Data_Modeling.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Data_Modeling.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "GkFVaenzEt1U",
        "5R_S7bHdElvy",
        "z3x8J3ZkxIzM",
        "SzkIao_n0TO2",
        "AEkvvMTI1hLl",
        "INfeMWgK7a7I",
        "z2wUKh83SBi0",
        "x8AlkPcsD4y6",
        "f7iMlxEBCDyJ",
        "gkoQeUISmn3h",
        "w_lOtqIVZIFK",
        "0T9rbWjrZPQB",
        "TwVXqv7fWAKZ",
        "Qj0Rc8gjBCXR"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N91y52jhl9GU"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Ucs7khewvm9"
      },
      "source": [
        "## * You Can Download Sankey Diagram of App From Link Below:\n",
        "# https://drive.google.com/file/d/1-0EPh034VQ6I5MGzOhGSowZbEj_RcYnB/view?usp=sharing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GkFVaenzEt1U"
      },
      "source": [
        "#imports\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfXxdHIYod9a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "%cd /content/gdrive/MyDrive/Colab Notebooks/Data_Modeling"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hq3vdGCOXolV",
        "outputId": "e59b982c-547c-4a09-a237-031a30e9d9b3"
      },
      "source": [
        "!pip install chart_studio"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: chart_studio in /usr/local/lib/python3.7/dist-packages (1.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from chart_studio) (1.15.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from chart_studio) (2.23.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from chart_studio) (1.3.3)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from chart_studio) (4.4.1)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->chart_studio) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->chart_studio) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->chart_studio) (2021.5.30)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->chart_studio) (2.10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5R_S7bHdElvy"
      },
      "source": [
        "#Basic"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hOJIdiQFG1WS"
      },
      "source": [
        "Some basic data prepration and visulaization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uPMTqrRZpyZ-"
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import pyplot\n",
        "from pandas.plotting import scatter_matrix\n",
        "plt.style.use('seaborn')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "EyLoJBo-qNbx",
        "outputId": "cb7d76a0-2c69-4b72-a9df-f5eb059586b3"
      },
      "source": [
        "path ='Ads_Data.xlsx'\n",
        "Data = pd.read_excel(path)\n",
        "Data.head()"
      ],
      "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>action</th>\n",
              "      <th>created_at</th>\n",
              "      <th>source_event_id</th>\n",
              "      <th>device_id</th>\n",
              "      <th>post_page_offset</th>\n",
              "      <th>tokens</th>\n",
              "      <th>post_index_in_post_list</th>\n",
              "      <th>post_token</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>1609545001150</td>\n",
              "      <td>1575558c-a702-46ef-8e18-bc5cef761473</td>\n",
              "      <td>Ed9EADRZRXCHcrcEP_Hnkg</td>\n",
              "      <td>26.0</td>\n",
              "      <td>[wXvP3enu, wXvHXzUS, wXvPHXVe, wXvPHRs3, wXvH3...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>1609545121641</td>\n",
              "      <td>0fd004f4-ce9b-4766-ac96-7578dedebacb</td>\n",
              "      <td>5aVIp_PNSAWxqwBETYpmxQ</td>\n",
              "      <td>19.0</td>\n",
              "      <td>[wXujANbk, wXurAJsf, wXufwlh1, wXtr_4tD, wXujg...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>1609545009147</td>\n",
              "      <td>479905f2-3bb4-4c90-8e88-5ce4b60d0a73</td>\n",
              "      <td>mt0UAT4AQe2dECdXBvKYBA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[wXvHbNqU, wXvDrof9, wXv_aXHk, wXvDrSIa, wXv_a...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>1609546776979</td>\n",
              "      <td>b9b609bd-a486-42fb-b1a9-5ca03cf4a215</td>\n",
              "      <td>huSKUX-mSwyUcGzgSbXX6g</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[wXvb7cpv, wXvH3hL0, wXvjmDaE, wXvT0sOS, wXv_j...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>1609547182717</td>\n",
              "      <td>6c390fce-34b6-4e19-837e-6bc295a72514</td>\n",
              "      <td>sL5fLDdoR9a7aDGPP8N-jA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>[wXvjJTIc, wXvb5_SX, wXvb54Gs, wXvb5t6J, wXvbJ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           action     created_at  ... post_index_in_post_list post_token\n",
              "0  load_post_page  1609545001150  ...                     NaN        NaN\n",
              "1  load_post_page  1609545121641  ...                     NaN        NaN\n",
              "2  load_post_page  1609545009147  ...                     NaN        NaN\n",
              "3  load_post_page  1609546776979  ...                     NaN        NaN\n",
              "4  load_post_page  1609547182717  ...                     NaN        NaN\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PK5VpQ0SXL-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "a7315e14-5df5-4a9f-eaa3-f2ee75ecf018"
      },
      "source": [
        "Data.sort_values('created_at').head(10)"
      ],
      "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>action</th>\n",
              "      <th>created_at</th>\n",
              "      <th>source_event_id</th>\n",
              "      <th>device_id</th>\n",
              "      <th>post_page_offset</th>\n",
              "      <th>tokens</th>\n",
              "      <th>post_index_in_post_list</th>\n",
              "      <th>post_token</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>26178</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>1609461369213</td>\n",
              "      <td>1fc5ef66-aaad-4cf5-8e36-1e974de66fdf</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[wXtbOKF-, wXtLeB6p, wXpbfeIJ, wXt38PoX, wXt7K...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56013</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>1609461378164</td>\n",
              "      <td>79910930-7296-4342-ae09-8e55e4803940</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[wXvPByYA, wXmHw9qd, wXsPnrBu, wXsTzSjv, wXrXX...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68800</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>1609461425007</td>\n",
              "      <td>79910930-7296-4342-ae09-8e55e4803940</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>1.0</td>\n",
              "      <td>[wXWrwAAW, wXVXrn3e, wXUrG55o, wXRvrx9F, wXIX6...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102202</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>1609461448740</td>\n",
              "      <td>21a5377e-c7c2-4b08-805c-6b580e0f074d</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[wXtT-_XF, wXs_6TLh, wXpneaBg, wXpX9Pdu, wXoL-...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24308</th>\n",
              "      <td>click_post</td>\n",
              "      <td>1609461463452</td>\n",
              "      <td>21a5377e-c7c2-4b08-805c-6b580e0f074d</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>wXoL-Kbx</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102919</th>\n",
              "      <td>click_post</td>\n",
              "      <td>1609461490096</td>\n",
              "      <td>21a5377e-c7c2-4b08-805c-6b580e0f074d</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>wXgfv-Z0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76544</th>\n",
              "      <td>click_post</td>\n",
              "      <td>1609461516071</td>\n",
              "      <td>21a5377e-c7c2-4b08-805c-6b580e0f074d</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.0</td>\n",
              "      <td>wXbnjxCm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24534</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>1609461565359</td>\n",
              "      <td>d1dbb841-5cad-462a-9c0d-3688c3213dcd</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[wXvT74zf, wXvHquvw, wXv7Zrpa, wXvzJls9, wXvbZ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12324</th>\n",
              "      <td>click_post</td>\n",
              "      <td>1609461575426</td>\n",
              "      <td>d1dbb841-5cad-462a-9c0d-3688c3213dcd</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>wXvbZtZU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88850</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>1609461612388</td>\n",
              "      <td>d1dbb841-5cad-462a-9c0d-3688c3213dcd</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>1.0</td>\n",
              "      <td>[wXvTVW9G, wXvT1TPg, wXrnib-6, wXvzWgkP, wXsnk...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                action     created_at  ... post_index_in_post_list post_token\n",
              "26178   load_post_page  1609461369213  ...                     NaN        NaN\n",
              "56013   load_post_page  1609461378164  ...                     NaN        NaN\n",
              "68800   load_post_page  1609461425007  ...                     NaN        NaN\n",
              "102202  load_post_page  1609461448740  ...                     NaN        NaN\n",
              "24308       click_post  1609461463452  ...                     5.0   wXoL-Kbx\n",
              "102919      click_post  1609461490096  ...                     8.0   wXgfv-Z0\n",
              "76544       click_post  1609461516071  ...                    12.0   wXbnjxCm\n",
              "24534   load_post_page  1609461565359  ...                     NaN        NaN\n",
              "12324       click_post  1609461575426  ...                     5.0   wXvbZtZU\n",
              "88850   load_post_page  1609461612388  ...                     NaN        NaN\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "yT97xsIY33W5",
        "outputId": "423db939-b40c-4073-a6dc-6d97489820f7"
      },
      "source": [
        "Data.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",
              "        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>created_at</th>\n",
              "      <th>post_page_offset</th>\n",
              "      <th>post_index_in_post_list</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.110830e+05</td>\n",
              "      <td>35287.000000</td>\n",
              "      <td>75796.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.609545e+12</td>\n",
              "      <td>8.673761</td>\n",
              "      <td>127.616418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.895533e+06</td>\n",
              "      <td>16.059840</td>\n",
              "      <td>272.981195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.609461e+12</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.609544e+12</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.609545e+12</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>41.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.609546e+12</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>124.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.609634e+12</td>\n",
              "      <td>161.000000</td>\n",
              "      <td>5176.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         created_at  post_page_offset  post_index_in_post_list\n",
              "count  1.110830e+05      35287.000000             75796.000000\n",
              "mean   1.609545e+12          8.673761               127.616418\n",
              "std    2.895533e+06         16.059840               272.981195\n",
              "min    1.609461e+12          0.000000                 1.000000\n",
              "25%    1.609544e+12          0.000000                11.000000\n",
              "50%    1.609545e+12          3.000000                41.000000\n",
              "75%    1.609546e+12          9.000000               124.000000\n",
              "max    1.609634e+12        161.000000              5176.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yOGf8Hya5bYA",
        "outputId": "75e5382b-08c6-4cc5-de3b-bbd6b73d9721"
      },
      "source": [
        "Data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(111083, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WeF1gKPHtxio",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "2375f2e9-3cfa-4829-b627-2f24dadab4d8"
      },
      "source": [
        "Data.hist(figsize=(10,10))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYTfByb_uTp9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "b488919f-5e00-4bdf-88be-bd36202c38b7"
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "corr = Data.corr()\n",
        "sns.heatmap(corr,annot=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb1090fc490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bdQSKrRHuqQz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "a464e7bd-472c-4509-c66b-c01d595e9c9b"
      },
      "source": [
        "plt.figure(figsize=(16, 8))\n",
        "ax = sns.jointplot(x=Data['action'],y=Data['post_page_offset'],data=Data,s=10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxHCSfJyvMa2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "1bb37aed-506c-4ad8-e831-2628ac1948fe"
      },
      "source": [
        "plt.figure(figsize=(16, 8))\n",
        "ax = sns.jointplot(x=Data['action'],y=Data['post_index_in_post_list'],data=Data,s=10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pUy2D8ncvZTq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "be12c3e5-30d4-4d4c-816e-f76feb0df802"
      },
      "source": [
        "plt.figure(figsize=(16, 8))\n",
        "ax = sns.jointplot(x=Data['post_page_offset'],y=Data['post_index_in_post_list'],data=Data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDHrmWjIvgSe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "outputId": "5c67642d-d763-428c-e95b-b6b7773d5c41"
      },
      "source": [
        "sns.pairplot(Data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f325d23a7d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 174
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3x8J3ZkxIzM"
      },
      "source": [
        "#Preprocess\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5oQ56ph8G-oC"
      },
      "source": [
        "Making data standard like: changing time format and ..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Te9UygyIvAiN"
      },
      "source": [
        "Data['created_at']=pd.to_datetime(Data['created_at'], unit='ms')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3gwgfZBA6v7"
      },
      "source": [
        "Data=Data.sort_values('created_at')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "OlSLihwMA9fU",
        "outputId": "a9ffbec4-41fe-49e5-b9a2-aa88babdd9ba"
      },
      "source": [
        "Data.head()"
      ],
      "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>action</th>\n",
              "      <th>created_at</th>\n",
              "      <th>source_event_id</th>\n",
              "      <th>device_id</th>\n",
              "      <th>post_page_offset</th>\n",
              "      <th>tokens</th>\n",
              "      <th>post_index_in_post_list</th>\n",
              "      <th>post_token</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>26178</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>2021-01-01 00:36:09.213</td>\n",
              "      <td>1fc5ef66-aaad-4cf5-8e36-1e974de66fdf</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[wXtbOKF-, wXtLeB6p, wXpbfeIJ, wXt38PoX, wXt7K...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56013</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>2021-01-01 00:36:18.164</td>\n",
              "      <td>79910930-7296-4342-ae09-8e55e4803940</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[wXvPByYA, wXmHw9qd, wXsPnrBu, wXsTzSjv, wXrXX...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68800</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>2021-01-01 00:37:05.007</td>\n",
              "      <td>79910930-7296-4342-ae09-8e55e4803940</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>1.0</td>\n",
              "      <td>[wXWrwAAW, wXVXrn3e, wXUrG55o, wXRvrx9F, wXIX6...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102202</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>2021-01-01 00:37:28.740</td>\n",
              "      <td>21a5377e-c7c2-4b08-805c-6b580e0f074d</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[wXtT-_XF, wXs_6TLh, wXpneaBg, wXpX9Pdu, wXoL-...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24308</th>\n",
              "      <td>click_post</td>\n",
              "      <td>2021-01-01 00:37:43.452</td>\n",
              "      <td>21a5377e-c7c2-4b08-805c-6b580e0f074d</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>wXoL-Kbx</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                action  ... post_token\n",
