Visualization_For_Medium.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Visualization For Medium.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM1f1IEhBYE/e26dzmBe6lT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kaancceylan/medium-notebooks/blob/main/Visualization_For_Medium.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup"
      ],
      "metadata": {
        "id": "hS-6Jucmfybe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "PlkPXINeVtID"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import altair as alt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tips = pd.read_csv('tips.csv')"
      ],
      "metadata": {
        "id": "iO4etJ5b9Ez3"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bar Plots"
      ],
      "metadata": {
        "id": "THJnbisGfvCX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here's the matplotlib bar chart. We need to set the axis labels manually, which are done for us in Seaborn and Plotly."
      ],
      "metadata": {
        "id": "dT21T1ht_xuD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.hist(x=tips['day'])\n",
        "plt.xlabel('Day')\n",
        "plt.ylabel('Count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "Rkye8Hlz-MNZ",
        "outputId": "e4da9667-783b-482a-9d2c-186a51af47eb"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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6O8kLgQ3Aaq9fr6XEUz3S3tkP+ESS4xlc5OyXAarqq91lqMcYXCfqBqOvpcbwS0NK8mIGkd/G4Dz/w8ArGZwy/fm0p14F/DFwLvDWRR5Tmpfhl4bQHcFfxuAXyVR3GmdLVT2VZC2wYtrTrwC+BmytKn+3rZYcwy/N7YAkG3j67Zz/AHyke+xS4IYkbwZuZdp106vq4SQbgX9e5HmlofjDXWmBJTmQwfv/T6iqH496Hmkm384pLaAkrwM2ApcYfS1VHvFLUmM84pekxhh+SWqM4Zekxhh+SWqM4ZekxvwfDFWOk/VUaZIAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here's the Seaborn bar chart, looking a bit tighter."
      ],
      "metadata": {
        "id": "T8YT6oB-_8xX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=tips, x='day')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "rPTZTBG3--3M",
        "outputId": "46ec6eac-f95d-4a81-d923-f115a5624b4d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe5ac63810>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If you pass the variable to y, you can get a horizontal graph, which might help you out if the category labels are overlapping on the graph."
      ],
      "metadata": {
        "id": "6c0Pd3aFAbl5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=tips, y='day')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "dNno-fUMAXsG",
        "outputId": "9df4aae7-a786-4cb6-b61f-6a93e144cfab"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe5a0f0510>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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kb0tyZXefJ3RvxPj7wGuTXJ3kmSP82aT7WT7/TSTNcBqDd/ac7TeBDQw+a2E1cEWSry3g8fZU1RlJ/pDBq71fmeRC4K6qeueiTS0dJY8kpMXxDODjVXWgqn4E/CvwlAXc7+CbLk4D64c0m3TUjIR0ZK4HzjyC2+/nvr9nK2etP/gePgfwyF5jyEhIR+YrwAOTbDm4IMnpwB3A5u6zsh/J4JPcvsngje9OTfLAJCcweGfQ+dwJPGTxR5eOnP9ykY5AVVWS3wDeneT1wP8AO4DXAKuAbzH48J3XdW+xTZJPAtcBNzN4F9D5fBa4JMmLgT+uqq8v+g8iLZDvAitJavJ0kySpyUhIkpqMhCSpyUhIkpqMhCSpyUhIkpqMhCSp6X8B5X2qtiVzm9oAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lastly the Plotly bar chart. You can select a specific area with your cursor and zoom in to take a closer look. Hover over the data to see more details. Double click to zoom-out. You can also download your plot as a PNG file."
      ],
      "metadata": {
        "id": "R0Jh_F6qAEr1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.bar(tips, x='day')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "VJfyIA7w_myM",
        "outputId": "da8b2833-8b0b-4d74-c8bd-9b5aa395694b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Line Plots"
      ],
      "metadata": {
        "id": "2Z0bSMXof2DU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can just use plt.plot in Matplotlib and it will generate a lineplot for you by default."
      ],
      "metadata": {
        "id": "Nba1GpoQBPzD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(tips['tip'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "5R_66SAaApNK",
        "outputId": "562734eb-e8ae-4e9f-fc2a-c567974e5c2e"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7efe59402c90>]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lineplot(data=tips, x='tip', y='total_bill')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "F3_KS05jBMfi",
        "outputId": "9713a1ea-10e0-4e66-ec4a-a253cfb5aa99"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe593b0d50>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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SWRGG7UNEOGthDd7a14GAlHjYSKGRr01ezNiGxZ8pSIQAnKbukWcvqMFgUMamQ/Yr+czmJC9m/MLizxQksrAK3mlzJsHjdNjS+sn2JC9mfMLizxQUsiIghICiCJjbxpd4XThl9kS8usd+Sd9cTPJixh/8G8EUFHc8uR03PrRJrfaJiHbPWlCDfW0DONo1lKfV5Qcu9WSiweLPFAyDAQnPbDuOw11Dqu0TMTHq7AXqQKBYG74KFVnmxm7MSPg3gikYXtndhoCkICDJUJTwJi+dWVUlmD6x2HatHkKKwtU+zAhY/JmC4YUdLQDU+b1qnb/1cSLC2Quq8fb+DvhD9in5lLmxGxMFFn8mYwhtelY+GA7KeFmr5AlISlTPHwDOWlgDf0jBuwc6Yx7rZxv24KM/ex33v74/a+vNJZIi4GbxZyJg8WdS4pGNR3Dtf79rfN/W78eib/8DHxzJTx39q7vbMBySsWxqBYKa+DtopOCdOnsSvC5H3FYPr+xuw+7Wfvzw+V14qbE1m8vOOmr1E+Bkz5+JgH8jmJRobO7DFpPQN3UPwx9ScLR7ZPO0XPDcjmZMKvHg9HlVCEhyTKvD53bitDmTEiZ9186dhMV15bj9ie3juvOnpKhDbNjzZyJh8WdSQlIU+EMKFEW1egb8EgAY3+cSf0i1fD66dDKK3U4oQrV+okX+gNro7VDnEA52DEZ9XFYEfC4nfnnNSgwHZXz5sa15+bkyQXu/2rW0osid55UwYw0WfyYlgpIqhgFJjSz7NfGX8yCSr+1px1BQxoVL6+B1q7/SwyE5pvifNV/t8hlrt6/eDnpuTSnuumQx3trXid+9fiA7i88yjc39AIBFdeV5Xgkz1mDxZ1JCtxP0qpmBQAhAfsT/+R3NqCx2Y83sifC6nAC03j4xfrunTyrG7OoSvBLD+hEiXCl09UnTcNGyOtyzfje2Hu3JxvKzSsPxPhABCyeX5XspzBiDxZ9JCX0g+rAm/kbkn+OKH39IxkuNbfjokslwOR3wusK/0pF1/mbOXlCDjQe6opZ8mttBExF+eMUy1Jb7cNujH6DfH8r8D5FFGpv7MHNSCUq8rnwvhRljsPgzKRGSVZEfNiL//Ng+b+ztwEBAwseW1QEAPGbxj2H7AKr4B2UF25tGRvOKEJYTR0WRG7/4pxVo6h7Ct576MIOrzz6NLX1YVMdRPzMS24r/5sNdGNQEy64c7BjEf63fnVJ9viRbbZ98ef4v7GhGRZEbp82ZBACG7QNgRHsHMyfNqkSxx4lNh0eWpgox8sSxeuZEfPHc+Xhq63H8dUtThlafXfr9IRzuHMJi9vuZKNhS/IeCEj75u3fxxObx8UecLT71wEb88uV96BwMjvq1euRveP55EP+AJGNDYyvOX1wLt2bwJ2v7eF1OrJ1bFVX8FTFydzAA3HrOXJw8ayK+9dSHOBSjUmgssbuFk71MbGwq/mod+HjzbzNNlyb68SLkWBief1D9qts+Sg49/7f2daDfL+FCzfIBYFT7AIl/rlNnT0J7f2BEHb8cY4OY00H4+dUr4HI6cNufP0BQq3RKl3cPdGJDQ+Y3kzU09wEAFk9h8WdGYkvx1/9oM/XHO17R/fpUkrShSNtHE38ph5H/8ztaUOZzYe3cKuM+j9Ps+cd//fL6CgDA9qZey/2KEjtfMGVCEX78iWXY3tSLe9bvTnHlVn70wi7c8sgWHOnMbKvpxuY+TCh2Y3K5L6PHZQoDW4q/XpsekO0t/jqpWDW6yBsJX39uSz2DkoL1O1uwbnGtJcnrdYc9/3i2D6BGxA4CdkQkfUUM20fngqV1uPaU6fjd6wfweprDYSRZwa7mPgQlBd99tiGtY0XS0NyPRZPLQSlc2TGFjy3FnyN/K6kItv7ZRZZ65mon7Nv7O9Dnl3Dh0jrL/WbPP1Eny2KPC9Mqi7HjmDXyj2X7mPnWRYsxr6YUX35sGzoGAqNcfZiDHYMISAqW11fgxcbWjI2ZlBWB3S19bPkwMYkr/kT0dyJ6Jta/XC0y07D4W9HFfzQ9bPTIPxBR6pkr2+ed/Z3wOB04fV6V5X6L+CcR8c6tKcWOY72Wiid9h288ijxO3HvtSvT5Q/jq49tSPuntPK768j+8fBlmV5fgO3/fiYCUfrvpgx2D8IcUTvYyMUkU+f8UwD1x/o1LgrL6xxVg8QcQFv+2vuQjWClik5fR2ydHCd+tR3uwqK4MPpPNA1jr/JNxO+bVlKJjIIjm3vCJL5Hto7NwcjnuvGgRXt3djv95+1CyS7fQ0NwHj8uBhZPL8B+XLMGhziE88MbBlI4VeVwAXObJxCTutj8hxGu5WkguCXDkbyG1hK/m+QfV5m4DwdyVesqKwIfHevGJE+tHPGap809CwefWqBugdhzrxZQJRcbxE9k+Op9eMwOv7+nAj1/YhVNmTcTSqRVJvU5n5/FeLJxcBpfTgTPnV+OCJZNx38v7cPnKqcZ6UqGxuQ9up9qfiGGikcj22UFE22P9y9UiMw2Lv5VUBNuo9pFkDIVk6OePXIj/gfYBDAZlLK+fMOIxS6lnEuI/s6oYTgdhh6niRxHJvRZQ2z/85MrlqCxx47Y/f4ChYPIbB4UQ2Hm8D0tMvvydFy+CIgR+8Hxj0seJRmNzH+ZUl1quhBjGTKLfjIsBXBLnX1yIyEdE7xHRNiLaSUTf0e6fRUQbiWgfEf2FiDzp/Rijw/D8bVztY/ao0xH/4aBs2S+RC/Hfpgn1CfUjo2xvku0dws93Yn5tGbYfM4u/SMoy0plY4sHPrl6Bgx2D+M4zyVfsNPf60TMUslgz9ZXFuOXsuXhuezPe3teR/CIiaDjOyV4mPnHFXwhxON6/JI4fAHCOEOIEACsAXEBEawD8GMDPhBBzAXQDuCHdH2Q0cMIXGDRFqCmVepp2+Op+P5CbhO/2ph6UeJyYXT3S0rDW+Sen4MunVmBHU4+R9I3W3iERp82pwuc+Mgd/2XQUH0ZUD8VCT/YunmI9id105mxMn1iMu57ZaZxkR0PHQABt/QH2+5m4JLJ93tS+9hNRX+TXRAcXKgPat27tnwBwDoAntPsfAnBZyj9BCrD4h6tzgBQjf1NL537TsXKR8N12tAdLp1ZEtWaIyLA6YrV0jmRZfQW6h0Jo0qaQRRv+ngz6TuNjPclNM4vVbtnnduLbFy/G3rYBPJRCIrlRS/ZypQ8Tj0SR/+na1zIhRHnk12TegIicRLQVQBuADQD2A+gRQuiK0QRgaozX3kREm4hoU3t7eptpzOh2j503eemRO5Bmwjci8k/X9tnb2o/ndzTHfDwoKWhs7seKaSP9fh3d+kk2etePdfZPX8XZP31VHQSTgvqX+dT6CfPnEY+dx3sxqyp6u+VzF9Xg7AXV+PmLe9HWP7oxkiz+TDIknQ0iolVEdBsRfYGIVib7OiGELIRYAaAewMkAFo7itfcLIVYLIVZXV1cn+7KEcORvjdBHK9iKIozXDIcUY4NXKseK5I/vHsaX/rI15nF2tfQhKCtRk706esVPsknbJVPKcd+1K3HTmbPRO6zmL0Zr+wBAmU8dlZhsz6iG5j4smRK9OoiI8O1LliAoKbj7hV2jWkdjcz8ml/swsSSnqTRmnJGU+BPRt6HaM5MAVAH4AxHdOZo3EkL0AHgFwKkAJhCRHu7UAzg2mmOli76JJpiBzTTjFXOwP1rB1i0fAPAHZWOKVyrHikRSBIKSgmMxBsHryd7lUZK9OqON/IkIFy+fgq9dsBDTKou0145m1SqlWgQ/kESr8F7NZorny8+qKsG/nTkLf91yDJsOdSW9Dk72MsmQbOR/HYCThBB3CSHuArAGwKcTvYiIqologna7CMA6AI1QTwJXak+7HsDTo114OnC1T3qRv9ky8kuyEfmX+VxpT/LSk6772weiPr79aA8mlnhQXxm7Bt4Q/1QUXDthpNLp1ONSJ4n1J2H77GxWT2JLEoj0LWfPRV2FD99+emdS/0/+kIz97QM8wIVJSLLifxyAuTWgF8lF63UAXtH2BLwPYIMQ4lkAXwfwZSLaB/Vq4sHkl5w+bPuotew6o478TSfN4aBsRLoVRe60I3/9oiKW+G9r6sHy+oq4zcqMhG8KAq6/ItVmaGU+lyUBHgu91/7CBCJd7HHh/120CA3NfXjkvSMJj7uvbQCSItjvZxISd4cvEd0LtTqnF8BOItqgfb8OwHuJDi6E2A5gRH5ACHEAqv+fF/REb67FXwiBAx2DmBOlRDH3pJ7wNZeJ6gnfEo8THqcjffGPE/kPBiTsaxvAxyKauUWid/ZMttrHjH7lkYrnD6i+fzKR/5GuIRR7nKgu9SZ87kXL6vDw7CO4Z/1uXLysDpVxvHxu68AkS6I/j00ANgP4G4BvQrVrXgXw/5BjqyaT5Cvy33S4G+fe81rMqDYZFEXglke2YPPh5D3gqMcxR/5y8oIthDCiVqeD4NcSvqU+FxwOSrvUU1/X/raRk7I+PNYLRQAnTIvfQsGrqX4q0bv+/qmcOADV9x9IIuF7tGsY0yqLk1ojEeE7ly5Bv1/CfyaYIdDY3IcitxMzJpUkvWbGniTq7fNQMgchoieFEJ/IzJKyTyBPnn/ngDo5q3swCKRYvNQ5GMRz25ux8UAnNt25LuW1WDz/UQi2IoCgpD6/zOdSN3kFJJR6XXA5yJIPSGdd0U6Q241kb+xKHyDc4iEV20doV0Tp2D7JJHybuocwbWLyvXvm15bhM6fNxO/fOohrTpqOZTES3g3H+7CwrizpSifGvmSq8cfsDB0nJ+gRf0gWOes/DwCSMnYSzalW+yhCGB09y3wuDGubvEp9bjgoE5G/+vrOwaB6kjSxrakHUycUoSqBVeI1NnmlIP7a8lO1fUq9roS2jxACR7uGUF9ZPKpjf/G8eZhU4sW3n/kw6u9tS+8wGpv72O9nkiJT4p87Bc0AZrsnl0KsR8WhNKJjkaGPOtVqH0UIoyV2mVdN8PYMBVHuc8HlpAx4/uHbkdG/nuxNhF7nn0q1T1j8R/1SAMl5/t1DIQwGZUyfODrxL/e58Y2PLcQHR3rw5JamEY/vPNaHPr/E4s8khS1b/pnFP5c9/fUqmVAa75mpxmnmAH000boQ4ZOYvqO1rS+AUq8LDqK0e/soQhjVOmbx7xoM4mjXME6Is7NXx2PU+af2/kBqVw2AVu2TwPM/2qXO6p02SvEHgMtXTsWJMyrx43/sMjak6RzsVPMknOxlkiFT4j+uDEZztJ/LpK8u3Kk069JJ11PXMev9aI6pCGHYV+VF6o7W9gFV/J0ZSPgKIVBfWQSPy4H97eGk73Ztzm5ykX/qnr9Oup6/iPM5HO3WxX/0/fodDsJ3Pr4EnYNB/PzFPZbHDnYMRu0VxDDRyJT4fz1Dx8kJ5jF5ubR9Qpr4p/OemeqaaRbprghvPf7rgGBE5C8rAqU+VfwzUefvdjgwu6oE+9vCkf/2pl4QAcuSGJYymk1eNeXW/EG6tk+p1wVFAEPB2LvHj+iR/yg9f52lUytw3SnT8b/vHMaulnB/xQPtg5gxsThqryCGiSTVYS47zMNchBDrs7/UzGHx/HMY+euJ0nQ8fylDJyuz+P/g+Ub4Q8m1ulATvupry7VeNgBQ5nXBSZnw/NVe+nOqSy22z/amHsyuKjH658RDr/NPJmlbW+6zfJ+u7VPqS9zi4WjXMCaWeNIS6a+evwDlPhfuenqncZVxqHOQ2zowSZPqMBf9/nFJ/sQ/A7ZPpjx/7ave/GswjlgdaB/AOT99FW39fggFCEnWyF+97c5M5K/10p9TXYIjXUMISDKEENh6tBcnJCjx1NF7+qe0yUv7mrrtk7i5W1P3kNFDKFUmFHtw+0cXYuPBLvx9ezP6/SE09/qxaDKLP5Mc2R7mMiYJSIpxWZ9L8dcboo0Nz189zuoZlQDi1/rvbx/EgY5B7GkZsHj+ZvE3bJ80lyeEgMMBzKkphSKAw51DaO71o2MgkFSyFxh9YzczeuSfcrWPFs3Hq/g52jWE+hSSvZFcfdI0LJtagR8814AtR9ScCEf+TLIk29VzDRG9T0QDRBQkIjmZYS5jlaCsGJfcetliLtCFO50TjrmjZjroAbrLqapcvMOGa+8DUIQwTl5mC0ZP+Mpprk8RQov81RYY+9sGRl1vYjEAACAASURBVJXsBUybvPJQ56+fEGOJv6wIHOsZTtnvN+N0qDt/W/sC+OZfdwDgHv5M8iR7YXwfgGsA7AVQBOBGAL/K1qKyTVBSDL86l6WeklHtk3p4nOlST5dD/RX46uPbcKhjZEsF9bnqkzsGglBE+OeIjPwdREg3JaEI1XKZVaW2J9jfPoBtTb1wOShpYTPq/FPZ4Ztm5J/I82/t8yMki1HX+Mdi1fRKXHViPY71DKPU60JdhS/xixgGo6j2EULsA+DUhrP8D4ALsres7BKQFEO48pPwTSPyN16bXnWtHs3rkf+b+zrwwxcaYzxX/do5EICw2D7WhK/LQWnvmFYjf6DE68KUCh/2tw9ie1MPFtaVwaclchPhScP20cmW5x+u8U/P8zfztQsWosznwpzqkpTXzdiPZMsNhojIA2ArEf0EQDPG8QaxoKQYgzdyKv4ZqPPPVORviL8pxI11RaIYkX8AsiJGbPJSb6sJXylJ26e1zz+i0gawDk+fU6NW/HQNBo3cRDKk095BJ9VXlibw/I9qQ2oyYfvoVJd58ei/rUFTV3KzgxkGSF7AP60991YAgwCmAbgiW4vKNkFJMS7Pc1rnr7eSHgMJXxief/hXIFYZaTjyD0IyjXAsj7R9HARFAD1DwYS7XNv6AjHeKzw8fVZVCQ52DGIoKBv/X8kwv7YMk8t9qC1P3C45FqlG0InE/0jXEIiAKRMyF/kDau3/1DQriBh7kaz4XyaE8Ash+oQQ3xFCfBlquee4JCjnKfLXSz2lNOr8MxD5t/b5DUF3WyL/6J+F4fkPBtXI3/D8rQlfl1bqueK7G3DyD15KaW1qnb+6pmKPC4GQgoGANKqa+KVTK/DuN8/FhOLUZ9imGvk7HYQSjzOm59/UNYS6cp9hTTFMvkj2N/D6KPd9JoPryBmSrEBWhCFcubV9MlHqmf562/oCps1M4V+BWLaPHul3DgQgKYrxfbEn7MHrvX3Cg91Tq6JS6/yhrU09UQclBSWe3O5aTcc6L/O5MRAl8m/p9WPjwa6UevowTKZJNMnrGgDXAphFRM+YHioHkN40kTwRlK016rm1fTJR6pnZTV5uZ1jlYn0WZttHEeGTgdtkGTkdBKcj/ZyEWueve/bh4+e6ZUE64l/qc6E/YLW9Go734V//8D4GAhL+86rlaa6OYdIn0V/U21CTu1UA7jHd3w9ge9RXjHF04dVtn0BofDV2S6WOfl/bACqK3KguC3vgkdU+8dCfOxySMRySjSsYd8QWWqfDkfYAd1kR8LlHDlEv8SRX6ZMu+urTqRRSO3uGI/9Xdrfh1oe3oLzIjcc/eyrX4jNjgmR2+L4qhDgVwC4AZdq/JiFE4nFFY5BI8R9vCd9U9ghc//v38O2nP7TcJ6LYPrEwd6jsGVJ9fweFq2lmTirWjpV+5K/X+evH08lV5J/muQuAdaDLwxsP48aHNmFmVQn+9vm1LPzMmCGpvygiugrAT6HO7yUA9xLR7UKIJ7K4tqwQiIz8x1lvn9GKa+9wCMd6htHnD0GSFaO6R0RJ+Jp5Y287ugaDuHTFVMuAlZ6hECRZGMfZ8q118JnGJmbE9tGWZO7KWeLNTeSvk069fLnPjWM9w/jR84343esHcM7CGtx7zUrutsmMKZL9bbwTwElCiDYAIKJqAC8CGHfir0fdXrcDbiflKeGbukD2DKntl+P1izezT2uL3O+XsONYL1ZOV+vlw+0dokf+v3/zIPa1D2jib478Q5CFME4aemM4QL2KSLY7aCz0xm6AdQ9CzhO+aby21OvCgfZB/K79AD69ZgbuumRxzM+ZYfJFsr+RDl34NTpH8doxhS72XpcDHqcjt43dMhD59w6picRkTx/72vqN22/t6zBuR9vkZWYgIKG1T93Ra438VdvHHaVU0elI/0rKXOdv9t3HU8J3UqkHRMCdFy3Cdy9dwsLPjEmS/Yt6gYj+D8Cj2vdXA3g+O0vKLro4eVwOeFyO3DZ20we4pzPGURPiZCdm7W0dgNflwKyqEry5rwO3njMPgKm3T4yEb79fQlBS0D0Usnr+w5rtEyVXkMrM3Eisnn8exT+N2P/mj8zBxcun5LzDZuRgGoaJR7IhiQDwOwDLtX/3Z21FWUYXXo/TqYp/niP/63//Hh5440DSx9BFP9nE5N62AcypLsVH5ldjy+EeDAUl7fV6tY/1VyAgyfCHZAxqz2vp9Vv69ei2jyfKSSPWVcRoMHv+Vtsn155/6q+tKHLnpbVytHYZDBOLZMV/nRDir0KIL2v//gbgY9lcWLYImiJ/r8uZpxm+uoALvHOgE7tb+uO9zIIuxKPx/OfVlmLt3CoEZQXvH+pWj2N09bSq3J6WAexp7Tc2KZl3A1cWu9EzFLQkjs2kMzNXR2/pDEQmfMeP588w44FEYxw/R0Q7ACyIGON4EOO1zl+zecK2T/66evZp1spoKmT0pybzisGAhGM9w5hXU4qTZ02Ex+UwfH+B6J7/cEiCogijPUFLn9+42qgu8+K9Q11450CXMYfWTDJlo4kwJ3zNJ5OiJDt6ZgpujskUOon+Wh+BOq7xGVjHOJ4ohPhUlteWFcZCwlc/4bT3q83NRrNrVx6F7bNXq/SZW1MKn9uJ1TMq8eZeVfyN3j4REfxwUEZIFsZaW3r9xntVl3kxGIidI8lEXlOf4aseT71R4nFmJJ+QDCI8yDEn78cw+SLRJq9eIcQhIcQ1ESMcx2VrB2Bkwje3w1yskX/HgCr+o9m1OxrbR5+AtXSqOgFr7dwqNDT3oXc4ZNrkZRW5oZBs5AUA3fbRIv/S+AnFTAi0uaWzvrbiHFo+4UleOXtLhskLttt1Yoi/05HzhG9kV08j8h9F3b8uxMm0Udh6pAdVpV5M1doHr5k9CQDQ2NxntHpwRyRuA0HZEhG09PkxY5I6VcvcHiIa8RK+IVnB15/cjtpyHy5fOTXm88ylnrr465vIcgkPRWEKHduJv9n28bocMVvvZoPIAe7hyH/0nn8yr9l6tAcrpk0whEzfkOUPyaY6/wjbJyRbSi1beq2efzzi9cN5ZVcb/rrlGADgN6/ux4LaMvz5pjWoLLG2XTYnfHXxj7SmcgFLP1Po2G73ibnaJ9eevxzD8x9Nj37d9kn0mp6hIA50DGLl9AnGfXqUL8nCsDeczsiEr2K0Y54xsVi1fbT3qimLX0oYb3LWY5uaUF3mxcZvnos7L1qEvW39+MVLe6P8fKY6f+1rrpO9ACd8mcLHfuIvR2zyymXCN6KrpyH+o6g4Mtf5x4v+tx5V/f6V08Li79Ei6JCiGMdxR0T+/pCMYc3zn1NTiu6hkHEyGI3tY7aT2vr8eGV3Gz6xqh615T7ceMZsrFs8GX969zAOtA9YjhGttw+LP8NkHvuJf4Tnn9vGbtbePrrtIykCjc19aOn1R33d7Y9vw/eebQBg3dkbb17u1qM9IAKW1VcY9+n2iTnyj9zh2zscQseA2j9obk0pABjrqhpFwtds1Ty55RhkReCTq+uN+647eTq8LgfufmEXWvvCP3e03j5FOd7gBaS3w5dhxgNZFX8imkZErxBRAxHtJKIvavdPJKINRLRX+5r8dO40CUgynA6Cy5l720dP+MraHNx2k+cfkhVLlY1Ovz+Ep7YeMyp3zNF+vHm+W4/