Python has long established itself as one of the most powerful tools in modern controlling – far beyond classic Excel. In this article, we’ll explore how controllers can utilize Python for forecasting, automation, and data analysis, as well as where the greatest potentials lie.
Opportunities and Getting Started with Python
Python is not just a powerful and universal programming language; in the context of controlling, it serves as an open and highly efficient platform for data analysis, automation, and machine learning. For controllers aiming to evolve beyond Excel and handle complex issues more efficiently, Python represents a true multitool. It couples powerful functionality with a relatively simple syntax and a vast community.
In practice, Python is employed where Excel reaches its limits: processing large datasets, automating forecasting, detecting outliers, conducting scenario analyses, scraping web data, integrating APIs, or establishing completely automated reporting workflows. With libraries like pandas for data manipulation, matplotlib and seaborn for visualizations, scikit-learn for machine learning, or prophet for time series forecasting, almost every use case can be represented, from exploratory analysis to production models.
Combining with Jupyter Notebooks is Recommended
A particularly practical tool in the Python ecosystem is Jupyter Notebooks. They allow for an interactive approach, with data import, computation, visualization, and documentation occurring within a single interface in the browser. For controllers, this means step-by-step traceability, transparent commentary, and the ability to build analyses modularly. Notebooks are excellent for exploratory data analysis, training purposes, or prototyping new models without a traditional development environment.
An interesting bridge between Excel and Python is the newly available Python integration in Microsoft Excel (currently in Microsoft 365). This allows direct embedding of Python code within Excel, for instance, using matplotlib for visualizations, pandas for calculations, or conducting statistical analyses. This functionality offers exciting opportunities to combine the best of both worlds. For educational purposes, this article will primarily discuss Python with Jupyter Notebooks, as documentation is simpler and clearer in this context. However, with proper Python knowledge, the Excel integration can provide advantages over notebooks in certain scenarios.
Strengths and Weaknesses of Python
Flexibility and Scalability
Python’s strengths lie prominently in its flexibility, scalability, and the capacity to solve complex or unique requirements efficiently. Once you have built an automated forecasting workflow with prophet, email notifications, and database integration, you often don’t want to go back. Jupyter Notebooks make it easy to document these processes appealingly and present them interactively, which also facilitates coordination with other departments.
Basic Knowledge and Installation Efforts
However, Python also has its hurdles: getting started requires programming knowledge and a certain understanding of data structures and logic. Installing the right packages, handling errors, or setting up a stable environment can be non-trivial for beginners. Additionally, a conceptual leap is required—from the mouse-driven world of Excel to a code-driven methodology, which must be learned and embraced.
| Practical Example |
A BI-savvy controller uses Python to forecast material cost trends based on historical purchasing data. After importing and cleaning the data with pandas, she trains a prophet model that detects seasonal patterns and outputs weekly forecasts. The results are visualized, commented on, and exported as an HTML file for internal presentation or coordination with the purchasing department within a Jupyter Notebook. |
Thus, Python serves as the ideal tool for advanced controllers, BI analysts, and controlling teams with data affinity who seek more than just pivot tables. It is suitable for innovation projects where quick results, high adaptability, and technical depth are required.
Combination with Other Tools is Beneficial
The combination with other tools further enhances Python’s capabilities: with Excel for output and distribution of results, with KNIME for GUI-based preprocessing, or with Power BI for dashboards. In this context, LLMs like ChatGPT can provide enormous assistance when starting out, for example, in writing Python code, debugging, or explaining models.
A significant advantage is that Python is open source—meaning it’s free, vendor-independent, and continuously developed by a global community. For controllers, this translates into considerable freedom in tool selection, rapid innovation cycles, and a wealth of learning resources. However, working with open-source tools also requires personal responsibility, especially regarding installation, security, and maintenance. We will discuss these aspects in detail in Chapter 6.3.
Conclusion: Python isn’t a one-size-fits-all tool but is a powerful instrument for anyone who regularly works with data and wants to elevate their controlling efforts to the next level.
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This text is an excerpt from the book “Understanding and Applying Artificial Intelligence and Data Science in Controlling” by Florian Bliefert, published in 2026 by VCW.
