def summarize(self, dframe, groups=[], no_cache=False):
"""Build and return a summary of the data in this dataset.
Return a summary of dframe grouped by `groups`, or the overall
summary if no groups are specified.
:param dframe: An optional dframe to summarize, if None fetch a dframe
:param groups: A list of columns to group on.
:param no_cache: Do not fetch a cached summary.
:returns: A summary of the dataset as a dict. Numeric columns will be
summarized by the arithmetic mean, standard deviation, and
percentiles. Dimensional columns will be summarized by counts.
"""
self.reload()
return summarize(self, dframe, groups, no_cache)
def summarize(self,
dframe,
groups=[],
no_cache=False,
update=False,
flat=False):
"""Build and return a summary of the data in this dataset.
Return a summary of dframe grouped by `groups`, or the overall
summary if no groups are specified.
:param dframe: dframe to summarize
:param groups: A list of columns to group on.
:param no_cache: Do not fetch a cached summary.
:param flat: Return a flattened list of groups.
:returns: A summary of the dataset as a dict. Numeric columns will be
summarized by the arithmetic mean, standard deviation, and
percentiles. Dimensional columns will be summarized by counts.
"""
self.reload()
summary = summarize(self, dframe, groups, no_cache, update=update)
if flat:
flat_summary = []
for cols, v in summary.iteritems():
cols = self.split_groups(cols)
for k, data in v.iteritems():
col_values = self.split_groups(k)
col_values = [
strip_pattern.sub(',', i)[1:-1] for i in col_values
]
flat_summary.append(
combine_dicts(dict(zip(cols, col_values)), data))
summary = flat_summary
return summary
def summarize(self, query=None, select=None,
group_str=None, limit=0, order_by=None):
"""Build and return a summary of the data in this dataset.
Return a summary of the rows/values filtered by `query` and `select`
and grouped by `group_str`, or the overall summary if no group is
specified.
:param query: An optional MongoDB query to limit the data summarized.
:param select: An optional select to limit the columns summarized.
:param group_str: A column in the dataset as a string or a list comma
separated columns to group on.
:returns: A JSON summary of the dataset. Numeric columns will be
summarized by the arithmetic mean, standard deviation, and
percentiles. Dimensional columns will be summarized by counts.
"""
# interpret none as all
if not group_str:
group_str = self.ALL
# split group in case of multigroups
groups = self.split_groups(group_str)
# if select append groups to select
if select:
select = json.loads(select)
if not isinstance(select, dict):
raise ArgumentError('select argument must be a JSON dictionary'
', found: %s.' % select)
select.update(dict(zip(groups, [1] * len(groups))))
select = json.dumps(select)
self.reload()
dframe = self.dframe(query=query, select=select,
limit=limit, order_by=order_by)
return summarize(self, dframe, groups, group_str, query or select)
def summarize(self, dframe, groups=[], no_cache=False, update=False,
flat=False):
"""Build and return a summary of the data in this dataset.
Return a summary of dframe grouped by `groups`, or the overall
summary if no groups are specified.
:param dframe: dframe to summarize
:param groups: A list of columns to group on.
:param no_cache: Do not fetch a cached summary.
:param flat: Return a flattened list of groups.
:returns: A summary of the dataset as a dict. Numeric columns will be
summarized by the arithmetic mean, standard deviation, and
percentiles. Dimensional columns will be summarized by counts.
"""
self.reload()
summary = summarize(self, dframe, groups, no_cache, update=update)
if flat:
flat_summary = []
for cols, v in summary.iteritems():
cols = self.split_groups(cols)
for k, data in v.iteritems():
col_values = self.split_groups(k)
col_values = [strip_pattern.sub(',', i)[1:-1]
for i in col_values]
flat_summary.append(
combine_dicts(dict(zip(cols, col_values)), data))
summary = flat_summary
return summary