Iterating over groups in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

grouped = df.groupby([1])

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

A'Category'

B'Value'

C'Index'

D'Date'

Attempts:

3 left

💡 Hint

Common Mistakes

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

Not using quotes around the column name.

The groupby function groups data by the specified column. Here, we want to group by the 'Category' column.

2fill in blank

medium

Complete the code to iterate over groups and print the group name.

Pandas

for [1], group in grouped:
    print([1])

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

Agroup

Bkey

Cname

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using the group DataFrame instead of the group name to print.

Using a variable name that is not assigned in the loop.

When iterating over groups, the first variable is the group key (name), which identifies the group.

3fill in blank

hard

Fix the error in the code to get the size of each group.

Pandas

sizes = grouped.[1]()

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

Asize

Bsum

Clength

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() which counts non-null entries per column, not group size.

Using non-existent methods like length().

The size() method returns the number of rows in each group.

4fill in blank

hard

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

Pandas

mean_values = { [1]: group['Value'].[2]() for [1] , group in grouped }

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Aname

Bmean

Csum

Dkey

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong variable name for the group key.

Using sum() instead of mean().

We use the group key as the dictionary key and calculate the mean of the 'Value' column for each group.

5fill in blank

hard

Fill all three blanks to filter groups where the mean of 'Value' is greater than 10 and create a dictionary with group names and their mean values.

Pandas

filtered_means = { [1]: group['Value'].[2]() for [1], group in grouped if group['Value'].[2]() [3] 10 }

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

Agrp

Bmean

C>

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of mean().

Using '<' instead of '>' in the condition.

Using inconsistent variable names for the group key.

We use 'grp' as the group name variable, calculate the mean of 'Value', and filter groups where this mean is greater than 10.