trad_rental.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "french-respondent",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "careful-rapid",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': 'success',\n",
       " 'content': {'months': [{'year': 2021,\n",
       "    'month': 4,\n",
       "    'zero_room_value': 1180,\n",
       "    'one_room_value': 1506.6,\n",
       "    'two_room_value': 1860,\n",
       "    'three_room_value': 2503.25,\n",
       "    'four_room_value': 2604},\n",
       "   {'year': 2021,\n",
       "    'month': 3,\n",
       "    'zero_room_value': 1180,\n",
       "    'one_room_value': 1506.6,\n",
       "    'two_room_value': 1860,\n",
       "    'three_room_value': 2418,\n",
       "    'four_room_value': 2604},\n",
       "   {'year': 2021,\n",
       "    'month': 2,\n",
       "    'zero_room_value': 1180,\n",
       "    'one_room_value': 1534.5,\n",
       "    'two_room_value': 1860,\n",
       "    'three_room_value': 2418,\n",
       "    'four_room_value': 2604},\n",
       "   {'year': 2021,\n",
       "    'month': 1,\n",
       "    'zero_room_value': 1180,\n",
       "    'one_room_value': 1534.5,\n",
       "    'two_room_value': 1953,\n",
       "    'three_room_value': 2511,\n",
       "    'four_room_value': 2604},\n",
       "   {'year': 2020,\n",
       "    'month': 12,\n",
       "    'zero_room_value': 1060,\n",
       "    'one_room_value': 1516.35,\n",
       "    'two_room_value': 1929.9,\n",
       "    'three_room_value': 2481.3,\n",
       "    'four_room_value': 2573.2},\n",
       "   {'year': 2020,\n",
       "    'month': 11,\n",
       "    'zero_room_value': 2665.1,\n",
       "    'one_room_value': 1525.54,\n",
       "    'two_room_value': 1929.9,\n",
       "    'three_room_value': 2412.38,\n",
       "    'four_room_value': 2573.2},\n",
       "   {'year': 2020,\n",
       "    'month': 10,\n",
       "    'zero_room_value': 3676,\n",
       "    'one_room_value': 1525.54,\n",
       "    'two_room_value': 1929.9,\n",
       "    'three_room_value': 2389.4,\n",
       "    'four_room_value': 2573.2},\n",
       "   {'year': 2020,\n",
       "    'month': 9,\n",
       "    'zero_room_value': 3997.65,\n",
       "    'one_room_value': 1527.84,\n",
       "    'two_room_value': 1929.9,\n",
       "    'three_room_value': 2389.4,\n",
       "    'four_room_value': 2665.1},\n",
       "   {'year': 2020,\n",
       "    'month': 8,\n",
       "    'zero_room_value': 4411.2,\n",
       "    'one_room_value': 1525.54,\n",
       "    'two_room_value': 1929.9,\n",
       "    'three_room_value': 2343.45,\n",
       "    'four_room_value': 2480.84},\n",
       "   {'year': 2020,\n",
       "    'month': 7,\n",
       "    'zero_room_value': 1516.35,\n",
       "    'one_room_value': 1530.14,\n",
       "    'two_room_value': 1962.06,\n",
       "    'three_room_value': 2251.55,\n",
       "    'four_room_value': 2665.1},\n",
       "   {'year': 2020,\n",
       "    'month': 6,\n",
       "    'zero_room_value': 1516.35,\n",
       "    'one_room_value': 1427.51,\n",
       "    'two_room_value': 1746.1,\n",
       "    'three_room_value': 2282.18,\n",
       "    'four_room_value': 2757},\n",
       "   {'year': 2020,\n",
       "    'month': 5,\n",
       "    'zero_room_value': 1447.43,\n",
       "    'one_room_value': 1102.8,\n",
       "    'two_room_value': 1631.22,\n",
       "    'three_room_value': 2018.74,\n",
       "    'four_room_value': 2297.5}],\n",
       "  'averages': {'zero_room_value': 2084.17,\n",
       "   'one_room_value': 1480.29,\n",
       "   'two_room_value': 1876.82,\n",
       "   'three_room_value': 2368.22,\n",
       "   'four_room_value': 2583.43}},\n",
       " 'message': 'Historical Data fetched successfully'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mia_trad = requests.get(\"https://api.mashvisor.com/v1.1/client/neighborhood/268938/historical/traditional?state=FL\", headers={'x-api-key':'7b9581e3-5b45-4fbc-8f59-d3379db49449'})\n",
    "mia_trent = mia_trad.json()\n",
    "mia_trent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "republican-workshop",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['months', 'averages'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mia_trent[\"content\"].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "hollow-guinea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>zero_room_value</th>\n",
       "      <th>one_room_value</th>\n",
       "      <th>two_room_value</th>\n",
       "      <th>three_room_value</th>\n",
       "      <th>four_room_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1506.60</td>\n",
       "      <td>1860.00</td>\n",
       "      <td>2503.25</td>\n",
       "      <td>2604.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1506.60</td>\n",
       "      <td>1860.00</td>\n",
       "      <td>2418.00</td>\n",
       "      <td>2604.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1534.50</td>\n",
       "      <td>1860.00</td>\n",
       "      <td>2418.00</td>\n",
       "      <td>2604.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1534.50</td>\n",
       "      <td>1953.00</td>\n",
       "      <td>2511.00</td>\n",
       "      <td>2604.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>1060.00</td>\n",
       "      <td>1516.35</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2481.30</td>\n",
