semvec/pypi-packages huggingface dataset:
apriorib1 1.0.2
A Python library that implements smooth and easy apriori for association rule mining. Currently limited for maximum 4 items/transaction.
License: MIT
Requires Python: >=3.5

# apriorib1

apriorib1 is a Python library that applies the very famous unsupervised learning algorithm, apriori, for Association Rule Mining(ARM) on a dataset of transaction/purchase logs and shows the accepted association rules. 

Currently, this version is limited to a maximum of 4 items in a certain transaction.

![Demo](https://user-images.githubusercontent.com/49288068/90564473-10814100-e1c3-11ea-81c4-4a5a37abff75.png)

## New in this version

1. Displays stage-wise final itemset as pandas DataFrames.

![Demo 2](https://user-images.githubusercontent.com/49288068/90564809-8d141f80-e1c3-11ea-8e2c-d81c08567643.png)

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install apriorib1.

```bash
pip install apriorib1
```

## Quick Start

```python
from apriorib1 import Apriori

data = [['MILK', 'BREAD', 'BISCUIT'],
    ['BREAD', 'MILK', 'BISCUIT', 'CORNFLAKES'],
    ['BREAD', 'TEA', 'BOURNVITA'],
    ['JAM', 'MAGGI', 'BREAD', 'MILK'],
    ['MAGGI', 'TEA', 'BISCUIT'],
    ['BREAD', 'TEA', 'BOURNVITA'],
    ['MAGGI', 'TEA', 'CORNFLAKES'],
    ['MAGGI', 'BREAD', 'TEA', 'BISCUIT'],
    ['JAM', 'MAGGI', 'BREAD', 'TEA'],
    ['BREAD', 'MILK'],
    ['COFFEE', 'COCK', 'BISCUIT', 'CORNFLAKES'],
    ['COFFEE', 'COCK', 'BISCUIT', 'CORNFLAKES'],
    ['COFFEE', 'SUGER', 'BOURNVITA'],
    ['BREAD', 'COFFEE', 'COCK'],
    ['BREAD', 'SUGER', 'BISCUIT'],
    ['COFFEE', 'SUGER', 'CORNFLAKES'],
    ['BREAD', 'SUGER', 'BOURNVITA'],
    ['BREAD', 'COFFEE', 'SUGER'],
    ['BREAD', 'COFFEE', 'SUGER'],
    ['TEA', 'MILK', 'COFFEE', 'CORNFLAKES']]

# Testing the Apriori class
apr = Apriori(records=data,min_sup=2,min_conf=50)
df1,df2,df3,df4 = apr.show_as_df(stage=1),apr.show_as_df(stage=2),apr.show_as_df(stage=3),apr.show_as_df(stage=4)
print("VIEWING THE ITEMSET DATAFRAMES AT THE DIFFERENT STAGES :\nSTAGE 1\n{}\nSTAGE 2\n{}\nSTAGE 3\n{}\nSTAGE 4\n{}".format(df1,df2,df3,df4))
apr.checkAssc()
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)
drf-dynamic-serializers 1.0
Dynamic serializers for the Django REST Framework
License: MIT
Requires Python: >=3.6

drf-dynamic-serializers
=======================

.. image:: https://img.shields.io/github/stars/csdenboer/drf-dynamic-serializers.svg?label=Stars&style=socialcA
   :target: https://github.com/csdenboer/drf-dynamic-serializers
   :alt: GitHub

.. image:: https://img.shields.io/pypi/v/drf-dynamic-serializers.svg
   :target: https://pypi.org/project/drf-dynamic-serializers/
   :alt: PyPI release

.. image:: https://img.shields.io/readthedocs/drf-dynamic-serializers.svg
   :target: https://drf-dynamic-serializers.readthedocs.io/
   :alt: Documentation

Dynamic serializers and view(set)s for the Django REST Framework.

Functionality
-------------
DRF Dynamic Serializers is a package that aims to increase the reusability of Django REST Framework's serializer classes. Serializers that inherit from `DynamicFieldsSerializer` are dynamic. The fields to include and/or exclude can be overriden as well as the `allow_null` and `required` property of fields. A common use case is a list and a detail endpoint sharing the same serializer class but with different fields included.

Furthermore, the package provides a viewset class (`DynamicFieldsModelViewSet`) that extends Django REST Framework's ModelViewSet with the the ability to dynamically select the fields to include or exclude in a response by reading the `fields` and `omit` query parameters of a request.

Documentation
-------------

For more information on installation and configuration see the documentation at:

https://drf-dynamic-serializers.readthedocs.io/


Issues
------

If you have questions or have trouble using the app please file a bug report at:

https://github.com/csdenboer/drf-dynamic-serializers/issues


Contributions
-------------

It is best to separate proposed changes and PRs into small, distinct patches
by type so that they can be merged faster into upstream and released quicker:

* features,
* bugfixes,
* code style improvements, and
* documentation improvements.

All contributions are required to pass the quality gates configured
with the CI. This includes running tests and linters successfully
on the currently officially supported Python and Django versions.

The test automation is run automatically by Travis CI, but you can
run it locally with the ``tox`` command before pushing commits.
mottaa 0.0.1
Detecting emotions behind the text, pyemotion package will help you to understand the emotions in textual meassages.
License: MIT

# What is emotionpy?
Emotions are biological states associated with the nervous system brought on by neurophysiological changes variously associated with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure.
(Source: Wikipedia)

Human being can easily identify the emotions from text and experience it. But what about the machines, are they able to identify the emotions from text?

#### Emotionpy is the python package which will help you to extract the emotions from the content.

- Processes any textual message and recognize the emotions embedded in it.
- Compatible with 5 different emotion categories as Happy, Angry, Sad, Surprise and Fear.

## Features
> ##### 1. Text Pre-processing
> At first we have the major goal to perform data cleaning and make the content suitable for emotion analysis.
> - Remove the unwanted textual part from the message.
> - Perform the natural language processing techniques.
> - Bring out the well pre-processed text from the text pre-processing.
> ##### 2. Emotion Investigation
> Detect emotion from every word that we got from pre-processed text and take a count of it for further analytical process.
> - Find the appropriate words that express emotions or feelings.
> - Check the emotion category of each word.
> - Store the count of emotions relevant to the words found.
> ##### 3. Emotion Analysis
> After emotion investigation, there is the time of getting the significant output for the textual message we input earlier.
> - The output will be in the form of dictionary.
> - There will be keys as emotion categories and values as emotion score.
> - Higher the score of a particular emotion category, we can conclude that the message belongs to that category.

## [Check Demo](https://pyemotion.herokuapp.com/)

Let's get experience with the library, test your multiple use cases on web app and check whether the library performs as your expectations level.
apriorib 1.0.1
A Python library that implements smooth and easy apriori for association rule mining. Currently limited for maximum 4 items/transaction.
License: MIT
Requires Python: >=3.5

# apriorib

apriorib is a Python library that applies the very famous unsupervised learning algorithm, apriori, for Association Rule Mining(ARM) on a dataset of transaction/purchase logs and shows the accepted association rules. 

Currently, this version is limited to a maximum of 4 items in a certain transaction.

![Demo](https://user-images.githubusercontent.com/49288068/90564473-10814100-e1c3-11ea-81c4-4a5a37abff75.png)

## New in this version

1. Displays stage-wise final itemset as pandas DataFrames.

![Demo 2](https://user-images.githubusercontent.com/49288068/90564809-8d141f80-e1c3-11ea-8e2c-d81c08567643.png)

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install apriorib.

