text: 
  import pandas as pd
  import numpy as np
  
  
  X_unclassified = pd.read_csv("data/raw/cytodata/validation_data.csv");
  X_classified = pd.read_csv("data/raw/cytodata/training_data.csv");
  
  X_classified['known'] = 1
  X_unclassified['known'] = 0
  
  X_unclassified = X_unclassified.set_index("cell_code")
  X_classified = X_classified.set_index("cell_code")
  X = pd.concat([X_classified, X_unclassified], sort = True)
  
  targets = list(X.target.unique())
  targets.remove(np.nan)
  y = pd.concat([X.target == target for target in targets], axis = 1)
  y.fillna(0.0)
  y.columns = targets
  y.astype('int')
  
  metadata_columns =  [
                       'cell_id',
                       'dist.10.nn',
                       'dist.20.nn',
                       'dist.30.nn',
                       'field',
                       'nuclear.displacement',
                       'plate',
                       'replicate',
                       'target',
                       'well',
                       'well_code',
                       'known'
                      ]
  metadata = X[metadata_columns]
  features = X[[i for i in X.columns if i not in metadata_columns]]
  
  features_unscaled = features.copy()
  features = features_unscaled / features_unscaled.std(axis = 0)
  
  features.to_csv("data/intermediate/cytodata/features.csv")
  features_unscaled.to_csv("data/intermediate/cytodata/features_unscaled.csv")
  metadata.to_csv("data/intermediate/cytodata/metadata.csv")
  y.to_csv("data/intermediate/cytodata/labels.csv")
  