Why grouping data matters in Pandas - 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 and sums the amounts?

Pandas

import pandas as pd

data = {'product': ['apple', 'banana', 'apple', 'banana', 'apple'],
        'amount': [10, 5, 15, 10, 5]}
df = pd.DataFrame(data)
result = df.groupby('product')['amount'].sum()
print(result)

A

product
apple     10
banana    5
Name: amount, dtype: int64

B

product
apple     15
banana    15
Name: amount, dtype: int64

C

product
apple     30
banana    25
Name: amount, dtype: int64

D

product
apple     30
banana    15
Name: amount, dtype: int64

💡 Hint

Sum all amounts for each product separately.

The code groups the data by 'product' and sums the 'amount' values for each group. Apple has 10+15+5=30, banana has 5+10=15.

❓ data_output

intermediate

Number of Groups Created by GroupBy

How many groups are created when grouping this DataFrame by the 'category' column?

Pandas

import pandas as pd

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

A 3

B 7

C 2

D 4

💡 Hint

Count unique values in 'category'.

There are three unique categories: A, B, and C, so three groups are created.

❓ visualization

advanced

Visualizing Grouped Data with Mean Values

Which plot correctly shows the average score per class from the grouped data?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'class': ['X', 'Y', 'X', 'Y', 'X'], 'score': [80, 90, 70, 85, 75]}
df = pd.DataFrame(data)
grouped = df.groupby('class')['score'].mean()

plt.bar(grouped.index, grouped.values)
plt.xlabel('Class')
plt.ylabel('Average Score')
plt.title('Average Score per Class')
plt.show()

A A line chart with points at X=80 and Y=90.

B A bar chart with two bars labeled X and Y, heights 75 and 87.5 respectively.

C A scatter plot with points for each individual score.

D A pie chart showing proportions of total scores per class.

💡 Hint

Mean scores are 75 for X and 87.5 for Y.

The code groups by class and calculates mean scores, then plots a bar chart with these means.

🔧 Debug

advanced

Identify the Error in GroupBy Usage

What error does this code raise when grouping by a non-existent column?

Pandas

import pandas as pd

data = {'name': ['Anna', 'Bob'], 'age': [25, 30]}
df = pd.DataFrame(data)
result = df.groupby('salary').mean()

A KeyError: 'salary'

B TypeError: unhashable type: 'list'

C ValueError: No numeric types to aggregate

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

💡 Hint

Check if the column exists in the DataFrame.

The code tries to group by 'salary' which is not a column in the DataFrame, causing a KeyError.

🚀 Application

expert

Calculate Total Sales per Region and Month

Given sales data with 'region', 'month', and 'sales' columns, which code correctly groups by region and month and sums sales?

Pandas

import pandas as pd

data = {'region': ['North', 'South', 'North', 'South', 'North'],
        'month': ['Jan', 'Jan', 'Feb', 'Feb', 'Jan'],
        'sales': [100, 200, 150, 250, 50]}
df = pd.DataFrame(data)

A df.groupby(['region', 'month']).sum()['sales']

B df.groupby('region', 'month')['sales'].sum()

C df.groupby(['region', 'month'])['sales'].sum()

D df.groupby('region')['month']['sales'].sum()

💡 Hint

Group by both columns using a list, then select 'sales' to sum.

Option C correctly groups by both 'region' and 'month' using a list and sums the 'sales' column.