Python Polars: The Definitive Guide. Transforming, Analyzing, and Visualizing Data with a Fast and Expressive DataFrame API Jeroen Janssens, Thijs Nieuwdorp

Spis treści książki

• Foreword
• Preface
  • Who This Book Is For
    • Hanna: The Data Analyst
    • Kosjo: The Data Engineer
    • A Broader Audience
  • Get More Out of This Book
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Begin
• 1. Introducing Polars
  • What Is This Thing Called Polars?
    • Key Features
    • Key Concepts
    • Advantages
  • Why You Should Use Polars
    • Performance
    • Usability
    • Popularity
    • Sustainability
  • Polars Compared to Other Data Processing Packages
  • Why We Focus on Python Polars
  • How This Book Is Organized
  • An ETL Showcase
    • Extract
      • Import packages
      • Download and extract Citi Bike trips
      • Read Citi Bike trips into a Polars DataFrame
      • Read in neighborhoods from GeoJSON
    • Bonus: Visualizing Neighborhoods and Stations
    • Transform
      • Clean up columns
      • Clean up rows
      • Add trip distance
      • Add borough and neighborhood
    • Bonus: Visualizing Daily Trips per Borough
    • Load
      • Write partitions
      • Verify
    • Bonus: Becoming Faster by Being Lazy
  • Takeaways
• 2. Getting Started
  • Setting Up Your Environment
    • Downloading the Project
    • Installing uv
    • Installing the Project
    • Working with the Virtual Environment
    • Verifying Your Installation
  • Crash Course in JupyterLab
    • Keyboard Shortcuts
      • Any mode
      • Command mode
      • Edit mode
  • Installing Polars on Other Projects
    • All Optional Dependencies
    • Optional Dependencies for Interoperability
    • Optional Dependencies for Working with Spreadsheets
    • Optional Dependencies for Working with Databases
    • Optional Dependencies for Working with Remote Filesystems
    • Optional Dependencies for Other I/O Formats
    • Optional Dependencies for Extra Functionality
    • Installing Optional Dependencies
  • Configuring Polars
    • Temporary Configuration Using a Context Manager
    • Local Configuration Using a Decorator
  • Compiling Polars from Scratch
    • Edge Case: Very Large Datasets
    • Edge Case: Processors Lacking AVX Support
  • Takeaways
• 3. Moving from pandas to Polars
  • Animals
  • Similarities to Recognize
  • Appearances to Appreciate
    • Differences in Code
    • Differences in Display
  • Concepts to Unlearn
    • Index
    • Axes
    • Indexing and Slicing
    • Eagerness
    • Relaxedness
  • Syntax to Forget
    • Common Operations Side By Side
      • Removing duplicate values
      • Removing missing values
      • Sorting rows
      • Casting an existing column
      • Aggregating rows
  • To and From pandas
  • Takeaways
• II. Form
• 4. Data Structures and Data Types
  • Series, DataFrames, and LazyFrames
  • Data Types
    • Nested Data Types
    • Missing Values
  • Data Type Conversion
  • Takeaways
• 5. Eager and Lazy APIs
  • Eager API: DataFrame
  • Lazy API: LazyFrame
  • Performance Differences
  • Functionality Differences
    • Attributes
    • Aggregation Methods
    • Computation Methods
    • Descriptive Methods
    • GroupBy Methods
    • Exporting Methods
    • Manipulation and Selection Methods
    • Miscellaneous Methods
  • Tips and Tricks
    • Going from LazyFrame to DataFrame and Vice Versa
    • Joining a DataFrame with a LazyFrame
    • Caching Intermittent Results
  • 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
    • JSON
    • NDJSON
  • Other File Formats
  • Querying Databases
  • Writing Data
    • CSV Format
    • Excel Format
    • Parquet Format
    • Other Considerations
  • Takeaways
• III. Express
• 7. Beginning Expressions
  • Methods and Namespaces
  • Expressions by Example
    • Selecting Columns with Expressions
    • Creating New Columns with Expressions
    • Filtering Rows with Expressions
    • Aggregating with Expressions
    • Sorting Rows with Expressions
  • The Definition of an Expression
    • Properties of Expressions
  • Creating Expressions
    • From Existing Columns
    • From Literal Values
    • From Ranges
    • Other Functions to Create Expressions
  • Renaming Expressions
  • Expressions Are Idiomatic
  • Takeaways
• 8. Continuing Expressions
  • Types of Operations
    • Example A: Element-Wise Operations
    • Example B: Operations That Summarize to One
    • Example C: Operations That Summarize to One or More
    • Example D: Operations That Extend
  • Element-Wise Operations
    • Operations That Perform Mathematical Transformations
    • Operations Related to Trigonometry
    • Operations That Round and Categorize
    • Operations for Missing or Infinite Values
    • Other Operations
  • Nonreducing Series-Wise Operations
    • Operations That Accumulate
    • Operations That Fill and Shift
    • Operations Related to Duplicate Values
    • Operations That Compute Rolling Statistics
    • Operations That Sort
    • Other Operations
  • Series-Wise Operations That Summarize to One
    • Operations That Are Quantifiers
    • Operations That Compute Statistics
    • Operations That Count
    • Other Operations
  • Series-Wise Operations That Summarize to One or More
    • Operations Related to Unique Values
    • Operations That Select
    • Operations That Drop Missing Values
    • Other Operations
  • Series-Wise Operations That Extend
  • Takeaways
• 9. Combining Expressions
  • Inline Operators Versus Methods
  • Arithmetic Operations
  • Comparison Operations
