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 subprocess
import typing

import pandas as pd
from google.cloud import storage


def main(
    source_url: str,
    source_file: pathlib.Path,
    column_name: str,
    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"World Bank Intl Education {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 {source_url}")
    download_file(source_url, source_file)

    logging.info(f"Opening file {source_file}")
    df = pd.read_csv(source_file, skip_blank_lines=True)

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

    logging.info(f"Transform: Dropping column {column_name} ...")
    delete_column(df, column_name)

    logging.info(f"Transform: Renaming columns for {pipeline_name} ...")
    rename_headers(df, rename_mappings)

    if pipeline_name == "country_summary":
        logging.info("Transform: converting to integer ... ")
        columns = [
            "latest_industrial_data",
            "latest_trade_data",
            "national_accounts_reference_year",
        ]
        convert_to_integer_string(df, columns)
    else:
        df = df

    logging.info(f"Transform: Reordering headers for {pipeline_name} ...")
    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"World Bank Intl Education {pipeline_name} process completed at "
        + str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
    )


def download_file(source_url: str, source_file: pathlib.Path) -> None:
    subprocess.check_call(["gsutil", "cp", f"{source_url}", f"{source_file}"])


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


def delete_column(df: pd.DataFrame, column_name: str) -> None:
    df = df.drop(column_name, axis=1, inplace=True)


def save_to_new_file(df: pd.DataFrame, file_path: str) -> None:
    df.to_csv(file_path, index=False)


def change_to_integer_string(input: typing.Union[str, float]) -> str:
    str_val = ""
    if not input or (math.isnan(input)):
        str_val = ""
    else:
        str_val = str(int(round(input, 0)))
    return str_val


def convert_to_integer_string(df: pd.DataFrame, columns: typing.List[str]):
    for col in columns:
        df[col] = df[col].apply(change_to_integer_string)


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(),
        column_name=os.environ["COLUMN_TO_REMOVE"],
        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"],
    )