Data_Cleaning Summary.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data_Cleaning Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "data = pd.read_csv(\"C:/data/dirtydata.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicate Rows :\n"
     ]
    }
   ],
   "source": [
    "#Cheking for duplicates \n",
    "duplicate = data[data.duplicated()]\n",
    "  \n",
    "print(\"Duplicate Rows :\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove duplcates \n",
    "data.drop_duplicates(inplace= True) #when we use in place we modify the data and we sont require to save a copy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0      0.000000\n",
       "id              0.000000\n",
       "url             0.000000\n",
       "region          0.000000\n",
       "region_url      0.000000\n",
       "price           0.000000\n",
       "year            0.002292\n",
       "manufacturer    0.039763\n",
       "model           0.010576\n",
       "condition       0.421071\n",
       "cylinders       0.373494\n",
       "fuel            0.007064\n",
       "odometer        0.120693\n",
       "title_status    0.005624\n",
       "transmission    0.005329\n",
       "VIN             0.409305\n",
       "drive           0.292851\n",
       "size            0.701307\n",
       "type            0.246038\n",
       "paint_color     0.307375\n",
       "image_url       0.000061\n",
       "description     0.000153\n",
       "state           0.000000\n",
       "lat             0.016254\n",
       "long            0.016254\n",
       "posting_date    0.000061\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for nulls / percentage  of nulls \n",
    "\n",
    "data.isnull().any()\n",
    "data.isnull().sum()/ data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>url</th>\n",
       "      <th>region</th>\n",
       "      <th>region_url</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>...</th>\n",
       "      <th>drive</th>\n",
       "      <th>size</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>image_url</th>\n",
       "      <th>description</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>posting_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7240372487</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>35990</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>corvette grand sport</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>rwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00N0N_ipkbHVZYf4...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-12-02T08:11:30-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7240309422</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-201...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>7500</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>sonata</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>fwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00s0s_gBHYmJ5o7y...</td>\n",
       "      <td>I'll move to another city and try to sell my c...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>-85.468200</td>\n",
       "      <td>2020-12-02T02:11:50-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7240224296</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>4900</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>x3 3.0i</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SUV</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00B0B_5zgEGWPOrt...</td>\n",
       "      <td>Clean 2006 BMW X3 3.0I.  Beautiful and rare Bl...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.616807</td>\n",
       "      <td>-85.464149</td>\n",
       "      <td>2020-12-01T19:50:41-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7240103965</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/lanett-tru...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>2000</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>c-10</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>rwd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>pickup</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00M0M_6o7KcDpArw...</td>\n",
       "      <td>1974 chev. truck (LONG BED) NEW starter front ...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.861600</td>\n",
       "      <td>-85.216100</td>\n",
       "      <td>2020-12-01T15:54:45-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7239983776</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>19500</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f350 lariat</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>pickup</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00p0p_b95l1EgUfl...</td>\n",
       "      <td>2005 Ford F350 Lariat (Bullet Proofed). This t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>-85.468200</td>\n",
       "      <td>2020-12-01T12:53:56-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458206</th>\n",
       "      <td>458206</td>\n",
       "      <td>7240967086</td>\n",
       "      <td>https://wausau.craigslist.org/cto/d/wittenberg...</td>\n",
       "      <td>wausau</td>\n",
       "      <td>https://wausau.craigslist.org</td>\n",
       "      <td>5300</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>captiva</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>fwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SUV</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00g0g_71rrf48ZM3...</td>\n",
       "      <td>2013 Chevy Captiva - Brand new battery - Runs ...</td>\n",
       "      <td>wi</td>\n",
       "      <td>44.823200</td>\n",
       "      <td>-89.166500</td>\n",
       "      <td>2020-12-03T09:10:33-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458208</th>\n",
       "      <td>458208</td>\n",
       "      <td>7240959076</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wisconsin-...</td>\n",
       "      <td>wausau</td>\n",
       "      <td>https://wausau.craigslist.org</td>\n",
       "      <td>14995</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>srx</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>SUV</td>\n",
       "      <td>silver</td>\n",
       "      <td>https://images.craigslist.org/00808_1bVcaYZtCy...</td>\n",
       "      <td>2014 Cadillac SRX All Wheel Drive Luxury Colle...</td>\n",
       "      <td>wi</td>\n",
       "      <td>44.408867</td>\n",
       "      <td>-89.881210</td>\n",
       "      <td>2020-12-03T08:56:29-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458209</th>\n",
       "      <td>458209</td>\n",
       "      <td>7240954682</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wisconsin-...</td>\n",
       "      <td>wausau</td>\n",
       "      <td>https://wausau.craigslist.org</td>\n",
       "      <td>17895</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>tucson</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>SUV</td>\n",
       "      <td>white</td>\n",
       "      <td>https://images.craigslist.org/00l0l_gikkP11dEF...</td>\n",
       "      <td>2018 Hyundai Tucson SE 2.0 I4 AWD.  Carfax ver...</td>\n",
       "      <td>wi</td>\n",
       "      <td>44.408867</td>\n",
       "      <td>-89.882240</td>\n",
       "      <td>2020-12-03T08:48:08-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458210</th>\n",
       "      <td>458210</td>\n",
       "      <td>7240645700</td>\n",
       "      <td>https://wyoming.craigslist.org/cto/d/sheridan-...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>17900</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>cts coupe awd</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>coupe</td>\n",
       "      <td>black</td>\n",
       "      <td>https://images.craigslist.org/00t0t_lNnW4cboOG...</td>\n",
       "      <td>53,000 miles. Cosmetically, in near mint condi...</td>\n",
       "      <td>wy</td>\n",
       "      <td>44.784900</td>\n",
       "      <td>-106.964800</td>\n",
       "      <td>2020-12-02T14:09:45-0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458211</th>\n",
       "      <td>458211</td>\n",
       "      <td>7240600465</td>\n",
       "      <td>https://wyoming.craigslist.org/cto/d/sheridan-...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>1300</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>jeep</td>\n",
       "      <td>grand cherokee</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>SUV</td>\n",
       "      <td>white</td>\n",
       "      <td>https://images.craigslist.org/00C0C_fl0NW1IeJw...</td>\n",
       "      <td>PRICE REDUCTION  Turns out the engine is toast...</td>\n",
       "      <td>wy</td>\n",
       "      <td>44.773500</td>\n",
       "      <td>-106.939600</td>\n",
       "      <td>2020-12-02T13:01:04-0700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>385758 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0          id  \\\n",
       "0                0  7240372487   \n",
       "1                1  7240309422   \n",
       "2                2  7240224296   \n",
       "3                3  7240103965   \n",
       "4                4  7239983776   \n",
       "...            ...         ...   \n",
       "458206      458206  7240967086   \n",
       "458208      458208  7240959076   \n",
       "458209      458209  7240954682   \n",
       "458210      458210  7240645700   \n",
       "458211      458211  7240600465   \n",
       "\n",
       "                                                      url   region  \\\n",
       "0       https://auburn.craigslist.org/ctd/d/auburn-uni...   auburn   \n",
       "1       https://auburn.craigslist.org/cto/d/auburn-201...   auburn   \n",
       "2       https://auburn.craigslist.org/cto/d/auburn-200...   auburn   \n",
       "3       https://auburn.craigslist.org/cto/d/lanett-tru...   auburn   \n",
       "4       https://auburn.craigslist.org/cto/d/auburn-200...   auburn   \n",
       "...                                                   ...      ...   \n",
       "458206  https://wausau.craigslist.org/cto/d/wittenberg...   wausau   \n",
       "458208  https://wausau.craigslist.org/ctd/d/wisconsin-...   wausau   \n",
       "458209  https://wausau.craigslist.org/ctd/d/wisconsin-...   wausau   \n",
       "458210  https://wyoming.craigslist.org/cto/d/sheridan-...  wyoming   \n",
       "458211  https://wyoming.craigslist.org/cto/d/sheridan-...  wyoming   \n",
       "\n",
       "                            region_url  price    year manufacturer  \\\n",
       "0        https://auburn.craigslist.org  35990  2010.0    chevrolet   \n",
       "1        https://auburn.craigslist.org   7500  2014.0      hyundai   \n",
       "2        https://auburn.craigslist.org   4900  2006.0          bmw   \n",
       "3        https://auburn.craigslist.org   2000  1974.0    chevrolet   \n",
       "4        https://auburn.craigslist.org  19500  2005.0         ford   \n",
       "...                                ...    ...     ...          ...   \n",
       "458206   https://wausau.craigslist.org   5300  2013.0    chevrolet   \n",
       "458208   https://wausau.craigslist.org  14995  2014.0     cadillac   \n",
       "458209   https://wausau.craigslist.org  17895  2018.0      hyundai   \n",
       "458210  https://wyoming.craigslist.org  17900  2014.0     cadillac   \n",
       "458211  https://wyoming.craigslist.org   1300  2008.0         jeep   \n",
       "\n",
       "                       model  condition  ... drive       size    type  \\\n",
       "0       corvette grand sport       good  ...   rwd        NaN   other   \n",
       "1                     sonata  excellent  ...   fwd        NaN   sedan   \n",
       "2                    x3 3.0i       good  ...   NaN        NaN     SUV   \n",
       "3                       c-10       good  ...   rwd  full-size  pickup   \n",
       "4                f350 lariat  excellent  ...   4wd  full-size  pickup   \n",
       "...                      ...        ...  ...   ...        ...     ...   \n",
       "458206               captiva  excellent  ...   fwd        NaN     SUV   \n",
       "458208                   srx  excellent  ...   4wd   mid-size     SUV   \n",
       "458209                tucson  excellent  ...   4wd   mid-size     SUV   \n",
       "458210         cts coupe awd  excellent  ...   4wd   mid-size   coupe   \n",
       "458211        grand cherokee       good  ...   4wd   mid-size     SUV   \n",
       "\n",
       "       paint_color                                          image_url  \\\n",
       "0              NaN  https://images.craigslist.org/00N0N_ipkbHVZYf4...   \n",
       "1              NaN  https://images.craigslist.org/00s0s_gBHYmJ5o7y...   \n",
       "2             blue  https://images.craigslist.org/00B0B_5zgEGWPOrt...   \n",
       "3             blue  https://images.craigslist.org/00M0M_6o7KcDpArw...   \n",
       "4             blue  https://images.craigslist.org/00p0p_b95l1EgUfl...   \n",
       "...            ...                                                ...   \n",
       "458206        blue  https://images.craigslist.org/00g0g_71rrf48ZM3...   \n",
       "458208      silver  https://images.craigslist.org/00808_1bVcaYZtCy...   \n",
       "458209       white  https://images.craigslist.org/00l0l_gikkP11dEF...   \n",
       "458210       black  https://images.craigslist.org/00t0t_lNnW4cboOG...   \n",
       "458211       white  https://images.craigslist.org/00C0C_fl0NW1IeJw...   \n",
       "\n",
       "                                              description state        lat  \\\n",
       "0       Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "1       I'll move to another city and try to sell my c...    al  32.547500   \n",
       "2       Clean 2006 BMW X3 3.0I.  Beautiful and rare Bl...    al  32.616807   \n",
       "3       1974 chev. truck (LONG BED) NEW starter front ...    al  32.861600   \n",
       "4       2005 Ford F350 Lariat (Bullet Proofed). This t...    al  32.547500   \n",
       "...                                                   ...   ...        ...   \n",
       "458206  2013 Chevy Captiva - Brand new battery - Runs ...    wi  44.823200   \n",
       "458208  2014 Cadillac SRX All Wheel Drive Luxury Colle...    wi  44.408867   \n",
       "458209  2018 Hyundai Tucson SE 2.0 I4 AWD.  Carfax ver...    wi  44.408867   \n",
       "458210  53,000 miles. Cosmetically, in near mint condi...    wy  44.784900   \n",
       "458211  PRICE REDUCTION  Turns out the engine is toast...    wy  44.773500   \n",
       "\n",
       "              long              posting_date  \n",
       "0       -85.480000  2020-12-02T08:11:30-0600  \n",
       "1       -85.468200  2020-12-02T02:11:50-0600  \n",
       "2       -85.464149  2020-12-01T19:50:41-0600  \n",
       "3       -85.216100  2020-12-01T15:54:45-0600  \n",
       "4       -85.468200  2020-12-01T12:53:56-0600  \n",
       "...            ...                       ...  \n",
       "458206  -89.166500  2020-12-03T09:10:33-0600  \n",
       "458208  -89.881210  2020-12-03T08:56:29-0600  \n",
       "458209  -89.882240  2020-12-03T08:48:08-0600  \n",
       "458210 -106.964800  2020-12-02T14:09:45-0700  \n",
       "458211 -106.939600  2020-12-02T13:01:04-0700  \n",
       "\n",
       "[385758 rows x 26 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove columns with certain threshold of nulls\n",
    "#threshold is the number of columns or rows without nulls \n",
    "thresh = len(data)*.6\n",
    "data.dropna(thresh = thresh, axis = 1)\n",
