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numpy之多维数组的创建全过程

目录
  • numpy多维数组的创建
    • 1.1 随机抽样创建
    • 1.2 序列创建
    • 1.3 数组重新排列
    • 1.4 数据类型的转换
    • 1.5 数组转列表
  • numpy 多维数组相关问题
    • 创建(多维)数组
    • 数组赋值
    • np数组保存
    • 读取np数组
  • 总结

    numpy多维数组的创建

    多维数组(矩阵ndarray)

    ndarray的基本属性

    • shape维度的大小
    • ndim维度的个数
    • dtype数据类型

    1.1 随机抽样创建

    1.1.1 rand

    生成指定维度的随机多维度浮点型数组,区间范围是[0,1)

    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.ndi开发者_Go入门m)
    print('维度的大小',nd1.shape)
    print('数据类型',nd1.dtype)   # float 64

    1.1.2 uniform

    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
    javascript                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
                    ``mpython * 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 编程客栈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
           www.devze.com     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)

    运行结果:

    numpy之多维数组的创建全过程

    1.1.3 randint

    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 ofjs 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

    注意点:

    1、如果没有指定最大值,只是指定了最小值,范围是[0,最小值)

    2、如果有最小值,也有最大值,范围为[最小值,最大值)

    1.2 序列创建

    1.2.1 array

    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

    1.2.2 zeros

    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0
    2、默认的数据类型是float
    3、可以指定其他的数据类型

    1.2.3 ones

    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.]]

    1.2.4 arange

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

    结果:

    [0 1 2 3 4 5 6 7 8 9]

    [1 2 3 4 5 6 7 8 9]

    [1 3 5 7 9]

    注意点:

    • 1、只填写一位数,范围:[0,填写的数字)
    • 2、填写两位,范围:[最低位,最高位)
    • 3、填写三位,填写的是(最低位,最高位,步长)
    • 4、创建的是一位数组
    • 5、等同于np.array(range())

    1.3 数组重新排列

    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

    两个可以配合使用,先打乱,在重新排列

    1.4 数据类型的转换

    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

    1.5 数组转列表

    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]

    numpy 多维数组相关问题

    创建(多维)数组

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

    numpy之多维数组的创建全过程

    得到全零的多维数组。

    数组赋值

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

    np数组保存

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

    读取np数组

    x = np.load("path")

    总结

    以上为个人经验,希望能给大家一个参考,也希望大家多多支持我们。

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