Left join behavior in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a left join of df1 and df2 on the 'key' column.

Pandas

result = df1.merge(df2, how=[1], on='key')

Drag options to blanks, or click blank then click option'

A'outer'

B'inner'

C'right'

D'left'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'inner' instead of 'left' will drop rows not matching in df2.

Using 'right' or 'outer' will change which rows are kept.

The left join keeps all rows from the left DataFrame (df1) and adds matching rows from df2.

2fill in blank

medium

Complete the code to join df1 and df2 on columns 'key1' and 'key2' using a left join.

Pandas

result = df1.merge(df2, how='left', on=[1])

Drag options to blanks, or click blank then click option'

A'key'

B'key2'

C['key1', 'key2']

D['key']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single string instead of a list for multiple keys.

Using incorrect column names.

To join on multiple columns, pass a list of column names to the on parameter.

3fill in blank

hard

Fix the error in the code to perform a left join on 'id' column.

Pandas

result = df1.merge(df2, how='[1]', on='id')

Drag options to blanks, or click blank then click option'

Aleftt

Bleft

Clef

Dright

Attempts:

3 left

💡 Hint

Common Mistakes

Typo in the join type string.

Using 'right' instead of 'left' changes the join behavior.

The correct spelling for the left join is 'left'. Misspelling causes an error.

4fill in blank

hard

Fill both blanks to create a left join that adds suffix '_df2' to overlapping columns from df2.

Pandas

result = df1.merge(df2, how=[1], on='key', suffixes=([2], '_df2'))

Drag options to blanks, or click blank then click option'

A'left'

B'_df1'

C'_left'

D'_right'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong join type like 'right' or 'inner'.

Not providing suffixes or using wrong suffix format.

Use how='left' for left join and suffixes=('_df1', '_df2') to rename overlapping columns.

5fill in blank

hard

Fill all three blanks to left join df1 and df2 on 'key', keep all df1 rows, and fill missing values in 'value' column with 0.

Pandas

result = df1.merge(df2, how=[1], on=[2])
result['value'] = result['value'].fillna([3])

Drag options to blanks, or click blank then click option'

A'left'

B'key'

C0

D'value'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong join type like 'inner'.

Joining on wrong column name.

Not filling missing values or using wrong fill value.

Use how='left' to keep all rows from df1, join on 'key', and fill missing 'value' with 0 using fillna(0).

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