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

Sorting by values

📖 Scenario: You work in a small bookstore. You have a list of books with their sales numbers. You want to find out which books sold the most and which sold the least.

🎯 Goal: You will create a pandas DataFrame with book sales data, then sort the books by their sales numbers to see the best and worst sellers.

📋 What You'll Learn

Create a pandas DataFrame with book titles and their sales numbers

Create a variable to decide sorting order

Sort the DataFrame by sales numbers using the sorting order variable

Print the sorted DataFrame

💡 Why This Matters

🌍 Real World

Sorting data by values is common in sales reports, rankings, and any list where order matters.

💼 Career

Data analysts and scientists often sort data to find top performers or trends quickly.

1

Create the book sales DataFrame

Import pandas as pd. Create a DataFrame called book_sales with two columns: 'Book' and 'Sales'. Use these exact entries: 'The Alchemist' with 150, '1984' with 200, 'The Catcher in the Rye' with 120, 'To Kill a Mockingbird' with 180, and 'Moby Dick' with 90.

Pandas

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

Hint

Use pd.DataFrame with a dictionary containing the book names and sales numbers.

2

Set the sorting order

Create a variable called ascending_order and set it to False to sort from highest to lowest sales.

Pandas

import pandas as pd

book_sales = pd.DataFrame({
    'Book': ['The Alchemist', '1984', 'The Catcher in the Rye', 'To Kill a Mockingbird', 'Moby Dick'],
    'Sales': [150, 200, 120, 180, 90]
})

# Create the variable ascending_order and set it to False
# Your code here

Hint

Set ascending_order to False to sort from highest to lowest.

3

Sort the DataFrame by sales

Create a new DataFrame called sorted_sales by sorting book_sales by the 'Sales' column using the ascending_order variable.

Pandas

import pandas as pd

book_sales = pd.DataFrame({
    'Book': ['The Alchemist', '1984', 'The Catcher in the Rye', 'To Kill a Mockingbird', 'Moby Dick'],
    'Sales': [150, 200, 120, 180, 90]
})

ascending_order = False

# Sort book_sales by 'Sales' using ascending_order and save to sorted_sales
# Your code here

Hint

Use book_sales.sort_values(by='Sales', ascending=ascending_order) to sort.

4

Print the sorted DataFrame

Print the sorted_sales DataFrame to see the books sorted by sales.

Pandas

import pandas as pd

book_sales = pd.DataFrame({
    'Book': ['The Alchemist', '1984', 'The Catcher in the Rye', 'To Kill a Mockingbird', 'Moby Dick'],
    'Sales': [150, 200, 120, 180, 90]
})

ascending_order = False

sorted_sales = book_sales.sort_values(by='Sales', ascending=ascending_order)

# Print the sorted_sales DataFrame
# Your code here

Hint

Use print(sorted_sales) to show the sorted DataFrame.

Practice

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

easy

A. It filters rows based on a condition.

B. It groups data by a column.

C. It sorts the DataFrame by one or more columns.

D. It merges two DataFrames.

Solution

1. Step 1: Understand the function purpose

   The sort_values() function is designed to reorder rows in a DataFrame based on the values in one or more columns.

2. Step 2: Compare with other functions

   Filtering, grouping, and merging are done by different pandas functions like filter(), groupby(), and merge().

3. Final Answer:

   It sorts the DataFrame by one or more columns. -> Option C

4. Quick Check:

   sort_values() = reorder rows by column values [OK]

Hint: Remember: sort_values orders rows by column data [OK]

Common Mistakes:

• Confusing sorting with filtering
• Thinking it groups data
• Mixing it up with merging DataFrames

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

easy

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

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

C. df.sort('age', ascending=False)

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

Solution

1. Step 1: Identify correct parameter names

   The correct parameter to specify the column is by='age', and to sort descending, use ascending=False.

2. Step 2: Check syntax correctness

   df.sort_values(by='age', ascending=False) uses the correct function name sort_values and correct parameters. df.sort_values('age', ascending=True) sorts ascending, C uses a wrong function sort, and B uses a wrong parameter descending=True.

3. Final Answer:

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

4. Quick Check:

   sort_values(by=col, ascending=False) = descending sort [OK]

Hint: Use ascending=False for descending order in sort_values [OK]

Common Mistakes:

• Using ascending=True for descending sort
• Using wrong parameter names like descending=True
• Using non-existent sort() method

3. Given the DataFrame:

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

What will be the output?

medium

A. Original DataFrame without sorting

B. Rows sorted by 'score' descending: Bob, Anna, Cara

C. Error because 'score' is not a column

D. Rows sorted by 'score' ascending: Cara, Anna, Bob

Solution

1. Step 1: Understand the DataFrame content

   The DataFrame has names and scores: Anna(88), Bob(92), Cara(85).

2. Step 2: Apply sort_values on 'score'

   Sorting by 'score' ascending orders rows as Cara(85), Anna(88), Bob(92).

3. Final Answer:

   Rows sorted by 'score' ascending: Cara, Anna, Bob -> Option D

4. Quick Check:

   sort_values('score') sorts ascending by default [OK]

Hint: Default sort_values sorts ascending unless told otherwise [OK]

Common Mistakes:

• Assuming descending order by default
• Thinking original order is preserved
• Believing 'score' column does not exist

4. What is wrong with this code snippet?

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

medium

A. The column name 'age' must 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 function should be sort_by_values, not sort_values.

Solution

1. Step 1: Check parameter types

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

2. Step 2: Validate other parts

   The column name can be a string or list, sort_values is the correct function, and it accepts ascending.

3. Final Answer:

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

4. Quick Check:

   ascending must be True/False boolean, not 'False' string [OK]

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

Common Mistakes:

• Passing 'False' as a string instead of boolean
• Thinking column name must be a list
• Using wrong function name

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 within each city. Which code correctly does this?

hard

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

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

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

D. df.sort_values(['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 requirements

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

3. Step 3: Check options

   df.sort_values(by=['city', 'temperature'], ascending=[True, False]) matches the correct order and ascending list. df.sort_values(by=['temperature', 'city'], ascending=[False, True]) reverses columns. df.sort_values(by=['city', 'temperature'], ascending=False) uses a single boolean for two columns. df.sort_values(['city', 'temperature'], ascending=[False, True]) has wrong ascending order.

4. Final Answer:

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

5. Quick Check:

   Multi-column sort uses lists for by and ascending [OK]

Hint: Use lists for by and ascending to sort multiple columns [OK]

Common Mistakes:

• Using single boolean for multiple columns
• Mixing column order in by list
• Wrong ascending order list