The Station is a weekly newsletter dedicated to all things transportation. Sign up here — just click The Station — to receive it every weekend in your inbox. Hi friends and new readers, welcome back to The Station, a newsletter dedicated to all the present and future ways people and packages move from Point A to […]

TechCrunch is embarking on a major project to survey the venture capital investors of Europe and their cities. Our survey of VCs in Bucharest and Romania will capture how the country is faring, and what changes are being wrought amongst investors by the coronavirus pandemic. We’d like to know how Romania’s startup scene is evolving, […]

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This tutorial details how to set up social auth with Django and Django Allauth.

Game programming is a great way to learn how to program. You use many tools that you’ll see in the real world, plus you get to play a game to test your results! An ideal game to start your Python game programming journey is rock paper scissors. In this tutorial, you’ll learn how to: Code your own rock paper scissors game Take in user input with input() Play several games in a row using a while loop Clean up your code with Enum and functions Define more complex rules with a dictionary Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you’ll need to take your Python skills to the next level. What Is Rock Paper Scissors? You may have played rock paper scissors before. Maybe you’ve used it to decide who pays for dinner or who gets first choice of players for a team. If you’re unfamiliar, rock paper scissors is a hand game for two or more players. Participants say “rock, paper, scissors” and then simultaneously form their hands into the shape of a rock (a fist), a piece of paper (palm facing downward), or a pair of scissors (two fingers extended). The rules are straightforward: Rock smashes scissors. Paper covers rock. Scissors cut paper. Now that you have the rules down, you can start thinking about how they might translate to Python code. Play a Single Game of Rock Paper Scissors in Python Using the description and rules above, you can make a game of rock paper scissors. Before you dive in, you’re going to need to import the module you’ll use to simulate the computer’s choices: import random Awesome! Now you’re able to use the different tools inside random to randomize the computer’s actions in the game. Now what? Since your users will also need to be able to choose their actions, the first logical thing you need is a way to take in user input. Take User Input Taking input from a user is pretty straightforward in Python. The goal here is to ask the user what they would like to choose as an action and then assign that choice to a variable: user_action = input("Enter a choice (rock, paper, scissors): ") This will prompt the user to enter a selection and save it to a variable for later use. Now that the user has selected an action, the computer needs to decide what to do. Make the Computer Choose A competitive game of rock paper scissors involves strategy. Rather than trying to develop a model for that, though, you can save yourself some time by having the computer select a random action. Random selections are a great way to have the computer choose a pseudorandom value. You can use random.choice() to have the computer randomly select between the actions: possible_actions = ["rock", "paper", "scissors"] computer_action = random.choice(possible_actions) This allows a random element to be selected from the list. You can also print the choices that the user and the computer made: print(f"\nYou chose {user_action}, computer chose {computer_action}.\n") Printing the user and computer actions can be helpful to the user, and it can also help you debug later on in case something isn’t quite right with the outcome. Determine a Winner Read the full article at https://realpython.com/python-rock-paper-scissors/ » [ Improve Your Python With ? Python Tricks ? – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Introduction I enjoy hearing from readers that have used concepts from this blog to solve their own problems. It always amazes me when I see examples where only a few lines of python code can solve a real business problem and save organizations a lot of time and money. I am also impressed when people figure out how to do this with no formal training - just with some hard work and willingness to persevere through the learning curve. This example comes from Mark Doll. I’ll turn it over to him to give his background: I have been learning/using Python for about 3 years to help automate business processes and reporting. I’ve never had any formal training in Python, but found it to be a reliable tool that has helped me in my work. Read on for more details on how Mark used Python to automate a very manual process of collecting and sorting Excel files to email to 100’s of users. The Problem Here’s Mark’s overview of the problem: A business need