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

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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')

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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)

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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)

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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.