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An 'active' business is defined as a registered business whose owner has not notified the Office of Finance of a cease of business operations. Update Interval: Monthly. Data source: https://data.lacity.org/A-Prosperous-City/Listing-of-Active-Businesses/6rrh-rzua This script fetches the data and saves it locally as a CSV file. It also selects a subset of businesses with operation starting date within the last NDAYS days (default 30) and saves this as a separate CSV file. Finally, it creates and saves a KML file from the subset, useful for importing into Google Maps or similar software to visualize the distribution of recent businesses opened in the Los Angeles area. # Installation Install with pip. The package installs as a command-line script. ``` pip install la-businesses ``` # Usage Run from the command line (it installs as as script). All downloaded and generated files will be stored in a directory `files` inside the current working directory. ``` usage: la-businesses [-h] [-u] [-d NDAYS] optional arguments: -h, --help show this help message and exit -u, --update update data (default: False) -d NDAYS, --days NDAYS days since the last N days (default: 30) ``` # Known issues ## Locations with missing coordinates are omitted from KML file The script relies on coordinate data already provided in the downloaded dataset. Some businesses contain addresses but no coordinates; in these cases, the business is ignored when creating the KML (but is included in any saved CSV file). Future implementations should include a function to look up location coordinates from a given address (e.g., using the Open Street Maps API). ## Locations with no DBA name simply show NaN in the KML file The script could use better handling of business name / DBA combinations to omit NaN from KML when it does not have a business name. ## No phone numbers The data does not include any phone or email contact information; merging this dataset with one that includes contact information would be more useful for market research. """ # ----------------------------------------------------------------------------# # LIBRARIES # ----------------------------------------------------------------------------# from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter from pathlib import Path import logging import datetime as dt import pytz # for timezone handling import pandas as pd import requests import simplekml # ----------------------------------------------------------------------------# # CONSTANTS AND CONFIGURATION # ----------------------------------------------------------------------------# logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") URL = "https://data.lacity.org/api/views/6rrh-rzua/rows.csv?accessType=DOWNLOAD" COMPLETE_LIST = "all_businesses.csv" # full database of businesses RECENT_LIST = "recent_businesses.csv" # subset of recent businesses DEFAULT_NDAYS = 30 # select businesses opened since this many days ago WRITE_CHUNK_SIZE = 1024 # bytes OUTPUT_DIR = Path.cwd() / 'files' # save all files here OUTPUT_DIR.mkdir(parents=True, exist_ok=True) # make the dir if not exist # ----------------------------------------------------------------------------# # CORE FUNCTIONS # ----------------------------------------------------------------------------# def get_business_list(): """Download latest businesses database.""" response = requests.get(URL, stream=True) # Throw an error for bad status codes response.raise_for_status() with open(OUTPUT_DIR / COMPLETE_LIST, "wb") as handle: for block in response.iter_content(DEFAULT_NDAYS): handle.write(block) logging.info(f"Saved complete business list as {COMPLETE_LIST}.") return OUTPUT_DIR / COMPLETE_LIST def load_business_list(file=None, update=False): """Load (optionally identified) database from file or download it first.""" business_list_file = OUTPUT_DIR / COMPLETE_LIST if file: logging.info(f"Loading business list {file.name} ...") df = pd.read_csv(file) df["LOCATION START DATE"] = pd.to_datetime(df["LOCATION START DATE"]) df["LOCATION END DATE"] = pd.to_datetime(df["LOCATION END DATE"]) logging.debug("Converted dates") return df if update or not business_list_file.exists(): logging.info("Downloading database of businesses ...") get_business_list() logging.info( f"Loading all businesses ...\n" \ f"Using cached data from {last_mod(business_list_file)}. " \ f"Use -u flag to update.") df = pd.read_csv(business_list_file) df["LOCATION START DATE"] = pd.to_datetime(df["LOCATION START DATE"]) df["LOCATION END DATE"] = pd.to_datetime(df["LOCATION END DATE"]) logging.debug("Converted dates") return df def last_mod(file): """Returns a string of the last modified time of a Path() in local timezone""" fmt = "%d %b %Y at %I:%M %p %Z" # time format return pytz.utc.localize(dt.datetime.utcfromtimestamp( file.stat().st_mtime)).astimezone().strftime(fmt) def select_recent(df, outfile=None, ndays=DEFAULT_NDAYS): logging.info(f"Selecting businesses starting {ndays} days ago or later ...") cutoff_date = dt.datetime.now() - dt.timedelta(days=ndays) df = df[df["LOCATION START DATE"] > cutoff_date] logging.debug(f"Selected recent since {cutoff_date.date()}: {len(df)} items") df = df.sort_values(by="LOCATION START DATE", ascending=False) logging.debug("Sorted by start date") output_filename = outfile or RECENT_LIST output_file = OUTPUT_DIR / output_filename df.to_csv(output_file, index=False) logging.info(f"Saved {len(df)} recent businesses to {output_file}.") return df def df_to_kml(df, outfile=None): """Make a KML file from pd.DataFrame of addresses""" df = df.dropna(subset=["LOCATION"]) df = df.reset_index(drop=True) logging.debug("Ignoring places with no lat-long") kml = simplekml.Kml() for id, row in df.iterrows(): long, lat = eval(row["LOCATION"]) kml.newpoint( name=str(row["BUSINESS NAME"]) + "\n" + str(row["DBA NAME"]), description=", ".join( [row["STREET ADDRESS"], row["CITY"], row["ZIP CODE"]] ), coords=[(lat, long)], ) output_filename = outfile or "recent_businesses" output_file = OUTPUT_DIR / (output_filename + ".kml") kml.save(output_file) logging.debug("made points") logging.info("Created KML file " + str(output_file)) # ----------------------------------------------------------------------------# # USER INTERFACE FUNCTIONS # ----------------------------------------------------------------------------# def get_parser(): parser = ArgumentParser( description=__doc__, formatter_class=ArgumentDefaultsHelpFormatter ) parser.add_argument( "-u", "--update", action="store_true", dest="update", help="update data" ) parser.add_argument( "-d", "--days", action="store", dest="ndays", type=int, default=DEFAULT_NDAYS, help="since the last N days", ) return parser def main(): try: args = get_parser().parse_args() df = load_business_list(update=args.update) df = select_recent(df, ndays=args.ndays) df_to_kml(df) except KeyboardInterrupt: logging.error("!!! 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