duplicated() for finding duplicates in Pandas - Step-by-Step Execution

Concept Flow - duplicated() for finding duplicates

Start with DataFrame

↓

Call duplicated()

↓

Check each row if duplicate

↓

Mark True for duplicates, False for first occurrences

↓

Return Boolean Series

↓

Use result to filter or analyze duplicates

The duplicated() method checks each row in a DataFrame and marks True if it is a duplicate of a previous row, otherwise False.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 2, 3, 1],
    'B': ['x', 'y', 'y', 'z', 'x']
})

result = df.duplicated()

This code creates a DataFrame and uses duplicated() to find which rows are duplicates of earlier rows.

Execution Table

Step	Row Index	Row Data	Is Duplicate?	Reason
1	0	{'A': 1, 'B': 'x'}	False	First occurrence of this row
2	1	{'A': 2, 'B': 'y'}	False	First occurrence of this row
3	2	{'A': 2, 'B': 'y'}	True	Duplicate of row 1
4	3	{'A': 3, 'B': 'z'}	False	First occurrence of this row
5	4	{'A': 1, 'B': 'x'}	True	Duplicate of row 0
6	End			All rows checked

💡 All rows processed, duplicated() returns Boolean Series marking duplicates

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	Final
result	empty	[False]	[False, False]	[False, False, True]	[False, False, True, False]	[False, False, True, False, True]	[False, False, True, False, True]

Key Moments - 2 Insights

Why is the first occurrence of a row marked False, not True?

duplicated() marks only repeated rows as True. The first time a row appears, it is not a duplicate, so it is False (see execution_table rows 1 and 2).

How does duplicated() decide which rows are duplicates?

It compares each row to all previous rows. If it matches any earlier row exactly, it marks True (see execution_table row 3 and 5).

Visual Quiz - 3 Questions

Look at the execution_table, what is the 'Is Duplicate?' value for row index 3?

A True

B False

C None

D Error

💡 Hint

Check execution_table row 4 for row index 3's duplicate status.

At which step does duplicated() first mark a row as True?

A Step 3

B Step 2

C Step 4

D Step 5

💡 Hint

Look at execution_table rows 2 and 3 to see when True first appears.

If the DataFrame had no repeated rows, what would the final 'result' variable look like?

A [True, True, True, True, True]

B [False, True, False, True, False]

C [False, False, False, False, False]

D [True, False, True, False, True]

💡 Hint

duplicated() marks only duplicates as True, first occurrences are False (see variable_tracker final).

Concept Snapshot

duplicated() checks each row in a DataFrame
Returns a Boolean Series: True for duplicates, False for first occurrences
By default, compares all columns
Use to filter or analyze repeated rows
Example: df.duplicated()

Full Transcript

The duplicated() method in pandas helps find duplicate rows in a DataFrame. It looks at each row and checks if it has appeared before. If yes, it marks that row as True, meaning it is a duplicate. If not, it marks it as False, meaning it is the first time this row appears. This method returns a Boolean Series that matches the DataFrame's rows. You can use this result to filter out duplicates or analyze them. For example, in the sample DataFrame, rows 2 and 4 are duplicates of earlier rows, so duplicated() marks them True. The first occurrences are always False. This helps you quickly spot repeated data.

Practice

1. What does the duplicated() function in pandas do?

easy

A. It identifies duplicate rows in a DataFrame.

B. It removes duplicate rows from a DataFrame.

C. It sorts the DataFrame by duplicate values.

D. It counts the number of unique rows in a DataFrame.

Solution

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

   The duplicated() function checks each row and marks it as True if it is a duplicate of a previous row.

2. Step 2: Differentiate from other functions

   Unlike functions that remove duplicates or count unique rows, duplicated() only flags duplicates without changing the DataFrame.

