Medium Stats Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Medium Stats Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous notebook, we worked on scraping and cleaning the data from Mediums stats page by leveraging `Selenium` and `BeautifulSoup`. Now that we have our data looking good, we've finally made it to the fun part. Enter exploratory data analysis. This is going to be the meat of this project where we'll dive into the more subtle aspects of analyzing engagement in respect to my work and later on a larger scale. This notebook directly corresponds to the Medium post linked below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_palette('coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read data\n",
    "df = pd.read_csv('mystats.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Publication</th>\n",
       "      <th>Read Time</th>\n",
       "      <th>Views</th>\n",
       "      <th>Reads</th>\n",
       "      <th>Read Ratio</th>\n",
       "      <th>Fans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>An Ode to the Type A Data Scientist</td>\n",
       "      <td>Towards Data Science</td>\n",
       "      <td>7</td>\n",
       "      <td>3224</td>\n",
       "      <td>699</td>\n",
       "      <td>21.681141</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Choosing Your First Job: Size Matters</td>\n",
       "      <td>Hacker Noon</td>\n",
       "      <td>7</td>\n",
       "      <td>391</td>\n",
       "      <td>144</td>\n",
       "      <td>36.828645</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Python for Data Science and Machine Learning B...</td>\n",
       "      <td>Towards Data Science</td>\n",
       "      <td>6</td>\n",
       "      <td>2175</td>\n",
       "      <td>795</td>\n",
       "      <td>36.551724</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Key to Optimal Internships</td>\n",
       "      <td>Hacker Noon</td>\n",
       "      <td>5</td>\n",
       "      <td>197</td>\n",
       "      <td>80</td>\n",
       "      <td>40.609137</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9 Essential Newsletters for Data Scientists</td>\n",
       "      <td>Towards Data Science</td>\n",
       "      <td>5</td>\n",
       "      <td>5401</td>\n",
       "      <td>1216</td>\n",
       "      <td>22.514349</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title           Publication  \\\n",
       "0                An Ode to the Type A Data Scientist  Towards Data Science   \n",
       "1              Choosing Your First Job: Size Matters           Hacker Noon   \n",
       "2  Python for Data Science and Machine Learning B...  Towards Data Science   \n",
       "3                     The Key to Optimal Internships           Hacker Noon   \n",
       "4        9 Essential Newsletters for Data Scientists  Towards Data Science   \n",
       "\n",
       "   Read Time  Views  Reads  Read Ratio  Fans  \n",
       "0          7   3224    699   21.681141    72  \n",
       "1          7    391    144   36.828645    19  \n",
       "2          6   2175    795   36.551724    48  \n",
       "3          5    197     80   40.609137     9  \n",
       "4          5   5401   1216   22.514349    59  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove newsletter bias and reset index\n",
    "df = df[~df['Title'].str.contains('Self Driven Data Science')]\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "This section will largely be free form; a combination of analyzing metrics and visualizations before diving deeper down interesting rabbit holes and examining possible relationships and statistics further. With that being said, here's an initial list of some questions that I wanted to further pursue:\n",
    "* General overview of top performers from different metrics\n",
    "* Distribution of views, reads, fans, etc\n",
    "* Feature engineering new metrics ex. read-fan ratio\n",
    "* Relationship between length of article and different engagement metrics\n",
    "* Average claps and other engagement metrics by segments like publication"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting Started with an Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get started by looking at which of my posts have performed the best in different categories as well as some high level numerical stats too. We'll use these simple plots and stats as a jumping off point for more interesting analysis later in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Read Time</th>\n",
       "      <th>Views</th>\n",
       "      <th>Reads</th>\n",
       "      <th>Read Ratio</th>\n",
       "      <th>Fans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6668.827586</td>\n",
       "      <td>1417.862069</td>\n",
       "      <td>35.919137</td>\n",
       "      <td>136.068966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.908627</td>\n",
       "      <td>18470.585968</td>\n",
       "      <td>3651.076971</td>\n",
       "      <td>13.979027</td>\n",
       "      <td>356.633458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>10.426731</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>26.635656</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>967.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>36.828645</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>2484.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>42.553191</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>85373.000000</td>\n",
       "      <td>14945.000000</td>\n",
       "      <td>63.893511</td>\n",
       "      <td>1447.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Read Time         Views         Reads  Read Ratio         Fans\n",
       "count  29.000000     29.000000     29.000000   29.000000    29.000000\n",
       "mean    6.000000   6668.827586   1417.862069   35.919137   136.068966\n",
       "std     1.908627  18470.585968   3651.076971   13.979027   356.633458\n",
