#288 Performance benchmarks for Python 3.11 are amazing

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Brian #1: Polars: Lightning-fast DataFrame library for Rust and Python

• Suggested by a several listeners
• “Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model.
  • Lazy | eager execution
  • Multi-threaded
  • SIMD (Single Instruction/Multiple Data)
  • Query optimization
  • Powerful expression API
  • Rust | Python | ...”
• Python API syntax set up to allow parallel and execution while sidestepping GIL issues, for both lazy and eager use cases. From the docs: Do not kill parallelization

• The syntax is very functional and pipeline-esque:

  import polars as pl q = ( pl.scan_csv("iris.csv") .filter(pl.col("sepal_length") > 5) .groupby("species") .agg(pl.all().sum()) ) df = q.collect()

• Polars User Guide is excellent and looks like it’s entirely written with Python examples.

• Includes a 30 min intro video from PyData Global 2021

Michael #2: PSF Survey is out

• Have a look, their page summarizes it better than my bullet points will.

Brian #3: Gin Config: a lightweight configuration framework for Python

• Found through Vincent D. Warmerdam’s excellent intro videos on gin on calmcode.io
• Quickly make parts of your code configurable through a configuration file with the @gin.configurable decorator.

• It’s in interesting take on config files. (Example from Vincent)

  # simulate.py @gin.configurable def simulate(n_samples): ... # config.py simulate.n_samples = 100

• You can specify:

  • required settings: def simulate(n_samples=gin.REQUIRED)`
  • blacklisted settings: @gin.configurable(blacklist=["n_samples"])
  • external configurations (specify values to functions your code is calling)
  • can also references to other functions: dnn.activation_fn = @tf.nn.tanh
• Documentation suggests that it is especially useful for machine learning.
• From motivation section:
  • “Modern ML experiments require configuring a dizzying array of hyperparameters, ranging from small details like learning rates or thresholds all the way to parameters affecting the model architecture.
  • Many choices for representing such configuration (proto buffers, tf.HParams, ParameterContainer, ConfigDict) require that model and experiment parameters are duplicated: at least once in the code where they are defined and used, and again when declaring the set of configurable hyperparameters.
  • Gin provides a lightweight dependency injection driven approach to configuring experiments in a reliable and transparent fashion. It allows functions or classes to be annotated as @gin.configurable, which enables setting their parameters via a simple config file using a clear and powerful syntax. This approach reduces configuration maintenance, while making experiment configuration transparent and easily repeatable.”

Michael #4: Performance benchmarks for Python 3.11 are amazing

• via Eduardo Orochena
• Performance may be the biggest feature of all
• Python 3.11 has
  • task groups in asyncio
  • fine-grained error locations in tracebacks
  • the self-type to return an instance of their class
• The "Faster CPython Project" to speed-up the reference implementation.
  • See my interview with Guido and Mark: talkpython.fm/339
  • Python 3.11 is 10~60% faster than Python 3.10 according to the official figures
  • And a 1.22x speed-up with their standard benchmark suite.
• Arriving as stable until October

Extras

Michael:

• Python 3.10.5 is available (changelog)
• Raycast (vs Spotlight)
  • e.g. CMD+Space => pypi search:

Joke: Why wouldn't you choose a parrot for your next application