Omni_1.ipynb

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      "name": "Omni_1.ipynb",
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      "authorship_tag": "ABX9TyNY/U0bPrg1wMfB6c3Dbe/k",
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    "language_info": {
      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JSchoonmaker/blockchain_projects/blob/main/Omni_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zs_DP9uy-spP"
      },
      "source": [
        "#Python version of Omni Analytics' Data Science on the Ethereum Blockchain Module 1: Motivating Example.\n",
        "\n",
        "\n",
        "## All datasets, code ideas, concepts, and text used are based on Omni Analytics' great work that can be found here: https://github.com/Omni-Analytics-Group/eth-data-science-course\n",
        "\n",
        "### Translation from R into Python by Jeanna Schoonmaker, Oct 2021\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J4rrPKg5NheO"
      },
      "source": [
        "# Let's get started\n",
        "\n",
        "Since this notebook uses Python instead of R, we need to start with importing pandas, a Python library for data analysis. It is common practice to import pandas with the alias \"pd\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYDCg74BiKW4"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kT2Yc1oljx0L"
      },
      "source": [
        "# Load the Data\n",
        "\n",
        "First, we set the url where the dataset can be found as the \"punks_url\" variable. <br>\n",
        "Next, we use pandas' read_csv method to load the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0z8mjIUJibW9"
      },
      "source": [
        "punks_url = 'https://raw.githubusercontent.com/Omni-Analytics-Group/eth-data-science-course/master/courses/Module%201/punks.csv'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KN8R87_rikDC"
      },
      "source": [
        "df_punks = pd.read_csv(punks_url, encoding='unicode_escape')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ieZ70oVZlOPJ"
      },
      "source": [
        "# First look at the data in Python\n",
        "Use Python to look at the first few rows of data in the Punks data set. The head() function will allow you to look at the first 5 rows of the dataset. The tail() function will allow you to look at the last 5 rows of the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "BYpk4g95kVGV",
        "outputId": "ebdbe526-6fb2-43ea-9c5a-c7c0a7d55b0b"
      },
      "source": [
        "df_punks.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Transaction</th>\n",
              "      <th>From</th>\n",
              "      <th>To</th>\n",
              "      <th>Crypto</th>\n",
              "      <th>USD</th>\n",
              "      <th>Txn</th>\n",
              "      <th>ID</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Type</th>\n",
              "      <th>Skin</th>\n",
              "      <th>Slots</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sold</td>\n",
              "      <td>0xf5099e</td>\n",
              "      <td>14715954</td>\n",
              "      <td>25.00</td>\n",
              "      <td>2822.0</td>\n",
              "      <td>2018-11-30</td>\n",
              "      <td>0</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Mid</td>\n",
              "      <td>3</td>\n",
              "      <td>3682560000%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sold</td>\n",
              "      <td>0x00d7c9</td>\n",
              "      <td>10528156</td>\n",
              "      <td>1.60</td>\n",
              "      <td>386.0</td>\n",
              "      <td>2017-07-07</td>\n",
              "      <td>0</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Mid</td>\n",
              "      <td>3</td>\n",
              "      <td>3682560000%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sold</td>\n",
              "      <td>0xc352b5</td>\n",
              "      <td>55241</td>\n",
              "      <td>0.98</td>\n",
              "      <td>320.0</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>0</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Mid</td>\n",
              "      <td>3</td>\n",
              "      <td>3682560000%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Claimed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12800693</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>0</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Mid</td>\n",
              "      <td>3</td>\n",
              "      <td>3682560000%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sold</td>\n",
              "      <td>EliteCat</td>\n",
              "      <td>0xcf6165</td>\n",
              "      <td>60.00</td>\n",
              "      <td>36305.0</td>\n",
              "      <td>2020-11-30</td>\n",
              "      <td>1</td>\n",
              "      <td>Guy</td>\n",
              "      <td>Male</td>\n",
              "      <td>Dark</td>\n",
              "      <td>2</td>\n",
              "      <td>2050240500%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Transaction       From        To  Crypto  ...    Type  Skin  Slots         Rank\n",
              "0        Sold   0xf5099e  14715954   25.00  ...  Female   Mid      3  3682560000%\n",
