README.md

@@ -1,10 +1,10 @@
 # aemp-seattle
 
-Initial repository for building up Seattle database for anti-eviction mapping. 
+Initial repository for building up Seattle database for anti-eviction mapping.
 
-Modelled off the [evictorbase pipeline code](https://github.com/antievictionmappingproject/eb-data-pipeline). 
+Modelled off the [evictorbase pipeline code](https://github.com/antievictionmappingproject/eb-data-pipeline).
 
-Relevant but not 1-1 walkthrough of how to programmatically find building owners: [350 Seattle BEPS Repo](https://github.com/BenBagBag/350_seattle_building_ownership/blob/main/How%20to%20Find%20Building%20Owners.ipynb). 
+Relevant but not 1-1 walkthrough of how to programmatically find building owners: [350 Seattle BEPS Repo](https://github.com/BenBagBag/350_seattle_building_ownership/blob/main/How%20to%20Find%20Building%20Owners.ipynb).
 
 [AEMP Seattle Community Agreements](https://docs.google.com/document/d/1ZMeRmPWmhxynBXZ-aV6R2sQBktjNYRL9Xw9PHpkVpJE/edit?usp=drive_link)
 
@@ -18,11 +18,12 @@ Relevant but not 1-1 walkthrough of how to programmatically find building owners
     - `to_load/`: directory for files that can be loaded directly into the PostgreSQL database
 - `experiments/`: directory for Jupyter notebooks for data exploration and script development
 
-## Data Inputs: 
-[eRealProperty](https://kingcounty.gov/en/dept/kcit/data-information-services/gis-center/property-research): King County assessor data for finding the owner of a given parcel. 
-[Washington State Corporations and Charities Filing Database (CCFS)](https://kingcounty.gov/en/dept/kcit/data-information-services/gis-center/property-research): For looking up a parcel owner name and finding the related business listing and related info. 
- 
-TODO: Find a good source for eviction filing data. Those with access can refer to the [potential data source list](https://docs.google.com/spreadsheets/d/1Ew0UrZvP-S74velkWSKaiSGBYcxIAoRH6IGpEzNWX6s/edit?gid=0#gid=0) to find new data sources. 
+## Data Inputs:
+[King County Assessor:](https://info.kingcounty.gov/assessor/DataDownload/default.aspx) Download records of all apartment complexes in King County.
+[eRealProperty](https://kingcounty.gov/en/dept/kcit/data-information-services/gis-center/property-research): King County assessor data for finding the owner of a given parcel.
+[Washington State Corporations and Charities Filing Database (CCFS)](https://kingcounty.gov/en/dept/kcit/data-information-services/gis-center/property-research): For looking up a parcel owner name and finding the related business listing and related info.
+
+TODO: Find a good source for eviction filing data. Those with access can refer to the [potential data source list](https://docs.google.com/spreadsheets/d/1Ew0UrZvP-S74velkWSKaiSGBYcxIAoRH6IGpEzNWX6s/edit?gid=0#gid=0) to find new data sources.
 
 ## Object Storage
 
@@ -34,4 +35,4 @@ Use `lib/minio_helper.py` to extend the functionality
 
 Run `test_minio` in `lib/main.py` to test out that it works (TODO: move this to own testing script, perhaps unit tests)
 
-Note: You will need to have access_key and secret_key in your env before running for this to work, contact @linnealovespie or @ammaratef45 to obtain these keys)
+Note: You will need to have minio_access_key and minio_secret_key in your env before running for this to work, contact @linnealovespie or @ammaratef45 to obtain these keys)

experiments/aemp.org

@@ -85,3 +85,11 @@ Can then determine which one to use
 ** TODO: Do some data cleaning to have names be the same
 eg. Seattle city of is #1 and #4 most common property owner names, should be standardized
 eg. LLC, LLP, L.L.C. etc. all to one format
+
+* 28 August
+From Dox: 610 Harvard Ave East
+Seattle, WA 981027
+Intense management mispractice, would like to know more about the above address.
+** TODO: At some point, cross-reference with registered rental data
+https://data.seattle.gov/Built-Environment/Rental-Property-Registration/j2xh-c7vt/about_data
+lf

lib/minio_helper.py

@@ -7,8 +7,8 @@ class MinioHelper:
     def __init__(self, bucket_name: str):
         self.client = Minio(
             "minio.radmin.live",
-            access_key=os.environ['access_key'],
-            secret_key=os.environ['secret_key']
+            access_key=os.environ['minio_access_key'],
+            secret_key=os.environ['minio_secret_key']
         )
         self.bucket_name = bucket_name
 

processors/corp_owners.py

@@ -1,5 +1,10 @@
 """
-    Utility functions for extracting owners. 
+    Utility functions for 
+    1. LookupCompaniesHelper: looking up a parcel owner in the WA Corporations and Charities Database, 
+    and extracting the best search result. 
+    2. GroupCompaniesHelper (WIP): given a company's stated governors and addresses, 
+    groups together addresses that likely share the same landlord. 
+    
 """
 
 import pandas as pd
@@ -8,13 +13,10 @@ import requests
 import json
 import os
 import re
-# import geopandas as gp
 import urllib.parse
 
