Histograms

Reviewed & published by Brayan K

Matplotlib is a Python library for creating charts and visualizations — and the histogram is the tool you reach for when you want to see the shape of a single numeric variable.

Part of the free Matplotlib course at LearnCodingFast — hands-on lessons with worked examples and the output they print, plus practice exercises and a quick quiz.

In this lesson you'll bucket data into bins, tune the bin count, normalize with density, and overlay two distributions to compare them.

What You'll Learn in This Lesson

• Building a histogram with plt.hist()
• Controlling detail with the bins argument
• Outlining bars with edgecolor
• Normalizing with density=True
• Overlaying two distributions with alpha
• Reading the shape of a distribution

1 Your First Histogram

A histogram takes one list of numbers, slices the range into equal bins, and counts how many values fall in each bin. Pass your data to plt.hist() and add edgecolor so the bars stay distinct.

import matplotlib.pyplot as plt
import numpy as np

# 500 exam scores centered around 70
np.random.seed(0)
scores = np.random.normal(loc=70, scale=10, size=500)

# Slice the range into 20 bins
plt.hist(scores, bins=20, edgecolor="black", color="cornflowerblue")

plt.title("Distribution of Exam Scores")
plt.xlabel("Score")
plt.ylabel("Number of Students")

plt.show()

What you'll see: a bell-shaped cluster of blue bars, tallest near a score of 70 and tapering off toward 40 and 100. Each bar is outlined in black, and the y-axis tells you how many students landed in each score range.

2 Bin Count & Density

The bins argument controls resolution: fewer bins give a smooth summary, more bins reveal fine detail. Setting density=True rescales the y-axis so the bars represent probability density (total area equals 1) instead of raw counts.

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(1)
data = np.random.normal(loc=0, scale=1, size=1000)

# More bins + density normalization
plt.hist(data, bins=40, density=True,
         edgecolor="black", color="mediumseagreen")

plt.title("Standard Normal Distribution (Density)")
plt.xlabel("Value")
plt.ylabel("Probability Density")

plt.show()

What you'll see: a finer, smoother bell curve with 40 narrow green bars peaking near 0. Because density=True is on, the y-axis now reads as probability density rather than counts, so the values are small decimals.

3 Comparing Two Distributions

To compare two groups, draw two histograms on the same axes and set alpha below 1 so the overlap is visible through the transparency. A legend tells the two groups apart.

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(2)
class_a = np.random.normal(65, 8, 400)
class_b = np.random.normal(75, 8, 400)

# Same bin edges for a fair comparison
bins = np.linspace(40, 100, 25)

plt.hist(class_a, bins=bins, alpha=0.6, label="Class A", edgecolor="black")
plt.hist(class_b, bins=bins, alpha=0.6, label="Class B", edgecolor="black")

plt.title("Class A vs Class B Scores")
plt.xlabel("Score")
plt.ylabel("Count")
plt.legend()

plt.show()

What you'll see: two semi-transparent bell shapes. Class A peaks around 65 and Class B around 75, and where they overlap in the middle the colors blend, making it obvious that Class B generally scored higher.

🎯 Your Turn: Fill in the Blanks

Replace each ___ to bucket the data into 15 outlined bins.

import matplotlib.pyplot as plt
import numpy as np

heights = np.random.normal(170, 7, 300)

# Draw a histogram with 15 bins and black edges
plt.___(heights, bins=___, edgecolor="black")

# Label the x-axis
plt.xlabel("Height (cm)")

# Display the chart
plt.___()

📋 Common Errors & Quick Reference

❌ Only one giant bar appears

You passed already-counted data. A histogram needs the raw values; it counts them for you.

❌ Bars all blur together

Add edgecolor="black" so each bin gets an outline, and lower alpha when overlaying.

🏆 Mini Challenge: Commute Times

Plot the distribution of 600 commute times across 25 bins with a title and labels.

import matplotlib.pyplot as plt
import numpy as np

np.random.seed(7)
commute = np.random.normal(30, 8, 600)

plt.hist(commute, bins=25, edgecolor="black", color="indianred")

plt.title("Daily Commute Times")
plt.xlabel("Minutes")
plt.ylabel("Number of People")

plt.show()

import matplotlib.pyplot as plt

ages = [12, 15, 15, 18, 21, 22, 22, 22, 25, 31, 34, 39]

fig, ax = plt.subplots()
counts, edges, patches = ax.hist(ages, bins=4, color="seagreen", edgecolor="white")

print("Bins:", len(counts))
print("Counts per bin:", [int(c) for c in counts])
print("Every value counted:", int(sum(counts)) == len(ages))
print("First bin starts at:", edges[0])
print("Last bin ends at:", edges[-1])
print("Bin width:", round(edges[1] - edges[0], 2))

# ✅ Expected output:
# Bins: 4
# Counts per bin: [4, 5, 1, 2]
# Every value counted: True
# First bin starts at: 12.0
# Last bin ends at: 39.0
# Bin width: 6.75

❓ Frequently Asked Questions

Lesson complete — you can read a distribution's shape!

You built histograms, tuned the bin count, normalized with density, and overlaid two groups to compare them side by side.

Practice quiz

Continue this course

• Previous: Bar Charts
• Next: Scatter Plots — Plot relationships between two variables point by point
• Quick reference: Matplotlib cheat sheet › Plot Types

Frequently asked questions

What is the difference between a bar chart and a histogram?

A bar chart compares separate categories and leaves gaps between bars. A histogram shows the distribution of a single numeric variable by grouping values into ranges called bins, so the bars usually touch each other.

How do I choose the number of bins?

Pass bins= to plt.hist(). Too few bins hide detail; too many make the chart noisy. A common starting point is 20–30 bins, then adjust. You can also pass a list of explicit bin edges for full control.

What does density=True do?

By default a histogram shows raw counts on the y-axis. With density=True the bars are normalized so the total area equals 1, turning the histogram into a probability density — handy for comparing distributions of different sample sizes.

Why add edgecolor='black'?

Without an edge color the bars blend together and you cannot tell where one bin ends and the next begins. Setting edgecolor='black' outlines each bar so the buckets stay visually distinct.