Add Python3 support

Edits:
 - Replace print statement with print function
 - Replace xrange with range
 - Replace iter* in dict methods (e.g. iteritems())
 - Replace generator.next() with next(generator) (available since python 2.6 release in 2008)
 - Explicit id based hash function for Pmf

code/brfss.py

@@ -4,6 +4,7 @@
 Copyright 2010 Allen B. Downey
 License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
 """
+from __future__ import print_function
 
 import math
 import sys
@@ -58,7 +59,7 @@ def Recode(self):
         """Recode variables that need cleaning."""
 
         def CleanWeight(weight):
-            if weight in [7777, 9999]:
+            if weight in [7777, 9999, 'NA']:
                 return 'NA'
             elif weight < 1000:
                 return weight / 2.2
@@ -95,13 +96,13 @@ def SummarizeHeight(self):
         [d[r.sex].append(r.htm3) for r in self.records if r.htm3 != 'NA']
         [d['all'].append(r.htm3) for r in self.records if r.htm3 != 'NA']
 
-        print 'Height (cm):'
-        print 'key n     mean     var    sigma     cv'
-        for key, t in d.iteritems():
+        print('Height (cm):')
+        print('key n     mean     var    sigma     cv')
+        for key, t in d.items():
             mu, var = thinkstats.TrimmedMeanVar(t)
             sigma = math.sqrt(var)
             cv = sigma / mu
-            print key, len(t), mu, var, sigma, cv
+            print(key, len(t), mu, var, sigma, cv)
 
         return d
 
@@ -113,13 +114,13 @@ def SummarizeWeight(self):
         [d[r.sex].append(r.weight2) for r in self.records if r.weight2 != 'NA']
         [d['all'].append(r.weight2) for r in self.records if r.weight2 != 'NA']
 
-        print 'Weight (kg):'
-        print 'key n     mean     var    sigma     cv'
-        for key, t in d.iteritems():
+        print('Weight (kg):')
+        print('key n     mean     var    sigma     cv')
+        for key, t in d.items():
             mu, var = thinkstats.TrimmedMeanVar(t)
             sigma = math.sqrt(var)
             cv = sigma / mu
-            print key, len(t), mu, var, sigma, cv
+            print(key, len(t), mu, var, sigma, cv)
 
 
     def SummarizeWeightChange(self):
@@ -130,7 +131,7 @@ def SummarizeWeightChange(self):
 
         changes = [(curr - prev) for curr, prev in data]
 
-        print 'Mean change', thinkstats.Mean(changes)
+        print('Mean change', thinkstats.Mean(changes))
 
 
 def main(name, data_dir='.'):

code/columns.py

@@ -4,6 +4,8 @@
 Copyright 2012 Allen B. Downey
 License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
 """
+from __future__ import print_function
+
 
 import csv
 
@@ -16,7 +18,7 @@ def read_csv(filename, constructor):
     fp = open(filename)
     reader = csv.reader(fp)
 
-    header = reader.next()
+    header = next(reader)
     names = [s.lower() for s in header]
 
     objs = [make_object(t, names, constructor) for t in reader]
@@ -47,7 +49,7 @@ def print_cols(cols):
     cols: list of columns
     """
     for i, col in enumerate(cols):
-        print i, col[0], col[1]
+        print(i, col[0], col[1])
 
 
 def make_col_dict(cols, names):

code/cookie.py

@@ -4,6 +4,7 @@
 Copyright 2012 Allen B. Downey
 License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
 """
+from __future__ import print_function
 
 from thinkbayes import Pmf
 
@@ -16,4 +17,4 @@
 
 pmf.Normalize()
 
-print pmf.Prob('Bowl 1')
+print(pmf.Prob('Bowl 1'))

code/cookie2.py

@@ -4,6 +4,7 @@
 Copyright 2012 Allen B. Downey
 License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
 """
+from __future__ import print_function
 
 from thinkbayes import Pmf
 
@@ -55,7 +56,7 @@ def main():
     pmf.Update('vanilla')
 
     for hypo, prob in pmf.Items():
-        print hypo, prob
+        print(hypo, prob)
 
 
 if __name__ == '__main__':

code/correlation.py

@@ -4,6 +4,7 @@
 Copyright 2010 Allen B. Downey
 License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
 """
+from __future__ import print_function
 
 import math
 import random
@@ -171,7 +172,7 @@ def CorrelatedGenerator(rho):
     x = random.gauss(0, 1)
     yield x
 
-    sigma = math.sqrt(1 - rho**2);    
+    sigma = math.sqrt(1 - rho**2)
     while True:
         x = random.gauss(x * rho, sigma)
         yield x

code/dice.py

@@ -4,6 +4,7 @@
 Copyright 2012 Allen B. Downey
 License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
 """
+from __future__ import print_function
 
 from thinkbayes import Suite
 
@@ -27,13 +28,13 @@ def main():
     suite = Dice([4, 6, 8, 12, 20])
 
     suite.Update(6)
-    print 'After one 6'
+    print('After one 6')
     suite.Print()
 
     for roll in [4, 8, 7, 7, 2]:
         suite.Update(roll)
 
-    print 'After more rolls'
+    print('After more rolls')
     suite.Print()
 
 

code/dungeons.py

@@ -4,6 +4,7 @@
 Copyright 2012 Allen B. Downey
 License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
 """
+from __future__ import print_function
 
 import random
 
@@ -23,7 +24,7 @@ def __init__(self, sides, name=''):
         name: string
         """
         thinkbayes.Pmf.__init__(self, name=name)
-        for x in xrange(1, sides+1):
+        for x in range(1, sides+1):
             self.Set(x, 1)
         self.Normalize()
 

code/euro.py

@@ -4,6 +4,7 @@
 Copyright 2012 Allen B. Downey
 License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
 """
+from __future__ import print_function
 
 """This file contains a partial solution to a problem from
 MacKay, "Information Theory, Inference, and Learning Algorithms."
@@ -59,7 +60,7 @@ def Likelihood(self, data, hypo):
 
 def UniformPrior():
     """Makes a Suite with a uniform prior."""
-    suite = Euro(xrange(0, 101))
+    suite = Euro(range(0, 101))
     return suite
 
 
@@ -89,17 +90,17 @@ def RunUpdate(suite, heads=140, tails=110):
 
 def Summarize(suite):
     """Prints summary statistics for the suite."""
-    print suite.Prob(50)
+    print(suite.Prob(50))
 
-    print 'MLE', suite.MaximumLikelihood()
+    print('MLE', suite.MaximumLikelihood())
 
-    print 'Mean', suite.Mean()
-    print 'Median', thinkbayes.Percentile(suite, 50) 
+    print('Mean', suite.Mean())
+    print('Median', thinkbayes.Percentile(suite, 50))
 
-    print '5th %ile', thinkbayes.Percentile(suite, 5) 
-    print '95th %ile', thinkbayes.Percentile(suite, 95) 
+    print('5th %ile', thinkbayes.Percentile(suite, 5))
+    print('95th %ile', thinkbayes.Percentile(suite, 95))
 
-    print 'CI', thinkbayes.CredibleInterval(suite, 90)
+    print('CI', thinkbayes.CredibleInterval(suite, 90))
 
 
 def PlotSuites(suites, root):

code/euro2.py

@@ -4,6 +4,7 @@
 Copyright 2012 Allen B. Downey
 License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
 """
+from __future__ import print_function
 
 """This file contains a partial solution to a problem from
 MacKay, "Information Theory, Inference, and Learning Algorithms."
@@ -58,7 +59,7 @@ def Likelihood(self, data, hypo):
 
 
 def Version1():
-    suite = Euro(xrange(0, 101))
+    suite = Euro(range(0, 101))
     heads, tails = 140, 110
     dataset = 'H' * heads + 'T' * tails
 
@@ -69,7 +70,7 @@ def Version1():
 
 
 def Version2():
-    suite = Euro(xrange(0, 101))
+    suite = Euro(range(0, 101))
     heads, tails = 140, 110
     dataset = 'H' * heads + 'T' * tails
 
@@ -78,7 +79,7 @@ def Version2():
 
 
 def Version3():
-    suite = Euro2(xrange(0, 101))
+    suite = Euro2(range(0, 101))
     heads, tails = 140, 110
 
     suite.Update((heads, tails))
@@ -88,7 +89,7 @@ def Version3():
 def main():
 
     suite = Version3()
-    print suite.Mean()
+    print(suite.Mean())
 
     thinkplot.Pmf(suite)
     thinkplot.Show()

code/euro3.py

@@ -4,6 +4,7 @@
 Copyright 2012 Allen B. Downey
 License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
 """
+from __future__ import print_function
 
 """This file contains a partial solution to a problem from
 MacKay, "Information Theory, Inference, and Learning Algorithms."
@@ -72,32 +73,32 @@ def Main():
 
     suite = Euro()
     like_f = suite.Likelihood(data, 50)
-    print 'p(D|F)', like_f
+    print('p(D|F)', like_f)
 
     actual_percent = 100.0 * 140 / 250
     likelihood = suite.Likelihood(data, actual_percent)
-    print 'p(D|B_cheat)', likelihood
-    print 'p(D|B_cheat) / p(D|F)', likelihood / like_f
+    print('p(D|B_cheat)', likelihood)
+    print('p(D|B_cheat) / p(D|F)', likelihood / like_f)
 
     like40 = suite.Likelihood(data, 40)
     like60 = suite.Likelihood(data, 60)
     likelihood = 0.5 * like40 + 0.5 * like60
-    print 'p(D|B_two)', likelihood
-    print 'p(D|B_two) / p(D|F)', likelihood / like_f
+    print('p(D|B_two)', likelihood)
+    print('p(D|B_two) / p(D|F)', likelihood / like_f)
 
-    b_uniform = Euro(xrange(0, 101))
+    b_uniform = Euro(range(0, 101))
     b_uniform.Remove(50)
     b_uniform.Normalize()
     likelihood = SuiteLikelihood(b_uniform, data)
-    print 'p(D|B_uniform)', likelihood
-    print 'p(D|B_uniform) / p(D|F)', likelihood / like_f
+    print('p(D|B_uniform)', likelihood)
+    print('p(D|B_uniform) / p(D|F)', likelihood / like_f)
 
     b_tri = TrianglePrior()
     b_tri.Remove(50)
     b_tri.Normalize()
     likelihood = b_tri.Update(data)
-    print 'p(D|B_tri)', likelihood
-    print 'p(D|B_tri) / p(D|F)', likelihood / like_f
+    print('p(D|B_tri)', likelihood)
+    print('p(D|B_tri) / p(D|F)', likelihood / like_f)
 
 
 if __name__ == '__main__':