feat: simplify new constructions processing

processed/new_constructions/final.jsonl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4cbd1713a383959a9c43afb152cf8dd169b584b809b1c940c9b48ae8b8d8a8e6
-size 9865188
+oid sha256:e0a487f1b425d392a62c6dd88655fe958c42a937d901efdd5dbc2ebf055826f3
+size 10854928

processors/process_new_constructions.ipynb

@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -71,9 +71,9 @@
        "      <th>StateName</th>\n",
        "      <th>Home Type</th>\n",
        "      <th>Date</th>\n",
-       "      <th>Sale Price</th>\n",
-       "      <th>Sale Price per Sqft</th>\n",
-       "      <th>Count</th>\n",
+       "      <th>Median Sale Price per Sqft</th>\n",
+       "      <th>Median Sale Price</th>\n",
+       "      <th>Sales Count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -86,8 +86,8 @@
        "      <td>NaN</td>\n",
        "      <td>SFR</td>\n",
        "      <td>2018-01-31</td>\n",
-       "      <td>309000.0</td>\n",
        "      <td>137.412316</td>\n",
+       "      <td>309000.0</td>\n",
        "      <td>33940.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -99,8 +99,8 @@
        "      <td>NaN</td>\n",
        "      <td>all homes</td>\n",
        "      <td>2018-01-31</td>\n",
-       "      <td>314596.0</td>\n",
        "      <td>140.504620</td>\n",
+       "      <td>314596.0</td>\n",
        "      <td>37135.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -112,8 +112,8 @@
        "      <td>NaN</td>\n",
        "      <td>condo/co-op only</td>\n",
        "      <td>2018-01-31</td>\n",
-       "      <td>388250.0</td>\n",
        "      <td>238.300000</td>\n",
+       "      <td>388250.0</td>\n",
        "      <td>3195.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -125,8 +125,8 @@
        "      <td>NaN</td>\n",
        "      <td>SFR</td>\n",
        "      <td>2018-02-28</td>\n",
-       "      <td>309072.5</td>\n",
        "      <td>137.199170</td>\n",
+       "      <td>309072.5</td>\n",
        "      <td>33304.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -138,8 +138,8 @@
        "      <td>NaN</td>\n",
        "      <td>all homes</td>\n",
        "      <td>2018-02-28</td>\n",
-       "      <td>314608.0</td>\n",
        "      <td>140.304966</td>\n",
+       "      <td>314608.0</td>\n",
        "      <td>36493.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -239,30 +239,41 @@
        "49485    845162       535   Granbury, TX        msa        TX   \n",
        "49486    845162       535   Granbury, TX        msa        TX   \n",
        "\n",
-       "              Home Type        Date  Sale Price  Sale Price per Sqft    Count  \n",
-       "0                   SFR  2018-01-31    309000.0           137.412316  33940.0  \n",
-       "1             all homes  2018-01-31    314596.0           140.504620  37135.0  \n",
-       "2      condo/co-op only  2018-01-31    388250.0           238.300000   3195.0  \n",
-       "3                   SFR  2018-02-28    309072.5           137.199170  33304.0  \n",
-       "4             all homes  2018-02-28    314608.0           140.304966  36493.0  \n",
-       "...                 ...         ...         ...                  ...      ...  \n",
-       "49482         all homes  2023-09-30         NaN                  NaN     26.0  \n",
-       "49483               SFR  2023-10-31         NaN                  NaN     24.0  \n",
-       "49484         all homes  2023-10-31         NaN                  NaN     24.0  \n",
-       "49485               SFR  2023-11-30         NaN                  NaN     16.0  \n",
-       "49486         all homes  2023-11-30         NaN                  NaN     16.0  \n",
+       "              Home Type        Date  Median Sale Price per Sqft  \\\n",
+       "0                   SFR  2018-01-31                  137.412316   \n",
+       "1             all homes  2018-01-31                  140.504620   \n",
+       "2      condo/co-op only  2018-01-31                  238.300000   \n",
+       "3                   SFR  2018-02-28                  137.199170   \n",
+       "4             all homes  2018-02-28                  140.304966   \n",
