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Maxime Beauchemin
2016-03-17 23:44:58 -07:00
parent 8f4f5b126a
commit be6b2fe556
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"""Loads datasets, dashboards and slices in a new dashed instance"""
import gzip
import json
import os
import textwrap
import pandas as pd
from sqlalchemy import String, DateTime
from dashed import app, db, models, utils
# Shortcuts
DB = models.Database
Slice = models.Slice
TBL = models.SqlaTable
Dash = models.Dashboard
config = app.config
DATA_FOLDER = os.path.join(config.get("BASE_DIR"), 'data')
def get_or_create_db(session):
print("Creating database reference")
dbobj = session.query(DB).filter_by(database_name='main').first()
if not dbobj:
dbobj = DB(database_name="main")
print(config.get("SQLALCHEMY_DATABASE_URI"))
dbobj.sqlalchemy_uri = config.get("SQLALCHEMY_DATABASE_URI")
session.add(dbobj)
session.commit()
return dbobj
def merge_slice(slc):
o = db.session.query(Slice).filter_by(slice_name=slc.slice_name).first()
if o:
db.session.delete(o)
db.session.add(slc)
db.session.commit()
def get_slice_json(defaults, **kwargs):
d = defaults.copy()
d.update(kwargs)
return json.dumps(d, indent=4, sort_keys=True)
def load_world_bank_health_n_pop():
"""Loads the world bank health dataset, slices and a dashboard"""
tbl_name = 'wb_health_population'
with gzip.open(os.path.join(DATA_FOLDER, 'countries.json.gz')) as f:
pdf = pd.read_json(f)
pdf.columns = [col.replace('.', '_') for col in pdf.columns]
pdf.year = pd.to_datetime(pdf.year)
pdf.to_sql(
tbl_name,
db.engine,
if_exists='replace',
chunksize=500,
dtype={
'year': DateTime(),
'country_code': String(3),
'country_name': String(255),
'region': String(255),
},
index=False)
print("Creating table [wb_health_population] reference")
tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
if not tbl:
tbl = TBL(table_name=tbl_name)
tbl.description = utils.readfile(os.path.join(DATA_FOLDER, 'countries.md'))
tbl.main_dttm_col = 'year'
tbl.is_featured = True
tbl.database = get_or_create_db(db.session)
db.session.merge(tbl)
db.session.commit()
tbl.fetch_metadata()
defaults = {
"compare_lag": "10",
"compare_suffix": "o10Y",
"datasource_id": "1",
"datasource_name": "birth_names",
"datasource_type": "table",
"limit": "25",
"granularity": "year",
"groupby": [],
"metric": 'sum__SP_POP_TOTL',
"metrics": ["sum__SP_POP_TOTL"],
"row_limit": config.get("ROW_LIMIT"),
"since": "2014-01-01",
"until": "2014-01-01",
"where": "",
"markup_type": "markdown",
"country_fieldtype": "cca3",
"secondary_metric": "sum__SP_POP_TOTL",
"entity": "country_code",
"show_bubbles": "y",
}
print("Creating slices")
slices = [
Slice(
slice_name="Region Filter",
viz_type='filter_box',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
viz_type='filter_box',
groupby=['region'],
)),
Slice(
slice_name="World's Population",
viz_type='big_number',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
since='2000',
viz_type='big_number',
compare_lag="10",
metric='sum__SP_POP_TOTL',
compare_suffix="over 10Y")),
Slice(
slice_name="Most Populated Countries",
viz_type='table',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
viz_type='table',
metrics=["sum__SP_POP_TOTL"],
groupby=['country_name'])),
Slice(
slice_name="Growth Rate",
viz_type='line',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
viz_type='line',
since="1960-01-01",
metrics=["sum__SP_POP_TOTL"],
num_period_compare="10",
groupby=['country_name'])),
Slice(
slice_name="% Rural",
viz_type='world_map',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
viz_type='world_map',
metric= "sum__SP_RUR_TOTL_ZS",
num_period_compare="10",)),
Slice(
slice_name="Life Expexctancy VS Rural %",
viz_type='bubble',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
viz_type='bubble',
since= "2011-01-01",
until= "2011-01-01",
series="region",
limit="0",
entity="country_name",
x="sum__SP_RUR_TOTL_ZS",
y="sum__SP_DYN_LE00_IN",
size="sum__SP_POP_TOTL",
max_bubble_size="50",
flt_col_1="country_code",
flt_op_1= "not in",
flt_eq_1="TCA,MNP,DMA,MHL,MCO,SXM,CYM,TUV,IMY,KNA,ASM,ADO,AMA,PLW",
