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other:python:jyp_steps [2023/12/15 13:54]
jypeter Added Sweetviz and AutoViz
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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 ===== Data analysis ===== ===== Data analysis =====
 +
 +==== EDA (Exploratory Data Analysis) ? ====
 +
 +<note tip>
 +The //EDA concept// seems to apply to **time series** (and tabular data), which is not exactly the case of full climate model output data</​note>​
 +
 +  * [[https://​www.geeksforgeeks.org/​what-is-exploratory-data-analysis/​|What is Exploratory Data Analysis ?]]
 +    * //The method of studying and exploring record sets to apprehend their predominant traits, discover patterns, locate outliers, and identify relationships between variables. EDA is normally carried out as a preliminary step before undertaking extra formal statistical analyses or modeling.//
 +
 +  * [[https://​medium.com/​codex/​automate-the-exploratory-data-analysis-eda-to-understand-the-data-faster-not-better-2ed6ff230eed|Automate the exploratory data analysis (EDA) to understand the data faster and easier]]: a nice comparison of some Python libraries listed below (''​ydata-profiling'',​ ''​D-Tale'',​ ''​sweetviz'',​ ''​autoviz''​)
 +
 +  * [[https://​www.geeksforgeeks.org/​exploratory-data-analysis-in-python/​|EDA in Python]]
 +
  
 ==== 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]]
 +
 +
 ==== 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
 +
 +
 +==== scikit-learn ====
 +
 +[[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
 +
 +Note: check the example in [[https://​lectures.scientific-python.org/​packages/​scikit-learn/​index.html|scikit-learn:​ machine learning in Python]]
 +
 +
 +==== scikit-image ====
 +
 +[[https://​scikit-image.org/​|scikit-image]] is a collection of algorithms for image processing in Python
 +
 +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 ==== 
- 
-[[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 
- 
-Note: check the example in [[https://​lectures.scientific-python.org/​packages/​scikit-learn/​index.html|scikit-learn:​ machine learning in Python]] 
-==== scikit-image ==== 
- 
-[[https://​scikit-image.org/​|scikit-image]] is a collection of algorithms for image processing in Python 
- 
-Note: check the example in [[https://​lectures.scientific-python.org/​packages/​scikit-image/​index.html|scikit-image:​ image processing]] 
  
  
other/python/jyp_steps.txt · Last modified: 2024/08/27 12:51 by jypeter