This fundamental CSV tracks all financial transactions, similar to your paper journal entries:

This represents the raw CSV contents, which would look like this in a text editor:

Date,Transaction_ID,Description,Category,Account,Debit,Credit,Balance,Notes
2025-05-01,T001,Initial Balance,Equity,Checking,5000.00,0.00,5000.00,Starting balance
2025-05-05,T002,Office Supplies,Expense,Checking,0.00,125.00,4875.00,"Printer paper, ink, folders"
2025-05-10,T003,Client Payment,Income,Checking,750.00,0.00,5625.00,Invoice #103 - ABC Company
2025-05-15,T004,Rent Payment,Expense,Checking,0.00,1200.00,4425.00,May office rent
2025-05-20,T005,Utility Bill,Expense,Checking,0.00,95.00,4330.00,Electric bill
      

If you're using a formal double-entry system, this format maintains proper debits and credits:

This represents the raw CSV contents, which would look like this in a text editor:

Date,Entry_ID,Account_Code,Account_Name,Debit,Credit,Reference,Description
2025-05-05,JE001,530,Office Supplies,125.00,0.00,INV-456,Purchase of office supplies
2025-05-05,JE001,101,Cash,0.00,125.00,INV-456,Purchase of office supplies
2025-05-10,JE002,101,Cash,750.00,0.00,INV-103,Payment from ABC Company
2025-05-10,JE002,401,Service Revenue,0.00,750.00,INV-103,Payment from ABC Company
2025-05-15,JE003,510,Rent Expense,1200.00,0.00,CHK-1201,May office rent
2025-05-15,JE003,101,Cash,0.00,1200.00,CHK-1201,May office rent
      

A digital chart of accounts maintains your account structure and categorization:

This represents the raw CSV contents, which would look like this in a text editor:

Account_Code,Account_Name,Account_Type,Account_Category,Parent_Account,Description,Is_Active
101,Cash,Asset,Current Asset,,Main bank account,Yes
110,Accounts Receivable,Asset,Current Asset,,Money owed by customers,Yes
201,Accounts Payable,Liability,Current Liability,,Money owed to vendors,Yes
301,Owner's Capital,Equity,Equity,,Owner's investment in business,Yes
401,Service Revenue,Revenue,Income,,Income from services,Yes
510,Rent Expense,Expense,Operating Expense,,Office rent,Yes
530,Office Supplies,Expense,Operating Expense,,Consumable office items,Yes
      

Track your accounts receivable with a dedicated invoice CSV:

This represents the raw CSV contents, which would look like this in a text editor:

Invoice_Number,Date_Issued,Customer_ID,Customer_Name,Due_Date,Amount,Status,Payment_Date,Description
INV-101,2025-04-20,CUST001,XYZ Corporation,2025-05-20,1200.00,Paid,2025-05-18,Website development
INV-102,2025-04-25,CUST002,Smith Consulting,2025-05-25,750.00,Overdue,,IT support services
INV-103,2025-04-30,CUST003,ABC Company,2025-05-30,750.00,Paid,2025-05-10,Database optimization
INV-104,2025-05-05,CUST004,Johnson LLC,2025-06-04,500.00,Pending,,Cloud migration consultation
INV-105,2025-05-10,CUST001,XYZ Corporation,2025-06-09,350.00,Pending,,Maintenance services
      

1. Choose Your Tool: Select the spreadsheet application you'll use:
   • Microsoft Excel: Most powerful for advanced features, formulas, and formatting
   • Google Sheets: Excellent for cloud storage and collaboration
   • Apple Numbers: User-friendly with elegant templates (Mac/iOS only)
   • LibreOffice Calc: Free, open-source alternative with solid CSV support
2. Create Headers: Set up your CSV file with appropriate column headers (as shown in the examples above).
3. Transfer Historical Data:
   • Start with your current balance sheet accounts to establish opening balances
   • Enter the last 3 months of transactions for meaningful reporting
   • Transfer data systematically by transaction date (oldest to newest)
   • Double-check all entries against your paper records
4. Verify Data Integrity:
   • Confirm ending balances match your most recent paper records
   • Ensure all transactions have appropriate classifications
   • Run totals for various categories to compare with your paper summaries
5. Export as CSV: Save your completed spreadsheet in CSV format for maximum compatibility.

1. Create a New Spreadsheet: Open your chosen spreadsheet application and start a blank document.
2. Set Up Headers: In the first row, enter the column headers as shown in the transaction.csv example above.
3. Format Columns:
   • Date columns: Use date format (YYYY-MM-DD for universal compatibility)
   • Amount columns: Format as currency with 2 decimal places
   • Text columns: Format as text
4. Start with Opening Balances: Enter your starting account balances as the first entries.
5. Create Automatic Balance Formula: In the Balance column, create a formula that adds the Debit value and subtracts the Credit value from the previous balance:
   • For Excel/Google Sheets, a formula like: =IF(ROW()=2,F2-G2,H1+F2-G2)
   • This automatically calculates the running balance as you add new transactions
6. Implement Data Validation: Add validation rules to ensure data integrity:
   • Drop-down lists for categories and accounts
   • Date validation to prevent future-dated entries
   • Number validation for amount fields
7. Save Periodically: Create regular backups using "Save As" with date-stamped filenames.
8. Export as CSV: Use "Save As" or "Export" and select CSV format.

Create a separate sheet that pulls data from your transactions.csv to generate an income statement:

This would be generated using SUMIFS formulas to total transactions by category and month:

// Formula to calculate Service Revenue
=SUMIFS(transactions!F2:F100,transactions!D2:D100,"Income",transactions!E2:E100,"Service Revenue",
        transactions!A2:A100,">2025-05-01",transactions!A2:A100,"<2025-06-01")

// Formula to calculate Rent Expense
=SUMIFS(transactions!G2:G100,transactions!D2:D100,"Expense",transactions!E2:E100,"Rent",
        transactions!A2:A100,">2025-05-01",transactions!A2:A100,"<2025-06-01")

// Formula for percentage of revenue
=B4/B8*100
      

import pandas as pd
import matplotlib.pyplot as plt

# Load transaction data
transactions = pd.read_csv('transactions.csv', parse_dates=['Date'])

# Monthly expense summary
monthly_expenses = transactions[transactions['Category'] == 'Expense'].groupby(
    pd.Grouper(key='Date', freq='M'))['Credit'].sum()

# Monthly income summary
monthly_income = transactions[transactions['Category'] == 'Income'].groupby(
    pd.Grouper(key='Date', freq='M'))['Debit'].sum()

# Calculate monthly profit/loss
monthly_profit = monthly_income.subtract(monthly_expenses, fill_value=0)

# Plot the results
plt.figure(figsize=(12, 6))
monthly_income.plot(label='Income')
monthly_expenses.plot(label='Expenses')
monthly_profit.plot(label='Profit/Loss', linestyle='--')
plt.title('Monthly Financial Summary')
plt.xlabel('Month')
plt.ylabel('Amount ($)')
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.savefig('financial_summary.png')
plt.show()

# Category breakdown
category_expenses = transactions[transactions['Category'] == 'Expense'].groupby('Description')['Credit'].sum().sort_values(ascending=False)
print("Top Expense Categories:")
print(category_expenses.head(5))