Keeping first vs last vs none in Pandas - Quick Revision & Key Differences

Recall & Review

beginner

What does the 'keep' parameter do in pandas' drop_duplicates() method?

The 'keep' parameter decides which duplicate to keep: 'first' keeps the first occurrence, 'last' keeps the last occurrence, and False drops all duplicates.

intermediate

In pandas, what happens if you set keep=False in drop_duplicates()?

All rows that have duplicates are removed, so only unique rows remain with no duplicates at all.

beginner

How does keep='first' differ from keep='last' in drop_duplicates()?

keep='first' keeps the first occurrence of each duplicate group and removes the rest, while keep='last' keeps the last occurrence and removes earlier ones.

beginner

True or False: Using keep=False in drop_duplicates() will keep one row from each duplicate group.

False. keep=False removes all duplicates, so no rows from duplicate groups are kept.

intermediate

Why might you use keep=False instead of 'first' or 'last' when removing duplicates?

To ensure that only completely unique rows remain, removing all rows that appear more than once, which can be important for clean data analysis.

What does keep='first' do in pandas drop_duplicates()?

A Keeps the first occurrence of duplicates and removes the rest

B Keeps the last occurrence of duplicates and removes the rest

C Removes all duplicates completely

D Keeps all duplicates

keep='first' keeps the first row of each duplicate group and removes the others.

If you want to remove all rows that have duplicates, which keep option should you use?

A last

B False

C first

D all

keep=False removes all rows that have duplicates, leaving only unique rows.

What is the default value of the keep parameter in drop_duplicates()?

A first

B last

C all

D none

By default, keep='first' in drop_duplicates(), so the first occurrence is kept.

Which keep option keeps the last duplicate row?

A all

B first

C none

D last

keep='last' keeps the last occurrence of each duplicate group.

What happens if you set keep=False and there are no duplicates in the data?

A All rows are removed

B Only the first row is kept

C No rows are removed

D Only the last row is kept

If there are no duplicates, keep=False removes nothing because no rows are duplicated.

Explain the difference between keep='first', keep='last', and keep=False in pandas drop_duplicates().

Think about which rows remain after duplicates are removed.

Describe a situation where using keep=False would be better than keep='first' or 'last'.

Consider data cleaning for accurate results.

Practice

1.

In pandas, what does keep='first' do when removing duplicates?

easy

A. Keeps the first occurrence of each duplicate and removes the rest

B. Keeps the last occurrence of each duplicate and removes the rest

C. Removes all duplicates, keeping only unique rows

D. Keeps all duplicates without removing any

Solution

1. Step 1: Understand the meaning of keep='first'

   This option keeps the first row of each set of duplicates and removes the later ones.

2. Step 2: Compare with other options

   Unlike keep='last' which keeps the last occurrence, keep='first' keeps the earliest one.

3. Final Answer:

   Keeps the first occurrence of each duplicate and removes the rest -> Option A

4. Quick Check:

   keep='first' = Keep first duplicate [OK]

Hint: Remember: 'first' keeps earliest duplicate row [OK]

Common Mistakes:

• Confusing keep='first' with keep='last'
• Thinking keep='first' removes all duplicates
• Assuming keep='first' keeps all duplicates

2.

Which of the following is the correct syntax to remove duplicates in a pandas DataFrame df keeping only the last occurrence?

df._____('column_name', keep='last')

easy

A. drop_dupes

B. remove_duplicates

C. drop_duplicates

D. delete_duplicates

Solution

1. Step 1: Identify the pandas method for removing duplicates

   The correct method is drop_duplicates to remove duplicate rows.

2. Step 2: Confirm syntax for keeping last occurrence

   Using keep='last' inside drop_duplicates keeps the last duplicate row.

3. Final Answer:

   drop_duplicates -> Option C

4. Quick Check:

   Method to remove duplicates = drop_duplicates [OK]

Hint: Use df.drop_duplicates() to remove duplicates [OK]

Common Mistakes:

• Using non-existent methods like remove_duplicates
• Confusing method names with similar words
• Forgetting to specify keep='last'

3.

What will be the output of the following code?

import pandas as pd

data = {'A': [1, 2, 2, 3, 3, 3], 'B': ['x', 'y', 'y', 'z', 'z', 'z']}
df = pd.DataFrame(data)
result = df.drop_duplicates(keep=False)
print(result)

medium

A. Rows with unique values only, no duplicates

B. All rows except the first occurrence of duplicates

C. Rows with A=1 B='x', A=2 B='y', A=3 B='z' only

D. Empty DataFrame

Solution

1. Step 1: Understand keep=False behavior

   Using keep=False removes all duplicates, keeping only rows that appear once.

2. Step 2: Identify unique rows in the DataFrame

   In the data, only the row with A=1 and B='x' is unique; others appear multiple times.

3. Final Answer:

   Rows with unique values only, no duplicates -> Option A

4. Quick Check:

   keep=False = Keep only unique rows [OK]

Hint: keep=False drops all duplicates, keeps only unique rows [OK]

Common Mistakes:

• Thinking keep=False keeps first or last duplicates
• Expecting some duplicates to remain
• Confusing output with keep='first' or keep='last'

4.

Consider this code snippet:

import pandas as pd

data = {'col': [1, 1, 2, 2, 3]}
df = pd.DataFrame(data)
result = df.drop_duplicates(keep='middle')
print(result)

What is the error in this code?

medium

A. DataFrame missing required columns

B. Invalid value for the 'keep' parameter

C. Syntax error in DataFrame creation

D. No error, code runs fine

Solution

1. Step 1: Check the 'keep' parameter values

   The valid values for 'keep' are 'first', 'last', or False. 'middle' is invalid.

2. Step 2: Identify the error raised by invalid 'keep'

   Using 'middle' causes a ValueError because pandas does not recognize it.

3. Final Answer:

   Invalid value for the 'keep' parameter -> Option B

4. Quick Check:

   keep must be 'first', 'last', or False [OK]

Hint: Use only 'first', 'last', or False for keep parameter [OK]

Common Mistakes:

• Using unsupported strings for keep
• Assuming 'middle' is valid
• Ignoring error messages

5.

You have a DataFrame df with duplicate rows based on column 'ID'. You want to remove all duplicates completely, so only rows with unique 'ID' remain. Which code snippet achieves this?

1) df.drop_duplicates(subset=['ID'], keep='first')
2) df.drop_duplicates(subset=['ID'], keep='last')
3) df.drop_duplicates(subset=['ID'], keep=False)
4) df.drop_duplicates()

hard

A. Option 1

B. Option 4

C. Option 2

D. Option 3

Solution

1. Step 1: Understand the goal

   The goal is to remove all duplicates so only unique 'ID' rows remain.

2. Step 2: Analyze each option

   Options 1 and 2 keep one duplicate (first or last). Option 4 removes duplicates but keeps first by default. Only option 3 with keep=False removes all duplicates completely.

3. Final Answer:

   Option 3 -> Option D

4. Quick Check:

   keep=False removes all duplicates [OK]

Hint: Use keep=False to remove all duplicates fully [OK]

Common Mistakes:

• Using keep='first' or 'last' when full removal needed
• Not specifying subset for duplicate check
• Assuming default drop_duplicates removes all duplicates