Binning and Discretization

Why bin values?

Binning (discretization) converts numeric values into categories:

text

- Age → {0–18, 19–35, 36–60, 60+}
- Spend → {low, medium, high}

Use cases:

Easier reporting and segmentation
Non-linear relationship capture (sometimes)
Feature engineering

`pd.cut` (fixed bins)

cut

import pandas as pd
 
age = pd.Series([12, 19, 25, 38, 52, 67])
 
bins = [0, 18, 35, 60, 200]
labels = ["0-18", "19-35", "36-60", "60+"]
 
age_group = pd.cut(age, bins=bins, labels=labels, right=True, include_lowest=True)
print(age_group)

`pd.qcut` (quantile bins)

Quantile binning tries to put roughly equal number of rows in each bin.

qcut

import pandas as pd
 
values = pd.Series([10, 15, 20, 25, 30, 40, 60, 100])
 
bucket = pd.qcut(values, q=3, labels=["low", "mid", "high"])
print(bucket)

Tips

Choose bins based on domain meaning.
Quantile bins are useful when distributions are skewed.
Always check counts per bin after binning.

Visualize it

diagram Binning continuous values mermaid

  flowchart LR
  A["Continuous values"] --> B{"cut or qcut?"}
  B -->|"fixed edges"| C["pd.cut(values, bins)"]
  B -->|"equal-sized groups"| D["pd.qcut(values, q)"]
  C --> E["Category per row"]
  D --> E

Continuous numeric values are grouped into a small number of discrete bins, either fixed-width or by quantile.

sketch Scattered ages sorted into bins p5.js

Each age lands in exactly one bin based on the bin edges; dot color matches the band it falls into.

🧪 Try It Yourself

Exercise 1 – Bin with fixed edges

Exercise 2 – Bin into equal-sized quantile groups

Exercise 3 – Count rows per bin

Bins turn numbers into categories — pair this with Train-Test Split Concepts before you build and evaluate a model on them.

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