semvec/pypi-packages huggingface dataset:
shared-data-handling 0.1.3
a lib to facilitate file handling and data extraction from log files
Requires Python: >=3.6

# Shared data handling
This lib simplifies the python code in other projects.

## Installation
````
pip3 install shared-data-handling
````

## Usage
tbd

## Publish
Run ```python -m build```
--> Packages will be stored under dist
Run ```py -m twine upload --repository pypi dist/*```


## ToDos
[ ] [setup a gitlab pipeline](https://docs.gitlab.com/ee/user/packages/pypi_repository/)


## Version History
### Current Version
### 0.01
- Initial Version
aviv-aws-costexplorer 0.2.3
Aviv AWS CostExplorer python library
Requires Python: >=3.8

# Aviv AWS CostExplorer

Aims to provide a quick and comprehensive interface to AWS costexplorer api.
This is useful to extract cost and usage (aka CAU) data, save it and to make it available for reporting and analysis.

## Requirements

- python >= 3.8
- boto3
- Access to AWS ce:cost_and_usage

## Usage

```shell
pip install aviv-aws-costexplorer

# Install additional libraries required to save/read data on AWS S3: pandas, awswrangler
pip install aviv-aws-costexplorer[datastore]
```

Sample code

```python
from aviv_aws_costexplorer import costreporter

cr = costreporter.CostReporter()
costs = cr.get_cost_and_usage()
# Will print you last 3 months costs
print(costs)

from aviv_aws_costexplorer import datastore
ds = datastore.DataStore(database='test', bucket='my-s3-bucket')
ds.to_parquet(data=costs, path='monthly/last3', database='monthly')

# Show it nicely
import pandas as pd
df = pd.DataFrame(costs)
df.head()


# Store on S3 and make it available through Athena (uses awswrangler)

```

## Development

```bash
pipenv install -d
```

## Test, Build, Release

We typically follow the [standard python packaging and distribution](https://packaging.python.org/en/latest/tutorials/packaging-projects/#generating-distribution-archives) process to release this package.

```bash
# Run tests
pipenv run pytest -v tests/

# Build python package
python3 -m pip install --upgrade build
python3 -m build

# Release on testpypi
python3 -m pip install --upgrade twine
python3 -m twine upload --repository testpypi dist/*
```

Note: the Pypi release is also done during the CICD process.

## Contribute

Yes please! Send us your PR's
arge-consumption 1.0.2
Python Client zum Abruf unterjähriger Verbrauchsinformationen über die *ARGE consumption-data API*.
Requires Python: >=3.8,<4.0

# ARGE Consumption Data Client
Python Client zum Abruf unterjähriger Verbrauchsinformationen über die *ARGE consumption-data API*.

## Hintergrund

Im Rahmen der [EED](https://de.wikipedia.org/wiki/Richtlinie_2012/27/EU_(Energieeffizienz-Richtlinie)) veröffentlicht die [ARGE HeiWaKo](https://arge-heiwako.de/) einen Webservice mit dem monatliche Verbrauchsdaten auf Nutzeinheitenebene vom Wärmedienstunternehmen abgerufen werden können.

Dieser Service solle es der Wohnungswirtschaft ermöglichen, Nutzern/Bewohnern eine unterjährige Verbrauchsinformation im Rahmen der EED in eigenen IT-Systemen bereitzustellen.

Mehr Infos hier: [ARGE HEIWAKO: Datenaustausch](https://arge-heiwako.de/veroeffentlichungen/datenaustausch/)
housing-pred-mle 0.0.1
MLE Training Package
Requires Python: >=3.6

# 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. 

## To set up the project

1. Clone the repository.


2. Create and activate virtual python development environment. On terminal, run


3. Install the package
    

4. Verify correct installation


5. Generate Sphinx Documentation



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.