创建数组
1- np.array()
参数众多，初学时只要关注基本用法。
array(...) array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0, like=None) Create an array. Parameters ---------- object : array_like An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence. dtype : data-type, optional The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence. copy : bool, optional If true (default), then the object is copied. Otherwise, a copy will only be made if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (`dtype`, `order`, etc.). order : {'K', 'A', 'C', 'F'}, optional Specify the memory layout of the array. If object is not an array, the newly created array will be in C order (row major) unless 'F' is specified, in which case it will be in Fortran order (column major). If object is an array the following holds. ===== ========= =================================================== order no copy copy=True ===== ========= =================================================== 'K' unchanged F & C order preserved, otherwise most similar order 'A' unchanged F order if input is F and not C, otherwise C order 'C' C order C order 'F' F order F order ===== ========= =================================================== When ``copy=False`` and a copy is made for other reasons, the result is the same as if ``copy=True``, with some exceptions for 'A', see the Notes section. The default order is 'K'. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default). ndmin : int, optional Specifies the minimum number of dimensions that the resulting array should have. Ones will be pre-pended to the shape as needed to meet this requirement. like : array_like Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as ``like`` supports the ``__array_function__`` protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument. .. versionadded:: 1.20.0元组、列表转换
>>> import numpy as np>>> np.array((1,2,3))array([1, 2, 3])>>> np.array([3,2,3])array([3, 2, 3])>>> np.array([[3,2,3],[4,5,6]])array([[3, 2, 3], [4, 5, 6]])内置函数 range()
>>> import numpy as np>>> np.array(range(5))array([0, 1, 2, 3, 4])>>> np.array(range(2,11,2))array([ 2, 4, 6, 8, 10])>>> np.array([range(1,5),range(5,9)])array([[1, 2, 3, 4], [5, 6, 7, 8]])数组副本copy，开辟一块新内存复制原数组
>>> import numpy as np>>> a = np.array([1,2,3])>>> b = np.array(a)>>> barray([1, 2, 3])>>> a[0] = 3>>> a,b(array([3, 2, 3]), array([1, 2, 3]))主要参数：
dtype= 数组元素的数据类型，可选
copy= 对象是否需要复制，可选
order= 创建数组的样式，C为行方向，F为列方向，A为任意方向（默认）
subok= 默认返回一个与基类类型一致的数组
ndmin= 指定生成数组的最小维度
>>> import numpy as np>>> np.array([[1, 2, 3, 4]], dtype=float)array([[1., 2., 3., 4.]])>>> np.array([[1, 2], [3, 4]], dtype=complex)array([[1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j]])>>> np.array([[1, 2, 3, 4]], dtype=np.int64)array([[1, 2, 3, 4]], dtype=int64)>>> np.array({1, 2, 3, 4})array({1, 2, 3, 4}, dtype=object)>>> np.array({1, 2, 3, 4}).dtypedtype('O') #集合只能作一个整体，大写字母O，即object>>> np.array([[1, 2, 3, 4]], dtype=np.int64).dtypedtype('int64')>>> np.array([[1, 2], [3, 4, 5]])array([list([1, 2]), list([3, 4, 5])], dtype=object)>>> np.array([[1, 2], [3, 4, 5]]).dtypedtype('O')>>> >>> np.array([1, 2, 3, 4, 5], ndmin = 1)array([1, 2, 3, 4, 5])>>> np.array([1, 2, 3, 4, 5], ndmin = 2)array([[1, 2, 3, 4, 5]])>>> np.array([1, 2, 3, 4, 5], ndmin = 3)array([[[1, 2, 3, 4, 5]]])>>>2.1- 基本属性 .shape .ndim .dtype .size等
>>> a = np.array(range(2,11,2))>>> b = np.array([range(1,5),range(5,9)])>>> a.shape(5,)>>> b.shape(2, 4)>>> a.ndim, b.ndim(1, 2)>>> np.array(1)array(1)>>> np.array(1).ndim0 #常数为0维>>> a.dtype.name, b.dtype.name('int32', 'int32')>>> a.size, b.size(5, 8)>>> type(a), type(b)(, )>>> aarray([ 2, 4, 6, 8, 10])>>> barray([[1, 2, 3, 4], [5, 6, 7, 8]])>>> print(a)[ 2 4 6 8 10]>>> print(b)[[1 2 3 4] [5 6 7 8]] .ndim 秩，即轴的数量或维度的数量
.shape 数组的维度，对于矩阵，n 行 m 列
.size 数组元素的总个数，相当于 .shape 中 n*m 的值
.dtype 对象的元素类型
.itemsize 对象中每个元素的大小，以字节为单位
.flags 对象的内存信息
.real 元素的实部
.imag 元素的虚部
.data 包含实际数组元素的缓冲区，由于一般通过数组的索引获取元素，所以通常不需要使用这个属性。
2.2- 与属性同名的方法
除.itemsize .flags .data外者有同名方法，其它有方法的参数都为ndarray，dtype()除外。
>>> a = np.array([*range(5)],dtype=complex)>>> np.ndim(a)1>>> np.shape(a)(5,)>>> np.size(a)5>>> np.real(a)array([0., 1., 2., 3., 4.])>>> np.imag(a)array([0., 0., 0., 0., 0.])>>> np.dtype(int)dtype('int32')>>> np.dtype(complex)dtype('complex128')>>> np.dtype(float)dtype('float64')>>> a.itemsize16>>> a.flags C_CONTIGUOUS : True F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False UPDATEIFCOPY : False>>> a.data3- np.arange()
arange(...) arange([start,] stop[, step,], dtype=None, *, like=None) Return evenly spaced values within a given interval. Values are generated within the half-open interval ``[start, stop)`` (in other words, the interval including `start` but excluding `stop`). For integer arguments the function is equivalent to the Python built-in `range` function, but returns an ndarray rather than a list. When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use `numpy.linspace` for these cases. Parameters ---------- start : integer or real, optional Start of interval. The interval includes this value. The default start value is 0. stop : integer or real End of interval. The interval does not include this value, except in some cases where `step` is not an integer and floating point round-off affects the length of `out`. step : integer or real, optional Spacing between values. For any output `out`, this is the distance between two adjacent values, ``out[i+1] - out[i]``. The default step size is 1. If `step` is specified as a position argument, `start` must also be given. dtype : dtype The type of the output array. If `dtype` is not given, infer the data type from the other input arguments. like : array_like Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as ``like`` supports the ``__array_function__`` protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument. .. versionadded:: 1.20.0np.arange() 与 np.array(range()) 类似，但前者允许用浮点数
>>> np.arange(12)array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])>>> np.arange(0,1.1,0.1)array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])>>> np.arange(2,5,0.3)array([2. , 2.3, 2.6, 2.9, 3.2, 3.5, 3.8, 4.1, 4.4, 4.7])4- np.reshape()
reshape(a, newshape, order='C') Gives a new shape to an array without changing its data. Parameters ---------- a : array_like Array to be reshaped. newshape : int or tuple of ints The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions. order : {'C', 'F', 'A'}, optional Read the elements of `a` using this index order, and place the elements into the reshaped array using this index order. 'C' means to read / write the elements using C-like index order, with the last axis index changing fastest, back to the first axis index changing slowest. 'F' means to read / write the elements using Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the 'C' and 'F' options take no account of the memory layout of the underlying array, and only refer to the order of indexing. 