In [1]:

%load_ext autoreload
%autoreload 2
from synthpop.census_helpers import Census
from synthpop import categorizer as cat
import pandas as pd
import numpy as np
import os
pd.set_option('display.max_columns', 500)

http://api.census.gov/data/key_signup.html ¶

In [2]:

c = Census(os.environ["CENSUS"])

Here we get aggregate information on households from ACS - note some variables are associated with block groups and others with tracts¶

In [3]:

income_columns = ['B19001_0%02dE'%i for i in range(1, 18)]
vehicle_columns = ['B08201_0%02dE'%i for i in range(1, 7)]
workers_columns = ['B08202_0%02dE'%i for i in range(1, 6)]
families_columns = ['B11001_001E', 'B11001_002E']
block_group_columns = income_columns + families_columns
tract_columns = vehicle_columns + workers_columns
h_acs = c.block_group_and_tract_query(block_group_columns,
                tract_columns, "06", "075", 
                merge_columns=['tract', 'county', 'state'],
                block_group_size_attr="B11001_001E",
                tract_size_attr="B08201_001E",
                tract="030600")
h_acs

Out[3]:

	B11001_001E	B11001_002E	B19001_001E	B19001_002E	B19001_003E	B19001_004E	B19001_005E	B19001_006E	B19001_007E	B19001_010E	B19001_011E	B19001_012E	B19001_013E	B19001_014E	B19001_015E	B19001_016E	B19001_017E	NAME	block group	county	state	tract	B08201_001E	B08201_002E	B08201_003E	B08201_004E	B08201_005E	B08201_006E	B08202_001E	B08202_002E	B08202_003E	B08202_004E	B08202_005E
0	294	183	294	0	4	8	28	0	8	0	27	10	36	33	28	34	78	Block Group 1, Census Tract 306, San Francisco...	1	075	06	030600	294	14	86	125	55	12	294	65	89	118	20
1	226	138	226	0	11	10	9	0	20	0	9	11	0	25	19	31	81	Block Group 2, Census Tract 306, San Francisco...	2	075	06	030600	226	11	66	96	42	9	226	50	68	91	15
2	287	237	287	3	0	0	8	20	22	21	7	0	12	22	6	89	77	Block Group 3, Census Tract 306, San Francisco...	3	075	06	030600	287	14	84	122	53	12	287	64	87	115	19

3 rows × 35 columns

And here is aggregate information on people from ACS¶

In [4]:

population = ['B01001_001E']
sex = ['B01001_002E', 'B01001_026E']
race = ['B02001_0%02dE'%i for i in range(1,11)]
male_age_columns = ['B01001_0%02dE'%i for i in range(3,26)]
female_age_columns = ['B01001_0%02dE'%i for i in range(27,50)]
all_columns = population + sex + race + male_age_columns + female_age_columns
p_acs = c.block_group_query(all_columns, "06", "075", tract="030600")
p_acs

Out[4]:

	B01001_001E	B01001_002E	B01001_003E	B01001_004E	B01001_005E	B01001_006E	B01001_007E	B01001_009E	B01001_010E	B01001_011E	B01001_012E	B01001_013E	B01001_014E	B01001_015E	B01001_016E	B01001_017E	B01001_018E	B01001_019E	B01001_020E	B01001_021E	B01001_022E	B01001_023E	B01001_024E	B01001_025E	B01001_026E	B01001_027E	B01001_028E	B01001_029E	B01001_030E	B01001_031E	B01001_034E	B02001_001E	B02001_002E	B02001_003E	B02001_005E	B02001_007E	B02001_008E	B02001_010E	NAME	block group	county	state	tract	B01001_036E	B01001_037E	B01001_038E	B01001_039E	B01001_040E	B01001_041E	B01001_042E	B01001_043E	B01001_044E	B01001_046E	B01001_047E	B01001_048E	B01001_049E
0	655	321	8	11	8	0	0	0	8	0	48	28	21	51	18	38	4	0	0	39	23	12	0	4	334	46	33	23	0	0	0	655	423	11	187	12	22	22	Block Group 1, Census Tract 306, San Francisco...	1	075	06	030600	6	20	65	41	0	14	7	0	23	37	4	0	15
1	528	236	7	17	11	0	4	0	0	0	0	14	53	39	24	12	19	9	0	17	8	2	0	0	292	48	7	0	0	3	25	528	448	9	11	0	60	60	Block Group 2, Census Tract 306, San Francisco...	2	075	06	030600	9	40	17	41	10	38	18	0	7	9	11	9	0
2	858	493	22	44	0	44	0	11	0	6	44	57	50	43	38	38	0	10	21	15	20	10	20	0	365	0	0	49	31	13	3	858	623	11	218	0	6	6	Block Group 3, Census Tract 306, San Francisco...	3	075	06	030600	14	0	51	38	66	30	2	8	7	20	9	21	3

3 rows × 64 columns

Get the puma for our test tracts - this actually downloads the mapping file from the census website so it might take a few seconds¶

In [5]:

puma = c.tract_to_pums("06", "075", "030600")

Download PUMS for people records for a PUMA from our server (we processed the large files into smaller ones for you)¶

In [6]:

p_pums = c.download_population_pums("06", puma)
p_pums.head(5)

Out[6]:

	Unnamed: 0	serialno	RT	SPORDER	PUMA00	PUMA10	ST	ADJINC	PWGTP	AGEP	CIT	CITWP05	CITWP12	COW	DDRS	DEAR	DEYE	DOUT	DPHY	DRAT	DRATX	DREM	ENG	FER	GCL	GCM	GCR	HINS1	HINS2	HINS3	HINS4	HINS5	HINS6	HINS7	INTP	JWMNP	JWRIP	JWTR	LANX	MAR	MARHD	MARHM	MARHT	MARHW	MARHYP05	MARHYP12	MIG	MIL	MLPA	MLPB	MLPC	MLPD	MLPE	MLPF	MLPG	MLPH	MLPI	MLPJ	MLPK	NWAB	NWAV	NWLA	NWLK	NWRE	OIP	PAP	RELP	RETP	SCH	SCHG	SCHL	SEMP	SEX	SSIP	SSP	WAGP	WKHP	WKL	WKW	YOEP05	YOEP12	ANC	ANC1P05	ANC1P12	ANC2P05	ANC2P12	DECADE	DIS	DRIVESP	ESP	ESR	HICOV	HISP	INDP	JWAP	JWDP	LANP05	LANP12	MIGPUMA00	MIGPUMA10	MIGSP05	MIGSP12	MSP	NAICSP	NATIVITY	NOP	OC	OCCP02	OCCP10	OCCP12	PAOC	PERNP	PINCP	POBP05	POBP12	POVPIP	POWPUMA00	POWPUMA10	POWSP05	POWSP12	PRIVCOV	PUBCOV	QTRBIR	RAC1P	RAC2P05	RAC2P12	RAC3P05	RAC3P12	RACASN	RACNUM	RACWHT	RC	SFN	SFR	SOCP00	SOCP10	SOCP12	VPS	WAOB	FCITP	FCOWP	FDDRSP	FDEARP	FDEYEP	FDOUTP	FDPHYP	FDREMP	FENGP	FESRP	FFERP	FHINS1P	FHINS2P	FHINS3C	FHINS3P	FHINS4C	FHINS4P	FHINS5C	FHINS5P	FHINS6P	FHINS7P	FHISP	FINDP	FINTP	FMARHDP	FMARHMP	FMARHTP	FMARHWP	FMARHYP	FMIGP	FMILSP	FOCCP	FOIP	FPAP	FPOBP	FRACP	FRETP	FSCHGP	FSCHP	FSEMP	FSSIP	FSSP	FWAGP	FWKHP	FWKLP	FWKWP	PWGTP1	PWGTP2	PWGTP3	PWGTP4	PWGTP5	PWGTP6	PWGTP7	PWGTP8	PWGTP9	PWGTP10	PWGTP11	PWGTP12	PWGTP13	PWGTP14	PWGTP15	PWGTP16	PWGTP17	PWGTP18	PWGTP19	PWGTP20	PWGTP21	PWGTP22	PWGTP23	PWGTP24	PWGTP25	PWGTP26	PWGTP27	PWGTP28	PWGTP29	PWGTP30	PWGTP31	PWGTP32	PWGTP33	PWGTP34	PWGTP35	PWGTP36	PWGTP37	PWGTP38	PWGTP39	PWGTP40	PWGTP41	PWGTP42	PWGTP43	PWGTP44	PWGTP45	PWGTP46	PWGTP47	PWGTP48	PWGTP49	PWGTP50	PWGTP51	PWGTP52	PWGTP53	PWGTP54	PWGTP55	PWGTP56	PWGTP57	PWGTP58	PWGTP59	PWGTP60	PWGTP61	PWGTP62	PWGTP63	PWGTP64	PWGTP65	PWGTP66	PWGTP67	PWGTP68	PWGTP69	PWGTP70	PWGTP71	PWGTP72	PWGTP73	PWGTP74	PWGTP75	PWGTP76	PWGTP77	PWGTP78	PWGTP79	PWGTP80