       "min     4.000000    109.000000     60.000000   10.426731     3.000000\n",
       "25%     5.000000    391.000000    140.000000   26.635656    10.000000\n",
       "50%     6.000000    967.000000    334.000000   36.828645    29.000000\n",
       "75%     7.000000   2484.000000    789.000000   42.553191    51.000000\n",
       "max    13.000000  85373.000000  14945.000000   63.893511  1447.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# General overview\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a couple things that I found interesting here. First of all, after removing the posts associated with my weekly data science newsletter, I've published 29 stories here on Medium. Certainly not an ideal sample size, but for our purposes - not bad! \n",
    "\n",
    "I can see that on average, my posts are 6 minutes long and gather about 6,500 views with around 1,300 reads. This leads to an average read ratio of ~35% and roughly 132 fans per post. I suspect these numbers are heavily skewed due to the success of a couple posts in particular, which would pull the mean up considerably, but let's look into that more later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Erl27xqJFi4iIiGDatGmlju9mv79HEhISrEEnNzeXa9eukZOTc8t3AXv06MH27dvZv39/\nkdW9tWvX4uTkxLvvvsuQIUOs90O9evVwcnJi4cKF1n8LZRUaGsrMmTOJiIigdu3a1vvLz8+PTz/9\nlKysLNzc3HB1daVJkyYsXboUi8WCj48PzZo1K1MftWvX5ujRowDs27fP+sjn7/+91KlTxzonP/zw\nAwCurq4cOnSI3NxcCgoK2LdvH40bN8bGxsb67/z3P5d2T5Z0T4uIiIj81dzRO1Y38/b2ZuvWrda/\ndD/yyCPMmjXL+pf5m/n5+REWFoa/vz/Z2dmMHDkSZ2fnO+rz9ddfZ+LEiXz55ZdkZWUxderUYu8M\n3azw8SgbGxuqVKnCe++9B8Dzzz/PiBEjcHZ2pk6dOqSlpQHw+OOPM378eD788EMWLFiAn58fDg4O\nNGzYsNhjc1OmTGHKlCksX76cihUrUr16dcLDw3FxceHJJ5/E39+f/Px8/P39rasDNjY2TJo0iWHD\nhlFQUECVKlWYNWsWZ8+eLbH+KVOmMHHiRGxsbKhVqxYBAQHUrVuX4OBg8vLygBurUkCJ71hNnz6d\nwMBA7OzssLe3LxZSHB0diYiIIDQ01PoeVrdu3Rg4cCAmk4m9e/fSv39/rl+/Ts+ePa3v0N2sRo0a\nvPbaawwePBiTyUS3bt2oX78+AQEBmM1m8vLyqF+/vnW1JiEhgZdffpn//ve/1pp+P/d79+7liy++\nwN7entGjR9/yGv/e+PHjCQsLIyYmhtzcXOvcvPTSS/Tv358GDRrc8ksdnJycqFOnDg0bNiwSPjp0\n6MDYsWOJj4+nUqVKPPjgg1y4cAEXFxf8/PyYPn06s2fPLnOdAH369MHPz48HHniAmjVrcuHCBQCe\neOIJwsLCrI/NtWjRgg4dOuDv709OTg6tWrUq8kjqrUyfPp1p06ZRUFCAra0tM2fOLHbM5MmTmThx\nIpUrV8be3h4XFxeaN2/Os88+a72HPTw88PLy4sKFCyQlJREbG8sTTzzBhx9+SMuWLQkPDy92TxaO\nR0REROSvzlRg4Du1P/74Y6pXr37Hf6UXiY6OpmbNmvj7+5d3KffE5s2bSU5OZsyYMeVdyl1ZtmwZ\nzz77LDVq1CAqKgp7e3tGjhz5h/QdHx/Pxvjaf0hfIiIi8tc0eVjD2x90D8THx+Ph4VHivrtesQoJ\nCSEtLY3o6Oi7Lkzkr2Du3Lns37+fBQsWlHcpd83Z2ZkhQ4ZQuXJlnJyciIiIKO+SRERERO4rhlas\nRESM0oqViIiIGPVnWLG66y+vEBERERERkRsUrERERERERAxSsBIRERERETFIwUpERERERMQgBSsR\nERERERGDFKxEREREREQMUrASERERERExSMFKRERERETEIAUrERERERERgxSsREREREREDFKwEhER\nERERMUjBSkRERERExCAFKxEREREREYMUrERERERERAyyK+8CREQmD2tY3iX86SUkJODu7l7eZfyp\naY7KRvN0e5qjstE83Z7mqGz+KvOkFSsRERERERGDFKxEREREREQMUrASERERERExSMFKRERERETE\nIAUrERERERERgxSsREREREREDFKwEhERERERMUjBSkRERERExCAFKxEREREREYMUrERERERERAwy\nFRQUFJR3ESLy9xUfH8/5/OblXYaIiIgY1Kut412dl5CQgLu7+z2u5n8jPj4eDw+PEvdpxUpERERE\nRMQgBSsRERERERGDFKxEREREREQMUrASERERERExSMFKRERERETEIAUrERERERERgxSsRERERERE\nDFKwEhERERERMUjBSkRERERExCAFKxEREREREYMUrERERERERAxSsBIRERERETFIwUpERERERMQg\nBSsRERERERGDFKxEREREREQMUrASERERERExSMFKRERERETEIAUrERERERERg+7bYBUREYHZbKZn\nz5489dRTmM1mRo8ezZ49ewgMDLzj9tauXcucOXOKbAsMDGTPnj3s3LmTVatWAbBq1SquX79eYhul\n9W02m0lJSSmyLSEhgfnz5wPQqVOnO6735MmTvPnmm2U6Njc3l/nz5+Pr68vgwYMZPHiwdTylWbRo\nEYcOHSqyLTs7m+7du5epz/T0dDZs2FCmY8+cOUPz5s1ZtGhRke3Dhw/HbDaXqY1CpV2DGTNmkJqa\nekdt/V50dDQrVqy46/PLauTIkYbOv5NxPvLII5jNZsxmM/7+/oSGhpKbm1visb+/niEhIezcudNQ\nnSIiIiJ/NXblXcDdCgkJAW4EouPHjzNu3Djgxgfre61Lly7Wnz/66CNeeOEFw226u7vj7u5+1+c7\nOTnRuXPnMh0bFRVFfn4+K1euxNbWlmvXrvH666/j6emJm5tbiecMGzbsrmsDSExM5Ouvv8bb27tM\nxzdq1Igvv/zS2m96ejonT56kZs2ahuooNGnSpHvSzv9aYdi+W3cyzqpVq2KxWKy/v/XWW+zYsYOn\nn3662LF3ej1FRERE/m7u22B1KydPnuTVV1/l8uXLdOvWjVGjRpGYmMj06dMBqFatGjNnzsTJyalM\n7RWGtwcffJCLFy8SGBjI/PnzmT59OocOHeL69euMGjUKJyenEvsG+OCDD7h06RL//e9/mTt3Lqmp\nqaxcuZKoqChrP3PnzuXq1au8/fbbbN26ldjYWGxsbPDw8GDcuHFER0dz8OBBfvvtN2bMmIG/vz9w\nIzjt3r2b/Px8nnvuOQICAqxt5ubmsmXLFv71r39ha2sLQJUqVbBYLJhMJvbs2VOkjk6dOrFr1y5C\nQkLo1auXte+MjAwaNWpkbfd287lw4UKOHj3KqlWr6NSpE5MmTSI3NxeTyURoaCgtWrQoMsfVq1en\nWrVqpKSk4ObmxubNm+nZsyf79+8HYOvWrSxbtsx6/Lx586hWrVqZr4HZbCY8PJzNmzdz5swZfv31\nV1JTU5kwYQJPPvkke/fuJSoqCltbWxo2bMjUqVOxt7e/7b1hsVjYuHEjJpOJXr168dJLL5GUlERE\nRAT5+flkZGQQGhpKmzZt6NatG66urri6unL16lUcHBz45ZdfuHDhAhEREbRs2dI6/2azmRYtWpCc\nnExmZibz5s2jfv36fPDBB2zbto0aNWrw3//+lzFjxtCuXTtrPbcbZ2muX7/Ob7/9RuXKlRkwYADT\npk2jadOm7Nixg2+//ZYTJ05YryfcWLldvHgxmZmZhIeH06pVK2JiYti0aRN2dnZ4enoSFBREdHT0\nHdUhIiIicr+6bx8FvJXs7GwWLFjAsmXLiIuLAyAsLIzJkydjsVjo0qULixcvLnbexo0brY9Gmc1m\ndu/eXWS/r68vtWrVIioqiu3bt5OWlsZnn33G4sWLOXz4cKl9A3Tt2pWlS5fSpUsXtm7dWqzvyMhI\ncnNzmTx5MleuXCE6OprY2FhWrFjB+fPn2bVrFwCurq6sXLmyyErTF198wZw5c1i2bBkVK1Ys0m5a\nWhpVq1bFzu5Ghl6+fDlms5m+ffsSGxt727lct24dzZo1Y9myZQwYMMC6/XbzOXz4cNq3b0///v2Z\nNWsWZrOZZcuWMWnSJCZOnFhiX8899xybNm0CYPv27Xh5eVn3nThxgkWLFmGxWGjcuDH//ve/7/ga\nFHJwcGDx4sVMmjSJ2NhYCgoKCAsLY/78+cTFxeHi4sK6detuOzfHjh1j8+bNLF++nOXLl7Nt2zaO\nHz/OsWPHCA4OJjY2lldeeYW1a9cCcPbsWebMmWNdVapXrx5LlizBbDaX+Ghmq1atiI2NpVOnTmza\ntImjR4/y3Xff8dlnn/HBBx9w8eLFW9Z38zhvduXKFeu9PnToUJ544gk6dOiAr6+vdfyff/45/fr1\nK3I9AVq2bMnSpUsZPHgwa9euJTExkS1btrBy5UpWrlzJyZMn+eabb8pUh4iIiMhfwV9yxapp06Y4\nODgAWANFSkoKU6ZMAW78db5x48bFzuvdu7f1kULglu9q/fzzzzz22GMA1KpVy/o+Vkl9w433WQBq\n1qzJpUuXirR16dIlEhMTrStCp06d4vLly9bH4q5du8bp06cBSqx77ty5zJ07l0uXLhVbDahWrRrp\n6enk5eVha2vLwIEDGThwICtWrChWB0BBQUGR35OTk61ttm7d+o7ms1BKSgpt27YFbjwCee7cuRKP\n8/LyYtCgQfj4+FCrVq0iIdHZ2Zng4GCqVKnC8ePHeeyxx+74GhQqfASzTp065OTkcPnyZS5cuMBb\nb70FQFZWVpnee0tKSiI1NdW6QnjlyhVOnTpF7dq1WbBgARUrVuTatWs4OjoCN1blqlevXmIdBw4c\nKNb+ww8/bN1/6dIlUlJSePTRR7G1tcXW1tZ6T5Xm5nHe7OZHAQv16tWLF198kaFDh3Lu3DlatmxZ\n7BHbli1bAjfu56ysLI4fP07r1q2tq3yenp4kJyeXqQ4RERGRv4K/5IqVyWQqtq1x48ZERkZisVgI\nCgqia9eud912fn4+rq6u1hWSq1evMnTo0FL7vp2aNWuyZMkSjh07xs6dO2nQoAF169YlJiYGi8XC\n4MGDad26NQA2NkUvWU5ODlu3bmXu3Ll8+umnrFu3jl9++cW6397enn/84x+899575OfnAzdWdH74\n4QdMJhMVKlSwrnz88ssvXLlypUj7rq6u/Oc//wHgyJEj1i83uN182tjYWPtzc3OzPtKXkJBQ6ntT\nVapUoXHjxsyePZvevXtbt1+9epX333+fqKgopk+fToUKFSgoKLjra3Dz/urVq1OnTh0WLFiAxWJh\n+PDhRR6vK42rqytNmjRh6dKlWCwWfHx8aNasGTNmzGD06NFERkbSrFkza1i9+drd6b3SpEkTDh8+\nTH5+Pjk5ORw5cuSOxllWlSpVol27dsyYMYM+ffoARa9nSW27urpy6NAhcnNzKSgoYN++fdawfbd1\niIiIiNxP/pIrViUJDw8nODiYvLw84Ma3p90NT09Phg0bxtKlS/n+++/x9/cnLy+vzN/QVxqTycTM\nmTMZOnQoq1evJiAgALPZTF5eHvXr1+fZZ58t8TwHBweqVq1Knz59qFq1Kp06daJevXpFjgkKCmLx\n4sUMGjQIOzs7MjMz8fLy4pVXXsHBwQEnJyd8fX1xc3OjQYMGRc4dNGgQEyZMwN/fH1dXV+uKxO3m\ns1GjRiQlJREbG8v48eMJCwsjJiaG3NzcW869t7c3b7/9NnPnzuXEiRMAODo60qZNG1588UUqV67M\nAw88wIULF/Dx8bkn18DGxoZJkyYxbNgwCgoKqFKlCrNmzSp23KJFi1izZg3w/99T69ChA/7+/uTk\n5NCqVStcXFx4/vnnGTFiBM7OztSpU4e0tLS7qutmzZs3p2vXrvj5+VG9enXs7e1LXJG7F/z8/PD3\n9yc8PBwoej1Lq+3ZZ5/F39+f/Px8PDw88PLy4ujRo/+T+kRERET+bEwFNz/7JSJ/Sr/++itbt25l\n0KBB5OTk8Nxzz/Hpp58WC9L3wqFDh4iLiysxYN5r8fHxnM9v/j/vR0RERP63erV1vKvzEhISDH1b\n9h8pPj4eDw+PEvf9bVasRO531atX58cff6Rv376YTCZ8fX3/J6EqLi6Ozz//nPfff/+ety0iIiLy\nV6UVKxEpV1qxEhER+Wv4u69Y/SW/vEJEREREROSPpGAlIiIiIiJikIKViIiIiIiIQQpWIiIiIiIi\nBilYiYiIiIiIGKRgJSIiIiIiYpCClYiIiIiIiEEKViIiIiIiIgYpWImIiIiIiBikYCUiIiIiImKQ\ngpWIiIiIiIhBClYiIiIiIiIGKViJiIiIiIgYpGAlIiIiIiJikIKViIiIiIiIQXblXYCISK+2juVd\nwp9eQkIC7u7u5V3Gn5rmqGw0T7enOSobzdPtaY7+XrRiJSIiIiIiYpCClYiIiIiIiEEKViIiIiIi\nIgYpWImIiIiIiBikYCUiIiIiImKQgpWIiIiIiIhBClYiIiIiIiIGKViJiIiIiIgYpGAlIiIiIiJi\nkIKViIiIiIiIQXblXYCIyPGUlPIu4U+vgoPDfTdPrm5u5V2CiIjIH0YrViIiIiIiIgYpWImIiIiI\niBikYCUiIiIiImKQgpWIiIiIiIhBClYiIiIiIiIGKViJiIiIiIgYpGAlIiIiIiJikIKViIiIiIiI\nQQpWIiIiIiIiBilYiYiIiIiIGKRgJSIiIiIiYpCClYiIiIiIiEEKViIiIiIiIgYpWImIiIiIiBik\nYCUiIiIiImKQgpWIiIiIiIi7u19uAAAgAElEQVRBClYiIiIiIiIGKViJiIiIiIgYpGBVjiIiIjCb\nzfTs2ZOnnnoKs9nM6NGj2bNnD4GBgXfc3tq1a63tDBw4kICAAC5cuABAYGAgOTk5t23jzJkz+Pn5\nFds+Y8YMUlNTSzwnOzubNWvWlLnOqKgofHx82LNnT4m1Dxo0iMGDB/P9999b9y9atIiAgACGDBnC\n0KFD+fHHH637YmJi2L17N927d+fVV18t0tcnn3xC8+bNAYiOjmbFihWl1lXa2H9v1apVXL9+vcxj\nvdnOnTtZtWrVXZ9fKDo6mh49emA2mzGbzXh7e/Phhx8abldERERE7o5deRfwdxYSEgLcCBXHjx9n\n3LhxAEUCx53q3bu3tZ1Vq1axcOFC3n77baKiogzVOmnSpFL3Xbx4kTVr1uDr61umtjZv3sy6detw\ndHQssv33tV+6dIlBgwYRFxfHlStX+Prrr1mxYgUmk4mEhASCg4NZv349APHx8ZjNZgDOnz/P5cuX\nqVGjBgA7duygatWqdzze0nz00Ue88MILd31+ly5d7lktAQEB+Pv7A5CTk0OvXr3w8/PD2dn5nvUh\nIiIiImWjFas/qZMnT/Lqq6/i4+NDdHQ0AImJidYVilGjRnH16tVbtnHlyhXq168PQPfu3cnOzubk\nyZP4+/tjNpsJCQmxBpLbMZvNpKSkEB8fj5+fHwMHDmT48OFkZmaycOFCjh07xvz584ucc+TIEfz9\n/Rk8eDBDhw4lNTWV+fPnc+7cOV5//XWysrJK7a9mzZr06NGDb7/9lho1apCamspnn33G+fPncXd3\n57PPPgPg6tWrVK5cGXt7ewB69OjB1q1bAUhJSaFRo0bWfXfCbDYzY8YMAgIC6NevH7/88gtr1qzh\n4sWL1tXEd999lwEDBtC/f3+2bNliPW/06NEEBATw5ptvsnfvXgAOHTrEG2+8wdq1a5kzZw4AFouF\n/v37M2DAAJYuXUpaWhp9+vQB4ODBgzzxxBPk5eVx7tw5hg4dest609LSyM3NpUKFCly9epXRo0db\n75XExETgRpAfOHAgffv2ZfPmzQDs2rULX19fBg8ezMiRI8nIyCi2YtqpUyfr+cOHD2fAgAGkpaUx\ndepU+vXrR58+fdi2bVupc7Js2TJ8fX3p378/kZGRd3wtRERERO4HClZ/UtnZ2SxYsIBly5YRFxcH\nQFhYGJMnT8ZisdClSxcWL15c7LyNGzdiNpvx8fFhyZIlxVZIZs2axfDhw7FYLLRp0+aO69q2bRvP\nPPMMcXFx9OvXj4yMDIYPH06TJk0YOXJkkWNDQ0N5++23iYuLw9/fn4iICEaOHEmtWrWIiYmhYsWK\nt+zL2dmZtLQ0atSowYcffsiBAwfo378/PXv25JtvvgHgu+++s37whxurXoUf6NevX4+3t/cdj7FQ\nq1atiI2NpVOnTmzatAlfX19q1apFVFQUO3bs4MyZM6xcuZKlS5eycOFCMjIyAPD29iY2NhY/Pz/W\nrVsHwLp164o8Znjs2DE2b97M8uXLWb58Odu2bSMtLY1q1apx9uxZvvvuO+rUqcNPP/3E9u3b8fLy\nKlZfbGwsgwcP5umnnyYwMJDp06fj6OjIwoULad++PRaLhWnTphEeHk5mZiZ79uxh/vz5fPzxx+Tl\n5VFQUEBYWBjz588nLi6Otm3b3vZxwvbt27Ny5Ur2799PWloan332GYsXL+bw4cOlzsnatWuZNGkS\nq1atomHDhuTm5t71NRERERH5s1Kw+pNq2rQpDg4OVKpUCTu7G09spqSkMGXKFMxmM59//rn1/anf\n6927NxaLhbVr1/Lee+8xYsSIIvtTUlJ4/PHHAfDw8LjjuoYPH87ly5d5+eWX2bp1q7W2kly4cAF3\nd3cA2rZtS3Jy8h31lZqaiouLCydPnsTR0ZF33nmHb7/9ltmzZxMeHk56ejo7d+4sEh7r1q0LwNmz\nZzlw4ACenp53PMZCDz/8MAB16tQhOzu7yL6kpCR++uknzGYzr776Krm5udZ30Bo3bgzAk08+yeHD\nh0lPT2f//v1F6kxKSiI1NZWAgABefvll0tPTOXXqFM888ww7duzg4MGDvPbaa+zatYsdO3aUGKwC\nAgKIi4tj3rx5XLp0iYceesja9ueff47ZbCYsLIyMjAwcHR0JCwsjLCzM+r5dWloajo6OuLi4AKVf\no4KCAuvPhWP7+eefeeyxxwCoVasWgYGBpc7JO++8w8qVKxk8eDCpqalF2hMRERH5q1Cw+pMymUzF\ntjVu3JjIyEgsFgtBQUF07dr1lm3UrVu32BctNGvWjIMHDwLwww8/3HFdGzZs4MUXX8RisdC0aVNW\nr16NjY0N+fn5xY6tXbs2R48eBWDfvn3WD/5lceHCBbZv307Xrl1JTEwkPDzcGm4aN26Mk5MTtra2\nZGRkWN+nKtSrVy8iIiJ4/PHHS5xHI0wmE/n5+bi6utKuXTssFguffvopzz77LA0aNLAeA2BjY0PP\nnj0JDw/Hy8sLW1tbazuurq40adKEpUuXYrFY8PHxoVmzZnh5ebFx40YcHR3p0qUL27ZtIycnh1q1\napVa0yOPPMJrr73G2LFjrbUFBARgsVh477338Pb25sKFC/z000988MEHLFq0iNmzZ+Pk5ERmZqY1\noO/du5eHHnqIChUqcPHiRQB++eUXrly5UmT8hfUfPnwYuPE45tChQ0udk9WrVzNlyhTi4uJISEiw\n3n8iIiIifyX68or7SHh4OMHBweTl5QE3vqnvZhs3buSHH37A1taWa9euMWXKlCL7x40bx8SJE4mJ\nicHJyanEFafk5GR8fHysvxd+yQbAo48+SkhIiPW9pqlTp+Ls7Mz169eZPXs2QUFB1mOnT5/OtGnT\nKCgowNbWlpkzZ95yfIW129jYUFBQwDvvvEO1atX4xz/+QUpKCr6+vlSuXJmCggLGjx9PcnIyrVu3\nLtZOz549mTFjBl988UWxfYsWLbJ+g2GVKlWwWCy3rOlmnp6eDBs2jKVLl7J3714GDhzIb7/9hpeX\nV7Ev4wDo27cvXl5efPnll0W2t2jRgg4dOuDv709OTg6tWrXCxcUFW1tbsrOzad++PVWrVsXOzo6n\nnnrqtnX5+vqyZcsWVqxYwfDhw5k0aRKrV68mMzPT+vjlxYsXeeGFF6hcuTJDhgzB3t6e6dOnM2rU\nKEwmE1WrVuWdd97hgQcewMnJCV9fX9zc3KyB8feefvppvv/+e/z9/cnLy+PNN9+kS5cuJc5J8+bN\n6devH9WrV8fFxaXEayYiIiJyvzMV6Lmcv5X169fTunVrHnzwQdasWcOBAwd45513yrss+RuLj4+n\nerVq5V2G/A+4urn9of0lJCRYHz+W0mmebk9zVDaap9vTHJXN/TRP8fHxpb5OoxWrv5m6desSGBhI\npUqVsLGxue0qkoiIiIiI3J6C1d9M27ZtWbt2bXmXISIiIiLyl6IvrxARERERETFIwUpERERERMQg\nBSsRERERERGDFKxEREREREQMUrASERERERExSMFKRERERETEIAUrERERERERgxSsREREREREDFKw\nEhERERERMUjBSkRERERExCAFKxEREREREYMUrERERERERAxSsBIRERERETFIwUpERERERMQgu/Iu\nQETE1c2tvEv400tISMDd3b28yxAREZFSaMVKRERERETEIAUrERERERERgxSsREREREREDFKwEhER\nERERMUjBSkRERERExCAFKxEREREREYMUrERERERERAxSsBIRERERETFIwUpERERERMQgBSsRERER\nERGD7Mq7ABGRX7evLNf+nZ8eUK79i4iIyP1PK1YiIiIiIiIGKViJiIiIiIgYpGAlIiIiIiJikIKV\niIiIiIiIQQpWIiIiIiIiBilYiYiIiIiIGKRgJSIiIiIiYpCClYiIiIiIiEEKViIiIiIiIgYpWImI\niIiIiBikYCUiIiIiImKQgpWIiIiIiIhBClYiIiIiIiIGKViJiIiIiIgYpGAlIiIiIiJikIKViIiI\niIiIQQpWIiIiIiIiBilYiYiIiIiIGHRfBKu1a9diNpsxm834+fnx6KOPkpGRUeSYTp06WX9OSUmh\nR48efP/993fd5+/bK7Rz505CQkLuus3SmM1m+vXrZx2j2WwmJyfnnvZx+fJla9uenp7W/tasWcOi\nRYs4dOjQHbf5yCOPFKk5PDz8rmoLDAy85+O9nezsbLp3715se/fu3cnOzrb+npKSgtlsBspW563m\n+fcSEhKYP38+UPK9drdOnz5Nnz59CA4OLrK9e/fuvPrqq0W2ffLJJzRv3vyO+0hPT2fDhg2G6hQR\nERH5q7Er7wLKwsfHBx8fHwCmTJlC3759eeCBB0o8Njk5mVGjRhEREcHjjz/+R5ZpSGRkJG5ubv+z\n9mvUqIHFYgGwhiCj/VWtWtXaphFRUVGG2/gjlKXOss6zu7s77u7u97zGAwcO0KFDhxL/AHD+/Hku\nX75MjRo1ANixYwdVq1a94z4SExP5+uuv8fb2NlyviIiIyF/FfRGsCh0+fJhjx44xefLkEvcfPXqU\nMWPGMG/ePFq0aAHA1atXmTRpEmlpaQCEhoZy8eJFVq9ezfvvvw/AgAEDeP/996ldu3axNlNSUpg4\ncSKVKlWiUqVK1g+iW7ZsITY2FhsbGzw8PBg3bhznzp0jPDyc7Oxs0tPTefPNN/Hy8iIqKordu3eT\nn5/Pc889R0BAQJnG27t3bx566CEcHBwIDw8nKCiIzMxM8vLyGDNmDB06dMDb2xtPT0+SkpJo3Lgx\nzs7O7N+/HwcHBxYtWoS9vf1t+wkJCaFXr15cunSJb775hqysLC5evMhLL73E9u3bSU5OZvz48Xh5\neZWpbovFwsaNGzGZTPTq1YuXXnqJkJAQ0tPTSU9PZ+jQodba/Pz8eP/999myZQs7duzg448/xs7O\njvr16zNr1iwOHjxIZGQkdnZ2PPDAA8yZMwdHR0drX0lJSURERJCfn09GRgahoaG0adOGf/zjH7Rp\n04aff/4ZZ2dnoqOjycrKYty4cWRkZNCoUaMyjeX3unfvXmqdNja3X/y9eQ42b95MVFQUOTk5BAYG\ncvbsWZo3b054eDiZmZnF7tubV5ciIiKIj48HbtwrzzzzDB9++CFZWVk0atSIgQMHFjm+R48ebN26\nlYEDB5KSkkKjRo1ITk4G4OzZs4SFhZGdnU2FChWYNm0aeXl5/POf/6ROnTqcPn2aRx99lClTprBw\n4UKOHj3KqlWr6NSpE5MmTSI3NxeTyURoaCgtWrSgW7duuLq64urqStu2be9qvkRERETuJ/dVsPro\no4948803S9x37do1QkJCsLW15erVq9btCxcupH379gwcOJATJ04wYcIEli9fzvTp07ly5QoXL16k\nevXqJYYqgHnz5jF69Gg6derEokWLOH78OOnp6URHR/P5559TqVIlgoKC2LVrFyaTiVdeeYV27dpx\n4MABoqOj8fLy4osvviAuLg4XFxfWrl1bYj/BwcFUqlQJgOeffx5fX19+++03RowYwcMPP0xkZCQd\nO3bk5Zdf5vz58/j7+7Nt2zauXbtG79698fDwoGfPnkyYMIHAwEAGDx7MsWPH7nhV5Nq1a8TExLBp\n0yZiY2NZvXo1e/bsYenSpcWC1ZUrV6yPyRWOoWLFimzevJnly5djMpkICAigc+fOALRv356AgAD2\n7NlDdna29fG4woC7ceNGAgICeO655/jiiy/IzMxk27ZtPPPMMwwdOpSvv/6ajIyMIsHq2LFjBAcH\n07x5czZs2MDatWtp06YNp0+f5tNPP6Vu3boMGDCAw4cP8+OPP9KsWTMCAwP54Ycf2LNnT4lzMGTI\nEOsH///+97/W61KopDpLW0G92e/noFBh4Ktfvz5jxozh66+/5sCBA8Xu2xUrVljP+eabbzhz5gyr\nV68mNzeXgQMH0r59e4YNG8bx48eLhSq4Eb7CwsIYOHAg69evx9vbm+3btwM3VkzNZjNdu3bl+++/\nZ86cOQQGBnLixAmWLFlCpUqV8PLy4uLFiwwfPpyVK1fSv39/Ro8ejdlsxsvLi4SEBCZOnMjatWs5\ne/Ysa9eupXr16owePfqu50tERETkfnHfBKuMjAyOHz9O+/btS9xvMpn44IMPSE9PZ9SoUaxZswZn\nZ2eSkpLYvXs3W7ZssbZjMpl4/vnn2bhxI2fOnKFfv36l9pucnEyrVq0AaNOmDcePH+fUqVNcvnyZ\nYcOGATfCyOnTp/Hw8ODDDz/ks88+w2QykZubC8DcuXOZO3culy5d4sknnyyxn9IeBWzcuDFwY+Ws\n8NErFxcXHB0duXz5MgAtW7YE4IEHHrC28cADDxR5V6isCoOYk5MTbm5umEwmqlatWmJbJT0KuHnz\nZlJTU62rcleuXOHUqVNFxnLzz4UmTJjARx99xIoVK3B1dcXLy4vhw4ezcOFCXn75ZVxcXKzXolDt\n2rVZsGABFStW5Nq1a9bQVb16derWrQtA3bp1yc7OJjk52Tr/rVu3xs6u5Ns/JiaGChUqADfm/eZ3\nx0qqs6xKGne9evWoX78+AI8//jg///xzifft76WkpODp6YnJZMLe3p7WrVuTkpJyy74L5+Ps2bMc\nOHCAt956y7ovKSmJjz76iMWLF1NQUGBd6WzUqJF1TmvVqlXsPkhJSaFt27bAjXvn3LlzwI35r169\nOmBsvkRERETuF/fN8zj79u2jY8eOpe6vXLky9evXp2XLlgwaNIhx48aRn5+Pq6srAQEBWCwW3nvv\nPWs46du3L1u3bmXfvn107dq11HZdXV05ePAgAD/++CMADRo0oG7dusTExGCxWBg8eDCtW7dm3rx5\n9OnTh9mzZ9OuXTsKCgrIyclh69atzJ07l08//ZR169bxyy+/lHnchSsnbm5u7N+/H7jxrkxGRgbV\nqlUDboTKe8VoW66urjRp0oSlS5disVjw8fGhWbNmxdou6VGwVatWMWrUKOLi4gD46quv2LBhAy++\n+CIWi4WmTZuyevXqIufMmDGD0aNHExkZSbNmzSgoKCh1HK6urvznP/8B4MiRI9bge6dKqrOsSqrr\n3LlzXLhwAbjxjlTTpk1LvW8Lubm5WR8DvH79OgcPHuTBBx+8bf+9evWyvn/4+1pcXV0ZN24cFouF\nKVOm0KNHj1LrtbGxIT8/31pH4X2ZkJBAzZo1rccUMjJfIiIiIveL+2bF6ueff6ZBgwZlOnbIkCHs\n2rWLBQsWMHz4cCZNmsTq1avJzMxk5MiRwI1VnypVqvDYY4+VunIBMHnyZAIDA1myZAk1atSgQoUK\n1KhRg4CAAMxmM3l5edSvX59nn32Wnj17MmPGDD766CPq1q1LWloaDg4OVK1alT59+lC1alU6depE\nvXr17nj8r7/+OhMnTuTLL78kKyuLqVOn3rLu8tKiRQs6dOiAv78/OTk5tGrVChcXlzKd26pVK155\n5RWqVatGlSpVeOqppzh16hQhISFUrlwZe3t7pk6dWuSc559/nhEjRuDs7EydOnWs7ySVZNCgQUyY\nMAF/f39cXV3L9P5ZWes0olq1akyfPp3z58/z+OOP07VrV1q1alXifVuoW7du7N27l/79+3P9+nV6\n9uxJy5YtSUxMvGVfhffoF198UWR7cHCw9f3ArKwsJk2aVGobjRo1IikpidjYWMaPH09YWBgxMTHk\n5uYyY8aMYsff6/kSERER+TMyFRT+if9vqDCslOUv/SLyvxEfH89D6cnlWoPz0wPKtf+ySEhI+J98\nk+RfieaobDRPt6c5KhvN0+1pjsrmfpqn+Ph4PDw8Stx33zwKeC9lZWXh4+NDixYtFKpERERERMSw\nP9+zZH+AihUrlvrtfCIiIiIiInfqb7liJSIiIiIici8pWImIiIiIiBikYCUiIiIiImKQgpWIiIiI\niIhBClYiIiIiIiIGKViJiIiIiIgYpGAlIiIiIiJikIKViIiIiIiIQQpWIiIiIiIiBilYiYiIiIiI\nGKRgJSIiIiIiYpCClYiIiIiIiEEKViIiIiIiIgYpWImIiIiIiBikYCUiIiIiImKQXXkXICLi/PSA\n8i5BRERExBCtWImIiIiIiBikYCUiIiIiImKQgpWIiIiIiIhBClYiIiIiIiIGKViJiIiIiIgYpGAl\nIiIiIiJikIKViIiIiIiIQQpWIiIiIiIiBilYiYiIiIiIGGRX3gWIiBwJH/WH9fVwePQf1peIiIj8\nfWjFSkRERERExCAFKxEREREREYMUrERERERERAxSsBIRERERETFIwUpERERERMQgBSsRERERERGD\nFKxEREREREQMUrASERERERExSMFKRERERETEIAUrERERERERgxSsREREREREDFKwEhERERERMUjB\nSkRERERExCAFKxEREREREYMUrERERERERAxSsBIRERERETFIwUpERERERMQgBSsRERERERGDFKzK\nwUsvvcShQ4cAyMnJwcPDgyVLllj3Dx48mKNHj9K9e3eys7MN95eXl4efnx+bNm2ybjt37hxPP/00\n58+fN9z+zW5Xd6dOnW55fnR0NCtWrLhtP2fOnKFNmzaYzWYGDx6Mn58fcXFxd1xveQoJCcHb2xuz\n2Yy/vz8jRozg9OnTtzznq6++KvN1u3z5MqNGjWLo0KEMGTKE0NBQsrKySjz24sWLhIeH3+kQSExM\nZN++fQAEBgaSk5NT4nHp6els2LDhjtsXERERuR8oWJWDzp07s3//fgDi4+Pp3Lkz3377LQDZ2dmc\nPXuWFi1a3LP+bG1tiYyMZPbs2Vy6dAmA0NBQxo8fj4uLyz3rpzw0adIEi8VCXFwcy5YtY+fOnXz9\n9dflXdYdCQoKwmKxsGLFCoYMGcJbb711y+OXLl1KZmZmmdpevHgxHTt2ZMmSJcTExFCpUiVWrlxZ\n4rG1atW6q2D1r3/9i2PHjgEQFRWFg4NDicclJibed9dGREREpKzsyruAv6OOHTuyYMEChgwZwo4d\nO/D19WXOnDlcvXqVn376iSeeeMJ6bHh4OGfOnAFg/vz5VK5cmcmTJ3Py5Eny8/N56623aNeuHd7e\n3jzxxBMkJiZiMplYsGABTk5O1nYaN27M0KFDmTlzJl26dKF27dr06NEDgF27dvHee+9RoUIFqlWr\nxsyZM0lISGDlypVERUUBN1aZdu3aRUhICOnp6aSnp/PRRx9RtWrVUsd55swZJk2aRG5uLiaTidDQ\nUFq0aEFOTg6BgYGcPXuW5s2bEx4ejslkKnZ+dnY2Y8aMITMzk6ysLIKCgmjXrl2p/dnb2/PSSy/x\nxRdf0L17d2JiYti0aRN2dnZ4enoSFBREdHQ0Z86c4ddffyU1NZUJEybw5JNPsnfvXqKiorC1taVh\nw4ZMnToVe3t7a9tJSUlERESQn59PRkYGoaGhtGnThpCQEE6dOkV2djZDhw6lV69eRWp69913+fHH\nH7l27Rpubm688847t7o18PT0xN7enpMnT5KdnV2sz4yMDBISEggODmb58uVER0ffsv369evz5Zdf\n8uCDD9KmTRuCg4Otc71gwQK2bdtGXl4e/v7+dO7cmbFjx7J69eoS52PDhg3s2LGDrKz/1969B0VV\nNnAc/y7SogIm5gU1rfAyoQ6laKl5a6aL5iUzDFzdQp3KTIkspcQ1NbXUYrooClRiaKKm1XRxqmkq\n0vIymDmKVmiDk2mrsWVLcVn2ef9w3Fdw0Xy31x3x9/nLPfvsOc/5cVl++5xdyzl8+DAPPvggt9xy\nC++88w5XXHEFXbt2JTU1lc2bN/Pll1+Sk5NDaGgobdu2ZfHixaxYsYIDBw6wbt06EhMTz5mDiIiI\nyKVGK1ZB0KVLFw4dOoQxhp07d3LTTTfRp08fvv76a3bs2EH//v19Y++9917y8vJo27YtW7duZcOG\nDURFRbFmzRoyMzOZN28eAGVlZQwdOpTVq1fTsmVLCgoKzjruuHHjcLlcrFq1ivT0dACMMTgcDpYu\nXcrq1avp1asXy5cvP+f8e/fuTX5+/jlLFcDixYux2+2sWbOG9PR0Zs6cCUB5eTlPPvkk+fn5/P77\n73WuYhw+fJgTJ06wYsUKXnzxxTovYTtT8+bNcblcfP/992zevJn8/Hzy8/MpKSnh888/B8BqtfLa\na6+Rnp5Obm7uWRm0atWKd955p8Z+i4uLSUtLIzc3l/Hjx7Np0ybcbjfbt29n6dKl5OTkUF1dXeMx\nbrebJk2asHLlSvLz89m9e/c/uoTvqquuwuVy+T3moEGDiI2NZdGiRVRWVp53/2PGjGHYsGG8/vrr\n9O/fnylTpuB0OikqKqKgoIANGzaQn59PcXExxhjg7O+JM/Nwu91kZWWxfPlysrOzadWqFffccw/J\nycnExcX5jvvBBx+QnJzM2rVr6devH263m0mTJtG7d2+VKhEREamXtGIVBCEhIVx//fUUFBTQokUL\nrFYrAwYM4IsvvuDAgQPcf//9vrHdunUDThWG8vJyfvjhBwoLC33v0fJ4PLhcLuBUYQNo3bq13/c4\nWSwWhg8fzqFDhwgPDwfA5XIRERHhuySwV69eZGRkMGjQoBqPPf1HN5xa/art5MmTREZG+lZDLBYL\nBw8epFevXgDExsZy7NgxANq0aUPbtm0B6N69Oz/99JPfnDp16sTYsWOZNm0aHo8Hu91eZ6anHTly\nhOjoaA4dOsQNN9zgW3Xq2bMnP/74o28uANHR0VRWVlJaWorT6fRdgldeXn7W+8BatmxJZmYmDRs2\npKysjIiICCIiInA4HDgcDtxuNyNGjKjxmLCwMEpLS5k2bRqNGzfmr7/+oqqq6rzn8MsvvxAdHY3H\n4znrmBe6/+3btzNy5EgSEhKorKwkJyeHhQsXMnjwYOLi4mjQoAGNGjVi1qxZvpXRuvJo37697xLV\n1q1b1/leKoCnn36arKws1q5dS0xMDLfddtt5z1tERETkUqYVqyC55ZZbyMrK8q1OxcfHU1RUBEDT\npk1942pfIhcTE8PQoQqOvYQAAAwHSURBVEPJy8sjJyeHwYMH+1aO/F1Odz5RUVG43W6cTicAO3bs\n4NprryUsLIzjx48Dp8rKH3/8UeecAJKTk/n5558pLy/H6/VitVrp0KGD771k+/fvp3nz5sCpD844\nfbxdu3bRqVMnv3P7/vvvKSsrIzs7m+eff55nn332nOdSWVnJm2++ydChQ4mJiWHPnj14PB7fyuDp\nQlh7/lFRUURHR5OZmUleXh6TJk0665LDBQsWkJKSwqJFi+jcuTPGGJxOJ/v27WPZsmVkZ2ezZMkS\nPB6P7zEFBQUcPXqUjIwMpk2bRnl5eY2C6s/WrVtp2LAh0dHRfo95ev7GmH+0/1WrVrFp0ybg1Epd\np06dsFqtxMTEUFRUhNfrpaqqivHjx/uK0rny8Pe1t1gseL3eGtvWrVvH1KlTfR8m8umnnxISEnLW\nOBEREZH6QitWQdK3b19mzZrF4sWLgVN/9EZGRvpWneqSlJTErFmzGDduHG63G5vNRkjI/96PLRYL\n8+fPZ+rUqVgsFq688kqee+45mjRpQmRkJKNHj6ZDhw5cffXV59zP5MmTeeyxx7Barb6VjhkzZuBw\nOHjjjTfweDwsWLAAOFUc58+fz6+//kr37t0ZOHCg331ee+21LFu2jHfffZcrrriClJSUs8YUFxdj\nt9uxWCx4PB6GDx9O3759ARgyZAhjxozB6/USHx/PbbfdxoEDB87aR0hICOnp6Tz00EMYYwgPD/d9\nXU4bMWIEkydP5qqrriI6OhqXy0WLFi04fvw4I0eOpHHjxkyYMIHQ0P/+SMXFxZGZmcl9992H1Wql\nXbt2OJ1O2rVrV2PfS5YsIScnh5CQEMLDw3nppZfqPCacWuWbMWMGy5cvP+/+586dy9y5c3nrrbdo\n2LAhUVFRzJkzh1atWtG/f39fPmPGjPF96ERdeRw9etTv16lbt24sXryYDh061Dj38ePH07RpU8LD\nwxk0aBCVlZX88MMP5Obmkpyc7HdfIiIiIpcqiznfS+giIv9HhYWFNHo/96Idr8ucVy/asf5N+/fv\n913GKv4po39GOZ2fMvpnlNP5KaN/5lLKqbCwkPj4eL/36VJAERERERGRAKlYiYiIiIiIBEjFSkRE\nREREJEAqViIiIiIiIgFSsRIREREREQmQipWIiIiIiEiAVKxEREREREQCpGIlIiIiIiISIBUrERER\nERGRAKlYiYiIiIiIBEjFSkREREREJEAqViIiIiIiIgFSsRIREREREQmQipWIiIiIiEiAVKxERERE\nREQCpGIlIiIiIiISIBUrERERERGRAIUGewIiIl3mvBrsKYiIiIgERCtWIiIiIiIiAVKxEhERERER\nCZCKlYiIiIiISIBUrERERERERAJkMcaYYE9CRC5fhYWFwZ6CiIiIyD8WHx/vd7uKlYiIiIiISIB0\nKaCIiIiIiEiAVKxEREREREQCpGIlIiIiIiISIBUrEQkKr9fL7NmzSUxMxG63U1JSEuwpBVVVVRXT\np0/HZrORkJDAZ599RklJCWPGjMFms/HMM8/g9XoBWLp0KQkJCSQlJbFnz54gz/zi++233xg4cCAH\nDx5URnXIysoiMTGRUaNGsWHDBuVUS1VVFU888QRJSUnYbDZ9L/nx3XffYbfbAS4om7rG1kdnZrR/\n/35sNht2u52JEydy4sQJANavX8+oUaO47777+PzzzwEoLS1lwoQJ2Gw2UlNT+fvvv4N2DhfDmTmd\n9v7775OYmOi7XW9yMiIiQfDxxx+btLQ0Y4wx3377rZk0aVKQZxRcb7/9tpk/f74xxpjS0lIzcOBA\n8/DDD5tt27YZY4xxOBzmk08+MXv37jV2u914vV5z5MgRM2rUqGBO+6KrrKw0kydPNnfccYcpLi5W\nRn5s27bNPPzww6a6utq43W7zyiuvKKdaPv30U5OSkmKMMWbLli1mypQpyugM2dnZZtiwYWb06NHG\nGHNB2fgbWx/Vzmjs2LGmqKjIGGPM2rVrzcKFC43T6TTDhg0zFRUV5uTJk75/P/vss2bjxo3GGGOy\nsrLMypUrg3Ua/3e1czLGmKKiInP//ff7ttWnnLRiJSJBUVhYSP/+/QG48cYb2bt3b5BnFFyDBw/m\nscce891u0KAB+/bt46abbgJgwIABfP311xQWFtKvXz8sFgtt2rShurqa0tLSYE37olu0aBFJSUm0\nbNkSQBn5sWXLFjp37syjjz7KpEmTGDRokHKq5brrrqO6uhqv14vb7SY0NFQZnaF9+/a8+uqrvtsX\nko2/sfVR7YwyMjKIjY0FoLq6mrCwMPbs2UP37t2xWq1ERkbSvn17Dhw4UOP5rz5nBGfn5HK5eOGF\nF5g5c6ZvW33KScVKRILC7XYTERHhu92gQQM8Hk8QZxRc4eHhRERE4Ha7SUlJITU1FWMMFovFd/+f\nf/55Vm6nt18ONm3aRLNmzXxPtIAy8sPlcrF3715efvll5s6dy5NPPqmcamncuDFHjhxhyJAhOBwO\n7Ha7MjrDnXfeSWhoqO/2hWTjb2x9VDuj0y/27Nq1i9WrV5OcnIzb7SYyMtI3Jjw8HLfbXWN7fc4I\nauZUXV1Neno6M2fOJDw83DemPuUUev4hIiL/voiICMrKyny3vV5vjSepy9HRo0d59NFHsdlsDB8+\nnCVLlvjuKysro0mTJmflVlZWVuMJqT7buHEjFouFb775hv3795OWllZj9UAZndK0aVNiYmKwWq3E\nxMQQFhbGsWPHfPcrJ8jNzaVfv3488cQTHD16lAceeICqqirf/cqoppCQ/74Of75s/I29XHz00Ucs\nX76c7OxsmjVrVmdGp7c3bNjwsspo3759lJSUMGfOHCoqKiguLmbBggX07t273uSkFSsRCYoePXpQ\nUFAAwO7du+ncuXOQZxRcJ06cYMKECUyfPp2EhAQAunTpwvbt2wEoKCigZ8+e9OjRgy1btuD1evnl\nl1/wer00a9YsmFO/aNasWcPq1avJy8sjNjaWRYsWMWDAAGVUS3x8PF999RXGGH799Vf+/vtv+vTp\no5zO0KRJE19BuvLKK/F4PPp5O4cLycbf2MvBe++95/v91K5dOwDi4uIoLCykoqKCP//8k4MHD9K5\nc2d69OjBl19+CZzKKD4+PphTv2ji4uL48MMPycvLIyMjg44dO5Kenl6vcrq8Xx4WkaC5/fbb2bp1\nK0lJSRhjWLhwYbCnFFQrVqzg5MmTZGZmkpmZCUB6ejrz588nIyODmJgY7rzzTho0aEDPnj1JTEz0\nfbLi5SwtLQ2Hw6GMznDrrbeyc+dOEhISMMYwe/Zsrr76auV0huTkZGbOnInNZqOqqorHH3+cbt26\nKaM6XMjPmb+x9V11dTULFiygdevWTJ06FYBevXqRkpKC3W7HZrNhjOHxxx8nLCyMRx55hLS0NNav\nX09UVBQvvvhikM8guFq0aFFvcrIYY0ywJyEiIiIiInIp06WAIiIiIiIiAVKxEhERERERCZCKlYiI\niIiISIBUrERERERERAKkTwUUERGRS8bPP//MiBEj6Nq1q2/bzTffzJQpU4I4KxERFSsRERG5xHTs\n2JG8vLxgT0NEpAYVKxEREbmkVVdXM3v2bI4dO4bL5WLAgAGkpqby1FNPYbVaOXLkCE6nk+eff56u\nXbvy1FNPcfjwYSoqKpg4cSJ33XVXsE9BROoBFSsRERG5pBQXF2O32323U1NTufHGGxk9ejQVFRW+\nYgXQpk0b5s2bx/r161m3bh0zZsxg+/btbNy4EYCtW7cG5RxEpP5RsRIREZFLSu1LAd1uN++99x7b\ntm0jIiKCyspK332xsbEAREdHs2vXLiIiInA4HDgcDtxuNyNGjLjo8xeR+knFSkRERC5pmzZtIjIy\nknnz5lFSUsL69esxxgBgsVhqjHU6nezbt49ly5ZRUVHBwIEDufvuuwkN1Z9EIhIY/RYRERGRS1qf\nPn2YNm0ahYWFNGrUiGuuuQan0+l3bIsWLTh+/DgjR46kcePGTJgwQaVKRP4VFnP6JR0RERERERH5\nn+g/CBYREREREQmQipWIiIiIiEiAVKxEREREREQCpGIlIiIiIiISIBUrERERERGRAKlYiYiIiIiI\nBEjFSkREREREJEAqViIiIiIiIgH6D80q9DwLmVqdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a11c4db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top 5 performers by Fans\n",
    "plt.figure(figsize=(10,5))\n",
    "temp = df.sort_values(by='Fans', ascending=False).head()\n",
    "g = sns.barplot('Fans', 'Title', data=temp, palette='coolwarm');\n",
    "plt.ylabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see here that even within my top 5 posts of all time (ranking by `Fans`), there is a huge descrepency between my top two posts and the next few. Note that there is also some bias here since Medium switched to the 'clap' feature after a couple of my earlier posts had already gone out. I've found that users are more likely to 'clap' than they were previously to 'like' a post. Just a hunch though. \n",
    "\n",