              "26178   load_post_page  ...        NaN\n",
              "56013   load_post_page  ...        NaN\n",
              "68800   load_post_page  ...        NaN\n",
              "102202  load_post_page  ...        NaN\n",
              "24308       click_post  ...   wXoL-Kbx\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzkIao_n0TO2"
      },
      "source": [
        "#Dark Query percent:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kSZOH02MHIfv"
      },
      "source": [
        "We calculate total number of tokens in dataframe and saving result as new column and calculate ratio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYP5g5mXxqzT"
      },
      "source": [
        "Data.loc[:,'tokens_length']= Data['tokens'].map(lambda x: len(re.sub(\n",
        "    \"[^\\w\\-]\", \" \",  x).split()) if type(x)!=float else np.nan)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IuKkWzlCEMW4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "66451f1b-c195-48ea-cada-986b5446a6a1"
      },
      "source": [
        "query_results=Data[Data['action']=='load_post_page'].groupby(\n",
        "    'source_event_id')['tokens_length'].sum().reset_index()\n",
        "query_results.head()"
      ],
      "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>source_event_id</th>\n",
              "      <th>tokens_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00049ef3-c027-4f55-869c-46711f0ea0ad</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00069dda-4175-4bb0-8ef0-c0e1031d4da6</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0017c208-e016-4231-a022-598ba020f1ff</td>\n",
              "      <td>168.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>002222cf-9e4b-4432-9e36-ad500a753850</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>004dc60d-2288-453f-bcda-1ad5e8d8211f</td>\n",
              "      <td>25.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        source_event_id  tokens_length\n",
              "0  00049ef3-c027-4f55-869c-46711f0ea0ad           24.0\n",
              "1  00069dda-4175-4bb0-8ef0-c0e1031d4da6           24.0\n",
              "2  0017c208-e016-4231-a022-598ba020f1ff          168.0\n",
              "3  002222cf-9e4b-4432-9e36-ad500a753850           24.0\n",
              "4  004dc60d-2288-453f-bcda-1ad5e8d8211f           25.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Md4hNTPYQERm",
        "outputId": "f2246149-5e53-4d7a-81e6-90fd9e63f700"
      },
      "source": [
        "print(query_results.shape)\n",
        "print(Data['source_event_id'].nunique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(9381, 2)\n",
            "14296\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aWpOWlSGBOvP",
        "outputId": "ef147519-42cf-4f4c-b646-eeed73f50cb6"
      },
      "source": [
        "query_results[query_results['tokens_length']<10].shape[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1138"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7uCGzw0ryBUA",
        "outputId": "b1f25313-1646-4126-e05c-cb689487260c"
      },
      "source": [
        "#Unique_queries/load_page_post_queries\n",
        "(query_results[query_results['tokens_length']<10].shape[0]/query_results['source_event_id'].shape[0])*100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.130902888817824"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dG8JnYhlUl_B",
        "outputId": "27443341-196b-460e-be9a-2d1676cddaec"
      },
      "source": [
        "#Unique_queries/Total_unique_queries\n",
        "(query_results[query_results['tokens_length']<10].shape[0]/Data['source_event_id'].nunique())*100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.960268606603246"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "oYdlsCMpxlpG",
        "outputId": "2dee8077-bb30-4b16-d2c1-fd21aaf90a61"
      },
      "source": [
        "plt.figure(figsize = (25,8))\n",
        "plt.subplot(1, 4, 1)\n",
        "plt.title('loaded post of all queries', fontsize=15)\n",
        "query_results['tokens_length'].plot.hist(fontsize=10)\n",
        "plt.subplot(1, 4, 2)\n",
        "plt.title('lower than 20 loaded post', fontsize=15)\n",
        "query_results['tokens_length'][query_results['tokens_length']<20].plot.hist(fontsize=10)\n",
        "plt.subplot(1, 4, 3)\n",
        "plt.title('lower than 20 loaded post comulative', fontsize=15)\n",
        "query_results['tokens_length'][query_results['tokens_length']<20].plot.hist(cumulative=True,fontsize=10)\n",
        "plt.subplot(1, 4, 4)\n",
        "plt.yscale('log')\n",
        "plt.title('log of frequency of token_lenght', fontsize=15)\n",
        "query_results['tokens_length'].plot.hist( fontsize=10)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1800x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEkvvMTI1hLl"
      },
      "source": [
        "#Query bounce rate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pV_DrMaEbOE_"
      },
      "source": [
        "For this query first  we should calculate total clicks/ total data and then we should calculate dual of this metric so we minus calculated value from 1.\n",
        "Result is the ratio of not clicked queries / total clicked and not clicked queries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "90gB7iWF48eY",
        "outputId": "05fbe08c-63a7-4152-a989-f179c6e8b36e"
      },
      "source": [
        "#Unique Queries with action ='click_post'\n",
        "Clicked_queries=Data[Data['action']=='click_post'].groupby(\n",
        "    by=['source_event_id'])['post_token'].apply(list).reset_index()\n",
        "Clicked_queries.head()"
      ],
      "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>source_event_id</th>\n",
              "      <th>post_token</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00142c59-745c-4004-a955-698ddcf1faa6</td>\n",
              "      <td>[wXuDbcdY, wXn_gOQB, wX_mPSOb, wXbDYFRf]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0016f59f-9fb3-4ab5-ae78-9783314b81fc</td>\n",
              "      <td>[wXvfYPCE, wXvH4j2o, wXvHIUus, wXv3XPy_, wXvn3...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0017b9ef-5903-40f9-a219-85728eb78436</td>\n",
              "      <td>[wXvrEhKR, wXvnwYiP, wXvrEhKR, wXsDHFZ9, wXuf9...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0017c208-e016-4231-a022-598ba020f1ff</td>\n",
              "      <td>[wXj3iwnq, wXvXn34I, wXv_2DE5, wXorqAO7]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>00240c5c-9d06-4fcd-93f5-93b842fdab63</td>\n",
              "      <td>[wXR7b2eM]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        source_event_id                                         post_token\n",
              "0  00142c59-745c-4004-a955-698ddcf1faa6           [wXuDbcdY, wXn_gOQB, wX_mPSOb, wXbDYFRf]\n",
              "1  0016f59f-9fb3-4ab5-ae78-9783314b81fc  [wXvfYPCE, wXvH4j2o, wXvHIUus, wXv3XPy_, wXvn3...\n",
              "2  0017b9ef-5903-40f9-a219-85728eb78436  [wXvrEhKR, wXvnwYiP, wXvrEhKR, wXsDHFZ9, wXuf9...\n",
              "3  0017c208-e016-4231-a022-598ba020f1ff           [wXj3iwnq, wXvXn34I, wXv_2DE5, wXorqAO7]\n",
              "4  00240c5c-9d06-4fcd-93f5-93b842fdab63                                         [wXR7b2eM]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3MIKJ7kIE4A",
        "outputId": "d3da82b0-d06e-4772-fd53-8de9d00e1d32"
      },
      "source": [
        "Clicked_queries['source_event_id'].value_counts().size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10820"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BhfPxVVTIGp_",
        "outputId": "88a756e7-b180-4cdb-a150-2a82388a7bb0"
      },
      "source": [
        "Data['source_event_id'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14296"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXmjQhMy4Mkw",
        "outputId": "7ad593ae-6542-45fc-8a77-4cbd50224436"
      },
      "source": [
        "100-(Clicked_queries['source_event_id'].value_counts().size / Data['source_event_id'].nunique())*100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24.314493564633466"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INfeMWgK7a7I"
      },
      "source": [
        "#Number of Click/Loaded for one query"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "mUpa0iosRmre",
        "outputId": "4b206216-cddb-4168-f50d-d2a74148e3f6"
      },
      "source": [
        "#number of clicks\n",
        "click_ratio=Data.groupby(['source_event_id'])['post_token'].nunique().reset_index()\n",
        "click_ratio.head()"
      ],
      "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>source_event_id</th>\n",
              "      <th>post_token</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00049ef3-c027-4f55-869c-46711f0ea0ad</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00069dda-4175-4bb0-8ef0-c0e1031d4da6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00142c59-745c-4004-a955-698ddcf1faa6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0016f59f-9fb3-4ab5-ae78-9783314b81fc</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0017b9ef-5903-40f9-a219-85728eb78436</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        source_event_id  post_token\n",
              "0  00049ef3-c027-4f55-869c-46711f0ea0ad           0\n",
              "1  00069dda-4175-4bb0-8ef0-c0e1031d4da6           0\n",
              "2  00142c59-745c-4004-a955-698ddcf1faa6           4\n",
              "3  0016f59f-9fb3-4ab5-ae78-9783314b81fc          10\n",
              "4  0017b9ef-5903-40f9-a219-85728eb78436           4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "0uv49rteqORK",
        "outputId": "608bdaa1-2beb-449b-d543-00eb15081c6f"
      },
      "source": [
        "click_ratio.loc[:,'tokens_length']=Data.groupby('source_event_id')['tokens_length'].sum().reset_index()\n",
        "click_ratio.head()"
      ],
      "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>source_event_id</th>\n",
              "      <th>post_token</th>\n",
              "      <th>tokens_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00049ef3-c027-4f55-869c-46711f0ea0ad</td>\n",
              "      <td>0</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00069dda-4175-4bb0-8ef0-c0e1031d4da6</td>\n",
              "      <td>0</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00142c59-745c-4004-a955-698ddcf1faa6</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0016f59f-9fb3-4ab5-ae78-9783314b81fc</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0017b9ef-5903-40f9-a219-85728eb78436</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        source_event_id  post_token  tokens_length\n",
              "0  00049ef3-c027-4f55-869c-46711f0ea0ad           0           24.0\n",
              "1  00069dda-4175-4bb0-8ef0-c0e1031d4da6           0           24.0\n",
              "2  00142c59-745c-4004-a955-698ddcf1faa6           4            0.0\n",
              "3  0016f59f-9fb3-4ab5-ae78-9783314b81fc          10            0.0\n",
              "4  0017b9ef-5903-40f9-a219-85728eb78436           4            0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TP74uu4zBJiW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "cda70a6c-56b4-443e-e39f-a8f4344d3078"
      },
      "source": [
        "click_ratio.loc[:,'Unique_Click_Ratio']=click_ratio['post_token']/(click_ratio['tokens_length'])\n",
        "click_ratio.head()"
      ],
      "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>source_event_id</th>\n",
              "      <th>post_token</th>\n",
              "      <th>tokens_length</th>\n",
              "      <th>Unique_Click_Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00049ef3-c027-4f55-869c-46711f0ea0ad</td>\n",
              "      <td>0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00069dda-4175-4bb0-8ef0-c0e1031d4da6</td>\n",
              "      <td>0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00142c59-745c-4004-a955-698ddcf1faa6</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0016f59f-9fb3-4ab5-ae78-9783314b81fc</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0017b9ef-5903-40f9-a219-85728eb78436</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        source_event_id  ...  Unique_Click_Ratio\n",
              "0  00049ef3-c027-4f55-869c-46711f0ea0ad  ...                 0.0\n",
              "1  00069dda-4175-4bb0-8ef0-c0e1031d4da6  ...                 0.0\n",
              "2  00142c59-745c-4004-a955-698ddcf1faa6  ...                 inf\n",
              "3  0016f59f-9fb3-4ab5-ae78-9783314b81fc  ...                 inf\n",
              "4  0017b9ef-5903-40f9-a219-85728eb78436  ...                 inf\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FObKfiyrP4Sr"
      },
      "source": [
        "click_ratio.replace([np.inf, -np.inf], np.nan, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "VxIjLRxw7Zt-",
        "outputId": "03f58ae2-e663-4966-c07f-04e0fec068f2"
      },
      "source": [
        "click_ratio.head()"
      ],
      "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>source_event_id</th>\n",
              "      <th>post_token</th>\n",
              "      <th>tokens_length</th>\n",
              "      <th>Unique_Click_Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00049ef3-c027-4f55-869c-46711f0ea0ad</td>\n",
              "      <td>0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00069dda-4175-4bb0-8ef0-c0e1031d4da6</td>\n",
              "      <td>0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00142c59-745c-4004-a955-698ddcf1faa6</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0016f59f-9fb3-4ab5-ae78-9783314b81fc</td>\n",
              "      <td>10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0017b9ef-5903-40f9-a219-85728eb78436</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        source_event_id  ...  Unique_Click_Ratio\n",
              "0  00049ef3-c027-4f55-869c-46711f0ea0ad  ...                 0.0\n",
              "1  00069dda-4175-4bb0-8ef0-c0e1031d4da6  ...                 0.0\n",
              "2  00142c59-745c-4004-a955-698ddcf1faa6  ...                 NaN\n",
              "3  0016f59f-9fb3-4ab5-ae78-9783314b81fc  ...                 NaN\n",
              "4  0017b9ef-5903-40f9-a219-85728eb78436  ...                 NaN\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpULLG5DQytg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c440d77-e92e-47cb-f410-952bc6e6793f"
      },
      "source": [
        "click_ratio['Unique_Click_Ratio']=click_ratio['Unique_Click_Ratio']*100\n",
        "click_ratio['Unique_Click_Ratio'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.77932654764141"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "dQHVmCRXb1bO",
        "outputId": "d1022154-a362-45c2-ed74-e1b27ff0e0a1"
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "corr = click_ratio.corr()\n",
        "sns.heatmap(corr,annot=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f324df81e50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "yc56L7JgdIwV",
        "outputId": "624de81e-02a4-4d99-c625-3c1a158aaf9d"
      },
      "source": [
        "plt.scatter(click_ratio['tokens_length'],click_ratio['post_token'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f324deaba90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "sxvNW326EwtQ",
        "outputId": "58deaadf-ee35-4098-c506-6bb972f856aa"
      },
      "source": [
        "plt.figure(figsize = (25,8))\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.yscale('log')\n",
        "plt.title('logaritmic frequency of number of loaded post', fontsize=20)\n",
        "click_ratio['tokens_length'].plot.hist()\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.yscale('log')\n",
        "plt.title('logaritmic frequency of number of clicked post', fontsize=20)\n",
        "click_ratio['post_token'].plot.hist()\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.yscale('log')\n",
        "plt.title('logaritmic frequency of ratio of clicked post/loaded post', fontsize=20)\n",
        "click_ratio['Unique_Click_Ratio'].plot.hist()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1800x576 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z2wUKh83SBi0"
      },
      "source": [
        "#Rank of first Click\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUujvhd7Dgki"
      },
      "source": [
        "Ad_rank=Data.groupby('source_event_id')['post_index_in_post_list'].apply(\n",
        "    lambda x: list(x) if type(x)!=float else np.nan).reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPWpp31VdeLd"
      },
      "source": [
        "Ad_rank[\"post_index_in_post_list\"] = Ad_rank[\"post_index_in_post_list\"].apply(\n",
        "    lambda x: [i for i in x if str(i) != \"nan\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9uiPB1q4sTfv"
      },
      "source": [
        "Ad_rank['post_index_in_post_list']=Ad_rank['post_index_in_post_list'].sort_values().apply(lambda x: sorted(x))\n",
        "Ad_rank.loc[:, 'first_click'] = Ad_rank['post_index_in_post_list'].map(lambda x: x[0] if len(x)!=0 else np.nan)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HZoE9a-ska0",
        "outputId": "e2126ecb-730c-4e78-e495-f2ef42296b07"
      },
      "source": [
        "Ad_rank.head()"
      ],
      "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>source_event_id</th>\n",
              "      <th>post_index_in_post_list</th>\n",
              "      <th>first_click</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00049ef3-c027-4f55-869c-46711f0ea0ad</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00069dda-4175-4bb0-8ef0-c0e1031d4da6</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00142c59-745c-4004-a955-698ddcf1faa6</td>\n",
              "      <td>[7.0, 17.0, 82.0, 181.0]</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0016f59f-9fb3-4ab5-ae78-9783314b81fc</td>\n",
              "      <td>[2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 11.0, 12.0, 20...</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0017b9ef-5903-40f9-a219-85728eb78436</td>\n",
              "      <td>[5.0, 5.0, 10.0, 11.0, 19.0]</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        source_event_id  ... first_click\n",
              "0  00049ef3-c027-4f55-869c-46711f0ea0ad  ...         NaN\n",
              "1  00069dda-4175-4bb0-8ef0-c0e1031d4da6  ...         NaN\n",
              "2  00142c59-745c-4004-a955-698ddcf1faa6  ...         7.0\n",
              "3  0016f59f-9fb3-4ab5-ae78-9783314b81fc  ...         2.0\n",
              "4  0017b9ef-5903-40f9-a219-85728eb78436  ...         5.0\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVOgrxjuK5Gc",
        "outputId": "8350d2e5-4e3b-4fbf-bb2f-6507f1f1f67a"
      },
      "source": [
        "Ad_rank.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",
              "        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>first_click</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10820.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>25.201571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>105.121353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3510.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        first_click\n",
              "count  10820.000000\n",
              "mean      25.201571\n",
              "std      105.121353\n",
              "min        1.000000\n",
              "25%        2.000000\n",
              "50%        4.000000\n",
              "75%       13.000000\n",
              "max     3510.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HeW-gYgXtPcr",
        "outputId": "3eb27a0f-5a41-4ea9-e92d-392ca4a058f5"
      },
      "source": [
        "Ad_rank[Ad_rank['first_click']<25]['first_click'].plot.hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f325fb502d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TpFdteZ26w20",
        "outputId": "3ef12a8c-d821-4edd-f2ae-07ff81f4711b"
      },
      "source": [
        "Ad_rank['first_click'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25.201571164510167"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x8AlkPcsD4y6"
      },
      "source": [
        "#Avg of click distances/first 3 clicked?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2T67tgwuDrL0"
      },
      "source": [
        "Ad_rank['post_index_in_post_list']=Ad_rank['post_index_in_post_list'].apply(sorted)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7V2Yo86UI6E2",
        "outputId": "9bd1d09e-ec5b-4808-d875-630e62c312cf"
      },
      "source": [
        "Ad_rank.head()"
      ],
      "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>source_event_id</th>\n",
              "      <th>post_index_in_post_list</th>\n",
              "      <th>first_click</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00049ef3-c027-4f55-869c-46711f0ea0ad</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00069dda-4175-4bb0-8ef0-c0e1031d4da6</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00142c59-745c-4004-a955-698ddcf1faa6</td>\n",
              "      <td>[7.0, 17.0, 82.0, 181.0]</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0016f59f-9fb3-4ab5-ae78-9783314b81fc</td>\n",
              "      <td>[2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 11.0, 12.0, 20...</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0017b9ef-5903-40f9-a219-85728eb78436</td>\n",
              "      <td>[5.0, 5.0, 10.0, 11.0, 19.0]</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        source_event_id  ... first_click\n",
              "0  00049ef3-c027-4f55-869c-46711f0ea0ad  ...         NaN\n",
              "1  00069dda-4175-4bb0-8ef0-c0e1031d4da6  ...         NaN\n",
              "2  00142c59-745c-4004-a955-698ddcf1faa6  ...         7.0\n",
              "3  0016f59f-9fb3-4ab5-ae78-9783314b81fc  ...         2.0\n",
              "4  0017b9ef-5903-40f9-a219-85728eb78436  ...         5.0\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsUKXzDQXSnU"
      },
      "source": [
        "Ad_rank.loc[:, 'click_dist_avg']= Ad_rank['post_index_in_post_list'].map(\n",
        "    lambda x: (x[-1]-x[0])/len(x) if (len(x)!=0 and len(set(x))>1) else \n",
        "    0 if len(set(x))==1 else np.nan)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTaA28iwZ5cL",
        "outputId": "a8af9ee4-40c8-40b3-c5c0-0687c8379020"
      },
      "source": [
        "Ad_rank.click_dist_avg.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.874658894127465"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aSCXmav1jxQ",
        "outputId": "776b4516-6629-4159-c017-83d471025864"
      },
      "source": [
        "plt.figure(figsize = (20,8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title('Distance with lower than 20')\n",
        "Ad_rank['click_dist_avg'][Ad_rank['click_dist_avg']<20].plot.hist()\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title('log of distances')\n",
        "plt.yscale('log')\n",
        "Ad_rank['click_dist_avg'].plot.hist()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpTVM_eqj2Km"
      },
      "source": [
        "Ad_rank.loc[:, 'clicked_first_3']= Ad_rank['post_index_in_post_list'].map(\n",
        "    lambda x: True if (x.count(1)>0 or x.count(2)>0 or\n",
        "                         x.count(3)>0  ) else False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CrDflm3XsIGJ",
        "outputId": "cc5b819f-98e6-4b66-fd6c-42e48db33f3a"
      },
      "source": [
        "Ad_rank.head()"
      ],
      "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>source_event_id</th>\n",
              "      <th>post_index_in_post_list</th>\n",
              "      <th>first_click</th>\n",
              "      <th>click_dist_avg</th>\n",
              "      <th>clicked_first_3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00049ef3-c027-4f55-869c-46711f0ea0ad</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00069dda-4175-4bb0-8ef0-c0e1031d4da6</td>\n",
              "      <td>[]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>00142c59-745c-4004-a955-698ddcf1faa6</td>\n",
              "      <td>[7.0, 17.0, 82.0, 181.0]</td>\n",
              "      <td>7.0</td>\n",
              "      <td>43.5</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0016f59f-9fb3-4ab5-ae78-9783314b81fc</td>\n",
              "      <td>[2.0, 3.0, 4.0, 5.0, 7.0, 10.0, 11.0, 12.0, 20...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0017b9ef-5903-40f9-a219-85728eb78436</td>\n",
              "      <td>[5.0, 5.0, 10.0, 11.0, 19.0]</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.8</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        source_event_id  ... clicked_first_3\n",
              "0  00049ef3-c027-4f55-869c-46711f0ea0ad  ...           False\n",
              "1  00069dda-4175-4bb0-8ef0-c0e1031d4da6  ...           False\n",
              "2  00142c59-745c-4004-a955-698ddcf1faa6  ...           False\n",
              "3  0016f59f-9fb3-4ab5-ae78-9783314b81fc  ...            True\n",
              "4  0017b9ef-5903-40f9-a219-85728eb78436  ...           False\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25_Ev_zm6vPv",
        "outputId": "280c46d6-ef06-4c5f-d73b-ed7ad5b9efa0"
      },
      "source": [
        "Ad_rank.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14296, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1f6N76dsPdKv",
        "outputId": "00c920de-2034-4ffe-f8ed-0080c67798f6"
      },
      "source": [
        "clicked=[Ad_rank.clicked_first_3.sum(),Ad_rank.shape[0]-Ad_rank.clicked_first_3.sum()]\n",
        "names=['True','False']\n",
        "plt.hist(names,weights=clicked)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([5062.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "        9234.]),\n",
              " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
              " <a list of 10 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dygNKSwL8QFJ",
        "outputId": "5c7d0c98-b11b-49e5-f58e-969a07fcce83"
      },
      "source": [
        "Ad_rank['clicked_first_3'].mean()*100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35.40850587576945"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7iMlxEBCDyJ"
      },
      "source": [
        "#Compare Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "Nux5cc1oBouE",
        "outputId": "b4e1cdda-2e80-4689-e025-af020979cc06"
      },
      "source": [
        "Ad_rank.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",
              "        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>first_click</th>\n",
              "      <th>click_dist_avg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10820.000000</td>\n",
              "      <td>10820.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>25.201571</td>\n",
              "      <td>6.874659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>105.121353</td>\n",
              "      <td>13.201312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>8.064732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3510.000000</td>\n",
              "      <td>487.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        first_click  click_dist_avg\n",
              "count  10820.000000    10820.000000\n",
              "mean      25.201571        6.874659\n",
              "std      105.121353       13.201312\n",
              "min        1.000000        0.000000\n",
              "25%        2.000000        0.500000\n",
              "50%        4.000000        3.000000\n",
              "75%       13.000000        8.064732\n",
              "max     3510.000000      487.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "iRFmwodzB8-K",
        "outputId": "0e8c9d6e-4546-4732-8a5d-a82b615012d3"
      },
      "source": [
        "click_ratio.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",
              "        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>post_token</th>\n",
              "      <th>tokens_length</th>\n",
              "      <th>Unique_Click_Ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>14296.000000</td>\n",
              "      <td>14296.000000</td>\n",
              "      <td>9381.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.026161</td>\n",
              "      <td>56.213556</td>\n",
              "      <td>8.779327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.732287</td>\n",
              "      <td>132.665078</td>\n",
              "      <td>17.584805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.884615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>8.823529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>173.000000</td>\n",
              "      <td>2376.000000</td>\n",
              "      <td>325.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         post_token  tokens_length  Unique_Click_Ratio\n",
              "count  14296.000000   14296.000000         9381.000000\n",
              "mean       5.026161      56.213556            8.779327\n",
              "std        8.732287     132.665078           17.584805\n",
              "min        0.000000       0.000000            0.000000\n",
              "25%        1.000000       0.000000            0.000000\n",
              "50%        2.000000      24.000000            2.884615\n",
              "75%        6.000000      48.000000            8.823529\n",
              "max      173.000000    2376.000000          325.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gkoQeUISmn3h"
      },
      "source": [
        "#Probability"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w_lOtqIVZIFK"
      },
      "source": [
        "##First Metric:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6lRI42tgWnpd"
      },
      "source": [
        "p=click_ratio['Unique_Click_Ratio'].mean()/100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gfutmutbYiJS",
        "outputId": "e552abda-7cff-45f9-b53e-ed4c53efd6f3"
      },
      "source": [
        "p"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08779326547641411"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0T9rbWjrZPQB"
      },
      "source": [
        "##Second Metric:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IOtdxp2xXtRT"
      },
      "source": [
        "Geometric=[]\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAkBkNovXb2b"
      },
      "source": [
        "for i in range(1,int(Data['post_index_in_post_list'].max())):\n",
        "  Geometric.insert(i-1,((1-p)**(i-1))*p)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eAQFvVZkYGx7",
        "outputId": "5f60bcba-0348-425a-cb2f-5e694efc58bf"
      },
      "source": [
        "\n",
        "plt.figure(figsize = (15,4))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title('Geometric Distribution first 200 elements', fontsize=20)\n",
        "plt.plot(Geometric[1:200])\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title('Geometric Distribution', fontsize=20)\n",
        "plt.plot(Geometric)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f325db29a10>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ragMSD8xdorH"
      },
      "source": [
        "##Third Metric"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zBFMkbCCdsU2",
        "outputId": "b2f03604-08f5-4529-b59c-59ac46683c99"
      },
      "source": [
        "1/p"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11.390395317607279"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5hJyRKfWU3I"
      },
      "source": [
        "##Fourth Metric"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8MSX_8PsWUnB"
      },
      "source": [
        "Geo_CDF=[]\n",
        "for i in range(1,int(Data['post_index_in_post_list'].max())):\n",
        "  Geo_CDF.insert(i-1,1-(1 - p)**(i-1) )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "uT4fvj3xXAH5",
        "outputId": "8c240a50-913b-4b53-80a9-20fd1bc72963"
      },
      "source": [
        "plt.figure(figsize = (15,4))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.title('Comulative Geometric Distribution first 30 elements', fontsize=20)\n",
        "plt.plot(Geo_CDF[1:30])\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.title('Comulative Geometric Distribution', fontsize=20)\n",
