Python Numpy入门基础（一）创建数组

矫情吗；* 2023-10-14 09:16 37阅读 0赞

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## 入门基础（一） ##

### 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)
    >>> b
    array([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}).dtype
    dtype('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).dtype
    dtype('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]]).dtype
    dtype('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).ndim
    0 #常数为0维
    >>> a.dtype.name, b.dtype.name
    ('int32', 'int32')
    >>> a.size, b.size
    (5, 8)
    >>> type(a), type(b)
    (<class 'numpy.ndarray'>, <class 'numpy.ndarray'>)
    >>> a
    array([ 2,  4,  6,  8, 10])
    >>> b
    array([[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.itemsize
    16
    >>> a.flags
      C_CONTIGUOUS : True
      F_CONTIGUOUS : True
      OWNDATA : True
      WRITEABLE : True
      ALIGNED : True
      WRITEBACKIFCOPY : False
      UPDATEIFCOPY : False
    
    >>> a.data
    <memory at 0x0000000002D79DC0>

#### 3- 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:: 1.20.0

np.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)
    >>> a
    array([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]])
    >>> a
    array([0, 1, 2, 3, 4, 5, 6, 7])
    >>> a.reshape(2,4)
    array([[0, 1, 2, 3],
           [4, 5, 6, 7]])
    >>> a
    array([0, 1, 2, 3, 4, 5, 6, 7])
    >>> a.reshape(4,2)
    array([[0, 1],
           [2, 3],
           [4, 5],
           [6, 7]])
    >>> a
    array([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', '<f4')])
    >>> import numpy as np
    >>> student = np.dtype([('name','S20'), ('age', 'i1'), ('marks', 'f4')])
    >>> student
    dtype([('name', 'S20'), ('age', 'i1'), ('marks', '<f4')])
    >>> 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', '<f4')])
    >>> 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 "<pyshell#68>", line 1, in <module>
        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 "<pyshell#70>", line 1, in <module>
        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 line
    array([0.   +1.j, 0.  +10.j, 0. +100.j, 0.+1000.j])
    >>> np.geomspace(-1+0j, 1+0j, num=5)  # Circle
    array([-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.pi
    3.141592653589793
    >>> np.e
    2.718281828459045
    >>> np.nan
    nan
    >>> np.inf
    inf
    >>> np.Inf
    inf
    >>> np.Infinity
    inf
    >>> np.PINF
    inf
    >>> np.NINF
    -inf
    >>> np.PZERO
    0.0
    >>> np.NZERO
    -0.0

#### 10.2- 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))*3
    array([[3, 3, 3, 3],
           [3, 3, 3, 3],
           [3, 3, 3, 3]])
    >>> np.linspace(1, 1, 12, dtype=int).reshape((3,4))*np.pi
    array([[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]])

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