# Test that the environment variables can be loaded
test(ACCESS_KEY, None, nequals)
test(SECRET_ACCCESS_KEY, None, nequals)
test(REGION_NAME, None, nequals)
test_eq(REGION_NAME, 'ap-southeast-2')Load Data
Functions that load the data for the map.
Contents
Solomon Geospatial Data - SolomonGeo: A class that cleans the solomon islandas census and geography data - SolomonGeo.read_test: Loads and transforms the test data - SolomonGeo.get_geojson: Returns the geo_df as a geojson datatset
Solomon Geospatial Data
Load the geography and census data ### Geography Data Solomons islands geography data is organised at the levels - adm0 - The country as as whole, Solomon Islands - adm1 - Also referred to as the province e.g. Honiara, Malaita - adm2 - The Consituency e.g. Central Honiara - adm3 - Ward, the smallest geography I am reporting. E.g. Cruz
Census Data
Solomon islands census data has been used from the 2009 and 2019 census. For the respective census: 2009 - We have the total population in for each of the administration regions 2019 - There is only data available down to the province level
Test that the .env variables exist
Function for connecting to AWS S3 client
Load and Prepare Elections Data
Load and manipulate the elections data. Below data will be manipulated in SolomonGeo.__clean_elections function.
elec = pd.read_csv(fp + 'Solomon_Elections_Data_April_2024.csv', thousands=',')
#elec['Party'] =
candParty = elec['Candidate'].str.split("(", expand = True)
elec['candParty'] = elec['Candidate']
elec['Candidate'] = candParty.loc[:, 0]
elec['Party'] = candParty.loc[:, 1].str.strip(")")
# Fix formatting
#elec.Total.astype(int)
elec.sort_values(by = ['Type','Year', 'Geo', 'loc_name', 'Total',], inplace = True, ascending= False)
elec['Order'] = elec.groupby(by = 'loc_name').cumcount()
elec.sort_values(by = ['Type','Year', 'Geo', 'loc_name', 'Total',], inplace = True, ascending= True)
elec.head()| Type | Year | Geo | loc_name | Candidate | Total | Proportion | candParty | Party | Order | |
|---|---|---|---|---|---|---|---|---|---|---|
| 4 | National Parliament | 2024 | Constituency | Auki-Langalanga | Joseph Waleanisia | 108 | 1.7 | Joseph Waleanisia (UP) | UP | 4 |
| 3 | National Parliament | 2024 | Constituency | Auki-Langalanga | Obadiah Koti | 257 | 3.9 | Obadiah Koti (PLD) | PLD | 3 |
| 2 | National Parliament | 2024 | Constituency | Auki-Langalanga | Talauburi Anisi | 488 | 7.5 | Talauburi Anisi (KAD) | KAD | 2 |
| 1 | National Parliament | 2024 | Constituency | Auki-Langalanga | David Filia | 1348 | 20.7 | David Filia (OUR) | OUR | 1 |
| 0 | National Parliament | 2024 | Constituency | Auki-Langalanga | Cooper Wale | 4314 | 66.2 | Cooper Wale (SID) | SID | 0 |
Make a wide dataset that has the top three finishing candidates per election and electorate.
elec_wide = elec.copy()
elec_wide = elec_wide[elec_wide['Order'] <= 2]
elec_wide['displayValue'] = elec_wide.candParty.map(str) + " - " + elec_wide.Total.map(str) + " (" + elec_wide.Proportion.map(str) + "%)"
elec_wide = pd.pivot(elec_wide, index = ['Type', 'Year', 'Geo', 'loc_name',], columns = ['Order', ], values = ['displayValue']).fillna("")
elec_wide = elec_wide.droplevel(0, axis = 1)
elec_wide.head()| Order | 0 | 1 | 2 | |||
|---|---|---|---|---|---|---|
| Type | Year | Geo | loc_name | |||
| National Parliament | 2024 | Constituency | Auki-Langalanga | Cooper Wale (SID) - 4314 (66.2%) | David Filia (OUR) - 1348 (20.7%) | Talauburi Anisi (KAD) - 488 (7.5%) |
| Baegu-Asifola | Tagini Makario (OUR) - 2391 (37.4%) | Celsus Talifilu (U4C) - 2147 (33.6%) | Douglas Buga (SIP) - 1009 (15.8%) | |||
| Central Guadalcanal | Shanel Agovaka (OUR) - 3454 (43.1%) | Manengelea Rex (IND) - 1592 (19.8%) | Denson Denni (KAD) - 1381 (17.2%) | |||
| Central Honiara | Darcy Lilo (SIP) - 8223 (63.4%) | Alfred Efona (UP) - 3632 (28.0%) | Francis Idu (OUR) - 669 (5.2%) | |||
| Central Kwara'ae | Ricky Fuo'o (UP) - 5208 (47.9%) | Jackson Fiulaua (OUR) - 1831 (16.8%) | Young Wate (IND) - 1537 (14.1%) |
Find winning candidate in each location and election
elec_wide2 = elec.copy().loc[elec.Order == 0, :]
elec_wide2.set_index(['Type', 'Year', 'Geo', 'loc_name',], inplace = True)
elec_wide2.head()| Candidate | Total | Proportion | candParty | Party | Order | ||||
|---|---|---|---|---|---|---|---|---|---|
| Type | Year | Geo | loc_name | ||||||
| National Parliament | 2024 | Constituency | Auki-Langalanga | Cooper Wale | 4314 | 66.2 | Cooper Wale (SID) | SID | 0 |
| Baegu-Asifola | Tagini Makario | 2391 | 37.4 | Tagini Makario (OUR) | OUR | 0 | |||
| Central Guadalcanal | Shanel Agovaka | 3454 | 43.1 | Shanel Agovaka (OUR) | OUR | 0 | |||
| Central Honiara | Darcy Lilo | 8223 | 63.4 | Darcy Lilo (SIP) | SIP | 0 | |||
| Central Kwara'ae | Ricky Fuo'o | 5208 | 47.9 | Ricky Fuo'o (UP) | UP | 0 |
Merge back together the two datasets
elec_wide2 = pd.DataFrame(elec_wide2.Party)
elec_wide2.rename(columns = {"Party": "Winning Party"}, inplace = True)
elec_wide = pd.merge(elec_wide, elec_wide2, how = 'left', left_index = True, right_index = True)
# remove the index
elec_wide.reset_index(inplace = True)elec.Party.unique()array(['UP', 'PLD', 'KAD', 'OUR', 'SID', 'IND', 'SIP', 'U4C', 'PAP',
'PFP', 'DAP', 'NTP', 'GPS'], dtype=object)Make the Proportion column a decimal to be displayed properly
elec.Proportion = elec.Proportion / 100SolomonGeo Data Class
This data class manipulates and stores the geospatial and regular data relating to each geography. It includes the necessary data and methods to manipulate that data to build the resulting dash app.
