1. Data Analysis > 
  2. Data Visualization with Python > 
  3. Next Steps

Next Steps

Next we will be learning how to use pandas with a sqlite3 database. Before continuing on, here are some of our favorite additional resources on data visualization in Python:

  1. Johns Hopkins’ Data Visualization Guides
  2. Differences Between Matplotlib and Seaborn
LaunchCode
  • Home
  • 1. About LaunchCode Courses
    • Reading
      • About LaunchCode Programs
      • Blended Learning
      • Class Platforms
      • Using This Book
  • 2. Our Approach to Learning Data Analysis
    • Reading
      • Why Learn Spreadsheets?
      • Why Learn to Code?
      • Why Learn Python?
      • Why Learn SQL?
      • Why Learn Tableau?
      • How Much Math You Need
  • 3. Intro to Spreadsheets
    • Reading
      • Getting Started with Google Sheets
      • Adding onto an Existing Spreadsheet
      • Calculations
    • Next Steps
  • 4. Introduction to Data
    • Reading
      • Data Project Life Cycle
      • Data Analysis Methods and Techniques
      • Datasets and KPIs
    • Exercises: Introduction to Data
    • Studio: Introduction to Data
    • Next Steps
  • 5. Exploratory Data Analysis With Spreadsheets
    • Reading
      • An Introduction to Exploratory Data Analysis
      • Summary Statistics with Google Sheets
      • Creating Visualizations with Google Sheets
      • Locating Outliers
    • Exercises
    • Studio
    • Next Steps
  • 6. Cleaning Data with Spreadsheets
    • Reading
      • Cleaning Data
      • Data Cleaning Techniques
      • Types of Dirty Data
    • Exercises: Cleaning Data
    • Studio: Cleaning Data
    • Next Steps
  • 7. SQL Part 1: Introduction and Azure Data Studio
    • Reading
      • What is SQL?
      • SQL Queries
      • Table Relationships
    • Exercises: SQL Intro
    • Studio
    • Next Steps
  • 8. SQL Part 2: String and Date Functions
    • Reading
      • String Functions
      • Date and Time Functions
      • Aggregation Functions
    • Exercises: SQL Part 2
    • Studio: SQL Part 2
    • Next Steps
  • 9. SQL Part 3: Joins
    • Reading
      • What is a Join?
      • Inner Joins
      • Left and Right Joins
      • Full Outer Joins
    • Exercises
    • Studio
    • Next Steps
  • 10. SQL Part 4: Subqueries, CTEs, UNION, INTERSECT, EXCEPT
    • Reading
      • SQL Subqueries
      • Nested Subqueries
      • Common Table Expressions
      • UNION, INTERSECT, EXCEPT
        • Table Data
    • Exercises
    • Studio
    • Next Steps
  • 11. SQL Part 5: Schemas and CRUD Operations
    • Reading
      • Schemas
      • Create
      • Update
      • Delete
    • Exercises: Schemas and CRUD Operations
    • Studio: Schemas and CRUD Operations
    • Next Steps
  • 12. How Programs Work
    • Reading
      • Introduction
      • Programming Languages
      • The Python Language
      • Setting Up Your Coding Tools
      • Your First Program
      • Workflow Recap
      • GitHub Repository: Data Analysis Projects
    • Next Steps
  • 13. How to Write and Run Code
    • Reading
      • 1. How to Write and Run Code
        • What is Code?
        • Syntax Rules
        • Comments
        • Output with print
        • Fixing Errors in Your Code
      • 2. Terminal
        • What is a Terminal anyways?
        • Filesystems and Paths
        • How to Do Stuff in the Terminal
        • Running Programs in the Terminal
    • Exercises: How to Write Code
    • Next Steps
  • 14. Data and Variables
    • Reading
      • Values and Data Types
      • Type Conversion
      • Variables
      • More on Variables
      • Expressions and Evaluation
      • Operations
      • Other Operators
      • Input with input()
    • Exercises: Data and Variables
    • Next Steps
  • 15. Making Decisions with Conditionals
    • Reading
      • Decision Making
      • Data Type for True and False
      • Boolean Expressions
      • Can We Do Math with Boolean Values?
      • Conditionals
      • Logical Operators
      • Truth Tables
      • Nested Conditionals
      • Chained Conditionals
    • Exercises: Booleans and Conditionals
    • Next Steps
  • 16. Loops
    • Reading
      • Iteration
      • For Loops
      • More on Range
      • Loops with Conditions
      • While Loops
      • Infinite Loops
      • Ending Loops with a Break
    • Exercises
    • Next Steps
  • 17. Collections
    • Reading
      • 1. Strings
        • Strings as Collections
        • Bracket Notation
      • 2. Lists
        • 1. List Basics
        • 2. Working with Lists
        • 3. Iterating with Lists
        • 4. Cloning Lists
        • 5. Lists within Lists
      • 3. Dictionaries
        • 1. Dictionary Basics
