Duplicates on specific columns in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the pandas function duplicated() do when used on a DataFrame?

It returns a Boolean Series indicating whether each row is a duplicate of a previous row. By default, it checks all columns.

beginner

How can you check for duplicates only on specific columns in a pandas DataFrame?

Use df.duplicated(subset=[column_names]) where column_names is a list of columns to check for duplicates.

beginner

What does the parameter keep='first' do in duplicated()?

It marks all duplicates as True except for the first occurrence, which is marked as False.

beginner

How do you remove duplicate rows based on specific columns in pandas?

Use df.drop_duplicates(subset=[column_names]) to keep only the first occurrence of duplicates in those columns.

beginner

Why might you want to check duplicates on specific columns instead of the whole DataFrame?

Because sometimes only certain columns define the uniqueness of a row, and other columns may have different data but still represent the same entity.

Which pandas method helps identify duplicate rows based on specific columns?

A fillna()

B dropna()

C groupby()

D duplicated(subset=[...])

The duplicated() method with the subset parameter checks duplicates on specific columns.

What does df.duplicated(subset=['col1', 'col2'], keep='last') do?

A Marks all duplicates except the last occurrence as True

B Marks all duplicates except the first occurrence as True

C Removes duplicates from the DataFrame

D Checks duplicates on all columns

The keep='last' parameter marks duplicates as True except for the last occurrence.

How do you remove duplicate rows based on columns 'A' and 'B' in pandas?

A df.duplicated(['A', 'B'])

B df.dropna(['A', 'B'])

C df.drop_duplicates(subset=['A', 'B'])

D df.fillna(['A', 'B'])

drop_duplicates with subset=['A', 'B'] removes duplicates based on those columns.

If you want to find duplicates considering all columns, what should you do?

A Call duplicated() without subset parameter

B Use duplicated(subset=[])

C Use drop_duplicates(subset=[])

D Use duplicated(subset=None)

By default, duplicated() checks duplicates on all columns if subset is not provided.

Why is it useful to specify columns in the subset parameter when checking duplicates?

A To sort the DataFrame

B To focus on columns that define uniqueness

C To change data types

D To speed up the DataFrame

Specifying subset focuses duplicate detection on columns that matter for uniqueness.

Explain how to find and remove duplicate rows based on specific columns in a pandas DataFrame.

Think about how to tell pandas which columns to check for duplicates.

Why might checking duplicates on the entire DataFrame be less useful than checking on specific columns?

Consider real-life examples where only some details matter for identifying duplicates.

Practice

1. What does the subset parameter do in the duplicated() method in pandas?

easy

A. It specifies which columns to check for duplicates.

B. It removes duplicates from the DataFrame.

C. It sorts the DataFrame before checking duplicates.

D. It changes the data type of columns before checking duplicates.

Solution

1. Step 1: Understand the purpose of duplicated()

   The duplicated() method identifies duplicate rows in a DataFrame.

2. Step 2: Role of subset parameter

   The subset parameter limits the duplicate check to specific columns instead of all columns.

3. Final Answer:

   It specifies which columns to check for duplicates. -> Option A

4. Quick Check:

   subset = columns to check duplicates [OK]

Hint: Use subset to focus duplicate check on chosen columns [OK]

Common Mistakes:

• Thinking subset removes duplicates
• Confusing subset with sorting
• Assuming subset changes data types

2. Which of the following is the correct syntax to find duplicates based on columns 'A' and 'B' in a pandas DataFrame df?

easy

A. df.duplicated(subset=['A', 'B'])

B. df.duplicates(columns=['A', 'B'])

C. df.duplicated(columns=['A', 'B'])

D. df.find_duplicates(['A', 'B'])

Solution

1. Step 1: Recall pandas method for duplicates

   The correct method is duplicated() to find duplicate rows.

2. Step 2: Correct use of subset parameter

   The subset parameter takes a list of column names to check duplicates on.

