IoB_13_Singa_repairNaN_bl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "730b0fcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a549b477",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>osmid</th>\n",
       "      <th>mp_prog</th>\n",
       "      <th>far</th>\n",
       "      <th>osmlanduse</th>\n",
       "      <th>mp_plotare</th>\n",
       "      <th>heritage_n</th>\n",
       "      <th>amenity</th>\n",
       "      <th>name</th>\n",
       "      <th>levels</th>\n",
       "      <th>age</th>\n",
       "      <th>...</th>\n",
       "      <th>hdb_1roo_1</th>\n",
       "      <th>hdb_2roo_1</th>\n",
       "      <th>hdb_3roo_1</th>\n",
       "      <th>hdb_4roo_1</th>\n",
       "      <th>hdb_5roo_1</th>\n",
       "      <th>hdb_exec_1</th>\n",
       "      <th>hdb_mult_1</th>\n",
       "      <th>hdb_stud_1</th>\n",
       "      <th>age_proxy</th>\n",
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       "      <th>0</th>\n",
       "      <td>1337396</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1362701</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>parking</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.0</td>\n",
       "      <td>None</td>\n",
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       "      <th>2</th>\n",
       "      <td>1407265</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>None</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1447583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>None</td>\n",
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       "      <td>1447589</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>None</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1975.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2006.0</td>\n",
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       "      <th>112963</th>\n",
       "      <td>1165450373</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>None</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>None</td>\n",
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       "<p>112965 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             osmid mp_prog  far osmlanduse  mp_plotare heritage_n  amenity   \n",
       "0          1337396     NaN  NaN        NaN         NaN        NaN      NaN  \\\n",
       "1          1362701     NaN  NaN        NaN         NaN        NaN  parking   \n",
       "2          1407265     NaN  NaN        NaN         NaN        NaN      NaN   \n",
       "3          1447583     NaN  NaN        NaN         NaN        NaN      NaN   \n",
       "4          1447589     NaN  NaN        NaN         NaN        NaN      NaN   \n",
       "...            ...     ...  ...        ...         ...        ...      ...   \n",
       "112960  1165422507     NaN  NaN        NaN         NaN        NaN      NaN   \n",
       "112961  1165422508     NaN  NaN        NaN         NaN        NaN      NaN   \n",
       "112962  1165450372     NaN  NaN        NaN         NaN        NaN      NaN   \n",
       "112963  1165450373     NaN  NaN        NaN         NaN        NaN      NaN   \n",
       "112964  1165450374     NaN  NaN        NaN         NaN        NaN      NaN   \n",
       "\n",
       "       name levels  age  ... hdb_1roo_1  hdb_2roo_1  hdb_3roo_1  hdb_4roo_1   \n",
       "0       NaN    NaN  NaN  ...        NaN         NaN         NaN         NaN  \\\n",
       "1       NaN      3  NaN  ...        NaN         NaN         NaN         NaN   \n",
       "2       NaN    NaN  NaN  ...        NaN         NaN         NaN         NaN   \n",
       "3        20     12  NaN  ...        NaN         NaN         NaN         NaN   \n",
       "4        22     12  NaN  ...        NaN         NaN         NaN         NaN   \n",
       "...     ...    ...  ...  ...        ...         ...         ...         ...   \n",
       "112960  NaN    NaN  NaN  ...        NaN         NaN         NaN         NaN   \n",
       "112961  NaN    NaN  NaN  ...        NaN         NaN         NaN         NaN   \n",
       "112962  NaN     29  NaN  ...        NaN         NaN         NaN         NaN   \n",
       "112963  NaN     29  NaN  ...        NaN         NaN         NaN         NaN   \n",
       "112964  NaN     29  NaN  ...        NaN         NaN         NaN         NaN   \n",
       "\n",
       "       hdb_5roo_1 hdb_exec_1 hdb_mult_1 hdb_stud_1 age_proxy  geometry  \n",
       "0             NaN        NaN        NaN        NaN    1961.0      None  \n",
