Florencia-97/finger1-100033.ipynb

## finger1-100033.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importacion general de librerias y de visualizacion (matplotlib y seaborn)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import cufflinks as cf\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('default') # haciendo los graficos un poco mas bonitos en matplotlib\n",
    "#plt.rcParams['figure.figsize'] = (20, 10)\n",
    "\n",
    "sns.set(style=\"whitegrid\") # seteando tipo de grid en seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vista previa de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/flox/.local/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2785: DtypeWarning:\n",
      "\n",
      "Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "eventos = pd.read_csv(\"events.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['timestamp', 'event', 'person', 'url', 'sku', 'model', 'condition',\n",
       "       'storage', 'color', 'skus', 'search_term', 'staticpage',\n",
       "       'campaign_source', 'search_engine', 'channel', 'new_vs_returning',\n",
       "       'city', 'region', 'country', 'device_type', 'screen_resolution',\n",
       "       'operating_system_version', 'browser_version'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventos.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            NaN\n",
       "1            NaN\n",
       "2           2694\n",
       "3           2694\n",
       "4          15338\n",
       "5            NaN\n",
       "6            NaN\n",
       "7          15338\n",
       "8            NaN\n",
       "9            NaN\n",
       "           ...  \n",
       "1011278      NaN\n",
       "1011279      NaN\n",
       "1011280      NaN\n",
       "1011281     8513\n",
       "1011282     8512\n",
       "1011283     8512\n",
       "1011284      NaN\n",
       "1011285      NaN\n",
       "1011286     8527\n",
       "1011287     8527\n",
       "Name: sku, Length: 1011288, dtype: object"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_rows', 20)\n",
    "eventos['sku']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23259624"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventos.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventos.columns.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iPhone 6                                     50916\n",
       "iPhone 5s                                    44878\n",
       "iPhone 6S                                    39296\n",
       "iPhone 7                                     27556\n",