arose to send out emails with Excel attachments to a list of ~500 users and presented us with a large task to complete manually. Making this task harder was the fact that we had to split data up by user from a master Excel file to create their own specific file, then email that file out to the correct user. Imagine the time it would take to manually filter, cut and paste the data into a file, then save it and email it out - 500 times! Using this Python approach we were able to automate the entire process and save valuable time. I have seen this type of problem multiple times in my experience. If you don’t have experience with a programming language, then it can seem daunting. With Python, it’s very feasible to automate this tedious process. Here’s a graphical view of what Mark was able to do: Solving the Problem The first step is getting the imports in place: import datetime import os import shutil from pathlib import Path import pandas as pd import win32com.client as win32 Now we will set up some strings with the current date and our directory structure: ## Set Date Formats today_string = datetime.datetime.today().strftime('%m%d%Y_%I%p') today_string2 = datetime.datetime.today().strftime('%b %d, %Y') ## Set Folder Targets for Attachments and Archiving attachment_path = Path.cwd() / 'data' / 'attachments' archive_dir = Path.cwd() / 'archive' src_file = Path.cwd() / 'data' / 'Example4.xlsx' Let’s take a look at the data file we need to process: df = pd.read_excel(src_file) df.head() The next step is to group all of the CUSTOMER_ID transactions together. We start by doing a groupby on CUSTOMER_ID . customer_group = df.groupby('CUSTOMER_ID') It might not be apparent to you what customer_group is in this case. A loop shows how we can process this grouped object: for ID, group_df in customer_group: print(ID) A1000 A1001 A1002 A1005 Here’s the last group_df that shows all of the transactions for customer A1005: We have everything we need to create an Excel file for each customer and store in a directory for future use: ## Write each ID, Group to Individual Excel files and use ID to name each file with Today's Date attachments = [] for ID, group_df in customer_group: attachment = attachment_path / f'{ID}_{today_string}.xlsx' group_df.to_excel(attachment, index=False) attachments.append((ID, str(attachment))) The attachments list contains the customer ID and the full path to the file: [('A1000', 'c:\\Users\\chris\\notebooks\\2020-10\\data\\attachments\\A1000_01162021_12PM.xlsx'), ('A1001', 'c:\\Users\\chris\\notebooks\\2020-10\\data\\attachments\\A1001_01162021_12PM.xlsx'), ('A1002', 'c:\\Users\\chris\\notebooks\\2020-10\\data\\attachments\\A1002_01162021_12PM.xlsx'), ('A1005', 'c:\\Users\\chris\\notebooks\\2020-10\\data\\attachments\\A1005_01162021_12PM.xlsx')] To make the processing easier, we convert the list to a DataFrame: df2 = pd.DataFrame(attachments, columns=['CUSTOMER_ID', 'FILE']) The final data prep stage is to generate a list of files with their email addresses by merging the DataFrames together: email_merge = pd.merge(df, df2, how='left') combined = email_merge[['CUSTOMER_ID', 'EMAIL', 'FILE']].drop_duplicates() Which gives this simple DataFrame: We’ve gathered the list of customers, their emails and the attachments. Now we need to send an email with Outlook. Refer to this article for additional explanation of this code: # Email Individual Reports to Respective Recipients class EmailsSender: def __init__(self): self.outlook = win32.Dispatch('outlook.application') def send_email(self, to_email_address, attachment_path): mail = self.outlook.CreateItem(0) mail.To = to_email_address mail.Subject = today_string2 + ' Report' mail.Body = """Please find today's report attached.""" mail.Attachments.Add(Source=attachment_path) # Use this to show the email #mail.Display(True) # Uncomment to send #mail.Send() We can use this simple class to generate the emails and attach the Excel file. email_sender = EmailsSender() for index, row in combined.iterrows(): email_sender.send_email(row['EMAIL'], row['FILE']) The last step is to move the files to our archive directory: # Move the files to the archive location for f in attachments: shutil.move(f[1], archive_dir) Summary This example does a nice job of automating a highly manual process where someone likely did a lot of copying and pasting and manual file manipulation. I hope the solution that Mark developed can help you figure out how to automate some of the more painful parts of your job. I encourage you to use this example to identify similar challenges in your day to day work. Maybe you don’t have to work with 100’s of files but you might have a manual process you run once a week. Even if that process only takes 1 hour, use that as a jumping off point to figure out how to use Python to make it easier. There is no better way to learn Python than to apply it to one of your own problems. Thanks again to Mark for taking the time to walk us through this content example!

