implementing changes to visualizations #1

fetch_new_builds/fetch_new_builds.py

@@ -3,12 +3,37 @@
 from lib_db.longlet_fund.models import PropertyHull
 import pandas as pd
 
-# pd.set_option('display.max_rows', 500)
-# pd.set_option("display.max_columns", 500)
+pd.set_option('display.max_rows', 500)
+pd.set_option("display.max_columns", 500)
 # pd.set_option('display.width', 1000)
 
 
 class FetchNewBuild:
+    zoopla_historic_prop_type_mapping = {
+        "terraced house": "house", 
+        "semi-detached house": "house", 
+        "bungalow": "house", 
+        "link-detached house": "house", 
+        "detached house": "house", 
+        "detached bungalow": "house", 
+        "terraced bungalow": "house", 
+        "end terrace house": "house", 
+        "semi-detached bungalow": "house", 
+        "town house": "house", 
+        "cottage": "house", 
+        "town house": "house", 
+        "mews house": "house", 
+        "houseboat": "house", 
+        "country house": "house", 
+        "barn conversion/farmhouse": "house", 
+        "barn conversion": "house", 
+        "house": "house",
+        "flat": "flat", 
+        "maisonette": "flat", 
+        "studio": "flat", 
+        "block of flats": "flat",
+    }
+
     def fetch_new_builds(self):
         new_build_object = (
             ZooplaSale.objects.select_related("zoopla", "zoopla__postcode")
@@ -38,39 +63,45 @@ def fetch_new_builds(self):
         print()
 
         new_build_df = pd.DataFrame.from_records(new_build_object.values()).fillna(0)
+        new_build_df["property_type"] = new_build_df.apply(lambda x: (self.zoopla_historic_prop_type_mapping[x.property_type] or None), axis=1)
 
         print("Records: ")        
         print(new_build_df)
         print()
 
+        # new_build_df.apply(lambda x: (self._fetch_matching_hulls(x) or {}), axis=1)
+
         new_build_df["average_income"] = new_build_df.apply(lambda x: (self.fetch_matching_hulls(x) or {}).get("average_income") or 0, axis=1) #or 0
         new_build_df.drop(new_build_df[new_build_df.average_income == 0].index, inplace=True)
         new_build_df["yield"] = round(pd.to_numeric(new_build_df["average_income"]/new_build_df["price"]*100), 2)
-
+        new_build_df["postal_sector"] = new_build_df["postcode"].str[:-2]
+
         print("Records with yield: ")
         print(new_build_df)
 
         new_build_df.to_csv("new_builds_in_hulls_with_yields.csv", sep=",", encoding="utf-8", index=False)
 
-    def fetch_matching_hulls(self, row):
-        # print(row)
+
+
+    def _fetch_matching_hulls(self, row):
         record_filter = {
             "geometry__intersects": row.point,
             "bedrooms": row.bedrooms,
             # "property_type": row.property_type,
         }
-        return (
+        item = (
             PropertyHull.objects
             .prefetch_related("propertyhullstatistic_set")
-            .filter(**record_filter)
-
+            .filter(**record_filter)    
             .annotate(
                 average_income=F("propertyhullstatistic__average_income"),
             )
             .values("average_income")
             .order_by("income_band")
             .first()
         )
+        print(item)
+        return item
 
 
 if __name__ == "__main__":

