Practical Business Python

Taking care of business, one python script at a time

Tue 06 September 2016

Creating Pandas DataFrames from Lists and Dictionaries

Posted by Chris Moffitt in articles   

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 analysis. In these cases it is helpful to know how to create DataFrames from standard python data structures such as lists or dictionaries. The basic process is not difficult but because there are several different options it is helpful to understand how each works. I can never remember whether I should use from_dict, from_records, from_items or the default DataFrame constructor. Normally, through some trial and error, I figure it out. Since it is still confusing to me, I thought I would walk through several examples below to clarify the different approaches. At the end of the article, I briefly show how this can be useful when generating Excel reports.


Wed 06 April 2016

Interactive Data Analysis with Python and Excel

Posted by Chris Moffitt in articles   

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.


Tue 26 January 2016

Learn More About Pandas By Building and Using a Weighted Average Function

Posted by Chris Moffitt in articles   

Pandas includes multiple built in functions such as sum, mean, max, 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 groupby.

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.


Mon 07 December 2015

Creating Advanced Excel Workbooks with Python

Posted by Chris Moffitt in articles   

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