def test_filter_unknown_keyword(self, model, index):
filterString = '(Foo < 10) and (Bar > 1)'
search = DataSearch("Test", filterString)
preFilterRows = model.rowCount()
model.setFilter(search)
postFilterRows = model.rowCount()
assert preFilterRows == postFilterRows
def test_extentSearch(self, geoDataFrame, dataFrame):
datasearch = DataSearch('Test', dataFrame=geoDataFrame)
filterString = 'extentSearch(51, 9, 55, 14)'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert valid
assert sum(ret) == 1
datasearch = DataSearch('Test', dataFrame=dataFrame)
filterString = 'extentSearch(51, 9, 55, 14)'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert valid
assert sum(ret) == 0
def test_init(self, dataFrame):
filterString = 'Foo < 10'
datasearch = DataSearch("Test", filterString)
assert datasearch._filterString == filterString
assert isinstance(datasearch._dataFrame, pandas.DataFrame)
assert datasearch.name == 'Test'
datasearch = DataSearch("Test2")
assert datasearch._filterString == ''
assert isinstance(datasearch._dataFrame, pandas.DataFrame)
assert datasearch.name == 'Test2'
datasearch = DataSearch("Test3", dataFrame=dataFrame)
assert datasearch._filterString == ''
assert isinstance(datasearch._dataFrame, pandas.DataFrame)
assert datasearch.name == 'Test3'
assert len(datasearch._dataFrame.index) == 3
def test_setFilterString(self):
datasearch = DataSearch("Test")
filterString = 'foo bar'
datasearch.setFilterString(filterString)
assert datasearch.filterString() == filterString
filterString = ' foo bar '
datasearch.setFilterString(filterString)
assert datasearch.filterString() != filterString
assert datasearch.filterString() == filterString.strip()
def test_dataFrame(self, dataFrame):
datasearch = DataSearch("Test")
assert datasearch.dataFrame().empty
assert isinstance(datasearch.dataFrame(), pandas.DataFrame)
datasearch = DataSearch("Test", dataFrame=dataFrame)
assert len(datasearch.dataFrame()) == 3
def test_filter_multiColumn(self, model, index):
filterString = '(Foo < 10) & (Bar > 1)'
search = DataSearch("Test", filterString)
preFilterRows = model.rowCount()
model.setFilter(search)
postFilterRows = model.rowCount()
assert preFilterRows > postFilterRows
assert preFilterRows == (postFilterRows + 2)
def test_filter_single_column(self, model, index):
filterString = 'Foo < 10'
search = DataSearch("Test", filterString)
preFilterRows = model.rowCount()
model.setFilter(search)
postFilterRows = model.rowCount()
assert preFilterRows > postFilterRows
assert preFilterRows == (postFilterRows + 1)
def test_filter_freeSearch(self, model, index):
filterString = 'freeSearch("10")'
search = DataSearch("Test", filterString)
preFilterRows = model.rowCount()
model.setFilter(search)
postFilterRows = model.rowCount()
assert preFilterRows > postFilterRows
assert preFilterRows == (postFilterRows + 2)
def __init__(self, dataFrame=None, copyDataFrame=False):
"""the __init__ method.
Args:
dataFrame (pandas.core.frame.DataFrame, optional): initializes the model with given DataFrame.
If none is given an empty DataFrame will be set. defaults to None.
copyDataFrame (bool, optional): create a copy of dataFrame or use it as is. defaults to False.
If you use it as is, you can change it from outside otherwise you have to reset the dataFrame
after external changes.
"""
super(DataFrameModel, self).__init__()
self._dataFrame = pandas.DataFrame()
if dataFrame is not None:
self.setDataFrame(dataFrame, copyDataFrame=copyDataFrame)
self.dataChanged.emit()
self._dataFrameOriginal = None
self._search = DataSearch("nothing", "")
self.editable = False
def __init__(self, dataFrame=None, copyDataFrame=False):
"""the __init__ method.
Args:
dataFrame (pandas.core.frame.DataFrame, optional): initializes the model with given DataFrame.
If none is given an empty DataFrame will be set. defaults to None.
copyDataFrame (bool, optional): create a copy of dataFrame or use it as is. defaults to False.
If you use it as is, you can change it from outside otherwise you have to reset the dataFrame
after external changes.
"""
super(DataFrameModel, self).__init__()
self._dataFrame = pandas.DataFrame()
if dataFrame is not None:
self.setDataFrame(dataFrame, copyDataFrame=copyDataFrame)
self.dataChanged.emit()
self._dataFrameOriginal = None
self._search = DataSearch("nothing", "")
self.editable = False
def setFilter(self):
#filterIndex = eval(self.lineEditFilterCondition.text())
search = DataSearch("Test", self.lineEditFilterCondition.text())
self.dataTableView.view().model().setFilter(search)
class DataFrameModel(QtCore.QAbstractTableModel):
"""data model for use in QTableView, QListView, QComboBox, etc.
Attributes:
timestampFormat (unicode): formatting string for conversion of timestamps to QtCore.QDateTime.
Used in data method.
sortingAboutToStart (QtCore.pyqtSignal): emitted directly before sorting starts.
sortingFinished (QtCore.pyqtSignal): emitted, when sorting finished.
dtypeChanged (Signal(columnName)): passed from related ColumnDtypeModel
if a columns dtype has changed.
changingDtypeFailed (Signal(columnName, index, dtype)):
passed from related ColumnDtypeModel.
emitted after a column has changed it's data type.
dataChanged (Signal):
Emitted, if data has changed, e.x. finished loading, new columns added or removed.
It's not the same as layoutChanged.
Usefull to reset delegates in the view.
"""
_float_precisions = {
"float16": numpy.finfo(numpy.float16).precision - 2,
"float32": numpy.finfo(numpy.float32).precision - 1,
"float64": numpy.finfo(numpy.float64).precision - 1
}
"""list of int datatypes for easy checking in data() and setData()"""
_intDtypes = SupportedDtypes.intTypes() + SupportedDtypes.uintTypes()
"""list of float datatypes for easy checking in data() and setData()"""
_floatDtypes = SupportedDtypes.floatTypes()
"""list of bool datatypes for easy checking in data() and setData()"""
_boolDtypes = SupportedDtypes.boolTypes()
"""list of datetime datatypes for easy checking in data() and setData()"""
_dateDtypes = SupportedDtypes.datetimeTypes()
_timestampFormat = Qt.ISODate
sortingAboutToStart = Signal()
sortingFinished = Signal()
dtypeChanged = Signal(int, object)
changingDtypeFailed = Signal(object, QtCore.QModelIndex, object)
dataChanged = Signal()
dataFrameChanged = Signal()
def __init__(self, dataFrame=None, copyDataFrame=False):
"""the __init__ method.
Args:
dataFrame (pandas.core.frame.DataFrame, optional): initializes the model with given DataFrame.
If none is given an empty DataFrame will be set. defaults to None.
copyDataFrame (bool, optional): create a copy of dataFrame or use it as is. defaults to False.
If you use it as is, you can change it from outside otherwise you have to reset the dataFrame
after external changes.
"""
super(DataFrameModel, self).__init__()
self._dataFrame = pandas.DataFrame()
if dataFrame is not None:
self.setDataFrame(dataFrame, copyDataFrame=copyDataFrame)
self.dataChanged.emit()
self._dataFrameOriginal = None
self._search = DataSearch("nothing", "")
self.editable = False
def dataFrame(self):
"""getter function to _dataFrame. Holds all data.
Note:
It's not implemented with python properties to keep Qt conventions.
"""
return self._dataFrame
def setDataFrame(self, dataFrame, copyDataFrame=False):
"""setter function to _dataFrame. Holds all data.
Note:
It's not implemented with python properties to keep Qt conventions.
Raises:
TypeError: if dataFrame is not of type pandas.core.frame.DataFrame.
Args:
dataFrame (pandas.core.frame.DataFrame): assign dataFrame to _dataFrame. Holds all the data displayed.
copyDataFrame (bool, optional): create a copy of dataFrame or use it as is. defaults to False.
If you use it as is, you can change it from outside otherwise you have to reset the dataFrame
after external changes.
"""
if not isinstance(dataFrame, pandas.core.frame.DataFrame):
raise TypeError("not of type pandas.core.frame.DataFrame")
self.layoutAboutToBeChanged.emit()
if copyDataFrame:
self._dataFrame = dataFrame.copy()
else:
self._dataFrame = dataFrame
self._columnDtypeModel = ColumnDtypeModel(dataFrame)
self._columnDtypeModel.dtypeChanged.connect(self.propagateDtypeChanges)
self._columnDtypeModel.changeFailed.connect(
lambda columnName, index, dtype: self.changingDtypeFailed.emit(columnName, index, dtype)
)
self.layoutChanged.emit()
self.dataChanged.emit()
self.dataFrameChanged.emit()
@Slot(int, object)
def propagateDtypeChanges(self, column, dtype):
self.dtypeChanged.emit(column, dtype)
@property
def timestampFormat(self):
"""getter to _timestampFormat"""
return self._timestampFormat
@timestampFormat.setter
def timestampFormat(self, timestampFormat):
"""setter to _timestampFormat. Formatting string for conversion of timestamps to QtCore.QDateTime
Raises:
AssertionError: if timestampFormat is not of type unicode.
Args:
timestampFormat (unicode): assign timestampFormat to _timestampFormat.
Formatting string for conversion of timestamps to QtCore.QDateTime. Used in data method.
"""
if not isinstance(timestampFormat, (unicode, )):
raise TypeError('not of type unicode')
#assert isinstance(timestampFormat, unicode) or timestampFormat.__class__.__name__ == "DateFormat", "not of type unicode"
self._timestampFormat = timestampFormat
def headerData(self, section, orientation, role=Qt.DisplayRole):
"""return the header depending on section, orientation and Qt::ItemDataRole
Args:
section (int): For horizontal headers, the section number corresponds to the column number.
Similarly, for vertical headers, the section number corresponds to the row number.
orientation (Qt::Orientations):
role (Qt::ItemDataRole):
Returns:
None if not Qt.DisplayRole
_dataFrame.columns.tolist()[section] if orientation == Qt.Horizontal
section if orientation == Qt.Vertical
None if horizontal orientation and section raises IndexError
"""
if role != Qt.DisplayRole:
return None
if orientation == Qt.Horizontal:
try:
label = self._dataFrame.columns.tolist()[section]
if label == section:
label = section
return label
except (IndexError, ):
return None
elif orientation == Qt.Vertical:
return section
def data(self, index, role=Qt.DisplayRole):
"""return data depending on index, Qt::ItemDataRole and data type of the column.
Args:
index (QtCore.QModelIndex): Index to define column and row you want to return
role (Qt::ItemDataRole): Define which data you want to return.
Returns:
None if index is invalid
None if role is none of: DisplayRole, EditRole, CheckStateRole, DATAFRAME_ROLE
if role DisplayRole:
unmodified _dataFrame value if column dtype is object (string or unicode).
_dataFrame value as int or long if column dtype is in _intDtypes.
_dataFrame value as float if column dtype is in _floatDtypes. Rounds to defined precision (look at: _float16_precision, _float32_precision).
None if column dtype is in _boolDtypes.
QDateTime if column dtype is numpy.timestamp64[ns]. Uses timestampFormat as conversion template.
if role EditRole:
unmodified _dataFrame value if column dtype is object (string or unicode).
_dataFrame value as int or long if column dtype is in _intDtypes.
_dataFrame value as float if column dtype is in _floatDtypes. Rounds to defined precision (look at: _float16_precision, _float32_precision).
_dataFrame value as bool if column dtype is in _boolDtypes.
QDateTime if column dtype is numpy.timestamp64[ns]. Uses timestampFormat as conversion template.
if role CheckStateRole:
Qt.Checked or Qt.Unchecked if dtype is numpy.bool_ otherwise None for all other dtypes.
if role DATAFRAME_ROLE:
unmodified _dataFrame value.
raises TypeError if an unhandled dtype is found in column.
"""
if not index.isValid():
return None
def convertValue(row, col, columnDtype):
value = None
if columnDtype == object:
value = self._dataFrame.ix[row, col]
elif columnDtype in self._floatDtypes:
value = round(float(self._dataFrame.ix[row, col]), self._float_precisions[str(columnDtype)])
elif columnDtype in self._intDtypes:
value = int(self._dataFrame.ix[row, col])
elif columnDtype in self._boolDtypes:
# TODO this will most likely always be true
# See: http://stackoverflow.com/a/715455
# well no: I am mistaken here, the data is already in the dataframe
# so its already converted to a bool
value = bool(self._dataFrame.ix[row, col])
elif columnDtype in self._dateDtypes:
#print numpy.datetime64(self._dataFrame.ix[row, col])
value = pandas.Timestamp(self._dataFrame.ix[row, col])
value = QtCore.QDateTime.fromString(str(value), self.timestampFormat)
#print value
# else:
# raise TypeError, "returning unhandled data type"
return value
row = self._dataFrame.index[index.row()]
col = self._dataFrame.columns[index.column()]
columnDtype = self._dataFrame[col].dtype
if role == Qt.DisplayRole:
# return the value if you wanne show True/False as text
if columnDtype == numpy.bool:
result = self._dataFrame.ix[row, col]
else:
result = convertValue(row, col, columnDtype)
elif role == Qt.EditRole:
result = convertValue(row, col, columnDtype)
elif role == Qt.CheckStateRole:
if columnDtype == numpy.bool_:
if convertValue(row, col, columnDtype):
result = Qt.Checked
else:
result = Qt.Unchecked
else:
result = None
elif role == DATAFRAME_ROLE:
result = self._dataFrame.ix[row, col]
else:
result = None
return result
def flags(self, index):
"""Returns the item flags for the given index as ored value, e.x.: Qt.ItemIsUserCheckable | Qt.ItemIsEditable
If a combobox for bool values should pop up ItemIsEditable have to set for bool columns too.