       "      <td>2573.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>2665.10</td>\n",
       "      <td>1525.54</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2412.38</td>\n",
       "      <td>2573.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>3676.00</td>\n",
       "      <td>1525.54</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2389.40</td>\n",
       "      <td>2573.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>3997.65</td>\n",
       "      <td>1527.84</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2389.40</td>\n",
       "      <td>2665.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>4411.20</td>\n",
       "      <td>1525.54</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2343.45</td>\n",
       "      <td>2480.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>1516.35</td>\n",
       "      <td>1530.14</td>\n",
       "      <td>1962.06</td>\n",
       "      <td>2251.55</td>\n",
       "      <td>2665.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "      <td>1516.35</td>\n",
       "      <td>1427.51</td>\n",
       "      <td>1746.10</td>\n",
       "      <td>2282.18</td>\n",
       "      <td>2757.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>1447.43</td>\n",
       "      <td>1102.80</td>\n",
       "      <td>1631.22</td>\n",
       "      <td>2018.74</td>\n",
       "      <td>2297.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month  zero_room_value  one_room_value  two_room_value  \\\n",
       "0   2021      4          1180.00         1506.60         1860.00   \n",
       "1   2021      3          1180.00         1506.60         1860.00   \n",
       "2   2021      2          1180.00         1534.50         1860.00   \n",
       "3   2021      1          1180.00         1534.50         1953.00   \n",
       "4   2020     12          1060.00         1516.35         1929.90   \n",
       "5   2020     11          2665.10         1525.54         1929.90   \n",
       "6   2020     10          3676.00         1525.54         1929.90   \n",
       "7   2020      9          3997.65         1527.84         1929.90   \n",
       "8   2020      8          4411.20         1525.54         1929.90   \n",
       "9   2020      7          1516.35         1530.14         1962.06   \n",
       "10  2020      6          1516.35         1427.51         1746.10   \n",
       "11  2020      5          1447.43         1102.80         1631.22   \n",
       "\n",
       "    three_room_value  four_room_value  \n",
       "0            2503.25          2604.00  \n",
       "1            2418.00          2604.00  \n",
       "2            2418.00          2604.00  \n",
       "3            2511.00          2604.00  \n",
       "4            2481.30          2573.20  \n",
       "5            2412.38          2573.20  \n",
       "6            2389.40          2573.20  \n",
       "7            2389.40          2665.10  \n",
       "8            2343.45          2480.84  \n",
       "9            2251.55          2665.10  \n",
       "10           2282.18          2757.00  \n",
       "11           2018.74          2297.50  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent_miafl = pd.DataFrame.from_dict(mia_trent['content']['months'])\n",
    "rent_miafl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "unavailable-review",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zero_room_value</th>\n",
       "      <th>one_room_value</th>\n",
       "      <th>two_room_value</th>\n",
       "      <th>three_room_value</th>\n",
       "      <th>four_room_value</th>\n",
       "      <th>dates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1447.43</td>\n",
       "      <td>1102.80</td>\n",
       "      <td>1631.22</td>\n",
       "      <td>2018.74</td>\n",
       "      <td>2297.50</td>\n",
       "      <td>2020-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1516.35</td>\n",
       "      <td>1427.51</td>\n",
       "      <td>1746.10</td>\n",
       "      <td>2282.18</td>\n",
       "      <td>2757.00</td>\n",
       "      <td>2020-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1516.35</td>\n",
       "      <td>1530.14</td>\n",
       "      <td>1962.06</td>\n",
       "      <td>2251.55</td>\n",
       "      <td>2665.10</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4411.20</td>\n",
       "      <td>1525.54</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2343.45</td>\n",
       "      <td>2480.84</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3997.65</td>\n",
       "      <td>1527.84</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2389.40</td>\n",
       "      <td>2665.10</td>\n",
       "      <td>2020-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3676.00</td>\n",
       "      <td>1525.54</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2389.40</td>\n",
       "      <td>2573.20</td>\n",
       "      <td>2020-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2665.10</td>\n",
       "      <td>1525.54</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2412.38</td>\n",
       "      <td>2573.20</td>\n",
       "      <td>2020-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1060.00</td>\n",
       "      <td>1516.35</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2481.30</td>\n",
       "      <td>2573.20</td>\n",
       "      <td>2020-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1180.00</td>\n",
