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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 glob
import json
import logging
import math
import os
import pathlib
import re
import subprocess
import typing
import pandas as pd
from google.cloud import storage
def main(
source_url: typing.List[str],
source_file: typing.List[pathlib.Path],
source_files_path: str,
target_file: pathlib.Path,
target_gcs_bucket: str,
target_gcs_path: str,
headers: typing.List[str],
rename_mappings: dict,
) -> None:
logging.info(
"Austin crime 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("Downloading file ...")
download_file(source_url, source_file)
logging.info("Opening files...")
df = read_files(source_files_path)
logging.info("Transform: Rename columns...")
rename_headers(df, rename_mappings)
logging.info("Transform: Cleaning up column location_description...")
# Removing two consecutive white soaces from location_description column
df["location_description"] = (
df["location_description"]
.astype("|S")
.str.decode("utf-8")
.apply(reg_exp_tranformation, args=(r"\s{2,}", ""))
)
logging.info("Transform: Converting to integer string...")
df["zipcode"] = df["zipcode"].apply(convert_to_integer_string)
df["council_district_code"] = df["council_district_code"].apply(
convert_to_integer_string
)
df["x_coordinate"] = df["x_coordinate"].apply(convert_to_integer_string)
df["y_coordinate"] = df["y_coordinate"].apply(convert_to_integer_string)
logging.info("Transform: Creating a new column - address...")
df["address"] = df["temp_address"]
df["address"] = (
df["address"]
.fillna(
df["location_description"].replace("nan", "")
+ " Austin, TX "
+ df["zipcode"]
)
.str.strip()
)
logging.info("Transform: Converting date format...")
df["timestamp"] = df["timestamp"].apply(convert_dt_format)
df["clearance_date"] = df["clearance_date"].apply(convert_dt_format)
logging.info("Transform: Creating a new column - year...")
df["year"] = df["timestamp"].apply(extract_year)
logging.info("Transform: Replacing values...")
df["address"] = df["address"].apply(reg_exp_tranformation, args=(r"\n", " "))
df = df.replace(
to_replace={
"clearance_status": {
"C": "Cleared by Arrest",
"O": "Cleared by Exception",
"N": "Not cleared",
},
"address": {"sAustin": "Austin"},
}
)
logging.info("Transform: Converting exponential values to integer...")
df["unique_key"] = (
df["unique_key"]
.apply(convert_exp_to_float)
.astype(float)
.apply(convert_to_integer_string)
)
logging.info("Transform: Creating a new column - latitude...")
# If address is 'Austin, TX (30.264979, -97.746598)' below code will extract
# value 30.264979 from the address and assign it to latitude column
df["latitude"] = (
df["address"]
.apply(search_string)
.apply(extract_lat_long, args=[r".*\((\d+\.\d+),.*"])
)
logging.info("Transform: Creating a new column - longitude...")
# If address is 'Austin, TX (30.264979, -97.746598)' below code will extract
# value -97.746598 from the address and assign it to longitude column
df["longitude"] = (
df["address"]
.apply(search_string)
.apply(extract_lat_long, args=[r".*(\-\d+\.\d+)\)"])
)
logging.info("Transform: Creating a new column - location...")
df["location"] = "(" + df["latitude"] + "," + df["longitude"] + ")"
df["location"] = df["location"].replace("(,)", "")
logging.info("Transform: Dropping column - temp_address...")
delete_column(df, "temp_address")
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(
"Austin crime process completed at "
+ str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
)
def download_file(
source_url: typing.List[str], source_file: typing.List[pathlib.Path]
) -> None:
for url, file in zip(source_url, source_file):
logging.info(f"Downloading file from {url} ...")
subprocess.check_call(["gsutil", "cp", f"{url}", f"{file}"])
def read_files(path: pathlib.Path) -> pd.DataFrame:
all_files = glob.glob(path + "/*.csv")
df_temp = []
for filename in all_files:
frame = pd.read_csv(filename, index_col=None, header=0)
df_temp.append(frame)
df = pd.concat(df_temp, axis=0, ignore_index=True)
return df
def rename_headers(df: pd.DataFrame, rename_mappings: dict) -> None:
df.rename(columns=rename_mappings, inplace=True)
def reg_exp_tranformation(str_value: str, search_pattern: str, replace_val: str) -> str:
str_value = re.sub(search_pattern, replace_val, str_value)
return str_value
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 convert_dt_format(dt_str: str) -> str:
a = ""
if not dt_str or str(dt_str) == "nan":
return str(a)
else:
return datetime.datetime.strptime(str(dt_str), "%m/%d/%Y %H:%M:%S %p").strftime(
"%Y-%m-%d %H:%M:%S"
)
def extract_year(string_val: str) -> str:
string_val = string_val[0:4]
return string_val
def convert_exp_to_float(exp_val: str) -> str:
float_val = "{:f}".format(float(exp_val))
return float_val
def search_string(str_value: str) -> str:
if re.search(r".*\(.*\)", str_value):
return str(str_value)
else:
return str("")
def extract_lat_long(str_val: str, patter: str) -> str:
m = re.match(patter, str_val)
if m:
return m.group(1)
else:
return ""
def delete_column(df: pd.DataFrame, column_name: str) -> None:
df = df.drop(column_name, axis=1, inplace=True)
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)
def save_to_new_file(df: pd.DataFrame, file_path: str) -> None:
df.to_csv(file_path, index=False)
if __name__ == "__main__":
logging.getLogger().setLevel(logging.INFO)
main(
source_url=json.loads(os.environ["SOURCE_URL"]),
source_file=json.loads(os.environ["SOURCE_FILE"]),
source_files_path=os.environ["FILE_PATH"],
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"]),
)