importnumpyasnp# 创建一个示例数组arr=np.array([1,2,3,4,5,6,7,8,9,10])# 使用where函数找出大于5的元素的索引indices=np.where(arr>5)print("numpyarray.com - 大于5的元素的索引:",indices)# 使用where函数返回大于5的元素,小于等于5的元素用0替代result=np.where(arr>5,arr,0)print("numpyarr...
# returns element of x when True# returns element of y when Falseresult = np.where([[True,False], [False,True]], x, y) print(result) Run Code Output [[1 2] [-3 -4]] [[1 -2] [-3 4]] Example 4: where() with Multiple Conditions The test condition in awhere()method may ...
Because NumPy provides an easy-to-use C API, it is straightforward(直接) to pass data to external(外部的) libraries wirtten in a low-level language and also for external libraries to return data to Python as NumPy arrays(通过扩展库函数, 将数据运行在低级语言(速度快)中,然后再返回给python)....
[20 25 30]] #We can also perform comparisons with multiple conditions vector = numpy.array([5, 10, 15, 20]) equal_to_ten_and_five = (vector == 10) & (vector == 5) print equal_to_ten_and_five [False False False False] vector = numpy.array([5, 10, 15, 20]) equal_to_...
nddary, an efficient multidimensional array providing fast array-oriented(面向数组编程) arithmetic operations and flexible broadcasting capabilitles.(强大而灵活的广播机制) Mathematical functions for fast operations on entire arrays of data without having to write loops.(高效的数学函数, 面向数组编程而不用...
To make it simpler, we can use the where statement: result = np.where(cond, xarr, yarr) result array([1.1, 2.2, 1.3, 1.4, 2.5]) We can also modify the value of the array according to the conditions of where: arr = np.random.randn(4, 4) ...
If these conditions are not met, aValueError:operandscouldnotbebroadcasttogetherexception is thrown, indicating that the arrays have incompatible shapes. Input arrays do not need to have the samenumberof dimensions. The resulting array will have the same number of dimensions as the input array with...
An array allows us to store a collection of multiple values in a single data structure.An array allows us to store a collection of multiple values in a single data structure. The NumPy array is similar to a list, but with added benefits such as being fas
The Predictive utility conditions the unobserved mu and tau sites to values drawn from the posterior distribution from our last MCMC run, and runs the model forward to generate predictions. >>> from numpyro.infer import Predictive >>> # New School ... def new_school(): ... mu = numpy...
Frequently we have a smaller array and a larger array, and we want to use the smaller array multiple times to perform some operation on the larger array. For example, suppose that we want to add a constant vector to each row of a matrix. We could do it like this: import numpy as ...