def summarize(dataset, dframe, groups, no_cache, update=False):
"""Raises a ColumnTypeError if grouping on a non-dimensional column."""
# do not allow group by numeric types
for group in groups:
if not dataset.is_factor(group):
raise ColumnTypeError("group: '%s' is not a dimension." % group)
group_str = dataset.join_groups(groups) or dataset.ALL
# check cached stats for group and update as necessary
stats = dataset.stats
group_stats = stats.get(group_str)
if no_cache or not group_stats or update:
group_stats = summarize_with_groups(dframe, groups, dataset) if groups else summarize_df(dframe, dataset)
if not no_cache:
if update:
original_group_stats = stats.get(group_str, {})
group_stats = combine_dicts(original_group_stats, group_stats)
stats.update({group_str: group_stats})
dataset.update({dataset.STATS: dict_for_mongo(stats)})
stats_dict = dict_from_mongo(group_stats)
if groups:
stats_dict = {group_str: stats_dict}
return stats_dict
def summarize(dataset, dframe, groups, no_cache, update=False):
"""Raises a ColumnTypeError if grouping on a non-dimensional column."""
# do not allow group by numeric types
for group in groups:
if not dataset.is_factor(group):
raise ColumnTypeError("group: '%s' is not a dimension." % group)
group_str = dataset.join_groups(groups) or dataset.ALL
# check cached stats for group and update as necessary
stats = dataset.stats
group_stats = stats.get(group_str)
if no_cache or not group_stats or update:
group_stats = summarize_with_groups(dframe, groups, dataset) if\
groups else summarize_df(dframe, dataset)
if not no_cache:
if update:
original_group_stats = stats.get(group_str, {})
group_stats = combine_dicts(original_group_stats, group_stats)
stats.update({group_str: group_stats})
dataset.update({dataset.STATS: dict_for_mongo(stats)})
stats_dict = dict_from_mongo(group_stats)
if groups:
stats_dict = {group_str: stats_dict}
return stats_dict
def summarize(dataset, dframe, groups, group_str, no_cache):
"""Raises a ColumnTypeError if grouping on a non-dimensional column."""
# do not allow group by numeric types
for group in groups:
if group != dataset.ALL and not dataset.schema.is_dimension(group):
raise ColumnTypeError("group: '%s' is not a dimension." % group)
# check cached stats for group and update as necessary
stats = dataset.stats
if no_cache or not stats.get(group_str):
group_stats = summarize_df(dframe, dataset=dataset) if\
group_str == dataset.ALL else\
summarize_with_groups(dframe, groups, dataset)
stats.update({group_str: group_stats})
if not no_cache:
dataset.update({dataset.STATS: dict_for_mongo(stats)})
stats_to_return = dict_from_mongo(stats.get(group_str))
return stats_to_return if group_str == dataset.ALL else {
group_str: stats_to_return}
