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csv_transform.py
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csv_transform.py
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# 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 typing
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"Austin bikeshare {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}...")
df = pd.read_csv(str(source_file))
logging.info(f"Transforming {source_file}... ")
logging.info(f"Transform: Rename columns.. {source_file}")
rename_headers(df, rename_mappings)
logging.info(f"Transform: Converting date values for {source_file}... ")
df["modified_date"] = df["modified_date"].apply(convert_dt_format)
logging.info(f"Transform: filtering rows for {source_file}... ")
filter_null_rows(df)
logging.info(f"Transform: converting to integer {source_file}... ")
df["city_asset_number"] = df["city_asset_number"].apply(convert_to_integer_string)
df["number_of_docks"] = df["number_of_docks"].apply(convert_to_integer_string)
df["footprint_length"] = df["footprint_length"].apply(convert_to_integer_string)
df["council_district"] = df["council_district"].apply(convert_to_integer_string)
logging.info(f"Transform: removing NaN values {source_file}... ")
df["footprint_width"] = df["footprint_width"].apply(resolve_nan)
logging.info("Transform: Reordering headers..")
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"Austin bikeshare {pipeline_name} process completed at "
+ str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
)
def resolve_nan(input: typing.Union[str, float]) -> str:
str_val = ""
# if input == "" or (math.isnan(input)):
if not input or (math.isnan(input)):
str_val = ""
else:
str_val = str(input)
return str_val.replace("None", "")
def convert_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 rename_headers(df: pd.DataFrame, rename_mappings: dict) -> None:
df.rename(columns=rename_mappings, inplace=True)
def convert_dt_format(dt_str: str) -> str:
# Old format: MM/dd/yyyy hh:mm:ss aa
# New format: yyyy-MM-dd HH:mm:ss
if not dt_str:
return dt_str
else:
return datetime.datetime.strptime(dt_str, "%m/%d/%Y %H:%M:%S %p").strftime(
"%Y-%m-%d %H:%M:%S"
)
def filter_null_rows(df: pd.DataFrame) -> None:
df = df[df.station_id != ""]
def save_to_new_file(df: pd.DataFrame, file_path: str) -> None:
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 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"],
)