In this Python GUI Frameworks tutorial, you'll see top 5 Python GUI Frameworks for Developers in 2020. Python Top GUI Frameworks For Learning in 2020, also iam going to show you the introduction, installation process with simple example on each GUI Framework. So as you know python is one the most top and trending programming language, and there are alot of gui frameworks that you can use, but in this article we want to know about the top gui frameworks in python. Python Top GUI Frameworks For 2020 | Introduction & Installation
In this article iam going to talk about Python Top GUI Frameworks For Learning , also iam going to show you the installation process with simple example on each GUI Framework. so as you know python is one the most top and trending programming language, and there are alot of gui frameworks that you can use, but in this article we want to know about the top gui frameworks in python, so now let’s get started.
Also you can check the complete tutorial for python GUI Frameworks
1: PyQt5 GUI Development Complete Tutorials
2: TKinter GUI Development For Beginners
3: Pyside2 GUI Development For Beginners
4: wxPython GUI Full Course For Beginners
PyQt5 is a Graphical User Interface GUI Framework for python. It is one of the best, powerful and popular Python GUI Framework. PyQt is a binding of Qt5 C++ a gui framework for c++ developers. You can create program in pyqt5 using coding or a using qt designer a visual dialog that you can drag and drop UI widgets
PyQt5 is a free Python bindings software open-source widget-toolkit Qt, implemented for cross-platform application development framework. In the free version, certain features may not be available but if your application is open source then you can use it under a free license. PyQt is available on Windows, MacOSX, Linux, Android iOS and Raspberry Pi.
pip install pyqt5
from PyQt5.QtWidgets import QApplication, QMainWindow import sys from PyQt5 import QtGui class Window(QMainWindow): def __init__(self): super().__init__() self.setGeometry(300, 300, 500, 400) self.setWindowTitle("PyQt5 Window") self.show() App = QApplication(sys.argv) window = Window() sys.exit(App.exec())
Run the above code and this will be the result2: TKinter
Tkinter is the most popular programming package for graphical user interface or desktop apps. It is so named because of its simplicity. Tkinter is the combination of Tk and Python’s standard GUI framework.
Tkinter It provides diverse widgets, such as labels, buttons, and text boxes used in a graphical user interface application. The Button control also called widgets are used to display buttons in developed application while the Canvas widget is used to draw shapes (lines, ovals, polygon…) in your application. It is a built in library for python.
It is a built in library in python, no need to install
from tkinter import * class Root(Tk): def __init__(self): super(Root, self).__init__() self.title("Python Tkinter First Window") self.minsize(640, 400) root = Root() root.mainloop()
Run the above code and this will be the result3: Pyside2 (Qt For Python)
Qt for Python offers the official Python bindings for Qt (PySide2), enabling the use of its APIs in Python applications, and a binding generator tool (Shiboken2) which can be used to expose C++ projects into Python.
Qt for Python is available under the LGPLv3/GPLv3 and the Qt commercial license.
pip install PySide2
from PySide2.QtWidgets import QApplication,QWidget import sys import time class Window(QWidget): def __init__(self): super().__init__() self.setWindowTitle("Pyside2 Simple Application") self.setGeometry(300,300,500,400) myApp = QApplication(sys.argv) window = Window() window.show() myApp.exec_() sys.exit(0)
So run the above code and this will be the result4: Kivy
Kivy is Open source Python library for rapid development of applications that make use of innovative user interfaces, such as multi-touch apps. There are some features for kivy like.
Before installing Kivy, you need to install the following dependency and after that you can install kivy
pip install docutils pygments pypiwin32 kivy.deps.sdl2 kivy.deps.glew
pip install Kivy
from kivy.app import App from kivy.uix.button import Button class TestApp(App): def build(self): return Button(text = "Hello Kivy World") TestApp().run()
So run the above code and this will be the result5: wxPython
wxPython is a cross-platform GUI toolkit for the Python programming language. It allows Python programmers to create programs with a robust, highly functional graphical user interface, simply and easily. It is implemented as a set of Python extension modules that wrap the GUI components of the popular wxWidgets cross platform library, which is written in C++.