'A' means to read / write the elements in Fortran-like index order if `a` is Fortran *contiguous* in memory, C-like order otherwise. Returns ------- reshaped_array : ndarray This will be a new view object if possible; otherwise, it will be a copy. Note there is no guarantee of the *memory layout* (C- or Fortran- contiguous) of the returned array.>>> a = np.arange(8)>>> aarray([0, 1, 2, 3, 4, 5, 6, 7])>>> np.reshape(a,(2,4))array([[0, 1, 2, 3], [4, 5, 6, 7]])>>> np.reshape(a,(4,2))array([[0, 1], [2, 3], [4, 5], [6, 7]])>>> np.reshape(a,(8,1))array([[0], [1], [2], [3], [4], [5], [6], [7]])>>> aarray([0, 1, 2, 3, 4, 5, 6, 7])>>> a.reshape(2,4)array([[0, 1, 2, 3], [4, 5, 6, 7]])>>> aarray([0, 1, 2, 3, 4, 5, 6, 7])>>> a.reshape(4,2)array([[0, 1], [2, 3], [4, 5], [6, 7]])>>> aarray([0, 1, 2, 3, 4, 5, 6, 7])5- 数据类型
dtype对应的类型除了内置的int,float,complex等，可以用 np.bool_, np.int8, np.uint64：
bool_ 布尔型数据类型（True 或者 False）
int_ 默认的整数类型（类似于 C 语言中的 long，int32 或 int64）
intc 与 C 的 int 类型一样，一般是 int32 或 int 64
intp 用于索引的整数类型（类似于 C 的 ssize_t，一般情况下仍然是 int32 或 int64）
int8 字节（-128 to 127）
int16 整数（-32768 to 32767）
int32 整数（-2147483648 to 2147483647）
int64 整数（-9223372036854775808 to 9223372036854775807）
uint8 无符号整数（0 to 255）
uint16 无符号整数（0 to 65535）
uint32 无符号整数（0 to 4294967295）
uint64 无符号整数（0 to 18446744073709551615）
float_ float64 类型的简写
float16 半精度浮点数，包括：1 个符号位，5 个指数位，10 个尾数位
float32 单精度浮点数，包括：1 个符号位，8 个指数位，23 个尾数位
float64 双精度浮点数，包括：1 个符号位，11 个指数位，52 个尾数位
complex_ complex128 类型的简写，即 128 位复数
complex64 复数，表示双 32 位浮点数（实数部分和虚数部分）
complex128 复数，表示双 64 位浮点数（实数部分和虚数部分）
每个内建类型都有一个唯一定义它的字符代码：
b 布尔型
i (有符号) 整型
u 无符号整型 integer
f 浮点型
c 复数浮点型
m timedelta（时间间隔）
M datetime（日期时间）
O (Python) 对象
S, a (byte-)字符串
U Unicode
V 原始数据 (void)
int8, int16, int32, int64 -- i1, i2, i4, i8
uint8,uint16,uint32,uint64 -- u1, u2, u4, u8
float16,float32,float64,float128 -- f2, f4, f8, f16
或： float32,float64,float128 -- f, d, g
complex64,complex128,complex256 -- c8,c16,c32
bool -- ?
>>> import numpy as np>>> np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')])dtype([('name', 'S20'), ('age', 'i1'), ('marks', '>> import numpy as np>>> student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')])>>> studentdtype([('name', 'S20'), ('age', 'i1'), ('marks', '>> np.array([('abc', 21, 50),('xyz', 18, 75)], dtype = student)array([(b'abc', 21, 50.), (b'xyz', 18, 75.)], dtype=[('name', 'S20'), ('age', 'i1'), ('marks', '>> a = np.array([('abc', 21, 50),('xyz', 18, 75)], dtype = student)>>> print(a)[(b'abc', 21, 50.) (b'xyz', 18, 75.)] 6- np.asarray()
asarray(...) asarray(a, dtype=None, order=None, *, like=None) Convert the input to an array. Parameters ---------- a : array_like Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays. dtype : data-type, optional By default, the data-type is inferred from the input data. order : {'C', 'F', 'A', 'K'}, optional Memory layout. 'A' and 'K' depend on the order of input array a. 'C' row-major (C-style), 'F' column-major (Fortran-style) memory representation. 'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise 'K' (keep) preserve input order Defaults to 'C'. like : array_like Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as ``like`` supports the ``__array_function__`` protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument. .. versionadded:: 1.20.0>>> import numpy as np>>> a = np.array([1,2,3])>>> b = np.asarray(a)>>> a,b(array([1, 2, 3]), array([1, 2, 3]))>>> a[0]=3>>> a,b(array([3, 2, 3]), array([3, 2, 3]))注意 b=asarray(a) 与 b=array(a) 的区别，前者两数组指向同一内存地址。
7- np.fromiter()
