Scatter plots with regression (regplot) in Data Analysis Python - Mini Project: Build & Apply

Scatter plots with regression (regplot)

📖 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 study hours and exam scores using a scatter plot. Additionally, you want to add a regression line to see the trend clearly.

🎯 Goal: Create a scatter plot with a regression line using the seaborn library to visualize the relationship between study hours and exam scores.

📋 What You'll Learn

Create a dictionary called data with keys 'Hours' and 'Scores' and the exact values specified.

Create a pandas DataFrame called df from the data dictionary.

Create a variable called plot_color and set it to the string 'blue'.

Use seaborn.regplot with df['Hours'] as x, df['Scores'] as y, and color=plot_color to create the scatter plot with regression line.

Use plt.show() to display the plot.

💡 Why This Matters

🌍 Real World

Scatter plots with regression lines help analysts understand relationships between two variables, such as study hours and exam scores, to make data-driven decisions.

💼 Career

Data analysts and scientists often use scatter plots with regression lines to visualize trends and correlations in data, which is essential for reporting and predictive modeling.

1

Create the data dictionary and DataFrame

Create a dictionary called data with keys 'Hours' and 'Scores'. Set 'Hours' to the list [1, 2, 3, 4, 5, 6, 7, 8] and 'Scores' to the list [35, 40, 50, 55, 65, 70, 75, 80]. Then create a pandas DataFrame called df from the data dictionary.

Data Analysis Python

import pandas as pd
# Create the data dictionary and DataFrame
# Your code here

Hint

Use data = {'Hours': [...], 'Scores': [...]} and then df = pd.DataFrame(data).

2

Set the plot color

Create a variable called plot_color and set it to the string 'blue'.

Data Analysis Python

import pandas as pd

data = {'Hours': [1, 2, 3, 4, 5, 6, 7, 8], 'Scores': [35, 40, 50, 55, 65, 70, 75, 80]}
df = pd.DataFrame(data)
# Create a variable called plot_color and set it to 'blue'
# Your code here

Hint

Just write plot_color = 'blue'.

3

Create the scatter plot with regression line

Import seaborn as sns and matplotlib.pyplot as plt. Use sns.regplot with df['Hours'] as x, df['Scores'] as y, and color=plot_color to create the scatter plot with regression line.

Data Analysis Python

import pandas as pd

data = {'Hours': [1, 2, 3, 4, 5, 6, 7, 8], 'Scores': [35, 40, 50, 55, 65, 70, 75, 80]}
df = pd.DataFrame(data)

plot_color = 'blue'
# Create the scatter plot with regression line using sns.regplot
# Your code here

Hint

Use sns.regplot(x=df['Hours'], y=df['Scores'], color=plot_color).

4

Display the plot

Use plt.show() to display the scatter plot with regression line.

Data Analysis Python

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

data = {'Hours': [1, 2, 3, 4, 5, 6, 7, 8], 'Scores': [35, 40, 50, 55, 65, 70, 75, 80]}
df = pd.DataFrame(data)

plot_color = 'blue'

sns.regplot(x=df['Hours'], y=df['Scores'], color=plot_color)
# Display the plot
# Your code here

Hint

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

Practice

1. What does the sns.regplot() function primarily do in data analysis?

easy

A. It creates a scatter plot with a regression line showing the relationship between two variables.

B. It calculates the correlation coefficient between two variables.

C. It generates a histogram of a single variable.

D. It fits a clustering model to the data.

Solution

1. Step 1: Understand the purpose of sns.regplot()

   The function sns.regplot() is designed to plot data points as a scatter plot and overlay a regression line to show the trend.

2. Step 2: Compare with other options

   Options A, C, and D describe different functions: correlation calculation, histogram plotting, and clustering, which are not the main purpose of sns.regplot().

