httpx
Pandas
httpx | Pandas | |
---|---|---|
53 | 395 | |
12,274 | 41,983 | |
1.2% | 0.6% | |
8.9 | 10.0 | |
8 days ago | 4 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
httpx
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A Retrospective on Requests
For reference, it's a butterfly, not a moth.
Source: https://github.com/encode/httpx/issues/834
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Show HN: Twitter API Wrapper for Python – No API Keys Needed
Very cool, first I'm hearing of httpx https://www.python-httpx.org/
I think most people would start with trying out requests or something for this kind of work, I'm guessing that didn't work out? You've got a star from me.
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Harlequin: SQL IDE for Your Terminal
To access 10 different commands at the same time, that is tricky but definitely doable.
First thing that comes to mind, you can use aliases.
To keep it simple, lets use 3 examples instead of 10: harlequin (this project), pgcli (https://www.pgcli.com/) and httpx (https://www.python-httpx.org/)
Setup a main home for all your venvs:
cd ~
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HTTP Rate Limit
There are already some implementations for Python HTTP clients. One of them is aiometer. But it's not suitable for my use case. Since httpx already has the internal pool, it would be better to reuse the design.
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Introducing Flama for Robust Machine Learning APIs
Besides, flama also provides support for SQL databases via SQLAlchemy, an SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. Finally, flama also provides support for HTTP clients to perform requests via httpx, a next generation HTTP client for Python.
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Embracing Modern Python for Web Development
We can use the async HTTP client provided by httpx, a fully featured HTTP client for Python with an API broadly compatible with requests, so it can be used in pretty much the same way in most cases.
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Didn't want to click on refresh to see updates, this is what I did!
httpx in place of requests library
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Python Requests 3
The main value of Requests is that it provided an abstract interface on top of HTTP, which was designed well-enough to become a standard. But today it has fallen way behind in its field, and there are much better alternatives such as HTTPX [0].
[0] https://www.python-httpx.org/
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Unlocking Performance: A Guide to Async Support in Django
HTTPX is a popular Python library that provides an asynchronous HTTP client, and it can be beneficial for enabling async support in Django. While Django itself does not require HTTPX for async support, using HTTPX in combination with Django's async views can bring several advantages:
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Show HN: Python package for interfacing with ChatGPT with minimized complexity
The underlying library for both sync and async is httpx (https://www.python-httpx.org/) which may be limited from the HTTP Client perspective but it may be possible to add rate limiting at a Session level.
Pandas
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AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.
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Pandas reset_index(): How To Reset Indexes in Pandas
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.
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Deploying a Serverless Dash App with AWS SAM and Lambda
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
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Help Us Build Our Roadmap – Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
[1]: https://github.com/pandas-dev/pandas/issues/53999
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
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Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
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Mastering Pandas read_csv() with Examples - A Tutorial by Codes With Pankaj
Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.
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What Would Go in Your Dream Documentation Solution?
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:
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How do people know when to use what programming language?
Weirdly most of my time spent with data analysis was in the C layers in pandas.
What are some alternatives?
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
Niquests - Requests but with HTTP/3, HTTP/2, Multiplexed Connections, System CAs, Certificate Revocation, DNS over HTTPS / TLS / QUIC or UDP, Async, DNSSEC, and (much) pain removed!
tensorflow - An Open Source Machine Learning Framework for Everyone
requests-html - Pythonic HTML Parsing for Humans™
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
requests - A simple, yet elegant, HTTP library.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Flask - The Python micro framework for building web applications.
Keras - Deep Learning for humans
starlette - The little ASGI framework that shines. 🌟
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration