Scatter plots in Data Analysis Python - Mini Project: Build & Apply

Scatter plots

📖 Scenario: You are a data analyst working with a small dataset of students' study hours and their exam scores. You want to visualize the relationship between how much they studied and how well they scored.

🎯 Goal: Create a scatter plot to show the relationship between study_hours and exam_scores.

📋 What You'll Learn

Create two lists called study_hours and exam_scores with exact values

Create a variable called point_color to set the color of the scatter points

Use matplotlib.pyplot.scatter() to create the scatter plot with the given data and color

Use plt.xlabel() and plt.ylabel() to label the axes

Use plt.title() to add a title to the plot

Use plt.show() to display the plot

💡 Why This Matters

🌍 Real World

Scatter plots are used in many fields like education, business, and science to see how two things relate to each other visually.

💼 Career

Data analysts and scientists use scatter plots to quickly understand patterns and relationships in data, which helps in making decisions.

1

Create the data lists

Create two lists called study_hours and exam_scores with these exact values: study_hours = [1, 2, 3, 4, 5] and exam_scores = [50, 55, 65, 70, 80].

Data Analysis Python

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

Hint

Use square brackets to create lists and separate numbers with commas.

2

Set the color for scatter points

Create a variable called point_color and set it to the string 'blue' to use as the color for the scatter plot points.

Data Analysis Python

study_hours = [1, 2, 3, 4, 5]
exam_scores = [50, 55, 65, 70, 80]
# Create the variable point_color and set it to 'blue'
# Your code here

Hint

Use quotes around the color name to make it a string.

3

Create the scatter plot

Import matplotlib.pyplot as plt. Then use plt.scatter() with study_hours, exam_scores, and color=point_color to create the scatter plot. Add labels using plt.xlabel() with text 'Study Hours' and plt.ylabel() with text 'Exam Scores'. Add a title using plt.title() with text 'Study Hours vs Exam Scores'.

Data Analysis Python

study_hours = [1, 2, 3, 4, 5]
exam_scores = [50, 55, 65, 70, 80]
point_color = 'blue'
# Import matplotlib.pyplot as plt
# Create scatter plot with plt.scatter()
# Add x and y labels and title
# Your code here

Hint

Remember to import matplotlib.pyplot as plt before using it.

4

Display the scatter plot

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

Data Analysis Python

import matplotlib.pyplot as plt

study_hours = [1, 2, 3, 4, 5]
exam_scores = [50, 55, 65, 70, 80]
point_color = 'blue'

plt.scatter(study_hours, exam_scores, color=point_color)
plt.xlabel('Study Hours')
plt.ylabel('Exam Scores')
plt.title('Study Hours vs Exam Scores')
# Display the plot
# Your code here

Hint

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

Practice

1. What does a scatter plot primarily show in data analysis?

easy

A. The relationship between two numerical variables

B. The total count of categories

C. The average value of a dataset

D. The sum of all data points

Solution

1. Step 1: Understand scatter plot purpose

   A scatter plot is used to display how two numerical variables relate to each other by plotting points on a graph.

2. Step 2: Compare options to definition

   Options B, C, and D describe other chart types or calculations, not relationships between two variables.

3. Final Answer:

   The relationship between two numerical variables -> Option A

4. Quick Check:

   Scatter plot = relationship between two numbers [OK]

Hint: Scatter plots always compare two numeric sets [OK]

Common Mistakes:

• Confusing scatter plots with bar charts
• Thinking scatter plots show totals or averages
• Ignoring that scatter plots need two numeric variables

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

easy

A. plt.bar(x, y)

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

C. plt.scatterplot(x, y)

D. plt.scatter(x, y)

Solution

1. Step 1: Recall matplotlib scatter syntax

   Matplotlib uses plt.scatter(x, y) to create scatter plots.

2. Step 2: Check other options for correctness

   plt.plot(x, y, kind='scatter') is incorrect syntax; plt.plot does not take kind='scatter'. plt.bar(x, y) creates bar charts. plt.scatterplot(x, y) is not a valid matplotlib function.

