فهرست مطالب

• Foreword
• Preface
• I. Begin
• 1. Introducing Polars
• What Is This Thing Called Polars?
• Why You Should Use Polars
• Polars Compared to Other Data Processing Packages
• Why We Focus on Python Polars
• How This Book is Organized
• An ETL Showcase
• Takeaways
• 2. Getting Started
• Setting Up Your Environment
• Crash Course JupyterLab
• Installing Polars on Other Projects
• Configuring Polars
• Compiling Polars from Scratch
• Takeaways
• 3. Moving from Pandas to Polars
• Animals
• Similarities to Recognize
• Appearances to Appreciate
• Concepts to Unlearn
• Syntax to Forget
• To and From Pandas
• Takeaways
• II. Form
• 4. Data Structures and Data Types
• Series, DataFrames, and LazyFrames
• Data Types
• Data Type Conversion
• Takeaways
• 5. Eager and Lazy APIs
• Eager API: DataFrame
• Lazy API: LazyFrame
• Performance Differences
• Functionality Differences
• Tips and Tricks
• Takeaways
• 6. Reading and Writing Data
• Format Overview
• Reading CSV Files
• Parsing Missing Values Correctly
• Reading Files with Encodings Other than UTF-8
• Reading Excel Spreadsheets
• Working with Multiple Files
• Reading Parquet
• Reading JSON and NDJSON
• Other File Formats
• Querying Databases
• Writing Data
• Takeaways
• III. Express
• 7. Beginning Expressions
• 8. Continuing Expressions
• Types of Operations
• Element-Wise Operations
• Nonreducing Series-Wise Operations
• Series-Wise Operations that Summarize to One
• Series-Wise Operations that Summarize to One or More
• Series-Wise Operations that Extend
• Takeaways
• 9. Combining Expressions
• Inline Operators Versus Methods
• Arithmetic Operations
• Comparison Operations
• Boolean Algebra Operations
• Bitwise Operations
• Using Functions
• Takeaways
• IV. Transform
• 10. Selecting and Creating Columns
• Selecting Columns
• Creating Columns
• Related Column Operations
• Takeaways
• 11. Filtering and Sorting Rows
• Filtering Rows
• Sorting Rows
• Related Row Operations
• Takeaways
• 12. Working with Textual, Temporal, and Nested Data Types
• String
• Categorical
• Enum
• Temporal
• List
• Array
• Struct
• Takeaways
• 13. Summarizing and Aggregating
• Split, Apply, and Combine
• GroupBy Context
• The Descriptives
• The Advanced
• Aggregate Values to a List
• Rename Aggregated Columns
• Apply Multiple Aggregations At Once
• Row-Wise Aggregations
• Window Functions in Selection Context
• Dynamic Grouping
• Rolling Aggregations
• Upsampling
• Takeaways
• 14. Joining and Concatenating
• Joining
• Inexact Joining
• Vertical and Horizontal Concatenation
• Takeaways
• 15. Reshaping
• Wide Versus Long DataFrames
• Pivot to Wider DataFrame
• Unpivot to Longer DataFrame
• Transposing
• Exploding
• Partition into Multiple DataFrames
• Takeaways
• V. Advance
• 16. Visualizing Data
• NYC Bike Trips
• Built-in Plotting with Altair
• Pandas-like Plotting With hvPlot
• Publication-Quality Graphics with Plotnine
• Styling DataFrames With Great Tables
• Takeaways
• 17. Extending Polars
• User Defined Functions in Python
• Registering Your Own Namespace
• Polars Plug-Ins in Rust
• Takeaways
• 18. Polars Internals
• Polars’ Architecture
• Arrow
• Multi-Threaded Computations and SIMD Operations
• The String Data Type in Memory
• ChunkedArrays in Series
• Query Optimization
• Checking Your Expressions
• Profiling Polars
• Tests in Polars
• Common Anti-patterns
• Takeaways
• Appendix A. Accelerating Polars with the GPU
• Index