        "plt.plot(Geo_CDF)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f325c821d10>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAENCAYAAACy3KwrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeVwU9f8H8Ney3IfIsSgioKKLyiHggUleiHdWaiameWt2WGn1zbSf+k2z+lqaaaaplQcmKniVppV3eeWBgleiIopyX8vN7vz+wF1cQQ6B3R14PR+PHsns7Mx7PrM7+3nPfA6JIAgCiIiIiIiIqF4w0ncAREREREREVHuY5BEREREREdUjTPKIiIiIiIjqESZ5RERERERE9QiTPCIiIiIionqESR4REREREVE90iCTvFOnTsHT0xPLly+v1e16enri1VdfrdVtUt0KDg5GcHCwvsPA8uXL4enpiVOnTtX5vmbNmgVPT0/cvXsXAHD37l14enpi1qxZdb5vAIiMjISnpyciIyN1sr/ypKWlYcqUKfD29oa/vz+AsuVCpQzhnBEZKtYpSI11ioZZpzBUxlVdURAE7Nu3D7t370Z0dDQyMjJgY2MDZ2dnBAcHY8SIEWjSpEldxmpQNm3ahI4dO6Jdu3aaZcuWLYO9vb0eowLy8/Oxbds2/Pnnn7h+/ToyMzNhYmICFxcXdOzYEWPGjIFcLtdrjHXpn3/+wZ07dzBs2LAqrT9v3rxa3f+sWbOwY8cOrWUWFhawt7eHt7c3Bg0ahL59+0IqlWqtM3DgQLRp0wZt2rSp9j4fPHiAbdu2Yfr06VVaf/To0ejVqxccHByqva/qKi+2wMBALFu2DD4+PnW+/ydZu3Ytjh49iuHDhyMwMBBA3ZXLoUOHoFQqERISUum6CoUCYWFh+PXXXxEfHw9BENCqVSsMGzYMr7zyCoyMtO/LHT58GGvWrMHly5ehUqnQpk0bjB8/Hs8991ytHkN9lpOTgx9++AHjxo1Do0aN9B1Og8E6hTbWKQwT6xSVY53CcFUpycvMzMT06dNx6tQptG/fHmPHjkXTpk2RmpqKU6dOYcWKFdi4cSOWLVuGrl271nXMeldYWIjPP/8cn3zyidYFecCAAXqMCrh27Rpef/11JCQkoGfPnpg2bRrs7OyQmpqKCxcuIDIyEpGRkfjkk0+qfMESm23btiEhIaHKx9ezZ886iWPmzJlwd3cHAOTl5eHOnTs4ePAg3nnnHfj4+GDZsmVwcXHRrN+6dWu0bt36qfb1119/YcWKFVW+IPv4+OjsYlhebC4uLlrHrg/Xrl0DAHz88cewtLQEUHflsm7dOjRv3rzSJC8/Px+TJ0/GxYsXMXToUEyYMAEKhQLbt2/HggULEBsbq1WB2LlzJ2bNmoV27drhP//5D0xNTbFr1y689957SElJwfjx42v9WOqjS5cuYcWKFRg6dCiTPB1hnUIb6xSGi3WKyrFOYbgqTfIEQcDMmTNx6tQpzJgxA6+99hokEonm9QkTJuDYsWN466238Pbbb2Pfvn06yeb16erVqygqKtJ3GFrS0tIwadIkzV3pbt26lVnnypUrmDx5Mj7++GO0adOmXt71uHTpkkF8/gIDA+Hn56e17J133kFERAT+7//+DxMnTkRERASsra1rvK9Lly7VeBt1xVBjKywsBABNgldXVCoVYmJi0Lx580rX3bJlC86fP4/3338fU6ZM0SwfNmwYBg4ciC1btmD69Omwt7dHXl4eFi1ahGbNmiEsLExzHC+++CJGjBiBJUuWYMiQIQbxXTB0hvoZra9YpyiLdQrDxTqFYTHk2AxRpX3yDh8+jOPHj6Nfv36YNm2a1sVYrXv37pg5cyYGDhwIhUKhWa5SqbBhwwa8+OKL6NChAzp06IDnn38e69atQ3FxsWY9dfvdOXPmICoqCqGhoejQoQOeffZZfPPNNxAEAZcuXcKYMWPg7++Pnj17Yt68eZqKGlBx++MBAwbA09Oz0sL466+/MGnSJHTp0gXe3t4ICQnBnDlzkJiYqFln1qxZGDFiBADgo48+0trno+3nP/jgA3h6euL8+fNl9lNQUICAgAAEBwdDEAQAJXdmli5div79+8Pb2xudO3fG+PHjcfTo0UrjBoDvv/8eycnJ+OCDD8q9GANAu3btsGTJEixYsKDcux7bt2/HSy+9hA4dOsDf3x9Dhw7Fxo0boVKpyqy7e/duhIaGwt/fHz4+PhgwYACWLl2K3NxcrfU8PT0xfvx43Lp1CxMnToS/vz8CAwMxf/58FBYWIi4uDlOnTkXHjh0RFBSEGTNmIDs7u9qxqftExMbG4vTp01rtwdXtxa9du4bJkyfDz88Phw8fBlB++/mioiKsXLkSgwYNgo+PD7p164a5c+ciOTm58hNRieHDh+Ptt9/G7du3sXbtWs3y8j6/8fHxmDNnDvr06QNfX1907doVY8eOxcGDB7XK9+eff9b8W30s6jbqu3btwqJFi9ClSxd88cUXWuVRXt+z8+fPa75nAQEBmDZtGm7fvq15vaK29t9//71Wu/jKYnu8/XxUVBSmTZuGwMBAeHt7o0ePHvjoo4/KxPnqq6+iffv2KCwsxP/+9z/07NkT3t7eCA4Oxk8//VRh+as/J6dPn9bEpb42PKlfwZw5c7Bz50706tULL730kmZbf/zxB1599VV069YNPj4+6N27Nz7++GMkJCRojrNdu3bIzc3Fjh07Ku2z4+HhgenTp2P48OFay62srODn5weVSoWkpCQAJU1AMzMzMWLECK1EVSqVYtSoUSgoKMBvv/1WYVkANb/uXL16FW+//Ta6du0Kb29v9OrVC3PnztW6ZgKl5yw3NxezZ89GYGAg/P39MWnSJDx48AC5ubmYP38+unXrpmkCpn7a+qh//vkHU6ZMQefOneHj44O+ffviiy++QGZmptZ6wcHB6Nu3L7Kzs/Hxxx8jKCgI3t7eGDhwIPbs2aO13pdffgkA6NOnj9bvxOnTpzF16lR0794dPj4+6NGjB2bMmIHr169XqWyofKxTsE7BOgXrFGpir1MYukqf5O3cuRNAyd21iowbN67Mso8//hgRERHo3r07RowYAalUiiNHjuB///sfrly5ovlxVXvw4AFmzJiBkSNHYvjw4diwYQO+/fZbSKVShIeHIzQ0FC+++CIiIyOxZcsWuLq6YvLkydU53ic6cuQIpk2bBg8PD7z99tto1KgRrl27ho0bN+Lvv//GL7/8AisrK4wePRqWlpYICwvD6NGj0aVLl3LbPD/33HPYvXs3Dhw4oBnYQe3YsWPIycnBmDFjIJFIUFhYiAkTJuDy5ct46aWX4Ovri4yMDGzfvh1Tp07F559/jhdffLHC+Hfv3g1bW1utSmh5AgMDNX2QHvX555/jxx9/RJ8+fTBy5EgUFxfj0KFDWLhwIa5evYpPP/1Us+63336Lb775Bn5+fnjnnXdgZWWFs2fPYtWqVTh79iw2bNig1XcoNzcX06ZNw4ABAzBo0CDs2LEDP//8MywsLHDgwAEMHDgQAwYMwO+//469e/eicePGWs3SqhJbmzZtsGzZMrzzzjto3bo1pk+fXuZHZ+nSpXBycsLChQsrbMYwffp0HD16FK+88gp8fX1x69Yt/PTTTzh58iS2bdsGW1vbCsu4MuPGjcN3332H3bt349133y13nezsbISGhqKwsBDjxo2Du7s7srKyEBkZiTfeeAMrVqxASEgIli1bhuXLl+PGjRtYtmwZLCwstLazb98+ZGZmYs6cOWjRokWFcd29exfvvPMOXnjhBQwbNgyXL1/Gzz//jMuXL2Pv3r3VvkNYWWyPOnHiBKZMmQIHBweMGzcOzZo1w40bNxAWFoYjR45gx44dZfrnfPTRR0hNTcXrr7+OoqIi/PTTT/jss88qbBqp/pw8GldlHjx4gOXLl+P111+HTCYDAOzduxczZsxAhw4d8NZbb8HGxgY3b97Ehg0bcPz4cfz6668IDAzEvHnz8N///hddunTB6NGjK/zcde/eHd27dy+zXKVSIS4uDpaWlnB1dQUAXLx4EQDK3NkFAF9fXwAlP3CjR49+4v5qet2JiorC2LFj0aRJE0yaNAkymQzXrl3Dli1bcPjwYURERGjKS+0///kPLC0t8eGHH+LMmTOIjIzERx99BFNTU1hbW+P999/HtWvXsGHDBsycORO//vqr5r1//PEH3n77bcjlckyfPh3W1ta4cOECNm7ciOPHj2Pbtm0wNzfXKrepU6dCJpNhxowZyMjIwLp16/Cf//wHrVq1gpeXF+bNm4e1a9fi9OnTmDdvnqb/07lz5zB+/Hi0aNECkydPhr29Pe7du4dNmzbh+PHj2LVrF5o1a/bEsqEnY52CdQrWKVinqC91CoMnVKJXr16Cr6+vUFRUVNmqWi5cuCDI5XJh4sSJgkql0npt6tSpglwuFy5cuCAIgiDEx8cLcrlckMvlwqlTpzTrRUVFaZYfO3ZMszwpKUlo27at8Morr2iWffPNN4JcLhdOnjxZJpb+/fsLcrlc8/fJkycFuVwufPPNN5pl69atE0aNGiXcvHlT671ffvmlIJfLhZ07d2qWRURECHK5XIiIiNBaVy6XC2PGjBEEQRAKCwuFLl26CMHBwWXimTlzpiCXy4V///1XEARBWL9+vSCXy4W9e/dqradQKITevXsLgYGBQmFhYZntqN25c0dT1k/jypUrglwuF+bPn1/mtenTpwtyuVyIiYkRBEEQHjx4ILRv31547rnnhIKCAq1158+fL8jlcuHXX3/VLFOfv8jISM0y9fmTy+XCli1bNMsLCgqEzp07Cz179nyq2NT7U58DtQ8//PCJ5dO7d2+hd+/emr//+OOPMp8NQRCELVu2CHK5XFi1alWZbZS3r/Pnz1e43pgxYwS5XC4kJiYKglD283vgwAFBLpcLa9eu1Xpffn6+MGXKFK3l6m09Sv0ZDQwMFLKzs8uNMT4+XhAE7e/f33//rbXu0qVLBblcLmzatElr3Q8//LDMMa1evbrM96Ki2B5db8CAAYKPj49w584drXX37t0ryOVyYe7cuWW2OXXqVK1ry5kzZwS5XC588MEHZWJ7XHlxPalcPD09hX/++Udr3WnTpglyuVxITU3VWn706FFh0qRJwqVLl7S2UV55VSQ3N1dITU0Vzp07J7z++utC+/btha1bt2pef+uttwS5XF7meiUIgpCVlSXI5XIhNDS0wn1U57pT3jl78cUXhZ49ewppaWla7z948KAgl8uFBQsWaJapy3vWrFla6w4aNEiQy+XCzJkztZZPmTJFkMvlms9DQUGBEBQUJAwdOlTIz8/XWnfjxo2CXC4XfvzxR82y3r17l3vd2LFjhyCXy4Wvv/5as+zx8y4IgrBgwQJBLpcLFy9e1Hr/lStXhPHjxwtHjhwR6OmwTsE6BesUrFPUtzqFoaq0uWZKSgocHBxgbFzlgTgBAL///jsAIDQ0tExzDHUH1kOHDmktd3FxQZcuXTR/t23bFgAgk8nw7LPPapbLZDI4ODjUyqNutYkTJ2Lz5s1o2bIlBEGAQqFAVlaW5s75vXv3qrU9ExMTDBgwAHfv3kV0dLRmeUFBAQ4dOoT27dtr7vyo72gEBQUhKytL859SqUSvXr2Qnp6Of//994n7Sk1NBQA4OTmV+/qj21T/92gTmH379gEABg0aVGa9/v37A4CmedvBgwdRXFyM4cOHw9TUVGs/6mZmj59XExMTrdH+1OdPIpFo3U00NTVFq1attM5rdWKrTL9+/SpdZ+/evQCAF154QWv5kCFDEBYWhueff75K+6qM+glHWlpaua+rR8qKioqCUqnULDczM8P333+PSZMmVWk/QUFBVb5b5u7ujmeeeUZr2cCBAwGgTodhjo2Nxc2bNxEUFKT5vqn169cPNjY2mqYwjxo/frzWtUXdH6Q2rwtAybnq2LGj1jL19fDcuXNay7t37461a9fC29u7Rvv873//i2eeeQahoaGIj4/H+vXrNU26gJIRIQGUeydTvUy9zpPU5Lpz+/ZtXL58GT169IBUKtV6f8eOHdG4ceNyv5dDhw7V+lt9jX/8qYJ6ubp56pkzZ5CcnIx+/fqhoKBAa3/BwcEwMjIq9zP6+NOiqn5G1Of37NmzZeL68ccf0aNHjwrfT0/GOgXrFADrFKxTNNw6hS5VepU1MjLStPGujps3bwJAuc0OWrZsCQBa7XIBlHkUrv7Cl9fW29TUVKsNfk0VFRVhzZo12LNnD+Lj48t0gn70S1FVzz33HLZs2YIDBw5oKn3qZhWPfrFjY2OhUCjQuXPnJ24rISEB7du3L/c1dTOG8tq5FxcXl7tdFxcXTTvsGzduAADGjBlT4f6BpzuvTk5OMDEx0VpmamoKR0dHmJmZaS03MTHROq/Via0yVRn84vr165BIJGU+c5aWlujUqVOV9lMV6mN8fNhjtWeffRb+/v7Yv38/goOD0adPH3Tt2hXdunWrVhOHqhyzWnnn9GkrJNVx69YtACh3GG6pVAo3NzfExMQgPz9fqzne4xdv9WepNq8LQPnXn0mTJmkGhwgICED37t3RrVs3+Pr6ltvHqLqmTJmC559/HgkJCdi1axfGjRuHd955B1OnTq3xttVqct1Rfy/Dw8MRHh5e7nvLux49Xpbq68KTlqvPpXp/S5cuxdKlS8vd3/3797X+lkqlZbZb1c/IqFGjsGvXLnz22WfYtWsXevTooekvWN3khLSxTlGCdQrWKVinqBuGXqfQpUp/rZycnHD//n0UFhaWuctSEXVn2fLuNKsLNS8vT2v5k7Zfnf0+rdmzZ2P37t3o0KED5s2bh2bNmsHY2BgnT57EypUrn2qbnTp1grOzM/bv34+ZM2cCKLmLJJVKMXjwYM16OTk5cHR0xJIlS564LQ8Pjye+pm5XXN6XRiqVYsOGDVrL1LE8un8AWLJkCRwdHcvdh/ouka7Pa3Viq4yVlVWl6xQUFEAqlT7xQllb1B1/nxS7qakpfvjhB2zZsgU7duxAWFgYwsLCYGZmhpdfflkzZH5lqnLMFa2rPqcFBQVV3k51VfRU6tEYcnNztS7Ij/+Y15XyysXPzw8RERH44Ycf8Mcff+Ds2bP4+uuv0bx5c3z44YdVusNbEQ8PD813ftiwYXj33Xfx1Vdfwd/fH507d9b8KD8+KMGjyyr74a7JdUd9zoYOHVrm6Zxaecnu014L1PubMmVKuf0WAWh9NgDU6Hvs7u6OHTt2YN26ddi/fz9WrVqFVatWwcHBAdOnT8eoUaOearvEOgXrFCVYp6hdrFOUMvQ6hS5VmuT5+/vjzp07OH36tFbzhvKkp6fDzs4OQOnQ5BVVQqrzYamJyj5MSUlJ2LNnD1q0aIENGzZonXT1HYGnIZFIMHDgQPzwww+4fv063N3dcejQIXTt2lWrGYSVlRUUCkW5nZerokmTJnBxccHFixeRlZWlNdeTRCIps93HP8jq8+Dq6qoZtOFJKjqv6gtxbZ7X6sRWG+zt7REXF4fc3Nw6G14/OTkZV69ehVwuR+PGjZ+4nqWlJSZOnIiJEyfiwYMHOHr0KMLCwrBx40bk5eVpdVyvDfn5+WWWqc/p4xXoqr6/KtTnuLzP1KMx6Op6UVUeHh749NNPsWDBAkRHR+P3339HWFgY3n77bWzatKnW7tIaGRlh2LBh2L9/P44fP47OnTtr7qYmJiaiVatWWuur70Kr51R6kppcd9Tnwtzc/KmvW0+zP1tbW53sDwCaNm2KOXPmYM6cObh69SoOHTqETZs2Yf78+bC0tCzT/IqqhnUK1ikexTpFzbFOoU2sdYq6UGmfPPVd2lWrVlXYxCIiIgLBwcGadvPqtuHlDTcdGxsLAGUqJzWhbkLz6BDIQMmH5MGDBxW+NyEhAYIgICAgoMwH78yZMzWKa8iQIQBKRoYrr1kFUFJW+fn5uHz5cpn3p6enV6lpy4gRI1BQUIB169ZVO0b1uXq8fxFQckfk0R+0is6ruhlEbZ7X6sRWG9RNKtTHolZYWIjIyEicOHGixvtYvXo1lEolXn755Sq/p2nTpnj55Zexbds2yGQyHDhwoMZxPE79vXzUnTt3AJQ2Y1A3kXn8ewaUbVJTVeo7yuV9poqLixEXF4fmzZsb7F02IyMj+Pr64r333sPixYshCMJTnZ/XXnsNgYGBZe5aAyV9YIDSZiMBAQEAyvYZA0qmGQBQph/h42py3VE3wynvewk8uV/I09L1/h7Xtm1bvP7665rra118/xoK1ilYp2CdgnUK1il0o9Ik75lnnkFISAjOnDmDTz75pNwJO48cOYJPPvlEq42xugNreHi41gVFEARs3boVQNU6rVaV+hH1ox2SAWDTpk3ltit/lHqiy8fnzjhx4gSOHz8OQPuOgrq9elUuBu3bt0erVq1w9OhR/PHHH7CwsEDfvn211lF3RP3hhx+0lhcWFmLixIkYMmRIpccwbtw4tGrVCmvWrMG2bdvKXaegoABff/01EhIStD7c6v3//PPPZe6cLF68GF27dtV8MYODg2FiYoKIiIgyX0p13xz1ua8N1YkNKDk3NblI9+nTBwDKlOGff/6Jjz76qNxKdXVs3LgRmzZtgpeXF0aOHPnE9VasWIHg4OAylVdjY2OYmZlpNauozuexIjdu3CgzB5N6CHt152k7OzsYGxsjJiZG63udkJCAP//8s8w2qxJby5Yt4enpib/++gvx8fFar+3Zswc5OTm1eq2oqfz8fLz88sv48MMPy7ymbiKpPj/qJjpVOTfu7u7IyMjA5s2by7y2a9cuAKXJXY8ePSCTybB9+3atAQ8KCwsRFhaGRo0aYcCAARXurybXHXd3d7Rr1w7Xrl3D33//rfVaVFQUgoKC8P3331dyxFXXqVMnODg44OjRo2UqDnv37sWzzz6rNf9ddZT3GZ06dSomTJhQ5vgfP79UfaxTsE7BOgXrFKxT6EaVepD/73//w8yZM7F582b8/fffGDJkCNzc3JCWloYTJ07g8OHDcHNzw6pVqzRNK7y8vPDKK69g8+bNmDZtGoKDg1FcXIyDBw/i5MmTmDBhQrmdIp9W9+7dYWZmhjVr1gAAnJ2dce7cOZw6dQodOnRAVFTUE9/bvHlz+Pr64vTp01i4cCF8fHxw5coV7Nq1C1988QVee+01HDhwAHK5HAMHDtQ0lQoLC0N+fj4CAgLQoUOHJ25/8ODBWLlyJW7duoU+ffqUeUQcGhqKPXv2YM+ePSgoKECfPn2gUCgQERGBy5cvY+HChVpzxJTH0tISP/zwA9544w3NXEJ9+vRB06ZNkZOTgxs3buC3335DcnIy/P39sXjxYs1727Zti3HjxmH9+vUYNWoURo4cCWNjYxw5cgQHDhzA888/Dzc3NwAlP3zvvvsuFi9ejLFjx2LIkCEwMTHBiRMnsHfvXvTr1w+9evWqMNbqqE5sQMm5jImJwfLly+Hs7FzpHD+PGzhwILZt24atW7dCqVQiMDAQd+7cwU8//QRXV1eMHz++Sts5deqU5m5vcXExEhISsH//fkRHR8PX1xfffvtthRXFrl27YtWqVRg5ciRGjBgBZ2dn5Obm4sCBA7h7967WXDjqz+O8efPQqlWrKsf4uM6dO+Pdd9/F0KFD4ebmhujoaM3cUeo7xaampujevTsOHTqEmTNnomfPnkhKSsKmTZsQEhKCX375RWubVY1t7ty5mDBhAsaOHYtRo0Zp5lzbvHkz3NzcMG3atKc6prpgbm4OLy8vbN68GVlZWejVqxesrKxw7949bN68GZaWlprR/hwdHWFubo5jx45h9erVcHd3f2Ly9dprr+HPP//E4sWLcf36dXTq1AmFhYXYt28fzpw5g8DAQPTu3RtAyXmYP38+pk+fjtGjR2PUqFGQSqWIiIjArVu38Pnnn1faJ6+m15158+Zh/PjxeOuttzBhwgS4ubnh5s2bCAsLg4ODg+aJQ21QH++7776LsWPHYvz48ZDJZIiOjkZ4eDhatmypKZvqUn9GFy9ejM6dO+OFF15Aly5dNNe4gQMHwtbWFikpKdi6dSuMjY0RGhpaa8fWELFOwToF6xSsUwCsU9S1KiV5VlZWWL16NX7//Xfs3LkTW7ZsQUZGBkxNTdGmTRvMnz8fL774YplOjnPnzoWHhwe2bt2KTz/9FEZGRmjdujUWLlyoNRx4bWjSpAnWrl2LJUuWYM2aNTAxMUFgYCDWr1+PWbNmVfheiUSCr7/+GgsWLMDu3buxZ88e+Pv7Y/369ZDL5Rg5ciR2796NJUuWoG/fvujUqROGDRuGffv2YeXKlfjvf/9b4QX5ueeew/Lly5GRkVHucLmmpqb46aef8P333+O3337DoUOHYGJiAi8vL6xYsaLMXboncXZ2xvbt27F7927s27cP69evR0ZGBiwsLCCTyRAcHIz+/fsjKCiozHtnz56NNm3aIDw8HJ999hlUKhVatGiBDz74oMyXaPLkyXB2dsb69evx5ZdfQqlUwt3dvdx1a0N1Yvvwww8xf/58fP/99+jVq1e1L8gSiQTfffcdVq9ejV9//RW7d++GjY0NBg0ahBkzZlR5FKpHO7wbGRnB1tYWXl5e+PzzzzFkyJBKR+jr1KkTNm3ahLVr12LDhg2a75unpye++OILrWGiJ0+ejEuXLuGXX36BTCbD2LFjq3XMau3atcOMGTPw1Vdf4aeffoKRkRF69eqF2bNnazU5WrRoERYvXozjx4/j4MGD8PDwwNy5c5GTk1PmglzV2Dp16oTNmzdjxYoVWLt2LXJzc+Hk5ISXX34Zb7zxRo0ni61tc+fORatWrbBz5058+eWXyM3Nhb29Pbp06YLXX39d07zIxMQEs2bNwtdff42VK1fi5ZdffmKS5+DggIiICKxevRoHDx7E3r17YWRkBDc3N8yYMQMTJ07UqpiFhIRg7dq1+O677/DFF19AEAS0bdsWK1euRHBwcKXHUNPrjr+/P8LDw7Fy5UqEhYUhOzsbdnZ26NOnD6ZPnw5nZ+dqlGjl+vXrh/Xr12P16tX4/vvvtT4jb775ZrUn1lULDQ3F8ePHcfz4cURHR6Nfv36YPHkynJycEB4ejm+++QYKhQKNGjWCn31iGUUAACAASURBVJ8fFi5cqHmiSk+HdQrWKR7FOkXlWKeoXmxiq1PUFYnwNGMZExERERERkUGqtE8eERERERERiQeTPCIiIiIionqESR4REREREVE9wiSPiIiIiIioHqnS6JqGJjk5u1a2Y2dnifT03FrZllg0xGMGeNwNTUM87vp8zDKZjb5DEJXa+I2sz5+n6mJZlGJZlGJZlGA5lNJHWVT0+9ign+QZG0v1HYLONcRjBnjcDU1DPO6GeMxUd/h5KsWyKMWyKMWyKMFyKGVoZdGgkzwiIiIiIqL6hkkeERERERFRPcIkj4iIiIiIqB5hkkdERERERFSPMMkjIiIiIiKqR5jkERER6dn169cREhKCTZs2lXnt77//xksvvYSRI0fi22+/1UN0REQkNkzyiIiI9Cg3NxcLFizAM888U+7rCxcuxPLly/Hzzz/jr7/+wo0bN3QcIRERiY0oJ0MnIiLDUlSsQmpWPlIy85CSkY/kzDyoVAKG9fCAiTHvJ1bE1NQUa9aswZo1a8q8Fh8fD1tbWzg7OwMAevbsiRMnTqB169Y6i08QBFy7k4ErcelQ5BWhsEgJodz1dBaSTpmbGyM/v1jfYRgElkUplkUJlkOp6pZF57ZO8GvjWGfxMMkjIqJKKVUqpGUVICUjDymZ+UjOzEdqZh6SM/ORkpGHDEVhmfcYSyXo7e8CJztLPUQsHsbGxjA2Lv/nODk5Gfb29pq/7e3tER8fX+H27Owsa2VSXpnMBgCwZucl7D52s8bbIyKiUsYmUvTt1rLutl9nWyYiIlEpKlYiOSMfSel5SMrIQ1J6rubfqZn5UKrKPqqRSAB7G3O0dWsMB1tzyGwtSv7f2AJN7S3RyMpUD0fSsKWn59Z4GzKZDZKTs3HrfhZ2H7sJZwdLhPZpA3sbM5iaSCGRlP8+CZ7wgojZO1ghLTVH32EYBJZFKZZFCZZDqeqWhZ2NGZKTs2u0T/XNuPIwySMiakCKipVITM/Dg9Tch4ncw2QuIw/pWQXlNsNrZGmCFs42kDW2gKOtBWS25nC0NYdjYwvY2ZjBWMrmmHXFyckJKSkpmr8TExPh5OSks/3/dek+AGBUSBt4t3TQ2X4NiczOEpJipb7DMAgsi1IsixIsh1KGVhZM8oiI6hlBEJChKMSDtNyS/1JL/n8/NQepWfnl9p2yszGD3LUxnOwsHv5nCafGJf+2MONPhb40b94cCoUCd+/eRdOmTXHo0CF8+eWXOtv/9fhMmBoboa2bnc72SURENcdfbiIikSpWqpCUnoeElBxkXkhAbHw67qfmIjEtF/mFZe8mNrI0QRsXWzR1sERTeys0eZjQyRpbwNSk5n246OlER0fjiy++wL1792BsbIz9+/cjODgYzZs3R9++fTF//ny89957AIBBgwahZcu668PxqKJiJe4lK9CmuS2f1hIRiQyTPCIiA6dUlSZz91JyNP9/kJpbpp+csVSCJnaWaGpv+TCZK/2/lbmJno6AKuLt7Y2NGzc+8fXOnTsjPDxchxGVSMnMhwDAyZ4D5xARiQ2TPCIiAyEIAlIz8xGfpMBddTKXnIMHabkoVqq01jUzkcKtiTWaOVrBxdEa7TwcYWEsgWMjcxgZ1b/BL0j3UrPyAQCOtuZ6joSIiKqLSR4RkR4UFilxLyUH8UkKxCcqEJ+UjfjkHOQVaM+xY2pihOYyK7g4WqGZzArNHEr+bW9rDqNHhjhUj4ZIVFtSMkuSPIdGTPKIiMSGSR4RUR3LUBTgjjqRS1IgPkmBB2m5WgOgSCRAEztL+LSyh6uTNVxk1nBxtILDY8kcka5kZBcAKBmUh4iIxIVJHhFRLcpUFODWg2zEPcjG7ftZuJ2YjczHJgq3MJOitYstXJ2sH/5nAxeZFcw4+AkZkNz8kqfK7MtJRCQ+TPKIiJ5SVk4hbj/Iwu0H2bh9PxtxidlIf/j0Q83Oxgz+bRzh1sRGk9Q52ppDwqdzZOByC9RJHqsKRERiwys3EVEV5BcW4/b9bMQmZOJmQhbiErORlqWd0DW2NoVfa0e0aGqDFs42cG/aCLZWpnqKmKhm1E/yLJnkERGJDq/cRESPEQQBiel5iL2XidiELNy8l4n4ZIVWHzpbK1N08HBAC+dGcG9qgxZNbdDYmn2XqP7IyS+CBIC5GasKRERiwys3ETV4eQXFuHU/S5PUxd7LRE5+6SiXJsZGaO1iC49mtvBwaYRWzWw5GAXVe7n5xbA0N+bAP0REIsQkj4ganExFAa7fzcT1Oxm4Fp+Be8kKPDqluKOtOXxaOaBVs0bweDhAirHUSG/xEulDTn4Rm2oSEYkUr95EVO+lZObhenwGrsdn4Fp8JhLTcjWvmRgbQe7aGB4utvBo1gitXGzZj44IQF6hEo0s+V0gIhIjJnlEVK8IgoC7Sdk4GXVPk9ilPjJAirmpFD6tHCB3tYWnmx1aNLXhUzqixwiCgMIiJUw5rQcRkSgxySMi0UvNzMfl22m4HJeOK3HpyMopnZfO2sIEAXIZ5K6N4enaGK5O1jAyYh8jooooVQIEoeRJNxERiQ+TPCISHUVeEa4+TOgu305DYnqe5jVba1P08HeBu5M15K6N0czBknPSEVVTYZESAGDKJI+ISJSY5BGRwSsqVuLfu5m4fLskqYt7kK0ZKMXcVAq/1o5o38IO7VvYw9nBEk5OjZCcnK3XmInErOBhkmfC5ppERKLEJI+IDI4gCEhIzcWl2FRE30rFv3czUVSsAgBIjSRo49pYk9S1dLaB1IhPG4hqU2FRyffNhP1ViYhEiUkeERmEgiIlrt1JR1RsKi7FpiIlM1/zWnOZtSapk7vawtyUly6iuqRprmnCJI+ISIxYUyIivUnJyCtJ6m6m4kpcuuZpnYWZMTq3dYKvhwO8W9rD1poTjxPpkjrJ48ArRETixCSPiHSmWKnCv3czcSk2FVGxKbifWjpfnYvMCr6tHODr4QAPF1tOa0CkR+rmmqbG7JNHRCRGTPKIqE4VFCoRfSsVZ68nI+pGKvIKigGUNAPza+0IHw8H+LSyh6OthZ4jJSI1jq5JRCRuTPKIqNbl5Bch6kYKzl5LRsytNBQ+bIbp0MgM3bybooOHAzzdGsOETwmIDFJBMZtrEhGJGZM8IqoVGYoCnP83BeeuJeHqnQwoVSWTHDg7WKKjpwwd5U5wa2LNOeuIRIB98oiIxI1JHhE9taSMPJy7loxz15MRey9TM3ddi6Y26OgpQ4BcBmcHK73GSETVp+mTx3nyiIhESWdJ3qJFixAVFQWJRILZs2fD19dX81pYWBh2794NIyMjeHt7Y86cOboKi4iqKT27AGeuJOLk5UTcflAy4bhEAshdGyPAU4aANjI42JrrOUoiqgk+ySMiEjedJHmnT59GXFwcwsPDERsbi9mzZyM8PBwAoFAosG7dOhw4cADGxsaYOHEiLly4AD8/P12ERkRVkJNfhLPXknEy5gGu3cmAAMBIIoF3S3t0ausEvzaOaGRpqu8wiaiWFCs5GToRkZjpJMk7ceIEQkJCAAAeHh7IzMyEQqGAtbU1TExMYGJigtzcXFhaWiIvLw+2tra6CIuIKlBQqMSFGyk4dTkRl26mavrYtW5ui67tm6BTWycmdkT1VLGy5PtuZMQ+tEREYqSTJC8lJQVeXl6av+3t7ZGcnAxra2uYmZnhzTffREhICMzMzDB48GC0bNmywu3Z2VnCuJZG5ZPJbGplO2LSEI8Z4HFXRbFShfPXknDk3D2cirmP/MKSJlstnBuhZ0Bz9PBzgZO9ZV2FWqsa4vluiMdMdUP58EmelEkeEZEo6WXgFUEQNP9WKBRYvXo1fvvtN1hbW2PcuHG4evUq2rZt+8T3p6fnPvG16pDJbJCcnF0r2xKLhnjMAI+7IoIgIC4xG8cu3seZK0lQ5BWVvLexOUI6NUdguyZwkVmXrKxUiqIcG+L5rs/HzORV99RP7qVSJnlERGKkkyTPyckJKSkpmr+TkpIgk8kAALGxsXB1dYW9vT0AoFOnToiOjq4wySOimlPkFeFE9AMcu3gfd5MVAIBGVqYliV37Jmjl3IjTHRA1UJonebwGEBGJkk6SvKCgICxfvhyhoaGIiYmBk5MTrK1Lngy4uLggNjYW+fn5MDc3R3R0NHr27KmLsIgaHJVKQMztNBy7eB8X/k1GsVKA1EiCjnIZnvV1hncre0iNONACUUNXrHmSx+sBEZEY6STJCwgIgJeXF0JDQyGRSDBv3jxERkbCxsYGffv2xaRJkzB27FhIpVL4+/ujU6dOugiLqMFITM/FX5fu469LD5CeXQAAcHG0QndfZ3T1aopGVhxAhYhKqZ/kceAVIiJx0lmfvPfff1/r70ebY4aGhiI0NFRXoRA1CAWFShz85w72Hr+Fa/EZAAALMyl6+TXDs77N0NLZhs0xiahcmj55TPKIiERJLwOvEFHduZ+ag0Pn7uGv6PvIKygZHbOtW2N0922GAE8ZzExqZ2RaIqodixYtQlRUFCQSCWbPng1fX1/Na2FhYdi9ezeMjIzg7e2NOXPm6CSmYo6uSUQkakzyiOoBlUpAVGwKDp69i5jb6QAAW2tTPN/dA/6tHeDU2ELPERJReU6fPo24uDiEh4cjNjYWs2fPRnh4OICS0afXrVuHAwcOwNjYGBMnTsSFCxfg5+dX53HxSR4RkbgxySMSsezcQhy7eB+Hzt1DalY+AEDu2hjBAS4IkMvg3NS23g6rT1QfnDhxAiEhIQAADw8PZGZmQqFQwNraGiYmJjAxMUFubi4sLS2Rl5cHW1tbncSlVHLgFSIiMWOSRyRCt+5n4eDZuzh1JQnFShVMTYzQy68ZggOao7mTtb7DI6IqSklJgZeXl+Zve3t7JCcnw9raGmZmZnjzzTcREhICMzMzDB48GC1btqx0m3Z2ljA2rlmzbKWqpLmmzNEaMgerGm2rPuBcjaVYFqVYFiVYDqUMqSyY5BGJRFGxCmeuJuLguXu4mZAFAGhiZ4HeAc3xrE9TWJqb6DlCIqopQRA0/1YoFFi9ejV+++03WFtbY9y4cbh69Wql88imp+fWOA71k7zMjFxIHyZ8DZVMZsMWEQ+xLEqxLEqwHErpoywqSiqZ5BEZuOzcQhw8dw8Hz91Fdm4RJAD8WjsiOMAF7Vvaw4gjZBKJlpOTE1JSUjR/JyUlQSaTAQBiY2Ph6uoKe3t7AECnTp0QHR1daZJXGzjwChGRuDHJIzJQyRl5OHA6HscuJqCwWAUrc2MMDHRDL38XyDiQClG9EBQUhOXLlyM0NBQxMTFwcnKCtXVJk2sXFxfExsYiPz8f5ubmiI6ORs+ePXUSl5KToRMRiRqTPCIDE/cgG/tOxeHM1SQIAuDQyBz9uriiu68zzE35lSWqTwICAuDl5YXQ0FBIJBLMmzcPkZGRsLGxQd++fTFp0iSMHTsWUqkU/v7+6NSpk07iUjfXZEsBIiJxYo2RyAAIgoCY22n47dQdXH44BYKbkzUGdHVD57ZOkBrxbjpRffX+++9r/f1oc8zQ0FCEhobqOiQUP+yHJ5UyySMiEiMmeUR6pFSpcOZKEvaduoP4JAUAoH0LOwwMdEf7FnaQ8C46EemBSsl58oiIxIxJHpEeFBQqcfRiAg6cjkdqVj4kEqBLOycMDHSHe1PDGX6XiBomzZM8JnlERKLEJI9IhwoKlTh0/h72noyDIq8IpsZG6BPQHP26uHIwFSIyGEqlACOJhK0JiIhEikkekQ4UFClx+Pw97DsZh6zcIliYGeP5oBbo07E5bCxN9R0eEZEWpUoFIz7FIyISLSZ5RHWosEiJwxcSsPdkHLJyCmFhJsXzQS3Qr7MrJy8nIoNVrBQ46AoRkYgxySOqA0XFShy5kIBfT8YhU1EIM1Mpnuvmjn6d3WBtweSOiAybSiXAmE/yiIhEi0keUS0qKlbhaFTJk7v07AKYmUgx+Bl39O/C5I6IxKNYyeaaRERixiSPqBYUK1U4dvE+fvn7NtKzC2BqYoSBgW7oH+iGRuxzR0Qio1QKHFmTiEjEmOQR1YBKEHDqciIij9xEalY+TI2N0L+LKwYGuqORFZM7IhInpUrFJI+ISMSY5BE9patx6Qg/eANxidkwlkrQt5MrBnV1g621mb5DIyKqEZVKYHNNIiIRY5JHVE0JKTnYfjgWF26kAAC6tm+CYT1awZHz3BFRPaESBBgbGek7DCIiekpM8oiqKDOnELuO38LRCwlQCQLkro0xMrg1Wjo30ndoRES1SiUARpwInYhItJjkEVWioEiJA2fisfdkHAoKlWhqb4kRvT3g19oRElaCiKgeUqkE8PJGRCReTPKInkClEvB39APsOHYT6dkFsLYwwYh+HujRoRmMpWzGRET1lyAIfJJHRCRiTPKIyhFzOw1bD95AfJICJsZGGPyMOwYGusPSnF8ZIqr/VALYUoGISMRYYyV6REpGHjb/8S8u3EiBBEA376YY1qMV7BuZ6zs0IiKdUakEcHBNIiLxYpJHBKCoWIX9p+/gl79vo7BYBblrY4zq0wbuTW30HRoRkc4JggAJszwiItFikkcNXsztNGw6cB2JabloZGmCsQM88YxXUzZVIqIGi0/yiIjEjUkeNVjp2QUIP/gvTl9JgkQC9AlojqE9WsLS3ETfoRER6RWnUCAiEjcmedTgKFUq/PnPXew8fgv5hUq0dG6Esf092TSTiOghQRDYmoGISMSY5FGDcj0+A5sOXMPd5BxYmRtj3ABPdO/QjHesiYgeoRI4Tx4RkZgxyaMGISO7AOt+vYy/Lj0AAHT3dcZLvTxgY2mq58iIiAyLIAgQ2FyTiEjUmORRvSYIAo5EJSDiyE3k5BXB1ckar/b3RGsXW32HRkRkkASh5P/M8YiIxItJHtVbKRl5+HHfVVyJS4eFmTFGhbRBcIALpEZG+g6NiMhgqR5meUYcXpOISLSY5FG9oxIEHLmQgK2HbqCgUAlfDwfMHN0RqsJifYdGRGTwhIdJHgdeISISLyZ5VK8kZ+Thp4dP7yzNjDFpcDt0824KB1sLJCdn6zs8IiKDp2JzTSIi0WOSR/WCShBw+Pw9bDsUi4IiJTp4OGDsgLawszHTd2hERKKifpLHgVeIiMSLSR6JXnJGHn7cewVX72TA0swYk59rh2e8mrKpERHRU1CpSv7PJI+ISLyY5JFoqQQBh87dw/bDJU/v/Fo7YuwATzS25tM7IqKnJUDdJ0/PgRAR0VPTWZK3aNEiREVFQSKRYPbs2fD19dW8dv/+fcycORNFRUVo3749PvnkE12FRSKVlJGHH3+9gmvxGbAyN8bYAe3RtX0TPr0jIqohlYrNNYmIxE4nY8mfPn0acXFxCA8Px6effopPP/1U6/XPP/8cEydOxPbt2yGVSpGQkKCLsEiEVIKAP8/exdx1p3AtPgN+rR2xYHIgm2cSkWgtWrQII0eORGhoKC5evKj12v379zFq1Ci89NJLmDt3rk7i0cyTxykUiIhESydJ3okTJxASEgIA8PDwQGZmJhQKBQBApVLh7NmzCA4OBgDMmzcPzZo100VYJDJZOYVYtu0iwn6/DhOpEaYOaY/pw33YPJOIRMsQb4KWDrxS57siIqI6opPmmikpKfDy8tL8bW9vj+TkZFhbWyMtLQ1WVlb47LPPEBMTg06dOuG9996rcHt2dpYwNpbWSmwymU2tbEdMxHjMF64nYcnmc0jPLoC/XIYZowJg18i8WtsQ43HXBh53w9EQj1nsnnQT1NraWnMTdMmSJQBKboLqQukUCszyiIjESi8Dr6jvEqr/nZiYiLFjx8LFxQVTp07F4cOH0atXrye+Pz09t1bikMlsGtzcaWI75mKlCjuP3cK+k3EwMpJgRG8P9O/ihuKCIiQnF1V5O2I77trC42446vMx1+fktbZvggK1cCPUuKRqYGFhUq/LvjpYDqVYFqVYFiVYDqUMqSx0kuQ5OTkhJSVF83dSUhJkMhkAwM7ODs2aNYObmxsA4JlnnsG///5bYZJHDUNyRh5W747BzYQsyBqbY9oL3mjp3EjfYRER1Zma3gQFan4jNCUjDwBQWFBcb28cVEd9voFSXSyLUiyLEiyHUvooi4qSSp30yQsKCsL+/fsBADExMXBycoK1tTUAwNjYGK6urrh9+7bm9ZYtW+oiLDJgp68kYv6Pp3EzIQtd2zfB/AldmOARUb1T1ZugUqlUcxO0rj2cJo+jaxIRiZhOnuQFBATAy8sLoaGhkEgkmDdvHiIjI2FjY4O+ffti9uzZmDVrFgRBgFwu1wzCQg1PQaESm/+4jmMX78PMRIpJg9uhmzdHziSi+ikoKAjLly9HaGhohTdBW7RogZiYGAwePLjOY1I/TeRll4hIvHTWJ+/999/X+rtt27aaf7u7u+Pnn3/WVShkoO4kZmP17hjcT82FWxNrvPa8F5wdrPQdFhFRnTHEm6DqefJ4c42ISLz0MvAK0aMEQcDBc/cQfvAGipUqhHRqjhG9WsPEWCetiYmI9MrQboKquwUacQ4FIiLRYpJHeqXIK8KPe6/g/L8psLYwwcTB3vBr7ajvsIiIGiwVm2sSEYkekzzSmzuJ2VgReQkpmflo69YYU4Z4wc6GE5sTEemT5kkeszwiItFikkd6cepyIn7cewWFxSoM6dYCLzzbkk2DiIgMQGmfPD0HQkRET41JHumUUqVCxJGb+O3UHZibSvHWMB8EyGX6DouIiB4SUJLk8UkeEZF4MckjnVHkFWHVrmhcvp2OpvaWeGuYD5o5cvRMIiJDwuaaRETixySPdOLR/nd+rR0x+bn2sDTnx4+IyNCwuSYRkfixlk117tH+d88HtcDzz7bkHWIiIgPFKRSIiMSPSR7VGaVKhYjDN/Hb6ZL+d9OH+cCf/e+IiAwap1AgIhI/JnlUJx7vfzd9uA+cHdj/jojI0AkCB14hIhI7JnlU69j/johIvB52yYOESR4RkWix5k216vSVRPzwa0n/uxeebYkhQS14N5iISETYXJOISPyY5FGtEAQBv56IQ+TRmyX974b7wL8N+98REYkNm2sSEYkfkzyqMaVKhU0HruPIhQQ4NDLDuyM6wEVmre+wiIjoKahUJf9njkdEJF5M8qhG8guLsWpXDC7GpsKtiTXeeakD7GzM9B0WERE9Jc2TPE6hQEQkWkzy6KllKgrw9baLiEvMhncre7z+gjcszPiRIiISM02fPDDJIyISK9bI6akkpORg6dYopGblo0cHZ4zp5wljqZG+wyIiohrSTIbOHI+ISLSY5FG1XbuTjuURl5BbUIyhPVrhuWfcOdQ2EVE9oW6uKWGWR0QkWkzyqFpOXU7Eul8vQxCAyc+1QzdvZ32HREREtUileZLHJI+ISKyY5FGVCIKAfafuYPvhWFiYSfHmUB+0b2Gv77CIiKiWlU6hoOdAiIjoqTHJo0opVSps/v1fHDp/D3Y2ZpgxogOaO3GKBCKi+kg98ArnUCAiEi8meVShgkIlVu2KRlRsKprLrDHjZU6RQERUnzHHIyISPyZ59ERZuYX4emsUbj/IhlcLO7wx1IdTJBAR1XfqJE+/URARUQ2wxk7lylAU4MstF5CQkoMgn6YYN6Atp0ggImoAhIdZHkdNJiISLyZ5VEZqZj4WbzmPpPQ89O3kitA+rfljT0TUQAh8kkdEJHpM8khLUnouFv98HqlZBXiumzuGdm/FBI+IqAFRJ3nM8oiIxItJHmkkpORg8ZbzyFQUYmiPVhjSrYW+QyIiIh3TNNdklkdEJFpM8ggAcCcxG1+FX0B2bhFC+7RBv86u+g6JiIj0gaNrEhGJHpM8wq37WVgSfgE5+cUY298Tvfxd9B0SERHpiaa1JpM8IiLRYpLXwF2Pz8DX26JQUKTEpMHtEOTjrO+QiIhIjwSBzTWJiMSOSV4Ddvl2Gr6JuAilUsC0F7zRua2TvkMiIiI9Uz/JY45HRCReTPIaqKgbKfh2RzQAAW8O9YFfG0d9h0RERIaAffKIiESPs1s3QP9cTcKKyEswkgDvvNSBCR4RkR4tWrQII0eORGhoKC5evFjuOl999RVeffVVncTD5ppEROLHJ3kNzOGz8Vi1KwYmJkZ49yVfeLrZ6TskIqIG6/Tp04iLi0N4eDhiY2Mxe/ZshIeHa61z48YNnDlzBiYmJjqJiQOvEBGJH5/kNSDHLiZgyc/nYG4qxfuhfkzwiIj07MSJEwgJCQEAeHh4IDMzEwqFQmudzz//HDNmzNBZTIKmuSazPCIiseKTvAbi9JVE/LT3KqwtTTDzZT+4N7XRd0hERA1eSkoKvLy8NH/b29sjOTkZ1tbWAIDIyEh06dIFLi5Vn9rGzs4SxsbSp47JysoMAGDbyAIyGX8rALAcHsGyKMWyKMFyKGVIZcEkrwG48G8K1uy5DHMzKT6Z2g225k//409ERHVH3R8OADIyMhAZGYkff/wRiYmJVd5GenpujWLIVuQDALKy8pCcnF2jbdUHMpkNy+EhlkUplkUJlkMpfZRFRUklm2vWc5dvp2HlzmhIjSR456UOaO3aWN8hERHRQ05OTkhJSdH8nZSUBJlMBgA4efIk0tLSMHr0aLz11luIiYnBokWL6j4ojq5JRCR6OkvyDG30sIbgxr1MLI+4BEDA9OG+kDPBIyIyKEFBQdi/fz8AICYmBk5OTpqmmgMGDMDevXuxdetWrFixAl5eXpg9e3adx1Q68AqzPCIisdJJc01DHD2svot7kI2lW6NQVKzCm0O94dXSXt8hERHRYwICAuDl5YXQ0FBIJBLMmzcPkZGRsLGxQd++ffUSU+kUCkREJFY6SfKeNHqY+m4lUDp62IoVK3QRUr2WkJKDr8IvIL+gGFOGtIe/XKbvkIiI6Anef/99rb/btm1bZp3mzZtj48aNOolH0y2QWR4RkWjpJMmr7dHDajpy2KMMaRSc2vAgNQdLtkZBkVeEN1/qgAHPtCizTn075qricTcsDfG4G+Ix/ozHjAAAFf1JREFUU93hZOhEROKll9E1azp6WE1HDlOrbyMCpWcX4LNNZ5GWlY+Rwa3RsbVDmeOrb8dcVTzuhqUhHnd9PmYmr7olPOyVxy55RETipZOBVwxy9LB6JiunEF9uOY+UzHy88GxL9O/ipu+QiIhIhASOrklEJHo6SfIMcfSw+iQ3vwhLwi/gfmou+ndxxfNBLfQdEhERiZTATnlERKKnk+aahjh6WH2RX1iMpVujcCdJgV5+zfBy79Yc9pqIiGrMiD8lRESipbM+eYY2elh9UFSsxPKIS4hNyEJXryYY09+TCR4REdWIig/yiIhET2eToVPtUqpU+G5nDK7EpcO/jSMmDW4HIyZ4RERUU5p58vibQkQkVkzyREgQBGz+/V9cuJECrxZ2mPaCN6RGPJVERFRzmvGvmeMREYkWMwMR2n86HofO30NzmTXeGOoDE2OeRiIiqiUPszz+shARiRev4SLzz9UkbD10A42tTfHuCF9YmOllqkMiIqqnVJxDgYhI9JjkiciNe5lY88tlmJlK8e6IDrBvZK7vkIiIqJ5iikdEJF5M8kQiKT0X32y/CKVSwOsveMOtiY2+QyIionqID/KIiMSPSZ4IKPKKsHTbRSjyijCmvxy+Hg76DomIiOop4WGnPE7JQ0QkXkzyDFxRsQorIi4iMS0XAwPd0MvPRd8hERFRPSYIla9DRESGjUmeAVMJAn7YewXX72aiU1snDO/loe+QiIiogeCDPCIi8WKSZ8B2HruJU5cT0drFFpM52TkREelAaZ88/uYQEYkVkzwDdSwqAb/8HQcnOwtMH+4DUxOpvkMiIqIGQHiY5THFIyISLyZ5BijmVho27L8GK3NjzBjRATaWpvoOiYiIGghNlzxmeUREosUkz8DcTVJg5c5LkEiA6cN90cTeUt8hERFRQ/Iwy2MXASIi8WKSZ0DSswvw9fYo5BUoMWlwe8hdG+s7JCIiamBUHF6TiEj0mOQZiPzCYizbHoW0rAIM79kKge2b6DskIiJqwPggj4hIvJjkGQBBEPDj3qu4k6hAjw7OGNTVXd8hERFRA6UZXZOd8oiIRItJngHYfzoeZ64moU1zW4zp58lhq4mISG+Eh53y+FNERCReTPL07MrtNGw7fAO21qZ4/UVvGEt5SoiISH/YJY+ISPyYUehRamY+vtsVAyOJBG++6IPG1mb6DomIiBo6ToZORCR6TPL0pKhYiW93XIIirwivhLRB6+a2+g6JiIiotLmmnuMgIqKnxyRPDwRBwMYD13H7QTaCfJqil7+LvkMiIiICUDoZOh/kERGJF5M8PTgSlYDjF+/DvYkNXuVAK0REZEAEZnlERKLHJE/HYu9lIuzAdVhbmODNYd4wNZHqOyQiIqJSAptrEhGJHZM8HcrMKcS3Oy5BJQh47QUvONpa6DskIiIiLXyQR0Qkfsb6DqChKFaq8N3OaGQoCjGilwe8WtjrOyQiIjIAixYtQlRUFCQSCWbPng1fX1/NaydPnsSSJUtgZPT/7d1/VNR1vsfx1wyIipAyOIOWtXLJgkgrbt2uQtIP9G5ktteTSvd4tbtpebx6bc+yG8c16Y9Qa6tTWZ1Nj97t5G5SyPHaL3FLazmEuuZdEzINuxEWCzNA4CAowvf+4TpoyxYCM1++830+/pHvjAyvz/ur85k338/3+3UqMTFRBQUFcjqD+/tZ49yRPLo8ALAsjuSFyBu7j+lozbe68Wq3fnzzFWbHAQAMAvv27VN1dbUKCwtVUFCggoKCC55ftWqVnn/+eW3ZskWtra0qLS0NeqZz5+TR4gGAddHkhcCeyr/oD/trdOnoEfqP7BR+OwoAkCSVl5crKytLkpSUlKTm5mb5/f7A88XFxRozZowkyeVyqampKeiZAvdCZ6oCAMtiuWaQfVV3Qr999zMNHxqh//zXazV8KCUHAJzl8/mUmpoa2Ha5XPJ6vYqJiZGkwJ/19fUqKyvT8uXLf/A14+KiFRnZ94t6DRs6RJI0Oj5G7vgRfX6dcOJ2x5odYdCgFt2oxVnUodtgqgUdRxD52zr0QvEhnT7TpWX3TNRYJksAwPcwAvcv6NbQ0KDFixcrPz9fcXFxP/gaTU0n+5Whre20JKmxsVURXV39eq1w4HbHyus9YXaMQYFadKMWZ1GHbmbU4vuaSpZrBklXl6H1b1bK19yuu6eM1w0T3GZHAgAMMh6PRz6fL7BdX18vt7t7vvD7/Vq0aJEefvhhZWRkhCRT4OqaIflpAIBgoMkLku1l/6eKLxo18R/idU9GotlxAACDUHp6ukpKSiRJlZWV8ng8gSWakrR27VotWLBAU6dODVmmwIVXOH8cACyL5ZpBcOSrJr350ZcaPXKYFt19jZxOJkoAwN9KS0tTamqqcnJy5HA4lJ+fr+LiYsXGxiojI0Pbtm1TdXW1ioqKJEkzZszQ3Llzg5rJ0LlbKAT1xwAAgogmb4C1tndow1ufyiGHHpyZqpjhQ8yOBAAYxHJzcy/YTk5ODnxdUVER6jjnXV4TAGBVLNccQIZh6JV3P1NjyynNzBivKy8baXYkAAAuSuCcPA7lAYBl0eQNoNJParX/iFdXjRupGZPHmx0HAICLdu4Kn/R4AGBdNHkDpLahVb9/76iih0Zq0d2pnIcHALCkwIVXzI0BAOgHmrwB0HGmSy//T6VOd3Tp/juTFT9ymNmRAADok8ApeRzKAwDLCtmFV1avXq2DBw/K4XBoxYoVmjRpUuC5PXv26JlnnpHT6VRiYqIKCgrkdFqn/9z64TF9Ve/X1OvG6sZkj9lxAADou3PLNU2OAQDou5B0Uvv27VN1dbUKCwtVUFCggoKCC55ftWqVnn/+eW3ZskWtra0qLS0NRawBceiLBu38U43GuKJ13x1XmR0HAIB+6b5Pnrk5AAB9F5Imr7y8XFlZWZKkpKQkNTc3y+/3B54vLi7WmDFjJEkul0tNTU2hiNVvza2ntfGtTxUZ4dBDM1M1NCrC7EgAAPQLV9cEAOsLSZPn8/kUFxcX2Ha5XPJ6vYHtmJgYSVJ9fb3KysqUmZkZilj90mUY2vT2YbWc7NC9mUn60ZhYsyMBANBv566uCQCwLlNuht7TBNLQ0KDFixcrPz//goawJ3Fx0YqMHJijZm5335qz7X88pkNfNCjtao/uu/MaS11Ns69jtjrGbS92HLcdx4zgsdC0BgD4jpA0eR6PRz6fL7BdX18vt9sd2Pb7/Vq0aJEefvhhZWRk/ODrNTWdHJBcbnesvN4TF/19X9Wd0H+/ValLoofo36dNUEOD/4e/aZDo65itjnHbix3HHc5jpnkNre7fw9LlAYBVhWS5Znp6ukpKSiRJlZWV8ng8gSWakrR27VotWLBAU6dODUWcfjnV0amXt1fqTKehn96VopExQ82OBADAgOFm6ABgfSE5kpeWlqbU1FTl5OTI4XAoPz9fxcXFio2NVUZGhrZt26bq6moVFRVJkmbMmKG5c+eGItpF2/L+56ptOKmsG8dpUtJos+MAADCgOCMPAKwvZOfk5ebmXrCdnJwc+LqioiJUMfrl4yP1+vDP3+hyT4xm33ql2XEAAAgaJ4fyAMCyrHPHcZM1trTrt+9+pqhIpx6amaohkZQOABB+ugI3yjM3BwCg7+hUeqHLMLThzU/V2n5GOVkTdOnoEWZHAgAgOOjxAMDyaPJ6YdfHx3Wk5lvdMGG0Mq+71Ow4AAAEDTdDBwDro8n7Ad5v21T04TGNGBap+T9OZtIDAIQ1rq4JANZHk/c9DMPQb9/9TKc7uvRv067SyBFRZkcCACCoDC6vCQCWR5P3PT48+I0OVzfpuqR4/fM1CWbHAQAgZDiSBwDWRZP3dzS2tOv1XVUaPpRlmgAA++hersm8BwBWRZPXA8Mw9MqOI2o/3amc269UXOxQsyMBABAS3EEBAKyPJq8HH1X8RYe+aFBqoksZk8aaHQcAgJDh6poAYH00ed/xrf+UXnvvcw2NitCCH1/NJAcAsBfD4Hw8ALA4mrzzGIahV0uO6OSpM5pza5JGjxxudiQAAEKqSyzVBACro8k7z58+q9f/fu5T8hWjlHnDZWbHAQAg9AxxaU0AsDiavL9qOXlam3ceVVSkU/ffmSwnExwAwIYMGRzJAwCLo8n7q9//4aj8bR2alZkkT1y02XEAADCHwUVXAMDqaPIkfXzEq32H65V02SXK+sdxZscBAMA0XQarNQHA6mzf5PnbOvTqziOKjHDqp9kpcjqZ2QAAdsZyTQCwOts3eVve/1wtraf1k1sSNTZ+hNlxAAAwlcGFVwDA8mzd5O0/XKePKv6iH42J1b/80+VmxwEAwHSGJBa1AIC12bbJO9l+Ri+88WdFOB16IDtFEU7blgIAYKLVq1dr7ty5ysnJ0SeffHLBcx999JHuvfdezZ07Vy+++GJI8hjcDB0ALM+2nc3ru6vU0Nyuu6eM1zhPjNlxAAA2tG/fPlVXV6uwsFAFBQUqKCi44PnHH39c69at02uvvaaysjJVVVUFP5QhcTt0ALA2WzZ5X9Wd0B8PfqPxYy9R9uQfmR0HAGBT5eXlysrKkiQlJSWpublZfr9fklRTU6ORI0dq7NixcjqdyszMVHl5edAzcUoeAFhfpNkBzDBi2BBdf+VoLfzJREVGMJMBAMzh8/mUmpoa2Ha5XPJ6vYqJiZHX65XL5brguZqamh98zbi4aEVGRvQ5U3Z6ohpb2uV2x/b5NcINtehGLbpRi7OoQ7fBVAtbNnnxI4fpv+6dJLc7Vl7vCbPjAAAg6ez5cP3V1HSyX99/89Vu5sfzUItu1KIbtTiLOnQzoxbf11TacrkmAACDgcfjkc/nC2zX19fL7Xb3+FxdXZ08Hk/IMwIArIcmDwAAk6Snp6ukpESSVFlZKY/Ho5iYsxcDGzdunPx+v44fP64zZ85o9+7dSk9PNzMuAMAibLlcEwCAwSAtLU2pqanKycmRw+FQfn6+iouLFRsbq2nTpumxxx7Tz3/+c0lSdna2EhMTTU4MALACmjwAAEyUm5t7wXZycnLg65tuukmFhYWhjgQAsDiWawIAAABAGKHJAwAAAIAwQpMHAAAAAGGEJg8AAAAAwojDGIg7rwIAAAAABgWO5AEAAABAGKHJAwAAAIAwQpMHAAAAAGGEJg8AAAAAwghNHgAAAACEEZo8AAAAAAgjNHkAAAAAEEYizQ5ghtWrV+vgwYNyOBxasWKFJk2aZHakoNu7d6+WL1+uCRMmSJKuuuoqPfrooyanCp6jR49qyZIluv/++zVv3jzV1tbql7/8pTo7O+V2u/XrX/9aUVFRZscccN8dd15eniorKzVq1ChJ0gMPPKBbb73V3JBB8OSTT+rjjz/WmTNn9NBDD2nixIlhv7+/O+Zdu3bZYl8j+OwyR/Z2nti+fbteeeUVOZ1OzZkzR7Nnz1ZHR4fy8vL0zTffKCIiQmvWrNHll19u9pD6rLfvoeFei7a2NuXl5amhoUGnTp3SkiVLlJycbMtaSFJ7e7tmzJihJUuWaPLkybasQ0+fnxcuXGiNWhg2s3fvXuPBBx80DMMwqqqqjDlz5picKDT27NljLFu2zOwYIdHa2mrMmzfPWLlypfHqq68ahmEYeXl5xjvvvGMYhmE8/fTTxu9+9zszIwZFT+N+5JFHjF27dpmcLLjKy8uNhQsXGoZhGI2NjUZmZmbY7++exmyHfY3gs8sc2dt5orW11Zg+fbrR0tJitLW1GXfddZfR1NRkFBcXG4899phhGIZRWlpqLF++3LSx9Fdv30PtUIu3337bWL9+vWEYhnH8+HFj+vTptq2FYRjGM888Y8yaNcvYunWrbevQ0+dnq9TCdss1y8vLlZWVJUlKSkpSc3Oz/H6/yakwkKKiorRhwwZ5PJ7AY3v37tUdd9whSbrttttUXl5uVryg6WncdnDTTTfpueeekyRdcsklamtrC/v93dOYOzs7TU6FcGCXObK388TBgwc1ceJExcbGatiwYUpLS9OBAwdUXl6uadOmSZKmTJmiAwcOmDKOgdDb91A71CI7O1uLFi2SJNXW1iohIcG2tTh27JiqqqoCK0LsWoeeWKUWtmvyfD6f4uLiAtsul0ter9fERKFTVVWlxYsX67777lNZWZnZcYImMjJSw4YNu+Cxtra2wHK9+Pj4sNznPY1bkjZv3qz58+frZz/7mRobG01IFlwRERGKjo6WJBUVFWnq1Klhv797GnNERETY72sEn13myN7OEz6fTy6XK/B3ztXj/MedTqccDodOnz4dugEMoN6+h9qhFufk5OQoNzdXK1assG0tnnjiCeXl5QW27VoH6W8/P1ulFrY8J+98hmGYHSEkxo8fr6VLl+rOO+9UTU2N5s+fr507d4bdeUq9YZd9Lkn33HOPRo0apZSUFK1fv14vvPCCVq1aZXasoHjvvfdUVFSkTZs2afr06YHHw3l/nz/miooK2+xrhE44///5Pn9v3Bf7uJVc7HtoONdiy5YtOnz4sH7xi19cMB671GLbtm26/vrr/+65Y3apg9Tz5+fzV84M5lrY7kiex+ORz+cLbNfX18vtdpuYKDQSEhKUnZ0th8OhK664QqNHj1ZdXZ3ZsUImOjpa7e3tkqS6ujrbLGmcPHmyUlJSJEm33367jh49anKi4CgtLdVvfvMbbdiwQbGxsbbY398ds132NYLLrnOk1PM80VM9zj1+7ghnR0eHDMOw9C9Ne/MeaodaVFRUqLa2VpKUkpKizs5OjRgxwna1+OCDD/T+++9rzpw5euONN/TSSy/Z9t9ET5+fm5ubLVEL2zV56enpKikpkSRVVlbK4/EoJibG5FTBt337dm3cuFGS5PV61dDQoISEBJNThc6UKVMC+33nzp265ZZbTE4UGsuWLVNNTY2ks2vIz10dKpycOHFCTz75pF5++eXAlSXDfX/3NGY77GsEn13nSKnn943rrrtOhw4dUktLi1pbW3XgwAHdeOONSk9P144dOyRJu3fv1s0332xm9H7p7XuoHWqxf/9+bdq0SdLZpcsnT560ZS2effZZbd26Va+//rpmz56tJUuW2LIOUs+fn2fNmmWJWjgMKx9D7aOnnnpK+/fvl8PhUH5+vpKTk82OFHR+v1+5ublqaWlRR0eHli5dqszMTLNjBUVFRYWeeOIJff3114qMjFRCQoKeeuop5eXl6dSpU7r00ku1Zs0aDRkyxOyoA6qncc+bN0/r16/X8OHDFR0drTVr1ig+Pt7sqAOqsLBQ69atU2JiYuCxtWvXauXKlWG7v3sa86xZs7R58+aw3tcIDTvMkRczT+zYsUMbN26Uw+HQvHnzNHPmTHV2dmrlypX68ssvFRUVpbVr12rs2LFmD6tPLuY9NNxr0d7erl/96leqra1Ve3u7li5dqmuvvVaPPPKI7Wpxzrp163TZZZcpIyPDlnXo6fNzSkqKJWphyyYPAAAAAMKV7ZZrAgAAAEA4o8kDAAAAgDBCkwcAAAAAYYQmDwAAAADCCE0eAAAAAIQRmjwAAAAACCM0eQAAAAAQRv4fVvh1YO625TAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TwVXqv7fWAKZ"
      },
      "source": [
        "#Data Statistics\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "y-kNEnhuWVoB",
        "outputId": "55bdc38e-529f-4401-a06d-6fecc94ea89f"
      },
      "source": [
        "Data.sort_values('created_at').head(6)"
      ],
      "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>action</th>\n",
              "      <th>created_at</th>\n",
              "      <th>source_event_id</th>\n",
              "      <th>device_id</th>\n",
              "      <th>post_page_offset</th>\n",
              "      <th>tokens</th>\n",
              "      <th>post_index_in_post_list</th>\n",
              "      <th>post_token</th>\n",
              "      <th>tokens_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>26178</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>2021-01-01 00:36:09.213</td>\n",
              "      <td>1fc5ef66-aaad-4cf5-8e36-1e974de66fdf</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[wXtbOKF-, wXtLeB6p, wXpbfeIJ, wXt38PoX, wXt7K...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56013</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>2021-01-01 00:36:18.164</td>\n",
              "      <td>79910930-7296-4342-ae09-8e55e4803940</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[wXvPByYA, wXmHw9qd, wXsPnrBu, wXsTzSjv, wXrXX...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68800</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>2021-01-01 00:37:05.007</td>\n",
              "      <td>79910930-7296-4342-ae09-8e55e4803940</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>1.0</td>\n",
              "      <td>[wXWrwAAW, wXVXrn3e, wXUrG55o, wXRvrx9F, wXIX6...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102202</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>2021-01-01 00:37:28.740</td>\n",
              "      <td>21a5377e-c7c2-4b08-805c-6b580e0f074d</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>0.0</td>\n",
              "      <td>[wXtT-_XF, wXs_6TLh, wXpneaBg, wXpX9Pdu, wXoL-...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24308</th>\n",
              "      <td>click_post</td>\n",
              "      <td>2021-01-01 00:37:43.452</td>\n",
              "      <td>21a5377e-c7c2-4b08-805c-6b580e0f074d</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>wXoL-Kbx</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102919</th>\n",
              "      <td>click_post</td>\n",
              "      <td>2021-01-01 00:38:10.096</td>\n",
              "      <td>21a5377e-c7c2-4b08-805c-6b580e0f074d</td>\n",
              "      <td>lJgu0b3FQG6_grFbXenl2Q</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>wXgfv-Z0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                action              created_at  ... post_token tokens_length\n",
              "26178   load_post_page 2021-01-01 00:36:09.213  ...        NaN          24.0\n",
              "56013   load_post_page 2021-01-01 00:36:18.164  ...        NaN          24.0\n",
              "68800   load_post_page 2021-01-01 00:37:05.007  ...        NaN          11.0\n",
              "102202  load_post_page 2021-01-01 00:37:28.740  ...        NaN          17.0\n",
              "24308       click_post 2021-01-01 00:37:43.452  ...   wXoL-Kbx           NaN\n",
              "102919      click_post 2021-01-01 00:38:10.096  ...   wXgfv-Z0           NaN\n",
              "\n",
              "[6 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1HONcsUk-y_-",
        "outputId": "d6b345aa-1abc-47be-81e7-589f936b7565"
      },
      "source": [
        "Data['post_token'].unique().size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "56805"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cP3_jQHC_TsH"
      },
      "source": [
        "Data[Data['post_token']=='wXvz5Kzy'].sort_values('created_at')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5aDTz7C8_Hgw",
        "outputId": "6d01d89a-286c-413b-e22c-2ae1789cfd48"
      },
      "source": [
        "Data['post_token'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "wXvz5Kzy    32\n",
              "wXv3aQrM    31\n",
              "wXvX4mek    28\n",
              "wXv_56iR    28\n",
              "wXvHqew1    24\n",
              "            ..\n",
              "wXjrc6zh     1\n",
              "wXoLVxjx     1\n",
              "wXXLjBF2     1\n",
              "wXbwzyZ_     1\n",
              "wXv_0RXM     1\n",
              "Name: post_token, Length: 56804, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQbAvRZJ_Ch3",
        "outputId": "61e029e3-50ab-4d70-e79e-4c47f215d983"
      },
      "source": [
        "Data['post_token'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "56804"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81SkR4fGtuS7",
        "outputId": "f8a74341-edf7-4787-d2e6-83c5956ee03b"
      },
      "source": [
        "Data['source_event_id'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14296"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rgKnVrMiWH0R",
        "outputId": "6b44b4c5-7117-41ad-f4a7-5eafaecc6329"
      },
      "source": [
        "Data['device_id'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5012"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AgYzVSgfWrMB",
        "outputId": "ecef95f7-7459-409a-cbfb-417f33d44f6d"
      },
      "source": [
        "Data[Data['action']=='load_post_page'].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(35287, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_942um2YWiuJ",
        "outputId": "d22d3d22-4235-4d1d-fa04-91886e8ea182"
      },
      "source": [
        "Data[Data['action']=='click_post'].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(75796, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wceksLuYV_ep",
        "outputId": "706917aa-b485-4559-e332-d0d167f9fda1"
      },
      "source": [
        "Data['source_event_id'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14296"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3ERmw-8W8Lo",
        "outputId": "067c5d2f-90c7-434a-eccf-a302d4029121"
      },
      "source": [
        "Data[Data['action']=='click_post']['source_event_id'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10820"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l963qqDnWzKx",
        "outputId": "d48a7292-5a17-4709-9978-3034cbcfd1e2"
      },
      "source": [
