#2.e Numpy Basics

🔢 NumPy Concepts with Demo Programs

This blog covers essential NumPy concepts for data science and numerical computing. Each demo has syntax highlighting and a copy button for easy learning and sharing.

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1. Creating NumPy Arrays

import numpy as np

# 1D array
arr1 = np.array([1, 2, 3, 4])
print("1D array:", arr1)

# 2D array
arr2 = np.array([[1, 2], [3, 4]])
print("2D array:\n", arr2)

# Using arange and linspace
arr_arange = np.arange(0, 10, 2)  # start, stop, step
arr_linspace = np.linspace(0, 1, 5)  # start, stop, number of points
print("arange:", arr_arange)
print("linspace:", arr_linspace)

# Array of zeros, ones, identity
zeros = np.zeros((2,3))
ones = np.ones((2,2))
identity = np.eye(3)
print("Zeros:\n", zeros)
print("Ones:\n", ones)
print("Identity:\n", identity)

2. Array Indexing and Slicing

# 1D array slicing
arr = np.array([10, 20, 30, 40, 50])
print("Original array:", arr)
print("Slice [1:4]:", arr[1:4])
print("Every 2nd element:", arr[::2])

# 2D array indexing
arr2d = np.array([[1,2,3],[4,5,6],[7,8,9]])
print("Element at (0,2):", arr2d[0,2])
print("First row:", arr2d[0])
print("Second column:", arr2d[:,1])

3. Array Operations

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

# Element-wise operations
print("a + b =", a + b)
print("a - b =", a - b)
print("a * b =", a * b)
print("a / b =", a / b)

# Universal functions
print("sqrt(a):", np.sqrt(a))
print("exp(a):", np.exp(a))
print("sin(a):", np.sin(a))

4. Array Aggregations

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

print("Sum:", np.sum(arr))
print("Sum axis=0 (columns):", np.sum(arr, axis=0))
print("Sum axis=1 (rows):", np.sum(arr, axis=1))
print("Mean:", np.mean(arr))
print("Max:", np.max(arr))
print("Min:", np.min(arr))
print("Std dev:", np.std(arr))

5. Reshaping and Transpose

arr = np.arange(12)
print("Original array:", arr)

reshaped = arr.reshape(3,4)
print("Reshaped (3x4):\n", reshaped)

flattened = reshaped.flatten()
print("Flattened:", flattened)

print("Transpose:\n", reshaped.T)

6. Boolean Indexing and Filtering

arr = np.array([10, 15, 20, 25, 30])

# Boolean mask
mask = arr > 20
print("Mask:", mask)
print("Filtered array:", arr[mask])

# Or directly
print("Values > 20:", arr[arr > 20])

7. Random Numbers with NumPy

# Random numbers
rand_float = np.random.rand(5)  # uniform [0,1)
rand_int = np.random.randint(0, 10, size=5)
rand_norm = np.random.randn(5)  # standard normal
print("Random floats:", rand_float)
print("Random integers:", rand_int)
print("Random normal:", rand_norm)

8. Linear Algebra & Dot Products

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

# Matrix multiplication
dot = np.dot(A,B)
print("Dot product:\n", dot)

# Determinant and inverse
det = np.linalg.det(A)
inv = np.linalg.inv(A)
print("Determinant:", det)
print("Inverse:\n", inv)

9. Stacking Arrays

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

# Vertical and horizontal stacking
vstacked = np.vstack([a,b])
hstacked = np.hstack([a,b])
print("VStacked:\n", vstacked)
print("HStacked:", hstacked)

10. Saving and Loading Arrays

arr = np.arange(10)

# Save to file
np.save("my_array.npy", arr)

# Load back
loaded_arr = np.load("my_array.npy")
print("Loaded array:", loaded_arr)

✅ With these 10 demos, you now have a full **NumPy cheat sheet with practical code examples**, ready for learning !