[Python] NumPy 배열 연산
- 파이썬(Python)
- 2022. 2. 18. 21:49
참조
배열 연산
- NumPy의 배열 연산은 벡터화 연산을 사용
- 일반적으로 NumPy의 범용 함수(universal functions) 를 통해 구현
- 배열 요소에 대한 반복적인 계산을 효율적으로 수행
브로드캐스팅(Broadcasting)
import numpy as np
a1 = np.array([1, 2, 3])
print(a1)
print(a1 + 5)
a2 = np.arange(1, 10).reshape(3, 3)
print(a2)
print(a1 + a2)
b2 = np.array([1, 2, 3]).reshape(3, 1)
print(b2)
print(a1 + b2)[1 2 3]
[6 7 8]
[[1 2 3]
[4 5 6]
[7 8 9]]
[[ 2 4 6]
[ 5 7 9]
[ 8 10 12]]
[[1]
[2]
[3]]
[[2 3 4]
[3 4 5]
[4 5 6]]산술 연산(Arithmetic Operators)
import numpy as np
a1 = np.arange(1, 10)
print(a1)
print(a1 + 1)
print(np.add(a1, 10))
print(a1 - 2)
print(np.subtract(a1, 10))
print(-a1)
print(np.negative(a1))
print(a1 * 2)
print(np.multiply(a1, 2))
print(a1 / 2)
print(np.divide(a1, 2))
print(a1 // 2)
print(np.floor_divide(a1, 2)) # 나눗셈 내림
print(a1 ** 2)
print(np.power(a1, 2))
print(a1 % 2)
print(np.mod(a1, 2))
b1 = np.random.randint(1, 10, size=9)
print(b1)
print(a1 + b1)
print(a1 - b1)
print(a1 * b1)
print(a1 / b1)
print(a1 // b1)
print(a1 ** b1)
print(a1 % b1)
a2 = np.arange(1, 10).reshape(3, 3)
print(a2)
b2 = np.random.randint(1, 10, size=(3, 3))
print(b2)
print(a2 + b2)
print(a2 - b2)
print(a2 * b2)
print(a2 / b2)
print(a2 // b2)
print(a2 ** b2)
print(a2 % b2)[1 2 3 4 5 6 7 8 9]
[ 2 3 4 5 6 7 8 9 10]
[11 12 13 14 15 16 17 18 19]
[-1 0 1 2 3 4 5 6 7]
[-9 -8 -7 -6 -5 -4 -3 -2 -1]
[-1 -2 -3 -4 -5 -6 -7 -8 -9]
[-1 -2 -3 -4 -5 -6 -7 -8 -9]
[ 2 4 6 8 10 12 14 16 18]
[ 2 4 6 8 10 12 14 16 18]
[0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5]
[0.5 1. 1.5 2. 2.5 3. 3.5 4. 4.5]
[0 1 1 2 2 3 3 4 4]
[0 1 1 2 2 3 3 4 4]
[ 1 4 9 16 25 36 49 64 81]
[ 1 4 9 16 25 36 49 64 81]
[1 0 1 0 1 0 1 0 1]
[1 0 1 0 1 0 1 0 1]
[4 8 6 7 1 9 7 2 5]
[ 5 10 9 11 6 15 14 10 14]
[-3 -6 -3 -3 4 -3 0 6 4]
[ 4 16 18 28 5 54 49 16 45]
[0.25 0.25 0.5 0.57142857 5. 0.66666667
1. 4. 1.8 ]
[0 0 0 0 5 0 1 4 1]
[ 1 256 729 16384 5 10077696 823543 64
59049]
[1 2 3 4 0 6 0 0 4]
[[1 2 3]
[4 5 6]
[7 8 9]]
[[1 6 4]
[4 2 8]
[5 9 3]]
[[ 2 8 7]
[ 8 7 14]
[12 17 12]]
[[ 0 -4 -1]
[ 0 3 -2]
[ 2 -1 6]]
[[ 1 12 12]
[16 10 48]
[35 72 27]]
[[1. 0.33333333 0.75 ]
[1. 2.5 0.75 ]
[1.4 0.88888889 3. ]]
[[1 0 0]
[1 2 0]
[1 0 3]]
[[ 1 64 81]
[ 256 25 1679616]
[ 16807 134217728 729]]
[[0 2 3]
[0 1 6]
[2 8 0]]절대값 함수(Absolute Function)
- absolute(), abs() : 내장된 절대값 함수
import numpy as np
a1 = np.random.randint(-10, 10, size=5)
print(a1)
print(np.absolute(a1))
print(np.abs(a1))[-6 -9 -5 5 9]
[6 9 5 5 9]
[6 9 5 5 9]제곱/제곱근 함수
- square, squr : 제곱, 제곱근 함수
import numpy as np
a1 = np.random.randint(-10, 10, size=5)
print(a1)
print(np.square(a1))
print(np.sqrt(a1)) print(np.sqrt(a1))
[ nan nan 2.64575131 2. nan]지수와 로그 함수(Exponential and Log Function)
import numpy as np
a1 = np.random.randint(1, 10, size=5)
print(a1)
print(np.exp(a1))
print(np.exp2(a1))
print(np.power(a1, 2))
print(np.log(a1))
print(np.log2(a1))
print(np.log10(a1))[1 6 7 1 1]
[ 2.71828183 403.42879349 1096.63315843 2.71828183 2.71828183]
[ 2. 64. 128. 2. 2.]
