Enrich Variable Name Lookup¶
Enrich variable names can be represented using a variety of string representations depending on where you are looking. Frequently there is a need to cross reference between these. The Country.enrich_variables property provides the ability to easily retrieve a Pandas data frame. This data frame can be used to perform cross reference lookups, but there also is a hidden method you can use as well, Country._ba_cntry.get_enrich_variables_from_name_list. This method accepts any iterabe of
strings, and will look up the values by checking for a sequence of case insensitive matches in the name, alias, field_name and enrich_field_name columns.
NOTE: This requires at least version 1.9.1 of the Python API. This was included with the Pro 2.9 release, but if you have a previous release of Pro, you can get the most recent release of the Python API by cloning your default Python environment, setting your new environment to be the default and updating the ArcGIS Python API (arcgis) package.
[1]:
from pathlib import Path
from arcgis.geoenrichment import Country
from arcgis.gis import GIS
import pandas as pd
Load Excel Table with Various Representations of Enrich Names¶
This is a table of name variations for enrich variables I was passed to use as an example of different variations needing to be looked up.
[2]:
dir_prj = Path.cwd().parent
dir_ref = dir_prj/'references'
xlsx_pth = dir_ref/'arcmap_pro_variables.xlsx'
assert xlsx_pth.exists()
[3]:
xlsx_df = pd.read_excel(xlsx_pth)
drop_cols = [c for c in xlsx_df.columns if c.startswith('Unnamed:')]
xlsx_df.drop(columns=drop_cols, inplace=True)
xlsx_df.columns = ['guided_workflow_names', 'pro_enrich_names', 'fieldnames_arcmap', 'fieldnames_pro']
xlsx_df
[3]:
| guided_workflow_names | pro_enrich_names | fieldnames_arcmap | fieldnames_pro | |
|---|---|---|---|---|
| 0 | TOTPOP_CY.2021 Key Demographic Indicators (Esr... | populationtotals.totpop_cy | TOTPOP_CY | populationtotals_totpop_cy |
| 1 | HHPOP_CY.2021 Key Demographic Indicators (Esri... | householdtotals.tothh_cy | HHPOP_CY | populationtotals_hhpop_cy |
| 2 | FAMPOP_CY.2021 Key Demographic Indicators (Esr... | populationtotals.fampop_cy | FAMPOP_CY | populationtotals_fampop_cy |
| 3 | MP14129a_B.2021 Health (Market Potential) (Esr... | healthpersonalcare.mp14129a_b | MP14129a_B | healthpersonalcare_mp14129a_b |
| 4 | X11001_X.2021 Education (Consumer Spending) (E... | education.x11001_x | X11001_X | education_x11001_x |
| 5 | X11001_A.2021 Education (Consumer Spending) (E... | education.x11001_x_a | X11001_A | education_x11001_x_a |
| 6 | X11001_I.2021 Education (Consumer Spending) (E... | education.x11001_x_i | X11001_I | education_x11001_x_i |
| 7 | MP09147a_B.2021 Electronics (Market Potential)... | electronicsinternet.mp09147a_b | MP09147a_B | electronicsinternet_mp09147a_b |
| 8 | MP09147a_I.2021 Electronics (Market Potential)... | electronicsinternet.mp09147a_b_i | MP09147a_I | electronicsinternet_mp09147a_b_i |
| 9 | MP09148a_B.2021 Electronics (Market Potential)... | electronicsinternet.mp09148a_b | MP09148a_B | electronicsinternet_mp09148a_b |
| 10 | MP09148a_I.2021 Electronics (Market Potential)... | electronicsinternet.mp09148a_b_i | MP09148a_I | electronicsinternet_mp09148a_b_i |
| 11 | MP09134a_B.2021 Electronics (Market Potential)... | electronicsinternet.mp09134a_b | MP09134a_B | electronicsinternet_mp09134a_b |
| 12 | MP09134a_I.2021 Electronics (Market Potential)... | electronicsinternet.mp09134a_b_i | MP09134a_I | electronicsinternet_mp09134a_b_i |
| 13 | ACSTOTPOP.2015-2019 Key Demographic Indicators... | veterans.acstotpop | ACSTOTPOP | veterans_acstotpop |
| 14 | MOETOTPOP.2015-2019 Key Demographic Indicators... | veterans.acstotpop_e | MOETOTPOP | veterans_acstotpop_e |
| 15 | RELTOTPOP.2015-2019 Key Demographic Indicators... | veterans.acstotpop_r | RELTOTPOP | veterans_acstotpop_r |
| 16 | INDAGRI_CY.2021 Labor Force (Esri) (US 2021 Re... | industry.indagri_cy | INDAGRI_CY | industry_indagri_cy |
| 17 | INDMIN_CY.2021 Labor Force (Esri) (US 2021 Ret... | industry.indmin_cy | INDMIN_CY | industry_indmin_cy |
| 18 | INDCONS_CY.2021 Labor Force (Esri) (US 2021 Re... | industry.indcons_cy | INDCONS_CY | industry_indcons_cy |
| 19 | HHWHTHHR10.2010 Households by Race_Hispanic Or... | householdsbyraceofhouseholder.hhwhthhr10 | HHWHTHHR10 | householdsbyraceofhouseholder_hhwhthhr10 |
| 20 | HHBLKHHR10.2010 Households by Race_Hispanic Or... | householdsbyraceofhouseholder.hhblkhhr10 | HHBLKHHR10 | householdsbyraceofhouseholder_hhblkhhr10 |
| 21 | HHAIHHR10.2010 Households by Race_Hispanic Ori... | householdsbyraceofhouseholder.hhaihhr10 | HHAIHHR10 | householdsbyraceofhouseholder_hhaihhr10 |
| 22 | HHASNHHR10.2010 Households by Race_Hispanic Or... | householdsbyraceofhouseholder.hhasnhhr10 | HHASNHHR10 | householdsbyraceofhouseholder_hhasnhhr10 |
| 23 | HHPIHHR10.2010 Households by Race_Hispanic Ori... | householdsbyraceofhouseholder.hhpihhr10 | HHPIHHR10 | householdsbyraceofhouseholder_hhpihhr10 |
| 24 | HHOTHHHR10.2010 Households by Race_Hispanic Or... | householdsbyraceofhouseholder.hhothhhr10 | HHOTHHHR10 | householdsbyraceofhouseholder_hhothhhr10 |
| 25 | TADULTBASE.2021 Tapestry Market Segmentation (... | tapestryadultsnew.tadultbase | TADULTBASE | tapestryadultsnew_tadultbase |
| 26 | TADULT01.2021 Tapestry Market Segmentation (Ad... | tapestryadultsnew.tadult01 | TADULT01 | tapestryadultsnew_tadult01 |
| 27 | TADULT02.2021 Tapestry Market Segmentation (Ad... | tapestryadultsnew.tadult02 | TADULT02 | tapestryadultsnew_tadult02 |
| 28 | POPGRW10CY.2021 Key Demographic Indicators (Es... | populationtotals.popgrw10cy | POPGRW10CY | populationtotals_popgrw10cy |
| 29 | DIVINDX_CY.2021 Race and Hispanic Origin (Esri... | raceandhispanicorigin.divindx_cy | DIVINDX_CY | raceandhispanicorigin_divindx_cy |
| 30 | MEDHINC_CY.2021 Income (Esri) (Esri USA 2021) | householdincome.medhinc_cy | MEDHINC_CY | householdincome_medhinc_cy |
| 31 | ACSMEDHINC.2015-2019 Income (ACS) (Esri USA 20... | householdincome.acsmedhinc | ACSMEDHINC | householdincome_acsmedhinc |
| 32 | MOEMEDHINC.2015-2019 Income (ACS) (Esri USA 20... | householdincome.acsmedhinc_e | MOEMEDHINC | householdincome_acsmedhinc_e |
| 33 | RELMEDHINC.2015-2019 Income (ACS) (Esri USA 20... | householdincome.acsmedhinc_r | RELMEDHINC | householdincome_acsmedhinc_r |
Create a Country Object Instance¶
Create a Country object instance referencing the country you are looking for variables in.
