Data Wrangling with Python

Spis treści książki

• Preface
  • About the Book
    • About the Authors
    • Learning Objectives
    • Approach
    • Audience
    • Minimum Hardware Requirements
    • Software Requirements
    • Conventions
    • Installation and Setup
    • Installing the Code Bundle
    • Additional Resources
• Chapter 1
• Introduction to Data Wrangling with Python
  • Introduction
    • Importance of Data Wrangling
  • Python for Data Wrangling
  • Lists, Sets, Strings, Tuples, and Dictionaries
    • Lists
    • Exercise 1: Accessing the List Members
    • Exercise 2: Generating a List
    • Exercise 3: Iterating over a List and Checking Membership
    • Exercise 4: Sorting a List
    • Exercise 5: Generating a Random List
    • Activity 1: Handling Lists
    • Sets
    • Introduction to Sets
    • Union and Intersection of Sets
    • Creating Null Sets
    • Dictionary
    • Exercise 6: Accessing and Setting Values in a Dictionary
    • Exercise 7: Iterating Over a Dictionary
    • Exercise 8: Revisiting the Unique Valued List Problem
    • Exercise 9: Deleting Value from Dict
    • Exercise 10: Dictionary Comprehension
    • Tuples
    • Creating a Tuple with Different Cardinalities
    • Unpacking a Tuple
    • Exercise 11: Handling Tuples
    • Strings
    • Exercise 12: Accessing Strings
    • Exercise 13: String Slices
    • String Functions
    • Exercise 14: Split and Join
    • Activity 2: Analyze a Multiline String and Generate the Unique Word Count
  • Summary
• Chapter 2
• Advanced Data Structures and File Handling
  • Introduction
  • Advanced Data Structures
    • Iterator
    • Exercise 15: Introduction to the Iterator
    • Stacks
    • Exercise 16: Implementing a Stack in Python
    • Exercise 17: Implementing a Stack Using User-Defined Methods
    • Exercise 18: Lambda Expression
    • Exercise 19: Lambda Expression for Sorting
    • Exercise 20: Multi-Element Membership Checking
    • Queue
    • Exercise 21: Implementing a Queue in Python
    • Activity 3: Permutation, Iterator, Lambda, List
  • Basic File Operations in Python
    • Exercise 22: File Operations
    • File Handling
    • Exercise 23: Opening and Closing a File
    • The with Statement
    • Opening a File Using the with Statement
    • Exercise 24: Reading a File Line by Line
    • Exercise 25: Write to a File
    • Activity 4: Design Your Own CSV Parser
  • Summary
• Chapter 3
• Introduction to NumPy, Pandas,and Matplotlib
  • Introduction
  • NumPy Arrays
    • NumPy Array and Features
    • Exercise 26: Creating a NumPy Array (from a List)
    • Exercise 27: Adding Two NumPy Arrays
    • Exercise 28: Mathematical Operations on NumPy Arrays
    • Exercise 29: Advanced Mathematical Operations on NumPy Arrays
    • Exercise 30: Generating Arrays Using arange and linspace
    • Exercise 31: Creating Multi-Dimensional Arrays
    • Exercise 32: The Dimension, Shape, Size, and Data Type of the Two-dimensional Array
    • Exercise 33: Zeros, Ones, Random, Identity Matrices, and Vectors
    • Exercise 34: Reshaping, Ravel, Min, Max, and Sorting
    • Exercise 35: Indexing and Slicing
    • Conditional Subsetting
    • Exercise 36: Array Operations (array-array, array-scalar, and universal functions)
    • Stacking Arrays
  • Pandas DataFrames
    • Exercise 37: Creating a Pandas Series
    • Exercise 38: Pandas Series and Data Handling
    • Exercise 39: Creating Pandas DataFrames
    • Exercise 40: Viewing a DataFrame Partially
    • Indexing and Slicing Columns
    • Indexing and Slicing Rows
    • Exercise 41: Creating and Deleting a New Column or Row
  • Statistics and Visualization with NumPy and Pandas
    • Refresher of Basic Descriptive Statistics (and the Matplotlib Library for Visualization)
    • Exercise 42: Introduction to Matplotlib Through a Scatter Plot
    • Definition of Statistical Measures Central Tendency and Spread
    • Random Variables and Probability Distribution
    • What Is a Probability Distribution?
