1,3,8,5])# an array of indices>>>a[i]# the elements of `a` at the positions `i`array([1,1,9,64,25])>>>>>>j=np.array([[3,4],[9,7]])# a bidimensional array of indices>>>a[j]# the same shape as `j`array([[9,16],[81
>>> # Create an empty array with 2 elements >>> np.empty(2) array([3.14, 42\. ]) # may vary 您可以创建一个具有元素范围的数组: 代码语言:javascript 代码运行次数:0 运行 复制 >>> np.arange(4) array([0, 1, 2, 3]) 甚至可以创建一个包含一系列均匀间隔的区间的数组。为此,您需要...
void 类型的元素现在以十六进制表示](release/1.14.0-notes.html#void-datatype-elements-are-now-printed-in-hex-notation) void 数据类型的打印样式现在可以独立自定义 np.loadtxt 的内存使用量减少 变更 结构化数组的多字段索引/赋值 整数和 Void 标量现在不受 np.set_string_function 影响 0 维数组打印...
subtract Subtract elements in second array from first array multiply Multiply array elements divide, floor_divide Divide or floor divide (truncating the remainder) power Raise elements in first array to powers indicated in second array maximum, fmax Element-wise maximum; fmax ignores NaN minimum, fmi...
>>> a = np.arange(12).reshape(3, 4) >>> b = a > 4 >>> b # `b` is a boolean with `a`'s shape array([[False, False, False, False], [False, True, True, True], [ True, True, True, True]]) >>> a[b] # 1d array with the selected elements array([ 5, 6, 7,...
numpy.exp(x, *args, **kwargs) Calculate the exponential of all elements in the input array. numpy.log(x, *args, **kwargs) Natural logarithm, element-wise. numpy.exp2(x, *args, **kwargs) Calculate 2**p for all p in the input array. ...
注意numpy.array和标准Python库类array.array并不相同,后者只处理一维数组和提供少量功能。numpy 数组的属性ndarray.shape 数组的维度。这是一个指示数组在每个维度上大小的整数元组。例如一个n排m列的矩阵,它的shape属性将是(2,3),这个元组的长度显然是秩,即维度或者ndim属性 import numpy as np a = np.array(...
numpy.exp(x, args, kwargs) Calculate the exponential of all elements in the input array. numpy.log numpy.log(x, args, kwargs) Natural logarithm, element-wise. numpy.exp2 numpy.exp2(x, *args, kwargs) * Calculate 2p for all p in the input array. ...
| *x : array_like | Input arrays. | out : ndarray, None, or tuple of ndarray and None, optional | Alternate array object(s) in which to put the result; if provided, it | must have a shape that the inputs broadcast to. A tuple of arrays ...
>>> b =array( [ (1.5,2,3), (4,5,6) ] ) >>> barray([[1.5,2. ,3. ], [4. ,5. ,6. ]]) 数组类型可以在创建时显示指定 >>> c =array( [ [1,2], [3,4] ], dtype=complex ) >>> carray([[1.+0.j,2.+0.j], ...