fix: adjust processors to share more code

processors/days_on_market.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,6 +10,7 @@
     "import os\n",
     "\n",
     "from helpers import (\n",
+    "    get_data_path_for_config,\n",
     "    get_combined_df,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
@@ -19,20 +20,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "DATA_DIR = \"../data\"\n",
-    "PROCESSED_DIR = \"../processed/\"\n",
-    "FACET_DIR = \"days_on_market/\"\n",
-    "FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)\n",
-    "FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)"
+    "CONFIG_NAME = \"days_on_market\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -379,7 +376,7 @@
        "[586714 rows x 13 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -403,15 +400,16 @@
     "    \"_perc_listings_price_cut_\": \"Percent Listings Price Cut\",\n",
     "}\n",
     "\n",
+    "data_dir_path = get_data_path_for_config(CONFIG_NAME)\n",
     "\n",
-    "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
+    "for filename in os.listdir(data_dir_path):\n",
     "    if filename.endswith(\".csv\"):\n",
     "        print(\"processing \" + filename)\n",
     "        # skip month files for now since they are redundant\n",
     "        if \"month\" in filename:\n",
     "            continue\n",
     "\n",
-    "        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
+    "        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))\n",
     "\n",
     "        cur_df[\"RegionName\"] = cur_df[\"RegionName\"].astype(str)\n",
     "        cur_df = set_home_type(cur_df, filename)\n",
@@ -439,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -729,7 +727,7 @@
        "[586714 rows x 13 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -753,11 +751,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
+    "save_final_df_as_jsonl(CONFIG_NAME, final_df)"
    ]
   }
  ],

processors/days_on_market.py

@@ -1,13 +1,14 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# In[6]:
+# In[4]:
 
 
 import pandas as pd
 import os
 
 from helpers import (
+    get_data_path_for_config,
     get_combined_df,
     save_final_df_as_jsonl,
     handle_slug_column_mappings,
@@ -15,17 +16,13 @@ from helpers import (
 )
 
 
-# In[7]:
+# In[5]:
 
 
-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)
+CONFIG_NAME = "days_on_market"
 
 
-# In[8]:
+# In[6]:
 
 
 data_frames = []
@@ -46,15 +43,16 @@ slug_column_mappings = {
     "_perc_listings_price_cut_": "Percent Listings Price Cut",
 }
 
+data_dir_path = get_data_path_for_config(CONFIG_NAME)
 
-for filename in os.listdir(FULL_DATA_DIR_PATH):
+for filename in os.listdir(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))
+        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))
 
         cur_df["RegionName"] = cur_df["RegionName"].astype(str)
         cur_df = set_home_type(cur_df, filename)
@@ -80,7 +78,7 @@ combined_df = get_combined_df(
 combined_df
 
 
-# In[4]:
+# In[7]:
 
 
 # Adjust column names
@@ -99,8 +97,8 @@ final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
 final_df
 
 
-# In[5]:
+# In[8]:
 
 
-save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+save_final_df_as_jsonl(CONFIG_NAME, final_df)
 