```bash
pip install apriorib
```

## Quick Start

```python
from apriorib.apriorib import Apriori

data = [['MILK', 'BREAD', 'BISCUIT'],
    ['BREAD', 'MILK', 'BISCUIT', 'CORNFLAKES'],
    ['BREAD', 'TEA', 'BOURNVITA'],
    ['JAM', 'MAGGI', 'BREAD', 'MILK'],
    ['MAGGI', 'TEA', 'BISCUIT'],
    ['BREAD', 'TEA', 'BOURNVITA'],
    ['MAGGI', 'TEA', 'CORNFLAKES'],
    ['MAGGI', 'BREAD', 'TEA', 'BISCUIT'],
    ['JAM', 'MAGGI', 'BREAD', 'TEA'],
    ['BREAD', 'MILK'],
    ['COFFEE', 'COCK', 'BISCUIT', 'CORNFLAKES'],
    ['COFFEE', 'COCK', 'BISCUIT', 'CORNFLAKES'],
    ['COFFEE', 'SUGER', 'BOURNVITA'],
    ['BREAD', 'COFFEE', 'COCK'],
    ['BREAD', 'SUGER', 'BISCUIT'],
    ['COFFEE', 'SUGER', 'CORNFLAKES'],
    ['BREAD', 'SUGER', 'BOURNVITA'],
    ['BREAD', 'COFFEE', 'SUGER'],
    ['BREAD', 'COFFEE', 'SUGER'],
    ['TEA', 'MILK', 'COFFEE', 'CORNFLAKES']]

