Why grouping data matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

grouped = df.[1]('Category')

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

Asort

Bfilter

Cgroupby

Dmerge

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sort' instead of 'groupby' will only sort data, not group it.

Using 'filter' or 'merge' does not create groups.

The groupby method groups data based on column values, which is essential for aggregation.

2fill in blank

medium

Complete the code to calculate the average sales for each group.

Pandas

average_sales = grouped.[1]()['Sales']

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

Amean

Bsum

Ccount

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' will add values instead of averaging.

Using 'count' returns the number of items, not the average.

The mean function calculates the average value for each group.

3fill in blank

hard

Fix the error in the code to get the total sales per group.

Pandas

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

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

Acount

Baverage

Cmean

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'average' is not a valid pandas method.

Using 'mean' calculates average, not total.

The sum method adds up all sales values in each group.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Pandas

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' will select words shorter than 3 characters.

Using 'word' instead of 'len(word)' will store the word itself, not its length.

We use len(word) to get the length and filter words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their counts for words with count greater than 1.

Pandas

result = [1]: [2] for word, count in word_counts.items() if count [3] 1

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

Aword.upper()

Bcount

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' instead of 'word.upper()' will not change case.

Using '<' will filter counts less than or equal to 1.

We convert words to uppercase with word.upper(), keep the count, and filter counts greater than 1 using >.

Practice

1. Why do we use grouping in pandas when working with data?

easy

A. To organize data into smaller parts based on shared values

B. To delete rows with missing values

C. To change the data type of columns

D. To sort data alphabetically

Solution

1. Step 1: Understand the purpose of grouping

   Grouping divides data into parts where rows share common values in one or more columns.

2. Step 2: Identify why grouping is useful

   This helps calculate summaries like totals or averages for each group, making data easier to analyze.

3. Final Answer:

   To organize data into smaller parts based on shared values -> Option A

4. Quick Check:

   Grouping = Organizing data by shared values [OK]

Hint: Grouping splits data by shared values for easy summaries [OK]

Common Mistakes:

• Confusing grouping with sorting
• Thinking grouping deletes data
• Believing grouping changes data types

2. Which of the following is the correct syntax to group a pandas DataFrame df by the column 'Category'?

easy

A. df.groupby('Category')

B. df.group_by('Category')

C. df.group('Category')

D. df.groupby_by('Category')

Solution

1. Step 1: Recall pandas grouping syntax

   The correct method to group data is groupby without underscores or extra words.

2. Step 2: Match the correct method call

   Only df.groupby('Category') matches pandas syntax exactly.

3. Final Answer:

   df.groupby('Category') -> Option A

4. Quick Check:

   Correct method is groupby() [OK]

Hint: Use df.groupby('column') to group data correctly [OK]

Common Mistakes:

• Using underscores incorrectly in method name
• Adding extra words like 'by' twice
• Using group() instead of groupby()

3. Given the DataFrame df below:

import pandas as pd
data = {'Team': ['A', 'A', 'B', 'B', 'C'], 'Points': [10, 15, 10, 5, 20]}
df = pd.DataFrame(data)
result = df.groupby('Team').sum()

What is the output of result?

medium

A. Team as column with Points averaged per team

B. Error because sum() cannot be used after groupby

C. Original DataFrame unchanged

D. Team as index with Points summed per team: A=25, B=15, C=20

Solution

1. Step 1: Group data by 'Team'

   The groupby('Team') groups rows by team names: A, B, and C.

2. Step 2: Sum the 'Points' for each group

   Points for A: 10+15=25, B: 10+5=15, C: 20.

3. Final Answer:

   Team as index with Points summed per team: A=25, B=15, C=20 -> Option D

4. Quick Check:

   Sum points per team = 25, 15, 20 [OK]

Hint: Sum after groupby adds values per group [OK]

Common Mistakes:

• Expecting average instead of sum
• Thinking groupby returns original data
• Believing sum() causes error after groupby

4. What is wrong with this code snippet?

import pandas as pd
data = {'City': ['X', 'Y', 'X', 'Z'], 'Sales': [100, 200, 150, 300]}
df = pd.DataFrame(data)
result = df.group('City').mean()

Choose the best explanation.

medium

A. The DataFrame is missing a column for grouping

B. The method group does not exist; should be groupby

C. The mean() function cannot be used after grouping

D. The data dictionary is incorrectly formatted

Solution

1. Step 1: Check the grouping method name

   The code uses df.group('City'), but pandas requires groupby.

2. Step 2: Confirm mean() is valid after grouping

   mean() works correctly after groupby, so no error there.

3. Final Answer:

   The method group does not exist; should be groupby -> Option B

4. Quick Check:

   Use groupby(), not group() [OK]

Hint: Use groupby(), not group(), to avoid errors [OK]

Common Mistakes:

• Using group() instead of groupby()
• Thinking mean() is invalid after grouping
• Assuming data format is wrong

5. You have a sales DataFrame df with columns 'Store', 'Product', and 'Revenue'. You want to find the total revenue per store but only for products with revenue above 100. Which pandas code correctly does this?

hard

A. df.groupby('Store').filter(lambda x: x['Revenue'] > 100).sum()

B. df.groupby('Store')['Revenue'].sum()[df['Revenue'] > 100]

C. df[df['Revenue'] > 100].groupby('Store')['Revenue'].sum()

D. df.groupby('Product')['Revenue'].sum().loc[df['Revenue'] > 100]

Solution

1. Step 1: Filter rows where Revenue > 100

   Use df[df['Revenue'] > 100] to keep only rows with revenue above 100.

2. Step 2: Group filtered data by 'Store' and sum 'Revenue'

   Then group by 'Store' and sum the 'Revenue' column to get totals per store.

3. Final Answer:

   df[df['Revenue'] > 100].groupby('Store')['Revenue'].sum() -> Option C

4. Quick Check:

   Filter first, then group and sum [OK]

Hint: Filter rows first, then group and sum [OK]

Common Mistakes:

• Grouping before filtering, causing wrong sums
• Filtering on grouped sums incorrectly
• Using filter() without returning boolean mask