Args:
index (QtCore.QModelIndex): Index to define column and row
Returns:
if column dtype is not boolean Qt.ItemIsSelectable | Qt.ItemIsEnabled | Qt.ItemIsEditable
if column dtype is boolean Qt.ItemIsSelectable | Qt.ItemIsEnabled | Qt.ItemIsUserCheckable
"""
flags = super(DataFrameModel, self).flags(index)
if not self.editable:
return flags
col = self._dataFrame.columns[index.column()]
if self._dataFrame[col].dtype == numpy.bool:
flags |= Qt.ItemIsUserCheckable
else:
# if you want to have a combobox for bool columns set this
flags |= Qt.ItemIsEditable
return flags
def setData(self, index, value, role=Qt.DisplayRole):
"""Set the value to the index position depending on Qt::ItemDataRole and data type of the column
Args:
index (QtCore.QModelIndex): Index to define column and row.
value (object): new value.
role (Qt::ItemDataRole): Use this role to specify what you want to do.
Raises:
TypeError: If the value could not be converted to a known datatype.
Returns:
True if value is changed. Calls layoutChanged after update.
False if value is not different from original value.
"""
if not index.isValid() or not self.editable:
return False
if value != index.data(role):
self.layoutAboutToBeChanged.emit()
row = self._dataFrame.index[index.row()]
col = self._dataFrame.columns[index.column()]
#print 'before change: ', index.data().toUTC(), self._dataFrame.iloc[row][col]
columnDtype = self._dataFrame[col].dtype
if columnDtype == object:
pass
elif columnDtype in self._intDtypes:
dtypeInfo = numpy.iinfo(columnDtype)
if value < dtypeInfo.min:
value = dtypeInfo.min
elif value > dtypeInfo.max:
value = dtypeInfo.max
elif columnDtype in self._floatDtypes:
value = numpy.float64(value).astype(columnDtype)
elif columnDtype in self._boolDtypes:
value = numpy.bool_(value)
elif columnDtype in self._dateDtypes:
# convert the given value to a compatible datetime object.
# if the conversation could not be done, keep the original
# value.
if isinstance(value, QtCore.QDateTime):
value = value.toString(self.timestampFormat)
try:
value = pandas.Timestamp(value)
except Exception:
raise Exception, u"Can't convert '{0}' into a datetime".format(value)
return False
else:
raise TypeError, "try to set unhandled data type"
self._dataFrame.set_value(row, col, value)
#print 'after change: ', value, self._dataFrame.iloc[row][col]
self.layoutChanged.emit()
return True
else:
return False
def rowCount(self, index=QtCore.QModelIndex()):
"""returns number of rows
Args:
index (QtCore.QModelIndex, optional): Index to define column and row. defaults to empty QModelIndex
Returns:
number of rows
"""
# len(df.index) is faster, so use it:
# In [12]: %timeit df.shape[0]
# 1000000 loops, best of 3: 437 ns per loop
# In [13]: %timeit len(df.index)
# 10000000 loops, best of 3: 110 ns per loop
# %timeit df.__len__()
# 1000000 loops, best of 3: 215 ns per loop
return len(self._dataFrame.index)
def columnCount(self, index=QtCore.QModelIndex()):
"""returns number of columns
Args:
index (QtCore.QModelIndex, optional): Index to define column and row. defaults to empty QModelIndex
Returns:
number of columns
"""
# speed comparison:
# In [23]: %timeit len(df.columns)
# 10000000 loops, best of 3: 108 ns per loop
# In [24]: %timeit df.shape[1]
# 1000000 loops, best of 3: 440 ns per loop
return len(self._dataFrame.columns)
def sort(self, columnId, order=Qt.AscendingOrder):
"""sort the model column
After sorting the data in ascending or descending order, a signal
`layoutChanged` is emitted.
Args:
columnId (int): columnIndex
order (Qt::SortOrder, optional): descending(1) or ascending(0). defaults to Qt.AscendingOrder
"""
self.layoutAboutToBeChanged.emit()
self.sortingAboutToStart.emit()
column = self._dataFrame.columns[columnId]
self._dataFrame.sort(column, ascending=not bool(order), inplace=True)
self.layoutChanged.emit()
self.sortingFinished.emit()
def setFilter(self, search):
"""apply a filter and hide rows.
The filter must be a `DataSearch` object, which evaluates a python
expression.
If there was an error while parsing the expression, the data will remain
unfiltered.
Args:
search(pandasqt.DataSearch): data search object to use.
Raises:
TypeError: An error is raised, if the given parameter is not a
`DataSearch` object.
"""
if not isinstance(search, DataSearch):
raise TypeError('The given parameter must an `pandasqt.DataSearch` object')
self._search = search
self.layoutAboutToBeChanged.emit()
if self._dataFrameOriginal is not None:
self._dataFrame = self._dataFrameOriginal
self._dataFrameOriginal = self._dataFrame.copy()
self._search.setDataFrame(self._dataFrame)
searchIndex, valid = self._search.search()
if valid:
self._dataFrame = self._dataFrame[searchIndex]
self.layoutChanged.emit()
else:
self.clearFilter()
self.layoutChanged.emit()
self.dataFrameChanged.emit()
def clearFilter(self):
"""clear all filters.