       "1             NaN        NaN        NaN        NaN    1969.0      None  \n",
       "2             NaN        NaN        NaN        NaN    1982.0      None  \n",
       "3             NaN        NaN        NaN        NaN    2007.0      None  \n",
       "4             NaN        NaN        NaN        NaN    1986.0      None  \n",
       "...           ...        ...        ...        ...       ...       ...  \n",
       "112960        NaN        NaN        NaN        NaN    1997.0      None  \n",
       "112961        NaN        NaN        NaN        NaN    1975.0      None  \n",
       "112962        NaN        NaN        NaN        NaN    2006.0      None  \n",
       "112963        NaN        NaN        NaN        NaN    1994.0      None  \n",
       "112964        NaN        NaN        NaN        NaN    1962.0      None  \n",
       "\n",
       "[112965 rows x 53 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf = gpd.read_file(r'C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_07_proxy.shp', encoding='latin-1')\n",
    "gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "31851309",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the count of non-NaN values for each column\n",
    "gdfy = gdf\n",
    "counts = gdfy.count()\n",
    "\n",
    "# create a bar plot of non-NaN counts with red color\n",
    "ax = counts.plot(kind='bar', color='green')\n",
    "\n",
    "# add the count percentage above each bar\n",
    "for i, v in enumerate(counts):\n",
    "    ax.text(i, v+5, str(round((v/gdfy.shape[0])*100, 2)) + '%', ha='center', rotation=90, size=8)\n",
    "    \n",
    "# set the x-label and y-label\n",
    "ax.set_xlabel('Columns')\n",
    "ax.set_ylabel('Number of non-NaN values')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0c88b4d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 112965 entries, 0 to 112964\n",
      "Data columns (total 53 columns):\n",
      " #   Column      Non-Null Count   Dtype   \n",
      "---  ------      --------------   -----   \n",
      " 0   osmid       112965 non-null  int64   \n",
      " 1   mp_prog     111211 non-null  object  \n",
      " 2   far         44540 non-null   float64 \n",
      " 3   osmlanduse  85026 non-null   object  \n",
      " 4   mp_plotare  111424 non-null  float64 \n",
      " 5   heritage_n  7770 non-null    object  \n",
      " 6   amenity     3054 non-null    object  \n",
      " 7   name        11150 non-null   object  \n",
      " 8   levels      28415 non-null   object  \n",
      " 9   age         14367 non-null   float64 \n",
      " 10  build_prog  110687 non-null  object  \n",
      " 11  build_foot  111424 non-null  float64 \n",
      " 12  gfa         111424 non-null  float64 \n",
      " 13  levels_pro  112965 non-null  int64   \n",
      " 14  gm_name     1072 non-null    object  \n",
      " 15  gm_phase    1072 non-null    object  \n",
      " 16  postcode    1072 non-null    object  \n",
      " 17  gm_award    1072 non-null    object  \n",
      " 18  gm_link     777 non-null     object  \n",
      " 19  building_l  6107 non-null    float64 \n",
      " 20  build_fo_1  10183 non-null   float64 \n",
      " 21  hdb_street  10183 non-null   object  \n",
      " 22  hdb_max_fl  10183 non-null   float64 \n",
      " 23  hdb_year_c  10183 non-null   float64 \n",
      " 24  hdb_reside  10183 non-null   object  \n",
      " 25  hdb_commer  10183 non-null   object  \n",
      " 26  hdb_total_  10183 non-null   float64 \n",
      " 27  hdb_1room_  10183 non-null   float64 \n",
      " 28  hdb_2room_  10183 non-null   float64 \n",
      " 29  hdb_3room_  10183 non-null   float64 \n",
      " 30  hdb_4room_  10183 non-null   float64 \n",
      " 31  hdb_5room_  10183 non-null   float64 \n",
      " 32  hdb_exec_s  10183 non-null   float64 \n",
      " 33  hdb_multig  10183 non-null   float64 \n",
      " 34  hdb_studio  10183 non-null   float64 \n",
      " 35  building_1  6879 non-null    object  \n",
      " 36  build_fo_2  11554 non-null   float64 \n",
      " 37  hdb_stre_1  11554 non-null   object  \n",
      " 38  hdb_max__1  11554 non-null   float64 \n",
      " 39  hdb_year_1  11554 non-null   float64 \n",
      " 40  hdb_resi_1  11554 non-null   object  \n",
      " 41  hdb_comm_1  11554 non-null   object  \n",
      " 42  hdb_tota_1  11554 non-null   float64 \n",
      " 43  hdb_1roo_1  11554 non-null   float64 \n",
      " 44  hdb_2roo_1  11554 non-null   float64 \n",
