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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.

This week we welcome Claudia Regio (@ClaudiaRegio) as our PyDev of the Week! Claudia is a program manager for Python Data Science with a focus on Python Notebooks in Visual Studio Code at Microsoft. She also blogs on Microsoft’s dev blog. Let’s spend some time getting to know Claudia better! Can you tell us a little about yourself (hobbies, education, etc): I am originally from Italy and moved to the Greater Seattle Area when I was 4 years old. Growing up I lived and breathed squash, and had it not been for COVID I still would be! I have been and always will be a huge math nerd and have been tutoring in math for over 10 years now. I attended the University of Washington where I majored in Applied Physics and received two minors in Comprehensive Mathematics and Applied Mathematics while a member of both the Delta Zeta Sorority and the UW Men’s Squash Team. After graduating I pursued a Data Science Certificate from the University of Washington to enhance my data analysis + data science skills while working at T-Mobile as a Systems Network Architecture Engineer. Two years after working in that role, I transitioned to Program Manager at Microsoft for the Python Extension in VS Code, focusing on the development of the Data Science & AI components and features. Why did you start using Python? The courses in my data science certificate got me started on Python back in 2017. What other programming languages do you know and which is your favorite? I learned Java during my time in college and while I enjoyed Java being a strongly typed language, no language beats Python when it comes to data science. What projects are you working on now? I am currently managing the Python + Jupyter Extension partnership in VS Code. While our recently released Jupyter Extension provides Jupyter Notebook support for other languages in VS Code Insiders, I focus on the collaboration of the two extensions to create an optimal notebooks experience for data scientists using Python. Which Python libraries are your favorite (core or 3rd party)? Scikit-learn will forever have my heart 3 What do you see as the best features of Python Notebooks? Our best features in Python Notebooks currently include the variable explorer, data viewer, and my personal favorite, Gather (a complimentary VS Code extension). When experimenting and prototyping in a notebook, it can often become busy as a user explores different approaches. After eventually reaching the desired result (for instance a specific visualization of the data) a user would then need to manually curate the cells involved with this specific flow. This task can be laborious and error-prone, leaving users without a strong approach for aggregating related code. A second scenario that is common among Notebooks users is that software engineers are tasked with turning an existing notebook into a production-ready script. The process of pulling out unneeded imports, graphs, outputs is often highly time consuming and can lead to errors as well. Gather is a complimentary VS Code extension that grabs all the relevant and dependent code required to create the contents of a selected cell and extracts those code dependencies into a new notebook or Python script. This helps save data scientists and developers a lot of notebook cleaning time! Why should Python developers and data scientists use Visual Studio Code over another editor? VS Code is a free and open source editor with a family of extensions (from both Microsoft and the open source community), products, and features that aim to make a seamless experience for developers and data scientists. A few examples include: Python (Comes with the Jupyter Extension): Includes features such as IntelliSense, linting, debugging, code navigation, code formatting, Jupyter notebook support, refactoring, variable explorer, test explorer, snippets, and more! Pylance: Language server that supercharges your Python IntelliSense experience with rich type information, helping you write better code faster. Live Share: Enables you to collaboratively edit and debug with others in real-time, regardless of what programming languages you’re using or app types you’re building. It allows you to instantly (and securely) share your current project, and then as needed, share debugging sessions, terminal instances, localhost webapps, voice calls, and more! Gather: A code cleaning tool that uses a static analysis technique to find and then copy all of the dependent code that was used to generate that cell’s result into a new notebook or script. Coding Pack for Python Installer: An installer pack that helps students and new coders quickly get started by installing VS Code, all of the extensions above, as well as Python and common packages such as numpy and pandas. Azure Machine Learning: Easily build, train, and deploy machine learning models to the cloud or the edge with Azure Machine Learning service from the Visual Studio Code interface. Over 350+ community contributed Python-related extensions on the VS Code Marketplace! It is the partnership constructed amongst these extensions and the open-source community as well as the Developer Division mindset to always build for the customer that creates an unmatchable experience for both developers and data scientists in VS Code. Is there anything else you’d like to say? I would like to thank the incredible team I get to work with (David Kutugata, Don Jayamanne, Ian Huff, Jim Griesmer, Joyce Er, Rich Chiodo, Rong Lu) who make this tool come to life and a thank you to all the customers who engage with us and are helping us build the best tool for data scientists! If anyone would like to provide any additional feedback, feature requests, or help contribute back to the product you can do so here! Thanks for doing the interview, Claudia! The post PyDev of the Week: Claudia Regio appeared first on Mouse Vs Python.

