Python NumPy

NumPy (Numerical Python) is one of the most fundamental packages for numerical computations in Python. It provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

1. Installation

Before diving into its functionality, ensure you have NumPy installed:

pip install numpy

Then, in your Python script or notebook:

import numpy as np

2. Basics of NumPy Arrays

2.1 Creating Arrays

From a Python list or tuple:

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

Using built-in functions:

zeros_array = np.zeros((2, 3))
ones_array = np.ones((3, 3))
identity_matrix = np.eye(3)
range_array = np.arange(10)

2.2 Array Attributes

arr = np.array([[1, 2, 3], [4, 5, 6]])
arr.shape    # (2, 3)
arr.size     # 6
arr.ndim     # 2
arr.dtype    # dtype('int64')

3. Array Operations

Arrays can be used in arithmetic operations:

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

c = a + b  # array([5, 7, 9])
d = a * b  # array([ 4, 10, 18])

You can also apply mathematical operations element-wise:

squared = np.square(a)  # array([1, 4, 9])

4. Indexing and Slicing

NumPy provides powerful ways to work with array subsets:

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Get a single element
arr[1, 2]   # 6

# Get a row
arr[1]      # array([4, 5, 6])

# Slicing
arr[0:2, 1:3]

5. Reshaping and Transposing

Easily change the shape of arrays:

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

# Transpose
transposed = reshaped.T

6. Broadcasting

Allows NumPy to work with arrays of different shapes:

a = np.array([1, 2, 3])
b = 2

result = a * b  # array([2, 4, 6])

7. Mathematical and Statistical Functions

NumPy provides many useful functions:

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

np.mean(arr)     # 3.0
np.median(arr)   # 3.0
np.sum(arr)      # 15
np.std(arr)      # 1.414

8. Linear Algebra

Matrix operations are straightforward:

a = np.array([[1, 2], [3, 4]])
b = np.array([[2, 0], [0, 2]])

dot_product = np.dot(a, b)

9. Final Thoughts

This tutorial only scratches the surface of what NumPy can do. Given its vast array of functionalities and the key role it plays in Python's scientific ecosystem (e.g., Pandas, Scikit-learn), having a solid understanding of NumPy's capabilities is crucial for anyone working in data science, machine learning, or related fields.