https://sandipanweb.wordpress.com/2017/05/27/some-image-processing-information-and-coding-theory/
http://www.scipy-lectures.org/index.html
http://agate.readthedocs.io/ Agate
http://www.learndatasci.com/data-science-statistics-using-python/
https://medium.com/pythonalytics
https://habrahabr.ru/company/ods/blog/323210/
conda list
conda update conda
conda config --set ssl_verify false
conda update pyopenssl
conda update anaconda
http://conda.pydata.org/docs/using/pkgs.html
conda install -c conda-forge jupyter_core=4.2.1
Categorical values in python and ML
The obvious answer is to just assign an integer to each category (we are assuming we know all of the possible categories up front). This is called ordinal coding. It does not add any dimensions to the problem, but implies an order to the variable that may not actually exist.
https://medium.com/@harshit.knit/exploratory-analysis-of-categorical-data-77de54cd620c#.p9iefaa2w
http://pbpython.com/categorical-encoding.html
http://www.willmcginnis.com/2015/11/29/beyond-one-hot-an-exploration-of-categorical-variables/
one hot encoding (but also goes by several different names shown below). Despite the different names, the basic strategy is to convert each category value into a new column and assigns a 1 or 0 (True/False) value to the column. This has the benefit of not weighting a value improperly but does have the downside of adding more columns to the data set. One hot encoding, is very useful but it can cause the number of columns to expand greatly if you have very many unique values in a column.
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
https://www.continuum.io/blog/developer-blog/learning-python-data-science-cheat-sheets
scikit-learn
https://www.datacamp.com/community/tutorials/machine-learning-python
https://github.com/amueller/scipy_2015_sklearn_tutorial/tree/master/notebooks
https://github.com/reiinakano/scikit-plot
https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A
https://www.youtube.com/watch?v=OB1reY6IX-o
https://www.youtube.com/watch?v=Cte8FYCpylk
https://github.com/jakevdp/PythonDataScienceHandbook
https://github.com/amueller/introduction_to_ml_with_python
http://www.kdnuggets.com/2015/03/the-grammar-data-science-python-vs-r.html
https://www.youtube.com/channel/UCnVzApLJE2ljPZSeQylSEyg
https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A
http://www.oreilly.com/programming/free/python-for-scientists.csp
https://github.com/ujjwalkarn/DataSciencePython
http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/
DASK
http://dask.pydata.org/en/latest/
http://pythondata.com/dask-large-csv-python/
http://matthewrocklin.com/blog/archive.html
IBIS
http://dan.iel.fm/emcee/current/ MCMC Markov Chain Monte Carlo
from IPython import __version__ as ipython_version
from pandas import __version__ as pandas_version
from bokeh import __version__ as bokeh_version
print("IPython - %s" % ipython_version)
print("Pandas - %s" % pandas_version)
print("Bokeh - %s" % bokeh_version)
https://ipgp.github.io/scientific_python_cheat_sheet/
https://www.yhat.com/products/rodeo
https://github.com/donnemartin/data-science-ipython-notebooks
NumPy
https://www.machinelearningplus.com/numpy-tutorial-part1-array-python-examples/
import numpy as np
np.version.version
https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python/learn/v4/overview
http://habrahabr.ru/post/274331/ NumPy
http://www.marsja.se/how-to-python-descriptives-statistics-numpy/
https://www.datacamp.com/community/tutorials/python-numpy-tutorial#gs.TOGup9c
https://www.datacamp.com/community/tutorials/python-numpy-tutorial#gs.pfIgUvU
https://www.dataquest.io/blog/numpy-tutorial-python/
http://www.labri.fr/perso/nrougier/from-python-to-numpy/
http://www.jamalmoir.com/2016/05/scientific-python-numpy.html
http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf
http://www.loria.fr/~rougier/teaching/numpy.100/index.html
http://blog.hackerearth.com/prerequisites-linear-algebra-machine-learning
SciPy
https://www.datacamp.com/community/tutorials/python-scipy-tutorial#gs.6NdA8R4
http://www.scipy-lectures.org/
http://scipy-lectures.github.com/
https://www.datacamp.com/community/blog/python-scipy-cheat-sheet#gs.j4YOPT8 SciPy
https://www.ibm.com/developerworks/community/blogs/jfp/entry/Python_Is_Not_C_Take_Two?lang=en
https://www.reddit.com/r/Python/comments/44r5hi/fantastic_talk_about_parallelism_in_python/
https://plot.ly/ipython-notebooks/big-data-analytics-with-pandas-and-sqlite/
https://pypi.python.org/pypi/pandas-highcharts/
https://github.com/hangtwenty/dive-into-machine-learning/blob/master/README.md
http://pythonforengineers.com/machine-learning-for-complete-beginners/
http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/Python%20vs%20Java.ipynb
http://bigdatauniversity.com/events/beginners-exploratory-data-analysis-in-python-hands-on/
http://slendermeans.org/category/will-it-python.html
http://www.johndcook.com/blog/2015/07/16/scientific-computing-in-python/
http://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html/2
http://www.pysnap.com/reinforcement-learning-in-python/
https://github.com/lmcinnes/hdbscan/blob/master/notebooks/Comparing%20Clustering%20Algorithms.ipynb
https://github.com/hangtwenty/dive-into-machine-learning
http://habrahabr.ru/post/270449/
http://bicorner.com/2015/11/16/time-series-analysis-using-ipython/
http://svaksha.github.io/pythonidae/
https://www.youtube.com/watch?v=__s45TTXxps ML in Python
https://www.youtube.com/watch?v=L7R4HUQ-eQ0&feature=youtu.be
http://blog.brakmic.com/data-science-for-losers-part-2/
https://www.youtube.com/watch?v=RTiAMB2tQjo
http://matthewrocklin.com/blog/archive.html
Books
https://www.reddit.com/r/MachineLearning/comments/2xptdg/best_book_for_machine_learning_in_python/
https://github.com/mrocklin/pydata-toolz
Matrix storage
https://medium.com/@jmaxg3/101-ways-to-store-a-sparse-matrix-c7f2bf15a229#.nngqpdulg
Pandas
https://habrahabr.ru/company/ods/blog/322626/ online course
https://www.reddit.com/r/Python/comments/645tyh/modern_pandas_tutorials/
https://www.amazon.com/Pandas-Cookbook-Ted-Petrou/dp/1784393878
https://medium.com/dunder-data/how-to-learn-pandas-108905ab4955
https://sandipanweb.wordpress.com/2017/11/25/some-deep-learning-with-python-tensorflow-and-keras
https://tomaugspurger.github.io/modern-1-intro.html
http://www.dataschool.io/best-python-pandas-resources/
http://pythondata.com/pandas-cheat-sheet/
https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf
http://machinelearningmastery.com/resample-interpolate-time-series-data-python/
import pandas as pd
wiki_df = pd.read_html("https://en.wikipedia.org/w/index.php?title=List_of_James_Bond_films&oldid=688916363", header=0)
pd.read_html returns a list of dataframes, with each table found on the web page being a dataframe
http://www.swegler.com/becky/blog/2014/08/06/useful-pandas-snippets/
panda, ipython plotly processing huge csv
https://plot.ly/ipython-notebooks/big-data-analytics-with-pandas-and-sqlite/
http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.DataFrame.to_html.html
http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.DataFrame.to_json.html
https://github.com/mritchie712/dfToHTML frame2HTML
https://www.dataquest.io/blog/python-vs-r/
https://www.youtube.com/watch?v=5JnMutdy6Fw
http://blog.brakmic.com/data-science-for-losers-part-5-spark-dataframes/
http://twiecki.github.io/blog/2014/11/18/python-for-data-science/
