NUMPY Tutorial

🔢 Introduction to NumPy Arrays

NumPy Arrays are the heart of the NumPy library. They are powerful, flexible, and optimized for numerical operations. Unlike Python lists, NumPy arrays are faster, memory-efficient, and support vectorized operations.

📌 What is a NumPy Array?

A NumPy array (also called ndarray) is a grid of values, all of the same type, and indexed by a tuple of non-negative integers.

import numpy as np

# Creating a NumPy array
arr = np.array([1, 2, 3, 4])
print(arr)

Output:

[1 2 3 4]

📚 Types of Arrays

• 1D Array: A single row of data
• 2D Array: Matrix of rows and columns
• 3D Array: Collection of matrices

# 1D array
a = np.array([10, 20, 30])

# 2D array
b = np.array([[1, 2], [3, 4]])

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

📏 Array Attributes

Every array has attributes that give you more information:

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

print(arr.shape)    # (2, 3)
print(arr.ndim)     # 2
print(arr.size)     # 6
print(arr.dtype)    # int64 (depends on system)

🧪 Useful Array Creation Functions

np.zeros((2, 3))     # 2x3 array of zeros
np.ones((3, 2))      # 3x2 array of ones
np.eye(3)            # Identity matrix
np.arange(0, 10, 2)  # [0 2 4 6 8]
np.linspace(0, 1, 5) # [0.  0.25 0.5  0.75 1. ]

🧮 Array Operations

NumPy allows element-wise operations without looping.

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

print(arr1 + arr2)    # [5 7 9]
print(arr1 * arr2)    # [4 10 18]
print(arr1 ** 2)      # [1 4 9]

🔍 Summary

• NumPy arrays are powerful data containers for numerical operations.
• They are faster, more efficient, and easier to use than Python lists for mathematical tasks.
• You can perform element-wise operations, reshaping, and statistical computations easily.