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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 logging
import os
import pathlib
import pandas as pd
from google.cloud import storage
def main(
source_url: str,
source_file: pathlib.Path,
target_file: pathlib.Path,
chunksize: str,
target_gcs_bucket: str,
target_gcs_path: str,
) -> None:
logging.info("Sunroof solar potential started")
pathlib.Path("./files").mkdir(parents=True, exist_ok=True)
download_file_gs(source_url, 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("Sunroof solar potential process completed")
def process_chunk(
df: pd.DataFrame, target_file_batch: str, target_file: str, skip_header: bool
) -> None:
df = rename_headers(df)
df = remove_nan_cols(df)
df = generate_location(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 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 generate_location(df: pd.DataFrame) -> pd.DataFrame:
logging.info("Generating location data")
df["center_point"] = (
"POINT( " + df["lng_avg"].map(str) + " " + df["lat_avg"].map(str) + " )"
)
return df
def reorder_headers(df: pd.DataFrame) -> pd.DataFrame:
logging.info("Reordering headers..")
df = df[
[
"region_name",
"state_name",
"lat_max",
"lat_min",
"lng_max",
"lng_min",
"lat_avg",
"lng_avg",
"yearly_sunlight_kwh_kw_threshold_avg",
"count_qualified",
"percent_covered",
"percent_qualified",
"number_of_panels_n",
"number_of_panels_s",
"number_of_panels_e",
"number_of_panels_w",
"number_of_panels_f",
"number_of_panels_median",
"number_of_panels_total",
"kw_median",
"kw_total",
"yearly_sunlight_kwh_n",
"yearly_sunlight_kwh_s",
"yearly_sunlight_kwh_e",
"yearly_sunlight_kwh_w",
"yearly_sunlight_kwh_f",
"yearly_sunlight_kwh_median",
"yearly_sunlight_kwh_total",
"install_size_kw_buckets",
"carbon_offset_metric_tons",
"existing_installs_count",
"center_point",
]
]
return df
def remove_nan(dt_str: str) -> int:
if not dt_str or str(dt_str) == "nan":
return int()
else:
return int(dt_str)
def remove_nan_cols(df: pd.DataFrame) -> pd.DataFrame:
logging.info("Resolve NaN data")
cols = {
"count_qualified",
"existing_installs_count",
"number_of_panels_n",
"number_of_panels_s",
"number_of_panels_e",
"number_of_panels_w",
"number_of_panels_f",
"number_of_panels_median",
"number_of_panels_total",
}
for col in cols:
df[col] = df[col].apply(remove_nan)
return df
def rename_headers(df: pd.DataFrame) -> pd.DataFrame:
logging.info("Renaming columns")
header_names = {"install_size_kw_buckets_json": "install_size_kw_buckets"}
df = df.rename(columns=header_names)
return df
def save_to_new_file(df: pd.DataFrame, file_path) -> None:
df.to_csv(file_path, index=False)
def download_file_gs(source_url: str, source_file: pathlib.Path) -> None:
with open(source_file, "wb+") as file_obj:
storage.Client().download_blob_to_file(source_url, file_obj)
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(),
chunksize=os.environ["CHUNKSIZE"],
target_gcs_bucket=os.environ["TARGET_GCS_BUCKET"],
target_gcs_path=os.environ["TARGET_GCS_PATH"],
)