def test_get_stat_data_nulls_with_denominator_key(self):
table = FieldTable(['household goods'],
universe='Households',
denominator_key='total households')
self.load_data(
table, """
lev,code,fridge,10
lev,code,computer,5
lev,code,total households,
""")
data, total = get_stat_data(['household goods'], self.geo, self.s)
self.assertEqual(total, None)
self.assertEqual(data['Fridge']['numerators']['this'], 10)
self.assertIsNone(data['Fridge']['values']['this'])
self.assertEqual(data['Computer']['numerators']['this'], 5)
self.assertIsNone(data['Computer']['values']['this'])
from wazimap.data.tables import FieldTable
# Define our tables so the data API can discover them.
# TODO: Add comments so that we can quickly see categories/topics
# TODO: Rework format to a standard
FieldTable(
['rural or urban', 'sex', 'age in completed years'],
year='2009'
)
FieldTable(
['employment activity status', 'sex'],
universe='People aged 5 years and older',
year='2009'
)
FieldTable(
['school attendance', 'sex'],
universe='People aged 3 years and older',
year='2009'
)
FieldTable(
['highest education level reached'],
universe='People aged 3 years and older',
year='2009'
)
FieldTable(
['main mode of human waste disposal'],
universe='Households',
year='2009'
)
FieldTable(
from django.conf import settings
from wazimap.data.tables import FieldTable, SimpleTable
# Define our tables for each profile so the data API can discover them.
# All profiles
# Census tables
if settings.WAZIMAP['default_profile'] == 'census':
FieldTable(['age groups in 5 years'])
FieldTable(['age in completed years'])
FieldTable([
'electricity for cooking', 'electricity for heating',
'electricity for lighting'
])
FieldTable(['energy or fuel for cooking'])
FieldTable(['energy or fuel for heating'])
FieldTable(['energy or fuel for lighting'])
FieldTable(['gender'])
FieldTable(['gender', 'marital status'])
FieldTable(['gender', 'population group'])
FieldTable(['gender', 'age groups in 5 years'])
FieldTable(['highest educational level'])
FieldTable(['highest educational level'],
id="highesteducationallevel20",
universe='Individuals 20 and older')
FieldTable(['language'],
description='Population by primary language spoken at home')
FieldTable(['employed individual monthly income'],
universe='Employed individuals')
FieldTable(['employed individual annual income'],
from wazimap.data.tables import FieldTable
# Define our tables so the data API can discover them.
FieldTable(['main type of cooking fuel'],
universe='Households',
table_per_level=False)
from wazimap.data.tables import FieldTable
# Define our tables so the data API can discover them.
# TODO: Add comments so that we can quickly see categories/topics
# TODO: Rework format to a standard
# FieldTable(['code', 'name', 'region', 'district', 'ward', 'ownership', 'latitude', 'longitude',
# 'pass_rate', 'change_previous_year_pass_rate', 'avg_gpa', 'chane_previous_year_gpa', 'rank',
# 'year_of_result', 'more_than_40', 'national_rank_all', 'regional_rank_all',
# 'district_rank_all'], id='secondary_school', dataset="School's League", year='2017')
FieldTable([
'code', 'name', 'region', 'district', 'ward', 'ownership', 'gender',
'latitude', 'longitude', 'avg_gpa', 'year_of_result', 'more_than_40',
'national_rank_all', 'regional_rank_all'
],
id='secondary_school',
dataset="School's League",
year='2017')
from wazimap.data.tables import FieldTable FieldTable(['rural or urban'], year='2014') FieldTable(['sex'], year='2014') FieldTable(['household'], year='2014') FieldTable(['household distribution by energy source'], year='2014') FieldTable(['household distribution by light source'], year='2014') FieldTable(['household percentage by permanency'], year='2014') FieldTable(['presidential candidate'], year='2014') FieldTable(['disability'], year='2014') FieldTable(['disabled or not'], year='2014')
from wazimap.data.tables import FieldTable FieldTable(['rural or urban'], table_per_level=False) FieldTable(['sex'], table_per_level=False) FieldTable(['household'], table_per_level=False) FieldTable(['household distribution by energy source'], table_per_level=False) FieldTable(['household distribution by light source'], table_per_level=False) FieldTable(['presidential candidate'], table_per_level=False) FieldTable(['disability'], table_per_level=False) FieldTable(['disabled or not'], table_per_level=False)
from wazimap.data.tables import FieldTable
# Define our tables so the data API can discover them.
FieldTable(['rural or urban', 'sex', 'age in completed years'],
table_per_level=False)
FieldTable(['employment activity status', 'sex'],
universe='People aged 5 years and older',
table_per_level=False)
FieldTable(['school attendance', 'sex'],
universe='People aged 3 years and older',
table_per_level=False)
FieldTable(['highest education level reached'],
universe='People aged 3 years and older',
table_per_level=False)
FieldTable(['main mode of human waste disposal'],
universe='Households',
table_per_level=False)
FieldTable(['main source of water'],
universe='Households',
table_per_level=False)
FieldTable(['main type of lighting fuel'],
universe='Households',
table_per_level=False)
FieldTable(['main type of floor material'],
universe='Households',
table_per_level=False)
FieldTable(['main type of wall material'],
universe='Households',
table_per_level=False)
FieldTable(['main type of roofing material'],
universe='Households',
from wazimap.data.tables import FieldTable # Define our tables so the data API can discover them. # Household tables FieldTable(['rural population'], universe='Population', table_per_level=False) FieldTable(['area', 'sex'], universe='Population', table_per_level=False) FieldTable(['census_year', 'measure'], universe='A2-Decadal Variation', table_per_level=False) FieldTable(['census_year', 'sex_vis'], universe='VISUAL', table_per_level=False) FieldTable(['area', 'sex', 'literacy'], universe='Population', table_per_level=False) FieldTable(['area','village_town_comparison'], universe='A3APPENDIX', table_per_level=False) FieldTable(['religion', 'area', 'sex'], universe='Religion', table_per_level=False) FieldTable(['age', 'area', 'sex'], universe='Age', table_per_level=False) FieldTable(['village_town_measures','area'], universe='A1-', table_per_level=False) FieldTable(['education', 'area', 'sex'], universe='Education', table_per_level=False) FieldTable(['houseless_population','area', 'sex'], universe='A7-Houseless', table_per_level=False) FieldTable(['sc_houseless_population','area', 'sex'], universe='A8-SC_Houseless', table_per_level=False) FieldTable(['st_houseless_population','area', 'sex'], universe='A9-ST_Houseless', table_per_level=False) FieldTable(['village_measures','population_range'], universe='A3-Inhabited Villages', table_per_level=False) FieldTable(['maritalstatus', 'area', 'sex'], universe='Relation', table_per_level=False) FieldTable(['workertype','age_group','area','sex'], universe='B1-Workerstype', table_per_level=False) FieldTable(['sc_workertype','age_group','area','sex'], universe='B1SC-Workerstype', table_per_level=False) FieldTable(['st_workertype','age_group','area','sex'], universe='B1ST-Workerstype', table_per_level=False) FieldTable(['workers', 'area', 'workerssex'], universe='Workers', table_per_level=False) FieldTable(['workertype','education_level', 'area', 'sex'], universe='B3', table_per_level=False) FieldTable(['education_level', 'area', 'sex_vis'], universe='VISUAL', table_per_level=False)
