How to reshape spatiotemporal data as lstm input?
You cannot easily use reshape when you have a different number of temporal steps for each example. What you typically do with LSTMs is that you have batches of examples and each batch is padded to the same length, usually with zeros. Use np.zeros(shape) and then iteratively assign to respective rows.
I have a dataset with columns like ['station_id', 'feature1', 'feature2',...]
Each row is a time step. And it is sorted by station_id.
The main problem is that station_ids have different number of timesteps ...
I want to shape it for an LSTM layer, like (NumberOfExamples, TimeSteps, FeaturesPerStep). 
Can someone help me to use np.reshape() in this case please ?