(Python) Anaconda Introduction

Today begins what will likely result in a long series of articles pertaining to the programming language: Python.

Python is lightweight scripting language which is useful for many purposes. It may not be too much of an exaggeration to state that Python is now almost primarily used for data science purposes. If this is not indeed the case, it is not inaccurate to state that Python is heavily utilized for the purposes of data mining and data science.

You will need to make two installations prior to beginning the exercises featured within the subsequent entries. First, you will need to install the Python programming language. Next, you will need to install an integrated development environment (IDE). The latter will enable your ability to interact with the Python language from outside of the console. These two elements interact in a manner which is similar to the way in which R-Studio and the R programming language communicate.

The subsequent exercises utilize the Anaconda Python distribution. Anaconda Python is a version of Python 3 which includes within its installation, various IDEs and Python packages. As a result of such, you only need to make a single installation. I have chosen this distribution as it includes binaries which enable the R package "Keras", to function.

"Keras" is a machine learning package which will be discussed in later articles.

I would recommend viewing a tutorial video as it relates to exactly how to download and install Anaconda Python.

However, once the program is installed, you can initiate the platform by double clicking the desktop icon:

Or, additionally, it can be accessed from the start menu:

The result should be the following interface:

Spyder is installed as an aspect of the Anaconda package, and it is this particular program that I utilize as my IDE when creating Python exercises. If you require more information as to how this interface functions, there are many resources which can be found online to assist you.

There are additional options as it relates to distribution and IDEs, and you are more than welcome to explore these. In the next article we will begin to discuss Python data types. Stay tuned, Data Heads.