# TODO - do a need a state class that tracks the state of different website objects to help with
# updating etc.
# TODO - not even state, should it be an object that contains everythin to track. Execpt if I am patching,
# then the tracking in mem is unessary, so better to just track necesary values like: last changes card etc.Generate Map
Class that generates the data map
Load Data
sol_geo = SolomonGeo.load_pickle("/testData/")Build the Cloreopath Map
go.
z_vals = sol_geo.get_census(geo_filter = 'Province', var = 'Key Statistics',
measure = 'Total Households').values
np.min(z_vals)688fig = define_map(sol_geo)print(fig['data'][0]['zmin'])688Election Maps
Slightly different as they have less geographies and colour based on winning party
sol_geo.elec_wide.head()| Type | Year | Geo | loc_name | 0 | 1 | 2 | Winning Party | |
|---|---|---|---|---|---|---|---|---|
| 0 | National Parliament | 2024 | Constituency | Auki-Langalanga | Cooper Wale (SID) - 4314 (66.2%) | David Filia (OUR) - 1348 (20.7%) | Talauburi Anisi (KAD) - 488 (7.5%) | SID |
| 1 | National Parliament | 2024 | Constituency | Baegu-Asifola | Tagini Makario (OUR) - 2391 (37.4%) | Celsus Talifilu (U4C) - 2147 (33.6%) | Douglas Buga (SIP) - 1009 (15.8%) | OUR |
| 2 | National Parliament | 2024 | Constituency | Central Guadalcanal | Shanel Agovaka (OUR) - 3454 (43.1%) | Manengelea Rex (IND) - 1592 (19.8%) | Denson Denni (KAD) - 1381 (17.2%) | OUR |
| 3 | National Parliament | 2024 | Constituency | Central Honiara | Darcy Lilo (SIP) - 8223 (63.4%) | Alfred Efona (UP) - 3632 (28.0%) | Francis Idu (OUR) - 669 (5.2%) | SIP |
| 4 | National Parliament | 2024 | Constituency | Central Kwara'ae | Ricky Fuo'o (UP) - 5208 (47.9%) | Jackson Fiulaua (OUR) - 1831 (16.8%) | Young Wate (IND) - 1537 (14.1%) | UP |
sol_geo.elec_wide["Winning Party"].unique()array(['SID', 'OUR', 'SIP', 'UP', 'IND', 'PFP', 'U4C', 'KAD', 'DAP'],
dtype=object)sol_geo.elec["Party"].unique()array(['UP', 'PLD', 'KAD', 'OUR', 'SID', 'IND', 'SIP', 'U4C', 'PAP',
'PFP', 'DAP', 'NTP', 'GPS'], dtype=object)TODO
I need to make a unique color for each party, then colorscale will need to have all as an option. Probably a dictionary is better. Use the color brewer colors plus maybe another colour like black https://colorbrewer2.org/#type=qualitative&scheme=Set3&n=12
election_map
def election_map(
sol_df:SolomonGeo, # Solomon geo object containing census data to input into map
)->Figure: # Returns a graph object figureCreates and returns a chloreopath map for election
#election_map(sol_geo).show()election_map(sol_geo)['data'][0]['colorscale']((0.0, '#8dd3c7'), (1.0, '#8dd3c7'))Create a bar graph to display current data
gen_bar_plot(sol_geo, 'Ward', "Main source of household income in last 12 months", 'Wages Salary', ['Cruz', 'Vavaea'], 'Proportion')Bar Plot for Elections Data
Example plot for Auki-Langalanga
df = sol_geo.elec
loc = 'Auki-Langalanga'
df.loc[df['loc_name'] == loc, 'candParty'].valuesarray(['Joseph Waleanisia (UP)', 'Obadiah Koti (PLD)',
'Talauburi Anisi (KAD)', 'David Filia (OUR)', 'Cooper Wale (SID)'],
dtype=object)fig = go.Figure()
measures = list(df.columns)
fig.add_trace(go.Bar(
x = df.loc[df['loc_name'] == loc, 'Candidate'].values,
y = df.loc[df['loc_name'] == loc, 'Total'].values,
name = loc,
marker = dict(color = list(map(lambda y: sol_geo.colormap[y], df.loc[df['loc_name'] == loc, 'Party'].values))),
hovertemplate = '%{x} <extra>%{y}</extra>',
text = df.loc[df['loc_name'] == loc, 'Total'].values,
textposition='auto',
))
fig.update_layout(barmode='group', xaxis_tickangle=-45, title_text="Title",
xaxis={'categoryorder':'total descending'}, )
fig.update_traces(textfont_size=12, textangle=0, textposition="outside", cliponaxis=False)election_bar_plot
def election_bar_plot(
sol_geo:SolomonGeo, # Solomon geo object containing census data to input into map
geo_filter:str='Constituency', # The desired aggregation of the geography
year:int=2024, # The year of the elections data
election:str='National Parliament', # The type of election.
