add workflows

Reviewed-on: #17

.drone.yml

@@ -11,25 +11,32 @@ steps:
   - name: build-and-publish
     image: python:3.11
     commands:
+      - echo ${DRONE_TAG}
+      - sed -i 's/version = "[^"]*"/version = "${DRONE_TAG}"/g' pyproject.toml
       - curl -sSL https://install.python-poetry.org | python3 -
       - export PATH="/root/.local/bin:$PATH"
       - poetry --version
-      - poetry config pypi-token.pypi $PYPI_PASSWORD
       - poetry build
-      - poetry publish --username $PYPI_USERNAME --password $PYPI_PASSWORD
+      - poetry publish --username __token__ --password $PYPI_TOKEN
     environment:
-      PYPI_USERNAME:
-        from_secret: pypi_username
-      PYPI_PASSWORD:
-        from_secret: pypi_password
+      PYPI_TOKEN:
+        from_secret: pypi_token
 
-    when:
-      event:
-        include:
-          - tag
+  - name: Notify on matrix
+    image: plugins/matrix
+    environment:
+      MATRIX_ROOMID:
+        from_secret: MATRIX_ROOMID
+      MATRIX_ACCESSTOKEN:
+        from_secret: MATRIX_ACCESSTOKEN
+      MATRIX_USERID:
+        from_secret: MATRIX_USERID
+    settings:
+      homeserver: https://matrix.poneyworld.net
+      template: "Une nouvelle version (${DRONE_TAG}) de pdf-oralia est publiée!"
 
 # Déclencheur de la pipeline
 trigger:
   event:
-    exclude:
+    include:
       - tag

.gitea/workflows/publish_tag.yml

@@ -0,0 +1,28 @@
+name: pdf-auralia-build-and-publish
+
+on:
+  push:
+    tags:
+      - "v*"
+
+jobs:
+  build-and-publish:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+      - name: Build and publish
+        run: |
+          echo $GITHUB_REF
+          sed -i 's/version = "[^"]*"/version = "${GITHUB_REF##*/}"/g' pyproject.toml
+          curl -sSL https://install.python-poetry.org | python3 -
+          export PATH="/root/.local/bin:$PATH"
+          poetry --version
+          poetry build
+          poetry publish --username __token__ --password ${{ secrets.PYPI_TOKEN }}
+        env:
+          PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }}

README.md

@@ -0,0 +1,3 @@
+# PDF AURALIA
+
+Extraction de fichiers de comptabilité en pdf vers xlsx.

pdf_oralia/__init__.py

@@ -0,0 +1 @@
+from .extract import from_pdf

pdf_oralia/extract.py

@@ -2,13 +2,11 @@ import logging
 from datetime import datetime
 from pathlib import Path
 
-import pandas as pd
 import pdfplumber
 
-from .extract_charge import extract_charge, extract_remise_com
-from .extract_locataire import extract_situation_loc
+from pdf_oralia.pages import charge, locataire, patrimoine, recapitulatif
 
-charge_table_settings = {
+extract_table_settings = {
     "vertical_strategy": "lines",
     "horizontal_strategy": "text",
 }
@@ -27,45 +25,64 @@ def extract_date(page_text):
             return datetime.strptime(words[-1], "%d/%m/%Y")
 
 
-def extract_from_pdf(pdf, charge_dest, location_dest):
-    """Build charge_dest and location_dest xlsx file from pdf"""
+def extract_building(page_text, buildings=["bloch", "marietton", "servient"]):
+    for building in buildings:
+        if building in page_text.lower():
+            return building
+    raise ValueError("Pas d'immeuble trouvé")
+
+
+def catch_malformed_table(tables):
+    if len(tables) == 2:
+        return tables[0] + tables[1]
+    return tables[0]
+
+
+def from_pdf(pdf_file):
+    """Build dataframes one about charges and another on loc"""
+    pdf = pdfplumber.open(pdf_file)
+    recapitulatif_tables = []
     loc_tables = []
-    charge_table = []
+    charge_tables = []
+    patrimoie_tables = []
 
