1.
Introduction
2.
Getting started
3.
Polars expressions
❱
3.1.
Expressions
3.2.
Contexts
3.3.
GroupBy
3.4.
Folds
3.5.
Window functions
3.6.
List context and row-wise compute
3.7.
Numpy universal functions
3.8.
Custom functions
3.9.
Python Examples
3.10.
Rust Examples
3.11.
API
3.12.
Video introduction
4.
Data Types
5.
Coming from Pandas
6.
Coming from Apache Spark
7.
How can I?
❱
7.1.
IO
❱
7.1.1.
CSV files
7.1.2.
Parquet files
7.1.3.
Multiple files
7.1.4.
Read from a database
7.1.5.
Interact with AWS
7.1.6.
Interact with Google BigQuery
7.1.7.
Interact with Postgres
7.1.8.
Interoperability
❱
7.1.8.1.
Arrow
7.1.8.2.
NumPy
7.2.
Selecting data
❱
7.2.1.
Selecting with expressions
7.2.2.
Selecting with indexing
7.3.
Data handling
❱
7.3.1.
Process strings
7.3.2.
Process timestamps
7.3.3.
Process missing data
7.4.
Time-series
❱
7.4.1.
Parsing dates and times
7.4.2.
Filtering by dates
7.4.3.
Fixed and rolling temporal groupby
7.4.4.
Resampling
7.5.
Combining data
❱
7.5.1.
Concatenation
7.5.2.
Joining
8.
Performance
❱
8.1.
Strings
9.
Optimizations
❱
9.1.
Lazy API
❱
9.1.1.
Predicate pushdown
9.1.2.
Projection pushdown
9.1.3.
Other optimizations
10.
Testing
11.
Reference guides
12.
Contributing
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Polars - User Guide
Video Introduction
Don't enjoy reading? Take a look at this introduction video on
Polars
and its expressions.