Multiple histograms overlay in Matplotlib - Mini Project: Build & Apply

Multiple histograms overlay

📖 Scenario: You are analyzing the sales data of two different stores. You want to compare the distribution of daily sales amounts for both stores visually.

🎯 Goal: Create two lists of daily sales amounts for Store A and Store B. Then, overlay their histograms on the same plot to compare their sales distributions.

📋 What You'll Learn

Create two lists named store_a_sales and store_b_sales with given sales data.

Create a variable bins to set the number of bins for the histograms.

Use matplotlib.pyplot.hist to plot both histograms on the same figure with transparency.

Add a legend to distinguish Store A and Store B histograms.

Display the plot using plt.show().

💡 Why This Matters

🌍 Real World

Overlaying histograms helps compare distributions of data from different groups, like sales from different stores or test scores from different classes.

💼 Career

Data scientists often compare multiple datasets visually to find patterns or differences, making histogram overlays a useful skill.

1

Create sales data lists

Create two lists called store_a_sales and store_b_sales with these exact values: store_a_sales = [20, 35, 30, 35, 27, 25, 32] and store_b_sales = [25, 32, 34, 20, 25, 40, 38].

Matplotlib

# Create the sales data lists for Store A and Store B
# Your code here

Hint

Use square brackets to create lists with the exact numbers given.

2

Set number of bins for histograms

Create a variable called bins and set it to 5 to define the number of bins for the histograms.

Matplotlib

store_a_sales = [20, 35, 30, 35, 27, 25, 32]
store_b_sales = [25, 32, 34, 20, 25, 40, 38]
# Create a variable bins and set it to 5
# Your code here

Hint

Just assign the number 5 to a variable named bins.

3

Plot the histograms overlay

Import matplotlib.pyplot as plt. Use plt.hist to plot store_a_sales and store_b_sales on the same figure with bins bins, set alpha=0.5 for transparency, and add labels 'Store A' and 'Store B' respectively. Then add a legend with plt.legend().

Matplotlib

store_a_sales = [20, 35, 30, 35, 27, 25, 32]
store_b_sales = [25, 32, 34, 20, 25, 40, 38]
bins = 5
# Import matplotlib.pyplot as plt
# Plot histograms for store_a_sales and store_b_sales with alpha=0.5 and labels
# Add legend
# Your code here

Hint

Use plt.hist() twice with the correct parameters and then call plt.legend().

4

Show the plot

Add a line to display the plot using plt.show().

Matplotlib

import matplotlib.pyplot as plt

store_a_sales = [20, 35, 30, 35, 27, 25, 32]
store_b_sales = [25, 32, 34, 20, 25, 40, 38]
bins = 5

plt.hist(store_a_sales, bins=bins, alpha=0.5, label='Store A')
plt.hist(store_b_sales, bins=bins, alpha=0.5, label='Store B')
plt.legend()
# Display the plot
# Your code here

Hint

Use plt.show() to display the histogram plot window.

Practice

1. What is the main purpose of overlaying multiple histograms in a single plot using matplotlib?

easy

A. To sort data values in ascending order

B. To create a pie chart from histogram data

C. To calculate the mean of multiple datasets

D. To visually compare the distribution of different data groups in one plot

Solution

1. Step 1: Understand histogram purpose

   A histogram shows how data values are distributed across ranges.

2. Step 2: Purpose of overlaying

   Overlaying multiple histograms lets us see and compare distributions of different groups in one plot.

3. Final Answer:

   To visually compare the distribution of different data groups in one plot -> Option D

4. Quick Check:

   Overlay = Compare distributions [OK]

Hint: Overlay histograms to compare groups visually [OK]

Common Mistakes:

• Thinking overlay sorts data
• Confusing histogram with pie chart
• Assuming overlay calculates statistics

2. Which of the following matplotlib code snippets correctly overlays two histograms with transparency?

easy

A. plt.hist(data1); plt.hist(data2, alpha=2)

B. plt.hist(data1, alpha=0.5); plt.hist(data2, alpha=0.5)

C. plt.hist(data1, transparency=0.5); plt.hist(data2, transparency=0.5)

D. plt.hist(data1, alpha=1.5); plt.hist(data2, alpha=1.5)

Solution

1. Step 1: Check correct parameter for transparency

   In matplotlib, alpha controls transparency and must be between 0 and 1.

2. Step 2: Validate each option

   plt.hist(data1, alpha=0.5); plt.hist(data2, alpha=0.5) uses alpha=0.5 correctly for both histograms. Options B and D use invalid alpha values (>1). plt.hist(data1, transparency=0.5); plt.hist(data2, transparency=0.5) uses a wrong parameter name transparency.

3. Final Answer:

   plt.hist(data1, alpha=0.5); plt.hist(data2, alpha=0.5) -> Option B

4. Quick Check:

   Use alpha between 0 and 1 for transparency [OK]

Hint: Use alpha between 0 and 1 for transparency [OK]

Common Mistakes:

• Using alpha values greater than 1
• Using wrong parameter names like transparency
• Omitting alpha causing no overlay visibility

3. What will be the output of this code?

import matplotlib.pyplot as plt
import numpy as np
np.random.seed(0)
data1 = np.random.normal(0, 1, 100)
data2 = np.random.normal(2, 1, 100)
plt.hist(data1, bins=20, alpha=0.6, label='Group 1')
plt.hist(data2, bins=20, alpha=0.6, label='Group 2')
plt.legend()
plt.show()

medium

A. Two overlapping histograms with semi-transparent bars and a legend showing 'Group 1' and 'Group 2'

B. A single histogram combining data1 and data2 without transparency

C. An error because bins parameter is missing

D. Two histograms plotted side by side without overlap

Solution

1. Step 1: Analyze histogram parameters

   Both histograms use 20 bins and alpha=0.6 for transparency, so bars will overlap but remain visible.

2. Step 2: Check legend and plot behavior

   Labels are set and legend is called, so legend will show 'Group 1' and 'Group 2'. Histograms overlay, not side by side.

3. Final Answer:

   Two overlapping histograms with semi-transparent bars and a legend showing 'Group 1' and 'Group 2' -> Option A

4. Quick Check:

   Alpha + legend = overlapping histograms with labels [OK]

Hint: Alpha makes bars transparent; legend shows labels [OK]

Common Mistakes:

• Thinking histograms combine data automatically
• Expecting side-by-side bars without specifying
• Missing legend despite label parameter

4. Identify the error in this code that tries to overlay two histograms:

plt.hist(data1, bins=15, alpha=0.4, label='A')
plt.hist(data2, bins=15, alpha=0.4)
plt.legend()
plt.show()

medium

A. Alpha value 0.4 is invalid and causes runtime error

B. Bins parameter must be different for each histogram

C. Missing label for the second histogram causes legend to show only one label

D. plt.legend() must be called before plotting histograms

Solution

1. Step 1: Check labels for legend

   The first histogram has label 'A', but the second histogram has no label, so legend will show only one entry.

2. Step 2: Validate other parameters

   Alpha=0.4 is valid. Bins can be same for overlay. Legend can be called after plotting.

3. Final Answer:

   Missing label for the second histogram causes legend to show only one label -> Option C

4. Quick Check:

   All histograms need labels for legend entries [OK]

Hint: Add labels to all histograms for complete legend [OK]

Common Mistakes:

• Assuming alpha=0.4 is invalid
• Thinking bins must differ for overlay
• Calling legend before plotting causes error

5. You have three datasets with very different sizes: data1 (1000 points), data2 (100 points), and data3 (10 points). You want to overlay their histograms fairly so that the smaller datasets are visible and comparable. Which approach is best?

hard

A. Use density=True in plt.hist for all datasets and set alpha=0.5 with labels

B. Plot histograms without transparency and use default counts

C. Use different bin sizes for each dataset without normalization

D. Plot only the largest dataset to avoid clutter

Solution

1. Step 1: Understand size difference impact

   Datasets with different sizes produce histograms with very different counts, making small datasets hard to see.

2. Step 2: Use density normalization and transparency

   Setting density=True normalizes histograms to show probability density, making them comparable regardless of size. Using alpha=0.5 allows overlapping visibility. Adding labels helps identify each.

3. Final Answer:

   Use density=True in plt.hist for all datasets and set alpha=0.5 with labels -> Option A

4. Quick Check:

   Normalize with density=True + alpha for visibility [OK]

Hint: Normalize histograms with density=True for fair comparison [OK]

Common Mistakes:

• Not normalizing causes small datasets to be invisible
• Using opaque bars hides overlaps
• Changing bins without normalization confuses comparison