How to Count Missing Values in pandas DataFrames

To count missing values in pandas, use df.isna() to find missing entries and then sum() to count them. For total missing values per column, use df.isna().sum(), and for the entire DataFrame, use df.isna().sum().sum().

Syntax

The main methods to count missing values in pandas are:

• df.isna(): Returns a DataFrame of the same shape with True where values are missing (NaN) and False otherwise.
• df.isna().sum(): Counts the number of missing values in each column.
• df.isna().sum().sum(): Counts the total number of missing values in the entire DataFrame.

python

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

Example

This example shows how to create a DataFrame with missing values and count them per column and overall.

python

import pandas as pd
import numpy as np

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

# Count missing values per column
missing_per_column = df.isna().sum()

# Count total missing values in DataFrame
total_missing = df.isna().sum().sum()

print('Missing values per column:')
print(missing_per_column)
print('\nTotal missing values in DataFrame:')
print(total_missing)

Output

Missing values per column:
A    1
B    1
C    0
dtype: int64

Total missing values in DataFrame:
2

Common Pitfalls

Common mistakes when counting missing values include:

• Using df.isnull() and df.isna() interchangeably is fine, but mixing them with other methods like df.count() without understanding their behavior can cause confusion.
• For counting missing values per row, you must specify axis=1 in sum(), e.g., df.isna().sum(axis=1).
• Not calling sum() after isna() returns a boolean DataFrame, not counts.

python

import pandas as pd
import numpy as np

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

# Wrong: returns boolean DataFrame, not counts
wrong = df.isna()

# Right: sum counts missing values per column
right = df.isna().sum()

print('Wrong output:')
print(wrong)
print('\nRight output:')
print(right)

Output

Wrong output:
       A      B
0  False   True
1   True  False

Right output:
A    1
B    1
dtype: int64

Quick Reference

Method	Description	Example
df.isna()	Detect missing values (NaN) as True/False	df.isna()
df.isna().sum()	Count missing values per column	df.isna().sum()
df.isna().sum().sum()	Count total missing values in DataFrame	df.isna().sum().sum()
df.isna().sum(axis=1)	Count missing values per row	df.isna().sum(axis=1)

Key Takeaways

Use df.isna().sum() to count missing values per column in a DataFrame.

Use df.isna().sum().sum() to get the total count of missing values in the entire DataFrame.

Remember to call sum() after isna() to get counts, not just booleans.

To count missing values per row, use sum(axis=1) after isna().

df.isna() and df.isnull() are interchangeable for detecting missing values.