NumPy Essentials

import numpy as np

# Creating arrays
arr = np.array([1, 2, 3, 4, 5])
print(type(arr))  # <class 'numpy.ndarray'>

# From list
a = np.array([1, 2, 3])

# Zeros and Ones
zeros = np.zeros(5)          # [0. 0. 0. 0. 0.]
ones = np.ones((2, 3))       # 2x3 matrix of 1s

# Ranges
range_arr = np.arange(0, 10, 2)  # [0, 2, 4, 6, 8]
linspace = np.linspace(0, 1, 5)  # 5 points between 0 and 1

# Random
rand = np.random.rand(3, 3)      # 3x3 random values [0, 1)
randn = np.random.randn(5)       # Standard normal distribution
randint = np.random.randint(0, 10, 5) # 5 random ints [0, 10)

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

# Math operations apply to ALL elements at once
print(a + b)      # [5 7 9]
print(a * 2)      # [2 4 6]
print(a ** 2)     # [1 4 9]
print(np.sqrt(a)) # Square root of each element

matrix = np.ones((3, 3))
row = np.array([1, 2, 3])

# Adds row to EVERY row of matrix
result = matrix + row

arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Access element [row, col]
print(arr[0, 0])  # 1
print(arr[1, 2])  # 6

# Slicing
print(arr[0, :])  # First row: [1 2 3]
print(arr[:, 1])  # Second column: [2 5 8]
print(arr[0:2, 0:2]) # Top-left 2x2 sub-array

arr = np.array([1, 10, 20, 3, 40])

# Create mask
mask = arr > 10
print(mask) # [False False  True False  True]

# Filter array
filtered = arr[arr > 10]
print(filtered) # [20 40]

arr = np.random.randn(1000)

print(np.mean(arr))   # Average
print(np.median(arr)) # Median
print(np.std(arr))    # Standard Deviation
print(np.min(arr))    # Minimum
print(np.max(arr))    # Maximum
print(np.sum(arr))    # Sum

import numpy as np

# 1. Create a 5x5 matrix of random integers between 1 and 100
matrix = np.random.randint(1, 101, (5, 5))

# 2. Find the mean of the entire matrix
print("Mean:", np.mean(matrix))

# 3. Find the max value in each row
print("Row maxs:", np.max(matrix, axis=1))

# 4. Filter values greater than 50
high_values = matrix[matrix > 50]
print("Values > 50 count:", len(high_values))