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Book
Table of Contents
Book Index
Scratch ActiveCode
Chapters
1. Introduction
2. Values and Variables (Single Pieces of Data)
3. Control Structures
4. Functions
5. Strings, Lists, and Files (Multiple Pieces of Data)
6. Pandas: Series
7. Pandas: Dataframes
8. Statistics
9. Visualization with Seaborn
10. Data Cleaning
11. Classes (Defining New Kinds of Objects)
Contributions
License
The Python and Pandas Field Guide
An Introduction to Computer and Data Science
anaconda by parkjisun; panda by Liane Kirschner; both from the Noun Project
Table of Contents
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1. Introduction
1.1. What is This?
1.2. What is a Computer?
1.3. What is Computer Science?
1.4. What is Data Science?
1.5. An Example Program: Word Count
1.6. How to Use This Book
1.7. Conventions Used in This Book
2. Values and Variables (Single Pieces of Data)
2.1. Values and Data Types
2.2. Variables
2.3. Expressions
2.4. Operators and Operands
2.5. Input
2.6. Updating Variables
2.7. Good Coding Practices: Comments
2.8. Word Count Example Program (Again)
2.9. Glossary
3. Control Structures
3.1. For Loops
3.2. Conditionals
3.3. While Loops
3.4. Booleans
3.5. Loop Control Keywords
3.6. Common Loop Patterns
3.7. Good Coding Practices: Debugging
3.8. Glossary
4. Functions
4.1. Function Calls
4.2. Built-In Functions
4.3. Function Definitions
4.4. Glossary
5. Strings, Lists, and Files (Multiple Pieces of Data)
5.1. Strings
5.2. Lists
5.3. Files
5.4. Glossary
6. Pandas: Series
6.1. Series: Introduction
6.2. Series: Selecting Data
6.3. Series: Updating and Sorting
6.4. Series: Examples
6.5. Series: String Methods
6.6. Series: Grouping
6.7. Series: Other Methods and Examples
7. Pandas: Dataframes
7.1. Dataframes: Basics
7.2. Dataframes: Selecting Data
7.3. Dataframes: Basic Operations
7.4. Dataframes: Boolean Combinations and Negations
7.5. Dataframes: Grouping Methods
8. Statistics
8.1. Descriptive Statistics
8.2. Correlations
9. Visualization with Seaborn
9.1. Introduction
9.2. Basic Figure Creation with Seaborn
9.3. Line Plots
9.4. Categorical Plots
9.5. Histograms
9.6. Box Plots
9.7. Scatter Plots
10. Data Cleaning
10.1. Introduction
10.2. Examining the Dataframe for Errors
10.3. Missing or Null Values
10.4. Duplicate Entries
10.5. Converting String and Mixed Columns to Numeric
10.6. Dealing with Whitespace in Strings
10.7. Example with ‘hangout’
11. Classes (Defining New Kinds of Objects)
11.1. Objects (Refresher)
11.2. Classes
Appendices
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Contributions
License
Index and Search
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Index
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