num_period_compare="10",)),
Slice(
slice_name="Rural Breakdown",
viz_type='sunburst',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
viz_type='sunburst',
groupby=["region", "country_name"],
secondary_metric="sum__SP_RUR_TOTL",
since= "2011-01-01",
until= "2011-01-01",)),
Slice(
slice_name="World's Pop Growth",
viz_type='area',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
since="1960-01-01",
until="now",
viz_type='area',
groupby=["region"],)),
]
for slc in slices:
merge_slice(slc)
print("Creating a World's Health Bank dashboard")
dash_name = "World's Health Bank Dashboard"
dash = db.session.query(Dash).filter_by(dashboard_title=dash_name).first()
if dash:
db.session.delete(dash)
js = """\
[
{
"size_y": 1,
"size_x": 3,
"col": 1,
"slice_id": "269",
"row": 1
},
{
"size_y": 3,
"size_x": 3,
"col": 1,
"slice_id": "270",
"row": 2
},
{
"size_y": 7,
"size_x": 3,
"col": 10,
"slice_id": "271",
"row": 1
},
{
"size_y": 3,
"size_x": 6,
"col": 1,
"slice_id": "272",
"row": 5
},
{
"size_y": 4,
"size_x": 6,
"col": 4,
"slice_id": "273",
"row": 1
},
{
"size_y": 4,
"size_x": 6,
"col": 7,
"slice_id": "274",
"row": 8
},
{
"size_y": 3,
"size_x": 3,
"col": 7,
"slice_id": "275",
"row": 5
},
{
"size_y": 4,
"size_x": 6,
"col": 1,
"slice_id": "276",
"row": 8
}
]
"""
l = json.loads(js)
for i, pos in enumerate(l):
pos['slice_id'] = str(slices[i].id)
dash = Dash(
dashboard_title=dash_name,
position_json=json.dumps(l, indent=4),
slug="world_health",
)
for s in slices:
dash.slices.append(s)
db.session.commit()
def load_css_templates():
"""Loads 2 css templates to demonstrate the feature"""
print('Creating default CSS templates')
CSS = models.CssTemplate
obj = db.session.query(CSS).filter_by(template_name='Flat').first()
if not obj:
obj = CSS(template_name="Flat")
css = textwrap.dedent("""\
.gridster li.widget {
transition: background-color 0.5s ease;
background-color: #FAFAFA;
border: 1px solid #CCC;
overflow: hidden;
box-shadow: none;
border-radius: 0px;
}
.gridster li.widget:hover {
border: 1px solid #000;
background-color: #EAEAEA;
}
.navbar {
transition: opacity 0.5s ease;
opacity: 0.05;
}
.navbar:hover {
opacity: 1;
}
.chart-header .header{
font-weight: normal;
font-size: 12px;
}
/*
var bnbColors = [
//rausch hackb kazan babu lima beach tirol
'#ff5a5f', '#7b0051', '#007A87', '#00d1c1', '#8ce071', '#ffb400', '#b4a76c',
'#ff8083', '#cc0086', '#00a1b3', '#00ffeb', '#bbedab', '#ffd266', '#cbc29a',
'#ff3339', '#ff1ab1', '#005c66', '#00b3a5', '#55d12e', '#b37e00', '#988b4e',
];
*/
""")
obj.css = css
db.session.merge(obj)
db.session.commit()
obj = (
db.session.query(CSS).filter_by(template_name='Courier Black').first())
if not obj:
obj = CSS(template_name="Courier Black")
css = textwrap.dedent("""\
.gridster li.widget {
transition: background-color 0.5s ease;
background-color: #EEE;
border: 2px solid #444;
overflow: hidden;
border-radius: 15px;
box-shadow: none;
}
h2 {
color: white;
font-size: 52px;
}
.navbar {
box-shadow: none;
}
.gridster li.widget:hover {
border: 2px solid #000;
background-color: #EAEAEA;
}
.navbar {
transition: opacity 0.5s ease;
opacity: 0.05;
}
.navbar:hover {
opacity: 1;
}
.chart-header .header{
font-weight: normal;
font-size: 12px;
}
.nvd3 text {
font-size: 12px;
font-family: inherit;
}
body{
background: #000;
font-family: Courier, Monaco, monospace;;
}
/*
var bnbColors = [
//rausch hackb kazan babu lima beach tirol
'#ff5a5f', '#7b0051', '#007A87', '#00d1c1', '#8ce071', '#ffb400', '#b4a76c',
'#ff8083', '#cc0086', '#00a1b3', '#00ffeb', '#bbedab', '#ffd266', '#cbc29a',
'#ff3339', '#ff1ab1', '#005c66', '#00b3a5', '#55d12e', '#b37e00', '#988b4e',
];
*/
""")
obj.css = css
db.session.merge(obj)
db.session.commit()
def load_birth_names():
with gzip.open(os.path.join(DATA_FOLDER, 'birth_names.json.gz')) as f:
pdf = pd.read_json(f)
pdf.ds = pd.to_datetime(pdf.ds, unit='ms')
pdf.to_sql(
'birth_names',
db.engine,
if_exists='replace',
chunksize=500,
dtype={
'ds': DateTime,
'gender': String(16),
'state': String(10),
'name': String(255),
},
index=False)
l = []
print("Done loading table!")
