pandas-express:使用Pandas和其他Python机器学习工具清理和分析Yelp数据的Python教程

## Pandas Express ###An Express Guide to becoming a Kung Fu Pandas Master After defeating the evil snow leopard Tai Lung, our favorite kung fu panda master Po returns to the Valley of Peace to help his father Mr. Ping with his noodle restaurant. Mr. Ping's noodle restaurant hasn't been doing so well, so Po is determined to help his dad figure out what he can do to improve his restaurant. Luckily, Po has been trained in the revered and ancient Python style of Shaolin martial arts and will analyze a dataset from Yelp to save his father's restaurant, like a true Kung Fu Pandas master. This is a Python tutorial for cleaning and analyzing the Yelp Data Challenge dataset with Pandas and other Python machine learning tools ### The Dataset We will be using a dataset of Yelp reviews provided by the [Yelp Dataset Challenge](http://www.yelp.com/dataset_challenge). The download consists of the following files in JSON format: * business.json - information on businesses * review.json - text and metadata of reviews * tip.json - text and metadata of tips * user.json - information on users * checkin.json - number of checkins at each business ###The Tools This tutorial will walk you through doing some basic data cleaning and exploratory analysis with Pandas and a suite of other Python data analysis tools. Below are a few of the tools we will be using: * [numpy](http://docs.scipy.org/doc/numpy-dev/user/index.html), for arrays * [pandas](http://pandas.pydata.org/), for data frames * [matplotlib](http://matplotlib.org/), for plotting * [seaborn](http://stanford.edu/~mwaskom/software/seaborn/), for making plots pretty * [statsmodels](http://statsmodels.sourceforge.net/), for statistical analysis * [sklearn](http://scikit-learn.org), for machine learning Follow-up tutorials on NLP, sentiment analysis, and more coming soon!