From eb2d69a5e6de3f882108a1f222358fc1ed78f561 Mon Sep 17 00:00:00 2001 From: "Michael S. Molina" <70410625+michael-s-molina@users.noreply.github.com> Date: Thu, 22 Aug 2024 14:46:26 -0300 Subject: [PATCH] fix: Duplicated example dataset (#29993) --- .../charts/Featured Charts/World_Map.yaml | 53 - .../configs/dashboards/Featured_Charts.yaml | 25 - .../examples/wb_health_population.yaml | 4317 ----------------- 3 files changed, 4395 deletions(-) delete mode 100644 superset/examples/configs/charts/Featured Charts/World_Map.yaml delete mode 100644 superset/examples/configs/datasets/examples/wb_health_population.yaml diff --git a/superset/examples/configs/charts/Featured Charts/World_Map.yaml b/superset/examples/configs/charts/Featured Charts/World_Map.yaml deleted file mode 100644 index e7ae235eb..000000000 --- a/superset/examples/configs/charts/Featured Charts/World_Map.yaml +++ /dev/null @@ -1,53 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -slice_name: World Map -description: null -certified_by: null -certification_details: null -viz_type: world_map -params: - datasource: 1__table - viz_type: world_map - entity: country_name - country_fieldtype: name - metric: sum__SP_POP_TOTL - adhoc_filters: - - clause: WHERE - subject: year - operator: TEMPORAL_RANGE - comparator: No filter - expressionType: SIMPLE - row_limit: 10000 - show_bubbles: true - secondary_metric: sum__SP_RUR_TOTL - max_bubble_size: "25" - color_picker: - r: 0 - g: 122 - b: 135 - a: 1 - color_by: metric - linear_color_scheme: superset_seq_1 - color_scheme: supersetColors - y_axis_format: SMART_NUMBER - extra_form_data: {} - dashboards: - - 13 -cache_timeout: null -uuid: f0606234-1644-426f-8fc6-3eaf8e77fd58 -version: 1.0.0 -dataset_uuid: 3cee7b7f-0009-4d9d-8de4-1da4cad4569e diff --git a/superset/examples/configs/dashboards/Featured_Charts.yaml b/superset/examples/configs/dashboards/Featured_Charts.yaml index 598db88ad..20155692b 100644 --- a/superset/examples/configs/dashboards/Featured_Charts.yaml +++ b/superset/examples/configs/dashboards/Featured_Charts.yaml @@ -261,20 +261,6 @@ position: - GRID_ID - ROW-LIWnqpnIk5 type: CHART - CHART-aAhaxRYu_t: - children: [] - id: CHART-aAhaxRYu_t - meta: - chartId: 116 - height: 50 - sliceName: World Map - uuid: f0606234-1644-426f-8fc6-3eaf8e77fd58 - width: 4 - parents: - - ROOT_ID - - GRID_ID - - ROW-we3i1eOT75 - type: CHART CHART-gfrGP3BD76: children: [] id: CHART-gfrGP3BD76 @@ -384,7 +370,6 @@ position: - ROW-Jq9auQfs6- - ROW-3XARWMYOfz - ROW-ux6j1ePT8I - - ROW-we3i1eOT75 id: GRID_ID parents: - ROOT_ID @@ -495,16 +480,6 @@ position: - ROOT_ID - GRID_ID type: ROW - ROW-we3i1eOT75: - children: - - CHART-aAhaxRYu_t - id: ROW-we3i1eOT75 - meta: - background: BACKGROUND_TRANSPARENT - parents: - - ROOT_ID - - GRID_ID - type: ROW metadata: color_scheme: supersetAndPresetColors refresh_frequency: 0 diff --git a/superset/examples/configs/datasets/examples/wb_health_population.yaml b/superset/examples/configs/datasets/examples/wb_health_population.yaml deleted file mode 100644 index ea89020b8..000000000 --- a/superset/examples/configs/datasets/examples/wb_health_population.yaml +++ /dev/null @@ -1,4317 +0,0 @@ -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -table_name: wb_health_population -main_dttm_col: year -description: - "\nThis data was\ - \ downloaded from the\n[World's Health Organization's website](https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics)\n\ - \nHere's the script that was used to massage the data:\n\n DIR = \"\"\n df_country\ - \ = pd.read_csv(DIR + '/HNP_Country.csv')\n df_country.columns = ['country_code']\ - \ + list(df_country.columns[1:])\n df_country = df_country[['country_code', 'Region']]\n\ - \ df_country.columns = ['country_code', 'region']\n\n df = pd.read_csv(DIR\ - \ + '/HNP_Data.csv')\n del df['Unnamed: 60']\n df.columns = ['country_name',\ - \ 'country_code'] + list(df.columns[2:])\n ndf = df.merge(df_country, how='inner')\n\ - \n dims = ('country_name', 'country_code', 'region')\n vv = [str(i) for i\ - \ in range(1960, 2015)]\n mdf = pd.melt(ndf, id_vars=dims + ('Indicator Code',),\ - \ value_vars=vv)\n mdf['year'] = mdf.variable + '-01-01'\n dims = dims + ('year',)\n\ - \n pdf = mdf.pivot_table(values='value', columns='Indicator Code', index=dims)\n\ - \ pdf = pdf.reset_index()\n pdf.to_csv(DIR + '/countries.csv')\n pdf.to_json(DIR\ - \ + '/countries.json', orient='records')\n\nHere's the description of the metrics\ - \ available:\n\nSeries | Code Indicator Name\n--- | ---\nNY.GNP.PCAP.CD | GNI per\ - \ capita, Atlas method (current US$)\nSE.ADT.1524.LT.FM.ZS | Literacy rate, youth\ - \ (ages 15-24), gender parity index (GPI)\nSE.ADT.1524.LT.MA.ZS | Literacy rate,\ - \ youth male (% of males ages 15-24)\nSE.ADT.1524.LT.ZS | Literacy rate, youth total\ - \ (% of people ages 15-24)\nSE.ADT.LITR.FE.ZS | Literacy rate, adult female (% of\ - \ females ages 15 and above)\nSE.ADT.LITR.MA.ZS | Literacy rate, adult male (% of\ - \ males ages 15 and above)\nSE.ADT.LITR.ZS | Literacy rate, adult total (% of people\ - \ ages 15 and above)\nSE.ENR.ORPH | Ratio of school attendance of orphans to school\ - \ attendance of non-orphans ages 10-14\nSE.PRM.CMPT.FE.ZS | Primary completion rate,\ - \ female (% of relevant age group)\nSE.PRM.CMPT.MA.ZS | Primary completion rate,\ - \ male (% of relevant age group)\nSE.PRM.CMPT.ZS | Primary completion rate, total\ - \ (% of relevant age group)\nSE.PRM.ENRR | School enrollment, primary (% gross)\n\ - SE.PRM.ENRR.FE | School enrollment, primary, female (% gross)\nSE.PRM.ENRR.MA |\ - \ School enrollment, primary, male (% gross)\nSE.PRM.NENR | School enrollment, primary\ - \ (% net)\nSE.PRM.NENR.FE | School enrollment, primary, female (% net)\nSE.PRM.NENR.MA\ - \ | School enrollment, primary, male (% net)\nSE.SEC.ENRR | School enrollment, secondary\ - \ (% gross)\nSE.SEC.ENRR.FE | School enrollment, secondary, female (% gross)\nSE.SEC.ENRR.MA\ - \ | School enrollment, secondary, male (% gross)\nSE.SEC.NENR | School enrollment,\ - \ secondary (% net)\nSE.SEC.NENR.FE | School enrollment, secondary, female (% net)\n\ - SE.SEC.NENR.MA | School enrollment, secondary, male (% net)\nSE.TER.ENRR | School\ - \ enrollment, tertiary (% gross)\nSE.TER.ENRR.FE | School enrollment, tertiary,\ - \ female (% gross)\nSE.XPD.TOTL.GD.ZS | Government expenditure on education, total\ - \ (% of GDP)\nSH.ANM.CHLD.ZS | Prevalence of anemia among children (% of children\ - \ under 5)\nSH.ANM.NPRG.ZS | Prevalence of anemia among non-pregnant women (% of\ - \ women ages 15-49)\nSH.CON.1524.FE.ZS | Condom use, population ages 15-24, female\ - \ (% of females ages 15-24)\nSH.CON.1524.MA.ZS | Condom use, population ages 15-24,\ - \ male (% of males ages 15-24)\nSH.CON.AIDS.FE.ZS | Condom use at last high-risk\ - \ sex, adult female (% ages 15-49)\nSH.CON.AIDS.MA.ZS | Condom use at last high-risk\ - \ sex, adult male (% ages 15-49)\nSH.DTH.COMM.ZS | Cause of death, by communicable\ - \ diseases and maternal, prenatal and nutrition conditions (% of total)\nSH.DTH.IMRT\ - \ | Number of infant deaths\nSH.DTH.INJR.ZS | Cause of death, by injury (% of total)\n\ - SH.DTH.MORT | Number of under-five deaths\nSH.DTH.NCOM.ZS | Cause of death, by non-communicable\ - \ diseases (% of total)\nSH.DTH.NMRT | Number of neonatal deaths\nSH.DYN.AIDS |\ - \ Adults (ages 15+) living with HIV\nSH.DYN.AIDS.DH | AIDS estimated deaths (UNAIDS\ - \ estimates)\nSH.DYN.AIDS.FE.ZS | Women's share of population ages 15+ living with\ - \ HIV (%)\nSH.DYN.AIDS.ZS | Prevalence of HIV, total (% of population ages 15-49)\n\ - SH.DYN.MORT | Mortality rate, under-5 (per 1,000 live births)\nSH.DYN.MORT.FE |\ - \ Mortality rate, under-5, female (per 