print("-" * 80)
print("Creating table reference")
obj = db.session.query(TBL).filter_by(table_name='birth_names').first()
if not obj:
obj = TBL(table_name = 'birth_names')
obj.main_dttm_col = 'ds'
obj.database = get_or_create_db(db.session)
obj.is_featured = True
db.session.merge(obj)
db.session.commit()
obj.fetch_metadata()
tbl = obj
defaults = {
"compare_lag": "10",
"compare_suffix": "o10Y",
"datasource_id": "1",
"datasource_name": "birth_names",
"datasource_type": "table",
"flt_op_1": "in",
"limit": "25",
"granularity": "ds",
"groupby": [],
"metric": 'sum__num',
"metrics": ["sum__num"],
"row_limit": config.get("ROW_LIMIT"),
"since": "100 years ago",
"until": "now",
"viz_type": "table",
"where": "",
"markup_type": "markdown",
}
print("Creating some slices")
slices = [
Slice(
slice_name="Girls",
viz_type='table',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
groupby=['name'],
flt_col_1='gender',
flt_eq_1="girl", row_limit=50)),
Slice(
slice_name="Boys",
viz_type='table',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
groupby=['name'],
flt_col_1='gender',
flt_eq_1="boy",
row_limit=50)),
Slice(
slice_name="Participants",
viz_type='big_number',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
viz_type="big_number", granularity="ds",
compare_lag="5", compare_suffix="over 5Y")),
Slice(
slice_name="Genders",
viz_type='pie',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
viz_type="pie", groupby=['gender'])),
Slice(
slice_name="Genders by State",
viz_type='dist_bar',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
flt_eq_1="other", viz_type="dist_bar",
metrics=['sum__sum_girls', 'sum__sum_boys'],
groupby=['state'], flt_op_1='not in', flt_col_1='state')),
Slice(
slice_name="Trends",
viz_type='line',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
viz_type="line", groupby=['name'],
granularity='ds', rich_tooltip='y', show_legend='y')),
Slice(
slice_name="Title",
viz_type='markup',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
viz_type="markup", markup_type="html",
code="""\
<div style="text-align:center">
<h1>Birth Names Dashboard</h1>
<p>
The source dataset came from
<a href="https://github.com/hadley/babynames">[here]</a>
</p>
<img src="http://monblog.system-linux.net/image/tux/baby-tux_overlord59-tux.png">
</div>
"""
)),
Slice(
slice_name="Name Cloud",
viz_type='word_cloud',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
viz_type="word_cloud", size_from="10",
series='name', size_to="70", rotation="square",
limit='100')),
Slice(
slice_name="Pivot Table",
viz_type='pivot_table',
datasource_type='table',
table=tbl,
params=get_slice_json(
defaults,
viz_type="pivot_table", metrics=['sum__num'],
groupby=['name'], columns=['state'])),
]
for slc in slices:
merge_slice(slc)
print("Creating a dashboard")
dash = db.session.query(Dash).filter_by(dashboard_title="Births").first()
if dash:
db.session.delete(dash)
js = """
[
{
"size_y": 4,
"size_x": 2,
"col": 8,
"slice_id": "85",
"row": 7
},
{
"size_y": 4,
"size_x": 2,
"col": 10,
"slice_id": "86",
"row": 7
},
{
"size_y": 2,
"size_x": 2,
"col": 1,
"slice_id": "87",
"row": 1
},
{
"size_y": 2,
"size_x": 2,
"col": 3,
"slice_id": "88",
"row": 1
},
{
"size_y": 3,
"size_x": 7,
"col": 5,
"slice_id": "89",
"row": 4
},
{
"size_y": 4,
"size_x": 7,
"col": 1,
"slice_id": "90",
"row": 7
},
{
"size_y": 3,
"size_x": 3,
"col": 9,
"slice_id": "91",
"row": 1
},
{
"size_y": 3,
"size_x": 4,
"col": 5,
"slice_id": "92",
"row": 1
},
{
"size_y": 4,
"size_x": 4,
"col": 1,
"slice_id": "93",
"row": 3
}
]
"""
l = json.loads(js)
for i, pos in enumerate(l):
pos['slice_id'] = str(slices[i].id)
dash = Dash(
dashboard_title="Births",
position_json=json.dumps(l, indent=4),
slug="births",
)
for s in slices:
dash.slices.append(s)
db.session.commit()

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This data was download from the
[World's Health Organization's website](http://data.worldbank.org/data-catalog/health-nutrition-and-population-statistics)
Here's the script that was used to massage the data:
DIR = ""
df_country = pd.read_csv(DIR + '/HNP_Country.csv')
df_country.columns = ['country_code'] + list(df_country.columns[1:])
df_country = df_country[['country_code', 'Region']]
df_country.columns = ['country_code', 'region']
df = pd.read_csv(DIR + '/HNP_Data.csv')
del df['Unnamed: 60']
df.columns = ['country_name', 'country_code'] + list(df.columns[2:])
ndf = df.merge(df_country, how='inner')
dims = ('country_name', 'country_code', 'region')
vv = [str(i) for i in range(1960, 2015)]
mdf = pd.melt(ndf, id_vars=dims + ('Indicator Code',), value_vars=vv)
mdf['year'] = mdf.variable + '-01-01'
dims = dims + ('year',)
pdf = mdf.pivot_table(values='value', columns='Indicator Code', index=dims)
pdf = pdf.reset_index()
pdf.to_csv(DIR + '/countries.csv')
pdf.to_json(DIR + '/countries.json', orient='records')
Here's the description of the metrics available:
Series | Code Indicator Name
--- | ---
NY.GNP.PCAP.CD | GNI per capita, Atlas method (current US$)
SE.ADT.1524.LT.FM.ZS | Literacy rate, youth (ages 15-24), gender parity index (GPI)
SE.ADT.1524.LT.MA.ZS | Literacy rate, youth male (% of males ages 15-24)
SE.ADT.1524.LT.ZS | Literacy rate, youth total (% of people ages 15-24)
SE.ADT.LITR.FE.ZS | Literacy rate, adult female (% of females ages 15 and above)
SE.ADT.LITR.MA.ZS | Literacy rate, adult male (% of males ages 15 and above)
SE.ADT.LITR.ZS | Literacy rate, adult total (% of people ages 15 and above)
SE.ENR.ORPH | Ratio of school attendance of orphans to school attendance of non-orphans ages 10-14
SE.PRM.CMPT.FE.ZS | Primary completion rate, female (% of relevant age group)
SE.PRM.CMPT.MA.ZS | Primary completion rate, male (% of relevant age group)
SE.PRM.CMPT.ZS | Primary completion rate, total (% of relevant age group)
SE.PRM.ENRR | School enrollment, primary (% gross)
SE.PRM.ENRR.FE | School enrollment, primary, female (% gross)
SE.PRM.ENRR.MA | School enrollment, primary, male (% gross)
SE.PRM.NENR | School enrollment, primary (% net)
SE.PRM.NENR.FE | School enrollment, primary, female (% net)
SE.PRM.NENR.MA | School enrollment, primary, male (% net)
SE.SEC.ENRR | School enrollment, secondary (% gross)
SE.SEC.ENRR.FE | School enrollment, secondary, female (% gross)
SE.SEC.ENRR.MA | School enrollment, secondary, male (% gross)
SE.SEC.NENR | School enrollment, secondary (% net)
SE.SEC.NENR.FE | School enrollment, secondary, female (% net)
SE.SEC.NENR.MA | School enrollment, secondary, male (% net)
SE.TER.ENRR | School enrollment, tertiary (% gross)
SE.TER.ENRR.FE | School enrollment, tertiary, female (% gross)
SE.XPD.TOTL.GD.ZS | Government expenditure on education, total (% of GDP)
SH.ANM.CHLD.ZS | Prevalence of anemia among children (% of children under 5)
SH.ANM.NPRG.ZS | Prevalence of anemia among non-pregnant women (% of women ages 15-49)
SH.CON.1524.FE.ZS | Condom use, population ages 15-24, female (% of females ages 15-24)
SH.CON.1524.MA.ZS | Condom use, population ages 15-24, male (% of males ages 15-24)
SH.CON.AIDS.FE.ZS | Condom use at last high-risk sex, adult female (% ages 15-49)
SH.CON.AIDS.MA.ZS | Condom use at last high-risk sex, adult male (% ages 15-49)
SH.DTH.COMM.ZS | Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)
SH.DTH.IMRT | Number of infant deaths
SH.DTH.INJR.ZS | Cause of death, by injury (% of total)
SH.DTH.MORT | Number of under-five deaths
SH.DTH.NCOM.ZS | Cause of death, by non-communicable diseases (% of total)
SH.DTH.NMRT | Number of neonatal deaths
SH.DYN.AIDS | Adults (ages 15+) living with HIV
SH.DYN.AIDS.DH | AIDS estimated deaths (UNAIDS estimates)
SH.DYN.AIDS.FE.ZS | Women's share of population ages 15+ living with HIV (%)
SH.DYN.AIDS.ZS | Prevalence of HIV, total (% of population ages 15-49)
SH.DYN.MORT | Mortality rate, under-5 (per 1,000 live births)
SH.DYN.MORT.FE | Mortality rate, under-5, female (per 1,000 live births)
SH.DYN.MORT.MA | Mortality rate, under-5, male (per 1,000 live births)
SH.DYN.NMRT | Mortality rate, neonatal (per 1,000 live births)
SH.FPL.SATI.ZS | Met need for contraception (% of married women ages 15-49)
SH.H2O.SAFE.RU.ZS | Improved water source, rural (% of rural population with access)
SH.H2O.SAFE.UR.ZS | Improved water source, urban (% of urban population with access)
SH.H2O.SAFE.ZS | Improved water source (% of population with access)
SH.HIV.0014 | Children (0-14) living with HIV
SH.HIV.1524.FE.ZS | Prevalence of HIV, female (% ages 15-24)
SH.HIV.1524.KW.FE.ZS | Comprehensive correct knowledge of HIV/AIDS, ages 15-24, female (2 prevent ways and reject 3 misconceptions)
SH.HIV.1524.KW.MA.ZS | Comprehensive correct knowledge of HIV/AIDS, ages 15-24, male (2 prevent ways and reject 3 misconceptions)
SH.HIV.1524.MA.ZS | Prevalence of HIV, male (% ages 15-24)
SH.HIV.ARTC.ZS | Antiretroviral therapy coverage (% of people living with HIV)
SH.HIV.KNOW.FE.ZS | % of females ages 15-49 having comprehensive correct knowledge about HIV (2 prevent ways and reject 3 misconceptions)
SH.HIV.KNOW.MA.ZS | % of males ages 15-49 having comprehensive correct knowledge about HIV (2 prevent ways and reject 3 misconceptions)
SH.HIV.ORPH | Children orphaned by HIV/AIDS
SH.HIV.TOTL | Adults (ages 15+) and children (0-14 years) living with HIV
SH.IMM.HEPB | Immunization, HepB3 (% of one-year-old children)
SH.IMM.HIB3 | Immunization, Hib3 (% of children ages 12-23 months)
SH.IMM.IBCG | Immunization, BCG (% of one-year-old children)
SH.IMM.IDPT | Immunization, DPT (% of children ages 12-23 months)
SH.IMM.MEAS | Immunization, measles (% of children ages 12-23 months)
SH.IMM.POL3 | Immunization, Pol3 (% of one-year-old children)
SH.MED.BEDS.ZS | Hospital beds (per 1,000 people)
SH.MED.CMHW.P3 | Community health workers (per 1,000 people)
SH.MED.NUMW.P3 | Nurses and midwives (per 1,000 people)
SH.MED.PHYS.ZS | Physicians (per 1,000 people)
SH.MLR.NETS.ZS | Use of insecticide-treated bed nets (% of under-5 population)
SH.MLR.PREG.ZS | Use of any antimalarial drug (% of pregnant women)
SH.MLR.SPF2.ZS | Use of Intermittent Preventive Treatment of malaria, 2+ doses of SP/Fansidar (% of pregnant women)
SH.MLR.TRET.ZS | Children with fever receiving antimalarial drugs (% of children under age 5 with fever)
SH.MMR.DTHS | Number of maternal deaths
SH.MMR.LEVE | Number of weeks of maternity leave
SH.MMR.RISK | Lifetime risk of maternal death (1 in: rate varies by country)
SH.MMR.RISK.ZS | Lifetime risk of maternal death (%)
SH.MMR.WAGE.ZS | Maternal leave benefits (% of wages paid in covered period)
SH.PRG.ANEM | Prevalence of anemia among pregnant women (%)
SH.PRG.ARTC.ZS | Antiretroviral therapy coverage (% of pregnant women living with HIV)
SH.PRG.SYPH.ZS | Prevalence of syphilis (% of women attending antenatal care)
SH.PRV.SMOK.FE | Smoking prevalence, females (% of adults)
SH.PRV.SMOK.MA | Smoking prevalence, males (% of adults)
SH.STA.ACSN | Improved sanitation facilities (% of population with access)
SH.STA.ACSN.RU | Improved sanitation facilities, rural (% of rural population with access)
SH.STA.ACSN.UR | Improved sanitation facilities, urban (% of urban population with access)
SH.STA.ANV4.ZS | Pregnant women receiving prenatal care of at least four visits (% of pregnant women)
SH.STA.ANVC.ZS | Pregnant women receiving prenatal care (%)
SH.STA.ARIC.ZS | ARI treatment (% of children under 5 taken to a health provider)
SH.STA.BFED.ZS | Exclusive breastfeeding (% of children under 6 months)
SH.STA.BRTC.ZS | Births attended by skilled health staff (% of total)
SH.STA.BRTW.ZS | Low-birthweight babies (% of births)
SH.STA.DIAB.ZS | Diabetes prevalence (% of population ages 20 to 79)
SH.STA.IYCF.ZS | Infant and young child feeding practices, all 3 IYCF (% children ages 6-23 months)
SH.STA.MALN.FE.ZS | Prevalence of underweight, weight for age, female (% of children under 5)
SH.STA.MALN.MA.ZS | Prevalence of underweight, weight for age, male (% of children under 5)
SH.STA.MALN.ZS | Prevalence of underweight, weight for age (% of children under 5)
SH.STA.MALR | Malaria cases reported
SH.STA.MMRT | Maternal mortality ratio (modeled estimate, per 100,000 live births)
SH.STA.MMRT.NE | Maternal mortality ratio (national estimate, per 100,000 live births)
SH.STA.ORCF.ZS | Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding)
SH.STA.ORTH | Diarrhea treatment (% of children under 5 who received ORS packet)
SH.STA.OW15.FE.ZS | Prevalence of overweight, female (% of female adults)
SH.STA.OW15.MA.ZS | Prevalence of overweight, male (% of male adults)
SH.STA.OW15.ZS | Prevalence of overweight (% of adults)
SH.STA.OWGH.FE.ZS | Prevalence of overweight, weight for height, female (% of children under 5)
SH.STA.OWGH.MA.ZS | Prevalence of overweight, weight for height, male (% of children under 5)
SH.STA.OWGH.ZS | Prevalence of overweight, weight for height (% of children under 5)
SH.STA.PNVC.ZS | Postnatal care coverage (% mothers)
SH.STA.STNT.FE.ZS | Prevalence of stunting, height for age, female (% of children under 5)
SH.STA.STNT.MA.ZS | Prevalence of stunting, height for age, male (% of children under 5)
SH.STA.STNT.ZS | Prevalence of stunting, height for age (% of children under 5)
SH.STA.WAST.FE.ZS | Prevalence of wasting, weight for height, female (% of children under 5)