      " 45  hdb_3roo_1  11554 non-null   float64 \n",
      " 46  hdb_4roo_1  11554 non-null   float64 \n",
      " 47  hdb_5roo_1  11554 non-null   float64 \n",
      " 48  hdb_exec_1  11554 non-null   float64 \n",
      " 49  hdb_mult_1  11554 non-null   float64 \n",
      " 50  hdb_stud_1  11554 non-null   float64 \n",
      " 51  age_proxy   112965 non-null  float64 \n",
      " 52  geometry    111424 non-null  geometry\n",
      "dtypes: float64(31), geometry(1), int64(2), object(19)\n",
      "memory usage: 45.7+ MB\n"
     ]
    }
   ],
   "source": [
    "gdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df4bebf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert level to integer\n",
    "gdf['levels'] = pd.to_numeric(gdf['levels'], errors='coerce').astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f916a99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matching values:  23758\n",
      "Non-matching values:  4657\n"
     ]
    }
   ],
   "source": [
    "#analyze if levels and levels_proxy are equal\n",
    "# compare values in col1 and col2 and create a new column with the result\n",
    "gdf['match'] = gdf['levels'] == gdf['levels_pro']\n",
    "\n",
    "# count the number of matching and non-matching values\n",
    "result_counts = gdf['match'].value_counts()\n",
    "result_match_count = result_counts.get(True, 0)\n",
    "result_caramba_count = result_counts.get(False, 0)\n",
    "\n",
    "print(\"Matching values: \", result_match_count)\n",
    "print(\"Non-matching values: \", result_caramba_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ed3e124b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#if no, replace levels_proxy with levels\n",
    "mask = (gdf['levels'].notnull()) & (gdf['match'] == 'False')\n",
    "gdf.loc[mask, 'levels_pro'] = gdf['levels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94c20a7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = gdf.drop(columns = ['match'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3cbd792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['small_box', nan, 'medic', 'resident_high', 'deco_box', 'civic',\n",
       "       'education', 'transport', 'glass_box', 'industrial',\n",
       "       'construction_site', 'resident_low', 'religious', 'sport', 'ruins',\n",
       "       'RESIDENTIAL'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf['build_prog'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6150b881",
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace with NaN items without program\n",
    "mask = gdf['build_prog'].isna()\n",
    "\n",
    "# update the values in the 'levels_pro' column using the boolean mask\n",
    "gdf.loc[mask, 'levels_pro'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6ba3da2a",
   "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>blk_no</th>\n",
       "      <th>street</th>\n",
       "      <th>max_floor_lvl</th>\n",
       "      <th>year_completed</th>\n",
       "      <th>residential</th>\n",
       "      <th>commercial</th>\n",
       "      <th>market_hawker</th>\n",
       "      <th>miscellaneous</th>\n",
       "      <th>multistorey_carpark</th>\n",
       "      <th>precinct_pavilion</th>\n",
       "      <th>...</th>\n",
       "      <th>3room_sold</th>\n",
       "      <th>4room_sold</th>\n",
       "      <th>5room_sold</th>\n",
       "      <th>exec_sold</th>\n",
       "      <th>multigen_sold</th>\n",
       "      <th>studio_apartment_sold</th>\n",
       "      <th>1room_rental</th>\n",
       "      <th>2room_rental</th>\n",
       "      <th>3room_rental</th>\n",
       "      <th>other_room_rental</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>BEACH RD</td>\n",
       "      <td>16</td>\n",
       "      <td>1970</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>BEDOK STH AVE 1</td>\n",
       "      <td>14</td>\n",
       "      <td>1975</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>CANTONMENT RD</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CHAI CHEE RD</td>\n",
       "      <td>15</td>\n",
       "      <td>1982</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>CHANGI VILLAGE RD</td>\n",
       "      <td>4</td>\n",
       "      <td>1975</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>DELTA AVE</td>\n",
       "      <td>25</td>\n",
       "      <td>1982</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>DOVER RD</td>\n",
       "      <td>12</td>\n",