        • 2. Working with Dictionaries
        • 3. Iterating with Dictionaries
        • 5. Searching Dictionaries
      • 4. Tuples
    • Exercises: Collections
    • Studio: Strings and Lists
    • Next Steps
  • 18. Functions are at Your Beck and Call
    • Reading
      • Introduction
      • Calling Functions
      • Defining Our Own Functions
      • Function Input
      • Function Output
      • A Good Function-Writing Process
      • Parameters and Variables
      • Functions Calling Other Functions
      • Why Create Functions?
    • Exercises: Functions
    • Studio: Functions
    • Next Steps
  • 19. Exploratory Data Analysis with pandas
    • Reading
      • Exploring Data with pandas
      • Introduction to NumPy
      • pandas Series
      • pandas DataFrames
      • pandas Functions
      • Conditional Formatting
    • Exercises: EDA with pandas
    • Studio: EDA with pandas
    • Next Steps
  • 20. Cleaning Data with Pandas
    • Reading
      • Revisiting Cleaning Data
      • Handling Missing Data
      • Handling Irregular Data
      • Handling Unnecessary Data
      • Handling Inconsistent Data
    • Exercises: Cleaning Data with Pandas
    • Studio: Cleaning Data with Pandas
    • Next Steps
  • 21. Data Manipulation
    • Reading
      • Aggregation
      • Recoding Data
      • Reshaping Tables
    • Exercises: Data Manipulation
    • Studio: Data Manipulation
    • Next Steps
  • 22. Data Visualization with Python
    • Reading
      • Best Practices for Data Visualization
      • Choosing a Chart Style
      • Matplotlib and Seaborn
    • Exercises: Data Visualization with Python
    • Studio: Data Visualization with Python
    • Next Steps
  • 23. Databases with Python and pandas
    • Reading
      • Databases with Python
      • Databases with pandas
    • Exercises: Working with Databases in Python
    • Studio: Working with Databases in Python
    • Next Steps
  • 24. Tableau Part 1: Intro to Visualization
    • Reading
      • Viz of the Day
      • Intro to Visualization with Tableau
      • Tableau Workspace Navigation
      • Connecting Data
      • Build a Chart
    • Exercises: Tableau Part 1
    • Studio: Tableau Part 1
    • Next Steps
  • 25. Tableau Part 2: Data Preparation
    • Reading
      • Filtering and Sorting
      • Hierarchies
      • Groups and Sets
    • Exercises: Tableau Part Two
    • Studio: Data Preparation
    • Next Steps
  • 26. Tableau Part 3: Calculations, Parameters and Relationships
    • Reading
      • Calculations
      • Parameters
      • Relationships
    • Exercises: Tableau Part 3
    • Studio: Tableau Part 3
    • Next Steps
  • 27. Tableau Part 4: Stories and Dates
    • Reading
      • Dates and Times
      • Tips for Making the Most of Tableau
      • Tableau Stories and Dashboards
    • Exercises: Tableau Part Four
    • Studio: Tableau Part Four
    • Next Steps
  • GA: Graded Assignments
    • A0: Hello, Google Sheets!
      • Task 1: Project Introduction
      • Task 2: Hello, Google Sheets!
      • Task 3: Verify and Submit
    • A1: Fintech Fun
      • Task 1: The Business Issue
      • Task 2: Tackling Debt
      • Task 3: Housing Prices
      • Task 4: Submitting Your Work
    • A2: 9 to 5
      • Task 1: Database Exploration
      • Task 2: Aggregate Your Friends and Code Some SQL
      • Task 3: Join in on the Fun
      • Task 4: Subqueries, Unions, Derived Tables, Oh My!
    • A3: Call Center Cleanup
      • Task 1: EDA
      • Task 2: Cleaning Data
      • Task 3: Data Manipulation
      • Task 4: Data Visualization
      • Task 5: Summarize and Bonus Mission
    • A4: The Final Project
      • Checkpoint 1: Choose Your Data
      • Checkpoint 2: EDA
      • Checkpoint 3: Cleaning Data
      • Checkpoint 4: Tableau Story
  • INSTL. Installations
    • Terminal Setup
    • Installing Git
    • Installing Python
    • Installing Visual Studio Code
    • Installing Jupyter Notebook
    • Installing Azure Data Studio
    • Setup and Introduction to Tableau
  • Appx. Appendices
    • Terminal Commands Tutorial
    • Common Python List Methods
      • Adding Elements To a List
      • Removing Elements From a List
      • count Examples
      • index Examples
      • join Examples
      • Rearranging List Elements
    • Spreadsheet Mastery
      • Spreadsheet Mastery: Exercises
      • Spreadsheet Mastery: Studio
      • Spreadsheet Mastery: Exercises Part 2
      • Spreadsheet Mastery: Studio Part 2
    • String Methods
      • Count Examples
      • find and index examples
      • lower and upper examples
      • replace Examples
      • split Examples
      • strip Examples
    • Dictionary Methods
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