16 best Python Accounting libraries in 2024

Python accounting provides software components. It enables powerful features like invoice generation, tax management, tax reminders, and risk management. 

Python development is ideal for analytics tools because of its gentle learning curve, flexibility, and open-sourced environment. It helps you build applications of many specializations, like machine learning, data analysis, financials, and cloud computing. Accountants can perform financial modeling in quantitative finance and tax solutions. A Python package enables the effective analysis of big data sets, large lists of numbers, and major financial data. You can also use these accounting libraries to simplify data visualization and statistical calculations. It makes working with invoices and structured data easier.

Below are our 16 best Python accounting open-source libraries in 2021. InvoiceNet is a deep neural network that can view PDF, JPG, and PNG invoices and extract intelligent information from them. You can train custom models, add or remove fields, and save the extracted information to your system. Another library, Rotki, is a portfolio tracking, analytics, accounting, and tax reporting application that works in the crypto and financial sectors and protects your privacy. On the other hand, LNbits is a free and open-source wallet or accounts system that has unique API keys for each wallet. It has a lightning-network functionality via LNbits extension framework.

erpnext  

• Integrated Enterprise Resource Planning (ERP) system with modules for various business functions.  
• Supports multi-currency and multi-language capabilities.  
• Offers features for managing accounts, inventory, sales, and more.  

InvoiceNet  

• Utilizes deep learning to extract structured information from invoices.  
• Can handle invoices in various languages and formats.  
• Provides a pre-trained model for invoice data extraction.  

beancount  

• A double-entry accounting tool that uses plain text files for data entry.  
• Provides powerful support for handling complex financial transactions.  
• Offers extensibility through plugins and scripting.  

timestrap  

• Specialized time tracking and invoicing system.  
• Allows for flexible project and task management.  
• Generates invoices directly from time entries.  

rotki  

• Cryptocurrency portfolio tracker with support for many exchanges and wallets.  
• Provides tax reporting and accounting features for crypto assets.  
• Offers real-time price data and portfolio performance metrics.  

django-userena  

• A customizable user management system for Django web applications.  
• Supports user profiles, account activation, and more.  
• Provides an extensible framework for authentication and user registration.  

fava  

• A web interface for Beancount, designed for easy financial data visualization.  
• Offers interactive charts and graphs for financial analysis.  
• Supports custom reporting and budget tracking.  

invoice2data  

• A Python library for extracting structured data from invoices.  
• Supports many invoice formats and templates.  
• Can output data in various formats, including JSON and CSV.  

QRLJacking  

• A security tool for testing QR code-based attacks.  
• Demonstrates vulnerabilities related to QR code authentication.  
• Raises awareness about the potential risks of QR code usage.  

receipt-parser  

• A Python library for parsing receipt data from images or scanned documents.  
• Extracts key information such as dates, items, prices, and taxes.  
• Can use it for expense tracking and accounting.  

django-ledger  

• A Django-based double-entry accounting system.  
• Provides a user-friendly web interface for managing financial data.  
• Supports many currencies and account types.  

koalixcrm  

• A CRM (Customer Relationship Management) system tailored for small and medium-sized businesses.  
• Offers features for managing contacts, sales, and projects.  
• Provides a customizable workflow engine.  

lnbits  

• A Lightning Network wallet and application platform for Bitcoin.  
• Supports Lightning Network payments and microtransactions.  
• Offers a user-friendly interface for managing Lightning wallets.  

django-hordak  

• A double-entry accounting library for Django web applications.  
• Provides a Django-friendly API for managing financial data.  
• Supports ledger-style accounting and reconciliation.  

bookkeeper  

• A financial ledger and accounting tool.  
• Offers double-entry bookkeeping capabilities.  
• Supports importing data from various formats, including CSV and QIF.  

django-invoice  

• A Django app for creating and managing invoices.  
• Provides features for invoice generation, tracking, and payment.  
• Offers customizable invoice templates and PDF generation.  

FAQ:  

1. Which data sources can data scientists use in Python accounting libraries?  

Data scientists who use accounting libraries can access many data sources in Python. You can find information in files, databases, web scraping, and market data providers. Data scientists study financial data from different sources to fully understand it.  

2. Which Python Libraries are most commonly used when working with financial data?  

The most used Python libraries for financial data are Pandas and NumPy. Pandas are great for manipulating and analyzing data, especially financial data. It helps clean, transform, and organize the data. NumPy is great for financial numerical computations. It supports operations on arrays, matrices, and multidimensional data. Together, Pandas and NumPy form a powerful duo for financial data analysis.  

3. What data types can the Python Library Numpy import and manipulate?  

Financial data scientists find NumPy invaluable because it can handle all data types. This software works with numbers and helps with organizing and changing data sets. You can easily import financial data into NumPy arrays for analysis and computation.  

4. How can a Data Scientist best use financial market datasets?  

Data scientists should employ a multifaceted approach to harness financial market datasets. To understand data, we clean and structure it. We analyze time series to find trends. We use machine learning for forecasting and risk assessment. We use data visualization to show insights. These methods help data scientists make smart decisions and achieve financial success.  

5. Can people use Python in an open source project to make their accounting apps?  

Users can create their accounting apps with Python using open-source projects. Notable examples include "Ledger" and "Beancount." These libraries form a base for double-entry accounting and financial management. You can adjust them to fit specific accounting requirements. You can customize your accounting solutions and enjoy the open-source community's contributions.  

6. Is Django better than other frameworks for creating Accounting Apps with Python?  

Django is a web framework that helps with developing accounting applications. It has advantages and can integrate Python libraries. It excels in providing a robust and secure foundation for web-based financial tools. Django offers built-in features for user authentication, data security, and efficient database management. You can use Python libraries to create secure and user-friendly accounting applications. They are scalable, modular, and great for financial data analysis and management.