SH.STA.WAST.MA.ZS | Prevalence of wasting, weight for height, male (% of children under 5)
SH.STA.WAST.ZS | Prevalence of wasting, weight for height (% of children under 5)
SH.SVR.WAST.FE.ZS | Prevalence of severe wasting, weight for height, female (% of children under 5)
SH.SVR.WAST.MA.ZS | Prevalence of severe wasting, weight for height, male (% of children under 5)
SH.SVR.WAST.ZS | Prevalence of severe wasting, weight for height (% of children under 5)
SH.TBS.CURE.ZS | Tuberculosis treatment success rate (% of new cases)
SH.TBS.DTEC.ZS | Tuberculosis case detection rate (%, all forms)
SH.TBS.INCD | Incidence of tuberculosis (per 100,000 people)
SH.TBS.MORT | Tuberculosis death rate (per 100,000 people)
SH.TBS.PREV | Prevalence of tuberculosis (per 100,000 population)
SH.VAC.TTNS.ZS | Newborns protected against tetanus (%)
SH.XPD.EXTR.ZS | External resources for health (% of total expenditure on health)
SH.XPD.OOPC.TO.ZS | Out-of-pocket health expenditure (% of total expenditure on health)
SH.XPD.OOPC.ZS | Out-of-pocket health expenditure (% of private expenditure on health)
SH.XPD.PCAP | Health expenditure per capita (current US$)
SH.XPD.PCAP.PP.KD | Health expenditure per capita, PPP (constant 2011 international $)
SH.XPD.PRIV | Health expenditure, private (% of total health expenditure)
SH.XPD.PRIV.ZS | Health expenditure, private (% of GDP)
SH.XPD.PUBL | Health expenditure, public (% of total health expenditure)
SH.XPD.PUBL.GX.ZS | Health expenditure, public (% of government expenditure)
SH.XPD.PUBL.ZS | Health expenditure, public (% of GDP)
SH.XPD.TOTL.CD | Health expenditure, total (current US$)
SH.XPD.TOTL.ZS | Health expenditure, total (% of GDP)
SI.POV.NAHC | Poverty headcount ratio at national poverty lines (% of population)
SI.POV.RUHC | Rural poverty headcount ratio at national poverty lines (% of rural population)
SI.POV.URHC | Urban poverty headcount ratio at national poverty lines (% of urban population)
SL.EMP.INSV.FE.ZS | Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)
SL.TLF.TOTL.FE.ZS | Labor force, female (% of total labor force)
SL.TLF.TOTL.IN | Labor force, total
SL.UEM.TOTL.FE.ZS | Unemployment, female (% of female labor force) (modeled ILO estimate)
SL.UEM.TOTL.MA.ZS | Unemployment, male (% of male labor force) (modeled ILO estimate)
SL.UEM.TOTL.ZS | Unemployment, total (% of total labor force) (modeled ILO estimate)
SM.POP.NETM | Net migration
SN.ITK.DEFC | Number of people who are undernourished
SN.ITK.DEFC.ZS | Prevalence of undernourishment (% of population)
SN.ITK.SALT.ZS | Consumption of iodized salt (% of households)
SN.ITK.VITA.ZS | Vitamin A supplementation coverage rate (% of children ages 6-59 months)
SP.ADO.TFRT | Adolescent fertility rate (births per 1,000 women ages 15-19)
SP.DYN.AMRT.FE | Mortality rate, adult, female (per 1,000 female adults)
SP.DYN.AMRT.MA | Mortality rate, adult, male (per 1,000 male adults)
SP.DYN.CBRT.IN | Birth rate, crude (per 1,000 people)
SP.DYN.CDRT.IN | Death rate, crude (per 1,000 people)
SP.DYN.CONU.ZS | Contraceptive prevalence (% of women ages 15-49)
SP.DYN.IMRT.FE.IN | Mortality rate, infant, female (per 1,000 live births)
SP.DYN.IMRT.IN | Mortality rate, infant (per 1,000 live births)
SP.DYN.IMRT.MA.IN | Mortality rate, infant, male (per 1,000 live births)
SP.DYN.LE00.FE.IN | Life expectancy at birth, female (years)
SP.DYN.LE00.IN | Life expectancy at birth, total (years)
SP.DYN.LE00.MA.IN | Life expectancy at birth, male (years)
SP.DYN.SMAM.FE | Mean age at first marriage, female
SP.DYN.SMAM.MA | Mean age at first marriage, male
SP.DYN.TFRT.IN | Fertility rate, total (births per woman)
SP.DYN.TO65.FE.ZS | Survival to age 65, female (% of cohort)
SP.DYN.TO65.MA.ZS | Survival to age 65, male (% of cohort)
SP.DYN.WFRT | Wanted fertility rate (births per woman)
SP.HOU.FEMA.ZS | Female headed households (% of households with a female head)
SP.MTR.1519.ZS | Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant)
SP.POP.0004.FE | Population ages 0-4, female
SP.POP.0004.FE.5Y | Population ages 0-4, female (% of female population)
SP.POP.0004.MA | Population ages 0-4, male
SP.POP.0004.MA.5Y | Population ages 0-4, male (% of male population)
SP.POP.0014.FE.ZS | Population ages 0-14, female (% of total)
SP.POP.0014.MA.ZS | Population ages 0-14, male (% of total)
SP.POP.0014.TO | Population ages 0-14, total
SP.POP.0014.TO.ZS | Population ages 0-14 (% of total)
