# MIT License # # Copyright (c) 2024 dataforgood # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # Standard imports import glob import uuid from pathlib import Path # External imports import pandas as pd class FromCSV: def __init__(self, csv_directory: str) -> None: self.csv_directory = csv_directory def __call__(self, pdf_filepath: str) -> dict: """ Returns asset that contain: """ # Load the tables from matching csv files # Given a report /path/to/{company_name}_{year}*.pdf # Tables are searched in /csv_directory/{company_name}_{year}*.csv report_basename = "_".join(Path(pdf_filepath).stem.split("_")[0:2]) tables_files = glob.glob(f"{self.csv_directory}/{report_basename}*.csv") tables_list = [pd.read_csv(f) for f in tables_files] # Create asset new_asset = { "id": uuid.uuid4(), "type": "from_csv", "params": {"csv_directory": self.csv_directory}, "tables": tables_list, } return new_asset