       "      <td>1975</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>118</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>EUNOS CRES</td>\n",
       "      <td>14</td>\n",
       "      <td>1977</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  blk_no             street  max_floor_lvl  year_completed residential   \n",
       "0      1           BEACH RD             16            1970           Y  \\\n",
       "1      1    BEDOK STH AVE 1             14            1975           Y   \n",
       "2      1      CANTONMENT RD              2            2010           N   \n",
       "3      1       CHAI CHEE RD             15            1982           Y   \n",
       "4      1  CHANGI VILLAGE RD              4            1975           Y   \n",
       "5      1          DELTA AVE             25            1982           Y   \n",
       "6      1           DOVER RD             12            1975           Y   \n",
       "7      1         EUNOS CRES             14            1977           Y   \n",
       "\n",
       "  commercial market_hawker miscellaneous multistorey_carpark   \n",
       "0          Y             N             N                   N  \\\n",
       "1          N             N             Y                   N   \n",
       "2          Y             N             N                   N   \n",
       "3          N             N             N                   N   \n",
       "4          Y             N             N                   N   \n",
       "5          N             N             N                   N   \n",
       "6          N             N             Y                   N   \n",
       "7          N             N             Y                   N   \n",
       "\n",
       "  precinct_pavilion  ... 3room_sold  4room_sold  5room_sold  exec_sold   \n",
       "0                 N  ...        138           1           2          0  \\\n",
       "1                 N  ...        204           0           2          0   \n",
       "2                 N  ...          0           0           0          0   \n",
       "3                 N  ...          0          10          92          0   \n",
       "4                 N  ...         54           0           1          0   \n",
       "5                 N  ...          0           0          96          0   \n",
       "6                 N  ...        118           0           7          0   \n",
       "7                 N  ...          0           0           0          0   \n",
       "\n",
       "   multigen_sold  studio_apartment_sold  1room_rental  2room_rental   \n",
       "0              0                      0             0             0  \\\n",
       "1              0                      0             0             0   \n",
       "2              0                      0             0             0   \n",
       "3              0                      0             0             0   \n",
       "4              0                      0             0             0   \n",
       "5              0                      0             0             0   \n",
       "6              0                      0             0             0   \n",
       "7              0                      0             0           247   \n",
       "\n",
       "   3room_rental  other_room_rental  \n",
       "0             0                  0  \n",
       "1             0                  0  \n",
       "2             0                  0  \n",
       "3             0                  0  \n",
       "4             0                  0  \n",
       "5             0                  0  \n",
       "6             0                  0  \n",
       "7             0                  0  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_Singa/hdb-property-information/hdb-property-information.csv', encoding='utf-8')\n",
    "df.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "415294a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#copy values from hdb_max__1 to levels_pro\n",
    "# create a boolean mask for the rows to update\n",
    "mask2 = gdf['hdb_max__1'].notnull()\n",
    "\n",
    "# update the values in the 'levels_pro' column using the boolean mask\n",
    "gdf.loc[mask2, 'levels_pro'] = gdf['hdb_max__1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "35ba4bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a boolean mask for the rows to update\n",
    "mask3 = gdf['build_prog'] == 'RESIDENTIAL'\n",
    "\n",
