/
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 json
import logging
import os
import pathlib
import pandas as pd
import requests
from google.cloud import storage
def main(
source_url_json: str,
source_file: pathlib.Path,
target_file: pathlib.Path,
chunksize: str,
target_gcs_bucket: str,
target_gcs_path: str,
) -> None:
logging.info("San Francisco - Bikeshare Status process started")
pathlib.Path("./files").mkdir(parents=True, exist_ok=True)
logging.info(f"Extracting URL for status: {source_url_json}")
source_file_status_json = str(source_file).replace(".csv", "") + "_status.json"
logging.info(f"Downloading states json file {source_url_json}")
download_file_json(source_url_json, source_file_status_json, source_file)
chunksz = int(chunksize)
logging.info(f"Opening batch file {source_file}")
with pd.read_csv(
source_file, # path to main source file to load in batches
engine="python",
encoding="utf-8",
quotechar='"', # string separator, typically double-quotes
chunksize=chunksz, # size of batch data, in no. of records
sep=",", # data column separator, typically ","
) as reader:
for chunk_number, chunk in enumerate(reader):
target_file_batch = str(target_file).replace(
".csv", "-" + str(chunk_number) + ".csv"
)
df = pd.DataFrame()
df = pd.concat([df, chunk])
process_chunk(df, target_file_batch, target_file, (not chunk_number == 0))
upload_file_to_gcs(target_file, target_gcs_bucket, target_gcs_path)
logging.info("San Francisco - Bikeshare Status process completed")
def process_chunk(
df: pd.DataFrame, target_file_batch: str, target_file: str, skip_header: bool
) -> None:
df = rename_headers(df)
df = filter_empty_data(df)
df = reorder_headers(df)
save_to_new_file(df, file_path=str(target_file_batch))
append_batch_file(target_file_batch, target_file, skip_header, not (skip_header))
def rename_headers(df: pd.DataFrame) -> None:
header_names = {
"data.stations.eightd_has_available_keys": "eightd_has_available_keys",
"data.stations.is_installed": "is_installed",
"data.stations.is_renting": "is_renting",
"data.stations.is_returning": "is_returning",
"data.stations.last_reported": "last_reported",
"data.stations.num_bikes_available": "num_bikes_available",
"data.stations.num_bikes_disabled": "num_bikes_disabled",
"data.stations.num_docks_available": "num_docks_available",
"data.stations.num_docks_disabled": "num_docks_disabled",
"data.stations.num_ebikes_available": "num_ebikes_available",
"data.stations.station_id": "station_id",
}
df.rename(columns=header_names, inplace=True)
return df
def filter_empty_data(df: pd.DataFrame) -> pd.DataFrame:
logging.info("Filter rows with empty key data")
df = df[df["station_id"] != ""]
df = df[df["num_bikes_available"] != ""]
df = df[df["num_docks_available"] != ""]
df = df[df["is_installed"] != ""]
df = df[df["is_renting"] != ""]
df = df[df["is_returning"] != ""]
df = df[df["last_reported"] != ""]
return df
def reorder_headers(df: pd.DataFrame) -> pd.DataFrame:
logging.info("Re-ordering Headers")
df = df[
[
"station_id",
"num_bikes_available",
"num_bikes_disabled",
"num_docks_available",
"num_docks_disabled",
"is_installed",
"is_renting",
"is_returning",
"last_reported",
"num_ebikes_available",
"eightd_has_available_keys",
]
]
return df
def save_to_new_file(df, file_path) -> None:
df.to_csv(file_path, index=False)
def append_batch_file(
batch_file_path: str, target_file_path: str, skip_header: bool, truncate_file: bool
) -> None:
data_file = open(batch_file_path, "r")
if truncate_file:
target_file = open(target_file_path, "w+").close()
target_file = open(target_file_path, "a+")
if skip_header:
logging.info(
f"Appending batch file {batch_file_path} to {target_file_path} with skip header"
)
next(data_file)
else:
logging.info(f"Appending batch file {batch_file_path} to {target_file_path}")
target_file.write(data_file.read())
data_file.close()
target_file.close()
if os.path.exists(batch_file_path):
os.remove(batch_file_path)
def download_file_json(
source_url_json: str, source_file_json: str, source_file_csv: str
) -> None:
# This function extracts the json from a source url and creates
# a csv file from that data to be used as an input file
# Download json url into object r
r = requests.get(source_url_json + ".json", stream=True)
# Push object r (json) into json file
try:
with open(source_file_json, "wb") as f:
for chunk in r:
f.write(chunk)
except ValueError:
print(f"Writing JSON to {source_file_json} has failed")
f = open(
source_file_json.strip(),
)
json_data = json.load(f)
df = pd.DataFrame(json_data["data"]["stations"])
df.to_csv(source_file_csv, index=False)
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_json=os.environ["SOURCE_URL_JSON"],
source_file=pathlib.Path(os.environ["SOURCE_FILE"]).expanduser(),
target_file=pathlib.Path(os.environ["TARGET_FILE"]).expanduser(),
chunksize=os.environ["CHUNKSIZE"],
target_gcs_bucket=os.environ["TARGET_GCS_BUCKET"],
target_gcs_path=os.environ["TARGET_GCS_PATH"],
)