Constructors for SolomonGeo class
These constudctors are used to build the SolomonGeo objects from various inputs
SolomonGeo.load_pickle
def load_pickle(
folder:str='/testData/', # file path of the folder to save in
aws:bool=True, # Whether to load from github or local
file_name:str='sol_geo.pickle', # file name of the saved class
):A constuctor that initialises the object from aws pickle
Test that dataset and geography merge perfectly
# TODO should this method be callable from the class
def test_geo(df:pd.DataFrame, # The dataframe to test
geo:gpd.GeoDataFrame, # The geographys
):
'''
Tests that the provided geography and data merge perfectly. I.e. all data
has a coresponding geography. The primary key to merge id and agg
'''
# Use outer join to see if anything is missing
geo_df = geo.merge(df, left_on=['id', 'agg'], right_on=['core: id', 'core: agg'], how = 'outer', indicator = True)
test_eq(sum(geo_df['_merge'] != 'both'), 0) # The total of non-joined rows should be 0Save SolomonGeo
Save as a pickled object
SolomonGeo.save_pickle
def save_pickle(
aws:bool=True, # Whether to save to aws or locally
folder:str='/testData/', # file path of the folder to save in, only necesasry for local saving
file_name:str='sol_geo.pickle', # file name of the saved class
):Save a pickle of the SolomonGeo class in backblaze b2
SolomonGeo.save_pickle
def save_pickle(
aws:bool=True, # Whether to save to aws or locally
folder:str='/testData/', # file path of the folder to save in, only necesasry for local saving
file_name:str='sol_geo.pickle', # file name of the saved class
):Save a pickle of the SolomonGeo class in backblaze b2
Get Geo JSON
A getter method for the geometry portion of the dataset that returns a geoJson formated geography.
It only includes the geography and location name as id
SolomonGeo.get_geojson
def get_geojson(
geo_filter:str=None, # Filters the geojson to the requested aggregation
)->dict: # Geo JSON formatted datasetA getter method for the SolomonGeo class that returns a Geo JSON formatted dataset
Store JSON
A getter method that returns a dcc.Store object with the data of the SolomonGeo class converted to a dictionary for storing with dash. We use a dictionary instead of geojson as the time to convert dictionary back to geopandas is much faster than geojson. (0.01s vs 1.2s) This makes the map much more snappy and responsive
On storing, we drop the geometry as it won’t store as json. Note that you can get around this by using a geojson, but this increases reload time by a lot.
SolomonGeo.get_store
def get_store()->Store: # Geo JSON formatted datasetA getter method that returns a dcc.Store object with the data of the SolomonGeo class converted to a dictionary for storing with dash.
Get Census Data
Returns the census dataset for a particular aggregation
SolomonGeo.get_census
def get_census(
geo_filter:str=None, # Filters the dataframe to the requested geography
var:str='Key Statistics', # Selects an upper level
measure:str=None, # Selects the lower level variable, if var 1 is used, measure must be used.
loc_filter:[<class 'str'>]=None, # Filters one of more locations
type_filter:str='Total', # Return either number of proportion
agg:bool=False, # Whether to return the dataset aggregated for the given selection
)->DataFrame: # Pandas Dataframe containing population dataA getter method for the SolomonGeo class that returns a pandas dataset containg the id variable and the requested census data. This is the minimal data required to display on the map. - Optionally can aggregate the dataset, uses weighted aggregation for proportional data - You should always provide a variable to this function or proportional aggregation won’t work
Get Population Data
Returns the population projection data for a given aggregation
SolomonGeo.get_pop
def get_pop(
years:[<class 'str'>], # Selects the year/years of data to return
var:str=None, # Selects an upper level variable
measure:str=None, # Selects the lower level variable, if var 1 is used, measure must be used.
loc_filter:[<class 'str'>]=None, # Filters one of more locations
type_filter:str='Total', # Return either number of proportion
agg:bool=False, # Whether to return the dataset aggregated for the given selection
agg_location:bool=False, # If true, don't aggregate the population data by location
agg_ages:bool=False, # If true, don't aggregate the population data by age
ages:[<class 'str'>]=None, # Filters for one or more Age Brackets, if none returns all
)->DataFrame: # Pandas Dataframe containing population dataA getter method for the SolomonGeo class that returns a pandas dataset containg the id variable and the requested popultion data. This is the minimal data required to display on the map.
Testing
sol_geo = SolomonGeo.read_test()/tmp/ipykernel_4212/1241796792.py:276: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
df['pk'] = df['location'] + "_" + df["type"]test = sol_geo.population.copy()test['Age']['Age_Bracket'](core, location)
Central 0-4
Central 0-4
Central 5-9
Central 5-9
Central 10-14
...
