orama-utils 0.1.7

Utility functions and tools for data processing and analysis

Orama Utils

A collection of utility functions for data processing and feature engineering.

Features

• Date feature generation: Add time-based features to your DataFrame
• Holiday feature generation: Add holiday information based on country and region
• Weather feature generation: Add weather data based on location and date

Installation

You can install the package using pip:

pip install orama-utils

For development installation:

git clone https://github.com/Orama-Solutions/utils.git
cd utils
pip install -e .[dev]

Usage

Date Features

Add date-related features to your DataFrame:

import pandas as pd
from orama_utils import add_date_features

# Create your DataFrame
df = pd.DataFrame({
    'date': ['2023-01-01', '2023-12-25'],
    'value': [100, 200]
})

# Add all available date features
result = add_date_features(df, date_column='date')

# Add only specific features
selected_features = ['year', 'month', 'week_of_month', 'is_monday', 'is_weekend', 'season']
result = add_date_features(df, date_column='date', features=selected_features)

Available Date Features

• Basic Components: year, month, day, week_of_month, week_of_year, quarter
• Day of Week Flags: is_monday, is_tuesday, is_wednesday, is_thursday, is_friday, is_saturday, is_sunday, is_weekend
• Calendar Flags: is_month_start, is_month_end, is_quarter_start, is_quarter_end, is_year_start, is_year_end
• Season: season (1=Winter, 2=Spring, 3=Summer, 4=Fall for Northern Hemisphere)

Holiday Features

The holiday_features module provides functions to add holiday-related features to your data. It supports:

• Public holidays
• Local holidays
• Day before/after holiday flags
• Many counties holiday flags

Currently supports:

• Spain (ES) with regional holidays
• Italy (IT) - basic support (to be expanded)

Important Note: The holiday data has a limited date range. If your data contains dates outside this range, the package will raise a clear error message with instructions to contact [e-mail] to request an update of the holiday data.

Example usage:

import pandas as pd
from orama_utils.holiday_features import add_holiday_features

# Create a sample DataFrame
df = pd.DataFrame({
    'date': ['2023-01-01', '2023-12-25'],  # New Year's Day and Christmas
    'country': ['ES', 'ES'],
    'county': ['ES-MD', 'ES-CT']
})

# Add holiday features
result = add_holiday_features(df)

# The result will include new columns:
# - is_public_holiday: True for national holidays
# - is_local_holiday: True for regional holidays
# - many_counties_holiday: True if many regions celebrate the holiday
# - is_day_before_holiday: True if the next day is a holiday
# - is_day_after_holiday: True if the previous day is a holiday

Weather Features

The weather_features module provides functions to add weather-related features to your data. It uses the Visual Crossing Weather API to fetch weather data and caches it locally to minimize API calls.

Important Notes:

• You need a Visual Crossing Weather API key (get it from https://www.visualcrossing.com/weather-api)
• Currently only supports future dates (historical data support coming soon)
• Weather data is cached locally and updated every 5 hours
• Location names should be in the format "City, Country" (e.g., "Barcelona, Spain")

Example usage:

import pandas as pd
from orama_utils.weather_features import add_weather_features

# Create a sample DataFrame
df = pd.DataFrame({
    'date': pd.date_range(start='2024-03-20', periods=5),
    'location': ['Barcelona, Spain'] * 5
})

# Add all available weather features
result = add_weather_features(
    df=df,
    api_key='your_visual_crossing_api_key'
)

# Add only specific weather features
selected_features = ['temp', 'precip', 'windspeed']
result = add_weather_features(
    df=df,
    api_key='your_visual_crossing_api_key',
    features=selected_features
)

Available Weather Features

• Temperature: temp, tempmax, tempmin, feelslike
• Precipitation: precip, snow
• Wind: windspeed
• Cloud Cover: cloudcover

The function will add these features as new columns with the prefix weather_ (e.g., weather_temp, weather_precip).

Local Caching

The weather data is cached locally in a weather_db directory (configurable via the weather_db_path parameter). For each location, it creates:

• A CSV file with the weather data (e.g., barcelona_spain.csv)
• A last_update.json file tracking when each location's data was last updated

The cache is automatically updated when:

• The requested date range is not covered by existing data
• The data is older than 5 hours
• The location's data doesn't exist yet

Development

Running Tests

python -m pytest tests/

Updating the Package Version

When releasing a new version of the package, you need to update the version number in three places:

1. In pyproject.toml:

   [project]
   name = "orama-utils"
   version = "0.1.1"  # Update this
   

2. In setup.py:

   setup(
       name="orama-utils",
       version="0.1.1",  # Update this
       # ...
   )
   

3. In orama_utils/__init__.py:

   __version__ = '0.1.1'  # Update this
   

Make sure to update all three files with the same version number to avoid build inconsistencies.

Building the Package

To build the package:

Windows:

# Clean previous builds
Remove-Item -Path dist\* -Force -ErrorAction SilentlyContinue
Remove-Item -Path *.egg-info -Recurse -Force -ErrorAction SilentlyContinue

# Build new package
python -m build

macOS/Linux:

# Clean previous builds
rm -rf dist/* *.egg-info/

# Build new package
python -m build

The build output will be in the dist/ directory:

• orama_utils-x.y.z-py3-none-any.whl (wheel)
• orama_utils-x.y.z.tar.gz (source distribution)

Publishing to PyPI

First, ensure you have configured your PyPI credentials in ~/.pypirc or as environment variables.

Windows/macOS/Linux:

# Upload to PyPI
python -m twine upload dist/*

For test uploads, use TestPyPI:

python -m twine upload --repository testpypi dist/*

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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