def line_or_bar_or_geo(ldf, dimension: Clause, measure: Clause):
dim_type = dimension.data_type
# If no aggregation function is specified, then default as average
if measure.aggregation == "":
measure.set_aggregation("mean")
if dim_type == "temporal" or dim_type == "oridinal":
return "line", {"x": dimension, "y": measure}
else: # unordered categorical
# if cardinality large than 5 then sort bars
if ldf.cardinality[dimension.attribute] > 5:
dimension.sort = "ascending"
if utils.like_geo(dimension.get_attr()):
return "geographical", {"x": dimension, "y": measure}
return "bar", {"x": measure, "y": dimension}
def line_or_bar_or_geo(ldf, dimension: Clause, measure: Clause):
dim_type = dimension.data_type
# If no aggregation function is specified, then default as average
if measure.aggregation == "":
measure.set_aggregation("mean")
if dim_type == "temporal" or dim_type == "oridinal":
if isinstance(dimension.attribute, pd.Timestamp):
# If timestamp, use the _repr_ (e.g., TimeStamp('2020-04-05 00.000')--> '2020-04-05')
attr = str(dimension.attribute._date_repr)
else:
attr = dimension.attribute
if ldf.cardinality[attr] == 1:
return "bar", {"x": measure, "y": dimension}
else:
return "line", {"x": dimension, "y": measure}
else: # unordered categorical
# if cardinality large than 5 then sort bars
if ldf.cardinality[dimension.attribute] > 5:
dimension.sort = "ascending"
if utils.like_geo(dimension.get_attr()):
return "geographical", {"x": dimension, "y": measure}
return "bar", {"x": measure, "y": dimension}
