Removing duplicates (drop_duplicates) in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the drop_duplicates() function do in pandas?

It removes duplicate rows from a DataFrame, keeping only unique rows.

beginner

How can you keep the last occurrence of a duplicate row using drop_duplicates()?

Use the parameter keep='last' to keep the last duplicate and remove earlier ones.

intermediate

What parameter do you use to remove duplicates based on specific columns only?

Use the subset parameter with a list of column names to check duplicates only on those columns.

intermediate

What happens if you set inplace=True in drop_duplicates()?

The DataFrame is modified directly without creating a new copy, so duplicates are removed in the original DataFrame.

beginner

Why is removing duplicates important in data analysis?

Duplicates can cause incorrect analysis results, like counting the same data multiple times or biasing statistics.

What is the default behavior of drop_duplicates() when duplicates are found?

A Removes all duplicates including the first

B Keeps the last occurrence and removes the rest

C Keeps the first occurrence and removes the rest

D Keeps all duplicates

By default, drop_duplicates() keeps the first occurrence of each duplicate row.

Which parameter lets you specify columns to check for duplicates?

A columns

B subset

C filter

D axis

The subset parameter takes a list of columns to consider when identifying duplicates.

What does setting inplace=True do in drop_duplicates()?

A Modifies the original DataFrame directly

B Duplicates are marked but not removed

C Does nothing

D Returns a new DataFrame without duplicates

Setting inplace=True changes the original DataFrame by removing duplicates without returning a new one.

If you want to keep the last duplicate row instead of the first, which argument do you use?

A keep='first'

B keep='none'

C keep='all'

D keep='last'

Use keep='last' to keep the last occurrence of duplicates.

Why should duplicates be removed before analysis?

A To avoid biased or incorrect results

B To reduce file size only

C Duplicates improve accuracy

D Duplicates are always needed

Duplicates can skew results by counting the same data multiple times, so removing them improves accuracy.

Explain how to use drop_duplicates() to remove duplicate rows based on specific columns and keep the last occurrence.

Think about which columns to check and which duplicate to keep.

Describe why removing duplicates is important in data analysis and what problems duplicates can cause.

Consider how duplicates affect statistics and decisions.

Practice

1. What does the drop_duplicates() function do in a pandas DataFrame?

easy

A. Fills missing values with zeros

B. Removes repeated rows based on all columns by default

C. Sorts the DataFrame rows in ascending order

D. Changes the data type of columns

Solution

1. Step 1: Understand the function purpose

   drop_duplicates() is used to remove repeated rows in a DataFrame.

2. Step 2: Default behavior

   By default, it checks all columns to find duplicates and keeps the first occurrence.

3. Final Answer:

   Removes repeated rows based on all columns by default -> Option B

4. Quick Check:

   drop_duplicates() removes repeated rows [OK]

Hint: Remember: drop_duplicates removes repeated rows [OK]

Common Mistakes:

• Thinking it sorts data
• Confusing with fillna()
• Assuming it changes data types

2. Which of the following is the correct syntax to remove duplicate rows from a DataFrame df?

easy

A. df.drop_duplicates[]

B. df.remove_duplicates()

C. df.drop_duplicates()

D. df.drop_duplicates{}

Solution

1. Step 1: Identify correct method call

   The method to remove duplicates is drop_duplicates(), called with parentheses.

2. Step 2: Check syntax correctness

   Only df.drop_duplicates() is valid syntax; brackets or braces are incorrect.

3. Final Answer:

   df.drop_duplicates() -> Option C

4. Quick Check:

   Use parentheses to call methods [OK]

Hint: Use parentheses () to call drop_duplicates method [OK]

Common Mistakes:

• Using square brackets instead of parentheses
• Using a non-existent method name
• Using curly braces instead of parentheses

3. Given the DataFrame df below, what will df.drop_duplicates(subset=['Name']) return?

import pandas as pd
data = {'Name': ['Anna', 'Bob', 'Anna', 'Cody'], 'Age': [25, 30, 25, 22]}
df = pd.DataFrame(data)

medium

A. [{'Name': 'Anna', 'Age': 25}, {'Name': 'Bob', 'Age': 30}, {'Name': 'Cody', 'Age': 22}]

B. [{'Name': 'Anna', 'Age': 25}, {'Name': 'Anna', 'Age': 25}, {'Name': 'Bob', 'Age': 30}, {'Name': 'Cody', 'Age': 22}]

C. [{'Name': 'Bob', 'Age': 30}, {'Name': 'Cody', 'Age': 22}]

D. [{'Name': 'Anna', 'Age': 25}, {'Name': 'Bob', 'Age': 30}]

Solution

1. Step 1: Understand subset parameter

   Using subset=['Name'] means duplicates are checked only on the 'Name' column.

2. Step 2: Identify duplicates by 'Name'

   'Anna' appears twice; only the first occurrence is kept, others dropped.

3. Final Answer:

   [{'Name': 'Anna', 'Age': 25}, {'Name': 'Bob', 'Age': 30}, {'Name': 'Cody', 'Age': 22}] -> Option A

4. Quick Check:

   Duplicates dropped by 'Name' column [OK]

Hint: Use subset to check duplicates on specific columns [OK]

Common Mistakes:

• Not using subset and dropping all duplicates
• Keeping all duplicates instead of first
• Confusing which rows are dropped

4. What is wrong with this code snippet to remove duplicates from DataFrame df?

df.drop_duplicates(inplace=True)

After running it, duplicates still appear in df. Why?

medium

A. The DataFrame has no duplicates to remove

B. drop_duplicates() does not modify the original DataFrame even with inplace=True

C. The DataFrame was not reassigned and inplace=True is ignored

D. The inplace parameter is misspelled

Solution

1. Step 1: Check inplace parameter usage

   inplace=True modifies the original DataFrame, so no reassignment needed.

2. Step 2: Consider DataFrame content

   If duplicates still appear, likely the DataFrame had no duplicates to remove.

3. Final Answer:

   The DataFrame has no duplicates to remove -> Option A

4. Quick Check:

   inplace=True modifies original; no duplicates means no change [OK]

Hint: Check if DataFrame actually has duplicates before drop_duplicates [OK]

Common Mistakes:

• Assuming inplace=True does nothing
• Thinking drop_duplicates always changes data
• Not verifying if duplicates exist

5. You have a DataFrame df with columns ['ID', 'Name', 'Score']. Some rows have duplicate ID values but different Score. How do you keep only the row with the highest Score for each ID?

hard

A. Use df.drop_duplicates(subset=['Score'])

B. Use df.drop_duplicates(subset=['ID']) without sorting

C. Use df.drop_duplicates(keep='last') without sorting

D. Use df.sort_values('Score', ascending=False).drop_duplicates(subset=['ID'])

Solution

1. Step 1: Sort DataFrame by Score descending

   Sorting by 'Score' descending puts highest scores first for each ID.

2. Step 2: Drop duplicates by ID keeping first

   After sorting, dropping duplicates by 'ID' keeps the first (highest score) row per ID.

3. Final Answer:

   df.sort_values('Score', ascending=False).drop_duplicates(subset=['ID']) -> Option D

4. Quick Check:

   Sort then drop duplicates keeps highest score per ID [OK]

Hint: Sort by score descending before drop_duplicates to keep max [OK]

Common Mistakes:

• Not sorting before dropping duplicates
• Dropping duplicates on wrong column
• Using keep='last' without sorting