Dropping columns and rows in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to drop the column named 'Age' from the DataFrame df.

Pandas

df = df.drop([1], axis=1)

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

A'Age', axis=1

B'age'

C'Age'

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name with incorrect case.

Not specifying axis=1 when dropping a column.

To drop a column by name, pass the column name as a string and set axis=1.

2fill in blank

medium

Complete the code to drop the row with index label 3 from the DataFrame df.

Pandas

df = df.drop([1], axis=0)

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

A3

B'3'

C'index=3'

Ddf.index[3]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the index as a string instead of an integer.

Confusing axis=0 and axis=1.

To drop a row by index label, pass the label as an integer and use axis=0 (rows).

3fill in blank

hard

Fix the error in the code to drop the column 'Salary' from df.

Pandas

df.drop([1], axis=1, inplace=True)

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

A['Salary']

BSalary

C'salary'

D'Salary'

Attempts:

3 left

💡 Hint

Common Mistakes

Not using quotes around the column name.

Using wrong case for the column name.

The column name must be a string matching exactly the column label. Use 'Salary'.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

Pandas

{word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the word in the condition instead of its length.

The dictionary maps each word to its length (len(word)) only if the length is greater than 3 (len(word) > 3).

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts only if the count is greater than 0.

Pandas

{ [1]: [2] for [3] in data.items() if [2] > 0 }

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

Ak.upper()

Bv

Ck

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using data instead of k or v in the comprehension.

Not converting keys to uppercase.

The dictionary maps uppercase keys (k.upper()) to their values (v) only if the value is greater than 0. The loop variable is k.

Practice

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

easy

A. Removes specified rows or columns from a DataFrame

B. Adds new rows or columns to a DataFrame

C. Sorts the DataFrame by index or column values

D. Calculates summary statistics of the DataFrame

Solution

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

   The drop() function is used to remove data, either rows or columns, from a DataFrame.

2. Step 2: Identify what drop() removes

   It specifically removes rows or columns based on labels, not adding or sorting data.

3. Final Answer:

   Removes specified rows or columns from a DataFrame -> Option A

4. Quick Check:

   drop() removes rows/columns [OK]

Hint: Remember: drop means remove rows or columns [OK]

Common Mistakes:

• Thinking drop adds data instead of removing
• Confusing drop with sort or describe functions
• Assuming drop changes data without specifying inplace

2. Which of the following is the correct syntax to drop the column named 'Age' from a DataFrame df?

easy

A. df.drop('Age', axis=0)

B. df.drop('Age', axis=1)

C. df.drop(column='Age')

D. df.drop('Age')

Solution

1. Step 1: Identify axis for columns

   In pandas, axis=1 means columns, so to drop a column, use axis=1.

2. Step 2: Check syntax correctness

   The syntax df.drop('Age', axis=1) correctly drops the column named 'Age'. df.drop(column='Age') is close but incorrect syntax; it should be df.drop(columns=['Age']) with a list.

3. Final Answer:

   df.drop('Age', axis=1) -> Option B

4. Quick Check:

   Drop column uses axis=1 [OK]

Hint: Use axis=1 to drop columns, axis=0 for rows [OK]

Common Mistakes:

• Using axis=0 to drop columns
• Forgetting to specify axis parameter
• Using drop with wrong parameter names

3. Given the DataFrame df below:

   Name  Age  City
0  John   25  NY
1  Anna   30  LA
2  Mike   22  SF

What is the output of df.drop(1, axis=0)?

medium

A. Name Age City
0 John 25 NY
1 Anna 30 LA

B. Name Age City
1 Anna 30 LA

C. Name Age City
0 John 25 NY
2 Mike 22 SF

D. Name Age City
0 John 25 NY
1 Anna 30 LA
2 Mike 22 SF

Solution

1. Step 1: Understand dropping rows by index

   Using drop(1, axis=0) removes the row with index 1, which is the row for Anna.

2. Step 2: Identify remaining rows

   Rows with index 0 (John) and 2 (Mike) remain, so the output excludes Anna's row.

3. Final Answer:

   Name Age City 0 John 25 NY 2 Mike 22 SF -> Option C

4. Quick Check:

   Drop row index 1 removes Anna [OK]

Hint: Drop row by index with axis=0 [OK]

Common Mistakes:

• Confusing axis=0 and axis=1
• Expecting row to be removed by label instead of index
• Not realizing drop returns a new DataFrame by default

4. What is wrong with this code snippet to drop the column 'Salary' from DataFrame df?

df.drop('Salary')

medium

A. drop() cannot remove columns

B. Should use inplace=True to drop the column

C. Column name should be in a list like ['Salary']

D. Missing axis=1 to specify dropping a column

Solution

1. Step 1: Check default axis in drop()

   By default, drop() assumes axis=0, which means it tries to drop a row, not a column.

2. Step 2: Specify axis=1 to drop a column

   To drop a column, you must specify axis=1. Without it, pandas looks for a row named 'Salary' and fails if none exists.

3. Final Answer:

   Missing axis=1 to specify dropping a column -> Option D

4. Quick Check:

   Drop columns requires axis=1 [OK]

Hint: Always specify axis=1 to drop columns [OK]

Common Mistakes:

• Forgetting axis=1 when dropping columns
• Assuming drop changes DataFrame without inplace=True
• Using drop with a single string but wrong axis

5. You have a DataFrame df with columns ['Name', 'Age', 'City', 'Salary']. You want to remove the rows with index 0 and 2, and also drop the column 'Salary' in one line without changing the original DataFrame. Which code does this correctly?

hard

A. df.drop([0, 2], axis=0).drop('Salary', axis=1)

B. df.drop([0, 2, 'Salary'], axis=0)

C. df.drop(['Salary'], axis=1).drop([0, 2], axis=0, inplace=True)

D. df.drop([0, 2], axis=1).drop('Salary', axis=0)

Solution

1. Step 1: Drop rows with index 0 and 2

   Use df.drop([0, 2], axis=0) to remove rows 0 and 2 without changing original DataFrame.

2. Step 2: Drop column 'Salary' from the result

   Chain .drop('Salary', axis=1) to remove the column from the intermediate DataFrame.

3. Step 3: Verify no inplace=True used

   Since we want to keep original DataFrame unchanged, do not use inplace=True. The chained drops return a new DataFrame.

4. Final Answer:

   df.drop([0, 2], axis=0).drop('Salary', axis=1) -> Option A

5. Quick Check:

   Chain drops with correct axis, no inplace [OK]

Hint: Chain drop with axis=0 for rows, axis=1 for columns [OK]

Common Mistakes:

• Mixing axis values for rows and columns
• Using inplace=True which changes original DataFrame
• Trying to drop rows and columns in one drop call incorrectly