{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XBRL US API - FERC 714 Part III - Schedule 2. Planning Area Hourly Demand\n",
    "\n",
    "### Authenticate for access token \n",
    "Click in the gray code cell below, then click the Run button above to execute the cell. Type your XBRL US Web account email, account password, Client ID, and secret as noted, pressing the Enter key on the keyboard after each entry.\n",
    "\n",
    "XBRL US limits records returned for a query to improve efficiency; this script loops to collect all data from the Public Filings Database for a query. **Non-members might not be able to return all data for a query** - join XBRL US for comprehensive access - https://xbrl.us/join."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "# @title\n",
    "import os, re, sys, json\n",
    "import requests\n",
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "import numpy as np\n",
    "import getpass\n",
    "from datetime import datetime\n",
    "import urllib\n",
    "from urllib.parse import urlencode\n",
    "\n",
    "\n",
    "class tokenInfoClass:\n",
    "    access_token = None\n",
    "    refresh_token = None\n",
    "    email = None\n",
    "    username = None\n",
    "    client_id = None\n",
    "    client_secret = None\n",
    "    url = 'https://api.xbrl.us/oauth2/token'\n",
    "    headers = {\"Content-Type\": \"application/x-www-form-urlencoded\"}\n",
    "\n",
    "def refresh(info):\n",
    "    refresh_auth = {\n",
    "                'client_id': info.client_id,\n",
    "                'client_secret' : info.client_secret,\n",
    "                'grant_type' : 'refresh_token',\n",
    "                'platform' : 'ipynb',\n",
    "                'refresh_token' : info.refresh_token\n",
    "                }\n",
    "    refreshres = requests.post(info.url, data=refresh_auth, headers=info.headers)\n",
    "    refresh_json = refreshres.json()\n",
    "    info.access_token = refresh_json.get('access_token')\n",
    "    info.refresh_token = refresh_json.get('refresh_token')\n",
    "    print('Your access token(%s) is refreshed for 60 minutes. If it expires again, run this cell to generate a new token and continue to use the query cells below.' % (info.access_token))\n",
    "    return info\n",
    "\n",
    "tokenInfo = tokenInfoClass()\n",
    "\n",
    "# Helper to prompt only if value is missing\n",
    "def prompt_if_missing(value, prompt_text, secret=False):\n",
    "    if value:\n",
    "        return value\n",
    "    if secret:\n",
    "        return getpass.getpass(prompt=prompt_text)\n",
    "    return input(prompt_text)\n",
    "\n",
    "# Load credentials (if .json exists)\n",
    "creds = {}\n",
    "if os.path.exists('creds.json'):\n",
    "    try:\n",
    "        with open('creds.json', 'r') as f:\n",
    "            creds = json.load(f)\n",
    "        print(\"Loaded .json\")\n",
    "    except Exception as e:\n",
    "        print(\"Warning: failed to read from .json:\", e)\n",
    "\n",
    "if creds:\n",
    "    # Try nested prod object first\n",
    "    selected = None\n",
    "    if isinstance(creds.get('prod'), dict):\n",
    "        selected = creds['prod']\n",
    "    \n",
    "    # Next, try prod-prefixed keys\n",
    "    if not selected:\n",
    "        selected = {}\n",
    "        keys = ['email', 'password', 'client_id', 'client_secret']\n",
    "        for k in keys:\n",
    "            prefixed_key = 'prod' + k\n",
    "            if creds.get(prefixed_key):\n",
    "                selected[k] = creds.get(prefixed_key)\n",
    "            # fall back to top-level key if prod variant not found\n",
    "            elif creds.get(k):\n",
    "                selected[k] = creds.get(k)\n",
    "    \n",
    "    # Verify we have all required keys\n",
    "    if not all(selected.get(k) for k in ('email', 'password', 'client_id', 'client_secret')):\n",
    "        # Fill in missing values from prompts\n",
    "        selected = {\n",
    "            'email': selected.get('email'),\n",
    "            'password': selected.get('password'),\n",
    "            'client_id': selected.get('client_id'),\n",
    "            'client_secret': selected.get('client_secret')\n",
    "        }\n",
    "    \n",
    "    # Assign values, prompting for any missing ones\n",
    "    tokenInfo.email = prompt_if_missing(selected.get('email'), 'Enter your XBRL US Web account email: ')\n",
    "    tokenInfo.password = prompt_if_missing(selected.get('password'), 'Password: ', secret=True)\n",
    "    tokenInfo.client_id = prompt_if_missing(selected.get('client_id'), 'Client ID: ', secret=True)\n",
    "    tokenInfo.client_secret = prompt_if_missing(selected.get('client_secret'), 'Secret: ', secret=True)\n",
    "\n",
    "    print('Using credentials from .json as available.')\n",
    "else:\n",
    "    # No creds.json — prompt the user\n",
    "    tokenInfo.email = input('Enter your XBRL US Web account email: ')\n",
    "    tokenInfo.password = getpass.getpass(prompt='Password: ')\n",
    "    tokenInfo.client_id = getpass.getpass(prompt='Client ID: ')\n",
    "    tokenInfo.client_secret = getpass.getpass(prompt='Secret: ')\n",
    "\n",
    "body_auth = {'username' : tokenInfo.email,\n",
    "            'client_id': tokenInfo.client_id,\n",
    "            'client_secret' : tokenInfo.client_secret,\n",
    "            'password' : tokenInfo.password,\n",
    "            'grant_type' : 'password',\n",
    "            'platform' : 'ipynb' }\n",
    "\n",
    "#print(body_auth)\n",
    "\n",
    "payload = urlencode(body_auth)\n",
    "res = requests.request(\"POST\", tokenInfo.url, data=payload, headers=tokenInfo.headers)\n",
    "auth_json = res.json()\n",
    "\n",
    "if 'error' in auth_json:\n",
    "    print(\"\\n\\nThere was a problem generating the access token: %s  Run the first cell again and enter the credentials.\" % (auth_json.get('error_description', auth_json)))\n",
    "else:\n",
    "    tokenInfo.access_token = auth_json.get('access_token')\n",
    "    tokenInfo.refresh_token = auth_json.get('refresh_token')\n",
    "    if tokenInfo.access_token and tokenInfo.refresh_token:\n",
    "        print (\"\\n\\nYour access token expires in 60 minutes. After it expires, it should be regenerated automatically.  If not, run the cell rerun the first query cell. \\n\\nFor now, skip ahead to the section 'Make a Query'.\")\n",
    "    else:\n",
    "        print(\"\\n\\nAuthentication completed but tokens were not returned. Response: {}\".format(auth_json))\n",
    "\n",
    "#print(vars(tokenInfo))\n",
    "if tokenInfo.access_token and tokenInfo.refresh_token:\n",
    "    print('\\n\\naccess token: ' + tokenInfo.access_token + ' refresh token: ' + tokenInfo.refresh_token)\n",
    "else:\n",
    "    print('\\n\\nNo access token was generated. Check the messages above for errors.')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a query \n",
    "After the access token confirmation appears above, you can modify the query below and use the **_Cell >> Run_** menu option with the cell **immediately below this text** to run the query for updated results. \n",
    "\n",
    "The sample results are for one concept reported hourly for two fiscal years of data by an entity filing data on Form 714 for 2022.  **To get more entities, paste the query for _a list of Form 714 filer entity codes_ (below) into a browser and login to XBRL US**, then copy value(s) as a comma-separated list into the corresponding line below.\n",
    "  \n",
    "Refer to XBRL API documentation at https://xbrlus.github.io/xbrl-api/#/Facts/getFactDetails for other endpoints and parameters to filter and return. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Example Queries (these can open in a browser for quick reference)\n",
    "# query taxonomies published for a specific year\n",
    "# https://api.xbrl.us/api/v1/dts/search?fields=dts.id,dts.version.sort(DESC),dts.taxonomy-name.sort(DESC)&dts.version=2023\n",
    "\n",
    "# query unique Statements in the 2023 FERC Form 714 Taxonomy\n",
    "# https://api.xbrl.us/api/v1/dts/731060/network/search?network.link-name=presentationLink&fields=network.role-description.sort(ASC),dts.id&unique\n",
    "\n",
    "# query for a list of Form 714 filer entity codes:\n",
    "# https://api.xbrl.us/api/v1/report/search?report.document-type=714&fields=report.entity-name.sort(ASC),entity.code&unique\n",
    "\n",
    "\n",
    "### Define the parameters for the filter and fields to be returned\n",
    "\n",
    "# Define endpoint (common values: fact, entity, report, cube, label, concept, relationship - see https://xbrlus/github.io/xbrl-api for additional endpoint options)\n",
    "\n",
    "endpoint = 'fact'\n",
    "\n",