Introduction It’s been quite some time since we wrote on any “engineering-like” topic. As we all want to stay efficient and productive, it is a good time to revisit Google Colaboratory. Google Colaboratory, or Colab for short, has been a great platform for data scientists or machine-learning enthusiasts in general. It offers a free instance of GPU and TPU for a limited time plus it serves a prettifiied version of a Jupyter notebook. It is a great combination of variouofmaller or mid-size projects. Unfortunately, it comes with certain limitations. The biggest ones are the lack of storage persistency, as well as being sort of confined to a single document. Both limitations complicate the development and make working with multiple files less straightforward. While some good solutions have been developed by the community (including my previous work here and here), many of us are still on the lookout for something like a “data studio” aka Jupyter Lab. In this article, we will show how to install and run a Jupyter Lab instance on the Google machine through Colab, turning it into a custom solution with Jupyter Lab frontend and GPU/TPU backend for free. What is more, the approach presented here is generic and will allow you to other services such as Flask as well. It differs from solutions presented here or here, as they show how to connect the Jupyter Colab frontend to a local instance. Here, we will do the exact opposite, so stay on! General idea The main idea is to utilize the server that resides behind the Colab notebook, and uses its backend powers, but replacing the frontend. For it to work, the steps go as follows: Tap to the server behind the notebook. Install all the packages we need (e.g. Jupyter Lab). Establish a communication channel. Connect to it and have fun. Getting started Go over to https://colab.research.google.com to start a new instance, connect to it, and wait for the resources to be allocated. If you want, now is the time to switch the backend to either GPU or TPU (unless you want to repeat all the steps). Figure 1. The proof we have connected to the Google backend. ✕ Figure 1. The proof we have connected to the Google backend. Preparing the workspace The first “hack” Now, we need to go deeper and talk to the machine behind the notebook rather than with the notebook itself. The standard way to interact with the shell underneath is to prefix bash commands with ! (e.g. !ls -la). However, it may generate some problems later, so it is better to use an alternative way, mainly execute 1 eval "$SHELL" in a cell, which will let us communicate directly with the console behind. Installing Jupyter Lab Next, we install Jupyter Lab or any other thing for that matter. Natively, Colab does not have it installed, which you can confirm by executing: 1 2 3 4 5 6 7 8 9 !pip list | egrep jupyter # output jupyter 1.0.0 jupyter-client 5.3.5 jupyter-console 5.2.0 jupyter-core 4.7.0 jupyterlab-pygments 0.1.2 jupyterlab-widgets 1.0.0 1 2 3 4 5 6 7 8 9 10 11 12 13 !pip install jupyterlab !pip list | egrep jupyter # output jupyter 1.0.0 jupyter-client 6.1.11 jupyter-console 5.2.0 jupyter-core 4.7.0 jupyter-server 1.2.2 jupyterlab 3.0.5 jupyterlab-pygments 0.1.2 jupyterlab-server 2.1.2 jupyterlab-widgets 1.0.0 So now, we have all we need when it comes to the Python environment, but we still need to expose it outside of the notebook. For this, we will do the so-called reverse ssh tunnel. Reverse SSH tunnel The reverse SSH tunneling allows using the existing connection between two machines to set up a new connection channel back from the local machine to the remote one. As this article explains: Because the original connection came from the remote computer to you, using it to go in the other direction is using it “in reverse.” And because SSH is secure, you’re putting a secure connection inside an existing secure connection. This means your connection to the remote computer acts as a private tunnel inside the original connection. Now, as per the vocabulary used by the article, the local machine is actually the Google server that runs Colab. It is this machine’s port we would like to expose to the outside world. However, as we don’t know the outside address of our local machine (or the “remote” one as per the article’s vocabulary), we use a third-party service, namely http://localhost.run/. This solution was suggested by haqpl, who is a professional pentester and a friend of mine. It acts as both the end to the reverse SSH tunnel and a normal HTTP server, allowing us to use it as a bridge in communication. In other words, the service completes the SSH tunnel on one end, and an HTTP server on the other connecting our local PC to whatever service we run on Colab. Generate a public key Before we start, there is one thing we n ed to take care of. We need a key pair to secure the SSH channel without a password. This is the easy part. Detailed instructions can be found on GitHub. For us, it is enough we execute the following commands. Don’t worry about a passphrase. Just hit enter. 1 ssh-keygen -t ed25519 -C "your_email@example.com" By default, the keys are stored under /root/.ssh/id_ed25519.pub. Next, confirm we have the ssh-agent and register the key. 1 2 3 4 5 eval "$(ssh-agent -s)" ssh-add # expected response Identity added: /root/.ssh/id_ed25519 (your_email@example.com) At this point, we are ready to test the tunnel. Test the connection To initialize the connection, we need to pick a port that is unlikely to be used by the system already. For example 9999. It’s a nice number, isn’t it? Then, the command to execute will map this port to port 80 (the standard port for HTTP connection). Additionally, we need to make the system turn a blind eye to who the host is. Hence the -o flag. 1 ssh -o StrictHostKeyChecking=no -R 80:localhost:9999 ssh.localhost.run If all goes well, the last line of the response should give you the URL of where to point your local machine to. Figure 2. The SSH reverse tunnel has been established. In our case, the URL is `root-3e42408d.localhost.run`. ✕ Figure 2. The SSH reverse tunnel has been established. In our case, the URL is `root-3e42408d.localhost.run`. However, when you copy-paste it to your browser, the most likely response you will get is Something went wrong opening the port forward, check your SSH command output for clues!. This is OK, as there is really no service running at this port (yet). Let’s start a small python server under this port (or change it if you used it before). 1 python -m http.server 9999 & ssh -o StrictHostKeyChecking=no -R 80:localhost:9999 ssh.localhost.run When the connection is established, you should be able to browse through the files on Colab in your browser, seeing lines like this: 1 2 3 4 5 =============================================================================== root-be893e68.localhost.run tunneled with tls termination 127.0.0.1 - - [14/Jan/2021 22:39:21] "GET / HTTP/1.1" 200 - 127.0.0.1 - - [14/Jan/2021 22:39:22] code 404, message File not found 127.0.0.1 - - [14/Jan/2021 22:39:22] "GET /favicon.ico HTTP/1.1" 404 - printed out in the notebook. This is a very good sign! It means that if we can run python -m http.server, we can just as well replace it with Flask or Jupyter Lab, and this is exactly what we are about to do! The final hit Now, let’s change the port and start the Lab instead. The command to run is a bit lengthy, and the reason is that we must pass the necessary arguments: --ip=0.0.0.0 to stress this is a local machine (local to Colab). --port=8989, of course, --allow-root, otherwise Lab will start but refuse to communicate with you. 1 jupyter lab --ip=0.0.0.0 --port=8989 --allow-root & ssh -o StrictHostKeyChecking=no -R 80:localhost:8989 ssh.localhost.run Again, if all goes correctly, then by virtue of the SSH tunnel, we should access the Jupyter Lab externally using the URL issued earlier. The only add-ons are will be the parameters and the token you can read from the response. Figure 3. The confirmation that the Jupyter Lab instance is running. ✕ Figure 3. The confirmation that the Jupyter Lab instance is running. Take the URL given and copy-paste it to another tab in your browser. Remember to replace the localhost:8989 with the URL received earlier. Figure 4. The confirmation that the Jupyter Lab is indeed connected to the Colab backend. ✕ Figure 4. The confirmation that the Jupyter Lab is indeed connected to the Colab backend. Conclusion This is it! The combination of Jupyter Lab and Google Colaboratory that we created thanks to reverse SSH tunneling (and haqpl), gives probably the ultimate freedom, as now you have simplified access to upload/download of your files, convenience of organizing your project across multiple files, and support of really powerful hardware to your calculations… for free. One word, before we go… Remember that despite the SSH channel is secure, the session is open to whoever knows of your URL. If, as a part of your work, you decided to attach e.g. Google Drive to the machine, there is a chance someone may access your files even without you knowing. So please, use this “hack” carefully. Alternatively, you may consider using a virtual private server (VPS) to replace the localhost.run and give exclusive ownership of the endpoint. Thanks for reading! Please, let me know in the comments in case you stumble across problems or have any suggestions. Good luck and have fun!

Flourish is a visual toy app that draws harmonographs, sinuous curves simulating a multi-pendulum trace: Each harmonograph is determined by a few dozen parameter values, which are chosen randomly. The number of parameters depends on the number of pendulums, which defaults to 3.Click a thumbnail to see a larger version. The large-image pages have thumbnails of “adjacent” images. Each harmonograph is determined by a few dozen parameter values. For each parameter, four nearby values are substituted, giving four thumbnails for each parameter. Clicking an adjacent thumbnail continues your exploration of the parameter space:The settings dialog lets you adjust the number of pendulums (which determines the number of parameters) and the kinds of symmetry you are interested in.I started this because I wanted to understand how the parameters affected the outcome, but I was also interested to give it a purely visual design. As an engineer, it was tempting to present the values of the parameters quantitatively, but I like the simplicity of just clicking curves you like.I repeated a trick I’ve used in other mathy visual toys: when you download a PNG file of an image, the parameter values are stored in a data island in the file. You can re-upload the image, and Flourish will extract the parameters and put you back into the parameter-space exploration at that point.This is one of those side projects that let me use different sorts of things than I usually do: numpy, SVG, sass, Docker, and so on. I had more ideas for things to add (there is color in the code but not the UI). Maybe someday I will build them.BTW, I am happy that my first post of 2021 is called “Flourish.” I hope it is a harbinger of things to come.

Hello coders!! In this article, we will be learning how one can capitalize the first letter in the string in Python. There are different ways to do this, and we will be discussing them in detail. Let us begin! Method 1: str.capitalize() to capitalize the first letter of a string in python: Syntax: string.capitalize()Parameters: no parametersReturn Value: string with the first capital first letter string = "python pool" print("Original string:") print(string) print("After capitalizing first letter:") print(string.capitalize()) Output & Explanation: Output When we use the capitalize() function, we convert the first letter of the string to uppercase. In this example, the string we took was “python pool.” The function capitalizes the first letter, giving the above result. Method 2: string slicing + upper(): Synatx: string.upper()Parameters: No parametersReturn Value:  string where all characters are in upper case string = "python pool" print("Original string:") print(string) result = string[0].upper() + string print("After capitalizing first letter:") print(result) Output & Explanation: Output We used the slicing technique to extract the string’s first letter in this example. We then used the upper() method to convert it into uppercase. Method 3: str.title(): Syntax: str.title()Parameters: a string that needs to be convertedReturn Value: String with every first letter of every word in capital string = "python pool" print("Original string:") print(string) print("After capitalizing first letter:") print(str.title(string)) Output & Explanation: Output str.title() method capitalizes the first letter of every word and changes the others to lowercase, thus giving the desired output. Method 4: capitalize() Function to Capitalize the first letter of each word in a string in Python string = "python pool" print("Original string:") print(string) print("After capitalizing first letter:") result = ' '.join(elem.capitalize() for elem in string.split()) print(result) Output & Explanation: Output In this example, we used the split() method to split the string into words. We then iterated through it with the help of a generator expression. While iterating, we used the capitalize() method to convert each word’s first letter into uppercase, giving the desired output. Method 5: string.capwords() to Capitalize first letter of every word in Python: Syntax: string.capwords(string)Parameters: a string that needs formattingReturn Value: String with every first letter of each word in capital import string txt = "python pool" print("Original string:") print(txt) print("After capitalizing first letter:") result = string.capwords(txt) print(result) Output & Explanation: Output capwords() function not just convert the first letter of every word into uppercase. It also converts every other letter to lowercase. Method 6: Capitalize the first letter of every word in the list in Python: colors=['red','blue','yellow','pink'] print('Original List:') print(colors) colors = [i.title() for i in colors] print('List after capitalizing each word:') print(colors) Output & Explanation: Output Iterate through the list and use the title() method to convert the first letter of each word in the list to uppercase. Method 7:Capitalize first letter of every word in a file in Python file = open('sample1.txt', 'r') for line in file: output = line.title() print(output) Output & Explanation: Python Pool Is Your Ultimate Destination For Your Python Knowledge We use the open() method to open the file in read mode. Then we iterate through the file using a loop. After that, we capitalize on every word’s first letter using the title() method. You might be interested in reading: How to Convert String to Lowercase in PythonHow to use Python find() | Python find() String MethodPython Pass Statement| What Does Pass Do In PythonHow to Count Words in a String in Python Conclusion: The various ways to convert the first letter in the string to uppercase are discussed above. All functions have their own application, and the programmer must choose the one which is apt for his/her requirement. 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 7 Ways in Python to Capitalize First Letter of a String appeared first on Python Pool.

