Why scatter plots show relationships in Matplotlib - See It in Action

Why scatter plots show relationships

📖 Scenario: Imagine you are a teacher who wants to understand if students' study hours affect their test scores. You have collected data on how many hours each student studied and their scores. You want to see if there is a pattern or relationship between these two things.

🎯 Goal: You will create a scatter plot to visually show the relationship between study hours and test scores. This will help you see if more study hours generally mean higher scores.

📋 What You'll Learn

Create two lists: study_hours and test_scores with exact values

Create a variable title with the exact string for the plot title

Use matplotlib.pyplot.scatter to plot the points with correct variables

Use plt.title to set the plot title

Use plt.xlabel and plt.ylabel to label axes

Use plt.show() to display the plot

💡 Why This Matters

🌍 Real World

Scatter plots help people see if two things are connected, like hours studied and test results, or temperature and ice cream sales.

💼 Career

Data scientists and analysts use scatter plots to quickly understand relationships in data before doing deeper analysis.

1

Create the data lists

Create a list called study_hours with these exact values: [1, 2, 3, 4, 5, 6, 7, 8]. Then create a list called test_scores with these exact values: [50, 55, 65, 70, 75, 80, 85, 90].

Matplotlib

# Create the lists study_hours and test_scores with the exact values
# Your code here

Hint

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

2

Create the plot title variable

Create a variable called title and set it to the string 'Study Hours vs Test Scores'.

Matplotlib

study_hours = [1, 2, 3, 4, 5, 6, 7, 8]
test_scores = [50, 55, 65, 70, 75, 80, 85, 90]
# Create the title variable with the exact string
# Your code here

Hint

Use single or double quotes to create the string exactly as shown.

3

Create the scatter plot

Import matplotlib.pyplot as plt. Then use plt.scatter with study_hours and test_scores to create the scatter plot. Use plt.title(title) to set the plot title. Use plt.xlabel('Study Hours') and plt.ylabel('Test Scores') to label the axes.

Matplotlib

study_hours = [1, 2, 3, 4, 5, 6, 7, 8]
test_scores = [50, 55, 65, 70, 75, 80, 85, 90]
title = 'Study Hours vs Test Scores'
# Import matplotlib.pyplot as plt
# Create scatter plot with study_hours and test_scores
# Set title and axis labels
# Your code here

Hint

Remember to import matplotlib.pyplot as plt before plotting.

4

Display the scatter plot

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

Matplotlib

import matplotlib.pyplot as plt

study_hours = [1, 2, 3, 4, 5, 6, 7, 8]
test_scores = [50, 55, 65, 70, 75, 80, 85, 90]
title = 'Study Hours vs Test Scores'

plt.scatter(study_hours, test_scores)
plt.title(title)
plt.xlabel('Study Hours')
plt.ylabel('Test Scores')
# Display the plot
# Your code here

Hint

Use plt.show() to open the plot window and see the scatter plot.

Practice

1. What is the main reason we use scatter plots in data science?

easy

A. To sort data in ascending order

B. To see how two variables relate to each other

C. To calculate the average of a dataset

D. To create a list of unique values

Solution

1. Step 1: Understand the purpose of scatter plots

   Scatter plots show points representing two variables on a graph to visualize their relationship.

2. Step 2: Compare options with scatter plot use

   Calculating averages, sorting, or listing unique values are not visual tasks done by scatter plots.

3. Final Answer:

   To see how two variables relate to each other -> Option B

4. Quick Check:

   Scatter plots show relationships = C [OK]

Hint: Scatter plots show points for two variables to find relationships [OK]

Common Mistakes:

• Confusing scatter plots with bar charts
• Thinking scatter plots calculate statistics
• Believing scatter plots sort data

2. Which of the following is the correct way to create a scatter plot using matplotlib?

easy

A. plt.scatterplot(x, y)

B. plt.plot(x, y, 'scatter')

C. plt.bar(x, y)

D. plt.scatter(x, y)

Solution

1. Step 1: Recall matplotlib scatter plot syntax

   The correct function to create scatter plots in matplotlib is plt.scatter(x, y).

2. Step 2: Check other options for correctness

   plt.plot with 'scatter' is invalid, plt.bar creates bar charts, plt.scatterplot does not exist in matplotlib.

3. Final Answer:

   plt.scatter(x, y) -> Option D

4. Quick Check:

   Correct scatter plot function = D [OK]

Hint: Use plt.scatter(x, y) to plot scatter plots in matplotlib [OK]

Common Mistakes:

• Using plt.plot with wrong style
• Confusing bar charts with scatter plots
• Trying non-existent functions like plt.scatterplot

3. What will the following code show?

import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
plt.scatter(x, y)
plt.show()

medium

A. A scatter plot showing points forming a straight line going up

B. A scatter plot with points randomly scattered with no pattern

C. A bar chart with bars increasing in height

D. An error because plt.scatter needs labels

Solution

1. Step 1: Analyze the data points

   x and y values increase proportionally: y is 2 times x, so points lie on a straight line.

2. Step 2: Understand scatter plot output

   Scatter plot will show points aligned diagonally upwards, not random or bars, and no error occurs without labels.

3. Final Answer:

   A scatter plot showing points forming a straight line going up -> Option A

4. Quick Check:

   Proportional points form line = B [OK]

Hint: Check if y changes proportionally with x for line pattern [OK]

Common Mistakes:

• Thinking scatter plots create bar charts
• Expecting error without labels
• Assuming points are random without checking data

4. The code below is intended to plot a scatter plot but shows an error. What is the problem?

import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [4, 5]
plt.scatter(x, y)
plt.show()

medium

A. plt.scatter cannot plot integers

B. plt.scatter requires labels before plotting

C. x and y lists have different lengths

D. plt.show() is missing

Solution

1. Step 1: Check the lengths of x and y

   x has 3 values, y has 2 values; scatter plot needs equal length lists for x and y.

2. Step 2: Verify other options

   Labels are optional, integers are valid, plt.show() is present, so these are not errors.

3. Final Answer:

   x and y lists have different lengths -> Option C

4. Quick Check:

   Equal length x and y needed = A [OK]

Hint: Ensure x and y lists have same length before plotting [OK]

Common Mistakes:

• Thinking labels are mandatory
• Believing plt.scatter can't plot integers
• Forgetting to check list lengths

5. You want to show a negative relationship between two variables using a scatter plot. Which data and code will best show this?

hard

A. x = [1, 2, 3, 4]; y = [10, 8, 6, 4]; plt.scatter(x, y)

B. x = [1, 2, 3, 4]; y = [2, 4, 6, 8]; plt.scatter(x, y)

C. x = [1, 2, 3, 4]; y = [4, 4, 4, 4]; plt.scatter(x, y)

D. x = [1, 2, 3, 4]; y = [1, 3, 2, 4]; plt.scatter(x, y)

Solution

1. Step 1: Identify negative relationship in data

   In x = [1, 2, 3, 4]; y = [10, 8, 6, 4]; plt.scatter(x, y), as x increases, y decreases, showing a negative relationship.

2. Step 2: Check other options for relationship type

   x = [1, 2, 3, 4]; y = [2, 4, 6, 8]; plt.scatter(x, y) shows positive relationship, C shows no change, D shows no clear pattern.

3. Final Answer:

   x = [1, 2, 3, 4]; y = [10, 8, 6, 4]; plt.scatter(x, y) -> Option A

4. Quick Check:

   y decreases as x increases = A [OK]

Hint: Negative relationship means y goes down as x goes up [OK]

Common Mistakes:

• Confusing positive and negative trends
• Choosing data with no clear pattern
• Ignoring constant y values