<jupyter_start><jupyter_text>1. Install DependenciesFirst install the libraries needed to execute recipes, this only needs to be done once, then click play.<jupyter_code>!pip install git+https://github.com/google/starthinker<jupyter_output><empty_output><jupyter_text>2. Get Cloud Project IDTo run this recipe [requires a Google Cloud Project](https://github.com/google/starthinker/blob/master/tutorials/cloud_project.md), this only needs to be done once, then click play.<jupyter_code>CLOUD_PROJECT = 'PASTE PROJECT ID HERE'

print("Cloud Project Set To: %s" % CLOUD_PROJECT)<jupyter_output><empty_output><jupyter_text>3. Get Client CredentialsTo read and write to various endpoints requires [downloading client credentials](https://github.com/google/starthinker/blob/master/tutorials/cloud_client_installed.md), this only needs to be done once, then click play.<jupyter_code>CLIENT_CREDENTIALS = 'PASTE CREDENTIALS HERE'

print("Client Credentials Set To: %s" % CLIENT_CREDENTIALS)<jupyter_output><empty_output><jupyter_text>4. Enter Census Data Normalized ParametersConvert given census table to percent of population, normalizing it. 1. Specify the geography, year, and span that make up the census table names. 1. Not every combination of geography, year, and span exists or contains all the required fields. 1. Every time the query runs it will overwrite the table.Modify the values below for your use case, can be done multiple times, then click play.<jupyter_code>FIELDS = {
  'auth_write': 'service',  # Credentials used for writing data.
  'census_geography': 'zip_codes',  # Census table to get data from.
  'census_year': '2018',  # Census table to get data from.
  'census_span': '5yr',  # Census table to get data from.
  'dataset': '',  # Existing BigQuery dataset.
  'table': '',  # Table to create from this census_data.
}

print("Parameters Set To: %s" % FIELDS)<jupyter_output><empty_output><jupyter_text>5. Execute Census Data NormalizedThis does NOT need to be modified unles you are changing the recipe, click play.<jupyter_code>from starthinker.util.project import project
from starthinker.script.parse import json_set_fields, json_expand_includes

USER_CREDENTIALS = '/content/user.json'

TASKS = [
  {
    'bigquery': {
      'auth': 'user',
      'from': {
        'query': '           SELECT             geo_id AS Geo_ID,              /* gender */             SAFE_DIVIDE(female_pop, total_pop) AS Female_Percent,             SAFE_DIVIDE(male_pop, total_pop) AS Male_Percent,                          /* age */             SAFE_DIVIDE(population_1_year_and_over , total_pop) AS Population_1_Year_And_Over_Percent,             SAFE_DIVIDE(population_3_years_over , total_pop) AS Population_3_Years_And_Over_Percent,             SAFE_DIVIDE(pop_5_years_over , total_pop) AS Population_5_Years_And_Over_Percent,             SAFE_DIVIDE(pop_15_and_over , total_pop) AS Population_15_Years_And_Over_Percent,             SAFE_DIVIDE(pop_16_over , total_pop) AS Population_16_Years_And_Over_Percent,             SAFE_DIVIDE(pop_25_years_over , total_pop) AS Population_25_Years_And_Over_Percent,             SAFE_DIVIDE(pop_25_64 , total_pop) AS Population_25_To_64_Percent,                              /* male age */             SAFE_DIVIDE(male_5_to_9 , total_pop) AS Male_5_To_9_Percent,             SAFE_DIVIDE(male_10_to_14 , total_pop) AS Male_10_To_14_Percent,             SAFE_DIVIDE(male_15_to_17 , total_pop) AS Male_15_To_17_Percent,             SAFE_DIVIDE(male_18_to_19 , total_pop) AS