2YF5NqWXUot6PR1KUmHX+v3v9AH716n4AQL02E7ZnOAQioKo0/lhEc12+2+geKvD4pqM4eeZEzK4uNR6vLPHg5o/MwfqGVmw53G3crwgB/WLEYSR8cy/+rP1MoZPtyF8C8BUhxGIAawDcQkSLAdwB4CUhxDwAL2nf54SgpBj2Ry43eXUPBi3vFZIVdPSrEbYkKxAieoXJ63s6EJKFYb2YnZ5ovn9rnx9CCGxv6sUKk+UDWD3/RF09gXCkPxSU4CBKOBPX7PnrVyibDnfjQMcgrjJF/TrnLKwBABzuHDK9box4/jl/R4bJLVkVfyFEsxBii3a7H0AjgKkALgXwkPa0hwBcls11mAlKijE8O5eef8dAALIijEg7ICkW2ydWDL+hoQUA4Ncmjllsnygnrra+ANr6A+gaDGLJlArLY7q/Lyki7PnHaOzmdBAmFKlXDcNB2Rjesry+Iurz9dfo6Eniv7x/FKVeFy5aXjfi+bOq1BLSYz3D4ddF6e2TH8+f5Z8pbHLm+RPRTAArAWwEUCuEaNYeagFQG+M1NxHRJiLa1N7enpF1BOX8iL8iBCRFGBZGe3/AiNx1KydSbkKygpd3qZ20h4Na5G+K9mMlfBub+wAACyeXWe53OAguByGkXWkA4SRwJKVel7HWoWC4/POnV50Q82c0i78sBPr9ITy3vRmXnFCH4ij9+Eu8LtSWe3G0ewj/+LAFV/32bXQNBo0+Po48eP7658LSzxQ6OanzJ6JSAE8C+JIQos8cVQkhBBFFVTEhxP3QOoiuXr06I5PLAybbx+tUbR8hRNYjPV3ofW4HBgJAc68a7TpIq/4xtTXQ2XSoG31+CTVlXmNIilnvY7WF2KU1ils4eWRnSbfToV5pGJF/WPy9pgS41+WwiH8yO16tkT/w3PZmDIdkXLV6WszXzKoqwau72/Hq7nZMnVCEOy9ahGtPmW45Xj48/3Rm+DLMeCDr4k9EbqjC/7AQ4q/a3a1EVCeEaCaiOgBtsY+QWQKSAq+2Y9SriUpQVuB1ZVdgQoaoqu9zXLM6asp8kDXbJ7LC5MXGVnhcDpy7qBbPbFU3SJl39soxEr6NzX2YUuFDRbF7xGNuJ0GSFeMk4jENZfG5nYb4C4R31g6HZIsY3nLWHFwaZZeuOeErC4G/bDqKeTWllnLTSD5+wlQMB2Xc/JE5OH9xrSUH4eRST4bJGtmu9iEADwJoFEL8l+mhZwBcr92+HsDT2VyHGUvCV/uaC+snqLV08Gpie6xHLW+srfBpkbhVcIQQeLGxFWvnTMKkEg+GQ7I2VSss+KEYpZ67mvuxMMagcI8rduRvbqMgBEyRv2QR/5NnTcT8WqulBFhLPWVF4IMjPbj6pGlxr6quPWU6vn/ZMly4rG5E8jns+fMOX4bJNNn+q1oL4NMAziGirdq/CwHcDWAdEe0FcJ72fU4ISoohwHrUaxb/7sEgjkZpV5z2+2qtpPWrDT3yryv3qXX3ESnfvW0DONw5hPMW16LI44Qi1CsUs95H8/xDsoL97QMj/H4dt9MBl5NMXT3DMme1VwR82lWKP6QkFQlHlo26HITL4vTxScTiunJ8Zd18Y+5wTmH1ZwqcrNo+Qog3EfvP6NxsvncsIqt9AFhKMO/ZsBvvHujCi1/+SEbfV9/Y5dPe83jPMDxOBypLPJAUgbb+gCG2rX1+bGhoBQCcu7AWz+1Qc+P+kGKxfaLNAm7qHoKkCCyKEfm7nQ64HA7jCsIclftM1pcQMOwxIDkPPLKr53mLahNuDIuHy+nAF86dhx1NvSkfI1V4kxdT6Nhvh6+52ieK7dM1GLTsOM0UettmXVCbe/2oLvOqHryi4EfPN+JnL+4BoJZrvtjYiuX1FZhc4TM8b39ITljtc7BDvWpZVBcr8ifLyc6s126XtU7f63IYEb/+PCFEzNN55CSvq0+Knegd67DnzxQ69hN/KZzcjWb7+EMKBgJS0mMSk8Wo9jElfKtKPXA6CLIsMBCQsflwN4QQ6B4MYuvRHqxbpFbAGonXoGz1/KMkfA91DsLjcmCm1oY5ErfTgZAU7u1jjujN0a6AelWgW2T68xQROyrWbZ+5NaW45uTpOGNeVYJPZezC2s8UOrYT/4AkW3b4qveZxV+GEMBgMPbEqkje2d+Jq377dtwGbXofHq/pPdXIX03AyopA91AI+9sHselwF4QAzlusir8R+UtW8Y8W+R/qGMT82tKYO3c9LgdCsmKcOMzlmdaEs/pVzwPo9pC58Vokuu1z0sxK/OiKZXF3D5upKU/dGsoWvMmLKXRsJ/6JPH+9nr7fH0r6mF95bCveP9SNVq1LZzT0PjxeU1K1uswLp0O1ffSTw3sHu9DUPQyvy2EkbX3aJic18g8fc09rP6749Vt4ZOMR48RzsHMwan2/jtvpQEgW6BkKwud2WJO8pmM/eP1q9b21KxVdx2O1oVCfk1pdfm15/FbRuUQYuZA8L4Rhsowth7noou+N4vkPa20U+v0S6kydDFr7/DFFShfkeBuh9Ijda6qrryr1gqBaQk7tIO8d7ETvcEh9TFMgXYCv/e+NMHdg3niwC1uO9GDLkR488MYBXLisDj1DoZiVPoDq+YdkBZ2DQUyM6NWja//3Ll2C1TMnqu/tjrR9RExLZDTiPxajfTOs/UyhU/CR/1BQMtodACPbOwBW8Q/EiPzb+mJH9dH880h0z39k5O+AEGH/fuPBLnQPhVBl6p2vtzcYDskYDMgo1r7vGVIbw/30qhNwuGsI972yDwBiVvoAeuSvoGswiIkRXTr15ZvFW7+t/2x6LiAao2nENpaifTPG+HYO/ZkCp+DF//FNTfj4fW+id0gV80BEV0/9Ph3d9unzJz/eUY/84+WIFSPha4389X76siJQXuRCc68fe1r7LcPSI8VU733TrYn/ZSumWHr0xI/8VduneyiEysgundr6zSWeYc8//DPG0sV8ztzNNKz9TKEz/v9KEzAQkBCSBXa1qNG/ub1D1GofKWz7JEsyQ9X1lszmNhLVZV7LxqjV2mamoaCM6rKwMEeKaaku/oMhFLmdcDkdlsEuk+LU1uu2jz8YvoIw0JZS7A67gfp7h8V/ZA8inXz24sk0rP1MoVPw4q8n8Ha39kMIoZZ6RrZ3kMOVPakkfHXxV2I0WgOA7/y9EYA1qq4q9VqqbeZNLkOZz2U8phPZEbPEG7Z99OfrttGVq0b2zTfj1prZBWUl5mD0aLaPPuhdnbQV/dgFJf4c+jMFTsGLv67Hu1r6DYGM5fkLIUzin3zkrwf8yQxiN9s+kZG/2+kwonrzvNzqMi/++59XGycr/WQwGJQN8deZqk3fioVH8/zNie9IzOeEcLWPOeEbXRgnlXjgjbPHIFXykRxm6WcKnYKv9tGj8t0t/Zbh7eavuviHTBOuUon84w1U19Fr331uB0o8TkstvNtBxloi2yKsW1yLyhI3WvsCxgkCAEp91s6diaJudZOXOlvAGyH+uuCZr0Z028cVYz+AmUmlXuz4j4/GPKmkSj6Swxz4M4WODcRf/bqnpd+o5DH6+WtRrZ7w1X15ABhIIfJXYnj+5t3CeuVOdZlaymmJ/E1jJaP1xNHXW2IS//KIyD9S0CNxu1TPX1KE8TkUuZ0R4j6y2Zv5hBCvqinTwp8vuLcPU+gUxl9qHHTh7Q9IONQ5CADwaCLqjdjkFTCJfzTb58Njvfjta+pw8xcbWo0oPVHkb7aDJpaoiVy9mscsquaB8mbbR0ePwku94eg+0vZJKP6652+yfbbddT42f2sdbjt3HnxuBxbUhgetG55/lD77hQxH/kyhY4PIPyy827XukEZL54hNXvqcXCB6qefF974JAFg1vRI3/u8m3Hj6LNx58eKE4q/nEWrKvFi3qBYel8OI7M1JV5cz3G2zqnTksHSnw+r5A7BYQEBi20f3/EOyGGF/rZ1bhUf/bQ3Ki8Lv7Y1i+0S2bi4kMtzSiWHGLAUf+ctKOIrTWwPrYqfPtDXEXzJH/rE9f73rp34locSxfV7Y0Yxl/7EeAPCJVfVwONRmaXoS0xr5Ex79tzW4aFndCFEHwp682fYpi/D8k4n8/SEFsiLgcVpPFEqUcZZ6wtd8byFbIvpcBR7jyBQ6BR/5C6F621WlXuw4ZhV//XY48lfF30HRbR8iNTLUcwR6pC/iRP5PbG4ybustk//zyhOwQNuIZYmonQ6snjkRXpczbqlhiSee7RM/8jcPQ4/mz0cG9fqVhNm6cjkLXxhZ+5lCp+Ajf0UIOIiwYHIZ9rUPAIgi/rLV9qkq9aI/MDLy122igGQdph4v8jc3jdOF+YKlkzGrSi2HtFT7JCmq5sg/8gohUeR/5rzqmO9X7HEZJyUdPc9g3kRmB2G0w8/I2BsbiL8azS6YXGb4uV6T4JqTrHrkX13mtUT++9oG0DscCreDCFkTvXrkL0Xpr2+etuWJsqkqss4/GUpMCd/ySNsnQWuFpVPLjQqhaCeKyCsHI/KXw3aIHTZAFbK1xTCALcRfwOEgS7+bWLbPcIT466J+w0Pv44/vHjbEW3+eiIj8o7V3MJ8QotksZgtl+sTipH4m87jF0do+RISLlk8BALTHaUFtvJd2MtFPYkT28MNt8CMyNqfgxV8IGLaPjlkgPS4HArI18q8q9UJWhCHyvcMhNPcOG5G5flUQafOYh6vrWCL/KOJvae9QUzricTO6IJmj+9II8U+mDPOG02cBAFbNSDwYXT/R6J4/kT1iYjv8jIy9KfiEr6JNnppdVQqXg9TNTa7oto9u5+g19v1+CcUeF4KSgo7+gPG6Pq0SqGcohMFA2B6KFvkHTZF/5IxbIHzCmFzuGzEAPRLDtrJE/u4Yz47N3JpSHPjhhQnfDxhp+xA48meYQqDgI3894etxOTCnWo2szeLvdTmM6h291FPfgKVH+AFJQcdA0Hid3h66obkPa374Uvi9olT7mCP/aAndSVo9/8dXTEnaZzZ79dFKQpMhGeEHwlcZ+s/hdBCKveO/cVtiWP2ZwsYGkX+4XcGCyWXY3dofxfNXRd/YjFWui38IsjZfV4YwxF2v8wfUncM60Uo9LeIfxfZZVFeO31y3Ej6PK2G0qT9uXr/uyd91yWJjE1smiSz1XDq1It7Txz361RVH/kyhU/Dibx44vqiuHM9sO24ZjuJxOTAc1MXf2len3y9Zev0PaELfYhJ/M4kSvrGSsSVeF2QleTvFfIWgJ6H/Za3q47fGWFuq6J5/KM5w+kKEtZ8pdApe/GVFGKJ63ZrpmDmp2OivA6ji2SurNo4/JMPlIGPCVaT4dw6qk7NaeqMLbDTbx1znX1M2smWDjttJcWcAxyIyiZzpDphGnX+UMtZCRP8p7ZDXYOyNDTz/8K7Vcp8bH1tWZ3nc63JaSj19bqdRPtnvDxkbuoCwrROrb/+/P7YVX39iu+U+c8Q8tyb6eEUCob6yOGH9/Jnz1Q1ak0x9f5LdG5AqYdvHZpE/az9T4BR85B+tX40Za3sHBT63wyT+kmW+byL8IQWbj3Rb7gtJ5h2+0YW61OdKamDJV89fgGtPno4pE8IDW7LdQjlaewc7YI+CVsbOFHzkL0T82nez+Ae0yL9ES772+0MW2yYZIj1388nDFSNKXzC5bMSoxmg4HYRpERvBst1hU8+P2K3bJUf+TKFT8OKvmBK+0bD09pFU8Xc4CKVeF/oiPP9k6PdLGAqqieGgpCQVMScj/LHIdquFRL2CGIYZnxT8X7YiEkyecprq/DXbBwDKvC6L7TOaevq2PrVtgnkD2GjIx8zaWDgchGkTi/D9y5bmeyk5hSN/ptCxiecf+3FvREtnvbSxzOfGQCBkPDZlgg97WgeSes/WPj9mVpUYpaGjJR8za+PxxtfOyfcSco4dmtcx9qbgI38hRMKZs0FZgRBCFX+3Lv4uS6mn3oI5FrOqSvDk504DALRqDdOGgnK8lzBjGJZ+ptApePFXlNiD1QHV9hFCrWYZNts+mvh/6sGNAIAFk8vjvs+VJ9ZjrtaYrU1L+uqR//cuXYJnv3B62j8Lk314hy9jF7Iq/kT0eyJqI6IPTfdNJKINRLRX+5q4tWQayELEnK0LhEslg5KCQEiG1x22fbq0TV0AMHOSWmUzbWLRyINA3aRV7nPB53YYFT+657+wrrzg2yIUGlzqyRQ62Y78/wDggoj77gDwkhBiHoCXtO+zRjK2D6CKvz8kG6WNZT4XjvUMG8+bqdk+y+sn4LXbz8LnzppjOY7L4QARobbch9Y+3fZRxb8kjWoeJtfoQ2vyvAyGyTJZVSUhxOtENDPi7ksBnKXdfgjAqwC+nq01qI3dYj9uiL+swC+ZbR9rq+TqUi+K3E44iTBjUgm+cM5cnDSzEg+8cRBv7+802gLUlvtwsEMd7D4QUD3/kix0wXzuttMxGOCcQtZg8WcKnHx4/rVCiGbtdguA2lhPJKKbiGgTEW1qb29P6c2URJG/tvHq4Y1HMBCQTNU+I2fjnjGvCounqN5/sceFcxbW4mJtKtbRriEAwAVLJmPHsV68f6jLsH1KUmy7HI8lUypw8qyJGT8uo8K2D1Po5NWPEEIIIoppyAsh7gdwPwCsXr06pT2mier8Z1aVwEHAL1/aCwCYWql6+uVRxiPe/8+rR7z+yhPrcahzEP986gwAwDUnT8evXtmH+17eh1Nmq+Kcas99hmGYbJEPVWolojohRDMR1QFoy+abiQR1/ifNnIjHbz4Nc2pKEJIFqrSmaZHjEWP10PG4HPjmhYuM74s8Ttxwxiz85B+7IaB6x7xLlmGYsUY+VOkZANdrt68H8HQ23yxRYzdAFfAJxR5Ul3mN55Z53SOekyyfXjMD5T4XXt/TjhKvizcMMQwz5sh2qeejAN4BsICImojoBgB3A1hHRHsBnKd9nzUUJbXKjUjPP5nB6OHXuvEZbbgKWz4Mw4xFsl3tc02Mh87N5vuaUYRIKXWXymB0M/9y2kw8+MYBFHvsMO+2cLBb91LGvhR8WCoSJHyB6I3UzJF/Kj3zK0s8uOuSJejzh0b9Wib/sFPHFDoFL/5ygoQvEL2RWrkp8t/z/Y+l9N6fPGlaSq9jGIbJNgVfhpJMwjcakdU+jD1g14exCzYQ/9SGcY8mwcsUHvy/zxQ6BR/eCiHgSFHI77xoEU6YNiHDK2IYhsk/Noj8E3v+sbjxjNk4aSa3ULATevdVN2/MYwqcgo/8l02dAGmUQ9gZ+/Lr61Zhd0u/JeHPMIVIwYv/j65Yhh1NvfleBjNOKPW6cOKMrI6YYJgxAV/bMgzD2BAWf4ZhGBvC4s8wDGNDWPwZhmFsCIs/wzCMDWHxilS+zwAABKVJREFUZxiGsSEs/gzDMDaExZ9hGMaGsPgzDMPYEBZ/hmEYG8LizzAMY0NsIf7RxjQyDMPYGVuIf7QxjQzDMHbGFuLPMAzDWGHxZxiGsSEs/gzDMDaExZ9hGMaGsPgzDMPYEBZ/hmEYG8LizzAMY0NY/BmGYWwICSHyvYakIKJ2AIfzvY40qQLQke9FjBH4s7DCn4cV/jzCpPtZzBBCVEfeOW7EvxAgok1CiNX5XsdYgD8LK/x5WOHPI0y2Pgu2fRiGYWwIiz/DMIwNYfHPLffnewFjCP4srPDnYYU/jzBZ+SzY82cYhrEhHPkzDMPYEBZ/hmEYG8Lin2WIaBoRvUJEDUS0k4i+mO81jQWIyElEHxDRs/leS74hoglE9AQR7SKiRiI6Nd9ryhdE9O/a38mHRPQoEdlqEhMR/Z6I2ojoQ9N9E4loAxHt1b5WZuK9WPyzjwTgK0KIxQDWALiFiBbneU1jgS8CaMz3IsYIvwDwDyHEQgAnwKafCxFNBXAbgNVCiKUAnAD+Kb+ryjl/AHBBxH13AHhJCDEPwEva92nD4p9lhBDNQogt2u1+qH/YU/O7qvxCRPUALgLwQL7Xkm+IqALAmQAeBAAhRFAI0ZPfVeUVF4AiInIBKAZwPM/rySlCiNcBdEXcfSmAh7TbDwG4LBPvxeKfQ4hoJoCVADbmdyV55+cAvgZAyfdCxgCzALQD+B/NBnuAiEryvah8IIQ4BuCnAI4AaAbQK4RYn99VjQlqhRDN2u0WALWZOCiLf44golIATwL4khCiL9/ryRdEdDGANiHE5nyvZYzgArAKwG+EECsBDCJDl/XjDc3LvhTqCXEKgBIi+lR+VzW2EGptfkbq81n8cwARuaEK/8NCiL/mez15Zi2AjxPRIQB/BnAOEf0pv0vKK00AmoQQ+tXgE1BPBnbkPAAHhRDtQogQgL8COC3PaxoLtBJRHQBoX9sycVAW/yxDRATVz20UQvxXvteTb4QQ3xBC1AshZkJN5r0shLBtdCeEaAFwlIgWaHedC6Ahj0vKJ0cArCGiYu3v5lzYNPkdwTMArtduXw/g6UwclMU/+6wF8GmoEe5W7d+F+V4UM6b4AoCHiWg7gBUAfpjn9eQF7ernCQBbAOyAqk+2avNARI8CeAfAAiJqIqIbANwNYB0R7YV6dXR3Rt6L2zswDMPYD478GYZhbAiLP8MwjA1h8WcYhrEhLP4MwzA2hMWfYRjGhrD4M0ySaN03P6/dnkJET+R7TQyTKlzqyTBJovVmelbrOMkw4xqO/Bkmee4GMEfbqPe43nOdiD5DRE8T0ataz/W78rxOhkmIK98LYJhxxB0AlgohVuhXAabHTgawFMAQgPeJ6DkhxKbcL5FhkoMjf4bJDBuEEJ1CiGGoDclOz/eCGCYeLP4Mkxkik2ecTGPGNCz+DJM8/QDKYjy2Tpu1WgR10tJbuVsWw4we9vwZJkmEEJ1E9JaW6I1sNfwe1JkN9QD+xH4/M9Zh8WeYUSCEuDbGQ01CiIzMVmWYXMC2D8MwjA3hTV4MwzA2hCN/hmEYG8LizzAMY0NY/BmGYWwIiz/DMIwNYfFnGIaxIf8fOU0xFKnBtZcAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "px.line(tips, x='total_bill')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "OuBhh5NFCTnr",
        "outputId": "083fefa2-83e7-44e6-9adb-7a9cf8332827"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Scatter Plots"
      ],
      "metadata": {
        "id": "CY0_y-cWDhb4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's take a look at the relationship between the total bill and tip amount with the scatter plot. Looks like as the total bill amount increases, the tip usually increases too."
      ],
      "metadata": {
        "id": "mAFpQJrNDVlp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(tips['tip'], tips['total_bill'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "8o3I9LE9kUiW",
        "outputId": "311d386b-161a-4b13-99e4-51fff39db69e"
      },
      "execution_count": 73,
      "outputs": [
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              "<matplotlib.collections.PathCollection at 0x7efe5914f2d0>"
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          },
          "metadata": {},
          "execution_count": 73
        },
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's get a bit fancy with Seaborn and include a hue parameter passing it the days in the dataset. You can see that the highes bills and tips are mostly on Saturdays."
      ],
      "metadata": {
        "id": "nSUXO3Q2ERQN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=tips['tip'], y=tips['total_bill'], hue=tips['day'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "hWvbDVJ6DsfC",
        "outputId": "7a782b94-cda7-4d86-e8bb-7a19b755cc6a"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe58f81490>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can get the same plot but a lot bigger with plotly so it's easier to read. You can hover on any point to see the bill and tip amounts and zoom in just like before to take a closer look."
