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=50, 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.database = get_or_create_main_db() tbl.filter_select_enabled = True db.session.merge(tbl) db.session.commit() tbl.fetch_metadata() defaults = { "compare_lag": "10", "compare_suffix": "o10Y", "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": "2014-01-02", "where": "", "markup_type": "markdown", "country_fieldtype": "cca3", "secondary_metric": "sum__SP_POP_TOTL", "entity": "country_code", "show_bubbles": True, } print("Creating slices") slices = [ Slice( slice_name="Region Filter", viz_type='filter_box', datasource_type='table', datasource_id=tbl.id, params=get_slice_json( defaults, viz_type='filter_box', groupby=['region', 'country_name'])), Slice( slice_name="World's Population", viz_type='big_number', datasource_type='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='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='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='table', datasource_id=tbl.id, params=get_slice_json( defaults, viz_type='world_map', metric="sum__SP_RUR_TOTL_ZS", num_period_compare="10")), Slice( slice_name="Life Expectancy VS Rural %", viz_type='bubble', datasource_type='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", filters=[{ "col": "country_code", "val": [ "TCA", "MNP", "DMA", "MHL", "MCO", "SXM", "CYM", "TUV", "IMY", "KNA", "ASM", "ADO", "AMA", "PLW", ], "op": "not in"}], )), Slice( slice_name="Rural Breakdown", viz_type='sunburst', datasource_type='table', datasource_id=tbl.id, 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', datasource_id=tbl.id, params=get_slice_json( defaults, since="1960-01-01", until="now", viz_type='area', groupby=["region"],)), Slice( slice_name="Box plot", viz_type='box_plot', datasource_type='table', datasource_id=tbl.id, params=get_slice_json( defaults, since="1960-01-01", until="now", whisker_options="Min/max (no outliers)", viz_type='box_plot', groupby=["region"],)), Slice( slice_name="Treemap", viz_type='treemap', datasource_type='table', datasource_id=tbl.id, params=get_slice_json( defaults, since="1960-01-01", until="now", viz_type='treemap', metrics=["sum__SP_POP_TOTL"], groupby=["region", "country_code"],)), Slice( slice_name="Parallel Coordinates", viz_type='para', datasource_type='table', datasource_id=tbl.id, params=get_slice_json( defaults, since="2011-01-01", until="2011-01-01", 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",)), ] misc_dash_slices.append(slices[-1].slice_name) for slc in slices: merge_slice(slc) print("Creating a World's Health Bank dashboard") dash_name = "World's Bank Data" slug = "world_health" dash = db.session.query(Dash).filter_by(slug=slug).first() if not dash: dash = Dash() js = textwrap.dedent("""\ [ { "col": 1, "row": 0, "size_x": 2, "size_y": 2, "slice_id": "1231" }, { "col": 1, "row": 2, "size_x": 2, "size_y": 2, "slice_id": "1232" }, { "col": 10, "row": 0, "size_x": 3, "size_y": 7, "slice_id": "1233" }, { "col": 1, "row": 4, "size_x": 6, "size_y": 3, "slice_id": "1234" }, { "col": 3, "row": 0, "size_x": 7, "size_y": 4, "slice_id": "1235" }, { "col": 5, "row": 7, "size_x": 8, "size_y": 4, "slice_id": "1236" }, { "col": 7, "row": 4, "size_x": 3, "size_y": 3, "slice_id": "1237" }, { "col": 1, "row": 7, "size_x": 4, "size_y": 4, "slice_id": "1238" }, { "col": 9, "row": 11, "size_x": 4, "size_y": 4, "slice_id": "1239" }, { "col": 1, "row": 11, "size_x": 8, "size_y": 4, "slice_id": "1240" } ] """) l = json.loads(js) for i, pos in enumerate(l): pos['slice_id'] = str(slices[i].id) dash.dashboard_title = dash_name dash.position_json = json.dumps(l, indent=4) dash.slug = slug dash.slices = slices[:-1] db.session.merge(dash) db.session.commit()