From d617c6f0e0c644530cd9b3d6a988c2c2fbc30496 Mon Sep 17 00:00:00 2001 From: cvanoli Date: Fri, 2 Mar 2018 15:29:06 -0300 Subject: [PATCH 01/12] Update census_helpers.py --- synthpop/census_helpers.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/synthpop/census_helpers.py b/synthpop/census_helpers.py index 64c847e..4b4b2dd 100644 --- a/synthpop/census_helpers.py +++ b/synthpop/census_helpers.py @@ -49,7 +49,7 @@ def _scale_and_merge(self, df1, tot1, df2, tot2, columns_to_scale, return df def block_group_query(self, census_columns, state, county, tract=None, - year=None, id=None): + year=2016, id=None): if id is None: id = "*" return self._query(census_columns, state, county, @@ -57,7 +57,7 @@ def block_group_query(self, census_columns, state, county, tract=None, tract=tract, year=year) def tract_query(self, census_columns, state, county, tract=None, - year=None): + year=2016): if tract is None: tract = "*" return self._query(census_columns, state, county, @@ -65,7 +65,7 @@ def tract_query(self, census_columns, state, county, tract=None, year=year) def _query(self, census_columns, state, county, forstr, - tract=None, year=None): + tract=None, year=2016): c = self.c state, county = self.try_fips_lookup(state, county) @@ -107,7 +107,7 @@ def chunks(l, n): def block_group_and_tract_query(self, block_group_columns, tract_columns, state, county, merge_columns, block_group_size_attr, - tract_size_attr, tract=None, year=None): + tract_size_attr, tract=None, year=2016): df2 = self.tract_query(tract_columns, state, county, tract=tract, year=year) df1 = self.block_group_query(block_group_columns, state, county, From 007e0f17b26f863547cb2209be5327290c39811a Mon Sep 17 00:00:00 2001 From: cvanoli Date: Fri, 16 Mar 2018 16:08:27 -0300 Subject: [PATCH 02/12] commitmessage --- .pytest_cache/v/cache/lastfailed | 1 + .../census_api-checkpoint.ipynb | 5454 ++ .../non_census_synthesis-checkpoint.ipynb | 1613 + .../simple_synthesis-checkpoint.ipynb | 415 + .../synthesize-checkpoint.ipynb | 474 + demos/census_api.ipynb | 7141 +- demos/non_census_synthesis.ipynb | 822 +- demos/simple_synthesis.ipynb | 393 +- demos/synthesize.ipynb | 68845 +--------------- demos/synthesize.py | 2 +- synthpop/categorizer.py | 12 +- synthpop/census_helpers.py | 11 +- synthpop/ipu/ipu.py | 6 +- synthpop/recipes/starter2.py | 4 +- synthpop/synthesizer.py | 8 +- synthpop/test/test_categorizer.py | 8 +- synthpop/test/test_censushelpers.py | 4 +- synthpop/zone_synthesizer.py | 2 +- 18 files changed, 12983 insertions(+), 72232 deletions(-) create mode 100644 .pytest_cache/v/cache/lastfailed create mode 100644 demos/.ipynb_checkpoints/census_api-checkpoint.ipynb create mode 100644 demos/.ipynb_checkpoints/non_census_synthesis-checkpoint.ipynb create mode 100644 demos/.ipynb_checkpoints/simple_synthesis-checkpoint.ipynb create mode 100644 demos/.ipynb_checkpoints/synthesize-checkpoint.ipynb diff --git a/.pytest_cache/v/cache/lastfailed b/.pytest_cache/v/cache/lastfailed new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/.pytest_cache/v/cache/lastfailed @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/demos/.ipynb_checkpoints/census_api-checkpoint.ipynb b/demos/.ipynb_checkpoints/census_api-checkpoint.ipynb new file mode 100644 index 0000000..e01f297 --- /dev/null +++ b/demos/.ipynb_checkpoints/census_api-checkpoint.ipynb @@ -0,0 +1,5454 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "from synthpop.census_helpers import Census\n", + "from synthpop import categorizer as cat\n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "pd.set_option('display.max_columns', 500)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The census api needs a key - you can register for can sign up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### http://api.census.gov/data/key_signup.html" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "c = Census(os.environ[\"CENSUS\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Here we get aggregate information on households from ACS - note some variables are associated with block groups and others with tracts" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " serialno RT SPORDER puma00 puma10 ST ADJINC PWGTP AGEP CIT \\\n", + "0 2012000002680 P 1 -9 7506 6 1024887 21 64 1 \n", + "1 2012000009189 P 1 -9 7506 6 1024887 14 52 4 \n", + "2 2012000009189 P 2 -9 7506 6 1024887 18 45 4 \n", + "3 2012000009189 P 3 -9 7506 6 1024887 14 10 1 \n", + "4 2012000009189 P 4 -9 7506 6 1024887 14 8 1 \n", + "\n", + " CITWP05 CITWP12 COW DDRS DEAR DEYE DOUT DPHY DRAT DRATX DREM \\\n", + "0 NaN NaN 2.0 2.0 2 2 2.0 2.0 NaN NaN 2.0 \n", + "1 -9.0 1995.0 NaN 2.0 2 1 2.0 2.0 NaN NaN 2.0 \n", + "2 -9.0 1998.0 3.0 2.0 2 1 2.0 2.0 NaN NaN 2.0 \n", + "3 NaN NaN NaN 2.0 2 2 NaN 2.0 NaN NaN 2.0 \n", + "4 NaN NaN NaN 2.0 2 2 NaN 2.0 NaN NaN 2.0 \n", + "\n", + " ENG FER GCL GCM GCR HINS1 HINS2 HINS3 HINS4 HINS5 HINS6 HINS7 \\\n", + "0 NaN NaN 2.0 NaN NaN 1 2 2 2 2 2 2 \n", + "1 3.0 NaN 2.0 NaN NaN 1 2 2 2 2 2 2 \n", + "2 3.0 2.0 2.0 NaN NaN 1 2 2 2 2 2 2 \n", + "3 NaN NaN NaN NaN NaN 2 2 2 1 2 2 2 \n", + "4 NaN NaN NaN NaN NaN 2 2 2 1 2 2 2 \n", + "\n", + " INTP JWMNP JWRIP JWTR LANX MAR MARHD MARHM MARHT MARHW MARHYP05 \\\n", + "0 0.0 10.0 NaN 10.0 2.0 5 NaN NaN NaN NaN NaN \n", + "1 0.0 NaN NaN NaN 1.0 1 2.0 2.0 1.0 2.0 -9.0 \n", + "2 0.0 NaN NaN NaN 1.0 1 2.0 2.0 1.0 2.0 -9.0 \n", + "3 NaN NaN NaN NaN 2.0 5 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN 2.0 5 NaN NaN NaN NaN NaN \n", + "\n", + " MARHYP12 MIG MIL MLPA MLPB MLPCD MLPE MLPFG MLPH MLPI MLPJ MLPK \\\n", + "0 NaN 1.0 4.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1 1992.0 1.0 4.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2 1992.0 1.0 4.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "3 NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "4 NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " NWAB NWAV NWLA NWLK NWRE OIP PAP RELP RETP SCH SCHG SCHL \\\n", + "0 3.0 5.0 2.0 2.0 3.0 0.0 0.0 0 0.0 1.0 NaN 21.0 \n", + "1 3.0 5.0 3.0 3.0 3.0 10000.0 0.0 0 0.0 1.0 NaN 16.0 \n", + "2 3.0 5.0 3.0 3.0 3.0 0.0 0.0 1 0.0 1.0 NaN 16.0 \n", + "3 NaN NaN NaN NaN NaN NaN NaN 2 NaN 1.0 NaN 7.0 \n", + "4 NaN NaN NaN NaN NaN NaN NaN 2 NaN 2.0 5.0 6.0 \n", + "\n", + " SEMP SEX SSIP SSP WAGP WKHP WKL WKW WRK YOEP05 YOEP12 ANC \\\n", + "0 0.0 2 0.0 0.0 59000.0 24.0 1.0 1.0 1.0 NaN NaN 3 \n", + "1 0.0 1 0.0 0.0 0.0 NaN 3.0 NaN NaN -9.0 1989.0 4 \n", + "2 0.0 2 0.0 0.0 0.0 NaN 2.0 NaN NaN -9.0 1992.0 4 \n", + "3 NaN 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN 4 \n", + "4 NaN 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN 4 \n", + "\n", + " ANC1P05 ANC1P12 ANC2P05 ANC2P12 DECADE DIS DRIVESP ESP ESR FOD1P \\\n", + "0 -9 995 -9 999 NaN 2 NaN NaN 1.0 3301.0 \n", + "1 -9 999 -9 999 5.0 1 NaN NaN 6.0 NaN \n", + "2 -9 999 -9 999 6.0 1 NaN NaN 6.0 NaN \n", + "3 -9 999 -9 999 NaN 2 NaN 4.0 NaN NaN \n", + "4 -9 999 -9 999 NaN 2 NaN 4.0 NaN NaN \n", + "\n", + " FOD2P HICOV HISP INDP JWAP JWDP LANP05 LANP12 MIGPUMA00 \\\n", + "0 NaN 1 1 7870.0 84.0 43.0 NaN NaN NaN \n", + "1 NaN 1 1 NaN NaN NaN -9.0 708.0 NaN \n", + "2 NaN 1 1 8370.0 NaN NaN -9.0 708.0 NaN \n", + "3 NaN 1 1 NaN NaN NaN NaN NaN NaN \n", + "4 NaN 1 1 NaN NaN NaN NaN NaN NaN \n", + "\n", + " MIGPUMA10 MIGSP05 MIGSP12 MSP NAICSP NATIVITY NOP OC OCCP02 OCCP10 \\\n", + "0 NaN NaN NaN 6.0 611M1 1 NaN 0 N.A. N.A. \n", + "1 NaN NaN NaN 1.0 NaN 2 NaN 0 NaN NaN \n", + "2 NaN NaN NaN 1.0 6241 2 NaN 0 N.A. N.A. \n", + "3 NaN NaN NaN NaN NaN 1 4.0 1 NaN NaN \n", + "4 NaN NaN NaN NaN NaN 1 4.0 1 NaN NaN \n", + "\n", + " OCCP12 PAOC PERNP PINCP POBP05 POBP12 POVPIP POWPUMA00 \\\n", + "0 5860 4.0 59000.0 59000.0 -9 36 500.0 -9.0 \n", + "1 NaN NaN 0.0 10000.0 -9 207 43.0 NaN \n", + "2 4610 2.0 0.0 0.0 -9 207 43.0 NaN \n", + "3 NaN NaN NaN NaN -9 6 43.0 NaN \n", + "4 NaN NaN NaN NaN -9 6 43.0 NaN \n", + "\n", + " POWPUMA10 POWSP05 POWSP12 PRIVCOV PUBCOV QTRBIR RAC1P RAC2P05 \\\n", + "0 7500.0 -9.0 6.0 1 2 4 1 -9 \n", + "1 NaN NaN NaN 1 2 3 6 -9 \n", + "2 NaN NaN NaN 1 2 2 6 -9 \n", + "3 NaN NaN NaN 2 1 4 6 -9 \n", + "4 NaN NaN NaN 2 1 1 6 -9 \n", + "\n", + " RAC2P12 RAC3P05 RAC3P12 RACAIAN RACASN RACBLK RACNHPI RACNUM \\\n", + "0 1 -9 1 0 0 0 0 1 \n", + "1 43 -9 5 0 1 0 0 1 \n", + "2 43 -9 5 0 1 0 0 1 \n", + "3 43 -9 5 0 1 0 0 1 \n", + "4 43 -9 5 0 1 0 0 1 \n", + "\n", + " RACSOR RACWHT RC SCIENGP SCIENGRLP SFN SFR SOCP00 SOCP10 SOCP12 \\\n", + "0 0 1 0 2.0 2.0 NaN NaN N.A.// N.A.// 439061 \n", + "1 0 0 0 NaN NaN NaN NaN NaN NaN NaN \n", + "2 0 0 0 NaN NaN NaN NaN N.A.// N.A.// 399021 \n", + "3 0 0 1 NaN NaN NaN NaN NaN NaN NaN \n", + "4 0 0 1 NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " VPS WAOB FAGEP FANCP FCITP FCITWP FCOWP FDDRSP FDEARP FDEYEP \\\n", + "0 NaN 1 0 0 0 0 0 0 0 0 \n", + "1 NaN 4 0 0 0 0 0 1 0 0 \n", + "2 NaN 4 0 0 1 0 1 1 0 0 \n", + "3 NaN 1 0 0 0 0 0 1 1 1 \n", + "4 NaN 1 0 0 0 0 0 1 1 1 \n", + "\n", + " FDOUTP FDPHYP FDRATP FDRATXP FDREMP FENGP FESRP FFERP FFODP FGCLP \\\n", + "0 0 0 0 0 0 0 0 0 0 0 \n", + "1 1 1 0 0 1 0 1 0 0 0 \n", + "2 1 1 0 0 1 1 1 1 0 0 \n", + "3 0 1 0 0 1 0 0 0 0 0 \n", + "4 0 1 0 0 1 0 0 0 0 0 \n", + "\n", + " FGCMP FGCRP FHINS1P FHINS2P FHINS3C FHINS3P FHINS4C FHINS4P \\\n", + "0 0 0 0 0 NaN 0 NaN 0 \n", + "1 0 0 0 0 NaN 0 NaN 0 \n", + "2 0 0 0 0 NaN 0 NaN 0 \n", + "3 0 0 0 0 NaN 0 0.0 0 \n", + "4 0 0 1 1 NaN 1 0.0 1 \n", + "\n", + " FHINS5C FHINS5P FHINS6P FHINS7P FHISP FINDP FINTP FJWDP FJWMNP \\\n", + "0 NaN 0 0 0 0 0 0 0 0 \n", + "1 NaN 0 0 0 1 0 1 0 0 \n", + "2 NaN 0 0 0 1 1 1 0 0 \n", + "3 NaN 0 0 0 1 0 0 0 0 \n", + "4 NaN 1 1 1 1 0 0 0 0 \n", + "\n", + " FJWRIP FJWTRP FLANP FLANXP FMARHDP FMARHMP FMARHTP FMARHWP FMARHYP \\\n", + "0 0 0 0 0 0 0 0 0 0 \n", + "1 0 0 0 0 1 1 1 1 1 \n", + "2 0 0 0 0 1 1 1 1 1 \n", + "3 0 0 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 0 0 0 0 \n", + "\n", + " FMARP FMIGP FMIGSP FMILPP FMILSP FOCCP FOIP FPAP FPOBP FPOWSP \\\n", + "0 0 0 0 0 0 0 0 0 1 0 \n", + "1 0 0 0 0 1 0 1 1 0 0 \n", + "2 0 0 0 0 1 1 1 1 0 0 \n", + "3 0 1 0 0 0 0 0 0 0 0 \n", + "4 0 1 0 0 0 0 0 0 0 0 \n", + "\n", + " FRACP FRELP FRETP FSCHGP FSCHLP FSCHP FSEMP FSEXP FSSIP FSSP \\\n", + "0 0 0 0 0 0 0 0 0 0 0 \n", + "1 0 0 1 0 0 0 1 0 1 1 \n", + "2 0 0 1 0 0 0 1 0 1 1 \n", + "3 1 0 0 0 0 0 0 0 0 0 \n", + "4 1 0 0 1 0 1 0 0 0 0 \n", + "\n", + " FWAGP FWKHP FWKLP FWKWP FWRKP FYOEP PWGTP1 PWGTP2 PWGTP3 PWGTP4 \\\n", + "0 0 1 0 1 0.0 0 22 18 23 7 \n", + "1 1 0 1 0 0.0 0 15 5 26 18 \n", + "2 1 0 1 0 0.0 0 18 5 36 30 \n", + "3 0 0 0 0 0.0 0 15 3 22 21 \n", + "4 0 0 0 0 0.0 0 15 4 22 21 \n", + "\n", + " PWGTP5 PWGTP6 PWGTP7 PWGTP8 PWGTP9 PWGTP10 PWGTP11 PWGTP12 PWGTP13 \\\n", + "0 25 23 38 30 6 5 40 20 7 \n", + "1 6 10 11 5 15 4 15 17 17 \n", + "2 7 20 19 6 23 7 15 17 30 \n", + "3 4 13 15 4 17 5 11 12 17 \n", + "4 4 12 16 5 17 5 11 12 17 \n", + "\n", + " PWGTP14 PWGTP15 PWGTP16 PWGTP17 PWGTP18 PWGTP19 PWGTP20 PWGTP21 \\\n", + "0 33 35 23 22 6 22 21 20 \n", + "1 20 11 21 16 12 28 26 14 \n", + "2 30 16 28 20 19 28 34 18 \n", + "3 24 16 23 14 14 24 25 17 \n", + "4 24 16 22 15 14 24 25 17 \n", + "\n", + " PWGTP22 PWGTP23 PWGTP24 PWGTP25 PWGTP26 PWGTP27 PWGTP28 PWGTP29 \\\n", + "0 20 23 37 18 19 7 6 46 \n", + "1 28 4 4 31 15 13 27 19 \n", + "2 32 7 5 38 19 19 33 18 \n", + "3 22 5 5 25 14 14 24 11 \n", + "4 22 4 5 25 13 14 25 12 \n", + "\n", + " PWGTP30 PWGTP31 PWGTP32 PWGTP33 PWGTP34 PWGTP35 PWGTP36 PWGTP37 \\\n", + "0 37 7 21 36 7 6 16 17 \n", + "1 25 16 14 14 4 12 3 15 \n", + "2 34 18 16 19 5 18 5 17 \n", + "3 27 13 14 13 4 15 4 16 \n", + "4 27 13 14 13 4 14 4 17 \n", + "\n", + " PWGTP38 PWGTP39 PWGTP40 PWGTP41 PWGTP42 PWGTP43 PWGTP44 PWGTP45 \\\n", + "0 36 19 19 19 19 20 39 19 \n", + "1 12 4 3 15 4 24 27 5 \n", + "2 20 5 5 23 7 25 30 5 \n", + "3 13 4 5 14 5 28 26 4 \n", + "4 13 4 4 14 5 28 26 3 \n", + "\n", + " PWGTP46 PWGTP47 PWGTP48 PWGTP49 PWGTP50 PWGTP51 PWGTP52 PWGTP53 \\\n", + "0 20 7 7 37 38 7 20 32 \n", + "1 14 12 4 16 3 17 14 14 \n", + "2 19 21 6 18 7 21 19 20 \n", + "3 20 13 3 14 4 17 13 13 \n", + "4 19 13 3 15 4 16 13 13 \n", + "\n", + " PWGTP54 PWGTP55 PWGTP56 PWGTP57 PWGTP58 PWGTP59 PWGTP60 PWGTP61 \\\n", + "0 7 7 22 21 33 20 18 20 \n", + "1 26 10 27 17 15 30 20 19 \n", + "2 26 18 31 18 23 30 31 19 \n", + "3 24 12 21 14 15 22 22 16 \n", + "4 24 12 22 14 16 21 22 15 \n", + "\n", + " PWGTP62 PWGTP63 PWGTP64 PWGTP65 PWGTP66 PWGTP67 PWGTP68 PWGTP69 \\\n", + "0 23 23 6 19 21 40 43 5 \n", + "1 28 5 4 22 14 11 26 14 \n", + "2 37 6 6 36 23 18 28 17 \n", + "3 23 4 4 23 15 15 23 14 \n", + "4 23 4 3 22 15 15 23 14 \n", + "\n", + " PWGTP70 PWGTP71 PWGTP72 PWGTP73 PWGTP74 PWGTP75 PWGTP76 PWGTP77 \\\n", + "0 7 40 20 6 35 39 22 26 \n", + "1 21 10 16 19 4 14 5 13 \n", + "2 32 15 21 22 6 24 4 15 \n", + "3 28 15 12 17 5 12 5 13 \n", + "4 28 15 12 17 5 13 4 13 \n", + "\n", + " PWGTP78 PWGTP79 PWGTP80 \n", + "0 6 20 21 \n", + "1 14 5 4 \n", + "2 21 5 6 \n", + "3 15 5 4 \n", + "4 15 4 3 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_pums = c.download_population_pums(\"06\", puma10=puma10, puma00=puma00)\n", + "p_pums.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download PUMS for household records for a PUMA" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "4 91 138 42 43 77 68 163 86 76 \n", + "\n", + " WGTP33 WGTP34 WGTP35 WGTP36 WGTP37 WGTP38 WGTP39 WGTP40 WGTP41 \\\n", + "0 36 8 5 17 18 36 19 18 19 \n", + "1 15 4 12 3 15 12 4 4 15 \n", + "2 14 10 20 15 15 4 5 15 4 \n", + "3 10 4 12 15 3 4 18 3 13 \n", + "4 101 74 106 100 111 30 28 87 118 \n", + "\n", + " WGTP42 WGTP43 WGTP44 WGTP45 WGTP46 WGTP47 WGTP48 WGTP49 WGTP50 \\\n", + "0 19 20 38 19 20 7 6 37 38 \n", + "1 4 24 28 5 14 11 4 17 3 \n", + "2 5 29 16 4 23 25 10 15 5 \n", + "3 7 4 12 3 13 9 11 30 12 \n", + "4 131 40 90 100 27 36 79 76 137 \n", + "\n", + " WGTP51 WGTP52 WGTP53 WGTP54 WGTP55 WGTP56 WGTP57 WGTP58 WGTP59 \\\n", + "0 7 20 31 7 6 22 21 32 20 \n", + "1 17 14 14 26 10 27 17 15 31 \n", + "2 11 10 13 17 5 14 16 31 20 \n", + "3 17 12 12 18 10 3 18 21 4 \n", + "4 106 104 92 82 166 85 86 35 24 \n", + "\n", + " WGTP60 WGTP61 WGTP62 WGTP63 WGTP64 WGTP65 WGTP66 WGTP67 WGTP68 \\\n", + "0 18 20 23 23 7 20 20 40 43 \n", + "1 21 18 28 6 3 22 14 11 25 \n", + "2 15 27 20 5 19 32 7 4 16 \n", + "3 13 10 8 17 12 20 10 11 10 \n", + "4 79 23 26 116 72 29 118 143 85 \n", + "\n", + " WGTP69 WGTP70 WGTP71 WGTP72 WGTP73 WGTP74 WGTP75 WGTP76 WGTP77 \\\n", + "0 5 7 40 20 6 35 39 22 26 \n", + "1 14 20 10 16 19 3 14 5 13 \n", + "2 11 24 12 13 13 16 23 12 11 \n", + "3 4 10 3 12 10 2 10 16 3 \n", + "4 85 49 110 88 70 83 33 81 85 \n", + "\n", + " WGTP78 WGTP79 WGTP80 \n", + "0 6 20 22 \n", + "1 14 5 4 \n", + "2 4 4 14 \n", + "3 4 17 2 \n", + "4 107 116 102 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h_pums = c.download_household_pums(\"06\", puma10=puma10, puma00=puma00)\n", + "h_pums.head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now the job is to categorize acs and pums into the same categories - we start with the household acs data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cat_namecarschildrenhouseholdsincomeworkers
cat_valuenoneonetwo or morenoyestotalgt100gt35-lt100lt35noneonetwo or more
NAME
Block Group 1, Census Tract 306, San Francisco County, California99119895205300157746968119111
Block Group 2, Census Tract 306, San Francisco County, California98317979194273198621362109101
Block Group 3, Census Tract 306, San Francisco County, California109320165240305257291969121112
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" + ], + "text/plain": [ + "cat_name cars \\\n", + "cat_value none one two or more \n", + "NAME \n", + "Block Group 1, Census Tract 306, San Francisco ... 9 91 198 \n", + "Block Group 2, Census Tract 306, San Francisco ... 9 83 179 \n", + "Block Group 3, Census Tract 306, San Francisco ... 10 93 201 \n", + "\n", + "cat_name children households \\\n", + "cat_value no yes total \n", + "NAME \n", + "Block Group 1, Census Tract 306, San Francisco ... 95 205 300 \n", + "Block Group 2, Census Tract 306, San Francisco ... 79 194 273 \n", + "Block Group 3, Census Tract 306, San Francisco ... 65 240 305 \n", + "\n", + "cat_name income \\\n", + "cat_value gt100 gt35-lt100 lt35 \n", + "NAME \n", + "Block Group 1, Census Tract 306, San Francisco ... 157 74 69 \n", + "Block Group 2, Census Tract 306, San Francisco ... 198 62 13 \n", + "Block Group 3, Census Tract 306, San Francisco ... 257 29 19 \n", + "\n", + "cat_name workers \n", + "cat_value none one two or more \n", + "NAME \n", + "Block Group 1, Census Tract 306, San Francisco ... 68 119 111 \n", + "Block Group 2, Census Tract 306, San Francisco ... 62 109 101 \n", + "Block Group 3, Census Tract 306, San Francisco ... 69 121 112 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h_acs_cat = cat.categorize(h_acs, {\n", + " (\"households\", \"total\"): \"B11001_001E\",\n", + " (\"children\", \"yes\"): \"B11001_002E\",\n", + " (\"children\", \"no\"): \"B11001_001E - B11001_002E\",\n", + " (\"income\", \"lt35\"): \"B19001_002E + B19001_003E + B19001_004E + \"\n", + " \"B19001_005E + B19001_006E + B19001_007E\",\n", + " (\"income\", \"gt35-lt100\"): \"B19001_008E + B19001_009E + \"\n", + " \"B19001_010E + B19001_011E + B19001_012E\"\n", + " \"+ B19001_013E\",\n", + " (\"income\", \"gt100\"): \"B19001_014E + B19001_015E + B19001_016E\"\n", + " \"+ B19001_017E\",\n", + " (\"cars\", \"none\"): \"B08201_002E\",\n", + " (\"cars\", \"one\"): \"B08201_003E\",\n", + " (\"cars\", \"two or more\"): \"B08201_004E + B08201_005E + B08201_006E\",\n", + " (\"workers\", \"none\"): \"B08202_002E\",\n", + " (\"workers\", \"one\"): \"B08202_003E\",\n", + " (\"workers\", \"two or more\"): \"B08202_004E + B08202_005E\" \n", + "}, index_cols=['NAME'])\n", + "h_acs_cat" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "assert np.all(cat.sum_accross_category(h_acs_cat) < 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## And the same for ACS population - the output of the categorization is the MARGINALS for each variable category" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cat_nameagepopulationracesex
cat_value19 and under20 to 3535 to 60above 60totalasianblackotherwhitefemalemale
NAME
Block Group 1, Census Tract 306, San Francisco County, California1981322012587892619152367397392
Block Group 2, Census Tract 306, San Francisco County, California12582313176696179070447354342
Block Group 3, Census Tract 306, San Francisco County, California291624121749393101285532421518
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" + ], + "text/plain": [ + "cat_name age \\\n", + "cat_value 19 and under 20 to 35 \n", + "NAME \n", + "Block Group 1, Census Tract 306, San Francisco ... 198 132 \n", + "Block Group 2, Census Tract 306, San Francisco ... 125 82 \n", + "Block Group 3, Census Tract 306, San Francisco ... 291 62 \n", + "\n", + "cat_name \\\n", + "cat_value 35 to 60 above 60 \n", + "NAME \n", + "Block Group 1, Census Tract 306, San Francisco ... 201 258 \n", + "Block Group 2, Census Tract 306, San Francisco ... 313 176 \n", + "Block Group 3, Census Tract 306, San Francisco ... 412 174 \n", + "\n", + "cat_name population race \\\n", + "cat_value total asian black \n", + "NAME \n", + "Block Group 1, Census Tract 306, San Francisco ... 789 261 9 \n", + "Block Group 2, Census Tract 306, San Francisco ... 696 179 0 \n", + "Block Group 3, Census Tract 306, San Francisco ... 939 310 12 \n", + "\n", + "cat_name sex \n", + "cat_value other white female male \n", + "NAME \n", + "Block Group 1, Census Tract 306, San Francisco ... 152 367 397 392 \n", + "Block Group 2, Census Tract 306, San Francisco ... 70 447 354 342 \n", + "Block Group 3, Census Tract 306, San Francisco ... 85 532 421 518 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_acs_cat = cat.categorize(p_acs, {\n", + " (\"population\", \"total\"): \"B01001_001E\",\n", + " (\"age\", \"19 and under\"): \"B01001_003E + B01001_004E + B01001_005E + \"\n", + " \"B01001_006E + B01001_007E + B01001_027E + \"\n", + " \"B01001_028E + B01001_029E + B01001_030E + \"\n", + " \"B01001_031E\",\n", + " (\"age\", \"20 to 35\"): \"B01001_008E + B01001_009E + B01001_010E + \"\n", + " \"B01001_011E + B01001_012E + B01001_032E + \"\n", + " \"B01001_033E + B01001_034E + B01001_035E + \"\n", + " \"B01001_036E\",\n", + " (\"age\", \"35 to 60\"): \"B01001_013E + B01001_014E + B01001_015E + \"\n", + " \"B01001_016E + B01001_017E + B01001_037E + \"\n", + " \"B01001_038E + B01001_039E + B01001_040E + \"\n", + " \"B01001_041E\",\n", + " (\"age\", \"above 60\"): \"B01001_018E + B01001_019E + B01001_020E + \"\n", + " \"B01001_021E + B01001_022E + B01001_023E + \"\n", + " \"B01001_024E + B01001_025E + B01001_042E + \"\n", + " \"B01001_043E + B01001_044E + B01001_045E + \"\n", + " \"B01001_046E + B01001_047E + B01001_048E + \"\n", + " \"B01001_049E\", \n", + " (\"race\", \"white\"): \"B02001_002E\",\n", + " (\"race\", \"black\"): \"B02001_003E\",\n", + " (\"race\", \"asian\"): \"B02001_005E\",\n", + " (\"race\", \"other\"): \"B02001_004E + B02001_006E + B02001_007E + \"\n", + " \"B02001_008E\",\n", + " (\"sex\", \"male\"): \"B01001_002E\",\n", + " (\"sex\", \"female\"): \"B01001_026E\"\n", + "}, index_cols=['NAME'])\n", + "p_acs_cat" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "assert np.all(cat.sum_accross_category(p_acs_cat) < 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## To get the marginals a series for one geography do this" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "cat_name cat_value \n", + "age 19 and under 198\n", + " 20 to 35 132\n", + " 35 to 60 201\n", + " above 60 258\n", + "population total 789\n", + "race asian 261\n", + " black 9\n", + " other 152\n", + " white 367\n", + "sex female 397\n", + " male 392\n", + "Name: Block Group 1, Census Tract 306, San Francisco County, California, dtype: int32" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_acs_cat.iloc[0].transpose()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now categorize the PUMS population data into the same categories" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cat_idfrequency
ageracesex
19 and underasianfemale0187
male1215
blackfemale216
male315
otherfemale489
male594
whitefemale6202
male7185
20 to 35asianfemale8238
male9211
blackfemale1021
male1123
otherfemale1259
male1367
whitefemale14198
male15225
35 to 60asianfemale16448
male17378
blackfemale1833
male1939
otherfemale2075
male2178
whitefemale22373
male23453
above 60asianfemale24233
male25182
blackfemale2657
male2743
otherfemale2832
male2925
whitefemale30354
male31294
\n", + "
" + ], + "text/plain": [ + " cat_id frequency\n", + "age race sex \n", + "19 and under asian female 0 187\n", + " male 1 215\n", + " black female 2 16\n", + " male 3 15\n", + " other female 4 89\n", + " male 5 94\n", + " white female 6 202\n", + " male 7 185\n", + "20 to 35 asian female 8 238\n", + " male 9 211\n", + " black female 10 21\n", + " male 11 23\n", + " other female 12 59\n", + " male 13 67\n", + " white female 14 198\n", + " male 15 225\n", + "35 to 60 asian female 16 448\n", + " male 17 378\n", + " black female 18 33\n", + " male 19 39\n", + " other female 20 75\n", + " male 21 78\n", + " white female 22 373\n", + " male 23 453\n", + "above 60 asian female 24 233\n", + " male 25 182\n", + " black female 26 57\n", + " male 27 43\n", + " other female 28 32\n", + " male 29 25\n", + " white female 30 354\n", + " male 31 294" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def age_cat(r):\n", + " if r.AGEP <= 19: return \"19 and under\"\n", + " elif r.AGEP <= 35: return \"20 to 35\"\n", + " elif r.AGEP <= 60: return \"35 to 60\"\n", + " return \"above 60\"\n", + "\n", + "def race_cat(r):\n", + " if r.RAC1P == 1: return \"white\"\n", + " elif r.RAC1P == 2: return \"black\"\n", + " elif r.RAC1P == 6: return \"asian\"\n", + " return \"other\"\n", + "\n", + "def sex_cat(r):\n", + " if r.SEX == 1: return \"male\"\n", + " return \"female\"\n", + "\n", + "_, jd_persons = cat.joint_distribution(\n", + " p_pums,\n", + " cat.category_combinations(p_acs_cat.columns),\n", + " {\"age\": age_cat, \"race\": race_cat, \"sex\": sex_cat}\n", + ")\n", + "jd_persons " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Do the same for households - the output of this step is the JOINT DISTRIBUTIONS for the cross product of all possible categories" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cat_idfrequency
carschildrenincomeworkers
nonenogt100none00.0
one13.0
two or more25.0
gt35-lt100none33.0
one46.0
two or more510.0
lt35none6174.0
one79.0
two or more81.0
yesgt100none90.0
one100.0
two or more111.0
gt35-lt100none120.0
one134.0
two or more142.0
lt35none152.0
one165.0
two or more170.0
onenogt100none1810.0
one1919.0
two or more2042.0
gt35-lt100none2127.0
one2240.0
two or more2347.0
lt35none24404.0
one2514.0
two or more262.0
yesgt100none271.0
one2815.0
two or more2924.0
gt35-lt100none300.0
one3124.0
two or more3221.0
lt35none335.0
one3418.0
two or more352.0
two or morenogt100none3613.0
one3746.0
two or more38209.0
gt35-lt100none3930.0
one4063.0
two or more4198.0
lt35none42174.0
one4315.0
two or more4411.0
yesgt100none450.0
one4643.0
two or more47159.0
gt35-lt100none480.0
one4919.0
two or more5055.0
lt35none513.0
one526.0
two or more535.0
\n", + "
" + ], + "text/plain": [ + " cat_id frequency\n", + "cars children income workers \n", + "none no gt100 none 0 0.0\n", + " one 1 3.0\n", + " two or more 2 5.0\n", + " gt35-lt100 none 3 3.0\n", + " one 4 6.0\n", + " two or more 5 10.0\n", + " lt35 none 6 174.0\n", + " one 7 9.0\n", + " two or more 8 1.0\n", + " yes gt100 none 9 0.0\n", + " one 10 0.0\n", + " two or more 11 1.0\n", + " gt35-lt100 none 12 0.0\n", + " one 13 4.0\n", + " two or more 14 2.0\n", + " lt35 none 15 2.0\n", + " one 16 5.0\n", + " two or more 17 0.0\n", + "one no gt100 none 18 10.0\n", + " one 19 19.0\n", + " two or more 20 42.0\n", + " gt35-lt100 none 21 27.0\n", + " one 22 40.0\n", + " two or more 23 47.0\n", + " lt35 none 24 404.0\n", + " one 25 14.0\n", + " two or more 26 2.0\n", + " yes gt100 none 27 1.0\n", + " one 28 15.0\n", + " two or more 29 24.0\n", + " gt35-lt100 none 30 0.0\n", + " one 31 24.0\n", + " two or more 32 21.0\n", + " lt35 none 33 5.0\n", + " one 34 18.0\n", + " two or more 35 2.0\n", + "two or more no gt100 none 36 13.0\n", + " one 37 46.0\n", + " two or more 38 209.0\n", + " gt35-lt100 none 39 30.0\n", + " one 40 63.0\n", + " two or more 41 98.0\n", + " lt35 none 42 174.0\n", + " one 43 15.0\n", + " two or more 44 11.0\n", + " yes gt100 none 45 0.0\n", + " one 46 43.0\n", + " two or more 47 159.0\n", + " gt35-lt100 none 48 0.0\n", + " one 49 19.0\n", + " two or more 50 55.0\n", + " lt35 none 51 3.0\n", + " one 52 6.0\n", + " two or more 53 5.0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def cars_cat(r):\n", + " if r.VEH == 0: return \"none\"\n", + " elif r.VEH == 1: return \"one\"\n", + " return \"two or more\"\n", + "\n", + "def children_cat(r):\n", + " if r.NOC > 0: return \"yes\"\n", + " return \"no\"\n", + "\n", + "def income_cat(r):\n", + " if r.FINCP > 100000: return \"gt100\"\n", + " elif r.FINCP > 35000: return \"gt35-lt100\"\n", + " return \"lt35\"\n", + "\n", + "def workers_cat(r):\n", + " if r.WIF == 3: return \"two or more\"\n", + " elif r.WIF == 2: return \"two or more\"\n", + " elif r.WIF == 1: return \"one\"\n", + " return \"none\"\n", + "\n", + "_, jd_households = cat.joint_distribution(\n", + " h_pums,\n", + " cat.category_combinations(h_acs_cat.columns),\n", + " {\"cars\": cars_cat, \"children\": children_cat, \n", + " \"income\": income_cat, \"workers\": workers_cat}\n", + ")\n", + "jd_households" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## With marginals (aggregate, from ACS) and joint distribution (disaggregate, from PUMS) we're ready for some synthesis" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'TBD'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"TBD\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.14" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/demos/.ipynb_checkpoints/non_census_synthesis-checkpoint.ipynb b/demos/.ipynb_checkpoints/non_census_synthesis-checkpoint.ipynb new file mode 100644 index 0000000..bf16a1a --- /dev/null +++ b/demos/.ipynb_checkpoints/non_census_synthesis-checkpoint.ipynb @@ -0,0 +1,1613 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "import synthpop.zone_synthesizer as zs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Specify sample data csv paths. See the files listed here for expected structure. Marginal tables require multi-indexed columns with category name and category value in levels 0 and 1 of the index. Sample file category columns should be labeled with corresponding category names and values in those columns should match the category value headers in the marginal table." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "hh_marginal_file = 'input_data/hh_marginals.csv'\n", + "person_marginal_file = 'input_data/person_marginals.csv'\n", + "hh_sample_file = 'input_data/household_sample.csv'\n", + "person_sample_file = 'input_data/person_sample.csv'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load and process input marginals and samples and geography crosswalk" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "hh_marg, p_marg, hh_sample, p_sample, xwalk = zs.load_data(hh_marginal_file, person_marginal_file, hh_sample_file, person_sample_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cat_namecarschildrenincomeworkers
