import numpy as np
import pandas as pd
import datetime
from dateutil.rrule import rrule, DAILY, MONTHLY
import matplotlib.pyplot as plt
import json
from sklearn import metrics
from pyclustering.cluster.kmedoids import kmedoids
from pyclustering.cluster import cluster_visualizer
from LoadData import LoadData
borrow_file_name = LoadData.load_month_borrow_fname()
return_file_name = LoadData.load_month_return_fname()
target_timestamp = np.array([])
start_date = datetime.datetime(2015, 1, 1)
end_date = datetime.datetime(2016, 12, 31)
for dt in rrule(MONTHLY, dtstart=start_date, until=end_date):
target_timestamp = np.append(target_timestamp, dt.strftime("%Y-%m"))
print('target_timestamp', target_timestamp)
x=input()
total_borrow_data = pd.DataFrame()
total_return_data = pd.DataFrame()
for count in range(int(len(target_timestamp) / 12)):
title_name = ""
total_borrow_data.drop(total_borrow_data.index, inplace=True)
total_return_data.drop(total_return_data.index, inplace=True)
