1BedRoom-Chicago Craigslist Rent DataScrape v1.0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updated: 12/8/2020\n",
    "\n",
    "Scraping Craigslist's rental section to get accurate representation of the prices and square footage of 1 bedrooms throughout different Chicago neighorhoods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "id": "_jGyBgvjXlXo",
    "outputId": "a5319979-e33b-460f-e3f5-73cc2bb58cb8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bs4.element.ResultSet'>\n",
      "120\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#import get to call a get request on the site\n",
    "from requests import get\n",
    "\n",
    "#get the 1st page of the chicago housing prices \n",
    "#get rid of posts without a pic using their filter\n",
    "response = get('https://chicago.craigslist.org/search/chc/apa?hasPic=1&availabilityMode=0')\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "html_soup = BeautifulSoup(response.text, 'html.parser')\n",
    "\n",
    "#get the macro-container for the housing posts\n",
    "posts = html_soup.find_all('li', class_= 'result-row')\n",
    "print(type(posts)) #to double check that I got a ResultSet\n",
    "print(len(posts)) #to double check I got 120 (elements/page)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "b1vwc4vZXlaF"
   },
   "outputs": [],
   "source": [
    "#get first post\n",
    "post_one = posts[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "BaxtdUl4Xlci",
    "outputId": "9d9cb440-03e9-4eb4-dbb6-2729989a78e9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'$1,500'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#price of 1st post\n",
    "post_one_price = post_one.a.text\n",
    "#remove comma from listing. Need to do later for all the posts\n",
    "#post_one_price = post_one.a.text.replace(',', '')\n",
    "post_one_price.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pqmoMN5VXlg4"
   },
   "outputs": [],
   "source": [
    "#get the time of the post in datetime format\n",
    "post_one_time = post_one.find('time', class_= 'result-date')\n",
    "post_one_datetime = post_one_time['datetime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xjHdFsk7XljA"
   },
   "outputs": [],
   "source": [
    "#title is a and that class, link is grabbing the href attribute of that variable\n",
    "post_one_title = post_one.find('a', class_='result-title hdrlnk')\n",
    "post_one_link = post_one_title['href']\n",
    "\n",
    "#easy to grab the post title by taking the text element of the title variable\n",
    "post_one_title_text = post_one_title.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#execute multiple statements per cell\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 200
    },
    "colab_type": "code",
    "id": "aCOC0VNZXlpN",
    "outputId": "f2483405-30ba-476d-9bc7-7240b120064c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2br'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<span class=\"housing\">\n",
       "                    2br -\n",
       "                </span>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-48c7016b7f3d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0mpost_one_sqft\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0mpost_one_sqft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpost_one\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'span'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclass_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'housing'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[0mpost_one_sqft\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;31m#post_one_sqft = post_one.find('span', class_ = 'housing').text.split()[2][:-3] #cleans the ft2 at the end\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "#grabs the whole segment of housing details. We will need missing value handling in the loop as this kind of detail is not common in posts\n",
    "#the text can be split, and we can use indexing to grab the elements we want. number of bedrooms is the first element.\n",
    "#sqft is the third element\n",
    "post_one_num_bedrooms = post_one.find('span', class_ = 'housing').text.split()[0]\n",
    "post_one_num_bedrooms\n",
    "\n",
    "post_one_sqft = post_one.find('span', class_ = 'housing')\n",
    "post_one_sqft\n",
    "\n",
    "post_one_sqft = post_one.find('span', class_ = 'housing').text.split()[2][:-3]\n",
    "post_one_sqft\n",
    "#post_one_sqft = post_one.find('span', class_ = 'housing').text.split()[2][:-3] #cleans the ft2 at the end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-x-QMD_NXlsF"
   },
   "outputs": [],
   "source": [
    "#the neighborhood is grabbed by finding the span class 'result-hood' and pulling the text element from there\n",
    "\n",
    "post_one_hood = posts[0].find('span', class_='result-hood').text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 520
    },
    "colab_type": "code",
    "id": "-kqViHwzXlwa",
    "outputId": "26f8f94e-c23a-4fd7-9abf-4c7bada70b24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\n",
      "\n",
      "Scrape complete!\n"
     ]
    }
   ],
   "source": [
    "#build out the loop\n",
    "from time import sleep\n",
    "import re\n",
    "##this will allow us to scrape without getting blocked by craigslist\n",
    "from random import randint #avoid throttling by not sending too many requests one after the other\n",
    "from warnings import warn\n",
    "from time import time\n",
    "from IPython.core.display import clear_output\n",
    "import numpy as np\n",
    "\n",
    "#find the total number of posts to find the limit of the pagination\n",
    "results_num = html_soup.find('div', class_= 'search-legend')\n",
    "results_total = int(results_num.find('span', class_='totalcount').text) #pulled the total count of posts as the upper bound of the pages array\n",
    "\n",
    "#each page has 119 posts so each new page is defined as follows: s=120, s=240, s=360, and so on. So we need to step in size 120 in the np.arange function\n",
    "pages = np.arange(0, results_total+1, 120)\n",
    "\n",
    "iterations = 0\n",
    "\n",
    "post_timing = []\n",
    "post_hoods = []\n",
    "post_title_texts = []\n",
    "bedroom_counts = []\n",
    "sqfts = []\n",
    "post_links = []\n",
    "post_prices = []\n",
    "\n",
    "for page in pages:\n",
    "    \n",
    "    #get request\n",
    "    response = get(\"https://chicago.craigslist.org/search/chc/apa?\" \n",
    "                   + \"s=\" #the parameter for defining the page number \n",
    "                   + str(page) #the page number in the pages array from earlier\n",
    "                   + \"&hasPic=1\"\n",
    "                   + \"&availabilityMode=0\")\n",
    "\n",
    "    sleep(randint(1,5))\n",
    "     \n",
    "    #throw warning for status codes that are not 200\n",
    "    if response.status_code != 200:\n",
    "        warn('Request: {}; Status code: {}'.format(requests, response.status_code))\n",
    "        \n",
    "    #define the html text\n",
    "    page_html = BeautifulSoup(response.text, 'html.parser')\n",
    "    \n",
    "    #define the posts\n",
    "    posts = html_soup.find_all('li', class_= 'result-row')\n",
    "        \n",
    "    #extract data item-wise\n",
    "    for post in posts:\n",
    "\n",
    "        if post.find('span', class_ = 'result-hood') is not None:\n",
    "\n",
    "            #posting date\n",
    "            #grab the datetime element 0 for date and 1 for time\n",
    "            post_datetime = post.find('time', class_= 'result-date')['datetime']\n",
    "            post_timing.append(post_datetime)\n",
    "\n",
    "            #neighborhoods\n",
    "            post_hood = post.find('span', class_= 'result-hood').text\n",
    "            post_hoods.append(post_hood)\n",
    "\n",
    "            #title text\n",
    "            post_title = post.find('a', class_='result-title hdrlnk')\n",
    "            post_title_text = post_title.text\n",
    "            post_title_texts.append(post_title_text)\n",
    "\n",
    "            #post link\n",
    "            post_link = post_title['href']\n",
    "            post_links.append(post_link)\n",
    "            \n",
    "            #removes the \\n whitespace from each side, removes the currency symbol, the comma \n",
    "            #and turns it into an int\n",
    "            post_price = int(post.a.text.strip().replace(\"$\", \"\").replace(\",\",\"\"))\n",
    "            #post_price = int(post.a.text.strip().replace(',', '')\n",
    "            post_prices.append(post_price)\n",
    "                        \n",
    "            if post.find('span', class_ = 'housing') is not None:\n",
    "                \n",
    "                #if the first element is accidentally square footage\n",
    "                if 'ft2' in post.find('span', class_ = 'housing').text.split()[0]:\n",
    "                    \n",
    "                    #make bedroom nan\n",
    "                    bedroom_count = np.nan\n",
    "                    bedroom_counts.append(bedroom_count)\n",
    "                    \n",
    "                    #make sqft the first element\n",
    "                    sqft = int(post.find('span', class_ = 'housing').text.split()[0][:-3])\n",
    "                    sqfts.append(sqft)\n",
    "                    \n",
    "                #if the length of the housing details element is more than 2\n",
    "                elif len(post.find('span', class_ = 'housing').text.split()) > 2:\n",
    "                    \n",
    "                    #therefore element 0 will be bedroom count\n",
    "                    bedroom_count = post.find('span', class_ = 'housing').text.replace(\"br\", \"\").split()[0]\n",
    "                    bedroom_counts.append(bedroom_count)\n",
    "                    \n",
    "                    #and sqft will be number 3, so set these here and append\n",
    "                    sqft = int(post.find('span', class_ = 'housing').text.split()[2][:-3])\n",
    "                    sqfts.append(sqft)\n",
    "                    \n",
    "                #if there is num bedrooms but no sqft\n",
    "                elif len(post.find('span', class_ = 'housing').text.split()) == 2:\n",
    "                    \n",
    "                    #therefore element 0 will be bedroom count\n",
    "                    bedroom_count = post.find('span', class_ = 'housing').text.replace(\"br\", \"\").split()[0]\n",
    "                    bedroom_counts.append(bedroom_count)\n",
    "                    \n",
    "                    #and sqft will be number 3, so set these here and append\n",
    "                    sqft = np.nan\n",
    "                    sqfts.append(sqft)                    \n",
    "                \n",
    "                else:\n",
    "                    bedroom_count = np.nan\n",
    "                    bedroom_counts.append(bedroom_count)\n",
    "                \n",
    "                    sqft = np.nan\n",
    "                    sqfts.append(sqft)\n",
    "                \n",
    "            #if none of those conditions catch, make bedroom nan, this won't be needed    \n",
    "            else:\n",
    "                bedroom_count = np.nan\n",
    "                bedroom_counts.append(bedroom_count)\n",
    "                \n",
    "                sqft = np.nan\n",
    "                sqfts.append(sqft)\n",
    "            #    bedroom_counts.append(bedroom_count)\n",
    "                \n",
    "            #    sqft = np.nan\n",
    "            #    sqfts.append(sqft)\n",
    "                \n",
    "    iterations += 1\n",
    "    print(\"Page \" + str(iterations) + \" scraped successfully!\")\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Scrape complete!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 754
    },
    "colab_type": "code",
    "id": "Qhfokzt7XlzC",
    "outputId": "e418b495-e965-4c4b-e0f9-02892661535a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2626 entries, 0 to 2625\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Posted        2626 non-null   object \n",
      " 1   Neighborhood  2626 non-null   object \n",
      " 2   Post_Title    2626 non-null   object \n",
      " 3   Num_Bedrooms  2314 non-null   object \n",
      " 4   Sqft          624 non-null    float64\n",
      " 5   URL           2626 non-null   object \n",
      " 6   Price         2626 non-null   int64  \n",
      "dtypes: float64(1), int64(1), object(5)\n",
      "memory usage: 143.7+ KB\n",
      "None\n"
     ]
    },
    {
     "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>Posted</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Post_Title</th>\n",
       "      <th>Num_Bedrooms</th>\n",
       "      <th>Sqft</th>\n",
       "      <th>URL</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-08 12:56</td>\n",
       "      <td>(CHICAGO)</td>\n",
       "      <td>AVAILABLE NOW!!!!-3802 N SEELEY-2S 2 BED/ 1 BATH</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-08 12:55</td>\n",
       "      <td>(Edgewater/ Chicago)</td>\n",
       "      <td>1 bedroom in Edgewater neighborhood 1ST MONTH ...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-08 12:50</td>\n",
       "      <td>(South Loop)</td>\n",
       "      <td>SOUTH LOOP! AMAZING LOFT STYLE Jr 1 bed WD, hd...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12-08 12:50</td>\n",
       "      <td>(Pilsen/UIC Area/ Pink Line)</td>\n",
       "      <td>RENOVATED Pilsen 1 Bed with in-unit washer/dryer</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-12-08 12:48</td>\n",
       "      <td>(CHICAGO)</td>\n",
       "      <td>AVAILABLE NOW!!!!-HOT SOUTHPORT CORRIDOR LOCAT...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-12-08 12:44</td>\n",
       "      <td>(On 70th St. and Crandon)</td>\n",
       "      <td>2209-2211 E. 70th St: HUGE 2BR/1BA Apartment</td>\n",
       "      <td>2</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/70th-...</td>\n",
       "      <td>1175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-12-08 12:42</td>\n",
       "      <td>(Uptown/Andersonvile)</td>\n",
       "      <td>1 bedroom in west uptown/andersonvile 1ST MONT...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-12-08 12:41</td>\n",
       "      <td>(Pilsen)</td>\n",
       "      <td>Pilsen apartment for rent</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-12-08 12:40</td>\n",
       "      <td>(CHICAGO)</td>\n",
       "      <td>HOT SOUTHPORT CORRIDOR LOCATION-1417 W CORNELI...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-12-08 12:40</td>\n",
       "      <td>(South Shore)</td>\n",
       "      <td>2209 E 70th St - LOVELY GARDEN UNIT 1BD/1BTH</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Posted                   Neighborhood  \\\n",
       "0  2020-12-08 12:56                      (CHICAGO)   \n",
       "1  2020-12-08 12:55           (Edgewater/ Chicago)   \n",
       "2  2020-12-08 12:50                   (South Loop)   \n",
       "3  2020-12-08 12:50   (Pilsen/UIC Area/ Pink Line)   \n",
       "4  2020-12-08 12:48                      (CHICAGO)   \n",
       "5  2020-12-08 12:44      (On 70th St. and Crandon)   \n",
       "6  2020-12-08 12:42          (Uptown/Andersonvile)   \n",
       "7  2020-12-08 12:41                       (Pilsen)   \n",
       "8  2020-12-08 12:40                      (CHICAGO)   \n",
       "9  2020-12-08 12:40                  (South Shore)   \n",
       "\n",
       "                                          Post_Title Num_Bedrooms    Sqft  \\\n",
       "0   AVAILABLE NOW!!!!-3802 N SEELEY-2S 2 BED/ 1 BATH            2     NaN   \n",
       "1  1 bedroom in Edgewater neighborhood 1ST MONTH ...            1     NaN   \n",
       "2  SOUTH LOOP! AMAZING LOFT STYLE Jr 1 bed WD, hd...            1     NaN   \n",
       "3   RENOVATED Pilsen 1 Bed with in-unit washer/dryer            1     NaN   \n",
       "4  AVAILABLE NOW!!!!-HOT SOUTHPORT CORRIDOR LOCAT...            1     NaN   \n",
       "5       2209-2211 E. 70th St: HUGE 2BR/1BA Apartment            2  1000.0   \n",
       "6  1 bedroom in west uptown/andersonvile 1ST MONT...            1     NaN   \n",
       "7                          Pilsen apartment for rent            2     NaN   \n",
       "8  HOT SOUTHPORT CORRIDOR LOCATION-1417 W CORNELI...            1     NaN   \n",
       "9       2209 E 70th St - LOVELY GARDEN UNIT 1BD/1BTH            1     NaN   \n",
       "\n",
       "                                                 URL  Price  \n",
       "0  https://chicago.craigslist.org/chc/apa/d/chica...   1500  \n",
       "1  https://chicago.craigslist.org/chc/apa/d/chica...    875  \n",
       "2  https://chicago.craigslist.org/chc/apa/d/chica...   1170  \n",
       "3  https://chicago.craigslist.org/chc/apa/d/chica...   1400  \n",
       "4  https://chicago.craigslist.org/chc/apa/d/chica...   1495  \n",
       "5  https://chicago.craigslist.org/chc/apa/d/70th-...   1175  \n",
       "6  https://chicago.craigslist.org/chc/apa/d/chica...    895  \n",
       "7  https://chicago.craigslist.org/chc/apa/d/chica...    950  \n",
       "8  https://chicago.craigslist.org/chc/apa/d/chica...   1495  \n",
       "9  https://chicago.craigslist.org/chc/apa/d/chica...    800  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "ch_apts = pd.DataFrame({'Posted': post_timing,\n",
    "                       'Neighborhood': post_hoods,\n",
    "                       'Post_Title': post_title_texts,\n",
    "                       'Num_Bedrooms': bedroom_counts,\n",
    "                        'Sqft': sqfts,\n",
    "                        'URL': post_links,\n",
    "                       'Price': post_prices})\n",
    "print(ch_apts.info())\n",
    "ch_apts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TE8SXNrUXl5u"
   },
   "outputs": [],
   "source": [
    "########################DATA CLEANING\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HTnHEt61Xl82"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Deleting duplicate postings bc people spam Craigslist with fake postings\n",
    "# the duplicate postings are most likely spam\n",
    "#Displays uniqe posts\n",
    "ch_apts = ch_apts.drop_duplicates(subset='URL')\n",
    "len(ch_apts.drop_duplicates(subset='URL'))\n",
    "\n",
    "#converts the # of bedrooms to a float (since np.nan is a float too)\n",
    "ch_apts['Num_Bedrooms'] = ch_apts['Num_Bedrooms'].apply(lambda x: float(x))\n",
    "\n",
    "#convert datetime string into datetime object to work with it\n",
    "from datetime import datetime\n",
    "ch_apts['Posted'] = pd.to_datetime(ch_apts['Posted'])\n",
    "\n",
    "#Checking neighborhoods that there are with ch_apts['neighborhood'].unique()\n",
    "#Seeing that allowed me to know what I needed to deal with in terms of cleaning those\n",
    "\n",
    "#remove the parenthesis from the left and right of the neighborhoods\n",
    "ch_apts['Neighborhood'] = ch_apts['Neighborhood'].map(lambda x: x.lstrip('(').rstrip(')'))\n",
    "\n",
    "ch_apts['Neighborhood'] = ch_apts['Neighborhood'].apply(lambda x: re.sub(r'[()]+', '', x))\n",
    "\n",
    "#titlecase them\n",
    "ch_apts['Neighborhood'] = ch_apts['Neighborhood'].str.title()\n",
    "\n",
    "#just take the first name of the neighborhood list, splitting on the '/' delimiter\n",
    "ch_apts['Neighborhood'] = ch_apts['Neighborhood'].apply(lambda x: x.split('/')[0])\n",
    "\n",
    "#fix one-offs that\n",
    "########################\n",
    "#####################\n",
    "\n",
    "#UPDATE\n",
    "###################\n",
    "########################\n",
    "#remove whitespaces\n",
    "ch_apts['Neighborhood'] = ch_apts['Neighborhood'].apply(lambda x: x.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 97
    },
    "colab_type": "code",
    "id": "XgR7WxPvXmAG",
    "outputId": "b83d69e4-30a2-4f76-f20d-5e3d04716d1e"
   },
   "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>Posted</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Post_Title</th>\n",
       "      <th>Num_Bedrooms</th>\n",
       "      <th>Sqft</th>\n",
       "      <th>URL</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-08 12:56:00</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>AVAILABLE NOW!!!!-3802 N SEELEY-2S 2 BED/ 1 BATH</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Posted Neighborhood  \\\n",
       "0 2020-12-08 12:56:00      Chicago   \n",
       "\n",
       "                                         Post_Title  Num_Bedrooms  Sqft  \\\n",
       "0  AVAILABLE NOW!!!!-3802 N SEELEY-2S 2 BED/ 1 BATH           2.0   NaN   \n",
       "\n",
       "                                                 URL  Price  \n",
       "0  https://chicago.craigslist.org/chc/apa/d/chica...   1500  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### EXPLORATORY DATA ANALYSIS\n",
    "#Checking first record\n",
    "ch_apts.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Chicago', 'Edgewater', 'South Loop', 'Pilsen', 'Uptown',\n",
       "       'South Shore', 'North Kenwood', 'Bucktown', 'Little Italy',\n",
       "       'Rogers Park', 'Lincoln Park', 'West Lakeview', 'Lakeview',\n",
       "       'Buena Park', 'Chicago & Lasalle', 'River North', 'Irving Park',\n",
       "       'Chicago, Illinois', 'Fulton Market, Chicago', 'Lincoln Square',\n",
       "       'Andersonville', 'All Pets', 'Hyde Park',\n",
       "       'South Loop Mccormick Square', 'Evanston', 'Jefferson Park',\n",
       "       'Lakeview, Chicago', 'Sheridan Park', 'Roscoe Village',\n",
       "       'Gold Coast', 'Logan Square', 'Auburn Gresham', 'Glenview',\n",
       "       'Wrigleyville', 'Ukrainian Village', 'Chicago - Uptown',\n",
       "       'Midway Area', 'Chicago Prime Bridgeport', 'Beacon & Wilson',\n",
       "       'Chicago, Il'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array(['Chicago', 'Edgewater', 'South Loop', 'Pilsen', 'Uptown',\n",
       "       'South Shore', 'North Kenwood', 'Bucktown', 'Little Italy',\n",
       "       'Rogers Park', 'Lincoln Park', 'West Lakeview', 'Lakeview',\n",
       "       'Buena Park', 'Chicago & Lasalle', 'River North', 'Irving Park',\n",
       "       'Chicago, Illinois', 'Fulton Market, Chicago', 'Lincoln Square',\n",
       "       'Andersonville', 'Hyde Park', 'South Loop Mccormick Square',\n",
       "       'Evanston', 'Jefferson Park', 'Lakeview, Chicago', 'Sheridan Park',\n",
       "       'Roscoe Village', 'Gold Coast', 'Logan Square', 'Auburn Gresham',\n",
       "       'Glenview', 'Wrigleyville', 'Ukrainian Village',\n",
       "       'Chicago - Uptown', 'Midway Area', 'Chicago Prime Bridgeport',\n",
       "       'Beacon & Wilson', 'Chicago, Il'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking unique neighborhood names\n",
    "ch_apts.Neighborhood.unique()\n",
    "\n",
    "#removing listings that have addresses as listings\n",
    "ch_apts = ch_apts[(ch_apts.Neighborhood != 'On 70Th St. And Crandon')]\n",
    "ch_apts = ch_apts[(ch_apts.Neighborhood != '1317 W. 18Th Street | #1F | Chicago, Il')]\n",
    "ch_apts = ch_apts[(ch_apts.Neighborhood != '1550 S Hamlin  North Lawndale')]\n",
    "ch_apts = ch_apts[(ch_apts.Neighborhood != 'Augusta Blvd At California Ave')]\n",
    "ch_apts = ch_apts[(ch_apts.Neighborhood != 'All Pets')]\n",
    "ch_apts = ch_apts[(ch_apts.Neighborhood != '3144 W 39Th Pl')]\n",
    "ch_apts = ch_apts[(ch_apts.Neighborhood != '1550 S Hamlin  North Lawndale')]\n",
    "ch_apts = ch_apts[(ch_apts.Neighborhood != '8901 S Loomis - Brainerd Senior Center')]\n",
    "ch_apts = ch_apts[(ch_apts.Neighborhood != '737 E 69Th')]\n",
    "ch_apts = ch_apts[(ch_apts.Neighborhood != '737 E 69Th')]\n",
    "ch_apts = ch_apts[(ch_apts.Neighborhood != '3144 W 39Th Pl, Chicago Il.')]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#(ch_apts.Neighborhood != 10)]\n",
    "\n",
    "ch_apts.Neighborhood.unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "xm8yaMzelE_J",
    "outputId": "44a8000c-67df-4648-d5a2-2572a78994f0"
   },
   "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>Posted</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Post_Title</th>\n",
       "      <th>Num_Bedrooms</th>\n",
       "      <th>Sqft</th>\n",
       "      <th>URL</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-12-08 12:56:00</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>AVAILABLE NOW!!!!-3802 N SEELEY-2S 2 BED/ 1 BATH</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-12-08 12:55:00</td>\n",
       "      <td>Edgewater</td>\n",
       "      <td>1 bedroom in Edgewater neighborhood 1ST MONTH ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-08 12:50:00</td>\n",
       "      <td>South Loop</td>\n",
       "      <td>SOUTH LOOP! AMAZING LOFT STYLE Jr 1 bed WD, hd...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-12-08 12:50:00</td>\n",
       "      <td>Pilsen</td>\n",
       "      <td>RENOVATED Pilsen 1 Bed with in-unit washer/dryer</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-12-08 12:48:00</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>AVAILABLE NOW!!!!-HOT SOUTHPORT CORRIDOR LOCAT...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1495</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2020-12-08 09:17:00</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Amazing One   Bedroom Apartments  @Willard Squ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2020-12-08 09:16:00</td>\n",
       "      <td>Lakeview</td>\n",
       "      <td>521 W. Brompton in Lakeview: Studio - Free Hea...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2020-12-08 09:15:00</td>\n",
       "      <td>Lakeview</td>\n",
       "      <td>517 W. Brompton in Lakeview: Studio- Free Heat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2020-12-08 09:14:00</td>\n",
       "      <td>Uptown</td>\n",
       "      <td>Cheerful 1 Bedroom, 1 Bath in Uptown, Availabl...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2020-12-08 09:14:00</td>\n",
       "      <td>Rogers Park</td>\n",
       "      <td>2 bedroom in West Rogers Park</td>\n",
       "      <td>2.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Posted Neighborhood  \\\n",
       "0  2020-12-08 12:56:00      Chicago   \n",
       "1  2020-12-08 12:55:00    Edgewater   \n",
       "2  2020-12-08 12:50:00   South Loop   \n",
       "3  2020-12-08 12:50:00       Pilsen   \n",
       "4  2020-12-08 12:48:00      Chicago   \n",
       "..                 ...          ...   \n",
       "94 2020-12-08 09:17:00      Chicago   \n",
       "95 2020-12-08 09:16:00     Lakeview   \n",
       "97 2020-12-08 09:15:00     Lakeview   \n",
       "98 2020-12-08 09:14:00       Uptown   \n",
       "99 2020-12-08 09:14:00  Rogers Park   \n",
       "\n",
       "                                           Post_Title  Num_Bedrooms   Sqft  \\\n",
       "0    AVAILABLE NOW!!!!-3802 N SEELEY-2S 2 BED/ 1 BATH           2.0    NaN   \n",
       "1   1 bedroom in Edgewater neighborhood 1ST MONTH ...           1.0    NaN   \n",
       "2   SOUTH LOOP! AMAZING LOFT STYLE Jr 1 bed WD, hd...           1.0    NaN   \n",
       "3    RENOVATED Pilsen 1 Bed with in-unit washer/dryer           1.0    NaN   \n",
       "4   AVAILABLE NOW!!!!-HOT SOUTHPORT CORRIDOR LOCAT...           1.0    NaN   \n",
       "..                                                ...           ...    ...   \n",
       "94  Amazing One   Bedroom Apartments  @Willard Squ...           1.0    NaN   \n",
       "95  521 W. Brompton in Lakeview: Studio - Free Hea...           NaN    NaN   \n",
       "97  517 W. Brompton in Lakeview: Studio- Free Heat...           NaN    NaN   \n",
       "98  Cheerful 1 Bedroom, 1 Bath in Uptown, Availabl...           1.0  408.0   \n",
       "99                      2 bedroom in West Rogers Park           2.0  800.0   \n",
       "\n",
       "                                                  URL  Price  \n",
       "0   https://chicago.craigslist.org/chc/apa/d/chica...   1500  \n",
       "1   https://chicago.craigslist.org/chc/apa/d/chica...    875  \n",
       "2   https://chicago.craigslist.org/chc/apa/d/chica...   1170  \n",
       "3   https://chicago.craigslist.org/chc/apa/d/chica...   1400  \n",
       "4   https://chicago.craigslist.org/chc/apa/d/chica...   1495  \n",
       "..                                                ...    ...  \n",
       "94  https://chicago.craigslist.org/chc/apa/d/chica...    934  \n",
       "95  https://chicago.craigslist.org/chc/apa/d/chica...   1015  \n",
       "97  https://chicago.craigslist.org/chc/apa/d/chica...   1055  \n",
       "98  https://chicago.craigslist.org/chc/apa/d/chica...   1075  \n",
       "99  https://chicago.craigslist.org/chc/apa/d/chica...   1375  \n",
       "\n",
       "[94 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correcting neighborhood names\n",
    "for Post_Title in ch_apts:\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Andersonville'), 'Neighborhood'] = 'Andersonville'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('andersonville'), 'Neighborhood'] = 'Andersonville'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Archer Heights'), 'Neighborhood'] = 'Archer Heights'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('archer Heights'), 'Neighborhood'] = 'Archer Heights'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Ashburn'), 'Neighborhood'] = 'Ashburn'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('ashburn'), 'Neighborhood'] = 'Ashburn'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Austin'), 'Neighborhood'] = 'Austin'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('austin'), 'Neighborhood'] = 'Austin'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('avon'), 'Neighborhood'] = 'Avondale'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Avon'), 'Neighborhood'] = 'Avondale'  \n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Albany'), 'Neighborhood'] = 'Albany Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('albany'), 'Neighborhood'] = 'Albany Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Beverly'), 'Neighborhood'] = 'Beverly'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('beverly'), 'Neighborhood'] = 'Beverly'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('buck'), 'Neighborhood'] = 'Bucktown'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Buck'), 'Neighborhood'] = 'Bucktown'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Boys'), 'Neighborhood'] = 'Boystown'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('boys'), 'Neighborhood'] = 'Boystown'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('China'), 'Neighborhood'] = 'Chinatown'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('china'), 'Neighborhood'] = 'Chinatown'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Depaul'), 'Neighborhood'] = 'Depaul'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('depaul'), 'Neighborhood'] = 'Depaul'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Edison'), 'Neighborhood'] = 'Edison Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('edison'), 'Neighborhood'] = 'Edison Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Edgewater'), 'Neighborhood'] = 'Edgewater'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('edgewater'), 'Neighborhood'] = 'Edgewater'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Englewood'), 'Neighborhood'] = 'Englewood'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('englewood'), 'Neighborhood'] = 'Englewood'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Galewood'), 'Neighborhood'] = 'Galewood'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('galewood'), 'Neighborhood'] = 'Galewood'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Garfield Park'), 'Neighborhood'] = 'Garfield Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('garfield Park'), 'Neighborhood'] = 'Garfield Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Gold Coast'), 'Neighborhood'] = 'Gold Coast'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('gold'), 'Neighborhood'] = 'Gold Coast'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Gresham'), 'Neighborhood'] = 'Gresham'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('gresham'), 'Neighborhood'] = 'Gresham'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Humb'), 'Neighborhood'] = 'Humboldt Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('humb'), 'Neighborhood'] = 'Humboldt Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Hyde'), 'Neighborhood'] = 'Hyde Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('hyde'), 'Neighborhood'] = 'Hyde Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Jefferson'), 'Neighborhood'] = 'Jefferson Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('jefferson'), 'Neighborhood'] = 'Jefferson Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Kenwood'), 'Neighborhood'] = 'Kenwood'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('kenwood'), 'Neighborhood'] = 'Kenwood'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Lakeview'), 'Neighborhood'] = 'Lakeview'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('lakeview'), 'Neighborhood'] = 'Lakeview'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Little Village'), 'Neighborhood'] = 'Little Village'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Little village'), 'Neighborhood'] = 'Little Village'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('little village'), 'Neighborhood'] = 'Little Village'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('lil village'), 'Neighborhood'] = 'Little Village'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Lil village'), 'Neighborhood'] = 'Little Village'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Lincoln Park'), 'Neighborhood'] = 'Lincoln Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('lincoln park'), 'Neighborhood'] = 'Lincoln Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Lincoln Square'), 'Neighborhood'] = 'Lincoln Square'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('lincoln square'), 'Neighborhood'] = 'Lincoln Square'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Logan'), 'Neighborhood'] = 'Logan Square'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('logan'), 'Neighborhood'] = 'Logan Square'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Marquette Park'), 'Neighborhood'] = 'Marquette Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('marquette park'), 'Neighborhood'] = 'Marquette Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Midway'), 'Neighborhood'] = 'Midway'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('midway'), 'Neighborhood'] = 'Midway'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Mount Greenwood'), 'Neighborhood'] = 'Mount Greenwood'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('mount greenwood'), 'Neighborhood'] = 'Mount Greenwood'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Norwood'), 'Neighborhood'] = 'Norwood Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('norwood'), 'Neighborhood'] = 'Norwood Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Irving'), 'Neighborhood'] = 'Irving Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('irving'), 'Neighborhood'] = 'Irving Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Old Town'), 'Neighborhood'] = 'Old Town'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('old town'), 'Neighborhood'] = 'Old Town'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Pilsen'), 'Neighborhood'] = 'Pilsen'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('pilsen'), 'Neighborhood'] = 'Pilsen'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Portage'), 'Neighborhood'] = 'Portage Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('portage'), 'Neighborhood'] = 'Portage Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Pullman'), 'Neighborhood'] = 'Pullman'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('pullman'), 'Neighborhood'] = 'Pullman'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Ravens'), 'Neighborhood'] = 'Ravenswood'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('ravens'), 'Neighborhood'] = 'Ravenswood'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('River North'), 'Neighborhood'] = 'River North'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('river north'), 'Neighborhood'] = 'River North'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('River West'), 'Neighborhood'] = 'River West'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('river west'), 'Neighborhood'] = 'River West'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Rogers'), 'Neighborhood'] = 'Rogers Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('rogers'), 'Neighborhood'] = 'Rogers Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('rodgers'), 'Neighborhood'] = 'Rogers Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Roscoe Village'), 'Neighborhood'] = 'Roscoe Village'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('roscoe village'), 'Neighborhood'] = 'Roscoe Village'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Sauganash'), 'Neighborhood'] = 'Sauganash'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('sauganash'), 'Neighborhood'] = 'Sauganash'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('South Loop'), 'Neighborhood'] = 'South Loop'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('south loop'), 'Neighborhood'] = 'South Loop'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('South Shore'), 'Neighborhood'] = 'South Shore'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('south shore'), 'Neighborhood'] = 'South Shore'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Streeter'), 'Neighborhood'] = 'Streeterville'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('streeter'), 'Neighborhood'] = 'Streeterville'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Uptown'), 'Neighborhood'] = 'Uptown'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('uptown'), 'Neighborhood'] = 'Uptown'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('West Loop'), 'Neighborhood'] = 'West Loop'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('west loop'), 'Neighborhood'] = 'West Loop'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('West Town'), 'Neighborhood'] = 'West Town'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('west town'), 'Neighborhood'] = 'West Town'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Wicker'), 'Neighborhood'] = 'Wicker Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('wicker'), 'Neighborhood'] = 'Wicker Park'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Wrigleyville'), 'Neighborhood'] = 'Wrigleyville'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('wrigleyville'), 'Neighborhood'] = 'Wrigleyville'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('wrigley'), 'Neighborhood'] = 'Wrigleyville'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('Ukrainian Village'), 'Neighborhood'] = 'Ukrainian Village'\n",
    "  ch_apts.loc[ch_apts['Post_Title'].str.contains('ukrainian village'), 'Neighborhood'] = 'Ukrainian Village'\n",
    "\n",
    "ch_apts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "colab_type": "code",
    "id": "LoRKReDVXmDd",
    "outputId": "bc2140ee-1e95-4432-b6f2-8ec4625c4689"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 94 entries, 0 to 99\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   Posted        94 non-null     datetime64[ns]\n",
      " 1   Neighborhood  94 non-null     object        \n",
      " 2   Post_Title    94 non-null     object        \n",
      " 3   Num_Bedrooms  82 non-null     float64       \n",
      " 4   Sqft          21 non-null     float64       \n",
      " 5   URL           94 non-null     object        \n",
      " 6   Price         94 non-null     int64         \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(3)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "ch_apts.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 414
    },
    "colab_type": "code",
    "id": "fnfRnj7TXog2",
    "outputId": "e87b5fa2-e06c-4fd4-94b4-fbe196ca01e8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\chris\\\\2020'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd, numpy as np\n",
    "\n",
    "#get current directory\n",
    "%pwd\n",
    "\n",
    "#export dataframe to csv file\n",
    "ch_apts.to_csv(r'C:\\\\Users\\\\chris\\\\2020\\Craigslist-Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 94 entries, 0 to 93\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Unnamed: 0    94 non-null     int64  \n",
      " 1   Posted        94 non-null     object \n",
      " 2   Neighborhood  94 non-null     object \n",
      " 3   Post_Title    94 non-null     object \n",
      " 4   Num_Bedrooms  82 non-null     float64\n",
      " 5   Sqft          21 non-null     float64\n",
      " 6   URL           94 non-null     object \n",
      " 7   Price         94 non-null     int64  \n",
      "dtypes: float64(2), int64(2), object(4)\n",
      "memory usage: 6.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(94, 8)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Posted', 'Neighborhood', 'Post_Title', 'Num_Bedrooms',\n",
       "       'Sqft', 'URL', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use created file\n",
    "chicago_apts = pd.read_csv(\"Craigslist-Data.csv\")\n",
    "\n",
    "chicago_apts.info()\n",
    "chicago_apts.shape\n",
    "chicago_apts.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "colab_type": "code",
    "id": "wUJGdnDxXo1b",
    "outputId": "998c602a-36bb-4094-aa40-5724ad331ef7",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#start to look at the distributions\n",
    "from matplotlib import figure\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(chicago_apts['Price'], edgecolor='black');\n",
    "plt.xlabel(\"Price\")\n",
    "plt.ylabel('Count')\n",
    "plt.title(\"Distribution of Rent\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "colab_type": "code",
    "id": "2Eor2W_qXoz9",
    "outputId": "8af2344e-9691-4744-cc93-99c89f85aac6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as pylab\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (15, 5),\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'x-large',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "pylab.rcParams.update(params)\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.scatterplot(x='Price', y='Sqft', hue='Num_Bedrooms', palette='summer', x_jitter=True, y_jitter=True, s=100, data=ch_apts.dropna())\n",
    "plt.legend(fontsize=12)\n",
    "plt.xlabel(\"Price\", fontsize=18)\n",
    "plt.ylabel(\"Square Footage\", fontsize=18);\n",
    "plt.title(\"Price vs. Square Footage Colored by Number of Bedrooms\", fontsize=18);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 522
    },
    "colab_type": "code",
    "id": "JxFiuvMgXow_",
    "outputId": "2025d02c-67af-41b2-bda5-5b853987432d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.regplot(x='Price', y='Sqft', data=ch_apts.dropna());\n",
    "plt.title('Price vs. Square Footage Regression Plot');\n",
    "plt.xlabel(\"Price (USD)\");\n",
    "plt.ylabel(\"Square Feet\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "colab_type": "code",
    "id": "382JUVRFXoup",
    "outputId": "02b5696c-05e2-489f-ef93-a26279be8df6"
   },
   "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>Num_Bedrooms</th>\n",
       "      <th>Sqft</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Num_Bedrooms</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.473636</td>\n",
       "      <td>0.423594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sqft</th>\n",
       "      <td>0.473636</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.774407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0.423594</td>\n",
       "      <td>0.774407</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Num_Bedrooms      Sqft     Price\n",
       "Num_Bedrooms      1.000000  0.473636  0.423594\n",
       "Sqft              0.473636  1.000000  0.774407\n",
       "Price             0.423594  0.774407  1.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correlations of # of bedrooms, sqft and price\n",
    "ch_apts.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "EVFEsr5pXosE",
    "outputId": "eb662494-eec7-4abe-9fb1-950f9487372f"
   },
   "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>Num_Bedrooms</th>\n",
       "      <th>Sqft</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Andersonville</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1070.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auburn Gresham</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>525.0</td>\n",
       "      <td>740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bucktown</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>980.0</td>\n",
       "      <td>1365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buena Park</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>850.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago</th>\n",
       "      <td>1.833333</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>1287.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago &amp; Lasalle</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1695.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago - Uptown</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago Prime Bridgeport</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>650.0</td>\n",
       "      <td>995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago, Illinois</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>885.0</td>\n",
       "      <td>875.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edgewater</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>951.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evanston</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1507.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glenview</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold Coast</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1465.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyde Park</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1435.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Irving Park</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>1750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jefferson Park</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1475.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakeview</th>\n",
       "      <td>1.476190</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>1427.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lincoln Park</th>\n",
       "      <td>1.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1671.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lincoln Square</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>2695.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Little Italy</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>3000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logan Square</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Midway Area</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>610.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Kenwood</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>895.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pilsen</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1245.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>River North</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1571.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rogers Park</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>1691.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roscoe Village</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1395.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sheridan Park</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1292.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Loop</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1170.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Loop Mccormick Square</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>2265.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Shore</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukrainian Village</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1575.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uptown</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>408.0</td>\n",
       "      <td>973.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Lakeview</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Loop</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>2900.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Num_Bedrooms    Sqft        Price\n",
       "Neighborhood                                                  \n",
       "Andersonville                    1.000000     NaN  1070.000000\n",
       "Auburn Gresham                   1.000000   525.0   740.000000\n",
       "Bucktown                         1.666667   980.0  1365.000000\n",
       "Buena Park                            NaN     NaN   850.000000\n",
       "Chicago                          1.833333  1089.0  1287.083333\n",
       "Chicago & Lasalle                2.000000  1000.0  1695.000000\n",
       "Chicago - Uptown                 1.000000     NaN   950.000000\n",
       "Chicago Prime Bridgeport         2.000000   650.0   995.000000\n",
       "Chicago, Illinois                1.000000   885.0   875.000000\n",
       "Edgewater                        1.000000     NaN   951.000000\n",
       "Evanston                         2.000000     NaN  1507.500000\n",
       "Glenview                         1.000000   700.0  1100.000000\n",
       "Gold Coast                       1.000000  1000.0  1465.000000\n",
       "Hyde Park                        1.000000     NaN  1435.000000\n",
       "Irving Park                      3.000000  1600.0  1750.000000\n",
       "Jefferson Park                   3.000000     NaN  1475.000000\n",
       "Lakeview                         1.476190  1050.0  1427.571429\n",
       "Lincoln Park                     1.750000     NaN  1671.250000\n",
       "Lincoln Square                   3.000000  2800.0  2695.000000\n",
       "Little Italy                     1.000000  2400.0  3000.000000\n",
       "Logan Square                     1.000000   900.0  1750.000000\n",
       "Midway Area                      1.000000     NaN   610.000000\n",
       "North Kenwood                    1.000000     NaN   895.000000\n",
       "Pilsen                           1.333333     NaN  1245.000000\n",
       "River North                      1.000000     NaN  1571.000000\n",
       "Rogers Park                      2.333333  1350.0  1691.666667\n",
       "Roscoe Village                   1.000000   900.0  1395.000000\n",
       "Sheridan Park                    2.000000     NaN  1292.000000\n",
       "South Loop                       1.000000     NaN  1170.000000\n",
       "South Loop Mccormick Square      2.000000  1125.0  2265.000000\n",
       "South Shore                      1.000000     NaN   800.000000\n",
       "Ukrainian Village                2.000000     NaN  1575.000000\n",
       "Uptown                           1.000000   408.0   973.333333\n",
       "West Lakeview                    2.000000     NaN  2100.000000\n",
       "West Loop                        1.000000  1100.0  2900.000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group by neighborhood\n",
    "ch_apts.groupby('Neighborhood').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 901
    },
    "colab_type": "code",
    "id": "29mw0QmmXonp",
    "outputId": "0311e2cc-1497-4959-fa79-107f4d340fa2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neighborhood\n",
       "Midway Area                     610.000000\n",
       "Auburn Gresham                  740.000000\n",
       "South Shore                     800.000000\n",
       "Buena Park                      850.000000\n",
       "Chicago, Illinois               875.000000\n",
       "North Kenwood                   895.000000\n",
       "Chicago - Uptown                950.000000\n",
       "Edgewater                       951.000000\n",
       "Uptown                          973.333333\n",
       "Chicago Prime Bridgeport        995.000000\n",
       "Andersonville                  1070.000000\n",
       "Glenview                       1100.000000\n",
       "South Loop                     1170.000000\n",
       "Pilsen                         1245.000000\n",
       "Chicago                        1287.083333\n",
       "Sheridan Park                  1292.000000\n",
       "Bucktown                       1365.000000\n",
       "Roscoe Village                 1395.000000\n",
       "Lakeview                       1427.571429\n",
       "Hyde Park                      1435.000000\n",
       "Gold Coast                     1465.000000\n",
       "Jefferson Park                 1475.000000\n",
       "Evanston                       1507.500000\n",
       "River North                    1571.000000\n",
       "Ukrainian Village              1575.000000\n",
       "Lincoln Park                   1671.250000\n",
       "Rogers Park                    1691.666667\n",
       "Chicago & Lasalle              1695.000000\n",
       "Logan Square                   1750.000000\n",
       "Irving Park                    1750.000000\n",
       "West Lakeview                  2100.000000\n",
       "South Loop Mccormick Square    2265.000000\n",
       "Lincoln Square                 2695.000000\n",
       "West Loop                      2900.000000\n",
       "Little Italy                   3000.000000\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort price to find cheapest\n",
    "ch_apts.groupby('Neighborhood').mean()['Price'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 842
    },
    "colab_type": "code",
    "id": "kuHF75vEXl3Z",
    "outputId": "275d0023-82f1-4e09-c595-2e046eb98a12"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (15, 5),\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'x-large',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "pylab.rcParams.update(params)\n",
    "\n",
    "sns.boxplot(x='Neighborhood', y='Price', data=ch_apts)\n",
    "plt.xlabel(\"Neighborhood\");\n",
    "plt.xticks(rotation=75)\n",
    "plt.ylabel(\"Price USD\");\n",
    "plt.title(\"Prices by Neighborhood - Boxplots\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HXfJa2EVXl2E"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Lakeview', 'Chicago', 'Sheridan Park', 'Lincoln Park', 'Gold Coast']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "############################\n",
    "############################\n",
    "# 1 bedroom apts\n",
    "###########################\n",
    "###########################\n",
    "###########################\n",
    "\n",
    "# get 5 most popular neighborhoods \n",
    "chicago_apts['Neighborhood'].value_counts()[:5].index.tolist()\n",
    "\n",
    "#break it down and do a sns barplot below with the Neighborhood hue so you can compare those different areas\n",
    "#sns.stripplot(data=chicago_apts, y=\"Price\",x=\"Num_Bedrooms\", jitter=True)\n",
    "#sns.barplot(data=chicago_apts, y= \"Price\", x=\"Num_Bedrooms\",hue=\"Neighborhood\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 94 entries, 0 to 93\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Unnamed: 0    94 non-null     int64  \n",
      " 1   Posted        94 non-null     object \n",
      " 2   Neighborhood  94 non-null     object \n",
      " 3   Post_Title    94 non-null     object \n",
      " 4   Num_Bedrooms  82 non-null     float64\n",
      " 5   Sqft          21 non-null     float64\n",
      " 6   URL           94 non-null     object \n",
      " 7   Price         94 non-null     int64  \n",
      "dtypes: float64(2), int64(2), object(4)\n",
      "memory usage: 6.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(94, 8)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Posted', 'Neighborhood', 'Post_Title', 'Num_Bedrooms',\n",
       "       'Sqft', 'URL', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>Posted</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Post_Title</th>\n",
       "      <th>Num_Bedrooms</th>\n",
       "      <th>Sqft</th>\n",
       "      <th>URL</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-12-08 12:56:00</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>AVAILABLE NOW!!!!-3802 N SEELEY-2S 2 BED/ 1 BATH</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-12-08 12:55:00</td>\n",
       "      <td>Edgewater</td>\n",
       "      <td>1 bedroom in Edgewater neighborhood 1ST MONTH ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-12-08 12:50:00</td>\n",
       "      <td>South Loop</td>\n",
       "      <td>SOUTH LOOP! AMAZING LOFT STYLE Jr 1 bed WD, hd...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-12-08 12:50:00</td>\n",
       "      <td>Pilsen</td>\n",
       "      <td>RENOVATED Pilsen 1 Bed with in-unit washer/dryer</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-12-08 12:48:00</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>AVAILABLE NOW!!!!-HOT SOUTHPORT CORRIDOR LOCAT...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://chicago.craigslist.org/chc/apa/d/chica...</td>\n",
       "      <td>1495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0               Posted Neighborhood  \\\n",
       "0           0  2020-12-08 12:56:00      Chicago   \n",
       "1           1  2020-12-08 12:55:00    Edgewater   \n",
       "2           2  2020-12-08 12:50:00   South Loop   \n",
       "3           3  2020-12-08 12:50:00       Pilsen   \n",
       "4           4  2020-12-08 12:48:00      Chicago   \n",
       "\n",
       "                                          Post_Title  Num_Bedrooms  Sqft  \\\n",
       "0   AVAILABLE NOW!!!!-3802 N SEELEY-2S 2 BED/ 1 BATH           2.0   NaN   \n",
       "1  1 bedroom in Edgewater neighborhood 1ST MONTH ...           1.0   NaN   \n",
       "2  SOUTH LOOP! AMAZING LOFT STYLE Jr 1 bed WD, hd...           1.0   NaN   \n",
       "3   RENOVATED Pilsen 1 Bed with in-unit washer/dryer           1.0   NaN   \n",
       "4  AVAILABLE NOW!!!!-HOT SOUTHPORT CORRIDOR LOCAT...           1.0   NaN   \n",
       "\n",
       "                                                 URL  Price  \n",
       "0  https://chicago.craigslist.org/chc/apa/d/chica...   1500  \n",
       "1  https://chicago.craigslist.org/chc/apa/d/chica...    875  \n",
       "2  https://chicago.craigslist.org/chc/apa/d/chica...   1170  \n",
       "3  https://chicago.craigslist.org/chc/apa/d/chica...   1400  \n",
       "4  https://chicago.craigslist.org/chc/apa/d/chica...   1495  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Focusing on 1 bedroom apts\n",
    "#use created file\n",
    "chicago_apts = pd.read_csv(\"Craigslist-Data.csv\")\n",
    "\n",
    "chicago_apts.info()\n",
    "chicago_apts.shape\n",
    "chicago_apts.columns\n",
    "chicago_apts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47, 8)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one = chicago_apts[chicago_apts.Num_Bedrooms.eq(1.0)]\n",
    "one.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#start to look at the distributions\n",
    "from matplotlib import figure\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(one['Price'], edgecolor='black');\n",
    "plt.xlabel(\"Price\")\n",
    "plt.ylabel('Count')\n",
    "plt.title(\"Distribution of Rent for 1 bedroom Apts in Chicago\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as pylab\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (15, 5),\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'x-large',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "pylab.rcParams.update(params)\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.scatterplot(x='Price', y='Sqft', hue='Num_Bedrooms', palette='summer', x_jitter=True, y_jitter=True, s=125, data=one.dropna())\n",
    "plt.legend(fontsize=12)\n",
    "plt.xlabel(\"Price\", fontsize=18)\n",
    "plt.ylabel(\"Square Footage\", fontsize=18);\n",
    "plt.title(\"Price vs. Square Footage Colored by Number of Bedrooms\", fontsize=18);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "sns.regplot(x='Price', y='Sqft', data=one.dropna());\n",
    "plt.title('Price vs. Square Footage Regression Plot');\n",
    "plt.xlabel(\"Price (USD)\");\n",
    "plt.ylabel(\"Square Feet\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Num_Bedrooms</th>\n",
       "      <th>Sqft</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Andersonville</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1070.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auburn Gresham</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>525.0</td>\n",
       "      <td>740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bucktown</th>\n",
       "      <td>1.666667</td>\n",
       "      <td>980.0</td>\n",