    "data.dropna(thresh = 21, axis = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          32742.0\n",
       "1          93600.0\n",
       "2          87046.0\n",
       "3         190000.0\n",
       "4         116000.0\n",
       "            ...   \n",
       "458208     83790.0\n",
       "458209     31888.0\n",
       "458210     53000.0\n",
       "458211    164000.0\n",
       "458212    164000.0\n",
       "Name: odometer, Length: 458213, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#imputing nulls fillna()----- using measures of central tendancy \n",
    "data.odometer.fillna(data.odometer.median())# Using median \n",
    "data.odometer.fillna(data.odometer.mean())# Using Mean \n",
    "data.odometer.fillna(data.odometer.mode()) # Using Mode "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan      171140\n",
       "6        105677\n",
       "4         94767\n",
       "8         81179\n",
       "5          2058\n",
       "10         1543\n",
       "other      1112\n",
       "3           550\n",
       "12          187\n",
       "Name: cylinders, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use regex .extract\n",
    "#use strip()\n",
    "#use replace()\n",
    "#split \n",
    "\n",
    "data.cylinders.dtype\n",
    "data.cylinders.value_counts()\n",
    "data.cylinders = data.cylinders.apply(lambda x: str(x).replace('cylinders','').strip())\n",
    "data.cylinders.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change data type \n",
    "data.cylinders = pd.to_numeric(data.cylinders, errors = 'coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEDCAYAAAAlRP8qAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAATyUlEQVR4nO3dfYxld33f8fcHzxqQTZcKW5OVH1haW+k4Q4KTkQPJqpohojXuinV5UFkgwfFEG0V4Q9WgKmQk01oatWmjRHWWh6wylu0qvYFCcO2pW4LKTPFUwbBr+XmgrEDGD4gHO17YZcGZ4ds/5tgdj2d37njv7J05fr+kqznnd373nK+tq49//p2nVBWSpK3vZf0uQJLUGwa6JLWEgS5JLWGgS1JLGOiS1BIGuiS1RF8DPclNSb6b5MEu+r42yf9Kcn+S2SQXnokaJWmr6PcI/Wbgyi77/hFwa1X9PHAD8O82qihJ2or6GuhV9UXgqeVtSf5hkv+Z5HCSu5L8o2bTZcAXmuUZYM8ZLFWSNr1+j9BXcxDYX1W/BHwI+FjTfh/w9mb5nwOvSvKaPtQnSZvSQL8LWC7JucCvAP81ybPNL2/+fgg4kOQa4IvA48Dima5RkjarTRXoLP0fw9NV9YaVG6rqCZoRehP876iqp89odZK0iW2qKZeq+gHwzSTvAsiSX2iWz0vybL0fBm7qU5mStCn1+7LFDvA3wM8meSzJOPBeYDzJfcBD/P+Tn6PA15L8X2AQmOxDyZK0acXH50pSO2yqKRdJ0ovXt5Oi5513Xu3cubNfh5dO6ejRo2zfvr3fZUgvcPjw4e9X1fmrbetboO/cuZNDhw716/DSKU1PT7N79+5+lyG9QJJHTrbNKRdJagkDXZJawkCXpJYw0CWpJQx0SWoJA11aptPpMDw8zJ49exgeHqbT6fS7JKlrm+3hXFLfdDodJiYmmJqaeu469PHxcQD27t3b5+qktTlClxqTk5NMTU0xNjbGwMAAY2NjTE1NMTnpY4O0NRjoUmN+fp5du3Y9r23Xrl3Mz8/3qSJpfQx0qTE0NMTc3Nzz2ubm5hgaGupTRdL6GOhSY2JigvHxcWZmZlhYWGBmZobx8XEmJib6XZrUlTVPiiZ5BUuvfHt50//TVfWRFX2uAf4jS6+FAzhQVX/e21KljfXsic/9+/czPz/P0NAQk5OTnhDVlrHm89Cz9HLPc6rqWJJtwBzwwar60rI+1wAjVXVdtwceGRkpH86lzcqHc2mzSnK4qkZW27bmCL2WEv9Ys7qt+fhWDEnaZLqaQ09yVpJ7ge8Cn6+qu1fp9o4k9yf5dJKLelmkJGltXd1YVFWLwBuSvBr4bJLhqnpwWZc7gE5V/STJbwO3AG9euZ8k+4B9AIODg8zOzp5m+dLGWFxc9PepLWfd7xRNcj3wo6r6o5NsPwt4qqpO+boX59C1mTmHrs3qVHPoa065JDm/GZmT5JXAW4CvruizY9nq2wDvxJCkM6ybKZcdwC3NyPtlwKeqajrJDcChqrod+N0kbwMWgKeAazaqYEnS6rq5yuV+4PJV2q9ftvxh4MO9LU2StB7eKSpJLWGgS1JLGOiS1BIGuiS1hIEuSS1hoEtSSxjoktQSBroktYSBLkktYaBLUksY6JLUEga6JLWEgS5JLWGgS1JLGOiS1BIGuiS1hIEuSS1hoEtSSxjoktQSawZ6klck+XKS+5I8lOTfrtLn5Uk+meRIkruT7NyQaiVJJ9XNCP0nwJur6heANwBXJnnjij7jwN9W1SXAnwB/2NMqJUlrWjPQa8mxZnVb86kV3fYAtzTLnwZ+LUl6VqUkaU0D3XRKchZwGLgE+GhV3b2iywXAowBVtZDkKPAa4Psr9rMP2AcwODjI7OzsaRUvbZTFxUV/n9pyugr0qloE3pDk1cBnkwxX1YPrPVhVHQQOAoyMjNTo6Oh6dyGdEdPT0/j71FazrqtcquppYAa4csWmx4GLAJIMANuBJ3tQnySpS91c5XJ+MzInySuBtwBfXdHtduD9zfI7gS9U1cp5dknSBupmymUHcEszj/4y4FNVNZ3kBuBQVd0OTAH/OckR4Cng3RtWsSRpVd1c5XJ/VV1eVT9fVcNVdUPTfn0T5lTVj6vqXVV1SVVdUVXf2OjCpY3Q6XQYHh5mz549DA8P0+l0+l2S1LWuTopKLwWdToeJiQmmpqY4evQo27dvZ3x8HIC9e/f2uTppbenXVPfIyEgdOnSoL8eWVjM8PMzVV1/Nbbfdxvz8PENDQ8+tP/jgui/qkjZEksNVNbLaNkfoUuPhhx/m+PHj3HTTTc+N0K+99loeeeSRfpcmdcWHc0mNs88+m/379zM2NsbAwABjY2Ps37+fs88+u9+lSV1xhC41nnnmGQ4cOMDll1/OwsICMzMzHDhwgGeeeabfpUldMdClxmWXXcbVV1/N/v37n5tDf8973sNtt93W79KkrhjoUmNiYmLVq1wmJyf7XZrUFQNdajx7aeLyEfrk5KSXLGrL8LJFaRXT09Ps3r2732VIL3Cqyxa9ykWSWsJAl6SWMNAlqSUMdElqCQNdklrCQJekljDQJaklDHRJagkDXZJaopuXRF+UZCbJw0keSvLBVfqMJjma5N7mc/3GlCtJOplunuWyAPxeVd2T5FXA4SSfr6qHV/S7q6q8V1qS+qSbl0R/u6ruaZZ/CMwDF2x0YZKk9VnX0xaT7AQuB+5eZfObktwHPAF8qKoeWuX7+4B9AIODg8zOzq63XumMWFxc9PepLafrpy0mORf438BkVf3Vim1/D/hpVR1LchXwn6rq0lPtz6ctajPzaYvarE77aYtJtgGfAf5iZZgDVNUPqupYs3wnsC3JeadRsyRpnbq5yiXAFDBfVX98kj4/0/QjyRXNfp/sZaGSpFPrZg79V4FfBx5Icm/T9gfAxQBV9QngncDvJFkATgDvrn69OUOSXqLWDPSqmgOyRp8DwIFeFSVJWj/vFJWkljDQJaklDHRJagkDXZJawkCXpJYw0CWpJQx0SWoJA12SWsJAl6SWMNAlqSUMdElqCQNdklrCQJekljDQJaklDHRJagkDXZJawkCXlul0OgwPD7Nnzx6Gh4fpdDr9LknqWjevoJNeEjqdDhMTE0xNTXH06FG2b9/O+Pg4AHv37u1zddLaunlJ9EVJZpI8nOShJB9cpU+S3JjkSJL7k/zixpQrbZzJyUmmpqYYGxtjYGCAsbExpqammJyc7HdpUle6GaEvAL9XVfckeRVwOMnnq+rhZX3eClzafH4Z+HjzV9oy5ufn2bVr1/Padu3axfz8fJ8qktZnzRF6VX27qu5pln8IzAMXrOi2B7i1lnwJeHWSHT2vVtpAQ0NDzM3NPa9tbm6OoaGhPlUkrc+6Toom2QlcDty9YtMFwKPL1h/jhaEvbWoTExOMj48zMzPDwsICMzMzjI+PMzEx0e/SpK50fVI0ybnAZ4B/WVU/eDEHS7IP2AcwODjI7Ozsi9mNtCF27NjBe9/7Xq699lq+9a1vcfHFF/O+972PHTt2+FvVlpCqWrtTsg2YBj5XVX+8yvY/A2arqtOsfw0Yrapvn2yfIyMjdejQoRdduLSRpqen2b17d7/LkF4gyeGqGlltWzdXuQSYAuZXC/PG7cBvNFe7vBE4eqowlyT1XjdTLr8K/DrwQJJ7m7Y/AC4GqKpPAHcCVwFHgB8Bv9nzSiVJp7RmoFfVHJA1+hTwgV4VJUlaP2/9l6SWMNAlqSUMdElqCQNdklrCQJekljDQJaklDHRJagkDXZJawkCXpJYw0CWpJQx0SWoJA12SWsJAl6SWMNAlqSUMdElqCQNdklrCQJekljDQJaklDHRJaok1Az3JTUm+m+TBk2wfTXI0yb3N5/relylJWsuaL4kGbgYOALeeos9dVbW7JxVJkl6UNUfoVfVF4KkzUIsk6TR0M0LvxpuS3Ac8AXyoqh5arVOSfcA+gMHBQWZnZ3t0eKm3FhcX/X1qy+lFoN8DvLaqjiW5CrgNuHS1jlV1EDgIMDIyUqOjoz04vNR709PT+PvUVnPaV7lU1Q+q6lizfCewLcl5p12ZJGldTjvQk/xMkjTLVzT7fPJ09ytJWp81p1ySdIBR4LwkjwEfAbYBVNUngHcCv5NkATgBvLuqasMqliStas1Ar6q9a2w/wNJljZKkPvJOUUlqCQNdklrCQJekljDQJaklDHRJagkDXVqm0+kwPDzMnj17GB4eptPp9LskqWu9epaLtOV1Oh0mJiaYmpri6NGjbN++nfHxcQD27j3l1bvSpuAIXWpMTk4yNTXF2NgYAwMDjI2NMTU1xeTkZL9Lk7pioEuN+fl5du3a9by2Xbt2MT8/36eKpPUx0KXG0NAQc3Nzz2ubm5tjaGioTxVJ62OgS42JiQnGx8eZmZlhYWGBmZkZxsfHmZiY6HdpUlc8KSo1nj3xuX//fubn5xkaGmJyctITotoy0q8HI46MjNShQ4f6cmxpLdPT0+ze7WtytfkkOVxVI6ttc8pFklrCQJekljDQJaklDHRJagkDXZJaYs1AT3JTku8mefAk25PkxiRHktyf5Bd7X6YkaS3djNBvBq48xfa3Apc2n33Ax0+/LEnSeq0Z6FX1ReCpU3TZA9xaS74EvDrJjl4VKEnqTi/uFL0AeHTZ+mNN27dXdkyyj6VRPIODg8zOzvbg8FLvLS4u+vvUlnNGb/2vqoPAQVi6U3R0dPRMHl7q2vT0NP4+tdX04iqXx4GLlq1f2LRJks6gXgT67cBvNFe7vBE4WlUvmG6RJG2sNadcknSAUeC8JI8BHwG2AVTVJ4A7gauAI8CPgN/cqGIlSSe3ZqBX1SmfHVpLj2v8QM8qkiS9KN4pKkktYaBLUksY6JLUEga6JLWEgS5JLWGgS1JLGOiS1BIGuiS1hIEuSS1hoEtSSxjoktQSBroktYSBLkktYaBLUksY6JLUEga6JLWEgS5JLWGgS1JLdBXoSa5M8rUkR5L8/irbr0nyvST3Np/f6n2pkqRT6eYl0WcBHwXeAjwGfCXJ7VX18Iqun6yq6zagRklSF7oZoV8BHKmqb1TVM8BfAns2tixJ0np1E+gXAI8uW3+saVvpHUnuT/LpJBf1pDpJUtfWnHLp0h1Ap6p+kuS3gVuAN6/slGQfsA9gcHCQ2dnZHh1e6q3FxUV/n9pyugn0x4HlI+4Lm7bnVNWTy1b/HPgPq+2oqg4CBwFGRkZqdHR0PbVKZ8z09DT+PrXVdDPl8hXg0iSvS3I28G7g9uUdkuxYtvo2YL53JUqSurHmCL2qFpJcB3wOOAu4qaoeSnIDcKiqbgd+N8nbgAXgKeCaDaxZkrSKrubQq+pO4M4VbdcvW/4w8OHeliZJWg/vFJWkljDQJaklDHRJagkDXZJawkCXpJYw0CWpJQx0SWoJA12SWsJAl6SWMNAlqSUMdElqCQNdklqiVy+4kFrh3HPP5fjx48+tn3POORw7dqyPFUndc4QuNZ4N84GBpXHOwMAAx48f59xzz+1zZVJ3DHSpcfz4cZJw4YUXPu/v8hG7tJkZ6NIyr3jFK065Lm1mBrq0zIkTJwBI8rx1aSsw0KUVTpw4wY033miYa8vxKhdphe985ztcd911/S5DWreuRuhJrkzytSRHkvz+KttfnuSTzfa7k+zseaWSpFNaM9CTnAV8FHgrcBmwN8llK7qNA39bVZcAfwL8Ya8LlSSdWjcj9CuAI1X1jap6BvhLYM+KPnuAW5rlTwO/lmfPKklbzM6dO0nCzp07+12KtC7dzKFfADy6bP0x4JdP1qeqFpIcBV4DfH95pyT7gH0Ag4ODzM7Ovriq9ZK1/5H9G7bv4ZuHn1v+OX5uqY2lttff8voNOeafvvZPN2S/emk6oydFq+ogcBBgZGSkRkdHz+Th1QIP8MCG7Xv5/1ReeumlfP3rX39uvao27LhSr3Qz5fI4cNGy9QubtlX7JBkAtgNP9qJAqR+Wh7m0VXQT6F8BLk3yuiRnA+8Gbl/R53bg/c3yO4EvlEMabTEn+8n6U9ZWsWagV9UCcB3wOWAe+FRVPZTkhiRva7pNAa9JcgT4V8ALLm2UtoKqoqq44447nluWtoqu5tCr6k7gzhVt1y9b/jHwrt6WJklaD2/9l6SWMNAlqSUMdElqCQNdkloi/TqLn+R7wCN9Obi0ttcB3+x3EdIqXltV56+2oW+BLm1mSY5X1Tn9rkNaD6dcJKklDHRJagkDXVrdX/W7AGm9nEOXpJZwhC5JLWGgS1JLGOhqrSSzSe7Y4GN8biP3L62HgS6dnn+y3i8k2bYRhUgGurasJP8tyY+bz2ebtr9O8kzzXtvXLev7L5L8MMmJJE8k2dm0P53kniTHk/wkyfuTPN7s465l3/9YkmPN9x9Osi3J3zTbTiT55sn6Ne2V5FCSE8BvnbF/SXpJMdC1JSV5L/BPWXr14WuBtzZt/xjYAVzW/H3WzcBEVb0S+Drw2WXbnmnuCp0GbgKuYukl529KckmSfwa8HTi/+f5PgQNV9SaAqnplVb3uZP2WHeeupu/He/nvQnrWGX1JtNRDbwfurqrvAST5MnBt0/Zk0/ZA8/ciYFtV3dh8998A/33Zvm5u/v4fYLSq7mu+dwz4JeAdwPnAU82LpM8Cvr9KTe9fo9+/ftH/tFIXDHQJjjd/fwosrNh2NhCW/kPxK2vs55T9qurvTqtKaQ1OuWir+gxwRZLXJDkfuIKl6ZIrkvz9JDuA1wNU1aPA3yX5QPPdjwBfXcexbgVGklwGkOQfJHk2tCvJK7voJ204A11bUlX9F+CvgceBR4H/UVV/AdwFfIelwH5i2VeuAf59c1LyZ1masun2WHcAHwMON99/ABhqNn8ZeDrJN9foJ204b/2XpJZwhC5JLWGgS1JLGOiS1BIGuiS1hIEuSS1hoEtSSxjoktQS/w+iabN8l0e/dQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot \n",
    "data.boxplot('price')\n",
    "data.boxplot('odometer')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#outlier detection and normalization remove rows with > 99% / z score \n",
    "numeric = data._get_numeric_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>odometer</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7240372487</td>\n",
       "      <td>35990</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32742.0</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7240309422</td>\n",
       "      <td>7500</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>93600.0</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>-85.468200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7240224296</td>\n",
       "      <td>4900</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>87046.0</td>\n",
       "      <td>32.616807</td>\n",
       "      <td>-85.464149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7240103965</td>\n",
       "      <td>2000</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>190000.0</td>\n",
       "      <td>32.861600</td>\n",
       "      <td>-85.216100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7239983776</td>\n",
       "      <td>19500</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>116000.0</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>-85.468200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458208</th>\n",
       "      <td>458208</td>\n",
       "      <td>7240959076</td>\n",
       "      <td>14995</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>83790.0</td>\n",
       "      <td>44.408867</td>\n",
       "      <td>-89.881210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458209</th>\n",
       "      <td>458209</td>\n",
       "      <td>7240954682</td>\n",
       "      <td>17895</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31888.0</td>\n",
       "      <td>44.408867</td>\n",
       "      <td>-89.882240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458210</th>\n",
       "      <td>458210</td>\n",
       "      <td>7240645700</td>\n",
       "      <td>17900</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>44.784900</td>\n",
       "      <td>-106.964800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458211</th>\n",
       "      <td>458211</td>\n",
       "      <td>7240600465</td>\n",
       "      <td>1300</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>164000.0</td>\n",
       "      <td>44.773500</td>\n",
       "      <td>-106.939600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458212</th>\n",
       "      <td>458212</td>\n",
       "      <td>7240562359</td>\n",
       "      <td>22900</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164000.0</td>\n",
       "      <td>42.117656</td>\n",
       "      <td>-88.436954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458213 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0          id  price    year  cylinders  odometer        lat  \\\n",
       "0                0  7240372487  35990  2010.0        8.0   32742.0  32.590000   \n",
       "1                1  7240309422   7500  2014.0        4.0   93600.0  32.547500   \n",
       "2                2  7240224296   4900  2006.0        6.0   87046.0  32.616807   \n",
       "3                3  7240103965   2000  1974.0        4.0  190000.0  32.861600   \n",
       "4                4  7239983776  19500  2005.0        8.0  116000.0  32.547500   \n",
       "...            ...         ...    ...     ...        ...       ...        ...   \n",
       "458208      458208  7240959076  14995  2014.0        6.0   83790.0  44.408867   \n",
       "458209      458209  7240954682  17895  2018.0        4.0   31888.0  44.408867   \n",
       "458210      458210  7240645700  17900  2014.0        6.0   53000.0  44.784900   \n",
       "458211      458211  7240600465   1300  2008.0        8.0  164000.0  44.773500   \n",
       "458212      458212  7240562359  22900  2008.0        NaN  164000.0  42.117656   \n",
       "\n",
       "              long  \n",
       "0       -85.480000  \n",
       "1       -85.468200  \n",
       "2       -85.464149  \n",
       "3       -85.216100  \n",
       "4       -85.468200  \n",
       "...            ...  \n",
       "458208  -89.881210  \n",
       "458209  -89.882240  \n",
       "458210 -106.964800  \n",
       "458211 -106.939600  \n",
       "458212  -88.436954  \n",
       "\n",
       "[458213 rows x 8 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 28,
     "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": [
    "# with no null values \n",
    "from scipy import stats\n",
    "import numpy as np \n",
    "\n",
    "data_outliers = data[(data.price < data.price.quantile(.995)) & (data.price > data.price.quantile(.005))]\n",
    "\n",
    "data_outliers.boxplot('price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>url</th>\n",
       "      <th>region</th>\n",
       "      <th>region_url</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>...</th>\n",
       "      <th>drive</th>\n",
       "      <th>size</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>image_url</th>\n",
       "      <th>description</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>posting_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7240372487</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>35990</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>corvette grand sport</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>rwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00N0N_ipkbHVZYf4...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-12-02T08:11:30-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7240309422</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-201...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>7500</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>sonata</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>fwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00s0s_gBHYmJ5o7y...</td>\n",
       "      <td>I'll move to another city and try to sell my c...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>-85.468200</td>\n",
       "      <td>2020-12-02T02:11:50-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7240224296</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>4900</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>x3 3.0i</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SUV</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00B0B_5zgEGWPOrt...</td>\n",
       "      <td>Clean 2006 BMW X3 3.0I.  Beautiful and rare Bl...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.616807</td>\n",
       "      <td>-85.464149</td>\n",
       "      <td>2020-12-01T19:50:41-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7240103965</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/lanett-tru...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>2000</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>c-10</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>rwd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>pickup</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00M0M_6o7KcDpArw...</td>\n",
