Why histograms show distributions in Matplotlib - See It in Action

Why Histograms Show Distributions

📖 Scenario: Imagine you work at a bakery and want to understand how many cookies customers buy each day. You have a list of cookie counts sold daily for a week. You want to see how these sales numbers are spread out.

🎯 Goal: You will create a histogram using matplotlib to visualize the distribution of cookie sales over a week. This will help you see which sales counts happen most often.

📋 What You'll Learn

Create a list called cookie_sales with exact daily sales numbers

Create a variable called bins to set the number of histogram bins

Use matplotlib.pyplot.hist() with cookie_sales and bins

Display the histogram with plt.show()

💡 Why This Matters

🌍 Real World

Histograms help businesses understand patterns in their data, like how often certain sales amounts happen, which can guide decisions.

💼 Career

Data scientists and analysts use histograms to quickly see data distributions and spot trends or outliers.

1

Create the cookie sales data

Create a list called cookie_sales with these exact values: [5, 7, 8, 5, 6, 9, 7] representing cookies sold each day.

Matplotlib

# Create the cookie_sales list with daily sales numbers
# Your code here

Hint

Use square brackets to create a list with the exact numbers given.

2

Set the number of bins for the histogram

Create a variable called bins and set it to 4 to divide the sales data into 4 groups.

Matplotlib

cookie_sales = [5, 7, 8, 5, 6, 9, 7]
# Create the bins variable and set it to 4
# Your code here

Hint

Just assign the number 4 to the variable bins.

3

Create the histogram using matplotlib

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

Matplotlib

cookie_sales = [5, 7, 8, 5, 6, 9, 7]
bins = 4
# Import matplotlib.pyplot as plt
# Create the histogram with plt.hist(cookie_sales, bins=bins)
# Your code here

Hint

Use import matplotlib.pyplot as plt and then plt.hist() with the right arguments.

4

Display the histogram

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

Matplotlib

import matplotlib.pyplot as plt

cookie_sales = [5, 7, 8, 5, 6, 9, 7]
bins = 4

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

Hint

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

Practice

1. What does a histogram primarily show about a dataset?

easy

A. The average value of the dataset

B. The exact values of each data point

C. The order in which data was collected

D. How data values are spread or grouped

Solution

1. Step 1: Understand histogram purpose

   A histogram groups data into ranges (bins) to show how values spread.

2. Step 2: Compare options to definition

   Only How data values are spread or grouped describes this grouping and spread, others describe different concepts.

3. Final Answer:

   How data values are spread or grouped -> Option D

4. Quick Check:

   Histogram = Data spread [OK]

Hint: Histograms show data grouping, not exact values [OK]

Common Mistakes:

• Thinking histograms show exact data points
• Confusing histogram with mean or order
• Assuming histograms show data collection sequence

2. Which of the following is the correct way to create a histogram using matplotlib for a list named data?

easy

A. plt.plot(data)

B. plt.hist(data)

C. plt.scatter(data)

D. plt.bar(data)

Solution

1. Step 1: Recall matplotlib histogram function

   The function to create histograms is plt.hist().

2. Step 2: Match options with correct function

   Only plt.hist(data) uses plt.hist(data), others create different 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 scatter or bar plots with histograms
• Forgetting to import matplotlib.pyplot as plt

3. Given the code:

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()

What does the histogram show about the data distribution?

medium

A. There are more data points at 1 than at 4

B. Data points are evenly spread across all values

C. Most data points are around 4

D. Data points cluster mostly at 2 and 3

Solution

1. Step 1: Count data frequency per value

   Data has 1 one, 2 twos, 3 threes, and 4 fours; highest count is 4.

2. Step 2: Understand histogram bins and heights

   With 4 bins, each value likely forms a bin; tallest bar corresponds to value 4.

3. Final Answer:

   Most data points are around 4 -> Option C

4. Quick Check:

   Highest frequency = 4 [OK]

Hint: Look for tallest bar to find where data clusters [OK]

Common Mistakes:

• Assuming equal spread without checking counts
• Confusing bin count with data values
• Ignoring frequency differences

4. What is wrong with this code snippet for plotting a histogram?

import matplotlib.pyplot as plt
data = [1, 2, 2, 3, 3, 3]
plt.hist(data, bins='five')
plt.show()

medium

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

B. plt.hist() cannot plot lists, only arrays

C. plt.show() is missing

D. Data list contains duplicate values which histograms can't handle

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 of code

   Data as list is valid, plt.show() is present, duplicates are allowed.

3. Final Answer:

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

4. Quick Check:

   Bins type error = The bins parameter should be an integer, not a string [OK]

Hint: Bins must be number, not text [OK]

Common Mistakes:

• Passing string instead of integer for bins
• Thinking duplicates cause errors
• Missing plt.show() call

5. You have a dataset with ages of 1000 people ranging from 0 to 100. You want to see detailed age groups but also avoid too much noise. Which bin setting for plt.hist() best balances detail and clarity?

hard

A. bins=20 (moderate groups)

B. bins=1000 (one bin per person)

C. bins='auto' (let matplotlib decide)

D. bins=5 (very few large groups)

Solution

1. Step 1: Understand bin size impact

   Few bins (5) lose detail; too many bins (1000) cause noise.

2. Step 2: Choose balanced bin count

   20 bins give moderate detail without clutter; 'auto' can vary unpredictably.

3. Final Answer:

   bins=20 (moderate groups) -> Option A

4. Quick Check:

   Balanced bins = 20 [OK]

Hint: Choose moderate bins for clear, detailed histograms [OK]

Common Mistakes:

• Using too few bins losing detail
• Using too many bins causing noisy plots
• Relying on 'auto' without checking result