Where() 与 SQL 中使用的 where condition 类似,如以下示例所示: y = np.array([1,5,6,8,1,7,3,6,9])# Where y is greater than 5, returns index positionnp.where(y>5)array([2, 3, 5, 7, 8], dtype=int64),)# First will replace the val...
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))>>> a[np.nonzero(a > 3)] array([4, 5, 6, 7, 8, 9]) np.where( condition, x, y ) np.where方法根据condition的条件,为真时返回x中对应的值,为假时返回y中对应的值。 >>> a = np.arange(10)>>>a array(...
| where : array_like of bool, optional | A boolean array which is broadcasted to match the dimensions | of `array`, and selects elements to include in the reduction. Note | that for ufuncs like ``minimum`` that do not have an identity | defined, one has to pass in also ``initial...
y = np.array([1,5,6,8,1,7,3,6,9])# Where y is greater than 5, returns index positionnp.where(y>5)array([2, 3, 5, 7, 8], dtype=int64),)# First will replace the values that match the condition, # second will replace the values that does notnp.where(y>5, "Hit", "M...
array([[ 1.5610358 , 1.47201866, 0.64378465], [ 0.39354435, -1.35112498, -3.12279483]]) 1. 2. Then I wirte mathematical operations with data: data*10 1. array([[ 15.61035804, 14.72018662, 6.4378465 ], [ 3.93544348, -13.51124975, -31.22794833]]) ...
array([[24, 25, 26, 27, 28, 29], [18, 19, 20, 21, 22, 23], [12, 13, 14, 15, 16, 17]]) 给定输入2D矩阵arr,此函数应返回形状为(top_n,arr.shape[-1])的np.ndarray。 以下是我尝试过的: def select_rows(arr, top_n): ...
y = np.array([1,5,6,8,1,7,3,6,9])# Where y is greater than 5, returns index position np.where(y>5) array([2, 3, 5, 7, 8], dtype=int64),)# First will replace the values that match the condition, # second will replace the values that does not ...
y = np.array([1,5,6,8,1,7,3,6,9])# Where y is greater than 5, returns index position np.where(y>5) array([2,3,5,7,8], dtype=int64),)# First will replace the values that match the condition, # second will replace the values that does not ...
array([[ 3.12207161, 2.94403732, 1.2875693 ], [ 0.7870887 , -2.70224995, -6.24558967]]) In the first example, all of the elements have been multiplied by 10. In the second, the corresponding values(相应值) in each "cell" in the array have been added to each oher. ...
y = np.array([1,5,6,8,1,7,3,6,9])# Where y is greater than 5, returns index position np.where(y》5) array([2, 3, 5, 7, 8], dtype=int64),)# First will replace the values that match the condition, # second will replace the values that does not ...