Western 70-74
Western 75-79
Western 75-79
Western 80+
Western 80+
Name: Age_Bracket, Length: 5440, dtype: objecttester = sol_geo.get_pop(years = [2010, 2011, 2012], var = "Population", measure='Total').values[:, -1]
tester
# TODO work out how to aggregate all the columns other than age.array([4156.0, 3988.0, 3289.0, 2521.0, 2118.0, 2102.0, 2022.0, 1940.0,
1382.0, 1126.0, 821.0, 694.0, 563.0, 428.0, 293.0, 212.0, 199.0,
4115.0, 4064.0, 3444.0, 2638.0, 2131.0, 2060.0, 2022.0, 1953.0,
1498.0, 1139.0, 879.0, 695.0, 585.0, 442.0, 306.0, 209.0, 209.0,
4062.0, 4112.0, 3594.0, 2772.0, 2164.0, 2018.0, 2022.0, 1944.0,
1623.0, 1150.0, 944.0, 697.0, 606.0, 456.0, 321.0, 210.0, 217.0,
4339.0, 4133.0, 3418.0, 2721.0, 2275.0, 2072.0, 1916.0, 1841.0,
1431.0, 1147.0, 835.0, 706.0, 509.0, 483.0, 311.0, 207.0, 136.0,
4366.0, 4211.0, 3580.0, 2842.0, 2345.0, 2103.0, 1946.0, 1863.0,
1528.0, 1186.0, 892.0, 715.0, 543.0, 471.0, 344.0, 206.0, 151.0,
4397.0, 4262.0, 3739.0, 2971.0, 2426.0, 2137.0, 1981.0, 1873.0,
1629.0, 1224.0, 958.0, 722.0, 584.0, 454.0, 378.0, 206.0, 167.0,
16116.0, 14274.0, 11675.0, 9959.0, 10071.0, 8908.0, 7662.0, 6667.0,
4865.0, 3772.0, 2509.0, 2035.0, 1456.0, 1248.0, 878.0, 561.0,
404.0, 16545.0, 14767.0, 12222.0, 10344.0, 10327.0, 9569.0, 8114.0,
7060.0, 5352.0, 3998.0, 2795.0, 2095.0, 1582.0, 1254.0, 944.0,
583.0, 430.0, 17002.0, 15222.0, 12794.0, 10778.0, 10513.0, 10227.0,
8595.0, 7434.0, 5874.0, 4224.0, 3115.0, 2154.0, 1724.0, 1257.0,
1012.0, 602.0, 461.0, 8338.0, 7292.0, 6381.0, 7062.0, 9399.0,
8235.0, 6289.0, 5060.0, 3667.0, 2806.0, 1995.0, 1393.0, 910.0,
577.0, 312.0, 173.0, 114.0, 8578.0, 7491.0, 6504.0, 6806.0, 9079.0,
8811.0, 6668.0, 5315.0, 3968.0, 2946.0, 2161.0, 1488.0, 989.0,
623.0, 348.0, 178.0, 125.0, 8794.0, 7688.0, 6689.0, 6628.0, 8620.0,
9341.0, 7078.0, 5569.0, 4292.0, 3090.0, 2335.0, 1589.0, 1075.0,
671.0, 388.0, 188.0, 135.0, 4023.0, 3898.0, 3206.0, 2615.0, 2188.0,
2053.0, 2039.0, 1893.0, 1339.0, 1137.0, 955.0, 823.0, 634.0, 499.0,
350.0, 268.0, 228.0, 4029.0, 3965.0, 3364.0, 2712.0, 2261.0,
2063.0, 2047.0, 1948.0, 1457.0, 1145.0, 995.0, 839.0, 666.0, 511.0,
367.0, 262.0, 246.0, 4035.0, 4001.0, 3523.0, 2815.0, 2346.0,
2080.0, 2051.0, 1984.0, 1587.0, 1155.0, 1036.0, 854.0, 700.0,
522.0, 387.0, 260.0, 260.0, 7400.0, 6377.0, 5042.0, 3807.0, 3671.0,
3553.0, 3199.0, 2734.0, 1892.0, 1542.0, 1132.0, 958.0, 740.0,
629.0, 399.0, 295.0, 257.0, 7434.0, 6625.0, 5301.0, 3990.0, 3613.0,
3587.0, 3263.0, 2831.0, 2050.0, 1566.0, 1212.0, 964.0, 776.0,
630.0, 433.0, 282.0, 275.0, 7420.0, 6855.0, 5560.0, 4220.0, 3563.0,
3612.0, 3321.0, 2909.0, 2227.0, 1590.0, 1301.0, 969.0, 816.0,
627.0, 469.0, 274.0, 288.0, 22242.0, 21688.0, 18652.0, 14923.0,
12290.0, 10767.0, 9563.0, 8157.0, 6171.0, 5141.0, 4105.0, 3477.0,
3054.0, 2348.0, 1540.0, 1056.0, 969.0, 21921.0, 21930.0, 19311.0,
15420.0, 12354.0, 10667.0, 9526.0, 8273.0, 6423.0, 5177.0, 4244.0,
3487.0, 3078.0, 2429.0, 1613.0, 1039.0, 1014.0, 21554.0, 22035.0,
19931.0, 15990.0, 12490.0, 10579.0, 9467.0, 8346.0, 6698.0, 5209.0,
4394.0, 3509.0, 3091.0, 2504.0, 1691.0, 1046.0, 1041.0, 409.0,
414.0, 448.0, 301.0, 251.0, 169.0, 204.0, 188.0, 134.0, 134.0,
138.0, 146.0, 111.0, 58.0, 62.0, 62.0, 46.0, 409.0, 417.0, 445.0,
341.0, 255.0, 190.0, 193.0, 201.0, 144.0, 130.0, 138.0, 145.0,
120.0, 66.0, 56.0, 60.0, 51.0, 411.0, 421.0, 436.0, 383.0, 258.0,
215.0, 181.0, 212.0, 156.0, 128.0, 138.0, 142.0, 128.0, 75.0, 51.0,
60.0, 56.0, 3048.0, 3293.0, 2702.0, 2184.0, 1727.0, 1555.0, 1390.0,
1416.0, 1153.0, 967.0, 783.0, 687.0, 499.0, 386.0, 320.0, 267.0,
302.0, 2985.0, 3294.0, 2849.0, 2245.0, 1776.0, 1537.0, 1393.0,
1389.0, 1211.0, 980.0, 811.0, 694.0, 532.0, 388.0, 319.0, 262.0,
306.0, 2933.0, 3251.0, 2995.0, 2312.0, 1839.0, 1520.0, 1405.0,
1348.0, 1269.0, 993.0, 842.0, 697.0, 567.0, 393.0, 320.0, 258.0,
310.0, 11916.0, 11508.0, 11564.0, 11520.0, 10650.0, 8704.0, 7373.0,
6083.0, 5447.0, 4995.0, 4729.0, 4054.0, 3076.0, 2080.0, 1468.0,
959.0, 897.0, 11911.0, 11346.0, 9841.0, 8485.0, 7192.0, 6216.0,
5623.0, 5244.0, 4673.0, 3734.0, 2723.0, 2070.0, 1621.0, 1250.0,
906.0, 630.0, 466.0, 11914.0, 11481.0, 10203.0, 8634.0, 7427.0,
6268.0, 5690.0, 5252.0, 4801.0, 3904.0, 2894.0, 2131.0, 1689.0,
1278.0, 944.0, 632.0, 508.0], dtype=object)sol_geo.get_pop(years = [2010, 2011, 2012], var = 'Population', measure = 'Total', ages = ["0-4"])#, agg = True) loc_filter = ['Honiara', 'Choiseul'],| core | Age | Population | |
|---|---|---|---|
| year | Age_Bracket | Total | |
| (core, location) | |||
| Central | 2010 | 0-4 | 4156.0 |
| Central | 2011 | 0-4 | 4115.0 |
| Central | 2012 | 0-4 | 4062.0 |
| Choiseul | 2010 | 0-4 | 4339.0 |
| Choiseul | 2011 | 0-4 | 4366.0 |
| Choiseul | 2012 | 0-4 | 4397.0 |
| Guadalcanal | 2010 | 0-4 | 16116.0 |
| Guadalcanal | 2011 | 0-4 | 16545.0 |
| Guadalcanal | 2012 | 0-4 | 17002.0 |
| Honiara | 2010 | 0-4 | 8338.0 |
| Honiara | 2011 | 0-4 | 8578.0 |
| Honiara | 2012 | 0-4 | 8794.0 |
| Isabel | 2010 | 0-4 | 4023.0 |
| Isabel | 2011 | 0-4 | 4029.0 |
| Isabel | 2012 | 0-4 | 4035.0 |
| Makira-Ulawa | 2010 | 0-4 | 7400.0 |
| Makira-Ulawa | 2011 | 0-4 | 7434.0 |
| Makira-Ulawa | 2012 | 0-4 | 7420.0 |
| Malaita | 2010 | 0-4 | 22242.0 |
| Malaita | 2011 | 0-4 | 21921.0 |
| Malaita | 2012 | 0-4 | 21554.0 |
| Rennell-Bell | 2010 | 0-4 | 409.0 |
| Rennell-Bell | 2011 | 0-4 | 409.0 |
| Rennell-Bell | 2012 | 0-4 | 411.0 |
| Temotu | 2010 | 0-4 | 3048.0 |
| Temotu | 2011 | 0-4 | 2985.0 |
| Temotu | 2012 | 0-4 | 2933.0 |
| Western | 2010 | 0-4 | 11916.0 |
| Western | 2011 | 0-4 | 11911.0 |
| Western | 2012 | 0-4 | 11914.0 |
sol_geo.locations{'Province': array(['Central', 'Choiseul', 'Guadalcanal', 'Honiara', 'Isabel',
'Makira-Ulawa', 'Malaita', 'Rennell-Bell', 'Temotu', 'Western'],
dtype=object),
'Constituency': array(['Auki-Langalanga', 'Baegu-Asifola', 'Central Guadalcanal',
'Central Honiara', "Central Kwara'ae", 'Central Makira',
'East AreAre', 'East Choiseul', 'East Guadalcanal', 'East Honiara',
'East Kwaio', 'East Makira', 'East Malaita', 'East-Central',
'Fataleka', 'Gao-Bugotu', 'Gizo-Kolombangara',
'Hograno-Kia-Havulei', 'Lau-Mbaelela', 'Malaita Outer Island',
'Marigne-Kokota', 'Marovo', 'Nggela', 'North Guadalcanal',
'North Malaita', 'North New Georgia', 'North Vella la Vella',
'North West Choiseul', 'North West Guadalcanl', 'North-East',
'Ranongga-Simbo', 'Rennell-Bellona', 'Savo-Russull Islands',
'Shortland Islands', 'Small Malaita', 'South Choiseul',
'South Guadalcanal', 'South New Georgia-Rendova',
'South Vella la Vella', 'Temotu Pele', 'Temotu Vatu',
'Temotu-Nende', 'Ulawa-Ugi', 'West Areare', 'West Guadalcanal',
'West Honiara', 'West Kwaio', "West Kwara'ae", 'West Makira',
'West New Georgia-Vonavona'], dtype=object),
'Ward': array(['Aba - Asimeuru', 'Aiaisi', 'Aimela', 'Aola', 'Areare',
'Arosi East', 'Arosi North', 'Arosi South', 'Arosi West', 'Asimae',
'Auki', 'Avuavu', 'Babatana', 'Bangara', 'Batava', 'Bauro Central',
'Bauro East', 'Bauro West', 'Bilua', 'Birao', 'Buala',
'Buini Tusu', 'Buma', 'Central Ranongga', 'Cruz', 'Dovele',
'Duff Islands', 'Duidui', 'East Baegu', 'East Gela',
'East Ghaobata', 'East Ghongau', 'East Tasimboko',
'East Te Nggano', 'Fauabu', 'Faumamanu - Kwai', 'Fenualoa',
'Foondo - Gwaiau', 'Fouenda', 'Gizo', 'Graciosa Bay', 'Gulalafou',
'Haununu', 'Havulei', 'Hovukoilo', 'Inner Shortlands', 'Iringgila',
'Japuana', 'Kaloka', 'Kangava', 'Katupika', 'Kerepangara', 'Kia',
'Kirugela', 'Kmaga', 'Kokota', "Kola'A", 'Kolokarako',
'Kolombaghea', 'Kolomola', 'Kolotubi', 'Koviloko', 'Kukum',
'Kusaghe', 'Kwaimela - Radefasu', 'Kwarekwareo', 'Langalanga',
'Lipe-Temua', 'Longgu', 'Lovukol', 'Luaniua', 'Lughu',
'Luva Station', 'Malango', "Malu'U", 'Mandalua - Folotana',
'Manuopo', 'Mareho', 'Matakwalao', 'Matangi', 'Mataniko',
'Mbanika', 'Mbumburu', 'Moli', 'Mugihenua', 'Munda', 'Nafinua',
'Naha', 'Nanggu-Lord Howe', 'Nea-Noole', 'Nenumpo', 'Neo',
'Nevenema', 'Nggatokae', 'Nggosi', 'Nipua-Nopoli', 'Nono', 'Noro',
'North East Gela', 'North East Santa Cruz', 'North Kolombangara',
'North Ranongga', 'North Rendova', 'North Savo', 'North Ulawa',
'North Vangunu', 'North West Gela', 'Nusa Roviana',
'Outer Shortlands', 'Panatina', 'Paripao', 'Pavuvu', 'Pelau',
'Polo', 'Polynesian Outer Islands', 'Raroisuu', 'Rawo',
'Rove - Lengakiki', 'Roviana Lagoon', "Sa'Aiho", 'Saghalu',
'Samasodu', 'Sandfly-Buenavista', 'Santa Ana', 'Santa Catalina',
'Savulei', 'Senga', 'Siesie', 'Sigana', 'Sikaiana', 'Simbo',
'South East Gela', 'South Kolombangara', 'South Ranongga',
'South Rendova', 'South Savo', 'South Ulawa', 'South West Gela',
'Star Harbour North', 'Star Harbour South', 'Sububenu- Burianias',
'Sulufou - Kwarande', 'Susubona', 'Susuka', 'Tai', 'Takwa',
'Talise', 'Tandai', 'Tangarare', 'Tatamba', 'Tavula', 'Tepakaza',
'Tetau Nangoto', 'Tetekanji', 'Tikopia', 'Tirotonga', 'Tulagi',
'Ugi And Pio', 'Utupua', 'Valasi', 'Vanikoro', 'Vasiduki',
'Vatukulau', 'Vavaea', 'Viviru', 'Vonavona', 'Vonunu', 'Vuhokesa',
'Vulolo', 'Vura', 'Waghina', 'Wainoni East', 'Wainoni West',
'Wanderer Bay', 'Waneagu - Taelanasin', 'Weagu Silana Sina',
'Weather Coast', 'West Baegu \xa0- Fatale', 'West Gela',
'West Ghaobata', 'West Ghongau', 'West Te Nggano', 'West Ulawa'],
dtype=object)}Save and Load
Test that the newly created solomon geo object can be saved to aws
sol_geo.save_pickle()Test that we can connect to the aws s3 bucket
# Create the S3 object
obj = s3_client().get_object(
Bucket = 'hobby-data',
Key = 'test.txt',
)
# Read in the pickle
try:
data = obj['Body'].read()
except:
raise ValueError("Issue dowloading test file from AWS.")SolomonGeo.load_pickle('/testData/', aws=True)<__main__.SolomonGeo>Test that gen_stored creates a copy correctly from a json serialised dataframe
stored_geo = sol_geo.get_store()
# TODO I need to potentially created a function to check if two objects are the samepd.DataFrame(stored_geo.data['data']['elec'])| Type | Year | Geo | loc_name | Candidate | Total | Proportion | candParty | Party | Order | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | National Parliament | 2024 | Constituency | Auki-Langalanga | Joseph Waleanisia | 108 | 0.017 | Joseph Waleanisia (UP) | UP | 4 |
| 1 | National Parliament | 2024 | Constituency | Auki-Langalanga | Obadiah Koti | 257 | 0.039 | Obadiah Koti (PLD) | PLD | 3 |
| 2 | National Parliament | 2024 | Constituency | Auki-Langalanga | Talauburi Anisi | 488 | 0.075 | Talauburi Anisi (KAD) | KAD | 2 |
| 3 | National Parliament | 2024 | Constituency | Auki-Langalanga | David Filia | 1348 | 0.207 | David Filia (OUR) | OUR | 1 |