-# Utils for finding principals
-
-
 search_for_business_url = 'https://ccfs-api.prod.sos.wa.gov/api/BusinessSearch/GetBusinessSearchList'
+# Old search URL, holding onto in case the above gets blocked
 # search_for_business_url = 'https://cfda.sos.wa.gov/api/BusinessSearch/GetBusinessSearchList'
 principal_url = 'https://ccfs-api.prod.sos.wa.gov/api/BusinessSearch/GetAdvanceBusinessSearchList'
 
@@ -40,11 +42,30 @@ def get_business_search_payload(business_name, page_count, page_num):
     }
 
 def get_business_details(business_id):
-    """ Get business details from the Corporation and charities filing database. """
+    """ Get business details from the Corporation and charities filing database. 
+    """
     url = f"https://ccfs-api.prod.sos.wa.gov/api/BusinessSearch/BusinessInformation?businessID={business_id}"
+    # Old search URL, holding onto in case the above gets blocked
     # url = 'https://cfda.sos.wa.gov/#/BusinessSearch/BusinessInformation?businessID={business_id}'.format(business_id=business_id)
-    r = requests.get(url)
-    return json.loads(r.text)
+    if(os.path.exists(f"../data/inputs/principals_json/{business_id}.json")):
+        with open(f"../data/inputs/principals_json/{business_id}.json", 'r') as f: 
+            return json.load(f)
+    else: 
+        r = requests.get(url)
+        # Try to read the response text
+        try: 
+            r_json = json.loads(r.text)
+        except: 
+            r_json = {}
+    
+        try:
+            # TODO: Will this write an empty string if no actual request result? 
+            with open(f"../data/inputs/principals_json/{business_id}.json", 'r') as f: 
+                str_json = json.dumps(r_json)
+                f.write(str_json)
+        except:
+            pass
+        return r_json
 
 
 class LookupCompaniesHelper:
@@ -53,26 +74,68 @@ class LookupCompaniesHelper:
 
     def _get_empty_df(self):
         return pd.DataFrame([], columns = ['SearchTerm', 'BusinessName', 'UBINumber', 'BusinessId', 
-                                           'Address', 'Status', 'address_match', 'ubi_match', 'id_match'])
+                                           'Address', 'Status', 'address_match'])
     
-    def _get_business_search_results(self, business_name, page_num):
-        r = requests.post(search_for_business_url, get_business_search_payload(business_name, 100, page_num))
-        try:
+    def _get_business_search_results(self, business_name_orig, page_num):
+        business_name = business_name_orig.strip()
+        no_result = True
+        result = {}
+        while no_result and len(business_name) > 0:
+            print(f"searching with name {business_name}")
+            r = requests.post(search_for_business_url, get_business_search_payload(business_name, 100, page_num))
+            # TODO: add some more error handling in case of connectivity issues. 
+            if r.status_code == 429: 
+                # TODO: Raise an error
+                print("This IP address has likely been blocked by CCFS, try using a vpn")
             result = json.loads(r.text)
-        #return json.loads(r.text)
-        except:
-            result = {}
+            if len(result) > 0:
+                no_result = False
+            else:
+                # Strip off the last word from the search term and try again next iteration
+                try: 
+                    # Get the index of the last space in the name
+                    last_space = business_name[::-1].index(" ") 
+                    business_name = business_name[: -1 - last_space].strip()
+                except ValueError: 
+                    # TODO: In this case, try with the LastBuyer in stead of ListedOwner? 
+                    print(f"Found no business with name {business_name_orig}\n")
+                    business_name = ""
+                    
+                
         return result
 
