I have heard from various people that my previous articles on common Excel tasks in pandas were useful in helping new pandas users translate Excel processes into equivalent pandas code. This article will continue that tradition by illustrating various pandas indexing examples using Excel’s Filter function as a model for understanding the process.
I have written several times about the usefulness of pandas as a data manipulation/wrangling tool and how it can be used to efficiently move data to and from Excel. There are cases, however, where you need an interactive environment for data analysis and trying to pull that together in pure python, in a user-friendly manner would be difficult. This article will discuss how to use xlwings to tie Excel, Python and pandas together to build a data analysis tool that pulls information from an external database, manipulates it and presents it to the user in a familiar spreadsheet format.
I have written several articles about using python and pandas to manipulate data and create useful Excel output. In my experience, no matter how strong the python tools are, there are times when you need to rely on Excel as the vehicle to communicate your message or further analyze the data. This article will walk through some additional improvements you can make to your Excel-based output by:
Adding Excel tables with XlsxWriter
Inserting custom VBA into your Excel file
Using COM for merging multiple Excel worksheets
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
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.
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.