              "1        Sold   0x00d7c9  10528156    1.60  ...  Female   Mid      3  3682560000%\n",
              "2        Sold   0xc352b5     55241    0.98  ...  Female   Mid      3  3682560000%\n",
              "3     Claimed        NaN  12800693     NaN  ...  Female   Mid      3  3682560000%\n",
              "4        Sold  EliteCat\n",
              "  0xcf6165   60.00  ...    Male  Dark      2  2050240500%\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "M-yZqo6clhOu",
        "outputId": "1a208eb4-93bf-4e2e-f30f-ab168ee7f514"
      },
      "source": [
        "df_punks.tail()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Transaction</th>\n",
              "      <th>From</th>\n",
              "      <th>To</th>\n",
              "      <th>Crypto</th>\n",
              "      <th>USD</th>\n",
              "      <th>Txn</th>\n",
              "      <th>ID</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Type</th>\n",
              "      <th>Skin</th>\n",
              "      <th>Slots</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17549</th>\n",
              "      <td>Sold</td>\n",
              "      <td>TJ2010</td>\n",
              "      <td>gaus</td>\n",
              "      <td>59.0</td>\n",
              "      <td>21062.0</td>\n",
              "      <td>2020-09-27</td>\n",
              "      <td>9997</td>\n",
              "      <td>Guy</td>\n",
              "      <td>Zombie</td>\n",
              "      <td>Zombie</td>\n",
              "      <td>2</td>\n",
              "      <td>0.023188000%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17550</th>\n",
              "      <td>Claimed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>TJ2010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>9997</td>\n",
              "      <td>Guy</td>\n",
              "      <td>Zombie</td>\n",
              "      <td>Zombie</td>\n",
              "      <td>2</td>\n",
              "      <td>0.023188000%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17551</th>\n",
              "      <td>Sold</td>\n",
              "      <td>7595170</td>\n",
              "      <td>TokenAng</td>\n",
              "      <td>15.0</td>\n",
              "      <td>9499.0</td>\n",
              "      <td>2020-12-27</td>\n",
              "      <td>9998</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Mid</td>\n",
              "      <td>3</td>\n",
              "      <td>1452800000%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17552</th>\n",
              "      <td>Claimed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0x73e4a2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>9998</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Mid</td>\n",
              "      <td>3</td>\n",
              "      <td>1452800000%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17553</th>\n",
              "      <td>Claimed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8269084</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>9999</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Dark</td>\n",
              "      <td>2</td>\n",
              "      <td>1752960000%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Transaction     From         To  ...    Skin  Slots          Rank\n",
              "17549        Sold   TJ2010       gaus  ...  Zombie      2  0.023188000%\n",
              "17550     Claimed      NaN     TJ2010  ...  Zombie      2  0.023188000%\n",
              "17551        Sold  7595170  TokenAng\n",
              "  ...     Mid      3   1452800000%\n",
              "17552     Claimed      NaN   0x73e4a2  ...     Mid      3   1452800000%\n",
              "17553     Claimed      NaN    8269084  ...    Dark      2   1752960000%\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdNaZ3bQlvct"
      },
      "source": [
        "# Crypto Punks Data Attributes\n",
        "\n",
        "The command .info() gives us information about the dataframe including number of rows and name, number, and data type for each column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jthVzwKIllqU",
        "outputId": "6adf1de9-a75b-4f9b-c066-0d8ced9b1299"
      },
      "source": [
        "df_punks.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17554 entries, 0 to 17553\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Transaction  17554 non-null  object \n",
            " 1   From         7554 non-null   object \n",
            " 2   To           17554 non-null  object \n",
            " 3   Crypto       7554 non-null   float64\n",
            " 4   USD          7554 non-null   float64\n",
            " 5   Txn          17554 non-null  object \n",
            " 6   ID           17554 non-null  int64  \n",
            " 7   Sex          17554 non-null  object \n",
            " 8   Type         17554 non-null  object \n",
            " 9   Skin         17554 non-null  object \n",
            " 10  Slots        17554 non-null  int64  \n",
            " 11  Rank         17554 non-null  object \n",
            "dtypes: float64(2), int64(2), object(8)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QP5v3NX-mysW"
      },
      "source": [
        "# Crypto Punks Statistical Summary\n",
        "\n",
        "Let's summarize the value for each variable in Punks by using .describe(). <br><br>\n",