This article is an excerpt from the broader set of changes to our documentation in preparation for Zato. High-level overview Zato is a highly scalable, Python-based integration platform for APIs, SOA and microservices. It is used to connect distributed systems or data sources and to build API-first, backend applications. The platform is designed and built specifically with Python users in mind. Zato is used for enterprise, business integrations, data science, IoT and other scenarios that require integrations of multiple systems. Real-world, production Zato environments include: A platform for processing payments from consumer devices A system for a telecommunication operator integrating CRM, ERP, Billing and other systems as well as applications of the operator's external partners A data science system for processing of information related to securities transactions (FIX) A platform for public administration systems, helping achieve healthcare data interoperability through the integration of independent data sources, databases and health information exchanges (HIE) A global IoT platform integrating medical devices A platform to process events produced by early warning systems Backend e-commerce systems managing multiple suppliers, marketplaces and process flows B2B platforms to accept and process multi-channel orders in cooperation with backend ERP and CRM systems Platforms integrating real-estate applications, collecting data from independent data sources to present unified APIs to internal and external applications A system for the management of hardware resources of an enterprise cloud provider Online auction sites E-learning platforms Zato offers connectors to all the popular technologies, such as REST, SOAP, AMQP, IBM MQ, SQL, Odoo, SAP, HL7, Redis, MongoDB, WebSockets, S3 and many more. Running on premises, in the cloud, or under Docker, Kubernetes and other container technologies, Zato services are optimised for high performance - it is easily possible to run hundreds and thousands of services on typical server instances as offered by Amazon, Google Cloud, Azure or other cloud providers. Zato servers offer high availability and no-downtime deployment. Servers form clusters that are used to scale systems both horizontally and vertically. The software is 100% Open Source with commercial and community support available A platform and language for interesting, reusable and atomic services Zato promotes the design of, and helps you build, solutions composed of services which are interesting, reusable and atomic (IRA): I for Interesting - each service should make its clients want to use it more and more. People should immediately see the value of using the service in their processes. An interesting service is one that strikes everyone as immediately useful in wider contexts, preferably with few or no conditions, prerequisites and obligations. An interesting service is aesthetically pleasing, both in terms of its technical usage as well as in its potential applicability in fields broader than originally envisaged. If people check the service and say "I know, we will definitely use it" or "Why don't we use it" you know that the service is interesting. If they say "Oh no, not this one again" or "No, thanks, but no" then it is the opposite. R for Reusable - services can be used in different, independent business processes A for Atomic - each service fullfils a single, atomic business need Each service is deployed independently and, as a whole, they constitute an implementation of business processes taking place in your company or organisation. With Zato, developers use Python to focus on the business logic exclusively and the platform takes care of scalability, availability, communication protocols, messaging, security or routing. This lets developers concentrate only on what is the very core of systems integrations - making sure their services are IRA. Python is the perfect choice for API integrations, SOA and microservices, because it hits the sweet spot under several key headings: It is a very high level language, with syntax close to how grammar of various spoken languages works, which makes it easy to translate business requirements into implementation Yet, it is a solid, mainstream and full-featured, real programming language rather than a domain-specific one which means that it offers to developers a great degree of flexibility and choice in expressing their needs Many Python developers have a strong web programming / open source background which means that it is little effort to take a step further, towards API integrations and backend servers. In turn, this means that it is easy to find good people for API projects. Many Python developers have knowledge of multiple programming languages - this is very useful in the context of integration projects where one is typically faced with dozens of technologies, vendors or integration methods and techniques Lower maintenance costs - thanks to the language's unique design, Python programmers tend to produce code that is easy to read and understand. From the perspective of multi-year maintenance, reading and analysing code, rather than writing it, is what most programmers do most of the time so it makes sense to use a language which makes it easy to carry out the most common tasks. In short, Python can be construed as executable pseudo-code with many of its users already having roots in modern server-side programming so Zato, both from a technical and strategic perspective, is