semvec/pypi-packages huggingface dataset:
dataramp 0.3.5
A Data science library for data science / data analysis teams
Requires Python: >=3.7
Keywords: data science, machine learning, data analysis

# Dataramp

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Pylint](https://img.shields.io/badge/pylint-enabled-brightgreen.svg)](https://github.com/PyCQA/pylint)
[![Flake8](https://img.shields.io/badge/flake8-enabled-blue.svg)](https://flake8.pycqa.org/en/latest/)
[![Scikit-learn](https://img.shields.io/badge/scikit--learn-v0.24.2-blue)](https://scikit-learn.org/stable/)

Welcome to the Dataramp documentation! Here you will find information about Dataramp, including some examples to get you started.

## Dataramp

Dataramp is a Python library designed to streamline data science and data analysis workflows. It offers a collection of utility functions and tools tailored to assist data science teams in various aspects of their projects.

By providing a range of functionalities, Dataramp aims to enhance productivity and efficiency in data science projects, empowering teams to focus on deriving meaningful insights from their data.

## Getting Started

Read the quick start guide [here](docs/quickstart.md).

If you want to see some examples, you can look at the examples in the [examples](examples/) directory.

You can install Dataramp and learn more from [PyPi](https://pypi.org/project/dataramp/).


# Example
```python
# Create and register a model pipeline
preprocessor = Pipeline([
    ('scaler', StandardScaler()),
    ('imputer', SimpleImputer())
])

pipeline = Pipeline([
    ('preprocess', preprocessor),
    ('classifier', LogisticRegression())
])

model_save(pipeline, "classifier", method="joblib", metadata={"dataset": "2023_sales"})
register_model(
    pipeline,
    name="sales_classifier",
    version="v1.0",
    metadata={
        "metrics": {"accuracy": 0.89},
        "serialization_method": "joblib"
    }
)

# Create versioned dataset
df = pd.read_csv("data.csv")
data_save(df, "processed_data", versioning=True, description="Initial cleaned version")
```

# Potential Use Cases
- Data Science Projects : Initialize projects with a standardized structure and manage datasets and models effectively.
- Team Collaboration : Facilitate collaboration by providing clear project organization and versioning.
- Reproducibility : Ensure reproducibility by tracking dataset versions, model metadata, and dependencies.
- Automation : Integrate into CI/CD pipelines for automated testing, deployment, and dependency updates.
monepy 0.5
Currency classes to work with monetary values
Requires Python: >=3.11

# Monepy

[![tests](https://github.com/vsbits/currency/actions/workflows/tests.yml/badge.svg)](https://github.com/vsbits/currency/actions/workflows/tests.yml)
[![codecov](https://codecov.io/gh/vsbits/currency/graph/badge.svg?token=8mHqn1neMk)](https://codecov.io/gh/vsbits/currency)
[![CodeFactor](https://www.codefactor.io/repository/github/vsbits/currency/badge)](https://www.codefactor.io/repository/github/vsbits/currency)

[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/vsbits/currency/blob/main/LICENSE)

A python package that implements currency classes to work with monetary values.

## Install

From github using `pip`:

```
pip install git+https://github.com/vsbits/monepy
```

## Behavior

The currency classes behave like a numeric type, with a `formatted()` method
that returns the value in the currency format:

```
>>> from py_currency import USD
>>> x = USD(10)
>>> x
<USD 10.00>
>>> x.formatted()
'$10.00'
>>> x == 1
False
>>> x == 10
True
>>> x > 10
False
>>> x < 10.1
True
```
The class constructor does not round the values recieved. If a value with more 
significant digits than the currency supports is provided, a `ValueError` will
be raised.

### Operators

Only supported operators are `+`, `-`, `*` and `/`.

#### The `+` and `-`

Operations with `+` and `-` can only be done with currencies of the same type
and will return an object of the same currency:

```
>>> x = USD(10)
>>> y = USD(0.1)
>>> x + y
<USD 10.10>
>>> x - y
<USD 9.90>
```

#### The `*`

The `*` is only possible with a numeric value:

```
>>> x = USD(1.99)
>>> x * 1.23
<USD 2.44>
```

#### The `/`
The `/` operator is possible with both numeric and a currency of the same type:

```
>>> x = USD(10)
>>> x / 2
<USD 5.00>
>>> y = USD(3)
>>> x / y
3.3333333333333335
```

#### The `%`

The `%` is only supports numeric values

```
>>> x = USD(100)
>>> x / 15
<USD 6.66>
>>> x % 15
<USD 0.10>
```