processors/for_sale_listings.ipynb

@@ -10,6 +10,7 @@
     "import os\n",
     "\n",
     "from helpers import (\n",
+    "    get_data_path_for_config,\n",
     "    get_combined_df,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
@@ -23,11 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "DATA_DIR = \"../data\"\n",
-    "PROCESSED_DIR = \"../processed/\"\n",
-    "FACET_DIR = \"for_sale_listings/\"\n",
-    "FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)\n",
-    "FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)"
+    "CONFIG_NAME = \"for_sale_listings\""
    ]
   },
   {
@@ -345,6 +342,8 @@
     }
    ],
    "source": [
+    "data_frames = []\n",
+    "\n",
     "exclude_columns = [\n",
     "    \"RegionID\",\n",
     "    \"SizeRank\",\n",
@@ -360,13 +359,12 @@
     "    \"new_pending\": \"New Pending\",\n",
     "}\n",
     "\n",
+    "data_dir_path = get_data_path_for_config(CONFIG_NAME)\n",
     "\n",
-    "data_frames = []\n",
-    "\n",
-    "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
+    "for filename in os.listdir(data_dir_path):\n",
     "    if filename.endswith(\".csv\"):\n",
     "        print(\"processing \" + filename)\n",
-    "        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
+    "        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))\n",
     "\n",
     "        # ignore monthly data for now since it is redundant\n",
     "        if \"month\" in filename:\n",
@@ -378,7 +376,6 @@
     "            data_frames, slug_column_mappings, exclude_columns, filename, cur_df\n",
     "        )\n",
     "\n",
-    "\n",
     "combined_df = get_combined_df(\n",
     "    data_frames,\n",
     "    [\n",
@@ -702,7 +699,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
+    "save_final_df_as_jsonl(CONFIG_NAME, final_df)"
    ]
   }
  ],

processors/for_sale_listings.py

@@ -8,6 +8,7 @@ import pandas as pd
 import os
 
 from helpers import (
+    get_data_path_for_config,
     get_combined_df,
     save_final_df_as_jsonl,
     handle_slug_column_mappings,
@@ -18,16 +19,14 @@ from helpers import (
 # 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)
+CONFIG_NAME = "for_sale_listings"
 
 
 # In[3]:
 
 
+data_frames = []
+
 exclude_columns = [
     "RegionID",
     "SizeRank",
@@ -43,13 +42,12 @@ slug_column_mappings = {
     "new_pending": "New Pending",
 }
 
+data_dir_path = get_data_path_for_config(CONFIG_NAME)
 
-data_frames = []
-
-for filename in os.listdir(FULL_DATA_DIR_PATH):
+for filename in os.listdir(data_dir_path):
     if filename.endswith(".csv"):
         print("processing " + filename)
-        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))
 
         # ignore monthly data for now since it is redundant
         if "month" in filename:
@@ -61,7 +59,6 @@ for filename in os.listdir(FULL_DATA_DIR_PATH):
             data_frames, slug_column_mappings, exclude_columns, filename, cur_df
         )
 
-
 combined_df = get_combined_df(
     data_frames,
     [
@@ -100,5 +97,5 @@ final_df
 # In[5]:
 
 
-save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+save_final_df_as_jsonl(CONFIG_NAME, final_df)
 

processors/helpers.py

@@ -2,6 +2,11 @@ import pandas as pd
 import os
 
 
+def get_data_path_for_config(config_name):
+    data_dir = "../data"
+    return os.path.join(data_dir, config_name)
+
+
 def coalesce_columns(
     df,
 ):
@@ -82,13 +87,15 @@ def get_melted_df(
     return df
 
 
-def save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df):
-    if not os.path.exists(FULL_PROCESSED_DIR_PATH):
-        os.makedirs(FULL_PROCESSED_DIR_PATH)
+def save_final_df_as_jsonl(config_name, df):
+    processed_dir = "../processed/"
 
-    final_df.to_json(
-        FULL_PROCESSED_DIR_PATH + "final.jsonl", orient="records", lines=True
-    )
+    if not os.path.exists(processed_dir):
+        os.makedirs(processed_dir)
+
+    full_path = os.path.join(processed_dir, config_name + ".jsonl")
+
+    df.to_json(full_path, orient="records", lines=True)
 
 
 def handle_slug_column_mappings(

processors/home_values.ipynb

@@ -10,6 +10,7 @@
     "import os\n",
     "\n",
     "from helpers import (\n",
+    "    get_data_path_for_config,\n",
     "    get_combined_df,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
@@ -23,11 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "DATA_DIR = \"../data\"\n",
-    "PROCESSED_DIR = \"../processed/\"\n",
-    "FACET_DIR = \"home_values/\"\n",
-    "FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)\n",
-    "FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)"
+    "CONFIG_NAME = \"home_values\""
    ]
   },
   {
@@ -375,10 +372,12 @@
     "    \"\": \"ZHVI\",\n",
     "}\n",
     "\n",
-    "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
+    "data_dir_path = get_data_path_for_config(CONFIG_NAME)\n",
+    "\n",
+    "for filename in os.listdir(data_dir_path):\n",
     "    if filename.endswith(\".csv\"):\n",
     "        print(\"processing \" + filename)\n",
-    "        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
+    "        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))\n",
     "        exclude_columns = [\n",
     "            \"RegionID\",\n",
     "            \"SizeRank\",\n",
@@ -1054,11 +1053,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
+    "save_final_df_as_jsonl(CONFIG_NAME, final_df)"
    ]
   }
  ],

processors/home_values.py

@@ -8,6 +8,7 @@ import pandas as pd
 import os
 
 from helpers import (
+    get_data_path_for_config,
     get_combined_df,
     save_final_df_as_jsonl,
     handle_slug_column_mappings,
@@ -18,14 +19,10 @@ from helpers import (
 # 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)
+CONFIG_NAME = "home_values"
 
 
-# In[5]:
+# In[3]:
 
 
 data_frames = []
@@ -37,10 +34,12 @@ slug_column_mappings = {
     "": "ZHVI",
 }
 
-for filename in os.listdir(FULL_DATA_DIR_PATH):
+data_dir_path = get_data_path_for_config(CONFIG_NAME)
+
+for filename in os.listdir(data_dir_path):
     if filename.endswith(".csv"):
         print("processing " + filename)
-        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))
         exclude_columns = [
             "RegionID",
             "SizeRank",
@@ -126,7 +125,7 @@ combined_df = get_combined_df(
 combined_df
 
 
-# In[11]:
+# In[4]:
 
 
 final_df = combined_df
@@ -152,7 +151,7 @@ for index, row in final_df.iterrows():
 final_df
 
 
-# In[12]:
+# In[5]:
 
 
 final_df = final_df.rename(
@@ -172,8 +171,8 @@ final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
 final_df
 
 
-# In[13]:
+# In[6]:
 
 
-save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+save_final_df_as_jsonl(CONFIG_NAME, final_df)
 

processors/home_values_forecasts.ipynb

@@ -9,7 +9,7 @@
     "import pandas as pd\n",
     "import os\n",
     "\n",
-    "from helpers import get_combined_df, save_final_df_as_jsonl"
+    "from helpers import get_data_path_for_config, get_combined_df, save_final_df_as_jsonl"
    ]
   },
   {
@@ -18,11 +18,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "DATA_DIR = \"../data/\"\n",
-    "PROCESSED_DIR = \"../processed/\"\n",
-    "FACET_DIR = \"home_values_forecasts/\"\n",
-    "FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)\n",
-    "FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)"
+    "CONFIG_NAME = \"home_values_forecasts\""
    ]
   },
   {
@@ -384,10 +380,12 @@
    "source": [
     "data_frames = []\n",
     "\n",
-    "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
+    "data_dir_path = get_data_path_for_config(CONFIG_NAME)\n",
+    "\n",
+    "for filename in os.listdir(data_dir_path):\n",
     "    if filename.endswith(\".csv\"):\n",
     "        print(\"processing \" + filename)\n",
-    "        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
+    "        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))\n",
     "\n",
     "        cols = [\"Month Over Month %\", \"Quarter Over Quarter %\", \"Year Over Year %\"]\n",
     "        if filename.endswith(\"sm_sa_month.csv\"):\n",
@@ -786,11 +784,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
+    "save_final_df_as_jsonl(CONFIG_NAME, final_df)"
    ]
   }
  ],

processors/home_values_forecasts.py

@@ -7,17 +7,13 @@
 import pandas as pd
 import os
 
-from helpers import get_combined_df, save_final_df_as_jsonl
+from helpers import get_data_path_for_config, 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)
+CONFIG_NAME = "home_values_forecasts"
 
 
 # In[3]:
@@ -25,10 +21,12 @@ FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)
 
 data_frames = []
 
-for filename in os.listdir(FULL_DATA_DIR_PATH):
+data_dir_path = get_data_path_for_config(CONFIG_NAME)
+
+for filename in os.listdir(data_dir_path):
     if filename.endswith(".csv"):
         print("processing " + filename)
-        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))
 
         cols = ["Month Over Month %", "Quarter Over Quarter %", "Year Over Year %"]
         if filename.endswith("sm_sa_month.csv"):
@@ -59,7 +57,7 @@ combined_df = get_combined_df(
 combined_df
 
 
-# In[1]:
+# In[4]:
 
 
 # Adjust columns
@@ -93,8 +91,8 @@ final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
 final_df
 
 
-# In[9]:
+# In[5]:
 
 
-save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+save_final_df_as_jsonl(CONFIG_NAME, final_df)
 

processors/new_construction.ipynb

@@ -10,6 +10,7 @@
     "import os\n",
     "\n",
     "from helpers import (\n",
+    "    get_data_path_for_config,\n",
     "    get_combined_df,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
@@ -23,11 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "DATA_DIR = \"../data\"\n",
-    "PROCESSED_DIR = \"../processed/\"\n",
-    "FACET_DIR = \"new_construction/\"\n",
-    "FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)\n",
-    "FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)"
+    "CONFIG_NAME = \"new_construction\""
    ]
   },
   {
@@ -268,6 +265,8 @@
     }
    ],
    "source": [
+    "data_frames = []\n",
+    "\n",
     "exclude_columns = [\n",
     "    \"RegionID\",\n",
     "    \"SizeRank\",\n",
@@ -283,12 +282,12 @@
     "    \"sales_count\": \"Sales Count\",\n",
     "}\n",
     "\n",
-    "data_frames = []\n",
+    "data_dir_path = get_data_path_for_config(CONFIG_NAME)\n",
     "\n",
-    "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
+    "for filename in os.listdir(data_dir_path):\n",
     "    if filename.endswith(\".csv\"):\n",
     "        print(\"processing \" + filename)\n",
-    "        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
+    "        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))\n",
     "\n",
     "        cur_df = set_home_type(cur_df, filename)\n",
     "\n",
@@ -558,7 +557,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
+    "save_final_df_as_jsonl(CONFIG_NAME, final_df)"
    ]
   }
  ],

processors/new_construction.py

@@ -8,6 +8,7 @@ import pandas as pd
 import os
 
 from helpers import (
+    get_data_path_for_config,
     get_combined_df,
     save_final_df_as_jsonl,
     handle_slug_column_mappings,
@@ -18,16 +19,14 @@ from helpers import (
 # 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)
+CONFIG_NAME = "new_construction"
 
 
 # In[3]:
 
 
+data_frames = []
+
 exclude_columns = [
     "RegionID",
     "SizeRank",
@@ -43,12 +42,12 @@ slug_column_mappings = {
     "sales_count": "Sales Count",
 }
 
-data_frames = []
+data_dir_path = get_data_path_for_config(CONFIG_NAME)
 
-for filename in os.listdir(FULL_DATA_DIR_PATH):
+for filename in os.listdir(data_dir_path):
     if filename.endswith(".csv"):
         print("processing " + filename)
-        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))
 
         cur_df = set_home_type(cur_df, filename)
 
@@ -95,5 +94,5 @@ final_df.sort_values(by=["Region ID", "Home Type", "Date"])
 # In[5]:
 
