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)

Using Python via Jupyter

Here, we show you how to write simple Python code via Jupyter:

  1. From the menu, after clicking Anaconda, we can find an entry called Jupyter Notebook. After clicking on that entry, we can see the following:
  1. If we click New on the right-hand side, we can find several choices. After we choose Python 3, we end up with the following starting file:
  1. We can now type our Python commands in the box. Remember to press Shift + Enter if you want to execute the command (shown here):
  1. We can also type multiple commands and execute them, as shown here:

The colorful and distinct treatment of keywords, parentheses, and values makes our programming a little easier.

We could save our programs by choosing File|Save and Checkpoint on the menu bar. Similarly, we could load our presaved programs by choosing File|Revert to Checkpoint directly from the menu bar or finding...