Scatter plots with regression (regplot) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a simple regplot

What will be the output of this code snippet that uses seaborn's regplot to plot data points and a regression line?

Data Analysis Python

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

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

A A scatter plot with points roughly increasing and a regression line fitting the points upward

B A scatter plot with points randomly scattered and a flat horizontal regression line

C A bar chart showing counts of x values

D A line plot connecting the points without any regression line

💡 Hint

Think about what regplot does: it shows points and fits a regression line.

Seaborn's regplot plots scatter points and fits a linear regression line through them. Since y generally increases with x, the line slopes upward.

❓ data_output

intermediate

Number of points plotted by regplot

Given this dataset and regplot code, how many points will be shown on the scatter plot?

Data Analysis Python

import seaborn as sns
import pandas as pd

data = pd.DataFrame({'x': range(10), 'y': [i**2 for i in range(10)]})
sns.regplot(x='x', y='y', data=data)

A 10

B 11

C 9

D 0

💡 Hint

Count the number of rows in the data.

The data has 10 rows, so regplot will plot 10 points.

🔧 Debug

advanced

Error when using regplot with missing data

What error will this code raise when running regplot with missing values in the data?

Data Analysis Python

import seaborn as sns
import pandas as pd
import numpy as np

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

A TypeError because data is not numeric

B ValueError due to NaN values in x

C No error, plot shows ignoring NaN

D KeyError because 'x' column is missing

💡 Hint

Think about how regression handles missing values.

Seaborn's regplot automatically drops rows with NaN values in the x or y columns before fitting the regression. No error is raised; it plots the remaining 3 points.

❓ visualization

advanced

Effect of changing order parameter in regplot

What is the visual difference when using order=2 in regplot compared to the default order=1?

Data Analysis Python

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

data = pd.DataFrame({'x': range(6), 'y': [1, 4, 9, 16, 25, 36]})
plt.subplot(1,2,1)
sns.regplot(x='x', y='y', data=data, order=1)
plt.title('order=1')
plt.subplot(1,2,2)
sns.regplot(x='x', y='y', data=data, order=2)
plt.title('order=2')
plt.tight_layout()
plt.show()

A order=1 shows a curved line; order=2 shows a straight line

B order=1 shows a straight line; order=2 shows a curved parabola fitting the data better

C Both plots show identical straight lines

D order=2 plot raises an error because order must be 1

💡 Hint

Order controls polynomial degree of regression line.

order=1 fits a linear regression line; order=2 fits a quadratic curve, which better fits the squared y values.

🚀 Application

expert

Interpreting regression line slope from regplot

Given this code plotting a regression line, what is the approximate slope of the regression line?

Data Analysis Python

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

data = pd.DataFrame({'x': [0, 1, 2, 3, 4], 'y': [1, 3, 5, 7, 9]})
sns.regplot(x='x', y='y', data=data)
plt.show()

A Approximately -1

B Approximately 1

C Approximately 0.5

D Approximately 2

💡 Hint

Look at how y changes when x increases by 1.

y increases by 2 for every increase of 1 in x, so slope is about 2.

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