Left join behavior in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does a left join do in pandas?

A left join keeps all rows from the left DataFrame and adds matching rows from the right DataFrame. If there is no match, it fills with NaN for the right DataFrame's columns.

beginner

Which pandas function is used to perform a left join?

The pandas function merge() is used with the argument how='left' to perform a left join.

beginner

What happens to rows in the right DataFrame that do not match any row in the left DataFrame during a left join?

Rows in the right DataFrame that do not match any row in the left DataFrame are ignored and do not appear in the result.

beginner

How does pandas represent missing data after a left join?

Pandas uses NaN (Not a Number) to represent missing data in columns from the right DataFrame when there is no matching row.

intermediate

If you want to keep all rows from both DataFrames, which join type should you use instead of left join?

You should use an outer join by setting how='outer' in merge() to keep all rows from both DataFrames.

What does how='left' do in pandas merge()?

A Keeps all rows from the left DataFrame and matches from the right

B Keeps only matching rows from both DataFrames

C Keeps all rows from the right DataFrame and matches from the left

D Keeps all rows from both DataFrames

A left join keeps all rows from the left DataFrame and adds matching rows from the right DataFrame.

What value fills columns from the right DataFrame when there is no match in a left join?

A 0

B NaN

C Empty string

D False

Pandas fills missing values with NaN when there is no matching row in the right DataFrame.

Which argument in pandas merge() specifies a left join?

A how='inner'

B how='outer'

C how='right'

D how='left'

Use how='left' to perform a left join in pandas merge.

If a row in the right DataFrame has no matching key in the left DataFrame, will it appear in the left join result?

A Yes, always

B Only if specified with an extra argument

C No, it will be excluded

D Only if the right DataFrame is smaller

Rows in the right DataFrame without a match in the left are excluded in a left join.

Which join type keeps all rows from both DataFrames?

A outer

B inner

C right

D left

An outer join keeps all rows from both DataFrames.

Explain how a left join works in pandas and what happens to unmatched rows from the right DataFrame.

Think about which DataFrame's rows always appear in the result.

Describe how to perform a left join in pandas and how missing data is represented after the join.

Focus on the function and the argument for left join.

Practice

1. What does a left join do in pandas when merging two DataFrames?

easy

A. Removes all rows that do not have matches in either DataFrame.

B. Keeps all rows from the left DataFrame and adds matching rows from the right DataFrame.

C. Keeps all rows from the right DataFrame and adds matching rows from the left DataFrame.

D. Keeps only rows that match in both DataFrames.

Solution

1. Step 1: Understand left join behavior

   A left join keeps all rows from the left DataFrame regardless of matches.

2. Step 2: Check how matching rows are added

   Matching rows from the right DataFrame are added; unmatched right rows are ignored.

3. Final Answer:

   Keeps all rows from the left DataFrame and adds matching rows from the right DataFrame. -> Option B

4. Quick Check:

   Left join = all left rows + matching right rows [OK]

Hint: Left join keeps all left rows, adds matching right rows [OK]

Common Mistakes:

• Confusing left join with inner join
• Thinking unmatched right rows appear in output
• Mixing up left and right join behavior

2. Which of the following is the correct syntax to perform a left join of DataFrames df1 and df2 on column 'id' in pandas?

easy

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

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

C. df1.join(df2, on='id', how='right')

D. pd.concat([df1, df2], join='left')

Solution

1. Step 1: Identify correct merge function and parameters

   Use pd.merge with 'on' for join column and 'how="left"' for left join.

2. Step 2: Check each option's correctness

   pd.merge(df1, df2, on='id', how='left') uses correct function and parameters; others use wrong join type or method.

3. Final Answer:

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

4. Quick Check:

   Left join syntax = pd.merge(..., how='left') [OK]

Hint: Use pd.merge with how='left' for left join [OK]

Common Mistakes:

• Using how='inner' instead of 'left'
• Using pd.concat instead of pd.merge
• Confusing join methods like right join

3. Given these DataFrames:

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

What is the result of pd.merge(df1, df2, on='id', how='left')?

medium

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

B. [{'id':2,'val1':'B','val2':'X'},{'id':3,'val1':'C','val2':'Y'},{'id':4,'val1':NaN,'val2':'Z'}]

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

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

Solution

1. Step 1: Identify left join behavior on 'id'

   All rows from df1 (ids 1,2,3) appear; matching df2 rows added.

2. Step 2: Match rows and fill unmatched with NaN

   id=1 has no match in df2, so val2 is NaN; id=2 and 3 match with val2 'X' and 'Y'.

3. Final Answer:

   [{'id':1,'val1':'A','val2':NaN},{'id':2,'val1':'B','val2':'X'},{'id':3,'val1':'C','val2':'Y'}] -> Option A

4. Quick Check:

   Left join keeps all left rows, unmatched right columns = NaN [OK]

Hint: Left join keeps all left rows; unmatched right columns become NaN [OK]

Common Mistakes:

• Including rows from right DataFrame not in left
• Filling unmatched right columns with wrong values
• Confusing left join with inner join output

4. You wrote this code:

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

but got an error: KeyError: 'id'. What is the likely cause?

medium

A. The DataFrames have different lengths.

B. You used 'how="left"' instead of 'how="inner"'.

C. You forgot to import pandas as pd.

D. The 'id' column is missing in one or both DataFrames.

Solution

1. Step 1: Understand KeyError meaning

   KeyError means the specified column 'id' does not exist in one or both DataFrames.

2. Step 2: Check other options for relevance

   Join type or import issues cause different errors; DataFrame length does not cause KeyError.

3. Final Answer:

   The 'id' column is missing in one or both DataFrames. -> Option D

4. Quick Check:

   KeyError 'id' = missing column [OK]

Hint: KeyError on column means column missing in DataFrame [OK]

Common Mistakes:

• Assuming join type causes KeyError
• Ignoring import errors
• Thinking DataFrame length affects merge keys

5. You have two DataFrames:

df1 = pd.DataFrame({'key': [1, 2, 2, 3], 'val1': ['A', 'B', 'C', 'D']})
df2 = pd.DataFrame({'key': [2, 2, 4], 'val2': ['X', 'Y', 'Z']})

You perform pd.merge(df1, df2, on='key', how='left'). How many rows will the result have?

hard

A. 5

B. 4

C. 6

D. 3

Solution

1. Step 1: Count rows in left DataFrame

   df1 has 4 rows with keys [1, 2, 2, 3].

2. Step 2: Understand left join with duplicate keys

   Each df1 row matches all df2 rows with the same key. Key=2 appears twice in df1 and twice in df2, creating 2x2=4 rows for key=2.

3. Step 3: Calculate total rows

   Key=1 and key=3 have no matches, so they remain as single rows with NaN for val2. Total rows = 1 (key=1) + 4 (key=2 matches) + 1 (key=3) = 6.

4. Final Answer:

   6 -> Option C

5. Quick Check:

   Duplicates multiply rows in left join [OK]

Hint: Duplicates in keys multiply rows in left join output [OK]

Common Mistakes:

• Ignoring duplicate keys causing row multiplication
• Counting only unique keys instead of rows
• Assuming unmatched keys are dropped