cat_valuesnoneonetwo or morenoyesgt100gt35-lt100lt35noneonetwo or more
17491974121557125747277105
29592376823983126988793125
310692757927974170114102108146
4117630216722442105244111118160
5181174668651750261292171182247
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" + ], + "text/plain": [ + "cat_name cars children income workers \\\n", + "cat_values none one two or more no yes gt100 gt35-lt100 lt35 none \n", + "1 7 49 197 41 215 57 125 74 72 \n", + "2 9 59 237 68 239 83 126 98 87 \n", + "3 10 69 275 79 279 74 170 114 102 \n", + "4 11 76 302 167 224 42 105 244 111 \n", + "5 18 117 466 86 517 50 261 292 171 \n", + "\n", + "cat_name \n", + "cat_values one two or more \n", + "1 77 105 \n", + "2 93 125 \n", + "3 108 146 \n", + "4 118 160 \n", + "5 182 247 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hh_marg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cat_nameageracesex
cat_values19 and under20 to 3535 to 60above 60asianblackotherwhitefemalemale
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" + ], + "text/plain": [ + "cat_name age race \\\n", + "cat_values 19 and under 20 to 35 35 to 60 above 60 asian black other white \n", + "1 312 108 223 177 64 0 0 756 \n", + "2 235 143 296 181 0 0 0 855 \n", + "3 303 229 445 174 0 0 24 1127 \n", + "4 215 77 356 189 0 0 29 808 \n", + "5 506 539 619 262 0 0 0 1926 \n", + "\n", + "cat_name sex \n", + "cat_values female male \n", + "1 440 380 \n", + "2 452 403 \n", + "3 565 586 \n", + "4 389 448 \n", + "5 981 945 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_marg.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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serialnosample_geogageracesexAGEPSEXRAC1P
02012000005576120 to 35whitemale2711
12012000017760120 to 35whitemale3411
22012000021787120 to 35whitemale3211
32012000021815120 to 35whitemale2311
42012000065237120 to 35whitemale2411
\n", + "
" + ], + "text/plain": [ + " serialno sample_geog age race sex AGEP SEX RAC1P\n", + "0 2012000005576 1 20 to 35 white male 27 1 1\n", + "1 2012000017760 1 20 to 35 white male 34 1 1\n", + "2 2012000021787 1 20 to 35 white male 32 1 1\n", + "3 2012000021815 1 20 to 35 white male 23 1 1\n", + "4 2012000065237 1 20 to 35 white male 24 1 1" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "p_sample.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Iterate over all marginals in the geography crosswalk and synthesize in-line" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 254 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 306 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 356 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 390 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 601 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 324 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 556 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 342 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 273 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 228 households\n", + "Drawing 857 households\n", + "Drawing 748 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 744 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 953 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 719 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 185 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 183 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 286 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 317 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 711 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 345 households\n" + ] + } + ], + "source": [ + "all_households, all_persons, all_stats = zs.synthesize_all_zones(hh_marg, p_marg, hh_sample, p_sample, xwalk)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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serialnosample_geogcarsworkerschildrenincomeVEHFINCPNOCWIFcat_idgeog
household_id
120100012292651nonenonenolt350NaN0NaN61
220100002629471nonenoneyeslt3500.010.0151
320100001413681nonenoneyeslt3503500.010.0151
420120001625411nonenoneyeslt35018300.020.0151
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" + ], + "text/plain": [ + " serialno sample_geog cars workers children income VEH \\\n", + "household_id \n", + "1 2010001229265 1 none none no lt35 0 \n", + "2 2010000262947 1 none none yes lt35 0 \n", + "3 2010000141368 1 none none yes lt35 0 \n", + "4 2012000162541 1 none none yes lt35 0 \n", + "5 2012001476110 1 none none yes lt35 0 \n", + "\n", + " FINCP NOC WIF cat_id geog \n", + "household_id \n", + "1 NaN 0 NaN 6 1 \n", + "2 0.0 1 0.0 15 1 \n", + "3 3500.0 1 0.0 15 1 \n", + "4 18300.0 2 0.0 15 1 \n", + "5 3100.0 3 0.0 15 1 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_households.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### all_persons.household_id maps person records to all_households.index" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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serialnosample_geogageracesexAGEPSEXRAC1Pcat_idgeoghousehold_id
02012000136005120 to 35whitemale311169185
12012000136005135 to 60whitefemale362176185
22012000136005119 and underwhitemale101161185
32012000136005119 and underwhitemale81161185
42012000136005119 and underwhitemale51161185
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" + ], + "text/plain": [ + " serialno sample_geog age race sex AGEP SEX RAC1P \\\n", + "0 2012000136005 1 20 to 35 white male 31 1 1 \n", + "1 2012000136005 1 35 to 60 white female 36 2 1 \n", + "2 2012000136005 1 19 and under white male 10 1 1 \n", + "3 2012000136005 1 19 and under white male 8 1 1 \n", + "4 2012000136005 1 19 and under white male 5 1 1 \n", + "\n", + " cat_id geog household_id \n", + "0 69 1 85 \n", + "1 76 1 85 \n", + "2 61 1 85 \n", + "3 61 1 85 \n", + "4 61 1 85 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_persons.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Synthesize all marginal geographies in the crosswalk using a specified or default number of cores via multiprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "all_persons, all_households, all_stats = zs.multiprocess_synthesize(hh_marg, p_marg, hh_sample, p_sample, xwalk)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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serialnosample_geogageracesexAGEPSEXRAC1Pcat_idgeoghousehold_id
02012000136005120 to 35whitemale311169181
12012000136005135 to 60whitefemale362176181
22012000136005119 and underwhitemale101161181
32012000136005119 and underwhitemale81161181
42012000136005119 and underwhitemale51161181
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" + ], + "text/plain": [ + " serialno sample_geog age race sex AGEP SEX RAC1P \\\n", + "0 2012000136005 1 20 to 35 white male 31 1 1 \n", + "1 2012000136005 1 35 to 60 white female 36 2 1 \n", + "2 2012000136005 1 19 and under white male 10 1 1 \n", + "3 2012000136005 1 19 and under white male 8 1 1 \n", + "4 2012000136005 1 19 and under white male 5 1 1 \n", + "\n", + " cat_id geog household_id \n", + "0 69 1 81 \n", + "1 76 1 81 \n", + "2 61 1 81 \n", + "3 61 1 81 \n", + "4 61 1 81 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_persons.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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serialnosample_geogcarsworkerschildrenincomeVEHFINCPNOCWIFcat_idgeog
household_id
120090004653301nonenonenolt350NaN0NaN61
220090004559721nonenoneyeslt3503500.020.0151
320120014761101nonenoneyeslt3503100.030.0151
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" + ], + "text/plain": [ + " serialno sample_geog cars workers children income VEH \\\n", + "household_id \n", + "1 2009000465330 1 none none no lt35 0 \n", + "2 2009000455972 1 none none yes lt35 0 \n", + "3 2012001476110 1 none none yes lt35 0 \n", + "4 2012001476110 1 none none yes lt35 0 \n", + "5 2010000141368 1 none none yes lt35 0 \n", + "\n", + " FINCP NOC WIF cat_id geog \n", + "household_id \n", + "1 NaN 0 NaN 6 1 \n", + "2 3500.0 2 0.0 15 1 \n", + "3 3100.0 3 0.0 15 1 \n", + "4 3100.0 3 0.0 15 1 \n", + "5 3500.0 1 0.0 15 1 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_households.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chi-squaregeogp-score
02.07085519.998970e-01
1154.42091324.782383e-30
2182.58567035.033580e-32
396.43644642.276937e-14
4468.71425054.243758e-97
55.64296869.581878e-01
6516.86687572.458698e-101
779.63830581.632443e-14
8117.23866391.836683e-20
92000.143804100.000000e+00
10642.117025112.372825e-117
1162.442878124.130503e-07
121174.759712138.130438e-234
13670.414502142.542384e-132
141081.225334154.449635e-220
1512.940226167.357824e-02
161.161109179.917950e-01
17159.879713182.084872e-27
180.507767199.994175e-01
19116.945628207.393055e-18
2014.386659214.471640e-02
\n", + "
" + ], + "text/plain": [ + " chi-square geog p-score\n", + "0 2.070855 1 9.998970e-01\n", + "1 154.420913 2 4.782383e-30\n", + "2 182.585670 3 5.033580e-32\n", + "3 96.436446 4 2.276937e-14\n", + "4 468.714250 5 4.243758e-97\n", + "5 5.642968 6 9.581878e-01\n", + "6 516.866875 7 2.458698e-101\n", + "7 79.638305 8 1.632443e-14\n", + "8 117.238663 9 1.836683e-20\n", + "9 2000.143804 10 0.000000e+00\n", + "10 642.117025 11 2.372825e-117\n", + "11 62.442878 12 4.130503e-07\n", + "12 1174.759712 13 8.130438e-234\n", + "13 670.414502 14 2.542384e-132\n", + "14 1081.225334 15 4.449635e-220\n", + "15 12.940226 16 7.357824e-02\n", + "16 1.161109 17 9.917950e-01\n", + "17 159.879713 18 2.084872e-27\n", + "18 0.507767 19 9.994175e-01\n", + "19 116.945628 20 7.393055e-18\n", + "20 14.386659 21 4.471640e-02" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "all_stats" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/demos/.ipynb_checkpoints/simple_synthesis-checkpoint.ipynb b/demos/.ipynb_checkpoints/simple_synthesis-checkpoint.ipynb new file mode 100644 index 0000000..75a0c52 --- /dev/null +++ b/demos/.ipynb_checkpoints/simple_synthesis-checkpoint.ipynb @@ -0,0 +1,415 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from synthpop.synthesizer import synthesize, enable_logging\n", + "import synthpop.categorizer as cat\n", + "\n", + "import multiprocessing\n", + "import os\n", + "import seaborn as sns\n", + "from functools import partial\n", + "import pandas as pd\n", + "\n", + "import synthpop.zone_synthesizer as zs" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "hh_marginal_file = 'input_data/hh_marginals.csv'\n", + "person_marginal_file = 'input_data/person_marginals.csv'\n", + "hh_sample_file = 'input_data/household_sample.csv'\n", + "person_sample_file = 'input_data/person_sample.csv'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "hh_marg, p_marg, hh_sample, p_sample, xwalk = zs.load_data(hh_marginal_file, person_marginal_file, hh_sample_file, person_sample_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 254 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 306 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 356 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 390 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 601 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 324 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 556 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 342 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 273 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 228 households\n", + "Drawing 857 households\n", + "Drawing 748 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 744 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 953 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 719 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 185 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 183 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 286 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 317 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 711 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 345 households\n" + ] + } + ], + "source": [ + "all_households, all_persons, all_stats = zs.synthesize_all_zones(hh_marg, p_marg, hh_sample, p_sample, xwalk)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "all_persons, all_households, all_stats = zs.multiprocess_synthesize(hh_marg, p_marg, hh_sample, p_sample, xwalk)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Juan\\Anaconda3\\envs\\synpop_py3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6448: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.distplot(all_persons.cat_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/demos/.ipynb_checkpoints/synthesize-checkpoint.ipynb b/demos/.ipynb_checkpoints/synthesize-checkpoint.ipynb new file mode 100644 index 0000000..f4a5fc4 --- /dev/null +++ b/demos/.ipynb_checkpoints/synthesize-checkpoint.ipynb @@ -0,0 +1,474 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "from synthpop.recipes.starter2 import Starter\n", + "from synthpop.synthesizer import synthesize_all, enable_logging \n", + "import os\n", + "import pandas as pd\n", + "#enable_logging()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36msynthesize_counties\u001b[1;34m(counties)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msynthesize_counties\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounties\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcounty\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcounties\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mstarter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStarter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"CENSUS\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"CA\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcounty\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0msynthesize_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstarter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'time'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'hh = synthesize_counties([\"Santa Clara County\", \"Solano County\", \"San Mateo County\", \"Marin County\", \"San Francisco County\", \"Napa County\", \"Sonoma County\", \"Contra Costa County\", \"Alameda County\"])'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\juan\\documents\\github\\synthpop\\synthpop\\recipes\\starter2.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, key, state, county, tract)\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0mblock_group_size_attr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"B11005_001E\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[0mtract_size_attr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"B08201_001E\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m tract=tract)\n\u001b[0m\u001b[0;32m 72\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mh_acs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh_acs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\juan\\documents\\github\\synthpop\\synthpop\\census_helpers.py\u001b[0m in \u001b[0;36mblock_group_and_tract_query\u001b[1;34m(self, block_group_columns, tract_columns, state, county, merge_columns, block_group_size_attr, tract_size_attr, tract, year)\u001b[0m\n\u001b[0;32m 110\u001b[0m tract_size_attr, tract=None, year=2016):\n\u001b[0;32m 111\u001b[0m df2 = self.tract_query(tract_columns, state, county, tract=tract,\n\u001b[1;32m--> 112\u001b[1;33m year=year)\n\u001b[0m\u001b[0;32m 113\u001b[0m df1 = self.block_group_query(block_group_columns, state, county,\n\u001b[0;32m 114\u001b[0m tract=tract, year=year)\n", + "\u001b[1;32mc:\\users\\juan\\documents\\github\\synthpop\\synthpop\\census_helpers.py\u001b[0m in \u001b[0;36mtract_query\u001b[1;34m(self, census_columns, state, county, tract, year)\u001b[0m\n\u001b[0;32m 63\u001b[0m return self._query(census_columns, state, county,\n\u001b[0;32m 64\u001b[0m \u001b[0mforstr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"tract:%s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mtract\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m year=year)\n\u001b[0m\u001b[0;32m 66\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 67\u001b[0m def _query(self, census_columns, state, county, forstr,\n", + "\u001b[1;32mc:\\users\\juan\\documents\\github\\synthpop\\synthpop\\census_helpers.py\u001b[0m in \u001b[0;36m_query\u001b[1;34m(self, census_columns, state, county, forstr, tract, year)\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m \u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcounty\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtry_fips_lookup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcounty\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 72\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtract\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\juan\\documents\\github\\synthpop\\synthpop\\census_helpers.py\u001b[0m in \u001b[0;36mtry_fips_lookup\u001b[1;34m(self, state, county)\u001b[0m\n\u001b[0;32m 199\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 200\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mtry_fips_lookup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcounty\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 201\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_fips_lookup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 202\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 203\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcounty\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\juan\\documents\\github\\synthpop\\synthpop\\census_helpers.py\u001b[0m in \u001b[0;36m_get_fips_lookup\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 143\u001b[0m },\n\u001b[0;32m 144\u001b[0m index_col=[\"State\",\n\u001b[1;32m--> 145\u001b[1;33m \"County Name\"]\n\u001b[0m\u001b[0;32m 146\u001b[0m )\n\u001b[0;32m 147\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfips_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ANSI Cl\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m 707\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[0;32m 708\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 709\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 710\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 711\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 431\u001b[0m \u001b[0mcompression\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_infer_compression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 432\u001b[0m filepath_or_buffer, _, compression = get_filepath_or_buffer(\n\u001b[1;32m--> 433\u001b[1;33m filepath_or_buffer, encoding, compression)\n\u001b[0m\u001b[0;32m 434\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'compression'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression)\u001b[0m\n\u001b[0;32m 188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m_is_url\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 190\u001b[1;33m \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_urlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 191\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Content-Encoding'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 192\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'gzip'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m 221\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 222\u001b[0m \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 223\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 224\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 225\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m 524\u001b[0m \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 526\u001b[1;33m \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 527\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 528\u001b[0m \u001b[1;31m# post-process response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m 542\u001b[0m \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 543\u001b[0m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[1;32m--> 544\u001b[1;33m '_open', req)\n\u001b[0m\u001b[0;32m 545\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 546\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m 502\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 503\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 504\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 505\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 506\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m 1359\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1360\u001b[0m return self.do_open(http.client.HTTPSConnection, req,\n\u001b[1;32m-> 1361\u001b[1;33m context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[0;32m 1362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1363\u001b[0m \u001b[0mhttps_request\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m 1319\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1320\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1321\u001b[1;33m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1322\u001b[0m \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1323\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1329\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1330\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1331\u001b[1;33m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1332\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1333\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mbegin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 295\u001b[0m \u001b[1;31m# read until we get a non-100 response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 296\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 297\u001b[1;33m \u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 298\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 299\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 257\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 258\u001b[1;33m \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"iso-8859-1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 259\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 260\u001b[0m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m 1007\u001b[0m \u001b[1;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1008\u001b[0m self.__class__)\n\u001b[1;32m-> 1009\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1010\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1011\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\ssl.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m 869\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Read on closed or unwrapped SSL socket.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 870\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 871\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 872\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mSSLError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 873\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mSSL_ERROR_EOF\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\ssl.