It has long been established that accountants can now write computer programs just like software engineers do. Accountants can use Python programming language in various ways to streamline their work, automate repetitive tasks, and analyze financial data more efficiently.
I didn’t know how to properly shutdown a computer when I graduated from the university with a Bachelor’s Degree in accounting. It all started during my one year compulsory national service when I met with someone who introduced me to the exciting world of computer.
Well, this article is not about how I learnt how to program as an accountant. You can read the hyperlinked article for that – this article is about how accountants use python programming language, so let’s get started. Below are some common things accountants use Python programming language for in accounting:
Automatic Data Import & Extraction: Python can be used to extract data from various sources, such as spreadsheets, databases, and online platforms. Libraries like pandas can help accountants import data in different formats (e.g., CSV, Excel) and clean and manipulate it for analysis.
Data Analysis: Python provides powerful tools for data analysis and statistical calculations. I know that some people still prefer R programming language for this but as an accountant, I can tell you that many accountants can use libraries like pandas, NumPy, and SciPy to perform financial analysis, identify trends, calculate ratios, and generate financial reports.
Automation of Repetitive Tasks: Python allows accountants to automate routine tasks like data entry, report generation, and reconciliation. Automation can significantly reduce the risk of human error, prevent/reduce fraud and free up time for more strategic work.
Financial Modeling: Python can be used to create financial models, perform sensitivity analysis, and forecast future financial outcomes. Libraries like NumPy and pandas are helpful for handling time-series data and building financial models.
Visualization: Python offers libraries such as Matplotlib and Seaborn for creating visual representations of financial data. Data visualization helps accountants (especially those in the audit space) present their findings effectively and gain insights from complex data sets.
Machine Learning and Predictive Analytics: Accountants with advanced computer usage skills can leverage machine learning libraries like scikit-learn and TensorFlow to develop predictive models for financial forecasting, fraud detection, and risk assessment.
Reporting and Dashboards: It’s beautiful to behold when i see accountants using unique dashboards in their presentations. Python can be used to generate customized financial reports and interactive dashboards. Libraries like Plotly and Dash are popular choices for creating interactive data visualizations and web-based reporting tools.
Auditing and Fraud Detection: I personally do not see any reason why any auditor should still rely on sampling when 100% of the dataset can be used or audited. Python can assist in automating audit procedures and detecting anomalies or fraud in financial data. Machine learning algorithms can be trained to identify suspicious patterns and transactions.
Legal, Taxation and Compliance: Accountants can use Python to calculate and track tax liabilities, automate tax return preparation, and ensure compliance with changing tax regulations.
Integration into Accounting Software: Python can be used to integrate accounting software with other business systems or external data sources. APIs (Application Programming Interfaces) can be used to connect accounting software or ERPs like QuickBooks, Sage, SAP or Xero with custom Python scripts.
If you are wondering where to start learning python from, I recommend you start with online courses or tutorials specifically designed for finance professionals or hire developers with expertise in Python to assist in developing custom solutions tailored to their needs. The choice of tools and libraries will depend on the specific requirements of the accounting tasks at hand
Challenges accountants face when learning to code in python
When learning and implementing Python in the field of accounting, there are several challenges that you should anticipate. Some of the common challenges are discussed below.
Learning Curve: Learning a new programming language and its associated libraries can be challenging, especially if you have no prior programming experience. Be prepared to invest time and effort into acquiring the necessary skills.
Imposter Syndrome: We all experienced this. The only difference between some of us that successfully made it into programming as accountants and those who didn’t make is perseverance. I can assure you that you will doubt yourself but just hang in there and keep learning while you practice to become perfect.
Data Quality and Cleaning: Financial data can be messy and inconsistent. You may encounter challenges related to data cleaning, handling missing values, and ensuring data accuracy and integrity.
Integration with Existing Systems: Integrating Python scripts and tools with existing accounting systems or databases can be complex. Compatibility issues and data transfer can be challenging to address.
Security Concerns: Handling sensitive financial data requires strict security measures. Ensure that your Python scripts and applications adhere to data security best practices to protect sensitive information.
Compliance and Regulations: Depending on your industry and region, you may need to comply with specific regulations (e.g., GDPR, HIPAA) when handling financial data. Ensuring compliance can be a complex task.
Version Control: Managing code changes and collaborating with others on Python projects can be challenging without proper version control practices (e.g., Git). Learning how to use version control is essential. Learning to use a version control tool like git is quite simple. You can become proficient in a day.
