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Python Introduction

One of the most versatile languages to use in the analysis of data is Python.

In addition to being a very simple and easy to learn language, the high number of available libraries (and in progress) make it a multipurpose tool.

This is one of its main advantages over R, the other most important tool at the moment in data analysis. Once we learn Python we can use it exploratory analysis but also write scripts for connecting to servers, security or any other purpose you can imagine.

Lots of information can be found in the web about this language. If you don’t know it you can start from its official site (www.python.org).

In this first post, I just want to show its power using it through Iphyton Notebook, a browsed-based tool to use it. Ideal for exploratory data analysis.

Once Ipython is installed (see ipython.org for more information), we will only have to open a command window and type ipython notebook.

Creating a new notebook and entering the code in this webpage we would be able to obtain the prices for the ticker SPY (ETF that follows the S&P index) and running it, we would see this prices chart:

Python Introduction - Prices Chart

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