otava/importer.py [495:556]:
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        try:
            time_index: int = columns.index(test_conf.time_column)
            attr_indexes: List[int] = [columns.index(c) for c in test_conf.attributes]
            metric_names = [m.name for m in metrics.values()]
            metric_columns = [m.column for m in metrics.values()]
            metric_indexes: List[int] = [columns.index(c) for c in metric_columns]
        except ValueError as err:
            raise DataImportError(f"Column not found {err.args[0]}")

        time: List[float] = []
        data: Dict[str, List[float]] = {}
        for n in metric_names:
            data[n] = []
        attributes: Dict[str, List[str]] = {}
        for i in attr_indexes:
            attributes[columns[i]] = []

        for row in rows:
            ts: datetime = row[time_index]
            if since_time is not None and ts < since_time:
                continue
            if until_time is not None and ts >= until_time:
                continue
            time.append(ts.timestamp())

            # Read metric values. Note we can still fail on conversion to float,
            # because the user is free to override the column selection and thus
            # they may select a column that contains non-numeric data:
            for name, i in zip(metric_names, metric_indexes):
                try:
                    data[name].append(float(row[i]))
                except ValueError as err:
                    raise DataImportError(
                        "Could not convert value in column " + columns[i] + ": " + err.args[0]
                    )

            # Attributes are just copied as-is, with no conversion:
            for i in attr_indexes:
                attributes[columns[i]].append(row[i])

        # Convert metrics to series.Metrics
        metrics = {m.name: Metric(m.direction, m.scale) for m in metrics.values()}

        # Leave last n points:
        time = time[-selector.last_n_points :]
        tmp = data
        data = {}
        for k, v in tmp.items():
            data[k] = v[-selector.last_n_points :]
        tmp = attributes
        attributes = {}
        for k, v in tmp.items():
            attributes[k] = v[-selector.last_n_points :]

        return Series(
            test_conf.name,
            branch=None,
            time=time,
            metrics=metrics,
            data=data,
            attributes=attributes,
        )
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otava/importer.py [712:773]:
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        try:
            time_index: int = columns.index(test_conf.time_column)
            attr_indexes: List[int] = [columns.index(c) for c in test_conf.attributes]
            metric_names = [m.name for m in metrics.values()]
            metric_columns = [m.column for m in metrics.values()]
            metric_indexes: List[int] = [columns.index(c) for c in metric_columns]
        except ValueError as err:
            raise DataImportError(f"Column not found {err.args[0]}")

        time: List[float] = []
        data: Dict[str, List[float]] = {}
        for n in metric_names:
            data[n] = []
        attributes: Dict[str, List[str]] = {}
        for i in attr_indexes:
            attributes[columns[i]] = []

        for row in rows:
            ts: datetime = row[time_index]
            if since_time is not None and ts < since_time:
                continue
            if until_time is not None and ts >= until_time:
                continue
            time.append(ts.timestamp())

            # Read metric values. Note we can still fail on conversion to float,
            # because the user is free to override the column selection and thus
            # they may select a column that contains non-numeric data:
            for name, i in zip(metric_names, metric_indexes):
                try:
                    data[name].append(float(row[i]))
                except ValueError as err:
                    raise DataImportError(
                        "Could not convert value in column " + columns[i] + ": " + err.args[0]
                    )

            # Attributes are just copied as-is, with no conversion:
            for i in attr_indexes:
                attributes[columns[i]].append(row[i])

        # Convert metrics to series.Metrics
        metrics = {m.name: Metric(m.direction, m.scale) for m in metrics.values()}

        # Leave last n points:
        time = time[-selector.last_n_points :]
        tmp = data
        data = {}
        for k, v in tmp.items():
            data[k] = v[-selector.last_n_points :]
        tmp = attributes
        attributes = {}
        for k, v in tmp.items():
            attributes[k] = v[-selector.last_n_points :]

        return Series(
            test_conf.name,
            branch=None,
            time=time,
            metrics=metrics,
            data=data,
            attributes=attributes,
        )
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