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