### Pandas integration

Currency classes can be used with the [pandas library](https://pandas.pydata.org/):

```
>>> import pandas as pd
>>> from py_currency import EUR
>>> df = pd.DataFrame({
... "product": ["x", "y", "z"],
... "price": [10, 0.99, 25],
... "quantity": [8, 50, 200]
... })
>>> df
  product  price  quantity
0       x  10.00         8
1       y   0.99        50
2       z  25.00       200
>>> df["price"] = df["price"].apply(EUR)
>>> df
  product  price  quantity
0       x  10,00         8
1       y   0,99        50
2       z  25,00       200
>>> df["subtotal"] = df["price"] * df["quantity"]
>>> df
  product  price  quantity  subtotal
0       x  10,00         8     80,00
1       y   0,99        50     49,50
2       z  25,00       200  5 000,00
>>> df[df["price"] >= 10]
  product  price  quantity  subtotal
0       x  10,00         8     80,00
2       z  25,00       200  5 000,00
>>> total = df["subtotal"].sum()
>>> print(f"The total is {total.formatted()}.")
The total is 5 129,50 €.
```
ottos-oranges 0.0.1
Otto's Oranges
Requires Python: >=3.12

# Otto's Oranges

Data generation for Otto's Oranges.

## Setup

Clone the repo:

```bash
git clone git@github.com:ascend-io/ascend-community.git
```

or:

```bash
gh repo clone ascend-io/ascend-community
```

Change into the directory:

```bash
cd ascend-community/ottos-oranges
```

Install `just` and `uv`:

```
brew install just uv
```

`just setup`:

```bash
just setup
```

Source the Python vritual environment:

```bash
source .venv/bin/activate
```

Run the data generation:

```bash
ottos-oranges datagen --days 1
```
saltriverprojectenergyapi 1.0.5
Unofficial api for the Arizona Salt River Project (SRP) API
Requires Python: >=3.8

# Salt River Project Client

This module provides a Python client for interacting with the Salt River Project (SRP) energy API. It allows users to retrieve energy usage data, billing information, and other related data from SRP.

## Usage

Here is an example of how to use the Salt River Project Client:

```python
from saltriverprojectenergyapi import SaltRiverProjectClient

# Initialize the client with your Billing Account and login info
client = SaltRiverProjectClient(
    billing_account="Billing Account Number",
    username="SRP email_address or login name",
    password="SRP password"
)

# Authenticate and Authorize the client.
# This is a boolean return value, so you can verify if the authentication
# and authorisation were successful or not.
is_authorised = client.authorise_login()

# Retrieve energy usage data
# The dates are passed in as string values with the format dd-mm-yyyy
energy_usage_data = client.get_hourly_usage(startDate='01-01-2025', endDate='01-05-2025')
# energy_usage_data is a EnergyUsageData object *See objects/data_classes.py
# Hourly Usage for the object is in the .energy_usage property, which is a List[] type of Hourly Energy Usage data.
for hourly_usage in energy_usage_data.energy_usage:
    print(hourly_usage)
# Weather data does not take any parameters, and just returns a full weather dataset.
weather_data = client.get_daily_weather()
# myWeatherData is a List[WeatherData] * See objects/weather_data.py

# Retrieve outage data for your billing account.
# The Outage Data endpoint does not take any parameters, and returns a SelfOutageData object.
outage_data = client.get_user_outage()
if outage_data.is_in_outage_area:
    print("User is in an outage area.)
```

## Currently supported methods

Currently, this client api supports the following methods.
1. getHourlyUsage
1. getDailyWeather
1. getUserOutage

## Features

- Retrieve energy usage data for a specified date range
- Retrieve weather data associated with your account
geckodriver-wrapper 0.4
A packaged version of geckodriver for offline use.

# Geckodriver Wrapper

This package provides a wrapped version of `geckodriver.exe` for use in offline environments.

## Installation
```bash
pip install geckodriver-wrapper
```

## Usage
```python
from geckodriver_wrapper import get_geckodriver_path
from selenium import webdriver

driver = webdriver.Firefox(executable_path=get_geckodriver_path())
driver.get("https://example.com")
```

## Driver Source
Driver Release: https://github.com/mozilla/geckodriver/releases/tag/v0.35.0 geckodriver-v0.35.0-win32.zip

## Driver Licence
Driver License: https://github.com/mozilla/geckodriver?tab=MPL-2.0-1-ov-file#readme MPL-2.0 license