Complete NumPy Tutorial

# Install: pip install numpy
import numpy as np
print("NumPy version:", np.__version__)

# From lists
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([[1, 2], [3, 4]])  # 2D array

# Built-in functions
zeros = np.zeros(5)           # [0. 0. 0. 0. 0.]
ones = np.ones((2, 3))        # 2x3 array of ones
range_arr = np.arange(0, 10, 2)  # [0 2 4 6 8]
linear = np.linspace(0, 1, 5)    # 5 numbers from 0 to 1

# Random arrays
random_floats = np.random.random(5)      # Random 0-1
random_ints = np.random.randint(1, 10, 5) # Random integers
normal = np.random.normal(0, 1, 5)       # Normal distribution

arr = np.array([[1, 2, 3], [4, 5, 6]])
print("Shape:", arr.shape)      # (2, 3)
print("Size:", arr.size)        # 6
print("Dimensions:", arr.ndim)  # 2
print("Data type:", arr.dtype)  # int64

arr = np.array([10, 20, 30, 40, 50])
print(arr[0])      # 10 (first element)
print(arr[-1])     # 50 (last element)
print(arr[1:4])    # [20 30 40] (slice)

# 2D indexing
arr2d = np.array([[1, 2, 3], [4, 5, 6]])
print(arr2d[0, 1])  # 2 (row 0, column 1)
print(arr2d[:, 0])  # [1 4] (all rows, column 0)

arr = np.array([1, 5, 3, 8, 2, 9])
mask = arr > 4
print(mask)        # [False True False True False True]
print(arr[mask])   # [5 8 9] (elements > 4)
print(arr[arr % 2 == 0])  # [8 2] (even numbers)

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

print(a + b)      # [6 8 10 12]
print(a * b)      # [5 12 21 32]
print(a ** 2)     # [1 4 9 16]
print(np.sqrt(a)) # [1. 1.41 1.73 2.]

arr = np.array([1, 4, 9, 16])
print(np.sqrt(arr))    # [1. 2. 3. 4.]
print(np.exp(arr))     # Exponential
print(np.log(arr))     # Natural log
print(np.sin(arr))     # Sine function

scores = np.array([85, 90, 78, 92, 88, 76, 95])
print("Mean:", scores.mean())      # 86.29
print("Median:", np.median(scores)) # 88.0
print("Std:", scores.std())        # 6.42
print("Min/Max:", scores.min(), scores.max())

matrix = np.array([[1, 2, 3], [4, 5, 6]])
print("Sum all:", matrix.sum())        # 21
print("Sum rows:", matrix.sum(axis=1)) # [6 15]
print("Sum cols:", matrix.sum(axis=0)) # [5 7 9]
print("Mean cols:", matrix.mean(axis=0)) # [2.5 3.5 4.5]

arr = np.arange(12)  # [0 1 2 3 4 5 6 7 8 9 10 11]
reshaped = arr.reshape(3, 4)  # 3x4 matrix
print(reshaped)

# Transpose
print(reshaped.T)  # Flip rows and columns
print(arr.reshape(-1, 1))  # Column vector (-1 = auto)

matrix = np.array([[1, 2, 3], [4, 5, 6]])
flat = matrix.flatten()    # [1 2 3 4 5 6]
ravel = matrix.ravel()     # Same as flatten
print("Original shape:", matrix.shape)
print("Flat shape:", flat.shape)

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

# Horizontal and vertical stacking
h_stack = np.hstack([a, b])  # [1 2 3 4 5 6]
v_stack = np.vstack([a, b])  # [[1 2 3], [4 5 6]]
print("Horizontal:", h_stack)
print("Vertical:\n", v_stack)

arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])

# Concatenate along different axes
concat_rows = np.concatenate([arr1, arr2], axis=0)  # Stack rows
concat_cols = np.concatenate([arr1, arr2], axis=1)  # Stack columns
print("Rows:\n", concat_rows)
print("Cols:\n", concat_cols)

# Broadcasting allows operations on different sized arrays
arr = np.array([[1, 2, 3], [4, 5, 6]])
scalar = 10
vector = np.array([1, 2, 3])

print(arr + scalar)  # Add 10 to all elements
print(arr + vector)  # Add vector to each row
print(arr * vector)  # Multiply each row by vector

# Simulate a grayscale image (values 0-255)
image = np.random.randint(0, 256, (5, 5))
print("Original image:\n", image)

# Brighten image
bright = np.clip(image + 50, 0, 255)
print("Brightened:\n", bright)

# Stock prices over 5 days
prices = np.array([100, 102, 98, 105, 103])
returns = (prices[1:] - prices[:-1]) / prices[:-1] * 100

print("Prices:", prices)
print("Daily returns (%):", returns.round(2))
print("Average return:", returns.mean().round(2))

# Student grades: rows=students, cols=subjects
grades = np.array([[85, 90, 78], [92, 88, 95], [76, 82, 89]])

student_avg = grades.mean(axis=1)  # Average per student
subject_avg = grades.mean(axis=0)  # Average per subject

print("Student averages:", student_avg.round(1))
print("Subject averages:", subject_avg.round(1))
print("Class average:", grades.mean().round(1))