reset_index(), default dict

from collections import defaultdict

a = np.arange(1, 11 ,1)
b = np.arange(1, 20, 2)
df = pd.DataFrame({'a': a, 'b': b})

now_df = defaultdict()
for i in range(2):
    now_df[i] = df[df['a']% 2 == i].reset_index()

using reset_index() we can divide data, fit the model and then add again (esp on time axis)

hdfs = []
df = []
for i in range(7):
  now_df = df[df['ds'].dt.dayofweek == i].reset_index()
  .
  . model fitting(params = sm.optimizing(data = data, iter = 1000)
  . 
  now_hdf = pd.concat([now_df, pd.DataFrame(params['yhat'], columns =[ 'yhat'], axis = 1)

divide the dates based on weekday and divide -> fit -> concat (axis =1)