semvec/pypi-packages huggingface dataset:
zogutils 1.5.1
General utilities to be use in my base-fastapi template. Why ZOG? It's sound like joke and look like zoo.
Requires Python: >=3.7.0,<3.8.0

# ZOG Utils

Utilities to use in my base-api project template. https://github.com/tienhm0202/base-fastapi/

Why ZOG? Because I can't named it as `utils` only, so I have to add a prefix.
ZOG sounds like `joke` and looks like `zoo`. I found that funny enough to use.

# Usage

```bash
$ pip install zogutils
```

## To generate short unique id string

```python
from zogutils import secret

secret.unique_id(8, "ID_")
# return: ID_a7uFg9k0
```

## To shorten package name like Java's Logback

```python
from zogutils import package_name

package_name.shorten("company.scope.modules.Function", 9)
# return: (something like) c.s.m.Function - depends on max length
```

## To init some middlewares

```python
from zogutils import middlewares
from your.app import settings, fastapi_app

middlewares.init_app(fastapi_app, settings)
```

### Configs:

```
# Sentry
SENTRY_DSN: Optional[HttpUrl] = None
SENTRY_INCLUDE: Optional[List[str]] = ["src"]
SENTRY_SAMPLE_RATE: Optional[float] = 0.5

# CSRF
SECURITY_CSRF: bool = False

# Rate limit
RATE_LIMIT: int = 100
RATE_LIMIT_TIME_SPAN: int = 30
RATE_LIMIT_BLOCK_DURATION: int = 300

# Prometheus
PROMETHEUS_ENABLE: bool = True
PROMETHEUS_PATH: str = "/metrics/"

# Cors
BACKEND_CORS_ORIGINS: List[AnyHttpUrl] = []
```
xhpflow-1.0 0.0.1
This is a mini but function competed neural networks framwork
Requires Python: >=3.5

##ABOUT
This is a mini deep learning framework created by xiaohaipeng @tianjin

##Features

+ Define Dynamic Computing Graph
+ Define Neural Network Operator
+ Define Placeholder,Linear,Sigmoid,Relu,MSE
+ Auto-Diff Computing
+ Auto-Feedforward and Backward

#Remain
+ Classification
+ Cross-Entropy
+ CNN, RNN

# Connection
wechat:18651271973
Mail:307153814@qq.com
docscov 1.1.3
Sphinx extension to make a dynamic documentation coverage badge
Requires Python: >=3.8

DocsCov
=======

DocsCov is a Sphinx extension that allows you to show a badge showing
documentation coverage for C++ (or any other Doxygen supported) code.


Getting Started
---------------

First, install it by (requires python 3.6+)::

    pip install docscov

You need to use Doxygen to generate XML data about the source files. To do that,
add the following to your Doxygen configuration: (not needed if you're using
tools like Breathe or Exhale, you should have this already)

    GENERATE_XML = YES

This will generate XML data about source files. Now just add ``docscov`` to your
Sphinx extensions and add the following to your ``conf.py``:

.. code-block:: python

    docscov_config = {
        "xml_dir": "<path to generated XML by Doxygen>"
    }

It'll places a JSON file inside built documentations, which can be used with
`Shields <https://shields.io>`_ to make a dynamic badge, updated each time
documentations are built.


Getting the badge
+++++++++++++++++

To get the badge do the following:

1. Get the URL to your generated documentations (with ``http://`` or
   ``https://``)
2. Replace the ``:`` with ``%3A`` in the URL
3. Replace all slashes (``/``) in the text you got by completing step 2
4. Replace ``<url>`` in the following text with the text you got by completing
   step 3, and the text you'll get is the URL to your badge that can be placed in
   your README:

    https://img.shields.io/endpoint?url=<url>%2F_static%2Fdocscov.json

Example: `<https://pypp.readthedocs.io/en/latest>`_ will be converted to this
after following above:
`<https://img.shields.io/endpoint?url=https%3A%2F%2Fpypp.readthedocs.io%2Fen%2Flatest%2F_static%2Fdocscov.json>`_


Configurable options
--------------------

You can configure DocsCov with the following options:

``xml_dir``:

    **Value type:** ``str``

    **Description:** Path to Doxygen XML output

``root_dir``:

    **Value type:** ``str``

    **Description:** Path to process relative directories.

    **Notes:** *Optional*.

    **Default:** ``'.'``

``badge_label``:

