自学教程：numpy之多维数组的创建全过程

Random values in a given shape.            Create an array of the given shape and populate it with            random samples from a uniform distribution            over ``[0, 1)``.nd1 = np.random.rand(1,1)print(nd1)print('维度的个数',nd1.ndim)print('维度的大小',nd1.shape)print('数据类型',nd1.dtype)   # float 64

def uniform(low=0.0, high=1.0, size=None): # real signature unknown; restored from __doc__    """    uniform(low=0.0, high=1.0, size=None)            Draw samples from a uniform distribution.            Samples are uniformly distributed over the half-open interval            ``[low, high)`` (includes low, but excludes high).  In other words,            any value within the given interval is equally likely to be drawn            by `uniform`.            Parameters            ----------            low : float or array_like of floats, optional                Lower boundary of the output interval.  All values generated will be                greater than or equal to low.  The default value is 0.            high : float or array_like of floats                Upper boundary of the output interval.  All values generated will be                less than high.  The default value is 1.0.            size : int or tuple of ints, optional                Output shape.  If the given shape is, e.g., ``(m, n, k)``, then                ``m * n * k`` samples are drawn.  If size is ``None`` (default),                a single value is returned if ``low`` and ``high`` are both scalars.                Otherwise, ``np.broadcast(low, high).size`` samples are drawn.            Returns            -------            out : ndarray or scalar                Drawn samples from the parameterized uniform distribution.            See Also            --------            randint : Discrete uniform distribution, yielding integers.            random_integers : Discrete uniform distribution over the closed                              interval ``[low, high]``.            random_sample : Floats uniformly distributed over ``[0, 1)``.            random : Alias for `random_sample`.            rand : Convenience function that accepts dimensions as input, e.g.,                   ``rand(2,2)`` would generate a 2-by-2 array of floats,                   uniformly distributed over ``[0, 1)``.            Notes            -----            The probability density function of the uniform distribution is            .. math:: p(x) = /frac{1}{b - a}            anywhere within the interval ``[a, b)``, and zero elsewhere.            When ``high`` == ``low``, values of ``low`` will be returned.            If ``high`` < ``low``, the results are officially undefined            and may eventually raise an error, i.e. do not rely on this            function to behave when passed arguments satisfying that            inequality condition.            Examples            --------            Draw samples from the distribution:            >>> s = np.random.uniform(-1,0,1000)            All values are within the given interval:            >>> np.all(s >= -1)            True            >>> np.all(s < 0)            True            Display the histogram of the samples, along with the            probability density function:            >>> import matplotlib.pyplot as plt            >>> count, bins, ignored = plt.hist(s, 15, density=True)            >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')            >>> plt.show()    """    pass

nd2 = np.random.uniform(-1,5,size = (2,3))print(nd2)print('维度的个数',nd2.ndim)print('维度的大小',nd2.shape)print('数据类型',nd2.dtype)

def randint(low, high=None, size=None, dtype='l'): # real signature unknown; restored from __doc__    """    randint(low, high=None, size=None, dtype='l')            Return random integers from `low` (inclusive) to `high` (exclusive).            Return random integers from the "discrete uniform" distribution of            the specified dtype in the "half-open" interval [`low`, `high`). If            `high` is None (the default), then results are from [0, `low`).            Parameters            ----------            low : int                Lowest (signed) integer to be drawn from the distribution (unless                ``high=None``, in which case this parameter is one above the                *highest* such integer).            high : int, optional                If provided, one above the largest (signed) integer to be drawn                from the distribution (see above for behavior if ``high=None``).            size : int or tuple of ints, optional                Output shape.  If the given shape is, e.g., ``(m, n, k)``, then                ``m * n * k`` samples are drawn.  Default is None, in which case a                single value is returned.            dtype : dtype, optional                Desired dtype of the result. All dtypes are determined by their                name, i.e., 'int64', 'int', etc, so byteorder is not available                and a specific precision may have different C types depending                on the platform. The default value is 'np.int'.                .. versionadded:: 1.11.0            Returns            -------            out : int or ndarray of ints                `size`-shaped array of random integers from the appropriate                distribution, or a single such random int if `size` not provided.            See Also            --------            random.random_integers : similar to `randint`, only for the closed                interval [`low`, `high`], and 1 is the lowest value if `high` is                omitted. In particular, this other one is the one to use to generate                uniformly distributed discrete non-integers.            Examples            --------            >>> np.random.randint(2, size=10)            array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])            >>> np.random.randint(1, size=10)            array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])            Generate a 2 x 4 array of ints between 0 and 4, inclusive:            >>> np.random.randint(5, size=(2, 4))            array([[4, 0, 2, 1],                   [3, 2, 2, 0]])    """    pass