location:str='Auki-Langalanga', # Desired location within aggregation
type_filter:str='Total', # The type of data, eitehr Total of Proportion
)->Figure: # Returns a graph object figure of a barplotCreate a bar plot that show the census selected election data
election_bar_plot(sol_geo, type_filter = 'Proportion')Create Data Tables
Census Data Table
A method that creates data tables for census
gen_census_grid
def gen_census_grid(
sol_geo:SolomonGeo, # Solomon geo object containing census data to input into map
geo_filter:str, # The desired aggregation of the geography
variable:str, # The variable to use to create the bar plot
measure:str, # The measure to highlight on the bar graph
locations:[<class 'str'>]=None, # Desired location within aggregation
type_filter:str='Total', # The type aggregartion
grid_rows:int=10, # The number of rows to display
)->AgGrid: # Returns a graph object figure of a barplotCreates a basic data table using dash grid
gen_census_grid(sol_geo, 'Province', "Main source of household income in last 12 months", 'Wages Salary')AgGrid(id='dash-grid', columnDefs=[{'field': 'Province', 'headerName': 'Province', 'filter': True, 'lockPinned': True, 'cellClass': 'lock-pinned'}, {'field': 'No income', 'headerName': 'No income'}, {'field': 'Wages Salary', 'headerName': 'Wages Salary'}, {'field': 'Own business', 'headerName': 'Own business'}, {'field': 'Sale fish crop craft', 'headerName': 'Sale fish crop craft'}, {'field': 'Land lease', 'headerName': 'Land lease'}, {'field': 'House rent', 'headerName': 'House rent'}, {'field': 'Remittances', 'headerName': 'Remittances'}, {'field': 'Other source', 'headerName': 'Other source'}], columnSize='sizeToFit', csvExportParams={'fileName': 'Solomons 2009 Census Data Main source of household income in last 12 months by Province - Total.csv'}, dashGridOptions={'pagination': True, 'domLayout': 'autoHeight', 'paginationPageSize': 10, 'wrapHeaderText': True, 'autoHeaderHeight': True}, defaultColDef={'resizable': True, 'sortable': True, 'minWidth': 100}, rowData=[{'Province': 'Central', 'No income': 66.0, 'Wages Salary': 665.0, 'Own business': 373.0, 'Sale fish crop craft': 2518.0, 'Land lease': 1.0, 'House rent': 5.0, 'Remittances': 88.0, 'Other source': 1189.0}, {'Province': 'Choiseul', 'No income': 88.0, 'Wages Salary': 892.0, 'Own business': 206.0, 'Sale fish crop craft': 2443.0, 'Land lease': 6.0, 'House rent': 16.0, 'Remittances': 206.0, 'Other source': 855.0}, {'Province': 'Guadalcanal', 'No income': 538.0, 'Wages Salary': 3978.0, 'Own business': 868.0, 'Sale fish crop craft': 7702.0, 'Land lease': 20.0, 'House rent': 102.0, 'Remittances': 182.0, 'Other source': 3773.0}, {'Province': 'Honiara', 'No income': 75.0, 'Wages Salary': 6984.0, 'Own business': 783.0, 'Sale fish crop craft': 393.0, 'Land lease': 54.0, 'House rent': 179.0, 'Remittances': 48.0, 'Other source': 465.0}, {'Province': 'Isabel', 'No income': 92.0, 'Wages