Basic histogram with plt.hist in Matplotlib - Step-by-Step Execution

Concept Flow - Basic histogram with plt.hist

Start with data array

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Call plt.hist(data)

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Calculate bins and counts

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Draw bars for each bin

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Show histogram plot

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End

The flow starts with data, then plt.hist calculates bins and counts, draws bars, and finally shows the histogram.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt

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

This code creates a histogram of the data list with 4 bins and displays the plot.

Execution Table

Step	Action	Data State	Bins	Bin Counts	Plot Action
1	Start with data	[1,2,2,3,3,3,4,4,4,4]	N/A	N/A	No plot yet
2	Call plt.hist(data, bins=4)	[1,2,2,3,3,3,4,4,4,4]	[1.0-1.75,1.75-2.5,2.5-3.25,3.25-4.0]	Counts calculated	Bars prepared
3	Count data in bins	Same data	Same bins	[1,2,3,4]	Bars heights set
4	Draw histogram bars	Same data	Same bins	Same counts	Bars drawn on plot
5	Show plot	Same data	Same bins	Same counts	Histogram displayed
6	End	Same data	Same bins	Same counts	Plot window open

💡 Plot is shown and program waits for user to close window

Variable Tracker

Variable	Start	After plt.hist call	After counts calculation	After plot draw	Final
data	[1,2,2,3,3,3,4,4,4,4]	Same	Same	Same	Same
bins	N/A	[1.0-1.75,1.75-2.5,2.5-3.25,3.25-4.0]	Same	Same	Same
counts	N/A	N/A	[1,2,3,4]	Same	Same

Key Moments - 3 Insights

Why do the bin ranges not exactly match the data values?

Bins are calculated to cover the data range evenly; for example, with 4 bins from 1 to 4, each bin covers a range like 1.0-1.75. This is shown in execution_table step 2.

Why does the count for the last bin include 4 values?

Because the last bin includes the upper edge, so all data points equal to 4 fall into the last bin, as seen in execution_table step 3.

What happens if plt.show() is not called?

The histogram bars are prepared but the plot window does not appear, so the user cannot see the histogram. This is implied between steps 4 and 5.

Visual Quiz - 3 Questions

Look at the execution table at step 3, what is the count of data points in the second bin?

A 1

B 3

C 2

D 4

💡 Hint

Check the 'Bin Counts' column at step 3 in the execution_table.

At which step does the histogram bars get drawn on the plot?

A Step 3

B Step 4

C Step 2

D Step 5

💡 Hint

Look at the 'Plot Action' column in the execution_table.

If the number of bins is increased to 5, what will happen to the counts in the bins?

A Counts will be distributed into 5 smaller bins

B Counts will remain the same as 4 bins

C Counts will double

D Counts will be zero

💡 Hint

Refer to how bins split data in the execution_table and variable_tracker.

Concept Snapshot

plt.hist(data, bins=n) creates a histogram.
Data is split into n bins covering the data range.
Counts of data points per bin are calculated.
Bars are drawn with heights = counts.
plt.show() displays the histogram plot.

Full Transcript

This visual execution traces how matplotlib's plt.hist function creates a histogram. Starting with a data list, plt.hist divides the data range into bins, counts how many data points fall into each bin, draws bars representing these counts, and finally shows the plot window. The execution table shows each step: starting with data, calling plt.hist, calculating bins and counts, drawing bars, and displaying the plot. Variables like data, bins, and counts are tracked to see their values change or stay the same. Key moments clarify why bins cover ranges and how counts are assigned. The quiz tests understanding of bin counts, drawing steps, and effects of changing bin numbers. The snapshot summarizes the main steps and syntax for quick reference.