Airbnb econ_state dataset clean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load datasets\n",
    "source=\"./airbnb_data/\"\n",
    "calendar=pd.read_csv(source+\"calendar.csv\")\n",
    "demographics=pd.read_csv(source+\"demographics.csv\")\n",
    "econ_state=pd.read_csv(source+\"econ_state.csv\")\n",
    "listings=pd.read_csv(source+\"listings.csv\")\n",
    "real_estate=pd.read_csv(source+\"real_estate.csv\")\n",
    "venues=pd.read_csv(source+\"venues.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>2005Q1_gdp</th>\n",
       "      <th>2005Q2_gdp</th>\n",
       "      <th>2005Q3_gdp</th>\n",
       "      <th>2005Q4_gdp</th>\n",
       "      <th>2006Q1_gdp</th>\n",
       "      <th>2006Q2_gdp</th>\n",
       "      <th>2006Q3_gdp</th>\n",
       "      <th>2006Q4_gdp</th>\n",
       "      <th>2007Q1_gdp</th>\n",
       "      <th>...</th>\n",
       "      <th>2016/03_ur</th>\n",
       "      <th>2016/04_ur</th>\n",
       "      <th>2016/05_ur</th>\n",
       "      <th>2016/06_ur</th>\n",
       "      <th>2016/07_ur</th>\n",
       "      <th>2016/08_ur</th>\n",
       "      <th>2016/09_ur</th>\n",
       "      <th>2016/10_ur</th>\n",
       "      <th>2016/11_ur</th>\n",
       "      <th>2016/12_ur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>153332</td>\n",
       "      <td>155940</td>\n",
       "      <td>157437</td>\n",
       "      <td>160293</td>\n",
       "      <td>161934</td>\n",
       "      <td>163974</td>\n",
       "      <td>165470</td>\n",
       "      <td>166495</td>\n",
       "      <td>166821</td>\n",
       "      <td>...</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.6</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>37517</td>\n",
       "      <td>38907</td>\n",
       "      <td>40691</td>\n",
       "      <td>43138</td>\n",
       "      <td>42872</td>\n",
       "      <td>44653</td>\n",
       "      <td>45349</td>\n",
       "      <td>45840</td>\n",
       "      <td>46658</td>\n",
       "      <td>...</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>218206</td>\n",
       "      <td>224496</td>\n",
       "      <td>231629</td>\n",
       "      <td>235099</td>\n",
       "      <td>241787</td>\n",
       "      <td>244659</td>\n",
       "      <td>250886</td>\n",
       "      <td>256505</td>\n",
       "      <td>258078</td>\n",
       "      <td>...</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>88446</td>\n",
       "      <td>89264</td>\n",
       "      <td>90515</td>\n",
       "      <td>93050</td>\n",
       "      <td>93413</td>\n",
       "      <td>95259</td>\n",
       "      <td>95481</td>\n",
       "      <td>95203</td>\n",
       "      <td>94289</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>1722091</td>\n",
       "      <td>1747827</td>\n",
       "      <td>1787427</td>\n",
       "      <td>1809426</td>\n",
       "      <td>1857944</td>\n",
       "      <td>1865835</td>\n",
       "      <td>1886549</td>\n",
       "      <td>1907754</td>\n",
       "      <td>1915172</td>\n",
       "      <td>...</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 519 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  2005Q1_gdp  2005Q2_gdp  2005Q3_gdp  2005Q4_gdp  2006Q1_gdp  \\\n",
       "0    AL      153332      155940      157437      160293      161934   \n",
       "1    AK       37517       38907       40691       43138       42872   \n",
       "2    AZ      218206      224496      231629      235099      241787   \n",
       "3    AR       88446       89264       90515       93050       93413   \n",
       "4    CA     1722091     1747827     1787427     1809426     1857944   \n",
       "\n",
       "   2006Q2_gdp  2006Q3_gdp  2006Q4_gdp  2007Q1_gdp  ...  2016/03_ur  \\\n",
       "0      163974      165470      166495      166821  ...         6.6   \n",
       "1       44653       45349       45840       46658  ...         5.9   \n",
       "2      244659      250886      256505      258078  ...         4.1   \n",
       "3       95259       95481       95203       94289  ...         5.5   \n",
       "4     1865835     1886549     1907754     1915172  ...         5.6   \n",
       "\n",
       "   2016/04_ur  2016/05_ur  2016/06_ur  2016/07_ur  2016/08_ur  2016/09_ur  \\\n",
       "0         6.6         6.6         6.7         6.7         6.7         6.6   \n",
       "1         5.8         5.8         5.8         5.8         5.9         6.0   \n",
       "2         4.1         4.1         4.1         4.0         4.0         4.0   \n",
       "3         5.4         5.3         5.3         5.2         5.1         5.1   \n",
       "4         5.5         5.5         5.5         5.4         5.4         5.3   \n",
       "\n",
       "   2016/10_ur  2016/11_ur  2016/12_ur  \n",
       "0         6.6         6.6         6.6  \n",
       "1         6.1         6.2         6.3  \n",
       "2         4.0         4.0         3.9  \n",
       "3         5.0         5.0         5.0  \n",
       "4         5.3         5.3         5.2  \n",
       "\n",
       "[5 rows x 519 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "econ_state.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>AL</th>\n",
       "      <th>AK</th>\n",
       "      <th>AZ</th>\n",
       "      <th>AR</th>\n",
       "      <th>CA</th>\n",
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       "      <th>...</th>\n",
       "      <th>SD</th>\n",
       "      <th>TN</th>\n",
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       "      <th>UT</th>\n",
       "      <th>VT</th>\n",
       "      <th>VA</th>\n",
       "      <th>WA</th>\n",
       "      <th>WV</th>\n",
       "      <th>WI</th>\n",
       "      <th>WY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005Q1_gdp</td>\n",
       "      <td>153332</td>\n",
       "      <td>37517</td>\n",
       "      <td>218206</td>\n",
       "      <td>88446</td>\n",
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       "      <td>25586</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005Q2_gdp</td>\n",
       "      <td>155940</td>\n",
       "      <td>38907</td>\n",
       "      <td>224496</td>\n",
       "      <td>89264</td>\n",
       "      <td>1747827</td>\n",
       "      <td>218710</td>\n",
       "      <td>210134</td>\n",
       "      <td>51156</td>\n",
       "      <td>83530</td>\n",
       "      <td>...</td>\n",
       "      <td>30828</td>\n",
       "      <td>224899</td>\n",
       "      <td>970290</td>\n",
       "      <td>92512</td>\n",
       "      <td>23371</td>\n",
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       "      <td>295759</td>\n",
       "      <td>53033</td>\n",
       "      <td>224820</td>\n",
       "      <td>26992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005Q3_gdp</td>\n",
       "      <td>157437</td>\n",
       "      <td>40691</td>\n",
       "      <td>231629</td>\n",
       "      <td>90515</td>\n",
       "      <td>1787427</td>\n",
       "      <td>222780</td>\n",
       "      <td>210233</td>\n",
       "      <td>51480</td>\n",
       "      <td>83845</td>\n",
       "      <td>...</td>\n",
       "      <td>31474</td>\n",
       "      <td>229409</td>\n",
       "      <td>998029</td>\n",
       "      <td>95443</td>\n",
       "      <td>23584</td>\n",
       "      <td>364188</td>\n",
       "      <td>296253</td>\n",
       "      <td>53825</td>\n",
       "      <td>229139</td>\n",
       "      <td>27684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005Q4_gdp</td>\n",
       "      <td>160293</td>\n",
       "      <td>43138</td>\n",
       "      <td>235099</td>\n",
       "      <td>93050</td>\n",
       "      <td>1809426</td>\n",
       "      <td>223421</td>\n",
       "      <td>213052</td>\n",
       "      <td>58232</td>\n",
       "      <td>84446</td>\n",
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       "      <td>231917</td>\n",
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       "      <td>29556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006Q1_gdp</td>\n",