       "      <td>1974 chev. truck (LONG BED) NEW starter front ...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.861600</td>\n",
       "      <td>-85.216100</td>\n",
       "      <td>2020-12-01T15:54:45-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7239983776</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>19500</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f350 lariat</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>pickup</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00p0p_b95l1EgUfl...</td>\n",
       "      <td>2005 Ford F350 Lariat (Bullet Proofed). This t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>-85.468200</td>\n",
       "      <td>2020-12-01T12:53:56-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458208</th>\n",
       "      <td>458208</td>\n",
       "      <td>7240959076</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wisconsin-...</td>\n",
       "      <td>wausau</td>\n",
       "      <td>https://wausau.craigslist.org</td>\n",
       "      <td>14995</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>srx</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>SUV</td>\n",
       "      <td>silver</td>\n",
       "      <td>https://images.craigslist.org/00808_1bVcaYZtCy...</td>\n",
       "      <td>2014 Cadillac SRX All Wheel Drive Luxury Colle...</td>\n",
       "      <td>wi</td>\n",
       "      <td>44.408867</td>\n",
       "      <td>-89.881210</td>\n",
       "      <td>2020-12-03T08:56:29-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458209</th>\n",
       "      <td>458209</td>\n",
       "      <td>7240954682</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wisconsin-...</td>\n",
       "      <td>wausau</td>\n",
       "      <td>https://wausau.craigslist.org</td>\n",
       "      <td>17895</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>tucson</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>SUV</td>\n",
       "      <td>white</td>\n",
       "      <td>https://images.craigslist.org/00l0l_gikkP11dEF...</td>\n",
       "      <td>2018 Hyundai Tucson SE 2.0 I4 AWD.  Carfax ver...</td>\n",
       "      <td>wi</td>\n",
       "      <td>44.408867</td>\n",
       "      <td>-89.882240</td>\n",
       "      <td>2020-12-03T08:48:08-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458210</th>\n",
       "      <td>458210</td>\n",
       "      <td>7240645700</td>\n",
       "      <td>https://wyoming.craigslist.org/cto/d/sheridan-...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>17900</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>cts coupe awd</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>coupe</td>\n",
       "      <td>black</td>\n",
       "      <td>https://images.craigslist.org/00t0t_lNnW4cboOG...</td>\n",
       "      <td>53,000 miles. Cosmetically, in near mint condi...</td>\n",
       "      <td>wy</td>\n",
       "      <td>44.784900</td>\n",
       "      <td>-106.964800</td>\n",
       "      <td>2020-12-02T14:09:45-0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458211</th>\n",
       "      <td>458211</td>\n",
       "      <td>7240600465</td>\n",
       "      <td>https://wyoming.craigslist.org/cto/d/sheridan-...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>1300</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>jeep</td>\n",
       "      <td>grand cherokee</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>SUV</td>\n",
       "      <td>white</td>\n",
       "      <td>https://images.craigslist.org/00C0C_fl0NW1IeJw...</td>\n",
       "      <td>PRICE REDUCTION  Turns out the engine is toast...</td>\n",
       "      <td>wy</td>\n",
       "      <td>44.773500</td>\n",
       "      <td>-106.939600</td>\n",
       "      <td>2020-12-02T13:01:04-0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458212</th>\n",
       "      <td>458212</td>\n",
       "      <td>7240562359</td>\n",
       "      <td>https://wyoming.craigslist.org/ctd/d/gilberts-...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>22900</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>International 4300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00k0k_77qSSp6sQC...</td>\n",
       "      <td>*** SHIPPING AVAILABLE DIRECTLY TO YOU. CALL F...</td>\n",
       "      <td>wy</td>\n",
       "      <td>42.117656</td>\n",
       "      <td>-88.436954</td>\n",
       "      <td>2020-12-02T12:03:45-0700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458213 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0          id  \\\n",
       "0                0  7240372487   \n",
       "1                1  7240309422   \n",
       "2                2  7240224296   \n",
       "3                3  7240103965   \n",
       "4                4  7239983776   \n",
       "...            ...         ...   \n",
       "458208      458208  7240959076   \n",
       "458209      458209  7240954682   \n",
       "458210      458210  7240645700   \n",
       "458211      458211  7240600465   \n",
       "458212      458212  7240562359   \n",
       "\n",
       "                                                      url   region  \\\n",
       "0       https://auburn.craigslist.org/ctd/d/auburn-uni...   auburn   \n",
       "1       https://auburn.craigslist.org/cto/d/auburn-201...   auburn   \n",
       "2       https://auburn.craigslist.org/cto/d/auburn-200...   auburn   \n",
       "3       https://auburn.craigslist.org/cto/d/lanett-tru...   auburn   \n",
       "4       https://auburn.craigslist.org/cto/d/auburn-200...   auburn   \n",
       "...                                                   ...      ...   \n",
       "458208  https://wausau.craigslist.org/ctd/d/wisconsin-...   wausau   \n",
       "458209  https://wausau.craigslist.org/ctd/d/wisconsin-...   wausau   \n",
       "458210  https://wyoming.craigslist.org/cto/d/sheridan-...  wyoming   \n",
       "458211  https://wyoming.craigslist.org/cto/d/sheridan-...  wyoming   \n",
       "458212  https://wyoming.craigslist.org/ctd/d/gilberts-...  wyoming   \n",
       "\n",
       "                            region_url  price    year manufacturer  \\\n",
       "0        https://auburn.craigslist.org  35990  2010.0    chevrolet   \n",
       "1        https://auburn.craigslist.org   7500  2014.0      hyundai   \n",
       "2        https://auburn.craigslist.org   4900  2006.0          bmw   \n",
       "3        https://auburn.craigslist.org   2000  1974.0    chevrolet   \n",
       "4        https://auburn.craigslist.org  19500  2005.0         ford   \n",
       "...                                ...    ...     ...          ...   \n",
       "458208   https://wausau.craigslist.org  14995  2014.0     cadillac   \n",
       "458209   https://wausau.craigslist.org  17895  2018.0      hyundai   \n",
       "458210  https://wyoming.craigslist.org  17900  2014.0     cadillac   \n",
       "458211  https://wyoming.craigslist.org   1300  2008.0         jeep   \n",
       "458212  https://wyoming.craigslist.org  22900  2008.0          NaN   \n",
       "\n",
       "                       model  condition  ...  drive       size    type  \\\n",
       "0       corvette grand sport       good  ...    rwd        NaN   other   \n",
       "1                     sonata  excellent  ...    fwd        NaN   sedan   \n",
       "2                    x3 3.0i       good  ...    NaN        NaN     SUV   \n",
       "3                       c-10       good  ...    rwd  full-size  pickup   \n",
       "4                f350 lariat  excellent  ...    4wd  full-size  pickup   \n",
       "...                      ...        ...  ...    ...        ...     ...   \n",
       "458208                   srx  excellent  ...    4wd   mid-size     SUV   \n",
       "458209                tucson  excellent  ...    4wd   mid-size     SUV   \n",
       "458210         cts coupe awd  excellent  ...    4wd   mid-size   coupe   \n",
       "458211        grand cherokee       good  ...    4wd   mid-size     SUV   \n",
       "458212    International 4300        NaN  ...    NaN        NaN     NaN   \n",
       "\n",
       "       paint_color                                          image_url  \\\n",
       "0              NaN  https://images.craigslist.org/00N0N_ipkbHVZYf4...   \n",
       "1              NaN  https://images.craigslist.org/00s0s_gBHYmJ5o7y...   \n",
       "2             blue  https://images.craigslist.org/00B0B_5zgEGWPOrt...   \n",
       "3             blue  https://images.craigslist.org/00M0M_6o7KcDpArw...   \n",
       "4             blue  https://images.craigslist.org/00p0p_b95l1EgUfl...   \n",
       "...            ...                                                ...   \n",
       "458208      silver  https://images.craigslist.org/00808_1bVcaYZtCy...   \n",
       "458209       white  https://images.craigslist.org/00l0l_gikkP11dEF...   \n",
       "458210       black  https://images.craigslist.org/00t0t_lNnW4cboOG...   \n",
       "458211       white  https://images.craigslist.org/00C0C_fl0NW1IeJw...   \n",
       "458212         NaN  https://images.craigslist.org/00k0k_77qSSp6sQC...   \n",
       "\n",
       "                                              description state        lat  \\\n",
       "0       Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "1       I'll move to another city and try to sell my c...    al  32.547500   \n",
       "2       Clean 2006 BMW X3 3.0I.  Beautiful and rare Bl...    al  32.616807   \n",
       "3       1974 chev. truck (LONG BED) NEW starter front ...    al  32.861600   \n",
       "4       2005 Ford F350 Lariat (Bullet Proofed). This t...    al  32.547500   \n",
       "...                                                   ...   ...        ...   \n",
       "458208  2014 Cadillac SRX All Wheel Drive Luxury Colle...    wi  44.408867   \n",
       "458209  2018 Hyundai Tucson SE 2.0 I4 AWD.  Carfax ver...    wi  44.408867   \n",
       "458210  53,000 miles. Cosmetically, in near mint condi...    wy  44.784900   \n",
       "458211  PRICE REDUCTION  Turns out the engine is toast...    wy  44.773500   \n",
       "458212  *** SHIPPING AVAILABLE DIRECTLY TO YOU. CALL F...    wy  42.117656   \n",
       "\n",
       "              long              posting_date  \n",
       "0       -85.480000  2020-12-02T08:11:30-0600  \n",
       "1       -85.468200  2020-12-02T02:11:50-0600  \n",
       "2       -85.464149  2020-12-01T19:50:41-0600  \n",
       "3       -85.216100  2020-12-01T15:54:45-0600  \n",
       "4       -85.468200  2020-12-01T12:53:56-0600  \n",
       "...            ...                       ...  \n",
       "458208  -89.881210  2020-12-03T08:56:29-0600  \n",
       "458209  -89.882240  2020-12-03T08:48:08-0600  \n",
       "458210 -106.964800  2020-12-02T14:09:45-0700  \n",
       "458211 -106.939600  2020-12-02T13:01:04-0700  \n",
       "458212  -88.436954  2020-12-02T12:03:45-0700  \n",
       "\n",
       "[458213 rows x 26 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove duplcates, subset, keep, etc.\n",
    "data.drop_duplicates()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "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": [
    "#histogram\n",
    "data_outliers.price.hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.55555556],\n",
       "       [0.11111111],\n",
       "       [0.33333333],\n",
       "       ...,\n",
       "       [0.33333333],\n",
       "       [0.55555556],\n",
       "       [       nan]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#types of normalization \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(data.cylinders.values.reshape(-1,1))\n",
    "scaler.transform(data.cylinders.values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding Relationships\n",
    "A great aspect of the Pandas module is the corr() method.\n",
    "\n",
    "The corr() method calculates the relationship between each column in your data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>Duration</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Maxpulse</th>\n",
       "      <th>Calories</th>\n",
       "      <th>calpul</th>\n",
       "      <th>dupul</th>\n",
       "      <th>Durationprice2x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Duration</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155408</td>\n",
       "      <td>0.009403</td>\n",
       "      <td>0.922717</td>\n",
       "      <td>0.946365</td>\n",
       "      <td>0.985333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulse</th>\n",
       "      <td>-0.155408</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.786535</td>\n",
       "      <td>0.025121</td>\n",
       "      <td>-0.120408</td>\n",
       "      <td>-0.275275</td>\n",
       "      <td>-0.155408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maxpulse</th>\n",
       "      <td>0.009403</td>\n",
       "      <td>0.786535</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203813</td>\n",
       "      <td>0.078419</td>\n",
       "      <td>-0.099643</td>\n",
       "      <td>0.009403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calories</th>\n",
       "      <td>0.922717</td>\n",
       "      <td>0.025121</td>\n",
       "      <td>0.203813</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980602</td>\n",
       "      <td>0.872975</td>\n",
       "      <td>0.922717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calpul</th>\n",
       "      <td>0.946365</td>\n",
       "      <td>-0.120408</td>\n",
       "      <td>0.078419</td>\n",
       "      <td>0.980602</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.928288</td>\n",
       "      <td>0.946365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dupul</th>\n",
       "      <td>0.985333</td>\n",
       "      <td>-0.275275</td>\n",
       "      <td>-0.099643</td>\n",
       "      <td>0.872975</td>\n",
       "      <td>0.928288</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.985333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Durationprice2x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155408</td>\n",
       "      <td>0.009403</td>\n",
       "      <td>0.922717</td>\n",
       "      <td>0.946365</td>\n",
       "      <td>0.985333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Duration     Pulse  Maxpulse  Calories    calpul     dupul  \\\n",
       "Duration         1.000000 -0.155408  0.009403  0.922717  0.946365  0.985333   \n",
       "Pulse           -0.155408  1.000000  0.786535  0.025121 -0.120408 -0.275275   \n",
       "Maxpulse         0.009403  0.786535  1.000000  0.203813  0.078419 -0.099643   \n",
       "Calories         0.922717  0.025121  0.203813  1.000000  0.980602  0.872975   \n",
       "calpul           0.946365 -0.120408  0.078419  0.980602  1.000000  0.928288   \n",
       "dupul            0.985333 -0.275275 -0.099643  0.872975  0.928288  1.000000   \n",
       "Durationprice2x  1.000000 -0.155408  0.009403  0.922717  0.946365  0.985333   \n",
       "\n",
       "                 Durationprice2x  \n",
       "Duration                1.000000  \n",
       "Pulse                  -0.155408  \n",
       "Maxpulse                0.009403  \n",
       "Calories                0.922717  \n",
       "calpul                  0.946365  \n",
       "dupul                   0.985333  \n",
       "Durationprice2x         1.000000  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Result Explained\n",
    "The Result of the corr() method is a table with a lot of numbers that represents how well the relationship is between two columns.\n",
    "\n",
    "The number varies from -1 to 1.\n",
    "\n",
    "1 means that there is a 1 to 1 relationship (a perfect correlation), and for this data set, each time a value went up in the first column, the other one went up as well.\n",
    "\n",
    "0.9 is also a good relationship, and if you increase one value, the other will probably increase as well.\n",
    "\n",
    "-0.9 would be just as good relationship as 0.9, but if you increase one value, the other will probably go down.\n",
    "\n",
    "0.2 means NOT a good relationship, meaning that if one value goes up does not mean that the other will."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perfect Correlation:\n",
    "We can see that \"Duration\" and \"Duration\" got the number 1.000000, which makes sense, each column always has a perfect relationship with itself.\n",
    "\n",
    "Good Correlation:\n",
    "\"Duration\" and \"Calories\" got a 0.922721 correlation, which is a very good correlation, and we can predict that the longer you work out, the more calories you burn, and the other way around: if you burned a lot of calories, you probably had a long work out.\n",
    "\n",
    "Bad Correlation:\n",
    "\"Duration\" and \"Maxpulse\" got a 0.009403 correlation, which is a very bad correlation, meaning that we can not predict the max pulse by just looking at the duration of the work out, and vice versa.\n",
    "\n",
    "**** What is a good correlation?*** It depends on the use, but I think it is safe to say you have to have at least 0.6 (or -0.6) to call it a good correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting\n",
    "Pandas uses the ***plot()*** method to create diagrams.\n",
    "\n",
    "Pythons uses Pyplot, a submodule of the Matplotlib library to visualize the diagram on the screen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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": [
    "#ploting \n",
    "df.plot() #creates te plot \n",
    "\n",
    "plt.show() # showing th eplot on to the screan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter Plot\n",
    "Specify that you want a scatter plot with the kind argument:\n",
    "\n",
    "kind = 'scatter'\n",
    "\n",
    "A scatter plot needs an x- and a y-axis.\n",
    "\n",
    "In the example below we will use \"Duration\" for the x-axis and \"Calories\" for the y-axis.\n",
    "\n",
    "Include the x and y arguments like this:\n",
    "\n",
    "x = 'Duration', y = 'Calories'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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": [
    "df.plot(kind = 'scatter', x = 'Maxpulse', y = 'Pulse')\n",
    "\n",
    "plt.show() #displaying plots\n",
    "\n",
    "AUTOMATION OF EDA \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Duration</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Maxpulse</th>\n",
       "      <th>Calories</th>\n",
       "      <th>calpul</th>\n",
       "      <th>dupul</th>\n",
       "      <th>Durationprice2x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Duration</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155408</td>\n",
       "      <td>0.009403</td>\n",
       "      <td>0.922717</td>\n",
       "      <td>0.946365</td>\n",
       "      <td>0.985333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulse</th>\n",
       "      <td>-0.155408</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.786535</td>\n",
       "      <td>0.025121</td>\n",
       "      <td>-0.120408</td>\n",
       "      <td>-0.275275</td>\n",
       "      <td>-0.155408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maxpulse</th>\n",
       "      <td>0.009403</td>\n",
       "      <td>0.786535</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203813</td>\n",
       "      <td>0.078419</td>\n",
       "      <td>-0.099643</td>\n",
       "      <td>0.009403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calories</th>\n",
       "      <td>0.922717</td>\n",
       "      <td>0.025121</td>\n",
       "      <td>0.203813</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980602</td>\n",
       "      <td>0.872975</td>\n",
       "      <td>0.922717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calpul</th>\n",
       "      <td>0.946365</td>\n",
       "      <td>-0.120408</td>\n",
       "      <td>0.078419</td>\n",
       "      <td>0.980602</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.928288</td>\n",
       "      <td>0.946365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dupul</th>\n",
       "      <td>0.985333</td>\n",
       "      <td>-0.275275</td>\n",
       "      <td>-0.099643</td>\n",
       "      <td>0.872975</td>\n",
       "      <td>0.928288</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.985333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Durationprice2x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155408</td>\n",
       "      <td>0.009403</td>\n",
       "      <td>0.922717</td>\n",
       "      <td>0.946365</td>\n",
       "      <td>0.985333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Duration     Pulse  Maxpulse  Calories    calpul     dupul  \\\n",
       "Duration         1.000000 -0.155408  0.009403  0.922717  0.946365  0.985333   \n",
       "Pulse           -0.155408  1.000000  0.786535  0.025121 -0.120408 -0.275275   \n",
       "Maxpulse         0.009403  0.786535  1.000000  0.203813  0.078419 -0.099643   \n",
       "Calories         0.922717  0.025121  0.203813  1.000000  0.980602  0.872975   \n",
       "calpul           0.946365 -0.120408  0.078419  0.980602  1.000000  0.928288   \n",
       "dupul            0.985333 -0.275275 -0.099643  0.872975  0.928288  1.000000   \n",
       "Durationprice2x  1.000000 -0.155408  0.009403  0.922717  0.946365  0.985333   \n",
       "\n",
       "                 Durationprice2x  \n",
       "Duration                1.000000  \n",
       "Pulse                  -0.155408  \n",
       "Maxpulse                0.009403  \n",
       "Calories                0.922717  \n",
       "calpul                  0.946365  \n",
       "dupul                   0.985333  \n",
       "Durationprice2x         1.000000  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": [
    "df.plot()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Duration</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Maxpulse</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Duration</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.155408</td>\n",
       "      <td>0.009403</td>\n",
       "      <td>0.922717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pulse</th>\n",
       "      <td>-0.155408</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.786535</td>\n",
       "      <td>0.025121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maxpulse</th>\n",
       "      <td>0.009403</td>\n",
       "      <td>0.786535</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calories</th>\n",
       "      <td>0.922717</td>\n",
       "      <td>0.025121</td>\n",
       "      <td>0.203813</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Duration     Pulse  Maxpulse  Calories\n",
       "Duration  1.000000 -0.155408  0.009403  0.922717\n",
       "Pulse    -0.155408  1.000000  0.786535  0.025121\n",
       "Maxpulse  0.009403  0.786535  1.000000  0.203813\n",
       "Calories  0.922717  0.025121  0.203813  1.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting corelation \n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is Matplotlib?\n",
    "Matplotlib is a low level graph plotting library in python that serves as a visualization utility.\n",
    "\n",
    "Matplotlib was created by John D. Hunter.\n",
    "\n",
    "Matplotlib is open source and we can use it freely.\n",
    "\n",
    "Matplotlib is mostly written in python, a few segments are written in C, Objective-C and Javascript for Platform compatibility.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#view columns & rename columns \n",
    "df.columns\n",
    "df3 = df.rename(index=str,columns={\"Duration\":\"du\"}) #check how we rename multiple columns in a dataset \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['du', 'Pulse', 'Maxpulse', 'Calories'], dtype='object')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>Duration</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>max</th>\n",
       "      <th>Calories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>110</td>\n",
       "      <td>130</td>\n",
       "      <td>409.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>117</td>\n",
       "      <td>145</td>\n",
       "      <td>479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>103</td>\n",
       "      <td>135</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>109</td>\n",
       "      <td>175</td>\n",
       "      <td>282.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>117</td>\n",
       "      <td>148</td>\n",
       "      <td>406.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>60</td>\n",
       "      <td>105</td>\n",
       "      <td>140</td>\n",
       "      <td>290.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>60</td>\n",
       "      <td>110</td>\n",
       "      <td>145</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>60</td>\n",
       "      <td>115</td>\n",
       "      <td>145</td>\n",
       "      <td>310.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>75</td>\n",
       "      <td>120</td>\n",
       "      <td>150</td>\n",
       "      <td>320.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>75</td>\n",
       "      <td>125</td>\n",
       "      <td>150</td>\n",
       "      <td>330.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Duration  Pulse  max  Calories\n",
       "0          60    110  130     409.1\n",
       "1          60    117  145     479.0\n",
       "2          60    103  135     340.0\n",
       "3          45    109  175     282.4\n",
       "4          45    117  148     406.0\n",
       "..        ...    ...  ...       ...\n",
       "164        60    105  140     290.8\n",
       "165        60    110  145     300.0\n",
       "166        60    115  145     310.2\n",
       "167        75    120  150     320.4\n",
       "168        75    125  150     330.4\n",
       "\n",
       "[169 rows x 4 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      409.1\n",
       "1      479.0\n",
       "2      340.0\n",
       "3      282.4\n",
       "4      406.0\n",
       "       ...  \n",
       "164    290.8\n",
       "165    300.0\n",
       "166    310.2\n",
       "167    320.4\n",
       "168    330.4\n",
       "Name: Calories, Length: 169, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view all rows for one column\n",
    "df['Calories']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>url</th>\n",
       "      <th>region</th>\n",
       "      <th>region_url</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>...</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>image_url</th>\n",
       "      <th>description</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>age</th>\n",
       "      <th>price2x</th>\n",
       "      <th>price3x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7240372487</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>35990</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>corvette grand sport</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00N0N_ipkbHVZYf4...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-12-02T08:11:30-0600</td>\n",
       "      <td>9.0</td>\n",
       "      <td>71980</td>\n",
       "      <td>107970</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7240309422</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-201...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>7500</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>sonata</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00s0s_gBHYmJ5o7y...</td>\n",
       "      <td>I'll move to another city and try to sell my c...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>-85.468200</td>\n",
       "      <td>2020-12-02T02:11:50-0600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15000</td>\n",
       "      <td>22500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7240224296</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>4900</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>x3 3.0i</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00B0B_5zgEGWPOrt...</td>\n",
       "      <td>Clean 2006 BMW X3 3.0I.  Beautiful and rare Bl...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.616807</td>\n",
       "      <td>-85.464149</td>\n",
       "      <td>2020-12-01T19:50:41-0600</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9800</td>\n",
       "      <td>14700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7240103965</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/lanett-tru...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>2000</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>c-10</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00M0M_6o7KcDpArw...</td>\n",
       "      <td>1974 chev. truck (LONG BED) NEW starter front ...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.861600</td>\n",
       "      <td>-85.216100</td>\n",
       "      <td>2020-12-01T15:54:45-0600</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4000</td>\n",
       "      <td>6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7239983776</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>19500</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f350 lariat</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00p0p_b95l1EgUfl...</td>\n",
       "      <td>2005 Ford F350 Lariat (Bullet Proofed). This t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>-85.468200</td>\n",
       "      <td>2020-12-01T12:53:56-0600</td>\n",
       "      <td>14.0</td>\n",
       "      <td>39000</td>\n",
       "      <td>58500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>7239776805</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>29590</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>tacoma double cab sr5</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>red</td>\n",
       "      <td>https://images.craigslist.org/00k0k_9nUNSlCBLM...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-12-01T07:27:25-0600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59180</td>\n",
       "      <td>88770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7239425036</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>39990</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>mustang shelby gt500 coupe</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/01515_fL9MWURsaZ...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-11-30T13:34:00-0600</td>\n",
       "      <td>7.0</td>\n",
       "      <td>79980</td>\n",
       "      <td>119970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7238667661</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>41990</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>camaro zl1 coupe 2d</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>red</td>\n",
       "      <td>https://images.craigslist.org/00p0p_eXZbgunMDy...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-11-29T07:39:33-0600</td>\n",
       "      <td>7.0</td>\n",
       "      <td>83980</td>\n",
       "      <td>125970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>7238127696</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>31990</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>jeep</td>\n",
       "      <td>wrangler unlimited sport s</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00g0g_lCDDDlgwtZ...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-11-28T07:21:09-0600</td>\n",
       "      <td>2.0</td>\n",
       "      <td>63980</td>\n",
       "      <td>95970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>7237779886</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/santa-cruz...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>490</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>transit connect wagon xlt</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00D0D_foGOQzDTOx...</td>\n",
       "      <td>2019 Ford Transit Connect Wagon XLT FOR ONLY $...</td>\n",
       "      <td>al</td>\n",
       "      <td>36.967357</td>\n",
       "      <td>-122.024254</td>\n",
       "      <td>2020-11-27T12:42:28-0600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>980</td>\n",
       "      <td>1470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>7237759157</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/opelika-20...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>27500</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f-250</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>silver</td>\n",
       "      <td>https://images.craigslist.org/00707_54YNEBNci6...</td>\n",
       "      <td>189k miles Leather interior  Tires are basical...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.639000</td>\n",
       "      <td>-85.380300</td>\n",
       "      <td>2020-11-27T12:12:30-0600</td>\n",
       "      <td>7.0</td>\n",
       "      <td>55000</td>\n",
       "      <td>82500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>7237595428</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>36990</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>m3 coupe 2d</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>black</td>\n",
       "      <td>https://images.craigslist.org/00606_kQUEArnEuL...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-11-27T07:21:42-0600</td>\n",
       "      <td>6.0</td>\n",
       "      <td>73980</td>\n",
       "      <td>110970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>7237366792</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>24990</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>ram</td>\n",
       "      <td>1500 crew cab slt pickup 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00O0O_7r7iltwH7G...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-11-26T12:50:29-0600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>49980</td>\n",
       "      <td>74970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>7237318515</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-201...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>5995</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>tucson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00O0O_hmpZECNGvn...</td>\n",
       "      <td>2010 Tucson AWD with 126k miles.  Vehicle has ...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.623299</td>\n",
       "      <td>-85.481787</td>\n",
       "      <td>2020-11-26T10:57:06-0600</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11990</td>\n",
       "      <td>17985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>7237009212</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/roanoke-20...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>4900</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>expedition</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00Z0Z_cU2ijvO9NH...</td>\n",
       "      <td>2003 Ford Expedition Eddie Baurer. 5.4L 2x4, S...</td>\n",
       "      <td>al</td>\n",
       "      <td>33.151200</td>\n",
       "      <td>-85.372200</td>\n",
       "      <td>2020-11-25T14:42:30-0600</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9800</td>\n",
       "      <td>14700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0          id                                                url  \\\n",
       "0            0  7240372487  https://auburn.craigslist.org/ctd/d/auburn-uni...   \n",
       "1            1  7240309422  https://auburn.craigslist.org/cto/d/auburn-201...   \n",
       "2            2  7240224296  https://auburn.craigslist.org/cto/d/auburn-200...   \n",
       "3            3  7240103965  https://auburn.craigslist.org/cto/d/lanett-tru...   \n",
       "4            4  7239983776  https://auburn.craigslist.org/cto/d/auburn-200...   \n",
       "5            5  7239776805  https://auburn.craigslist.org/ctd/d/auburn-uni...   \n",
       "6            6  7239425036  https://auburn.craigslist.org/ctd/d/auburn-uni...   \n",
       "7            7  7238667661  https://auburn.craigslist.org/ctd/d/auburn-uni...   \n",
       "8            8  7238127696  https://auburn.craigslist.org/ctd/d/auburn-uni...   \n",
       "9            9  7237779886  https://auburn.craigslist.org/ctd/d/santa-cruz...   \n",
       "10          10  7237759157  https://auburn.craigslist.org/cto/d/opelika-20...   \n",
       "11          11  7237595428  https://auburn.craigslist.org/ctd/d/auburn-uni...   \n",
       "12          12  7237366792  https://auburn.craigslist.org/ctd/d/auburn-uni...   \n",
       "13          13  7237318515  https://auburn.craigslist.org/cto/d/auburn-201...   \n",
       "14          14  7237009212  https://auburn.craigslist.org/cto/d/roanoke-20...   \n",
       "\n",
       "    region                     region_url  price    year manufacturer  \\\n",
       "0   auburn  https://auburn.craigslist.org  35990  2010.0    chevrolet   \n",
       "1   auburn  https://auburn.craigslist.org   7500  2014.0      hyundai   \n",
       "2   auburn  https://auburn.craigslist.org   4900  2006.0          bmw   \n",
       "3   auburn  https://auburn.craigslist.org   2000  1974.0    chevrolet   \n",
       "4   auburn  https://auburn.craigslist.org  19500  2005.0         ford   \n",
       "5   auburn  https://auburn.craigslist.org  29590  2016.0       toyota   \n",
       "6   auburn  https://auburn.craigslist.org  39990  2012.0         ford   \n",
       "7   auburn  https://auburn.craigslist.org  41990  2012.0    chevrolet   \n",
       "8   auburn  https://auburn.craigslist.org  31990  2017.0         jeep   \n",
       "9   auburn  https://auburn.craigslist.org    490  2019.0         ford   \n",
       "10  auburn  https://auburn.craigslist.org  27500  2012.0         ford   \n",
       "11  auburn  https://auburn.craigslist.org  36990  2013.0          bmw   \n",
       "12  auburn  https://auburn.craigslist.org  24990  2016.0          ram   \n",
       "13  auburn  https://auburn.craigslist.org   5995  2010.0      hyundai   \n",
       "14  auburn  https://auburn.craigslist.org   4900  2003.0         ford   \n",
       "\n",
       "                          model  condition  ... paint_color  \\\n",
       "0          corvette grand sport       good  ...         NaN   \n",
       "1                        sonata  excellent  ...         NaN   \n",
       "2                       x3 3.0i       good  ...        blue   \n",
       "3                          c-10       good  ...        blue   \n",
       "4                   f350 lariat  excellent  ...        blue   \n",
       "5         tacoma double cab sr5       good  ...         red   \n",
       "6    mustang shelby gt500 coupe       good  ...        blue   \n",
       "7           camaro zl1 coupe 2d       good  ...         red   \n",
       "8    wrangler unlimited sport s       good  ...         NaN   \n",
       "9     transit connect wagon xlt  excellent  ...         NaN   \n",
       "10                        f-250  excellent  ...      silver   \n",
       "11                  m3 coupe 2d       good  ...       black   \n",
       "12  1500 crew cab slt pickup 4d       good  ...         NaN   \n",
       "13                       tucson        NaN  ...         NaN   \n",
       "14                   expedition       good  ...        blue   \n",
       "\n",
       "                                            image_url  \\\n",
       "0   https://images.craigslist.org/00N0N_ipkbHVZYf4...   \n",
       "1   https://images.craigslist.org/00s0s_gBHYmJ5o7y...   \n",
       "2   https://images.craigslist.org/00B0B_5zgEGWPOrt...   \n",
       "3   https://images.craigslist.org/00M0M_6o7KcDpArw...   \n",
       "4   https://images.craigslist.org/00p0p_b95l1EgUfl...   \n",
       "5   https://images.craigslist.org/00k0k_9nUNSlCBLM...   \n",
       "6   https://images.craigslist.org/01515_fL9MWURsaZ...   \n",
       "7   https://images.craigslist.org/00p0p_eXZbgunMDy...   \n",
       "8   https://images.craigslist.org/00g0g_lCDDDlgwtZ...   \n",
       "9   https://images.craigslist.org/00D0D_foGOQzDTOx...   \n",
       "10  https://images.craigslist.org/00707_54YNEBNci6...   \n",
       "11  https://images.craigslist.org/00606_kQUEArnEuL...   \n",
       "12  https://images.craigslist.org/00O0O_7r7iltwH7G...   \n",
       "13  https://images.craigslist.org/00O0O_hmpZECNGvn...   \n",
       "14  https://images.craigslist.org/00Z0Z_cU2ijvO9NH...   \n",
       "\n",
       "                                          description state        lat  \\\n",
       "0   Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "1   I'll move to another city and try to sell my c...    al  32.547500   \n",
       "2   Clean 2006 BMW X3 3.0I.  Beautiful and rare Bl...    al  32.616807   \n",
       "3   1974 chev. truck (LONG BED) NEW starter front ...    al  32.861600   \n",
       "4   2005 Ford F350 Lariat (Bullet Proofed). This t...    al  32.547500   \n",
       "5   Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "6   Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "7   Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "8   Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "9   2019 Ford Transit Connect Wagon XLT FOR ONLY $...    al  36.967357   \n",
       "10  189k miles Leather interior  Tires are basical...    al  32.639000   \n",
       "11  Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "12  Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "13  2010 Tucson AWD with 126k miles.  Vehicle has ...    al  32.623299   \n",
       "14  2003 Ford Expedition Eddie Baurer. 5.4L 2x4, S...    al  33.151200   \n",
       "\n",
       "          long              posting_date   age price2x price3x  \n",
       "0   -85.480000  2020-12-02T08:11:30-0600   9.0   71980  107970  \n",
       "1   -85.468200  2020-12-02T02:11:50-0600   5.0   15000   22500  \n",
       "2   -85.464149  2020-12-01T19:50:41-0600  13.0    9800   14700  \n",
       "3   -85.216100  2020-12-01T15:54:45-0600  45.0    4000    6000  \n",
       "4   -85.468200  2020-12-01T12:53:56-0600  14.0   39000   58500  \n",
       "5   -85.480000  2020-12-01T07:27:25-0600   3.0   59180   88770  \n",
       "6   -85.480000  2020-11-30T13:34:00-0600   7.0   79980  119970  \n",
       "7   -85.480000  2020-11-29T07:39:33-0600   7.0   83980  125970  \n",
       "8   -85.480000  2020-11-28T07:21:09-0600   2.0   63980   95970  \n",
       "9  -122.024254  2020-11-27T12:42:28-0600   0.0     980    1470  \n",
       "10  -85.380300  2020-11-27T12:12:30-0600   7.0   55000   82500  \n",
       "11  -85.480000  2020-11-27T07:21:42-0600   6.0   73980  110970  \n",
       "12  -85.480000  2020-11-26T12:50:29-0600   3.0   49980   74970  \n",
       "13  -85.481787  2020-11-26T10:57:06-0600   9.0   11990   17985  \n",
       "14  -85.372200  2020-11-25T14:42:30-0600  16.0    9800   14700  \n",
       "\n",
       "[15 rows x 29 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#view all columns for select group of rows\n",
    "data[0:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7240372487</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7240309422</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-201...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7240224296</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7240103965</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/lanett-tru...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7239983776</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458208</th>\n",
       "      <td>458208</td>\n",
       "      <td>7240959076</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wisconsin-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458209</th>\n",
       "      <td>458209</td>\n",
       "      <td>7240954682</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wisconsin-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458210</th>\n",
       "      <td>458210</td>\n",
       "      <td>7240645700</td>\n",
       "      <td>https://wyoming.craigslist.org/cto/d/sheridan-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458211</th>\n",
       "      <td>458211</td>\n",
       "      <td>7240600465</td>\n",
       "      <td>https://wyoming.craigslist.org/cto/d/sheridan-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458212</th>\n",
       "      <td>458212</td>\n",
       "      <td>7240562359</td>\n",
       "      <td>https://wyoming.craigslist.org/ctd/d/gilberts-...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458213 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0          id  \\\n",
       "0                0  7240372487   \n",
       "1                1  7240309422   \n",
       "2                2  7240224296   \n",
       "3                3  7240103965   \n",
       "4                4  7239983776   \n",
       "...            ...         ...   \n",
       "458208      458208  7240959076   \n",
       "458209      458209  7240954682   \n",
       "458210      458210  7240645700   \n",
       "458211      458211  7240600465   \n",
       "458212      458212  7240562359   \n",
       "\n",
       "                                                      url  \n",
       "0       https://auburn.craigslist.org/ctd/d/auburn-uni...  \n",
       "1       https://auburn.craigslist.org/cto/d/auburn-201...  \n",
       "2       https://auburn.craigslist.org/cto/d/auburn-200...  \n",
       "3       https://auburn.craigslist.org/cto/d/lanett-tru...  \n",
       "4       https://auburn.craigslist.org/cto/d/auburn-200...  \n",
       "...                                                   ...  \n",
       "458208  https://wausau.craigslist.org/ctd/d/wisconsin-...  \n",
       "458209  https://wausau.craigslist.org/ctd/d/wisconsin-...  \n",
       "458210  https://wyoming.craigslist.org/cto/d/sheridan-...  \n",
       "458211  https://wyoming.craigslist.org/cto/d/sheridan-...  \n",
       "458212  https://wyoming.craigslist.org/ctd/d/gilberts-...  \n",
       "\n",
       "[458213 rows x 3 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter for multiple columns (all below do the same thing ) \n",
    "data[['url','lat','price']]\n",
    "data.loc[:,['url','lat','price']]\n",
    "data.iloc[:,0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filtering \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7240372487</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7240309422</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-201...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7240224296</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7240103965</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/lanett-tru...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7239983776</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>95</td>\n",
       "      <td>7240566722</td>\n",
       "      <td>https://bham.craigslist.org/ctd/d/summerville-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>96</td>\n",
       "      <td>7240548971</td>\n",
       "      <td>https://bham.craigslist.org/ctd/d/cartersville...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>7240543174</td>\n",
       "      <td>https://bham.craigslist.org/ctd/d/cartersville...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98</td>\n",
       "      <td>7240485219</td>\n",
       "      <td>https://bham.craigslist.org/ctd/d/stokesdale-2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>7240483721</td>\n",
       "      <td>https://bham.craigslist.org/ctd/d/birmingham-2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0          id                                                url\n",
       "0            0  7240372487  https://auburn.craigslist.org/ctd/d/auburn-uni...\n",
       "1            1  7240309422  https://auburn.craigslist.org/cto/d/auburn-201...\n",
       "2            2  7240224296  https://auburn.craigslist.org/cto/d/auburn-200...\n",
       "3            3  7240103965  https://auburn.craigslist.org/cto/d/lanett-tru...\n",
       "4            4  7239983776  https://auburn.craigslist.org/cto/d/auburn-200...\n",
       "..         ...         ...                                                ...\n",
       "95          95  7240566722  https://bham.craigslist.org/ctd/d/summerville-...\n",
       "96          96  7240548971  https://bham.craigslist.org/ctd/d/cartersville...\n",
       "97          97  7240543174  https://bham.craigslist.org/ctd/d/cartersville...\n",
       "98          98  7240485219  https://bham.craigslist.org/ctd/d/stokesdale-2...\n",
       "99          99  7240483721  https://bham.craigslist.org/ctd/d/birmingham-2...\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter by rows and columns \n",
    "data.loc[0:100,['url','lat','price']]\n",
    "data.iloc[0:100,0:3]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>url</th>\n",
       "      <th>region</th>\n",
       "      <th>region_url</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>...</th>\n",
       "      <th>drive</th>\n",
       "      <th>size</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>image_url</th>\n",
       "      <th>description</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>posting_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7240372487</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>35990</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>corvette grand sport</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>rwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00N0N_ipkbHVZYf4...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-12-02T08:11:30-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7240309422</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-201...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>7500</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>sonata</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>fwd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00s0s_gBHYmJ5o7y...</td>\n",
       "      <td>I'll move to another city and try to sell my c...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>-85.468200</td>\n",
       "      <td>2020-12-02T02:11:50-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7240224296</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>4900</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>x3 3.0i</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SUV</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00B0B_5zgEGWPOrt...</td>\n",
       "      <td>Clean 2006 BMW X3 3.0I.  Beautiful and rare Bl...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.616807</td>\n",
       "      <td>-85.464149</td>\n",
       "      <td>2020-12-01T19:50:41-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7240103965</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/lanett-tru...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>2000</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>c-10</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>rwd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>pickup</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00M0M_6o7KcDpArw...</td>\n",
       "      <td>1974 chev. truck (LONG BED) NEW starter front ...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.861600</td>\n",
       "      <td>-85.216100</td>\n",
       "      <td>2020-12-01T15:54:45-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7239983776</td>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>19500</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f350 lariat</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>full-size</td>\n",
       "      <td>pickup</td>\n",
       "      <td>blue</td>\n",
       "      <td>https://images.craigslist.org/00p0p_b95l1EgUfl...</td>\n",
       "      <td>2005 Ford F350 Lariat (Bullet Proofed). This t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>-85.468200</td>\n",
       "      <td>2020-12-01T12:53:56-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458208</th>\n",
       "      <td>458208</td>\n",
       "      <td>7240959076</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wisconsin-...</td>\n",
       "      <td>wausau</td>\n",
       "      <td>https://wausau.craigslist.org</td>\n",
       "      <td>14995</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>srx</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>SUV</td>\n",
       "      <td>silver</td>\n",
       "      <td>https://images.craigslist.org/00808_1bVcaYZtCy...</td>\n",
       "      <td>2014 Cadillac SRX All Wheel Drive Luxury Colle...</td>\n",
       "      <td>wi</td>\n",
       "      <td>44.408867</td>\n",
       "      <td>-89.881210</td>\n",
       "      <td>2020-12-03T08:56:29-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458209</th>\n",
       "      <td>458209</td>\n",
       "      <td>7240954682</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wisconsin-...</td>\n",
       "      <td>wausau</td>\n",
       "      <td>https://wausau.craigslist.org</td>\n",
       "      <td>17895</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>tucson</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>SUV</td>\n",
       "      <td>white</td>\n",
       "      <td>https://images.craigslist.org/00l0l_gikkP11dEF...</td>\n",
       "      <td>2018 Hyundai Tucson SE 2.0 I4 AWD.  Carfax ver...</td>\n",
       "      <td>wi</td>\n",
       "      <td>44.408867</td>\n",
       "      <td>-89.882240</td>\n",
       "      <td>2020-12-03T08:48:08-0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458210</th>\n",
       "      <td>458210</td>\n",
       "      <td>7240645700</td>\n",
       "      <td>https://wyoming.craigslist.org/cto/d/sheridan-...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>17900</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>cadillac</td>\n",
       "      <td>cts coupe awd</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>coupe</td>\n",
       "      <td>black</td>\n",
       "      <td>https://images.craigslist.org/00t0t_lNnW4cboOG...</td>\n",
       "      <td>53,000 miles. Cosmetically, in near mint condi...</td>\n",
       "      <td>wy</td>\n",
       "      <td>44.784900</td>\n",
       "      <td>-106.964800</td>\n",
       "      <td>2020-12-02T14:09:45-0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458211</th>\n",
       "      <td>458211</td>\n",
       "      <td>7240600465</td>\n",
       "      <td>https://wyoming.craigslist.org/cto/d/sheridan-...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>1300</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>jeep</td>\n",
       "      <td>grand cherokee</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>4wd</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>SUV</td>\n",
       "      <td>white</td>\n",
       "      <td>https://images.craigslist.org/00C0C_fl0NW1IeJw...</td>\n",
       "      <td>PRICE REDUCTION  Turns out the engine is toast...</td>\n",
       "      <td>wy</td>\n",
       "      <td>44.773500</td>\n",
       "      <td>-106.939600</td>\n",
       "      <td>2020-12-02T13:01:04-0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458212</th>\n",
       "      <td>458212</td>\n",
       "      <td>7240562359</td>\n",
       "      <td>https://wyoming.craigslist.org/ctd/d/gilberts-...</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>https://wyoming.craigslist.org</td>\n",
       "      <td>22900</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>International 4300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00k0k_77qSSp6sQC...</td>\n",
       "      <td>*** SHIPPING AVAILABLE DIRECTLY TO YOU. CALL F...</td>\n",
       "      <td>wy</td>\n",
       "      <td>42.117656</td>\n",
       "      <td>-88.436954</td>\n",
       "      <td>2020-12-02T12:03:45-0700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458213 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0          id  \\\n",
       "0                0  7240372487   \n",
       "1                1  7240309422   \n",
       "2                2  7240224296   \n",
       "3                3  7240103965   \n",
       "4                4  7239983776   \n",
       "...            ...         ...   \n",
       "458208      458208  7240959076   \n",
       "458209      458209  7240954682   \n",
       "458210      458210  7240645700   \n",
       "458211      458211  7240600465   \n",
       "458212      458212  7240562359   \n",
       "\n",
       "                                                      url   region  \\\n",
       "0       https://auburn.craigslist.org/ctd/d/auburn-uni...   auburn   \n",
       "1       https://auburn.craigslist.org/cto/d/auburn-201...   auburn   \n",
       "2       https://auburn.craigslist.org/cto/d/auburn-200...   auburn   \n",
       "3       https://auburn.craigslist.org/cto/d/lanett-tru...   auburn   \n",
       "4       https://auburn.craigslist.org/cto/d/auburn-200...   auburn   \n",
       "...                                                   ...      ...   \n",
       "458208  https://wausau.craigslist.org/ctd/d/wisconsin-...   wausau   \n",
       "458209  https://wausau.craigslist.org/ctd/d/wisconsin-...   wausau   \n",
       "458210  https://wyoming.craigslist.org/cto/d/sheridan-...  wyoming   \n",
       "458211  https://wyoming.craigslist.org/cto/d/sheridan-...  wyoming   \n",
       "458212  https://wyoming.craigslist.org/ctd/d/gilberts-...  wyoming   \n",
       "\n",
       "                            region_url  price    year manufacturer  \\\n",
       "0        https://auburn.craigslist.org  35990  2010.0    chevrolet   \n",
       "1        https://auburn.craigslist.org   7500  2014.0      hyundai   \n",
       "2        https://auburn.craigslist.org   4900  2006.0          bmw   \n",
       "3        https://auburn.craigslist.org   2000  1974.0    chevrolet   \n",
       "4        https://auburn.craigslist.org  19500  2005.0         ford   \n",
       "...                                ...    ...     ...          ...   \n",
       "458208   https://wausau.craigslist.org  14995  2014.0     cadillac   \n",
       "458209   https://wausau.craigslist.org  17895  2018.0      hyundai   \n",
       "458210  https://wyoming.craigslist.org  17900  2014.0     cadillac   \n",
       "458211  https://wyoming.craigslist.org   1300  2008.0         jeep   \n",
       "458212  https://wyoming.craigslist.org  22900  2008.0          NaN   \n",
       "\n",
       "                       model  condition  ... drive       size    type  \\\n",
       "0       corvette grand sport       good  ...   rwd        NaN   other   \n",
       "1                     sonata  excellent  ...   fwd        NaN   sedan   \n",
       "2                    x3 3.0i       good  ...   NaN        NaN     SUV   \n",
       "3                       c-10       good  ...   rwd  full-size  pickup   \n",
       "4                f350 lariat  excellent  ...   4wd  full-size  pickup   \n",
       "...                      ...        ...  ...   ...        ...     ...   \n",
       "458208                   srx  excellent  ...   4wd   mid-size     SUV   \n",
       "458209                tucson  excellent  ...   4wd   mid-size     SUV   \n",
       "458210         cts coupe awd  excellent  ...   4wd   mid-size   coupe   \n",
       "458211        grand cherokee       good  ...   4wd   mid-size     SUV   \n",
       "458212    International 4300        NaN  ...   NaN        NaN     NaN   \n",
       "\n",
       "       paint_color                                          image_url  \\\n",
       "0              NaN  https://images.craigslist.org/00N0N_ipkbHVZYf4...   \n",
       "1              NaN  https://images.craigslist.org/00s0s_gBHYmJ5o7y...   \n",
       "2             blue  https://images.craigslist.org/00B0B_5zgEGWPOrt...   \n",
       "3             blue  https://images.craigslist.org/00M0M_6o7KcDpArw...   \n",
       "4             blue  https://images.craigslist.org/00p0p_b95l1EgUfl...   \n",
       "...            ...                                                ...   \n",
       "458208      silver  https://images.craigslist.org/00808_1bVcaYZtCy...   \n",
       "458209       white  https://images.craigslist.org/00l0l_gikkP11dEF...   \n",
       "458210       black  https://images.craigslist.org/00t0t_lNnW4cboOG...   \n",
       "458211       white  https://images.craigslist.org/00C0C_fl0NW1IeJw...   \n",
       "458212         NaN  https://images.craigslist.org/00k0k_77qSSp6sQC...   \n",
       "\n",
       "                                              description state        lat  \\\n",
       "0       Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "1       I'll move to another city and try to sell my c...    al  32.547500   \n",
       "2       Clean 2006 BMW X3 3.0I.  Beautiful and rare Bl...    al  32.616807   \n",
       "3       1974 chev. truck (LONG BED) NEW starter front ...    al  32.861600   \n",
       "4       2005 Ford F350 Lariat (Bullet Proofed). This t...    al  32.547500   \n",
       "...                                                   ...   ...        ...   \n",
       "458208  2014 Cadillac SRX All Wheel Drive Luxury Colle...    wi  44.408867   \n",
       "458209  2018 Hyundai Tucson SE 2.0 I4 AWD.  Carfax ver...    wi  44.408867   \n",
       "458210  53,000 miles. Cosmetically, in near mint condi...    wy  44.784900   \n",
       "458211  PRICE REDUCTION  Turns out the engine is toast...    wy  44.773500   \n",
       "458212  *** SHIPPING AVAILABLE DIRECTLY TO YOU. CALL F...    wy  42.117656   \n",
       "\n",
       "              long              posting_date  \n",
       "0       -85.480000  2020-12-02T08:11:30-0600  \n",
