New to python and trying to create a simple calculator of my own design just wondering how I could get x to times input1 and input2 together (x is whatever is inputed)(intended to be either / or * or +/-) submitted by /u/-Doodoofard [link] [comments]

I need to migrate around 1 billion records from one MongoDB cluster to another MongoDB cluster using Python. What's the best way to do it using Python. Basically I need to insert data in batches and batch size should be controllable, and multi threads/process should be running. Logging should be present, and if the script stops in between it should continue process from where it stopped. Exception handling and retries are required. Any other approach / libraries which would simplify this? submitted by /u/vmanel96 [link] [comments]

Hello everybody, Thank you for having us here, and a huge "Thank you" to the moderators for letting us post. We have just released the latest edition of The Quick Python Book by the one-and-only Naomi Ceder, and I wanted to share that news with the community. Many of you are already familiar with Naomi's work and her massive contributions to the world of Python programming language. The Quick Python Book has aided over 100,000 developers in mastering Python. The Fourth Edition of the book has been revised to include the latest features, control structures, and libraries of Python, along with new coverage of working with AI-generated Python code. Naomi, the author, has beautifully balanced the details of the language with the insights and advice required to accomplish any task. Her personal touch has made learning Python an enjoyable experience for countless developers. 📚 You can find the book here: https://mng.bz/aEQj 📖 Get into the liveBook: https://mng.bz/gvee And last but not the least, get 46% off with code: receder46 Hope you find the book helpful. Thank you. Cheers, submitted by /u/ManningBooks [link] [comments]

Python Streamlit is terrific for putting together interactive dashboards. Combined with the geopandas library, streamlit can easily display GIS data points on a map for you. Forest fires in my home province of British Columbia, Canada have been really bad recently. NASA has a terrific dataset that keeps track of forest fires by country. Can I use Streamlit to access this dataset and display a map off all the fires within a certain area (BC) for a particular time frame (2021)? And can I give the user the ability to choose a month? You bet! Let me step you through how! FREE tutorial (with code): https://johnloewen.substack.com/p/simple-interactive-python-streamlit submitted by /u/jgloewen [link] [comments]

Excited to share my pypi package typedparser I have been working on for around 1 year now. What My Project Does: It enables writing CLI scripts and create an "args" variable with autocompleted members and type checks, but still keeps the simple and universally understood syntax of the stdlib argarse module. Target Audience: For stability, I battletested it in my research projects and added automatic builds as well as 80%+ test coverage. So I believe it is pretty stable. Comparison: For typing functionality it uses the attrs package as backend. It also provides some additional features for object and dictionary manipulation. Of course there are many other CLI argument packages out there, but this one stands out in that it tries to keep the syntax of the argparse standard library as much as possible, making it easy for others to figure out what your script does. Check it out and let me know what you think. submitted by /u/gings7 [link] [comments]

Weekly Thread: Meta Discussions and Free Talk Friday 🎙️ Welcome to Free Talk Friday on /r/Python! This is the place to discuss the r/Python community (meta discussions), Python news, projects, or anything else Python-related! How it Works: Open Mic: Share your thoughts, questions, or anything you'd like related to Python or the community. Community Pulse: Discuss what you feel is working well or what could be improved in the /r/python community. News & Updates: Keep up-to-date with the latest in Python and share any news you find interesting. Guidelines: All topics should be related to Python or the /r/python community. Be respectful and follow Reddit's Code of Conduct. Example Topics: New Python Release: What do you think about the new features in Python 3.11? Community Events: Any Python meetups or webinars coming up? Learning Resources: Found a great Python tutorial? Share it here! Job Market: How has Python impacted your career? Hot Takes: Got a controversial Python opinion? Let's hear it! Community Ideas: Something you'd like to see us do? tell us. Let's keep the conversation going. Happy discussing! 🌟 submitted by /u/AutoModerator [link] [comments]

Where can I find a decent comparison (pros and cons) of these 5 solutions? They seem to be solving the same problem, which is, afaiu, separating the frontend ‘annoyance’ from Python scripting / math. ⁠Reflex (used to be called Pynecone) https://reflex.dev ⁠Streamlit https://streamlit.io ⁠Gradio https://gradio.app ⁠Dash https://dash.plotly.com ⁠Panel https://panel.holoviz.org/ ⁠Anvil https://anvil.works/ Quarto My use case: user access the web app, choose some parameters, selects things that go or not into a model. Python returns results of my math. Needs to be somewhat eye-candy and I need to use a lot of pictures to get the user input (i.e. “which of these figures you like most? 1,2,3. User clicks on “3”, 3 is considered in the model. submitted by /u/Sea_Split_1182 [link] [comments]