3. Final Answer:

   df.duplicated(subset=['A', 'B']) -> Option A

4. Quick Check:

   Correct method and subset syntax [OK]

Hint: Use duplicated(subset=[...]) to specify columns [OK]

Common Mistakes:

• Using non-existent methods like duplicates()
• Passing columns instead of subset
• Wrong method names like find_duplicates()

3. Given the DataFrame:

import pandas as pd
data = {'A': [1, 2, 1, 3], 'B': [4, 5, 4, 6], 'C': [7, 8, 9, 10]}
df = pd.DataFrame(data)
print(df.duplicated(subset=['A', 'B']))

What is the output?

medium

A. [False, True, False, False]

B. [True, False, True, False]

C. [False, False, True, False]

D. [False, False, False, True]

Solution

1. Step 1: Understand the DataFrame and subset

   The DataFrame has rows with columns A and B: (1,4), (2,5), (1,4), (3,6).

2. Step 2: Identify duplicates on columns 'A' and 'B'

   The third row (index 2) duplicates the first row (index 0) on columns A and B.

3. Final Answer:

   [False, False, True, False] -> Option C

4. Quick Check:

   Only third row is duplicate on A and B [OK]

Hint: Check repeated (A,B) pairs for True in duplicated() output [OK]

Common Mistakes:

• Assuming duplicates on all columns
• Marking first occurrence as duplicate
• Confusing index positions

4. You run this code:

df.duplicated(subset=['A', 'B'], keep='last')

but get unexpected results. What is a common mistake causing this?

medium

A. Forgetting to pass a list to subset causes an error.

B. Using keep='last' marks the last occurrence as False, others as True.

C. Using keep='last' marks the first duplicates as True, not the last.

D. The duplicated() method does not accept keep parameter.

Solution

1. Step 1: Understand keep parameter behavior

   keep='last' marks all duplicates except the last occurrence as True.

2. Step 2: Identify common confusion

   Many expect it to mark the last duplicate as True, but it actually keeps the last as False (not duplicate).

3. Final Answer:

   Using keep='last' marks the last occurrence as False, others as True. -> Option B

4. Quick Check:

   keep='last' keeps last duplicate False [OK]

Hint: Remember keep='last' keeps last duplicate False, others True [OK]

Common Mistakes:

• Thinking keep='last' marks last duplicate True
• Passing subset as string instead of list
• Believing duplicated() lacks keep parameter

5. You have a DataFrame df with columns 'Name', 'Age', 'City'. You want to remove rows where 'Name' and 'City' are duplicates but keep the first occurrence. Which code correctly does this?

hard

A. df[~df.duplicated(subset=['Name', 'City'], keep='first')]

B. df = df.duplicated(subset=['Name', 'City'], keep='first')

C. df = df.drop_duplicates(columns=['Name', 'City'], keep='last')

D. df = df.drop_duplicates(subset=['Name', 'City'], keep='first')

Solution

1. Step 1: Identify method to remove duplicates

   Use drop_duplicates() to remove duplicate rows.

2. Step 2: Use subset and keep parameters correctly

   Pass subset=['Name', 'City'] and keep='first' to keep first occurrence and remove others.

3. Step 3: Check options

   df = df.drop_duplicates(subset=['Name', 'City'], keep='first') uses correct method and parameters. df[~df.duplicated(subset=['Name', 'City'], keep='first')] uses duplicated() with negation but does not assign back to df. df = df.drop_duplicates(columns=['Name', 'City'], keep='last') uses wrong parameter name. df = df.duplicated(subset=['Name', 'City'], keep='first') returns boolean Series, not DataFrame.

4. Final Answer:

   df = df.drop_duplicates(subset=['Name', 'City'], keep='first') -> Option D

5. Quick Check:

   drop_duplicates with subset and keep='first' removes duplicates [OK]

Hint: Use drop_duplicates(subset=[...], keep='first') to remove duplicates [OK]

Common Mistakes:

• Using duplicated() instead of drop_duplicates() to remove rows
• Passing columns instead of subset
• Not assigning result back to df