.. _Data preparation for NeurEco Classification python API:

Data preparation for NeurEco Classification with python API
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The python API expects the data for model construction or evaluation in form of NumPy arrays containing the data.

• allowed types of arrays: int, float, double

• **input** array contains a table with:

  • number of lines equal to a number of samples
  
  • number of columns equal to a number of input features
  
• **output** array contains a table with:

  • number of lines equal to a number of samples
  
  • number of columns equal to a number of output features, for Classification these features are the classes
  
  • the **output** is one-hot encoded: each line contains '0' on all positions, except for one containing '1'. This position corresponds to a class to which belong the sample on the line.
  
• **input** array and the corresponding **output** array have the same number of samples

There is no need to normalize the data, as the normalization is handled by NeurEco, :std:ref:`Normalizing the data`.