Pandas
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Pandas | orange | |
---|---|---|
393 | 27 | |
41,923 | 4,604 | |
1.4% | 1.7% | |
10.0 | 9.6 | |
3 days ago | 6 days ago | |
Python | Python | |
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.
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]
[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.
- Read files from s3 using Pandas/s3fs or AWS Data Wrangler?
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10 Github repositories to achieve Python mastery
Explore here.
orange
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Hierarchical Clustering
I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.
Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.
https://orangedatamining.com/
https://orange3.readthedocs.io/projects/orange-visual-progra...
- Orange Data Mining
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The Graph of Wikipedia [video]
For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.
[1] https://orangedatamining.com/
[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.
- Ask HN: What Underrated Open Source Project Deserves More Recognition?
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Taxonomy Management?
First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.
- Orange: Open-source machine learning and data visualization
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What exactly is AutoGPT?
Both tools are ripoffs of a data mining framework named Orange 3
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Why don't more people use Altair for python Visualizations instead of Plotly?
You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.
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Advice on Transitioning to Data Science/ML/AI without Coding Experience
You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.
- Has anybody used Orange?
What are some alternatives?
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
glue - Linked Data Visualizations Across Multiple Files
tensorflow - An Open Source Machine Learning Framework for Everyone
RDKit - The official sources for the RDKit library
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Keras - Deep Learning for humans
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
NumPy - The fundamental package for scientific computing with Python.
pyexcel - Single API for reading, manipulating and writing data in csv, ods, xls, xlsx and xlsm files
Dask - Parallel computing with task scheduling