Bản_sao_của_Lab_2_Pandas_Seaborn.ipynb

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  "metadata": {
    "colab": {
      "name": "Bản sao của Lab_2_Pandas-Seaborn.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nB9SkJ8AdMVF",
        "colab_type": "text"
      },
      "source": [
        "![](https://i.imgur.com/0AUxkXt.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mhi70e4CdNf4",
        "colab_type": "text"
      },
      "source": [
        "# Lab 2 - Pandas & Seaborn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdjCl3H3dWbX",
        "colab_type": "text"
      },
      "source": [
        "## Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mlRxipHtc3Bh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fdAwfjMIeW3k",
        "colab_type": "text"
      },
      "source": [
        "## Import data\n",
        "\n",
        "In this Lab we will use a dataset from seaborn library. The dataset contains tips data from different customers females and males, smokers and non smokers, from days, dinner or lunch and from different table size."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QArJ6Nt4ech-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Run this code\n",
        "tips = sns.load_dataset('tips')"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Kt_GDz6e2K4",
        "colab_type": "text"
      },
      "source": [
        "### Overview\n",
        "\n",
        "**Show the first 3 rows of the data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oW0zZRWKezAK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "2bd3b460-85fd-4f59-f361-c4beb0b49b7a"
      },
      "source": [
        "tips.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KELAOLL2fFeW",
        "colab_type": "text"
      },
      "source": [
        "### Show the last 2 rows of the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NDmmOi7xfJhA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "730da38b-6f21-4689-cb37-d9b0a38617c4"
      },
      "source": [
        "tips.tail(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>242</th>\n",
              "      <td>17.82</td>\n",
              "      <td>1.75</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>243</th>\n",
              "      <td>18.78</td>\n",
              "      <td>3.00</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Thur</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     total_bill   tip     sex smoker   day    time  size\n",
              "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
              "243       18.78  3.00  Female     No  Thur  Dinner     2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pYKHwnDnfMXk",
        "colab_type": "text"
      },
      "source": [
        "### Show 4 randoms rows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6hL9EeTAfQ3L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "ad41c2ec-f81d-4d93-ca2f-22ad0e0d4a8d"
      },
      "source": [
        "tips.sample(4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>13.94</td>\n",
              "      <td>3.06</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>226</th>\n",
              "      <td>10.09</td>\n",
              "      <td>2.00</td>\n",
              "      <td>Female</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fri</td>\n",
              "      <td>Lunch</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>23.17</td>\n",
              "      <td>6.50</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>14.26</td>\n",
              "      <td>2.50</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Thur</td>\n",
              "      <td>Lunch</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     total_bill   tip     sex smoker   day    time  size\n",
              "42        13.94  3.06    Male     No   Sun  Dinner     2\n",
              "226       10.09  2.00  Female    Yes   Fri   Lunch     2\n",
              "183       23.17  6.50    Male    Yes   Sun  Dinner     4\n",
              "122       14.26  2.50    Male     No  Thur   Lunch     2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vKwm4bp1fd9a",
        "colab_type": "text"
      },
      "source": [
        "### Print a concise summary of the data\n",
        "\n",
        "Print information about the data including the index dtype and column dtypes, non-null values and memory usage."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0mcL7RLflEm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "2f43ed24-7d26-4b57-f3f2-1576d4ed311a"
      },
      "source": [
        "tips.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 244 entries, 0 to 243\n",
            "Data columns (total 7 columns):\n",
            " #   Column      Non-Null Count  Dtype   \n",
            "---  ------      --------------  -----   \n",
            " 0   total_bill  244 non-null    float64 \n",
            " 1   tip         244 non-null    float64 \n",
            " 2   sex         244 non-null    category\n",
            " 3   smoker      244 non-null    category\n",
            " 4   day         244 non-null    category\n",
            " 5   time        244 non-null    category\n",
            " 6   size        244 non-null    int64   \n",
            "dtypes: category(4), float64(2), int64(1)\n",
            "memory usage: 7.3 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GMFkZD2NgEW_",
        "colab_type": "text"
      },
      "source": [
        "### Generate descriptive statistics\n",
        "\n",
        "A descriptive statistics that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding NaN values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VGcw9_DogYVh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "d571e9a6-7d80-464d-f567-9a23d89bf58b"
      },
      "source": [
        "tips.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>244.000000</td>\n",
              "      <td>244.000000</td>\n",
              "      <td>244.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>19.785943</td>\n",
              "      <td>2.998279</td>\n",
              "      <td>2.569672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.902412</td>\n",
              "      <td>1.383638</td>\n",
