lxml
spaCy
lxml | spaCy | |
---|---|---|
17 | 107 | |
2,588 | 28,887 | |
1.4% | 1.1% | |
9.6 | 9.2 | |
7 days ago | 7 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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lxml
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8 Most Popular Python HTML Web Scraping Packages with Benchmarks
lxml
- Looking for someone to web scrape housing data needed research. Will pay you for your work!!
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13 ways to scrape any public data from any website
Parsel is a library build to extract data from XML/HTML documents with XPath and CSS selectors support, and could be combined with regular expressions. It's usees lxml parser under the hood by default.
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lazy and fast .mpd file parser - for video streaming
So, now that I no longer work in that industry, and I had some free time, I created a lazy parsing package using lxml instead of the xml parser in the standard library, which can help people who want to have a python only parsing solution.
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Guide to working with fancier XML documents with python?
Seriously, use LXML.
- There is framework for everything.
- how to find text in website ?
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Parsing XML file deletes whitespace. How to avoid it?
I got curious about this now so I did some tests on my own, and it appears that the XML parser implementation in Python does indeed strip all newline characters from attributes. Whether this is according to XML standard I do not know; I also briefly tried an alternative XML implementation for Python and it behaves the same, so I would assume that this is standard behavior, but I'm not knowledgable enough about XML to say for certain.
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Use case for ETL over ELT?
I use lxml for the XML parsing and pyodbc as the ODBC library. We have a small team so I just keep it as simple as possible: 1. A cursor yields the XML documents from a SQL query as a stream 2. A generator function parses the XML document and yields the rows (you could parallelize this step) 3. Stream each of the resulting rows to a single CSV file 4. Scoop up the resulting CSV file into the target database (usually with the DB engine's loader; bulk insert isn't so fast over ODBC) It ends up being a straight forward, low-overhead approach.
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CompactLogix: Implementing HTTP requests & XML Data Transfer via TCP/IP
If that sounds too weird maybe take a look at pycomm3, python also has lxml as well as requests. You could write a script that retrieves the data from the clx using the appropriate pycomm3 driver for cplx and then do xml things with the data using lxml and transmit the data over http using requests.
spaCy
- How I discovered Named Entity Recognition while trying to remove gibberish from a string.
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Step by step guide to create customized chatbot by using spaCy (Python NLP library)
Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):
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Best AI SEO Tools for NLP Content Optimization
SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.
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Who has the best documentation you’ve seen or like in 2023
spaCy https://spacy.io/
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A beginner’s guide to sentiment analysis using OceanBase and spaCy
In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.
- Retrieval Augmented Generation (RAG): How To Get AI Models Learn Your Data & Give You Answers
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Against LLM Maximalism
Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.
The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?
[0] https://spacy.io/
[1] https://prodi.gy/
- Swirl: An open-source search engine with LLMs and ChatGPT to provide all the answers you need 🌌
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How to predict this sequence?
spaCy
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What do you all think about (setq sentence-end-double-space nil)?
I chose spacy. Although it's not state of the art, it's very well established and stable.
What are some alternatives?
xmltodict - Python module that makes working with XML feel like you are working with JSON
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
selectolax - Python binding to Modest and Lexbor engines (fast HTML5 parser with CSS selectors).
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
html5lib - Standards-compliant library for parsing and serializing HTML documents and fragments in Python
NLTK - NLTK Source
untangle - Converts XML to Python objects
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
bleach - Bleach is an allowed-list-based HTML sanitizing library that escapes or strips markup and attributes
polyglot - Multilingual text (NLP) processing toolkit
pyquery - A jquery-like library for python
textacy - NLP, before and after spaCy