Why combining datasets creates complete pictures in Data Analysis Python - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of merging two datasets with an inner join?

Consider two data tables representing sales data and customer data. What will be the result of merging them using an inner join on the 'customer_id' column?

Data Analysis Python

import pandas as pd

sales = pd.DataFrame({'customer_id': [1, 2, 3], 'amount': [100, 200, 300]})
customers = pd.DataFrame({'customer_id': [2, 3, 4], 'name': ['Alice', 'Bob', 'Charlie']})

merged = pd.merge(sales, customers, on='customer_id', how='inner')
print(merged)

A

   customer_id  amount   name
0            1     100    NaN
1            4     NaN  Charlie

B

   customer_id  amount   name
0            1     100    NaN
1            2     200  Alice
2            3     300    Bob

C

   customer_id  amount   name
0            2     200  Alice
1            3     300    Bob

D

   customer_id  amount   name
0            2     200  Alice
1            3     300    Bob
2            4     NaN  Charlie

💡 Hint

Inner join keeps only rows with matching keys in both tables.

An inner join returns only rows where the 'customer_id' exists in both datasets. Here, customer_id 2 and 3 are common, so only those rows appear.

❓ data_output

intermediate

How many rows result from a left join combining product and review data?

Given product data and review data, if you perform a left join on 'product_id', how many rows will the resulting dataset have?

Data Analysis Python

import pandas as pd

products = pd.DataFrame({'product_id': [101, 102, 103], 'product_name': ['Pen', 'Pencil', 'Eraser']})
reviews = pd.DataFrame({'product_id': [101, 101, 104], 'review': ['Good', 'Excellent', 'Bad']})

left_joined = pd.merge(products, reviews, on='product_id', how='left')
print(len(left_joined))

A 4

B 3

C 2

D 5

💡 Hint

Left join keeps all rows from the left table.

Left join keeps all 3 products. Reviews for product_id 101 appear twice, but since only one product row exists per product_id, the join expands rows accordingly. Here, product_id 101 has two reviews, so it creates two rows for that product, resulting in 4 rows total.

❓ visualization

advanced

Which plot best shows the relationship after combining sales and region data?

You combined sales data with region data to analyze sales by region. Which plot below best visualizes total sales per region?

Data Analysis Python

import pandas as pd
import matplotlib.pyplot as plt

sales = pd.DataFrame({'region': ['North', 'South', 'North', 'East'], 'sales': [100, 150, 200, 130]})
total_sales = sales.groupby('region').sum().reset_index()

plt.bar(total_sales['region'], total_sales['sales'])
plt.xlabel('Region')
plt.ylabel('Total Sales')
plt.title('Total Sales by Region')
plt.show()

A A scatter plot with sales on x-axis and region on y-axis

B A line plot showing sales over time (time not in data)

C A pie chart showing percentage of sales per product (product not in data)

D A bar chart with regions on x-axis and total sales on y-axis

💡 Hint

Bar charts are good for comparing totals across categories.

The bar chart clearly shows total sales per region, which is the goal after combining sales and region data.

🔧 Debug

advanced

What error occurs when merging datasets with missing keys without specifying join type?

What error will this code raise when merging two datasets with no common keys and no join type specified?

Data Analysis Python

import pandas as pd

left = pd.DataFrame({'id': [1, 2]})
right = pd.DataFrame({'key': [3, 4]})

merged = pd.merge(left, right, on='id')

A KeyError: 'id'

B ValueError: columns overlap but no suffix specified

C TypeError: merge() missing 1 required positional argument

D MergeError: No common columns to perform merge on

💡 Hint

Check if the columns used for merging exist in both datasets.

The right DataFrame does not have the 'id' column, so pandas raises a KeyError: 'id'.

🚀 Application

expert

How does combining datasets improve data analysis completeness?

You have customer purchase data and customer support data in separate tables. Which reason best explains why combining these datasets helps create a complete picture?

A Combining datasets reduces data size, making analysis faster but less detailed.

B Combining datasets allows analysis of customer behavior and support issues together, revealing patterns missed when separate.

C Combining datasets removes duplicate customers, which always improves data quality.

D Combining datasets automatically fixes missing values in both tables.

💡 Hint

Think about how different data sources add context to each other.

Combining purchase and support data lets analysts see how buying habits relate to support needs, giving a fuller understanding of customers.