        "Note that stats such as mean, min, and max are shown only for columns containing numeric data. <br><br>\n",
        "Columns containing string, or text data are summarized with a count, count of unique items, the most common value (top) and the frequency at which the top value occurs.   "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "-4atIOtal1s6",
        "outputId": "d0fb43be-86ca-49f9-d042-a169b6cfe5be"
      },
      "source": [
        "df_punks.describe(include='all')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Transaction</th>\n",
              "      <th>From</th>\n",
              "      <th>To</th>\n",
              "      <th>Crypto</th>\n",
              "      <th>USD</th>\n",
              "      <th>Txn</th>\n",
              "      <th>ID</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Type</th>\n",
              "      <th>Skin</th>\n",
              "      <th>Slots</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17554</td>\n",
              "      <td>7554</td>\n",
              "      <td>17554</td>\n",
              "      <td>7554.000000</td>\n",
              "      <td>7554.000000</td>\n",
              "      <td>17554</td>\n",
              "      <td>17554.000000</td>\n",
              "      <td>17554</td>\n",
              "      <td>17554</td>\n",
              "      <td>17554</td>\n",
              "      <td>17554.000000</td>\n",
              "      <td>17554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>2</td>\n",
              "      <td>682</td>\n",
              "      <td>1524</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>898</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Claimed</td>\n",
              "      <td>Hemba</td>\n",
              "      <td>12800693</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Guy</td>\n",
              "      <td>Male</td>\n",
              "      <td>Mid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2213293500%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>10000</td>\n",
              "      <td>1058</td>\n",
              "      <td>990</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11379</td>\n",
              "      <td>11152</td>\n",
              "      <td>5307</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.389854</td>\n",
              "      <td>867.305033</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5193.788481</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.778227</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.118331</td>\n",
              "      <td>2833.265913</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2757.690393</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.799278</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2906.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>232.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5189.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1003.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7557.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>189.990000</td>\n",
              "      <td>137522.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9999.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Transaction   From        To  ...   Skin         Slots         Rank\n",
              "count        17554   7554     17554  ...  17554  17554.000000        17554\n",
              "unique           2    682      1524  ...      7           NaN         4007\n",
              "top        Claimed  Hemba  12800693  ...    Mid           NaN  2213293500%\n",
              "freq         10000   1058       990  ...   5307           NaN           45\n",
              "mean           NaN    NaN       NaN  ...    NaN      2.778227          NaN\n",
              "std            NaN    NaN       NaN  ...    NaN      0.799278          NaN\n",
              "min            NaN    NaN       NaN  ...    NaN      0.000000          NaN\n",
              "25%            NaN    NaN       NaN  ...    NaN      2.000000          NaN\n",
              "50%            NaN    NaN       NaN  ...    NaN      3.000000          NaN\n",
              "75%            NaN    NaN       NaN  ...    NaN      3.000000          NaN\n",
              "max            NaN    NaN       NaN  ...    NaN      7.000000          NaN\n",
              "\n",
              "[11 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mifctIqwpLMl"
      },
      "source": [
        "# Scatterplots\n",
        "\n",
        "Let's look at the relationship between Crypto and USD.\n",
        "\n",
        "matplotlib and seaborn are both open source libraries for creating visualizations in python."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dKW5GV2lnJz-"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "Zq7rh6lupRxI",
        "outputId": "283a1bd3-4e7c-4a75-cf2d-07885bb24ef1"
      },
      "source": [
        "sns.scatterplot(x=df_punks.Crypto, y=df_punks.USD).set(title='USD vs. Crypto (ETH)')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'USD vs. Crypto (ETH)')]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "na4axB3IqPeG"
      },
      "source": [
        "# Another Scatterplot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "fgCgMT25poVZ",
        "outputId": "dacb8c82-d4b7-4dc5-8dde-1ce6a58f8172"
      },
      "source": [
