Series arithmetic and alignment in Data Analysis Python - Mini Project: Build & Apply

Series arithmetic and alignment

📖 Scenario: You work in a small store that tracks daily sales of two products. Sometimes, sales data for a product is missing on some days. You want to add the sales of both products day by day, but only where data for both products exists.

🎯 Goal: Build a Python program using pandas Series to add sales data of two products with automatic alignment by date. You will create the sales data, set a threshold for minimum sales, add the sales with alignment, and print the result.

📋 What You'll Learn

Create two pandas Series named sales_product_a and sales_product_b with sales data indexed by dates.

Create a variable min_sales to hold the minimum sales threshold.

Add the two Series using the add() method with fill_value=0 to handle missing dates.

Print the resulting Series showing total sales per date.

💡 Why This Matters

🌍 Real World

Stores and businesses often have sales data for different products recorded on different days. Adding sales data with alignment helps combine information correctly even if some days are missing for some products.

💼 Career

Data analysts and scientists use pandas Series arithmetic and alignment to clean, combine, and analyze time series data in retail, finance, and many other fields.

1

Create sales data for two products

Import pandas as pd. Create a pandas Series called sales_product_a with these values: 10 on '2024-06-01', 15 on '2024-06-02', and 7 on '2024-06-04'. Create another Series called sales_product_b with these values: 5 on '2024-06-01', 8 on '2024-06-03', and 12 on '2024-06-04'. Use the dates as the index for both Series.

Data Analysis Python

# Import pandas and create sales_product_a and sales_product_b Series with given dates and values
# Your code here

Hint

Use pd.Series with index=pd.to_datetime([...]) to create Series with date indexes.

2

Set minimum sales threshold

Create a variable called min_sales and set it to 10. This will be used later to filter total sales.

Data Analysis Python

import pandas as pd

sales_product_a = pd.Series([10, 15, 7], index=pd.to_datetime(['2024-06-01', '2024-06-02', '2024-06-04']))
sales_product_b = pd.Series([5, 8, 12], index=pd.to_datetime(['2024-06-01', '2024-06-03', '2024-06-04']))
# Create min_sales variable and set it to 10
# Your code here

Hint

Just create a variable named min_sales and assign the value 10.

3

Add sales data with alignment

Create a new Series called total_sales by adding sales_product_a and sales_product_b using the add() method. Use fill_value=0 to treat missing sales as zero.

Data Analysis Python

import pandas as pd

sales_product_a = pd.Series([10, 15, 7], index=pd.to_datetime(['2024-06-01', '2024-06-02', '2024-06-04']))
sales_product_b = pd.Series([5, 8, 12], index=pd.to_datetime(['2024-06-01', '2024-06-03', '2024-06-04']))

min_sales = 10
# Add sales_product_a and sales_product_b using add() with fill_value=0 and store in total_sales
# Your code here

Hint

Use sales_product_a.add(sales_product_b, fill_value=0) to add with alignment.

4

Print total sales

Print the total_sales Series to see the combined sales per date.

Data Analysis Python

import pandas as pd

sales_product_a = pd.Series([10, 15, 7], index=pd.to_datetime(['2024-06-01', '2024-06-02', '2024-06-04']))
sales_product_b = pd.Series([5, 8, 12], index=pd.to_datetime(['2024-06-01', '2024-06-03', '2024-06-04']))

min_sales = 10

total_sales = sales_product_a.add(sales_product_b, fill_value=0)
# Print total_sales
# Your code here

Hint

Use print(total_sales) to show the combined sales.

Practice

1. What happens when you add two pandas Series with different labels using + operator?

easy

A. The result is a Series with all zeros

B. Values are added ignoring labels, just by position

C. An error is raised due to label mismatch

D. Values are added only where labels match; others become NaN

Solution

1. Step 1: Understand pandas Series label alignment

   When adding two Series, pandas aligns data by their labels (index) before performing arithmetic.

2. Step 2: Effect of label mismatch on addition

   If a label is missing in one Series, the result for that label is NaN because pandas cannot add missing data.

3. Final Answer:

   Values are added only where labels match; others become NaN -> Option D

4. Quick Check:

   Series addition aligns labels = Values are added only where labels match; others become NaN [OK]

Hint: Remember: pandas adds by matching labels, not positions [OK]

Common Mistakes:

• Assuming addition ignores labels and adds by position
• Expecting an error on label mismatch
• Thinking missing labels fill with zero automatically

2. Which of the following is the correct syntax to add two pandas Series s1 and s2 with label alignment, allowing specification of fill_value for missing labels?

easy

A. s1 + s2

B. s1 + s2.values

C. s1.add(s2)

D. s1.append(s2)

Solution

1. Step 1: Check syntax for adding Series with alignment and fill_value

   Using s1.add(s2) adds Series with label alignment and allows fill_value for missing labels.

2. Step 2: Compare other options

   s1 + s2 adds with alignment but no fill_value option. s1 + s2.values ignores labels (positional). s1.append(s2) concatenates.

3. Final Answer:

   s1.add(s2) -> Option C

4. Quick Check:

   Use .add() for aligned addition with fill_value [OK]

Hint: Use .add() method for safe label-aligned addition [OK]

Common Mistakes:

• Using + with raw values ignoring labels
• Confusing append with addition
• Not knowing .add() exists for Series

3. Given the code:

import pandas as pd
s1 = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
s2 = pd.Series([1, 3, 3], index=['b', 'c', 'd'])
result = s1 + s2
print(result)

What is the output?

medium

A. a NaN
b 21.0
c 33.0
d NaN

B. a 10
b 22
c 33
d 3

C. a 10
b 21
c 33
d 3

D. a NaN
b 22.0
c 33.0
d NaN

Solution

1. Step 1: Align Series by labels

   s1 has 'a':10, 'b':20, 'c':30; s2 has 'b':1, 'c':3, 'd':3. Union index: 'a','b','c','d'.

2. Step 2: Add values where labels match, NaN otherwise

   'a': 10 + NaN = NaN, 'b':20+1=21.0, 'c':30+3=33.0, 'd': NaN + 3 = NaN. Floats due to NaN.

3. Final Answer:

   a NaN b 21.0 c 33.0 d NaN -> Option A

4. Quick Check:

   Series addition aligns labels, unmatched get NaN [OK]

Hint: Add Series and check labels; unmatched labels become NaN [OK]

Common Mistakes:

• Adding values by position ignoring labels
• Expecting no NaN for unmatched labels
• Misreading index labels and values

4. Consider the code:

import pandas as pd
s1 = pd.Series([5, 10], index=['x', 'y'])
s2 = pd.Series([2, 4, 6], index=['x', 'y', 'z'])
result = s1 + s2
print(result)

The result contains NaN. What is the problem and how to fix it to treat missing values as zero?

medium

A. No error; code runs fine

B. NaN due to different lengths; fix by using s1.add(s2, fill_value=0)

C. Error because + cannot add Series; use concat instead

D. Error due to missing index in s1; fix by reindexing s1 to s2's index

Solution

1. Step 1: Check the code execution

   The code runs but produces NaN for 'z' (missing in s1). Alignment by labels causes NaN for unmatched.

2. Step 2: Fix with fill_value

   s1.add(s2, fill_value=0) fills missing labels with 0: 'x':7, 'y':14, 'z':6.

3. Final Answer:

   NaN due to different lengths; fix by using s1.add(s2, fill_value=0) -> Option B

4. Quick Check:

   Use .add() with fill_value to avoid NaN [OK]

Hint: Use .add(fill_value=0) to handle missing labels safely [OK]

Common Mistakes:

• Thinking + raises error on length mismatch
• Using concat instead of arithmetic
• Ignoring missing labels causing NaN

5. You have two Series representing sales in two stores:

store1 = pd.Series([100, 200, 300], index=['apple', 'banana', 'orange'])
store2 = pd.Series([50, 150, 100], index=['banana', 'orange', 'grape'])

How do you create a Series showing total sales per fruit, treating missing fruits as zero?

hard

A. store1.add(store2, fill_value=0)

B. store1 + store2

C. store1.append(store2).sum()

D. store1.combine(store2, lambda x, y: x + y)

Solution

1. Step 1: Understand the problem

   We want to add sales per fruit, but some fruits are missing in one store's Series.

2. Step 2: Use addition with fill_value=0

   Using store1.add(store2, fill_value=0) adds values label-wise, treating missing labels as zero, so no NaN appears.

3. Final Answer:

   store1.add(store2, fill_value=0) -> Option A

4. Quick Check:

   Add Series with fill_value=0 to handle missing labels [OK]

Hint: Use .add(fill_value=0) to sum with missing labels as zero [OK]

Common Mistakes:

• Using + causing NaN for missing labels
• Using append and sum incorrectly
• Using combine without fill_value causing NaN