semvec/pypi-packages huggingface dataset:
loyverse 0.1.0
Loyverse API wrapper
License: MIT
Requires Python: >3.7.0

The loyverse package provides a wrapper around the [Loyverse API](https://developer.loyverse.com/docs/).
It is intended to help Loyverse users to access their data using Python, without the need for 
boilerplate code. Furthermore, the package provides tools to convert the API responses into 
[pandas](https://pandas.pydata.org/pandas-docs/stable/index.html) DataFrames for easier manipulation. 

### Getting started
The package relies on a central Client object to access all available endpoints. 
To start a new client, the user either needs to provide the access token explicitly, or 
store it in the environmental variables under LOYVERSE_ACCESS_TOKEN. For how to generate your access token, please
 check 
 [here](https://help.loyverse.com/help/loyverse-api#:~:text=To%20create%20a%20new%20token,Access%20token%20will%20be%20created.).

The example below shows how to initialize a client:

```python
from loyverse import Client

# Pass API access token explicitly
client = Client(access_token='your_access_token')

# Pass API access token using environment variable LOYVERSE_ACCESS_TOKEN
client = Client()
```

### Access an endpoint

All exposed API endpoints are available as properties of the Client class. Currently, the following 
endpoints are implemented:

* Customers
* Receipts

The example below shows how to retrieve receipts information for a specific date:

```python
from datetime import datetime
from loyverse import Client

target_date = datetime(2020, 10, 31)

# Retrieve receipts data
client = Client()
response = client.receipts.get_by_date(target_date)

# Convert to dataframes
receipts, items, payments = client.receipts.to_dataframes(response)
```
pymmails 0.3.319
A module to download emails from an IMAP4 server
License: MIT
Keywords: pymmails


.. image:: https://github.com/sdpython/pymmails/blob/master/_doc/sphinxdoc/source/phdoc_static/project_ico.png?raw=true
    :target: https://github.com/sdpython/pymmails/

.. _l-README:

pymmails: send and grab mails
=============================


.. image:: https://travis-ci.org/sdpython/pymmails.svg?branch=master
    :target: https://travis-ci.org/sdpython/pymmails
    :alt: Build status

.. image:: https://ci.appveyor.com/api/projects/status/hqhhdndvayrx0r9k?svg=true
    :target: https://ci.appveyor.com/project/sdpython/pymmails
    :alt: Build Status Windows

.. image:: https://circleci.com/gh/sdpython/pymmails/tree/master.svg?style=svg
    :target: https://circleci.com/gh/sdpython/pymmails/tree/master

.. image:: https://badge.fury.io/py/pymmails.svg
    :target: http://badge.fury.io/py/pymmails

.. image:: http://img.shields.io/github/issues/sdpython/pymmails.png
    :alt: GitHub Issues
    :target: https://github.com/sdpython/pymmails/issues

.. image:: https://img.shields.io/badge/license-MIT-blue.svg
    :alt: MIT License
    :target: http://opensource.org/licenses/MIT

.. image:: https://requires.io/github/sdpython/pymmails/requirements.svg?branch=master
    :target: https://requires.io/github/sdpython/pymmails/requirements/?branch=master
    :alt: Requirements Status

.. image:: https://codecov.io/github/sdpython/pymmails/coverage.svg?branch=master
    :target: https://codecov.io/github/sdpython/pymmails?branch=master

The module was started to grab emails using IMAP and to store them on a local disk.
It is now used to download material sent by students before an oral presentation,
which is quite annoying to do manually.

::

    from pymmails import MailBoxImap, EmailMessageRenderer

    user = "your.email"
    pwd = "passsword"
    server = "imap.your_provider.ext"

    box = MailBoxImap(user, pwd, server)
    render = EmailMessageRenderer()
    box.login()
    for mail in box.enumerate_mails_in_folder("saved", pattern="<pattern>") :
        mail.dump(render, location=temp, fLOG=fLOG)
    box.logout()
    render.flush()

Some examples of patterns::

    pattern='FROM "xavier" SINCE 1-Feb-2013'
    pattern='FROM "xavier" SINCE 1-Feb-2013 BEFORE 5-May-2013'
    pattern='FROM "xavier" SINCE 1-Feb-2013 BEFORE 5-May-2013 UNANSWERED'
    pattern='CC "jacques" FROM "xavier" DELETED'
    pattern='TEXT "github"'
    pattern='LARGER 10000 SMALLER 1000000'
    pattern='SUBJECT "programmation"'
    pattern='TO "student" FLAGGED'
    pattern='UNSEEN'

