Why Seaborn creates statistical visualizations in Data Analysis Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import Seaborn with its common alias.

Data Analysis Python

import [1] as sns

Drag options to blanks, or click blank then click option'

Apandas

Bsns

Cseaborn

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sns' directly instead of 'seaborn' in import.

Importing pandas or matplotlib instead of seaborn.

Seaborn is imported using the name 'seaborn' and commonly aliased as 'sns'.

2fill in blank

medium

Complete the code to create a scatter plot using Seaborn.

Data Analysis Python

sns.[1](data=df, x='age', y='income')

Drag options to blanks, or click blank then click option'

Ascatterplot

Bhistplot

Cbarplot

Dboxplot

Attempts:

3 left

💡 Hint

Common Mistakes

Using barplot or boxplot which are for categorical data.

Using histplot which is for distributions.

The function scatterplot creates scatter plots showing relationships between two variables.

3fill in blank

hard

Fix the error in the code to show a regression line with Seaborn.

Data Analysis Python

sns.lmplot(x='height', y='[1]', data=df)

Drag options to blanks, or click blank then click option'

Aweight

Bplt

Csns

Dage

Attempts:

3 left

💡 Hint

Common Mistakes

Passing library names or objects instead of column names.

Using variables not present in the data frame.

The y-axis variable must be a column name like 'weight' from the data frame.

4fill in blank

hard

Fill both blanks to create a boxplot grouped by category.

Data Analysis Python

sns.[1](x='[2]', y='score', data=df)

Drag options to blanks, or click blank then click option'

Aboxplot

Bbarplot

Ccategory

Dgroup

Attempts:

3 left

💡 Hint

Common Mistakes

Using barplot instead of boxplot for distribution.

Using wrong column names for grouping.

Use boxplot to show distributions and 'category' as the grouping variable on x-axis.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that filters and maps data.

Data Analysis Python

result = { [1]: [2] for k, v in data.items() if v [3] 10 }

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Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong operators in filter condition.

Not transforming keys correctly.

This comprehension creates a new dictionary with keys uppercased, values kept, filtering values greater than 10.

Practice

1. Why do data scientists often use Seaborn to create visualizations?

easy

A. Because it automatically adds statistical information to charts

B. Because it only creates simple line charts

C. Because it requires no data preparation

D. Because it does not support customization

Solution

1. Step 1: Understand Seaborn's purpose

   Seaborn is designed to create charts that include statistical details like confidence intervals or regression lines.

2. Step 2: Compare options

   The other options are incorrect: Seaborn does support customization, it creates more than just simple line charts, and it does require data preparation.

3. Final Answer:

   Because it automatically adds statistical information to charts -> Option A

4. Quick Check:

   Seaborn adds stats to charts = D [OK]

Hint: Seaborn adds stats to charts automatically [OK]

Common Mistakes:

• Thinking Seaborn only makes simple charts
• Believing Seaborn needs no data prep
• Assuming Seaborn can't be customized

2. Which of the following is the correct way to create a Seaborn scatter plot with a regression line?

easy

A. sns.scatterplot(data=df, x='age', y='income', fit_reg=True)

B. sns.barplot(data=df, x='age', y='income', regression=True)

C. sns.lineplot(data=df, x='age', y='income', reg=True)

D. sns.regplot(data=df, x='age', y='income')

Solution

1. Step 1: Identify the correct function for regression plots

   The function sns.regplot is used to create scatter plots with regression lines in Seaborn.

2. Step 2: Check other options for correctness

   sns.scatterplot(data=df, x='age', y='income', fit_reg=True) uses scatterplot which does not have fit_reg parameter. lineplot and barplot use wrong functions or parameters.

3. Final Answer:

   sns.regplot(data=df, x='age', y='income') -> Option D

4. Quick Check:

   Use sns.regplot for scatter + regression [OK]

Hint: Use sns.regplot for scatter with regression line [OK]

Common Mistakes:

• Using scatterplot with fit_reg parameter
• Confusing lineplot with regression
• Using barplot for regression

3. What will the following code display?

import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('tips')
sns.boxplot(x='day', y='total_bill', data=df)
plt.show()

medium

A. A box plot showing total bill distribution for each day

B. A scatter plot of total bill vs day

C. A line chart of total bill over days

D. An error because 'day' is not numeric

Solution

1. Step 1: Understand sns.boxplot usage

   The code uses sns.boxplot with x as categorical 'day' and y as numeric 'total_bill', which creates box plots per day.

2. Step 2: Analyze output type

   Box plots show distribution summaries, not scatter or line charts. 'day' can be categorical, so no error occurs.

3. Final Answer:

   A box plot showing total bill distribution for each day -> Option A

4. Quick Check:

   Boxplot with categorical x = A [OK]

Hint: Boxplot shows distribution per category [OK]

Common Mistakes:

• Thinking boxplot is scatter or line chart
• Assuming categorical x causes error
• Confusing x and y roles

4. Identify the error in this Seaborn code:

import seaborn as sns
sns.barplot(x='category', y='value', data=mydata, stat='mean')

medium

A. The x and y parameters are reversed

B. The parameter 'stat' is invalid in sns.barplot

C. DataFrame 'mydata' must be converted to numpy array first

D. sns.barplot does not accept string column names

Solution

1. Step 1: Check sns.barplot parameters

   The parameter 'stat' is not valid for sns.barplot; it does not exist in its API.

2. Step 2: Verify other options

   x and y usage is correct, DataFrame input is allowed, and string column names are accepted.

3. Final Answer:

   The parameter 'stat' is invalid in sns.barplot -> Option B

4. Quick Check:

   Invalid parameter 'stat' in barplot = A [OK]

Hint: Check parameter names carefully in Seaborn docs [OK]

Common Mistakes:

• Using non-existent parameters
• Confusing parameter names
• Thinking DataFrame input is invalid

5. You want to visualize the relationship between two numeric variables with a confidence interval. Which Seaborn function should you use and why?

hard

A. sns.scatterplot, because it shows points clearly but no confidence interval

B. sns.barplot, because it summarizes data with bars and confidence intervals

C. sns.regplot, because it plots points and fits a regression line with confidence interval

D. sns.histplot, because it shows distribution and confidence intervals

Solution

1. Step 1: Identify function for numeric relationship with confidence

   sns.regplot fits a regression line and shows confidence intervals around it, ideal for two numeric variables.

2. Step 2: Compare other functions

   Scatterplot shows points but no confidence interval. Barplot summarizes categories, not numeric relationships. Histplot shows distribution, not relationships.

3. Final Answer:

   sns.regplot, because it plots points and fits a regression line with confidence interval -> Option C

4. Quick Check:

   Regression plot with confidence = C [OK]

Hint: Use sns.regplot for numeric relation + confidence [OK]

Common Mistakes:

• Choosing scatterplot expecting confidence intervals
• Using barplot for numeric variable relationships
• Confusing histplot with regression