Pandas DataFrame

Putting Python Pandas DataFrames Together

If you have worked with Pandas for any length of time you have probably come across the need to stick a dataframe together with another dataframe.  It turns out it’s not as simple as you might think.

I’m going to show you a couple of ways to accomplish putting one or more dataframes together in Pandas depending on the situation and desired results.


the best basketball players in the NBA

The Best 25 ( or so ) Players in the NBA 2018 and the 2018 NBA MVP

There is little doubt that advanced metrics have helped the Houston Rockets become the best team in the NBA so far in 2018.  In terms of how the team plays and was built, modern analytics have significantly contributed to their success.    In the spirit of celebrating NBA analytics I thought that I would compile as much advanced player evaluation data as I could to answer the un-answerable: Who is the best player in the NBA this year?

Most Valuable” is such a loaded term these days.  Everybody has their own idea about what makes an MVP.  Some people think it’s the best player on the best team.  Other people focus on old-timey, box score statistics like who’s scored the most points or gotten the most rebounds.  I’ve heard people focus on the word valuable as in this good team would not be good without player x because he adds the most value…


Only 14 NBA Teams Matter Now. Okay, Would You Believe 4…

Due to my situationally forced absence from all things digital (nothing horrible, just a very difficult move + personal milestone) I have had allot of time to think.  I have re-written my Pigskin Prognosticator in Python over and over again in my head. When I got back at it for real however,  I went a little sideways.

After talking to my brother for days on end about the NCAA Tournament, I was in full hoops mode.  Since the tourney was wrapping up all I had left of interest was The Association.  So I once again attempted to get some good NBA data; which seems to be more difficult than obtaining reliable football data.  While not as rich as my NFL data source, I did manage to get my virtual hands on some solid NBA team data anyway.


pandas for python

Python Pandas Prolonged

The other day I posted a Pandas for Python Primer . I promised a follow up before the end of the week; and away we go…

Pandas for Python, A Slight Return

Previously I talked about creating DataFrames, their rows and columns, and how one might apply a function to them. In my experience, more often than not you’ll want to import some data. You start by importing Pandas of course. A note here – by convention some coders import Pandas as pd. You’ll want to replace my pandas. to pd. if you are doing this.


Pandas for Python

In my continuing education / love affair with Python I have moved past the basics. I have started exploring the rich Python ecosystem, and my latest fascination is Pandas.

What is Pandas for Python?

Pandas for Python helps coders with data manipulation and analysis. It offers data structures and operations for manipulating numerical tables and time series. It’s free software released under a BSD license.