**Links:**

* `GitHub/pymmails <https://github.com/sdpython/pymmails/>`_
* `documentation <http://www.xavierdupre.fr/app/pymmails/helpsphinx/index.html>`_
* `Blog <http://www.xavierdupre.fr/app/pymmails/helpsphinx/blog/main_0000.html#ap-main-0>`_

.. _l-HISTORY:

=======
History
=======

current - 2020-11-02 - 0.00Mb
=============================

0.2.290 - 2019-01-18 - 0.03Mb
=============================

* `3`: add list of attachements at the end of the summary (2019-01-15)
* `1`: rename flake8 into code_style (2018-04-14)
textslack 0.1.5
Play with text data
License: MIT

# textslack
A text cleaning pipeline to perform text cleaning, along with additional functionalities for sentiment, pos extraction, and word count.

After pip install, please follow the below step to access the functionalities:
* from textslack.textslack import TextSlack
* slack = TextSlack()

Below are the key functionalities currently available in the all the versions:

1. transform(text): normalises and cleans unstructured text
2. extract_nouns(text): cleans text to keep only nouns
3. extract_verbs(text): cleans text to keep only verbs
4. extract_adjectives(text): cleans text to keep only adjectives
5. extract_adverbs(text): cleans text to keep only adverbs
6. sentiment(text): returns a string as sentiment
7. word_occurances(word, text): returns frequency of a word mentioned in the text

v.0.1.5 updates: 
1. transform method can now be used on a list and an entire pandas dataframe column.
2. textslack can perform basic text cleaning for some non-english languages as well, just pass the language while creating the object as shown below.
   
   slack = TextSlack(lang='spanish')

Please refer the below medium article for a detailed explanation of textslack functionalities.
https://medium.com/analytics-vidhya/text-processing-made-easy-with-textslack-4214ae6bc67a
hl7-transform 0.1.6
Library that transforms HL7 messages using mapping schemes
License: MIT
Requires Python: >=3
Keywords: HL7,hospital IT,infrastructure,message,transform

# Introduction

``hl7_transform`` is a Python package that allows for transformation of HL7 messages
using a field mapping dictionary.

[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity) ![Docs build status](https://readthedocs.org/projects/hl7-transform/badge/?version=latest) ![GitHub Workflow Status](https://img.shields.io/github/workflow/status/pdyban/hl7_transform/CI) [![PyPI license](https://img.shields.io/pypi/l/hl7-transform.svg)](https://pypi.python.org/pypi/hl7-transform/) [![PyPI pyversions](https://img.shields.io/pypi/pyversions/hl7-transform.svg)](https://pypi.python.org/pypi/hl7-transform/)

As a standard, HL7 permits different ways of implementing message interfaces
between systems. For example two systems that exchange ADT or SIU messages often
pass the same information in different fields.
In a hospital, an integration engine would map one type of HL7 messages
to another, transforming the messages on the fly as they are passed through
the interfaces.
This Python package allows you to test message transformation without having
an integration engine in place. You can modify your HL7 message structure freely,
by using a JSON-encoded mapping of fields,
then evaluate the conformance of the newly created transformed messages
to the target software.

A live example of this package at work can be found in [hl7_transform_web](https://github.com/pdyban/hl7_transform_web) repository and online.

# How to use

The easiest way to use this library is to install it from Pypi:

```py
pip install hl7_transform
```

Alternatively, you can download and build this package from source:

```bash
git clone https://github.com/pdyban/hl7_transform.git
python setup.py build
python setup.py install
```

After installation, you can use the Python library in your own projects as well as call standalone script hl7_transform in the shell console of your choice.

```bash
hl7_transform --help
```

You can also build your own projects or experiment in Jupyter notebooks by importing the library in your Python code:

```py
from hl7_transform.mapping import HL7Mapping
from hl7_transform.transform import HL7Transform
from hl7_transform.message import HL7Message

mapping = HL7Mapping.from_json('test_transform.json')
message = HL7Message.from_file('test_msg.hl7')
transform = HL7Transform(mapping)
transformed_message = transform(message)
```

For example code, see inside [test](hl7_transform/test) module, in particular [test_transform.py](hl7_transform/test/test_transform.py).

# Documentation

This project is documented using [sphinx](https://www.sphinx-doc.org). The documentation pages can be found in [ReadTheDocs](https://hl7-transform.readthedocs.io/en/latest/).

To understand how the package works, we suggest to start by reading the [Mapping rules](https://hl7-transform.readthedocs.io/en/latest/mapping.html) documentation page.