/
main.py
89 lines (63 loc) · 2.46 KB
/
main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
import pandas as pd
import numpy as np
import random
from random import randint
from faker import Faker
# Set seed for reproducibility
np.random.seed(0)
fake = Faker()
# Generate a random date
def generate_date(n):
start_date = pd.to_datetime('01-01-2020')
end_date = pd.to_datetime('31-12-2022')
return start_date + (end_date - start_date) * np.random.rand(n)
# Generate a random product price
def generate_price(n):
return np.random.uniform(5, 200, n)
# Generate a random quantity
def generate_quantity(n):
return np.random.randint(1, 5, n)
# Generate a random age
def generate_age(n):
return np.random.randint(18, 70, n)
# Generate a random product category
def generate_product_category(n):
categories = ['Electronics', 'Clothing', 'Home & Kitchen', 'Books', 'Beauty & Health']
return np.random.choice(categories, n)
# Generate a random product name
def generate_product_name(n):
product_names = ['Smartphone', 'Laptop', 'Headphones', 'T-shirt', 'Jeans', 'Dress', 'Shoes', 'Coffee Maker',
'Blender', 'Cookware Set', 'Book1', 'Book2', 'Book3', 'Lipstick', 'Skincare Set', 'Perfume']
return np.random.choice(product_names, n)
# Generate a random location
def generate_location(n):
return [fake.city() for _ in range(n)]
# Generate a random customer ID
def generate_customer_id(n):
return [fake.unique.random_number(digits=5, fix_len=True) for _ in range(n)]
n = 1000 # Number of records
data = {
'CustomerID': generate_customer_id(n),
'PurchaseDate': generate_date(n),
'ProductID': np.arange(1, n + 1),
'ProductName': generate_product_name(n),
'ProductCategory': generate_product_category(n),
'ProductPrice': generate_price(n),
'Quantity': generate_quantity(n),
'TotalPrice': [0] * n, # We'll calculate this later
'CustomerAge': generate_age(n),
'CustomerLocation': generate_location(n)
}
# Create DataFrame
df = pd.DataFrame(data)
# Calculate TotalPrice
df['TotalPrice'] = df['ProductPrice'] * df['Quantity']
# Introduce some missing values
df.loc[df.sample(frac=0.05).index, 'ProductPrice'] = np.nan
df.loc[df.sample(frac=0.03).index, 'CustomerAge'] = np.nan
df.loc[df.sample(frac=0.02).index, 'CustomerLocation'] = np.nan
# Introduce some outliers in ProductPrice
df.loc[df.sample(frac=0.01).index, 'ProductPrice'] = df['ProductPrice'] * 10
# Convert PurchaseDate to datetime format
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
df.to_csv('retail_data.csv', index=False)