sort_values() by multiple columns in Pandas - Mini Project: Build & Apply

Sorting Data with pandas using sort_values() by multiple columns

📖 Scenario: You work in a small bookstore. You have a list of books with their title, author, and year published. You want to organize this list so that books are sorted first by author name alphabetically, and then by year published from oldest to newest.

🎯 Goal: Build a pandas DataFrame with book data and sort it by author and year using sort_values() with multiple columns.

📋 What You'll Learn

Create a pandas DataFrame called books with columns title, author, and year using the exact data provided.

Create a list called sort_columns with the column names 'author' and 'year' in that order.

Use sort_values() on the books DataFrame with the by parameter set to sort_columns.

Print the sorted DataFrame.

💡 Why This Matters

🌍 Real World

Sorting data by multiple columns is common when organizing records like books, sales, or customer data to find patterns or prepare reports.

💼 Career

Data analysts and scientists often sort data by multiple criteria to clean, explore, and present data clearly.

1

Create the books DataFrame

Import pandas as pd and create a DataFrame called books with these exact entries: title as ['The Hobbit', '1984', 'Brave New World', 'The Catcher in the Rye', 'Animal Farm'], author as ['Tolkien', 'Orwell', 'Huxley', 'Salinger', 'Orwell'], and year as [1937, 1949, 1932, 1951, 1945].

Pandas

# Import pandas and create the DataFrame 'books' with the given data
# Your code here

Hint

Use pd.DataFrame() with a dictionary where keys are column names and values are lists of data.

2

Create the list of columns to sort by

Create a list called sort_columns with the exact values 'author' and 'year' in that order.

Pandas

import pandas as pd

books = pd.DataFrame({
    'title': ['The Hobbit', '1984', 'Brave New World', 'The Catcher in the Rye', 'Animal Farm'],
    'author': ['Tolkien', 'Orwell', 'Huxley', 'Salinger', 'Orwell'],
    'year': [1937, 1949, 1932, 1951, 1945]
})

# Create the list 'sort_columns' with 'author' and 'year'
# Your code here

Hint

Use square brackets to create a list with the two column names as strings.

3

Sort the DataFrame by multiple columns

Use the sort_values() method on the books DataFrame with the by parameter set to the list sort_columns. Assign the sorted DataFrame back to books.

Pandas

import pandas as pd

books = pd.DataFrame({
    'title': ['The Hobbit', '1984', 'Brave New World', 'The Catcher in the Rye', 'Animal Farm'],
    'author': ['Tolkien', 'Orwell', 'Huxley', 'Salinger', 'Orwell'],
    'year': [1937, 1949, 1932, 1951, 1945]
})

sort_columns = ['author', 'year']

# Sort the 'books' DataFrame by 'author' and 'year' using sort_values()
# Your code here

Hint

Use books.sort_values(by=sort_columns) and assign it back to books.

4

Print the sorted DataFrame

Write a print statement to display the sorted books DataFrame.

Pandas

import pandas as pd

books = pd.DataFrame({
    'title': ['The Hobbit', '1984', 'Brave New World', 'The Catcher in the Rye', 'Animal Farm'],
    'author': ['Tolkien', 'Orwell', 'Huxley', 'Salinger', 'Orwell'],
    'year': [1937, 1949, 1932, 1951, 1945]
})

sort_columns = ['author', 'year']

books = books.sort_values(by=sort_columns)

# Print the sorted DataFrame
# Your code here

Hint

Use print(books) to show the sorted DataFrame.

Practice

1.

What does the sort_values() function do when used on a pandas DataFrame?

easy

A. It sorts the DataFrame rows based on the values in one or more columns.

B. It filters rows that match a condition.

C. It changes the data types of columns.

D. It adds new columns to the DataFrame.

Solution

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

   This function organizes the rows of a DataFrame by sorting them according to the values in specified columns.

2. Step 2: Compare with other DataFrame operations

   Filtering selects rows, changing data types modifies columns, and adding columns is unrelated to sorting.

3. Final Answer:

   It sorts the DataFrame rows based on the values in one or more columns. -> Option A

4. Quick Check:

   Sorting = Organizing rows by column values [OK]

Hint: Remember: sort_values() arranges rows by column values [OK]

Common Mistakes:

• Confusing sorting with filtering rows
• Thinking it changes data types
• Assuming it adds or removes columns

2.

Which of the following is the correct syntax to sort a DataFrame df by columns 'A' ascending and 'B' descending?

df.sort_values(??? )

easy

A. columns=['A', 'B'], ascending=[False, True]

B. by=['A', 'B'], ascending=[True, False]

C. by=['A', 'B'], ascending=True

D. sort=['A', 'B'], order=[True, False]

Solution

1. Step 1: Identify correct parameter names

   The method uses by to specify columns and ascending to specify order per column.

