classification(full address)
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AnastasiaOnimovma
parent
cb422b9a2f
commit
d822c5012b
@ -7,16 +7,17 @@ import pandas as pd
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T = TypeVar("T")
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CLASSES = ("s", "h", "b", "l", "?")
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SETTLEMENTS_PREFIXES = (
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"г", "мо", "р-н", "п", "д", "гп", "c", "хутор", "массив", "тер", "СНТ", "СТ", "ДСК", "ДНП", "ДПК", "НП",
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"садоводство")
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CLASSES = ("d", "c", "t", "s", "h", "b", "l", "r", "w")
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DISTRICTS_PREFIXES = ("мо ", "р-н")
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COUNTRYSIDE_PREFIXES = (
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" г", " п", " д", " гп", " рп", " кп", " пгт", " c", "хутор", " урочище"
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"г.", "п.", "д.", "гп.", "рп.", "кп.", "пгт.", "c.")
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TERRITORY_PREFIXES =("тер.", " тер", "снт ", "ст ", "дск ", "днп ", "дпк ", "нп ", "пдк ", "т/б ", "садоводство", "массив", "хоз","сад-во","с-во")
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STREET_PREFIXES = (
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" ул", " бул", " пр", " ш", " пер", " дор", " маг", " наб", " пл", " просп", " туп", "шоссе","линия","аллея", "мост", "парк", "кольцо","проезд",
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" ул", " бул", " пр", " ш", " пер", " дор", " маг", " наб", " пл", " просп", " туп", "шоссе","линия","аллея", "мост", " парк", "кольцо","проезд", "съезд",
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"ул.", "бул.", "пр.", "ш.", "пер.", "дор.", "маг.", "наб.", "пл.", "просп.", "туп.")
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HOUSES_PREFIXES = ("д.", "уч.", "участок")
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BUILDING_PREFIXES = ("к.", "корп", 'стр.', "строение")
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HOUSES_PREFIXES = ("д.", "уч.", "участок","мкд","тп")
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BUILDING_PREFIXES = ("к.", "корп", 'стр.', "строение","корпус")
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LETTER = ("лит.", "литера"," л.")
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@ -58,46 +59,67 @@ def any_of_in(substrings: Iterable[str], string: str) -> bool:
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def flatten(arr: Iterable[List[T]]) -> List[T]:
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return sum(arr, [])
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def find_room(token: pd.Series, pre_token: pd.Series) -> str:
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if re.search(r"пом\.?", token['obj']):
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return "r"
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return ""
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def find_litera(token: pd.Series, pre_token: pd.Series) -> str:
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if any_of_in(LETTER, token['obj']) \
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or re.search(r"\d{1,3}[А-Яа-я]( |$)", token['obj']):
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if any_of_in(LETTER, token['obj'].lower()) \
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or re.search(r"\d{1,3}([А-Я]|[а-я])( |$)", token['obj']):
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return "l"
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# не работает
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if (re.search(r"\b[А-Яа-я]{1}\b", token['obj']) and "l" in pre_token['class']):
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if (re.search(r"\b([А-Я]|[а-я]){1}$", token['obj']) \
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and ("l" in pre_token['class'] or "h" in pre_token['class'])) \
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and not (" ш" in token["obj"]) \
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and not find_countryside(token,pre_token):
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return "l"
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return ""
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def find_building(token: pd.Series, pre_token: pd.Series) -> str:
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if any_of_in(BUILDING_PREFIXES, token['obj']) \
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or (re.search(r"\d", token['obj']) and "b" in pre_token['class']) \
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or re.search(r"к\.*\d", token['obj']) \
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or re.search(r"\d", token['obj']) and "b" in pre_token['class']:
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if re.search(r"\d", token['obj']):
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if any_of_in(BUILDING_PREFIXES, token['obj'].lower()) \
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or "b" in pre_token['class'] and not ("h" in token['class'])\
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or re.search(r"к\.* ?\d", token['obj']):
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return "b"
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return ""
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def find_house(token: pd.Series, pre_token: pd.Series) -> str:
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if any_of_in(HOUSES_PREFIXES, token['obj']):
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if re.search(r"\d{1,4}", token['obj']):
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if any_of_in(HOUSES_PREFIXES, token['obj'].lower()):
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return "h"
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if re.search(r"(д|д\.) ?\d{1,3} ?\/*\d* ?", token['obj']) and not ("-я" in token['obj']):
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if "h" in pre_token['class'] \
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or "s" in pre_token['class'] \
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or "s" in token['class']:
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if re.search(r"(д|д\.) ?\d{1,4} ?\/*\d* ?", token['obj']):
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return "h"
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# не работает
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if re.search(r"\d{1,3}", token['obj']) and ("s" in pre_token['class'] or "h" in pre_token['class']):
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if ("s" in pre_token['class'] or "h" in pre_token['class'] or "s" in token['class']) \
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and not any_of_in(("-я", "-й", "-Я"), token['obj'])\
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and not find_building(token,pre_token):
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return "h"
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return ""
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def find_street(token: pd.Series, pre_token: pd.Series) -> str:
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if any_of_in(STREET_PREFIXES, token['obj']) \
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or (re.search(r"[А-Я]{1}[а-я]+", token['obj']) and "s" in pre_token['class']):
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if any_of_in(STREET_PREFIXES, token['obj'].lower()) \
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or re.search(r"[А-Я]{1}[а-я]+ая", token['obj']):
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return "s"
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return ""
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def find_territory(token: pd.Series, pre_token: pd.Series) -> str:
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if any_of_in(TERRITORY_PREFIXES, token['obj'].lower()):
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return "t"
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return ""
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def find_countryside(token: pd.Series, pre_token: pd.Series) -> str:
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if any_of_in(COUNTRYSIDE_PREFIXES, token['obj'].lower()) \
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and not find_house(token,pre_token) \
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and not find_street(token,pre_token):
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return "c"
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return ""
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def find_district(token: pd.Series, pre_token: pd.Series) -> str:
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if any_of_in(DISTRICTS_PREFIXES, token['obj'].lower()):
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return "d"
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return ""
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# TODO: переработать систему из if в нормальный вид и классификация чисел/букв
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def split_address(address: str) -> List[str]:
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@ -125,43 +147,68 @@ def split_address(address: str) -> List[str]:
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pre_token = pd.Series(data=["", ""], index=['obj', 'class'])
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else:
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pre_token = tokens.iloc[i - 1]
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obj_class = find_district(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "d" in pre_token['class']:
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res.append(accumulator)
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accumulator = ""
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accumulator += cur_tk["obj"]
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obj_class = find_countryside(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "c" in pre_token['class']:
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res.append(accumulator)
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accumulator = ""
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accumulator += cur_tk["obj"]
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obj_class = find_territory(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "t" in pre_token['class']:
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res.append(accumulator)
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accumulator = ""
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accumulator +=cur_tk["obj"]
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obj_class = find_street(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "s" in tokens['class'].iloc[i - 1]:
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if "s" in pre_token['class']:
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res.append(accumulator)
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accumulator = ""
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accumulator += tokens["obj"].iloc[i]
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accumulator += cur_tk["obj"]
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obj_class = find_house(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "h" in tokens['class'].iloc[i - 1]:
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if "h" in pre_token["class"]:
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res.append(accumulator)
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num = re.findall("\d{,3}", tokens['obj'].iloc[i])[-1]
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accumulator = re.sub(r"\d{,3} ?\/*\d* ?", num,accumulator)
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num = re.findall("\d{1,4}", cur_tk['obj'])[-1]
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accumulator = re.sub(r"\d{1,4} ?\/*\d* ?", num, accumulator)
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else:
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accumulator += tokens["obj"].iloc[i]
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accumulator += cur_tk["obj"]
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obj_class = find_building(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "b" in tokens['class'].iloc[i - 1]:
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if "b" in pre_token["class"]:
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res.append(accumulator)
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num = re.findall("\d", tokens['obj'].iloc[i])[-1]
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accumulator = re.sub(r"\d$", num, accumulator)
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else:
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accumulator += tokens["obj"].iloc[i]
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accumulator += pre_token["obj"]
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obj_class = find_litera(cur_tk, pre_token)
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if obj_class:
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cur_tk["class"] += obj_class
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if "l" in tokens['class'].iloc[i - 1]:
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if "l" in pre_token["class"]:
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res.append(accumulator)
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num = re.findall("[А-яа-я]", tokens['obj'].iloc[i].strip())[-1]
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num = re.findall("[А-яа-я]", cur_tk["obj"].strip())[-1]
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accumulator = re.sub(r"[А-яа-я]$", num, accumulator)
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else:
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accumulator += tokens["obj"].iloc[i]
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accumulator += cur_tk["obj"]
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if cur_tk['class'] == "":
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cur_tk['class'] = "w"
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print(cur_tk)
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tokens.iloc[i] = cur_tk
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print(tokens.iloc[i])
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# print(cur_tk)
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return res
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return [address]
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@ -10,12 +10,16 @@ from . import (
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def pipeline(parser: Optional[LenenergoParser] = None) -> LenenergoParser:
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if parser is None:
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parser = LenenergoParser()
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parser = LenenergoParser(ndays=15)
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print(parser)
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parser.df = split_addresses(parser.df)
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for i in range(len(parser.df)):
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print(parser.df['Улица'].iloc[i])
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parser.df = concurrent_fetch_builing_ids(parser.df)
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parser.df = preprocess_df(parser.df)
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