| 4 | National Parliament | 2024 | Constituency | Auki-Langalanga | Cooper Wale | 4314 | 0.662 | Cooper Wale (SID) | SID | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 329 | National Parliament | 2024 | Constituency | West New Georgia-Vonavona | Usa Kama | 11 | 0.001 | Usa Kama (IND) | IND | 4 |
| 330 | National Parliament | 2024 | Constituency | West New Georgia-Vonavona | Ben Anikwai | 45 | 0.006 | Ben Anikwai (IND) | IND | 3 |
| 331 | National Parliament | 2024 | Constituency | West New Georgia-Vonavona | Trevor Veo | 1514 | 0.190 | Trevor Veo (OUR) | OUR | 2 |
| 332 | National Parliament | 2024 | Constituency | West New Georgia-Vonavona | V Tausinga | 2524 | 0.317 | V Tausinga (UP) | UP | 1 |
| 333 | National Parliament | 2024 | Constituency | West New Georgia-Vonavona | George Temahua | 3869 | 0.486 | George Temahua (SID) | SID | 0 |
334 rows × 10 columns
restored_geo = SolomonGeo.gen_stored(stored_geo.data)restored_geo.get_census(geo_filter = 'Constituency', loc_filter=['Nggela', 'East AreAre'])| Total Households | Total population | Male population | Female population | |
|---|---|---|---|---|
| (core, location) | ||||
| East AreAre | 1105 | 7099 | 3567.0 | 3532.0 |
| Nggela | 3315 | 16811 | 8500.0 | 8311.0 |
# TODO create a test to check that two object are the same. Probably need to write defualt behaviour into
# object. Maybe use fastcore test_eq
#test_eq(stored_geo.geo_df, sol_geo.geo_df)Test filtering of down to multiple locations
#test_eq(sol_geo.get_census(geo_filter = 'constituency')['total_pop'].sum(), const_df['total_pop'].sum())
sol_geo.get_census(geo_filter = 'Constituency', loc_filter=['Nggela', 'East AreAre'])| Total Households | Total population | Male population | Female population | |
|---|---|---|---|---|
| (core, location) | ||||
| East AreAre | 1105 | 7099 | 3567.0 | 3532.0 |
| Nggela | 3315 | 16811 | 8500.0 | 8311.0 |
Test Proportions
#test_eq(sol_geo.get_census(geo_filter = 'constituency')['total_pop'].sum(), const_df['total_pop'].sum())
pd.DataFrame(sol_geo.get_census(geo_filter = 'Constituency', loc_filter=['Nggela', 'East AreAre'], agg = True, type_filter='Proportion'))| 0 | |
|---|---|
| Total Households | 0.084609 |
| Total population | 0.457695 |
| Male population | 0.230992 |
| Female population | 0.226704 |
# TODO work out how to check save and load are the same as a test
# Might need to wreite and equality function
restored_geo.population| core | Age | Population | |||||||
|---|---|---|---|---|---|---|---|---|---|
| id | agg | type | year | Age_Bracket | Numerical_Bracket | Males | Females | Total | |
| (core, location) | |||||||||
| Central | 4 | Province | Total | 2010 | 0-4 | 0 | 2119.000000 | 2037.000000 | 4156.000000 |
| Central | 4 | Province | Proportion | 2010 | 0-4 | 0 | 0.007116 | 0.007210 | 0.007162 |
| Central | 4 | Province | Total | 2010 | 5-9 | 5 | 2074.000000 | 1914.000000 | 3988.000000 |
| Central | 4 | Province | Proportion | 2010 | 5-9 | 5 | 0.006965 | 0.006775 | 0.006872 |
| Central | 4 | Province | Total | 2010 | 10-14 | 10 | 1732.000000 | 1556.000000 | 3289.000000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| Western | 2 | Province | Proportion | 2025 | 70-74 | 70 | 0.001877 | 0.001965 | 0.001920 |
| Western | 2 | Province | Total | 2025 | 75-79 | 75 | 466.000000 | 493.000000 | 959.000000 |
| Western | 2 | Province | Proportion | 2025 | 75-79 | 75 | 0.001197 | 0.001315 | 0.001255 |
| Western | 2 | Province | Total | 2025 | 80+ | 80 | 448.000000 | 449.000000 | 897.000000 |
| Western | 2 | Province | Proportion | 2025 | 80+ | 80 | 0.001150 | 0.001197 | 0.001173 |
5440 rows × 9 columns
test = sol_geo.population
test.loc[:, ('core', 'location')] = test.indextest| core | Age | Population | core | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| id | agg | type | year | Age_Bracket | Numerical_Bracket | Males | Females | Total | location | |
| (core, location) | ||||||||||
| Central | 4 | Province | Total | 2010 | 0-4 | 0 | 2119.000000 | 2037.000000 | 4156.000000 | Central |
| Central | 4 | Province | Proportion | 2010 | 0-4 | 0 | 0.007116 | 0.007210 | 0.007162 | Central |
| Central | 4 | Province | Total | 2010 | 5-9 | 5 | 2074.000000 | 1914.000000 | 3988.000000 | Central |
| Central | 4 | Province | Proportion | 2010 | 5-9 | 5 | 0.006965 | 0.006775 | 0.006872 | Central |
| Central | 4 | Province | Total | 2010 | 10-14 | 10 | 1732.000000 | 1556.000000 | 3289.000000 | Central |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| Western | 2 | Province | Proportion | 2025 | 70-74 | 70 | 0.001877 | 0.001965 | 0.001920 | Western |
| Western | 2 | Province | Total | 2025 | 75-79 | 75 | 466.000000 | 493.000000 | 959.000000 | Western |
| Western | 2 | Province | Proportion | 2025 | 75-79 | 75 | 0.001197 | 0.001315 | 0.001255 | Western |
| Western | 2 | Province | Total | 2025 | 80+ | 80 | 448.000000 | 449.000000 | 897.000000 | Western |
| Western | 2 | Province | Proportion | 2025 | 80+ | 80 | 0.001150 | 0.001197 | 0.001173 | Western |
5440 rows × 10 columns
# TODO should do a count check for the geojson similar to this
sol_geo.census[sol_geo.census['core']['agg'] == 'Constituency'].count()core id 100
location 100
agg 100
type 100
Key Statistics Total Households 100
...
