superset/examples/world_bank.py
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# distributed with this work for additional information
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# 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
#
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# software distributed under the License is distributed on an
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# KIND, either express or implied. See the License for the
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import os
import pandas as pd
from sqlalchemy import DateTime, inspect, String
from sqlalchemy.sql import column
import superset.utils.database
from superset import app, db
from superset.connectors.sqla.models import BaseDatasource, SqlMetric
from superset.examples.helpers import (
get_example_url,
get_examples_folder,
get_slice_json,
get_table_connector_registry,
merge_slice,
misc_dash_slices,
update_slice_ids,
)
from superset.models.dashboard import Dashboard
from superset.models.slice import Slice
from superset.sql_parse import Table
from superset.utils import core as utils, json
from superset.utils.core import DatasourceType
def load_world_bank_health_n_pop( # pylint: disable=too-many-locals
only_metadata: bool = False,
force: bool = False,
sample: bool = False,
) -> None:
"""Loads the world bank health dataset, slices and a dashboard"""
tbl_name = "wb_health_population"
database = superset.utils.database.get_example_database()
with database.get_sqla_engine() as engine:
schema = inspect(engine).default_schema_name
table_exists = database.has_table(Table(tbl_name, schema))
if not only_metadata and (not table_exists or force):
url = get_example_url("countries.json.gz")
pdf = pd.read_json(url, compression="gzip")
pdf.columns = [col.replace(".", "_") for col in pdf.columns]
if database.backend == "presto":
pdf.year = pd.to_datetime(pdf.year)
pdf.year = pdf.year.dt.strftime("%Y-%m-%d %H:%M%:%S")
else:
pdf.year = pd.to_datetime(pdf.year)
pdf = pdf.head(100) if sample else pdf
pdf.to_sql(
tbl_name,
engine,
schema=schema,
if_exists="replace",
chunksize=50,
dtype={
# TODO(bkyryliuk): use TIMESTAMP type for presto
"year": DateTime if database.backend != "presto" else String(255),
"country_code": String(3),
"country_name": String(255),
"region": String(255),
},
method="multi",
index=False,
)
print("Creating table [wb_health_population] reference")
table = get_table_connector_registry()
tbl = db.session.query(table).filter_by(table_name=tbl_name).first()
if not tbl:
tbl = table(table_name=tbl_name, schema=schema)
db.session.add(tbl)
tbl.description = utils.readfile(
os.path.join(get_examples_folder(), "countries.md")
)
tbl.main_dttm_col = "year"
tbl.database = database
tbl.filter_select_enabled = True
metrics = [
"sum__SP_POP_TOTL",
"sum__SH_DYN_AIDS",
"sum__SH_DYN_AIDS",
"sum__SP_RUR_TOTL_ZS",
"sum__SP_DYN_LE00_IN",
"sum__SP_RUR_TOTL",
]
for metric in metrics:
if not any(col.metric_name == metric for col in tbl.metrics):
aggr_func = metric[:3]
col = str(column(metric[5:]).compile(db.engine))
tbl.metrics.append(
SqlMetric(metric_name=metric, expression=f"{aggr_func}({col})")
)
tbl.fetch_metadata()
slices = create_slices(tbl)
misc_dash_slices.add(slices[-1].slice_name)
for slc in slices:
merge_slice(slc)
print("Creating a World's Health Bank dashboard")
dash_name = "World Bank's Data"
slug = "world_health"
dash = db.session.query(Dashboard).filter_by(slug=slug).first()
if not dash:
dash = Dashboard()
db.session.add(dash)
dash.published = True
pos = dashboard_positions
slices = update_slice_ids(pos)
dash.dashboard_title = dash_name
dash.position_json = json.dumps(pos, indent=4)
dash.slug = slug
dash.slices = slices
def create_slices(tbl: BaseDatasource) -> list[Slice]:
metric = "sum__SP_POP_TOTL"
metrics = ["sum__SP_POP_TOTL"]
secondary_metric = {
"aggregate": "SUM",
"column": {
"column_name": "SP_RUR_TOTL",
"optionName": "_col_SP_RUR_TOTL",
"type": "DOUBLE",
},
"expressionType": "SIMPLE",
"hasCustomLabel": True,
"label": "Rural Population",
}
defaults = {
"compare_lag": "10",
"compare_suffix": "o10Y",
"limit": "25",
"granularity_sqla": "year",
"groupby": [],
"row_limit": app.config["ROW_LIMIT"],
"since": "2014-01-01",
"until": "2014-01-02",
"time_range": "2014-01-01 : 2014-01-02",
"markup_type": "markdown",
"country_fieldtype": "cca3",
"entity": "country_code",
"show_bubbles": True,
}
return [
Slice(
slice_name="World's Population",
viz_type="big_number",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
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=DatasourceType.TABLE,
datasource_id=tbl.id,
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=DatasourceType.TABLE,
datasource_id=tbl.id,
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=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="world_map",
metric="sum__SP_RUR_TOTL_ZS",