    "# update the values in the 'build_prog' column using the boolean mask\n",
    "gdf.loc[mask3, 'build_prog'] = 'resident_high'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0f5354d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask5 = (gdf['build_prog'] == 'resident_high') & (gdf['levels_pro'] < 9)\n",
    "\n",
    "# update the values in the 'levels_pro' column using the boolean mask\n",
    "gdf.loc[mask5, 'levels_pro'] = np.random.randint(12, 31, size=mask5.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9a059c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'commercial', 'park', 'residential', 'industrial', 'retail',\n",
       "       'nature_reserve', 'military', 'grass', 'meadow', 'forest',\n",
       "       'recreation_ground', 'scrub', 'allotments', 'farmyard', 'farmland',\n",
       "       'cemetery', 'quarry'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf['osmlanduse'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "26efbb1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask6 = (gdf['osmlanduse'] == 'residential') & (gdf['levels_pro'].isna())\n",
    "\n",
    "gdf.loc[mask6, 'levels_pro'] = np.random.randint(12, 31, size=mask6.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "589bd736",
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf.to_file('C:/WORK_IAAC/submissions/Internet of Buildings/IoB_WIP/IoB_QGIS/buildings/buildings_08_proxy.shp', crs='EPSG:4326')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "732e3154",
   "metadata": {},
   "outputs": [
    {
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       "        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>osmid</th>\n",
       "      <th>mp_prog</th>\n",
       "      <th>far</th>\n",
       "      <th>osmlanduse</th>\n",
       "      <th>mp_plotare</th>\n",
       "      <th>heritage_n</th>\n",
       "      <th>amenity</th>\n",
       "      <th>name</th>\n",
       "      <th>levels</th>\n",
       "      <th>age</th>\n",
       "      <th>...</th>\n",
       "      <th>hdb_1roo_1</th>\n",
       "      <th>hdb_2roo_1</th>\n",
       "      <th>hdb_3roo_1</th>\n",
       "      <th>hdb_4roo_1</th>\n",
       "      <th>hdb_5roo_1</th>\n",
       "      <th>hdb_exec_1</th>\n",
       "      <th>hdb_mult_1</th>\n",
       "      <th>hdb_stud_1</th>\n",
       "      <th>age_proxy</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty GeoDataFrame\n",
       "Columns: [osmid, mp_prog, far, osmlanduse, mp_plotare, heritage_n, amenity, name, levels, age, build_prog, build_foot, gfa, levels_pro, gm_name, gm_phase, postcode, gm_award, gm_link, building_l, build_fo_1, hdb_street, hdb_max_fl, hdb_year_c, hdb_reside, hdb_commer, hdb_total_, hdb_1room_, hdb_2room_, hdb_3room_, hdb_4room_, hdb_5room_, hdb_exec_s, hdb_multig, hdb_studio, building_1, build_fo_2, hdb_stre_1, hdb_max__1, hdb_year_1, hdb_resi_1, hdb_comm_1, hdb_tota_1, hdb_1roo_1, hdb_2roo_1, hdb_3roo_1, hdb_4roo_1, hdb_5roo_1, hdb_exec_1, hdb_mult_1, hdb_stud_1, age_proxy, geometry]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 53 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#by mask get rows that have non-NaN values in all columns\n",
    "maskx = gdf.dropna(how='any').index\n",
    "\n",
    "# select the rows with non-NaN values in all columns and show the first 10 rows\n",
    "gdf.loc[maskx].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a0aa608d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Subordinate Courts', 'Dairy Farm', 'Clarke Quay',\n",
       "       'Emerald Hill', 'Orchard Road No. 14 to 38',\n",
       "       'Bras Basah Road Conservation Area', 'Singapore Cricket Club',\n",
       "       'Capitol Theatre, Capitol Building and Stamford House',\n",
       "       'Fullerton Building', 'Joo Chiat Conservation Area',\n",
       "       'Singapore Botanic Gardens',\n",
       "       'University of Singapore, Former (Bukit Timah Campus)',\n",
       "       'Chinatown (Kreta Ayer)', 'Chinatown (Bukit Pasoh)',\n",
       "       'China Square', 'Asia Insurance Building, Former',\n",
       "       'Robinson Road No. 35 (TAS Building, Former)', 'Sentosa',\n",
       "       'Sri Krishnan Temple', 'Margaret  Drive No 53', 'Southern Ridges',\n",
       "       'Pulau Ubin No. 1', 'Chinatown (Tanjong Pagar)',\n",
       "       'Jurong Town Hall, Former',\n",
       "       'Convent of the Holy Infant Jesus, Former',\n",
       "       'CLIFFORD PIER, FORMER CUSTOMS HARBOUR BRANCH BUILDING AND CHANGE ALLEY PLAZA AT COLLYER QUAY',\n",
