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)

Parallel processing in Python

The following example is about computing π digits and is borrowed from the website http://ipyparallel.readthedocs.io/en/latest/demos.html#parallel-examples. Since the first part needs a program called one_digit_freqs() function, we could run a Python program called pidigits.py contained at .../ipython-ipython-in-depth-4d98937\examples\Parallel Computing\pi, and this path depends on where the reader downloaded and saved his/her files.

To complete our part, we simply include it in the first part of the program, as shown here:

import matplotlib.pyplot as plt
import sympy
import numpy as np
#
def plot_one_digit_freqs(f1):
"""
Plot one digit frequency counts using matplotlib.
"""
ax = plt.plot(f1,'bo-')
plt.title('Single digit counts in pi')
plt.xlabel('Digit')
plt.ylabel...