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)

Data sorting

In R, we have several ways to sort data. The easiest way is to use the sort() function (see the code for the simplest one-dimensional data):

> set.seed(123) 
> x<-rnorm(100) 
> head(x) 
[1] -0.56047565 -0.23017749  1.55870831  0.07050839  0.12928774  1.71506499 
> y<-sort(x) 
> head(y) 
[1] -2.309169 -1.966617 -1.686693 -1.548753 -1.265396 -1.265061 

Let's look at another way to sort data. The dataset used is called nyseListing, which is included in the R package called fImport, shown here:

library(fImport) 
data(nyseListing) 
dim(nyseListing) 
head(nyseListing) 

The output is shown here:

In total, we have 3,387 observations, each with 4 variables. The dataset is sorted by Symbol, as in the tickers of individual stocks. Assume that we want to sort them by Name, as shown here:

> x<-nyseListing[order(nyseListing$Name),] 
> head(x...