NumPy asoslari: Massivlar va matematik amallar

NumPy (Numerical Python) ilmiy hisob-kitoblar uchun eng muhim kutubxona.

Massiv yaratish

import numpy as np

# Ro'yxatdan massiv
arr = np.array([1, 2, 3, 4, 5])

# 2D massiv
matrix = np.array([[1, 2, 3], [4, 5, 6]])

# Maxsus massivlar
nollar = np.zeros((3, 3))
birlar = np.ones((2, 4))
range_arr = np.arange(0, 10, 2)   # [0, 2, 4, 6, 8]
linspace = np.linspace(0, 1, 5)   # 0 dan 1 gacha 5 ta son

Massiv xususiyatlari

print(arr.shape)    # (5,)
print(arr.dtype)    # int64
print(arr.size)     # 5
print(arr.ndim)     # 1

Matematik amallar

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]
print(np.sqrt(a))   # [1., 1.41, 1.73]

Statistik funksiyalar

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

print(np.mean(data))    # o'rtacha
print(np.median(data))  # mediana
print(np.std(data))     # standart og'ish
print(np.var(data))     # dispersiya
print(np.min(data))     # minimum
print(np.max(data))     # maksimum

Massivni filtrlash

data = np.array([10, 25, 5, 30, 15, 20])
katta = data[data > 15]   # [25, 30, 20]