a natural choice for complex and sophisticated API solutions as a platform built in the language and designed for Python developers from day one. More than services Systems integrations commonly require two more features that Zato offers as well: File transfer - allows you to move batch data between locations and to distribute it among systems and APIs Single Sign-On (SSO) - a convenient REST interface lets you easily provide authentication and authorisation to users across multiple systems Next steps Start the tutorial to learn more technical details about Zato, including its architecture, installation and usage. After completing it, you will have a multi-protocol service representing a sample scenario often seen in banking systems with several applications cooperating to provide a single, consistent API to its callers. Visit the support page if you would like to discuss anything about Zato with its creators

Hello coders!! In this article, we will learn how to make a circle using matplotlib in Python. A circle is a figure of round shape with no corners. There are various ways in which one can plot a circle in matplotlib. Let us discuss them in detail. Method 1: matplotlib.patches.Circle(): SYNTAX: class matplotlib.patches.Circle(xy, radius=r, **kwargs)PARAMETERS: xy: (x,y) center of the circler: radius of the circleRESULT: a circle of radius r with center at (x,y) import matplotlib.pyplot as plt figure, axes = plt.subplots() cc = plt.Circle(( 0.5 , 0.5 ), 0.4 ) axes.set_aspect( 1 ) axes.add_artist( cc ) plt.title( 'Colored Circle' ) plt.show() Output & Explanation: Output Here, we have used the circle() method of the matplotlib module to draw the circle. We adjusted the ratio of y unit to x unit using the set_aspect() method. We set the radius of the circle as 0.4 and made the coordinate (0.5,0.5) as the center of the circle. Method 2: Using the equation of circle: The equation of circle is: x = r cos θy = r sin θ r: radius of the circle This equation can be used to draw the circle using matplotlib. import numpy as np import matplotlib.pyplot as plt angle = np.linspace( 0 , 2 * np.pi , 150 ) radius = 0.4 x = radius * np.cos( angle ) y = radius * np.sin( angle ) figure, axes = plt.subplots( 1 ) axes.plot( x, y ) axes.set_aspect( 1 ) plt.title( 'Parametric Equation Circle' ) plt.show() Output & Explanation: Output In this example, we used the parametric equation of the circle to plot the figure using matplotlib. For this example, we took the radius of the circle as 0.4 and set the aspect ratio as 1. Method 3: Scatter Plot to plot a circle: A scatter plot is a graphical representation that makes use of dots to represent values of the two numeric values.  Each dot on the xy axis indicates value for an individual data point. SYNTAX: matplotlib.pyplot.scatter(x_axis_data, y_axis_data, s=None, c=None, marker=None, cmap=None, vmin=None, vmax=None, alpha=None, linewidths=None, edgecolors=None)PARAMETERS:x_axis_data-  x-axis datay_axis_data- y-axis datas- marker sizec- color of sequence of colors for markersmarker- marker stylecmap- cmap namelinewidths- width of marker borderedgecolor- marker border-coloralpha- blending value import matplotlib.pyplot as plt plt.scatter( 0 , 0 , s = 7000 ) plt.xlim( -0.85 , 0.85 ) plt.ylim( -0.95 , 0.95 ) plt.title( "Scatter plot of points Circle" ) plt.show() Output & Explanation: Output Here, we have used the scatter plot to draw the circle. The xlim() and the ylim() methods are used to set the x limits and the y limits of the axes respectively. We’ve set the marker size as 7000 and got the circle as the output. Method 4: Matplotlib hollow circle: import matplotlib.pyplot as plt plt.scatter( 0 , 0 , s=10000 , facecolors='none', edgecolors='blue' ) plt.xlim( -0.5 , 0.5 ) plt.ylim( -0.5 , 0.5 ) plt.show() Output & Explanation: Output To make the circle hollow, we have set the facecolor parameter as none, so that the circle is hollow. To differentiate the circle from the plane we have set the edgecolor as blue for better visualization. Method 5: Matplotlib draw circle on image: import matplotlib.pyplot as plt import matplotlib.patches as patches import matplotlib.cbook as cb with cb.get_sample_data('C:\\Users\\Prachee\\Desktop\\cds\\img1.jpg') as image_file: image = plt.imread(image_file) fig, ax = plt.subplots() im = ax.imshow(image) patch = patches.Circle((100, 100), radius=80, transform=ax.transData) im.set_clip_path(patch) ax.axis('off') plt.show() Output & Explanation: Output In this example, we first loaded our data and then used the axes.imshow() method. This method is used to display data as an image. We then set the radius and the center of the circle. Then using the set_clip_path() method we set the artist’s clip-path. Method 6: Matplotlib transparent circle: import matplotlib.pyplot as plt figure, axes = plt.subplots() cc = plt.Circle(( 0.5 , 0.5 ), 0.4 , alpha=0.1) axes.set_aspect( 1 ) axes.add_artist( cc ) plt.title( 'Colored Circle' ) plt.show() Output & Explanation: Output To make the circle transparent we changed the value of the alpha parameter which is used to control the transparency of our figure. Conclusion: With this, we come to an end with this article. These are the various ways in which one can plot a circle using matplotlib in Python. However, if you have any doubts or questions, do let me know in the comment section below. I will try to help you as soon as possible. Happy Pythoning! The post 6 Ways to Plot a Circle in Matplotlib appeared first on Python Pool.

