Pivot tables with pivot_table() in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a pivot table from the DataFrame df using the pivot_table() method.

Data Analysis Python

pivot = df.[1](index='Category', values='Sales', aggfunc='sum')

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

Apivot_table

Bgroupby

Cmerge

Dconcat

Attempts:

3 left

💡 Hint

Common Mistakes

Using groupby instead of pivot_table.

Trying to use merge or concat which combine dataframes but don't create pivot tables.

The pivot_table() method is used to create pivot tables in pandas. Here, it groups data by 'Category' and sums the 'Sales'.

2fill in blank

medium

Complete the code to create a pivot table that uses mean as the aggregation function.

Data Analysis Python

pivot = df.pivot_table(index='Region', values='Profit', aggfunc=[1])

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

A'mean'

B'count'

C'sum'

D'max'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' instead of 'mean' when average is needed.

Forgetting to put the aggregation function in quotes.

The aggfunc parameter defines how to aggregate data. Using 'mean' calculates the average.

3fill in blank

hard

Fix the error in the code to create a pivot table with multiple index columns.

Data Analysis Python

pivot = df.pivot_table(index=[1], values='Sales', aggfunc='sum')

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

A'Region, Category'

B['Region', 'Category']

C'Region'

DRegion, Category

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single string with commas instead of a list.

Not using quotes around column names.

The index parameter accepts a list of column names to group by multiple columns. It must be a list like ['Region', 'Category'].

4fill in blank

hard

Fill both blanks to create a pivot table with Region as index and Category as columns, aggregating Profit by sum.

Data Analysis Python

pivot = df.pivot_table(index=[1], columns=[2], values='Profit', aggfunc='sum')

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

A'Region'

B'Category'

C'Sales'

D'Date'

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping index and columns values.

Using a wrong column name like 'Sales' or 'Date'.

The index is the row grouping, here 'Region'. The columns parameter creates columns for each 'Category'.

5fill in blank

hard

Fill all three blanks to create a pivot table that groups by Region and Category, aggregates Sales by mean, and fills missing values with 0.

Data Analysis Python

pivot = df.pivot_table(index=[1], columns=[2], values=[3], aggfunc='mean', fill_value=0)

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

A['Region']

B['Category']

C'Sales'

D'Profit'

Attempts:

3 left

💡 Hint

Common Mistakes

Using strings instead of lists for index or columns.

Choosing the wrong column for values.

Forgetting to set fill_value to handle missing data.

The index groups rows by 'Region', columns groups columns by 'Category', and values selects 'Sales' to aggregate by mean. Missing values are replaced by 0.