       "      <td>1534.50</td>\n",
       "      <td>1953.00</td>\n",
       "      <td>2511.00</td>\n",
       "      <td>2604.00</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1180.00</td>\n",
       "      <td>1534.50</td>\n",
       "      <td>1860.00</td>\n",
       "      <td>2418.00</td>\n",
       "      <td>2604.00</td>\n",
       "      <td>2021-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1180.00</td>\n",
       "      <td>1506.60</td>\n",
       "      <td>1860.00</td>\n",
       "      <td>2418.00</td>\n",
       "      <td>2604.00</td>\n",
       "      <td>2021-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1180.00</td>\n",
       "      <td>1506.60</td>\n",
       "      <td>1860.00</td>\n",
       "      <td>2503.25</td>\n",
       "      <td>2604.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    zero_room_value  one_room_value  two_room_value  three_room_value  \\\n",
       "11          1447.43         1102.80         1631.22           2018.74   \n",
       "10          1516.35         1427.51         1746.10           2282.18   \n",
       "9           1516.35         1530.14         1962.06           2251.55   \n",
       "8           4411.20         1525.54         1929.90           2343.45   \n",
       "7           3997.65         1527.84         1929.90           2389.40   \n",
       "6           3676.00         1525.54         1929.90           2389.40   \n",
       "5           2665.10         1525.54         1929.90           2412.38   \n",
       "4           1060.00         1516.35         1929.90           2481.30   \n",
       "3           1180.00         1534.50         1953.00           2511.00   \n",
       "2           1180.00         1534.50         1860.00           2418.00   \n",
       "1           1180.00         1506.60         1860.00           2418.00   \n",
       "0           1180.00         1506.60         1860.00           2503.25   \n",
       "\n",
       "    four_room_value      dates  \n",
       "11          2297.50 2020-05-01  \n",
       "10          2757.00 2020-06-01  \n",
       "9           2665.10 2020-07-01  \n",
       "8           2480.84 2020-08-01  \n",
       "7           2665.10 2020-09-01  \n",
       "6           2573.20 2020-10-01  \n",
       "5           2573.20 2020-11-01  \n",
       "4           2573.20 2020-12-01  \n",
       "3           2604.00 2021-01-01  \n",
       "2           2604.00 2021-02-01  \n",
       "1           2604.00 2021-03-01  \n",
       "0           2604.00 2021-04-01  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent_miafl['dates'] =pd.to_datetime(rent_miafl['year'].astype(str) + \"-\" + rent_miafl['month'].astype(str))\n",
    "rent_miafl_df = rent_miafl.drop(columns=[\"year\", \"month\"])\n",
    "miami_rent = rent_miafl_df.sort_values('dates', ascending=True)\n",
    "miami_rent\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "centered-education",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zero_room_value</th>\n",
       "      <th>one_room_value</th>\n",
       "      <th>two_room_value</th>\n",
       "      <th>three_room_value</th>\n",
       "      <th>four_room_value</th>\n",
       "      <th>dates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1180.00</td>\n",
       "      <td>1506.60</td>\n",
       "      <td>1860.00</td>\n",
       "      <td>2503.25</td>\n",
       "      <td>2604.00</td>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1180.00</td>\n",
       "      <td>1506.60</td>\n",
       "      <td>1860.00</td>\n",
       "      <td>2418.00</td>\n",
       "      <td>2604.00</td>\n",
       "      <td>2021-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1180.00</td>\n",
       "      <td>1534.50</td>\n",
       "      <td>1860.00</td>\n",
       "      <td>2418.00</td>\n",
       "      <td>2604.00</td>\n",
       "      <td>2021-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1180.00</td>\n",
       "      <td>1534.50</td>\n",
       "      <td>1953.00</td>\n",
       "      <td>2511.00</td>\n",
       "      <td>2604.00</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1060.00</td>\n",
       "      <td>1516.35</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2481.30</td>\n",
       "      <td>2573.20</td>\n",
       "      <td>2020-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2665.10</td>\n",
       "      <td>1525.54</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2412.38</td>\n",
       "      <td>2573.20</td>\n",
       "      <td>2020-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3676.00</td>\n",
       "      <td>1525.54</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2389.40</td>\n",
       "      <td>2573.20</td>\n",
       "      <td>2020-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3997.65</td>\n",
       "      <td>1527.84</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2389.40</td>\n",
       "      <td>2665.10</td>\n",
       "      <td>2020-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4411.20</td>\n",
       "      <td>1525.54</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>2343.45</td>\n",
       "      <td>2480.84</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1516.35</td>\n",
       "      <td>1530.14</td>\n",
       "      <td>1962.06</td>\n",
       "      <td>2251.55</td>\n",
