We demo a script that converts python numerical commands to LaTeX format. A notebook available on our GitHub page will take this and pretty print the result.
Reinstalling software and configuring settings on a new computer is a pain. After my latest hard drive failure set the stage for yet another round of download-extract-install and configuration file twiddling, it was time to overhaul my approach. "Enough is enough!"
This post walks through
- how to back up and automate the installation and configuration process
- how to set up a minimal framework for data science
We’ll use a dotfiles repository on Github to illustrate both points in parallel.
We have made use of Python’s Pandas package in a variety of posts on the site. These have showcased some of Pandas’ abilities including the following:
- DataFrames for data manipulation with built in indexing
- Handling of missing data
- Data alignment
- Melting/stacking and Pivoting/unstacking data sets
- Groupby feature allowing split -> apply -> combine operations on data sets
- Data merging and joining
Pandas is also a high performance library, with much of its code written in Cython or C. Unfortunately, Pandas can have a bit of a steep learning curve — In this post, I’ll cover some introductory tips and tricks to help one get started with this excellent package.
- This post was partially inspired by Tom Augspurger’s Pandas tutorial, which has a youtube video that can be viewed along side it. We also suggest some other excellent resource materials — where relevant — below.
- The notebook we use below can be downloaded from our github page. Feel free to grab it and follow along.
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Code, references, and examples of this project are on Github.
In this post, I’ll describe the soup to nuts process of automating a literature search in Pubmed Central using R.
It feels deeply satisfying to sit back and let the code do the dirty work.
Is it as satisfying as a bowl of red-braised beef noodle soup with melt-in-your-mouth tendons from Taipei’s Yong Kang Restaurant (featured image)?
If you have to do a lit search like this more than once, then I have to say the answer is yes — unequivocally, yes.
A guest post, contributed by Cathy Yeh.
Can you format some data in Excel for me?
If you’re as excited as this tapir about the prospect of formatting data in Excel, read on!
Today, we’ll talk about reshaping data in R. At the same time, we’ll see how for-loops can be avoided by using R functionals (functions of functions). Functionals are faster than for-loops and make code easier to read by clearly laying out the intent of a loop.