Fastest way to populate QTableView from Pandas dat

问题:

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.

回答1:

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.values[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_())

回答2:

This works:

class PandasModel(QtCore.QAbstractTableModel):
    """
    Class to populate a table view with a pandas dataframe
    """
    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=QtCore.Qt.DisplayRole):
        if index.isValid():
            if role == QtCore.Qt.DisplayRole:
                return str(self._data.values[index.row()][index.column()])
        return None

    def headerData(self, col, orientation, role):
        if orientation == QtCore.Qt.Horizontal and role == QtCore.Qt.DisplayRole:
            return self._data.columns[col]
        return None

Using it like this:

model = PandasModel(your_pandas_data_frame)
your_tableview.setModel(model)

I read here to avoid QVariant() from PyQT 4.6 on.

回答3:

I've found all of the proposed answers painfully slow for DataFrames with 1000+ rows. What works for me blazingly fast:

class PandasModel(QtCore.QAbstractTableModel):
    """
    Class to populate a table view with a pandas dataframe
    """
    def __init__(self, data, parent=None):
        QtCore.QAbstractTableModel.__init__(self, parent)
        self._data = data

    def rowCount(self, parent=None):
        return self._data.shape[0]

    def columnCount(self, parent=None):
        return self._data.shape[1]

    def data(self, index, role=QtCore.Qt.DisplayRole):
        if index.isValid():
            if role == QtCore.Qt.DisplayRole:
                return str(self._data.iloc[index.row(), index.column()])
        return None

    def headerData(self, col, orientation, role):
        if orientation == QtCore.Qt.Horizontal and role == QtCore.Qt.DisplayRole:
            return self._data.columns[col]
        return None

回答4:

There is actually some code in pandas supporting integration with Qt.

At the time of writing this answer, the latest pandas version is 0.18.1 and you could do:

from pandas.sandbox.qtpandas import DataFrameModel, DataFrameWidget

That code seems to be coupled to PySide, however it should be relatively trivial to make it work with PyQt. Also, that code has been deprecated and the warning says that the module will be removed in the future.

Luckily they extracted that into a separated project in GitHub called pandas-qt:

https://github.com/datalyze-solutions/pandas-qt

I would try to use that before trying to roll out my own model and view implementation.