0	2165691	2012000002680	P	1	-9	07506	06	1010207	20	64	1	NaN	NaN	2	2	2	2	2	2	NaN	NaN	2	NaN	NaN	2	NaN	NaN	1	2	2	2	2	2	2	0	10	NaN	10	2	5	NaN	NaN	NaN	NaN	NaN	NaN	1	5	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3	5	2	2	3	0	0	0	0	1	NaN	21	0	2	0	0	59000	24	1	1	NaN	NaN	3	-9	995	-9	999	NaN	2	NaN	NaN	1	1	1	7870	84	43	NaN	NaN	NaN	NaN	NaN	NaN	6	611M1	1	NaN	0	N.A.	N.A.	5860	4	59000	59000	-9	36	500	-9	7500	-9	6	1	2	4	1	-9	1	-9	1	0	1	1	0	NaN	NaN	N.A.//	N.A.//	439061	NaN	1	0	0	0	0	0	0	0	0	0	0	0	0	0	NaN	0	NaN	0	NaN	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	21	19	20	6	22	23	31	29	7	6	39	21	6	29	30	22	20	7	22	21	18	17	26	44	18	19	7	5	35	33	7	16	32	7	5	22	16	34	20	14	17	16	22	33	18	17	6	6	41	41	7	17	29	6	6	21	19	30	19	17	18	24	25	6	24	24	32	45	5	7	40	21	6	33	36	18	24	7	16	16
1	2167411	2012000009189	P	1	-9	07506	06	1010207	16	52	4	-9	1995	NaN	2	2	1	2	2	NaN	NaN	2	3	NaN	2	NaN	NaN	1	2	2	2	2	2	2	0	NaN	NaN	NaN	1	1	2	2	1	2	-9	1992	1	5	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3	5	3	3	3	10000	0	0	0	1	NaN	16	0	1	0	0	0	NaN	3	NaN	-9	1989	4	-9	999	-9	999	5	1	NaN	NaN	6	1	1	NaN	NaN	NaN	-9	708	NaN	NaN	NaN	NaN	1	NaN	2	NaN	0	NaN	NaN	NaN	NaN	0	10000	-9	207	43	NaN	NaN	NaN	NaN	1	2	3	6	-9	43	-9	5	1	1	0	0	NaN	NaN	NaN	NaN	NaN	NaN	4	0	0	1	0	0	1	1	1	0	1	0	0	0	NaN	0	NaN	0	NaN	0	0	0	1	0	1	1	1	1	1	1	0	1	0	1	1	0	0	1	0	0	1	1	1	1	0	1	0	19	11	23	20	7	16	11	7	20	4	21	14	16	27	11	20	19	15	18	26	13	33	4	4	50	16	12	40	25	37	19	17	10	3	12	3	11	14	5	5	16	4	28	24	6	19	14	5	17	3	17	15	12	27	13	33	15	19	47	18	22	28	6	5	28	12	12	25	21	15	12	12	15	5	14	7	15	13	7	4
2	2167412	2012000009189	P	2	-9	07506	06	1010207	13	45	4	-9	1998	3	2	2	1	2	2	NaN	NaN	2	3	2	2	NaN	NaN	1	2	2	2	2	2	2	0	NaN	NaN	NaN	1	1	2	2	1	2	-9	1992	1	5	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	3	5	3	3	3	0	0	1	0	1	NaN	16	0	2	0	0	0	NaN	2	NaN	-9	1992	4	-9	999	-9	999	6	1	NaN	NaN	6	1	1	8370	NaN	NaN	-9	708	NaN	NaN	NaN	NaN	1	6241	2	NaN	0	N.A.	