3. Final Answer:

   It creates a scatter plot with a regression line showing the relationship between two variables. -> Option A

4. Quick Check:

   sns.regplot() = scatter + regression line [OK]

Hint: Remember regplot = scatter plot + regression line [OK]

Common Mistakes:

• Confusing regplot with correlation calculation
• Thinking regplot creates histograms
• Assuming regplot fits clustering models

2. Which of the following is the correct syntax to create a scatter plot with a regression line using seaborn for variables x and y in DataFrame df?

easy

A. sns.regplot(df, x=x, y=y)

B. sns.regplot(data=df, x='x', y='y')

C. sns.regplot(x=x, y=y, data=df)

D. sns.regplot(x=df['x'], y='y')

Solution

1. Step 1: Check seaborn regplot parameter order

   The correct way is to specify the DataFrame with data= and then the column names as strings for x and y.

2. Step 2: Validate each option

   sns.regplot(data=df, x='x', y='y') uses the correct keyword arguments. sns.regplot(df, x=x, y=y) passes undefined variables x and y. sns.regplot(x=x, y=y, data=df) passes undefined variables x and y. sns.regplot(x=df['x'], y='y') mixes pandas Series for x and string literal for y, causing a type error.

3. Final Answer:

   sns.regplot(data=df, x='x', y='y') -> Option B

4. Quick Check:

   Use data= for DataFrame, x and y as column names [OK]

Hint: Use data= for DataFrame, x and y as column names [OK]

Common Mistakes:

• Omitting data= keyword
• Passing column names without quotes
• Mixing parameter order

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

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

data = pd.DataFrame({
    'height': [150, 160, 170, 180, 190],
    'weight': [50, 60, 65, 80, 90]
})
sns.regplot(data=data, x='height', y='weight')
plt.show()

medium

A. An error because plt.show() is missing.

B. A scatter plot of height vs weight without any regression line.

C. A bar chart showing average weight for each height.

D. A scatter plot of height vs weight with a regression line showing positive correlation.

Solution

1. Step 1: Understand the code

   The code creates a DataFrame with height and weight, then uses sns.regplot() to plot height on x-axis and weight on y-axis with a regression line.

2. Step 2: Predict the plot output

   The plot will show points scattered according to the data and a regression line indicating the positive trend between height and weight.

3. Final Answer:

   A scatter plot of height vs weight with a regression line showing positive correlation. -> Option D

4. Quick Check:

   regplot shows scatter + regression line [OK]

Hint: regplot always adds regression line by default [OK]

Common Mistakes:

• Thinking regplot does not add regression line
• Confusing scatter plot with bar chart
• Assuming plt.show() is optional but causes error

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

import seaborn as sns
import pandas as pd

data = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
sns.regplot(x='x', y='y')

medium

A. Incorrect argument order; data= keyword is missing before DataFrame.

B. Missing plt.show() to display the plot.

C. Column names should not be strings.

D. sns.regplot does not accept DataFrame input.

Solution

1. Step 1: Check function call syntax

   The function call sns.regplot(x='x', y='y') passes column names without the data= keyword for the DataFrame.

2. Step 2: Understand the error

   Without data=, seaborn cannot locate the columns 'x' and 'y' and treats the strings as data, causing a TypeError.

3. Final Answer:

   Incorrect argument order; data= keyword is missing before DataFrame. -> Option A

4. Quick Check:

   Always use data= for DataFrame input [OK]

Hint: Always use data= when passing DataFrame to regplot [OK]

Common Mistakes:

• Omitting data= keyword
• Thinking plt.show() omission causes error
• Misunderstanding column name types

5. You have a dataset with variables age and income. You want to plot a scatter plot with a regression line but exclude the confidence interval shading. Which sns.regplot() parameter should you set to False to achieve this?

hard

A. scatter

B. fit_reg

C. ci

D. line_kws

Solution

1. Step 1: Identify parameter for confidence interval

   The ci parameter controls the confidence interval shading around the regression line in sns.regplot().

2. Step 2: Set ci to False to remove shading

   Setting ci=False disables the confidence interval shading, leaving only the regression line and scatter points.

3. Final Answer:

   ci -> Option C

4. Quick Check:

   ci=False removes confidence interval shading [OK]

Hint: Set ci=False to hide confidence interval shading [OK]

Common Mistakes:

• Using fit_reg instead of ci
• Trying to remove scatter points instead
• Misusing line_kws parameter