SP.POP.0509.FE | Population ages 5-9, female
SP.POP.0509.FE.5Y | Population ages 5-9, female (% of female population)
SP.POP.0509.MA | Population ages 5-9, male
SP.POP.0509.MA.5Y | Population ages 5-9, male (% of male population)
SP.POP.1014.FE | Population ages 10-14, female
SP.POP.1014.FE.5Y | Population ages 10-14, female (% of female population)
SP.POP.1014.MA | Population ages 10-14, male
SP.POP.1014.MA.5Y | Population ages 10-14, male (% of male population)
SP.POP.1519.FE | Population ages 15-19, female
SP.POP.1519.FE.5Y | Population ages 15-19, female (% of female population)
SP.POP.1519.MA | Population ages 15-19, male
SP.POP.1519.MA.5Y | Population ages 15-19, male (% of male population)
SP.POP.1564.FE.ZS | Population ages 15-64, female (% of total)
SP.POP.1564.MA.ZS | Population ages 15-64, male (% of total)
SP.POP.1564.TO | Population ages 15-64, total
SP.POP.1564.TO.ZS | Population ages 15-64 (% of total)
SP.POP.2024.FE | Population ages 20-24, female
SP.POP.2024.FE.5Y | Population ages 20-24, female (% of female population)
SP.POP.2024.MA | Population ages 20-24, male
SP.POP.2024.MA.5Y | Population ages 20-24, male (% of male population)
SP.POP.2529.FE | Population ages 25-29, female
SP.POP.2529.FE.5Y | Population ages 25-29, female (% of female population)
SP.POP.2529.MA | Population ages 25-29, male
SP.POP.2529.MA.5Y | Population ages 25-29, male (% of male population)
SP.POP.3034.FE | Population ages 30-34, female
SP.POP.3034.FE.5Y | Population ages 30-34, female (% of female population)
SP.POP.3034.MA | Population ages 30-34, male
SP.POP.3034.MA.5Y | Population ages 30-34, male (% of male population)
SP.POP.3539.FE | Population ages 35-39, female
SP.POP.3539.FE.5Y | Population ages 35-39, female (% of female population)
SP.POP.3539.MA | Population ages 35-39, male
SP.POP.3539.MA.5Y | Population ages 35-39, male (% of male population)
SP.POP.4044.FE | Population ages 40-44, female
SP.POP.4044.FE.5Y | Population ages 40-44, female (% of female population)
SP.POP.4044.MA | Population ages 40-44, male
SP.POP.4044.MA.5Y | Population ages 40-44, male (% of male population)
SP.POP.4549.FE | Population ages 45-49, female
SP.POP.4549.FE.5Y | Population ages 45-49, female (% of female population)
SP.POP.4549.MA | Population ages 45-49, male
SP.POP.4549.MA.5Y | Population ages 45-49, male (% of male population)
SP.POP.5054.FE | Population ages 50-54, female
SP.POP.5054.FE.5Y | Population ages 50-54, female (% of female population)
SP.POP.5054.MA | Population ages 50-54, male
SP.POP.5054.MA.5Y | Population ages 50-54, male (% of male population)
SP.POP.5559.FE | Population ages 55-59, female
SP.POP.5559.FE.5Y | Population ages 55-59, female (% of female population)
SP.POP.5559.MA | Population ages 55-59, male
SP.POP.5559.MA.5Y | Population ages 55-59, male (% of male population)
SP.POP.6064.FE | Population ages 60-64, female
SP.POP.6064.FE.5Y | Population ages 60-64, female (% of female population)
SP.POP.6064.MA | Population ages 60-64, male
SP.POP.6064.MA.5Y | Population ages 60-64, male (% of male population)
SP.POP.6569.FE | Population ages 65-69, female
SP.POP.6569.FE.5Y | Population ages 65-69, female (% of female population)
SP.POP.6569.MA | Population ages 65-69, male
SP.POP.6569.MA.5Y | Population ages 65-69, male (% of male population)
SP.POP.65UP.FE.ZS | Population ages 65 and above, female (% of total)
SP.POP.65UP.MA.ZS | Population ages 65 and above, male (% of total)
SP.POP.65UP.TO | Population ages 65 and above, total
SP.POP.65UP.TO.ZS | Population ages 65 and above (% of total)
SP.POP.7074.FE | Population ages 70-74, female
SP.POP.7074.FE.5Y | Population ages 70-74, female (% of female population)
SP.POP.7074.MA | Population ages 70-74, male
SP.POP.7074.MA.5Y | Population ages 70-74, male (% of male population)
SP.POP.7579.FE | Population ages 75-79, female
SP.POP.7579.FE.5Y | Population ages 75-79, female (% of female population)
SP.POP.7579.MA | Population ages 75-79, male
SP.POP.7579.MA.5Y | Population ages 75-79, male (% of male population)
SP.POP.80UP.FE | Population ages 80 and above, female
SP.POP.80UP.FE.5Y | Population ages 80 and above, female (% of female population)
SP.POP.80UP.MA | Population ages 80 and above, male
SP.POP.80UP.MA.5Y | Population ages 80 and above, male (% of male population)
SP.POP.AG00.FE.IN | Age population, age 0, female, interpolated