     def _extract_search_results(self, search_term, search_req_response):
-        res_list = [[search_term, res['BusinessName'], res['UBINumber'], res['BusinessID'],
-                    res['PrincipalOffice']['PrincipalStreetAddress']['FullAddress'], res["BusinessStatus"]] 
-                    for res in search_req_response]
-        res_df = pd.DataFrame(res_list, columns=['SearchTerm', 'BusinessName', 'UBINumber', 'BusinessId', 'Address', "Status"])
-        # Basically keep a list of exact matches, and build a list of potential matches that we give to human verifiers
+        res_list = []
+        for res in search_req_response:
+            # build up the known responses
+            # get more business data from that id
+            business_info = get_business_details(res["BusinessID"])
+            res_list += [[search_term.strip(), 
+                          res.get('BusinessName').strip(), 
+                          res.get('UBINumber'), 
+                          res.get('BusinessID'),
+                          res.get('PrincipalOffice')['PrincipalStreetAddress']['FullAddress'], 
+                          res.get("BusinessStatus"), 
+                          business_info.get("BINAICSCodeDesc", "NOT_FOUND")]]
+        # return an empty row if no search results
+        if len(search_req_response) == 0: 
+            res_list += [[search_term, "NOT_FOUND", "NOT_FOUND", "NOT_FOUND", "NOT_FOUND", "NOT_FOUND", "NOT_FOUND"]]
+        
+        res_df = pd.DataFrame(res_list, columns=['SearchTerm', 'BusinessName', 'UBINumber', 'BusinessId', 'Address', "Status", "BusinessNature"])
+        
+        # Clean some of the results a bit more:
+        # Keep only active companies and searches that yielded no results
+        res_df = res_df[(res_df["Status"]=="Active") | (res_df["Status"]=="NOT_FOUND")]
+
+        # TODO: Maybe add a filter on BusinessNature for only real estate/ property investments
+        # TODO: First need to get an idea of all the BusinessNature types
+
+        # Keep a list of exact matches, or later build a list of potential matches that we give to human verifiers
+        # This check is very simple heuristic and more robust matching will occur later in processing
         exact_match = res_df.index[res_df['BusinessName'] == search_term].tolist()
         if exact_match:
             res_df = pd.concat([res_df.iloc[[exact_match[0]],:], res_df.drop(exact_match[0], axis=0)], axis=0)
+            
         return res_df
 
     def _determine_search_matches(self, search_results_df):
@@ -82,24 +145,16 @@ class LookupCompaniesHelper:
             and result have the same address. Could add search terms as a subset for duplicated call
         """
         search_results_df['address_match'] = search_results_df.duplicated(subset=['Address'], keep=False) 
-        search_results_df['ubi_match'] = search_results_df.duplicated(subset=['UBINumber'], keep=False)
-        search_results_df['id_match'] = search_results_df.duplicated(subset=['BusinessId'], keep=False)
 
     def _get_all_company_name_match_search_results(self, owner_name):
         n = 1
         res_length = 100
         search_results = []
         
-        while res_length == 100:
-            res = self._get_business_search_results(owner_name, n)
-            search_results += (res)
-            n += 1
-            res_length = len(res)
-        
-        return search_results
+        res = self._get_business_search_results(owner_name, n)     
+        return res
 
     """
-    TODO: Remove the ubi and address match, this does nothing to help
     """
     def _get_potential_company_name_matches(self, owner_name):        
         all_search_results = self._get_all_company_name_match_search_results(owner_name)
@@ -111,40 +166,50 @@ class LookupCompaniesHelper:
         """
             utils to separate search results into exact match, potential match (where no exact match was found), 
             and additional matches (extra matches if there was an exact match and additional matches)
+            TODO: Give more robust answers here! Other abbreviations include: 
+                - Apartment: APTS -> Apartments
+                - Partnership
+                - etc.
         """
         def is_exact_match(row):
             """ Extract exact matches, including some regex magic. """
             search = row["SearchTerm"]
             result = row["BusinessName"]
-
+            
             # examples: LLC, LLP, L L C, L.L.C., L.L.C. L.L.P., L.L.P, LLC.
             # Limited Partnership, Limited liability company
             p = re.compile("L[\s.]?L[\s,.]?[PC][.]" ,flags=re.IGNORECASE)
-            result=result.replace(",", "")
+            
+            replace_map = {
+                ",": "", 
+                "LIMITED LIABILITY COMPANY":"LLC",
+                "LIMITED PARTNERSHIP": "LLC",
+                "APARTMENTS": "APTS", 
+                "LTD PS": "LLC", 
+                "LTD PARTNERSHIP": "LLC",
+             }
+            
             result= re.sub(p, "LLC", result)
-            result=result.replace("LIMITED LIABILITY COMPANY", "LLC") 
-            result=result.replace("LIMITED PARTNERSHIP", "LLC") 
-
-            search=search.replace(",", "")
             search=re.sub(p, "LLC", search)
-            search=search.replace("LIMITED PARTNERSHIP", "LLC") 
-            search=search.replace("LIMITED LIABILITY COMPANY", "LLC") 
+
+            for k,v in replace_map.items(): 
+                result = result.replace(k, v)
+                search = search.replace(k, v)
 
             return search == result
         
         exact_matches = self._get_empty_df()
-        exact_matches.columns
         potential_matches = self._get_empty_df()
-        additional_matches = self._get_empty_df()
         
         exact_match = results[results.apply(lambda row: is_exact_match(row), axis=1)]
-        if len(exact_match) > 0:
-            exact_matches = pd.concat([exact_matches, exact_match], ignore_index=True)
-            additional_matches = pd.concat([additional_matches, results[results['SearchTerm'] != results['BusinessName']]], ignore_index=True)
+        # TODO: If going to do len(results) check, then need to filter by business nature sooner
+        # Len results heuristic doesn't work for empty searches, or the recursive search
+        if len(exact_match) > 0: #or len(results) == 1:
+            exact_matches = pd.DataFrame(results.iloc[0]).T
         else:
             potential_matches = pd.concat([potential_matches, results], ignore_index=True)
         
-        return exact_matches, potential_matches, additional_matches
+        return exact_matches, potential_matches
 
     def get_company_list_name_matches(self, owner_list: list):
         """
@@ -152,19 +217,18 @@ class LookupCompaniesHelper:
             owner_list: a list of owner names that will be searched in the CCFS database for matches.
             Exact_matches: when search term exactly matches a result in CCFS database. 
             Potential_matches: when search term doesn't exactly match, there needs to be some human verification here to determine. 
-            Additional_matches: extraneous matches in case potential_matches didn't yield enough results. 
         """
         exact_matches = self._get_empty_df()
         potential_matches = self._get_empty_df()
-        additional_matches = self._get_empty_df()
+        # TODO: Make a df for search terms with no matches and how to make it mesh well with recursive search
         
         for owner in owner_list:
+            owner = owner.strip() # Clean owner name slightly
             matches = self._get_potential_company_name_matches(owner)
-            temp_exact, temp_potential, temp_add = self._separate_search_results(matches)
+            temp_exact, temp_potential = self._separate_search_results(matches)
             exact_matches = pd.concat([temp_exact, exact_matches], ignore_index=True)
             potential_matches = pd.concat([temp_potential, potential_matches], ignore_index=True)
-            additional_matches = pd.concat([temp_add, additional_matches], ignore_index=True)
-        return exact_matches, potential_matches, additional_matches
+        return exact_matches, potential_matches
     
 
     def get_company_matches_and_export(self, owner_list: list, x: int):
@@ -173,11 +237,10 @@ class LookupCompaniesHelper:
             match CSV's in the folder determined by `output_path`
         """
         print(f"Saving output files to {self.output_path}")
-        exact_matches, potential_matches, additional_matches = self.get_company_list_name_matches(owner_list)
+        exact_matches, potential_matches = self.get_company_list_name_matches(owner_list)
         
         exact_matches.to_csv(f'{self.output_path}/exact_matches_{x}.csv')
         potential_matches.to_csv(f'{self.output_path}/potential_matches_{x}.csv')
-        additional_matches.to_csv(f'{self.output_path}/additional_matches_{x}.csv')
 
 class GroupCompaniesHelper:
     def __init__(self, out_path: str, out_name: str):
@@ -261,7 +324,7 @@ class GroupCompaniesHelper:
             return principals
         business_ids = [res['BusinessID'] for res in search_results]
         business_names = [res['BusinessName'] for res in search_results]
-        ubi_nums = [res['UBINumber'] for res in search_results]
+        # ubi_nums = [res['UBINumber'] for res in search_results]
         
         for id, name in zip(business_ids, business_names):
             business_json = get_business_details(id)

processors/gre-llc.py

@@ -5,44 +5,39 @@ Created on Fri Aug 15 19:06:45 2025
 
 @author: linnea
 
+Script to find exact and potential search results for a parcel owner in the CCFS database
+
+A representative example for the parcel owner (assessor) data scraping step
 Address: 308 4th Ave S, Seattle, WA, 98104
 ParcelNumber: 5247801370
 ListedOwner: GRE DOWNTOWNER LLC      
 PreviousBuyer: CENTRAL PUGET SOUND REGIONAL TRASNSIT AUTHORITY
-         
 
+We happen to already know the answer, 
+which is this address is part of Goodman Real Estate's extensive portfolio   
 GRE List: https://goodmanre.com/our-projects/
 
 TODO: 
     - Make a flag that shows if the buywer / owner are similar
-    - Check the fuzzy wuzzy matching in utils
     - Get the address field from CCFS, put in corp_owners
     - If the previous buyer doesn't make sense, 
         get the year of the last buying to see if it's at all recent for sanity checks
-
-1. Load in the whole dataframe of owners and buyers
-2. Get the whole list of responses for the listed owner
-    - This shows all the companies that match the listed owner in assessor data
-    - Need to find the most likely company in CCFS to match the listed owner
-3. Make a df out of? 
-4. 
 """
 
 from corp_owners import LookupCompaniesHelper, GroupCompaniesHelper
 import pandas as pd
 
 lookup_helper = LookupCompaniesHelper(("../data/intermediates"))
-df = pd.read_csv("../data/intermediates/owners_listed.csv")
 
-# Almost never need additional matches, as it's only populated if there's an exact match
-exact, potential, additional = lookup_helper.get_company_list_name_matches(["GRE DOWNTOWNER LLC"])
-owner_names = df["ListedOwner"].unique()
-# exact, potential, additional = lookup_helper.get_company_list_name_matches(owner_names[:10])
+# Option 1: Uncomment the two lines to run the full script. 
+# df = pd.read_csv("../data/intermediates/owners_listed.csv")
+# owner_names = df["ListedOwner"].unique()
+
+# Option 2: Uncomment two lines to run with a specific subset for debugging
+df = pd.read_excel("../experiments/gre_apartments.ods", engine='odf')
+df = df.iloc[1]
+owner_names = [df["ListedOwner"]]
+
+exact, potential = lookup_helper.get_company_list_name_matches(owner_names)
 
-if(len(exact) >= 1):
-    ubi = exact.loc[0, "UBINumber"]
-    
-group_helper= GroupCompaniesHelper("../data/intermediates", "principals")
-# TODO: Figure out how to format the url for proper response
-res_group = group_helper.get_companies_principals(exact)
 

processors/merge.py

@@ -2,9 +2,12 @@
 # -*- coding: utf-8 -*-
 """
 Created on Tue Aug 12 18:17:47 2025
-
 @author: linnea
 
+One-time script for cleaning up parcel lookup data.
+If everything went 100% smoothly in scrape.py (ie. no search results came back empty), 
+then this script shouldn't be needed. 
+
 1. Load intermediate results
 2. Load original data with unmodified parcelid
 3. Add a taxparcelid to unmodified so can merge
@@ -66,9 +69,7 @@ if __name__ == "__main__":
     # Add address from df_apts to df_raw
     df_join = df_apts.merge(df_raw, 'left', on="ParcelNumber")
     df_join["ListedOwner"] = "NOT_FOUND"
-    # df_join["ListedOwner"] = df_join.apply(lambda row: get_listed_owner(row), axis=1)
     for idx, row in df_join.iterrows():
-        # df_join.loc[idx, "ListedOwner"] = get_listed_owner(row)
         row.ListedOwner = get_listed_owner(row)
         df_join.loc[idx] = row
         if idx % 500 == 0: 
@@ -76,7 +77,6 @@ if __name__ == "__main__":
             df_join.to_csv(f"{intermediates_path}/owners_listed.csv")
             
     df_join.to_csv(f"{intermediates_path}/owners_listed.csv")
-    # df_join = df_join.rename(columns={"Owner":"RecentBuyer"})
     
     
     

processors/parcel_owners.py

@@ -1,3 +1,8 @@
+"""
+    Utils for finding a parcel owner given an address. 
+    Data source is King County Assessor. 
+"""
+
 import pandas as pd
 from bs4 import BeautifulSoup
 import requests
@@ -43,13 +48,7 @@ class ParcelLookupHelper:
         if data_not_found:
             return None
         return html_soup
-    
-    # TODO: Maybe include sales history AND current owner? 
-    # Example: 308 4TH AVE S 98104
-    # Current owner = GRE DOWNTOWNER LLC 
-    # Sales history = CENTRAL PUGET SOUND REGIONAL TRASNSIT AUTHORITY
-    # Website also shows GRE DOWNTOWNER https://www.addisononfourth.com/
-    # TODO: cache the whole soup object so can lookup later? 
+    s
     def _get_owner_name_from_soup(self, soup: object):
         """
         Extract the owner name from a given BeautifulSoup object, `soup`, of a Property Detail page.
@@ -60,34 +59,8 @@ class ParcelLookupHelper:
         parent = title.parent
         next_tr = title and parent.find_next('tr')
         table = next_tr and next_tr.table
-        return table and table.find_all('td')[5].text
-    
-    def _get_num_units_and_types_from_soup(self, soup: object):
-        """
-        Given a BeautifulSoup object, `soup`, of a Property Detail page, extract:
-            - the number of units in the building
-            - the unit types
-            - the sq ft of each unit type
-            - number of bed/bath rooms in each unit type
-        """
-        title = soup.find('span', text = 'Unit Breakdown')
-        if not title:
-            return { 'numUnits': 'NOT_FOUND', 'unitDetails': 'NOT_FOUND' }
-                     
-        table = title and title.find_next('div').table
-        table_rows = table and table.find_all('tr')[1:]
-        cells = table_rows and [row.find_all('td') for row in table_rows]
-        table_data = []
-        
-        for c in cells:
-            table_data.append([span.text for span in c])
-            total_units = sum([int(row[1]) for row in table_data])
-            dict_keys = ['type', 'number', 'sqft', 'bed', 'bath']
-            units = [dict(zip(dict_keys, row)) for row in table_data]
-        return { 'numUnits': total_units, 'unitDetails': units }
+        return table and table.find_all('td')[5].texts
 