fromiter(...) fromiter(iter, dtype, count=-1, *, like=None) Create a new 1-dimensional array from an iterable object. Parameters ---------- iter : iterable object An iterable object providing data for the array. dtype : data-type The data-type of the returned array. count : int, optional The number of items to read from *iterable*. The default is -1, which means all data is read. like : array_like Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as ``like`` supports the ``__array_function__`` protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument. .. versionadded:: 1.20.0 Returns ------- out : ndarray The output array. Notes ----- Specify `count` to improve performance. It allows ``fromiter`` to pre-allocate the output array, instead of resizing it on demand.>>> import numpy as np>>> np.fromiter(range(5),dtype=int)array([0, 1, 2, 3, 4])>>> np.fromiter(range(5),dtype=float)array([0., 1., 2., 3., 4.])>>> iterable = (x*x for x in range(5))>>> np.fromiter(iterable, float)array([ 0., 1., 4., 9., 16.])>>> np.fromiter({1,2,3,4}, float)array([1., 2., 3., 4.])>>> np.array({1,2,3,4})array({1, 2, 3, 4}, dtype=object)#注意：array()不能从集合中取出元素，只能作为一个整体>>> np.fromiter('Hann Yang',dtype='S1')array([b'H', b'a', b'n', b'n', b' ', b'Y', b'a', b'n', b'g'], dtype='|S1')>>> np.fromiter(b'Hann Yang',dtype=np.uint8)array([ 72, 97, 110, 110, 32, 89, 97, 110, 103], dtype=uint8)#注意：字节串b''与字符串str的区别8- np.frombuffer()
流的形式读入转化成 ndarray 对象，还可以分批读入。
frombuffer(...) frombuffer(buffer, dtype=float, count=-1, offset=0, *, like=None) Interpret a buffer as a 1-dimensional array. Parameters ---------- buffer : buffer_like An object that exposes the buffer interface. dtype : data-type, optional Data-type of the returned array; default: float. count : int, optional Number of items to read. ``-1`` means all data in the buffer. offset : int, optional Start reading the buffer from this offset (in bytes); default: 0. like : array_like Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as ``like`` supports the ``__array_function__`` protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument. .. versionadded:: 1.20.0>>> np.frombuffer('Hann Yang',dtype='S1')Traceback (most recent call last): File "", line 1, in np.frombuffer('Hann Yang',dtype='S1')TypeError: a bytes-like object is required, not 'str'>>> np.frombuffer(b'Hann Yang',dtype='S1')array([b'H', b'a', b'n', b'n', b' ', b'Y', b'a', b'n', b'g'], dtype='|S1')>>> np.frombuffer(b'Hann Yang',dtype=int)Traceback (most recent call last): File "", line 1, in np.frombuffer(b'Hann Yang',dtype=int)ValueError: buffer size must be a multiple of element size>>> np.frombuffer(b'Hann Yang',dtype=np.uint8)array([ 72, 97, 110, 110, 32, 89, 97, 110, 103], dtype=uint8)>>> np.frombuffer(b'Hann Yang',dtype='S1')array([b'H', b'a', b'n', b'n', b' ', b'Y', b'a', b'n', b'g'], dtype='|S1')>>> np.frombuffer(b'Hann Yang',dtype=np.uint8)array([ 72, 97, 110, 110, 32, 89, 97, 110, 103], dtype=uint8)>>> np.frombuffer(b'Hann Yang',dtype=np.uint8,count=4)array([ 72, 97, 110, 110], dtype=uint8)>>> np.frombuffer(b'Hann Yang',dtype=np.uint8,count=4,offset=4)array([ 32, 89, 97, 110], dtype=uint8)>>> np.frombuffer(b'Hann Yang',dtype=np.uint8,count=-1,offset=8)array([103], dtype=uint8) 9.1- np.linspace()
以等差数列创建数组
linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0) Return evenly spaced numbers over a specified interval. Returns `num` evenly spaced samples, calculated over the interval [`start`, `stop`]. The endpoint of the interval can optionally be excluded. .. versionchanged:: 1.16.0 Non-scalar `start` and `stop` are now supported. .. versionchanged:: 1.20.0 Values are rounded towards ``-inf`` instead of ``0`` when an integer ``dtype`` is specified. The old behavior can still be obtained with ``np.linspace(start, stop, num).astype(int)`` Parameters ---------- start : array_like The starting value of the sequence. stop : array_like The end value of the sequence, unless `endpoint` is set to False. In that case, the sequence consists of all but the last of ``num + 1`` evenly spaced samples, so that `stop` is excluded. Note that the step size changes when `endpoint` is False. num : int, optional Number of samples to generate. Default is 50. Must be non-negative. endpoint : bool, optional If True, `stop` is the last sample. Otherwise, it is not included. Default is True. retstep : bool, optional If True, return (`samples`, `step`), where `step` is the spacing between samples. dtype : dtype, optional The type of the output array. If `dtype` is not given, the data type is inferred from `start` and `stop`. The inferred dtype will never be an integer; `float` is chosen even if the arguments would produce an array of integers. .. versionadded:: 1.9.0 axis : int, optional The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. .. versionadded:: 1.16.0创建区间可以是全开区间，也可以前开后闭区间。
>>> np.linspace(2.0, 3.0, num=5)array([2. , 2.25, 2.5 , 2.75, 3. ])>>> np.linspace(2.0, 3.0, num=5, endpoint=False)array([2. , 2.2, 2.4, 2.6, 2.8])>>> np.linspace(2.0, 3.0, num=5, retstep=True)(array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)>>> np.linspace(1, 1, 10, dtype=int)array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])9.2- np.logspace()
以对数数列创建数组
logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0) Return numbers spaced evenly on a log scale. In linear space, the sequence starts at ``base ** start`` (`base` to the power of `start`) and ends with ``base ** stop`` (see `endpoint` below). .. versionchanged:: 1.16.0 Non-scalar `start` and `stop` are now supported. Parameters ---------- start : array_like ``base ** start`` is the starting value of the sequence. stop : array_like ``base ** stop`` is the final value of the sequence, unless `endpoint` is False. In that case, ``num + 1`` values are spaced over the interval in log-space, of which all but the last (a sequence of length `num`) are returned. num : integer, optional Number of samples to generate. Default is 50. endpoint : boolean, optional If true, `stop` is the last sample. Otherwise, it is not included. Default is True. base : array_like, optional The base of the log space. The step size between the elements in ``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform. Default is 10.0. dtype : dtype The type of the output array. If `dtype` is not given, the data type is inferred from `start` and `stop`. The inferred type will never be an integer; `float` is chosen even if the arguments would produce an array of integers. axis : int, optional The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. .. versionadded:: 1.16.0>>> np.logspace(2.0, 3.0, num=4)array([ 100. , 215.443469 , 464.15888336, 1000. ])>>> np.logspace(2.0, 3.0, num=4, endpoint=False)array([100. , 177.827941 , 316.22776602, 562.34132519])>>> np.logspace(2.0, 3.0, num=4, base=2.0)array([4. , 5.0396842 , 6.34960421, 8. ])9.3- np.geomspace()