Male_18_To_19_Percent,             SAFE_DIVIDE(male_20 , total_pop) AS Male_20_Percent,             SAFE_DIVIDE(male_21 , total_pop) AS Male_21_Percent,             SAFE_DIVIDE(male_22_to_24 , total_pop) AS Male_22_To_24_Percent,             SAFE_DIVIDE(male_25_to_29 , total_pop) AS Male_25_To_29_Percent,             SAFE_DIVIDE(male_30_to_34 , total_pop) AS Male_30_To_34_Percent,             SAFE_DIVIDE(male_35_to_39 , total_pop) AS Male_35_To_39_Percent,             SAFE_DIVIDE(male_40_to_44 , total_pop) AS Male_40_To_44_Percent,             SAFE_DIVIDE(male_45_to_49 , total_pop) AS Male_45_To_49_Percent,             SAFE_DIVIDE(male_50_to_54 , total_pop) AS Male_50_To_54_Percent,             SAFE_DIVIDE(male_55_to_59 , total_pop) AS Male_55_To_59_Percent,             SAFE_DIVIDE(male_65_to_66 , total_pop) AS Male_65_To_66_Percent,             SAFE_DIVIDE(male_67_to_69 , total_pop) AS Male_67_To_69_Percent,             SAFE_DIVIDE(male_70_to_74 , total_pop) AS Male_70_To_74_Percent,             SAFE_DIVIDE(male_75_to_79 , total_pop) AS Male_75_To_79_Percent,             SAFE_DIVIDE(male_80_to_84 , total_pop) AS Male_80_To_84_Percent,             SAFE_DIVIDE(male_85_and_over , total_pop) AS Male_85_And_Over_Percent,                          /* female age */             SAFE_DIVIDE(female_5_to_9 , total_pop) AS Female_5_To_9_Percent,             SAFE_DIVIDE(female_10_to_14 , total_pop) AS Female_10_To_14_Percent,             SAFE_DIVIDE(female_15_to_17 , total_pop) AS Female_15_To_17_Percent,             SAFE_DIVIDE(female_18_to_19 , total_pop) AS Female_18_To_19_Percent,             SAFE_DIVIDE(female_20 , total_pop) AS Female_20_Percent,             SAFE_DIVIDE(female_21 , total_pop) AS Female_21_Percent,             SAFE_DIVIDE(female_22_to_24 , total_pop) AS Female_22_To_24_Percent,             SAFE_DIVIDE(female_25_to_29 , total_pop) AS Female_25_To_29_Percent,             SAFE_DIVIDE(female_30_to_34 , total_pop) AS Female_30_To_34_Percent,             SAFE_DIVIDE(female_35_to_39 , total_pop) AS Female_35_To_39_Percent,             SAFE_DIVIDE(female_40_to_44 , total_pop) AS Female_40_To_44_Percent,             SAFE_DIVIDE(female_45_to_49 , total_pop) AS Female_45_To_49_Percent,             SAFE_DIVIDE(female_50_to_54 , total_pop) AS Female_50_To_54_Percent,             SAFE_DIVIDE(female_55_to_59 , total_pop) AS Female_55_To_59_Percent,             SAFE_DIVIDE(female_65_to_66 , total_pop) AS Female_65_To_66_Percent,             SAFE_DIVIDE(female_67_to_69 , total_pop) AS Female_67_To_69_Percent,             SAFE_DIVIDE(female_70_to_74 , total_pop) AS Female_70_To_74_Percent,             SAFE_DIVIDE(female_75_to_79 , total_pop) AS Female_75_To_79_Percent,             SAFE_DIVIDE(female_80_to_84 , total_pop) AS Female_80_To_84_Percent,             SAFE_DIVIDE(female_85_and_over , total_pop) AS Female_85_And_Over_Percent,                          /* race */             SAFE_DIVIDE(white_pop , total_pop) AS White_Percent,             SAFE_DIVIDE(black_pop , total_pop) AS Black_Percent,             SAFE_DIVIDE(asian_pop , total_pop) AS Asian_Percent,             SAFE_DIVIDE(hispanic_pop , total_pop) AS Hispanic_Percent,             SAFE_DIVIDE(amerindian_pop , total_pop) AS American_Indian_Percent,             SAFE_DIVIDE(other_race_pop , total_pop) AS Other_Percent,               SAFE_DIVIDE(two_or_more_races_pop , total_pop) AS Two_or_More_Races_Percent,               SAFE_DIVIDE(hispanic_any_race , total_pop) AS Hipanic_Any_Percent,               SAFE_DIVIDE(not_hispanic_pop , total_pop) AS Not_Hispanic_Percent,                            /* race age */             SAFE_DIVIDE(asian_male_45_54 , total_pop) AS Asian_Male_45_54_Percent,             SAFE_DIVIDE(asian_male_55_64 , total_pop) AS Asian_Male_55_64_Percent,             SAFE_DIVIDE(black_male_45_54 , total_pop) AS Black_Male_45_54_Percent,             SAFE_DIVIDE(black_male_55_64 , total_pop) AS Black_Male_55_64_Percent,             SAFE_DIVIDE(hispanic_male_45_54 , total_pop) AS Hispanic_Male_45_54_Percent,             SAFE_DIVIDE(hispanic_male_55_64 , total_pop) AS Hispanic_Male_55_64_Percent,               SAFE_DIVIDE(white_male_45_54 , total_pop) AS White_Male_45_54_Percent,               SAFE_DIVIDE(white_male_55_64 , total_pop) AS White_Male_55_64_Percent,                            /* income */             SAFE_DIVIDE(income_less_10000 , total_pop) AS Income_Less_10000_Percent,             SAFE_DIVIDE(income_10000_14999 , total_pop) AS Income_10000_14999_Percent,             SAFE_DIVIDE(income_15000_19999 , total_pop) AS Income_15000_19999_Percent,             SAFE_DIVIDE(income_20000_24999 , total_pop) AS Income_20000_24999_Percent,             SAFE_DIVIDE(income_25000_29999 , total_pop) AS Income_25000_29999_Percent,             SAFE_DIVIDE(income_30000_34999 , total_pop) AS Income_30000_34999_Percent,               SAFE_DIVIDE(income_35000_39999 , total_pop) AS Income_35000_39999_Percent,               SAFE_DIVIDE(income_40000_44999 , total_pop) AS Income_40000_44999_Percent,               SAFE_DIVIDE(income_45000_49999 , total_pop) AS Income_45000_49999_Percent,               SAFE_DIVIDE(income_50000_59999 , total_pop) AS Income_50000_59999_Percent,             SAFE_DIVIDE(income_60000_74999 , total_pop) AS Income_60000_74999_Percent,             SAFE_DIVIDE(income_75000_99999 , total_pop) AS Income_75000_99999_Percent,             SAFE_DIVIDE(income_100000_124999 , total_pop) AS Income_100000_129999_Percent,             SAFE_DIVIDE(income_125000_149999 , total_pop) AS Income_125000_149999_Percent,             SAFE_DIVIDE(income_150000_199999 , total_pop) AS Income_150000_199999_Percent,             SAFE_DIVIDE(income_200000_or_more , total_pop) AS Income_200000_Or_More_Percent,               SAFE_DIVIDE(poverty , total_pop) AS Poverty_Percent,             gini_index as Gini_Index,                          /* housing */             SAFE_DIVIDE(occupied_housing_units , housing_units ) AS Occupied_Housing_Units_Percent,             SAFE_DIVIDE(housing_units_renter_occupied , housing_units) AS Housing_Units_Renter_Occupied_Percent,             SAFE_DIVIDE(vacant_housing_units , housing_units) AS Vacant_Housing_Units_Percent,             SAFE_DIVIDE(vacant_housing_units_for_rent , housing_units) AS Vacant_Housing_Units_For_Rent_Percent,             SAFE_DIVIDE(vacant_housing_units_for_sale , housing_units) AS Vacant_Housing_Units__For_Sale_Percent,             SAFE_DIVIDE(dwellings_1_units_detached , housing_units) AS Dwellings_1_Units_Detached_Percent,               SAFE_DIVIDE(dwellings_1_units_attached , housing_units) AS Dwellings_1_Units_Attached_Percent,               SAFE_DIVIDE(dwellings_2_units , housing_units) AS Dwellings_2_Units_Percent,               SAFE_DIVIDE(dwellings_3_to_4_units , housing_units) AS Dwellings_3_To_4_Units_Percent,               SAFE_DIVIDE(dwellings_5_to_9_units , housing_units) AS Dwellings_5_To_9_Units_Percent,             SAFE_DIVIDE(dwellings_10_to_19_units , housing_units) AS Dwellings_10_To_19_Units_Percent,             