garystafford/residential_address_data_gen.py

## residential_address_data_gen.py
# Purpose: Generate US residential address data
# Author: Gary A. Stafford and GitHub Copilot
# Date: 2023-04-13
# Usage: python3 residential_address_data_gen.py 100
# Command-line argument(s): rec_count (number of records to generate as an integer)

# Write an application that create a random list of united states addresses.
# The application should accept a command line argument that specifies the number of records to generate.
# Include address, city, state, zip code, country, and property type.
# Write the data to a csv file named 'address_data.csv'.
# The application should contain the following functions:
#   - main() function that calls the other functions
#   - function that returns a list of common street names in the United States
#   - function that returns a list of common street types in the United States
#   - function that returns a list of common city, state, zip code, and population in the United States
#   - function that returns a property type


import csv
import random
import argparse

cities_final = []


def main():
    parser = argparse.ArgumentParser(description="Generate coffee shop sales data")
    parser.add_argument(
        "rec_count", type=int, help="The number of records to generate", default=100
    )

    # add population calculations to the city data
    cities = get_cities()
    prepare_cities(cities)

    rec_count = parser.parse_args().rec_count
    write_data(rec_count)


# Write a function that creates a list of common street names
# in the United States, in alphabetical order.
# Each one should be unique.
# Return a random street name.
def get_street_name():
    street_names = [
        "Ash", "Bend", "Bluff", "Branch", "Bridge", "Broadway", "Brook", "Burg",
        "Bury", "Canyon", "Cape", "Cedar", "Cove", "Creek", "Crest", "Crossing",
        "Dale", "Dam", "Divide", "Downs", "Elm", "Estates", "Falls", "Fifth",
        "First", "Fork", "Fourth", "Glen", "Green", "Grove", "Harbor", "Heights",
        "Hickory", "Hill", "Hollow", "Island", "Isle", "Knoll", "Lake", "Landing",
        "Lawn", "Main", "Manor", "Maple", "Meadow", "Meadows", "Mill", "Mills",
        "Mission", "Mount", "Mountain", "Oak", "Oaks", "Orchard", "Park", "Parkway",
        "Pass", "Path", "Pike", "Pine", "Place", "Plain", "Plains", "Port", "Prairie",
        "Ridge", "River", "Road", "Rock", "Rocks", "Second", "Seventh", "Shoals",
        "Shore", "Shores", "Sixth", "Skyway", "Spring", "Springs", "Spur", "Station",
        "Summit", "Sunset", "Terrace", "Third", "Trace", "Track", "Trail", "Tunnel",
        "Turnpike", "Vale", "Valley", "View", "Village", "Ville", "Vista", "Walk",
        "Way", "Well", "Wells", "Wood", "Woods", "Worth"
    ]

    return random.choice(street_names)


# Write a function that creates a list of common street types
# in the United States, in alphabetical order.
# Each one should be unique.
# Return a random street type.
def get_street_type():
    street_types = [
        "Alley", "Avenue", "Bend", "Bluff", "Boulevard", "Branch", "Bridge", "Brook",
        "Burg", "Circle", "Commons", "Court", "Drive", "Highway", "Lane", "Parkway",
        "Place", "Road", "Square", "Street", "Terrace", "Trail", "Way"
    ]

    return random.choice(street_types)


# Write a function that calculates the total population of the list of cities.
# Add a 'pcnt_of_total_population' and 'pcnt_running_total' columns to list.
# Returns a sorted list of cities by population.
def prepare_cities(cities):
    total_population = 0  # 51,035,885
    for city in cities:
        total_population += city["population"]

    for city in cities:
        city["pcnt_of_total_population"] = city["population"] / total_population

    global cities_final
    cities_final = sorted(cities, key=lambda d: d["population"], reverse=True)

    running_total = 1
    for city in cities_final:
        running_total -= city["pcnt_of_total_population"]
        city["pcnt_running_total"] = running_total


