semvec/pypi-packages huggingface dataset:
django-randomcharfield 0.0.3
An alternative to UUIDs for model fields
License: MIT
Requires Python: >=3.6

# django-randomcharfield

## What's this?

This is a custom Django model field written as an alternative to UUIDs.

It works by base64-encoding 20 bytes (160 bits) from `urandom`, as recommended by Neil Madden's fantastic blog post, ["Moving away from UUIDs"](https://neilmadden.blog/2018/08/30/moving-away-from-uuids/)

## Installation

Use any package manager of choice. If using pip, install with this command:

```
pip install django-randomcharfield
```

## Usage

Add this to your model definition as follows:

```python
from django.db import models
from randomcharfield import (
    RandomCharField, generate_random_field,
)


class SomeModel(models.Model):
    uid = RandomCharField(default=generate_random_field)
```

Note that this doesn't make any assumptions about your current database state. That means you are responsible for creating the database index for this field, adding a uniqueness constraint, or making it a primary key.

If you have existing rows in your table and want to add a uniqueness constraint, you may want to set this field as nullable (i.e. passing in `null=True`) and to devise a plan to populate this. A good guide for such a plan is one written in the official Django documentation to deal with the same situation for `UUIDField`: https://docs.djangoproject.com/en/3.0/howto/writing-migrations/#migrations-that-add-unique-fields

## Notes

This isn't a lot of code, so you should probably be just copying the field to your project if you want customisations, rather than proposing changes.

## License

MIT
gmplot 1.4.1
A matplotlib-like interface to plot data with Google Maps.
License: MIT
Keywords: python google-maps visualization

gmplot |PyVersions|_
====================
.. |PyVersions| image:: https://img.shields.io/pypi/pyversions/gmplot?style=flat-square
.. _PyVersions: https://www.python.org/

A matplotlib-like interface to render all the data you'd like on top of Google Maps.

Several plotting methods make creating exploratory map views effortless.

To install: ``pip install gmplot``

Documentation (with examples): `API Reference <https://github.com/gmplot/gmplot/wiki>`_

Crash course
------------

.. code:: python

    import gmplot

    # Create the map plotter:
    apikey = '' # (your API key here)
    gmap = gmplot.GoogleMapPlotter(37.766956, -122.448481, 14, apikey=apikey)

    # Outline the Golden Gate Park:
    golden_gate_park = zip(*[
        (37.771269, -122.511015),
        (37.773495, -122.464830),
        (37.774797, -122.454538),
        (37.771988, -122.454018),
        (37.773646, -122.440979),
        (37.772742, -122.440797),
        (37.771096, -122.453889),
        (37.768669, -122.453518),
        (37.766227, -122.460213),
        (37.764028, -122.510347)
    ])
    gmap.polygon(*golden_gate_park, color='cornflowerblue', edge_width=10)

    # Highlight some attractions:
    attractions_lats, attractions_lngs = zip(*[
        (37.769901, -122.498331),
        (37.768645, -122.475328),
        (37.771478, -122.468677),
        (37.769867, -122.466102),
        (37.767187, -122.467496),
        (37.770104, -122.470436)
    ])
    gmap.scatter(attractions_lats, attractions_lngs, color='#3B0B39', size=40, marker=False)

    # Mark a hidden gem:
    gmap.marker(37.770776, -122.461689, color='cornflowerblue')

    # Draw the map:
    gmap.draw('map.html')

.. image:: https://raw.githubusercontent.com/gmplot/gmplot/master/docs/images/README_Example.png

----

Inspired by Yifei Jiang's (jiangyifei@gmail.com) `pygmaps <http://code.google.com/p/pygmaps/>`_ module.
stat-prob-dist 0.2.4
Statistical distributions - Gaussian, Binomial etc..
License: MIT

# stat_prob_dist
A python package to calculate Statistics and Probability Distributions. Currently provides Gaussian and Binomial distribution support. Please note that this is very early stage and is certainly not ready for production use.


## Prerequisites
* Python3
* Matplotlib


## Installation
Package can be installed using pip
```python
 pip install stat_prob_dist
 ```


## Usage
```python
>>> from stat_prob_dist import Gaussian, Binomial
>>> Binomial(.4, 25)
mean 10.0, standard deviation 2.449489742783178, p 0.4, n 25
>>>
>>> Gaussian(10, 7)
mean 10, standard deviation 7
>>>
```

## Author(s)
* [Amanpreet Singh](http://amanpreetsingh459.github.io/)

## License
This project is licensed under the MIT License - see the LICENSE.txt file for details