    **Value type:** ``str``

    **Description:** The label of badge.

    **Notes:** *Optional*.

    **Default:** ``'docscov'``

``scope``:

    **Value type:** ``list``

    **Description:** Coverage scope. Possible values of elements:

    * ``public``: Include public members of classes
    * ``protected``: Include protected members of classes
    * ``private``: Include private members of classes
    * ``all``: All of above

    **Notes:** *Optional*.

    **Default:** ``['all']``

``kind``:

    **Value type:** ``list``

    **Description:** Kind of definitions to include in coverage. Possible values
    of elements:
    
    * ``class``
    * ``define``
    * ``enum``
    * ``enumvalue``
    * ``file``
    * ``friend``
    * ``function``
    * ``namespace``
    * ``page``
    * ``signal``
    * ``slot``
    * ``struct``
    * ``typedef``
    * ``union``
    * ``variable``
    * ``all``: All of above

    **Notes:** *Optional*.

    **Default:** ``['all']``
cacho 0.0.7
A tiny C++ in-memory cache system with Least recently Used algorithm!
Requires Python: >=3.6

# CACHO

A tiny C++ in-memory cache system with Least recently Used algorithm as a python shared-object library !

## How to install

To install the pyhon lib, just hit :
```shell
pip3 install cacho
```

## How to use it

```python
import cacho as c

# To set a value
c.set("key1", "value1")

# We can also check if the key exist
if c.exists("key1"):
    # And get it
    print(c.get("key1"))

```

## To build and deploy the lib on pypi

You just have to run these bash files :
```shell
# cmake build
sh ./shells/build.sh
# building the wheel
sh ./shells/build_whl.sh

# And to deploy on Pypi :
sh ./shells/pypi_deploy.sh
```

## LICENSE

- [MIT](./LICENSE.txt)
alqtendpy 0.1.1
Extras for working with PyQt5.

alqtendpy
===========

|PyPI| |Pythons| |Travis| |AppVeyor| |Circle| |codecov| |GitHub|

Miscellanious extras that I like to use when working with PyQt5.

.. |PyPI| image:: https://img.shields.io/pypi/v/alqtendpy.svg
   :alt: PyPI version
   :target: https://pypi.python.org/pypi/alqtendpy

.. |Pythons| image:: https://img.shields.io/pypi/pyversions/alqtendpy.svg
   :alt: Supported Python versions
   :target: https://pypi.python.org/pypi/alqtendpy

.. |Travis| image:: https://travis-ci.org/altendky/alqtendpy.svg?branch=develop
   :alt: Travis build status
   :target: https://travis-ci.org/altendky/alqtendpy

.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/565xmcrd3rl7ark7/branch/develop?svg=true
   :alt: AppVeyor build status
   :target: https://ci.appveyor.com/project/KyleAltendorf/alqtendpy

.. |Circle| image:: https://circleci.com/gh/altendky/alqtendpy.svg?style=svg
   :alt: Circle build status
   :target: https://circleci.com/gh/altendky/alqtendpy

.. |codecov| image:: https://codecov.io/gh/altendky/alqtendpy/branch/develop/graph/badge.svg
   :alt: codecov coverage status
   :target: https://codecov.io/gh/altendky/alqtendpy

.. |GitHub| image:: https://img.shields.io/github/last-commit/altendky/alqtendpy/develop.svg
   :alt: Source on GitHub
   :target: https://github.com/altendky/alqtendpy
easydataframe-lipsum 0.0.3
A library that handles small xlsx files as a DataFrame.
Requires Python: >=3.8

#   Easy Dataframe

##   What is this library?
This library is used when using the xlsx file as pandas.DataFrame.

It is intended for use in small-scale development.

##  What kind of function does it have?

* A function to judge whether it is xlsx or csv from the file path and read it as a data frame(from dataframe_in import read)


* Ability to read a specific cell(from dataframe_helper import read_cell)


* Check if there is NaN or None in the data frame


* ※ Most functions try to treat the elements of the data frame as str(from dataframe_helper import _any_to_str).
##   use
` pip install easydataframe-lipsum`

python3

`>>> import lipsum_easydataframe.dataframe_helper as helper`

`>>> helper.example()`


##  Required library
pandas

##  Required System
Windows10 == useWindows and usePython >= Python3.8