num_period_compare="10",
secondary_metric=secondary_metric,
),
),
Slice(
slice_name="Life Expectancy VS Rural %",
viz_type="bubble",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="bubble",
since="2011-01-01",
until="2011-01-02",
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",
adhoc_filters=[
{
"clause": "WHERE",
"expressionType": "SIMPLE",
"filterOptionName": "2745eae5",
"comparator": [
"TCA",
"MNP",
"DMA",
"MHL",
"MCO",
"SXM",
"CYM",
"TUV",
"IMY",
"KNA",
"ASM",
"ADO",
"AMA",
"PLW",
],
"operator": "NOT IN",
"subject": "country_code",
}
],
),
),
Slice(
slice_name="Rural Breakdown",
viz_type="sunburst_v2",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
viz_type="sunburst_v2",
columns=["region", "country_name"],
since="2011-01-01",
until="2011-01-02",
metric=metric,
secondary_metric=secondary_metric,
),
),
Slice(
slice_name="World's Pop Growth",
viz_type="area",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since="1960-01-01",
until="now",
viz_type="area",
groupby=["region"],
metrics=metrics,
),
),
Slice(
slice_name="Box plot",
viz_type="box_plot",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since="1960-01-01",
until="now",
whisker_options="Min/max (no outliers)",
x_ticks_layout="staggered",
viz_type="box_plot",
groupby=["region"],
metrics=metrics,
),
),
Slice(
slice_name="Treemap",
viz_type="treemap_v2",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since="1960-01-01",
until="now",
viz_type="treemap_v2",
metric="sum__SP_POP_TOTL",
groupby=["region", "country_code"],
),
),
Slice(
slice_name="Parallel Coordinates",
viz_type="para",
datasource_type=DatasourceType.TABLE,
datasource_id=tbl.id,
params=get_slice_json(
defaults,
since="2011-01-01",
until="2012-01-01",
viz_type="para",
limit=100,
metrics=["sum__SP_POP_TOTL", "sum__SP_RUR_TOTL_ZS", "sum__SH_DYN_AIDS"],
secondary_metric="sum__SP_POP_TOTL",
series="country_name",
),
),
]
dashboard_positions = {
"CHART-37982887": {
"children": [],
"id": "CHART-37982887",
"meta": {
"chartId": 41,
"height": 52,
"sliceName": "World's Population",
"width": 2,
},
"type": "CHART",
},
"CHART-17e0f8d8": {
"children": [],
"id": "CHART-17e0f8d8",
"meta": {
"chartId": 42,
"height": 92,
"sliceName": "Most Populated Countries",
"width": 3,
},
"type": "CHART",
},
"CHART-2ee52f30": {
"children": [],
"id": "CHART-2ee52f30",
"meta": {"chartId": 43, "height": 38, "sliceName": "Growth Rate", "width": 6},
"type": "CHART",
},
"CHART-2d5b6871": {
"children": [],
"id": "CHART-2d5b6871",
"meta": {"chartId": 44, "height": 52, "sliceName": "% Rural", "width": 7},
"type": "CHART",
},
"CHART-0fd0d252": {
"children": [],
"id": "CHART-0fd0d252",
"meta": {
"chartId": 45,
"height": 50,
"sliceName": "Life Expectancy VS Rural %",
"width": 8,
},
"type": "CHART",
},
"CHART-97f4cb48": {
"children": [],
"id": "CHART-97f4cb48",
"meta": {
"chartId": 46,
"height": 38,
"sliceName": "Rural Breakdown",
"width": 3,
},
"type": "CHART",
},
"CHART-b5e05d6f": {
"children": [],
"id": "CHART-b5e05d6f",
"meta": {
"chartId": 47,
"height": 50,
"sliceName": "World's Pop Growth",
"width": 4,
},
"type": "CHART",
},
"CHART-e76e9f5f": {
"children": [],
"id": "CHART-e76e9f5f",
"meta": {"chartId": 48, "height": 50, "sliceName": "Box plot", "width": 4},
"type": "CHART",
},
"CHART-a4808bba": {
"children": [],
"id": "CHART-a4808bba",
"meta": {"chartId": 49, "height": 50, "sliceName": "Treemap", "width": 8},
"type": "CHART",
},
"COLUMN-071bbbad": {
"children": ["ROW-1e064e3c", "ROW-afdefba9"],
"id": "COLUMN-071bbbad",
"meta": {"background": "BACKGROUND_TRANSPARENT", "width": 9},
"type": "COLUMN",
},
"COLUMN-fe3914b8": {
"children": ["CHART-37982887"],
"id": "COLUMN-fe3914b8",
"meta": {"background": "BACKGROUND_TRANSPARENT", "width": 2},
"type": "COLUMN",
},
"GRID_ID": {
"children": ["ROW-46632bc2", "ROW-3fa26c5d", "ROW-812b3f13"],
"id": "GRID_ID",
"type": "GRID",
},
"HEADER_ID": {
"id": "HEADER_ID",
"meta": {"text": "World's Bank Data"},
"type": "HEADER",
},
"ROOT_ID": {"children": ["GRID_ID"], "id": "ROOT_ID", "type": "ROOT"},
"ROW-1e064e3c": {
"children": ["COLUMN-fe3914b8", "CHART-2d5b6871"],
"id": "ROW-1e064e3c",
"meta": {"background": "BACKGROUND_TRANSPARENT"},
"type": "ROW",
},
"ROW-3fa26c5d": {
"children": ["CHART-b5e05d6f", "CHART-0fd0d252"],
"id": "ROW-3fa26c5d",
"meta": {"background": "BACKGROUND_TRANSPARENT"},
"type": "ROW",
},
"ROW-46632bc2": {
"children": ["COLUMN-071bbbad", "CHART-17e0f8d8"],
"id": "ROW-46632bc2",
"meta": {"background": "BACKGROUND_TRANSPARENT"},
"type": "ROW",
},
"ROW-812b3f13": {
"children": ["CHART-a4808bba", "CHART-e76e9f5f"],
"id": "ROW-812b3f13",
"meta": {"background": "BACKGROUND_TRANSPARENT"},
"type": "ROW",
},
"ROW-afdefba9": {
"children": ["CHART-2ee52f30", "CHART-97f4cb48"],
"id": "ROW-afdefba9",
"meta": {"background": "BACKGROUND_TRANSPARENT"},
"type": "ROW",
},
"DASHBOARD_VERSION_KEY": "v2",
}