Practical data wrangling : expert techniques for transforming your raw data into a valuable source for analytics / Allan Visochek.

Format:

Book

Author/Creator:

Visochek, Allan, author.

Language:

English

Subjects (All):

Data mining.

R (Computer program language).

Physical Description:

1 online resource (204 pages) : illustrations

Edition:

1st edition

Other Title:

Expert techniques for transforming your raw data into a valuable source for analytics

Place of Publication:

Birmingham ; Mumbai : Packt, 2017.

System Details:

text file

Biography/History:

Visochek Allan: Allan Visochek is a freelance web developer and data analyst in New Haven, Connecticut. Outside of work, Allan has a deep interest in machine learning and artificial intelligence. Allan thoroughly enjoys teaching and sharing knowledge. After graduating from the Udacity Data Analyst Nanodegree program, he was contracted to Udacity for several months as a forum mentor and project reviewer, offering guidance to students working on data analysis projects. He has also written technical content for LearnToProgram.

Summary:

Turn your noisy data into relevant, insight-ready information by leveraging the data wrangling techniques in Python and R About This Book This easy-to-follow guide takes you through every step of the data wrangling process in the best possible way Work with different types of datasets, and reshape the layout of your data to make it easier for analysis Get simple examples and real-life data wrangling solutions for data pre-processing Who This Book Is For If you are a data scientist, data analyst, or a statistician who wants to learn how to wrangle your data for analysis in the best possible manner, this book is for you. As this book covers both R and Python, some understanding of them will be beneficial. What You Will Learn Read a csv file into python and R, and print out some statistics on the data Gain knowledge of the data formats and programming structures involved in retrieving API data Make effective use of regular expressions in the data wrangling process Explore the tools and packages available to prepare numerical data for analysis Find out how to have better control over manipulating the structure of the data Create a dexterity to programmatically read, audit, correct, and shape data Write and complete programs to take in, format, and output data sets In Detail Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages that can be best used to manipulate different kinds of data, as per your requirements. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them. You'll start by understanding the data wrangling process and get a solid foundation to work with different types of data. You'll work with different data structures and acquire and parse data from various locations. You'll also see how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, we conclude with a quick primer on accessing and processing data from databases, conducting data exploration, and storing and retrieving data quickly using databases. The book includes practical examples on each of these points using simple and real-world data sets to give you an easier understanding. By the en...

Contents:

Cover

Title Page

Copyright

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Programming with Data

Understanding data wrangling

Getting and reading data

Cleaning data

Shaping and structuring data

Storing data

The tools for data wrangling

Python

R

Summary

Chapter 2: Introduction to Programming in Python

External resources

Logistical overview

Installation requirements

Using other learning resources

Python 2 versus Python 3

Running programs in python

Using text editors to write and manage programs

Writing the hello world program

Using the terminal to run programs

Running the Hello World program

What if it didn't work?

Data types, variables, and the Python shell

Numbers - integers and floats

Why integers?

Strings

Booleans

The print function

Variables

Adding to a variable

Subtracting from a variable

Multiplication

Division

Naming variables

Arrays (lists, if you ask Python)

Dictionaries

Compound statements

Compound statement syntax and indentation level

For statements and iterables

If statements

Else and elif clauses

Functions

Passing arguments to a function

Returning values from a function

Making annotations within programs

A programmer's resources

Documentation

Online forums and mailing lists

Chapter 3: Reading, Exploring, and Modifying Data - Part I

Data

File system setup

Introducing a basic data wrangling work flow

Introducing the JSON file format

Opening and closing a file in Python using file I/O

The open function and file objects

File structure - best practices to store your data

Opening a file.

Reading the contents of a file

Modules in Python

Parsing a JSON file using the json module

Exploring the contents of a data file

Extracting the core content of the data

Listing out all of the variables in the data

Modifying a dataset

Extracting data variables from the original dataset

Using a for loop to iterate over the data

Using a nested for loop to iterate over the data variables

Outputting the modified data to a new file

Specifying input and output file names in the Terminal

Specifying the filenames from the Terminal

Chapter 4: Reading, Exploring, and Modifying Data - Part II

Installing pandas

Understanding the CSV format

Introducing the CSV module

Using the CSV module to read CSV data

Using the CSV module to write CSV data

Using the pandas module to read and process data

Counting the total road length in 2011 revisited

Handling non-standard CSV encoding and dialect

Understanding XML

XML versus JSON

Using the XML module to parse XML data

XPath

Chapter 5: Manipulating Text Data - An Introduction to Regular Expressions

File structure setup

Understanding the need for pattern recognition

Introducting regular expressions

Writing and using a regular expression

Special characters

Matching whitespace

Matching the start of string

Matching the end of a string

Matching a range of characters

Matching any one of several patterns

Matching a sequence instead of just one character

Putting patterns together

Extracting a pattern from a string

The regex split() function

Python regex documentation

Looking for patterns

Quantifying the existence of patterns

Creating a regular expression to match the street address.

Counting the number of matches

Verifying the correctness of the matches

Extracting patterns

Outputting the data to a new file

Chapter 6: Cleaning Numerical Data - An Introduction to R and RStudio

Directory structure

Installing R and RStudio

Introducing R and RStudio

Familiarizing yourself with RStudio

Running R commands

Setting the working directory

Reading data

The R dataframe

R vectors

Indexing R dataframes

Finding the 2011 total in R

Conducting basic outlier detection and removal

Handling NA values

Deleting missing values

Replacing missing values with a constant

Imputation of missing values

Variable names and contents

Chapter 7: Simplifying Data Manipulation with dplyr

Installing the dplyr and tibble packages

Introducing dplyr

Getting started with dplyr

Chaining operations together

Filtering the rows of a dataframe

Summarizing data by category

Rewriting code using dplyr

Chapter 8: Getting Data from the Web

Filesystem setup

Installing the requests module

Internet connection

Introducing APIs

Using Python to retrieve data from APIs

Using URL parameters to filter the results

Chapter 9: Working with Large Datasets

System requirements

Installing MongoDB

Planning out your time

Cleaning up

Understanding computer memory

Understanding databases

Introducing MongoDB

Interfacing with MongoDB from Python

Index.

Notes:

Description based on print version record.

OCLC:

1017738649