def get_demographics_profile(geo, session):
year = current_context().get('year')
with dataset_context(year=year):
# gender
gender_dist_data, total_pop = get_stat_data(
'gender', geo, session,
table_fields=['gender', 'age group'])
# age group
age_group_dist_data, _ = get_stat_data(
'age group', geo, session,
table_fields=['gender', 'age group'])
total_under_15 = age_group_dist_data['0-14 Years']['numerators']['this']
# rural or urban
rural_dist_data, _ = get_stat_data(
['rural or urban','gender'], geo, session,
table_fields=['gender', 'rural or urban'])
final_data = {
'gender_ratio': gender_dist_data,
'age_group_distribution': age_group_dist_data,
'under_15': {
'name': 'Under 15 years',
'values': {'this': total_under_15}
},
'rural_distribution': rural_dist_data,
'total_population': {
"name": "People",
"values": {"this": total_pop}
}}
return final_data
def dataset_context_stat_data(self):
"""
Calulate statistical data with a particular context
"""
with dataset_context(year=str(self.profile.dataset_context)):
try:
self.distribution, self.total = get_stat_data(
[self.profile.field_name],
self.geo,
self.session,
table_name=self.profile.table_name.name,
exclude_zero=self.profile.exclude_zero,
percent=self.profile.percent,
recode=self.profile.recode,
key_order=self.profile.key_order,
exclude=self.profile.exclude,
order_by=self.profile.order_by,
)
self.compare_geos()
if self.profile.group_remainder:
group_remainder(self.distribution, self.profile.group_remainder)
self.distribution = enhance_api_data(self.distribution)
return {"stat_values": self.distribution}
except DataNotFound:
return {}
def get_households_profile(geo, session, year):
with dataset_context(year=year):
try:
permanency, _ = get_stat_data('household percentage by permanency', geo,
session,
table_fields=[
'household percentage by permanency'])
except Exception:
permanency = LOCATIONNOTFOUND
try:
light_source, total_households = get_stat_data(
'household distribution by light source', geo,
session, table_fields=['household distribution by light source'])
energy_source, _ = get_stat_data(
'household distribution by energy source', geo, session,
table_fields=['household distribution by energy source'])
except Exception:
total_households = 0
light_source = LOCATIONNOTFOUND
energy_source, _ = LOCATIONNOTFOUND, 0
is_missing = permanency.get('is_missing') and light_source.get('is_missing') and energy_source.get('is_missing')
final_data = {
'is_missing': is_missing,
'percentage_by_permanency': permanency,
'light_source_distribution': light_source,
'energy_source_distribution': energy_source,
'total_households': {
"name": "Households",
"values": {"this": total_households}
}
}
return final_data
def get(self, request, geo_level, geo_code):
results = {}
version = self.request.GET.get("geo_version",
settings.WAZIMAP["default_geo_version"])
geography = Geography.objects.get(geo_level=geo_level,
geo_code=geo_code,
version=version)
year = self.request.GET.get("release",
geo_data.primary_release_year(geography))
results["geograhy"] = geography.as_dict_deep()
if settings.WAZIMAP["latest_release_year"] == year:
year = "latest"
build_indicator = settings.DYNAMIC_PROFILE_INDICATOR
if build_indicator:
module = importlib.import_module(build_indicator["path"])
build_indicator = getattr(module, build_indicator["class"])
else:
build_indicator = BuildIndicator
session = get_session()
with dataset_context(year=year):
section = Section(geography, session)
section_results = section.build(BuildProfile, build_indicator)
results["data"] = section_results
return Response(results, status=status.HTTP_200_OK)
def get_context_data(self, *args, **kwargs):
json_data = open("takwimu/fixtures/sdg.json")
data = json.load(json_data)
# get profileData aka summaries from wagtail
summary_data = ProfileData.objects.filter(
country_iso_code=self.geo.geo_code).values('id', 'chart_id',
'summary')
page_context = {"profile_data_summaries": list(summary_data)}
# get data by topic page text description
try:
page = DataByTopicPage.objects.get(country=self.geo)
page_context['profile_data_description'] = page.description
except DataByTopicPage.DoesNotExist:
page_context['profile_data_description'] = ''
# load the profile
profile_method = takwimu_settings.HURUMAP.get("profile_builder", None)
self.profile_name = takwimu_settings.HURUMAP.get(
"default_profile", "default")
if not profile_method:
raise ValueError(
"You must define WAZIMAP.profile_builder in settings.py")
profile_method = import_string(profile_method)
year = self.request.GET.get(
"release", get_primary_release_year_per_geography(self.geo))
with dataset_context(year=year):
profile_data = profile_method(self.geo, self.profile_name,
self.request)
profile_data["geography"] = self.geo.as_dict_deep()
profile_data["primary_releases"] = get_page_releases_per_country(
takwimu_settings.HURUMAP["primary_dataset_name"], self.geo, year)
profile_data = enhance_api_data(profile_data)
page_context.update(profile_data)
profile_data_json = SafeString(
json.dumps(profile_data, cls=DjangoJSONEncoder))
page_context.update({"profile_data_json": profile_data_json})
# is this a head-to-head view?
page_context["head2head"] = "h2h" in self.request.GET
page_context["sdgs"] = data
page_context.update(settings(self.request))
page_context.update(takwimu_countries(self.request))
page_context.update(takwimu_stories(self.request))
page_context.update(takwimu_topics(self.request))
return page_context
def get_context_data(self, *args, **kwargs):
latest_release_year = self.release.year
with dataset_context(year=latest_release_year):
table_columns = self.table.columns()
return {
'table': self.table,
'latest_release_year': latest_release_year,
'table_columns': table_columns
}
def get_elections2016_profile(geo, session):
with dataset_context(year='2016'):
try:
candidate, total_votes = get_stat_data('presidential candidate', geo,
session,
table_fields=[
'presidential candidate'],
order_by="-total")
except Exception:
candidate, total_votes = LOCATIONNOTFOUND, 0
total_besigye = 0
for data, value in candidate.get('Kizza besigye', {}).items():
if data == 'numerators':
total_besigye += value['this']
total_museveni = 0
for data, value in candidate.get('Yoweri museveni', {}).items():
if data == 'numerators':
total_museveni += value['this']
is_missing = candidate.get('is_missing')
final_data = {
'is_missing': is_missing,
'candidate_distribution': candidate,
'museveni_votes': {
'name': 'Museveni Votes',
'numerators': {'this': total_museveni}
},
'besigye_votes': {
'name': 'Besigye Votes',
'numerators': {'this': total_besigye},
},
'total_votes': {
"name": "Votes",
"values": {"this": total_votes}
}
}
try:
final_data['besigye_votes']['values'] = {
'this': round(total_besigye / total_votes * 100, 2)}
final_data['museveni_votes']['values'] = {
'this': round(total_museveni / total_votes * 100, 2)
}
except ZeroDivisionError:
final_data['besigye_votes']['values'] = {'this': 0}
final_data['museveni_votes']['values'] = {'this': 0}
return final_data
def get(self, request, *args, **kwargs):
try:
self.geo_ids = request.GET.get('geo_ids', '').split(',')
geo_version = request.GET.get('geo_version', None)
self.data_geos, self.info_geos = self.get_geos(
self.geo_ids, geo_version)
except LocationNotFound as e:
return render_json_error(e.message, 404)
try:
self.table_ids = request.GET.get('table_ids', '').split(',')
self.tables = [get_datatable(t) for t in self.table_ids]
except KeyError as e:
return render_json_error('Unknown table: %s' % e.message, 404)
# tables should all be from the same dataset
datasets = set(t.dataset for t in self.tables)
if len(datasets) > 1:
return render_json_error(
"All tables must belong to the same dataset.", 400)
self.dataset = list(datasets)[0]
self.year = kwargs['release']
if settings.WAZIMAP['latest_release_year'] == self.year:
self.year = 'latest'
self.available_releases = get_page_releases(self.dataset.name,
self.data_geos[0],
self.year,
filter_releases=False)
self.release = None
for table in self.tables:
release = table.get_release(year=self.year)
if not release:
return render_json_error(
"No release %s for table %s." %
(kwargs['release'], table.name.upper()), 400)
# different?
if self.release and self.release != release:
return render_json_error(
"All tables must have the same release.", 400)
self.release = release
with dataset_context(year=self.release.year):
if kwargs.get('action') == 'show':
return self.show(request)
if kwargs.get('action') == 'download':
return self.download(request)
def get_land_audit_profile(geo, session):
year = current_context().get('year')
with dataset_context(year=year):
land_use_dist = LOCATIONNOTFOUND
land_user_dist = LOCATIONNOTFOUND
land_distribution_gender = LOCATIONNOTFOUND
land_ownership = LOCATIONNOTFOUND
try:
land_use_dist, _ = get_stat_data('land_use', geo, session,
table_name='landuse',
table_fields=['land_use'])
except Exception as e:
pass
try:
land_user_dist, _ = get_stat_data('land_user', geo, session,
table_name='landuser',
table_fields=['land_user'])
except Exception:
pass
try:
land_distribution_gender, _ = get_stat_data(
'land_ownership_by_gender', geo, session,
table_name='privatelanddistributionbygender',
table_fields=['land_ownership_by_gender'])
except Exception:
pass
try:
land_ownership, _ = get_stat_data('private_vs_state_ownership', geo,
session,
table_name='landownership',
table_fields=[
'private_vs_state_ownership'])
except Exception:
pass
is_missing = land_user_dist.get('is_missing') and \
land_use_dist.get('is_missing') and \
land_distribution_gender.get('is_missing') and \
land_ownership.get('is_missing')
return {
'is_missing': is_missing,
'land_user_dist': land_user_dist,
'land_use_dist': land_use_dist,
'land_distribution_gender': land_distribution_gender,
'land_ownership': land_ownership,
}
def get_demographics_profile(geo, session, year):
with dataset_context(year=year):
geo.version = str(geo.version)
# sex
try:
sex_dist_data, total_pop = get_stat_data(
'sex', geo, session,
table_fields=['sex'])
except Exception:
sex_dist_data = LOCATIONNOTFOUND
total_pop = 0
# urban/rural by sex
total_urbanised = 0
try:
urban_dist_data, _ = get_stat_data(
['rural or urban'], geo, session,
table_fields=['rural or urban'])
for data in urban_dist_data['Urban'].values():
if 'numerators' in data:
total_urbanised += data['numerators']['this']
except Exception:
urban_dist_data = LOCATIONNOTFOUND
is_missing = sex_dist_data.get('is_missing') and urban_dist_data.get('is_missing')
final_data = {
'is_missing': is_missing,
'sex_ratio': sex_dist_data,
'urban_distribution': urban_dist_data,
'urbanised': {
'name': 'In urban areas',
'numerators': {'this': total_urbanised},
},
'total_population': {
"name": "People",
"values": {"this": total_pop}
}}
try:
final_data['urbanised']['values'] = {
'this': round(total_urbanised / total_pop * 100, 2)}
except ZeroDivisionError:
final_data['urbanised']['values'] = {'this': 0}
return final_data
def get_population(geo, session):
with dataset_context(year='2017'):
sex_dist, total_population_sex = LOCATIONNOTFOUND, 0
residence_dist, total_population_residence = LOCATIONNOTFOUND, 0
religion_dist, total_population_religion = LOCATIONNOTFOUND, 0
try:
sex_dist, total_population_sex = get_stat_data(
'sex', geo, session, table_fields=['sex'])
except Exception:
pass
try:
residence_dist, total_population_residence = get_stat_data(
'residence', geo, session,
table_fields=['residence'])
except Exception:
pass
try:
religion_dist, total_population_religion = get_stat_data(
'religion', geo, session,
table_fields=['religion'])
except Exception:
pass
total_population = 0
is_missing = sex_dist.get('is_missing') and \
residence_dist.get('is_missing') and \
religion_dist.get('is_missing')
if not is_missing:
total_population = total_population_sex if total_population_sex > 0 else total_population_residence
total_population_dist = _create_single_value_dist(
'People', total_population)
demographics_data = {
'is_missing': is_missing,
'sex_dist': sex_dist,
'residence_dist': residence_dist,
'religion_dist': religion_dist,
'total_population': total_population_dist,
}
if geo.square_kms:
demographics_data['population_density'] = {
'name': "people per square kilometre",
'values': {"this": total_population / geo.square_kms},
}
return demographics_data
def get_demographics_profile(geo, session):
with dataset_context(year='2016'):
pop_dist_data = LOCATIONNOTFOUND
total_pop = 0
try:
pop_dist_data, total_pop = get_stat_data(
['population group'], geo, session)
except Exception:
pass
return {
'total_population': {
"name": "People",
"values": {"this": total_pop},
},
'is_missing': pop_dist_data.get('is_missing', False)
}
def get_crime_profile(geo, session):
with dataset_context(year="2014"):
child_crime, total = get_stat_data(
["crime"],
geo,
session,
table_universe="Crimes",
only=["Neglect and ill-treatment of children"],
percent=False,
)
return {
"metadata": child_crime["metadata"],
"crime_against_children": {
"name": "Crimes of neglect and ill-treatment of children in 2014",
"values": {"this": total},
"metadata": child_crime["metadata"],
},
}
def get_county_expenditure_ceilings(geo, session):
try:
with dataset_context(year='2016'):
ceilings, _ = get_stat_data(['expenditure', 'year'],
geo, session, table_fields=['expenditure',
'year'],
percent=False)
final_data = {}
county_assembly = ceilings['County Assembly']
county_exec = ceilings['County Executive']
final_data['county_assembly'] = {
'fy2015_2016': {
'name': county_assembly['FY 2015/2016'].get('name'),
'numerators': county_assembly['FY 2015/2016'].get('numerators'),
'values': county_assembly['FY 2015/2016'].get('values')
},
'fy2016_2017': {
'name': county_assembly['FY 2016/2017'].get('name'),
'numerators': county_assembly['FY 2016/2017'].get('numerators'),
'values': county_assembly['FY 2016/2017'].get('values')
}
}
final_data['county_executive'] = {
'fy2015_2016': {
'name': county_exec['FY 2015/2016'].get('name'),
'numerators': county_exec['FY 2015/2016'].get('numerators'),
'values': county_exec['FY 2015/2016'].get('values')
},
'fy2016_2017': {
'name': county_exec['FY 2016/2017'].get('name'),
'numerators': county_exec['FY 2016/2017'].get('numerators'),
'values': county_exec['FY 2016/2017'].get('values')
}
}
return final_data
except Exception:
ceilings, _ = LOCATIONNOTFOUND, 0
def context_comparative_geo(self, comp_geo):
"""
Calculte the dataset_context stat_data for a comparative geo.
"""
with dataset_context(year=str(self.profile.dataset_context)):
try:
distribution, total = get_stat_data(
[self.profile.field_name],
comp_geo,
self.session,
table_name=self.profile.table_name.name,
exclude_zero=self.profile.exclude_zero,
percent=self.profile.percent,
recode=self.profile.recode,
key_order=self.profile.key_order,
exclude=self.profile.exclude,
order_by=self.profile.order_by,
)
return distribution
except DataNotFound:
return {}
def get_conditional_allocation_2015_2016(geo, session):
try:
with dataset_context(year='2016'):
conditional, _ = get_stat_data(['conditional_fund'], geo, session,
table_fields=['conditional_fund'],
percent=False)
final_data = {}
data = ['DANIDA Grant', 'Other Conditional Allocations',
'World Bank Loan']
for i in data:
fund = conditional.get(i)
final_data[i.lower().replace(' ', '_')] = {
'name': i,
'numerators': fund['numerators'],
'values': fund['values']
}
return final_data
except Exception:
conditional, _ = LOCATIONNOTFOUND, 0
def get_sectionaltitleland_profile(geo, session):
year = current_context().get('year')
with dataset_context(year=year):
topic_profiles = SECTIONS['sectionaltitleland']['profiles']
profiles_data = {'is_missing': True}
for profile in topic_profiles:
try:
profile_table = profile.lower()
profile_name = profile.lower().replace(' ', '_')
profiles_data[profile_name] = LOCATIONNOTFOUND
profiles_data[profile_name], _ = get_stat_data([profile_table],
geo, session)
except Exception:
pass
profiles_data['is_missing'] = profiles_data.get('is_missing') and \
profiles_data[profile_name].get(
'is_missing')
return profiles_data
def get_disabilities_profile(geo, session, year):
with dataset_context(year=year):
try:
disabled_or_not, total_ = get_stat_data('disabled or not', geo, session,
table_fields=[
'disabled or not'])
disability, _ = get_stat_data('disability', geo, session,
table_fields=['disability'])
except Exception:
disabled_or_not, total_ = LOCATIONNOTFOUND, 0
disability = LOCATIONNOTFOUND
total_disabled = 0
for data, value in disabled_or_not.get('With disability', {}).items():
if data == 'numerators':
total_disabled += value['this']
is_missing = disability.get('is_missing') and disabled_or_not.get('is_missing')
final_data = {
'is_missing': is_missing,
'disabled_or_not_distribution': disabled_or_not,
'disability': disability,
'total_disabled': {
'name': 'Disabled',
'numerators': {'this': total_disabled},
},
'total_': {
"name": "Population",
"values": {"this": total_}
}
}
try:
final_data['total_disabled']['values'] = {
'this': round(total_disabled / total_ * 100, 2)}
except ZeroDivisionError:
final_data['total_disabled']['values'] = {'this': 0}
return final_data
def get_context_data(self, **kwargs):
page_context = {}
# load the profile
profile_method = settings.HURUMAP.get('profile_builder', None)
self.profile_name = settings.HURUMAP.get('default_profile', 'default')
if not profile_method:
raise ValueError(
"You must define WAZIMAP.profile_builder in settings.py")
profile_method = import_string(profile_method)
year = self.request.GET.get(
'release', get_primary_release_year_per_geography(self.geo))
if settings.HURUMAP['latest_release_year'] == year:
year = 'latest'
with dataset_context(year=year):
profile_data = profile_method(
self.geo, self.profile_name, self.request)
profile_data['geography'] = self.geo.as_dict_deep()
profile_data['primary_releases'] = get_page_releases_per_country(
settings.HURUMAP['primary_dataset_name'], self.geo, year)
profile_data = enhance_api_data(profile_data)
page_context.update(profile_data)
profile_data_json = SafeString(
json.dumps(profile_data, cls=DjangoJSONEncoder))
page_context.update({
'profile_data_json': profile_data_json
})
# is this a head-to-head view?
page_context['head2head'] = 'h2h' in self.request.GET
return page_context
def get_election_data(geo, election, session):
with dataset_context(year=election['release_year']):
party_data, total_valid_votes = get_stat_data(
['party'], geo, session,
table_dataset=election['dataset'],
recode=lambda f, v: make_party_acronym(v),
order_by='-total')
results = {
'name': election['name'],
'party_distribution': party_data,
'geo_version': election['geo_version']
}
# voter registration and turnout
table = get_datatable('voter_turnout_%s' % election['table_code'])
results.update(table.get_stat_data(geo, 'registered_voters', percent=False,
year=election['release_year'],
recode={'registered_voters': 'Number of registered voters'})[0])
results.update(table.get_stat_data(geo, 'total_votes', percent=True, total='registered_voters',
year=election['release_year'],
recode={'total_votes': 'Of registered voters cast their vote'})[0])
return results
def get_context_data(self, *args, **kwargs):
page_context = {}
# load the profile
profile_method = settings.WAZIMAP.get('profile_builder', None)
self.profile_name = settings.WAZIMAP.get('default_profile', 'default')
if not profile_method:
raise ValueError(
"You must define WAZIMAP.profile_builder in settings.py")
profile_method = import_string(profile_method)
year = self.request.GET.get('release',
geo_data.primary_release_year(self.geo))
if settings.WAZIMAP['latest_release_year'] == year:
year = 'latest'
with dataset_context(year=year):
profile_data = profile_method(self.geo, self.profile_name,
self.request)
profile_data['geography'] = self.geo.as_dict_deep()
profile_data['primary_releases'] = get_page_releases(
settings.WAZIMAP['primary_dataset_name'], self.geo, year)
profile_data = enhance_api_data(profile_data)
page_context.update(profile_data)
profile_data_json = SafeString(
json.dumps(profile_data, cls=DjangoJSONEncoder))
page_context.update({'profile_data_json': profile_data_json})
# is this a head-to-head view?
page_context['head2head'] = 'h2h' in self.request.GET
return page_context
def get_schools_profile(geo, session, year):
schools_dist = LOCATIONNOTFOUND
region_dist = LOCATIONNOTFOUND
category_dist = LOCATIONNOTFOUND
top_schools_40_more = []
top_schools_40_less = []
lowest_schools_40_less = []
lowest_schools_40_more = []
gpa_dist_data = []
gender_dist = []
total_schools = 0
median = 0
reg = 'region'
if geo.geo_level == "country":
reg = 'region'
elif geo.geo_level == "region":
reg = 'district'
elif geo.geo_level == "district":
reg = 'ward'
with dataset_context(year='2017'):
try:
schools_dist, total_schools = get_stat_data(
['ownership'],
geo=geo,
table_name='secondary_school',
session=session,
only={'year_of_result': [year]})
except Exception as e:
pass
try:
region_dist, total_schools = get_stat_data(
[reg],
geo=geo,
session=session,
only={'year_of_result': [year]})
except Exception as e:
pass
try:
category_dist, _ = get_stat_data(['more_than_40'],
geo=geo,
session=session,
only={'year_of_result': [year]})
except Exception as e:
pass
try:
gender_dist, _ = get_stat_data(['gender'],
geo=geo,
session=session,
only={'year_of_result': [year]})
except Exception:
pass
try:
# ownership status
# school_dist_data, _ = get_stat_data('age in completed years',geo=geo, session=session, only={'year_of_result': [year]})
# Choosing sorting option
# Sorting will only be done using national_rank all, as regional and district ranks are unknown for some result esp historical
Base.metadata.reflect()
rank_column = Base.metadata.tables[
'secondary_school'].c.national_rank_all
# Getting top for schools with more than 40 students
top_schools_40_more = session.query(
Base.metadata.tables['secondary_school']) \
.filter(Base.metadata.tables[
'secondary_school'].c.geo_level == geo.geo_level) \
.filter(
Base.metadata.tables['secondary_school'].c.geo_code == geo.geo_code) \
.filter(
Base.metadata.tables['secondary_school'].c.year_of_result == year) \
.filter(
Base.metadata.tables['secondary_school'].c.more_than_40.like(
"yes%")) \
.order_by(asc(cast(rank_column, Integer))) \
.all()
# Getting top for schools with less than 40 students
top_schools_40_less = session.query(
Base.metadata.tables['secondary_school']) \
.filter(Base.metadata.tables[
'secondary_school'].c.geo_level == geo.geo_level) \
.filter(
Base.metadata.tables['secondary_school'].c.geo_code == geo.geo_code) \
.filter(
Base.metadata.tables['secondary_school'].c.year_of_result == year) \
.filter(
Base.metadata.tables['secondary_school'].c.more_than_40.like("no%")) \
.order_by(asc(cast(rank_column, Integer))) \
.all()
# Getting lowest schools with more than 40 students
lowest_schools_40_more = session.query(
Base.metadata.tables['secondary_school']) \
.filter(Base.metadata.tables[
'secondary_school'].c.geo_level == geo.geo_level) \
.filter(
Base.metadata.tables['secondary_school'].c.geo_code == geo.geo_code) \
.filter(
Base.metadata.tables['secondary_school'].c.year_of_result == year) \
.filter(
Base.metadata.tables['secondary_school'].c.more_than_40.like(
"yes%")) \
.order_by(desc(cast(rank_column, Integer))) \
.all()
# Getting lowest for schools with less than 40 students
lowest_schools_40_less = session.query(
Base.metadata.tables['secondary_school']) \
.filter(Base.metadata.tables[
'secondary_school'].c.geo_level == geo.geo_level) \
.filter(
Base.metadata.tables['secondary_school'].c.geo_code == geo.geo_code) \
.filter(
Base.metadata.tables['secondary_school'].c.year_of_result == year) \
.filter(
Base.metadata.tables['secondary_school'].c.more_than_40.like("no%")) \
.order_by(desc(cast(rank_column, Integer))) \
.all()
# median gpa
db_model_age = get_datatable('code_name_avg_gpa')
objects = db_model_age.get_rows_for_geo(geo, session)
median = calculate_median(objects, 'avg_gpa')
# gpa in 1 point groups
def gpa_recode(f, x):
gpa = x
if gpa >= 4:
return '4+'
bucket = 1 * (gpa / 1)
return '%d-%d' % (bucket, bucket + 2)
gpa_dist_data, total_schools = get_stat_data(
'avg_gpa',
geo,
session,
table_fields=['code', 'name', 'avg_gpa'],
recode=gpa_recode,
exclude=['unspecified'],
only={'year_of_result': [year]})
total_private = 0.0
for data in schools_dist.get('Non-Government', {}).values():
if 'numerators' in data:
total_private += data['numerators']['this']
except Exception as e:
pass
return {
'schools_distribution': schools_dist,
'region_distribution': region_dist,
'category_distribution': category_dist,
'best_schools_more_40': top_schools_40_more,
'worst_schools_more_40': lowest_schools_40_more,
'best_schools_less_40': top_schools_40_less,
'worst_schools_less_40': lowest_schools_40_less,
'gpa_group_distribution': gpa_dist_data,
'gender_distribution': gender_dist,
'total_schools': {
"name": "Schools",
"values": {
"this": total_schools
}
},
'median_gpa': {
"name": "Median GPA",
"values": {
"this": median
},
},
}
def setUp(self):
self.s = get_session()
self.ctxt = dataset_context(year='latest')
self.ctxt.__enter__()
def get_afrobarometer_profile(geo, session):
with dataset_context(year='2016'):
own_radio = LOCATIONNOTFOUND
own_motor_vehicle = LOCATIONNOTFOUND
own_television = LOCATIONNOTFOUND
own_mobile_phone = LOCATIONNOTFOUND
election_work_for_a_candidate_or_party = LOCATIONNOTFOUND
election_attend_a_campaign_rally = LOCATIONNOTFOUND
last_national_election_attend_a_campaign_meeting = LOCATIONNOTFOUND
courts_make_binding_decisions = LOCATIONNOTFOUND
vote_for_which_party = LOCATIONNOTFOUND
level_of_corruption = LOCATIONNOTFOUND
chinas_assistance_meets_countrys_needs = LOCATIONNOTFOUND
employment_status = LOCATIONNOTFOUND
main_reason_for_not_reporting_corruption = LOCATIONNOTFOUND
education_of_respondent = LOCATIONNOTFOUND
people_must_pay_taxes = LOCATIONNOTFOUND
negative_image_of_china = LOCATIONNOTFOUND
most_effective_way_to_combat_corruption = LOCATIONNOTFOUND
people_can_fight_corruption = LOCATIONNOTFOUND
people_must_obey_the_law = LOCATIONNOTFOUND
chinas_influence_on_economy = LOCATIONNOTFOUND
chinas_influence_positive_or_negative = LOCATIONNOTFOUND
positive_image_of_china = LOCATIONNOTFOUND
occupation_of_respondent = LOCATIONNOTFOUND
employer_of_respondent = LOCATIONNOTFOUND
how_often_use_a_mobile_phone = LOCATIONNOTFOUND
how_often_use_the_internet = LOCATIONNOTFOUND
freeness_and_fairness_of_last_national_election = LOCATIONNOTFOUND
extent_of_democracy = LOCATIONNOTFOUND
elections_ensure_voters_views_are_reflected = LOCATIONNOTFOUND
corruption_judges_and_magistrates = LOCATIONNOTFOUND
election_enable_voters_remove_leaders_from_office = LOCATIONNOTFOUND
corruption_tax_officials = LOCATIONNOTFOUND
corruption_police = LOCATIONNOTFOUND
corruption_local_government_councilors = LOCATIONNOTFOUND
corruption_government_officials = LOCATIONNOTFOUND
corruption_members_of_parliament = LOCATIONNOTFOUND
corruption_office_of_the_presidency = LOCATIONNOTFOUND
satisfaction_with_democracy = LOCATIONNOTFOUND
corruption_religious_leaders = LOCATIONNOTFOUND
corruption_traditional_leaders = LOCATIONNOTFOUND
difficulty_to_obtain_public_school_services = LOCATIONNOTFOUND
voting_in_the_most_recent_national_election = LOCATIONNOTFOUND
difficulty_to_obtain_medical_treatment = LOCATIONNOTFOUND
pay_bribe_for_school_services = LOCATIONNOTFOUND
difficulty_to_obtain_identity_document = LOCATIONNOTFOUND
bribe_for_treatment_at_public_health = LOCATIONNOTFOUND
difficulty_to_obtain_household_services = LOCATIONNOTFOUND
pay_bribe_for_document_or_permit = LOCATIONNOTFOUND
pay_bribe_for_household_services = LOCATIONNOTFOUND
try:
own_radio, _ = get_stat_data('own_radio', geo, session,
table_fields=['own_radio'])
except LocationNotFound:
pass
try:
own_motor_vehicle, _ = get_stat_data('own_motor_vehicle', geo, session,
table_fields=['own_motor_vehicle'])
except LocationNotFound:
pass
try:
own_television, _ = get_stat_data('own_television', geo, session,
table_fields=['own_television'])
except LocationNotFound:
pass
try:
own_mobile_phone, _ = get_stat_data('own_mobile_phone', geo, session,
table_fields=['own_mobile_phone'])
except LocationNotFound:
pass
try:
election_work_for_a_candidate_or_party, _ = get_stat_data(
'election_work_for_a_candidate_or_party', geo,
session, table_fields=[
'election_work_for_a_candidate_or_party'])
except LocationNotFound:
pass
try:
election_attend_a_campaign_rally, _ = get_stat_data(
'election_attend_a_campaign_rally', geo, session,
table_fields=['election_attend_a_campaign_rally'])
except LocationNotFound:
pass
try:
last_national_election_attend_a_campaign_meeting, _ = get_stat_data(
'last_national_election_attend_a_campaign_meeting', geo, session,
table_fields=['last_national_election_attend_a_campaign_meeting'])
except LocationNotFound:
pass
try:
courts_make_binding_decisions, _ = get_stat_data(
'courts_make_binding_decisions', geo, session,
table_fields=['courts_make_binding_decisions'])
except LocationNotFound:
pass
try:
vote_for_which_party, _ = get_stat_data('vote_for_which_party', geo,
session, table_fields=[
'vote_for_which_party'])
except LocationNotFound:
pass
try:
level_of_corruption, _ = get_stat_data('level_of_corruption', geo,
session, table_fields=[
'level_of_corruption'])
except LocationNotFound:
pass
try:
chinas_assistance_meets_countrys_needs, _ = get_stat_data(
'chinas_assistance_meets_countrys_needs', geo,
session, table_fields=[
'chinas_assistance_meets_countrys_needs'])
except LocationNotFound:
pass
try:
employment_status, _ = get_stat_data('employment_status', geo, session,
table_fields=['employment_status'])
except LocationNotFound:
pass
try:
main_reason_for_not_reporting_corruption, _ = get_stat_data(
'main_reason_for_not_reporting_corruption', geo, session,
table_fields=['main_reason_for_not_reporting_corruption'])
except LocationNotFound:
pass
try:
education_of_respondent, _ = get_stat_data('education_of_respondent',
geo, session, table_fields=[
'education_of_respondent'])
except LocationNotFound:
pass
try:
people_must_pay_taxes, _ = get_stat_data('people_must_pay_taxes', geo,
session, table_fields=[
'people_must_pay_taxes'])
except LocationNotFound:
pass
try:
negative_image_of_china, _ = get_stat_data('negative_image_of_china',
geo, session, table_fields=[
'negative_image_of_china'])
except LocationNotFound:
pass
try:
most_effective_way_to_combat_corruption, _ = get_stat_data(
'most_effective_way_to_combat_corruption', geo, session,
table_fields=['most_effective_way_to_combat_corruption'])
except LocationNotFound:
pass
try:
people_can_fight_corruption, _ = get_stat_data(
'people_can_fight_corruption', geo, session,
table_fields=['people_can_fight_corruption'])
except LocationNotFound:
pass
try:
people_must_obey_the_law, _ = get_stat_data('people_must_obey_the_law',
geo, session, table_fields=[
'people_must_obey_the_law'])
except LocationNotFound:
pass
try:
chinas_influence_on_economy, _ = get_stat_data(
'chinas_influence_on_economy', geo, session,
table_fields=['chinas_influence_on_economy'])
except LocationNotFound:
pass
try:
chinas_influence_positive_or_negative, _ = get_stat_data(
'chinas_influence_positive_or_negative', geo, session,
table_fields=['chinas_influence_positive_or_negative'])
except LocationNotFound:
pass
try:
positive_image_of_china, _ = get_stat_data('positive_image_of_china',
geo, session, table_fields=[
'positive_image_of_china'])
except LocationNotFound:
pass
try:
occupation_of_respondent, _ = get_stat_data('occupation_of_respondent',
geo, session, table_fields=[
'occupation_of_respondent'])
except LocationNotFound:
pass
try:
employer_of_respondent, _ = get_stat_data('employer_of_respondent', geo,
session, table_fields=[
'employer_of_respondent'])
except LocationNotFound:
pass
try:
how_often_use_a_mobile_phone, _ = get_stat_data(
'how_often_use_a_mobile_phone', geo, session,
table_fields=['how_often_use_a_mobile_phone'])
except LocationNotFound:
pass
try:
how_often_use_the_internet, _ = get_stat_data(
'how_often_use_the_internet', geo, session,
table_fields=['how_often_use_the_internet'])
except LocationNotFound:
pass
try:
freeness_and_fairness_of_last_national_election, _ = get_stat_data(
'freeness_and_fairness_of_last_national_election', geo, session,
table_fields=[
'freeness_and_fairness_of_last_national_election'])
except LocationNotFound:
pass
try:
extent_of_democracy, _ = get_stat_data('extent_of_democracy', geo,
session, table_fields=[
'extent_of_democracy'])
except LocationNotFound:
pass
try:
elections_ensure_voters_views_are_reflected, _ = get_stat_data(
'elections_ensure_voters_views_are_reflected', geo, session,
table_fields=['elections_ensure_voters_views_are_reflected'])
except LocationNotFound:
pass
try:
corruption_judges_and_magistrates, _ = get_stat_data(
'corruption_judges_and_magistrates', geo, session,
table_fields=['corruption_judges_and_magistrates'])
except LocationNotFound:
pass
try:
election_enable_voters_remove_leaders_from_office, _ = get_stat_data(
'election_enable_voters_remove_leaders_from_office', geo,
session, table_fields=[
'election_enable_voters_remove_leaders_from_office'])
except LocationNotFound:
pass
try:
corruption_tax_officials, _ = get_stat_data('corruption_tax_officials',
geo, session, table_fields=[
'corruption_tax_officials'])
except LocationNotFound:
pass
try:
corruption_police, _ = get_stat_data('corruption_police', geo, session,
table_fields=['corruption_police'])
except LocationNotFound:
pass
try:
corruption_local_government_councilors, _ = get_stat_data(
'corruption_local_government_councilors', geo, session,
table_fields=['corruption_local_government_councilors'])
except LocationNotFound:
pass
try:
corruption_government_officials, _ = get_stat_data(
'corruption_government_officials', geo, session,
table_fields=['corruption_government_officials'])
except LocationNotFound:
pass
try:
corruption_members_of_parliament, _ = get_stat_data(
'corruption_members_of_parliament', geo, session,
table_fields=['corruption_members_of_parliament'])
except LocationNotFound:
pass
try:
corruption_office_of_the_presidency, _ = get_stat_data(
'corruption_office_of_the_presidency', geo, session,
table_fields=['corruption_office_of_the_presidency'])
except LocationNotFound:
pass
try:
satisfaction_with_democracy, _ = get_stat_data(
'satisfaction_with_democracy', geo, session,
table_fields=['satisfaction_with_democracy'])
except LocationNotFound:
pass
try:
corruption_religious_leaders, _ = get_stat_data(
'corruption_religious_leaders', geo, session,
table_fields=['corruption_religious_leaders'])
except LocationNotFound:
pass
try:
corruption_traditional_leaders, _ = get_stat_data(
'corruption_traditional_leaders', geo, session,
table_fields=['corruption_traditional_leaders'])
except LocationNotFound:
pass
try:
difficulty_to_obtain_public_school_services, _ = get_stat_data(
'difficulty_to_obtain_public_school_services', geo, session,
table_fields=['difficulty_to_obtain_public_school_services'])
except LocationNotFound:
pass
try:
voting_in_the_most_recent_national_election, _ = get_stat_data(
'voting_in_the_most_recent_national_election', geo, session,
table_fields=['voting_in_the_most_recent_national_election'])
except LocationNotFound:
pass
try:
difficulty_to_obtain_medical_treatment, _ = get_stat_data(
'difficulty_to_obtain_medical_treatment', geo, session,
table_fields=['difficulty_to_obtain_medical_treatment'])
except LocationNotFound:
pass
try:
pay_bribe_for_school_services, _ = get_stat_data(
'pay_bribe_for_school_services', geo, session,
table_fields=['pay_bribe_for_school_services'])
except LocationNotFound:
pass
try:
difficulty_to_obtain_identity_document, _ = get_stat_data(
'difficulty_to_obtain_identity_document', geo, session,
table_fields=['difficulty_to_obtain_identity_document'])
except LocationNotFound:
pass
try:
bribe_for_treatment_at_public_health, _ = get_stat_data(
'bribe_for_treatment_at_public_health', geo,
session, table_fields=[
'bribe_for_treatment_at_public_health'])
except LocationNotFound:
pass
try:
difficulty_to_obtain_household_services, _ = get_stat_data(
'difficulty_to_obtain_household_services', geo, session,
table_fields=['difficulty_to_obtain_household_services'])
except LocationNotFound:
pass
try:
pay_bribe_for_document_or_permit, _ = get_stat_data(
'pay_bribe_for_document_or_permit', geo, session,
table_fields=['pay_bribe_for_document_or_permit'])
except LocationNotFound:
pass
try:
pay_bribe_for_household_services, _ = get_stat_data(
'pay_bribe_for_household_services', geo, session,
table_fields=['pay_bribe_for_household_services'])
except LocationNotFound:
pass
is_missing = own_radio.get('is_missing') and own_motor_vehicle.get(
'is_missing') and own_television.get(
'is_missing') and own_mobile_phone.get(
'is_missing') and election_work_for_a_candidate_or_party.get(
'is_missing') and election_attend_a_campaign_rally.get(
'is_missing') and last_national_election_attend_a_campaign_meeting.get(
'is_missing') and courts_make_binding_decisions.get(
'is_missing') and vote_for_which_party.get(
'is_missing') and level_of_corruption.get(
'is_missing') and chinas_assistance_meets_countrys_needs.get(
'is_missing') and employment_status.get(
'is_missing') and main_reason_for_not_reporting_corruption.get(
'is_missing') and education_of_respondent.get(
'is_missing') and people_must_pay_taxes.get(
'is_missing') and negative_image_of_china.get(
'is_missing') and most_effective_way_to_combat_corruption.get(
'is_missing') and people_can_fight_corruption.get(
'is_missing') and people_must_obey_the_law.get(
'is_missing') and chinas_influence_on_economy.get(
'is_missing') and chinas_influence_positive_or_negative.get(
'is_missing') and positive_image_of_china.get(
'is_missing') and occupation_of_respondent.get(
'is_missing') and employer_of_respondent.get(
'is_missing') and how_often_use_a_mobile_phone.get(
'is_missing') and how_often_use_the_internet.get(
'is_missing') and freeness_and_fairness_of_last_national_election.get(
'is_missing') and extent_of_democracy.get(
'is_missing') and elections_ensure_voters_views_are_reflected.get(
'is_missing') and corruption_judges_and_magistrates.get(
'is_missing') and election_enable_voters_remove_leaders_from_office.get(
'is_missing') and corruption_tax_officials.get(
'is_missing') and corruption_police.get(
'is_missing') and corruption_local_government_councilors.get(
'is_missing') and corruption_government_officials.get(
'is_missing') and corruption_members_of_parliament.get(
'is_missing') and corruption_office_of_the_presidency.get(
'is_missing') and satisfaction_with_democracy.get(
'is_missing') and corruption_religious_leaders.get(
'is_missing') and corruption_traditional_leaders.get(
'is_missing') and difficulty_to_obtain_public_school_services.get(
'is_missing') and voting_in_the_most_recent_national_election.get(
'is_missing') and difficulty_to_obtain_medical_treatment.get(
'is_missing') and pay_bribe_for_school_services.get(
'is_missing') and difficulty_to_obtain_identity_document.get(
'is_missing') and bribe_for_treatment_at_public_health.get(
'is_missing') and difficulty_to_obtain_household_services.get(
'is_missing') and pay_bribe_for_document_or_permit.get(
'is_missing') and pay_bribe_for_household_services.get('is_missing')
if not is_missing:
free_and_fair_key_order = ["Don't know", "Not free and fair",
"Free and fair, with major problems",
"Free and fair, but with minor problems",
"Completely free and fair",
"Do not understand the question",
"metadata"]
elections_key_order = ["Don't know", "Not at all well", "Not very well",
"Well", "Very well", "metadata"]
chinas_influence_key_order = ["Don't know / Haven't heard enough",
"Very negative", "Somewhat negative",
"Neither positive nor negative",
"Somewhat positive", "Very positive",
"metadata"]
china_influence_on_economy_key_order = ["Don't know / Haven't heard enough",
"None", "Some", "A little", "A lot",
"metadata"]
mobile_phone_usage_key_order = ["Don't know", "Never",
"A few times a month", "A few times a week",
"Less than once a month", "Everyday",
"metadata"]
internet_usage_key_order = mobile_phone_usage_key_order + ["Missing"]
corruption_amongst_key_order = ["Don't know/ Haven't heard enough", "None",
"Some of them", "Most of them",
"All of them", "metadata"]
corruption_level_key_order = ["Don't know", "Decreased a lot",
"Decreased somewhat", "Stayed the same",
"Increased somewhat", "Increased a lot",
"metadata"]
pay_bribes_key_order = ["Don't know", "No contact", "Never",
"Once or Twice", "A Few times", "Often", "metadata"]
difficulty_key_order = ["Don't Know", "No contact", "Very Easy", "Easy",
"Difficult", "Very Difficult", "metadata"]
corruption_government_officials = sort_keys(
corruption_amongst_key_order + ["Missing"],
corruption_government_officials)
corruption_judges_and_magistrates = sort_keys(corruption_amongst_key_order,
corruption_judges_and_magistrates)
corruption_local_government_councilors = sort_keys(
corruption_amongst_key_order, corruption_local_government_councilors)
corruption_members_of_parliament = sort_keys(corruption_amongst_key_order,
corruption_members_of_parliament)
corruption_office_of_the_presidency = sort_keys(
corruption_amongst_key_order, corruption_office_of_the_presidency)
corruption_police = sort_keys(corruption_amongst_key_order,
corruption_police)
corruption_religious_leaders = sort_keys(corruption_amongst_key_order,
corruption_religious_leaders)
corruption_traditional_leaders = sort_keys(corruption_amongst_key_order,
corruption_traditional_leaders)
corruption_tax_officials = sort_keys(corruption_amongst_key_order,
corruption_tax_officials)
level_of_corruption = sort_keys(corruption_level_key_order,
level_of_corruption)
pay_bribe_for_document_or_permit = sort_keys(pay_bribes_key_order,
pay_bribe_for_document_or_permit)
pay_bribe_for_household_services = sort_keys(pay_bribes_key_order,
pay_bribe_for_household_services)
pay_bribe_for_school_services = sort_keys(pay_bribes_key_order,
pay_bribe_for_school_services)
bribe_for_treatment_at_public_health = sort_keys(
pay_bribes_key_order,
bribe_for_treatment_at_public_health)
freeness_and_fairness_of_last_national_election = sort_keys(
free_and_fair_key_order,
freeness_and_fairness_of_last_national_election)
elections_ensure_voters_views_are_reflected = sort_keys(elections_key_order,
elections_ensure_voters_views_are_reflected)
election_enable_voters_remove_leaders_from_office = sort_keys(
elections_key_order,
election_enable_voters_remove_leaders_from_office)
chinas_influence_positive_or_negative = sort_keys(
chinas_influence_key_order, chinas_influence_positive_or_negative)
chinas_influence_on_economy = sort_keys(
china_influence_on_economy_key_order, chinas_influence_on_economy)
how_often_use_a_mobile_phone = sort_keys(mobile_phone_usage_key_order,
how_often_use_a_mobile_phone)
how_often_use_the_internet = sort_keys(internet_usage_key_order,
how_often_use_the_internet)
difficulty_to_obtain_household_services = sort_keys(difficulty_key_order,
difficulty_to_obtain_household_services)
difficulty_to_obtain_identity_document = sort_keys(difficulty_key_order,
difficulty_to_obtain_identity_document)
difficulty_to_obtain_medical_treatment = sort_keys(difficulty_key_order,
difficulty_to_obtain_medical_treatment)
difficulty_to_obtain_public_school_services = sort_keys(
difficulty_key_order, difficulty_to_obtain_public_school_services)
people_must_obey_the_law = sort_keys(
["Don't Know", "Strongly Disagree", "Disagree",
"Neither Agree Nor Disagree", "Agree", "Strongly Agree", "metadata"],
people_must_obey_the_law)
extent_of_democracy = sort_keys(
["Don't know", "Not a democracy", "A democracy, with major problems",
"A democracy, but with minor problems",
"Do not understand question / democracy", "A full democracy",
"metadata"], extent_of_democracy)
satisfaction_with_democracy = sort_keys(
["Do not know", "The country is not a democracy",
"Not at all satisfied", "Not very satisfied", "Fairly satisfied",
"Very satisfied", "metadata"], satisfaction_with_democracy)
final_data = {
'is_missing': is_missing,
'own_radio': own_radio,
'own_motor_vehicle': own_motor_vehicle,
'own_television': own_television,
'own_mobile_phone': own_mobile_phone,
'election_work_for_a_candidate_or_party': election_work_for_a_candidate_or_party,
'election_attend_a_campaign_rally': election_attend_a_campaign_rally,
'last_national_election_attend_a_campaign_meeting': last_national_election_attend_a_campaign_meeting,
'courts_make_binding_decisions': courts_make_binding_decisions,
'vote_for_which_party': vote_for_which_party,
'level_of_corruption': level_of_corruption,
'chinas_assistance_meets_countrys_needs': chinas_assistance_meets_countrys_needs,
'employment_status': employment_status,
'main_reason_for_not_reporting_corruption': main_reason_for_not_reporting_corruption,
'education_of_respondent': education_of_respondent,
'people_must_pay_taxes': people_must_pay_taxes,
'negative_image_of_china': negative_image_of_china,
'most_effective_way_to_combat_corruption': most_effective_way_to_combat_corruption,
'people_can_fight_corruption': people_can_fight_corruption,
'people_must_obey_the_law': people_must_obey_the_law,
'chinas_influence_on_economy': chinas_influence_on_economy,
'chinas_influence_positive_or_negative': chinas_influence_positive_or_negative,
'positive_image_of_china': positive_image_of_china,
'occupation_of_respondent': occupation_of_respondent,
'employer_of_respondent': employer_of_respondent,
'how_often_use_a_mobile_phone': how_often_use_a_mobile_phone,
'how_often_use_the_internet': how_often_use_the_internet,
'freeness_and_fairness_of_last_national_election': freeness_and_fairness_of_last_national_election,
'extent_of_democracy': extent_of_democracy,
'elections_ensure_voters_views_are_reflected': elections_ensure_voters_views_are_reflected,
'corruption_judges_and_magistrates': corruption_judges_and_magistrates,
'election_enable_voters_remove_leaders_from_office': election_enable_voters_remove_leaders_from_office,
'corruption_tax_officials': corruption_tax_officials,
'corruption_police': corruption_police,
'corruption_local_government_councilors': corruption_local_government_councilors,
'corruption_government_officials': corruption_government_officials,
'corruption_members_of_parliament': corruption_members_of_parliament,
'corruption_office_of_the_presidency': corruption_office_of_the_presidency,
'satisfaction_with_democracy': satisfaction_with_democracy,
'corruption_religious_leaders': corruption_religious_leaders,
'corruption_traditional_leaders': corruption_traditional_leaders,
'difficulty_to_obtain_public_school_services': difficulty_to_obtain_public_school_services,
'voting_in_the_most_recent_national_election': voting_in_the_most_recent_national_election,
'difficulty_to_obtain_medical_treatment': difficulty_to_obtain_medical_treatment,
'pay_bribe_for_school_services': pay_bribe_for_school_services,
'difficulty_to_obtain_identity_document': difficulty_to_obtain_identity_document,
'bribe_for_treatment_at_public_health': bribe_for_treatment_at_public_health,
'difficulty_to_obtain_household_services': difficulty_to_obtain_household_services,
'pay_bribe_for_document_or_permit': pay_bribe_for_document_or_permit,
'pay_bribe_for_household_services': pay_bribe_for_household_services,
}
return final_data
def get_afrobarometer_profile(geo, session):
with dataset_context(year='2016'):
own_radio = LOCATIONNOTFOUND
own_motor_vehicle = LOCATIONNOTFOUND
own_television = LOCATIONNOTFOUND
own_mobile_phone = LOCATIONNOTFOUND
election_work_for_candidate_or_party = LOCATIONNOTFOUND
election_attend_campaign_rally = LOCATIONNOTFOUND
election_attend_campaign_meeting = LOCATIONNOTFOUND
courts_make_binding_decisions = LOCATIONNOTFOUND
vote_for_which_party = LOCATIONNOTFOUND
level_of_corruption = LOCATIONNOTFOUND
chinas_assistance_meets_country_needs = LOCATIONNOTFOUND
employment_status = LOCATIONNOTFOUND
main_reason_for_not_reporting_corruption = LOCATIONNOTFOUND
education_of_respondent = LOCATIONNOTFOUND
people_must_pay_taxes = LOCATIONNOTFOUND
negative_image_of_china = LOCATIONNOTFOUND
most_effective_way_to_combat_corruption = LOCATIONNOTFOUND
people_can_fight_corruption = LOCATIONNOTFOUND
people_must_obey_the_law = LOCATIONNOTFOUND
chinas_influence_on_economy = LOCATIONNOTFOUND
chinas_influence_positive_or_negative = LOCATIONNOTFOUND
positive_image_of_china = LOCATIONNOTFOUND
occupation_of_respondent = LOCATIONNOTFOUND
employer_of_respondent = LOCATIONNOTFOUND
how_often_use_a_mobile_phone = LOCATIONNOTFOUND
how_often_use_the_internet = LOCATIONNOTFOUND
freeness_and_fairness_last_elections = LOCATIONNOTFOUND
extent_of_democracy = LOCATIONNOTFOUND
elections_ensure_voters_views_are_reflected = LOCATIONNOTFOUND
corruption_judges_and_magistrates = LOCATIONNOTFOUND
elections_to_remove_leaders_from_office = LOCATIONNOTFOUND
corruption_tax_officials = LOCATIONNOTFOUND
corruption_police = LOCATIONNOTFOUND
corruption_local_gov_councilors = LOCATIONNOTFOUND
corruption_gov_officials = LOCATIONNOTFOUND
corruption_mps = LOCATIONNOTFOUND
corruption_president_office = LOCATIONNOTFOUND
satisfaction_with_democracy = LOCATIONNOTFOUND
corruption_business_executives = LOCATIONNOTFOUND
corruption_religious_leaders = LOCATIONNOTFOUND
corruption_traditional_leaders = LOCATIONNOTFOUND
difficulty_to_obtain_public_school_services = LOCATIONNOTFOUND
voting_recent_national_election = LOCATIONNOTFOUND
difficulty_to_obtain_medical_treatment = LOCATIONNOTFOUND
pay_bribe_for_school_services = LOCATIONNOTFOUND
difficulty_to_obtain_identity_document = LOCATIONNOTFOUND
pay_bribe_treatment_public_health = LOCATIONNOTFOUND
difficulty_to_obtain_household_services = LOCATIONNOTFOUND
pay_bribe_for_document_or_permit = LOCATIONNOTFOUND
pay_bribe_for_household_services = LOCATIONNOTFOUND
try:
own_radio, _ = get_stat_data('own_radio', geo, session,
table_fields=['own_radio'])
except LocationNotFound:
pass
try:
own_motor_vehicle, _ = get_stat_data('own_motor_vehicle', geo, session,
table_fields=['own_motor_vehicle'])
except LocationNotFound:
pass
try:
own_television, _ = get_stat_data('own_television', geo, session,
table_fields=['own_television'])
except LocationNotFound:
pass
try:
own_mobile_phone, _ = get_stat_data('own_mobile_phone', geo, session,
table_fields=['own_mobile_phone'])
except LocationNotFound:
pass
try:
election_work_for_candidate_or_party, _ = get_stat_data(
'election_work_for_candidate_or_party', geo, session,
table_fields=['election_work_for_candidate_or_party'])
except LocationNotFound:
pass
try:
election_attend_campaign_rally, _ = get_stat_data(
'election_attend_campaign_rally', geo, session,
table_fields=['election_attend_campaign_rally'])
except LocationNotFound:
pass
try:
election_attend_campaign_meeting, _ = get_stat_data(
'election_attend_campaign_meeting', geo, session,
table_fields=['election_attend_campaign_meeting'])
except LocationNotFound:
pass
try:
courts_make_binding_decisions, _ = get_stat_data(
'courts_make_binding_decisions', geo, session,
table_fields=['courts_make_binding_decisions'])
except LocationNotFound:
pass
try:
vote_for_which_party, _ = get_stat_data('vote_for_which_party', geo,
session, table_fields=[
'vote_for_which_party'])
except LocationNotFound:
pass
try:
level_of_corruption, _ = get_stat_data('level_of_corruption', geo,
session, table_fields=[
'level_of_corruption'])
except LocationNotFound:
pass
try:
chinas_assistance_meets_country_needs, _ = get_stat_data(
'chinas_assistance_meets_country_needs', geo, session,
table_fields=['chinas_assistance_meets_country_needs'])
except LocationNotFound:
pass
try:
employment_status, _ = get_stat_data('employment_status', geo, session,
table_fields=['employment_status'])
except LocationNotFound:
pass
try:
main_reason_for_not_reporting_corruption, _ = get_stat_data(
'main_reason_for_not_reporting_corruption', geo, session,
table_fields=['main_reason_for_not_reporting_corruption'])
except LocationNotFound:
pass
try:
education_of_respondent, _ = get_stat_data('education_of_respondent',
geo, session, table_fields=[
'education_of_respondent'])
except LocationNotFound:
pass
try:
people_must_pay_taxes, _ = get_stat_data('people_must_pay_taxes', geo,
session, table_fields=[
'people_must_pay_taxes'])
except LocationNotFound:
pass
try:
negative_image_of_china, _ = get_stat_data('negative_image_of_china',
geo, session, table_fields=[
'negative_image_of_china'])
except LocationNotFound:
pass
try:
most_effective_way_to_combat_corruption, _ = get_stat_data(
'most_effective_way_to_combat_corruption', geo, session,
table_fields=['most_effective_way_to_combat_corruption'])
except LocationNotFound:
pass
try:
people_can_fight_corruption, _ = get_stat_data(
'people_can_fight_corruption', geo, session,
table_fields=['people_can_fight_corruption'])
except LocationNotFound:
pass
try:
