def get_household_joint_dist_for_geography(self, ind):
# check the cache to see if we've done it already
puma = self.tazToPUMA2010.loc[ind.SFTAZ,'PUMA2010']
if puma in self.h_pums.keys():
return self.h_pums[puma], self.jd_households[puma]
# if not, get the superclass to do a bunch of variable setting
h_pums, p_pums = SFCTAStarter.get_pums(self, puma)
orig_len = len(h_pums)
# filter to housing unit only with number of persons > 0
h_pums = h_pums[h_pums['NP']>0]
# Only Housing units
h_pums = h_pums[h_pums['TYPE']==1]
print "Filtered to %d households from %d originally" % (len(h_pums), orig_len)
# Household income
h_pums['hhinc_2012dollars'] = h_pums['HINCP']*(0.000001*h_pums['ADJINC']) # ADJINC has 6 implied decimal places
h_pums['hhinc_1989dollars'] = 0.54*h_pums['hhinc_2012dollars']
h_pums['hhinc'] = h_pums['hhinc_1989dollars']/1000.0 # in thousands of dollars
# print sum(h_pums.loc[:,'hhinc']<0)
h_pums.loc[h_pums.loc[:,'hhinc']<0, 'hhinc'] = 0.0 # no negatives
# print sum(h_pums.loc[:,'hhinc']>255)
h_pums.loc[h_pums.loc[:,'hhinc']>255,'hhinc'] = 255.0 # max = 255
# For the following, r is a pandas.Series
# It's basically a row from h_pums, so any variables defined above will be available
def hhsize_cat(r):
# NP = number of persons
if r.NP >=5:
return "5+"
elif r.NP == 4:
return "4"
elif r.NP == 3:
return "3"
elif r.NP == 2:
return "2"
elif r.NP == 1:
return "1"
return "1"
def income_cat(r):
if r.hhinc < 25.0:
return "0-25k"
elif r.hhinc < 45.0:
return "25-45k"
elif r.hhinc < 75.0:
return "45-75k"
else:
return "75k+"
def workers_cat(r):
# hmm... WIF = Workers in Family. What about non-family households?
if r.workers >= 3:
return "3+"
elif r.workers == 2:
return "2"
elif r.workers == 1:
return "1"
return "0"
def htype_cat(r):
if r.hhage < 65 and r.NOC==0:
return "HAGE1K0"
elif r.hhage < 65 and r.NOC>0:
return "HAGE1K1"
else:
return "HAGE65KALL"
h_pums, jd_households = cat.joint_distribution(
h_pums,
cat.category_combinations(self.hh_controls.columns),
{"hhsize_cat": hhsize_cat,
"income_cat": income_cat,
"workers_cat": workers_cat,
"htype_cat": htype_cat}
)
# cache them
self.h_pums[puma] = h_pums
self.jd_households[puma] = jd_households
return h_pums, jd_households
def get_household_joint_dist_for_geography(self, ind):
# check the cache to see if we've done it already
puma = self.tazToPUMA2010.loc[ind.SFTAZ,'PUMA2010']
if puma in self.h_pums.keys():
return self.h_pums[puma], self.jd_households[puma]
# if not, get the superclass to do a bunch of variable setting
h_pums, p_pums = SFCTAStarter.get_pums(self, puma)
orig_len = len(h_pums)
# Don't bother filter number of persons -- this should happen with TYPE filter
# h_pums = h_pums[h_pums['NP']==1]
# Only Non-Institutional Group Quarters
h_pums = h_pums[h_pums['TYPE']>2]
print "Filtered to %d GQ 'households' from %d originally" % (len(h_pums), orig_len)
np_bad = (h_pums.NP != 1)
assert(np_bad.sum() == 0)
# Group quarters income -- use PINCP
h_pums.loc[pd.isnull(h_pums.loc[:,'PINCP']),'PINCP'] = 0.0 # no null
h_pums['hhinc_2012dollars'] = h_pums['PINCP']*(0.000001*h_pums['ADJINC']) # ADJINC has 6 implied decimal places
h_pums['hhinc_1989dollars'] = 0.54*h_pums['hhinc_2012dollars']
h_pums['hhinc'] = h_pums['hhinc_1989dollars']/1000.0 # in thousands of dollars
# print sum(h_pums.loc[:,'hhinc']<0)
h_pums.loc[h_pums.loc[:,'hhinc']<0, 'hhinc'] = 0.0 # no negatives
# print sum(h_pums.loc[:,'hhinc']>255)
h_pums.loc[h_pums.loc[:,'hhinc']>255,'hhinc'] = 255.0 # max = 255
# For the following, r is a pandas.Series
# It's basically a row from h_pums, so any variables defined above will be available
def hhsize_cat(r):
# NP = number of persons
if r.NP >=5:
return "5+"
elif r.NP == 4:
return "4"
elif r.NP == 3:
return "3"
elif r.NP == 2:
return "2"
elif r.NP == 1:
return "1"
return "1"
def income_cat(r):
if r.hhinc < 25.0:
return "0-25k"
elif r.hhinc < 45.0:
return "25-45k"
elif r.hhinc < 75.0:
return "45-75k"
else:
return "75k+"
def workers_cat(r):
# hmm... WIF = Workers in Family. What about non-family households?
if r.workers >= 3:
return "3+"
elif r.workers == 2:
return "2"
elif r.workers == 1:
return "1"
return "0"
def htype_cat(r):
if r.hhage < 65 and r.gqchild==0:
return "HAGE1K0"
elif r.hhage < 65 and r.gqchild > 0:
return "HAGE1K1"
else:
return "HAGE65KALL"
category_df = pd.DataFrame({'cat_id':[0], 'hhsize_cat':["1"]})
category_df.set_index(['hhsize_cat'], inplace=True)
h_pums, jd_households = cat.joint_distribution(
h_pums,
category_df,
{"hhsize_cat": hhsize_cat,
"income_cat": income_cat,
"workers_cat": workers_cat,
"htype_cat": htype_cat}
)
# cache them
self.h_pums[puma] = h_pums
self.jd_households[puma] = jd_households
return h_pums, jd_households
def get_household_joint_dist_for_geography(self, ind):
# check the cache to see if we've done it already
puma = self.tazToPUMA2010.loc[ind.SFTAZ, 'PUMA2010']
if puma in self.h_pums.keys():
return self.h_pums[puma], self.jd_households[puma]
# if not, get the superclass to do a bunch of variable setting
h_pums, p_pums = SFCTAStarter.get_pums(self, puma)
orig_len = len(h_pums)
# filter to housing unit only with number of persons > 0
h_pums = h_pums[h_pums['NP'] > 0]
# Only Housing units
h_pums = h_pums[h_pums['TYPE'] == 1]
print "Filtered to %d households from %d originally" % (len(h_pums),
orig_len)
# Household income
h_pums['hhinc_2012dollars'] = h_pums['HINCP'] * (
0.000001 * h_pums['ADJINC']) # ADJINC has 6 implied decimal places
h_pums['hhinc_1989dollars'] = 0.54 * h_pums['hhinc_2012dollars']
h_pums['hhinc'] = h_pums[
'hhinc_1989dollars'] / 1000.0 # in thousands of dollars
# print sum(h_pums.loc[:,'hhinc']<0)
h_pums.loc[h_pums.loc[:, 'hhinc'] < 0, 'hhinc'] = 0.0 # no negatives
# print sum(h_pums.loc[:,'hhinc']>255)
h_pums.loc[h_pums.loc[:, 'hhinc'] > 255, 'hhinc'] = 255.0 # max = 255
# For the following, r is a pandas.Series
# It's basically a row from h_pums, so any variables defined above will be available
def hhsize_cat(r):
# NP = number of persons
if r.NP >= 5:
return "5+"
elif r.NP == 4:
return "4"
elif r.NP == 3:
return "3"
elif r.NP == 2:
return "2"
elif r.NP == 1:
return "1"
return "1"
def income_cat(r):
if r.hhinc < 25.0:
return "0-25k"
elif r.hhinc < 45.0:
return "25-45k"
elif r.hhinc < 75.0:
return "45-75k"
else:
return "75k+"
def workers_cat(r):
# hmm... WIF = Workers in Family. What about non-family households?
if r.workers >= 3:
return "3+"
elif r.workers == 2:
return "2"
elif r.workers == 1:
return "1"
return "0"
def htype_cat(r):
if r.hhage < 65 and r.NOC == 0:
return "HAGE1K0"
elif r.hhage < 65 and r.NOC > 0:
return "HAGE1K1"
else:
return "HAGE65KALL"
h_pums, jd_households = cat.joint_distribution(
h_pums, cat.category_combinations(self.hh_controls.columns), {
"hhsize_cat": hhsize_cat,
"income_cat": income_cat,
"workers_cat": workers_cat,
"htype_cat": htype_cat
})
# cache them
self.h_pums[puma] = h_pums
self.jd_households[puma] = jd_households
return h_pums, jd_households
def get_household_joint_dist_for_geography(self, ind):
# check the cache to see if we've done it already
puma = self.tazToPUMA2010.loc[ind.SFTAZ, 'PUMA2010']
if puma in self.h_pums.keys():
return self.h_pums[puma], self.jd_households[puma]
# if not, get the superclass to do a bunch of variable setting
h_pums, p_pums = SFCTAStarter.get_pums(self, puma)
orig_len = len(h_pums)
# Don't bother filter number of persons -- this should happen with TYPE filter
# h_pums = h_pums[h_pums['NP']==1]
# Only Non-Institutional Group Quarters
h_pums = h_pums[h_pums['TYPE'] > 2]
print "Filtered to %d GQ 'households' from %d originally" % (
len(h_pums), orig_len)
np_bad = (h_pums.NP != 1)
assert (np_bad.sum() == 0)
# Group quarters income -- use PINCP
h_pums.loc[pd.isnull(h_pums.loc[:, 'PINCP']), 'PINCP'] = 0.0 # no null
h_pums['hhinc_2012dollars'] = h_pums['PINCP'] * (
0.000001 * h_pums['ADJINC']) # ADJINC has 6 implied decimal places
h_pums['hhinc_1989dollars'] = 0.54 * h_pums['hhinc_2012dollars']
h_pums['hhinc'] = h_pums[
'hhinc_1989dollars'] / 1000.0 # in thousands of dollars
# print sum(h_pums.loc[:,'hhinc']<0)
h_pums.loc[h_pums.loc[:, 'hhinc'] < 0, 'hhinc'] = 0.0 # no negatives
# print sum(h_pums.loc[:,'hhinc']>255)
h_pums.loc[h_pums.loc[:, 'hhinc'] > 255, 'hhinc'] = 255.0 # max = 255
# For the following, r is a pandas.Series
# It's basically a row from h_pums, so any variables defined above will be available
def hhsize_cat(r):
# NP = number of persons
if r.NP >= 5:
return "5+"
elif r.NP == 4:
return "4"
elif r.NP == 3:
return "3"
elif r.NP == 2:
return "2"
elif r.NP == 1:
return "1"
return "1"
def income_cat(r):
if r.hhinc < 25.0:
return "0-25k"
elif r.hhinc < 45.0:
return "25-45k"
elif r.hhinc < 75.0:
return "45-75k"
else:
return "75k+"
def workers_cat(r):
# hmm... WIF = Workers in Family. What about non-family households?
if r.workers >= 3:
return "3+"
elif r.workers == 2:
return "2"
elif r.workers == 1:
return "1"
return "0"
def htype_cat(r):
if r.hhage < 65 and r.gqchild == 0:
return "HAGE1K0"
elif r.hhage < 65 and r.gqchild > 0:
return "HAGE1K1"
else:
return "HAGE65KALL"
category_df = pd.DataFrame({'cat_id': [0], 'hhsize_cat': ["1"]})
category_df.set_index(['hhsize_cat'], inplace=True)
h_pums, jd_households = cat.joint_distribution(
h_pums, category_df, {
"hhsize_cat": hhsize_cat,
"income_cat": income_cat,
"workers_cat": workers_cat,
"htype_cat": htype_cat
})
# cache them
self.h_pums[puma] = h_pums
self.jd_households[puma] = jd_households
return h_pums, jd_households