Salary': 907.0, 'Own business': 310.0, 'Sale fish crop craft': 2900.0, 'Land lease': 4.0, 'House rent': 18.0, 'Remittances': 174.0, 'Other source': 738.0}, {'Province': 'Makira-Ulawa', 'No income': 315.0, 'Wages Salary': 1007.0, 'Own business': 459.0, 'Sale fish crop craft': 3293.0, 'Land lease': 14.0, 'House rent': 28.0, 'Remittances': 90.0, 'Other source': 1967.0}, {'Province': 'Malaita', 'No income': 1404.0, 'Wages Salary': 2942.0, 'Own business': 1290.0, 'Sale fish crop craft': 11075.0, 'Land lease': 15.0, 'House rent': 154.0, 'Remittances': 584.0, 'Other source': 6957.0}, {'Province': 'Rennell-Bell', 'No income': 114.0, 'Wages Salary': 147.0, 'Own business': 40.0, 'Sale fish crop craft': 244.0, 'Land lease': 1.0, 'House rent': 13.0, 'Remittances': 82.0, 'Other source': 47.0}, {'Province': 'Temotu', 'No income': 159.0, 'Wages Salary': 612.0, 'Own business': 166.0, 'Sale fish crop craft': 2505.0, 'Land lease': 10.0, 'House rent': 14.0, 'Remittances': 120.0, 'Other source': 717.0}, {'Province': 'Western', 'No income': 182.0, 'Wages Salary': 3732.0, 'Own business': 618.0, 'Sale fish crop craft': 7518.0, 'Land lease': 8.0, 'House rent': 48.0, 'Remittances': 410.0, 'Other source': 1246.0}], style={'height': None})Create a Population Grid
gen_pop_grid
def gen_pop_grid(
sol_geo:SolomonGeo, # Solomon geo object containing census data to input into map
years:str, # Selected data years
variable:str, # The variable to use to create the bar plot
measure:str, # The measure to highlight on the bar graph
geo_filter:str='Province', # The desired aggregation of the geography
locations:[<class 'str'>]=None, # Desired location within aggregation
type_filter:str='Total', # The type aggregartion
grid_rows:int=10, # The number of rows to display
)->AgGrid: # Returns a graph object figure of a barplotCreates a basic data table using dash grid
gen_pop_grid(sol_geo, [2023, 2024, 2025], 'Population', 'Total',)AgGrid(id='dash-grid', columnDefs=[{'field': 'Province', 'headerName': 'Province', 'filter': True, 'lockPinned': True, 'cellClass': 'lock-pinned'}, {'field': 'Year', 'headerName': 'Year', 'filter': True, 'lockPinned': True, 'cellClass': 'lock-pinned'}, {'field': '0-4', 'headerName': '0-4'}, {'field': '5-9', 'headerName': '5-9'}, {'field': '10-14', 'headerName': '10-14'}, {'field': '15-19', 'headerName': '15-19'}, {'field': '20-24', 'headerName': '20-24'}, {'field': '25-29', 'headerName': '25-29'}, {'field': '30-34', 'headerName': '30-34'}, {'field': '35-39', 'headerName': '35-39'}, {'field': '40-44', 'headerName': '40-44'}, {'field': '45-49', 'headerName': '45-49'}, {'field': '50-54', 'headerName': '50-54'}, {'field': '55-59', 'headerName': '55-59'}, {'field': '60-64', 'headerName': '60-64'}, {'field': '65-69', 'headerName': '65-69'}, {'field': '70-74', 'headerName': '70-74'}, {'field': '75-79', 'headerName': '75-79'}, {'field': '80+', 'headerName': '80+'}], columnSize='sizeToFit', csvExportParams={'fileName': 