 
-save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+save_final_df_as_jsonl(CONFIG_NAME, final_df)
 

processors/rentals.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -10,6 +10,7 @@
     "import os\n",
     "\n",
     "from helpers import (\n",
+    "    get_data_path_for_config,\n",
     "    get_combined_df,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
@@ -19,347 +20,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "DATA_DIR = \"../data\"\n",
-    "PROCESSED_DIR = \"../processed/\"\n",
-    "FACET_DIR = \"rentals/\"\n",
-    "FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)\n",
-    "FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)"
+    "CONFIG_NAME = \"rentals\""
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>RegionID</th>\n",
-       "      <th>SizeRank</th>\n",
-       "      <th>RegionName</th>\n",
-       "      <th>RegionType</th>\n",
-       "      <th>StateName</th>\n",
-       "      <th>Home Type</th>\n",
-       "      <th>State</th>\n",
-       "      <th>Metro</th>\n",
-       "      <th>StateCodeFIPS</th>\n",
-       "      <th>MunicipalCodeFIPS</th>\n",
-       "      <th>Date</th>\n",
-       "      <th>Rent (Smoothed)</th>\n",
-       "      <th>CountyName</th>\n",
-       "      <th>Rent (Smoothed) (Seasonally Adjusted)</th>\n",
-       "      <th>City</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>66</td>\n",
-       "      <td>146</td>\n",
-       "      <td>Ada County</td>\n",
-       "      <td>county</td>\n",
-       "      <td>ID</td>\n",
-       "      <td>all homes plus multifamily</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>Boise City, ID</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2015-01-31</td>\n",
-       "      <td>927.493763</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>927.493763</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>66</td>\n",
-       "      <td>146</td>\n",
-       "      <td>Ada County</td>\n",
-       "      <td>county</td>\n",
-       "      <td>ID</td>\n",
-       "      <td>all homes plus multifamily</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>Boise City, ID</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2015-02-28</td>\n",
-       "      <td>931.690623</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>931.690623</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>66</td>\n",
-       "      <td>146</td>\n",
-       "      <td>Ada County</td>\n",
-       "      <td>county</td>\n",
-       "      <td>ID</td>\n",
-       "      <td>all homes plus multifamily</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>Boise City, ID</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2015-03-31</td>\n",
-       "      <td>932.568601</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>932.568601</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>66</td>\n",
-       "      <td>146</td>\n",
-       "      <td>Ada County</td>\n",
-       "      <td>county</td>\n",
-       "      <td>ID</td>\n",
-       "      <td>all homes plus multifamily</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>Boise City, ID</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2015-04-30</td>\n",
-       "      <td>933.148134</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>933.148134</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>66</td>\n",
-       "      <td>146</td>\n",
-       "      <td>Ada County</td>\n",
-       "      <td>county</td>\n",
-       "      <td>ID</td>\n",
-       "      <td>all homes plus multifamily</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>Boise City, ID</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2015-05-31</td>\n",
-       "      <td>941.045724</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>941.045724</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1258735</th>\n",
-       "      <td>857850</td>\n",
-       "      <td>713</td>\n",
-       "      <td>Cherry Hill</td>\n",
-       "      <td>city</td>\n",
-       "      <td>NJ</td>\n",
-       "      <td>all homes plus multifamily</td>\n",
-       "      <td>NJ</td>\n",
-       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023-08-31</td>\n",
-       "      <td>2291.604800</td>\n",
-       "      <td>Camden County</td>\n",
-       "      <td>2244.961006</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1258736</th>\n",
-       "      <td>857850</td>\n",
-       "      <td>713</td>\n",
-       "      <td>Cherry Hill</td>\n",
-       "      <td>city</td>\n",
-       "      <td>NJ</td>\n",
-       "      <td>all homes plus multifamily</td>\n",
-       "      <td>NJ</td>\n",
-       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023-09-30</td>\n",
-       "      <td>2296.188906</td>\n",
-       "      <td>Camden County</td>\n",
-       "      <td>2254.213172</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1258737</th>\n",
-       "      <td>857850</td>\n",
-       "      <td>713</td>\n",
-       "      <td>Cherry Hill</td>\n",
-       "      <td>city</td>\n",
-       "      <td>NJ</td>\n",
-       "      <td>all homes plus multifamily</td>\n",
-       "      <td>NJ</td>\n",
-       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023-10-31</td>\n",
-       "      <td>2292.270938</td>\n",
-       "      <td>Camden County</td>\n",
-       "      <td>2261.540446</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1258738</th>\n",
-       "      <td>857850</td>\n",
-       "      <td>713</td>\n",
-       "      <td>Cherry Hill</td>\n",
-       "      <td>city</td>\n",
-       "      <td>NJ</td>\n",
-       "      <td>all homes plus multifamily</td>\n",
-       "      <td>NJ</td>\n",
-       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023-11-30</td>\n",
-       "      <td>2253.417140</td>\n",
-       "      <td>Camden County</td>\n",
-       "      <td>2257.956024</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1258739</th>\n",
-       "      <td>857850</td>\n",
-       "      <td>713</td>\n",
-       "      <td>Cherry Hill</td>\n",
-       "      <td>city</td>\n",
-       "      <td>NJ</td>\n",
-       "      <td>all homes plus multifamily</td>\n",
-       "      <td>NJ</td>\n",
-       "      <td>Philadelphia-Camden-Wilmington, PA-NJ-DE-MD</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2023-12-31</td>\n",
-       "      <td>2280.830303</td>\n",
-       "      <td>Camden County</td>\n",
-       "      <td>2280.830303</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1258740 rows × 15 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         RegionID  SizeRank   RegionName RegionType StateName  \\\n",
-       "0              66       146   Ada County     county        ID   \n",
-       "1              66       146   Ada County     county        ID   \n",
-       "2              66       146   Ada County     county        ID   \n",
-       "3              66       146   Ada County     county        ID   \n",
-       "4              66       146   Ada County     county        ID   \n",
-       "...           ...       ...          ...        ...       ...   \n",
-       "1258735    857850       713  Cherry Hill       city        NJ   \n",
-       "1258736    857850       713  Cherry Hill       city        NJ   \n",
-       "1258737    857850       713  Cherry Hill       city        NJ   \n",
-       "1258738    857850       713  Cherry Hill       city        NJ   \n",
-       "1258739    857850       713  Cherry Hill       city        NJ   \n",
-       "\n",
-       "                          Home Type State  \\\n",
-       "0        all homes plus multifamily  16.0   \n",
-       "1        all homes plus multifamily  16.0   \n",
-       "2        all homes plus multifamily  16.0   \n",
-       "3        all homes plus multifamily  16.0   \n",
-       "4        all homes plus multifamily  16.0   \n",
-       "...                             ...   ...   \n",
-       "1258735  all homes plus multifamily    NJ   \n",
-       "1258736  all homes plus multifamily    NJ   \n",
-       "1258737  all homes plus multifamily    NJ   \n",
-       "1258738  all homes plus multifamily    NJ   \n",
-       "1258739  all homes plus multifamily    NJ   \n",
-       "\n",
-       "                                               Metro  StateCodeFIPS  \\\n",
-       "0                                     Boise City, ID           16.0   \n",
-       "1                                     Boise City, ID           16.0   \n",
-       "2                                     Boise City, ID           16.0   \n",
-       "3                                     Boise City, ID           16.0   \n",
-       "4                                     Boise City, ID           16.0   \n",
-       "...                                              ...            ...   \n",
-       "1258735  Philadelphia-Camden-Wilmington, PA-NJ-DE-MD            NaN   \n",
-       "1258736  Philadelphia-Camden-Wilmington, PA-NJ-DE-MD            NaN   \n",
-       "1258737  Philadelphia-Camden-Wilmington, PA-NJ-DE-MD            NaN   \n",
-       "1258738  Philadelphia-Camden-Wilmington, PA-NJ-DE-MD            NaN   \n",
-       "1258739  Philadelphia-Camden-Wilmington, PA-NJ-DE-MD            NaN   \n",
-       "\n",
-       "         MunicipalCodeFIPS        Date  Rent (Smoothed)     CountyName  \\\n",
-       "0                      1.0  2015-01-31       927.493763            NaN   \n",
-       "1                      1.0  2015-02-28       931.690623            NaN   \n",
-       "2                      1.0  2015-03-31       932.568601            NaN   \n",
-       "3                      1.0  2015-04-30       933.148134            NaN   \n",
-       "4                      1.0  2015-05-31       941.045724            NaN   \n",
-       "...                    ...         ...              ...            ...   \n",
-       "1258735                NaN  2023-08-31      2291.604800  Camden County   \n",
-       "1258736                NaN  2023-09-30      2296.188906  Camden County   \n",
-       "1258737                NaN  2023-10-31      2292.270938  Camden County   \n",
-       "1258738                NaN  2023-11-30      2253.417140  Camden County   \n",
-       "1258739                NaN  2023-12-31      2280.830303  Camden County   \n",
-       "\n",
-       "         Rent (Smoothed) (Seasonally Adjusted) City  \n",
-       "0                                   927.493763  NaN  \n",
-       "1                                   931.690623  NaN  \n",
-       "2                                   932.568601  NaN  \n",
-       "3                                   933.148134  NaN  \n",
-       "4                                   941.045724  NaN  \n",
-       "...                                        ...  ...  \n",
-       "1258735                            2244.961006  NaN  \n",
-       "1258736                            2254.213172  NaN  \n",
-       "1258737                            2261.540446  NaN  \n",
-       "1258738                            2257.956024  NaN  \n",
-       "1258739                            2280.830303  NaN  \n",
-       "\n",
-       "[1258740 rows x 15 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data_frames = []\n",
     "\n",
     "slug_column_mappings = {\"\": \"Rent\"}\n",
     "\n",
-    "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
+    "data_dir_path = get_data_path_for_config(CONFIG_NAME)\n",
+    "\n",
+    "for filename in os.listdir(data_dir_path):\n",
     "    if filename.endswith(\".csv\"):\n",
-    "        # print(\"processing \" + filename)\n",
-    "        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
+    "        print(\"processing \" + filename)\n",
+    "        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))\n",
     "        exclude_columns = [\n",
     "            \"RegionID\",\n",
     "            \"SizeRank\",\n",
@@ -1095,7 +778,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
+    "save_final_df_as_jsonl(CONFIG_NAME, final_df)"
    ]
   }
  ],