Disability walking Some Difficulty 100
Cannot perform the task 100
Disability remembering No Difficulty at all 100
Some Difficulty 100
Cannot perform the task 100
Length: 339, dtype: int64TODO check that all proportions are less than 0
# TODO filter and test equality?
test = sol_geo.get_geojson(geo_filter = 'Ward')sol_geo.geo| geometry | id | agg | year | location | |
|---|---|---|---|---|---|
| location | |||||
| Aba - Asimeuru | MULTIPOLYGON (((161.54336 -9.54751, 161.54367 ... | 722 | Ward | 2009 | Aba - Asimeuru |
| Aiaisi | MULTIPOLYGON (((161.26900 -9.21229, 161.27144 ... | 719 | Ward | 2009 | Aiaisi |
| Aimela | MULTIPOLYGON (((160.68653 -8.71158, 160.67020 ... | 702 | Ward | 2009 | Aimela |
| Aola | MULTIPOLYGON (((160.50139 -9.52996, 160.50196 ... | 616 | Ward | 2009 | Aola |
| Areare | MULTIPOLYGON (((161.35878 -9.46664, 161.35859 ... | 720 | Ward | 2009 | Areare |
| ... | ... | ... | ... | ... | ... |
| West Makira | MULTIPOLYGON (((161.50441 -10.51180, 161.50559... | 844 | Constituency | 2009 | West Makira |
| West New Georgia-Vonavona | MULTIPOLYGON (((157.27598 -8.35143, 157.26991 ... | 209 | Constituency | 2009 | West New Georgia-Vonavona |
| West Te Nggano | MULTIPOLYGON (((160.35206 -11.66366, 160.30721... | 502 | Ward | 2009 | West Te Nggano |
| West Ulawa | MULTIPOLYGON (((161.97730 -9.77521, 161.94899 ... | 803 | Ward | 2009 | West Ulawa |
| Western | MULTIPOLYGON (((158.25148 -8.78861, 158.24885 ... | 2 | Province | 2009 | Western |
243 rows × 5 columns
The sum after merging and filtering should equal the sum from the raw dataset.
test_eq(sol_geo.get_census(geo_filter = 'Constituency',var = 'Key Statistics', measure = 'Total Households', agg = True)[0],
const_df.loc[const_df['type'] == 'Total'].loc[const_df['agg'] == 'Constituency']['Key Statistics: Total Households'].sum())/tmp/ipykernel_4212/2628698497.py:1: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
test_eq(sol_geo.get_census(geo_filter = 'Constituency',var = 'Key Statistics', measure = 'Total Households', agg = True)[0],sol_geo.get_census(geo_filter = 'Constituency', var='Key Statistics', measure = 'Total Households', agg = True)Total Households 91251
dtype: int64sol_geo.get_census(geo_filter = 'Constituency', var='Key Statistics', measure = 'Total Households', loc_filter=['West Guadalcanal']).values[0]array([1671])sol_geo.census['core'].filter(items = ['id'])| id | |
|---|---|
| pk | |
| Aba - Asimeuru_Proportion | 722 |
| Aba - Asimeuru_Total | 722 |
| Aiaisi_Total | 719 |
| Aiaisi_Proportion | 719 |
| Aimela_Proportion | 702 |
| ... | ... |
| West Te Nggano_Proportion | 502 |
| West Ulawa_Proportion | 803 |
| West Ulawa_Total | 803 |
| Western_Total | 2 |
| Western_Proportion | 2 |
486 rows × 1 columns
# TODO -test that they all sum to one
test_agg = sol_geo.get_census(geo_filter = 'Constituency', type_filter='Proportion', var = "Main source of household drinking water",
agg = True)
test_eq(test_agg.sum(), 1.0)
test_aggmetered SIWA drinking water 0.091681
communal standpipe 0.351141
private water tank 0.124711
communal water tank 0.106191
well - protected 0.020811
well - unprotected 0.017819
river or stream 0.245433
bottled water 0.006115
other source of drinking water 0.036098
dtype: float64sol_geo.get_census(geo_filter = 'Constituency', type_filter='Proportion', var = "Key Statistics",
agg = True)Total Households 1.000
Total population 1.000
Male population 0.513
Female population 0.487
dtype: float64test_agg.sum()1.0sol_geo.ages['0-4',
'10-14',
'15-19',
'20-24',
'25-29',
'30-34',
'35-39',
'40-44',
'45-49',
'5-9',
'50-54',
'55-59',
'60-64',
'65-69',
'70-74',
'75-79',
'80+']sol_geo.pop_years[2010,
2011,
2012,
2013,
2014,
2015,
2016,
2017,
2018,
2019,
2020,
2021,
2022,
2023,
2024,
2025]Testing of Population Getter
sol_geo.get_pop(years = [2024],var='Population',
measure='Total',
loc_filter=None,
type_filter='Total',
agg=False,
ages=['0-4']
)| core | Age | Population | |
|---|---|---|---|
| year | Age_Bracket | Total | |
| (core, location) | |||
| Central | 2024 | 0-4 | 3641.0 |
| Choiseul | 2024 | 0-4 | 4973.0 |
| Guadalcanal | 2024 | 0-4 | 22087.0 |
| Honiara | 2024 | 0-4 | 8535.0 |
| Isabel | 2024 | 0-4 | 4203.0 |
| Makira-Ulawa | 2024 | 0-4 | 7242.0 |
| Malaita | 2024 | 0-4 | 19039.0 |
| Rennell-Bell | 2024 | 0-4 | 581.0 |
| Temotu | 2024 | 0-4 | 2697.0 |
| Western | 2024 | 0-4 | 11834.0 |
test = sol_geo.get_pop(years = [2023, 2024],var='Population',
measure='Total',
loc_filter=None,
type_filter='Total',
agg=False,
ages=['0-4']
)test = sol_geo.get_pop(years = sol_geo.pop_years,var='Population',
measure='Total',
type_filter='Total',
agg=True,
agg_location = True,
ages=sol_geo.ages
)Check that sum of proportions rounded is 100
prop = sol_geo.get_pop(years = [sol_geo.pop_years[-1]],
var='Population',
measure='Total',
type_filter='Proportion',
agg=True,
ages=sol_geo.ages,
).values[0, 0]
test_eq(1, int(prop))sol_geo.get_pop(years = [2024],
var='Population',
measure='Total',
type_filter='Total',
agg=True,
ages=sol_geo.ages,
agg_location = True,
)| Population | ||
|---|---|---|
| Total | ||
| (core, year) | (core, location) | |
| 2024 | Central | 34359.0 |
| Choiseul | 40226.0 | |
| Guadalcanal | 178238.0 | |
| Honiara | 97801.0 | |
| Isabel | 38134.0 | |
| Makira-Ulawa | 59705.0 | |
| Malaita | 165610.0 | |
| Rennell-Bell | 4702.0 | |
| Temotu | 26182.0 | |
| Western | 105367.0 |
sol_geo.locations['Province']array(['Central', 'Choiseul', 'Guadalcanal', 'Honiara', 'Isabel',
'Makira-Ulawa', 'Malaita', 'Rennell-Bell', 'Temotu', 'Western'],
dtype=object)sol_geo.census| core | Key Statistics | Main source of household drinking water | ... | Disability seeing | Disability hearing | Disability walking | Disability remembering | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | location | agg | type | Total Households | metered SIWA drinking water | communal standpipe | private water tank | communal water tank | well - protected | ... | Cannot perform the task | No Difficulty at all | Some Difficulty | Cannot perform the task | No Difficulty at all | Some Difficulty | Cannot perform the task | No Difficulty at all | Some Difficulty | Cannot perform the task | |
| pk | |||||||||||||||||||||
| Aba - Asimeuru_Proportion | 722 | Aba - Asimeuru | Ward | Proportion | 1 | 0.0000 | 0.5378 | 0.0245 | 0.0085 | 0.0011 | ... | 0.003849 | 0.940843 | 0.054295 | 0.004862 | 0.880065 | 0.105754 | 0.014182 | 0.906807 | 0.081645 | 0.011548 |
| Aba - Asimeuru_Total | 722 | Aba - Asimeuru | Ward | Total | 939 | 0.0000 | 505.0000 | 23.0000 | 8.0000 | 1.0000 | ... | 19.000000 | 4644.000000 | 268.000000 | 24.000000 | 4344.000000 | 522.000000 | 70.000000 | 4476.000000 | 403.000000 | 57.000000 |
| Aiaisi_Total | 719 | Aiaisi | Ward | Total | 561 | 1.0000 | 403.0000 | 9.0000 | 3.0000 | 1.0000 | ... | 9.000000 | 3415.000000 | 149.000000 | 10.000000 | 3316.000000 | 239.000000 | 19.000000 | 3349.000000 | 205.000000 | 20.000000 |
| Aiaisi_Proportion | 719 | Aiaisi | Ward | Proportion | 1 | 0.0018 | 0.7184 | 0.0160 | 0.0053 | 0.0018 | ... | 0.002518 | 0.955512 | 0.041690 | 0.002798 | 0.927812 | 0.066872 | 0.005316 | 0.937045 | 0.057359 | 0.005596 |
| Aimela_Proportion | 702 | Aimela | Ward | Proportion | 1 | 0.0164 | 0.2937 | 0.0762 | 0.1921 | 0.1360 | ... | 0.001571 | 0.959288 | 0.038749 | 0.001964 | 0.954444 | 0.039796 | 0.005760 | 0.951172 | 0.043330 | 0.005498 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| West Te Nggano_Proportion | 502 | West Te Nggano | Ward | Proportion | 1 | 0.0000 | 0.0000 | 0.3704 | 0.4568 | 0.0000 | ... | 0.000000 | 0.978836 | 0.018519 | 0.002646 | 0.968254 | 0.031746 | 0.000000 | 0.986772 | 0.013228 | 0.000000 |
| West Ulawa_Proportion | 803 | West Ulawa | Ward | Proportion | 1 | 0.0099 | 0.7673 | 0.0050 | 0.0050 | 0.0000 | ... | 0.000000 | 0.929070 | 0.069767 | 0.001163 | 0.886047 | 0.113953 | 0.000000 | 0.920930 | 0.077907 | 0.001163 |
| West Ulawa_Total | 803 | West Ulawa | Ward | Total | 202 | 2.0000 | 155.0000 | 1.0000 | 1.0000 | 0.0000 | ... | 0.000000 | 799.000000 | 60.000000 | 1.000000 | 762.000000 | 98.000000 | 0.000000 | 792.000000 | 67.000000 | 1.000000 |
| Western_Total | 2 | Western | Province | Total | 13762 | 37.0000 | 4151.0000 | 4596.0000 | 2426.0000 | 46.0000 | ... | 159.000000 | 72641.000000 | 3788.000000 | 220.000000 | 71496.000000 | 4644.000000 | 509.000000 | 69162.000000 | 6901.000000 | 586.000000 |
| Western_Proportion | 2 | Western | Province | Proportion | 1 | 0.0027 | 0.3016 | 0.3340 | 0.1763 | 0.0033 | ... | 0.002074 | 0.947710 | 0.049420 | 0.002870 | 0.932771 | 0.060588 | 0.006641 | 0.902321 | 0.090034 | 0.007645 |
486 rows × 339 columns
Check Some of the datasets
necessary??