+       "...                 ...         ...                         ...   \n",
+       "49482         all homes  2023-09-30                         NaN   \n",
+       "49483               SFR  2023-10-31                         NaN   \n",
+       "49484         all homes  2023-10-31                         NaN   \n",
+       "49485               SFR  2023-11-30                         NaN   \n",
+       "49486         all homes  2023-11-30                         NaN   \n",
+       "\n",
+       "       Median Sale Price  Sales Count  \n",
+       "0               309000.0      33940.0  \n",
+       "1               314596.0      37135.0  \n",
+       "2               388250.0       3195.0  \n",
+       "3               309072.5      33304.0  \n",
+       "4               314608.0      36493.0  \n",
+       "...                  ...          ...  \n",
+       "49482                NaN         26.0  \n",
+       "49483                NaN         24.0  \n",
+       "49484                NaN         24.0  \n",
+       "49485                NaN         16.0  \n",
+       "49486                NaN         16.0  \n",
        "\n",
        "[49487 rows x 10 columns]"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data_frames = []\n",
-    "\n",
     "# base cols RegionID,SizeRank,RegionName,RegionType,StateName\n",
     "\n",
     "exclude_columns = [\n",
@@ -271,13 +282,10 @@
     "    \"RegionName\",\n",
     "    \"RegionType\",\n",
     "    \"StateName\",\n",
-    "    # \"Value Type\",\n",
     "    \"Home Type\",\n",
     "]\n",
     "\n",
-    "price_data_frames = []\n",
-    "price_per_sqft_data_frames = []\n",
-    "count_data_frames = []\n",
+    "batches = {\"median_sale_price_per_sqft\": [], \"median_sale_price\": [], \"sales_count\": []}\n",
     "\n",
     "for filename in os.listdir(FULL_DATA_DIR_PATH):\n",
     "    if filename.endswith(\".csv\"):\n",
@@ -294,45 +302,37 @@
     "        # Identify columns to pivot\n",
     "        columns_to_pivot = [col for col in cur_df.columns if col not in exclude_columns]\n",
     "\n",
-    "        if \"sale_price_per_sqft\" in filename:\n",
-    "            # cur_df[\"Value Type\"] = \"Sale Price Per Sqft\"\n",
-    "            # Perform pivot\n",
+    "        if \"median_sale_price_per_sqft\" in filename:\n",
     "            cur_df = pd.melt(\n",
     "                cur_df,\n",
     "                id_vars=exclude_columns,\n",
     "                value_vars=columns_to_pivot,\n",
     "                var_name=\"Date\",\n",
-    "                value_name=\"Sale Price per Sqft\",\n",
+    "                value_name=\"Median Sale Price per Sqft\",\n",
     "            )\n",
-    "            price_per_sqft_data_frames.append(cur_df)\n",
+    "            batches[\"median_sale_price_per_sqft\"].append(cur_df)\n",
     "\n",
-    "        elif \"sale_price_uc\" in filename:\n",
-    "            # cur_df[\"Value Type\"] = \"Sale Price\"\n",
+    "        elif \"median_sale_price\" in filename:\n",
     "            cur_df = pd.melt(\n",
     "                cur_df,\n",
     "                id_vars=exclude_columns,\n",
     "                value_vars=columns_to_pivot,\n",
     "                var_name=\"Date\",\n",
-    "                value_name=\"Sale Price\",\n",
+    "                value_name=\"Median Sale Price\",\n",
     "            )\n",
-    "            price_data_frames.append(cur_df)\n",
+    "            batches[\"median_sale_price\"].append(cur_df)\n",
     "\n",
-    "        elif \"count\" in filename:\n",
-    "            # cur_df[\"Value Type\"] = \"Count\"\n",
+    "        elif \"sales_count\" in filename:\n",
     "            cur_df = pd.melt(\n",
     "                cur_df,\n",
     "                id_vars=exclude_columns,\n",
     "                value_vars=columns_to_pivot,\n",
     "                var_name=\"Date\",\n",
-    "                value_name=\"Count\",\n",
+    "                value_name=\"Sales Count\",\n",
     "            )\n",
-    "            count_data_frames.append(cur_df)\n",
+    "            batches[\"sales_count\"].append(cur_df)\n",
     "\n",
     "\n",
-    "combined_price = pd.concat(price_data_frames)\n",