        "Data[Data['action']=='load_post_page']['source_event_id'].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9381"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "28huQ4V-29_b",
        "outputId": "2cd41b29-626b-495d-d225-6ffead534783"
      },
      "source": [
        "print(Data['source_event_id'].nunique())\n",
        "print(Data[Data['action']=='click_post']['source_event_id'].nunique())\n",
        "print(Data[Data['action']=='click_post'].shape)\n",
        "print(Data[Data['action']=='load_post_page'].shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "14296\n",
            "10820\n",
            "(75796, 9)\n",
            "(35287, 9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcjs52Ta2Wjf"
      },
      "source": [
        "action_names=['click_post','load_post_page']\n",
        "action_size=[Data[Data['action']=='click_post'].shape[0],Data[Data['action']=='load_post_page'].shape[0]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "q7D_wetUuuu0",
        "outputId": "b212912f-5117-4581-c221-b8f5b10e30ea"
      },
      "source": [
        "#@title\n",
        "plt.figure(figsize = (25,5))\n",
        "plt.subplot(1, 4, 1)\n",
        "plt.title('Page Offsets', fontsize=15)\n",
        "Data['post_page_offset'].plot.hist(fontsize=10)\n",
        "plt.subplot(1, 4, 2)\n",
        "plt.title('Click/Load', fontsize=15)\n",
        "plt.hist(action_names,weights=action_size)\n",
        "plt.subplot(1, 4, 3)\n",
        "plt.title('Post_index', fontsize=15)\n",
        "Data['post_index_in_post_list'].plot.hist(fontsize=10)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABEYAAAFACAYAAACx2XmvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdf1SUZd7H8fcwMJEKKTS4Zv7INPVJQQ01UfxtIrVJJQYkta5ZrGaalBqrhJlhGmYZZaUiS6Ku1Lpkhm6FbCphSJn9sNbNp/BHMiSIgggCzx8d54lQG42Bkfm8zukc5ppr7vt7scvtdT5z39dlqKmpqUFERERERERExAm5NHYBIiIiIiIiIiKNRcGIiIiIiIiIiDgtBSMiIiIiIiIi4rQUjIiIiIiIiIiI01IwIiIiIiIiIiJOS8GIiIiIiIiIiDgtBSMidrZnzx5GjhyJr68vR48erfNaREQgJyeHrl278v333wMwevRoXn75ZZs+27VrVzZu3HjRPk8++SRLliz53XVero0bN9K1a9dGO7+IiIhcmGtjFyByPpGRkeTm5uLq+v//FzWbzfTv358ZM2bQunXrRqwOSktLSU5OJiMjg/z8fADatWtHUFAQf/rTn2jWrJm1b1JSEl5eXrz33nu4ubmxcOHCWq9/r9zcXCorKxkwYMDvPpaIiL0UFBTwxhtvsH37dgoKCnB3d+emm24iLCyM22+/vU7/rVu31uv5d+zYweLFizl06BAjRowgKSmJgICAej2HiIjUj1deeYV//OMf/Otf/7rsY4SHh9OhQwcWLVpUj5VJU6VgRBzW7bffzvPPPw9ATU0NP/zwA3PnzuXhhx/m7bffxsWlcW54OnHiBPfddx/NmzcnNjaWXr16AfDZZ5/x3HPPkZGRwdq1a/Hw8LD2b9++vTUE+fXr3ys5OZlOnTopGBERh/XDDz8QHh5Or169ePXVV7nxxhs5ceIEW7ZsISYmhv379zNo0CC7nf/bb7+lpKSEW265hYKCArudR0TkStQQX0hmZmbi4+PDzTffbFP/KVOmMGXKlN99XhFb6VEauSIYDAY6dOjAzJkz+frrrzl48CAA3333HQ899BC33nort9xyC/fddx9ffvml9XPHjx8nKioKPz8/Bg8ezNq1a3nooYeYM2eOtc/HH39MREQE/v7+9O3bl8ceewyLxXLBWhISEigqKmLVqlX4+/vj6uqKq6sr/v7+rFmzhtLSUpYtWwb8HO588sknvPvuu/Ts2ZPhw4fXen348GF27dpFaGgot9xyC/7+/kycOJEDBw7YVF9YWBjbtm3jjTfewN/fH4Avv/ySyMhI+vbtS+/evQkLCyM3N7f+/scQEblE8+fP59prr2X58uV07twZg8FAy5YtiYiI4LnnnqO6uprq6upanxk+fDgvvPCC9fU///lP7rjjDnr16sUdd9zBu+++e8HzxcXFMXr0aH766ScAdu7cib+/PyaTyaZ6i4qKePLJJxk6dCi+vr7ceeedZGRkWN8/e/Yszz//PMOHD6d3796MHDmS5OTkWsd48803GTFiBL179yYqKoqioiKbzi0i0hhuv/129u3bx759+/j8889JSkri0KFDPPzww3Wuz5dj+fLlfPXVV/VQqYh9KBiRK0pVVRWANdGePn0611xzDdu3b2fnzp1cf/31TJs2zdp/8eLFfPfdd7zzzjts2bKFTz/9lL1791rfP3DgAA8//DB33nkn2dnZbNmyhZKSEqKjo897/urqarZs2UJ4eDgtWrSo837z5s2555572Lx5MzU1Nbz77rv07dvX+o/Nhx9+WOu1j48PU6dO5Z577mH37t1s376dG264gblz59pU3/r162nbti2TJ0+2hh/R0dH06dOHnTt38vHHHzNs2DAef/xx6+9ORKQhHT9+nJ07dzJx4sTz3ukXFBTEE088cdG7AHfs2EFsbCxz5swhNzeXmTNnMmvWrPOGvq+88grbt28nKSkJb29v6+cHDhxoc82PPvoohw4dYt26deTm5nLvvfcyY8YMPvvsMwD+9re/8fbbb7NmzRry8vKYN28ezz77LB9//DHw8yOOCxYsYObMmezevZs///nP/O1vf7P5/CIijel8X0j+VmCcn59PVFQU/fv3p3fv3oSEhPD+++8DMHjwYL788kvi4uK48847baph+fLlDB48GIBDhw7RtWtXPvroIyZOnEjv3r0ZNmwYb731lrV/SUkJM2bMoG/fvgwaNIjXX3+9zjEv9mXjP//5T3r37s2hQ4es/WNiYrj77ruprKy89F+iXHEUjMgVobq6moMHD7J06VL69u1L+/btAVi3bh0LFizA3d0dd3d3goODOXz4MBaLhZqaGjIyMoiIiKB9+/a0aNGC2NhYysvLrcf9+9//Tvfu3QkLC8PNzQ2z2cysWbPIycnhhx9+qFPH8ePHOXnyJJ07d75grZ06daK4uJjjx4//5rgqKio4c+YMV111FUajkRYtWjBv3jzWr19/WfUBnDx5Ejc3N9zc3Ljqqqt4+OGH2b59O0aj8TfrERGpb/n5+dTU1HDjjTde9jHWrVvH4MGDGTRoEK6urgwfPpwXX3yRVq1a1er31ltvsXbtWpKSkrjuuusAOHPmDLm5uTYHI99++y27d+9m1qxZtGnTBpPJxH333UeXLl3YtGkT8PNt5++99x7t27fHYDAwZMgQvLy8+PzzzwF499136d69O7fffjtubm7069eP22677bLHLyLSGH75heRvBcZxcXG0bNmS7du388knnzBx4kSeeOIJiouL+fe//23tk56eftn1vPjii8yePZvc3FzGjh1LXFyc9W68RYsW8fXXX/P222/zr3/9ixMnTrB//37rZ3/ry8axY8cSGBjI/PnzgZ8XBH/33XdZsmRJvT3+Lo5Na4yIw3r33Xeti+8ZDAZ8fHwIDAzk0UcfxWAwAPDpp5+SmJjIgQMHOHPmDDU1NcDPE+Hi4mJOnz5Nu3btrMf09PSsNTn/7rvv2Lt3Lz179qx1bqPRyKFDh6wBzDnnznuxWwrPvXeu78U0b96cmTNnEhsby2uvvcaAAQMYNWqUdUHAS60PYNasWTz99NO89dZbDBgwgOHDhzNs2DAFIyLSKM5dC3/PxPL7778nMDCwVtvIkSNrvf7oo4/44IMPiIuL44YbbrC25+bm4uHhYfOOMOdC5y5dutRqv/HGG62LbZ88eZL4+Hiys7M5ceIE8P9BN8DRo0e5/vrra33+YoG6iIgjqa6u5vvvv7d+IVleXs7u3btJS0ujTZs2ANx3332sX7+eTZs20atXL06ePMkf/vAHTCYTRqORsWPHcuedd9o0H7bVXXfdRbdu3QC44447ePXVVzl48CCtWrXivffeY8aMGdZ5//Tp02vtVvbLLxsB65eNd955Jz/88APt27dn/vz5/PGPf+Ttt9/mtddeY+bMmb8r1Jcri4IRcVi/XHz1fA4ePMhf/vIXIiMjWbFiBS1btuSjjz7iwQcfBLCGJBebjLu7uzN06FBeffVVm2ry8vKiZcuWfPvttwQHB5+3z4EDB2jVqlWdbzIv5MEHH2TcuHHs3LmTjz76iKlTpzJ8+HASEhIuuT74OfEeOXIk2dnZ7Nixg7/+9a906dKF5ORkhSMi0uA6duyIi4sL+/bts05oL5WLi8tvPuOek5PDqFGjWL58OSNGjMDLywv4+TGaS9l95ly4ce7fkHN+ef7p06dTXFxMcnIyN9xwAy4uLrUWj62oqKiznsmvjyci4kgu9oXknj17gIsHxjNmzODxxx8nMDCQAQMGMHjwYMaMGWPz2k626NChg/Vnd3d3AMrLyykqKqKsrKxWIG0ymWr1t+XLxlatWvHMM88QFRVF3759uf/+++utdnF8epRGrlhfffUVlZWVPPzww7Rs2RKg1vohLVu2xM3NzXrBhp+fP/zuu++srzt27Mg333xTa8J75swZjh07dt5zGgwG/vjHP7Ju3TqKi4vrvF9aWsqGDRsICQmxOSE/fvw4LVu25Pbbb2fRokW88sorbN68meLi4kuu79zxmjdvzsiRI4mLi2Pjxo188skntW4nFBFpKJ6engwZMoTXX3+dioqKOu9nZmbyxz/+kdOnT1/wGB07dqx17QbYtGkTn3zyifX1Y489xvPPP0+bNm2YPXu2NYjYuXPnJQUjHTt2BOCbb76p1X7gwAHrnSifffYZd999NzfeeCMuLi7WRzjP+cMf/sDhw4drff7XxxMRcSS/Xnz1/fff56mnnqJVq1Y2BcYBAQFs376d5557Dh8fHxISEhg7diynTp2qtxovtBbVuX9bfv3+L+s792XjuTGe+++rr76q9W/E999/T7NmzTh69ChlZWX1Vrs4PgUjcsU6d6vcnj17OHPmDO+99551knz06FFcXFwYPnw4qampHDlyhFOnTrFw4UKuvvpq6zHCwsKwWCwsW7aMU6dOceLECebPn88DDzxwwW8nZ8yYQZs2bZg4cSK7d+/m7NmznD17lry8PP785z/Tpk0bHn30UZvGsGfPHkaMGMGOHTuoqqqioqKCzz77jGuvvZZrrrnGpvquvvpqfvjhB06ePMnhw4cZPHgw77zzDhUVFZw9e5Y9e/Zw1VVXWZ+3FxFpaHPnzuXMmTNERETw+eefU11dzYkTJ1i7di0zZ84kJCSk1rX518LDw8nOzmbbtm1UVlayc+dO5s2bV6uP0WjE1dWVhIQEPv30U1auXInFYuHbb7+9pIVXb775Znx9fVm8eDEFBQWcOXOGpKQkvv/+e8aNGwdA+/bt2bt3LxUVFfz3v/9l4cKFtG3bliNHjgA/76jzxRdfsHXrViorK8nOziYzM/MyfnMiIo3PlsD4+PHjmEwmAgMDmT17Nlu2bOHHH39k165ddq/P29sbNzc36zUYfg5Lvv/++1pj+K0vG7/77juWLVvG66+/zh/+8AeeffZZu9cujkPBiFyxfH19iYqKIiYmhkGDBvHvf/+bl19+mVtuuYXJkyeze/duZs+ejZeXF7fddhshISEMGDCADh06WO/muP7663nttdfIzs4mICCA0aNHc+LECd54440LptItWrQgNTWVkSNHsmDBAuvK1vPnz2fEiBGsXbuWZs2a2TSGW265hTlz5rBw4UL69OlDYGAgu3fvZsWKFRgMBpvqi4iIYPv27YwYMYKrrrqKF154gVWrVtGvXz9uvfVWNmzYwKuvvmrzoz0iIvXt+uuv5x//+Ae9e/dm5syZ9OrVizFjxvDhhx+yfPlyJk2adNHPDxgwgMWLF7NkyRJuueUWFi5cyMKFC+nbt+95z/X000/z4osv8ve//50uXbpgNpvr9HvooYfo2bNnrf9WrVoF/LyzjY+PD/fccw+DBg3iX//6F8nJydZHgebPn8+3335L3759mTVrFn/5y1944IEH2LJlC7GxsYwcOZJZs2bx3HPP0bdvX1avXk1UVFQ9/CZFRBrebwXGZWVl3HbbbaxevZrTp09TXV3N559/TkVFhTVUufrqqzl48KB1Xab65OrqypAhQ1i7di1HjhyhtLSUpUuX1gpBfuvLxqqqKmbPns0999yDv78/zzzzDJs3b+bDDz+s93rFMRlq9NCrNHHndn05Z/jw4YSEhNh8V4eIiIiISFMVGRlJ69atL7q2n8ViYeHChezZs4fy8nK6dOnCzJkz8ff3B35e6Pr555/nm2++wWAw0L59ex588EHuuOMOABISEkhOTsbT05MdO3b8Zk3Lly9n48aN/Pvf/+bQoUOMGDGCpKQk62Mvv247fvw48+bNIycnB3d3d+677z4+/fRTvLy8WLRoEQC7du3ihRde4JtvvqFZs2bWLyjbtWvHK6+8QlpaGps3b7Z+wblq1SpWrVrF5s2bretWSdOlYESatAULFrBz505Wr16N2WwmLS2Np59+mo0bN9KjR4/GLk9EREREREQamXalkSbtscceo7S0lLvvvpszZ87Qrl07EhISFIqIiIiIiIgIoDtGREREREREpAEcO3aMkSNHXrTPHXfcQXx8fANVJPIzBSMiIiIiIiIi4rS0K42IiIiIiIiIOK0mv8aIxXLysj7XqlUziorK6rmaK7cOcJxaVIdj1gGOU0tTqcNs9qjHahzflX69FpEr3+VcT5ztWg26Xv+SxnTlaIrj0pguzcWu17pj5AJcXY2NXQLgOHWA49SiOmpzlDrAcWpRHc5Fv2cRqS+6nthXU/z9akxXjqY4Lo2p/igYERERERERERGnpWBERERERERERJyWghERERERERERcVoKRkRERERERETEaSkYERERERERERGnpWBERERERERERJyWghERERERERERcVoKRkRERERERETEabna68CnT59mzpw5/PTTT5w5c4YpU6bQrVs3Zs2aRVVVFWazmSVLlmAymUhPTyc5ORkXFxfGjx9PaGgolZWVzJkzhyNHjmA0GomPj6ddu3bs37+fuLg4ALp27cr8+fPtNQQRERERERERaeLsdsdIZmYmPXr04M0332TZsmUsWrSIl156iYiICFJTU+nQoQNpaWmUlZWRmJjImjVrSElJITk5meLiYjZv3oynpyfr1q0jKiqKhIQEABYuXEhMTAzr16/n1KlTZGVl2WsIIiIiIiIiItLE2S0YCQ4OZvLkyQAcPXqU1q1bk5OTw4gRIwAYNmwY2dnZ7N27l549e+Lh4YG7uzt9+vQhLy+P7OxsRo0aBUBAQAB5eXlUVFRw+PBhfH19ax1DRERERERERORy2O1RmnPCwsL48ccfWbFiBRMnTsRkMgHg7e2NxWKhsLAQLy8va38vL6867S4uLhgMBgoLC/H09LT2PXcMe/hj9D/tctwLWT1neIOeT0SkqdD1WkTkyqDrtYg4KrsHI+vXr+frr7/miSeeoKamxtr+y59/6VLaL9T3l1q1aoarq9HGahuP2exxWe81NEepRXXU5ih1gOPUojpERERERMQWdgtGvvjiC7y9vWnTpg3du3enqqqK5s2bU15ejru7O8eOHcPHxwcfHx8KCwutnysoKKBXr174+PhgsVjo1q0blZWV1NTUYDabKS4utvY9d4yLKSoqs9cQ65XFcvK87WazxwXfa2iOUovqcMw6wHFqaSp1KFQREREREbE/u60xkpuby+rVqwEoLCykrKyMgIAAtm7dCsC2bdsIDAzEz8+Pffv2UVJSQmlpKXl5efj7+zNw4EAyMjKAnxdy7d+/P25ubnTq1Inc3NxaxxARERERaeo2btxIZGSk9b/evXuzf/9+wsLCCAsL46mnnrL2XblyJePGjSM0NNS6WcHJkyd56KGHCA8PZ9KkSdYvHHft2sW4ceO49957SUxMbJSxiYg0JrvdMRIWFsZf//pXIiIiKC8vJzY2lh49ejB79mw2bNjAddddR0hICG5ubkRHRzNp0iQMBgNTp07Fw8OD4OBgdu3aRXh4OCaTiUWLFgEQExNDbGws1dXV+Pn5ERAQYK8hiIiIiIg4jNDQUEJDQwHYvXs37733nnXHRl9fX6Kjo8nKyqJTp05s2bLFuotjREQEgwYNIjk5mX79+vHggw+yYcMG3njjDZ544gmeeeYZVq1aRevWrZkwYQKjR4+mc+fOjTxaEZGGY7dgxN3d3brF7i8lJSXVaQsKCiIoKKhWm9FoJD4+vk7fzp07k5qaWn+FioiIiIhcYRITE4mPj2fChAl1dmy0WCwEBgZiMpnw8vKibdu2HDhwgOzsbJ599llr36ioKPLz87nmmmto06YNAEOGDCE7O1vBiIg4Fbs9SiMiIiIiIvXv888/p02bNhiNxvPu2GjLro/e3t4UFBRgsVjO21dExJnYfVcaERERERGpP2lpadx111112ut7d8cLaQq7Pl6J52lITXFM0DTHpTHVDwUjIiIiIiJXkJycHObOnYvBYDjvjo0+Pj4cPHjwvO0WiwUPD48L7hDpDLs+1idH2QmvPjXFMUHTHJfGdOnHvhA9SiMiIiIicoU4duwYzZs3x2QyXXDHxltvvZXt27dTUVHBsWPHKCgooHPnzrV2fTzX9/rrr+fUqVMcOnSIs2fPkpmZycCBAxtziCIiDU53jIiIiIiIXCF+vSbIhXZsHD9+PBMmTMBgMBAXF4eLiwuRkZE88cQTRERE4OnpyZIlSwCIi4sjOjoagODgYG644YaGH5iISCNSMCIiIiIicoXo0aMHK1eutL6+0I6NkZGRREZG1mpr3rw5r7zySp2+ffv2ZcOGDfVfrIjIFUKP0oiIiIiIiIiI01IwIiIiIiIiIiJOS8GIiIiIiIiIiDgtBSMiIiIiIiIi4rS0+KqIiJPbuHEj6enp1tdffPEF69atIy4uDoCuXbsyf/58AFauXElGRgYGg4FHHnmEIUOGcPLkSaKjozl58iTNmjUjISGBli1bsmvXLpYuXYrRaGTw4MFMnTq1MYYnIiIiInJRumNERMTJhYaGkpKSQkpKCtOmTSMkJISFCxcSExPD+vXrOXXqFFlZWeTn57NlyxZSU1N57bXXiI+Pp6qqiuTkZPr168e6deu47bbbeOONNwB45plnWL58OevWrWPnzp0cOHCgkUcqIiIiIlKXghEREbFKTExk8uTJHD58GF9fXwCGDRtGdnY2OTk5BAYGYjKZ8PLyom3bthw4cIDs7GxGjRpVq29+fj7XXHMNbdq0wcXFhSFDhpCdnd2YQxMREREROS8FIyIiAsDnn39OmzZtMBqNeHp6Wtu9vb2xWCwUFhbi5eVlbffy8qrT7u3tTUFBARaL5bx9RUREREQcjdYYERERANLS0rjrrrvqtNfU1Jy3//naL9TXFq1aNcPV1XjZn28oZrNHY5cgInakv3EREeejYERERADIyclh7ty5GAwGiouLre3Hjh3Dx8cHHx8fDh48eN52i8WCh4dHrbbCwsI6fS+mqKis/gdlBxbLycYuQUTsxGz2uOS/cQUpIiJXPj1KIyIiHDt2jObNm2MymXBzc6NTp07k5uYCsG3bNgIDA7n11lvZvn07FRUVHDt2jIKCAjp37szAgQPJyMio1ff666/n1KlTHDp0iLNnz5KZmcnAgQMbc4giIiIiIuelO0ZERKTOmiAxMTHExsZSXV2Nn58fAQEBAIwfP54JEyZgMBiIi4vDxcWFyMhInnjiCSIiIvD09GTJkiUAxMXFER0dDUBwcDA33HBDww9MREREROQ3KBgRERF69OjBypUrra87d+5MampqnX6RkZFERkbWamvevDmvvPJKnb59+/Zlw4YN9V+siIiIiEg90qM0IiIiIiIiIuK0FIyIiIiIiIiIiNNSMCIiIiIiIiIiTkvBiIiIiIiIiIg4LQUjIiIiIiIiIuK0FIyIiIiIiIiIiNNSMCIiIiIiIiIiTkvBiIiIiIiIiIg4LQUjIiIiIiIiIuK0FIyIiIiIiIiIiNNSMCIiIiIiIiIiTkvBiIiIiIiIiIg4LQUjIiIiIiIiIuK0FIyIiIiIiIiIiNNybewCRERERETENunp6axcuRJXV1ceffRRunbtyqxZs6iqqsJsNrNkyRJMJhPp6ekkJyfj4uLC+PHjCQ0NpbKykjlz5nDkyBGMRiPx8fG0a9eO/fv3ExcXB0DXrl2ZP39+4w5SRKSB2TUYWbx4MXv27OHs2bM8/PDDfPjhh3z55Ze0bNkSgEmTJjF06FBduEVEREREfkNRURGJiYm89dZblJWVsXz5crZu3UpERARjxoxh6dKlpKWlERISQmJiImlpabi5uTFu3DhGjRpFZmYmnp6eJCQksGPHDhISEli2bBkLFy4kJiYGX19foqOjycrKYsiQIY09XBGRBmO3R2k+/vhj/vOf/7BhwwZWrlzJs88+C8DMmTNJSUkhJSWFoUOHUlZWRmJiImvWrCElJYXk5GSKi4vZvHkznp6erFu3jqioKBISEgCsF+7169dz6tQpsrKy7DUEERERERGHkZ2dzYABA2jRogU+Pj4sWLCAnJwcRowYAcCwYcPIzs5m79699OzZEw8PD9zd3enTpw95eXlkZ2czatQoAAICAsjLy6OiooLDhw/j6+tb6xgiIs7EbsFI3759efHFFwHw9PTk9OnTVFVV1emnC7eIiIiIyG87dOgQ5eXlREVFERERQXZ2NqdPn8ZkMgHg7e2NxWKhsLAQLy8v6+e8vLzqtLu4uGAwGCgsLMTT09Pa99wxREScid0epTEajTRr1gyAtLQ0Bg8ejNFo5M033yQpKQlvb2/mzZunC7eIiIiIiI2Ki4t5+eWXOXLkCPfffz81NTXW93758y9dSvuF+v5Sq1bNcHU12lhx4zGbPZrUeRpSUxwTNM1xaUz1w+6Lr77//vukpaWxevVqvvjiC1q2bEn37t15/fXXefnll+ndu3et/rpwX9p7Dc1RalEdtTlKHeA4tagOERFpary9venduzeurq60b9+e5s2bYzQaKS8vx93dnWPHjuHj44OPjw+FhYXWzxUUFNCrVy98fHywWCx069aNyspKampqMJvNFBcXW/ueO8bFFBWV2W2M9cliOWn3c5jNHg1ynobUFMcETXNcGtOlH/tC7BqMfPTRR6xYsYKVK1fi4eHBgAEDrO8NHz6cuLg4Ro8erQs3F75wO9L/2R2lFtXhmHWA49TSVOpQqCIiIr80aNAg5syZw+TJkzlx4gRlZWUMGjSIrVu3MnbsWLZt20ZgYCB+fn7MnTuXkpISjEYjeXl5xMTEcOrUKTIyMggMDCQzM5P+/fvj5uZGp06dyM3Nxd/fn23bthEZGdnYQxURaVB2W2Pk5MmTLF68mNdee826C820adPIz88HICcnhy5duuDn58e+ffsoKSmhtLSUvLw8/P39GThwIBkZGQDnvXAD1ou/iIiIiEhT17p1a0aPHs348eOZPHkyc+fOZdq0aWzatImIiAiKi4sJCQnB3d2d6OhoJk2axMSJE5k6dSoeHh4EBwdTXV1NeHg4a9euJTo6GoCYmBiWLl1KWFgY7du3JyAgoJFHKiLSsOx2x8iWLVsoKipixowZ1ra7776bGTNmcPXVV9OsWTPi4+NrXbgNBkOtC/euXbsIDw/HZDKxaNEi4OcLd2xsLNXV1fj5+enCLSIiIiJOI30SvZIAACAASURBVCwsjLCwsFptSUlJdfoFBQURFBRUq81oNBIfH1+nb+fOnUlNTa3fQkVEriB2C0buvfde7r333jrtd911V502XbhFREREREREpDHY7VEaERERERERERFHZ/ddaURExPGlp6ezcuVKXF1defTRR+natSuzZs2iqqoKs9nMkiVLMJlMpKenk5ycjIuLC+PHjyc0NJTKykrmzJnDkSNHrHf7tWvXjv379xMXFwdA165dmT9/fuMOUkRERETkPHTHiIiIkysqKiIxMZHU1FRWrFjBBx98wEsvvURERASpqal06NCBtLQ0ysrKSExMZM2aNaSkpJCcnExxcTGbN2/G09OTdevWERUVRUJCAgALFy4kJiaG9evXc+rUKbKyshp5pCIiIiIidSkYERFxctnZ2QwYMIAWLVrg4+PDggULyMnJYcSIEQAMGzaM7Oxs9u7dS8+ePfHw8MDd3Z0+ffqQl5dHdnY2o0aNAiAgIIC8vDwqKio4fPgwvr6+tY4hIiIiIuJo9CiNiIiTO3ToEOXl5URFRVFSUsK0adM4ffo0JpMJAG9vbywWC4WFhXh5eVk/5+XlVafdxcUFg8FAYWEhnp6e1r7njiEiIiIi4mgUjIiICMXFxbz88sscOXKE+++/n5qaGut7v/z5ly6l/UJ9f6lVq2a4uhptrLjxmM0ejV2CiNiR/sZFRJyPghERESfn7e1N7969cXV1pX379jRv3hyj0Uh5eTnu7u4cO3YMHx8ffHx8KCwstH6uoKCAXr164ePjg8VioVu3blRWVlJTU4PZbKa4uNja99wxLqaoqMxuY6xPFsvJxi5BROzEbPa45L9xBSkiIlc+rTEiIuLkBg0axMcff0x1dTVFRUWUlZUREBDA1q1bAdi2bRuBgYH4+fmxb98+SkpKKC0tJS8vD39/fwYOHEhGRgYAmZmZ9O/fHzc3Nzp16kRubm6tY4iIiIiIOBrdMSIi4uRat27N6NGjGT9+PABz586lZ8+ezJ49mw0bNnDdddcREhKCm5sb0dHRTJo0CYPBwNSpU/Hw8CA4OJhdu3YRHh6OyWRi0aJFAMTExBAbG0t1dTV+fn4EBAQ05jBFRERERM5LwYiIiBAWFkZYWFittqSkpDr9goKCCAoKqtVmNBqJj4+v07dz586kpqbWb6EiIiIiIvVMj9KIiIiIiIiIiNNSMCIiIiIiIiIiTkvBiIiIiIiIiIg4LQUjIiIiIiIiIuK0FIyIiIiIiIiIiNNSMCIiIiIiIiIiTkvBiIiIiIiIiIg4LQUjIiIiIiIiIuK0FIyIiIiIiIiIiNNSMCIiIiIiIiIiTkvBiIiIiIiIiIg4LQUjIiIiIiIiIuK0FIyIiIiIiIiIiNNSMCIiIiIiIiIiTkvBiIiIiIiIiIg4LQUjIiIiIiIiIuK0XBu7ABERERERubicnBymT59Oly5dALjpppt48MEHmTVrFlVVVZjNZpYsWYLJZCI9PZ3k5GRcXFwYP348oaGhVFZWMmfOHI4cOYLRaCQ+Pp527dqxf/9+4uLiAOjatSvz589vxFGKiDQO3TEiIiIiInIF6NevHykpKaSkpDBv3jxeeuklIiIiSE1NpUOHDqSlpVFWVkZiYiJr1qwhJSWF5ORkiouL2bx5M56enqxbt46oqCgSEhIAWLhwITExMaxfv55Tp06RlZXVyKMUEWl4CkZERERERK5AOTk5jBgxAoBhw4aRnZ3N3r176dmzJx4eHri7u9OnTx/y8vLIzs5m1KhRAAQEBJCXl0dFRQWHDx/G19e31jFERJyNHqUREREREbkCHDhwgKioKE6cOMEjjzzC6dOnMZlMAHh7e2OxWCgsLMTLy8v6GS8vrzrtLi4uGAwGCgsL8fT0tPY9dwwREWejYERERERExMF17NiRRx55hDFjxpCfn8/9999PVVWV9f2amprzfu5S2i/U99datWqGq6vRpr6NyWz2aFLnaUhNcUzQNMelMdUPBSMiIiIiIg6udevWBAcHA9C+fXuuvfZa9u3bR3l5Oe7u7hw7dgwfHx98fHwoLCy0fq6goIBevXrh4+ODxWKhW7duVFZWUlNTg9lspri42Nr33DF+S1FRWf0P0A4slpN2P4fZ7NEg52lITXFM0DTHpTFd+rEvRGuMiIiIiIg4uPT0dFatWgWAxWLhp59+4u6772br1q0AbNu2jcDAQPz8/Ni3bx8lJSWUlpaSl5eHv78/AwcOJCMjA4DMzEz69++Pm5sbnTp1Ijc3t9YxREScje4YERERERFxcMOHD+fxxx/ngw8+oLKykri4OLp3787s2bPZsGED1113HSEhIbi5uREdHc2kSZMwGAxMnToVDw8PgoOD2bVrF+Hh4ZhMJhYtWgRATEwMsbGxVFdX4+fnR0BAQCOPVESk4dk1GFm8eDF79uzh7NmzPPzww/Ts2VN7rYuIiIiIXKIWLVqwYsWKOu1JSUl12oKCgggKCqrVdm4+/WudO3cmNTW1/goVEbkC2S0Y+fjjj/nPf/7Dhg0bKCoq4q677mLAgAFEREQwZswYli5dSlpaGiEhISQmJpKWloabmxvjxo1j1KhRZGZm4unpSUJCAjt27CAhIYFly5ZZ91r39fUlOjqarKwshgwZYq9hiIg0eTk5OUyfPp0uXboAcNNNN/Hggw8qyBYRERERp2C3NUb69u3Liy++CICnpyenT5/WXusiIg6qX79+pKSkkJKSwrx583jppZeIiIggNTWVDh06kJaWRllZGYmJiaxZs4aUlBSSk5MpLi5m8+bNeHp6sm7dOqKiokhISACwBtnr16/n1KlTZGVlNfIoRURERETqslswYjQaadasGQBpaWkMHjxYe62LiFwhFGSLiIiIiLOw++Kr77//PmlpaaxevZrbbrvN2t5Qe603hX3WHWlvakepRXXU5ih1gOPUojouzYEDB4iKiuLEiRM88sgjCrJFRERExGnYNRj56KOPWLFiBStXrsTDw4NmzZo1+F7rV/o+6460N7Wj1KI6HLMOcJxamkodDRWqdOzYkUceeYQxY8aQn5/P/fffT1VVlfV9Bdm1XSlhl4hcHv2Ni4g4H7sFIydPnmTx4sWsWbOGli1bAj/fYr1161bGjh1ba6/1uXPnUlJSgtFoJC8vj5iYGE6dOkVGRgaBgYHn3Wvd39+fbdu2ERkZaa8hiIg4hdatWxMcHAxA+/btufbaa9m3b5+C7AtwhNBNROzjcgJtBSkiIlc+u60xsmXLFoqKipgxYwaRkZFERkYSFRXFpk2biIiIoLi4mJCQENzd3a17rU+cOLHWXuvV1dWEh4ezdu1aoqOjgZ/3Wl+6dClhYWG0b99ee62LiPxO6enprFq1CgCLxcJPP/3E3XffzdatWwFqBdn79u2jpKSE0tJS8vLy8Pf3Z+DAgWRkZACcN8j+5TFERERERByN3e4Yuffee7n33nvrtGuvdRERxzJ8+HAef/xxPvjgAyorK4mLi6N79+7Mnj2bDRs2cN111xESEoKbm5s1yDYYDLWC7F27dhEeHo7JZGLRokXAz0F2bGws1dXV+Pn5KcgWEREREYdk98VXRUTEsbVo0YIVK1bUaVeQLSIiIiLOwG6P0oiIiIiIiIiIODoFIyIiIiIiIiLitBSMiIiIiIiIiIjTUjAiIiIiIiIiIk5LwYiIiIiIiIiIOC2bgpGamhp71yEiIiIi4jQ0vxYRcRw2BSPDhg3jhRdeID8/3971iIiIiIg0eZpfi4g4DpuCkY0bN2I2m4mJiWHixIm88847VFRU2Ls2EREREZEmSfNrERHHYVMwYjabmTBhAikpKcTFxbFu3ToCAwN54YUXOHPmjL1rFBERERFpUjS/FhFxHDYvvvrJJ5/w5JNPMnnyZPr06UNqaiqenp5Mnz7dnvWJiIiIiDRJml+LiDgGV1s6jRo1irZt2zJ+/Hiefvpp3NzcALjxxht5//337VqgiIiIiEhTo/m1iIjjsCkYWblyJTU1NXTs2BGAr776iv/5n/8BIDU11W7FiYiIiIg0RZpfi4g4DpsepXn77bd57bXXrK9ff/11nn/+eQAMBoN9KhMRERERaaI0vxYRcRw2BSM5OTnEx8dbXy9btow9e/bYrSgRERERkaZM82sREcdhUzBSWVlZa/uw0tJSzp49a7eiRERERESaMs2vRUQch01rjISFhREcHEyPHj2orq5m3759PPLII/auTURERESkSdL8WkTEcdgUjISGhjJw4ED27duHwWDgySefpE2bNvauTURERESkSdL8WkTEcdgUjJw5c4avvvqKU6dOUVNTw86dOwEYN26cXYsTEREREWmKNL8WEXEcNgUjkyZNwsXFhbZt29Zq14VbREREROTSXe78ury8nDvuuIMpU6YwYMAAZs2aRVVVFWazmSVLlmAymUhPTyc5ORkXFxfGjx9PaGgolZWVzJkzhyNHjmA0GomPj6ddu3bs37+fuLg4ALp27cr8+fPtNWQREYdlUzBy9uxZ1q9fb+9aREREREScwuXOr1999VWuueYaAF566SUiIiIYM2YMS5cuJS0tjZCQEBITE0lLS8PNzY1x48YxatQoMjMz8fT0JCEhgR07dpCQkMCyZctYuHAhMTEx+Pr6Eh0dTVZWFkOGDKnv4YqIODSbdqXp3LkzRUVF9q5FRERERMQpXM78+r///S8HDhxg6NChwM9b/o4YMQKAYcOGkZ2dzd69e+nZsyceHh64u7vTp08f8vLyyM7OZtSoUQAEBASQl5dHRUUFhw8fxtfXt9YxREScjU13jPz444/cdttt3HjjjRiNRmv72rVr7VaYiIiIiEhTdTnz6+eee4558+axadMmAE6fPo3JZALA29sbi8VCYWEhXl5e1s94eXnVaXdxccFgMFBYWIinp6e177ljiIg4G5uCkYceesjedYiIiIiIOI1LnV9v2rSJXr160a5du/O+X1NT87vbL9T311q1aoarq/G3OzYys9mjSZ2nITXFMUHTHJfGVD9sCkb69evH9u3bOXToEBMmTOCHH3644EVZREREREQu7lLn19u3byc/P5/t27fz448/YjKZaNasGeXl5bi7u3Ps2DF8fHzw8fGhsLDQ+rmCggJ69eqFj48PFouFbt26UVlZSU1NDWazmeLiYmvfc8f4LUVFZb9v8A3EYjlp93OYzR4Ncp6G1BTHBE1zXBrTpR/7QmxaY2TJkiWkpaXx9ttvA/DOO+/wzDPP1E91IiIiIiJO5lLn18uWLeOtt97i73//O6GhoUyZMoWAgAC2bt0KwLZt2wgMDMTPz499+/ZRUlJCaWkpeXl5+Pv7M3DgQDIyMgDIzMykf//+uLm50alTJ3Jzc2sdQ0TE2dgUjHzyySe8/PLLNG/eHICpU6fy5Zdf2rUwERFpOOXl5YwcOZK3336bo0ePEhkZSUREBNOnT6eiogKA9PR07rnnHkJDQ9m4cSMAlZWVREdHEx4ezoQJE8jPzwdg//79hIWFERYWxlNPPdVo4xIRcVT1Mb+eNm0amzZtIiIiguLiYkJCQnB3dyc6OppJkyYxceJEpk6dioeHB8HBwVRXVxMeHs7atWuJjo4GICYmhqVLlxIWFkb79u0JCAio97GKiDg6mx6lueqqqwAwGAwAVFVVUVVVZb+qRESkQWn7RxGRhvV75tfTpk2z/pyUlFTn/aCgIIKCgmq1GY1G4uPj6/Tt3LkzqampNtctItIU2XTHSJ8+fXjyyScpKCggKSmJCRMm0K9fP3vXJiIiDUDbP4qINDzNr0VEHIdNd4w89thjZGRk4O7uzo8//sjEiRO57bbb7F2biIg0AG3/KCLS8DS/FhFxHDYFI/n5+dx8883cfPPNtdq0M42IyJVN2z9euqa4LZ6I/L+G+hvX/FpExHHYFIw88MAD1ucfKyoqOH78OF26dLF+uygiIlcmbf946Zratngi8v8uZ5vIyw1SNL8WEXEcNgUjH374Ya3X//nPf0hLS7NLQSIi0nCWLVtm/Xn58uW0bduWTz/9lK1btzJ27Nha2z/OnTuXkpISjEYjeXl5xMTEcOrUKTIyMggMDDzv9o/+/v5s27aNyMjIRhyliIjj0fxaRMRx2BSM/FqXLl20Xa+ISBM1bdo0Zs+ezYYNG7juuusICQnBzc3Nuv2jwWCotf3jrl27CA8Px2QysWjRIuDn7R9jY2Oprq7Gz89P2z+KiPwGza9FRBqPTcHIiy++WOv1jz/+SElJyW9+7ttvv2XKlCn86U9/YsKECcyZM4cvv/ySli1bAjBp0iSGDh1Keno6ycnJuLi4MH78eEJDQ6msrGTOnDkcOXLEur1Yu3bt2L9/P3FxcQB07dqV+fPnX+KQRUTkfLT9o4hIw7nc+bWIiNQ/m4IRo7H2Ynhdu3ZlxowZF/1MWVkZCxYsYMCAAbXaZ86cybBhw2r1S0xMJC0tDTc3N8aNG8eoUaPIzMzE09OThIQEduzYQUJCAsuWLWPhwoXExMTg6+tLdHQ0WVlZDBkyxNbxioiIiIg0usuZX4uIiH3YFIxMmTLlvO3V1dXAz1s0/prJZOKNN97gjTfeuOix9+7dS8+ePfHw+Hnhqj59+pCXl0d2djYhISEABAQEEBMTQ0VFBYcPH8bX1xeAYcOGkZ2drWBERERERK4olzO/FhER+7ApGPH19aWqqqpOe01NDQaDga+//rrugV1dcXWte/g333yTpKQkvL29mTdvHoWFhXh5eVnf9/LywmKx1Gp3cXHBYDBQWFiIp6enta+3tzcWi8WWIYiIiIiIOIzLmV+LiIh92BSMTJ06lc6dOzNw4EAMBgOZmZn87//+7wWT7gsZO3YsLVu2pHv37rz++uu8/PLL9O7du1afmpqa8372fO0X6vtLrVo1w9XV+Jv9GtvFtnq73G3g7MFRalEdtTlKHeA4tagOERFxZPU1vxYRkd/PpmDk448/5i9/+Yv1dXBwMA888MAlX7h/ud7I8OHDiYuLY/To0RQWFlrbCwoK6NWrFz4+PlgsFrp160ZlZSU1NTWYzWaKi4utfY8dO4aPj89Fz1lUVHZJNTYWi+XkedvNZo8LvtfQHKUW1eGYdYDj1NJU6lCoIiLSdNXX/FpERH4/mx5eLC4uJisri9LSUkpLS8nKyuL48eOXfLJp06aRn58PQE5ODl26dMHPz499+/ZRUlJCaWkpeXl5+Pv7M3DgQDIyMgDIzMykf//+uLm50alTJ3JzcwHYtm0bgYGBl1yHiIiIiEhjqq/5tYiI/H423TGyYMECFi1axGOPPQbATTfdxFNPPXXRz3zxxRc899xzHD58GFdXV7Zu3cqECROYMWMGV199Nc2aNSM+Ph53d3eio6OZNGkSBoOBqVOn4uHhQXBwMLt27SI8PByTycSiRYsAiImJITY2lurqavz8/AgICPidvwIRERERkYZ1OfNrERGxD5sXX01NTbUuBmWLHj16kJKSUqd99OjRddqCgoIICgqq1WY0GomPj6/Tt3PnzqSmptpUg4iIiIiII7qc+bWIiNiHTY/S7N+/n7vvvpsxY8YA8Morr7B37167FiYiIiIi0lRpfi0i4jhsCkaefvppnn32WcxmMwBjxow5790cIiIiIiLy2zS/FhFxHDYFI66urnTr1s36+oYbbsDV1aancERERERE5Fc0vxYRcRw2XX1dXV3Jz8+3Pv+YlZVFTU2NXQsTEREREef250UfNuj53kkY22Dn0vxaRMRx2BSMzJ49mylTpnDw4EFuueUW2rZty+LFi+1dm4iIiIhIk6T5tYiI47ApGGnVqhXvvPMOx48fx2Qy0aJFC3vXJSIiIiLSZGl+LSLiOGxaY+Txxx8HwMvLSxdtEREREZHfSfNrERHHYdMdIx07dmTWrFn07t0bNzc3a/u4cePsVpiIiIiISFOl+bWIiOO4aDCyf/9+unXrRmVlJUajkaysLFq1amV9XxduERERERHbaX4tIuJ4LhqMPPvss/ztb3+z7ql+//33s2LFigYpTERERESkqdH8WkTE8Vx0jRFtGSYiIiIiUn80vxYRcTwXDUbO7at+ji7kIiIiIiKXT/NrERHHY9Piq+f8+kIuIiIiIiKXz9b59enTp5kzZw4//fQTZ86cYcqUKXTr1o1Zs2ZRVVWF2WxmyZIlmEwm0tPTSU5OxsXFhfHjxxMaGkplZSVz5szhyJEjGI1G4uPjadeuHfv37ycuLg6Arl27Mn/+fDuOVkTEMV00GPn0008ZOnSo9fVPP/3E0KFDqampwWAwsH37djuXJyIiIiLSdFzu/DozM5MePXowefJkDh8+zJ///Gf69OlDREQEY8aMYenSpaSlpRESEkJiYiJpaWm4ubkxbtw4Ro0aRWZmJp6eniQkJLBjxw4SEhJYtmwZCxcuJCYmBl9fX6Kjo8nKymLIkCEN88sQEXEQFw1GMjIyGqoOEREREZEm73Ln18HBwdafjx49SuvWrcnJybHe4TFs2DBWr17NDTfcQM+ePfHw8ACgT58+5OXlkZ2dTUhICAABAQHExMRQUVHB4cOH8fX1tR4jOztbwYiIOJ2LBiNt27ZtqDpERERERJq83zu/DgsL48cff2TFihVMnDgRk8kEgLe3NxaLhcLCQry8vKz9vby86rS7uLhgMBgoLCzE09PT2vfcMUREnM0lrTEiIiIiIiKNZ/369Xz99dc88cQTtRZuvdAirpfSbutCsK1aNcPV1WhT38ZkNns0qfM0pKY4Jmia49KY6oeCERERERERB/fFF1/g7e1NmzZt6N69O1VVVTRv3pzy8nLc3d05duwYPj4++Pj4UFhYaP1cQUEBvXr1wsfHB4vFQrdu3aisrKSmpgaz2UxxcbG177lj/JaiojK7jLG+WSwn7X4Os9mjQc7TkJrimKBpjktjuvRjX8hFt+sVEZGm7fTp00yfPp0JEyYQGhpKZmYmR48eJTIykoiICKZPn05FRQUA6enp3HPPPYSGhrJx40YAKisriY6OJjw8nAkTJpCfnw/A/v37CQsLIywsjKeeeqrRxici0lTk5uayevVqAAoLCykrKyMgIICtW7cCsG3bNgIDA/Hz82Pfvn2UlJRQWlpKXl4e/v7+DBw40Lq+SWZmJv3798fNzY1OnTqRm5tb6xgiIs5Gd4yIiDgx7XIgInJlCAsL469//SsRERGUl5cTGxtLjx49mD17Nhs2bOC6664jJCQENzc3oqOjmTRpEgaDgalTp+Lh4UFwcDC7du0iPDwck8nEokWLAIiJiSE2Npbq6mr8/PwICAho5JGKiDQ8BSMiIk5MuxyIiFwZ3N3dSUhIqNOelJRUpy0oKIigoKBabUajkfj4+Dp9O3fuTGpqav0VKiJyBVIwIiIiDrHLgRbzExFHoL9xERHno2BEREQcYpcDLeYnIo7gUv/GFaSIiFz5tPiqiIgT++KLLzh69ChAnV0OgIvucnCu/dzdIL93lwMRERERkcagYERExIlplwMRERERcXZ6lEZExIlplwMRERERcXYKRkREnJh2ORARERERZ6dHaURERERERETEaSkYERERERERERGnpWBERERERERERJyWghERERERERERcVoKRkRERERERETEaSkYERERERERERGnpWBERERERERERJyWXYORb7/9lpEjR/Lmm28CcPToUSIjI4mIiGD69OlUVFQAkJ6ezj333ENoaCgbN24EoLKykujoaMLDw5kwYQL5+fkA7N+/n7CwMMLCwnjqqafsWb6IiIiIiIiINHF2C0bKyspYsGABAwYMsLa99NJLREREkJqaSocOHUhLS6OsrIzExETWrFlDSkoKycnJFBcXs3nzZjw9PVm3bh1RUVEkJCQA/9fe3cdVWd9/HH8fOBAPFKboOT5y8650WYk4ZrEw6+cNhm4VUxGwQ/WY3ViybMMMycxmBpb4cJrLokzESJKc2c2U6chsntgck4mNFdacooODgQiCIF6/P3xwJnmTppzD4bye/53vdZ3r+/lccK5zXZ/r+72OtHDhQqWmpmrdunWqq6vT9u3b2ysFAAAAAADQybVbYcTf31+ZmZmyWq3OtsLCQo0ZM0aSNGrUKNntdhUXFys0NFRBQUEKCAhQeHi4ioqKZLfbFRUVJUmKjIxUUVGRmpqaVF5erqFDh7bZBgAAAAAAwHdhbrcNm80ym9tuvqGhQf7+/pKkHj16yOFwqKqqSiEhIc51QkJCzmr38fGRyWRSVVWVgoODneu2bgMAAAAAAOC7aLfCyLcxDOOy28+37pm6dw+U2ex7acG5gcUS9J2WuVpHiYU42uoocUgdJxbiAAAAAHAxXFoYCQwMVGNjowICAlRRUSGr1Sqr1aqqqirnOpWVlRo2bJisVqscDocGDx6s5uZmGYYhi8Wimpoa57qt27iQ6urj7ZbPleRwHDtnu8USdN5lrtZRYiGOjhmH1HFi6SxxUFQBAAAA2p9Lf643MjJSW7ZskSTl5+dr5MiRCgsL0549e1RbW6v6+noVFRVp+PDhGjFihDZv3ixJKigoUEREhPz8/HTNNddo165dbbYBAAAAAADwXbTbiJGSkhItWrRI5eXlMpvN2rJlixYvXqyUlBTl5uaqd+/eiomJkZ+fn5KTkzVt2jSZTCbNmDFDQUFBmjBhgnbu3KmEhAT5+/srPT1dkpSamqp58+bp1KlTCgsLU2RkZHulAAAAAAAAOrl2K4wMGTJE2dnZZ7W/8cYbZ7VFR0crOjq6TZuvr6/S0tLOWnfgwIHKycm5coECAAAAAACv5dKpNAAAAAAAAB0JhREAAAAAAOC1KIwAAAAAAACvRWEEAAAAAAB4LQojAAAAAADAa7Xbr9IAAAAAuLJeeOEF/e1vf9PJkyf18MMPKzQ0VLNnz1ZLS4ssFotefPFF+fv7a9OmTcrKypKPj4+mTJmi2NhYNTc3KyUlRYcOHXL+AmSfPn1UWlqq+fPnS5Kuu+46PfvsaiZJSgAAHlBJREFUs+5NEgBcjBEjAAAAgAf49NNP9cUXXyg3N1evvfaann/+eS1btkxTp05VTk6O+vXrp7y8PB0/flwrVqzQ6tWrlZ2draysLNXU1Oj9999XcHCw3nrrLU2fPl0ZGRmSpIULFyo1NVXr1q1TXV2dtm/f7uZMAcC1KIwAAAAAHuCmm27Sb3/7W0lScHCwGhoaVFhYqDFjxkiSRo0aJbvdruLiYoWGhiooKEgBAQEKDw9XUVGR7Ha7oqKiJEmRkZEqKipSU1OTysvLNXTo0DbbAABvQmEEAAAA8AC+vr4KDAyUJOXl5em2225TQ0OD/P39JUk9evSQw+FQVVWVQkJCnO8LCQk5q93Hx0cmk0lVVVUKDg52rtu6DQDwJjxjBADAnHUA8CBbt25VXl6eVq1apXHjxjnbDcM45/qX0n6+dc/UvXugzGbfi4zWfSyWoE7Vjyt1xpykzpkXOV0ZFEYAwMudOWe9urpaP//5z3XLLbdo6tSpGj9+vJYsWaK8vDzFxMRoxYoVysvLk5+fnyZPnqyoqCgVFBQoODhYGRkZ+uSTT5SRkaGlS5c656wPHTpUycnJ2r59u26//XZ3pwsAHm3Hjh1auXKlXnvtNQUFBSkwMFCNjY0KCAhQRUWFrFarrFarqqqqnO+prKzUsGHDZLVa5XA4NHjwYDU3N8swDFksFtXU1DjXbd3GhVRXH2+3/K4kh+NYu/dhsQS5pB9X6ow5SZ0zL3K69G2fD1NpAMDLMWcdADzDsWPH9MILL+iVV15Rt27dJJ0+7m7ZskWSlJ+fr5EjRyosLEx79uxRbW2t6uvrVVRUpOHDh2vEiBHavHmzJKmgoEARERHy8/PTNddco127drXZBgB4E0aMAICXO9ec9U8++cTlc9YZmg2gI+jIn/EPP/xQ1dXVevzxx51t6enpmjt3rnJzc9W7d2/FxMTIz89PycnJmjZtmkwmk2bMmKGgoCBNmDBBO3fuVEJCgvz9/ZWeni5JSk1N1bx583Tq1CmFhYUpMjLSXSkCgFtQGAEASHL/nHWGZgPoCC71M+7KQkpcXJzi4uLOan/jjTfOaouOjlZ0dHSbttbnQH3TwIEDlZOTc+UCBQAPw1QaAIBzznpmZmabOeuSLjhnvbW9dTTI5cxZBwAAANyBwggAeDnmrAMAAMCbMZUGALwcc9YBAADgzSiMAICXY846AAAAvBlTaQAAAAAAgNeiMAIAAAAAALwWhREAAAAAAOC1KIwAAAAAAACvRWEEAAAAAAB4LQojAAAAAADAa1EYAQAAAAAAXovCCAAAAAAA8FoURgAAAAAAgNeiMAIAAAAAALwWhREAAAAAAOC1KIwAAAAAAACvRWEEAAAAAAB4LQojAAAAAADAa1EYAQAAAAAAXovCCAAAAAAA8FpmV3ZWWFiomTNnatCgQZKkH/7wh3rggQc0e/ZstbS0yGKx6MUXX5S/v782bdqkrKws+fj4aMqUKYqNjVVzc7NSUlJ06NAh+fr6Ki0tTX369HFlCgAAAAAAoBNxaWFEkm6++WYtW7bM+XrOnDmaOnWqxo8fryVLligvL08xMTFasWKF8vLy5Ofnp8mTJysqKkoFBQUKDg5WRkaGPvnkE2VkZGjp0qWuTgEAAAAAAHQSbp9KU1hYqDFjxkiSRo0aJbvdruLiYoWGhiooKEgBAQEKDw9XUVGR7Ha7oqKiJEmRkZEqKipyZ+gAAAAAAMDDuXzESFlZmaZPn66jR48qKSlJDQ0N8vf3lyT16NFDDodDVVVVCgkJcb4nJCTkrHYfHx+ZTCY1NTU53w8AAAAAAHApXFoY6d+/v5KSkjR+/HgdOHBA9957r1paWpzLDcM45/sutf1M3bsHymz2/W4Bu5DFEvSdlrlaR4mFONrqKHFIHScW4gAAAABwMVxaGOnVq5cmTJggSerbt6969uypPXv2qLGxUQEBAaqoqJDVapXValVVVZXzfZWVlRo2bJisVqscDocGDx6s5uZmGYbxraNFqquPt2tOV4rDceyc7RZL0HmXuVpHiYU4OmYcUseJpbPEQVEFAAAAaH8ufcbIpk2b9Prrr0uSHA6Hjhw5ookTJ2rLli2SpPz8fI0cOVJhYWHas2ePamtrVV9fr6KiIg0fPlwjRozQ5s2bJUkFBQWKiIhwZfgAAAAAAKCTcemIkdGjR2vWrFnatm2bmpubNX/+fF1//fV68sknlZubq969eysmJkZ+fn5KTk7WtGnTZDKZNGPGDAUFBWnChAnauXOnEhIS5O/vr/T0dFeGDwAAALjV559/rkcffVT333+/bDabDh8+rNmzZ6ulpUUWi0Uvvvii/P39tWnTJmVlZcnHx0dTpkxRbGysmpublZKSokOHDsnX11dpaWnq06ePSktLNX/+fEnSddddp2effda9SQKAi7m0MNK1a1etXLnyrPY33njjrLbo6GhFR0e3aWs9gAMAAADe5vjx41qwYIFuueUWZ9uyZcs0depUjR8/XkuWLFFeXp5iYmK0YsUK5eXlyc/PT5MnT1ZUVJQKCgoUHBysjIwMffLJJ8rIyNDSpUu1cOFCpaamaujQoUpOTtb27dt1++23uzFTAHAtt/9cLwDA/T7//HONHTtWa9eulSQdPnxYiYmJmjp1qmbOnKmmpiZJp6dETpo0SbGxsVq/fr0kqbm5WcnJyUpISJDNZtOBAwckSaWlpYqPj1d8fLyeeeYZ9yQGAJ2Iv7+/MjMzZbVanW2FhYUaM2aMJGnUqFGy2+0qLi5WaGiogoKCFBAQoPDwcBUVFclutysqKkqSFBkZqaKiIjU1Nam8vFxDhw5tsw0A8CYURgDAy13oDmROTo769eunvLw8HT9+XCtWrNDq1auVnZ2trKws1dTU6P3331dwcLDeeustTZ8+XRkZGZLkvAO5bt061dXVafv27e5KEQA6BbPZrICAgDZtDQ0Nzh8j6NGjhxwOh6qqqhQSEuJcJyQk5Kx2Hx8fmUwmVVVVKTg42Llu6zYAwJu4dCoNAKDjab0DmZmZ6WwrLCx0zjEfNWqUVq1apQEDBjjvQEpqcwcyJiZG0uk7kKmpqee9A8nQbABoP4ZhXHb7+dY9U/fugTKbfS8tODdw1a+7dcZfkeuMOUmdMy9yujIojACAlzObzTKb234dcAcSADxDYGCgGhsbFRAQoIqKClmtVlmtVlVVVTnXqays1LBhw2S1WuVwODR48GA1NzfLMAxZLBbV1NQ4123dxoVUVx9vt3yuJIfjWLv3YbEEuaQfV+qMOUmdMy9yuvRtnw+FEQDABXEHsq3OeGcGwP942mc8MjJSW7Zs0d133638/HyNHDlSYWFhmjt3rmpra+Xr66uioiKlpqaqrq5Omzdv1siRI1VQUKCIiAj5+fnpmmuu0a5duzR8+HDl5+crMTHR3WkBgEtRGAEAnIU7kOfX2e7MAGjrUj/jriyklJSUaNGiRSovL5fZbNaWLVu0ePFipaSkKDc3V71791ZMTIz8/PyUnJysadOmyWQyacaMGQoKCtKECRO0c+dOJSQkyN/fX+np6ZKk1NRUzZs3T6dOnVJYWJgiIyNdlhMAdAQURgAAZ+EOJAB0PEOGDFF2dvZZ7W+88cZZbdHR0YqOjm7T5uvrq7S0tLPWHThwoHJycq5coADgYSiMAICX4w4kAAAAvBmFEQDwctyBBAAAgDfzcXcAAAAAAAAA7kJhBAAAAAAAeC0KIwAAAAAAwGtRGAEAAAAAAF6LwggAAAAAAPBa/CpNB/GL9D+5rK9VKaNd1hcAAAAAAB0ZI0YAAAAAAIDXojACAAAAAAC8FoURAAAAAADgtSiMAAAAAAAAr0VhBAAAAAAAeC0KIwAAAAAAwGtRGAEAAAAAAF6LwggAAAAAAPBaFEYAAAAAAIDXojACAAAAAAC8FoURAAAAAADgtSiMAAAAAAAAr0VhBAAAAAAAeC0KIwAAAAAAwGuZ3R0AXO8X6X9yaX+rUka7tD8AAADAlee8nO8Cno0RIwAAAAAAwGtRGAEAAAAAAF6LqTRodwxjBAAAAAB0VIwYAQAAAAAAXssjR4w8//zzKi4ulslkUmpqqoYOHerukNBB8GBZoGPheA0AnoHjNQBv5nGFkb/85S/av3+/cnNztW/fPqWmpio3N9fdYQEAvoHjNQB4Bo7Xl4+bc4Bn87jCiN1u19ixYyVJ1157rY4ePaq6ujp17drVzZHBG/ElCJwfx2sA8AwcrwF4O48rjFRVVenGG290vg4JCZHD4eDADa/Ag2zhSTheA4Bn4HjteVx9c86VOAeFO3hcYeSbDMO44HKLJeg7bfe9jLu/0/sAb/ddP3NXGnF0PByvAVwqd3y+OW5zvEbn1Rk/3+R0ZXjcr9JYrVZVVVU5X1dWVspisbgxIgDAuXC8BgDPwPEagLfzuMLIiBEjtGXLFknS3r17ZbVaGeYHAB0Qx2sA8AwcrwF4O4+bShMeHq4bb7xR8fHxMplMeuaZZ9wdEgDgHDheA4Bn4HgNwNuZjG+bRAgAAAAAANBJedxUGgAAAAAAgCuFwggAAAAAAPBaFEbO4fnnn1dcXJzi4+P1j3/8w6V9v/DCC4qLi9OkSZOUn5+vw4cPKzExUVOnTtXMmTPV1NTkslgaGxs1duxYbdiwwa1xbNq0SXfddZcmTpyojz76yC2x1NfXKykpSYmJiYqPj9eOHTtUWlqq+Ph4xcfHu2Qu7ueff66xY8dq7dq1knTe/bBp0yZNmjRJsbGxWr9+vUviuP/++2Wz2XT//ffL4XC4JY5WO3bs0HXXXed87eo4mpublZycrMmTJ+u+++7T0aNHXRKHJ9qwYYMWLVokh8OhefPmnXe90aNHq76+3iUxbd682SX9APif1mPBdzVx4kQdPHjwCkbU1rZt21x63tMZufPc+nJczrlX6/lAQkKCbDabDhw44LY8znSx1xqeklNDQ4Nmzpwpm82m2NhYFRQUeHxOrS7mWsxTciosLNRPfvITJSYmKjExUQsWLOh4ORloo7Cw0HjooYcMwzCMsrIyY8qUKS7r2263Gw888IBhGIbx9ddfG7fffruRkpJifPjhh4ZhGEZGRobx5ptvuiyeJUuWGBMnTjTeeecdt8Xx9ddfG+PGjTOOHTtmVFRUGHPnznVLLNnZ2cbixYsNwzCM//73v8Ydd9xh2Gw2o7i42DAMw/j1r39tfPTRR+3Wf319vWGz2Yy5c+ca2dnZhmEY59wP9fX1xrhx44za2lqjoaHB+OlPf2pUV1e3axyzZ882PvjgA8MwDGPt2rXGokWL3BKHYRhGY2OjYbPZjBEjRjjXc3Uca9euNRYsWGAYhmGsW7fO2Lp1a7vH4aneeecdIz09/VvXGzVqlFFXV9fu8Zw4ccKIi4tr934AtHWxx4Lz+fnPf24cOHDgCkbUls1mc8kxqLNy57n15bjcc68NGzYY8+fPNwzDMHbs2GHMnDnTbbm0uthrDU/K6YMPPjBeffVVwzAM4+DBg8a4ceM8PqdW33Yt5kk5ffrpp8Yvf/nLNm0dLSdGjHyD3W7X2LFjJUnXXnutjh49qrq6Opf0fdNNN+m3v/2tJCk4OFgNDQ0qLCzUmDFjJEmjRo2S3W53SSz79u1TWVmZ/u///k+S3BaH3W7XLbfcoq5du8pqtWrBggVuiaV79+6qqamRJNXW1qpbt24qLy/X0KFDXRKHv7+/MjMzZbVanW3n2g/FxcUKDQ1VUFCQAgICFB4erqKionaN45lnntEdd9wh6X/7yR1xSNLKlSs1depU+fv7S5Jb4igoKNBdd90lSYqLi9OYMWPaPQ5P0Vrtj4+P13333aeKigpJ0sGDBzVx4kRJ0p///GdNmjRJU6ZM0erVq9u8//Dhw5o4caIqKyvPuf3CwkI98MADevTRRxUTE6OXX35ZkvSvf/1L99xzjxITEzV9+nTV1NTo2LFjmjZtmhITExUXF6e9e/cqLS1N//rXvzR//vx22wcAzi8rK0txcXGKi4vTq6++KkkqLS1VQkKCEhMTdd999zm/i5977jlNmjRJs2bNUnNz8wW3O3LkSD333HOKi4vTY489pqamJh07dkwzZsxQYmKiEhIStHfvXud24+PjFRsbqw0bNmjjxo3avXu3HnzwwfOOGlm+fLmeeuop/eIXv9Cdd96pjz/+WJK0atUqxcXFKTY2Vi+99JIk6b///a/i4uJks9m0dOlSJSYmSpLy8/MVHx8vm82m9PT0y9+ZHYg7z60vx+Wee9ntdkVFRUmSIiMjO8T3/sVea3hSThMmTNCDDz4o6fR5Qq9evTw+J+nirsU8Ladv6mg5URj5hqqqKnXv3t35OiQkxDk1oL35+voqMDBQkpSXl6fbbrtNDQ0Nzou8Hj16uCyWRYsWKSUlxfnaXXEcPHhQjY2Nmj59uqZOnSq73e6WWH7605/q0KFDioqKks1m0+zZsxUcHOxc3t5xmM1mBQQEtGk7136oqqpSSEiIc50r/f97rjgCAwPl6+urlpYW5eTk6M4773RLHF999ZVKS0s1fvx4Z5s74igvL9fHH3+sxMRE/epXv1JNTU27x+EpNm7cqJ49e2rdunWaMmWKvve977VZbhiGnn32WWVmZuqtt96S3W5XY2OjJOnEiROaPXu2nnvuubMKYmcqKSnRiy++qNzcXK1fv17V1dVauHChZs+erezsbN10001as2aN7Ha7evXqpezsbC1evFhHjhzRtGnTNGDAAAojgBscPHhQv//97/Xmm2/qzTff1B/+8Af95z//0ZEjR/T0008rOztb4eHheu+991RWVqaioiKtX79eycnJ+uqrry647crKSv3sZz9Tbm6uDMPQxx9/rKysLIWFhSk7O1upqalKS0tTTU2NPvroI61bt045OTk6efKkYmJiZLFYlJmZ6fzOPZeKigqtWrVKixcv1pIlS5ztOTk5evvtt7VhwwbV1dVp9erVGj9+vNauXesstNTX1+vll1/WmjVrtHbtWh0+fFh/+9vfrsyO7QDceW59OS733OvMdh8fH5lMJrdPybrYaw1PyqlVfHy8Zs2apdTU1E6R08Vci3laTmVlZZo+fboSEhL05z//ucPlRGHkWxhu+DXjrVu3Ki8v76w5966KZePGjRo2bJj69OlzzuWu3ic1NTV66aWXlJ6erjlz5rTp31WxvPvuu+rdu7f++Mc/KisrS0888USb5e74P7mY/l0VV0tLi2bPnq2f/OQnuuWWW9wSR1pamubMmXPBdVwRh2EYGjBggLKzszVo0CC98sorbomjI9q7d6/Cw8MlnS42fvOE8+uvv9ZVV12lkJAQ+fr66pVXXnGuM3/+fI0ePVo33HDDBfsICwtTly5ddNVVV2nQoEE6cOCA9u3bp7CwMElSRESEPvvsMw0bNky7d+/WvHnztH//ft12223tkDGAi/XZZ58pLCxMZrNZZrNZ4eHhKi0tVY8ePbRkyRLZbDZ98MEHqqmpUVlZmcLCwuTj46Orr776vOcrrQIDAzVs2DBJ0rBhw/TVV1+ppKREERERkqTQ0FDt379f3bp1U//+/fXII4/oww8/VExMzEXH3/rdd9111zlHwwUEBMhms+nee+9VdXW1ampqtG/fPudxcPTo0ZJOXywcOnTIOYpt//79OnTo0KXtQA/SWb4DL/XcqyPlfanXGp6Q07p16/Tyyy/riSeeuKhrhY6c03e9FuvIOfXv319JSUl6+eWXtWjRIj311FNqaWlxLu8IOVEY+Qar1aqqqirn68rKSlksFpf1v2PHDq1cuVKZmZkKCgpSYGCg845pRUXFBe+UXikfffSRtm3bpilTpmj9+vX63e9+55Y4pNPVwx/96Ecym83q27evunTpoi5durg8lqKiIt16662SpMGDB+vEiROqrq52LnflPml1rr/Juf5/XRHXnDlz1K9fPyUlJUk69+eoPeOoqKjQl19+qVmzZmnKlCmqrKyUzWZzy/7o2bOnbrrpJknSrbfeqrKyMrf9XToaX19fnTp16rzLfXx8zru8V69eevfdd7/17sCZ7z/Xl2Zzc7N8fHxktVr17rvvaty4cXrrrbecw9wBuIfJZGrzmW39rC5cuFD33nuv1q5dq7i4OEmnP9s+Pv87hb3QceWbyw3DkMlkOqu/1nVee+01JSUlqbS0VNOnT7/o+L8ZQ3l5uVavXq3XXntN2dnZ+v73v9+m/9acJcnPz09DhgxRdna2srOztXHjRt15550X3XdH5+5z6yvpUs69rFarc2RMc3OzDMO44KgjV7mYaw1PyqmkpESHDx+WJF1//fVqaWk557WCJ+V0sddinpRTr169NGHCBJlMJvXt21c9e/bU0aNHO1ROFEa+YcSIEdqyZYuk03c3rVarunbt6pK+jx07phdeeEGvvPKKunXrJun0HKrWePLz8zVy5Mh2j2Pp0qV655139Pbbbys2NlaPPvqoW+KQTl9Yfvrppzp16pSqq6t1/Phxt8TSr18/FRcXSzp9stOlSxdde+212rVrl0vjONO59kNYWJj27Nmj2tpa1dfXq6ioSMOHD2/XODZt2iQ/Pz899thjzjZXx9GrVy9t3bpVb7/9tt5++21ZrVatXbvWLfvjtttu044dOySdPoYMGDDALXF0RKGhofr0008lnX4WyzefFdK9e3e1tLSooqJChmHo4YcfVm1trSTp8ccf1+jRo7VixYoL9vHZZ5+poaFBJ06cUFlZmfr3769Bgwbp73//uyTpr3/9q4YMGaKdO3dq586duvXWW/X000+rpKREPj4+be5eAHCdG264Qbt379bJkyd18uRJFRcX6/rrr1dNTY369u2rpqYmbd++Xc3NzRowYID27t0rwzBUXl6u8vLyC267sbFRJSUlkqTdu3dr4MCBCg0NVWFhobNt0KBBOnjwoNasWaMbb7xRTz75pPN5JiaT6VuPDa1TX0pLS9W7d29VV1crJCREXbp00d69e1VeXq7m5mb17dvXGUvrs0gGDBigffv26ciRI5KkZcuWOUeddAbuPLe+0i7l3GvEiBHOXzorKChwjlByp4u91vCknHbt2qVVq1ZJOj1t63zXCp6U08Vei3lSTps2bdLrr78uSXI4HDpy5IgmTpzYoXIyGR1hbE0Hs3jxYu3atUsmk0nPPPOMBg8e7JJ+c3NztXz5cg0YMMDZlp6errlz5+rEiRPq3bu30tLS5Ofn55J4pNMPFPv+97+vW2+9VU8++aRb4li3bp3y8vIkSY888ohCQ0NdHkt9fb1SU1N15MgRnTx5UjNnzpTFYtG8efN06tQphYWFfes0jstRUlKiRYsWqby8XGazWb169dLixYuVkpJy1n7YvHmzXn/9dZlMJtlsNueDQNsrjiNHjuiqq65ynuRce+21mj9/vsvjWL58ufNLfvTo0frTn/4kSS6PY/HixVq4cKEcDocCAwO1aNEi9ezZs13j8BRNTU2aO3euDh06JLPZrIiICNXW1uqee+7RY489pg0bNshut2vp0qWSpPHjx+v+++/X6NGj9d5778nPz09xcXFasGCBhgwZctb2CwsLtWLFCvXo0UP//ve/NX78eD300EMqKyvTs88+K5PJpO9973vOZwk88cQTMpvNMplMeuyxxxQWFqa7775bAwcO1LJly1y9ewCvtWHDBn3xxRf6wQ9+oPfee0+GYejOO++UzWZTbm6u1qxZoz59+mjSpEn6zW9+o8zMTOXk5Ki0tFT9+/fXl19+qaVLl+oHP/jBObcfERGhu+++WyUlJbJYLMrIyFBjY6NSU1NVU1MjwzA0b9489evXT08++aQOHz4sPz8/RUdH65577tGcOXO0Z88erVmzps3c91bLly/X/v37VVdXp/LycqWmpurmm2/WQw89pPr6ev34xz/WqVOn9M9//lPz58/X448/rm7duiksLEy7d+9WVlaW8vPztXLlSvn7++uGG27Q008/7RxR0hm469z6clzuuVdLS4vmzp2rf//73/L391d6erquvvpqt+Z0KdcanpJTY2OjnnrqKR0+fFiNjY1KSkrSkCFDznmt4Ck5nenbrsU8Jae6ujrNmjVLtbW1am5uVlJSkq6//voOlROFEQBAp1BYWKg333yTogaANiIiIpyjQ9rD8uXL1b17d9lstm9d94svvlBtba1+/OMf6/3331dhYaEWLFjQbrEBAC6O2d0BAABwKV566aVzXuRcyoMSAXQu27ZtO+snviXp3nvvvWJ9JCUl6ejRo23aunbt+q0PhT5Tly5dNG/ePJlMJvn4+CgtLe2KxQcA+O4YMQIAAAAAALwWD18FAAAAAABei8IIAAAAAADwWhRGAAAAAACA16IwAgAAAAAAvBaFEQAAAAAA4LUojAAAAAAAAK/1/ytXiOoE34o/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1800x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2N4k8seo09fW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qj0Rc8gjBCXR"
      },
      "source": [
        "#Sankey Users Activities"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZkTeuc_KjWy"
      },
      "source": [