SAFE_DIVIDE(dwellings_20_to_49_units , housing_units) AS Dwellings_20_To_49_Units_Percent,             SAFE_DIVIDE(dwellings_50_or_more_units , housing_units) AS Dwellings_50_or_More_Units_Percent,             SAFE_DIVIDE(mobile_homes , housing_units) AS Mobile_Homes_Percent,             SAFE_DIVIDE(housing_built_2005_or_later , housing_units) AS Housing_Built_2005_Or_Later_Percent,             SAFE_DIVIDE(housing_built_2000_to_2004 , housing_units) AS Housing_Built_2000_To_2004_Percent,               SAFE_DIVIDE(housing_built_1939_or_earlier , housing_units ) AS Housing_Built_1939_Or_Earlier_Percent,                          SAFE_DIVIDE(rent_over_50_percent , housing_units) AS Rent_Over_50_Percent,             SAFE_DIVIDE(rent_40_to_50_percent , housing_units) AS Rent_40_To_50_Percent,             SAFE_DIVIDE(rent_35_to_40_percent , housing_units) AS Rent_35_To_40_Percent,             SAFE_DIVIDE(rent_30_to_35_percent , housing_units) AS Rent_30_To_35_Percent,             SAFE_DIVIDE(rent_25_to_30_percent , housing_units) AS Rent_25_To_30_Percent,               SAFE_DIVIDE(rent_20_to_25_percent , housing_units) AS Rent_20_To_25_Percent,               SAFE_DIVIDE(rent_15_to_20_percent , housing_units) AS Rent_15_To_20_Percent,               SAFE_DIVIDE(rent_10_to_15_percent , housing_units) AS Rent_10_To_15_Percent,               SAFE_DIVIDE(rent_under_10_percent , housing_units) AS Rent_Under_10_Percent,             SAFE_DIVIDE(rent_burden_not_computed , housing_units) AS Rent_Burden_Not_Computed_Percent,                        SAFE_DIVIDE(owner_occupied_housing_units , housing_units) AS Owner_occupied_Housing_Units_Percent,             SAFE_DIVIDE(million_dollar_housing_units , housing_units) AS Million_Dollar_Housing_Units_Percent,             SAFE_DIVIDE(mortgaged_housing_units , housing_units) AS Mortgaged_Housing_Units_Percent,             percent_income_spent_on_rent AS Spent_on_Rent_Percent,             SAFE_DIVIDE(group_quarters , total_pop) AS Group_Quarters_Percent,                         SAFE_DIVIDE(different_house_year_ago_different_city , housing_units) AS Different_House_year_Ago_Different_City_Percent,             SAFE_DIVIDE(different_house_year_ago_same_city , housing_units) AS Different_House_year_Ago_Same_City_Percent,                                         /* family */             SAFE_DIVIDE(married_households , households ) AS Married_Households_Percent,             SAFE_DIVIDE(nonfamily_households , households) AS NonFamily_Households_Percent,             SAFE_DIVIDE(family_households , households) AS Family_Households_Percent,             SAFE_DIVIDE(households_public_asst_or_food_stamps , households) AS Households_Public_Assist_or_Food_Stamps_Percent,             SAFE_DIVIDE(male_male_households , households) AS Male_Male_Households_Percent,               SAFE_DIVIDE(female_female_households , households) AS Female_female_Households_Percent,               SAFE_DIVIDE(children , households) AS Children_Percent,               SAFE_DIVIDE(children_in_single_female_hh , households) AS Childern_In_Single_Female_Household_Percent,               SAFE_DIVIDE(families_with_young_children , households) AS Families_With_Young_Children_Percent,             SAFE_DIVIDE(two_parent_families_with_young_children , households) AS Two_Parent_Families_With_Young_Children_Percent,             SAFE_DIVIDE(two_parents_in_labor_force_families_with_young_children , households) AS Two_Parents_In_Labor_Force_Families_With_Young_Children_Percent,             