Data_Lab/Scripts/Generate_transactions.py
2025-12-09 16:37:53 -07:00

115 lines
4 KiB
Python

from faker import Faker
from dotenv import load_dotenv
from datetime import datetime
import os
import random
import pandas as pd
import boto3
# ---- Setup ----
fake = Faker()
load_dotenv()
s3 = boto3.resource(
"s3",
endpoint_url=os.getenv("STORAGE_ENDPOINT"),
aws_access_key_id=os.getenv("STORAGE_ACCESS_KEY"),
aws_secret_access_key=os.getenv("STORAGE_SECRET_KEY")
)
bucket_name = os.getenv("STORAGE_BUCKET")
accounts_key = "DataLab/accounts/accounts.csv"
transactions_s3_key = "DataLab/transactions/transactions.csv"
# ---- Ensure local data folder exists ----
os.makedirs("../Data", exist_ok=True)
# ---- Download accounts.csv from S3 ----
local_accounts_file = "../Data/accounts.csv"
try:
s3.Bucket(bucket_name).download_file(accounts_key, local_accounts_file)
print("Downloaded accounts.csv from S3.")
except Exception as e:
print("ERROR: Could not download accounts.csv:", e)
raise SystemExit()
# ---- Load accounts DataFrame ----
accounts_df = pd.read_csv(local_accounts_file)
# ---- Sample vendors ----
vendors = ["Amazon", "Walmart", "Target", "Starbucks", "Apple", "Netflix", "Uber", "Lyft", "BestBuy", "Costco"]
# ---- Helper Functions ----
def generate_transaction_id(account_id, idx):
"""Generate a unique transaction ID combining account ID and index."""
return f"{account_id}{str(idx).zfill(5)}"
def generate_transaction(account):
"""Generate a realistic transaction for a given account."""
t_type = random.choices(
["Deposit", "Withdrawal", "Payment", "Transfer"],
weights=[0.4, 0.3, 0.2, 0.1], k=1
)[0]
transaction_data = {
"transaction_id": None, # fill later
"account_id": account['account_id'],
"branch_id": None,
"transaction_type": t_type,
"amount": 0,
"date": fake.date_between(start_date=pd.to_datetime(account['open_date']), end_date=datetime.today()),
"balance_after": 0,
"vendor": None,
"transaction_location": None
}
if t_type in ["Deposit", "Withdrawal"]:
# Pick one of the branches for deposit/withdrawal
transaction_data["branch_id"] = account['branch_id']
amount = round(random.uniform(50, 7000), 2) if t_type == "Withdrawal" else round(random.uniform(20, 10000), 2)
if t_type == "Withdrawal":
amount = min(amount, account['balance'])
account['balance'] -= amount
else:
account['balance'] += amount
transaction_data["amount"] = amount
transaction_data["balance_after"] = round(account['balance'], 2)
transaction_data["transaction_location"] = f"Branch {account['branch_id']}"
else: # Payment or Transfer
transaction_data["branch_id"] = None
transaction_data["vendor"] = random.choice(vendors)
amount = round(random.uniform(5, 1000), 2)
account['balance'] = max(account['balance'] - amount, 0)
transaction_data["amount"] = amount
transaction_data["balance_after"] = round(account['balance'], 2)
transaction_data["transaction_location"] = "POS / Online"
return transaction_data
# ---- Generate transactions ----
transactions = []
idx = 1
for _, account in accounts_df.iterrows():
account_transactions_count = random.randint(5, 20)
for _ in range(account_transactions_count):
txn = generate_transaction(account)
txn['transaction_id'] = generate_transaction_id(account['account_id'], idx)
transactions.append(txn)
idx += 1
# ---- Convert to DataFrame ----
transactions_df = pd.DataFrame(transactions)
# ---- Save locally ----
local_transactions_file = "../Data/transactions.csv"
transactions_df.to_csv(local_transactions_file, index=False)
print("Generated transactions.csv locally with realistic branch/vendor data.")
# ---- Upload to S3 ----
try:
s3.Bucket(bucket_name).upload_file(local_transactions_file, transactions_s3_key)
print(f"Uploaded transactions.csv to s3://{bucket_name}/{transactions_s3_key}")
except Exception as e:
print("ERROR: Could not upload transactions.csv to S3:", e)