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)
铺示：
[[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

通过列表入行创建
nd4 = np.array([1,2,3])
铺示：
[1 2 3]
通过列表嵌套列表创建
nd5 = np.array([[1,2,3],[4,5]])
铺示：
[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)
铺示：
[1 2 3]
1
(3,)
int32
[[1 2 3]
 [4 5 6]]
2
(2, 3)
int32

nd6 = np.zeros((4,4))
print(nd6)
铺示：
[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]
注重点：
1、创建的数里面的数据为0
2、默认的数据类型是float
3、可以指定其他的数据类型

nd7 = np.ones((4,4))
print(nd7)
铺示：
[[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)
铺示：
[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]
注重点：
1、有返归值，返归新的数组，原始数组不受影响
2、入行维度大小的设置过程中，要注重数据的个数，注重元素的个数
nd13 = np.arange(10)
print(nd13)
nd14 = np.random.shuffle(nd13)
print(nd14)
print(nd13)
铺示：
[0 1 2 3 4 5 6 7 8 9]
None
[8 2 6 7 9 3 5 1 0 4]
注重点：
1、在原始数据集上做的操作
2、将原始数组的元素入行重新排列，打乱顺序
3、shuffle这个是没有返归值的

nd15 = np.arange(10,dtype=np.int64)
print(nd15)
nd16 = nd15.astype(np.float64)
print(nd16)
print(nd15)
铺示：
[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]
注重点：
1、astype()不在原始数组做操作，有返归值，返归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参数入行指定

arr1 = np.arange(10)
# 数组转列表
print(list(arr1))
print(arr1.tolist())
铺示：
[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")