       "Samsung Galaxy S7 Edge                       23926\n",
       "Samsung Galaxy S7                            19337\n",
       "iPhone 7 Plus                                18792\n",
       "Samsung Galaxy S8                            16959\n",
       "iPhone SE                                    16531\n",
       "Samsung Galaxy J5                            13044\n",
       "                                             ...  \n",
       "iPad Mini 2 Wi-Fi + 4G                           4\n",
       "Motorola Moto G1 3G                              4\n",
       "LG L80 Dual                                      3\n",
       "Samsung Galaxy Tab A 2016 10.1  W-Fi + 4G        3\n",
       "iPad 2 Wi-Fi                                     3\n",
       "Quantum Muv Up                                   2\n",
       "Quantum GO 3G                                    2\n",
       "Samsung Galaxy Young 2 Duos TV                   1\n",
       "Blackberry Blackberry Z30                        1\n",
       "Motorola RAZR I                                  1\n",
       "Name: model, Length: 202, dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_rows', 20)\n",
    "eventos['model'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows 7           19675\n",
       "Windows 10          17110\n",
       "Android 7            9964\n",
       "Android 6.0.1        7805\n",
       "Android 6            6871\n",
       "Android 5.1.1        4123\n",
       "Android 7.1.1        3325\n",
       "Windows 8.1          2781\n",
       "Android 5.0.2        1640\n",
       "Android 4.4.2        1402\n",
       "                    ...  \n",
       "Android 6.3             1\n",
       "Mac OS X 10.10.3        1\n",
       "iOS 9.1                 1\n",
       "Mac OS X 10.11.1        1\n",
       "iOS 8.1                 1\n",
       "iOS 11.4.1              1\n",
       "Mac OS X 10.6.4         1\n",
       "Mac OS X 10.10.4        1\n",
       "Mac OS X 10.10.1        1\n",
       "iOS 9.2.1               1\n",
       "Name: operating_system_version, Length: 121, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_rows', 20)\n",
    "eventos['operating_system_version'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Ejercicio 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "viewed product       528931\n",
       "brand listing         98635\n",
       "visited site          87378\n",
       "ad campaign hit       82827\n",
       "generic listing       67534\n",
       "searched products     56073\n",
       "search engine hit     50957\n",