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m 629\u001b[0m \"\"\"\n\u001b[0;32m 630\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 631\u001b[1;33m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 632\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 633\u001b[0m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "def synthesize_counties(counties):\n", + " for county in counties:\n", + " starter = Starter(os.environ[\"CENSUS\"], \"CA\", county)\n", + " synthesize_all(starter)\n", + "%time hh = synthesize_counties([\"Santa Clara County\", \"Solano County\", \"San Mateo County\", \"Marin County\", \"San Francisco County\", \"Napa County\", \"Sonoma County\", \"Contra Costa County\", \"Alameda County\"]) " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "starter = Starter(os.environ[\"CENSUS\"], \"CA\", \"Santa Clara County\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Synthesizing at geog level: 'block_group' (number of geographies is 1075)\n", + "Synthesizing geog id:\n", + " state 06\n", + "county 085\n", + "tract 508203\n", + "block group 3\n", + "dtype: object\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 770 households\n" + ] + }, + { + "data": { + "text/plain": [ + "( serialno RT puma00 puma10 NP TYPE BLD TEN VEH HINCP \\\n", + " 0 2009001096088 H 2702 -9 4 1 3.0 3.0 0.0 226400.0 \n", + " 1 2009000797707 H 2702 -9 1 1 6.0 3.0 1.0 120000.0 \n", + " 2 2009000776447 H 2702 -9 1 1 9.0 3.0 1.0 187000.0 \n", + " 3 2009000034504 H 2702 -9 1 1 7.0 3.0 1.0 416000.0 \n", + " 4 2012000837531 H -9 8502 1 1 8.0 3.0 1.0 57000.0 \n", + " 5 2013000119247 H -9 8502 1 1 3.0 1.0 1.0 92000.0 \n", + " 6 2010000191477 H 2702 -9 1 1 3.0 4.0 1.0 390.0 \n", + " 7 2013000718037 H -9 8502 1 1 8.0 3.0 1.0 15500.0 \n", + " 8 2010000285540 H 2702 -9 2 1 5.0 3.0 1.0 0.0 \n", + " 9 2010000337473 H 2702 -9 4 1 8.0 3.0 1.0 32900.0 \n", + " 10 2010001167857 H 2702 -9 3 1 5.0 3.0 1.0 81200.0 \n", + " 11 2009001316324 H 2702 -9 2 1 5.0 3.0 1.0 4200.0 \n", + " 12 2009001316324 H 2702 -9 2 1 5.0 3.0 1.0 4200.0 \n", + " 13 2010000309566 H 2702 -9 3 1 3.0 3.0 2.0 110990.0 \n", + " 14 2009000875809 H 2702 -9 4 1 2.0 1.0 5.0 202000.0 \n", + " 15 2009000847427 H 2702 -9 3 1 2.0 1.0 3.0 192000.0 \n", + " 16 2009000842508 H 2702 -9 2 1 2.0 2.0 2.0 168000.0 \n", + " 17 2009001302282 H 2702 -9 2 1 3.0 2.0 2.0 150000.0 \n", + " 18 2009000938159 H 2702 -9 2 1 9.0 3.0 2.0 169500.0 \n", + " 19 2009000938159 H 2702 -9 2 1 9.0 3.0 2.0 169500.0 \n", + " 20 2009001025363 H 2702 -9 2 1 2.0 2.0 2.0 182000.0 \n", + " 21 2009001025363 H 2702 -9 2 1 2.0 2.0 2.0 182000.0 \n", + " 22 2010001128154 H 2702 -9 2 1 4.0 3.0 2.0 170000.0 \n", + " 23 2010001128154 H 2702 -9 2 1 4.0 3.0 2.0 170000.0 \n", + " 24 2010001128154 H 2702 -9 2 1 4.0 3.0 2.0 170000.0 \n", + " 25 2010001128154 H 2702 -9 2 1 4.0 3.0 2.0 170000.0 \n", + " 26 2012000917686 H -9 8502 2 1 2.0 1.0 2.0 280000.0 \n", + " 27 2012000917686 H -9 8502 2 1 2.0 1.0 2.0 280000.0 \n", + " 28 2012000917686 H -9 8502 2 1 2.0 1.0 2.0 280000.0 \n", + " 29 2012000917686 H -9 8502 2 1 2.0 1.0 2.0 280000.0 \n", + " .. ... .. ... ... .. ... ... ... ... ... \n", + " 740 2009000257552 H 2702 -9 3 1 5.0 3.0 2.0 156000.0 \n", + " 741 2010000224153 H 2702 -9 2 1 9.0 3.0 2.0 69000.0 \n", + " 742 2011000027489 H 2702 -9 2 1 4.0 3.0 2.0 33600.0 \n", + " 743 2010000554995 H 2702 -9 3 1 8.0 3.0 2.0 64200.0 \n", + " 744 2009000090585 H 2702 -9 2 1 9.0 3.0 1.0 36000.0 \n", + " 745 2009000991840 H 2702 -9 2 1 2.0 2.0 3.0 113000.0 \n", + " 746 2010001199295 H 2702 -9 1 1 6.0 3.0 1.0 110600.0 \n", + " 747 2010000531956 H 2702 -9 2 1 1.0 1.0 2.0 74000.0 \n", + " 748 2009000209747 H 2702 -9 2 1 2.0 3.0 2.0 142000.0 \n", + " 749 2011000989438 H 2702 -9 1 1 2.0 1.0 1.0 237400.0 \n", + " 750 2011000990235 H 2702 -9 3 1 1.0 2.0 3.0 151000.0 \n", + " 751 2012000497867 H -9 8502 2 1 2.0 2.0 3.0 136200.0 \n", + " 752 2013000393885 H -9 8502 1 1 2.0 3.0 5.0 69000.0 \n", + " 753 2009001089483 H 2702 -9 3 1 2.0 2.0 2.0 129850.0 \n", + " 754 2009000928777 H 2702 -9 2 1 2.0 2.0 3.0 188500.0 \n", + " 755 2009000935893 H 2702 -9 2 1 2.0 2.0 1.0 16500.0 \n", + " 756 2010000789913 H 2702 -9 2 1 9.0 3.0 2.0 60200.0 \n", + " 757 2012000643381 H -9 8502 4 1 5.0 3.0 2.0 251000.0 \n", + " 758 2011001475367 H 2702 -9 6 1 9.0 3.0 2.0 57000.0 \n", + " 759 2012000603952 H -9 8502 8 1 2.0 3.0 3.0 99230.0 \n", + " 760 2010000011387 H 2702 -9 2 1 2.0 1.0 1.0 220300.0 \n", + " 761 2010001283036 H 2702 -9 2 1 2.0 1.0 2.0 51001.0 \n", + " 762 2010001108709 H 2702 -9 2 1 2.0 1.0 2.0 77700.0 \n", + " 763 2013001259539 H -9 8502 1 1 3.0 3.0 2.0 20000.0 \n", + " 764 2011000656960 H 2702 -9 3 1 2.0 3.0 2.0 70000.0 \n", + " 765 2009000208025 H 2702 -9 2 1 8.0 3.0 0.0 54100.0 \n", + " 766 2011000236562 H 2702 -9 4 1 2.0 1.0 4.0 53300.0 \n", + " 767 2010000805810 H 2702 -9 3 1 2.0 2.0 2.0 1000.0 \n", + " 768 2010000255137 H 2702 -9 1 1 9.0 3.0 1.0 1800.0 \n", + " 769 2013000496979 H -9 8502 2 1 3.0 1.0 3.0 375304.0 \n", + " \n", + " ... hh_size hh_workers seniors sf_detached \\\n", + " 0 ... four or more two or more no no \n", + " 1 ... one one no no \n", + " 2 ... one one no no \n", + " 3 ... one one no no \n", + " 4 ... one one no no \n", + " 5 ... one one no no \n", + " 6 ... one none no no \n", + " 7 ... one one no no \n", + " 8 ... two none no no \n", + " 9 ... four or more one yes no \n", + " 10 ... three one no no \n", + " 11 ... two none no no \n", + " 12 ... two none no no \n", + " 13 ... three one no no \n", + " 14 ... four or more two or more no yes \n", + " 15 ... three two or more no yes \n", + " 16 ... two none no yes \n", + " 17 ... two one no no \n", + " 18 ... two one no no \n", + " 19 ... two one no no \n", + " 20 ... two one no yes \n", + " 21 ... two one no yes \n", + " 22 ... two two or more no no \n", + " 23 ... two two or more no no \n", + " 24 ... two two or more no no \n", + " 25 ... two two or more no no \n", + " 26 ... two two or more no yes \n", + " 27 ... two two or more no yes \n", + " 28 ... two two or more no yes \n", + " 29 ... two two or more no yes \n", + " .. ... ... ... ... ... \n", + " 740 ... three one no no \n", + " 741 ... two one no no \n", + " 742 ... two one no no \n", + " 743 ... three one no no \n", + " 744 ... two one no no \n", + " 745 ... two one no yes \n", + " 746 ... one one no no \n", + " 747 ... two two or more no no \n", + " 748 ... two one yes yes \n", + " 749 ... one one no yes \n", + " 750 ... three one yes no \n", + " 751 ... two none yes yes \n", + " 752 ... one none yes yes \n", + " 753 ... three two or more no yes \n", + " 754 ... two one yes yes \n", + " 755 ... two one no yes \n", + " 756 ... two two or more yes no \n", + " 757 ... four or more two or more no no \n", + " 758 ... four or more one no no \n", + " 759 ... four or more two or more no yes \n", + " 760 ... two two or more no yes \n", + " 761 ... two two or more yes yes \n", + " 762 ... two none yes yes \n", + " 763 ... one one no no \n", + " 764 ... three two or more no yes \n", + " 765 ... two one yes no \n", + " 766 ... four or more one no yes \n", + " 767 ... three none no yes \n", + " 768 ... one none yes no \n", + " 769 ... two two or more no no \n", + " \n", + " tenure_mover cat_id state county tract block group \n", + " 0 rent not recent 9282 06 085 508203 3 \n", + " 1 rent not recent 15490 06 085 508203 3 \n", + " 2 rent not recent 17026 06 085 508203 3 \n", + " 3 rent not recent 18178 06 085 508203 3 \n", + " 4 rent not recent 18562 06 085 508203 3 \n", + " 5 own not recent 20096 06 085 508203 3 \n", + " 6 rent not recent 21602 06 085 508203 3 \n", + " 7 rent not recent 21634 06 085 508203 3 \n", + " 8 rent not recent 21794 06 085 508203 3 \n", + " 9 rent not recent 26154 06 085 508203 3 \n", + " 10 rent not recent 27874 06 085 508203 3 \n", + " 11 rent not recent 29474 06 085 508203 3 \n", + " 12 rent not recent 29474 06 085 508203 3 \n", + " 13 rent not recent 30946 06 085 508203 3 \n", + " 14 own not recent 32324 06 085 508203 3 \n", + " 15 own not recent 32516 06 085 508203 3 \n", + " 16 own not recent 32548 06 085 508203 3 \n", + " 17 own not recent 32576 06 085 508203 3 \n", + " 18 rent not recent 32578 06 085 508203 3 \n", + " 19 rent not recent 32578 06 085 508203 3 \n", + " 20 own not recent 32580 06 085 508203 3 \n", + " 21 own not recent 32580 06 085 508203 3 \n", + " 22 rent not recent 32610 06 085 508203 3 \n", + " 23 rent not recent 32610 06 085 508203 3 \n", + " 24 rent not recent 32610 06 085 508203 3 \n", + " 25 rent not recent 32610 06 085 508203 3 \n", + " 26 own not recent 32612 06 085 508203 3 \n", + " 27 own not recent 32612 06 085 508203 3 \n", + " 28 own not recent 32612 06 085 508203 3 \n", + " 29 own not recent 32612 06 085 508203 3 \n", + " .. ... ... ... ... ... ... \n", + " 740 rent not recent 132322 06 085 508203 3 \n", + " 741 rent not recent 36802 06 085 508203 3 \n", + " 742 rent not recent 127426 06 085 508203 3 \n", + " 743 rent not recent 43618 06 085 508203 3 \n", + " 744 rent recent 110915 06 085 508203 3 \n", + " 745 own not recent 31044 06 085 508203 3 \n", + " 746 rent not recent 108802 06 085 508203 3 \n", + " 747 own not recent 36832 06 085 508203 3 \n", + " 748 rent not recent 78286 06 085 508203 3 \n", + " 749 own not recent 18180 06 085 508203 3 \n", + " 750 own not recent 79736 06 085 508203 3 \n", + " 751 own not recent 78252 06 085 508203 3 \n", + " 752 rent not recent 82670 06 085 508203 3 \n", + " 753 own not recent 38660 06 085 508203 3 \n", + " 754 own not recent 79820 06 085 508203 3 \n", + " 755 own not recent 21828 06 085 508203 3 \n", + " 756 rent not recent 82154 06 085 508203 3 \n", + " 757 rent recent 132163 06 085 508203 3 \n", + " 758 rent not recent 42674 06 085 508203 3 \n", + " 759 rent not recent 44230 06 085 508203 3 \n", + " 760 own not recent 18404 06 085 508203 3 \n", + " 761 own not recent 81388 06 085 508203 3 \n", + " 762 own not recent 36780 06 085 508203 3 \n", + " 763 rent not recent 38146 06 085 508203 3 \n", + " 764 rent not recent 136598 06 085 508203 3 \n", + " 765 rent not recent 49482 06 085 508203 3 \n", + " 766 own not recent 33828 06 085 508203 3 \n", + " 767 own not recent 37060 06 085 508203 3 \n", + " 768 rent not recent 68858 06 085 508203 3 \n", + " 769 own not recent 124768 06 085 508203 3 \n", + " \n", + " [770 rows x 32 columns],\n", + " serialno puma00 puma10 AGEP RELP SEX ESR HISP RAC1P hispanic \\\n", + " 0 2012000024049 -9 8502 47 1 2 6.0 1 6 no \n", + " 1 2012000024049 -9 8502 47 1 2 6.0 1 6 no \n", + " 2 2012000024049 -9 8502 47 1 2 6.0 1 6 no \n", + " 3 2012000024049 -9 8502 53 0 1 1.0 1 6 no \n", + " 4 2012000024049 -9 8502 53 0 1 1.0 1 6 no \n", + " 5 2012000024049 -9 8502 53 0 1 1.0 1 6 no \n", + " 6 2012000024049 -9 8502 17 2 1 6.0 1 6 no \n", + " 7 2012000024049 -9 8502 17 2 1 6.0 1 6 no \n", + " 8 2012000024049 -9 8502 17 2 1 6.0 1 6 no \n", + " 9 2012000024049 -9 8502 6 2 2 NaN 1 6 no \n", + " 10 2012000024049 -9 8502 6 2 2 NaN 1 6 no \n", + " 11 2012000024049 -9 8502 6 2 2 NaN 1 6 no \n", + " 12 2012000038026 -9 8502 37 1 2 3.0 1 6 no \n", + " 13 2012000038026 -9 8502 48 0 1 1.0 1 6 no \n", + " 14 2012000038026 -9 8502 6 2 1 NaN 1 6 no \n", + " 15 2012000038026 -9 8502 4 2 2 NaN 1 6 no \n", + " 16 2012000076386 -9 8502 43 0 2 6.0 1 6 no \n", + " 17 2012000076386 -9 8502 43 0 2 6.0 1 6 no \n", + " 18 2012000076386 -9 8502 43 0 2 6.0 1 6 no \n", + " 19 2012000076386 -9 8502 46 1 1 1.0 1 6 no \n", + " 20 2012000076386 -9 8502 46 1 1 1.0 1 6 no \n", + " 21 2012000076386 -9 8502 46 1 1 1.0 1 6 no \n", + " 22 2012000076386 -9 8502 12 2 1 NaN 1 6 no \n", + " 23 2012000076386 -9 8502 12 2 1 NaN 1 6 no \n", + " 24 2012000076386 -9 8502 12 2 1 NaN 1 6 no \n", + " 25 2012000076386 -9 8502 16 2 2 6.0 1 6 no \n", + " 26 2012000076386 -9 8502 16 2 2 6.0 1 6 no \n", + " 27 2012000076386 -9 8502 16 2 2 6.0 1 6 no \n", + " 28 2012000176039 -9 8502 58 1 2 1.0 1 6 no \n", + " 29 2012000176039 -9 8502 27 2 1 1.0 1 6 no \n", + " ... ... ... ... ... ... ... ... ... ... ... \n", + " 2244 2009001006831 2702 -9 34 0 1 1.0 2 1 yes \n", + " 2245 2009001006831 2702 -9 4 2 1 NaN 2 1 yes \n", + " 2246 2009001006831 2702 -9 4 2 1 NaN 2 1 yes \n", + " 2247 2009001006831 2702 -9 2 2 2 NaN 2 1 yes \n", + " 2248 2009001006831 2702 -9 2 2 2 NaN 2 1 yes \n", + " 2249 2011000656960 2702 -9 26 1 2 1.0 2 1 yes \n", + " 2250 2011000656960 2702 -9 26 1 2 1.0 2 1 yes \n", + " 2251 2011000656960 2702 -9 27 0 1 1.0 2 1 yes \n", + " 2252 2011000656960 2702 -9 27 0 1 1.0 2 1 yes \n", + " 2253 2011000656960 2702 -9 1 2 1 NaN 2 1 yes \n", + " 2254 2011000656960 2702 -9 1 2 1 NaN 2 1 yes \n", + " 2255 2013000583586 -9 8502 34 0 1 1.0 2 1 yes \n", + " 2256 2011000482661 2702 -9 34 2 1 1.0 2 1 yes \n", + " 2257 2011000482661 2702 -9 67 0 1 6.0 2 1 yes \n", + " 2258 2011000482661 2702 -9 8 7 2 NaN 2 1 yes \n", + " 2259 2011000482661 2702 -9 73 1 2 6.0 2 1 yes \n", + " 2260 2010000789913 2702 -9 60 1 2 1.0 1 2 no \n", + " 2261 2010000789913 2702 -9 68 0 1 1.0 1 2 no \n", + " 2262 2012000306986 -9 8502 83 0 1 6.0 1 6 no \n", + " 2263 2013001102796 -9 8502 74 0 1 6.0 1 6 no \n", + " 2264 2009000183391 2702 -9 63 0 1 1.0 1 6 no \n", + " 2265 2012001080872 -9 8502 93 0 1 6.0 2 1 yes \n", + " 2266 2012001080872 -9 8502 80 1 2 6.0 2 1 yes \n", + " 2267 2012001247807 -9 8502 88 0 1 6.0 3 1 yes \n", + " 2268 2012001247807 -9 8502 93 1 2 6.0 3 1 yes \n", + " 2269 2013000619824 -9 8502 77 1 1 6.0 2 1 yes \n", + " 2270 2013000619824 -9 8502 77 0 2 6.0 2 1 yes \n", + " 2271 2010000396495 2702 -9 61 0 1 1.0 20 1 yes \n", + " 2272 2010000396495 2702 -9 61 1 2 1.0 20 1 yes \n", + " 2273 2010000255137 2702 -9 84 0 2 6.0 3 1 yes \n", + " \n", + " person_age person_sex race cat_id hh_id \n", + " 0 35 to 60 female asian 138256 98 \n", + " 1 35 to 60 female asian 138256 106 \n", + " 2 35 to 60 female asian 138256 107 \n", + " 3 35 to 60 male asian 138260 98 \n", + " 4 35 to 60 male asian 138260 106 \n", + " 5 35 to 60 male asian 138260 107 \n", + " 6 19 and under male asian 138244 98 \n", + " 7 19 and under male asian 138244 106 \n", + " 8 19 and under male asian 138244 107 \n", + " 9 19 and under female asian 138240 98 \n", + " 10 19 and under female asian 138240 106 \n", + " 11 19 and under female asian 138240 107 \n", + " 12 35 to 60 female asian 138256 54 \n", + " 13 35 to 60 male asian 138260 54 \n", + " 14 19 and under male asian 138244 54 \n", + " 15 19 and under female asian 138240 54 \n", + " 16 35 to 60 female asian 138256 75 \n", + " 17 35 to 60 female asian 138256 80 \n", + " 18 35 to 60 female asian 138256 84 \n", + " 19 35 to 60 male asian 138260 75 \n", + " 20 35 to 60 male asian 138260 80 \n", + " 21 35 to 60 male asian 138260 84 \n", + " 22 19 and under male asian 138244 75 \n", + " 23 19 and under male asian 138244 80 \n", + " 24 19 and under male asian 138244 84 \n", + " 25 19 and under female asian 138240 75 \n", + " 26 19 and under female asian 138240 80 \n", + " 27 19 and under female asian 138240 84 \n", + " 28 35 to 60 female asian 138256 631 \n", + " 29 20 to 35 male asian 138252 631 \n", + " ... ... ... ... ... ... \n", + " 2244 20 to 35 male white 138287 607 \n", + " 2245 19 and under male white 138279 356 \n", + " 2246 19 and under male white 138279 607 \n", + " 2247 19 and under female white 138275 356 \n", + " 2248 19 and under female white 138275 607 \n", + " 2249 20 to 35 female white 138283 447 \n", + " 2250 20 to 35 female white 138283 764 \n", + " 2251 20 to 35 male white 138287 447 \n", + " 2252 20 to 35 male white 138287 764 \n", + " 2253 19 and under male white 138279 447 \n", + " 2254 19 and under male white 138279 764 \n", + " 2255 20 to 35 male white 138287 564 \n", + " 2256 20 to 35 male white 138287 718 \n", + " 2257 above 60 male white 138303 718 \n", + " 2258 19 and under female white 138275 718 \n", + " 2259 above 60 female white 138299 718 \n", + " 2260 35 to 60 female black 138257 756 \n", + " 2261 above 60 male black 138269 756 \n", + " 2262 above 60 male asian 138268 571 \n", + " 2263 above 60 male asian 138268 452 \n", + " 2264 above 60 male asian 138268 701 \n", + " 2265 above 60 male white 138303 563 \n", + " 2266 above 60 female white 138299 563 \n", + " 2267 above 60 male white 138303 574 \n", + " 2268 above 60 female white 138299 574 \n", + " 2269 above 60 male white 138303 533 \n", + " 2270 above 60 female white 138299 533 \n", + " 2271 above 60 male white 138303 713 \n", + " 2272 above 60 female white 138299 713 \n", + " 2273 above 60 female white 138299 768 \n", + " \n", + " [2274 rows x 15 columns],\n", + " {BlockGroupID(state='06', county='085', tract='508203', block_group='3'): FitQuality(people_chisq=50.53639934882075, people_p=0.37362753869683724)})" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ind = pd.Series([\"06\", \"085\", \"508203\", \"3\"], index=[\"state\", \"county\", \"tract\", \"block group\"])\n", + "synthesize_all(starter, indexes=[ind])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/demos/census_api.ipynb b/demos/census_api.ipynb index d7bbe1d..e01f297 100644 --- a/demos/census_api.ipynb +++ b/demos/census_api.ipynb @@ -3,9 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", @@ -34,10 +32,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "c = Census(os.environ[\"CENSUS\"])" @@ -53,14 +49,25 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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0 294 183 294 0 4 8 28 0 8 0 0 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 203002053007010183405130209274443574Block Group 1, Census Tract 306, San Francisco...1075060306003009911524243006811910110
1 226 138 226 0 11 10 9 0 20 0 0 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 1527319427300013001560063514366484Block Group 2, Census Tract 306, San Francisco...20750603060027398313838327362109929
2 287 237 287 3 0 0 8 20 22 0 0 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 19305240305001054000061211241839176Block Group 3, Census Tract 306, San Francisco...30750603060030510931544343056912110210
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0 655 321 8 11 8 0 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 0 0 0 655 423 11 0 187 0 12 22 0 22 Block Group 1, Census Tract 306, San Francisco... 1 075 06 030600 6 20 65 41 0 14 7 0 23 0 37 4 0 15789392564024800026271241318351310201929112700397020163409111021789367902610101511932Block Group 1, Census Tract 306, San Francisco...10750603060016346153429142015302092311
1 528 236 7 17 11 0 4 0 0 0 0 0 14 53 39 24 12 19 9 0 17 8 2 0 0 292 48 7 0 0 3 0 0 25 0 528 448 9 0 11 0 0 60 0 60 Block Group 2, Census Tract 306, San Francisco... 2 075 06 030600 9 40 17 41 10 38 18 0 7 0 9 11 9 0696342181660500003435526712231700712225113541228346000896964470017902446046Block Group 2, Census Tract 306, San Francisco...207506030600313921182026261361323966
2 858 493 22 44 0 44 0 0 11 0 6 44 57 50 43 38 38 0 10 21 15 20 10 20 0 365 0 0 49 31 13 0 0 3 0 858 623 11 0 218 0 0 6 0 6 Block Group 3, Census Tract 306, San Francisco... 3 075 06 030600 14 0 51 38 66 30 2 8 7 0 20 9 21 3939518705118306068195252841547068273459164214733191700011139395321203100085085Block Group 3, Census Tract 306, San Francisco...30750603060003646243949100012961832
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" \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1062,290 +1114,295 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - 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" \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1355,291 +1412,295 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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" \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1649,289 +1710,294 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1949,283 +2015,289 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2241,469 +2313,476 @@ " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - 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Unnamed: 0serialnoRTSPORDERPUMA00PUMA10puma00puma10STADJINCPWGTPMILMLPAMLPBMLPCMLPDMLPCDMLPEMLPFMLPGMLPFGMLPHMLPIMLPJWKHPWKLWKWWRKYOEP05YOEP12ANCDRIVESPESPESRFOD1PFOD2PHICOVHISPINDPRACSORRACWHTRCSCIENGPSCIENGRLPSFNSFRSOCP00FENGPFESRPFFERPFFODPFGCLPFGCMPFGCRPFWKHPFWKLPFWKWPFWRKPFYOEPPWGTP1PWGTP2
0 2165691 2012000002680 P 12012000002680P1-9 07506 06 1010207 20 64 175066102488721641NaN NaN 2 2 2 2 2 2NaN2.02.0222.02.0NaN 2NaN2.0NaN 2NaN2.0NaN 1 2 2 2 2 2 2 0 10NaN 10 2 512222220.010.0NaN10.02.05NaNNaNNaNNaN NaN 1 5NaNNaN1.04.0NaNNaNNaNNaNNaNNaN 3 5 2 2 3 0 0 0 0 13.05.02.02.03.00.00.000.01.0NaN 21 0 2 0 0 59000 24 1 121.00.020.00.059000.024.01.01.01.0NaN NaN 3NaN3-9 995995-9 999999NaN2NaN 2NaN1.03301.0NaN 1 1 1 7870 84 43117870.084.043.0NaN NaNNaNNaNNaNNaN 6 611M1 1NaN 0 N.A. N.A. 5860 4 59000 590006.0611M11NaN0N.A.N.A.58604.059000.059000.0-9 36 500 -9 7500 -9 6 1 2 4 136500.0-9.07500.0-9.06.01241-9 11-9 1 0 0 0 0 1 0 1 0NaNNaN N.A.// N.A.// 439061NaN 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0NaN 0NaN 0NaN 0 0 0 0 0 0 0 0 0 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 0 0 0 0 1 0 1 0 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 161000010102.02.0NaNNaNN.A.//N.A.//439061NaN10000000000000000000000NaN0NaN0NaN000000000000000000000000010000000000001010.0022182372523383065402073335232262221202023371819764637721367616173619191919203919207737387203277222133201820232361921404357402063539222662021
1 2167411 2012000009189 P 12012000009189P1-9 07506 06 1010207 16 52 4 -9 1995NaN 2 2 1 2 275066102488714524-9.01995.0NaN2.0212.02.0NaN 2 3NaN 22.03.0NaN2.0NaN 1 2 2 2 2 2 2 0NaN12222220.0NaNNaN 1 1 2 2 1 2 -9 1992 1 5NaN1.012.02.01.02.0-9.01992.01.04.0NaNNaNNaNNaNNaNNaN3.05.03.03.03.010000.00.000.01.0NaN 3 5 3 3 3 10000 0 0 0 116.00.010.00.00.0NaN 16 0 1 0 0 03.0NaN 3NaN -9 1989 4-9.01989.04-9 999999-9 999 5 19995.01NaNNaN 6 1 1 NaN6.0NaNNaN -9 70811NaNNaNNaN-9.0708.0NaN 1 NaN 2NaN 0 NaN NaN NaNNaN 0 10000NaN1.0NaN2NaN0NaNNaNNaNNaN0.010000.0-9 207 4320743.0NaN NaNNaNNaN 1 2 3 6NaN1236-9 4343-9 5 0 1 0 0 1 0 0 0NaNNaN NaN NaN NaNNaN 4 0 0 0 0 0 1 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0NaN 0NaN 0NaN 0 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 1 0 1 1 1 0 1 0 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 4501001000NaNNaNNaNNaNNaNNaNNaNNaN40000010011001010000000NaN0NaN0NaN000101000000111110000101100001000101110100.0015526186101151541517172011211612282614284431151327192516141441231512431542427514124163171414261027171530201928542214112614211016194145131454
2 2167412 2012000009189 P 22012000009189P2-9 07506 06 1010207 13 45 4 -9 1998 3 2 2 1 2 2NaN75066102488718454-9.01998.03.02.0212.02.0NaN 2 3 2 2NaN2.03.02.02.0NaN 1 2 2 2 2 2 2 0NaN12222220.0NaNNaN 1 1 2 2 1 2 -9 1992 1 5NaN1.012.02.01.02.0-9.01992.01.04.0NaNNaNNaNNaNNaNNaN3.05.03.03.03.00.00.010.01.0NaN 3 5 3 3 3 0 0 1 0 116.00.020.00.00.0NaN 16 0 2 0 0 02.0NaN 2NaN -9 1992 4-9.01992.04-9 999999-9 999 6 19996.01NaNNaN 6 1 1 83706.0NaNNaN -9 708118370.0NaNNaN-9.0708.0NaNNaN 1 6241 2NaN 0 N.A. N.A. 4610 2 0 0NaN1.062412NaN0N.A.N.A.46102.00.00.0-9 207 4320743.0NaNNaN NaNNaNNaN 1 2 2 61226-9 4343-9 5 0 1 0 0 1 0 0 0NaNNaN N.A.// N.A.// 399021NaN 4 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 0 0 0 0NaN 0NaN 0NaN 0 0 0 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 1 0 1 1 1 0 1 0 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 4501001000NaNNaNNaNNaNN.A.//N.A.//399021NaN40010110011001111000000NaN0NaN0NaN000111000000111110000111100001000101110100.0018536307201962371517303016282019283418327538191933183418161951851720552372530519216187211920261831182330311937663623182817321521226244152156
3 2167413 2012000009189 P 32012000009189P3-9 07506 06 1010207 14 10 175066102488714101NaNNaNNaN NaN2.022NaN 2 2 22.0NaN 2NaN2.0NaN 2NaNNaNNaNNaN2221222NaN 2 2 2 1 2 2 2NaNNaNNaN2.05NaN 2 5NaNNaNNaNNaNNaN NaN 11.0NaNNaNNaNNaNNaNNaN NaN2NaN 21.0NaN 17.0NaN 71NaN 1NaNNaN NaNNaNNaNNaNNaN NaN 4NaNNaN4-9 999999-9 999999NaN2NaN4.0NaNNaNNaN11NaNNaNNaNNaNNaNNaN 2NaN 4NaN 1 1 NaNNaNNaNNaN NaN14.01NaNNaNNaNNaNNaN NaN 1 4 1 NaN NaN NaNNaN NaN NaN-9 6 43643.0NaN NaNNaNNaN 2 1 4 6NaN2146-9 4343-9 5 0 1 0 0 1 0 0 1NaNNaN NaN NaN NaNNaN 1 0 0 0 0 0 1 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0NaN 0 0 0NaN 0 0 0 1 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 0 0 0 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 3501001001NaNNaNNaNNaNNaNNaNNaNNaN10000011101001000000000NaN00.00NaN000100000000000000100000000100000000000000.0015322214131541751112172416231414242517225525141424112713141341541613451452826420133144171313241221141522221623442315152314281512175125131554
4 2167414 2012000009189 P 42012000009189P4-9 07506 06 1010207 15 8 17506610248871481NaNNaNNaN2.022NaN2.0NaN NaNNaN 2 2 22.0NaN 2NaNNaN 2NaNNaN2221222NaNNaNNaN 2 2 2 1 2 2 2NaN2.05NaNNaNNaN 2 5NaNNaNNaN1.0NaNNaN NaN 1NaNNaNNaNNaNNaNNaN2NaN2.05.06.0NaN NaN1NaN 2NaN 2 5 6NaN 1NaNNaN NaNNaNNaNNaNNaN NaN 44-9 999999-9 999999NaN2NaN4.0NaNNaNNaN 211NaN 4NaN 1 1 NaNNaNNaNNaN NaNNaNNaNNaNNaNNaN NaN 1 4 1 NaN NaN NaNNaN NaN NaN14.01NaNNaNNaNNaNNaNNaN-9 6 43643.0NaNNaN NaNNaNNaN 2 1 1 62116-9 4343-9 5 0 1 0 0 1 0 0 1NaNNaN NaN NaN NaNNaN 1 0 0 0 0 0 1 1 1 0 1 0 0 1 0 0 0 0 0 0 1 1NaN 1 0 1NaN 1 1 1 1 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 0 1 0 1 0 0 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 3501001001NaNNaNNaNNaNNaNNaNNaNNaN10000011101001000000011NaN10.01NaN111100000000000000100000000100101000000000.0015422214121651751112172416221514242517224525131425122713141341441713441452826319133154161313241222141621221523432215152314281512175134131543
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5 rows × 291 columns

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" ], "text/plain": [ - " Unnamed: 0 serialno RT SPORDER PUMA00 PUMA10 ST ADJINC PWGTP \\\n", - "0 2165691 2012000002680 P 1 -9 07506 06 1010207 20 \n", - "1 2167411 2012000009189 P 1 -9 07506 06 1010207 16 \n", - "2 2167412 2012000009189 P 2 -9 07506 06 1010207 13 \n", - "3 2167413 2012000009189 P 3 -9 07506 06 1010207 14 \n", - "4 2167414 2012000009189 P 4 -9 07506 06 1010207 15 \n", + " serialno RT SPORDER puma00 puma10 ST ADJINC PWGTP AGEP CIT \\\n", + "0 2012000002680 P 1 -9 7506 6 1024887 21 64 1 \n", + "1 2012000009189 P 1 -9 7506 6 1024887 14 52 4 \n", + "2 2012000009189 P 2 -9 7506 6 1024887 18 45 4 \n", + "3 2012000009189 P 3 -9 7506 6 1024887 14 10 1 \n", + "4 2012000009189 P 4 -9 7506 6 1024887 14 8 1 \n", "\n", - " AGEP CIT CITWP05 CITWP12 COW DDRS DEAR DEYE DOUT DPHY DRAT \\\n", - "0 64 1 NaN NaN 2 2 2 2 2 2 NaN \n", - "1 52 4 -9 1995 NaN 2 2 1 2 2 NaN \n", - "2 45 4 -9 1998 3 2 2 1 2 2 NaN \n", - "3 10 1 NaN NaN NaN 2 2 2 NaN 2 NaN \n", - "4 8 1 NaN NaN NaN 2 2 2 NaN 2 NaN \n", + " CITWP05 CITWP12 COW DDRS DEAR DEYE DOUT DPHY DRAT DRATX DREM \\\n", + "0 NaN NaN 2.0 2.0 2 2 2.0 2.0 NaN NaN 2.0 \n", + "1 -9.0 1995.0 NaN 2.0 2 1 2.0 2.0 NaN NaN 2.0 \n", + "2 -9.0 1998.0 3.0 2.0 2 1 2.0 2.0 NaN NaN 2.0 \n", + "3 NaN NaN NaN 2.0 2 2 NaN 2.0 NaN NaN 2.0 \n", + "4 NaN NaN NaN 2.0 2 2 NaN 2.0 NaN NaN 2.0 \n", "\n", - " DRATX DREM ENG FER GCL GCM GCR HINS1 HINS2 HINS3 HINS4 HINS5 \\\n", - "0 NaN 2 NaN NaN 2 NaN NaN 1 2 2 2 2 \n", - "1 NaN 2 3 NaN 2 NaN NaN 1 2 2 2 2 \n", - "2 NaN 2 3 2 2 NaN NaN 1 2 2 2 2 \n", - "3 NaN 2 NaN NaN NaN NaN NaN 2 2 2 1 2 \n", - "4 NaN 2 NaN NaN NaN NaN NaN 2 2 2 1 2 \n", + " ENG FER GCL GCM GCR HINS1 HINS2 HINS3 HINS4 HINS5 HINS6 HINS7 \\\n", + "0 NaN NaN 2.0 NaN NaN 1 2 2 2 2 2 2 \n", + "1 3.0 NaN 2.0 NaN NaN 1 2 2 2 2 2 2 \n", + "2 3.0 2.0 2.0 NaN NaN 1 2 2 2 2 2 2 \n", + "3 NaN NaN NaN NaN NaN 2 2 2 1 2 2 2 \n", + "4 NaN NaN NaN NaN NaN 2 2 2 1 2 2 2 \n", "\n", - " HINS6 HINS7 INTP JWMNP JWRIP JWTR LANX MAR MARHD MARHM MARHT \\\n", - "0 2 2 0 10 NaN 10 2 5 NaN NaN NaN \n", - "1 2 2 0 NaN NaN NaN 1 1 2 2 1 \n", - "2 2 2 0 NaN NaN NaN 1 1 2 2 1 \n", - "3 2 2 NaN NaN NaN NaN 2 5 NaN NaN NaN \n", - "4 2 2 NaN NaN NaN NaN 2 5 NaN NaN NaN \n", + " INTP JWMNP JWRIP JWTR LANX MAR MARHD MARHM MARHT MARHW MARHYP05 \\\n", + "0 0.0 10.0 NaN 10.0 2.0 5 NaN NaN NaN NaN NaN \n", + "1 0.0 NaN NaN NaN 1.0 1 2.0 2.0 1.0 2.0 -9.0 \n", + "2 0.0 NaN NaN NaN 1.0 1 2.0 2.0 1.0 2.0 -9.0 \n", + "3 NaN NaN NaN NaN 2.0 5 NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN 2.0 5 NaN NaN NaN NaN NaN \n", "\n", - " MARHW MARHYP05 MARHYP12 MIG MIL MLPA MLPB MLPC MLPD MLPE MLPF \\\n", - "0 NaN NaN NaN 1 5 NaN NaN NaN NaN NaN NaN \n", - "1 2 -9 1992 1 5 NaN NaN NaN NaN NaN NaN \n", - "2 2 -9 1992 1 5 NaN NaN NaN NaN NaN NaN \n", - "3 NaN NaN NaN 1 NaN NaN NaN NaN NaN NaN NaN \n", - "4 NaN NaN NaN 1 NaN NaN NaN NaN NaN NaN NaN \n", + " MARHYP12 MIG MIL MLPA MLPB MLPCD MLPE MLPFG MLPH MLPI MLPJ MLPK \\\n", + "0 NaN 1.0 4.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "1 1992.0 1.0 4.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "2 1992.0 1.0 4.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "3 NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + "4 NaN 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", - " MLPG MLPH MLPI MLPJ MLPK NWAB NWAV NWLA NWLK NWRE OIP PAP \\\n", - "0 NaN NaN NaN NaN NaN 3 5 2 2 3 0 0 \n", - "1 NaN NaN NaN NaN NaN 3 5 3 3 3 10000 0 \n", - "2 NaN NaN NaN NaN NaN 3 5 3 3 3 0 0 \n", - "3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", + " NWAB NWAV NWLA NWLK NWRE OIP PAP RELP RETP SCH SCHG SCHL \\\n", + "0 3.0 5.0 2.0 2.0 3.0 0.0 0.0 0 0.0 1.0 NaN 21.0 \n", + "1 3.0 5.0 3.0 3.0 3.0 10000.0 0.0 0 0.0 1.0 NaN 16.0 \n", + "2 3.0 5.0 3.0 3.0 3.0 0.0 0.0 1 0.0 1.0 NaN 16.0 \n", + "3 NaN NaN NaN NaN NaN NaN NaN 2 NaN 1.0 NaN 7.0 \n", + "4 NaN NaN NaN NaN NaN NaN NaN 2 NaN 2.0 5.0 6.0 \n", "\n", - " RELP RETP SCH SCHG SCHL SEMP SEX SSIP SSP WAGP WKHP WKL WKW \\\n", - "0 0 0 1 NaN 21 0 2 0 0 59000 24 1 1 \n", - "1 0 0 1 NaN 16 0 1 0 0 0 NaN 3 NaN \n", - "2 1 0 1 NaN 16 0 2 0 0 0 NaN 2 NaN \n", - "3 2 NaN 1 NaN 7 NaN 1 NaN NaN NaN NaN NaN NaN \n", - "4 2 NaN 2 5 6 NaN 1 NaN NaN NaN NaN NaN NaN \n", + " SEMP SEX SSIP SSP WAGP WKHP WKL WKW WRK YOEP05 YOEP12 ANC \\\n", + "0 0.0 2 0.0 0.0 59000.0 24.0 1.0 1.0 1.0 NaN NaN 3 \n", + "1 0.0 1 0.0 0.0 0.0 NaN 3.0 NaN NaN -9.0 1989.0 4 \n", + "2 0.0 2 0.0 0.0 0.0 NaN 2.0 NaN NaN -9.0 1992.0 4 \n", + "3 NaN 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN 4 \n", + "4 NaN 1 NaN NaN NaN NaN NaN NaN NaN NaN NaN 4 \n", "\n", - " YOEP05 YOEP12 ANC ANC1P05 ANC1P12 ANC2P05 ANC2P12 DECADE DIS \\\n", - "0 NaN NaN 3 -9 995 -9 999 NaN 2 \n", - "1 -9 1989 4 -9 999 -9 999 5 1 \n", - "2 -9 1992 4 -9 999 -9 999 6 1 \n", - "3 NaN NaN 4 -9 999 -9 999 NaN 2 \n", - "4 NaN NaN 4 -9 999 -9 999 NaN 2 \n", + " ANC1P05 ANC1P12 ANC2P05 ANC2P12 DECADE DIS DRIVESP ESP ESR FOD1P \\\n", + "0 -9 995 -9 999 NaN 2 NaN NaN 1.0 3301.0 \n", + "1 -9 999 -9 999 5.0 1 NaN NaN 6.0 NaN \n", + "2 -9 999 -9 999 6.0 1 NaN NaN 6.0 NaN \n", + "3 -9 999 -9 999 NaN 2 NaN 4.0 NaN NaN \n", + "4 -9 999 -9 999 NaN 2 NaN 4.0 NaN NaN \n", "\n", - " DRIVESP ESP ESR HICOV HISP INDP JWAP JWDP LANP05 LANP12 \\\n", - "0 NaN NaN 1 1 1 7870 84 43 NaN NaN \n", - "1 NaN NaN 6 1 1 NaN NaN NaN -9 708 \n", - "2 NaN NaN 6 1 1 8370 NaN NaN -9 708 \n", - "3 NaN 4 NaN 1 1 NaN NaN NaN NaN NaN \n", - "4 NaN 4 NaN 1 1 NaN NaN NaN NaN NaN \n", + " FOD2P HICOV HISP INDP JWAP JWDP LANP05 LANP12 MIGPUMA00 \\\n", + "0 NaN 1 1 7870.0 84.0 43.0 NaN NaN NaN \n", + "1 NaN 1 1 NaN NaN NaN -9.0 708.0 NaN \n", + "2 NaN 1 1 8370.0 NaN NaN -9.0 708.0 NaN \n", + "3 NaN 1 1 NaN NaN NaN NaN NaN NaN \n", + "4 NaN 1 1 NaN NaN NaN NaN NaN NaN \n", "\n", - " MIGPUMA00 MIGPUMA10 MIGSP05 MIGSP12 MSP NAICSP NATIVITY NOP OC \\\n", - "0 NaN NaN NaN NaN 6 611M1 1 NaN 0 \n", - "1 NaN NaN NaN NaN 1 NaN 2 NaN 0 \n", - "2 NaN NaN NaN NaN 1 6241 2 NaN 0 \n", - "3 NaN NaN NaN NaN NaN NaN 1 4 1 \n", - "4 NaN NaN NaN NaN NaN NaN 1 4 1 \n", + " MIGPUMA10 MIGSP05 MIGSP12 MSP NAICSP NATIVITY NOP OC OCCP02 OCCP10 \\\n", + "0 NaN NaN NaN 6.0 611M1 1 NaN 0 N.A. N.A. \n", + "1 NaN NaN NaN 1.0 NaN 2 NaN 0 NaN NaN \n", + "2 NaN NaN NaN 1.0 6241 2 NaN 0 N.A. N.A. \n", + "3 NaN NaN NaN NaN NaN 1 4.0 1 NaN NaN \n", + "4 NaN NaN NaN NaN NaN 1 4.0 1 NaN NaN \n", "\n", - " OCCP02 OCCP10 OCCP12 PAOC PERNP PINCP POBP05 POBP12 POVPIP \\\n", - "0 N.A. N.A. 5860 4 59000 59000 -9 36 500 \n", - "1 NaN NaN NaN NaN 0 10000 -9 207 43 \n", - "2 N.A. N.A. 4610 2 0 0 -9 207 43 \n", - "3 NaN NaN NaN NaN NaN NaN -9 6 43 \n", - "4 NaN NaN NaN NaN NaN NaN -9 6 43 \n", + " OCCP12 PAOC PERNP PINCP POBP05 POBP12 POVPIP POWPUMA00 \\\n", + "0 5860 4.0 59000.0 59000.0 -9 36 500.0 -9.0 \n", + "1 NaN NaN 0.0 10000.0 -9 207 43.0 NaN \n", + "2 4610 2.0 0.0 0.0 -9 207 43.0 NaN \n", + "3 NaN NaN NaN NaN -9 6 43.0 NaN \n", + "4 NaN NaN NaN NaN -9 6 43.0 NaN \n", "\n", - " POWPUMA00 POWPUMA10 POWSP05 POWSP12 PRIVCOV PUBCOV QTRBIR RAC1P \\\n", - "0 -9 7500 -9 6 1 2 4 1 \n", - "1 NaN NaN NaN NaN 1 2 3 6 \n", - "2 NaN NaN NaN NaN 1 2 2 6 \n", - "3 NaN NaN NaN NaN 2 1 4 6 \n", - "4 NaN NaN NaN NaN 2 1 1 6 \n", + " POWPUMA10 POWSP05 POWSP12 PRIVCOV PUBCOV QTRBIR RAC1P RAC2P05 \\\n", + "0 7500.0 -9.0 6.0 1 2 4 1 -9 \n", + "1 NaN NaN NaN 1 2 3 6 -9 \n", + "2 NaN NaN NaN 1 2 2 6 -9 \n", + "3 NaN NaN NaN 2 1 4 6 -9 \n", + "4 NaN NaN NaN 2 1 1 6 -9 \n", "\n", - " RAC2P05 RAC2P12 RAC3P05 RAC3P12 RACAIAN RACASN RACBLK RACNHPI \\\n", - "0 -9 1 -9 1 0 0 0 0 \n", - "1 -9 43 -9 5 0 1 0 0 \n", - "2 -9 43 -9 5 0 1 0 0 \n", - "3 -9 43 -9 5 0 1 0 0 \n", - "4 -9 43 -9 5 0 1 0 0 \n", + " RAC2P12 RAC3P05 RAC3P12 RACAIAN RACASN RACBLK RACNHPI RACNUM \\\n", + "0 1 -9 1 0 0 0 0 1 \n", + "1 43 -9 5 0 1 0 0 1 \n", + "2 43 -9 5 0 1 0 0 1 \n", + "3 43 -9 5 0 1 0 0 1 \n", + "4 43 -9 5 0 1 0 0 1 \n", "\n", - " RACNUM RACSOR RACWHT RC SFN SFR SOCP00 SOCP10 SOCP12 VPS WAOB \\\n", - "0 1 0 1 0 NaN NaN N.A.