1. Data Analysis > 
  2. Exploratory Data Analysis with pandas > 
  3. Next Steps

Next Steps

The next chapter will dive into cleaning data with pandas. Before proceeding, if you would like to further your understanding of pandas, here are some of our favorite resources.

  1. pandas Documentation: 10 minutes to pandas
  2. pandas Documenation: Intro to data structures
  3. pandas Documentation: Indexing and selecting data
  4. Kaggle: EDA with Pandas
  5. NumPy Documentation
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
  •  
  •  
  •