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README.md

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* [Statistics](#statistics)
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* [Distributed Computing](#distributed-computing)
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* [Experimentation](#experimentation)
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* [Data Validation](#data-validation)
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* [Evaluation](#evaluation)
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* [Computations](#computations)
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* [Spatial Analysis](#spatial-analysis)
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## Data Manipulation
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### Data Containers
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### Data Frames
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* [pandas](https://pandas.pydata.org/pandas-docs/stable/) - Powerful Python data analysis toolkit.
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* [pandas_profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects
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* [cuDF](https://github.com/rapidsai/cudf) - GPU DataFrame Library. <img height="20" src="img/pandas_big.png" alt="pandas compatible"> <img height="20" src="img/gpu_big.png" alt="GPU accelerated">
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* [Sacred](https://github.com/IDSIA/sacred) - A tool to help you configure, organize, log and reproduce experiments.
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* [Ax](https://github.com/facebook/Ax) - Adaptive Experimentation Platform. <img height="20" src="img/sklearn_big.png" alt="sklearn">
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## Data validation
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## Data Validation
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* [great_expectations](https://github.com/great-expectations/great_expectations) - Always know what to expect from your data.
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* [pandera](https://github.com/unionai-oss/pandera) - A light-weight, flexible, and expressive statistical data testing library.
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* [deepchecks](https://github.com/deepchecks/deepchecks) - Validation & testing of ML models and data during model development, deployment, and production. <img height="20" src="img/sklearn_big.png" alt="sklearn">

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