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)

Dealing with missing data

First, let's look at the missing codes for different languages:

Languages
Missing code
Explanation or examples

R

NA

NA stands for Not Available

Python

nan

import scipy as sp

misingCode=sp.nan

Jullia

missing

julia> missing + 5

missing

Octave

NaN

Same for MATLAB as well

Table 3.7: Missing codes for R, Python, Julia, and Octave

For R, the missing code is NA. Here are several functions we could use to remove those missing observations, shown in an example:

> head(na_example,20) 
[1]  2  1  3  2  1  3  1  4  3  2  2 NA  2  2  1  4 NA  1  1  2 
> length(na_example) 
[1] 1000 
> x<-na.exclude(na_example) 
> length(x) 
[1] 855 
> head(x,20) 
[1] 2 1 3 2 1 3 1 4 3 2 2 2 2 1 4 1 1 2 1 2 

In the previous example, we removed 145 missing values by using the R function called na.exclude(). We could...