csv_transform.py

# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import datetime
import json
import logging
import math
import os
import pathlib
import re
import typing
from urllib.parse import urlparse

import pandas as pd
import requests
from google.cloud import storage


def main(
    source_url: str,
    source_file: pathlib.Path,
    target_file: pathlib.Path,
    target_gcs_bucket: str,
    target_gcs_path: str,
    headers: typing.List[str],
    rename_mappings: dict,
    pipeline_name: str,
) -> None:

    logging.info(
        f"irs 990 {pipeline_name} process started at "
        + str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
    )

    logging.info("creating 'files' folder")
    pathlib.Path("./files").mkdir(parents=True, exist_ok=True)

    logging.info(f"Downloading file from {source_url}... ")
    download_file(source_url, source_file)

    logging.info(f"Opening file {source_file} ... ")
    str_value = os.path.basename(urlparse(source_url).path)

    if re.search("zip", str_value):
        df = pd.read_csv(
            str(source_file), compression="zip", encoding="utf-8", sep=r"\s+"
        )
    else:
        df = pd.read_csv(str(source_file), encoding="utf-8", sep=r"\s+")

    logging.info(f"Transforming {source_file} ...")

    logging.info(f"Transform: Rename columns {source_file} ...")
    rename_headers(df, rename_mappings)

    logging.info(f"Transform: filtering null values {source_file} ...")
    filter_null_rows(df)

    logging.info(f"Transform: converting to integer {source_file} ...")

    if re.search("pf", pipeline_name):
        df.invstexcisetx = df.invstexcisetx.replace("N", 0)
        df.crelamt = df.crelamt.replace("N", 0)
        df.dvdndsinte = df.dvdndsinte.replace("N", 0)
        df.intrstrvnue = df.intrstrvnue.replace("N", 0)
    else:
        df["totsupp509"] = df["totsupp509"].apply(convert_to_int)

    logging.info(
        f"Transform: Reordering headers for {os.path.basename(urlparse(source_url).path)} ..."
    )

    df = df[headers]

    logging.info(f"Saving to output file {target_file} ...")
    try:
        save_to_new_file(df, file_path=str(target_file))
    except Exception as e:
        logging.error(f"Error saving output file: {e}.")

    logging.info(
        f"Uploading output file to.. gs://{target_gcs_bucket}/{target_gcs_path}"
    )
    upload_file_to_gcs(target_file, target_gcs_bucket, target_gcs_path)

    logging.info(
        f"irs 990 {pipeline_name} process completed at "
        + str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
    )


def rename_headers(df: pd.DataFrame, rename_mappings: dict) -> None:
    df = df.rename(columns=rename_mappings, inplace=True)


def filter_null_rows(df: pd.DataFrame) -> None:
    df = df[df.ein != ""]


def save_to_new_file(df: pd.DataFrame, file_path: pathlib.Path) -> None:
    # df.export_csv(file_path)
    df.to_csv(file_path, index=False)


def download_file(source_url: str, source_file: pathlib.Path) -> None:
    logging.info(f"Downloading {source_url} into {source_file}")
    r = requests.get(source_url, stream=True)
    if r.status_code == 200:
        with open(source_file, "wb") as f:
            for chunk in r:
                f.write(chunk)
    else:
        logging.error(f"Couldn't download {source_url}: {r.text}")


def convert_to_int(input: str) -> str:
    str_val = ""
    if input == "" or (math.isnan(input)):
        str_val = ""
    else:
        str_val = str(int(round(input, 0)))
    return str_val


def upload_file_to_gcs(file_path: pathlib.Path, gcs_bucket: str, gcs_path: str) -> None:
    storage_client = storage.Client()
    bucket = storage_client.bucket(gcs_bucket)
    blob = bucket.blob(gcs_path)
    blob.upload_from_filename(file_path)


if __name__ == "__main__":
    logging.getLogger().setLevel(logging.INFO)

    main(
        source_url=os.environ["SOURCE_URL"],
        source_file=pathlib.Path(os.environ["SOURCE_FILE"]).expanduser(),
        target_file=pathlib.Path(os.environ["TARGET_FILE"]).expanduser(),
        target_gcs_bucket=os.environ["TARGET_GCS_BUCKET"],
        target_gcs_path=os.environ["TARGET_GCS_PATH"],
        headers=json.loads(os.environ["CSV_HEADERS"]),
        rename_mappings=json.loads(os.environ["RENAME_MAPPINGS"]),
        pipeline_name=os.environ["PIPELINE_NAME"],
    )