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----
-language: Statistical computing with Python
-contributors:
-    - ["e99n09", "https://github.com/e99n09"]
-filename: pythonstatcomp.py
----
-
-This is a tutorial on how to do some typical statistical programming tasks using Python. It's intended for people basically familiar with Python and experienced at statistical programming in a language like R, Stata, SAS, SPSS, or MATLAB.
-
-```python
-
-# 0. Getting set up ====
-
-""" Get set up with IPython and pip install the following: numpy, scipy, pandas,
-    matplotlib, seaborn, requests.
-        Make sure to do this tutorial in the IPython notebook so that you get
-    the inline plots and easy documentation lookup.
-"""
-
-# 1. Data acquisition ====
-
-""" One reason people choose Python over R is that they intend to interact a lot
-    with the web, either by scraping pages directly or requesting data through
-    an API. You can do those things in R, but in the context of a project
-    already using Python, there's a benefit to sticking with one language.
-"""
-
-import requests # for HTTP requests (web scraping, APIs)
-import os
-
-# web scraping
-r = requests.get("https://github.com/adambard/learnxinyminutes-docs")
-r.status_code # if 200, request was successful
-r.text # raw page source
-print(r.text) # prettily formatted
-# save the page source in a file:
-os.getcwd() # check what's the working directory
-f = open("learnxinyminutes.html","wb")
-f.write(r.text.encode("UTF-8"))
-f.close()
-
-# downloading a csv
-fp = "https://raw.githubusercontent.com/adambard/learnxinyminutes-docs/master/"
-fn = "pets.csv"
-r = requests.get(fp + fn)
-print(r.text)
-f = open(fn,"wb")
-f.write(r.text.encode("UTF-8"))
-f.close()
-
-""" for more on the requests module, including APIs, see
-    http://docs.python-requests.org/en/latest/user/quickstart/
-"""
-
-# 2. Reading a CSV file ====
-
-""" Wes McKinney's pandas package gives you 'DataFrame' objects in Python. If
-    you've used R, you will be familiar with the idea of the "data.frame" already.
-"""
-
-import pandas as pd, numpy as np, scipy as sp
-pets = pd.read_csv(fn)
-pets
-#        name  age  weight species
-# 0    fluffy    3      14     cat
-# 1  vesuvius    6      23    fish
-# 2       rex    5      34     dog
-
-""" R users: note that Python, like most normal programming languages, starts
-    indexing from 0. R is the unusual one for starting from 1.
-"""
-
-# two different ways to print out a column
-pets.age
-pets["age"]
-
-pets.head(2) # prints first 2 rows
-pets.tail(1) # prints last row
-
-pets.name[1] # 'vesuvius'
-pets.species[0] # 'cat'
-pets["weight"][2] # 34
-
-# in R, you would expect to get 3 rows doing this, but here you get 2:
-pets.age[0:2]
-# 0    3
-# 1    6
-
-sum(pets.age)*2 # 28
-max(pets.weight) - min(pets.weight) # 20
-
-""" If you are doing some serious linear algebra and number-crunching, you may
-    just want arrays, not DataFrames. DataFrames are ideal for combining columns
-    of different types.
-"""
-
-# 3. Charts ====
-
-import matplotlib as mpl, matplotlib.pyplot as plt
-%matplotlib inline
-
-# To do data vizualization in Python, use matplotlib
-
-plt.hist(pets.age);
-
-plt.boxplot(pets.weight);
-
-plt.scatter(pets.age, pets.weight); plt.xlabel("age"); plt.ylabel("weight");
-
-# seaborn sits atop matplotlib and makes plots prettier
-
-import seaborn as sns
-
-plt.scatter(pets.age, pets.weight); plt.xlabel("age"); plt.ylabel("weight");
-
-# there are also some seaborn-specific plotting functions
-# notice how seaborn automatically labels the x-axis on this barplot
-sns.barplot(pets["age"])
-
-# R veterans can still use ggplot
-from ggplot import *
-ggplot(aes(x="age",y="weight"), data=pets) + geom_point() + labs(title="pets")
-# source: https://pypi.python.org/pypi/ggplot
-
-# there's even a d3.js port: https://github.com/mikedewar/d3py
-
-# 4. Simple data cleaning and exploratory analysis ====
-
-""" Here's a more complicated example that demonstrates a basic data
-    cleaning workflow leading to the creation of some exploratory plots
-    and the running of a linear regression.
-        The data set was transcribed from Wikipedia by hand. It contains
-    all the Holy Roman Emperors and the important milestones in their lives
-    (birth, death, coronation, etc.).
