Grouping by multiple columns in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to group the DataFrame by 'city' and calculate the mean of 'temperature'.

Pandas

grouped = df.groupby([1])['temperature'].mean()

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

A'city'

B'humidity'

C'date'

D'temperature'

Attempts:

3 left

💡 Hint

Common Mistakes

Grouping by 'temperature' instead of 'city' will not group locations.

Forgetting quotes around the column name causes errors.

The groupby function groups data by the specified column. Here, we group by 'city' to find the average temperature per city.

2fill in blank

medium

Complete the code to group the DataFrame by both 'city' and 'date' columns.

Pandas

grouped = df.groupby([1]).sum()

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

A['humidity']

B['city']

C['city', 'date']

D['temperature']

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single string instead of a list for multiple columns.

Grouping by columns not present in the DataFrame.

Grouping by multiple columns requires a list of column names. Here, we group by both 'city' and 'date' to aggregate data for each city on each date.

3fill in blank

hard

Fix the error in the code to group by 'city' and 'date' and calculate the mean temperature.

Pandas

result = df.groupby([1]).mean()['temperature']

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

A'city', 'date'

B['city', 'date']

C('city', 'date')

D'city date'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing multiple columns as a single string separated by commas.

Using parentheses instead of square brackets for the list.

The groupby method expects a list of column names to group by multiple columns. Using a list like ['city', 'date'] is correct.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each city to the count of dates where temperature is above 20.

Pandas

counts = {city: df[(df['city'] == city) & (df['temperature'] [1] 20)]['date'].[2]() for city in df['city'].unique()}

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

A>

Bcount

Cmean

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for the temperature condition.

Using 'mean' instead of 'count' to count dates.

The condition checks if temperature is greater than 20. Then count() counts the number of dates matching the condition for each city.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each city to the average humidity on days when temperature is below 15.

Pandas

avg_humidity = {city: df[(df['city'] == city) & (df['temperature'] [1] 15)]['[2]'].[3]() for city in df['city'].unique()}

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

A<

Bhumidity

Cmean

Dtemperature

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>' instead of '<' for the temperature condition.

Selecting 'temperature' column instead of 'humidity'.

Using 'count' instead of 'mean' to calculate average.

The condition checks if temperature is less than 15. Then the mean humidity is calculated for those days per city.

Practice

1. What does grouping by multiple columns in pandas allow you to do?

easy

A. Filter rows based on a single condition

B. Sort data alphabetically by one column

C. Analyze data by several categories at the same time

D. Change the data type of multiple columns

Solution

1. Step 1: Understand grouping concept

   Grouping by multiple columns means splitting data into groups based on combinations of those columns.

2. Step 2: Identify the purpose of grouping

   This helps analyze data by several categories at once, like sales by region and product.

3. Final Answer:

   Analyze data by several categories at the same time -> Option C

4. Quick Check:

   Grouping multiple columns = multi-category analysis [OK]

Hint: Grouping by list of columns splits data by combined categories [OK]

Common Mistakes:

• Thinking grouping changes data order
• Confusing grouping with filtering
• Assuming grouping changes data types

2. Which of the following is the correct syntax to group a DataFrame df by columns 'A' and 'B'?

easy

A. df.groupby(['A', 'B'])

B. df.groupby('A', 'B')

C. df.group_by(['A', 'B'])

D. df.groupby(['A' & 'B'])

Solution

1. Step 1: Recall pandas groupby syntax

   To group by multiple columns, pass a list of column names inside groupby().

2. Step 2: Check each option

   df.groupby(['A', 'B']) uses a list ['A', 'B'], which is correct syntax. Others have syntax errors or wrong method names.

3. Final Answer:

   df.groupby(['A', 'B']) -> Option A

4. Quick Check:

   List of columns inside groupby() = correct syntax [OK]

Hint: Use a list of column names inside groupby() [OK]

Common Mistakes:

• Passing columns as separate arguments instead of a list
• Using wrong method name like group_by
• Using bitwise operators inside list

3. Given the DataFrame df below:

   City  Product  Sales
0  NY    A        10
1  NY    B        20
2  LA    A        15
3  LA    B        25
4  NY    A        5

What is the output of df.groupby(['City', 'Product']).sum()?

medium

A. A DataFrame with total sales for each City and Product combination

B. A DataFrame with average sales for each City

C. A Series with sales summed only by City

D. An error because sum() cannot be used after groupby

Solution

1. Step 1: Group by City and Product

   The data is grouped by both City and Product columns, creating groups like (NY, A), (NY, B), (LA, A), (LA, B).

2. Step 2: Apply sum() aggregation

   Sum sales within each group: (NY, A) = 10+5=15, (NY, B) = 20, (LA, A) = 15, (LA, B) = 25.

3. Final Answer:

   A DataFrame with total sales for each City and Product combination -> Option A

4. Quick Check:

   Group by multiple columns + sum() = grouped sums [OK]

Hint: Sum after groupby sums values per group combination [OK]

Common Mistakes:

• Expecting average instead of sum
• Grouping by only one column
• Thinking sum() causes error

4. What is wrong with this code snippet?

df.groupby('City', 'Product').mean()

medium

A. groupby() requires column names as integers

B. mean() cannot be used after groupby

C. The columns 'City' and 'Product' do not exist

D. groupby() expects a single argument, so multiple columns must be passed as a list

Solution

1. Step 1: Check groupby argument format

   groupby() accepts one argument for columns; multiple columns must be inside a list.

2. Step 2: Identify error cause

   Passing 'City', 'Product' as separate arguments causes a TypeError.

3. Final Answer:

   groupby() expects a single argument, so multiple columns must be passed as a list -> Option D

4. Quick Check:

   Multiple columns in groupby() = list argument [OK]

Hint: Pass multiple columns as a list inside groupby() [OK]

Common Mistakes:

• Passing multiple columns as separate arguments
• Thinking mean() is invalid after groupby
• Assuming column names must be integers

5. You have a sales DataFrame df with columns Region, Category, and Revenue. You want to find the average revenue per region and category, but only for categories where the total revenue exceeds 1000. Which code correctly does this?

hard

A. df.groupby(['Region', 'Category']).Revenue.mean()[df.groupby(['Region', 'Category']).Revenue.mean() > 1000]

B. df.groupby(['Region', 'Category']).filter(lambda x: x['Revenue'].sum() > 1000).groupby(['Region', 'Category']).Revenue.mean()

C. df.groupby(['Region', 'Category']).Revenue.mean().filter(lambda x: x > 1000)

D. df.groupby(['Region', 'Category']).Revenue.sum().mean() > 1000

Solution

1. Step 1: Filter groups by total revenue

   Use groupby with filter and a lambda to keep only groups where sum of Revenue > 1000.

2. Step 2: Calculate average revenue per filtered group

   After filtering, group again by Region and Category and calculate mean Revenue.

3. Final Answer:

   df.groupby(['Region', 'Category']).filter(lambda x: x['Revenue'].sum() > 1000).groupby(['Region', 'Category']).Revenue.mean() -> Option B

4. Quick Check:

   Filter groups by sum, then compute mean [OK]

Hint: Use filter() on groupby to keep groups before aggregation [OK]

Common Mistakes:

• Trying to filter after mean instead of before
• Using boolean indexing on Series with mismatched index
• Calculating mean of sums instead of per group