        "sns.scatterplot(x=df_punks.Crypto, y=df_punks.USD, hue = df_punks.Type).set(title='USD vs. Crypto (ETH)')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'USD vs. Crypto (ETH)')]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZbgF_dVLq_hQ"
      },
      "source": [
        "# Creating a New Variable\n",
        "\n",
        "We will make a new variable in the Punks data set to account for the ratio between Crypto and USD that is `Ratio of Currency = USD / Crypto`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_gzbv72sqUop"
      },
      "source": [
        "df_punks['Ratio of Currency'] = df_punks['USD']/df_punks['Crypto']"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fR2nyft_rgkM"
      },
      "source": [
        "# Summary of 'Ratio of Currency' Variable\n",
        "\n",
        "One way we can interpret this is 1 ETH is worth $328.00 (the mean at the time of writing)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IM8G6qYrrUKT",
        "outputId": "8e5b3dc6-82ea-4568-ed23-1627d32f9e7f"
      },
      "source": [
        "df_punks['Ratio of Currency'].describe()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    7554.000000\n",
              "mean      327.962994\n",
              "std       188.430207\n",
              "min         1.000000\n",
              "25%       202.857143\n",
              "50%       326.887464\n",
              "75%       367.200000\n",
              "max      1409.090909\n",
              "Name: Ratio of Currency, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pvqxp4PprlE7"
      },
      "source": [
        "# Column Names\n",
        "\n",
        "We can look at all of the column names found in the dataset with this method."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LI0Sq5RIrWCc",
        "outputId": "7bd1ee1e-8522-46ba-b4ca-ac76164ccc18"
      },
      "source": [
        "df_punks.columns"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Transaction', 'From', 'To', 'Crypto', 'USD', 'Txn', 'ID', 'Sex',\n",
              "       'Type', 'Skin', 'Slots', 'Rank', 'Ratio of Currency'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Oeh4McRruw8"
      },
      "source": [
        "# Crypto Histogram\n",
        "\n",
        "We now plot a histogram of Crypto to see its distribution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "MVEeJwqSrn5U",
        "outputId": "f9d73418-a132-4569-c394-f59e0fca64c2"
      },
      "source": [
        "sns.histplot(x=df_punks.Crypto, binwidth=2.5).set(title='Histogram of Crypto') # binwidth is the length of each rectangular bar"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Histogram of Crypto')]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQdoeQFSswt4"
      },
      "source": [
        "# Finding the most expensive punk that was last sold in the sample data\n",
        "\n",
        "We can look at the most expensive punk that was last sold by using the .loc (location) command and the .idxmax (index for the max value) command."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0oqxAJYDsR69",
        "outputId": "504609f1-d29b-452f-d736-9f64013fb2f8"
      },
      "source": [
        "df_punks.loc[df_punks.Crypto.idxmax()]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Transaction                 Sold\n",
              "From                         jmg\n",
              "To                      0x7224a1\n",
              "Crypto                    189.99\n",
              "USD                       137522\n",
              "Txn                   2020-12-30\n",
              "ID                          3306\n",
              "Sex                          Guy\n",
              "Type                        Male\n",
              "Skin                         Mid\n",
              "Slots                          3\n",
              "Rank                 6437574000%\n",
              "Ratio of Currency        723.838\n",
              "Name: 5234, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xu1bp-RDw9os"
      },
      "source": [
        "# Average Slots By Sex\n",
        "### As a breakdown: <br>\n",
        "df_punks.loc - locates the values in the dataframe meeting the criteria inside the brackets <br>\n",
        "df_punks.Sex == 'XX' - subsets the dataset with the rows that meet the critera<br>\n",
        "'Slots' - selects the Slots column from the dataset<br>\n",
        ".mean() - provides the mean"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJ926DjZs6P0",
        "outputId": "c35f9169-217c-4f65-973f-2040118504c1"
      },
      "source": [
        "df_punks.loc[df_punks.Sex == 'Girl', 'Slots'].mean()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.763076923076923"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "thDX7iOFwmxy",
        "outputId": "b0ebcdcc-ab01-45aa-f61a-1be860e25390"
      },
      "source": [
        "df_punks.loc[df_punks.Sex == 'Guy', 'Slots'].mean()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.7864487213287634"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zWOMKTGpya3L"
      },
      "source": [
        "# Combine Types\n",
        "\n",
        "We can label Alien, Ape and Zombie as non_human, and similarly we can label Female and Male as human.\n",
        "\n",
        "This code works by creating new columns \"Non_human\" and \"Human\" by selecting the rows that contain \"Alien\", \"Ape\", \"Zombie\" and \"Female\", \"Male\" in the Type column.\n",
