Sorting by index in Pandas - Mini Project: Build & Apply

Sorting DataFrames by Index with pandas

📖 Scenario: You work in a small bookstore. You have a list of books with their prices stored in a table. You want to organize this list by the book titles to find books quickly.

🎯 Goal: Build a pandas DataFrame with book titles as the index and prices as the values. Then sort the DataFrame by its index (book titles) in ascending order.

📋 What You'll Learn

Create a pandas DataFrame called books with the given data and set the book titles as the index.

Create a variable called sorted_books that stores the DataFrame sorted by its index in ascending order.

💡 Why This Matters

🌍 Real World

Sorting data by index helps organize and quickly find information in tables, like sorting books by title in a bookstore inventory.

💼 Career

Data analysts and database managers often sort data by index or keys to improve data retrieval and reporting.

1

Create the books DataFrame with index

Import pandas as pd. Create a DataFrame called books with these entries: titles 'Python Basics', 'Data Science 101', 'Machine Learning', and prices 25, 40, 55 respectively. Set the book titles as the index of the DataFrame.

Pandas

# Import pandas and create the DataFrame with book titles as index
# Your code here

Hint

Use pd.DataFrame with a dictionary for prices and set index to the list of book titles.

2

Set sorting order variable

Create a variable called ascending_order and set it to True to indicate sorting in ascending order.

Pandas

import pandas as pd
books = pd.DataFrame({'Price': [25, 40, 55]}, index=['Python Basics', 'Data Science 101', 'Machine Learning'])
# Create a variable ascending_order and set it to True
# Your code here

Hint

Just assign True to the variable ascending_order.

3

Sort the DataFrame by index

Create a new DataFrame called sorted_books by sorting books by its index in ascending order using the variable ascending_order.

Pandas

import pandas as pd
books = pd.DataFrame({'Price': [25, 40, 55]}, index=['Python Basics', 'Data Science 101', 'Machine Learning'])
ascending_order = True
# Sort books by index using ascending_order and assign to sorted_books
# Your code here

Hint

Use books.sort_index(ascending=ascending_order) to sort by index.

4

Complete the sorting project

Ensure the variable sorted_books contains the DataFrame sorted by index in ascending order. This completes the sorting by index task.

Pandas

import pandas as pd
books = pd.DataFrame({'Price': [25, 40, 55]}, index=['Python Basics', 'Data Science 101', 'Machine Learning'])
ascending_order = True
sorted_books = books.sort_index(ascending=ascending_order)
# Final step: confirm sorted_books is ready
# Your code here

Hint

No extra code needed here. Just confirm the sorting is done.

Practice

1. What does sorting by index in pandas do?

easy

A. It changes the data values to alphabetical order.

B. It filters rows based on a condition.

C. It removes duplicate rows from the DataFrame.

D. It arranges data rows or columns based on their labels.

Solution

1. Step 1: Understand the meaning of sorting by index

   Sorting by index means arranging the data based on the row or column labels, not the data values.

2. Step 2: Compare options with the definition

   Only It arranges data rows or columns based on their labels. correctly describes sorting by index as arranging rows or columns by their labels.

3. Final Answer:

   It arranges data rows or columns based on their labels. -> Option D

4. Quick Check:

   Sorting by index = arrange by labels [OK]

Hint: Sorting by index means ordering by labels, not values [OK]

Common Mistakes:

• Confusing sorting by index with sorting by values
• Thinking it filters or removes data
• Assuming it changes data values

2. Which of the following is the correct syntax to sort a DataFrame df by its row index in descending order without changing the original DataFrame?

easy

A. df.sort_index(ascending=False, inplace=False)

B. df.sort_index(axis=1, ascending=False)

C. df.sort_index(ascending=True, inplace=False)

D. df.sort_index(ascending=False, inplace=True)

Solution

1. Step 1: Identify parameters for sorting by index

   To sort by row index descending, use ascending=False. To avoid changing original, use inplace=False.

2. Step 2: Check each option

   df.sort_index(ascending=False, inplace=False) uses ascending=False and inplace=False, which matches the requirement. df.sort_index(ascending=False, inplace=True) changes original (inplace=True). df.sort_index(ascending=True, inplace=False) sorts ascending. df.sort_index(axis=1, ascending=False) sorts columns (axis=1).

3. Final Answer:

   df.sort_index(ascending=False, inplace=False) -> Option A

4. Quick Check:

   Descending + no inplace = ascending=False, inplace=False [OK]

Hint: Use inplace=False to keep original unchanged [OK]

Common Mistakes:

• Using inplace=True when original should not change
• Confusing ascending=True with descending order
• Sorting columns instead of rows by mistake

3. Given the DataFrame df with index labels [3, 1, 2] and values ["a", "b", "c"], what will df.sort_index() return as the index order?

medium

A. [a, b, c]

B. [1, 2, 3]

C. [3, 2, 1]

D. [c, b, a]

Solution

1. Step 1: Understand default sort_index behavior

   By default, sort_index sorts row labels in ascending order.

2. Step 2: Apply sorting to index [3, 1, 2]

   Sorting ascending gives [1, 2, 3]. Values remain associated with their labels.

3. Final Answer:

   [1, 2, 3] -> Option B

4. Quick Check:

   Default sort_index sorts index ascending [OK]

Hint: Default sort_index sorts index ascending [OK]

Common Mistakes:

• Confusing index labels with values
• Assuming sort_index sorts values instead of index
• Thinking sort_index reverses order by default

4. You run df.sort_index(axis=2) on a DataFrame but get an error. What is the likely cause?

medium

A. axis=2 is invalid; axis must be 0 or 1.

B. sort_index does not accept axis parameter.

C. DataFrame index labels are missing.

D. inplace parameter is required.

Solution

1. Step 1: Check valid axis values for sort_index

   sort_index accepts axis=0 (rows) or axis=1 (columns) only.

2. Step 2: Identify error cause with axis=2

   axis=2 is invalid and causes an error.

3. Final Answer:

   axis=2 is invalid; axis must be 0 or 1. -> Option A

4. Quick Check:

   axis must be 0 or 1 in sort_index [OK]

Hint: axis can only be 0 (rows) or 1 (columns) [OK]

Common Mistakes:

• Using axis values other than 0 or 1
• Assuming sort_index ignores invalid axis
• Forgetting axis parameter is optional but must be valid

5. You have a DataFrame df with a multi-level index (two levels). How do you sort df by the second level of the index in ascending order without changing the original DataFrame?

hard

A. df.sort_values(level=1, ascending=True)

B. df.sort_index(level=0, ascending=True, inplace=True)

C. df.sort_index(level=1, ascending=True, inplace=False)

D. df.sort_index(axis=1, level=1)

Solution

1. Step 1: Understand sorting multi-level index

   Use sort_index with level parameter to specify which index level to sort.

2. Step 2: Choose correct parameters

   To sort by second level (level=1), ascending order, and keep original unchanged, use inplace=False.

3. Final Answer:

   df.sort_index(level=1, ascending=True, inplace=False) -> Option C

4. Quick Check:

   Sort multi-level index by level=1, inplace=False [OK]

Hint: Use level parameter to sort multi-level index [OK]

Common Mistakes:

• Using sort_values instead of sort_index for index sorting
• Sorting wrong index level (level=0 instead of 1)
• Using inplace=True when original should not change