Like Python and wxWidgets, wxPython is Open Source, which means that it is free for anyone to use and the source code is available for anyone to look at and modify. And anyone can contribute fixes or enhancements to the project.
wxPython is a cross-platform toolkit. This means that the same program will run on multiple platforms without modification. Currently Supported platforms are Microsoft Windows, Mac OS X and macOS, and Linux.
pip install wxPython
import wx class MyFrame(wx.Frame): def __init__(self, parent, title): super(MyFrame, self).__init__(parent, title=title, size = (400,300)) self.panel = MyPanel(self) class MyPanel(wx.Panel): def __init__(self, parent): super(MyPanel, self).__init__(parent) class MyApp(wx.App): def OnInit(self): self.frame = MyFrame(parent=None, title="wxPython Window") self.frame.Show() return True app = MyApp() app.MainLoop()
So now run the above code and this will be the result
Also you can watch the complete video tutorial for this article (Python Top GUI Frameworks For Learning )
Python tutorial for beginners - Learn Python for Machine Learning and Web Development. Can Python be used for machine learning? Python is widely considered as the preferred language for teaching and learning ML (Machine Learning). Can I use Python for web development? Python can be used to build server-side web applications. Why Python is suitable for machine learning? How Python is used in AI? What language is best for machine learning?Python tutorial for beginners - Learn Python for Machine Learning and Web Development
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In this "Python Tutorial: Data Science vs. Web Development" to provide a comparison on the two completely different purposes of using Python language and help understand that it is not necessary to know Python as a web programming language for doing data science in Python.
Python programming has various frameworks and features to expand in web application development, graphical user interfaces, data analysis, data visualization, etc. Python programming language might not be an ideal choice for web application development, but is extensively used by many organizations for evaluating large datasets, for data visualization, for running data analysis or prototyping. Python programming language is gaining traction amongst users for data science whilst being outmoded as a web programming language. The idea of this blog post is to provide a comparison on the two completely different purposes of using Python language and help understand that it is not necessary to know Python as a web programming language for doing data science in Python.Python for Data Science :
Organizations of all sizes and industries — from the top financial institutions to the smallest big data start-ups are using Python programming language to run their business.
Python language is among the popular data science programming languages not only with the top big data companies but also with the tech start up crowd. Python language ranks among the top 10 programming languages to learn in 2019.
Python language comes in the former category and is finding increased adoption in numerical computations, machine learning and several data science applications. Python language can do anything, excluding performance dependent and low level stuff. The best bet to use Python programming language is for data analysis and statistical computations. Learning Python programming for web development requires programmers to master various web frameworks like Django that can help the build websites whereas learning Python for data science requires data scientists to learn the usage of regular expressions, get working with the scientific libraries and master the data visualization concepts. With completely different purposes, programmers or professionals who are not knowledgeable about web programming concepts with Python language can easily go ahead and pursue data science in Python programming language without any difficulty.
Python is a 23-year-old powerful expressive dynamic programming language where a programmer can write the code once and execute it without using a separate compiler for the purpose. Python in web development supports various programming paradigms such as structured programming, functional programming and object oriented programming. Python language code can be easily embedded into various existing web application that require a programming interface. However, Python language is a preeminent choice for academic, research and scientific applications which need faster execution and precise mathematical calculations.
Python web programming requires programmers to learn about the various python web development frameworks, which can be intimidating because the documentation available for the python web development frameworks might be somewhat difficult to understand. However, it is undeniable that to develop a dynamic website or a web application using Python language, learning a web framework is essential.Python Web Development Frameworks
There are several Python web application frameworks available for free like-
Django is the python web development framework for perfectionists with deadlines. Python web development with django is best suited for developing database driven web applications with attractive features like automatic admin interface and a templating system. For web development projects that don’t require extensive features, Django may be an overkill because of its confusing file system and strict directory structure. Some companies that are using python web development with django are The New York Times, Instagram, and Pinterest.
It is a simple and lightweight solution for beginners who want to get started with developing single-page web applications. This framework does not support for validation, data abstraction layer and many other components that various other frameworks include. It is not a full stack framework and is used only in the development of small websites.
It emphasizes on Pythonic conventions so that programmers can build web applications just the way they would do it using object oriented Python programming. CherryPy is the base template for other popular full stack frameworks like TurboBears and Web2py.
There are so many other web frameworks like Pyramid, Bottle, and Pylons etc. but regardless of the fact, whichever web framework a python programmer uses, the challenge is that he/she needs to pay close attention to detailing on the tutorials and documentation.Why Web Development with Python is an impractical choice?
Python programming language probably is an impractical choice for being chosen as a web programming language –
Python for web development requires non-standard and expensive hosting particularly when programmers use popular python web frameworks for building websites. With PHP language being so expedient for web programming, most of the users are not interested in investing in Python programming language for web development.
Python language for web development is not a commonly demanded skill unlike demand for other web development languages like PHP, Java or Ruby on Rails. Python for Data science is gaining traction and is the most sought after skill companies are looking for in data scientists, with its increased adoption in machine learning and various other data science applications.
Python for web development has come a long way but it does not have a steep learning curve as compared to other web programming languages like PHP.
Why Python for Data Science is the best fit?
Python programming is the core technology that powers big data, finance, statistics and number crunching with English like syntax. The recent growth of the rich Python data science ecosystem with multiple packages for Machine learning, natural language processing, data visualization, data exploration, data analysis and data mining is resulting in Pythonification of the data science community. Today, Python data science language has all the nuts and bolts for cleaning, transforming, processing and crunching big data. Python is the most in-demand skill for data scientist job role. A data scientist with python programming skills in New York earns an average salary of $180,000Why data scientists love doing data science in Python language?
Data Scientists like to work in a programming environment that can quickly prototype by helping them jot down their ideas and models easily. They like to get their stuff done by analysing huge datasets to draw conclusions. Python programming is the most versatile and capable all-rounder for data science applications as it helps data scientists do all this productively by taking optimal minimal time for coding, debugging, executing and getting the results.
The real value of a great enterprise data scientist is to use various data visualizations that can help communicate the data patterns and predictions to various stakeholders of the business effectively, otherwise it is just a zero-sum game. Python has almost every aspect of scientific computing with high computational intensity which makes it a supreme choice for programming across different data science applications, as programmers can do all the development and analysis in one language. Python for data science links between various units of a business and provides a direct medium for data sharing and processing language.
Data analysis and Python programming language go hand in hand. If you have taken a decision to learn Data Science in Python language, then the next question in your mind would be –What are the best data science in Python libraries that do most of the data analysis task? Here are top data analysis libraries in Python used by enterprise data scientists across the world-
It is the foundation base for the higher level tools built in Python programming language. This library cannot be used for high level data analysis but in-depth understanding of array oriented computing in NumPy helps data scientists use the Pandas library effectively.
SciPy is used for technical and scientific computing with various modules for integration, special functions, image processing, interpolation, linear algebra, optimizations, ODE solvers and various other tasks. This library is used to work with NumPy arrays with various efficient numerical routines.
This is the best library for doing data munging as this library makes it easier to handle missing data, supports automatic data alignment, supports working with differently indexed data gathered from multiple data sources.
This is a popular machine learning library with various regression, classification and clustering algorithms with support for gradient boosting, vector machines, naïve Bayes, and logistic regression. This library is designed to interoperate with NumPy and SciPy.
It is a 2D plotting library with interactive features for zooming and panning for publication quality figures in different hard copy formats and in interactive environments across various platforms.
Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks. Introducing Tensorflow, Using Tensorflow, Introducing Keras, Using Keras, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Learning Deep Learning, Machine Learning with Neural Networks, Deep Learning Tutorial with PythonMachine Learning, Data Science and Deep Learning with Python
Explore the full course on Udemy (special discount included in the link): http://learnstartup.net/p/BkS5nEmZg
In less than 3 hours, you can understand the theory behind modern artificial intelligence, and apply it with several hands-on examples. This is machine learning on steroids! Find out why everyone’s so excited about it and how it really works – and what modern AI can and cannot really do.
In this course, we will cover:
• Deep Learning Pre-requistes (gradient descent, autodiff, softmax)
• The History of Artificial Neural Networks
• Deep Learning in the Tensorflow Playground
• Deep Learning Details
• Introducing Tensorflow
• Using Tensorflow
• Introducing Keras
• Using Keras to Predict Political Parties
• Convolutional Neural Networks (CNNs)
• Using CNNs for Handwriting Recognition
• Recurrent Neural Networks (RNNs)
• Using a RNN for Sentiment Analysis
• The Ethics of Deep Learning
• Learning More about Deep Learning
At the end, you will have a final challenge to create your own deep learning / machine learning system to predict whether real mammogram results are benign or malignant, using your own artificial neural network you have learned to code from scratch with Python.
Separate the reality of modern AI from the hype – by learning about deep learning, well, deeply. You will need some familiarity with Python and linear algebra to follow along, but if you have that experience, you will find that neural networks are not as complicated as they sound. And how they actually work is quite elegant!
This is hands-on tutorial with real code you can download, study, and run yourself.