Python Automation for Data Reconciliation

Manual data reconciliation is one of the biggest time drains in finance and operations teams. A well-written Python script can eliminate hours of spreadsheet work every day ??? and do it more accurately.

The Stack

We use pandas for data manipulation, SQLAlchemy for database connectivity, and schedule or cron for automation. The script reads source data, applies transformation rules, compares against target data, and flags discrepancies in a report.

Sample Workflow

import pandas as pd
from sqlalchemy import create_engine

engine = create_engine("mysql+pymysql://user:pass@localhost/db")
df_source = pd.read_sql("SELECT * FROM transactions", engine)
df_target = pd.read_csv("target_file.csv")
discrepancies = df_source[~df_source["id"].isin(df_target["id"])]
print(discrepancies)

This approach reduced our team's monthly close time by 3 days.