def from_dataframe()

in hiplot/experiment.py [0:0]

• 11 lines of code
• 9 McCabe index (conditional complexity)

    def from_dataframe(dataframe: "pd.DataFrame") -> "Experiment":  # No type hint to avoid having pandas as an additional dependency
        """
        Creates a HiPlot experiment from a pandas DataFrame.

        :param dataframe: Pandas DataFrame
        """
        # Check if from_uid and uid is both in columns
        if {'from_uid', 'uid'}.issubset(dataframe.columns):
            # Check if there are any NaN values to handle
            if dataframe['from_uid'].isnull().values.any():

                # NaN values forces integer columns to become float, if uid is integer and from_uid is float, it crashes.
                # The line below changes uid to match from_uid type (either float or string), since NaN cannot be integer.
                dataframe['uid'] = dataframe['uid'].astype(dataframe['from_uid'].dtypes)
                dataframe = dataframe.fillna({'from_uid': '', 'uid': ''})

                # Replaces their dtypes accordingly to str, which is handled better with lesser errors with no change to functionality
                dataframe['uid'] = dataframe['uid'].astype(str)
                dataframe['from_uid'] = dataframe['from_uid'].astype(str)

        experiment = Experiment.from_iterable(dataframe.to_dict(orient='records'))

        # Restore columns order
        experiment.display_data(Displays.PARALLEL_PLOT)['order'] = list(dataframe.columns)
        experiment.display_data(Displays.TABLE)['order'] = list(dataframe.columns)

        return experiment