N.A.	4610	2	0	0	-9	207	43	NaN	NaN	NaN	NaN	1	2	2	6	-9	43	-9	5	1	1	0	0	NaN	NaN	N.A.//	N.A.//	399021	NaN	4	1	1	1	0	0	1	1	1	1	1	1	0	0	NaN	0	NaN	0	NaN	0	0	0	1	1	1	1	1	1	1	1	0	1	1	1	1	0	0	1	0	0	1	1	1	1	0	1	0	11	6	25	18	4	15	16	5	12	5	14	13	20	25	9	19	13	16	19	23	12	19	4	4	21	12	12	20	16	21	14	14	11	4	15	3	11	17	4	3	13	5	19	21	4	12	14	4	11	3	19	13	17	25	12	16	12	15	19	27	16	24	5	4	24	12	11	19	13	24	11	16	14	3	15	3	15	12	3	4
3	2167413	2012000009189	P	3	-9	07506	06	1010207	14	10	1	NaN	NaN	NaN	2	2	2	NaN	2	NaN	NaN	2	NaN	NaN	NaN	NaN	NaN	2	2	2	1	2	2	2	NaN	NaN	NaN	NaN	2	5	NaN	NaN	NaN	NaN	NaN	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	NaN	1	NaN	7	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4	-9	999	-9	999	NaN	2	NaN	4	NaN	1	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1	4	1	NaN	NaN	NaN	NaN	NaN	NaN	-9	6	43	NaN	NaN	NaN	NaN	2	1	4	6	-9	43	-9	5	1	1	0	1	NaN	NaN	NaN	NaN	NaN	NaN	1	0	0	1	1	1	0	1	1	0	0	0	0	0	NaN	0	0	0	NaN	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	18	4	24	25	5	12	14	6	14	4	10	12	20	28	16	23	18	18	22	23	18	23	4	3	23	14	15	25	12	19	14	19	13	3	16	4	16	17	4	5	11	5	21	23	6	23	13	4	15	4	19	16	13	26	11	21	13	18	22	23	12	26	5	4	23	14	13	21	15	22	15	13	16	5	12	6	12	12	3	3
4	2167414	2012000009189	P	4	-9	07506	06	1010207	15	8	1	NaN	NaN	NaN	2	2	2	NaN	2	NaN	NaN	2	NaN	NaN	NaN	NaN	NaN	2	2	2	1	2	2	2	NaN	NaN	NaN	NaN	2	5	NaN	NaN	NaN	NaN	NaN	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2	NaN	2	5	6	NaN	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	4	-9	999	-9	999	NaN	2	NaN	4	NaN	1	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1	4	1	NaN	NaN	NaN	NaN	NaN	NaN	-9	6	43	NaN	NaN	NaN	NaN	2	1	1	6	-9	43	-9	5	1	1	0	1	NaN	NaN	NaN	NaN	NaN	NaN	1	0	0	1	1	1	0	1	1	0	0	0	1	1	NaN	1	0	1	NaN	1	1	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1	1	0	0	0	0	0	0	0	18	4	25	24	5	11	13	6	14	4	11	11	20	28	15	23	18	18	22	23	18	23	4	4	23	14	16	25	12	19	15	19	13	4	16	3	16	17	4	5	12	7	21	23	5	23	13	5	16	3	20	16	13	26	12	22	13	17	22	22	13	27	5	3	23	14	13	20	14	22	14	13	17	5	13	6	12	13	4	3