3. Final Answer:

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

4. Quick Check:

   Matplotlib scatter = plt.scatter(x, y) [OK]

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

Common Mistakes:

• Using plt.plot with kind='scatter' (wrong syntax)
• Confusing scatter with bar plot functions
• Trying to use plt.scatterplot which doesn't exist

3. What will the following code output?

import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [10, 20, 25, 30]
plt.scatter(x, y)
plt.title('Test Plot')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.show()

medium

A. An error because plt.scatter requires more arguments

B. A scatter plot with points at (1,10), (2,20), (3,25), (4,30) and labeled axes

C. A bar chart with bars at x positions 1 to 4

D. A line plot connecting points (1,10), (2,20), (3,25), (4,30)

Solution

1. Step 1: Analyze the code commands

   The code uses plt.scatter(x, y) which plots points at given x and y coordinates. Titles and labels are added correctly.

2. Step 2: Understand the plot output

   The plot will show points at (1,10), (2,20), (3,25), (4,30) with axis labels and a title. No lines or bars are drawn.

3. Final Answer:

   A scatter plot with points at (1,10), (2,20), (3,25), (4,30) and labeled axes -> Option B

4. Quick Check:

   plt.scatter plots points, not lines or bars [OK]

Hint: plt.scatter plots points, not lines or bars [OK]

Common Mistakes:

• Thinking plt.scatter draws lines between points
• Confusing scatter plot with bar chart
• Assuming plt.scatter needs extra arguments

4. Identify the error in this code snippet that tries to create a scatter plot:

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

medium

A. plt.scatter requires a third argument for color

B. plt.show() is missing parentheses

C. x and y lists have different lengths causing an error

D. No error, code runs fine

Solution

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

   x has 3 elements, y has 2 elements. Scatter plot requires equal length for x and y.

2. Step 2: Understand matplotlib error behavior

   Matplotlib will raise a ValueError because x and y lengths differ. Options A and C are incorrect because color is optional and plt.show() is correctly called.

3. Final Answer:

   x and y lists have different lengths causing an error -> Option C

4. Quick Check:

   Equal length x and y needed for scatter [OK]

Hint: x and y must be same length for scatter plots [OK]

Common Mistakes:

• Ignoring length mismatch between x and y
• Thinking color argument is mandatory
• Assuming plt.show() can be called without parentheses

5. You have a dataset with columns 'Age' and 'Income'. You want to create a scatter plot to find if older people tend to earn more. Which steps should you follow to create a clear scatter plot in Python using pandas and matplotlib?

hard

A. Import pandas and matplotlib, extract 'Age' and 'Income' columns as x and y, use plt.scatter(x, y), add title and axis labels, then plt.show()

B. Use plt.bar with 'Age' as x and 'Income' as height, then add labels

C. Plot a line graph with 'Age' on x-axis and 'Income' on y-axis without labels

D. Create a pie chart showing distribution of 'Income' grouped by 'Age' categories

Solution

1. Step 1: Extract relevant columns for scatter plot

   Use pandas to get 'Age' and 'Income' columns as x and y arrays for plotting.

2. Step 2: Plot scatter and add labels

   Use plt.scatter(x, y) to plot points, add a title and axis labels for clarity, then display with plt.show().

3. Final Answer:

   Import pandas and matplotlib, extract 'Age' and 'Income' columns as x and y, use plt.scatter(x, y), add title and axis labels, then plt.show() -> Option A

4. Quick Check:

   Scatter plot with labels = Import pandas and matplotlib, extract 'Age' and 'Income' columns as x and y, use plt.scatter(x, y), add title and axis labels, then plt.show() [OK]

Hint: Extract columns, use plt.scatter, add labels, then show plot [OK]

Common Mistakes:

• Using bar or pie charts instead of scatter for relationships
• Skipping axis labels and titles
• Plotting line graph which implies continuous trend