    • Discrete Distributions
    • Continuous Distributions
    • Data Wrangling in Statistics and Visualization
    • Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame
    • Random Number Generation Using NumPy
    • Exercise 43: Generating Random Numbers from a Uniform Distribution
    • Exercise 44: Generating Random Numbers from a Binomial Distribution and Bar Plot
    • Exercise 45: Generating Random Numbers from Normal Distribution and Histograms
    • Exercise 46: Calculation of Descriptive Statistics from a DataFrame
    • Exercise 47: Built-in Plotting Utilities
    • Activity 5: Generating Statistics from a CSV File
  • Summary
• Chapter 4
• A Deep Dive into Data Wrangling with Python
  • Introduction
  • Subsetting, Filtering, and Grouping
    • Exercise 48: Loading and Examining a Superstores Sales Data from an Excel File
    • Subsetting the DataFrame
    • An Example Use Case: Determining Statistics on Sales and Profit
    • Exercise 49: The unique Function
    • Conditional Selection and Boolean Filtering
    • Exercise 50: Setting and Resetting the Index
    • Exercise 51: The GroupBy Method
  • Detecting Outliers and Handling Missing Values
    • Missing Values in Pandas
    • Exercise 52: Filling in the Missing Values with fillna
    • Exercise 53: Dropping Missing Values with dropna
    • Outlier Detection Using a Simple Statistical Test
  • Concatenating, Merging, and Joining
    • Exercise 54: Concatenation
    • Exercise 55: Merging by a Common Key
    • Exercise 56: The join Method
  • Useful Methods of Pandas
    • Exercise 57: Randomized Sampling
    • The value_counts Method
    • Pivot Table Functionality
    • Exercise 58: Sorting by Column Values the sort_values Method
    • Exercise 59: Flexibility for User-Defined Functions with the apply Method
    • Activity 6: Working with the Adult Income Dataset (UCI)
  • Summary
• Chapter 5
• Getting Comfortable with Different Kinds of Data Sources
  • Introduction
  • Reading Data from Different Text-Based (and Non-Text-Based) Sources
    • Data Files Provided with This Chapter
    • Libraries to Install for This Chapter
    • Exercise 60: Reading Data from a CSV File Where Headers Are Missing
    • Exercise 61: Reading from a CSV File where Delimiters are not Commas
    • Exercise 62: Bypassing the Headers of a CSV File
    • Exercise 63: Skipping Initial Rows and Footers when Reading a CSV File
    • Reading Only the First N Rows (Especially Useful for Large Files)
    • Exercise 64: Combining Skiprows and Nrows to Read Data in Small Chunks
    • Setting the skip_blank_lines Option
    • Read CSV from a Zip file
    • Reading from an Excel File Using sheet_name and Handling a Distinct sheet_name
    • Exercise 65: Reading a General Delimited Text File
    • Reading HTML Tables Directly from a URL
    • Exercise 66: Further Wrangling to Get the Desired Data
    • Exercise 67: Reading from a JSON File
    • Reading a Stata File
    • Exercise 68: Reading Tabular Data from a PDF File
  • Introduction to Beautiful Soup 4 and Web Page Parsing
    • Structure of HTML
    • Exercise 69: Reading an HTML file and Extracting its Contents Using BeautifulSoup
    • Exercise 70: DataFrames and BeautifulSoup
    • Exercise 71: Exporting a DataFrame as an Excel File
    • Exercise 72: Stacking URLs from a Document using bs4
    • Activity 7: Reading Tabular Data from a Web Page and Creating DataFrames
  • Summary
• Chapter 6
• Learning the Hidden Secrets of Data Wrangling
  • Introduction
    • Additional Software Required for This Section
  • Advanced List Comprehension and the zip Function
    • Introduction to Generator Expressions
    • Exercise 73: Generator Expressions
    • Exercise 74: One-Liner Generator Expression
    • Exercise 75: Extracting a List with Single Words
    • Exercise 76: The zip Function
    • Exercise 77: Handling Messy Data
  • Data Formatting
    • The % operator
    • Using the format Function
    • Exercise 78: Data Representation Using {}
  • Identify and Clean Outliers
    • Exercise 79: Outliers in Numerical Data
    • Z-score
    • Exercise 80: The Z-Score Value to Remove Outliers
    • Exercise 81: Fuzzy Matching of Strings
  • Activity 8: Handling Outliers and Missing Data
  • Summary
• Chapter 7
• Advanced Web Scraping and Data Gathering
  • Introduction
  • The Basics of Web Scraping and the Beautiful Soup Library
    • Libraries in Python
    • Exercise 81: Using the Requests Library to Get a Response from the Wikipedia Home Page
    • Exercise 82: Checking the Status of the Web Request
    • Checking the Encoding of the Web Page
    • Exercise 83: Creating a Function to Decode the Contents of the Response and Check its Length
    • Exercise 84: Extracting Human-Readable Text From a BeautifulSoup Object
    • Extracting Text from a Section
    • Extracting Important Historical Events that Happened on Today's Date
    • Exercise 85: Using Advanced BS4 Techniques to Extract Relevant Text
