numpy

funciton	interpretation
np.array()
np.arange(start, end, step)
np.linspace(start, end, count)	from start to end, equal margin
np.copy()
np.hstack(tuple)	Stack arrays in sequence horizontally (column wise)
np.vstack(tuple)	Stack arrays in sequence vertically (row wise)
numpy.random.rand(d0,d1,d2,...,dN)	d is dimention,float values betweent 0 and 1
numpy.zeros(shape, dtype=float, order='C')	order = 'C' is row-major, F is column-major storage in memory
numpy.nditer()	iterator for array
np.dot()	dot product
np.cross()	cross product
np.max()	get maximum value from tensor
np.amax(arr, axis)	get maximum value from given axis
np.sum(arr, axis)	sum of all elements if using default axis
np.average(arr, axis，weights)	average of all elements if using default axis
np.mean(arr, axis)	equal to np.average while weights is 1
np.std()	standard deviation
np.tolist()
np.isnan(value)
np.full()	fill all ndarray with one value
np.ones()	fill all adarray with 1
np.flatten()