PRAW
Pandas
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PRAW | Pandas | |
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528 | 393 | |
3,294 | 41,678 | |
1.2% | 1.6% | |
7.8 | 10.0 | |
3 days ago | 4 days ago | |
Python | Python | |
BSD 2-clause "Simplified" 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.
PRAW
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PRAW VS redditwarp - a user suggested alternative
2 projects | 21 Jun 2023
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Migrating subreddits to Lemmy communities
To get the relevant IDs, you can use something like PRAW to query the subreddit for the top 1000 posts for example.
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Reddit Comment Nuke: A Python script to edit and save your Reddit comment history en masse
Huge thanks to the contributors to PRAW, which is the Python package that does all the heavy lifting relating to Reddit's API that I need for this script.
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Why does PRAW's stream_generator() use a BoundedSet limit of 301?
However, in practice duplicate items were yielded with these smaller numbers. So I increased the limit briefly to 250 in October 2016, and then increased it finally to 301 in December 2016 in order to resolve https://github.com/praw-dev/praw/issues/673. That issue provides an explanation for how 301 came to be.
I have a pretty specific question about PRAW about the code shown here in the source repository. Perhaps this is best directed at one of the authors like /u/bboe.
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reddit downloader in python
Woah, dude, you're doing a lot of the heavy lifting trying to make sense of reddit's API yourself. Why not use an existing API wrapper for reddit such as praw? That way, authenticate() isn't really needed and main() could be shortened substantially.
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Python projects with best practices on Github?
the reddit API is actually really nice: https://github.com/praw-dev/praw
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[Script] Save Reddit posts to Obsidian
Ha, nearly a year ago now I started implementing something similar in Python using the excellent PRAW, but it never got finished.
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Understanding class attribute creation
That makes sense. For the second one you might take some design tips from praw: https://github.com/praw-dev/praw/tree/master/praw
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Introduction
praw is sequential and he is looking to leverage asyncpraw to work on analytics like comments, submissions, votes etc., any suggestions on this front would help as well.
Pandas
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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]
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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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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:
- Read files from s3 using Pandas/s3fs or AWS Data Wrangler?
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10 Github repositories to achieve Python mastery
Explore here.
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Interacting with Amazon S3 using AWS Data Wrangler (awswrangler) SDK for Pandas: A Comprehensive Guide
AWS Data Wrangler is a Python library that simplifies the process of interacting with various AWS services, built on top of some useful data tools and open-source projects such as Pandas, Apache Arrow and Boto3. It offers streamlined functions to connect to, retrieve, transform, and load data from AWS services, with a strong focus on Amazon S3.
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How to Build and Deploy a Machine Learning model using Docker
Pandas
What are some alternatives?
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
tensorflow - An Open Source Machine Learning Framework for Everyone
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Keras - Deep Learning for humans
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
pyexcel - Single API for reading, manipulating and writing data in csv, ods, xls, xlsx and xlsm files
SymPy - A computer algebra system written in pure Python
Dask - Parallel computing with task scheduling
NumPy - The fundamental package for scientific computing with Python.
asyncpraw - Async PRAW, an abbreviation for "Asynchronous Python Reddit API Wrapper", is a python package that allows for simple access to Reddit's API.
blaze - NumPy and Pandas interface to Big Data