Why aggregation matters in NumPy - See It in Action

Why aggregation matters

📖 Scenario: Imagine you work in a small grocery store. You have daily sales data for different fruits. You want to understand the total sales for each fruit to know which ones sell the most.

🎯 Goal: You will create a NumPy array with sales data, set a threshold for high sales, calculate total sales for each fruit, and finally print the fruits that have total sales above the threshold.

📋 What You'll Learn

Use NumPy to handle sales data

Create a 2D NumPy array with exact sales numbers

Set a threshold variable for high sales

Calculate total sales per fruit using aggregation

Print the fruits with total sales above the threshold

💡 Why This Matters

🌍 Real World

Stores and businesses use aggregation to understand total sales, helping them decide what products to stock more.

💼 Career

Data analysts and scientists often aggregate data to summarize and find important insights quickly.

1

Create sales data array

Create a NumPy array called sales with these exact daily sales numbers for fruits (rows are days, columns are fruits): [[10, 5, 8], [7, 3, 6], [12, 4, 9]]

NumPy

import numpy as np
# Create the sales array with the exact values
# Your code here

Hint

Use np.array() and pass the list of lists exactly as shown.

2

Set sales threshold

Create a variable called threshold and set it to 20 to represent high total sales.

NumPy

import numpy as np
sales = np.array([[10, 5, 8], [7, 3, 6], [12, 4, 9]])
# Set the threshold variable to 20
# Your code here

Hint

Just assign the number 20 to the variable threshold.

3

Calculate total sales per fruit

Use NumPy's aggregation to create a variable called total_sales that sums sales for each fruit (sum over rows). Use np.sum with axis=0.

NumPy

import numpy as np
sales = np.array([[10, 5, 8], [7, 3, 6], [12, 4, 9]])
threshold = 20
# Calculate total sales per fruit
# Your code here

Hint

Use np.sum with axis=0 to add sales vertically for each fruit.

4

Print fruits with high total sales

Create a list called fruits with these exact names: ["Apples", "Bananas", "Oranges"]. Then print the names of fruits whose total sales in total_sales are greater than threshold. Use a for loop with variables fruit and sale iterating over zip(fruits, total_sales). Print each fruit if sale > threshold.

NumPy

import numpy as np
sales = np.array([[10, 5, 8], [7, 3, 6], [12, 4, 9]])
threshold = 20
total_sales = np.sum(sales, axis=0)
# Create fruits list and print fruits with total sales above threshold
# Your code here

Hint

Use a for loop with zip(fruits, total_sales) and print the fruit name if its sale is greater than threshold.

Practice

1. What is the main purpose of aggregation in data analysis using numpy?

easy

A. To remove missing values from data

B. To create new columns in a dataset

C. To summarize many data points into simple values like sums or averages

D. To sort data in ascending order

Solution

1. Step 1: Understand aggregation meaning

   Aggregation means combining many values into one summary value.

2. Step 2: Identify common aggregation uses

   Common aggregations are sum, mean, max, min, which summarize data quickly.

3. Final Answer:

   To summarize many data points into simple values like sums or averages -> Option C

4. Quick Check:

   Aggregation = summary values [OK]

Hint: Aggregation means summarizing data points simply [OK]

Common Mistakes:

• Confusing aggregation with sorting
• Thinking aggregation creates new columns
• Mixing aggregation with data cleaning

2. Which of the following is the correct syntax to calculate the mean of a numpy array arr?

easy

A. arr.mean()

B. mean(arr)

C. numpy.mean(arr)

D. arr.average()

Solution

1. Step 1: Recall numpy array methods

   Numpy arrays have a method called mean() to calculate average.

2. Step 2: Check syntax correctness

   Calling arr.mean() is correct; mean(arr) is invalid without import, numpy.mean(arr) requires prefix, arr.average() does not exist.

3. Final Answer:

   arr.mean() -> Option A

4. Quick Check:

   Array method for mean = arr.mean() [OK]

Hint: Use array.method() for aggregation in numpy [OK]

Common Mistakes:

• Using mean(arr) without numpy prefix or import
• Using arr.average() which is not a numpy method
• Confusing function and method syntax

3. What is the output of this code?

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(arr.sum())

medium

A. 5

B. 15

C. [1 2 3 4 5]

D. Error

Solution

1. Step 1: Understand the array values

   The array contains numbers 1, 2, 3, 4, 5.

2. Step 2: Calculate the sum of array elements

   Sum = 1+2+3+4+5 = 15.

3. Final Answer:

   15 -> Option B

4. Quick Check:

   Sum of 1 to 5 = 15 [OK]

Hint: Sum adds all elements in the array [OK]

Common Mistakes:

• Confusing sum with max or count
• Expecting array output instead of single value
• Syntax errors from missing import

4. Identify the error in this code snippet:

import numpy as np
arr = np.array([1, 2, 3, 4])
result = arr.mean
print(result)

medium

A. mean() method does not exist in numpy

B. Using print instead of return

C. Array should be a list for mean to work

D. Missing parentheses when calling mean method

Solution

1. Step 1: Check how mean is called

   In the code, arr.mean is assigned without parentheses, so it is a method reference, not a value.

2. Step 2: Understand method call syntax

   To get the mean value, arr.mean() must be called with parentheses before assignment.

3. Final Answer:

   Missing parentheses when calling mean method -> Option D

4. Quick Check:

   Method call needs () [OK]

Hint: Call methods with () to get results [OK]

Common Mistakes:

• Assigning method without calling it
• Thinking print causes error
• Believing mean only works on lists

5. You have a 2D numpy array representing sales data for 3 products over 4 days:

import numpy as np
sales = np.array([[10, 20, 30, 40],
                  [5, 15, 25, 35],
                  [2, 4, 6, 8]])

Which code correctly calculates the total sales per product?

hard

A. sales.sum(axis=1)

B. sales.sum(axis=0)

C. sales.mean(axis=1)

D. sales.max(axis=0)

Solution

1. Step 1: Understand array shape and axis meaning

   The array shape is (3 products, 4 days). Axis 0 is down rows (products), axis 1 is across columns (days per product).

2. Step 2: Choose axis to sum per product

   Summing along axis=1 adds sales across days for each product, giving total sales per product.

3. Final Answer:

   sales.sum(axis=1) -> Option A

4. Quick Check:

   Sum per product = sum across columns (axis=1) [OK]

Hint: Sum axis=1 sums rows (per product) in 2D array [OK]

Common Mistakes:

• Using axis=0 sums per day, not per product
• Using mean instead of sum for totals
• Confusing axis numbering in numpy