cm010.Rmd

---
title: "More introduction to Python"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(cache=TRUE)
```

# cm010 - October 26, 2016

## Overview

* Demonstrate methods for working with data frames in `pandas`
* Visualize basic statistics using `matplotlib` and `seaborn`
* Practice analyzing the `gapminder` data set in Python

## Slides and links

* [Repo from class today](https://github.com/jmausolf/Gentle_R_to_Python)

* [Python for Data Analysis](http://proquestcombo.safaribooksonline.com.proxy.uchicago.edu/book/programming/python/9781449323592) -- Wes McKinney. Excellent companion for those who wish to use Python for data analysis. Covers similar topics to what we will be doing in R, through use of the [`NumPy`](http://www.numpy.org/), [`scipy`](https://www.scipy.org/scipylib/index.html), and [`pandas`](http://pandas.pydata.org/) libraries. Downside is that it is written for Python 2, but most examples and code are still usable. Also getting a bit dated (written in 2012) but a second edition is expected in 2017.
* [Python for Data Science Cheat Sheet: Pandas Basics](https://assets.datacamp.com/blog_assets/PandasPythonForDataScience.pdf)


## To do for Monday

* [Start homework 5](hw05-python-data-analysis.html)
* Make sure you are working on your final projects