fix:update

processors/days_on_market.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import os
+
+from helpers import (
+    get_combined_df,
+    save_final_df_as_jsonl,
+    handle_slug_column_mappings,
+)
+
+
+# In[2]:
+
+
+DATA_DIR = "../data"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "days_on_market/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[3]:
+
+
+data_frames = []
+
+exclude_columns = [
+    "RegionID",
+    "SizeRank",
+    "RegionName",
+    "RegionType",
+    "StateName",
+    "Home Type",
+]
+
+slug_column_mappings = {
+    "_mean_listings_price_cut_amt_": "Mean Listings Price Cut Amount",
+    "_med_doz_pending_": "Median Days on Pending",
+    "_median_days_to_pending_": "Median Days to Close",
+    "_perc_listings_price_cut_": "Percent Listings Price Cut",
+}
+
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        print("processing " + filename)
+        # skip month files for now since they are redundant
+        if "month" in filename:
+            continue
+
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+
+        if "_uc_sfrcondo_" in filename:
+            cur_df["Home Type"] = "all homes (SFR + Condo)"
+            # change column type to string
+            cur_df["RegionName"] = cur_df["RegionName"].astype(str)
+        elif "_uc_sfr_" in filename:
+            cur_df["Home Type"] = "SFR"
+
+        data_frames = handle_slug_column_mappings(
+            data_frames, slug_column_mappings, exclude_columns, filename, cur_df
+        )
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "SizeRank",
+        "RegionName",
+        "RegionType",
+        "StateName",
+        "Home Type",
+        "Date",
+    ],
+)
+
+combined_df
+
+
+# In[9]:
+
+
+# Adjust column names
+final_df = combined_df.rename(
+    columns={
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "StateName": "State",
+    }
+)
+
+final_df
+
+
+# In[5]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

processors/for_sale_listings.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import os
+
+from helpers import (
+    get_combined_df,
+    save_final_df_as_jsonl,
+    handle_slug_column_mappings,
+)
+
+
+# In[2]:
+
+
+DATA_DIR = "../data"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "for_sale_listings/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[3]:
+
+
+exclude_columns = [
+    "RegionID",
+    "SizeRank",
+    "RegionName",
+    "RegionType",
+    "StateName",
+    "Home Type",
+]
+
+slug_column_mappings = {
+    "_mlp_": "Median Listing Price",
+    "_new_listings_": "New Listings",
+    "new_pending": "New Pending",
+}
+
+
+data_frames = []
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        print("processing " + filename)
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+
+        # ignore monthly data for now since it is redundant
+        if "month" in filename:
+            continue
+
+        if "sfrcondo" in filename:
+            cur_df["Home Type"] = "all homes"
+        elif "sfr" in filename:
+            cur_df["Home Type"] = "SFR"
+        elif "condo" in filename:
+            cur_df["Home Type"] = "condo/co-op only"
+
+        data_frames = handle_slug_column_mappings(
+            data_frames, slug_column_mappings, exclude_columns, filename, cur_df
+        )
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "SizeRank",
+        "RegionName",
+        "RegionType",
+        "StateName",
+        "Home Type",
+        "Date",
+    ],
+)
+
+combined_df
+
+
+# In[4]:
+
+
+# Adjust column names
+final_df = combined_df.rename(
+    columns={
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "StateName": "State",
+    }
+)
+
+final_df
+
+
+# In[5]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

processors/helpers.py

@@ -12,6 +12,7 @@ def coalesce_columns(
                 if column_to_coalesce in col and "_" in col:
                     if not pd.isna(row[col]):
                         df.at[index, column_to_coalesce] = row[col]
+                        continue
 
     # remove columns with underscores
     combined_df = df[columns_to_coalesce]
@@ -62,6 +63,7 @@ def get_melted_df(
         var_name="Date",
         value_name=col_name,
     )
+
     return df
 
 
@@ -70,7 +72,7 @@ def save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df):
         os.makedirs(FULL_PROCESSED_DIR_PATH)
 
     final_df.to_json(
-        FULL_PROCESSED_DIR_PATH + "final.jsonl", orient="records", lines=True
+        FULL_PROCESSED_DIR_PATH + "final2.jsonl", orient="records", lines=True
     )
 