geomspace(start, stop, num=50, endpoint=True, dtype=None, axis=0) Return numbers spaced evenly on a log scale (a geometric progression). This is similar to `logspace`, but with endpoints specified directly. Each output sample is a constant multiple of the previous. .. versionchanged:: 1.16.0 Non-scalar `start` and `stop` are now supported. Parameters ---------- start : array_like The starting value of the sequence. stop : array_like The final value of the sequence, unless `endpoint` is False. In that case, ``num + 1`` values are spaced over the interval in log-space, of which all but the last (a sequence of length `num`) are returned. num : integer, optional Number of samples to generate. Default is 50. endpoint : boolean, optional If true, `stop` is the last sample. Otherwise, it is not included. Default is True. dtype : dtype The type of the output array. If `dtype` is not given, the data type is inferred from `start` and `stop`. The inferred dtype will never be an integer; `float` is chosen even if the arguments would produce an array of integers. axis : int, optional The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end. .. versionadded:: 1.16.0>>> np.geomspace(1, 1000, num=4)array([ 1., 10., 100., 1000.])>>> np.geomspace(1, 1000, num=3, endpoint=False)array([ 1., 10., 100.])>>> np.geomspace(1, 1000, num=4, endpoint=False)array([ 1. , 5.62341325, 31.6227766 , 177.827941 ])>>> np.geomspace(1, 256, num=9)array([ 1., 2., 4., 8., 16., 32., 64., 128., 256.]) #Note that the above may not produce exact integers: >>> np.geomspace(1, 256, num=9, dtype=int)array([ 1, 2, 4, 7, 16, 32, 63, 127, 256])>>> np.around(np.geomspace(1, 256, num=9)).astype(int)array([ 1, 2, 4, 8, 16, 32, 64, 128, 256]) #Negative, decreasing, and complex inputs are allowed: >>> np.geomspace(1000, 1, num=4)array([1000., 100., 10., 1.])>>> np.geomspace(-1000, -1, num=4)array([-1000., -100., -10., -1.])>>> np.geomspace(1j, 1000j, num=4) # Straight linearray([0. +1.j, 0. +10.j, 0. +100.j, 0.+1000.j])>>> np.geomspace(-1+0j, 1+0j, num=5) # Circlearray([-1.00000000e+00+1.22464680e-16j, -7.07106781e-01+7.07106781e-01j, 6.12323400e-17+1.00000000e+00j, 7.07106781e-01+7.07106781e-01j, 1.00000000e+00+0.00000000e+00j])10.1- 常量np.pi np.e np.nan np.inf 等
>>> np.pi3.141592653589793>>> np.e2.718281828459045>>> np.nannan>>> np.infinf>>> np.Infinf>>> np.Infinityinf>>> np.PINFinf>>> np.NINF-inf>>> np.PZERO0.0>>> np.NZERO-0.010.2- 常量数组 zeros() ones() empty()
>>> np.zeros((2,5))array([[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.]])>>> np.zeros((2,5),dtype=int)array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]])>>> np.linspace(0, 0, 10, dtype=int).reshape((2,5))array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]])>>> >>> np.ones((3,4))array([[1., 1., 1., 1.], [1., 1., 1., 1.], [1., 1., 1., 1.]])>>> np.ones((3,4),dtype=int)array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])>>> np.linspace(1, 1, 12, dtype=int).reshape((3,4))array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])>>>>>> np.linspace(1, 1, 12, dtype=int).reshape((3,4))*3array([[3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 3, 3]])>>> np.linspace(1, 1, 12, dtype=int).reshape((3,4))*np.piarray([[3.14159265, 3.14159265, 3.14159265, 3.14159265], [3.14159265, 3.14159265, 3.14159265, 3.14159265], [3.14159265, 3.14159265, 3.14159265, 3.14159265]])10.3- 常量数组 zeros_like() ones_like() empty_like()
>>> arr = np.ones((3,4))>>> np.zeros_like(arr)array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]])10.4- 单位矩阵 np.eye() 或 np.identity() 对角线为1，其余为0
>>> np.eye(4)array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0., 0., 0., 1.]])>>> np.identity(4, dtype=int)array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
来源地址：https://blog.csdn.net/boysoft2002/article/details/131990717
