CHAPTER 01 / 07
DataFrames and expressions
Understand the table and expression model, create DataFrames, and inspect data before transforming it.
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
Select and transform columns, filter records, handle missing values, and clean strings and dates.
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
Aggregate and join records, work with windows, reshape tables, and handle nested values.
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
Compose reusable queries, read and write files, exchange data, and work with partitioned and 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
Inspect query plans, control memory use, stream larger workloads, and understand UDF costs.
CHAPTER 06 / 07
Data pipeline examples
Follow complete examples for sales cleanup, schema drift, reconciliation, and larger-than-memory files.
CHAPTER 07 / 07
SQL, databases, and data quality
Test data quality, connect database tools, use SQL and visualization, and consult reference material.