Book Image

Hands-On Data Science with Anaconda

By : Yuxing Yan, James Yan
Book Image

Hands-On Data Science with Anaconda

By: Yuxing Yan, James Yan

Overview of this book

Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world. The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You’ll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You’ll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Once you’re accustomed to all this, you’ll start with operations in data science such as cleaning, sorting, and data classification. You’ll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you’ll learn how to visualize data using the packages available for Julia, Python, and R.
Table of Contents (15 chapters)

Anaconda add-on

The following information is from the Anaconda Addon Development Guide.

An Anaconda add-on is a Python package containing a directory with an __init__.py file and other source directories (sub packages) inside. Because Python allows importing each package name only once, the package top-level directory name must be unique. At the same time, the name can be arbitrary, because add-ons are loaded regardless of their name; the only requirement is that they must be placed in a specific directory.

The suggested naming convention for add-ons is therefore similar to that of Java packages or D-Bus service names: prefix the add-on name with the reversed domain name of your organization, using underscores (_) instead of dots so that the directory name is a valid identifier for a Python package. An example add-on name following these suggestions would therefore be, for example...