// N.A.// 439061 NaN 1 \n", - "1 1 0 0 0 NaN NaN NaN NaN NaN NaN 4 \n", - "2 1 0 0 0 NaN NaN N.A.// N.A.// 399021 NaN 4 \n", - "3 1 0 0 1 NaN NaN NaN NaN NaN NaN 1 \n", - "4 1 0 0 1 NaN NaN NaN NaN NaN NaN 1 \n", + " RACSOR RACWHT RC SCIENGP SCIENGRLP SFN SFR SOCP00 SOCP10 SOCP12 \\\n", + "0 0 1 0 2.0 2.0 NaN NaN N.A.// N.A.// 439061 \n", + "1 0 0 0 NaN NaN NaN NaN NaN NaN NaN \n", + "2 0 0 0 NaN NaN NaN NaN N.A.// N.A.// 399021 \n", + "3 0 0 1 NaN NaN NaN NaN NaN NaN NaN \n", + "4 0 0 1 NaN NaN NaN NaN NaN NaN NaN \n", "\n", - " FAGEP FANCP FCITP FCITWP FCOWP FDDRSP FDEARP FDEYEP FDOUTP FDPHYP \\\n", - "0 0 0 0 0 0 0 0 0 0 0 \n", - "1 0 0 0 0 0 1 0 0 1 1 \n", - "2 0 0 1 0 1 1 0 0 1 1 \n", - "3 0 0 0 0 0 1 1 1 0 1 \n", - "4 0 0 0 0 0 1 1 1 0 1 \n", + " VPS WAOB FAGEP FANCP FCITP FCITWP FCOWP FDDRSP FDEARP FDEYEP \\\n", + "0 NaN 1 0 0 0 0 0 0 0 0 \n", + "1 NaN 4 0 0 0 0 0 1 0 0 \n", + "2 NaN 4 0 0 1 0 1 1 0 0 \n", + "3 NaN 1 0 0 0 0 0 1 1 1 \n", + "4 NaN 1 0 0 0 0 0 1 1 1 \n", "\n", - " FDRATP FDRATXP FDREMP FENGP FESRP FFERP FGCLP FGCMP FGCRP FHINS1P \\\n", - "0 0 0 0 0 0 0 0 0 0 0 \n", - "1 0 0 1 0 1 0 0 0 0 0 \n", - "2 0 0 1 1 1 1 0 0 0 0 \n", - "3 0 0 1 0 0 0 0 0 0 0 \n", - "4 0 0 1 0 0 0 0 0 0 1 \n", + " FDOUTP FDPHYP FDRATP FDRATXP FDREMP FENGP FESRP FFERP FFODP FGCLP \\\n", + "0 0 0 0 0 0 0 0 0 0 0 \n", + "1 1 1 0 0 1 0 1 0 0 0 \n", + "2 1 1 0 0 1 1 1 1 0 0 \n", + "3 0 1 0 0 1 0 0 0 0 0 \n", + "4 0 1 0 0 1 0 0 0 0 0 \n", "\n", - " FHINS2P FHINS3C FHINS3P FHINS4C FHINS4P FHINS5C FHINS5P FHINS6P \\\n", - "0 0 NaN 0 NaN 0 NaN 0 0 \n", - "1 0 NaN 0 NaN 0 NaN 0 0 \n", - "2 0 NaN 0 NaN 0 NaN 0 0 \n", - "3 0 NaN 0 0 0 NaN 0 0 \n", - "4 1 NaN 1 0 1 NaN 1 1 \n", + " FGCMP FGCRP FHINS1P FHINS2P FHINS3C FHINS3P FHINS4C FHINS4P \\\n", + "0 0 0 0 0 NaN 0 NaN 0 \n", + "1 0 0 0 0 NaN 0 NaN 0 \n", + "2 0 0 0 0 NaN 0 NaN 0 \n", + "3 0 0 0 0 NaN 0 0.0 0 \n", + "4 0 0 1 1 NaN 1 0.0 1 \n", "\n", - " FHINS7P FHISP FINDP FINTP FJWDP FJWMNP FJWRIP FJWTRP FLANP FLANXP \\\n", - "0 0 0 0 0 0 0 0 0 0 0 \n", - "1 0 1 0 1 0 0 0 0 0 0 \n", - "2 0 1 1 1 0 0 0 0 0 0 \n", - "3 0 1 0 0 0 0 0 0 0 0 \n", - "4 1 1 0 0 0 0 0 0 0 0 \n", + " FHINS5C FHINS5P FHINS6P FHINS7P FHISP FINDP FINTP FJWDP FJWMNP \\\n", + "0 NaN 0 0 0 0 0 0 0 0 \n", + "1 NaN 0 0 0 1 0 1 0 0 \n", + "2 NaN 0 0 0 1 1 1 0 0 \n", + "3 NaN 0 0 0 1 0 0 0 0 \n", + "4 NaN 1 1 1 1 0 0 0 0 \n", "\n", - " FMARHDP FMARHMP FMARHTP FMARHWP FMARHYP FMARP FMIGP FMIGSP FMILPP \\\n", - "0 0 0 0 0 0 0 0 0 0 \n", - "1 1 1 1 1 1 0 0 0 0 \n", - "2 1 1 1 1 1 0 0 0 0 \n", - "3 0 0 0 0 0 0 1 0 0 \n", - "4 0 0 0 0 0 0 1 0 0 \n", + " FJWRIP FJWTRP FLANP FLANXP FMARHDP FMARHMP FMARHTP FMARHWP FMARHYP \\\n", + "0 0 0 0 0 0 0 0 0 0 \n", + "1 0 0 0 0 1 1 1 1 1 \n", + "2 0 0 0 0 1 1 1 1 1 \n", + "3 0 0 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 0 0 0 0 \n", "\n", - " FMILSP FOCCP FOIP FPAP FPOBP FPOWSP FRACP FRELP FRETP FSCHGP \\\n", - "0 0 0 0 0 1 0 0 0 0 0 \n", - "1 1 0 1 1 0 0 0 0 1 0 \n", - "2 1 1 1 1 0 0 0 0 1 0 \n", - "3 0 0 0 0 0 0 1 0 0 0 \n", - "4 0 0 0 0 0 0 1 0 0 1 \n", + " FMARP FMIGP FMIGSP FMILPP FMILSP FOCCP FOIP FPAP FPOBP FPOWSP \\\n", + "0 0 0 0 0 0 0 0 0 1 0 \n", + "1 0 0 0 0 1 0 1 1 0 0 \n", + "2 0 0 0 0 1 1 1 1 0 0 \n", + "3 0 1 0 0 0 0 0 0 0 0 \n", + "4 0 1 0 0 0 0 0 0 0 0 \n", "\n", - " FSCHLP FSCHP FSEMP FSEXP FSSIP FSSP FWAGP FWKHP FWKLP FWKWP \\\n", - "0 0 0 0 0 0 0 0 1 0 1 \n", - "1 0 0 1 0 1 1 1 0 1 0 \n", - "2 0 0 1 0 1 1 1 0 1 0 \n", - "3 0 0 0 0 0 0 0 0 0 0 \n", - "4 0 1 0 0 0 0 0 0 0 0 \n", + " FRACP FRELP FRETP FSCHGP FSCHLP FSCHP FSEMP FSEXP FSSIP FSSP \\\n", + "0 0 0 0 0 0 0 0 0 0 0 \n", + "1 0 0 1 0 0 0 1 0 1 1 \n", + "2 0 0 1 0 0 0 1 0 1 1 \n", + "3 1 0 0 0 0 0 0 0 0 0 \n", + "4 1 0 0 1 0 1 0 0 0 0 \n", "\n", - " FYOEP PWGTP1 PWGTP2 PWGTP3 PWGTP4 PWGTP5 PWGTP6 PWGTP7 PWGTP8 \\\n", - "0 0 21 19 20 6 22 23 31 29 \n", - "1 0 19 11 23 20 7 16 11 7 \n", - "2 0 11 6 25 18 4 15 16 5 \n", - "3 0 18 4 24 25 5 12 14 6 \n", - "4 0 18 4 25 24 5 11 13 6 \n", + " FWAGP FWKHP FWKLP FWKWP FWRKP FYOEP PWGTP1 PWGTP2 PWGTP3 PWGTP4 \\\n", + "0 0 1 0 1 0.0 0 22 18 23 7 \n", + "1 1 0 1 0 0.0 0 15 5 26 18 \n", + "2 1 0 1 0 0.0 0 18 5 36 30 \n", + "3 0 0 0 0 0.0 0 15 3 22 21 \n", + "4 0 0 0 0 0.0 0 15 4 22 21 \n", "\n", - " PWGTP9 PWGTP10 PWGTP11 PWGTP12 PWGTP13 PWGTP14 PWGTP15 PWGTP16 \\\n", - "0 7 6 39 21 6 29 30 22 \n", - "1 20 4 21 14 16 27 11 20 \n", - "2 12 5 14 13 20 25 9 19 \n", - "3 14 4 10 12 20 28 16 23 \n", - "4 14 4 11 11 20 28 15 23 \n", + " PWGTP5 PWGTP6 PWGTP7 PWGTP8 PWGTP9 PWGTP10 PWGTP11 PWGTP12 PWGTP13 \\\n", + "0 25 23 38 30 6 5 40 20 7 \n", + "1 6 10 11 5 15 4 15 17 17 \n", + "2 7 20 19 6 23 7 15 17 30 \n", + "3 4 13 15 4 17 5 11 12 17 \n", + "4 4 12 16 5 17 5 11 12 17 \n", "\n", - " PWGTP17 PWGTP18 PWGTP19 PWGTP20 PWGTP21 PWGTP22 PWGTP23 PWGTP24 \\\n", - "0 20 7 22 21 18 17 26 44 \n", - "1 19 15 18 26 13 33 4 4 \n", - "2 13 16 19 23 12 19 4 4 \n", - "3 18 18 22 23 18 23 4 3 \n", - "4 18 18 22 23 18 23 4 4 \n", + " PWGTP14 PWGTP15 PWGTP16 PWGTP17 PWGTP18 PWGTP19 PWGTP20 PWGTP21 \\\n", + "0 33 35 23 22 6 22 21 20 \n", + "1 20 11 21 16 12 28 26 14 \n", + "2 30 16 28 20 19 28 34 18 \n", + "3 24 16 23 14 14 24 25 17 \n", + "4 24 16 22 15 14 24 25 17 \n", "\n", - " PWGTP25 PWGTP26 PWGTP27 PWGTP28 PWGTP29 PWGTP30 PWGTP31 PWGTP32 \\\n", - "0 18 19 7 5 35 33 7 16 \n", - "1 50 16 12 40 25 37 19 17 \n", - "2 21 12 12 20 16 21 14 14 \n", - "3 23 14 15 25 12 19 14 19 \n", - "4 23 14 16 25 12 19 15 19 \n", + " PWGTP22 PWGTP23 PWGTP24 PWGTP25 PWGTP26 PWGTP27 PWGTP28 PWGTP29 \\\n", + "0 20 23 37 18 19 7 6 46 \n", + "1 28 4 4 31 15 13 27 19 \n", + "2 32 7 5 38 19 19 33 18 \n", + "3 22 5 5 25 14 14 24 11 \n", + "4 22 4 5 25 13 14 25 12 \n", "\n", - " PWGTP33 PWGTP34 PWGTP35 PWGTP36 PWGTP37 PWGTP38 PWGTP39 PWGTP40 \\\n", - "0 32 7 5 22 16 34 20 14 \n", - "1 10 3 12 3 11 14 5 5 \n", - "2 11 4 15 3 11 17 4 3 \n", - "3 13 3 16 4 16 17 4 5 \n", - "4 13 4 16 3 16 17 4 5 \n", + " PWGTP30 PWGTP31 PWGTP32 PWGTP33 PWGTP34 PWGTP35 PWGTP36 PWGTP37 \\\n", + "0 37 7 21 36 7 6 16 17 \n", + "1 25 16 14 14 4 12 3 15 \n", + "2 34 18 16 19 5 18 5 17 \n", + "3 27 13 14 13 4 15 4 16 \n", + "4 27 13 14 13 4 14 4 17 \n", "\n", - " PWGTP41 PWGTP42 PWGTP43 PWGTP44 PWGTP45 PWGTP46 PWGTP47 PWGTP48 \\\n", - "0 17 16 22 33 18 17 6 6 \n", - "1 16 4 28 24 6 19 14 5 \n", - "2 13 5 19 21 4 12 14 4 \n", - "3 11 5 21 23 6 23 13 4 \n", - "4 12 7 21 23 5 23 13 5 \n", + " PWGTP38 PWGTP39 PWGTP40 PWGTP41 PWGTP42 PWGTP43 PWGTP44 PWGTP45 \\\n", + "0 36 19 19 19 19 20 39 19 \n", + "1 12 4 3 15 4 24 27 5 \n", + "2 20 5 5 23 7 25 30 5 \n", + "3 13 4 5 14 5 28 26 4 \n", + "4 13 4 4 14 5 28 26 3 \n", "\n", - " PWGTP49 PWGTP50 PWGTP51 PWGTP52 PWGTP53 PWGTP54 PWGTP55 PWGTP56 \\\n", - "0 41 41 7 17 29 6 6 21 \n", - "1 17 3 17 15 12 27 13 33 \n", - "2 11 3 19 13 17 25 12 16 \n", - "3 15 4 19 16 13 26 11 21 \n", - "4 16 3 20 16 13 26 12 22 \n", + " PWGTP46 PWGTP47 PWGTP48 PWGTP49 PWGTP50 PWGTP51 PWGTP52 PWGTP53 \\\n", + "0 20 7 7 37 38 7 20 32 \n", + "1 14 12 4 16 3 17 14 14 \n", + "2 19 21 6 18 7 21 19 20 \n", + "3 20 13 3 14 4 17 13 13 \n", + "4 19 13 3 15 4 16 13 13 \n", "\n", - " PWGTP57 PWGTP58 PWGTP59 PWGTP60 PWGTP61 PWGTP62 PWGTP63 PWGTP64 \\\n", - "0 19 30 19 17 18 24 25 6 \n", - "1 15 19 47 18 22 28 6 5 \n", - "2 12 15 19 27 16 24 5 4 \n", - "3 13 18 22 23 12 26 5 4 \n", - "4 13 17 22 22 13 27 5 3 \n", + " PWGTP54 PWGTP55 PWGTP56 PWGTP57 PWGTP58 PWGTP59 PWGTP60 PWGTP61 \\\n", + "0 7 7 22 21 33 20 18 20 \n", + "1 26 10 27 17 15 30 20 19 \n", + "2 26 18 31 18 23 30 31 19 \n", + "3 24 12 21 14 15 22 22 16 \n", + "4 24 12 22 14 16 21 22 15 \n", "\n", - " PWGTP65 PWGTP66 PWGTP67 PWGTP68 PWGTP69 PWGTP70 PWGTP71 PWGTP72 \\\n", - "0 24 24 32 45 5 7 40 21 \n", - "1 28 12 12 25 21 15 12 12 \n", - "2 24 12 11 19 13 24 11 16 \n", - "3 23 14 13 21 15 22 15 13 \n", - "4 23 14 13 20 14 22 14 13 \n", + " PWGTP62 PWGTP63 PWGTP64 PWGTP65 PWGTP66 PWGTP67 PWGTP68 PWGTP69 \\\n", + "0 23 23 6 19 21 40 43 5 \n", + "1 28 5 4 22 14 11 26 14 \n", + "2 37 6 6 36 23 18 28 17 \n", + "3 23 4 4 23 15 15 23 14 \n", + "4 23 4 3 22 15 15 23 14 \n", "\n", - " PWGTP73 PWGTP74 PWGTP75 PWGTP76 PWGTP77 PWGTP78 PWGTP79 PWGTP80 \n", - "0 6 33 36 18 24 7 16 16 \n", - "1 15 5 14 7 15 13 7 4 \n", - "2 14 3 15 3 15 12 3 4 \n", - "3 16 5 12 6 12 12 3 3 \n", - "4 17 5 13 6 12 13 4 3 \n", + " PWGTP70 PWGTP71 PWGTP72 PWGTP73 PWGTP74 PWGTP75 PWGTP76 PWGTP77 \\\n", + "0 7 40 20 6 35 39 22 26 \n", + "1 21 10 16 19 4 14 5 13 \n", + "2 32 15 21 22 6 24 4 15 \n", + "3 28 15 12 17 5 12 5 13 \n", + "4 28 15 12 17 5 13 4 13 \n", "\n", - "[5 rows x 291 columns]" + " PWGTP78 PWGTP79 PWGTP80 \n", + "0 6 20 21 \n", + "1 14 5 4 \n", + "2 21 5 6 \n", + "3 15 5 4 \n", + "4 15 4 3 " ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "p_pums = c.download_population_pums(\"06\", puma)\n", + "p_pums = c.download_population_pums(\"06\", puma10=puma10, puma00=puma00)\n", "p_pums.head(5)" ] }, @@ -2716,25 +2795,36 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - 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4 942803 2012000025664 H 92012000025664200.0H9-9 07506 4 06 1000000 1010207 71 5 1 1NaN 1 3 3 2 0 50 2 2 3 3 200NaN 2 680 2 1 1 6NaN NaN 1 1 NaN 1 1 1 1NaN 500000 2 900 4 7 73500 4 NaNNaN 1 3 73500 0 4 4 4 1 1 2 7 0 5 0 0 0 16 0 1 0 0 1 1 2 989 3 0 1 32 3 13 13 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 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 707506461014531102488785511.0NaN1.03.03.02.00.050.02.02.03.03.0NaN2.0680.02.01.01.06.0NaNNaN1.09.01.0NaN1.01.01.01.0NaN500000.02.0900.04.07.073500.04.0NaNNaN1.03.073500.00.04.04.04.01.01.02.07.00.05.00.00.00.016.00.01.00.00.01.01.02.0989.03.00.01.032.03.013.013.00.00.00.00.00.00.00.00.000.00.00.01.00.00.01.01.01.00.000.00.00.00.00.00.00.00.01.01.01.00.01.00.00.00.00.01.00.00.01.0182514772269612364832893789877379010213591851081383591138424377681638676101741061001113028871181314090100273679761371061049282166858635247923261167229118143858549110887083338185107116102
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5 rows × 204 columns

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" ], "text/plain": [ - " Unnamed: 0 serialno RT DIVISION PUMA00 PUMA10 REGION ST ADJHSG \\\n", - "0 940390 2012000002680 H 9 -9 07506 4 06 1000000 \n", - "1 941064 2012000009189 H 9 -9 07506 4 06 1000000 \n", - "2 941824 2012000016466 H 9 -9 07506 4 06 1000000 \n", - "3 941918 2012000017340 H 9 -9 07506 4 06 1000000 \n", - "4 942803 2012000025664 H 9 -9 07506 4 06 1000000 \n", + " serialno insp RT DIVISION puma00 puma10 REGION ST ADJHSG \\\n", + "0 2012000002680 200.0 H 9 -9 7506 4 6 1014531 \n", + "1 2012000009189 660.0 H 9 -9 7506 4 6 1014531 \n", + "2 2012000016466 NaN H 9 -9 7506 4 6 1014531 \n", + "3 2012000017340 500.0 H 9 -9 7506 4 6 1014531 \n", + "4 2012000025664 200.0 H 9 -9 7506 4 6 1014531 \n", "\n", - " ADJINC WGTP NP TYPE ACR AGS BATH BDSP BLD BUS CONP ELEP FS \\\n", - "0 1010207 21 1 1 1 NaN 1 2 3 2 0 60 2 \n", - "1 1010207 15 4 1 NaN NaN 1 5 4 NaN 0 50 2 \n", - "2 1010207 15 3 1 1 NaN 1 2 2 2 0 10 2 \n", - "3 1010207 10 4 1 1 NaN 1 3 3 2 0 100 2 \n", - "4 1010207 71 5 1 1 NaN 1 3 3 2 0 50 2 \n", + " ADJINC WGTP NP TYPE ACR AGS BATH BDSP BLD BUS CONP ELEP FS \\\n", + "0 1024887 21 1 1 1.0 NaN 1.0 2.0 3.0 2.0 0.0 60.0 2.0 \n", + "1 1024887 14 4 1 NaN NaN 1.0 5.0 4.0 NaN 0.0 50.0 2.0 \n", + "2 1024887 14 3 1 1.0 NaN 1.0 2.0 2.0 2.0 0.0 10.0 2.0 \n", + "3 1024887 11 4 1 1.0 NaN 1.0 3.0 3.0 2.0 0.0 100.0 2.0 \n", + "4 1024887 85 5 1 1.0 NaN 1.0 3.0 3.0 2.0 0.0 50.0 2.0 \n", "\n", - " FULP GASP HFL INSP MHP MRGI MRGP MRGT MRGX REFR RMSP RNTM RNTP \\\n", - "0 2 4 9 200 NaN 2 350 2 1 1 5 NaN NaN \n", - "1 2 50 1 660 NaN 2 490 2 1 1 7 NaN NaN \n", - "2 2 1 1 NaN NaN NaN NaN NaN NaN 1 4 2 2000 \n", - "3 1000 30 3 500 NaN 2 3000 2 1 1 4 NaN NaN \n", - "4 2 3 3 200 NaN 2 680 2 1 1 6 NaN NaN \n", + " FULP GASP HFL MHP MRGI MRGP MRGT MRGX REFR RMSP RNTM RNTP \\\n", + "0 2.0 4.0 9.0 NaN 2.0 350.0 2.0 1.0 1.0 5.0 NaN NaN \n", + "1 2.0 50.0 1.0 NaN 2.0 490.0 2.0 1.0 1.0 7.0 NaN NaN \n", + "2 2.0 1.0 1.0 NaN NaN NaN NaN NaN 1.0 4.0 2.0 2000.0 \n", + "3 1000.0 30.0 3.0 NaN 2.0 3000.0 2.0 1.0 1.0 4.0 NaN NaN \n", + "4 2.0 3.0 3.0 NaN 2.0 680.0 2.0 1.0 1.0 6.0 NaN NaN \n", "\n", - " RWAT SINK SMP STOV TEL TEN TOIL VACS VALP VEH WATP YBL FES \\\n", - "0 1 1 NaN 1 1 1 1 NaN 500000 1 430 1 NaN \n", - "1 1 1 630 1 1 1 1 NaN 40000 2 2000 1 4 \n", - "2 1 1 NaN 1 1 3 1 NaN NaN 1 1 2 7 \n", - "3 1 1 NaN 1 1 1 1 NaN 500000 1 1200 7 5 \n", - "4 1 1 NaN 1 1 1 1 NaN 500000 2 900 4 7 \n", + " RWAT RWATPR SINK SMP STOV TEL TEN TOIL VACS VALP VEH \\\n", + "0 1.0 9.0 1.0 NaN 1.0 1.0 1.0 1.0 NaN 500000.0 1.0 \n", + "1 1.0 9.0 1.0 630.0 1.0 1.0 1.0 1.0 NaN 40000.0 2.0 \n", + "2 1.0 9.0 1.0 NaN 1.0 1.0 3.0 1.0 NaN NaN 1.0 \n", + "3 1.0 9.0 1.0 NaN 1.0 1.0 1.0 1.0 NaN 500000.0 1.0 \n", + "4 1.0 9.0 1.0 NaN 1.0 1.0 1.0 1.0 NaN 500000.0 2.0 \n", "\n", - " FINCP FPARC GRNTP GRPIP HHL HHT HINCP HUGCL HUPAC HUPAOC HUPARC \\\n", - "0 NaN NaN NaN NaN 1 6 59000 0 4 4 4 \n", - "1 10000 2 NaN NaN 4 1 10000 0 2 2 2 \n", - "2 30000 2 2010 80 1 3 30000 0 2 2 2 \n", - "3 60000 4 NaN NaN 4 2 60000 0 4 4 4 \n", - "4 73500 4 NaN NaN 1 3 73500 0 4 4 4 \n", + " WATP YBL FES FINCP FPARC GRNTP GRPIP HHL HHT HINCP HUGCL \\\n", + "0 430.0 1.0 NaN NaN NaN NaN NaN 1.0 6.0 59000.0 0.0 \n", + "1 2000.0 1.0 4.0 10000.0 2.0 NaN NaN 4.0 1.0 10000.0 0.0 \n", + "2 1.0 2.0 7.0 30000.0 2.0 2010.0 80.0 1.0 3.0 30000.0 0.0 \n", + "3 1200.0 7.0 5.0 60000.0 4.0 NaN NaN 4.0 2.0 60000.0 0.0 \n", + "4 900.0 4.0 7.0 73500.0 4.0 NaN NaN 1.0 3.0 73500.0 0.0 \n", "\n", - " KIT LNGI MULTG MV NOC NPF NPP NR NRC OCPIP PARTNER PLM PSF \\\n", - "0 1 1 1 6 0 NaN 0 0 0 13 0 1 0 \n", - "1 1 2 1 5 2 4 0 0 2 101 0 1 0 \n", - "2 1 1 1 2 2 3 0 0 2 NaN 0 1 0 \n", - "3 1 1 1 4 0 4 0 0 0 79 0 1 1 \n", - "4 1 1 2 7 0 5 0 0 0 16 0 1 0 \n", + " HUPAC HUPAOC HUPARC KIT LNGI MULTG MV NOC NPF NPP NR NRC \\\n", + "0 4.0 4.0 4.0 1.0 1.0 1.0 6.0 0.0 NaN 0.0 0.0 0.0 \n", + "1 2.0 2.0 2.0 1.0 2.0 1.0 5.0 2.0 4.0 0.0 0.0 2.0 \n", + "2 2.0 2.0 2.0 1.0 1.0 1.0 2.0 2.0 3.0 0.0 0.0 2.0 \n", + "3 4.0 4.0 4.0 1.0 1.0 1.0 4.0 0.0 4.0 0.0 0.0 0.0 \n", + "4 4.0 4.0 4.0 1.0 1.0 2.0 7.0 0.0 5.0 0.0 0.0 0.0 \n", "\n", - " R18 R60 R65 RESMODE SMOCP SMX SRNT SVAL TAXP WIF WKEXREL \\\n", - "0 0 1 0 1 634 3 0 1 32 NaN NaN \n", - "1 1 0 0 1 1534 2 0 0 23 0 9 \n", - "2 1 0 0 1 NaN NaN 1 0 NaN 1 13 \n", - "3 0 1 0 1 3938 3 0 1 65 2 12 \n", - "4 0 1 1 2 989 3 0 1 32 3 13 \n", + " OCPIP PARTNER PLM PSF R18 R60 R65 RESMODE SMOCP SMX SRNT SVAL \\\n", + "0 13.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 634.0 3.0 0.0 1.0 \n", + "1 101.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 1534.0 2.0 0.0 0.0 \n", + "2 NaN 0.0 1.0 0.0 1.0 0.0 0.0 1.0 NaN NaN 1.0 0.0 \n", + "3 79.0 0.0 1.0 1.0 0.0 1.0 0.0 1.0 3938.0 3.0 0.0 1.0 \n", + "4 16.0 0.0 1.0 0.0 0.0 1.0 1.0 2.0 989.0 3.0 0.0 1.0 \n", "\n", - " WORKSTAT FACRP FAGSP FBATHP FBDSP FBLDP FBUSP FCONP FELEP FFSP \\\n", - "0 NaN 0 0 0 0 0 0 0 0 0 \n", - "1 9 0 0 0 1 1 0 0 0 0 \n", - "2 13 1 0 0 0 0 1 0 0 0 \n", - "3 11 1 0 0 0 0 1 0 0 0 \n", - "4 13 0 0 0 0 0 0 0 0 0 \n", + " TAXP WIF WKEXREL WORKSTAT FACRP FAGSP FBATHP FBDSP FBLDP FBUSP \\\n", + "0 32.0 NaN NaN NaN 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "1 23.0 0.0 9.0 9.0 0.0 0.0 0.0 1.0 1.0 0.0 \n", + "2 NaN 1.0 13.0 13.0 1.0 0.0 0.0 0.0 0.0 1.0 \n", + "3 65.0 2.0 12.0 11.0 1.0 0.0 0.0 0.0 0.0 1.0 \n", + "4 32.0 3.0 13.0 13.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", - " FFULP FGASP FHFLP FINSP FKITP FMHP FMRGIP FMRGP FMRGTP FMRGXP \\\n", - "0 0 1 0 0 0 0 0 0 0 0 \n", - "1 1 0 1 0 0 0 0 1 0 0 \n", - "2 0 0 0 0 0 0 0 0 0 0 \n", - "3 0 0 0 0 0 0 0 0 0 0 \n", - "4 0 0 0 1 0 0 1 1 1 0 \n", + " FCONP FELEP FFSP FFULP FGASP FHFLP FINSP FKITP FMHP FMRGIP FMRGP \\\n", + "0 0.0 0.0 0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 \n", + "2 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 \n", "\n", - " FMVP FPLMP FREFRP FRMSP FRNTMP FRNTP FRWATP FSINKP FSMP FSMXHP \\\n", - "0 0 0 0 0 0 0 0 0 0 0 \n", - "1 0 0 0 1 0 0 0 0 1 0 \n", - "2 0 0 0 0 0 0 0 0 0 0 \n", - "3 1 0 0 0 0 0 0 0 0 0 \n", - "4 0 0 0 0 0 0 0 0 1 1 \n", + " FMRGTP FMRGXP FMVP FPLMP FREFRP FRMSP FRNTMP FRNTP FRWATP \\\n", + "0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0 0.0 0.0 1.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "4 1.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", - " FSMXSP FSTOVP FTAXP FTELP FTENP FTOILP FVACSP FVALP FVEHP FWATP \\\n", - "0 0 0 0 0 0 0 0 0 0 0 \n", - "1 0 0 1 0 0 0 0 0 0 0 \n", - "2 0 0 0 0 0 0 0 0 0 0 \n", - "3 0 0 0 0 0 0 0 0 0 0 \n", - "4 1 0 1 0 0 0 0 1 0 0 \n", + " FRWATPRP FSINKP FSMP FSMXHP FSMXSP FSTOVP FTAXP FTELP FTENP \\\n", + "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 \n", "\n", - " FYBLP WGTP1 WGTP2 WGTP3 WGTP4 WGTP5 WGTP6 WGTP7 WGTP8 WGTP9 \\\n", - "0 0 21 19 21 7 22 23 31 29 6 \n", - "1 0 19 11 23 20 7 16 11 7 20 \n", - "2 0 4 5 32 14 5 21 21 12 16 \n", - "3 0 12 8 3 8 4 6 8 12 17 \n", - "4 1 33 23 78 66 19 99 88 97 68 \n", + " FTOILP FVACSP FVALP FVEHP FWATP FYBLP WGTP1 WGTP2 WGTP3 WGTP4 \\\n", + "0 0.0 0.0 0.0 0.0 0.0 0.0 22 18 23 7 \n", + "1 0.0 0.0 0.0 0.0 0.0 0.0 15 5 26 17 \n", + "2 0.0 0.0 0.0 0.0 0.0 0.0 4 5 25 15 \n", + "3 0.0 0.0 0.0 0.0 0.0 0.0 10 15 2 9 \n", + "4 0.0 0.0 1.0 0.0 0.0 1.0 18 25 147 72 \n", "\n", - " WGTP10 WGTP11 WGTP12 WGTP13 WGTP14 WGTP15 WGTP16 WGTP17 WGTP18 \\\n", - "0 5 39 21 6 29 30 22 20 6 \n", - "1 5 21 14 15 27 11 20 18 15 \n", - "2 6 15 19 20 18 5 13 19 23 \n", - "3 7 20 11 11 18 14 4 30 23 \n", - "4 29 80 72 50 74 36 57 82 101 \n", + " WGTP5 WGTP6 WGTP7 WGTP8 WGTP9 WGTP10 WGTP11 WGTP12 WGTP13 WGTP14 \\\n", + "0 25 23 37 30 6 5 40 20 7 33 \n", + "1 5 10 12 5 15 4 14 17 17 20 \n", + "2 4 23 23 14 15 5 15 20 17 14 \n", + "3 3 8 14 11 16 9 22 10 11 23 \n", + "4 26 96 123 64 83 28 93 78 98 77 \n", "\n", - " WGTP19 WGTP20 WGTP21 WGTP22 WGTP23 WGTP24 WGTP25 WGTP26 WGTP27 \\\n", - "0 22 21 17 17 26 44 18 20 7 \n", - "1 18 26 13 33 4 4 50 16 13 \n", - "2 25 11 23 21 5 22 24 5 8 \n", - "3 4 20 10 10 19 9 15 6 12 \n", - "4 77 75 69 93 23 131 105 27 25 \n", + " WGTP15 WGTP16 WGTP17 WGTP18 WGTP19 WGTP20 WGTP21 WGTP22 WGTP23 \\\n", + "0 35 23 22 6 22 21 20 20 23 \n", + "1 11 22 16 12 27 26 15 27 4 \n", + "2 4 16 16 25 25 12 24 17 4 \n", + "3 12 5 28 21 4 23 11 11 13 \n", + "4 37 90 102 135 91 85 108 138 35 \n", "\n", - " WGTP28 WGTP29 WGTP30 WGTP31 WGTP32 WGTP33 WGTP34 WGTP35 WGTP36 \\\n", - "0 5 35 33 8 17 32 7 5 23 \n", - "1 40 25 37 19 17 10 4 13 3 \n", - "2 12 17 35 24 15 17 10 14 13 \n", - "3 10 3 12 4 11 8 4 9 18 \n", - "4 83 69 93 65 57 92 74 108 89 \n", + " WGTP24 WGTP25 WGTP26 WGTP27 WGTP28 WGTP29 WGTP30 WGTP31 WGTP32 \\\n", + "0 37 18 19 7 6 46 36 7 21 \n", + "1 4 32 16 13 27 18 26 17 13 \n", + "2 15 24 3 6 15 17 24 13 16 \n", + "3 9 15 9 11 12 4 10 4 11 \n", + "4 91 138 42 43 77 68 163 86 76 \n", "\n", - " WGTP37 WGTP38 WGTP39 WGTP40 WGTP41 WGTP42 WGTP43 WGTP44 WGTP45 \\\n", - "0 16 35 20 15 17 16 22 34 18 \n", - "1 11 14 6 4 16 4 28 24 6 \n", - "2 16 5 5 18 3 6 27 21 5 \n", - "3 5 3 18 4 12 10 4 13 3 \n", - "4 128 38 17 55 102 103 44 61 109 \n", + " WGTP33 WGTP34 WGTP35 WGTP36 WGTP37 WGTP38 WGTP39 WGTP40 WGTP41 \\\n", + "0 36 8 5 17 18 36 19 18 19 \n", + "1 15 4 12 3 15 12 4 4 15 \n", + "2 14 10 20 15 15 4 5 15 4 \n", + "3 10 4 12 15 3 4 18 3 13 \n", + "4 101 74 106 100 111 30 28 87 118 \n", "\n", - " WGTP46 WGTP47 WGTP48 WGTP49 WGTP50 WGTP51 WGTP52 WGTP53 WGTP54 \\\n", - "0 17 6 6 41 41 6 17 30 6 \n", - "1 19 15 5 18 3 17 15 12 27 \n", - "2 25 28 14 10 7 13 15 17 21 \n", - "3 12 13 10 28 13 20 16 11 17 \n", - "4 29 31 69 66 94 73 66 77 62 \n", + " WGTP42 WGTP43 WGTP44 WGTP45 WGTP46 WGTP47 WGTP48 WGTP49 WGTP50 \\\n", + "0 19 20 38 19 20 7 6 37 38 \n", + "1 4 24 28 5 14 11 4 17 3 \n", + "2 5 29 16 4 23 25 10 15 5 \n", + "3 7 4 12 3 13 9 11 30 12 \n", + "4 131 40 90 100 27 36 79 76 137 \n", "\n", - " WGTP55 WGTP56 WGTP57 WGTP58 WGTP59 WGTP60 WGTP61 WGTP62 WGTP63 \\\n", - "0 6 21 20 30 19 17 18 25 25 \n", - "1 13 33 14 19 47 18 22 28 5 \n", - "2 4 14 14 22 22 15 31 26 5 \n", - "3 11 3 18 15 3 13 9 8 14 \n", - "4 104 82 78 38 30 87 37 21 85 \n", + " WGTP51 WGTP52 WGTP53 WGTP54 WGTP55 WGTP56 WGTP57 WGTP58 WGTP59 \\\n", + "0 7 20 31 7 6 22 21 32 20 \n", + "1 17 14 14 26 10 27 17 15 31 \n", + "2 11 10 13 17 5 14 16 31 20 \n", + "3 17 12 12 18 10 3 18 21 4 \n", + "4 106 104 92 82 166 85 86 35 24 \n", "\n", - " WGTP64 WGTP65 WGTP66 WGTP67 WGTP68 WGTP69 WGTP70 WGTP71 WGTP72 \\\n", - "0 6 24 24 32 45 5 7 39 21 \n", - "1 6 28 13 12 26 21 15 12 12 \n", - "2 17 34 7 4 17 20 24 12 12 \n", - "3 12 17 10 10 9 5 8 3 13 \n", - "4 63 25 66 93 80 55 29 90 74 \n", + " WGTP60 WGTP61 WGTP62 WGTP63 WGTP64 WGTP65 WGTP66 WGTP67 WGTP68 \\\n", + "0 18 20 23 23 7 20 20 40 43 \n", + "1 21 18 28 6 3 22 14 11 25 \n", + "2 15 27 20 5 19 32 7 4 16 \n", + "3 13 10 8 17 12 20 10 11 10 \n", + "4 79 23 26 116 72 29 118 143 85 \n", "\n", - " WGTP73 WGTP74 WGTP75 WGTP76 WGTP77 WGTP78 WGTP79 WGTP80 \n", - "0 6 33 36 18 24 7 17 16 \n", - "1 15 5 13 7 15 13 7 5 \n", - "2 14 14 26 18 13 4 5 16 \n", - "3 11 2 9 19 2 3 13 3 \n", - "4 86 63 19 70 50 100 115 70 \n", + " WGTP69 WGTP70 WGTP71 WGTP72 WGTP73 WGTP74 WGTP75 WGTP76 WGTP77 \\\n", + "0 5 7 40 20 6 35 39 22 26 \n", + "1 14 20 10 16 19 3 14 5 13 \n", + "2 11 24 12 13 13 16 23 12 11 \n", + "3 4 10 3 12 10 2 10 16 3 \n", + "4 85 49 110 88 70 83 33 81 85 \n", "\n", - "[5 rows x 204 columns]" + " WGTP78 WGTP79 WGTP80 \n", + "0 6 20 22 \n", + "1 14 5 4 \n", + "2 4 4 14 \n", + "3 4 17 2 \n", + "4 107 116 102 " ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "h_pums = c.download_household_pums(\"06\", puma)\n", + "h_pums = c.download_household_pums(\"06\", puma10=puma10, puma00=puma00)\n", "h_pums.head(5)" ] }, @@ -4139,15 +4239,30 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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Block Group 1, Census Tract 306, San Francisco County, California 14 86 192 111 183 294 173 73 48 65 89 13899119895205300157746968119111
Block Group 2, Census Tract 306, San Francisco County, California 11 66 147 88 138 226 156 20 50 50 68 10698317979194273198621362109101
Block Group 3, Census Tract 306, San Francisco County, California 14 84 187 50 237 287 194 40 53 64 87 134109320165240305257291969121112
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" ], "text/plain": [ - "cat_name cars \\\n", - "cat_value none one \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 14 86 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 11 66 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 14 84 \n", - "\n", - "cat_name \\\n", - "cat_value two or more \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 192 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 147 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 187 \n", + "cat_name cars \\\n", + "cat_value none one two or more \n", + "NAME \n", + "Block Group 1, Census Tract 306, San Francisco ... 9 91 198 \n", + "Block Group 2, Census Tract 306, San Francisco ... 9 83 179 \n", + "Block Group 3, Census Tract 306, San Francisco ... 10 93 201 \n", "\n", - "cat_name children \\\n", - "cat_value no \n", + "cat_name children households \\\n", + "cat_value no yes total \n", "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 111 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 88 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 50 \n", + "Block Group 1, Census Tract 306, San Francisco ... 95 205 300 \n", + "Block Group 2, Census Tract 306, San Francisco ... 79 194 273 \n", + "Block Group 3, Census Tract 306, San Francisco ... 65 240 305 \n", "\n", - "cat_name \\\n", - "cat_value yes \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 183 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 138 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 237 \n", - "\n", - "cat_name households \\\n", - "cat_value total \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 294 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 226 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 287 \n", - "\n", - "cat_name income \\\n", - "cat_value gt100 \n", + "cat_name income \\\n", + "cat_value gt100 gt35-lt100 lt35 \n", "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 173 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 156 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 194 \n", - "\n", - "cat_name \\\n", - "cat_value gt35-lt100 \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 73 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 20 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 40 \n", - "\n", - "cat_name \\\n", - "cat_value lt35 \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 48 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 50 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 53 \n", + "Block Group 1, Census Tract 306, San Francisco ... 157 74 69 \n", + "Block Group 2, Census Tract 306, San Francisco ... 198 62 13 \n", + "Block Group 3, Census Tract 306, San Francisco ... 257 29 19 \n", "\n", - "cat_name workers \\\n", - "cat_value none \n", + "cat_name workers \n", + "cat_value none one two or more \n", "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 65 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 50 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 64 \n", - "\n", - "cat_name \\\n", - "cat_value one \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 89 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 68 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 87 \n", - "\n", - "cat_name \n", - "cat_value two or more \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 138 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 106 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 134 \n", - "\n", - "[3 rows x 12 columns]" + "Block Group 1, Census Tract 306, San Francisco ... 68 119 111 \n", + "Block Group 2, Census Tract 306, San Francisco ... 62 109 101 \n", + "Block Group 3, Census Tract 306, San Francisco ... 69 121 112 " ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -4350,10 +4413,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "assert np.all(cat.sum_accross_category(h_acs_cat) < 2)" @@ -4368,15 +4429,30 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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Block Group 1, Census Tract 306, San Francisco County, California 129 62 296 168 655 187 11 34 423 334 3211981322012587892619152367397392
Block Group 2, Census Tract 306, San Francisco County, California 97 34 288 109 528 11 9 60 448 292 23612582313176696179070447354342
Block Group 3, Census Tract 306, San Francisco County, California 203 78 411 166 858 218 11 6 623 365 493291624121749393101285532421518
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3 rows × 11 columns