 

processors/home_value_forecasts.py

@@ -0,0 +1,96 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import os
+
+from helpers import get_combined_df, save_final_df_as_jsonl
+
+
+# In[2]:
+
+
+DATA_DIR = "../data/"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "home_values_forecasts/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[3]:
+
+
+data_frames = []
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        print("processing " + filename)
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+
+        cols = ["Month Over Month %", "Quarter Over Quarter %", "Year Over Year %"]
+        if filename.endswith("sm_sa_month.csv"):
+            # print('Smoothed')
+            cur_df.columns = list(cur_df.columns[:-3]) + [
+                x + " (Smoothed) (Seaonally Adjusted)" for x in cols
+            ]
+        else:
+            # print('Raw')
+            cur_df.columns = list(cur_df.columns[:-3]) + cols
+        cur_df["RegionName"] = cur_df["RegionName"].astype(str)
+
+        data_frames.append(cur_df)
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "RegionType",
+        "SizeRank",
+        "StateName",
+        "BaseDate",
+    ],
+)
+
+combined_df
+
+
+# In[4]:
+
+
+# Adjust columns
+final_df = combined_df
+final_df = combined_df.drop("StateName", axis=1)
+final_df = final_df.rename(
+    columns={
+        "CountyName": "County",
+        "BaseDate": "Date",
+        "RegionName": "Region",
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+    }
+)
+
+# iterate over rows of final_df and populate State and City columns if the regionType is msa
+for index, row in final_df.iterrows():
+    if row["RegionType"] == "msa":
+        regionName = row["Region"]
+        # final_df.at[index, 'Metro'] = regionName
+
+        city = regionName.split(", ")[0]
+        final_df.at[index, "City"] = city
+
+        state = regionName.split(", ")[1]
+        final_df.at[index, "State"] = state
+
+final_df
+
+
+# In[9]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

processors/home_values.py

@@ -0,0 +1,181 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import os
+
+from helpers import (
+    get_combined_df,
+    save_final_df_as_jsonl,
+    handle_slug_column_mappings,
+)
+
+
+# In[2]:
+
+
+DATA_DIR = "../data"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "home_values/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[5]:
+
+
+data_frames = []
+
+slug_column_mappings = {
+    "_tier_0.0_0.33_": "Bottom Tier ZHVI",
+    "_tier_0.33_0.67_": "Mid Tier ZHVI",
+    "_tier_0.67_1.0_": "Top Tier ZHVI",
+    "": "ZHVI",
+}
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        print("processing " + filename)
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+        exclude_columns = [
+            "RegionID",
+            "SizeRank",
+            "RegionName",
+            "RegionType",
+            "StateName",
+            "Bedroom Count",
+            "Home Type",
+        ]
+
+        if "Zip" in filename:
+            continue
+        if "Neighborhood" in filename:
+            continue
+        if "City" in filename:
+            continue
+        if "Metro" in filename:
+            continue
+        if "County" in filename:
+            continue
+
+        if "City" in filename:
+            exclude_columns = exclude_columns + ["State", "Metro", "CountyName"]
+        elif "Zip" in filename:
+            exclude_columns = exclude_columns + [
+                "State",
+                "City",
+                "Metro",
+                "CountyName",
+            ]
+        elif "County" in filename:
+            exclude_columns = exclude_columns + [
+                "State",
+                "Metro",
+                "StateCodeFIPS",
+                "MunicipalCodeFIPS",
+            ]
+        elif "Neighborhood" in filename:
+            exclude_columns = exclude_columns + [
+                "State",
+                "City",
+                "Metro",
+                "CountyName",
+            ]
+
+        if "_bdrmcnt_1_" in filename:
+            cur_df["Bedroom Count"] = "1-Bedroom"
+        elif "_bdrmcnt_2_" in filename:
+            cur_df["Bedroom Count"] = "2-Bedrooms"
+        elif "_bdrmcnt_3_" in filename:
+            cur_df["Bedroom Count"] = "3-Bedrooms"
+        elif "_bdrmcnt_4_" in filename:
+            cur_df["Bedroom Count"] = "4-Bedrooms"
+        elif "_bdrmcnt_5_" in filename:
+            cur_df["Bedroom Count"] = "5+-Bedrooms"
+        else:
+            cur_df["Bedroom Count"] = "All Bedrooms"
+
+        if "_uc_sfr_" in filename:
+            cur_df["Home Type"] = "SFR"
+        elif "_uc_sfrcondo_" in filename:
+            cur_df["Home Type"] = "all homes (SFR/condo)"
+        elif "_uc_condo_" in filename:
+            cur_df["Home Type"] = "condo"
+
+        cur_df["StateName"] = cur_df["StateName"].astype(str)
+        cur_df["RegionName"] = cur_df["RegionName"].astype(str)
+
+        data_frames = handle_slug_column_mappings(
+            data_frames, slug_column_mappings, exclude_columns, filename, cur_df
+        )
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "SizeRank",
+        "RegionName",
+        "RegionType",
+        "StateName",
+        "Bedroom Count",
+        "Home Type",
+        "Date",
+    ],
+)
+
+combined_df
+
+
+# In[11]:
+
+
+final_df = combined_df
+
+for index, row in final_df.iterrows():
+    if row["RegionType"] == "city":
+        final_df.at[index, "City"] = row["RegionName"]
+    elif row["RegionType"] == "county":
+        final_df.at[index, "County"] = row["RegionName"]
+    if row["RegionType"] == "state":
+        final_df.at[index, "StateName"] = row["RegionName"]
+
+# coalesce State and StateName columns
+# final_df["State"] = final_df["State"].combine_first(final_df["StateName"])
+# final_df["County"] = final_df["County"].combine_first(final_df["CountyName"])
+
+# final_df = final_df.drop(
+#     columns=[
+#         "StateName",
+#         # "CountyName"
+#     ]
+# )
+final_df
+
+
+# In[12]:
+
+
+final_df = final_df.rename(
+    columns={
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "StateCodeFIPS": "State Code FIPS",
+        "StateName": "State",
+        "MunicipalCodeFIPS": "Municipal Code FIPS",
+    }
+)
+
+final_df
+
+
+# In[13]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