-    "combined_price_per = pd.concat(price_per_sqft_data_frames)\n",
-    "combined_count = pd.concat(count_data_frames)\n",
-    "\n",
     "matching_cols = [\n",
     "    \"RegionID\",\n",
     "    \"Date\",\n",
@@ -340,29 +340,27 @@
     "    \"RegionName\",\n",
     "    \"RegionType\",\n",
     "    \"StateName\",\n",
-    "    # \"Value Type\",\n",
     "    \"Home Type\",\n",
     "]\n",
     "\n",
-    "combined_df = pd.merge(\n",
-    "    combined_price,\n",
-    "    combined_price_per,\n",
-    "    on=matching_cols,\n",
-    "    how=\"outer\",\n",
-    ")\n",
-    "combined_df = pd.merge(\n",
-    "    combined_df,\n",
-    "    combined_count,\n",
-    "    on=matching_cols,\n",
-    "    how=\"outer\",\n",
-    ")\n",
+    "combined_batches = [pd.concat(cur_batch) for cur_batch in batches.values()]\n",
+    "\n",
+    "if len(combined_batches) > 0:\n",
+    "    combined_df = combined_batches[0]\n",
+    "    for batch in combined_batches[1:]:\n",
+    "        combined_df = pd.merge(\n",
+    "            combined_df,\n",
+    "            batch,\n",
+    "            on=matching_cols,\n",
+    "            how=\"outer\",\n",
+    "        )\n",
     "\n",
     "combined_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -393,9 +391,9 @@
        "      <th>State</th>\n",
        "      <th>Home Type</th>\n",
        "      <th>Date</th>\n",
-       "      <th>Sale Price</th>\n",
-       "      <th>Sale Price per Sqft</th>\n",
-       "      <th>Count</th>\n",
+       "      <th>Median Sale Price per Sqft</th>\n",
+       "      <th>Median Sale Price</th>\n",
+       "      <th>Sales Count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -408,8 +406,8 @@
        "      <td>NaN</td>\n",
        "      <td>SFR</td>\n",
        "      <td>2018-01-31</td>\n",
-       "      <td>309000.0</td>\n",
        "      <td>137.412316</td>\n",
+       "      <td>309000.0</td>\n",
        "      <td>33940.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -421,8 +419,8 @@
        "      <td>NaN</td>\n",
        "      <td>all homes</td>\n",
        "      <td>2018-01-31</td>\n",
-       "      <td>314596.0</td>\n",
        "      <td>140.504620</td>\n",
+       "      <td>314596.0</td>\n",
        "      <td>37135.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -434,8 +432,8 @@
        "      <td>NaN</td>\n",
        "      <td>condo/co-op only</td>\n",
        "      <td>2018-01-31</td>\n",
-       "      <td>388250.0</td>\n",
        "      <td>238.300000</td>\n",
+       "      <td>388250.0</td>\n",
        "      <td>3195.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -447,8 +445,8 @@
        "      <td>NaN</td>\n",
        "      <td>SFR</td>\n",
        "      <td>2018-02-28</td>\n",
-       "      <td>309072.5</td>\n",
        "      <td>137.199170</td>\n",
+       "      <td>309072.5</td>\n",
        "      <td>33304.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -460,8 +458,8 @@
        "      <td>NaN</td>\n",
        "      <td>all homes</td>\n",
        "      <td>2018-02-28</td>\n",
-       "      <td>314608.0</td>\n",
        "      <td>140.304966</td>\n",
+       "      <td>314608.0</td>\n",
        "      <td>36493.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -561,23 +559,36 @@
        "49485     845162        535   Granbury, TX         msa    TX   \n",
        "49486     845162        535   Granbury, TX         msa    TX   \n",
        "\n",
-       "              Home Type        Date  Sale Price  Sale Price per Sqft    Count  \n",
-       "0                   SFR  2018-01-31    309000.0           137.412316  33940.0  \n",
-       "1             all homes  2018-01-31    314596.0           140.504620  37135.0  \n",
-       "2      condo/co-op only  2018-01-31    388250.0           238.300000   3195.0  \n",
-       "3                   SFR  2018-02-28    309072.5           137.199170  33304.0  \n",
-       "4             all homes  2018-02-28    314608.0           140.304966  36493.0  \n",
-       "...                 ...         ...         ...                  ...      ...  \n",
-       "49482         all homes  2023-09-30         NaN                  NaN     26.0  \n",
