Q&A: How can I enable editing on a QTableView in PySide6?

Modifying your model to allow editing of your data source
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In the Model-Views course, we covered Displaying Tabular Data in Qt ModelViews. This takes a data source, such as a list of lists, a NumPy array, or a Pandas DataFrame, and displays it in a QTableView. But often, displaying is just the first step—you also want your users to be able to add and edit the table, updating the underlying data object.

Reader Vic T asked:

I have been trying for a few days to get edit mode to work with a QTableView using Pandas for the model via QAbstractTableModel. Having searched all over the internet although, I found suggestions to implement the flags() method, but it doesn't seem to work.

This is correct. You need to implement the flags() method on your model to inform Qt that your model supports editing. To do this, your method needs to return the Qt.ItemFlag.ItemIsEditable flag, which you or together (using the pipe | character) with the other flags.

For example:

python
    def flags(self, index):
        return (
            Qt.ItemFlag.ItemIsSelectable
            | Qt.ItemFlag.ItemIsEnabled
            | Qt.ItemFlag.ItemIsEditable
        )

However, to get the editing working, you also need to implement a setData() method. This method is the model's interface between Qt and your data object. It takes care of making the changes to the data.

Remember, Qt views don't know anything about your data beyond what you tell them via the model. Likewise, they also don't know how to update your list, NumPy array, or DataFrame objects with the new data that has been provided. You need to handle that yourself!

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Below are some example setData() methods for lists of lists, NumPy, and Pandas. The only difference is how we index into the data object:

python
    def setData(self, index, value, role):
        if role == Qt.ItemDataRole.EditRole:
            self._data[index.row()][index.column()] = value
            return True
python
    def setData(self, index, value, role):
        if role == Qt.ItemDataRole.EditRole:
            self._data.iloc[index.row(),index.column()] = value
            return True
python
    def setData(self, index, value, role):
        if role == Qt.ItemDataRole.EditRole:
            self._data[index.row(), index.column()] = value
            return True

Notice that we first need to check whether the role is Qt.ItemDataRole.EditRole to determine if an edit is currently being made. After making the edit, we return True to confirm this.

If you try the above on your model, you should be able to edit the values. However, you'll notice that when editing, it clears the current value of the cell -- you have to start from an empty cell. To display the current value when editing, you need to modify the data() method to return the current value when the role is Qt.ItemDataRole.EditRole as well as when it is Qt.ItemDataRole.DisplayRole.

For example:

python
    def data(self, index, role=Qt.ItemDataRole.DisplayRole):
        if index.isValid():
            if role == Qt.ItemDataRole.DisplayRole or role == Qt.ItemDataRole.EditRole:
                value = self._data[index.row()][index.column()]
                return str(value)
python
    def data(self, index, role=Qt.ItemDataRole.DisplayRole):
        if index.isValid():
            if role == Qt.ItemDataRole.DisplayRole or role == Qt.ItemDataRole.EditRole:
                value = self._data.iloc[index.row(), index.column()]
                return str(value)
python
    def data(self, index, role=Qt.ItemDataRole.DisplayRole):
        if index.isValid():
            if role == Qt.ItemDataRole.DisplayRole or role == Qt.ItemDataRole.EditRole:
                value = self._data[index.row(), index.column()]
                return str(value)

That's it, you should now have a properly editable table view.

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Below are some complete working examples for list, NumPy, and Pandas, with PySide6.

List of Lists

The following examples use a nested list of lists as a data source:

python
from PySide6.QtCore import QAbstractTableModel, Qt
from PySide6.QtWidgets import QApplication, QMainWindow, QTableView

class ListModel(QAbstractTableModel):
    def __init__(self, data):
        super().__init__()
        self._data = data

    def rowCount(self, index):
        return len(self._data)

    def columnCount(self, index):
        # The following takes the first sub-list, and returns
        # the length (only works if all rows are an equal length)
        return len(self._data[0])

    def data(self, index, role=Qt.ItemDataRole.DisplayRole):
        if index.isValid():
            if role == Qt.ItemDataRole.DisplayRole or role == Qt.ItemDataRole.EditRole:
                value = self._data[index.row()][index.column()]
                return str(value)

    def setData(self, index, value, role):
        if role == Qt.ItemDataRole.EditRole:
            self._data[index.row()][index.column()] = value
            return True
        return False

    def flags(self, index):
        return (
            Qt.ItemFlag.ItemIsSelectable
            | Qt.ItemFlag.ItemIsEnabled
            | Qt.ItemFlag.ItemIsEditable
        )

class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()

        self.table = QTableView()

        data = [
            [1, 9, 2],
            [1, 0, -1],
            [3, 5, 2],
            [3, 3, 2],
            [5, 8, 9],
        ]

        self.model = ListModel(data)
        self.table.setModel(self.model)

        self.setCentralWidget(self.table)

app = QApplication([])
window = MainWindow()
window.show()
app.exec_()

