parsel
Pandas
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parsel | Pandas | |
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5 | 395 | |
1,077 | 41,983 | |
2.0% | 1.6% | |
6.4 | 10.0 | |
22 days ago | 1 day 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.
parsel
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What web scraping tools do ya'll use?
An alternative for beautifulsoup is https://github.com/scrapy/parsel also from the scrapy team.
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13 ways to scrape any public data from any website
variable.css(".X5PpBb::text").get() # returns a text value variable.css(".gs_a").xpath("normalize-space()").get() # https://github.com/scrapy/parsel/issues/192#issuecomment-1042301716 variable.css(".gSGphe img::attr(srcset)").get() # returns a attribute value variable.css(".I9Jtec::text").getall() # returns a list of strings values variable.xpath('th/text()').get() # returns text value using xpath
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Web Scraping With Python (An Ultimate Guide)
Something I don't see discussed when this topic is brought up is that Scrapy's HTML parsing library, parsel, can be installed separately from scrapy itself. You can use it in place of beautifulsoup and, imo, it's much easier to use.
- Looking for a nicer html parser to use with python other than BeautifulSoup4
- How to Crawl the Web with Scrapy
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?
parsel-cli - cli for evaluating css and xpath selectors
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
soupsieve - A modern CSS selector implementation for BeautifulSoup
tensorflow - An Open Source Machine Learning Framework for Everyone
insomnia - The open-source, cross-platform API client for GraphQL, REST, WebSockets, SSE and gRPC. With Cloud, Local and Git storage.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
CSS-Minifier - This CSS Minifier tries to reduce the length of code by renaming class names and id names.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
author-tools - Author Tools
Keras - Deep Learning for humans
FnF-Spritesheet-and-XML-Maker - A Friday Night Funkin' mod making helper tool that allows you to generate XML files and spritesheets from induvidual pngs
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration