def _set_nims_and_mdes()

in spotify_confidence/analysis/frequentist/nims_and_mdes.py [0:0]

• 26 lines of code
• 15 McCabe index (conditional complexity)

    def _set_nims_and_mdes(grp: DataFrame) -> DataFrame:
        nim = grp[nim_column].astype(float)
        input_preference = grp[preferred_direction_column].values[0]
        mde = None if mde_column is None else grp[mde_column]

        nim_is_na = nim.isna().all()
        mde_is_na = True if mde is None else mde.isna().all()
        if input_preference is None or (type(input_preference) is float and isnan(input_preference)):
            signed_nim = 0.0 if nim_is_na else nim * grp[ORIGINAL_POINT_ESTIMATE]
            preference = TWO_SIDED
            signed_mde = None if mde_is_na else mde * grp[ORIGINAL_POINT_ESTIMATE]
        elif input_preference.lower() == INCREASE_PREFFERED:
            signed_nim = 0.0 if nim_is_na else -nim * grp[ORIGINAL_POINT_ESTIMATE]
            preference = "larger"
            signed_mde = None if mde_is_na else mde * grp[ORIGINAL_POINT_ESTIMATE]
        elif input_preference.lower() == DECREASE_PREFFERED:
            signed_nim = 0.0 if nim_is_na else nim * grp[ORIGINAL_POINT_ESTIMATE]
            preference = "smaller"
            signed_mde = None if mde_is_na else -mde * grp[ORIGINAL_POINT_ESTIMATE]
        else:
            raise ValueError(f"{input_preference.lower()} not in " f"{[INCREASE_PREFFERED, DECREASE_PREFFERED]}")

        return (
            grp.assign(**{NIM: nim})
            .assign(**{PREFERENCE: preference})
            .assign(**{NULL_HYPOTHESIS: signed_nim})
            .assign(**{ALTERNATIVE_HYPOTHESIS: signed_mde if nim_is_na else 0.0})
        )