This article covers the most popular GUI Frameworks in Python. One of Python’s strongest selling points is the vast number of GUI libraries available for GUI development. GUI development can be a tricky task, but thanks to the tools these Python GUI frameworks provide us, things become much simpler. While some of the below GUI libraries are similar and directly compete with each other, each library has it’s own pros and cons. Sometimes you have special libraries designed for a specific situation, like Kivy is for touchscreen devices. So you don’t have to learn just one. There are a large number of GUI frameworks in Python and we couldn’t possibly cover all of them. Hence we’ll just be discussing 5 of the most popular and important GUI frameworks in Python. Tkinter GUI I decided to start with Tkinter as it’s probably the oldest and most well known GUI framework in Python. Tkinter was released in 1991 and quickly gained popularity due to its simplicity and ease of use compared to other GUI toolkits at the time. In fact, Tkinter is now included in the standard Python Library, meaning you don’t have to download and install it separately. Other plus points include the fact that Tkinter has a pretty small memory footprint and a quick start up time. If you were to convert a Tkinter application into an exe with something like pyinstaller, it’s size would smaller than the other GUI library equivalents. The only downsides to Tkinter are it’s rather outdated and old design. If you’re goal is to create a sleek and modern-looking GUI, Tkinter probably isn’t the best choice. Another possible downside is that Tkinter has fewer “special” widgets than the others, such as a VideoPlayer widget. Such widgets are used rarely, but still important. You can begin learning it with our very own Tkinter Tutorial series. PyQt5 PyQt5 is the Python binding of the popular Qt GUI framework which is written in C++. PyQt5’s main plus points is it’s cross platform ability and modern looking GUI. Personally I’ve noticed quite a few people switching from Tkinter to PyQt5 to be able to create for stylish GUI’s. Another one of PyQt5’s plus points is the Qt Designer. The Qt Designer is a drag and drop kind of tool where you don’t have to code in each widget individually. Instead, you can simply “drag” the widget and “drop” it onto the screen to create a GUI. It’s similar to Windows Form (VB.NET) and the Scene Builder (JavaFX). PyQt5 downsides include it’s relatively large package size and slow start up speed. Furthermore, PyQt was released under the GPL License. This means you cannot distribute any software containing PyQt code without bundling the source code with it as well. For someone selling commercial software, this a significant set back. You’ll have to buy a special commercial license which gives you the right to withhold the source code. The license issue isn’t something just should bother the average programmer though. You can begin learning PyQt from our very own tutorial series here! If you’ve narrowed down your GUI of choice between Tkinter and PyQt5 and are having a hard time picking one, I suggest you read this comparison article that compares both in a very detailed manner. PySide2 We’re bringing up PySide right after PyQt5 due to their strong connection. PySide is also a Python binding of the popular Qt GUI framework. Because of this reason the syntax is almost the exact same with some very minor differences. The reason why PyQt is used more nowadays is because it’s development was faster than that of PySide. When Qt5 was released, PyQt released their binding for it (called PyQt5) in 2016. Whereas it took PySide an extra 2 years to release PySide2 in 2018. If both had released at the same time, things might have been a bit different today. All the plus points for PyQt5 also apply for PySide2, with an extra addition. Unlike PyQt5, PySide was released under the LGPL license, allowing you to keep the source code for your distributed programs private. This makes the selling of commercial applications easier than it would be using PyQt5. You can learn more about PyQt5 vs PySide2 from this article here. Kivy Kivy is an opensource multi-platform GUI development library for Python and can run on iOS, Android, Windows, OS X, and Linux. The Kivy framework is well known for it’s support for touchscreen devices and it’s clean and modern looking GUI’s. It’s GUI and widgets have the interactive, multi-touch kind of ability that’s required for any decent GUI on a touchscreen device like a mobile. The one possible downside to GUI’s created with Kivy is the non-native look. This may or may not be something you wish to have. Other issues may include the smaller community and lack of documentation compared to more popular GUI libraries like Tkinter. If you’re looking to be developing on Desktop mostly, then it’s better to stick to one of the Qt options. Mobile support is Kivy’s greatest draw after all. wxPython wxPython is a Python open source cross platform GUI toolkit. Similar to how PyQt5 is based of the Qt GUI framework, WxPython is also based of a GUI framework called wxWidgets written in C++. It’s purpose is to allow Python developers to create native user interfaces for their GUI applications on a wide variety of different operating systems. The native GUI ability makes GUI’s created by wxPython looks very natural on any Operating system that they are run. Although some people may not want to have this native GUI look, instead preferring to have one look/style that is the exact same across all platforms. This marks the end of the Python GUI Frameworks article. Any suggestions or contributions for CodersLegacy are more than welcome. Questions regarding the article content can be asked in the comments section below. The post Python GUI Frameworks appeared first on CodersLegacy.

Selenium 4 is driving a lot of curiosity as it follows a different architecture compared to its predecessor. In this blog, we will see how to work with Python in Selenium 4.

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