def flatten_dataset_frequent_strings(dataset_summary: DatasetSummary):
"""
Flatten frequent strings summaries from a dataset summary
"""
frequent_strings = {}
for col_name, col in dataset_summary.columns.items():
try:
item_summary = getter(getter(col, "string_summary"),
"frequent").items
items = {item.value: int(item.estimate) for item in item_summary}
if items:
frequent_strings[col_name] = items
except KeyError:
continue
return frequent_strings
def flatten_dataset_histograms(dataset_summary: DatasetSummary):
"""
Flatten histograms from a dataset summary
"""
histograms = {}
for col_name, col in dataset_summary.columns.items():
try:
hist = getter(getter(col, "number_summary"), "histogram")
if len(hist.bins) > 1:
histograms[col_name] = {
"bin_edges": list(hist.bins),
"counts": list(hist.counts),
}
except KeyError:
continue
return histograms
def flatten_dataset_string_quantiles(dataset_summary: DatasetSummary):
"""
Flatten quantiles from a dataset summary
"""
quants = {}
for col_name, col in dataset_summary.columns.items():
try:
quant = getter(getter(col, "number_summary"), "quantiles")
x = OrderedDict()
for q, qval in zip(_quantile_strings(quant.quantiles),
quant.quantile_values):
x[q] = qval
quants[col_name] = x
except KeyError:
pass
return quants
