semvec/pypi-packages huggingface dataset:
pydbots 1.0.0
A Python wrapper for the Discord API
License: MIT
Requires Python: >=3.8.0

PyDBota
==========

.. image:: https://discord.com/api/guilds/881799755339010049/embed.png
   :target: https://discord.gg/wbQkNfEjdP
   :alt: Discord server invite
.. image:: https://img.shields.io/pypi/v/pydbots.svg
   :target: https://pypi.python.org/pypi/pydbots
   :alt: PyPI version info
.. image:: https://img.shields.io/pypi/pyversions/pydbots.svg
   :target: https://pypi.python.org/pypi/pydbots
   :alt: PyPI supported Python versions

A modern, easy to use, feature-rich, and async ready API wrapper for Discord written in Python.

Key Features
-------------

- Modern Pythonic API using ``async`` and ``await``.
- Proper rate limit handling.
- Optimised in both speed and memory.

Installing
----------

**Python 3.8 or higher is required**

To install the library without full voice support, you can just run the following command:

.. code:: sh

    # Linux/macOS
    python3 -m pip install -U pydbots

    # Windows
    py -3 -m pip install -U pydbots

Otherwise to get voice support you should run the following command:

.. code:: sh

    # Linux/macOS
    python3 -m pip install -U "pydbots[voice]"

    # Windows
    py -3 -m pip install -U pydbots[voice]


To install the development version, do the following:

.. code:: sh

    $ git clone https://github.com/anshtyagi0/pydbots
    $ cd pydbots
    $ python3 -m pip install -U .[voice]


Optional Packages
~~~~~~~~~~~~~~~~~~

* `PyNaCl <https://pypi.org/project/PyNaCl/>`__ (for voice support)

Please note that on Linux installing voice you must install the following packages via your favourite package manager (e.g. ``apt``, ``dnf``, etc) before running the above commands:

* libffi-dev (or ``libffi-devel`` on some systems)
* python-dev (e.g. ``python3.6-dev`` for Python 3.6)

Quick Example
--------------

.. code:: py

    import discord

    class MyClient(discord.Client):
        async def on_ready(self):
            print('Logged on as', self.user)

        async def on_message(self, message):
            # don't respond to ourselves
            if message.author == self.user:
                return

            if message.content == 'ping':
                await message.channel.send('pong')

    client = MyClient()
    client.run('token')

Bot Example
~~~~~~~~~~~~~

.. code:: py

    import discord
    from discord.ext import commands

    bot = commands.Bot(command_prefix='>')

    @bot.command()
    async def ping(ctx):
        await ctx.send('pong')

    bot.run('token')

You can find more examples in the examples directory.

Links
------

- `Documentation <https://discordpy.readthedocs.io/en/latest/index.html>`_
- `Official Discord Server <https://discord.gg/wbQkNfEjdP>`_
- `Discord API <https://discord.gg/discord-api>`_
settree 0.2.1
A framework for learning tree-based models over sets
License: MIT

Set-Tree
Extending decision trees to process sets

This is the official repository for the paper: "Trees with Attention for Set Prediction Tasks" (ICML21).
This repository contains a prototypical implementaion of Set-Tree and GBeST (Gradient Boosted Set-Tree) algorithms
The Set-Tree package can be downloaded from PIP: pip install settree
We also supply the code and datasets for reproducing our experimetns under exps folder.

In many machine learning applications, each record represents a set of items. A set is an unordered group of items, the number of items may differ between different sets. Problems comprised from sets of items are present in diverse fields, from particle physics and cosmology to statistics and computer graphics. In this work, we present a novel tree-based algorithm for processing sets.

Set-Tree model comprised from two components:
Set-compatible split creteria: we specifically support the familly of split creteria defined by the following equation and parametrized by alpha and beta.
Attention-Sets: a mechanism for allplying the split creteria to subsets of the input. The attention-sets are derived forn previous split-creteria and allows the model to learn more complex set-functions.
safegraph-ql 0.1.0
graphQL API of safegraph.com using Python functions
License: MIT
Keywords: api graphql safegraph gql safegraph-api

============
safegraph_ql
============
.. image:: https://github.com/datamade/census/workflows/Python%20package/badge.svg

API of https://api.safegraph.com/v1/graphql

Installation
============
::

    pip install safegraph_ql

Usage
=====
First, get yourself a `SafeGraph API key <https://shop.safegraph.com/api>`_.

::

    from safegraph_ql import client
    graph = client.HTTP_Client("MY_API_KEY")
    placekeys = [
        "zzw-222@8fy-fjg-b8v", # (Disney World)
        "zzy-227@5sb-8cw-pjv", # (O'Hare Airport)
        "222-223@65y-rxx-djv", # (Walmart in Albany, NY)
        ] 
    # returns pandas dataframe
    df = graph.places(placekeys, columns="*")

Datasets
========
* CORE PLACES

Reliable POI For Accuracy In Your Data Models.
With global coverage for any brand you request, our Core dataset provides high quality data for the POI you care about.

* GEOMETRY

Machine Generated, Human Verified Building Footprint Polygons.
SafeGraph Geometry allows data leaders to understand POI footprints with spatial hierarchy metadata.

* PATTERNS

Understand Consumer Behavior With Verified Foot Traffic Data.
Aggregate foot fall data to either specific POIs or to Census Block Groups (CBGs) with SafeGraph Patterns.

Examples
========
* places
* search
* place_by_name