1,000 live births)\nSH.DYN.MORT.MA | Mortality\ - \ rate, under-5, male (per 1,000 live births)\nSH.DYN.NMRT | Mortality rate, neonatal\ - \ (per 1,000 live births)\nSH.FPL.SATI.ZS | Met need for contraception (% of married\ - \ women ages 15-49)\nSH.H2O.SAFE.RU.ZS | Improved water source, rural (% of rural\ - \ population with access)\nSH.H2O.SAFE.UR.ZS | Improved water source, urban (% of\ - \ urban population with access)\nSH.H2O.SAFE.ZS | Improved water source (% of population\ - \ with access)\nSH.HIV.0014 | Children (0-14) living with HIV\nSH.HIV.1524.FE.ZS\ - \ | Prevalence of HIV, female (% ages 15-24)\nSH.HIV.1524.KW.FE.ZS | Comprehensive\ - \ correct knowledge of HIV/AIDS, ages 15-24, female (2 prevent ways and reject 3\ - \ misconceptions)\nSH.HIV.1524.KW.MA.ZS | Comprehensive correct knowledge of HIV/AIDS,\ - \ ages 15-24, male (2 prevent ways and reject 3 misconceptions)\nSH.HIV.1524.MA.ZS\ - \ | Prevalence of HIV, male (% ages 15-24)\nSH.HIV.ARTC.ZS | Antiretroviral therapy\ - \ coverage (% of people living with HIV)\nSH.HIV.KNOW.FE.ZS | % of females ages\ - \ 15-49 having comprehensive correct knowledge about HIV (2 prevent ways and reject\ - \ 3 misconceptions)\nSH.HIV.KNOW.MA.ZS | % of males ages 15-49 having comprehensive\ - \ correct knowledge about HIV (2 prevent ways and reject 3 misconceptions)\nSH.HIV.ORPH\ - \ | Children orphaned by HIV/AIDS\nSH.HIV.TOTL | Adults (ages 15+) and children\ - \ (0-14 years) living with HIV\nSH.IMM.HEPB | Immunization, HepB3 (% of one-year-old\ - \ children)\nSH.IMM.HIB3 | Immunization, Hib3 (% of children ages 12-23 months)\n\ - SH.IMM.IBCG | Immunization, BCG (% of one-year-old children)\nSH.IMM.IDPT | Immunization,\ - \ DPT (% of children ages 12-23 months)\nSH.IMM.MEAS | Immunization, measles (%\ - \ of children ages 12-23 months)\nSH.IMM.POL3 | Immunization, Pol3 (% of one-year-old\ - \ children)\nSH.MED.BEDS.ZS | Hospital beds (per 1,000 people)\nSH.MED.CMHW.P3 |\ - \ Community health workers (per 1,000 people)\nSH.MED.NUMW.P3 | Nurses and midwives\ - \ (per 1,000 people)\nSH.MED.PHYS.ZS | Physicians (per 1,000 people)\nSH.MLR.NETS.ZS\ - \ | Use of insecticide-treated bed nets (% of under-5 population)\nSH.MLR.PREG.ZS\ - \ | Use of any antimalarial drug (% of pregnant women)\nSH.MLR.SPF2.ZS | Use of\ - \ Intermittent Preventive Treatment of malaria, 2+ doses of SP/Fansidar (% of pregnant\ - \ women)\nSH.MLR.TRET.ZS | Children with fever receiving antimalarial drugs (% of\ - \ children under age 5 with fever)\nSH.MMR.DTHS | Number of maternal deaths\nSH.MMR.LEVE\ - \ | Number of weeks of maternity leave\nSH.MMR.RISK | Lifetime risk of maternal\ - \ death (1 in: rate varies by country)\nSH.MMR.RISK.ZS | Lifetime risk of maternal\ - \ death (%)\nSH.MMR.WAGE.ZS | Maternal leave benefits (% of wages paid in covered\ - \ period)\nSH.PRG.ANEM | Prevalence of anemia among pregnant women (%)\nSH.PRG.ARTC.ZS\ - \ | Antiretroviral therapy coverage (% of pregnant women living with HIV)\nSH.PRG.SYPH.ZS\ - \ | Prevalence of syphilis (% of women attending antenatal care)\nSH.PRV.SMOK.FE\ - \ | Smoking prevalence, females (% of adults)\nSH.PRV.SMOK.MA | Smoking prevalence,\ - \ males (% of adults)\nSH.STA.ACSN | Improved sanitation facilities (% of population\ - \ with access)\nSH.STA.ACSN.RU | Improved sanitation facilities, rural (% of rural\ - \ population with access)\nSH.STA.ACSN.UR | Improved sanitation facilities, urban\ - \ (% of urban population with access)\nSH.STA.ANV4.ZS | Pregnant women receiving\ - \ prenatal care of at least four visits (% of pregnant women)\nSH.STA.ANVC.ZS |\ - \ Pregnant women receiving prenatal care (%)\nSH.STA.ARIC.ZS | ARI treatment (%\ - \ of children under 5 taken to a health provider)\nSH.STA.BFED.ZS | Exclusive breastfeeding\ - \ (% of children under 6 months)\nSH.STA.BRTC.ZS | Births attended by skilled