Polars-Cookbook

B21621 - Polars Cookbook - Amazon link for pre-order

Chapter 1: Getting Started with Python Polars

• Introducing Key Features in Polars
• The Polars DataFrame
• The Polars Series
• The Polars LazyFrame
• Selecting columns and filtering data
• Creating, modifying, and deleting columns
• Understanding method chaining
• Processing datasets larger than RAM

Chapter 2: Reading and Writing Files

• Reading and writing CSV files
• Reading and writing parquet files
• Reading and writing delta tables
• Reading and writing JSON files
• Reading and writing excel files
• Reading and writing other data file formats
• Reading and writing multiple files
• Working with databases

Chapter 3: An Introduction to Data Analysis in Python Polars

• Inspecting the DataFrame
• Casting data types
• Handling duplicate values
• Masking sensitive data
• Visualizing data using Plotly
• Detecting and handling outliers

Chapter 4: Data Transformation Techniques

• Exploring basic aggregations
• Using group by aggregations
• Aggregating values across multiple columns
• Computing with window functions
• Applying UDFs
• Using SQL for data transformations

Chapter 5: Handling Missing Data

• Identifying missing data
• Deleting rows and columns containing missing data
• Filling missing data

Chapter 6: Performing String Manipulations

• Filtering strings
• Converting strings into a Date/Datetime/Time
• Extracting substrings
• Cleaning strings
• Splitting strings into lists and structs
• Concatenating and combining strings

Chapter 7: Working with Nested Data Structures

• Creating lists
• Aggregating elements in lists
• Accessing and selecting elements in lists
• Applying logic to each element in lists
• Working with structs and JSON data

Chapter 8: Reshaping and Tidying Data

• Turning columns into rows
• Turning rows into columns
• Joining DataFrames
• Concatenating DataFrames
• Other reshaping techniques

Chapter 9: Time Series Analysis

• Working with date and time
• Applying rolling windows calculations
• Resampling techniques
• Time series forecasting with the functime library

Chapter 10: Interoperability With Other Python Libraries

• Converting to and from a pandas DataFrame
• Converting to and from NumPy arrays
• Interoperating with PyArrow
• Integration with DuckDB

Chapter 11: Working With Common Cloud Sources

• Amazon S3
• Azure Blog Storage
• Google Cloud Storage
• BigQuery
• Snowflake

Chapter 12: Testing and Debugging in Polars

• Debugging chained operations
• Inspecting and optimizing the query plan
• Testing data quality with cuallee
• Running unit tests with Pytest