Name: Practical Data Wrangling
Author: Allan Visochek
ISBN: 9781787283671

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Practical Data Wrangling

Allan Visochek

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Title Page
Copyright
Practical Data Wrangling
Credits
About the Author
About the Reviewer
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
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
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
Summary
Reading, Exploring, and Modifying Data - Part I
External resources
Logistical overview
Installation requirements
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
Summary
Reading, Exploring, and Modifying Data - Part II
Logistical overview
File system setup
Data
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
Summary
Manipulating Text Data - An Introduction to Regular Expressions
Logistical overview
Data
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
Summary
Cleaning Numerical Data - An Introduction to R and RStudio
Logistical overview
Data
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
Summary
Simplifying Data Manipulation with dplyr
Logistical overview
Data
File system setup
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
Summary
Getting Data from the Web
Logistical overview
Filesystem setup
Installing the requests module
Internet connection
Introducing APIs
Using Python to retrieve data from APIs
Using URL parameters to filter the results
Summary
Working with Large Datasets
Logistical overview
System requirements
Data
File system setup
Installing MongoDB
Planning out your time
Cleaning up
Understanding computer memory
Understanding databases
Introducing MongoDB
Interfacing with MongoDB from Python
Summary

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Practical Data Wrangling