Scatter plots in Data Analysis Python

Introduction

Scatter plots help us see how two things relate to each other by showing points on a graph.

To check if there is a pattern between hours studied and exam scores.

To see if taller people tend to weigh more.

To explore the relationship between temperature and ice cream sales.

To find out if advertising budget affects product sales.

To spot groups or clusters in data points.

Syntax

Data Analysis Python

plt.scatter(x, y, color='blue', marker='o')
plt.xlabel('X-axis label')
plt.ylabel('Y-axis label')
plt.title('Title of the plot')
plt.show()

x and y are lists or arrays of numbers.

You can change the color and shape of points with color and marker.

Examples

Basic scatter plot with default blue circles.

Data Analysis Python

plt.scatter([1, 2, 3], [4, 5, 6])
plt.show()

Scatter plot with red 'x' markers.

Data Analysis Python

plt.scatter([10, 20, 30], [15, 25, 35], color='red', marker='x')
plt.show()

Scatter plot using columns from a data table with labels and title.

Data Analysis Python

plt.scatter(data['age'], data['income'])
plt.xlabel('Age')
plt.ylabel('Income')
plt.title('Age vs Income')
plt.show()

Sample Program

This program shows how exam scores increase as hours studied increase using a scatter plot.

Data Analysis Python

import matplotlib.pyplot as plt

# Sample data: hours studied and exam scores
hours = [1, 2, 3, 4, 5, 6, 7, 8]
scores = [50, 55, 65, 70, 75, 80, 85, 90]

plt.scatter(hours, scores, color='green', marker='o')
plt.xlabel('Hours Studied')
plt.ylabel('Exam Score')
plt.title('Scatter Plot of Hours Studied vs Exam Score')
plt.grid(True)
plt.show()

Important Notes

Scatter plots do not connect points; they just show their position.

Adding labels and a title helps others understand your plot.

Use grid lines to make reading values easier.

Summary

Scatter plots show relationships between two sets of numbers.

They help find patterns, trends, or groups in data.

Labels and colors make scatter plots clearer and more informative.

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