notebooks/00_All-meters-dataset.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building Data Genome 2.0\n",
    "## All-meters datasets creation: hourly and daily timestamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biam! (pic.biam@gmail.com)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "# Meters data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To join all meters raw dataets run the following code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write custom path here\n",
    "path_raw = \"..\\\\data\\\\meters\\\\raw\\\\\"\n",
    "path_proc = \"..\\\\data\\\\meters\\\\processed\\\\\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# files in directory\n",
    "files = glob(path_raw + \"*.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['..\\\\data\\\\meters\\\\raw\\\\chilledwater.csv',\n",
       " '..\\\\data\\\\meters\\\\raw\\\\electricity.csv',\n",
       " '..\\\\data\\\\meters\\\\raw\\\\gas.csv',\n",
       " '..\\\\data\\\\meters\\\\raw\\\\hotwater.csv',\n",
       " '..\\\\data\\\\meters\\\\raw\\\\irrigation.csv',\n",
       " '..\\\\data\\\\meters\\\\raw\\\\solar.csv',\n",
       " '..\\\\data\\\\meters\\\\raw\\\\steam.csv',\n",
       " '..\\\\data\\\\meters\\\\raw\\\\water.csv']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = [] # empty list of the dataframes to create\n",
    "for file in files: # for each file in directory\n",
    "    meter_type = file.split(\"\\\\\")[4].split(\".\")[0] # meter_type to rename the value feature\n",
    "    meter = pd.read_csv(file) # load the dataset\n",
    "    meter = pd.melt(meter, id_vars = \"timestamp\", var_name = \"building_id\", value_name = \"meter_reading\") # melt dataset\n",
    "    meter[\"meter\"] = str(meter_type) # adds column with the meter type\n",
    "    dfs.append(meter) # append to list\n",
    "complete_data = pd.concat(dfs, axis=0, ignore_index=True) # concatenate all meter\n",
    "del(dfs, meter, file, files, meter_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>building_id</th>\n",
       "      <th>meter_reading</th>\n",
       "      <th>meter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>Panther_office_Clementine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chilledwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01 01:00:00</td>\n",
       "      <td>Panther_office_Clementine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chilledwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 02:00:00</td>\n",
       "      <td>Panther_office_Clementine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chilledwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01 03:00:00</td>\n",
       "      <td>Panther_office_Clementine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chilledwater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01 04:00:00</td>\n",
       "      <td>Panther_office_Clementine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chilledwater</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp                building_id  meter_reading         meter\n",
       "0  2016-01-01 00:00:00  Panther_office_Clementine            NaN  chilledwater\n",
       "1  2016-01-01 01:00:00  Panther_office_Clementine            NaN  chilledwater\n",
       "2  2016-01-01 02:00:00  Panther_office_Clementine            NaN  chilledwater\n",
       "3  2016-01-01 03:00:00  Panther_office_Clementine            NaN  chilledwater\n",
       "4  2016-01-01 04:00:00  Panther_office_Clementine            NaN  chilledwater"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "electricity     27684432\n",
       "chilledwater     9736920\n",
       "steam            6491280\n",
       "hotwater         3245640\n",
       "gas              3105288\n",
       "water            2561424\n",
       "irrigation        649128\n",
       "solar              87720\n",
       "Name: meter, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the meters\n",
    "complete_data[\"meter\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1636"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check number of buildings\n",
    "len(np.unique(complete_data[\"building_id\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rounds to 4 decimals\n",
    "complete_data[\"meter_reading\"] = round(complete_data[\"meter_reading\"],4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hourly timestamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Meters reading are currently hourly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_data.to_csv(path_proc + \"allmeters.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daily timestamp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Daily readings are summarized as the sum of all the readings of that day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converts timestamp to datetime object\n",
    "complete_data[\"timestamp\"] = pd.to_datetime(complete_data[\"timestamp\"], format='%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sums all readings per day\n",
    "complete_data = complete_data.groupby(by=[\"building_id\",\"meter\",complete_data['timestamp'].dt.date]).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset indexes to ungroup\n",
    "complete_data = complete_data.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "complete_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# export csv\n",
    "complete_data.to_csv(path_proc + \"allmeters_daily.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "del complete_data"
   ]
  }
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