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When preparing data for machine learning or statistical analysis, you often need to transform continuous variables into categorical bins. This is where pandas.cut becomes invaluable.

What is pd.cut?

pd.cut is a Pandas utility to segment and sort data values into discrete bins:

import pandas as pd
import numpy as np

ages = pd.Series([5, 12, 17, 24, 35, 45, 52, 63, 78])
binned = pd.cut(ages, bins=[0, 18, 35, 60, 100], labels=["Child", "Young Adult", "Adult", "Senior"])
print(binned)

Output:

0       Child
1       Child
2       Child
3    Young Adult
4    Young Adult
5         Adult
6         Adult
7         Adult
8        Senior

Why Use cut?

  • Converts continuous variables into categorical features
  • Facilitates stratified sampling by group
  • Makes histograms and frequency plots more interpretable
  • Useful for feature engineering in ML pipelines

Stratified Sampling Use Case

Suppose you want to split a population dataset for training/testing with even age distribution. You can first bin the data using cut:

df['age_group'] = pd.cut(df['age'], bins=[0, 18, 35, 60, 100])
stratified_sample = df.groupby('age_group', group_keys=False).apply(lambda x: x.sample(frac=0.2))

This ensures each age group is proportionally represented in your sample — a critical aspect of stratified sampling.

Additional Features

  • Automatic bin calculation with bins=int:
pd.cut(data, bins=5)  # Creates 5 equally spaced bins
  • Right-inclusive or not via right=True|False
  • Return bin edges with retbins=True
  • Works well with plotting:
df['binned'] = pd.cut(df['value'], bins=5)
df['binned'].value_counts().plot(kind='bar')

When Not to Use It

  • If bins should have equal number of observations, use pd.qcut instead (quantile-based)
  • If input is ordinal but non-numeric, consider mapping directly via pd.Categorical

Final Thoughts

pd.cut is a deceptively simple API that solves an essential data preparation need: turning continuous measurements into stratified or grouped categories. Whether you’re building ML pipelines or preparing reports, it gives you expressive control over how to discretize your data.

With just one line, pd.cut brings structure and clarity to numeric chaos.

Published on Jan 28, 2019 — written by Ganesh Raman.

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