SAFE_DIVIDE(two_parents_father_in_labor_force_families_with_young_children , households) AS Two_Parents_Father_In_Labor_Force_Families_With_Young_Children_Percent,             SAFE_DIVIDE(two_parents_mother_in_labor_force_families_with_young_children , households) AS Two_Parents_Mother_In_Labor_Force_Families_With_Young_Children_Percent,             SAFE_DIVIDE(two_parents_not_in_labor_force_families_with_young_children , households) AS Two_Parents_Not_In_Labor_Force_Families_With_Young_Children_Percent,             SAFE_DIVIDE(one_parent_families_with_young_children , households) AS One_Parent_Families_With_Young_Children_Percent,             SAFE_DIVIDE(father_one_parent_families_with_young_children , households) AS Father_One_Parent_Families_With_Young_Children_Percent,             SAFE_DIVIDE(father_in_labor_force_one_parent_families_with_young_children , households) AS Father_In_Labor_Force_One_Parent_Families_With_Young_Children_Percent,                          /* commute */             SAFE_DIVIDE(commute_less_10_mins , total_pop) AS Commute_Less_10_Mins_Percent,             SAFE_DIVIDE(commute_10_14_mins , total_pop) AS Commute_10_14_Mins_Percent,             SAFE_DIVIDE(commute_15_19_mins , total_pop) AS Commute_15_19_Mins_Percent,             SAFE_DIVIDE(commute_20_24_mins , total_pop) AS Commute_20_24_Mins_Percent,             SAFE_DIVIDE(commute_25_29_mins , total_pop) AS Commute_25_29_Mins_Percent,             SAFE_DIVIDE(commute_30_34_mins , total_pop) AS Commute_30_34_Mins_Percent,             SAFE_DIVIDE(commute_35_44_mins , total_pop) AS Commute_35_44_Mins_Percent,             SAFE_DIVIDE(commute_45_59_mins , total_pop) AS Commute_45_59_Mins_Percent,             SAFE_DIVIDE(commuters_16_over , total_pop) AS Commuters_16_Over_Percent,             SAFE_DIVIDE(walked_to_work , total_pop) AS Walked_To_Work_Percent,             SAFE_DIVIDE(worked_at_home , total_pop) AS Worked_At_Home_Percent,             SAFE_DIVIDE(no_car , total_pop) AS No_Car_Percent,             SAFE_DIVIDE(one_car , total_pop) AS One_Car_Percent,             SAFE_DIVIDE(two_cars , total_pop) AS Two_cars_Percent,             SAFE_DIVIDE(three_cars , total_pop) AS Three_cars_Percent,             SAFE_DIVIDE(four_more_cars , total_pop) AS Four_more_Cars_Percent,             SAFE_DIVIDE(commuters_by_public_transportation , total_pop) AS Commuters_By_Public_Transportation_Percent,             SAFE_DIVIDE(commuters_by_bus , total_pop) AS Commuters_By_Bus_Percent,             SAFE_DIVIDE(commuters_by_car_truck_van , total_pop) AS Commuters_By_Car_truck_Van_Percent,             SAFE_DIVIDE(commuters_by_carpool , total_pop) AS Commuters_By_Carpool_Percent,             SAFE_DIVIDE(commuters_by_subway_or_elevated , total_pop) AS Commuters_By_Subway_Or_Elevated_Percent,             SAFE_DIVIDE(commuters_drove_alone , total_pop) AS Commuters_Drove_Alone_Percent,                          /* education */             SAFE_DIVIDE(associates_degree , total_pop) AS Associates_Degree_Percent,             SAFE_DIVIDE(bachelors_degree , total_pop) AS Bachelors_Degree_Percent,             SAFE_DIVIDE(high_school_diploma , total_pop) AS High_School_Diploma_Percent,             SAFE_DIVIDE(less_one_year_college , total_pop) AS Less_One_year_College_Percent,             SAFE_DIVIDE(masters_degree , total_pop) AS Masters_Degree_Percent,             SAFE_DIVIDE(one_year_more_college , total_pop) AS One_Year_More_College_Percent,             SAFE_DIVIDE(less_than_high_school_graduate , total_pop) AS Less_Than_high_School_Graduate_Percent,                            SAFE_DIVIDE(high_school_including_ged , total_pop) AS High_School_including_Ged_Percent,             SAFE_DIVIDE(bachelors_degree_2 , total_pop) AS Backelors_Degree_2_Percent,             SAFE_DIVIDE(bachelors_degree_or_higher_25_64 , total_pop) AS Backelors_Degree_Or_Higher_25_64_Percent,             SAFE_DIVIDE(graduate_professional_degree , total_pop) AS GRaduate_professional_Degree_Percent,             SAFE_DIVIDE(some_college_and_associates_degree , total_pop) AS Some_College_And_Associates_Percent,                          SAFE_DIVIDE(male_45_64_associates_degree , total_pop) AS Males_45_64_Associates_Degree_Percent,             SAFE_DIVIDE(male_45_64_bachelors_degree , total_pop) AS Males_45_64_Bachelors_Degree_Percent,             SAFE_DIVIDE(male_45_64_graduate_degree , total_pop) AS Males_45_65_Graduate_Degree_Percent,             SAFE_DIVIDE(male_45_64_less_than_9_grade , total_pop) AS Male_45_64_Less_Than_9_Grade_Percent,             SAFE_DIVIDE(male_45_64_grade_9_12 , total_pop) AS Male_45_64_Grade_9_12_Percent,             SAFE_DIVIDE(male_45_64_high_school , total_pop) AS Males_45_64_High_School_Percent,             SAFE_DIVIDE(male_45_64_some_college , total_pop) AS Male_45_64_Some_College_Percent,                          SAFE_DIVIDE(in_grades_1_to_4 , total_pop) AS In_Grades_1_To_4_Percent,             SAFE_DIVIDE(in_grades_5_to_8 , total_pop) AS In_Grades_5_To_8_Percent,             SAFE_DIVIDE(in_grades_9_to_12 , total_pop) AS In_Grades_9_To_12_Percent,             SAFE_DIVIDE(in_school , total_pop) AS OIn_School_Percent,             SAFE_DIVIDE(in_undergrad_college , total_pop) AS In_Undergrad_College_Percent,                          /* labor */             SAFE_DIVIDE(employed_pop , total_pop) AS Employed_Percent,             SAFE_DIVIDE(unemployed_pop , total_pop) AS Unemployed_Percent,             SAFE_DIVIDE(pop_in_labor_force , total_pop) AS In_Labor_Force_Percent,             SAFE_DIVIDE(not_in_labor_force , total_pop) AS Not_In_Labor_Force_Percent,             SAFE_DIVIDE(workers_16_and_over , total_pop) AS Workers_16_And_Over_Percent,             SAFE_DIVIDE(armed_forces , total_pop) AS Armed_Forces_Percent,             SAFE_DIVIDE(civilian_labor_force , total_pop) AS Civilian_labor_Force_Percent,             SAFE_DIVIDE(employed_agriculture_forestry_fishing_hunting_mining , total_pop) AS Employed_agriculture_Forestry_Fishing_Hunting_Mining_Percent,             SAFE_DIVIDE(employed_arts_entertainment_recreation_accommodation_food , total_pop) AS Employed_Entertainment_Recreation_Accommodation_Food_Percent,             SAFE_DIVIDE(employed_construction , total_pop) AS Employed_Construction_Percent,             SAFE_DIVIDE(employed_education_health_social , total_pop) AS Employed_Education_Health_Social_Percent,             SAFE_DIVIDE(employed_finance_insurance_real_estate , total_pop) AS Employed_Finance_Insurance_Real_Estate_Percent,             SAFE_DIVIDE(employed_information , total_pop) AS Employed_Information_Percent,             SAFE_DIVIDE(employed_manufacturing , total_pop) AS Employed_Manufacturing_Percent,             SAFE_DIVIDE(employed_other_services_not_public_admin , total_pop) AS Employed_Other_Services_Not_Public_Admin_Percent,             SAFE_DIVIDE(employed_public_administration , total_pop) AS Employed_Public_Administration_Percent,             SAFE_DIVIDE(employed_retail_trade , total_pop) AS Eomployed_Retail_Trade_Percent,             