'Solomons 2009 Population Projections for Population Population by Province - Total.csv'}, dashGridOptions={'pagination': True, 'domLayout': 'autoHeight', 'paginationPageSize': 10, 'wrapHeaderText': True, 'autoHeaderHeight': True}, defaultColDef={'resizable': True, 'sortable': True, 'minWidth': 100}, rowData=[{'Year': 2023, 'Province': 'Central', '0-4': 3621.0, '5-9': 3642.0, '10-14': 3915.0, '15-19': 4036.0, '20-24': 3534.0, '25-29': 2622.0, '30-34': 1920.0, '35-39': 1740.0, '40-44': 1830.0, '45-49': 1786.0, '50-54': 1610.0, '55-59': 1073.0, '60-64': 891.0, '65-69': 597.0, '70-74': 483.0, '75-79': 321.0, '80+': 296.0}, {'Year': 2024, 'Province': 'Central', '0-4': 3641.0, '5-9': 3603.0, '10-14': 3861.0, '15-19': 4019.0, '20-24': 3656.0, '25-29': 2773.0, '30-34': 1995.0, '35-39': 1719.0, '40-44': 1811.0, '45-49': 1776.0, '50-54': 1689.0, '55-59': 1127.0, '60-64': 933.0, '65-69': 620.0, '70-74': 493.0, '75-79': 334.0, '80+': 309.0}, {'Year': 2025, 'Province': 'Central', '0-4': 3671.0, '5-9': 3576.0, '10-14': 3788.0, '15-19': 4001.0, '20-24': 3757.0, '25-29': 2928.0, '30-34': 2091.0, '35-39': 1716.0, '40-44': 1779.0, '45-49': 1773.0, '50-54': 1732.0, '55-59': 1212.0, '60-64': 958.0, '65-69': 659.0, '70-74': 499.0, '75-79': 348.0, '80+': 322.0}, {'Year': 2023, 'Province': 'Choiseul', '0-4': 4900.0, '5-9': 4586.0, '10-14': 4396.0, '15-19': 4266.0, '20-24': 3885.0, '25-29': 3128.0, '30-34': 2539.0, '35-39': 2187.0, '40-44': 2007.0, '45-49': 1851.0, '50-54': 1668.0, '55-59': 1208.0, '60-64': 937.0, '65-69': 641.0, '70-74': 499.0, '75-79': 309.0, '80+': 330.0}, {'Year': 2024, 'Province': 'Choiseul', '0-4': 4973.0, '5-9': 4630.0, '10-14': 4458.0, '15-19': 4258.0, '20-24': 4013.0, '25-29': 3274.0, '30-34': 2640.0, '35-39': 2238.0, '40-44': 2040.0, '45-49': 1865.0, '50-54': 1735.0, '55-59': 1268.0, '60-64': 989.0, '65-69': 665.0, '70-74': 522.0, '75-79': 313.0, '80+': 345.0}, {'Year': 2025, 'Province': 'Choiseul', '0-4': 5048.0, '5-9': 4682.0, '10-14': 4479.0, '15-19': 4289.0, '20-24': 4117.0, '25-29': 3429.0, '30-34': 2750.0, '35-39': 2299.0, '40-44': 2071.0, '45-49': 1888.0, '50-54': 1777.0, '55-59': 1346.0, '60-64': 1032.0, '65-69': 704.0, '70-74': 536.0, '75-79': 328.0, '80+': 355.0}, {'Year': 2023, 'Province': 'Guadalcanal', '0-4': 21745.0, '5-9': 20158.0, '10-14': 17946.0, '15-19': 16342.0, '20-24': 15215.0, '25-29': 14197.0, '30-34': 13723.0, '35-39': 13159.0, '40-44': 10683.0, '45-49': 8749.0, '50-54': 6879.0, '55-59': 4715.0, '60-64': 3450.0, '65-69': 2140.0, '70-74': 1608.0, '75-79': 941.0, '80+': 870.0}, {'Year': 2024, 'Province': 'Guadalcanal', '0-4': 22087.0, '5-9': 20561.0, '10-14': 18484.0, '15-19': 16701.0, '20-24': 15766.0, '25-29': 14668.0, '30-34': 13976.0, '35-39': 13660.0, '40-44': 11240.0, '45-49': 9147.0, '50-54': 7337.0, '55-59': 5032.0, '60-64': 3717.0, '65-69': 2273.0, '70-74': 1697.0, '75-79': 980.0, '80+': 912.0}, {'Year': 2025, 'Province': 'Guadalcanal', '0-4': 22419.0, '5-9': 20948.0, '10-14': 18986.0, '15-19': 17102.0, '20-24': 16290.0, '25-29': 15166.0, '30-34': 14301.0, '35-39': 14036.0, '40-44': 11849.0, '45-49': 9569.0, '50-54': 7749.0, '55-59': 5425.0, '60-64': 3947.0, '65-69': 2466.0, '70-74': 1760.0, '75-79': 1044.0, '80+': 947.0}, {'Year': 2023, 'Province': 'Honiara', '0-4': 8666.0, '5-9': 9207.0, '10-14': 9041.0, '15-19': 8060.0, '20-24': 7380.0, '25-29': 7291.0, '30-34': 8878.0, '35-39': 10220.0, '40-44': 7840.0, '45-49': 5991.0, '50-54': 4628.0, '55-59': 3193.0, '60-64': 2359.0, '65-69': 1509.0, '70-74': 943.0, '75-79': 507.0, '80+': 313.0}, {'Year': 2024, 'Province': 'Honiara', '0-4': 8535.0, '5-9': 9146.0, '10-14': 9172.0, '15-19': 8285.0, '20-24': 7595.0, '25-29': 7252.0, '30-34': 8475.0, '35-39': 10381.0, '40-44': 8319.0, '45-49': 6300.0, '50-54': 4918.0, '55-59': 3392.0, '60-64': 2507.0, '65-69': 1620.0, '70-74': 1014.0, '75-79': 547.0, '80+': 343.0}, {'Year': 2025, 'Province': 'Honiara', '0-4': 8404.0, '5-9': 9065.0, '10-14': 9241.0, '15-19': 8536.0, '20-24': 7802.0, '25-29': 7291.0, '30-34': 8159.0, '35-39': 10294.0, '40-44': 8849.0, '45-49': 6643.0, '50-54': 5182.0, '55-59': 3636.0, '60-64': 2642.0, '65-69': 1746.0, '70-74': 1085.0, '75-79': 593.0, '80+': 374.0}, {'Year': 2023, 'Province': 'Isabel', '0-4': 4165.0, '5-9': 4041.0, '10-14': 4026.0, '15-19': 4005.0, '20-24': 3680.0, '25-29': 2958.0, '30-34': 2468.0, '35-39': 2128.0, '40-44': 2054.0, '45-49': 1985.0, '50-54': 1666.0, '55-59': 1131.0, '60-64': 993.0, '65-69': 764.0, '70-74': 590.0, '75-79': 379.0, '80+': 377.0}, {'Year': 2024, 'Province': 'Isabel', '0-4': 4203.0, '5-9': 4051.0, '10-14': 4047.0, '15-19': 3992.0, '20-24': 3804.0, '25-29': 3087.0, '30-34': 2562.0, '35-39': 2171.0, '40-44': 2059.0, '45-49': 2004.0, '50-54': 1764.0, '55-59': 1185.0, '60-64': 1020.0, '65-69': 786.0, '70-74': 613.0, '75-79': 394.0, '80+': 392.0}, {'Year': 2025, 'Province': 'Isabel', '0-4': 4244.0, '5-9': 4068.0, '10-14': 4038.0, '15-19': 4002.0, '20-24': 3900.0, '25-29': 3232.0, '30-34': 2657.0, '35-39': 2228.0, '40-44': 2067.0, '45-49': 2019.0, '50-54': 1838.0, '55-59': 1271.0, '60-64': 1036.0, '65-69': 816.0, '70-74': 632.0, '75-79': 413.0, '80+': 407.0}, {'Year': 2023, 'Province': 'Makira-Ulawa', '0-4': 7179.0, '5-9': 7037.0, '10-14': 7244.0, '15-19': 6960.0, '20-24': 5670.0, '25-29': 4277.0, '30-34': 3355.0, '35-39': 3440.0, '40-44': 3220.0, '45-49': 2842.0, '50-54': 2267.0, '55-59': 1512.0, '60-64': 1238.0, '65-69': 832.0, '70-74': 658.0, '75-79': 417.0, '80+': 398.0}, {'Year': 2024, 'Province': 'Makira-Ulawa', '0-4': 7242.0, '5-9': 7021.0, '10-14': 7177.0, '15-19': 7117.0, '20-24': 5929.0, '25-29': 4533.0, '30-34': 3405.0, '35-39': 3428.0, '40-44': 3263.0, '45-49': 2910.0, '50-54': 2410.0, '55-59': 1589.0, '60-64': 1297.0, '65-69': 864.0, '70-74': 679.0, '75-79': 427.0, '80+': 414.0}, {'Year': 2025, 'Province': 'Makira-Ulawa', '0-4': 7321.0, '5-9': 