semvec/pypi-packages huggingface dataset:
Esipraisal 0.1.5
An Eve Online appraisal tool which uses ESI
License: MIT
Keywords: Esi Eve Python Api

# Esipraisal

Esipraisal is a simple tool designed to figure out the value of an item ("type" in the eve DB) based on several sources.


## To Use


[Install with PyPi](https://pypi.org/project/Esipraisal/)


```python
from Esipraisal import Esipraisal
ep = Esipraisal()
await ep.appraise(type_id, region_ids)
```


type_id = The id of the type you want to get the value of

region_ids = a list of region ids in which you want to pull order information from.  Usefull if you say only want mineral prices in Delve.


Returns: Appraisal object


The Appraisal object includes the following info:


* type = the type_id that was appraised
* region_list = the list of region_ids from where the data was pulled
* value = the estimated value of the type
* volume = volume of orders
* buy_value = average value of "buy" orders
* sell_value = average value of "sell" orders
* buy_volume = volume of "buy" orders
* sell_volume = volume of "sell" orders
* source = How the estimate was determined (enum):
    * Market Orders - Most common, from market orders currently active
    * Historical Orders - Past orders which were recently completed, only used if there are not enough market orders for accurate data
    * CCP - CCP's estimate, only used if the above 2 are not valid

Note: volumes listed may be different than those currently listed on market as the volume is after outlier removal has been done
imputena 1.0
Package that allows both automated and customized treatment of missing values in datasets using Python.
License: MIT
Requires Python: >=3.5


This package allows both automated and customized treatment of missing
values in datasets using Python. The treatments that are implemented in this
package are:

* Listwise deletion
* Pairwise deletion
* Dropping variables
* Random sample imputation
* Random hot-deck imputation
* LOCF
* NOCB
* Most frequent substitution
* Mean and median substitution
* Constant value imputation
* Random value imputation
* Interpolation
* Interpolation with seasonal adjustment
* Linear regression imputation
* Stochastic regression imputation
* Logistic regression imputation
* K-nearest neighbors imputation
* Sequential regression multiple imputation
* Multiple imputation by chained equations

All these treatments can be applied to whole datasets or parts of them and
 allow for extensive customization. The package can also recommend a
  treatment for a given dataset, inform about the treatments that are
   applicable to it, and automatically apply the best treatment.
adafruit-circuitpython-ble-pulse-oximeter 1.0.1
BLE Support for BerryMed Pulse Oximeter Service
License: MIT
Keywords: adafruit blinka circuitpython micropython ble_contec_pulse_oximeter ble pulse oximeter berrymed spo2

Introduction
============

.. image:: https://readthedocs.org/projects/adafruit-circuitpython-ble-berrymed_pulse_oximeter/badge/?version=latest
    :target: https://circuitpython.readthedocs.io/projects/ble_berrymed_pulse_oximeter/en/latest/
    :alt: Documentation Status

.. image:: https://img.shields.io/discord/327254708534116352.svg
    :target: https://discord.gg/nBQh6qu
    :alt: Discord

.. image:: https://github.com/adafruit/Adafruit_CircuitPython_BLE_BerryMed_Pulse_Oximeter/workflows/Build%20CI/badge.svg
    :target: https://github.com/adafruit/Adafruit_CircuitPython_BLE_BerryMed_Pulse_Oximeter/actions
    :alt: Build Status

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
    :target: https://github.com/psf/black
    :alt: Code Style: Black

BLE support for BerryMed Pulse Oximeters


Dependencies
=============
This driver depends on:

* `Adafruit CircuitPython <https://github.com/adafruit/circuitpython>`_

Please ensure all dependencies are available on the CircuitPython filesystem.
This is easily achieved by downloading
`the Adafruit library and driver bundle <https://circuitpython.org/libraries>`_.

Installing from PyPI
=====================
.. note:: This library is not available on PyPI yet. Install documentation is included
   as a standard element. Stay tuned for PyPI availability!

On supported GNU/Linux systems like the Raspberry Pi, you can install the driver locally `from
PyPI <https://pypi.org/project/adafruit-circuitpython-ble-berrymed_pulse_oximeter/>`_. To install for current user:

.. code-block:: shell

    pip3 install adafruit-circuitpython-ble-berrymed-pulse-oximeter

To install system-wide (this may be required in some cases):

.. code-block:: shell

    sudo pip3 install adafruit-circuitpython-ble-berrymed-pulse-oximeter

To install in a virtual environment in your current project:

.. code-block:: shell

    mkdir project-name && cd project-name
    python3 -m venv .env
    source .env/bin/activate
    pip3 install adafruit-circuitpython-ble-berrymed-pulse-oximeter

Contributing
============

Contributions are welcome! Please read our `Code of Conduct
<https://github.com/adafruit/Adafruit_CircuitPython_BLE_BerryMed_Pulse_Oximeter/blob/master/CODE_OF_CONDUCT.md>`_
before contributing to help this project stay welcoming.

Documentation
=============

For information on building library documentation, please check out `this guide <https://learn.adafruit.com/creating-and-sharing-a-circuitpython-library/sharing-our-docs-on-readthedocs#sphinx-5-1>`_.