In case you missed it, github recently announced that Jupyter notebooks will be natively rendered by github. This useful new feature will make it easier for followers of pbpython to view notebooks through github as well as download them to your local system and follow along.

I have moved over 4 notebooks to github and set up the associated files so that it should be pretty straightforward for anyone to checkout the pbpython repo and work with the notebooks. This will also make it easier for others to follow along and help spot issues and make this collection of tips and tricks even more robust.

This post also contains a couple of helpful links I wanted to pass on and keep record of because I think they are really useful.

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The combination of IPython + Jupyter + Pandas makes it easy to interact with and display your data. Not surprisingly, these tools are easy to customize and configure for your own needs. This article summarizes some of the most useful and interesting options.

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Pandas makes it very easy to output a DataFrame to Excel. However, there are limited options for customizing the output and using Excel’s features to make your output as useful as it could be. Fortunately, it is easy to use the excellent XlsxWriter module to customize and enhance the Excel workbooks created by Panda’s to_excel function. This article will describe how to use XlsxWriter and Pandas to make complex, visually appealing and useful Excel workbooks. As an added bonus, the article will briefly discuss the use of the new assign function that has been introduced in pandas 0.16.0.

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Pandas is excellent at manipulating large amounts of data and summarizing it in multiple text and visual representations. Without much effort, pandas supports output to CSV, Excel, HTML, json and more. Where things get more difficult is if you want to combine multiple pieces of data into one document. For example, if you want to put two DataFrames on one Excel sheet, you need to use the Excel libraries to manually construct your output. It is certainly possible but not simple. This article will describe one method to combine multiple pieces of information into an HTML template and convert it to a standalone PDF document using Jinja templates and WeasyPrint.

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The previous pivot table article described how to use the pandas pivot_table function to combine and present data in an easy to view manner. This concept is probably familiar to anyone that has used pivot tables in Excel. However, pandas has the capability to easily take a cross section of the data and manipulate it. This cross section capability makes a pandas pivot table really useful for generating custom reports. This article will give a short example of how to manipulate the data in a pivot table to create a custom Excel report with a subset of pivot table data.

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