SAFE_DIVIDE(employed_science_management_admin_waste , total_pop) AS Employed_Science_Management_Admin_Waste_Percent,             SAFE_DIVIDE(employed_transportation_warehousing_utilities , total_pop) AS Employed_Transportation_WareHousing_Utilities_Percent,             SAFE_DIVIDE(employed_wholesale_trade , total_pop) AS Employed_wholesale_Arts_Percent,             SAFE_DIVIDE(occupation_management_arts , total_pop) AS Occupation_Managment_Arts_Percent,             SAFE_DIVIDE(occupation_natural_resources_construction_maintenance , total_pop) AS Occupation_Natural_Resources_Construction_Maintenance_Percent,             SAFE_DIVIDE(occupation_production_transportation_material , total_pop) AS Occupation_Production_Transportation_Material_Percent,             SAFE_DIVIDE(occupation_sales_office , total_pop) AS Occupation_sales_office_Percent,             SAFE_DIVIDE(occupation_services , total_pop) AS Occupation_Services_Percent,             SAFE_DIVIDE(management_business_sci_arts_employed , total_pop) AS Management_Business_Sci_Arts_Employed_Percent,             SAFE_DIVIDE(sales_office_employed , total_pop) AS Sales_office_Eomployed_Percent,                         /* language */             SAFE_DIVIDE(speak_only_english_at_home , total_pop) AS Speak_Only_English_Percent,             SAFE_DIVIDE(speak_spanish_at_home , total_pop) AS Speak_Spanish_Percent,             SAFE_DIVIDE(speak_spanish_at_home_low_english , total_pop) AS Speak_Spanish_Low_English_Percent,                        total_pop           FROM             `bigquery-public-data.census_bureau_acs.{census_geography}_{census_year}_{census_span}`         ',
        'legacy': False,
        'parameters': {
          'census_geography': {'field': {'name': 'census_geography','kind': 'choice','order': 1,'default': 'zip_codes','description': 'Census table to get data from.','choices': ['zip_codes','state','zcta5','schooldistrictunified','puma','place','county','congressionaldistrict','censustract','cbsa']}},
          'census_year': {'field': {'name': 'census_year','kind': 'choice','order': 2,'default': '2018','description': 'Census table to get data from.','choices': [2018,2017,2016,2015,2014,2013,2012,2011,2010,2009,2008,2007]}},
          'census_span': {'field': {'name': 'census_span','kind': 'choice','order': 3,'default': '5yr','description': 'Census table to get data from.','choices': ['1yr','3yr','5yr']}}
        }
      },
      'to': {
        'dataset': {'field': {'name': 'dataset','kind': 'string','order': 4,'default': '','description': 'Existing BigQuery dataset.'}},
        'table': {'field': {'name': 'table','kind': 'string','order': 5,'default': '','description': 'Table to create from this census_data.'}}
      }
    }
  }
]

json_set_fields(TASKS, FIELDS)
json_expand_includes(TASKS)

project.initialize(_recipe={ 'tasks':TASKS }, _project=CLOUD_PROJECT, _user=USER_CREDENTIALS, _client=CLIENT_CREDENTIALS, _verbose=True)
project.execute()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.059203712143182666, 'fraction_numerical': 0.04362596843021088, 'mean_word_length': 4.079545454545454, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 21, 'https://': 3, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 6, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '505082', 'license': 'Apache-2.0', 'n_tokens_mistral': 9000, 'n_tokens_neox': 7847, 'n_words': 1181, 'path': 'colabs/bigquery_census_normalize.ipynb', 'repo_name': 'Gregorfran/starthinker', 'size': '24137'}