from wazimap.data.tables import FieldTable, SimpleTable
# Define our tables so the data API can discover them.
# Household tables
FieldTable(['main type of cooking fuel'],
universe='Households',
description='Main type of cooking fuel',
dataset='National Population and Housing Census 2011',
year='2011',
table_per_level=False)
FieldTable(['drinking water source'],
universe='Households',
description='Drinking water source',
dataset='National Population and Housing Census 2011',
year='2011',
table_per_level=False)
FieldTable(['lighting fuel'],
universe='Households',
description='Main type of lighting fuel',
dataset='National Population and Housing Census 2011',
year='2011',
table_per_level=False)
FieldTable(['foundation type'],
universe='Households',
description='Building foundation',
dataset='National Population and Housing Census 2011',
year='2011',
table_per_level=False)
from wazimap.data.tables import FieldTable
# Census data tables
FieldTable(['area', 'sex', 'year'],
id='population_2001',
universe='Population')
FieldTable(['area', 'sex', 'year'],
id='population_2011',
universe='Population')
FieldTable(['area', 'sex', 'year'],
id='population_default',
universe='Population')
FieldTable(['area', 'literacy', 'sex', 'year'],
id='literacy_2001',
universe='Literacy')
FieldTable(['area', 'literacy', 'sex', 'year'],
id='literacy_2011',
universe='Literacy')
FieldTable(['area', 'literacy', 'sex', 'year'],
id='literacy_default',
universe='Literacy')
FieldTable(['religion', 'year'], id='religion_2011', universe='Religion')
FieldTable(['religion', 'year'], id='religion_default', universe='Religion')
FieldTable(['area', 'sex', 'education', 'year'],
id='education_2011',
universe='Education')
FieldTable(['area', 'sex', 'education', 'year'],
from wazimap.data.tables import FieldTable
# Define our tables so the data API can discover them.
# Household tables
FieldTable(['rural population'], universe='Population', table_per_level=False)
FieldTable(['area', 'sex'], universe='Population', table_per_level=False)
FieldTable(['area', 'sex', 'literacy'],
universe='Population',
table_per_level=False)
FieldTable(['religion', 'area', 'sex'],
universe='Religion',
table_per_level=False)
FieldTable(['age', 'area', 'sex'], universe='Age', table_per_level=False)
FieldTable(['education', 'area', 'sex'],
universe='Education',
table_per_level=False)
FieldTable(['maritalstatus', 'area', 'sex'],
universe='Relation',
table_per_level=False)
FieldTable(['workers', 'area', 'workerssex'],
universe='Workers',
table_per_level=False)
from wazimap.data.tables import FieldTable
# Define our tables so the data API can discover them.
# TODO: Add comments so that we can quickly see categories/topics
# TODO: Rework format to a standard
FieldTable(['rural or urban', 'sex', 'age in completed years'],
year='2009',
dataset='Census')
FieldTable(['employment activity status', 'sex'],
universe='People aged 5 years and older',
year='2009',
dataset='Census')
FieldTable(['school attendance', 'sex'],
universe='People aged 3 years and older',
year='2009',
dataset='Census')
FieldTable(['highest education level reached'],
universe='People aged 3 years and older',
year='2009',
dataset='Census')
FieldTable(['main mode of human waste disposal'],
universe='Households',
year='2009',
dataset='Census')
FieldTable(id='religion', fields=['religion'], year=2009, dataset='Census')
FieldTable(id='household_heads',
fields=['Household_Heads'],
year=2009,
dataset='Census')
from wazimap.data.tables import FieldTable, SimpleTable
# Define our tables so the data API can discover them.
FieldTable(['rural or urban', 'sex', 'age in completed years'])
#tz wards do not have population breakdown by age so they have a separate table
FieldTable(['rural or urban', 'sex'])
FieldTable(['employment activity status', 'sex'],
universe='People aged 5 years and older')
FieldTable(['school attendance', 'sex'],
universe='People aged 3 years and older')
FieldTable(['highest education level reached'],
universe='People aged 3 years and older')
FieldTable(['main mode of human waste disposal'], universe='Households')
FieldTable(['main source of water'], universe='Households')
FieldTable(['main type of lighting fuel'], universe='Households')
FieldTable(['main type of floor material'], universe='Households')
FieldTable(['main type of wall material'], universe='Households')
FieldTable(['main type of roofing material'], universe='Households')
FieldTable(['literacy test'])
FieldTable(['pepfar'])
FieldTable(['school attendance'])
FieldTable(['pupil teacher ratios'])
FieldTable(['school amenity'])
FieldTable(['causes of death under five'])
FieldTable(['causes of death over five'])
FieldTable(['inpatient diagnosis over five'])
FieldTable(['inpatient diagnosis under five'])
FieldTable(['outpatient diagnosis over five'])
FieldTable(['outpatient diagnosis under five'])
FieldTable(['family planning clients'])
FieldTable(['place of delivery'])
from wazimap.data.tables import FieldTable
# Define our tables so the data API can discover them.
# Household tables
FieldTable(['main type of cooking fuel'],
universe='Households',
table_per_level=False)
FieldTable(['drinking water source'],
universe='Households',
table_per_level=False)
FieldTable(['lighting fuel'], universe='Households', table_per_level=False)
FieldTable(['foundation type'], universe='Households', table_per_level=False)
FieldTable(['outer wall type'], universe='Households', table_per_level=False)
FieldTable(['roof type'], universe='Households', table_per_level=False)
FieldTable(['toilet type'], universe='Households', table_per_level=False)
FieldTable(['home ownership'], universe='Households', table_per_level=False)
# Education tables
FieldTable(['education level passed', 'sex'],
universe='People aged 5 years and older',
table_per_level=False)
FieldTable(['literacy', 'sex'],
universe='People aged 5 years and older',
from wazimap.data.tables import FieldTable FieldTable(['GNI_Year']) FieldTable(['Life_Expectancy_Year']) FieldTable(['Population_Year']) FieldTable(['Crop_Production_Year']) FieldTable(['GDP_Year'], value_type='Float') FieldTable(['Rank', 'year']) # Percentage of population ages 15-49 FieldTable(['HIV_Prevalence_Year'], value_type='Float') # literate Percentage of population above 15 years FieldTable(['Literacy_Year']) FieldTable(['Indicator'], id='hd_landscape', value_type='Float')
from wazimap.data.tables import FieldTable, SimpleTable FieldTable(['population group'], id='populationgroup_2016', year='2016', dataset='Census')
from wazimap.data.tables import FieldTable, SimpleTable
# Ipv4
FieldTable(['users_or_not'],
id='users_in_country',
universe='Internet users',
value_type='BIGINT',
description='What percentage of a country are internet users',
dataset='Stats from stats.labs.apnic.net',
year='2017')
FieldTable(['country_or_world'],
id='users_in_world',
universe='Internet users',
value_type='BIGINT',
description='How much of the worlds internet users does this country make make up of',
dataset='Stats from stats.labs.apnic.net',
year='2017')
FieldTable(['asn'],
id='market_share',
universe='Internet users',
value_type='BIGINT',
description='The market share in a given country.',
dataset='Stats from stats.labs.apnic.net',
from wazimap.data.tables import FieldTable
# Define our tables so the data API can discover them.
FieldTable(['rural or urban', 'sex', 'age in completed years'],
table_per_level=False)
#FieldTable(['employment activity status', 'sex'], universe='People aged 5 years and older', table_per_level=False)
#FieldTable(['school attendance', 'sex'], universe='People aged 3 years and older', table_per_level=False)
#FieldTable(['highest education level reached'], universe='People aged 3 years and older', table_per_level=False)
#FieldTable(['main mode of human waste disposal'], universe='Households', table_per_level=False)
#FieldTable(['main source of water'], universe='Households', table_per_level=False)
#FieldTable(['main type of lighting fuel'], universe='Households', table_per_level=False)
#FieldTable(['main type of floor material'], universe='Households', table_per_level=False)
#FieldTable(['main type of wall material'], universe='Households', table_per_level=False)
#FieldTable(['main type of roofing material'], universe='Households', table_per_level=False)
from wazimap.data.tables import FieldTable
FieldTable(['population_sex_2006'],
id='population_sex_2006',
year='2006',
dataset='Census')
FieldTable(['population_sex_2007'],
id='population_sex_2007',
year='2007',
dataset='Census')
FieldTable(['population_sex_2009'],
id='population_sex_2009',
year='2009',
dataset='Census')
FieldTable(['population_sex_2011'],
id='population_sex_2011',
year='2011',
dataset='Census')
FieldTable(['population_sex_2012'],
id='population_sex_2012',
year='2012',
dataset='Census')
FieldTable(['population_sex_2013'],
id='population_sex_2013',
year='2013',
dataset='Census')
FieldTable(['population_residence_2009'],
id='population_residence_2009',
year='2006',
dataset='Census')
from wazimap.data.tables import FieldTable
# Define our tables so the data API can discover them.
# TODO: Add comments so that we can quickly see categories/topics
# TODO: Rework format to a standard
FieldTable([
'code', 'name', 'region', 'district', 'ward', 'ownership', 'latitude',
'longitude', 'pass_rate', 'change_previous_year_pass_rate', 'avg_gpa',
'chane_previous_year_gpa', 'rank', 'year_of_result', 'more_than_40',
'national_rank_all', 'regional_rank_all', 'district_rank_all'
],
id='secondary_schools')
FieldTable([
'code', 'name', 'region', 'district', 'ward', 'ownership', 'gender',
'latitude', 'longitude', 'avg_gpa', 'year_of_result', 'more_than_40',
'national_rank_all', 'regional_rank_all'
],
id='secondary_school')
#FieldTable(['university_name', 'course_name', 'general_major', 'cumpulsory_subjects_ar', 'other_subjects_ar'], id='universityfinder')
#
# FieldTable([], id='olevel_subject_performance')
# FieldTable([], id='alevel_subject_performance')
# FieldTable([], id='olevel_student_performance')
# FieldTable([], id='alevel_student_performance')
# FieldTable([], id='olevel_overall_performance')
# FieldTable([], id='alevel_overall_performance')
from wazimap.data.tables import FieldTable # Define our tables so the data API can discover them. FieldTable(['gender', 'age group']) FieldTable(['gender', 'rural or urban']) # FieldTable(['rural or urban', 'sex', 'age in completed years'], table_per_level=False) # FieldTable(['employment activity status', 'sex'], universe='People aged 5 years and older', table_per_level=False) # FieldTable(['school attendance', 'sex'], universe='People aged 3 years and older', table_per_level=False) # FieldTable(['highest education level reached'], universe='People aged 3 years and older', table_per_level=False) # FieldTable(['main mode of human waste disposal'], universe='Households', table_per_level=False) # FieldTable(['main source of water'], universe='Households', table_per_level=False) # FieldTable(['main type of lighting fuel'], universe='Households', table_per_level=False) # FieldTable(['main type of floor material'], universe='Households', table_per_level=False) # FieldTable(['main type of wall material'], universe='Households', table_per_level=False) # FieldTable(['main type of roofing material'], universe='Households', table_per_level=False)
def field_table(self, fields, data_str):
table = FieldTable(fields)
self.load_data(table, data_str)
from django.conf import settings
from wazimap.data.tables import SimpleTable, FieldTable
FieldTable(
['gender', 'population group'],
id='youth_gender_population_group',
universe='Youth',
year='2011')
FieldTable(
['population group', 'gender'],
id='youth_population_group_gender',
universe='Youth',
year='2011',
db_table='youth_gender_population_group')
FieldTable(
['age groups in 10 years'],
id='youth_age_groups_in_10_years',
universe='Population',
year='2011')
FieldTable(['language'], id='youth_language', universe='Youth', year='2011')
FieldTable(
['province of birth'],
id='youth_province_of_birth',
universe='Youth',
year='2011')
FieldTable(
['region of birth'],
id='youth_region_of_birth',
universe='Youth',
year='2011')
FieldTable(
from wazimap.data.tables import FieldTable, SimpleTable
# Define our tables so the data API can discover them.
# Household tables
FieldTable(['main type of cooking fuel'],
universe='Households',
description='Main type of cooking fuel',
dataset='National Population and Housing Census 2011',
year='2011')
FieldTable(['drinking water source'],
universe='Households',
description='Drinking water source',
dataset='National Population and Housing Census 2011',
year='2011')
FieldTable(['lighting fuel'],
universe='Households',
description='Main type of lighting fuel',
dataset='National Population and Housing Census 2011',
year='2011')
FieldTable(['foundation type'],
universe='Households',
description='Building foundation',
dataset='National Population and Housing Census 2011',
year='2011')
FieldTable(['outer wall type'],
universe='Households',
description='Outer wall of building',
from wazimap.data.tables import FieldTable FieldTable(['financial_year'], value_type='Float') FieldTable(['expenditure', 'year'], value_type='Float') FieldTable(['conditional_fund'], value_type='Float')
