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)

Drawing simple graphs

The simplest graph will be a straight line. For R, we have the following example:

x<-seq(-3,3,by=0.05) 
y<-2+2.5*x 
plot(x,y,type="b") 

In this simple program, type would specify the format of the line, and b would specify both the spot and the line. The corresponding graph is shown here:

The possible values for type are given in the following table:

Value
Description

p

for points

l

for lines

b

for both

c

for the lines part, alone of b

o

for both overplotted

h

for histogram-like (or high-density) vertical lines

s

for stair steps

S

for other steps (see the following details)

n

for no plotting

Table 4.1 Possible values for type in the R function plot()

For Python, we have the following simple example for a future value, given the present value and interest rate:

import numpy as...