What my project does: It basically takes a formula string like you'd get from Openpyxl like "=SUM(A1:B2)" and breaks it all out into a dictionary structure for you to then navigate through, modify, and then reformat that modified structure back into an excel friendly formula string again! Target Audience: (People who modify Excel formula strings in automated spreadsheet modification scripts. Or people who need to analyze formulas in a spreadsheet to do some kind of logic based on that analysis). Disclaimer: For most people some simple regex pattern matching and str replaces would be fine to modify formulas but if you need a more structured approach to working with these strings, this package has you covered! How does it differ compared to other projects: There are libraries like Openpyxl that allow you to tokenize and translate formulas but that's currently where it ends. It doesn't allow you to systematically parse out a formula and replace those pieces and add new structures and what not into it. Currently the best you can really do is translate formulas and anything other than that would need to rely on regex string matching logic or string replacements. (Which still would be fine for most people, but this just adds another layer of organization and scalability to the format). More info about it here: https://github.com/Voltaic314/ExcelFormulaParser To install, just do: pip install ExcelFormulaParser Thank you for reading this!! Hope you guys find it useful if you're ever systematically modifying (or analyzing) spreadsheets! submitted by /u/MPGaming9000 [link] [comments]

I made a tutorial on how to build a server-to-server Zoom OAuth application using Python. This application can transcribe Zoom meeting recordings, print the transcripts to the terminal, and save the transcripts as text files. video tutorial repo written tutorial This tutorial covers: Setting up OAuth authentication for server-to-server apps Utilizing the Zoom API to access recordings Implementing automatic transcription using Python submitted by /u/SleekEagle [link] [comments]

Hey everyone 👋 TL;DR Since everyone is talking about the Humane AI Pin and the Rabbit R1, I decided to make a short 5 minute tutorial on how people can setup and customize their own little AI assistant on their machine. I've uploaded a video tutorial here: https://www.youtube.com/watch?v=2fD_SAouoOs&ab_channel=2BytesGoat And the Github code is here: https://github.com/2BYTESGOAT/AI-ASSISTANT Longer version What my project does: It's the starter code for an AI assistant that you can run locally. More precisely, it's a ChatGPT / Llama 2 agent that has access to Google Search and can get businesses nearby based on your location. The tool can be easily extended to support other APIs. Target audience: Pythoneers that are curious about LLMs and LLM related libraries. Comparison: It was inspired by projects such as the Humane AI Pin and the Rabbit R1. Though it's a inferior version to those, it serves more as a playground for people to develop their own AI assistants. submitted by /u/2bytesgoat [link] [comments]

What my project does: I wanted to share an open-source project I’ve been working on called k8sAI. It’s a personal AI Kubernetes expert that can answer questions about your cluster, suggests commands, and even executes relevant kubectl commands to help diagnose and suggest fixes to your cluster, all in the CLI! Target Audience: As a relative newcomer to k8s, this tool has really streamlined my workflow. I can ask questions about my cluster, k8sAI will run kubectl commands to gather info, and then answer those question. It’s also found several issues in my cluster for me - all I’ve had to do is point it in the right direction. I’ve really enjoyed making and using this so I thought it could be useful for others. Added bonus is that you don’t need to copy and paste into ChatGPT anymore! k8sAI operates with read-only kubectl commands to make sure your cluster stays safe. All you need is an OpenAI API key and a valid kubectl config. Start chatting with k8sAI using: $ pip install k8sAI $ k8sAI chat or to fix an issue: $ k8sAI fix -p="take a look at the failing pod in the test namespace" Would love to get any feedback you guys have! Here's the repo for anyone who wants to take a look Comparison: I found a tool (k8sGPT) that I enjoyed using, but I felt it was still missing a few pieces on the chatbot side. You can't chat back and forth with k8sGPT and it doesn't suggest commands for you to execute, so I decided to make this. submitted by /u/Wild_Plantain528 [link] [comments]

I created this tutorial after overcoming a difficult challenge myself: uploading 5GB+ files to AWS. This approach allows the browser to securely upload directly to an S3 bucket without the file having to travel through the backend server. The implementation is written in python (backend) and vanilla js (frontend). submitted by /u/tylersavery [link] [comments]

Hello everyone! I'd like to announce version 1.10.0: https://hatch.pypa.io/latest/blog/2024/05/02/hatch-v1100/ Feel free to provide any feedback either here or as a discussion on the repo: https://github.com/pypa/hatch submitted by /u/Ofekmeister [link] [comments]

The Python on Microcontrollers (and Raspberry Pi) Newsletter: subscribe for free With the Python on Microcontrollers newsletter, you get all the latest information on Python running on hardware in one place! MicroPython, CircuitPython and Python on single Board Computers like Raspberry Pi & many more. The Python on Microcontrollers newsletter is the place for the latest news. It arrives Monday morning with all the week’s happenings. No advertising, no spam, easy to unsubscribe. 10,958 subscribers - the largest Python on hardware newsletter out there. Catch all the weekly news on Python for Microcontrollers with adafruitdaily.com. This ad-free, spam-free weekly email is filled with CircuitPython, MicroPython, and Python information that you may have missed, all in one place! Ensure you catch the weekly Python on Hardware roundup– you can cancel anytime – try our spam-free newsletter today! https://www.adafruitdaily.com/ submitted by /u/HP7933 [link] [comments]

I'd like to explore other people's setup and perhaps try need things or extra tools. What kind IDE, any extra tools to make it easier for you, etc. Looking forward to everyone's responses! submitted by /u/Working_Noise_6043 [link] [comments]

I have a simple backend REST API service that is serving a few ML models. I have made it "secured" by implementing an API key in order call those endpoints. I was wondering, how common it is for people to use services that can be self-hosted as their authentication/authorization. If it is common and reliable, what are the best options to go for? I've read that building your own authentication/authorization service with email verification, password reset, and social auth can be a pain. Also, did some googling and found this General - Fief. Has anyone ever tried using this? If so, how was the experience? Thanks in advance. submitted by /u/FlyingRaijinEX [link] [comments]

Hello Pythonistas! I just wrote a blog post about measuring performance inside pytest test cases. We dive into why it’s important to test for performance and how to integrate the measurements in the CI. Here is the link to the blog: https://codspeed.io/blog/one-pytest-marker-to-track-the-performance-of-your-tests ​ submitted by /u/toodarktoshine [link] [comments]

