A crude Hy handle on Pandas library

Related tags

Data Analysis Hyenas
Overview

Quickstart

Hyenas is a curde Hy handle written on top of Pandas API to allow for more elegant access to data-scientist's powerhouse that is Pandas.

Installation

To quickstart with Hyenas you will need to:

  1. Install Hy
  2. Download Hyenas by pulling this repository and executing the following commands:
cd ./hyenas
pip install -r requirements.txt
python -m build
pip install ./dist/hyenas-0.0.1-py3-none-any.whl

Usage

Hyenas - while very humble in its Pandas API coverage as of now - currently boasts an SQL like interface to access dataframe structures.

(import [hyenas[*]])
(import [pandas [read_csv]])

(
    select  :cols [(count_agg "long_name") (mean_agg "weight_kg")] 
            :from_df (read_csv "/some/data/players_21.csv") 
            :group_by ["height_cm"]
)

Project Status

Hyenas is currently in its infancy. Collaboratos, Pull Requests and Issues are welcome.

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Comments
  • chore(deps): bump numpy from 1.21.2 to 1.22.0

    chore(deps): bump numpy from 1.21.2 to 1.22.0

    Bumps numpy from 1.21.2 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

    Commits

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Owner
Peter Výboch
Developer by profession and at heart. Close to data and/or close to metal is where I thrive.
Peter Výboch
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