ऐरे संक्रियाएँ और ब्रॉडकास्टिंग

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b)    # [5 7 9]
print(a * b)    # [4 10 18]
print(a ** 2)   # [1 4 9]

import numpy as np

x = np.linspace(0, np.pi, 5)
print(np.sin(x))   # [0. 0.707 1. 0.707 0.]
print(np.exp([1.0, 2.0, 3.0]))   # [2.718 7.389 20.086]

import numpy as np

m = np.array([[1, 2, 3], [4, 5, 6]])
print(np.sum(m))          # 21
print(np.sum(m, axis=0))  # [5 7 9]  — sum each column
print(np.sum(m, axis=1))  # [6 15]   — sum each row

import numpy as np

a = np.array([[1], [2], [3]])  # shape (3,1)
b = np.array([10, 20, 30])     # shape (3,)

print(a + b)
# [[11 21 31]
#  [12 22 32]
#  [13 23 33]]

import numpy as np

arr = np.arange(6).reshape(2, 3)
print(arr + 10)
# [[10 11 12]
#  [13 14 15]]
print(arr * 0.5)
# [[0.  0.5 1. ]
#  [1.5 2.  2.5]]

import numpy as np

arr = np.array([1, 5, 3, 8, 2])
print(arr > 3)           # [False  True False  True False]
print(arr[(arr > 2) & (arr < 7)])  # [5 3]

import numpy as np

arr = np.array([-2, 3, -1, 4, 0])
result = np.where(arr >= 0, arr, 0)  # clip negatives to 0
print(result)   # [0 3 0 4 0]

import numpy as np

data = np.arange(12).reshape(3, 4)
print(data.mean(axis=0))   # mean of each column → shape (4,)
print(data.mean(axis=1))   # mean of each row → shape (3,)

import numpy as np

prices = np.array([100, 110, 105, 115])
print(np.cumsum(prices))          # [100 210 315 430]
print(np.diff(prices))            # [10 -5 10] (daily changes)

import numpy as np

words = np.array(["apple", "banana", "cherry"])
print(np.char.upper(words))       # ['APPLE' 'BANANA' 'CHERRY']
print(np.char.startswith(words, "a"))  # [True False False]

import numpy as np, time

n = 1_000_000

start = time.perf_counter()
python_sum = sum(i**2 for i in range(n))
print(f"Python: {time.perf_counter()-start:.3f}s")

arr = np.arange(n)
start = time.perf_counter()
numpy_sum = (arr**2).sum()
print(f"NumPy:  {time.perf_counter()-start:.3f}s")