      ],
      "metadata": {
        "id": "gqn7YlE6E6NA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "px.scatter(x=tips['tip'], y=tips['total_bill'], color=tips['day'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "BYEghQ6JEeXp",
        "outputId": "3708cda7-2085-4b17-fa7e-62e5f3667b8d"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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        "#Box Plots"
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        "You get pretty much the same plot with both Matplotlib and Seaborn here."
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe58cda490>"
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          "execution_count": 89
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(tips['total_bill'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "fB7Z3ktLFZNf",
        "outputId": "b814f3a0-00a0-44ee-dbc1-3cf64c747d11"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning:\n",
            "\n",
            "Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe58bd4b50>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotly is again a lot more useful especially with boxplots thanks to the interactive plots by default. You can hover and see the median value, Q1 and Q3 values that I've talked about in the blog post. You can also pinpoint the outlier values directly on the plot."
      ],
      "metadata": {
        "id": "JwFCOdSWF4g-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "px.box(x=tips['total_bill'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "uW2jc9jAFgWx",
        "outputId": "af6c8dba-503f-4660-e91f-36bfa93de441"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "markdown",
      "source": [
        "Now that we've taken a look at all the graph types from the blog post, I'm going to show you some cool tricks you can do with Plotly 😎"
      ],
      "metadata": {
        "id": "pZLPP_StGc4q"
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        "# Something Extra"
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      "cell_type": "markdown",
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        "You can add the points parameter and see all the individual datapoints alongside the boxplot so that you can get a better understanding of the variable's distribution. Though this does take the outlier points away, you can set the points variable to 'outliers' or delete it completely to get them back."
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        "px.box(x=tips['total_bill'], points='all')"
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          "height": 542
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        "id": "FN0MrIAOGV0j",
        "outputId": "dcc11965-3896-499d-cccf-47188ad06fe8"
      },
      "execution_count": 100,
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    {
      "cell_type": "markdown",
      "source": [
        "You can pass a categorical variable to the color argument and get different boxplots for each category. Now the cooles thing you can do here is if you click on the categories on the right side of the plot, you can toggle them on and off on the plot."
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        "px.box(x=tips['total_bill'], points='outliers', color=tips['day'])"
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        "outputId": "c465bc8e-dca9-4cff-f117-b6d24c68ff3b"
      },
      "execution_count": 101,
      "outputs": [
        {
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