-    
-    # TODO: pass maybe a list of features want to extract? 
     def _scrape_parcel_owners(self, 
                               tax_parcel_id_numbers: list, 
                               file_name: str):
@@ -110,6 +83,10 @@ class ParcelLookupHelper:
         self._write_parcel_owner_csv(parcel_df, file_name)
     
     def _save_html(self, soup, id):
+        """
+            Given a 'soup' type response for an address lookup, save
+            as an html file for future lookups. 
+        """
         table = soup.find("table", attrs={"class":"_table2", "id":"TABLE1"})
         with open(f"{self.output_path}/html/{id}.html", 'w') as f: 
             f.write(str(table))
@@ -122,7 +99,6 @@ class ParcelLookupHelper:
             return "NOT FOUND"
         else:
             return self._get_owner_name_from_soup(parcel_soup)
-            # parcel_df.loc[len(parcel_df.index)] = [id, owner_name]
         
                      
     def _scrape_parcel_owners_and_unit_details(self, tax_parcel_id_numbers: list, file_name: str):

processors/requirements-conda.txt

@@ -0,0 +1,69 @@
+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: linux-64
+# created-by: conda 25.5.1
+_libgcc_mutex=0.1=main
+_openmp_mutex=5.1=1_gnu
+beautifulsoup4=4.13.5=py313h06a4308_0
+blas=1.0=mkl
+bottleneck=1.4.2=py313hf0014fa_0
+brotlicffi=1.0.9.2=py313h6a678d5_1
+bs4=4.13.5=py39hd3eb1b0_0
+bzip2=1.0.8=h5eee18b_6
+ca-certificates=2025.9.9=h06a4308_0
+certifi=2025.8.3=py313h06a4308_0
+cffi=2.0.0=pypi_0
+charset-normalizer=3.3.2=pyhd3eb1b0_0
+defusedxml=0.7.1=pyhd3eb1b0_0
+expat=2.7.1=h6a678d5_0
+idna=3.7=py313h06a4308_0
+intel-openmp=2025.0.0=h06a4308_1171
+ld_impl_linux-64=2.40=h12ee557_0
+libffi=3.4.4=h6a678d5_1
+libgcc-ng=11.2.0=h1234567_1
+libgomp=11.2.0=h1234567_1
+libmpdec=4.0.0=h5eee18b_0
+libstdcxx-ng=11.2.0=h1234567_1
+libuuid=1.41.5=h5eee18b_0
+libxcb=1.17.0=h9b100fa_0
+libzlib=1.3.1=hb25bd0a_0
+mkl=2025.0.0=hacee8c2_941
+mkl-service=2.4.0=py313h5eee18b_3
+mkl_fft=1.3.11=py313hacdc0fc_1
+mkl_random=1.2.8=py313h8928b4f_1
+ncurses=6.5=h7934f7d_0
+numexpr=2.11.0=py313h41d4191_1
+numpy=2.3.3=py313h720eef7_0
+numpy-base=2.3.3=py313h95072fd_0
+odfpy=1.4.1=pyhd8ed1ab_1
+openssl=3.0.17=h5eee18b_0
+pandas=2.3.2=py313h280b501_0
+pip=25.2=pyhc872135_0
+pthread-stubs=0.3=h0ce48e5_1
+pycparser=2.23=py313h06a4308_0
+pysocks=1.7.1=py313h06a4308_0
+python=3.13.7=h7e8bc2b_100_cp313
+python-dateutil=2.9.0post0=py313h06a4308_2
+python-tzdata=2025.2=pyhd3eb1b0_0
+python_abi=3.13=1_cp313
+pytz=2025.2=py313h06a4308_0
+readline=8.3=hc2a1206_0
+requests=2.32.5=py313h06a4308_0
+setuptools=72.1.0=py313h06a4308_0
+six=1.17.0=py313h06a4308_0
+soupsieve=2.5=py313h06a4308_0
+sqlite=3.50.2=hb25bd0a_1
+tbb=2022.0.0=hdb19cb5_0
+tbb-devel=2022.0.0=hdb19cb5_0
+tk=8.6.15=h54e0aa7_0
+typing-extensions=4.15.0=py313h06a4308_0
+typing_extensions=4.15.0=py313h06a4308_0
+tzdata=2025b=h04d1e81_0
+urllib3=2.5.0=py313h06a4308_0
+wheel=0.45.1=py313h06a4308_0
+xorg-libx11=1.8.12=h9b100fa_1
+xorg-libxau=1.0.12=h9b100fa_0
+xorg-libxdmcp=1.1.5=h9b100fa_0
+xorg-xorgproto=2024.1=h5eee18b_1
+xz=5.6.4=h5eee18b_1
+zlib=1.3.1=hb25bd0a_0

