weightedcalcsThe example below uawa weightedcalcs to analyze a slice of the American Community Survey's 2015 data for Wyoming.
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import weightedcalcs as wc
import pandas as pd
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responses = pd.read_csv("../data/acs-2015-pums-wy-simple.csv")
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responses.head()
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In addition to the full list of responses, let's create a subset including only adult respondents, since we'll be focusing on income later.
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adults = responses[responses["age"] >= 18]
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adults.head()
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calc = wc.Calculator("PWGTP")
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calc.mean(adults, "income").round()
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calc.std(adults, "income").round()
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calc.median(adults, "income")
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calc.quantile(adults, "income", 0.75)
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~43% of Wyoming residents are married:
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calc.distribution(responses, "marriage_status").round(3).sort_values(ascending=False)
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~56% of adult Wyoming residents are married:
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calc.distribution(adults, "marriage_status").round(3).sort_values(ascending=False)
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grp_marriage_sex = adults.groupby(["marriage_status", "gender"])
For reference, here's how many responses fall into each category:
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grp_marriage_sex.size().unstack()
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calc.mean(grp_marriage_sex, "income").round().astype(int)
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calc.std(grp_marriage_sex, "income").round()
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calc.median(grp_marriage_sex, "income")
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calc.quantile(grp_marriage_sex, "income", 0.75)
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