diff options
Diffstat (limited to 'pythonstatcomp.html.markdown')
| -rw-r--r-- | pythonstatcomp.html.markdown | 110 |
1 files changed, 60 insertions, 50 deletions
diff --git a/pythonstatcomp.html.markdown b/pythonstatcomp.html.markdown index 78b62e33..8ee3aa64 100644 --- a/pythonstatcomp.html.markdown +++ b/pythonstatcomp.html.markdown @@ -1,14 +1,16 @@ --- -language: Statistical computing with Python +category: tool +tool: 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, @@ -25,17 +27,17 @@ This is a tutorial on how to do some typical statistical programming tasks using already using Python, there's a benefit to sticking with one language. """ -import requests # for HTTP requests (web scraping, APIs) +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 +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") +os.getcwd() # check what's the working directory +f = open("learnxinyminutes.html", "wb") f.write(r.text.encode("UTF-8")) f.close() @@ -44,7 +46,7 @@ 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 = open(fn, "wb") f.write(r.text.encode("UTF-8")) f.close() @@ -58,7 +60,9 @@ f.close() 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 +import pandas as pd +import numpy as np +import scipy as sp pets = pd.read_csv(fn) pets # name age weight species @@ -74,20 +78,20 @@ pets pets.age pets["age"] -pets.head(2) # prints first 2 rows -pets.tail(1) # prints last row +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 +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 +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 @@ -96,7 +100,8 @@ max(pets.weight) - min(pets.weight) # 20 # 3. Charts ==== -import matplotlib as mpl, matplotlib.pyplot as plt +import matplotlib as mpl +import matplotlib.pyplot as plt %matplotlib inline # To do data vizualization in Python, use matplotlib @@ -105,13 +110,17 @@ plt.hist(pets.age); plt.boxplot(pets.weight); -plt.scatter(pets.age, pets.weight); plt.xlabel("age"); plt.ylabel("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"); +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 @@ -141,41 +150,40 @@ ggplot(aes(x="age",y="weight"), data=pets) + geom_point() + labs(title="pets") 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() +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 + 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 +import re # module for regular expressions -rx = re.compile(r'\d+$') # match trailing digits +rx = re.compile(r'\d+$') # 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 @@ -185,8 +193,9 @@ rx = re.compile(r'\d+$') # match trailing digits - 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)) + 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) @@ -199,17 +208,17 @@ 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) +(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 +slope # 0.0057672618839073328 # check the R^2 value: -rval**2 # 0.020363950027333586 +rval**2 # 0.020363950027333586 # check the p-value -pval # 0.34971812581498452 +pval # 0.34971812581498452 # use seaborn to make a scatterplot and plot the linear regression trend line sns.lmplot("BirthY", "EstAge", data=hre); @@ -223,6 +232,7 @@ sns.lmplot("BirthY", "EstAge", data=hre); 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. |