       "      <td>161934</td>\n",
       "      <td>42872</td>\n",
       "      <td>241787</td>\n",
       "      <td>93413</td>\n",
       "      <td>1857944</td>\n",
       "      <td>229476</td>\n",
       "      <td>221025</td>\n",
       "      <td>55333</td>\n",
       "      <td>86260</td>\n",
       "      <td>...</td>\n",
       "      <td>31931</td>\n",
       "      <td>235324</td>\n",
       "      <td>1058764</td>\n",
       "      <td>102201</td>\n",
       "      <td>24217</td>\n",
       "      <td>376028</td>\n",
       "      <td>310192</td>\n",
       "      <td>55802</td>\n",
       "      <td>232421</td>\n",
       "      <td>30619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "state   Indicator      AL     AK      AZ     AR       CA      CO      CT  \\\n",
       "0      2005Q1_gdp  153332  37517  218206  88446  1722091  216906  207262   \n",
       "1      2005Q2_gdp  155940  38907  224496  89264  1747827  218710  210134   \n",
       "2      2005Q3_gdp  157437  40691  231629  90515  1787427  222780  210233   \n",
       "3      2005Q4_gdp  160293  43138  235099  93050  1809426  223421  213052   \n",
       "4      2006Q1_gdp  161934  42872  241787  93413  1857944  229476  221025   \n",
       "\n",
       "state     DE     DC  ...     SD      TN       TX      UT     VT      VA  \\\n",
       "0      52121  82522  ...  30851  223795   960570   90903  23276  349134   \n",
       "1      51156  83530  ...  30828  224899   970290   92512  23371  356828   \n",
       "2      51480  83845  ...  31474  229409   998029   95443  23584  364188   \n",
       "3      58232  84446  ...  32368  231917  1031329   99043  23924  366941   \n",
       "4      55333  86260  ...  31931  235324  1058764  102201  24217  376028   \n",
       "\n",
       "state      WA     WV      WI     WY  \n",
       "0      290007  51722  223206  25586  \n",
       "1      295759  53033  224820  26992  \n",
       "2      296253  53825  229139  27684  \n",
       "3      303593  55234  228133  29556  \n",
       "4      310192  55802  232421  30619  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "econ_state_t = econ_state.transpose()\n",
    "econ_state_t.columns = econ_state_t.iloc[0]\n",
    "econ_state_t = econ_state_t.iloc[1:]\n",
    "econ_state_t = econ_state_t.reset_index()\n",
    "econ_state_t.rename(columns={'index':'Indicator'}, inplace=True)\n",
    "econ_state_t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>AL</th>\n",
       "      <th>AK</th>\n",
       "      <th>AZ</th>\n",
       "      <th>AR</th>\n",
       "      <th>CA</th>\n",
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       "      <th>WV</th>\n",
       "      <th>WI</th>\n",
       "      <th>WY</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gdp</td>\n",
       "      <td>153332</td>\n",
       "      <td>37517</td>\n",
       "      <td>218206</td>\n",
       "      <td>88446</td>\n",
       "      <td>1722091</td>\n",
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       "      <td>2005Q1</td>\n",
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       "      <th>1</th>\n",
       "      <td>gdp</td>\n",
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       "      <td>83530</td>\n",
       "      <td>...</td>\n",
       "      <td>224899</td>\n",
       "      <td>970290</td>\n",
       "      <td>92512</td>\n",
       "      <td>23371</td>\n",
       "      <td>356828</td>\n",
       "      <td>295759</td>\n",
       "      <td>53033</td>\n",
       "      <td>224820</td>\n",
       "      <td>26992</td>\n",
       "      <td>2005Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gdp</td>\n",
       "      <td>157437</td>\n",
       "      <td>40691</td>\n",
       "      <td>231629</td>\n",
       "      <td>90515</td>\n",
       "      <td>1787427</td>\n",
       "      <td>222780</td>\n",
       "      <td>210233</td>\n",
       "      <td>51480</td>\n",
       "      <td>83845</td>\n",
       "      <td>...</td>\n",
       "      <td>229409</td>\n",
       "      <td>998029</td>\n",
       "      <td>95443</td>\n",
       "      <td>23584</td>\n",
       "      <td>364188</td>\n",
       "      <td>296253</td>\n",
       "      <td>53825</td>\n",
       "      <td>229139</td>\n",
       "      <td>27684</td>\n",
       "      <td>2005Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gdp</td>\n",
       "      <td>160293</td>\n",
       "      <td>43138</td>\n",
       "      <td>235099</td>\n",
       "      <td>93050</td>\n",
       "      <td>1809426</td>\n",
       "      <td>223421</td>\n",
       "      <td>213052</td>\n",
       "      <td>58232</td>\n",
       "      <td>84446</td>\n",
       "      <td>...</td>\n",
       "      <td>231917</td>\n",
       "      <td>1031329</td>\n",
       "      <td>99043</td>\n",
       "      <td>23924</td>\n",
       "      <td>366941</td>\n",
       "      <td>303593</td>\n",
       "      <td>55234</td>\n",
       "      <td>228133</td>\n",
       "      <td>29556</td>\n",
       "      <td>2005Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gdp</td>\n",
       "      <td>161934</td>\n",
       "      <td>42872</td>\n",
       "      <td>241787</td>\n",
       "      <td>93413</td>\n",
       "      <td>1857944</td>\n",
       "      <td>229476</td>\n",
       "      <td>221025</td>\n",
       "      <td>55333</td>\n",
       "      <td>86260</td>\n",
       "      <td>...</td>\n",
       "      <td>235324</td>\n",
       "      <td>1058764</td>\n",
       "      <td>102201</td>\n",
       "      <td>24217</td>\n",
       "      <td>376028</td>\n",
       "      <td>310192</td>\n",
       "      <td>55802</td>\n",
       "      <td>232421</td>\n",
       "      <td>30619</td>\n",
       "      <td>2006Q1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "state Indicator      AL     AK      AZ     AR       CA      CO      CT     DE  \\\n",
       "0           gdp  153332  37517  218206  88446  1722091  216906  207262  52121   \n",
       "1           gdp  155940  38907  224496  89264  1747827  218710  210134  51156   \n",
       "2           gdp  157437  40691  231629  90515  1787427  222780  210233  51480   \n",
       "3           gdp  160293  43138  235099  93050  1809426  223421  213052  58232   \n",
       "4           gdp  161934  42872  241787  93413  1857944  229476  221025  55333   \n",
       "\n",
       "state     DC  ...      TN       TX      UT     VT      VA      WA     WV  \\\n",
       "0      82522  ...  223795   960570   90903  23276  349134  290007  51722   \n",
       "1      83530  ...  224899   970290   92512  23371  356828  295759  53033   \n",
       "2      83845  ...  229409   998029   95443  23584  364188  296253  53825   \n",
       "3      84446  ...  231917  1031329   99043  23924  366941  303593  55234   \n",
       "4      86260  ...  235324  1058764  102201  24217  376028  310192  55802   \n",
       "\n",
       "state      WI     WY    Date  \n",
       "0      223206  25586  2005Q1  \n",
       "1      224820  26992  2005Q2  \n",
       "2      229139  27684  2005Q3  \n",
       "3      228133  29556  2005Q4  \n",
       "4      232421  30619  2006Q1  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "econ_state_t['Date'] = econ_state_t['Indicator'].str.split('_').str[0]\n",
    "econ_state_t['Indicator'] = econ_state_t['Indicator'].str.split('_').str[1]\n",
    "econ_state_t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "#econ_state_t['Indicator'].unique().tolist()\n",
    "gdp = econ_state_t[econ_state_t['Indicator']=='gdp']\n",
    "income = econ_state_t[econ_state_t['Indicator']=='personal']\n",
    "unemp = econ_state_t[econ_state_t['Indicator']=='ur']\n",
    "\n",
    "# Drop first column 'Indicator' and reorder columns to bring date column to start i.e. column index 0\n",
    "def reordercol(data):\n",
    "    data = data.drop('Indicator', axis=1)\n",
    "    cols = data.columns.tolist()\n",
    "    cols = cols[-1:] + cols[:-1]\n",
    "    return data[cols]\n",
    "\n",
    "gdp = reordercol(gdp)\n",
    "income = reordercol(income)\n",
