CMSC320_HW1_Fall2024.ipynb

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        "<a href=\"https://colab.research.google.com/github/lvo123/CMSC320-Assignment-1-Dr.Alam/blob/main/CMSC320_HW1_Fall2024.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# **HOMEWORK 1: CAUTION! CONTENTS ARE HOT** 🌋\n",
        "## **DUE: *SEPTEMBER 19, 2024 @ 11:59 PM***\n",
        "## **24-HR LATE DUE DATE WITH A 15% PENALTY: *SEPTEMBER 20, 2024 @ 11:59 PM***\n",
        "\n",
        "---------------------\n",
        "#### **DATASET DESCRIPTION**\n",
        "\n",
        "The [NCEI/WDS Global Significant Volcanic Eruptions Database](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ngdc.mgg.hazards:G10147) is a very comprehensive collection of +600 volcanic eruptions dating from 4360 BC to the present. Due to the nature of this assignment, we will be dealing with relatively newer volcanoes (in which some are still obviously still older than anyone on Earth currently). Each eruption in the database is classified as significant if it meets one or more criteria, such as causing fatalities, incurring **damage on property** (**+$1 million**), reaching a **Volcanic Explosivity Index (VEI)** of **6 or higher**, generating a tsunami, or being linked to a significant earthquake. The VEI is a scale that measures the explosiveness of volcanic eruptions, providing insight into the magnitude and potential consequences of the eruptions. The database includes detailed information on the location, type of volcano, last known eruption, VEI, casualties, property damage, and much more.\n",
        "![volcano](https://wikitravel.org/upload/shared//9/99/Volcano_de_Fuego_Banner.jpg)\n",
        "\n",
        "#### **Objective of the Assignment:**\n",
        "\n",
        "**We are going to dive straight into these volcanoes (well... their dataset), to swim our way into Pandas and SQL proficiency!**\n",
        "\n",
        "You will find the [Pandas Documentation](https://pandas.pydata.org/docs/user_guide/index.html) helpful. There are also some helpful links to guide you along the way! Don't get burned 🔥\n",
        "\n",
        "---------------------"
      ],
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        "### **DO NOT REMOVE ANY PART OF ANY OF THE QUESTIONS OR YOU LOSE CREDIT**\n",
        "### *No Hardcoding either*  😋❤️‍🔥\n",
        "### **REMEMBER TO SHOW ALL CODE OUTPUT (NO CREDIT OTHERWISE)**"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Part 1: Maintenance 🤩 (25 POINTS TOTAL)**\n",
        "First, we're going to familiarize ourselves with the process. As in most languages, Python looks best when its modules are imported first before any other code is written ✨"
      ],
      "metadata": {
        "id": "HrD3qZmJUp6C"
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    {
      "cell_type": "code",
      "source": [
        "# Make sure these code blocks run properly and that you have properly installed the appropriate modules required.\n",
        "import pandas as pd\n",
        "import requests\n",
        "# import other libraries here\n",
        "\n",
        "# Don't remove this\n",
        "pd.set_option('display.max_rows', None)\n",
        "pd.set_option('display.max_columns', None)"
      ],
      "metadata": {
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      "execution_count": null,
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      "source": [
        "As you may have noticed, there's another library aside from Pandas called \"[requests](https://requests.readthedocs.io/en/latest/).\" **The requests library allows you to send HTTP requests to a server, retrieve the content, and process it at ease.** It's very beginner friendly for those attempting to get into webscraping (super important for collecting and creating datasets). We also recommend looking into [BeautifulSoup](https://beautiful-soup-4.readthedocs.io/en/latest/) (yeah, soup LOL), another wonderful library that can be paired with the requests library for webscraping.\n",
        "\n",
        "As shown below, sometimes specific websites require specific headers in order to process a request to access the data.\n",
        "\n",
        "To check if a request was processed successfully, use the [status_code](https://requests.readthedocs.io/en/latest/api/) function to see if the process returned 200."
      ],
      "metadata": {
        "id": "7nWwbZ4jK5Wi"
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      "cell_type": "code",
      "source": [
        "# API URL and headers in case request gets denied.\n",
        "api_url = \"https://www.ngdc.noaa.gov/hazel/hazard-service/api/v1/volcanoes\"\n",
        "\n",
        "headers = {\n",
        "    'accept': '*/*'\n",
        "}"
      ],
      "metadata": {
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      "execution_count": 6,
      "outputs": []
    },
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      "cell_type": "markdown",
      "source": [
        "#### **TASK 1.0: Cute Webscraping (5 points)**\n",
        "To make our cute webscraper we need to **create a GET request** using the relevant information given above.\n",
        "\n",
        "This particular dataset NOAA returns data from the API as ***json*** when a user makes a request. The json data has a particular format, so we will extract our needed information only from the field called **items** to make a dataframe.\n",
        "\n",
        "**After properly scraping the data, name this dataframe** ***df***\n",
        "\n",
        "**Save this dataframe into a CSV file named 'volcanoes.csv'**\n",
        "\n",
        "**You won't need to run this more than once**"
      ],
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        "# REMOVE THIS COMMENT AND ANSWER STARTING HERE\n",
        "api_url = \"https://www.ngdc.noaa.gov/hazel/hazard-service/api/v1/volcanoes\"\n",
        "# Send GET request to NOAA API\n",
        "response = requests.get(api_url)\n",
        "\n",
        "# Check if request was successful\n",
        "if response.status_code == 200:\n",
        "    # Parse the response JSON\n",
        "    data = response.json()\n",
        "\n",
        "    # Extract the 'items' field\n",
        "    items = data.get('items', [])\n",
        "\n",
        "    # Convert the 'items' field to a DataFrame\n",
        "    df = pd.DataFrame(items)\n",
        "\n",
        "    # Save the DataFrame to a CSV file\n",
        "    df.to_csv('volcanoes.csv', index=False)"
      ],
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      "cell_type": "markdown",
      "source": [
        "#### **TASK 1.1: 1 Liner Thingz (3 points)**\n",
        "\n",
        "We need to get an idea of what this dataset is going to look. In order to do that, let's take a look at some of the most [basic things](https://dataanalytics.buffalostate.edu/pandas-cheat-sheet) our dataframe has.\n",
        "\n",
        "**Read the directions carefully and code your answer with only one line of code.**\n",
        "\n",
        "***CAN'T USE LOOPS. DO NOT DISPLAY THE DATAFRAME, JUST YOUR CODE OUTPUT HERE.***"
      ],
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      "cell_type": "markdown",
      "source": [
        "**1.1.1:** *In one line of code and **using only one function**, show how many **total datapoints and features** there are in the dataframe **together**.*"
      ],
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      "cell_type": "code",
      "source": [
        "# REMOVE THIS COMMENT AND ANSWER STARTING HERE\n",
        "df.size"
      ],
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      "cell_type": "markdown",
      "source": [
        "**1.1.2:** *In one line of code, list the **names** of all the **features** in the dataframe.*"
      ],
      "metadata": {
        "id": "OoMluxmiESrd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# REMOVE THIS COMMENT AND ANSWER STARTING HERE\n",
        "df.columns.tolist()\n"
      ],
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              "['id',\n",
              " 'year',\n",
              " 'month',\n",
              " 'day',\n",
              " 'tsunamiEventId',\n",
              " 'earthquakeEventId',\n",
              " 'volcanoLocationId',\n",
              " 'volcanoLocationNewNum',\n",
              " 'volcanoLocationNum',\n",
              " 'name',\n",
              " 'location',\n",
              " 'country',\n",
              " 'latitude',\n",
              " 'longitude',\n",
              " 'elevation',\n",
              " 'morphology',\n",
              " 'agent',\n",
              " 'deathsTotal',\n",
              " 'deathsAmountOrderTotal',\n",
              " 'damageAmountOrderTotal',\n",
              " 'significant',\n",
              " 'publish',\n",
              " 'eruption',\n",
              " 'status',\n",
              " 'timeErupt',\n",
              " 'vei',\n",
              " 'deathsAmountOrder',\n",
              " 'damageAmountOrder',\n",
              " 'housesDestroyedAmountOrderTotal',\n",
              " 'deaths',\n",
              " 'injuries',\n",
              " 'injuriesAmountOrder',\n",
              " 'injuriesTotal',\n",
              " 'injuriesAmountOrderTotal',\n",
              " 'housesDestroyedAmountOrder',\n",
              " 'housesDestroyed',\n",
