Pandas GroupBy Columns with NaN (missing) Values

python

dataframe

pandas

best-practices

byAlex Kataev·Mar 2, 2025

To deal with NaN in pandas groupby(), use a placeholder. Implement this by using fillna(), and pick a unique value such as 'missing'. Following this, apply groupby() as usual.

import pandas as pd
import numpy as np

# Sample DataFrame with NaN values
df = pd.DataFrame({'A': [1, np.nan, 3], 'B': [4, np.nan, 6]})

# Replace NaN values with 'missing' and group
result = df.fillna('missing').groupby(['A', 'B']).size()
print(result)  # Prints out grouped counts with NaNs intact. Bingo!

This ensures that each NaN is regarded as a distinct group, facilitating precise aggregations.

Handling NaN inclusion during grouping

To ensure the completeness of data analysis, never overlook the NaN values:

Since pandas 1.1, use dropna=False in groupby(). This discards the default behavior of excluding NaN.
Before grouping, replacing NaN values with placeholders or converting them to str permits their retention during grouping!

df['column'] = df['column'].astype(str)  # Transforms NaN values into 'nan'. Clever, huh?

Apply df.fillna() with a dummy or placeholder value that doesn't exist in your dataset. This keeps NaN values in their unique groups.
For critical data integrity, avoid dummy values that can be mistaken for valid data. Pick distinguishable placeholders instead.

Advanced handling strategies for NaN grouping

Taking it a notch higher with advanced techniques:

Merge methods: Merge a unique index from pd.drop_duplicates() to the original dataframe before grouping. Will protect your data integrity, like an insurance policy!
Custom functions: Write your own aggregation function to handle NaNs in a controlled manner. Be the master of your NaNs!
Data type awareness: Ensuring unity in group keys' type can save you from NaN-induced grouping nightmares.

Navigating potential pitfalls

Vigilance is key when working with NaN values:

Watch out for 'placeholder-clashes'. Pick a placeholder dissimilar to the original data values.
Carefully examine grouped output. Spot check for unintentional data loss or misinterpretation, especially where NaNs abound.
Lookout for odd behavior in aggregations with NaN in your teams.

Best practices and additional methods

Here are some best practices and additional methods:

Preserving NaN as a separate category is often vital. Doing this can help maintain meaningful insights in your data.
Consult GitHub issue discussions on pandas. Hidden treasures of innovative solutions for handling NaNs lie there.
Always test your grouping logic with a subset containing NaNs to gauge its behavior with the full dataset.