Python Pandas: How to Check for NaN Values in a Pandas DataFrame

Missing data, represented as NaN (Not a Number) in Pandas, is one of the most common issues you will encounter when working with real-world datasets. Values can be missing because of incomplete data collection, failed API responses, merge mismatches, or simply empty fields in a CSV file. Identifying where and how much data is missing is a critical first step before any analysis, because undetected NaN values can silently distort calculations, break aggregations, and produce misleading results.

In this guide, you will learn how to detect, count, locate, and filter missing values in a Pandas DataFrame using a variety of built-in methods.

Counting NaN Values per Column

The most common starting point for data quality assessment is checking how many missing values exist in each column:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, np.nan, 3, np.nan],
    'B': [4, 5, np.nan, 6],
    'C': [7, 8, 9, 10]
})

print(df.isna().sum())

Output:

A    2
B    1
C    0
dtype: int64

The .isna() method returns a DataFrame of boolean values (True where a value is NaN, False otherwise), and .sum() counts the True values in each column. This immediately tells you that column A has 2 missing values, column B has 1, and column C is complete.

Checking if Any NaN Exists

Sometimes you just need a quick yes-or-no answer about whether your DataFrame contains any missing data at all:

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, np.nan], 'B': [3, 4]})

has_nan = df.isna().any().any()
print(f"DataFrame has missing values: {has_nan}")

print(df.isna().any())

Output:

DataFrame has missing values: True
A     True
B    False
dtype: bool

The first .any() checks each column, returning a boolean Series. The second .any() collapses that Series into a single boolean. This two-step chain is a useful pattern for quick validation checks in data pipelines.

Getting the Total NaN Count

To find the total number of missing values across the entire DataFrame, chain two .sum() calls:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, np.nan, 3],
    'B': [np.nan, np.nan, 6]
})

total_nan = df.isna().sum().sum()
print(f"Total NaN values: {total_nan}")

Output:

Total NaN values: 3

The first .sum() counts per column, and the second .sum() adds those counts together.

Calculating the Percentage of Missing Values

Raw counts are useful, but percentages give a clearer picture of data quality, especially when columns have different lengths or when comparing across datasets:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, np.nan, np.nan, 4],
    'B': [np.nan, 2, 3, 4],
    'C': [1, 2, 3, 4]
})

pct_missing = (df.isna().sum() / len(df)) * 100
print(pct_missing.round(1))

Output:

A    50.0
B    25.0
C     0.0
dtype: float64

A column with 50% missing values may need different treatment than one with only 25% missing. You can also use df.isna().mean() * 100 as a more concise equivalent.

Filtering Rows That Contain NaN

To see exactly which rows have missing data, use boolean indexing with .isna().any(axis=1):

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, np.nan, 35],
    'City': ['NYC', 'LA', np.nan]
})

rows_with_nan = df[df.isna().any(axis=1)]
print("Rows with missing values:")
print(rows_with_nan)

Output:

Rows with missing values:
      Name   Age City
1      Bob   NaN   LA
2  Charlie  35.0  NaN

The axis=1 parameter tells .any() to check across columns for each row, returning True for any row that contains at least one NaN.

Filtering Rows Without NaN

To get only the complete rows, negate the condition or use .dropna():

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, np.nan, 35],
    'City': ['NYC', 'LA', np.nan]
})

complete_rows = df[~df.isna().any(axis=1)]
print(complete_rows)

complete_rows = df.dropna()
print(complete_rows)

Output:

    Name   Age City
0  Alice  25.0  NYC
    Name   Age City
0  Alice  25.0  NYC

Checking a Specific Column

When you already know which column to investigate, apply the checks directly to that single column:

import pandas as pd
import numpy as np

df = pd.DataFrame({'Price': [10.5, np.nan, 20.0, np.nan]})

nan_count = df['Price'].isna().sum()
print(f"Missing prices: {nan_count}")

has_missing = df['Price'].isna().any()
print(f"Has missing: {has_missing}")

print(df['Price'].isna())

Output:

Missing prices: 2
Has missing: True
0    False
1     True
2    False
3     True
Name: Price, dtype: bool

The boolean mask from .isna() can be used directly for filtering or further analysis.

Using df.info() for a Visual Summary

The .info() method provides a quick overview of the DataFrame's structure, including the non-null count for each column:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'A': [1, np.nan, 3],
    'B': [4, 5, np.nan],
    'C': [7, 8, 9]
})

df.info()

Output:

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
---  ------  --------------  -----  
 0   A       2 non-null      float64
 1   B       2 non-null      float64
 2   C       3 non-null      int64  
dtypes: float64(2), int64(1)
memory usage: 204.0 bytes

Columns A and B show "2 non-null" out of 3 entries, indicating one missing value each. This method is especially helpful during initial data exploration because it also shows data types and memory usage.

Quick Reference

• Start with df.isna().sum() as your standard first step in data cleaning to see how missing values are distributed across columns.
• Use df.isna().any().any() for a quick boolean check in automated pipelines.
• For deeper investigation, filter rows with df[df.isna().any(axis=1)] to see exactly which records have gaps and decide how to handle them.