NumPy arrays are the cornerstone of numerical computing in Python. They provide efficient storage and manipulation of numerical data, making them a powerful tool for data scientists, machine learning engineers, and researchers.
Creating NumPy Arrays
There are several ways to create NumPy arrays:
1. From Python Lists
python
import numpy as np
# Create a 1D array
arr1 = np.array([1, 2, 3, 4, 5])
# Create a 2D array
arr2 = np.array([[1, 2, 3], [4, 5, 6]])
2. Using NumPy’s Array Creation Functions
NumPy provides several functions for creating arrays:
np.zeros(): Creates an array filled with zeros:np.ones(): Creates an array filled with ones:np.full(): Creates an array filled with a specific value:np.random.rand(): Creates an array with random values between 0 and 1:np.linspace(): Creates an array with evenly spaced numbers within a given interval:
Key Attributes of NumPy Arrays
NumPy arrays have key attributes that define their structure:
- Shape: The dimensions of the array.
- Data Type: The data type of the elements in the array.
- Size: The total number of elements in the array.
Basic Array Operations
NumPy provides a rich set of operations for working with arrays:
Arithmetic Operations
python
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
# Addition
print(arr1 + arr2)
# Subtraction
print(arr1 - arr2)
# Multiplication
print(arr1 * arr2)
# Division
print(arr1 / arr2)
Array Indexing and Slicing
python
arr = np.array([[1, 2, 3], [4, 5, 6]])
# Accessing elements
print(arr[0, 1]) # Access the element at the first row, second column
# Slicing
print(arr[1:, 1:]) # Slice the array from the second row and second column onwards
Reshaping Arrays
python
arr = np.array([1, 2, 3, 4, 5, 6])
new_arr = arr.reshape(2, 3) # Reshape into a 2x3 array
By understanding the fundamentals of NumPy arrays, you can effectively leverage their power for a wide range of numerical computing tasks.