       "checkout              33735\n",
       "staticpage             3598\n",
       "conversion             1172\n",
       "Name: event, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_rows', 20)\n",
    "eventos['event'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "eventosPopulares = eventos['event'].value_counts().head(10).to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "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>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>viewed product</th>\n",
       "      <td>528931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand listing</th>\n",
       "      <td>98635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visited site</th>\n",
       "      <td>87378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ad campaign hit</th>\n",
       "      <td>82827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generic listing</th>\n",
       "      <td>67534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>searched products</th>\n",
       "      <td>56073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>search engine hit</th>\n",
       "      <td>50957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>checkout</th>\n",
       "      <td>33735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>staticpage</th>\n",
       "      <td>3598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conversion</th>\n",
       "      <td>1172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    event\n",
       "viewed product     528931\n",
       "brand listing       98635\n",
       "visited site        87378\n",
       "ad campaign hit     82827\n",
       "generic listing     67534\n",
       "searched products   56073\n",
       "search engine hit   50957\n",
       "checkout            33735\n",
       "staticpage           3598\n",
       "conversion           1172"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventosPopulares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Cantidad'), Text(0.5,0,'Evento')]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Cw8Mxbtw4fP7552jatKmYDrzu0Ll8+TLGjh2LevXq4eTJk5g0aRJMTU3FYTWOHz+OWbNmoUePHujcuTOuX78u3jjNy8WLFzFjxgz4+PigdevWSE1NxZUrV3K9QJw9ezYmTZqEcuXKYcqUKQAgPh16+PBhTJw4ET169ICfnx/i4+OxbNkyqFQqrFixIt+8vC/++ecfODk5oX///jAwMMDly5cxc+ZMCIKA7t27A3jdePTx8YGhoaF4ky80NBTJycmoXbt2nsu/dOkShgwZAgcHByxYsACmpqa4du0a/vnnH3Gap0+fYuTIkahcuTKePXuGjRs3YtCgQTh48CDKlPmvqXbjxg08f/4ckydPRnJyMhYsWIBZs2YhLi4O3bp1w7Bhw7BmzRr4+fnh+PHjKF++vDjvkSNHUKNGDcyZMwcqlQpLly6Fn58fduzYUeB8ZFeQmBMTE4Nnz54hMDAQmZmZWLhwISZNmiTJQ25mz54Nb29vREREIDIyEkuXLoW1tTVatWqlMW1hYmtxOXr0KGbPno3evXujQ4cOiImJwYQJEzSm27hxI5YsWYIhQ4Zg6tSpuHv3rth5ExgYKJk2MDAQffr0wbBhw/D9999j6tSpUCqV4lAEBTm3Z82ahU8++QQRERG5vvEqp4wXVHJyMkaOHInGjRtj+fLlMDAwwB9//AGVSgUg79ilTXp6OgIDAzF48GCMGTMG69atg7+/P44dOwYLCwsAwLJly7B9+3b4+/ujYcOG+N///legNufbMGrUKFSsWBFffPEFjI2N8ffff+Phw4fi79HR0Rg0aBBat26NFStW4OXLlwgJCcGYMWMKdW5fu3YNcXFxGD9+PExNTVGtWrV826dqx44dw/379xEUFITnz58jODgY8+fPF8tUWloahg0bhqdPn2LChAmoUqUKfvzxR4wcOVK86Qi8edt6+fLlaNGiBUJCQnDjxg2EhoZCX19f4zwJCAhA3759MXLkSHGIqMDAQJw+fRoTJkxA3bp1sW/fPvj5+SEiIkJ8KKOw9bM2aWlpGDJkCOLi4jB27FgoFAo8fPgQly5dApB7Oc/v/HgTpanMaSP3GlTO8lNTU+Hj4wNTU1MsWbIEwOuHGlUqlaxrtPzqNbn7urDLz+59qjeLQl71Vq1atQDkf80VGxuLfv36oX79+li4cCF0dHSwevVqDB06FIcPHxb33/z587Fjxw4MGTIEzZs3x4sXL3DixAmkpKTAxMREI2+LFi3C9u3bsXbtWvEa4ujRo/D394eXlxcCAgJw7949rFixAv/++y9CQ0Nlb7e6A/LevXtYunQpALw3IweUVLnFzLz2dX7XHo0aNUJQUBDmzZuH4OBg1K1bN888yClj6enpmDhxInx8fGBlZYVt27Zh3Lhxkvr3wYMH8PDwgI+PD3R1dXHq1Cn4+vpi06ZNYll8/PgxRowYARsbG4SEhCApKQlz5sxBUlISGjZsKObpTeMX5R2n5N6vBPK//vLy8sLu3bsxY8YM6OnpAXj9xsyhQ4fQuXNnsf0v5x4G8LqMLFiwAKNHj0a1atVQvXp1rFmzBtu3b0dgYCDq16+P58+fIyoqKs97VHLaZMDre4oLFiyAr68vTExMsGzZMowbNw5HjhwpXW/yCvTBGDt2rNC3b19J2qBBgwRfX1/x/ylTpgheXl7i/ytXrhQaN24sPHnyREx79eqV4O7uLixatEgQBEFITU0VbG1thd27dwuCIAgbN24U+vbtK3Tr1k3YunWrIAiC8P333wsODg5Cenq67OU+f/5csLOzE0JCQiR5HjBggKBQKITY2Nhct/XcuXOCQqEQJk2aJEmfNGmS0LJlSyEzM1MQBEEIDQ0VFAqFcPDgQcl0kZGRgkKhEE6dOiWmpaWlCe7u7sK4ceMEQRCEjIwMoUWLFlrXoVAohHPnzgmCIAixsbGCQqEQfvrpJ8l0CxYsEDw8PMT/ly9fLtja2ua5XR4eHsLcuXNz/f19MmXKFEGhUAhnzpwR01QqleDo6CgsXbpUTPPw8BBcXFyEFy9e5LqsrKwsIT09Xfjqq6+E5s2bi+nqfe/v7y+ZfuDAgcKQIUPE/ydMmCC0adNGyMjIENN27twpKBQKITQ0NM/tyHlMcp5DXl5eQnBwcK7z79q1S1AoFMLTp0/zTJe7Lf7+/oKnp6dYxgVBEPbu3StrWz4UhYmFBT2OuZ33Z8+eFRQKhXD69GlJ+oQJE4SePXuK//fu3Vv49NNPJdOEhIQICoVC2LVrV675WL9+veDi4pLr74IgCAqFQli/fr34/8CBAyXbLgivzykPDw/h888/l6SfPHlSsLa2Fm7fvp3nOt5X6lgya9YsSRnZunWr0KBBA+HPP/8U0/766y+hQYMGwsCBA/NcZt++fYXOnTtL4kteMjIyhIcPH2qUk4EDBwoODg6SWLFw4UJBoVCIda0gCMKtW7cEhUIhHDlyREzz8PAQHB0dBZVKJaadOXNGo56Tm4/s5UVuzNGWf/V58++//+a6P9T1ubptIAj/lc/p06eLaTnP2dxia0nTs2dPYfDgwZK0iIgIybmelJQkODg4CMuWLZNMt3XrVsHOzk549uyZIAj/bfOWLVvEaV68eCHY29sLERERgiDIP7fV+z0oKEgjzznrvYKW8ezLv3LlipiWPTZduXJFUCgUws2bN3NdhrbYpS1d3dY7ceKEmKaO0Xv37hUE4XWbU6lUCuHh4ZJlDRkyJN8259vy9OlTQaFQCEePHs11mgEDBgh9+/YVsrKyxLQ7d+4I1tbWku3NLq9zu1GjRsI///wjmV5u+7RVq1bCq1evxLTQ0FChUaNGYmz44YcfBBsbG+HOnTuSeXv37i22beS2rbVRH1Ntdae9vb2QkJAgCMJ/58maNWsk08XExAgKhULYtm2bJL1v375C9+7dJfmVUz/nrGsF4fU1kkKhEP/fsWOHoFAohMuXL+e6XdrKuZzzozBKW5nTRs41qNzlb9myRWjYsKEkn7GxsULDhg0lbYPQ0FDBwcFB/F9uvVaYfV2Q5b+v9WZRyKveknvNNXnyZKFt27ZCamqqmPb06VPBwcFBrIfv3bsnWFtbC6tXr841L+rykZWVJQQFBQnOzs7Cb7/9JpmmW7duGtcu27dvl8SB3I7fJ598IkyZMkX8P+dxp8LLL2bK2de5XXtoaydpS5dbxhQKhbBz504xLSMjQ2jTpo0wceJErfNkZmYK6enpgo+Pj6TNuGjRIqFx48ZCUlKSmHbx4kVBoVBIyllh4xf9J684Jed+pSDIu/5St39++eUXcZoLFy5Iypncexjqe3w5Y5ivr68kXznlLNdy22RTpkzRuDehXtbFixdzXd/7iMOmfUC8vLzw22+/iU9CPn78GBcvXoSXl1eu80RFRaFp06YwMzNDRkYGMjIyoKurC2dnZ/GVtrJly0KpVIqv6P36669wcXGBs7OzmHbx4kU4ODiIT1rJWe7t27eRmpoKT09PSZ7at28ve5tzztuhQwc8evRI8gQZAI2PSP76668wNjZGy5YtxTR9fX14enqKT8M9fPgQjx8/1rqOwjh79iyaNWsGKyurQs3/PjIxMZEMr2diYgI3Nzf8/vvvkumaNm0KIyMjSdqrV68QGhoKT09PKJVKNGrUCCtWrEB8fLzG0HotWrSQ/F+vXj1JGfj999/h4eEhPmkA5P4GRkHZ2Nhgz549+Prrr3H79u03Xl5+23L16lW4u7tLnpDO/mQCFS4WFtVxjIqKgrm5OZo1aybGvoyMDLi5uSEmJgaZmZnIzMzE9evXCxVbbGxskJCQgKlTpyIqKgovX74sVD7//PNPxMXFoVOnTpJ8uri4QFdXt1S9ipyYmIgFCxbAw8MDjRo1Ej+Y+Oeff4rTXLlyBfXr15e8ZVOrVi00aNAgz2W/fPkSv//+O7p16yaJLzmdPHkS/fr1Q+PGjWFjYyM+FfvXX39JpmvQoIHkCUl1ftzc3DTSctZzTZs2lTzh6erqCnNzc0m8lZuP7AoSc3LmX/1tkpx51SZ77NPR0dGIfe+jzMxMxMTEaAxNk3P/RUdHIyUlBR07dtSIG6mpqbhz545k+uz7ysjICNWrVxf3VUHP7ezD4mojt4wXVM2aNWFsbIw5c+bg0KFDsoZCyouurq6kvWFlZYVy5crh0aNHAF63OV+9eqWx74uz/rSwsECNGjWwfPly7NmzR6O8v3z5EpcvX0bHjh2RmZkpHsvatWujWrVqkqEn5J7bCoUC1apVk6TJbZ86OztLntSvV68e0tPTxSFSoqKioFAoULt2bY1yrM5rUbSttc378uVLjbo7Z9lWt+9ztv86deqEGzduICUl5Y3qZ23Onj2LevXqSYbUkqOozw+10lbmtJFzDSp3+deuXYNCoZDk08rKKt+2gVpe9VpB9nVhlv8hk1tv5XfNFRUVhTZt2kBPT088PqamprCxsRHr0nPnzkEQBPTq1SvPPAmCgMmTJ+PIkSP49ttvYW9vL/724sULxMTEaMSZzp07A/gvdtG7l1/MzI2caw+55JYxQFo/6unpoV27dpLrgIcPH2LKlClo2bIlbGxs0KhRI/zyyy+SfF29ehVNmzaFsbGxmNakSROYm5uL/xdF/PrQ5Ren5NyvVMvv+qtBgwb4+OOPcfDgQXGagwcPonbt2lAqlQDk3cNQMzc3l8Qw4PU9ipMnTyIsLAxXrlyRfOpAGzltMrXKlSujfv36GtunbuOXFhw27QPi4eEBQ0NDHDx4ECNGjMBPP/2EsmXLol27drnO8/z5c/z2229o1KiRxm/ZXwd3dnbGTz/9BOD1idanTx9xnFjgdXDp2bNngZYbHx8PAJLX1wGgYsWKcjdZ4zVg9bzx8fGoXr06AMDQ0FAytAzweozgnOtVz5+YmCjJX27rKKiEhARJ0PkQaHtNu0KFCrh7965GWk5LlizBzp07MXbsWNja2sLExARHjx7FV199hVevXkmOac5X0vX19SWvaMbHx2usw9jYGGXLli3UdmU3a9YsmJmZYePGjVi8eDGqVasGX19ffPrpp4Vanpxtyblfi2pbSovCxMKiOo7Pnz9HQkKC1tgHvD5+6ovAwsQWV1dXLF68GN9++y2GDx+OsmXLokOHDpg+fbqkUS0nnwAwduxYrb//+++/spdV0k2dOhXR0dEYO3YsPv74YxgbG2Pbtm1inQa87uDTFocqVKggGW88J5VKhaysLHHIPG2uXLmCMWPGoG3bthgxYgQqVKgAHR0dsR7NztTUVPK/+lXw7HFBfaMp57za8m9paSnWZQXJR3YFiTm55T+v5atpi315jRf/Pnj27JnWcz3nsVKfj+ph/HLKeT7mVU8U9NzWVm6yk1PGC0Mdb0NDQzF58mRkZmaiSZMmmDlzpsY3Q+QoV66cxhBA+vr6YtlTnwfqIdTU8tv+t0lHRwdff/01VqxYgXnz5iElJQWNGjXCtGnT4OzsDJVKhczMTAQHByM4OFhjfvWxLMi5ra2ekds+zXl+54xFz58/x40bN7TWf+obE0XRts6r7Z/XMhMTE6Gvr69RV1asWBGCICApKQkvXrwodP2sTUJCQqHOnaI+P9RKW5nTRu61rZzlP378WOu1jKWl5RvXa3L3dWGX/yGTW2/ld831/PlzbNq0Seu3mNTtm4SEBJQpUybfuiQ9PR3Hjh2Dm5sbFAqF5LekpCQIgqCxDPXQdup7E/Tu5RczcyPn2kMuuWVMX