Series sorting in Data Analysis Python - Mini Project: Build & Apply

Series sorting

📖 Scenario: You work in a small shop and keep track of daily sales using a list of numbers. You want to organize these sales numbers to see which days had the highest and lowest sales.

🎯 Goal: Sort a pandas Series of daily sales numbers in ascending and descending order to understand sales trends.

📋 What You'll Learn

Create a pandas Series with exact daily sales values

Create a variable to hold the sorting order

Sort the Series using the variable for ascending or descending order

Print the sorted Series

💡 Why This Matters

🌍 Real World

Sorting sales data helps shop owners quickly see which days perform best or worst, aiding in planning and stock management.

💼 Career

Data analysts often sort data to find trends and make decisions based on ordered information.

1

Create the sales data Series

Create a pandas Series called sales with these exact daily sales values: 250, 400, 150, 300, 500.

Data Analysis Python

import pandas as pd
# Create the sales Series with the exact values
# Your code here

Hint

Use pd.Series and pass the list of sales numbers inside square brackets.

2

Set the sorting order

Create a variable called ascending_order and set it to True to sort the sales in ascending order.

Data Analysis Python

import pandas as pd
sales = pd.Series([250, 400, 150, 300, 500])
# Create the variable ascending_order and set it to True
# Your code here

Hint

Just assign the value True to the variable ascending_order.

3

Sort the sales Series

Create a new Series called sorted_sales by sorting the sales Series using the ascending_order variable to decide the order.

Data Analysis Python

import pandas as pd
sales = pd.Series([250, 400, 150, 300, 500])
ascending_order = True
# Sort the sales Series using ascending_order and save it as sorted_sales
# Your code here

Hint

Use the sort_values() method on sales and pass ascending=ascending_order.

4

Print the sorted sales

Print the sorted_sales Series to see the sales sorted in ascending order.

Data Analysis Python

import pandas as pd
sales = pd.Series([250, 400, 150, 300, 500])
ascending_order = True
sorted_sales = sales.sort_values(ascending=ascending_order)
# Print the sorted_sales Series
# Your code here

Hint

Use print(sorted_sales) to display the sorted Series.

Practice

1. What does the sort_values() method do when applied to a pandas Series?

easy

A. Sorts the Series by its data values in ascending order by default.

B. Sorts the Series by its index labels in ascending order by default.

C. Removes duplicate values from the Series.

D. Converts the Series into a DataFrame.

Solution

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

   This method sorts the Series based on the data values it contains, not the index labels.

2. Step 2: Default sorting order

   By default, sort_values() sorts in ascending order, meaning smallest to largest values.

3. Final Answer:

   Sorts the Series by its data values in ascending order by default. -> Option A

4. Quick Check:

   sort_values() sorts by values = D [OK]

Hint: Remember: sort_values() sorts by data values, not index [OK]

Common Mistakes:

• Confusing sort_values() with sort_index()
• Thinking it sorts by index labels
• Assuming it removes duplicates

2. Which of the following is the correct syntax to sort a pandas Series named data by its index labels in descending order?

easy

A. data.sort_index(ascending=False)

B. data.sort_values(ascending=False)

C. data.sort(ascending=False)

D. data.sort_by_index(desc=True)

Solution

1. Step 1: Identify method for sorting by index

   To sort by index labels, use sort_index(), not sort_values().

2. Step 2: Apply descending order

   The parameter ascending=False sorts in descending order.

3. Final Answer:

   data.sort_index(ascending=False) -> Option A

4. Quick Check:

   sort_index() + ascending=False = B [OK]

Hint: Use sort_index() to sort by index, add ascending=False for descending [OK]

Common Mistakes:

• Using sort_values() instead of sort_index()
• Using non-existent methods like sort_by_index()
• Missing the ascending parameter

3. Given the following pandas Series:

import pandas as pd
s = pd.Series([30, 10, 20], index=['c', 'a', 'b'])
print(s.sort_values())

What will be the output?

medium

A. a 10
c 30
b 20
dtype: int64

B. c 30
b 20
a 10
dtype: int64

C. a 10
b 20
c 30
dtype: int64

D. b 20
a 10
c 30
dtype: int64

Solution

1. Step 1: Understand the Series and sort_values()

   The Series has values [30, 10, 20] with indexes ['c', 'a', 'b']. Sorting by values arranges them as 10, 20, 30.

2. Step 2: Match sorted values with their indexes

   Value 10 corresponds to index 'a', 20 to 'b', and 30 to 'c'. So sorted Series is:
   a 10
   b 20
   c 30

3. Final Answer:

   a 10 b 20 c 30 dtype: int64 -> Option C

4. Quick Check:

   sort_values() sorts values ascending = A [OK]

Hint: Match sorted values with their original indexes carefully [OK]

Common Mistakes:

• Sorting by index instead of values
• Mixing up index labels with values
• Assuming default sort is descending

4. What is wrong with the following code snippet if the goal is to sort a pandas Series s by its index in ascending order?

sorted_s = s.sort_values(ascending=True)

medium

A. The ascending parameter should be False to sort ascending.

B. It sorts by values, not by index, so index order is not guaranteed.

C. The method sort_values() does not exist for Series.

D. The code will raise a syntax error.

Solution

1. Step 1: Identify the method used

   The code uses sort_values() which sorts by data values, not index labels.

2. Step 2: Understand the goal

   The goal is to sort by index ascending, so sort_index() should be used instead.

3. Final Answer:

   It sorts by values, not by index, so index order is not guaranteed. -> Option B

4. Quick Check:

   sort_values() sorts values, not index = C [OK]

Hint: Use sort_index() to sort by index, not sort_values() [OK]

Common Mistakes:

• Confusing sort_values() with sort_index()
• Thinking ascending=False sorts ascending
• Expecting syntax error when none occurs

5. You have a pandas Series sales with product codes as index and sales numbers as values:

product_codes = ['p3', 'p1', 'p2', 'p4']
sales_numbers = [200, 450, 300, 150]
sales = pd.Series(sales_numbers, index=product_codes)

How do you sort this Series so that the products are ordered by their codes alphabetically, but the sales numbers remain correctly matched?

hard

A. sales.sort_index(ascending=False)

B. sales.sort_values()

C. sales.sort_values(ascending=False)

D. sales.sort_index()

Solution

1. Step 1: Understand the data and goal

   The Series has product codes as index and sales numbers as values. Sorting by product codes means sorting by index labels.

2. Step 2: Choose correct sorting method

   Use sort_index() to sort by index labels alphabetically, preserving the correct sales numbers with their products.

3. Final Answer:

   sales.sort_index() -> Option D

4. Quick Check:

   Sort by index to order products alphabetically = A [OK]

Hint: Sort by index to reorder labels, not by values [OK]

Common Mistakes:

• Using sort_values() which sorts by sales numbers
• Sorting descending when ascending is needed
• Mixing up index and values sorting