Numpy Array Creation (original) (raw)

Last Updated : 24 Jan, 2025

**Numpy Arrays are grid-like structures similar to lists in Python but optimized for numerical operations. The most straightforward way to create a NumPy array is by converting a regular Python list into an array using the np.array() function.

Let's understand this with the help of an example:

Python `

import numpy as np

One-dimensional array

arr1 = np.array([1, 2, 3, 4, 5]) print(arr1)

Two-dimensional array

arr2 = np.array([[1, 2], [3, 4]]) print(arr2)

`

Output

[1 2 3 4 5] [[1 2] [3 4]]

Creating Arrays with Specific Values

For assigning a specific values. NumPy provides several function to create arrays filled with zeros, ones, or a specific constant value.

• **Zeros Array: np.zeros() function creates an array filled with zeros. It requires the shape of the array as a parameter.

**Example:

Python `

import numpy as np

3x4 array filled with zeros

arr_zero = np.zeros((3, 4))
print(arr_zero)

`

Output

[[0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.]]

• **Ones Array: np.ones() creates an array filled with ones.

**Example:

Python `

import numpy as np

2x3 array filled with ones

arr_one = np.ones((2, 3))
print(arr_one)

`

Output

[[1. 1. 1.] [1. 1. 1.]]

• **Full Array : np.full() function allows you to create an array filled with a specific value.

**Example:

Python `

import numpy as np

2x2 array filled with 7

arr_full = np.full((2, 2), 7) print(arr_full)

`

Creating Arrays with Random Values

NumPy also has functions for generating arrays with random values, useful for simulations and testing.

• **Random Float Array : np.random.rand() function generates an array of random values between 0 and 1.

**Example:

Python `

import numpy as np

2x3 array of random floats

arr_rand = np.random.rand(2, 3) print(arr_rand)

`

Output

[[0.67820861 0.64484802 0.48673431] [0.00263043 0.55383721 0.43240166]]

• **Random Integers : If we need random integers, we can use **np.random.randint() to create arrays with integer values in a specified range.

**Example:

Python `

import numpy as np

3x3 array of random integers from 1 to 9

arr_int = np.random.randint(1, 10, size=(3, 3)) print(arr_int)

`

Output

[[4 6 5] [7 4 8] [8 5 2]]

Creating Arrays with a Range of Values

Another common method of creating arrays is using a range of values. NumPy provides functions like np.arange() and np.linspace() for this purpose.

• **Using np.arange() : np.arange() creates arrays with values spaced according to a given interval. It’s similar to Python’s built-in range() but returns a NumPy array.

**Example:

Python `

import numpy as np

Array from 0 to 10 with step 2

arr_range = np.arange(0, 10, 2)
print(arr_range)

`

• **Using np.linspace(): np.linspace() generates an array with a specified number of evenly spaced values over a specified range.

**Example:

Python `

import numpy as np

5 values from 0 to 1

arr_linspace = np.linspace(0, 1, 5)
print(arr_linspace)

`

Output

[0. 0.25 0.5 0.75 1. ]

Identity and Diagonal Matrices

NumPy also provides functions for creating identity matrices and diagonal matrices, which are often used in linear algebra.

• **Identity Matrix : np.eye() function creates an identity matrix, a square matrix with ones on the diagonal and zeros elsewhere.

**Example:

Python `

import numpy as np

3x3 identity matrix

identity_matrix = np.eye(3)
print(identity_matrix)

`

Output

[[1. 0. 0.] [0. 1. 0.] [0. 0. 1.]]

• **Diagonal Matrix : Use np.diag() to create a diagonal matrix, where the provided array elements form the diagonal.

**Example:

Python `

import numpy as np

Diagonal matrix with [1, 2, 3] on the diagonal

diag_matrix = np.diag([1, 2, 3])
print(diag_matrix)

`

Output

[[1 0 0] [0 2 0] [0 0 3]]

Methods for array creation in Numpy