-       "49483               SFR  2023-10-31         NaN                  NaN     24.0  \n",
-       "49484         all homes  2023-10-31         NaN                  NaN     24.0  \n",
-       "49485               SFR  2023-11-30         NaN                  NaN     16.0  \n",
-       "49486         all homes  2023-11-30         NaN                  NaN     16.0  \n",
+       "              Home Type        Date  Median Sale Price per Sqft  \\\n",
+       "0                   SFR  2018-01-31                  137.412316   \n",
+       "1             all homes  2018-01-31                  140.504620   \n",
+       "2      condo/co-op only  2018-01-31                  238.300000   \n",
+       "3                   SFR  2018-02-28                  137.199170   \n",
+       "4             all homes  2018-02-28                  140.304966   \n",
+       "...                 ...         ...                         ...   \n",
+       "49482         all homes  2023-09-30                         NaN   \n",
+       "49483               SFR  2023-10-31                         NaN   \n",
+       "49484         all homes  2023-10-31                         NaN   \n",
+       "49485               SFR  2023-11-30                         NaN   \n",
+       "49486         all homes  2023-11-30                         NaN   \n",
+       "\n",
+       "       Median Sale Price  Sales Count  \n",
+       "0               309000.0      33940.0  \n",
+       "1               314596.0      37135.0  \n",
+       "2               388250.0       3195.0  \n",
+       "3               309072.5      33304.0  \n",
+       "4               314608.0      36493.0  \n",
+       "...                  ...          ...  \n",
+       "49482                NaN         26.0  \n",
+       "49483                NaN         24.0  \n",
+       "49484                NaN         24.0  \n",
+       "49485                NaN         16.0  \n",
+       "49486                NaN         16.0  \n",
        "\n",
        "[49487 rows x 10 columns]"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -599,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -608,13 +619,6 @@
     "\n",
     "final_df.to_json(FULL_PROCESSED_DIR_PATH + \"final.jsonl\", orient=\"records\", lines=True)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

zillow.py

@@ -132,11 +132,13 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                     "State": datasets.Value(dtype="string", id="State"),
                     "Home Type": datasets.Value(dtype="string", id="Home Type"),
                     "Date": datasets.Value(dtype="string", id="Date"),
-                    "Sale Price": datasets.Value(dtype="float32", id="Sale Price"),
-                    "Sale Price per Sqft": datasets.Value(
-                        dtype="float32", id="Sale Price per Sqft"
+                    "Median Sale Price": datasets.Value(
+                        dtype="float32", id="Median Sale Price"
                     ),
-                    "Count": datasets.Value(dtype="int32", id="Count"),
+                    "Median Sale Price per Sqft": datasets.Value(
+                        dtype="float32", id="Median Sale Price per Sqft"
+                    ),
+                    "Sales Count": datasets.Value(dtype="int32", id="Sales Count"),
                     # These are the features of your dataset like images, labels ...
                 }
             )
@@ -255,9 +257,11 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                         "State": data["State"],
                         "Home Type": data["Home Type"],
                         "Date": data["Date"],
-                        "Sale Price": data["Sale Price"],
-                        "Sale Price per Sqft": data["Sale Price per Sqft"],
-                        "Count": data["Count"],
+                        "Median Sale Price": data["Median Sale Price"],
+                        "Median Sale Price per Sqft": data[
+                            "Median Sale Price per Sqft"
+                        ],
+                        "Sales Count": data["Sales Count"],
                         # "answer": "" if split == "test" else data["answer"],
                     }
                 # else: