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
# 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 ...
Knowing how to use multiple conditions and understanding how to use parentheses to control operator precedence allows you to create some really complex analysis conditions and unleash the real power of where(). As another example, suppose you wanted to flip the signs in your original test_array,...
[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_...
Numpy, short for Numerical Python, is one of the most important foundational(基本的) packages for numerical computing in Python. Most computational packages providing scientific functionality use NumPy's array object as thelinaua franca(通用语言)for data exchange. ...
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) ...
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...
Selecting two of the three names to combine multiple boolean conditions, use boolean arithmetic operators like & (and) and | (or): In [110]: mask = (names == 'Bob') | (names == 'Will') In [111]: mask Out[111]: array([ True, False, True, True, True, False, False]) In [...
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 elements satisfying both conditions are returned.Go to: ...