Counting duplicates in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the duplicated() function in pandas do?

It returns a Boolean Series indicating whether each row is a duplicate of a previous row.
True means the row is a duplicate, False means it is unique so far.

beginner

How can you count the total number of duplicate rows in a DataFrame?

Use df.duplicated().sum().
This counts how many rows are duplicates.

intermediate

What does the keep parameter in duplicated() control?

It controls which duplicates to mark as True:
- 'first' marks duplicates except the first occurrence
- 'last' marks duplicates except the last occurrence
- False marks all duplicates as True

intermediate

How do you count duplicates based on specific columns only?

Pass the column names to the subset parameter in duplicated().
Example: df.duplicated(subset=['col1', 'col2']).sum()

beginner

What is the difference between duplicated() and drop_duplicates()?

duplicated() marks duplicate rows with True/False.
drop_duplicates() removes duplicate rows from the DataFrame.

Which pandas function returns a Boolean Series marking duplicate rows?

A duplicated()

B drop_duplicates()

C count_duplicates()

D is_duplicate()

duplicated() returns True for duplicate rows, False otherwise.

How do you count the number of duplicate rows in a DataFrame df?

A df.count_duplicates()

B df.drop_duplicates().count()

C df.duplicated().sum()

D df.duplicated().count()

df.duplicated() returns a Boolean Series; summing it counts duplicates.

What does df.duplicated(keep=False) do?

A Marks all duplicates as True, including first occurrences

B Marks only the first duplicate as True

C Marks only the last duplicate as True

D Removes duplicates from df

keep=False marks all duplicates as True, not keeping any unique.

To check duplicates based on columns 'A' and 'B' only, which is correct?

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

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

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

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

The subset parameter specifies columns to check for duplicates.

Which function removes duplicate rows from a DataFrame?

A duplicated()

B drop_duplicates()

C remove_duplicates()

D delete_duplicates()

drop_duplicates() returns a DataFrame without duplicate rows.

Explain how to find and count duplicate rows in a pandas DataFrame.

Think about marking duplicates first, then counting them.

Describe the difference between duplicated() and drop_duplicates() in pandas.

One marks duplicates, the other removes them.

Practice

1.

What does the duplicated() function in pandas do?

easy

A. It counts how many times each value appears in a column.

B. It removes all duplicate rows from the DataFrame.

C. It marks duplicate rows as True and unique rows as False.

D. It sorts the DataFrame by duplicate values.

Solution

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

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

2. Step 2: Compare with other functions

   Unlike drop_duplicates() which removes duplicates, duplicated() only marks them. value_counts() counts values, not duplicates.

3. Final Answer:

   It marks duplicate rows as True and unique rows as False. -> Option C

4. Quick Check:

   duplicated() = True/False mask [OK]

Hint: duplicated() flags duplicates, does not remove them [OK]

Common Mistakes:

• Thinking duplicated() removes duplicates
• Confusing duplicated() with value_counts()
• Assuming duplicated() sorts data

2.

Which of the following is the correct syntax to count duplicate rows in a pandas DataFrame df?

?

easy

A. df.duplicated().sum()

B. df.duplicate().count()

C. df.value_counts().duplicated()

D. df.count_duplicates()

Solution

1. Step 1: Identify correct function names

   The correct function to find duplicates is duplicated(). There is no duplicate() or count_duplicates() in pandas.

2. Step 2: Count duplicates using sum()

   duplicated() returns a boolean Series. Using sum() counts how many True values (duplicates) exist.

3. Final Answer:

   df.duplicated().sum() -> Option A

4. Quick Check:

   duplicated() + sum() = count duplicates [OK]

Hint: Use duplicated() then sum() to count duplicates [OK]

Common Mistakes:

• Using wrong function names like duplicate()
• Trying to count duplicates with value_counts() directly
• Using count() instead of sum() on boolean Series

3.

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

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

medium

A. 3

B. 2

C. 1

D. 0

Solution

1. Step 1: Identify duplicate rows

   Rows 0 and 4 are identical (A=1, B='x'), and rows 1 and 2 are identical (A=2, B='y'). The first occurrence is not counted as duplicate.

2. Step 2: Count duplicates marked True

   Row 2 and row 4 are duplicates (second occurrences), so duplicated() marks these two rows True. Summing gives 2.

3. Final Answer:

   2 -> Option B

4. Quick Check:

   Two duplicate rows found = 2 [OK]

Hint: duplicated() counts second+ occurrences only [OK]

Common Mistakes:

• Counting all duplicates including first occurrence
• Confusing duplicated() with value_counts()
• Ignoring that duplicated() marks only later duplicates

4.

What is wrong with this code to count duplicates in DataFrame df?

duplicates = df.duplicated.count()

medium

A. duplicated is a method and needs parentheses before count()

B. count() cannot be used on duplicated() output

C. duplicated() does not exist in pandas

D. sum() should be used instead of count()

Solution

1. Step 1: Check method call syntax

   duplicated is a method and must be called with parentheses: duplicated().

2. Step 2: Understand method chaining

   Without parentheses, df.duplicated.count() tries to access count attribute of the method itself, causing error. Correct is df.duplicated().count(), but this counts all rows, not duplicates.

3. Final Answer:

   duplicated is a method and needs parentheses before count() -> Option A

4. Quick Check:

   Call duplicated() with () before count() [OK]

Hint: Always use parentheses to call duplicated() method [OK]

Common Mistakes:

• Forgetting parentheses on duplicated()
• Using count() instead of sum() to count duplicates
• Assuming duplicated is a property, not a method

5.

You have a DataFrame df with a column 'Category'. You want to find which categories appear more than once and how many times. Which code snippet correctly does this?

?

hard

A. df['Category'].count_duplicates()

B. df.duplicated('Category').sum()

C. df['Category'].duplicated().count()

D. df['Category'].value_counts()[df['Category'].value_counts() > 1]

Solution

1. Step 1: Use value_counts() to count category occurrences

   value_counts() counts how many times each category appears.

2. Step 2: Filter categories with counts greater than 1

   Using boolean indexing [value_counts() > 1] selects only categories appearing more than once.

3. Final Answer:

   df['Category'].value_counts()[df['Category'].value_counts() > 1] -> Option D

4. Quick Check:

   value_counts() + filter > 1 = repeated categories [OK]

Hint: Use value_counts() then filter counts > 1 [OK]

Common Mistakes:

• Using duplicated() on a column returns booleans, not counts
• Trying to call non-existent count_duplicates()
• Using count() on duplicated() output incorrectly