fix: remove old processors

processors/days_on_market.py

@@ -1,107 +0,0 @@
-#!/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

@@ -1,106 +0,0 @@
-#!/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/home_values.py

@@ -1,181 +0,0 @@
-#!/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/home_values_forecasts.py

@@ -1,96 +0,0 @@
-#!/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) (Seasonally 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",
-        "RegionType": "Region Type",
-        "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["Region Type"] == "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/new_construction.py

@@ -1,101 +0,0 @@
-#!/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

@@ -1,160 +0,0 @@
-#!/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

@@ -1,106 +0,0 @@
-#!/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)
-