    "# Define parameters of the query\n",
    "\n",
    "document_types = [\n",
    "    '714'\n",
    "    ]\n",
    "\n",
    "concepts = [\n",
    "    'PlanningAreaHourlyDemandMegawatts'\n",
    "    ]\n",
    "\n",
    "entity_codes = [\n",
    "    'C000030','C000029',\n",
    "    ]\n",
    "\n",
    "report_years = [\n",
    "    '2021','2022'\n",
    "    ]\n",
    "\n",
    "# Define data fields to return (multi-sort based on order)\n",
    "fields = [ \n",
    "\t\t'entity.name.sort(ASC)',\n",
    "\t\t'entity.code',\n",
    "\t\t'period.instant.sort(ASC)',\n",
    "\t\t'fact.value',\n",
    "\t\t'report.year-focus',\n",
    "\t\t'period.fiscal-year',\n",
    "\t\t'concept.local-name',\n",
    "\t\t'report.html-url'\n",
    "        ]\n",
    "\n",
    "# Set unique rows as True of False (True drops any duplicate rows)\n",
    "unique = True\n",
    "\n",
    "# Limit the number of rows displayed by the notebook (does not impact the data frame)\n",
    "rows_to_display = 10 # Set as '' to display all rows in the notebook\n",
    "\n",
    "# Below is the list of what's being queried using the search endpoint.\n",
    " \n",
    "params = {  \n",
    "         'report.document-type': ','.join(document_types),\n",
    "         'concept.local-name': ','.join(concepts),\n",
    "         'entity.code': ','.join(entity_codes),\n",
    "         'report.year-focus': ','.join(report_years),\n",
    "         'fact.ultimus': 'TRUE',\n",
    "         'fields': ','.join(fields)\n",
    "         }\n",
    "\n",
    "\n",
    "### Execute the query with loop for all results \n",
    "### THIS SECTION DOES NOT NEED TO BE EDITED\n",
    "\n",
    "search_endpoint = 'https://api.xbrl.us/api/v1/' + endpoint + '/search'\n",
    "if unique:\n",
    "    search_endpoint += \"?unique\"\n",
    "orig_fields = params['fields']\n",
    "offset_value = 0\n",
    "res_df = []\n",
    "count = 0\n",
    "query_start = datetime.now()\n",
    "printed = False\n",
    "run_query = True\n",
    "print(\"Run the cell below to execute the query.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On Mon Nov 18 15:37:43 2024 info@xbrl.us (client ID: 69e1257c ...) started the query and\n",
      "up to 5000 records are found so far ...\n",
      "up to 10000 records are found so far ...\n",
      "up to 15000 records are found so far ...\n",
      "up to 20000 records are found so far ...\n",
      "up to 25000 records are found so far ...\n",
      "up to 30000 records are found so far ...\n",
      "up to 35000 records are found so far ...\n",
      "up to 40000 records are found so far ...\n",
      " - this set contained fewer than the 5000 possible, only 38 records.\n",
      "\n",
      "At Mon Nov 18 15:37:50 2024, the query finished with   35038   rows returned in 0:00:06.730763 for \n",
      "https://api.xbrl.us/api/v1/fact/search?unique&report.document-type=714&concept.local-name=PlanningAreaHourlyDemandMegawatts&entity.code=C000030,C000029&report.year-focus=2021,2022&fact.ultimus=TRUE&fields=entity.name.sort(ASC),entity.code,period.instant.sort(ASC),fact.value,report.year-focus,period.fiscal-year,concept.local-name,report.html-url,fact.offset(35000)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entity.name</th>\n",
       "      <th>entity.code</th>\n",
       "      <th>period.instant</th>\n",
       "      <th>fact.value</th>\n",
       "      <th>report.year-focus</th>\n",
       "      <th>period.fiscal-year</th>\n",
       "      <th>concept.local-name</th>\n",
       "      <th>report.html-url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ISO New England Inc.</td>\n",
       "      <td>C000029</td>\n",
       "      <td>2021-01-01 01:00:00</td>\n",
       "      <td>12,192.00</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>PlanningAreaHourlyDemandMegawatts</td>\n",
       "      <td>https://ecollection.ferc.gov/api/DownloadDocument/148344/3?filename=XBRL_4_48_20220601073855_92735.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ISO New England Inc.</td>\n",
       "      <td>C000029</td>\n",
       "      <td>2021-01-01 02:00:00</td>\n",
       "      <td>11,740.00</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>PlanningAreaHourlyDemandMegawatts</td>\n",
       "      <td>https://ecollection.ferc.gov/api/DownloadDocument/148344/3?filename=XBRL_4_48_20220601073855_92735.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ISO New England Inc.</td>\n",
