awesome-python
Section: Data Science and Analytics · N-D labeled arrays and datasets in Python
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Appears in 4 awesome lists
An open source project and Python package that introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays, which allows for more intuitive, more concise, and less error-prone user experience.
Section: Data Science and Analytics · N-D labeled arrays and datasets in Python
Section: Data Frames · Xarray combines the best features of NumPy and pandas for multidimensional data selection by supplementing numerical axis labels with named dimensions for more intuitive, concise, and less error-prone indexing routines.
Section: xarray (32 · 4.2K) - N-D labeled arrays and datasets in Python. Apache-2 · (👨💻 640 · 🔀 1.3K · 📦 46K):
Section: Climate Data Standards · An open source project and Python package that introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays, which allows for more intuitive, more concise, and less error-prone user experience.
Python module for building complex pipelines of batch jobs. Handles dependency resolution, workflow management, visualization, and Hadoop integration. Built at Spotify and battle-tested in production. Apache 2.0 licensed.
"Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it…
Cloud-native orchestration platform for developing and maintaining data assets including ML models. Declarative programming model with integrated lineage and observability. Apache 2.0 licensed.
Build and share delightful machine learning apps, all in Python. The de facto standard for creating interactive ML demos with automatic UI generation from function signatures. Powers thousands of Hugging Face Spaces.
Workflow management system that makes it easy to take your data pipelines and add semantics like retries, logging, dynamic mapping, caching, failure notifications, and more.
🆓 Connect and query your data sources, build dashboards to visualize data and share them with your company. Owned by Databricks but the hosted SaaS shut down in 2021, so the project is now community-maintained under the Databricks org with no paid Redash product.