       "      <td>2665.10</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1516.35</td>\n",
       "      <td>1427.51</td>\n",
       "      <td>1746.10</td>\n",
       "      <td>2282.18</td>\n",
       "      <td>2757.00</td>\n",
       "      <td>2020-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1447.43</td>\n",
       "      <td>1102.80</td>\n",
       "      <td>1631.22</td>\n",
       "      <td>2018.74</td>\n",
       "      <td>2297.50</td>\n",
       "      <td>2020-05-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    zero_room_value  one_room_value  two_room_value  three_room_value  \\\n",
       "0           1180.00         1506.60         1860.00           2503.25   \n",
       "1           1180.00         1506.60         1860.00           2418.00   \n",
       "2           1180.00         1534.50         1860.00           2418.00   \n",
       "3           1180.00         1534.50         1953.00           2511.00   \n",
       "4           1060.00         1516.35         1929.90           2481.30   \n",
       "5           2665.10         1525.54         1929.90           2412.38   \n",
       "6           3676.00         1525.54         1929.90           2389.40   \n",
       "7           3997.65         1527.84         1929.90           2389.40   \n",
       "8           4411.20         1525.54         1929.90           2343.45   \n",
       "9           1516.35         1530.14         1962.06           2251.55   \n",
       "10          1516.35         1427.51         1746.10           2282.18   \n",
       "11          1447.43         1102.80         1631.22           2018.74   \n",
       "\n",
       "    four_room_value      dates  \n",
       "0           2604.00 2021-04-01  \n",
       "1           2604.00 2021-03-01  \n",
       "2           2604.00 2021-02-01  \n",
       "3           2604.00 2021-01-01  \n",
       "4           2573.20 2020-12-01  \n",
       "5           2573.20 2020-11-01  \n",
       "6           2573.20 2020-10-01  \n",
       "7           2665.10 2020-09-01  \n",
       "8           2480.84 2020-08-01  \n",
       "9           2665.10 2020-07-01  \n",
       "10          2757.00 2020-06-01  \n",
       "11          2297.50 2020-05-01  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "miami_rent_sort = miami_rent.sort_index()\n",
    "miami_rent_sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "white-pencil",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "miami_rent_sort[['four_room_value','three_room_value','two_room_value', 'one_room_value']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "confused-discretion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': 'success',\n",
       " 'content': {'months': [{'year': 2021,\n",
       "    'month': 4,\n",
       "    'zero_room_value': 1080,\n",
       "    'one_room_value': 1116,\n",
       "    'two_room_value': 1116,\n",
       "    'three_room_value': 1185.75,\n",
       "    'four_room_value': 1474.05},\n",
       "   {'year': 2021,\n",
       "    'month': 3,\n",
       "    'zero_room_value': 1080,\n",
       "    'one_room_value': 1116,\n",
       "    'two_room_value': 1162.5,\n",
       "    'three_room_value': 1185.75,\n",
       "    'four_room_value': 1474.05},\n",
       "   {'year': 2021,\n",
       "    'month': 2,\n",
       "    'zero_room_value': 1080,\n",
       "    'one_room_value': 1116,\n",
       "    'two_room_value': 1209,\n",
       "    'three_room_value': 1185.75,\n",
       "    'four_room_value': 1474.05},\n",
       "   {'year': 2021,\n",
       "    'month': 1,\n",
       "    'zero_room_value': 1080,\n",
       "    'one_room_value': 1116,\n",
       "    'two_room_value': 1209,\n",
       "    'three_room_value': 1174.12,\n",
       "    'four_room_value': 1474.05},\n",
       "   {'year': 2020,\n",
       "    'month': 12,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1132.8,\n",
       "    'two_room_value': 1227.2,\n",
       "    'three_room_value': 1180,\n",
       "    'four_room_value': 1496.24},\n",
       "   {'year': 2020,\n",
       "    'month': 11,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1132.8,\n",
       "    'two_room_value': 1227.2,\n",
       "    'three_room_value': 1180,\n",
       "    'four_room_value': 1496.24},\n",
       "   {'year': 2020,\n",
       "    'month': 10,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1132.8,\n",
       "    'two_room_value': 1227.2,\n",
       "    'three_room_value': 1215.87,\n",
       "    'four_room_value': 1496.24},\n",
       "   {'year': 2020,\n",
       "    'month': 9,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1132.8,\n",
       "    'two_room_value': 1227.2,\n",
       "    'three_room_value': 1274.4,\n",
       "    'four_room_value': 1496.24},\n",
       "   {'year': 2020,\n",
       "    'month': 8,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1132.8,\n",
       "    'two_room_value': 1274.4,\n",
       "    'three_room_value': 1180,\n",
       "    'four_room_value': 1203.6},\n",
       "   {'year': 2020,\n",
       "    'month': 7,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1132.8,\n",
       "    'two_room_value': 1321.6,\n",
       "    'three_room_value': 1180,\n",
       "    'four_room_value': 1203.6},\n",
       "   {'year': 2020,\n",
       "    'month': 6,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1098.53,\n",
       "    'two_room_value': 1215.87,\n",
       "    'three_room_value': 1334.19,\n",
       "    'four_room_value': 1687.87},\n",
       "   {'year': 2020,\n",
       "    'month': 5,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1098.53,\n",
       "    'two_room_value': 1144.76,\n",
       "    'three_room_value': 1697.63,\n",
       "    'four_room_value': 1687.87}],\n",
       "  'averages': {'zero_room_value': 1000,\n",
       "   'one_room_value': 1121.49,\n",
       "   'two_room_value': 1213.49,\n",
       "   'three_room_value': 1247.79,\n",
       "   'four_room_value': 1472.01}},\n",
       " 'message': 'Historical Data fetched successfully'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orl_trad = requests.get(\"https://api.mashvisor.com/v1.1/client/neighborhood/268950/historical/traditional?state=FL\", headers={'x-api-key':'7b9581e3-5b45-4fbc-8f59-d3379db49449'})\n",
    "orl_trent = orl_trad.json()\n",
    "orl_trent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "naval-budapest",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['months', 'averages'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orl_trent[\"content\"].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "refined-hawaii",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>zero_room_value</th>\n",
       "      <th>one_room_value</th>\n",
       "      <th>two_room_value</th>\n",
       "      <th>three_room_value</th>\n",
       "      <th>four_room_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1185.75</td>\n",
       "      <td>1474.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1162.50</td>\n",
       "      <td>1185.75</td>\n",
       "      <td>1474.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1209.00</td>\n",
       "      <td>1185.75</td>\n",
       "      <td>1474.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1209.00</td>\n",
       "      <td>1174.12</td>\n",
       "      <td>1474.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1496.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1496.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1215.87</td>\n",
       "      <td>1496.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1274.40</td>\n",
       "      <td>1496.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1274.40</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1203.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1321.60</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1203.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "      <td>960</td>\n",
       "      <td>1098.53</td>\n",
       "      <td>1215.87</td>\n",
       "      <td>1334.19</td>\n",
       "      <td>1687.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>960</td>\n",
       "      <td>1098.53</td>\n",
       "      <td>1144.76</td>\n",
       "      <td>1697.63</td>\n",
       "      <td>1687.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month  zero_room_value  one_room_value  two_room_value  \\\n",
       "0   2021      4             1080         1116.00         1116.00   \n",
       "1   2021      3             1080         1116.00         1162.50   \n",
       "2   2021      2             1080         1116.00         1209.00   \n",
       "3   2021      1             1080         1116.00         1209.00   \n",
       "4   2020     12              960         1132.80         1227.20   \n",
       "5   2020     11              960         1132.80         1227.20   \n",
       "6   2020     10              960         1132.80         1227.20   \n",
       "7   2020      9              960         1132.80         1227.20   \n",
       "8   2020      8              960         1132.80         1274.40   \n",
       "9   2020      7              960         1132.80         1321.60   \n",
       "10  2020      6              960         1098.53         1215.87   \n",
       "11  2020      5              960         1098.53         1144.76   \n",
       "\n",
       "    three_room_value  four_room_value  \n",
       "0            1185.75          1474.05  \n",
       "1            1185.75          1474.05  \n",
       "2            1185.75          1474.05  \n",
       "3            1174.12          1474.05  \n",
       "4            1180.00          1496.24  \n",
       "5            1180.00          1496.24  \n",
       "6            1215.87          1496.24  \n",
       "7            1274.40          1496.24  \n",
       "8            1180.00          1203.60  \n",
       "9            1180.00          1203.60  \n",
       "10           1334.19          1687.87  \n",
       "11           1697.63          1687.87  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent_orlfl = pd.DataFrame.from_dict(orl_trent['content']['months'])\n",
    "rent_orlfl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "worse-translation",
   "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>zero_room_value</th>\n",
       "      <th>one_room_value</th>\n",
       "      <th>two_room_value</th>\n",
       "      <th>three_room_value</th>\n",
       "      <th>four_room_value</th>\n",
       "      <th>dates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>960</td>\n",
       "      <td>1098.53</td>\n",
       "      <td>1144.76</td>\n",
       "      <td>1697.63</td>\n",
       "      <td>1687.87</td>\n",
       "      <td>2020-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>960</td>\n",
       "      <td>1098.53</td>\n",
