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.
# 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 # 'vesuvius' pets.species # 'cat' pets["weight"] # 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 visualization 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 Probabilistic Programming and Bayesian Methods for Hackers.
Some more modules to research: - text analysis and natural language processing: nltk, http://www.nltk.org - social network analysis: igraph, http://igraph.org/python/
Got a suggestion? A correction, perhaps? Open an Issue on the Github Repo, or make a pull request yourself!
Originally contributed by e99n09, and updated by 1 contributor(s).
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