Histograms in Data Analysis Python - Mini Project: Build & Apply

Histograms

📖 Scenario: You work as a data analyst for a small bakery. You have collected data on the number of cookies sold each day for a week. You want to understand how the sales are distributed to help plan baking quantities better.

🎯 Goal: Build a simple program that creates a histogram of cookie sales data to visualize the frequency of sales numbers.

📋 What You'll Learn

Create a list of daily cookie sales numbers

Set a variable for the number of bins in the histogram

Use matplotlib to create a histogram of the sales data

Display the histogram plot

💡 Why This Matters

🌍 Real World

Histograms help businesses understand how often certain values occur, like daily sales, so they can make better decisions.

💼 Career

Data analysts and scientists use histograms to explore data distributions and communicate insights visually.

1

Create the sales data list

Create a list called cookie_sales with these exact daily sales numbers: 12, 15, 7, 10, 20, 13, 9.

Data Analysis Python

# Create the list cookie_sales with the given numbers
# Your code here

Hint

Use square brackets [] to create a list and separate numbers with commas.

2

Set the number of bins

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

Data Analysis Python

cookie_sales = [12, 15, 7, 10, 20, 13, 9]
# Create the variable bins and set it to 5
# Your code here

Hint

Just assign the number 5 to the variable bins.

3

Create the histogram plot

Import matplotlib.pyplot as plt. Then use plt.hist() with cookie_sales and bins to create the histogram.

Data Analysis Python

cookie_sales = [12, 15, 7, 10, 20, 13, 9]
bins = 5
# Import matplotlib.pyplot as plt
# Create histogram with plt.hist(cookie_sales, bins=bins)
# Your code here

Hint

Use import matplotlib.pyplot as plt to import. Then call plt.hist() with the list and bins.

4

Display the histogram

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

Data Analysis Python

import matplotlib.pyplot as plt

cookie_sales = [12, 15, 7, 10, 20, 13, 9]
bins = 5

plt.hist(cookie_sales, bins=bins)
# Display the plot with plt.show()
# Your code here

Hint

Call plt.show() to open the histogram window.

Practice

1. What does a histogram primarily show when analyzing data?

easy

A. The average value of the dataset

B. The exact values of each data point

C. The relationship between two variables

D. The frequency distribution of data values grouped into bins

Solution

1. Step 1: Understand histogram purpose

   A histogram groups data into bins and counts how many data points fall into each bin.

2. Step 2: Identify what histogram shows

   This grouping shows the frequency distribution, not exact values or relationships.

3. Final Answer:

   The frequency distribution of data values grouped into bins -> Option D

4. Quick Check:

   Histogram = frequency distribution [OK]

Hint: Histograms show counts per bin, not individual values [OK]

Common Mistakes:

• Confusing histogram with scatter plot
• Thinking histogram shows exact data points
• Assuming histogram shows averages

2. Which of the following is the correct way to create a histogram using matplotlib in Python?

easy

A. plt.plot(data)

B. plt.hist(data)

C. plt.bar(data)

D. plt.scatter(data)

Solution

1. Step 1: Recall matplotlib histogram function

   Matplotlib uses plt.hist() to create histograms from data arrays.

2. Step 2: Differentiate from other plot types

   plt.plot() is for line plots, plt.bar() for bar charts, plt.scatter() for scatter plots.

3. Final Answer:

   plt.hist(data) -> Option B

4. Quick Check:

   Histogram function = plt.hist() [OK]

Hint: Use plt.hist() to plot histograms in matplotlib [OK]

Common Mistakes:

• Using plt.plot() instead of plt.hist()
• Confusing bar chart with histogram
• Trying plt.scatter() for histograms

3. What will be the output of this code snippet?

import matplotlib.pyplot as plt
import numpy as np

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

medium

A. A histogram with 4 bars showing counts: 1, 2, 3, 4

B. A histogram with 4 bars showing counts: 1, 2, 3, 4 but bars are unevenly spaced

C. A histogram with 4 bars showing counts: 1, 2, 3, 4 but bars represent ranges, not exact values

D. An error because bins must be equal to the number of unique values

Solution

1. Step 1: Understand bins in histogram

   Bins divide the data range into intervals. Here, bins=4 divides data from 1 to 4 into 4 intervals.

2. Step 2: Analyze counts per bin

   Counts per bin correspond to how many data points fall into each interval, not just counts of exact values.

3. Final Answer:

   A histogram with 4 bars showing counts: 1, 2, 3, 4 but bars represent ranges, not exact values -> Option C

4. Quick Check:

   Bins = ranges, counts per range [OK]

Hint: Bins group ranges, not just unique values [OK]

Common Mistakes:

• Assuming bins equal unique values
• Thinking bars show exact counts per value
• Expecting error due to bins count

4. Identify the error in this code that tries to plot a histogram:

import matplotlib.pyplot as plt

values = [5, 7, 8, 5, 6]
plt.hist(values, bins='five')
plt.show()

medium

A. bins parameter should be an integer, not a string

B. plt.hist() does not accept lists as data

C. plt.show() is missing

D. values list must be converted to numpy array first

Solution

1. Step 1: Check bins parameter type

   bins must be an integer or sequence, not a string like 'five'.

2. Step 2: Verify other parts

   plt.hist accepts lists, plt.show() is present, numpy array conversion is optional.

3. Final Answer:

   bins parameter should be an integer, not a string -> Option A

4. Quick Check:

   bins must be int or sequence [OK]

Hint: bins must be int or list, not string [OK]

Common Mistakes:

• Passing bins as string causes error
• Thinking plt.hist needs numpy array
• Forgetting plt.show()

5. You have a dataset with ages: [22, 25, 29, 35, 40, 42, 45, 50, 55, 60]. You want to create a histogram with bins representing decades (20-29, 30-39, 40-49, 50-59, 60-69). Which bins parameter should you use in plt.hist() to get this grouping?

hard

A. [20, 30, 40, 50, 60, 70]

B. [20, 29, 39, 49, 59, 69]

C. [20, 25, 30, 35, 40, 45, 50, 55, 60, 65]

D. [21, 31, 41, 51, 61, 71]

Solution

1. Step 1: Define bin edges for decades

   Bins edges must cover ranges: 20-29, 30-39, etc. Using edges [20, 30, 40, 50, 60, 70] creates intervals [20-30), [30-40), etc.

2. Step 2: Confirm bins cover all ages

   These edges include all ages from 22 to 60 correctly in decade groups.

3. Final Answer:

   [20, 30, 40, 50, 60, 70] -> Option A

4. Quick Check:

   Bins edges = decade boundaries [OK]

Hint: Bins edges define intervals; use decade boundaries [OK]

Common Mistakes:

• Using bin edges that exclude upper bounds
• Using single values instead of edges
• Misaligning bins with decade ranges