The generating array function of the introduction of NumPy functions

1, empty (shape[, Dtype, order])

Returns a new empty array based on the given shape and type (shape[, Dtype, order]).

Parameters:

Shape: integer or integral tuple

Defines the shape of the returned array;

Dtype: Data type, optional

Defines the type of the returned array.

Order: {' C ', ' F '}, optional

Specifies the order in which the array elements are stored in memory: C (C language)-row-major;f (Fortran) column-major.

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>>> np.empty([2, 2])array([[ -9.74499359e+001, 6.69583040e-309],[ 2.13182611e-314, 3.06959433e-309]])    #random>>> np.empty([2, 2], dtype=int)array([[-1073741821, -1067949133],[ 496041986, 19249760]])    #random

2. Empty_like (a)

Returns a new, empty array based on the shape and type of the given array (a).

Parameters:

A: Array

Its shape and type are used to specify the shape and type of the returned function.

return value:

Output: Ndarray

An array of the same shape and type as the array A.

1234	>>> 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]])

3. Eye (n[, M, K, Dtype])

Returns a two-dimensional array with a diagonal element of 1 and another element of 0.

Parameters:

N: Integer

Returns the number of rows in the array;

M: integer, optional

Returns the number of columns in the array. If you do not assign a value, the default equals n;

K: integer, optional

Diagonal Serial Number: 0 corresponds to the main diagonal, the integer corresponds to the upper diagonal, the negative numbers correspond to lower diagonal;

Dtype:dtype, optional

Returns the data type of the array

return value:

I:ndarray (N,M)

The element for the K-diagonal of the array is 1, and the other element is 0.

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>>> 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.]])

4. Identity (n[, Dtype])

Returns an n-dimensional unit square.

Parameters:

N: Integer

Returns the number of rows of a phalanx;

Dtype: Data type, optional

Returns the data type of the square, by default, float.

return value:

Output: Ndarray

n x N unit square.

1234	>>> np.identity (/ Code>3 array ([[ 1. 0. 0. [ 0. 1. 0. [ 0. 0. 1.

5, Ones (shape[, Dtype, order])

Returns an array of all 1 elements of a new element, based on the given shape and type (shape[, Dtype, order]).

please refer to zeros for parameter setting.

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>>> np.ones(5)array([ 1., 1., 1., 1., 1.]) >>> np.ones((5,), dtype=np.int)array([1, 1, 1, 1, 1]) >>> np.ones((2, 1))array([[ 1.],[ 1.]])>>> s =(2,2)>>> np.ones(s)array([[ 1., 1.],[ 1., 1.]])

6, Ones_like ()

Returns an array of all 1 elements of a new element, based on the shape and type of the given array (a).

equivalent to A.copy (). Fill (1), please refer to Zeros_like's documentation for specific use.

1234	>>> a =np.array([[1, 2, 3], [4, 5, 6]])>>> np.ones_like(a)array([[1, 1, 1],[1, 1, 1]])

7, zeros (shape[, Dtype, order])

Returns an array of all 0 elements of a new element, based on the given shape and type (shape[, Dtype, order]).

Parameters:

Shape:int or ints tuples;

Defines the shape of the returned array, such as: (2, 3), or 2.

Dtype: Data type, optional.

Returns the data type of the array, for example: Numpy.int8, default is Numpy.float64.

order:{' C ', ' F '}, optionally, returns the array as multidimensional, in which the elements are arranged in memory in C or Fortran order (row-or columnwise).

Output: Ndarray

An array of data types for the given shape.

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>>> np.zeros(5)array([ 0., 0., 0., 0., 0.])>>> np.zeros((5,), dtype=numpy.int)array([0, 0, 0, 0, 0])>>> np.zeros((2, 1))array([[ 0.],[ 0.]])>>> s =(2,2)>>> np.zeros(s)array([[ 0., 0.],[ 0., 0.]])>>> np.zeros((2,), dtype=[(’x’, ’i4’), (’y’, ’i4’)]) # custom dtypearray([(0, 0), (0, 0)],dtype=[(’x’, ’<i4’), (’y’, ’<i4’)])

8. Zeros_like (a)

Returns an array of all 1 elements of a new element, based on the shape and type of the given array (a).

Equivalent to A.copy (). Fill (0).

Parameters:

A:array_like

Output: Ndarray

A 0 array that is consistent with the shape type of a array.

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>>> x =np.arange(6)>>> x =x.reshape((2, 3))>>> xarray([[0, 1, 2],[3, 4, 5]])>>> np.zeros_like(x)array([[0, 0, 0],[0, 0, 0]])>>> y =np.arange(3, dtype=np.float)>>> yarray([ 0., 1., 2.])>>> np.zeros_like(y)array([ 0., 0., 0.])

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The generating array function of the introduction of NumPy functions