processors/new_construction.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import os
+
+from helpers import (
+    get_combined_df,
+    save_final_df_as_jsonl,
+    handle_slug_column_mappings,
+)
+
+
+# In[2]:
+
+
+DATA_DIR = "../data"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "new_construction/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[3]:
+
+
+exclude_columns = [
+    "RegionID",
+    "SizeRank",
+    "RegionName",
+    "RegionType",
+    "StateName",
+    "Home Type",
+]
+
+slug_column_mappings = {
+    "_median_sale_price_per_sqft": "Median Sale Price per Sqft",
+    "_median_sale_price": "Median Sale Price",
+    "sales_count": "Sales Count",
+}
+
+data_frames = []
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        print("processing " + filename)
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+
+        if "sfrcondo" in filename:
+            cur_df["Home Type"] = "all homes"
+        elif "sfr" in filename:
+            cur_df["Home Type"] = "SFR"
+        elif "condo" in filename:
+            cur_df["Home Type"] = "condo/co-op only"
+
+        data_frames = handle_slug_column_mappings(
+            data_frames, slug_column_mappings, exclude_columns, filename, cur_df
+        )
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "SizeRank",
+        "RegionName",
+        "RegionType",
+        "StateName",
+        "Home Type",
+        "Date",
+    ],
+)
+
+combined_df
+
+
+# In[4]:
+
+
+final_df = combined_df
+final_df = final_df.rename(
+    columns={
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "StateName": "State",
+    }
+)
+
+final_df.sort_values(by=["Region ID", "Home Type", "Date"])
+
+
+# In[5]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