        "\n",
        "Note the | operator is interpreted as 'or', so the non_human column assignment looks at the Type column in the Punks data and assigns a 'True' value if the Punk Type is Alien OR Ape OR Zombie. Similarly, the Punk is assigned a 'True' value in the Human column if the Type is Female OR Male. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsghbEMFw8lU"
      },
      "source": [
        "df_punks['Non_human'] = (df_punks['Type'] == 'Alien') | (df_punks['Type'] == 'Ape') | (df_punks['Type'] == 'Zombie')"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "MEvomxcAM38w",
        "outputId": "fe5eba68-7638-4181-da83-0871dd2be9c9"
      },
      "source": [
        "df_punks"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Transaction</th>\n",
              "      <th>From</th>\n",
              "      <th>To</th>\n",
              "      <th>Crypto</th>\n",
              "      <th>USD</th>\n",
              "      <th>Txn</th>\n",
              "      <th>ID</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Type</th>\n",
              "      <th>Skin</th>\n",
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              "      <th>Rank</th>\n",
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              "      <th>Non_human</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sold</td>\n",
              "      <td>0xf5099e</td>\n",
              "      <td>14715954</td>\n",
              "      <td>25.00</td>\n",
              "      <td>2822.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>112.880000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>241.250000</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sold</td>\n",
              "      <td>0xc352b5</td>\n",
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              "      <td>0</td>\n",
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              "      <td>Mid</td>\n",
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              "      <td>326.530612</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sold</td>\n",
              "      <td>EliteCat</td>\n",
              "      <td>0xcf6165</td>\n",
              "      <td>60.00</td>\n",
              "      <td>36305.0</td>\n",
              "      <td>2020-11-30</td>\n",
              "      <td>1</td>\n",
              "      <td>Guy</td>\n",
              "      <td>Male</td>\n",
              "      <td>Dark</td>\n",
              "      <td>2</td>\n",
              "      <td>2050240500%</td>\n",
              "      <td>605.083333</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>gaus</td>\n",
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              "      <td>21062.0</td>\n",
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              "      <td>9997</td>\n",
              "      <td>Guy</td>\n",
              "      <td>Zombie</td>\n",
              "      <td>Zombie</td>\n",
              "      <td>2</td>\n",
              "      <td>0.023188000%</td>\n",
              "      <td>356.983051</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>17550</th>\n",
              "      <td>Claimed</td>\n",
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              "      <td>9997</td>\n",
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              "      <td>Zombie</td>\n",
              "      <td>Zombie</td>\n",
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              "      <td>0.023188000%</td>\n",
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              "      <td>Sold</td>\n",
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              "      <th>17552</th>\n",
              "      <td>Claimed</td>\n",
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              "      <td>9999</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Dark</td>\n",
              "      <td>2</td>\n",
              "      <td>1752960000%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17554 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Transaction       From  ... Ratio of Currency  Non_human\n",
              "0            Sold   0xf5099e  ...        112.880000      False\n",
              "1            Sold   0x00d7c9  ...        241.250000      False\n",
              "2            Sold   0xc352b5  ...        326.530612      False\n",
              "3         Claimed        NaN  ...               NaN      False\n",
              "4            Sold  EliteCat\n",
              "  ...        605.083333      False\n",
              "...           ...        ...  ...               ...        ...\n",
              "17549        Sold     TJ2010  ...        356.983051       True\n",
              "17550     Claimed        NaN  ...               NaN       True\n",
              "17551        Sold    7595170  ...        633.266667      False\n",
              "17552     Claimed        NaN  ...               NaN      False\n",
              "17553     Claimed        NaN  ...               NaN      False\n",
              "\n",
              "[17554 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
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    {
      "cell_type": "code",
      "metadata": {
        "id": "EzQeSGI9LvNd"
      },
      "source": [
