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Practical Data Wrangling

By : Visochek

Buy this Book

Practical Data Wrangling

By: Visochek

Buy this Book

Overview of this book

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 end of the book, you’ll have a thorough understanding of all the data wrangling concepts and how to implement them in the best possible way.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Programming with Data

Understanding data wrangling

The tools for data wrangling

Summary

Introduction to Programming in Python

External resources

Logistical overview

Running programs in python

Data types, variables, and the Python shell

Compound statements

Making annotations within programs

A programmer's resources

Summary

Reading, Exploring, and Modifying Data - Part I

External resources

Logistical overview

Introducing a basic data wrangling work flow

Introducing the JSON file format

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

Reading the contents of a file

Exploring the contents of a data file

Modifying a dataset

Outputting the modified data to a new file

Specifying input and output file names in the Terminal

Summary

Reading, Exploring, and Modifying Data - Part II

Logistical overview

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

Handling non-standard CSV encoding and dialect

Understanding XML

Using the XML module to parse XML data

Summary

Manipulating Text Data - An Introduction to Regular Expressions

Logistical overview

Understanding the need for pattern recognition

Introducting regular expressions

Looking for patterns

Quantifying the existence of patterns

Extracting patterns

Summary

Cleaning Numerical Data - An Introduction to R and RStudio

Logistical overview

Introducing R and RStudio

Familiarizing yourself with RStudio

Conducting basic outlier detection and removal

Handling NA values

Variable names and contents

Summary

Simplifying Data Manipulation with dplyr

Logistical overview

Introducing dplyr

Getting started with dplyr

Chaining operations together

Filtering the rows of a dataframe

Summarizing data by category

Rewriting code using dplyr

Summary

Getting Data from the Web

Logistical overview

Introducing APIs

Using Python to retrieve data from APIs

Using URL parameters to filter the results

Summary

Working with Large Datasets

Logistical overview

Understanding computer memory

Understanding databases

Introducing MongoDB

Interfacing with MongoDB from Python

Summary

Customer Reviews

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Simplifying Data Manipulation with dplyr

Both R and pandas go a step further to make data manipulation a bit more expressive than most programming languages. For example, many iterative tasks that would otherwise require a for loop (such as selecting a column) can be done using a single line of code.

However, there are still aspects of data manipulation that could be expressed a bit more directly. Recall that in previous chapter, a number of processing steps and variables were used to filter the data and find the result. It can be hard to express a large number of data manipulation operations in a way that is descriptive and contained.

Ideally, it should be possible to express each of the steps for processing data in one sequence of code, and in a way that reflects the function of each processing step. A number of packages build on the R programming language and environment in...