np.sum() and axis parameter in NumPy - Mini Project: Build & Apply

Summing Data with np.sum() and the axis Parameter

📖 Scenario: Imagine you work at a small store that tracks daily sales of different products. You have sales data for 3 products over 4 days. You want to find total sales for each product and total sales for each day.

🎯 Goal: You will create a 2D numpy array with sales data, then use np.sum() with the axis parameter to calculate total sales per product and per day.

📋 What You'll Learn

Create a numpy array called sales with the exact data given.

Create a variable called axis_0 set to 0 to represent summing over rows (days).

Use np.sum() with axis=axis_0 to find total sales per product.

Print the total sales per product.

Create a variable called axis_1 set to 1 to represent summing over columns (products).

Use np.sum() with axis=axis_1 to find total sales per day.

Print the total sales per day.

💡 Why This Matters

🌍 Real World

Stores and businesses often track sales data in tables and need to quickly find totals by product or by day to understand performance.

💼 Career

Data analysts and scientists use numpy and its sum function with axis to summarize and analyze multi-dimensional data efficiently.

1

Create the sales data array

Create a numpy array called sales with this exact 2D data:
[[5, 3, 2], [6, 7, 1], [4, 5, 3], [8, 2, 4]] representing sales of 3 products over 4 days.

NumPy

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

Hint

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

2

Set axis variables for summing

Create a variable called axis_0 and set it to 0 to sum over rows (days). Then create a variable called axis_1 and set it to 1 to sum over columns (products).

NumPy

import numpy as np
sales = np.array([[5, 3, 2], [6, 7, 1], [4, 5, 3], [8, 2, 4]])
# Create axis_0 and axis_1 variables
# Your code here

Hint

Remember, axis 0 means down the rows, axis 1 means across the columns.

3

Calculate total sales per product and per day

Use np.sum() with axis=axis_0 on sales to calculate total sales per product and save it in total_per_product. Then use np.sum() with axis=axis_1 on sales to calculate total sales per day and save it in total_per_day.

NumPy

import numpy as np
sales = np.array([[5, 3, 2], [6, 7, 1], [4, 5, 3], [8, 2, 4]])
axis_0 = 0
axis_1 = 1
# Calculate total_per_product and total_per_day
# Your code here

Hint

Use np.sum() with the correct axis variable for each total.

4

Print the total sales results

Print total_per_product and then print total_per_day to show the sums.

NumPy

import numpy as np
sales = np.array([[5, 3, 2], [6, 7, 1], [4, 5, 3], [8, 2, 4]])
axis_0 = 0
axis_1 = 1
total_per_product = np.sum(sales, axis=axis_0)
total_per_day = np.sum(sales, axis=axis_1)
# Print the totals
# Your code here

Hint

Use two print() statements, one for each total array.

Practice

1. What does the axis parameter in np.sum() control?

easy

A. The data type of the output array.

B. The direction along which the sum is calculated (rows or columns).

C. The size of the array to sum.

D. The number of times the sum is repeated.

Solution

1. Step 1: Understand the role of axis in np.sum()

   The axis parameter tells numpy whether to sum across rows (axis=1) or columns (axis=0).

2. Step 2: Clarify what axis does not affect

   It does not change data type, size, or repetition of sums.

3. Final Answer:

   The direction along which the sum is calculated (rows or columns). -> Option B

4. Quick Check:

   axis controls sum direction [OK]

Hint: Axis decides row or column sum direction [OK]

Common Mistakes:

• Thinking axis changes data type
• Confusing axis with array size
• Assuming axis repeats sums

2. Which of the following is the correct syntax to sum all elements of a 2D numpy array arr?

easy

A. np.sum(arr)

B. np.sum(arr, axis=1, 0)

C. np.sum(arr, axis=2)

D. np.sum(arr, axis='all')

Solution

1. Step 1: Recall default behavior of np.sum()

   Without axis, np.sum(arr) sums all elements in the array.

2. Step 2: Check invalid axis values

   axis=2 is invalid for 2D arrays, axis=1,0 is incorrect syntax, axis='all' is invalid.

3. Final Answer:

   np.sum(arr) -> Option A

4. Quick Check:

   Default np.sum sums all elements [OK]

Hint: No axis sums all elements [OK]

Common Mistakes:

• Using invalid axis values
• Passing multiple axis values incorrectly
• Using string instead of integer for 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. [5 7 9]

B. [3 7]

C. [6 15]

D. [1 2 3 4 5 6]

Solution

1. Step 1: Understand axis=0 sums columns

   Summing along axis=0 adds elements vertically: (1+4), (2+5), (3+6).

2. Step 2: Calculate sums for each column

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

3. Final Answer:

   [5 7 9] -> Option A

4. Quick Check:

   Sum columns = [5 7 9] [OK]

Hint: axis=0 sums columns vertically [OK]

Common Mistakes:

• Confusing axis=0 with axis=1
• Adding rows instead of columns
• Expecting a scalar output

4. What is wrong with this code snippet?

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

medium

A. The array is not defined correctly.

B. np.sum() cannot be used on 2D arrays.

C. The axis value 2 is invalid for a 2D array.

D. The import statement is missing.

Solution

1. Step 1: Check array dimensions

   The array has shape (2, 2), so valid axis values are 0 or 1.

2. Step 2: Validate axis parameter

   axis=2 is out of bounds for this 2D array, causing an error.

3. Final Answer:

   The axis value 2 is invalid for a 2D array. -> Option C

4. Quick Check:

   Axis must be less than array dimensions [OK]

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

Common Mistakes:

• Using axis larger than array dimensions
• Thinking np.sum can't handle 2D arrays
• Ignoring error messages

5. You have a 3D numpy array arr = np.array([[[1,2],[3,4]], [[5,6],[7,8]]]). What is the shape of the result when you run np.sum(arr, axis=1)?

hard

A. (1, 2)

B. (3, 2)

C. (2, 4)

D. (2, 2)

Solution

1. Step 1: Understand the shape of arr

   arr has shape (2, 2, 2): 2 blocks, each with 2 rows and 2 columns.

2. Step 2: Summing along axis=1

   axis=1 sums over the second dimension (rows), reducing it from 2 to 1, so shape changes from (2, 2, 2) to (2, 2).

3. Final Answer:

   (2, 2) -> Option D

4. Quick Check:

   Sum axis=1 reduces dimension 2 to 1 [OK]

Hint: Sum along axis removes that dimension [OK]

Common Mistakes:

• Confusing which axis is summed
• Expecting shape to stay same
• Mixing up dimensions in 3D arrays