-    df_1st_charge = extract_remise_com(
-        pdf.pages[0].extract_table(charge_table_settings)
-    )
-
-    for page in pdf.pages[1:]:
+    for page_number, page in enumerate(pdf.pages):
         page_text = page.extract_text()
-        situation_loc_line = [
-            l for l in page_text.split("\n") if "SITUATION DES LOCATAIRES" in l
-        ]
         date = extract_date(page_text)
-        mois = date.strftime("%m")
-        annee = date.strftime("%Y")
-        if situation_loc_line:
-            # mois, annee = situation_loc_line[0].split(" ")[-2:]
-            if loc_tables:
-                loc_tables.append(page.extract_table()[1:])
-            else:
-                loc_tables.append(page.extract_table())
+        additionnal_fields = {
+            "immeuble": extract_building(page_text),
+            "mois": date.strftime("%m"),
+            "annee": date.strftime("%Y"),
+        }
 
-        elif "RECAPITULATIF DES OPERATIONS" in page_text:
-            if charge_table:
-                charge_table += page.extract_table(charge_table_settings)[1:]
-            else:
-                charge_table = page.extract_table(charge_table_settings)
+        if recapitulatif.is_it(page_text):
+            table = page.extract_tables()[0]
+            extracted = recapitulatif.extract(table, additionnal_fields)
+            if extracted:
+                recapitulatif_tables.append(extracted)
 
-    df_charge = extract_charge(charge_table)
-    df_charge_with_1st = pd.concat([df_1st_charge, df_charge])
-    df_charge_with_1st.to_excel(charge_dest, sheet_name="Charges", index=False)
-    logging.info(f"{charge_dest} saved")
+        elif locataire.is_it(page_text):
+            tables = page.extract_tables(extract_table_settings)[1:]
+            table = catch_malformed_table(tables)
+            extracted = locataire.extract(table, additionnal_fields)
+            loc_tables.append(extracted)
 
-    df_loc = extract_situation_loc(loc_tables, mois=mois, annee=annee)
-    df_loc = df_loc.assign()
-    df_loc.to_excel(location_dest, sheet_name="Location", index=False)
-    logging.info(f"{location_dest} saved")
+        elif charge.is_it(page_text):
+            tables = page.extract_tables(extract_table_settings)[1:]
+            table = catch_malformed_table(tables)
+            extracted = charge.extract(table, additionnal_fields)
+            charge_tables.append(extracted)
+
+        elif patrimoine.is_it(page_text):
+            pass
+
+        else:
+            logging.warning(f"Page {page_number+1} non reconnu. Page ignorée.")
+
+    df_charge = charge.table2df(recapitulatif_tables + charge_tables)
+    df_loc = locataire.table2df(loc_tables)
+
+    return df_charge, df_loc
 
 
 def extract_save(pdf_file, dest):
@@ -74,5 +91,9 @@ def extract_save(pdf_file, dest):
     xls_charge = Path(dest) / f"{pdf_file.stem.replace(' ', '_')}_charge.xlsx"
     xls_locataire = Path(dest) / f"{pdf_file.stem.replace(' ', '_')}_locataire.xlsx"
 
-    pdf = pdfplumber.open(pdf_file)
-    extract_from_pdf(pdf, xls_charge, xls_locataire)
+    df_charge, df_loc = from_pdf(pdf_file)
+
+    df_charge.to_excel(xls_charge, sheet_name="Charges", index=False)
+    logging.info(f"{xls_charge} saved")
+    df_loc.to_excel(xls_locataire, sheet_name="Location", index=False)
+    logging.info(f"{xls_locataire} saved")