5 rows × 291 columns

Download PUMS for household records for a PUMA¶

In [7]:

h_pums = c.download_household_pums("06", puma)
h_pums.head(5)

Out[7]:

	Unnamed: 0	serialno	RT	DIVISION	PUMA00	PUMA10	REGION	ST	ADJHSG	ADJINC	WGTP	NP	TYPE	ACR	AGS	BATH	BDSP	BLD	BUS	ELEP	FS	FULP	GASP	HFL	INSP	MHP	MRGI	MRGP	MRGT	MRGX	REFR	RMSP	RNTM	RNTP	RWAT	SINK	SMP	STOV	TEL	TEN	TOIL	VACS	VALP	VEH	WATP	YBL	FES	FINCP	FPARC	GRNTP	GRPIP	HHL	HHT	HINCP	HUPAC	HUPAOC	HUPARC	KIT	LNGI	MULTG	MV	NOC	NPF	NRC	OCPIP	PLM	PSF	R18	R60	R65	RESMODE	SMOCP	SMX	SRNT	SVAL	TAXP	WIF	WKEXREL	WORKSTAT	FACRP	FBDSP	FBLDP	FBUSP	FFULP	FGASP	FHFLP	FINSP	FMRGIP	FMRGP	FMRGTP	FMVP	FRMSP	FSMP	FSMXHP	FSMXSP	FTAXP	FVALP	FYBLP	WGTP1	WGTP2	WGTP3	WGTP4	WGTP5	WGTP6	WGTP7	WGTP8	WGTP9	WGTP10	WGTP11	WGTP12	WGTP13	WGTP14	WGTP15	WGTP16	WGTP17	WGTP18	WGTP19	WGTP20	WGTP21	WGTP22	WGTP23	WGTP24	WGTP25	WGTP26	WGTP27	WGTP28	WGTP29	WGTP30	WGTP31	WGTP32	WGTP33	WGTP34	WGTP35	WGTP36	WGTP37	WGTP38	WGTP39	WGTP40	WGTP41	WGTP42	WGTP43	WGTP44	WGTP45	WGTP46	WGTP47	WGTP48	WGTP49	WGTP50	WGTP51	WGTP52	WGTP53	WGTP54	WGTP55	WGTP56	WGTP57	WGTP58	WGTP59	WGTP60	WGTP61	WGTP62	WGTP63	WGTP64	WGTP65	WGTP66	WGTP67	WGTP68	WGTP69	WGTP70	WGTP71	WGTP72	WGTP73	WGTP74	WGTP75	WGTP76	WGTP77	WGTP78	WGTP79	WGTP80
0	940390	2012000002680	H	9	-9	07506	4	06	1000000	1010207	21	1	1	1	NaN	1	2	3	2	60	2	2	4	9	200	NaN	2	350	2	1	1	5	NaN	NaN	1	1	NaN	1	1	1	1	NaN	500000	1	430	1	NaN	NaN	NaN	NaN	NaN	1	6	59000	4	4	4	1	1	1	6	0	NaN	0	13	1	0	0	1	0	1	634	3	0	1	32	NaN	NaN	NaN	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	21	19	21	7	22	23	31	29	6	5	39	21	6	29	30	22	20	6	22	21	17	17	26	44	18	20	7	5	35	33	8	17	32	7	5	23	16	35	20	15	17	16	22	34	18	17	6	6	41	41	6	17	30	6	6	21	20	30	19	17	18	25	25	6	24	24	32	45	5	7	39	21	6	33	36	18	24	7	17	16
