I'm very new to PyQt and I am struggling to populate a QTableView control.
My code is the following:
def data_frame_to_ui(self, data_frame):
"""
Displays a pandas data frame into the GUI
"""
list_model = QtGui.QStandardItemModel()
i = 0
for val in data_frame.columns:
# for the list model
if i > 0:
item = QtGui.QStandardItem(val)
#item.setCheckable(True)
item.setEditable(False)
list_model.appendRow(item)
i += 1
self.ui.profilesListView.setModel(list_model)
# for the table model
table_model = QtGui.QStandardItemModel()
# set table headers
table_model.setColumnCount(data_frame.columns.size)
table_model.setHorizontalHeaderLabels(data_frame.columns.tolist())
self.ui.profileTableView.horizontalHeader().setStretchLastSection(True)
# fill table model data
for row_idx in range(10): #len(data_frame.values)
row = list()
for col_idx in range(data_frame.columns.size):
val = QtGui.QStandardItem(str(data_frame.values[row_idx][col_idx]))
row.append(val)
table_model.appendRow(row)
# set table model to table object
self.ui.profileTableView.setModel(table_model)
Actually in the code I succeed to populate a QListView, but the values I set to the QTableView are not displayed, also you can see that I truncated the rows to 10 because it takes forever to display the hundreds of rows of the data frame.
So, What is the fastest way to populate the table model from a pandas data frame?
Thanks in advance.
Modin is a new library designed to accelerate Pandas by automatically distributing the computation across all of the system's available CPU cores. With that, Modin claims to be able to get nearly linear speedup to the number of CPU cores on your system for Pandas DataFrames of any size.
The query function seams more efficient than the loc function. DF2: 2K records x 6 columns. The loc function seams much more efficient than the query function.
Dask runs faster than pandas for this query, even when the most inefficient column type is used, because it parallelizes the computations. pandas only uses 1 CPU core to run the query. My computer has 4 cores and Dask uses all the cores to run the computation.
While the process takes 16.62 seconds for Pandas, Datatable is only at 6.55 seconds. Overall Datatable is 2 times faster than Pandas.
Personally I would just create my own model class to make handling it somewhat easier.
For example:
import sys
from PyQt4 import QtCore, QtGui
Qt = QtCore.Qt
class PandasModel(QtCore.QAbstractTableModel):
def __init__(self, data, parent=None):
QtCore.QAbstractTableModel.__init__(self, parent)
self._data = data
def rowCount(self, parent=None):
return len(self._data.values)
def columnCount(self, parent=None):
return self._data.columns.size
def data(self, index, role=Qt.DisplayRole):
if index.isValid():
if role == Qt.DisplayRole:
return QtCore.QVariant(str(
self._data.iloc[index.row()][index.column()]))
return QtCore.QVariant()
if __name__ == '__main__':
application = QtGui.QApplication(sys.argv)
view = QtGui.QTableView()
model = PandasModel(your_pandas_data)
view.setModel(model)
view.show()
sys.exit(application.exec_())
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