  • Boolean Algebra Operations
  • Bitwise Operations
  • Using Functions
    • When, Then, Otherwise
  • Takeaways
• IV. Transform
• 10. Selecting and Creating Columns
  • Selecting Columns
    • Introducing Selectors
    • Selecting Based on Name
    • Selecting Based on Data Type
    • Selecting Based on Position
    • Combining Selectors
  • Creating Columns
  • Related Column Operations
    • Dropping
    • Renaming
    • Stacking
    • Adding Row Indices
  • Takeaways
• 11. Filtering and Sorting Rows
  • Filtering Rows
    • Filtering Based on Expressions
    • Filtering Based on Column Names
    • Filtering Based on Constraints
  • Sorting Rows
    • Sorting Based on a Single Column
    • Sorting in Reverse
    • Sorting Based on Multiple Columns
    • Sorting Based on Expressions
    • Sorting Nested Data Types
  • Related Row Operations
    • Filtering Missing Values
    • Slicing
    • Top and Bottom
    • Sampling
    • Semi-Joins
  • Takeaways
• 12. Working with Textual, Temporal, and Nested Data Types
  • String
    • String Methods
      • String methods for conversion
      • String methods for describing and querying
      • String methods for manipulation
    • String Examples
  • Categorical
    • Categorical Methods
    • Categorical Examples
  • Enum
  • Temporal
    • Temporal Methods
      • Temporal methods for conversion
      • Temporal methods for describing and querying
      • Temporal methods for manipulation
    • Temporal Examples
      • Loading from a CSV file
      • Converting to and from a String
      • Generating date ranges
      • Time zones
  • List
    • List Methods
    • List Examples
  • Array
    • Array Methods
    • Array Examples
  • Struct
    • Struct Methods
    • Struct Examples
  • Takeaways
• 13. Summarizing and Aggregating
  • Split, Apply, and Combine
  • GroupBy Context
    • The Descriptives
    • Advanced Methods
      • 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
    • Join Strategies
      • Inner
      • Full
      • Left
      • Right
      • Cross
      • Semi
      • Anti
    • Joining on Multiple Columns
    • Validation
      • Many-to-many
      • One-to-many
      • Many-to-one
      • One-to-one
  • Inexact Joining
    • Inexact Join Strategies
    • Additional Fine-Tuning
    • Use Case: Marketing Campaign Attribution
  • Vertical and Horizontal Concatenation
    • Vertical
    • Horizontal
    • Diagonal
    • Align
    • Relaxed
    • Stacking
    • Appending
    • Extending
  • Takeaways
• 15. Reshaping
  • Wide Versus Long DataFrames
  • Pivot to a Wider DataFrame
  • Unpivot to a Longer DataFrame
  • Transposing
  • Exploding
  • Partition into Multiple DataFrames
  • Takeaways
• V. Advance
• 16. Visualizing Data
  • NYC Bike Trips
  • Built-In Plotting with Altair
    • Introducing Altair
    • Methods in the Plot Namespaces
    • Plotting DataFrames
    • Too Large to Handle
    • Plotting Series
  • pandas-Like Plotting with hvPlot
    • Introducing hvPlot
    • A First Plot
    • Methods in the hvPlot Namespace
    • pandas as Backup
    • Manual Transformations
    • Changing the Plotting Backend
    • Plotting Points on a Map
    • Composing Plots
    • Adding Interactive Widgets
  • Publication-Quality Graphics with plotnine
    • Introducing plotnine
    • Plots for Exploration
    • Plots for Communication
  • Styling DataFrames With Great Tables
  • Takeaways
• 17. Extending Polars
  • User-Defined Functions in Python
    • Applying a Function to Elements
    • Applying a Function to a Series
    • Applying a Function to Groups
    • Applying a Function to an Expression
    • Applying a Function to a DataFrame or LazyFrame
  • Registering Your Own Namespace
  • Polars Plugins in Rust
    • Prerequisites
    • The Anatomy of a Plugin Project
    • The Plugin
    • Compiling the Plugin
    • Performance Benchmark
    • Register Arguments
      • Working with multiple arguments as input
      • Other register arguments
    • Using a Rust Crate
    • Use Case: geo
      • Adding the geo crate
      • The Rust code
      • The Python code
      • Making the custom namespace
  • Takeaways
• 18. Polars Internals
  • Polars Architecture
  • Arrow
  • Multithreaded Computations and SIMD Operations
  • The String Data Type in Memory
  • ChunkedArrays in Series
  • Query Optimization
    • LazyFrame Scan-Level Optimizations
    • Other Optimizations
  • Checking Your Expressions
    • meta Namespace Overview
    • meta Namespace Examples
  • Profiling Polars
  • Tests in Polars
    • Comparing DataFrames and Series
  • Common Antipatterns
    • Using Brackets for Column Selection
    • Misusing Collect
    • Using Python Code in your Polars Queries
  • Takeaways
• A. Accelerating Polars with the GPU
  • NVIDIA RAPIDS
  • Installing the GPU Engine
    • Step 1: Install WSL2 on Windows
    • Step 2: Install Ubuntu Linux on WSL2
    • Step 3: Install Prerequisite Ubuntu Linux Packages
    • Step 4: Install the CUDA Toolkit
    • Step 5: Install Python Dependencies
    • Step 6: Test Your Installation
  • Using the Polars GPU Engine
    • Configuration
    • Unsupported Features
  • Benchmarking the Polars GPU Engine
    • Solutions
    • Queries and Data
    • Method
    • Results and Discussion
      • Polars GPU engine versus CPU engine
      • Performance on different hardware
      • Polars GPU engine versus other packages
      • The effect of the Polars optimizer
    • Conclusion
  • The Future of Polars on the GPU
  • Takeaways
• Index

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