processors/rentals.py

@@ -8,6 +8,7 @@ import pandas as pd
 import os
 
 from helpers import (
+    get_data_path_for_config,
     get_combined_df,
     save_final_df_as_jsonl,
     handle_slug_column_mappings,
@@ -15,27 +16,25 @@ from helpers import (
 )
 
 
-# In[3]:
+# In[1]:
 
 
-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)
+CONFIG_NAME = "rentals"
 
 
-# In[7]:
+# In[3]:
 
 
 data_frames = []
 
 slug_column_mappings = {"": "Rent"}
 
-for filename in os.listdir(FULL_DATA_DIR_PATH):
+data_dir_path = get_data_path_for_config(CONFIG_NAME)
+
+for filename in os.listdir(data_dir_path):
     if filename.endswith(".csv"):
-        # print("processing " + filename)
-        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))
+        print("processing " + filename)
+        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))
         exclude_columns = [
             "RegionID",
             "SizeRank",
@@ -112,7 +111,7 @@ combined_df = get_combined_df(
 combined_df
 
 
-# In[8]:
+# In[4]:
 
 
 final_df = combined_df
@@ -131,7 +130,7 @@ final_df = final_df.drop(columns=["StateName", "CountyName"])
 final_df
 
 
-# In[6]:
+# In[5]:
 