    "I'd imagine this is a common theme out there. Not every post that you put out will be a homerun, you are always going to have a couple that outperform the rest. If you're interested, here's the link to each post below:\n",
    "* https://towardsdatascience.com/python-for-data-science-8-concepts-you-may-have-forgotten-i-did-825966908393\n",
    "* https://medium.freecodecamp.org/the-hitchhikers-guide-to-machine-learning-algorithms-in-python-bfad66adb378"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rc+f27t2rdevWaf369VqzZo1WrVqleDyu1atXa+7cuXrggQc0ZcoUdXR06PDhw7lr//3f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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a121d2668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of Fans per post\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(df['Fans'], bins=12);"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Percentage of total views\n",
    "df.sort_values('Views', ascending=False).head(2).sum()['Views'] / df.sum()['Views']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This confirms our thoughts about the top two posts significantly outperforming the others from a `Fans` perspective. We see that my top two performing posts alone make up over 70% of my total views, even with ~30 stories. Next we'll go into whether or not this is really a reliable metric and brainstorm possible other metrics that we can produce as a result of feature engineering.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering and Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, I'll go into more detail on each specific metric while analyzing the pros and cons along with what it is trying to tell us. This includes engineering new features and discussing them along with exploring relationships between other features.\n",
    "\n",
    "Initial ideas: fans/read ratio, adjusted reads for read time, weighted average of reads/views and fans/reads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reads/Views Ratio\n",
    "This metric has already found it's home on the current Medium stats dashboard, but let's look into a bit deeper. What exactly does this tell us? In a nutshell, this is trying to communicate how effective a post was at turning views into reads. It tells us how engaging a post is. This is probably the most valuable metric given to us at the moment, as it actually tells us something about how a post was percieved, contrary to counting vanity metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jI4Nhw4bdlbhuZcuWLWRmZjJ//nwANmzYwPjx45k5c+Zdjat///7mD+Tp6ekM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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a17806ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top read ratios\n",
    "plt.figure(figsize=(10,5))\n",
    "temp = df.sort_values(by='Read Ratio', ascending=False).head(10)\n",
    "g = sns.barplot('Read Ratio', 'Title', data=temp, palette='coolwarm');\n",
    "plt.ylabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is super interesting. My very first Medium post ever has the highest read ratio. Looking at these posts, I remember these all being somewhat short, mostly under 6 minutes or so. I also remember these all having plenty of pictures and bolded headers or lists for easy skimming which I'm guessing plays a big part as well. Let's follow that initial hunch and check out the relationship between read time and read ratio to check for any bias there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y5Mg8+OCD+eY3v5np06e3veTP7vvN91s1Nzd7ifoNOvTQQ/Ptb387EyZMqLlP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1a4196a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read time vs. read ratio\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(x='Read Time', y='Read Ratio', data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There appears to be a clear trend here. The shorter the post, the more likely a reader is to read it. This is intuitive, but nice to see that the data backs it up. What would be interesting to check out here is if longer posts are more likely to convert to fans? This would create a trade-off between engagement and value proposition. Which leads us to our next point..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Fans/Reads Ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We already looks at the more traditional `Read Ratio` metric above that you've probably already seen on the dashboard. I believe that it doesn't tell the whole picture however. By `Read Ratio` alone, you are simply seeing how engaging your post is, not necessarily how valuable it is to your readers, or how much they enjoyed it. You can litter buzzwords and infographics throughout your post, but chances are that readers didn't find it particularly special. \n",
    "\n",
    "So how do we judge how much readers enjoyed your post using what we have? With the data I have, I suggest `Fans Ratio`. This is simply calculated with `Fans/Reads` and I believe it will much more effectively describe how well recieved a given post is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Publication</th>\n",
       "      <th>Read Time</th>\n",
       "      <th>Views</th>\n",
       "      <th>Reads</th>\n",
       "      <th>Read Ratio</th>\n",
       "      <th>Fans</th>\n",
       "      <th>Fan Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>The 5 Proven Best Ways to Start Your Day</td>\n",
       "      <td>The Ascent</td>\n",
       "      <td>4</td>\n",
       "      <td>109</td>\n",
       "      <td>69</td>\n",
       "      <td>63.302752</td>\n",
       "      <td>10</td>\n",
       "      <td>9.174312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Everything I Wish I Knew My Freshman Year</td>\n",
       "      <td>The Ascent</td>\n",
       "      <td>6</td>\n",
       "      <td>112</td>\n",
       "      <td>60</td>\n",
       "      <td>53.571429</td>\n",
       "      <td>9</td>\n",
       "      <td>8.035714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Stop Cheating Yourself out of Great Ideas</td>\n",