              "      <td>0.951100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.070000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>13.347500</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>17.795000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>24.127500</td>\n",
              "      <td>3.562500</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.810000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       total_bill         tip        size\n",
              "count  244.000000  244.000000  244.000000\n",
              "mean    19.785943    2.998279    2.569672\n",
              "std      8.902412    1.383638    0.951100\n",
              "min      3.070000    1.000000    1.000000\n",
              "25%     13.347500    2.000000    2.000000\n",
              "50%     17.795000    2.900000    2.000000\n",
              "75%     24.127500    3.562500    3.000000\n",
              "max     50.810000   10.000000    6.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fX18JXY6hUpi",
        "colab_type": "text"
      },
      "source": [
        "## EDA - Exploratory Data Analysis\n",
        "\n",
        "**What is the min/max of tip?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPc8Vdfyg-3g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "921c7d59-6233-4fa9-f316-8aaa83f73482"
      },
      "source": [
        "tips['tip'].agg(['min', 'max'])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "min     1.0\n",
              "max    10.0\n",
              "Name: tip, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-ZzN78Rilnt",
        "colab_type": "text"
      },
      "source": [
        "**The restaurant is open on which days of the week?**\n",
        "\n",
        "*Hint: unique values of the column `day`*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gSrW3rnzjTva",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "ff5fee9b-3a0b-4022-dfbf-85e00a939cb7"
      },
      "source": [
        "tips.day.unique()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Sun, Sat, Thur, Fri]\n",
              "Categories (4, object): [Sun, Sat, Thur, Fri]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0jftAWtQv_xW",
        "colab_type": "text"
      },
      "source": [
        "**List of unique values in column `time`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-dxGTqBwMLA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3e4a1419-1f3d-4368-a322-ed35835324f1"
      },
      "source": [
        "tips.time.unique()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Dinner, Lunch]\n",
              "Categories (2, object): [Dinner, Lunch]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGVtUnvSjg9b",
        "colab_type": "text"
      },
      "source": [
        "**What is the range of table size?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTE9tQ_wjltu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "516a0ab2-1744-4b21-eb2d-fa52c6d6954f"
      },
      "source": [
        "tips.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(244, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Gx8UIMepNsp",
        "colab_type": "text"
      },
      "source": [
        "**How many man/woman paid for the dish?**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67yMlr22pjUL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "77803a30-2928-44a3-fc49-c0943baf76ab"
      },
      "source": [
        "tips.sex.value_counts()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      157\n",
              "Female     87\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pBVDe59QpqDM",
        "colab_type": "text"
      },
      "source": [
        "**What is the percentage of smoker table and their revenue?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qy1Q250upxwG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "08987fd5-c4a9-4d0f-b16d-2c8d0871c1e0"
      },
      "source": [
        "tips.smoker.value_counts(normalize=True)*100\n",
        "tips.loc[tips['smoker']=='Yes', 'total_bill'].sum()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1930.3400000000001"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h1Izjo0HqI-9",
        "colab_type": "text"
      },
      "source": [
        "**List 3 tables that have the most expensive bill**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TTvXeHxsqT3q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "105d9274-245a-4bfa-fe55-fb571269f89d"
      },
      "source": [
        "tips.total_bill.nlargest(3)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "170    50.81\n",
              "212    48.33\n",
              "59     48.27\n",
              "Name: total_bill, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HyYXxw8Fqy-7",
        "colab_type": "text"
      },
      "source": [
        "### Group by - Example\n",
        "\n",
        "**What is the best day to work - maximum tips (sum and percents)?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwE2LP4sqVtf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "7cfa6067-290e-4de3-bebd-083ec402cfb6"
      },
      "source": [
        "tips2 = tips.groupby('day').sum()\n",
        "tips2['percent'] = tips2['tip'] / tips2['total_bill'] * 100\n",
        "tips2"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>size</th>\n",
              "      <th>percent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Thur</th>\n",
              "      <td>1096.33</td>\n",
              "      <td>171.83</td>\n",
              "      <td>152</td>\n",
              "      <td>15.673201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fri</th>\n",
              "      <td>325.88</td>\n",
              "      <td>51.96</td>\n",
              "      <td>40</td>\n",
              "      <td>15.944519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sat</th>\n",
              "      <td>1778.40</td>\n",
              "      <td>260.40</td>\n",
              "      <td>219</td>\n",
              "      <td>14.642375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sun</th>\n",
              "      <td>1627.16</td>\n",
              "      <td>247.39</td>\n",
              "      <td>216</td>\n",
              "      <td>15.203791</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      total_bill     tip  size    percent\n",
