Practical Business Python

Taking care of business, one python script at a time

Mon 19 December 2016

Building a Financial Model with Pandas - Version 2

Posted by Chris Moffitt in articles   

In my last article, I discussed building a financial model in pandas that could be used for multiple amortization scenarios. Unfortunately, I realized that I made a mistake in that approach so I had to rethink how to solve the problem. Thanks to the help of several individuals, I have a new solution that resolves the issues and produces the correct results.

In addition to posting the updated solution, I have taken this article as an opportunity to take a step back and examine what I should have done differently in approaching the original problem. While it is never fun to make a mistake in front of thousands of people, I’ll try to swallow my pride and learn from it.

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Mon 21 November 2016

Building a Financial Model with Pandas

Posted by Chris Moffitt in articles   

In my previous articles, I have discussed how to use pandas as a replacement for Excel when it comes to data wrangling. In many cases, a python + pandas solution is superior to the highly manual processes many people use for manipulating data in Excel. However, Excel is used for many scenarios in a business environment - not just data wrangling. This specific post will discuss how to do financial modeling in pandas instead of Excel. For this example, I will build a simple amortization table in pandas and show how to model various outcomes.

In some ways, building the model is easier in Excel (there are many examples just a google search away). However, as an exercise in learning about pandas, it is useful because it forces you to think about how to use pandas strengths to solve a problem in a way different from the Excel solution. In my opinion the solution is more powerful because you can build on it to run multiple scenarios, easily chart various outcomes and focus on aggregating the data in a way most useful for your needs.

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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 though 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.

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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.

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