5 Python Automations Every CA Should Know in 2025
Introduction to Python Automations
In today’s fast-paced accounting world, Chartered Accountants (CAs) are expected to deliver faster, more accurate results while handling complex data and compliance requirements. Fortunately, Python—an easy-to-learn, powerful programming language—is helping CAs automate repetitive tasks and reduce errors.
Whether you manage GST filings, perform TDS reconciliations, or handle audit sampling, Python can make your life significantly easier. This guide introduces 5 must-know Python automations tailored for CAs in India, complete with code examples and real-world applications.
1. GST Filing Automation with Python
With Python, CAs can streamline GST data validation, JSON generation, and upload to the GSTN portal. Using libraries like pandas for data cleaning and APIs provided by the GST Network, you can automate entire GST workflows.
Key Steps:
Read GST invoice data from Excel using
pandas.Format and clean the data (remove duplicates, standardize fields).
Generate JSON in the format accepted by GSTN.
Use authentication to connect with the GST API portal.
Sample Script:
import pandas as pd
import json
df = pd.read_excel(‘invoices.xlsx’)
df.drop_duplicates(inplace=True)
# Format the GST JSON
gst_data = {“gstin”: “29ABCDE1234F2Z5”, “data”: df.to_dict(orient=’records’)}
with open(‘gst_return.json’, ‘w’) as f:
json.dump(gst_data, f)
2. TDS Reconciliation Python Script
Manually reconciling TDS entries with Form 26Q can be time-consuming. A Python script can automatically compare entries from your internal records against TDS statements and highlight mismatches.
Steps:
Read Form 26Q (PDF or Excel) and ledger entries.
Use
pandas.merge()to align transactions.Flag unmatched entries for review.
Sample Code:
df_26q = pd.read_excel(‘form_26q.xlsx’)
df_ledger = pd.read_excel(‘tds_ledger.xlsx’)
merged = pd.merge(df_ledger, df_26q, on=’PAN’, how=’outer’, indicator=True)
discrepancies = merged[merged[‘_merge’] != ‘both’]
discrepancies.to_excel(‘tds_discrepancy_report.xlsx’)
3. Bank Reconciliation via Python
Bank reconciliations often involve matching large volumes of Excel transactions. Python can automate this by matching transaction IDs, dates, and amounts between bank statements and your ledger.
Implementation:
Read bank and ledger Excel files.
Standardize formats.
Identify unmatched or duplicate transactions.
Code Example:
bank_df = pd.read_excel(‘bank_statement.xlsx’)
ledger_df = pd.read_excel(‘company_ledger.xlsx’)
# Inner join on common keys
matched = pd.merge(bank_df, ledger_df, on=[‘Date’, ‘Amount’], how=’inner’)
unmatched = bank_df[~bank_df.index.isin(matched.index)]
unmatched.to_excel(‘unmatched_bank_entries.xlsx’)
4. Audit Sampling Scripts
Audit procedures often require sampling from large financial datasets. With Python, CAs can perform random or stratified sampling, ensuring statistically sound selections.
Random Sampling:
sample = df.sample(n=50, random_state=1)
Stratified Sampling (by vendor or amount group):
sample = df.groupby(‘Vendor’).apply(lambda x: x.sample(frac=0.1)).reset_index(drop=True)
5. Automated Financial Report Generator
Imagine turning raw Excel data into a client-ready PDF or PowerPoint with a single click. With pandas, matplotlib, and libraries like python-pptx or fpdf, you can automate:
Trend charts
Cash flow summaries
Month-on-month comparisons
import smtplib
from email.message import EmailMessage
msg = EmailMessage()
msg[‘Subject’] = ‘Weekly Financial Report’
msg[‘From’] = ‘you@example.com’
msg[‘To’] = ‘client@example.com’
msg.set_content(‘Please find attached your weekly financial report.’)
with open(‘report.pdf’, ‘rb’) as f:
msg.add_attachment(f.read(), maintype=’application’, subtype=’pdf’, filename=’report.pdf’)
# SMTP sending logic here
Why Every CA Needs These Scripts
These Python automations aren’t just time-savers—they’re accuracy boosters. Whether you’re an independent CA or part of a mid-size firm, automation helps with:
Time savings on repetitive tasks.
Reduced manual errors.
Standardization across clients and engagements.
Audit trail with automated logs.
When clients expect speed and reliability, automation is your competitive advantage.
Conclusion
Python is no longer just for developers—it’s a powerful ally for Chartered Accountants who want to save time, improve accuracy, and stay ahead in a competitive market. From GST filing automation to bank reconciliation and audit sampling, these five Python scripts address common pain points in a CA’s workflow, especially in the Indian regulatory context.
By adopting these automations, you’re not just streamlining tasks—you’re setting the foundation for scalable, error-free financial operations. Whether you’re working solo or leading a team, mastering these tools can transform your productivity and client service quality.