       "1       -85.468200  2020-12-02T02:11:50-0600  \n",
       "2       -85.464149  2020-12-01T19:50:41-0600  \n",
       "3       -85.216100  2020-12-01T15:54:45-0600  \n",
       "4       -85.468200  2020-12-01T12:53:56-0600  \n",
       "...            ...                       ...  \n",
       "458208  -89.881210  2020-12-03T08:56:29-0600  \n",
       "458209  -89.882240  2020-12-03T08:48:08-0600  \n",
       "458210 -106.964800  2020-12-02T14:09:45-0700  \n",
       "458211 -106.939600  2020-12-02T13:01:04-0700  \n",
       "458212  -88.436954  2020-12-02T12:03:45-0700  \n",
       "\n",
       "[458213 rows x 26 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter by column list \n",
    "data[data.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>url</th>\n",
       "      <th>region</th>\n",
       "      <th>region_url</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>condition</th>\n",
       "      <th>...</th>\n",
       "      <th>size</th>\n",
       "      <th>type</th>\n",
       "      <th>paint_color</th>\n",
       "      <th>image_url</th>\n",
       "      <th>description</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>posting_date</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>7239776805</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>29590</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>toyota</td>\n",
       "      <td>tacoma double cab sr5</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pickup</td>\n",
       "      <td>red</td>\n",
       "      <td>https://images.craigslist.org/00k0k_9nUNSlCBLM...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-12-01T07:27:25-0600</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>7238127696</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>31990</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>jeep</td>\n",
       "      <td>wrangler unlimited sport s</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00g0g_lCDDDlgwtZ...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-11-28T07:21:09-0600</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>7237779886</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/santa-cruz...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>490</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>transit connect wagon xlt</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>van</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00D0D_foGOQzDTOx...</td>\n",
       "      <td>2019 Ford Transit Connect Wagon XLT FOR ONLY $...</td>\n",
       "      <td>al</td>\n",
       "      <td>36.967357</td>\n",
       "      <td>-122.024254</td>\n",
       "      <td>2020-11-27T12:42:28-0600</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>7237366792</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>24990</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>ram</td>\n",
       "      <td>1500 crew cab slt pickup 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pickup</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00O0O_7r7iltwH7G...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-11-26T12:50:29-0600</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>7236210088</td>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>auburn</td>\n",
       "      <td>https://auburn.craigslist.org</td>\n",
       "      <td>32990</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>ford</td>\n",
       "      <td>f150 supercrew cab xlt</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pickup</td>\n",
       "      <td>silver</td>\n",
       "      <td>https://images.craigslist.org/00r0r_iV4TYppIUV...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>al</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>-85.480000</td>\n",
       "      <td>2020-11-24T07:23:47-0600</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458201</th>\n",
       "      <td>458201</td>\n",
       "      <td>7241009387</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wausau-201...</td>\n",
       "      <td>wausau</td>\n",
       "      <td>https://wausau.craigslist.org</td>\n",
       "      <td>14590</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>i3 hatchback 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00H0H_e25REbgf6W...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>wi</td>\n",
       "      <td>44.960000</td>\n",
       "      <td>-89.640000</td>\n",
       "      <td>2020-12-03T10:18:21-0600</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458203</th>\n",
       "      <td>458203</td>\n",
       "      <td>7240977929</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wausau-201...</td>\n",
       "      <td>wausau</td>\n",
       "      <td>https://wausau.craigslist.org</td>\n",
       "      <td>14590</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>bmw</td>\n",
       "      <td>i3 hatchback 4d</td>\n",
       "      <td>good</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00l0l_cQCZp0X2QB...</td>\n",
       "      <td>Carvana is the safer way to buy a car During t...</td>\n",
       "      <td>wi</td>\n",
       "      <td>44.960000</td>\n",
       "      <td>-89.640000</td>\n",
       "      <td>2020-12-03T09:29:49-0600</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458205</th>\n",
       "      <td>458205</td>\n",
       "      <td>7240969563</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/eau-claire...</td>\n",
       "      <td>wausau</td>\n",
       "      <td>https://wausau.craigslist.org</td>\n",
       "      <td>20950</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>jeep</td>\n",
       "      <td>grand cherokee overland 4x4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00606_inlZnX05CG...</td>\n",
       "      <td>2015 Jeep Grand Cherokee Overland 4x4 ✔  We TA...</td>\n",
       "      <td>wi</td>\n",
       "      <td>44.843195</td>\n",
       "      <td>-91.550371</td>\n",
       "      <td>2020-12-03T09:15:05-0600</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458207</th>\n",
       "      <td>458207</td>\n",
       "      <td>7240964712</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/eau-claire...</td>\n",
       "      <td>wausau</td>\n",
       "      <td>https://wausau.craigslist.org</td>\n",
       "      <td>19950</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>a4 premium plus quattro awd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://images.craigslist.org/00o0o_elNZEbATqG...</td>\n",
       "      <td>2017 Audi A4 Premium Plus Quattro AWD ✔  We TA...</td>\n",
       "      <td>wi</td>\n",
       "      <td>44.843195</td>\n",
       "      <td>-91.550371</td>\n",
       "      <td>2020-12-03T09:06:21-0600</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458209</th>\n",
       "      <td>458209</td>\n",
       "      <td>7240954682</td>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wisconsin-...</td>\n",
       "      <td>wausau</td>\n",
       "      <td>https://wausau.craigslist.org</td>\n",
       "      <td>17895</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>hyundai</td>\n",
       "      <td>tucson</td>\n",
       "      <td>excellent</td>\n",
       "      <td>...</td>\n",
       "      <td>mid-size</td>\n",
       "      <td>SUV</td>\n",
       "      <td>white</td>\n",
       "      <td>https://images.craigslist.org/00l0l_gikkP11dEF...</td>\n",
       "      <td>2018 Hyundai Tucson SE 2.0 I4 AWD.  Carfax ver...</td>\n",
       "      <td>wi</td>\n",
       "      <td>44.408867</td>\n",
       "      <td>-89.882240</td>\n",
       "      <td>2020-12-03T08:48:08-0600</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169062 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0          id  \\\n",
       "5                5  7239776805   \n",
       "8                8  7238127696   \n",
       "9                9  7237779886   \n",
       "12              12  7237366792   \n",
       "18              18  7236210088   \n",
       "...            ...         ...   \n",
       "458201      458201  7241009387   \n",
       "458203      458203  7240977929   \n",
       "458205      458205  7240969563   \n",
       "458207      458207  7240964712   \n",
       "458209      458209  7240954682   \n",
       "\n",
       "                                                      url  region  \\\n",
       "5       https://auburn.craigslist.org/ctd/d/auburn-uni...  auburn   \n",
       "8       https://auburn.craigslist.org/ctd/d/auburn-uni...  auburn   \n",
       "9       https://auburn.craigslist.org/ctd/d/santa-cruz...  auburn   \n",
       "12      https://auburn.craigslist.org/ctd/d/auburn-uni...  auburn   \n",
       "18      https://auburn.craigslist.org/ctd/d/auburn-uni...  auburn   \n",
       "...                                                   ...     ...   \n",
       "458201  https://wausau.craigslist.org/ctd/d/wausau-201...  wausau   \n",
       "458203  https://wausau.craigslist.org/ctd/d/wausau-201...  wausau   \n",
       "458205  https://wausau.craigslist.org/ctd/d/eau-claire...  wausau   \n",
       "458207  https://wausau.craigslist.org/ctd/d/eau-claire...  wausau   \n",
       "458209  https://wausau.craigslist.org/ctd/d/wisconsin-...  wausau   \n",
       "\n",
       "                           region_url  price    year manufacturer  \\\n",
       "5       https://auburn.craigslist.org  29590  2016.0       toyota   \n",
       "8       https://auburn.craigslist.org  31990  2017.0         jeep   \n",
       "9       https://auburn.craigslist.org    490  2019.0         ford   \n",
       "12      https://auburn.craigslist.org  24990  2016.0          ram   \n",
       "18      https://auburn.craigslist.org  32990  2019.0         ford   \n",
       "...                               ...    ...     ...          ...   \n",
       "458201  https://wausau.craigslist.org  14590  2015.0          bmw   \n",
       "458203  https://wausau.craigslist.org  14590  2016.0          bmw   \n",
       "458205  https://wausau.craigslist.org  20950  2015.0         jeep   \n",
       "458207  https://wausau.craigslist.org  19950  2017.0         audi   \n",
       "458209  https://wausau.craigslist.org  17895  2018.0      hyundai   \n",
       "\n",
       "                              model  condition  ...      size       type  \\\n",
       "5             tacoma double cab sr5       good  ...       NaN     pickup   \n",
       "8        wrangler unlimited sport s       good  ...       NaN      other   \n",
       "9         transit connect wagon xlt  excellent  ...       NaN        van   \n",
       "12      1500 crew cab slt pickup 4d       good  ...       NaN     pickup   \n",
       "18           f150 supercrew cab xlt       good  ...       NaN     pickup   \n",
       "...                             ...        ...  ...       ...        ...   \n",
       "458201              i3 hatchback 4d       good  ...       NaN  hatchback   \n",
       "458203              i3 hatchback 4d       good  ...       NaN  hatchback   \n",
       "458205  grand cherokee overland 4x4        NaN  ...       NaN        NaN   \n",
       "458207  a4 premium plus quattro awd        NaN  ...       NaN        NaN   \n",
       "458209                       tucson  excellent  ...  mid-size        SUV   \n",
       "\n",
       "        paint_color                                          image_url  \\\n",
       "5               red  https://images.craigslist.org/00k0k_9nUNSlCBLM...   \n",
       "8               NaN  https://images.craigslist.org/00g0g_lCDDDlgwtZ...   \n",
       "9               NaN  https://images.craigslist.org/00D0D_foGOQzDTOx...   \n",
       "12              NaN  https://images.craigslist.org/00O0O_7r7iltwH7G...   \n",
       "18           silver  https://images.craigslist.org/00r0r_iV4TYppIUV...   \n",
       "...             ...                                                ...   \n",
       "458201          NaN  https://images.craigslist.org/00H0H_e25REbgf6W...   \n",
       "458203          NaN  https://images.craigslist.org/00l0l_cQCZp0X2QB...   \n",
       "458205          NaN  https://images.craigslist.org/00606_inlZnX05CG...   \n",
       "458207          NaN  https://images.craigslist.org/00o0o_elNZEbATqG...   \n",
       "458209        white  https://images.craigslist.org/00l0l_gikkP11dEF...   \n",
       "\n",
       "                                              description state        lat  \\\n",
       "5       Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "8       Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "9       2019 Ford Transit Connect Wagon XLT FOR ONLY $...    al  36.967357   \n",
       "12      Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "18      Carvana is the safer way to buy a car During t...    al  32.590000   \n",
       "...                                                   ...   ...        ...   \n",
       "458201  Carvana is the safer way to buy a car During t...    wi  44.960000   \n",
       "458203  Carvana is the safer way to buy a car During t...    wi  44.960000   \n",
       "458205  2015 Jeep Grand Cherokee Overland 4x4 ✔  We TA...    wi  44.843195   \n",
       "458207  2017 Audi A4 Premium Plus Quattro AWD ✔  We TA...    wi  44.843195   \n",
       "458209  2018 Hyundai Tucson SE 2.0 I4 AWD.  Carfax ver...    wi  44.408867   \n",
       "\n",
       "              long              posting_date  age  \n",
       "5       -85.480000  2020-12-01T07:27:25-0600  3.0  \n",
       "8       -85.480000  2020-11-28T07:21:09-0600  2.0  \n",
       "9      -122.024254  2020-11-27T12:42:28-0600  0.0  \n",
       "12      -85.480000  2020-11-26T12:50:29-0600  3.0  \n",
       "18      -85.480000  2020-11-24T07:23:47-0600  0.0  \n",
       "...            ...                       ...  ...  \n",
       "458201  -89.640000  2020-12-03T10:18:21-0600  4.0  \n",
       "458203  -89.640000  2020-12-03T09:29:49-0600  3.0  \n",
       "458205  -91.550371  2020-12-03T09:15:05-0600  4.0  \n",
       "458207  -91.550371  2020-12-03T09:06:21-0600  2.0  \n",
       "458209  -89.882240  2020-12-03T08:48:08-0600  1.0  \n",
       "\n",
       "[169062 rows x 27 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filtering data by columns & boolean indexing \n",
    "data[(data['age'] < 5)]\n",
    "\n",
    "data.loc[(data.age <5),:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add column \n",
    "data['age'] = 2019 - data['year']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# basic operators on columns \n",
    "data['price_per_mile'] = data['price'] / data['odometer']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['dupul'] = df['Duration']/df['Pulse']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>Duration</th>\n",
       "      <th>Pulse</th>\n",
       "      <th>Maxpulse</th>\n",
       "      <th>Calories</th>\n",
       "      <th>calpul</th>\n",
       "      <th>dupul</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>110</td>\n",
       "      <td>130</td>\n",
       "      <td>409.1</td>\n",
       "      <td>3.719091</td>\n",
       "      <td>0.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>117</td>\n",
       "      <td>145</td>\n",
       "      <td>479.0</td>\n",
       "      <td>4.094017</td>\n",
       "      <td>0.512821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>103</td>\n",
       "      <td>135</td>\n",
       "      <td>340.0</td>\n",
       "      <td>3.300971</td>\n",
       "      <td>0.582524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45</td>\n",
       "      <td>109</td>\n",
       "      <td>175</td>\n",
       "      <td>282.4</td>\n",
       "      <td>2.590826</td>\n",
       "      <td>0.412844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45</td>\n",
       "      <td>117</td>\n",
       "      <td>148</td>\n",
       "      <td>406.0</td>\n",
       "      <td>3.470085</td>\n",
       "      <td>0.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>60</td>\n",
       "      <td>105</td>\n",
       "      <td>140</td>\n",
       "      <td>290.8</td>\n",
       "      <td>2.769524</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>60</td>\n",
       "      <td>110</td>\n",
       "      <td>145</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2.727273</td>\n",
       "      <td>0.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>60</td>\n",
       "      <td>115</td>\n",
       "      <td>145</td>\n",
       "      <td>310.2</td>\n",
       "      <td>2.697391</td>\n",
       "      <td>0.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>75</td>\n",
       "      <td>120</td>\n",
       "      <td>150</td>\n",
       "      <td>320.4</td>\n",
       "      <td>2.670000</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>75</td>\n",
       "      <td>125</td>\n",
       "      <td>150</td>\n",
       "      <td>330.4</td>\n",
       "      <td>2.643200</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Duration  Pulse  Maxpulse  Calories    calpul     dupul\n",
       "0          60    110       130     409.1  3.719091  0.545455\n",
       "1          60    117       145     479.0  4.094017  0.512821\n",
       "2          60    103       135     340.0  3.300971  0.582524\n",
       "3          45    109       175     282.4  2.590826  0.412844\n",
       "4          45    117       148     406.0  3.470085  0.384615\n",
       "..        ...    ...       ...       ...       ...       ...\n",
       "164        60    105       140     290.8  2.769524  0.571429\n",
       "165        60    110       145     300.0  2.727273  0.545455\n",
       "166        60    115       145     310.2  2.697391  0.521739\n",
       "167        75    120       150     320.4  2.670000  0.625000\n",
       "168        75    125       150     330.4  2.643200  0.600000\n",
       "\n",
       "[169 rows x 6 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    71980\n",
       "1    15000\n",
       "2     9800\n",
       "3     4000\n",
       "4    39000\n",
       "Name: price2x, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply function \n",
    "\n",
    "def timex2(x):\n",
    "    return 2*x\n",
    "\n",
    "data['price2x'] = data['price'].apply(timex2)\n",
    "data['price'].head()\n",
    "data['price2x'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-108-5ed3dffbeb24>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['Duration2x'] = df['Duration'].apply(timex2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    120.0\n",
       "1    120.0\n",
       "2    120.0\n",
       "3     90.0\n",
       "4     90.0\n",
       "Name: Duration2x, dtype: float64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def timex2(x):\n",
    "    return 2*x\n",
    "\n",
    "df2['Duration2x'] = df['Duration'].apply(timex2)\n",
    "df2['Duration'].head()\n",
    "df2['Duration2x'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    107970\n",
       "1     22500\n",
       "2     14700\n",
       "3      6000\n",
       "4     58500\n",
       "Name: price3x, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lambda function \n",
    "data['price3x'] = data['price'].apply(lambda x: x*3)\n",
    "data['price3x'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dummy variables \n",
    "data_dummies = pd.get_dummies(data[['price','year','fuel','transmission','type']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 70,
     "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": [
    "#pivot table / sort_index / sort_values \n",
    "data.pivot_table(index='year',columns='type',values='price',aggfunc ='mean').sort_index(ascending=False)\n",
    "data.pivot_table(index='year',columns='type',values='price',aggfunc ='count').sort_index(ascending=False)\n",
    "data.pivot_table(index='year',columns='type',values='price',aggfunc ='count').sort_index(ascending=False).plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>type</th>\n",
       "      <th>fuel</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>odometer</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>age</th>\n",
       "      <th>price2x</th>\n",
       "      <th>price3x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SUV</td>\n",
       "      <td>diesel</td>\n",
       "      <td>226911.695373</td>\n",
       "      <td>7.235279e+09</td>\n",
       "      <td>19343.570694</td>\n",
       "      <td>2009.791506</td>\n",
       "      <td>110524.967699</td>\n",
       "      <td>39.529982</td>\n",
       "      <td>-100.737849</td>\n",
       "      <td>9.208494</td>\n",
       "      <td>38687.141388</td>\n",
       "      <td>58030.712082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SUV</td>\n",
       "      <td>electric</td>\n",
       "      <td>179992.729167</td>\n",
       "      <td>7.236947e+09</td>\n",
       "      <td>50290.791667</td>\n",
       "      <td>2016.562500</td>\n",
       "      <td>40988.844444</td>\n",
       "      <td>35.952523</td>\n",
       "      <td>-105.176048</td>\n",
       "      <td>2.437500</td>\n",
       "      <td>100581.583333</td>\n",
       "      <td>150872.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SUV</td>\n",
       "      <td>gas</td>\n",
       "      <td>231729.442345</td>\n",
       "      <td>7.235166e+09</td>\n",
       "      <td>13825.767596</td>\n",
       "      <td>2011.946895</td>\n",
       "      <td>101347.305668</td>\n",
       "      <td>39.308835</td>\n",
       "      <td>-96.240308</td>\n",
       "      <td>7.053105</td>\n",
       "      <td>27651.535191</td>\n",
       "      <td>41477.302787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SUV</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>248902.971660</td>\n",
       "      <td>7.235729e+09</td>\n",
       "      <td>15323.455466</td>\n",
       "      <td>2012.060976</td>\n",
       "      <td>106299.243802</td>\n",
       "      <td>40.153276</td>\n",
       "      <td>-101.216078</td>\n",
       "      <td>6.939024</td>\n",
       "      <td>30646.910931</td>\n",
       "      <td>45970.366397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SUV</td>\n",
       "      <td>other</td>\n",
       "      <td>222833.205933</td>\n",
       "      <td>7.235288e+09</td>\n",
       "      <td>16299.396463</td>\n",
       "      <td>2013.011409</td>\n",
       "      <td>106406.657310</td>\n",
       "      <td>38.445257</td>\n",
       "      <td>-95.952305</td>\n",
       "      <td>5.988591</td>\n",
       "      <td>32598.792926</td>\n",
       "      <td>48898.189390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>wagon</td>\n",
       "      <td>diesel</td>\n",
       "      <td>210396.864111</td>\n",
       "      <td>7.233975e+09</td>\n",
       "      <td>11596.076655</td>\n",
       "      <td>2011.857143</td>\n",
       "      <td>78858.714286</td>\n",
       "      <td>40.254986</td>\n",
       "      <td>-98.186943</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>23192.153310</td>\n",
       "      <td>34788.229965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>wagon</td>\n",
       "      <td>electric</td>\n",
       "      <td>203509.113043</td>\n",
       "      <td>7.235415e+09</td>\n",
       "      <td>21395.313043</td>\n",
       "      <td>2018.826087</td>\n",
       "      <td>14357.373913</td>\n",
       "      <td>39.434653</td>\n",
       "      <td>-97.901405</td>\n",
       "      <td>0.173913</td>\n",
       "      <td>42790.626087</td>\n",
       "      <td>64185.939130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>wagon</td>\n",
       "      <td>gas</td>\n",
       "      <td>233940.897237</td>\n",
       "      <td>7.235440e+09</td>\n",
       "      <td>11391.198489</td>\n",
       "      <td>2011.137860</td>\n",
       "      <td>101120.572999</td>\n",
       "      <td>39.738757</td>\n",
       "      <td>-95.682538</td>\n",
       "      <td>7.862140</td>\n",
       "      <td>22782.396978</td>\n",
       "      <td>34173.595467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>wagon</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>224780.085106</td>\n",
       "      <td>7.235462e+09</td>\n",
       "      <td>9636.457447</td>\n",
       "      <td>2014.632979</td>\n",
       "      <td>84546.445652</td>\n",
       "      <td>37.462859</td>\n",
       "      <td>-99.404609</td>\n",
       "      <td>4.367021</td>\n",
       "      <td>19272.914894</td>\n",
       "      <td>28909.372340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>wagon</td>\n",
       "      <td>other</td>\n",
       "      <td>257037.572816</td>\n",
       "      <td>7.236071e+09</td>\n",
       "      <td>14707.888350</td>\n",
       "      <td>2012.834951</td>\n",
       "      <td>76685.732323</td>\n",
       "      <td>38.131310</td>\n",
       "      <td>-92.486885</td>\n",
       "      <td>6.165049</td>\n",
       "      <td>29415.776699</td>\n",
       "      <td>44123.665049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     type      fuel     Unnamed: 0            id         price         year  \\\n",
       "0     SUV    diesel  226911.695373  7.235279e+09  19343.570694  2009.791506   \n",
       "1     SUV  electric  179992.729167  7.236947e+09  50290.791667  2016.562500   \n",
       "2     SUV       gas  231729.442345  7.235166e+09  13825.767596  2011.946895   \n",
       "3     SUV    hybrid  248902.971660  7.235729e+09  15323.455466  2012.060976   \n",
       "4     SUV     other  222833.205933  7.235288e+09  16299.396463  2013.011409   \n",
       "..    ...       ...            ...           ...           ...          ...   \n",
       "56  wagon    diesel  210396.864111  7.233975e+09  11596.076655  2011.857143   \n",
       "57  wagon  electric  203509.113043  7.235415e+09  21395.313043  2018.826087   \n",
       "58  wagon       gas  233940.897237  7.235440e+09  11391.198489  2011.137860   \n",
       "59  wagon    hybrid  224780.085106  7.235462e+09   9636.457447  2014.632979   \n",
       "60  wagon     other  257037.572816  7.236071e+09  14707.888350  2012.834951   \n",
       "\n",
       "         odometer        lat        long       age        price2x  \\\n",
       "0   110524.967699  39.529982 -100.737849  9.208494   38687.141388   \n",
       "1    40988.844444  35.952523 -105.176048  2.437500  100581.583333   \n",
       "2   101347.305668  39.308835  -96.240308  7.053105   27651.535191   \n",
       "3   106299.243802  40.153276 -101.216078  6.939024   30646.910931   \n",
       "4   106406.657310  38.445257  -95.952305  5.988591   32598.792926   \n",
       "..            ...        ...         ...       ...            ...   \n",
       "56   78858.714286  40.254986  -98.186943  7.142857   23192.153310   \n",
       "57   14357.373913  39.434653  -97.901405  0.173913   42790.626087   \n",
       "58  101120.572999  39.738757  -95.682538  7.862140   22782.396978   \n",
       "59   84546.445652  37.462859  -99.404609  4.367021   19272.914894   \n",
       "60   76685.732323  38.131310  -92.486885  6.165049   29415.776699   \n",
       "\n",
       "          price3x  \n",
       "0    58030.712082  \n",
       "1   150872.375000  \n",
       "2    41477.302787  \n",
       "3    45970.366397  \n",
       "4    48898.189390  \n",
       "..            ...  \n",
       "56   34788.229965  \n",
       "57   64185.939130  \n",
       "58   34173.595467  \n",
       "59   28909.372340  \n",
       "60   44123.665049  \n",
       "\n",
       "[61 rows x 12 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#groupby \n",
    "data.groupby('type').mean()\n",
    "data.groupby(['type','fuel']).mean()\n",
    "data.groupby(['type','fuel'],as_index = False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>url</th>\n",
       "      <th>lat</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://auburn.craigslist.org/ctd/d/auburn-uni...</td>\n",
       "      <td>32.590000</td>\n",
       "      <td>35990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-201...</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "      <td>32.616807</td>\n",
       "      <td>4900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://auburn.craigslist.org/cto/d/lanett-tru...</td>\n",
       "      <td>32.861600</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://auburn.craigslist.org/cto/d/auburn-200...</td>\n",
       "      <td>32.547500</td>\n",
       "      <td>19500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458208</th>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wisconsin-...</td>\n",
       "      <td>44.408867</td>\n",
       "      <td>14995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458209</th>\n",
       "      <td>https://wausau.craigslist.org/ctd/d/wisconsin-...</td>\n",
       "      <td>44.408867</td>\n",
       "      <td>17895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458210</th>\n",
       "      <td>https://wyoming.craigslist.org/cto/d/sheridan-...</td>\n",
       "      <td>44.784900</td>\n",
       "      <td>17900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458211</th>\n",
       "      <td>https://wyoming.craigslist.org/cto/d/sheridan-...</td>\n",
       "      <td>44.773500</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458212</th>\n",
       "      <td>https://wyoming.craigslist.org/ctd/d/gilberts-...</td>\n",
       "      <td>42.117656</td>\n",
       "      <td>22900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>458213 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      url        lat  price\n",
       "0       https://auburn.craigslist.org/ctd/d/auburn-uni...  32.590000  35990\n",
       "1       https://auburn.craigslist.org/cto/d/auburn-201...  32.547500   7500\n",
       "2       https://auburn.craigslist.org/cto/d/auburn-200...  32.616807   4900\n",
       "3       https://auburn.craigslist.org/cto/d/lanett-tru...  32.861600   2000\n",
       "4       https://auburn.craigslist.org/cto/d/auburn-200...  32.547500  19500\n",
       "...                                                   ...        ...    ...\n",
       "458208  https://wausau.craigslist.org/ctd/d/wisconsin-...  44.408867  14995\n",
       "458209  https://wausau.craigslist.org/ctd/d/wisconsin-...  44.408867  17895\n",
       "458210  https://wyoming.craigslist.org/cto/d/sheridan-...  44.784900  17900\n",
       "458211  https://wyoming.craigslist.org/cto/d/sheridan-...  44.773500   1300\n",
       "458212  https://wyoming.craigslist.org/ctd/d/gilberts-...  42.117656  22900\n",
       "\n",
       "[458213 rows x 3 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.merge == to join in sql  \n",
    "df1 = data[['url','lat']]\n",
    "df2 = data[['url','price']]\n",
    "df_merged = pd.merge(df1,df2,on='url')\n",
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write to a csv file pd.to_csv() or exporting it to csv \n",
    "df2.to_csv('C:/data/steve.csv')"
   ]
  }
 ],
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