    "unemp = reordercol(unemp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "def splitdate(data, time_unit, split_by):\n",
    "    data['Year'] = data['Date'].str[:4]\n",
    "    data[time_unit] = data['Date'].str[split_by:]\n",
    "    data = data.drop('Date',axis=1)\n",
    "    data[['Year', time_unit]] = data[['Year', time_unit]].apply(pd.to_numeric)\n",
    "    return data\n",
    "\n",
    "gdp = splitdate(gdp, 'Quarter', -1)\n",
    "income = splitdate(income, 'Quarter', -1)\n",
    "unemp = splitdate(unemp, 'Month', -2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "ny_gdp = gdp[['NY','Year','Quarter']]\n",
    "ny_income = income[['NY','Year','Quarter']]\n",
    "ny_unemp = unemp[['NY','Year','Month']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>state</th>\n",
       "      <th>NY</th>\n",
       "      <th>Year</th>\n",
       "      <th>Quarter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>996072</td>\n",
       "      <td>2005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1017192</td>\n",
       "      <td>2005</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1031009</td>\n",
       "      <td>2005</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1049458</td>\n",
       "      <td>2005</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1070271</td>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state       NY  Year  Quarter\n",
       "0       996072  2005        1\n",
       "1      1017192  2005        2\n",
       "2      1031009  2005        3\n",
       "3      1049458  2005        4\n",
       "4      1070271  2006        1"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_gdp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ny_gdp['NY'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ny_income['NY'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ny_unemp['NY'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Listings data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>metro_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>t</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-04</td>\n",
       "      <td>t</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-03</td>\n",
       "      <td>t</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>t</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>t</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NYC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date available  price metro_area\n",
       "0        2515  2018-03-05         t   69.0        NYC\n",
       "1        2515  2018-03-04         t   69.0        NYC\n",
       "2        2515  2018-03-03         t   69.0        NYC\n",
       "3        2515  2018-03-02         t   69.0        NYC\n",
       "4        2515  2018-03-01         t   69.0        NYC"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calendar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-04</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-03</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-02</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2515</td>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id        date  price\n",
       "0        2515  2018-03-05   69.0\n",
       "1        2515  2018-03-04   69.0\n",
       "2        2515  2018-03-03   69.0\n",
       "3        2515  2018-03-02   69.0\n",
       "4        2515  2018-03-01   69.0"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calendar['metro_area'].unique().tolist()\n",
    "ny_calendar = calendar[calendar['metro_area']=='NYC']\n",
    "ny_calendar = ny_calendar.drop(['metro_area','available'], axis=1)\n",
    "ny_calendar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ny_calendar['date'] = pd.to_datetime(ny_calendar.date)\n",
    "ny_calendar['date_mean'] = ny_calendar.groupby(ny_calendar.date.dt.day)['price'].transform('mean')\n",
    "ny_cal_mean = ny_calendar['date'] + ny_cal_mean.to_frame()\n",
    "ny_cal_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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