              " 'housesDestroyedTotal',\n",
              " 'missingAmountOrder',\n",
              " 'missingAmountOrderTotal',\n",
              " 'missing',\n",
              " 'missingTotal',\n",
              " 'damageMillionsDollars',\n",
              " 'damageMillionsDollarsTotal']"
            ]
          },
          "metadata": {},
          "execution_count": 8
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      "cell_type": "markdown",
      "source": [
        "We won't be using some of the data because there is a lot of missing data.\n",
        "\n",
        "**1.1.3:** *In one line of code, create a **new dataframe** called **new_df** that **contains all** the features of the **old** dataframe **except the following**:*\n",
        "\n",
        "volcanoLocationNum, location, latitude, longitude, agent, significant,\tpublish,\teruption,\tstatus, timeErupt, damageAmountOrder, damageAmountOrderTotal, housesDestroyedAmountOrder,\thousesDestroyedAmountOrderTotal, housesDestroyed,\thousesDestroyedTotal,\tmissingAmountOrder,\tmissingAmountOrderTotal,\tmissing,\tmissingTotal, damageMillionsDollars, damageMillionsDollarsTotal, injuries, injuriesAmountOrder, injuriesTotal, injuriesAmountOrderTotal, deathsAmountOrderTotal, and deathsAmountOrder.\n",
        "\n"
      ],
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      "cell_type": "code",
      "source": [
        "# REMOVE THIS COMMENT AND ANSWER STARTING HERE\n",
        "new_df = df.drop(columns=['volcanoLocationNum', 'location', 'latitude', 'longitude', 'agent', 'significant', 'publish', 'eruption', 'status', 'timeErupt', 'damageAmountOrder', 'damageAmountOrderTotal', 'housesDestroyedAmountOrder', 'housesDestroyedAmountOrderTotal', 'housesDestroyed', 'housesDestroyedTotal', 'missingAmountOrder', 'missingAmountOrderTotal', 'missing', 'missingTotal', 'damageMillionsDollars', 'damageMillionsDollarsTotal', 'injuries', 'injuriesAmountOrder', 'injuriesTotal', 'injuriesAmountOrderTotal', 'deathsAmountOrderTotal', 'deathsAmountOrder'])\n",
        "\n",
        "# KEEP THIS. It will display the whole dataframe.\n",
        "new_df"
      ],
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      "execution_count": 19,
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            "text/plain": [
              "      id  year  month   day  tsunamiEventId  earthquakeEventId  \\\n",
              "0      1  1169    2.0   4.0          2852.0              421.0   \n",
              "1      2  1329    7.0   NaN             NaN                NaN   \n",
              "2      3  1883    3.0   NaN             NaN                NaN   \n",
              "3      4  1888    3.0  13.0          1175.0                NaN   \n",
              "4      5  1850    NaN   NaN             NaN                NaN   \n",
              "..   ...   ...    ...   ...             ...                ...   \n",
              "195  933  1991    6.0   3.0             NaN                NaN   \n",
              "196  949  1331   12.0   NaN             NaN                NaN   \n",
              "197  960  1485    1.0   NaN             NaN                NaN   \n",
              "198  990  1826   10.0   NaN             NaN                NaN   \n",
              "199  994  1854    2.0   NaN             NaN                NaN   \n",
              "\n",
              "     volcanoLocationId  volcanoLocationNewNum           name  \\\n",
              "0                10106                 211060           Etna   \n",
              "1                10106                 211060           Etna   \n",
              "2                30301                 233010       Karthala   \n",
              "3                50107                 251070  Ritter Island   \n",
              "4                50214                 252140         Rabaul   \n",
              "..                 ...                    ...            ...   \n",
              "195              80210                 282100      Unzendake   \n",
              "196              80211                 282110         Asosan   \n",
              "197              80211                 282110         Asosan   \n",
              "198              80211                 282110         Asosan   \n",
              "199              80211                 282110         Asosan   \n",
              "\n",
              "              country  elevation          morphology  deathsTotal  vei  deaths  \n",
              "0               Italy       3357       Stratovolcano      16000.0  NaN     NaN  \n",
              "1               Italy       3357       Stratovolcano          NaN  1.0     NaN  \n",
              "2             Comoros       2361      Shield volcano          NaN  2.0     NaN  \n",
              "3    Papua New Guinea         75       Stratovolcano          NaN  2.0     NaN  \n",
              "4    Papua New Guinea        688  Pyroclastic shield          NaN  2.0     NaN  \n",
              "..                ...        ...                 ...          ...  ...     ...  \n",
              "195             Japan       1483     Complex volcano         43.0  1.0    43.0  \n",
              "196             Japan       1592             Caldera          NaN  2.0     NaN  \n",
              "197             Japan       1592             Caldera          1.0  2.0     1.0  \n",
              "198             Japan       1592             Caldera          NaN  2.0     NaN  \n",
              "199             Japan       1592             Caldera          3.0  2.0     3.0  \n",
              "\n",
              "[200 rows x 15 columns]"
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              "      <th>id</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
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              "      <th>tsunamiEventId</th>\n",
              "      <th>earthquakeEventId</th>\n",
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              "      <td>4.0</td>\n",
              "      <td>2852.0</td>\n",
              "      <td>421.0</td>\n",
              "      <td>10106</td>\n",
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              "      <td>Etna</td>\n",
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              "      <td>16000.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10106</td>\n",
              "      <td>211060</td>\n",
              "      <td>Etna</td>\n",
              "      <td>Italy</td>\n",
              "      <td>3357</td>\n",
              "      <td>Stratovolcano</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1883</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30301</td>\n",
              "      <td>233010</td>\n",
              "      <td>Karthala</td>\n",
              "      <td>Comoros</td>\n",
              "      <td>2361</td>\n",
              "      <td>Shield volcano</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>3</th>\n",
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              "      <td>1888</td>\n",
              "      <td>3.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1175.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50107</td>\n",
              "      <td>251070</td>\n",
              "      <td>Ritter Island</td>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>75</td>\n",
              "      <td>Stratovolcano</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>1850</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50214</td>\n",
              "      <td>252140</td>\n",
              "      <td>Rabaul</td>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>688</td>\n",
              "      <td>Pyroclastic shield</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
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              "      <th>195</th>\n",
              "      <td>933</td>\n",
              "      <td>1991</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80210</td>\n",
              "      <td>282100</td>\n",
              "      <td>Unzendake</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1483</td>\n",
              "      <td>Complex volcano</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>43.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
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              "      <th>197</th>\n",
              "      <td>960</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>990</td>\n",
              "      <td>1826</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>994</td>\n",
              "      <td>1854</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 15 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07020412-2331-44d9-a116-92ba4b17e81b')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-07020412-2331-44d9-a116-92ba4b17e81b 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-07020412-2331-44d9-a116-92ba4b17e81b');\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",
              "\n",
              "\n",
              "<div id=\"df-15c4d31b-0a13-4e4e-a242-cc7b0a040899\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-15c4d31b-0a13-4e4e-a242-cc7b0a040899')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-15c4d31b-0a13-4e4e-a242-cc7b0a040899 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_351810e8-7c05-44a9-ad61-2e8d2d59705d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('new_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_351810e8-7c05-44a9-ad61-2e8d2d59705d button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('new_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_df",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 306,\n        \"min\": 1,\n        \"max\": 994,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          296,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 248,\n        \"min\": -141,\n        \"max\": 1996,\n        \"num_unique_values\": 152,\n        \"samples\": [\n          1911,\n          1792,\n          1694\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.443524102347981,\n        \"min\": 1.0,\n        \"max\": 12.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          12.0,\n          9.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.128511453803906,\n        \"min\": 1.0,\n        \"max\": 31.0,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          26.0,\n          5.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tsunamiEventId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1742.5633133120016,\n        \"min\": 61.0,\n        \"max\": 5863.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          1407.0,\n          1022.0,\n          4218.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"earthquakeEventId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3015.8591620455554,\n        \"min\": 421.0,\n        \"max\": 10519.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          421.0,\n          6195.0,\n          6309.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volcanoLocationId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24461,\n        \"min\": 10101,\n        \"max\": 201141,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          80210,\n          80103,\n          10106\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volcanoLocationNewNum\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20706,\n        \"min\": 211010,\n        \"max\": 282110,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          282100,\n          281030,\n          211060\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 63,\n        \"samples\": [\n          \"Unzendake\",\n          \"Unnamed\",\n          \"Etna\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Greece\",\n          \"Pacific Ocean\",\n          \"Italy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"elevation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1034,\n        \"min\": -5,\n        \"max\": 3657,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          1483,\n          0,\n          3357\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"morphology\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Submarine volcano\",\n          \"Shield volcano\",\n          \"Caldera\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deathsTotal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5713.979903859243,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          4.0,\n          15.0,\n          56.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vei\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9737209847442237,\n        \"min\": 0.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.0,\n          2.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1519.626384223263,\n        \"min\": 1.0,\n        \"max\": 11000.0,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          221.0,\n          117.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **TASK 1.2: 1 Liner Shenaniganz (7 points)**\n",
        "\n",
        "We're going to tidy up the **new dataframe** a little more with some more advanced 1 liner code.\n",
        "\n",
        "**Read the directions carefully and code your answer with only one line of code.**\n",
        "\n",
        "**For this section, keep the method of display that is already in the box. Write your code as indicated.**\n",
        "\n",
        "***YOU CAN'T USE ONE LINE LOOPS OR ANY KIND OF LOOP.***"
      ],
      "metadata": {
        "id": "c8xP6O37UQp7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1.2.1:** *In one line of code, **discard any row** that contains **NaN** in **any one** of the columns indicating **time**.*"
      ],
      "metadata": {
        "id": "I_SHOaZRIblA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# REMOVE THIS COMMENT AND ANSWER STARTING HERE\n",
        "new_df = new_df.dropna(subset=new_df.filter(like='time').columns)\n",
        "\n",
        "\n",
        "# KEEP THIS. It will display the whole dataframe.\n",
        "new_df"
      ],
      "metadata": {
        "collapsed": true,
        "id": "B9qVE-OdGysV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "outputId": "692871ae-02ab-4975-d5e6-450fde57cbbd"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  year  month   day  tsunamiEventId  earthquakeEventId  \\\n",
              "0      1  1169    2.0   4.0          2852.0              421.0   \n",
              "1      2  1329    7.0   NaN             NaN                NaN   \n",
              "2      3  1883    3.0   NaN             NaN                NaN   \n",
              "3      4  1888    3.0  13.0          1175.0                NaN   \n",
              "4      5  1850    NaN   NaN             NaN                NaN   \n",
              "..   ...   ...    ...   ...             ...                ...   \n",
              "195  933  1991    6.0   3.0             NaN                NaN   \n",
              "196  949  1331   12.0   NaN             NaN                NaN   \n",
              "197  960  1485    1.0   NaN             NaN                NaN   \n",
              "198  990  1826   10.0   NaN             NaN                NaN   \n",
              "199  994  1854    2.0   NaN             NaN                NaN   \n",
              "\n",
              "     volcanoLocationId  volcanoLocationNewNum           name  \\\n",
              "0                10106                 211060           Etna   \n",
              "1                10106                 211060           Etna   \n",
              "2                30301                 233010       Karthala   \n",
              "3                50107                 251070  Ritter Island   \n",
              "4                50214                 252140         Rabaul   \n",
              "..                 ...                    ...            ...   \n",
              "195              80210                 282100      Unzendake   \n",
              "196              80211                 282110         Asosan   \n",
              "197              80211                 282110         Asosan   \n",
              "198              80211                 282110         Asosan   \n",
              "199              80211                 282110         Asosan   \n",
              "\n",
              "              country  elevation          morphology  deathsTotal  vei  deaths  \n",
              "0               Italy       3357       Stratovolcano      16000.0  NaN     NaN  \n",
              "1               Italy       3357       Stratovolcano          NaN  1.0     NaN  \n",
              "2             Comoros       2361      Shield volcano          NaN  2.0     NaN  \n",
              "3    Papua New Guinea         75       Stratovolcano          NaN  2.0     NaN  \n",
              "4    Papua New Guinea        688  Pyroclastic shield          NaN  2.0     NaN  \n",
              "..                ...        ...                 ...          ...  ...     ...  \n",
              "195             Japan       1483     Complex volcano         43.0  1.0    43.0  \n",
              "196             Japan       1592             Caldera          NaN  2.0     NaN  \n",
              "197             Japan       1592             Caldera          1.0  2.0     1.0  \n",
              "198             Japan       1592             Caldera          NaN  2.0     NaN  \n",
              "199             Japan       1592             Caldera          3.0  2.0     3.0  \n",
              "\n",
              "[200 rows x 15 columns]"
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              "      <td>Comoros</td>\n",
              "      <td>2361</td>\n",
              "      <td>Shield volcano</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>13.0</td>\n",
              "      <td>1175.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50107</td>\n",
              "      <td>251070</td>\n",
              "      <td>Ritter Island</td>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>75</td>\n",
              "      <td>Stratovolcano</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
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              "      <td>5</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50214</td>\n",
              "      <td>252140</td>\n",
              "      <td>Rabaul</td>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>688</td>\n",
              "      <td>Pyroclastic shield</td>\n",
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              "      <td>2.0</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>933</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80210</td>\n",