[4]:
usa = Country('usa')
Perform Lookups¶
Now, we just need to provide an interable of strings to be matched into the get_enrich_variables_from_list function.
ArcGIS Pro Enrich Names¶
If using a list of names used to enrich with ArcGIS Pro, you can easily look up these values.
[5]:
print(list(xlsx_df.pro_enrich_names))
['populationtotals.totpop_cy', 'householdtotals.tothh_cy', 'populationtotals.fampop_cy', 'healthpersonalcare.mp14129a_b', 'education.x11001_x', 'education.x11001_x_a', 'education.x11001_x_i', 'electronicsinternet.mp09147a_b', 'electronicsinternet.mp09147a_b_i', 'electronicsinternet.mp09148a_b', 'electronicsinternet.mp09148a_b_i', 'electronicsinternet.mp09134a_b', 'electronicsinternet.mp09134a_b_i', 'veterans.acstotpop', 'veterans.acstotpop_e', 'veterans.acstotpop_r', 'industry.indagri_cy', 'industry.indmin_cy', 'industry.indcons_cy', 'householdsbyraceofhouseholder.hhwhthhr10', 'householdsbyraceofhouseholder.hhblkhhr10', 'householdsbyraceofhouseholder.hhaihhr10', 'householdsbyraceofhouseholder.hhasnhhr10', 'householdsbyraceofhouseholder.hhpihhr10', 'householdsbyraceofhouseholder.hhothhhr10', 'tapestryadultsnew.tadultbase', 'tapestryadultsnew.tadult01', 'tapestryadultsnew.tadult02', 'populationtotals.popgrw10cy', 'raceandhispanicorigin.divindx_cy', 'householdincome.medhinc_cy', 'householdincome.acsmedhinc', 'householdincome.acsmedhinc_e', 'householdincome.acsmedhinc_r']
[6]:
usa._ba_cntry.get_enrich_variables_from_iterable(xlsx_df.pro_enrich_names)
[6]:
| name | alias | data_collection | enrich_name | enrich_field_name | |
|---|---|---|---|---|---|
| 0 | ACSMEDHINC | 2019 Median HH Income (ACS 5-Yr) | householdincome | householdincome.ACSMEDHINC | householdincome_ACSMEDHINC |
| 1 | MEDHINC_CY | 2021 Median Household Income | householdincome | householdincome.MEDHINC_CY | householdincome_MEDHINC_CY |
| 2 | MP09147a_B | 2021 Own Any Tablet | ElectronicsInternet | ElectronicsInternet.MP09147a_B | ElectronicsInternet_MP09147a_B |
| 3 | MP09148a_B | 2021 Own Any E-Reader | ElectronicsInternet | ElectronicsInternet.MP09148a_B | ElectronicsInternet_MP09148a_B |
| 4 | MP09134a_B | 2021 Own E-Reader/Tablet: Amazon Kindle | ElectronicsInternet | ElectronicsInternet.MP09134a_B | ElectronicsInternet_MP09134a_B |
| 5 | X11001_X | 2021 Education | education | education.X11001_X | education_X11001_X |
| 6 | MP14129a_B | 2021 Typically Spend 1-3 Hrs Exercising/Wk | HealthPersonalCare | HealthPersonalCare.MP14129a_B | HealthPersonalCare_MP14129a_B |
| 7 | INDAGRI_CY | 2021 Industry: Agriculture | industry | industry.INDAGRI_CY | industry_INDAGRI_CY |
| 8 | INDMIN_CY | 2021 Industry: Mining | industry | industry.INDMIN_CY | industry_INDMIN_CY |
| 9 | INDCONS_CY | 2021 Industry: Construction | industry | industry.INDCONS_CY | industry_INDCONS_CY |
| 10 | HHWHTHHR10 | 2010 HHs w/White HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHWHTHHR10 | householdsbyraceofhouseholder_HHWHTHHR10 |
| 11 | HHBLKHHR10 | 2010 HHs w/Black HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHBLKHHR10 | householdsbyraceofhouseholder_HHBLKHHR10 |
| 12 | HHAIHHR10 | 2010 HHs w/Amer Indian HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHAIHHR10 | householdsbyraceofhouseholder_HHAIHHR10 |
| 13 | HHASNHHR10 | 2010 HHs w/Asian HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHASNHHR10 | householdsbyraceofhouseholder_HHASNHHR10 |
| 14 | HHPIHHR10 | 2010 HHs w/Pacific Isl HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHPIHHR10 | householdsbyraceofhouseholder_HHPIHHR10 |
| 15 | HHOTHHHR10 | 2010 HHs w/Other Race HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHOTHHHR10 | householdsbyraceofhouseholder_HHOTHHHR10 |
| 16 | TOTHH_CY | 2021 Total Households | householdtotals | householdtotals.TOTHH_CY | householdtotals_TOTHH_CY |
| 17 | TADULTBASE | 2021 Tapestry Adult Pop Base | tapestryadultsNEW | tapestryadultsNEW.TADULTBASE | tapestryadultsNEW_TADULTBASE |
| 18 | TADULT01 | 2021 Pop 18+ in Tapestry Seg 1A | tapestryadultsNEW | tapestryadultsNEW.TADULT01 | tapestryadultsNEW_TADULT01 |
| 19 | TADULT02 | 2021 Pop 18+ in Tapestry Seg 1B | tapestryadultsNEW | tapestryadultsNEW.TADULT02 | tapestryadultsNEW_TADULT02 |
| 20 | TOTPOP_CY | 2021 Total Population | populationtotals | populationtotals.TOTPOP_CY | populationtotals_TOTPOP_CY |
| 21 | FAMPOP_CY | 2021 Family Population | populationtotals | populationtotals.FAMPOP_CY | populationtotals_FAMPOP_CY |
| 22 | POPGRW10CY | 2010-2021 Growth Rate: Population | populationtotals | populationtotals.POPGRW10CY | populationtotals_POPGRW10CY |
| 23 | DIVINDX_CY | 2021 Diversity Index | raceandhispanicorigin | raceandhispanicorigin.DIVINDX_CY | raceandhispanicorigin_DIVINDX_CY |
| 24 | ACSTOTPOP | 2019 Total Population (ACS 5-Yr) | veterans | veterans.ACSTOTPOP | veterans_ACSTOTPOP |
ArcMap Enrich Names¶
ArcMap required a slightly different string format. Hence, if migrating from ArcMap, the same workflow can be used to retrieve these values as well.
[7]:
print(list(xlsx_df.fieldnames_arcmap))
['TOTPOP_CY', 'HHPOP_CY', 'FAMPOP_CY', 'MP14129a_B', 'X11001_X', 'X11001_A', 'X11001_I', 'MP09147a_B', 'MP09147a_I', 'MP09148a_B', 'MP09148a_I', 'MP09134a_B', 'MP09134a_I', 'ACSTOTPOP', 'MOETOTPOP', 'RELTOTPOP', 'INDAGRI_CY', 'INDMIN_CY', 'INDCONS_CY', 'HHWHTHHR10', 'HHBLKHHR10', 'HHAIHHR10', 'HHASNHHR10', 'HHPIHHR10', 'HHOTHHHR10', 'TADULTBASE', 'TADULT01', 'TADULT02', 'POPGRW10CY', 'DIVINDX_CY', 'MEDHINC_CY', 'ACSMEDHINC', 'MOEMEDHINC', 'RELMEDHINC']
[8]:
usa._ba_cntry.get_enrich_variables_from_iterable(xlsx_df.fieldnames_arcmap)
[8]:
| name | alias | data_collection | enrich_name | enrich_field_name | |
|---|---|---|---|---|---|
| 0 | MEDHINC_CY | 2021 Median Household Income | Health | Health.MEDHINC_CY | Health_MEDHINC_CY |
| 1 | MOETOTPOP | 2019 Total Population MOE (ACS 5-Yr) | ACS_Housing_Summary_rep | ACS_Housing_Summary_rep.MOETOTPOP | ACS_Housing_Summary_rep_MOETOTPOP |
| 2 | TADULTBASE | 2021 Tapestry Adult Pop Base | travelMPI | travelMPI.TADULTBASE | travelMPI_TADULTBASE |
| 3 | TOTPOP_CY | 2021 Total Population | Age_by_Sex_by_Race_Profile_rep | Age_by_Sex_by_Race_Profile_rep.TOTPOP_CY | Age_by_Sex_by_Race_Profile_rep_TOTPOP_CY |
| 4 | DIVINDX_CY | 2021 Diversity Index | Policy | Policy.DIVINDX_CY | Policy_DIVINDX_CY |
| 5 | ACSMEDHINC | 2019 Median HH Income (ACS 5-Yr) | householdincome | householdincome.ACSMEDHINC | householdincome_ACSMEDHINC |
| 6 | MOEMEDHINC | 2019 Median HH Income MOE (ACS 5-Yr) | householdincome | householdincome.MOEMEDHINC | householdincome_MOEMEDHINC |
| 7 | RELMEDHINC | 2019 Median HH Income REL (ACS 5-Yr) | householdincome | householdincome.RELMEDHINC | householdincome_RELMEDHINC |
| 8 | X11001_I | 2021 Index: Education | Community_Profile_rep | Community_Profile_rep.X11001_I | Community_Profile_rep_X11001_I |
| 9 | MP09147a_B | 2021 Own Any Tablet | ElectronicsInternet | ElectronicsInternet.MP09147a_B | ElectronicsInternet_MP09147a_B |
| 10 | MP09147a_I | 2021 Index: Own Any Tablet | ElectronicsInternet | ElectronicsInternet.MP09147a_I | ElectronicsInternet_MP09147a_I |
| 11 | MP09148a_B | 2021 Own Any E-Reader | ElectronicsInternet | ElectronicsInternet.MP09148a_B | ElectronicsInternet_MP09148a_B |
| 12 | MP09148a_I | 2021 Index: Own Any E-Reader | ElectronicsInternet | ElectronicsInternet.MP09148a_I | ElectronicsInternet_MP09148a_I |
| 13 | MP09134a_B | 2021 Own E-Reader/Tablet: Amazon Kindle | ElectronicsInternet | ElectronicsInternet.MP09134a_B | ElectronicsInternet_MP09134a_B |
| 14 | MP09134a_I | 2021 Index: Own E-Reader/Tablet: Amazon Kindle | ElectronicsInternet | ElectronicsInternet.MP09134a_I | ElectronicsInternet_MP09134a_I |
| 15 | X11001_X | 2021 Education | education | education.X11001_X | education_X11001_X |
| 16 | X11001_A | 2021 Avg: Education | education | education.X11001_A | education_X11001_A |
| 17 | MP14129a_B | 2021 Typically Spend 1-3 Hrs Exercising/Wk | HealthPersonalCare | HealthPersonalCare.MP14129a_B | HealthPersonalCare_MP14129a_B |
| 18 | INDAGRI_CY | 2021 Industry: Agriculture | industry | industry.INDAGRI_CY | industry_INDAGRI_CY |
| 19 | INDMIN_CY | 2021 Industry: Mining | industry | industry.INDMIN_CY | industry_INDMIN_CY |
| 20 | INDCONS_CY | 2021 Industry: Construction | industry | industry.INDCONS_CY | industry_INDCONS_CY |
| 21 | HHWHTHHR10 | 2010 HHs w/White HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHWHTHHR10 | householdsbyraceofhouseholder_HHWHTHHR10 |
| 22 | HHBLKHHR10 | 2010 HHs w/Black HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHBLKHHR10 | householdsbyraceofhouseholder_HHBLKHHR10 |
| 23 | HHAIHHR10 | 2010 HHs w/Amer Indian HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHAIHHR10 | householdsbyraceofhouseholder_HHAIHHR10 |
| 24 | HHASNHHR10 | 2010 HHs w/Asian HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHASNHHR10 | householdsbyraceofhouseholder_HHASNHHR10 |
| 25 | HHPIHHR10 | 2010 HHs w/Pacific Isl HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHPIHHR10 | householdsbyraceofhouseholder_HHPIHHR10 |
| 26 | HHOTHHHR10 | 2010 HHs w/Other Race HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHOTHHHR10 | householdsbyraceofhouseholder_HHOTHHHR10 |
| 27 | POPGRW10CY | 2010-2021 Growth Rate: Population | KeyUSFacts | KeyUSFacts.POPGRW10CY | KeyUSFacts_POPGRW10CY |
| 28 | TADULT01 | 2021 Pop 18+ in Tapestry Seg 1A | tapestryadultsNEW | tapestryadultsNEW.TADULT01 | tapestryadultsNEW_TADULT01 |
| 29 | TADULT02 | 2021 Pop 18+ in Tapestry Seg 1B | tapestryadultsNEW | tapestryadultsNEW.TADULT02 | tapestryadultsNEW_TADULT02 |
| 30 | ACSTOTPOP | 2019 Total Population (ACS 5-Yr) | population | population.ACSTOTPOP | population_ACSTOTPOP |
| 31 | RELTOTPOP | 2019 Total Population REL (ACS 5-Yr) | population | population.RELTOTPOP | population_RELTOTPOP |
| 32 | HHPOP_CY | 2021 Household Population | populationtotals | populationtotals.HHPOP_CY | populationtotals_HHPOP_CY |
| 33 | FAMPOP_CY | 2021 Family Population | populationtotals | populationtotals.FAMPOP_CY | populationtotals_FAMPOP_CY |
Field Names¶
Once enriched, the enrich_name string has the period replaced for the output attibute field name (enrich_field_name). We can use also use these output field name values to lookup relevant rows in the enrich_variables table.
[9]:
print(list(xlsx_df.fieldnames_pro))
['populationtotals_totpop_cy', 'populationtotals_hhpop_cy', 'populationtotals_fampop_cy', 'healthpersonalcare_mp14129a_b', 'education_x11001_x', 'education_x11001_x_a', 'education_x11001_x_i', 'electronicsinternet_mp09147a_b', 'electronicsinternet_mp09147a_b_i', 'electronicsinternet_mp09148a_b', 'electronicsinternet_mp09148a_b_i', 'electronicsinternet_mp09134a_b', 'electronicsinternet_mp09134a_b_i', 'veterans_acstotpop', 'veterans_acstotpop_e', 'veterans_acstotpop_r', 'industry_indagri_cy', 'industry_indmin_cy', 'industry_indcons_cy', 'householdsbyraceofhouseholder_hhwhthhr10', 'householdsbyraceofhouseholder_hhblkhhr10', 'householdsbyraceofhouseholder_hhaihhr10', 'householdsbyraceofhouseholder_hhasnhhr10', 'householdsbyraceofhouseholder_hhpihhr10', 'householdsbyraceofhouseholder_hhothhhr10', 'tapestryadultsnew_tadultbase', 'tapestryadultsnew_tadult01', 'tapestryadultsnew_tadult02', 'populationtotals_popgrw10cy', 'raceandhispanicorigin_divindx_cy', 'householdincome_medhinc_cy', 'householdincome_acsmedhinc', 'householdincome_acsmedhinc_e', 'householdincome_acsmedhinc_r']
[10]:
usa._ba_cntry.get_enrich_variables_from_iterable(xlsx_df.fieldnames_pro)
[10]:
| name | alias | data_collection | enrich_name | enrich_field_name | |
|---|---|---|---|---|---|
| 0 | ACSMEDHINC | 2019 Median HH Income (ACS 5-Yr) | householdincome | householdincome.ACSMEDHINC | householdincome_ACSMEDHINC |
| 1 | MEDHINC_CY | 2021 Median Household Income | householdincome | householdincome.MEDHINC_CY | householdincome_MEDHINC_CY |
| 2 | MP09147a_B | 2021 Own Any Tablet | ElectronicsInternet | ElectronicsInternet.MP09147a_B | ElectronicsInternet_MP09147a_B |
| 3 | MP09148a_B | 2021 Own Any E-Reader | ElectronicsInternet | ElectronicsInternet.MP09148a_B | ElectronicsInternet_MP09148a_B |
| 4 | MP09134a_B | 2021 Own E-Reader/Tablet: Amazon Kindle | ElectronicsInternet | ElectronicsInternet.MP09134a_B | ElectronicsInternet_MP09134a_B |
| 5 | X11001_X | 2021 Education | education | education.X11001_X | education_X11001_X |
| 6 | MP14129a_B | 2021 Typically Spend 1-3 Hrs Exercising/Wk | HealthPersonalCare | HealthPersonalCare.MP14129a_B | HealthPersonalCare_MP14129a_B |
| 7 | INDAGRI_CY | 2021 Industry: Agriculture | industry | industry.INDAGRI_CY | industry_INDAGRI_CY |
| 8 | INDMIN_CY | 2021 Industry: Mining | industry | industry.INDMIN_CY | industry_INDMIN_CY |
| 9 | INDCONS_CY | 2021 Industry: Construction | industry | industry.INDCONS_CY | industry_INDCONS_CY |
| 10 | HHWHTHHR10 | 2010 HHs w/White HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHWHTHHR10 | householdsbyraceofhouseholder_HHWHTHHR10 |
| 11 | HHBLKHHR10 | 2010 HHs w/Black HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHBLKHHR10 | householdsbyraceofhouseholder_HHBLKHHR10 |
| 12 | HHAIHHR10 | 2010 HHs w/Amer Indian HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHAIHHR10 | householdsbyraceofhouseholder_HHAIHHR10 |
| 13 | HHASNHHR10 | 2010 HHs w/Asian HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHASNHHR10 | householdsbyraceofhouseholder_HHASNHHR10 |
| 14 | HHPIHHR10 | 2010 HHs w/Pacific Isl HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHPIHHR10 | householdsbyraceofhouseholder_HHPIHHR10 |
| 15 | HHOTHHHR10 | 2010 HHs w/Other Race HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHOTHHHR10 | householdsbyraceofhouseholder_HHOTHHHR10 |
| 16 | TADULTBASE | 2021 Tapestry Adult Pop Base | tapestryadultsNEW | tapestryadultsNEW.TADULTBASE | tapestryadultsNEW_TADULTBASE |
| 17 | TADULT01 | 2021 Pop 18+ in Tapestry Seg 1A | tapestryadultsNEW | tapestryadultsNEW.TADULT01 | tapestryadultsNEW_TADULT01 |
| 18 | TADULT02 | 2021 Pop 18+ in Tapestry Seg 1B | tapestryadultsNEW | tapestryadultsNEW.TADULT02 | tapestryadultsNEW_TADULT02 |
| 19 | TOTPOP_CY | 2021 Total Population | populationtotals | populationtotals.TOTPOP_CY | populationtotals_TOTPOP_CY |
| 20 | HHPOP_CY | 2021 Household Population | populationtotals | populationtotals.HHPOP_CY | populationtotals_HHPOP_CY |
| 21 | FAMPOP_CY | 2021 Family Population | populationtotals | populationtotals.FAMPOP_CY | populationtotals_FAMPOP_CY |
| 22 | POPGRW10CY | 2010-2021 Growth Rate: Population | populationtotals | populationtotals.POPGRW10CY | populationtotals_POPGRW10CY |
| 23 | DIVINDX_CY | 2021 Diversity Index | raceandhispanicorigin | raceandhispanicorigin.DIVINDX_CY | raceandhispanicorigin_DIVINDX_CY |
| 24 | ACSTOTPOP | 2019 Total Population (ACS 5-Yr) | veterans | veterans.ACSTOTPOP | veterans_ACSTOTPOP |
NOTE: Another way to get this list is simply using a list comprehension listing the field names using arcpy.ListFields.
import arcpy
from arcgis.gis import Country
pth_to_fc = r'C:/to/somwhere/somedb.fdgb/fc_of_stuff'
field_name_list = [f.name for f in arcpy.ListFields(pth_to_fc)]
usa = Country('usa')
usa._ba_cntry.get_enrich_variables_from_name_list(field_name_list)
Preprocessing with Lookup¶
The first column we are saving for last. These values do not have explicit matches. However, using pandas, we can split the string at the period and only keep the first part. This first part can then be looked up since it matches the name column.
[11]:
xlsx_df.guided_workflow_names.head()
[11]:
0 TOTPOP_CY.2021 Key Demographic Indicators (Esr...
1 HHPOP_CY.2021 Key Demographic Indicators (Esri...
2 FAMPOP_CY.2021 Key Demographic Indicators (Esr...
3 MP14129a_B.2021 Health (Market Potential) (Esr...
4 X11001_X.2021 Education (Consumer Spending) (E...
Name: guided_workflow_names, dtype: object
We do it slightly differently here. Instead of simply creating an iterable to use for lookup, in this case, we add the search_names to the existing dataframe generated from the Excel table we read in.
[12]:
xlsx_df['search_names'] = xlsx_df.guided_workflow_names.str.split('.').apply(lambda val: val[0])
xlsx_df.head()
[12]:
| guided_workflow_names | pro_enrich_names | fieldnames_arcmap | fieldnames_pro | search_names | |
|---|---|---|---|---|---|
| 0 | TOTPOP_CY.2021 Key Demographic Indicators (Esr... | populationtotals.totpop_cy | TOTPOP_CY | populationtotals_totpop_cy | TOTPOP_CY |
| 1 | HHPOP_CY.2021 Key Demographic Indicators (Esri... | householdtotals.tothh_cy | HHPOP_CY | populationtotals_hhpop_cy | HHPOP_CY |
| 2 | FAMPOP_CY.2021 Key Demographic Indicators (Esr... | populationtotals.fampop_cy | FAMPOP_CY | populationtotals_fampop_cy | FAMPOP_CY |
| 3 | MP14129a_B.2021 Health (Market Potential) (Esr... | healthpersonalcare.mp14129a_b | MP14129a_B | healthpersonalcare_mp14129a_b | MP14129a_B |
| 4 | X11001_X.2021 Education (Consumer Spending) (E... | education.x11001_x | X11001_X | education_x11001_x | X11001_X |
Since the function just needs an Iterable object, we can use the column (Pandas Series) we just created to look up valid values.
[13]:
enrich_vars = usa._ba_cntry.get_enrich_variables_from_iterable(xlsx_df.search_names)
enrich_vars
[13]:
| name | alias | data_collection | enrich_name | enrich_field_name | |
|---|---|---|---|---|---|
| 0 | MEDHINC_CY | 2021 Median Household Income | Health | Health.MEDHINC_CY | Health_MEDHINC_CY |
| 1 | MOETOTPOP | 2019 Total Population MOE (ACS 5-Yr) | ACS_Housing_Summary_rep | ACS_Housing_Summary_rep.MOETOTPOP | ACS_Housing_Summary_rep_MOETOTPOP |
| 2 | TADULTBASE | 2021 Tapestry Adult Pop Base | travelMPI | travelMPI.TADULTBASE | travelMPI_TADULTBASE |
| 3 | TOTPOP_CY | 2021 Total Population | Age_by_Sex_by_Race_Profile_rep | Age_by_Sex_by_Race_Profile_rep.TOTPOP_CY | Age_by_Sex_by_Race_Profile_rep_TOTPOP_CY |
| 4 | DIVINDX_CY | 2021 Diversity Index | Policy | Policy.DIVINDX_CY | Policy_DIVINDX_CY |
| 5 | ACSMEDHINC | 2019 Median HH Income (ACS 5-Yr) | householdincome | householdincome.ACSMEDHINC | householdincome_ACSMEDHINC |
| 6 | MOEMEDHINC | 2019 Median HH Income MOE (ACS 5-Yr) | householdincome | householdincome.MOEMEDHINC | householdincome_MOEMEDHINC |
| 7 | RELMEDHINC | 2019 Median HH Income REL (ACS 5-Yr) | householdincome | householdincome.RELMEDHINC | householdincome_RELMEDHINC |
| 8 | X11001_I | 2021 Index: Education | Community_Profile_rep | Community_Profile_rep.X11001_I | Community_Profile_rep_X11001_I |
| 9 | MP09147a_B | 2021 Own Any Tablet | ElectronicsInternet | ElectronicsInternet.MP09147a_B | ElectronicsInternet_MP09147a_B |
| 10 | MP09147a_I | 2021 Index: Own Any Tablet | ElectronicsInternet | ElectronicsInternet.MP09147a_I | ElectronicsInternet_MP09147a_I |
| 11 | MP09148a_B | 2021 Own Any E-Reader | ElectronicsInternet | ElectronicsInternet.MP09148a_B | ElectronicsInternet_MP09148a_B |
| 12 | MP09148a_I | 2021 Index: Own Any E-Reader | ElectronicsInternet | ElectronicsInternet.MP09148a_I | ElectronicsInternet_MP09148a_I |
| 13 | MP09134a_B | 2021 Own E-Reader/Tablet: Amazon Kindle | ElectronicsInternet | ElectronicsInternet.MP09134a_B | ElectronicsInternet_MP09134a_B |
| 14 | MP09134a_I | 2021 Index: Own E-Reader/Tablet: Amazon Kindle | ElectronicsInternet | ElectronicsInternet.MP09134a_I | ElectronicsInternet_MP09134a_I |
| 15 | X11001_X | 2021 Education | education | education.X11001_X | education_X11001_X |
| 16 | X11001_A | 2021 Avg: Education | education | education.X11001_A | education_X11001_A |
| 17 | MP14129a_B | 2021 Typically Spend 1-3 Hrs Exercising/Wk | HealthPersonalCare | HealthPersonalCare.MP14129a_B | HealthPersonalCare_MP14129a_B |
| 18 | INDAGRI_CY | 2021 Industry: Agriculture | industry | industry.INDAGRI_CY | industry_INDAGRI_CY |
| 19 | INDMIN_CY | 2021 Industry: Mining | industry | industry.INDMIN_CY | industry_INDMIN_CY |
| 20 | INDCONS_CY | 2021 Industry: Construction | industry | industry.INDCONS_CY | industry_INDCONS_CY |
| 21 | HHWHTHHR10 | 2010 HHs w/White HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHWHTHHR10 | householdsbyraceofhouseholder_HHWHTHHR10 |
| 22 | HHBLKHHR10 | 2010 HHs w/Black HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHBLKHHR10 | householdsbyraceofhouseholder_HHBLKHHR10 |
| 23 | HHAIHHR10 | 2010 HHs w/Amer Indian HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHAIHHR10 | householdsbyraceofhouseholder_HHAIHHR10 |
| 24 | HHASNHHR10 | 2010 HHs w/Asian HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHASNHHR10 | householdsbyraceofhouseholder_HHASNHHR10 |
| 25 | HHPIHHR10 | 2010 HHs w/Pacific Isl HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHPIHHR10 | householdsbyraceofhouseholder_HHPIHHR10 |
| 26 | HHOTHHHR10 | 2010 HHs w/Other Race HHr | householdsbyraceofhouseholder | householdsbyraceofhouseholder.HHOTHHHR10 | householdsbyraceofhouseholder_HHOTHHHR10 |
| 27 | POPGRW10CY | 2010-2021 Growth Rate: Population | KeyUSFacts | KeyUSFacts.POPGRW10CY | KeyUSFacts_POPGRW10CY |
| 28 | TADULT01 | 2021 Pop 18+ in Tapestry Seg 1A | tapestryadultsNEW | tapestryadultsNEW.TADULT01 | tapestryadultsNEW_TADULT01 |
| 29 | TADULT02 | 2021 Pop 18+ in Tapestry Seg 1B | tapestryadultsNEW | tapestryadultsNEW.TADULT02 | tapestryadultsNEW_TADULT02 |
| 30 | ACSTOTPOP | 2019 Total Population (ACS 5-Yr) | population | population.ACSTOTPOP | population_ACSTOTPOP |
| 31 | RELTOTPOP | 2019 Total Population REL (ACS 5-Yr) | population | population.RELTOTPOP | population_RELTOTPOP |
| 32 | HHPOP_CY | 2021 Household Population | populationtotals | populationtotals.HHPOP_CY | populationtotals_HHPOP_CY |
| 33 | FAMPOP_CY | 2021 Family Population | populationtotals | populationtotals.FAMPOP_CY | populationtotals_FAMPOP_CY |
Concantenating for Local Enrich¶
The arcpy.ba.EnrichLayer tool in ArcGIS Pro needs a semicolon separated string of enrich_name values for input. From any of the above results, this can quickly be created from the enich_name column.
[14]:
vars_str = ';'.join(enrich_vars.enrich_name.values)
print(vars_str)
Health.MEDHINC_CY;ACS_Housing_Summary_rep.MOETOTPOP;travelMPI.TADULTBASE;Age_by_Sex_by_Race_Profile_rep.TOTPOP_CY;Policy.DIVINDX_CY;householdincome.ACSMEDHINC;householdincome.MOEMEDHINC;householdincome.RELMEDHINC;Community_Profile_rep.X11001_I;ElectronicsInternet.MP09147a_B;ElectronicsInternet.MP09147a_I;ElectronicsInternet.MP09148a_B;ElectronicsInternet.MP09148a_I;ElectronicsInternet.MP09134a_B;ElectronicsInternet.MP09134a_I;education.X11001_X;education.X11001_A;HealthPersonalCare.MP14129a_B;industry.INDAGRI_CY;industry.INDMIN_CY;industry.INDCONS_CY;householdsbyraceofhouseholder.HHWHTHHR10;householdsbyraceofhouseholder.HHBLKHHR10;householdsbyraceofhouseholder.HHAIHHR10;householdsbyraceofhouseholder.HHASNHHR10;householdsbyraceofhouseholder.HHPIHHR10;householdsbyraceofhouseholder.HHOTHHHR10;KeyUSFacts.POPGRW10CY;tapestryadultsNEW.TADULT01;tapestryadultsNEW.TADULT02;population.ACSTOTPOP;population.RELTOTPOP;populationtotals.HHPOP_CY;populationtotals.FAMPOP_CY
Adding Onto Existing Table¶
Also, if we want, we can add the values needed for the Enrich Layer tool onto our existing table.
