other:python:jyp_steps
Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
|
other:python:jyp_steps [2021/09/22 13:38] jypeter [cdms2 and netCDF4] Added xarray |
other:python:jyp_steps [2021/09/22 14:23] jypeter Added statsmodels, scikit-learn and scikit-image |
||
|---|---|---|---|
| Line 156: | Line 156: | ||
| - you have to replace //cdms// with **cdms2**, and //MV// with **MV2** (sooorry about that, the tutorial was written when CDAT was based on //Numeric// instead of //numpy// to handle array data) | - you have to replace //cdms// with **cdms2**, and //MV// with **MV2** (sooorry about that, the tutorial was written when CDAT was based on //Numeric// instead of //numpy// to handle array data) | ||
| - read the [[http://cdms.readthedocs.io/en/docstanya/index.html|official cdms documentation]] (link may change) | - read the [[http://cdms.readthedocs.io/en/docstanya/index.html|official cdms documentation]] (link may change) | ||
| + | |||
| + | ==== xarray ==== | ||
| + | |||
| + | Summary: [[http://xarray.pydata.org/en/stable/|xarray]] is an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun! [...] It is particularly tailored to working with netCDF files | ||
| Line 342: | Line 346: | ||
| This document will teach you even more things about python, numpy and matplotlib, debugging and optimizing scripts, and about using python for statistics, image processing, machine learning, washing dishes (this is just to check if you have read this page), etc... | This document will teach you even more things about python, numpy and matplotlib, debugging and optimizing scripts, and about using python for statistics, image processing, machine learning, washing dishes (this is just to check if you have read this page), etc... | ||
| * Example: the [[http://www.scipy-lectures.org/packages/statistics/index.html|Statistics in Python]] tutorial that combines [[other:python:jyp_steps#pandas|Pandas]], [[http://statsmodels.sourceforge.net/|Statsmodels]] and [[http://seaborn.pydata.org/|Seaborn]] | * Example: the [[http://www.scipy-lectures.org/packages/statistics/index.html|Statistics in Python]] tutorial that combines [[other:python:jyp_steps#pandas|Pandas]], [[http://statsmodels.sourceforge.net/|Statsmodels]] and [[http://seaborn.pydata.org/|Seaborn]] | ||
| + | |||
| + | ===== statsmodels ===== | ||
| + | |||
| + | [[https://www.statsmodels.org/|statsmodels ]] is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. | ||
| + | |||
| + | ===== scikit-learn ===== | ||
| + | |||
| + | [[http://scikit-learn.org/|scikit-learn]] is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities. | ||
| + | |||
| + | ===== scikit-image ===== | ||
| + | |||
| + | [[https://scikit-image.org/|scikit-image]] is a collection of algorithms for image processing in Python | ||
| ===== Quick Reference and cheat sheets ===== | ===== Quick Reference and cheat sheets ===== | ||
other/python/jyp_steps.txt · Last modified: 2024/03/07 10:15 by jypeter
