Calculating Daily Highs and Lows with Intraday Data

Question

I am looking to calculate daily highs and lows that are updated as they happen (intraday). Meaning that the first value of the day is taken as the high/low then updated as new highs/lows are achieved. The values should reset each day.

I saw some code in Pinescript that does exactly what I need but I have been unable to figure out how to do this in Python:

//New day bars calculation
is_new_day() =>
    d = dayofweek
    na(d[1]) or d != d[1]

bar_num = int(barssince(is_new_day()))

// Find the highest and lowest price of the day
sofar_high = 0.0
sofar_low = 9999999999.9

for i = 0 to bar_num
    sofar_high := high[i] > sofar_high ? high[i] : sofar_high

for i = 0 to bar_num
    sofar_low := low[i] < sofar_low ? low[i] : sofar_low

The piece of Python Code that I have come up with is below. I take the return values of each function and outer join into my main dataframe, that part is working at least..

import pandas as pd


class DailyRange:

    def __init__(self, high, low, length, time, day):
        self.high = high
        self.low = low
        self.length = length
        self.time = time
        self.day = day

    def daily_high(self):
        keys = [] # List of keys or i to be used later to outer join to main dataframe
        highs = []
        times = []
        date_times = []
        current_high = 0
        for i in range(1, self.length - 1, 1):
            # New Day
            if self.day[i] > self.day[i-1] and \
                    self.day[i] == self.day[i+1] and \
                    self.high[i] > current_high:
                current_high = self.high[i]
                keys.append(i-1)
                highs.append(current_high)
                times.append(self.day[i])
                date_times.append(self.time[i])
            # Same Day
            elif self.day[i] == self.day[i-1] and \
                    self.high[i] > current_high:
                current_high = self.high[i]
                keys.append(i-1)
                highs.append(current_high)
                times.append(self.day[i])
                date_times.append(self.time[i])
            else:
                continue
        high_data = {'Key': keys,
                     'Current Daily High': highs,
                     'Day of Month - High': times,
                     'Datetime - High': date_times}
        hd_df = pd.DataFrame(high_data, columns=['Key',
                                                 'Current Daily High',
                                                 'Day of Month - High',
                                                 'Datetime - High'])
        return hd_df

    def daily_low(self):
        keys = []  # List of keys or i to be used later to outer join to main dataframe
        lows = []
        times = []
        date_times = []
        current_low = 999999999
        for i in range(1, self.length - 1, 1):
            # New Day
            if self.day[i] > self.day[i - 1] and \
                    self.day[i] == self.day[i+1] and \
                    self.low[i] < current_low:
                current_low = self.low[i]
                keys.append(i-1)
                lows.append(current_low)
                times.append(self.day[i])
                date_times.append(self.time[i])
            # Same Day
            elif self.day[i] == self.day[i-1] and \
                    self.low[i] < current_low:
                current_low = self.low[i]
                keys.append(i-1)
                lows.append(current_low)
                times.append(self.day[i])
                date_times.append(self.time[i])
            else:
                continue
        low_data = {'Key': keys,
                    'Current Daily Low': lows,
                    'Day of Month - Low': times,
                    'Datetime - Low': date_times}
        ld_df = pd.DataFrame(low_data, columns=['Key',
                                                'Current Daily Low',
                                                'Day of Month - Low',
                                                'Datetime - Low'])
        return ld_df

I have a few issues with this code and I think they are related to my if statements and the day[i-1] arguments but I have been unable to crack it.

1. The days are not resetting which I thought would have been taken care of by if self.day[i] > self.day[i - 1] and self.day[i] == self.day[i+1]

2. The appending of the [i] values happens on the preceding row for some reason.

I am fairly new to Python and would appreciate any leads. I can't help but think there is a more simple way to do what I have tried to do above.