def bulk_intent_to_df()

in src/dfcx_scrapi/core/intents.py [0:0]

• 27 lines of code
• 8 McCabe index (conditional complexity)

    def bulk_intent_to_df(
        self,
        agent_id: str = None,
        mode: str = "basic",
        intent_subset: List[str] = None,
        transpose: bool = False,
        language_code: str = None) -> pd.DataFrame:
        """Extracts all Intents and Training Phrases into a Pandas DataFrame.

        Args:
          agent_id (str):
            agent to pull list of intents
          mode (str):
            "basic" returns display name and training phrase as plain text.
            "advanced" returns training phrases broken out by parts
            with their parameters included.
          intent_subset (List[str]):
            A subset of intents to extract the intents from.
          transpose (bool):
            Return the transposed DataFrame. If this flag passed as True,
            mode won't affect the result and the result would be like basic.
          language_code (str):
            Language code of the intents being uploaded. Ref:
            https://cloud.google.com/dialogflow/cx/docs/reference/language

        Returns:
          In basic mode, a Pandas DataFrame with columns:
            display_name, training phrase
          In advanced mode, a Pandas DataFrame with columns:
            name, display_name, description, priority,
            is_fallback, labels, id, repeat_count,
            training_phrase_idx, text, text_idx,
            parameter_id, entity_type, is_list, redact
        """

        if not agent_id:
            agent_id = self.agent_id

        if transpose:
            _, intents_dict = self.intents_to_df_cosine_prep(agent_id)
            transposed_df = pd.DataFrame.from_dict(
                intents_dict, "index"
            ).transpose()
            if intent_subset:
                transposed_df = transposed_df[intent_subset]

            return transposed_df

        if mode not in ["basic", "advanced"]:
            raise ValueError("Mode types: [basic, advanced]")

        main_df = pd.DataFrame()
        intents = self.list_intents(agent_id, language_code=language_code)

        for obj in intents:
            if (intent_subset) and (obj.display_name not in intent_subset):
                continue
            intent_df = self.intent_proto_to_dataframe(obj, mode=mode)
            main_df = pd.concat([main_df, intent_df], ignore_index=True)

        return main_df