semvec/pypi-packages huggingface dataset:
vk-exchange-rates 0.0.3
A small library for getting information on cash exchange rates of PrivatBank and the NBU on the selected date range
License: MIT
Keywords: python,exchange rate,privatbank,nbu,graph,diagram

![PyPI - Python Version](https://img.shields.io/pypi/pyversions/vk_exchange_rates?style=for-the-badge)

![PyPI - Developer](https://img.shields.io/badge/Developer-VitoldKliain-red)

**Official Repo:** https://gtlb.jetsoftpro.com/vitoldkliain/vk-exchange-rates

The library allows you to collect exchange rates from two Ukrainian banks: PrivatBank and National Bank of Ukraine. What
can you do:

- Get the exchange rate of each currency for a certain period
- Save data in csv and json format
- Get a graph how a currency changed in the selected period

## Libraries

It uses the following libraries:

- [requests](https://pypi.org/project/requests/) for requests to exchange rates
- [numpy](https://pypi.org/project/numpy/) to calculation for better data visualization
- [matplotlib](https://pypi.org/project/matplotlib/) to draw a graph
- [pandas](https://pypi.org/project/pandas/) to set a DataFrame

# Quick Install / Usage

```bash
pip install vk-exchange-rates
```

```python
from datetime import datetime

from vk_exchange_rates import get_exchange_rates, show_graph, save_to_csv, save_to_json


def main():
    get_exchange_rates(datetime(2022, 4, 20), datetime(2022, 4, 27))
    # show_graph(currency='PLN')
    # save_to_json(datetime(2022, 4, 25))
    # save_to_csv(datetime(2022, 4, 20), datetime(2022, 4, 27), currency='USD')

    # Output:
    # Exchange rates (sale) National Bank of Ukraine:
    # {'date': '20.04.2022', 'rates': {'AUD': 21.5418, 'CAD': 23.1906, 'CNY': 4.5801, 'HRK': 4.176, 'CZK': 1.293, ... }
    # Exchange rates (sale) PrivatBank:
    # {'date': '20.04.2022', 'rates': {'AZN': 17.2433, 'BYN': 10.6335, 'CAD': 23.1906, 'CHF': 33.82, 'CNY': 4.5801, ... }
```

# Most popular currencies

- USD
- EUR
- PLN
- GBP
- CHF
- CZK
keras-bed-sequence 1.1.9
Lazily one-hot encoding bed sequences using Keras Sequence.
License: MIT
Requires Python: >3.5.2

keras_bed_sequence
=========================================================================================
|travis| |sonar_quality| |sonar_maintainability| |codacy|
|code_climate_maintainability| |pip| |downloads|

Lazily one-hot encoding bed sequences using Keras Sequence.

How do I install this package?
----------------------------------------------
As usual, just download it using pip:

.. code:: shell

    pip install keras_bed_sequence

Tests Coverage
----------------------------------------------
Since some software handling coverages sometimes get
slightly different results, here's three of them:

|coveralls| |sonar_coverage| |code_climate_coverage|



.. |travis| image:: https://travis-ci.org/LucaCappelletti94/keras_bed_sequence.png
   :target: https://travis-ci.org/LucaCappelletti94/keras_bed_sequence
   :alt: Travis CI build

.. |sonar_quality| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_keras_bed_sequence&metric=alert_status
    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_keras_bed_sequence
    :alt: SonarCloud Quality

.. |sonar_maintainability| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_keras_bed_sequence&metric=sqale_rating
    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_keras_bed_sequence
    :alt: SonarCloud Maintainability

.. |sonar_coverage| image:: https://sonarcloud.io/api/project_badges/measure?project=LucaCappelletti94_keras_bed_sequence&metric=coverage
    :target: https://sonarcloud.io/dashboard/index/LucaCappelletti94_keras_bed_sequence
    :alt: SonarCloud Coverage

.. |coveralls| image:: https://coveralls.io/repos/github/LucaCappelletti94/keras_bed_sequence/badge.svg?branch=master
    :target: https://coveralls.io/github/LucaCappelletti94/keras_bed_sequence?branch=master
    :alt: Coveralls Coverage

.. |pip| image:: https://badge.fury.io/py/keras-bed-sequence.svg
    :target: https://badge.fury.io/py/keras-bed-sequence
    :alt: Pypi project

.. |downloads| image:: https://pepy.tech/badge/keras-bed-sequence
    :target: https://pepy.tech/badge/keras-bed-sequence
    :alt: Pypi total project downloads

.. |codacy| image:: https://api.codacy.com/project/badge/Grade/6bb591f3d405443a9549967eac35b723
    :target: https://www.codacy.com/manual/LucaCappelletti94/keras_bed_sequence?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=LucaCappelletti94/keras_bed_sequence&amp;utm_campaign=Badge_Grade
    :alt: Codacy Maintainability

.. |code_climate_maintainability| image:: https://api.codeclimate.com/v1/badges/d601fb2c7485f1ac3433/maintainability
    :target: https://codeclimate.com/github/LucaCappelletti94/keras_bed_sequence/maintainability
    :alt: Maintainability

.. |code_climate_coverage| image:: https://api.codeclimate.com/v1/badges/d601fb2c7485f1ac3433/test_coverage
    :target: https://codeclimate.com/github/LucaCappelletti94/keras_bed_sequence/test_coverage
    :alt: Code Climate
automataPy 0.0.1
Deterministic, non-deterministic, and stochastic final-state automata implementation with auxiliary functions.
License: MIT
Requires Python: >=3.6

<div align="center">
    <h1 align="center">AutomataPy</h1>
    <p align="center">Final state automata library</p>

[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
</div>
<hr />

***automataPy*** is an finite-state automata library written in Python. It supports varaity of formalisms, and provides useful functions
such as random automata generation, saving and loading automata from/to file using well defined syntax as well as visualization of automata.

***automataPy*** is a subset of [AALpy](https://github.com/DES-Lab/AALpy), an active automata learning library. 

|        Type       |                              Supported Formalism                              |                                                           Features                                                           |
|:-----------------:|:-----------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------:|
| Deterministic     | DFAs<br />  Mealy Machines<br /> Moore Machines                                           | Shortest path computation between 2 states<br /> Characterization set computation                                                  |
| Non-Deterministic | Observable Non-Deterministic  Finite-State Machines (ONFSM)                   | -                                                                                                                            |
| Stochastic        | Markov Decision Processes (MDP)<br /> Stochastic Mealy Machines (SMM)<br /> Markov Chains | Computing input sequence probability<br /> Conversion from SMM to MDPs<br /> Conversion to PRISM format<br /> Scheduler computation with PRISM |
| All models        | -                                                                             | Saving to file<br /> Loading from file<br /> Generation of random automata<br /> Visualization of automata<br /> Execution of sequences <br />             |

## Install
Use the package manager [pip](https://pip.pypa.io/en/stable/):
```
pip install automataPy
```
Ensure that you have [Graphviz](https://graphviz.org/) installed and added to your path if you want to visualize models.

## Short example
Examples demonstrating all ***automataPy*** functionalities can be found in [Example.py]().

## Contributing
Pull requests are welcome. For significant changes, please open an issue first to discuss what you would like to change.
In case of any questions or possible bugs, please open issues.