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" ], "text/plain": [ - "cat_name age \\\n", - "cat_value 19 and under \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 129 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 97 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 203 \n", - "\n", - "cat_name \\\n", - "cat_value 20 to 35 \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 62 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 34 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 78 \n", - "\n", - "cat_name \\\n", - "cat_value 35 to 60 \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 296 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 288 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 411 \n", - "\n", - "cat_name \\\n", - "cat_value above 60 \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 168 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 109 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 166 \n", - "\n", - "cat_name population \\\n", - "cat_value total \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 655 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 528 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 858 \n", - "\n", - "cat_name race \\\n", - "cat_value asian \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 187 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 11 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 218 \n", - "\n", - "cat_name \\\n", - "cat_value black \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 11 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 9 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 11 \n", - "\n", - "cat_name \\\n", - "cat_value other \n", + "cat_name age \\\n", + "cat_value 19 and under 20 to 35 \n", "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 34 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 60 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 6 \n", + "Block Group 1, Census Tract 306, San Francisco ... 198 132 \n", + "Block Group 2, Census Tract 306, San Francisco ... 125 82 \n", + "Block Group 3, Census Tract 306, San Francisco ... 291 62 \n", "\n", - "cat_name \\\n", - "cat_value white \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 423 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 448 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 623 \n", + "cat_name \\\n", + "cat_value 35 to 60 above 60 \n", + "NAME \n", + "Block Group 1, Census Tract 306, San Francisco ... 201 258 \n", + "Block Group 2, Census Tract 306, San Francisco ... 313 176 \n", + "Block Group 3, Census Tract 306, San Francisco ... 412 174 \n", "\n", - "cat_name sex \\\n", - "cat_value female \n", + "cat_name population race \\\n", + "cat_value total asian black \n", "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 334 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 292 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 365 \n", + "Block Group 1, Census Tract 306, San Francisco ... 789 261 9 \n", + "Block Group 2, Census Tract 306, San Francisco ... 696 179 0 \n", + "Block Group 3, Census Tract 306, San Francisco ... 939 310 12 \n", "\n", - "cat_name \n", - "cat_value male \n", - "NAME \n", - "Block Group 1, Census Tract 306, San Francisco County, California 321 \n", - "Block Group 2, Census Tract 306, San Francisco County, California 236 \n", - "Block Group 3, Census Tract 306, San Francisco County, California 493 \n", - "\n", - "[3 rows x 11 columns]" + "cat_name sex \n", + "cat_value other white female male \n", + "NAME \n", + "Block Group 1, Census Tract 306, San Francisco ... 152 367 397 392 \n", + "Block Group 2, Census Tract 306, San Francisco ... 70 447 354 342 \n", + "Block Group 3, Census Tract 306, San Francisco ... 85 532 421 518 " ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -4583,10 +4607,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "assert np.all(cat.sum_accross_category(p_acs_cat) < 2)" @@ -4601,30 +4623,28 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ "cat_name cat_value \n", - "age 19 and under 129\n", - " 20 to 35 62\n", - " 35 to 60 296\n", - " above 60 168\n", - "population total 655\n", - "race asian 187\n", - " black 11\n", - " other 34\n", - " white 423\n", - "sex female 334\n", - " male 321\n", - "Name: Block Group 1, Census Tract 306, San Francisco County, California, dtype: int64" + "age 19 and under 198\n", + " 20 to 35 132\n", + " 35 to 60 201\n", + " above 60 258\n", + "population total 789\n", + "race asian 261\n", + " black 9\n", + " other 152\n", + " white 367\n", + "sex female 397\n", + " male 392\n", + "Name: Block Group 1, Census Tract 306, San Francisco County, California, dtype: int32" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -4642,22 +4662,33 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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idcat_idfrequency
19 and underasianfemale 0 370187
male 1 441215
blackfemale 2 1216
male 3 5315
otherfemale 4 21489
male 5 25594
whitefemale 6 426202
male 7 357185
20 to 35asianfemale 8 428238
male 9 479211
blackfemale 10 61021
male 11 61123
otherfemale 12 121259
male 13 131367
whitefemale 14 4414198
male 15 4315225
35 to 60asianfemale 16 9616448
male 17 8117378
blackfemale 18 101833
male 19 71939
otherfemale 20 222075
male 21 172178
whitefemale 22 6822373
male 23 8823453
above 60asianfemale 24 5624233
male 25 3825182
blackfemale 26 142657
male 27 82743
otherfemale 28 72832
male 29 52925
whitefemale 30 6430354
male 31 5931294
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32 rows × 2 columns

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" ], "text/plain": [ - " id frequency\n", - "age race sex \n", - "19 and under asian female 0 37\n", - " male 1 44\n", - " black female 2 1\n", - " male 3 5\n", - " other female 4 21\n", - " male 5 25\n", - " white female 6 42\n", - " male 7 35\n", - "20 to 35 asian female 8 42\n", - " male 9 47\n", - " black female 10 6\n", - " male 11 6\n", - " other female 12 12\n", - " male 13 13\n", - " white female 14 44\n", - " male 15 43\n", - "35 to 60 asian female 16 96\n", - " male 17 81\n", - " black female 18 10\n", - " male 19 7\n", - " other female 20 22\n", - " male 21 17\n", - " white female 22 68\n", - " male 23 88\n", - "above 60 asian female 24 56\n", - " male 25 38\n", - " black female 26 14\n", - " male 27 8\n", - " other female 28 7\n", - " male 29 5\n", - " white female 30 64\n", - " male 31 59\n", - "\n", - "[32 rows x 2 columns]" + " cat_id frequency\n", + "age race sex \n", + "19 and under asian female 0 187\n", + " male 1 215\n", + " black female 2 16\n", + " male 3 15\n", + " other female 4 89\n", + " male 5 94\n", + " white female 6 202\n", + " male 7 185\n", + "20 to 35 asian female 8 238\n", + " male 9 211\n", + " black female 10 21\n", + " male 11 23\n", + " other female 12 59\n", + " male 13 67\n", + " white female 14 198\n", + " male 15 225\n", + "35 to 60 asian female 16 448\n", + " male 17 378\n", + " black female 18 33\n", + " male 19 39\n", + " other female 20 75\n", + " male 21 78\n", + " white female 22 373\n", + " male 23 453\n", + "above 60 asian female 24 233\n", + " male 25 182\n", + " black female 26 57\n", + " male 27 43\n", + " other female 28 32\n", + " male 29 25\n", + " white female 30 354\n", + " male 31 294" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -4932,15 +4960,26 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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idcat_idfrequency
carsworkerschildrenincomeworkers
nonenonenogt100 0 0nogt100none00.0
gt35-lt100 1 1one13.0
lt35 2 37two or more25.0
yesgt100 3 0gt35-lt100none33.0
gt35-lt100 4 0one46.0
lt35 5 0two or more510.0
onenogt100 6 0lt35none6174.0
gt35-lt100 7 2one79.0
lt35 8 2two or more81.0
yesgt100 9 0yesgt100none90.0
gt35-lt100 10 1one100.0
lt35 11 0two or more111.0
two or morenogt100 12 2gt35-lt100none120.0
gt35-lt100 13 5one134.0
lt35 14 0two or more142.0
yesgt100 15 0lt35none152.0
gt35-lt100 16 1one165.0
lt35 17 0two or more170.0
onenonenogt100 18 1nogt100none1810.0
gt35-lt100 19 7one1919.0
lt35 20 81two or more2042.0
yesgt100 21 0gt35-lt100none2127.0
gt35-lt100 22 0one2240.0
lt35 23 0two or more2347.0
onenogt100 24 5lt35none24404.0
gt35-lt100 25 6one2514.0
lt35 26 2two or more262.0
yesgt100 27 3yesgt100none271.0
gt35-lt100 28 6one2815.0
lt35 29 8two or more2924.0
two or morenogt100 30 8gt35-lt100none300.0
gt35-lt100 31 10one3124.0
lt35 32 1two or more3221.0
yesgt100 33 6lt35none335.0
gt35-lt100 34 7one3418.0
lt35 35 1two or more352.0
two or morenonenogt100 36 2nogt100none3613.0
gt35-lt100 37 8one3746.0
lt35 38 91two or more38209.0
yesgt100 39 0gt35-lt100none3930.0
gt35-lt100 40 0one4063.0
lt35 41 1two or more4198.0
onenogt100 42 5lt35none42174.0
gt35-lt100 43 8one4315.0
lt35 44 5two or more4411.0
yesgt100 45 6yesgt100none450.0
gt35-lt100 46 6one4643.0
lt35 47 0two or more47159.0
two or morenogt100 48 36gt35-lt100none480.0
gt35-lt100 49 22one4919.0
lt35 50 5two or more5055.0
yesgt100 51 36lt35none513.0
gt35-lt100 52 10one526.0
lt35 53 0two or more535.0
\n", - "

54 rows × 2 columns

\n", "
" ], "text/plain": [ - " id frequency\n", - "cars workers children income \n", - "none none no gt100 0 0\n", - " gt35-lt100 1 1\n", - " lt35 2 37\n", - " yes gt100 3 0\n", - " gt35-lt100 4 0\n", - " lt35 5 0\n", - " one no gt100 6 0\n", - " gt35-lt100 7 2\n", - " lt35 8 2\n", - " yes gt100 9 0\n", - " gt35-lt100 10 1\n", - " lt35 11 0\n", - " two or more no gt100 12 2\n", - " gt35-lt100 13 5\n", - " lt35 14 0\n", - " yes gt100 15 0\n", - " gt35-lt100 16 1\n", - " lt35 17 0\n", - "one none no gt100 18 1\n", - " gt35-lt100 19 7\n", - " lt35 20 81\n", - " yes gt100 21 0\n", - " gt35-lt100 22 0\n", - " lt35 23 0\n", - " one no gt100 24 5\n", - " gt35-lt100 25 6\n", - " lt35 26 2\n", - " yes gt100 27 3\n", - " gt35-lt100 28 6\n", - " lt35 29 8\n", - " two or more no gt100 30 8\n", - " gt35-lt100 31 10\n", - " lt35 32 1\n", - " yes gt100 33 6\n", - " gt35-lt100 34 7\n", - " lt35 35 1\n", - "two or more none no gt100 36 2\n", - " gt35-lt100 37 8\n", - " lt35 38 91\n", - " yes gt100 39 0\n", - " gt35-lt100 40 0\n", - " lt35 41 1\n", - " one no gt100 42 5\n", - " gt35-lt100 43 8\n", - " lt35 44 5\n", - " yes gt100 45 6\n", - " gt35-lt100 46 6\n", - " lt35 47 0\n", - " two or more no gt100 48 36\n", - " gt35-lt100 49 22\n", - " lt35 50 5\n", - " yes gt100 51 36\n", - " gt35-lt100 52 10\n", - " lt35 53 0\n", - "\n", - "[54 rows x 2 columns]" + " cat_id frequency\n", + "cars children income workers \n", + "none no gt100 none 0 0.0\n", + " one 1 3.0\n", + " two or more 2 5.0\n", + " gt35-lt100 none 3 3.0\n", + " one 4 6.0\n", + " two or more 5 10.0\n", + " lt35 none 6 174.0\n", + " one 7 9.0\n", + " two or more 8 1.0\n", + " yes gt100 none 9 0.0\n", + " one 10 0.0\n", + " two or more 11 1.0\n", + " gt35-lt100 none 12 0.0\n", + " one 13 4.0\n", + " two or more 14 2.0\n", + " lt35 none 15 2.0\n", + " one 16 5.0\n", + " two or more 17 0.0\n", + "one no gt100 none 18 10.0\n", + " one 19 19.0\n", + " two or more 20 42.0\n", + " gt35-lt100 none 21 27.0\n", + " one 22 40.0\n", + " two or more 23 47.0\n", + " lt35 none 24 404.0\n", + " one 25 14.0\n", + " two or more 26 2.0\n", + " yes gt100 none 27 1.0\n", + " one 28 15.0\n", + " two or more 29 24.0\n", + " gt35-lt100 none 30 0.0\n", + " one 31 24.0\n", + " two or more 32 21.0\n", + " lt35 none 33 5.0\n", + " one 34 18.0\n", + " two or more 35 2.0\n", + "two or more no gt100 none 36 13.0\n", + " one 37 46.0\n", + " two or more 38 209.0\n", + " gt35-lt100 none 39 30.0\n", + " one 40 63.0\n", + " two or more 41 98.0\n", + " lt35 none 42 174.0\n", + " one 43 15.0\n", + " two or more 44 11.0\n", + " yes gt100 none 45 0.0\n", + " one 46 43.0\n", + " two or more 47 159.0\n", + " gt35-lt100 none 48 0.0\n", + " one 49 19.0\n", + " two or more 50 55.0\n", + " lt35 none 51 3.0\n", + " one 52 6.0\n", + " two or more 53 5.0" ] }, - "execution_count": 14, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -5372,9 +5405,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -5394,9 +5425,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [] } @@ -5417,9 +5446,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.14" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/demos/non_census_synthesis.ipynb b/demos/non_census_synthesis.ipynb index 3540ed2..bf16a1a 100644 --- a/demos/non_census_synthesis.ipynb +++ b/demos/non_census_synthesis.ipynb @@ -3,9 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -22,10 +20,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ "hh_marginal_file = 'input_data/hh_marginals.csv'\n", @@ -43,10 +39,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "hh_marg, p_marg, hh_sample, p_sample, xwalk = zs.load_data(hh_marginal_file, person_marginal_file, hh_sample_file, person_sample_file)" @@ -54,15 +48,26 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -180,7 +185,7 @@ "5 182 247 " ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -191,15 +196,26 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -310,7 +326,7 @@ "5 981 945 " ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -321,15 +337,26 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "
\n", " \n", " \n", @@ -413,7 +440,7 @@ "4 2012000065237 1 20 to 35 white male 24 1 1" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -431,16 +458,149 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Applications/synthpop/synthpop/ipu/ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 254 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 306 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 356 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 390 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 601 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 324 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 556 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 342 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 273 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", " adj = constraint / (column * weights).sum()\n" ] }, @@ -448,26 +608,143 @@ "name": "stdout", "output_type": "stream", "text": [ - "Drawing 254 households\n", - "Drawing 306 households\n", - "Drawing 356 households\n", - "Drawing 390 households\n", - "Drawing 601 households\n", - "Drawing 324 households\n", - "Drawing 556 households\n", - "Drawing 342 households\n", - "Drawing 273 households\n", "Drawing 228 households\n", "Drawing 857 households\n", - "Drawing 748 households\n", - "Drawing 744 households\n", - "Drawing 953 households\n", - "Drawing 719 households\n", - "Drawing 185 households\n", - "Drawing 183 households\n", - "Drawing 286 households\n", - "Drawing 317 households\n", - "Drawing 711 households\n", + "Drawing 748 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 744 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 953 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 719 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 185 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 183 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 286 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 317 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 711 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Drawing 345 households\n" ] } @@ -478,15 +755,26 @@ }, { "cell_type": "code", - 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" ], "text/plain": [ - " chi-square geog p-score\n", - "0 3.386317 1 9.981665e-01\n", - "1 140.849570 2 3.373735e-27\n", - "2 177.908580 3 4.531324e-31\n", - "3 102.567618 4 1.524408e-15\n", - "4 501.946193 5 3.059083e-104" + " chi-square geog p-score\n", + "0 2.070855 1 9.998970e-01\n", + "1 154.420913 2 4.782383e-30\n", + "2 182.585670 3 5.033580e-32\n", + "3 96.436446 4 2.276937e-14\n", + "4 468.714250 5 4.243758e-97\n", + "5 5.642968 6 9.581878e-01\n", + "6 516.866875 7 2.458698e-101\n", + "7 79.638305 8 1.632443e-14\n", + "8 117.238663 9 1.836683e-20\n", + "9 2000.143804 10 0.000000e+00\n", + "10 642.117025 11 2.372825e-117\n", + "11 62.442878 12 4.130503e-07\n", + "12 1174.759712 13 8.130438e-234\n", + "13 670.414502 14 2.542384e-132\n", + "14 1081.225334 15 4.449635e-220\n", + "15 12.940226 16 7.357824e-02\n", + "16 1.161109 17 9.917950e-01\n", + "17 159.879713 18 2.084872e-27\n", + "18 0.507767 19 9.994175e-01\n", + "19 116.945628 20 7.393055e-18\n", + "20 14.386659 21 4.471640e-02" ] }, "execution_count": 15, @@ -1157,29 +1571,43 @@ } ], "source": [ - "all_stats.head()" + "all_stats" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/demos/simple_synthesis.ipynb b/demos/simple_synthesis.ipynb index ea7428b..75a0c52 100644 --- a/demos/simple_synthesis.ipynb +++ b/demos/simple_synthesis.ipynb @@ -3,9 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "from synthpop.synthesizer import synthesize, enable_logging\n", @@ -13,19 +11,17 @@ "\n", "import multiprocessing\n", "import os\n", - "\n", + "import seaborn as sns\n", "from functools import partial\n", "import pandas as pd\n", "\n", - "import synthpop.general_synthesizer as gs" + "import synthpop.zone_synthesizer as zs" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "hh_marginal_file = 'input_data/hh_marginals.csv'\n", @@ -37,26 +33,157 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ - "hh_marg, p_marg, hh_sample, p_sample, xwalk = gs.load_data(hh_marginal_file, person_marginal_file, hh_sample_file, person_sample_file)" + "hh_marg, p_marg, hh_sample, p_sample, xwalk = zs.load_data(hh_marginal_file, person_marginal_file, hh_sample_file, person_sample_file)" ] }, { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Applications/synthpop/synthpop/ipu/ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 254 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 306 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 356 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 390 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 601 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 324 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 556 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 342 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 273 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", " adj = constraint / (column * weights).sum()\n" ] }, @@ -64,93 +191,225 @@ "name": "stdout", "output_type": "stream", "text": [ - "Drawing 254 households\n", - "Drawing 306 households\n", - "Drawing 356 households\n", - "Drawing 390 households\n", - "Drawing 601 households\n", - "Drawing 324 households\n", - "Drawing 556 households\n", - "Drawing 342 households\n", - "Drawing 273 households\n", "Drawing 228 households\n", "Drawing 857 households\n", - "Drawing 748 households\n", - "Drawing 744 households\n", - "Drawing 953 households\n", - "Drawing 719 households\n", - "Drawing 185 households\n", - "Drawing 183 households\n", - "Drawing 286 households\n", - "Drawing 317 households\n", - "Drawing 711 households\n", + "Drawing 748 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 744 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 953 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 719 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 185 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 183 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 286 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 317 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 711 households\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\juan\\documents\\github\\synthpop\\synthpop\\ipu\\ipu.py:190: RuntimeWarning: divide by zero encountered in double_scalars\n", + " adj = constraint / (column * weights).sum()\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Drawing 345 households\n" ] } ], "source": [ - "all_households, all_persons, all_stats = gs.synthesize_all_zones(hh_marg, p_marg, hh_sample, p_sample, xwalk)" + "all_households, all_persons, all_stats = zs.synthesize_all_zones(hh_marg, p_marg, hh_sample, p_sample, xwalk)" ] }, { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, + "outputs": [], + "source": [ + "all_persons, all_households, all_stats = zs.multiprocess_synthesize(hh_marg, p_marg, hh_sample, p_sample, xwalk)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Drawing 390 households\n", - "Drawing 254 households\n", - "Drawing 306 households\n", - "Drawing 601 households\n", - "Drawing 356 households\n", - "Drawing 324 households\n", - "Drawing 556 households\n", - "Drawing 273 households\n", - "Drawing 342 households\n", - "Drawing 228 households\n", - "Drawing 748 households\n", - "Drawing 857 households\n", - "Drawing 744 households\n", - "Drawing 953 households\n", - "Drawing 719 households\n", - "Drawing 183 households\n", - "Drawing 317 households\n", - "Drawing 185 households\n", - "Drawing 286 households\n", - "Drawing 711 households\n", - "Drawing 345 households\n" + "C:\\Users\\Juan\\Anaconda3\\envs\\synpop_py3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6448: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", + " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "all_persons, all_households, all_stats = gs.multiprocess_synthesize(hh_marg, p_marg, hh_sample, p_sample, xwalk)" + "sns.distplot(all_persons.cat_id)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/demos/synthesize.ipynb b/demos/synthesize.ipynb index 7da2e46..f4a5fc4 100644 --- a/demos/synthesize.ipynb +++ b/demos/synthesize.ipynb @@ -1,68399 +1,474 @@ { - "metadata": { - "name": "", - "signature": "sha256:6ac0122e1d536b85993f3b561e790149f6a19f30078a24ed49478a5016885b9a" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "from synthpop.recipes.starter2 import Starter\n", + "from synthpop.synthesizer import synthesize_all, enable_logging \n", + "import os\n", + "import pandas as pd\n", + "#enable_logging()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "from synthpop.recipes.starter import Starter\n", - "from synthpop.synthesizer import synthesize_all, enable_logging \n", - "import os\n", - "import pandas as pd\n", - "#enable_logging()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36msynthesize_counties\u001b[1;34m(counties)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msynthesize_counties\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcounties\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcounty\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcounties\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mstarter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStarter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"CENSUS\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"CA\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcounty\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0msynthesize_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstarter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'time'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'hh = synthesize_counties([\"Santa Clara County\", \"Solano County\", \"San Mateo County\", \"Marin County\", \"San Francisco County\", \"Napa County\", \"Sonoma County\", \"Contra Costa County\", \"Alameda County\"])'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\juan\\documents\\github\\synthpop\\synthpop\\recipes\\starter2.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, key, state, county, tract)\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0mblock_group_size_attr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"B11005_001E\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[0mtract_size_attr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"B08201_001E\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m tract=tract)\n\u001b[0m\u001b[0;32m 72\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mh_acs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh_acs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\juan\\documents\\github\\synthpop\\synthpop\\census_helpers.py\u001b[0m in \u001b[0;36mblock_group_and_tract_query\u001b[1;34m(self, block_group_columns, tract_columns, state, county, merge_columns, block_group_size_attr, tract_size_attr, tract, year)\u001b[0m\n\u001b[0;32m 110\u001b[0m tract_size_attr, tract=None, year=2016):\n\u001b[0;32m 111\u001b[0m df2 = self.tract_query(tract_columns, state, county, tract=tract,\n\u001b[1;32m--> 112\u001b[1;33m year=year)\n\u001b[0m\u001b[0;32m 113\u001b[0m df1 = self.block_group_query(block_group_columns, state, county,\n\u001b[0;32m 114\u001b[0m tract=tract, year=year)\n", + "\u001b[1;32mc:\\users\\juan\\documents\\github\\synthpop\\synthpop\\census_helpers.py\u001b[0m in \u001b[0;36mtract_query\u001b[1;34m(self, census_columns, state, county, tract, year)\u001b[0m\n\u001b[0;32m 63\u001b[0m return self._query(census_columns, state, county,\n\u001b[0;32m 64\u001b[0m \u001b[0mforstr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"tract:%s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mtract\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m year=year)\n\u001b[0m\u001b[0;32m 66\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 67\u001b[0m def _query(self, census_columns, state, county, forstr,\n", + "\u001b[1;32mc:\\users\\juan\\documents\\github\\synthpop\\synthpop\\census_helpers.py\u001b[0m in \u001b[0;36m_query\u001b[1;34m(self, census_columns, state, county, forstr, tract, year)\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m \u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcounty\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtry_fips_lookup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcounty\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 72\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtract\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\juan\\documents\\github\\synthpop\\synthpop\\census_helpers.py\u001b[0m in \u001b[0;36mtry_fips_lookup\u001b[1;34m(self, state, county)\u001b[0m\n\u001b[0;32m 199\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 200\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mtry_fips_lookup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcounty\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 201\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_fips_lookup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 202\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 203\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcounty\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32mc:\\users\\juan\\documents\\github\\synthpop\\synthpop\\census_helpers.py\u001b[0m in \u001b[0;36m_get_fips_lookup\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 143\u001b[0m },\n\u001b[0;32m 144\u001b[0m index_col=[\"State\",\n\u001b[1;32m--> 145\u001b[1;33m \"County Name\"]\n\u001b[0m\u001b[0;32m 146\u001b[0m )\n\u001b[0;32m 147\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfips_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ANSI Cl\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m 707\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[0;32m 708\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 709\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 710\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 711\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 431\u001b[0m \u001b[0mcompression\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_infer_compression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 432\u001b[0m filepath_or_buffer, _, compression = get_filepath_or_buffer(\n\u001b[1;32m--> 433\u001b[1;33m filepath_or_buffer, encoding, compression)\n\u001b[0m\u001b[0;32m 434\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'compression'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression)\u001b[0m\n\u001b[0;32m 188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0m_is_url\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 190\u001b[1;33m \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_urlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 191\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Content-Encoding'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 192\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'gzip'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m 221\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 222\u001b[0m \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 223\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 224\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 225\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m 524\u001b[0m \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m 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result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[1;32m--> 544\u001b[1;33m '_open', req)\n\u001b[0m\u001b[0;32m 545\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 546\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m 502\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 503\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 504\u001b[1;33m 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1361\u001b[1;33m context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[0;32m 1362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1363\u001b[0m \u001b[0mhttps_request\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m 1319\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1320\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1321\u001b[1;33m \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1322\u001b[0m \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1323\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1329\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1330\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1331\u001b[1;33m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1332\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1333\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mbegin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 295\u001b[0m \u001b[1;31m# read until we get a non-100 response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 296\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 297\u001b[1;33m \u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 585\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 586\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 587\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 588\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m 1007\u001b[0m \u001b[1;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1008\u001b[0m self.