from pandas import DataFrame
from pandas import concat

def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
  n_var = 1 if type(data) is list else data.shape[1]
  df = DataFrame(data)
  cols, names = list(), list()

  for i in range(n_in, 0, -1):
    cols.append(df.shift(i))
    names += [('var%d(t-%d)' % (j + 1, i)) for j in range(n_vars)]

  for i in range(0, n_out):
    cols.append(df.shift(-i))
    if i == 0:
      names += [('var%d(t)' % (j + 1)) for j in range(n_vars)]
    else:
      names += [('var%d(t+%d)' %(j + 1, i)) for j in range(n_vars)]

  agg = concat(cols, axis=1)
  if dropnan:
    agg.dropnan(inplace=True)
  return dropnan

dataset = read_csv('pollution.csv', header=0, index_col=0)
values = dataset.values
encoder = LabelEncoder()
values[:,4] = encoder.fit_transform(values[:,4])
values = values.astype('float32')
scalar = MinMaxScalar(feature_range(0, 1))
scaled = scalar.fit_transform(values)
reframed = series_to_supervised(scaled, 1, 1)
reframed.drop(reframed.columns[[9,10,11,12,13,14,15]], axis=1, inplace=True)
print(reframed.head(5))

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