Introduction:
Welcome to another exciting tutorial on PyQt5! In this blog post, we will explore how to create a graphical user interface (GUI) using PyQt5 to display a Pandas dataframe as a table. We’ll focus on implementing column filtering options, similar to the filters found in Microsoft Excel. By the end of this tutorial, you’ll be able to build a PyQt5 application that allows users to filter data based on multiple columns, providing a powerful data exploration and analysis tool.
Table of Contents:
- Setting Up the Environment
-
Building the GUI
- 1. Creating the Main Window
- 2. Loading and Displaying Data
- 3. Adding Filter Options
-
Implementing Column Filtering
- 1. Customizing the Proxy Model
- 2. Handling Filter Operations
-
Enhancing the User Experience
- 1. Highlighting Filtered Columns
- 2. Adding Personalization and Interactivity
Setting Up the Environment
Before we dive into the implementation, we need to set up our development environment. We’ll guide you through the process of installing the necessary dependencies and creating a new PyQt5 project.
Building the GUI
In this section, we’ll start building our GUI by creating the main window and setting up the basic structure. We’ll also load a sample dataset and display it as a table using PyQt5’s QTableView widget.
Creating the Main Window
We’ll walk you through the steps of creating the main window for our application using the QMainWindow class. We’ll add necessary widgets and set up the layout to accommodate the table and filtering options.
Loading and Displaying Data
To populate our table, we’ll load a sample dataset using Pandas. We’ll create a custom model, PandasModel, that inherits from the QAbstractTableModel class. This model will handle data manipulation and provide data to the table view.
Adding Filter Options
To enable filtering, we’ll add a QLineEdit widget for text-based filtering and a QComboBox widget for column selection. We’ll connect signals and slots to update the filter based on user input.
Implementing Column Filtering
In this section, we’ll dive into the implementation details of column filtering. We’ll create a custom proxy model, CustomProxyModel, that inherits from QSortFilterProxyModel. This model will handle the filtering logic based on user input and interact with the main model.
Customizing the Proxy Model
We’ll override the filterAcceptsRow method in CustomProxyModel to implement our custom filtering logic. We’ll retrieve the texts of each column and check if they match the filter condition. We’ll also add methods to set and update filters.
Handling Filter Operations
We’ll connect signals and slots to handle filter operations triggered by user actions. We’ll update the filter based on text input, column selection, and the “All” option. We’ll also implement font customization to highlight filtered columns.
Enhancing the User Experience
In this section, we’ll focus on improving the user experience and adding personalization to our application. We’ll explore how to highlight filtered columns visually and introduce interactive elements to make the GUI more engaging.
Highlighting Filtered Columns
To provide visual cues to the user, we can highlight the columns that are currently being filtered. We’ll customize the appearance of the filtered columns by changing their background color.
To achieve this, we’ll modify the CustomProxyModel
class by adding a method called getFilteredColumns()
. This method will return a set of column indexes that match the current filter criteria. We’ll also override the data()
method to customize the appearance of the filtered columns.
Here’s the updated code for the CustomProxyModel
class:
class CustomProxyModel(QtCore.QSortFilterProxyModel):
def init(self, parent=None):
super().init(parent)
self.filterText = “”
self.filterColumn = -1
def setFilterText(self, text):
self.filterText = text
self.invalidateFilter()
def setFilterColumn(self, column):
self.filterColumn = column
self.invalidateFilter()
def getFilteredColumns(self):
filteredColumns = set()
for row in range(self.sourceModel().rowCount()):
index = self.sourceModel().index(row, self.filterColumn)
if self.filterAcceptsRow(row, index):
filteredColumns.add(index.column())
return filteredColumns
def filterAcceptsRow(self, sourceRow, sourceParent):
if self.filterText == "":
return True
if self.filterColumn == -1:
return False
index = self.sourceModel().index(sourceRow, self.filterColumn, sourceParent)
value = index.data(QtCore.Qt.DisplayRole)
return self.filterText.lower() in value.lower()
def data(self, index, role=QtCore.Qt.DisplayRole):
if role == QtCore.Qt.BackgroundRole:
if index.column() in self.getFilteredColumns():
return QtGui.QColor(255, 255, 0) # Yellow background color for filtered columns
return super().data(index, role)
In the data()
method, we check if the BackgroundRole
is requested for the given index. If the index’s column is in the set of filtered columns returned by getFilteredColumns()
, we return a yellow background color. Otherwise, we call the data()
method of the base class to handle other roles and non-filtered columns.
Adding Personalization and Interactivity
To enhance the user experience and make the application more interactive, we can introduce additional features such as:
- Allowing users to change the font and font size of the displayed table.
- Providing options to sort the table based on a specific column.
- Adding pagination functionality to handle large datasets.
These features can be implemented by extending the functionality of our existing code. Here are some steps you can follow:
- Font Customization:
- Add a font selection widget (e.g., QFontComboBox) to the main window.
- Connect the font selection widget’s
currentFontChanged
signal to a slot that updates the font of the QTableView widget.
- Sorting:
- Add a QComboBox widget for column selection and sorting order (ascending/descending) to the main window.
- Connect the sorting options’ signals to slots that update the sorting behavior of the QTableView widget.
- Pagination:
- Add navigation buttons (e.g., “Next” and “Previous”) to the main window.
- Implement logic to handle pagination by updating the displayed rows based on the current page number.
By implementing these features, you can provide users with more control over the displayed data, making the application more interactive and personalized.
Conclusion:
In this tutorial, we have explored how to create an interactive Excel-like table in PyQt. We started by designing the GUI using Qt Designer and then wrote the necessary code to implement the desired functionality. We learned about using QTableView and QSortFilterProxyModel to display and filter data efficiently. We also covered how to customize the appearance of filtered columns and discussed additional features for personalization and interactivity. By following this tutorial, you should now have a good foundation for creating data-driven applications with tables in PyQt. Remember to consult the official PyQt documentation and explore other PyQt resources to deepen your understanding and explore more advanced topics.