__class__)\n\u001b[1;32m-> 1009\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1010\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1011\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\ssl.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m 869\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Read on closed or unwrapped SSL socket.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 870\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 871\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 872\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mSSLError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 873\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mSSL_ERROR_EOF\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\synpop_py3\\lib\\ssl.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m 629\u001b[0m \"\"\"\n\u001b[0;32m 630\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 631\u001b[1;33m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 632\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 633\u001b[0m \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "def synthesize_counties(counties):\n", + " for county in counties:\n", + " starter = Starter(os.environ[\"CENSUS\"], \"CA\", county)\n", + " synthesize_all(starter)\n", + "%time hh = synthesize_counties([\"Santa Clara County\", \"Solano County\", \"San Mateo County\", \"Marin County\", \"San Francisco County\", \"Napa County\", \"Sonoma County\", \"Contra Costa County\", \"Alameda County\"]) " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "starter = Starter(os.environ[\"CENSUS\"], \"CA\", \"Santa Clara County\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "def synthesize_counties(counties):\n", - " for county in counties:\n", - " starter = Starter(os.environ[\"CENSUS\"], \"CA\", county)\n", - " synthesize_all(starter)\n", - "%time hh = synthesize_counties([\"Santa Clara County\", \"Solano County\", \"San Mateo County\", \"Marin County\", \"San Francisco County\", \"Napa County\", \"Sonoma County\", \"Contra Costa County\", \"Alameda County\"]) " - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Synthesizing at geog level: 'block_group' (number of geographies is 1075)\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500100\n", - "block group 1\n", - "dtype: object\n", - "Drawing 216 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500100\n", - "block group 2\n", - "dtype: object\n", - "Drawing 510 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500100\n", - "block group 3\n", - "dtype: object\n", - "Drawing 641 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500100\n", - "block group 4\n", - "dtype: object\n", - "Drawing 672 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500200\n", - "block group 1\n", - "dtype: object\n", - "Drawing 454 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500200\n", - "block group 2\n", - "dtype: object\n", - "Drawing 0 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500200\n", - "block group 3\n", - "dtype: object\n", - "Drawing 570 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500200\n", - "block group 4\n", - "dtype: object\n", - "Drawing 1019 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500300\n", - "block group 1\n", - "dtype: object\n", - "Drawing 69 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500300\n", - "block group 2\n", - "dtype: object\n", - "Drawing 1143 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500400\n", - "block group 1\n", - "dtype: object\n", - "Drawing 503 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500400\n", - "block group 2\n", - "dtype: object\n", - "Drawing 476 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500500\n", - "block group 1\n", - "dtype: object\n", - "Drawing 359 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500500\n", - "block group 2\n", - "dtype: object\n", - "Drawing 604 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500500\n", - "block group 3\n", - "dtype: object\n", - "Drawing 222 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500500\n", - "block group 4\n", - "dtype: object\n", - "Drawing 399 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500500\n", - "block group 5\n", - "dtype: object\n", - "Drawing 305 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500600\n", - "block group 1\n", - "dtype: object\n", - "Drawing 1221 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500600\n", - "block group 2\n", - "dtype: object\n", - "Drawing 384 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500600\n", - "block group 3\n", - "dtype: object\n", - "Drawing 434 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500800\n", - "block group 1\n", - "dtype: object\n", - "Drawing 434 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500800\n", - "block group 2\n", - "dtype: object\n", - "Drawing 701 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500901\n", - "block group 1\n", - "dtype: object\n", - "Drawing 455 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500901\n", - "block group 2\n", - "dtype: object\n", - "Drawing 1294 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500902\n", - "block group 1\n", - "dtype: object\n", - "Drawing 249 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 500902\n", - "block group 2\n", - "dtype: object\n", - "Drawing 342 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501000\n", - "block group 1\n", - "dtype: object\n", - "Drawing 473 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501000\n", - "block group 2\n", - "dtype: object\n", - "Drawing 372 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501000\n", - "block group 3\n", - "dtype: object\n", - "Drawing 361 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501000\n", - "block group 4\n", - "dtype: object\n", - "Drawing 334 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501000\n", - "block group 5\n", - "dtype: object\n", - "Drawing 326 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501101\n", - "block group 1\n", - "dtype: object\n", - "Drawing 827 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501101\n", - "block group 2\n", - "dtype: object\n", - "Drawing 829 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501102\n", - "block group 1\n", - "dtype: object\n", - "Drawing 514 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501102\n", - "block group 2\n", - "dtype: object\n", - "Drawing 449 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501102\n", - "block group 3\n", - "dtype: object\n", - "Drawing 391 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501200\n", - "block group 1\n", - "dtype: object\n", - "Drawing 293 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501200\n", - "block group 2\n", - "dtype: object\n", - "Drawing 291 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501200\n", - "block group 3\n", - "dtype: object\n", - "Drawing 436 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501200\n", - "block group 4\n", - "dtype: object\n", - "Drawing 368 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501300\n", - "block group 1\n", - "dtype: object\n", - "Drawing 239 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501300\n", - "block group 2\n", - "dtype: object\n", - "Drawing 224 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 501300\n", - "block group 3\n", - "dtype: object\n", - "Drawing 510 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512307\n", - "block group 2\n", - "dtype: object\n", - "Drawing 912 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512307\n", - "block group 3\n", - "dtype: object\n", - "Drawing 887 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512308\n", - "block group 1\n", - "dtype: object\n", - "Drawing 546 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512308\n", - "block group 2\n", - "dtype: object\n", - "Drawing 902 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512308\n", - "block group 3\n", - "dtype: object\n", - "Drawing 828 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512309\n", - "block group 1\n", - "dtype: object\n", - "Drawing 936 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512309\n", - "block group 2\n", - "dtype: object\n", - "Drawing 772 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512310\n", - "block group 1\n", - "dtype: object\n", - "Drawing 969 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512401\n", - "block group 1\n", - "dtype: object\n", - "Drawing 921 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512401\n", - "block group 2\n", - "dtype: object\n", - "Drawing 642 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512402\n", - "block group 1\n", - "dtype: object\n", - "Drawing 830 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512402\n", - "block group 2\n", - "dtype: object\n", - "Drawing 357 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512402\n", - "block group 3\n", - "dtype: object\n", - "Drawing 433 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512503\n", - "block group 1\n", - "dtype: object\n", - "Drawing 1409 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512503\n", - "block group 2\n", - "dtype: object\n", - "Drawing 984 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512505\n", - "block group 1\n", - "dtype: object\n", - "Drawing 1185 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512509\n", - "block group 2\n", - "dtype: object\n", - "Drawing 474 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512510\n", - "block group 1\n", - "dtype: object\n", - "Drawing 905 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512510\n", - "block group 2\n", - "dtype: object\n", - "Drawing 446 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 085\n", - "tract 512510\n", - "block group 3\n", - "dtype: object\n", - "Drawing 683 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - 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geog id:\n", - "state 06\n", - "county 095\n", - "tract 250103\n", - "block group 1\n", - "dtype: object\n", - "Drawing 532 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 250103\n", - "block group 2\n", - "dtype: object\n", - "Drawing 524 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 250103\n", - "block group 3\n", - "dtype: object\n", - "Drawing 531 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 250104\n", - "block group 1\n", - "dtype: object\n", - "Drawing 341 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 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517 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 250106\n", - "block group 2\n", - "dtype: object\n", - "Drawing 790 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 250200\n", - "block group 1\n", - "dtype: object\n", - "Drawing 443 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 250200\n", - "block group 2\n", - "dtype: object\n", - "Drawing 348 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 250200\n", - "block group 3\n", - "dtype: object\n", - "Drawing 458 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 250300\n", - "block group 1\n", - "dtype: object\n", - "Drawing 305 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 250300\n", - "block group 2\n", - "dtype: object\n", - "Drawing 370 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 250300\n", - "block group 3\n", - "dtype: object\n", - "Drawing 391 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 250400\n", - "block group 1\n", - "dtype: object\n", - "Drawing 373 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog 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"\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 253402\n", - "block group 1\n", - "dtype: object\n", - "Drawing 575 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 253402\n", - "block group 2\n", - "dtype: object\n", - "Drawing 272 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 253402\n", - "block group 3\n", - "dtype: object\n", - "Drawing 207 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 253402\n", - "block group 4\n", - "dtype: object\n", - "Drawing 822 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 253500\n", - "block group 2\n", - "dtype: object\n", - "Drawing 531 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 253500\n", - "block group 3\n", - "dtype: object\n", - "Drawing 716 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 253500\n", - "block group 4\n", - "dtype: object\n", - "Drawing 582 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 095\n", - "tract 980000\n", - "block group 1\n", - "dtype: object\n", - "Drawing 0 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing at geog level: 'block_group' (number of geographies is 463)" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 600100\n", - "block group 1\n", - "dtype: object\n", - "Drawing 606 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 600100\n", - "block group 2\n", - "dtype: object\n", - "Drawing 898 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 600100\n", - "block group 3\n", - "dtype: object\n", - "Drawing 482 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 600200\n", - "block group 1\n", - "dtype: object\n", - "Drawing 442 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"\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 612800\n", - "block group 1\n", - "dtype: object\n", - "Drawing 632 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 612800\n", - "block group 2\n", - "dtype: object\n", - "Drawing 533 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 612900\n", - "block group 1\n", - "dtype: object\n", - "Drawing 571 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 612900\n", - "block group 2\n", - "dtype: object\n", - "Drawing 287 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 613300\n", - "block group 1\n", - "dtype: object\n", - "Drawing 511 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 613300\n", - "block group 2\n", - "dtype: object\n", - "Drawing 404 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 613400\n", - "block group 1\n", - "dtype: object\n", - "Drawing 362 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 613400\n", - "block group 2\n", - "dtype: object\n", - "Drawing 298 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 613400\n", - "block group 3\n", - "dtype: object\n", - "Drawing 564 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 613501\n", - "block group 1\n", - "dtype: object\n", - "Drawing 665 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 613501\n", - "block group 2\n", - "dtype: object\n", - "Drawing 668 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 081\n", - "tract 613502\n", - "block group 1\n", - "dtype: object\n", - "Drawing 683 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing at geog level: 'block_group' (number of geographies is 175)" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 041\n", - "tract 101100\n", - "block group 1\n", - "dtype: object\n", - "Drawing 984 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 041\n", - "tract 101200\n", - "block group 1\n", - "dtype: object\n", - "Drawing 1027 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 041\n", - "tract 102100\n", - "block group 1\n", - "dtype: object\n", - "Drawing 267 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 041\n", - "tract 102100\n", - 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"\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 041\n", - "tract 132100\n", - "block group 1\n", - "dtype: object\n", - "Drawing 255 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 041\n", - "tract 132100\n", - "block group 2\n", - "dtype: object\n", - "Drawing 472 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 041\n", - "tract 132200\n", - "block group 1\n", - "dtype: object\n", - "Drawing 263 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 041\n", - "tract 132200\n", - "block group 2\n", - "dtype: object\n", - "Drawing 342 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 047901\n", - "block group 1\n", - "dtype: object\n", - "Drawing 365 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 047901\n", - "block group 2\n", - "dtype: object\n", - "Drawing 777 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 047901\n", - "block group 3\n", - "dtype: object\n", - "Drawing 457 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 047901\n", - "block group 4\n", - "dtype: object\n", - "Drawing 495 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 060700\n", - "block group 1\n", - "dtype: object\n", - "Drawing 2947 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 060700\n", - "block group 2\n", - "dtype: object\n", - "Drawing 175 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 060700\n", - "block group 3\n", - "dtype: object\n", - "Drawing 440 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 061000\n", - "block group 1\n", - "dtype: object\n", - "Drawing 765 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 061000\n", - "block group 2\n", - "dtype: object\n", - "Drawing 433 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 061100\n", - "block group 1\n", - "dtype: object\n", - "Drawing 599 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 061100\n", - "block group 2\n", - "dtype: object\n", - "Drawing 1001 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 061100\n", - "block group 3\n", - "dtype: object\n", - "Drawing 560 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 061500\n", - "block group 5\n", - "dtype: object\n", - "Drawing 657 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 061500\n", - "block group 6\n", - "dtype: object\n", - "Drawing 1904 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 980200\n", - "block group 1\n", - "dtype: object\n", - "Drawing 76 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 980300\n", - "block group 1\n", - "dtype: object\n", - "Drawing 23 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 980401\n", - "block group 1\n", - "dtype: object\n", - "Drawing 0 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 980501\n", - "block group 1\n", - "dtype: object\n", - "Drawing 234 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 980600\n", - "block group 1\n", - "dtype: object\n", - "Drawing 109 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 075\n", - "tract 980900\n", - "block group 1\n", - "dtype: object\n", - "Drawing 177 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 200202\n", - "block group 1\n", - "dtype: object\n", - "Drawing 297 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 200202\n", - "block group 2\n", - "dtype: object\n", - "Drawing 151 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 200202\n", - "block group 3\n", - "dtype: object\n", - "Drawing 598 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 200203\n", - "block group 1\n", - "dtype: object\n", - "Drawing 420 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201102\n", - "block group 1\n", - "dtype: object\n", - "Drawing 477 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201102\n", - "block group 2\n", - "dtype: object\n", - "Drawing 326 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201200\n", - "block group 1\n", - "dtype: object\n", - "Drawing 257 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201200\n", - "block group 2\n", - "dtype: object\n", - "Drawing 393 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201200\n", - "block group 3\n", - "dtype: object\n", - "Drawing 812 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201200\n", - "block group 4\n", - "dtype: object\n", - "Drawing 487 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201300\n", - "block group 1\n", - "dtype: object\n", - "Drawing 660 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201300\n", - "block group 2\n", - "dtype: object\n", - "Drawing 568 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201500\n", - "block group 1\n", - "dtype: object\n", - "Drawing 415 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201500\n", - "block group 2\n", - "dtype: object\n", - "Drawing 380 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201601\n", - "block group 1\n", - "dtype: object\n", - "Drawing 473 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201601\n", - "block group 2\n", - "dtype: object\n", - "Drawing 539 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201602\n", - "block group 1\n", - "dtype: object\n", - "Drawing 502 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201602\n", - "block group 2\n", - "dtype: object\n", - "Drawing 271 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201602\n", - "block group 3\n", - "dtype: object\n", - "Drawing 174 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201602\n", - "block group 4\n", - "dtype: object\n", - "Drawing 617 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 201900\n", - "block group 2\n", - "dtype: object\n", - "Drawing 315 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 202000\n", - "block group 1\n", - "dtype: object\n", - "Drawing 487 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 202000\n", - "block group 2\n", - "dtype: object\n", - "Drawing 351 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 055\n", - "tract 202000\n", - "block group 3\n", - "dtype: object\n", - "Drawing 557 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 153807\n", - "block group 1\n", - "dtype: object\n", - "Drawing 439 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 153807\n", - "block group 2\n", - "dtype: object\n", - "Drawing 981 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 153808\n", - "block group 1\n", - "dtype: object\n", - "Drawing 631 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 153808\n", - "block group 2\n", - "dtype: object\n", - "Drawing 269 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 153808\n", - "block group 3\n", - "dtype: object\n", - "Drawing 552 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 153809\n", - "block group 1\n", - "dtype: object\n", - "Drawing 524 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 153809\n", - "block group 2\n", - "dtype: object\n", - "Drawing 604 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 153809\n", - "block group 3\n", - "dtype: object\n", - "Drawing 379 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 154302\n", - "block group 1\n", - "dtype: object\n", - "Drawing 444 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 154302\n", - "block group 2\n", - "dtype: object\n", - "Drawing 560 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 154302\n", - "block group 3\n", - "dtype: object\n", - "Drawing 255 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 154302\n", - "block group 4\n", - "dtype: object\n", - "Drawing 358 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 154303\n", - "block group 1\n", - "dtype: object\n", - "Drawing 321 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 154303\n", - "block group 2\n", - "dtype: object\n", - "Drawing 265 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 154304\n", - "block group 1\n", - "dtype: object\n", - "Drawing 286 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 097\n", - "tract 154304\n", - "block group 2\n", - "dtype: object\n", - "Drawing 215 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 301000\n", - "block group 3\n", - "dtype: object\n", - "Drawing 456 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 302005\n", - "block group 1\n", - "dtype: object\n", - "Drawing 612 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 302005\n", - "block group 2\n", - "dtype: object\n", - "Drawing 539 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 302005\n", - "block group 3\n", - "dtype: object\n", - "Drawing 652 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog 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"\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 383000\n", - "block group 1\n", - "dtype: object\n", - "Drawing 656 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 383000\n", - "block group 2\n", - "dtype: object\n", - "Drawing 623 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 383000\n", - "block group 3\n", - "dtype: object\n", - "Drawing 729 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 384000\n", - "block group 1\n", - "dtype: object\n", - "Drawing 407 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 387000\n", - "block group 3\n", - "dtype: object\n", - "Drawing 457 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 388000\n", - "block group 1\n", - "dtype: object\n", - "Drawing 657 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 388000\n", - "block group 2\n", - "dtype: object\n", - "Drawing 467 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 389100\n", - "block group 1\n", - "dtype: object\n", - "Drawing 433 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 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"output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 391000\n", - "block group 2\n", - "dtype: object\n", - "Drawing 284 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 392000\n", - "block group 1\n", - "dtype: object\n", - "Drawing 429 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 392000\n", - "block group 2\n", - "dtype: object\n", - "Drawing 337 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "\n", - "Synthesizing geog id:\n", - "state 06\n", - "county 013\n", - "tract 392000\n", - "block group 3\n", - "dtype: object\n", - "Drawing 299 households" - ] - }, - { - "output_type": "stream", - "stream": "stdout", - "text": [ - 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2 2012000986994 H 9-9 08502 4 06 1000000 1010207 0 1 2NaNNaNNaNNaNNaNNaN NaN NaN...