health\ - \ staff (% of total)\nSH.STA.BRTW.ZS | Low-birthweight babies (% of births)\nSH.STA.DIAB.ZS\ - \ | Diabetes prevalence (% of population ages 20 to 79)\nSH.STA.IYCF.ZS | Infant\ - \ and young child feeding practices, all 3 IYCF (% children ages 6-23 months)\n\ - SH.STA.MALN.FE.ZS | Prevalence of underweight, weight for age, female (% of children\ - \ under 5)\nSH.STA.MALN.MA.ZS | Prevalence of underweight, weight for age, male\ - \ (% of children under 5)\nSH.STA.MALN.ZS | Prevalence of underweight, weight for\ - \ age (% of children under 5)\nSH.STA.MALR | Malaria cases reported\nSH.STA.MMRT\ - \ | Maternal mortality ratio (modeled estimate, per 100,000 live births)\nSH.STA.MMRT.NE\ - \ | Maternal mortality ratio (national estimate, per 100,000 live births)\nSH.STA.ORCF.ZS\ - \ | Diarrhea treatment (% of children under 5 receiving oral rehydration and continued\ - \ feeding)\nSH.STA.ORTH | Diarrhea treatment (% of children under 5 who received\ - \ ORS packet)\nSH.STA.OW15.FE.ZS | Prevalence of overweight, female (% of female\ - \ adults)\nSH.STA.OW15.MA.ZS | Prevalence of overweight, male (% of male adults)\n\ - SH.STA.OW15.ZS | Prevalence of overweight (% of adults)\nSH.STA.OWGH.FE.ZS | Prevalence\ - \ of overweight, weight for height, female (% of children under 5)\nSH.STA.OWGH.MA.ZS\ - \ | Prevalence of overweight, weight for height, male (% of children under 5)\n\ - SH.STA.OWGH.ZS | Prevalence of overweight, weight for height (% of children under\ - \ 5)\nSH.STA.PNVC.ZS | Postnatal care coverage (% mothers)\nSH.STA.STNT.FE.ZS |\ - \ Prevalence of stunting, height for age, female (% of children under 5)\nSH.STA.STNT.MA.ZS\ - \ | Prevalence of stunting, height for age, male (% of children under 5)\nSH.STA.STNT.ZS\ - \ | Prevalence of stunting, height for age (% of children under 5)\nSH.STA.WAST.FE.ZS\ - \ | Prevalence of wasting, weight for height, female (% of children under 5)\nSH.STA.WAST.MA.ZS\ - \ | Prevalence of wasting, weight for height, male (% of children under 5)\nSH.STA.WAST.ZS\ - \ | Prevalence of wasting, weight for height (% of children under 5)\nSH.SVR.WAST.FE.ZS\ - \ | Prevalence of severe wasting, weight for height, female (% of children under\ - \ 5)\nSH.SVR.WAST.MA.ZS | Prevalence of severe wasting, weight for height, male\ - \ (% of children under 5)\nSH.SVR.WAST.ZS | Prevalence of severe wasting, weight\ - \ for height (% of children under 5)\nSH.TBS.CURE.ZS | Tuberculosis treatment success\ - \ rate (% of new cases)\nSH.TBS.DTEC.ZS | Tuberculosis case detection rate (%, all\ - \ forms)\nSH.TBS.INCD | Incidence of tuberculosis (per 100,000 people)\nSH.TBS.MORT\ - \ | Tuberculosis death rate (per 100,000 people)\nSH.TBS.PREV | Prevalence of tuberculosis\ - \ (per 100,000 population)\nSH.VAC.TTNS.ZS | Newborns protected against tetanus\ - \ (%)\nSH.XPD.EXTR.ZS | External resources for health (% of total expenditure on\ - \ health)\nSH.XPD.OOPC.TO.ZS | Out-of-pocket health expenditure (% of total expenditure\ - \ on health)\nSH.XPD.OOPC.ZS | Out-of-pocket health expenditure (% of private expenditure\ - \ on health)\nSH.XPD.PCAP | Health expenditure per capita (current US$)\nSH.XPD.PCAP.PP.KD\ - \ | Health expenditure per capita, PPP (constant 2011 international $)\nSH.XPD.PRIV\ - \ | Health expenditure, private (% of total health expenditure)\nSH.XPD.PRIV.ZS\ - \ | Health expenditure, private (% of GDP)\nSH.XPD.PUBL | Health expenditure, public\ - \ (% of total health expenditure)\nSH.XPD.PUBL.GX.ZS | Health expenditure, public\ - \ (% of government expenditure)\nSH.XPD.PUBL.ZS | Health expenditure, public (%\ - \ of GDP)\nSH.XPD.TOTL.CD | Health expenditure, total (current US$)\nSH.XPD.TOTL.ZS\ - \ | Health expenditure, total (% of GDP)\nSI.POV.NAHC | Poverty headcount ratio\ - \ at national poverty lines (% of population)\nSI.POV.RUHC | Rural poverty headcount\ - \ ratio at national poverty lines (% of rural population)\nSI.POV.URHC | Urban poverty\ - \ headcount ratio at national poverty lines (% of urban population)\nSL.EMP.INSV.FE.ZS\ - \ | Share of women in wage employment in the nonagricultural sector (% of total\ - \ nonagricultural employment)\nSL.TLF.TOTL.FE.ZS | Labor force, female (% of total\ - \ labor force)\nSL.TLF.TOTL.IN | Labor force, total\nSL.UEM.TOTL.FE.ZS | Unemployment,\ - \ female (% of female labor force) (modeled ILO estimate)\nSL.UEM.TOTL.MA.ZS | Unemployment,\ - \ male (% of male labor force) (modeled ILO estimate)\nSL.UEM.TOTL.ZS | Unemployment,\ - \ total (% of total labor force) (modeled ILO estimate)\nSM.POP.NETM | Net migration\n\ - SN.ITK.DEFC | Number of people who are undernourished\nSN.ITK.DEFC.ZS | Prevalence\ - \ of undernourishment (% of population)\nSN.ITK.SALT.ZS | Consumption of iodized\ - \ salt (% of households)\nSN.ITK.VITA.ZS | Vitamin A supplementation coverage rate\ - \ (% of children ages 6-59 months)\nSP.ADO.TFRT | Adolescent fertility rate (births\ - \ per 1,000 women ages 15-19)\nSP.DYN.AMRT.FE | Mortality rate, adult, female (per\ - \ 1,000 female adults)\nSP.DYN.AMRT.MA | Mortality rate, adult, male (per 1,000\ - \ male adults)\nSP.DYN.CBRT.IN | Birth rate, crude (per 1,000 people)\nSP.DYN.CDRT.IN\ - \ | Death rate, crude (per 1,000 people)\nSP.DYN.CONU.ZS | Contraceptive prevalence\ - \ (% of women ages 15-49)\nSP.DYN.IMRT.FE.IN | Mortality rate, infant, female (per\ - \ 1,000 live births)\nSP.DYN.IMRT.IN | Mortality rate, infant (per 1,000 live births)\n\ - SP.DYN.IMRT.MA.IN | Mortality rate, infant, male (per 1,000 live births)\nSP.DYN.LE00.FE.IN\ - \ | Life expectancy at birth, female (years)\nSP.DYN.LE00.IN | Life expectancy at\ - \ birth, total (years)\nSP.DYN.LE00.MA.IN | Life expectancy at birth, male (years)\n\ - SP.DYN.SMAM.FE | Mean age at first marriage, female\nSP.DYN.SMAM.MA | Mean age at\ - \ first marriage, male\nSP.DYN.TFRT.IN | Fertility rate, total (births per woman)\n\ - SP.DYN.TO65.FE.ZS | Survival to age 65, female (% of cohort)\nSP.DYN.TO65.MA.ZS\ - \ | Survival to age 65, male (% of cohort)\nSP.DYN.WFRT | Wanted fertility rate\ - \ (births per woman)\nSP.HOU.FEMA.ZS | Female headed households (% of households\ - \ with a female head)\nSP.MTR.1519.ZS | Teenage mothers (% of women ages 15-19 who\ - \ have had children or are currently pregnant)\nSP.POP.0004.FE | Population ages\ - \ 0-4, female\nSP.POP.0004.FE.5Y | Population ages 0-4, female (% of female population)\n\ - SP.POP.0004.MA | Population ages 0-4, male\nSP.POP.0004.MA.5Y | Population ages\ - \ 0-4, male (% of male population)\nSP.POP.0014.FE.ZS | Population ages 0-14, female\ - \ (% of total)\nSP.POP.0014.MA.ZS | Population ages 0-14, male (% of total)\nSP.POP.0014.TO\ - \ | Population ages 0-14, total\nSP.POP.0014.TO.ZS | Population ages 0-14 (% of\ - \ total)\nSP.POP.0509.FE | Population ages 5-9, female\nSP.POP.0509.FE.5Y | Population\ - \ ages 5-9, female (% of female population)\nSP.POP.0509.MA | Population ages 5-9,\ - \ male\nSP.POP.0509.MA.5Y | Population ages 5-9, male (% of male population)\nSP.POP.1014.FE\ - \ | Population ages 10-14, female\nSP.POP.1014.FE.5Y | Population ages 10-14, female\ - \ (% of female population)\nSP.POP.1014.MA | Population ages 10-14, male\nSP.POP.1014.MA.5Y\ - \ | Population ages 10-14, male (% of male population)\nSP.POP.1519.FE | Population\ - \ ages 15-19, female\nSP.POP.1519.FE.5Y | Population ages 15-19, female (% of female\ - \ population)\nSP.POP.1519.MA | Population ages 15-19, male\nSP.POP.1519.MA.5Y |\ - \ Population ages 15-19, male (% of male population)\nSP.POP.1564.FE.ZS | Population\ - \ ages 15-64, female (% of total)\nSP.POP.1564.MA.ZS | Population ages 15-64, male\ - \ (% of total)\nSP.POP.1564.TO | Population ages 15-64, total\nSP.POP.1564.TO.ZS\ - \ | Population ages 15-64 (% of total)\nSP.POP.2024.FE | Population ages 20-24,\ - \ female\nSP.POP.2024.FE.5Y | Population ages 20-24, female (% of female population)\n\ - SP.POP.2024.MA | Population ages 20-24, male\nSP.POP.2024.MA.5Y | Population ages\ - \ 20-24, male (% of male population)\nSP.POP.2529.FE | Population ages 25-29, female\n\ - SP.POP.2529.FE.5Y | Population ages 25-29, female (% of female population)\nSP.POP.2529.MA\ - \ | Population ages 25-29, male\nSP.POP.2529.MA.5Y | Population ages 25-29, male\ - \ (% of male population)\nSP.POP.3034.FE | Population ages 30-34, female\nSP.POP.3034.FE.5Y\ - \ | Population ages 30-34, female (% of female population)\nSP.POP.3034.MA | Population\ - \ ages 30-34, male\nSP.POP.3034.MA.5Y | Population ages 30-34, male (% of male population)\n\ - SP.POP.3539.FE | Population ages 35-39, female\nSP.POP.3539.FE.5Y | Population ages\ - \ 35-39, female (% of female population)\nSP.POP.3539.MA | Population ages 35-39,\ - \ male\nSP.POP.3539.MA.5Y | Population ages 35-39, male (% of male population)\n\ - SP.POP.4044.FE | Population ages 40-44, female\nSP.POP.4044.FE.5Y | Population ages\ - \ 40-44, female (% of female population)\nSP.POP.4044.MA | Population ages 40-44,\ - \ male\nSP.POP.4044.MA.5Y | Population ages 40-44, male (% of male population)\n\ - SP.POP.4549.FE | Population ages 45-49, female\nSP.POP.4549.FE.5Y | Population ages\ - \ 45-49, female (% of female population)\nSP.POP.4549.MA | Population ages 45-49,\ - \ male\nSP.POP.4549.MA.5Y | Population ages 45-49, male (% of male population)\n\ - SP.POP.5054.FE | Population ages 50-54, female\nSP.POP.5054.FE.5Y | Population ages\ - \ 50-54, female (% of female population)\nSP.POP.5054.MA | Population ages 50-54,\ - \ male\nSP.POP.5054.MA.5Y | Population ages 50-54, male (% of male population)\n\ - SP.POP.5559.FE | Population ages 55-59, female\nSP.POP.5559.FE.5Y | Population ages\ - \ 55-59, female (% of female population)\nSP.POP.5559.MA | Population ages 55-59,\ - \ male\nSP.POP.5559.MA.5Y | Population ages 55-59, male (% of male population)\n\ - SP.POP.6064.FE | Population ages 60-64, female\nSP.POP.6064.FE.5Y | Population ages\ - \ 60-64, female (% of female population)\nSP.POP.6064.MA | Population ages 60-64,\ - \ male\nSP.POP.6064.MA.5Y | Population ages 60-64, male (% of male population)\n\ - SP.POP.6569.FE | Population ages 65-69, female\nSP.POP.6569.FE.5Y | Population ages\ - \ 65-69, female (% of female population)\nSP.POP.6569.MA | Population ages 65-69,\ - \ male\nSP.POP.6569.MA.5Y | Population ages 65-69, male (% of male population)\n\ - SP.POP.65UP.FE.ZS | Population ages 65 and above, female (% of total)\nSP.POP.65UP.MA.ZS\ - \ | Population ages 65 and above, male (% of total)\nSP.POP.65UP.TO | Population\ - \ ages 65 and above, total\nSP.POP.65UP.TO.ZS | Population ages 65 and above (%\ - \ of total)\nSP.POP.7074.FE | Population ages 70-74, female\nSP.POP.7074.FE.5Y |\ - \ Population ages 70-74, female (% of female population)\nSP.POP.7074.MA | Population\ - \ ages 70-74, male\nSP.POP.7074.MA.5Y | Population ages 70-74, male (% of male population)\n\ - SP.POP.7579.FE | Population ages 75-79, female\nSP.POP.7579.FE.5Y | Population ages\ - \ 75-79, female (% of female population)\nSP.POP.7579.MA | Population ages 75-79,\ - \ male\nSP.POP.7579.MA.5Y | Population ages 75-79, male (% of male population)\n\ - SP.POP.80UP.FE | Population ages 80 and above, female\nSP.POP.80UP.FE.5Y | Population\ - \ ages 80 and above, female (% of female population)\nSP.POP.80UP.MA | Population\ - \ ages 80 and above, male\nSP.POP.80UP.MA.5Y | Population ages 80 and above, male\ - \ (% of male population)\nSP.POP.AG00.FE.IN | Age population, age 0, female, interpolated\n\ - SP.POP.AG00.MA.IN | Age population, age 0, male, interpolated\nSP.POP.AG01.FE.IN\ - \ | Age population, age 