Awesome Data Analysis
Section: Useful Python Tools for Data Analysis · Reads data from various online sources into pandas DataFrames.
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Appears in 5 awesome lists
Python - Python module to get data from various sources (Google Finance, Yahoo Finance, FRED, OECD, Fama/French, World Bank, Eurostat...) into Pandas datastructures such as DataFrame, Panel with a caching mechanism.
Section: Useful Python Tools for Data Analysis · Reads data from various online sources into pandas DataFrames.
Section: Data Science and Analytics · Extract data from a wide range of Internet sources into a pandas DataFrame.
Section: Market Data & Data Sources · Python - Python module to get data from various sources (Google Finance, Yahoo Finance, FRED, OECD, Fama/French, World Bank, Eurostat...) into Pandas datastructures such as DataFrame, Panel with a caching mechanism.
Section: General · Up to date remote data access for pandas, works for multiple versions of pandas.
Section: Stocks and General · |Python| - Up to date remote data access for pandas, works for multiple versions of pandas.
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.
| Python | - Parallel computing with task scheduling in Python with a Pandas like API
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.