drop_duplicates() for removal in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to remove duplicate rows from the DataFrame df.

Pandas

df_clean = df.[1]()

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

Adrop_duplicates

Bsort_values

Cfillna

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna() which removes missing values, not duplicates.

Using fillna() which fills missing values.

Using sort_values() which sorts rows but does not remove duplicates.

The drop_duplicates() method removes duplicate rows from a DataFrame.

2fill in blank

medium

Complete the code to remove duplicates based only on the 'Name' column.

Pandas

df_unique = df.drop_duplicates(subset=[1])

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

A'City'

B'Salary'

C'Name'

D'Age'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column unrelated to duplicates like 'Age' or 'City'.

Not passing the column name as a string.

The subset parameter specifies columns to consider for identifying duplicates. Here, only 'Name' is used.

3fill in blank

hard

Fix the error in the code to remove duplicates and keep the last occurrence.

Pandas

df_last = df.drop_duplicates(keep=[1])

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

A'first'

B'last'

C'none'

D'all'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'first' which keeps the first occurrence instead.

Using invalid options like 'none' or 'all'.

The keep parameter controls which duplicate to keep. 'last' keeps the last occurrence.

4fill in blank

hard

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

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 the word itself as the value instead of its length.

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

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 keys and values greater than zero.

Pandas

result = [1]: [2] for k, v in data.items() if v [3] 0}

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys without uppercase.

Using wrong comparison operators like '<'.

The dictionary comprehension uses uppercase keys, original values, and filters values greater than zero.

Practice

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

easy

A. Removes repeated rows from a DataFrame

B. Adds new rows to a DataFrame

C. Sorts the DataFrame by column values

D. Changes the data type of columns

Solution

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

   This function is designed to remove rows that appear more than once in a DataFrame.

2. Step 2: Compare options with function behavior

   Only Removes repeated rows from a DataFrame correctly describes removing repeated rows. Other options describe different functions.

3. Final Answer:

   Removes repeated rows from a DataFrame -> Option A

4. Quick Check:

   drop_duplicates() removes repeats [OK]

Hint: Remember: drop_duplicates() cleans repeated rows fast [OK]

Common Mistakes:

• Confusing drop_duplicates() with sorting functions
• Thinking it adds or modifies data instead of removing duplicates
• Assuming it changes data types

2. Which of the following is the correct syntax to remove duplicate rows from a DataFrame df?

easy

A. df.drop_duplicate_rows()

B. df.remove_duplicates()

C. df.delete_duplicates()

D. df.drop_duplicates()

Solution

1. Step 1: Recall the exact pandas method name

   The correct method to remove duplicates is drop_duplicates().

2. Step 2: Check each option for correct syntax

   Only df.drop_duplicates() uses the correct method name and syntax. Others are invalid pandas methods.

3. Final Answer:

   df.drop_duplicates() -> Option D

4. Quick Check:

   Correct method name is drop_duplicates() [OK]

Hint: Use exact method name: drop_duplicates() [OK]

Common Mistakes:

• Using incorrect method names like remove_duplicates()
• Adding extra words like delete or rows
• Confusing with other DataFrame methods

3. Given the DataFrame df below:

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

What will be the output?

medium

A. Rows with index 1, 2, and 3

B. Rows with index 0, 1, and 3

C. Rows with index 0, 2, and 3

D. All rows remain unchanged

Solution

1. Step 1: Identify duplicate rows in the DataFrame

   Rows 2 and 3 have the same values (A=2, B='y'). So one will be removed.

2. Step 2: Understand default behavior of drop_duplicates()

   By default, it keeps the first occurrence and removes later duplicates. So row 1 stays, row 2 is removed.

3. Final Answer:

   Rows with index 0, 1, and 3 -> Option B

4. Quick Check:

   First duplicates kept, later removed [OK]

Hint: drop_duplicates() keeps first by default [OK]

Common Mistakes:

• Assuming last duplicate is kept instead of first
• Thinking all duplicates are removed including first
• Confusing row indices after removal

4. What is wrong with this code snippet to remove duplicates from DataFrame df?

df.drop_duplicates(inplace=True)
print(df.drop_duplicates())

medium

A. Calling drop_duplicates() twice is redundant

B. Using inplace=True and then printing drop_duplicates() returns original data

C. drop_duplicates() does not have inplace parameter

D. print statement syntax is incorrect

Solution

1. Step 1: Understand inplace=True effect

   Using inplace=True modifies df directly by removing duplicates.

2. Step 2: Analyze print statement

   After the first call, df is already deduplicated. The second call to df.drop_duplicates() is redundant as it returns the same DataFrame since there are no duplicates left.

3. Final Answer:

   Calling drop_duplicates() twice is redundant -> Option A

4. Quick Check:

   Second call redundant after inplace [OK]

Hint: inplace=True changes df; extra drop_duplicates() returns copy [OK]

Common Mistakes:

• Thinking drop_duplicates() lacks inplace parameter
• Assuming print statement is wrong syntax
• Believing calling twice causes error

5. You have a DataFrame df with columns ['Name', 'Age', 'City']. You want to remove duplicates only based on Name and City, keeping the last occurrence. Which code achieves this?

hard

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

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

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

D. df.drop_duplicates(subset=['Age'], keep='last')

Solution

1. Step 1: Use subset parameter to specify columns

   To check duplicates only on 'Name' and 'City', use subset=['Name', 'City'].

2. Step 2: Set keep='last' to keep last occurrence

   keep='last' keeps the last duplicate row, removing earlier ones.

3. Final Answer:

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

4. Quick Check:

   subset selects columns; keep='last' keeps last duplicate [OK]

Hint: Use subset for columns, keep='last' to keep last duplicate [OK]

Common Mistakes:

• Using 'columns' instead of 'subset' parameter
• Forgetting to set keep='last' to keep last occurrence
• Selecting wrong columns for subset