              "day                                      \n",
              "Thur     1096.33  171.83   152  15.673201\n",
              "Fri       325.88   51.96    40  15.944519\n",
              "Sat      1778.40  260.40   219  14.642375\n",
              "Sun      1627.16  247.39   216  15.203791"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_XtpGXPerFoB",
        "colab_type": "text"
      },
      "source": [
        "**Your turn: Who eats more (and tips more)? smokers or non smokers?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ttatVLMIrM1e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "35a7fdee-5253-4da4-adea-12a0b8522cff"
      },
      "source": [
        "x = tips.groupby('smoker').sum()\n",
        "x['bill_per_size'] = x['total_bill']/x['size']\n",
        "x['tip_per_size'] = x['tip']/x['size']\n",
        "x"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>size</th>\n",
              "      <th>bill_per_size</th>\n",
              "      <th>tip_per_size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoker</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>1930.34</td>\n",
              "      <td>279.81</td>\n",
              "      <td>224</td>\n",
              "      <td>8.617589</td>\n",
              "      <td>1.249152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>2897.43</td>\n",
              "      <td>451.77</td>\n",
              "      <td>403</td>\n",
              "      <td>7.189653</td>\n",
              "      <td>1.121017</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        total_bill     tip  size  bill_per_size  tip_per_size\n",
              "smoker                                                       \n",
              "Yes        1930.34  279.81   224       8.617589      1.249152\n",
              "No         2897.43  451.77   403       7.189653      1.121017"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NBZuVHsprnmv",
        "colab_type": "text"
      },
      "source": [
        "### Data Visualization using Seaborn\n",
        "\n",
        "**Visualize # of tables per day**\n",
        "\n",
        "*Hint: using the seaborn's function **countplot()** with `x=day` and `data=tips`*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "POjYBe4zsAGm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "7ba35d11-5afb-44a6-cefa-00ad96327c70"
      },
      "source": [
        "sns.countplot(x = \"day\", data=tips)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6680fa58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4pfm5HHhTVT0TeD/d1Rqr6qvAiiQvAZZU1S1jm1DaDcMvze3LwFlJliU5EHh1t/5A4N7u/P05s7a5EvgbPNrXIuVF2qQ9SPJeBtdj3wLcBXydwd8LeCewFbgROLCqzu0e/1TgduDwqto2jpmluRh+aYF1f8/2zKp6w7hnkXbF6/FLCyjJJcArGLwCSFqUPOKXpMb45K4kNcbwS1JjDL8kNcbwS1JjDL8kNeb/AZAIY3fngD8aAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yu9PNmzRvJxS",
        "colab_type": "text"
      },
      "source": [
        "**Visualize # of smoker and non-smoker tables**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yFFZxn0vO0L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "c96bb231-5e3e-409c-c17b-e3276bfd360d"
      },
      "source": [
        "sns.countplot(x = \"smoker\", data=tips)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d662a3c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bZPzcdrqvTWm",
        "colab_type": "text"
      },
      "source": [
        "**Visualize # of male/female customers who pay**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1xEJMbMsvdIR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "4361d0d9-9ba9-4553-b9de-cebe77391a39"
      },
      "source": [
        "sns.countplot(x = \"sex\", data=tips)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d66285c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bKtSBv_rsCbI",
        "colab_type": "text"
      },
      "source": [
        "**Analyze the distribution of payments of male and female in different days**\n",
        "\n",
        "*Hint: countplot with `hue='sex'`*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UoNGFMKosld-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "715ac5e6-be2d-489f-97c2-c71574a99814"
      },
      "source": [
        "sns.countplot(x = \"day\", data=tips, hue = 'sex')"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6672d390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2EiVJPanWK/4NEXFeRAwqvs4DNpRZmCSpHLUG/4XAh4Bn6Jih8yzg/JJqkiSVqNahnquAGZm5ESAiDgCuoeMPgqQ+ri9MGQ5OG95X1HrFf8S20AfIzOfoYvxektT31Rr8+0XEqG0rxRV/rf8tSJL6kFrD+38ASyLix8X6fwW+XE5JkqQy1frO3VsiooWOCdYAzszM35RXliSpLDUP1xRBb9hLUj+319MyS5L6N4NfkirG4JekijH4JaliDH5JqhiDX5IqxuCXpIox+CWpYgx+SaoYg1+SKsbgl6SKMfglqWIMfkmqGINfkirG4JekijH4JaliDH5JqhiDX5IqprTgj4g3R8T9EfGbiFgREZ8q2g+IiPsiYlXxOKqsGiRJuyrzir8d+ExmHg68G/h4RBwOXAYsysxDgUXFuiSpl5QW/Jm5NjMfKpZfAFYCBwOnAzcXu90MnFFWDZKkXfXKGH9ENAFHAQ8AB2bm2mLTM8CBvVGDJKlD6cEfEa8H7gBmZeZftt+WmQnkbvrNjIiWiGhZv3592WVKUmWUGvwRMYSO0L8tM/+taP5zRIwtto8F1nXVNzPnZmZzZjY3NDSUWaYkVUqZd/UE8D1gZWZ+dbtNC4EZxfIM4M6yapAk7Wpwid/7b4EPA49FxLKi7XPA1cCPIuIi4EngQyXWIEnaSWnBn5m/BGI3m08o67iSpO75zl1JqhiDX5IqpswxfkkakJ66alK9S+CQzz+2z3294pekijH4JaliDH5JqhiDX5IqxuCXpIox+CWpYgx+SaoYg1+SKsbgl6SKMfglqWIMfkmqGINfkirG4JekijH4JaliDH5JqhiDX5IqxuCXpIox+CWpYgx+SaoYg1+SKsbgl6SKMfglqWIMfkmqGINfkiqmtOCPiBsjYl1ELN+u7YCIuC8iVhWPo8o6viSpa2Ve8d8EnLhT22XAosw8FFhUrEuSelFpwZ+ZPwee26n5dODmYvlm4Iyyji9J6trgXj7egZm5tlh+BjhwdztGxExgJsAhhxzSC6Vpmymzb6l3CbT+6z/WuwRpwKrbi7uZmUB2s31uZjZnZnNDQ0MvViZJA1tvB/+fI2IsQPG4rpePL0mV19tDPQuBGcDVxeOdvXx89RNPXTWp3iUAcMjnH6t3CVKPK/N2ztuBJcD4iFgTERfREfjTImIV8P5iXZLUi0q74s/Mc3az6YSyjilJ2jPfuStJFWPwS1LFGPySVDEGvyRVjMEvSRVj8EtSxRj8klQxBr8kVYzBL0kVY/BLUsUY/JJUMQa/JFWMwS9JFWPwS1LFGPySVDEGvyRVjMEvSRVj8EtSxRj8klQxBr8kVYzBL0kVY/BLUsUY/JJUMQa/JFWMwS9JFWPwS1LF1CX4I+LEiPhdRDweEZfVowZJqqpeD/6IGARcD5wEHA6cExGH93YdklRV9bjiPxp4PDOfyMy/Aj8ETq9DHZJUSZGZvXvAiLOAEzPzvxXrHwaOycxP7LTfTGBmsToe+F2vFrpvxgDP1ruIAcJz2bM8nz2rv5zPt2Rmw86Ng+tRSS0ycy4wt9517I2IaMnM5nrXMRB4LnuW57Nn9ffzWY+hnj8Cb95uvbFokyT1gnoE/4PAoRExLiKGAmcDC+tQhyRVUq8P9WRme0R8AvjfwCDgxsxc0dt1lKRfDU31cZ7LnuX57Fn9+nz2+ou7kqT68p27klQxBr8kVYzB342IGB0Ry4qvZyLij8Xy8xHxm3rXN1BExNbtzvOyiGjqYp+fRsQbe7+6/iUiLo+IFRHxaHEuj+lm3/Mj4qDerK+/2Jvz2B/12fv4+4LM3ABMBoiILwIvZuY1RTDdta/fNyIGZ2Z7T9Q4QLyUmZO72hARQcdrUSf3ck39TkQcC5wKvDMzX46IMcDQbrqcDywH/tQL5fUb+3Ae+x2v+PfdoIj4bnFVcG9EDAeIiMUR0Vwsj4mI1cXy+RGxMCL+A1hUv7L7vohoKibxu4WOYHpzRKwufgG1e2OBZzPzZYDMfDYz/xQRn4+IByNieUTMjQ5nAc3AbcUV7fC6Vt637O48dv4MRkRzRCwulr8YETcWv/tPRMTF9Su9Ngb/vjsUuD4zJwLPA/9QQ593Amdl5nGlVtb/DN9umGdB0XYo8M3MnJiZT9azuH7kXjr+SP4+Ir4ZEdt+zr6Rme/KzHcAw4FTM3M+0AKcm5mTM/OlehXdB+3uPHbnMGA6HXORfSEihpRa4WvkUM+++0NmLiuWW4GmGvrcl5nPlVdSv7XDUE8xlPZkZv66bhX1Q5n5YkRMAf4OeB8wr5j2/IWIuAQYARwArAD+vX6V9m3dnMfu3F38h/ByRKwDDgTWlFzqPjP4993L2y1vpeNKCqCdV/+TGrZTn01lFzWAeK72QWZuBRYDiyPiMeCjwBFAc2Y+XbxWtfPPpXbSxXmcQfe/2zvnQZ/OVod6et5qYEqxfFYd61DFRMT4iDh0u6bJvDqr7bMR8Xp2/Jl8ARjZW/X1F7s5j0+y4+92LUO7fVaf/qvUT10D/KiYVvruehejSnk9cF1x22s78DgdU5s/T8eL5M/QMVfWNjcB346Il4BjHefvtLvzOAH4XkT8Mx3/DfRbTtkgSRXjUI8kVYzBL0kVY/BLUsUY/JJUMQa/JFWMwS/thWJels/Wuw7ptTD4JaliDH5pD4q52X8fEb8ExhdtHylmvHwkIu6IiBERMTIi/rBtgq6IeMP261JfYfBL3Sgm6zqbjrftnwy8q9j0b8WMl0cCK4GLMvMFOt7ReUqxz9nFflt6t2qpewa/1L2/AxZk5v/LzL8AC4v2d0TEL4oJvM4FJhbtNwAXFMsXAN/v1WqlGhj80r65CfhEZk4CvkQxW2Nm/gpoioipwKDMXF63CqXdMPil7v0cOCMihkfESODvi/aRwNpi/P7cnfrcAvwAr/bVRzlJm7QHEXE5HfOxrwOeAh6i4/MCLgHWAw8AIzPz/GL/vwH+AIzNzOfrUbPUHYNf6mHF59menpkfrnctUlecj1/qQRFxHXASHXcASX2SV/ySVDG+uCtJFWPwS1LFGPySVDEGvyRVjMEvSRXz/wEQNHR1m3zgwwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcehBnDcv0J0",
        "colab_type": "text"
      },
      "source": [
        "**Analyze the # of tables in different time (lunch or dinner) and days**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_200tzWls4qG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "954bc05f-41fd-4414-b600-71a4811525fa"
      },
      "source": [
        "sns.countplot(x = \"day\", data=tips, hue = \"time\")"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d66192828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zIiRuD1lwXIM",
        "colab_type": "text"
      },
      "source": [
        "**Example: Visualize the distribution of the total bill**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQV66iZquyN1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "a66e01f6-e7bd-4389-f77d-85c38410ac09"
      },
      "source": [
        "sns.distplot(tips['total_bill'], bins=5)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6617f8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ShsMMKhSwsEQ",
        "colab_type": "text"
      },
      "source": [
        "**Visualize the distribution of the tip**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWF4EKDfwzeA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "4b805a7c-91f7-4102-b16a-a07baf892642"
      },
      "source": [
        "sns.distplot(tips['tip'], bins=5)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d660ef0b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T9QjEDMbw8iG",
        "colab_type": "text"
      },
      "source": [
        "**Example: Scatter plot of tip and total bill**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlrpjaLQxCAY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "62b4a2ee-bd50-4442-82e4-9230de933ea6"
      },
      "source": [
        "sns.jointplot(x='tip', y='total_bill', data=tips)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f1d6609ef28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-qLLbwYxIGL",
        "colab_type": "text"
      },
      "source": [
        "**Add one more parameter `kind='hex'` to the previous   `jointplot()` function**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jctY9-d4xUhM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "3be941fb-0b56-405b-bdde-22b8f9348149"
      },
      "source": [
        "sns.jointplot(x='tip', y='total_bill', data=tips, kind = 'hex')"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f1d634f8470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WSv8X8EtA+4UlB4GSqoatt58Hrt/oC0XkPSLyoIg8OD8/v/0jNcaYFFg7962UFnZ7ODtqW4uSiPwQMKeqD/Xz9ar6AVU9raqnZ2Zmhjw6Y4xJp7Vz3/jUgd0ezo7a7hsdvgf4ERH5H4AcMAH8HjAlIl5rtXQceGGbx2GMMWYP2NaVkqr+qqoeV9WTwDuAz6nqTwGfB36i9Wn3AZ/YznEYY4zZG3brdUq/DPyCiDxDco3pj3ZpHCOlGSnPLQXMlgMaYf8ZFzlPcB0GiimIYmWxFjFbDqkPMJZ6GDOWdZjKufTbps4RODLm9f31o+5I0aU4wAu3PQeK1s3BDMmOdXRQ1S8AX2j9+9vAK3fqe486VWW2HHKhHK6+sn6xFpFxYyZzbtdNR5O+cA6e03/nA1WlEsSsNK60rVmsxfhu3Oqp191xw1hZqkc0InBEyHmQ9TxWGjHVoPsiN513OTHp42zQlNUkXEcYzzoUM0nLoG67ZwgwnnEoZux5NcNjbYb2uJVGxJlSQBjrulYvCjSipCHpWMahmNm8wevVk0u/k3czSrKionh9m6Kk9RHMVSLGMsL4FmNRTbqarzSv6pwu0mpWmzyWUi0i2KK3Tc4TTk755DzrBN4NRwRH4EDeoREqS414y9ZB7S7rVuzNsFlR2qOCSDm71GS5vnUshQLlZrK6mMq5ZLz12yztyUUGmFxiTSI4al30UCs3lWoQMZVzyF01lkYYs1jfejIUETyBgwWXeqgsN6J1n+8IXDfuMVP0LFKhD1dWpS4rjZhKsP6H4QpM5Rx8d7DGwcZsxorSHqOqzFVCzq+EXTfBVCBSWKhFZL0kAC/jysCTi6pSDWKWG711mI51/ZaecGXbqOs03g229KZzLiemkq06mzD7116VXtnSiwgiGMs4jA24mjamEytKe0isypNzDYKrtuq6pSQdofNezPUTmYFWEqrKpWpEGPfXUXztll6/1m7pnZzyybhiW3VD1N7SO5h3V3/GVozMdrOitIeEMTTjJN58EONZdygriR7uN9hWyarJYim2i4iAqj2/ZkfYfZx7jE0LZjdYQTI7xYqSMcaY1LCiZIwxJjWsKBljjEkNK0rGGGNSw4qSMcaY1LCiZIwxe9D9D5zj/gfO7fYwhs6KkjHGmNSwomRMSg0SHWLMXmVFaQ9xh/T6xWaoA094sSpCOiZOVSWKIe6n91JKxa3nNQ3PrzE7yYrSHuI6wu2Hc6uRAb0SIOMKE7nNoyM6UVXiWFmsxcyVA+p9FjiB1aiEA/n+Hg8kk3epHvH5M2XOLQVE8eAFdzepKrEqy42Y+VZvwXgPPx5jemW97/aYjCu86GCW5UbEmcVmskLo4uscgaNjHkfHvP4jKmKlESnPlprUWpEGpXqE7whTeRe3h/iLgi9MZK8UxyNFYaUZU252NwHHsRLEyrOlgEozeQYem2twZingZUdzFDNO14GCaaCqqw1zl9dkGc1XI/KeMJlzLIrD7AtWlPaoiazLHUdyXCyHzJY3j7FwJAnwOzGVIdPn/l+sSRPY55YCLteu7eodxEmYYMF3GM9uPXlmXDZMoBURJrIuBV9XoxI2ekjJSgLOrwQbdhhfacR86WyV4xMedxzO4Qip7xyuqkSapAVv1OS2Fir1csRkziHf+j/WipMZVVaU9jBHhOvGfQ7lXc4uBZQb8eqqyQFcB05OZZjIuX0dv332vlCLeH4poFNKdjWIqYdJXlPOWz9xOsBkzunYzdtzhEMFj3oYU6rHqF4pTrEqy/WIs0sBYYfl4fPLIRfLZW6fyXL9RDrj0NvP73Ijphps/eQqSeZUxUmKuuuoZUaZkWRFaQRkPIdbDmZZqkecLTUJYzgynmzVDRLgVw+VM6VmxwlzrVhbW3quMJ1zcR2h6AvjWaenseQ8hyNFYbkRU24mAYBnSgHlZvd5GWEMX5tt8Gwp4OXHcoxtEcO+k9rXvOpdxI5fLYiTLb2CnyQGQ/qKrTGDsKI0QiZzyZZerAx8PeUb8w2qXcSbbyaIklTam6Z9fLe/+2lEhMmcyxNzdS7X+g9vWm7EPHi+zqtPFPD6WzQOVaxJYRnkZsFqoHiOMpaxe5XMaLGiNGLaaaGD6rRV161hbDGN0J3eq0bxMRkzDHaaZYwxJjWsKBljjEkNK0rGGGNSw4qSMcaY1LCiZIwxJjWsKBljjEkNK0rGGGNSY98Xpb3eVfpqsSrBEF5k5KXoN8ORpKv4oIbxvMStLt6DSnk7PmN2zb598WwYK0v1iEYEvgPT+WubhO41pVaboSiGI2Mex8b7bzP04kNZGpFyZjGgslGX0A7GMg6npjMDTb6xKiuNmBunMlw3kbQZWmn0PhbfgaIvPHmpwYG8yw2Tfs8/a1Wl3IxZaWrXffw24wgcKbp9tRlqK/pJ+yZVtTZDZqTsu6LUnlzKTV1t9BnEMFeJ+urRlgaNMOZMKaAaXJngZsshl6ohJ6cyTPbRkFVEyHnCrQczlOoRzy13boIKSQE4MekzkXMH7ru3VE8azIpIEtkxnWGlGXO21Nywm/Y1jwFanbWvFI/FWkSpHnF8wuNQobsYj3Zz2PZzG5M0R/U36XjecVyt75nzIOu5rDRiKl32F2yfQKWxwawxw7CvilIjjFmsb35mWg2UWhgxme3/LHgnxapcWEmiK65+SErSkPTbi02KvsONUz7ZPvbkHEeYzrtM5lyeXw64VL02LqLtcNHluvHBOnKHsbJYS8Ltrn5MjpNkMN1++Epkx2ZTeTuv6erCqIBq0kV8rhJxaipDYZP+cVGcxGg0N4jRUKAZDXYyIyIIMJ51KGZY/V4bcQQmWr+Xe+2kyWyv+x84B8C995zY5ZEMx74oSsnkknSa3up8tD1hDXIWvFOW6hFnWlt1Wz2mWGGlGfPEXCMJ+etjS09EcAWOT/gcKXo8e1Xn8LGMw8kpH98RnD6fr/ZWXacVQ3ssx8Y9ZgpusqW3pnO477R/blsXxliTLt3fuNTgQMHl+MSVLb2NVtNbaZ/MTOUcsm7vJzPtfoUH8i6NDbb0VgMRsdWRGX0jXZR6nVxWv44rZ8FjrbPgtEwGjTDmbCm5ztPLtQhl/ZZePxlLrpNMni8+lGWxFnFhJeD6CZ/JrNt3MVJN0mzb2UndckTIeMKLDmZYbkQ8Vwoo+A55v7eioMBCNWKxFnHDhMdYxqXU43We9snMYm2wkxlHZN2WXjPSVnbScBrbGrMXjHRRWqzHNMLeCtLVaqEynh3akAYSxcqT842+O0zHQBwnK6d+C217y2k673Ig766+r1+1MClI/XJEmMy6NMa07+elXVRmyxGNfP+Pp30yEyt934DQfn4nss669xmzX6Toxt/hi+LBChKAyNbbYztpWHESmT62mK7WPnMf9DhR//VolYgMJQpChvR/gzuEmxBkSM+vMXvNSBcls71swtxe9vya/ciKkjHGmNSwomSMMSY1rCgZY4xJDStKxhhjUsOKkjHGmNSwomSMMSY1rCgZY4xJDStKO2RYmU0jFP1kjDHXGOmilPMGf/HhMDoOxLES6eCBgo4ovisDB94tNSJi3f1wQ1UdSpigquIP4TjDCAGEpNHrMIIAjdmPRrr33XjWJesl8QOdumlfTUjiAqYGzAWKFZ5bCvj6pQY5z+HuoznGs05PDTvbx1mqKwfyLtUgZqUR993+aKmedA2/cdKnmHFwd6ETeqxKs9URu1+qSRup5XrUVb7SZgTwXWEy5w6li8JSI6YWClM5Zygth4zZT0a6KEHS522mkEzky43ueuEJMJ4Ripn+u4OHsVJpxjx8ob4arVBuxnz5XJXrxj3uPJLDE7bsrt1eyay0Op1DMsEVMy45z2G5EVEP+ytNzUj55kKTiazDyanMjnWiVk1Wje0okT6PgirUgpjlAYpz+9FO5VyyQ87PakaadJnPCGMZi50wplsjX5TgykSe95M009oWE3nOFSZz/a8e2lt1j8/VeX453PBzzq+EzJbL3DaT5cTkxqF4sSrNKBnvRnO36wjTeY9mGFOqR0ln6j7Gu9yIeWy2nmQtjXnINp3Zt1c15TUFtt/jhHGSJxUM2IW14Avj2eGsjjZTbiq1IGIy55BxLYLCmE72RVFqcyRJUS1GyZZeO9577VZdto/rUO0VTazw/HLAk/ONjtHhSeFqcLYU8LJjOYqZZEuvvVVXqsc0ulhJZDyHmaJQacaUm/2tGhS4UA65XIs4OeVT8Ie7pdepwHY1xvZWXSOi1mV0+EYE8BxhKr9zAY6RwkItJusmW3oWZW7M5ra1KIlIDvgSkG19r4+q6ntF5BTwYeAg8BDwLlVtbudY1mpv6VWC5Ky96Le2WPqcKOphElT3tYt1lnu8RrLSjPnS2SrXj3vcdSRHLdR1SardEBHGsi5532GxFvZ9faUZKU9fbjKZc7hpOjPwllM7OmSpywK71XEakbLciAa6+zCJFHd3Leq+ESmzlYjxzGC/b8aMsu1eKTWA16tqWUR84Msi8ingF4DfVdUPi8h/Ad4N/OdtHss6IsJYxmUsM/ixLpbDgcL3AF5YCTlUDAc6e3edZJtyqR4NlAG1VI8JIiU74K1xYaws1Pq/5tM2SKFt8xw4WOg9Cn47VAIdyu+dMWvd/8C5Dd9/7z0ndngkg9nWW8I1UW696bf+KPB64KOt938QeOt2jsMYY8zesO2vUxIRV0QeAeaATwPfAkqq2r4L4Hng+k2+9j0i8qCIPDg/P7/dQzXGmFRYO/etlBZ2ezg7asvtOxH5K7a4qUtVf6TTN1DVCLhbRKaAjwMv6XZwqvoB4AMAp0+ftlcjGmP2hbVz30233bWv5r5O15T+j2F9I1UticjngVcBUyLitVZLx4EXhvV9jDHG7F1bFiVV/eIgBxeRGSBoFaQ88Ebgt4HPAz9BcgfefcAnBvk+xhhjRkOn7bvH2Hr77q4Oxz8GfFBEXJLrVx9R1b8WkSeBD4vIbwAPA3/U27CNMcaMok7bdz80yMFV9VHgZRu8/9vAKwc5tjHGmNHTafvu7E4NxBhjjNnylnAR+XLr7xURWb76750Z4vaJVakG8cAxAwKMZQa7u96RpBfboJ1v6mFMLYwHjqWoh0o4YG+5ahCzWIsGHstE1iHrDvbEqEI9GPx5yboycNxGu5nsbkeHGJNGnVZK39v6e3xnhrMzVJV6qJTqSbcBASayQsHvrfVLrEk/t4zncPN0hkoQc6YU9Nz9eqbgcvexHBlHQJImqdUe+7tFsfLCcsClagQkHQymci5+j5O57wpTuaQFUyXQvlriRLHy9OUG314MgKSt08mpDMUeC7fnwHTOxXVcUJirhpxfCftqNRRp8ryWmzGTObfnbhWuwHQ+6U8oQDXQvjuUK1BqKMvNiKmcQ24YoVLGjIiu2wyJyMuB7yX5f+rLqvrwto1qG4WxslhLmrG2J5Sk0adSCSKmu5jItbXCWhuF4TjJ5H374SwXV0IulsOOE1bOE156JMeBwvrmoJNZh7EMLNY65wSpKov1iHOlYF2n8CCG+WpE0RfGs07H9jqb9YVbabafF6eriXy2HPK1i3WCWFfbLtVD5alLDQ7kXY5P+h1bKSUnCQ4FP/k8kaRYHy54HMx7nF1qslTvve+Q0m6OGpF1k+LUqfFsEmPiUMysGQtQ8CHvux27zm8lVlisxfhuzFRu5xrEGpNmXRUlEfk14CeBv2y9609E5C9U9Te2bWRDFquy0kjO/jeiQNiayAu+MLHJRN6MNg8NFEnOoo+Oe8wUXc6Ugg0btApw84EMtxzMbBgCJyK4KAcLLvUwOSPfaCetFsScLTWphbpp371qoFTDiKmss2kj0oLvMJ7dPPMnVrhci8m6mgTXbTB5VoOYr12ss1iLNuwEriTFYLEecXzC41DB2/B75b0kOmSjsTiO4ACnpjJU+1yVtjUiZb4SMpZxNs3NynnCZNbZMM6j/bOezDkUY9Z1ne+FAs2I1eylcWvUava5bldKPwW8VFXrACLyPuARIPVFqb1Vt1TvfqulGlzJwMm3JvL2Vl03Z8WOCI4r3DydodyMObMUrEZtHyy4vOxojowrW56ltye9vAc5z123pRfFyvmVgPlK58ar2vpPqR7jO6yLbPCdZKvOdbrrBt64OrhOhChWnllo8sxCE/ob430AACAASURBVO2Q6aQk11OeXw6Zr0ScnM5QaOWYt7cbvS7CBt32qnQmy2wl5MJK51XpZuMpN2OqQbJSybRWgq7AVM7Bd6WLFabgO9rqOq8DJQKXm0o1sC09s791W5TOAzmg3no7yx7owhDGyaomiHoPwGtHLpQlOWOuBN2l1q7lOMnW2e0zWRaqIUfGPWYKXk9ZRe3iNJF1KPrw7cUm31poJhN8j4+nGSdn5OMZ4fiET77Ha2jt47QnzyhWHp9rEMabr9Q2EivUQuUb8w1mii7fMZNdvVGk2/GICCJwZCxZdT2z0Oj5Olz78UQKl2sReS/m+ITP2Barxs3GAlD0oeC7LNQimlHPQwGubOll3JjpnLtlMrExo6jTi2f/E635GXhCRD7devuNwD9t//AGU6pF9BhNtI4CoUJ5kFA5EVyBWw5lV1dd/XBEaMYR37zcHDgKYjLn9lWQ2pKtTuWrF+odP7fTcca32D7rRrIqpe9Y+LXyvsNYF9ffNtM+gYgGjNloBd9ju3hmP+q0Unqw9fdDJM1U276wLaMZsjTdcOsweNqoktwyPkBeHgCuDB5yN6zn1mlN5IMaxt3Vw1qUDOO5EWH1zlBj9pNOt4R/sJuDiMjHVPWfDWdIxhhj9qthXU29aUjHMcYYs48NqyilaafMGGPMHmX3nRpjjEmNrjs6dGDXY40xJoXuf+Dchu+/954TOzyS7gxrpfTLQzqOMcaYfazfkD8BtB3yp6p/tw1jM8YYs89sa8ifMcYY0wsL+dshw7o9cRjHGbDhgDHGbJuurimJyHeJyFdEpCwiTRGJ9kLIX7vz9aD8Aa+8tRuoVoO47+A8VU0aqmYHvww4XwlphP2HG2qr82rRH+zZFeBiOaAWKNEAz0usykRm8LEsNyKCSAd6XlSVIfyIaIRKc4CxGLNXdXv33e8D7wD+AjgN/HPg1u0a1LDkPIcjY9JXaB5ciVFwRGhGSQ7T2syiTqI4SW99dLbObDnEFXjZsRx3Hc3jSOdu2G1xrCw1Is4tBeR8hxlHVqMS+pmy6mHSSPVw0eW6cR9ng2iGrR5TJYg5O0BsBLRyirJC0Xf4+qUGBwsuN0z4SA/PSxQr9TDm2VJAY8Ded8WMw1jG4XItJucJU5vEZ2wmViVqRVh0ysDqRhL1EZN1k7H08jMyZi/r+pZwVX1GRFxVjYA/FpGHgV/dvqENhyNJPEPR164mcgHcVoxCZk3YX8YVDhddKs2YlebWHcOTs3c4s9jkqcuN1Q7akcKD5+s8danJa08VOVTwtgwUjGMliJUzpYDyms6yviscKrirQYPQX3Gaq0Qs1iJOTPpM5Nwti0F70j1Tam6YEdUtIem6PpFdn8t0uRpRqkXcMOkznXe3LAhx6/k9VwpYrPfZjrs1lnbS7tqx1ENlthwx3urMzhZjUdVWSGR/Jz6dNCJldm1cyBZjMWYUdFuUqiKSAR4RkfcDF9hjL7xtT+S1IGapsXFR6RSLLiKMZV3yvrLUiGmE1x4nipXlRsQjF+pUNjllXmnG/NVTK5yY9Pm+k0V8V9aljraL2vmVkLlKuOExRIRiJhnLIBNiEMO3FgPGMhGnpny8qzKEki2pJFH2QhdpupsRkoan0/n1xX6tSOFMKWCuEnJqKoPvsq5YtAvApUrICythT3EZV49FJDnx2CxN90qhaec86TXPCyQRHL1kdfWr3EwyvpKcp+5Xk8bsNd0WpXeRFKF/A/xb4Abgx7drUNtFRChkXHIbTORrt+o6cR3hQN6lEbZSaDVZ1bS36i6WNy4kVzu3FHD/YyVefizPnUdyuJJ0u15ubdV1sw3UXgkW/M0TcbtRbsY8PtfgyJjH0TEPR5Jsn0ETXqEdKS5dR1RUA+WJ+QaHCi7HW1t6qskK5kypOXBMRXurrpuxhDFcqkbrEnEVhrpV161oNQH4ypYe2MrJjJZui9JbVfX3SEL+fh1ARH4e+L3tGth2WrulV27GFDPOpmfvW8l6yZbetxaanF8J+eblRs+xElEMX3mhxlOXGrz+VJFSPWaljxCojCvMFFxK9YhadzXxGgpcLIdcrkYcG/dYqkcsDbBV5wpkXRjPuj0FG7ZdqkaU6hHHxjwqgbJQ63+rzhHIuslK1+tjLLVQqZcjxjMOoeq2bNV1q50AfCDf3++tMWnW7RbcfRu876eHOI5d4buy5XZSN0SEeqQ83UdBWmu5kVyw76cgrR1LznMGzgUKYuXcUjBQQYJkRTmZ668gtYUxPLccDlSQIClKE7n+ClKbAsvN7bl21M9YgthWSWb0dOro8E7gXuCUiHxyzYcmgIXtHJgxxpj9p9P23T+Q3NRwCPidNe9fAR7drkEZY4zZn7rp6HAWeJWIHAG+s/Whr6tqn1cujDHGmI1129HhJ4F/An4SeBvwgIj8xHYOzBhjzP7T7d13/wH4TlWdAxCRGeAzwEe3a2DGGGP2n27vvnPaBanlcg9fa4wxxnSl25XSp0TkvwEfar39duBvtmdIxhhj9qtuVzsK/AFwV+vPB7ZtRMYYY/atbldKb1TVXwb+sv0OEfl1LAYdVSXvCi8+mB3oBbRZVwaO2VBVnl8KuFyLuG7C7/uFotUg5unLTY4UXY6OeX29QFNVeW4p4Im5mNPX5Slm+tvt9Rw4NuZTCeKBXkAbKyzXo747OkC7XdLgHR0yrlDwhHIQE/b5+mQhiVRRVXsBrenL/Q+c6+rz7r3nxDaPZL1OL579V8C/Bm4SkbWvSxoH/n47B7YXNMKYUj0mn3E5Ne1ww5TPY7N1Lqx0f7e8I3Bs3ONw0RuoyWapHvHwhTq1IEYVLtcijk94HCp0X1RiVZ653OSZhWbSEHY5YCrn8tKjuZ6Kykoj4pGLdcqNmBh4ZqHJ6ety3Hkk19NjnCm4XD+RRGscVJcjRY9n++x9F2vSKqgWhoz10PuubX3vO2EsA4u13nrfOQKTWYesl5yA5H2XSqCsNHpr6JrzhMns4J07jEmjTiul+4FPAb8F/Mqa96+o6r7t6BDFSWfo+pplkeMIGYS7j+a5aTqZlCsdWgZN5hxOTmZwnP67Pjcj5cm5OudXwvWrNIXnl0PmKxEnpzIUOhSV+UrIIxfrhLGuHidSWKhFfOFMhZumfW49mN2yZVAYK09danCmFKBrcqdihYfO13lirsnrThU5Nr71r13RF05OZ8is6VjuCuR9uG0my6VKxAsrQd9dwivNmGoQM5lzyW3SJbzNc9pdwq/8jAQQlIMFl3qYNPftNJaiL6uhk2uLYdGHgudSasQdi60rtLqEi3UJNyOr04tnl4Al4J07M5x0U9WOeUqukzR7fc2NRc6Wmjx16dotvYwrnJryKWS660q+2VjOLQU8OZ/kNW00KbZXB99ohehdv8GWXi2IeWy2zqVqtOHWo5J06H62FPDcUshdR3McHVv/a6OqXCiHPHqxvulYghiCZszffDOJ7PieEwUKV0X6ug6cmPCZyrnIBqF2IskK41DR5UDB5dxSwGIfW3rtx1SqRfhufE2eErRDCJ3VdN3NxpL3IOe5m8aHZNykqG0W6igirRgNhzBWFmvxhj+H8YzDWGbjsRgzSroO+dvv2jEV3STPigiuwI1TGY5P+KtxFkKyVXdkbLCtuqX6lZVYN9ewkhTTJNDv+ITPwYKLAt9aaPLNy01UodMuVBRDhPLV8zWm8y4vPZKjkHEoN2O+dqHGUqO7sYQxnC0FnFta4pXX57n9cBZHZF1MRafnxhHBEbhx0udI0eVMKehrS09JVppzlfVbemu36joVgHZxmmgFArbjLNpbdTlPuioijgi+QytIUllpJlt67ZiKjYq0MaPIilIHqspivfPWykZcR3Ad4WXH8izWQ2pNxXX6n1xUlUdnGzy/3PvWVfvzn1sOeH4pYL4WEUbae9SGJimxnz9TYTrnsFiP123VdXsMFP7phRoXV0J+7rsOkPOcnruJu04SyHjbTJZvLzRYavR/80GlGdOMYm49mO1re8wRQRzlUMGlGSl+68aVXn7W7c8tZJLrTZGybtvQmP3AilIHYZyskgbhOoIgeO5gY6mH2ldBWivWZKU1SFBee/vrcm2waIswhpfMZMn7/W9jtlcqywMUJEge00TWHeh6TbuoZNzBVjWOCAg4dmed2YesK8MOGdbUMopT1Kg9pmEVEitIZj+yomSMMSY1rCgZY4xJDStKxhhjUsOKkjHGmNSwomSMMSY1rCgZY4xJDStKxhhjUsOKkjHGmNSwotSBsnnz1Z3miA7UzaFNVUEH68YwLFE8nMc0jNeZalp+0MbsY1aUthDGSrkRc365SRhrMpn3SFUJY2W+ElKqhcR9znxRrDSjJEsH+uyCoDFh0OSZr3yec098hSho0lvXuoQrkHXh+IRHxk3e7pWQ9HUrNaKk+aj29/y23XIwS9aVvjOGhKQFkxUmY3bXtva+E5EbgD8FjpDMfh9Q1d8TkQPAnwMngTPA21R1cTvH0ouolSn02GySUwQwV0k6bB8ouF032oxi5XItiXRo95oby0ScmvLxuuyxppqM5fG5Bs8tBVfe3+NjCpsN5s8/x6f+/L9SujQHwIHrPserfuxnKEwcwPUzHY+RPG44NuZyuNXp/HDR5WI55GI56roxq+/AwYLLW2+b4OiYTzVQmlHUyi3SvnrPjWUcbj+cZa4Scn4l7Hr15Qj4jnBy2mcsM2BzQmPMwGSQs9OOBxc5BhxT1a+KyDjwEPBW4KeBBVV9n4j8CjDdilvf1OnTp/XBBx/ctrFCUgBihXNLAV+f3zjaPO8Jp6YzyVn5JqflUawEkfLIxRqXqhvn/Rwtehwd93C2iCSIYuXCSsjjc/VNE06TJNTNxWFIo1Hn0x/9U555/KvXfr0IN7/81bz0jT+O7/ngbDwxt6MYbpj0yWywNGqEyrmlJivNzbfjPAdcEd5yyxgvPbpxCm0vsRGbCSLluaUmSx3C9xyB61qpv9Znzuywrn/hbrrtLv2NP/nr7RzLlrYpDn3Tx7+tRemabybyCeD3W39eq6oXWoXrC6r64q2+druLUhgr5WbMwxfqlDskxgIczLvcMOmvKyqxKqrwzEKDb7UixbfiO0nm0njGWVfgoliphclYSvXur/2sK1AaE4Yhj/7jF/nypz5OGDS3/NpsYYzTP/B2rrv1pbi+T/t3xpUkfO/klM9EtvNKYqkecaaUdDJfW9Q9B+48kuNNN4+R97feNe4UsNetlUYylvCq61ZXCmwGv5+9R2MGt2eKUjf6KFybPv4di64QkZPAy4AHgCOqeqH1oYsk23sbfc17gPcAnDixLdV69UL7Y7N1Xmht1XXjci2iVI+4YTJJSgVYrIV87WKdWpexEEEMzyw0Gc84nJz2cUVQ4Mm5BmfXbNV1K4mUUMKgycLsef7mQ3/E4vzFrr62US3z9x/7Iw4eP8Wr3vozjE0dxHFdjo65PYUSTuZc7jzicHEl5GIlwhWYzidbddeN+10/jiTJFaZbW3rQe3Eaz7rccdhZ3dID8Bzh5JTPeBcF1pjdsnbuO3T0+l0ezc7akZWSiIwBXwR+U1X/UkRKqjq15uOLqjq91TG2a6X01fM1Zish4QA3owlKM1Qu9RHNfeUYiflqRLPX5L01nv3GYzz2wJd45vGH+x+LCD/3H3+Ho4emN9yq61beE46O+9w2kx0oqG6m4OC7g92TE0TKSjNiOufaVp1JA1spbWLb774TER/4GPBnqvqXrXfPtrbt2ted5rZ7HJs5vzJYQQIoN2MuD1CQIFkdXCiHAxUkgGajzvPPfH2wsahSdMKBChJAwXd48cHBClIynoG+HADfFQ7k7dqRMWm3rUVJkhngj4Cvq+r/ueZDnwTua/37PuAT2zkOY4wxe8N2X1P6HuBdwGMi8kjrff8z8D7gIyLybuAs8LZtHocxxpg9YFuLkqp+mc33Dt+wnd/bGGPM3mMdHYwxxqSGFSVjjDGpYUXJGGNMalhRMsYYkxpWlIwxxqTGvi1KqspsOeBw0WUs0/8LKi9XmvzpA8/zpw88x+xyo++xPD1f4zPfXOIbczWiPgOGKpUKTzz9LMzcjJMb6+sYAC+64+XU/QlK9f6jNgCWGxFfPFNmttx7y6S2rCt4rgwUa2GM2Tt2rPddmpSbEWcWA4JYcR1hPONQ9GGxHnfdUSGIYj771GW++MwCUZw0Yv3wgy9w29ExXnPLQXJ+d73VLldDvvTsCsv1iCCGc6UGzy81ufNogcNj3XUgiKKIxx59lK88+CAax4ifI3vsFrS2Qn3+DETd9fSbOnSYH3j7z3LouhsQ16MWQC2ImMwKed/puRtCEEMQK189X2M63+Suo3kKHZqxtjkCU1mHjNddxIcxZjTsq6KURBoElOrRurgHEcEVOJh3aERKqb515MHXL5b5i69eoB7GBGuKWBgrX79Y5qnZCq+79SC3Xze+6UTejGK+8nyFp+cbSbjc6jEAlEcuVJjKudx5tEBhi5yf8+fP8/nPf45arUYYrik+4uAWJsndcAfh4nnCpc07OXl+hu9+0w9z9/e8Ac/zQJLC0R7TUkOpBEneUT9dtSOFy9WILzxb5kUHMtx8IIu7RRpf0RfGs4PFVxhj9qZ9UZRUk+TXF1bCLYPoRIScJxwuCiuNmEqw/jMXqgEfe/gCz16ubbqiCmMFlM89fYmHn1/izd9xmMPj2XVj+dZCg384WyFuBfhtJIphsRbxpWdXuOlAlpsP5tZN5NVqlb//8pc5c/bs+mK0RgyI45I5eBx/8jCN2WeJG5V1n3PTbS/lTW+7j2wuj+tt3MVbSVY9l6oRhVbB6HX1knQwh28tNDm3FPDSo3lmiut//TKuMJVzcARbHRmzT418Uao0Y86UmjSjzcPnruaIMJF1KGagVI+oNmM+//RlPvfNBeJ480KyVhAp8ytNPvSVF7j92BivvuUg1UD50rMrlGrRpqF9a7XHe2axwXOtLb1DBZfHH3+cBx54IMlMijofSBHwsmSvuxWtLVOfO8vE5BQ/8I6f4fDxk/iZbMdjJMeBaqBUg4ipnEPOk9WVTKewwbZIIQqVB1+ocqDgcteRPMWMw2TWIWtbdcbseyNdlM4vB1wshz1Hh0OyavIEcg689zPfptKI1m3VdUNJVk5PXijzzctNsrlc15Hha4VxcpyHzi0z+9DfEjaqm66OtiQObnGK73jd3bzhDW/Adz1wervXpT32Uj3mUMFZ3c7r9TFFCpcqEU/N13nzLeNbJvAaY/aPkS5KS42or4K01qVKQD3o/gaIjQSxknPdrldqmx6n2aBeraBx/zEZscKNJ27E9zMDjUVJAvMGPcbM2NaR8MaY/WXf3hK+42zONcaYjqwoGWOMSY2R3r4zxhiz/e5/4FxXn9dNbLqtlIwxxqSGFSVjjDGpYUXJGGNMalhRMsYYkxpWlIwxxqSGFSVjjDGpMZJFSVWZKwdUg0H7OUAx6xLGymANB5SgGaBhc6CxuF5yB/+g3Q/K5TIad9F8b6uxSNKRYdBfoFoQD9x1wxgzOkauKFWaMU/ON3hhpY/ecOsosSqBCu965XGum8z2FdtA2CC4/DxP/959nP2L3yCqV5C4t7EJSaENY2X8O16DP3EIcbrLa1or43tMjI9xx83HmS54STREz0dJCtLNBzK89mSRI+NJm6B+jpHzhBsmM9bswhizamRePBvGyvNLAQu1wfvdxbFSCWLOlAKakTJdzPD2V1zPN+cqfPob80SREnRoZCcaEQVNLv3t/03pHz8KGlM/9yiLX/07Tr7t3zP50jfiZLJ0LgtKFMVUqlXiOMbJ5Cnc/J0ES/PUzj2KxCFRtHUvPNdxcByHV9/zCr73la/A85KClvOSiI5qoF09Z54DYxmH154a43ArduIV13ksVEMeuVijEXbuoC4kAX63H87xiuvy/RV6Y8zI2vNFSVW5VA15fnnrrKRuxKpEMZwtNVlqrN/eEhFuPTLGyYMF/uFbC3ztheUkcXajMQV1Kk/9Axc/8X6i8sK6j4WVRZ7541+kePIubv7p/53M9FHEz11zDGmNp1arEQTXxon7kzN4t7+W5uy3qM1+G0d1w+jyjO9x8vh1/PCbX8/UxPi6jzkiTOZcCr5SqkeE8cbPnytJIbnneIGXzGSviZc4UPB47akxziw2eerS+tDCtTwHDuQ9XnuqyFSu95WeMWb07emiVA1inl3sLStpM7Eqs+WQCytbR11kPIfXvvgQdx6f4L89McelSvNKpEXYIFy5zIWP/EdqZx7Z8vtVzjzKo7/+Axx+zTs5/tZfxPMzqJP8OFSVIGhSrdW3PIY4Ltljt+IdOE7juccJyourHcQzvkc2m+HH3vL93HLTjVsex3eFQwWXahCz3EgeS/s5cAVOTWf47hsK5LaIMndEuOlAlusmfB6frTNfCVdXTa4kHcVffWORU9O+dQQ3xmxqzxalhVrImcVg4K061dZW3WJAo4d4ioPFDO/8zut5arbM3z1+kUazweXPfIDFv/8wdBstoTFzX/wzFh76FKfe+V4m734zGl/ZquuWmy1QeNErCZZmqZ97HIlDvvs7X8Zrvus0vtfdj1hEKGZc8r6ubulNZJOtuiNj3f+a5DyH09cXuFwN+dqFGvVIuW0my3deX7CtOmNMR3u2KA3rrq3HZutdpcBuRER4ydFxnv/SR/jkB36bxtLlvo4Tlhf45h/+PC967+dwcmP9DQbwJ49w22tv5HUvmmJivNjXMdpbem9+UZ4DBa/vJNiDBY833TJOwRfGMrZVZ4zpzsjdfderfgvSWi4xcb088HEGCe9rc1yXQiE/8HHyvjNwNLkjQmGLLT9jjLmazRjGGGNSw4qSMcaY1LCiZIwxJjWsKBljjEkNK0rGGGNSY8/eEm6MMSb97r3nRE+fbyslY4wxqbHvi1KlGaEb9IzrRRg0Bz4GwETOH/gYvitkvXR0TtBN+vEZY8xm9uz2Xd53EPrvCL5YC7n/a4s8MVvn6LjHq0+OM9Fjk1BV5cFP/7/89QfeT9jsPytpYvog//LXfofvfvPdXFxucv9DF7i43Oj5OK+5aZLf/qFTTOQ8vnahxsMX6z33BPQcuG0mO5QXvQYxzFViir4ynh38xbjGmNG3Z4vSgbxH1nU4U+qtIWsYK595ZoVPPX2ly/dsOeRjTyxy55E8d19XwOsiIOjimaf54P/yc7zwzJM0alUAHMdBVbteNTmOw1ve8W7u+3e/Tiabw3Ecjk1m+fnX3shXzi7x/z0xTyPs3HLi+skMv/kDJ7n7+jHyflJYX3osz0tmcnzxTIXnl6/tMr7hcSZ87jicw3UGDxJcqxIo1SBiKueQ88QashpjNrVnixJAMePwHTPZrqMrnpqv86cPL1BuxjTXNF+NFVB4YrbGU/N1Xn1qnBNTmQ2P0ahV+OR/+S2++LE/Jgqa6xqnrv234zhbNlW95c5X8Avv/wCHjh0nm7vSFkhE8F3hnpOT3H18gr985CKPvLCy4TF8V/iXrzrG/3jPMXxXcNcUU88RvIzwppvHuFAO+NKZKpVNeiqNZxzuPpZnLOOsO8YwKVCqx3gOTOfdrgq/MWb/2dNFCZJJfKboM533eG4pYHGDkL9SLeLDjy7w9fnGumJ0tSCGIFY+961ljoz5fO/JMcazycpDVXn483/Fn/1v/46gUSNobB0rsZnxqQO85z+8n+/6/h8mk81tumpwHYdCBt72imN83y0H+NBDF5hbubJF+D0nJ/itHzzFZM7bMlLCc4Xrx33edsckj1yo8bXZK1t6riRbdTdMZnBkuKujjSjtLb2Ioi+2pWeMucaeL0ptniOcms5wuBjzbKlJEGqrwKzw108lW3XdJlOEMVxYCfjo44vcdTTPseAC9//mz3PuqUdXt+o6aa+S2lt6AG/6yfv42V/+TTLZHK7X3U0NGdfh+FSOf/u6kzxwpsRD50q89003cvqG8dWtuk4cR3CAl12X57bDWb7wbAUF7jySwxPB2YVVSzVQakHEpG3pGWPWGJmi1FbMONw+k2W+EvJrn73I47P1LVdHm2lv6T305Ld5+L1vhjgk7hA7vuFxWsXpX/z79/Omn/znZHOFno/R3tJ79c1T/MabT+AIfW2zeU4SI/HqE0VWgnhXVymtp5dGqORScregMWb3jeQt4SLC4TGf6lXXjvpRW1nE8fy+CtJax0+9qK+CtFbWc5E+C9JaMZqabTNnJH8DjTH9Gu0pIR3zburYVpkxJq1GuygZY4zZU6woGWOMSQ0rSsYYY1LDipIxxpjUsKJkjDEmNawoGWOMSY1tLUoi8l9FZE5EHl/zvgMi8mkR+Wbr7+ntHIMxxpi9Y7tXSn8CvOWq9/0K8FlVvQX4bOvtbdFrbMOmUhQJZK8wMsaMsm0tSqr6JWDhqnf/KPDB1r8/CLx12N+3GSnPLDS49WAWf8DuB/mD1+Pmx8gM0I0hk83x8Jc/t2XX8G5EsQ7coQIg4w6ntAmDF8lgsEYZxpgRsxvXlI6o6oXWvy8CRzb7RBF5j4g8KCIPzs/PdzywqnJxJeCJuTpL9ZgffMkkv/C9MxwZ88j2OBG7kkzet544yk/94T9w14//K9xMDtftvl2giJDJ5nj9j76Dt/3rX8Lps6eOI5B1hZcdy5MfQvhewXc4XHTJuP0VFQGmcw5Hig4TWen7GFkXpnKOdZgw5ipr576V0tXn9aNNhhHjveU3EDkJ/LWq3tF6u6SqU2s+vqiqHa8rnT59Wh988MFNP77SiDhTCgjjawP/YlX++5kyH39iiUiVrXLzHACBo+MZZsb8dRPmyuxz/P1//iUuPPkVwkZty/HmCkWO3nCS/+l9f8BNt93V6eFtSAARuGna59aD2aFnHakqjUgp1eOOWVRtBV+YuCpyIlZluRFTDboMNxRagX92n43Zt7r+n/mm2+7S3/iTv+7rm9x7z4m+vm4HbPr4d6NL+KyIHFPVCyJyDJgb5GBBpJxbarLUSCbWjTgifN+p3eGkuwAACy9JREFUcV5+XYG/eKzEIxdrBBtsgzkC41mX66eyZNxrJ8zxIzfwlv/4Ic49+Bn+++//IkGtTFBfX5wy2Syen+Hdv/o+3vBjP9X36sgVmMy53H00RzGzPZO3iJDzhMNFYaURU9miqPgOTOVc/A1WnI4IUzmXgq+U6hFRvHGBE6CYEcYztjoyxmxsN05VPwnc1/r3fcAn+jmIqjJbDnh8rr56pt/JeNblZ08f5Oe/e4aZ4pUtvfZW3akDOU4dzG9YkNY6cfr7efsH/pE7fuRf4GVyOG6Sa5TN5XnND/4kf/iZx3jjP3tXXwXJEci48PJjOb77hvy2FaT131OYzLkcLrr4zvpTGAEms8KhwsYFaa2MK8wU3Gu29ITkMc0UXSayrhUkY8ymtnWlJCIfAl4LHBKR54H3Au8DPiIi7wbOAm/r59iz5ZDzK2FfN8bdfCDLe19/lC88W+YTTy5xaMxjZizTU5yDl8lx+qd+mVvf8A6+8ge/jFZL/Jv/9T9xy50v72NEiYwrHJ/wePGh7K7EhXtOUnzqobLUiMl5127VdSIiFDMueV9ZqifRIZO2VWeM6dK2FiVVfecmH3rDoMeOVAe6U9t1hDfcPM63FvsrbG0TR2/k3b/7Me44nB14BfC6U4WOq7TtJiLkfRn4hgpHhOl8d8m4xhjTZqevxhhjUsOKkjHGmNSwomSMMSY1rCgZY4xJDStKxhhjUsOKkjHGmNSwomSMMSY1rCgZY4xJjX1flG4/nO25g/jVDhddDg74QlEhaTFktkcUK7UgZrsbEBtjBrMbDVmHYirnMV+JiLvsbn21rCdMZl3eetsEQaT8zdMrPDbX6OkYBV942+0TnL6+gCOwUIt49GKdWtjbiA4Vksarjgiqar3hhkhVqTRjVppJBxBXksayWc+eY2PSaM8WpWLG4c4jOV5YDrhUjbouTO1JyXdldfL3HOFHXjLBq24I+fjXl5mvbp08J8Crbsjz9jsm8V1Z7VN3qODyulNFnrnc5JsLzY5jynnCXUeyHCp4Q4+lMNAIk67la09cIoXLtYicJ0xmHXvejUmZPVuUIOlfd2Iqw0wx5kypST28NktprbGMw1jGIalF6ycj3xWOjXu85/QBvnq+xmefrWyY8np8wuNnXzbNTNHl/2/v3kLlOsswjv+fWbPnsCc7ZxqbxLRFS2qNmEpoWiuC1oJoMb3wQoJSwTtFqwhSr7yTXIjolSD1UGipYCxUWiiWqghF1FpLbRuliLWN5rTTQ2KSfZp5vVhrS9g57Z09s741M88Pwl6ZGYZ3McN61vetWd/bXLLIaE0Cwbs3NdixfoLnj84wfZGAE0WPpM1NMuGRUZ91e/mCsjOXGbHOLASzC12mGqLjVhpmlTHUobSoPVHjps1N3jjX5fW35y+Y0mtmeWuG2hUCQBITGezZ1uZ972jxxN9P89KJfEqvXReffu9a9m6fzNs7XOZ9sppo18St29pMn13ghWOz/z9Abmpn7L62RTOTz9L7bOlU3RVfD5yaC87Md9nQzvrWJt7Mrt5IhBLkIbFpss76VsbhU/OcPNulVjTKa5w3VbccWU1M1sS+96zl9h0LvPrmHHfvnKJx3lTdct/nmk6dj9xQ5x8nZ1nbyrim46m6QZjrBm+e63KRwe0VdQOmz3Zp18W61spadZhZf41MKC3KauK6Ykpv+myX+mJP8avQyMR16+rs2dq66gOVJOqCGzc3i/bmPuANwskVXFe8HH86ZmmNXCgtmpyo0a73mO+t7n36FSI++x6svgSSPyKrsP17d6QuoRRjf5+SmZlVh0PJzMwqw6FkZmaV4VAyM7PKcCiZmVllOJTMzKwyHEpmZlYZDiUzM6uMkQ2liLiqJWfMzCydkQylmYUex4peS6vV7UGvH29kA7W2oVUvETTXXd5CrmY2OCO1zNBCL++fM9/tz7IzXqBzeKxpZrQmrv7zn2qoaGviz9ospZEIpYjg9FyP/8715zy3XssbAbqVwXCp18TmyTozCz3emuldcaQsYCLLP+uVrP5uZoMz9KG03APQcgiYaorOhM+Yh1mrXmNLR5c9UakJ1rdqtOojOYNtNrSGNpS6xVTdXJ+m6twee7RIYm0zY/IiU3prGmLKU3VmlTS0oXRmvsfshZ3GV6xVh85ERrPuA9QoOn9K79x8MNWsearOrMKGNpT69TOpDa3MZ8xjoFWv0Rreb7vZ2PCEupmZVYZDyczMKsOhZGZmleFQMjOzynAomZlV2MZOg/17d6QuozQOJTMzqwyHkpmZVYZDyczMKsOhZGZmlTG0odRp1GhkrKqHjoBZdwI0M6uMoV14JVthm4KlvACrmVn1DG0oLVpsU3BqtseZ+csnk8hbFmxou1eSmVkVDX0oQd6mYF0ro9O4dOdRkXcX7bhlgZlZZY1EKC2q18SmdsbMQvD2TI8eeRg1PVVnZjYURiqUIB81tSdEsy7OzPVoZDX3SjIzGxIjF0qLahJTzSx1GWZmtgJD+5NwMzMbPQ4lMzOrDEUMx82jkk4A/0pdxwptBqZTF5HAuO43jO++e79XZjoiPr6cF0p6crmvHQVDE0rDSNKzEbEndR1lG9f9hvHdd++39Yun78zMrDIcSmZmVhkOpcH6YeoCEhnX/Ybx3Xfvt/WFrymZmVlleKRkZmaV4VAyM7PKcCgNgKR3SvqNpJclvSTpvtQ1lUlSJukvkh5PXUtZJK2XdFDS3yQdknR76prKIulrxff8RUmPSGqlrmkQJP1Y0nFJL5732EZJT0l6pfi7IWWNo8ChNBgLwNcj4mbgNuBLkm5OXFOZ7gMOpS6iZN8HnoyIm4D3Myb7L2kb8BVgT0TsAjLgM2mrGpifAktvYr0feDoibgSeLv5vq+BQGoCIOBIRzxXbp8kPUNvSVlUOSduBTwIPpK6lLJLWAR8GfgQQEXMR8VbaqkpVB9qS6sAk8J/E9QxERPwOeGPJw/uAB4vtB4F7Si1qBDmUBkzS9cAtwB/SVlKa7wHfAHqpCynRDcAJ4CfFtOUDkjqpiypDRPwb+A7wGnAEeDsifpW2qlJtiYgjxfZRYEvKYkaBQ2mAJK0BfgF8NSJOpa5n0CTdDRyPiD+nrqVkdeADwA8i4hbgDGMyjVNcQ9lHHsxbgY6kz6atKo3I76/xPTar5FAaEEkT5IH0cEQ8mrqektwBfErSq8DPgI9KeihtSaU4DByOiMXR8EHykBoHHwP+GREnImIeeBT4YOKaynRM0rUAxd/jiesZeg6lAZAk8usLhyLiu6nrKUtEfDMitkfE9eQXu38dESN/1hwRR4HXJe0sHroTeDlhSWV6DbhN0mTxvb+TMfmRR+GXwL3F9r3AYwlrGQkOpcG4A/gc+Ujh+eLfJ1IXZQP1ZeBhSS8Au4FvJ66nFMXo8CDwHPBX8mPKSC69I+kR4PfATkmHJX0BOADcJekV8lHjgZQ1jgIvM2RmZpXhkZKZmVWGQ8nMzCrDoWRmZpXhUDIzs8pwKJmZWWU4lGwsFat6f7HY3irpYOqazMw/CbcxVaxJ+HixsrWZVYRHSjauDgDvKm5s/vlijxxJn5f0mKTfFj1yvpW4TrOxUk9dgFki9wO7ImL34qjpvOduBXYBZ4E/SXoiIp4tv0Sz8eORktmFnoqIkxFxjnyB0Q+lLshsXDiUzC609EKrL7yalcShZOPqNDB1iefukrRRUpu8k+gz5ZVlNt58TcnGUkSclPRM8QOHpa0W/kjeC2s78JCvJ5mVx6FkYysi9l/iqcMRcU+pxZgZ4Ok7MzOrEN88a2ZmleGRkpmZVYZDyczMKsOhZGZmleFQMjOzynAomZlZZfwPoYg99+NczI0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S48PZXDcxgXE",
        "colab_type": "text"
      },
      "source": [
        "**Let's try with `kind='kde'` (KDE - Kernel Density Estimation)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tj50I_K4xwHI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "5b76e11f-4f55-4c57-ec79-8202974a3e2a"