        "df_punks['Human'] = (df_punks['Type'] == 'Female')| (df_punks['Type'] == 'Male')"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "cHMYYx7XzQf3",
        "outputId": "6397276d-5853-4c6c-b30f-f67a529b0673"
      },
      "source": [
        "df_punks"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>17552</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>0x73e4a2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>9998</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Mid</td>\n",
              "      <td>3</td>\n",
              "      <td>1452800000%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17553</th>\n",
              "      <td>Claimed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8269084</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>9999</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Dark</td>\n",
              "      <td>2</td>\n",
              "      <td>1752960000%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17554 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Transaction       From         To  ...  Ratio of Currency  Non_human  Human\n",
              "0            Sold   0xf5099e   14715954  ...         112.880000      False   True\n",
              "1            Sold   0x00d7c9   10528156  ...         241.250000      False   True\n",
              "2            Sold   0xc352b5      55241  ...         326.530612      False   True\n",
              "3         Claimed        NaN   12800693  ...                NaN      False   True\n",
              "4            Sold  EliteCat\n",
              "   0xcf6165  ...         605.083333      False   True\n",
              "...           ...        ...        ...  ...                ...        ...    ...\n",
              "17549        Sold     TJ2010       gaus  ...         356.983051       True  False\n",
              "17550     Claimed        NaN     TJ2010  ...                NaN       True  False\n",
              "17551        Sold    7595170  TokenAng\n",
              "  ...         633.266667      False   True\n",
              "17552     Claimed        NaN   0x73e4a2  ...                NaN      False   True\n",
              "17553     Claimed        NaN    8269084  ...                NaN      False   True\n",
              "\n",
              "[17554 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QKa-t_1kHmyO"
      },
      "source": [
        "#Average Slots by Types\n",
        "\n",
        "Finding the mean of each based on the newly created Types\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyEaxEJhHl-a",
        "outputId": "a036e61c-e607-4e59-962c-fbb86e8f049f"
      },
      "source": [
        "df_punks.loc[df_punks['Non_human'] == True, 'Slots'].mean()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.2246696035242293"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zmlTOJkfH5cR",
        "outputId": "9262d1c5-0414-44d8-9f86-b81b5e9a398d"
      },
      "source": [
        "df_punks.loc[df_punks['Human'] == True, 'Slots'].mean()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.7854793097477923"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8z7bZdFUID2X"
      },
      "source": [
        "We can see that **human** type punks have more slots than **non_human** type punks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vFSpB8MiIPdd"
      },
      "source": [
        "#Box Plots\n",
        "\n",
        "We could compare the slots for the different types of punks with a side by side boxplot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "cdf5O3M9IQwE",
        "outputId": "c15dbb1c-49d6-4232-bdf3-80be5aea7635"
      },
      "source": [
        "sns.boxplot(x=df_punks.Type, y=df_punks.Slots).set(title='Box Plot of Slots by Types')"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Box Plot of Slots by Types')]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVl_2xoUJgFc"
      },
      "source": [
        "#Your Turn\n",
        "\n",
        "Try playing with chunks of code from this session to further investigate the Punks data:\n",
        "\n",
        "1. Summarize the Crypto values.\n",
        "2. Make a boxplot comparing Crypto for different Skins.\n",
        "3. Find the average slots for the pale (Alien, Albino, Light) and dark (Dark, Mid)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X1JBDZ3-JuQT"
      },
      "source": [
        "#Answers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTiwMvPYJye4"
      },
      "source": [
        "#1. Summarize the Crypto values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FPHRB605JZ7G",
        "outputId": "0fe617e1-b398-416f-d07f-967d06b966e2"
      },
      "source": [
        "df_punks['Crypto'].describe()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    7554.000000\n",
              "mean        2.389854\n",
              "std         6.118331\n",
              "min         0.010000\n",
              "25%         0.300000\n",
              "50%         1.000000\n",
              "75%         2.900000\n",
              "max       189.990000\n",
              "Name: Crypto, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ys7Ebgg1KCBd"
      },
      "source": [
        "#2. Make a boxplot comparing Crypto for different Skins."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "vhrrjx2pJ9uL",