       "      <td>1365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buena Park</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>850.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago</th>\n",
       "      <td>1.833333</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>1287.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago &amp; Lasalle</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1695.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago - Uptown</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago Prime Bridgeport</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>650.0</td>\n",
       "      <td>995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago, Illinois</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>885.0</td>\n",
       "      <td>875.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edgewater</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>951.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Evanston</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1507.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glenview</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold Coast</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1465.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hyde Park</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1435.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Irving Park</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>1750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jefferson Park</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1475.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakeview</th>\n",
       "      <td>1.476190</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>1427.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lincoln Park</th>\n",
       "      <td>1.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1671.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lincoln Square</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>2695.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Little Italy</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>3000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logan Square</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Midway Area</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>610.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Kenwood</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>895.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pilsen</th>\n",
       "      <td>1.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1245.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>River North</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1571.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rogers Park</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>1691.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roscoe Village</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1395.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sheridan Park</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1292.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Loop</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1170.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Loop Mccormick Square</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>2265.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Shore</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukrainian Village</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1575.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uptown</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>408.0</td>\n",
       "      <td>973.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Lakeview</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Loop</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>2900.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Num_Bedrooms    Sqft        Price\n",
       "Neighborhood                                                  \n",
       "Andersonville                    1.000000     NaN  1070.000000\n",
       "Auburn Gresham                   1.000000   525.0   740.000000\n",
       "Bucktown                         1.666667   980.0  1365.000000\n",
       "Buena Park                            NaN     NaN   850.000000\n",
       "Chicago                          1.833333  1089.0  1287.083333\n",
       "Chicago & Lasalle                2.000000  1000.0  1695.000000\n",
       "Chicago - Uptown                 1.000000     NaN   950.000000\n",
       "Chicago Prime Bridgeport         2.000000   650.0   995.000000\n",
       "Chicago, Illinois                1.000000   885.0   875.000000\n",
       "Edgewater                        1.000000     NaN   951.000000\n",
       "Evanston                         2.000000     NaN  1507.500000\n",
       "Glenview                         1.000000   700.0  1100.000000\n",
       "Gold Coast                       1.000000  1000.0  1465.000000\n",
       "Hyde Park                        1.000000     NaN  1435.000000\n",
       "Irving Park                      3.000000  1600.0  1750.000000\n",
       "Jefferson Park                   3.000000     NaN  1475.000000\n",
       "Lakeview                         1.476190  1050.0  1427.571429\n",
       "Lincoln Park                     1.750000     NaN  1671.250000\n",
       "Lincoln Square                   3.000000  2800.0  2695.000000\n",
       "Little Italy                     1.000000  2400.0  3000.000000\n",
       "Logan Square                     1.000000   900.0  1750.000000\n",
       "Midway Area                      1.000000     NaN   610.000000\n",
       "North Kenwood                    1.000000     NaN   895.000000\n",
       "Pilsen                           1.333333     NaN  1245.000000\n",
       "River North                      1.000000     NaN  1571.000000\n",
       "Rogers Park                      2.333333  1350.0  1691.666667\n",
       "Roscoe Village                   1.000000   900.0  1395.000000\n",
       "Sheridan Park                    2.000000     NaN  1292.000000\n",
       "South Loop                       1.000000     NaN  1170.000000\n",
       "South Loop Mccormick Square      2.000000  1125.0  2265.000000\n",
       "South Shore                      1.000000     NaN   800.000000\n",
       "Ukrainian Village                2.000000     NaN  1575.000000\n",
       "Uptown                           1.000000   408.0   973.333333\n",
       "West Lakeview                    2.000000     NaN  2100.000000\n",
       "West Loop                        1.000000  1100.0  2900.000000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#group by neighborhood\n",
    "ch_apts.groupby('Neighborhood').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neighborhood\n",
       "Midway Area           610.0\n",
       "Auburn Gresham        740.0\n",
       "South Shore           800.0\n",
       "Chicago, Illinois     875.0\n",
       "Chicago - Uptown      950.0\n",
       "Edgewater             951.0\n",
       "Uptown                985.0\n",
       "North Kenwood        1000.0\n",
       "Andersonville        1070.0\n",
       "Glenview             1100.0\n",
       "Sheridan Park        1145.0\n",
       "South Loop           1170.0\n",
       "Bucktown             1200.0\n",
       "Chicago              1256.0\n",
       "Lakeview             1376.8\n",
       "Pilsen               1392.5\n",
       "Roscoe Village       1395.0\n",
       "Hyde Park            1435.0\n",
       "Gold Coast           1465.0\n",
       "River North          1571.0\n",
       "Lincoln Park         1600.0\n",
       "Logan Square         1750.0\n",
       "West Loop            2900.0\n",
       "Little Italy         3000.0\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sort price to find cheapest\n",
    "one.groupby('Neighborhood').mean()['Price'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sLu8ltkzXlus"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (15, 5),\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'x-large',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "pylab.rcParams.update(params)\n",
    "\n",
    "sns.boxplot(x='Neighborhood', y='Price', data=one)\n",
    "plt.xlabel(\"Neighborhood\");\n",
    "plt.xticks(rotation=75)\n",
    "plt.ylabel(\"Price USD\");\n",
    "plt.title(\"Prices by Neighborhood of One Bedrooms - Boxplots\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MMaWO5vgXlnc"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "14HUxGXDXllp"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-siXGg8RXlf6"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Welcome To Colaboratory",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}