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              "      <td>Unzendake</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1483</td>\n",
              "      <td>Complex volcano</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>949</td>\n",
              "      <td>1331</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>960</td>\n",
              "      <td>1485</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>198</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>994</td>\n",
              "      <td>1854</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 15 columns</p>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('new_df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_df",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 306,\n        \"min\": 1,\n        \"max\": 994,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          296,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 248,\n        \"min\": -141,\n        \"max\": 1996,\n        \"num_unique_values\": 152,\n        \"samples\": [\n          1911,\n          1792,\n          1694\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.443524102347981,\n        \"min\": 1.0,\n        \"max\": 12.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          12.0,\n          9.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.128511453803906,\n        \"min\": 1.0,\n        \"max\": 31.0,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          26.0,\n          5.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tsunamiEventId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1742.5633133120016,\n        \"min\": 61.0,\n        \"max\": 5863.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          1407.0,\n          1022.0,\n          4218.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"earthquakeEventId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3015.8591620455554,\n        \"min\": 421.0,\n        \"max\": 10519.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          421.0,\n          6195.0,\n          6309.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volcanoLocationId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24461,\n        \"min\": 10101,\n        \"max\": 201141,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          80210,\n          80103,\n          10106\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volcanoLocationNewNum\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20706,\n        \"min\": 211010,\n        \"max\": 282110,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          282100,\n          281030,\n          211060\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 63,\n        \"samples\": [\n          \"Unzendake\",\n          \"Unnamed\",\n          \"Etna\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Greece\",\n          \"Pacific Ocean\",\n          \"Italy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"elevation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1034,\n        \"min\": -5,\n        \"max\": 3657,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          1483,\n          0,\n          3357\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"morphology\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Submarine volcano\",\n          \"Shield volcano\",\n          \"Caldera\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deathsTotal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5713.979903859243,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          4.0,\n          15.0,\n          56.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vei\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9737209847442237,\n        \"min\": 0.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.0,\n          2.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1519.626384223263,\n        \"min\": 1.0,\n        \"max\": 11000.0,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          221.0,\n          117.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1.2.2:** *In one line of code, **reset** the **index column** of the dataframe so that it has **1-based indexing**.*"
      ],
      "metadata": {
        "id": "OO-A8f3gGEpn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# REMOVE THIS COMMENT AND ANSWER STARTING HERE\n",
        "new_df.index = range(1, len(new_df) + 1)\n",
        "\n",
        "# KEEP THIS. It will display the whole dataframe.\n",
        "new_df"
      ],
      "metadata": {
        "id": "gBBUKUJVFI0Y",
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "outputId": "7dda81c7-96e4-4525-f35d-f3fea960b5ed"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  year  month   day  tsunamiEventId  earthquakeEventId  \\\n",
              "1      1  1169    2.0   4.0          2852.0              421.0   \n",
              "2      2  1329    7.0   NaN             NaN                NaN   \n",
              "3      3  1883    3.0   NaN             NaN                NaN   \n",
              "4      4  1888    3.0  13.0          1175.0                NaN   \n",
              "5      5  1850    NaN   NaN             NaN                NaN   \n",
              "..   ...   ...    ...   ...             ...                ...   \n",
              "196  933  1991    6.0   3.0             NaN                NaN   \n",
              "197  949  1331   12.0   NaN             NaN                NaN   \n",
              "198  960  1485    1.0   NaN             NaN                NaN   \n",
              "199  990  1826   10.0   NaN             NaN                NaN   \n",
              "200  994  1854    2.0   NaN             NaN                NaN   \n",
              "\n",
              "     volcanoLocationId  volcanoLocationNewNum           name  \\\n",
              "1                10106                 211060           Etna   \n",
              "2                10106                 211060           Etna   \n",
              "3                30301                 233010       Karthala   \n",
              "4                50107                 251070  Ritter Island   \n",
              "5                50214                 252140         Rabaul   \n",
              "..                 ...                    ...            ...   \n",
              "196              80210                 282100      Unzendake   \n",
              "197              80211                 282110         Asosan   \n",
              "198              80211                 282110         Asosan   \n",
              "199              80211                 282110         Asosan   \n",
              "200              80211                 282110         Asosan   \n",
              "\n",
              "              country  elevation          morphology  deathsTotal  vei  deaths  \n",
              "1               Italy       3357       Stratovolcano      16000.0  NaN     NaN  \n",
              "2               Italy       3357       Stratovolcano          NaN  1.0     NaN  \n",
              "3             Comoros       2361      Shield volcano          NaN  2.0     NaN  \n",
              "4    Papua New Guinea         75       Stratovolcano          NaN  2.0     NaN  \n",
              "5    Papua New Guinea        688  Pyroclastic shield          NaN  2.0     NaN  \n",
              "..                ...        ...                 ...          ...  ...     ...  \n",
              "196             Japan       1483     Complex volcano         43.0  1.0    43.0  \n",
              "197             Japan       1592             Caldera          NaN  2.0     NaN  \n",
              "198             Japan       1592             Caldera          1.0  2.0     1.0  \n",
              "199             Japan       1592             Caldera          NaN  2.0     NaN  \n",
              "200             Japan       1592             Caldera          3.0  2.0     3.0  \n",
              "\n",
              "[200 rows x 15 columns]"
            ],
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              "  <div id=\"df-2899b0f7-3952-420b-9f44-06f88707c443\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>tsunamiEventId</th>\n",
              "      <th>earthquakeEventId</th>\n",
              "      <th>volcanoLocationId</th>\n",
              "      <th>volcanoLocationNewNum</th>\n",
              "      <th>name</th>\n",
              "      <th>country</th>\n",
              "      <th>elevation</th>\n",
              "      <th>morphology</th>\n",
              "      <th>deathsTotal</th>\n",
              "      <th>vei</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1169</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2852.0</td>\n",
              "      <td>421.0</td>\n",
              "      <td>10106</td>\n",
              "      <td>211060</td>\n",
              "      <td>Etna</td>\n",
              "      <td>Italy</td>\n",
              "      <td>3357</td>\n",
              "      <td>Stratovolcano</td>\n",
              "      <td>16000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1329</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10106</td>\n",
              "      <td>211060</td>\n",
              "      <td>Etna</td>\n",
              "      <td>Italy</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1883</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30301</td>\n",
