Practical Data Wrangling (source Code)

Key Features* 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 Book DescriptionAround 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. 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 About the Author**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. Table of Contents1. Programming with Data 2. An Introduction to Programming in Python 3. Reading, Writing and Modifying Data in Python I 4. Reading, Writing and Modifying Data in Python II 5. Text Data and Regular expressions 6. Cleaning Numerical Data: An Introduction To R and Rstudio 7. Data Munging in R using Dplyr 8. Getting data from the web 9. Working with really large datasets Key Features 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 Book Description 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. 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 About the Author 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. Table of Contents Programming with Data An Introduction to Programming in Python Reading, Writing and Modifying Data in Python I Reading, Writing and Modifying Data in Python II Text Data and Regular expressions Cleaning Numerical Data: An Introduction To R and Rstudio Data Munging in R using Dplyr Getting data from the web Working with really large datasets Turn your noisy data into relevant, insight-ready information by leveraging the data wrangling techniques in Python and RAbout This BookThis easy-to-follow guide takes you through every step of the data wrangling process in the best possible wayWork with different types of datasets, and reshape the layout of your data to make it easier for analysisGet simple examples and real-life data wrangling solutions for data pre-processingWho This Book Is ForIf 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 LearnRead a csv file into python and R, and print out some statistics on the dataGain knowledge of the data formats and programming structures involved in retrieving API dataMake effective use of regular expressions in the data wrangling processExplore the tools and packages available to prepare numerical data for analysisFind out how to have better control over manipulating the structure of the dataCreate a dexterity to programmatically read, audit, correct, and shape dataWrite and complete programs to take in, format, and output data setsIn DetailAround 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.Style and approachThis is a practical book on data wrangling designed to give you an insight into the practical application of data wrangling. It takes you through complex concepts and tasks in an accessible way, featuring information on a wide range of data wrangling techniques with Python and R 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 ..