How to align data of two dataframes in a single dataframe based on common dates [duplicate]

I have two csv files from which I have created a single dataframe. Problem is first it adds data of 'Tatasteel' first and then data of 'Tatamotors'. What I want is to order them according to date. For example, first data of Tatasteel at 9:15 and then data of Tatamotors at 9:15 and so on.

Here is the code:

import pandas as pd
import datetime as dt
from datetime import timedelta
import os
import glob


exclude_days = [dt.datetime(2019, 1, 5), dt.datetime(2019, 1, 6), dt.datetime(2019, 1, 12), dt.datetime(2019, 1, 13), dt.datetime(2019, 1, 19), dt.datetime(2019, 1, 20),
dt.datetime(2019, 1, 26), dt.datetime(2019, 1, 27) ]
backtest_start = dt.datetime(2019, 1, 1)
backtest_end = dt.datetime(2019, 1, 2)

path = os.getcwd()
path = os.path.join(path,"2019/*")
dat=[]
data3 =[]
stock_list = glob.glob(path)
for stock in stock_list:
   rdata= pd.read_csv(stock, parse_dates=['Date'])
   dat.append(rdata)
data = pd.concat(dat, ignore_index=True)

   
curr_day = backtest_start

while curr_day < backtest_end:
   if curr_day not in exclude_days:
      day_start = dt.datetime(curr_day.year,curr_day.month,curr_day.day, 9, 15)
      day_end = dt.datetime(curr_day.year,curr_day.month,curr_day.day, 15, 15)
      data2 =  data[data['Date'].between(day_start,day_end)]
      data2 = data2.reset_index(drop=True)
      data2 = pd.DataFrame(data2)
      print(data2)         
        
  curr_day += timedelta(days=1)

Here is the output: