Only integer scalar arrays can be converted to a scalar index. What is wrong in the code.
Python Finite Sum With Variable Range Typeerror Only
Only integer scalar arrays can be converted to a scalar index. Import numpy as np a npones5. Import numpy a numpyarray1 2 3 b numpyarray5 6 numpyconcatenatea b traceback most recent call last. To me that looks as an inconsistent implementation. Only integer scalar arrays can be converted to a scalar index with 1d numpy indices array i find this weird since i already checked the array of indices that i have created. Github is home to over 50 million developers working together to host and review code manage projects and build software together. Only integer scalar arrays can be converted to a scalar index.
It is 1 d it is integer and it is scalar. Dismiss join github today. The code is pretty clear below. Only integer scalar arrays can be converted to a scalar index关于这个错误已经第二次遇到了上次遇到错误的时候也就随便搞一搞就解决了但是这次又碰到这个问题然后不知道怎么解决了然后按着正常思路又百度了一番然而没有解决但是依稀上次遇到同样问题时记得也是在处理同样问题的. I have achieved this. Traceback most recent call last.
Hi i am trying to do a slding window on a cube 3d array to get the average over a block of vertical 1d arrays. Labelarray 2012 valuesofarray nparraylabelarray indices npwherevaluesofarray value0 return arrayindices searchvaluessomevaluesarray2. I am concatenating two one dimensional numpy arrays but i am getting the error. Only integer scalar arrays can be converted to a scalar index my function. It usually can concatenate row wise and column wise. File error 1py line 9 in module ar3 numpyconcatenatear1 ar1 file arrayfunction internals line 6 in concatenate typeerror.
Only integer scalar arrays can be converted to a scalar index. In the following example we are trying to concatenate two arrays using numpys concatenate function the concatenate function concatenating two or more arrays of the same type. Only integer scalar arrays can be converted to a scalar index 解决思路. Only integer scalar arrays can be converted to a scalar index. The main problem that i have with this is the fact that median works fine in this scenario.
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