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: