Read the pregnancy file.



In [1]:

    
import nsfg
preg = nsfg.ReadFemPreg()

Select live births, then make a CDF of totalwgt_lb.



In [2]:

    
import thinkstats2
live = preg[preg.outcome == 1]
firsts = live[live.birthord == 1]
others = live[live.birthord != 1]
cdf = thinkstats2.Cdf(live.totalwgt_lb)

Display the CDF.



In [43]:

    
import thinkplot
thinkplot.Cdf(cdf, label='totalwgt_lb')
thinkplot.Show(loc='lower right')

Find out how much you weighed at birth, if you can, and compute CDF(x).



In [44]:

    
cdf.Prob(8.4)









    Out[44]:





0.81422881168400085

If you are a first child, look up your birthweight in the CDF of first children; otherwise use the CDF of other children.



In [59]:

    
other_cdf = thinkstats2.Cdf(others.totalwgt_lb)
other_cdf.Prob(8.4)









    Out[59]:





0.79657754010695192

Compute the percentile rank of your birthweight



In [46]:

    
cdf.PercentileRank(8.4)









    Out[46]:





81.422881168400082

Compute the median birth weight by looking up the value associated with p=0.5.



In [45]:

    
cdf.Value(0.5)









    Out[45]:





7.375

Compute the interquartile range (IQR) by computing percentiles corresponding to 25 and 75.



In [47]:

    
cdf.Percentile(25), cdf.Percentile(75)









    Out[47]:





(6.5, 8.125)

Make a random selection from cdf.



In [48]:

    
cdf.Random()









    Out[48]:





7.0

Draw a random sample from cdf.



In [49]:

    
cdf.Sample(10)









    Out[49]:





[6.25, 5.1875, 8.1875, 6.5, 7.9375, 6.6875, 5.75, 6.5625, 7.8125, 5.25]

Draw a random sample from cdf, then compute the percentile rank for each value, and plot the distribution of the percentile ranks.



In [50]:

    
t = [cdf.PercentileRank(x) for x in cdf.Sample(1000)]
cdf2 = thinkstats2.Cdf(t)
thinkplot.Cdf(cdf2)
thinkplot.Show(legend=False)

Generate 1000 random values using random.random() and plot their PMF.



In [55]:

    
import random
t = [random.random() for _ in range(1000)]
pmf = thinkstats2.Pmf(t)
thinkplot.Pmf(pmf, linewidth=0.1)
thinkplot.Show()

Assuming that the PMF doesn't work very well, try plotting the CDF instead.



In [56]:

    
cdf = thinkstats2.Cdf(t)
thinkplot.Cdf(cdf)
thinkplot.Show()



In [60]:

    
import scipy.stats



In [64]:

    
scipy.stats.norm.cdf(0)









    Out[64]:





0.5



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