Exercise from Think Stats, 2nd Edition (thinkstats2.com)
Allen Downey

Read the female respondent file and display the variables names.



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%matplotlib inline

import chap01soln
resp = chap01soln.ReadFemResp()
resp.columns

Make a histogram of totincr the total income for the respondent's family. To interpret the codes see the codebook.



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import thinkstats2
hist = thinkstats2.Hist(resp.totincr)

Display the histogram.



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import thinkplot
thinkplot.Hist(hist, label='totincr')
thinkplot.Show()

Make a histogram of age_r, the respondent's age at the time of interview.



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# 回答者の年齢をヒストグラムで表示する
import thinkstats2
import chap01soln
import thinkplot
    
def ex2pr1():
    df = chap01soln.ReadFemResp()
    #print(df.age_r.value_counts().sort_index())  #年齢別回答者数を表示（年齢順）
    #print(df.age_r.value_counts())  #年齢別回答者数を表示（人数順）
    hist = thinkstats2.Hist(df.age_r, label='age_r')
    thinkplot.Hist(hist)
    thinkplot.Show(xlabel='Age of respondent', ylabel='Frequency')


ex2pr1()

Make a histogram of numfmhh, the number of people in the respondent's household.



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Make a histogram of parity, the number children the respondent has borne. How would you describe this distribution?



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Use Hist.Largest to find the largest values of parity.



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Use totincr to select the respondents with the highest income. Compute the distribution of parity for just the high income respondents.



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Find the largest parities for high income respondents.



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Compare the mean parity for high income respondents and others.



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Investigate any other variables that look interesting.



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