"""
if self._dataFrameOriginal is not None:
self.layoutAboutToBeChanged.emit()
self._dataFrame = self._dataFrameOriginal
self._dataFrameOriginal = None
self.layoutChanged.emit()
def columnDtypeModel(self):
"""Getter for a ColumnDtypeModel.
Returns:
ColumnDtypeModel
"""
return self._columnDtypeModel
def enableEditing(self, editable):
self.editable = editable
self._columnDtypeModel.setEditable(self.editable)
def dataFrameColumns(self):
return self._dataFrame.columns.tolist()
def addDataFrameColumn(self, columnName, dtype, defaultValue):
if not self.editable or dtype not in SupportedDtypes.allTypes():
return False
elements = self.rowCount()
columnPosition = self.columnCount()
newColumn = pandas.Series([defaultValue]*elements, index=self._dataFrame.index, dtype=dtype)
self.beginInsertColumns(QtCore.QModelIndex(), columnPosition - 1, columnPosition - 1)
try:
self._dataFrame.insert(columnPosition, columnName, newColumn, allow_duplicates=False)
except ValueError, e:
# columnName does already exist
return False
self.endInsertColumns()
self.propagateDtypeChanges(columnPosition, newColumn.dtype)
return True
def test_search(self, dataFrame):
datasearch = DataSearch('Test', dataFrame=dataFrame)
filterString = 'Foo < 10'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert valid
assert sum(ret) == 2
filterString = 'Foo < 10 and Bar'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert not valid
filterString = '(Foo < 10) & (Bar > 1)'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert valid
assert sum(ret) == 1
filterString = '(Monty < 10) & (Bar > 1)'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert not valid
class DataFrameModel(QtCore.QAbstractTableModel):
"""data model for use in QTableView, QListView, QComboBox, etc.
Attributes:
timestampFormat (unicode): formatting string for conversion of timestamps to QtCore.QDateTime.
Used in data method.
sortingAboutToStart (QtCore.pyqtSignal): emitted directly before sorting starts.
sortingFinished (QtCore.pyqtSignal): emitted, when sorting finished.
dtypeChanged (Signal(columnName)): passed from related ColumnDtypeModel
if a columns dtype has changed.
changingDtypeFailed (Signal(columnName, index, dtype)):
passed from related ColumnDtypeModel.
emitted after a column has changed it's data type.
dataChanged (Signal):
Emitted, if data has changed, e.x. finished loading, new columns added or removed.
It's not the same as layoutChanged.
Usefull to reset delegates in the view.
"""
_float_precisions = {
"float16": numpy.finfo(numpy.float16).precision - 2,
"float32": numpy.finfo(numpy.float32).precision - 1,
"float64": numpy.finfo(numpy.float64).precision - 1
}
"""list of int datatypes for easy checking in data() and setData()"""
_intDtypes = SupportedDtypes.intTypes() + SupportedDtypes.uintTypes()
"""list of float datatypes for easy checking in data() and setData()"""
_floatDtypes = SupportedDtypes.floatTypes()
"""list of bool datatypes for easy checking in data() and setData()"""
_boolDtypes = SupportedDtypes.boolTypes()
"""list of datetime datatypes for easy checking in data() and setData()"""
_dateDtypes = SupportedDtypes.datetimeTypes()
_timestampFormat = Qt.ISODate
sortingAboutToStart = Signal()
sortingFinished = Signal()
dtypeChanged = Signal(int, object)
changingDtypeFailed = Signal(object, QtCore.QModelIndex, object)
dataChanged = Signal()
dataFrameChanged = Signal()
def __init__(self, dataFrame=None, copyDataFrame=False):
"""the __init__ method.
Args:
dataFrame (pandas.core.frame.DataFrame, optional): initializes the model with given DataFrame.
If none is given an empty DataFrame will be set. defaults to None.
copyDataFrame (bool, optional): create a copy of dataFrame or use it as is. defaults to False.
If you use it as is, you can change it from outside otherwise you have to reset the dataFrame
after external changes.
"""
super(DataFrameModel, self).__init__()
self._dataFrame = pandas.DataFrame()
if dataFrame is not None:
self.setDataFrame(dataFrame, copyDataFrame=copyDataFrame)
self.dataChanged.emit()
self._dataFrameOriginal = None
self._search = DataSearch("nothing", "")
self.editable = False
def dataFrame(self):
"""getter function to _dataFrame. Holds all data.
Note:
It's not implemented with python properties to keep Qt conventions.
"""
return self._dataFrame
def setDataFrame(self, dataFrame, copyDataFrame=False):
"""setter function to _dataFrame. Holds all data.
Note:
It's not implemented with python properties to keep Qt conventions.
Raises:
TypeError: if dataFrame is not of type pandas.core.frame.DataFrame.
Args:
dataFrame (pandas.core.frame.DataFrame): assign dataFrame to _dataFrame. Holds all the data displayed.
copyDataFrame (bool, optional): create a copy of dataFrame or use it as is. defaults to False.
If you use it as is, you can change it from outside otherwise you have to reset the dataFrame
after external changes.
"""
if not isinstance(dataFrame, pandas.core.frame.DataFrame):
raise TypeError("not of type pandas.core.frame.DataFrame")
self.layoutAboutToBeChanged.emit()
if copyDataFrame:
self._dataFrame = dataFrame.copy()
else:
self._dataFrame = dataFrame
self._columnDtypeModel = ColumnDtypeModel(dataFrame)
self._columnDtypeModel.dtypeChanged.connect(self.propagateDtypeChanges)
self._columnDtypeModel.changeFailed.connect(
lambda columnName, index, dtype: self.changingDtypeFailed.emit(
columnName, index, dtype))
self.layoutChanged.emit()
self.dataChanged.emit()
self.dataFrameChanged.emit()
@Slot(int, object)
def propagateDtypeChanges(self, column, dtype):
self.dtypeChanged.emit(column, dtype)
@property
def timestampFormat(self):
"""getter to _timestampFormat"""
return self._timestampFormat
@timestampFormat.setter
def timestampFormat(self, timestampFormat):
"""setter to _timestampFormat. Formatting string for conversion of timestamps to QtCore.QDateTime
Raises:
AssertionError: if timestampFormat is not of type unicode.
Args:
timestampFormat (unicode): assign timestampFormat to _timestampFormat.
Formatting string for conversion of timestamps to QtCore.QDateTime. Used in data method.
"""
if not isinstance(timestampFormat, (unicode, )):
raise TypeError('not of type unicode')
#assert isinstance(timestampFormat, unicode) or timestampFormat.__class__.__name__ == "DateFormat", "not of type unicode"
self._timestampFormat = timestampFormat
def headerData(self, section, orientation, role=Qt.DisplayRole):
"""return the header depending on section, orientation and Qt::ItemDataRole
Args:
section (int): For horizontal headers, the section number corresponds to the column number.
Similarly, for vertical headers, the section number corresponds to the row number.
orientation (Qt::Orientations):
role (Qt::ItemDataRole):
Returns:
None if not Qt.DisplayRole
_dataFrame.columns.tolist()[section] if orientation == Qt.Horizontal
section if orientation == Qt.Vertical
None if horizontal orientation and section raises IndexError
"""
if role != Qt.DisplayRole:
return None
if orientation == Qt.Horizontal:
try:
label = self._dataFrame.columns.tolist()[section]
if label == section:
label = section
return label
except (IndexError, ):
return None
elif orientation == Qt.Vertical:
return section
def data(self, index, role=Qt.DisplayRole):
"""return data depending on index, Qt::ItemDataRole and data type of the column.
Args:
index (QtCore.QModelIndex): Index to define column and row you want to return
role (Qt::ItemDataRole): Define which data you want to return.
Returns:
None if index is invalid
None if role is none of: DisplayRole, EditRole, CheckStateRole, DATAFRAME_ROLE
if role DisplayRole:
unmodified _dataFrame value if column dtype is object (string or unicode).
_dataFrame value as int or long if column dtype is in _intDtypes.
_dataFrame value as float if column dtype is in _floatDtypes. Rounds to defined precision (look at: _float16_precision, _float32_precision).
None if column dtype is in _boolDtypes.
QDateTime if column dtype is numpy.timestamp64[ns]. Uses timestampFormat as conversion template.
if role EditRole:
unmodified _dataFrame value if column dtype is object (string or unicode).
_dataFrame value as int or long if column dtype is in _intDtypes.
_dataFrame value as float if column dtype is in _floatDtypes. Rounds to defined precision (look at: _float16_precision, _float32_precision).
_dataFrame value as bool if column dtype is in _boolDtypes.
QDateTime if column dtype is numpy.timestamp64[ns]. Uses timestampFormat as conversion template.
if role CheckStateRole:
Qt.Checked or Qt.Unchecked if dtype is numpy.bool_ otherwise None for all other dtypes.
if role DATAFRAME_ROLE:
unmodified _dataFrame value.
raises TypeError if an unhandled dtype is found in column.
"""
if not index.isValid():
return None
def convertValue(row, col, columnDtype):
value = None
if columnDtype == object:
value = self._dataFrame.ix[row, col]
elif columnDtype in self._floatDtypes:
value = round(float(self._dataFrame.ix[row, col]),
self._float_precisions[str(columnDtype)])
elif columnDtype in self._intDtypes:
value = int(self._dataFrame.ix[row, col])
elif columnDtype in self._boolDtypes:
# TODO this will most likely always be true
# See: http://stackoverflow.com/a/715455
# well no: I am mistaken here, the data is already in the dataframe
# so its already converted to a bool
value = bool(self._dataFrame.ix[row, col])
elif columnDtype in self._dateDtypes:
#print numpy.datetime64(self._dataFrame.ix[row, col])
value = pandas.Timestamp(self._dataFrame.ix[row, col])
value = QtCore.QDateTime.fromString(str(value),
self.timestampFormat)
#print value
# else:
# raise TypeError, "returning unhandled data type"
return value
row = self._dataFrame.index[index.row()]
col = self._dataFrame.columns[index.column()]
columnDtype = self._dataFrame[col].dtype
if role == Qt.DisplayRole:
# return the value if you wanne show True/False as text
if columnDtype == numpy.bool:
result = self._dataFrame.ix[row, col]
else:
result = convertValue(row, col, columnDtype)
elif role == Qt.EditRole:
result = convertValue(row, col, columnDtype)
elif role == Qt.CheckStateRole:
if columnDtype == numpy.bool_:
if convertValue(row, col, columnDtype):
result = Qt.Checked
else:
result = Qt.Unchecked
else:
result = None
elif role == DATAFRAME_ROLE:
result = self._dataFrame.ix[row, col]
else:
result = None
return result
def flags(self, index):
"""Returns the item flags for the given index as ored value, e.x.: Qt.ItemIsUserCheckable | Qt.ItemIsEditable
If a combobox for bool values should pop up ItemIsEditable have to set for bool columns too.
Args:
index (QtCore.QModelIndex): Index to define column and row
Returns:
if column dtype is not boolean Qt.ItemIsSelectable | Qt.ItemIsEnabled | Qt.ItemIsEditable
if column dtype is boolean Qt.ItemIsSelectable | Qt.ItemIsEnabled | Qt.ItemIsUserCheckable
"""
flags = super(DataFrameModel, self).flags(index)
if not self.editable:
return flags
col = self._dataFrame.columns[index.column()]
if self._dataFrame[col].dtype == numpy.bool:
flags |= Qt.ItemIsUserCheckable
else:
# if you want to have a combobox for bool columns set this
flags |= Qt.ItemIsEditable
return flags
def setData(self, index, value, role=Qt.DisplayRole):
"""Set the value to the index position depending on Qt::ItemDataRole and data type of the column
Args:
index (QtCore.QModelIndex): Index to define column and row.
value (object): new value.
role (Qt::ItemDataRole): Use this role to specify what you want to do.
Raises:
TypeError: If the value could not be converted to a known datatype.
Returns:
True if value is changed. Calls layoutChanged after update.
False if value is not different from original value.
"""
if not index.isValid() or not self.editable:
return False
if value != index.data(role):
self.layoutAboutToBeChanged.emit()
row = self._dataFrame.index[index.row()]
col = self._dataFrame.columns[index.column()]
#print 'before change: ', index.data().toUTC(), self._dataFrame.iloc[row][col]
columnDtype = self._dataFrame[col].dtype
if columnDtype == object:
pass
elif columnDtype in self._intDtypes:
dtypeInfo = numpy.iinfo(columnDtype)
if value < dtypeInfo.min:
value = dtypeInfo.min
elif value > dtypeInfo.max:
value = dtypeInfo.max
elif columnDtype in self._floatDtypes:
value = numpy.float64(value).astype(columnDtype)
elif columnDtype in self._boolDtypes:
value = numpy.bool_(value)
elif columnDtype in self._dateDtypes:
# convert the given value to a compatible datetime object.
# if the conversation could not be done, keep the original
# value.
if isinstance(value, QtCore.QDateTime):
value = value.toString(self.timestampFormat)
try:
value = pandas.Timestamp(value)
except Exception:
raise Exception, u"Can't convert '{0}' into a datetime".format(
value)
return False
else:
raise TypeError, "try to set unhandled data type"
self._dataFrame.set_value(row, col, value)
#print 'after change: ', value, self._dataFrame.iloc[row][col]
self.layoutChanged.emit()
return True
else:
return False
def rowCount(self, index=QtCore.QModelIndex()):
"""returns number of rows
Args:
index (QtCore.QModelIndex, optional): Index to define column and row. defaults to empty QModelIndex
Returns:
number of rows
"""
# len(df.index) is faster, so use it:
# In [12]: %timeit df.shape[0]
# 1000000 loops, best of 3: 437 ns per loop
# In [13]: %timeit len(df.index)
# 10000000 loops, best of 3: 110 ns per loop
# %timeit df.__len__()
# 1000000 loops, best of 3: 215 ns per loop
return len(self._dataFrame.index)
def columnCount(self, index=QtCore.QModelIndex()):
"""returns number of columns
Args:
index (QtCore.QModelIndex, optional): Index to define column and row. defaults to empty QModelIndex
Returns:
number of columns
"""
# speed comparison:
# In [23]: %timeit len(df.columns)
# 10000000 loops, best of 3: 108 ns per loop
# In [24]: %timeit df.shape[1]
# 1000000 loops, best of 3: 440 ns per loop
return len(self._dataFrame.columns)
def sort(self, columnId, order=Qt.AscendingOrder):
"""sort the model column
After sorting the data in ascending or descending order, a signal
`layoutChanged` is emitted.
Args:
columnId (int): columnIndex
order (Qt::SortOrder, optional): descending(1) or ascending(0). defaults to Qt.AscendingOrder
"""
self.layoutAboutToBeChanged.emit()
self.sortingAboutToStart.emit()
column = self._dataFrame.columns[columnId]
self._dataFrame.sort(column, ascending=not bool(order), inplace=True)
self.layoutChanged.emit()
self.sortingFinished.emit()
def setFilter(self, search):
"""apply a filter and hide rows.
The filter must be a `DataSearch` object, which evaluates a python
expression.
If there was an error while parsing the expression, the data will remain
unfiltered.
Args:
search(pandasqt.DataSearch): data search object to use.
Raises:
TypeError: An error is raised, if the given parameter is not a
`DataSearch` object.
"""
if not isinstance(search, DataSearch):
raise TypeError(
'The given parameter must an `pandasqt.DataSearch` object')
self._search = search
self.layoutAboutToBeChanged.emit()
if self._dataFrameOriginal is not None:
self._dataFrame = self._dataFrameOriginal
self._dataFrameOriginal = self._dataFrame.copy()
self._search.setDataFrame(self._dataFrame)
searchIndex, valid = self._search.search()
if valid:
self._dataFrame = self._dataFrame[searchIndex]
self.layoutChanged.emit()
else:
self.clearFilter()
self.layoutChanged.emit()
self.dataFrameChanged.emit()
def clearFilter(self):
"""clear all filters.
"""
if self._dataFrameOriginal is not None:
self.layoutAboutToBeChanged.emit()
self._dataFrame = self._dataFrameOriginal
self._dataFrameOriginal = None
self.layoutChanged.emit()
def columnDtypeModel(self):
"""Getter for a ColumnDtypeModel.
Returns:
ColumnDtypeModel
"""
return self._columnDtypeModel
def enableEditing(self, editable):
self.editable = editable
self._columnDtypeModel.setEditable(self.editable)
def dataFrameColumns(self):
return self._dataFrame.columns.tolist()
def addDataFrameColumn(self, columnName, dtype, defaultValue):
if not self.editable or dtype not in SupportedDtypes.allTypes():
return False
elements = self.rowCount()
columnPosition = self.columnCount()
newColumn = pandas.Series([defaultValue] * elements,
index=self._dataFrame.index,
dtype=dtype)
self.beginInsertColumns(QtCore.QModelIndex(), columnPosition - 1,
columnPosition - 1)
try:
self._dataFrame.insert(columnPosition,
columnName,
newColumn,
allow_duplicates=False)
except ValueError, e:
# columnName does already exist
return False
self.endInsertColumns()
self.propagateDtypeChanges(columnPosition, newColumn.dtype)
return True
def test_filterString(self):
datasearch = DataSearch("Test")
assert datasearch.filterString() == ''
datasearch = DataSearch('Test2', filterString='Hello World')
assert datasearch.filterString() == 'Hello World'
def test_repr(self, dataFrame):
datasearch = DataSearch("Test2")
assert str(datasearch).startswith('DataSearch(')
assert str(datasearch).endswith('Test2 ()')
def test_indexSearch(self, dataFrame):
datasearch = DataSearch('Test', dataFrame=dataFrame)
filterString = 'indexSearch([0])'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert valid
assert sum(ret) == 1
filterString = 'indexSearch([0, 2])'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert valid
assert sum(ret) == 2
filterString = 'indexSearch([0, 1, 2])'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert valid
assert sum(ret) == 3
filterString = 'indexSearch([99])'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert valid
assert sum(ret) == 0
def test_freeSearch(self, dataFrame):
datasearch = DataSearch('Test', dataFrame=dataFrame)
filterString = 'freeSearch("0")'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert valid
assert sum(ret) == 2
filterString = 'freeSearch(1)'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert not valid
filterString = 'freeSearch("12")'
datasearch.setFilterString(filterString)
ret, valid = datasearch.search()
assert valid
assert sum(ret) == 1