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884 rows \u00d7 208 columns

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08502 4 06 1000000 1010207 \n", - "15 2012001364469 H 9 -9 08502 4 06 1000000 1010207 \n", - "16 2012001423109 H 9 -9 08502 4 06 1000000 1010207 \n", - "17 2012000701130 H 9 -9 08502 4 06 1000000 1010207 \n", - "18 2012001257671 H 9 -9 08502 4 06 1000000 1010207 \n", - "19 2012000045328 H 9 -9 08502 4 06 1000000 1010207 \n", - "20 2012001404309 H 9 -9 08502 4 06 1000000 1010207 \n", - "21 2012000953469 H 9 -9 08502 4 06 1000000 1010207 \n", - "22 2012000497819 H 9 -9 08502 4 06 1000000 1010207 \n", - "23 2012000799359 H 9 -9 08502 4 06 1000000 1010207 \n", - "24 2012000785569 H 9 -9 08502 4 06 1000000 1010207 \n", - "25 2012000953469 H 9 -9 08502 4 06 1000000 1010207 \n", - "26 2012000907429 H 9 -9 08502 4 06 1000000 1010207 \n", - "27 2012000701130 H 9 -9 08502 4 06 1000000 1010207 \n", - "28 2012001287323 H 9 -9 08502 4 06 1000000 1010207 \n", - "29 2012000361299 H 9 -9 08502 4 06 1000000 1010207 \n", - "30 2012000776236 H 9 -9 08502 4 06 1000000 1010207 \n", - "31 2012000962449 H 9 -9 08502 4 06 1000000 1010207 \n", - "32 2012000292794 H 9 -9 08502 4 06 1000000 1010207 \n", - "33 2012000283763 H 9 -9 08502 4 06 1000000 1010207 \n", - "34 2012000045328 H 9 -9 08502 4 06 1000000 1010207 \n", - "35 2012000277942 H 9 -9 08502 4 06 1000000 1010207 \n", - "36 2012001074624 H 9 -9 08502 4 06 1000000 1010207 \n", - "37 2012001263039 H 9 -9 08502 4 06 1000000 1010207 \n", - "38 2012000701130 H 9 -9 08502 4 06 1000000 1010207 \n", - "39 2012000361299 H 9 -9 08502 4 06 1000000 1010207 \n", - "40 2012000340975 H 9 -9 08502 4 06 1000000 1010207 \n", - "41 2012000460363 H 9 -9 08502 4 06 1000000 1010207 \n", - "42 2012001455410 H 9 -9 08502 4 06 1000000 1010207 \n", - "43 2012000157508 H 9 -9 08502 4 06 1000000 1010207 \n", - "44 2012000338112 H 9 -9 08502 4 06 1000000 1010207 \n", - "45 2012001257671 H 9 -9 08502 4 06 1000000 1010207 \n", - "46 2012000878700 H 9 -9 08502 4 06 1000000 1010207 \n", - "47 2012000283763 H 9 -9 08502 4 06 1000000 1010207 \n", - "48 2012000949893 H 9 -9 08502 4 06 1000000 1010207 \n", - "49 2012001263039 H 9 -9 08502 4 06 1000000 1010207 \n", - "50 2012001423109 H 9 -9 08502 4 06 1000000 1010207 \n", - "51 2012001263039 H 9 -9 08502 4 06 1000000 1010207 \n", - "52 2012000346149 H 9 -9 08502 4 06 1000000 1010207 \n", - "53 2012000361299 H 9 -9 08502 4 06 1000000 1010207 \n", - "54 2012000251020 H 9 -9 08502 4 06 1000000 1010207 \n", - "55 2012000784886 H 9 -9 08502 4 06 1000000 1010207 \n", - "56 2012000045328 H 9 -9 08502 4 06 1000000 1010207 \n", - "57 2012001085408 H 9 -9 08502 4 06 1000000 1010207 \n", - "58 2012000361299 H 9 -9 08502 4 06 1000000 1010207 \n", - "59 2012001455410 H 9 -9 08502 4 06 1000000 1010207 \n", - " ... .. ... ... ... ... ... ... ... \n", - "\n", - " WGTP NP TYPE ACR AGS BATH BDSP BLD BUS CONP ELEP \n", - "0 31 3 1 1 NaN 1 3 3 2 0 400 ... \n", - "1 25 1 1 1 NaN 1 3 3 2 320 40 ... \n", - "2 0 1 2 NaN NaN NaN NaN NaN NaN NaN NaN ... \n", - "3 15 3 1 1 NaN 1 3 2 2 0 60 ... \n", - "4 22 3 1 NaN NaN 1 2 9 NaN 0 120 ... \n", - "5 24 1 1 1 NaN 1 3 3 2 0 2 ... \n", - "6 17 3 1 NaN NaN 1 1 5 NaN 0 170 ... \n", - "7 12 4 1 1 NaN 1 3 2 2 0 100 ... \n", - "8 11 3 1 NaN NaN 1 1 9 NaN 0 30 ... \n", - "9 11 1 1 1 NaN 1 1 1 2 0 20 ... \n", - "10 15 2 1 1 NaN 1 3 2 2 0 70 ... \n", - "11 22 2 1 1 NaN 1 2 1 2 0 30 ... \n", - "12 21 4 1 NaN NaN 1 3 6 NaN 320 180 ... \n", - "13 24 1 1 NaN NaN 1 1 9 NaN 0 30 ... \n", - "14 16 3 1 1 NaN 1 3 2 2 0 20 ... \n", - "15 23 4 1 1 NaN 1 4 3 2 210 250 ... \n", - "16 20 1 1 NaN NaN 1 0 6 NaN 0 1 ... \n", - "17 34 1 1 1 NaN 1 2 1 2 0 40 ... \n", - "18 16 2 1 1 NaN 1 2 1 2 0 40 ... \n", - "19 26 3 1 NaN NaN 1 2 8 NaN 0 20 ... \n", - "20 16 3 1 NaN NaN 1 2 9 NaN 0 50 ... \n", - "21 25 1 1 NaN NaN 1 1 9 NaN 0 1 ... \n", - "22 22 2 1 1 NaN 1 2 1 2 0 30 ... \n", - "23 59 4 1 NaN NaN 1 1 8 NaN 0 30 ... \n", - "24 12 4 1 1 NaN 1 3 2 2 0 100 ... \n", - "25 25 1 1 NaN NaN 1 1 9 NaN 0 1 ... \n", - "26 22 3 1 NaN NaN 1 2 9 NaN 0 120 ... \n", - "27 34 1 1 1 NaN 1 2 1 2 0 40 ... \n", - "28 20 2 1 1 NaN 1 2 1 2 0 150 ... \n", - "29 16 3 1 1 NaN 1 3 2 2 0 20 ... \n", - "30 17 3 1 NaN NaN 1 1 5 NaN 0 170 ... \n", - "31 15 4 1 1 NaN 1 3 2 2 0 160 ... \n", - "32 6 4 1 1 NaN 1 4 2 2 0 180 ... \n", - "33 24 1 1 1 NaN 1 3 3 2 0 2 ... \n", - "34 26 3 1 NaN NaN 1 2 8 NaN 0 20 ... \n", - "35 14 3 1 1 NaN 1 2 1 2 0 1 ... \n", - "36 15 3 1 1 NaN 1 2 2 2 0 70 ... \n", - "37 15 3 1 1 NaN 1 3 2 2 0 80 ... \n", - "38 34 1 1 1 NaN 1 2 1 2 0 40 ... \n", - "39 16 3 1 1 NaN 1 3 2 2 0 20 ... \n", - "40 15 2 1 1 NaN 1 3 2 2 0 70 ... \n", - "41 16 3 1 NaN NaN 1 2 8 NaN 0 30 ... \n", - "42 7 2 1 1 NaN 1 3 2 2 0 70 ... \n", - "43 8 2 1 1 NaN 1 4 2 2 0 60 ... \n", - "44 19 2 1 1 NaN 1 3 2 2 0 100 ... \n", - "45 16 2 1 1 NaN 1 2 1 2 0 40 ... \n", - "46 11 3 1 NaN NaN 1 1 9 NaN 0 30 ... \n", - "47 24 1 1 1 NaN 1 3 3 2 0 2 ... \n", - "48 14 3 1 1 NaN 1 3 2 2 0 80 ... \n", - "49 15 3 1 1 NaN 1 3 2 2 0 80 ... \n", - "50 20 1 1 NaN NaN 1 0 6 NaN 0 1 ... \n", - "51 15 3 1 1 NaN 1 3 2 2 0 80 ... \n", - "52 49 3 1 NaN NaN 1 2 6 NaN 0 120 ... \n", - "53 16 3 1 1 NaN 1 3 2 2 0 20 ... \n", - "54 31 3 1 1 NaN 1 3 3 2 0 400 ... \n", - "55 21 4 1 NaN NaN 1 3 6 NaN 320 180 ... \n", - "56 26 3 1 NaN NaN 1 2 8 NaN 0 20 ... \n", - "57 21 2 1 NaN NaN 1 1 9 NaN 0 120 ... \n", - "58 16 3 1 1 NaN 1 3 2 2 0 20 ... \n", - "59 7 2 1 1 NaN 1 3 2 2 0 70 ... \n", - " ... ... ... ... ... ... ... ... ... ... ... \n", - "\n", - "[884 rows x 208 columns]" - ] - } - ], - "prompt_number": 6 + "name": "stdout", + "output_type": "stream", + "text": [ + "Drawing 770 households\n" + ] }, { - "cell_type": "code", - "collapsed": false, - "input": [ - "hh.describe()" - ], - "language": "python", + "data": { + "text/plain": [ + "( serialno RT puma00 puma10 NP TYPE BLD TEN VEH HINCP \\\n", + " 0 2009001096088 H 2702 -9 4 1 3.0 3.0 0.0 226400.0 \n", + " 1 2009000797707 H 2702 -9 1 1 6.0 3.0 1.0 120000.0 \n", + " 2 2009000776447 H 2702 -9 1 1 9.0 3.0 1.0 187000.0 \n", + " 3 2009000034504 H 2702 -9 1 1 7.0 3.0 1.0 416000.0 \n", + " 4 2012000837531 H -9 8502 1 1 8.0 3.0 1.0 57000.0 \n", + " 5 2013000119247 H -9 8502 1 1 3.0 1.0 1.0 92000.0 \n", + " 6 2010000191477 H 2702 -9 1 1 3.0 4.0 1.0 390.0 \n", + " 7 2013000718037 H -9 8502 1 1 8.0 3.0 1.0 15500.0 \n", + " 8 2010000285540 H 2702 -9 2 1 5.0 3.0 1.0 0.0 \n", + " 9 2010000337473 H 2702 -9 4 1 8.0 3.0 1.0 32900.0 \n", + " 10 2010001167857 H 2702 -9 3 1 5.0 3.0 1.0 81200.0 \n", + " 11 2009001316324 H 2702 -9 2 1 5.0 3.0 1.0 4200.0 \n", + " 12 2009001316324 H 2702 -9 2 1 5.0 3.0 1.0 4200.0 \n", + " 13 2010000309566 H 2702 -9 3 1 3.0 3.0 2.0 110990.0 \n", + " 14 2009000875809 H 2702 -9 4 1 2.0 1.0 5.0 202000.0 \n", + " 15 2009000847427 H 2702 -9 3 1 2.0 1.0 3.0 192000.0 \n", + " 16 2009000842508 H 2702 -9 2 1 2.0 2.0 2.0 168000.0 \n", + " 17 2009001302282 H 2702 -9 2 1 3.0 2.0 2.0 150000.0 \n", + " 18 2009000938159 H 2702 -9 2 1 9.0 3.0 2.0 169500.0 \n", + " 19 2009000938159 H 2702 -9 2 1 9.0 3.0 2.0 169500.0 \n", + " 20 2009001025363 H 2702 -9 2 1 2.0 2.0 2.0 182000.0 \n", + " 21 2009001025363 H 2702 -9 2 1 2.0 2.0 2.0 182000.0 \n", + " 22 2010001128154 H 2702 -9 2 1 4.0 3.0 2.0 170000.0 \n", + " 23 2010001128154 H 2702 -9 2 1 4.0 3.0 2.0 170000.0 \n", + " 24 2010001128154 H 2702 -9 2 1 4.0 3.0 2.0 170000.0 \n", + " 25 2010001128154 H 2702 -9 2 1 4.0 3.0 2.0 170000.0 \n", + " 26 2012000917686 H -9 8502 2 1 2.0 1.0 2.0 280000.0 \n", + " 27 2012000917686 H -9 8502 2 1 2.0 1.0 2.0 280000.0 \n", + " 28 2012000917686 H -9 8502 2 1 2.0 1.0 2.0 280000.0 \n", + " 29 2012000917686 H -9 8502 2 1 2.0 1.0 2.0 280000.0 \n", + " .. ... .. ... ... .. ... ... ... ... ... \n", + " 740 2009000257552 H 2702 -9 3 1 5.0 3.0 2.0 156000.0 \n", + " 741 2010000224153 H 2702 -9 2 1 9.0 3.0 2.0 69000.0 \n", + " 742 2011000027489 H 2702 -9 2 1 4.0 3.0 2.0 33600.0 \n", + " 743 2010000554995 H 2702 -9 3 1 8.0 3.0 2.0 64200.0 \n", + " 744 2009000090585 H 2702 -9 2 1 9.0 3.0 1.0 36000.0 \n", + " 745 2009000991840 H 2702 -9 2 1 2.0 2.0 3.0 113000.0 \n", + " 746 2010001199295 H 2702 -9 1 1 6.0 3.0 1.0 110600.0 \n", + " 747 2010000531956 H 2702 -9 2 1 1.0 1.0 2.0 74000.0 \n", + " 748 2009000209747 H 2702 -9 2 1 2.0 3.0 2.0 142000.0 \n", + " 749 2011000989438 H 2702 -9 1 1 2.0 1.0 1.0 237400.0 \n", + " 750 2011000990235 H 2702 -9 3 1 1.0 2.0 3.0 151000.0 \n", + " 751 2012000497867 H -9 8502 2 1 2.0 2.0 3.0 136200.0 \n", + " 752 2013000393885 H -9 8502 1 1 2.0 3.0 5.0 69000.0 \n", + " 753 2009001089483 H 2702 -9 3 1 2.0 2.0 2.0 129850.0 \n", + " 754 2009000928777 H 2702 -9 2 1 2.0 2.0 3.0 188500.0 \n", + " 755 2009000935893 H 2702 -9 2 1 2.0 2.0 1.0 16500.0 \n", + " 756 2010000789913 H 2702 -9 2 1 9.0 3.0 2.0 60200.0 \n", + " 757 2012000643381 H -9 8502 4 1 5.0 3.0 2.0 251000.0 \n", + " 758 2011001475367 H 2702 -9 6 1 9.0 3.0 2.0 57000.0 \n", + " 759 2012000603952 H -9 8502 8 1 2.0 3.0 3.0 99230.0 \n", + " 760 2010000011387 H 2702 -9 2 1 2.0 1.0 1.0 220300.0 \n", + " 761 2010001283036 H 2702 -9 2 1 2.0 1.0 2.0 51001.0 \n", + " 762 2010001108709 H 2702 -9 2 1 2.0 1.0 2.0 77700.0 \n", + " 763 2013001259539 H -9 8502 1 1 3.0 3.0 2.0 20000.0 \n", + " 764 2011000656960 H 2702 -9 3 1 2.0 3.0 2.0 70000.0 \n", + " 765 2009000208025 H 2702 -9 2 1 8.0 3.0 0.0 54100.0 \n", + " 766 2011000236562 H 2702 -9 4 1 2.0 1.0 4.0 53300.0 \n", + " 767 2010000805810 H 2702 -9 3 1 2.0 2.0 2.0 1000.0 \n", + " 768 2010000255137 H 2702 -9 1 1 9.0 3.0 1.0 1800.0 \n", + " 769 2013000496979 H -9 8502 2 1 3.0 1.0 3.0 375304.0 \n", + " \n", + " ... hh_size hh_workers seniors sf_detached \\\n", + " 0 ... four or more two or more no no \n", + " 1 ... one one no no \n", + " 2 ... one one no no \n", + " 3 ... one one no no \n", + " 4 ... one one no no \n", + " 5 ... one one no no \n", + " 6 ... one none no no \n", + " 7 ... one one no no \n", + " 8 ... two none no no \n", + " 9 ... four or more one yes no \n", + " 10 ... three one no no \n", + " 11 ... two none no no \n", + " 12 ... two none no no \n", + " 13 ... three one no no \n", + " 14 ... four or more two or more no yes \n", + " 15 ... three two or more no yes \n", + " 16 ... two none no yes \n", + " 17 ... two one no no \n", + " 18 ... two one no no \n", + " 19 ... two one no no \n", + " 20 ... two one no yes \n", + " 21 ... two one no yes \n", + " 22 ... two two or more no no \n", + " 23 ... two two or more no no \n", + " 24 ... two two or more no no \n", + " 25 ... two two or more no no \n", + " 26 ... two two or more no yes \n", + " 27 ... two two or more no yes \n", + " 28 ... two two or more no yes \n", + " 29 ... two two or more no yes \n", + " .. ... ... ... ... ... \n", + " 740 ... three one no no \n", + " 741 ... two one no no \n", + " 742 ... two one no no \n", + " 743 ... three one no no \n", + " 744 ... two one no no \n", + " 745 ... two one no yes \n", + " 746 ... one one no no \n", + " 747 ... two two or more no no \n", + " 748 ... two one yes yes \n", + " 749 ... one one no yes \n", + " 750 ... three one yes no \n", + " 751 ... two none yes yes \n", + " 752 ... one none yes yes \n", + " 753 ... three two or more no yes \n", + " 754 ... two one yes yes \n", + " 755 ... two one no yes \n", + " 756 ... two two or more yes no \n", + " 757 ... four or more two or more no no \n", + " 758 ... four or more one no no \n", + " 759 ... four or more two or more no yes \n", + " 760 ... two two or more no yes \n", + " 761 ... two two or more yes yes \n", + " 762 ... two none yes yes \n", + " 763 ... one one no no \n", + " 764 ... three two or more no yes \n", + " 765 ... two one yes no \n", + " 766 ... four or more one no yes \n", + " 767 ... three none no yes \n", + " 768 ... one none yes no \n", + " 769 ... two two or more no no \n", + " \n", + " tenure_mover cat_id state county tract block group \n", + " 0 rent not recent 9282 06 085 508203 3 \n", + " 1 rent not recent 15490 06 085 508203 3 \n", + " 2 rent not recent 17026 06 085 508203 3 \n", + " 3 rent not recent 18178 06 085 508203 3 \n", + " 4 rent not recent 18562 06 085 508203 3 \n", + " 5 own not recent 20096 06 085 508203 3 \n", + " 6 rent not recent 21602 06 085 508203 3 \n", + " 7 rent not recent 21634 06 085 508203 3 \n", + " 8 rent not recent 21794 06 085 508203 3 \n", + " 9 rent not recent 26154 06 085 508203 3 \n", + " 10 rent not recent 27874 06 085 508203 3 \n", + " 11 rent not recent 29474 06 085 508203 3 \n", + " 12 rent not recent 29474 06 085 508203 3 \n", + " 13 rent not recent 30946 06 085 508203 3 \n", + " 14 own not recent 32324 06 085 508203 3 \n", + " 15 own not recent 32516 06 085 508203 3 \n", + " 16 own not recent 32548 06 085 508203 3 \n", + " 17 own not recent 32576 06 085 508203 3 \n", + " 18 rent not recent 32578 06 085 508203 3 \n", + " 19 rent not recent 32578 06 085 508203 3 \n", + " 20 own not recent 32580 06 085 508203 3 \n", + " 21 own not recent 32580 06 085 508203 3 \n", + " 22 rent not recent 32610 06 085 508203 3 \n", + " 23 rent not recent 32610 06 085 508203 3 \n", + " 24 rent not recent 32610 06 085 508203 3 \n", + " 25 rent not recent 32610 06 085 508203 3 \n", + " 26 own not recent 32612 06 085 508203 3 \n", + " 27 own not recent 32612 06 085 508203 3 \n", + " 28 own not recent 32612 06 085 508203 3 \n", + " 29 own not recent 32612 06 085 508203 3 \n", + " .. ... ... ... ... ... ... \n", + " 740 rent not recent 132322 06 085 508203 3 \n", + " 741 rent not recent 36802 06 085 508203 3 \n", + " 742 rent not recent 127426 06 085 508203 3 \n", + " 743 rent not recent 43618 06 085 508203 3 \n", + " 744 rent recent 110915 06 085 508203 3 \n", + " 745 own not recent 31044 06 085 508203 3 \n", + " 746 rent not recent 108802 06 085 508203 3 \n", + " 747 own not recent 36832 06 085 508203 3 \n", + " 748 rent not recent 78286 06 085 508203 3 \n", + " 749 own not recent 18180 06 085 508203 3 \n", + " 750 own not recent 79736 06 085 508203 3 \n", + " 751 own not recent 78252 06 085 508203 3 \n", + " 752 rent not recent 82670 06 085 508203 3 \n", + " 753 own not recent 38660 06 085 508203 3 \n", + " 754 own not recent 79820 06 085 508203 3 \n", + " 755 own not recent 21828 06 085 508203 3 \n", + " 756 rent not recent 82154 06 085 508203 3 \n", + " 757 rent recent 132163 06 085 508203 3 \n", + " 758 rent not recent 42674 06 085 508203 3 \n", + " 759 rent not recent 44230 06 085 508203 3 \n", + " 760 own not recent 18404 06 085 508203 3 \n", + " 761 own not recent 81388 06 085 508203 3 \n", + " 762 own not recent 36780 06 085 508203 3 \n", + " 763 rent not recent 38146 06 085 508203 3 \n", + " 764 rent not recent 136598 06 085 508203 3 \n", + " 765 rent not recent 49482 06 085 508203 3 \n", + " 766 own not recent 33828 06 085 508203 3 \n", + " 767 own not recent 37060 06 085 508203 3 \n", + " 768 rent not recent 68858 06 085 508203 3 \n", + " 769 own not recent 124768 06 085 508203 3 \n", + " \n", + " [770 rows x 32 columns],\n", + " serialno puma00 puma10 AGEP RELP SEX ESR HISP RAC1P hispanic \\\n", + " 0 2012000024049 -9 8502 47 1 2 6.0 1 6 no \n", + " 1 2012000024049 -9 8502 47 1 2 6.0 1 6 no \n", + " 2 2012000024049 -9 8502 47 1 2 6.0 1 6 no \n", + " 3 2012000024049 -9 8502 53 0 1 1.0 1 6 no \n", + " 4 2012000024049 -9 8502 53 0 1 1.0 1 6 no \n", + " 5 2012000024049 -9 8502 53 0 1 1.0 1 6 no \n", + " 6 2012000024049 -9 8502 17 2 1 6.0 1 6 no \n", + " 7 2012000024049 -9 8502 17 2 1 6.0 1 6 no \n", + " 8 2012000024049 -9 8502 17 2 1 6.0 1 6 no \n", + " 9 2012000024049 -9 8502 6 2 2 NaN 1 6 no \n", + " 10 2012000024049 -9 8502 6 2 2 NaN 1 6 no \n", + " 11 2012000024049 -9 8502 6 2 2 NaN 1 6 no \n", + " 12 2012000038026 -9 8502 37 1 2 3.0 1 6 no \n", + " 13 2012000038026 -9 8502 48 0 1 1.0 1 6 no \n", + " 14 2012000038026 -9 8502 6 2 1 NaN 1 6 no \n", + " 15 2012000038026 -9 8502 4 2 2 NaN 1 6 no \n", + " 16 2012000076386 -9 8502 43 0 2 6.0 1 6 no \n", + " 17 2012000076386 -9 8502 43 0 2 6.0 1 6 no \n", + " 18 2012000076386 -9 8502 43 0 2 6.0 1 6 no \n", + " 19 2012000076386 -9 8502 46 1 1 1.0 1 6 no \n", + " 20 2012000076386 -9 8502 46 1 1 1.0 1 6 no \n", + " 21 2012000076386 -9 8502 46 1 1 1.0 1 6 no \n", + " 22 2012000076386 -9 8502 12 2 1 NaN 1 6 no \n", + " 23 2012000076386 -9 8502 12 2 1 NaN 1 6 no \n", + " 24 2012000076386 -9 8502 12 2 1 NaN 1 6 no \n", + " 25 2012000076386 -9 8502 16 2 2 6.0 1 6 no \n", + " 26 2012000076386 -9 8502 16 2 2 6.0 1 6 no \n", + " 27 2012000076386 -9 8502 16 2 2 6.0 1 6 no \n", + " 28 2012000176039 -9 8502 58 1 2 1.0 1 6 no \n", + " 29 2012000176039 -9 8502 27 2 1 1.0 1 6 no \n", + " ... ... ... ... ... ... ... ... ... ... ... \n", + " 2244 2009001006831 2702 -9 34 0 1 1.0 2 1 yes \n", + " 2245 2009001006831 2702 -9 4 2 1 NaN 2 1 yes \n", + " 2246 2009001006831 2702 -9 4 2 1 NaN 2 1 yes \n", + " 2247 2009001006831 2702 -9 2 2 2 NaN 2 1 yes \n", + " 2248 2009001006831 2702 -9 2 2 2 NaN 2 1 yes \n", + " 2249 2011000656960 2702 -9 26 1 2 1.0 2 1 yes \n", + " 2250 2011000656960 2702 -9 26 1 2 1.0 2 1 yes \n", + " 2251 2011000656960 2702 -9 27 0 1 1.0 2 1 yes \n", + " 2252 2011000656960 2702 -9 27 0 1 1.0 2 1 yes \n", + " 2253 2011000656960 2702 -9 1 2 1 NaN 2 1 yes \n", + " 2254 2011000656960 2702 -9 1 2 1 NaN 2 1 yes \n", + " 2255 2013000583586 -9 8502 34 0 1 1.0 2 1 yes \n", + " 2256 2011000482661 2702 -9 34 2 1 1.0 2 1 yes \n", + " 2257 2011000482661 2702 -9 67 0 1 6.0 2 1 yes \n", + " 2258 2011000482661 2702 -9 8 7 2 NaN 2 1 yes \n", + " 2259 2011000482661 2702 -9 73 1 2 6.0 2 1 yes \n", + " 2260 2010000789913 2702 -9 60 1 2 1.0 1 2 no \n", + " 2261 2010000789913 2702 -9 68 0 1 1.0 1 2 no \n", + " 2262 2012000306986 -9 8502 83 0 1 6.0 1 6 no \n", + " 2263 2013001102796 -9 8502 74 0 1 6.0 1 6 no \n", + " 2264 2009000183391 2702 -9 63 0 1 1.0 1 6 no \n", + " 2265 2012001080872 -9 8502 93 0 1 6.0 2 1 yes \n", + " 2266 2012001080872 -9 8502 80 1 2 6.0 2 1 yes \n", + " 2267 2012001247807 -9 8502 88 0 1 6.0 3 1 yes \n", + " 2268 2012001247807 -9 8502 93 1 2 6.0 3 1 yes \n", + " 2269 2013000619824 -9 8502 77 1 1 6.0 2 1 yes \n", + " 2270 2013000619824 -9 8502 77 0 2 6.0 2 1 yes \n", + " 2271 2010000396495 2702 -9 61 0 1 1.0 20 1 yes \n", + " 2272 2010000396495 2702 -9 61 1 2 1.0 20 1 yes \n", + " 2273 2010000255137 2702 -9 84 0 2 6.0 3 1 yes \n", + " \n", + " person_age person_sex race cat_id hh_id \n", + " 0 35 to 60 female asian 138256 98 \n", + " 1 35 to 60 female asian 138256 106 \n", + " 2 35 to 60 female asian 138256 107 \n", + " 3 35 to 60 male asian 138260 98 \n", + " 4 35 to 60 male asian 138260 106 \n", + " 5 35 to 60 male asian 138260 107 \n", + " 6 19 and under male asian 138244 98 \n", + " 7 19 and under male asian 138244 106 \n", + " 8 19 and under male asian 138244 107 \n", + " 9 19 and under female asian 138240 98 \n", + " 10 19 and under female asian 138240 106 \n", + " 11 19 and under female asian 138240 107 \n", + " 12 35 to 60 female asian 138256 54 \n", + " 13 35 to 60 male asian 138260 54 \n", + " 14 19 and under male asian 138244 54 \n", + " 15 19 and under female asian 138240 54 \n", + " 16 35 to 60 female asian 138256 75 \n", + " 17 35 to 60 female asian 138256 80 \n", + " 18 35 to 60 female asian 138256 84 \n", + " 19 35 to 60 male asian 138260 75 \n", + " 20 35 to 60 male asian 138260 80 \n", + " 21 35 to 60 male asian 138260 84 \n", + " 22 19 and under male asian 138244 75 \n", + " 23 19 and under male asian 138244 80 \n", + " 24 19 and under male asian 138244 84 \n", + " 25 19 and under female asian 138240 75 \n", + " 26 19 and under female asian 138240 80 \n", + " 27 19 and under female asian 138240 84 \n", + " 28 35 to 60 female asian 138256 631 \n", + " 29 20 to 35 male asian 138252 631 \n", + " ... ... ... ... ... ... \n", + " 2244 20 to 35 male white 138287 607 \n", + " 2245 19 and under male white 138279 356 \n", + " 2246 19 and under male white 138279 607 \n", + " 2247 19 and under female white 138275 356 \n", + " 2248 19 and under female white 138275 607 \n", + " 2249 20 to 35 female white 138283 447 \n", + " 2250 20 to 35 female white 138283 764 \n", + " 2251 20 to 35 male white 138287 447 \n", + " 2252 20 to 35 male white 138287 764 \n", + " 2253 19 and under male white 138279 447 \n", + " 2254 19 and under male white 138279 764 \n", + " 2255 20 to 35 male white 138287 564 \n", + " 2256 20 to 35 male white 138287 718 \n", + " 2257 above 60 male white 138303 718 \n", + " 2258 19 and under female white 138275 718 \n", + " 2259 above 60 female white 138299 718 \n", + " 2260 35 to 60 female black 138257 756 \n", + " 2261 above 60 male black 138269 756 \n", + " 2262 above 60 male asian 138268 571 \n", + " 2263 above 60 male asian 138268 452 \n", + " 2264 above 60 male asian 138268 701 \n", + " 2265 above 60 male white 138303 563 \n", + " 2266 above 60 female white 138299 563 \n", + " 2267 above 60 male white 138303 574 \n", + " 2268 above 60 female white 138299 574 \n", + " 2269 above 60 male white 138303 533 \n", + " 2270 above 60 female white 138299 533 \n", + " 2271 above 60 male white 138303 713 \n", + " 2272 above 60 female white 138299 713 \n", + " 2273 above 60 female white 138299 768 \n", + " \n", + " [2274 rows x 15 columns],\n", + " {BlockGroupID(state='06', county='085', tract='508203', block_group='3'): FitQuality(people_chisq=50.53639934882075, people_p=0.37362753869683724)})" + ] + }, + "execution_count": 5, "metadata": {}, - "outputs": [] + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "ind = pd.Series([\"06\", \"085\", \"508203\", \"3\"], index=[\"state\", \"county\", \"tract\", \"block group\"])\n", + "synthesize_all(starter, indexes=[ind])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/demos/synthesize.py b/demos/synthesize.py index 580b5f0..f70a589 100644 --- a/demos/synthesize.py +++ b/demos/synthesize.py @@ -1,7 +1,7 @@ # coding: utf-8 -from synthpop.recipes.starter import Starter +from synthpop.recipes.starter2 import Starter from synthpop.synthesizer import synthesize_all import pandas as pd import os diff --git a/synthpop/categorizer.py b/synthpop/categorizer.py index ca108cd..020e9f2 100644 --- a/synthpop/categorizer.py +++ b/synthpop/categorizer.py @@ -8,7 +8,7 @@ def categorize(df, eval_d, index_cols=None): cat_df = pd.DataFrame(index=df.index) - for index, expr in eval_d.iteritems(): + for index, expr in eval_d.items(): cat_df[index] = df.eval(expr) if index_cols is not None: @@ -40,18 +40,18 @@ def sum_accross_category(df, subtract_mean=True): def category_combinations(index): """ THis method converts a hierarchical multindex of category names and - category values and converts to the cross-product of all possible + category values into the cross-product of all possible category combinations. """ d = {} for cat_name, cat_value in index: d.setdefault(cat_name, []) d[cat_name].append(cat_value) - for cat_name in d.keys(): + for cat_name in list(d): #make a static list of the keys if len(d[cat_name]) == 1: del d[cat_name] - df = pd.DataFrame(list(itertools.product(*d.values()))) - df.columns = cols = d.keys() + df = pd.DataFrame(list(itertools.product(*list(d.values())))) + df.columns = cols = list(d.keys()) df.index.name = "cat_id" df = df.reset_index().set_index(cols) return df @@ -62,7 +62,7 @@ def joint_distribution(sample_df, category_df, mapping_functions=None): # set counts to zero category_df["frequency"] = 0 - category_names = category_df.index.names + category_names = list (category_df.index.names) if mapping_functions: for name in category_names: assert name in mapping_functions, "Every category needs to have " \ diff --git a/synthpop/census_helpers.py b/synthpop/census_helpers.py index 4b4b2dd..f3d1a07 100644 --- a/synthpop/census_helpers.py +++ b/synthpop/census_helpers.py @@ -82,7 +82,7 @@ def _query(self, census_columns, state, county, forstr, def chunks(l, n): """ Yield successive n-sized chunks from l. """ - for i in xrange(0, len(l), n): + for i in range(0, len(l), n): yield l[i:i+n] for census_column_batch in chunks(census_columns, 45): @@ -99,7 +99,7 @@ def chunks(l, n): df = dfs[0] for mdf in dfs[1:]: df = pd.merge(df, mdf, on="NAME", suffixes=("", "_ignore")) - drop_cols = filter(lambda x: "_ignore" in x, df.columns) + drop_cols = list(filter(lambda x: "_ignore" in x, df.columns)) df = df.drop(drop_cols, axis=1) return df @@ -116,7 +116,7 @@ def block_group_and_tract_query(self, block_group_columns, df = self._scale_and_merge(df1, block_group_size_attr, df2, tract_size_attr, tract_columns, merge_columns, suffixes=("", "_ignore")) - drop_cols = filter(lambda x: "_ignore" in x, df.columns) + drop_cols = list(filter(lambda x: "_ignore" in x, df.columns)) df = df.drop(drop_cols, axis=1) return df @@ -152,9 +152,10 @@ def tract_to_puma(self, state, county, tract): state, county = self.try_fips_lookup(state, county) df = self._get_pums_relationship() - q = "statefp == '%s' and countyfp == '%s' and tractce == '%s'" % \ - (state, county, tract) + q = "statefp == '%s' and countyfp == '%s' and tractce == '%s'" % (state, county, tract) r = df.query(q) + # Another way: + # pums_relat[(pums_relat.statefp==state) & (pums_relat.countyfp==county) & (pums_relat.tractce==tract)] return r["puma10_id"].values[0], r["puma00_id"].values[0] def _read_csv(self, loc, **kargs): diff --git a/synthpop/ipu/ipu.py b/synthpop/ipu/ipu.py index e2258a5..4fdf10f 100644 --- a/synthpop/ipu/ipu.py +++ b/synthpop/ipu/ipu.py @@ -25,7 +25,7 @@ def for_each_col(col): nz = col.nonzero()[0] return col[nz], nz - for (col_idx, (col, nz)) in df.apply(for_each_col, axis=0, raw=True).iteritems(): + for (col_idx, (col, nz)) in df.apply(for_each_col, axis=0, raw=True).items(): yield (col_idx, col, nz) @@ -99,7 +99,7 @@ def iter_columns(self): The returned column contains only the non-zero elements. """ - return self._everything.itervalues() + return list(self._everything.values()) def get_column(self, key): """ @@ -187,7 +187,7 @@ def _update_weights(column, weights, constraint): new_weights : ndarray """ - adj = constraint / (column * weights).sum() + adj = constraint / float((column * weights).sum()) return weights * adj diff --git a/synthpop/recipes/starter2.py b/synthpop/recipes/starter2.py index 04a76fe..62b3c18 100644 --- a/synthpop/recipes/starter2.py +++ b/synthpop/recipes/starter2.py @@ -134,7 +134,8 @@ def __init__(self, key, state, county, tract=None): all_columns = population + sex + race + male_age_columns + \ female_age_columns + hh_population + hispanic p_acs = c.block_group_query(all_columns, state, county, tract=tract) - + self.p_acs = p_acs + self.p_acs_cat = cat.categorize(p_acs, { ("person_age", "19 and under"): "(B01001_003E + B01001_004E + B01001_005E + " @@ -203,6 +204,7 @@ def get_household_joint_dist_for_geography(self, ind): c = self.c puma10, puma00 = c.tract_to_puma(ind.state, ind.county, ind.tract) + # this is cached so won't download more than once if type(puma00) == str: h_pums = self.c.download_household_pums(ind.state, puma10, puma00, diff --git a/synthpop/synthesizer.py b/synthpop/synthesizer.py index b2b5e5d..e02202f 100644 --- a/synthpop/synthesizer.py +++ b/synthpop/synthesizer.py @@ -81,7 +81,7 @@ def synthesize(h_marg, p_marg, h_jd, p_jd, h_pums, p_pums, logger.debug(iterations) num_households = int(h_marg.groupby(level=0).sum().mean()) - print "Drawing %d households" % num_households + print ("Drawing %d households" % num_households) best_chisq = np.inf @@ -102,8 +102,8 @@ def synthesize_all(recipe, num_geogs=None, indexes=None, and ``people_p``. """ - print "Synthesizing at geog level: '{}' (number of geographies is {})".\ - format(recipe.get_geography_name(), recipe.get_num_geographies()) + print ("Synthesizing at geog level: '{}' (number of geographies is {})".\ + format(recipe.get_geography_name(), recipe.get_num_geographies())) if indexes is None: indexes = recipe.get_available_geography_ids() @@ -116,7 +116,7 @@ def synthesize_all(recipe, num_geogs=None, indexes=None, # TODO will parallelization work here? for geog_id in indexes: - print "Synthesizing geog id:\n", geog_id + print ("Synthesizing geog id:\n", geog_id) h_marg = recipe.get_household_marginal_for_geography(geog_id) logger.debug("Household marginal") diff --git a/synthpop/test/test_categorizer.py b/synthpop/test/test_categorizer.py index 6631a3a..35d9eee 100644 --- a/synthpop/test/test_categorizer.py +++ b/synthpop/test/test_categorizer.py @@ -1,12 +1,12 @@ import pytest import numpy as np -from ..census_helpers import Census -from .. import categorizer as cat - +from synthpop.census_helpers import Census +from synthpop import categorizer as cat +import os @pytest.fixture def c(): - return Census("827402c2958dcf515e4480b7b2bb93d1025f9389") + return Census(os.environ['CENSUS']) @pytest.fixture diff --git a/synthpop/test/test_censushelpers.py b/synthpop/test/test_censushelpers.py index bb35cd9..04fd1e8 100644 --- a/synthpop/test/test_censushelpers.py +++ b/synthpop/test/test_censushelpers.py @@ -2,11 +2,11 @@ from ..census_helpers import Census import numpy as np from pandas.util.testing import assert_series_equal - +import os @pytest.fixture def c(): - return Census("827402c2958dcf515e4480b7b2bb93d1025f9389") + return Census(os.environ['CENSUS']) def test_block_group_and_tract_query(c): diff --git a/synthpop/zone_synthesizer.py b/synthpop/zone_synthesizer.py index 8c9a263..af7382b 100644 --- a/synthpop/zone_synthesizer.py +++ b/synthpop/zone_synthesizer.py @@ -42,7 +42,7 @@ def load_data(hh_marginal_file, person_marginal_file, hh_marg.columns.levels[0].name = 'cat_name' hh_marg.columns.levels[1].name = 'cat_values' - xwalk = zip(hh_marg.index, hh_marg.sample_geog.unstack().values) + xwalk = list(zip(hh_marg.index, hh_marg.sample_geog.unstack().values)) hh_marg = hh_marg.drop('sample_geog', axis=1, level=0) p_marg = pd.read_csv(person_marginal_file, header=[0, 1], index_col=0) From 3ccfbe7117bde6284efa69844360b0835cacd482 Mon Sep 17 00:00:00 2001 From: cvanoli Date: Fri, 16 Mar 2018 16:53:39 -0300 Subject: [PATCH 03/12] pycodestyle formatting fixes --- synthpop/categorizer.py | 5 +++-- synthpop/census_helpers.py | 2 -- synthpop/recipes/starter2.py | 1 - synthpop/recipes/tests/test_starter.py | 2 +- synthpop/synthesizer.py | 8 ++++---- synthpop/test/test_categorizer.py | 3 ++- synthpop/test/test_censushelpers.py | 3 ++- 7 files changed, 12 insertions(+), 12 deletions(-) diff --git a/synthpop/categorizer.py b/synthpop/categorizer.py index 020e9f2..b720196 100644 --- a/synthpop/categorizer.py +++ b/synthpop/categorizer.py @@ -30,6 +30,7 @@ def sum_accross_category(df, subtract_mean=True): cells in the table should be close to zero. The reason why it's not exactly zero is because of rounding errors in the scaling of any tract variables down to block group variables + """ df = df.stack(level=1).fillna(0).groupby(level=0).sum() if subtract_mean: @@ -47,7 +48,7 @@ def category_combinations(index): for cat_name, cat_value in index: d.setdefault(cat_name, []) d[cat_name].append(cat_value) - for cat_name in list(d): #make a static list of the keys + for cat_name in list(d): if len(d[cat_name]) == 1: del d[cat_name] df = pd.DataFrame(list(itertools.product(*list(d.values())))) @@ -62,7 +63,7 @@ def joint_distribution(sample_df, category_df, mapping_functions=None): # set counts to zero category_df["frequency"] = 0 - category_names = list (category_df.index.names) + category_names = list(category_df.index.names) if mapping_functions: for name in category_names: assert name in mapping_functions, "Every category needs to have " \ diff --git a/synthpop/census_helpers.py b/synthpop/census_helpers.py index f3d1a07..a522954 100644 --- a/synthpop/census_helpers.py +++ b/synthpop/census_helpers.py @@ -154,8 +154,6 @@ def tract_to_puma(self, state, county, tract): df = self._get_pums_relationship() q = "statefp == '%s' and countyfp == '%s' and tractce == '%s'" % (state, county, tract) r = df.query(q) - # Another way: - # pums_relat[(pums_relat.statefp==state) & (pums_relat.countyfp==county) & (pums_relat.tractce==tract)] return r["puma10_id"].values[0], r["puma00_id"].values[0] def _read_csv(self, loc, **kargs): diff --git a/synthpop/recipes/starter2.py b/synthpop/recipes/starter2.py index 62b3c18..b59baf0 100644 --- a/synthpop/recipes/starter2.py +++ b/synthpop/recipes/starter2.py @@ -135,7 +135,6 @@ def __init__(self, key, state, county, tract=None): female_age_columns + hh_population + hispanic p_acs = c.block_group_query(all_columns, state, county, tract=tract) self.p_acs = p_acs - self.p_acs_cat = cat.categorize(p_acs, { ("person_age", "19 and under"): "(B01001_003E + B01001_004E + B01001_005E + " diff --git a/synthpop/recipes/tests/test_starter.py b/synthpop/recipes/tests/test_starter.py index bbe5c0a..06bd5b5 100644 --- a/synthpop/recipes/tests/test_starter.py +++ b/synthpop/recipes/tests/test_starter.py @@ -1,6 +1,6 @@ import pytest from ...synthesizer import * -from ..starter import Starter +from ..starter2 import Starter # from ..starter2 import Starter as Starter2 diff --git a/synthpop/synthesizer.py b/synthpop/synthesizer.py index e02202f..f5532b1 100644 --- a/synthpop/synthesizer.py +++ b/synthpop/synthesizer.py @@ -81,7 +81,7 @@ def synthesize(h_marg, p_marg, h_jd, p_jd, h_pums, p_pums, logger.debug(iterations) num_households = int(h_marg.groupby(level=0).sum().mean()) - print ("Drawing %d households" % num_households) + print("Drawing %d households" % num_households) best_chisq = np.inf @@ -102,8 +102,8 @@ def synthesize_all(recipe, num_geogs=None, indexes=None, and ``people_p``. """ - print ("Synthesizing at geog level: '{}' (number of geographies is {})".\ - format(recipe.get_geography_name(), recipe.get_num_geographies())) + print("Synthesizing at geog level: '{}' (number of geographies is {})" + .format(recipe.get_geography_name(), recipe.get_num_geographies())) if indexes is None: indexes = recipe.get_available_geography_ids() @@ -116,7 +116,7 @@ def synthesize_all(recipe, num_geogs=None, indexes=None, # TODO will parallelization work here? for geog_id in indexes: - print ("Synthesizing geog id:\n", geog_id) + print("Synthesizing geog id:\n", geog_id) h_marg = recipe.get_household_marginal_for_geography(geog_id) logger.debug("Household marginal") diff --git a/synthpop/test/test_categorizer.py b/synthpop/test/test_categorizer.py index 35d9eee..88ddf84 100644 --- a/synthpop/test/test_categorizer.py +++ b/synthpop/test/test_categorizer.py @@ -4,9 +4,10 @@ from synthpop import categorizer as cat import os + @pytest.fixture def c(): - return Census(os.environ['CENSUS']) + return Census('bfa6b4e541243011fab6307a31aed9e91015ba90') @pytest.fixture diff --git a/synthpop/test/test_censushelpers.py b/synthpop/test/test_censushelpers.py index 04fd1e8..e2a1b27 100644 --- a/synthpop/test/test_censushelpers.py +++ b/synthpop/test/test_censushelpers.py @@ -4,9 +4,10 @@ from pandas.util.testing import assert_series_equal import os + @pytest.fixture def c(): - return Census(os.environ['CENSUS']) + return Census('bfa6b4e541243011fab6307a31aed9e91015ba90') def test_block_group_and_tract_query(c): From 052d0d16671d1f9dec3daa55a9834c0f8eab792f Mon Sep 17 00:00:00 2001 From: cvanoli Date: Fri, 16 Mar 2018 18:28:48 -0300 Subject: [PATCH 04/12] fixes on the test files --- synthpop/ipu/test/test_ipu.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/synthpop/ipu/test/test_ipu.py b/synthpop/ipu/test/test_ipu.py index 4e955ef..721e9a1 100644 --- a/synthpop/ipu/test/test_ipu.py +++ b/synthpop/ipu/test/test_ipu.py @@ -179,7 +179,7 @@ def test_FrequencyAndConstraints(freq_wrap): assert freq_wrap.ncols == 5 assert len(list(freq_wrap.iter_columns())) == 5 - iter_cols = freq_wrap.iter_columns() + iter_cols = iter(freq_wrap.iter_columns()) key, col, constraint, nz = next(iter_cols) assert key == ('yes', 'blue') From b9d250fb1efb28ec0cae1c102d7bad654a11fd8e Mon Sep 17 00:00:00 2001 From: cvanoli Date: Fri, 16 Mar 2018 18:39:10 -0300 Subject: [PATCH 05/12] uncommented test_starter to test starter2 --- synthpop/recipes/tests/test_starter.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/synthpop/recipes/tests/test_starter.py b/synthpop/recipes/tests/test_starter.py index 06bd5b5..b862556 100644 --- a/synthpop/recipes/tests/test_starter.py +++ b/synthpop/recipes/tests/test_starter.py @@ -1,7 +1,7 @@ import pytest from ...synthesizer import * -from ..starter2 import Starter -# from ..starter2 import Starter as Starter2 +# from ..starter import Starter +from ..starter2 import Starter as Starter2 @pytest.fixture @@ -9,14 +9,14 @@ def key(): return "827402c2958dcf515e4480b7b2bb93d1025f9389" -def test_starter(key): - st = Starter(key, "CA", "Napa County") - # just run it for now - synthesize_all(st, num_geogs=1) +# def test_starter(key): +# st = Starter(key, "CA", "Napa County") +# # just run it for now +# synthesize_all(st, num_geogs=1) # commented out as it is to slow for travis -# def test_starter2(key): -# st = Starter2(key, "CA", "Napa County") -# # just run it for now -# synthesize_all(st, num_geogs=1) +def test_starter2(key): + st = Starter2(key, "CA", "Napa County") + # just run it for now + synthesize_all(st, num_geogs=1) From 63315d61025ac5252a3645a9a0bb0519b858da10 Mon Sep 17 00:00:00 2001 From: cvanoli Date: Fri, 16 Mar 2018 19:12:40 -0300 Subject: [PATCH 06/12] run pytest again --- .pytest_cache/v/cache/lastfailed | 1 - 1 file changed, 1 deletion(-) delete mode 100644 .pytest_cache/v/cache/lastfailed diff --git a/.pytest_cache/v/cache/lastfailed b/.pytest_cache/v/cache/lastfailed deleted file mode 100644 index 9e26dfe..0000000 --- a/.pytest_cache/v/cache/lastfailed +++ /dev/null @@ -1 +0,0 @@ -{} \ No newline at end of file From fc7a6e96380b3903740b61f0be502d625ca929f4 Mon Sep 17 00:00:00 2001 From: cvanoli Date: Fri, 16 Mar 2018 19:51:27 -0300 Subject: [PATCH 07/12] run pytest again with pytest-cov installed --- .travis.yml | 1 + setup.py | 4 +++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 0d7ea3b..9568f66 100644 --- a/.travis.yml +++ b/.travis.yml @@ -2,6 +2,7 @@ language: python sudo: false python: - '2.7' +- '3.5' install: - if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh; else wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh diff --git a/setup.py b/setup.py index 7f6478f..09b785b 100644 --- a/setup.py +++ b/setup.py @@ -13,7 +13,9 @@ url='https://github.com/udst/synthpop', classifiers=[ 'Development Status :: 4 - Beta', - 'Programming Language :: Python :: 2.7' + 'Programming Language :: Python :: 2.7', + 'Programming Language :: Python :: 3.5', + 'Programming Language :: Python :: 3.6' ], packages=find_packages(exclude=['*.tests']), install_requires=[ From dfccfa28adc8a3506d01ccdb8e150658f0d99dd5 Mon Sep 17 00:00:00 2001 From: cvanoli Date: Mon, 19 Mar 2018 09:21:59 -0300 Subject: [PATCH 08/12] pytest and coverage report --- .pytest_cache/v/cache/lastfailed | 1 + synthpop/census_helpers.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) create mode 100644 .pytest_cache/v/cache/lastfailed diff --git a/.pytest_cache/v/cache/lastfailed b/.pytest_cache/v/cache/lastfailed new file mode 100644 index 0000000..9e26dfe --- /dev/null +++ b/.pytest_cache/v/cache/lastfailed @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/synthpop/census_helpers.py b/synthpop/census_helpers.py index a522954..9df3499 100644 --- a/synthpop/census_helpers.py +++ b/synthpop/census_helpers.py @@ -209,4 +209,4 @@ def try_fips_lookup(self, state, county=None): return df.loc[(state, county)] except: pass - return state, county + return state, county \ No newline at end of file From 90886eb72fcbb9119abd60c2837e3db621e7dc6d Mon Sep 17 00:00:00 2001 From: cvanoli Date: Mon, 19 Mar 2018 09:43:42 -0300 Subject: [PATCH 09/12] fixed: cov report term-missing --- synthpop/census_helpers.py | 3 ++- synthpop/recipes/tests/test_starter.py | 10 +++++----- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/synthpop/census_helpers.py b/synthpop/census_helpers.py index 9df3499..8324f10 100644 --- a/synthpop/census_helpers.py +++ b/synthpop/census_helpers.py @@ -209,4 +209,5 @@ def try_fips_lookup(self, state, county=None): return df.loc[(state, county)] except: pass - return state, county \ No newline at end of file + return state, county + \ No newline at end of file diff --git a/synthpop/recipes/tests/test_starter.py b/synthpop/recipes/tests/test_starter.py index b862556..7a91150 100644 --- a/synthpop/recipes/tests/test_starter.py +++ b/synthpop/recipes/tests/test_starter.py @@ -1,6 +1,6 @@ import pytest from ...synthesizer import * -# from ..starter import Starter +from ..starter import Starter from ..starter2 import Starter as Starter2 @@ -9,10 +9,10 @@ def key(): return "827402c2958dcf515e4480b7b2bb93d1025f9389" -# def test_starter(key): -# st = Starter(key, "CA", "Napa County") -# # just run it for now -# synthesize_all(st, num_geogs=1) +def test_starter(key): + st = Starter(key, "CA", "Napa County") + # just run it for now + synthesize_all(st, num_geogs=1) # commented out as it is to slow for travis From b14955683e6d031dba695cd2a891fb2579ec3a1e Mon Sep 17 00:00:00 2001 From: cvanoli Date: Mon, 19 Mar 2018 09:54:55 -0300 Subject: [PATCH 10/12] starter and starter 2 tested in different files --- synthpop/recipes/tests/test_starter.py | 8 -------- synthpop/recipes/tests/test_starter2.py | 16 ++++++++++++++++ 2 files changed, 16 insertions(+), 8 deletions(-) create mode 100644 synthpop/recipes/tests/test_starter2.py diff --git a/synthpop/recipes/tests/test_starter.py b/synthpop/recipes/tests/test_starter.py index 7a91150..bd206c5 100644 --- a/synthpop/recipes/tests/test_starter.py +++ b/synthpop/recipes/tests/test_starter.py @@ -1,7 +1,6 @@ import pytest from ...synthesizer import * from ..starter import Starter -from ..starter2 import Starter as Starter2 @pytest.fixture @@ -13,10 +12,3 @@ def test_starter(key): st = Starter(key, "CA", "Napa County") # just run it for now synthesize_all(st, num_geogs=1) - - -# commented out as it is to slow for travis -def test_starter2(key): - st = Starter2(key, "CA", "Napa County") - # just run it for now - synthesize_all(st, num_geogs=1) diff --git a/synthpop/recipes/tests/test_starter2.py b/synthpop/recipes/tests/test_starter2.py new file mode 100644 index 0000000..b29157f --- /dev/null +++ b/synthpop/recipes/tests/test_starter2.py @@ -0,0 +1,16 @@ +import pytest +from ...synthesizer import * +from ..starter2 import Starter as Starter2 + + +@pytest.fixture +def key(): + return "827402c2958dcf515e4480b7b2bb93d1025f9389" + + +# commented out as it is to slow for travis +def test_starter2(key): + st = Starter2(key, "CA", "Napa County") + # just run it for now + synthesize_all(st, num_geogs=1) + \ No newline at end of file From b8b0b4fd32dfbd28a24b1b96c4798052b4ef97e6 Mon Sep 17 00:00:00 2001 From: cvanoli Date: Mon, 19 Mar 2018 10:27:20 -0300 Subject: [PATCH 11/12] fixed codestyle --- .pytest_cache/v/cache/lastfailed | 1 - synthpop/census_helpers.py | 1 - synthpop/recipes/tests/test_starter.py | 1 - synthpop/recipes/tests/test_starter2.py | 3 --- 4 files changed, 6 deletions(-) delete mode 100644 .pytest_cache/v/cache/lastfailed diff --git a/.pytest_cache/v/cache/lastfailed b/.pytest_cache/v/cache/lastfailed deleted file mode 100644 index 9e26dfe..0000000 --- a/.pytest_cache/v/cache/lastfailed +++ /dev/null @@ -1 +0,0 @@ -{} \ No newline at end of file diff --git a/synthpop/census_helpers.py b/synthpop/census_helpers.py index 8324f10..a522954 100644 --- a/synthpop/census_helpers.py +++ b/synthpop/census_helpers.py @@ -210,4 +210,3 @@ def try_fips_lookup(self, state, county=None): except: pass return state, county - \ No newline at end of file diff --git a/synthpop/recipes/tests/test_starter.py b/synthpop/recipes/tests/test_starter.py index bd206c5..c31b1df 100644 --- a/synthpop/recipes/tests/test_starter.py +++ b/synthpop/recipes/tests/test_starter.py @@ -10,5 +10,4 @@ def key(): def test_starter(key): st = Starter(key, "CA", "Napa County") - # just run it for now synthesize_all(st, num_geogs=1) diff --git a/synthpop/recipes/tests/test_starter2.py b/synthpop/recipes/tests/test_starter2.py index b29157f..1efe3f6 100644 --- a/synthpop/recipes/tests/test_starter2.py +++ b/synthpop/recipes/tests/test_starter2.py @@ -8,9 +8,6 @@ def key(): return "827402c2958dcf515e4480b7b2bb93d1025f9389" -# commented out as it is to slow for travis def test_starter2(key): st = Starter2(key, "CA", "Napa County") - # just run it for now synthesize_all(st, num_geogs=1) - \ No newline at end of file From 6b6c70d394c8c296c6852a00de8b45825c64d267 Mon Sep 17 00:00:00 2001 From: cvanoli Date: Mon, 19 Mar 2018 10:37:52 -0300 Subject: [PATCH 12/12] commented out test_starter2 --- synthpop/recipes/tests/test_starter2.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/synthpop/recipes/tests/test_starter2.py b/synthpop/recipes/tests/test_starter2.py index 1efe3f6..b57444b 100644 --- a/synthpop/recipes/tests/test_starter2.py +++ b/synthpop/recipes/tests/test_starter2.py @@ -8,6 +8,6 @@ def key(): return "827402c2958dcf515e4480b7b2bb93d1025f9389" -def test_starter2(key): - st = Starter2(key, "CA", "Napa County") - synthesize_all(st, num_geogs=1) +# def test_starter2(key): +# st = Starter2(key, "CA", "Napa County") +# synthesize_all(st, num_geogs=1)