semvec/pypi-packages huggingface dataset:
kpler 0.0.3
A Python wrapper around Kpler client api
License: MIT

Python Client for Kpler data

````python
from kpler import KplerClient, Platform
from datetime import date, timedelta

# Connect to Kpler client using your Kpler credentials
client = KplerClient(Platform.Oil, "<your email>", "<your password>")

# Get all possible columns returned by the get_trades query
trades_columns = client.get_trades_columns()

# Do a get trades with default columns and with a maximum size of 5 for the dataframe
trades_df = client.get_trades(size=5)

# Get US exports with a specific columns hash and a history of one year
us_exports_df = client.get_trades(
    origin_zones=["united states"],
    columns_hash="9f0eb7222f991f1cdd285a994c039a35",
    with_forecast=True,
    start_date=date.today() - timedelta(days=365),
)
````
vixtor 0.0.3
Solves questions like 3D vector.
License: MIT
Keywords: custom,python package,function and class,3D line,3D plane,angle bw planes or line,distance bw point and plane

# [vixtor](https://github.com/imvickykumar999/vixtor/blob/master/vixtor.ipynb)

## [Available functions you can try](https://github.com/imvickykumar999/vixtor/blob/master/vector/vixtor/__init__.py)

## [pip install vixtor](https://github.com/imvickykumar999/vixtor/blob/master/pip%20install%20vixtor.ipynb)

#### ...made with love and hardwork ;)

#### [Post on LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:6715639149992394752/)

[![pypi](https://raw.githubusercontent.com/imvickykumar999/vixtor/master/pypi.png)](https://pypi.org/project/vixtor/)

# Package : Vixtor = Vix (my nickname) + 3D_Vec.tor

...this package can solve most of the questions of chapter 11, [NCERT](https://github.com/imvickykumar999/vixtor/blob/master/NCERT-Books-for-class%2012-Maths-Chapter%2011.pdf) (3D Vector) of class 12th.

...checkout my linkedin post for tutorial on how to use package's function and fully explained by solving questions.

...repo on github ( username : imvickykumar999 )

![plane](https://raw.githubusercontent.com/imvickykumar999/vixtor/master/vixtor.png)
pyap 0.3.1
Pyap is an MIT Licensed text processing library, written in Python, for detecting and parsing addresses. Currently it supports USA, Canadian and British addresses.
License: MIT
Requires Python: >=2.7

Pyap: Python address parser
===========================


Pyap is an MIT Licensed text processing library, written in Python, for
detecting and parsing addresses. Currently it supports US 🇺🇸, Canadian 🇨🇦 and British 🇬🇧 addresses. 


.. code-block:: python

    >>> import pyap
    >>> test_address = """
        Lorem ipsum
        225 E. John Carpenter Freeway, 
        Suite 1500 Irving, Texas 75062
        Dorem sit amet
        """
    >>> addresses = pyap.parse(test_address, country='US')
    >>> for address in addresses:
            # shows found address
            print(address)
            # shows address parts
            print(address.as_dict())
    ...




Installation
------------

To install Pyap, simply:

.. code-block:: bash

    $ pip install pyap



About
-----
This library has been created because i couldn't find any reliable and
opensource solution for detecting addresses on web pages when writing my
web crawler. Currently available solutions have drawbacks when it comes
to using them to process really large amounts of data fast. You'll
either have to buy some proprietary software; use third-party
pay-per-use services or use address detecting which is slow and
unsuitable for real-time processing.

Pyap is an alternative to all these methods. It is really fast because
it is based on using regular expressions and it allows to find addresses
in text in real time with low error rates.


Future work
-----------
- Add rules for parsing FR addresses
- ...


Typical workflow
----------------
Pyap should be used as a first thing when you need to detect an address
inside a text when you don't know for sure whether the text contains
addresses or not.

To achieve the most accuracy Pyap results could be reverified using
geocoding process.


Limitations
-----------
Because Pyap is based on regular expressions it provides fast results.
This is also a limitation because regexps intentionally do not use too
much context to detect an address.

In other words in order to detect US address, the library doesn't
use any list of US cities or a list of typical street names. It
looks for a pattern which is most likely to be an address.

For example the string below would be detected as a valid address: 
"1 SPIRITUAL HEALER DR SHARIF NSAMBU SPECIALISING IN"

It happens because this string has all the components of a valid
address: street number "1", street name "SPIRITUAL HEALER" followed
by a street identifier "DR" (Drive), city "SHARIF NSAMBU SPECIALISING"
and a state name abbreviation "IN" (Indiana).

The good news is that the above mentioned errors are **quite rare**.