<jupyter_start><jupyter_text>B17001 Poverty Status by Sex by AgeFor the [Poverty Status by Sex by Age](https://censusreporter.org/tables/B17001/) we'll select the columns for male and female, below poverty, 65 and older. **NOTE** if you want to get seniors of a particular race, use table `C17001a-g`, condensed race iterations. The 'C' tables have fewer age ranges, but there is no 'C' table for all races: There is a `C17001a` for Whites, a condensed version of `B17001a`, but there is no `C17001` for a condensed version of `B17001`<jupyter_code>[e for e in b17001.columns if '65 to 74' in str(e) or '75 years' in str(e) ]
# Now create a subset dataframe with just the columns we need. 
b17001s = b17001[['geoid', 'B17001015', 'B17001016','B17001029','B17001030']]
b17001s.head()<jupyter_output><empty_output><jupyter_text>Senior poverty ratesCreating the sums for the senior below poverty rates at the tract level is easy, but there is a *serious problem* with the results: the numbers are completely unstable. The minimum RSE is 22%, and the median is about 60%. These are useless results.<jupyter_code>b17001_65mf = pr.CensusDataFrame()
b17001_65mf['geoid'] = b17001['geoid']
b17001_65mf['poverty_65'], b17001_65mf['poverty_65_m90'] = b17001.sum_m('B17001015', 'B17001016','B17001029','B17001030')
b17001_65mf.add_rse('poverty_65')
b17001_65mf.poverty_65_rse.replace([np.inf, -np.inf], np.nan).dropna().describe()<jupyter_output><empty_output>
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