sol_geo.census| core | Key Statistics | Main source of household drinking water | ... | Disability seeing | Disability hearing | Disability walking | Disability remembering | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | location | agg | type | Total Households | metered SIWA drinking water | communal standpipe | private water tank | communal water tank | well - protected | ... | Cannot perform the task | No Difficulty at all | Some Difficulty | Cannot perform the task | No Difficulty at all | Some Difficulty | Cannot perform the task | No Difficulty at all | Some Difficulty | Cannot perform the task | |
| pk | |||||||||||||||||||||
| Aba - Asimeuru_Proportion | 722 | Aba - Asimeuru | Ward | Proportion | 1 | 0.0000 | 0.5378 | 0.0245 | 0.0085 | 0.0011 | ... | 0.003849 | 0.940843 | 0.054295 | 0.004862 | 0.880065 | 0.105754 | 0.014182 | 0.906807 | 0.081645 | 0.011548 |
| Aba - Asimeuru_Total | 722 | Aba - Asimeuru | Ward | Total | 939 | 0.0000 | 505.0000 | 23.0000 | 8.0000 | 1.0000 | ... | 19.000000 | 4644.000000 | 268.000000 | 24.000000 | 4344.000000 | 522.000000 | 70.000000 | 4476.000000 | 403.000000 | 57.000000 |
| Aiaisi_Total | 719 | Aiaisi | Ward | Total | 561 | 1.0000 | 403.0000 | 9.0000 | 3.0000 | 1.0000 | ... | 9.000000 | 3415.000000 | 149.000000 | 10.000000 | 3316.000000 | 239.000000 | 19.000000 | 3349.000000 | 205.000000 | 20.000000 |
| Aiaisi_Proportion | 719 | Aiaisi | Ward | Proportion | 1 | 0.0018 | 0.7184 | 0.0160 | 0.0053 | 0.0018 | ... | 0.002518 | 0.955512 | 0.041690 | 0.002798 | 0.927812 | 0.066872 | 0.005316 | 0.937045 | 0.057359 | 0.005596 |
| Aimela_Proportion | 702 | Aimela | Ward | Proportion | 1 | 0.0164 | 0.2937 | 0.0762 | 0.1921 | 0.1360 | ... | 0.001571 | 0.959288 | 0.038749 | 0.001964 | 0.954444 | 0.039796 | 0.005760 | 0.951172 | 0.043330 | 0.005498 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| West Te Nggano_Proportion | 502 | West Te Nggano | Ward | Proportion | 1 | 0.0000 | 0.0000 | 0.3704 | 0.4568 | 0.0000 | ... | 0.000000 | 0.978836 | 0.018519 | 0.002646 | 0.968254 | 0.031746 | 0.000000 | 0.986772 | 0.013228 | 0.000000 |
| West Ulawa_Proportion | 803 | West Ulawa | Ward | Proportion | 1 | 0.0099 | 0.7673 | 0.0050 | 0.0050 | 0.0000 | ... | 0.000000 | 0.929070 | 0.069767 | 0.001163 | 0.886047 | 0.113953 | 0.000000 | 0.920930 | 0.077907 | 0.001163 |
| West Ulawa_Total | 803 | West Ulawa | Ward | Total | 202 | 2.0000 | 155.0000 | 1.0000 | 1.0000 | 0.0000 | ... | 0.000000 | 799.000000 | 60.000000 | 1.000000 | 762.000000 | 98.000000 | 0.000000 | 792.000000 | 67.000000 | 1.000000 |
| Western_Total | 2 | Western | Province | Total | 13762 | 37.0000 | 4151.0000 | 4596.0000 | 2426.0000 | 46.0000 | ... | 159.000000 | 72641.000000 | 3788.000000 | 220.000000 | 71496.000000 | 4644.000000 | 509.000000 | 69162.000000 | 6901.000000 | 586.000000 |
| Western_Proportion | 2 | Western | Province | Proportion | 1 | 0.0027 | 0.3016 | 0.3340 | 0.1763 | 0.0033 | ... | 0.002074 | 0.947710 | 0.049420 | 0.002870 | 0.932771 | 0.060588 | 0.006641 | 0.902321 | 0.090034 | 0.007645 |
486 rows × 339 columns
sol_geo.geo| geometry | id | agg | year | location | |
|---|---|---|---|---|---|
| location | |||||
| Aba - Asimeuru | MULTIPOLYGON (((161.54336 -9.54751, 161.54367 ... | 722 | Ward | 2009 | Aba - Asimeuru |
| Aiaisi | MULTIPOLYGON (((161.26900 -9.21229, 161.27144 ... | 719 | Ward | 2009 | Aiaisi |
| Aimela | MULTIPOLYGON (((160.68653 -8.71158, 160.67020 ... | 702 | Ward | 2009 | Aimela |
| Aola | MULTIPOLYGON (((160.50139 -9.52996, 160.50196 ... | 616 | Ward | 2009 | Aola |
| Areare | MULTIPOLYGON (((161.35878 -9.46664, 161.35859 ... | 720 | Ward | 2009 | Areare |
| ... | ... | ... | ... | ... | ... |
| West Makira | MULTIPOLYGON (((161.50441 -10.51180, 161.50559... | 844 | Constituency | 2009 | West Makira |
| West New Georgia-Vonavona | MULTIPOLYGON (((157.27598 -8.35143, 157.26991 ... | 209 | Constituency | 2009 | West New Georgia-Vonavona |
| West Te Nggano | MULTIPOLYGON (((160.35206 -11.66366, 160.30721... | 502 | Ward | 2009 | West Te Nggano |
| West Ulawa | MULTIPOLYGON (((161.97730 -9.77521, 161.94899 ... | 803 | Ward | 2009 | West Ulawa |
| Western | MULTIPOLYGON (((158.25148 -8.78861, 158.24885 ... | 2 | Province | 2009 | Western |
243 rows × 5 columns
# TODO write testing that checks that different methods create equilvalent objects.
# TODO - need to write an equality objecttest.loc[2024]| Population | |
|---|---|
| Total | |
| (core, location) | |
| Central | 34359.0 |
| Choiseul | 40226.0 |
| Guadalcanal | 178238.0 |
| Honiara | 97801.0 |
| Isabel | 38134.0 |
| Makira-Ulawa | 59705.0 |
| Malaita | 165610.0 |
| Rennell-Bell | 4702.0 |
| Temotu | 26182.0 |
| Western | 105367.0 |
len(sol_geo.pop_years)16Test that the election data Exists
sol_geo.elec.head()| Type | Year | Geo | loc_name | Candidate | Total | Proportion | candParty | Party | Order | |
|---|---|---|---|---|---|---|---|---|---|---|
| 4 | National Parliament | 2024 | Constituency | Auki-Langalanga | Joseph Waleanisia | 108 | 0.017 | Joseph Waleanisia (UP) | UP | 4 |
| 3 | National Parliament | 2024 | Constituency | Auki-Langalanga | Obadiah Koti | 257 | 0.039 | Obadiah Koti (PLD) | PLD | 3 |
| 2 | National Parliament | 2024 | Constituency | Auki-Langalanga | Talauburi Anisi | 488 | 0.075 | Talauburi Anisi (KAD) | KAD | 2 |
| 1 | National Parliament | 2024 | Constituency | Auki-Langalanga | David Filia | 1348 | 0.207 | David Filia (OUR) | OUR | 1 |
| 0 | National Parliament | 2024 | Constituency | Auki-Langalanga | Cooper Wale | 4314 | 0.662 | Cooper Wale (SID) | SID | 0 |
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 |