69 lines
1.8 KiB
Python
69 lines
1.8 KiB
Python
import logging
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import numpy as np
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import pandas as pd
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def get_lot(x):
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"""Return lot number from "RECAPITULATIF DES OPERATIONS" """
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if x[:2].isdigit():
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return x[:2]
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if x[:1].isdigit():
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return "0" + x[:1]
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if x[:2] == "PC":
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return "PC"
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return ""
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def extract_charge(table):
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"""From pdfplumber table extract the charge dataframe"""
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df = (
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pd.DataFrame(table[1:], columns=table[0])
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.replace("", np.nan)
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.dropna(subset=["Débits", "Crédits"], how="all")
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)
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drop_index = df[
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df["RECAPITULATIF DES OPERATIONS"].str.contains("TOTAUX", case=False)
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| df["RECAPITULATIF DES OPERATIONS"].str.contains("Solde créditeur", case=False)
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| df["RECAPITULATIF DES OPERATIONS"].str.contains("Solde débiteur", case=False)
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| df["RECAPITULATIF DES OPERATIONS"].str.contains(
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"Total des reglements locataires", case=False
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)
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].index
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df.drop(drop_index, inplace=True)
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df[""].mask(
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df["RECAPITULATIF DES OPERATIONS"].str.contains("honoraires", case=False),
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"IMI GERANCE",
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inplace=True,
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)
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df = df.assign(lot=df["RECAPITULATIF DES OPERATIONS"].map(get_lot))
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df = df.astype(
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{
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"Débits": "float64",
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"Crédits": "float64",
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"Dont T.V.A.": "float64",
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"Locatif": "float64",
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"Déductible": "float64",
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}
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)
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df.columns.values[0] = "Fournisseur"
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return df
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def extract_remise_com(table):
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"""Extract "remise commercial" from first page"""
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df = pd.DataFrame(table[1:], columns=table[0]).replace("", np.nan)
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df = df[
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df["RECAPITULATIF DES OPERATIONS"].str.contains(
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"Remise commerciale gérance", case=False, na=False
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)
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]
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df.columns.values[0] = "Fournisseur"
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return df
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