       "      <td>C000029</td>\n",
       "      <td>2021-01-01 03:00:00</td>\n",
       "      <td>11,476.00</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>PlanningAreaHourlyDemandMegawatts</td>\n",
       "      <td>https://ecollection.ferc.gov/api/DownloadDocument/148344/3?filename=XBRL_4_48_20220601073855_92735.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ISO New England Inc.</td>\n",
       "      <td>C000029</td>\n",
       "      <td>2021-01-01 04:00:00</td>\n",
       "      <td>11,399.00</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>PlanningAreaHourlyDemandMegawatts</td>\n",
       "      <td>https://ecollection.ferc.gov/api/DownloadDocument/148344/3?filename=XBRL_4_48_20220601073855_92735.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ISO New England Inc.</td>\n",
       "      <td>C000029</td>\n",
       "      <td>2021-01-01 05:00:00</td>\n",
       "      <td>11,508.00</td>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
       "      <td>PlanningAreaHourlyDemandMegawatts</td>\n",
       "      <td>https://ecollection.ferc.gov/api/DownloadDocument/148344/3?filename=XBRL_4_48_20220601073855_92735.html</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35033</th>\n",
       "      <td>PJM Interconnection, LLC</td>\n",
       "      <td>C000030</td>\n",
       "      <td>2022-12-31 20:00:00</td>\n",
       "      <td>85,172.38</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022</td>\n",
       "      <td>PlanningAreaHourlyDemandMegawatts</td>\n",
       "      <td>https://ecollection.ferc.gov/api/DownloadDocument/180486/3?filename=XBRL_4_83_20230612090954_112777.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35034</th>\n",
       "      <td>PJM Interconnection, LLC</td>\n",
       "      <td>C000030</td>\n",
       "      <td>2022-12-31 21:00:00</td>\n",
       "      <td>82,952.54</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022</td>\n",
       "      <td>PlanningAreaHourlyDemandMegawatts</td>\n",
       "      <td>https://ecollection.ferc.gov/api/DownloadDocument/180486/3?filename=XBRL_4_83_20230612090954_112777.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35035</th>\n",
       "      <td>PJM Interconnection, LLC</td>\n",
       "      <td>C000030</td>\n",
       "      <td>2022-12-31 22:00:00</td>\n",
       "      <td>80,383.59</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022</td>\n",
       "      <td>PlanningAreaHourlyDemandMegawatts</td>\n",
       "      <td>https://ecollection.ferc.gov/api/DownloadDocument/180486/3?filename=XBRL_4_83_20230612090954_112777.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35036</th>\n",
       "      <td>PJM Interconnection, LLC</td>\n",
       "      <td>C000030</td>\n",
       "      <td>2022-12-31 23:00:00</td>\n",
       "      <td>77,960.75</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022</td>\n",
       "      <td>PlanningAreaHourlyDemandMegawatts</td>\n",
       "      <td>https://ecollection.ferc.gov/api/DownloadDocument/180486/3?filename=XBRL_4_83_20230612090954_112777.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35037</th>\n",
       "      <td>PJM Interconnection, LLC</td>\n",
       "      <td>C000030</td>\n",
       "      <td>2023-01-01 00:00:00</td>\n",
       "      <td>75,490.48</td>\n",
       "      <td>2022</td>\n",
       "      <td>2022</td>\n",
       "      <td>PlanningAreaHourlyDemandMegawatts</td>\n",
       "      <td>https://ecollection.ferc.gov/api/DownloadDocument/180486/3?filename=XBRL_4_83_20230612090954_112777.html</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# @title\n",
    "while True:\n",
    "    if not printed:\n",
    "        print(\"On\", query_start.strftime(\"%c\"), tokenInfo.email, \"(client ID:\", str(tokenInfo.client_id.split('-')[0]), \"...) started the query and\")\n",
    "        printed = True\n",
    "    retry = 0\n",
    "    while retry < 3:\n",
    "        res = requests.get(search_endpoint, params=params, headers={'Authorization' : 'Bearer {}'.format(tokenInfo.access_token)})\n",
    "        res_json = res.json()\n",
    "        if 'error' in res_json:\n",
    "            if res_json['error_description'] == 'Bad or expired token':\n",
    "                tokenInfo = refresh(tokenInfo)\n",
    "            else: \n",