-        The goal of the analysis will be to explore whether a relationship
-    exists between emperor birth year and emperor lifespan.
-    data source: https://en.wikipedia.org/wiki/Holy_Roman_Emperor
-"""
-
-# load some data on Holy Roman Emperors
-url = "https://raw.githubusercontent.com/e99n09/R-notes/master/data/hre.csv"
-r = requests.get(url)
-fp = "hre.csv"
-f = open(fp,"wb")
-f.write(r.text.encode("UTF-8"))
-f.close()
-
-hre = pd.read_csv(fp)
-
-hre.head()
-"""
-   Ix      Dynasty        Name        Birth             Death Election 1  
-0 NaN  Carolingian   Charles I  2 April 742    28 January 814        NaN   
-1 NaN  Carolingian     Louis I          778       20 June 840        NaN   
-2 NaN  Carolingian   Lothair I          795  29 September 855        NaN   
-3 NaN  Carolingian    Louis II          825     12 August 875        NaN   
-4 NaN  Carolingian  Charles II  13 June 823     6 October 877        NaN   
-
-  Election 2      Coronation 1   Coronation 2 Ceased to be Emperor  
-0        NaN   25 December 800            NaN       28 January 814   
-1        NaN  11 September 813  5 October 816          20 June 840   
-2        NaN       5 April 823            NaN     29 September 855   
-3        NaN        Easter 850     18 May 872        12 August 875   
-4        NaN   29 December 875            NaN        6 October 877   
-
-  Descent from whom 1 Descent how 1 Descent from whom 2 Descent how 2  
-0                 NaN           NaN                 NaN           NaN  
-1           Charles I           son                 NaN           NaN  
-2             Louis I           son                 NaN           NaN  
-3           Lothair I           son                 NaN           NaN  
-4             Louis I           son                 NaN           NaN  
-"""
-
-# clean the Birth and Death columns
-
-import re # module for regular expressions
-
-rx = re.compile(r'\d+$') # match trailing digits
-
-""" This function applies the regular expression to an input column (here Birth,
-    Death), flattens the resulting list, converts it to a Series object, and
-    finally converts the type of the Series object from string to integer. For
-    more information into what different parts of the code do, see:
-      - https://docs.python.org/2/howto/regex.html
-      - http://stackoverflow.com/questions/11860476/how-to-unlist-a-python-list
-      - http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.html
-"""
-def extractYear(v):
-    return(pd.Series(reduce(lambda x,y: x+y,map(rx.findall,v),[])).astype(int))
-
-hre["BirthY"] = extractYear(hre.Birth)
-hre["DeathY"] = extractYear(hre.Death)
-
-# make a column telling estimated age
-hre["EstAge"] = hre.DeathY.astype(int) - hre.BirthY.astype(int)
-
-# simple scatterplot, no trend line, color represents dynasty
-sns.lmplot("BirthY", "EstAge", data=hre, hue="Dynasty", fit_reg=False);
-
-# use scipy to run a linear regression
-from scipy import stats
-(slope,intercept,rval,pval,stderr)=stats.linregress(hre.BirthY,hre.EstAge)
-# code source: http://wiki.scipy.org/Cookbook/LinearRegression
-
-# check the slope
-slope # 0.0057672618839073328
-
-# check the R^2 value:
-rval**2 # 0.020363950027333586
-
-# check the p-value
-pval # 0.34971812581498452
-
-# use seaborn to make a scatterplot and plot the linear regression trend line
-sns.lmplot("BirthY", "EstAge", data=hre);
-
-""" For more information on seaborn, see
-      - http://web.stanford.edu/~mwaskom/software/seaborn/
-      - https://github.com/mwaskom/seaborn
-    For more information on SciPy, see
-      - http://wiki.scipy.org/SciPy
-      - http://wiki.scipy.org/Cookbook/
-    To see a version of the Holy Roman Emperors analysis using R, see
-      - http://github.com/e99n09/R-notes/blob/master/holy_roman_emperors_dates.R
-"""
-```
-
-If you want to learn more, get _Python for Data Analysis_ by Wes McKinney. It's a superb resource and I used it as a reference when writing this tutorial.
-
-You can also find plenty of interactive IPython tutorials on subjects specific to your interests, like Cam Davidson-Pilon's <a href="http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/" Title="Probabilistic Programming and Bayesian Methods for Hackers">Probabilistic Programming and Bayesian Methods for Hackers</a>.
-
-Some more modules to research:
-   - text analysis and natural language processing: nltk, http://www.nltk.org
-   - social network analysis: igraph, http://igraph.org/python/