nd3 = np.random.randint(1,20,size=(3,4))print(nd3)print('维度的个数',nd3.ndim)print('维度的大小',nd3.shape)print('数据类型',nd3.dtype)展示：[[11 17  5  6] [17  1 12  2] [13  9 10 16]]维度的个数 2维度的大小 (3, 4)数据类型 int32

通过列表进行创建nd4 = np.array([1,2,3])展示：[1 2 3]通过列表嵌套列表创建nd5 = np.array([[1,2,3],[4,5]])展示：[list([1, 2, 3]) list([4, 5])]综合nd4 = np.array([1,2,3])print(nd4)print(nd4.ndim)print(nd4.shape)print(nd4.dtype)nd5 = np.array([[1,2,3],[4,5,6]])print(nd5)print(nd5.ndim)print(nd5.shape)print(nd5.dtype)展示：[1 2 3]1(3,)int32[[1 2 3] [4 5 6]]2(2, 3)int32

nd6 = np.zeros((4,4))print(nd6)展示：[[0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.]]注意点：1、创建的数里面的数据为02、默认的数据类型是float3、可以指定其他的数据类型

nd7 = np.ones((4,4))print(nd7)展示：[[1. 1. 1. 1.] [1. 1. 1. 1.] [1. 1. 1. 1.] [1. 1. 1. 1.]]

nd8 = np.arange(10)print(nd8)nd9 = np.arange(1,10)print(nd9)nd10 = np.arange(1,10,2)print(nd10)

nd11 = np.arange(10)print(nd11)nd12 = nd11.reshape(2,5)print(nd12)print(nd11)展示：[0 1 2 3 4 5 6 7 8 9][[0 1 2 3 4] [5 6 7 8 9]][0 1 2 3 4 5 6 7 8 9]注意点：1、有返回值，返回新的数组，原始数组不受影响2、进行维度大小的设置过程中，要注意数据的个数，注意元素的个数nd13 = np.arange(10)print(nd13)nd14 = np.random.shuffle(nd13)print(nd14)print(nd13)展示：[0 1 2 3 4 5 6 7 8 9]None[8 2 6 7 9 3 5 1 0 4]注意点：1、在原始数据集上做的操作2、将原始数组的元素进行重新排列，打乱顺序3、shuffle这个是没有返回值的

nd15 = np.arange(10,dtype=np.int64)print(nd15)nd16 = nd15.astype(np.float64)print(nd16)print(nd15)展示：[0 1 2 3 4 5 6 7 8 9][0. 1. 2. 3. 4. 5. 6. 7. 8. 9.][0 1 2 3 4 5 6 7 8 9]注意点：1、astype()不在原始数组做操作，有返回值，返回的是更改数据类型的新数组2、在创建新数组的过程中，有dtype参数进行指定

arr1 = np.arange(10)# 数组转列表print(list(arr1))print(arr1.tolist())展示：[0, 1, 2, 3, 4, 5, 6, 7, 8, 9][0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

x = np.zeros(shape=[10, 1000, 1000], dtype='int')

x[*,*,*] = ***

np.save("./**.npy",x)

x = np.load("path")