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2761c3ed7b
Author | SHA1 | Date | |
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2761c3ed7b | |||
5692898137 | |||
44d4150910 | |||
223f25130d | |||
1a86b7bc26 |
@ -12,7 +12,7 @@ steps:
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image: python:3.11
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commands:
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- echo ${DRONE_TAG}
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- sed -i "s/VERSION_PLACEHOLDER/${DRONE_TAG}/g" pyproject.toml
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- sed -i 's/version = "[^"]*"/version = "${DRONE_TAG}"/g' pyproject.toml
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- curl -sSL https://install.python-poetry.org | python3 -
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- export PATH="/root/.local/bin:$PATH"
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- poetry --version
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@ -3,7 +3,20 @@ import re
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import numpy as np
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import pandas as pd
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RECAPITULATIF_DES_OPERATION = 1
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RECAPITULATIF_DES_OPERATIONS = 1
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DF_TYPES = {
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"Fournisseur": str,
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"RECAPITULATIF DES OPERATIONS": str,
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"Débits": float,
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"Crédits": float,
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"Dont T.V.A.": float,
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"Locatif": float,
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"Déductible": float,
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"immeuble": str,
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"mois": str,
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"annee": str,
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"lot": str,
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}
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def is_it(page_text):
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@ -17,7 +30,7 @@ def is_it(page_text):
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def get_lot(txt):
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"""Return lot number from "RECAPITULATIF DES OPERATIONS" """
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regex = r"[BSM](\d+)\s-"
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regex = r"[BSM](\d+)(?=\s*-)"
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result = re.findall(regex, txt)
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if result:
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return "{:02d}".format(int(result[0]))
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@ -27,14 +40,14 @@ def get_lot(txt):
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def keep_row(row):
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return not any(
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[
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word.lower() in row[RECAPITULATIF_DES_OPERATION].lower()
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word.lower() in row[RECAPITULATIF_DES_OPERATIONS].lower()
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for word in ["TOTAL", "TOTAUX", "Solde créditeur", "Solde débiteur"]
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]
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)
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def extract(table, additionnal_fields: dict = {}):
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"""Turn table to dictionary with additionnal fields"""
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"""Turn table to dictionary with additional fields"""
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extracted = []
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header = table[0]
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for row in table[1:]:
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@ -49,9 +62,7 @@ def extract(table, additionnal_fields: dict = {}):
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for k, v in additionnal_fields.items():
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r[k] = v
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r["lot"] = get_lot(row[RECAPITULATIF_DES_OPERATION])
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if "honoraire" in row[RECAPITULATIF_DES_OPERATION]:
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if "honoraire" in row[RECAPITULATIF_DES_OPERATIONS]:
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r["Fournisseur"] = "IMI GERANCE"
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extracted.append(r)
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@ -69,4 +80,9 @@ def table2df(tables):
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)
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df["Fournisseur"] = df["Fournisseur"].fillna(method="ffill")
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dfs.append(df)
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return pd.concat(dfs)
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df = pd.concat(dfs)
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df["immeuble"] = df["immeuble"].apply(lambda x: x[0].capitalize())
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print(df.columns)
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df["lot"] = df["RECAPITULATIF DES OPERATIONS"].apply(get_lot)
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return df.astype(DF_TYPES, errors="ignore")
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@ -1,5 +1,22 @@
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import pandas as pd
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DF_TYPES = {
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"Locataires": str,
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"Période": str,
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"Loyers": float,
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"Taxes": float,
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"Provisions": float,
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"Divers": str,
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"Total": float,
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"Réglés": float,
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"Impayés": float,
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"immeuble": str,
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"mois": str,
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"annee": str,
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"Lot": str,
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"Type": str,
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}
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def is_it(page_text):
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if "SITUATION DES LOCATAIRES" in page_text:
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@ -67,6 +84,12 @@ def parse_lot(string):
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return {"Lot": "{:02d}".format(int(words[1])), "Type": " ".join(words[2:])}
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def clean_type(string):
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if "appartement" in string.lower():
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return string[-2:]
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return string
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def join_row(table):
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joined = []
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for row in table:
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@ -116,7 +139,7 @@ def join_row(table):
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)
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joined.append(row)
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else:
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print(row)
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pass
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return joined
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@ -131,4 +154,9 @@ def flat_tables(tables):
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def table2df(tables):
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tables = flat_tables(tables)
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joined = join_row(tables)
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return pd.DataFrame.from_records(joined)
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df = pd.DataFrame.from_records(joined)
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df["immeuble"] = df["immeuble"].apply(lambda x: x[0].capitalize())
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df["Type"] = df["Type"].apply(clean_type)
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return df.astype(DF_TYPES, errors="ignore")
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@ -3,10 +3,8 @@ from logging.config import dictConfig
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from pathlib import Path
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import click
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import pandas as pd
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from .extract import extract_save
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from .join import join_excel
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logging_config = dict(
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version=1,
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@ -1,6 +1,6 @@
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[tool.poetry]
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name = "pdf-oralia"
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version = "VERSION_PLACEHOLDER"
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version = "dev"
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description = ""
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authors = ["Bertrand Benjamin <benjamin.bertrand@opytex.org>"]
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readme = "README.md"
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