# Write a function to returns the 50 largest cities in the United States.
# Include the city, state abbreviation, zip code, and population.
# List should be sorted in descending order by population.
# Return a list of dictionaries.
def get_cities():
    cities = [
        {"city": "Albuquerque", "state": "NM", "zip": "87102", "population": 559277},
        {"city": "Anaheim", "state": "CA", "zip": "92801", "population": 345012},
        {"city": "Anchorage", "state": "AK", "zip": "99501", "population": 291826},
        {"city": "Arlington", "state": "TX", "zip": "76010", "population": 398121},
        {"city": "Atlanta", "state": "GA", "zip": "30303", "population": 486290},
        {"city": "Aurora", "state": "CO", "zip": "80010", "population": 325078},
        {"city": "Austin", "state": "TX", "zip": "78701", "population": 931830},
        {"city": "Bakersfield", "state": "CA", "zip": "93301", "population": 372576},
        {"city": "Baltimore", "state": "MD", "zip": "21202", "population": 602495},
        {"city": "Boston", "state": "MA", "zip": "02108", "population": 667137},
        {"city": "Buffalo", "state": "NY", "zip": "14202", "population": 258959},
        {"city": "Charlotte", "state": "NC", "zip": "28202", "population": 872498},
        {"city": "Chicago", "state": "IL", "zip": "60602", "population": 2695598},
        {"city": "Cincinnati", "state": "OH", "zip": "45202", "population": 296943},
        {"city": "Cleveland", "state": "OH", "zip": "44113", "population": 390113},
        {"city": "Colorado Springs", "state": "CO", "zip": "80903", "population": 456568},
        {"city": "Columbus", "state": "OH", "zip": "43215", "population": 822553},
        {"city": "Dallas", "state": "TX", "zip": "75201", "population": 1345047},
        {"city": "Denver", "state": "CO", "zip": "80202", "population": 682545},
        {"city": "Detroit", "state": "MI", "zip": "48226", "population": 672662},
        {"city": "El Paso", "state": "TX", "zip": "79901", "population": 674433},
        {"city": "Fort Worth", "state": "TX", "zip": "76102", "population": 792727},
        {"city": "Fresno", "state": "CA", "zip": "93721", "population": 509924},
        {"city": "Houston", "state": "TX", "zip": "77002", "population": 2296224},
        {"city": "Indianapolis", "state": "IN", "zip": "46204", "population": 843393},
        {"city": "Jacksonville", "state": "FL", "zip": "32202", "population": 842583},
        {"city": "Kansas City", "state": "MO", "zip": "64102", "population": 467007},
        {"city": "Las Vegas", "state": "NV", "zip": "89101", "population": 603488},
        {"city": "Long Beach", "state": "CA", "zip": "90802", "population": 462257},
        {"city": "Los Angeles", "state": "CA", "zip": "90001", "population": 3971883},
        {"city": "Louisville", "state": "KY", "zip": "40202", "population": 609893},
        {"city": "Memphis", "state": "TN", "zip": "38103", "population": 653450},
        {"city": "Mesa", "state": "AZ", "zip": "85201", "population": 508958},
        {"city": "Miami", "state": "FL", "zip": "33128", "population": 463347},
        {"city": "Milwaukee", "state": "WI", "zip": "53202", "population": 594833},
        {"city": "Minneapolis", "state": "MN", "zip": "55402", "population": 410939},
        {"city": "Nashville", "state": "TN", "zip": "37203", "population": 654610},
        {"city": "New York", "state": "NY", "zip": "10007", "population": 8405837},
        {"city": "Newark", "state": "NJ", "zip": "07102", "population": 281944},
        {"city": "Oakland", "state": "CA", "zip": "94607", "population": 406253},
        {"city": "Oklahoma City", "state": "OK", "zip": "73102", "population": 631346},
        {"city": "Omaha", "state": "NE", "zip": "68102", "population": 434353},
        {"city": "Philadelphia", "state": "PA", "zip": "19107", "population": 1526006},
        {"city": "Phoenix", "state": "AZ", "zip": "85003", "population": 1445632},
        {"city": "Pittsburgh", "state": "PA", "zip": "15222", "population": 305841},
        {"city": "Portland", "state": "OR", "zip": "97201", "population": 609456},
        {"city": "Raleigh", "state": "NC", "zip": "27601", "population": 403892},
        {"city": "Sacramento", "state": "CA", "zip": "95814", "population": 479686},
        {"city": "San Antonio", "state": "TX", "zip": "78205", "population": 1327407},
        {"city": "San Diego", "state": "CA", "zip": "92101", "population": 1307402},
        {"city": "San Francisco", "state": "CA", "zip": "94102", "population": 805235},
        {"city": "San Jose", "state": "CA", "zip": "95113", "population": 998537},
        {"city": "Seattle", "state": "WA", "zip": "98101", "population": 608660},
        {"city": "St. Louis", "state": "MO", "zip": "63102", "population": 319294},
        {"city": "Tampa", "state": "FL", "zip": "33602", "population": 335709},
        {"city": "Tucson", "state": "AZ", "zip": "85701", "population": 520116},
        {"city": "Virginia Beach", "state": "VA", "zip": "23451", "population": 448479},
        {"city": "Washington", "state": "DC", "zip": "20001", "population": 601723},
    ]

    return cities


# write a function to return a random city
# accept a random value between 0 and 1 as an input parameter
def get_city(rnd_value):
    for city in cities_final:
        if rnd_value >= city["pcnt_running_total"]:
            return city


# Write a function to return a random property type.
# Accept a random value between 0 and 1 as an input parameter.
# The function must return one of the following values based on the %:
# 63% Single-family, 26% Multi-family, 4% Condo,
# 3% Townhouse, 2% Mobile home, 1% Farm, 1% Other.
def get_property_type(rnd_value):
    if rnd_value < 0.63:
        return "Single-family"
    elif rnd_value < 0.89:
        return "Multi-family"
    elif rnd_value < 0.93:
        return "Condo"
    elif rnd_value < 0.96:
        return "Townhouse"
    elif rnd_value < 0.98:
        return "Mobile home"
    elif rnd_value < 0.99:
        return "Farm"
    else:
        return "Other"


# Create a function to write the address records to a csv file called 'address_data.csv'.
# Use an input parameter to specify the number of records to write.
# The csv file must have a header row and be comma delimited.
# All string values must be enclosed in double quotes.
def write_data(rec_count):
    address_id = 0
    with open("output/address_data.csv", "w", newline="") as csv_file:
        csv_writer = csv.writer(
            csv_file, delimiter=",", quotechar='"', quoting=csv.QUOTE_NONNUMERIC
        )

        csv_writer.writerow(
            [
                "id",
                "address",
                "city",
                "state",
                "zip",
                "country",
                "property_type",
                "assessed_value",
            ]
        )

        for i in range(rec_count):
            address_id += 1
            street_name = get_street_name()
            street_type = get_street_type()
            street_address = f"{random.randint(1, 9999)} {street_name} {street_type}"
            city_state_zip = get_city(random.random())
            country = "United States"
            property_type = get_property_type(random.random())
            assessed_value = random.randint(52500, 1950000)

            csv_writer.writerow(
                [
                    address_id,
                    street_address,
                    city_state_zip["city"],
                    city_state_zip["state"],
                    city_state_zip["zip"],
                    country,
                    property_type,
                    assessed_value,
                ]
            )


if __name__ == "__main__":
    main()
	# Purpose: Generate US residential address data
	# Author: Gary A. Stafford and GitHub Copilot
	# Date: 2023-04-13
	# Usage: python3 residential_address_data_gen.py 100
	# Command-line argument(s): rec_count (number of records to generate as an integer)

	# Write an application that create a random list of united states addresses.
	# The application should accept a command line argument that specifies the number of records to generate.
	# Include address, city, state, zip code, country, and property type.
	# Write the data to a csv file named 'address_data.csv'.
	# The application should contain the following functions:
	# - main() function that calls the other functions
	# - function that returns a list of common street names in the United States
	# - function that returns a list of common street types in the United States
	# - function that returns a list of common city, state, zip code, and population in the United States
	# - function that returns a property type


	import csv
	import random
	import argparse

	cities_final = []


	def main():
	parser = argparse.ArgumentParser(description="Generate coffee shop sales data")
	parser.add_argument(
	"rec_count", type=int, help="The number of records to generate", default=100
	)

	# add population calculations to the city data
	cities = get_cities()
	prepare_cities(cities)

	rec_count = parser.parse_args().rec_count
	write_data(rec_count)


	# Write a function that creates a list of common street names
	# in the United States, in alphabetical order.
	# Each one should be unique.
	# Return a random street name.
	def get_street_name():
	street_names = [
	"Ash", "Bend", "Bluff", "Branch", "Bridge", "Broadway", "Brook", "Burg",
	"Bury", "Canyon", "Cape", "Cedar", "Cove", "Creek", "Crest", "Crossing",
	"Dale", "Dam", "Divide", "Downs", "Elm", "Estates", "Falls", "Fifth",
	"First", "Fork", "Fourth", "Glen", "Green", "Grove", "Harbor", "Heights",
	"Hickory", "Hill", "Hollow", "Island", "Isle", "Knoll", "Lake", "Landing",
	"Lawn", "Main", "Manor", "Maple", "Meadow", "Meadows", "Mill", "Mills",
	"Mission", "Mount", "Mountain", "Oak", "Oaks", "Orchard", "Park", "Parkway",
	"Pass", "Path", "Pike", "Pine", "Place", "Plain", "Plains", "Port", "Prairie",
	"Ridge", "River", "Road", "Rock", "Rocks", "Second", "Seventh", "Shoals",
	"Shore", "Shores", "Sixth", "Skyway", "Spring", "Springs", "Spur", "Station",
	"Summit", "Sunset", "Terrace", "Third", "Trace", "Track", "Trail", "Tunnel",
	"Turnpike", "Vale", "Valley", "View", "Village", "Ville", "Vista", "Walk",
	"Way", "Well", "Wells", "Wood", "Woods", "Worth"
	]

	return random.choice(street_names)


	# Write a function that creates a list of common street types
	# in the United States, in alphabetical order.
	# Each one should be unique.
	# Return a random street type.
	def get_street_type():
	street_types = [
	"Alley", "Avenue", "Bend", "Bluff", "Boulevard", "Branch", "Bridge", "Brook",
	"Burg", "Circle", "Commons", "Court", "Drive", "Highway", "Lane", "Parkway",
	"Place", "Road", "Square", "Street", "Terrace", "Trail", "Way"
	]

	return random.choice(street_types)


	# Write a function that calculates the total population of the list of cities.
	# Add a 'pcnt_of_total_population' and 'pcnt_running_total' columns to list.
	# Returns a sorted list of cities by population.
	def prepare_cities(cities):
	total_population = 0 # 51,035,885
	for city in cities:
	total_population += city["population"]

	for city in cities:
	city["pcnt_of_total_population"] = city["population"] / total_population

	global cities_final
	cities_final = sorted(cities, key=lambda d: d["population"], reverse=True)

	running_total = 1
	for city in cities_final:
	running_total -= city["pcnt_of_total_population"]
	city["pcnt_running_total"] = running_total


	# Write a function to returns the 50 largest cities in the United States.
	# Include the city, state abbreviation, zip code, and population.
	# List should be sorted in descending order by population.
	# Return a list of dictionaries.
	def get_cities():
	cities = [
	{"city": "Albuquerque", "state": "NM", "zip": "87102", "population": 559277},
	{"city": "Anaheim", "state": "CA", "zip": "92801", "population": 345012},
	{"city": "Anchorage", "state": "AK", "zip": "99501", "population": 291826},
	{"city": "Arlington", "state": "TX", "zip": "76010", "population": 398121},
	{"city": "Atlanta", "state": "GA", "zip": "30303", "population": 486290},
	{"city": "Aurora", "state": "CO", "zip": "80010", "population": 325078},
	{"city": "Austin", "state": "TX", "zip": "78701", "population": 931830},
	{"city": "Bakersfield", "state": "CA", "zip": "93301", "population": 372576},
	{"city": "Baltimore", "state": "MD", "zip": "21202", "population": 602495},
	{"city": "Boston", "state": "MA", "zip": "02108", "population": 667137},
	{"city": "Buffalo", "state": "NY", "zip": "14202", "population": 258959},
	{"city": "Charlotte", "state": "NC", "zip": "28202", "population": 872498},
	{"city": "Chicago", "state": "IL", "zip": "60602", "population": 2695598},
	{"city": "Cincinnati", "state": "OH", "zip": "45202", "population": 296943},
	{"city": "Cleveland", "state": "OH", "zip": "44113", "population": 390113},
	{"city": "Colorado Springs", "state": "CO", "zip": "80903", "population": 456568},
	{"city": "Columbus", "state": "OH", "zip": "43215", "population": 822553},
	{"city": "Dallas", "state": "TX", "zip": "75201", "population": 1345047},
	{"city": "Denver", "state": "CO", "zip": "80202", "population": 682545},
	{"city": "Detroit", "state": "MI", "zip": "48226", "population": 672662},
	{"city": "El Paso", "state": "TX", "zip": "79901", "population": 674433},
	{"city": "Fort Worth", "state": "TX", "zip": "76102", "population": 792727},
	{"city": "Fresno", "state": "CA", "zip": "93721", "population": 509924},
	{"city": "Houston", "state": "TX", "zip": "77002", "population": 2296224},
	{"city": "Indianapolis", "state": "IN", "zip": "46204", "population": 843393},
	{"city": "Jacksonville", "state": "FL", "zip": "32202", "population": 842583},
	{"city": "Kansas City", "state": "MO", "zip": "64102", "population": 467007},
	{"city": "Las Vegas", "state": "NV", "zip": "89101", "population": 603488},
	{"city": "Long Beach", "state": "CA", "zip": "90802", "population": 462257},
	{"city": "Los Angeles", "state": "CA", "zip": "90001", "population": 3971883},
	{"city": "Louisville", "state": "KY", "zip": "40202", "population": 609893},
	{"city": "Memphis", "state": "TN", "zip": "38103", "population": 653450},
	{"city": "Mesa", "state": "AZ", "zip": "85201", "population": 508958},
	{"city": "Miami", "state": "FL", "zip": "33128", "population": 463347},
	{"city": "Milwaukee", "state": "WI", "zip": "53202", "population": 594833},
	{"city": "Minneapolis", "state": "MN", "zip": "55402", "population": 410939},
	{"city": "Nashville", "state": "TN", "zip": "37203", "population": 654610},
	{"city": "New York", "state": "NY", "zip": "10007", "population": 8405837},
	{"city": "Newark", "state": "NJ", "zip": "07102", "population": 281944},
	{"city": "Oakland", "state": "CA", "zip": "94607", "population": 406253},
	{"city": "Oklahoma City", "state": "OK", "zip": "73102", "population": 631346},
	{"city": "Omaha", "state": "NE", "zip": "68102", "population": 434353},
	{"city": "Philadelphia", "state": "PA", "zip": "19107", "population": 1526006},
	{"city": "Phoenix", "state": "AZ", "zip": "85003", "population": 1445632},
	{"city": "Pittsburgh", "state": "PA", "zip": "15222", "population": 305841},
	{"city": "Portland", "state": "OR", "zip": "97201", "population": 609456},
	{"city": "Raleigh", "state": "NC", "zip": "27601", "population": 403892},
	{"city": "Sacramento", "state": "CA", "zip": "95814", "population": 479686},
	{"city": "San Antonio", "state": "TX", "zip": "78205", "population": 1327407},
	{"city": "San Diego", "state": "CA", "zip": "92101", "population": 1307402},
	{"city": "San Francisco", "state": "CA", "zip": "94102", "population": 805235},
	{"city": "San Jose", "state": "CA", "zip": "95113", "population": 998537},
	{"city": "Seattle", "state": "WA", "zip": "98101", "population": 608660},
	{"city": "St. Louis", "state": "MO", "zip": "63102", "population": 319294},
	{"city": "Tampa", "state": "FL", "zip": "33602", "population": 335709},
	{"city": "Tucson", "state": "AZ", "zip": "85701", "population": 520116},
	{"city": "Virginia Beach", "state": "VA", "zip": "23451", "population": 448479},
	{"city": "Washington", "state": "DC", "zip": "20001", "population": 601723},
	]

	return cities


	# write a function to return a random city
	# accept a random value between 0 and 1 as an input parameter
	def get_city(rnd_value):
	for city in cities_final:
	if rnd_value >= city["pcnt_running_total"]:
	return city


	# Write a function to return a random property type.
	# Accept a random value between 0 and 1 as an input parameter.
	# The function must return one of the following values based on the %:
	# 63% Single-family, 26% Multi-family, 4% Condo,
	# 3% Townhouse, 2% Mobile home, 1% Farm, 1% Other.
	def get_property_type(rnd_value):
	if rnd_value < 0.63:
	return "Single-family"
	elif rnd_value < 0.89:
	return "Multi-family"
	elif rnd_value < 0.93:
	return "Condo"
	elif rnd_value < 0.96:
	return "Townhouse"
	elif rnd_value < 0.98:
	return "Mobile home"
	elif rnd_value < 0.99:
	return "Farm"
	else:
	return "Other"


	# Create a function to write the address records to a csv file called 'address_data.csv'.
	# Use an input parameter to specify the number of records to write.
	# The csv file must have a header row and be comma delimited.
	# All string values must be enclosed in double quotes.
	def write_data(rec_count):
	address_id = 0
	with open("output/address_data.csv", "w", newline="") as csv_file:
	csv_writer = csv.writer(
	csv_file, delimiter=",", quotechar='"', quoting=csv.QUOTE_NONNUMERIC
	)

	csv_writer.writerow(
	[
	"id",
	"address",
	"city",
	"state",
	"zip",
	"country",
	"property_type",
	"assessed_value",
	]
	)

	for i in range(rec_count):
	address_id += 1
	street_name = get_street_name()
	street_type = get_street_type()
	street_address = f"{random.randint(1, 9999)} {street_name} {street_type}"
	city_state_zip = get_city(random.random())
	country = "United States"
	property_type = get_property_type(random.random())
	assessed_value = random.randint(52500, 1950000)

	csv_writer.writerow(
	[
	address_id,
	street_address,
	city_state_zip["city"],
	city_state_zip["state"],
	city_state_zip["zip"],
	country,
	property_type,
	assessed_value,
	]
	)


	if __name__ == "__main__":
	main()