From 34861c602a30e0ede58a004d25f1ea21b9cad1d2 Mon Sep 17 00:00:00 2001 From: Menna Fateen Date: Tue, 30 Oct 2018 00:30:17 +0900 Subject: [PATCH] added pandas link --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index bc20eb7..b1bbace 100644 --- a/README.md +++ b/README.md @@ -113,6 +113,7 @@ A collection of workhorse libraries that are elemental for any python data scien * [Pandas](http://pandas.pydata.org/) Wes McKinney's pandas library for EDA on small to medium sized data sets when you don't want to put the infrastructure for SQL or when it isn't necessary. It has many other great applications other than just better than SQL on small to medium data sets. * [Numpy/Pandas/Scipy Cheatsheet](https://s3.amazonaws.com/quandl-static-content/Documents/Quandl+-+Pandas,+SciPy,+NumPy+Cheat+Sheet.pdf) - self explanatory * [SciPy](http://www.scipy.org/) - Open-source software for mathematics, science and engineering. +* [Pandas Cheatsheet](https://assets.datacamp.com/blog_assets/PandasPythonForDataScience.pdf) - Pandas Basics Cheat Sheet * [NumPy](http://www.numpy.org/) - Fundamental package for scientific computing with Python. * [StatsModels](http://statsmodels.sourceforge.net/) - Module that allows users to explore data, estimate statistical models and perform statistical tests. * [PyMC](https://pypi.python.org/pypi/pymc) - Bayesian estimation useful for Markov chain Monte Carlo analysis (among other things).