It’s incredibly easy to create Pandas DataFrames with data from databases, Excel and csv files or json responses from a web API. I am probably not exaggerating when I claim that almost all reporting in Python starts with Pandas. It’s very fast and powerful but comes with a steep learning curve. ReportLab creates direct PDF files without going through HTML or Excel first. It also offers a hosted solution so end users can change the input parameters that are used to create these reports. Like Pandas + HTML, it requires good HTML + CSS skills to make it look the way you want.ĭatapane allows you to create HTML reports with interactive elements. If formatted properly, it can be used as a source for PDFs, too. The Excel file can be exported to PDF.ĭash allows you to easily spin up a great looking web dashboard that is interactive without having to write any JavaScript code. It requires, however, an installation of Excel so it’s a good option when the report can be generated on a desktop, e.g. Xlwings allows the use of an Excel template so the formatting can be done by users without coding skills. it’s an ideal candidate for a “download to Excel” button in a web app. It can be run on a server where Excel is not installed, i.e. This is a great option if the report has to be in Excel. The HTML report can also be turned into a PDF for printing. You can generate beautiful reports in the form of static web pages if you know your way around HTML + CSS. Table of Contentsīefore we begin, here is a high level comparison of the libraries presented in this post: Library After reading this blog post, you should be able to pick the right library for your next reporting project according to your needs and skill set. There is a wealth of techniques and libraries available and we’re going to introduce five popular options here. Python is a popular tool for all kind of automation needs and therefore a great candidate for your reporting tasks.
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