CHAPTER 01 / 07
DataFrames and expressions
Create Polars Series and DataFrames in Python, inspect schemas, and understand expressions and LazyFrames before transforming a dataset.
Python reference
Polars
cheat sheet.
DataFrames, expressions, joins, file formats, and reporting with Polars. Includes lazy queries, streaming, and data quality checks.
50 topics52 code examplesUpdated
How Polars fits together
01Understand the tableKnow what a row means and which types each column needs.
02Describe the workUse expressions to clean, check, and transform values.
03Choose executionExplore eagerly or optimize and stream a file query.
04Check and exportValidate the result and write a suitable typed output.
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7 chapters · 50 topics
CHAPTER 02 / 07
Select, filter, and transform
Use Polars select, with_columns, and filter to transform data. Python examples cover nulls, duplicates, strings, dates, and time zones.
9 topics
- Select columns and build expressions
- Add columns and make conditional decisions
- Filter rows and sort results
- Types, casting, and exact money
- Null, NaN, and missing-value policies
- Duplicates and the latest customer record
- Clean strings and messy exports
- Parse dates and calculate intervals
- Time zones and daylight-saving changes
CHAPTER 03 / 07
Joins, grouping, and aggregation
Join and aggregate Polars DataFrames with group_by, windows, and as-of joins. Reshape tables and work with lists, structs, and nested JSON.
10 topics
- Group and aggregate sales
- Joins and duplicate-key cardinality
- Reconcile transactions from two systems
- Window functions and row-level context
- Calendar summaries, rolling windows, and missing days
- As-of joins and effective prices
- Reshape with unpivot and pivot
- Lists, arrays, and exploding line items
- Structs and nested JSON
- Categorical data and ordered enums
CHAPTER 04 / 07
File formats and data interchange
Read and write CSV, Parquet, JSON, and Excel with Polars. Exchange Arrow and pandas data, combine files, and scan partitioned cloud datasets.
13 topics
- Reusable expressions, selectors, and pipe
- Eager execution, lazy execution, read, and scan
- CSV input, schema inference, and malformed records
- Parquet and Arrow IPC file round trips
- JSON, NDJSON, and quarantine for bad records
- Excel input with fastexcel
- Excel reporting with XlsxWriter
- Arrow interchange with PyArrow
- NumPy and pandas interoperability
- Combine files and handle schema drift
- Partitioned datasets and Hive-style folders
- Cloud storage and credentials
- fsspec for Python filesystem workflows
CHAPTER 05 / 07
Lazy queries and performance
Optimize Polars lazy queries with explain, streaming, and native expressions. Understand memory use and when Python UDFs add overhead.
CHAPTER 06 / 07
Data pipeline examples
Follow complete Polars pipelines for messy sales exports, schema drift, ledger reconciliation, and files larger than memory.
CHAPTER 07 / 07
SQL, databases, and data quality
Query databases with Polars and SQLAlchemy, use SQLContext, and test data quality. Includes ConnectorX, ADBC, and Altair examples.
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