Analizzare i dati del Censimento USA con Python
Lee Hachadoorian
Asst. Professor of Instruction, Temple University
Modi diversi di calcolare l’affitto:
Onere dell’affitto:
Tabella B25074: Reddito familiare per affitto lordo come percentuale del reddito familiare negli ultimi 12 mesi
Totale
Meno di $10.000
Meno del 20,0 percento
20,0–24,9 percento
25,0–29,9 percento
30,0–34,9 percento
35,0–39,9 percento
40,0–49,9 percento
50,0 percento o più
Non calcolato
$10.000–$19.999
$20.000–$34.999
$35.000–$49.999
$50.000–$74.999
$75.000–$99.999
$100.000 o più
USA: quota d’affitto sul reddito, ACS 2012–2016
total 42835169
inc_under_10k 5558843
inc_under_10k_rent_under_20_pct 57052
inc_under_10k_rent_20_to_25_pct 58042
inc_under_10k_rent_25_to_30_pct 208806
inc_under_10k_rent_30_to_35_pct 177709
inc_under_10k_rent_35_to_40_pct 102565
inc_under_10k_rent_40_to_50_pct 150153
inc_under_10k_rent_over_50_pct 3381537
inc_under_10k_rent_not_computed 1422979
inc_10k_to_20k 7027373
inc_10k_to_20k_rent_under_20_pct 213000
etc...
print(rent.columns[10:19])
Index(['inc_10k_to_20k', 'inc_10k_to_20k_rent_under_20_pct',
'inc_10k_to_20k_rent_20_to_25_pct', 'inc_10k_to_20k_rent_25_to_30_pct',
'inc_10k_to_20k_rent_30_to_35_pct', 'inc_10k_to_20k_rent_35_to_40_pct',
'inc_10k_to_20k_rent_40_to_50_pct', 'inc_10k_to_20k_rent_over_50_pct',
'inc_10k_to_20k_rent_not_computed'],
dtype='object')
rent["inc_10k_to_20k_rent_burden"] = 100 * (rent["inc_10k_to_20k_rent_30_to_35_pct"] + rent["inc_10k_to_20k_rent_35_to_40_pct"] + rent["inc_10k_to_20k_rent_40_to_50_pct"] + rent["inc_10k_to_20k_rent_over_50_pct"]) / ( rent["inc_10k_to_20k"] - rent["inc_10k_to_20k_rent_not_computed"] )
print(rent["inc_10k_to_20k_rent_burden"])
0 87.008024
Name: inc_10k_to_20k_rent_burden, dtype: float64
# Create list with income category part of column names
incomes = ["inc_under_10k", "inc_10k_to_20k", "inc_20k_to_35k",
"inc_35k_to_50k", "inc_50k_to_75k", "inc_75k_to_100k",
"inc_over_100k"]
# Create new DataFrame with just the geography name rent_burden = rent["name"] # Loop over the list of income categories for income in incomes:# Construct column names rent_burden[income] =100 * (rent[income + "_rent_30_to_35_pct"] + rent[income + "_rent_35_to_40_pct"] + rent[income + "_rent_40_to_50_pct"] + rent[income + "_rent_over_50_pct"]) / ( rent[income] - rent[income + "_rent_not_computed"])
print(rent_burden.squeeze())
name United States
inc_under_10k 92.1685
inc_10k_to_20k 87.008
inc_20k_to_35k 74.7448
inc_35k_to_50k 43.0434
inc_50k_to_75k 21.0937
inc_75k_to_100k 9.11853
inc_over_100k 3.14882
Name: 0, dtype: object

Analizzare i dati del Censimento USA con Python