# Testing the Apriori class
apr = Apriori(records=data,min_sup=2,min_conf=50)
df1,df2,df3,df4 = apr.show_as_df(stage=1),apr.show_as_df(stage=2),apr.show_as_df(stage=3),apr.show_as_df(stage=4)
print("VIEWING THE ITEMSET DATAFRAMES AT THE DIFFERENT STAGES :\nSTAGE 1\n{}\nSTAGE 2\n{}\nSTAGE 3\n{}\nSTAGE 4\n{}".format(df1,df2,df3,df4))
apr.checkAssc()
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)
speedtimedistance 0.0.0.1
calculate speed, time and distance
License: MIT
Keywords: speed,time,distance

'Speedtimedistance' is an advanced tool that has already coded, programmed and prepared with multiple clases and functions using python 3.8.5 in a first-started library version 0.0.0.1; this library is supposed to assist computer scientists,programmers, mathematic students, scholars, and researchers to write their own softwares based on most accurate and standard scientific equations to caculate speed, time, and distance. The first version of this library though may be considerd simple and humble comparing with other scientific tools, but it is good start after all :). All scientific calculations inside this library has already been tested among other standard scientific resourses and convertor-tool applications during conding, and as before publishing and sharing....


Info and Instructions: 

1- Apparently this package contains three different modules .py namely: time, speed, and distance. Every module has its own class and functions, for example: 'time.py ------ Class Time: ------ function caculateTime()', and so on... 

2- Each module has been designed and programmed to stand-alone when called, and each has its own methods and mechanisim to calculate within its own sphere; for example, time module: calculate time, convert time... speed module: calculate speed, convert speed... distance module: caculate distance, convert distance...and so on.. 

3- Error handeling has been considered at the most: probably not covered all errors, but it could be helpful, simplify programming and using the functions when called and when parameter assignments. For example, consider the function: timeUnits() inside time module: time.py: 

- When it is called alone with no parameter, it prints all time units stored in the program: 

1 : s : second
2 : m̲i̲n : minute
3 : h̲r : hour

- Try to enter unwanted parameter like numbers for example it will print an error message with again all time units stored in the program: 

No value has been found in time units
Try again with these values: 

1 : s : second
2 : m̲i̲n : minute
3 : h̲r : hour

No value has been returned yet!

4- All calculations inside the program based on comparison alogorithim, eventhough it looks a large bunch of codes, but it works most accurently and very smart when called and manipulated.


By Ehssan Dannouf
Version 0.0.0.1 (19/08/2020): First Release 




Change Log


0.0.0.1 (19/08/2020)
- First Release
motta 0.0.1
Detecting emotions behind the text, pyemotion package will help you to understand the emotions in textual meassages.
License: MIT

# What is emotionpy?
Emotions are biological states associated with the nervous system brought on by neurophysiological changes variously associated with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure.
(Source: Wikipedia)

Human being can easily identify the emotions from text and experience it. But what about the machines, are they able to identify the emotions from text?

#### Emotionpy is the python package which will help you to extract the emotions from the content.

- Processes any textual message and recognize the emotions embedded in it.
- Compatible with 5 different emotion categories as Happy, Angry, Sad, Surprise and Fear.

## Features
> ##### 1. Text Pre-processing
> At first we have the major goal to perform data cleaning and make the content suitable for emotion analysis.
> - Remove the unwanted textual part from the message.
> - Perform the natural language processing techniques.
> - Bring out the well pre-processed text from the text pre-processing.
> ##### 2. Emotion Investigation
> Detect emotion from every word that we got from pre-processed text and take a count of it for further analytical process.
> - Find the appropriate words that express emotions or feelings.
> - Check the emotion category of each word.
> - Store the count of emotions relevant to the words found.
> ##### 3. Emotion Analysis
> After emotion investigation, there is the time of getting the significant output for the textual message we input earlier.
> - The output will be in the form of dictionary.
> - There will be keys as emotion categories and values as emotion score.
> - Higher the score of a particular emotion category, we can conclude that the message belongs to that category.

## [Check Demo](https://pyemotion.herokuapp.com/)

Let's get experience with the library, test your multiple use cases on web app and check whether the library performs as your expectations level.
motte 0.0.1
Detecting emotions behind the text, pyemotion package will help you to understand the emotions in textual meassages.
License: MIT

# What is emotionpy?
Emotions are biological states associated with the nervous system brought on by neurophysiological changes variously associated with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure.
(Source: Wikipedia)

Human being can easily identify the emotions from text and experience it. But what about the machines, are they able to identify the emotions from text?

