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pseudosales.py
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pseudosales.py
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# FILENAME: pseudosales.py
# AUTHOR: Jp Aldama
# DATE: 2/20/2020
# DESCRIPTION: TODO TODO TODO TODO FEST TODO CON
#
# NOTE BUG FIX TODO NOW
# If products[product][0] <= 1.00
# Then numpy.random.geometric(p,size) returns value error
# so if p <= 0 or p
import pandas as pd
import random
import datetime
import calendar
import numpy as np
import os
import time
import sys
"""
# For testing..
is_verbose = False
start_time = time.time()
processed_done = False
start_process = False
month_done = False
def verbose():
if is_verbose == True:
if start_process == True:
print(f'______PROCESSING_DATA_____')
if month_done == True:
print(f'___{month_}___Done!!___RUNTIME_{time.time()-start_time}')
if processed_done == True:
print(f'__PROCESSING_COMPLETE!!_RUNTIME_{time.time()-start_time}__')
else:
print(f'_{month_}_{order_size}CURRENT_PROCESS__')
"""
products = {
# TODO Add weighted values for each product in this set {DONE]
# {product : [price, weight]}
# The higher the weight value, the more likely a customer will it.
# Cheaper items would mean a higher weight value
# play around with these values or go crazy and assign random values
# all the values and have fun!
# NOTE FIX Weights!! [NO!]
'Sony Playstation 4 Pro': [399.99, 17],
'Sony Playstation 4 Controller': [59.99, 10],
'Microsoft XBox One Controller': [59.99, 9],
'Nintendo Switch Joycon Pair': [89.99, 11],
'Microsoft XBox One X': [399.99, 12],
'Microsoft Surprise Game': [59.99, 9],
'Sony Playstation 4 Surprise Game': [59.99, 10],
'Nintendo Switch Surprise Game': [59.99, 10],
'Nintendo Switch': [249.99, 20],
'JBL Bluetooth Wireless Speaker': [199.99, 15],
'Beats Wireless Headphones': [249.99, 1],
'LG 65 Inch 4k Smart TV': [399.99, 8],
'Apple iPhone 11 Pro': [1100, 14],
'Lenovo Thinkpad': [799.00, 14],
'Samsung Galaxy S10 Plus': [999.99, 15],
'Apple TV': [99.99, 30],
'Whirlpool Washer and Dryer': [699.00, 4],
'Epson printer': [99.99, 13],
'Apple Watch': [499.99, 10],
'Apple Air Pods': [149.00, 13],
'Apple Lightning USB Charger': [20.00, 12],
'USB C Android Charger': [11.99, 11],
'Duracell 9 Volt Batteries': [4.99, 40],
'Light Bulbs 3 PK': [2.49, 37],
'IooT Refrigerator': [1500.00, 2],
'Apple Macbook Pro': [2700.00, 13],
'Wired Headset for gaming': [49.99, 12],
'Wireless Headset for gaming': [69.99, 15],
'Sony wired Headset for MP3 and Movies': [12.99, 17],
'Candy': [1.99, 60],
'Soda': [2.00, 50],
'Hot Dog': [10.00, 30],
'Extended Warranty 3 Years': [49.00, 10]
}
columns = ['OrderID','Product','QuantityOrdered','Price','OrderDate','PurchaseAddress']
"""
def export_product_inventory():
inventory = products
product_index = pd.DataFrame(columns=[inventory])
sorted_product_index = product_index.sort_values()
sorted_product_index.to_csv('product_inventory.csv', index=None)
"""
def generate_random_datetime(month):
day = generate_random_day(month)
if random.random() < 0.5:
date = datetime.datetime(2019, month, day, 12,00)
else:
date = datetime.datetime(2019, month, day, 20, 00)
offset_time = np.random.normal(loc=0.0, scale=180)
datetime_result = date + datetime.timedelta(minutes=offset_time)
return datetime_result.strftime('%m-%d-%y %H:%M')
def generate_random_day(month):
days_span = calendar.monthrange(2019,month)[1]
return random.randint(1,days_span)
def generate_pseudo_address():
streets_avenues= [
'1st',
'2nd',
'3rd',
'4th',
'5th',
'6th',
'7th',
'8th',
'9th',
'Park',
'Madison',
'Lexington',
'Broadway',
'Amsterdam',
'St Nicholas',
'Wadsworth',
'Audobon' ]
cities = ['Bronx', 'New York City', 'Queens', 'Brooklyn', 'Staten Island']
weights = [6,9,5,4,6]
zipcodes = ['10032','10033','10473','12116','10023']
states = ['NY','NY','NY','NY','NY']
street = random.choice(streets_avenues)
index = random.choices(range(len(cities)), weights=weights)[0]
return (f"{random.randint(1,999)} {street} Avenue, {cities[index]}, {states[index]} {zipcodes[index]}")
# TODO take the data and create csv files
def data_to_csv():
pass
# Maybe json for API?
def data_to_json():
pass
# TODO WRITE ROWS FOR EXTRA PURCHASES
def write_row(order_number,product, thedate, address):
price_product = products[product][0]
quantity = np.random.geometric(p=1.0-(1.0/price_product), size=1)[0]
output = [order_number,product,quantity,price_product,thedate,address]
return output
# TODO wrap this up and test already!
# TODO create conditions to control what gets bought and what is added
# EX-> if its november, add purchases on certain days
# EX-> time of day of order. This is very important!
# NOTE Think it through!
if __name__ == '__main__':
#if is_verbose == True:
# verbose()
# start_process = True
order_number = 112358
for months in range(1,13):
# Normal distribution example
if months <= 9:
order_size = int(np.random.normal(loc=13500, scale=4000))
elif months == 10:
order_size = int(np.random.normal(loc=23750, scale=3000))
elif months == 11:
order_size = int(np.random.normal(loc=25000, scale=2500))
else: #december
order_size = int(np.random.normal(loc=30000, scale=2000))
product_all = [product for product in products]
weights = [products[product][1] for product in products]
df = pd.DataFrame(columns=columns)
i = 0
while order_size > 0:
#for i in range(order_size):
address = generate_pseudo_address()
thedate = generate_random_datetime(months)
product = random.choices(product_all, weights=weights)[0]
df.loc[i] = write_row(order_number, product,thedate,address)
i += 1
# Add more items with random chance
# If Iphone
if product == 'Apple iPhone 11 Pro':
if random.random() < 0.15:
# add related accesories
df.loc[i] = write_row(order_number,
'Apple Lightning USB Charger',
thedate,address)
i += 1
if random.random() < 0.05:
df.loc[i] = write_row(order_number,
'Apple Air Pods',
thedate, address)
i += 1
elif product == 'Sony Playstation 4 Pro':
if random.random() < 0.15:
df.loc[i] = write_row(order_number,
'Sony Playstation 4 Surprise Game',
thedate, address)
i += 1
if random.random() < 0.13:
df.loc[i] = write_row(order_number,
'Sony Playstation 4 Surprise Game',
thedate, address)
i += 1
if random.random() < 0.10:
df.loc[i] = write_row(order_number,
'Sony Playstation 4 Controller',
thedate, address)
i += 1
elif product == 'Microsoft XBox One X':
if random.random() < 0.15:
df.loc[i] = write_row(order_number,
'Microsoft Surprise Game',
thedate, address)
i += 1
if random.random() < 0.13:
df.loc[i] = write_row(order_number,
'Microsoft Surprise Game',
thedate, address)
i += 1
if random.random() < 0.10:
df.loc[i] = write_row(order_number,
'Microsoft XBox One Controller',
thedate, address)
i += 1
if random.random() <= 0.02:
product = random.choices(product_all,weights)[0]
df.loc[i] = write_row(order_number,product,thedate,address)
i += 1
order_number += 1
order_size -= 1
month_ = calendar.month_name[months]
df.to_csv(f"pseudocorp_{month_}_2019.csv", index=False)
print(f"{month_} Done!")
# month_done = True
# if month_done == True and months == 13 and order_size == 0:
# processed_done == True