def execute(self, df: data.Frame) -> data.Frame:
df = df.getRawFrame()
colOrder: List[str] = df.columns.to_list()
subDf = df.iloc[:, self.__selected]
duplicates = find_duplicates(subDf)
if duplicates:
df = df.copy(True)
df = df.drop(duplicates, axis=1)
# Keep original order
order = [c for c in colOrder if c not in duplicates]
df = df[order]
return data.Frame(df)
def execute(self, df: data.Frame) -> data.Frame:
columns = df.colnames
df = df.getRawFrame().copy(True)
# Notice that this timestamps are already set to a proper format (with default time/date) by
# the editor
intervals: Dict[int, pd.IntervalIndex] = \
{i: pd.IntervalIndex([pd.Interval(a, b, closed='right') for a, b in zip(opts[0],
opts[0][1:])])
for i, opts in self.__attributes.items()}
processedDict = dict()
for i, opts in self.__attributes.items():
_, labels, byDate, byTime = opts
applyCol = df.iloc[:, i]
if byTime and not byDate:
# Replace the date part with the default date in a way that every ts has the
# same date, but retains its original time. Nan values are propagated
applyCol = applyCol \
.map(lambda ts:
pd.Timestamp(QDateTime(_IntervalWidget.DEFAULT_DATE,
toQtDateTime(ts.to_pydatetime()).time()).toPython()),
na_action='ignore')
name = columns[i]
if self.__attributesSuffix:
name += self.__attributesSuffix
categoriesMap = dict(zip(intervals[i], labels))
processedDict[name] = pd.cut(
applyCol,
bins=intervals[i]).cat.rename_categories(categoriesMap)
if self.__attributesSuffix:
duplicateColumns: Set[str] = set(
processedDict.keys()) & set(columns)
else:
duplicateColumns: List[str] = list(processedDict.keys())
if duplicateColumns:
df = df.drop(columns=duplicateColumns)
processed = pd.DataFrame(processedDict).set_index(df.index)
df = pd.concat([df, processed], ignore_index=False, axis=1)
if not self.__attributesSuffix:
# Reorder columns
df = df[columns]
return data.Frame(df)