Chicago Craigslist Rent DataScrape v.02.ipynb

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  "metadata": {
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      "name": "Welcome To Colaboratory",
      "provenance": [],
      "collapsed_sections": []
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "_jGyBgvjXlXo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "a5319979-e33b-460f-e3f5-73cc2bb58cb8"
      },
      "source": [
        "#import get to call a get request on the site\n",
        "from requests import get\n",
        "\n",
        "#get the first page of the chicago housing prices\n",
        "response = get('https://chicago.craigslist.org/search/chc/apa?hasPic=1&availabilityMode=0') #get rid of posts without a pic using their filter\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)"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'bs4.element.ResultSet'>\n",
            "120\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1vwc4vZXlaF",
        "colab_type": "code",
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      },
      "source": [
        "#grab the first post\n",
        "post_one = posts[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BaxtdUl4Xlci",
        "colab_type": "code",
        "colab": {
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          "height": 34
        },
        "outputId": "9d9cb440-03e9-4eb4-dbb6-2729989a78e9"
      },
      "source": [
        "#grab the price of the first post\n",
        "post_one_price = post_one.a.text\n",
        "post_one_price.strip()"
      ],
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'$2000'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pqmoMN5VXlg4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#grab the time of the post in datetime format to save on cleaning efforts\n",
        "post_one_time = post_one.find('time', class_= 'result-date')\n",
        "post_one_datetime = post_one_time['datetime']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjHdFsk7XljA",
        "colab_type": "code",
        "colab": {}
      },
      "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"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCOC0VNZXlpN",
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          "height": 200
        },
        "outputId": "f2483405-30ba-476d-9bc7-7240b120064c"
      },
      "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",
        "\n",
        "post_one_num_bedrooms = post_one.find('span', class_ = 'housing').text.split()[0]\n",
        "\n",
        "post_one_sqft = post_one.find('span', class_ = 'housing').text.split()[2][:-3] #cleans the ft2 at the end"
      ],
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-228-8507a438cc20>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpost_one_num_bedrooms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpost_one\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'span'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'housing'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mpost_one_sqft\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpost_one\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'span'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'housing'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m#cleans the ft2 at the end\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-x-QMD_NXlsF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#the neighborhood is grabbed by finding the span class 'result-hood' and pulling the text element from that\n",
        "post_one_hood = posts[0].find('span', class_='result-hood').text"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-kqViHwzXlwa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "26f8f94e-c23a-4fd7-9abf-4c7bada70b24"
      },
      "source": [
        "#build out the loop\n",
        "from time import sleep\n",
        "import re\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, and turns it into an int\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!\")"
      ],
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\n",
            "\n",
            "Scrape complete!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qhfokzt7XlzC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "outputId": "e418b495-e965-4c4b-e0f9-02892661535a"
      },
      "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)"
      ],
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2652 entries, 0 to 2651\n",
            "Data columns (total 7 columns):\n",
            "Posted          2652 non-null object\n",
            "Neighborhood    2652 non-null object\n",
            "Post_Title      2652 non-null object\n",
            "Num_Bedrooms    2132 non-null object\n",
            "Sqft            832 non-null float64\n",
            "URL             2652 non-null object\n",
            "Price           2652 non-null int64\n",
            "dtypes: float64(1), int64(1), object(5)\n",
            "memory usage: 145.2+ KB\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "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>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>2019-12-11 15:13</td>\n",
              "      <td>(Chicago - River West)</td>\n",
              "      <td>True Timber Loft 1B/1B - Exposed Brick, Street Parking, Roof Deck</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-true-timber-loft-1b-1b-exposed/7036694247.html</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-12-11 15:13</td>\n",
              "      <td>(Chicago - River North)</td>\n",
              "      <td>River North 2/2, balcony, 1050 sq feet, outdoor pool, Chicago Red Line</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-river-north-2-2-balcony-1050-sq/7036693636.html</td>\n",
              "      <td>2385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-12-11 15:11</td>\n",
              "      <td>(Chicago - River North)</td>\n",
              "      <td>Beautiful Studio, Hardwood, W/D in unit, amazing location!</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-beautiful-studio-hardwood-d-in/7036692687.html</td>\n",
              "      <td>1560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-12-11 15:09</td>\n",
              "      <td>(River North)</td>\n",
              "      <td>State of the art building is having a special deal on studios</td>\n",
              "      <td>NaN</td>\n",
              "      <td>448.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-state-of-the-art-building-is/7036691329.html</td>\n",
              "      <td>1648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-12-11 15:04</td>\n",
              "      <td>(Chicago - The Loop)</td>\n",
              "      <td>Historic Loop Studio - Hardwood, Washer/Dryer, Indoor Pool, Gym</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-historic-loop-studio-hardwood/7036687683.html</td>\n",
              "      <td>1700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2019-12-11 15:04</td>\n",
              "      <td>(Belmont Cragin)</td>\n",
              "      <td>2.5 bed all bills included (se habla Espanol)</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-25-bed-all-bills-included-se/7036687582.html</td>\n",
              "      <td>1250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2019-12-11 15:03</td>\n",
              "      <td>(South Shore)</td>\n",
              "      <td>MAGNIFICENT🎁 UNIT! A MUST SEE!🎁</td>\n",
              "      <td>NaN</td>\n",
              "      <td>500.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-magnificent-unit-must-see/7036687126.html</td>\n",
              "      <td>705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2019-12-11 14:56</td>\n",
              "      <td>(South Chicago)</td>\n",
              "      <td>WINTER W/ FREE HEAT! MUST GIVE US A CALL 📞NOW! HWD FLRS!</td>\n",
              "      <td>3</td>\n",
              "      <td>975.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-winter-free-heat-must-give-us/7036681477.html</td>\n",
              "      <td>1075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2019-12-11 14:54</td>\n",
              "      <td>(Chicago - Fulton River District)</td>\n",
              "      <td>Fulton Market 2B/2B   W/D, Balcony, Gym, Pool/Deck</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-fulton-market-2b-2b-d-balcony/7036680705.html</td>\n",
              "      <td>2644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2019-12-11 14:53</td>\n",
              "      <td>(Chicago - Lakeshore East)</td>\n",
              "      <td>Great value downtown! Convertible near grocery, connected to PEDWAY</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-great-value-downtown/7036679571.html</td>\n",
              "      <td>1450</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Posted  ... Price\n",
              "0  2019-12-11 15:13  ...  2000\n",
              "1  2019-12-11 15:13  ...  2385\n",
              "2  2019-12-11 15:11  ...  1560\n",
              "3  2019-12-11 15:09  ...  1648\n",
              "4  2019-12-11 15:04  ...  1700\n",
              "5  2019-12-11 15:04  ...  1250\n",
              "6  2019-12-11 15:03  ...   705\n",
              "7  2019-12-11 14:56  ...  1075\n",
              "8  2019-12-11 14:54  ...  2644\n",
              "9  2019-12-11 14:53  ...  1450\n",
              "\n",
              "[10 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TE8SXNrUXl5u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "########################DATA CLEANING\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTnHEt61Xl82",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#first things first, drop duplicate URLs because people are spammy on Craigslist. \n",
        "#Let's see how many uniqe posts we really have.\n",
        "ch_apts = ch_apts.drop_duplicates(subset='URL')\n",
        "len(ch_apts.drop_duplicates(subset='URL'))\n",
        "\n",
        "#make the number 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 be able to work with it\n",
        "from datetime import datetime\n",
        "\n",
        "ch_apts['Posted'] = pd.to_datetime(ch_apts['Posted'])\n",
        "\n",
        "#Looking at what neighborhoods there are with ch_apts['neighborhood'].unique() allowed me to see what\n",
        "#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_rooms['neighborhood'] = ch_rooms['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",
        "###################\n",
        "########################\n",
        
        "#remove whitespaces\n",
        "ch_apts['Neighborhood'] = ch_apts['Neighborhood'].apply(lambda x: x.strip())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XgR7WxPvXmAG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 97
        },
        "outputId": "b83d69e4-30a2-4f76-f20d-5e3d04716d1e"
      },
      "source": [
        "### EXPLORATORY DATA ANALYSIS\n",
        "ch_apts.head(1)"
      ],
      "execution_count": 249,
      "outputs": [
        {
          "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>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>2019-12-11 15:13:00</td>\n",
              "      <td>Chicago - River West</td>\n",
              "      <td>True Timber Loft 1B/1B - Exposed Brick, Street Parking, Roof Deck</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-true-timber-loft-1b-1b-exposed/7036694247.html</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Posted  ... Price\n",
              "0 2019-12-11 15:13:00  ...  2000\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xm8yaMzelE_J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "44a8000c-67df-4648-d5a2-2572a78994f0"
      },
      "source": [
        "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"
      ],
      "execution_count": 251,
      "outputs": [
        {
          "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>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>2019-12-11 15:13:00</td>\n",
              "      <td>Chicago - River West</td>\n",
              "      <td>True Timber Loft 1B/1B - Exposed Brick, Street Parking, Roof Deck</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-true-timber-loft-1b-1b-exposed/7036694247.html</td>\n",
              "      <td>2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-12-11 15:13:00</td>\n",
              "      <td>River North</td>\n",
              "      <td>River North 2/2, balcony, 1050 sq feet, outdoor pool, Chicago Red Line</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-river-north-2-2-balcony-1050-sq/7036693636.html</td>\n",
              "      <td>2385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-12-11 15:11:00</td>\n",
              "      <td>Chicago - River North</td>\n",
              "      <td>Beautiful Studio, Hardwood, W/D in unit, amazing location!</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-beautiful-studio-hardwood-d-in/7036692687.html</td>\n",
              "      <td>1560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-12-11 15:09:00</td>\n",
              "      <td>River North</td>\n",
              "      <td>State of the art building is having a special deal on studios</td>\n",
              "      <td>NaN</td>\n",
              "      <td>448.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-state-of-the-art-building-is/7036691329.html</td>\n",
              "      <td>1648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-12-11 15:04:00</td>\n",
              "      <td>Chicago - The Loop</td>\n",
              "      <td>Historic Loop Studio - Hardwood, Washer/Dryer, Indoor Pool, Gym</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-historic-loop-studio-hardwood/7036687683.html</td>\n",
              "      <td>1700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2019-12-11 15:04:00</td>\n",
              "      <td>Belmont Cragin</td>\n",
              "      <td>2.5 bed all bills included (se habla Espanol)</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-25-bed-all-bills-included-se/7036687582.html</td>\n",
              "      <td>1250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2019-12-11 15:03:00</td>\n",
              "      <td>South Shore</td>\n",
              "      <td>MAGNIFICENT🎁 UNIT! A MUST SEE!🎁</td>\n",
              "      <td>NaN</td>\n",
              "      <td>500.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-magnificent-unit-must-see/7036687126.html</td>\n",
              "      <td>705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2019-12-11 14:56:00</td>\n",
              "      <td>South Chicago</td>\n",
              "      <td>WINTER W/ FREE HEAT! MUST GIVE US A CALL 📞NOW! HWD FLRS!</td>\n",
              "      <td>3.0</td>\n",
              "      <td>975.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-winter-free-heat-must-give-us/7036681477.html</td>\n",
              "      <td>1075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2019-12-11 14:54:00</td>\n",
              "      <td>Chicago - Fulton River District</td>\n",
              "      <td>Fulton Market 2B/2B   W/D, Balcony, Gym, Pool/Deck</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-fulton-market-2b-2b-d-balcony/7036680705.html</td>\n",
              "      <td>2644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2019-12-11 14:53:00</td>\n",
              "      <td>Chicago - Lakeshore East</td>\n",
              "      <td>Great value downtown! Convertible near grocery, connected to PEDWAY</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-great-value-downtown/7036679571.html</td>\n",
              "      <td>1450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2019-12-11 14:52:00</td>\n",
              "      <td>Old Town</td>\n",
              "      <td>Checkout this Old Town Loft,  Red, Brown and purple line 5min walk</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-checkout-this-old-town-loft-red/7036679206.html</td>\n",
              "      <td>1675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2019-12-11 14:50:00</td>\n",
              "      <td>South Loop</td>\n",
              "      <td>South Loop 1/1, luxury apartment, in-unit laundry, walk to Trader Joes</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-south-loop-1-1-luxury-apartment/7036677222.html</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2019-12-11 14:46:00</td>\n",
              "      <td>South Loop</td>\n",
              "      <td>Beautiful apartment available 2000 S Wabash Ave - South Loop</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-beautiful-apartment-available/7036674646.html</td>\n",
              "      <td>2500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2019-12-11 14:42:00</td>\n",
              "      <td>Gold Coast</td>\n",
              "      <td>Boutique Style Studio Minutes Away from Michigan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-boutique-style-studio-minutes/7036671771.html</td>\n",
              "      <td>1344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2019-12-11 14:28:00</td>\n",
              "      <td>Edgewater</td>\n",
              "      <td>Sunny 3 bdrms 2 bath duplex in  Edgewater Glen near Loyola</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-sunny-3-bdrms-2-bath-duplex-in/7036660995.html</td>\n",
              "      <td>1395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2019-12-11 14:26:00</td>\n",
              "      <td>Portage Park</td>\n",
              "      <td>Newly Remodeled 2br/1ba 2nd fl apartment for rent Portage Park Chicago</td>\n",
              "      <td>2.0</td>\n",
              "      <td>850.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-newly-remodeled-2br-1ba-2nd-fl/7036659307.html</td>\n",
              "      <td>1100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2019-12-11 14:26:00</td>\n",
              "      <td>Roscoe Village</td>\n",
              "      <td>3 BED 2 BATH IN ROSCOE. HIGH END FINISHES, SPLIT FP, WINE CELLAR!</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1800.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-3-bed-2-bath-in-roscoe-high-end/7036659248.html</td>\n",
              "      <td>3750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2019-12-11 14:24:00</td>\n",
              "      <td>Bucktown</td>\n",
              "      <td>2Bed/2Bath Apartment in Bucktown</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-2bed-2bath-apartment-in-bucktown/7036657786.html</td>\n",
              "      <td>2300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2019-12-11 14:23:00</td>\n",
              "      <td>Uptown</td>\n",
              "      <td>Very Large Studio off Wilson Redline</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-very-large-studio-off-wilson/7036657172.html</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2019-12-11 14:22:00</td>\n",
              "      <td>Lincoln Park</td>\n",
              "      <td>Large 2 bedroom in Lincoln Park with Parking Included</td>\n",
              "      <td>2.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-large-2-bedroom-in-lincoln-park/7036656356.html</td>\n",
              "      <td>1650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2019-12-11 14:16:00</td>\n",
              "      <td>South Loop</td>\n",
              "      <td>Modern South Loop 2/2, in-unit laundry, balcony, giant closet</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-modern-south-loop-2-2-in-unit/7036652105.html</td>\n",
              "      <td>2290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2019-12-11 14:14:00</td>\n",
              "      <td>Logan Square</td>\n",
              "      <td>LOGAN SQUARE / AVONDALE **ALL UTILITIES INCLUDED** NEWLY REMODELED!!!!</td>\n",
              "      <td>2.0</td>\n",
              "      <td>975.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-logan-square-avondale-all/7036650702.html</td>\n",
              "      <td>900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2019-12-11 14:03:00</td>\n",
              "      <td>Lakeview</td>\n",
              "      <td>14th Floor Studio In Full Amenity Building! Heat Included! Ready Now!</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-14th-floor-studio-in-full/7036641833.html</td>\n",
              "      <td>1250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2019-12-11 14:01:00</td>\n",
              "      <td>Chicago - Logan Square</td>\n",
              "      <td>One Month Free Move-In Special! Laundry IN UNIT + BALCONY!</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-one-month-free-move-in-special/7036640857.html</td>\n",
              "      <td>2675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2019-12-11 13:45:00</td>\n",
              "      <td>3422 Lavergne</td>\n",
              "      <td>Lovely One Bedroom Apartment - Pets Welcome</td>\n",
              "      <td>1.0</td>\n",
              "      <td>600.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-lovely-one-bedroom-apartment/7036627815.html</td>\n",
              "      <td>1050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2019-12-11 13:44:00</td>\n",
              "      <td>2711 N Kenmore St.</td>\n",
              "      <td>5 Beds 2 Baths - Pets Welcome</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-5-beds-2-baths-pets-welcome/7036626872.html</td>\n",
              "      <td>3495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2019-12-11 13:43:00</td>\n",
              "      <td>2126 N Sheffield Ave</td>\n",
              "      <td>4br - 1200ft2 - 4 Bed 2 Bath Spacious Apartment. Entire Floor! Unit WD</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/4br-ft2-4-bed-2-bath-spacious-apartment/7036626353.html</td>\n",
              "      <td>3400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2019-12-11 13:42:00</td>\n",
              "      <td>2126 N Sheffield</td>\n",
              "      <td>Spacious 4 Bedroom In Heart of DePaul Campus</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-spacious-4-bedroom-in-heart-of/7036625756.html</td>\n",
              "      <td>3250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2019-12-11 13:41:00</td>\n",
              "      <td>Bucktown</td>\n",
              "      <td>Bright and Newly Renovated 2 Bedroom in Bucktown! Pets Welcome!</td>\n",
              "      <td>2.0</td>\n",
              "      <td>650.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-bright-and-newly-renovated-2/7036624970.html</td>\n",
              "      <td>1395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2019-12-11 13:41:00</td>\n",
              "      <td>South Loop</td>\n",
              "      <td>**DEAL** on South Loop 1 bed w/WD, HRWD, pool, gym, more!</td>\n",
              "      <td>1.0</td>\n",
              "      <td>656.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-deal-on-south-loop-1-bed-wd/7036624658.html</td>\n",
              "      <td>1705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2019-12-11 13:39:00</td>\n",
              "      <td>Hermosa</td>\n",
              "      <td>$1,295 - 2 Bed / 1 Bath in Hermosa</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-bed-1-bath-in-hermosa/7036623677.html</td>\n",
              "      <td>1295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2019-12-11 13:30:00</td>\n",
              "      <td>Tri-Taylor</td>\n",
              "      <td>Beautiful 1BR in Little Italy / University Village / UIC</td>\n",
              "      <td>1.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-beautiful-1br-in-little-italy/7036616395.html</td>\n",
              "      <td>1750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2019-12-11 13:30:00</td>\n",
              "      <td>Uptown</td>\n",
              "      <td>Highrise 1 Bed!  Pet Friendly, Elevator, Close to Red Line</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-highrise-1-bed-pet-friendly/7036615659.html</td>\n",
              "      <td>1350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2019-12-11 13:27:00</td>\n",
              "      <td>Rogers Park East</td>\n",
              "      <td>Affordable Lakefront Home, a 6 min walk to the CTA Red Line Station!</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-affordable-lakefront-home-6-min/7036613850.html</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2019-12-11 13:21:00</td>\n",
              "      <td>Lincoln Square</td>\n",
              "      <td>Classic Chi-Town Vintage 2 bed, FREE HT, Sm Dog ok, Sun Room!</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-classic-chi-town-vintage-2-bed/7036608919.html</td>\n",
              "      <td>1275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2019-12-11 13:16:00</td>\n",
              "      <td>West Ridge</td>\n",
              "      <td>Heat incld!  Parking Avail!  Elevator &amp; Pet Friendly</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-heat-incld-parking-avail/7036605262.html</td>\n",
              "      <td>1025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2019-12-11 13:13:00</td>\n",
              "      <td>Depaul</td>\n",
              "      <td>Depaul Area</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-depaul-area/7036602988.html</td>\n",
              "      <td>1695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2019-12-11 13:13:00</td>\n",
              "      <td>Ukrainian Village</td>\n",
              "      <td>Beautiful House for Rent Between Ukrainian Village and West Town</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3000.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-beautiful-house-for-rent/7036602567.html</td>\n",
              "      <td>5990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2019-12-11 13:08:00</td>\n",
              "      <td>Bridgeport</td>\n",
              "      <td>2 Bed + Den &amp; 2 Full Bath</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-2-bed-den-2-full-bath/7036598915.html</td>\n",
              "      <td>1450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>2019-12-11 13:08:00</td>\n",
              "      <td>Rogers Park</td>\n",
              "      <td>Updated Kitchen &amp; Bath! In Unit Laundry! Central A/C! Dogs Ok!</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-updated-kitchen-bath-in-unit/7036598366.html</td>\n",
              "      <td>1280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>2019-12-11 13:06:00</td>\n",
              "      <td>Pilsen</td>\n",
              "      <td>2 Bedroom 1 Bath Pilsen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-2-bedroom-1-bath-pilsen/7036597130.html</td>\n",
              "      <td>1200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>2019-12-11 13:03:00</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>4.5 months - 1 bed/1 bath apartment 29th floor Fulton River District</td>\n",
              "      <td>1.0</td>\n",
              "      <td>750.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-45-months-1-bed-1-bath/7036594841.html</td>\n",
              "      <td>2150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>2019-12-11 12:56:00</td>\n",
              "      <td>Edgewater</td>\n",
              "      <td>1 Block from Loyola!  Heat/Cooking gas/Water incld!  Pet Friendly</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-1-block-from-loyola-heat/7036589262.html</td>\n",
              "      <td>1095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>2019-12-11 12:54:00</td>\n",
              "      <td>Lake View</td>\n",
              "      <td>Breathtaking Lake View 1 Bedroom - 3257 N. Sheffield #3257-306</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-breathtaking-lake-view-1/7036587655.html</td>\n",
              "      <td>1695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2019-12-11 12:53:00</td>\n",
              "      <td>Lake View</td>\n",
              "      <td>Perfect 3 Bedroom/2 Bath in Lake View - 1417 W. Belle Plaine Ave #1417-1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-perfect-3-bedroom-2-bath-in/7036586380.html</td>\n",
              "      <td>2145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>2019-12-11 12:51:00</td>\n",
              "      <td>Lincoln Park</td>\n",
              "      <td>Prime Lincoln Park Location - 2056 N. Cleveland Ave #2056-2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-prime-lincoln-park-location/7036585184.html</td>\n",
              "      <td>4095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2019-12-11 12:49:00</td>\n",
              "      <td>Edgewater</td>\n",
              "      <td>SUPER CLOSE TO LOYOLA!  Pet Friendly!  Move in Speical</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-super-close-to-loyola-pet/7036583103.html</td>\n",
              "      <td>1650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>2019-12-11 12:48:00</td>\n",
              "      <td>Lake View</td>\n",
              "      <td>Breathtaking 4 Bedroom/2 Bath in Lake View - 3722 N. Fremont St #3722-2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-breathtaking-4-bedroom-2-bath/7036582622.html</td>\n",
              "      <td>3345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2019-12-11 12:47:00</td>\n",
              "      <td>Wrigleyville</td>\n",
              "      <td>Wonderfully Updated Wrigleyville 3 Bedroom - 1338 W. Waveland Ave #1338-1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-wonderfully-updated/7036581499.html</td>\n",
              "      <td>2675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>2019-12-11 12:45:00</td>\n",
              "      <td>Edgewater</td>\n",
              "      <td>Close to Red Line!  Heat included!  Updated 1 Bedroom!</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-close-to-red-line-heat-included/7036579927.html</td>\n",
              "      <td>1395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>2019-12-11 12:45:00</td>\n",
              "      <td>Lake View</td>\n",
              "      <td>Prime Lake View Location - 849 W. Sheridan Rd #849S-G</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-prime-lake-view-location/7036579917.html</td>\n",
              "      <td>2265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>2019-12-11 12:44:00</td>\n",
              "      <td>River North</td>\n",
              "      <td>Slick River North 2B/2B W/D, Balcony, Gym, Pool/Spa</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-slick-river-north-2b-2b-d/7036579720.html</td>\n",
              "      <td>2875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>2019-12-11 12:43:00</td>\n",
              "      <td>Lincoln Park</td>\n",
              "      <td>Prime 3 Bedroom/2 Bath in Lincoln Park - 1817 N. Clybourn #2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-prime-3-bedroom-2-bath-in/7036578926.html</td>\n",
              "      <td>2845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>2019-12-11 12:43:00</td>\n",
              "      <td>West Rogers Park</td>\n",
              "      <td>Updated 1 bedroom w In Unit Laundry &amp; Central HVAC!</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-updated-1-bedroom-in-unit/7036578918.html</td>\n",
              "      <td>975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>2019-12-11 12:40:00</td>\n",
              "      <td>Lake View</td>\n",
              "      <td>Beautiful 1 Bedroom in Lake View - 611 W. Briar Pl #203</td>\n",
              "      <td>1.0</td>\n",
              "      <td>650.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-beautiful-1-bedroom-in-lake/7036576132.html</td>\n",
              "      <td>1495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>2019-12-11 12:36:00</td>\n",
              "      <td>Rogers Park</td>\n",
              "      <td>Prime Rogers Park location 1 bed-2 bed $1050-1350</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-prime-rogers-park-location-1/7036573002.html</td>\n",
              "      <td>1050</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>2019-12-11 12:20:00</td>\n",
              "      <td>Lincoln Square</td>\n",
              "      <td>HUGE Lincoln Square Duplex</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-huge-lincoln-square-duplex/7036560067.html</td>\n",
              "      <td>2100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>2019-12-11 12:13:00</td>\n",
              "      <td>Uptown</td>\n",
              "      <td>Classic Studio in Historic Uptown - Wilson Redline</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-classic-studio-in-historic/7036554387.html</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>2019-12-11 12:10:00</td>\n",
              "      <td>Logan Square</td>\n",
              "      <td>$1000 OFF FIRST MONTH - HUGE SUNNY with private deck, parking, pets ok</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-1000-off-first-month-huge-sunny/7036551573.html</td>\n",
              "      <td>2200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>2019-12-11 12:06:00</td>\n",
              "      <td>Uptown</td>\n",
              "      <td>Uptown/Buena Park NEW Construction!</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-uptown-buena-park-new/7036548308.html</td>\n",
              "      <td>1565</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>2019-12-11 12:04:00</td>\n",
              "      <td>Logan Square</td>\n",
              "      <td>Logan Sq 2BR/1bath renovated $1350  900sq ft</td>\n",
              "      <td>2.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-logan-sq-2br-1bath-renovated-sq/7036547340.html</td>\n",
              "      <td>1350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>2019-12-11 12:04:00</td>\n",
              "      <td>Uptown</td>\n",
              "      <td>Uptown Studio off the Wilson Redline-$850</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-uptown-studio-off-the-wilson/7036546852.html</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>2019-12-11 12:00:00</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>Apt. For the rent</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-apt-for-the-rent/7036543154.html</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>2019-12-11 11:56:00</td>\n",
              "      <td>Gold Coast</td>\n",
              "      <td>Very safe location--Gold Coast Condo---No car needed for this one!</td>\n",
              "      <td>1.0</td>\n",
              "      <td>650.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-very-safe-location-gold-coast/7036539756.html</td>\n",
              "      <td>1575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>2019-12-11 11:53:00</td>\n",
              "      <td>Lakeview</td>\n",
              "      <td>Amazing 3 bed with 3 parking spots!</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-amazing-3-bed-with-3-parking/7036537586.html</td>\n",
              "      <td>2950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>2019-12-11 11:49:00</td>\n",
              "      <td>Hyde Park</td>\n",
              "      <td>Hyde Park - New Construction 1 Bed 1 Bath with Private Balcony</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-hyde-park-new-construction-1/7036534055.html</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>2019-12-11 11:48:00</td>\n",
              "      <td>South Shore</td>\n",
              "      <td>♦♦COME ON DOWN &amp; TOUR THIS ONE♦♦3BR|ON-SITE LAUNDRY|HIGH-END FINISHES</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-come-on-down-tour-this-one3bron/7036533833.html</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>2019-12-11 11:48:00</td>\n",
              "      <td>Hyde Park</td>\n",
              "      <td>Hyde Park - New Construction 2 Bed 2 Bath with Condo Quality Finishes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-hyde-park-new-construction-2/7036533237.html</td>\n",
              "      <td>2300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>2019-12-11 11:45:00</td>\n",
              "      <td>Uptown</td>\n",
              "      <td>Uptown - New Construction 3 Bed 2 Bath in amenity filled building</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-uptown-new-construction-3-bed-2/7036530979.html</td>\n",
              "      <td>2650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>2019-12-11 11:43:00</td>\n",
              "      <td>Uptown</td>\n",
              "      <td>New Construction Uptown - 2 Bed 1 Bath One Month Free!</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-new-construction-uptown-2-bed-1/7036529777.html</td>\n",
              "      <td>1950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>2019-12-11 11:42:00</td>\n",
              "      <td>Midway Area</td>\n",
              "      <td>Cano Properties - Studio, 1 &amp; 2 Bdrms</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-cano-properties-studio-1-2-bdrms/7036529007.html</td>\n",
              "      <td>625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>2019-12-11 11:40:00</td>\n",
              "      <td>Gold Coast</td>\n",
              "      <td>Charming Gold Coast Studio -- Pool, Gym,  Steps to Oak Street Beach</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-charming-gold-coast-studio-pool/7036527010.html</td>\n",
              "      <td>1400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>2019-12-11 11:36:00</td>\n",
              "      <td>Pilsen</td>\n",
              "      <td>1 BR****AVAIL 1/1**pets ok**FITS KING***IN UNIT LAUNDRY!!!!**</td>\n",
              "      <td>1.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-1-bravail-1-1pets-okfits-kingin/7036524103.html</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>2019-12-11 11:35:00</td>\n",
              "      <td>Chicago - Lakeshore East</td>\n",
              "      <td>Riverfront 1B/1B -- Gym, Rooftop Atrium, Pedway Access</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-riverfront-1b-1b-gym-rooftop/7036523095.html</td>\n",
              "      <td>1643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>2019-12-11 11:34:00</td>\n",
              "      <td>Chicago - South Loop</td>\n",
              "      <td>Open, Modern 1/1 - Great value, W/D, Pool. Grant Park, 5min to CTA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-open-modern-great-value-d-pool/7036521779.html</td>\n",
              "      <td>1699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>2019-12-11 11:29:00</td>\n",
              "      <td>Chicago - South Loop</td>\n",
              "      <td>Quaint, Spacious 1B/1B - WD, Balcony, Stainless Steel, Gym. Green Line</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-quaint-spacious-1b-1b-wd/7036517757.html</td>\n",
              "      <td>1787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>2019-12-11 11:27:00</td>\n",
              "      <td>6725 S. East End</td>\n",
              "      <td>Newly Renovated 3 Bedroom 2 Bath Apt. at 6725 East End</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/newly-renovated-3-bedroom-2-bath-apt-at/7036516526.html</td>\n",
              "      <td>1550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>2019-12-11 11:26:00</td>\n",
              "      <td>South Loop</td>\n",
              "      <td>Modern Convertible -- WD, HW, Balcony! Amenity Packed, Shopping, CTA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-modern-convertible-wd-hw/7036515369.html</td>\n",
              "      <td>1680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2019-12-11 11:20:00</td>\n",
              "      <td>Irving Park</td>\n",
              "      <td>Large 2 Room Studio W/Heat &amp; Gas Included! New Bathroom! Ready!</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-large-2-room-studio-heat-gas/7036510980.html</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>2019-12-11 11:19:00</td>\n",
              "      <td>South Loop</td>\n",
              "      <td>Furnished, Smart South Loop 1BR w/ Gym, Pool, on Michigan Ave</td>\n",
              "      <td>1.0</td>\n",
              "      <td>710.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-furnished-smart-south-loop-1br/7036509532.html</td>\n",
              "      <td>2290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>2019-12-11 11:17:00</td>\n",
              "      <td>Uptown</td>\n",
              "      <td>Jr. One Bedroom in Uptown! - $995</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-jr-one-bedroom-in-uptown-995/7036507904.html</td>\n",
              "      <td>995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>2019-12-11 11:12:00</td>\n",
              "      <td>Uptown</td>\n",
              "      <td>micro studio w/Maximum Style FREE RENT SPECIAL</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-micro-studio-maximum-style-free/7036503830.html</td>\n",
              "      <td>877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>2019-12-11 11:09:00</td>\n",
              "      <td>Edgewater</td>\n",
              "      <td>Spacious Apartments -Price Drop - (Chicago-Edgewater)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-spacious-apartments-price-drop/7036501474.html</td>\n",
              "      <td>995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>2019-12-11 11:08:00</td>\n",
              "      <td>Albany Park</td>\n",
              "      <td>Beautiful Newly Renovated  Two  Bedrooms (Chicago-Albany Park)</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-beautiful-newly-renovated-two/7036500794.html</td>\n",
              "      <td>1375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>2019-12-11 11:06:00</td>\n",
              "      <td>Albany Park</td>\n",
              "      <td>Amazing Newly Renovated One Bedroom Apartments (Chicago-Albany Park)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-amazing-newly-renovated-one/7036499496.html</td>\n",
              "      <td>1150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>2019-12-11 11:05:00</td>\n",
              "      <td>South Loop</td>\n",
              "      <td>Must See!  South Loop Jr. 1B/1B with w/d, Balcony, Pool, Gym.</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-must-see-south-loop-jr-1b-1b/7036498281.html</td>\n",
              "      <td>1638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>2019-12-11 11:03:00</td>\n",
              "      <td>River North</td>\n",
              "      <td>Furnished Lux River North Studio w/ Gym, W/D, by Magnificent Mile</td>\n",
              "      <td>NaN</td>\n",
              "      <td>580.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-furnished-lux-river-north/7036496814.html</td>\n",
              "      <td>1590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>2019-12-11 11:02:00</td>\n",
              "      <td>Irving Park</td>\n",
              "      <td>The Seville In Old Irving Studio @ Keystone St.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-the-seville-in-old-irving/7036495790.html</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>2019-12-11 10:59:00</td>\n",
              "      <td>Rogers Park</td>\n",
              "      <td>Recently Remodeled Studio (Chicago-East Rogers Park)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-recently-remodeled-studio/7036493530.html</td>\n",
              "      <td>795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>2019-12-11 10:59:00</td>\n",
              "      <td>South Douglas Park West Pilsen</td>\n",
              "      <td>Vintage Large Two bedroom in Corner building with Sideyard</td>\n",
              "      <td>2.0</td>\n",
              "      <td>850.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-vintage-large-two-bedroom-in/7036493242.html</td>\n",
              "      <td>1195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>2019-12-11 10:55:00</td>\n",
              "      <td>Lincoln Square</td>\n",
              "      <td>Awesome 1 Bedroom in Heart of Lincoln Square</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-awesome-1-bedroom-in-heart-of/7036490186.html</td>\n",
              "      <td>1250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>2019-12-11 10:54:00</td>\n",
              "      <td>Lincoln Square</td>\n",
              "      <td>Awesome Huge Studio in Heart of Lincoln Square</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/awesome-huge-studio-in-heart-of-lincoln/7036489241.html</td>\n",
              "      <td>1200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>2019-12-11 10:53:00</td>\n",
              "      <td>Lincoln Square</td>\n",
              "      <td>Awesome 1 Bedroom in Heart of Lincoln Square</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-awesome-1-bedroom-in-heart-of/7036488247.html</td>\n",
              "      <td>1250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>2019-12-11 10:51:00</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>BIG Rehabbed South Douglas Park 2862_1F</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1300.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-big-rehabbed-south-douglas-park/7036487142.html</td>\n",
              "      <td>1595</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>2019-12-11 10:46:00</td>\n",
              "      <td>Edgewater</td>\n",
              "      <td>Beautiful One Bedroom Units (Chicago-Edgewater)</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-beautiful-one-bedroom-units/7036482792.html</td>\n",
              "      <td>1250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>2019-12-11 10:42:00</td>\n",
              "      <td>River North</td>\n",
              "      <td>Ideal River North 1BR w/ Gym, Pool, W/D, near MM, by Blueground</td>\n",
              "      <td>1.0</td>\n",
              "      <td>450.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-ideal-river-north-1br-gym-pool/7036479930.html</td>\n",
              "      <td>2990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>2019-12-11 10:37:00</td>\n",
              "      <td>62Nd &amp; Greenwood</td>\n",
              "      <td>Beautiful, Large 1 Bedroom 1 Bath. No Deposit, No Move-in Fee</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-beautiful-large-1-bedroom-1/7036475765.html</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>2019-12-11 10:33:00</td>\n",
              "      <td>River North</td>\n",
              "      <td>Ample 1BR in River North w/ Gym, near Riverwalk by Blueground</td>\n",
              "      <td>1.0</td>\n",
              "      <td>863.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-ample-1br-in-river-north-gym/7036472527.html</td>\n",
              "      <td>2590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>2019-12-11 10:32:00</td>\n",
              "      <td>810 Seward - Evanston</td>\n",
              "      <td>Beautiful  Two Bedroom Apartments @ 810 Seward</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/evanston-beautiful-two-bedroom/7036471563.html</td>\n",
              "      <td>1350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>2019-12-11 10:26:00</td>\n",
              "      <td>Old Town</td>\n",
              "      <td>Large 1 Bed in Heart of Old Town</td>\n",
              "      <td>1.0</td>\n",
              "      <td>750.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-large-1-bed-in-heart-of-old-town/7036466670.html</td>\n",
              "      <td>1700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>2019-12-11 10:26:00</td>\n",
              "      <td>Milwaukee</td>\n",
              "      <td>HUGE LUX 2BR plus Den and back porch</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-huge-lux-2br-plus-den-and-back/7036466442.html</td>\n",
              "      <td>1375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>2019-12-11 10:17:00</td>\n",
              "      <td>Old Town</td>\n",
              "      <td>1500 N Orleans 1 BR</td>\n",
              "      <td>1.0</td>\n",
              "      <td>800.0</td>\n",
              "      <td>https://chicago.craigslist.org/chc/apa/d/chicago-1500-orleans-1-br/7036459426.html</td>\n",
              "      <td>1795</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Posted  ... Price\n",
              "0   2019-12-11 15:13:00  ...  2000\n",
              "1   2019-12-11 15:13:00  ...  2385\n",
              "2   2019-12-11 15:11:00  ...  1560\n",
              "3   2019-12-11 15:09:00  ...  1648\n",
              "4   2019-12-11 15:04:00  ...  1700\n",
              "5   2019-12-11 15:04:00  ...  1250\n",
              "6   2019-12-11 15:03:00  ...   705\n",
              "7   2019-12-11 14:56:00  ...  1075\n",
              "8   2019-12-11 14:54:00  ...  2644\n",
              "9   2019-12-11 14:53:00  ...  1450\n",
              "10  2019-12-11 14:52:00  ...  1675\n",
              "11  2019-12-11 14:50:00  ...  1600\n",
              "12  2019-12-11 14:46:00  ...  2500\n",
              "13  2019-12-11 14:42:00  ...  1344\n",
              "14  2019-12-11 14:28:00  ...  1395\n",
              "15  2019-12-11 14:26:00  ...  1100\n",
              "16  2019-12-11 14:26:00  ...  3750\n",
              "17  2019-12-11 14:24:00  ...  2300\n",
              "18  2019-12-11 14:23:00  ...   950\n",
              "19  2019-12-11 14:22:00  ...  1650\n",
              "20  2019-12-11 14:16:00  ...  2290\n",
              "21  2019-12-11 14:14:00  ...   900\n",
              "22  2019-12-11 14:03:00  ...  1250\n",
              "23  2019-12-11 14:01:00  ...  2675\n",
              "24  2019-12-11 13:45:00  ...  1050\n",
              "25  2019-12-11 13:44:00  ...  3495\n",
              "26  2019-12-11 13:43:00  ...  3400\n",
              "27  2019-12-11 13:42:00  ...  3250\n",
              "28  2019-12-11 13:41:00  ...  1395\n",
              "29  2019-12-11 13:41:00  ...  1705\n",
              "30  2019-12-11 13:39:00  ...  1295\n",
              "31  2019-12-11 13:30:00  ...  1750\n",
              "32  2019-12-11 13:30:00  ...  1350\n",
              "33  2019-12-11 13:27:00  ...   850\n",
              "34  2019-12-11 13:21:00  ...  1275\n",
              "35  2019-12-11 13:16:00  ...  1025\n",
              "36  2019-12-11 13:13:00  ...  1695\n",
              "37  2019-12-11 13:13:00  ...  5990\n",
              "38  2019-12-11 13:08:00  ...  1450\n",
              "39  2019-12-11 13:08:00  ...  1280\n",
              "40  2019-12-11 13:06:00  ...  1200\n",
              "41  2019-12-11 13:03:00  ...  2150\n",
              "42  2019-12-11 12:56:00  ...  1095\n",
              "43  2019-12-11 12:54:00  ...  1695\n",
              "44  2019-12-11 12:53:00  ...  2145\n",
              "45  2019-12-11 12:51:00  ...  4095\n",
              "46  2019-12-11 12:49:00  ...  1650\n",
              "47  2019-12-11 12:48:00  ...  3345\n",
              "48  2019-12-11 12:47:00  ...  2675\n",
              "49  2019-12-11 12:45:00  ...  1395\n",
              "50  2019-12-11 12:45:00  ...  2265\n",
              "51  2019-12-11 12:44:00  ...  2875\n",
              "52  2019-12-11 12:43:00  ...  2845\n",
              "53  2019-12-11 12:43:00  ...   975\n",
              "54  2019-12-11 12:40:00  ...  1495\n",
              "55  2019-12-11 12:36:00  ...  1050\n",
              "56  2019-12-11 12:20:00  ...  2100\n",
              "57  2019-12-11 12:13:00  ...   850\n",
              "58  2019-12-11 12:10:00  ...  2200\n",
              "59  2019-12-11 12:06:00  ...  1565\n",
              "60  2019-12-11 12:04:00  ...  1350\n",
              "61  2019-12-11 12:04:00  ...   850\n",
              "62  2019-12-11 12:00:00  ...   950\n",
              "63  2019-12-11 11:56:00  ...  1575\n",
              "64  2019-12-11 11:53:00  ...  2950\n",
              "65  2019-12-11 11:49:00  ...  1600\n",
              "66  2019-12-11 11:48:00  ...   950\n",
              "67  2019-12-11 11:48:00  ...  2300\n",
              "68  2019-12-11 11:45:00  ...  2650\n",
              "69  2019-12-11 11:43:00  ...  1950\n",
              "70  2019-12-11 11:42:00  ...   625\n",
              "71  2019-12-11 11:40:00  ...  1400\n",
              "72  2019-12-11 11:36:00  ...  1000\n",
              "73  2019-12-11 11:35:00  ...  1643\n",
              "74  2019-12-11 11:34:00  ...  1699\n",
              "75  2019-12-11 11:29:00  ...  1787\n",
              "76  2019-12-11 11:27:00  ...  1550\n",
              "77  2019-12-11 11:26:00  ...  1680\n",
              "78  2019-12-11 11:20:00  ...   850\n",
              "79  2019-12-11 11:19:00  ...  2290\n",
              "80  2019-12-11 11:17:00  ...   995\n",
              "81  2019-12-11 11:12:00  ...   877\n",
              "82  2019-12-11 11:09:00  ...   995\n",
              "83  2019-12-11 11:08:00  ...  1375\n",
              "84  2019-12-11 11:06:00  ...  1150\n",
              "85  2019-12-11 11:05:00  ...  1638\n",
              "86  2019-12-11 11:03:00  ...  1590\n",
              "87  2019-12-11 11:02:00  ...   850\n",
              "88  2019-12-11 10:59:00  ...   795\n",
              "89  2019-12-11 10:59:00  ...  1195\n",
              "90  2019-12-11 10:55:00  ...  1250\n",
              "91  2019-12-11 10:54:00  ...  1200\n",
              "92  2019-12-11 10:53:00  ...  1250\n",
              "93  2019-12-11 10:51:00  ...  1595\n",
              "94  2019-12-11 10:46:00  ...  1250\n",
              "95  2019-12-11 10:42:00  ...  2990\n",
              "96  2019-12-11 10:37:00  ...   700\n",
              "97  2019-12-11 10:33:00  ...  2590\n",
              "98  2019-12-11 10:32:00  ...  1350\n",
              "99  2019-12-11 10:26:00  ...  1700\n",
              "100 2019-12-11 10:26:00  ...  1375\n",
              "101 2019-12-11 10:17:00  ...  1795\n",
              "\n",
              "[102 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LoRKReDVXmDd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "bc2140ee-1e95-4432-b6f2-8ec4625c4689"
      },
      "source": [
        "ch_apts.info()"
      ],
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 102 entries, 0 to 101\n",
            "Data columns (total 7 columns):\n",
            "Posted          102 non-null datetime64[ns]\n",
            "Neighborhood    102 non-null object\n",
            "Post_Title      102 non-null object\n",
            "Num_Bedrooms    82 non-null float64\n",
            "Sqft            32 non-null float64\n",
            "URL             102 non-null object\n",
            "Price           102 non-null int64\n",
            "dtypes: datetime64[ns](1), float64(2), int64(1), object(3)\n",
            "memory usage: 6.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fnfRnj7TXog2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "outputId": "e87b5fa2-e06c-4fd4-94b4-fbe196ca01e8"
      },
      "source": [
        "import pandas as pd, numpy as np\n",
        "#",
        "chicago_apts = pd.read_csv(\"chicago_apts.csv\")"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-237-7cc74a490525>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#I think Craigslist blocked me. Reload dataframe via file for now\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mch_apts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ch_apts_1642_Jan_2_19_clean.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    683\u001b[0m         )\n\u001b[1;32m    684\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 685\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    687\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 457\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    459\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1133\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1134\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1135\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1136\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1137\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1915\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1917\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1918\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File b'ch_apts.csv' does not exist: b'ch_apts_1642_Jan_2_19_clean.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wUJGdnDxXo1b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "outputId": "998c602a-36bb-4094-aa40-5724ad331ef7"
      },
      "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(ch_apts['Price'], edgecolor='black');\n",
        "plt.xlabel(\"Price\")\n",
        "plt.ylabel('Count')\n",
        "plt.title(\"Distribution of Prices\");"
      ],
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mvPVBbtUWB94JJVlTVSuGrmMxcu6H49wPw3kfjnM/jCHnvbVlzbXAsrG23Uf6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\nSZLUEMOZJElSQwxnkiRJDfn/G0GNxQWqHLMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Eor2W_qXoz9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "outputId": "8af2344e-9691-4744-cc93-99c89f85aac6"
      },
      "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=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);"
      ],
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4cbJdmTlmxu67787+++/PP/7xDyAU3X/6059YuXJl1aW8vJz99tuPLl268MUX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\n7SIiIiIiOU5Fu4iIiIhIjvv/h9xHo9Uwi1kAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JxFiuvMgXow_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "outputId": "2025d02c-67af-41b2-bda5-5b853987432d"
      },
      "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\");"
      ],
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IiEiFUZAuIiIiIlJhFKSLiIiIiFSYMQvSzewmM3vCzNrNLGFme8zsPWZmeW1eYmaPmVnS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\niIj4jEK6iIiIiIjP/P9uIhP/BiDrWQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "382JUVRFXoup",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "02b5696c-05e2-489f-ef93-a26279be8df6"
      },
      "source": [
        "ch_apts.corr()"
      ],
      "execution_count": 256,
      "outputs": [
        {
          "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>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>0.472064</td>\n",
              "      <td>0.550991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sqft</th>\n",
              "      <td>0.472064</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.727463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price</th>\n",
              "      <td>0.550991</td>\n",
              "      <td>0.727463</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  0.472064  0.550991\n",
              "Sqft              0.472064  1.000000  0.727463\n",
              "Price             0.550991  0.727463  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EVFEsr5pXosE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "eb662494-eec7-4abe-9fb1-950f9487372f"
      },
      "source": [
        "#group by neighborhood\n",
        "ch_apts.groupby('Neighborhood').mean()"
      ],
      "execution_count": 257,
      "outputs": [
        {
          "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>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>2126 N Sheffield</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>1000.00</td>\n",
              "      <td>3250.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2126 N Sheffield Ave</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>1200.00</td>\n",
              "      <td>3400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2711 N Kenmore St.</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>1000.00</td>\n",
              "      <td>3495.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3422 Lavergne</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>600.00</td>\n",
              "      <td>1050.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62Nd &amp; Greenwood</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6725 S. East End</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1550.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>810 Seward - Evanston</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1350.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albany Park</th>\n",
              "      <td>1.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1262.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Belmont Cragin</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1250.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bridgeport</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1450.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bucktown</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1025.00</td>\n",
              "      <td>1847.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chicago</th>\n",
              "      <td>1.666667</td>\n",
              "      <td>1025.00</td>\n",
              "      <td>1565.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chicago - Fulton River District</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2644.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chicago - Lakeshore East</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1546.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chicago - Logan Square</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2675.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chicago - River North</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1560.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chicago - River West</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chicago - South Loop</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1743.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chicago - The Loop</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Depaul</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1695.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Edgewater</th>\n",
              "      <td>1.600000</td>\n",
              "      <td>1100.00</td>\n",
              "      <td>1296.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gold Coast</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>650.00</td>\n",
              "      <td>1439.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hermosa</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1295.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hyde Park</th>\n",
              "      <td>1.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1950.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Irving Park</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>850.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lake View</th>\n",
              "      <td>2.400000</td>\n",
              "      <td>650.00</td>\n",
              "      <td>2189.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lakeview</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1500.00</td>\n",
              "      <td>2100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lincoln Park</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>900.00</td>\n",
              "      <td>2863.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lincoln Square</th>\n",
              "      <td>1.750000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1415.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Logan Square</th>\n",
              "      <td>2.333333</td>\n",
              "      <td>937.50</td>\n",
              "      <td>1483.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Midway Area</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>625.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Milwaukee</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1375.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Old Town</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>775.00</td>\n",
              "      <td>1723.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pilsen</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>900.00</td>\n",
              "      <td>1100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portage Park</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>850.00</td>\n",
              "      <td>1100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>River North</th>\n",
              "      <td>1.500000</td>\n",
              "      <td>585.25</td>\n",
              "      <td>2346.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rogers Park</th>\n",
              "      <td>1.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1041.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rogers Park East</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>850.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Roscoe Village</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1800.00</td>\n",
              "      <td>3750.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Chicago</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>975.00</td>\n",
              "      <td>1075.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Douglas Park West Pilsen</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>850.00</td>\n",
              "      <td>1195.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Loop</th>\n",
              "      <td>1.285714</td>\n",
              "      <td>683.00</td>\n",
              "      <td>1957.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>South Shore</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>760.00</td>\n",
              "      <td>827.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tri-Taylor</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>900.00</td>\n",
              "      <td>1750.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ukrainian Village</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3000.00</td>\n",
              "      <td>5990.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uptown</th>\n",
              "      <td>1.600000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1337.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West Ridge</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1025.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West Rogers Park</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>975.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wrigleyville</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2675.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 Num_Bedrooms     Sqft        Price\n",
              "Neighborhood                                                       \n",
              "2126 N Sheffield                     4.000000  1000.00  3250.000000\n",
              "2126 N Sheffield Ave                 4.000000  1200.00  3400.000000\n",
              "2711 N Kenmore St.                   5.000000  1000.00  3495.000000\n",
              "3422 Lavergne                        1.000000   600.00  1050.000000\n",
              "62Nd & Greenwood                     1.000000      NaN   700.000000\n",
              "6725 S. East End                     3.000000      NaN  1550.000000\n",
              "810 Seward - Evanston                2.000000      NaN  1350.000000\n",
              "Albany Park                          1.500000      NaN  1262.500000\n",
              "Belmont Cragin                       3.000000      NaN  1250.000000\n",
              "Bridgeport                           2.000000      NaN  1450.000000\n",
              "Bucktown                             2.000000  1025.00  1847.500000\n",
              "Chicago                              1.666667  1025.00  1565.000000\n",
              "Chicago - Fulton River District      2.000000      NaN  2644.000000\n",
              "Chicago - Lakeshore East             1.000000      NaN  1546.500000\n",
              "Chicago - Logan Square               2.000000      NaN  2675.000000\n",
              "Chicago - River North                     NaN      NaN  1560.000000\n",
              "Chicago - River West                 1.000000      NaN  2000.000000\n",
              "Chicago - South Loop                 1.000000      NaN  1743.000000\n",
              "Chicago - The Loop                        NaN      NaN  1700.000000\n",
              "Depaul                               2.000000      NaN  1695.000000\n",
              "Edgewater                            1.600000  1100.00  1296.666667\n",
              "Gold Coast                           1.000000   650.00  1439.666667\n",
              "Hermosa                              2.000000      NaN  1295.000000\n",
              "Hyde Park                            1.500000      NaN  1950.000000\n",
              "Irving Park                               NaN      NaN   850.000000\n",
              "Lake View                            2.400000   650.00  2189.000000\n",
              "Lakeview                             3.000000  1500.00  2100.000000\n",
              "Lincoln Park                         3.000000   900.00  2863.333333\n",
              "Lincoln Square                       1.750000      NaN  1415.000000\n",
              "Logan Square                         2.333333   937.50  1483.333333\n",
              "Midway Area                          2.000000      NaN   625.000000\n",
              "Milwaukee                                 NaN      NaN  1375.000000\n",
              "Old Town                             1.000000   775.00  1723.333333\n",
              "Pilsen                               1.000000   900.00  1100.000000\n",
              "Portage Park                         2.000000   850.00  1100.000000\n",
              "River North                          1.500000   585.25  2346.333333\n",
              "Rogers Park                          1.500000      NaN  1041.666667\n",
              "Rogers Park East                     1.000000      NaN   850.000000\n",
              "Roscoe Village                       3.000000  1800.00  3750.000000\n",
              "South Chicago                        3.000000   975.00  1075.000000\n",
              "South Douglas Park West Pilsen       2.000000   850.00  1195.000000\n",
              "South Loop                           1.285714   683.00  1957.571429\n",
              "South Shore                          3.000000   760.00   827.500000\n",
              "Tri-Taylor                           1.000000   900.00  1750.000000\n",
              "Ukrainian Village                    3.000000  3000.00  5990.000000\n",
              "Uptown                               1.600000      NaN  1337.444444\n",
              "West Ridge                                NaN      NaN  1025.000000\n",
              "West Rogers Park                     1.000000      NaN   975.000000\n",
              "Wrigleyville                         3.000000      NaN  2675.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29mw0QmmXonp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "outputId": "0311e2cc-1497-4959-fa79-107f4d340fa2"
      },
      "source": [
        "#sort price to find cheapest\n",
        "ch_apts.groupby('Neighborhood').mean()['Price'].sort_values()"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Neighborhood\n",
              "Midway Area                         625.000000\n",
              "62Nd & Greenwood                    700.000000\n",
              "South Shore                         827.500000\n",
              "Irving Park                         850.000000\n",
              "Rogers Park East                    850.000000\n",
              "West Rogers Park                    975.000000\n",
              "West Ridge                         1025.000000\n",
              "Rogers Park                        1041.666667\n",
              "3422 Lavergne                      1050.000000\n",
              "South Chicago                      1075.000000\n",
              "Portage Park                       1100.000000\n",
              "Pilsen                             1100.000000\n",
              "South Douglas Park West Pilsen     1195.000000\n",
              "Belmont Cragin                     1250.000000\n",
              "Albany Park                        1262.500000\n",
              "Hermosa                            1295.000000\n",
              "Edgewater                          1296.666667\n",
              "Uptown                             1337.444444\n",
              "810 Seward - Evanston              1350.000000\n",
              "Milwaukee                          1375.000000\n",
              "Lincoln Square                     1415.000000\n",
              "Gold Coast                         1439.666667\n",
              "Bridgeport                         1450.000000\n",
              "Logan Square                       1483.333333\n",
              "Chicago - Lakeshore East           1546.500000\n",
              "6725 S. East End                   1550.000000\n",
              "Chicago - River North              1560.000000\n",
              "Chicago                            1565.000000\n",
              "Depaul                             1695.000000\n",
              "Chicago - The Loop                 1700.000000\n",
              "Old Town                           1723.333333\n",
              "Chicago - South Loop               1743.000000\n",
              "Tri-Taylor                         1750.000000\n",
              "Bucktown                           1847.500000\n",
              "Hyde Park                          1950.000000\n",
              "South Loop                         1957.571429\n",
              "Chicago - River West               2000.000000\n",
              "Lakeview                           2100.000000\n",
              "Lake View                          2189.000000\n",
              "River North                        2346.333333\n",
              "Chicago - Fulton River District    2644.000000\n",
              "Wrigleyville                       2675.000000\n",
              "Chicago - Logan Square             2675.000000\n",
              "Lincoln Park                       2863.333333\n",
              "2126 N Sheffield                   3250.000000\n",
              "2126 N Sheffield Ave               3400.000000\n",
              "2711 N Kenmore St.                 3495.000000\n",
              "Roscoe Village                     3750.000000\n",
              "Ukrainian Village                  5990.000000\n",
              "Name: Price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kuHF75vEXl3Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 842
        },
        "outputId": "275d0023-82f1-4e09-c595-2e046eb98a12"
      },
      "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\");"
      ],
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JqurwJH9RVWe21r6xiLXsoapekeQVSfLoRz96KUsBAJaxjRs3ZvPmzfc937FjR1prs7av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\nOSOqBvrlmkwAYL91+eWXZ9e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pVMSSNO/HcZyW4W2B0xqMHTu26LkZM2Zw+umnc8oppzBg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\n2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tZL05c8fTwIoZEVyAN2ZJox2vw0sj3WC4EGz+DDmBJrOdn128fRdHDNmsj77M6HxiGv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\nP8zYidJrSz+5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXfJa2EVXl2E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLu8ltkzXlus",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMaWO5vgXlnc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14HUxGXDXllp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-siXGg8RXlf6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}