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There are various useful functions in the numpy library. Thus, I left them here to use later.
Numpy 라이브러리에는 다양한 유용한 기능이 있습니다. 나중에 사용하기 위해 여기에 간단한 설명과 함께 남겨 두었습니다.
1. shape
# shape : dimension of the array
print(arr1.shape)
2. zeros
# zeros(size, dtype=) : dtype in bool/float/int, default is float
arr6 = np.zeros(((3,2)), dtype=int)
3. random
# random(shape) : shape dimension random number array generator
print(np.random.random((3,2))
4. random.normal
# np.random.normal(mean, std, arry size) - generating array that consist of normal distribution samples
arr7 = np.random.normal(0, 1, 6)
arr8 = np.random.normal(0, 1, (5,3))
5. reshape
# horizon vector to vertical vector
# (array).reshape : changing array structure
print(str(arr4.reshape(6,1)))
print(str(arr4.reshape(-1,1)))
6. random.randint
randEx = np.random.randint(1,45, size=5)
7. sort
# up-down ascending
print(np.sort(arr8, axis=0), end="\n!!!\n")
# ascending
print(np.sort(arr8, axis=1), end="\n!!!\n")
# up-down descending
print(np.sort(arr8, axis=0)[::-1], end="\n!!!\n")
8. Mathematical expression
x = np.array([1,2,3,5])
# Calculate the exponential of all elements
print('exp' + str(np.exp(x)))
# Calculate `2**p` for all `p`
print('exp2' + str(np.exp2(x)))
# First array elements raised to powers from second array, element-wise
print('power' + str(np.power(3, x)))
# Natural logarithm, element-wise
print('log' + str(np.log(x)))
# Return the base 10 logarithm of the input array, element-wise
print('log10' + str(np.log10(x)))
9. Pythagorean theorem(hypot and linalg. norm - same result if there're 2 parameters)
arr = np.array([1,2,3,4])
# Pythagorean theorem
print(np.hypot(arr[0], arr[1])) # by 2 parameters
print(np.linalg.norm(arr)) # by more than 2 parameters
10. vector distance(np.linalg.norm)
arr = np.arange(3, 5)
arr2 = np.arange(12, 14)
print(np.linalg.norm(arr-arr2))
11. Identity matrix
# create identity matrix
print(np.eye(3,3)) # same with np.eye(3)
12. create the same random arrays by seed
np.random.seed(1)
print(np.random.rand(5))
np.random.seed(0)
print(np.random.rand(5))
np.random.seed(1)
print(np.random.rand(5))
13. np.argmax & np.max
a = np.array([[3,7,5], [6,7,8], [3,5,9]])
# to get index of max #
print(np.argmax(a, axis=1))
print(np.argmax(a, axis=0))
# to get value of max #
print(a.max(axis=0))
print(a.max(axis=1))
14. np.poly1d & np.polyder
eq = np.array([1, 0, -2, 1])
# create one-dimensional polynomial
p = np.poly1d(eq)
print(p)
# p.variable : name of the polynomial variable
print(p.variable)
# p.order : order or degree of the polynomial
print(p.order)
# p.roots : answer of the equation p
print(p.roots)
# np.linalg.solve(array1, array2) : answer of the system of equations, array1 and array2.
# differential equation by p(created by np.poly1d
print(np.polyder(p)) # same function with np.polyder : p.deriv()
15. np.diff
x = np.array([1,2,34,7,10,14])
print(np.diff(x))
# diff of diff x - diff two times by n=2 option
print(np.diff(x, n=2))
# three times by n=3 option
print(np.diff(x, n=3))
16. np.gradient
x = np.array([1,2,34,7,10,14])
# solving differential equation of array data
print(np.gradient(x))
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