Single and multiple column grouping in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ data_output

intermediate

Output of single column grouping with aggregation

Given the following DataFrame, what is the output of grouping by the 'Category' column and calculating the sum of 'Sales'?

Data Analysis Python

import pandas as pd

data = {'Category': ['A', 'B', 'A', 'B', 'C'], 'Sales': [100, 200, 150, 300, 250]}
df = pd.DataFrame(data)
result = df.groupby('Category')['Sales'].sum()
print(result)

A Category\nA 250\nB 500\nC 250\nName: Sales, dtype: int64

B Category\nA 100\nB 200\nC 250\nName: Sales, dtype: int64

C Category\nA 150\nB 300\nC 250\nName: Sales, dtype: int64

D Category\nA 350\nB 500\nC 250\nName: Sales, dtype: int64

💡 Hint

Sum the 'Sales' values for each unique 'Category'.

The sum of sales for Category A is 100 + 150 = 250, for B is 200 + 300 = 500, and for C is 250.

❓ data_output

intermediate

Output of multiple column grouping with mean aggregation

What is the output of grouping the DataFrame by 'Category' and 'Region' columns and calculating the mean of 'Sales'?

Data Analysis Python

import pandas as pd

data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'], 'Region': ['East', 'West', 'East', 'West', 'East', 'West'], 'Sales': [100, 150, 200, 300, 250, 350]}
df = pd.DataFrame(data)
result = df.groupby(['Category', 'Region'])['Sales'].mean()
print(result)

A Category Region\nA East 100\n West 150\nB East 200\n West 300\nC East 250\n West 350\nName: Sales, dtype: int64

B Category Region\nA East 125.0\n West 225.0\nB East 250.0\n West 325.0\nC East 300.0\n West 400.0\nName: Sales, dtype: float64

C Category Region\nA East 100.0\n West 150.0\nB East 200.0\n West 300.0\nC East 250.0\n West 350.0\nName: Sales, dtype: float64

D Category Region\nA East 150.0\n West 100.0\nB East 300.0\n West 200.0\nC East 350.0\n West 250.0\nName: Sales, dtype: float64

💡 Hint

Calculate the average sales for each combination of Category and Region.

Each group has one value, so the mean is the value itself.

🧠 Conceptual

advanced

Understanding groupby object behavior

What happens if you try to access a column that was not included in the aggregation after grouping a DataFrame by a column?

Data Analysis Python

import pandas as pd

data = {'Category': ['A', 'B', 'A'], 'Sales': [100, 200, 150], 'Profit': [30, 50, 40]}
df = pd.DataFrame(data)
grouped = df.groupby('Category')['Sales'].sum()
print(grouped['Profit'])

A Returns the sum of 'Profit' for each category.

B Raises a KeyError because 'Profit' is not part of the grouped object.

C Returns the original 'Profit' column without grouping.

D Returns NaN values for 'Profit' since it was not aggregated.

💡 Hint

Check if the grouped object contains the 'Profit' column.

After grouping and selecting 'Sales', the grouped object only contains 'Sales'. Accessing 'Profit' raises KeyError.

🔧 Debug

advanced

Identify the error in multiple column grouping code

What error will this code raise when grouping by multiple columns and aggregating?

Data Analysis Python

import pandas as pd

data = {'Category': ['A', 'B', 'A'], 'Region': ['East', 'West', 'East'], 'Sales': [100, 200, 150]}
df = pd.DataFrame(data)
result = df.groupby('Category', 'Region')['Sales'].sum()
print(result)

A KeyError: 'Region' not found

B No error, outputs grouped sums correctly

C SyntaxError: invalid syntax

D TypeError: groupby() takes 2 positional arguments but 3 were given

💡 Hint

Check the syntax of the groupby method arguments.

groupby expects a single argument for columns as a list or array, not multiple positional arguments.

🚀 Application

expert

Calculate total sales and average profit by category and region

Given the DataFrame below, which code snippet correctly groups by 'Category' and 'Region' to calculate total 'Sales' and average 'Profit'?

Data Analysis Python

import pandas as pd

data = {'Category': ['A', 'A', 'B', 'B', 'C', 'C'], 'Region': ['East', 'West', 'East', 'West', 'East', 'West'], 'Sales': [100, 150, 200, 300, 250, 350], 'Profit': [20, 30, 40, 50, 60, 70]}
df = pd.DataFrame(data)

A

result = df.groupby(['Category', 'Region']).agg({'Sales': 'sum', 'Profit': 'mean'})
print(result)

B

result = df.groupby('Category', 'Region').agg({'Sales': 'sum', 'Profit': 'mean'})
print(result)

C

result = df.groupby(['Category', 'Region'])[['Sales', 'Profit']].agg(['sum', 'mean'])
print(result)

D

result = df.groupby(['Category', 'Region']).agg({'Sales': 'mean', 'Profit': 'sum'})
print(result)

💡 Hint

Use a dictionary to specify different aggregation functions for each column.

Option A correctly groups by both columns and applies sum to Sales and mean to Profit. Option A has wrong groupby syntax. Option A uses deprecated syntax for selecting multiple columns. Option A swaps aggregation functions.