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)

Testing Python

The steps for testing Python are as follows:

  1. For PC users, after clicking All Programs, Anaconda3.6, and then Anaconda Prompt, we would see the following.

Note that different users would probably get a different path:

  1. Then, just type python, and we can launch it, as shown here:
  1. It tells us that Python 3.6.3 was operational. We could also try import scipy as sp to see if it is preinstalled:
  1. After we type import scipy as sp, no error message appears, which indicates that the package was preinstalled. The command of sp.sqrt(3) would offer us the square root of 3. Another example with the related graph is shown here:
import scipy as np 
from pylab import * 
x=np.linspace(-np.pi,np.pi,256,endpoint=True) 
c,s=np.cos(x),np.sin(x) 
plot(x,c),plot(x,s) 
show()  

The previous code will give an output such as the following:

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