This is a series of ipython notebooks for analyzing finance-related Big Data using Python's powerful PANDAS (Python Data Analysis) library.

If you are new to Python, you may wish to go through a series of tutorials I have created in order. If you want to go to some general data analysis tutorials, take a look at my other tutorial series on how how to analyze Twitter data PANDAS. In the first notebook in that series I show you how to set up your ipython working environment and import the Twitter data we have downloaded. And if you want to skip the data download and just use the sample data, but don't yet have Python set up on your computer, you may wish to go through the tutorial "Setting up Your Computer to Use My Python Code".

Also note that we are using the iPython notebook interactive computing framework for running the code in this tutorial. If you're unfamiliar with this see this tutorial "Four Ways to Run your Code".

For a more general set of PANDAS notebook tutorials, I'd recommend this cookbook by Julia Evans. I also have a growing list of "recipes" that contains frequently used PANDAS commands.

##Prerequisites As you may know from my other tutorials, I am a big fan of the free Anaconda version of Python 2.7. It contains all of the prerequisites you need and will save you a lot of headaches getting your system set up. Once it's all installed open up a terminal and run the following:

git clone https://github.com/gdsaxton/Financial-Analysis-using-Python.git
cd Financial-Analysis-using-Python
ipython notebook

A tab containing links to all of the available chapters will open up in your browser at http://localhost:8888

I hope you find these tutorials helpful. If so, then please share and spread the word and help build a community of PANDAS users.

Happy coding!