Why groupby summarizes data by category in Data Analysis Python - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of groupby sum aggregation

What is the output of this code that groups sales by product category and sums the sales?

Data Analysis Python

import pandas as pd

data = {'Category': ['Fruit', 'Fruit', 'Vegetable', 'Vegetable', 'Fruit'],
        'Sales': [10, 15, 7, 8, 5]}
df = pd.DataFrame(data)
result = df.groupby('Category')['Sales'].sum()
print(result)

A

Category
Fruit       15
Vegetable   15
Name: Sales, dtype: int64

B

Category
Fruit       30
Vegetable   15
Name: Sales, dtype: int64

C

Category
Fruit       10
Vegetable   7
Name: Sales, dtype: int64

D

Category
Fruit       5
Vegetable   8
Name: Sales, dtype: int64

💡 Hint

Sum adds all sales values within each category.

The groupby groups rows by 'Category'. Then sum adds all 'Sales' values in each group. Fruit has 10+15+5=30, Vegetable has 7+8=15.

❓ data_output

intermediate

Number of groups created by groupby

How many groups are created when grouping this data by 'Type'?

Data Analysis Python

import pandas as pd

data = {'Type': ['A', 'B', 'A', 'C', 'B', 'C', 'A'],
        'Value': [1, 2, 3, 4, 5, 6, 7]}
df = pd.DataFrame(data)
groups = df.groupby('Type')
print(len(groups))

A 3

B 7

C 4

D 1

💡 Hint

Count unique values in 'Type' column.

There are three unique types: A, B, and C, so groupby creates 3 groups.

🔧 Debug

advanced

Identify the error in groupby usage

What error does this code raise when trying to group by a non-existent column?

Data Analysis Python

import pandas as pd

data = {'Category': ['X', 'Y', 'X'], 'Value': [1, 2, 3]}
df = pd.DataFrame(data)
result = df.groupby('Type')['Value'].sum()
print(result)

A KeyError: 'Type'

B ValueError: No numeric types to aggregate

C TypeError: unhashable type: 'list'

D AttributeError: 'DataFrame' object has no attribute 'groupby'

💡 Hint

Check if the column name exists in the DataFrame.

The code tries to group by 'Type' which is not a column in df, causing KeyError.

❓ visualization

advanced

Visualizing grouped data with bar plot

Which option produces a bar plot showing total sales per region after grouping?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

data = {'Region': ['North', 'South', 'North', 'East', 'South'],
        'Sales': [100, 150, 200, 130, 170]}
df = pd.DataFrame(data)
grouped = df.groupby('Region')['Sales'].sum()

# Which code below plots grouped data correctly?

A

grouped.plot.bar()
plt.show()

B

df.plot(kind='bar', x='Region', y='Sales')
plt.show()

C

grouped.plot(kind='bar')
plt.show()

D

plt.bar(grouped)
plt.show()

💡 Hint

Use the grouped Series plot method with kind='bar'.

Option C calls plot on the grouped Series with kind='bar', producing correct bar chart. Option C plots raw data, not grouped sums. Option C is valid syntax and also works. Option C passes Series directly to plt.bar causing error.

🧠 Conceptual

expert

Why does groupby summarize data by category?

Why is grouping data by category useful in data analysis?

A It sorts the data alphabetically by category names.

B It converts all data types to strings for easier processing.

C It removes duplicate rows from the dataset automatically.

D It organizes data into meaningful groups to calculate summary statistics easily.

💡 Hint

Think about what grouping helps you do with data.

Grouping lets you split data into categories so you can compute sums, averages, counts, and other summaries per group. This helps understand patterns by category.