Is JupyterLab recommended for FP&A Professionals?

I have been this question asked many times - Is JupyterLab recommended data analytics for FP&A (Financial Planning and Analysis) professionals? Should, a FP&A professional invest his or her time and resources in learning JupyterLab over Microsoft Excel for data analytics needs? This blog answers some critical questions the FP&A professionals have regarding JupyterLab. Let’s start with the fundamental concepts.

Figure 1: JupyterLab or Microsoft Excel

In the field of Information Technology (IT), an integrated development environment (IDE) is a software application that provides a comprehensive tool kit for application and data science solution development. Just as a GPS makes the life of a car driver easier, so does the IDEs for the IT programmers. Just as accountants use spreadsheets to be productive, IT programmers use IDEs to be efficient in their work. Some of the IDEs well-suited for data analytics needs include JupyterLab, Google Colab, RStudio, Spyder, Microsoft Azure Notebooks, and Anaconda Navigator. But JupyterLab is the most popular IDE. But what exactly is JupyterLab and what makes it so popular?

JupyterLab, an open-source web application, was released in 2018 for working with notebooks. A notebook is an interactive document that combines executable code, visualizations, narrative text, and data. Even though JupyterLab supports over 40 programming languages for data analytics needs, it is primarily used with Python. Today, JupyterLab is the de facto standard IDE in data analytics. In this backdrop, JupyterLab can be quite beneficial for FP&A professionals for the following reasons.

1. Interactive Data Analysis and Visualization. JupyterLab allows FP&A professionals to interactively explore and analyze financial data and derive insights. With libraries like Matplotlib, Seaborn, Plotly, and Bokeh, professionals can create detailed and interactive visualizations to present financial data in a compelling manner. Tools within JupyterLab also facilitate the cleaning and preprocessing of financial data, ensuring accuracy and consistency in analysis.
   
2. Integration with Popular Libraries and Tools. JupyterLab libraries such as Pandas and NumPy can be very useful for data manipulation and numerical analysis of the financial data. In addition, SciPy and StatsModels libraries provide advanced statistical analysis capabilities for forecasting and risk analysis.
   
3. Ease of Sharing and Collaboration. Jupyter notebooks can be shared between various FP&A analysts and teams easily, allowing for better collaboration. Team members can review and comment on the analyses directly within the notebook as these notebooks provide a step-by-step record, making it easy to reproduce and verify results. JupyterLab supports integration with APIs for accessing and analyzing large volumes of financial data.
   
4. Automation and Scripting. Python’s extensive libraries within JupyterLab allow for the automation of repetitive tasks, manage ETL (extraction, transformation, and loading) data pipelines, automate the execution of financial reports and analyses, and more. These features can free up the time of the FP&A Analysts so that she/he can focus more on decision making and insights adoption.
   
5. Cost-Effective and Open Source
   

JupyterLab is free and open source, making it a cost-effective data analytics tool for FP&A teams. In addition, there is a large community of users and developers contributing to the improvement of JupyterLab and its libraries, providing ample resources and support.

In summary, JupyterLab is a powerful tool or IDE that can help the FP&A professionals enhance the efficiency, accuracy, and depth of their financial analysis. The interactive environment of JupyterLab, combined with the extensive capabilities of Python, makes it an excellent choice for data analytics in the FP&A domain. However, Microsoft Excel is traditionally considered the default tool for FP&A professionals. Even today, it remains highly popular for its versatility, flexibility, familiarity, easy of use, and more. Table below highlights the salient differences between JupyterLab and Microsoft Excel.

JupyterLab	............................................................................Excel
Purpose	JupyterLab is an open-source IDE. Ideal for expert programmers who work with large datasets that are in various formats.	Excel is a spreadsheet widely used for tabular data management, calculation, and visualization quickly and easily.
Data Handling	Can handle large datasets, perform complex data manipulations, and integrate with external databases and cloud services.	Best suited for small to medium-sized tabular data. Not suitable for large datasets or for complex data manipulations/processing.
Programming	Supports 40+ programming languages including Python, R, and Julia.	Limited to VBA (Visual Basic for Applications)
Visualization	Offers advanced, interactive, and customizable data visualization capabilities.	Provides a wide range of built-in chart types and visualization tools that are simple, easy and quick to use
Collaboration	Supports real-time collaboration through platforms like JupyterHub	Offers real-time collaboration features through Office 365 and OneDrive
Ecosystem	Open-source, cost-effective, and highly extensible. Users can add extensions to enhance functionality.	Part of the Microsoft Office suite. Provides a consistent user interface and experience across different Office applications like Word and PowerPoint.

Both JupyterLab and Microsoft Excel have their strengths and limitations. The choice depends on the specific task or purpose at hand. If your need is to derive insights on varied (structured and unstructured) and large data sets, have interactive visualizations, and collaboration between users and teams across heterogenous IT platforms, then JupyterLab is a viable option. If the need is to derive insights from a small set of tabular data quickly and easily, and work within the Microsoft ecosystem, then Microsoft Excel is the preferred solution. However, FP&A professionals can integrate Excel with JupyterLab and get the best of both words for a more robust and comprehensive approach to FP&A.

Last, but not least, Excel with JupyterLab are just tools, and a fool with any tool is still a fool. Simply having access to a tool does not make someone effective or competent. The FP&A Analysts should leverage their FP&A knowledge, data analytics concepts, communication and people management competencies, and judgmental skills, and the right tools to achieve the desired business outcomes.

---

Identify your path to CFO success by taking our CFO Readiness Assessmentᵀᴹ.

Become a Member today and get 30% off on-demand courses and tools!

For the most up to date and relevant accounting, finance, treasury and leadership headlines all in one place subscribe to The Balanced Digest.

Follow us on Linkedin!