processors/scrape.py

@@ -1,13 +1,15 @@
+"""
+Script for getting all apartment addressses in King County, 
+and looking up their parcel owners in KC Assessor. 
+Only runs in set increments to avoid being blocked by Assessor site. 
+"""
 import pandas as pd
-from selenium import webdriver
-from selenium.webdriver.common.by import By
-import time
 import os
 from parcel_owners import ParcelLookupHelper
 
-incr = 998
+incr = 998 # Number of addressses to look up per run. 
 
-class ParcelScraper: 
+class ApartmentDataLoader: 
     def __init__(self, path):
         self.path = path # path to the csv
         # self.driver = self.load_driver()
@@ -35,31 +37,6 @@ class ParcelScraper:
             if idx % 50 == 0: 
                 print(f"Saving row {idx}")
                 self.df.to_csv("apartments_with_owners.csv") 
-
-    def submit_parcel(self, parcel): 
-        self.driver.get("https://blue.kingcounty.com/Assessor/eRealProperty/default.aspx")
-        print(f"https://blue.kingcounty.com/Assessor/eRealProperty/Dashboard.aspx?ParcelNbr={parcel}")
-        self.driver.get(f"https://blue.kingcounty.com/Assessor/eRealProperty/Dashboard.aspx?ParcelNbr={parcel}")
-        parcel_name = ""
-        try:
-            # parcel_form = self.driver.find_element(By.ID, "cphContent_txtParcelNbr")
-            # parcel_form.send_keys(parcel)
-            
-            # search_box = self.driver.find_element(By.NAME, "kingcounty_gov$cphContent$btn_Search")
-            # search_box.click()
-            
-            # Wait until the table view has loaded
-            # table_loaded = self.driver.find_element(By.ID, "topnavlistbtn")
-            # wait = WebDriverWait(self.driver, timeout=5)
-            # wait.until(lambda _: table_loaded.is_displayed())
-            
-            name = self.driver.find_element(By.XPATH, "/html/body/form/table/tbody/tr/td[2]/table/tbody/tr[2]/td[1]/table/tbody/tr[2]/td/div/table/tbody/tr[2]/td[2]")
-            parcel_name = name.text
-            # print(name.text)
-        except:
-            print(f"Couldn't find parcel name for parcel number {parcel}")
-            
-        return parcel_name
     
     def get_parcel_number(self, major, minor):
         return str(major).rjust(6, "0") + str(minor).rjust(4,"0")
@@ -73,10 +50,9 @@ if __name__ == "__main__":
         pass
     
     print(f"starting at index {nrows}")
-
-    scraper = ParcelScraper("EXTR_AptComplex.csv")
     
-    df = scraper.df.loc[nrows:nrows + incr]
+    loader = ApartmentDataLoader("EXTR_AptComplex.csv")
+    df = loader.df.loc[nrows:nrows + incr]
     
     parcelHelper = ParcelLookupHelper(os.getcwd(), True)
     parcelHelper.scrape_parcel_owners(df["ParcelNumber"], f"raw/owners_{nrows // incr}", False)

