Files

132 lines
4.3 KiB
Python

import pandas as pd
import psycopg2
from psycopg2.extras import execute_values
import os
from dotenv import load_dotenv
# Load environment variables
load_dotenv()
# Database Configuration
DB_CONFIG = {
"user": os.getenv("DB_USER"),
"password": os.getenv("DB_PASSWORD"),
"name": os.getenv("DB_NAME"),
"port": os.getenv("DB_PORT"),
"host": os.getenv("DB_HOST")
}
def connect_to_db():
"""Establish connection to the database."""
return psycopg2.connect(
user=DB_CONFIG["user"],
password=DB_CONFIG["password"],
host=DB_CONFIG["host"],
port=DB_CONFIG["port"],
database=DB_CONFIG["name"]
)
def create_table_if_not_exists(conn):
"""Create the analytics_raw_transactions table if it doesn't exist."""
with conn.cursor() as cur:
cur.execute("""
CREATE TABLE IF NOT EXISTS analytics_raw_transactions (
id SERIAL PRIMARY KEY,
cust_id VARCHAR(10),
accountid VARCHAR(10),
tran_id VARCHAR(12),
entry_date TIMESTAMP NULL,
value_date TIMESTAMP NULL,
pstd_date TIMESTAMP NULL,
tran_date TIMESTAMP NULL,
tran_sub_ty VARCHAR(4),
part_tran_ty VARCHAR(4),
channel VARCHAR(32),
tran_amt DECIMAL(20,2),
balance DECIMAL(20,2),
isreverse VARCHAR(4),
reverse VARCHAR(4),
tran_particular VARCHAR(100)
)
""")
conn.commit()
def upload_xls_to_db(xls_path):
"""Upload data from XLS file to the database."""
try:
# Read XLS file
df = pd.read_excel(xls_path, dtype=str)
# Convert date columns to datetime
date_cols = ["ENTRY_DATE", "VALUE_DATE", "PSTD_DATE", "TRAN_DATE"]
for col in date_cols:
if col in df.columns:
df[col] = pd.to_datetime(df[col], errors='coerce')
df[col] = df[col].fillna(pd.Timestamp.now())
# Convert numeric columns
for col in ["TRAN_AMT", "BALANCE"]:
if col in df.columns:
df[col] = pd.to_numeric(df[col].str.replace(",", ""), errors='coerce')
# Truncate TRAN_PARTICULAR to 100 chars
if "TRAN_PARTICULAR" in df.columns:
df["TRAN_PARTICULAR"] = df["TRAN_PARTICULAR"].astype(str).str.slice(0, 100)
# Connect to database
conn = connect_to_db()
# Create table if it doesn't exist
create_table_if_not_exists(conn)
# Prepare data for insertion
data = df.to_dict('records')
# Insert data
with conn.cursor() as cur:
execute_values(
cur,
"""
INSERT INTO analytics_raw_transactions
(cust_id, accountid, tran_id, entry_date, value_date, pstd_date, tran_date, tran_sub_ty, part_tran_ty, channel, tran_amt, balance, isreverse, reverse, tran_particular)
VALUES %s
""",
[(
row.get('CUST_ID'),
row.get('ACCOUNTID'),
row.get('TRAN_ID'),
row.get('ENTRY_DATE'),
row.get('VALUE_DATE'),
row.get('PSTD_DATE'),
row.get('TRAN_DATE'),
row.get('TRAN_SUB_TY'),
row.get('PART_TRAN_TY'),
row.get('CHANNEL'),
row.get('TRAN_AMT'),
row.get('BALANCE'),
row.get('ISREVERSE'),
row.get('REVERSE'),
(row.get('TRAN_PARTICULAR') or '')[:100]
) for row in data]
)
conn.commit()
print(f"Successfully uploaded {len(data)} records to analytics_raw_transactions")
except Exception as e:
print(f"Error uploading data: {str(e)}")
if conn:
conn.rollback()
finally:
if conn:
conn.close()
if __name__ == "__main__":
import sys
if len(sys.argv) != 2:
print("Usage: python upload_xls.py <path_to_xls_file>")
sys.exit(1)
xls_path = sys.argv[1]
upload_xls_to_db(xls_path)