SP.POP.AG00.MA.IN | Age population, age 0, male, interpolated
SP.POP.AG01.FE.IN | Age population, age 01, female, interpolated
SP.POP.AG01.MA.IN | Age population, age 01, male, interpolated
SP.POP.AG02.FE.IN | Age population, age 02, female, interpolated
SP.POP.AG02.MA.IN | Age population, age 02, male, interpolated
SP.POP.AG03.FE.IN | Age population, age 03, female, interpolated
SP.POP.AG03.MA.IN | Age population, age 03, male, interpolated
SP.POP.AG04.FE.IN | Age population, age 04, female, interpolated
SP.POP.AG04.MA.IN | Age population, age 04, male, interpolated
SP.POP.AG05.FE.IN | Age population, age 05, female, interpolated
SP.POP.AG05.MA.IN | Age population, age 05, male, interpolated
SP.POP.AG06.FE.IN | Age population, age 06, female, interpolated
SP.POP.AG06.MA.IN | Age population, age 06, male, interpolated
SP.POP.AG07.FE.IN | Age population, age 07, female, interpolated
SP.POP.AG07.MA.IN | Age population, age 07, male, interpolated
SP.POP.AG08.FE.IN | Age population, age 08, female, interpolated
SP.POP.AG08.MA.IN | Age population, age 08, male, interpolated
SP.POP.AG09.FE.IN | Age population, age 09, female, interpolated
SP.POP.AG09.MA.IN | Age population, age 09, male, interpolated
SP.POP.AG10.FE.IN | Age population, age 10, female, interpolated
SP.POP.AG10.MA.IN | Age population, age 10, male
SP.POP.AG11.FE.IN | Age population, age 11, female, interpolated
SP.POP.AG11.MA.IN | Age population, age 11, male
SP.POP.AG12.FE.IN | Age population, age 12, female, interpolated
SP.POP.AG12.MA.IN | Age population, age 12, male
SP.POP.AG13.FE.IN | Age population, age 13, female, interpolated
SP.POP.AG13.MA.IN | Age population, age 13, male
SP.POP.AG14.FE.IN | Age population, age 14, female, interpolated
SP.POP.AG14.MA.IN | Age population, age 14, male
SP.POP.AG15.FE.IN | Age population, age 15, female, interpolated
SP.POP.AG15.MA.IN | Age population, age 15, male, interpolated
SP.POP.AG16.FE.IN | Age population, age 16, female, interpolated
SP.POP.AG16.MA.IN | Age population, age 16, male, interpolated
SP.POP.AG17.FE.IN | Age population, age 17, female, interpolated
SP.POP.AG17.MA.IN | Age population, age 17, male, interpolated
SP.POP.AG18.FE.IN | Age population, age 18, female, interpolated
SP.POP.AG18.MA.IN | Age population, age 18, male, interpolated
SP.POP.AG19.FE.IN | Age population, age 19, female, interpolated
SP.POP.AG19.MA.IN | Age population, age 19, male, interpolated
SP.POP.AG20.FE.IN | Age population, age 20, female, interpolated
SP.POP.AG20.MA.IN | Age population, age 20, male, interpolated
SP.POP.AG21.FE.IN | Age population, age 21, female, interpolated
SP.POP.AG21.MA.IN | Age population, age 21, male, interpolated
SP.POP.AG22.FE.IN | Age population, age 22, female, interpolated
SP.POP.AG22.MA.IN | Age population, age 22, male, interpolated
SP.POP.AG23.FE.IN | Age population, age 23, female, interpolated
SP.POP.AG23.MA.IN | Age population, age 23, male, interpolated
SP.POP.AG24.FE.IN | Age population, age 24, female, interpolated
SP.POP.AG24.MA.IN | Age population, age 24, male, interpolated
SP.POP.AG25.FE.IN | Age population, age 25, female, interpolated
SP.POP.AG25.MA.IN | Age population, age 25, male, interpolated
SP.POP.BRTH.MF | Sex ratio at birth (male births per female births)
SP.POP.DPND | Age dependency ratio (% of working-age population)
SP.POP.DPND.OL | Age dependency ratio, old (% of working-age population)
SP.POP.DPND.YG | Age dependency ratio, young (% of working-age population)
SP.POP.GROW | Population growth (annual %)
SP.POP.TOTL | Population, total
SP.POP.TOTL.FE.IN | Population, female
SP.POP.TOTL.FE.ZS | Population, female (% of total)
SP.POP.TOTL.MA.IN | Population, male
SP.POP.TOTL.MA.ZS | Population, male (% of total)
SP.REG.BRTH.RU.ZS | Completeness of birth registration, rural (%)
SP.REG.BRTH.UR.ZS | Completeness of birth registration, urban (%)
SP.REG.BRTH.ZS | Completeness of birth registration (%)
SP.REG.DTHS.ZS | Completeness of death registration with cause-of-death information (%)
SP.RUR.TOTL | Rural population
SP.RUR.TOTL.ZG | Rural population growth (annual %)
SP.RUR.TOTL.ZS | Rural population (% of total population)
SP.URB.GROW | Urban population growth (annual %)
SP.URB.TOTL | Urban population
SP.URB.TOTL.IN.ZS | Urban population (% of total)
SP.UWT.TFRT | Unmet need for contraception (% of married women ages 15-49)

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