Python Numeric Cook List: Do Not Procrastinate

Python Numeric Cook List

Alguns macetes na utilização do python para resolução de problemas numéricos/de engenharia/científicos:

Receitas numéricas

Otimização

Resumos dos métodos de otimização: http://scipy-lectures.github.io/advanced/mathematical_optimization/ (Se você não é familiarizado com métodos de otimização - Esta é uma ótima primeira leitura!)
Optimization (scipy.optimize) - Tutorial: http://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html
Todas as rotinas de otimização do scipy: http://docs.scipy.org/doc/scipy/reference/optimize.html

Álgebra

Calculando o rank e o nullspace: http://scipy-cookbook.readthedocs.io/items/RankNullspace.html

Numpy+Scipy

Lendo dados de arquivo texto direto para matriz numpy http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html
Rosetta stone Matlab x Numpy: http://mathesaurus.sourceforge.net/matlab-numpy.html
Mais uma ótima tabela comparativa da manipulação de matrizes Numpy x Matlab http://sebastianraschka.com/Articles/2014_matlab_vs_numpy.html
Slicing, Indexing, Filtering and selecting data from arrays:

Plotando gráficos

Tutorial básico do uso do pylab: http://jakevdp.github.io/mpl_tutorial/index.html
Página oficial do matplotlib: http://matplotlib.org/
Guia do usuário do matplotlib: http://matplotlib.org/users/index.html
Convertendo texto utf-8 para unicode (pau do matplotlib): http://stackoverflow.com/questions/2406700/accented-characters-in-matplotlib/2406765#2406765
Writing mathematical expressions http://matplotlib.org/users/mathtext.html
Plotando vetores: http://stackoverflow.com/questions/12265234/plotting-2d-vectors-in-python-matplotlib
Resolução de alguns problemas associados aos terminais de plotagem interativa e seleção dos mesmos https://daemoniolabs.wordpress.com/2014/12/09/instalando-pylab-matplotlib-no-opensuse-13-2/
Algumas opções para visualização de gráficos além da matplotlib http://pbpython.com/visualization-tools-1.html
Estilos para serem utilizados com a matplotlib http://matplotlib.org/examples/style_sheets/index.html
Plotando múltiplas figuras e mantendo a memória sã: usar plt.clf(); plt.close(figure)
Plotando múltiplas curvas no mesmo gráfico com degradée (gradiente): http://stackoverflow.com/questions/4971269/how-to-pick-a-new-color-for-each-plotted-line-within-a-figure-in-matplotlib
Remover bordas da legenda e tornar a caixa transparente: http://stackoverflow.com/questions/25540259/remove-or-adapt-border-of-frame-of-legend-using-matplotlib
Ajustando os "majors" e "minors" xticks e yticks: http://stackoverflow.com/questions/24943991/matplotlib-change-grid-interval-and-specify-tick-larocandobels
Mudando automaticamente os tipos de linhas: http://stackoverflow.com/questions/7799156/can-i-cycle-through-line-styles-in-matplotlib
Trocando o lado do eixo-y http://stackoverflow.com/questions/10354397/python-matplotlib-y-axis-ticks-on-right-side-of-plot
Colorbar: http://stackoverflow.com/questions/14777066/matplotlib-discrete-colorbar

Mixing Python com outras linguagens

Usando C e Fortran com Python (mantendo compatibilidade com os tipos do SciPy): http://nbviewer.ipython.org/urls/raw.github.com/jrjohansson/scientific-python-lectures/master/Lecture-6A-Fortran-and-C.ipynb
f2py (Fortran e Python):

Tutorial f2py (desenvolva rápido com python, execute rápido com fortran): http://cens.ioc.ee/projects/f2py2e/usersguide/
Ótimo material da Universidade de Cambridge para integrar o Fortran com o Python: http://www.ucs.cam.ac.uk/docs/course-notes/unix-courses/pythonfortran/files/f2py.pdf
F2PY Users Guide and Reference Manual: http://docs.scipy.org/doc/numpy-dev/f2py/
Python x Fortran 90 Rosetta stone: http://www.fortran90.org/src/rosetta.html