    • Exercise 86: Creating a Compact Function to Extract the "On this Day" Text from the Wikipedia Home Page
  • Reading Data from XML
    • Exercise 87: Creating an XML File and Reading XML Element Objects
    • Exercise 88: Finding Various Elements of Data within a Tree (Element)
    • Reading from a Local XML File into an ElementTree Object
    • Exercise 89: Traversing the Tree, Finding the Root, and Exploring all Child Nodes and their Tags and Attributes
    • Exercise 90: Using the text Method to Extract Meaningful Data
    • Extracting and Printing the GDP/Per Capita Information Using a Loop
    • Exercise 91: Finding All the Neighboring Countries for each Country and Printing Them
    • Exercise 92: A Simple Demo of Using XML Data Obtained by Web Scraping
  • Reading Data from an API
    • Defining the Base URL (or API Endpoint)
    • Exercise 93: Defining and Testing a Function to Pull Country Data from an API
    • Using the Built-In JSON Library to Read and Examine Data
    • Printing All the Data Elements
    • Using a Function that Extracts a DataFrame Containing Key Information
    • Exercise 94: Testing the Function by Building a Small Database of Countries' Information
  • Fundamentals of Regular Expressions (RegEx)
    • Regex in the Context of Web Scraping
    • Exercise 95: Using the match Method to Check Whether a Pattern matches a String/Sequence
    • Using the Compile Method to Create a Regex Program
    • Exercise 96: Compiling Programs to Match Objects
    • Exercise 97: Using Additional Parameters in Match to Check for Positional Matching
    • Finding the Number of Words in a List That End with "ing"
    • Exercise 98: The search Method in Regex
    • Exercise 99: Using the span Method of the Match Object to Locate the Position of the Matched Pattern
    • Exercise 100: Examples of Single Character Pattern Matching with search
    • Exercise 101: Examples of Pattern Matching at the Start or End of a String
    • Exercise 102: Examples of Pattern Matching with Multiple Characters
    • Exercise 103: Greedy versus Non-Greedy Matching
    • Exercise 104: Controlling Repetitions to Match
    • Exercise 105: Sets of Matching Characters
    • Exercise 106: The use of OR in Regex using the OR Operator
    • The findall Method
    • Activity 9: Extracting the Top 100 eBooks from Gutenberg
    • Activity 10: Building Your Own Movie Database by Reading an API
  • Summary
• Chapter 8
• RDBMS and SQL
  • Introduction
  • Refresher of RDBMS and SQL
    • How is an RDBMS Structured?
    • SQL
  • Using an RDBMS (MySQL/PostgreSQL/SQLite)
    • Exercise 107: Connecting to Database in SQLite
    • Exercise 108: DDL and DML Commands in SQLite
    • Reading Data from a Database in SQLite
    • Exercise 109: Sorting Values that are Present in the Database
    • Exercise 110: Altering the Structure of a Table and Updating the New Fields
    • Exercise 111: Grouping Values in Tables
    • Relation Mapping in Databases
    • Adding Rows in the comments Table
    • Joins
    • Retrieving Specific Columns from a JOIN query
    • Exercise 112: Deleting Rows
    • Updating Specific Values in a Table
    • Exercise 113: RDBMS and DataFrames
    • Activity 11: Retrieving Data Correctly From Databases
  • Summary
• Chapter 9
• Application of Data Wrangling in Real Life
  • Introduction
  • Applying Your Knowledge to a Real-life Data Wrangling Task
  • Activity 12: Data Wrangling Task Fixing UN Data
  • Activity 13: Data Wrangling Task Cleaning GDP Data
  • Activity 14: Data Wrangling Task Merging UN Data and GDP Data
  • Activity 15: Data Wrangling Task Connecting the New Data to the Database
  • An Extension to Data Wrangling
    • Additional Skills Required to Become a Data Scientist
    • Basic Familiarity with Big Data and Cloud Technologies
    • What Goes with Data Wrangling?
    • Tips and Tricks for Mastering Machine Learning
  • Summary
• Appendix
  • Solution of Activity 1: Handling Lists
    • Solution of Activity 2: Analyze a Multiline String and Generate the Unique Word Count
    • Solution of Activity 3: Permutation, Iterator, Lambda, List
    • Solution of Activity 4: Design Your Own CSV Parser
    • Solution of Activity 5: Generating Statistics from a CSV File
    • Solution of Activity 6: Working with the Adult Income Dataset (UCI)
    • Solution of Activity 7: Reading Tabular Data from a Web Page and Creating DataFrames
    • Solution of Activity 8: Handling Outliers and Missing Data
    • Solution of Activity 9: Extracting the Top 100 eBooks from Gutenberg
    • Solution of Activity 10: Extracting the top 100 eBooks from Gutenberg.org
    • Solution of Activity 11: Retrieving Data Correctly from Databases
    • Solution of Activity 12: Data Wrangling Task Fixing UN Data
    • Activity 13: Data Wrangling Task Cleaning GDP Data
    • Solution of Activity 14: Data Wrangling Task Merging UN Data and GDP Data
    • Activity 15: Data Wrangling Task Connecting the New Data to a Database

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