Inner join in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is an inner join in database terms?

An inner join combines rows from two tables where the join condition is true, returning only matching rows from both tables.

beginner

In an inner join, what happens to rows that do not have matching values in both tables?

Rows without matching values in both tables are excluded from the result set.

beginner

How do you perform an inner join using pandas in Python?

Use pandas.merge(left_df, right_df, on='key_column', how='inner') to join two dataframes on a key column.

intermediate

Why is an inner join useful in data analysis?

It helps combine related data from different tables, showing only the rows where there is a match, which is useful for focused analysis.

intermediate

What is the difference between an inner join and a left join?

An inner join returns only matching rows from both tables, while a left join returns all rows from the left table and matching rows from the right table (or NaN if no match).

What does an inner join return?

A Only rows with matching keys in both tables

B All rows from the left table

C All rows from the right table

D All rows from both tables

An inner join returns only rows where the join condition matches in both tables.

Which pandas function is used to perform an inner join?

A pandas.concat()

B pandas.append()

C pandas.join() with how='outer'

D pandas.merge() with how='inner'

Use pandas.merge() with the argument how='inner' to perform an inner join.

If a row in the left table has no matching row in the right table, will it appear in an inner join result?

A Yes, always

B No, it will be excluded

C Only if specified with a condition

D Only if the right table is empty

Inner join excludes rows without matching keys in both tables.

Which SQL keyword is used to specify an inner join?

A INNER JOIN

B OUTER JOIN

C LEFT JOIN

D JOIN

INNER JOIN explicitly specifies an inner join in SQL.

What is the main purpose of using an inner join?

A To remove duplicates from a table

B To combine all rows from two tables

C To combine only matching rows from two tables

D To sort data in a table

Inner join combines only rows with matching keys from both tables.

Explain what an inner join does and give a simple example of when you might use it.

Think about how you combine two lists but keep only the common items.

Describe how to perform an inner join using pandas in Python and what parameters are important.

Remember the function that combines dataframes based on a key.

Practice

1. What does an inner join do when combining two tables?

easy

A. Returns only rows with matching keys in both tables

B. Returns all rows from the first table, matching or not

C. Returns all rows from the second table, matching or not

D. Returns all rows from both tables, matching or not

Solution

1. Step 1: Understand join types

   Inner join returns rows where keys match in both tables only.

2. Step 2: Compare options with definition

   The option 'Returns only rows with matching keys in both tables' describes this behavior correctly.

3. Final Answer:

   Returns only rows with matching keys in both tables -> Option A

4. Quick Check:

   Inner join = matching rows only [OK]

Hint: Inner join keeps only matching rows from both tables [OK]

Common Mistakes:

• Confusing inner join with left or right join
• Thinking inner join returns unmatched rows
• Mixing up join types and their results

2. Which of the following is the correct syntax to perform an inner join of two pandas DataFrames df1 and df2 on column 'id'?

easy

A. df1.join(df2, on='id', how='outer')

B. pd.concat([df1, df2], axis=1, join='inner')

C. pd.merge(df1, df2, on='id', how='inner')

D. df1.merge(df2, on='id', how='left')

Solution

1. Step 1: Identify pandas inner join syntax

   The correct function is pd.merge() with how='inner' and on='id'.

2. Step 2: Check each option

   pd.merge(df1, df2, on='id', how='inner') matches this exactly; others use wrong methods or join types.

3. Final Answer:

   pd.merge(df1, df2, on='id', how='inner') -> Option C

4. Quick Check:

   pd.merge + how='inner' = inner join [OK]

Hint: Use pd.merge with how='inner' for inner joins [OK]

Common Mistakes:

• Using join() instead of merge() for inner join
• Using wrong join type like 'outer' or 'left'
• Using concat which does not perform join

3. Given two DataFrames:

df1 = pd.DataFrame({'id': [1, 2, 3], 'val1': ['A', 'B', 'C']})
df2 = pd.DataFrame({'id': [2, 3, 4], 'val2': ['X', 'Y', 'Z']})
result = pd.merge(df1, df2, on='id', how='inner')

What is the content of result?

medium

A. {'id': [2, 3], 'val1': ['B', 'C'], 'val2': ['X', 'Y']}

B. {'id': [1, 4], 'val1': ['A', None], 'val2': [None, 'Z']}

C. {'id': [2, 3, 4], 'val1': ['B', 'C', None], 'val2': ['X', 'Y', 'Z']}

D. {'id': [1, 2, 3], 'val1': ['A', 'B', 'C'], 'val2': ['X', 'Y', 'Z']}

Solution

1. Step 1: Identify matching keys for inner join

   Keys in both df1 and df2 are 2 and 3 only.

2. Step 2: Merge rows with matching keys

   Rows with id 2 and 3 combine val1 and val2 columns.

3. Final Answer:

   {'id': [2, 3], 'val1': ['B', 'C'], 'val2': ['X', 'Y']} -> Option A

4. Quick Check:

   Inner join keeps ids 2 and 3 only [OK]

Hint: Inner join keeps only common keys in both DataFrames [OK]

Common Mistakes:

• Including keys not present in both tables
• Confusing inner join with outer join
• Assuming all rows are kept

4. You wrote this code to inner join two DataFrames:

result = pd.merge(df1, df2, how='inner')

But it raises an error. What is the likely cause?

medium

A. Using 'how' parameter incorrectly

B. Missing the 'on' parameter to specify join key

C. DataFrames must be converted to lists first

D. Inner join is not supported by pandas

Solution

1. Step 1: Check required parameters for pd.merge

   pd.merge requires 'on' or 'left_on'/'right_on' to specify join keys.

2. Step 2: Identify missing 'on' parameter

   Code misses 'on' parameter, causing error.

3. Final Answer:

   Missing the 'on' parameter to specify join key -> Option B

4. Quick Check:

   pd.merge needs 'on' key [OK]

Hint: Always specify 'on' column for merge keys [OK]

Common Mistakes:

• Forgetting to specify join keys
• Assuming default join key exists
• Trying to join incompatible data types

5. You have two DataFrames:

df1 = pd.DataFrame({'id': [1, 2, 2, 3], 'val1': ['A', 'B', 'C', 'D']})
df2 = pd.DataFrame({'id': [2, 2, 3], 'val2': ['X', 'Y', 'Z']})
result = pd.merge(df1, df2, on='id', how='inner')

How many rows will result contain after the inner join?

hard

A. 6

B. 4

C. 3

D. 5

Solution

1. Step 1: Identify matching keys and duplicates

   Keys 2 and 3 appear multiple times in both DataFrames.

2. Step 2: Calculate Cartesian product for duplicates

   For id=2: df1 has 2 rows, df2 has 2 rows -> 2*2=4 rows.
   For id=3: df1 has 1 row, df2 has 1 row -> 1*1=1 row.
   Total rows = 4 + 1 = 5 rows.

3. Step 3: Recount carefully

   Recount confirms 5 rows total.

4. Final Answer:

   5 -> Option D

5. Quick Check:

   Duplicates multiply rows in inner join [OK]

Hint: Duplicates multiply rows in inner join result [OK]

Common Mistakes:

• Ignoring duplicate keys causing more rows
• Counting unique keys only
• Assuming one-to-one join always