Introduction to Jython -- A vantagem do Pyhton (linguagem) combinada com a vantagem do Java (ambiente): http://www.jython.org/jythonbook/en/1.0/
Python x Julia:

https://juliabyexample.helpmanual.io/
http://blog.leahhanson.us/post/julia/julia-calling-python.html
https://github.com/JuliaPy/PyCall.jl
https://github.com/JuliaPy/pyjulia
https://groups.google.com/forum/#!topic/julia-users/oAWYShR1Pks - from julia import XXX
https://github.com/JuliaPy/pyjulia/issues/39 - j.using("moduleName")

Processamento paralelo

Diversas API, módulos, técnicas, etc: https://wiki.python.org/moin/ParallelProcessing
Processamento paralelo em 1 (UMA) linha!!!: https://medium.com/building-things-on-the-internet/40e9b2b36148
Uso de Queues para retornar dados das threads: http://www.troyfawkes.com/learn-python-multithreading-queues-basics/
iPython paralelo: http://research-it.wharton.upenn.edu/news/parallel-ipython-with-univa-grid-engine-sge/
Parallel Programming with numpy and scipy: http://scipy.github.io/old-wiki/pages/ParallelProgramming
Numba is an open-source NumPy-aware optimizing compiler for Python: https://numba.pydata.org/
Installation of conda (numba package manager): https://conda.io/miniconda.html
GPU:

Python + GPU NVIDIA (PyCUDA): http://mathema.tician.de/software/pycuda
scikit-cuda, substituto do scikit na GPU: https://scikit-cuda.readthedocs.io/en/latest/
Theano, python library for math processing: http://deeplearning.net/software/theano/introduction.html#
Dask: provides advanced parallelism for analytics, enabling performance at scale for the tools you love https://dask.org/

http://matthewrocklin.com/blog/work/2019/01/03/dask-array-gpus-first-steps

Artigos variados:

First impressions of GPUs and PyData: http://matthewrocklin.com/blog/work/2018/12/17/gpu-python-challenges

Data Science

Pandas

Tutorials: https://pandas.pydata.org/pandas-docs/stable/tutorials.html
Tutorial Panda Datacamp: https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python
Filtro por data em Pandas: https://stackoverflow.com/questions/29370057/select-dataframe-rows-between-two-dates
Removendo horas de uma data: https://stackoverflow.com/questions/24786209/dropping-time-from-datetime-m8-in-pandas
Buscando valores únicos em Pandas: https://chrisalbon.com/python/data_wrangling/pandas_list_unique_values_in_column/
Encontrar NaN: https://stackoverflow.com/questions/14247586/python-pandas-how-to-select-rows-with-one-or-more-nulls-from-a-dataframe-without
Remover linhas com NaN: https://stackoverflow.com/questions/13413590/how-to-drop-rows-of-pandas-dataframe-whose-value-in-certain-columns-is-nan
Informações acerca do dataframe: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.info.html
groupby, aggregation and new DataFrame:

Ajustando o tipo de dados da coluna https://stackoverflow.com/questions/15125343/how-to-iterate-through-two-pandas-columns

Iterar sobre múltiplas colunas: https://stackoverflow.com/questions/15125343/how-to-iterate-through-two-pandas-columns
Criando novas colunas baseadas nas demais: https://chrisalbon.com/python/data_wrangling/pandas_create_column_using_conditional/
19 exemplos de manipulação de dados com pandas: https://jeffdelaney.me/blog/useful-snippets-in-pandas/
Alterar o valor de uma célula específica: https://stackoverflow.com/questions/13842088/set-value-for-particular-cell-in-pandas-dataframe
A Beginner’s Guide to Optimizing Pandas Code for Speed: https://engineering.upside.com/a-beginners-guide-to-optimizing-pandas-code-for-speed-c09ef2c6a4d6
Interseção de dois dataframes: https://stackoverflow.com/questions/26921943/pandas-intersection-of-two-data-frames-based-on-column-entries
Lendo arquivos json: https://hayd.github.io/2013/pandas-json
Lendo arquivos com FIXED COLUMN WITDH https://www.programcreek.com/python/example/101362/pandas.read_fwf https://pandas.pydata.org/pandas-docs/version/0.20/generated/pandas.read_fwf.html
Expandindo resolução de tempo: https://stackoverflow.com/questions/13726573/create-a-24-hour-1-min-resolution-data-set-in-pandas