       "      <td>1215.87</td>\n",
       "      <td>1334.19</td>\n",
       "      <td>1687.87</td>\n",
       "      <td>2020-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1321.60</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1203.60</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1274.40</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1203.60</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1274.40</td>\n",
       "      <td>1496.24</td>\n",
       "      <td>2020-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1215.87</td>\n",
       "      <td>1496.24</td>\n",
       "      <td>2020-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1496.24</td>\n",
       "      <td>2020-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1496.24</td>\n",
       "      <td>2020-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1209.00</td>\n",
       "      <td>1174.12</td>\n",
       "      <td>1474.05</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1209.00</td>\n",
       "      <td>1185.75</td>\n",
       "      <td>1474.05</td>\n",
       "      <td>2021-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1162.50</td>\n",
       "      <td>1185.75</td>\n",
       "      <td>1474.05</td>\n",
       "      <td>2021-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1185.75</td>\n",
       "      <td>1474.05</td>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    zero_room_value  one_room_value  two_room_value  three_room_value  \\\n",
       "11              960         1098.53         1144.76           1697.63   \n",
       "10              960         1098.53         1215.87           1334.19   \n",
       "9               960         1132.80         1321.60           1180.00   \n",
       "8               960         1132.80         1274.40           1180.00   \n",
       "7               960         1132.80         1227.20           1274.40   \n",
       "6               960         1132.80         1227.20           1215.87   \n",
       "5               960         1132.80         1227.20           1180.00   \n",
       "4               960         1132.80         1227.20           1180.00   \n",
       "3              1080         1116.00         1209.00           1174.12   \n",
       "2              1080         1116.00         1209.00           1185.75   \n",
       "1              1080         1116.00         1162.50           1185.75   \n",
       "0              1080         1116.00         1116.00           1185.75   \n",
       "\n",
       "    four_room_value      dates  \n",
       "11          1687.87 2020-05-01  \n",
       "10          1687.87 2020-06-01  \n",
       "9           1203.60 2020-07-01  \n",
       "8           1203.60 2020-08-01  \n",
       "7           1496.24 2020-09-01  \n",
       "6           1496.24 2020-10-01  \n",
       "5           1496.24 2020-11-01  \n",
       "4           1496.24 2020-12-01  \n",
       "3           1474.05 2021-01-01  \n",
       "2           1474.05 2021-02-01  \n",
       "1           1474.05 2021-03-01  \n",
       "0           1474.05 2021-04-01  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent_orlfl['dates'] =pd.to_datetime(rent_orlfl['year'].astype(str) + \"-\" + rent_orlfl['month'].astype(str))\n",
    "rent_orlfl_df = rent_orlfl.drop(columns=[\"year\", \"month\"])\n",
    "orlando_rent = rent_orlfl_df.sort_values('dates', ascending=True)\n",
    "orlando_rent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "furnished-behalf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "orlando_rent[['four_room_value','three_room_value','two_room_value', 'one_room_value']].plot(use_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "magnetic-ceremony",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': 'success',\n",
       " 'content': {'months': [{'year': 2021,\n",
       "    'month': 4,\n",
       "    'zero_room_value': 1080,\n",
       "    'one_room_value': 1116,\n",
       "    'two_room_value': 1116,\n",
       "    'three_room_value': 1185.75,\n",
       "    'four_room_value': 1474.05},\n",
       "   {'year': 2021,\n",
       "    'month': 3,\n",
       "    'zero_room_value': 1080,\n",
       "    'one_room_value': 1116,\n",
       "    'two_room_value': 1162.5,\n",
       "    'three_room_value': 1185.75,\n",
       "    'four_room_value': 1474.05},\n",
       "   {'year': 2021,\n",
       "    'month': 2,\n",
       "    'zero_room_value': 1080,\n",
       "    'one_room_value': 1116,\n",
       "    'two_room_value': 1209,\n",
       "    'three_room_value': 1185.75,\n",
       "    'four_room_value': 1474.05},\n",
       "   {'year': 2021,\n",
       "    'month': 1,\n",
       "    'zero_room_value': 1080,\n",
       "    'one_room_value': 1116,\n",
       "    'two_room_value': 1209,\n",
       "    'three_room_value': 1174.12,\n",
       "    'four_room_value': 1474.05},\n",
       "   {'year': 2020,\n",
       "    'month': 12,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1132.8,\n",
       "    'two_room_value': 1227.2,\n",
       "    'three_room_value': 1180,\n",
       "    'four_room_value': 1496.24},\n",
       "   {'year': 2020,\n",
       "    'month': 11,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1132.8,\n",
       "    'two_room_value': 1227.2,\n",
       "    'three_room_value': 1180,\n",
       "    'four_room_value': 1496.24},\n",
       "   {'year': 2020,\n",
