Practical Data Wrangling

Turn your noisy data into relevant, insight-ready information by leveraging the data wrangling techniques in Python and R
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Practical Data Wrangling

Allan Visochek

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Turn your noisy data into relevant, insight-ready information by leveraging the data wrangling techniques in Python and R
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Book Details

ISBN 139781787286139
Paperback204 pages

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.

Table of Contents

Chapter 1: Programming with Data
Understanding data wrangling
The tools for data wrangling
Summary
Chapter 2: 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
Chapter 3: 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
Chapter 4: 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
Chapter 5: 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
Chapter 6: 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
Chapter 7: 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
Chapter 8: Getting Data from the Web
Logistical overview
Introducing APIs
Using Python to retrieve data from APIs
Using URL parameters to filter the results
Summary
Chapter 9: Working with Large Datasets
Logistical overview 
Understanding computer memory
Understanding databases
Introducing MongoDB
Interfacing with MongoDB from Python
Summary

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

Authors

Table of Contents

Chapter 1: Programming with Data
Understanding data wrangling
The tools for data wrangling
Summary
Chapter 2: 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
Chapter 3: 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
Chapter 4: 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
Chapter 5: 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
Chapter 6: 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
Chapter 7: 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
Chapter 8: Getting Data from the Web
Logistical overview
Introducing APIs
Using Python to retrieve data from APIs
Using URL parameters to filter the results
Summary
Chapter 9: Working with Large Datasets
Logistical overview 
Understanding computer memory
Understanding databases
Introducing MongoDB
Interfacing with MongoDB from Python
Summary

Book Details

ISBN 139781787286139
Paperback204 pages
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