PyQt5 Tutorial: Comprehensive Guide to Developing Desktop GUIs in Python

PyQt is one of the most popular frameworks for building desktop GUI apps in Python. It provides Python bindings for Qt – a powerful cross-platform GUI toolkit. In this comprehensive PyQt5 tutorial, you‘ll learn the basics of creating graphical interfaces as well as more advanced concepts to develop fully-featured PyQt applications.

Additional Qt Widgets

While we have covered some common widgets earlier, PyQt offers many more options for various UI requirements:

QTableView

This displays data in a tabular format from data models:

table = QTableView()
data_model = QStandardItemModel(5,5) 
table.setModel(data_model)

We can enable sorting, filtering, resizing columns easily.

QTreeView

This shows data hierarchies from models: 

model = QDirModel()
tree = QTreeView()  
tree.setModel(model)

Useful for displaying folder structures, organizational charts etc.

Qt Charts

The QtCharts module provides ways to visualize data:

from PyQt5.QtCharts import QChart, QBarSeries

series = QBarSeries()
series.append([1, 2, 3, 4])

chart = QChart()
chart.addSeries(series)

Other chart types like pie, line, scatter are also available.

Databases and Model Views

PyQt provides extensive database support through the QtSql module and auto-generated model/view classes.

We can configure a SQLite connection by:

import sqlite3
from PyQt5.QtSql import QSqlDatabase

db = QSqlDatabase.addDatabase("QSQLITE")
db.setDatabaseName("mydb.sqlite")

if not db.open():
    print("Cannot open database")
    sys.exit(1)

Once connected, we can execute SQL statements directly:

query = QSqlQuery()
query.exec_("CREATE TABLE IF NOT EXISTS Contacts(...)")

For easier data handling, QtSql provides model/view classes that can be linked with UI elements like QTableView.

So using SQLite and predefined models simplifies common database use cases.

Advanced GUI Design Concepts

As we build more complex UIs, additional PyQt capabilities help enhance application ergonomics and workflow.

Model/View Architecture

This separates data from presentation logic for easier maintenance.

For example, QTabWidget shows different views bound to the same underlying `QDirModel**: 

model = QDirModel()

tree_view = QTreeView(model)
table_view = QTableView(model)  

tab_widget = QTabWidget()
tab_widget.addTab(tree_view, "Tree")  
tab_widget.addTab(table_view, "Table")

So data can be rendered via multiple compatible views.

Widget Animations

We can create interesting UI effects using QPropertyAnimation: 

animation = QPropertyAnimation(widget, b"geometry") 
animation.setDuration(1000)

animation.setStartValue(QRect(0, 0, 100, 30))
animation.setEndValue(QRect(250, 250, 100, 30)) 

animation.start()

This smoothly transitions geometry from (0,0) to (250, 250) over 1 second.

Background Threads

Tasks like data loading can cause the UI to freeze if on main thread. QThread enables multi-threading:

class Loader(QThread):
    def run(self):
        result = load_data()  

        self.signal.emit(result)  

thread = Loader()
thread.signal.connect(show_result)
thread.start()

So the UI remains responsive as loading happens separately.

These are just some of the advanced capabilities that facilitate building sophisticated, dynamic and robust GUIs using Python and PyQt.

Now let‘s look at how we can package and deploy PyQt applications for end-users.

Deploying PyQt Applications

For shipping standalone executable versions of PyQt apps to end-users instead of raw code, we can leverage:

  • pyinstaller – Generates binaries for Windows, Linux, MacOS
  • cx_Freeze – Build executables by packaging Python interpreter
  • fbs – Build and deploy apps with plugins like notifications, auto-updates

The overall workflow is:

  1. Design application UI and logic
  2. Package into executable using above tools
  3. Copy required DLLs for Qt to work
  4. Ship installer to end-users

So deploying is straightforward. Additionally for enterprise use cases, Qt framework itself can be statically compiled into the application removing external dependencies.

This covers the major aspects of building desktop applications using PyQt ranging from basic concepts to advanced capabilities to final deployment.

With its breadth of features and multitude of widgets and modules PyQt continues to be one of the most popular and powerful ways to create native UI applications using Python.

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