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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In the python world, there are multiple options for visualizing your data. Because of this variety, it can be really challenging to figure out which one to use when. This article contains a sample of some of the more popular ones and illustrates how to use them to create a simple bar chart. I will create examples of plotting data with: Pandas, Seaborn, ggplot, Bokeh, pygal and Plotly.

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More and more information from local, state and federal governments is being placed on the web. However, a lot of the data is not presented in a way that is easy to download and manipulate. I think it is an important civic duty for us all to be aware of how government money is spent. Having the data in a more accessible format is a first step in that process.

In this article, I’ll use BeautifulSoup to scrape some data from the Minnesota 2014 Capital Budget. Then I’ll load the data into a pandas DataFrame and create a simple plot showing where the money is going.

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Most people likely have experience with pivot tables in Excel. Pandas provides a similar function called (appropriately enough) pivot_table. While it is exceedingly useful, I frequently find myself struggling to remember how to use the syntax to format the output for my needs. This article will focus on explaining the pandas pivot_table function and how to use it for your data analysis.

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A very common tasks for python and pandas is to automate the process of aggregating data from multiple files and spreadsheets.

This article will walk through the basic flow required to parse multiple Excel files, combine the data, clean it up and analyze it. The combination of python + pandas can be extremely powerful for these activities and can be a very useful alternative to the manual processes or painful VBA scripts frequently used in business settings today.

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