Python Polars: The Definitive Guide - Helion

ebook

Autor: Jeroen Janssens, Thijs Nieuwdorp
ISBN: 9781098156046
stron: 504, Format: ebook
Data wydania: 2025-02-20
Księgarnia: Helion

Cena książki: 237,15 zł (poprzednio: 285,72 zł)
Oszczędzasz: 17% (-48,57 zł)

Osoby, które kupiły tę książkę, wybierały także »

Want to speed up your data analysis and work with larger-than-memory datasets? Python Polars offers a blazingly fast, multithreaded, and elegant API for data loading, manipulation, and processing. With this hands-on guide, you'll walk through every aspect of Polars and learn how to tackle practical use cases using real-world datasets.

Jeroen Janssens and Thijs Nieuwdorp from Xomnia in Amsterdam show you how this superfast DataFrame library is perfect for efficient data wrangling, ETL pipelines, and so much more. This book helps you quickly learn the syntax and understand Polars' underlying concepts. You don't need to have experience with pandas or Spark, but if you do, this book will help you make a smooth transition.

With this definitive guide at your side, you'll be able to:

Process larger-than-memory datasets at record speed
Apply the eager, lazy, and streaming APIs of Polars and decide when to use them
Transition smoothly from pandas or Spark to Polars
Integrate Polars into your existing code base
Work with Arrow and Parquet to efficiently read and write data
Translate complex ETL tasks into efficient and elegant queries

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Spis treści

Python Polars: The Definitive Guide eBook -- spis treści

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