Data Wrangling with Python Tips and Tools to Make Your Life Easier 1st Edition by Jacqueline Kazil, Katharine Jarmul ISBN 1491956809 9781491956809

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ISBN 10: 1491956809
ISBN 13: 9781491956809
Author: Jacqueline Kazil, Katharine Jarmul

How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don’t need to know a thing about the Python programming language to get started. Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain. Quickly learn basic Python syntax, data types, and language concepts Work with both machine-readable and human-consumable data Scrape websites and APIs to find a bounty of useful information Clean and format data to eliminate duplicates and errors in your datasets Learn when to standardize data and when to test and script data cleanup Explore and analyze your datasets with new Python libraries and techniques Use Python solutions to automate your entire data-wrangling process

Data Wrangling with Python Tips and Tools to Make Your Life Easier 1st Table of contents:

Chapter 1. Introduction to Python

Why Python

Getting Started with Python

Which Python Version

Setting Up Python on Your Machine

Test Driving Python

Install pip

Install a Code Editor

Optional: Install IPython

Summary

Chapter 2. Python Basics

Basic Data Types

Strings

Integers and Floats

Data Containers

Variables

Lists

Dictionaries

What Can the Various Data Types Do?

String Methods: Things Strings Can Do

Numerical Methods: Things Numbers Can Do

List Methods: Things Lists Can Do

Dictionary Methods: Things Dictionaries Can Do

Helpful Tools: type, dir, and help

type

dir

help

Putting It All Together

What Does It All Mean?

Summary

Chapter 3. Data Meant to Be Read by Machines

CSV Data

How to Import CSV Data

Saving the Code to a File; Running from Command Line

JSON Data

How to Import JSON Data

XML Data

How to Import XML Data

Summary

Chapter 4. Working with Excel Files

Installing Python Packages

Parsing Excel Files

Getting Started with Parsing

Summary

Chapter 5. PDFs and Problem Solving in Python

Avoid Using PDFs!

Programmatic Approaches to PDF Parsing

Opening and Reading Using slate

Converting PDF to Text

Parsing PDFs Using pdfminer

Learning How to Solve Problems

Exercise: Use Table Extraction, Try a Different Library

Exercise: Clean the Data Manually

Exercise: Try Another Tool

Uncommon File Types

Summary

Chapter 6. Acquiring and Storing Data

Not All Data Is Created Equal

Fact Checking

Readability, Cleanliness, and Longevity

Where to Find Data

Using a Telephone

US Government Data

Government and Civic Open Data Worldwide

Organization and Non-Government Organization (NGO) Data

Education and University Data

Medical and Scientific Data

Crowdsourced Data and APIs

Case Studies: Example Data Investigation

Ebola Crisis

Train Safety

Football Salaries

Child Labor

Storing Your Data: When, Why, and How?

Databases: A Brief Introduction

Relational Databases: MySQL and PostgreSQL

Non-Relational Databases: NoSQL

Setting Up Your Local Database with Python

When to Use a Simple File

Cloud-Storage and Python

Local Storage and Python

Alternative Data Storage

Summary

Chapter 7. Data Cleanup: Investigation, Matching, and Formatting

Why Clean Data?

Data Cleanup Basics

Identifying Values for Data Cleanup

Formatting Data

Finding Outliers and Bad Data

Finding Duplicates

Fuzzy Matching

RegEx Matching

What to Do with Duplicate Records

Summary

Chapter 8. Data Cleanup: Standardizing and Scripting

Normalizing and Standardizing Your Data

Saving Your Data

Determining What Data Cleanup Is Right for Your Project

Scripting Your Cleanup

Testing with New Data

Summary

Chapter 9. Data Exploration and Analysis

Exploring Your Data

Importing Data

Exploring Table Functions

Joining Numerous Datasets

Identifying Correlations

Identifying Outliers

Creating Groupings

Further Exploration

Analyzing Your Data

Separating and Focusing Your Data

What Is Your Data Saying?

Drawing Conclusions

Documenting Your Conclusions

Summary

Chapter 10. Presenting Your Data

Avoiding Storytelling Pitfalls

How Will You Tell the Story?