TensorFlow vs. scikit-learn: The Microbiome Challenge: http://alifar76.github.io/sklearn-metrics/
Azure Studio Machine Learning: https://studio.azureml.net
Ferramentas que podem ser utilizadas para trabalhar com grande conjuntos de dados: numpy, h5py, databases, HDF5 support, mpi4py, dask, blaze: https://datascience.stackexchange.com/questions/778/is-python-suitable-for-big-data/796
Scikit:

Decision Trees: http://scikit-learn.org/stable/modules/tree.html
Decision Trees Regression: http://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html
Parâmetros da Decision Tree Regression: http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html

ForestCI:

Docs: http://contrib.scikit-learn.org/forest-confidence-interval/reference/forestci.html
Repositório github: https://github.com/scikit-learn-contrib/forest-confidence-interval

Conectando ao SQL Server:

Miscelâneas

Constantes físicas, matemáticas e unidades no Scipy: http://docs.scipy.org/doc/scipy/reference/constants.html
Fazendo matemática simbólica no python: http://scipy-lectures.github.io/advanced/sympy.html
Handbook of the Physics Computing Course: http://pentangle.net/python/handbook/
Lista de diversas bibliotecas, módulos e pacotes de cunho numérico e científico: https://wiki.python.org/moin/NumericAndScientific
Abramowitz and Stegun. Handbook of Mathematical Functions.http://people.math.sfu.ca/~cbm/aands/toc.htm
Trabalhando com fasores no python: http://csserver.evansville.edu/~richardson/courses/Tutorials/complex_numbers/python/python.pdf
Biblioteca numérica de precisão arbitrária para Python http://docs.sympy.org/dev/modules/mpmath/index.html
Notas no uso científico do Python por Scott Sievert - Menos completa que esta, entretanto, mais detalhada: http://scottsievert.github.io/blog/2014/05/14/Scientific-Python-tips-and-tricks/
Primeiros passos raṕidos ipython + numpy: http://www.linuxuser.co.uk/tutorials/numpy-python-tutorial
Compilando/Congelando seu código em python: http://docs.python-guide.org/en/latest/shipping/freezing/

pyinstaller: https://pythonhosted.org/PyInstaller/

Executando scripts python dentro do iPython e salvando as variáveis para uso interativo: http://ipython.readthedocs.org/en/latest/interactive/magics.html#magic-run
Empurrando as variáveis da main(), ou qualquer outra função, para a shell do iPython: http://ipython.readthedocs.org/en/stable/api/generated/IPython.core.interactiveshell.html#IPython.core.interactiveshell.InteractiveShell.push http://ipython.readthedocs.org/en/stable/api/generated/IPython.core.getipython.html (thanks nbastin -> #ipython @ freenode)
Creating virtual enviroments: http://docs.python-guide.org/en/latest/dev/virtualenvs/ - Great for remote computers without root access!
Python packaging:

Fifteen Python libraries for data science: http://www.kdnuggets.com/2017/06/top-15-python-libraries-data-science.html#.WZB6larsoLA.twitter

Aprendendo Python

Tutorial interativo web, ótimo início:http://www.learnpython.org/
Ótima documentação:http://learnpythonthehardway.org/book/
Dive Into Python (A referência mais usadas por programadores python conhecidos: http://www.diveinto.org/python3/
Python for Bash scripters:A well-kept secret: http://magazine.redhat.com/2008/02/07/python-for-bash-scripters-a-well-kept-secret/
Dealing with tuples: http://www.tutorialspoint.com/python/python_tuples.htm
Dictionaries basic: http://www.tutorialspoint.com/python/dictionary_get.htm
GUI:

Tkinter 8.5 reference: a GUI for Python http://infohost.nmt.edu/tcc/help/pubs/tkinter/web/index.html

Basic debugging with pdb: https://pythonconquerstheuniverse.wordpress.com/2009/09/10/debugging-in-python/

Agradecimentos

Ao amigo @CKPYT pelas contribuições à lista.

Do Not Procrastinate : PythonCookList