tl; dr: Python was wrong ;-)After one day, I've had one valid entry submitted to the contest I announced yesterday; I've also had two other submissions from the same contributor that I deemed to be invalid for the contest. The submissions shared a similar characteristics to a different degree: the information provided by Python in the exception message did not tell the whole story and, taken on its own, might have been considered to be misleading. One such cases which I did not considered to be valid for this contest was the error message UnboundLocalError: local variable 'x' referenced before assignment  given for code containing the followingdef f(): x = 1 del x print(x) # raises the exceptionWhen the exception is raised, there is indeed no trace of variable "x". So while there was an assignment for such a variable before, after deletion it no longer exists. Reading this code, instead of an UnboundLocalError, the exception that should probably be raised at this point is NameError: name 'x' is not defined ; however, Friendly-traceback's role is not to second-guess Python but to explain what an exception means and, whenever possible, give more details using the information available when the exception was raised. I considered this case and another one to be beyond the scope of what Friendly-traceback could accomplish.The entry that I deemed to be valid was based on the following code:class F: __slots__ = ["a"]f = F()f.b = 1The error message given by Python in this case is AttributeError: 'F' object has no attribute 'b'. While technically correct, the problem is not that this object has no such attribute but that it cannot have such an attribute. This information can be easily obtained when the exception is raised and the information provided by Friendly-traceback now includes the following:The object f has no attribute named b. Note that object f uses __slots__ which prevents the creation of new attributes. The following are some of its known attributes: a. Reminder: the contest is open for 8 more days.

In this episode, we'll be discussing two powerful tools for data reporting and exploration: Datasette and Dogsheep. <br/> <br/> Datasette helps people take data of any shape or size, analyze and explore it, and publish it as an interactive website and accompanying API. <br/> <br/> Dogsheep is a collection of tools for personal analytics using SQLite and Datasette. Imagine a unified search engine for everything personal in your life such as twitter, photos, google docs, todoist, goodreads, and more, all in once place and outside of cloud companies. <br/> <br/> On this episode we talk with Simon Willison who created both of these projects. He's also one of the co-creators of Django and we'll discuss some early Django history!<br/> <br/> <strong>Links from the show</strong><br/> <br/> <div><b>Datasette</b>: <a href="https://datasette.io/" target="_blank" rel="noopener">datasette.io</a><br/> <b>Dogsheep</b>: <a href="https://dogsheep.github.io/" target="_blank" rel="noopener">dogsheep.github.io</a><br/> <b>Datasheet newsletter</b>: <a href="https://datasette.substack.com/" target="_blank" rel="noopener">datasette.substack.com</a><br/> <b>Video: Build your own data warehouse for personal analytics with SQLite and Datasette</b>: <a href="https://www.youtube.com/watch?v=CPQCD3Qxxik" target="_blank" rel="noopener">youtube.com</a><br/> <br/> <b>Examples</b><br/> <b>List</b>: <a href="https://github.com/simonw/datasette/wiki/Datasettes" target="_blank" rel="noopener">github.com</a><br/> <b>Personal data warehouses</b>: <a href="https://simonwillison.net/2020/Nov/14/personal-data-warehouses/" target="_blank" rel="noopener">github.com</a><br/> <b>Global power plants</b>: <a href="https://global-power-plants.datasettes.com/" target="_blank" rel="noopener">datasettes.com</a><br/> <b>SF data</b>: <a href="https://san-francisco.datasettes.com/" target="_blank" rel="noopener">datasettes.com</a><br/> <b>FiveThirtyEight</b>: <a href="https://fivethirtyeight.datasettes.com/" target="_blank" rel="noopener">fivethirtyeight.datasettes.com</a><br/> <b>Lahman’s Baseball Database</b>: <a href="https://baseballdb.lawlesst.net/" target="_blank" rel="noopener">baseballdb.lawlesst.net</a><br/> <b>Live demo of current main</b>: <a href="https://latest.datasette.io/" target="_blank" rel="noopener">datasette.io</a><br/></div><br/> <strong>Sponsors</strong><br/> <br/> <a href='https://talkpython.fm/linode'>Linode</a><br> <a href='https://talkpython.fm/training'>Talk Python Training</a>