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Python中reshape函数参数-1的意思？ 在Python的numpy库中，经常出现reshape (x， [-1，28，28，1])之类的表达，请问新shape中-1是什么含义？ 我在网上查不到详细… 显示全部 关注者 117 被浏览 Siguiendo con tu ejemplo array_2d = np.array. We can think of it as x (unknown)
Reshape() function is generally used to change the shape of an array without changing the data The amounts to jump to next row and column just change. The reshape comments and similar argument names aren't all that helpful
However, i have found that for long to wide, you need to provide data = your data.frame, idvar = the variable that identifies your groups, v.names = the variables that will become multiple columns in wide format, timevar = the variable containing the values that will be appended to v.names in wide format, direction = wide.
The semantics of reshape () are that it may or may not share the storage and you don't know beforehand Also see here about the meaning of contiguous. 注意transpose函数和reshape函数的区别：reshape后数组中元素的序号不会发生变化，而transpose后行列互换，数组中元素的序号也变了。 MATLAB支持复数，虚数单位为i或j。 在对复数数组进行转置时，有时需要同时对其取复共轭，这种操作称为共轭转置。 It is tempting to catch the reshape s and don't allow them, but a lot of the cool things you've come to love in numpy rely on altering the dimensions of the underlying data, e.g
Get rid of reshape and tile does not work any more May be this is a small, unavoidable, price to pay for reusing the numpy engine in pandas. The examples you provide are inconsistent (1) you cannot reshape a list with 8 elements into a 2x2 array
(2) what is np.shape = (28, 100) supposed to do?
B_new = b.reshape((10, 1)) the amount of memory used should not differ at all between the 2 shapes Numpy arrays use the concept of strides and so the dimensions (10,) and (10, 1) can both use the same buffer
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