Python - Numpy

Python NumPy

Introduction

NumPy, which stands for Numerical Python, is an open-source library fundamental to scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a vast collection of high-level mathematical functions to operate on these arrays efficiently. It is widely used in data science, machine learning, engineering, and scientific research.

Why Use NumPy?

• Efficient multidimensional array objects (ndarray)
• Functions for performing element-wise operations and linear algebra
• Tools for integrating C, C++, and Fortran code
• Support for a wide variety of numeric data types
• Broadcasting capabilities for operations on arrays of different shapes

Installation

pip install numpy

Importing NumPy

import numpy as np

Creating Arrays

From Python Lists

import numpy as np

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

Multidimensional Arrays

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

Using Built-in Functions

zeros = np.zeros((2, 3))
ones = np.ones((3, 3))
identity = np.eye(3)
arange = np.arange(0, 10, 2)
linspace = np.linspace(0, 1, 5)

Array Attributes

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

Indexing and Slicing

arr = np.array([10, 20, 30, 40, 50])
print(arr[1])      # 20
print(arr[1:4])    # [20 30 40]

Multidimensional Indexing

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

Data Types

arr = np.array([1, 2, 3], dtype='float32')
print(arr.dtype)

Reshaping Arrays

arr = np.arange(12)
reshaped = arr.reshape((3, 4))

Mathematical Operations

Element-wise Operations

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
print(arr1 + arr2)
print(arr1 * arr2)

Aggregate Functions

arr = np.array([[1, 2], [3, 4]])
print(arr.sum())
print(arr.mean())
print(arr.max())
print(arr.min())

Broadcasting

arr = np.array([[1, 2, 3], [4, 5, 6]])
broadcasted = arr + 10

Boolean Indexing

arr = np.array([10, 20, 30, 40])
bool_idx = arr > 20
print(arr[bool_idx])

Fancy Indexing

arr = np.array([10, 20, 30, 40])
print(arr[[0, 3]])

Copy vs View

arr = np.array([1, 2, 3])
copy = arr.copy()
view = arr.view()
arr[0] = 100
print(copy)  # [1 2 3]
print(view)  # [100 2 3]

Linear Algebra with NumPy

Matrix Multiplication

A = np.array([[1, 2], [3, 4]])
B = np.array([[2, 0], [1, 2]])
product = np.dot(A, B)

Determinant and Inverse

det = np.linalg.det(A)
inv = np.linalg.inv(A)

Random Numbers

rand_arr = np.random.rand(2, 3)
rand_int = np.random.randint(1, 10, size=(3, 3))
normal = np.random.randn(4)

Saving and Loading Data

np.save('my_array.npy', arr)
loaded = np.load('my_array.npy')

Useful NumPy Functions

np.unique(arr)
np.sort(arr)
np.argsort(arr)
np.where(arr > 2)

NumPy vs Lists Performance

import numpy as np
import time

size = 1000000

list1 = list(range(size))
list2 = list(range(size))
start = time.time()
result = [x + y for x, y in zip(list1, list2)]
print("List time:", time.time() - start)

arr1 = np.arange(size)
arr2 = np.arange(size)
start = time.time()
result = arr1 + arr2
print("NumPy time:", time.time() - start)

Applications of NumPy

• Data preprocessing in machine learning
• Image processing
• Scientific simulations
• Financial analysis
• Signal processing

NumPy is an essential tool for anyone working with numerical data in Python. Its efficient array representation and vectorized operations make it ideal for high-performance computing. Mastering NumPy is the first step toward becoming proficient in data science, numerical computing, and scientific programming in Python.