 
 # Adjust column names
@@ -154,5 +153,5 @@ final_df
 # In[7]:
 
 
-save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+save_final_df_as_jsonl(CONFIG_NAME, final_df)
 

processors/sales.ipynb

@@ -10,6 +10,7 @@
     "import os\n",
     "\n",
     "from helpers import (\n",
+    "    get_data_path_for_config,\n",
     "    get_combined_df,\n",
     "    save_final_df_as_jsonl,\n",
     "    handle_slug_column_mappings,\n",
@@ -23,11 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "DATA_DIR = \"../data\"\n",
-    "PROCESSED_DIR = \"../processed/\"\n",
-    "FACET_DIR = \"sales/\"\n",
-    "FULL_DATA_DIR_PATH = os.path.join(DATA_DIR, FACET_DIR)\n",
-    "FULL_PROCESSED_DIR_PATH = os.path.join(PROCESSED_DIR, FACET_DIR)"
+    "CONFIG_NAME = \"sales\""
    ]
   },
   {
@@ -448,6 +445,8 @@
     }
    ],
    "source": [
+    "data_frames = []\n",
+    "\n",
     "exclude_columns = [\n",
     "    \"RegionID\",\n",
     "    \"SizeRank\",\n",
@@ -466,16 +465,16 @@
     "    \"_sales_count_now_\": \"Nowcast\",\n",
     "}\n",
     "\n",
-    "data_frames = []\n",
+    "data_dir_path = get_data_path_for_config(CONFIG_NAME)\n",
     "\n",
-    "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
+    "for filename in os.listdir(data_dir_path):\n",
     "    if filename.endswith(\".csv\"):\n",
     "        print(\"processing \" + filename)\n",
     "        # ignore monthly data for now since it is redundant\n",
     "        if \"month\" in filename:\n",
     "            continue\n",
     "\n",
-    "        cur_df = pd.read_csv(os.path.join(FULL_DATA_DIR_PATH, filename))\n",
+    "        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))\n",
     "\n",
     "        cur_df = set_home_type(cur_df, filename)\n",
     "\n",
@@ -1294,7 +1293,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)"
+    "save_final_df_as_jsonl(CONFIG_NAME, final_df)"
    ]
   }
  ],

processors/sales.py

@@ -1,13 +1,14 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# In[2]:
+# In[1]:
 
 
 import pandas as pd
 import os
 
 from helpers import (
+    get_data_path_for_config,
     get_combined_df,
     save_final_df_as_jsonl,
     handle_slug_column_mappings,
@@ -15,19 +16,17 @@ from helpers import (
 )
 
 
-# In[3]:
+# 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)
+CONFIG_NAME = "sales"
 
 
-# In[4]:
+# In[3]:
 
 
+data_frames = []
+
 exclude_columns = [
     "RegionID",
     "SizeRank",
@@ -46,16 +45,16 @@ slug_column_mappings = {
     "_sales_count_now_": "Nowcast",
 }
 
-data_frames = []
+data_dir_path = get_data_path_for_config(CONFIG_NAME)
 
-for filename in os.listdir(FULL_DATA_DIR_PATH):
+for filename in os.listdir(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))
+        cur_df = pd.read_csv(os.path.join(data_dir_path, filename))
 
         cur_df = set_home_type(cur_df, filename)
 
@@ -80,7 +79,7 @@ combined_df = get_combined_df(
 combined_df
 
 
-# In[52]:
+# In[4]:
 
 
 # Adjust column names
@@ -98,7 +97,7 @@ final_df["Date"] = pd.to_datetime(final_df["Date"])
 final_df.sort_values(by=["Region ID", "Home Type", "Date"])
 
 
-# In[53]:
+# In[5]:
 
 
 final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
@@ -106,8 +105,8 @@ final_df["Date"] = pd.to_datetime(final_df["Date"], format="%Y-%m-%d")
 final_df
 
 
-# In[54]:
+# In[6]:
 
 
-save_final_df_as_jsonl(FULL_PROCESSED_DIR_PATH, final_df)
+save_final_df_as_jsonl(CONFIG_NAME, final_df)