Ascending and descending order in Pandas - Mini Project: Build & Apply

Sorting Data in Ascending and Descending Order with pandas

📖 Scenario: You work in a small bookstore. You have a list of books with their prices. You want to see the books sorted by price from cheapest to most expensive, and also from most expensive to cheapest.

🎯 Goal: Learn how to sort data in ascending and descending order using pandas DataFrame.

📋 What You'll Learn

Create a pandas DataFrame with book titles and prices

Create a variable to control sorting order

Sort the DataFrame by price in ascending or descending order based on the variable

Print the sorted DataFrame

💡 Why This Matters

🌍 Real World

Sorting data is common in stores to show products from cheapest to most expensive or vice versa, helping customers make choices.

💼 Career

Data scientists often sort data to analyze trends, prepare reports, or clean data before deeper analysis.

1

Create the books DataFrame

Create a pandas DataFrame called books with two columns: 'Title' and 'Price'. Use these exact entries: 'The Alchemist' priced at 10, '1984' priced at 15, 'Python 101' priced at 20, 'Data Science' priced at 25.

Pandas

import pandas as pd
# Create the books DataFrame with Title and Price columns
# Your code here

Hint

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

2

Set the 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({
    'Title': ['The Alchemist', '1984', 'Python 101', 'Data Science'],
    'Price': [10, 15, 20, 25]
})
# Create ascending_order variable and set it to True
# Your code here

Hint

Just create a variable named ascending_order and assign True to it.

3

Sort the DataFrame by price

Create a new DataFrame called sorted_books by sorting books by the 'Price' column. Use the ascending_order variable to decide if sorting is ascending or descending.

Pandas

import pandas as pd
books = pd.DataFrame({
    'Title': ['The Alchemist', '1984', 'Python 101', 'Data Science'],
    'Price': [10, 15, 20, 25]
})
ascending_order = True
# Sort books by Price using ascending_order variable
# Your code here

Hint

Use books.sort_values(by='Price', ascending=ascending_order) to sort.

4

Print the sorted DataFrame

Print the sorted_books DataFrame to see the books sorted by price.

Pandas

import pandas as pd
books = pd.DataFrame({
    'Title': ['The Alchemist', '1984', 'Python 101', 'Data Science'],
    'Price': [10, 15, 20, 25]
})
ascending_order = True
sorted_books = books.sort_values(by='Price', ascending=ascending_order)
# Print sorted_books DataFrame
# Your code here

Hint

Use print(sorted_books) to display the sorted DataFrame.

Practice

1. What does setting ascending=False do when sorting a pandas DataFrame?

easy

A. Sorts the data from largest to smallest values

B. Sorts the data from smallest to largest values

C. Leaves the data unsorted

D. Sorts the data alphabetically only

Solution

1. Step 1: Understand the ascending parameter

   The ascending parameter controls the order of sorting: True means smallest to largest, False means largest to smallest.

2. Step 2: Apply the meaning of ascending=False

   Setting ascending=False sorts the data from the largest values down to the smallest values.

3. Final Answer:

   Sorts the data from largest to smallest values -> Option A

4. Quick Check:

   ascending=False = largest to smallest [OK]

Hint: False means descending order, largest first [OK]

Common Mistakes:

• Confusing ascending=False with ascending=True
• Thinking it sorts alphabetically only
• Assuming it leaves data unsorted

2. Which of the following is the correct syntax to sort a DataFrame df by column 'age' in ascending order?

easy

A. df.sort('age', ascending=True)

B. df.sort_values('age', ascending=True)

C. df.sort_values(by='age', ascending=True)

D. df.sort_values(by='age', ascending=False)

Solution

1. Step 1: Recall the correct pandas sorting method

   The correct method to sort a DataFrame by column is sort_values() with the parameter by='column_name'.

2. Step 2: Check the ascending parameter and syntax

   To sort ascending, use ascending=True. df.sort_values(by='age', ascending=True) uses the correct method and parameters.

3. Final Answer:

   df.sort_values(by='age', ascending=True) -> Option C

4. Quick Check:

   sort_values(by=col, ascending=True) is correct [OK]

Hint: Use sort_values(by=col, ascending=True) for ascending sort [OK]

Common Mistakes:

• Using df.sort instead of df.sort_values
• Mixing ascending True/False values
• Omitting 'by=' keyword

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'name': ['Anna', 'Bob', 'Cara'], 'score': [88, 92, 85]})
print(df.sort_values(by='score', ascending=False))

What will be the output?

medium

A. Rows sorted by score from highest to lowest

B. Error because ascending must be True

C. Rows sorted by name alphabetically

D. Rows sorted by score from lowest to highest

Solution

1. Step 1: Understand the sorting code

   The code sorts the DataFrame by the 'score' column with ascending=False, meaning from highest to lowest score.

2. Step 2: Predict the sorted order

   Scores are 88, 92, 85. Sorted descending: 92 (Bob), 88 (Anna), 85 (Cara).

3. Final Answer:

   Rows sorted by score from highest to lowest -> Option A

4. Quick Check:

   ascending=False sorts descending [OK]

Hint: False sorts descending, highest first [OK]

Common Mistakes:

• Thinking ascending=False sorts ascending
• Confusing sorting by name vs score
• Expecting an error from ascending=False

4. What is wrong with this code snippet?

df.sort_values(by='height', ascending='False')

medium

A. sort_values does not accept ascending parameter

B. The column name must be a list, not a string

C. No error, code is correct

D. ascending should be a boolean, not a string

Solution

1. Step 1: Check the type of ascending parameter

   The ascending parameter expects a boolean value: True or False, not a string.

2. Step 2: Identify the error in the code

   Here, ascending='False' is a string, which will cause unexpected behavior or error.

3. Final Answer:

   ascending should be a boolean, not a string -> Option D

4. Quick Check:

   ascending must be True/False boolean [OK]

Hint: Use True/False without quotes for ascending [OK]

Common Mistakes:

• Passing 'False' as string instead of boolean
• Thinking column name must be list always
• Assuming no error with wrong ascending type

5. You have a DataFrame df with columns 'city' and 'temperature'. You want to sort it first by 'city' alphabetically ascending, then by 'temperature' descending. Which code does this correctly?

hard

A. df.sort_values(by=['city', 'temperature'], ascending=False)

B. df.sort_values(by=['city', 'temperature'], ascending=[True, False])

C. df.sort_values(by=['temperature', 'city'], ascending=[False, True])

D. df.sort_values(by=['city', 'temperature'], ascending=[False, True])

Solution

1. Step 1: Understand multi-column sorting

   To sort by multiple columns, pass a list to by and a list of booleans to ascending matching each column.

2. Step 2: Match sorting order to columns

   We want 'city' ascending (True) and 'temperature' descending (False), so ascending=[True, False].

3. Final Answer:

   df.sort_values(by=['city', 'temperature'], ascending=[True, False]) -> Option B

4. Quick Check:

   Multi-column sort uses list of booleans [OK]

Hint: Use ascending list matching columns for multi-sort [OK]

Common Mistakes:

• Using single boolean for multi-column ascending
• Swapping column order in by list
• Mixing ascending True/False order