people_must_obey_the_law, _ = get_stat_data('people_must_obey_the_law',
geo, session, table_fields=[
'people_must_obey_the_law'])
except LocationNotFound:
pass
try:
chinas_influence_on_economy, _ = get_stat_data(
'chinas_influence_on_economy', geo, session,
table_fields=['chinas_influence_on_economy'])
except LocationNotFound:
pass
try:
chinas_influence_positive_or_negative, _ = get_stat_data(
'chinas_influence_positive_or_negative', geo, session,
table_fields=['chinas_influence_positive_or_negative'])
except LocationNotFound:
pass
try:
positive_image_of_china, _ = get_stat_data('positive_image_of_china',
geo, session, table_fields=[
'positive_image_of_china'])
except LocationNotFound:
pass
try:
occupation_of_respondent, _ = get_stat_data('occupation_of_respondent',
geo, session, table_fields=[
'occupation_of_respondent'])
except LocationNotFound:
pass
try:
employer_of_respondent, _ = get_stat_data('employer_of_respondent', geo,
session, table_fields=[
'employer_of_respondent'])
except LocationNotFound:
pass
try:
how_often_use_a_mobile_phone, _ = get_stat_data(
'how_often_use_a_mobile_phone', geo, session,
table_fields=['how_often_use_a_mobile_phone'])
except LocationNotFound:
pass
try:
how_often_use_the_internet, _ = get_stat_data(
'how_often_use_the_internet', geo, session,
table_fields=['how_often_use_the_internet'])
except LocationNotFound:
pass
try:
freeness_and_fairness_last_elections, _ = get_stat_data(
'freeness_and_fairness_last_elections', geo, session,
table_fields=['freeness_and_fairness_last_elections'])
except LocationNotFound:
pass
try:
extent_of_democracy, _ = get_stat_data('extent_of_democracy', geo,
session, table_fields=[
'extent_of_democracy'])
except LocationNotFound:
pass
try:
elections_ensure_voters_views_are_reflected, _ = get_stat_data(
'elections_ensure_voters_views_are_reflected', geo, session,
table_fields=['elections_ensure_voters_views_are_reflected'])
except LocationNotFound:
pass
try:
corruption_judges_and_magistrates, _ = get_stat_data(
'corruption_judges_and_magistrates', geo, session,
table_fields=['corruption_judges_and_magistrates'])
except LocationNotFound:
pass
try:
elections_to_remove_leaders_from_office, _ = get_stat_data(
'elections_to_remove_leaders_from_office', geo,
session, table_fields=[
'elections_to_remove_leaders_from_office'])
except LocationNotFound:
pass
try:
corruption_tax_officials, _ = get_stat_data('corruption_tax_officials',
geo, session, table_fields=[
'corruption_tax_officials'])
except LocationNotFound:
pass
try:
corruption_police, _ = get_stat_data('corruption_police', geo, session,
table_fields=['corruption_police'])
except LocationNotFound:
pass
try:
corruption_local_gov_councilors, _ = get_stat_data(
'corruption_local_gov_councilors', geo, session,
table_fields=['corruption_local_gov_councilors'])
except LocationNotFound:
pass
try:
corruption_gov_officials, _ = get_stat_data('corruption_gov_officials',
geo, session, table_fields=[
'corruption_gov_officials'])
except LocationNotFound:
pass
try:
corruption_mps, _ = get_stat_data('corruption_mps', geo, session,
table_fields=['corruption_mps'])
except LocationNotFound:
pass
try:
corruption_president_office, _ = get_stat_data(
'corruption_president_office', geo, session,
table_fields=['corruption_president_office'])
except LocationNotFound:
pass
try:
satisfaction_with_democracy, _ = get_stat_data(
'satisfaction_with_democracy', geo, session,
table_fields=['satisfaction_with_democracy'])
except LocationNotFound:
pass
try:
corruption_business_executives, _ = get_stat_data(
'corruption_business_executives', geo, session,
table_fields=['corruption_business_executives'])
except LocationNotFound:
pass
try:
corruption_religious_leaders, _ = get_stat_data(
'corruption_religious_leaders', geo, session,
table_fields=['corruption_religious_leaders'])
except LocationNotFound:
pass
try:
corruption_traditional_leaders, _ = get_stat_data(
'corruption_traditional_leaders', geo, session,
table_fields=['corruption_traditional_leaders'])
except LocationNotFound:
pass
try:
difficulty_to_obtain_public_school_services, _ = get_stat_data(
'difficulty_to_obtain_public_school_services', geo, session,
table_fields=['difficulty_to_obtain_public_school_services'])
except LocationNotFound:
pass
try:
voting_recent_national_election, _ = get_stat_data(
'voting_recent_national_election', geo, session,
table_fields=['voting_recent_national_election'])
except LocationNotFound:
pass
try:
difficulty_to_obtain_medical_treatment, _ = get_stat_data(
'difficulty_to_obtain_medical_treatment', geo, session,
table_fields=['difficulty_to_obtain_medical_treatment'])
except LocationNotFound:
pass
try:
pay_bribe_for_school_services, _ = get_stat_data(
'pay_bribe_for_school_services', geo, session,
table_fields=['pay_bribe_for_school_services'])
except LocationNotFound:
pass
try:
difficulty_to_obtain_identity_document, _ = get_stat_data(
'difficulty_to_obtain_identity_document', geo, session,
table_fields=['difficulty_to_obtain_identity_document'])
except LocationNotFound:
pass
try:
pay_bribe_treatment_public_health, _ = get_stat_data(
'pay_bribe_treatment_public_health', geo, session,
table_fields=['pay_bribe_treatment_public_health'])
except LocationNotFound:
pass
try:
difficulty_to_obtain_household_services, _ = get_stat_data(
'difficulty_to_obtain_household_services', geo, session,
table_fields=['difficulty_to_obtain_household_services'])
except LocationNotFound:
pass
try:
pay_bribe_for_document_or_permit, _ = get_stat_data(
'pay_bribe_for_document_or_permit', geo, session,
table_fields=['pay_bribe_for_document_or_permit'])
except LocationNotFound:
pass
try:
pay_bribe_for_household_services, _ = get_stat_data(
'pay_bribe_for_household_services', geo, session,
table_fields=['pay_bribe_for_household_services'])
except LocationNotFound:
pass
is_missing = own_radio.get('is_missing') and own_motor_vehicle.get(
'is_missing') and own_television.get(
'is_missing') and own_mobile_phone.get(
'is_missing') and election_work_for_candidate_or_party.get(
'is_missing') and election_attend_campaign_rally.get(
'is_missing') and election_attend_campaign_meeting.get(
'is_missing') and courts_make_binding_decisions.get(
'is_missing') and vote_for_which_party.get(
'is_missing') and level_of_corruption.get(
'is_missing') and chinas_assistance_meets_country_needs.get(
'is_missing') and employment_status.get(
'is_missing') and main_reason_for_not_reporting_corruption.get(
'is_missing') and education_of_respondent.get(
'is_missing') and people_must_pay_taxes.get(
'is_missing') and negative_image_of_china.get(
'is_missing') and most_effective_way_to_combat_corruption.get(
'is_missing') and people_can_fight_corruption.get(
'is_missing') and people_must_obey_the_law.get(
'is_missing') and chinas_influence_on_economy.get(
'is_missing') and chinas_influence_positive_or_negative.get(
'is_missing') and positive_image_of_china.get(
'is_missing') and occupation_of_respondent.get(
'is_missing') and employer_of_respondent.get(
'is_missing') and how_often_use_a_mobile_phone.get(
'is_missing') and how_often_use_the_internet.get(
'is_missing') and freeness_and_fairness_last_elections.get(
'is_missing') and extent_of_democracy.get(
'is_missing') and elections_ensure_voters_views_are_reflected.get(
'is_missing') and corruption_judges_and_magistrates.get(
'is_missing') and elections_to_remove_leaders_from_office.get(
'is_missing') and corruption_tax_officials.get(
'is_missing') and corruption_police.get(
'is_missing') and corruption_local_gov_councilors.get(
'is_missing') and corruption_gov_officials.get(
'is_missing') and corruption_mps.get(
'is_missing') and corruption_president_office.get(
'is_missing') and satisfaction_with_democracy.get(
'is_missing') and corruption_business_executives.get(
'is_missing') and corruption_religious_leaders.get(
'is_missing') and corruption_traditional_leaders.get(
'is_missing') and difficulty_to_obtain_public_school_services.get(
'is_missing') and voting_recent_national_election.get(
'is_missing') and difficulty_to_obtain_medical_treatment.get(
'is_missing') and pay_bribe_for_school_services.get(
'is_missing') and difficulty_to_obtain_identity_document.get(
'is_missing') and pay_bribe_treatment_public_health.get(
'is_missing') and difficulty_to_obtain_household_services.get(
'is_missing') and pay_bribe_for_document_or_permit.get(
'is_missing') and pay_bribe_for_household_services.get('is_missing')
if not is_missing:
difficulty_to_obtain_household_services = sort_keys(difficulty_key_order, difficulty_to_obtain_household_services)
difficulty_to_obtain_identity_document = sort_keys(difficulty_key_order, difficulty_to_obtain_identity_document)
difficulty_to_obtain_medical_treatment = sort_keys(difficulty_key_order, difficulty_to_obtain_medical_treatment)
difficulty_to_obtain_public_school_services = sort_keys(difficulty_key_order, difficulty_to_obtain_public_school_services)
election_attend_campaign_meeting = sort_keys(elections_key_order, election_attend_campaign_meeting)
election_attend_campaign_rally = sort_keys(elections_key_order, election_attend_campaign_rally)
election_work_for_candidate_or_party = sort_keys(elections_key_order, election_work_for_candidate_or_party)
elections_to_remove_leaders_from_office = sort_keys(elections_key_order, elections_to_remove_leaders_from_office)
freeness_and_fairness_last_elections = sort_keys(free_and_fair_key_order, freeness_and_fairness_last_elections)
chinas_influence_positive_or_negative = sort_keys(chinas_influence_key_order, chinas_influence_positive_or_negative)
chinas_influence_on_economy = sort_keys(china_influence_on_economy_key_order, chinas_influence_on_economy)
pay_bribe_for_document_or_permit = sort_keys(pay_bribes_key_order, pay_bribe_for_document_or_permit)
pay_bribe_for_household_services = sort_keys(pay_bribes_key_order, pay_bribe_for_household_services)
pay_bribe_for_school_services = sort_keys(pay_bribes_key_order, pay_bribe_for_school_services)
pay_bribe_treatment_public_health = sort_keys(pay_bribes_key_order, pay_bribe_treatment_public_health)
how_often_use_a_mobile_phone = sort_keys(mobile_phone_usage_key_order, how_often_use_a_mobile_phone)
how_often_use_the_internet = sort_keys(internet_usage_key_order, how_often_use_the_internet)
education_of_respondent = sort_keys(education_of_respondent_key_order, education_of_respondent)
employment_status = sort_keys(employment_status_key_order, employment_status)
people_can_fight_corruption = sort_keys(can_people_fight_corruption_key_order, people_can_fight_corruption)
courts_make_binding_decisions = sort_keys(courts_make_binding_decisions_key_order, courts_make_binding_decisions)
people_must_pay_taxes = sort_keys(pay_taxes_key_order, people_must_pay_taxes)
people_must_obey_the_law = sort_keys(people_must_obey_the_law_key_order, people_must_obey_the_law)
final_data = {
'is_missing': is_missing,
'own_radio': own_radio,
'own_motor_vehicle': own_motor_vehicle,
'own_television': own_television,
'own_mobile_phone': own_mobile_phone,
'election_work_for_candidate_or_party': election_work_for_candidate_or_party,
'election_attend_campaign_rally': election_attend_campaign_rally,
'election_attend_campaign_meeting': election_attend_campaign_meeting,
'courts_make_binding_decisions': courts_make_binding_decisions,
'vote_for_which_party': vote_for_which_party,
'level_of_corruption': level_of_corruption,
'chinas_assistance_meets_country_needs': chinas_assistance_meets_country_needs,
'employment_status': employment_status,
'main_reason_for_not_reporting_corruption': main_reason_for_not_reporting_corruption,
'education_of_respondent': education_of_respondent,
'people_must_pay_taxes': people_must_pay_taxes,
'negative_image_of_china': negative_image_of_china,
'most_effective_way_to_combat_corruption': most_effective_way_to_combat_corruption,
'people_can_fight_corruption': people_can_fight_corruption,
'people_must_obey_the_law': people_must_obey_the_law,
'chinas_influence_on_economy': chinas_influence_on_economy,
'chinas_influence_positive_or_negative': chinas_influence_positive_or_negative,
'positive_image_of_china': positive_image_of_china,
'occupation_of_respondent': occupation_of_respondent,
'employer_of_respondent': employer_of_respondent,
'how_often_use_a_mobile_phone': how_often_use_a_mobile_phone,
'how_often_use_the_internet': how_often_use_the_internet,
'freeness_and_fairness_last_elections': freeness_and_fairness_last_elections,
'extent_of_democracy': extent_of_democracy,
'elections_ensure_voters_views_are_reflected': elections_ensure_voters_views_are_reflected,
'corruption_judges_and_magistrates': corruption_judges_and_magistrates,
'elections_to_remove_leaders_from_office': elections_to_remove_leaders_from_office,
'corruption_tax_officials': corruption_tax_officials,
'corruption_police': corruption_police,
'corruption_local_gov_councilors': corruption_local_gov_councilors,
'corruption_gov_officials': corruption_gov_officials,
'corruption_mps': corruption_mps,
'corruption_president_office': corruption_president_office,
'satisfaction_with_democracy': satisfaction_with_democracy,
'corruption_business_executives': corruption_business_executives,
'corruption_religious_leaders': corruption_religious_leaders,
'corruption_traditional_leaders': corruption_traditional_leaders,
'difficulty_to_obtain_public_school_services': difficulty_to_obtain_public_school_services,
'voting_recent_national_election': voting_recent_national_election,
'difficulty_to_obtain_medical_treatment': difficulty_to_obtain_medical_treatment,
'pay_bribe_for_school_services': pay_bribe_for_school_services,
'difficulty_to_obtain_identity_document': difficulty_to_obtain_identity_document,
'pay_bribe_treatment_public_health': pay_bribe_treatment_public_health,
'difficulty_to_obtain_household_services': difficulty_to_obtain_household_services,
'pay_bribe_for_document_or_permit': pay_bribe_for_document_or_permit,
'pay_bribe_for_household_services': pay_bribe_for_household_services,
}
return final_data
def get_education_profile(geo, session, display_profile, comparative=False):
youth_completed_grade9, _ = get_stat_data(
['completed grade9'], geo, session,
table_universe='Youth aged 16 to 17',
table_dataset='Census and Community Survey',
key_order=('Completed', 'Not completed'))
youth_completed_grade9_by_gender, _ = get_stat_data(
['completed grade9', 'gender'], geo, session,
table_universe='Youth aged 16 to 17',
table_dataset='Census and Community Survey',
percent_grouping=['gender'], slices=['Completed'],
key_order={'gender': GENDER_ORDER})
youth_education_level, youth_pop_20_to_24 = get_stat_data(
['education level'], geo, session,
table_universe='Youth aged 20 to 24',
table_dataset='Census and Community Survey',
key_order=EDUCATION_LEVEL_KEY_ORDER)
youth_perc_matric = None
if youth_education_level['Matric/matric equivalent']['numerators']['this']:
matric_or_equiv = (
youth_education_level['Matric/matric equivalent']['numerators']['this'] +
youth_education_level['Any tertiary']['numerators']['this'])
youth_perc_matric = percent(matric_or_equiv, youth_pop_20_to_24)
youth_education_attendance, _ = get_stat_data(
['attendance'], geo, session,
table_universe='Youth',
table_dataset='Census and Community Survey',
table_fields=['attendance', 'age in completed years', 'gender'])
youth_education_attending_by_age, _ = get_stat_data(
['attendance', 'age in completed years'], geo, session,
table_universe='Youth',
table_dataset='Census and Community Survey',
table_fields=['attendance', 'age in completed years', 'gender'],
table_name='youth_education_attendance_age_incompleted_years_gender',
percent_grouping=['age in completed years'], slices=['Yes'])
youth_education_attending_by_gender, _ = get_stat_data(
['attendance', 'gender'], geo, session,
table_universe='Youth',
table_dataset='Census and Community Survey',
table_fields=['attendance', 'age in completed years', 'gender'],
percent_grouping=['gender'], slices=['Yes'],
key_order={'gender': GENDER_ORDER})
final_data = {
'youth_completed_grade9': youth_completed_grade9,
'youth_perc_completed_grade9': {
'name': 'Of youth aged 16-17 have completed grade 9 or higher',
'values': {'this': youth_completed_grade9['Completed']['values']['this']}
},
'youth_completed_grade9_by_gender': youth_completed_grade9_by_gender,
'youth_education_level': youth_education_level,
'youth_perc_matric': {
"name": "Of youth aged 20-24 have completed matric/matric equivalent or higher",
"values": {"this": youth_perc_matric}
},
'youth_perc_attending': {
"name": "Of youth aged 15-24 attend an educational institution",
"values": {"this": youth_education_attendance['Yes']['values']['this']}
},
'youth_education_attending_by_age': youth_education_attending_by_age,
'youth_education_attending_by_gender': youth_education_attending_by_gender,
}
if display_profile == 'WC':
with dataset_context(year='2017'):
youth_average_mean_score_by_year, _ = get_stat_data(
['year'], geo, session,
table_universe='Average mean score in both language and mathematics',
percent=False)
youth_average_language_score_by_year, _ = get_stat_data(
['year'], geo, session,
table_universe='Average score in language',
percent=False)
youth_average_maths_score_by_year, _ = get_stat_data(
['year'], geo, session,
table_universe='Average score in mathematics',
percent=False)
youth_language_outcome_latest, _ = get_stat_data(
['year', 'outcome'], geo, session,
table_universe='Percentage passed in language',
key_order={'outcome': ['Passed', 'Failed']},
percent=False, slices=['2017'])
youth_maths_outcome_latest, _ = get_stat_data(
['year', 'outcome'], geo, session,
table_universe='Percentage passed in mathematics',
key_order={'outcome': ['Passed', 'Failed']},
percent=False, slices=['2017'])
with dataset_context(year='2017'):
youth_matric_outcome_by_year, _ = get_stat_data(
['year'], geo, session,
table_universe='Matric pass rate',
only={'outcome': ['Passed']},
percent=False)
youth_matric_outcome_latest, _ = get_stat_data(
['year', 'outcome'], geo, session,
table_universe='Matric pass rate',
key_order={'outcome': ['Passed', 'Failed']},
percent=False, slices=['2017'])
youth_matric_throughput_rate_by_year, _ = get_stat_data(
['year'], geo, session,
table_universe='Matric passes as a % of grade 8 enrolment',
only={'outcome': ['Passed']},
percent=False)
youth_matric_throughput_latest, _ = get_stat_data(
['year', 'outcome'], geo, session,
table_universe='Matric passes as a % of grade 8 enrolment',
key_order={'outcome': ['Passed', 'Dropped out or failed']},
percent=False, slices=['2017'])
youth_bachelor_passes_by_year, _ = get_stat_data(
['year'], geo, session,
table_universe='Bachelor passes as a % of grade 8 enrolment',
only={'outcome': ['Bachelor pass']},
percent=False)
youth_bachelor_outcome_latest, _ = get_stat_data(
['year', 'outcome'], geo, session,
table_universe='Bachelor passes as a % of grade 8 enrolment',
key_order={'outcome': ['Bachelor pass', 'No bachelor pass']},
percent=False, slices=['2017'])
youth_student_dropout_rate_by_year, _ = get_stat_data(
['year'], geo, session,
table_universe='Dropout rates between grade 10 and matric',
only={'outcome': ['Dropped out']},
percent=False)
youth_student_dropout_rate_latest, _ = get_stat_data(
['year', 'outcome'], geo, session,
table_universe='Dropout rates between grade 10 and matric',
percent=False, slices=['2017'])
final_data.update({
'youth_ave_mean_score_latest': {
"name": "Average mean score in both language and mathematics",
"values": {"this": youth_average_mean_score_by_year['2017']['values']['this']}
},
'youth_ave_mean_score_by_year': youth_average_mean_score_by_year,
'youth_ave_language_score_latest': {
"name": "Average score in language",
"values": {"this": youth_average_language_score_by_year['2017']['values']['this']}
},
'youth_ave_language_score_by_year': youth_average_language_score_by_year,
'youth_ave_maths_score_latest': {
"name": "Average score in mathematics",
"values": {"this": youth_average_maths_score_by_year['2017']['values']['this']}
},
'youth_ave_maths_score_by_year': youth_average_maths_score_by_year,
'youth_language_outcome_latest': youth_language_outcome_latest,
'youth_maths_outcome_latest': youth_maths_outcome_latest,
'youth_matric_pass_rate_latest': {
"name": "Of students writing matric passed",
"values": {"this": youth_matric_outcome_latest['Passed']['values']['this']}
},
'youth_matric_outcome_latest': youth_matric_outcome_latest,
'youth_matric_outcome_by_year': youth_matric_outcome_by_year,
'youth_matric_throughput_rate_latest': {
"name": "Of Grade 8 students go on to pass matric",
"values": {"this": youth_matric_throughput_latest['Passed']['values']['this']}
},
'youth_matric_throughput_latest': youth_matric_throughput_latest,
'youth_matric_throughput_rate_by_year': youth_matric_throughput_rate_by_year,
'youth_bachelor_passes_latest': {
"name": "Of Grade 8 students go on to pass matric with a bachelor's pass",
"values": {"this": youth_bachelor_outcome_latest['Bachelor pass']['values']['this']}
},
'youth_bachelor_outcome_latest': youth_bachelor_outcome_latest,
'youth_bachelor_passes_by_year': youth_bachelor_passes_by_year,
'youth_student_dropout_rate_latest': {
"name": "Of students drop out between grade 10 and matric",
"values": {"this": youth_student_dropout_rate_latest['Dropped out']['values']['this']}
},
'youth_student_dropout_rate_by_year': youth_student_dropout_rate_by_year
})
return final_data
def get_safety_profile(geo, session, display_profile, comparative=False):
def rate_per_10k_pop(value, population):
if not value:
return None
return value / population * 10000
final_data = {}
if display_profile == 'WC':
youth_pop_table = get_datatable('youth_population')
youth, pop_total = youth_pop_table.get_stat_data(
geo, total='total_pop', percent='False')
with dataset_context(year='2015'):
victims_by_age_group_per_10k_pop, total_victims_per_10k_pop = get_stat_data(
['age group'], geo, session,
table_universe='Victims of crime per 10,000 population in age group',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
accused_by_age_group_per_10k_pop, total_accused_per_10k_pop = get_stat_data(
['age group'], geo, session,
table_universe='Accused of crime per 10,000 population in age group',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
youth_victims_by_offence_per_10k_youth, _ = get_stat_data(
['type of offence'], geo, session,
table_universe='Youth victims of crime, per 10,000 youth',
table_dataset='Police Crime Statistics 2006-2015')
youth_accused_by_offence_per_10k_youth, _ = get_stat_data(
['type of offence'], geo, session,
table_universe='Youth accused of crimes, per 10,000 youth',
table_dataset='Police Crime Statistics 2006-2015')
youth_victims_by_pop_group_per_10k, _ = get_stat_data(
['population group'], geo, session,
table_universe='Youth victims of crime, per 10,000 youth',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
youth_accused_by_pop_group_per_10k, _ = get_stat_data(
['population group'], geo, session,
table_universe='Youth accused of crimes, per 10,000 youth',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
youth_victims_by_gender_per_10k, _ = get_stat_data(
['gender'], geo, session,
table_universe='Youth victims of crime, per 10,000 youth',
table_dataset='Police Crime Statistics 2006-2015',
percent=False,
key_order=GENDER_ORDER)
youth_accused_by_gender_per_10k, _ = get_stat_data(
['gender'], geo, session,
table_universe='Youth accused of crimes, per 10,000 youth',
table_dataset='Police Crime Statistics 2006-2015',
percent=False,
key_order=GENDER_ORDER)
youth_victims_by_year, _ = get_stat_data(
['year'], geo, session,
table_universe='Youth victims of crime',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
youth_accused_by_year, _ = get_stat_data(
['year'], geo, session,
table_universe='Youth accused of crimes',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
crimes_by_year, _ = get_stat_data(
['type of crime', 'year'], geo, session,
table_universe='Crimes',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
contact_crimes_by_year = crimes_by_year['Contact crime']
contact_crimes_by_year['metadata'] = crimes_by_year['metadata']
property_crimes_by_year = crimes_by_year['Property crime']
property_crimes_by_year['metadata'] = crimes_by_year['metadata']
contact_crimes_per_10k_pop = rate_per_10k_pop(contact_crimes_by_year['2014-15']['values']['this'], pop_total)
property_crimes_per_10k_pop = rate_per_10k_pop(property_crimes_by_year['2014-15']['values']['this'], pop_total)
final_data = {
'youth_victims_per_10k_youth': {
"name": "Youth victims of contact crime per 10,000 youth",
"values": {"this": victims_by_age_group_per_10k_pop['15-24']['values']['this']}
},
'youth_accused_per_10k_youth': {
"name": "Youth accused of contact crime per 10,000 youth",
"values": {"this": accused_by_age_group_per_10k_pop['15-24']['values']['this']}
},
'victims_by_age_group_per_10k_pop': victims_by_age_group_per_10k_pop,
'accused_by_age_group_per_10k_pop': accused_by_age_group_per_10k_pop,
'youth_victims_by_offence_per_10k_youth': youth_victims_by_offence_per_10k_youth,
'youth_accused_by_offence_per_10k_youth': youth_accused_by_offence_per_10k_youth,
'youth_victims_by_pop_group_per_10k': youth_victims_by_pop_group_per_10k,
'youth_accused_by_pop_group_per_10k': youth_accused_by_pop_group_per_10k,
'youth_victims_by_gender_per_10k': youth_victims_by_gender_per_10k,
'youth_accused_by_gender_per_10k': youth_accused_by_gender_per_10k,
'youth_victims_by_year': youth_victims_by_year,
'youth_accused_by_year': youth_accused_by_year,
'contact_crimes_per_10k_pop': {
"name": "Contact crimes per 10,000 population",
"values": {"this": contact_crimes_per_10k_pop}
},
'property_crimes_per_10k_pop': {
"name": "Property-related crime per 10,000 population",
"values": {"this": property_crimes_per_10k_pop}
},
'contact_crimes_by_year': contact_crimes_by_year,
'property_crimes_by_year': property_crimes_by_year
}
return final_data
def get_landsales_profile(geo, session):
#TODO: landsales profile for 2016 data context
with dataset_context(year='2018'):
landsales = {'is_missing': True}
landsalestransaction = landsaleshectares = LOCATIONNOTFOUND
landsalesaverageprice = landsalespricetrends = LOCATIONNOTFOUND
landsalestransaction_tot = landsaleshectares_tot = 0
try:
landsalestransaction, landsalestransaction_tot = get_stat_data(
['class_distribution'], geo, session,
table_name='land_traded_per_class_statistic_2018',
only={'statistic': ['Number of transactions in 12 months']},
key_order=LAND_CLASS,
percent=False)
except Exception as e:
pass
try:
landsaleshectares, landsaleshectares_tot = get_stat_data(
['class_distribution'], geo, session,
table_name='land_traded_per_class_statistic_2018',
only={'statistic': ['Total hectare traded in 12 months(ha)']},
key_order=LAND_CLASS,
percent=False)
except Exception as e:
pass
try:
landsalesaverageprice, _ = get_stat_data(
['class_distribution'], geo, session,
table_name='land_traded_per_class_statistic_2018',
only={'statistic': ['Average price per hectare in 12 months(R/ha)']},
key_order=LAND_CLASS,
percent=False)
except Exception as e:
pass
try:
landsalespricetrends, _ = get_stat_data(
['class_distribution'], geo, session,
table_name='land_traded_per_class_statistic_2018',
only={'statistic': ['Price trend per hectare in 12 months(R/ha)']},
key_order=LAND_CLASS,
percent=False)
except Exception as e:
pass
try:
landsalesaveragetrends, _ = get_stat_data(
['class_distribution'], geo, session, exclude_zero=True,
table_name='land_traded_per_class_statistic_2018',
only={'statistic': ['Average trend in 12 months(%)']},
key_order=LAND_CLASS,
percent=False)
except Exception as e:
pass
landsales['landsalestransaction'] = landsalestransaction
landsales['landsaleshectares'] = landsaleshectares
landsales['landsalesaverageprice'] = landsalesaverageprice
landsales['landsalespricetrends'] = landsalespricetrends
landsales['landsaleshectares_tot'] = \
{
"name": " Total number of sold hectares from Dec 2017 to Nov 2018",
"values": {"this": int(landsaleshectares_tot)},
}
landsales['landsalestransaction_tot'] = \
{
"name": "Total number of sales transactions from Dec 2017 to Nov 2018",
"values": {"this": int(landsalestransaction_tot)},
}
landsales['is_missing'] = landsalestransaction.get('is_missing') and \
landsaleshectares.get('is_missing')
return landsales
def get_redistributionandrestitution_profile(geo, session):
with dataset_context(year='2018'):
redistributedlandusebreakdown = redistributeprogrammeprojectsbyyear = LOCATIONNOTFOUND
redistributeprogrammehouseholdsbyyear = landcostrestitution = LOCATIONNOTFOUND
redistributeprogrammebeneficiariesbyyear = femalepartybenefited = LOCATIONNOTFOUND
youthpartybenefited = disabledpeoplepartybenefited = LOCATIONNOTFOUND
redistributedlandinhectares = redistributedlandcostinrands = LOCATIONNOTFOUND
redistributedlandaveragecostperhectares = householdsrestitution = LOCATIONNOTFOUND
hectarestransferredperprovincebyyear = hectaresacquiredrestitution = LOCATIONNOTFOUND
projectsrestitution = beneficiariesrestitution = LOCATIONNOTFOUND
claimssettledrestitution = disabilitiesrestitution = LOCATIONNOTFOUND
femaleheadedhouseholdsrestitution = financialcompensationrestitution = LOCATIONNOTFOUND
femaleheadedhouseholdsrestitution_tot = beneficiariesrestitution_tot = projectsrestitution_tot = 0
financialcompensationrestitution_tot = claimssettledrestitution_tot = 0
femalepartybenefited_tot = disabledpeoplepartybenefited_tot = youthpartybenefited_tot = 0
hectarestransferredperprovincebyyear_tot = hectaresacquiredrestitution_tot = 0
householdsrestitution_tot = disabilitiesrestitution_tot = 0
redistributedland_avg_cost = redistributedland_tot_cost = redistributedland_hect_tot = 0
redistributeprogrammehouseholdsbyyear_tot = redistributeprogrammeprojectsbyyear_tot = 0
redistributeprogrammebeneficiariesbyyear_tot = 0
redistributionrestitution = {'is_missing': True}
try:
redistributedlandusebreakdown, _ = get_stat_data(
['redistributed land use breakdown'], geo, session)
except Exception:
pass
try:
redistributeprogrammeprojectsbyyear, redistributeprogrammeprojectsbyyear_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'outcome of redistribution programme'],
only={'outcome of redistribution programme': [
'projects transferred']},
percent=False)
except Exception:
pass
try:
redistributeprogrammehouseholdsbyyear, redistributeprogrammehouseholdsbyyear_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'outcome of redistribution programme'],
only={'outcome of redistribution programme': [
'benefited households']},
percent=False)
except Exception:
pass
try:
redistributeprogrammebeneficiariesbyyear, redistributeprogrammebeneficiariesbyyear_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'outcome of redistribution programme'],
only={'outcome of redistribution programme': [
'benefited beneficiaries']},
percent=False)
except Exception:
pass
try:
femalepartybenefited, femalepartybenefited_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'party_benefited'],
only={'party_benefited': ['female']},
percent=False)
except Exception:
pass
try:
youthpartybenefited, youthpartybenefited_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'party_benefited'],
only={'party_benefited': ['youth']},
percent=False)
except Exception:
pass
try:
disabledpeoplepartybenefited, disabledpeoplepartybenefited_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'party_benefited'],
only={'party_benefited': ['disable people']},
percent=False)
except Exception:
pass
try:
hectarestransferredperprovincebyyear, hectarestransferredperprovincebyyear_tot = get_stat_data(
['year'], geo, session,
percent=False)
except Exception:
pass
try:
hectaresacquiredrestitution, hectaresacquiredrestitution_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'restitutionoutcomestatistic'],
only={'restitutionoutcomestatistic': ['hectares acquired']},
percent=False)
except Exception:
pass
try:
claimssettledrestitution, claimssettledrestitution_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'restitutionoutcomestatistic'],
only={'restitutionoutcomestatistic': ['claims settled']},
percent=False)
except Exception:
pass
try:
disabilitiesrestitution, disabilitiesrestitution_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'restitutionoutcomestatistic'],
only={'restitutionoutcomestatistic': [
'people with disabilities']},
percent=False)
except Exception:
pass
try:
beneficiariesrestitution, beneficiariesrestitution_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'restitutionoutcomestatistic'],
only={'restitutionoutcomestatistic': ['beneficiaries']},
percent=False)
except Exception:
pass
try:
householdsrestitution, householdsrestitution_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'restitutionoutcomestatistic'],
only={'restitutionoutcomestatistic': ['households']},
percent=False)
except Exception:
pass
try:
landcostrestitution, _ = get_stat_data(
['year'], geo, session,
table_fields=['year', 'restitutionoutcomestatistic'],
only={'restitutionoutcomestatistic': ['landcost']},
percent=False)
except Exception:
pass
try:
financialcompensationrestitution, financialcompensationrestitution_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'restitutionoutcomestatistic'],
only={
'restitutionoutcomestatistic': ['financial compensation']},
percent=False)
except Exception:
pass
try:
projectsrestitution, projectsrestitution_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'restitutionoutcomestatistic'],
only={'restitutionoutcomestatistic': ['projects']},
percent=False)
except Exception:
pass
try:
femaleheadedhouseholdsrestitution, femaleheadedhouseholdsrestitution_tot = get_stat_data(
['year'], geo, session,
table_fields=['year', 'restitutionoutcomestatistic'],
only={'restitutionoutcomestatistic': [
'female headed households']},
percent=False)
except Exception:
pass
try:
redistributedlandinhectares, redistributedland_hect_tot = get_stat_data(
['redistributedland'], geo, session, percent=False,
only={'redistributedland': ['number of hectares']})
except Exception:
pass
try:
redistributedlandcostinrands, redistributedland_tot_cost = get_stat_data(
['redistributedland'], geo, session, percent=False,
only={'redistributedland': ['cost in rands']})
except Exception:
pass
try:
redistributedlandaveragecostperhectares, redistributedland_avg_cost = get_stat_data(
['redistributedland'], geo, session, percent=False,
only={'redistributedland': ['average cost per hectares']})
except Exception:
pass
redistributionrestitution[
'redistributedlandusebreakdown'] = redistributedlandusebreakdown
redistributionrestitution['hectarestransferredperprovincebyyear_stat'] = \
{
"name": "Total land in hectares (ha) redistributed from the year 2009 to 2018",
"values": {"this": hectarestransferredperprovincebyyear_tot}
}
redistributionrestitution['redistributedlandhectares_stat'] = \
{"name": "Redistributed land in hectares for the year 2017/2018",
"values": {"this": redistributedland_hect_tot}
}
redistributionrestitution['redistributedlandcostinrands_stat'] = \
{
"name": "Cost in Rands (ZAR) of Redistributed Land for the year 2017/2018",
"values": {"this": redistributedland_tot_cost}
}
redistributionrestitution[
'redistributedlandaveragecostperhectares_stat'] = \
{
"name": "Average Cost in Rands (ZAR) per Hectares for Redistributed Land in 2017/2018",
"values": {"this": redistributedland_avg_cost}
}
redistributionrestitution[
'redistributeprogrammeprojectsbyyear'] = redistributeprogrammeprojectsbyyear
redistributionrestitution['redistributeprogrammeprojectsbyyear_stat'] = \
{
"name": "Total projects benefitted through Redistribution Programme from 2009/2010 to 2017/2018",
"values": {"this": redistributeprogrammeprojectsbyyear_tot}
}
redistributionrestitution[
'redistributeprogrammehouseholdsbyyear'] = redistributeprogrammehouseholdsbyyear
redistributionrestitution[
'redistributeprogrammehouseholdsbyyear_stat'] = \
{
"name": "Total households benefitted though Redistribution Programme from 2009/2010 to 2017/2018",
"values": {"this": redistributeprogrammehouseholdsbyyear_tot}
}
redistributionrestitution[
'redistributeprogrammebeneficiariesbyyear'] = redistributeprogrammebeneficiariesbyyear
redistributionrestitution[
'redistributeprogrammebeneficiariesbyyear_stat'] = \
{
"name": "Total number of beneficiaries of Redistribution Programme from the 2009/2010 to 2017/2018",
"values": {"this": redistributeprogrammebeneficiariesbyyear_tot}
}
redistributionrestitution['femalepartybenefited'] = femalepartybenefited
redistributionrestitution['femalepartybenefited_stat'] = \
{
"name": "Number of female benefited through Redistribution programme from 2009/2010 to 2017/2018",
"values": {"this": femalepartybenefited_tot}
}
redistributionrestitution['youthpartybenefited'] = youthpartybenefited
redistributionrestitution['youthpartybenefited_stat'] = \
{
"name": "Number of youth benefited through Redistribution programme from 2009/2010 to 2017/2018",
"values": {"this": youthpartybenefited_tot}
}
redistributionrestitution[
'disabledpeoplepartybenefited'] = disabledpeoplepartybenefited
redistributionrestitution['disabledpeoplepartybenefited_stat'] = \
{
"name": "People with disabilities benefited in Redistribution programme from 2009/2010 to 2017/2018",
"values": {"this": disabledpeoplepartybenefited_tot}
}
redistributionrestitution[
'hectarestransferredperprovincebyyear'] = hectarestransferredperprovincebyyear
redistributionrestitution[
'hectaresacquiredrestitution'] = hectaresacquiredrestitution
redistributionrestitution[
'claimssettledrestitution'] = claimssettledrestitution
redistributionrestitution[
'householdsrestitution'] = householdsrestitution
redistributionrestitution[
'femaleheadedhouseholdsrestitution'] = femaleheadedhouseholdsrestitution
redistributionrestitution[
'disabilitiesrestitution'] = disabilitiesrestitution
redistributionrestitution['projectsrestitution'] = projectsrestitution
redistributionrestitution[
'beneficiariesrestitution'] = beneficiariesrestitution
redistributionrestitution['landcostrestitution'] = landcostrestitution
redistributionrestitution[
'financialcompensationrestitution'] = financialcompensationrestitution
redistributionrestitution[
'hectarestransferredperprovincebyyear_tot'] = hectarestransferredperprovincebyyear_tot
redistributionrestitution[
'hectaresacquiredrestitution_tot'] = hectaresacquiredrestitution_tot
redistributionrestitution[
'claimssettledrestitution_tot'] = claimssettledrestitution_tot
redistributionrestitution['landcostrestitution'] = landcostrestitution
redistributionrestitution['hectaresacquiredrestitution_stat'] = \
{
"name": "Total hectares acquired in Restitution programme from 2009/2010 to 2017/2018",
"values": {"this": hectaresacquiredrestitution_tot}
}
redistributionrestitution['householdsrestitution_stat'] = \
{
"name": "Total households benefited in Restitution programme from 2009/2010 to 2017/2018",
"values": {"this": householdsrestitution_tot}
}
redistributionrestitution['femaleheadedhouseholdsrestitution_stat'] = \
{
"name": "Female headed households benefited in restitution programme from 2009/2010 to 2017/2018",
"values": {"this": femaleheadedhouseholdsrestitution_tot}
}
redistributionrestitution['disabilitiesrestitution_stat'] = \
{
"name": "Number of people with disabilities benefited in Restitution programme from 2009/2010 to 2017/2018",
"values": {"this": disabilitiesrestitution_tot}
}
redistributionrestitution['projectsrestitution_stat'] = \
{
"name": "Number of projects in the restitution programme from 2009/2010 to 2017/2018",
"values": {"this": projectsrestitution_tot}
}
redistributionrestitution['beneficiariesrestitution_stat'] = \
{
"name": "Number of beneficiaries in the restitution programme from 2009/2010 to 2017/2018",
"values": {"this": beneficiariesrestitution_tot}
}
# if total hectares of redistruted land is missing
# and total hectares acquired under restitution is missing, then there's no data
redistributionrestitution[
'is_missing'] = hectarestransferredperprovincebyyear.get(
'is_missing') \
and hectaresacquiredrestitution.get('is_missing')
return redistributionrestitution
def get_health_profile(geo, session, display_profile, comparative=False):
youth_difficulty_by_function, _ = get_stat_data(
['function type'],
geo,
session,
table_universe=
'Youth with non-missing responses to the general health and functioning questions',
table_dataset='Census and Community Survey',
key_order=DIFFICULTY_FUNCTIONING_KEY_ORDER)
youth_female_given_birth, _ = get_stat_data(
['given birth'],
geo,
session,
table_universe='Female youth',
table_dataset='Census and Community Survey',
key_order=GIVEN_BIRTH_KEY_ORDER)
youth_female_given_birth['Unspecified']['name'] = 'Unspecified *'
youth_female_given_birth_by_age_group, _ = get_stat_data(
['given birth', 'age in completed years'],
geo,
session,
table_universe='Female youth',
table_dataset='Census and Community Survey',
percent_grouping=['age in completed years'],
slices=['Given birth'],
key_order={'given birth': GIVEN_BIRTH_KEY_ORDER},
recode=GIVEN_BIRTH_AGE_GROUP_RECODE)
final_data = {
'youth_difficulty_seeing': {
"name":
"Of youth experience difficulty in seeing even when using eye glasses",
"values": {
"this":
youth_difficulty_by_function[
'Seeing, even when using eye glasses']['values']['this']
}
},
'youth_difficulty_by_function':
youth_difficulty_by_function,
'youth_female_have_given_birth': {
"name": "Of females aged 15-24 have given birth to a child",
"values": {
"this":
youth_female_given_birth['Given birth']['values']['this']
}
},
'youth_female_given_birth':
youth_female_given_birth,
'youth_female_given_birth_by_age_group':
youth_female_given_birth_by_age_group
}
if display_profile == 'WC' and geo.geo_level != 'ward':
# We don't have data on ward level for the following
with dataset_context(year='2017'):
youth_pregnancy_rate_by_year, _ = get_stat_data(
['year'],
geo,
session,
table_universe='Teenage pregnancy rate by year',
table_dataset='Department of Health Administrative data',
percent=False)
youth_delivery_rate_by_year, _ = get_stat_data(
['year'],
geo,
session,
table_universe='Teenage delivery rate by year',
table_dataset='Department of Health Administrative data',
percent=False)
youth_female_causes_of_death, _ = get_stat_data(
['cause of death'],
geo,
session,
table_universe='Female youth causes of death',
table_dataset='Department of Health Administrative data',
order_by='-total')
youth_male_causes_of_death, _ = get_stat_data(
['cause of death'],
geo,
session,
table_universe='Male youth causes of death',
table_dataset='Department of Health Administrative data',
order_by='-total')
if not comparative:
youth_female_top10_causes_of_death = OrderedDict()
youth_male_top10_causes_of_death = OrderedDict()
for key, value in youth_female_causes_of_death.items()[0:10]:
youth_female_top10_causes_of_death[key] = value
for key, value in youth_male_causes_of_death.items()[0:10]:
youth_male_top10_causes_of_death[key] = value
youth_female_top10_causes_of_death[
'metadata'] = youth_female_causes_of_death['metadata']
youth_male_top10_causes_of_death[
'metadata'] = youth_male_causes_of_death['metadata']
final_data.update({
'youth_preganacy_rate_latest': {
"name": "Of total pregnancies are to females under 18 years",
"values": {
"this":
youth_pregnancy_rate_by_year['2016-17']['values']['this']
}
},
'youth_pregnancy_rate_by_year':
youth_pregnancy_rate_by_year,
'youth_delivery_rate_latest': {
"name": "Of total deliveries are to females under 18 years",
"values": {
"this":
youth_delivery_rate_by_year['2016-17']['values']['this']
}
},
'youth_delivery_rate_by_year':
youth_delivery_rate_by_year,
'youth_female_HIV_deaths': {
"name": "Of female youth deaths were due to HIV/AIDS",
"values": {
"this":
youth_female_causes_of_death['HIV/AIDS']['values']['this']
}
},
'youth_male_interpersonal_violence_deaths': {
"name":
"Of male youth deaths were due to interpersonal violence",
"values": {
"this":
youth_male_causes_of_death['Interpersonal violence']
['values']['this']
}
},
'youth_female_top10_causes_of_death':
youth_female_top10_causes_of_death
if not comparative else youth_female_causes_of_death,
'youth_male_top10_causes_of_death':
youth_male_top10_causes_of_death
if not comparative else youth_male_causes_of_death
})
return final_data
def get_afrobarometer_profile(geo, session):
with dataset_context(year='2017'):
access_to_information = LOCATIONNOTFOUND
allow_farmers_retain_land_ownership = LOCATIONNOTFOUND
maintain_willing_buyer_willing_seller_policy = LOCATIONNOTFOUND
land_acquisation_challenges = LOCATIONNOTFOUND
land_to_prioritise_for_redistribution = LOCATIONNOTFOUND
women_have_equal_right_to_land = LOCATIONNOTFOUND
women_men_equal_chance_own_land = LOCATIONNOTFOUND
try:
access_to_information, _ = get_stat_data('access_to_information',
geo,
session, table_fields=[
'access_to_information'])
except Exception:
pass
try:
allow_farmers_retain_land_ownership, _ = get_stat_data(
'allow_farmers_retain_land_ownership', geo, session,
table_fields=['allow_farmers_retain_land_ownership'])
except Exception:
pass
try:
maintain_willing_buyer_willing_seller_policy, _ = get_stat_data(
'maintain_willing_buyer_willing_seller_policy', geo, session,
table_fields=['maintain_willing_buyer_willing_seller_policy'])
except Exception:
pass
try:
land_acquisation_challenges, _ = get_stat_data(
'land_acquisation_challenges', geo, session,
table_fields=['land_acquisation_challenges'])
except Exception:
pass
try:
land_to_prioritise_for_redistribution, _ = get_stat_data(
'land_to_prioritise_for_redistribution', geo, session,
table_fields=['land_to_prioritise_for_redistribution'])
except Exception:
pass
is_missing = access_to_information.get('is_missing') and \
allow_farmers_retain_land_ownership.get('is_missing') and \
maintain_willing_buyer_willing_seller_policy.get(
'is_missing') \
and land_acquisation_challenges.get('is_missing') and \
land_to_prioritise_for_redistribution.get('is_missing') and \
women_have_equal_right_to_land.get('is_missing') and \
women_men_equal_chance_own_land.get('is_missing')
final_data = {
'is_missing': is_missing,
'access_to_information': access_to_information,
'allow_farmers_retain_land_ownership': allow_farmers_retain_land_ownership,
'maintain_willing_buyer_willing_seller_policy': maintain_willing_buyer_willing_seller_policy,
'land_acquisation_challenges': land_acquisation_challenges,
'land_to_prioritise_for_redistribution': land_to_prioritise_for_redistribution,
'women_have_equal_right_to_land': women_have_equal_right_to_land,
'women_men_equal_chance_own_land': women_men_equal_chance_own_land,
}
return final_data
def get_education_profile(geo, session, display_profile, comparative=False):
youth_completed_grade9, _ = get_stat_data(
['completed grade9'],
geo,
session,
table_universe='Youth aged 16 to 17',
table_dataset='Census and Community Survey',
key_order=('Completed', 'Not completed'))
youth_completed_grade9_by_gender, _ = get_stat_data(
['completed grade9', 'gender'],
geo,
session,
table_universe='Youth aged 16 to 17',
table_dataset='Census and Community Survey',
percent_grouping=['gender'],
slices=['Completed'],
key_order={'gender': GENDER_ORDER})
youth_education_level, youth_pop_20_to_24 = get_stat_data(
['education level'],
geo,
session,
table_universe='Youth aged 20 to 24',
table_dataset='Census and Community Survey',
key_order=EDUCATION_LEVEL_KEY_ORDER)
youth_perc_matric = None
if youth_education_level['Matric/matric equivalent']['numerators']['this']:
matric_or_equiv = (
youth_education_level['Matric/matric equivalent']['numerators']
['this'] +
youth_education_level['Any tertiary']['numerators']['this'])
youth_perc_matric = percent(matric_or_equiv, youth_pop_20_to_24)
youth_education_attendance, _ = get_stat_data(
['attendance'],
geo,
session,
table_universe='Youth',
table_dataset='Census and Community Survey',
table_fields=['attendance', 'age in completed years', 'gender'])
youth_education_attending_by_age, _ = get_stat_data(
['attendance', 'age in completed years'],
geo,
session,
table_universe='Youth',
table_dataset='Census and Community Survey',
table_fields=['attendance', 'age in completed years', 'gender'],
table_name='youth_education_attendance_age_incompleted_years_gender',
percent_grouping=['age in completed years'],
slices=['Yes'])
youth_education_attending_by_gender, _ = get_stat_data(
['attendance', 'gender'],
geo,
session,
table_universe='Youth',
table_dataset='Census and Community Survey',
table_fields=['attendance', 'age in completed years', 'gender'],
percent_grouping=['gender'],
slices=['Yes'],
key_order={'gender': GENDER_ORDER})
final_data = {
'youth_completed_grade9': youth_completed_grade9,
'youth_perc_completed_grade9': {
'name': 'Of youth aged 16-17 have completed grade 9 or higher',
'values': {
'this': youth_completed_grade9['Completed']['values']['this']
}
},
'youth_completed_grade9_by_gender': youth_completed_grade9_by_gender,
'youth_education_level': youth_education_level,
'youth_perc_matric': {
"name":
"Of youth aged 20-24 have completed matric/matric equivalent or higher",
"values": {
"this": youth_perc_matric
}
},
'youth_perc_attending': {
"name": "Of youth aged 15-24 attend an educational institution",
"values": {
"this": youth_education_attendance['Yes']['values']['this']
}
},
'youth_education_attending_by_age': youth_education_attending_by_age,
'youth_education_attending_by_gender':
youth_education_attending_by_gender,
}
if display_profile == 'WC':
with dataset_context(year='2017'):
youth_average_mean_score_by_year, _ = get_stat_data(
['year'],
geo,
session,
table_universe=
'Average mean score in both language and mathematics',
percent=False)
youth_average_language_score_by_year, _ = get_stat_data(
['year'],
geo,
session,
table_universe='Average score in language',
percent=False)
youth_average_maths_score_by_year, _ = get_stat_data(
['year'],
geo,
session,
table_universe='Average score in mathematics',
percent=False)
youth_language_outcome_latest, _ = get_stat_data(
['year', 'outcome'],
geo,
session,
table_universe='Percentage passed in language',
key_order={'outcome': ['Passed', 'Failed']},
percent=False,
slices=['2017'])
youth_maths_outcome_latest, _ = get_stat_data(
['year', 'outcome'],
geo,
session,
table_universe='Percentage passed in mathematics',
key_order={'outcome': ['Passed', 'Failed']},
percent=False,
slices=['2017'])
with dataset_context(year='2017'):
youth_matric_outcome_by_year, _ = get_stat_data(
['year'],
geo,
session,
table_universe='Matric pass rate',
only={'outcome': ['Passed']},
percent=False)
youth_matric_outcome_latest, _ = get_stat_data(
['year', 'outcome'],
geo,
session,
table_universe='Matric pass rate',
key_order={'outcome': ['Passed', 'Failed']},
percent=False,
slices=['2017'])
youth_matric_throughput_rate_by_year, _ = get_stat_data(
['year'],
geo,
session,
table_universe='Matric passes as a % of grade 8 enrolment',
only={'outcome': ['Passed']},
percent=False)
youth_matric_throughput_latest, _ = get_stat_data(
['year', 'outcome'],
geo,
session,
table_universe='Matric passes as a % of grade 8 enrolment',
key_order={'outcome': ['Passed', 'Dropped out or failed']},
percent=False,
slices=['2017'])
youth_bachelor_passes_by_year, _ = get_stat_data(
['year'],
geo,
session,
table_universe='Bachelor passes as a % of grade 8 enrolment',
only={'outcome': ['Bachelor pass']},
percent=False)
youth_bachelor_outcome_latest, _ = get_stat_data(
['year', 'outcome'],
geo,
session,
table_universe='Bachelor passes as a % of grade 8 enrolment',
key_order={'outcome': ['Bachelor pass', 'No bachelor pass']},
percent=False,
slices=['2017'])
youth_student_dropout_rate_by_year, _ = get_stat_data(
['year'],
geo,
session,
table_universe='Dropout rates between grade 10 and matric',
only={'outcome': ['Dropped out']},
percent=False)
youth_student_dropout_rate_latest, _ = get_stat_data(
['year', 'outcome'],
geo,
session,
table_universe='Dropout rates between grade 10 and matric',
percent=False,
slices=['2017'])
final_data.update({
'youth_ave_mean_score_latest': {
"name": "Average mean score in both language and mathematics",
"values": {
"this":
youth_average_mean_score_by_year['2017']['values']['this']
}
},
'youth_ave_mean_score_by_year':
youth_average_mean_score_by_year,
'youth_ave_language_score_latest': {
"name": "Average score in language",
"values": {
"this":
youth_average_language_score_by_year['2017']['values']
['this']
}
},
'youth_ave_language_score_by_year':
youth_average_language_score_by_year,
'youth_ave_maths_score_latest': {
"name": "Average score in mathematics",
"values": {
"this":
youth_average_maths_score_by_year['2017']['values']['this']
}
},
'youth_ave_maths_score_by_year':
youth_average_maths_score_by_year,
'youth_language_outcome_latest':
youth_language_outcome_latest,
'youth_maths_outcome_latest':
youth_maths_outcome_latest,
'youth_matric_pass_rate_latest': {
"name": "Of students writing matric passed",
"values": {
"this":
youth_matric_outcome_latest['Passed']['values']['this']
}
},
'youth_matric_outcome_latest':
youth_matric_outcome_latest,
'youth_matric_outcome_by_year':
youth_matric_outcome_by_year,
'youth_matric_throughput_rate_latest': {
"name": "Of Grade 8 students go on to pass matric",
"values": {
"this":
youth_matric_throughput_latest['Passed']['values']['this']
}
},
'youth_matric_throughput_latest':
youth_matric_throughput_latest,
'youth_matric_throughput_rate_by_year':
youth_matric_throughput_rate_by_year,
'youth_bachelor_passes_latest': {
"name":
"Of Grade 8 students go on to pass matric with a bachelor's pass",
"values": {
"this":
youth_bachelor_outcome_latest['Bachelor pass']['values']
['this']
}
},
'youth_bachelor_outcome_latest':
youth_bachelor_outcome_latest,
'youth_bachelor_passes_by_year':
youth_bachelor_passes_by_year,
'youth_student_dropout_rate_latest': {
"name": "Of students drop out between grade 10 and matric",
"values": {
"this":
youth_student_dropout_rate_latest['Dropped out']['values']
['this']
}
},
'youth_student_dropout_rate_by_year':
youth_student_dropout_rate_by_year
})
return final_data
def get_equitable_allocation_data(geo, session):
try:
with dataset_context(year='2016'):
budget_allocation, _ = get_stat_data('financial_year', geo, session,
table_fields=[
'financial_year'],
percent=False)
yr2013_2014 = 0
for data, value in budget_allocation.get('FY 2013/2014',
{}).items():
if data == 'values':
yr2013_2014 += value['this']
yr2014_2015 = 0
for data, value in budget_allocation.get('FY 2014/2015',
{}).items():
if data == 'values':
yr2014_2015 += value['this']
yr2015_2016 = 0
for data, value in budget_allocation.get('FY 2015/2016',
{}).items():
if data == 'values':
yr2015_2016 += value['this']
yr2016_2017 = 0
for data, value in budget_allocation.get('FY 2016/2017',
{}).items():
if data == 'values':
yr2016_2017 += value['this']
final_data = {
'budget_allocation': budget_allocation,
'fy2013_2014': {
'name': 'FY 2013/2014',
'numerators': {'this': yr2013_2014},
'values': {'this': yr2013_2014}
},
'fy2014_2015': {
'name': 'FY 2014/2015',
'numerators': {'this': yr2014_2015},
'values': {'this': yr2014_2015}
},
'fy2015_2016': {
'name': 'FY 2015/2016',
'numerators': {'this': yr2015_2016},
'values': {'this': yr2015_2016}
},
'fy2016_2017': {
'name': 'FY 2016/2017',
'numerators': {'this': yr2016_2017},
'values': {'this': yr2016_2017}
},
}
return final_data
except Exception:
budget_allocation, _ = LOCATIONNOTFOUND, 0
def get_poverty_profile(geo, session, display_profile, comparative=False):
youth_income_poor_by_age_group, _ = get_stat_data(
['income poverty', 'age group'],
geo,
session,
table_universe='Youth',
table_dataset='Census and Community Survey',
percent_grouping=['age group'],
slices=['Income-poor'],
recode=INCOME_POVERTY_AGE_GROUP_RECODE)
youth_income_poor_by_pop_group, _ = get_stat_data(
['income poverty', 'population group'],
geo,
session,
table_universe='Youth',
table_dataset='Census and Community Survey',
table_name='youth_income_poverty_population_group_gender',
percent_grouping=['population group'],
slices=['Income-poor'],
key_order={'population group': POPULATION_GROUP_ORDER})
youth_income_poor_by_gender, _ = get_stat_data(
['income poverty', 'gender'],
geo,
session,
table_universe='Youth',
table_dataset='Census and Community Survey',
percent_grouping=['gender'],
slices=['Income-poor'],
key_order={'gender': GENDER_ORDER})
with dataset_context(year='2011'):
youth_multid_poverty, _ = get_stat_data(
['multidimensionally poor'],
geo,
session,
table_universe='Youth',
table_dataset='Multidimensional poverty',
key_order=('Multidimensionally poor', 'Non-poor'),
)
youth_multid_poor_by_pop_group, _ = get_stat_data(
['multidimensionally poor', 'population group'],
geo,
session,
table_universe='Youth',
table_dataset='Multidimensional poverty',
table_name='youth_multidimensionally_poor_population_group_gender',
percent_grouping=['population group'],
slices=['Multidimensionally poor'],
key_order={'population group': POPULATION_GROUP_ORDER})
youth_multid_poor_by_gender, _ = get_stat_data(
['multidimensionally poor', 'gender'],
geo,
session,
table_universe='Youth',
table_dataset='Multidimensional poverty',
percent_grouping=['gender'],
slices=['Multidimensionally poor'],
key_order={'gender': GENDER_ORDER})
youth_mpi_table = get_datatable('youth_mpi_score')
youth_mpi_score, _ = youth_mpi_table.get_stat_data(geo, percent=False)
youth_mpi_score['youth_mpi_score']['name'] = 'Youth MPI score (0-1)*'
final_data = {
'youth_income_poor': {
"name": "Of youth live in income-poor households *",
"values": {
"this":
youth_income_poor_by_age_group['Youth (15-24)']['values']
['this']
}
},
'youth_income_poor_by_age_group': youth_income_poor_by_age_group,
'youth_income_poor_by_pop_group': youth_income_poor_by_pop_group,
'youth_income_poor_by_gender': youth_income_poor_by_gender,
'youth_multid_poor': {
"name": "Of youth are multidimensionally poor*",
"values": {
"this":
youth_multid_poverty['Multidimensionally poor']['values']
['this']
}
},
'youth_multid_poor_by_pop_group': youth_multid_poor_by_pop_group,
'youth_multid_poor_by_gender': youth_multid_poor_by_gender,
'youth_multid_poverty': youth_multid_poverty,
'youth_mpi_score_stat': youth_mpi_score['youth_mpi_score'],
'youth_mpi_score': youth_mpi_score
}
return final_data
def get_health_profile(geo, session, display_profile, comparative=False):
youth_difficulty_by_function, _ = get_stat_data(
['function type'], geo, session,
table_universe='Youth with non-missing responses to the general health and functioning questions',
table_dataset='Census and Community Survey',
key_order=DIFFICULTY_FUNCTIONING_KEY_ORDER)
youth_female_given_birth, _ = get_stat_data(
['given birth'], geo, session,
table_universe='Female youth',
table_dataset='Census and Community Survey',
key_order=GIVEN_BIRTH_KEY_ORDER)
youth_female_given_birth['Unspecified']['name'] = 'Unspecified *'
youth_female_given_birth_by_age_group, _ = get_stat_data(
['given birth', 'age in completed years'], geo, session,
table_universe='Female youth',
table_dataset='Census and Community Survey',
percent_grouping=['age in completed years'], slices=['Given birth'],
key_order={'given birth': GIVEN_BIRTH_KEY_ORDER},
recode=GIVEN_BIRTH_AGE_GROUP_RECODE)
final_data = {
'youth_difficulty_seeing': {
"name": "Of youth experience difficulty in seeing even when using eye glasses",
"values": {"this": youth_difficulty_by_function['Seeing, even when using eye glasses']['values']['this']}
},
'youth_difficulty_by_function': youth_difficulty_by_function,
'youth_female_have_given_birth': {
"name": "Of females aged 15-24 have given birth to a child",
"values": {"this": youth_female_given_birth['Given birth']['values']['this']}
},
'youth_female_given_birth': youth_female_given_birth,
'youth_female_given_birth_by_age_group': youth_female_given_birth_by_age_group
}
if display_profile == 'WC' and geo.geo_level != 'ward':
# We don't have data on ward level for the following
with dataset_context(year='2017'):
youth_pregnancy_rate_by_year, _ = get_stat_data(
['year'], geo, session,
table_universe='Teenage pregnancy rate by year',
table_dataset='Department of Health Administrative data',
percent=False)
youth_delivery_rate_by_year, _ = get_stat_data(
['year'], geo, session,
table_universe='Teenage delivery rate by year',
table_dataset='Department of Health Administrative data',
percent=False)
youth_female_causes_of_death, _ = get_stat_data(
['cause of death'], geo, session,
table_universe='Female youth causes of death',
table_dataset='Department of Health Administrative data',
order_by='-total')
youth_male_causes_of_death, _ = get_stat_data(
['cause of death'], geo, session,
table_universe='Male youth causes of death',
table_dataset='Department of Health Administrative data',
order_by='-total')
if not comparative:
youth_female_top10_causes_of_death = OrderedDict()
youth_male_top10_causes_of_death = OrderedDict()
for key, value in youth_female_causes_of_death.items()[0:10]:
youth_female_top10_causes_of_death[key] = value
for key, value in youth_male_causes_of_death.items()[0:10]:
youth_male_top10_causes_of_death[key] = value
youth_female_top10_causes_of_death['metadata'] = youth_female_causes_of_death['metadata']
youth_male_top10_causes_of_death['metadata'] = youth_male_causes_of_death['metadata']
final_data.update({
'youth_preganacy_rate_latest': {
"name": "Of total pregnancies are to females under 18 years",
"values": {"this":youth_pregnancy_rate_by_year['2016-17']['values']['this']}
},
'youth_pregnancy_rate_by_year': youth_pregnancy_rate_by_year,
'youth_delivery_rate_latest': {
"name": "Of total deliveries are to females under 18 years",
"values": {"this":youth_delivery_rate_by_year['2016-17']['values']['this']}
},
'youth_delivery_rate_by_year': youth_delivery_rate_by_year,
'youth_female_HIV_deaths': {
"name": "Of female youth deaths were due to HIV/AIDS",
"values": {"this":youth_female_causes_of_death['HIV/AIDS']['values']['this']}
},
'youth_male_interpersonal_violence_deaths': {
"name": "Of male youth deaths were due to interpersonal violence",
"values": {"this":youth_male_causes_of_death['Interpersonal violence']['values']['this']}
},
'youth_female_top10_causes_of_death': youth_female_top10_causes_of_death if not comparative else youth_female_causes_of_death,
'youth_male_top10_causes_of_death': youth_male_top10_causes_of_death if not comparative else youth_male_causes_of_death
})
return final_data
def get_schools_profile(geo, session, year):
schools_dist = LOCATIONNOTFOUND
region_dist = LOCATIONNOTFOUND
category_dist = LOCATIONNOTFOUND
top_schools_40_more = []
top_schools_40_less = []
lowest_schools_40_less = []
lowest_schools_40_more = []
gpa_dist_data = []
gender_dist = []
total_schools = 0
median = 0
reg = 'region'
if geo.geo_level == "country":
reg = 'region'
elif geo.geo_level == "region":
reg = 'district'
elif geo.geo_level == "district":
reg = 'ward'
with dataset_context(year='2017'):
try:
schools_dist, total_schools = get_stat_data(['ownership'], geo=geo, table_name='secondary_school',
session=session, only={
'year_of_result': [year]})
except Exception as e:
pass
try:
region_dist, total_schools = get_stat_data([reg], geo=geo,
session=session, only={
'year_of_result': [year]})
except Exception as e:
pass
try:
category_dist, _ = get_stat_data(['more_than_40'], geo=geo,
session=session,
only={'year_of_result': [year]})
except Exception as e:
pass
try:
gender_dist, _ = get_stat_data(['gender'], geo=geo, session=session,
only={'year_of_result': [year]})
except Exception:
pass
try:
# ownership status
# school_dist_data, _ = get_stat_data('age in completed years',geo=geo, session=session, only={'year_of_result': [year]})
# Choosing sorting option
# Sorting will only be done using national_rank all, as regional and district ranks are unknown for some result esp historical
Base.metadata.reflect()
rank_column = Base.metadata.tables[
'secondary_school'].c.national_rank_all
# Getting top for schools with more than 40 students
top_schools_40_more = session.query(
Base.metadata.tables['secondary_school']) \
.filter(Base.metadata.tables[
'secondary_school'].c.geo_level == geo.geo_level) \
.filter(
Base.metadata.tables['secondary_school'].c.geo_code == geo.geo_code) \
.filter(
Base.metadata.tables['secondary_school'].c.year_of_result == year) \
.filter(
Base.metadata.tables['secondary_school'].c.more_than_40.like(
"yes%")) \
.order_by(asc(cast(rank_column, Integer))) \
.all()
# Getting top for schools with less than 40 students
top_schools_40_less = session.query(
Base.metadata.tables['secondary_school']) \
.filter(Base.metadata.tables[
'secondary_school'].c.geo_level == geo.geo_level) \
.filter(
Base.metadata.tables['secondary_school'].c.geo_code == geo.geo_code) \
.filter(
Base.metadata.tables['secondary_school'].c.year_of_result == year) \
.filter(
Base.metadata.tables['secondary_school'].c.more_than_40.like("no%")) \
.order_by(asc(cast(rank_column, Integer))) \
.all()
# Getting lowest schools with more than 40 students
lowest_schools_40_more = session.query(
Base.metadata.tables['secondary_school']) \
.filter(Base.metadata.tables[
'secondary_school'].c.geo_level == geo.geo_level) \
.filter(
Base.metadata.tables['secondary_school'].c.geo_code == geo.geo_code) \
.filter(
Base.metadata.tables['secondary_school'].c.year_of_result == year) \
.filter(
Base.metadata.tables['secondary_school'].c.more_than_40.like(
"yes%")) \
.order_by(desc(cast(rank_column, Integer))) \
.all()
# Getting lowest for schools with less than 40 students
lowest_schools_40_less = session.query(
Base.metadata.tables['secondary_school']) \
.filter(Base.metadata.tables[
'secondary_school'].c.geo_level == geo.geo_level) \
.filter(
Base.metadata.tables['secondary_school'].c.geo_code == geo.geo_code) \
.filter(
Base.metadata.tables['secondary_school'].c.year_of_result == year) \
.filter(
Base.metadata.tables['secondary_school'].c.more_than_40.like("no%")) \
.order_by(desc(cast(rank_column, Integer))) \
.all()
# median gpa
db_model_age = get_datatable('code_name_avg_gpa')
objects = db_model_age.get_rows_for_geo(geo, session)
median = calculate_median(objects, 'avg_gpa')
# gpa in 1 point groups
def gpa_recode(f, x):
gpa = x
if gpa >= 4:
return '4+'
bucket = 1 * (gpa / 1)
return '%d-%d' % (bucket, bucket + 2)
gpa_dist_data, total_schools = get_stat_data(
'avg_gpa', geo, session,
table_fields=['code', 'name', 'avg_gpa'],
recode=gpa_recode, exclude=['unspecified'],
only={'year_of_result': [year]})
total_private = 0.0
for data in schools_dist.get('Non-Government', {}).values():
if 'numerators' in data:
total_private += data['numerators']['this']
except Exception as e:
pass
return {
'schools_distribution': schools_dist,
'region_distribution': region_dist,
'category_distribution': category_dist,
'best_schools_more_40': top_schools_40_more,
'worst_schools_more_40': lowest_schools_40_more,
'best_schools_less_40': top_schools_40_less,
'worst_schools_less_40': lowest_schools_40_less,
'gpa_group_distribution': gpa_dist_data,
'gender_distribution': gender_dist,
'total_schools': {
"name": "Schools",
"values": {"this": total_schools}
},
'median_gpa': {
"name": "Median GPA",
"values": {"this": median},
},
}
def get_landsalescolour_profile(geo, session):
with dataset_context(year='2018'):
landsalescolourhectares = landsalescolourhectaresperMONTH_V2 = LOCATIONNOTFOUND
landsalescolourcostperMONTH_V2 = landsalescolourcostpermonthbreakdown = LOCATIONNOTFOUND
landsalescolourcostpermonthbreakdowngu = landsalescolourcostpermonthbreakdownga = LOCATIONNOTFOUND
landsalescolourcostpermonthbreakdownpr = landsalescolourcostpermonthbreakdownot = LOCATIONNOTFOUND
landsalescolourtransaction = landsalescolourtransactionpermonthbreakdown = LOCATIONNOTFOUND
landsalesallvscolourtransaction = landsalescolourhectarespermonthbreakdown = LOCATIONNOTFOUND
landsalescolourhectarespermonthbreakdowngu = landsalescolourhectarespermonthbreakdownga = LOCATIONNOTFOUND
landsalescolourhectarespermonthbreakdownpr = landsalescolourhectarespermonthbreakdownot = LOCATIONNOTFOUND
landsalescolourtransactionpermonthbreakdownga = landsalescolourtransactionpermonthbreakdowngu = LOCATIONNOTFOUND
landsalescolourtransactionpermonthbreakdownpr = landsalescolourtransactionpermonthbreakdownot = LOCATIONNOTFOUND
landsalescolourtransactionperMONTH_V2 = LOCATIONNOTFOUND
landsalescolourhectares_tot = 0
landsalescolourtransaction_tot = 0
landsalescolourcost_tot = 0
try:
landsalescolourhectares, landsalescolourhectares_tot = get_stat_data(
['breakdown_cc'], geo, session,
table_name='land_traded_colour_hectares_breakdown_2018',
only={'breakdown_cc': ['Government Agriculture',
'Government Urban', 'Private',
'Other']}
)
except Exception as e:
pass
try:
landsalescolourtransaction, landsalescolourtransaction_tot = get_stat_data(
['breakdown_cc'], geo, session,
table_name='land_traded_colour_transactions_2018',
only={'breakdown_cc': ['Government Agriculture',
'Government Urban', 'Private',
'Other']}
)
except Exception as e:
pass
try:
landsalesallvscolourtransaction, _ = get_stat_data(
['transaction_group'], geo, session,
table_name='land_traded_all_vs_colour_2018',
percent=False
)
except Exception as e:
pass
try:
landsalescolourtransactionperMONTH_V2, _ = get_stat_data(
['month', 'transaction_group'], geo, session,
table_name='land_traded_all_vs_colour_2018',
only={'transaction_group': ['all', 'colour']},
key_order={'month': MONTH_V2,
'transaction_group': ['All', 'Colour']},
percent=False
)
except Exception as e:
pass
try:
landsalescolourhectaresperMONTH_V2, _ = get_stat_data(
['month_cc'], geo, session,
table_name='land_traded_colour_hectares_breakdown_2018',
key_order=( 'Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May',\
'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov'),
percent=False
)
except Exception as e:
pass
try:
landsalescolourhectarespermonthbreakdown, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_hectares_breakdown_2018',
key_order={'month_cc': MONTH_V2},
percent=False
)
except Exception as e:
pass
try:
landsalescolourhectarespermonthbreakdownga, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_hectares_breakdown_2018',
only= {'breakdown_cc': ['Government Agriculture']},
key_order={'month_cc': MONTH_V2,
'breakdown_cc': ['Government Agriculture']},
percent=False
)
except Exception as e:
pass
try:
landsalescolourhectarespermonthbreakdowngu, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_hectares_breakdown_2018',
only= {'breakdown_cc': ['Government Urban']},
key_order={'month_cc': MONTH_V2},
percent=False
)
except Exception as e:
pass
try:
landsalescolourhectarespermonthbreakdownpr, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_hectares_breakdown_2018',
only= {'breakdown_cc': ['Private']},
key_order={'month_cc': MONTH_V2,
'breakdown_cc': ['Private']},
percent=False
)
except Exception as e:
pass
try:
landsalescolourhectarespermonthbreakdownot, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_hectares_breakdown_2018',
only= {'breakdown_cc': ['Other']},
key_order={'month_cc': MONTH_V2,
'breakdown_cc': ['Other']},
percent=False
)
except Exception as e:
pass
try:
landsalescolourtransactionpermonthbreakdown, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_transactions_2018',
only={'breakdown_cc': ['Government Agriculture',
'Government Urban', 'Private',
'Other']},
key_order={'month_cc': MONTH_V2,
'breakdown_cc': ['Government Agriculture', 'Private',
'Government Urban',
'Other']},
percent=False
)
except Exception as e:
pass
try:
landsalescolourtransactionpermonthbreakdowngu, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_transactions_2018',
only={'breakdown_cc': ['Government Urban']},
key_order={'month_cc': MONTH_V2},
percent=False
)
except Exception as e:
pass
try:
landsalescolourtransactionpermonthbreakdownga, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_transactions_2018',
only={'breakdown_cc': ['Government Agriculture']},
key_order={'month_cc': MONTH_V2},
percent=False
)
except Exception as e:
pass
try:
landsalescolourtransactionpermonthbreakdownot, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_transactions_2018',
only={'breakdown_cc': ['Other']},
key_order={'month_cc': MONTH_V2},
percent=False
)
except Exception as e:
pass
try:
landsalescolourtransactionpermonthbreakdownpr, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_transactions_2018',
only={'breakdown_cc': ['Private']},
key_order={'month_cc': MONTH_V2},
percent=False
)
except Exception as e:
pass
try:
landsalescolourcostperMONTH_V2, landsalescolourcost_tot = get_stat_data(
['month_cc'], geo, session,
table_name='land_traded_colour_cost_breakdown_2018',
key_order=( 'Dec', 'Jan', 'Feb', 'Mar', 'Apr', 'May',\
'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov'),
percent=False
)
except Exception as e:
pass
try:
landsalescolourcostpermonthbreakdown, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_cost_breakdown_2018',
key_order={'month_cc': MONTH_V2,
'breakdown_cc': ['Government Agriculture', 'Private',
'Government Urban',
'Other']},
percent=False
)
except Exception as e:
pass
try:
landsalescolourcostpermonthbreakdownot, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_cost_breakdown_2018',
only={'breakdown_cc': ['Other']},
key_order={'month_cc': MONTH_V2},
percent=False
)
except Exception as e:
pass
try:
landsalescolourcostpermonthbreakdownpr, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_cost_breakdown_2018',
only={'breakdown_cc': ['Private']},
key_order={'month_cc': MONTH_V2},
percent=False
)
except Exception as e:
pass
try:
landsalescolourcostpermonthbreakdowngu, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_cost_breakdown_2018',
only={'breakdown_cc': ['Government Urban']},
key_order={'month_cc': MONTH_V2},
percent=False
)
except Exception as e:
pass
try:
landsalescolourcostpermonthbreakdownga, _ = get_stat_data(
['breakdown_cc','month_cc'], geo, session,
table_name='land_traded_colour_cost_breakdown_2018',
only={'breakdown_cc': ['Government Agriculture']},
key_order={'month_cc': MONTH_V2},
percent=False
)
except Exception as e:
pass
return {
'landsalescolourhectares': landsalescolourhectares,
'landsalescolourtransaction': landsalescolourtransaction,
'landsalesallvscolourtransaction': landsalesallvscolourtransaction,
'landsalescolourtattransactionpermonth': landsalescolourtransactionperMONTH_V2,
'landsalescolourhectarespermonthbreakdown': landsalescolourhectarespermonthbreakdown,
'landsalescolourhectarespermonthbreakdowngu': landsalescolourhectarespermonthbreakdowngu,
'landsalescolourhectarespermonthbreakdownga': landsalescolourhectarespermonthbreakdownga,
'landsalescolourhectarespermonthbreakdownot': landsalescolourhectarespermonthbreakdownot,
'landsalescolourhectarespermonthbreakdownpr': landsalescolourhectarespermonthbreakdownpr,
'landsalescolourtransactionpermonthbreakdown': landsalescolourtransactionpermonthbreakdown,
'landsalescolourtransactionpermonthbreakdowngu': landsalescolourtransactionpermonthbreakdowngu,
'landsalescolourtransactionpermonthbreakdownga': landsalescolourtransactionpermonthbreakdownga,
'landsalescolourtransactionpermonthbreakdownpr': landsalescolourtransactionpermonthbreakdownpr,
'landsalescolourtransactionpermonthbreakdownot': landsalescolourtransactionpermonthbreakdownot,
'landsalescolourcostpermonthbreakdown': landsalescolourcostpermonthbreakdown,
'landsalescolourcostpermonthbreakdowngu': landsalescolourcostpermonthbreakdowngu,
'landsalescolourcostpermonthbreakdownga': landsalescolourcostpermonthbreakdownga,
'landsalescolourcostpermonthbreakdownpr': landsalescolourcostpermonthbreakdownpr,
'landsalescolourcostpermonthbreakdownot': landsalescolourcostpermonthbreakdownot,
'landsalescolourcostpermonth': landsalescolourcostperMONTH_V2,
'landsalescolourhectarespermonth': landsalescolourhectaresperMONTH_V2,
'landsalescolourhectares_stat': {
"name": "Total hectares (ha) traded from Dec 2017/Nov 2018 for transaction of colour",
"values": {"this": landsalescolourhectares_tot},
},
'landsalescolourcost_stat': {
"name": "Total Cost in R (million) traded from Dec 2017/Nov 2018 for transaction of colour",
"values": {"this": landsalescolourcost_tot},
},
'landsalescolourtransaction_stat': {
"name": "Total transactions traded from Dec 2017/Nov 2018 for transaction of colour",
"values": {"this": landsalescolourtransaction_tot},
},
'is_missing': landsalescolourhectares.get('is_missing')
}
def get_poverty_profile(geo, session, display_profile, comparative=False):
youth_income_poor_by_age_group, _ = get_stat_data(
['income poverty', 'age group'], geo, session,
table_universe='Youth',
table_dataset='Census and Community Survey',
percent_grouping=['age group'], slices=['Income-poor'],
recode=INCOME_POVERTY_AGE_GROUP_RECODE)
youth_income_poor_by_pop_group, _ = get_stat_data(
['income poverty', 'population group'], geo, session,
table_universe='Youth',
table_dataset='Census and Community Survey',
table_name='youth_income_poverty_population_group_gender',
percent_grouping=['population group'], slices=['Income-poor'],
key_order={'population group': POPULATION_GROUP_ORDER})
youth_income_poor_by_gender, _ = get_stat_data(
['income poverty', 'gender'], geo, session,
table_universe='Youth',
table_dataset='Census and Community Survey',
percent_grouping=['gender'], slices=['Income-poor'],
key_order={'gender': GENDER_ORDER})
with dataset_context(year='2011'):
youth_multid_poverty, _ = get_stat_data(
['multidimensionally poor'], geo, session,
table_universe='Youth',
table_dataset='Multidimensional poverty',
key_order=('Multidimensionally poor', 'Non-poor'),)
youth_multid_poor_by_pop_group, _ = get_stat_data(
['multidimensionally poor', 'population group'], geo, session,
table_universe='Youth',
table_dataset='Multidimensional poverty',
table_name='youth_multidimensionally_poor_population_group_gender',
percent_grouping=['population group'], slices=['Multidimensionally poor'],
key_order={'population group': POPULATION_GROUP_ORDER})
youth_multid_poor_by_gender, _ = get_stat_data(
['multidimensionally poor', 'gender'], geo, session,
table_universe='Youth',
table_dataset='Multidimensional poverty',
percent_grouping=['gender'], slices=['Multidimensionally poor'],
key_order={'gender': GENDER_ORDER})
youth_mpi_table = get_datatable('youth_mpi_score')
youth_mpi_score, _ = youth_mpi_table.get_stat_data(
geo, percent=False)
youth_mpi_score['youth_mpi_score']['name'] = 'Youth MPI score (0-1)*'
final_data = {
'youth_income_poor': {
"name": "Of youth live in income-poor households *",
"values": {"this": youth_income_poor_by_age_group['Youth (15-24)']['values']['this']}
},
'youth_income_poor_by_age_group': youth_income_poor_by_age_group,
'youth_income_poor_by_pop_group': youth_income_poor_by_pop_group,
'youth_income_poor_by_gender': youth_income_poor_by_gender,
'youth_multid_poor': {
"name": "Of youth are multidimensionally poor*",
"values": {"this": youth_multid_poverty['Multidimensionally poor']['values']['this']}
},
'youth_multid_poor_by_pop_group': youth_multid_poor_by_pop_group,
'youth_multid_poor_by_gender': youth_multid_poor_by_gender,
'youth_multid_poverty': youth_multid_poverty,
'youth_mpi_score_stat': youth_mpi_score['youth_mpi_score'],
'youth_mpi_score': youth_mpi_score
}
return final_data
def get_districtdistribution_profile(geo, session):
with dataset_context(year='2018'):
towndistrictdistributiontransactions = all_town = LOCATIONNOTFOUND
towndistrictdistributionhectares = towndistrictdistributionavgprice = LOCATIONNOTFOUND
towndistrictdistributionpricetrends = LOCATIONNOTFOUND
towndistrictdistributiontransactionsdata = towndistrictdistributionhectaresdata = LOCATIONNOTFOUND
towndistrictdistributionpricetrendsdata = towndistrictdistributionavgpricedata = LOCATIONNOTFOUND
towndistrictdistributionhectares_tot = towndistrictdistributiontransactions_tot = 0
dist = {}
towns = []
try:
#Get all towns in the geo
all_town, _ = get_stat_data(
['name'], geo, session,
table_name='land_traded_per_class_statistic_2018',
exclude_zero=True,
percent=False)
for keys, townname in all_town.items():
if keys != 'metadata':
towns.append(keys)
towndistrictdistributiontransactionsdata = {}
towndistrictdistributionhectaresdata = {}
towndistrictdistributionpricetrendsdata = towndistrictdistributionavgpricedata = {}
for town in towns:
town_code = town.replace(' ', '_').replace('-', '_').replace(
'/',
'_').replace(
'(', '').replace(')', '').lower()
try:
towndistrictdistributiontransactionsdata[
town_code], _ = get_stat_data(
['statistic', 'class_distribution'], geo, session,
table_name='land_traded_per_class_statistic_2018',
only={'name': [town], 'statistic': ['Number of transactions in 12 months']},
key_order={'class_distribution': LAND_CLASS},
percent=False)
except Exception as e:
pass
try:
towndistrictdistributionhectaresdata[
town_code], _ = get_stat_data(
['statistic', 'class_distribution'], geo, session,
table_name='land_traded_per_class_statistic_2018',
only={'name': [town], 'statistic': ['Total hectare traded in 12 months(ha)']},
key_order={'class_distribution': LAND_CLASS},
percent=False)
except Exception:
pass
try:
towndistrictdistributionavgpricedata[
town_code], _ = get_stat_data(
['statistic', 'class_distribution'], geo, session,
table_name='land_traded_per_class_statistic_2018',
only={'name': [town], 'statistic': ['Average price per hectare in 12 months(R/ha)']},
key_order={'class_distribution': LAND_CLASS},
percent=False)
except Exception:
pass
try:
towndistrictdistributionpricetrendsdata[
town_code], _ = get_stat_data(
['statistic', 'class_distribution'], geo, session,
table_name='land_traded_per_class_statistic_2018',
only={'name': [town], 'statistic': ['Price trend per hectare in 12 months(R/ha)']},
key_order={'class_distribution': LAND_CLASS},
percent=False)
except Exception as e:
pass
except Exception as e:
pass
dist[
'towndistrictdistributiontransactionsdata'] = towndistrictdistributiontransactionsdata
dist[
'towndistrictdistributionhectaresdata'] = towndistrictdistributionhectaresdata
dist[
'towndistrictdistributionavgpricedata'] = towndistrictdistributionavgpricedata
dist[
'towndistrictdistributionpricetrendsdata'] = towndistrictdistributionpricetrendsdata
dist['is_missing'] = all_town.get('is_missing')
return dist
def setUp(self):
self.s = get_session()
self.ctxt = dataset_context(year='latest')
self.ctxt.__enter__()
def get_safety_profile(geo, session, display_profile, comparative=False):
def rate_per_10k_pop(value, population):
if not value:
return None
return value / population * 10000
final_data = {}
if display_profile == 'WC':
youth_pop_table = get_datatable('youth_population')
youth, pop_total = youth_pop_table.get_stat_data(geo,
total='total_pop',
percent='False')
with dataset_context(year='2015'):
victims_by_age_group_per_10k_pop, total_victims_per_10k_pop = get_stat_data(
['age group'],
geo,
session,
table_universe=
'Victims of crime per 10,000 population in age group',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
accused_by_age_group_per_10k_pop, total_accused_per_10k_pop = get_stat_data(
['age group'],
geo,
session,
table_universe=
'Accused of crime per 10,000 population in age group',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
youth_victims_by_offence_per_10k_youth, _ = get_stat_data(
['type of offence'],
geo,
session,
table_universe='Youth victims of crime, per 10,000 youth',
table_dataset='Police Crime Statistics 2006-2015')
youth_accused_by_offence_per_10k_youth, _ = get_stat_data(
['type of offence'],
geo,
session,
table_universe='Youth accused of crimes, per 10,000 youth',
table_dataset='Police Crime Statistics 2006-2015')
youth_victims_by_pop_group_per_10k, _ = get_stat_data(
['population group'],
geo,
session,
table_universe='Youth victims of crime, per 10,000 youth',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
youth_accused_by_pop_group_per_10k, _ = get_stat_data(
['population group'],
geo,
session,
table_universe='Youth accused of crimes, per 10,000 youth',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
youth_victims_by_gender_per_10k, _ = get_stat_data(
['gender'],
geo,
session,
table_universe='Youth victims of crime, per 10,000 youth',
table_dataset='Police Crime Statistics 2006-2015',
percent=False,
key_order=GENDER_ORDER)
youth_accused_by_gender_per_10k, _ = get_stat_data(
['gender'],
geo,
session,
table_universe='Youth accused of crimes, per 10,000 youth',
table_dataset='Police Crime Statistics 2006-2015',
percent=False,
key_order=GENDER_ORDER)
youth_victims_by_year, _ = get_stat_data(
['year'],
geo,
session,
table_universe='Youth victims of crime',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
youth_accused_by_year, _ = get_stat_data(
['year'],
geo,
session,
table_universe='Youth accused of crimes',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
crimes_by_year, _ = get_stat_data(
['type of crime', 'year'],
geo,
session,
table_universe='Crimes',
table_dataset='Police Crime Statistics 2006-2015',
percent=False)
contact_crimes_by_year = crimes_by_year['Contact crime']
contact_crimes_by_year['metadata'] = crimes_by_year['metadata']
property_crimes_by_year = crimes_by_year['Property crime']
property_crimes_by_year['metadata'] = crimes_by_year['metadata']
contact_crimes_per_10k_pop = rate_per_10k_pop(
contact_crimes_by_year['2014-15']['values']['this'], pop_total)
property_crimes_per_10k_pop = rate_per_10k_pop(
property_crimes_by_year['2014-15']['values']['this'], pop_total)
final_data = {
'youth_victims_per_10k_youth': {
"name": "Youth victims of contact crime per 10,000 youth",
"values": {
"this":
victims_by_age_group_per_10k_pop['15-24']['values']['this']
}
},
'youth_accused_per_10k_youth': {
"name": "Youth accused of contact crime per 10,000 youth",
"values": {
"this":
accused_by_age_group_per_10k_pop['15-24']['values']['this']
}
},
'victims_by_age_group_per_10k_pop':
victims_by_age_group_per_10k_pop,
'accused_by_age_group_per_10k_pop':
accused_by_age_group_per_10k_pop,
'youth_victims_by_offence_per_10k_youth':
youth_victims_by_offence_per_10k_youth,
'youth_accused_by_offence_per_10k_youth':
youth_accused_by_offence_per_10k_youth,
'youth_victims_by_pop_group_per_10k':
youth_victims_by_pop_group_per_10k,
'youth_accused_by_pop_group_per_10k':
youth_accused_by_pop_group_per_10k,
'youth_victims_by_gender_per_10k': youth_victims_by_gender_per_10k,
'youth_accused_by_gender_per_10k': youth_accused_by_gender_per_10k,
'youth_victims_by_year': youth_victims_by_year,
'youth_accused_by_year': youth_accused_by_year,
'contact_crimes_per_10k_pop': {
"name": "Contact crimes per 10,000 population",
"values": {
"this": contact_crimes_per_10k_pop
}
},
'property_crimes_per_10k_pop': {
"name": "Property-related crime per 10,000 population",
"values": {
"this": property_crimes_per_10k_pop
}
},
'contact_crimes_by_year': contact_crimes_by_year,
'property_crimes_by_year': property_crimes_by_year
}
return final_data