       "       'Alexandra Hospital',\n",
       "       'Traffic Police Headquarters Building, Former and Former Customs House',\n",
       "       'Pepys Road No. 31K',\n",
       "       'MacTaggart Road No. 2 (Khong Guan Biscuit Factory)', 'Boat Quay',\n",
       "       'Singapore River Bridges', 'Upper Circular',\n",
       "       'Armenian Street No.45 to 53',\n",
       "       '40 to 44 Armenian Street and 71 Stamford Road', 'Loke Yew Street',\n",
       "       'Magazine Road Conservation Area', 'Mount  Sophia',\n",
       "       'Joo Chiat Conservation Area-Chapel Road/Marine Parade',\n",
       "       'St. Anthony058e+004', 'Kampong Glam', 'Little India',\n",
       "       'Sri Veeramakaliamman Temple', 'Beach Road',\n",
       "       'MacRitchie Reservoir', 'Former Royal Air Force (RAF) Seletar',\n",
       "       'Gallop Road/Tyersall Avenue', 'Rochester Park',\n",
       "       'Draycott Drive No.4', 'Chatsworth Park', 'Chinatown (Telok Ayer)',\n",
       "       'Maxwell Road No. 38', 'Red Cross House', 'Kampong Silat',\n",
       "       'Tiong Bahru', 'Outram Road No 226', 'Cantonment Road',\n",
       "       'Fort Canning and Coleman Street',\n",
       "       'Ban Siew San Temple and Koon Seng Ting Temple',\n",
       "       'Balestier Road Conservation Area', 'Cheang Jim Chwan Place',\n",
       "       'Former Beach Road Central Police Station',\n",
       "       'Waterloo Street No. 155 (Former Stamford Girlsap.html?areaid=SRBT01',\n",
       "       'Short Street', 'River Valley', 'Mohamed Sultan Road Nos 72-13',\n",
       "       'Blair Plain', 'Waterboat House', 'River Valley Road No. 338E',\n",
       "       'Church of St. Bernadette',\n",
       "       'Alexandra Road No. 332 (Princess House)', 'Bukit Chermin',\n",
       "       'Tudor Court', 'Grange Road No. 53 (Spring Grove)',\n",
       "       'Jellicoe Road Conservation Area',\n",
       "       'Ellison Building and Rex Cinema',\n",
       "       'Mount Sophia Conservation Area- 7 Mount Sophia',\n",
       "       'Mount Sophia Conservation Area- 2 Adis Road',\n",
       "       'Joo Chiat Coservation Area-25 Still Road South',\n",
       "       \"St. Patrick's Road No. 124 and 126\", 'Bukit Timah Road No. 501',\n",
       "       'Joo Chiat Conservation Area-25 Chapel Road',\n",
       "       'Joo Chiat Conservation Area -37 Marine Parade',\n",
       "       'Joo Chiat Conservation Area (Ph2) -26 Still Road South',\n",
       "       'Joo Chiat Conservation Area (Ph2)-272 to 292 East Coast Road',\n",
       "       'Toa Payoh Town Park', \"Pearl's Hill\",\n",
       "       'Bukit Timah Railway Station', 'Alexandra Road (Lot 1997pt MK1)',\n",
       "       'Queen of Peace Church',\n",
       "       'Chatsworth Park (11) Lot 55-4 TS 24 (3 Chatsworth Ave)',\n",
       "       'Cairnhill Road No.42', 'Cairnhill Conservation Area',\n",
       "       'Lot 884 TS 27 at No. 42 Cairnhill Road', 'Robertson Quay',\n",
       "       'Moulmein Road / Mandalay Road', 'Upper Seletar Reservoir Park',\n",
       "       'Holland Park and Ridout Road', 'St. Patrick948e+002',\n",
       "       'Fairy Point Hill', 'Gilstead Road No. 10',\n",
       "       'White House Park/Nassim Rd(5) Lot 55 TS 25 (24 Nassim Rd)',\n",
       "       'Tanjong Katong (includes 64, 65, 110 & 112 Tanjong Katong Road)',\n",
       "       'Joo Chiat Conservtion Area- 17 Pennefather Road',\n",
       "       'Sian Keng Tong Temple', 'Mountbatten Road -Amber Road No.23',\n",
       "       'Nepal Park', 'Mount Sophia Conservation Area- 11A  Mount Sophia',\n",
       "       'Mount Sophia Conservation Area- 12 Mount Sophia',\n",
       "       'White House Park/Nassim Rd(6) Lot 111 TS 25 (28 Nassim Rd)',\n",
       "       'Jalan Besar Conservation Area',\n",
       "       'White House Park/Nassim Rd(1) Lot 468 TS 25 (1 Dalvey Estate',\n",
       "       'White House Park/Nassim Rd(12) Lot 77-6 TS 25 (6 & 10 Lermit',\n",
       "       'White House Park/Nassim Rd(8) Lot 61-13 TS 25 (11 Ladyhill R',\n",
       "       'White House Park/Nassim Rd(4) Lot 99-24 TS 25 (12 Dalvey Est',\n",
       "       'White House Park/Nassim Rd(13) Lot 539 TS 25 (75 Whitehouse',\n",
       "       'White House Park/Nassim Rd(2) Lot 464 TS 25 (2 Dalvey Estate',\n",
       "       'White House Park/Nassim Rd(9) Lot 470 TS 25 (32 Nassim Rd)',\n",
       "       'White House Park/Nassim Rd(10) Lot 48-2 TS 25 (40 Nassim Rd)',\n",
