for itm in self._tests:
sel_data = select_trending_data(in_data, itm)
if sel_data is not None:
sel_data["test_name"] = itm["id"]
for itm in self._tests:
sel_data = select_trending_data(in_data, itm)
if sel_data is not None:
sel_data["test_name"] = itm["id"]
- df = pd.concat([df, sel_data], ignore_index=True, copy=False)
+ lst_items.append(sel_data)
+ df = pd.concat(lst_items, ignore_index=True, copy=False)
+
tmp_labels = dict()
for _, row in self._data.iterrows():
telemetry = row["telemetry"]
for itm in metrics:
df = telemetry.loc[(telemetry["metric"] == itm)]
tmp_labels = dict()
for _, row in self._data.iterrows():
telemetry = row["telemetry"]
for itm in metrics:
df = telemetry.loc[(telemetry["metric"] == itm)]
for _, tm in df.iterrows():
for label in tm["labels"]:
if label[0] not in tmp_labels:
tmp_labels[label[0]] = set()
tmp_labels[label[0]].add(label[1])
for _, tm in df.iterrows():
for label in tm["labels"]:
if label[0] not in tmp_labels:
tmp_labels[label[0]] = set()
tmp_labels[label[0]].add(label[1])
for _, row in self._unique_metrics_labels.iterrows():
if _is_selected(row["labels"], selection):
for _, row in self._unique_metrics_labels.iterrows():
if _is_selected(row["labels"], selection):
- self._selected_metrics_labels = pd.concat(
- [self._selected_metrics_labels, row.to_frame().T],
- ignore_index=True,
- axis=0,
- copy=False
- )
+ lst_items.append(row.to_frame().T)
+ self._selected_metrics_labels = \
+ pd.concat(lst_items, ignore_index=True, axis=0, copy=False)
"""Select telemetry data for trending based on user's 'selection'.
The output dataframe includes these columns:
"""Select telemetry data for trending based on user's 'selection'.
The output dataframe includes these columns:
for _, row in self._data.iterrows():
tm_row = row["telemetry"]
for _, tm_sel in df_sel.iterrows():
df_tmp = tm_row.loc[tm_row["metric"] == tm_sel["metric"]]
for _, tm in df_tmp.iterrows():
for _, row in self._data.iterrows():
tm_row = row["telemetry"]
for _, tm_sel in df_sel.iterrows():
df_tmp = tm_row.loc[tm_row["metric"] == tm_sel["metric"]]
for _, tm in df_tmp.iterrows():
- if tm["labels"] == tm_sel["labels"]:
- labels = ','.join(
- [f"{itm[0]}='{itm[1]}'" for itm in tm["labels"]]
- )
+ do_it = False
+ if ignore_host:
+ if tm["labels"][2:] == tm_sel["labels"][2:]:
+ labels = ','.join(
+ [f"{i[0]}='{i[1]}'" for i in tm["labels"][2:]]
+ )
+ do_it = True
+ else:
+ if tm["labels"] == tm_sel["labels"]:
+ labels = ','.join(
+ [f"{i[0]}='{i[1]}'" for i in tm["labels"]]
+ )
+ do_it = True
+ if do_it:
- new_row = row.drop(labels=["telemetry", ])
- df = pd.concat(
- [df, new_row.to_frame().T],
- ignore_index=True,
- axis=0,
- copy=False
+ lst_rows.append(
+ row.drop(labels=["telemetry", ]).to_frame().T