def _render_timeseries_data(group_df, metric_key): series = [] for dimension_values, y in group_df[metric_key].iteritems(): first_dimension_value = utils.wrap_list(dimension_values)[0] if pd.isnull(first_dimension_value): # Ignore totals on the x-axis. continue series.append((formats.date_as_millis(first_dimension_value), formats.metric_value(y))) return series
def _get_timeseries_positions(df: pd.DataFrame, dimension_alias: str): timeseries_positions = [] for dimensions, dimension_value in df[dimension_alias].iteritems(): datetime = utils.wrap_list(dimensions)[0] timeseries_positions.append({ "position": formats.date_as_millis(datetime), "label": dimension_value }) return timeseries_positions
def _render_timeseries_data(group_df, metric_alias, metric): series = [] for dimension_values, y in group_df[metric_alias].iteritems(): first_dimension_value = utils.wrap_list(dimension_values)[0] # Ignore empty result sets where the only row is totals if first_dimension_value in TOTALS_MARKERS: continue if pd.isnull(first_dimension_value): # Ignore totals on the x-axis. continue series.append(( formats.date_as_millis(first_dimension_value), formats.raw_value(y, metric), )) return series