1	941064	2012000009189	H	9	-9	07506	4	06	1000000	1010207	15	4	1	NaN	NaN	1	5	4	NaN	50	2	2	50	1	660	NaN	2	490	2	1	1	7	NaN	NaN	1	1	630	1	1	1	1	NaN	40000	2	2000	1	4	10000	2	NaN	NaN	4	1	10000	2	2	2	1	2	1	5	2	4	2	101	1	0	1	0	0	1	1534	2	0	0	23	0	9	9	0	1	1	0	1	0	1	0	0	1	0	0	1	1	0	0	1	0	0	19	11	23	20	7	16	11	7	20	5	21	14	15	27	11	20	18	15	18	26	13	33	4	4	50	16	13	40	25	37	19	17	10	4	13	3	11	14	6	4	16	4	28	24	6	19	15	5	18	3	17	15	12	27	13	33	14	19	47	18	22	28	5	6	28	13	12	26	21	15	12	12	15	5	13	7	15	13	7	5
2	941824	2012000016466	H	9	-9	07506	4	06	1000000	1010207	15	3	1	1	NaN	1	2	2	2	10	2	2	1	1	NaN	NaN	NaN	NaN	NaN	NaN	1	4	2	2000	1	1	NaN	1	1	3	1	NaN	NaN	1	1	2	7	30000	2	2010	80	1	3	30000	2	2	2	1	1	1	2	2	3	2	NaN	1	0	1	0	0	1	NaN	NaN	1	0	NaN	1	13	13	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	4	5	32	14	5	21	21	12	16	6	15	19	20	18	5	13	19	23	25	11	23	21	5	22	24	5	8	12	17	35	24	15	17	10	14	13	16	5	5	18	3	6	27	21	5	25	28	14	10	7	13	15	17	21	4	14	14	22	22	15	31	26	5	17	34	7	4	17	20	24	12	12	14	14	26	18	13	4	5	16
3	941918	2012000017340	H	9	-9	07506	4	06	1000000	1010207	10	4	1	1	NaN	1	3	3	2	100	2	1000	30	3	500	NaN	2	3000	2	1	1	4	NaN	NaN	1	1	NaN	1	1	1	1	NaN	500000	1	1200	7	5	60000	4	NaN	NaN	4	2	60000	4	4	4	1	1	1	4	0	4	0	79	1	1	0	1	0	1	3938	3	0	1	65	2	12	11	1	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	12	8	3	8	4	6	8	12	17	7	20	11	11	18	14	4	30	23	4	20	10	10	19	9	15	6	12	10	3	12	4	11	8	4	9	18	5	3	18	4	12	10	4	13	3	12	13	10	28	13	20	16	11	17	11	3	18	15	3	13	9	8	14	12	17	10	10	9	5	8	3	13	11	2	9	19	2	3	13	3
4	942803	2012000025664	H	9	-9	07506	4	06	1000000	1010207	71	5	1	1	NaN	1	3	3	2	50	2	2	3	3	200	NaN	2	680	2	1	1	6	NaN	NaN	1	1	NaN	1	1	1	1	NaN	500000	2	900	4	7	73500	4	NaN	NaN	1	3	73500	4	4	4	1	1	2	7	0	5	0	16	1	0	0	1	1	2	989	3	0	1	32	3	13	13	0	0	0	0	0	0	0	1	1	1	1	0	0	1	1	1	1	1	1	33	23	78	66	19	99	88	97	68	29	80	72	50	74	36	57	82	101	77	75	69	93	23	131	105	27	25	83	69	93	65	57	92	74	108	89	128	38	17	55	102	103	44	61	109	29	31	69	66	94	73	66	77	62	104	82	78	38	30	87	37	21	85	63	25	66	93	80	55	29	90	74	86	63	19	70	50	100	115	70