[15]:
add_df = enrich_vars[['name', 'enrich_name']].set_index('name')
add_df.head()
[15]:
| enrich_name | |
|---|---|
| name | |
| MEDHINC_CY | Health.MEDHINC_CY |
| MOETOTPOP | ACS_Housing_Summary_rep.MOETOTPOP |
| TADULTBASE | travelMPI.TADULTBASE |
| TOTPOP_CY | Age_by_Sex_by_Race_Profile_rep.TOTPOP_CY |
| DIVINDX_CY | Policy.DIVINDX_CY |
[16]:
joined_df = xlsx_df.join(add_df, on='search_names')
joined_df.info()
joined_df.head()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 34 entries, 0 to 33
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 guided_workflow_names 34 non-null object
1 pro_enrich_names 34 non-null object
2 fieldnames_arcmap 34 non-null object
3 fieldnames_pro 34 non-null object
4 search_names 34 non-null object
5 enrich_name 34 non-null object
dtypes: object(6)
memory usage: 1.7+ KB
[16]:
| guided_workflow_names | pro_enrich_names | fieldnames_arcmap | fieldnames_pro | search_names | enrich_name | |
|---|---|---|---|---|---|---|
| 0 | TOTPOP_CY.2021 Key Demographic Indicators (Esr... | populationtotals.totpop_cy | TOTPOP_CY | populationtotals_totpop_cy | TOTPOP_CY | Age_by_Sex_by_Race_Profile_rep.TOTPOP_CY |
| 1 | HHPOP_CY.2021 Key Demographic Indicators (Esri... | householdtotals.tothh_cy | HHPOP_CY | populationtotals_hhpop_cy | HHPOP_CY | populationtotals.HHPOP_CY |
| 2 | FAMPOP_CY.2021 Key Demographic Indicators (Esr... | populationtotals.fampop_cy | FAMPOP_CY | populationtotals_fampop_cy | FAMPOP_CY | populationtotals.FAMPOP_CY |
| 3 | MP14129a_B.2021 Health (Market Potential) (Esr... | healthpersonalcare.mp14129a_b | MP14129a_B | healthpersonalcare_mp14129a_b | MP14129a_B | HealthPersonalCare.MP14129a_B |
| 4 | X11001_X.2021 Education (Consumer Spending) (E... | education.x11001_x | X11001_X | education_x11001_x | X11001_X | education.X11001_X |
Finally, if all we want is the input values, and the enrich names, we can quickly filter to just these columns.
[17]:
vals_df = joined_df.loc[:,['guided_workflow_names', 'enrich_name']]
vals_df
[17]:
| guided_workflow_names | enrich_name | |
|---|---|---|
| 0 | TOTPOP_CY.2021 Key Demographic Indicators (Esr... | Age_by_Sex_by_Race_Profile_rep.TOTPOP_CY |
| 1 | HHPOP_CY.2021 Key Demographic Indicators (Esri... | populationtotals.HHPOP_CY |
| 2 | FAMPOP_CY.2021 Key Demographic Indicators (Esr... | populationtotals.FAMPOP_CY |
| 3 | MP14129a_B.2021 Health (Market Potential) (Esr... | HealthPersonalCare.MP14129a_B |
| 4 | X11001_X.2021 Education (Consumer Spending) (E... | education.X11001_X |
| 5 | X11001_A.2021 Education (Consumer Spending) (E... | education.X11001_A |
| 6 | X11001_I.2021 Education (Consumer Spending) (E... | Community_Profile_rep.X11001_I |
| 7 | MP09147a_B.2021 Electronics (Market Potential)... | ElectronicsInternet.MP09147a_B |
| 8 | MP09147a_I.2021 Electronics (Market Potential)... | ElectronicsInternet.MP09147a_I |
| 9 | MP09148a_B.2021 Electronics (Market Potential)... | ElectronicsInternet.MP09148a_B |
| 10 | MP09148a_I.2021 Electronics (Market Potential)... | ElectronicsInternet.MP09148a_I |
| 11 | MP09134a_B.2021 Electronics (Market Potential)... | ElectronicsInternet.MP09134a_B |
| 12 | MP09134a_I.2021 Electronics (Market Potential)... | ElectronicsInternet.MP09134a_I |
| 13 | ACSTOTPOP.2015-2019 Key Demographic Indicators... | population.ACSTOTPOP |
| 14 | MOETOTPOP.2015-2019 Key Demographic Indicators... | ACS_Housing_Summary_rep.MOETOTPOP |
| 15 | RELTOTPOP.2015-2019 Key Demographic Indicators... | population.RELTOTPOP |
| 16 | INDAGRI_CY.2021 Labor Force (Esri) (US 2021 Re... | industry.INDAGRI_CY |
| 17 | INDMIN_CY.2021 Labor Force (Esri) (US 2021 Ret... | industry.INDMIN_CY |
| 18 | INDCONS_CY.2021 Labor Force (Esri) (US 2021 Re... | industry.INDCONS_CY |
| 19 | HHWHTHHR10.2010 Households by Race_Hispanic Or... | householdsbyraceofhouseholder.HHWHTHHR10 |
| 20 | HHBLKHHR10.2010 Households by Race_Hispanic Or... | householdsbyraceofhouseholder.HHBLKHHR10 |
| 21 | HHAIHHR10.2010 Households by Race_Hispanic Ori... | householdsbyraceofhouseholder.HHAIHHR10 |
| 22 | HHASNHHR10.2010 Households by Race_Hispanic Or... | householdsbyraceofhouseholder.HHASNHHR10 |
| 23 | HHPIHHR10.2010 Households by Race_Hispanic Ori... | householdsbyraceofhouseholder.HHPIHHR10 |
| 24 | HHOTHHHR10.2010 Households by Race_Hispanic Or... | householdsbyraceofhouseholder.HHOTHHHR10 |
| 25 | TADULTBASE.2021 Tapestry Market Segmentation (... | travelMPI.TADULTBASE |
| 26 | TADULT01.2021 Tapestry Market Segmentation (Ad... | tapestryadultsNEW.TADULT01 |
| 27 | TADULT02.2021 Tapestry Market Segmentation (Ad... | tapestryadultsNEW.TADULT02 |
| 28 | POPGRW10CY.2021 Key Demographic Indicators (Es... | KeyUSFacts.POPGRW10CY |
| 29 | DIVINDX_CY.2021 Race and Hispanic Origin (Esri... | Policy.DIVINDX_CY |
| 30 | MEDHINC_CY.2021 Income (Esri) (Esri USA 2021) | Health.MEDHINC_CY |
| 31 | ACSMEDHINC.2015-2019 Income (ACS) (Esri USA 20... | householdincome.ACSMEDHINC |
| 32 | MOEMEDHINC.2015-2019 Income (ACS) (Esri USA 20... | householdincome.MOEMEDHINC |
| 33 | RELMEDHINC.2015-2019 Income (ACS) (Esri USA 20... | householdincome.RELMEDHINC |
Saving Results¶
Obviously, copying and pasting out of this notebook is a possibility, but Pandas also includes a large number of output options including back to Excel or one of my personal favorites, directly to the clipboard.