Know Your Audience

Visualizing Your Data

Charts

Time-Related Data

Maps

Interactives

Words

Images, Video, and Illustrations

Presentation Tools

Publishing Your Data

Using Available Sites

Open Source Platforms: Starting a New Site

Jupyter (Formerly Known as IPython Notebooks)

Summary

Chapter 11. Web Scraping: Acquiring and Storing Data from the Web

What to Scrape and How

Analyzing a Web Page

Inspection: Markup Structure

Network/Timeline: How the Page Loads

Console: Interacting with JavaScript

In-Depth Analysis of a Page

Getting Pages: How to Request on the Internet

Reading a Web Page with Beautiful Soup

Reading a Web Page with LXML

A Case for XPath

Summary

Chapter 12. Advanced Web Scraping: Screen Scrapers and Spiders

Browser-Based Parsing

Screen Reading with Selenium

Screen Reading with Ghost.Py

Spidering the Web

Building a Spider with Scrapy

Crawling Whole Websites with Scrapy

Networks: How the Internet Works and Why It’s Breaking Your Script

The Changing Web (or Why Your Script Broke)

A (Few) Word(s) of Caution

Summary

Chapter 13. APIs

API Features

REST Versus Streaming APIs

Rate Limits

Tiered Data Volumes

API Keys and Tokens

A Simple Data Pull from Twitter’s REST API

Advanced Data Collection from Twitter’s REST API

Advanced Data Collection from Twitter’s Streaming API

Summary

Chapter 14. Automation and Scaling

Why Automate?

Steps to Automate

What Could Go Wrong?

Where to Automate

Special Tools for Automation

Using Local Files, argv, and Config Files

Using the Cloud for Data Processing

Using Parallel Processing

Using Distributed Processing

Simple Automation

CronJobs

Web Interfaces

Jupyter Notebooks

Large-Scale Automation

Celery: Queue-Based Automation

Ansible: Operations Automation

Monitoring Your Automation

Python Logging

Adding Automated Messaging

Uploading and Other Reporting

Logging and Monitoring as a Service

No System Is Foolproof

Summary

Chapter 15. Conclusion

Duties of a Data Wrangler

Beyond Data Wrangling

Become a Better Data Analyst

Become a Better Developer

Become a Better Visual Storyteller

Become a Better Systems Architect

Where Do You Go from Here?

Appendix A. Comparison of Languages Mentioned

C, C++, and Java Versus Python

R or MATLAB Versus Python

HTML Versus Python

JavaScript Versus Python

Node.js Versus Python

Ruby and Ruby on Rails Versus Python

Appendix B. Python Resources for Beginners

Online Resources

In-Person Groups

Appendix C. Learning the Command Line

Bash

Navigation

Modifying Files

Executing Files

Searching with the Command Line

More Resources

Windows CMD/Power Shell

Navigation

Modifying Files

Executing Files

Searching with the Command Line

More Resources

Appendix D. Advanced Python Setup

Step 1: Install GCC

Step 2: (Mac Only) Install Homebrew

Step 3: (Mac Only) Tell Your System Where to Find Homebrew

Step 4: Install Python 2.7

Step 5: Install virtualenv (Windows, Mac, Linux)

Step 6: Set Up a New Directory

Step 7: Install virtualenvwrapper

Installing virtualenvwrapper (Mac and Linux)

Installing virtualenvwrapper-win (Windows)

Testing Your Virtual Environment (Windows, Mac, Linux)

Learning About Our New Environment (Windows, Mac, Linux)

Advanced Setup Review

Appendix E. Python Gotchas

Hail the Whitespace

The Dreaded GIL

= Versus == Versus is, and When to Just Copy

Default Function Arguments

Python Scope and Built-Ins: The Importance of Variable Names

Defining Objects Versus Modifying Objects

Changing Immutable Objects

Type Checking

Catching Multiple Exceptions

The Power of Debugging

Appendix F. IPython Hints

Why Use IPython?

Getting Started with IPython

Magic Functions

Final Thoughts: A Simpler Terminal

Appendix G. Using Amazon Web Services

Spinning Up an AWS Server

AWS Step 1: Choose an Amazon Machine Image (AMI)

AWS Step 2: Choose an Instance Type

AWS Step 7: Review Instance Launch

AWS Extra Question: Select an Existing Key Pair or Create a New One

Logging into an AWS Server

Get the Public DNS Name of the Instance

Prepare Your Private Key

Log into Your Server

Summary

Index

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Tags: Jacqueline Kazil, Katharine Jarmul, Data Wrangling, Life Easier