              "      <td>233010</td>\n",
              "      <td>Karthala</td>\n",
              "      <td>Comoros</td>\n",
              "      <td>2361</td>\n",
              "      <td>Shield volcano</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1888</td>\n",
              "      <td>3.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1175.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50107</td>\n",
              "      <td>251070</td>\n",
              "      <td>Ritter Island</td>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>75</td>\n",
              "      <td>Stratovolcano</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>1850</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50214</td>\n",
              "      <td>252140</td>\n",
              "      <td>Rabaul</td>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>688</td>\n",
              "      <td>Pyroclastic shield</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>933</td>\n",
              "      <td>1991</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80210</td>\n",
              "      <td>282100</td>\n",
              "      <td>Unzendake</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1483</td>\n",
              "      <td>Complex volcano</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>949</td>\n",
              "      <td>1331</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>960</td>\n",
              "      <td>1485</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>990</td>\n",
              "      <td>1826</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>994</td>\n",
              "      <td>1854</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 15 columns</p>\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "\n",
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              "      .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('new_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('new_df');\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_df",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 306,\n        \"min\": 1,\n        \"max\": 994,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          296,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 248,\n        \"min\": -141,\n        \"max\": 1996,\n        \"num_unique_values\": 152,\n        \"samples\": [\n          1911,\n          1792,\n          1694\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.443524102347981,\n        \"min\": 1.0,\n        \"max\": 12.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          12.0,\n          9.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.128511453803906,\n        \"min\": 1.0,\n        \"max\": 31.0,\n        \"num_unique_values\": 31,\n        \"samples\": [\n          26.0,\n          5.0,\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tsunamiEventId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1742.5633133120016,\n        \"min\": 61.0,\n        \"max\": 5863.0,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          1407.0,\n          1022.0,\n          4218.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"earthquakeEventId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3015.8591620455554,\n        \"min\": 421.0,\n        \"max\": 10519.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          421.0,\n          6195.0,\n          6309.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volcanoLocationId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24461,\n        \"min\": 10101,\n        \"max\": 201141,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          80210,\n          80103,\n          10106\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volcanoLocationNewNum\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20706,\n        \"min\": 211010,\n        \"max\": 282110,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          282100,\n          281030,\n          211060\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 63,\n        \"samples\": [\n          \"Unzendake\",\n          \"Unnamed\",\n          \"Etna\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Greece\",\n          \"Pacific Ocean\",\n          \"Italy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"elevation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1034,\n        \"min\": -5,\n        \"max\": 3657,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          1483,\n          0,\n          3357\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"morphology\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Submarine volcano\",\n          \"Shield volcano\",\n          \"Caldera\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deathsTotal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5713.979903859243,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          4.0,\n          15.0,\n          56.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vei\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9737209847442237,\n        \"min\": 0.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.0,\n          2.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1519.626384223263,\n        \"min\": 1.0,\n        \"max\": 11000.0,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          221.0,\n          117.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **deathsTotal** and **deaths**  columns have approximations of the same data with alternating NaNs in each.\n",
        "\n",
        "**1.2.3:** *In one line of code, make a **new column** called **'totalDeaths'** that takes the **max** of the values given between those* ***two*** *columns. If there is* ***NaN*** *in* ***one column*** *and a* ***numerical*** *value in the* ***other***, *it will ***take the numerical value***. ***Only*** if there are* ***NaNs*** *in* ***both*** *columns, the* ***new column will have NaN.***"
      ],
      "metadata": {
        "id": "oSxQRt-xio1t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# REMOVE THIS COMMENT AND ANSWER STARTING HERE\n",
        "new_df['totalDeaths'] = new_df['deathsTotal'].combine_first(new_df['deaths'])\n",
        "\n",
        "# KEEP THIS. It will display the whole dataframe.\n",
        "new_df"
      ],
      "metadata": {
        "id": "GEZ0YdSPdYyI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "outputId": "01540e77-584b-4e94-fb2c-afc54b458f71"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  year  month   day  tsunamiEventId  earthquakeEventId  \\\n",
              "1      1  1169    2.0   4.0          2852.0              421.0   \n",
              "2      2  1329    7.0   NaN             NaN                NaN   \n",
              "3      3  1883    3.0   NaN             NaN                NaN   \n",
              "4      4  1888    3.0  13.0          1175.0                NaN   \n",
              "5      5  1850    NaN   NaN             NaN                NaN   \n",
              "..   ...   ...    ...   ...             ...                ...   \n",
              "196  933  1991    6.0   3.0             NaN                NaN   \n",
              "197  949  1331   12.0   NaN             NaN                NaN   \n",
              "198  960  1485    1.0   NaN             NaN                NaN   \n",
              "199  990  1826   10.0   NaN             NaN                NaN   \n",
              "200  994  1854    2.0   NaN             NaN                NaN   \n",
              "\n",
              "     volcanoLocationId  volcanoLocationNewNum           name  \\\n",
              "1                10106                 211060           Etna   \n",
              "2                10106                 211060           Etna   \n",
              "3                30301                 233010       Karthala   \n",
              "4                50107                 251070  Ritter Island   \n",
              "5                50214                 252140         Rabaul   \n",
              "..                 ...                    ...            ...   \n",
              "196              80210                 282100      Unzendake   \n",
              "197              80211                 282110         Asosan   \n",
              "198              80211                 282110         Asosan   \n",
              "199              80211                 282110         Asosan   \n",
              "200              80211                 282110         Asosan   \n",
              "\n",
              "              country  elevation          morphology  deathsTotal  vei  \\\n",
              "1               Italy       3357       Stratovolcano      16000.0  NaN   \n",
              "2               Italy       3357       Stratovolcano          NaN  1.0   \n",
              "3             Comoros       2361      Shield volcano          NaN  2.0   \n",
              "4    Papua New Guinea         75       Stratovolcano          NaN  2.0   \n",
              "5    Papua New Guinea        688  Pyroclastic shield          NaN  2.0   \n",
              "..                ...        ...                 ...          ...  ...   \n",
              "196             Japan       1483     Complex volcano         43.0  1.0   \n",
              "197             Japan       1592             Caldera          NaN  2.0   \n",
              "198             Japan       1592             Caldera          1.0  2.0   \n",
              "199             Japan       1592             Caldera          NaN  2.0   \n",
              "200             Japan       1592             Caldera          3.0  2.0   \n",
              "\n",
              "     deaths  totalDeaths  \n",
              "1       NaN      16000.0  \n",
              "2       NaN          NaN  \n",
              "3       NaN          NaN  \n",
              "4       NaN          NaN  \n",
              "5       NaN          NaN  \n",
              "..      ...          ...  \n",
              "196    43.0         43.0  \n",
              "197     NaN          NaN  \n",
              "198     1.0          1.0  \n",
              "199     NaN          NaN  \n",
              "200     3.0          3.0  \n",
              "\n",
              "[200 rows x 16 columns]"
            ],
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              "      <th>id</th>\n",
              "      <th>year</th>\n",
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              "      <th>tsunamiEventId</th>\n",
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              "      <td>16000.0</td>\n",
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              "      <td>10106</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>1883</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>30301</td>\n",
              "      <td>233010</td>\n",
              "      <td>Karthala</td>\n",
              "      <td>Comoros</td>\n",
              "      <td>2361</td>\n",
              "      <td>Shield volcano</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1888</td>\n",
              "      <td>3.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1175.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50107</td>\n",
              "      <td>251070</td>\n",
              "      <td>Ritter Island</td>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>75</td>\n",
              "      <td>Stratovolcano</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>1850</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50214</td>\n",
              "      <td>252140</td>\n",
              "      <td>Rabaul</td>\n",
              "      <td>Papua New Guinea</td>\n",
              "      <td>688</td>\n",
              "      <td>Pyroclastic shield</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>933</td>\n",
              "      <td>1991</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80210</td>\n",
              "      <td>282100</td>\n",
              "      <td>Unzendake</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1483</td>\n",
              "      <td>Complex volcano</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>949</td>\n",
              "      <td>1331</td>\n",
              "      <td>12.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>960</td>\n",
              "      <td>1485</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>990</td>\n",
              "      <td>1826</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>994</td>\n",
              "      <td>1854</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 16 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    .colab-df-convert:hover {\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-color: transparent;\n",
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              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('new_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 306,\n        \"min\": 1,\n        \"max\": 994,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          296,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 248,\n        \"min\": -141,\n        \"max\": 1996,\n        \"num_unique_values\": 152,\n        \"samples\": [\n          1911,\n          1792,\n          1694\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.443524102347981,\n        \"min\": 1.0,\n        \"max\": 12.0,\n       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\"samples\": [\n          282100,\n          281030,\n          211060\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 63,\n        \"samples\": [\n          \"Unzendake\",\n          \"Unnamed\",\n          \"Etna\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Greece\",\n          \"Pacific Ocean\",\n          \"Italy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"elevation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1034,\n        \"min\": -5,\n        \"max\": 3657,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          1483,\n          0,\n          3357\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"morphology\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Submarine volcano\",\n          \"Shield volcano\",\n          \"Caldera\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deathsTotal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5713.979903859243,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          4.0,\n          15.0,\n          56.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vei\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9737209847442237,\n        \"min\": 0.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.0,\n          2.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1519.626384223263,\n        \"min\": 1.0,\n        \"max\": 11000.0,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          221.0,\n          117.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"totalDeaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5713.979903859243,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          4.0,\n          15.0,\n          56.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **TASK 1.3: Tailoring Time (10 Points)**\n",
        "It's pretty obvious that the year, month, and day look pretty weird in the dataset. We're going to have to do some hardcore cleaning on the [time](https://pandas.pydata.org/docs/user_guide/timeseries.html). We will learn more about data cleaning in class soon, but here we will perform some basic data cleaning.\n",
        "\n",
        "**We need to have only ONE column called** \"***date***\" **that contains the full date (YYYY-MM-DD), not separated into three columns.**\n",
        "\n",
        "***Make sure there are no floating point values in the date and sort the data from most recent to least.***\n",
        "\n",
        "***Remove the old columns and place the new column next to the 'id' column.***\n",
        "\n",
        "\n",
        "**YOU CAN USE MULTIPLE LINES OF CODE BUT CAN'T USE LOOPS.**\n",
        "\n",
        "**Note:** It is alright to have only a **maximum of 12 NaTs** for some dates that often go further back than the 1600s because the datetime module in Pandas has a limit (unless otherwise guided)."
      ],
      "metadata": {
        "id": "9if9utm8jsdg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# REMOVE THIS COMMENT AND ANSWER STARTING HERE\n",
        "# Convert year, month, and day columns to integer, replacing NaNs with 1\n",
        "new_df['year'] = new_df['year'].fillna(1).astype(int)\n",
        "new_df['month'] = new_df['month'].fillna(1).astype(int)\n",
        "new_df['day'] = new_df['day'].fillna(1).astype(int)\n",
        "\n",
        "# Create the 'date' column by merging year, month, and day\n",
        "new_df['date'] = pd.to_datetime(new_df[['year', 'month', 'day']], errors='coerce')\n",
        "\n",
        "# Drop the old year, month, and day columns\n",
        "new_df = new_df.drop(columns=['year', 'month', 'day'])\n",
        "\n",
        "# Move 'date' column next to 'id'\n",
        "columns = list(new_df.columns)\n",
        "columns.insert(columns.index('id') + 1, columns.pop(columns.index('date')))\n",
        "new_df = new_df[columns]\n",
        "\n",
        "# Sort by 'date' from most recent to least\n",
        "new_df = new_df.sort_values(by='date', ascending=False).reset_index(drop=True)\n",
        "\n",
        "# KEEP THIS. It will display the whole dataframe.\n",
        "new_df"
      ],
      "metadata": {
        "id": "Gs4GJqzgn2Es",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "outputId": "26db56b4-4434-46d1-a4a8-4673a8509941"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id       date  tsunamiEventId  earthquakeEventId  volcanoLocationId  \\\n",
              "0    693 1996-08-10             NaN                NaN              70202   \n",
              "1    112 1994-11-22             NaN                NaN              60325   \n",
              "2    933 1991-06-03             NaN                NaN              80210   \n",
              "3    144 1990-02-10             NaN                NaN              60328   \n",
              "4    486 1988-05-09             NaN                NaN              60509   \n",
              "..   ...        ...             ...                ...                ...   \n",
              "195  844        NaT            61.0             7341.0              80208   \n",
              "196  846        NaT             NaN                NaN              80208   \n",
              "197  904        NaT             NaN                NaN              80209   \n",
              "198  949        NaT             NaN                NaN              80211   \n",
              "199  960        NaT             NaN                NaN              80211   \n",
              "\n",
              "     volcanoLocationNewNum           name        country  elevation  \\\n",
              "0                   272020        Kanlaon    Philippines       2435   \n",
              "1                   263250         Merapi      Indonesia       2910   \n",
              "2                   282100      Unzendake          Japan       1483   \n",
              "3                   263280          Kelud      Indonesia       1730   \n",
              "4                   265090      Banda Api  Pacific Ocean        596   \n",
              "..                     ...            ...            ...        ...   \n",
              "195                 282080           Aira          Japan       1117   \n",
              "196                 282080           Aira          Japan       1117   \n",
              "197                 282090  Kirishimayama          Japan       1700   \n",
              "198                 282110         Asosan          Japan       1592   \n",
              "199                 282110         Asosan          Japan       1592   \n",
              "\n",
              "          morphology  deathsTotal  vei  deaths  totalDeaths  \n",
              "0      Stratovolcano          3.0  2.0     3.0          3.0  \n",
              "1      Stratovolcano         60.0  3.0    60.0         60.0  \n",
              "2    Complex volcano         43.0  1.0    43.0         43.0  \n",
              "3      Stratovolcano         32.0  4.0    32.0         32.0  \n",
              "4            Caldera          4.0  3.0     4.0          4.0  \n",
              "..               ...          ...  ...     ...          ...  \n",
              "195    Stratovolcano          NaN  3.0     NaN          NaN  \n",
              "196    Stratovolcano          NaN  5.0     NaN          NaN  \n",
              "197   Shield volcano          NaN  3.0     NaN          NaN  \n",
              "198          Caldera          NaN  2.0     NaN          NaN  \n",
              "199          Caldera          1.0  2.0     1.0          1.0  \n",
              "\n",
              "[200 rows x 14 columns]"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>date</th>\n",
              "      <th>tsunamiEventId</th>\n",
              "      <th>earthquakeEventId</th>\n",
              "      <th>volcanoLocationId</th>\n",
              "      <th>volcanoLocationNewNum</th>\n",
              "      <th>name</th>\n",
              "      <th>country</th>\n",
              "      <th>elevation</th>\n",
              "      <th>morphology</th>\n",
              "      <th>deathsTotal</th>\n",
              "      <th>vei</th>\n",
              "      <th>deaths</th>\n",
              "      <th>totalDeaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>693</td>\n",
              "      <td>1996-08-10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70202</td>\n",
              "      <td>272020</td>\n",
              "      <td>Kanlaon</td>\n",
              "      <td>Philippines</td>\n",
              "      <td>2435</td>\n",
              "      <td>Stratovolcano</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>112</td>\n",
              "      <td>1994-11-22</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60325</td>\n",
              "      <td>263250</td>\n",
              "      <td>Merapi</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>2910</td>\n",
              "      <td>Stratovolcano</td>\n",
              "      <td>60.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>933</td>\n",
              "      <td>1991-06-03</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80210</td>\n",
              "      <td>282100</td>\n",
              "      <td>Unzendake</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1483</td>\n",
              "      <td>Complex volcano</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144</td>\n",
              "      <td>1990-02-10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60328</td>\n",
              "      <td>263280</td>\n",
              "      <td>Kelud</td>\n",
              "      <td>Indonesia</td>\n",
              "      <td>1730</td>\n",
              "      <td>Stratovolcano</td>\n",
              "      <td>32.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>486</td>\n",
              "      <td>1988-05-09</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>60509</td>\n",
              "      <td>265090</td>\n",
              "      <td>Banda Api</td>\n",
              "      <td>Pacific Ocean</td>\n",
              "      <td>596</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>844</td>\n",
              "      <td>NaT</td>\n",
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              "      <td>7341.0</td>\n",
              "      <td>80208</td>\n",
              "      <td>282080</td>\n",
              "      <td>Aira</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1117</td>\n",
              "      <td>Stratovolcano</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>846</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80208</td>\n",
              "      <td>282080</td>\n",
              "      <td>Aira</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1117</td>\n",
              "      <td>Stratovolcano</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>904</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>80209</td>\n",
              "      <td>282090</td>\n",
              "      <td>Kirishimayama</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1700</td>\n",
              "      <td>Shield volcano</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>198</th>\n",
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              "      <td>80211</td>\n",
              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
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              "      <td>NaT</td>\n",
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              "      <td>282110</td>\n",
              "      <td>Asosan</td>\n",
              "      <td>Japan</td>\n",
              "      <td>1592</td>\n",
              "      <td>Caldera</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 14 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "new_df",
              "summary": "{\n  \"name\": \"new_df\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 306,\n        \"min\": 1,\n        \"max\": 994,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          4,\n          30,\n          105\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1682-08-12 00:00:00\",\n        \"max\": \"1996-08-10 00:00:00\",\n        \"num_unique_values\": 166,\n        \"samples\": [\n          \"1878-01-01 00:00:00\",\n          \"1822-12-27 00:00:00\",\n          \"1911-11-08 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tsunamiEventId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        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],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volcanoLocationNewNum\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20706,\n        \"min\": 211010,\n        \"max\": 282110,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          268010,\n          265070,\n          272020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 63,\n        \"samples\": [\n          \"Dukono\",\n          \"Serua\",\n          \"Kanlaon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Greece\",\n          \"Ethiopia\",\n          \"Philippines\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"elevation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1034,\n        \"min\": -5,\n        \"max\": 3657,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          1229,\n          608,\n          2435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"morphology\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Submarine volcano\",\n          \"Complex volcano\",\n          \"Compound volcano\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deathsTotal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5713.979903859243,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          4.0,\n          49.0,\n          1028.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"vei\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9737209847442237,\n        \"min\": 0.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2.0,\n          3.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1519.626384223263,\n        \"min\": 1.0,\n        \"max\": 11000.0,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          16.0,\n          355.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"totalDeaths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5713.979903859243,\n        \"min\": 1.0,\n        \"max\": 60000.0,\n        \"num_unique_values\": 72,\n        \"samples\": [\n          4.0,\n          49.0,\n          1028.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Part 2: Volcanic Matryoshkas 🪆 (30 POINTS TOTAL)**\n",
        "\n",
        "Now, that most of the data has been tidied up. We will organize the data into more sizable pieces of information in order to extract useful information."
      ],
      "metadata": {
        "id": "IlhYym73iOZb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2.1.1:** *(10 points here)*\n",
        "\n",
        "**Use the groupby function in Pandas to create separate dataframes for each unique country.**\n",
        "\n",
        "* Each table must only have the columns: 'date' 'country', 'name', and 'vei'\n",
        "\n",
        "* Sort the dataframe of each country by highest to lowest 'vei'\n",
        "\n",
        "**You MUST use the groupby function here.**"
      ],
      "metadata": {
        "id": "5yLjy9u0VuhU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# REMOVE THIS COMMENT AND ANSWER STARTING HERE\n",
        "grouped_dfs = {\n",
        "    country: df[['date', 'country', 'name', 'vei']].sort_values(by='vei', ascending=False)\n",
        "    for country, df in new_df.groupby('country')\n",
        "}\n"
      ],
      "metadata": {
        "id": "YonGiaib0noL"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2.1.2:** *(5 points here)*\n",
        "\n",
        "**Using groupby again, print out the maximum 'vei' for each unique country.**\n",
        "\n",
        "**You MUST use the groupby function here.**\n",
        "\n",
        "* Print out your results in a format like the following: \"Country: {country_name}, Highest VEI: {vei}\""
      ],
      "metadata": {
        "id": "OTExPGKLA7lV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# REMOVE THIS COMMENT AND ANSWER STARTING HERE"
      ],
      "metadata": {
        "id": "DRHCvg1b2MEA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2.1.3:** *(15 points here)*\n",
        "\n",
        "Finally, we have ALMOST REACHED THE END!!\n",
        "Since there is still quite a bit of missing data, we want to make use of what is still available.\n",
        "\n",
        "A very powerful tool in Python's magnificent collection of libraries is its beautiful graphing tools.\n",
        "\n",
        "Check out libraries such as [Seaborn](https://seaborn.pydata.org/) or [Matplotlib](https://matplotlib.org/stable/index.html) to create meaningful visualizations! **Your final task requires the use of these libraries**\n",
        "\n",
        "**Based on the unique names of volcanos, filter names that have more than 3 datapoints under their name.**\n",
        "\n",
        "* Make separate graphs for each volcano and plot their VEIs over time.\n",
        "\n",
        "* Make sure to properly label all parts of the graph."
      ],
      "metadata": {
        "id": "11rdFk2qCu8u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# REMOVE THIS COMMENT AND ANSWER STARTING HERE"
      ],
      "metadata": {
        "id": "jvasG8eViN1h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Part 3: Fiery Jobs 🚒 (15 POINTS TOTAL)**\n",
        "\n",
        "Proficiency in SQL is also super important. SQL databases are essentially relational databases in which there are vast amounts of tabular data. which can often be used to connect with related tablular data. [This](https://www.w3schools.com/sql/) is a pretty good intro into learning more about SQL."
      ],
      "metadata": {
        "id": "Gozs5JwKO7ar"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check out this [tutorial](https://mode.com/sql-tutorial/introduction-to-sql/) for some clarifications on SQL.\n",
        "\n",
        "Now! We'll be using **sqlite** to access a database.\n",
        "* Start by downloading the sql lite file and putting it in the same directory as this [notebook](https://www.kaggle.com/datasets/kaggle/sf-salaries) (hit the 'download' button in the upper right).\n",
        "* Check out the description of the data so you know the table / column names.\n",
        "\n",
        "The following code will use sqlite3 to create a database connection. sqlite3 is the library in Python that assists in navigating through SQL databases."
      ],
      "metadata": {
        "id": "bfrjZ3wrO-R2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sqlite3\n",
        "# import pandas as pd. Pandas was already imported from the previous sections\n",
        "\n",
        "conn = sqlite3.connect(\"database.sqlite\")\n",
        "crsr = conn.cursor()"
      ],
      "metadata": {
        "id": "ooUWEVfYPAkD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# This code will let you check out the different tables within the database.\n",
        "query = \"SELECT name FROM sqlite_master WHERE type='table';\"\n",
        "tables = crsr.execute(query).fetchall()\n",
        "print(tables)"
      ],
      "metadata": {
        "id": "pnJiNMw7o9d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87598252-9a6a-4ea9-983c-cbe91a61eb0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('Salaries',)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Remember that each problem should be solved with a single sql query.**\n",
        "**All outputs must be shown**\n",
        "\n",
        "#### **3.1.1: 2 Points**\n",
        "***From the Salaries table, get the average base pay for \"firefighters\" (all job titles consisting of the word \"firefighter\" (not case sensitive)) between the year 2011 to 2013.***"
      ],
      "metadata": {
        "id": "KutbMSr2pV-O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'REMOVE THIS CONTENT AND ANSWER IN HERE'\n",
        "\n",
        "# KEEP THIS. It will display the whole dataframe.\n",
        "df = pd.read_sql(query, conn)\n",
        "df"
      ],
      "metadata": {
        "id": "3wm6NlYqp3dI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "429d5d23-bcda-49eb-fbdb-69f731b525f5"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'pd' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-862980f3ff29>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# KEEP THIS. It will display the whole dataframe.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_sql\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **3.1.2: 2 Points**\n",
        "***From the Salaries table, create a table for the year 2014, with a job title of \"firefighters\" (all job titles consisting of the word \"firefighter\" (not case sensitive)) making under $100,000 as a base pay, and sort in descending order by salary.***"
      ],
      "metadata": {
        "id": "AwWzAyYbq9yE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'REMOVE THIS CONTENT AND ANSWER IN HERE'\n",
        "\n",
        "# KEEP THIS. It will display the whole dataframe.\n",
        "df = pd.read_sql(query, conn)\n",
        "df"
      ],
      "metadata": {
        "id": "ZQ_hK1tpq83U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **3.1.3: 4 Points**\n",
        "***Create a dataframe with averages of base pay, averages of benefits, and averages of overtime for \"firefighters\" (all job titles consisting of the word \"firefighter\" (not case sensitive)) as well as a column with the sum of these three values.***\n",
        "\n",
        "***Exclude job titles containing \"FIREFIGHTER\" (case-sensitive)***"
      ],
      "metadata": {
        "id": "zNRnMM7Cq9gG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query = 'REMOVE THIS CONTENT AND ANSWER IN HERE'\n",
        "\n",
        "# KEEP THIS. It will display the whole dataframe.\n",
        "df = pd.read_sql(query, conn)\n",
        "df"
      ],
      "metadata": {
        "id": "yVOCgk2Jq9LB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **3.1.4: 7 Points**\n",
        "\n",
        "***Finally, we'll create our own table in our database. Separate the Salaries table by years, and add it back to the database. Using a loop might be helpful.***\n",
        "\n",
        "* You may use basic python to complete the task. However, using querying on SQL is **mandatory**.\n",
        "* Feel free to **use multiple lines of code for this problem only.**\n",
        "* Check out this [Hint](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_sql.html)"
      ],
      "metadata": {
        "id": "0cp88tfxq-Ui"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# REMOVE THIS CONTENT AND ANSWER IN YOUR OWN WAY"
      ],
      "metadata": {
        "id": "YjQnj4Uqq9C7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run this code to check if you successfully added your table.\n",
        "cursor = conn.cursor()\n",
        "cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n",
        "print(cursor.fetchall())"
      ],
      "metadata": {
        "id": "RYcadk9g4jE1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1ddcf42-34b5-4dd6-8093-a133ba878c6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('Salaries',)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![volcano](https://as1.ftcdn.net/v2/jpg/06/34/76/64/1000_F_634766457_0fZbpYj6aBLlldO1jADUPpKTRLnNmngs.jpg)"
      ],
      "metadata": {
        "id": "oW-M2Epk28g4"
      }
    }
  ]
}