GitHub What My Project Does PkgInspect is a comprehensive tool designed to inspect and compare Python packages and Python versions effortlessly. It equips users with a comprehensive set of tools and utility classes to retrieve essential information from installed Python packages, compare versions seamlessly, and extract various details about Python installations with ease. Target Audience Developers and Python enthusiasts looking to streamline the process of inspecting Python packages, comparing versions, and extracting vital information from Python installations will find PkgInspect invaluable. Many current modules such as importlib_metadata and pkg_resources are fairly limited on what items can be inspected and retrieved for a specified python package. Also noticed pkg_resources has also deprecated some of its important retrieval methods. Comparison PkgInspect stands out from other Python package inspection tools due to its robust features. Unlike traditional methods that require manual inspection and comparison, PkgInspect automates the process, saving developers valuable time and effort. With PkgInspect, you can effortlessly retrieve package information, compare versions across different Python installations, and extract crucial details with just a few simple commands. Key Features Inspect Packages: Retrieve comprehensive information about installed Python packages. Compare Versions: Seamlessly compare package data across different Python versions. Retrieve Installed Pythons: Identify and list installed Python versions effortlessly. Inspect PyPI Packages: Gather detailed information about packages from the Python Package Index (PyPI). Fetch Available Updates: Stay up-to-date with available updates for a package from the current version. List Inspection Fieldnames: Access a list of available fieldnames for package inspection. Retrieve Package Metrics: Extract OS statistics about a package effortlessly. Fetch GitHub Statistics: Retrieve insightful statistics about a package from GitHub effortlessly. Retrieve all Python Packages: Easily list all installed Python packages for a given Python version. Main Components Core Modules PkgInspect: Inspects Python packages and retrieves package information. PkgVersions: Retrieves and compares package data across different Python versions. PkgMetrics: Extracts OS statistics about a package. Functions - inspect_package: Inspects a Python package and retrieves package information. - inspect_pypi: Inspects a package from the Python Package Index (PyPI). - get_available_updates: Fetches available updates for a package from the current version. - get_installed_pythons: Identifies and lists installed Python versions. - get_version_packages: Lists all installed Python packages for a given Python version. - pkg_version_compare: Compares package data across different Python versions. Inspection Field Options Any other field name will be treated as a file name to inspect from the packages' site-path directory. - `short_meta` (dict[str, Any]): Returns a dictionary of the most important metadata fields. - If only one field is needed, you can use any of the following metadata fields. - Possible Fields instead of `short_meta`: - `Metadata-Version` (PackageVersion) - `Name` (str) - `Summary` (str) - `Author-email` (str) - `Home-page` (str) - `Download-URL` (str) - `Platform(s)` (set) - `Author` (str) - `Classifier(s)` (set) - `Description-Content-Type` (str) - `short_license` (str): Returns the name of the license being used. - `metadata` (str): Returns the contents of the METADATA file. - `installer` (str): Returns the installer tool used for installation. - `license` (str): Returns the contents of the LICENSE file. - `record` (str): Returns the list of installed files. - `wheel` (str): Returns information about the Wheel distribution format. - `requested` (str): Returns information about the requested installation. - `authors` (str): Returns the contents of the AUTHORS.md file. - `entry_points` (str): Returns the contents of the entry_points.txt file. - `top_level` (str): Returns the contents of the top_level.txt file. - `source_file` (str): Returns the source file path for the specified package. - `source_code` (str): Returns the source code contents for the specified package. - `doc` (str): Returns the documentation for the specified package. - `Pkg` Custom Class Fields - `PkgInspect fields`: Possible Fields from the `PkgInspect` class. - `site_path` (Path): Returns the site path of the package. - `package_paths` (Iterable[Path]): Returns the package paths of the package. - `package_versions` (Generator[tuple[str, tuple[tuple[Any, str]]]]): Returns the package versions of the package. - `pyversions` (tuple[Path]): Returns the Python versions of the package. - `installed_pythons` (TupleOfPkgVersions): Returns the installed Python versions of the package. - `site_packages` (Iterable[str]): Returns the site packages of the package. - `islatest_version` (bool): Returns True if the package is the latest version. - `isinstalled_version` (bool): Returns True if the package is the installed version. - `installed_version` (PackageVersion): Returns the installed version of the package. - `available_updates` (TupleOfPkgVersions): Returns the available updates of the package. - `PkgVersions fields`: Possible Fields from the `PkgVersions` class. - `initial_version` (PackageVersion): Returns the initial version of the package. - `installed_version` (PackageVersion): Returns the installed version of the package. - `latest_version` (PackageVersion): Returns the latest version of the package. - `total_versions` (int): Returns the total number of versions of the package. - `version_history` (TupleOfPkgVersions): Returns the version history of the specified package. - `package_url`: Returns the URL of the package on PyPI. - `github_stats_url` (str): Returns the GitHub statistics URL of the