processors/rentals.py

@@ -0,0 +1,160 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import pandas as pd
+import os
+
+from helpers import (
+    get_combined_df,
+    save_final_df_as_jsonl,
+    handle_slug_column_mappings,
+)
+
+
+# In[3]:
+
+
+DATA_DIR = "../data"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "rentals/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[7]:
+
+
+data_frames = []
+
+slug_column_mappings = {"": "Rent"}
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        # print("processing " + filename)
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+        exclude_columns = [
+            "RegionID",
+            "SizeRank",
+            "RegionName",
+            "RegionType",
+            "StateName",
+            "Home Type",
+        ]
+
+        if "_sfrcondomfr_" in filename:
+            cur_df["Home Type"] = "all homes plus multifamily"
+            # change column type to string
+            cur_df["RegionName"] = cur_df["RegionName"].astype(str)
+            if "City" in filename:
+                exclude_columns = [
+                    "RegionID",
+                    "SizeRank",
+                    "RegionName",
+                    "RegionType",
+                    "StateName",
+                    "Home Type",
+                    # City Specific
+                    "State",
+                    "Metro",
+                    "CountyName",
+                ]
+            elif "Zip" in filename:
+                exclude_columns = [
+                    "RegionID",
+                    "SizeRank",
+                    "RegionName",
+                    "RegionType",
+                    "StateName",
+                    "Home Type",
+                    # Zip Specific
+                    "State",
+                    "City",
+                    "Metro",
+                    "CountyName",
+                ]
+            elif "County" in filename:
+                exclude_columns = [
+                    "RegionID",
+                    "SizeRank",
+                    "RegionName",
+                    "RegionType",
+                    "StateName",
+                    "Home Type",
+                    # County Specific
+                    "State",
+                    "Metro",
+                    "StateCodeFIPS",
+                    "MunicipalCodeFIPS",
+                ]
+
+        elif "_sfr_" in filename:
+            cur_df["Home Type"] = "SFR"
+        elif "_mfr_" in filename:
+            cur_df["Home Type"] = "multifamily"
+
+        data_frames = handle_slug_column_mappings(
+            data_frames, slug_column_mappings, exclude_columns, filename, cur_df
+        )
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "SizeRank",
+        "RegionName",
+        "RegionType",
+        "StateName",
+        "Home Type",
+        "Date",
+    ],
+)
+
+combined_df
+
+
+# In[8]:
+
+
+final_df = combined_df
+
+for index, row in final_df.iterrows():
+    if row["RegionType"] == "city":
+        final_df.at[index, "City"] = row["RegionName"]
+    elif row["RegionType"] == "county":
+        final_df.at[index, "County"] = row["RegionName"]
+
+# coalesce State and StateName columns
+final_df["State"] = final_df["State"].combine_first(final_df["StateName"])
+final_df["State"] = final_df["County"].combine_first(final_df["CountyName"])
+
+final_df = final_df.drop(columns=["StateName", "CountyName"])
+final_df
+
+
+# In[6]:
+
+
+# Adjust column names
+final_df = final_df.rename(
+    columns={
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "StateCodeFIPS": "State Code FIPS",
+        "MunicipalCodeFIPS": "Municipal Code FIPS",
+    }
+)
+
+final_df
+
+
+# In[7]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

processors/sales.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import os
+
+from helpers import (
+    get_combined_df,
+    save_final_df_as_jsonl,
+    handle_slug_column_mappings,
+)
+
+
+# In[2]:
+
+
+DATA_DIR = "../data"
+PROCESSED_DIR = "../processed/"
+FACET_DIR = "sales/"
+FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)
+FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
+
+
+# In[3]:
+
+
+exclude_columns = [
+    "RegionID",
+    "SizeRank",
+    "RegionName",
+    "RegionType",
+    "StateName",
+    "Home Type",
+]
+
+slug_column_mappings = {
+    "_median_sale_to_list_": "Median Sale to List Ratio",
+    "_mean_sale_to_list_": "Mean Sale to List Ratio",
+    "_median_sale_price_": "Median Sale Price",
+    "_pct_sold_above_list_": "% Sold Above List",
+    "_pct_sold_below_list_": "% Sold Below List",
+    "_sales_count_now_": "Nowcast",
+}
+
+data_frames = []
+
+for filename in os.listdir(FULL_DATA_DIR_PATH):
+    if filename.endswith(".csv"):
+        print("processing " + filename)
+        # ignore monthly data for now since it is redundant
+        if "month" in filename:
+            continue
+
+        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+
+        if "_sfrcondo_" in filename:
+            cur_df["Home Type"] = "all homes"
+        elif "_sfr_" in filename:
+            cur_df["Home Type"] = "SFR"
+
+        data_frames = handle_slug_column_mappings(
+            data_frames, slug_column_mappings, exclude_columns, filename, cur_df
+        )
+
+
+combined_df = get_combined_df(
+    data_frames,
+    [
+        "RegionID",
+        "SizeRank",
+        "RegionName",
+        "RegionType",
+        "StateName",
+        "Home Type",
+        "Date",
+    ],
+)
+
+combined_df
+
+
+# In[4]:
+
+
+# Adjust column names
+final_df = combined_df.rename(
+    columns={
+        "RegionID": "Region ID",
+        "SizeRank": "Size Rank",
+        "RegionName": "Region",
+        "RegionType": "Region Type",
+        "StateName": "State",
+    }
+)
+
+final_df.sort_values(by=["Region ID", "Home Type", "Date"])
+
+
+# In[5]:
+
+
+save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+

zillow.py

@@ -299,7 +299,7 @@ class Zillow(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        file_path = os.path.join("processed", self.config.name, "final1.jsonl")
+        file_path = os.path.join("processed", self.config.name, "final2.jsonl")
         file_train = dl_manager.download(file_path)
         # file_test = dl_manager.download(os.path.join(self.config.name, "test.csv"))
         # file_eval = dl_manager.download(os.path.join(self.config.name, "valid.csv"))