def __init__(self, dataFrame=None, copyDataFrame=False, filePath=None):
"""
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.
filePath (str, optional): stores the original path for tracking.
"""
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
self._filePath = filePath
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_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 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 setFilter(self):
#filterIndex = eval(self.lineEditFilterCondition.text())
search = DataSearch("Test", self.lineEditFilterCondition.text())
self.dataTableView.view().model().setFilter(search)
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
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
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, filePath=None):
"""
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.
filePath (str, optional): stores the original path for tracking.
"""
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
self._filePath = filePath
@property
def filePath(self):
"""
Access to the internal _filepath property (could be None)
:return: qtpandas.models.DataFrameModel._filepath
"""
return self._filePath
def dataFrame(self):
"""
getter function to _dataFrame. Holds all data.
Note:
It's not implemented with python properties to keep Qt conventions.
Not sure why??
"""
return self._dataFrame
def setDataFrameFromFile(self, filepath, **kwargs):
"""
Sets the model's dataFrame by reading a file.
Accepted file formats:
- .xlsx (sheet1 is read unless specified in kwargs)
- .csv (comma separated unless specified in kwargs)
- .txt (any separator)
:param filepath: (str)
The path to the file to be read.
:param kwargs:
pandas.read_csv(**kwargs) or pandas.read_excel(**kwargs)
:return: None
"""
df = superReadFile(filepath, **kwargs)
self.setDataFrame(df, filePath=filepath)
def setDataFrame(self, dataFrame, copyDataFrame=False, filePath=None):
"""
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))
if filePath is not None:
self._filePath = filePath
self.layoutChanged.emit()
self.dataChanged.emit()
self.dataFrameChanged.emit()
@Slot(int, object)
def propagateDtypeChanges(self, column, dtype):
"""
Emits a dtypeChanged signal with the column and dtype.
:param column: (str)
:param dtype: ??
:return: None
"""
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, str):
raise TypeError('not of type unicode')
#assert isinstance(timestampFormat, unicode) or timestampFormat.__class__.__name__ == "DateFormat", "not of type unicode"
self._timestampFormat = timestampFormat
def rename(self, index=None, columns=None, **kwargs):
"""
Renames the dataframe inplace calling appropriate signals.
Wraps pandas.DataFrame.rename(*args, **kwargs) - overrides
the inplace kwarg setting it to True.
Example use:
renames = {'colname1':'COLNAME_1', 'colname2':'COL2'}
DataFrameModel.rename(columns=renames)
:param args:
see pandas.DataFrame.rename
:param kwargs:
see pandas.DataFrame.rename
:return:
None
"""
kwargs['inplace'] = True
self.layoutAboutToBeChanged.emit()
self._dataFrame.rename(index, columns, **kwargs)
self.layoutChanged.emit()
self.dataChanged.emit()
self.dataFrameChanged.emit()
def applyFunction(self, func):
"""
Applies a function to the dataFrame with appropriate signals.
The function must return a dataframe.
:param func: A function (or partial function) that accepts a dataframe as the first argument.
:return: None
:raise:
AssertionError if the func is not callable.
AssertionError if the func does not return a DataFrame.
"""
assert callable(func), "function {} is not callable".format(func)
self.layoutAboutToBeChanged.emit()
df = func(self._dataFrame)
assert isinstance(df, pandas.DataFrame
), "function {} did not return a DataFrame.".format(
func.__name__)
self._dataFrame = df
self.layoutChanged.emit()
self.dataChanged.emit()
self.dataFrameChanged.emit()
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(
"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):
"""
Sorts the model column
After sorting the data in ascending or descending order, a signal
`layoutChanged` is emitted.
:param: columnId (int)
the index of the column to sort on.
:param: 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_values(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(qtpandas.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 `qtpandas.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.
:return:
qtpandas.models.ColumnDtypeModel
"""
return self._columnDtypeModel
def enableEditing(self, editable=True):
"""
Sets the DataFrameModel and columnDtypeModel's
editable properties.
:param editable: bool
defaults to True,
False disables most editing methods.
:return:
None
"""
self.editable = editable
self._columnDtypeModel.setEditable(self.editable)
def dataFrameColumns(self):
"""
:return: list containing dataframe columns
"""
return self._dataFrame.columns.tolist()
def addDataFrameColumn(self, columnName, dtype=str, defaultValue=None):
"""
Adds a column to the dataframe as long as
the model's editable property is set to True and the
dtype is supported.
:param columnName: str
name of the column.
:param dtype: qtpandas.models.SupportedDtypes option
:param defaultValue: (object)
to default the column's value to, should be the same as the dtype or None
:return: (bool)
True on success, False otherwise.
"""
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 as e:
# columnName does already exist
return False
self.endInsertColumns()
self.propagateDtypeChanges(columnPosition, newColumn.dtype)
return True
def addDataFrameRows(self, count=1):
"""
Adds rows to the dataframe.
:param count: (int)
The number of rows to add to the dataframe.
:return: (bool)
True on success, False on failure.
"""
# don't allow any gaps in the data rows.
# and always append at the end
if not self.editable:
return False
position = self.rowCount()
if count < 1:
return False
if len(self.dataFrame().columns) == 0:
# log an error message or warning
return False
# Note: This function emits the rowsAboutToBeInserted() signal which
# connected views (or proxies) must handle before the data is
# inserted. Otherwise, the views may end up in an invalid state.
self.beginInsertRows(QtCore.QModelIndex(), position,
position + count - 1)
defaultValues = []
for dtype in self._dataFrame.dtypes:
if dtype.type == numpy.dtype('<M8[ns]'):
val = pandas.Timestamp('')
elif dtype.type == numpy.dtype(object):
val = ''
else:
val = dtype.type()
defaultValues.append(val)
for i in range(count):
self._dataFrame.loc[position + i] = defaultValues
self._dataFrame.reset_index()
self.endInsertRows()
return True
def removeDataFrameColumns(self, columns):
"""
Removes columns from the dataframe.
:param columns: [(int, str)]
:return: (bool)
True on success, False on failure.
"""
if not self.editable:
return False
if columns:
deleted = 0
errored = False
for (position, name) in columns:
position = position - deleted
if position < 0:
position = 0
self.beginRemoveColumns(QtCore.QModelIndex(), position,
position)
try:
self._dataFrame.drop(name, axis=1, inplace=True)
except ValueError as e:
errored = True
continue
self.endRemoveColumns()
deleted += 1
self.dataChanged.emit()
if errored:
return False
else:
return True
return False
def removeDataFrameRows(self, rows):
"""
Removes rows from the dataframe.
:param rows: (list)
of row indexes to removes.
:return: (bool)
True on success, False on failure.
"""
if not self.editable:
return False
if rows:
position = min(rows)
count = len(rows)
self.beginRemoveRows(QtCore.QModelIndex(), position,
position + count - 1)
removedAny = False
for idx, line in self._dataFrame.iterrows():
if idx in rows:
removedAny = True
self._dataFrame.drop(idx, inplace=True)
if not removedAny:
return False
self._dataFrame.reset_index(inplace=True, drop=True)
self.endRemoveRows()
return True
return False