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)

Finding help

  1. In Chapter 1, Ecosystem of Anaconda, we showed that we could go to the link https://docs.anaconda.com/anaconda/user-guide/.
  2. After clicking the link, we would see four entries, shown here:
  1. We could type conda help to find information about the usages of Conda. In Windows, click All Programs | Anaconda | Anaconda Prompt. In the Prompt, type conda help, as shown here:
  1. To find all packages associated with a Conda environment, we could issue the conda list command.
  2. Since the number of packages is quite large, a better solution is to generate a text file. We could issue conda list >c:/temp/list.txt:
  1. The first several lines from the output file called list.txt are shown:
  1. We could write an R program to read this text file:
> x<-read.csv('c:/temp/list.txt',skip=2) 
> head(x) 
X_ipyw_jlab_nb_ext_conf....0.1.0............py36ha9200a3_0 
1...