[ 1 36 49 1 1]
[0. 1.79175947 1.94591015 0. 0. ]
[0. 2.5849625 2.80735492 0. 0. ]
[0. 0.77815125 0.84509804 0. 0. ]삼각함수(Trigonometrical Function)
import numpy as np
t = np.linspace(0, np.pi, 3)
print(t)
print(np.sin(t))
print(np.cos(t))
print(np.tan(t))
x = [-1, 0, 1]
print(x)
print(np.arcsin(x))
print(np.arccos(x))
print(np.arctan(x))[0.0000000e+00 1.0000000e+00 1.2246468e-16]
[ 1.000000e+00 6.123234e-17 -1.000000e+00]
[ 0.00000000e+00 1.63312394e+16 -1.22464680e-16]
[-1, 0, 1]
[-1.57079633 0. 1.57079633]
[3.14159265 1.57079633 0. ]
[-0.78539816 0. 0.78539816]
집계함수(Aggregate Functions)
sum() : 합 계산
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
print(a2.sum(), np.sum(a2))
print(a2.sum(axis=0), np.sum(a2, axis=0))
print(a2.sum(axis=1), np.sum(a2, axis=1))[[1 5 6]
[1 8 7]
[5 4 6]]
43 43
[ 7 17 19] [ 7 17 19]
[12 16 15] [12 16 15]cumsum(): 누적합 계산
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
print(np.cumsum(a2))
print(np.cumsum(a2, axis=0))
print(np.cumsum(a2, axis=1))[[1 4 2]
[6 8 1]
[2 2 5]]
[ 1 5 7 13 21 22 24 26 31]
[[ 1 4 2]
[ 7 12 3]
[ 9 14 8]]
[[ 1 5 7]
[ 6 14 15]
[ 2 4 9]]diff(): 차분 계산
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
print(np.diff(a2))
print(np.diff(a2, axis=0))
print(np.diff(a2, axis=1))[[2 2 1]
[3 1 5]
[7 7 7]]
[[ 0 -1]
[-2 4]
[ 0 0]]
[[ 1 -1 4]
[ 4 6 2]]
[[ 0 -1]
[-2 4]
[ 0 0]]prod(): 곱 계산
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
print(np.prod(a2))
print(np.prod(a2, axis=0))
print(np.prod(a2, axis=1))[[1 2 4]
[2 6 9]
[2 7 2]]
24192
[ 4 84 72]
[ 8 108 28]cumprod(): 누적곱 계산
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
print(np.cumprod(a2))
print(np.cumprod(a2, axis=0))
print(np.cumprod(a2, axis=1))[[4 9 8]
[2 5 1]
[2 6 2]]
[ 4 36 288 576 2880 2880 5760 34560 69120]
[[ 4 9 8]
[ 8 45 8]
[ 16 270 16]]
[[ 4 36 288]
[ 2 10 10]
[ 2 12 24]]dot()/matmul(): 점곱/행렬곱 계산
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
b2 = np.ones_like(a2)
print(b2)
print(np.dot(a2, b2))
print(np.matmul(a2, b2))[[1 3 5]
[1 7 5]
[9 8 9]]
[[1 1 1]
[1 1 1]
[1 1 1]]
[[ 9 9 9]
[13 13 13]
[26 26 26]]
[[ 9 9 9]
[13 13 13]
[26 26 26]]tensordot(): 텐서곱 계산
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
b2 = np.ones_like(a2)
print(b2)
print(np.tensordot(a2, b2))
print(np.tensordot(a2, b2, axes=0))
print(np.tensordot(a2, b2, axes=1))[[6 9 9]
[8 6 5]
[2 1 5]]
[[1 1 1]
[1 1 1]
[1 1 1]]
51
[[[[6 6 6]
[6 6 6]
[6 6 6]]
[[9 9 9]
[9 9 9]
[9 9 9]]
[[9 9 9]
[9 9 9]
[9 9 9]]]
[[[8 8 8]
[8 8 8]
[8 8 8]]
[[6 6 6]
[6 6 6]
[6 6 6]]
[[5 5 5]
[5 5 5]
[5 5 5]]]
[[[2 2 2]
[2 2 2]
[2 2 2]]
[[1 1 1]
[1 1 1]
[1 1 1]]
[[5 5 5]
[5 5 5]
[5 5 5]]]]
[[24 24 24]
[19 19 19]
