Exploratory-Data-Analysis.ipynb

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        "# Mission Rapid Analysis 🌎 (40 points)\n",
        "\n",
        "\n",
        "### 1.1 Overview ✉️\n",
        "\n",
        "Welcome to your mission! In this notebook, you will download a dataset containing restaurants' information in the state of California, US. The dataset will then be transformed, processed and prepared in a required format. This clean dataset will then be used to answer some analytical questions and create a few data visualizations in Python.\n",
        "\n",
        "This is a template notebook that has some code already filled-in to help you on your way. There are also cells that require you to fill in the python code to solve specific problems. There are sections of the notebook that contain a points tally for code written.\n",
        "\n",
        "**Each section of this notebook is largely independent, so if you get stuck on a problem you can always move on to the next one.**"
      ]
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      "source": [
        "### 1.2 Tools & Technologies 🪛\n",
        "\n",
        "- This exercise will be carried out using the [Python](https://www.python.org/) programming language and will rely hevily on the [Pandas](https://pandas.pydata.org/) library for data manipulation.\n",
        "- You may use any of [Matplotlib](https://matplotlib.org/), [Seaborn](https://seaborn.pydata.org/) or [Plotly](https://plotly.com/python/) packages for data visualization.\n",
        "- We will be using [Jupyter notebooks](https://jupyter.org/) to run Python code in order to view and interact better with our data and visualizations.\n",
        "- You are free to use [Google Colab](https://colab.research.google.com/) which provides an easy-to-use Jupyter interface.\n",
        "- When not in Colab, it is recommended to run this Jupyter Notebook within an [Anaconda](https://continuum.io/) environment\n",
        "- You can use any other Python packages that you deem fit for this project.\n",
        "\n",
        "> ⚠ **Ensure that your Python version is 3.9 or higher**\n",
        "\n",
        "![](https://upload.wikimedia.org/wikipedia/commons/1/1b/Blue_Python_3.9_Shield_Badge.svg)\n",
        "\n",
        "**Language**\n",
        "\n",
        "![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)\n",
        "\n",
        "**Environments & Packages**\n",
        "\n",
        "![Anaconda](https://img.shields.io/badge/Anaconda-%2344A833.svg?style=for-the-badge&logo=anaconda&logoColor=white)\n",
        "![Jupyter Notebook](https://img.shields.io/badge/jupyter-%23FA0F00.svg?style=for-the-badge&logo=jupyter&logoColor=white)\n",
        "![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white)\n",
        "![Matplotlib](https://img.shields.io/badge/Matplotlib-%23ffffff.svg?style=for-the-badge&logo=Matplotlib&logoColor=black)\n",
        "![Plotly](https://img.shields.io/badge/Plotly-%233F4F75.svg?style=for-the-badge&logo=plotly&logoColor=white)\n"
      ]
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      "source": [
        "### 2.1 Read California Restaurants 🔍 (3 points)\n",
        "\n",
        "In this section, we will load the dataset from this link :\n",
        "\n",
        "https://rapidopso365-my.sharepoint.com/:u:/g/personal/pushkar_ambatkar_rapidops_com/EZneJNDj5f1CrIY-ZjMDxakBJOYJjI2VAQqSZgBYdkCzjQ?e=2tpkQM\n",
        "\n",
        "\n",
        "- Ensure that pandas and plotly are installed (possibly via pip or poetry)\n",
        "- The dataset is about 300 MB in size and time-to-download depends on internet speed and availability\n",
        "- Download the dataset using Python into this notebook and load it into a pandas dataframe\n"
      ]
    },
    {
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      "source": [
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "import numpy as np\n",
        "\n",
        "CELL_HEIGHT: int = 50\n",
        "\n",
        "# Initialize helpers to ignore pandas warnings and resize columns and cells\n",
        "pd.set_option(\"chained_assignment\", None)\n",
        "pd.set_option(\"display.max_rows\", 50)\n",
        "pd.set_option(\"display.max_columns\", 500)\n",
        "pd.set_option('display.max_colwidth', CELL_HEIGHT)"
      ]
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      "id": "0e60d6b3-cea6-4a12-b85d-15748a6be4ea",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        {
          "output_type": "stream",
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          "text": [
            "CPU times: user 10.8 s, sys: 3.12 s, total: 13.9 s\n",
            "Wall time: 17.1 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "# ✏️ YOUR CODE HERE\n",
        "# df: pd.DataFrame = ?\n",
        "# Downloaded offline to load\n",
        "df = pd.read_json('/content/drive/MyDrive/Rapidops_Practice/california_restaurants.json')"
      ]
    },
    {
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      "id": "521e6f26-8256-4f7b-b662-4580448d07a0",
      "metadata": {
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      },
      "source": [
        "Create a restaurant ID column to uniquely index each restaurant\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hHww9EJXbYbd",
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      "id": "hHww9EJXbYbd",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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            "text/plain": [
              "         country   subregion          city           brand_name  categories  \\\n",
              "0  United States  California   Los Angeles  Luv2Eat Thai Bistro      [Thai]   \n",
              "1  United States  California  Sherman Oaks  Jerry's Famous Deli  [American]   \n",
              "2  United States  California      Pasadena        Terrace Sushi   [Spanish]   \n",
              "\n",
              "    latitude   longitude          area  \\\n",
              "0  34.097510 -118.335921     Hollywood   \n",
              "1  34.154596 -118.448700  Sherman Oaks   \n",
              "2  34.145800 -118.142900      Pasadena   \n",
              "\n",
              "                                             address  \\\n",
              "0  6660 W Sunset Blvd, Ste P, CA, Los Angeles, 90...   \n",
              "1                                               None   \n",
              "2  443 E Colorado Blvd, , CA, Pasadena, 91101, Un...   \n",
              "\n",
              "                                                menu  \\\n",
              "0  [{'name': 'Main Menu', 'sections': [{'name': '...   \n",
              "1  [{'name': 'Sample Menu', 'sections': [{'name':...   \n",
              "2  [{'name': 'Main Menu', 'sections': [{'name': '...   \n",
              "\n",
              "                                         description public_transit  \\\n",
              "0  Luv2Eat Thai Bistro is located in Los Angeles,...           None   \n",
              "1  <p>Feast on delicious grub at Jerry's Famous D...           None   \n",
              "2  <p>Terrace Sushi serves Japanese cuisines to i...           None   \n",
              "\n",
              "  cross_street                restaurant_website    phone_number  \\\n",
              "0         None       http://www.luv2eatthai.com/  (323) 498-5835   \n",
              "1         None  http://www.jerrysfamousdeli.com/  (818) 905-5774   \n",
              "2         None                              None  (626) 584-0503   \n",
              "\n",
              "  primary_cuisine   dining_style executive_chef_name    parking_info  \\\n",
              "0            Thai  Casual Dining                None  Street Parking   \n",
              "1        American  Casual Dining                None            None   \n",
              "2         Spanish  Casual Dining                None            None   \n",
              "\n",
              "        dress_code entertainment  \\\n",
              "0     Casual Dress          None   \n",
              "1  Business Casual          None   \n",
              "2  Business Casual          None   \n",
              "\n",
              "                                     operating_hours  price_range_id  \\\n",
              "0  Lunch\\nDaily 11:00 am–3:30 pm\\nDinner\\nDaily 4...               2   \n",
              "1                                               None               2   \n",
              "2                                               None               2   \n",
              "\n",
              "     price_range                     payment_options  \\\n",
              "0  $30 and under  [AMEX, Discover, MasterCard, Visa]   \n",
              "1  $30 and under                                  []   \n",
              "2  $30 and under                                  []   \n",
              "\n",
              "   maximum_days_advance_for_reservation  rating  rating_count  \\\n",
              "0                                    90     4.6           136   \n",
              "1                                    90     0.0             0   \n",
              "2                                    90     0.0             0   \n",
              "\n",
              "                                   rating_by_feature rating_distribution  \\\n",
              "0  {'food': 4.7, 'noise': 2.0, 'value': 4.6, 'ser...  [2, 3, 4, 23, 104]   \n",
              "1  {'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...     [0, 0, 0, 0, 0]   \n",
              "2  {'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...     [0, 0, 0, 0, 0]   \n",
              "\n",
              "   review_count                      review_topics awards experiences  \\\n",
              "0            18  [Spicy, Casual, Neighborhood Gem]     []          []   \n",
              "1             0                                 []     []          []   \n",
              "2             0                                 []     []          []   \n",
              "\n",
              "                                                tags editorial_lists  \\\n",
              "0  [Delivery, Gluten-free Options, Late Night, No...              []   \n",
              "1                                                 []              []   \n",
              "2                                                 []              []   \n",
              "\n",
              "                                           checklist  \\\n",
              "0  {'bar': False, 'counter': False, 'gifting': No...   \n",
              "1  {'bar': None, 'counter': None, 'gifting': None...   \n",
              "2  {'bar': None, 'counter': None, 'gifting': None...   \n",
              "\n",
              "                                  safety_precautions order_online_link  \\\n",
              "0  {'cleanMenus': None, 'limitedSeating': None, '...              None   \n",
              "1  {'cleanMenus': None, 'limitedSeating': None, '...              None   \n",
              "2  {'cleanMenus': None, 'limitedSeating': None, '...              None   \n",
              "\n",
              "                                     facebook  \\\n",
              "0  http://www.facebook.com/luv2eatthaibistro/   \n",
              "1                                        None   \n",
              "2                                        None   \n",
              "\n",
              "                                    menu_url popular_dishes  \\\n",
              "0  http://sappclub.com/restaurant.aspx?r=205             []   \n",
              "1                                       None             []   \n",
              "2                                       None             []   \n",
              "\n",
              "   daily_reservation_count  restaurant_id  \n",
              "0                      NaN              1  \n",
              "1                      NaN              2  \n",
              "2                      NaN              3  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>subregion</th>\n",
              "      <th>city</th>\n",
              "      <th>brand_name</th>\n",
              "      <th>categories</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>area</th>\n",
              "      <th>address</th>\n",
              "      <th>menu</th>\n",
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              "      <th>restaurant_website</th>\n",
              "      <th>phone_number</th>\n",
              "      <th>primary_cuisine</th>\n",
              "      <th>dining_style</th>\n",
              "      <th>executive_chef_name</th>\n",
              "      <th>parking_info</th>\n",
              "      <th>dress_code</th>\n",
              "      <th>entertainment</th>\n",
              "      <th>operating_hours</th>\n",
              "      <th>price_range_id</th>\n",
              "      <th>price_range</th>\n",
              "      <th>payment_options</th>\n",
              "      <th>maximum_days_advance_for_reservation</th>\n",
              "      <th>rating</th>\n",
              "      <th>rating_count</th>\n",
              "      <th>rating_by_feature</th>\n",
              "      <th>rating_distribution</th>\n",
              "      <th>review_count</th>\n",
              "      <th>review_topics</th>\n",
              "      <th>awards</th>\n",
              "      <th>experiences</th>\n",
              "      <th>tags</th>\n",
              "      <th>editorial_lists</th>\n",
              "      <th>checklist</th>\n",
              "      <th>safety_precautions</th>\n",
              "      <th>order_online_link</th>\n",
              "      <th>facebook</th>\n",
              "      <th>menu_url</th>\n",
              "      <th>popular_dishes</th>\n",
              "      <th>daily_reservation_count</th>\n",
              "      <th>restaurant_id</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Luv2Eat Thai Bistro</td>\n",
              "      <td>[Thai]</td>\n",
              "      <td>34.097510</td>\n",
              "      <td>-118.335921</td>\n",
              "      <td>Hollywood</td>\n",
              "      <td>6660 W Sunset Blvd, Ste P, CA, Los Angeles, 90...</td>\n",
              "      <td>[{'name': 'Main Menu', 'sections': [{'name': '...</td>\n",
              "      <td>Luv2Eat Thai Bistro is located in Los Angeles,...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>http://www.luv2eatthai.com/</td>\n",
              "      <td>(323) 498-5835</td>\n",
              "      <td>Thai</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>None</td>\n",
              "      <td>Street Parking</td>\n",
              "      <td>Casual Dress</td>\n",
              "      <td>None</td>\n",
              "      <td>Lunch\\nDaily 11:00 am–3:30 pm\\nDinner\\nDaily 4...</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[AMEX, Discover, MasterCard, Visa]</td>\n",
              "      <td>90</td>\n",
              "      <td>4.6</td>\n",
              "      <td>136</td>\n",
              "      <td>{'food': 4.7, 'noise': 2.0, 'value': 4.6, 'ser...</td>\n",
              "      <td>[2, 3, 4, 23, 104]</td>\n",
              "      <td>18</td>\n",
              "      <td>[Spicy, Casual, Neighborhood Gem]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[Delivery, Gluten-free Options, Late Night, No...</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': False, 'counter': False, 'gifting': No...</td>\n",
              "      <td>{'cleanMenus': None, 'limitedSeating': None, '...</td>\n",
              "      <td>None</td>\n",
              "      <td>http://www.facebook.com/luv2eatthaibistro/</td>\n",
              "      <td>http://sappclub.com/restaurant.aspx?r=205</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Sherman Oaks</td>\n",
              "      <td>Jerry's Famous Deli</td>\n",
              "      <td>[American]</td>\n",
              "      <td>34.154596</td>\n",
              "      <td>-118.448700</td>\n",
              "      <td>Sherman Oaks</td>\n",
              "      <td>None</td>\n",
              "      <td>[{'name': 'Sample Menu', 'sections': [{'name':...</td>\n",
              "      <td>&lt;p&gt;Feast on delicious grub at Jerry's Famous D...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>http://www.jerrysfamousdeli.com/</td>\n",
              "      <td>(818) 905-5774</td>\n",
              "      <td>American</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Business Casual</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[]</td>\n",
              "      <td>90</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>{'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...</td>\n",
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              "      <td>0</td>\n",
              "      <td>[]</td>\n",
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              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': None, 'counter': None, 'gifting': None...</td>\n",
              "      <td>{'cleanMenus': None, 'limitedSeating': None, '...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Pasadena</td>\n",
              "      <td>Terrace Sushi</td>\n",
              "      <td>[Spanish]</td>\n",
              "      <td>34.145800</td>\n",
              "      <td>-118.142900</td>\n",
              "      <td>Pasadena</td>\n",
              "      <td>443 E Colorado Blvd, , CA, Pasadena, 91101, Un...</td>\n",
              "      <td>[{'name': 'Main Menu', 'sections': [{'name': '...</td>\n",
              "      <td>&lt;p&gt;Terrace Sushi serves Japanese cuisines to i...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>(626) 584-0503</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Business Casual</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[]</td>\n",
              "      <td>90</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>{'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...</td>\n",
              "      <td>[0, 0, 0, 0, 0]</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
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              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': None, 'counter': None, 'gifting': None...</td>\n",
              "      <td>{'cleanMenus': None, 'limitedSeating': None, '...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-4c82dca0-1034-4155-b1b4-692ffdeaff52');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df[\"restaurant_id\"] = range(1, len(df) + 1)\n",
        "df.head(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0b5d359f-7c75-40b4-81a2-b294fd9e388c",
      "metadata": {
        "id": "0b5d359f-7c75-40b4-81a2-b294fd9e388c"
      },
      "source": [
        "### 2.2 Basic Operations 🔧 (4 points)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "47f2c62f-da56-40f1-abda-29ba3ee4a5a3",
      "metadata": {
        "id": "47f2c62f-da56-40f1-abda-29ba3ee4a5a3"
      },
      "source": [
        "#### 2.2.1 Restaurants by City 🌆 (1 point)\n",
        "\n",
        "For each city in california, find\n",
        "\n",
        "1. the number of restaurants in that city,\n",
        "2. mean `rating` of all restaurants in the city,\n",
        "3. mean `price_range_id` per city,\n",
        "4. mean `maximum_days_advance_for_reservation` per city\n",
        "\n",
        "sort by number of restaurants.\n",
        "\n",
        "The resulting dataframe's top 5 rows would look as follows:\n",
        "\n",
        "| city          | restaurant_count | avg_rating | avg_price_range_id | avg_reservation_advance |\n",
        "| :------------ | ---------------: | ---------: | -----------------: | ----------------------: |\n",
        "| San Francisco |             1645 |    2.59343 |             2.3617 |                 90.3453 |\n",
        "| Los Angeles   |             1604 |    2.31995 |            2.29052 |                  86.692 |\n",
        "| San Diego     |             1034 |    2.65493 |            2.28723 |                 94.5783 |\n",
        "| San Jose      |              372 |    1.54597 |            2.16398 |                 88.3011 |\n",
        "| Sacramento    |              329 |    1.68663 |            2.26748 |                 95.0274 |\n"
      ]
    },
    {
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            "text/plain": [
              "               restaurant_count  avg_rating  avg_price_range_id  \\\n",
              "city                                                              \n",
              "San Francisco              1645    2.593435            2.361702   \n",
              "Los Angeles                1604    2.319950            2.290524   \n",
              "San Diego                  1034    2.654932            2.287234   \n",
              "San Jose                    372    1.545968            2.163978   \n",
              "Sacramento                  329    1.686626            2.267477   \n",
              "\n",
              "               avg_reservation_advance  \n",
              "city                                    \n",
              "San Francisco                90.345289  \n",
              "Los Angeles                  86.692020  \n",
              "San Diego                    94.578337  \n",
              "San Jose                     88.301075  \n",
              "Sacramento                   95.027356  "
            ],
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              "      <th>restaurant_count</th>\n",
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              "      <th>avg_price_range_id</th>\n",
              "      <th>avg_reservation_advance</th>\n",
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              "    <tr>\n",
              "      <th>city</th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>San Francisco</th>\n",
              "      <td>1645</td>\n",
              "      <td>2.593435</td>\n",
              "      <td>2.361702</td>\n",
              "      <td>90.345289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Los Angeles</th>\n",
              "      <td>1604</td>\n",
              "      <td>2.319950</td>\n",
              "      <td>2.290524</td>\n",
              "      <td>86.692020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>San Diego</th>\n",
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              "      <th>San Jose</th>\n",
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              "      <td>1.545968</td>\n",
              "      <td>2.163978</td>\n",
              "      <td>88.301075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sacramento</th>\n",
              "      <td>329</td>\n",
              "      <td>1.686626</td>\n",
              "      <td>2.267477</td>\n",
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              "  "
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          "metadata": {},
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      ],
      "source": [
        "# ✏️ YOUR CODE HERE\n",
        "# Groupby is used and aggregate function is performed to get the average and count of different features\n",
        "\n",
        "df1=df.groupby('city').aggregate({'city':'count',\n",
        "                                 'rating':'mean',\n",
        "                                 'price_range_id':'mean',\n",
        "                                 'maximum_days_advance_for_reservation':'mean'})\n",
        "\n",
        " # Columns are renamed so to get the output as the table given above\n",
        "\n",
        "\n",
        "df1.rename(columns={'city':'restaurant_count',\n",
        "                    'rating':'avg_rating',\n",
        "                    'price_range_id':'avg_price_range_id',\n",
        "                    'maximum_days_advance_for_reservation':'avg_reservation_advance'},inplace=True)\n",
        "\n",
        "\n",
        "\n",
        "# df1.nlargest(5,columns=['restaurant_count'])\n",
        "\n",
        "# one more way to produce the same result\n",
        "\n",
        "df1.sort_values(by='restaurant_count',ascending=False)[:5]\n",
        "\n"
      ]
    },
    {
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      "metadata": {
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      },
      "source": [
        "#### 2.2.2 Restaurants by Brand 🍔 (1 point)\n",
        "\n",
        "For each brand (`brand_name`) in california, find\n",
        "\n",
        "1. the number of restaurants that belong to that brand,\n",
        "2. mean `price_range_id` of the brand across its restaurants\n",
        "\n",
        "sort by number of restaurants.\n",
        "\n",
        "The resulting dataframe's top 5 rows would look as follows:\n",
        "\n",
        "| brand_name               | restaurant_count | avg_price_range_id |\n",
        "| :----------------------- | ---------------: | -----------------: |\n",
        "| Denny's                  |               73 |                  2 |\n",
        "| Ihop                     |               37 |                  2 |\n",
        "| Buffalo Wild Wings       |               32 |                  2 |\n",
        "| Black Bear Diner         |               28 |                  2 |\n",
        "| Coco's Bakery Restaurant |               24 |                  2 |\n"
      ]
    },
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            "text/plain": [
              "                          restaurant_count  avg_price_range_id\n",
              "brand_name                                                    \n",
              "Denny's                                 73                   2\n",
              "Ihop                                    37                   2\n",
              "Buffalo Wild Wings                      32                   2\n",
              "Black Bear Diner                        28                   2\n",
              "Coco's Bakery Restaurant                24                   2"
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-661ba92a-4aa6-4eab-844b-47c9408b2dcc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-661ba92a-4aa6-4eab-844b-47c9408b2dcc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# ✏️ YOUR CODE HERE\n",
        "\n",
        "# Here, again groupby function is used and aggregate is used to perform the required operation\n",
        "df2=df.groupby('brand_name').aggregate({'brand_name':'count',\n",
        "                                     'price_range_id':'mean'})\n",
        "\n",
        "# Columns are renamed so to get the output as the table given above\n",
        "\n",
        "df2.rename(columns={'brand_name':'restaurant_count',\n",
        "                    'price_range_id':'avg_price_range_id'},inplace=True)\n",
        "\n",
        "# Convert the avg_price_range_id column into int\n",
        "\n",
        "df2['avg_price_range_id']=df2['avg_price_range_id'].astype(int)\n",
        "\n",
        "# Largest five values of restaurant count are taken\n",
        "\n",
        "df2.nlargest(5,columns=['restaurant_count'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8111c4e6-5655-487a-a2cc-179744d747cb",
      "metadata": {
        "id": "8111c4e6-5655-487a-a2cc-179744d747cb"
      },
      "source": [
        "#### 2.2.3 Visualize Brands 📊 (2 points)\n",
        "\n",
        "Create a bar chart of top 5 brands in california by average number of reviews where each brand has at least 5 restaurants\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "56fb034d-b9a6-4ad7-a498-17e75f5f8781",
      "metadata": {
        "id": "56fb034d-b9a6-4ad7-a498-17e75f5f8781",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "outputId": "97f389e6-57c3-4b1f-948a-552989d81e6b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Average number of reviews')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# ✏️ YOUR CODE HERE\n",
        "\n",
        "# First we need to import seaborn\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "# Perform groupby and aggregate to perform the operation required\n",
        "\n",
        "df3=df.groupby('brand_name').aggregate({'brand_name':'count',\n",
        "                                        'review_count':'mean'})\n",
        "\n",
        "# putting the condition for the count of brand name to be more than 5\n",
        "\n",
        "condition=df3[df3['brand_name']>=5]\n",
        "\n",
        "# sorting the values by top five review counts of those brands that have atleast 5 restaurants\n",
        "condition=condition.sort_values(by='review_count',ascending=False)[:5]\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "# using seaborn to plot barplot\n",
        "\n",
        "ax=sns.barplot(x=condition.index,y=condition.review_count)\n",
        "plt.xticks(rotation=90)\n",
        "plt.title('Top 5 brands with highest average number of reviews and minimum 5 restaurants')\n",
        "plt.xlabel('Name of the restaurants')\n",
        "plt.ylabel('Average number of reviews')\n",
        "# for i in ax.containers:\n",
        "#   ax.bar_label(i,)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ad7f78bb-91d8-4b1a-8cb0-0a60dd1b9e4c",
      "metadata": {
        "id": "ad7f78bb-91d8-4b1a-8cb0-0a60dd1b9e4c"
      },
      "source": [
        "### 2.3 Transform Columns 🚚 (15 Points)\n",
        "\n",
        "<img src=\"https://media.giphy.com/media/2f41Z7bhKGvbG/giphy.gif\" height=\"250px\" width=\"250px\" alt=\"harry potter\">"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8cf2c3d6-5a03-4eb5-b568-7c8e265fd448",
      "metadata": {
        "id": "8cf2c3d6-5a03-4eb5-b568-7c8e265fd448"
      },
      "source": [
        "#### 2.3.1 Safety Precautions 🦺 (2 points)\n",
        "\n",
        "Transform the entire safety precautions column into a new column based on the following rule:\n",
        "\n",
        "Convert from dictionary to list. Only include in the list, those keys in the dictionary which are true.\n",
        "For ex, for safety precautions of the type:\n",
        "\n",
        "```python\n",
        "{\n",
        "    'cleanMenus': True,\n",
        "    'limitedSeating': False,\n",
        "    'sealedUtensils': None,\n",
        "    'prohibitSickStaff': True,\n",
        "    'requireDinerMasks': True,\n",
        "    'staffIsVaccinated': None,\n",
        "    'proofOfVaccinationRequired': False,\n",
        "    'sanitizerProvidedForCustomers': None\n",
        "}\n",
        "```\n",
        "\n",
        "It should turn into a list of the form:\n",
        "\n",
        "```python\n",
        "[\"Clean Menus\", \"Prohibit Sick Staff\", \"Require Diner Masks\"]\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5201dc9",
      "metadata": {
        "id": "e5201dc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "outputId": "6ccf04c1-375c-4c02-fbcd-2b5c99e5e76c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         country   subregion          city                   brand_name  \\\n",
              "0  United States  California   Los Angeles          Luv2Eat Thai Bistro   \n",
              "1  United States  California  Sherman Oaks          Jerry's Famous Deli   \n",
              "2  United States  California      Pasadena                Terrace Sushi   \n",
              "3  United States  California      Pasadena  Hamburger Hamlet - Pasadena   \n",
              "4  United States  California       Burbank           Centanni - Burbank   \n",
              "\n",
              "                categories   latitude   longitude          area  \\\n",
              "0                   [Thai]  34.097510 -118.335921     Hollywood   \n",
              "1               [American]  34.154596 -118.448700  Sherman Oaks   \n",
              "2                [Spanish]  34.145800 -118.142900      Pasadena   \n",
              "3  [Contemporary American]  34.137800 -118.132300      Pasadena   \n",
              "4                [Italian]  34.174018 -118.317323       Burbank   \n",
              "\n",
              "                                             address  \\\n",
              "0  6660 W Sunset Blvd, Ste P, CA, Los Angeles, 90...   \n",
              "1                                               None   \n",
              "2  443 E Colorado Blvd, , CA, Pasadena, 91101, Un...   \n",
              "3  214 S Lake Ave, , CA, Pasadena, 91101, United ...   \n",
              "4  117 N. Victory Blvd, , CA, Burbank, 91502, Uni...   \n",
              "\n",
              "                                                menu  \\\n",
              "0  [{'name': 'Main Menu', 'sections': [{'name': '...   \n",
              "1  [{'name': 'Sample Menu', 'sections': [{'name':...   \n",
              "2  [{'name': 'Main Menu', 'sections': [{'name': '...   \n",
              "3  [{'name': 'Main Menu', 'sections': [{'name': '...   \n",
              "4  [{'name': 'Main Menu', 'sections': [{'name': '...   \n",
              "\n",
              "                                         description public_transit  \\\n",
              "0  Luv2Eat Thai Bistro is located in Los Angeles,...           None   \n",
              "1  <p>Feast on delicious grub at Jerry's Famous D...           None   \n",
              "2  <p>Terrace Sushi serves Japanese cuisines to i...           None   \n",
              "3  <p>Hamburger Hamlet serves traditional America...           None   \n",
              "4  <br />WE ARE STILL OPEN FOR TAKE OUT/ DELIVERY...           None   \n",
              "\n",
              "  cross_street                restaurant_website    phone_number  \\\n",
              "0         None       http://www.luv2eatthai.com/  (323) 498-5835   \n",
              "1         None  http://www.jerrysfamousdeli.com/  (818) 905-5774   \n",
              "2         None                              None  (626) 584-0503   \n",
              "3         None                              None  (626) 449-8520   \n",
              "4        Clark        http://www.centannila.com/  (818) 561-4643   \n",
              "\n",
              "         primary_cuisine   dining_style executive_chef_name    parking_info  \\\n",
              "0                   Thai  Casual Dining                None  Street Parking   \n",
              "1               American  Casual Dining                None            None   \n",
              "2                Spanish  Casual Dining                None            None   \n",
              "3  Contemporary American  Casual Dining                None            None   \n",
              "4                Italian  Casual Dining        Marin Santos     Private Lot   \n",
              "\n",
              "        dress_code entertainment  \\\n",
              "0     Casual Dress          None   \n",
              "1  Business Casual          None   \n",
              "2  Business Casual          None   \n",
              "3  Business Casual          None   \n",
              "4     Casual Dress          None   \n",
              "\n",
              "                                     operating_hours  price_range_id  \\\n",
              "0  Lunch\\nDaily 11:00 am–3:30 pm\\nDinner\\nDaily 4...               2   \n",
              "1                                               None               2   \n",
              "2                                               None               2   \n",
              "3                                               None               2   \n",
              "4  Dinner\\nMon–Thu 5:00 pm–8:30 pm\\nFri, Sat 5:00...               2   \n",
              "\n",
              "     price_range                     payment_options  \\\n",
              "0  $30 and under  [AMEX, Discover, MasterCard, Visa]   \n",
              "1  $30 and under                                  []   \n",
              "2  $30 and under                                  []   \n",
              "3  $30 and under                                  []   \n",
              "4  $30 and under  [AMEX, Discover, MasterCard, Visa]   \n",
              "\n",
              "   maximum_days_advance_for_reservation  rating  rating_count  \\\n",
              "0                                    90     4.6           136   \n",
              "1                                    90     0.0             0   \n",
              "2                                    90     0.0             0   \n",
              "3                                    90     0.0             0   \n",
              "4                                    90     4.7            83   \n",
              "\n",
              "                                   rating_by_feature rating_distribution  \\\n",
              "0  {'food': 4.7, 'noise': 2.0, 'value': 4.6, 'ser...  [2, 3, 4, 23, 104]   \n",
              "1  {'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...     [0, 0, 0, 0, 0]   \n",
              "2  {'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...     [0, 0, 0, 0, 0]   \n",
              "3  {'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...     [0, 0, 0, 0, 0]   \n",
              "4  {'food': 4.7, 'noise': 2.0, 'value': 4.6, 'ser...   [0, 0, 7, 14, 62]   \n",
              "\n",
              "   review_count                                review_topics  \\\n",
              "0            18            [Spicy, Casual, Neighborhood Gem]   \n",
              "1             0                                           []   \n",
              "2             0                                           []   \n",
              "3             0                                           []   \n",
              "4           692  [Neighborhood Gem, Casual, Good for a Date]   \n",
              "\n",
              "                                              awards experiences  \\\n",
              "0                                                 []          []   \n",
              "1                                                 []          []   \n",
              "2                                                 []          []   \n",
              "3                                                 []          []   \n",
              "4  [{'name': 'Neighborhood Gem', 'location': 'San...          []   \n",
              "\n",
              "                                                tags editorial_lists  \\\n",
              "0  [Delivery, Gluten-free Options, Late Night, No...              []   \n",
              "1                                                 []              []   \n",
              "2                                                 []              []   \n",
              "3                                                 []              []   \n",
              "4  [Beer, Corkage Fee, Delivery, Gluten-free Opti...              []   \n",
              "\n",
              "                                           checklist  \\\n",
              "0  {'bar': False, 'counter': False, 'gifting': No...   \n",
              "1  {'bar': None, 'counter': None, 'gifting': None...   \n",
              "2  {'bar': None, 'counter': None, 'gifting': None...   \n",
              "3  {'bar': None, 'counter': None, 'gifting': None...   \n",
              "4  {'bar': True, 'counter': False, 'gifting': Non...   \n",
              "\n",
              "                                  safety_precautions   order_online_link  \\\n",
              "0  {'cleanMenus': None, 'limitedSeating': None, '...                None   \n",
              "1  {'cleanMenus': None, 'limitedSeating': None, '...                None   \n",
              "2  {'cleanMenus': None, 'limitedSeating': None, '...                None   \n",
              "3  {'cleanMenus': None, 'limitedSeating': None, '...                None   \n",
              "4  {'cleanMenus': True, 'limitedSeating': False, ...  www.centannila.com   \n",
              "\n",
              "                                     facebook  \\\n",
              "0  http://www.facebook.com/luv2eatthaibistro/   \n",
              "1                                        None   \n",
              "2                                        None   \n",
              "3                                        None   \n",
              "4          http://www.facebook.com/centannila   \n",
              "\n",
              "                                    menu_url  \\\n",
              "0  http://sappclub.com/restaurant.aspx?r=205   \n",
              "1                                       None   \n",
              "2                                       None   \n",
              "3                                       None   \n",
              "4        http://www.centannila.com/menu.html   \n",
              "\n",
              "                                      popular_dishes  daily_reservation_count  \\\n",
              "0                                                 []                      NaN   \n",
              "1                                                 []                      NaN   \n",
              "2                                                 []                      NaN   \n",
              "3                                                 []                      NaN   \n",
              "4  [{'name': 'Ravioli Di', 'description': 'pumpki...                      1.0   \n",
              "\n",
              "   restaurant_id                            safety_precautions_list  \n",
              "0              1                                                 []  \n",
              "1              2                                                 []  \n",
              "2              3                                                 []  \n",
              "3              4                                                 []  \n",
              "4              5  [cleanMenus, prohibitSickStaff, requireDinerMa...  "
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              "      <th>country</th>\n",
              "      <th>subregion</th>\n",
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              "      <th>safety_precautions_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Luv2Eat Thai Bistro</td>\n",
              "      <td>[Thai]</td>\n",
              "      <td>34.097510</td>\n",
              "      <td>-118.335921</td>\n",
              "      <td>Hollywood</td>\n",
              "      <td>6660 W Sunset Blvd, Ste P, CA, Los Angeles, 90...</td>\n",
              "      <td>[{'name': 'Main Menu', 'sections': [{'name': '...</td>\n",
              "      <td>Luv2Eat Thai Bistro is located in Los Angeles,...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>http://www.luv2eatthai.com/</td>\n",
              "      <td>(323) 498-5835</td>\n",
              "      <td>Thai</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>None</td>\n",
              "      <td>Street Parking</td>\n",
              "      <td>Casual Dress</td>\n",
              "      <td>None</td>\n",
              "      <td>Lunch\\nDaily 11:00 am–3:30 pm\\nDinner\\nDaily 4...</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[AMEX, Discover, MasterCard, Visa]</td>\n",
              "      <td>90</td>\n",
              "      <td>4.6</td>\n",
              "      <td>136</td>\n",
              "      <td>{'food': 4.7, 'noise': 2.0, 'value': 4.6, 'ser...</td>\n",
              "      <td>[2, 3, 4, 23, 104]</td>\n",
              "      <td>18</td>\n",
              "      <td>[Spicy, Casual, Neighborhood Gem]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[Delivery, Gluten-free Options, Late Night, No...</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': False, 'counter': False, 'gifting': No...</td>\n",
              "      <td>{'cleanMenus': None, 'limitedSeating': None, '...</td>\n",
              "      <td>None</td>\n",
              "      <td>http://www.facebook.com/luv2eatthaibistro/</td>\n",
              "      <td>http://sappclub.com/restaurant.aspx?r=205</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Sherman Oaks</td>\n",
              "      <td>Jerry's Famous Deli</td>\n",
              "      <td>[American]</td>\n",
              "      <td>34.154596</td>\n",
              "      <td>-118.448700</td>\n",
              "      <td>Sherman Oaks</td>\n",
              "      <td>None</td>\n",
              "      <td>[{'name': 'Sample Menu', 'sections': [{'name':...</td>\n",
              "      <td>&lt;p&gt;Feast on delicious grub at Jerry's Famous D...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>http://www.jerrysfamousdeli.com/</td>\n",
              "      <td>(818) 905-5774</td>\n",
              "      <td>American</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Business Casual</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[]</td>\n",
              "      <td>90</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>{'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...</td>\n",
              "      <td>[0, 0, 0, 0, 0]</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': None, 'counter': None, 'gifting': None...</td>\n",
              "      <td>{'cleanMenus': None, 'limitedSeating': None, '...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Pasadena</td>\n",
              "      <td>Terrace Sushi</td>\n",
              "      <td>[Spanish]</td>\n",
              "      <td>34.145800</td>\n",
              "      <td>-118.142900</td>\n",
              "      <td>Pasadena</td>\n",
              "      <td>443 E Colorado Blvd, , CA, Pasadena, 91101, Un...</td>\n",
              "      <td>[{'name': 'Main Menu', 'sections': [{'name': '...</td>\n",
              "      <td>&lt;p&gt;Terrace Sushi serves Japanese cuisines to i...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>(626) 584-0503</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Business Casual</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[]</td>\n",
              "      <td>90</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>{'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...</td>\n",
              "      <td>[0, 0, 0, 0, 0]</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': None, 'counter': None, 'gifting': None...</td>\n",
              "      <td>{'cleanMenus': None, 'limitedSeating': None, '...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Pasadena</td>\n",
              "      <td>Hamburger Hamlet - Pasadena</td>\n",
              "      <td>[Contemporary American]</td>\n",
              "      <td>34.137800</td>\n",
              "      <td>-118.132300</td>\n",
              "      <td>Pasadena</td>\n",
              "      <td>214 S Lake Ave, , CA, Pasadena, 91101, United ...</td>\n",
              "      <td>[{'name': 'Main Menu', 'sections': [{'name': '...</td>\n",
              "      <td>&lt;p&gt;Hamburger Hamlet serves traditional America...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>(626) 449-8520</td>\n",
              "      <td>Contemporary American</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Business Casual</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[]</td>\n",
              "      <td>90</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>{'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...</td>\n",
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              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': None, 'counter': None, 'gifting': None...</td>\n",
              "      <td>{'cleanMenus': None, 'limitedSeating': None, '...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Burbank</td>\n",
              "      <td>Centanni - Burbank</td>\n",
              "      <td>[Italian]</td>\n",
              "      <td>34.174018</td>\n",
              "      <td>-118.317323</td>\n",
              "      <td>Burbank</td>\n",
              "      <td>117 N. Victory Blvd, , CA, Burbank, 91502, Uni...</td>\n",
              "      <td>[{'name': 'Main Menu', 'sections': [{'name': '...</td>\n",
              "      <td>&lt;br /&gt;WE ARE STILL OPEN FOR TAKE OUT/ DELIVERY...</td>\n",
              "      <td>None</td>\n",
              "      <td>Clark</td>\n",
              "      <td>http://www.centannila.com/</td>\n",
              "      <td>(818) 561-4643</td>\n",
              "      <td>Italian</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>Marin Santos</td>\n",
              "      <td>Private Lot</td>\n",
              "      <td>Casual Dress</td>\n",
              "      <td>None</td>\n",
              "      <td>Dinner\\nMon–Thu 5:00 pm–8:30 pm\\nFri, Sat 5:00...</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[AMEX, Discover, MasterCard, Visa]</td>\n",
              "      <td>90</td>\n",
              "      <td>4.7</td>\n",
              "      <td>83</td>\n",
              "      <td>{'food': 4.7, 'noise': 2.0, 'value': 4.6, 'ser...</td>\n",
              "      <td>[0, 0, 7, 14, 62]</td>\n",
              "      <td>692</td>\n",
              "      <td>[Neighborhood Gem, Casual, Good for a Date]</td>\n",
              "      <td>[{'name': 'Neighborhood Gem', 'location': 'San...</td>\n",
              "      <td>[]</td>\n",
              "      <td>[Beer, Corkage Fee, Delivery, Gluten-free Opti...</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': True, 'counter': False, 'gifting': Non...</td>\n",
              "      <td>{'cleanMenus': True, 'limitedSeating': False, ...</td>\n",
              "      <td>www.centannila.com</td>\n",
              "      <td>http://www.facebook.com/centannila</td>\n",
              "      <td>http://www.centannila.com/menu.html</td>\n",
              "      <td>[{'name': 'Ravioli Di', 'description': 'pumpki...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>[cleanMenus, prohibitSickStaff, requireDinerMa...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1539fab-dfff-44e6-98d6-9b2ea4ea80dc')\"\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# ✏️ YOUR CODE HERE\n",
        "# adding new column that has only th epositive key values of the dicitonary\n",
        "\n",
        "# The apply() method is being called on the df['safety_precautions'] column.\n",
        "# This method applies a function to each element of the column, and returns a new column with the results.\n",
        "\n",
        "df['new_column']=df['safety_precautions'].apply([lambda x:[ key for key,value in x.items() if value]])\n",
        "\n",
        "#renaming the column\n",
        "\n",
        "df.rename(columns={'new_column':'safety_precautions_list'},inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "28008039-4e56-4876-b4f3-faee50990b0f",
      "metadata": {
        "id": "28008039-4e56-4876-b4f3-faee50990b0f"
      },
      "source": [
        "#### 2.3.2 Clean up HTML text 🥜 (2 points)\n",
        "\n",
        "Find columns containing text / strings that have html text and remove those HTML texts\n",
        "\n",
        "ex:\n",
        "\n",
        "```html\n",
        "<p>\n",
        "  Feast on delicious grub at Jerry's Famous Deli.<br />\n",
        "  Its retro-style casual setting features comfortable booth seating.\n",
        "</p>\n",
        "```\n",
        "\n",
        "to:\n",
        "\n",
        "```\n",
        "Feast on delicious grub at Jerry's Famous Deli. Its retro-style casual setting features comfortable booth seating.\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3b807861-41ac-42c6-9792-6e8783d76539",
      "metadata": {
        "id": "3b807861-41ac-42c6-9792-6e8783d76539",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "outputId": "6ec73cf6-859d-42ec-e376-29fd6d266447"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['menu', 'description', 'public_transit', 'cross_street', 'entertainment', 'operating_hours', 'experiences']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/bs4/__init__.py:332: UserWarning: \"https://www.dumpling-empire.com/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/bs4/__init__.py:270: UserWarning: \"b'.'\" looks like a filename, not markup. You should probably open this file and pass the filehandle into Beautiful Soup.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/bs4/__init__.py:332: UserWarning: \"http://www.lbtransit.com/\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/bs4/__init__.py:332: UserWarning: \"http://www.lagunabeachcity.net/cityhall/transit_and_trolleys/default.htm\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/bs4/__init__.py:332: UserWarning: \"https://www.google.com/maps/place/Manzanita+Park+%26+Ride/@37.8793185,-122.5162724,15z/data=!4m5!1m2!2m1!1sbus+!3m1!1s0x0000000000000000:0x06e1fc7744a6f6de?hl=en\" looks like a URL. Beautiful Soup is not an HTTP client. You should probably use an HTTP client like requests to get the document behind the URL, and feed that document to Beautiful Soup.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         country   subregion          city                   brand_name  \\\n",
              "0  United States  California   Los Angeles          Luv2Eat Thai Bistro   \n",
              "1  United States  California  Sherman Oaks          Jerry's Famous Deli   \n",
              "2  United States  California      Pasadena                Terrace Sushi   \n",
              "3  United States  California      Pasadena  Hamburger Hamlet - Pasadena   \n",
              "4  United States  California       Burbank           Centanni - Burbank   \n",
              "\n",
              "                categories   latitude   longitude          area  \\\n",
              "0                   [Thai]  34.097510 -118.335921     Hollywood   \n",
              "1               [American]  34.154596 -118.448700  Sherman Oaks   \n",
              "2                [Spanish]  34.145800 -118.142900      Pasadena   \n",
              "3  [Contemporary American]  34.137800 -118.132300      Pasadena   \n",
              "4                [Italian]  34.174018 -118.317323       Burbank   \n",
              "\n",
              "                                             address  \\\n",
              "0  6660 W Sunset Blvd, Ste P, CA, Los Angeles, 90...   \n",
              "1                                               None   \n",
              "2  443 E Colorado Blvd, , CA, Pasadena, 91101, Un...   \n",
              "3  214 S Lake Ave, , CA, Pasadena, 91101, United ...   \n",
              "4  117 N. Victory Blvd, , CA, Burbank, 91502, Uni...   \n",
              "\n",
              "                                                menu  \\\n",
              "0  [{'name': 'Main Menu', 'sections': [{'name': '...   \n",
              "1  [{'name': 'Sample Menu', 'sections': [{'name':...   \n",
              "2  [{'name': 'Main Menu', 'sections': [{'name': '...   \n",
              "3  [{'name': 'Main Menu', 'sections': [{'name': '...   \n",
              "4  [{'name': 'Main Menu', 'sections': [{'name': '...   \n",
              "\n",
              "                                         description public_transit  \\\n",
              "0  Luv2Eat Thai Bistro is located in Los Angeles,...           None   \n",
              "1  Feast on delicious grub at Jerry's Famous Deli...           None   \n",
              "2  Terrace Sushi serves Japanese cuisines to its ...           None   \n",
              "3  Hamburger Hamlet serves traditional American c...           None   \n",
              "4  WE ARE STILL OPEN FOR TAKE OUT/ DELIVERY. PLEA...           None   \n",
              "\n",
              "  cross_street                restaurant_website    phone_number  \\\n",
              "0         None       http://www.luv2eatthai.com/  (323) 498-5835   \n",
              "1         None  http://www.jerrysfamousdeli.com/  (818) 905-5774   \n",
              "2         None                              None  (626) 584-0503   \n",
              "3         None                              None  (626) 449-8520   \n",
              "4        Clark        http://www.centannila.com/  (818) 561-4643   \n",
              "\n",
              "         primary_cuisine   dining_style executive_chef_name    parking_info  \\\n",
              "0                   Thai  Casual Dining                None  Street Parking   \n",
              "1               American  Casual Dining                None            None   \n",
              "2                Spanish  Casual Dining                None            None   \n",
              "3  Contemporary American  Casual Dining                None            None   \n",
              "4                Italian  Casual Dining        Marin Santos     Private Lot   \n",
              "\n",
              "        dress_code entertainment  \\\n",
              "0     Casual Dress          None   \n",
              "1  Business Casual          None   \n",
              "2  Business Casual          None   \n",
              "3  Business Casual          None   \n",
              "4     Casual Dress          None   \n",
              "\n",
              "                                     operating_hours  price_range_id  \\\n",
              "0  Lunch\\nDaily 11:00 am–3:30 pm\\nDinner\\nDaily 4...               2   \n",
              "1                                               None               2   \n",
              "2                                               None               2   \n",
              "3                                               None               2   \n",
              "4  Dinner\\nMon–Thu 5:00 pm–8:30 pm\\nFri, Sat 5:00...               2   \n",
              "\n",
              "     price_range                     payment_options  \\\n",
              "0  $30 and under  [AMEX, Discover, MasterCard, Visa]   \n",
              "1  $30 and under                                  []   \n",
              "2  $30 and under                                  []   \n",
              "3  $30 and under                                  []   \n",
              "4  $30 and under  [AMEX, Discover, MasterCard, Visa]   \n",
              "\n",
              "   maximum_days_advance_for_reservation  rating  rating_count  \\\n",
              "0                                    90     4.6           136   \n",
              "1                                    90     0.0             0   \n",
              "2                                    90     0.0             0   \n",
              "3                                    90     0.0             0   \n",
              "4                                    90     4.7            83   \n",
              "\n",
              "                                   rating_by_feature rating_distribution  \\\n",
              "0  {'food': 4.7, 'noise': 2.0, 'value': 4.6, 'ser...  [2, 3, 4, 23, 104]   \n",
              "1  {'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...     [0, 0, 0, 0, 0]   \n",
              "2  {'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...     [0, 0, 0, 0, 0]   \n",
              "3  {'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...     [0, 0, 0, 0, 0]   \n",
              "4  {'food': 4.7, 'noise': 2.0, 'value': 4.6, 'ser...   [0, 0, 7, 14, 62]   \n",
              "\n",
              "   review_count                                review_topics  \\\n",
              "0            18            [Spicy, Casual, Neighborhood Gem]   \n",
              "1             0                                           []   \n",
              "2             0                                           []   \n",
              "3             0                                           []   \n",
              "4           692  [Neighborhood Gem, Casual, Good for a Date]   \n",
              "\n",
              "                                              awards experiences  \\\n",
              "0                                                 []          []   \n",
              "1                                                 []          []   \n",
              "2                                                 []          []   \n",
              "3                                                 []          []   \n",
              "4  [{'name': 'Neighborhood Gem', 'location': 'San...          []   \n",
              "\n",
              "                                                tags editorial_lists  \\\n",
              "0  [Delivery, Gluten-free Options, Late Night, No...              []   \n",
              "1                                                 []              []   \n",
              "2                                                 []              []   \n",
              "3                                                 []              []   \n",
              "4  [Beer, Corkage Fee, Delivery, Gluten-free Opti...              []   \n",
              "\n",
              "                                           checklist  \\\n",
              "0  {'bar': False, 'counter': False, 'gifting': No...   \n",
              "1  {'bar': None, 'counter': None, 'gifting': None...   \n",
              "2  {'bar': None, 'counter': None, 'gifting': None...   \n",
              "3  {'bar': None, 'counter': None, 'gifting': None...   \n",
              "4  {'bar': True, 'counter': False, 'gifting': Non...   \n",
              "\n",
              "                                  safety_precautions   order_online_link  \\\n",
              "0  {'cleanMenus': None, 'limitedSeating': None, '...                None   \n",
              "1  {'cleanMenus': None, 'limitedSeating': None, '...                None   \n",
              "2  {'cleanMenus': None, 'limitedSeating': None, '...                None   \n",
              "3  {'cleanMenus': None, 'limitedSeating': None, '...                None   \n",
              "4  {'cleanMenus': True, 'limitedSeating': False, ...  www.centannila.com   \n",
              "\n",
              "                                     facebook  \\\n",
              "0  http://www.facebook.com/luv2eatthaibistro/   \n",
              "1                                        None   \n",
              "2                                        None   \n",
              "3                                        None   \n",
              "4          http://www.facebook.com/centannila   \n",
              "\n",
              "                                    menu_url  \\\n",
              "0  http://sappclub.com/restaurant.aspx?r=205   \n",
              "1                                       None   \n",
              "2                                       None   \n",
              "3                                       None   \n",
              "4        http://www.centannila.com/menu.html   \n",
              "\n",
              "                                      popular_dishes  daily_reservation_count  \\\n",
              "0                                                 []                      NaN   \n",
              "1                                                 []                      NaN   \n",
              "2                                                 []                      NaN   \n",
              "3                                                 []                      NaN   \n",
              "4  [{'name': 'Ravioli Di', 'description': 'pumpki...                      1.0   \n",
              "\n",
              "   restaurant_id                            safety_precautions_list  \n",
              "0              1                                                 []  \n",
              "1              2                                                 []  \n",
              "2              3                                                 []  \n",
              "3              4                                                 []  \n",
              "4              5  [cleanMenus, prohibitSickStaff, requireDinerMa...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6def235b-5261-4f0a-99e5-18eb64f6cc1f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>country</th>\n",
              "      <th>subregion</th>\n",
              "      <th>city</th>\n",
              "      <th>brand_name</th>\n",
              "      <th>categories</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>area</th>\n",
              "      <th>address</th>\n",
              "      <th>menu</th>\n",
              "      <th>description</th>\n",
              "      <th>public_transit</th>\n",
              "      <th>cross_street</th>\n",
              "      <th>restaurant_website</th>\n",
              "      <th>phone_number</th>\n",
              "      <th>primary_cuisine</th>\n",
              "      <th>dining_style</th>\n",
              "      <th>executive_chef_name</th>\n",
              "      <th>parking_info</th>\n",
              "      <th>dress_code</th>\n",
              "      <th>entertainment</th>\n",
              "      <th>operating_hours</th>\n",
              "      <th>price_range_id</th>\n",
              "      <th>price_range</th>\n",
              "      <th>payment_options</th>\n",
              "      <th>maximum_days_advance_for_reservation</th>\n",
              "      <th>rating</th>\n",
              "      <th>rating_count</th>\n",
              "      <th>rating_by_feature</th>\n",
              "      <th>rating_distribution</th>\n",
              "      <th>review_count</th>\n",
              "      <th>review_topics</th>\n",
              "      <th>awards</th>\n",
              "      <th>experiences</th>\n",
              "      <th>tags</th>\n",
              "      <th>editorial_lists</th>\n",
              "      <th>checklist</th>\n",
              "      <th>safety_precautions</th>\n",
              "      <th>order_online_link</th>\n",
              "      <th>facebook</th>\n",
              "      <th>menu_url</th>\n",
              "      <th>popular_dishes</th>\n",
              "      <th>daily_reservation_count</th>\n",
              "      <th>restaurant_id</th>\n",
              "      <th>safety_precautions_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>Luv2Eat Thai Bistro</td>\n",
              "      <td>[Thai]</td>\n",
              "      <td>34.097510</td>\n",
              "      <td>-118.335921</td>\n",
              "      <td>Hollywood</td>\n",
              "      <td>6660 W Sunset Blvd, Ste P, CA, Los Angeles, 90...</td>\n",
              "      <td>[{'name': 'Main Menu', 'sections': [{'name': '...</td>\n",
              "      <td>Luv2Eat Thai Bistro is located in Los Angeles,...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>http://www.luv2eatthai.com/</td>\n",
              "      <td>(323) 498-5835</td>\n",
              "      <td>Thai</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>None</td>\n",
              "      <td>Street Parking</td>\n",
              "      <td>Casual Dress</td>\n",
              "      <td>None</td>\n",
              "      <td>Lunch\\nDaily 11:00 am–3:30 pm\\nDinner\\nDaily 4...</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[AMEX, Discover, MasterCard, Visa]</td>\n",
              "      <td>90</td>\n",
              "      <td>4.6</td>\n",
              "      <td>136</td>\n",
              "      <td>{'food': 4.7, 'noise': 2.0, 'value': 4.6, 'ser...</td>\n",
              "      <td>[2, 3, 4, 23, 104]</td>\n",
              "      <td>18</td>\n",
              "      <td>[Spicy, Casual, Neighborhood Gem]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[Delivery, Gluten-free Options, Late Night, No...</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': False, 'counter': False, 'gifting': No...</td>\n",
              "      <td>{'cleanMenus': None, 'limitedSeating': None, '...</td>\n",
              "      <td>None</td>\n",
              "      <td>http://www.facebook.com/luv2eatthaibistro/</td>\n",
              "      <td>http://sappclub.com/restaurant.aspx?r=205</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Sherman Oaks</td>\n",
              "      <td>Jerry's Famous Deli</td>\n",
              "      <td>[American]</td>\n",
              "      <td>34.154596</td>\n",
              "      <td>-118.448700</td>\n",
              "      <td>Sherman Oaks</td>\n",
              "      <td>None</td>\n",
              "      <td>[{'name': 'Sample Menu', 'sections': [{'name':...</td>\n",
              "      <td>Feast on delicious grub at Jerry's Famous Deli...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>http://www.jerrysfamousdeli.com/</td>\n",
              "      <td>(818) 905-5774</td>\n",
              "      <td>American</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Business Casual</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[]</td>\n",
              "      <td>90</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>{'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...</td>\n",
              "      <td>[0, 0, 0, 0, 0]</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': None, 'counter': None, 'gifting': None...</td>\n",
              "      <td>{'cleanMenus': None, 'limitedSeating': None, '...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Pasadena</td>\n",
              "      <td>Terrace Sushi</td>\n",
              "      <td>[Spanish]</td>\n",
              "      <td>34.145800</td>\n",
              "      <td>-118.142900</td>\n",
              "      <td>Pasadena</td>\n",
              "      <td>443 E Colorado Blvd, , CA, Pasadena, 91101, Un...</td>\n",
              "      <td>[{'name': 'Main Menu', 'sections': [{'name': '...</td>\n",
              "      <td>Terrace Sushi serves Japanese cuisines to its ...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>(626) 584-0503</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Business Casual</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[]</td>\n",
              "      <td>90</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>{'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...</td>\n",
              "      <td>[0, 0, 0, 0, 0]</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': None, 'counter': None, 'gifting': None...</td>\n",
              "      <td>{'cleanMenus': None, 'limitedSeating': None, '...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Pasadena</td>\n",
              "      <td>Hamburger Hamlet - Pasadena</td>\n",
              "      <td>[Contemporary American]</td>\n",
              "      <td>34.137800</td>\n",
              "      <td>-118.132300</td>\n",
              "      <td>Pasadena</td>\n",
              "      <td>214 S Lake Ave, , CA, Pasadena, 91101, United ...</td>\n",
              "      <td>[{'name': 'Main Menu', 'sections': [{'name': '...</td>\n",
              "      <td>Hamburger Hamlet serves traditional American c...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>(626) 449-8520</td>\n",
              "      <td>Contemporary American</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>Business Casual</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[]</td>\n",
              "      <td>90</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>{'food': 0.0, 'noise': 0.0, 'value': 0.0, 'ser...</td>\n",
              "      <td>[0, 0, 0, 0, 0]</td>\n",
              "      <td>0</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': None, 'counter': None, 'gifting': None...</td>\n",
              "      <td>{'cleanMenus': None, 'limitedSeating': None, '...</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>[]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>United States</td>\n",
              "      <td>California</td>\n",
              "      <td>Burbank</td>\n",
              "      <td>Centanni - Burbank</td>\n",
              "      <td>[Italian]</td>\n",
              "      <td>34.174018</td>\n",
              "      <td>-118.317323</td>\n",
              "      <td>Burbank</td>\n",
              "      <td>117 N. Victory Blvd, , CA, Burbank, 91502, Uni...</td>\n",
              "      <td>[{'name': 'Main Menu', 'sections': [{'name': '...</td>\n",
              "      <td>WE ARE STILL OPEN FOR TAKE OUT/ DELIVERY. PLEA...</td>\n",
              "      <td>None</td>\n",
              "      <td>Clark</td>\n",
              "      <td>http://www.centannila.com/</td>\n",
              "      <td>(818) 561-4643</td>\n",
              "      <td>Italian</td>\n",
              "      <td>Casual Dining</td>\n",
              "      <td>Marin Santos</td>\n",
              "      <td>Private Lot</td>\n",
              "      <td>Casual Dress</td>\n",
              "      <td>None</td>\n",
              "      <td>Dinner\\nMon–Thu 5:00 pm–8:30 pm\\nFri, Sat 5:00...</td>\n",
              "      <td>2</td>\n",
              "      <td>$30 and under</td>\n",
              "      <td>[AMEX, Discover, MasterCard, Visa]</td>\n",
              "      <td>90</td>\n",
              "      <td>4.7</td>\n",
              "      <td>83</td>\n",
              "      <td>{'food': 4.7, 'noise': 2.0, 'value': 4.6, 'ser...</td>\n",
              "      <td>[0, 0, 7, 14, 62]</td>\n",
              "      <td>692</td>\n",
              "      <td>[Neighborhood Gem, Casual, Good for a Date]</td>\n",
              "      <td>[{'name': 'Neighborhood Gem', 'location': 'San...</td>\n",
              "      <td>[]</td>\n",
              "      <td>[Beer, Corkage Fee, Delivery, Gluten-free Opti...</td>\n",
              "      <td>[]</td>\n",
              "      <td>{'bar': True, 'counter': False, 'gifting': Non...</td>\n",
              "      <td>{'cleanMenus': True, 'limitedSeating': False, ...</td>\n",
              "      <td>www.centannila.com</td>\n",
              "      <td>http://www.facebook.com/centannila</td>\n",
              "      <td>http://www.centannila.com/menu.html</td>\n",
              "      <td>[{'name': 'Ravioli Di', 'description': 'pumpki...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>[cleanMenus, prohibitSickStaff, requireDinerMa...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6def235b-5261-4f0a-99e5-18eb64f6cc1f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-6def235b-5261-4f0a-99e5-18eb64f6cc1f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6def235b-5261-4f0a-99e5-18eb64f6cc1f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# ✏️ YOUR CODE HERE\n",
        "\n",
        "# find the columns containing HTML tags\n",
        "from bs4 import BeautifulSoup\n",
        "df_new=df.copy()\n",
        "cols_with_html = []\n",
        "for col in df_new.columns:\n",
        "    for cell in df_new[col]:\n",
        "        if '<' in str(cell):\n",
        "            cols_with_html.append(col)\n",
        "            break\n",
        "\n",
        "# print the columns that contain\n",
        "print(cols_with_html)\n",
        "\n",
        "# convert the columns that need to be parsed into string\n",
        "for x in cols_with_html:\n",
        "  df_new[x]=df_new[x].astype(str)\n",
        "\n",
        "# remove HTML tags from the columns containing them\n",
        "\n",
        "def htmlParser(text):\n",
        "  parser=BeautifulSoup(text,'html.parser')\n",
        "  return parser.get_text()\n",
        "\n",
        "for col in cols_with_html:\n",
        "  df_new[col]=df_new[col].apply(htmlParser)\n",
        "\n",
        "# giving the output that does not contain html tags in the column\n",
        "df_new.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ecb76263-094a-4ee6-842d-24da225eb72d",
      "metadata": {
        "id": "ecb76263-094a-4ee6-842d-24da225eb72d"
      },
      "source": [
        "#### 2.3.3 Imputing 📈 (3 points)\n",
        "\n",
        "Fill up missing values for rating, rating count and review count by imputing based on the following columns in order:\n",
        "\n",
        "1. `brand_name`\n",
        "2. `area`\n",
        "3. `city`\n",
        "\n",
        "This means that if `rating` is missing for a restaurant (null / 0), but that restaurant is part of a brand where\n",
        "other restaurants of the same brand have ratings, then a median rating is taken. If brands are complete, then missing values are filled using\n",
        "area where the restaurant is located (median rating) and finally filled using the city's rating\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "93ac70d9-6b5a-482e-9c49-92556ed0b704",
      "metadata": {
        "id": "93ac70d9-6b5a-482e-9c49-92556ed0b704"
      },
      "outputs": [],
      "source": [
        "# ✏️ YOUR CODE HERE\n",
        "\n",
        "# new_df=df.copy()\n",
        "# group_by_city=df.groupby(['city'])\n",
        "# city_rating=group_by_city['rating'].median()\n",
        "# city_rating_median= group_by_city['rating_count'].median()\n",
        "# city_review_median= group_by_city['review_count'].median()\n",
        "\n",
        "# group_by_area=df.groupby(['area'])\n",
        "# area_rating=group_by_area['rating'].median()\n",
        "# area_rating_median=group_by_area['rating_count'].median()\n",
        "# area_review_median=group_by_area['review_count'].median()\n",
        "\n",
        "# group_by_brand=df.groupby(['brand_name'])\n",
        "# brand_rating=group_by_brand['rating'].median()\n",
        "# brand_rating_median=group_by_brand['rating_count'].median()\n",
        "# brand_review_median=group_by_brand['review_count'].median()\n",
        "\n",
        "\n",
        "# brand_name_rating_mean=df.groupby('brand_name').aggregate({'rating':'mean'}).to_dict()\n",
        "# brand_name_ratingCount_mean=df.groupby('brand_name').aggregate({'rating_count':'mean'}).to_dict()\n",
        "\n",
        "# brand_name_reviewCount_mean=df.groupby('brand_name').aggregate({'review_count':'mean'}).to_dict()\n",
        "\n",
        "# new_slice_df_1=df[df['rating']>0]\n",
        "\n",
        "# grouping1=new_slice_df_1.groupby(['brand_name'])\n",
        "# grouping_1=grouping1['rating'].mean()\n",
        "\n",
        "# group_by_area=new_slice_df_1.groupby(['area'])\n",
        "# area_rating=group_by_area['rating'].median()\n",
        "\n",
        "\n",
        "# new_slice_df_2=df[df['rating_count']>0]\n",
        "\n",
        "# grouping2=new_slice_df_2.groupby(['brand_name'])\n",
        "# grouping_2=grouping2['rating_count'].median()\n",
        "\n",
        "# new_slice_df_3=df[df['review_count']>0]\n",
        "\n",
        "# grouping3=new_slice_df_3.groupby(['brand_name'])\n",
        "# grouping_3=grouping3['review_count'].median()\n",
        "\n",
        "\n",
        "# for i in range(len(new_df)):\n",
        "\n",
        "#     if new_df['rating'][i]==None or new_df['rating'][i]==0:\n",
        "#          if brand_rating[new_df['brand_name'][i]]!=0:\n",
        "#             new_df['rating'][i]=grouping_1[new_df['brand_name'][i]]\n",
        "#          else:\n",
        "#             if area_rating[new_df['area'][i]]!=0:\n",
        "#                 new_df['rating'][i]=area_rating[new_df['area'][i]]\n",
        "#             else:\n",
        "#                 if city_rating[new_df['city'][i]]!=0:\n",
        "#                     new_df['rating'][i]=city_rating[new_df['city'][i]]\n",
        "#                 else:\n",
        "#                     if new_df['brand_name'][i] in grouping_1:\n",
        "#                         new_df['rating'][i]=grouping_1[new_df['brand_name'][i]]\n",
        "#                     else:\n",
        "#                         new_df['rating'][i] = 0\n",
        "\n",
        "\n",
        "\n",
        "#     if new_df['rating_count'][i]==None or new_df['rating_count'][i]==0:\n",
        "#         if brand_rating_median[new_df['brand_name'][i]]!=0:\n",
        "#             new_df['rating_count'][i]=brand_rating_median[new_df['brand_name'][i]]\n",
        "#         else:\n",
        "#             if area_rating_median[new_df['area'][i]]!=0:\n",
        "#                 new_df['rating_count'][i]=area_rating_median[new_df['area'][i]]\n",
        "#             else:\n",
        "#                 if city_rating_median[new_df['city'][i]]!=0:\n",
        "#                     new_df['rating_count'][i]=city_rating[new_df['city'][i]]\n",
        "#                 else:\n",
        "#                     if new_df['brand_name'][i] in grouping_2:\n",
        "#                         new_df['rating_count'][i]=grouping_2[new_df['brand_name'][i]]\n",
        "#                     else:\n",
        "#                         new_df['rating_count'][i] = 0\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # if new_df['review_count'][i]==None or new_df['review_count'][i]==0:\n",
        "    #     if brand_review_median[new_df['brand_name'][i]]!=0:\n",
        "    #         new_df['review_count'][i]=brand_review_median[new_df['brand_name'][i]]\n",
        "    #     else:\n",
        "    #         if area_review_median[new_df['area'][i]]!=0:\n",
        "    #             new_df['review_count'][i]=area_review_median[new_df['area'][i]]\n",
        "    #         else:\n",
        "    #             if city_review_median[new_df['city'][i]]!=0:\n",
        "    #                 new_df['review_count'][i]=city_review_median[new_df['city'][i]]\n",
        "    #             else:\n",
        "    #                 if new_df['brand_name'][i] in grouping_3:\n",
        "    #                     new_df['review_count'][i]=grouping_1[new_df['brand_name'][i]]\n",
        "    #                 else:\n",
        "    #                     new_df['review_count'][i] = 0\n",
        "\n",
        "# first we need to groupby city and then find rating, rating_count and review count accordingly\n",
        "\n",
        "groupby_city=df.groupby(['city'])\n",
        "rating_city=groupby_city['rating'].median()\n",
        "rating_count_city=groupby_city['rating_count'].median()\n",
        "review_count_city=groupby_city['review_count'].median()\n",
        "\n",
        "# now we group according to the area and then take the median of the rating, rating count and review count of the restaurants\n",
        "# in that area\n",
        "groupby_area=df.groupby(['area'])\n",
        "rating_area=groupby_area['rating'].median()\n",
        "rating_count_area=groupby_area['rating_count'].median()\n",
        "revew_count_area=groupby_area['review_count'].median()\n",
        "\n",
        "# now we group bybrand name and take the median of the rating, rating count and review count of the restaurants with that\n",
        "# brand name\n",
        "\n",
        "groupby_brand=df.groupby(['brand_name'])\n",
        "rating_brand=groupby_brand['rating'].median()\n",
        "rating_count_brand=groupby_brand['rating_count'].median()\n",
        "review_count_brand=groupby_brand['review_count'].median()\n",
        "\n",
        "# even after reducing the number of zeroes in the rating, rating count and review count column, we are getting very less difference\n",
        "# in the number of zeroes. So, we can take the mean of the area of al the rating, rating count and review count in which the\n",
        "# restaurant is located\n",
        "\n",
        "\n",
        "areas_rating_mean=groupby_area['rating'].mean()\n",
        "areas_ratingCount_mean=groupby_area['rating_count'].mean()\n",
        "areas_reviewCount_mean=groupby_area['review_count'].mean()\n",
        "\n",
        "df_new=df.copy()\n",
        "\n",
        "for i in range(len(df_new)):\n",
        "\n",
        "# first the process is done fo rating\n",
        "    if df_new['rating'][i]==None or df_new['rating'][i]==0:\n",
        "        if rating_brand[df_new['brand_name'][i]]!=0:\n",
        "            df_new['rating'][i]=rating_brand[df_new['brand_name'][i]]\n",
        "        else:\n",
        "            if rating_area[df_new['area'][i]]!=0:\n",
        "                df_new['rating'][i]=rating_area[df_new['area'][i]]\n",
        "            else:\n",
        "                if rating_city[df_new['city'][i]]!=0:\n",
        "                    df_new['rating'][i]=rating_city[df_new['city'][i]]\n",
        "                else:\n",
        "                    df_new['rating'][i]=areas_rating_mean[df_new['area'][i]]\n",
        "\n",
        "# now the steps are done for rating count\n",
        "\n",
        "\n",
        "    if df_new['rating_count'][i]==None or df_new['rating_count'][i]==0:\n",
        "        if rating_count_brand[df_new['brand_name'][i]]!=0:\n",
        "            df_new['rating_count'][i]=rating_count_brand[df_new['brand_name'][i]]\n",
        "        else:\n",
        "            if rating_count_area[df_new['area'][i]]!=0:\n",
        "                df_new['rating_count'][i]=rating_count_area[df_new['area'][i]]\n",
        "            else:\n",
        "                if rating_count_city[df_new['city'][i]]!=0:\n",
        "                    df_new['rating_count'][i]=rating_city[df_new['city'][i]]\n",
        "                else:\n",
        "                    df_new['rating_count'][i]=areas_ratingCount_mean[df_new['area'][i]]\n",
        "\n",
        "# these are the steps for review count\n",
        "\n",
        "\n",
        "    if df_new['review_count'][i]==None or df_new['review_count'][i]==0:\n",
        "        if review_count_brand[df_new['brand_name'][i]]!=0:\n",
        "            df_new['review_count'][i]=review_count_brand[df_new['brand_name'][i]]\n",
        "        else:\n",
        "            if revew_count_area[df_new['area'][i]]!=0:\n",
        "                df_new['review_count'][i]=revew_count_area[df_new['area'][i]]\n",
        "            else:\n",
        "                if review_count_city[df_new['city'][i]]!=0:\n",
        "                    df_new['review_count'][i]=review_count_city[df_new['city'][i]]\n",
        "                else:\n",
        "                    df_new['review_count'][i]=areas_reviewCount_mean[df_new['area'][i]]\n",
        "\n",
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ccbc10eb",
      "metadata": {
        "id": "ccbc10eb"
      },
      "source": [
        "#### 2.3.4 Analytical Transformations (8 points)\n",
        "\n",
        "Choose any one sub-section only to answer. The choice is yours\n",
        "\n",
        "<img src=\"https://media.giphy.com/media/SCt3Miv6ugvSg/giphy.gif\" height=\"250px\" width=\"250px\" alt=\"the matrix\">\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8472e53d-b62e-4bb5-954c-b878d295b6af",
      "metadata": {
        "id": "8472e53d-b62e-4bb5-954c-b878d295b6af"
      },
      "source": [
        "##### 2.3.4a Operating Hours 🕰️\n",
        "\n",
        "Create an operating hours [bitmap](https://en.wikipedia.org/wiki/Bit_array) column from the operating hours text column for all restaurants. The bitmap would be a matrix of size 24 x 7 where a 1 or 0 on each cell indicates whether the restaurant is operating on a specific day at a specific hour\n",
        "\n",
        "Example: For operating hours text of the form:\n",
        "\n",
        "```tex\n",
        "Lunch\n",
        "Daily 11:00 am–3:30 pm\n",
        "Dinner\n",
        "Daily 4:30 pm–11:30 pm\n",
        "```\n",
        "\n",
        "Create a bitmap of the following form:\n",
        "\n",
        "```json\n",
        "{\n",
        "    \"Monday\" : [0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1],\n",
        "    \"Tuesday\" : [0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1],\n",
        "    .\n",
        "    .\n",
        "    .\n",
        "    \"Sunday\" : [0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1],\n",
        "\n",
        "}\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5138cd99-9cc4-4b67-8f37-3a3efe29853a",
      "metadata": {
        "id": "5138cd99-9cc4-4b67-8f37-3a3efe29853a"
      },
      "outputs": [],
      "source": [
        "# ✏️ YOUR CODE HERE"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4a5c110b-691b-4559-b82b-9b0ae02ec27b",
      "metadata": {
        "id": "4a5c110b-691b-4559-b82b-9b0ae02ec27b"
      },
      "source": [
        "##### 2.3.4b On my radar 🗺️\n",
        "\n",
        "For the following restaurant:\n",
        "\n",
        "- brand_name `Calzone's Pizza Cucina`\n",
        "- coordinates `37.799068, -122.408226`.\n",
        "\n",
        "Answer these questions:\n",
        "\n",
        "- How many restaurants exist within a 100 meter radius of this restaurant?\n",
        "- What is the most frequent cuisine (`category`) occurence in this 100m radius across the restaurants that exist in that range?"
      ]
    },
    {
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      "execution_count": null,
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      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e70945a5-8351-49a7-ffe1-75bbb5c4d22f"
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          "output_type": "stream",
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          "text": [
            "21\n",
            "['Italian']\n"
          ]
        }
      ],
      "source": [
        "# # ✏️ YOUR CODE HERE\n",
        "\n",
        "# importing geodesic from geopy.distance\n",
        "\n",
        "from geopy.distance import geodesic\n",
        "\n",
        "coordinates=[37.799068, -122.408226]\n",
        "df_new=df.copy()\n",
        "\n",
        "# converting latitude and longitude into a list\n",
        "\n",
        "all_points=list(zip(df_new.latitude,df_new.longitude))\n",
        "\n",
        "# checking how many restaurants are there within the radius of 100m\n",
        "\n",
        "count=0\n",
        "for x in all_points:\n",
        "  if geodesic(coordinates,x).m <=100:\n",
        "    count=count+1\n",
        "\n",
        "\n",
        "print(count)\n",
        "\n",
        "# the count 21 also consists of the restaurant itself\n",
        "\n",
        "# creating a new column in the dataframe and adding the distances of the restaurants from the reference restaurant\n",
        "\n",
        "df_new['distance'] = df_new.apply(lambda row: geodesic((row['latitude'], row['longitude']), (37.799068,-122.408226 )).meters, axis=1)\n",
        "\n",
        "# there are 21 restaurants within 100 points from the given restaurant\n",
        "\n",
        "# we filter the restaurants within a 100 meter radius\n",
        "df_100m = df_new[df_new['distance'] <= 100]\n",
        "\n",
        "# now we count the number of restaurants within the 100 meter radius\n",
        "num_restaurants = len(df_100m)\n",
        "\n",
        "# we will find the most frequent cuisine among the restaurants in the 100 meter radius\n",
        "most_frequent_cuisine = df_100m['categories'].value_counts().idxmax()\n",
        "\n",
        "#we print the most frequent cuisine\n",
        "\n",
        "print(most_frequent_cuisine)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "58a351f2-1975-4c02-a7dd-bc1ee4b60ab7",
      "metadata": {
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      "source": [
        "### 2.4 Menu-Level Table 🧾 (8 points)\n",
        "\n",
        "<img src=\"https://media.giphy.com/media/qpLuA97QGOsnK/giphy.gif\" height=\"250px\" width=\"250px\" alt=\"ratatouille\">\n",
        "\n",
        "**Create a menu-level table by parsing out menu items from the `menu` column per restaurant.**\n",
        "\n",
        "Every restaurant has a `menu` column that contains deeply nested JSON data on the restaurant's menu. The hierarchy is as follows:\n",
        "\n",
        "* One restaurant can have multiple menus (morning menu, evening menu, etc.)\n",
        "    * Each menu can have a description and provider\n",
        "* Each restaurant menu can have multiple sections (such as Appetizers, Desserts, etc.)\n",
        "    * Each section has a description\n",
        "* Each section can have multiple menu items (such as Latte, Apple Pie, Carrot Halwa, etc.)\n",
        "    * Each menu item has a price, currency and description\n",
        "\n",
        "You need to parse out the menu data from the JSON in the `menu` column for each restaurant and have a restaurants x menu table as shown below.\n",
        "\n",
        "| restaurant_id | menu_name | menu_description | menu_provider | section_name | section_description | item_name          | item_description                                                                                                      | item_price | item_price_currency |\n",
        "| ------------: | :-------- | :--------------- | ------------: | :----------- | :------------------ | :----------------- | :-------------------------------------------------------------------------------------------------------------------- | ---------: | :------------------ |\n",
        "|             1 | Main Menu |                  |           nan | Appetizers   |                     | Egg Rolls          | Deep fried mixed veggie egg rolls served with sweet & sour sauce                                                      |          8 | USD                 |\n",
        "|             1 | Main Menu |                  |           nan | Appetizers   |                     | Fried Tofu         | (Contains Peanut) Deep fried tofu, served with sweet & sour sauce and crushed peanut                                  |          8 | USD                 |\n",
        "|             1 | Main Menu |                  |           nan | Appetizers   |                     | Fried Meat Balls   | Deep fried fish, pork, beef balls or mixed served with sweet & sour sauce. Meat: Beef $1, Fish, Mixed Meat ball, Pork |        8.5 | USD                 |\n",
        "|             1 | Main Menu |                  |           nan | Appetizers   |                     | Pork Jerky         | Deep fried marinated pork served with special jaew sauce                                                              |        8.5 | USD                 |\n",
        "|             1 | Main Menu |                  |           nan | Appetizers   |                     | Thai Isaan Sausage | (Contains Peanut) Thai Style sausage served with fresh vegetables and peanuts                                         |          9 | USD                 |\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c90a62c3",
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      "source": [
        "# # # ✏️ YOUR CODE HERE\n",
        "# # df_2=df.copy()\n",
        "\n",
        "# # # flatten the JSON data in the menu column\n",
        "# # df_2 = pd.concat([df_2.drop(['menu'], axis=1)\n",
        "# # df_2=pd.json_normalize(df_2['menu'])], axis=1)\n",
        "\n",
        "# # print(df_2)\n",
        "\n",
        "# # # group the data by restaurant and menu\n",
        "# # grouped = df_2.groupby(['restaurant_id', 'menu'])\n",
        "\n",
        "# # # extract the relevant columns to create a menu-level table\n",
        "# # menu_df = grouped.apply(lambda x: x[['description', 'menu_provider', 'section_name', 'section_description', 'item_name','item_description','item_price','item_price_currency']].explode('section')).reset_index(drop=True)\n",
        "\n",
        "# # # reset the index\n",
        "# # menu_df = menu_df.reset_index(drop=True)\n",
        "\n",
        "# # # display the resulting menu-level table\n",
        "# # print(menu_df)\n",
        "\n",
        "# df_new1=pd.read_json('/content/drive/MyDrive/Rapidops_Practice/california_restaurants.json')\n",
        "# import json\n",
        "# import ast\n",
        "# # Define a function to extract the menu data from the JSON string\n",
        "# def extract_menu_data(menu_data):\n",
        "#     # menu_data = json.loads(menu_data)\n",
        "#     menu_data= ast.literal_eval(menu_data)\n",
        "\n",
        "#     result = []\n",
        "#     for menu in menu_data:\n",
        "#         menu_name = menu.get('name', '')\n",
        "#         menu_description = menu.get('description', '')\n",
        "#         menu_provider = menu.get('provider', '')\n",
        "#         for section in menu.get('sections', []):\n",
        "#             section_name = section.get('name', '')\n",
        "#             section_description = section.get('description', '')\n",
        "#             for item in section.get('items', []):\n",
        "#                 item_name = item.get('name', '')\n",
        "#                 item_description = item.get('description', '')\n",
        "#                 item_price = item.get('price', '')\n",
        "#                 item_price_currency = item.get('priceCurrency', '')\n",
        "#                 result.append({\n",
        "#                     'menu_name': menu_name,\n",
        "#                     'menu_description': menu_description,\n",
        "#                     'menu_provider': menu_provider,\n",
        "#                     'section_name': section_name,\n",
        "#                     'section_description': section_description,\n",
        "#                     'item_name': item_name,\n",
        "#                     'item_description': item_description,\n",
        "#                     'item_price': item_price,\n",
        "#                     'item_price_currency': item_price_currency\n",
        "#                 })\n",
        "#     return result\n",
        "\n",
        "# # Apply the function to the menu column to extract the menu data\n",
        "# df['menu_data'] = df['menu'].apply(extract_menu_data)\n",
        "\n",
        "# # Expand the menu_data column into a new dataframe\n",
        "# menu_df = pd.DataFrame(\n",
        "#     data = [item for menu_data in df['menu_data'] for item in menu_data]\n",
        "# )\n",
        "\n",
        "# # Merge the menu data with the restaurant data\n",
        "# menu_df = menu_df.merge(df[['id', 'name']], left_on=None, right_on=None, left_index=True, right_index=True, how='outer')\n",
        "# menu_df = menu_df.rename(columns={'id': 'restaurant_id', 'name': 'restaurant_name'})\n",
        "\n",
        "# # Drop the menu_data column\n",
        "# menu_df = menu_df.drop(columns='menu_data')\n",
        "\n",
        "# # Rearrange the columns in the desired order\n",
        "# menu_df = menu_df[['restaurant_id', 'restaurant_name', 'menu_name', 'menu_description', 'menu_provider', 'section_name', 'section_description', 'item_name', 'item_description', 'item_price', 'item_price_currency']]\n",
        "\n",
        "# # Display the resulting table\n",
        "# print(menu_df)\n",
        "\n"
      ]
    },
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      "id": "b1415f5d-2c5e-4cd6-a59a-9a6bb9d04771",
      "metadata": {
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      },
      "source": [
        "### 3.1 Analytical Questions ⚗️ (10 points)\n",
        "\n",
        "**Answer ONLY ONE of the Questions using the Data, i.e, choose between `3.1.1`, `3.1.2` or `3.1.3`**\n",
        "\n",
        "<img src=\"https://media.giphy.com/media/3o7TKVSE5isogWqnwk/giphy.gif\" height=\"250px\" width=\"250px\" alt=\"sherlock holmes\">\n",
        "\n",
        "> Note that the analytical questions may sometimes require converting categorical type columns that are lists or strings into numeric columns. For ex. \"Casual Dining\", \"Fine Dining\"..etc. would require you to generate a categorical encoding of 1,2..etc. For columns that contain lists like `categories`, which contain cuisine tags, a one-hot or multi-hot encoding technique may be required based on the situation. A numeric categorical encoding is required for these string or list based columns since pandas cannot (usually) automatically generate correlations or clusters based on text-based categories\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7ea37141-d478-4b97-879e-48ead1c08ba0",
      "metadata": {
        "id": "7ea37141-d478-4b97-879e-48ead1c08ba0"
      },
      "source": [
        "#### 3.1.1 Take me out for dinner 🕯️\n",
        "\n",
        "Which areas according to you have the best restaurants in California and why? You can define best based on whatever criteria you wish as long as it involves measuring more than a single column. For ex. You cannot merely claim that the restaurant with the highest rating is the best restaurant.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cd7bb2ec-cf0b-474c-8e22-ec25751f483c",
      "metadata": {
        "id": "cd7bb2ec-cf0b-474c-8e22-ec25751f483c"
      },
      "outputs": [],
      "source": [
        "# ✏️ YOUR CODE HERE\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "060f9bd8",
      "metadata": {
        "id": "060f9bd8"
      },
      "source": [
        "### 🍴 BEST Area to eat out goes to \"West Hollywood\"\n",
        "\n",
        "Their are various reason why West Hollywood is the best place to eat out,\\\n",
        "Because,\n",
        "1. We are already filter out those restaurants less reviews which less people have visited it.\n",
        "2. The Award is not only based on rating and reviews counts of the area but also the categories of food you can have\n",
        "3. One of the best reasons is the area is not pricey insplit of the categories of food it is providing\n",
        "4. Area have decent amount of reatuarants so one can easily hop on another place if not liked"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6fc6b3a5-e173-4720-8a80-216a7bf14e4f",
      "metadata": {
        "id": "6fc6b3a5-e173-4720-8a80-216a7bf14e4f"
      },
      "source": [
        "#### 3.1.2 Michelin Approves 🎖️\n",
        "\n",
        "Which columns seem to play / not play a major factor in whether or not the restaurant has an award? Justify your options\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ce8dd39a-ca91-43e2-bfd5-23cf96701efc",
      "metadata": {
        "id": "ce8dd39a-ca91-43e2-bfd5-23cf96701efc",
        "outputId": "4ffac6f3-49cf-4169-8950-549af24a4b1f"
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      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Most Booked</th>\n",
              "      <th>Best Ambiance</th>\n",
              "      <th>Best Food</th>\n",
              "      <th>Best Overall</th>\n",
              "      <th>Best Service</th>\n",
              "      <th>Best Value</th>\n",
              "      <th>Special Occasion</th>\n",
              "      <th>Romantic</th>\n",
              "      <th>Fit for Foodies</th>\n",
              "      <th>Vibrant Bar Scene</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>award_count</th>\n",
              "      <td>414</td>\n",
              "      <td>404</td>\n",
              "      <td>402</td>\n",
              "      <td>402</td>\n",
              "      <td>401</td>\n",
              "      <td>400</td>\n",
              "      <td>398</td>\n",
              "      <td>393</td>\n",
              "      <td>391</td>\n",
              "      <td>389</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Most Booked  Best Ambiance  Best Food  Best Overall  \\\n",
              "award_count          414            404        402           402   \n",
              "\n",
              "             Best Service  Best Value  Special Occasion  Romantic  \\\n",
              "award_count           401         400               398       393   \n",
              "\n",
              "             Fit for Foodies  Vibrant Bar Scene  \n",
              "award_count              391                389  "
            ]
          },
          "execution_count": 101,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# simple dataframe to look at distribution of awards across california by most awarded titles\n",
        "awards_df: pd.DataFrame = pd.json_normalize(df[\"awards\"].dropna().explode()).rename(\n",
        "    columns={\"name\": \"award_name\", \"location\": \"award_location\"}\n",
        ")\n",
        "awards_df[\"award_name\"].value_counts().to_frame().head(10).rename(\n",
        "    columns={\"award_name\": \"award_count\"}\n",
        ").transpose()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7910b3c6-88b2-41e3-9d68-a9c669e7ebd0",
      "metadata": {
        "id": "7910b3c6-88b2-41e3-9d68-a9c669e7ebd0"
      },
      "outputs": [],
      "source": [
        "# ✏️ YOUR CODE HERE"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a0ad2e08-ca7e-49d0-b57b-451af09bd925",
      "metadata": {
        "id": "a0ad2e08-ca7e-49d0-b57b-451af09bd925"
      },
      "source": [
        "#### 3.1.3 Principal Components 🥨\n",
        "\n",
        "Which columns are highly correlated between each other and can be treated as redundant?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e3481f40-02d8-4fea-a9e5-d55bdbc98479",
      "metadata": {
        "id": "e3481f40-02d8-4fea-a9e5-d55bdbc98479",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "outputId": "a12a4441-85dd-4e48-f846-4cef618f7d3b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The following columns are highly correlated and can be treated as redundant: ['longitude', 'area', 'primary_cuisine', 'price_range']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc8707cc70>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# ✏️ YOUR CODE HERE - may require encoding categorical string variables\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder=LabelEncoder()\n",
        "\n",
        "\n",
        "df_new_2=df_new.copy()\n",
        "\n",
        "# perform label encoding\n",
        "\n",
        "categorical_features=['city','brand_name','categories','area','primary_cuisine','dining_style','parking_info','dress_code',\n",
        "                      'price_range_id','price_range','payment_options','maximum_days_advance_for_reservation','rating','rating_count',\n",
        "                      'review_count','checklist','safety_precautions','popular_dishes']\n",
        "\n",
        "for column in categorical_features:\n",
        "  if column in df.columns:\n",
        "    df_new_2[column]=encoder.fit_transform(df_new_2[column].astype(str))\n",
        "\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "# Compute the correlation matrix\n",
        "corr_matrix = df_new_2.corr().abs()\n",
        "\n",
        "# Select upper triangle of correlation matrix\n",
        "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
        "\n",
        "# Find index of feature columns with correlation greater than threshold\n",
        "threshold = 0.7\n",
        "to_drop = [column for column in upper.columns if any(upper[column] > threshold)]\n",
        "\n",
        "print(f\"The following columns are highly correlated and can be treated as redundant: {to_drop}\")\n",
        "\n",
        "plt.figure(figsize=(15,10))\n",
        "\n",
        "sns.heatmap(corr_matrix,annot=True,cmap='coolwarm')\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "54f74bcd",
      "metadata": {
        "id": "54f74bcd"
      },
      "source": [
        "---\n",
        "\n",
        "Good job!\n",
        "\n",
        "<img src=\"https://media.giphy.com/media/qLhxN7Rp3PI8E/giphy.gif\" height=\"250px\" width=\"250px\" alt=\"legend of zelda\">"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "toc-showtags": false,
    "vscode": {
      "interpreter": {
        "hash": "5a95100a7d3b4e320db1551c68e327e796fcc58a9206dc42c48870a93129471d"
      }
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}