SathishN

# -*- coding: utf-8 -*-

import time
t0 = time.clock()

import pandas as pd
from pandas.tseries.offsets import BDay
import numpy as np
import datetime as dt
from copy import copy

SathishN

#IF YOU FOUND THIS USEFUL, Please Donate some Bitcoin .... 1FWt366i5PdrxCC6ydyhD8iywUHQ2C7BWC

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from subprocess import check_output
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.layers.advanced_activations import LeakyReLU, PReLU

SathishN

import pandas as pd
import os

file = os.path.abspath('C:\\Zerodha\\Pi\\Exported\\Nifty 50.csv')
df = pd.read_csv(file)
df['Date'] = pd.to_datetime(df['Date'], format = '%d/%m/%y %I:%M:%S %p')
df['Time'] = df['Date'].apply(lambda x: x.strftime('%H:%M'))
df['Date'] = df['Date'].apply(lambda x: x.strftime('%y-%m-%d'))
df.set_index('Date', drop=True, inplace=True)
df.columns = ['O', 'H', 'L', 'C', 'V', 'Time']