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More (data) science + virtualenv #7

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More (data) science + virtualenv
Jul 19, 2015
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Links to tutorials for the scientific libraries
Jul 19, 2015
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scientific libraries resources
Jul 19, 2015
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Jul 19, 2015
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3 changes: 3 additions & 0 deletions .gitignore
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__pycache__

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#*
16 changes: 16 additions & 0 deletions challenges/Readme.md
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* Tk for graphics and GUI stuff

and lots more...


_____

###Extras resources for Python Scientific libraries:
1- (Data) Science resources
* scientific Python lectures: http://github.com/jrjohansson/scientific-python-lectures
* data science Python: http://github.com/donnemartin/data-science-ipython-notebooks

2- Library specific
* official Pandas tutorial: http://pandas.pydata.org/pandas-docs/stable/tutorials.html
* Pandas lessons & tutorials: http://bitbucket.org/hrojas/learn-pandas
* Scikit-learn tutorial by Gael Varoquaux at EP 2014: https://github.com/GaelVaroquaux/sklearn_europython_2014
* simplified text processing with textBlob: https://textblob.readthedocs.org/en/dev/
* official Seaborn tutorial: http://web.stanford.edu/~mwaskom/software/seaborn/tutorial.html#tutorial
* official Bokeh tutorial: http://bokeh.pydata.org/en/latest/docs/tutorials.html
18 changes: 13 additions & 5 deletions python-ecosystem.md
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**pip**: the standard Python command-line package installer. "pip install package-name" is the basic command. There used to be one called `easy_install`, essentially the same, now is less used. You can also manually download the source code for a package and install it by running the setup.py file, `python setup.py install`.

**vitural environments** or [virtualenvs](http://docs.python-guide.org/en/latest/dev/virtualenvs/) constitute a very useful python tool to keeps dependencies in separate places and avoid conflicts. Packages can be installed globally or inside a virtualenv.


## Web dev:

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## Science stuff:
## (Data) Science stuff:

**numpy & scipy** are two of the most popular scientific python libraries. Numpy has lots of tools for doing maths on big arrays of numbers, and scipy has lots of stats and analysis functions. scipy depends on numpy. If you're on windows, they may not "pip install" cleanly, so go to the scipy website and download installers, or look into Conda.

**IPyton and the IPython Notebook** (now [Jupyter project](http://jupyter.org/)) is an HTML notebook environment based on iPython shell. As we saw earlier, Ipython is an enhanced Python interpreter. The Ipython Notebook allows to combine code execution, text and plots and has become the go-to coding environment for the scientific Python world.

**numpy & scipy** are two of the most popular scientific python libraries. Numpy has lots of tools for doing maths on big arrays of numbers, and scipy has lots of stats and ananalysis functions. scipy depends on numpy. If you're on windows, they may not "pip install" cleanly, so go to the scipy website and download installers, or look into Conda.
**pandas** is popular data analysis library that reproduces a lot of functionalities found in R and contains plotting functionalities. It is used a lot for dealing with tables of data (CSV, Excel) but also handles other formats like text files, HTML, json, SQL...

**IPyton and the IPython Notebook** - as we saw earlier, Ipython is an enhanced Python interpreter. The Ipython Notebook is the real darling of the scientific Python world though, it gives you a sort of interactive notebook interface, a bit like matlab I'm told.
**scikit-learn** is machine learning library built on top of SciPy/Numpy. It integrates a wide range of classification, clustering and regression algorithms.

**pandas** is popular for dealing with tables of data. Lets you do excel-style pivottables, for example
**nltk** is a natural language processing library. It provides tools for the classification, tokenization, stemming, tagging, parsing... of human language data.

**matplotlib** is a well-revered tool for drawing graphs
**matplotlib** is a well-revered plotting library for Python.

**seaborn** is a data visualisation library based on matplotlib (but much nicer visually).

**bokeh** is an interactive data visualisation tool in the browser (like D3.js).



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