Binning continuous variables in Data Analysis Python - Step-by-Step Execution

Concept Flow - Binning continuous variables

Start with continuous data

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Choose number of bins or bin edges

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Assign each value to a bin

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Create new categorical variable

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Use binned data for analysis

We start with continuous data, decide how to split it into bins, assign each value to a bin, and create a new categorical variable for easier analysis.

Execution Sample

Data Analysis Python

import pandas as pd

values = [1.5, 2.3, 3.7, 4.1, 5.6]
bins = [0, 2, 4, 6]
labels = ['Low', 'Medium', 'High']
binned = pd.cut(values, bins=bins, labels=labels)

This code splits a list of numbers into three bins labeled Low, Medium, and High.

Execution Table

Step	Value	Bin Edges	Bin Assigned	Label Assigned
1	1.5	[0, 2, 4, 6]	[0, 2)	Low
2	2.3	[0, 2, 4, 6]	[2, 4)	Medium
3	3.7	[0, 2, 4, 6]	[2, 4)	Medium
4	4.1	[0, 2, 4, 6]	[4, 6)	High
5	5.6	[0, 2, 4, 6]	[4, 6)	High
6	End	N/A	N/A	All values assigned bins

💡 All values processed and assigned to bins based on defined edges.

Variable Tracker

Variable	Start	After 1	After 2	After 3	After 4	After 5	Final
values	[1.5, 2.3, 3.7, 4.1, 5.6]	[1.5, 2.3, 3.7, 4.1, 5.6]	[1.5, 2.3, 3.7, 4.1, 5.6]	[1.5, 2.3, 3.7, 4.1, 5.6]	[1.5, 2.3, 3.7, 4.1, 5.6]	[1.5, 2.3, 3.7, 4.1, 5.6]	[1.5, 2.3, 3.7, 4.1, 5.6]
bin_edges	None	[0, 2, 4, 6]	[0, 2, 4, 6]	[0, 2, 4, 6]	[0, 2, 4, 6]	[0, 2, 4, 6]	[0, 2, 4, 6]
bin_assigned	None	[0, 2)	[2, 4)	[2, 4)	[4, 6)	[4, 6)	[0, 2), [2, 4), [4, 6)]
labels	None	Low	Medium	Medium	High	High	[Low, Medium, Medium, High, High]

Key Moments - 3 Insights

Why does the value 2.3 go into the bin [2, 4) and not [0, 2)?

Because bins are left-inclusive and right-exclusive by default in pd.cut, so 2.3 is >= 2 and < 4, placing it in [2, 4). See execution_table rows 2 and 3.

What happens if a value is exactly on a bin edge, like 2.0?

By default, the left edge is included and the right edge excluded, so 2.0 belongs to the bin starting at 2. For example, 2.0 would be in [2, 4).

Can bin labels be any strings or numbers?

Yes, labels can be any list of strings or numbers matching the number of bins minus one. They help name the bins for easier understanding.

Visual Quiz - 3 Questions

Look at the execution_table, what label is assigned to the value 3.7?

A Medium

B Low

C High

D None

💡 Hint

Check row 3 in the execution_table where value 3.7 is assigned a label.

At which step does the bin assignment process finish?

A Step 5

B Step 6

C Step 4

D Step 3

💡 Hint

Look at the exit note and the last row in execution_table.

If we add a bin edge at 5, how would the label for 5.6 change?

A It would be 'Medium'

B It would be 'Low'

C It would be 'High'

D It would be outside all bins

💡 Hint

Consider that 5.6 is greater than 5 and less than 6, so it stays in the highest bin.

Concept Snapshot

Binning continuous variables splits data into intervals called bins.
Use pd.cut() in Python with bin edges and optional labels.
Bins are left-inclusive, right-exclusive by default.
Assigns each value to a bin, creating a categorical variable.
Useful for grouping continuous data into categories.

Full Transcript

Binning continuous variables means dividing continuous data into groups called bins. We start with a list of numbers and decide how many bins or what edges to use. Then, each number is assigned to a bin based on where it falls between the edges. In Python, we use pandas' cut function to do this easily. The bins include the left edge but exclude the right edge, so a value exactly on the left edge belongs to that bin. We can also give names to bins to make the data easier to understand. This process helps turn continuous numbers into categories for analysis.