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other:python:jyp_steps [2023/12/15 14:16] jypeter Added the EDA section |
other:python:jyp_steps [2023/12/15 14:37] jypeter Moved scikit-learn and scikit-image in front of lesser known libraries |
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* [[https://www.geeksforgeeks.org/exploratory-data-analysis-in-python/|EDA in Python]] | * [[https://www.geeksforgeeks.org/exploratory-data-analysis-in-python/|EDA in Python]] | ||
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==== Easy to use datasets ==== | ==== Easy to use datasets ==== | ||
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* [[https://github.com/xCDAT/xcdat/issues/277|xCDAT test data GH discussion]] | * [[https://github.com/xCDAT/xcdat/issues/277|xCDAT test data GH discussion]] | ||
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==== Pandas ==== | ==== Pandas ==== | ||
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Note: check the example in the [[https://lectures.scientific-python.org/packages/statistics/index.html|Statistics in Python]] tutorial | Note: check the example in the [[https://lectures.scientific-python.org/packages/statistics/index.html|Statistics in Python]] tutorial | ||
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+ | ==== scikit-learn ==== | ||
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+ | [[http://scikit-learn.org/|scikit-learn]] is a Python library for machine learning, and is one of the most widely used tools for supervised and unsupervised machine learning. Scikit–learn provides an easy-to-use, consistent interface to a large collection of machine learning models, as well as tools for model evaluation and data preparation | ||
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+ | Note: check the example in [[https://lectures.scientific-python.org/packages/scikit-learn/index.html|scikit-learn: machine learning in Python]] | ||
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+ | ==== scikit-image ==== | ||
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+ | [[https://scikit-image.org/|scikit-image]] is a collection of algorithms for image processing in Python | ||
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+ | Note: check the example in [[https://lectures.scientific-python.org/packages/scikit-image/index.html|scikit-image: image processing]] | ||
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[[https://github.com/AutoViML/AutoViz|AutoViz]]: the One-Line Automatic Data Visualization Library. Automatically Visualize any dataset, any size with a single line of code | [[https://github.com/AutoViML/AutoViz|AutoViz]]: the One-Line Automatic Data Visualization Library. Automatically Visualize any dataset, any size with a single line of code | ||
- | ==== scikit-learn ==== | ||
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- | [[http://scikit-learn.org/|scikit-learn]] is a Python library for machine learning, and is one of the most widely used tools for supervised and unsupervised machine learning. Scikit–learn provides an easy-to-use, consistent interface to a large collection of machine learning models, as well as tools for model evaluation and data preparation | ||
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- | Note: check the example in [[https://lectures.scientific-python.org/packages/scikit-learn/index.html|scikit-learn: machine learning in Python]] | ||
- | ==== scikit-image ==== | ||
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- | [[https://scikit-image.org/|scikit-image]] is a collection of algorithms for image processing in Python | ||
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- | Note: check the example in [[https://lectures.scientific-python.org/packages/scikit-image/index.html|scikit-image: image processing]] | ||