Jupyter Notebooks are a widely used solution for quick analysis. As an alternative to creating code using scripts, they allow you to structure your code step by step and visualize the outputs of each code block. However, they are also powerful and sometimes underestimated tools for creating reports. Jupyter Notebooks allow you to combine code with rich text and interactive visualizations, which can be easily exported in a variety of formats, including HTML. In this way, a non-technical audience, who may not have tools such as integrated development environments installed on their computer and who have no interest in the code used in the analysis, can easily access the results from a browser.

In addition, in many projects, the use of Jupyter Notebooks is combined with Python scripts and pipelines. These Jupyter Notebooks are generally used to create interactive reports that support the analysis executed in the scripts. For this reason, it is interesting that the execution of the Notebooks is simultaneous to the execution of the pipeline, so that as we update, for example, several data sets, the interactive reports are also updated, ensuring that they always show the latest available data.

In this article, we will create a synthetic dataset that simulates the annual purchases we make at the supermarket. In addition, we will also create an interactive report where the purchases will be analyzed. Subsequently, we will simulate the update of this dataset with new purchases. At the same time that we update the dataset, we will also update the interactive report from a Python script. All this will be achieved without the need to run the notebook and export it manually as an HTML file.