      },
      "source": [
        "sns.jointplot(x='tip', y='total_bill', data=tips, kind = 'kde')"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f1d634a7cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oXZauHzuyqFo",
        "colab_type": "text"
      },
      "source": [
        "**Analyze the relationship between `size` and `tip`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F1fg3P_by0E5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "bc52269d-33be-4ba3-d1a3-be31abc74cdc"
      },
      "source": [
        "sns.relplot(x='tip', y='size', data=tips, size = 'size')"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f1d62ed89b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 415.375x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BlJRnGdOzUph",
        "colab_type": "text"
      },
      "source": [
        "**Correlation Matrix**\n",
        "\n",
        "Show the correlation matrix and comfirm the analytics you have from previous steps."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8N1itVCpzoZw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "87e58879-85a4-44d5-d17e-f8ae51968d3e"
      },
      "source": [
        "corr = tips.corr()\n",
        "sns.heatmap(corr, vmax=.3,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d62d26ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7f6HYYru6XRY",
        "colab_type": "text"
      },
      "source": [
        "**Box plot, Swarm plot and Violin plot**\n",
        "\n",
        "![](https://i.imgur.com/5Lo17qp.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "niLV2dVs62Ik",
        "colab_type": "text"
      },
      "source": [
        "**Implement the seaborn's function `boxplot()` with params: `x='sex'`, `y='total_bill'` and `data=tips`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2lvNCKB6YBS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "38dafbad-ea35-4afd-c2af-c2cfdbe30069"
      },
      "source": [
        "sns.boxplot(x=tips[\"sex\"], y=tips[\"total_bill\"])\n"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d62cae668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BcVs63bw7Mc2",
        "colab_type": "text"
      },
      "source": [
        "**Implement `boxplot()` with params: `x='sex'`, `y='total_bill'` and `data=tips`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpjbuI0x7a1U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "3bdb556f-fe41-4ac5-c06c-1aaf48463445"
      },
      "source": [
        "sns.boxplot(x=tips[\"sex\"], y=tips[\"total_bill\"])\n"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d62de4630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z6FvQWcR7gFS",
        "colab_type": "text"
      },
      "source": [
        "**Add `hue='smoker'` to the previous `boxplot()`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "40GWj8Uq7o5I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "b8cee242-3d12-4faf-dea7-34d0624b446d"
      },
      "source": [
        "sns.boxplot(x=tips[\"sex\"], y=tips[\"total_bill\"], hue = tips['smoker'])\n"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6339eba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fU3kztPk7t9S",
        "colab_type": "text"
      },
      "source": [
        "**Implement `swarmplot()` with: `x='day', y='tip', hue='smoker', data=tips`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "amtE0-nU74Y6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "4d8a36fe-e517-4bdd-a1fb-345ea0fe2438"
      },
      "source": [
        "sns.swarmplot(x=tips[\"sex\"], y=tips[\"total_bill\"], hue = tips['smoker'])"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d6339efd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cT6IXHgI8NtC",
        "colab_type": "text"
      },
      "source": [
        "**Implement previous chart with `violinplot()`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtXZkxV_8QUg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "164b9fcf-0eac-42f0-c0ad-c717171a5db1"
      },
      "source": [
        "sns.violinplot(x=tips[\"sex\"], y=tips[\"total_bill\"], hue = tips['smoker'])"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1d595b6080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "geDjBuE02C6S",
        "colab_type": "text"
      },
      "source": [
        "### Bonus"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJjq986a37p_",
        "colab_type": "text"
      },
      "source": [
        "**Total revenue per day**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQ-YKLls1Yca",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "7269b4d4-3586-486c-c244-6f09b4c0f57c"
      },
      "source": [
        "tips.groupby('day').sum()['total_bill'].plot(kind='bar')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f36b7a5bdd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9aEFDIMky4FPA+cCpwIYkpy5kD5KkFy30nsCZwFRVPVJVfwvcCly0wD1IkjoLHQIrgMcH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\nOm8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WVHZ4XE4u8U",
        "colab_type": "text"
      },
      "source": [
        "**Pie chart of table size in percentage**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-xcx85Dv4vZm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "96395fa2-9fc5-4c1f-fe48-9233790eddb2"
      },
      "source": [
        "# Pie chart\n",
        "labels = []\n",
        "sizes = []\n",
        "explode = []\n",
        "\n",
        "labels = tips['size'].value_counts().index.sort_values()\n",
        "for index, size in enumerate(labels):\n",
        "    sizes.append(size)\n",
        "    if size==3:\n",
        "        explode.append(0.2)\n",
        "    else:\n",
        "        explode.append(0)\n",
        " \n",
        "f, ax1 = plt.subplots()\n",
        "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "# Equal aspect ratio ensures that pie is drawn as a circle\n",
        "ax1.axis('equal')  \n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KccvPArC4KbFr9muRB3b4tW1prvOwLnFvlOax+UNu86WZBNQhKIpiVRRlJdAK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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t8U7mHTGz54d",
        "colab_type": "text"
      },
      "source": [
        "## Great Job!!"
      ]
    }
  ]
}