Prefect
Pandas
Prefect | Pandas | |
---|---|---|
19 | 397 | |
14,724 | 42,039 | |
2.3% | 0.7% | |
10.0 | 10.0 | |
1 day ago | 1 day ago | |
Python | Python | |
Apache License 2.0 | 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.
Prefect
- Prefect: A workflow orchestration tool for data pipelines
- self hosted Alternative to easycron.com?
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Example typescript project repos?
If I was answering this question but for python, I'd recommend something like prefect, boto3, or tortoise-orm -- not extremely complex and with a pretty comprehensible featureset.
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I have developed a simple Task Orchestrator
However, if you are looking for something like this, but much more mature and something of a bloat to be frank, there's Prefect. Honestly, woflo borrows a lot from Prefect conceptually.
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Dabbling with Dagster vs. Airflow
Disclaimer: I work for Prefect.
It looks like we added cron and other schedule types to the deployment CLI just under a month ago[1].
Over the last couple of releases, we've also made it easier to pull deployments from GitHub or bake your flow code into Docker images instead of needing S3-like storage.
As with any product, there's always more to do, so I appreciate you sharing your thoughts. More than anywhere else I've worked, community feedback is a huge driver of product enhancements and feature development. Feel free to join our Slack community[2] if you'd like to share more feedback or ask questions.
[1] https://github.com/PrefectHQ/prefect/blob/main/RELEASE-NOTES...
- Prefect - The easiest way to automate your data
- Ask HN: Codebases with great, easy to read code?
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Prefect CLI Action
GitHub Action for running Prefect commands using the Prefect CLI.
- Perfect – Data workflow automation with Python
Pandas
- PDEP-13: The Pandas Logical Type System
- PHP Doesn't Suck Anymore
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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.
What are some alternatives?
dagster - An orchestration platform for the development, production, and observation of data assets.
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
APScheduler - Task scheduling library for Python
tensorflow - An Open Source Machine Learning Framework for Everyone
airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
schedule - Python job scheduling for humans.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
doit - task management & automation tool
Keras - Deep Learning for humans
django-schedule - A calendaring app for Django. It is now stable, Please feel free to use it now. Active development has been taken over by bartekgorny.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration