exploratory_datascience_activity_mini_project.ipynb

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        "<a href=\"https://colab.research.google.com/github/akhilesh-kanmanthreddy/AI_Camp_2023/blob/main/exploratory_datascience_activity_mini_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      "source": [
        "# Welcome to your first EDA \\(Exploratory Data Analysis\\)\n",
        "\n"
      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "### Our task at hand \\-\\- I hope you like penguins!\n",
        "\n",
        "For our mini project, we are going to use Pandas and Numpy to conduct a basic Exploratory Data Analysis on a penguins dataset. Specifically, we are looking at the <u>Palmer Archipelago \\(Antarctica\\) Penguins</u> dataset, which you can find here:  [https://raw.githubusercontent.com/mcnakhaee/palmerpenguins/master/palmerpenguins/data/penguins\\-raw.csv](https://raw.githubusercontent.com/mcnakhaee/palmerpenguins/master/palmerpenguins/data/penguins-raw.csv)\n",
        "\n",
        "Not sure what an exploratory data analysis is? You can google it! The best definition I found is below:\n",
        "\n",
        "_**Exploratory Data Analysis**_ **refers to the critical process of performing initial investigations on data so as to discover patterns, to spot anomalies, to test hypotheses and to check assumptions with the help of summary statistics and graphical representations.**\n",
        "\n"
      ]
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    {
      "cell_type": "code",
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        "id": "qi-rmtq9I7Tl"
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      "source": [
        "#Before anything else let's import our library for data analysis, pandas.\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "Qzx0LbG3I7Tm"
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      "source": [
        "### Step 1: Loading in and understanding our data\n",
        "\n",
        "We need to read in our data. It is currently a .csv \\(comma\\-separated value\\) file, and we generally use the pd.read\\_csv\\(\\) function from pandas to read our data into a dataframe.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "JRCeAjhBI7Tn"
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      "outputs": [],
      "source": [
        "penguins_dataframe = pd.read_csv(\"https://raw.githubusercontent.com/mcnakhaee/palmerpenguins/master/palmerpenguins/data/penguins-raw.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "RP0YFvgCI7Tn"
      },
      "source": [
        "**TODO:** Can you run the .head\\(\\) and .tail\\(\\) function on our new dataframe? Can you print the name of all the columns in our dataframe? How about the number of rows?\n"
      ]
    },
    {
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              "  studyName  Sample Number                              Species  Region  \\\n",
              "0   PAL0708              1  Adelie Penguin (Pygoscelis adeliae)  Anvers   \n",
              "1   PAL0708              2  Adelie Penguin (Pygoscelis adeliae)  Anvers   \n",
              "2   PAL0708              3  Adelie Penguin (Pygoscelis adeliae)  Anvers   \n",
              "3   PAL0708              4  Adelie Penguin (Pygoscelis adeliae)  Anvers   \n",
              "4   PAL0708              5  Adelie Penguin (Pygoscelis adeliae)  Anvers   \n",
              "\n",
              "      Island               Stage Individual ID Clutch Completion    Date Egg  \\\n",
              "0  Torgersen  Adult, 1 Egg Stage          N1A1               Yes  2007-11-11   \n",
              "1  Torgersen  Adult, 1 Egg Stage          N1A2               Yes  2007-11-11   \n",
              "2  Torgersen  Adult, 1 Egg Stage          N2A1               Yes  2007-11-16   \n",
              "3  Torgersen  Adult, 1 Egg Stage          N2A2               Yes  2007-11-16   \n",
              "4  Torgersen  Adult, 1 Egg Stage          N3A1               Yes  2007-11-16   \n",
              "\n",
              "   Culmen Length (mm)  Culmen Depth (mm)  Flipper Length (mm)  Body Mass (g)  \\\n",
              "0                39.1               18.7                181.0         3750.0   \n",
              "1                39.5               17.4                186.0         3800.0   \n",
              "2                40.3               18.0                195.0         3250.0   \n",
              "3                 NaN                NaN                  NaN            NaN   \n",
              "4                36.7               19.3                193.0         3450.0   \n",
              "\n",
              "      Sex  Delta 15 N (o/oo)  Delta 13 C (o/oo)  \\\n",
              "0    MALE                NaN                NaN   \n",
              "1  FEMALE            8.94956          -24.69454   \n",
              "2  FEMALE            8.36821          -25.33302   \n",
              "3     NaN                NaN                NaN   \n",
              "4  FEMALE            8.76651          -25.32426   \n",
              "\n",
              "                         Comments  \n",
              "0  Not enough blood for isotopes.  \n",
              "1                             NaN  \n",
              "2                             NaN  \n",
              "3              Adult not sampled.  \n",
              "4                             NaN  "
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              "      <td>PAL0708</td>\n",
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              "      <td>39.5</td>\n",
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              "      <td>40.3</td>\n",
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          "metadata": {},
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      "source": [
        "penguins_dataframe.head()"
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            "text/plain": [
              "    studyName  Sample Number                                    Species  \\\n",
              "339   PAL0910             64  Chinstrap penguin (Pygoscelis antarctica)   \n",
              "340   PAL0910             65  Chinstrap penguin (Pygoscelis antarctica)   \n",
              "341   PAL0910             66  Chinstrap penguin (Pygoscelis antarctica)   \n",
              "342   PAL0910             67  Chinstrap penguin (Pygoscelis antarctica)   \n",
              "343   PAL0910             68  Chinstrap penguin (Pygoscelis antarctica)   \n",
              "\n",
              "     Region Island               Stage Individual ID Clutch Completion  \\\n",
              "339  Anvers  Dream  Adult, 1 Egg Stage         N98A2               Yes   \n",
              "340  Anvers  Dream  Adult, 1 Egg Stage         N99A1                No   \n",
              "341  Anvers  Dream  Adult, 1 Egg Stage         N99A2                No   \n",
              "342  Anvers  Dream  Adult, 1 Egg Stage        N100A1               Yes   \n",
              "343  Anvers  Dream  Adult, 1 Egg Stage        N100A2               Yes   \n",
              "\n",
              "       Date Egg  Culmen Length (mm)  Culmen Depth (mm)  Flipper Length (mm)  \\\n",
              "339  2009-11-19                55.8               19.8                207.0   \n",
              "340  2009-11-21                43.5               18.1                202.0   \n",
              "341  2009-11-21                49.6               18.2                193.0   \n",
              "342  2009-11-21                50.8               19.0                210.0   \n",
              "343  2009-11-21                50.2               18.7                198.0   \n",
              "\n",
              "     Body Mass (g)     Sex  Delta 15 N (o/oo)  Delta 13 C (o/oo)  \\\n",
              "339         4000.0    MALE            9.70465          -24.53494   \n",
              "340         3400.0  FEMALE            9.37608          -24.40753   \n",
              "341         3775.0    MALE            9.46180          -24.70615   \n",
              "342         4100.0    MALE            9.98044          -24.68741   \n",
              "343         3775.0  FEMALE            9.39305          -24.25255   \n",
              "\n",
              "                                  Comments  \n",
              "339                                    NaN  \n",
              "340  Nest never observed with full clutch.  \n",
              "341  Nest never observed with full clutch.  \n",
              "342                                    NaN  \n",
              "343                                    NaN  "
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              "    .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>studyName</th>\n",
              "      <th>Sample Number</th>\n",
              "      <th>Species</th>\n",
              "      <th>Region</th>\n",
              "      <th>Island</th>\n",
              "      <th>Stage</th>\n",
              "      <th>Individual ID</th>\n",
              "      <th>Clutch Completion</th>\n",
              "      <th>Date Egg</th>\n",
              "      <th>Culmen Length (mm)</th>\n",
              "      <th>Culmen Depth (mm)</th>\n",
              "      <th>Flipper Length (mm)</th>\n",
              "      <th>Body Mass (g)</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Delta 15 N (o/oo)</th>\n",
              "      <th>Delta 13 C (o/oo)</th>\n",
              "      <th>Comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>PAL0910</td>\n",
              "      <td>64</td>\n",
              "      <td>Chinstrap penguin (Pygoscelis antarctica)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Dream</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>N98A2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2009-11-19</td>\n",
              "      <td>55.8</td>\n",
              "      <td>19.8</td>\n",
              "      <td>207.0</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>MALE</td>\n",
              "      <td>9.70465</td>\n",
              "      <td>-24.53494</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>PAL0910</td>\n",
              "      <td>65</td>\n",
              "      <td>Chinstrap penguin (Pygoscelis antarctica)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Dream</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>N99A1</td>\n",
              "      <td>No</td>\n",
              "      <td>2009-11-21</td>\n",
              "      <td>43.5</td>\n",
              "      <td>18.1</td>\n",
              "      <td>202.0</td>\n",
              "      <td>3400.0</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>9.37608</td>\n",
              "      <td>-24.40753</td>\n",
              "      <td>Nest never observed with full clutch.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>PAL0910</td>\n",
              "      <td>66</td>\n",
              "      <td>Chinstrap penguin (Pygoscelis antarctica)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Dream</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>N99A2</td>\n",
              "      <td>No</td>\n",
              "      <td>2009-11-21</td>\n",
              "      <td>49.6</td>\n",
              "      <td>18.2</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3775.0</td>\n",
              "      <td>MALE</td>\n",
              "      <td>9.46180</td>\n",
              "      <td>-24.70615</td>\n",
              "      <td>Nest never observed with full clutch.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342</th>\n",
              "      <td>PAL0910</td>\n",
              "      <td>67</td>\n",
              "      <td>Chinstrap penguin (Pygoscelis antarctica)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Dream</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>N100A1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2009-11-21</td>\n",
              "      <td>50.8</td>\n",
              "      <td>19.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>4100.0</td>\n",
              "      <td>MALE</td>\n",
              "      <td>9.98044</td>\n",
              "      <td>-24.68741</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>PAL0910</td>\n",
              "      <td>68</td>\n",
              "      <td>Chinstrap penguin (Pygoscelis antarctica)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Dream</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>N100A2</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2009-11-21</td>\n",
              "      <td>50.2</td>\n",
              "      <td>18.7</td>\n",
              "      <td>198.0</td>\n",
              "      <td>3775.0</td>\n",
              "      <td>FEMALE</td>\n",
              "      <td>9.39305</td>\n",
              "      <td>-24.25255</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d701725-267e-4631-988d-eb1fd0735eb9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "\n",
              "\n",
              "\n",
              "    <div id=\"df-3a5ccf84-c5f7-45df-ba40-c577cc00be05\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3a5ccf84-c5f7-45df-ba40-c577cc00be05')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
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              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "      <script>\n",
              "\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\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",
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              "      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",
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              "\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-2d701725-267e-4631-988d-eb1fd0735eb9 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-2d701725-267e-4631-988d-eb1fd0735eb9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Run tail() on penguins_dataframe\n",
        "penguins_dataframe.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "u0Oq0HRgI7Tp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "589f400f-07cd-4520-b4f1-a66f8957b7c9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      PAL0708\n",
              "1      PAL0708\n",
              "2      PAL0708\n",
              "3      PAL0708\n",
              "4      PAL0708\n",
              "        ...   \n",
              "339    PAL0910\n",
              "340    PAL0910\n",
              "341    PAL0910\n",
              "342    PAL0910\n",
              "343    PAL0910\n",
              "Name: studyName, Length: 344, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Print the name of all the columns in our dataframe\n",
        "penguins_dataframe['studyName']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "dwZVpezMI7Tp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8be6fab4-8263-4846-ded9-d40cdf1cb66b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(344, 17)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# Use google to find out how to print the number of rows in this dataset.\n",
        "penguins_dataframe.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "EFcf6t1sI7Tq"
      },
      "source": [
        "### Step 2: Do your research\n",
        "\n",
        "What do the column names mean? At this point you should look up the meanings of any columns you don't know about and make some comments below.\n",
        "\n",
        "Typically at this point we would read up on the domain we are performing data analysis on. To do research on penguins you should probably get to know something about penguins!\n"
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "collapsed": false,
        "id": "VNRuL1OCI7Tq"
      },
      "source": [
        "What do these columns mean?\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "aDPOw_8oI7Tq"
      },
      "source": [
        "### Step 3: Cleaning up our Data\n",
        "\n",
        "This data is a lot to take in. How do we know if we can even use this data? I see some weird columns and lots of N/A values.\n",
        "\n",
        "Let's talk through some functions to clean our data and see if it is salvagable. To start, we cannot use all of these columns. I'm thinking I want to get rid of \"studyName\" and \"Comments\" since they are not super useful for understanding the data relationships. I'll do that below.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "VHJ7R5GrI7Tr",
        "outputId": "29065087-d8e0-4b20-9a47-9f30167656eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Sample Number                              Species  Region     Island  \\\n",
              "0              1  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen   \n",
              "\n",
              "                Stage Individual ID Clutch Completion    Date Egg  \\\n",
              "0  Adult, 1 Egg Stage          N1A1               Yes  2007-11-11   \n",
              "\n",
              "   Culmen Length (mm)  Culmen Depth (mm)  Flipper Length (mm)  Body Mass (g)  \\\n",
              "0                39.1               18.7                181.0         3750.0   \n",
              "\n",
              "    Sex  Delta 15 N (o/oo)  Delta 13 C (o/oo)  \n",
              "0  MALE                NaN                NaN  "
            ],
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "#Make a list of what you want to drop\n",
        "columns_to_drop = ['studyName', 'Comments']\n",
        "\n",
        "#Drop the columns using drop()\n",
        "penguins_dataframe.drop(columns_to_drop, axis=1, inplace = True) #axis = 1 lets pandas know we are dropping columns, not rows.\n",
        "\n",
        "#Check that they are dropped\n",
        "penguins_dataframe.head(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "FQgYja0DI7Tr"
      },
      "source": [
        "**What does inplace mean?** This argument in the `drop()` function means we are changing the dataframe in place! If we had `inplace = False`, we would need to create a new dataframe like so:\n",
        "\n",
        "`new_penguins_dataframe = penguins_dataframe.drop(columns_to_drop, axis=1, inplace = False)`\n",
        "\n",
        "**TODO:** Can you drop the Sample Number, Individual ID, Delta 15 N \\(o/oo\\), and Delta 13 C \\(o/oo\\) columns using the example above?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "penguins_dataframe.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cImljiBFnm46",
        "outputId": "687cb730-6c76-40ea-8879-ec2ce0546a79"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Sample Number', 'Species', 'Region', 'Island', 'Stage',\n",
              "       'Individual ID', 'Clutch Completion', 'Date Egg', 'Culmen Length (mm)',\n",
              "       'Culmen Depth (mm)', 'Flipper Length (mm)', 'Body Mass (g)', 'Sex',\n",
              "       'Delta 15 N (o/oo)', 'Delta 13 C (o/oo)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
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          "height": 81
        },
        "outputId": "1827db77-50a9-47a0-e578-a4cd1b41f399"
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      "outputs": [
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          "data": {
            "text/plain": [
              "                               Species  Region     Island               Stage  \\\n",
              "0  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "\n",
              "  Clutch Completion    Date Egg  Culmen Length (mm)  Culmen Depth (mm)  \\\n",
              "0               Yes  2007-11-11                39.1               18.7   \n",
              "\n",
              "   Flipper Length (mm)  Body Mass (g)   Sex  \n",
              "0                181.0         3750.0  MALE  "
            ],
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Drop the Sample Number, Individual ID, Delta 15 N (o/oo), and Delta 13 C (o/oo) columns using the example above.\n",
        "columns_to_drop = ['Sample Number', 'Individual ID', 'Delta 15 N (o/oo)', 'Delta 13 C (o/oo)']\n",
        "\n",
        "#Drop the columns using drop()\n",
        "penguins_dataframe.drop(columns_to_drop, axis=1, inplace = True)\n",
        "\n",
        "#Check that they are dropped\n",
        "penguins_dataframe.head(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "k5UI0_N3I7Ts"
      },
      "source": [
        "#### Getting Rid of N/A's\n",
        "\n",
        "I see lots of N/A's, which we typically cannot visualize or use in machine learning.\n",
        "Now, I want to get rid of all the rows that have NA's in them. I'll show you how. **TODO:** Can you print the number of rows our dataframe has after I drop the NAs?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "SYwAUlSkI7Ts"
      },
      "outputs": [],
      "source": [
        "# This is a very convenient function to drop all rows that have N/A values!\n",
        "penguins_dataframe.dropna(inplace=True)\n",
        "penguins_dataframe.reset_index(drop=True, inplace=True) #Very good practice to reset how your rows are counted when you drop rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "Px7P3jBOI7Ts",
        "outputId": "f873f94f-9e51-47a7-9216-826d6cf41cfc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "333"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "#Print the number of rows our new dataframe has.\n",
        "penguins_dataframe.shape[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "O2pLXqr8I7Tt"
      },
      "source": [
        "### Exploring the Data\n",
        "\n",
        "Pandas has some amazing tools for exploring your data. Since this is a **mini** project, we will walk through only a few key features of pandas exploration. You can expect to do lots of research on your own to best explore, clean, and visualize your data for your project.\n",
        "\n",
        "First we will learn how to access certain columns of data and use conditions to get subsets of data.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "dvtoDVsmI7Tt",
        "outputId": "0ae8fd2b-0f19-4e74-c0e3-f0cefcaa5962",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Adelie Penguin (Pygoscelis adeliae)\n",
              "1    Adelie Penguin (Pygoscelis adeliae)\n",
              "2    Adelie Penguin (Pygoscelis adeliae)\n",
              "3    Adelie Penguin (Pygoscelis adeliae)\n",
              "4    Adelie Penguin (Pygoscelis adeliae)\n",
              "Name: Species, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# access a column using df[\"<COLUMN_NAME\"] or df.COLUMN_NAME\n",
        "penguins_dataframe[\"Species\"].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "ZPmbaVyEI7Tt",
        "outputId": "1978889e-c927-4b07-b15a-0321d65dc57a",
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          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Adelie Penguin (Pygoscelis adeliae)\n",
              "1    Adelie Penguin (Pygoscelis adeliae)\n",
              "2    Adelie Penguin (Pygoscelis adeliae)\n",
              "3    Adelie Penguin (Pygoscelis adeliae)\n",
              "4    Adelie Penguin (Pygoscelis adeliae)\n",
              "Name: Species, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "penguins_dataframe.Species.head()"
      ]
    },
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z6lGTU3cI7Tt",
        "outputId": "b6b3d3cb-d91f-4b51-ad38-a6909fb1b904"
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              "      <th>Species</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Species  Region\n",
              "0  Adelie Penguin (Pygoscelis adeliae)  Anvers\n",
              "1  Adelie Penguin (Pygoscelis adeliae)  Anvers\n",
              "2  Adelie Penguin (Pygoscelis adeliae)  Anvers\n",
              "3  Adelie Penguin (Pygoscelis adeliae)  Anvers\n",
              "4  Adelie Penguin (Pygoscelis adeliae)  Anvers"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#You can also access multiple columns using double brackets (printing a \"list\" of columns)\n",
        "penguins_dataframe[[\"Species\",\"Region\"]].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "zTYdPIOjI7Tu"
      },
      "source": [
        "**TODO: Can you print the last five rows of the Island and Region columns?**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Island  Region\n",
              "0  Torgersen  Anvers\n",
              "1  Torgersen  Anvers\n",
              "2  Torgersen  Anvers\n",
              "3  Torgersen  Anvers\n",
              "4  Torgersen  Anvers"
            ],
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              "                                                     [key], {});\n",
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          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "#Print the last five rows of the Island and Region columns.\n",
        "penguins_dataframe[[\"Island\",\"Region\"]].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "h5bSdsxVI7Tu"
      },
      "source": [
        "<u>**Conditions in Pandas**</u>\n",
        "\n",
        "Pandas is also incredible for selecting subsets of columns that fit a certain condition. Check out the code below to get only the Adelie penguins.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "AUbqbGfTI7Tu",
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        "colab": {
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          "height": 206
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Species  Region     Island               Stage  \\\n",
              "0  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "1  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "2  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "3  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "4  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "\n",
              "  Clutch Completion    Date Egg  Culmen Length (mm)  Culmen Depth (mm)  \\\n",
              "0               Yes  2007-11-11                39.1               18.7   \n",
              "1               Yes  2007-11-11                39.5               17.4   \n",
              "2               Yes  2007-11-16                40.3               18.0   \n",
              "3               Yes  2007-11-16                36.7               19.3   \n",
              "4               Yes  2007-11-16                39.3               20.6   \n",
              "\n",
              "   Flipper Length (mm)  Body Mass (g)     Sex  \n",
              "0                181.0         3750.0    MALE  \n",
              "1                186.0         3800.0  FEMALE  \n",
              "2                195.0         3250.0  FEMALE  \n",
              "3                193.0         3450.0  FEMALE  \n",
              "4                190.0         3650.0    MALE  "
            ],
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              "\n",
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              "      <th></th>\n",
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              "      <td>MALE</td>\n",
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              "      <th>1</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
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              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
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              "      <td>3800.0</td>\n",
              "      <td>FEMALE</td>\n",
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              "    <tr>\n",
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              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
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              "      <td>2007-11-16</td>\n",
              "      <td>40.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
              "      <td>FEMALE</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
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              "      <td>2007-11-16</td>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
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              "      <td>3450.0</td>\n",
              "      <td>FEMALE</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
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              "      <td>2007-11-16</td>\n",
              "      <td>39.3</td>\n",
              "      <td>20.6</td>\n",
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              "      <td>3650.0</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "penguins_dataframe[penguins_dataframe[\"Species\"] == \"Adelie Penguin (Pygoscelis adeliae)\"].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "WdnJVtlgI7Tv"
      },
      "source": [
        "Conditions in pandas work just like `if` statements in python.\n",
        "\n",
        "**TODO: Can you tell me which species of penguin can have a flipper length greater than or equal to 230 by using conditions in pandas?**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "HFGw4vbVI7Tv",
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          "height": 206
        },
        "outputId": "764c878e-d463-42b1-f33e-ad2a39ec0e69"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Species  Region  Island               Stage  \\\n",
              "147  Gentoo penguin (Pygoscelis papua)  Anvers  Biscoe  Adult, 1 Egg Stage   \n",
              "178  Gentoo penguin (Pygoscelis papua)  Anvers  Biscoe  Adult, 1 Egg Stage   \n",
              "208  Gentoo penguin (Pygoscelis papua)  Anvers  Biscoe  Adult, 1 Egg Stage   \n",
              "210  Gentoo penguin (Pygoscelis papua)  Anvers  Biscoe  Adult, 1 Egg Stage   \n",
              "219  Gentoo penguin (Pygoscelis papua)  Anvers  Biscoe  Adult, 1 Egg Stage   \n",
              "\n",
              "    Clutch Completion    Date Egg  Culmen Length (mm)  Culmen Depth (mm)  \\\n",
              "147               Yes  2007-11-27                50.0               16.3   \n",
              "178               Yes  2007-12-03                59.6               17.0   \n",
              "208               Yes  2008-11-13                54.3               15.7   \n",
              "210               Yes  2008-11-04                49.8               16.8   \n",
              "219               Yes  2008-11-06                48.6               16.0   \n",
              "\n",
              "     Flipper Length (mm)  Body Mass (g)   Sex  \n",
              "147                230.0         5700.0  MALE  \n",
              "178                230.0         6050.0  MALE  \n",
              "208                231.0         5650.0  MALE  \n",
              "210                230.0         5700.0  MALE  \n",
              "219                230.0         5800.0  MALE  "
            ],
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              "      <th></th>\n",
              "      <th>Species</th>\n",
              "      <th>Region</th>\n",
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              "      <th>Stage</th>\n",
              "      <th>Clutch Completion</th>\n",
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              "      <th>147</th>\n",
              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
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              "      <td>Adult, 1 Egg Stage</td>\n",
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              "      <td>Anvers</td>\n",
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              "      <td>Adult, 1 Egg Stage</td>\n",
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              "      <td>59.6</td>\n",
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              "      <td>Anvers</td>\n",
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              "      <td>Adult, 1 Egg Stage</td>\n",
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              "      <td>15.7</td>\n",
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              "      <td>5650.0</td>\n",
              "      <td>MALE</td>\n",
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              "      <th>210</th>\n",
              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
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              "      <td>2008-11-04</td>\n",
              "      <td>49.8</td>\n",
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              "      <td>5700.0</td>\n",
              "      <td>MALE</td>\n",
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              "      <th>219</th>\n",
              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2008-11-06</td>\n",
              "      <td>48.6</td>\n",
              "      <td>16.0</td>\n",
              "      <td>230.0</td>\n",
              "      <td>5800.0</td>\n",
              "      <td>MALE</td>\n",
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              "\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "# Use pandas to find out which species of penguin can have a flipper length greater than or equal to 230\n",
        "penguins_dataframe[penguins_dataframe[\"Flipper Length (mm)\"] >= 230].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "axosD9FGI7Tv"
      },
      "source": [
        "**TODO:** Take 5 minutes to explore the dataset on your own. There's some space for you to code below.\n",
        "\n",
        "Think of what you want to learn. Maybe what the smallest flipper size penguin is? How about the smallest culmen width? What is the largest penguin by mass?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w7lCrtXAI7Tv"
      },
      "outputs": [],
      "source": [
        "#biggest number of flipper length\n",
        "penguins_dataframe[\"Flipper Length (mm)\"].max()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "toGI8nK_I7Tv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "563660de-8a0b-4ce3-a9fb-0f5065ac4053"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "# number of penguins with flipper size of 231 mm\n",
        "penguins_dataframe[penguins_dataframe[\"Flipper Length (mm)\"] == 231].shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "BLIwpSraI7Tv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6696c4b-0f6f-4228-d76e-42301ae03c26"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "168"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "# number of penguiins that are male and after before 7/7/07\n",
        "penguins_dataframe[(penguins_dataframe[\"Sex\"] == \"MALE\") & (penguins_dataframe[\"Date Egg\"] > \"2007-07-07\")].shape[0]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "qTuFEAJsI7Tv"
      },
      "source": [
        "### Visualize on the Fly with Pandas\n",
        "\n",
        "Before we create mind\\-blowing visualizations, it is sometimes worth it to create quick visualizations to get ourselves oriented with the data and it's underlying patterns.\n",
        "\n",
        "I put a simple histogram below that helps me understand how the dataset is distributed. Notice that is is pretty hard to read and ugly, but it gets the job done for the data scientist. I now know that there are three species of penguin we are worried about, and that we have a lot more samples of the Adelie penguin than the Gentoo and Chinstrap \\(a good to know for machine learning\\).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "vuIk7OI-I7Tw",
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          "height": 449
        },
        "outputId": "2ed8d788-d40f-478a-b0cb-b7a41a5806fa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a histogram of a column's data using the df.hist() function. I found it here: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.hist.html\n",
        "penguins_dataframe[\"Flipper Length (mm)\"].hist()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "-KXTWC_VI7Tw"
      },
      "source": [
        "**TODO: Create a histogram of another column of your choice below.**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "kyLZ4skOI7Tw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "outputId": "f03c88e4-d023-44ce-a013-97a30e192353"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Create a histogram of another column of your choice below.\n",
        "penguins_dataframe[\"Body Mass (g)\"].hist()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "YWSbTRg2I7Tw"
      },
      "source": [
        "**TODO:** Now, let's have some fun. Can you create plots using the following functions?\n",
        "\n",
        "1. Scatter Plot: [https://pandas.pydata.org/pandas\\-docs/version/0.25.0/reference/api/pandas.DataFrame.plot.scatter.html](https://pandas.pydata.org/pandas-docs/version/0.25.0/reference/api/pandas.DataFrame.plot.scatter.html)\n",
        "2. Pie Plot: [https://pandas.pydata.org/docs/user\\_guide/visualization.html\\#visualization\\-pie](https://pandas.pydata.org/docs/user_guide/visualization.html#visualization-pie)\n",
        "\n",
        "Pro challenge: Add a title to each!\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "5ohbib_MI7Tw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "2b29dea7-cbcf-4e94-b1a2-98d7bc15adf0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Body Mass (g)', ylabel='Flipper Length (mm)'>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Create a scatter here\n",
        "penguins_dataframe.plot.scatter('Species', 'Flipper Length (mm)')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#temp df\n",
        "df = penguins_dataframe.filter(items=['Body Mass (g)', 'Flipper Length (mm)']).head(20)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mvK9-vlALSxB",
        "outputId": "027b7da8-96f2-4d99-de79-dbe9b7146c5a"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Body Mass (g)  Flipper Length (mm)\n",
              "0         3750.0                181.0\n",
              "1         3800.0                186.0\n",
              "2         3250.0                195.0\n",
              "3         3450.0                193.0\n",
              "4         3650.0                190.0"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-428b7907-8936-4be9-9832-e667c2744837\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Body Mass (g)</th>\n",
              "      <th>Flipper Length (mm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3750.0</td>\n",
              "      <td>181.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3800.0</td>\n",
              "      <td>186.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3250.0</td>\n",
              "      <td>195.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3450.0</td>\n",
              "      <td>193.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3650.0</td>\n",
              "      <td>190.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-428b7907-8936-4be9-9832-e667c2744837')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-60a71ac8-7ad6-4317-ae10-085b3e6f8ea8\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-60a71ac8-7ad6-4317-ae10-085b3e6f8ea8')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-60a71ac8-7ad6-4317-ae10-085b3e6f8ea8 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-428b7907-8936-4be9-9832-e667c2744837 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-428b7907-8936-4be9-9832-e667c2744837');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "IkUbBCktI7T3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "5eff0747-fe55-4a73-d8e3-1e00f44cdaa6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Flipper Length (mm)'>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Create a pie plot here - might look more like art than data science. Is a pie chart a good choice for the column?\n",
        "df.plot.pie(y='Flipper Length (mm)')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "AWnyUOOaI7T3"
      },
      "source": [
        "### Creating Beautiful Visualizations with Plotly\n",
        "\n",
        "So far, it looks like a scatter plot might be our best bet! Now we want to make a beautiful scatter plot using `plotly.`Just like pandas and numpy, plotly is a module that lets us create beautiful visualizations in just a few lines of code.\n",
        "\n",
        "I'm only going to help with the import, so I need you to spend time on plotly's documentation in order to build the same scatter plot you did in the last section, but nicer. Maybe google \"Scatter plots in plotly python\" to start!\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "ECwqrCxsI7T3"
      },
      "outputs": [],
      "source": [
        "import plotly.express as px #importing plotly"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "ph8pHoKUI7T4"
      },
      "source": [
        "**TODO:** Create a scatter plot using plotly. Please add a title here!\n",
        "\n",
        "Maybe google \"Scatter plots in plotly python\" to start!\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "AT3hCoQFI7T4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "cc83f2b9-3a7d-4c6c-fb97-6c4be461ea74"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      ],
      "source": [
        "# Create a scatter plot\n",
        "fig = px.scatter(penguins_dataframe['Body Mass (g)'],penguins_dataframe['Flipper Length (mm)'])\n",
        "fig.show()"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "bL4FRMTSI7T4"
      },
      "source": [
        "**TODO:** Let's make it look even better. Copy your function from above but add another argument to the function by setting the `color=\"Species\"`. Not sure what this means? Get to the scatter plots page on plotly and scroll down to the \"Setting size and color with column names\" section.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "Hz68aMbiI7T4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "f662f49b-d9a8-4948-d3d1-954f197f6f4f"
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      ],
      "source": [
        "# Create the same scatter plot with color=\"Species\". Feeling crazy? Set a column name to the \"size\" argument as well!\n",
        "# Create a scatter plot\n",
        "fig = px.scatter(penguins_dataframe['Body Mass (g)'], penguins_dataframe['Flipper Length (mm)'], color = penguins_dataframe[\"Species\"])\n",
        "fig.show()\n"
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      "cell_type": "markdown",
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      "source": [
        "**Scatter Matrix:** I wanted to create a scatter plot for every possible numerical combination of 'Culmen Length \\(mm\\)', 'Culmen Depth \\(mm\\)', 'Flipper Length \\(mm\\)', and 'Body Mass \\(g\\)'. This could help me understand what is correlated and could mean a relationship between each other. The code to do so is below; are there any findings you can take away from this data?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        }
      },
      "outputs": [
        {
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# My scatter matrix!\n",
        "numerical_penguins_df = penguins_dataframe[['Culmen Length (mm)', 'Culmen Depth (mm)', 'Flipper Length (mm)',\n",
        "       'Body Mass (g)']]\n",
        "fig = px.scatter_matrix(numerical_penguins_df)\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "stIWNnORI7T5"
      },
      "source": [
        "### Putting the DA in EDA\n",
        "\n",
        "We now have explored our data some, and have built some beautiful visualizations to showcase patterns our data. But having a plot doesn't mean anything to someone who can't understand how to read plots.\n",
        "\n",
        "Below you can continue your analysis if you have hypotheses you'd like to explore with the penguins \\(i.e. is flipper length related to culmen depth? Which species has the largest culmen area?\\). After you are done, please write up some descriptions for your findings on this dataset. One question we answered earlier was _which species has the largest flipper length?_ You can start with you\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VCc29lDxI7T5"
      },
      "outputs": [],
      "source": [
        "#Continue your analysis below -- try something new!"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "penguins_dataframe.head()"
      ],
      "metadata": {
        "id": "elOaEiNrHpEP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "source": [
        "import numpy as np\n",
        "from google.colab import autoviz\n",
        "df_5045017119178890796 = autoviz.get_df('df_5045017119178890796')\n",
        "\n",
        "def violin_plot(df, value_colname, facet_colname, col_width=.3, col_length=3, mpl_palette_name='Dark2', **kwargs):\n",
        "  from matplotlib import pyplot as plt\n",
        "  import seaborn as sns\n",
        "  plt.figure(figsize=(col_length, col_width * len(df[facet_colname].unique())))\n",
        "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
        "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
        "  return autoviz.MplChart.from_current_mpl_state()\n",
        "\n",
        "chart = violin_plot(df_5045017119178890796, *['Flipper Length (mm)', 'Sex'], **{'inner': 'stick'})\n",
        "chart"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "FHO7mbv6H5hd",
        "outputId": "bd4b3407-e904-400f-85bf-01b489acbd28"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "YII=\n",
              "\">\n",
              "        <script></script>"
            ],
            "text/plain": [
              "<google.colab._quickchart_lib.MplChart at 0x7f4bfb55f850>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "source": [
        "import numpy as np\n",
        "from google.colab import autoviz\n",
        "df_5575262694553503838 = autoviz.get_df('df_5575262694553503838')\n",
        "\n",
        "def violin_plot(df, value_colname, facet_colname, col_width=.3, col_length=3, mpl_palette_name='Dark2', **kwargs):\n",
        "  from matplotlib import pyplot as plt\n",
        "  import seaborn as sns\n",
        "  plt.figure(figsize=(col_length, col_width * len(df[facet_colname].unique())))\n",
        "  sns.violinplot(df, x=value_colname, y=facet_colname, palette=mpl_palette_name, **kwargs)\n",
        "  sns.despine(top=True, right=True, bottom=True, left=True)\n",
        "  return autoviz.MplChart.from_current_mpl_state()\n",
        "\n",
        "chart = violin_plot(df_5575262694553503838, *['Culmen Length (mm)', 'Sex'], **{'inner': 'stick'})\n",
        "chart"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130
        },
        "id": "Mv76T8uCH0QL",
        "outputId": "d70389a8-33ad-412f-cd01-e4919f493754"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "0pJGjBhBKSkplJCQQK1ataKRI0fSxIkTKSMjg44fP05WVlYUGxurrfe3v/0tDRgwgBITEykzM5M2\n",
              "btxIMpmM7t69K6h3+PDhdO3aNbp+/Tp17tyZpk6dalb8utauXUs+Pj50+vRpysrKop07d5JMJqP4\n",
              "+HhB2/r160fx8fGUkZFBgwcPFgz2tnfvXmrevDnFxMSQUqmk1atXk729vTaBFhYW0sSJEwUDpJWV\n",
              "lWlj8/HxoRMnTpBSqaTx48eTh4eHybGCfH19KTo6WlAWGRlJdnZ2NH78eMrIyKBjx46RlZUVBQYG\n",
              "0vz58+nOnTsUExNDAOiHH37QbgeA2rVrR3FxcaRUKunDDz8kT09Pev/99+n06dP0448/Uv/+/Sko\n",
              "KEiwvxcvXpCFhYXgP7imhBMoM+nKlSsEQNA1miFiEygAwVnL7NmzycbGRjB6ZmBgIM2ePZuIiB48\n",
              "eEBSqZR+/vlnwf4DAgJo+fLlRuvdunUrubi4mBW/RmlpKdnY2Oid8UVERNCUKVMEbTt37px2+cmT\n",
              "JwmAtsu4fv360bx58wR1DBw4UJtAiQwPkKaJTXfAvIyMDAJAt2/fNtomBwcH2rNnj6AsMjKSbGxs\n",
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              "169f1/tmWTfpGqpD7H8GRUVFAICTJ0+iXbt2gmUymUwwr7tfiUQCANrYX5e5dTs5OSE/P99kPZq6\n",
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              "ZSXy8vLQoUMHwdSmTZta3x8AdOnSBTKZDDk5OXr7dHNzq3E9crlcbzTKqvNWVla19symn58ffvzx\n",
              "x1qpS6ysrCyUlpbqnak3FXwGyqq1detWDBw4EH379kVUVBR8fX1RUVGBs2fPYtu2bbh9+zasra3R\n",
              "v39/REdHo3379sjLy8PKlStrPZZOnTohNDQU06dPx6ZNm+Dn54f//ve/OH/+PHx9fREcHPxa9SuV\n",
              "Sr2yrl27YtGiRfj000+hVqsxaNAgFBQU4Pvvv4e9vT1mzJhRo7rnz5+PmTNnonfv3hgwYADi4uKQ\n",
              "np4OLy8v7Tqenp44c+YMlEolWrVqBQcHB9FtCQwMxO7du0VvXxsuXrwILy+vRj/Cq1icQFm1vLy8\n",
              "kJKSgi+++AILFy5Ebm4unJ2d0atXL2zbtk27XkxMDCIiItCrVy/I5XJs2LABI0eOrPV4du7cibVr\n",
              "12LhwoX4+eef4eTkhP79++OXv/zla9etO0SuxsOHD7FmzRo4Oztj/fr1+Omnn+Do6IiePXtixYoV\n",
              "Na47NDQUP/30ExYtWoTS0lJMnDgRYWFhuHr1qnadmTNnIj4+Hr1790ZRUREuXLgAT09PUW0JDQ3F\n",
              "kiVLoFQqIZfLRdXxuvbv34+ZM2c2yL7rAw/pwVgDGjFiBNq0aYN//OMfdVL/4sWLoVKpsGPHjjqp\n",
              "35SMjAy8//77uHv37mudSTdmfAbKWD0pLi7G9u3bERgYCKlUiv379+PcuXM4e/Zsne3zs88+w9df\n",
              "fw21Wi34lr0+5ObmYs+ePU02eQJ8BspYvSkpKcGYMWOQmpqK0tJSyOVyrFy5EuPGjWvo0JhInEAZ\n",
              "Y0wkfoyJMcZE4gTKGGMicQJljDGROIEyxphInEAZY0wkTqCMMSYSJ1DGGBOJEyhjjInECZQxxkT6\n",
              "H/yUZkWIUfm7AAAAAElFTkSuQmCC\n",
              "\">\n",
              "        <script></script>"
            ],
            "text/plain": [
              "<google.colab._quickchart_lib.MplChart at 0x7f4bf1920ee0>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "penguins_dataframe.plot.hexbin('Flipper Length (mm)','Body Mass (g)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "vyi9dlFDD0xS",
        "outputId": "7f84dec8-5ecc-4048-c01a-09acc1c8b697"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Flipper Length (mm)', ylabel='Body Mass (g)'>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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RioJeQnnGEwOl85NIJBKJpDzIyaDJh7SwlAFFUbCpENeFYTRZiUQima5YVMUwb/NFO84ayE9HKixlQlEUTIpAurJIJBLJxJGP862K8aKEiuigdMKMM1JhKRMJXZCQyopEMiUZHLuQLiJY6kFPCHFUgUKjDKwTTfrapSN/kooLDDX5Y6QoIamwSEqKEMI4dSkkEknBxHSNiJbgUMhHR3iAk2qacReYrCwf+mMR/PEIuwd6OMZTR7PTW/JjTFV88QiarrPDfxiLauKkmmYQQ4/XgmQtIauBBvPpgFGm6SQSiWTSEtc12oJ9HAz5CSRiBBKxcalFcyDk40Cwn2AiRm80OA5HmLr0x8LsD/bTGw1xOBIoKC/LZKbctQ/vvPNOTjnlFNxuN/X19Vx00UVHZawfzEMPPXRUsrrsXGr5Ii0sZULBQMW0JBJJQTjNVpZXNXFspSCciOO0jE8dneVVydpnwUQMxwSnvzcac1zV4IIllQ2oioJplBwrqkHihMo9JfTiiy+yevVqTjnlFBKJBN/61rc4//zz2b59O06nc9j9PB5PjmIzFpmlwlIGkrWEZJSQRDJVST98VUUZN2UlG+cEFhY0OtY8EtpZVcUQ/isTwdNPP52z/NBDD1FfX8+mTZs466yzht1PUZQhiw0XgpwSKhOKoqDK779EIpFMOKO93RvpUV2qWkJ+vz/nE41G8zq+z+cDoLq6esR2gUCA2bNn09LSwqc//Wm2bdtW8LlKhaUMCCGIa7qMEpJIJJIyko7e0oVACJFTFFGk1g1FVBdoBgmUUErwD6ClpQWv15v53HnnnaMeW9d1brjhBs444wyWLVs2bLtFixbxy1/+kj/84Q/893//N7quc/rpp3PgwIGCzlVOCZUJbaIFkEgkkmlEOBHHF4/QHvLTGR5gvqeWCpOZnf4ezKrCGfVzUJXh39k1ISZF5elysX///pxaQjbb6FFuq1evZuvWrWzYsGHEdq2trbS2tmaWTz/9dJYsWcJPf/pTvvvd7+Yto1RYJBKJRDIqQgg6wgP44xFmu6qwT3KnXyEEgXiUQCJGXOgE4lESuk5UixPXVYw18TM8pXK69Xg8BRU/vO6663jyySdZv349zc3NBR3SYrFwwgknsGvXroL2kwpLmZBRQhKJxKgkdJ2Nhz+gPxYGoD8a4ZS6lkntmFphtjDfXcMCTy1xXcOimhBCsNBTi6qMrq6YDKLQjCU0ObeDwpoLIbj++ut54oknWLduHXPnzi34kJqmsWXLFi688MKC9pMKSxlIRwklhECTWotEIjEY/bFwRlkBmOn0TPrhPNvqYElFBimKgonRnW5llNDwrF69mkceeYQ//OEPuN1uOjo6APB6vVRUVABw+eWXM3PmzIwfzHe+8x1OO+00FixYQH9/Pz/84Q/54IMP+OpXv1rQsaXCUiYURQGDOHFJJBLJVCWf6RMjqSqqUlx5BlHgrg888AAA55xzTs76X/3qV1x55ZUAtLW1oWbluenr6+Pqq6+mo6ODqqoqTjrpJF5++WWWLl1a0LGlwlIGhBAyB4tEIjEsXqsdt9nGQCIZ6toe8jPD4TXUwF4I0VQtISM43ZY7cdxwkVXZrFu3Lmf5nnvu4Z577inoOEMhFZYyIZUViURiVCyqibMb53Eg5MMfizDHXT3lp0yMEiUkix9KJBKJxDD0RwL873trQcDnl3yMSrur5MdQFIUWZyUMn309L3QhaAv20R+LMNdVjddaeE2ZYtnbf4jHd7zA/MqZfHLBmVhMcig0AvIulQkVaWWRSCSlZ8vh3dz0f/9GTIsD0OCs5qJjzp5gqYYmoWus79xLMBEDkkUjT65pLmsRwt/ueIH7Nz2OooBJMXF8wzHMq5x5VDsjWFcAKDJKqFAflolEKixlQFEUrCaFhC6z3UokktKyqeNdYloik8X1rJYTJlii4QnEYxllBaB+HCxBo/Fi21sIBEJAo7NySGXFphY5zVJGip0SMsp5gkzNXzaSaaEnWgqJRDJZyceZcYSdSyfIFGe0ATqmJfDH86ujIykvUmEpA0IIYjJKSCKRDEFaUYnpWqbmTSEKyEdmnUyjqzaz/Mdd64tTfsYRj9WeY1U5GOrPqe9TDv5m0UdxmJN+M+2BHjZ1vAck74MmdN7zHebZQ7vYM9BbVrnGSqmKHxoBOSVUJibn40MikUw0ewZ68MejHAj5qLJW8OGGwjKHzvY28utPfJu1+14DIfjY3FMn7SCkKgqn1s2iOxKkPxamxVmJSS3ve/OZLcdzfMNC/rx7I/MrZ3JS42IORwJ0R4LsD/qI6gkAOsMB5rlHrkA8GVAockrIQMHpUmGRSCSSCcRrrcBqMtMTDVE5xogZs2rignmtozecAIQQdEUC7B7oocbmZL67hlq7k1p7keFGReC2Ovn8kvMyyzbVTJ3dRSARoyM8MGFySUZGKixlQFEUTIpMyy+RSI6mxuZAAM0OL4qiIISYtBaSsbClr4MPgn0A+ONR5rqqEGJyTUW4LcnKxLV2J/uD/ezwdbHQUzvKXpOD6eR0KxWWMmFRVRQZJSSRSAahKEmjfNrvxEgDSD70RIOZvx1mC9ZJmPMk+5rPqPAwz12JSTGGi2exxQ+N9HUzxh2ZAgghpCO/RCIZlqmmqKTJ8ZEYp2egSDsrl4CkT83UvBdGRyosZSAdJaRNtCASiURSZpZU1mNNVUv2xSP0REIlUy7S/fhjQQ4FukvSJ0BcFyQMkodCRglJSo4xvvoSiURSWhoq3JzbtJCOsJ9KawWulL9IKdiw/23WH3iL9fvfIqrF+c9VtzHb21SSvnUERrC0SB8WiUQikUhKhFlVaXZWlrzfuVUzmFvZRIXZRnugm1pH5ZRzWh4NVVGKK0RpoGslFZYyoCgKZgUS0olFIpFISsZMVx0AN55yCYqioIvSpOdUALOBBvLpglRYyoRZVVB0QVzqLBKJRFISsi0pQgjUEkX2WA1VS2j6RAlJhaVMCCFkSLNEIhkT4xnyPJYplKHkGa6fcoVr59t/PvLoAlSMMbU0nXxYZJRQGRBCENWFdLyVSCQFkR5cfbEw/ngkZ12x6ELQFQ6QyEyjjN5v+tgD8Sj9sUhOP3E9HQcpctr2xkKZCs0TWeMofez+WJiBVHHD4eSJCyGn8CchE6qw3H777UeFVy1evDiz/Zxzzjlq+zXXXJPTR1tbG6tWrcLhcFBfX89NN91EIpHIabNu3TpOPPFEbDYbCxYs4KGHHirH6UkkEklR7B7oYUPnXv7atY/1nXuJa4mSvBG/driNF9p38Wp3G2/2HEytHb3fD4J9vNS5jxc797Chay9/7dzD/6X6eaP7QE4/7/m6WN+xh5e7PuD/OnYnt0zg2/zOgW42dO5lQ1dS/oSujyiPUdQVpQT/jMKETwkde+yxPPfcc5llszlXpKuvvprvfOc7mWWHw5H5W9M0Vq1aRWNjIy+//DLt7e1cfvnlWCwWvv/97wOwd+9eVq1axTXXXMPDDz/M888/z1e/+lWamppYuXLlOJ+dRCKZLOhCZNKuW1Uzy6oaJ7SejRCCgyE/7/k6URWVY70N1Fe4cgbRZoeXWc5Kdvq7UZRkzaBCp3D6oiG29XcSiEdZ6Kllrrua42tmEozH2O3vYZarMu++GuxuZjq87PJ3owmdhZ46Ilqcnf7uTBRQWr657mrmuKp533eYCrOl5FNDA/EI2/o76Y2GmOeuYYG7FvMIhRRnOSuZ7aziXV8X+4P9rD30Ph6LneVVjXjGWMNpMjCdpoQmXGExm800NjYOu93hcAy7/dlnn2X79u0899xzNDQ0cPzxx/Pd736Xm2++mdtvvx2r1cqDDz7I3LlzufvuuwFYsmQJGzZs4J577imbwqIoChY1mYxIIpFMDLv83ezwHwYgqmu4zNYJDYHdF+hja39HZrnCbDmqjS2Vxn5pZUNmXSHi+mMRNnTtS6b+J10GQMGiKHitdk6ua0YvYOrDbjInLeHe+sw6i2ri5NqWoxQSm5qU/biaGSW/zlEtwYsde4DkeWlCH1FZyZZH15NTYAmhE0hEcFqO/h6ogMVAA/l0YcJ9WHbu3MmMGTOYN28eX/ziF2lra8vZ/vDDD1NbW8uyZcu45ZZbCIVCmW0bN25k+fLlNDQc+TGvXLkSv9/Ptm3bMm3OO++8nD5XrlzJxo0bx/GsjsakKFjk918imTDCWjzH+G03Wyb07TIySJ6KIeTJfns+8nf+Mke05PR4WiWxmyyZvtK5OwrJ4ZEty+B+hpN9PJTCqJZAcOS8KkyWURWvtDxh7YjLgFkxYVLUo+Szmo5eN1mRmW7LxKmnnspDDz3EokWLaG9v54477uDMM89k69atuN1uLr30UmbPns2MGTN45513uPnmm9mxYwe/+93vAOjo6MhRVoDMckdHx4ht/H4/4XCYioqKo+SKRqNEo9HMst/vL/pcdRklJJFMKCZFzfFLSOg6pgl8YA+WR9N1zEMMnjB2s71pkNVBK1GeEshfpvG4voOtKZqu563GZe+rZdUgypYzoQtMijGmS2RYc5m44IILMn+vWLGCU089ldmzZ/Poo49y1VVX8bWvfS2zffny5TQ1NXHuueeye/du5s+fP25y3Xnnndxxxx0l6y9dS0gikUwcCz21aEKnLdiPqii0h/w0O70TJs9cdzUxXWNfoBdQaA/7meOqLukxqq0VLKtsZIevi7jQ6Y0GaarwGD4pmsNs5YTqmWzv7ySqJ+iLRYjrOlaTadR9l1c1sq2/k47wADqCw5EAdXZXTpuESEZ1ymmhycWE+7BkU1lZyTHHHMOuXbuG3H7qqacCsGvXLubPn09jYyOvvfZaTpvOzk6AjN9LY2NjZl12G4/HM6R1BeCWW25hzZo1mWW/309LS8vYTkoikUwKbCYzx1XP4BhPHSZFzWtwG08sqollVY0s8NSgoGT8VUpJ2vm1xeklpms4zNaC+4jr2rCWn4mk2emlyeEmnIgXVJ/IYbZySm0LwUQMm2oe1vfFKK+Y08npdsJ9WLIJBALs3r2bpqahi1dt3rwZILO9tbWVLVu20NXVlWmzdu1aPB4PS5cuzbR5/vnnc/pZu3Ytra2tw8phs9nweDw5H4lEMjWoMFsmXFnJxm6yjIuyko1ZNRWsrIQTcd7sOcjTB3ewvnMv/lTelcmESVHHXEzRabaO6KhrlGFcUdSiP0ZhQiX9h3/4B1588UX27dvHyy+/zGc+8xlMJhOXXHIJu3fv5rvf/S6bNm1i3759/PGPf+Tyyy/nrLPOYsWKFQCcf/75LF26lMsuu4y3336bZ555hltvvZXVq1djsyW/xNdccw179uzhm9/8Ju+99x4/+clPePTRR7nxxhvLdp6KomBVjfL1l0gkEni9ez+HQr7UksBjtU9o4rdyYlIMVEuoWIdbo5wnEzwldODAAS655BJ6enqoq6vjwx/+MK+88gp1dXVEIhGee+457r33XoLBIC0tLVx88cXceuutmf1NJhNPPvkk1157La2trTidTq644oqcvC1z587lqaee4sYbb+S+++6jubmZX/ziF2XPwaIqCmYEidGbSiSSaYlgMr3Xx3QtMy1iUZMWqfGZPijVeZfu+llGCZGWTAwTqrD85je/GXZbS0sLL7744qh9zJ49mz//+c8jtjnnnHN46623CpavlOhCKisSiWRo0qG/E5kXZjBW1UREiyMgk3a/VPJlny+kX/LHoHAIAYqCLgRKMf0MIq7rmA0S8quoCkoRCpZiIOu/VCPLgIwSkkgkw6HpOnFdZ4evi65IAJjYmjtpTq5tYYYjHUWl4I9FSjaAJ4SOEIK2YB+7B3oQQjCWU46n+jkY7GfXQA/6GPsZjCYwTC2h6eTDMqmihCQSiWQqENM1ErqOY4jstYPZ3HuInmiIqJ6gwmShocJd1jd7XQiCiRguszXnuA6zhRNrZrK8qrHkUUKHQj72Bfrwp4oQNthduMeQHr8jPMCegZ5MP3U2J5W2oaM/C8UY6sr0QiosEolEUiKEEOwe6OF9/2E0IZjnqmZxZT2mEd5iT6yZiUBwIOjHa7Vl+imH0tIZHmBbfwfBRJwqSwUn1TYfVSIg7b9SSpqdlcx2VdMVHiAudFwW25jOeYbDQ4uzku5IgIiWwJtyDC7FtTPKRMl0CmuWCksZSEYJJWsJSa1dIpm6dEYCvOs7kmah1u5EHWXoS9f3aclKYleOQSSh67zevT/zTPJa7djHObw6jSl1TdIJ28acyTfVT43NWVQ/gzErCiaDjOMKxU3rKAbyDDGOpAZHVRTMBvkBSCSSsZHQc1PfFzKVUu66Ljq5L1AmVS3ohSpfP5sh2x1VH2mMlKqfQZhVYzjcTjekhaVMaEIQl+YViaRspCNHBIUV+CuGCnPykZo+bliLT6rIn2xMioJZUdGEnpQ1ER/1Og2O7kkuw1ATKNk1eibrNRiOmKZjMYjSMp2mhKSFpQwIIYjLKCGJpCwIIUjoGocjATb1HCAQj46+U4mosTlprZuN22LHqpoyysBkxKSonN04jxkOD6qSLA0Q17QR94loCeK6xnu+Lrb1dyQrJg9zglE9QURL8HbvIXb6u1ORQJP1auSiA3GDyCqjhCQSicSgCOC17v0MxKPJ+jkmK0sstrK9SdbanZzdOK8sxyoWh9nKiTXNebfvjAxwMOinNxYCYJ67ZtiU/3sHejkcCeKLJ1P6H+OtK15gybRGKiwSiWRKoQCn188hoev0RkPU2B2GMntPZmY7q5jjqsYfi6AJHYfZOux0z2JvPUsqFXqiQWxqcqgx0tSQMaScXlNCUmEpA4qiYFMhJqOEJJJxJ/0ANqsq9RWuCZZmapG+tu6sgoPDDXjp9dVWx6htJxsWRcEoCWCLndaRU0KSo1AUBbMiHW8lEsnQlDrt/Xj2U0j/RlFSslEV48gtLSySkpPQBQmprEgkkkEIITKRTMUoG4OjctJ/j3TM9NbsdoPlGamfqUpMF1jU8kWXSfJDKixlQAhhmLoUEomkvAQSUaKaxg7fYeoqnBzjGZtzaiARJabr7OjvpMrmZEll/QhtYwTiUXb6u5nvrmFmVtK6QDxGXGjs8HXhsdhZWtkwJnmMjCBZS8hqAIVlOk0JGUdSiUQimYIcCPp433+Y3liIQ0H/mPs5GPSz03eYnliYgyFfzjZfLMKhkA899eK0x9/DTn83vniEjvBATtv2sJ+d/m66oyEOhPpzrCv+WISDQR+ayE2QVygD8SgHStCPhOT8VbEfgyAtLGUinUhKIpFIslmSsmAMxKLYzWOPplmcsqgE4lFsqRT7mq7zdl97RoGpt/s4uaaZ42pmAEkFxGXJDUtOhx8H4lGs6pF+tvS1sz/VT23QwSm1szCrhb3zCiHY0tfOB8F+AD4IOPhQXcu41CsqltFKKkjKj1RYykC6llBCF4yclkkikUxXshWHYnxGnFl5UXpjoRxrS7PTm+OX4RmhQnJ2P/3xSEZZSfZTiWkMMvrikYyyAjDT6cE8CackLKoypvObCKbTlJBUWMpE0pNbSDOLRCIZklI5tuY60A7aVoDVYLAjbm4/Y2QIebKdfycLxhnCk+WUiosSKqEw44yR7ouhieu6jBKSSCRlxW2x5Vgw+qKhMQ1uLosNS1Y/vdHwmPpxmC1Ys6Z/eqOhSaesAER1kfH3kUwepIWlDAgh0OR3XyKRlJkKs4Vzmxaw09/NQDxKQ4V7TP3YTWbOnbGQXf5u+mMRmhxj68dqMvPRpgXsHuihLxpmpsM7+k4ThIwSmnxIhUUikUgKRBM6+4M+LIrKDIdnQvKU9MfCHI4EaHZUUmG2DNvOopqotTuxmy14R/BZGQ2Laso4CBeDRTWx2DtCyHU8yqGQnxkOD66sjLqSoZEKi6TkyCghiWRq0B8L83r3fiJaAkhWJZ7rqi6b0iKE4O3eQxkn2ANBPx9umDNkpE1C13mtu42eaLJYYU8kyMm1LZM2Idq7/Z3sGugBYF+gl7Ma5mEfQRkbT2SU0OTDOKqVgUlGCSnyYkskU4ADQV9GWQGY7aoqq4UlqidyInZmONzDRtr0RkMZZQWS0T2TeRhOKysA9RXuCVNWrKqC2SD5SdKp+Yv5FMKdd97JKaecgtvtpr6+nosuuogdO3aMut9jjz3G4sWLsdvtLF++nD//+c8Fn6scQ8uEYqBiWhKJZGTG46c8OBKnoH1LKIdk8kUtjUR6SqiYTyG8+OKLrF69mldeeYW1a9cSj8c5//zzCQaDw+7z8ssvc8kll3DVVVfx1ltvcdFFF3HRRRexdevWgo4tFZYyIIQgpskoIYlkKlBnd6JkBQh3hAZGbD8aQgiEEOgptWM0xcWqmqm0VmSWD0eCDFcH3mu1YzcdmfnvTGW1TR8znWm2GGWplDTajzjz9kZDxHVtQmSL6gJtklyT0Si3heXpp5/myiuv5Nhjj+W4447joYceoq2tjU2bNg27z3333cfHP/5xbrrpJpYsWcJ3v/tdTjzxRO6///6Cji19WMqETEAtkUwNGircnDtjAbv8PZhVlfoKV1H9tYf9BBNxdg90U2dzcVJt84jtVUXhw/VzOBTy0xkZYLarGtMwb8m2VFTOvkAfgXiUee4aFEXhQLAffzzK3kAvC9y1LPKOrX5RqTm5tpmuSIADQR/NTi9mRZ2wwouaEIZJHlcK/P7cshA2mw2bbXSnZ58vOT1ZXV09bJuNGzeyZs2anHUrV67k97//fUEySoVFIpFICsRusrCsqrEkfWm6TkLX0LMsHqOhKAoznd6cooXDYVJU5rtrctYldJ2ErudYWSYDiqLQUOEec/j1tKTIKCFS+7a0tOSs/va3v83tt98+4q66rnPDDTdwxhlnsGzZsmHbdXR00NCQG2HW0NBAR0dHQaJKhaVMqEgri0QiOZpmZyUACz11mFV1THWECmW2qwqApZUNZTum0TCKdWUs0zqD9wfYv38/Ho8nsz4f68rq1avZunUrGzZsGPPxC0EqLGVAURSsJoW4rssEchKJJIf0gGEatFyOY5pT/0tlJRebWpwSYEQ8Hk+OwjIa1113HU8++STr16+nuXnkaczGxkY6Oztz1nV2dtLYWJiVUjrdlonJ4tQmkUgk+TCdn1lGOnNFUVHUIj4FTicJIbjuuut44okneOGFF5g7d+6o+7S2tvL888/nrFu7di2tra0FHVtaWMqAEIKYPpwfv0QiMTrpwV1RFOK6hkU15T3Nkt43IXTMqXfIiXy7T8ujCR01lT5tulkbYrrAooDJALkoSjUllC+rV6/mkUce4Q9/+ANutzvjh+L1eqmoSEavXX755cycOZM777wTgK9//eucffbZ3H333axatYrf/OY3vPHGG/zsZz8r6NhSYSkTUlmRSKYuoUSc9rCf9rCf/liEU2tnUWt35pXP44NAHwOJKG2BfqptDlrrZ4+7vCOxP9jPQDzKB8E+KkwWPtK0YELlmSg0BCZDZWQpDw888AAA55xzTs76X/3qV1x55ZUAtLW1oapHLDenn346jzzyCLfeeivf+ta3WLhwIb///e9HdNQdCqmwSCQSSZGoioLLYsMVtxGMx7Co+Q91FWYLgmSNnQrTxGR2zcZuSspjVc04JijTrCR/yl1LKJ+pwnXr1h217nOf+xyf+9znCjrWYKTCUiZMgDbRQkgkknGhwmzBbjLTWOFGF6Kg9/J6uwtBMnJHVZQJj9ipszsRwCxnJUpGHjBW/tfimW5RQkZAKixlQFEULCYFRZfZbiWSqUr6wV9oYUFFSWbNzfaDmUgmmzwTwXSMEjICUmEpE0IIdKmsSCQlZ6ItElOV6XxNBcaxJyUtLMVMCRnlTGVYc1lIRwnJxHESSWnIjmQJa/EJlqY0hLV42Wv7BOOxaR2+PBwxXZAwyBtmuWsJTSQTqrDcfvvtR124xYsXZ7ZHIhFWr15NTU0NLpeLiy+++KjkM21tbaxatQqHw0F9fT033XQTiUQip826des48cQTsdlsLFiwgIceeqgcp5eDMb76Eokx8MUibO3r4IX2XTzfvouuSMCwA+/7vi5e797P8+27eKlzHzD+b73b+jp4uWsfL3TsYnPvoXE9llHRDfLULne15olkwqeEjj32WJ577rnMstl8RKQbb7yRp556isceewyv18t1113HZz/7WV566SUANE1j1apVNDY28vLLL9Pe3s7ll1+OxWLh+9//PgB79+5l1apVXHPNNTz88MM8//zzfPWrX6WpqYmVK1eW92QlEklJsJpMtKTq6ByOBHCarBMs0djY0fsBd7/+v2gonDm7lQWNCzPbwok4O3xdBBMxFnrrqLcXV2QxmxkOD+64jWA8Sk80xIsde5jtqmSWsyrjgyOE4EDIx96BXursThZ4arGoplF6Hp2olmCnv5u+aIj5nhqaKjyGesuXTBwTrrCYzeYh0/P6fD7+4z/+g0ceeYSPfvSjQDLOe8mSJbzyyiucdtppPPvss2zfvp3nnnuOhoYGjj/+eL773e9y8803c/vtt2O1WnnwwQeZO3cud999NwBLlixhw4YN3HPPPWVTWBRFwaxAwqBvgBLJZKPCZKHCZOHYSjvQMGr7ycgLH7zBd176D1RFBSFYfdynWOytRwhBIBFjfcceBMmEk/3RcEkVlkprBW6Lnff93YS1OGEtTkdogNnOqkybTT0HaA8PABDVEyz01BXtLxTVErzQvotEauqrOxJihmP0Ao7lRuFI2YJJj6JmChiOeX+DMOGS7ty5kxkzZjBv3jy++MUv0tbWBsCmTZuIx+Ocd955mbaLFy9m1qxZbNy4EUiWrF6+fHlOFciVK1fi9/vZtm1bpk12H+k26T6GIhqN4vf7cz7FYlYVLAb5/kskk53B8+9Gm4sH2Nm3H5OiogsdHcGi6lmZ8wjEo+gcyY5dabWX9NiKoqAjcvx/PIOO0R+LZP52mq2YVbXoaxxOxDPKCoDXakefhC9yVlUpONprophOPiwFW1ii0SivvvoqH3zwAaFQiLq6Ok444YS86gkM5tRTT+Whhx5i0aJFtLe3c8cdd3DmmWeydetWOjo6sFqtVFZW5uyTXZJ6uJLV6W0jtfH7/YTD4Uwq4WzuvPNO7rjjjoLPZySSZdxL2qVEIsFYUQ7ZmBQ1x+8mIbSMBWPwOemUPhpqcE+DfYCyD1UqpWKw+ILCctaUCx1QZfTZpCNvheWll17ivvvu409/+hPxeDxTN6C3t5doNMq8efP42te+xjXXXIPb7c6rzwsuuCDz94oVKzj11FOZPXs2jz766JCKRLm45ZZbWLNmTWbZ7/fT0tIy5v6EEEQN4nEukUjKw6cXns27PfvY1PEeAA9vf46vrFiF3WShzuZklrOStmA/AAeD/dTZnCVNZmZRTSyrbORdXyeaEHRGAszTaqhIZbddXtnE232HiGgJfLEIfdEQVTZHUcf0WOwscNewe6AHARwK+pnp8GJRiveNKSVxXWBOTeVPdsqd6XYiyUth+dSnPsWbb77JpZdeyrPPPsvJJ5+co1Ds2bOHv/71r/zP//wPP/rRj/j1r3/Nxz72sYKFqays5JhjjmHXrl187GMfIxaL0d/fn2NlyS5J3djYyGuvvZbTRzqKKLvNUGWtPR7PsEqRzWbDZrMVLL9EIpHkS52jkrs/+nX+5/0NBBJxGt0NvNz1AR9tWoBJVTmuegbz3DWEE3Hq7M5xeduf665mpsNDdzREvd2FOav+S32Fi4/aF9AVDlBlq8BegrIBiqKwpLKB2a4q/PEo9XbXpJ160Q2SjUVVipu+mqzXfyjyUlhWrVrFb3/7WyyWob+w8+bNY968eVxxxRVs376d9vb2MQkTCATYvXs3l112GSeddBIWi4Xnn3+eiy++GIAdO3bQ1taWKUnd2trKP//zP9PV1UV9fT2QLFnt8XhYunRpps2f//znnOOMpay1RCKR5ENfNMy7vk7CiTiLvHXMdHhHVDY+Pe9DxHSN932HqbU5gaRV1h+PsL0/GSV0jKeOFufQ/QghOBwJ8q4v+WK21NuQLLyY50BkNZmZ4fAMuc2kqDQNs60YHGYrDrMxI7skE0deCsvf/d3f5d3h0qVLM8rCaPzDP/wDn/zkJ5k9ezaHDh3i29/+NiaTiUsuuQSv18tVV13FmjVrqK6uxuPxcP3119Pa2sppp50GwPnnn8/SpUu57LLLuOuuu+jo6ODWW29l9erVGQvJNddcw/333883v/lNvvKVr/DCCy/w6KOP8tRTT+V9TsUio4QkkulBbzTES137kqntyc/3Ix3x9KG6WRk/En88yvrOvZl+NKEPq4AcDPl4KyuXikk1jol/smKkKCEl9a+Y/Y3ChIY1HzhwgEsuuYSenh7q6ur48Ic/zCuvvEJdXR0A99xzD6qqcvHFFxONRlm5ciU/+clPMvubTCaefPJJrr32WlpbW3E6nVxxxRV85zvfybSZO3cuTz31FDfeeCP33Xcfzc3N/OIXvyh7DhazqqDogrjUWSSSKUsokYy6Sf/MnWbrqJaO9PZsp9pQIjaon+GnY4KJeEaxyfeYkpGxGqiWkPRhGYGqqqohb6SiKNjtdhYsWMCVV17Jl7/85VH7+s1vfjPidrvdzo9//GN+/OMfD9tm9uzZR035DOacc87hrbfeGlWe8UQXQhY+lEimOIOdYjUhjoruGS7aJ3udadAgMlI/JkXJycmqCV3WVyoSTYAJY1zD6VStuWDV6rbbbkNVVVatWsUdd9zBHXfcwapVq1BVldWrV3PMMcdw7bXX8vOf/3w85DUk6VpCUl+RSKY2DRVuFrhrUVNm9s7IQGZaKKFrHAz5cvKQDEed3ckxnroj/YQDmX4iiSj/98Em/LEgALOclcxyVmb2PRjylfKUpiUJIeQU/iSkYAvLhg0b+N73vsc111yTs/6nP/0pzz77LL/97W9ZsWIF//Zv/8bVV19dMkElEolkMpH2N8l+Q1UVhSWV9cx1VxHXddyWpC/dhs49BBIx4rrOzAovJ9bOHLFvRVFY5K1jjquKqJbIJHX7+tofsc/fji8a4KyWE/jOmV/DajJzXPUMFnpqAcbVmVWI5IuXkSJLxopR1BUFFaWIHLDF7FtuFFFgxTCXy8XmzZtZsGBBzvpdu3Zx/PHHZyJ9VqxYQTAYLKmwE4Xf78fr9eLz+fB4CveYl3lYJJKpgy4EHwT62OHrwqSqLKtspLHCPaJpXRc6A/Eo+wJ9zHVVH5VVNl+iWpyDA1088f6LfGL+GSyqmV226Z/uSJBt/Z0E4lHmu6tZ6Kmb0g6+JgUsYzy/YseMQo5x3m/+EbNj7Gk4EqEoz33hB+Mqa6ko+G5UV1fzpz/96aj1f/rTn6iurgYgGAzmnTxuOqAoChZ16r+RSCTTgd0DPWzt7yAudCJagkrr6EkuVUXFbbFzXPWMjNVlLNhMFmZ7mvjGhy5lQVUymWU5lJWBeJSNhz9gIB5BR2BWTVPayqJinCih6UTBU0L/9E//xLXXXsv//d//8aEPfQiA119/nT//+c88+OCDQDLPydlnn11aSQ1O0jFOkJhoQSQSSVFEtUROVI7VZMpLaUgP8MO3zS9RWdqqUU7rRlRLPrnS52w3TXjd3HHFajKO5UhRlGQBzSL2NwoFf+uuvvpqli5dyv3338/vfvc7ABYtWsSLL77I6aefDsA3vvGN0ko5BdCFVFYkksmKLgSqouQ1vWJR1Rz/hoSuo+YRBpvue7hjJCfnx2l6RwhIHRsKH6Qsam7q/Lg+uuOwkYnrOmaDFAaUeVhG4YwzzuCMM84otSxTlnSUkEQimVxoQsekqPTHwvRFQ8xxVaMy8oA+312DLgS7B3pRFYWu8ADNWVE6QyGEIKIlOBDy0ezwZur1QFJZ0oWgLdCHx2qn1u4suV9KQghMwIGgDxAZefM9htdq58TqmWz3dRLREgzEI2hCxzzJagCVCi2lPFoMoLBMJ/JSWILBIE6nM+9OC20vkUgk5SKmaaiKgllVGYhHeae3HV88AoCqQJOjcsQpD7NqYkllA/PdtahKcnk0/tq5h2A8TgIdXyzCybXNmW1v9RygNxomoieoMFk4b8bCkr/ZfxDoYX/Qx0AqIV2t3ZWjNOXDTKeXRoebuK5P+SkhMFCUkEwcl8uCBQv4+te/zhVXXEFTU9OQbYQQPPfcc/zoRz/irLPO4pZbbimpoBKJRFIM8VS9nr2BXkyKyoqqJhor3JzVOI+eSJC3eg+yvf8w2/q7OMZdywJv7Yi+AVZT/taFMxvmEdESHAz5mOnw5mw7oaY5Y2HxpqKHSm1hme2qYZ67NmVhSfqgjOUYJkXFZCD/jmIwim1lOiWOy0thWbduHd/61re4/fbbOe644zj55JOZMWMGdrudvr4+tm/fzsaNGzGbzdxyyy0F1R6aDiiKglVFTgtJJBPITn83ewK9ACSETn3FkUrBES1BWDviZVZld5R0bl9RFOwmMws8tQzOJJGutjvXXZ3TvpSYFQUUhWand1z6n2qYFBklNBnJS2FZtGgRv/3tb2lra+Oxxx7jr3/9Ky+//DLhcJja2lpOOOEEfv7zn3PBBRdgKuCtYzqhKgpmGSUkGUdkOvaRietaTnRPtiPp4OyzFjW/yJ9CSPc3XL/J1eN0/3KOLV+cRmOs+VcmAul0OwyzZs3iG9/4howCGgOajBKSjBPZiopUWobHZjLnDNVRLYE1pZgM9smIaImcqJ4043tty3Xf5PdjNGKajsUgBRCnkw+LcSQ1MEII4nI6SDJOxHWNhK6xw3eY3QM9yfTpsg7KUSz01LK0sgGzolJhstAfC2e21dtdnFTTTIXJgllRCSfiCJIRPP2xMG/3thNIOaxKpj46GKaWkJpJzj/2j1GY+q7eEskUZ+9AL+1hfyYCZI6rGrOBTNrlwqSozHfXMM+V9BXJfntWFIUZDg9NFe6cbes79hBMxEgIHUWB46pnZPY54O/CarZQ76gqSq5gIoaCgmOIqJ2+iJ/+SIC5lTOG2FMynhhDXZleSIVFIjE4C711LKqs53A4gElVMauqnBoagZGuy+BtZzXOI65rdIUD1Fe4gKQS8eNNj/PcB69jUlSuP+lv+dTCswpOVR/TNLb3d7A/VV352MoG5rqqURQFTdf59dY/8z/vPktMi7Nq/hlcf9LfYh/HwoaSXIzy61EockrIQBMtUmEpA+koobgupNYuKTnpgbLWfiT3kVRWSodZUZnp9Gam2R5973leaHsDSCaeO2/OKWMa3PYGejPKCsCMrHDnNzre5T+3PpVZPnXGsVinQe6TyYJZUTAZ5Cc0ncKajaNaGRxVUTAb53shMSDFPrgkQzM4uieWiOe8lVpNljFdd13oOYqOKev+xbR4TlubyVpUvRhJYZgUYw3k04WCfwFPP/00GzZsyCz/+Mc/5vjjj+fSSy+lr6+vpMJNJTRdEJfmFck0RKRSz08VZ+AmVy2a0DLhpO2BHvQxnJfDbM2xuIYSscz1aXTVoJD0uwE4ONBVAskl+RLTxZju6URQvMutcRThgiW96aab8Pv9AGzZsoVvfOMbXHjhhezdu5c1a9aUXMCpgBCCuEG+/BJJqYloCXqiQV453EZnODDR4hTNxYs+wnfO/Bq1jioa3PU8d+Bd2gKFv6zNclZySm0LbrMNr8WezBOTeqtfWNXCzz7+LY6rP4YGZzUOsw19ihccnEwIjBMllLasFvMxCgVPiu7du5elS5cC8Nvf/pZPfOITfP/73+fNN9/kwgsvLLmAEokkl4SucTgSpMbuxJpHHZuJZnt/J/54hEAihllRaXS4J0wWIQQ90RAW1ZRJg5+NPxYhpmvU2BwjJHhTOKvlBLzORnyxML54hLZgH3OyMtXmg6IoNFa4aawY+nosrG7hjrOuIazFqbM5ixpYAvEowUSMervrqH5iWoKeaIg6u0tGl0kmNQUrLFarlVAoBMBzzz3H5ZdfDkB1dXXG8iKRSMaHA0EfW/s7iOsaVtXEGfVzcFlsEy3WiJxU24wQgr5YGK8lrSQIyh2HEYzHeKv3IH2p/CvLKxuZ7apCURQSusbbve0cCiefYbOdlSyrahox8ue46mRdtb5oCKc5eQ9KFZ2lCZ2tfR20BfsBmFHh5vjqmZgKVCh0Idja18EHwaQFqNHu5oSamRnFZO9AL+/6OtGEwGm20lo3u+CiiFMVo9gdplPiuIIVlg9/+MOsWbOGM844g9dee43//d//BeD999+nubl5lL2nJ4qiYEtFCUmjrqQY3uk7hJYyVdfanJNeWUmjKArVNkf2mrLLsHugJ6OsAMxyHcmf0h4eyCgrAC3OyrwlrLRWZP4ulXn9cCSYUVYAZjorCw6bBuiJBjPKCsAMpwdTqp+4rrG1vyOzrcHumhZVmPPBohrHs0OhuPT6RlHMYAw+LPfffz9ms5nHH3+cBx54gJkzZwLwl7/8hY9//OMlF3CqoBgoTE4yuch2VM1OmGygqedJgUDkPJzVrPn7wQ6Whcztj4cfwFEFEhmbMjTYDSN7YBvsoWEkX4bxZqzXWzK+FKxOz5o1iyeffPKo9ffcc09JBJqqJHRBwhg+XBOCEOKoSYLp/MBID1jpwTC97LXY6I9HABiIR9FFchCeztcqX7xWO23B5N8KSX8Vt8WGoih4LLbMd08A/bFwjuVkNAYrGOnvcnaNJ13oqIqaUpxGVnJcFhsqCgKRkidC/TC+LiPhslhz+vHFIsxweAAyJQrCqRBqfyxScP9TlZgusKiMyapVbtLVvovZ3ygUbGF588032bJlS2b5D3/4AxdddBHf+ta3iMVkrY2hEEIYxuN8ouiPRdgf7OeF9t0cCvmn/QA8kIjii0d4s+cAGzr3Ek9VEz6jYS7HVjbgsdiY4fDKjLYFMMdVzen1s6mxOWiq8GBS1My1q7I5+EjjApoqPNTYHLgLnGqL6Rr+eJT3fF2s69hNIB7N2b6j9wP+tGsDlz15OxsOvD3qPXNbbHykaQHNDi9V1goqbfkrT9k4zFbOnbGAFmclVdYKqm0VGeVKVRTOaZzPIk8dHouNRodbJrZMYagooWkU1lywheXv/u7v+Md//EeWL1/Onj17+MIXvsBnPvMZHnvsMUKhEPfee+84iCmZ6hwM9tMfjxDSYvREg8x0ekffaQoTSsTpCgfoDAfQhE5M07BaTCjAPHcN89w1Ey1iXmhC52DQj8NsycnEO1HU2JycXj+0HE6LlZNq8/PD04WgPeTHrKrU213EdY22YB+d4QBhLU4gEcOVZbV5es8rvN/bxoGBLrYe3s1ZLSeMegyH2cLxNTPzPbVhsZssOTWQsjGrKsd46zjGW1f0cSQTw3TKdFuwwvL+++9z/PHHA/DYY49x1lln8cgjj/DSSy/xhS98QSosw6Agi2mNxLJUxEUwHsUh66XQYHfRWOFmSWU9mq5jN2DkRk80yKaeg0S1BAAn1sxkRoXHUA/IofDHIrzRc4Bgqtjk0sp65rlqWF7VxLGVgnAijtOS+x2+4ZQvAHBw4DBNrtqyyywpHCNVMZ4uFKywCCEyCYyee+45PvGJTwDQ0tJCd3d3aaWbImTXEpJRQiPjNEjUy3iTHtTNiorZZByTbTZ7B3ozyopFUZnpmBpWs7Zgf0ZZgeRUU/p+qYpylLKSzUy3tGQYAatanF9IWSkyrJmpHNZ88skn873vfY/zzjuPF198kQceeABIJpRraGgouYBTBUVRUBWRE+UhMR7ZPiPl8B8xsjUi56tu3NMYkslsMZV+TcVjpKuXLhFRzP5GoWDV6t577+XNN9/kuuuu4//7//4/FixYAMDjjz/O6aefXnIBpwJCCOKaLqOEDExuaHFunZHkNnlzB1OX8llRgISu48vKgTLepOsWDVXDKHtbejl7/Wj1jmpS+WTSj/nuSLD0JzAGhPxeloyoLjL5jiSTh4ItLCtWrMiJEkrzwx/+EJNp8qcJnyi0iRZAMmYGUmnNDwT76YmGWOipxaSo7PJ34zBbaK2fM9EiTkrmuKqpsjrY5e/GabGWzTdJF4LO8AC90RBtwX6Or56Rk/6+MzxAMBFn10A3tTYnM51eDgZ9dEeDnFY3C4/l6JT92TQ5PHzEuoCdvsOYVVNB4c/jSVck6fC7099NtdWRtwOxZGg0ITJJ9iYzMtPtGLDbR/6RSyRGRReCQDxKIBEjrmsE4lFMqomonoDEREs3ufFa7RMycA6k7ldC6Bk/mjTBRJyBRJS4rhFMxDL3NqZr7B7owW22McddjWWEOk1Os7UkETylJJKIZ84jpMkUE9OF6ZSHpWCFRdM07rnnHh599FHa2tqOyr3S29tbMuGmEpN5zlsyMl6rHY/Fxnx3DQmhY069kSzy1CYTgUmfgUmFAiz01KIoCjEtgdVkzpnmmedOOske623AYjIhhGCmw8v6zj0cDCXT80e0BMuqGg11X9OlBhZ56zDL72XRmAzk2zFdKNgWdMcdd/CjH/2Iz3/+8/h8PtasWcNnP/tZVFXl9ttvHwcRjU8ySkim5jcy6VwH5lSysWSpBTUnvbtkcpCdl8Kaqo2TfY/Sf1tSU9iKotAVCRDTj0zctrgqDXdfh/qOSsaGVVUwqca4ftMpcVzBkj788MP8/Oc/5xvf+AZms5lLLrmEX/ziF9x222288sor4yHjlEA+PKYGQw18EslkQn4vi8dIVzCtnBbzMQoFKywdHR0sX74cAJfLhc/nA+ATn/gETz31VGmlmyIIIYhpOpqcE5JIhiU7QieRsnaMFrFTKmrtTmxZlYoPBn1lO7Zk8iGjhCYnBSsszc3NtLe3AzB//nyeffZZAF5//XVstrEn/frBD36AoijccMMNmXXnnHPOUZrgNddck7NfW1sbq1atwuFwUF9fz0033UQiketkt27dOk488URsNhsLFizgoYceGrOcY0UmjJNIRkYTOvsCfbzevZ+/HNzBwaCvbMd2mq2c27SApZUNLPTUsiDlAyOZvhhFYVFK8K9Q1q9fzyc/+UlmzJiBoij8/ve/H7H9unXrhrTsdHR0FHTcgp1uP/OZz/D8889z6qmncv311/OlL32J//iP/6CtrY0bb7yx0O6ApLLz05/+lBUrVhy17eqrr+Y73/lOZtnhcGT+1jSNVatW0djYyMsvv0x7ezuXX345FouF73//+0Ayod2qVau45pprePjhh3n++ef56le/SlNTEytXrhyTvBLJZGUgHmHPQC8ei53ZripDRQD0xcJs7U8+wBTKH71gUlTmD1OjSReC/cF++qIh5rirJ00os0QyEWHNwWCQ4447jq985St89rOfzXu/HTt24PF4Msv19fUFHbdgheUHP/hB5u/Pf/7zzJo1i40bN7Jw4UI++clPFtodgUCAL37xi/z85z/ne9/73lHbHQ4HjY2NQ+777LPPsn37dp577jkaGho4/vjj+e53v8vNN9/M7bffjtVq5cEHH2Tu3LncfffdACxZsoQNGzZwzz33lFVhUZFWFsn4stvfw3ZfJwrJt64amwOP1RjpBrb3d7J7oAdIpvE/q3EeFabJUT8poets6NzLQCJZgTmqa5xS22IoZVBSOEbIwQITU/zwggsu4IILLih4v/r6eiorKwveL03R7sGtra2sWbNmTMoKwOrVq1m1ahXnnXfekNsffvhhamtrWbZsGbfccguhUCizbePGjSxfvjynJMDKlSvx+/1s27Yt02Zw3ytXrmTjxo3DyhSNRvH7/TmfYlAUBatJxWyM77/EoLSHk99TAdjNZsMoKwCHQkd+Yx6rHYfZOmkcAkOJWEZZgSMZfCVTF5uqGEZhKRWDx7xoNDr6TgVy/PHH09TUxMc+9jFeeumlgvfP28Kyfv36vNqdddZZeR/8N7/5DW+++Savv/76kNsvvfRSZs+ezYwZM3jnnXe4+eab2bFjB7/73e+ApAPw4PpF6eX03Nhwbfx+P+FwmIqKo027d955J3fccUfe55EPybTZJe1SIslhtMerzMtROuRVnNoIjHOPk4HJY7c9pKtSt7S05Kz/9re/XbJUJU1NTTz44IOcfPLJRKNRfvGLX3DOOefw6quvcuKJJ+bdT94KS9oBFob33FcUBU3LLwn9/v37+frXv87atWuHzZL7ta99LfP38uXLaWpq4txzz2X37t3Mnz8/X9EL5pZbbmHNmjWZZb/ff9TNLAQhBDFdyMRxknFlnrsGf+8hEkInlIjTHQlQa3cllWUEmq5jUU1D/o6jWgK72TJhSs0CTy3b+jrQEfRFw/hiEbxjtBClz0tRFMKJOBVFnpfLYqOxwk1HeACAgyE/s5xVmKXyN2WJ6QKzAmYD5GIp1ZTQ/v37c/xLigmiGcyiRYtYtGhRZvn0009n9+7d3HPPPfzXf/1X3v3krbBUVVXhdru58sorueyyy6itrS1M4kFs2rSJrq6uHO1K0zTWr1/P/fffTzQaPao20amnngrArl27mD9/Po2Njbz22ms5bTo7OwEyfi+NjY2ZddltPB7PkNYVSN6oUt4skFluJeNPk8NDjd3J/kA/HquNWruLrkiAnkiQ/UEfMT3B2Y3zcKdq5US0OB8E+umIDDAQj3Ji9UyaHJ4JebOc46qiscLF/qCPWptjzMoKJH9r7/m66IuG6I2FWeptYJ67esz9qYrCKbUt9ERD9EVDtDgrMavGSbYlGRu6oewsxePxeHIUlvHmQx/6EBs2bChon7x/de3t7fzLv/xLxm/kqquu4uWXX8bj8eD1ejOffDn33HPZsmULmzdvznxOPvlkvvjFL7J58+YhCylu3rwZSJqXIOk/s2XLFrq6ujJt1q5di8fjYenSpZk2zz//fE4/a9eupbW1NW9ZJZOTmJZgW18Hr3R9QE90clTMnWisqon5nhrq7C4A7KqZOruLKlsFTrMVi2rKWCBURaXW7qTW5sSmmnCYLXk9ngPxKG/2HODNngME4qWb57abLCz01FJlc4zeeBTq7E5q7MnzclnyK7oYTsR5u/cQr3W34YtFjtpeY3OwwFObk69FIplo0rWEivlMBJs3b86M5fmS9y/ParXy+c9/ns9//vO0tbXx0EMPcd111xGNRrniiiu44447MJvz/yG73W6WLVuWs87pdFJTU8OyZcvYvXs3jzzyCBdeeCE1NTW888473HjjjZx11lmZ8Ofzzz+fpUuXctlll3HXXXfR0dHBrbfeyurVqzMWkmuuuYb777+fb37zm3zlK1/hhRde4NFHHy1rkrtkGnchE8eVkFAixosde0iIZOyVO2SjxiadIQfjtiR/B7V251FTuTaTGatqosbm4NjKhqF2P4rO8ACvd+/PLDdWuHFZSmuNLBZVUai1JRWxRZ66vPbpj4V5qXMf6YnbGquzKCuPxPgYZcovnWC/mP0LJRAIsGvXrszy3r172bx5M9XV1cyaNYtbbrmFgwcP8utf/xqAe++9l7lz53LssccSiUT4xS9+wQsvvJDJ45a/rGNg1qxZ3HbbbTz33HMcc8wx/OAHPyg6kmYwVquV5557jvPPP5/FixfzjW98g4svvpg//elPmTYmk4knn3wSk8lEa2srX/rSl7j88stz8rbMnTuXp556irVr13Lcccdx991384tf/KLsOVgsqowSKiUD8WhGWQGotjlkZtIhGDy/Pdxyvmm6+2NhIDntImDSKomFnpcvFkFPqSsCqLEXb+WRGBebOnGWByPwxhtvcMIJJ3DCCScAsGbNGk444QRuu+02IDkj09bWlmkfi8X4xje+wfLlyzn77LN5++23ee655zj33HMLOq4iCnzKR6NRfvvb3/LLX/6SjRs3smrVKr7yla/w8Y9/vKADGwm/34/X68Xn8415jk8IQUIX5OeSLBmNrnCAV7uP/CBOqmmmqcI95aJgsh1IJ0M/7/sOs8N/OLP8sRkLsalmw1/3tkAfb/e1Z5Y/XD+HSmuF4c9LMjYsRYY1l2LMyPcYf/fsr7A6x65gx4Ihfnr+l8dV1lKRt4Xltdde49prr6WxsZEf/vCHfOpTn2L//v08+uijU1pZKQXpKCGprJSOGruDmY4jP66DwX70KWRhSSsY/liQQ4HuIbdpuo5/CF+LodrGdY1gIla0XC3OSqqysrzuC/QV3edE4Y9FSOhJK12d3UVFlm9KW6C/IEf5fO6JEAJ/LDKlvqdTlbiefME0AkqR/itGUsrzdjo57bTTmDVrFv/v//0/TjrpJIAhPXw/9alPlU66KYQxvvrGwaSonFjTzHx3mGAiRmOFZ0qZcNcf2MyG/ZtZv/8tYlqCX6/6Ni3epJ9JIBFjl7+bw5EAUV3jhOqZzHAMff6dkQHaQwO0h/1oQnBWw1y8RaSVrzBbOKN+Dt0pJ+dam9NQDzyALb3t+OIR+mJhrKqJ+lQ0VUzXmOeuptrqoLFAa91o9+StnoP44hEG4lFmO6tYUV2Ys6Gk/Ey3KCEjUJC7e1tbG9/97neH3V5IHhaJpBR4rRVFDcCTlfnemczzzsButtIZ7KXG4c3kErGqJo7x1uGyWOkIDVBptQ/7WHWb7bg8NsyqSiAepcJkLTrXiqIomSgkIzLHVU1Ii/G+/zD1djfNTi8Os5WuSIC5rmoqTJaCr49tlHuywFOLLxZm90AvDRXFX7uolmCnv5vO8ABz3NXMcVVhKqKejMS4KCm322L2Nwp5Kyy6LivhjBVFUTArkJCmYEmezHQno1vWnHIpiqKgZzkYW1UTNsXMAnctC9y1I1rvHOZkPZ5llY0oiiIdkwGXxYrTYqU+pXQpisIxnlqO8Yx8LUfCopqwjnBPXGYrTrOVmY5k6odilMaErrOuYzcxPfly2BcNDVu0UTI2FAwUJVRkaLKRLNMyoUCZMKsKii6Iy/FCkgfZg5kQAjXr7Tm9LfN/Af0YbfpmPFAU5ahrls+1HK3PkfoZ6phjJSG0jLICydB1XQhDDTyTHatqHN+O6aSwGMcWZHCEECSksiIZA8M9OAu1lhjlASwZmcGqjy6E9LQoMboo/PclGX+kwlIGhBBEZS0hSYlIP0hjukZXOCAfrINIX4+ErtMVDqCJqTWdbTOZWV7ViDlldesKB4jqiQmWamoRF8IwU/gKR+ccKuhjIHVXTglJJAYjkIixta+DvlgITQhWVDXR4qw0lGl3PBHAxq59DMSjxHSN+a4allTWTykL0xxXNTMdXvpjYWpsTnnvxwFjqCsTk+l2opAKi2TaEdc1dvm72RvopdbmZFlVIw5zfvVmyoE/FmG7r5PeaJh57moWumsxZRXbs5vMfKi2hUNhP53hARoqXGV75AghaA/7ebc/Wb9raWVDwSHAQzH4nhxb1YhzjPdEAT6UKlb4woGt3LPtTxwc6OCSxefz+aXnYTVZhtyvKxJge18nMT3BYm89zcMogUIIDoR8vNffhVlVWVbZSK29/OHdFtVk6GgtiaRQClZYrrjiCq666irOOuus8ZBnSqIoChY1mYxIMvG8eriNvlSK+WAiRoXJMmkcUiNanPWde4DkG54uRI6yAsmBCmCmw0uLs7KsU0L7g/05GWErzEMP/oXy2uH99MZCQPKeOIq4J8moPBO6FuPnb/w3INCFICESwysrgzInW1TTsErgnoEetvtSBVd1cORZXFFiHFTAMgmeB/mQT+mJ0fY3CgX7sPh8Ps477zwWLlzI97//fQ4ePDgeck05TIqCxTjfiylNWItn/raZzEX/4EtJTNMy9WwgaU0ZLjNq+u2/nPKHtXjOQD6WnCXD9ZumVPekPzKALvTM9atzVKHpQ+eJyj4+JBWx4Y4f1hI518BuMn5pAkkuVpNqmHuqKmrRH6NQsKS///3vOXjwINdeey3/+7//y5w5c7jgggt4/PHHicfjo3cwTdFllNCkwZz1A02nZp8sjquDrSlaAREgQohxPw+zoubM7Sd0vSTHHI97UjGoinQkER12EDIPuu4jnZdp0DXQ9PG/7pLykpD3dFIyJtWqrq6ONWvW8Pbbb/Pqq6+yYMECLrvsMmbMmMGNN97Izp07Sy2noUnXEpJf/8nBSTXNVFuTxcKieoL+WHjSvE05zVZOqJ6JTU3O1vZFw8RHSdqopxSVQ+EBuiIBYPwUsNmuaua5q1FI+op0RgZK0u+JNTOpsZX2nszxNnHzqZdRaXMDsK1nL6F4dMi2TRUeFnnqMlarw5FAKjX70cz31DDHVQUkHRbbw6WtVC+ZeBIGihJSM7lux/4xCkU53ba3t7N27VrWrl2LyWTiwgsvZMuWLSxdupS77rqLG2+8sVRySiQlw2O1c3r9bAKJGA6zZdSU5kIIQvEIzhKUAIjrWsYHZTianV4aK1wE4jEqbaMfc4evi46wn0AiaeG8YObioywGpcKsqhxb2cgCdy2QnL4pBR6rnda6/O9Jvlww/3Q+MvtkusP9NLvrh22nKgrHeOuY46pGE/qIvjlW1cTyqiYWuGtRFaVk10AyuTCGujK9fFgK/qXF43H++Mc/8qtf/Ypnn32WFStWcMMNN3DppZdmSlM/8cQTfOUrX5EKi2TSoigK7kFTBkOxs3c/P37zcTZ3vc/pM1bwjVMvpabCW/Dx/LEI2/s7ORwNUmd3clzVjGEHxcORANv6OxmIR5nl8LK0qnFEJWehp5ZjvHXsG+jFpJowpVLwj+eDaDwG6XzvSaHYzdYRlZVsrCYTMLJCmaZUDseSyYlxhvHpQ8FPnaamJnRd55JLLuG1117j+OOPP6rNRz7yESorK0sg3tQgWbAOYjJKyFDEtQTXP3c38ZRDptNqH5OyAvBS175MAjOzog472IUScV45fCRaxW625Ph3DIVJSToIzkvVkzHSG5NEMhkxKbKW0GSkYIXlnnvu4XOf+xx2u33YNpWVlezdu7cowaYaqqJgRiDzURoHTehEEkd8HtxWJ7rQx+RVn8jKtjqStSQxKIrFoppGLXKfU7/GQA8fiWSyYhmnKdXxwkjZaouhYIXlsssuGw85pjy6kMqK0TApKnazjZgWRxc6/lhwzCGAZkVFE0k3zvgwobUA5kHKTFzX8n8USWUlh/S02GTJsSMxDnFdxzyJ0h2MhLSwDOKzn/1s3h3+7ne/G7MwU5V0lJDEWFhMZv79vG9kfFjC8Qg9Yd+YpoXOqJ/Ddl8nhyNBEkInnIgPOS3kMFs4rW5WxoclkoiTEDoWJT+/CsmRCKmBeJSDIR9zXNXS30RSEJoAEIZJHjddyEth8XqPPKCFEDzxxBN4vV5OPvlkADZt2kR/f39Bio1EYgQWVrdwz7k3EIiHcadCoceCx2rn1NpZ9MVCVFkdQ765BeJR7CYLdXYXZzc4k4rKKBFFkqMRwF879xBIxJKWTV1nWVVjXm/L/miQiBaj3lE1/oJKJjVGecWUFpZB/OpXv8r8ffPNN/O3f/u3PPjgg5hMyYeppmn8/d//fSZKSCKZSiiKUpSyAtAdCbK1r4OBRBSP2cbJtS04UyndA/EoW/s7OBwJYlVNnFzTTI3dKa0qY0QBzm6cT0SL0xEeYKbDO6qyktA1Htn+DA9ve5qYluDSpSu5cvkqLDJkedpilGE8nU2lmP2NQsGS/vKXv+Qf/uEfMsoKgMlkYs2aNfzyl78sqXBThWSU0HRxi5IMxWvdbQykHHhdFltGWQF4u6+d7kgQSPq61NidMstmEaSVE5tqZo6retQoK4D1+9/il+/8iagWRyA4pWnJUVmHJdMHs6IYJkpoOlHwLzKRSPDee+8dtf69995DHyUj53RGVRTM8vs/pShEqdCy2g6ZBj71d3qQzLYI5HscqeTkkomeymPgCcUjOcsOi91QNVYkpcWsGsPhFo5MCRXzMQoF2zu//OUvc9VVV7F7924+9KEPAfDqq6/ygx/8gC9/+cslF3CqoAlBXI4nU4K0YlBIBIpNNRPTEwiSFZmzcZgs+FMDZlzT0FP1g7L7zz5mGl0IVEXJtE/LJiNjCqcu5bOioCAQdAR7WFjVIq/hNCWm6VgMorTITLcj8K//+q80NjZy9913096eLDPf1NTETTfdxDe+8Y2SCzgVEEIQl1FCU4aonkAIeN9/mAqThYWeZJr6kX74ZzbMZYf/MAeDPuwmCzEtgTXlH3FCzUz2DPSwe6AHl8VGMBHDoqqYFZXdA71EtATLqhoyNT/iuoaKwsGwn67wAE32ChxmK/+19WkqzFauOu7TcvoxT2K6hgmFBncTX1jxGf6692XC8TDBWBhdCEwGephLSocOxIXAKu//pEIRRdiR/f5k0a+p7mzr9/vxer34fL4xnasQgqhUWKYM2/s66Y4G8cUjKMAnWpaW/BgHgz4+CPbREw0BcHbDPDzWZLLGgXiUrX0d9MVCaELwzqHNHOw/wPaeZLLGZ/72Pmxm67B9S47QHwvzXn8XPbEQuhCcUD2DGQ6voczkkvFBVcA6Rj+mYseMQo7xnZf/hN3lHHM/kUCQ207/5LjKWirG7AJ/+PBhduzYAcDixYupra0tmVASyWRmSWU9iqLQEwlmauqUegpmhsPDTKeXQDxCVNPwWO2ZY7jMVlrrZxPVEvhiEVY1L0ZVVN7t3ovNbMVmtsopoTzxWuycVj+biBbHH4tSZ3fK6yYBjBMlVGzF5SldrTkYDHL99dfz61//OuNkazKZuPzyy/n3f/93HI7iwj+nIoqiYEvVEpJ2FuOTHtCqbY6j1pX6GE6zDac5d136f6tqor7Cldlncc2ccZNnqpIdUVRfIZPLSZJYFAVV/oQmHQXbu9asWcOLL77In/70J/r7++nv7+cPf/gDL774ovRhGQFFRglNGkoVTTOas1spjjPSMQavL9b5bjojr5skG1Uxznci/bsv5mMUCraw/Pa3v+Xxxx/nnHPOyay78MILqaio4G//9m954IEHSinflCGhCxLSvDKhCJG0cKnDRN2U6hjpfoeL7plupK97+gpMpmsxOAorLedkklFSfmK6wKIaIwusqqhFheAbKXy/YIUlFArR0NBw1Pr6+npCoVBJhJpqCCFIyBwZE04gHiUudHb4uvBY7Bxb1VjyYwQTMQB2DfQQ1RKcXNNsiIfeeBJIxAjEo+z0dzPfXcNMZ+G1mMaLmK6REDoHgj66IgFOqplJhUlODU13BJAwSJSQkvpXzP5GoWDVqrW1lW9/+9tEIkcSLYXDYe644w5aW1tLKpxEUkoOhQd433+Y7miIgyHfuBzjQMjH/3XsZn+wn65IgKiWKPnbekxLsD/YT0Qbv/rfuhAcCvnwxSJHbRNC8Hr7dt5of3fUaa+ErvHLbWt5at+b+OIROsID4yXymIhocXb6D7Mv0Et/LIwvFpF+ZhLJJKVgC8t9993HypUraW5u5rjjjgPg7bffxm6388wzz5RcwKmCgnGKaU1VFnnrgKSlxaqWNrpHCMGWvnY+CPYD4DLbOKlmJg5LacOLPwj0sa2/A00ILIqJDzfMwWWxlfQYvdEQb/YcJJxKcHdyTTONFW4URaEr2MsdL/0H27r3AHD5sgu5cvmqIc3K7/Xs47sv/ZKDgcMAfP2USzm+eXFJZS0Wj8XO8dUzSeg6oUQsEzoukRglekYpMlutkaY/C1ZYli1bxs6dO3n44YczKfovueQSvvjFL1JRUVFyAacCyVpCST8WbaKFkeDMylFSqh+rLx7JKCsA89zVuEusSABs7e9AT1k16uzOkisrADv93RllxaaaaHIcyc3w9J5X2N69N7P8qQVnDmtS/s27azkU6AbAZangMwvPLLmsxZK+/2ZVlcqKJINFVQyTNFBWax4Fh8PB1VdfXWpZpjRJJ0whzSyTgPF4oxhqaiTb0bR0xzny93g9Z7LPZfC10oSOqiiZ2kgmVR32eupCJx3Ib6S3OInEOG6o04uC70tPT0/m7/3793Pbbbdx0003sX79+qIE+cEPfoCiKNxwww2ZdZFIhNWrV1NTU4PL5eLiiy+ms7MzZ7+2tjZWrVqFw+Ggvr6em266iUQid25/3bp1nHjiidhsNhYsWMBDDz1UlKxjIa7rMkpoCuMwW7GoRyqY98XC4/LmUm07YsX0xaJoQi950cNq+5H8MjFNI5iIZY6xtHYOAjApKiZF5Z2uXcP2s6x2PpCMQogkorT5OmSBRokhiOoiY8mc7Cgl+GcU8rawbNmyhU9+8pPs37+fhQsX8pvf/IaPf/zjBINBVFXlnnvu4fHHH+eiiy4qWIjXX3+dn/70p6xYsSJn/Y033shTTz3FY489htfr5brrruOzn/0sL730EgCaprFq1SoaGxt5+eWXaW9v5/LLL8disfD9738fgL1797Jq1SquueYaHn74YZ5//nm++tWv0tTUxMqVKwuWdSwIIdCM8d2XjBGbycy5TQvYPdBDXzTMjArPuGSbba2bzf6gj/3BfmY6PMnHTYmPcYynjhqrg53+btwWGxbliBXl1BnL+PWqb/OrLU8CML+yedh+/nbJeSytnct/bf0Lze56PDaZRVZiHIwSJTSdpoTyriV0wQUXYDab+cd//Ef+67/+iyeffJKVK1fy85//HIDrr7+eTZs28corrxQkQCAQ4MQTT+QnP/kJ3/ve9zj++OO599578fl81NXV8cgjj/A3f/M3ALz33nssWbKEjRs3ctppp/GXv/yFT3ziExw6dCgTav3ggw9y8803c/jwYaxWKzfffDNPPfUUW7duzRzzC1/4Av39/Tz99NN5yShrCUnKTU80RF80RIuzMpP+v9SEEjEOhHw02N14B/lvxHWNtmA/LrOVervrqCrR6SirmZOg7o4QgkMhP3Gh0+L0YhpjXgkhBO1hPzFNo8VZiWmMdWQkUwOj1BL60evPUVFELaFwIMiaU84zRC2hvO/G66+/zj//8z9zxhln8K//+q8cOnSIv//7v0dVVVRV5frrr8844RbC6tWrWbVqFeedd17O+k2bNhGPx3PWL168mFmzZrFx40YANm7cyPLly3PywqxcuRK/38+2bdsybQb3vXLlykwf5cI4OqxkItGF4PXu/bzctY93fV281r2fRKoERinZ5e/mhfZd7PAd5uWufQTjscy2zvAAzx3ayfb+Tl7r3k9XJJDZNhCP8n/tu9jce4jNvYfY6T88odM84UScFzv28GbvQbb0tfNuf9eY5IloCdZ37mFTz0G29HewrV9OX013jBQlpChqER9jnCcUMCXU29tLY2My0ZbL5cLpdFJVVZXZXlVVxcBAYTkWfvOb3/Dmm2/y+uuvH7Wto6MDq9VKZWVlzvqGhgY6OjoybQYnsUsvj9bG7/cTDoeHjGyKRqNEo9HMcroq9VhJRwnFdUHphx7JVCIQj+bkKpnp8IxLtMJOf3fG/7vW7sSZFX69d6CXhEh+U+0mMw0V7sy2QyEfoVQEEcAcV/WEPvA6wgMMJI78Vme7qsYkT2d4AH/8SD+zxtiPZGpgVYubZikn06n4YUH2rqFql4yV/fv38/Wvf52HH34Yu31yhRPeeeedeL3ezKelpaXoPhVZTKtoyvXGW6rjTLY39Mkmj0QyWZGP6slJQZPjV155JTZbMu9DJBLhmmuuwelMzp1lWyTyYdOmTXR1dXHiiSdm1mmaxvr167n//vt55plniMVi9Pf351hZOjs7M5aexsZGXnvttZx+01FE2W0GRxZ1dnbi8XiGzRtzyy23sGbNmsyy3+8vSmkRQkjrShEMrvMyHs6sg+vdjLUG0GCloNB+HGYrLrOVQCrF/+FIMGnFKEiKo2VSFCVpUUnJ0+Rwsz+Y9EPpj4WJaQksqglFUWh0uDkcDQIQ1RL0x8JUWisQQlBnd+VYZ9pDfua4q4uQrrDzANARmBQVIQQ1NgdmRSUhdJSUPG5vXeacNaFn3iBHugc1NgcW1URc11L9DOC12KWVZZoSTdUSMkIuFlWhqJdhI71I562wXHHFFTnLX/rSl45qc/nll+d94HPPPZctW7bkrPvyl7/M4sWLufnmm2lpacFisfD8889z8cUXA7Bjxw7a2toyJQBaW1v553/+Z7q6uqivrwdg7dq1eDweli5dmmnz5z//Oec4a9euHbGMgM1myyhmpUIqK2OnIzxAMBFj90APldYKTq2bVfJj9ERDBBMx9gz0ogmdsxvn5YQp50tvqp+9gV7iusZZDfOwFuA0a1ZVzm6cz/5gP73REHPd1UWZpvuiIQKJGPsCfYQSMc5smIvDbOX46pk0OyppC/bRWOHJKCuQnOaptTnZPdCDy2LDbbHRHwsTiEf5INCHABor3DjN1pykcuPNwZCPgXiMvYEe5riqWVrZgMdq57wZC9k90ENC15nlqsw4BfdGQxwI9nNc9UxmjiKny2LjvKYF7BnoJaonmCOnhKY9mhCGUFiKrbhspO953k/SX/3qVyU9sNvtZtmyZTnrnE4nNTU1mfVXXXUVa9asobq6Go/Hw/XXX09rayunnXYaAOeffz5Lly7lsssu46677qKjo4Nbb72V1atXZxSOa665hvvvv59vfvObfOUrX+GFF17g0Ucf5amnnirp+UwHNKHTFugnosWZ564Zt+iVwSSETkLX0YVAE+Oj+ulCkBA6mtBTCc+OcDgSoD00QLPTS7XNMWwfkHz7Twg91ZcYU55AVVGY7apitqtq9MajoCNI6OnzEjkWoFq7k1r70NEFLouN46pnZJaTVsLkeZkVlcXe+nHJ5DsSCV0nIbSjvgcW1cRib31mWReCuK4R13V0kgns8kFRFEyqilmYDOO/IJFMJx+W8ow4Y+See+5BVVUuvvhiotEoK1eu5Cc/+Ulmu8lk4sknn+Taa6+ltbUVp9PJFVdcwXe+851Mm7lz5/LUU09x4403ct9999Hc3MwvfvGLsuVgSaNibCtLOBFnQ9feTMG9uK6zvKqxLNp5syNZ3XehpxaTqo7LlFB9hYs64WSuKzm9oQC6rvNm70HaU06wPdEgH26YO6Llpc7uotbmZI6rGhAoKOMib77U2JxUWx3McVWhI1DHKE+VzUGltSLZjxCoSvnPK63ALfHWY1ZNwx5fAea6qpnnVkjoWqbtSAQTMV7q2kc09f3Whc7SygZDvX1KSosRrCvTjUmlsKxbty5n2W638+Mf/5gf//jHw+4ze/bso6Z8BnPOOefw1ltvlULEMaEoClaTQlzXDZtAricazKkO3Oz0lu1hnj6OOmh5PI6T7SOjCZFRViCpjJjzyPFxpJ/JYW7NXL8i5cm+DxNxXpm6P4opZ3m4dgBmdeS2abojwYyyAjCzjN9vyeTDppY+IeN4UWzyyCmZ6VZSHDJCo3jKrSCNxmgWhlLJWypLRqnkMcqDXCIZK+NRB2y8mE6ZbmUqxzIghCCmT670/GkFKqZpOcvDUWt35lQ53h/sL6kSlq7bMZCKTpkMCp5JUWhxVgLJ6aGOsJ+eQPK8RcqPYrBfSCkRQqT8NUTmmOn16b/jen73b7j+C/0eTFXq7S6cZktmeX+gtN9vibGI6QJNZiefdEgLS5mYbF/9nf5u+mNhOiMBllbWM99dO2J7u8nCRxrncyDkI6LFS54w7H+2P8vOvjbWtb3JF5d+nKuP/3TJ+h4riqJwfPUMfJ2H+K8NT7L+lRfo6DzIL2//OYvnL6Et2I9NNXPujIUlP/bhSID+WJgPAv3EdI1zGufjSA2ooUSc9rCf9rCf/liEU2tnUWt3FvxGGNHiHAz56QgP0BcLc3JNMw0VbsO8WZaSCrOFcxoXcCCYvN5jTUAnmTpoCEwG+DUoRTrdyikhyaTHbbGREDo9kRB2k2X0HUgO4GmLQ6mZ5WkgpsVxWSqoc4zPMcZKk7uKzyz/MMGOdl4OBqj3VOG22LGp5owSUWrMigmX2YbDbEFJ5DoAqoqCy2LDFbcRjMeSIcljOEamn0SMgXgU6xj6EULQFQnwQaCP+go3s5yVhjIxZ6MqCrNKEJklkZST6RTWnHfxw+lMKYofJnSBNg6yjZXBydhgYr+4ySmh5BSFmnJsnUw/JF3XUVUVTdNAIVmDg6SMSR8TKOWsd3bCueyfaPqapP1adCGKun/F9vNWz0EOpAoh2lQz5zTNz6nuLJEYFYuqjDlSqJzFD3++eQMOt2vM/YQGAlx9/IcLknX9+vX88Ic/ZNOmTbS3t/PEE09w0UUXjbjPunXrWLNmDdu2baOlpYVbb72VK6+8siBZpQ9LGVAUBYtJxTyJnuFprVxN/T/RA0zScUzFlEpgNtHyDEZNVW1V1aSM2VaEpKyllXfwNRhqGSj6/hXbT3ZhRK/VnrTSTLJ7J5EUiq0IZaXcpPOwFPMplGAwyHHHHTdiBG82e/fuZdWqVXzkIx9h8+bN3HDDDXz1q1/lmWeeKei4ckqoTCQdKCdaCkmxDKU0jCelNICOR96U7N7GliZPIpl8GClKaCKmhC644AIuuOCCvNs/+OCDzJ07l7vvvhuAJUuWsGHDBu65556CcqJJC0sZSEcJGTlxnKS8pBWVhNBz8oOMuR9dy8mjUyoWe+szb6I90RD+WKTkx5BIyk1MT07jS0rDxo0bOe+883LWrVy5ko0bNxbUj7SwlAn51ZcUQmckQFckwMGgD03onNO4AJfFOvqOg+hK9xPyEdd1zm6Yh8dauuros1xVNFS4ORTyU2t3lj1dv0QyXugGsbOUKg+L3+/PWV/KmnodHR00NDTkrGtoaMDv9xMOh4ctRHyUrCWRRjItCCVivNlzgJe79tEdCU60OIQTcd7uPcSGzr10hgdypk9C8Qi/3vJnvvqXf+YPO9eT0Id3eY5oCd7pPcRfO/fSHvLn9JPQdXb6u3mxYzd7Bnoy+WIgab04EPSxvmMP2/s7M7lMhjxGLMI9j/+UU//+49z7258RjY1c3dxhtjDLWUlDhYtKawVW0+jp5Yfux0qLs5IGuxuvxY7NZB61HyEEneEBNnTu5e3eQ0S0+LBtdSHoCA/QFuznYMg34nWWSCSlp1Q+LC0tLXi93sznzjvvnOAzOxppYSkDiqJgViBh4IAsXyzMXzv3HlmOR4YtnFcOgokY69p3I1KeE32xMA0VbgDiWoKv/Pl7dAZ7EQheOvA2n1xw5pD9hBNx/q9jVzIBHMlqy+kKxEII1nfuIZiIAcmq0elaQwBv9R7kYCj5VhLW4iz01A7rJ3Lm1z/Ftr07EAjsVjvXX3TViOfnNiffbE6sac7IMhZcqWR/J9TMzLuf93xd7BroAcAfj3CMp27Y83q9u42ulPKa0LURr4FEYhQUwGyQ77CiQDGipvfdv39/TpRQqawrAI2NjXR2duas6+zsxOPx5G1dAamwlA2zqqDogrhBdZaBeDRnWqvKmv+XbDwIxKNk11T2WuyZgTKUiNAR7Mlsm1/ZjBA6KEcXLQwmYmhZg7jXWpFTSyitrAB4LLlTKf1Z/hoOs3XEoohb976X+fvYOYswmYZvC7mOcMUoAGPpx5d1XhbVRMUIuWayr4HLYsOUR60liWSyYzVQLaFS4fF4xi0Eu7W19aiaf2vXrqW1tbWgfuTTpUwkU7lPtBRjZ/CPdzxT0ufDYHmyI1TUQYOmJrS8CuUN7mfwLvogT6TscMDRrkU6LBogMcj5VYxyLUvx4CzkXmUfTx/tvMi/rURiFHSMU6Yi7cNSzKdQAoEAmzdvZvPmzUAybHnz5s20tbUBcMstt3D55Zdn2l9zzTXs2bOHb37zm7z33nv85Cc/4dFHH+XGG28s7FwLllRSMEIIogaPEmqwu2l2eDPL+4P9Rw3g5aTW5mR2Vtbdg0F/ZsB0Wx1cf9LnsKemQzYe3Epv2D9UN1RbK5jrqs4MuwdDSSdXAJOisryqMVOhuSscIJzlz3FsVQMVqSzB/niUnmhw2IfcAzfchdeZnLJ6dtOLvPnBEYvLQDya6bfUD8l0fxEtgS8eGXJbQtfpjYYyy4u8dZkpqZiuHeXXk83y6iZsKctSXyyELxYuqfwSyUQQn2S130ZCKcG/QnnjjTc44YQTOOGEEwBYs2YNJ5xwArfddhsA7e3tGeUFYO7cuTz11FOsXbuW4447jrvvvptf/OIXBYU0g8x0mxelyHQbnSIhcv5YhKieoNbmnBQm00A8SjARo97uOkqe/kiAt7ve50MzjqXCPPJ8bDARIxCPDtlPTNPojgaptzsxD5r20VOp6b0W+4hTJwBtfV389MXfs2LpCTgqnHgsNiyqiZ5oCAVY1byk5Ne0PeTnQMhHVziAjuDM+jlU2hxA0ol6e38nPdEQMV1jeWUjs1xVqKnsul2RAE6zFdcokT+artMVCVBtc2AzyVlmydRAVcCqju2dvpyZbh/Z+lrRmW4vXfahcZW1VMini6QghguJDSVivOfr4nAkyDx3DfNc1ZjG+GMvBJfFNuyAWml3cfasE/Pqx2m25lSjThPVErzvP8yhkJ9ZzkoWempzlBZVUWhMOfuORExL0KPHOe2kDwNJH6CllQ1YVRPv9XfRHhlg7aGdqfX1OMcQHpzQNf606688sv1ZFlY18/cn/A3VjkqqbA72mnsJJqI4LbZkqQihs2egh/bwAJD0z6nLKqCoKErGiXk0TKqacVSWSCTlpVRhzUZAKixlYCpECY1ETNdY17E7E2kTTsTKoqyMN7oQvNixh5ieQJCc9hlsYcmX9Z17CKd8V2ptTlrrZx+p35N6YET1BH2xEHazZUyOtv/66sM8vTeZiCmWiFFT4cFusqAoCou9dTl1iV7u2oc/ngyt9ljsnNUwV+YKkkhSGClKaKzp9bP3NwpSYSkTRo8SGomYlsiJtHGYrehCGEpzHwpdCKL6EQdZp9k65oidcJajbbrCc/r6BBNHcrJYTeYxR9ocDHRl/nZZHVRkRTUdKdKYPGYoccQXx2FOKjXGvlsSSemYjlFCRkAqLGVCF4LEFFRWgKMGWC2r8m8pya5gnG/74mpsJN+00rctoee6TQuRzAKTE5UkBGRZMjLFBVEyTsraIEtb9vVLOw6PRXabyYqaqrwcy3LiHVzxGVLKksg9pkQiSaIJMGGMfEITUUtoojC+3d4ApGsJTdVhwaKq1Gb5P3RHgsRKmPE0WThS56UDb9Pm78ysGw5N1zkU8mXJMLYrb1JUTqppxpJSKLb1tvHMvtdJaAl0ofP06//HO7u358iTVmn6YmEOpyoZCyE4ubY5E1HUHw0TjB/J77KiqimT1yaqJTgcCYzpIXL9SZ9jRd1CAALxMK8e2pY584F4hM5IIBNCfVJNcyapnC8eYSA+cuZdiWQ6kRDCMFP4SpEhzUZSWGSUUB7IKKHh+WsqE2xc12m0u1noqcVrtZf0R/DDV/6LNzvfpz3YTbXdw+8++y/Dtn25cx8DiSgxXaPO5uS0+tlFH//tzp3c9fr/cMjfgUAQ3t5BpLOf9/fvxmwyM/DUnkzbtkAf+wJ9mRDijzTMx2VNOtDqQtAfC1NprThqukwIgS8ewTlKAro0w025CSHY1X+ABkc1HpuTQyEfewZ66EsleDu9bhbVqQgvkZLHY7XLhG8SySCMEiX0+Ltv4iwiSig4EOBvlpwoo4QkU58z6ufgj0f5INDHXFd1SQvrpbn2xL8hpsX5zfZnmV/VPOLU0Kl1swhpcfYO9NLsTOeNKa6I2fyqmfzXqn/ihQ9e593uvVx8wUeoMFv50WMPMrO2KUeehgo3zc5K9gz0kNB1HJYjfi+qolCdCikejKIoVOaRPTgdjXUw5KfB7mZ5VWNOOLWiKCysasks19icNFZ42DfQy3v+Ll7rPoBJUVlW1UBThYeqYeSRSKY7RrE7KDCmXCrZ+xsFqbCUAUVRsKjJZERTDVVR8VjsrKhqGrcpL6fFjsNi5+9P+ht0oY9ovTGpKi7Fyorqpqxpo+J+ki5rclD/6KyTOW/Oh5IyoPCdL9+c5WuSPJZVNaEoCvPdNZn9S2lxfalrH9GUA68mdCpGiSiyqaakTw0i5TuTDGkeXGZAIpEcQcVAUUIyrFlSakypQSMxelPDkf7Cj9fXPh3BIsQgB9cR2mf/XypUVc2RQQiRlXI/95iDo3JKRVTTMoqhLVWPaMRjZEKmtRwHYpvJbKi5a4mknFhNxpkinU4Ki3HuisHRxdRUVspNPi5X6TYFuWdl7TPcfvogBWS0AX+47Xo+8g0pj8CiHsmaEE9FLeVznhbVlGMBS+i6YWqlSCTlJi5/H5MSqbCUgXSUkGRs6EKg6Tp7A710R4LA8IN08lpr7PJ3Z1VaHv3aJ1KKwaGQn7Zgf46ioKUUg95oiN3+njEXfkzXKOqPhdnl7x6xn3SEQkfYzweBvpQ8SR+dqtQUVVRLEIzH8rKUzHVVs9BTi4qCSVHoSkUwSSSSo9GEcRJ9TkQtoYlCTglJCiKh6+hCxzpEzZiEruGPBqiu8A6x59h5u/cg3ZEQET2BVTWxcuaiYQfpl7r2MRCPkhA6hyNBWutnk89k1QeBPvYH+xhIKTm1dmcmVX9fLMyGjp2oajLBmt1kYoaj8HP0xyK80XOASMoHxaqqNDurhpSuPexnj78HfyqpXJW1Aq+tgkprBafXzyaiJbAXMK1jVlUWe+uZ565BgbwikSSS6Ywx1JVkNJNahM5RzL7lRioskrwQQrAv0McOXxcJobPAXctCb20mHPaVg1u5/83HODDQxTktJ7LmQ5fisTlLcuzjqmcgBHwQ7MNttmfkGWqwPqN+DjFdY3+wP6vGz+hRQrNdVcxzV3Mo5EcTOg5T0pl1a/ce7n39N+zuP8BsbzN/f/IXaBqDstIXDfNOX3tGWZnrqmamo3JYqZoqPLQ4K2kP+YlqCTxWe+acFUUZtdDicFiloiKR5IWBxvFpg1RYyoCiKFhVDD0t1BUJsLW/I7NcaavI1KDoCfu45cWfZLY1expw5RGimy+qooKSHOTTDGdZSF5rEws8tQVFCZkVBRSFGakifoqiENcSfOP5+0iIZAK6xdXNnNowf0znsPHwB+ipKaGmCjfLqhpHdMpNRyikla7sSCSJRDK+mBTjRAkVO60jp4QkR6EqCmYDRwnFB2WuTYfvAoTjUbLz+DotdnSh5xXRUyj5TIGMKUooZ590Cn2dmH6k5o7TUjHm80r7r8CR6Zh8onty0+ob58EikRgZi4GKt8ooIUnJ0QweJWQb5LMS0eKZgdRlrcCsmjLTQ30RPyaldFMPaQfYoWrijA/JH7BZNeG0VOScl6qoI0YSDYdVNWXUjag2+jdhcKSTSOVQkUgk409Mk1FCkxGpsJQBIYThk8bV2pycXNOMw2zBpKhEEonM8Flpd/Pj829iWd18VEUlrmmEEpGSHTshdGK6xta+DvYHfWNSGMaCWTXxwMpvclLTUgA6wgNsaN+ZYy3Jlw83zM1M76iKSkSLj9g+pmvoQrA30Mu7vi4EmUhniUQyzugYJ0qomDpCxVpnyo2cEpLkhaIoNDk8VFrtxHX9qBT8i6pnc++5N6IJHXMejp1RLUFMT+AeIuNqQtcZiEepTNUk2tbXQXc0RFiLY1VNzHJV5vSTdkodjCZ0/LEIldaKMSdJm+Vp5ObWL7Ol9xCBRIy+RJyuSICmisJqbjjNVk6ubRmxBlB2LaHDkQB7B3rpT9Ukmunw4k2dY0LXeL+3jYVVLViGiNYqN8maRBHcFhtmA5nSJZKRMIa6In1YJJKj0IVgh6+L3QO9CARzXFUs9TZgyhqgFEXBPMpUkBCC9/2H2eXvQUcwy1nJssrGTD8Hgv1s6+8kpmtUWSs4ubaF46pnANAZHsBpSRUS1HV2BXrY5e9GE4LmCi/Lq5syA+ahkI+t/Z1EtQRei41TameNObKmylrBOU0LCMSjDMSjWdFHhTOUsuKPRXinr52+WBizonJybTMzHF6anZX0R0NEdQ1vKkpoU8d73PP6/3AwcJgmZw13feR6WjwNY5anWHqjId7pa2cgHsWmmji1bnZGsZJIjIxxhvHpg1RYykA6SiiuC8No7YPpCA+wa6Ans9xQ4R6TKbErEuB9f3dmud7uyvQT1RK81Xsos63G5sCW5dzbkKUo9MbC7PAdzizXVbgwpdrFdY1NPQcz26ptTuxFWCLS8jnNVpxma8lT2qeVFUj6CtXZXZkpL29WtJWiKNy24WeE48ncLAurZk2osgLwZs9BwqnpLZfFJpUVyZTArCiYDKKxTCenW6mwlAlVUTArgrhBNRZ9kN+GWVHHNHBrg+aFTVn96IO2qYqak0El+3iD/UhMWdsG+7eoipLTz1hr/IxX7Z3sc8nUZRom0immxTMRWVbzxP98s2U3jUNUmEQyEZiU8fu9l5p0bqZi9jcKE/qEeeCBB1ixYgUejwePx0Nrayt/+ctfMtvPOeeczM1If6655pqcPtra2li1ahUOh4P6+npuuukmEoncKIx169Zx4oknYrPZWLBgAQ899FA5Ti8HTTeusgLgSGV9TX+1A/HomBxfHalpmXQ/R9LnJ8N9zcqRWjmhRGxY7X+wPKFEPPPDM6kqlqyonHQ/5Y82Gpm0POmMugAxLYEmho9QaHLWZvLfdAZ7M/1MFNmyhxOxsjlESyTjSUwXR71ATVYUFNQiPkbyYZlQhaW5uZkf/OAHbNq0iTfeeIOPfvSjfPrTn2bbtm2ZNldffTXt7e2Zz1133ZXZpmkaq1atIhaL8fLLL/Of//mfPPTQQ9x2222ZNnv37mXVqlV85CMfYfPmzdxwww189atf5ZlnninbeQohiBvkyz8c1TYHH66fQ5XVgcNkGXOOlUprBWc2zKXa5qDCZMGcqoAMyfTx5zTOp9nhxaqacJqtmTo+g3FbbJzdMI9ae3K6x6yqmQeMSVE5p3EeLc5KrKoJl8VGQtcIaXEG4lHeOLyf9/2HJ3xwDSXiSYUtJYJNNVFldRDJUr4G85OVN/H5JR/Da3OxsKqFgWhwQt+QTqubxTGeWqyqiUprBVEtYag3NolkKATGiRKaTihikr0OVVdX88Mf/pCrrrqKc845h+OPP5577713yLZ/+ctf+MQnPsGhQ4doaEjO5T/44IPcfPPNHD58GKvVys0338xTTz3F1q1bM/t94QtfoL+/n6effjovmfx+P16vF5/Ph8dTWHQIJBWWqMHDmstJVEvQGw1RX+EadZohEI8SSsSpszuPGijT/dTZXZhVle39nfRGQ/TFwlgVEyubF2XaBhMxAvEo9XZXUQNuKBFjIB6lLss3J01c1zgcCVJnd2JRTbzvO0xPNEh3NIQCrGpektexE6l+auzOo1LtCyHoigRwWWw51o80vdEQqqJQWcJMxBLJVERVkvW+xkKxY0Yhx1i3dwcu99gDAQIDA5wzd9G4yloqJn4SPIWmaTz22GMEg0FaW1sz6x9++GH++7//m8bGRj75yU/yT//0TzgcyWq1GzduZPny5RllBWDlypVce+21bNu2jRNOOIGNGzdy3nnn5Rxr5cqV3HDDDcPKEo1GiUajmWW/31+is5SMxi5/Nzv8h9GFwG22cVr97CEdZnUh2NrXzgfBfgAa7W5OrJmZiTbaM9DDe74utNSUS2vdbJZWJr8nvlg4kwhP13W2+7rYG0hOr9TZk/lm8gnNzkYIwbu+LvYM9CBIOgyfUtuSyWrbFuhnW38HCaFjV82c3jCHY7x1QB3+WASzqmamqkZSWg4EfWzt7yCua1hVE2fUz8GVipzqi4Z4q/cQwUQMBTiltiWjgIUTcTb3HqI7mqx2vdhTxwJPrbSGSCTDYJRfhkJxshrlPGESKCxbtmyhtbWVSCSCy+XiiSeeYOnSZKKuSy+9lNmzZzNjxgzeeecdbr75Znbs2MHvfvc7ADo6OnKUFSCz3NHRMWIbv99POBymouLoN80777yTO+64o2TnqCgKtlSUUOEpx6YPES3Bu76uzHKjw41tGMWhOxrMKCsAM52ejEUjrmts6+/MbGuocOUoPZ6s3C998UhGWYFkvpOxOI/64hF2Z0VRzXB4MGf183bfkeinugpnjvXDnVI4YHQHuHf6DmUcl2ttzoyyAvCuryvjE2Q3WXKiqvYF+jLKCsDsrLpMEokkF4uqyKyqk5AJV1gWLVrE5s2b8fl8PP7441xxxRW8+OKLLF26lK997WuZdsuXL6epqYlzzz2X3bt3M3/+2IrQ5cMtt9zCmjVrMst+v5+Wlpai+lQUBZMikDNDwzN4dlJBGbbO8lBt8+0nWykYqZ/hZBxKqShE9sHHGE1JyT5m9vdn8G7ZToKDtwkE2eUTFQNFQUgk5UbFSL+P6WNjmXAl0mq1smDBAk466STuvPNOjjvuOO67774h25566qkA7Nq1C4DGxkY6Oztz2qSXGxsbR2zj8XiGtK4A2Gy2TORS+lMsiTJECQmR9GwXWR8jYVVNOTWL/PHIsD8ll8WW8nBP4otHMg8Ys2qiwnQkSdxI/TjNVkxKdj/hox5U6WuZVggSmnbUtXWYrTkWFV88kuPDkm1F8cej6Fn3aiiyj6coSiZyyJvVz0Cqn3TbbL+UmKYR1RJZ+VzsGQVKAXyxiOG+HxJJuTBalNDgaNqCPlJhGTu6ruf4j2SzefNmAJqamgBobW1ly5YtdHUdmUZYu3YtHo8nM63U2trK888/n9PP2rVrc/xkxhshRFk8zvtjYdpDftZ17GbPQI+B3hCSmFSVjzTOZ6GnFo/FRoPdNWyiPafZykebFtDirKTKWkGVtSIzAKuKwjmN81nkqcNjsdFY4R62nwqzhY82LWSWs5JKawU1NudRA/n+rkO8s2c7//DAtzn52vM5ePjQUf3YTGY+2rSAua5qKi12agf1c2bDXOampmH6Y2HWdewmlBXSPZgP/B3s7G3juy/9ktXP/pBwKkX/GQ1zObayAY/FxgyHN5PSvzcawpdKPue12Jnp8AJH3hJnOrycWT+XWpuTersrp9q2RCLJRUYJTU4mdErolltu4YILLmDWrFkMDAzwyCOPsG7dOp555hl2797NI488woUXXkhNTQ3vvPMON954I2eddRYrVqwA4Pzzz2fp0qVcdtll3HXXXXR0dHDrrbeyevVqbLbkm+g111zD/fffzze/+U2+8pWv8MILL/Doo4/y1FNPTeSpjwvt4QH88QiBRIzuSJD5ntoJk0UIQXs4Wd24oYDIG4tqYrG3nsXe+lHbVpgtmbT9gzGrKjMdXiyqiaYKz4jZHO0mMyuG6Qegq/8w//PCE/x+w19o7+1kd/sHtNTPPOqcbCYzy6oah+zDpKi0OCsBaA/7CSZiBBMxHGbrkO83Bwa6eO3Qdl45tIVIIoYvEsRldaAA89w1zHPXZNr2R8P0xZIfk6JwRv2cnJIJaSptFZxWP3vY85RIJMZj+kwITXBY81VXXcXzzz9Pe3s7Xq+XFStWcPPNN/Oxj32M/fv386UvfYmtW7cSDAZpaWnhM5/5DLfeemvOFM0HH3zAtddey7p163A6nVxxxRX84Ac/wJyVBXTdunXceOONbN++nebmZv7pn/6JK6+8Mm85SxHWHCtjWv5gPEaF2ZIapIfzpBg/BuJR3ujeTyBlQVjsrWeBu6asb/Tv9ndmSgk4zBY+XD83Z7qpENI+JJFYlK6+w8xqaC6qH13ohLXEkGHHg9uG4hHCiSg1Fd5R+49ocUyKmolMkkgkY8esKJjVsT2zyhnWvGHvLlyeIsKa/QN8eO4CQ4Q1T7o8LJORUnz5hBDTJkpoe39nTsTMx2cuKusgGtM0njm0I7M8313DEm99SRSmsab1L+Z4kJ8DYCFtJRLJ8FjV4urzSIVlfJjwKKHpgqIoqAVGCZV7cCwlOREpJeoz/+tRHh28HPenkP5LJYuRv3cSSSkw0rc/GfFX3P5GYdI53U5FhBDENZ1EnuNottHLiNE+NbZkYr/03GpXJDhi+9EQQqDpRyJzRrseZtWUk2ulNxoqWoXJPmY6wid3W35HyD6HyXZvxRDnla986Xb6JDwviaRQoro4qlDr5EUpwccYSAtLmdDybDcQjxJMxNgf7Kc3GuL0+jm4RvB1mIw0VLj5aNMCdvq7UVGothWXBv61Q9voCPby39v+wor6hfzTGV8Zsb2qKJzZMJcDwX46wgPMclUV9ZNM35MDwX56oiEWemoxKSq7/N04zBZa6+fk1U8wlbb/YNBHdzTIaXWzchSriaYnGiKqJdg5kLxvwznvDiauafREg3RFAhwM+Tm5pplau7MMEksk44cmRE4V+MmKUmQBQyOFNUuFZZKhCz0zQMZ1jbier6ozuXCYrcNG8BRKV6iPff52fNEAHcGe0XcgqbT4wr28dfAdGuaehlIx/BxvMqJpgIF4hNmu6qNKAehCEIhHCaTuSSAexaSaiOoJQtE47/m6qLY6hqxnlNuPntPPZLu3MT3BQCJKREvkTOmNhkAwkIgRSMRICJ3YJDsviUQyNZBOt3lQ7iih9C1JCD2TjGw6+xQIkbx24USECrMtkyhppPbfe/lXPP/B6ygozPI08pPzb8I5RMG/hK6z8fA++mPJPCf1dhen1LYc5XA31D3ZPdCTKSWgkrTqeKwjW0zS/iFxXZuU9zY5LaSDkio9X6Czb0zTsJpM0g9GYngsioLJAFFCG/ftKdrptnXOPOl0K0miKApWNZmISMtDa8lkbEWVD31IZWMEh9me1/Xwx4I8/8HrQPLt//SZy6nIyhCbTX8snFFWIJlgbch0+kPck7asWkYeq31UZSW7n8kaeqwoCmqB37vstlaT6ah1EonRKDZKqJykM9YWs79RkE63ZWIsXwojfZHKwXS9HuU2gk7X6yyRpJG/gMmJVFjKgBCCmKbnZV2ZrmSmXHQ9Z3ksuK0OTpuxDEhO1bzWvo1oIj5kW6/VnuPU3B72DxnpMlQUTDpzLcBAPEIgPnRJibGi6Tq60InrCfRULaFSk33dZXSPRJLEWFFC0wc5JVQmpkPCuGLYF+jFH4+yP9jPMZ46jvHWjbkvVVG58+y/5/X2d3np4NtcMO/0YaeELKqJcxrn82bPQQ6F/XSEB3i+fSfnNC3Ampq28cUi9MVCHAj68McjnF4/B6/FzkJPLY0Vbj4I9FFtqxgxc22hvNX5Pnv6D/KHnS/SHwnwHxfeSp2jsmT9A0QScToiA3SEB+iOBDmltoV6u6ukx5BIjIpxooSKi/SZ/Gd4BKmwSEqCEIIDIR/dkSBzXFVUpXKx5ItZMWFWkvWXS/WQaK5s5mybl9osS0iavmiIfYE+au1Omh1eZruqqLU72TPQiyb0nB+xqiiYFRVTSr7sh4PbYhu2ftBY0YXgUMjP+/4eIrqOxWTOez59IB5hz0AvHoud2a6qkfdTwKyoyeuecrKVSCSSyYpUWMqEytS1smhCZ0PnXvypKZGIluDUulkFOa01O72Al8WV9ZgUtagoEyEELx/+gN5oCIBAIsoZtjmYUlE52/o62RNIhkd3RgaotTmpsTmotjmYlaXcpGVwW2y4zFaanZXoQozrsJ7Qdf7auQfd7GBpw2I+OudDnFQzE1VRRr0mu/09bPd1Zt64amyOER2B7SYLMx1emp2VaEKXCotEkoURrCswvZxupcJSBhRFwWpSSOj5Z7s1EuFEPKOsANSNIWlY+kdjSg2axfyI4rqWUVYAam3OnMH4UNiX+dtjsVNhtiSPmVo3lGKQXlaKlG00Qql8JpCc2qqzO1EVNS/lrz3sB5L5UyrM5oKiltLKnEQiAZtanBJQTqZTtWb5lCoTyfwWEy1F+ZjMP4LRZBvpQZW9rVwOqvley8l8zSUSIzGNHtWGQiosZSCdOG6qTgk5zVZmVBxJOHQo5EcTE3e2FtXEbGdVZrkj5M/JKrvAU5exuPTFwviz8rDkS7L6toZW4ugdl8VGY1ZW3gNBHxEtkdcx5rlrMsnoQok43ZFAyeSSSKYTMV2QMMgbplKCf0ZBTgmVCWN89ceGoiicVNvMvGiI7miIFqcX8wQmRlMUhRXVTcx2VdEZGaDZ4cWalW5/jquKxgoX+4M+akfx8xiKfQO9+OIRDgR9WFSV82cuKpnsqqJwSm0L7/Z3smugB188wgvtuzincT5Oy8hRSE0ODzV2J/sD/XisNmplxI9EMmZ0BNJuObmQCss0JxSP8Oh7z/FO1y4+t/hcTpuxbMxzt1U2R8HRQeOFJnR6okEOh4M4TFYqTJac87KbLCz01I6pb7fFhtVkpjcaIpCI8XLXPmptzqSFI49igfkww+HJRC2FtThmNT9HZKtqYr6npiQySCSSyY90upWUFEVRMCn5peUvJ4FYmMufvJ2+yAAAlXZ3JuGakdGFYH3HnozzqhKEmY7S1ciotjkQwOHI/9/enUc3Veb/A3/fpFnbJt1bSluKLGWRtWgJnoOonRbscFgVGWQTZZkiIh5UGAS/OkdwFMVRBHGGxYUB0Ski6w+h7FWgFoa1QNkKdIFu6ZrtPr8/Si9NF5q0aZLbfF7n5Bxy75PnPp+maT4891nKUGY2osBQgVKTAdE+/mDMMYP1anZxDlb60GJuhLiAl4i+yD0FJSxOIpNIwLnZLKHc8gIUVumF548GPQKe8ZBy7rnPja3MvEVIVgDAr4FND1uiZm+jglozkdRecqvbTo64xsOeE0Jaj7hmCbVsHAqNYSH1VC977upWWKs7VZYHA1rhQ1p7N1+naMF17Glr7RK278VtP7H84SSkreABiOm/bZ7yF4JmCTlBzSwhS9NFnaqjNhyjuw4RMuzU6+mocvB+OABQZjYKs4accXtDLpFCzhthuT8zKLdCDwNvfuhratplsJhRZWl436G6uvuFWi3fX2CoeGh8tfftac7MJEKIc5hENEvIVVauXIno6GgolUrExcXh+PHjjZZdv369MNam5qFU2jfZAaAeFqdxx199juMwZ8A4jOo6BBcKrmNwZD8oHbgfzvniPBQbK1FgqICvlwJD2nVq9d6Cj377DpeLsnGp6CYGRfTBrNgXEKbyhayJWUu3ykuQX1WG3MpS8GD4U3gXKKWyh74mTOWLoHZdkFuph59cBZ9G9iuqoTdV4VpZIfIqy2DkLRgQGIFQla9otrEnxJOIZpYQx7WsZ7wZr928eTPmzZuH1atXIy4uDitWrEBiYiIyMzMREhLS4Gs0Gg0yMzNrXdb+61LCQhCpCUWkJrRFdRgsZlzS30V+ZRke8Q1EBx9/RKi1CFF6I7PkLkJUzpli+3z3Z3C9JAffn9+D+KgBVjsqP4yfQgU/hQoSjoOJt0DGSZuclcMzhlsVxbhaWohAhRox2uCHJjnVM5OCoZLKkV9VCq1cKYY/h4QQN+aKlW4/+eQTvPLKK5g6dSoAYPXq1dixYwfWrl2Lt99+u+HrcBzCwlq27xolLE7AcRy8OMDsboNYHMTEW7A/50r1ImoAio2V6MgFwFemAIMCT4Q6b6ZLlCYMEb4hGBzZz65xJT73e5b6BbYHYNutq/SCW8itrJ5hZeF5POoX9tAkRy6RQsF5oasmCF01QW7Z60YIqf4S97RZQnq93uq5QqGAQlG/19hoNCI9PR0LFiwQjkkkEsTHxyMtLa3R+svKytChQwfwPI/+/fvjgw8+QM+ePe1qI41hcRIvCQdZG/39N1jMMN9PVoDqdUr4+1/ctmza50gcx0Eqkd6/tu2/3rXXMqhpb1NtLq013kfpJYNUIrFpWf+auiUtXD+BENI65BJONLdqHbXSbWRkJLRarfBYunRpg9e7d+8eLBYLQkOte+VDQ0ORm5vb4GtiYmKwdu1a/Pzzz/juu+/A8zwGDRqEW7du2RUr9bA4SZXFjKv6QjzSzMXK3Fm92UYN7GjsiKTF3tlGzb2mra+pHTfvwq0ICCGOxTNAAuf9R6slHDWtOTs7GxrNg/WqGupdaS6dTgedTic8HzRoELp3746vvvoK77//vs31UA+LE/CMx/+7fRnnS/JxqeQu+DZ2a0jtJUdPv1BhO/a8qupBpUB10nDPUA6DxSw8b657VRWovD+D52H18Iwhv7IMZiGJaJ2fd2//dsKtpHKzEQVV5a1yHUKIc5kYa7O38Buj0WisHo0lLEFBQZBKpcjLy7M6npeXZ/MYFZlMhn79+uHKlSt2tZF6WJzAwhgM97/AM/V3wQB01QSJInu31SO+gYhQ+0FvqkKgQg2O43Cq8DaKDJUoMxuhlsrwTHiXZsX8v8IcFBqrV5NVSKRIaB/TaD2/372JUlMVKi1mhCp98HhwFFprpH+AQo0hYZ2qZ0HJFFA4cOE4QohriSVdcfYkIblcjtjYWOzbtw8jR44EAPA8j3379mH27Nk21WGxWHDmzBk8++yzdl2b/sI6QU2HHQOglSsR4cBl4t2JXCpFkNRbeN5dGwoTb8GlkrsIVFYfb85tmhhtMMyMx6WSu/CTq4TelSJjJc4X56HMZEAXTTA6+vqjX0B7lJsNyNIXoIOPfxM1txzHcQhSejddkBBCWoXz5wnNmzcPkydPxoABA/D4449jxYoVKC8vF2YNTZo0Ce3btxfGwbz33nsYOHAgOnfujOLiYnz00Ue4ceMGXn75ZbuuSwmLE3hJJBgYHIlLJfegC4munt3fhnpXGiOXSCGXSNE/KKJFq93KJVLIIUW/wPZCwqM3VuFo/nUhEeS46sRQJuHgJ1dhQHAk7cFDCGkWCQCZSP5Gu2Ja87hx43D37l0sXrwYubm56Nu3L3bv3i0MxL158yYktTaCLSoqwiuvvILc3Fz4+/sjNjYWx44dQ48ePexrK6O/6k3S6/XQarUoKSmxGpRkL5OFd7vVbp3BUbOEateTV1mK4/eyhXMDAiMQpvL1iESQENK6lNKWDe901HeGLdc4czsHvi24Rqlej17t27VqWx2FelichGcMnjqPpMEkglXvW2TX3j21ynjVmbJsplk6hBAHMfMMUk4cPeG2LMHQ1OvFgmYJOUHNXkLUlfVAzaotuZV65FWWAbBvBlGAQo2efqGQ3U9cCg0VsFBnISHEAcwimiXkqHVYxIB6WIhLXNYX4E5FMcrM1dOUE5UxwkaCtuA4Do/4BqK9WguDxQyN3P6NtEy8BV7cwxd7A6oTKTPjG92PyNZ6CCHiIY50xbNQwkJc4hHfAHTRBOF6aSE4joOMk9g91uVOhR4XivNQaTGhk28gumqCIZU03WlYYTbhYkkeblfooZUp0TcwHBpZwwlPibES54rzUGCoQKjSB70D2gn7BVWaTcgsyUd2RQk0MgX6BrSHthmJEyHE/Yjlvx+uGHTrKpSwOAHHcZBLACNtVy6o6ZHo6BsAwP77qEWGSqQXPFjWWe0lt3kp7ZP3sqE3VQEAGBg0MmWDyRLPGI7mXxcW+vOSSKw2N0wvuIViYyWA6rV2tPKG6yGEiIuUE9NeQp6TstAYFieRcBxlh7U82FfHnmTlQcJn5M1WZ+y5nWTgzUJNNbd5GmoDzxgs7MHYI7nEy2qcjZG3NFEPJaiEiJGsiX3BiGu4NGFZtWoVevfuLSwFrNPpsGvXLuF8VVUVkpOTERgYCB8fH4wZM6becsA3b95EUlIS1Go1QkJCMH/+fJjN1l9mBw4cQP/+/aFQKNC5c2esX7/eGeFZ4RmDueliHqjpPwqMsfuPBwNz5RLr9K9mKwBbKCRewlVNtbYQqEvCcZByD4akGXmz1R8xuUTaaD017b0fgc1tI4S4nonnRbOOU81Kty15iIVLE5aIiAgsW7YM6enpOHnyJJ5++mmMGDEC586dAwC8/vrr+OWXX7BlyxYcPHgQd+7cwejRo4XXWywWJCUlwWg04tixY9iwYQPWr1+PxYsXC2WuXbuGpKQkPPXUUzh16hTmzp2Ll19+GXv27HFanDWzhEjzWO4Per2kv4fcylIAgJ9cidjACKilMnAAKsxGm/doGhAUifD7qw1z4KA3VTX4vykJx+GJkGgEKNQAADPPo+r+XkYAEBsYgQi1FgAg5TiUGKuqd61mDDfLi5BVWlAncSGEiIGFgWYJuSG3WzguICAAH330EcaOHYvg4GBs3LgRY8eOBQBcvHgR3bt3R1paGgYOHIhdu3bhz3/+M+7cuSOssLd69Wq89dZbuHv3LuRyOd566y3s2LEDZ8+eFa7xwgsvoLi4GLt377apTS1dBIgxBgMlLM2Wce8W7hkqUMWbIZdIkdg+RjjHMwaeMXjZMNi2rtaYJXSjrBDXygpRajICAIaEPgJfGohLiOhIOEDejL8rgHMXjruYc7fFC8d1axcsioXj3GYMi8ViwaZNm1BeXg6dTof09HSYTCbEx8cLZbp164aoqCikpaUBANLS0tCrVy8hWQGAxMRE6PV6oZcmLS3Nqo6aMjV1NMRgMECv11s9iOv0DWyPZ8K7oK9/OzweFAngwa0XCcc1K1kBqsed2LpgXWPJSt16Irz9MCSsM+KCohAb2B4+MoVoupYJIQ+Ip9/Bc7g8YTlz5gx8fHygUCgwc+ZMpKSkoEePHsjNzYVcLoefn59V+dDQUOTm5gIAcnNzrZKVmvM15x5WRq/Xo7KyssE2LV26FFqtVnhERka2KMbqWUJi6nhzLxzHQcJxiPD2g59cJRxzRzWjWoKV3min0rR4FUpCiPN5cZxoZglxDniIhcsTlpiYGJw6dQq///47Zs2ahcmTJ+P8+fMubdOCBQtQUlIiPLKzs5t+URMkHAcvMf1muCFRfPkLs59E0FZCSIO8JOL5/HrSoFuXz7SVy+Xo3LkzACA2NhYnTpzAZ599hnHjxsFoNKK4uNiqlyUvLw9hYWEAgLCwMBw/ftyqvppZRLXL1J1ZlJeXB41GA5VK1WCbFAoFFAqFQ+KrYWEMJrozQAghbs9o4SETUdLiKVzew1IXz/MwGAyIjY2FTCbDvn37hHOZmZm4efMmdDodAECn0+HMmTPIz88XyuzduxcajUbYtlqn01nVUVOmpg5nYIzBRINuCSFEFHgAJhp75nZc2sOyYMECDBs2DFFRUSgtLcXGjRtx4MAB7NmzB1qtFtOmTcO8efMQEBAAjUaDV199FTqdDgMHDgQAJCQkoEePHpg4cSL+8Y9/IDc3F4sWLUJycrLQQzJz5kx88cUXePPNN/HSSy9h//79+OGHH7Bjxw5Xhk48kJnnUWisQKBcbdMWAoQQ0pSWTk0W0+hKlyYs+fn5mDRpEnJycqDVatG7d2/s2bMHf/rTnwAAn376KSQSCcaMGQODwYDExER8+eWXwuulUim2b9+OWbNmQafTwdvbG5MnT8Z7770nlOnYsSN27NiB119/HZ999hkiIiLwr3/9C4mJiU6Pl3iuOxV6nC3KgYG3QCX1gi44Gt4yuaubRQhphHi+xj2H263D4o4cMae+ZvE4+mF7pu3Z54X3PkKtRb/A9i5tDyGkcTKOg8SubUOsOXMdlsu591q8DkuXsCBRrMPi8kG3noLjOHhxNPDWHbhig8Lab7utmzQSQlyjJcmKs7V0po9IwgTghoNu2yoz717JCru/Qiyr9WgLGGMw85b78fH1zvH3kxVnx+zt9eD2T5nJ2GZ+3oS0RUae2bzVB3EeSlicoHppd/f65S83G3GrvBiH866iwFAhmv9NNOWmPg/Hbv0Ps/b8Awdu/GF1rsxkQLGxEmn513G+OM+pMQ8O7YiumiCopDIEKdWw1EmmCCHug0E8ewl5Erol5KEu6e+ixFiFMrMRd6vKEKT0bnGd5WYjCg0VaKfSNHu5fKB6I8MCQwXCVL4PXRK/IT9c+BWXi7Jxqegmjtw+jaejBwjn7lSWoshYgXuGCpSaDOjpH9bsNtrLSyJFjDYEMdoQp12TENL2tXS1WjH9V5USFifhALcacNs/MAKMMehNBmhkLVskjzGGCyX5uFpaAAbgqqwQuuAoyKX2/3pdLMnHFf09MAC+XnLoQqKhsKOe+QNfBABkFd1GlDa0poEAxyFGGwyguqdFLvES2t5WepcIIY4jEdVXuWeghMUJqvcSqh7HYnF1Y2rhOA5aB+wkXGExIau0QHgertbY3TMCAFUWEy7r7wnPw9QayJtRDwB08q81C6dOQlJ7PAklK4SQumQSDlKR/G3wpB4WGsPiJNV7y7i6Fa2j7gDS5vYm1b1lzIFrlV4p2ueHEPIw9MXonuh9cRITz8PsTveEHEgplUFZ67ZNkbGyWVm7XCqFWioTnhc3sx5CCGkJg4hmCdHmh8ShGGOwiON3v1m8JBI8FdYZ18oKcLeqHBFqbbPqkXISDGnXCddKC5FXWYZIbz/HNpQQQmxkZgxyMX2bewBKWIhDeEkk6KIJRhdNcIvqkXISdNYEobMmyEEtI4SQtsxzRrFQwuIk7jZLiBBCSOPENEtIPC1tGRrD4gTVs4Q4+mETQogIyCUcvCSekgaIB32HOgl3fzMtQggh7o3+VLsnuiXkBIwxmHgGWoydEELcn4FnkEkgirVYPGcEC/WwOA0lK4QQIh4WkUxr9iTUw0IIIYSIVEvXUhFBJ5KAelichH7QhBAiHmK4HeRp6HvUCTiOg1wqgZR+/wkhxO0pRLSXkCehW0JOUne/HdL20U7QhIgTg3gGo9KgW+JQjDEY+ba9PD95oHorBh4WxihRJUSEjDyDhafPrruhHhYnoV99z5BXWYoSYxVulBfBzPN4KqwTlF6ypl9ICHErFjBIRdD/0NLd58XUC0wJCyEOVGkxIVN/V3hu5C1QghIWQghpKbol5CRSVzeAOEWo0he+MoXw/GZ5Ed0WIkSEaNCt+6EeFifgOA4yKQcpY+AZIOGqBzrxrPpWUc3sIcv9cxKOuz/+wfocx1VnmOz+a22ph79/Tat6UH2+Xj1Ag9eUOqme2m1/WD21fwY15xr7Gdhaj6PeE61cjsTwzsguL4GJ5xHt43f/te75njT0s2xr74nYPif0nrjHeyKWWyUcPGfQLSUsTiSps59Q3WnOXlbnOKvfpNrnav/haqqeutd8aD01B11UT93nD6vHnp+Bw+qx4z2J8vFrtB53e0/qlm2r74mYPif0nrjHe0LcC90SIoQQQojbox4WQgghRKQ86ZYQ9bAQQgghxO1RDwshhBAiUrT5ISGEEEKIG6GEhRBCCCFuj24JEUIIISLlSYNuKWEhhBBCRMtzUhZKWAghhBCRokG3hBBCCCGNWLlyJaKjo6FUKhEXF4fjx48/tPyWLVvQrVs3KJVK9OrVCzt37rT7mpSwEEIIIcRmmzdvxrx587BkyRL88ccf6NOnDxITE5Gfn99g+WPHjmH8+PGYNm0aMjIyMHLkSIwcORJnz56167oco61km6TX66HValFSUgKNRuPq5hBCCHFjzvjOqLlGXmFRi66h1+sRGuBvV1vj4uLw2GOP4YsvvgAA8DyPyMhIvPrqq3j77bfrlR83bhzKy8uxfft24djAgQPRt29frF692ua20hgWG9TkdHq93sUtIYQQ4u5qviuc0R/Q0u+lmtfXrUehUEChUNQrbzQakZ6ejgULFgjHJBIJ4uPjkZaW1uA10tLSMG/ePKtjiYmJ2Lp1q11tpYTFBqWlpQCAyMhIF7eEEEKIWJSWlkKr1bZK3XK5HGFhYegS3aHFdfn4+NT7fluyZAnefffdemXv3bsHi8WC0NBQq+OhoaG4ePFig/Xn5uY2WD43N9eudlLCYoPw8HBkZ2fD19cXXK0h1Xq9HpGRkcjOzvaYW0UUs2fEDHhm3BQzxewIjDGUlpYiPDzc4XXXUCqVuHbtGoxGY4vrYoxZfbcBaLB3xdUoYbGBRCJBREREo+c1Go3HfNBrUMyewxPjppg9Q2vG3Fo9K7UplUoolcpWv05tQUFBkEqlyMvLszqel5eHsLCwBl8TFhZmV/nG0CwhQgghhNhELpcjNjYW+/btE47xPI99+/ZBp9M1+BqdTmdVHgD27t3baPnGUA8LIYQQQmw2b948TJ48GQMGDMDjjz+OFStWoLy8HFOnTgUATJo0Ce3bt8fSpUsBAK+99hqefPJJLF++HElJSdi0aRNOnjyJNWvW2HVdSlhaQKFQYMmSJW55r6+1UMyewxPjppg9gyfG7Ejjxo3D3bt3sXjxYuTm5qJv377YvXu3MLD25s2bkEge3MAZNGgQNm7ciEWLFmHhwoXo0qULtm7dikcffdSu69I6LIQQQghxezSGhRBCCCFujxIWQgghhLg9SlgIIYQQ4vYoYSGEEEKI26OEpQGHDh3C8OHDER4eDo7j6u13wHFcg4+PPvpIKFNYWIgJEyZAo9HAz88P06ZNQ1lZmZMjsV1TMZeVlWH27NmIiIiASqVCjx496m1aVVVVheTkZAQGBsLHxwdjxoypt1iQO2kq5ry8PEyZMgXh4eFQq9UYOnQoLl++bFVGbDEvXboUjz32GHx9fRESEoKRI0ciMzPTqowtMd28eRNJSUlQq9UICQnB/PnzYTabnRmKzWyJec2aNRgyZAg0Gg04jkNxcXG9esT0mW4q5sLCQrz66quIiYmBSqVCVFQU5syZg5KSEqt62tr7PGPGDHTq1AkqlQrBwcEYMWJEveXkxRSzp6GEpQHl5eXo06cPVq5c2eD5nJwcq8fatWvBcRzGjBkjlJkwYQLOnTuHvXv3Yvv27Th06BCmT5/urBDs1lTM8+bNw+7du/Hdd9/hwoULmDt3LmbPno1t27YJZV5//XX88ssv2LJlCw4ePIg7d+5g9OjRzgrBbg+LmTGGkSNH4urVq/j555+RkZGBDh06ID4+HuXl5UI5scV88OBBJCcn47fffsPevXthMpmQkJBgV0wWiwVJSUkwGo04duwYNmzYgPXr12Px4sWuCKlJtsRcUVGBoUOHYuHChY3WI6bPdFMx37lzB3fu3MHHH3+Ms2fPYv369di9ezemTZsm1NEW3+fY2FisW7cOFy5cwJ49e8AYQ0JCAiwWCwDxxexxGHkoACwlJeWhZUaMGMGefvpp4fn58+cZAHbixAnh2K5duxjHcez27dut1VSHaSjmnj17svfee8/qWP/+/dnf/vY3xhhjxcXFTCaTsS1btgjnL1y4wACwtLS0Vm9zS9WNOTMzkwFgZ8+eFY5ZLBYWHBzMvv76a8aY+GNmjLH8/HwGgB08eJAxZltMO3fuZBKJhOXm5gplVq1axTQaDTMYDM4NoBnqxlxbamoqA8CKioqsjov9M/2wmGv88MMPTC6XM5PJxBhr2+9zjdOnTzMA7MqVK4wx8cfc1lEPSwvl5eVhx44dVv8zSUtLg5+fHwYMGCAci4+Ph0Qiwe+//+6KZrbYoEGDsG3bNty+fRuMMaSmpuLSpUtISEgAAKSnp8NkMiE+Pl54Tbdu3RAVFdXoluPuzGAwAIDVPh0SiQQKhQJHjhwB0DZirrkFEBAQAMC2mNLS0tCrVy+r3VcTExOh1+tx7tw5J7a+eerGbAuxf6ZtibmkpAQajQZeXtXribb197m8vBzr1q1Dx44dhZ2KxR5zW0cJSwtt2LABvr6+Vl3mubm5CAkJsSrn5eWFgIAAu7fTdheff/45evTogYiICMjlcgwdOhQrV67E4MGDAVTHLJfL4efnZ/W65mwh7g5qvqQXLFiAoqIiGI1GfPjhh7h16xZycnIAiD9mnucxd+5cPPHEE8KKk7bE1NhW8TXn3FlDMdtCzJ9pW2K+d+8e3n//fatbXG31ff7yyy/h4+MDHx8f7Nq1C3v37oVcLgcg7pg9ASUsLbR27VpMmDDB6TtmOtvnn3+O3377Ddu2bUN6ejqWL1+O5ORk/Prrr65uWquQyWT473//i0uXLiEgIABqtRqpqakYNmyY1ZLTYpacnIyzZ89i06ZNrm6K01DM9en1eiQlJaFHjx549913ndu4VvKwmCdMmICMjAwcPHgQXbt2xfPPP4+qqioXtJLYi/YSaoHDhw8jMzMTmzdvtjoeFhaG/Px8q2NmsxmFhYV2b6ftDiorK7Fw4UKkpKQgKSkJANC7d2+cOnUKH3/8MeLj4xEWFgaj0Yji4mKr/503ZwtxdxEbG4tTp06hpKQERqMRwcHBiIuLE24LiDnm2bNnCwNHIyIihOO2xBQWFobjx49b1Vczi8id424sZluI9TPdVMylpaUYOnQofH19kZKSAplMJpxrq++zVquFVqtFly5dMHDgQPj7+yMlJQXjx48Xbcyeom38V9FF/v3vfyM2NhZ9+vSxOq7T6VBcXIz09HTh2P79+8HzPOLi4pzdzBYzmUwwmUz1ehakUil4ngdQ/eUuk8msthDPzMzEzZs37d5C3N1otVoEBwfj8uXLOHnyJEaMGAFAnDEzxjB79mykpKRg//796Nixo9V5W2LS6XQ4c+aM1Rf43r17odFo0KNHD+cEYoemYraF2D7TtsSs1+uRkJAAuVyObdu21esl9oT3mTEGxpgwZk1sMXscFw74dVulpaUsIyODZWRkMADsk08+YRkZGezGjRtCmZKSEqZWq9mqVasarGPo0KGsX79+7Pfff2dHjhxhXbp0YePHj3dWCHZrKuYnn3yS9ezZk6WmprKrV6+ydevWMaVSyb788kuhjpkzZ7KoqCi2f/9+dvLkSabT6ZhOp3NVSE1qKuYffviBpaamsqysLLZ161bWoUMHNnr0aKs6xBbzrFmzmFarZQcOHGA5OTnCo6KiQijTVExms5k9+uijLCEhgZ06dYrt3r2bBQcHswULFrgipCbZEnNOTg7LyMhgX3/9NQPADh06xDIyMlhBQYFQRkyf6aZiLikpYXFxcaxXr17sypUrVmXMZjNjrO29z1lZWeyDDz5gJ0+eZDdu3GBHjx5lw4cPZwEBASwvL48xJr6YPQ0lLA2omdpY9zF58mShzFdffcVUKhUrLi5usI6CggI2fvx45uPjwzQaDZs6dSorLS11UgT2ayrmnJwcNmXKFBYeHs6USiWLiYlhy5cvZzzPC3VUVlayv/71r8zf35+p1Wo2atQolpOT46KImtZUzJ999hmLiIhgMpmMRUVFsUWLFtWb2ii2mBuKFwBbt26dUMaWmK5fv86GDRvGVCoVCwoKYm+88YYwHdbd2BLzkiVLmiwjps90UzE39rsPgF27dk2opy29z7dv32bDhg1jISEhTCaTsYiICPaXv/yFXbx40aoeMcXsaTjGGHNkjw0hhBBCiKPRGBZCCCGEuD1KWAghhBDi9ihhIYQQQojbo4SFEEIIIW6PEhZCCCGEuD1KWAghhBDi9ihhIYQQQojbo4SFEAcZMmQI5s6dKzyPjo7GihUrXNYeMXv33XfRt2/fZr124sSJ+OCDDxzbIBsNHDgQP/30k0uuTUhbRwkLITaaMmUKOI6r97hy5UqD5U+cOIHp06c7uZW2a0lS4Egcx2Hr1q0Oqev06dPYuXMn5syZ45D67LVo0SK8/fbbwh5bhBDHoYSFEDsMHToUOTk5Vo/GNlkLDg6GWq12cgvrMxqNrm6C03z++ed47rnn4OPj45LrDxs2DKWlpdi1a5dLrk9IW0YJCyF2UCgUCAsLs3pIpdIGy9a9JcRxHFatWoVhw4ZBpVLhkUcewY8//iicv379OjiOw6ZNmzBo0CAolUo8+uijOHjwoFW9Z8+exbBhw+Dj44PQ0FBMnDgR9+7dE84PGTIEs2fPxty5cxEUFITExMRmxZqdnY3nn38efn5+CAgIwIgRI3D9+nXh/JQpUzBy5Eh8/PHHaNeuHQIDA5GcnAyTySSUycnJQVJSElQqFTp27IiNGzda/Vyio6MBAKNGjQLHccLzGt9++y2io6Oh1WrxwgsvoLS0tNH2WiwW/Pjjjxg+fLjV8ejoaPz973/HpEmT4OPjgw4dOmDbtm24e/cuRowYAR8fH/Tu3RsnT54UXrN+/Xr4+flh+/btiImJgVqtxtixY1FRUYENGzYgOjoa/v7+mDNnDiwWi/A6qVSKZ599Fps2bbLzp00IaQolLIQ40TvvvIMxY8bg9OnTmDBhAl544QVcuHDBqsz8+fPxxhtvICMjAzqdDsOHD0dBQQEAoLi4GE8//TT69euHkydPYvfu3cjLy8Pzzz9vVceGDRsgl8tx9OhRrF692u52mkwmJCYmwtfXF4cPH8bRo0fh4+ODoUOHWvXYpKamIisrC6mpqdiwYQPWr1+P9evXC+cnTZqEO3fu4MCBA/jpp5+wZs0a5OfnC+dPnDgBAFi3bh1ycnKE5wCQlZWFrVu3Yvv27di+fTsOHjyIZcuWNdrm//3vfygpKcGAAQPqnfv000/xxBNPICMjA0lJSZg4cSImTZqEF198EX/88Qc6deqESZMmofbWahUVFfjnP/+JTZs2Yffu3Thw4ABGjRqFnTt3YufOnfj222/x1VdfWSWdAPD444/j8OHDtv+wCSG2cfHmi4SIxuTJk5lUKmXe3t7CY+zYscL5J598kr322mvC8w4dOrBPP/1UeA6AzZw506rOuLg4NmvWLMYYY9euXWMA2LJly4TzJpOJRUREsA8//JAxxtj777/PEhISrOrIzs5mAFhmZqbQjn79+jUZz5IlS1ifPn0aPPftt9+ymJgYq924DQYDU6lUbM+ePcLPo0OHDsxsNgtlnnvuOTZu3DjGGGMXLlxgANiJEyeE85cvX2YA6v1cUlJS6rVNrVYzvV4vHJs/fz6Li4trNJ6UlBQmlUqt2sxY9fvw4osvCs9zcnIYAPbOO+8Ix9LS0hgAYVfqdevWMQDsypUrQpkZM2YwtVpttUNzYmIimzFjhtX1fv75ZyaRSJjFYmm0rYQQ+3m5LFMiRISeeuoprFq1Snju7e1t1+t1Ol2956dOnWq0jJeXFwYMGCD0wpw+fRqpqakNjtHIyspC165dAQCxsbF2tauu06dP48qVK/D19bU6XlVVhaysLOF5z549rW6JtWvXDmfOnAEAZGZmwsvLC/379xfOd+7cGf7+/ja1ITo62ur67dq1s+qdqauyshIKhQIcx9U717t3b+HfoaGhAIBevXrVO5afn4+wsDAAgFqtRqdOnazKREdHW/3sQ0ND67VJpVKB53kYDAaoVCqbYiWENI0SFkLs4O3tjc6dO7vs+mVlZRg+fDg+/PDDeufatWsn/NveRKqh68TGxuL777+vdy44OFj4t0wmszrHcZzDZsjYW3dQUBAqKipgNBohl8sbrasmoWnoWO36G7q+LW0qLCyEt7c3JSuEOBiNYSHEiX777bd6z7t3795oGbPZjPT0dKFM//79ce7cOURHR6Nz585Wj5YmKbX1798fly9fRkhISL3raLVam+qIiYmB2WxGRkaGcOzKlSsoKiqyKieTyawGrjZXzRTt8+fPt7iuljh79iz69evn0jYQ0hZRwkKIE23ZsgVr167FpUuXsGTJEhw/fhyzZ8+2KrNy5UqkpKTg4sWLSE5ORlFREV566SUAQHJyMgoLCzF+/HicOHECWVlZ2LNnD6ZOndqsL/3KykqcOnXK6pGVlYUJEyYgKCgII0aMwOHDh3Ht2jUcOHAAc+bMwa1bt2yqu1u3boiPj8f06dNx/PhxZGRkYPr06VCpVFa3baKjo7Fv3z7k5ubWS2bsERwcjP79++PIkSPNrsMRDh8+jISEBJe2gZC2iBIWQpzo//7v/7Bp0yb07t0b33zzDf7zn/+gR48eVmWWLVuGZcuWoU+fPjhy5Ai2bduGoKAgAEB4eDiOHj0Ki8WChIQE9OrVC3PnzoWfnx8kEvs/zpcuXUK/fv2sHjNmzIBarcahQ4cQFRWF0aNHo3v37pg2bRqqqqqg0Whsrv+bb75BaGgoBg8ejFGjRuGVV16Br68vlEqlUGb58uXYu3cvIiMjW9wz8fLLLzd4G8tZbt++jWPHjmHq1KkuawMhbRXHWK15fISQVsNxHFJSUjBy5MgGz1+/fh0dO3ZERkaGW6xA2xpu3bqFyMhI/Prrr3jmmWccXn9lZSViYmKwefPmegOcneGtt95CUVER1qxZ4/RrE9LW0aBbQkir2b9/P8rKytCrVy/k5OTgzTffRHR0NAYPHtwq11OpVPjmm2+sFtJzppCQEMybN88l1yakraOEhRDSakwmExYuXIirV6/C19cXgwYNwvfff19vto0jDRkypNXqbsobb7zhsmsT0tbRLSFCCCGEuD0adEsIIYQQt0cJCyGEEELcHiUshBBCCHF7lLAQQgghxO1RwkIIIYQQt0cJCyGEEELcHiUshBBCCHF7lLAQQgghxO1RwkIIIYQQt/f/AVKSIu8VmQ93AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#temp df\n",
        "dfmale = penguins_dataframe[penguins_dataframe[\"Sex\"] == \"MALE\"]\n",
        "dfmale.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wYmHDbDcOvn7",
        "outputId": "fc823131-94b1-47fc-c79a-49d66b74cb7c"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Species  Region     Island               Stage  \\\n",
              "0  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "4  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "6  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "8  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "9  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "\n",
              "  Clutch Completion    Date Egg  Culmen Length (mm)  Culmen Depth (mm)  \\\n",
              "0               Yes  2007-11-11                39.1               18.7   \n",
              "4               Yes  2007-11-16                39.3               20.6   \n",
              "6                No  2007-11-15                39.2               19.6   \n",
              "8               Yes  2007-11-15                38.6               21.2   \n",
              "9               Yes  2007-11-16                34.6               21.1   \n",
              "\n",
              "   Flipper Length (mm)  Body Mass (g)   Sex  \n",
              "0                181.0         3750.0  MALE  \n",
              "4                190.0         3650.0  MALE  \n",
              "6                195.0         4675.0  MALE  \n",
              "8                191.0         3800.0  MALE  \n",
              "9                198.0         4400.0  MALE  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-9e320ba9-9acd-4fe1-973a-eab1c01855c8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Species</th>\n",
              "      <th>Region</th>\n",
              "      <th>Island</th>\n",
              "      <th>Stage</th>\n",
              "      <th>Clutch Completion</th>\n",
              "      <th>Date Egg</th>\n",
              "      <th>Culmen Length (mm)</th>\n",
              "      <th>Culmen Depth (mm)</th>\n",
              "      <th>Flipper Length (mm)</th>\n",
              "      <th>Body Mass (g)</th>\n",
              "      <th>Sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-11</td>\n",
              "      <td>39.1</td>\n",
              "      <td>18.7</td>\n",
              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>MALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-16</td>\n",
              "      <td>39.3</td>\n",
              "      <td>20.6</td>\n",
              "      <td>190.0</td>\n",
              "      <td>3650.0</td>\n",
              "      <td>MALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>No</td>\n",
              "      <td>2007-11-15</td>\n",
              "      <td>39.2</td>\n",
              "      <td>19.6</td>\n",
              "      <td>195.0</td>\n",
              "      <td>4675.0</td>\n",
              "      <td>MALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-15</td>\n",
              "      <td>38.6</td>\n",
              "      <td>21.2</td>\n",
              "      <td>191.0</td>\n",
              "      <td>3800.0</td>\n",
              "      <td>MALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-16</td>\n",
              "      <td>34.6</td>\n",
              "      <td>21.1</td>\n",
              "      <td>198.0</td>\n",
              "      <td>4400.0</td>\n",
              "      <td>MALE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e320ba9-9acd-4fe1-973a-eab1c01855c8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-9012c420-9a8e-4c20-ae8b-b3c780b2ec0e\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9012c420-9a8e-4c20-ae8b-b3c780b2ec0e')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-9012c420-9a8e-4c20-ae8b-b3c780b2ec0e button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9e320ba9-9acd-4fe1-973a-eab1c01855c8 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-9e320ba9-9acd-4fe1-973a-eab1c01855c8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfmale.plot.scatter('Flipper Length (mm)','Body Mass (g)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "mjFiGBuDOWSy",
        "outputId": "6693eeef-60b3-4cb9-f9ec-64cd56855ef4"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Flipper Length (mm)', ylabel='Body Mass (g)'>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#temp df\n",
        "dffemale = penguins_dataframe[penguins_dataframe[\"Sex\"] == \"FEMALE\"]\n",
        "dffemale.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Et1uuo9XPPDf",
        "outputId": "f73c5cce-e578-43e7-b05f-4205ffb6ecfb"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Species  Region     Island               Stage  \\\n",
              "1  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "2  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "3  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "5  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "7  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "\n",
              "  Clutch Completion    Date Egg  Culmen Length (mm)  Culmen Depth (mm)  \\\n",
              "1               Yes  2007-11-11                39.5               17.4   \n",
              "2               Yes  2007-11-16                40.3               18.0   \n",
              "3               Yes  2007-11-16                36.7               19.3   \n",
              "5                No  2007-11-15                38.9               17.8   \n",
              "7               Yes  2007-11-15                41.1               17.6   \n",
              "\n",
              "   Flipper Length (mm)  Body Mass (g)     Sex  \n",
              "1                186.0         3800.0  FEMALE  \n",
              "2                195.0         3250.0  FEMALE  \n",
              "3                193.0         3450.0  FEMALE  \n",
              "5                181.0         3625.0  FEMALE  \n",
              "7                182.0         3200.0  FEMALE  "
            ],
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              "      <th></th>\n",
              "      <th>Species</th>\n",
              "      <th>Region</th>\n",
              "      <th>Island</th>\n",
              "      <th>Stage</th>\n",
              "      <th>Clutch Completion</th>\n",
              "      <th>Date Egg</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-11</td>\n",
              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
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              "      <td>3800.0</td>\n",
              "      <td>FEMALE</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
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              "      <td>40.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-16</td>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
              "      <td>FEMALE</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
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              "      <td>Anvers</td>\n",
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              "      <td>2007-11-15</td>\n",
              "      <td>38.9</td>\n",
              "      <td>17.8</td>\n",
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              "      <td>3625.0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>41.1</td>\n",
              "      <td>17.6</td>\n",
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              "      <td>3200.0</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
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              "\n",
              "      <script>\n",
              "\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a1fae660-36b0-4e2f-872f-932e4064164f 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-a1fae660-36b0-4e2f-872f-932e4064164f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "M4wZ__LuI7T5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "cc6cd547-246a-480b-adbe-954c432c39c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Flipper Length (mm)', ylabel='Body Mass (g)'>"
            ]
          },
          "metadata": {},
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Continue your analysis\n",
        "dffemale.plot.scatter('Flipper Length (mm)','Body Mass (g)')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#temp df\n",
        "dfadeliepenguinpygoscelisadeliae = penguins_dataframe[penguins_dataframe[\"Species\"] == \"Adelie Penguin (Pygoscelis adeliae)\"]\n",
        "dfadeliepenguinpygoscelisadeliae.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FXqET9zxPl_O",
        "outputId": "4ec5a6bf-2872-4f60-fa51-a19320dabd85"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Species  Region     Island               Stage  \\\n",
              "0  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "1  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "2  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "3  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "4  Adelie Penguin (Pygoscelis adeliae)  Anvers  Torgersen  Adult, 1 Egg Stage   \n",
              "\n",
              "  Clutch Completion    Date Egg  Culmen Length (mm)  Culmen Depth (mm)  \\\n",
              "0               Yes  2007-11-11                39.1               18.7   \n",
              "1               Yes  2007-11-11                39.5               17.4   \n",
              "2               Yes  2007-11-16                40.3               18.0   \n",
              "3               Yes  2007-11-16                36.7               19.3   \n",
              "4               Yes  2007-11-16                39.3               20.6   \n",
              "\n",
              "   Flipper Length (mm)  Body Mass (g)     Sex  \n",
              "0                181.0         3750.0    MALE  \n",
              "1                186.0         3800.0  FEMALE  \n",
              "2                195.0         3250.0  FEMALE  \n",
              "3                193.0         3450.0  FEMALE  \n",
              "4                190.0         3650.0    MALE  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-532d7185-5243-425c-8bcd-c84d5e363627\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Species</th>\n",
              "      <th>Region</th>\n",
              "      <th>Island</th>\n",
              "      <th>Stage</th>\n",
              "      <th>Clutch Completion</th>\n",
              "      <th>Date Egg</th>\n",
              "      <th>Culmen Length (mm)</th>\n",
              "      <th>Culmen Depth (mm)</th>\n",
              "      <th>Flipper Length (mm)</th>\n",
              "      <th>Body Mass (g)</th>\n",
              "      <th>Sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-11</td>\n",
              "      <td>39.1</td>\n",
              "      <td>18.7</td>\n",
              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>MALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-11</td>\n",
              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3800.0</td>\n",
              "      <td>FEMALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-16</td>\n",
              "      <td>40.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
              "      <td>FEMALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-16</td>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
              "      <td>FEMALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-16</td>\n",
              "      <td>39.3</td>\n",
              "      <td>20.6</td>\n",
              "      <td>190.0</td>\n",
              "      <td>3650.0</td>\n",
              "      <td>MALE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-532d7185-5243-425c-8bcd-c84d5e363627')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-86a7a391-0c8a-4d15-adc7-572c7a05d443\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-86a7a391-0c8a-4d15-adc7-572c7a05d443')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-86a7a391-0c8a-4d15-adc7-572c7a05d443 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-532d7185-5243-425c-8bcd-c84d5e363627 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-532d7185-5243-425c-8bcd-c84d5e363627');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfadeliepenguinpygoscelisadeliae.plot.hist()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "9E5gXmZ-PmGM",
        "outputId": "eddfb700-be54-439e-f308-dd5fe6e00935"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Frequency'>"
            ]
          },
          "metadata": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#temp df\n",
        "dfgentoopenguinpygoscelispapua = penguins_dataframe[penguins_dataframe[\"Species\"] == \"Gentoo penguin (Pygoscelis papua)\"]\n",
        "dfgentoopenguinpygoscelispapua.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cWYoXXtxP9cZ",
        "outputId": "7dfae7e6-cf97-4b49-f33f-3a85fa67da74"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Species  Region  Island               Stage  \\\n",
              "146  Gentoo penguin (Pygoscelis papua)  Anvers  Biscoe  Adult, 1 Egg Stage   \n",
              "147  Gentoo penguin (Pygoscelis papua)  Anvers  Biscoe  Adult, 1 Egg Stage   \n",
              "148  Gentoo penguin (Pygoscelis papua)  Anvers  Biscoe  Adult, 1 Egg Stage   \n",
              "149  Gentoo penguin (Pygoscelis papua)  Anvers  Biscoe  Adult, 1 Egg Stage   \n",
              "150  Gentoo penguin (Pygoscelis papua)  Anvers  Biscoe  Adult, 1 Egg Stage   \n",
              "\n",
              "    Clutch Completion    Date Egg  Culmen Length (mm)  Culmen Depth (mm)  \\\n",
              "146               Yes  2007-11-27                46.1               13.2   \n",
              "147               Yes  2007-11-27                50.0               16.3   \n",
              "148               Yes  2007-11-27                48.7               14.1   \n",
              "149               Yes  2007-11-27                50.0               15.2   \n",
              "150               Yes  2007-11-18                47.6               14.5   \n",
              "\n",
              "     Flipper Length (mm)  Body Mass (g)     Sex  \n",
              "146                211.0         4500.0  FEMALE  \n",
              "147                230.0         5700.0    MALE  \n",
              "148                210.0         4450.0  FEMALE  \n",
              "149                218.0         5700.0    MALE  \n",
              "150                215.0         5400.0    MALE  "
            ],
            "text/html": [
              "\n",
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Species</th>\n",
              "      <th>Region</th>\n",
              "      <th>Island</th>\n",
              "      <th>Stage</th>\n",
              "      <th>Clutch Completion</th>\n",
              "      <th>Date Egg</th>\n",
              "      <th>Culmen Length (mm)</th>\n",
              "      <th>Culmen Depth (mm)</th>\n",
              "      <th>Flipper Length (mm)</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-27</td>\n",
              "      <td>46.1</td>\n",
              "      <td>13.2</td>\n",
              "      <td>211.0</td>\n",
              "      <td>4500.0</td>\n",
              "      <td>FEMALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-27</td>\n",
              "      <td>50.0</td>\n",
              "      <td>16.3</td>\n",
              "      <td>230.0</td>\n",
              "      <td>5700.0</td>\n",
              "      <td>MALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-27</td>\n",
              "      <td>48.7</td>\n",
              "      <td>14.1</td>\n",
              "      <td>210.0</td>\n",
              "      <td>4450.0</td>\n",
              "      <td>FEMALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-27</td>\n",
              "      <td>50.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>218.0</td>\n",
              "      <td>5700.0</td>\n",
              "      <td>MALE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
              "      <td>Anvers</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>Adult, 1 Egg Stage</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2007-11-18</td>\n",
              "      <td>47.6</td>\n",
              "      <td>14.5</td>\n",
              "      <td>215.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>MALE</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-fef100ec-9d01-4163-943f-6762f5b04df7 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5db21a61-649e-4bd7-a989-5bb9c36cfe4d 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-5db21a61-649e-4bd7-a989-5bb9c36cfe4d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfgentoopenguinpygoscelispapua.plot.hist\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JFHPvZqVP9h7",
        "outputId": "8a612c11-0370-48c2-ab22-78df58e37c1a"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method PlotAccessor.hist of <pandas.plotting._core.PlotAccessor object at 0x7f4bebf8ee00>>"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "collapsed": false,
        "id": "FFLbZqKiI7T5"
      },
      "source": [
        "Write up some descriptions for your findings here....\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   Males have a very uniform Culem length, While females have a very wide range of culem lengths\n",
        "*   Females tend to have longer flipper lengths than males on average\n",
        "*   Females and Males have about the same correlation bewteen their Flipper lengths and Body Masses\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "sXDDVkwsNv8w"
      }
    }
  ],
  "metadata": {
    "interpreter": {
      "hash": "08210294964727e4010dccc398c44f22b92a2e77e2aceea574ad21eae77cd8e8"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "orig_nbformat": 4,
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}