#### Emotionpy is the python package which will help you to extract the emotions from the content.

- Processes any textual message and recognize the emotions embedded in it.
- Compatible with 5 different emotion categories as Happy, Angry, Sad, Surprise and Fear.

## Features
> ##### 1. Text Pre-processing
> At first we have the major goal to perform data cleaning and make the content suitable for emotion analysis.
> - Remove the unwanted textual part from the message.
> - Perform the natural language processing techniques.
> - Bring out the well pre-processed text from the text pre-processing.
> ##### 2. Emotion Investigation
> Detect emotion from every word that we got from pre-processed text and take a count of it for further analytical process.
> - Find the appropriate words that express emotions or feelings.
> - Check the emotion category of each word.
> - Store the count of emotions relevant to the words found.
> ##### 3. Emotion Analysis
> After emotion investigation, there is the time of getting the significant output for the textual message we input earlier.
> - The output will be in the form of dictionary.
> - There will be keys as emotion categories and values as emotion score.
> - Higher the score of a particular emotion category, we can conclude that the message belongs to that category.

## [Check Demo](https://pyemotion.herokuapp.com/)

Let's get experience with the library, test your multiple use cases on web app and check whether the library performs as your expectations level.
moteta 0.0.1
Detecting emotions behind the text, pyemotion package will help you to understand the emotions in textual meassages.
License: MIT

# What is emotionpy?
Emotions are biological states associated with the nervous system brought on by neurophysiological changes variously associated with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure.
(Source: Wikipedia)

Human being can easily identify the emotions from text and experience it. But what about the machines, are they able to identify the emotions from text?

#### Emotionpy is the python package which will help you to extract the emotions from the content.

- Processes any textual message and recognize the emotions embedded in it.
- Compatible with 5 different emotion categories as Happy, Angry, Sad, Surprise and Fear.

## Features
> ##### 1. Text Pre-processing
> At first we have the major goal to perform data cleaning and make the content suitable for emotion analysis.
> - Remove the unwanted textual part from the message.
> - Perform the natural language processing techniques.
> - Bring out the well pre-processed text from the text pre-processing.
> ##### 2. Emotion Investigation
> Detect emotion from every word that we got from pre-processed text and take a count of it for further analytical process.
> - Find the appropriate words that express emotions or feelings.
> - Check the emotion category of each word.
> - Store the count of emotions relevant to the words found.
> ##### 3. Emotion Analysis
> After emotion investigation, there is the time of getting the significant output for the textual message we input earlier.
> - The output will be in the form of dictionary.
> - There will be keys as emotion categories and values as emotion score.
> - Higher the score of a particular emotion category, we can conclude that the message belongs to that category.

## [Check Demo](https://pyemotion.herokuapp.com/)

Let's get experience with the library, test your multiple use cases on web app and check whether the library performs as your expectations level.
motet 0.0.1
Detecting emotions behind the text, pyemotion package will help you to understand the emotions in textual meassages.
License: MIT

# What is emotionpy?
Emotions are biological states associated with the nervous system brought on by neurophysiological changes variously associated with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure.
(Source: Wikipedia)

Human being can easily identify the emotions from text and experience it. But what about the machines, are they able to identify the emotions from text?

#### Emotionpy is the python package which will help you to extract the emotions from the content.

- Processes any textual message and recognize the emotions embedded in it.
- Compatible with 5 different emotion categories as Happy, Angry, Sad, Surprise and Fear.

## Features
> ##### 1. Text Pre-processing
> At first we have the major goal to perform data cleaning and make the content suitable for emotion analysis.
> - Remove the unwanted textual part from the message.
> - Perform the natural language processing techniques.
> - Bring out the well pre-processed text from the text pre-processing.
> ##### 2. Emotion Investigation
> Detect emotion from every word that we got from pre-processed text and take a count of it for further analytical process.
> - Find the appropriate words that express emotions or feelings.
> - Check the emotion category of each word.
> - Store the count of emotions relevant to the words found.
> ##### 3. Emotion Analysis
> After emotion investigation, there is the time of getting the significant output for the textual message we input earlier.
> - The output will be in the form of dictionary.
> - There will be keys as emotion categories and values as emotion score.
> - Higher the score of a particular emotion category, we can conclude that the message belongs to that category.

## [Check Demo](https://pyemotion.herokuapp.com/)

Let's get experience with the library, test your multiple use cases on web app and check whether the library performs as your expectations level.