       "    'month': 10,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1132.8,\n",
       "    'two_room_value': 1227.2,\n",
       "    'three_room_value': 1215.87,\n",
       "    'four_room_value': 1496.24},\n",
       "   {'year': 2020,\n",
       "    'month': 9,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1132.8,\n",
       "    'two_room_value': 1227.2,\n",
       "    'three_room_value': 1274.4,\n",
       "    'four_room_value': 1496.24},\n",
       "   {'year': 2020,\n",
       "    'month': 8,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1132.8,\n",
       "    'two_room_value': 1274.4,\n",
       "    'three_room_value': 1180,\n",
       "    'four_room_value': 1203.6},\n",
       "   {'year': 2020,\n",
       "    'month': 7,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1132.8,\n",
       "    'two_room_value': 1321.6,\n",
       "    'three_room_value': 1180,\n",
       "    'four_room_value': 1203.6},\n",
       "   {'year': 2020,\n",
       "    'month': 6,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1098.53,\n",
       "    'two_room_value': 1215.87,\n",
       "    'three_room_value': 1334.19,\n",
       "    'four_room_value': 1687.87},\n",
       "   {'year': 2020,\n",
       "    'month': 5,\n",
       "    'zero_room_value': 960,\n",
       "    'one_room_value': 1098.53,\n",
       "    'two_room_value': 1144.76,\n",
       "    'three_room_value': 1697.63,\n",
       "    'four_room_value': 1687.87}],\n",
       "  'averages': {'zero_room_value': 1000,\n",
       "   'one_room_value': 1121.49,\n",
       "   'two_room_value': 1213.49,\n",
       "   'three_room_value': 1247.79,\n",
       "   'four_room_value': 1472.01}},\n",
       " 'message': 'Historical Data fetched successfully'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpa_trad = requests.get(\"https://api.mashvisor.com/v1.1/client/neighborhood/268950/historical/traditional?state=FL\", headers={'x-api-key':'7b9581e3-5b45-4fbc-8f59-d3379db49449'})\n",
    "tpa_trent = tpa_trad.json()\n",
    "tpa_trent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "senior-waters",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>zero_room_value</th>\n",
       "      <th>one_room_value</th>\n",
       "      <th>two_room_value</th>\n",
       "      <th>three_room_value</th>\n",
       "      <th>four_room_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021</td>\n",
       "      <td>4</td>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1185.75</td>\n",
       "      <td>1474.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>3</td>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1162.50</td>\n",
       "      <td>1185.75</td>\n",
       "      <td>1474.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>2</td>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1209.00</td>\n",
       "      <td>1185.75</td>\n",
       "      <td>1474.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021</td>\n",
       "      <td>1</td>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1209.00</td>\n",
       "      <td>1174.12</td>\n",
       "      <td>1474.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1496.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1496.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1215.87</td>\n",
       "      <td>1496.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1274.40</td>\n",
       "      <td>1496.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1274.40</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1203.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020</td>\n",
       "      <td>7</td>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1321.60</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1203.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "      <td>960</td>\n",
       "      <td>1098.53</td>\n",
       "      <td>1215.87</td>\n",
       "      <td>1334.19</td>\n",
       "      <td>1687.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020</td>\n",
       "      <td>5</td>\n",
       "      <td>960</td>\n",
       "      <td>1098.53</td>\n",
       "      <td>1144.76</td>\n",
       "      <td>1697.63</td>\n",
       "      <td>1687.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  month  zero_room_value  one_room_value  two_room_value  \\\n",
       "0   2021      4             1080         1116.00         1116.00   \n",
       "1   2021      3             1080         1116.00         1162.50   \n",
       "2   2021      2             1080         1116.00         1209.00   \n",
       "3   2021      1             1080         1116.00         1209.00   \n",
       "4   2020     12              960         1132.80         1227.20   \n",
       "5   2020     11              960         1132.80         1227.20   \n",
       "6   2020     10              960         1132.80         1227.20   \n",
       "7   2020      9              960         1132.80         1227.20   \n",
       "8   2020      8              960         1132.80         1274.40   \n",
       "9   2020      7              960         1132.80         1321.60   \n",
       "10  2020      6              960         1098.53         1215.87   \n",
       "11  2020      5              960         1098.53         1144.76   \n",
       "\n",
       "    three_room_value  four_room_value  \n",
       "0            1185.75          1474.05  \n",