5 rows × 204 columns

Now the job is to categorize acs and pums into the same categories - we start with the household acs data¶

In [8]:

h_acs_cat = cat.categorize(h_acs, {
    ("households", "total"): "B11001_001E",
    ("children", "yes"): "B11001_002E",
    ("children", "no"): "B11001_001E - B11001_002E",
    ("income", "lt35"): "B19001_002E + B19001_003E + B19001_004E + "
                        "B19001_005E + B19001_006E + B19001_007E",
    ("income", "gt35-lt100"): "B19001_008E + B19001_009E + "
                        "B19001_010E + B19001_011E + B19001_012E"
                        "+ B19001_013E",
    ("income", "gt100"): "B19001_014E + B19001_015E + B19001_016E"
                        "+ B19001_017E",
    ("cars", "none"): "B08201_002E",
    ("cars", "one"): "B08201_003E",
    ("cars", "two or more"): "B08201_004E + B08201_005E + B08201_006E",
    ("workers", "none"): "B08202_002E",
    ("workers", "one"): "B08202_003E",
    ("workers", "two or more"): "B08202_004E + B08202_005E" 
}, index_cols=['NAME'])
h_acs_cat

Out[8]:

cat_name	cars			children		households	income			workers
cat_value	none	one	two or more	no	yes	total	gt100	gt35-lt100	lt35	none	one	two or more
NAME
Block Group 1, Census Tract 306, San Francisco County, California	14	86	192	111	183	294	173	73	48	65	89	138
Block Group 2, Census Tract 306, San Francisco County, California	11	66	147	88	138	226	156	20	50	50	68	106
Block Group 3, Census Tract 306, San Francisco County, California	14	84	187	50	237	287	194	40	53	64	87	134

3 rows × 12 columns

In [9]:

assert np.all(cat.sum_accross_category(h_acs_cat) < 2)

And the same for ACS population - the output of the categorization is the MARGINALS for each variable category¶

In [10]:

p_acs_cat = cat.categorize(p_acs, {
    ("population", "total"): "B01001_001E",
    ("age", "19 and under"): "B01001_003E + B01001_004E + B01001_005E + "
                             "B01001_006E + B01001_007E + B01001_027E + "
                             "B01001_028E + B01001_029E + B01001_030E + "
                             "B01001_031E",
    ("age", "20 to 35"): "B01001_008E + B01001_009E + B01001_010E + "
                         "B01001_011E + B01001_012E + B01001_032E + "
                         "B01001_033E + B01001_034E + B01001_035E + "
                         "B01001_036E",
    ("age", "35 to 60"): "B01001_013E + B01001_014E + B01001_015E + "
                         "B01001_016E + B01001_017E + B01001_037E + "
                         "B01001_038E + B01001_039E + B01001_040E + "
                         "B01001_041E",
    ("age", "above 60"): "B01001_018E + B01001_019E + B01001_020E + "
                         "B01001_021E + B01001_022E + B01001_023E + "
                         "B01001_024E + B01001_025E + B01001_042E + "
                         "B01001_043E + B01001_044E + B01001_045E + "
                         "B01001_046E + B01001_047E + B01001_048E + "
                         "B01001_049E", 
    ("race", "white"):   "B02001_002E",
    ("race", "black"):   "B02001_003E",
    ("race", "asian"):   "B02001_005E",
    ("race", "other"):   "B02001_004E + B02001_006E + B02001_007E + "
                         "B02001_008E",
    ("sex", "male"):     "B01001_002E",
    ("sex", "female"):   "B01001_026E"
}, index_cols=['NAME'])
p_acs_cat

Out[10]:

cat_name	age				population	race				sex
cat_value	19 and under	20 to 35	35 to 60	above 60	total	asian	black	other	white	female	male
NAME
Block Group 1, Census Tract 306, San Francisco County, California	129	62	296	168	655	187	11	34	423	334	321
Block Group 2, Census Tract 306, San Francisco County, California	97	34	288	109	528	11	9	60	448	292	236
Block Group 3, Census Tract 306, San Francisco County, California	203	78	411	166	858	218	11	6	623	365	493

3 rows × 11 columns

In [11]:

assert np.all(cat.sum_accross_category(p_acs_cat) < 2)

To get the marginals a series for one geography do this¶

In [12]:

p_acs_cat.iloc[0].transpose()

Out[12]:

cat_name    cat_value   
age         19 and under    129
            20 to 35         62
            35 to 60        296
            above 60        168
population  total           655
race        asian           187
            black            11
            other            34
            white           423
sex         female          334
            male            321
Name: Block Group 1, Census Tract 306, San Francisco County, California, dtype: int64

Now categorize the PUMS population data into the same categories¶

In [13]:

def age_cat(r):
    if r.AGEP <= 19: return "19 and under"
    elif r.AGEP <= 35: return "20 to 35"
    elif r.AGEP <= 60: return "35 to 60"
    return "above 60"

def race_cat(r):
    if r.RAC1P == 1: return "white"
    elif r.RAC1P == 2: return "black"
    elif r.RAC1P == 6: return "asian"
    return "other"

def sex_cat(r):
    if r.SEX == 1: return "male"
    return "female"

