Aggregation along specific axes in NumPy

Introduction

Aggregation helps us summarize data by combining values. Doing it along specific axes lets us focus on rows or columns separately.

You want to find the average score of each student across subjects.

You need the total sales per product from a sales table.

You want to find the maximum temperature each day from hourly data.

You want to count how many times each category appears in columns.

Syntax

NumPy

numpy_function(array, axis=axis_number)

array is your data in a numpy array.

axis=0 means aggregate down the rows (column-wise).

axis=1 means aggregate across the columns (row-wise).

Examples

This sums each column: result is [4, 6].

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4]])
np.sum(arr, axis=0)

This sums each row: result is [3, 7].

NumPy

np.sum(arr, axis=1)

This finds the average of each column: [2.0, 3.0].

NumPy

np.mean(arr, axis=0)

This finds the max value in each row: [2, 4].

NumPy

np.max(arr, axis=1)

Sample Program

This program shows how to sum scores for each test across all students and find each student's average score.

NumPy

import numpy as np

# Create a 3x4 array representing 3 students and 4 test scores
scores = np.array([
    [80, 90, 70, 85],
    [75, 85, 80, 90],
    [90, 95, 85, 100]
])

# Sum scores for each test (column-wise sum)
total_per_test = np.sum(scores, axis=0)

# Average score for each student (row-wise mean)
avg_per_student = np.mean(scores, axis=1)

print("Total scores per test:", total_per_test)
print("Average score per student:", avg_per_student)

Important Notes

Remember axis=0 collapses rows and keeps columns.

Axis numbering starts at 0 for the first dimension.

Aggregation functions include sum, mean, max, min, etc.

Summary

Aggregation combines data to summarize it.

Use axis=0 to aggregate down rows (per column).

Use axis=1 to aggregate across columns (per row).

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