Analyser les données du recensement des États-Unis avec Python
Lee Hachadoorian
Asst. Professor of Instruction, Temple University
Différentes façons de calculer le loyer :
Fardeau de loyer :
Table B25074 : Revenu du ménage selon le loyer brut en pourcentage du revenu du ménage au cours des 12 derniers mois
Total
Moins de $10,000
Moins de 20,0 %
20,0 à 24,9 %
25,0 à 29,9 %
30,0 à 34,9 %
35,0 à 39,9 %
40,0 à 49,9 %
50,0 % ou plus
Non calculé
$10,000 à $19,999
$20,000 à $34,999
$35,000 à $49,999
$50,000 à $74,999
$75,000 à $99,999
$100,000 ou plus
États-Unis : part du revenu consacrée au loyer, 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

Analyser les données du recensement des États-Unis avec Python