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) arr array([[ 0.7953, 0.1181, -0.7485, 0.585 ], [ 0.1527, -1.5657, -0.5625, -0.0327], [...
[ True True False False] #The matrix product can be performed using the dot function or method A = np.array( [[1,1], [0,1]] ) B = np.array( [[2,0], [3,4]] ) print A print B #print A*B print A.dot(B) print np.dot(A, B) [[1 1] [0 1]] [[2 0] [3 4]...
Creating ndarrays The easiest way to create an array is to use the array function. This accepts any sequence-like object (including other arrays) (往np.array()里面丢一个含类似列表的序列对象对象就可生成)and produces a new NumPy array containing(包含) the passed data. For example, a list i...
Expressing condtinal logic as array expressions instead of loops with if-elif-else branches(数组表达式代替if-elif-else结构). Group-wise data manipulations (aggregaton, transformation, function application)(分组聚合) While(尽管) NumPy provides a computaional foundation for general numerical data processin...
The numpy.where function is a vectorized version of the ternary expression x if condition else y. Suppose we had a boolean array and two arrays of values: In [165]: xarr = np.array([1.1, 1.2, 1.3, 1.4, 1.5]) In [166]: yarr = np.array([2.1, 2.2, 2.3, 2.4, 2.5]) In [167...
Create a function that uses np.logical_and to combine two conditions and then extracts matching elements. Implement a solution that applies np.where to locate indices of elements meeting both criteria and then retrieves them. Test the filtering on arrays with a mix of values to ensure only ele...
In the above example, we have created arrays using thenp.arange()function. np.arange(5)- create an array with 5 elements, where the values range from0to4 np.arange(1, 9, 2)- create an array with 5 elements, where the values range from1to8with a step of2. ...
, or as a higher order function: def fn(): x = numpyro.sample('x', dist.Beta(1, 1)) y = numpyro.sample('y', dist.Bernoulli(x)) return y print(handlers.seed(fn, rng_seed=0)()) Can I use the same Pyro model for doing inference in NumPyro? As you may have noticed from...
Write a NumPy program to select indices satisfying multiple conditions in a NumPy array.Sample array :a = np.array([97, 101, 105, 111, 117]) b = np.array(['a','e','i','o','u'])Note: Select the elements from the second array corresponding to elements in the first array that ...
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 ...