       "1            1185.75          1474.05  \n",
       "2            1185.75          1474.05  \n",
       "3            1174.12          1474.05  \n",
       "4            1180.00          1496.24  \n",
       "5            1180.00          1496.24  \n",
       "6            1215.87          1496.24  \n",
       "7            1274.40          1496.24  \n",
       "8            1180.00          1203.60  \n",
       "9            1180.00          1203.60  \n",
       "10           1334.19          1687.87  \n",
       "11           1697.63          1687.87  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent_tpafl = pd.DataFrame.from_dict(tpa_trent['content']['months'])\n",
    "rent_tpafl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "changed-creature",
   "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>zero_room_value</th>\n",
       "      <th>one_room_value</th>\n",
       "      <th>two_room_value</th>\n",
       "      <th>three_room_value</th>\n",
       "      <th>four_room_value</th>\n",
       "      <th>dates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>960</td>\n",
       "      <td>1098.53</td>\n",
       "      <td>1144.76</td>\n",
       "      <td>1697.63</td>\n",
       "      <td>1687.87</td>\n",
       "      <td>2020-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>960</td>\n",
       "      <td>1098.53</td>\n",
       "      <td>1215.87</td>\n",
       "      <td>1334.19</td>\n",
       "      <td>1687.87</td>\n",
       "      <td>2020-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1321.60</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1203.60</td>\n",
       "      <td>2020-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1274.40</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1203.60</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1274.40</td>\n",
       "      <td>1496.24</td>\n",
       "      <td>2020-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1215.87</td>\n",
       "      <td>1496.24</td>\n",
       "      <td>2020-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1496.24</td>\n",
       "      <td>2020-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>960</td>\n",
       "      <td>1132.80</td>\n",
       "      <td>1227.20</td>\n",
       "      <td>1180.00</td>\n",
       "      <td>1496.24</td>\n",
       "      <td>2020-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1209.00</td>\n",
       "      <td>1174.12</td>\n",
       "      <td>1474.05</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1209.00</td>\n",
       "      <td>1185.75</td>\n",
       "      <td>1474.05</td>\n",
       "      <td>2021-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1162.50</td>\n",
       "      <td>1185.75</td>\n",
       "      <td>1474.05</td>\n",
       "      <td>2021-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1080</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1116.00</td>\n",
       "      <td>1185.75</td>\n",
       "      <td>1474.05</td>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    zero_room_value  one_room_value  two_room_value  three_room_value  \\\n",
       "11              960         1098.53         1144.76           1697.63   \n",
       "10              960         1098.53         1215.87           1334.19   \n",
       "9               960         1132.80         1321.60           1180.00   \n",
       "8               960         1132.80         1274.40           1180.00   \n",
       "7               960         1132.80         1227.20           1274.40   \n",
       "6               960         1132.80         1227.20           1215.87   \n",
       "5               960         1132.80         1227.20           1180.00   \n",
       "4               960         1132.80         1227.20           1180.00   \n",
       "3              1080         1116.00         1209.00           1174.12   \n",
       "2              1080         1116.00         1209.00           1185.75   \n",
       "1              1080         1116.00         1162.50           1185.75   \n",
       "0              1080         1116.00         1116.00           1185.75   \n",
       "\n",
       "    four_room_value      dates  \n",
       "11          1687.87 2020-05-01  \n",
       "10          1687.87 2020-06-01  \n",
       "9           1203.60 2020-07-01  \n",
       "8           1203.60 2020-08-01  \n",
       "7           1496.24 2020-09-01  \n",
       "6           1496.24 2020-10-01  \n",
       "5           1496.24 2020-11-01  \n",
       "4           1496.24 2020-12-01  \n",
       "3           1474.05 2021-01-01  \n",
       "2           1474.05 2021-02-01  \n",
       "1           1474.05 2021-03-01  \n",
       "0           1474.05 2021-04-01  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent_tpafl['dates'] =pd.to_datetime(rent_tpafl['year'].astype(str) + \"-\" + rent_tpafl['month'].astype(str))\n",
    "rent_tpafl_df = rent_tpafl.drop(columns=[\"year\", \"month\"])\n",
    "tampa_rent = rent_tpafl_df.sort_values('dates', ascending=True)\n",
    "tampa_rent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "military-batman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tampa_rent[['four_room_value','three_room_value','two_room_value', 'one_room_value']].plot(use_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "double-charm",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}