PRAW
NetworkX
PRAW | NetworkX | |
---|---|---|
528 | 61 | |
3,321 | 14,200 | |
0.8% | 0.9% | |
7.7 | 9.6 | |
5 days ago | 2 days ago | |
Python | Python | |
BSD 2-clause "Simplified" License | GNU General Public License v3.0 or later |
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
- PRAW documentation
- Testing
- `resubmit=False` started resubmitting duplicate URLs Jul 24 2023
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Just curious which person is the most popular user flair.
I'm... not sure I understand the question? PRAW still works just fine for "personal use" of the reddit API.
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How to use use Praw library with access and refresh tokens?
Thank you for pointing out. So there is no need then for the access token? Only with the refresh token is enough? To be honest I took a look at it but I did not expect that to be under authentication as strictly speaking, the user already made the authentication. Also I took a look at the code at https://github.com/praw-dev/praw/blob/master/praw/reddit.py and I did not get a hint whether was possible to pass it or not. I am just saying this to let you know I tried to search for the answer before asking. Again thank you for the help.
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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.
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is there a list of http status code which reddit api returns?
Why? You gotta be ready for any status code. Even 777.
NetworkX
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Routes to LANL from 186 sites on the Internet
Built from this data... https://github.com/networkx/networkx/blob/main/examples/grap...
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The Hunt for the Missing Data Type
I think one of the elements that author is missing here is that graphs are sparse matrices, and thus can be expressed with Linear Algebra. They mention adjacency matrices, but not sparse adjacency matrices, or incidence matrices (which can express muti and hypergraphs).
Linear Algebra is how almost all academic graph theory is expressed, and large chunks of machine learning and AI research are expressed in this language as well. There was recent thread here about PageRank and how it's really an eigenvector problem over a matrix, and the reality is, all graphs are matrices, they're typically sparse ones.
One question you might ask is, why would I do this? Why not just write my graph algorithms as a function that traverses nodes and edges? And one of the big answers is, parallelism. How are you going to do it? Fork a thread at each edge? Use a thread pool? What if you want to do it on CUDA too? Now you have many problems. How do you know how to efficiently schedule work? By treating graph traversal as a matrix multiplication, you just say Ax = b, and let the library figure it out on the specific hardware you want to target.
Here for example is a recent question on the NetworkX repo for how to find the boundary of a triangular mesh, it's one single line of GraphBLAS if you consider the graph as a matrix:
https://github.com/networkx/networkx/discussions/7326
This brings a very powerful language to the table, Linear Algebra. A language spoken by every scientist, engineer, mathematician and researcher on the planet. By treating graphs like matrices graph algorithms become expressible as mathematical formulas. For example, neural networks are graphs of adjacent layers, and the operation used to traverse from layer to layer is matrix multiplication. This generalizes to all matrices.
There is a lot of very new and powerful research and development going on around sparse graphs with linear algebra in the GraphBLAS API standard, and it's best reference implementation, SuiteSparse:GraphBLAS:
https://github.com/DrTimothyAldenDavis/GraphBLAS
SuiteSparse provides a highly optimized, parallel and CPU/GPU supported sparse Matrix Multiplication. This is relevant because traversing graph edges IS matrix multiplication when you realize that graphs are matrices.
Recently NetworkX has grown the ability to have different "graph engine" backends, and one of the first to be developed uses the python-graphblas library that binds to SuiteSparse. I'm not a directly contributor to that particular work but as I understand it there has been great results.
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Build the dependency graph of your BigQuery pipelines at no cost: a Python implementation
In the project we used Python lib networkx and a DiGraph object (Direct Graph). To detect a table reference in a Query, we use sqlglot, a SQL parser (among other things) that works well with Bigquery.
- NetworkX – Network Analysis in Python
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Custom libraries and utility tools for challenges
If you program in Python, can use NetworkX for that. But it's probably a good idea to implement the basic algorithms yourself at least one time.
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Google open-sources their graph mining library
For those wanting to play with graphs and ML I was browsing the arangodb docs recently and I saw that it includes integrations to various graph libraries and machine learning frameworks [1]. I also saw a few jupyter notebooks dealing with machine learning from graphs [2].
Integrations include:
* NetworkX -- https://networkx.org/
* DeepGraphLibrary -- https://www.dgl.ai/
* cuGraph (Rapids.ai Graph) -- https://docs.rapids.ai/api/cugraph/stable/
* PyG (PyTorch Geometric) -- https://pytorch-geometric.readthedocs.io/en/latest/
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1: https://docs.arangodb.com/3.11/data-science/adapters/
2: https://github.com/arangodb/interactive_tutorials#machine-le...
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org-roam-pygraph: Build a graph of your org-roam collection for use in Python
org-roam-ui is a great interactive visualization tool, but its main use is visualization. The hope of this library is that it could be part of a larger graph analysis pipeline. The demo provides an example graph visualization, but what you choose to do with the resulting graph certainly isn't limited to that. See for example networkx.
What are some alternatives?
asyncpraw - Async PRAW, an abbreviation for "Asynchronous Python Reddit API Wrapper", is a python package that allows for simple access to Reddit's API.
Numba - NumPy aware dynamic Python compiler using LLVM
Pushshift API - Pushshift API
Dask - Parallel computing with task scheduling
pmaw - A multithread Pushshift.io API Wrapper for reddit.com comment and submission searches.
julia - The Julia Programming Language
boto3 - AWS SDK for Python
RDKit - The official sources for the RDKit library
Telethon - Pure Python 3 MTProto API Telegram client library, for bots too!
snap - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
django-wordpress - WordPress models and views for Django.
SymPy - A computer algebra system written in pure Python