numpy
Ones and zeros
np.empty(shape, dtype, order)
랜덤으로 채워진 배열 생성.
>>> np.empty([2, 2])
array([[ -9.74499359e+001, 6.69583040e-309],
[ 2.13182611e-314, 3.06959433e-309]]) #random
np.empty_like(shape, dtype, order, subok)
지정한 배열 형상에 따라 랜덤으로 채워진 배열 생성
>>> a = ([1,2,3], [4,5,6]) # a is array-like
>>> np.empty_like(a)
array([[-1073741821, -1073741821, 3], #random
[ 0, 0, -1073741821]])
>>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
>>> np.empty_like(a)
array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000],#random
[ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])
np.eye(N, M, k, dtype, order)
단위 행렬 생성. k는 대각에서의 거리.
>>> np.eye(2, dtype=int)
array([[1, 0],
[0, 1]])
>>> np.eye(3, k=1)
array([[ 0., 1., 0.],
[ 0., 0., 1.],
[ 0., 0., 0.]])
np.identity(n, dtype)
단위 행렬 생성.
>>> np.identity(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
np.ones(shape, dtype, order)
1로 채워진 배열 생성
>>> s = (2,2)
>>> np.ones(s)
array([[ 1., 1.],
[ 1., 1.]])
np.ones(a, dtype, order, subok)
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> np.ones_like(x)
array([[1, 1, 1],
[1, 1, 1]])
>>> y = np.arange(3, dtype=float)
>>> y
array([ 0., 1., 2.])
>>> np.ones_like(y)
array([ 1., 1., 1.])
np.ones_like(a, dtype, order, subok)
np.zeros(shape, dtype, order)
np.zeros_like(a, dtype, order, subok)
np.full(shape, fill_value, dtype, order)
np.full_like(a, fill_value, dtype, order, subok)
From existing data
np.array(object, dtype, copy, subok, ndmin)
np.asarray(a, dtype, order)
np.asanyarray(a, dtype, order)
np.ascontlguousarray(a, dtype)
np.asmatrix(data, dytpe)
np.copy(a, order)
np.frombuffer(buffer, dtype, count, offset)
np.fromfile(file, dtype, count, sep)
np.fromfunction(function, shape, **kwargs)
np.fromiter(iterable, dtype, count)
np.fromstring(string, dtype, count, sep)
np.loadtxt(fname, dtype, comments, delimiter, ...)
Creating record arrays (numpy.rec)
Creating character arrays (numpy.char)
Numerical ranges
np.arange(start, stop, step, dtype)
np.linspace(start, stop, num, endpoint, ...)
np.logspace(start, stop, num, endpoint, base, ...)
np.geomspace(start, stop, num, endpoint, ...)
np.meshgrid(*xi, **kwargs)
>>> nx, ny = (3, 2)
>>> x = np.linspace(0, 1, nx)
>>> x
array([0. , 0.5, 1. ])
>>> y = np.linspace(0, 1, ny)
>>> y
array([0., 1.])
>>> xv, yv = np.meshgrid(x, y)
>>> xv
array([[ 0. , 0.5, 1. ],
[ 0. , 0.5, 1. ]])
>>> yv
array([[ 0., 0., 0.],
[ 1., 1., 1.]])
>>> xv, yv = np.meshgrid(x, y, sparse=True) # make sparse output arrays
>>> xv
array([[ 0. , 0.5, 1. ]])
>>> yv
array([[ 0.],
[ 1.]])