Pandas
Airflow
Pandas | Airflow | |
---|---|---|
427 | 190 | |
45,967 | 40,957 | |
1.0% | 1.7% | |
9.9 | 10.0 | |
1 day ago | 4 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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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Don't Know These 6 Tools? No Wonder Your Python Development Is So Slow
👉 https://pandas.pydata.org/
- Open Source Can't Coordinate
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Top Programming Languages for AI Development in 2025
Libraries for data science and deep learning that are always changing
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How to import sample data into a Python notebook on watsonx.ai and other questions…
# Read the content of nda.txt try: import os, types import pandas as pd from botocore.client import Config import ibm_boto3 def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. cos_client = ibm_boto3.client(service_name='s3', ibm_api_key_id='api-generated', ibm_auth_endpoint="https://iam.cloud.ibm.com/identity/token", config=Config(signature_version='oauth'), endpoint_url='https://s3.direct.us-south.cloud-object-storage.appdomain.cloud') bucket = 'your-bucket-referenced-here' object_key = 'nda__da__crxq8b2hmy.txt' # load data of type "text/plain" into a botocore.response.StreamingBody object. # Please read the documentation of ibm_boto3 and pandas to learn more about the possibilities to load the data. # ibm_boto3 documentation: https://ibm.github.io/ibm-cos-sdk-python/ # pandas documentation: http://pandas.pydata.org/ streaming_body_1 = cos_client.get_object(Bucket=bucket, Key=object_key)['Body'] with open("nda.txt", "r") as f: nda_content = f.read() print("Content of nda.txt has been read.") except FileNotFoundError: print("Error: nda.txt not found in the current directory.") nda_content = "" # Initialize knowledge source content_source = CrewDoclingSource( file_paths=["..."] )
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How I Hacked Uber’s Hidden API to Download 4379 Rides
As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here).
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Show HN: Aiopandas – Async .apply() and .map() for Pandas, Faster API/LLMs Calls
Can this be merged into pandas?
Pandas does not currently install tqdm by default.
pandas-dev/pandas//pyproject.toml [project.optional-dependencies] https://github.com/pandas-dev/pandas/blob/main/pyproject.tom...
Dask solves for various adjacent problems; IDK if pandas, dask, or dask-cudf would be faster with async?
Dask docs > Scheduling > Dask Distributed (local) https://docs.dask.org/en/stable/scheduling.html#dask-distrib... :
> Asynchronous Futures API
Dask docs > Deploy Dask Clusters; local multiprocessing poll, k8s (docker desktop, podman-desktop,), public and private clouds, dask-jobqueue (SLURM,), dask-mpi:
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We Are Destroying Software
They are when the only reason they are flagged as security updates is because some a single group deems a very rare, obscure edge case as a HIGH severity vuln when in practice it rarely is => this leads to having to upgrade a minor version of a library that ends up causing breaking changes.
This is the recent thread I'm down. Pandas 2.2 broke SQLalchemy backwards compatibility: https://stackoverflow.com/questions/38332787/pandas-to-sql-t... + https://github.com/pandas-dev/pandas/issues/57049#issuecomme...
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Must-Know 2025 Developer’s Roadmap and Key Programming Trends
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python, try projects that combine data with everyday problems. For example, build a simple recommendation system using Pandas and scikit-learn.
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Sample Super Store Analysis Using Python & Pandas
This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of capabilities that the Pandas library, written in Python can offer.
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Bullish on AI infrastructure, bearish on AI developer frameworks
Data preprocessing and manipulation: Libraries like Pandas solve for the messy, real-world challenge of efficiently wrangling and cleaning large datasets. Without it, you'd be reinventing functionality for basic tasks like merging, filtering, or aggregating data.
Airflow
- MCP Specification – 2025-06-18
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Building Effective AI Agents \ Anthropic
You appear to be making the mistake of assuming that the only valid definition for the term "workflow" is the definition used by software such as https://airflow.apache.org/
https://www.merriam-webster.com/dictionary/workflow thinks the word dates back to 1921.
There no reason Anthropic can't take that word and present their own alternative definition for it in the context of LLM tool usage, which is what they've done here.
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Top 10 Open-source AI/ML platform engineering tools
Apache Airflow
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Airflow AI SDK to build simple LLM workflows
Hi HN,
We've built an SDK for building DAGs / data pipelines with LLMs in Apache Airflow [1] using Pydantic AI [2] under the hood. I've seen success across the board with Airflow users building simple LLM workflows before moving on to "AI agents". In my experience, the noise around building agents means that people forget that there are other ways to get more immediate value out of LLMs.
Coupling Airflow for orchestration and Pydantic AI for LLM interactions has turned out to be a very pragmatic approach to building these workflows (and agents). Neither tool "gets in the way" of what you're trying to do. Airflow's been around for 10+ years and has a very well-built orchestration engine rich with everything you need to write production grade data pipelines, and Pydantic AI's been a refreshing take on working with LLMs.
Would love some feedback from this community!
[1] https://github.com/apache/airflow
- The DOJ Still Wants Google to Sell Off Chrome
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10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows
Apache Airflow offers simplicity when it comes to scheduling, authoring, and monitoring ML workflows using Python. The tool's greatest advantage is its compatibility with any system or process you are running. This also eliminates manual intervention and increases team productivity, which aligns with the principles of Platform Engineering tools.
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AI Is Spamming Open Source Repos with Fake Issues
Examples: https://github.com/apache/airflow/issues?q=is%3Aissue%20stat...
Other than the content (which indeed makes no sense), these usually can be recognized by subjective adjectives and polish language[1].
[1] https://news.ycombinator.com/item?id=42864854
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Data Orchestration Tool Analysis: Airflow, Dagster, Flyte
Data orchestration tools are key for managing data pipelines in modern workflows. When it comes to tools, Apache Airflow, Dagster, and Flyte are popular tools serving this need, but they serve different purposes and follow different philosophies. Choosing the right tool for your requirements is essential for scalability and efficiency. In this blog, I will compare Apache Airflow, Dagster, and Flyte, exploring their evolution, features, and unique strengths, while sharing insights from my hands-on experience with these tools in a weather data pipeline project.
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AIOps, DevOps, MLOps, LLMOps – What’s the Difference?
Data pipelines: Apache Kafka and Airflow are often used for building data pipelines that can continuously feed data to models in production.
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Data Engineering with DLT and REST
This article demonstrates how to work with near real-time and historical data using the dlt package. Whether you need to scale data access across the enterprise or provide historical data for post-event analysis, you can use the same framework to provide customer data. In a future article, I'll demonstrate how to use dlt with a workflow orchestrator such as Apache Airflow or Dagster.``
What are some alternatives?
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
n8n - Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
dagster - An orchestration platform for the development, production, and observation of data assets.