Pandas DataFrame isna() Function

Pandas DataFrame.isna() function is used to check the missing values in a given DataFrame. It returns a same-sized DataFrame object where the values are replaced with a Boolean value True for every NAN (not-a-number) value, and otherwise False.This is the best way to check missing values in a DataFrame using the isna() function. Moreover, using isna() function along with sum() we can count the NaN values in a DataFrame.

Quick Example of isna() Function

If you are in a hurry, below are some quick examples of how of DataFrame.isna() function.


# Quick example of isna() function

# Example 1: Detect NaN values 
# Using df.isna()
df1 = df.isna()

# Example 2: Apply isna() function 
# To specific Column
df1 = df['Fee'].isna()

# Example 3: Count NaN values of DataFrame
nan_count = df.isna().sum().sum()

# Example 4: Count the NaN values in column-wise 
nan_count = df.isna().sum()

# Example 5: Count the NaN values in row-wise
nan_count = df.isna().sum(axis = 1)

Syntax of DataFrame.isna() Function

Following is the syntax of Pandas DataFrame.isna() function.


# Syntax of df.isna()
DataFrame.isna()

Parameters

This function doesn’t have any parameters.

Return Value

It returns a boolean DataFrame for DataFrame having missing values otherwise, it returns boolean Series for a specific column.

Pandas DataFrame.isna() Usage

Pandas DataFrame.isna() function is used to check whether missing values exist in a given DataFrame or not. Either you can apply this function to Pandas DataFrame that returns a boolean DataFrame or apply it to a specific column of DataFrame to return boolean Pandas Series.

Now, let’s create a DataFrame with a few rows and columns using Python Dictionary. Our DataFrame contains the column names Courses, Fee, Duration, and Discount and includes some NaN values on a string and integer columns.


# Create pandas DataFrame
import pandas as pd
import numpy as np
technologies = {
    'Courses':["Spark", np.nan, "PySpark", np.nan, "Hadoop"],
    'Fee' :[np.nan, 20000, np.nan, 25000, np.nan],
    'Duration':[np.nan,'40days','35days', np.nan, np.nan],
    'Discount':[np.nan, 1000, np.nan, np.nan, 1500]
               }
df = pd.DataFrame(technologies, index = ['r1', 'r2', 'r3', 'r4', 'r5'])
print(df)

Yields below output. Note that in Pandas nan can be defined by using NumPy np.nan.

Apply isna() Function to Whole DataFrame

Apply isna() function to a given DataFrame having some missing values then, it will check whether it contains missing values. If it contains it is replaced with True otherwise False.


# Detect NaN values using df.isna()
df1 = df.isna()
print(df1)

Yields below output.

Apply isna() Function to Specific Column

Apply isna() function to the specific column of the given DataFrame, it checks the NaN values and returns the boolean values in the form of Series. For example,


# Apply isna() Function to specific Column
df1 = df['Fee'].isna()
print(df1)

# Output:
# r1     True
# r2    False
# r3     True
# r4    False
# r5     True
# Name: Fee, dtype: boo

Count NaN Values using Pandas isna() along with sum()

So far, we have learned how to check the missing values using the isna() function now, we will learn how to count the NaN values in a DataFrame/row-wise/column-wise using isna() function along with the sum() function. Let’s apply these functions and count the NaN values.

Let’s count the total number of NaN values in the whole DataFrame, using df.isna().sum().sum(), this syntax returns the total number of NaN values in the entire DataFrame.


# Count NaN values of DataFrame
nan_count = df.isna().sum().sum()
print(nan_count )

# Output:
# 11

Count NaN Values in Pandas Column-Wise using isna()

To count the NaN values column-wise in a Pandas DataFrame using the isna() function, you can simply chain it with the sum() function along the desired axis, which is axis=0 for column-wise operations.


# Count the NaN values column-wise of DataFrame
nan_count = df.isna().sum(axis=0)
print(nan_count)

# Count the NaN values column-wise of DataFrame
nan_count = df.isna().sum()
print(nan_count )

# Output:
# Courses     2
# Fee         3
# Duration    3
# Discount    3
# dtype: int64

Count NaN Values in Pandas Row-Wise using isna()

To count NaN values row-wise in a Pandas DataFrame using the isna() function, you can utilize the sum() function along with axis=1.


# Count the NaN values in row-wise
nan_count = df.isna().sum(axis = 1)
print(nan_count)

# Output:
# r1    3
# r2    1
# r3    2
# r4    3
# r5    2
# dtype: int64

FAQ on Pandas DataFrame isna() Function

What does the isna() function do?

The isna() function checks for missing values in a DataFrame or Series and returns a DataFrame or Series of the same shape with True where the values are missing (NaN) and False elsewhere.

Can isna() be used on non-numeric columns?

The isna() function works on all data types, including strings, dates, and objects, identifying NaN or None values.

How is isna() different from isnull()?

Both isna() and isnull() are identical in functionality. They are interchangeable and serve the same purpose in Pandas.

How do I count missing values in a DataFrame using isna()?

To count missing values in a DataFrame using the isna() function, you can combine it with the sum() function.

How does isna() handle custom missing value indicators?

By default, isna() identifies NaN and None. To handle custom missing values (e.g., 'N/A' or ''), use the replace() or na_values parameter when reading data.

Does isna() modify the original DataFrame?

The isna() function does not modify the original DataFrame. Instead, it returns a new DataFrame or Series of the same shape, containing True where there are missing values and False elsewhere.

Conclusion

In this article, I have explained the Pandas DataFrame.isna() function and used this function to check whether the missing values are present in the DataFrame or not with examples.

Happy learning!!