>>> # 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...
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...
numpy.prod(a[, axis=None, dtype=None, out=None, …]) Return the product of array elements over a given axis. 【例】返回给定轴上数组元素的乘积。 numpy.cumprod 累乘 numpy.cumprod(a, axis=None, dtype=None, out=None) Return the cumulative product of elements along a given axis. 【例】...
x = np.array([1, 2, 3, 4, 5, 6, 7, 8]) y = x + 1 print(y) print(np.add(x, 1)) # [2 3 4 5 6 7 8 9] y = x - 1 print(y) print(np.subtract(x, 1)) # [0 1 2 3 4 5 6 7] y = x * 2 print(y) print(np.multiply(x, 2)) # [ 2 4 6 8 10 12...
(x, y))#Elementwise product; both produce the array#[[ 5.0 12.0]#[21.0 32.0]]print(x *y)print(np.multiply(x, y))#Elementwise division; both produce the array#[[ 0.2 0.33333333]#[ 0.42857143 0.5 ]]print(x /y)print(np.divide(x, y))#Elementwise square root; produces the array#...
import numpy as np arr1 = np.array([1, 2, 3]) arr2 = np.array([4, 5, 6]) elementwise_product = np.multiply(arr1, arr2) print(elementwise_product) [ 4 10 18] 练习54: 计算二维数组中每列的标准差。 import numpy as np matrix = np.random.random((4, 3)) column_stddev = ...
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. ...
Note that the ‘C’ and ‘F’ options take no account of the memory layout of the underlying array, and only refer to the order of indexing. ‘A’ means to read / write the elements in Fortran-like index order if a is Fortran contiguous in memory, C-like order otherwise. 一维数组重...
引用对象,允许创建不是 NumPy 数组的数组。如果传入作为like支持__array_function__协议的类似数组,结果将由其定义。在这种情况下,它确保创建与通过此参数传入的兼容的数组对象。 1.20.0 版的新功能。 返回: arrndarray 构建的数组。 引发: ValueError
In [21]: arr.reshape((4, 2)).reshape((2, 4)) Out[21]: array([[0, 1, 2, 3], [4, 5, 6, 7]]) 作为参数的形状的其中一维可以是-1,它表示该维度的大小由数据本身推断而来: In [22]: arr = np.arange(15) In [23]: arr.reshape((5, -1)) Out[23]: array([[ 0, 1, 2...