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Introduction I have talked quite a bit about how pandas is a great alternative to Excel for many tasks. One of Excel’s benefits is that it offers an intuitive and powerful graphical interface for viewing your data. In contrast, pandas + a Jupyter notebook offers a lot of programmatic power but limited abilities to graphically display and manipulate a DataFrame view. There are several tools in the
Taking care of business, one python script at a time Introduction Pandas offers several options for grouping and summarizing data but this variety of options can be a blessing and a curse. These approaches are all powerful data analysis tools but it can be confusing to know whether to use a groupby , pivot_table or crosstab to build a summary table. Since I have previously covered pivot_tables, th
Taking care of business, one python script at a time Introduction It is difficult to write a python script that does not have some interaction with the file system. The activity could be as simple as reading a data file into a pandas DataFrame or as complex as parsing thousands of files in a deeply nested directory structure. Python’s standard library has several helpful functions for these tasks
Taking care of business, one python script at a time Introduction Python’s visualization landscape is quite complex with many available libraries for various types of data visualization. In previous articles, I have covered several approaches for visualizing data in python. These options are great for static data but oftentimes there is a need to create interactive visualizations to more easily ex
Taking care of business, one python script at a time Introduction The python visualization world can be a frustrating place for a new user. There are many different options and choosing the right one is a challenge. For example, even after 2 years, this article is one of the top posts that lead people to this site. In that article, I threw some shade at matplotlib and dismissed it during the analy
Taking care of business, one python script at a time Introduction One of the compelling features of pandas is that it has a rich library of methods for manipulating data. However, there are times when it is not clear what the various functions do and how to use them. If you are approaching a problem from an Excel mindset, it can be difficult to translate the planned solution into the unfamiliar pa
Taking care of business, one python script at a time Introduction In many practical Data Science activities, the data set will contain categorical variables. These variables are typically stored as text values which represent various traits. Some examples include color (“Red”, “Yellow”, “Blue”), size (“Small”, “Medium”, “Large”) or geographic designations (State or Country). Regardless of what the
Taking care of business, one python script at a time Introduction Whenever I am doing analysis with pandas my first goal is to get data into a panda’s DataFrame using one of the many available options. For the vast majority of instances, I use read_excel , read_csv , or read_sql . However, there are instances when I just have a few lines of data or some calculations that I want to include in my an
Taking care of business, one python script at a time Introduction 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 wi
Taking care of business, one python script at a time After a hiatus from the blog, I’m back with a new post. While I’ve still been using Python and Pandas, I wanted to explore some new technologies. In this post, I’ll delve into polars. This article will cover some basic polars concepts, pointing out both its strengths and differences compared to Pandas. While I’m not ditching Pandas completely, I
Taking care of business, one python script at a time Introduction 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_
Taking care of business, one python script at a time Introduction I have been very excited by the response to the first post in this series. Thank you to all for the positive feedback. I want to keep the series going by highlighting some other tasks that you commonly execute in Excel and show how you can perform similar functions in pandas. In the first article, I focused on common math tasks in E
Taking care of business, one python script at a time Introduction The purpose of this article is to show some common Excel tasks and how you would execute similar tasks in pandas. Some of the examples are somewhat trivial but I think it is important to show the simple as well as the more complex functions you can find elsewhere. As an added bonus, I’m going to do some fuzzy string matching to show
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