[ 8 8 8]]cross(): 벡터곱
import numpy as np
x = [1, 2, 3]
y = [4, 5, 6]
print(np.cross(x, y))[-3 6 -3]inner()/outer(): 내적/외적
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
b2 = np.ones_like(a2)
print(b2)
print(np.inner(a2, b2))
print(np.outer(a2, b2))[[6 6 8]
[9 3 9]
[7 7 8]]
[[1 1 1]
[1 1 1]
[1 1 1]]
[[20 20 20]
[21 21 21]
[22 22 22]]
[[6 6 6 6 6 6 6 6 6]
[6 6 6 6 6 6 6 6 6]
[8 8 8 8 8 8 8 8 8]
[9 9 9 9 9 9 9 9 9]
[3 3 3 3 3 3 3 3 3]
[9 9 9 9 9 9 9 9 9]
[7 7 7 7 7 7 7 7 7]
[7 7 7 7 7 7 7 7 7]
[8 8 8 8 8 8 8 8 8]]mean(): 평균 계산
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
b2 = np.ones_like(a2)
print(b2)
print(np.mean(a2))
print(np.mean(a2, axis=0))
print(np.mean(a2, axis=1))[[1 4 9]
[1 2 6]
[3 3 7]]
[[1 1 1]
[1 1 1]
[1 1 1]]
4.0
[1.66666667 3. 7.33333333]
[4.66666667 3. 4.33333333]std() : 표준 편차 계산
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
b2 = np.ones_like(a2)
print(b2)
print(np.std(a2))
print(np.std(a2, axis=0))
print(np.std(a2, axis=1))[[4 8 2]
[5 5 8]
[4 4 8]]
[[1 1 1]
[1 1 1]
[1 1 1]]
2.0548046676563256
[0.47140452 1.69967317 2.82842712]
[2.49443826 1.41421356 1.88561808]var(): 분산 계산
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
b2 = np.ones_like(a2)
print(b2)
print(np.var(a2))
print(np.var(a2, axis=0))
print(np.var(a2, axis=1))[[1 3 1]
[8 8 8]
[1 3 6]]
[[1 1 1]
[1 1 1]
[1 1 1]]
8.88888888888889
[10.88888889 5.55555556 8.66666667]
[0.88888889 0. 4.22222222]min(): 최소값
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
b2 = np.ones_like(a2)
print(b2)
print(np.min(a2))
print(np.min(a2, axis=0))
print(np.min(a2, axis=1))[[2 6 7]
[4 7 4]
[6 4 1]]
[[1 1 1]
[1 1 1]
[1 1 1]]
1
[2 4 1]
[2 4 1]max(): 최대값
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
b2 = np.ones_like(a2)
print(b2)
print(np.max(a2))
print(np.max(a2, axis=0))
print(np.max(a2, axis=1))[[1 5 9]
[5 1 3]
[9 5 1]]
[[1 1 1]
[1 1 1]
[1 1 1]]
9
[9 5 9]
[9 5 9]argmin(): 최소값 인덱스
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
b2 = np.ones_like(a2)
print(b2)
print(np.argmin(a2))
print(np.argmin(a2, axis=0))
print(np.argmin(a2, axis=1))[[4 3 1]
[4 1 4]
[4 3 9]]
[[1 1 1]
[1 1 1]
[1 1 1]]
2
[0 1 0]
[2 1 1]argmax(): 최대값 인덱스
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
b2 = np.ones_like(a2)
print(b2)
print(np.argmax(a2))
print(np.argmax(a2, axis=0))
print(np.argmax(a2, axis=1))[[3 2 6]
[9 7 4]
[1 2 5]]
[[1 1 1]
[1 1 1]
[1 1 1]]
3
[1 1 0]
[2 0 2]median(): 중앙값
import numpy as np
a2 = np.random.randint(1, 10, size=(3, 3))
print(a2)
b2 = np.ones_like(a2)
print(b2)
print(np.median(a2))
print(np.median(a2, axis=0))
print(np.median(a2, axis=1))728x90
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