        "### * You Can Download Sankey Diagram of App From Link Below:\n",
        "## https://drive.google.com/file/d/1-0EPh034VQ6I5MGzOhGSowZbEj_RcYnB/view?usp=sharing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B_S_rr8DH-uB"
      },
      "source": [
        "First we should prepare data for using sankey plot "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjIgnqEtW5cC"
      },
      "source": [
        "Data_Sankey=Data.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CGG74EHzjDE0",
        "outputId": "9cd10c06-cb13-4418-ba00-43ea72ebbc95"
      },
      "source": [
        "Data.value_counts"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.value_counts of                 action              created_at  ... post_token tokens_length\n",
              "26178   load_post_page 2021-01-01 00:36:09.213  ...        NaN          24.0\n",
              "56013   load_post_page 2021-01-01 00:36:18.164  ...        NaN          24.0\n",
              "68800   load_post_page 2021-01-01 00:37:05.007  ...        NaN          11.0\n",
              "102202  load_post_page 2021-01-01 00:37:28.740  ...        NaN          17.0\n",
              "24308       click_post 2021-01-01 00:37:43.452  ...   wXoL-Kbx           NaN\n",
              "...                ...                     ...  ...        ...           ...\n",
              "49588       click_post 2021-01-03 00:25:06.532  ...   wXvzorm4           NaN\n",
              "44400       click_post 2021-01-03 00:26:23.942  ...   wXvTYk8B           NaN\n",
              "57126       click_post 2021-01-03 00:27:06.592  ...   wXvLYCQp           NaN\n",
              "5550        click_post 2021-01-03 00:27:22.914  ...   wXvLoezU           NaN\n",
              "22088       click_post 2021-01-03 00:28:32.004  ...   wXvjHt4X           NaN\n",
              "\n",
              "[111083 rows x 9 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2giH8jgCn0tc",
        "outputId": "d401ce6c-9c8d-4158-b2d8-a288ca6e17dd"
      },
      "source": [
        "Data_Sankey['action'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['load_post_page', 'click_post'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BdBDFLdDnqx0",
        "outputId": "7e75ba76-1c31-413d-ac60-498aa82ebd84"
      },
      "source": [
        "Data_Sankey['post_page_offset'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  0.,   1.,  nan,   2.,   3.,   5.,   6.,   7.,   4.,  30.,   8.,\n",
              "         9.,  10.,  11.,  12.,  13.,  14.,  15.,  16.,  24., 149.,  25.,\n",
              "        20.,  43.,  17.,  21., 150.,  27.,  22., 138.,  41.,  18., 151.,\n",
              "        34.,  62., 115.,  31.,  42.,  28.,  98., 152.,  99.,  44., 100.,\n",
              "        73., 101., 102., 139., 103.,  60.,  26., 104., 105., 106.,  19.,\n",
              "        23.,  33.,  29., 107., 108., 109.,  50., 110.,  37., 140.,  40.,\n",
              "        61., 111.,  36., 112., 113., 114., 116.,  51., 117., 118., 119.,\n",
              "       120., 141.,  38.,  63.,  52., 121., 122., 142.,  32.,  64.,  53.,\n",
              "        74.,  35.,  54.,  55., 123., 124., 143.,  65.,  45.,  66.,  46.,\n",
              "        47.,  48.,  49., 125.,  67., 126., 144.,  39.,  56.,  68.,  57.,\n",
              "        69., 145.,  75., 127., 146.,  70., 128., 129.,  71.,  72., 147.,\n",
              "       148.,  76., 130., 131., 132., 133., 134., 135., 136., 137.,  77.,\n",
              "        78.,  79., 153.,  80., 154.,  81.,  58.,  82., 155.,  83.,  84.,\n",
              "       156.,  85.,  59.,  86.,  87., 157.,  88., 158.,  89., 159., 160.,\n",
              "       161.,  90.,  91.,  92.,  93.,  94.,  95.,  96.,  97.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TVR-wkdnnljM",
        "outputId": "3c0e0908-4e02-47f6-b659-493fbcbc0923"
      },
      "source": [
        "Data_Sankey['post_index_in_post_list'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  nan,    5.,    8., ..., 1414., 2175., 2347.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jsaESIDcjqCd",
        "outputId": "2f8c50d0-f6fe-4ccd-9b4f-31422a4abcb4"
      },
      "source": [
        "Data_Sankey['tokens_length'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([24., 11., 17., nan,  5.,  2.,  7., 13., 10., 16., 23.,  1., 20.,\n",
              "        6., 22., 18.,  4.,  9., 19.,  3., 12.,  8., 14., 15., 21.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EV8IoeGgcQvD"
      },
      "source": [
        "Data_Sankey['tokens_length']= Data_Sankey['tokens_length'].apply(lambda x:  'No Post' if str(x)=='nan' else 'Length < 10' if (((int(x) < 11) and (\n",
        "    int(x)>0))) else '10 < Length < 24' if (((x>10) and (x<24))) else 'Length > 24' if int(x)==24  else 'nan')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsL411cEdVMi"
      },
      "source": [
        "Data_Sankey['post_page_offset']= Data_Sankey['post_page_offset'].apply(lambda x:  '-1' if str(x)=='nan'else 'offset > 10' if int(x)>=10 else str(x) )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFSJ0iEvenFk"
      },
      "source": [
        "Data_Sankey['post_index_in_post_list']= Data_Sankey['post_index_in_post_list'].apply(lambda x: 'nan' if str(x)=='nan' else 'Rank= '+str(x) if (\n",
        "    x<=10 and x>=0) else '10 < Rank < 25' if ((x>10)and(x<=24)) else '49 >Rank > 24' if  ((x>24)and(x<=48))  else 'Rank > 48' )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uveOD1klMRy"
      },
      "source": [
        "Data_Sankey=Data_Sankey[['action','post_page_offset','post_index_in_post_list','tokens_length']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "FZ8s7N2lq8-e",
        "outputId": "935e5792-dd58-4f80-ee7c-d69795714303"
      },
      "source": [
        "Data_Sankey.head()"
      ],
      "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>action</th>\n",
              "      <th>post_page_offset</th>\n",
              "      <th>post_index_in_post_list</th>\n",
              "      <th>tokens_length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>26178</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>0.0</td>\n",
              "      <td>nan</td>\n",
              "      <td>Length &gt; 24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56013</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>0.0</td>\n",
              "      <td>nan</td>\n",
              "      <td>Length &gt; 24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68800</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>1.0</td>\n",
              "      <td>nan</td>\n",
              "      <td>10 &lt; Length &lt; 24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102202</th>\n",
              "      <td>load_post_page</td>\n",
              "      <td>0.0</td>\n",
              "      <td>nan</td>\n",
              "      <td>10 &lt; Length &lt; 24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24308</th>\n",
              "      <td>click_post</td>\n",
              "      <td>-1</td>\n",
              "      <td>Rank= 5.0</td>\n",
              "      <td>No Post</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                action  ...     tokens_length\n",
              "26178   load_post_page  ...       Length > 24\n",
              "56013   load_post_page  ...       Length > 24\n",
              "68800   load_post_page  ...  10 < Length < 24\n",
              "102202  load_post_page  ...  10 < Length < 24\n",
              "24308       click_post  ...           No Post\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 169
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIYFRNJ1YCB6"
      },
      "source": [
        "Data_Sankey=Data_Sankey.groupby(['action','post_page_offset','post_index_in_post_list','tokens_length'], dropna=False).size().reset_index(name='count')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wN2jlNn3CQ1a",
        "outputId": "f0f30998-b333-4d06-fcc0-340199b9b6b8"
      },
      "source": [
        "Data_Sankey.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "action          post_page_offset  post_index_in_post_list  tokens_length     count\n",
              "load_post_page  offset > 10       nan                      Length > 24       8308     1\n",
              "click_post      -1                Rank= 8.0                No Post           1393     1\n",
              "load_post_page  2.0               nan                      Length < 10       96       1\n",
              "                                                           10 < Length < 24  107      1\n",
              "                1.0               nan                      Length > 24       4443     1\n",
              "                                                           Length < 10       225      1\n",
              "                                                           10 < Length < 24  244      1\n",
              "                0.0               nan                      Length > 24       7131     1\n",
              "                                                           Length < 10       1256     1\n",
              "                                                           10 < Length < 24  658      1\n",
              "click_post      -1                Rank= 9.0                No Post           1262     1\n",
              "                                  Rank= 7.0                No Post           1532     1\n",
              "load_post_page  offset > 10       nan                      Length < 10       44       1\n",
              "click_post      -1                Rank= 6.0                No Post           1593     1\n",
              "                                  Rank= 5.0                No Post           1721     1\n",
              "                                  Rank= 4.0                No Post           1968     1\n",
              "                                  Rank= 3.0                No Post           2273     1\n",
              "                                  Rank= 2.0                No Post           2519     1\n",
              "                                  Rank= 10.0               No Post           1125     1\n",
              "                                  Rank= 1.0                No Post           2704     1\n",
              "                                  Rank > 48                No Post           34973    1\n",
              "                                  49 >Rank > 24            No Post           10763    1\n",
              "load_post_page  2.0               nan                      Length > 24       3086     1\n",
              "                3.0               nan                      10 < Length < 24  70       1\n",
              "                                                           Length < 10       55       1\n",
              "                                                           Length > 24       2304     1\n",
              "                offset > 10       nan                      10 < Length < 24  47       1\n",
              "                9.0               nan                      Length > 24       731      1\n",
              "                                                           Length < 10       9        1\n",
              "                                                           10 < Length < 24  13       1\n",
              "                8.0               nan                      Length > 24       860      1\n",
              "                                                           Length < 10       2        1\n",
              "                                                           10 < Length < 24  11       1\n",
              "                7.0               nan                      Length > 24       995      1\n",
              "                                                           Length < 10       7        1\n",
              "                                                           10 < Length < 24  12       1\n",
              "                6.0               nan                      Length > 24       1182     1\n",
              "                                                           Length < 10       16       1\n",
              "                                                           10 < Length < 24  22       1\n",
              "                5.0               nan                      Length > 24       1440     1\n",
              "                                                           Length < 10       15       1\n",
              "                                                           10 < Length < 24  35       1\n",
              "                4.0               nan                      Length > 24       1787     1\n",
              "                                                           Length < 10       32       1\n",
              "                                                           10 < Length < 24  44       1\n",
              "click_post      -1                10 < Rank < 25           No Post           11970    1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "NOFlKkFLl0GO",
        "outputId": "1e764ec9-1b14-416b-9bf0-eccb598d1d8b"
      },
      "source": [
        "import plotly\n",
        "import chart_studio.plotly as py\n",
        "\n",
        "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
        "init_notebook_mode(connected=True)\n",
        "\n",
        "def genSankey(df,cat_cols=[],value_cols='',title='Sankey Diagram'):\n",
        "    # maximum of 6 value cols -> 6 colors\n",
        "    colorPalette = ['#4B8BBE','#BCB6FF','#FFE873','#D72483','#94FBAB','#646464']\n",
        "    labelList = []\n",
        "    colorNumList = []\n",
        "    for catCol in cat_cols:\n",
        "        labelListTemp =  list(set(df[catCol].values))\n",
        "        colorNumList.append(len(labelListTemp))\n",
        "        labelList = labelList + labelListTemp\n",
        "        \n",
        "    # remove duplicates from labelList\n",
        "    labelList = list(dict.fromkeys(labelList))\n",
        "    \n",
        "    # define colors based on number of levels\n",
        "    colorList = []\n",
        "    for idx, colorNum in enumerate(colorNumList):\n",
        "        colorList = colorList + [colorPalette[idx]]*colorNum\n",
        "        \n",
        "    # transform df into a source-target pair\n",
        "    for i in range(len(cat_cols)-1):\n",
        "        if i==0:\n",
        "            sourceTargetDf = df[[cat_cols[i],cat_cols[i+1],value_cols]]\n",
        "            sourceTargetDf.columns = ['source','target','count']\n",
        "        else:\n",
        "            tempDf = df[[cat_cols[i],cat_cols[i+1],value_cols]]\n",
        "            tempDf.columns = ['source','target','count']\n",
        "            sourceTargetDf = pd.concat([sourceTargetDf,tempDf])\n",
        "        sourceTargetDf = sourceTargetDf.groupby(['source','target']).agg({'count':'sum'}).reset_index()\n",
        "        \n",
        "    # add index for source-target pair\n",
        "    sourceTargetDf['sourceID'] = sourceTargetDf['source'].apply(lambda x: labelList.index(x))\n",
        "    sourceTargetDf['targetID'] = sourceTargetDf['target'].apply(lambda x: labelList.index(x))\n",
        "    \n",
        "    # creating the sankey diagram\n",
        "    data = dict(\n",
        "        type='sankey',\n",
        "        node = dict(\n",
        "          pad = 15,\n",
        "          thickness = 20,\n",
        "          line = dict(\n",
        "            color = \"black\",\n",
        "            width = 0.5\n",
        "          ),\n",
        "          label = labelList,\n",
        "          color = colorList\n",
        "        ),\n",
        "        link = dict(\n",
        "          source = sourceTargetDf['sourceID'],\n",
        "          target = sourceTargetDf['targetID'],\n",
        "          value = sourceTargetDf['count']\n",
        "        )\n",
        "      )\n",
        "    \n",
        "    layout =  dict(\n",
        "        title = title,\n",
        "        font = dict(\n",
        "          size = 10\n",
        "        )\n",
        "    )\n",
        "       \n",
        "    fig = dict(data=[data], layout=layout)\n",
        "    return fig"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Ts_niVjM0mrf",
        "outputId": "b0731daa-e033-4f5b-d0e5-1523d391f90a"
      },
      "source": [
        "import pandas as pd\n",
        "import plotly\n",
        "import chart_studio.plotly as py\n",
        "\n",
        "fig = genSankey(Data_Sankey,cat_cols=['action','post_page_offset','post_index_in_post_list','tokens_length'],value_cols='count',title='Users Activities')\n",
        "plotly.offline.plot(fig, validate=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'temp-plot.html'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8jdze07MH1K"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "giZam3hhd5O0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}