def binarize(df,cols,kpi_axis,time_axis,order):
    cols = cols.drop([kpi_axis,time_axis])
    features = []
    for k in range(0,order):
        features.append(cols)
    fs = []
    for f in itertools.product(*features):
      #  list(set(f)).sort()
        f = np.unique(f)
        fs.append(tuple(f))
    fs = tuple(set(i for i in fs))
    print(fs)
    for f in fs:
        print(len(f))
        states =[]
        for d in f:
            states.append(tuple(set(df[d].astype('category'))))
        for state in itertools.product(*states):
            z = 1
            name = str()
            for d in range(0,len(f)):
                z = z*df[f[d]]==state[d]
                name +=  f[d] + " == " +str(state[d])
                if d<len(f)-1:
                   name += " AND "
            df[name] = z
    for d in cols:
        df = df.drop([d],axis = 1)
    return df