sort_index() for index sorting in Pandas - Mini Project: Build & Apply

Sort DataFrame by Index Using sort_index()

📖 Scenario: You work in a small bookstore. You have a list of books with their prices, but the list is not sorted by the book titles. You want to organize your list alphabetically by the book titles to find books faster.

🎯 Goal: Create a pandas DataFrame with book titles as the index and prices as the values. Then, sort the DataFrame by the index (book titles) using sort_index().

📋 What You'll Learn

Create a pandas DataFrame called books with the exact index labels: 'Python Basics', 'Data Science', 'Machine Learning', and 'Deep Learning'.

The DataFrame must have a single column named Price with the exact values: 25, 40, 55, and 60 respectively.

Create a variable called sorted_books that contains the DataFrame sorted by its index using sort_index().

💡 Why This Matters

🌍 Real World

Sorting data by index helps organize information alphabetically or by date, making it easier to find and analyze data quickly.

💼 Career

Data analysts and scientists often need to sort data tables to prepare reports or perform analysis efficiently.

1

Create the initial DataFrame

Import pandas as pd and create a DataFrame called books with the index labels 'Python Basics', 'Data Science', 'Machine Learning', and 'Deep Learning'. The DataFrame should have one column named Price with values 25, 40, 55, and 60 respectively.

Pandas

# Import pandas and create the DataFrame 'books' with the specified index and prices
# Your code here

Hint

Use pd.DataFrame with a dictionary for the column and specify the index parameter for the row labels.

2

Add a variable for sorted DataFrame

Create a variable called sorted_books and assign it the value of books sorted by its index using the sort_index() method.

Pandas

import pandas as pd

books = pd.DataFrame({'Price': [25, 40, 55, 60]}, index=['Python Basics', 'Data Science', 'Machine Learning', 'Deep Learning'])
# Create 'sorted_books' by sorting 'books' by index
# Your code here

Hint

Use books.sort_index() and assign it to sorted_books.

3

Sort the DataFrame by index in descending order

Modify the sorted_books variable to sort the books DataFrame by its index in descending order by passing the argument ascending=False to sort_index().

Pandas

import pandas as pd

books = pd.DataFrame({'Price': [25, 40, 55, 60]}, index=['Python Basics', 'Data Science', 'Machine Learning', 'Deep Learning'])
# Sort 'books' by index in descending order and assign to 'sorted_books'
# Your code here

Hint

Use the ascending=False argument inside sort_index() to sort in descending order.

4

Reset the index after sorting

Reset the index of sorted_books using the reset_index() method and assign the result back to sorted_books. This will move the index labels into a column.

Pandas

import pandas as pd

books = pd.DataFrame({'Price': [25, 40, 55, 60]}, index=['Python Basics', 'Data Science', 'Machine Learning', 'Deep Learning'])
# Reset the index of 'sorted_books' and assign it back to 'sorted_books'
# Your code here

Hint

Use reset_index() on sorted_books and assign it back to sorted_books.

Practice

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

easy

A. It filters rows based on a condition.

B. It sorts the DataFrame by its values.

C. It sorts the DataFrame by its index labels.

D. It resets the index to default integers.

Solution

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

   This function organizes the DataFrame by sorting its row or column labels (index), not the data values.

2. Step 2: Compare options

   Only It sorts the DataFrame by its index labels. correctly describes sorting by index labels. Options B, C, and D describe other functions.

3. Final Answer:

   It sorts the DataFrame by its index labels. -> Option C

4. Quick Check:

   sort_index() = sort by index [OK]

Hint: Remember: sort_index() sorts labels, not data values [OK]

Common Mistakes:

• Confusing sort_index() with sort_values()
• Thinking it filters rows
• Assuming it resets the index

2. Which of the following is the correct syntax to sort a DataFrame df by its index in descending order?

easy

A. df.sort_index(order='desc')

B. df.sort_index(ascending=False)

C. df.sort_index(descending=True)

D. df.sort_index(reverse=True)

Solution

1. Step 1: Recall the parameter for sorting order

   The correct parameter is ascending, which accepts True or False.

2. Step 2: Check each option

   Only df.sort_index(ascending=False) uses ascending=False correctly. Options A, B, and D use invalid parameters.

3. Final Answer:

   df.sort_index(ascending=False) -> Option B

4. Quick Check:

   Use ascending=False for descending sort [OK]

Hint: Use ascending=False to sort index descending [OK]

Common Mistakes:

• Using invalid parameter names like descending or reverse
• Confusing ascending with descending
• Missing parentheses after sort_index

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?

medium

A. [1, 2, 3]

B. [3, 2, 1]

C. ["a", "b", "c"]

D. [2, 3, 1]

Solution

1. Step 1: Understand the initial index

   The DataFrame index is [3, 1, 2], unordered.

2. Step 2: Apply sort_index()

   This sorts the index labels in ascending order: [1, 2, 3].

3. Final Answer:

   [1, 2, 3] -> Option A

4. Quick Check:

   sort_index() sorts index ascending by default [OK]

Hint: sort_index() sorts index ascending by default [OK]

Common Mistakes:

• Confusing index labels with values
• Expecting descending order without specifying
• Thinking values get sorted instead of index

4. What is wrong with this code snippet to sort the columns of DataFrame df by their labels?

df.sort_index(axis=1, inplace=False)

medium

A. It does not modify df because inplace=False is used.

B. The axis parameter should be 0 to sort columns.

C. sort_index() cannot sort columns, only rows.

D. inplace must be True to return a sorted DataFrame.

Solution

1. Step 1: Understand inplace parameter

   With inplace=False, the original DataFrame df is not changed; a sorted copy is returned.

2. Step 2: Check axis parameter

   axis=1 correctly specifies sorting columns, so no error there.

3. Step 3: Identify the issue

   Since the returned sorted DataFrame is not assigned, df remains unsorted.

4. Final Answer:

   It does not modify df because inplace=False is used. -> Option A

5. Quick Check:

   inplace=False returns new DataFrame, original unchanged [OK]

Hint: Use inplace=True or assign result to keep changes [OK]

Common Mistakes:

• Assuming inplace=False modifies original DataFrame
• Confusing axis=0 and axis=1 for columns
• Thinking sort_index() can't sort columns

5. You have a DataFrame df with a MultiIndex on rows. How can you sort the DataFrame by the second level of the index using sort_index()?

hard

A. df.sort_index(index=1)

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

C. df.sort_index(by=1)

D. df.sort_index(level=1)

Solution

1. Step 1: Understand MultiIndex sorting

   To sort by a specific level in a MultiIndex, use the level parameter with the level number.

2. Step 2: Check each option

   df.sort_index(level=1) correctly uses level=1 to sort by the second index level. Options A, B, and C use invalid parameters.

3. Final Answer:

   df.sort_index(level=1) -> Option D

4. Quick Check:

   Use level parameter to sort MultiIndex levels [OK]

Hint: Use level=1 to sort by second index level [OK]

Common Mistakes:

• Using axis=1 to sort rows
• Using invalid parameters like by or index
• Not specifying level for MultiIndex sorting