Basic histogram with plt.hist in Matplotlib - Mini Project: Build & Apply

Basic histogram with plt.hist

📖 Scenario: You have collected the ages of a group of people at a community event. You want to see how these ages are spread out to understand the age distribution.

🎯 Goal: Build a simple histogram using plt.hist to visualize the distribution of ages.

📋 What You'll Learn

Create a list called ages with the exact values: 22, 25, 29, 22, 24, 30, 27, 25, 29, 31

Create a variable called bins and set it to 5

Use plt.hist with ages and bins to create the histogram

Use plt.show() to display the histogram

💡 Why This Matters

🌍 Real World

Histograms help us understand how data like ages, heights, or test scores are spread out in a group.

💼 Career

Data scientists use histograms to quickly see patterns and distributions in data, which helps in making decisions or building models.

1

Create the data list

Create a list called ages with these exact values: 22, 25, 29, 22, 24, 30, 27, 25, 29, 31

Matplotlib

# Create the list ages with the given values
# 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

Matplotlib

ages = [22, 25, 29, 22, 24, 30, 27, 25, 29, 31]
# 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

Import matplotlib.pyplot as plt and use plt.hist with ages and bins to create the histogram

Matplotlib

ages = [22, 25, 29, 22, 24, 30, 27, 25, 29, 31]
bins = 5
# Import matplotlib.pyplot as plt
# Use plt.hist with ages and bins
# Your code here

Hint

Use import matplotlib.pyplot as plt to import. Then call plt.hist(ages, bins=bins).

4

Display the histogram

Use plt.show() to display the histogram

Matplotlib

import matplotlib.pyplot as plt

ages = [22, 25, 29, 22, 24, 30, 27, 25, 29, 31]
bins = 5

plt.hist(ages, bins=bins)
# Use plt.show() to display the histogram
# Your code here

Hint

Call plt.show() after creating the histogram to see the plot.

Practice

1. What does the plt.hist() function do in matplotlib?

easy

A. It creates a scatter plot of two variables.

B. It plots a line graph connecting data points.

C. It creates a histogram to show data frequency distribution.

D. It generates a pie chart to show proportions.

Solution

1. Step 1: Understand the purpose of plt.hist()

   The function plt.hist() groups data into bins and shows how often values fall into each bin.

2. Step 2: Compare with other plot types

   Line graphs connect points, scatter plots show points, and pie charts show parts of a whole, which are different from histograms.

3. Final Answer:

   It creates a histogram to show data frequency distribution. -> Option C

4. Quick Check:

   Histogram = frequency bars [OK]

Hint: Histograms show frequency counts in bars [OK]

Common Mistakes:

• Confusing histogram with line plot
• Thinking plt.hist() creates scatter plots
• Mixing histogram with pie chart

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

easy

A. plt.histogram(data); plt.show()

B. plt.plot.hist(data); plt.show()

C. plt.bar(data); plt.show()

D. plt.hist(data); plt.show()

Solution

1. Step 1: Recall the correct function name

   The correct function to create a histogram is plt.hist(), not plt.plot.hist() or plt.histogram().

2. Step 2: Confirm usage with plt.show()

   After plotting, plt.show() displays the figure. Using plt.bar() creates bar charts, not histograms.

3. Final Answer:

   plt.hist(data); plt.show() -> Option D

4. Quick Check:

   Use plt.hist() for histograms [OK]

Hint: Use plt.hist() to plot histograms, then plt.show() [OK]

Common Mistakes:

• Using plt.plot.hist() which is invalid
• Calling plt.histogram() which doesn't exist
• Using plt.bar() instead of plt.hist()

3. What will be the output of the following code?

import matplotlib.pyplot as plt

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

medium

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

B. A histogram with 10 bars, one for each value

C. A line plot connecting the values 1 to 4

D. An error because bins must be greater than 4

Solution

1. Step 1: Understand bins parameter

   Setting bins=4 divides the data range into 4 intervals (bins).

2. Step 2: Count frequencies in each bin

   The values 1, 2, 3, and 4 fall into these bins with frequencies 1, 2, 3, and 4 respectively, so the histogram shows 4 bars with these heights.

3. Final Answer:

   A histogram with 4 bars showing frequencies: 1, 2, 3, and 4 -> Option A

4. Quick Check:

   Bins split data; bars show counts [OK]

Hint: Bins split data; bar height = count in bin [OK]

Common Mistakes:

• Thinking bins=4 means 10 bars
• Confusing histogram with line plot
• Assuming bins must be greater than data length

4. Identify the error in this code snippet 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. plt.show() is missing and needed to display the plot.

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

C. plt.hist() requires a color argument.

D. The values list contains invalid data types.

Solution

1. Step 1: Check bins parameter type

   The bins argument must be an integer or sequence of numbers, not a string like 'five'.

2. Step 2: Verify other parts

   The data list is valid, color is optional, and plt.show() is present, so no other errors.

3. Final Answer:

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

4. Quick Check:

   Bins must be int or list, not string [OK]

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

Common Mistakes:

• Passing bins as a string instead of integer
• Thinking color is required
• Forgetting plt.show() (not the case here)

5. You have a dataset of exam scores ranging from 0 to 100. You want to create a histogram with 5 equal-width bins and color the bars blue. Which code snippet correctly achieves this?

hard

A. plt.hist(scores, bins=5, color='blue'); plt.show()

B. plt.hist(scores, bins=[0,20,50,70,90,100], color='blue'); plt.show()

C. plt.hist(scores, bins=5, colors='blue'); plt.show()

D. plt.hist(scores, bins=5, color=blue); plt.show()

Solution

1. Step 1: Use bins=integer for equal-width

   bins=5 automatically divides the range (0-100) into 5 equal-width bins of 20 units each: [0-20, 20-40, 40-60, 60-80, 80-100].

2. Step 2: Color the bars blue

   The color='blue' parameter sets the bar color correctly.

3. Step 3: Eliminate distractors

   B specifies explicit bins [0,20,50,70,90,100] with unequal widths (20,30,20,20,10); C uses invalid 'colors'; D has unquoted 'blue' causing NameError.

4. Final Answer:

   plt.hist(scores, bins=5, color='blue'); plt.show() -> Option A

5. Quick Check:

   bins=5 (equal width) + color='blue' [OK]

Hint: bins=5 for equal-width over data range, color='blue' [OK]

Common Mistakes:

• Using explicit bins with unequal widths
• Using 'colors' instead of 'color'
• Using color=blue without quotes