semvec/pypi-packages huggingface dataset:
djenv 0.0.6
DjEnv (Django + Environment): Load Django Settings from Environmental Variables
Keywords: auto,automate,conf,configure,env,environment,django,loader,settings

# DjEnv: Django + Environment
Load Django Settings Directly from Environmental Variables

# features
- modify django configuration without modifying source code
- prevent hard-coding of Django settings
- works great with Docker

# install
### with pip
```bash
pip install djenv
```
### with pipenv
```bash
pipenv install djenv
```

# basic usage
```python3
# inside settings.py

# import settings from environment
from djenv.settings import *
```

Prepend Settings with `DJANGO_` to import them.  For example:
```bash
DJANGO_DEBUG=False python3 manage.py runserver
```
Will set DEBUG=False in the settings.py

# advanced usage
You can also replace nested settings like DATABASES by setting a JSON
```bash
DJANGO_DATABASES='{ "default": { "ENGINE": "django.db.backends.sqlite3", "NAME": "db.sqlite3" } }'  python3 manage.py runserver
```
housing-traning-5519 0.1
Sample code for coding practice
Requires Python: >=3.11
Keywords: housing,data training

# mle-training
# Median housing value prediction

The housing data can be downloaded from https://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data. 

The following techniques have been used: 

 - Linear regression
 - Decision Tree
 - Random Forest

## Steps performed
 - We prepare and clean the data. We check and impute for missing values.
 - Features are generated and the variables are checked for correlation.
 - Multiple sampling techinuqies are evaluated. The data set is split into train and test.
 - All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.

## To excute the script
python < scriptname.py >
## Command to create Virtual Enviornemnt:
conda --version
conda create --name mle-dev biopython
conda activate mle-dev

## install the necessary librabries like numpy,pandas , matplotlib and scikit learn
conda install numpy
conda install pandas
conda install matplotlib

## To excute the script
python < scriptname.py >
python nonstandardcode.py

## Exporting the enviorment
conda export --name MLE-training >env.yml
onesecmail-api 0.1.1
1secmail unofficial API, supporting random email generation, fetching messages and downloading attachments
Requires Python: >=3.9

<a href="https://www.buymeacoffee.com/st1vms"><img src="https://img.buymeacoffee.com/button-api/?text=1 Pizza Margherita&emoji=🍕&slug=st1vms&button_colour=0fa913&font_colour=ffffff&font_family=Bree&outline_colour=ffffff&coffee_colour=FFDD00" width="200" height="30" style="max-width:100%;"/></a>

# 1secmail unofficial API

This is an unofficial wrapper for 1secmail API.

It provides:

- `MailBoxMessage`, `MailMessage`, and `MailAttachment` convience dataclasses.

- **Wrapper** methods for:
    - Getting list of 1secmail active domains -> `get_domain_list`
    - Generating random emails -> `gen_random_emails`
    - Fetching mailbox for an email -> `fetch_mailbox`
    - Fetching a message out of an email -> `fetch_msg`
    - Downloading file attachments from email messages: `download_attachment`


# How to install
```
pip install onesecmail-api
```

# How to import

Import this library with:
```py
import onesecmail_api
```
or just some individual components with:
```py
from onesecmail_api import (
    MailBoxMessage,
    MailAttachment,
    MailMessage,
    get_domain_list,
    gen_random_emails,
    fetch_mailbox,
    fetch_msg,
    download_attachment,
)
```