        "outputId": "bfa5f018-c46f-450c-a659-fb0032d631a8"
      },
      "source": [
        "sns.boxplot(x=df_punks.Skin, y=df_punks.Crypto).set(title='Box Plot of Crypto by Skin')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 1.0, 'Box Plot of Crypto by Skin')]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j2taows0K02z"
      },
      "source": [
        "#3. Find the average slots for the pale (Alien, Albino, Light) and dark (Dark, Mid)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mEbn-RYmKjjw"
      },
      "source": [
        "df_punks['Pale'] = (df_punks['Skin'] == 'Alien') | (df_punks['Skin'] == 'Albino') | (df_punks['Skin'] == 'Light')"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFaKYOOXMcSY"
      },
      "source": [
        "df_punks['Dark'] = (df_punks['Skin'] == 'Dark') | (df_punks['Skin'] == 'Mid')"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "NHj20jOlMjLO",
        "outputId": "48ed7dbd-32e8-4003-f4a3-04a488c07fa0"
      },
      "source": [
        "df_punks.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Transaction</th>\n",
              "      <th>From</th>\n",
              "      <th>To</th>\n",
              "      <th>Crypto</th>\n",
              "      <th>USD</th>\n",
              "      <th>Txn</th>\n",
              "      <th>ID</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Type</th>\n",
              "      <th>Skin</th>\n",
              "      <th>Slots</th>\n",
              "      <th>Rank</th>\n",
              "      <th>Ratio of Currency</th>\n",
              "      <th>Non_human</th>\n",
              "      <th>Human</th>\n",
              "      <th>Pale</th>\n",
              "      <th>Dark</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sold</td>\n",
              "      <td>0xf5099e</td>\n",
              "      <td>14715954</td>\n",
              "      <td>25.00</td>\n",
              "      <td>2822.0</td>\n",
              "      <td>2018-11-30</td>\n",
              "      <td>0</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Mid</td>\n",
              "      <td>3</td>\n",
              "      <td>3682560000%</td>\n",
              "      <td>112.880000</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sold</td>\n",
              "      <td>0x00d7c9</td>\n",
              "      <td>10528156</td>\n",
              "      <td>1.60</td>\n",
              "      <td>386.0</td>\n",
              "      <td>2017-07-07</td>\n",
              "      <td>0</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Mid</td>\n",
              "      <td>3</td>\n",
              "      <td>3682560000%</td>\n",
              "      <td>241.250000</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sold</td>\n",
              "      <td>0xc352b5</td>\n",
              "      <td>55241</td>\n",
              "      <td>0.98</td>\n",
              "      <td>320.0</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>0</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Mid</td>\n",
              "      <td>3</td>\n",
              "      <td>3682560000%</td>\n",
              "      <td>326.530612</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Claimed</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12800693</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-06-23</td>\n",
              "      <td>0</td>\n",
              "      <td>Girl</td>\n",
              "      <td>Female</td>\n",
              "      <td>Mid</td>\n",
              "      <td>3</td>\n",
              "      <td>3682560000%</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sold</td>\n",
              "      <td>EliteCat</td>\n",
              "      <td>0xcf6165</td>\n",
              "      <td>60.00</td>\n",
              "      <td>36305.0</td>\n",
              "      <td>2020-11-30</td>\n",
              "      <td>1</td>\n",
              "      <td>Guy</td>\n",
              "      <td>Male</td>\n",
              "      <td>Dark</td>\n",
              "      <td>2</td>\n",
              "      <td>2050240500%</td>\n",
              "      <td>605.083333</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Transaction       From        To  Crypto  ...  Non_human Human   Pale  Dark\n",
              "0        Sold   0xf5099e  14715954   25.00  ...      False  True  False  True\n",
              "1        Sold   0x00d7c9  10528156    1.60  ...      False  True  False  True\n",
              "2        Sold   0xc352b5     55241    0.98  ...      False  True  False  True\n",
              "3     Claimed        NaN  12800693     NaN  ...      False  True  False  True\n",
              "4        Sold  EliteCat\n",
              "  0xcf6165   60.00  ...      False  True  False  True\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Shu-sVNQMjy0",
        "outputId": "8b45ec9e-0a81-4678-b88e-3284f91d1bea"
      },
      "source": [
        "df_punks.loc[df_punks['Pale'] == True, 'Slots'].mean()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.748796498905908"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pKccvXiBM6P1",
        "outputId": "9f955cb9-040d-466a-94c1-44a821cac0a3"
      },
      "source": [
        "df_punks.loc[df_punks['Dark'] == True, 'Slots'].mean()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.80833412796796"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    }
  ]
}