7021.0, '10-14': 7120.0, '15-19': 7209.0, '20-24': 6185.0, '25-29': 4788.0, '30-34': 3525.0, '35-39': 3390.0, '40-44': 3302.0, '45-49': 2977.0, '50-54': 2526.0, '55-59': 1706.0, '60-64': 1335.0, '65-69': 916.0, '70-74': 691.0, '75-79': 446.0, '80+': 427.0}, {'Year': 2023, 'Province': 'Malaita', '0-4': 18990.0, '5-9': 18896.0, '10-14': 20187.0, '15-19': 20945.0, '20-24': 18277.0, '25-29': 13286.0, '30-34': 9337.0, '35-39': 7805.0, '40-44': 7425.0, '45-49': 6991.0, '50-54': 5924.0, '55-59': 4453.0, '60-64': 3759.0, '65-69': 2804.0, '70-74': 2244.0, '75-79': 1627.0, '80+': 1394.0}, {'Year': 2024, 'Province': 'Malaita', '0-4': 19039.0, '5-9': 18690.0, '10-14': 19891.0, '15-19': 20718.0, '20-24': 18748.0, '25-29': 13942.0, '30-34': 9648.0, '35-39': 7769.0, '40-44': 7338.0, '45-49': 7010.0, '50-54': 6134.0, '55-59': 4578.0, '60-64': 3859.0, '65-69': 2871.0, '70-74': 2255.0, '75-79': 1667.0, '80+': 1453.0}, {'Year': 2025, 'Province': 'Malaita', '0-4': 19122.0, '5-9': 18543.0, '10-14': 19503.0, '15-19': 20502.0, '20-24': 19118.0, '25-29': 14609.0, '30-34': 10052.0, '35-39': 7776.0, '40-44': 7246.0, '45-49': 7006.0, '50-54': 6295.0, '55-59': 4766.0, '60-64': 3924.0, '65-69': 2967.0, '70-74': 2267.0, '75-79': 1696.0, '80+': 1516.0}, {'Year': 2023, 'Province': 'Rennell-Bell', '0-4': 566.0, '5-9': 491.0, '10-14': 424.0, '15-19': 428.0, '20-24': 444.0, '25-29': 449.0, '30-34': 299.0, '35-39': 264.0, '40-44': 189.0, '45-49': 227.0, '50-54': 172.0, '55-59': 127.0, '60-64': 132.0, '65-69': 126.0, '70-74': 113.0, '75-79': 65.0, '80+': 67.0}, {'Year': 2024, 'Province': 'Rennell-Bell', '0-4': 581.0, '5-9': 506.0, '10-14': 441.0, '15-19': 421.0, '20-24': 437.0, '25-29': 473.0, '30-34': 317.0, '35-39': 282.0, '40-44': 190.0, '45-49': 229.0, '50-54': 184.0, '55-59': 130.0, '60-64': 131.0, '65-69': 124.0, '70-74': 116.0, '75-79': 72.0, '80+': 68.0}, {'Year': 2025, 'Province': 'Rennell-Bell', '0-4': 595.0, '5-9': 521.0, '10-14': 453.0, '15-19': 420.0, '20-24': 435.0, '25-29': 483.0, '30-34': 346.0, '35-39': 293.0, '40-44': 201.0, '45-49': 223.0, '50-54': 197.0, '55-59': 137.0, '60-64': 129.0, '65-69': 124.0, '70-74': 117.0, '75-79': 78.0, '80+': 70.0}, {'Year': 2023, 'Province': 'Temotu', '0-4': 2686.0, '5-9': 2674.0, '10-14': 2813.0, '15-19': 3071.0, '20-24': 2905.0, '25-29': 2072.0, '30-34': 1578.0, '35-39': 1250.0, '40-44': 1220.0, '45-49': 1168.0, '50-54': 1197.0, '55-59': 910.0, '60-64': 765.0, '65-69': 586.0, '70-74': 460.0, '75-79': 269.0, '80+': 315.0}, {'Year': 2024, 'Province': 'Temotu', '0-4': 2697.0, '5-9': 2650.0, '10-14': 2805.0, '15-19': 2959.0, '20-24': 2996.0, '25-29': 2179.0, '30-34': 1646.0, '35-39': 1259.0, '40-44': 1221.0, '45-49': 1144.0, '50-54': 1219.0, '55-59': 941.0, '60-64': 789.0, '65-69': 598.0, '70-74': 479.0, '75-79': 283.0, '80+': 317.0}, {'Year': 2025, 'Province': 'Temotu', '0-4': 2710.0, '5-9': 2636.0, '10-14': 2756.0, '15-19': 2891.0, '20-24': 3043.0, '25-29': 