_, jd_persons = cat.joint_distribution(
    p_pums,
    cat.category_combinations(p_acs_cat.columns),
    {"age": age_cat, "race": race_cat, "sex": sex_cat}
)
jd_persons 

Out[13]:

			id	frequency
age	race	sex
19 and under	asian	female	0	37
	asian	male	1	44
	black	female	2	1
	black	male	3	5
	other	female	4	21
	other	male	5	25
	white	female	6	42
	white	male	7	35
20 to 35	asian	female	8	42
	asian	male	9	47
	black	female	10	6
	black	male	11	6
	other	female	12	12
	other	male	13	13
	white	female	14	44
	white	male	15	43
35 to 60	asian	female	16	96
	asian	male	17	81
	black	female	18	10
	black	male	19	7
	other	female	20	22
	other	male	21	17
	white	female	22	68
	white	male	23	88
above 60	asian	female	24	56
	asian	male	25	38
	black	female	26	14
	black	male	27	8
	other	female	28	7
	other	male	29	5
	white	female	30	64
	white	male	31	59

32 rows × 2 columns

Do the same for households - the output of this step is the JOINT DISTRIBUTIONS for the cross product of all possible categories¶

In [14]:

def cars_cat(r):
    if r.VEH == 0: return "none"
    elif r.VEH == 1: return "one"
    return "two or more"

def children_cat(r):
    if r.NOC > 0: return "yes"
    return "no"

def income_cat(r):
    if r.FINCP > 100000: return "gt100"
    elif r.FINCP > 35000: return "gt35-lt100"
    return "lt35"

def workers_cat(r):
    if r.WIF == 3: return "two or more"
    elif r.WIF == 2: return "two or more"
    elif r.WIF == 1: return "one"
    return "none"

_, jd_households = cat.joint_distribution(
    h_pums,
    cat.category_combinations(h_acs_cat.columns),
    {"cars": cars_cat, "children": children_cat, 
     "income": income_cat, "workers": workers_cat}
)
jd_households

Out[14]:

				id	frequency
cars	workers	children	income
none	none	no	gt100	0	0
			gt35-lt100	1	1
			lt35	2	37
		yes	gt100	3	0
			gt35-lt100	4	0
			lt35	5	0
	one	no	gt100	6	0
			gt35-lt100	7	2
			lt35	8	2
		yes	gt100	9	0
			gt35-lt100	10	1
			lt35	11	0
	two or more	no	gt100	12	2
			gt35-lt100	13	5
			lt35	14	0
		yes	gt100	15	0
			gt35-lt100	16	1
			lt35	17	0
one	none	no	gt100	18	1
			gt35-lt100	19	7
			lt35	20	81
		yes	gt100	21	0
			gt35-lt100	22	0
			lt35	23	0
	one	no	gt100	24	5
			gt35-lt100	25	6
			lt35	26	2
		yes	gt100	27	3
			gt35-lt100	28	6
			lt35	29	8
	two or more	no	gt100	30	8
			gt35-lt100	31	10
			lt35	32	1
		yes	gt100	33	6
			gt35-lt100	34	7
			lt35	35	1
two or more	none	no	gt100	36	2
			gt35-lt100	37	8
			lt35	38	91
		yes	gt100	39	0
			gt35-lt100	40	0
			lt35	41	1
	one	no	gt100	42	5
			gt35-lt100	43	8
			lt35	44	5
		yes	gt100	45	6
			gt35-lt100	46	6
			lt35	47	0
	two or more	no	gt100	48	36
			gt35-lt100	49	22
			lt35	50	5
		yes	gt100	51	36
			gt35-lt100	52	10
			lt35	53	0

54 rows × 2 columns

With marginals (aggregate, from ACS) and joint distribution (disaggregate, from PUMS) we're ready for some synthesis¶

In [16]:

"TBD"

Out[16]:

'TBD'

In [ ]:

The census api needs a key - you can register for can sign up¶

http://api.census.gov/data/key_signup.html¶