2. Step 2: Match order for each column

   To sort 'A' ascending and 'B' descending, use ascending=[True, False].

3. Final Answer:

   by=['A', 'B'], ascending=[True, False] -> Option B

4. Quick Check:

   Correct parameters = by=['A', 'B'], ascending=[True, False] [OK]

Hint: Use by=columns and ascending=list of booleans [OK]

Common Mistakes:

• Using wrong parameter names like columns or sort
• Passing a single boolean for multiple columns
• Mixing ascending order values

3.

Given the DataFrame:

import pandas as pd
df = pd.DataFrame({
  'Name': ['Anna', 'Bob', 'Anna', 'Bob'],
  'Score': [90, 92, 85, 80]
})

What is the result of df.sort_values(by=['Name', 'Score'], ascending=[True, False])?

medium

A. Rows sorted by Name ascending, then Score descending: Anna(90), Anna(85), Bob(92), Bob(80)

B. Rows sorted by Name descending, then Score ascending: Bob(80), Bob(92), Anna(85), Anna(90)

C. Rows sorted by Score ascending only: Bob(80), Anna(85), Anna(90), Bob(92)

D. Rows sorted by Score descending only: Bob(92), Anna(90), Anna(85), Bob(80)

Solution

1. Step 1: Sort by 'Name' ascending

   Names order: Anna, Anna, Bob, Bob.

2. Step 2: Within each Name, sort 'Score' descending

   For Anna: 90 then 85; for Bob: 92 then 80.

3. Final Answer:

   Rows sorted by Name ascending, then Score descending: Anna(90), Anna(85), Bob(92), Bob(80) -> Option A

4. Quick Check:

   Sort by Name ↑, Score ↓ = Rows sorted by Name ascending, then Score descending: Anna(90), Anna(85), Bob(92), Bob(80) [OK]

Hint: Sort first column ascending, second descending as list [OK]

Common Mistakes:

• Mixing ascending and descending order
• Sorting only by one column
• Confusing order of sorting columns

4.

What is wrong with this code snippet?

df.sort_values(by=['A', 'B'], ascending=True)

Assuming df has columns 'A' and 'B'.

medium

A. The method sort_values does not accept the ascending parameter.

B. The by parameter should be a string, not a list.

C. The ascending parameter should be a list when sorting by multiple columns.

D. The code is correct and will run without errors.

Solution

1. Step 1: Recall sort_values() parameters

   by accepts list of columns, ascending accepts bool (applies to all) or list of bools.

2. Step 2: Validate the code

   The code uses ascending=True, which correctly sorts both columns ascending. No errors.

3. Final Answer:

   The code is correct and will run without errors. -> Option D

4. Quick Check:

   ascending=True valid for multi-column sort [OK]

Hint: ascending=True sorts all columns ascending; use list for mixed orders [OK]

Common Mistakes:

• Believing ascending must be a list for multiple columns
• Using string instead of list for 'by' parameter
• Assuming sort_values lacks ascending parameter

5.

You have a DataFrame df with columns 'City', 'Temperature', and 'Humidity'. You want to sort the data so that cities are in alphabetical order, but within each city, rows with higher temperature come first, and if temperatures are equal, rows with lower humidity come first.

Which sort_values() call achieves this?

hard

A. df.sort_values(by=['City', 'Temperature', 'Humidity'], ascending=[False, True, False])

B. df.sort_values(by=['City', 'Humidity', 'Temperature'], ascending=[True, True, False])

C. df.sort_values(by=['City', 'Temperature', 'Humidity'], ascending=[True, False, True])

D. df.sort_values(by=['Temperature', 'City', 'Humidity'], ascending=[False, True, True])

Solution

1. Step 1: Sort 'City' alphabetically ascending

   Set ascending=True for 'City' to get alphabetical order.

2. Step 2: Sort 'Temperature' descending within each city

   Use ascending=False for 'Temperature' to have higher temperatures first.

3. Step 3: Sort 'Humidity' ascending when temperatures tie

   Use ascending=True for 'Humidity' to get lower humidity first.

4. Final Answer:

   df.sort_values(by=['City', 'Temperature', 'Humidity'], ascending=[True, False, True]) -> Option C

5. Quick Check:

   City ↑, Temp ↓, Humidity ↑ = df.sort_values(by=['City', 'Temperature', 'Humidity'], ascending=[True, False, True]) [OK]

Hint: Match ascending list to desired order per column [OK]

Common Mistakes:

• Mixing order of columns in 'by' list
• Using wrong ascending booleans
• Sorting humidity before temperature