NumPy Tutorial for Beginners: Arrays, Maths and Filtering in Python

P12.6 NumPy 2D Arrays: Rows, Columns and Axis

By Ravindra Bagale, trainer LinkedIn Last updated: 27 October 2026

Why. Tables have rows and columns. A 2D array is a table of numbers, and axis decides whether you total down columns or across rows.

What. arr[row, col] picks a cell. axis=0 works down the columns, axis=1 across the rows.

	Week 1	Week 2	Week 3
Pune	120	135	128
Mumbai	210	198	225

How to read this table: each row is one store; each column is one week; values are orders.

How.

Work with a 2D array

Create with a list of lists.
Check .shape → (rows, columns).
Pick a cell with [row, col], a row with [row], a column with [:, col].
Sum with axis=0 (per column) or axis=1 (per row).

Worked example A — Create and read.

Step 1 — What we do. The store-by-week table above.

Step 2 — Type and run this code.

import numpy as np
orders = np.array([[120, 135, 128],
                   [210, 198, 225]])
print(orders.shape)
print(orders[1, 2])
print(orders[:, 0])

Step 3 — Output you should see.

(2, 3)
225
[120 210]

Step 4 — What it means. Shape (2, 3) = 2 rows, 3 columns. [1, 2] = Mumbai week 3. [:, 0] = all rows, week 1.

Worked example B — Totals by store and by week.

Step 1 — What we do. We sum across and down.

Step 2 — Type and run this code.

import numpy as np
orders = np.array([[120, 135, 128],
                   [210, 198, 225]])
print("Per store:", orders.sum(axis=1))
print("Per week:", orders.sum(axis=0))

Step 3 — Output you should see.

Per store: [383 633]
Per week: [330 333 353]

Step 4 — What it means. axis=1 adds across each row (store totals). axis=0 adds down each column (week totals). pandas uses the same axis idea.

Ravindra Bagale's Tip

Remember axis like this: axis=0 collapses the rows (answer per column), axis=1 collapses the columns (answer per row).

Samajla ka? If yes, take the quiz for this chapter. If not, run every example again and change one value each time.