Tips for Customizing Your IPython and Pandas Display
Posted by Chris Moffitt in articles
Improving Pandas’s Excel Output
Posted by Chris Moffitt in articles
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.
Creating PDF Reports with Pandas, Jinja and WeasyPrint
Posted by Chris Moffitt in articles
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.
Generating Excel Reports from a Pandas Pivot Table
Posted by Chris Moffitt in articles
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.
Overview of Python Visualization Tools
Posted by Chris Moffitt in articles
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.