3. Final Answer:

   It identifies duplicate rows in a DataFrame. -> Option A

4. Quick Check:

   duplicated() = identifies duplicates [OK]

Hint: Remember: duplicated() flags duplicates, doesn't remove them [OK]

Common Mistakes:

• Confusing duplicated() with drop_duplicates()
• Thinking duplicated() sorts data
• Assuming duplicated() counts unique rows

2. Which of the following is the correct syntax to find duplicates in a pandas DataFrame df?

easy

A. df.duplicated()

B. df.duplicates()

C. df.find_duplicates()

D. df.duplicate()

Solution

1. Step 1: Recall the exact function name

   The correct pandas function to find duplicate rows is duplicated(), not any other variation.

2. Step 2: Verify syntax correctness

   The function is called on the DataFrame object as df.duplicated() with parentheses.

3. Final Answer:

   df.duplicated() -> Option A

4. Quick Check:

   Correct function name = df.duplicated() [OK]

Hint: Use exact function name: duplicated() with parentheses [OK]

Common Mistakes:

• Misspelling the function name
• Omitting parentheses after duplicated
• Using non-existent functions like duplicates()

3. Given the DataFrame df below, what is the output of df.duplicated()?

import pandas as pd
data = {'A': [1, 2, 2, 3], 'B': ['x', 'y', 'y', 'z']}
df = pd.DataFrame(data)
print(df.duplicated())

medium

A. [True, False, True, False]

B. [False, False, True, False]

C. [False, True, False, True]

D. [False, False, False, True]

Solution

1. Step 1: Identify duplicate rows in the DataFrame

   The rows are: 0: (1, 'x'), 1: (2, 'y'), 2: (2, 'y'), 3: (3, 'z'). Row 2 is a duplicate of row 1.

2. Step 2: Understand duplicated() output

   duplicated() returns False for the first occurrence and True for duplicates. So, rows 0,1,3 are False; row 2 is True.

3. Final Answer:

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

4. Quick Check:

   First duplicate row marked True = [False, False, True, False] [OK]

Hint: First occurrence is False, duplicates after are True [OK]

Common Mistakes:

• Marking first occurrence as True
• Confusing row indices with values
• Assuming all duplicates are True regardless of position

4. What is wrong with this code snippet to find duplicates in column 'A' of DataFrame df?

duplicates = df.duplicated['A']
print(duplicates)

medium

A. duplicated() cannot check specific columns

B. Missing import statement for pandas

C. The print statement is incorrect

D. Using square brackets instead of parentheses to call duplicated()

Solution

1. Step 1: Check function call syntax

   The code uses square brackets [] instead of parentheses () to call the duplicated() method, which is incorrect syntax.

2. Step 2: Correct way to specify columns

   To check duplicates based on column 'A', the correct syntax is df.duplicated(subset=['A']) with parentheses.

3. Final Answer:

   Using square brackets instead of parentheses to call duplicated() -> Option D

4. Quick Check:

   Call methods with () not [] [OK]

Hint: Call duplicated() with parentheses, not square brackets [OK]

Common Mistakes:

• Using [] instead of () to call functions
• Trying to access duplicated as an attribute
• Not using subset parameter for specific columns

5. You have a DataFrame df with columns 'Name', 'Age', and 'City'. You want to find duplicates only based on 'Name' and 'City', ignoring 'Age'. Which code correctly finds these duplicates?

hard

A. df.duplicated(['Name', 'City'], keep=True)

B. df.duplicated(columns=['Name', 'City'])

C. df.duplicated(subset=['Name', 'City'])

D. df.duplicated('Name', 'City')

Solution

1. Step 1: Use subset parameter to specify columns

   To check duplicates based on specific columns, use the subset parameter with a list of column names.

2. Step 2: Validate correct syntax

   The correct syntax is df.duplicated(subset=['Name', 'City']). df.duplicated(columns=['Name', 'City']) uses a wrong parameter name (columns), and options A and C use invalid syntax.

3. Final Answer:

   df.duplicated(subset=['Name', 'City']) -> Option C

4. Quick Check:

   Use subset=['col1', 'col2'] to check specific columns [OK]

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

Common Mistakes:

• Using wrong parameter name like columns instead of subset
• Passing list without parameter name
• Confusing keep parameter with subset