Creating and Shaping Arrays

The ndarray

A NumPy ndarray is a grid of values that all share one dtype (int64, float64, float32, bool), described by a shape tuple. Because the values are packed tightly in memory, operations on whole arrays run in optimised C instead of the Python interpreter. Create arrays from lists (np.array), with constructors (zeros, ones, arange, linspace), or from files and other libraries. reshape changes the shape without copying data when it can.

One type, many dimensions

NumPy arrays store numbers of one type in contiguous memory, so whole-array operations run in compiled code.

Creating, reshaping and choosing dtypes, run

I ran this with Python 3.12.3 and NumPy 2.5.3. The integer list becomes int64; reshape gives a 2 by 3 matrix; arange uses a step while linspace includes both ends; a float32 array of three values uses 12 bytes.

import numpy as np

a = np.array([1, 2, 3, 4, 5, 6])
print(a, a.dtype, a.shape)
m = a.reshape(2, 3)
print(m)
print("shape:", m.shape, "ndim:", m.ndim, "size:", m.size)
print(np.zeros((2, 2)))
print(np.arange(0, 1, 0.25))
print(np.linspace(0, 1, 5))
f = np.array([1, 2, 3], dtype=np.float32)
print(f.dtype, f.nbytes, "bytes")

Output:

[1 2 3 4 5 6] int64 (6,)
[[1 2 3]
 [4 5 6]]
shape: (2, 3) ndim: 2 size: 6
[[0. 0.]
 [0. 0.]]
[0.   0.25 0.5  0.75]
[0.   0.25 0.5  0.75 1.  ]
float32 12 bytes

An egg tray, not a shopping bag

A Python list is a bag that can hold anything, each item stored separately. An array is an egg tray: identical slots in a fixed grid, so you can process the whole tray at once.

त्वरित जाँच: What do all elements of a NumPy array share?

• A Python object type per element
• A single value
• A single dtype
• Nothing in particular