Python Programming in Context

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Presentation transcript:

Python Programming in Context Chapter 5

Objectives To use text files to store large data sets To access online data sources To introduce the while loop To introduce Boolean expressions

Text File Sequence of characters, organized into lines, stored on some external memory device (such as a hard drive) End of line markers (new line character)

Python Open a file Read from a file Write to a file open read readline readlines Write to a file Write

Python Iterate thru the file line by line Use split method to process the line

Listing 5.1 rainfile = open("rainfall.txt","r") for aline in rainfile: values = aline.split() print(values[0], "had",values[1],"inches of rain.") rainfile.close()

Listing 5.2 rainfile = open("rainfall.txt","r") outfile = open("rainfallInCM.txt","w") for aline in rainfile: values = aline.split() inches = float(values[1]) cm = 2.54 * inches outfile.write(values[0]+" "+str(cm)+"\n") rainfile.close() outfile.close()

Processing Earthquake Data 2.8 2006/10/19 02:02:10 62.391 -149.751 15.0 CENTRAL ALASKA 2.5 2006/10/19 00:31:15 20.119 -156.213 1.5 MAUI REGION, HAWAII 5.0 2006/10/18 21:15:51 4.823 -82.592 37.3 SOUTH OF PANAMA 2.6 2006/10/18 21:12:25 59.934 -147.904 30.0 GULF OF ALASKA 3.4 2006/10/18 20:59:21 36.540 -89.640 7.7 SOUTHEASTERN MISSOURI 2.7 2006/10/18 20:11:22 61.023 -151.418 60.0 SOUTHERN ALASKA 3.1 2006/10/18 16:40:15 20.282 -156.611 4.7 MAUI REGION, HAWAII 2.7 2006/10/18 14:12:19 59.808 -152.538 50.0 SOUTHERN ALASKA 2.8 2006/10/18 14:02:12 60.686 -151.871 90.0 KENAI PENINSULA, ALASKA 4.9 2006/10/18 12:10:01 1.758 127.488 127.0 HALMAHERA, INDONESIA 6.2 2006/10/18 10:45:36 -15.081 167.243 138.5 VANUATU

Listing 5.3 def makeMagnitudeList(): quakefile = open("earthquakes.txt","r") maglist = [ ] for aline in quakefile: vlist = aline.split() maglist.append(float(vlist[0])) return maglist

Processing Data from the Internet urllib.request module Internet behaves just like a text file

Listing 5.4 import urllib.request def countHead(url): page = urllib.request.urlopen(url) numHeadLines = 0 line = page.readline().decode('utf-8') while '<head>' not in line: while '</head>' not in line: numHeadLines = numHeadLines + 1 while "<body>" not in line: while line != "" and "</body>" not in line: print (line[:-1]) print ("number of lines in header = ", numHeadLines) page.close()

Correlating Data from Internet Using real data streams Stock market data

Listing 5.5 def correlation(xlist, ylist): xbar = mean(xlist) ybar = mean(ylist) xstd = standardDev(xlist) ystd = standardDev(ylist) num = 0.0 for i in range(len(xlist)): num = num + (xlist[i]-xbar) * (ylist[i]-ybar) corr = num / ((len(xlist)-1) * xstd * ystd) return corr

Listing 5.6 def stockCorrelate(ticker1, ticker2): url1 = urllib.request.urlopen('http://ichart.yahoo.com/table.csv?s=%s'%ticker1) url2 = urllib.request.urlopen('http://ichart.yahoo.com/table.csv?s=%s'%ticker2) t1Data = url1.readlines() t2Data = url2.readlines() t1Data = [line[0:-1].decode("utf-8").split(',') for line in t1Data[1:] ] t2Data = [line[0:-1].decode("utf-8").split(',') for line in t2Data[1:] ] t1Close = [] t2Close = [] for i in range(min(len(t1Data), len(t2Data))): if t1Data[i][0] == t2Data[i][0]: t1Close.append(float(t1Data[i][4])) t2Close.append(float(t2Data[i][4])) return correlation(t1Close, t2Close)