing value and False indicates a non-missing value.

There is no functional difference between isnull() and isna(); they can be used interchangeably based on personal preference. Both methods are aliases of each other, and Pandas provides them for user convenience.

3. Differences Between isnull() and isna()

As mentioned earlier, there is no actual difference in functionality between isnull() and isna(). They can be used interchangeably to achieve the same result. The choice between them typically comes down to user preference. Some users might find isnull() more intuitive, while others might prefer isna() due to its brevity.

4. Examples

In this section, we will explore two examples to illustrate the usage of isnull() and isna().

Example 1: Handling Missing Values in a DataFrame

Suppose we have a DataFrame containing information about students’ test scores. However, some of the test scores are missing. Let’s use isnull() (or isna()) to identify and handle these missing values.

import pandas as pd

# Sample data
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Math_Score': [85, None, 72, 90],
        'Science_Score': [92, 78, None, 88]}

df = pd.DataFrame(data)

# Identifying missing values
missing_values = df.isnull()  # Alternatively, you can use df.isna()

print(missing_values)

Output:

    Name  Math_Score  Science_Score
0  False       False          False
1  False        True          False
2  False       False           True
3  False       False          False

In this example, the isnull() method is applied to the DataFrame, and it returns a DataFrame of the same shape, where each cell contains True if the corresponding value is missing and False otherwise.

Example 2: Counting Missing Values in a Series

Another common scenario is counting the number of missing values in a Series. Let’s consider a Series representing daily temperature readings, where some values are missing. We will use both isnull() and isna() to count the missing values.

import pandas as pd

# Sample data
temperature_data = [25.5, None, 28.0, None, 26.8, 27.2, None, 24.9]

temperature_series = pd.Series(temperature_data)

# Counting missing values
missing_count_isnull = temperature_series.isnull().sum()
missing_count_isna = temperature_series.isna().sum()

print("Missing values count using isnull():", missing_count_isnull)
print("Missing values count using isna():", missing_count_isna)

Output:

Missing values count using isnull(): 3
Missing values count using isna(): 3

In this example, we first apply the isnull() method to the Series to create a Boolean Series, where each value is True if the corresponding value in the original Series is missing. Then, we use the .sum() method to count the number of True values, which corresponds to the number of missing values in the Series. The same procedure is followed using the isna() method.

5. Best Practices

• Consistency: Choose either isnull() or isna() and stick with it throughout your codebase to maintain consistency.
• Interchangeability: Remember that these methods are interchangeable, so you can use the one that you find more intuitive or readable.
• Method Chaining: These methods can be used in method chaining for more concise code. For example:

  missing_count = df.isnull().sum()

6. Conclusion

In this tutorial, we explored the usage of Pandas’ isnull() and isna() methods for identifying missing values in DataFrames and Series. We discussed their similarities and noted that they are functionally identical. Through examples, we demonstrated how to use these methods to identify missing values and count them. Remember that choosing between isnull() and isna() is a matter of personal preference, and you can use either one based on what you find more comfortable.

By using these methods effectively, you can efficiently manage and analyze datasets with missing values, ensuring that your data analysis and modeling tasks are accurate and reliable.