pdf_oralia/extract_charge.py

@@ -1,68 +0,0 @@
-import logging
-
-import numpy as np
-import pandas as pd
-
-
-def get_lot(x):
-    """Return lot number from "RECAPITULATIF DES OPERATIONS" """
-    if x[:2].isdigit():
-        return x[:2]
-    if x[:1].isdigit():
-        return "0" + x[:1]
-    if x[:2] == "PC":
-        return "PC"
-    return ""
-
-
-def extract_charge(table):
-    """From pdfplumber table extract the charge dataframe"""
-    df = (
-        pd.DataFrame(table[1:], columns=table[0])
-        .replace("", np.nan)
-        .dropna(subset=["Débits", "Crédits"], how="all")
-    )
-
-    drop_index = df[
-        df["RECAPITULATIF DES OPERATIONS"].str.contains("TOTAUX", case=False)
-        | df["RECAPITULATIF DES OPERATIONS"].str.contains("Solde créditeur", case=False)
-        | df["RECAPITULATIF DES OPERATIONS"].str.contains("Solde débiteur", case=False)
-        | df["RECAPITULATIF DES OPERATIONS"].str.contains(
-            "Total des reglements locataires", case=False
-        )
-    ].index
-    df.drop(drop_index, inplace=True)
-
-    df[""].mask(
-        df["RECAPITULATIF DES OPERATIONS"].str.contains("honoraires", case=False),
-        "IMI GERANCE",
-        inplace=True,
-    )
-
-    df = df.assign(lot=df["RECAPITULATIF DES OPERATIONS"].map(get_lot))
-
-    df = df.astype(
-        {
-            "Débits": "float64",
-            "Crédits": "float64",
-            "Dont T.V.A.": "float64",
-            "Locatif": "float64",
-            "Déductible": "float64",
-        }
-    )
-
-    df.columns.values[0] = "Fournisseur"
-    return df
-
-
-def extract_remise_com(table):
-    """Extract "remise commercial" from first page"""
-    df = pd.DataFrame(table[1:], columns=table[0]).replace("", np.nan)
-    df = df[
-        df["RECAPITULATIF DES OPERATIONS"].str.contains(
-            "Remise commerciale gérance", case=False, na=False
-        )
-    ]
-
-    df.columns.values[0] = "Fournisseur"
-    return df

pdf_oralia/extract_locataire.py

@@ -1,81 +0,0 @@
-import logging
-
-import pandas as pd
-
-
-def parse_above_loc(content):
-    row = {}
-    app, loc, *_ = content.split("\n")
-    app_ = app.split(" ")
-    row["lot"] = f"{int(app_[1]):02d}"
-    row["type"] = " ".join(app_[2:])
-    row["locataire"] = loc
-    return pd.Series(row)
-
-
-def join_row(last, next):
-    row = []
-    for i in range(len(last)):
-        if last[i] and next[i]:
-            row.append(f"{last[i]}\n{next[i]}")
-        elif last[i]:
-            row.append(last[i])
-        elif next[i]:
-            row.append(next[i])
-        else:
-            row.append("")
-    return row
-
-
-def join_tables(tables):
-
-    joined = tables[0]
-
-    for t in tables[1:]:
-        last_row = joined[-1]
-        if "Totaux" not in last_row[0]:
-            first_row = t[0]
-            joined_row = join_row(last_row, first_row)
-            joined = joined[:-1] + [joined_row] + t[1:]
-        else:
-            joined += t
-
-    return joined
-
-
-def extract_situation_loc(tables, mois, annee):
-    """From pdfplumber table extract locataire df"""
-    table = join_tables(tables)
-    try:
-        df = pd.DataFrame(table[1:], columns=table[0])
-    except IndexError:
-        print(table)
-    rows = []
-    for i, row in df[df["Locataires"] == "Totaux"].iterrows():
-        above_row_loc = df.iloc[i - 1]["Locataires"]
-        up_row = pd.concat(
-            [
-                row,
-                parse_above_loc(above_row_loc),
-            ]
-        )
-
-        rows.append(up_row)
-    df_cleaned = pd.concat(rows, axis=1).T
-    df_cleaned.drop(["Locataires", "", "Période"], axis=1, inplace=True)
-
-    df_cleaned = df_cleaned.astype(
-        {
-            "Loyers": "float64",
-            "Taxes": "float64",
-            "Provisions": "float64",
-            "Divers": "float64",
-            "Total": "float64",
-            "Réglés": "float64",
-            "Impayés": "float64",
-        },
-        errors="ignore",
-    )
-
-    df_cleaned = df_cleaned.assign(mois=mois, annee=annee)
-    return df_cleaned

pdf_oralia/join.py

@@ -1,30 +1,29 @@
+import glob
 import logging
-from pathlib import Path
 
 import pandas as pd
 
 
-def extract_excel_to_dfs(directory, df_names=["charge", "locataire"]):
-    p = Path(directory)
-    dfs = {name: [] for name in df_names}
+def join_excel(src, dest, file_pattern):
+    """Join every excel file in arc respecting file_pattern into on unique file in dist"""
+    filenames = list_files(src, file_pattern)
+    logging.debug(f"Concatenate {filenames}")
+    dfs = extract_dfs(filenames)
+    joined_df = pd.concat(dfs)
+    logging.debug(f"Writing joined excel to {dest}")
+    joined_df.to_excel(dest, index=False)
+    logging.debug(f"with {len(joined_df)} rows")
 
-    for file in p.glob("*.xlsx"):
-        year, month, immeuble, table = file.stem.split("_")
-        df = pd.read_excel(file, dtype={"lot": str}).assign(
-            annee=year, mois=month, immeuble=immeuble[:3]
-        )
-        dfs[table].append(df)
 
+def list_files(src, file_glob):
+    return list(glob.iglob(f"{src}/{file_glob}"))
+
+
+def extract_dfs(filenames):
+    dfs = []
+    for filename in filenames:
+        logging.debug(f"Extracting {filename}")
+        df = pd.read_excel(filename)
+        logging.debug(f"Found {len(df)} rows")
+        dfs.append(df)
     return dfs
-
-
-def join_excel(directory, dest, df_names=["charge", "locataire"]):
-    dfs = extract_excel_to_dfs(directory, df_names)
-    destinations = {}
-    for tablename, datas in dfs.items():
-        df = pd.concat(datas)
-        destination = Path(dest) / f"{tablename}.xlsx"
-        df.to_excel(destination, index=False)
-        destinations[tablename] = destination
-        logging.info(f"{destination} written")
-    return destinations

pdf_oralia/pages/__init__.py

@@ -0,0 +1 @@
+from . import charge, locataire, patrimoine, recapitulatif

pdf_oralia/pages/charge.py

@@ -0,0 +1,91 @@
+import re
+
+import numpy as np
+import pandas as pd
+
+RECAPITULATIF_DES_OPERATIONS = 1
+DF_TYPES = {
+    "Fournisseur": str,
+    "RECAPITULATIF DES OPERATIONS": str,
+    "Débits": float,
+    "Crédits": float,
+    "Dont T.V.A.": float,
+    "Locatif": float,
+    "Déductible": float,
+    "immeuble": str,
+    "mois": str,
+    "annee": str,
+    "lot": str,
+}
+DEFAULT_FOURNISSEUR = "ROSIER MODICA MOTTEROZ SA"
+
+
+def is_it(page_text):
+    if (
+        "RECAPITULATIF DES OPERATIONS" in page_text
+        and "COMPTE RENDU DE GESTION" not in page_text
+    ):
+        return True
+    return False
+
+
+def get_lot(txt):
+    """Return lot number from "RECAPITULATIF DES OPERATIONS" """
+    regex = r"[BSM](\d+)(?=\s*-)"
+    try:
+        result = re.findall(regex, txt)
+    except TypeError:
+        return "*"
+    if result:
+        return "{:02d}".format(int(result[0]))
+    return "*"
+
+
+def keep_row(row):
+    return not any(
+        [
+            word.lower() in row[RECAPITULATIF_DES_OPERATIONS].lower()
+            for word in ["TOTAL", "TOTAUX", "Solde créditeur", "Solde débiteur"]
+        ]
+    )
+
+
+def extract(table, additionnal_fields: dict = {}):
+    """Turn table to dictionary with additional fields"""
+    extracted = []
+    header = table[0]
+    for row in table[1:]:
+        if keep_row(row):
+            r = dict()
+            for i, value in enumerate(row):
+                if header[i] == "":
+                    r["Fournisseur"] = value
+                else:
+                    r[header[i]] = value
+
+            for k, v in additionnal_fields.items():
+                r[k] = v
+
+            if "honoraire" in row[RECAPITULATIF_DES_OPERATIONS].lower():
+                r["Fournisseur"] = DEFAULT_FOURNISSEUR
+
+            extracted.append(r)
+
+    return extracted
+
+
+def table2df(tables):
+    dfs = []
+    for table in tables:
+        df = (
+            pd.DataFrame.from_records(table)
+            .replace("", np.nan)
+            .dropna(subset=["Débits", "Crédits"], how="all")
+        )
+        df["Fournisseur"] = df["Fournisseur"].fillna(method="ffill")
+        dfs.append(df)
+    df = pd.concat(dfs)
+
+    df["immeuble"] = df["immeuble"].apply(lambda x: x[0].capitalize())
+    df["lot"] = df["RECAPITULATIF DES OPERATIONS"].apply(get_lot)
+    return df.astype(DF_TYPES)