requirements-conda.txt

@@ -0,0 +1,69 @@
+# This file may be used to create an environment using:
+# $ conda create --name <env> --file <this file>
+# platform: linux-64
+# created-by: conda 25.5.1
+_libgcc_mutex=0.1=main
+_openmp_mutex=5.1=1_gnu
+beautifulsoup4=4.13.5=py313h06a4308_0
+blas=1.0=mkl
+bottleneck=1.4.2=py313hf0014fa_0
+brotlicffi=1.0.9.2=py313h6a678d5_1
+bs4=4.13.5=py39hd3eb1b0_0
+bzip2=1.0.8=h5eee18b_6
+ca-certificates=2025.9.9=h06a4308_0
+certifi=2025.8.3=py313h06a4308_0
+cffi=1.17.1=py313h1fdaa30_1
+charset-normalizer=3.3.2=pyhd3eb1b0_0
+defusedxml=0.7.1=pyhd3eb1b0_0
+expat=2.7.1=h6a678d5_0
+idna=3.7=py313h06a4308_0
+intel-openmp=2025.0.0=h06a4308_1171
+ld_impl_linux-64=2.40=h12ee557_0
+libffi=3.4.4=h6a678d5_1
+libgcc-ng=11.2.0=h1234567_1
+libgomp=11.2.0=h1234567_1
+libmpdec=4.0.0=h5eee18b_0
+libstdcxx-ng=11.2.0=h1234567_1
+libuuid=1.41.5=h5eee18b_0
+libxcb=1.17.0=h9b100fa_0
+libzlib=1.3.1=hb25bd0a_0
+mkl=2025.0.0=hacee8c2_941
+mkl-service=2.4.0=py313h5eee18b_3
+mkl_fft=1.3.11=py313hacdc0fc_1
+mkl_random=1.2.8=py313h8928b4f_1
+ncurses=6.5=h7934f7d_0
+numexpr=2.11.0=py313h41d4191_1
+numpy=2.3.3=py313h720eef7_0
+numpy-base=2.3.3=py313h95072fd_0
+odfpy=1.4.1=pyhd8ed1ab_1
+openssl=3.0.17=h5eee18b_0
+pandas=2.3.2=py313h280b501_0
+pip=25.2=pyhc872135_0
+pthread-stubs=0.3=h0ce48e5_1
+pycparser=2.23=py313h06a4308_0
+pysocks=1.7.1=py313h06a4308_0
+python=3.13.7=h7e8bc2b_100_cp313
+python-dateutil=2.9.0post0=py313h06a4308_2
+python-tzdata=2025.2=pyhd3eb1b0_0
+python_abi=3.13=1_cp313
+pytz=2025.2=py313h06a4308_0
+readline=8.3=hc2a1206_0
+requests=2.32.5=py313h06a4308_0
+setuptools=72.1.0=py313h06a4308_0
+six=1.17.0=py313h06a4308_0
+soupsieve=2.5=py313h06a4308_0
+sqlite=3.50.2=hb25bd0a_1
+tbb=2022.0.0=hdb19cb5_0
+tbb-devel=2022.0.0=hdb19cb5_0
+tk=8.6.15=h54e0aa7_0
+typing-extensions=4.15.0=py313h06a4308_0
+typing_extensions=4.15.0=py313h06a4308_0
+tzdata=2025b=h04d1e81_0
+urllib3=2.5.0=py313h06a4308_0
+wheel=0.45.1=py313h06a4308_0
+xorg-libx11=1.8.12=h9b100fa_1
+xorg-libxau=1.0.12=h9b100fa_0
+xorg-libxdmcp=1.1.5=h9b100fa_0
+xorg-xorgproto=2024.1=h5eee18b_1
+xz=5.6.4=h5eee18b_1
+zlib=1.3.1=hb25bd0a_0

requirements.txt

@@ -1,9 +1,32 @@
+beautifulsoup4==4.13.5
+Bottleneck==1.4.2
+brotlicffi==1.0.9.2
+certifi==2025.8.3
+charset-normalizer==3.3.2
+defusedxml==0.7.1
+idna==3.7
+mkl_fft==1.3.11
+mkl_random==1.2.8
+mkl-service==2.4.0
+numexpr==2.11.0
+numpy==2.3.3
+odfpy==1.4.1
+pandas==2.3.2
+pip==25.2
+pycparser==2.23
+PySocks==1.7.1
+python-dateutil==2.9.0.post0
+pytz==2025.2
+requests==2.32.5
+setuptools==72.1.0
+six==1.17.0
+soupsieve==2.5
+typing_extensions==4.15.0
+tzdata==2025.2
+urllib3==2.5.0
+wheel==0.45.1
 argon2-cffi==25.1.0
 argon2-cffi-bindings==25.1.0
-certifi==2025.8.3
 cffi==2.0.0
 minio==7.2.16
-pycparser==2.23
 pycryptodome==3.23.0
-typing_extensions==4.15.0
-urllib3==2.5.0