Counting duplicates in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to count duplicate rows in the DataFrame.

Pandas

duplicate_count = df.[1]().sum()

Drag options to blanks, or click blank then click option'

Aunique

Bdrop_duplicates

Ccount

Dduplicated

Attempts:

3 left

💡 Hint

Common Mistakes

Using drop_duplicates() which removes duplicates instead of identifying them.

Using count() which counts non-null values, not duplicates.

The duplicated() method returns a boolean Series marking duplicate rows. Counting True values gives the number of duplicates.

2fill in blank

medium

Complete the code to count how many duplicate rows exist in the DataFrame.

Pandas

num_duplicates = [1](df[df.duplicated()])

Drag options to blanks, or click blank then click option'

Acount

Bsize

Clen

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() which sums values but the DataFrame rows are not numeric.

Using count() which counts non-null values per column, not rows.

Filtering the DataFrame with duplicated() returns only duplicate rows. Using len() counts how many rows are duplicates.

3fill in blank

hard

Fix the error in the code to count duplicate rows correctly.

Pandas

duplicate_rows = df.[1]().sum()

Drag options to blanks, or click blank then click option'

Adrop_duplicates

Bduplicated

Cunique

Dvalue_counts

Attempts:

3 left

💡 Hint

Common Mistakes

Using drop_duplicates() which returns a DataFrame, not booleans.

Using unique() which returns unique rows, not duplicates.

The duplicated() method returns a boolean Series where duplicates are True. Summing this Series counts duplicates. Using drop_duplicates() removes duplicates and does not return booleans.

4fill in blank

hard

Fill both blanks to create a dictionary with counts of duplicate rows and unique rows.

Pandas

counts = {'duplicates': df.[1]().sum(), 'unique': len(df.[2]())}

Drag options to blanks, or click blank then click option'

Aduplicated

Bdrop_duplicates

Ccount

Dunique

Attempts:

3 left

💡 Hint

Common Mistakes

Using unique() which returns unique values of a Series, not rows.

Using count() which counts non-null values, not unique rows.

duplicated() marks duplicates as True, summing counts duplicates. drop_duplicates() removes duplicates, so its length gives unique rows count.

5fill in blank

hard

Fill all three blanks to create a dictionary with counts of duplicate rows, unique rows, and total rows.

Pandas

counts = {'duplicates': df.[1]().sum(), 'unique': len(df.[2]()), 'total': df.[3]

Drag options to blanks, or click blank then click option'

Aduplicated

Bdrop_duplicates

Cshape[0]

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using count which counts non-null values per column, not total rows.

Using unique() which is for Series, not DataFrame rows.

duplicated() marks duplicates; summing counts them. drop_duplicates() removes duplicates; length counts unique rows. shape[0] gives total number of rows.

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