Introduction to NumPy

📌 What is NumPy?

NumPy stands for "Numerical Python". It provides support for large, multi-dimensional arrays and matrices.

🚀 Why use it?

• Speed: 50x faster than traditional Python lists.
• Memory: More efficient.
• Data Science Base: Pandas and Matplotlib are built ON TOP of NumPy.

🛠️ Setup

import numpy as np
arr = np.array([1, 2, 3])
print(arr)

Creating Arrays

📌 0-D to N-D Arrays

# 0-D
arr0 = np.array(42)

# 1-D
arr1 = np.array([1, 2, 3])

# 2-D (Matrix)
arr2 = np.array([[1, 2, 3], [4, 5, 6]])

# 3-D
arr3 = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

Check dimensions with arr.ndim.

Indexing & Slicing

📌 Indexing

arr = np.array([1, 2, 3, 4])
print(arr[0]) # 1
print(arr[2] + arr[3]) # 7

📌 Slicing

[start:end:step]

print(arr[1:5]) # Slice from index 1 to 5 (not included)
print(arr[::2]) # Return every other element

Data Types

Common types: i (integer), b (boolean), u (unsigned integer), f (float).

arr = np.array([1.1, 2.2, 3.3])
newarr = arr.astype('i') # Converts to integer
print(newarr) # [1 2 3]

Copy vs View

• Copy: New array, owns the data. Changes don't affect original.
• View: Just a view of original array. Changes affect original.

x = arr.copy()
y = arr.view()

Shape & Reshape

📌 Shape

Get the shape of an array.

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

📌 Reshape

Change from 1-D to 2-D.

arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
newarr = arr.reshape(4, 3)

Iterating

Looping through arrays.

for x in np.nditer(arr):
  print(x)

Joining & Splitting

📌 Join

arr = np.concatenate((arr1, arr2))
arr = np.stack((arr1, arr2), axis=1)

📌 Split

newarr = np.array_split(arr, 3)

Searching & Sorting

📌 Search

x = np.where(arr == 4)

📌 Sort

print(np.sort(arr))

Filtering

Getting some elements out of an existing array and creating a new array out of them.

arr = np.array([41, 42, 43, 44])
x = [True, False, True, False]
newarr = arr[x] # [41, 43]

Random Numbers

NumPy offers the random module to work with random numbers.

from numpy import random
x = random.randint(100)
y = random.rand() # Float 0 to 1

Ufuncs (Math)

Universal Functions.

# Vector Addition
x = [1, 2, 3]
y = [4, 5, 6]
z = np.add(x, y) # [5 7 9]

Also: subtract, multiply, divide, power, mod.

Data Distributions

Generate arrays with specific probability distributions.

• Normal (Gaussian): random.normal()
• Binomial: random.binomial()
• Poisson: random.poisson()

🚀 Real World Projects

🟢 Beginner: Matrix Calculator

Goal: Add, Subtract, Multiply 3x3 Matrices.

🟡 Intermediate: Image Manipulator

Goal: Load an image as a NumPy array and flip/rotate it.

🔴 Advanced: Neural Network from Scratch

Goal: Build a simple perceptron using dot products.

🎯 NumPy Mini Task

Goal: Array Operations.

📋 Requirements:

1. Create an array with numbers 1 to 10.
2. Reshape it into a 2x5 matrix.
3. Multiply every number by 2.
4. Print the result.

Crunch those numbers! 🔢