    "                print('There was an error: {}'.format(res_json['error_description']))\n",
    "                run_query = False\n",
    "                break\n",
    "        else: \n",
    "\t\t        break\n",
    "        retry +=1\n",
    "        if retry >= 3:\n",
    "            print(\"Can't refresh the access token.  Run the first query block, then rerun the query.\")\n",
    "            run_query = False\n",
    "\n",
    "    if not run_query:\n",
    "       break\n",
    "\n",
    "    print(\"up to\", str(offset_value + res_json['paging']['limit']), \"records are found so far ...\")\n",
    "\n",
    "    res_df += res_json['data']\n",
    "\n",
    "    if res_json['paging']['count'] < res_json['paging']['limit']:\n",
    "        print(\" - this set contained fewer than the\", res_json['paging']['limit'], \"possible, only\", str(res_json['paging']['count']), \"records.\")\n",
    "        break\n",
    "    else: \n",
    "        offset_value += res_json['paging']['limit'] \n",
    "        if 100 == res_json['paging']['limit']:\n",
    "                params['fields'] = orig_fields + ',' + endpoint + '.offset({})'.format(offset_value)\n",
    "                if offset_value == 10 * res_json['paging']['limit']:\n",
    "                        break \n",
    "        elif 500 == res_json['paging']['limit']:\n",
    "                params['fields'] = orig_fields + ',' + endpoint + '.offset({})'.format(offset_value)\n",
    "                if offset_value == 4 * res_json['paging']['limit']:\n",
    "                        break \n",
    "        params['fields'] = orig_fields + ',' + endpoint + '.offset({})'.format(offset_value)\n",
    "\n",
    "if not 'error' in res_json:\n",
    "    current_datetime = datetime.now().replace(microsecond=0)\n",
    "    time_taken = current_datetime - query_start\n",
    "    index = pd.DataFrame(res_df).index\n",
    "    total_rows = len(index)\n",
    "    your_limit = res_json['paging']['limit']\n",
    "    limit_message = \"If the results below match the limit noted above, you might not be seeing all rows, and should consider upgrading (https://xbrl.us/access-token).\\n\"\n",
    "    \n",
    "    if your_limit == 100:\n",
    "        print(\"\\nThis non-Member account has a limit of \" , 10 * your_limit, \" rows per query from our Public Filings Database. \" + limit_message)\n",
    "    elif your_limit == 500:\n",
    "        print(\"\\nThis Basic Individual Member account has a limit of \", 4 * your_limit, \" rows per query from our Public Filings Database. \" + limit_message)\n",
    "    \n",
    "    print(\"\\nAt \" + current_datetime.strftime(\"%c\") +  \", the query finished with  \", str(total_rows), \"  rows returned in \" + str(time_taken) + \" for \\n\" +  urllib.parse.unquote(res.url))\n",
    "    \n",
    "    df = pd.DataFrame(res_df)\n",
    "    # the format truncates the HTML display of numerical values to two decimals; .csv data is unaffected\n",
    "    pd.options.display.float_format = '{:,.2f}'.format\n",
    "    display(HTML(df.to_html(max_rows=rows_to_display)))\n",
    "\n",
    "    # set period.instant as datetimeindex for resampling\n",
    "    df['period.instant'] = pd.to_datetime(df['period.instant'], format='%Y-%m-%d %H:%M:%S')\n",
    "    df = df.set_index('period.instant')\n",
    "    df.index = pd.to_datetime(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#group data by entity and display MWH as line chart\n",
    "grouped = df.groupby('entity.name')['fact.value'].plot(legend=True, \n",
    "                                             xlabel = \"Month & Year\", \n",
    "                                             ylabel=\"MWH\", \n",
    "                                             title=\"Form 714 Planning Area Hourly Demand for Selected Entities\\n(daily total MWH)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you run this program locally, you can save the output to a file on your computer (modify D:\\results.csv to your system)\n",
    "df.to_csv(r\"D:\\results.csv\",sep=\",\")\n",
    "\n",
    "# Google Colab users - comment out the line above and uncomment the code below to save the data frame as a .csv in your Google Drive\n",
    "\n",
    "#from google.colab import drive\n",
    "#drive.mount('drive')\n",
    "#df.to_csv('data.csv')\n",
    "#!cp data.csv \"drive/My Drive/\""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}