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.

Practice

1. Why do we combine datasets in data analysis?

easy

A. To make data smaller by removing columns

B. To delete unnecessary data permanently

C. To get a more complete view by joining related information

D. To create random data for testing

Solution

1. Step 1: Understand the purpose of combining datasets

   Combining datasets helps bring together related pieces of information from different sources.

2. Step 2: Recognize the benefit of combining

   This creates a fuller, more complete picture for analysis.

3. Final Answer:

   To get a more complete view by joining related information -> Option C

4. Quick Check:

   Combining datasets = fuller picture [OK]

Hint: Combining means joining info to see the whole story [OK]

Common Mistakes:

• Thinking combining deletes data
• Confusing combining with filtering
• Believing combining creates random data

2. Which of the following is the correct syntax to merge two pandas DataFrames df1 and df2 on a common column 'id'?

easy

A. pd.merge(df1, df2, on='id')

B. df1.merge(df2, by='id')

C. merge(df1, df2, key='id')

D. pd.join(df1, df2, on='id')

Solution

1. Step 1: Recall pandas merge syntax

   The correct function is pd.merge() with parameters for the two DataFrames and the column name using on='id'.

2. Step 2: Check each option

   pd.merge(df1, df2, on='id') uses correct function and parameter names. Others use incorrect method names or parameters.

3. Final Answer:

   pd.merge(df1, df2, on='id') -> Option A

4. Quick Check:

   Correct merge syntax = pd.merge(df1, df2, on='id') [OK]

Hint: Use pd.merge() with on='column' to combine DataFrames [OK]

Common Mistakes:

• Using wrong method like df1.merge with 'by' instead of 'on'
• Using pd.join which does not exist
• Passing wrong parameter names like 'key'

3. Given these two DataFrames:

df1 = pd.DataFrame({'id': [1, 2, 3], 'value1': ['A', 'B', 'C']})
df2 = pd.DataFrame({'id': [2, 3, 4], 'value2': ['X', 'Y', 'Z']})
result = pd.merge(df1, df2, on='id', how='inner')

What is the content of result?

medium

A. {'id': [2, 3], 'value1': ['B', 'C'], 'value2': ['X', 'Y']}

B. {'id': [1, 2, 3, 4], 'value1': ['A', 'B', 'C', NaN], 'value2': [NaN, 'X', 'Y', 'Z']}

C. {'id': [1, 2, 3], 'value1': ['A', 'B', 'C'], 'value2': [NaN, 'X', 'Y']}

D. {'id': [2, 3, 4], 'value1': ['B', 'C', NaN], 'value2': ['X', 'Y', 'Z']}

Solution

1. Step 1: Understand inner join behavior

   Inner join keeps only rows with matching 'id' in both DataFrames. Here, ids 2 and 3 appear in both.

2. Step 2: Match rows and combine columns

   Rows with id 2 and 3 combine values from both DataFrames. Others are dropped.

3. Final Answer:

   {'id': [2, 3], 'value1': ['B', 'C'], 'value2': ['X', 'Y']} -> Option A

4. Quick Check:

   Inner join = only matching ids kept [OK]

Hint: Inner join keeps only rows with matching keys in both sets [OK]

Common Mistakes:

• Confusing inner join with outer join
• Expecting all ids to appear
• Mixing up which columns get NaN

4. You try to merge two DataFrames df1 and df2 on column 'key' using pd.merge(df1, df2, on='key') but get a KeyError: 'key'. What is the likely cause?

medium

A. You must use how='outer' to avoid KeyError

B. The column 'key' does not exist in one or both DataFrames

C. You forgot to import pandas as pd

D. The DataFrames have different numbers of rows

Solution

1. Step 1: Understand KeyError in merge context

   A KeyError for the merge column means that column is missing in one or both DataFrames.

2. Step 2: Check other options

   Using different join types or import issues do not cause KeyError on column name. Row counts do not matter for this error.

3. Final Answer:

   The column 'key' does not exist in one or both DataFrames -> Option B

4. Quick Check:

   KeyError means missing column [OK]

Hint: KeyError on merge means missing column in data [OK]

Common Mistakes:

• Thinking join type fixes KeyError
• Assuming import error causes KeyError
• Believing row count mismatch causes KeyError

5. You have two datasets:

sales = pd.DataFrame({'store_id': [1, 2, 3], 'sales': [100, 200, 300]})
stores = pd.DataFrame({'store_id': [2, 3, 4], 'location': ['NY', 'LA', 'SF']})

You want a combined table showing all stores with their sales if available, and location if available. Which merge command gives this complete picture?

hard

A. pd.merge(sales, stores, on='store_id', how='inner')

B. pd.merge(sales, stores, on='store_id', how='left')

C. pd.merge(sales, stores, on='store_id', how='right')

D. pd.merge(sales, stores, on='store_id', how='outer')

Solution

1. Step 1: Understand join types and goal

   You want all stores from both datasets, showing sales and location where available.

2. Step 2: Choose join type that keeps all rows

   Outer join keeps all rows from both DataFrames, filling missing values with NaN.

3. Step 3: Verify options

   Inner join keeps only matching stores. Left or right join keep only one side's stores. Outer join keeps all.

4. Final Answer:

   pd.merge(sales, stores, on='store_id', how='outer') -> Option D

5. Quick Check:

   Outer join = all data combined [OK]

Hint: Use outer join to keep all rows from both datasets [OK]

Common Mistakes:

• Using inner join and losing unmatched rows
• Confusing left/right join with full outer join
• Expecting missing data to be dropped automatically