http://www.oreilly.com/programming/free/files/python-data-for-developers.pdf
Book: Learning pandas http://avxhome.se/ebooks/programming_development/34499003449900.html
Book: Mastering pandas http://avxhome.se/ebooks/programming_development/35457253545725.html
Book: Python Data Analytics http://avxhome.se/ebooks/programming_development/1484209591s.html
http://habrahabr.ru/post/266289/
http://efavdb.com/pandas-tips-and-tricks/
http://chrisalbon.com/python/pandas_dataframe_importing_csv.html
http://nbviewer.jupyter.org/github/lmcinnes/hdbscan/blob/master/notebooks/Python%20vs%20Java.ipynb
https://github.com/hangtwenty/dive-into-machine-learning/blob/master/README.md
http://habrahabr.ru/search/?q=%5Bpandas%5D&target_type=posts Panda
http://habrahabr.ru/post/202090/ Panda + http://scikit-learn.org/
http://jvns.ca/blog/2013/12/22/cooking-with-pandas/
https://github.com/robdmc/pandashells
https://www.codementor.io/python/tutorial/python-vs-r-for-data-science-data-frames-i#/
http://dev.socrata.com/consumers/examples/data-visualization-with-python.html
https://www.youtube.com/watch?v=5JnMutdy6Fw
http://pbpython.com/pandas-pivot-table-explained.html
http://chrisalbon.com/python/pandas_index_select_and_filter.html
https://realpython.com/blog/python/analyzing-obesity-in-england-with-python/
https://jakevdp.github.io/blog/2015/07/23/learning-seattles-work-habits-from-bicycle-counts/
http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_table.html
pd.read_table(self.archive_path,
delimiter="\t",
error_bad_lines=True,
skip_blank_lines=self.skip_blank_lines,
header=self.column_header_line_index,
converters=self.get_unit_converters(),
date_parser=self.parse_dates,
parse_dates=['DPt Time']
)
http://fastml.com/predicting-sales-pandas-vs-sql/
https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects
http://pythonforengineers.com/introduction-to-web-scraping-and-data-analysis/
For stats plotting in python: https://github.com/mwaskom/seabornhttps://github.com/yhat/ggplot
For stats plotting and web apps in python: https://github.com/bokeh/bokeh
For calling r libraries in python: https://pypi.python.org/pypi/rpy2
For out of core datasets in python: https://github.com/blaze/dask
https://github.com/blaze/blaze
http://people.duke.edu/~ccc14/sta-663/index.html
https://news.ycombinator.com/item?id=9634966
https://github.com/hangtwenty/dive-into-machine-learning/blob/master/README.md
http://eng.climate.com/2015/04/09/numba-vs-cython-how-to-choose/
https://news.ycombinator.com/item?id=9004689
https://github.com/andsor/scientific-python-quick-primer
https://github.com/joelgrus/data-science-from-scratch
http://geoffboeing.com/2015/03/chaos-theory-logistic-map/
http://graydon2.dreamwidth.org/3186.html
http://graydon2.dreamwidth.org/189377.html
https://www.crumpington.com/blog/2014/10-19-high-performance-python-extensions-part-1.html
https://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/
http://www.stuartreid.co.za/random-walks-down-wall-street-stochastic-processes-in-python/
http://www.reddit.com/r/Python/comments/2uzj6i/pathomx_a_workflowbased_data_analysis_tool/
https://beta.oreilly.com/learning/intro-to-svm
http://alexey.radul.name/ideas/2013/introduction-to-automatic-differentiation/
BOOKS
http://www.amazon.com/gp/product/B00OZLVAW2/
http://www.amazon.com/gp/product/B00P0W7EC0/
http://www.amazon.com/gp/product/B00NXWI0QI/
http://www.amazon.com/gp/product/B00NXWI0PY/
https://github.com/joelgrus/data-science-from-scratch
http://habrahabr.ru/post/236689/ fast python
https://github.com/bagrow/datatools
https://www.gnu.org/software/datamash/manual/
http://tuulos.github.io/sf-python-meetup-sep-2013/#/