pdf_oralia/pages/locataire.py

@@ -0,0 +1,166 @@
+import numpy as np
+import pandas as pd
+
+DF_TYPES = {
+    "Locataires": str,
+    "Période": str,
+    "Loyers": float,
+    "Taxes": float,
+    "Provisions": float,
+    "Divers": str,
+    "Total": float,
+    "Réglés": float,
+    "Impayés": float,
+    "immeuble": str,
+    "mois": str,
+    "annee": str,
+    "Lot": str,
+    "Type": str,
+}
+
+
+def is_it(page_text):
+    if "SITUATION DES LOCATAIRES" in page_text:
+        return True
+    return False
+
+
+def is_drop(row):
+    if "totaux" in row[0].lower():
+        return True
+    if not any(row):
+        return True
+    return False
+
+
+def extract(table, additionnal_fields: dict = {}):
+    """Turn table to dictionary with additional fields"""
+    extracted = []
+    header = table[0]
+    for row in table[1:]:
+        if not is_drop(row):
+            r = dict()
+            for i, value in enumerate(row):
+                if header[i] != "":
+                    r[header[i]] = value
+            for k, v in additionnal_fields.items():
+                r[k] = v
+            extracted.append(r)
+    return extracted
+
+
+def join_row(last, next):
+    row = {}
+    for key in last:
+        if last[key] == next[key]:
+            row[key] = last[key]
+        elif last[key] and next[key]:
+            row[key] = f"{last[key]}\n{next[key]}"
+        elif last[key]:
+            row[key] = last[key]
+        elif next[key]:
+            row[key] = next[key]
+        else:
+            row[key] = ""
+    return row
+
+
+def join_tables(tables):
+    joined = tables[0]
+
+    for t in tables[1:]:
+        last_row = joined[-1]
+        if "totaux" not in last_row["Locataires"].lower():
+            first_row = t[0]
+            joined_row = join_row(last_row, first_row)
+            joined = joined[:-1] + [joined_row] + t[1:]
+        else:
+            joined += t
+
+    return joined
+
+
+def parse_lot(string):
+    words = string.split(" ")
+    return {"Lot": "{:02d}".format(int(words[1])), "Type": " ".join(words[2:])}
+
+
+def clean_type(string):
+    if "appartement" in string.lower():
+        return string[-2:]
+    return string
+
+
+def join_row(table):
+    joined = []
+    for row in table:
+        if row["Locataires"].startswith("Lot"):
+            row.update(parse_lot(row["Locataires"]))
+            row["Locataires"] = ""
+            joined.append(row)
+        elif row["Locataires"] == "Rappel de Loyer":
+            last_row = joined[-1]
+            row.update(
+                {
+                    "Lot": last_row["Lot"],
+                    "Type": last_row["Type"],
+                    "Locataires": last_row["Locataires"],
+                    "Divers": "Rappel de Loyer",
+                }
+            )
+            joined.append(row)
+
+        elif row["Locataires"]:
+            last_row = joined.pop()
+            row_name = row["Locataires"].replace("\n", " ")
+            row.update({k: v for k, v in last_row.items() if v})
+            row["Locataires"] = last_row["Locataires"] + " " + row_name
+            joined.append(row)
+
+        else:
+            if row["Période"].startswith("Solde"):
+                last_row = joined.pop()
+                row.update(
+                    {
+                        "Lot": last_row["Lot"],
+                        "Type": last_row["Type"],
+                        "Locataires": last_row["Locataires"],
+                    }
+                )
+                joined.append(row)
+
+            elif row["Période"].startswith("Du"):
+                last_row = joined[-1]
+                row.update(
+                    {
+                        "Lot": last_row["Lot"],
+                        "Type": last_row["Type"],
+                        "Locataires": last_row["Locataires"],
+                    }
+                )
+                joined.append(row)
+
+    return joined
+
+
+def flat_tables(tables):
+    tables_flat = []
+    for table in tables:
+        tables_flat.extend(table)
+    return tables_flat
+
+
+def table2df(tables):
+    tables = flat_tables(tables)
+    joined = join_row(tables)
+    df = pd.DataFrame.from_records(joined)
+
+    df["immeuble"] = df["immeuble"].apply(lambda x: x[0].capitalize())
+    df["Type"] = df["Type"].apply(clean_type)
+
+    numeric_cols = [k for k, v in DF_TYPES.items() if v == float]
+    df[numeric_cols] = df[numeric_cols].replace("", np.nan)
+
+    df = df.drop(df[(df["Locataires"] == "") & (df["Période"] == "")].index)
+
+    return df.astype(DF_TYPES)

pdf_oralia/pages/patrimoine.py

@@ -0,0 +1,4 @@
+def is_it(page_text):
+    if "VOTRE PATRIMOINE" in page_text:
+        return True
+    return False

pdf_oralia/pages/recapitulatif.py

@@ -0,0 +1,34 @@
+import numpy as np
+import pandas as pd
+
+
+def is_it(page_text):
+    if "COMPTE RENDU DE GESTION" in page_text:
+        return True
+    return False
+
+
+def extract(table, additionnal_fields: dict = {}):
+    """Extract "remise commercial" from first page"""
+    extracted = []
+    header = table[0]
+    for row in table[1:]:
+        if "Remise commerciale gérance" in row:
+            r = dict()
+            for i, value in enumerate(row):
+                r[header[i]] = value
+            for k, v in additionnal_fields.items():
+                r[k] = v
+            extracted.append(r)
+
+    return extracted
+
+    # df = pd.DataFrame(table[1:], columns=table[0]).replace("", np.nan)
+    # df = df[
+    #     df["RECAPITULATIF DES OPERATIONS"].str.contains(
+    #         "Remise commerciale gérance", case=False, na=False
+    #     )
+    # ]
+    #
+    # df.columns.values[0] = "Fournisseur"
+    # return df

pdf_oralia/scripts.py

@@ -3,33 +3,35 @@ from logging.config import dictConfig
 from pathlib import Path
 
 import click
-import pandas as pd
 
 from .extract import extract_save
 from .join import join_excel
 
-logging_config = dict(
-    version=1,
-    formatters={"f": {"format": "%(levelname)-8s %(name)-12s %(message)s"}},
-    handlers={
-        "h": {
-            "class": "logging.StreamHandler",
-            "formatter": "f",
-            "level": logging.DEBUG,
-        }
-    },
-    root={
-        "handlers": ["h"],
-        "level": logging.DEBUG,
-    },
-)
-
-dictConfig(logging_config)
-
 
 @click.group()
-def main():
-    pass
+@click.option("--debug/--no-debug", default=False)
+def main(debug):
+    if debug:
+        logging_level = logging.DEBUG
+    else:
+        logging_level = logging.INFO
+    logging_config = dict(
+        version=1,
+        formatters={"f": {"format": "%(levelname)-8s %(name)-12s %(message)s"}},
+        handlers={
+            "h": {
+                "class": "logging.StreamHandler",
+                "formatter": "f",
+                "level": logging_level,
+            }
+        },
+        root={
+            "handlers": ["h"],
+            "level": logging_level,
+        },
+    )
+
+    dictConfig(logging_config)
 
 
 @main.group()
@@ -66,5 +68,31 @@ def all(src, dest):
 @main.command()
 @click.option("--src", help="Tous les fichiers dans src", default="./")
 @click.option("--dest", help="Où mettre les fichiers produits", default="")
-def join(src, dest):
-    join_excel(src, dest, df_names=["charge", "locataire"])
+@click.option(
+    "--force",
+    help="Ecraser si le ficher destination existe.",
+    default=False,
+    is_flag=True,
+)
+def join(src, dest, force):
+    """Join tous les fichiers excel charge (resp locataire) de src dans un seul fichier charge.xlsx dans dist.
+
+    Exemple:
+
+        pdf-oralia join --src <dossier_source> --dest <dossier_destination>
+
+
+    """
+    dest_charge = f"{dest}/charge.xlsx"
+    if not force and Path(dest_charge).exists():
+        raise ValueError(f"The file {dest_charge} already exists")
+    dest_locataire = f"{dest}/locataire.xlsx"
+    if not force and Path(dest_locataire).exists():
+        raise ValueError(f"The file {dest_locataire} already exists")
+
+    if not Path(src).exists():
+        raise ValueError(f"The source directory ({src}) does not exists.")
+    join_excel(src, dest_charge, "*_charge.xlsx")
+    logging.info(f"Les données charges ont été concaténées dans {dest_charge}")
+    join_excel(src, dest_locataire, "*_locataire.xlsx")
+    logging.info(f"Les données locataires ont été concaténées dans {dest_locataire}")

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pdf-oralia"
-version = "0.2.6"
+version = "0"
 description = ""
 authors = ["Bertrand Benjamin <benjamin.bertrand@opytex.org>"]
 readme = "README.md"
@@ -13,13 +13,14 @@ pdf-oralia = "pdf_oralia.scripts:main"
 python = "^3.10"
 click = "^8.1.3"
 pdfplumber = "^0.7.4"
-pandas = "^1.5.0"
+pandas = "^2.2.3"
 openpyxl = "^3.0.10"
 
 
 [tool.poetry.group.dev.dependencies]
 pre-commit = "^2.20.0"
 jupyter = "^1.0.0"
+tabulate = "^0.9.0"
 
 [build-system]
 requires = ["poetry-core"]

renovate.json

@@ -0,0 +1,2 @@
+{
+}

requirements.txt

@@ -1,76 +1,3 @@
-argon2-cffi==21.3.0
-argon2-cffi-bindings==21.2.0
-asttokens==2.0.8
-attrs==22.1.0
-backcall==0.2.0
-beautifulsoup4==4.11.1
-bleach==5.0.1
-cffi==1.15.1
-charset-normalizer==2.1.1
-cryptography==38.0.1
-debugpy==1.6.3
-decorator==5.1.1
-defusedxml==0.7.1
-entrypoints==0.4
-et-xmlfile==1.1.0
-executing==1.1.0
-fastjsonschema==2.16.2
-ipykernel==6.16.0
-ipython==8.5.0
-ipython-genutils==0.2.0
-ipywidgets==8.0.2
-jedi==0.18.1
-Jinja2==3.1.2
-jsonschema==4.16.0
-jupyter==1.0.0
-jupyter-console==6.4.4
-jupyter-core==4.11.1
-jupyter_client==7.3.5
-jupyterlab-pygments==0.2.2
-jupyterlab-widgets==3.0.3
-lxml==4.9.1
-MarkupSafe==2.1.1
-matplotlib-inline==0.1.6
-mistune==2.0.4
-nbclient==0.6.8
-nbconvert==7.0.0
-nbformat==5.6.1
-nest-asyncio==1.5.5
-notebook==6.4.12
-numpy==1.23.3
-openpyxl==3.0.10
-packaging==21.3
-pandas==1.5.0
-pandocfilters==1.5.0
-parso==0.8.3
-pdfminer.six==20220524
-pdfplumber==0.7.4
-pexpect==4.8.0
-pickleshare==0.7.5
-Pillow==9.2.0
-prometheus-client==0.14.1
-prompt-toolkit==3.0.31
-psutil==5.9.2
-ptyprocess==0.7.0
-pure-eval==0.2.2
-pycparser==2.21
-Pygments==2.13.0
-pyparsing==3.0.9
-pyrsistent==0.18.1
-python-dateutil==2.8.2
-pytz==2022.2.1
-pyzmq==24.0.1
-qtconsole==5.3.2
-QtPy==2.2.0
-Send2Trash==1.8.0
-six==1.16.0
-soupsieve==2.3.2.post1
-stack-data==0.5.1
-terminado==0.15.0
-tinycss2==1.1.1
-tornado==6.2
-traitlets==5.4.0
-Wand==0.6.10
-wcwidth==0.2.5
-webencodings==0.5.1
-widgetsnbextension==4.0.3
+pdfplumber
+numpy
+pandas