2309.0, '30-34': 1709.0, '35-39': 1288.0, '40-44': 1210.0, '45-49': 1136.0, '50-54': 1215.0, '55-59': 986.0, '60-64': 806.0, '65-69': 617.0, '70-74': 490.0, '75-79': 301.0, '80+': 319.0}, {'Year': 2023, 'Province': 'Western', '0-4': 11762.0, '5-9': 11511.0, '10-14': 11679.0, '15-19': 11368.0, '20-24': 10110.0, '25-29': 8167.0, '30-34': 6990.0, '35-39': 5802.0, '40-44': 5338.0, '45-49': 4928.0, '50-54': 4599.0, '55-59': 3756.0, '60-64': 2749.0, '65-69': 1874.0, '70-74': 1368.0, '75-79': 889.0, '80+': 833.0}, {'Year': 2024, 'Province': 'Western', '0-4': 11834.0, '5-9': 11500.0, '10-14': 11631.0, '15-19': 11449.0, '20-24': 10408.0, '25-29': 8406.0, '30-34': 7198.0, '35-39': 5919.0, '40-44': 5398.0, '45-49': 4953.0, '50-54': 4678.0, '55-59': 3908.0, '60-64': 2914.0, '65-69': 1965.0, '70-74': 1420.0, '75-79': 921.0, '80+': 865.0}, {'Year': 2025, 'Province': 'Western', '0-4': 11916.0, '5-9': 11508.0, '10-14': 11564.0, '15-19': 11520.0, '20-24': 10650.0, '25-29': 8704.0, '30-34': 7373.0, '35-39': 6083.0, '40-44': 5447.0, '45-49': 4995.0, '50-54': 4729.0, '55-59': 4054.0, '60-64': 3076.0, '65-69': 2080.0, '70-74': 1468.0, '75-79': 959.0, '80+': 897.0}], style={'height': None})dashGridOptions={‘pagination’: True, ‘domLayout’: ‘autoHeight’, ‘paginationPageSize’: 10},
Create KPI
The kpi card is useful for displaying a particular value as a standout kpi
# Build and test a card
gen_kpi(sol_geo, 2022, 'Population', 'Total', ["0-4"])(Markdown('# 83,808'), Markdown('total persons in current selection'))Create function to make dropdowns
gen_dd
def gen_dd(
location_list:[<class 'str'>], # a list of locations
id:str, # Id of the dropdown
place_holder:str=None, # a placeholder message to display
val:str=None, # The starting value of the dropdown
clear:bool=False, # pick whether the ]
height:int=35, # height of the dropdown text
multi:bool=False, # Is the dropdown multi select
)->Dropdown: # Returns a dropdownCreate the location dropdown from given list
Create Population Pyramid
For use to demonstrate the population pyramid of solomon islands. It should highlight age groups based on selection
gen_pyramid
def gen_pyramid(
sol_geo:SolomonGeo, # Solomon geo object containing census data to input into map
geo_filter:str, # The desired aggregation of the geography
year:str, # Selected year to display on the graph
variable:str='Population', # The variable to use to create the bar plot
locations:[<class 'str'>]=[], # Desired location within aggregation
type_filter:str='Total', # The type aggregartion
ages:[<class 'str'>]=[], # Currenly selected ages for highlighting
)->Figure: # Returns a population pyramid and it's titleCreate a population pyramid of selected population data
gen_pyramid(sol_geo, geo_filter = 'Province', year = 2024, variable = "Population", locations = ['Western'], type_filter = "Proportion")Testing
# TODO implementing testing, everything should at least be run here and execute and be displayed