np.sum() and axis parameter in NumPy

Introduction

We use np.sum() to add up numbers in arrays quickly. The axis parameter helps us choose how to add: across rows, columns, or the whole array.

Adding all sales numbers in a table to get total sales.

Summing scores of each student across subjects to get total marks.

Calculating total pixels brightness in each row or column of an image.

Finding total expenses per category from a budget spreadsheet.

Syntax

NumPy

np.sum(array, axis=None)

array is the data you want to add up.

axis=None sums all elements into one number.

axis=0 sums down columns (vertical).

axis=1 sums across rows (horizontal).

Examples

Adds every number in the array to get one total.

NumPy

np.sum(arr)
# sums all elements in arr

Adds numbers down each column, returns sums per column.

NumPy

np.sum(arr, axis=0)
# sums each column

Adds numbers across each row, returns sums per row.

NumPy

np.sum(arr, axis=1)
# sums each row

Sample Program

This code creates a 3x3 array. It sums all numbers, then sums each column, and then sums each row. It prints all results clearly.

NumPy

import numpy as np

arr = np.array([[1, 2, 3],
                [4, 5, 6],
                [7, 8, 9]])

# Sum all elements
total_sum = np.sum(arr)

# Sum down columns (axis=0)
sum_columns = np.sum(arr, axis=0)

# Sum across rows (axis=1)
sum_rows = np.sum(arr, axis=1)

print(f"Total sum: {total_sum}")
print(f"Sum of each column: {sum_columns}")
print(f"Sum of each row: {sum_rows}")

Important Notes

If you forget axis, it adds everything into one number.

For 2D arrays, axis=0 means down columns, axis=1 means across rows.

For higher dimensions, axis chooses which dimension to sum over.

Summary

np.sum() adds numbers in arrays.

The axis parameter controls the direction of addition.

Use axis=0 for columns, axis=1 for rows, or None for all elements.