package. - `github_stats` (dict[str, Any]): Returns the GitHub statistics of the package. - The GitHub statistics are returned as a dictionary \ containing the following fields which can accessed using the `item` parameter: - `Forks` (int): Returns the number of forks on GitHub. - `Stars` (int): Returns the number of stars on GitHub. - `Watchers` (int): Returns the number of watchers on GitHub. - `Contributors` (int): Returns the number of contributors on GitHub. - `Dependencies` (int): Returns the number of dependencies on GitHub. - `Dependent repositories` (int): Returns the number of dependent repositories on GitHub. - `Dependent packages` (int): Returns the number of dependent packages on GitHub. - `Repository size` (NamedTuple): Returns the size of the repository on GitHub. - `SourceRank` (int): Returns the SourceRank of the package on GitHub. - `Total releases` (int): Returns the total number of releases on GitHub. - `PkgMetrics fields`: Possible Fields from the `PkgMetrics` class. - `all_metric_stats` (dict[str, Any]): Returns all the OS statistics of the package. - `total_size` (int): Returns the total size of the package. - `date_installed` (datetime): Returns the date the package was installed. - `pypistats fields`: Possible Fields from the `pypistats` module. - `all-pypi-stats` (dict[str, Any]): Returns all the statistics of the package on PyPI into a single dictionary. - `stats-overall` (dict[str, Any]): Returns the overall statistics of the package on PyPI. - `stats-major` (dict[str, Any]): Returns the major version statistics of the package on PyPI. - `stats-minor` (dict[str, Any]): Returns the minor version statistics of the package on PyPI. - `stats-recent` (dict[str, Any]): Returns the recent statistics of the package on PyPI. - `stats-system` (dict[str, Any]): Returns the system statistics of the package on PyPI. Downsides & Limitations My algorithms are fairly well but do come with some important downsides. PkgInspect will ONLY inspect packages that are python files or contains a dist-info folder in the site-packages folder for a given Python version. Was not able to efficiently figure out a way to retrieve all necessary packages without containing unrelevant folders/files. Some personal packages may be skipped otherwise. Beta (pre-releases) has not been implemented yet. As many files may be handled, the runtime may be slow for some users. The demand for a project like this is not so much in-demand but have noticed many people, including my self, still seeking for a project like this. However, this type of project does seem to exceed my experience level with Python and algorithms (hence the downsides) so not entirely sure how far this project may come in the future. Was hoping for it to be GUI based if possible. Usage Examples from pkg_inspect import inspect_package inspect_package("pkg_inspect", itemOrfile="initial_version") # Output (Format - DateTimeAndVersion): ('May 02, 2024', '0.1.0') inspect_package("pkg_inspect", itemOrfile="version_history") # Output (Format - tuple[DateTimeAndVersion]): (('May 02, 2024', '0.1.2'), ('May 02, 2024', '0.1.1'), ('May 02, 2024', '0.1.0')) inspect_package("pkg_inspect", pyversion="3.12", itemOrfile="short_meta") # Output (Format dict[str, Any]): {'Author': 'Yousef Abuzahrieh', 'Author-email': 'yousefzahrieh17@gmail.com', 'Classifiers': {'Development Status 4 Beta', 'Intended Audience Developers', 'License OSI Approved Apache Software License', 'Operating System OS Independent', 'Programming Language Python 3', 'Programming Language Python 3 Only', 'Topic Utilities'}, 'Description-Content-Type': 'text/markdown', 'Download-URL': 'https://github.com/yousefabuz17/PkgInspect.git', 'Home-page': 'https://github.com/yousefabuz17/PkgInspect', 'License': 'Apache Software License', 'Metadata-Version': <Version('2.1')>, 'Name': 'pkg-inspect', 'Platforms': {'Windows', 'MacOS', 'Linux'}, 'Summary': 'A comprehensive tools to inspect Python packages and Python ' 'installations.'} inspect_package("pandas", pyversion="3.12", itemOrfile="github_stats") # Output (Format - dict[str, Any]): {'Contributors': '1.09K', 'Dependencies': 3, 'Dependent packages': '41.3K', 'Dependent repositories': '38.4K', 'Forks': '17.3K', 'Repository size': Stats(symbolic='338.000 KB (Kilobytes)', calculated_size=338.0, bytes_size=346112.0), 'SourceRank': 32, 'Stars': '41.9K', 'Total releases': 126, 'Watchers': 1116} submitted by /u/yousefabuz [link] [comments]

Hello world,I wrote an article about creating diagrams from code on Jupyter Notebook inside VS Code. It will give you a brief on the setup and also an overview of concepts. Within 5 minutes, you should be able to start making cool architecture diagrams. [TO MODERATOR: This link does not contain any paywalled or paid content. All the contents are available for free] Article link: https://ashgaikwad.substack.com/p/how-to-create-architecture-diagrams submitted by /u/writer_on_rails [link] [comments]

Weekly Thread: Professional Use, Jobs, and Education 🏢 Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment. How it Works: Career Talk: Discuss using Python in your job, or the job market for Python roles. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources. Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally. Guidelines: This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar. Keep discussions relevant to Python in the professional and educational context. Example Topics: Career Paths: What kinds of roles are out there for Python developers? Certifications: Are Python certifications worth it? Course Recommendations: Any good advanced Python courses to recommend? Workplace Tools: What Python libraries are indispensable in your professional work? Interview Tips: What types of Python questions are commonly asked in interviews? Let's help each other grow in our careers and education. Happy discussing! 🌟 submitted by /u/AutoModerator [link] [comments]