duplicated() for finding duplicates in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to find duplicate rows in the DataFrame df.

Pandas

duplicates = df.[1]()

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

Aduplicated

Bfillna

Cisnull

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna() instead of duplicated()

Using isnull() which checks for missing values

The duplicated() method returns a boolean Series marking duplicate rows as True.

2fill in blank

medium

Complete the code to find duplicate rows in df based only on the 'Name' column.

Pandas

duplicates = df.duplicated(subset=[1])

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

A'Name'

B'Age'

C'Score'

D'City'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name in subset

Not passing the column name as a string

The subset parameter specifies columns to check for duplicates. Here, we check duplicates based on 'Name'.

3fill in blank

hard

Fix the error in the code to mark duplicate rows in df, keeping the first occurrence.

Pandas

duplicates = df.duplicated(keep=[1])

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

A'last'

BFalse

C'first'

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Using boolean True instead of string 'first'

Using None which is invalid here

The keep parameter accepts 'first', 'last', or False. 'first' marks duplicates except the first occurrence.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 letters.

Pandas

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' in the condition

Using 'word' instead of 'len(word)' for the value

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths for words longer than 4 letters.

Pandas

result = {{ [1]: [2] for w in words if len(w) [3] 4 }}

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

Aw.upper()

Blen(w)

C>

Dw

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'w' instead of 'w.upper()' for keys

Using '<' instead of '>' in the condition

This comprehension creates a dictionary with uppercase words as keys and their lengths as values, filtering words longer than 4.

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