Arrays in NumPy

In NumPy, an array is a powerful data structure that allows you to store and manipulate numerical data efficiently. It is called a NumPy array or ndarray (N-dimensional array).

Creating a NumPy Array

To create an array, you first need to import NumPy:

import numpy as np

1. Creating Arrays from Lists You can create a NumPy array from a Python list using np.array():

arr = np.array([1, 2, 3, 4, 5])
print(arr)
# Output: [1 2 3 4 5]

2. Creating Multi-Dimensional Arrays You can create a 2D (matrix) or higher-dimensional array:

arr_2d = np.array([[1, 2, 3], [4, 5, 6]])
print(arr_2d)

Output:

[[1 2 3]
 [4 5 6]]

3. Using NumPy Functions to Create Arrays NumPy provides several functions to generate arrays:

• Zeros Array:
  zeros = np.zeros((3, 3))  # 3x3 array of zeros
  

• Ones Array:
  ones = np.ones((2, 4))  # 2x4 array of ones
  

• Identity Matrix:
  identity = np.eye(3)  # 3x3 identity matrix
  

• Random Array:
  rand_arr = np.random.rand(2, 3)  # 2x3 array with random values
  

• Arange (Like Python range):
  arr = np.arange(0, 10, 2)  # [0, 2, 4, 6, 8]
  

• Linspace (Evenly Spaced Values):
  lin_arr = np.linspace(0, 10, 5)  # [0, 2.5, 5, 7.5, 10]
  

NumPy Array Properties

You can check the following properties of an array:

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

print(arr.shape)  # (2, 3) -> Rows x Columns
print(arr.ndim)   # 2 -> Number of dimensions
print(arr.size)   # 6 -> Total elements
print(arr.dtype)  # int64 (or int32) -> Data type of elements