5.1 Arrays and Matrices
Arrays are an essential part of NumPy, and understanding them is crucial to mastering this powerful library. An array is a data structure that can store multiple values simultaneously. By using arrays, you can perform operations on entire sets of data, making it an efficient way to process large amounts of data.
NumPy arrays are homogeneous, meaning that their elements must be of the same data type. This allows for faster computation and more efficient memory usage. Overall, mastering arrays in NumPy is a key step in becoming proficient in using this impressive library.
Here is how you can create a simple array in NumPy:
import numpy as np
# Create a 1-dimensional array
one_d_array = np.array([1, 2, 3, 4, 5])
print("1D Array:", one_d_array)
Output: 1D Array: [1 2 3 4 5]
Arrays can be multi-dimensional. For example, here's a 2-dimensional array, which you can think of as a matrix:
# Create a 2-dimensional array
two_d_array = np.array...