       "      <td>The Startup</td>\n",
       "      <td>6</td>\n",
       "      <td>394</td>\n",
       "      <td>140</td>\n",
       "      <td>35.532995</td>\n",
       "      <td>27</td>\n",
       "      <td>6.852792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Why I Spend $5 Monthly on Medium</td>\n",
       "      <td>The Startup</td>\n",
       "      <td>5</td>\n",
       "      <td>536</td>\n",
       "      <td>243</td>\n",
       "      <td>45.335821</td>\n",
       "      <td>31</td>\n",
       "      <td>5.783582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Want to Supercharge Your Productivity? Be Delu...</td>\n",
       "      <td>The Startup</td>\n",
       "      <td>4</td>\n",
       "      <td>177</td>\n",
       "      <td>76</td>\n",
       "      <td>42.937853</td>\n",
       "      <td>9</td>\n",
       "      <td>5.084746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Title  Publication  Read Time  \\\n",
       "25           The 5 Proven Best Ways to Start Your Day   The Ascent          4   \n",
       "22          Everything I Wish I Knew My Freshman Year   The Ascent          6   \n",
       "5           Stop Cheating Yourself out of Great Ideas  The Startup          6   \n",
       "9                    Why I Spend $5 Monthly on Medium  The Startup          5   \n",
       "17  Want to Supercharge Your Productivity? Be Delu...  The Startup          4   \n",
       "\n",
       "    Views  Reads  Read Ratio  Fans  Fan Ratio  \n",
       "25    109     69   63.302752    10   9.174312  \n",
       "22    112     60   53.571429     9   8.035714  \n",
       "5     394    140   35.532995    27   6.852792  \n",
       "9     536    243   45.335821    31   5.783582  \n",
       "17    177     76   42.937853     9   5.084746  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create feature\n",
    "df['Fan Ratio'] = (df['Fans'] / df['Views']) * 100\n",
    "df.sort_values('Fan Ratio', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yoqKi+OCDDyhbtiwBAQH06tWLF154gapVq/Lkk0/y1FNPsWnTJpycnIiMjGT5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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1230b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization\n",
    "plt.figure(figsize=(10,5))\n",
    "temp = df.sort_values(by='Fan Ratio', ascending=False).head(10)\n",
    "g = sns.barplot('Fan Ratio', 'Title', data=temp, palette='coolwarm');\n",
    "plt.ylabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears my posts on self improvement garnered a far better `Fan Ratio` than my technical data science posts. I'm really curious if this is simply a built in bias dealing with the genre of post or not. Luckily, all my posts are published based on their goals. Startup is business, entrepreneurship, and self improvement. Ascent is all self improvement. Hackernoon and Towards Data Science and Freecodecamp are generally are technical, though some posts differ. Could also be that some publications just have generally more active readers. Or a small sample size. Hard to tell, but let's look into it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Publication\n",
      "The Ascent              8.605013\n",
      "The Startup             3.985051\n",
      "Hacker Noon             3.608070\n",
      "Towards Data Science    2.440604\n",
      "UX Planet               1.711613\n",
      "freeCodeCamp            1.597695\n",
      "Name: Fan Ratio, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Group by publication\n",
    "temp = df.groupby('Publication').mean()['Fan Ratio']\n",
    "temp = temp.sort_values(ascending=False)\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H5bHHHsull16aJOnQoUPGjRuX6dOn54UXXsioUaMyatSozfFSAQCKEaSvoqqq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\n/n8hC8S7GONf6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a0f33c780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize publications effect on Fan Ratio\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.barplot(x=temp, y=temp.index);\n",
    "plt.ylabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping my publication reconfirms this thought. Honestly, my best guess is that self improvement posts get more claps in general. I mean they absolutely kill it on Medium through and through. Let's examine that trade-off we talked about earlier and check out the relationship between  `Fan Ratio` and `Read Ratio`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a122f06a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Qo0QpRUtIZUVl7HjyyRyTEEKMnKsUza0Ky5ZQGildA00DO8O/lxJMQmSAuiMW\n67dFudTsUlKks3pZICHHUQzEdRVXZDn4qNE0DZ+R+T2mhB0UGIvF+OIXv8hdd93F2rVraWhoSNRT\nC5HR6o5YrFsfpjnkkhuE5pDLuvVh6o5YSW2X43jHn2f6i2ii+Qwt4+eYEhZMr732GrZts27dOj79\n6U/zr//6r4l6aiEy2vptUQwfBPwamqYR8GsYPu/2ZInZXihl+8F+8eAzvKMvMlnChvKmTZuG4zi4\nrktbWxs+X99PXV9fn6gmDVskEkmLdo62bL1uSO1rP3uhlIBfEelRDUApOHtRo77+1IgeezjXHbMh\nFDFI5/f00Wg0rqM6E8bOGtL9Gxsbuz9R+YRCLo2Nl6kPhEe5ZYlTXV3d7/9LWDDl5uZy+vRp7rjj\nDq5cucIPf/jDPu93rcamivr6+rRo52jL1uuG1L72stpWmkMuAX93teloTFFWrI+4zUO97kjU2zib\n7hoaGpg+fXqym9GlvLy869/BkxE0oLy8iOrqQPIaFUcJG8r7z//8T1asWMErr7zCr371Kx588EGi\n0eQNNQiRKVYvC+DYXhgp5RVDdWzv9kQKRdyMCKVU55fFD6OnsLCQgoICAIqKirBtG0dOAhNixOZX\nmdy1OoeiPJ32CBTl6dy1Oiehq/Ja213a2tN58C59ZMPih4QN5X30ox/loYceYu3atcRiMe6//35y\nc3MT9fRCZLT5VWZSlofLxtnEy4bl4gkLpry8PP7t3/4tUU8nhIgzVyma2xSWnKOUUD6fJhtshRDi\naq6raGpTxDL8BTIVSY9JCCGu4jiKK20uMkWcHD5Dw1XeIYuZSoJJCDFocrhf8mXD0RcJW5UnhEhv\nVsyreyehNDy2o9j5lsX/91zbiB5n7jQ/AEtmJ78WYrxIj0kIMaBM2TibDDFbseNAjM17ojSHRj78\nlhPwNlKHozKUJ4TIUu0Rl1bZozRkEUvxxn6LLXUWobD3/dM1mD/dP6LHlWASQmS1cFSTUBqiUMRl\na53F9n0WkY4C74YOi2f5uXlRgLGFI5tB6Qym9kjm9mAlmIQQfWoJuURiMg09WC0hl817LN48YBHr\nKKhr+mBptcnyBSaFeaPzvZQekxAi66iOjbNR2Tg7KJdbXF7fHaX2YKzrmI+gCcvmmSybb5IXHN1w\nzw1KMAkhsohsnB28c5cdXt9tsbchhur4duXnaNw43+T6uSZBU7v2AwxT91Be5v6MJJiEEIC3YfNK\nq2ycHcjpCw4bdkU5cLz7AKyifI2bFwZYMtuP3xefQOrk92nkBjRaRmGFX6qSYBJCYDuyR+lalFIc\na3TYWBul4XR3cpcU6axcZLIjoIraAAAgAElEQVRwph9Dj28g9VSUr9Hclrk/LAkmIbKcFVM0t7lk\ncIWbYVNKceikzYZai5PnugOpbKzOypoAc6f50BMYSJ2K8nWaJJiESG91RyzWb4tyqdmlpEhn9bJA\nUo6JSDVRS2X0C9xwuQrqGmJsrI1y9nL392fyBINVNSYzJ/vQtMQHUqfiAp2DJ+yB75imJJhExqs7\nYrFufRjDB7lBaA65rFsf5q7VZHU4haOKFqnm0IvjKvYcivGHN8fQ0h7uun16hcHKmgBTy42kBlKn\nony9o5er0FOgPaNNgklkvPXbohg+CPi9P+CAH6Io1m+LZm0wtYXdrmoEwisbtPOtGJv2RGluU3S+\nNM6p9LFqcYCKcUZyG3iV4nwNx4W2dkVhngSTEGnnUrNLbrD3babPuz3buErRInuUuvRXNmjq+Ah3\nrChh/NjUCqROndUjLjW7o7ZxN5VIMImMV1Kk0xxyCfQoUWbZ3u3ZxHG8+aRMPi5hsNo7ygZt66ds\n0JULFxg/dnxyG3kNEzoC89xlh2kTM+9lPPOuSIirrF4WYN36MFEUps8LJcf2bs8WMVvR1Cor71pC\nLpv3Wuyot7A61g74fbB0jslNC7vLBl25kMRGDkJpsY6uwbnLmdnrl2ASGW9+lcldq8naVXlyZIVX\nNmjT7ii7ElQ2KN58hkZpsc65y5nZ/ZVgEllhfpWZNUHUUyjs0pbFixzOX3bYuNuiriHW1VvMy9FY\nHueyQYlQVmJw5oIEkxAiTSilaAkpIlZ2htLpC16VhvpjPcoG5WnctDDAkjl+zDiXDUqEKRMM9h6O\nEYkqgoH0v56eJJiEyDCO61UHz8ZCrMcabTbsuqpsUKHOyhqTBTP8+IzMeQGfUmaggJPnbWZOHtnh\ng6lGgkmIDBKzvZV32VTzTinF4VM2G3ZZnOhRNmjCWJ1VSSwbFG+VZd7L94mzjgSTECI1ZdsiB9dV\n1B+z2VAb5eylHmWDxhusWpz8skHxVpSvU5Svcawx80oTSTAJkQGyqZKD4yr2HI7xeq3FxR6bpKsq\nDFalUNmgRJg52cfBkzZKqYy6ZgkmIdJYNi1yeHvZIM+cSh8ra0wmjc++l7PZU/y8WR/j3GWXspLU\nrFIxHNn3kxQiQ2TLIoeopXij3mLLXqtr6bumwfwqHytrAl1VELLRrCneS/jBE7YEkxAiubJhkUN7\nxGXrPottdb3LBtXM8rNiUaCrXlw2Gz9Gpzhf48DxGKsWZ04lEwkmIdJMpi9yaG132bzH4s2eZYMM\nuK7a5KYFJkX5EkidNE1jXpWfHQcsYraK+7HuiZLQYPrRj37EH/7wB2KxGHfffTd33nlnIp9eiLTX\n1u4SimTm0N2VFpdNe7yyQZ2FZgN+r2zQjfNN8nIkkPqyaKafTXssDp6wmVeVGcvGExZM27ZtY9eu\nXfz85z8nHA7z5JNPJuqphUh7mXxcxfkrDq/XWuztWTYoqHHjApMb0rxsUCLMqfRj+mH34VjGBJOm\nlErIb/p3v/tdNE3j0KFDtLW18fd///csWLCg13127NhBbm5uIpozIpFIhGAwOPAdM0y2Xjck99od\nF0IRHcdN/At0NBolEIjP3MWlFh97j+dy4oIJeNeWG3CYNyXMzIlhfEmcy4/ndQPcdP2sQd93x44d\nNJwv6fp8wdTw2+7zm+3FnGvy87F3XSBdVo1XV1f3+/8S1mO6cuUKZ86c4Yc//CGnTp3iU5/6FL/9\n7W/ftvb+Wo1NFfX19WnRztGWrdcNybt2K6Y6jtBO+FMD0NDQwPTp00f1MY83eptiD5/qrtIwtlBn\nxSKTRTP9+Iwxo/p8wxGP6x6J8vLyrn9XV789MFvcKD/+TTv+ghkZUQUiYcFUXFxMVVUVpmlSVVVF\nIBDg8uXLlJSUDPzFQqSpuiPWsI/bCEcVLRmyyOFaZYNW1gSYl6Flg+JhY230bbfZtsJnwK82hFk2\nb3C/MytrUncVX8JmE6+77jo2btyIUopz584RDocpLi5O1NMLkXB1RyzWrQ/THPKOdm8OuaxbH6bu\niDXg17aE3IwIJVcp9h+N8aP/DvHT34a7QmnSeIO1q3P41AfyWDDdL6E0Qj6fxqTxBsfPOjhO+s9D\nJqzH9I53vIM33niDD37wgyileOSRRzCMzNkQJsTV1m+LYvgg4PdedAN+iKJYvy3ab6/JdRXNIYWV\n5oscHFex93CMjbstLjb1KBs00WDV4uwqG5QoVRN9HGt0OH3RYcqE9N4JlNDW//3f/30in06IpLrU\n7PWUejJ93u19sWLe/qR03jQbsxW7DsbYtDtKU4+yQbMrfaxcZDI5zV8wU9mEsTo5AY2G03Z2BFNT\nUxMnTpxg0qRJjB07Nt5tEiIjlBTpNIdcAj3moi3bu/1q6X7SrJQNSj5d16iqMNh/xCYUdtN639eA\nwfTyyy/zb//2b0yfPp1Dhw7xmc98hj//8z9PRNuESGurlwVYtz5MFIXp80LJsb3bO6V7Edb2iGLb\nvijb9lmEO+bkDd3b9LliUaDPEBbxM2OSj31HbA6fslk0c3CLbFLRgMH0k5/8hBdeeIG8vDza2tq4\n9957JZiEGIT5VSZ3rabfVXmuq2hK0yKsre0uW/ZavFFvYcW826RsUPLl5+hMLNVpOO2wYLpK20Ul\nAwaTpmnk5eUBkJ+fH9dNZ0JkmvlVZp8LHWxH0dTq4qTZfNKV1o6yQW/1Lht0Q0fZoPw0Hj7KFDMn\n+3htl8WpC+m7CGLAVk+ZMoVvfetbLF26lDfffJMpU6Ykol1CZKxIVNHS7pKYmiuj40KTw8Zai72H\nu8sG5QY1ls83uX6uSU4gPd+ZZ6KJ4wzycjTeOp6+iyAGbPU3vvENnnnmGTZv3sz06dP54he/mIh2\nCZFxlFK0hRXtaVSE9VKrjzd/3079UZvOVhfmady00OS62SamXwIp1eiaxuwpPna+FeNyi5uWx4P0\nG0x79+5lwYIFbN26lcrKSiorKwGvGOuKFSsS1kAhMkG6Hep3/KzNhl1RDp8aA3hnT4wt1FixKNBR\nNkgCKZVNr/Cx53CMA8dj3LQg/aZf+g2mLVu2sGDBAl566aW3/T8JJiEGL2p5pYWSVe9usJRSNJxy\n2FAb5fjZ7rJB48forKoJMLfKh5Gmk+nZxvRrTK/wceikzeJZKu2GWvsNpk984hMALFmypNe5Sf/1\nX/8V/1YJkSHS4fwkVykOHLPZWBvlzMXu1RgV43RmlV1h1bJJ6FKlIe3MmuLjrRM2h07GWDgjvZaO\n9xtMv/nNb/jDH/7Atm3b2Lp1KwCu63Lw4EE+8pGPJKyBQqS6vgq1Vk/1p/zQneMq6hpibKy1uNCj\nbNC0cq9s0LSJBkeOnJNQSlOFeToV4wwOnrSZN82PkUbDr/0G08qVKxk3bhxNTU2sWbMGAF3XmTx5\ncsIaJ0Sq6yzUavjoKtT681faeffNQWZMSs3jB/otGzTFx8oaKRuUSeZU+nj1TYdjZx2mV6TPz7Xf\nlhYVFbFs2TKWLVvG+fPnsW0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aN/TW2eMxelS6lvARqczv88pxvfpmlOY2l6L8xC2CkGAS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\nRKrCYCIiIlVhMBERkaowmIiISFX+H4gBUU3xK0e+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a120a4cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fan Ratio vs. Read Ratio\n",
    "plt.figure(figsize=(10,5));\n",
    "sns.jointplot('Read Ratio', 'Fan Ratio', df, kind='reg_fit');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With a nearly 0.6 pearson correlation, there's some strong evidence to suggest a relationship between `Read Ratio` and `Fan Ratio`. This is exciting stuff and makes a lot of sense intuitively. Better posts are not only engaging, but also enjoyable for the reader and more likely to receive applause."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrapping Up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I hope this was informative for anyone reading. I know I learned a lot. It's worth noting that this type of analysis should be available to all writers, not just those that are data science practioners. Data analysis should be democratized for writers and content creators. Even with my ~30 post sample size, I was able to walk away with some interesting insights:\n",
    "* 2 out of my 30+ posts make up over 70% of my total lifetime views\n",
    "* `Read Ratio` and `Read Time` appear to be strongly correlated\n",
    "* Publication choice matters for enjoyment stats like Fan Ratio\n",
    "* `Read Ratio` and `Fan Ratio` correlated - strong posts do both well\n",
    "\n",
    "Note that this analysis is just from using the fraction of data available to users on the Medium Stats page. Imagine if we had more of our data and information at our disposal; if writers were empowered to use Medium Stats for improving their work and understanding how readers perceive it rather than boosting their ego with simple vanity stats. It's no small task, but I believe it can be done.\n",
    "\n",
    "Hope you enjoyed this project, I know I did. Once again, reiterating that I am in no way affiliated with or working for Medium. However, you can follow my writing at the link below if interested. Thanks for reading!\n",
    "\n",
    "https://medium.com/@conordewey3"
   ]
  }
 ],
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