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Python for Accounting and Finance: A Mind-Mapping Approach is an innovative textbook written for accounting and finance students and professionals with no prior coding experience. It introduces Python programming through a mind-mapping approach that presents complex concepts in a clear and structured way to support understanding and retention.
The book is organised into four parts: Python Basics, Data Analysis and Visualisation, Automation, and Machine Learning. It places programming within practical accounting and finance contexts so that learners can see the direct application of coding in their field. The textbook uses mind maps as a core instructional method. These visual diagrams show programming concepts and their connections, helping learners to understand, organise and recall information, particularly those who learn through visual representation.
The framework is organized into four main parts with focused chapters. Part 1, Python Basics, covers Introduction to Python and Mind Mapping Approach, Fundamentals of Programming, Control Structures, Data Structures, and File Handling. Part 2, Data Analysis and Visualization, includes Working with DataFrames, Exploratory Data Analysis, Time Series and Panel Data Analysis, and Data Visualization Techniques. Part 3, Automation, presents Automating Financial Tasks and Web Scraping with Cryptocurrency Data Access. Part 4, Machine Learning, covers Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, and Advanced Machine Learning with Financial Modeling, supporting progressive learning from fundamentals to financial applications.
Python has become increasingly important in accounting and finance due to its capabilities in data manipulation, automation, and predictive modelling.
• Data Analysis and Visualisation: Python manages and processes accounting and financial datasets. Tasks include data import, cleaning, handling missing values, sorting, and grouping. Exploratory data analysis involves summarisation and anomaly 3identification. Time series and panel data analysis using Python can identify trends, cycles, and seasonality. Visualisation uses libraries such as Matplotlib and Seaborn to generate and customise charts for presenting accounting and financial information.
• Automation: Python automates routine accounting and financial tasks such as data entry, calculation, and report generation. Python manages files and directories and handles errors. Automation applies to processes including invoice handling, data extraction from systems, and production of summary reports. Web scraping tools such as BeautifulSoup collect data from online sources. Application Programming Interfaces (APIs) provide access to real-time financial data and support integration into financial models.
• Machine Learning: Python supports Machine Learning workflows in accounting and finance. Supervised learning methods include linear regression, logistic regression, and random forests used for credit scoring and forecasting. Unsupervised learning methods such as clustering and isolation forest support segmentation and anomaly detection. Other applications include neural networks, algorithmic trading, and financial modelling.
Enhanced with Google Colab notebooks, this book creates a highly supportive learning experience, equipping students of accounting and finance with essential programming skills for their speciality
