sort_values() by single column in Pandas - Mini Project: Build & Apply

Sorting Data with pandas sort_values() by Single Column

📖 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 to find the cheapest and most expensive ones easily.

🎯 Goal: Build a small program that creates a DataFrame of books and prices, then sorts the books by their price using pandas sort_values() method.

📋 What You'll Learn

Create a pandas DataFrame with book titles and prices

Create a variable to hold the column name to sort by

Use sort_values() to sort the DataFrame by the price column

Print the sorted DataFrame

💡 Why This Matters

🌍 Real World

Sorting data by a specific column is a common task in data analysis to organize and understand data better.

💼 Career

Data scientists and analysts often sort data to prepare reports, find trends, 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 10.99, '1984' priced at 8.99, 'Python 101' priced at 15.50, 'Data Science' priced at 12.75.

Pandas

import pandas as pd
# Create the DataFrame 'books' with the given titles and prices
# Your code here

Hint

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

2

Set the column to sort by

Create a variable called sort_column and set it to the string 'Price' to specify the column to sort the DataFrame by.

Pandas

import pandas as pd
books = pd.DataFrame({
    'Title': ['The Alchemist', '1984', 'Python 101', 'Data Science'],
    'Price': [10.99, 8.99, 15.50, 12.75]
})
# Create variable 'sort_column' with value 'Price'
# Your code here

Hint

Just assign the string 'Price' to the variable sort_column.

3

Sort the DataFrame by the price column

Create a new DataFrame called sorted_books by sorting the books DataFrame using the sort_values() method with the column name stored in sort_column.

Pandas

import pandas as pd
books = pd.DataFrame({
    'Title': ['The Alchemist', '1984', 'Python 101', 'Data Science'],
    'Price': [10.99, 8.99, 15.50, 12.75]
})
sort_column = 'Price'
# Sort 'books' by 'sort_column' and save to 'sorted_books'
# Your code here

Hint

Use books.sort_values(by=sort_column) to sort by the column stored in sort_column.

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': [10.99, 8.99, 15.50, 12.75]
})
sort_column = 'Price'
sorted_books = books.sort_values(by=sort_column)
# Print the sorted_books DataFrame
# Your code here

Hint

Use print(sorted_books) to display the sorted DataFrame.

Practice

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

easy

A. It filters rows based on a condition.

B. It changes the data type of a column.

C. It sorts the DataFrame rows based on the values in a specified column.

D. It adds a new column to the DataFrame.

Solution

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

   This function is used to reorder rows in a DataFrame based on the values in one or more columns.

2. Step 2: Compare with other options

   Changing data types, filtering rows, or adding columns are done by different functions, not sort_values().

3. Final Answer:

   It sorts the DataFrame rows based on the values in a specified column. -> Option C

4. Quick Check:

   sort_values() = reorder rows by column [OK]

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

Common Mistakes:

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

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

easy

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

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

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

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

Solution

1. Step 1: Check the correct function and parameters

   The function is sort_values() and it requires the parameter by='column_name'. The ascending parameter should be a boolean.

2. Step 2: Validate each option

   df.sort_values(by='age', ascending=True) uses correct function and parameters. df.sort_values('age', ascending=False) has ascending=False (descending). df.sort(by='age', ascending=True) uses wrong function sort(). df.sort_values(by='age', ascending='yes') uses a string instead of boolean for ascending.

3. Final Answer:

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

4. Quick Check:

   Correct syntax = df.sort_values(by='age', ascending=True) [OK]

Hint: Use by='column' and ascending=True/False in sort_values() [OK]

Common Mistakes:

• Using sort() instead of sort_values()
• Passing ascending as string instead of boolean
• Omitting 'by' keyword

3. Given the DataFrame:

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

What is the order of names in sorted_df?

medium

A. ['Cara', 'Anna', 'Bob']

B. ['Bob', 'Anna', 'Cara']

C. ['Anna', 'Bob', 'Cara']

D. ['Cara', 'Bob', 'Anna']

Solution

1. Step 1: Understand sorting by 'score'

   The scores are 88 (Anna), 92 (Bob), and 85 (Cara). Sorting ascending means smallest to largest.

2. Step 2: Order names by ascending scores

   Sorted scores: 85 (Cara), 88 (Anna), 92 (Bob). So names order is ['Cara', 'Anna', 'Bob'].

3. Final Answer:

   ['Cara', 'Anna', 'Bob'] -> Option A

4. Quick Check:

   Ascending sort by score = ['Cara', 'Anna', 'Bob'] [OK]

Hint: Sort ascending means smallest value first [OK]

Common Mistakes:

• Assuming descending order by default
• Mixing up names and scores
• Not using 'by' parameter

4. What is wrong with this code snippet?

df = pd.DataFrame({'A': [3, 1, 2]})
df.sort_values('A', ascending='False')

medium

A. The column name should be passed as a list.

B. The ascending parameter should be a boolean, not a string.

C. sort_values() does not accept the ascending parameter.

D. The DataFrame must be sorted inplace.

Solution

1. Step 1: Check the ascending parameter type

   The ascending parameter expects a boolean (True or False), but here it is passed as a string 'False'.

2. Step 2: Validate other options

   Passing column name as string is allowed. sort_values() accepts ascending. Sorting inplace is optional.

3. Final Answer:

   The ascending parameter should be a boolean, not a string. -> Option B

4. Quick Check:

   ascending must be True/False boolean [OK]

Hint: Use True/False, not 'True'/'False' for ascending [OK]

Common Mistakes:

• Passing ascending as string
• Thinking column name must be list
• Forcing inplace sorting unnecessarily

5. You have a DataFrame df with columns 'product' and 'price'. You want to sort the products by price descending but keep the original DataFrame unchanged. Which code achieves this?

hard

A. df.sort_values(by='price', ascending=False, inplace=True)

B. df.sort_values('price', inplace=False)

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

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

Solution

1. Step 1: Understand sorting without changing original

   To keep original DataFrame unchanged, do not use inplace=True. Instead, assign the sorted result to a new variable.

2. Step 2: Check options for descending order

   sorted_df = df.sort_values(by='price', ascending=False) sorts by 'price' descending and assigns to sorted_df. df.sort_values(by='price', ascending=False, inplace=True) changes original due to inplace=True. df.sort_values(by='price', ascending=True) sorts ascending, not descending. df.sort_values('price', inplace=False) sorts ascending by default and does not assign result.

3. Final Answer:

   sorted_df = df.sort_values(by='price', ascending=False) -> Option D

4. Quick Check:

   Assign sorted result without inplace for original unchanged [OK]

Hint: Assign sorted DataFrame to new variable, avoid inplace=True [OK]

Common Mistakes:

• Using inplace=True and losing original order
• Sorting ascending instead of descending
• Not assigning sorted DataFrame to a variable