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

P12.2 How to Create NumPy Arrays: array, arange, zeros, linspace

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Why. You need arrays from lists, ranges of numbers or empty starting arrays.

Function Gives Example → result
np.array(list) array from a list np.array([1,2]) → [1 2]
np.arange(a, b) a to b-1 np.arange(1,5) → [1 2 3 4]
np.zeros(n) n zeros np.zeros(3) → [0. 0. 0.]
np.linspace(a, b, n) n evenly spaced values np.linspace(0,1,3) → [0. 0.5 1.]

How to read this table: each row is one way to create an array, with a tiny example.

How.

Create and inspect

  1. Create the array.
  2. Check .shape (size) and .dtype (type).
  3. Read items by index like a list.

Worked example A — Create and check.

Step 1 — What we do. Monthly revenue (₹ lakh) of a retail chain.

Step 2 — Type and run this code.

import numpy as np
rev = np.array([12.5, 14.0, 13.2, 15.8])
print(rev)
print(rev.shape, rev.dtype)
print(rev[0], rev[-1])

Step 3 — Output you should see.

[12.5 14.  13.2 15.8]
(4,) float64
12.5 15.8

Step 4 — What it means. Shape (4,) means 4 values in one dimension. dtype float64 means decimal numbers.

Worked example B — Ranges and zeros.

Step 1 — What we do. Day numbers for a 7-day report and an empty counter array.

Step 2 — Type and run this code.

import numpy as np
days = np.arange(1, 8)
counts = np.zeros(7)
print(days)
print(counts)

Step 3 — Output you should see.

[1 2 3 4 5 6 7]
[0. 0. 0. 0. 0. 0. 0.]

Step 4 — What it means. arange works like range; zeros is a starting point you can fill later.

Ravindra Bagale's Tip

All values in an array share one dtype. If you mix numbers and text, everything becomes text — check .dtype after creating.