如何通过numpy创建一个复杂的多维数组并实现所有可能的初始化方式？

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目录 + numpy多维度数组的创建 + 1.1 随机抽样创建 + 1.2 序列创建 + 1.3 数组重新排序 + 1.4 数据类型转换 + 1.5 数组转列 + numpy多维度数组相关问题 + 创建（多维度）数组 + 数组赋值 + np数组保存 + 读取np数组

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.ndim) 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 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)

运行结果：

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 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

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')

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自由互联。