summaryrefslogtreecommitdiffhomepage
path: root/pythonstatcomp.markdown.html
diff options
context:
space:
mode:
Diffstat (limited to 'pythonstatcomp.markdown.html')
-rw-r--r--pythonstatcomp.markdown.html234
1 files changed, 234 insertions, 0 deletions
diff --git a/pythonstatcomp.markdown.html b/pythonstatcomp.markdown.html
new file mode 100644
index 00000000..78b62e33
--- /dev/null
+++ b/pythonstatcomp.markdown.html
@@ -0,0 +1,234 @@
+---
+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/