parse_it | python library for parsing multiple types | File Utils library

yourcode = input
tokens = []
cursor = 0
while cursor < len(yourcode):
    yourcode = yourcode[cursor:-1] # remove previously scanned tokens
    match token regex from list of regexes
    if match == token:
        add add token of match

pointsObj = driver.find_elements(By.XPATH,"//span[contains(@class, 'treeImg') and contains(., 'AHU-01_ahu_ChilledWtrVlvOutVolts')]")

pointsObj = driver.find_elements(By.XPATH,"//span[@class='treeImg v65point' and t

file = open("sections.txt", 'r')

last_header=''
output = {}
for line in file.readlines():
    if is_section_header(line):
        last_header = line
        output[line] = ""
    else:
        existing_data = output[last_header]
        

out = df['sports'].explode().unique().tolist()

import ast
out = df['sports'].apply(ast.literal_eval).explode().unique().tolist()

out = [*{x for lst in df['sports'].tolist() for x in ast.lit

_sample = sample.replace("\n", "").replace(" ", "").replace(",]", "]")

import re

_sample = re.sub(r",(\s)+]", "]", sample)

import json

outcome = json.loads(_sample)

>>> from datetime import datetime
>>> sorted(list_of_odered_dicts,key=lambda x: datetime.strptime(x['stampdate'], '%Y-%m-%dT%H:%M:%S.%f'))
[OrderedDict([('id', 56), ('werkzeug', 1006), ('stampdate', '2021-12-12T10:48:19.7

from datetime import datetime

mytime = '2021-12-06T13:52:41.864+0000'
datetime.strptime(mytime, '%Y-%m-%dT%H:%M:%S.%f%z')

datetime.datetime(2021, 12, 6, 13, 52, 41, 864000, tzinfo=datetime.timezone.utc)

input_string = r'00000:\nHHBBUUSSXXNJJDHCBSOXMJ X=-1323 Y=AA6D87CB78F8EE\nX=-908 Y=C87F32E6767\nX=-87 Y=AB67C78E23\n'

regex = r'(?:.*\s)?X=(?P-?\d+)\sY=(?P.*)'

parts = input_string.split(r'\n')

extracted_vals = []

for part in parts: