Long to wide format conversion in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the DataFrame from long to wide format using pivot.

Pandas

wide_df = long_df.pivot(index='[1]', columns='variable', values='value')

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

Aid

Bvalue

Cvariable

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'value' as index instead of 'id'.

Using 'variable' as index which is actually the column to spread.

We use the 'id' column as the index to reshape the DataFrame from long to wide format.

2fill in blank

medium

Complete the code to reset the index of the wide DataFrame to turn the index back into a column.

Pandas

wide_df = wide_df.[1]()

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

Areset_index

Bset_index

Cdrop

Dmelt

Attempts:

3 left

💡 Hint

Common Mistakes

Using set_index() which does the opposite.

Using drop() which removes columns.

reset_index() converts the index back into a regular column.

3fill in blank

hard

Fix the error in the code to pivot the DataFrame when there are duplicate entries for the index and columns combination.

Pandas

wide_df = long_df.pivot_table(index='id', columns='[1]', values='value', aggfunc='mean')

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

Aaggfunc

Bvariable

Cid

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'value' as columns which is incorrect.

Using 'id' as columns which is the index.

When duplicates exist, pivot_table with aggfunc is used. The columns parameter should be the 'variable' column.

4fill in blank

hard

Fill both blanks to create a wide DataFrame with 'date' as index and 'category' as columns, aggregating values by sum.

Pandas

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

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

Adate

Bcategory

Cregion

Dproduct

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping index and columns values.

Using columns that are not categorical.

We use 'date' as index and 'category' as columns to reshape sales data, summing duplicates.

5fill in blank

hard

Fill all three blanks to create a dictionary of average scores by student and subject from a long DataFrame.

Pandas

result = df.pivot_table(index='[1]', columns='[2]', values='[3]', aggfunc='mean').to_dict('index')

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

Astudent

Bsubject

Cscore

Dgrade

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'grade' instead of 'score' for values.

Mixing up student and subject keys.

The dictionary keys are students, inner keys are subjects, and values are scores.