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frequent/largest/smallest determined from source and compare combined #66

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33 changes: 23 additions & 10 deletions sweetviz/series_analyzer_numeric.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,17 +36,23 @@ def do_detail_numeric(series: pd.Series, counts: dict, counts_compare: dict, upd
detail["frequent_values"] = list()
detail["min_values"] = list()
detail["max_values"] = list()
frequent_values = pd.DataFrame(counts["value_counts_without_nan"].head(num_to_show))
min_values = pd.DataFrame(counts["value_counts_without_nan"].sort_index( \
ascending=True).head(num_to_show))
max_values = pd.DataFrame(counts["value_counts_without_nan"].sort_index( \
ascending=False)).head(num_to_show)

if counts_compare is not None:
this_compare_count = counts_compare["value_counts_without_nan"]
compare_total_num = float(updated_dict["compare"]["base_stats"]["num_values"])
total_counts = counts["value_counts_without_nan"].add(this_compare_count, fill_value=0)
frequent_values = pd.DataFrame(total_counts.head(num_to_show))
min_values = pd.DataFrame(total_counts.sort_index( \
ascending=True).head(num_to_show))
max_values = pd.DataFrame(total_counts.sort_index( \
ascending=False)).head(num_to_show)
else:
this_compare_count = None
frequent_values = pd.DataFrame(counts["value_counts_without_nan"].head(num_to_show))
min_values = pd.DataFrame(counts["value_counts_without_nan"].sort_index( \
ascending=True).head(num_to_show))
max_values = pd.DataFrame(counts["value_counts_without_nan"].sort_index( \
ascending=False)).head(num_to_show)

for frequent, min_value, max_value in zip(frequent_values.itertuples(), \
min_values.itertuples(), max_values.itertuples()):
def get_comparison_num(feature_name):
Expand All @@ -69,11 +75,18 @@ def get_comparison_num(feature_name):
# ("none" is the absence of value)
this_comparison = NumWithPercent(0, compare_total_num)
return this_comparison
detail["frequent_values"].append((frequent[0], NumWithPercent(frequent[1], total_num),

def get_num(feature_name):
if feature_name in counts["value_counts_without_nan"]:
return NumWithPercent(counts["value_counts_without_nan"][feature_name], total_num)
else:
return NumWithPercent(0, total_num)

detail["frequent_values"].append((frequent[0], get_num(frequent[0]),
get_comparison_num(frequent[0])))
detail["min_values"].append((min_value[0], NumWithPercent(min_value[1], total_num),
detail["min_values"].append((min_value[0], get_num(min_value[0]),
get_comparison_num(min_value[0])))
detail["max_values"].append((max_value[0], NumWithPercent(max_value[1], total_num),
detail["max_values"].append((max_value[0], get_num(max_value[0]),
get_comparison_num(max_value[0])))
# detail["min_values"] = pd.DataFrame(counts["value_counts_without_nan"].sort_index( \
# ascending=True).tail(num_to_show))
Expand Down Expand Up @@ -108,4 +121,4 @@ def analyze(to_process: FeatureToProcess, feature_dict: dict):
if to_process.is_target():
feature_dict["html_summary"] = sv_html.generate_html_summary_target_numeric(feature_dict, compare_dict)
else:
feature_dict["html_summary"] = sv_html.generate_html_summary_numeric(feature_dict, compare_dict)
feature_dict["html_summary"] = sv_html.generate_html_summary_numeric(feature_dict, compare_dict)