Histogram plots in Pandas - Step-by-Step Execution

Concept Flow - Histogram plots

Start with data

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Select numeric column

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Divide data range into bins

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Count data points in each bin

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Draw bars with heights = counts

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Display histogram plot

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End

The flow shows how histogram plots are created by dividing data into bins, counting points in each bin, and drawing bars to represent counts.

Execution Sample

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = pd.Series([1,2,2,3,3,3,4,4,4,4])
data.plot.hist(bins=4)
plt.show()

This code creates a histogram plot of the data with 4 bins, showing how many values fall into each bin.

Execution Table

Step	Action	Data Range	Bins	Bin Counts	Plot Bars Height
1	Start with data	[1,4]
2	Decide number of bins		4
3	Calculate bin edges		Bins: [1.0,1.75,2.5,3.25,4.0]
4	Count data in bin 1 (1.0 to 1.75)			1	Bar height = 1
5	Count data in bin 2 (1.75 to 2.5)			2	Bar height = 2
6	Count data in bin 3 (2.5 to 3.25)			3	Bar height = 3
7	Count data in bin 4 (3.25 to 4.0]			4	Bar height = 4
8	Draw bars with heights				Bars drawn with heights 1,2,3,4
9	Show plot				Histogram displayed
10	End				Plot complete

💡 All bins counted and bars drawn, histogram plot is complete

Variable Tracker

Variable	Start	After Step 4	After Step 5	After Step 6	After Step 7	Final
data	[1,2,2,3,3,3,4,4,4,4]	[1,2,2,3,3,3,4,4,4,4]	[1,2,2,3,3,3,4,4,4,4]	[1,2,2,3,3,3,4,4,4,4]	[1,2,2,3,3,3,4,4,4,4]	[1,2,2,3,3,3,4,4,4,4]
bins	Not set	4 bins set	4 bins set	4 bins set	4 bins set	4 bins set
bin_edges	Not set	[1.0,1.75,2.5,3.25,4.0]	[1.0,1.75,2.5,3.25,4.0]	[1.0,1.75,2.5,3.25,4.0]	[1.0,1.75,2.5,3.25,4.0]	[1.0,1.75,2.5,3.25,4.0]
bin_counts	Empty	[1]	[1,2]	[1,2,3]	[1,2,3,4]	[1,2,3,4]
plot_bars_height	None	None	None	None	None	[1,2,3,4]

Key Moments - 3 Insights

Why do the bin edges include decimal values like 1.75 and 2.5 instead of just integers?

The bin edges divide the data range evenly into 4 parts, so they are calculated as decimal values to split the range [1,4] into equal intervals, as shown in execution_table rows 3 and 4.

Why does the last bin count include values equal to 4 but the first bin count only includes values equal to 1?

By default, bins include the left edge but exclude the right edge, except the last bin which includes both edges. This is why the last bin counts all 4s, as seen in execution_table row 7.

What does the height of each bar represent in the histogram?

Each bar's height shows how many data points fall into that bin's range, as counted in execution_table rows 4 to 7.

Visual Quiz - 3 Questions

Look at the execution_table at step 6, what is the count of data points in the third bin?

A 2

B 3

C 4

D 1

💡 Hint

Check the 'Bin Counts' column at step 6 in the execution_table.

At which step does the histogram plot get displayed?

A Step 9

B Step 8

C Step 7

D Step 10

💡 Hint

Look for the action 'Show plot' in the execution_table.

If we change bins from 4 to 2, how would the bin counts change?

A Counts would be [5,5]

B Counts would be [4,6]

C Counts would be [3,7]

D Counts would be [2,8]

💡 Hint

Think about dividing the data range [1,4] into 2 bins and counting data points in each.

Concept Snapshot

Histogram plots show how data values are distributed across ranges called bins.
Use pandas Series or DataFrame column with .plot.hist(bins=number).
Bins split data range evenly; bar heights show counts per bin.
Useful to see data shape and frequency visually.
Call plt.show() to display the plot.

Full Transcript

This visual execution traces how a histogram plot is created using pandas. Starting with numeric data, the code decides the number of bins, calculates bin edges dividing the data range evenly, counts how many data points fall into each bin, and draws bars with heights equal to these counts. The plot is then displayed. Key points include understanding bin edges as decimal intervals, how the last bin includes its right edge, and that bar heights represent counts of data points in each bin. The execution table shows each step clearly, and the variable tracker follows changes in data, bins, bin edges, counts, and bar heights. This helps beginners see exactly how histogram plots work step-by-step.