Pandas DataFrame

The following examples use a Pandas DataFrame, adding column headings from the column names:

python
import pandas as pd
from PySide6.QtCore import QAbstractTableModel, Qt
from PySide6.QtWidgets import QApplication, QMainWindow, QTableView

class PandasModel(QAbstractTableModel):
    def __init__(self, data):
        super().__init__()
        self._data = data

    def rowCount(self, index):
        return self._data.shape[0]

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

    def data(self, index, role=Qt.ItemDataRole.DisplayRole):
        if index.isValid():
            if role == Qt.ItemDataRole.DisplayRole or role == Qt.ItemDataRole.EditRole:
                value = self._data.iloc[index.row(), index.column()]
                return str(value)

    def setData(self, index, value, role):
        if role == Qt.ItemDataRole.EditRole:
            self._data.iloc[index.row(), index.column()] = value
            return True
        return False

    def headerData(self, col, orientation, role):
        if (
            orientation == Qt.Orientation.Horizontal
            and role == Qt.ItemDataRole.DisplayRole
        ):
            return self._data.columns[col]

    def flags(self, index):
        return (
            Qt.ItemFlag.ItemIsSelectable
            | Qt.ItemFlag.ItemIsEnabled
            | Qt.ItemFlag.ItemIsEditable
        )

class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()

        self.table = QTableView()

        data = pd.DataFrame(
            [
                [1, 9, 2],
                [1, 0, -1],
                [3, 5, 2],
                [3, 3, 2],
                [5, 8, 9],
            ],
            columns=["A", "B", "C"],
        )

        self.model = PandasModel(data)
        self.table.setModel(self.model)

        self.setCentralWidget(self.table)

app = QApplication([])
window = MainWindow()
window.show()
app.exec_()

NumPy Array

The following examples use a NumPy array for their data source. The array will only accept valid values (in this case, integers) when setting, so we must first coerce the value to an integer before setting it on the error. If you enter something which isn't a valid integer (e.g. jdskfjdskjfndsf ), the int() call will throw a ValueError, which we catch. By returning False when this exception is thrown, we cancel the edit:

python
import numpy as np
from PySide6.QtCore import QAbstractTableModel, Qt
from PySide6.QtWidgets import QApplication, QMainWindow, QTableView

class NumPyModel(QAbstractTableModel):
    def __init__(self, data):
        super().__init__()
        self._data = data

    def rowCount(self, index):
        return self._data.shape[0]

    def columnCount(self, index):
        return self._data.shape[1]

    def data(self, index, role=Qt.ItemDataRole.DisplayRole):
        if index.isValid():
            if role == Qt.ItemDataRole.DisplayRole or role == Qt.ItemDataRole.EditRole:
                value = self._data[index.row(), index.column()]
                return str(value)

    def setData(self, index, value, role):
        if role == Qt.ItemDataRole.EditRole:
            try:
                value = int(value)
            except ValueError:
                return False
            self._data[index.row(), index.column()] = value
            return True
        return False

    def flags(self, index):
        return (
            Qt.ItemFlag.ItemIsSelectable
            | Qt.ItemFlag.ItemIsEnabled
            | Qt.ItemFlag.ItemIsEditable
        )

class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()

        self.table = QTableView()

        data = np.array(
            [
                [1, 9, 2],
                [1, 0, -1],
                [3, 5, 2],
                [3, 3, 2],
                [5, 8, 9],
            ]
        )

        self.model = NumPyModel(data)
        self.table.setModel(self.model)

        self.setCentralWidget(self.table)

app = QApplication([])
window = MainWindow()
window.show()
app.exec()

That's all for now!

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More info Get the book

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Martin Fitzpatrick

Q&A: How can I enable editing on a QTableView in PySide6? was written by Martin Fitzpatrick with contributions from Leo Well .

Martin Fitzpatrick has been developing Python/Qt apps for 8 years. Building desktop applications to make data-analysis tools more user-friendly, Python was the obvious choice. Starting with Tk, later moving to wxWidgets and finally adopting PyQt. Martin founded PythonGUIs to provide easy to follow GUI programming tutorials to the Python community. He has written a number of popular Python books on the subject.