https://www.youtube.com/playlist?list=PLYx7XA2nY5GfuhCvStxgbynFNrxr3VFog
http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/
http://scottsievert.github.io/blog/2014/05/14/Scientific-Python-tips-and-tricks/
https://news.ycombinator.com/item?id=7109982
http://kuznetsovin.livejournal.com/23302.html Cube in python
https://www.youtube.com/watch?v=wZEFoVUu8h0 HDF5
http://climateecology.wordpress.com/2014/07/15/in-defense-of-matplotlib/
http://blog.mikiobraun.de/2013/11/how-python-became-the-language-of-choice-for-data-science.html
https://store.continuum.io/cshop/anaconda/
https://sites.google.com/site/pydatalog/
http://blog.explainmydata.com/
http://strata.oreilly.com/2013/03/python-data-tools-just-keep-getting-better.html
https://speakerdeck.com/ogrisel/scaling-machine-learning-in-python
https://github.com/luispedro/BuildingMachineLearningSystemsWithPython
http://www.datasciencecentral.com/
http://hilpisch.com/YH_Performance_Python_Slides.html#/
https://store.continuum.io/cshop/anaconda/
http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
http://pyvideo.org/category/20/scipy_2012
http://conference.scipy.org/scipy2013/tutorials_schedule.php
http://www.agillo.net/loving-python/
http://glowingpython.blogspot.com/2012/11/first-steps-with-networx.html
http://en.wikipedia.org/wiki/Spyder_(software)
http://charlesleifer.com/blog/using-python-and-k-means-to-find-the-dominant-colors-in-images/
http://www.laurentluce.com/posts/twitter-sentiment-analysis-using-python-and-nltk/
http://vimeo.com/channels/pydata
Distributions
http://code.google.com/p/winpython/
https://store.continuum.io/cshop/anaconda
http://code.google.com/p/pythonxy/
https://code.google.com/p/spyderlib/
Python Scientific Tools
http://www.reddit.com/r/Python/comments/13vi7d/what_do_install_onto_a_windows_machine_that_will/
http://www.youtube.com/watch?v=lJw-obv5I8E&feature=related
http://aliquote.org/memos/2011/02/07/python-for-statistical-computing
http://www.reddit.com/r/Python/comments/i9kbs/what_are_the_best_scientificengineering_packages/
http://www.lfd.uci.edu/~gohlke/pythonlibs/
http://aliquote.org/memos/2011/02/07/python-for-statistical-computing
Sympy example:
%logstart or logstart -o myoutput.txt
from sympy.matrices import Matrix
a=Matrix([[(1,2),[3,4]])
inverse = a.inv()
In [20]: inverse
Out[20]:
[ -2, 1]
[3/2, -1/2]
In [21]: a * inverse
Out[21]:
[1, 0]
[0, 1]
In [17]: a.
a.C a.cross a.key2ij
a.D a.delRowCol a.limit
a.H a.det a.lower_triangular_solve
a.LDLdecomposition a.det_bareis a.mat
a.LDLsolve a.diagonal_solve a.minorEntry
a.LUdecomposition a.diagonalize a.minorMatrix
a.LUdecompositionFF a.diff a.multiply
a.LUdecomposition_Simple a.dot a.multiply_elementwise
a.LUsolve a.eigenvals a.norm
a.QRdecomposition a.eigenvects a.normalized
a.QRsolve a.evalf a.nullspace
a.T a.exp a.permuteBkwd
a.add a.expand a.permuteFwd
a.adjugate a.extract a.print_nonzero
a.applyfunc a.eye a.project
a.berkowitz a.fill a.reshape
a.berkowitz_charpoly a.get_diag_blocks a.row
a.berkowitz_det a.has a.row_del
a.berkowitz_eigenvals a.hash a.row_insert
a.berkowitz_minors a.integrate a.row_join
a.charpoly a.inv a.row_swap
a.cholesky a.inverse_ADJ a.rows
a.cholesky_solve a.inverse_GE a.rref
a.clone a.inverse_LU a.shape
a.cofactor a.is_diagonal a.simplify
a.cofactorMatrix a.is_diagonalizable a.singular_values
a.col a.is_lower a.slice2bounds
a.col_del a.is_lower_hessenberg a.submatrix
a.col_insert a.is_nilpotent a.subs
a.col_join a.is_square a.tolist
a.col_swap a.is_symbolic a.trace
a.cols a.is_symmetric a.transpose
a.combine a.is_upper a.upper_triangular_solve
a.condition_number a.is_upper_hessenberg a.vec
a.conjugate a.jacobian a.vech
a.copyin_list a.jordan_cells a.zeros
a.copyin_matrix a.jordan_form