Garbage Disposal Function¶
Taking advantage of a little iteration we can create a more universal function to try and find matches based on any of these input types. While we are at it, we can also add a bit of convenience, the option to copy the results directly to the clipboard.
[18]:
from pathlib import Path
from typing import Union, Iterable
from arcgis.geoenrichment import Country
from pandas.io.clipboards import to_clipboard
def get_variable_string(input_names: Union[Iterable[str], Path], country: Country, copy_to_clipboard: bool = False) -> str:
"""
Based on an iterable input identifying variables, return the string needed to perform enrichment using the Enrich Layer
geoprocessing tool.
Args:
input_names: Iterable (typically a list) of strings identifying the variable names for enrichment, or a path to a
Feature Class if trying to find variable names from the feature inputs.
country: Country to search in for enrichment variables.
copy_to_clipboard: Whether or not to copy the result to the clipboard. Default is false.
Return:
String formatted for input into the Enrich Layer geoprocessing tool.
"""
# if the input names is a path to a feature class, get the column names to use for lookup
if isinstance(input_names, Path):
input_names = [f.name for f in arcpy.ListFields(input_names)]
# try to find valid enrich variable strings based on the input names
enrich_vars = country._ba_cntry.get_enrich_variables_from_iterable(input_names)
# if nothing found, try to split on a period, and see if this provides any matches
if len(enrich_vars.index) == 0:
input_names = [var_str.split('.')[0] for var_str in input_names]
# try again - NOTE: not doing the split before trying, becuase another potential input format can include periods
enrich_vars = country._ba_cntry.get_enrich_variables_from_iterable(input_names)
# sort for consistency
enrich_vars.sort_values('name', inplace=True)
# combine the enrich variable strings into a single string
enrich_str = ';'.join(enrich_vars.enrich_name.values)
# now, if desiring making life easy by dropping right into the clipboard
if copy_to_clipboard:
to_clipboard(enrich_str, excel=False)
return enrich_str
Now, with this new function we can input the variations from above, and consistently get the results we need. Because of the repeat trying to find variables, you will encounter the warning below if passing in the variables needing to be modified (split on the period).
[19]:
get_variable_string(xlsx_df.guided_workflow_names, country=usa)
d:\projects\geosaurus\src\arcgis\geoenrichment\_business_analyst\_main.py:1157: UserWarning: It appears none of the input enrich enrich_variables were found.
warn(f"It appears none of the input enrich enrich_variables were found.")
[19]:
'householdincome.ACSMEDHINC;population.ACSTOTPOP;Policy.DIVINDX_CY;populationtotals.FAMPOP_CY;householdsbyraceofhouseholder.HHAIHHR10;householdsbyraceofhouseholder.HHASNHHR10;householdsbyraceofhouseholder.HHBLKHHR10;householdsbyraceofhouseholder.HHOTHHHR10;householdsbyraceofhouseholder.HHPIHHR10;populationtotals.HHPOP_CY;householdsbyraceofhouseholder.HHWHTHHR10;industry.INDAGRI_CY;industry.INDCONS_CY;industry.INDMIN_CY;Health.MEDHINC_CY;householdincome.MOEMEDHINC;ACS_Housing_Summary_rep.MOETOTPOP;ElectronicsInternet.MP09134a_B;ElectronicsInternet.MP09134a_I;ElectronicsInternet.MP09147a_B;ElectronicsInternet.MP09147a_I;ElectronicsInternet.MP09148a_B;ElectronicsInternet.MP09148a_I;HealthPersonalCare.MP14129a_B;KeyUSFacts.POPGRW10CY;householdincome.RELMEDHINC;population.RELTOTPOP;tapestryadultsNEW.TADULT01;tapestryadultsNEW.TADULT02;travelMPI.TADULTBASE;Age_by_Sex_by_Race_Profile_rep.TOTPOP_CY;education.X11001_A;Community_Profile_rep.X11001_I;education.X11001_X'
[20]:
get_variable_string(xlsx_df.fieldnames_arcmap, country=usa)
[20]:
'householdincome.ACSMEDHINC;population.ACSTOTPOP;Policy.DIVINDX_CY;populationtotals.FAMPOP_CY;householdsbyraceofhouseholder.HHAIHHR10;householdsbyraceofhouseholder.HHASNHHR10;householdsbyraceofhouseholder.HHBLKHHR10;householdsbyraceofhouseholder.HHOTHHHR10;householdsbyraceofhouseholder.HHPIHHR10;populationtotals.HHPOP_CY;householdsbyraceofhouseholder.HHWHTHHR10;industry.INDAGRI_CY;industry.INDCONS_CY;industry.INDMIN_CY;Health.MEDHINC_CY;householdincome.MOEMEDHINC;ACS_Housing_Summary_rep.MOETOTPOP;ElectronicsInternet.MP09134a_B;ElectronicsInternet.MP09134a_I;ElectronicsInternet.MP09147a_B;ElectronicsInternet.MP09147a_I;ElectronicsInternet.MP09148a_B;ElectronicsInternet.MP09148a_I;HealthPersonalCare.MP14129a_B;KeyUSFacts.POPGRW10CY;householdincome.RELMEDHINC;population.RELTOTPOP;tapestryadultsNEW.TADULT01;tapestryadultsNEW.TADULT02;travelMPI.TADULTBASE;Age_by_Sex_by_Race_Profile_rep.TOTPOP_CY;education.X11001_A;Community_Profile_rep.X11001_I;education.X11001_X'
[21]:
get_variable_string(xlsx_df.pro_enrich_names, country=usa)
[21]:
'householdincome.ACSMEDHINC;veterans.ACSTOTPOP;raceandhispanicorigin.DIVINDX_CY;populationtotals.FAMPOP_CY;householdsbyraceofhouseholder.HHAIHHR10;householdsbyraceofhouseholder.HHASNHHR10;householdsbyraceofhouseholder.HHBLKHHR10;householdsbyraceofhouseholder.HHOTHHHR10;householdsbyraceofhouseholder.HHPIHHR10;householdsbyraceofhouseholder.HHWHTHHR10;industry.INDAGRI_CY;industry.INDCONS_CY;industry.INDMIN_CY;householdincome.MEDHINC_CY;ElectronicsInternet.MP09134a_B;ElectronicsInternet.MP09147a_B;ElectronicsInternet.MP09148a_B;HealthPersonalCare.MP14129a_B;populationtotals.POPGRW10CY;tapestryadultsNEW.TADULT01;tapestryadultsNEW.TADULT02;tapestryadultsNEW.TADULTBASE;householdtotals.TOTHH_CY;populationtotals.TOTPOP_CY;education.X11001_X'