Aggregation along specific axes in NumPy - Mini Project: Build & Apply

Aggregation along specific axes

📖 Scenario: You work in a small bakery that tracks daily sales of three types of bread over a week. You want to analyze the total sales per bread type and per day to understand which bread is most popular and which day has the highest sales.

🎯 Goal: Build a program that creates a 2D array of sales data, then calculates the total sales per bread type and per day using aggregation along specific axes.

📋 What You'll Learn

Create a 2D numpy array called sales with exact values representing sales of three bread types over seven days.

Create a variable called axis_bread set to 0 to represent aggregation along bread types.

Use np.sum with axis=axis_bread to calculate total sales per day and store it in total_per_day.

Create a variable called axis_day set to 1 to represent aggregation along days.

Use np.sum with axis=axis_day to calculate total sales per bread type and store it in total_per_bread.

Print total_per_day and total_per_bread to show the results.

💡 Why This Matters

🌍 Real World

Aggregating sales data helps businesses understand trends and make decisions about inventory and marketing.

💼 Career

Data analysts and scientists often aggregate data along specific axes to summarize and interpret multi-dimensional datasets.

1

Create the sales data array

Create a 2D numpy array called sales with these exact values representing sales of three bread types over seven days:
[[10, 12, 9, 11, 13, 8, 7], [5, 7, 6, 8, 7, 5, 6], [3, 4, 2, 5, 4, 3, 2]]

NumPy

import numpy as np
# Create the 2D numpy array called sales with the exact values
# Your code here

Hint

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

2

Set axis variables for aggregation

Create a variable called axis_bread and set it to 0 to represent aggregation along bread types (rows). Then create a variable called axis_day and set it to 1 to represent aggregation along days (columns).

NumPy

import numpy as np
sales = np.array([[10, 12, 9, 11, 13, 8, 7], [5, 7, 6, 8, 7, 5, 6], [3, 4, 2, 5, 4, 3, 2]])
# Create axis_bread = 0 and axis_day = 1
# Your code here

Hint

Just assign the numbers 0 and 1 to the variables axis_bread and axis_day.

3

Calculate total sales per day and per bread type

Use np.sum with axis=axis_bread on sales to calculate total sales per day and store it in total_per_day. Then use np.sum with axis=axis_day on sales to calculate total sales per bread type and store it in total_per_bread.

NumPy

import numpy as np
sales = np.array([[10, 12, 9, 11, 13, 8, 7], [5, 7, 6, 8, 7, 5, 6], [3, 4, 2, 5, 4, 3, 2]])
axis_bread = 0
axis_day = 1
# Calculate total_per_day using np.sum with axis=axis_bread
# Calculate total_per_bread using np.sum with axis=axis_day
# Your code here

Hint

Use np.sum with the correct axis variables to get sums along rows or columns.

4

Print the results

Print the variables total_per_day and total_per_bread to display the total sales per day and per bread type.

NumPy

import numpy as np
sales = np.array([[10, 12, 9, 11, 13, 8, 7], [5, 7, 6, 8, 7, 5, 6], [3, 4, 2, 5, 4, 3, 2]])
axis_bread = 0
axis_day = 1
total_per_day = np.sum(sales, axis=axis_bread)
total_per_bread = np.sum(sales, axis=axis_day)
# Print total_per_day and total_per_bread
# Your code here

Hint

Use two print statements, one for each variable.

Practice

1. What does setting axis=0 do when aggregating a 2D NumPy array?

easy

A. Aggregates values down each column (per column)

B. Aggregates values across each row (per row)

C. Flattens the array before aggregation

D. Aggregates only the diagonal elements

Solution

1. Step 1: Understand axis parameter meaning

   In NumPy, axis=0 means aggregation happens down the rows, combining values in each column.

2. Step 2: Apply to 2D array

   Aggregating with axis=0 summarizes each column separately.

3. Final Answer:

   Aggregates values down each column (per column) -> Option A

4. Quick Check:

   axis=0 means down rows = per column aggregation [OK]

Hint: axis=0 aggregates columns, axis=1 aggregates rows [OK]

Common Mistakes:

• Confusing axis=0 with axis=1
• Thinking axis=0 aggregates rows instead of columns
• Assuming axis=0 flattens the array

2. Which of the following is the correct syntax to sum a 2D NumPy array arr along rows (per row)?

easy

A. np.sum(arr, axis=1)

B. np.sum(arr, axis=0)

C. arr.sum(axis=2)

D. np.sum(arr)

Solution

1. Step 1: Identify axis for row-wise sum

   Summing across columns (per row) requires axis=1.

2. Step 2: Check syntax correctness

   np.sum(arr, axis=1) correctly sums each row's elements.

3. Final Answer:

   np.sum(arr, axis=1) -> Option A

4. Quick Check:

   Sum per row uses axis=1 [OK]

Hint: Use axis=1 to sum across columns (per row) [OK]

Common Mistakes:

• Using axis=0 to sum rows
• Using invalid axis like 2 for 2D array
• Forgetting to specify axis

3. Given the array:

arr = np.array([[1, 2, 3], [4, 5, 6]])

What is the output of np.sum(arr, axis=0)?

medium

A. [3 7 11]

B. [5 7 9]

C. [6 15]

D. [1 2 3 4 5 6]

Solution

1. Step 1: Understand axis=0 sum

   Sum down rows means add elements in each column: (1+4), (2+5), (3+6).

2. Step 2: Calculate sums

   Calculations: 1+4=5, 2+5=7, 3+6=9, so result is [5 7 9].

3. Final Answer:

   [5 7 9] -> Option B

4. Quick Check:

   Sum axis=0 adds columns down rows [OK]

Hint: Sum axis=0 adds columns down rows [OK]

Common Mistakes:

• Adding rows instead of columns
• Confusing axis=0 with axis=1
• Returning flattened array instead of summed

4. What is wrong with this code snippet?

arr = np.array([[1, 2], [3, 4]])
result = np.mean(arr, axis=2)

medium

A. No error, code runs fine

B. np.mean cannot be used on arrays

C. Missing parentheses in np.array

D. axis=2 is invalid for 2D array

Solution

1. Step 1: Check array dimensions

   The array arr is 2D with shape (2,2), so valid axes are 0 and 1.

2. Step 2: Validate axis parameter

   Using axis=2 is invalid and causes an error because axis 2 does not exist.

3. Final Answer:

   axis=2 is invalid for 2D array -> Option D

4. Quick Check:

   Axis must be less than array dimensions [OK]

Hint: Axis must be less than array dimensions [OK]

Common Mistakes:

• Using axis number too large for array
• Thinking np.mean can't be used on arrays
• Ignoring error messages

5. You have a 3D NumPy array data with shape (4, 3, 2). You want to compute the sum of each 2D slice along the first axis (axis=0), resulting in a 2D array. Which code correctly does this?

hard

A. np.sum(data)

B. np.sum(data, axis=1)

C. np.sum(data, axis=0)

D. np.sum(data, axis=2)

Solution

1. Step 1: Understand array shape and axis

   Array shape is (4,3,2). Summing along axis=0 collapses the first dimension (4), resulting in shape (3,2).

2. Step 2: Apply sum along axis=0

   Using np.sum(data, axis=0) sums all 4 slices element-wise, producing a 2D array.

3. Final Answer:

   np.sum(data, axis=0) -> Option C

4. Quick Check:

   Sum axis=0 collapses first dimension to 2D [OK]

Hint: Sum axis=0 collapses first dimension in 3D arrays [OK]

Common Mistakes:

• Using wrong axis and getting wrong shape
• Summing all elements without axis
• Confusing axis numbering in 3D arrays