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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
+import numpy as np
+import 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
+import 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"
+with open(fp, "wb") as f:
+    f.write(r.text.encode("UTF-8"))
+
+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/