01, female, interpolated\nSP.POP.AG01.MA.IN | Age population,\ - \ age 01, male, interpolated\nSP.POP.AG02.FE.IN | Age population, age 02, female,\ - \ interpolated\nSP.POP.AG02.MA.IN | Age population, age 02, male, interpolated\n\ - SP.POP.AG03.FE.IN | Age population, age 03, female, interpolated\nSP.POP.AG03.MA.IN\ - \ | Age population, age 03, male, interpolated\nSP.POP.AG04.FE.IN | Age population,\ - \ age 04, female, interpolated\nSP.POP.AG04.MA.IN | Age population, age 04, male,\ - \ interpolated\nSP.POP.AG05.FE.IN | Age population, age 05, female, interpolated\n\ - SP.POP.AG05.MA.IN | Age population, age 05, male, interpolated\nSP.POP.AG06.FE.IN\ - \ | Age population, age 06, female, interpolated\nSP.POP.AG06.MA.IN | Age population,\ - \ age 06, male, interpolated\nSP.POP.AG07.FE.IN | Age population, age 07, female,\ - \ interpolated\nSP.POP.AG07.MA.IN | Age population, age 07, male, interpolated\n\ - SP.POP.AG08.FE.IN | Age population, age 08, female, interpolated\nSP.POP.AG08.MA.IN\ - \ | Age population, age 08, male, interpolated\nSP.POP.AG09.FE.IN | Age population,\ - \ age 09, female, interpolated\nSP.POP.AG09.MA.IN | Age population, age 09, male,\ - \ interpolated\nSP.POP.AG10.FE.IN | Age population, age 10, female, interpolated\n\ - SP.POP.AG10.MA.IN | Age population, age 10, male\nSP.POP.AG11.FE.IN | Age population,\ - \ age 11, female, interpolated\nSP.POP.AG11.MA.IN | Age population, age 11, male\n\ - SP.POP.AG12.FE.IN | Age population, age 12, female, interpolated\nSP.POP.AG12.MA.IN\ - \ | Age population, age 12, male\nSP.POP.AG13.FE.IN | Age population, age 13, female,\ - \ interpolated\nSP.POP.AG13.MA.IN | Age population, age 13, male\nSP.POP.AG14.FE.IN\ - \ | Age population, age 14, female, interpolated\nSP.POP.AG14.MA.IN | Age population,\ - \ age 14, male\nSP.POP.AG15.FE.IN | Age population, age 15, female, interpolated\n\ - SP.POP.AG15.MA.IN | Age population, age 15, male, interpolated\nSP.POP.AG16.FE.IN\ - \ | Age population, age 16, female, interpolated\nSP.POP.AG16.MA.IN | Age population,\ - \ age 16, male, interpolated\nSP.POP.AG17.FE.IN | Age population, age 17, female,\ - \ interpolated\nSP.POP.AG17.MA.IN | Age population, age 17, male, interpolated\n\ - SP.POP.AG18.FE.IN | Age population, age 18, female, interpolated\nSP.POP.AG18.MA.IN\ - \ | Age population, age 18, male, interpolated\nSP.POP.AG19.FE.IN | Age population,\ - \ age 19, female, interpolated\nSP.POP.AG19.MA.IN | Age population, age 19, male,\ - \ interpolated\nSP.POP.AG20.FE.IN | Age population, age 20, female, interpolated\n\ - SP.POP.AG20.MA.IN | Age population, age 20, male, interpolated\nSP.POP.AG21.FE.IN\ - \ | Age population, age 21, female, interpolated\nSP.POP.AG21.MA.IN | Age population,\ - \ age 21, male, interpolated\nSP.POP.AG22.FE.IN | Age population, age 22, female,\ - \ interpolated\nSP.POP.AG22.MA.IN | Age population, age 22, male, interpolated\n\ - SP.POP.AG23.FE.IN | Age population, age 23, female, interpolated\nSP.POP.AG23.MA.IN\ - \ | Age population, age 23, male, interpolated\nSP.POP.AG24.FE.IN | Age population,\ - \ age 24, female, interpolated\nSP.POP.AG24.MA.IN | Age population, age 24, male,\ - \ interpolated\nSP.POP.AG25.FE.IN | Age population, age 25, female, interpolated\n\ - SP.POP.AG25.MA.IN | Age population, age 25, male, interpolated\nSP.POP.BRTH.MF |\ - \ Sex ratio at birth (male births per female births)\nSP.POP.DPND | Age dependency\ - \ ratio (% of working-age population)\nSP.POP.DPND.OL | Age dependency ratio, old\ - \ (% of working-age population)\nSP.POP.DPND.YG | Age dependency ratio, young (%\ - \ of working-age population)\nSP.POP.GROW | Population growth (annual %)\nSP.POP.TOTL\ - \ | Population, total\nSP.POP.TOTL.FE.IN | Population, female\nSP.POP.TOTL.FE.ZS\ - \ | Population, female (% of total)\nSP.POP.TOTL.MA.IN | Population, male\nSP.POP.TOTL.MA.ZS\ - \ | Population, male (% of total)\nSP.REG.BRTH.RU.ZS | Completeness of birth registration,\ - \ rural (%)\nSP.REG.BRTH.UR.ZS | Completeness of birth registration, urban (%)\n\ - SP.REG.BRTH.ZS | Completeness of birth registration (%)\nSP.REG.DTHS.ZS | Completeness\ - \ of death registration with cause-of-death information (%)\nSP.RUR.TOTL | Rural\ - \ population\nSP.RUR.TOTL.ZG | Rural population growth (annual %)\nSP.RUR.TOTL.ZS\ - \ | Rural population (% of total population)\nSP.URB.GROW | Urban population growth\ - \ (annual %)\nSP.URB.TOTL | Urban population\nSP.URB.TOTL.IN.ZS | Urban population\ - \ (% of total)\nSP.UWT.TFRT | Unmet need for contraception (% of married women ages\ - \ 15-49)\n" -default_endpoint: null -offset: 0 -cache_timeout: null -schema: public -sql: null -params: null -template_params: null -filter_select_enabled: true -fetch_values_predicate: null -extra: null -normalize_columns: false -always_filter_main_dttm: false -uuid: 3cee7b7f-0009-4d9d-8de4-1da4cad4569e -metrics: - - metric_name: sum__SP_DYN_LE00_IN - verbose_name: null - metric_type: null - expression: sum("SP_DYN_LE00_IN") - 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verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SP_DYN_IMRT_FE_IN - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SP_REG_DTHS_ZS - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_MED_PHYS_ZS - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_XPD_EXTR_ZS - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SP_DYN_TFRT_IN - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SP_DYN_WFRT - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_DTH_INJR_ZS - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_DTH_IMRT - 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verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_XPD_PRIV_ZS - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_XPD_PRIV - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_XPD_TOTL_ZS - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_DYN_NMRT - 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verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SE_PRM_ENRR_FE - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SE_TER_ENRR - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SL_EMP_INSV_FE_ZS - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SL_UEM_TOTL_FE_ZS - 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verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_PRG_SYPH_ZS - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SP_RUR_TOTL_ZG - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SP_POP_GROW - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_HIV_ORPH - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_HIV_TOTL - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_MMR_RISK_ZS - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SH_MMR_RISK - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SP_POP_TOTL - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SP_RUR_TOTL_ZS - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: SP_RUR_TOTL - verbose_name: null - is_dttm: false - is_active: true - type: DOUBLE PRECISION - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: country_name - verbose_name: null - is_dttm: false - is_active: true - type: VARCHAR(255) - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: region - verbose_name: null - is_dttm: false - is_active: true - type: VARCHAR(255) - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null - - column_name: country_code - verbose_name: null - is_dttm: false - is_active: true - type: VARCHAR(3) - advanced_data_type: null - groupby: true - filterable: true - expression: null - description: null - python_date_format: null - extra: null -version: 1.0.0 -database_uuid: a2dc77af-e654-49bb-b321-40f6b559a1ee