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 would like to offer this blog as platform for people to share their success stories with python. Over the past couple of weeks, I have had a handful of conversations related to the topic of how to get python implemented in an organization. In these conversations, I have noticed a lot of common themes related to getting the process started and sustaining it over time.
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.
Pandas includes multiple built in functions such as
min, etc. that you can apply to a DataFrame or grouped data.
However, building and using your own function is a good way to learn more about
how pandas works and can increase your productivity with data wrangling and analysis.
The weighted average is a good example use case because it is easy to understand but useful formula
that is not included in pandas. I find that it can be more intuitive than a simple average
when looking at certain collections of data. Building a weighted average function
in pandas is relatively simple but can be incredibly useful when combined with
other pandas functions such as
This article will discuss the basics of why you might choose to use a weighted average to look at your data then walk through how to build and use this function in pandas. The basic principles shown in this article will be helpful for building more complex analysis in pandas and should also be helpful in understanding how to work with grouped data in pandas.
As we close out the year, I wanted to take a step back and write a post that will motivate people to learn python and apply it to their daily jobs. Based on some comments I’ve received (and my own personal observations), some people struggle to get started on this journey. They see the potential value of using python in their jobs but are not sure where to start and can not find the time to take the first steps. Closely related to this challenge is finding the perseverance to make it through the inevitable barriers you will encounter. My goal in this article is to provide some items to keep in mind so that you can be successful in your endeavors to learn python and apply it to your job. If you take the time (definitely no easy task) to develop your python skills, you can reap many benefits - outside of the obvious ones you may have started out seeking.