groupby() basics in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to group the DataFrame by the 'Category' column.

Data Analysis Python

grouped = df.groupby([1])

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

A'Category'

B'Value'

C'Date'

D'Index'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column name that does not exist in the DataFrame.

Forgetting to put the column name inside quotes.

The groupby() function groups data based on the column name given. Here, we want to group by the 'Category' column.

2fill in blank

medium

Complete the code to calculate the mean of each group after grouping by 'Category'.

Data Analysis Python

mean_values = df.groupby('Category').[1]()

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

Asum

Bmax

Ccount

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of mean().

Forgetting the parentheses after the function name.

The mean() function calculates the average value for each group.

3fill in blank

hard

Fix the error in the code to get the size of each group after grouping by 'Category'.

Data Analysis Python

group_sizes = df.groupby('Category').[1]

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

Asize()

Bsize

Ccount()

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using size without parentheses, which returns a method object.

Using count() which counts non-null values per column, not total rows.

The size() function returns the number of rows in each group. It requires parentheses to call the function.

4fill in blank

hard

Fill both blanks to create a dictionary with group names as keys and the sum of 'Value' as values.

Data Analysis Python

result = {group: data['Value'].[1]() for group, data in df.groupby([2])}

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

Asum

Bmean

C'Category'

D'Value'

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean instead of sum for aggregation.

Grouping by the wrong column name.

The dictionary comprehension loops over groups. We sum the 'Value' column for each group. The grouping is done by 'Category'.

5fill in blank

hard

Fill all three blanks to create a dictionary with group names as keys and the max 'Value' for each group.

Data Analysis Python

result = {group: data[[1]].[2]() for group, data in df.groupby([3])}

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

A'Value'

Bmax

C'Category'

D'Date'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name in the first blank.

Using min or mean instead of max.

Grouping by a column other than 'Category'.

We select the 'Value' column from each group, then find the maximum value using max(). The groups are formed by the 'Category' column.