Why pandas groupby count includes NaN values (and how to fix it)

Incorrect counts in pandas groupby usually appear in real-world datasets coming from SQL exports, logs, or APIs, where NaN values are present. This leads pandas to include NaNs in the count, often silently breaking downstream logic due to missing value handling.

Quick Answer

Pandas groupby count includes NaN because NaN values are counted by default. Fix by using dropna() before grouping or specifying dropna parameter in groupby.

TL;DR

  • Groupby counts NaN values by default
  • Use dropna() before grouping to exclude NaNs
  • Specify dropna=True in groupby for convenience

Problem Example

import pandas as pd

df = pd.DataFrame({'group': ['A', 'B', 'A', 'B'], 'value': [10, 20, None, 40]})
print(f"df: {len(df)} rows")
grouped = df.groupby('group')['value'].count()
print(f"Grouped counts: {grouped}")
# Output shows counts including NaN values

Root Cause Analysis

The presence of NaN values in the DataFrame causes pandas to include them in the count by default. Pandas treats NaN as a valid value for the purpose of counting, following standard behavior for missing data handling. This is consistent with how database engines handle grouping operations. Related factors:

  • NaN values in the group column
  • NaN values in the value column
  • Default dropna behavior in groupby

How to Detect This Issue

# Check for NaN values in the DataFrame
nan_count = df['value'].isna().sum()
print(f'NaN values in value column: {nan_count}')

Solutions

Solution 1: Remove NaN values before grouping

df_clean = df.dropna(subset='value')
grouped = df_clean.groupby('group')['value'].count()

Solution 2: Specify dropna in groupby

grouped = df.groupby('group')['value'].count(dropna=True)

Why validate Parameter Fails

Using groupby without handling NaN values will lead to incorrect counts. This behavior alerts developers to the presence of missing data. To avoid incorrect counts, explicitly handle NaN values before or during grouping.

Production-Safe Pattern

grouped = df.groupby('group')['value'].count(dropna=True)
assert not grouped.isna().any(), 'NaN values in grouped counts'

Wrong Fixes That Make Things Worse

❌ Using fillna() with a placeholder value: This distorts the count with arbitrary values

❌ Ignoring NaN values without removing them: NaNs still affect downstream calculations

❌ Not validating the presence of NaNs before grouping: Silent data corruption

Common Mistakes to Avoid

  • Not checking for NaN values before grouping
  • Not specifying dropna parameter in groupby
  • Incorrectly assuming NaNs are ignored by default

Frequently Asked Questions

Q: Why does pandas groupby count include NaN values?

Pandas treats NaN as a valid value for counting purposes, following standard behavior for missing data handling.

Q: Is this a pandas bug?

No. This behavior is consistent with how database engines handle grouping operations and is intended to highlight missing data.

Q: How do I prevent NaN values from being counted in pandas groupby?

Use dropna() before grouping or specify dropna=True in the groupby method.

→ Why pandas groupby size differs from count → Why pandas merge duplicates rows after groupby → Fix pandas fillna not working on specific columns → Fix pandas outer join creates NaN rows