Using Datapane, you can either generate one-off reports, or deploy your Jupyter Notebook or Python script so others can generate reports dynamically by entering parameters through an automatically generated web app. These components are compatible with Python objects such as pandas DataFrames, and many visualisation libraries, such as Altair:. This code renders a standalone HTML document with an interactive, searchable table and plot component.

Datapane can also be used to deploy Jupyter Notebooks and Python scripts so that other people who are not familiar with Python can generate custom reports. By adding a YAML file to your folder, you can specify input parameters as well as dependencies through pip, Docker, or local folders. Datapane also has support for managing secret variables, such as database passwords, and for storing and persisting files.

Here is a sample script stocks. For a full example see this example GitHub repository or read the docs. Required libraries : pandas , reportlab. ReportLab writes PDF files directly. In its most basic functionality, ReportLab uses a canvas where you can place objects using a coordinate system:. Within pages, Frames would then arrange Flowables e. Here is a sample of how it looks like, taken directly from the official documentation:.

Python offers various libraries to create professional reports and factsheets. It has a steep learning curve and requires to write quite some code but once the code has been written, it works at high speed. Last updated on June 3, 5 Python Libraries for Reporting and Factsheets Posted by Felix Zumstein - Comments Python is a popular tool for all kind of automation needs and therefore a great candidate for your reporting tasks.

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