💻Installation
Install
import sovai as sov
sov.token_auth(token="add_your_subscriber_token_here")
Full package
pip install sovai[full]
The best way to familiarize yourself with this powerful library is to head straight to the tutorial section.
Data package
If you only want to download data into pandas please use the much lighter package.
pip install sovai
import sovai as sov
sov.data("query")
Environment
Sovai is tested and supported on the following 64-bit systems:
Python 3.6 – 3.11
Python 3.9 for Ubuntu only
Ubuntu 16.04 or later
Windows 7 or later
In order to avoid potential conflicts with other packages, it is strongly recommended to use a virtual environment, e.g. python3 virtualenv.
# Create a virtual environment
python -m venv yourenvname
# Activate the virtual environment
source yourenvname/bin/activate # For Unix/Linux
yourenvname\Scripts\activate # For Windows
# Install sovai
pip install sovai
# Create notebook kernel
python -m ipykernel install --user --name yourenvname --display-name "display-name"
Dependencies
Default dependencies that are installed with pip install sovai
are listed here.
Select the tab
numpy>=1.20
scipy>=1.0
pandas>=1.0
python-dateutil>=2.8
python-dotenv>=0.10
requests>=2.20
joblib>=1.0
pyarrow>=5.0
matplotlib>=3.0
plotly>=5.0
scikit-learn>=1.0
numba>=0.50
boto3>=1.20
dash>=2.0
great-tables>=0.9
polars>=0.20.30
ruptures>=1.0
shap>=0.40
skfolio>=0.3
statsforecast>=1.0
tensorly>=0.6
openai>=1.0
mfles>=0.2
pexpect>=4.9.0
lightgbm>=4.5.0
ipywidgets>=8.1.3
polars-talib==0.1.3
dash-bootstrap-components>=1.6.0
Docker
Docker uses containers to create virtual environments that isolate a Sovai installation from the rest of the system. Sovai docker comes pre-installed with a Notebook environment that can share resources with its host machine (access directories, use the GPU, connect to the Internet, etc.). The Sovai Docker images are tested for each release.
docker run -p 8888:8888 sovai/slim
For docker image with full version:
docker run -p 8888:8888 sovai/full
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