Error Handling and Debugging: Something as simple as a semi colon (;) can make you spend a whole day debugging especially if you are not using the right methodology. Debugging Python code, especially in complex financial applications, can be time-consuming. Developing effective debugging skills is crucial.
Performance Optimization: As financial data sets can be large and complex, optimizing your Python code for performance may become necessary. Profiling tools and optimization techniques may be needed.
Maintaining Code: As your Python projects grow in complexity, maintaining and updating your code can become challenging. Good documentation and code organization practices can help with this.
Model Accuracy: If you are using machine learning or statistical models, be aware that model accuracy depends on data quality and model selection. Ensuring the models provide meaningful insights is important.
Resource Constraints: Depending on the scale of your projects, you may face limitations in terms of computational resources (e.g., memory, processing power). Be prepared to optimize your code for resource efficiency.
Training and Support: Access to training resources, support, and expertise in Python for accounting may be limited. Seek out online courses, forums, and communities for assistance.
Project Scope: Be cautious not to overcomplicate projects. Start with small, manageable tasks and gradually expand your Python capabilities as you become more proficient.
Ethical Considerations: When using Python for financial analysis, it’s important to be aware of ethical considerations, such as avoiding biases in your data analysis and maintaining data privacy.
Continuous Learning: Python is a dynamic language, and libraries evolve. Continuous learning is essential to stay up-to-date with the latest tools and best practices in Python programming for accounting.
Despite these challenges, the benefits of using Python in accounting, such as increased efficiency, reduced errors, and improved data analysis capabilities, can outweigh the initial difficulties. Be patient, persistent, and open to learning, and consider seeking advice and assistance from colleagues or professionals who have experience in using Python for accounting tasks
Required Skills or Areas to Pay More Attention on
As an accountant looking to use Python in your work, it’s important to focus on the following Python topics and libraries that are particularly relevant to accounting tasks:
Basic Python Syntax and Data Structures:
One cool thing about python as a programming language is that it’s syntaxes are easy to learn. Understand the fundamentals of Python syntax, variables, data types, and basic operations. Learning and mastering data structure could pose a little challenge but nothing you cannot handle.
Pandas:
Pandas is a powerful library for data manipulation and analysis. Learn how to work with DataFrames, filter, transform, and aggregate data.
NumPy:
NumPy is useful for numerical operations and working with arrays. It’s beneficial when dealing with financial calculations and modeling.
Matplotlib and Seaborn:
These libraries are essential for creating data visualizations and charts to present financial data effectively.
Working with Excel Files:
Learn how to read and write Excel files using libraries like pandas and openpyxl. This is particularly important for accountants who often work with spreadsheet data.
Data Cleaning and Transformation:
Understand how to clean and preprocess data, handle missing values, and convert data types. You cannot do any meaningful analysis with messy and dirty data – don’t say you weren’t warned.
Financial Analysis and Calculation:
Familiarize yourself with Python’s capabilities for performing financial calculations, such as NPV, IRR, and various financial ratios.
Time Series Analysis:
If you work with time-series data (e.g., stock prices, financial statements over time), learn how to handle time series data using pandas and perform time series analysis.
Automating Tasks:
Gain skills in automating repetitive tasks using Python scripts. This includes tasks like data import, report generation, and data reconciliation. Doing this was what sustained my passion to even learn more programming.
SQL and Database Integration (if applicable):
If your work involves databases, learn how to connect to databases, retrieve data using SQL, and work with Python libraries like SQLAlchemy.
Data Visualization and Reporting:
Explore libraries like Matplotlib, Seaborn, Plotly, and ReportLab for creating visual reports, charts, and dashboards.
API Integration:
Understand how to work with APIs to fetch data from online sources or integrate data from external systems with your Python scripts. Many things that you intend to do or implement are already residing somewhere in an API. In fact, there are somethings that you can only do through the use of APIs
Error Handling and Debugging:
Learn techniques for handling errors and debugging your Python code effectively.
Best Practices and Code Organization:
Adopt coding best practices, including code organization, documentation, and version control (e.g., Git) to maintain and share your code efficiently.
Compliance and Security:
Familiarize yourself with security best practices and compliance standards relevant to your field, especially when dealing with sensitive financial data.
Finance-Specific Libraries (optional):
Depending on your specific needs, consider exploring finance-specific libraries like QuantLib for advanced financial modeling.
These may at first sound scary but, It’s a good idea to start with the basics and gradually build your Python skills as you become more comfortable with the language. Online tutorials, courses, and books geared towards accountants and finance professionals can be valuable resources for learning Python in a finance context.