property_count_per_city/cities_by_property_number.csv

@@ -0,0 +1,221 @@
+,city,count
+65,East Cambridgeshire,1
+47,Chichester,1
+49,Chorley,1
+183,Stroud,1
+28,Bromsgrove,1
+63,Dover,1
+67,East Northamptonshire,1
+201,Waverley,1
+180,Stevenage,1
+72,Eden,1
+170,South Norfolk,1
+171,South Northamptonshire,1
+198,Walsall,1
+127,Newcastle-under-Lyme,1
+134,North West Leicestershire,1
+124,New Forest,1
+212,Windsor and Maidenhead,1
+92,Harrogate,1
+186,Sutton,2
+182,Stratford-on-Avon,2
+141,Oxford,2
+155,Rushcliffe,2
+156,Rushmoor,2
+91,Harlow,2
+161,Sevenoaks,2
+34,Cambridge,2
+88,Guildford,2
+23,Breckland,2
+75,Epping Forest,3
+151,Rossendale,3
+80,Forest of Dean,3
+172,South Oxfordshire,3
+94,Hart,3
+169,South Lakeland,3
+86,Great Yarmouth,3
+71,Eastleigh,3
+130,North Kesteven,3
+206,West Oxfordshire,3
+210,Wiltshire,3
+14,Blaby,3
+22,Braintree,3
+203,Welwyn Hatfield,3
+197,Vale of White Horse,4
+207,West Suffolk,4
+85,Gravesham,4
+99,Hillingdon,4
+140,Oadby and Wigston,4
+192,Test Valley,4
+193,Tewkesbury,4
+104,Kettering,5
+29,Broxbourne,5
+147,Redditch,5
+178,St Albans,5
+41,Chelmsford,5
+202,Wellingborough,5
+68,East Riding of Yorkshire,5
+121,Mid Sussex,5
+163,Slough,5
+215,Wokingham,5
+179,St. Helens,5
+185,Surrey Heath,5
+181,Stockport,6
+154,Runnymede,6
+51,Corby,6
+123,Mole Valley,6
+76,Epsom and Ewell,6
+48,Chiltern,7
+31,Burnley,7
+59,Daventry,7
+43,Cherwell,7
+33,Calderdale,7
+39,Central Bedfordshire,8
+167,South Holland,8
+173,South Ribble,8
+125,Newark and Sherwood,8
+17,Bolsover,9
+96,Havant,9
+10,Bath and North East Somerset,9
+211,Winchester,9
+187,Swale,9
+98,"Herefordshire, County of",9
+46,Chesterfield,9
+157,Rutland,9
+27,Bromley,9
+149,Ribble Valley,10
+190,Tameside,10
+8,Basingstoke and Deane,10
+40,Charnwood,10
+55,Croydon,10
+129,North Hertfordshire,11
+66,East Hertfordshire,11
+200,Warwick,11
+204,West Berkshire,11
+148,Reigate and Banstead,11
+20,Bracknell Forest,12
+100,Hinckley and Bosworth,12
+101,Huntingdonshire,12
+6,Barnsley,13
+106,Kingston upon Thames,13
+69,East Staffordshire,13
+58,Dartford,13
+189,Swindon,14
+164,Solihull,14
+1,Arun,14
+116,Maidstone,14
+113,Lincoln,14
+18,Bolton,15
+90,Haringey,15
+196,Trafford,16
+97,Havering,16
+12,Bexley,16
+62,Dorset,16
+7,Barrow-in-Furness,17
+194,Thanet,17
+209,Wigan,17
+166,South Gloucestershire,17
+52,Cornwall,17
+38,Carlisle,18
+30,Broxtowe,19
+119,Medway,19
+153,Rugby,19
+132,North Somerset,20
+219,Wyre Forest,20
+208,Westminster,21
+165,South Derbyshire,21
+73,Elmbridge,22
+214,Woking,22
+199,Waltham Forest,22
+146,Redbridge,23
+107,Kirklees,24
+174,South Somerset,25
+109,Lancaster,25
+105,"Kingston upon Hull, City of",25
+102,Ipswich,25
+77,Erewash,26
+2,Ashfield,27
+11,Bedford,27
+84,Gosport,27
+3,Aylesbury Vale,28
+56,Dacorum,30
+83,Gloucester,31
+93,Harrow,33
+216,Worthing,33
+145,Reading,35
+120,Merton,36
+78,Exeter,37
+0,Amber Valley,38
+74,Enfield,38
+191,Tendring,38
+205,West Lindsey,38
+15,Blackburn with Darwen,38
+131,North Lincolnshire,39
+79,Fenland,39
+150,Rochdale,41
+152,Rotherham,41
+168,South Kesteven,41
+217,Wycombe,41
+177,Southwark,42
+128,Newham,42
+218,Wyre,42
+42,Cheltenham,44
+5,Barnet,44
+25,Brighton and Hove,44
+139,Nuneaton and Bedworth,45
+184,Sunderland,45
+103,Islington,46
+142,Peterborough,47
+32,Bury,50
+24,Brent,53
+4,Barking and Dagenham,54
+95,Hastings,55
+36,Canterbury,55
+35,Camden,56
+64,Dudley,57
+82,Gedling,59
+19,"Bournemouth, Christchurch and Poole",65
+135,Northampton,67
+115,Luton,72
+50,Colchester,75
+89,Hackney,75
+112,Lewisham,76
+87,Greenwich,77
+9,Bassetlaw,80
+45,Cheshire West and Chester,83
+108,Knowsley,87
+136,Northumberland,88
+137,Norwich,99
+122,Milton Keynes,100
+159,Sandwell,118
+176,Southampton,119
+53,County Durham,122
+70,East Suffolk,122
+195,Tower Hamlets,126
+81,Gateshead,136
+118,Mansfield,141
+175,South Tyneside,147
+57,Darlington,149
+160,Sefton,173
+54,Coventry,189
+144,Portsmouth,192
+126,Newcastle upon Tyne,200
+158,Salford,202
+61,Doncaster,217
+213,Wirral,220
+133,North Tyneside,222
+111,Leicester,230
+44,Cheshire East,233
+37,Cardiff,268
+162,Sheffield,269
+21,Bradford,280
+188,Swansea,299
+16,Blackpool,337
+60,Derby,366
+143,Plymouth,458
+26,"Bristol, City of",597
+110,Leeds,670
+117,Manchester,692
+114,Liverpool,975
+138,Nottingham,992
+13,Birmingham,1402

property_count_per_city/compute_number_of_props_per_city.py

@@ -4,4 +4,4 @@
 
 df.groupby(["city"])["city"].count().reset_index(name="count").sort_values(
     "count"
-).tail(5).to_csv("top_five_cities_by_property_number.csv", sep=",", encoding="utf-8")
+).to_csv("cities_by_property_number.csv", sep=",", encoding="utf-8")