       "       'White House Park/Nassim Rd(3) Lot 96-2 TS 25 (5 Dalvey Estat',\n",
       "       'White House Park/Nassim Rd(11) Lot 44-8 TS 25 (56 Dalvey Rd)',\n",
       "       'Mahatma Gandhi Memorial Hall',\n",
       "       'Mountbatten Road- Amber Road No. 43',\n",
       "       'Beach Road NCO Club at Beach Rd Lot 200-1pt TS 11',\n",
       "       'Block 1 of Former Beach Road Camp',\n",
       "       'Block 9 of Former Beach Road Camp',\n",
       "       'Block 14 of Former Beach Road Camp', 'Geylang Conservation Area',\n",
       "       'Sri Manmatha Karuneshvarar Temple', 'Former Kallang Airport',\n",
       "       'Angullia Mosque Gatehouse', 'Matilda House', 'Wak Tanjong Mosque',\n",
       "       'Geylang Fire Station, Former', 'Former Victoria School',\n",
       "       \"Former St. Matthew's Church\", 'Pasir Panjang Road No. 222 to 240',\n",
       "       'Beaulieu House', 'Mountbatten Road', 'Meyer Place No. 2',\n",
       "       'Former Chee Kong Tong (Thekchen Choling) Entrance Gate',\n",
       "       'Townerville', 'Seventh-Day Adventist Church',\n",
       "       'Kwong Wai Shui Hosptial', 'Commonwealth Avenu No 38',\n",
       "       'Jalan Lim Tai See No. 27 (Hoon San Temple)',\n",
       "       'Jalan Jurong Kechil', 'Kiew Lee Tong Temple',\n",
       "       'Joo Chiat Conservation Area (Ph2) - 445 to 459 East Coast Ro',\n",
       "       'Joo Chiat Conservation Area (Ph2) - 417 to 431 East Coast Ro',\n",
       "       'Ramakrishna Mission', 'Sembawang Fire Station, Former',\n",
       "       'Buckley Road No.9', 'Leong San See Temple', 'Grange Road No. 25',\n",
       "       'Geylang Conservation Area-Lorong19/Geylang',\n",
       "       'Sims Avenue No. 514 to 530', \"St. Andrew's School\",\n",
       "       'Alexandra Road No 394', 'Singapore River Warehouses',\n",
       "       'Church of the Blessed Sacrament',\n",
       "       'Singapore Hakka Methodist Church',\n",
       "       'Jalan Besar Conservation area',\n",
       "       'Sri Vadapathira Kaliamman Temple', \"St. Gregory's Place\",\n",
       "       'Malabar Mosque', 'Tang Gah Beo Temple',\n",
       "       'Tanglin Officers Mess, Former (MFA Building)',\n",
       "       'Merbau Road No 20',\n",
       "       \"Former Fairfield Methodist Girls' School (Neil Road Conservation Area)\"],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf['heritage_n'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d70c66da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'parking', 'community_centre', 'hospital', 'school',\n",
       "       'place_of_worship', 'theatre', 'clinic', 'social_facility',\n",
       "       'college', 'police', 'marketplace', 'childcare', 'shelter',\n",
       "       'library', 'fire_station', 'fast_food', 'food_court', 'toilets',\n",
       "       'fuel', 'bank', 'conference_centre', 'restaurant', 'doctors',\n",
       "       'cafe', 'ice_cream', 'pharmacy', 'pub', 'bar', 'nursing_home',\n",
       "       'waste_transfer_station', 'waste_disposal', 'music_school',\n",
       "       'public_building', 'townhall', 'vehicle_inspection',\n",
       "       'kindergarten', 'arts_centre', 'ferry_terminal', 'atm',\n",
       "       'dressing_room', 'car_wash', 'driving_school', 'bus_station',\n",
       "       'cinema', 'bicycle_rental', 'monastery', 'commercial',\n",
       "       'crematorium', 'post_office', 'veterinary', 'dentist', 'studio',\n",
       "       'prep_school', 'car_rental', 'social_centre', 'language_school',\n",
       "       'university', 'events_venue', 'bicycle_parking', 'taxi',\n",
       "       'Clubhouse', 'shower', 'clubhouse', 'stage'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf['amenity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7ba4d875",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, '20', '22', ..., 'Prima Food Building', 'Revv',\n",
       "       'Westlite Jalan Tukang'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf['name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "013c3af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Changi Airport Terminal 1', 'Sim Lim Square', ...,\n",
       "       'Waterway Ridges', 'Floral Spring @ Yishun',\n",
       "       'FairPrice Supermarket at Bukit Batok Home Team'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf['gm_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86a633cb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
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