Pickle / Serialization
Chart objects are picklable via __getstate__ / __setstate__ — works with joblib, multiprocessing, Ray, Streamlit reruns.
Python
import seraplot as sp
import pickle
chart = sp.bar("Revenue", labels=["a", "b", "c"], values=[1, 2, 3])
blob = pickle.dumps(chart)
restored = pickle.loads(blob)
restored.save("restored.html")
Internally, only the HTML string is serialized — minimal payload, no transient state.
ML models
Trained models are a separate concern from Chart pickling — see ML Model Persistence for sp.ml_save_model() / sp.ml_load_model().
Les objets Chart sont sérialisables via __getstate__ / __setstate__ — compatible joblib, multiprocessing, Ray, reruns Streamlit.
Python
import seraplot as sp
import pickle
chart = sp.bar("Revenu", labels=["a", "b", "c"], values=[1, 2, 3])
blob = pickle.dumps(chart)
restored = pickle.loads(blob)
restored.save("restored.html")
En interne, seule la chaîne HTML est sérialisée — payload minimal, aucun état transitoire.
Modèles ML
Les modèles entraînés sont une préoccupation distincte du pickling de Chart — voir ML Model Persistence pour sp.ml_save_model() / sp.ml_load_model().