18fZmZmkrQKFSqIdWNWVhZGjx6NpKQk+Pv7o1atWjA0NERoaKgk3sTHx2sdMjp7LCyK+PWhyy9OyblfqSbn+svLywsbN27EnDlzoKuri//973+ST03IuYehHr5YW70+evRo6OrqYs+ePQgPD4elpSUGDBiAsWPHQkdHR2N6OW0y9cPdb3J9+T5h580HpFy5cmjXrh0OHTqEESNG4NChQ/Dw8NB4oyE7MzMztGzZUutYztkvgp2dnbF69WqcPXsWiYmJcHJyQlpaGuLj43H27FnExcWhSZMmBVpupUqVALweM7lKlSri7zk/NJ6XnE+iqedVLxuA1mBhZmam9amtJ0+eiJWeehm5rUNNfQMrPT1dkp5zXGpzc3OND3mWdtqeFHz69Knk+ADaj9Hhw4fRt29f+Pr6imnZP+pYEJUqVdI43snJyUUS8E1MTDBjxgzMmDEDt27dwrfffou5c+dCoVBIzomiUqlSJY39WlTbUloUJhYW1XE0MzODpaUl1q5dq/V3S0tL6OnpoUyZMvnGltx4e3vD29sbz549w9GjRxEcHIwyZcrgyy+/lJ1PdUMpKCgIdnZ2Gr8X9Y3a4vLq1SucOHECU6dOlXxceOvWrZLpKleujOvXr2vM//TpU8mTZzmZmJhAV1c3z9geGRkJY2NjhISEiG+vxMXFFXRT8qWtTnv27JkYbwubD8acwrO0tNR6ruc8Vup2R3h4uNbvuhTkjd2Cntva6t/s5JTxwrKzs8P69euRmpqK8+fPY9GiRRg7diwiIyOLfF3q8+D58+eSNmdxf1i3Tp06CA0NRXp6OqKjo7F8+XKMGjUKp06dgomJCXR0dDBy5EitDx+oO6IKcm5rO95F1T41MzODtbU1vvjii1ynkdu2zouctn9u+UtPT0diYqLkBteTJ0+go6MDExMTlC1bVnb9bGBgIKvtf+vWrfw3Sou3dX6UpjKnjdxrWzkqV66MmJgYjfRnz55pPBhYUHL3NRVcUdVbZmZmaN26tdYHudTH39zcHBkZGXj69GmeN9cNDAywZs0ajBgxAnPmzJF8PFxdFnLGnaSkJKSlpYnxSu49BypaecVMbeRee8glt4xpq9+y33e5f/8+bty4gYiICEnMSU1NlSxHW7sfkNa9jF9vLr84Jed+ZUF4eXlh5cqVOH36NAwMDPDs2TPJqCRy7mGoaavXDQwM4OfnBz8/P9y/fx+7du1CWFgYrKys0K1bN63bl1+b7EPDYdM+MF26dMGNGzdw+vRp/Pbbb3kOEwS8Horl7t27qFevHpRKpeQv+5Ndjo6OKFOmDFatWoWGDRvC2NgYlpaW+Pjjj7Fq1Sro6+vDwcGhQMtVKBQoV64cjhw5IsmT+sPicuSc93//+x8qV66c50dtAaBx48ZITk7GL7/8IqZlZGQgMjISjRs3BvD6g6GVKlXSuo7sKlSoAH19fcnbJGlpaeKQcmqurq44d+5cnh/4zf6UaGmQlJSEs2fPSv4/c+aMxmuW2rx69UryAbLMzEzJq54FYWdnh+PHj0te9zx8+HChlpUXa2trTJs2DQDE8qDehrw+1lYQSqUSJ06ckLyK+jZudL3vChoLs9N2HHPK7YkPNzc3PHv2DPr6+hqxT6lUwsDAAHp6erCxsck3tuTH0tISvXv3RvPmzXHv3r1cp9MWV+rWrYuqVasiNjZWaz6z39x8n6WlpSErK0sSS5KTk3Hs2DHJdEqlEnfu3MH9+/fFtPv37+PmzZt5Lt/IyAgODg7Yt2+fJL5kl5qaCn19fUlDd//+/YXZnDydP39e8sTt2bNnkZCQIMbbwuajpMacoo6tb4Oenh4aNmyIo0ePStJz7j9HR0cYGhri4cOHWs/HglwEF/W5LaeMv6ly5cqhdevW6N+/Px48eCDGq6JsE9WvXx9ly5bV2PcloSwDr7fVxcUFvr6+SE5OxuPHj8V9f+/ePa3HUt2p96YxRk77VA43NzfExsaicuXKWvMLyG9b50XbvIaGhhpPs+ekbt/nbP8dPnwYNjY2MDIyKlD9XLVqVY02wpkzZyT/q6+Hcg4XnF1+5Ty38+NNlYYyp43ca1s5bG1tcevWLcTGxoppDx48yLdtIIfcfV2U3od6sygUVb3l6uqKO3fuwMbGRuP4qD8w36xZM+jo6GDXrl35Lq9JkyZYtWoV9u7dK+nkLl++PBo2bKgRm9Rvaahjl7r+zt7mv3v3rsZbDqXtfkJJoS1matvXcq895L49UJAylr3uyszMRGRkpHgdkL1tpRYXF4fo6GjJMpRKJc6dO4fk5GQx7ddff0VCQoL4f3HEr9Imvzgl535lQdSqVQtKpRIHDx7EwYMH0bBhQ9SrV0/8Xc49jIKs6/PPP4e5uXmu9yjktMk+NHzz5gPj5uYGc3NzTJ8+HaampuL4w7kZOnQo9u/fj4EDB2Lw4MGoXr06nj17ht9//x1VqlTB0KFDAfzXqLhw4QKGDRsmzt+kSRNs27YNTk5OkiFU5CzX3Nwc/fr1w7p161CuXDnY2Njg4MGD+Pvvv2Vv77lz57Bo0SI0b94cUVFR2LdvH4KCgiRj82vj7u4OOzs7TJo0CQEBAahYsSI2b96Mx48fIzQ0FMDrmy6+vr744osvUKFCBXEd58+flyxLV1cXnp6e+O6771CrVi1YWFhgy5YtEARBclEzdOhQ7Nu3DwMHDsTo0aPx0UcfITY2Fn/99RcmTZoE4PVNl3PnziEqKgqmpqawsrJ6r59cMDc3x4wZM+Dv7w8TExOsW7cOgiBgyJAh+c7r5uaGnTt34uOPP4aFhQW2bt1a6IsNX19f9OrVC2PHjhUvfr/++usiGWqsX79+8PT0RP369aGnp4e9e/dCX19ffFtDXSl+9913aNeuHcqVK/dGQ16MHDkSvXr1gp+fH/r06YN//vkHGzZsQNmyZfN9gvpDUtBYmN9xzKlSpUowNTXFwYMHYWVlBQMDA1hbW6N58+bw8PDAZ599hs8++wzW1tZ4+fIl/vjjD9y/f1+8WBs1ahTGjBmDadOmoXPnzrh+/Tr27duX73aFhoYiISEBLi4uqFChAm7fvo3Tp0+LsVqbunXrYu/evTh27BgqVaqEypUro0qVKpg6dSoCAwORkpICd3d3GBoa4p9//sHJkycxceJE1KlTJ9/8lHQmJiZQKpVYt26d+BbE2rVrYWxsLHmCrEePHvjqq68wcuRI8Wnd0NBQWUPlBAQEYOjQoRg6dCg+/fS3EG11AAARBklEQVRTmJmZ4fr167CwsECvXr3QvHlzbNq0CfPnz4enpyeio6NlHeuCKl++PEaMGIERI0YgKSkJS5cuhZ2dnThWcmHzUVJjTlHH1rdl9OjRGDNmDGbOnImOHTvixo0b2Lt3LwCIbRVTU1P4+/tjyZIlePjwIVxcXKCnp4fY2FgcPXoUYWFhMDQ0lLU+HR2dIj+38yvjhXHixAn88MMPaNeuHapXr44nT55gy5YtkvZkbrGrMCwsLNC/f3+sXr0aZcuWFW+Uqb/PkV+78W24efMmFi1ahM6dO+Ojjz5CcnIy1qxZgxo1aojDO02ePBlDhgzBhAkT4OXlBVNTUzx8+BBnzpxBjx490LRp0zeOMXLap3J069YN27dvx+DBg+Hj44PatWsjKSkJN27cQHp6OgICAmS3rfPy999/i3XnjRs3sHbtWgwZMiTfJ1EbNGiA9u3bY+HChUhNTUWdOnXw448/Ijo6GqtWrRKnk1s/d+jQAZs2bYJSqRSXlXP8dW9vb2zduhW+vr4YN24c6tevj0ePHuHXX3/F/PnzAWgv5zExMfmeH4VR2spcbsuWc20rR8+ePbF69WqMGjUKfn5+AF6/IVmxYsUiqf/k7Oui9L7Um0Uhr3qrWbNmspbh7++PXr16Yfjw4ejTpw8qVqyIJ0+e4MKFC2jSpAm6dOmCOnXqoF+/fli5ciUSExPh6uqK1NRUnDhxAn5+fhp1lqurK8LCwjB27FgYGhri888/BwCMGzcOY8eORWBgID755BP8+eefWLFiBTp06CAeI3t7e1SrVg1ffvklAgICkJycjLVr12oMO1SvXj3s2rULBw4cEO9N8GZ64eQXM3Pb13KuPWrXrg09PT3s2rULZcqUgZ6envigQ3Zyy5i+vr44vLyVlRW2bduGhw8fIiIiAsB/D/csW7YMWVlZSElJQWhoqMYb2UOHDsW2bdswcuRIDB8+HCqVChEREbCwsJDEvXcdv0qjvOJU9+7d871fWVBdunTBypUroaenh1GjRkl+k3sPIzdjxoxBo0aNYGNjA0NDQxw/fhyJiYm5xlu5bbIPCTtvPjD6+vro0KEDduzYgV69euXbQ2phYYEdO3YgJCQES5cuRUJCAipUqAB7e3uNj3WqP/SY/Wami4sLtm3bpjHmp9zlBgQEIDMzE+vXr0dWVhY8PT0REBCAyZMny9reefPmYceOHdi2bRvKly+P8ePHY8CAAfnOp6enh7Vr12Lx4sVYsmSJOH7phg0bYGtrK043aNAgqFQqbN26Fdu2bYOrqysWLFiAzz77TLK8WbNmYdasWViwYAHKly+P4cOHo06dOpKnbS0sLLBt2zYsW7YMS5cuxcuXL1GjRg3Jq9iff/455syZAz8/P7x48QLBwcHo0aOHrH1RElWqVAmBgYFYvHgx/v77b9SvXx9ff/21rBuis2bNwuzZszF//nwYGhqie/fu8PT0xMyZMwucDxsbG6xcuRJLly4VL55XrFiB4cOHF2azJJycnLB37148ePAAurq6UCgUWL16tXiBZGNjAz8/P+zcuRPr169HtWrVNJ68Kei2hISEYNmyZeK2LFy4EIMHD/4gXy/NTUFjYX7HMSddXV0EBwdj+fLlGDp0KNLS0nD06FFYWVkhNDQUa9euxbZt2xAXFwcTExPUr19fci63bdsWc+fOxerVq3Hw4EHY29sjJCQEvXv3zjOfSqUSmzZtwk8//YTk5GRUrVoVw4cPx+jRo3OdZ8SIEfj7778xZcoUqFQqjBs3Dn5+fujUqRNMTU2xevVq8YnZGjVqoGXLloUe378kWrZsGYKCgjB16lSYm5tj0KBBSElJwYYNG8RpypUrhw0bNmDOnDmYNGkSqlSpgjFjxuDo0aP5jh/fpEkTfPvttwgJCcG0adOgq6uL+vXrY8KECQCA1q1bIzAwEFu2bMHu3bvh5OSENWvWFPoD2Lnx9PRE1apVMXv2bKhUKri5uWHu3Lni74XNR0mNOUUdW9+Wtm3bYs6cOVizZg1+/PFH2NvbY86cOfDx8ZEMyefj44MqVapg48aN2LJlC8qUKYOaNWvC3d1d8pSkHEV9budXxgujZs2a0NXVRUhICJ4+fQpzc3O0aNFCvJEF5B67CisgIAAZGRlYu3at2Ob09fXFvHnziqUsV6pUCRUrVsSaNWvw6NEjmJiYoEmTJliyZIn4jRgnJyds3boVYWFhmDZtGtLT01G1alU0a9YMtWrVAvDmMUZO+1QOAwMDfPvttwgLC8Pq1asRHx8Pc3Nz2NjYSJYlt22dm4kTJ+LChQsYP3489PT08Omnn2LixImy5l2yZAmWL1+OdevWISEhAXXr1kVoaCjatGkjTiO3fh4zZgyePn2KiIgI6OjooG/fvhg8eDAWLlwo2SfffPMNVqxYgTVr1iAhIQFVq1aVvA2srZx7eXnle34URmkrc7ktW+61bX7UbYPZs2cjMDBQbBvs3bu3SGKGnH1dlN6XerMoFEW9VatWLezcuRMhISGYO3cuUlJSUKlSJTg7O0s6vYKCgmBlZYWdO3di06ZNMDc3h7Ozc65D67Vu3RohISEYP348ypYti7Fjx6Jt27ZYuXIlIiIiMGbMGJibm6NPnz4ICAgQ59PX10d4eDjmzJmD8ePHo2bNmpg+fbok5gBAr169cOXKFcyfPx8JCQno3r27xjQkT34xM7d9Lefaw9LSEkFBQVi/fj1+/PFHZGRk5DrMppwypq+vj+XLl2Pu3Lm4ffu2eE3aoEEDAK/ro7CwMMybNw/jx49HtWrVMHr0aJw7dw7Xrl0Tl1O5cmWsW7cOCxYsgL+/P2rWrIkZM2ZotJXedfwqjfKKU3LvVxZEp06dsGjRIgiCoHVUEjn3MHLj5OSEn376CRs3bkRmZibq1KmDpUuXws3NLdd55LTJPiQ6giAIxZ0JoqJ2/vx5DB48GD/88IPWJxSIPiRnz57F0KFDsXnzZri4uBR3dojoHWvTpg3c3d0RFBT0TtbHmPNmdu7ciZkzZ4odvlR8Jk2ahEuXLpXaG5ilyYMHD8QbnB07dizu7NAHKiEhAe3atcPQoUMxbty44s4OERHCwsKwYcMGjSHQispff/2FTp064csvv0T37t3fyjqIPnR884aIqJSZM2cOXF1dYW5ujj/++AOrVq2CjY1NrkN8ERG9CcacwktISEB4eDiaNWuG8uXL4+rVq1i9ejXatm3Ljpt37MKFC7h8+TIaNWqErKwsnDhxAvv378fUqVOLO2tEVEKtXbsWFStWRI0aNRAfH48NGzYgMzMTPXv2LO6sERG9FcuWLYO1tTUqV66M2NhYrFmzBpUqVUL79u2LO2tEpRY7b4iIShmVSiW+nm1sbIyWLVtiypQpxTJmPxGVfow5hVemTBnExsbiwIEDSEpKgoWFBby9vREYGFjcWfvgGBkZ4cSJE1i3bh1evXqFGjVqYOrUqQX6BgYRfVh0dXXx1Vdf4dGjR9DT04O9vT02bdqEatWqFXfWiIjeivT0dCxduhRPnjxBuXLl4OLigsmTJ+c6FCARvTkOm0ZERERERERERERERFSC8JFIIiIiIiIiIiIiIiKiEoSdN0RERERERERERERERCUIO2+IiIiIiIiIiIiIiIhKEHbeEBERERERERERERERlSDsvCEiIiIiIiIiIiIiIipByhR3BoiIiIiIiEqS3bt3Y9q0abn+vmPHDjg4OLzDHEldvnwZUVFRGDJkCExNTYstH0RERERE9Paw84aIiIiIiEgLf39/WFlZaaTXrFmzGHLzn+joaISHh6N79+7svCEiIiIiKqXYeUNERERERKRFq1atoFQqizsbRERERET0AeI3b4iIiIiIiAogPT0dLi4uWodWS05OhlKpxKJFi8S0tLQ0hIaGwtPTE7a2tmjdujUWL16MtLQ0ybzW1taYN28eIiMj0aVLF9ja2sLLywunTp0SpwkLC8PixYsBAG3btoW1tTWsra3x4MEDAEBGRgYiIiLQrl072Nraok2bNli+fLnGuoiIiIiIqGTjmzdERERERERaJCcn49mzZ5I0HR0dWFhYoF27djhy5Ajmzp0LAwMD8ffIyEikpaWhc+fOAICsrCyMHj0aly5dQp8+fVCvXj3cvn0bmzZtwl9//YVVq1ZJln/p0iX8/PPP+PTTT1G+fHls3rwZ/v7+OH78OCwsLODp6Ym//voLBw4cwLRp02BhYQEAsLS0BADMnDkTe/bsQYcOHTBs2DBcuXIFa9aswd27dxEREfE2dxcRERERERUhdt4QERERERFpMXToUI00AwMDXL16FZ07d8auXbsQFRUFDw8P8fdDhw7ho48+Eodb279/P86cOYPNmzejSZMm4nT169fH7NmzcfnyZTg5OYnpd+/exaFDh8Tv6jRt2hTe3t44ePAgBg4ciAYNGsDGxgYHDhxAu3btJN/kuXnzJvbs2YPevXtjwYIFAIABAwbA0tISGzZswLlz59CsWbMi3UdERERERPR2sPOGiIiIiIhIi6CgINSpU0eSpqv7euTpZs2awcLCAocOHRI7bxITE3HmzBn4+PiI0x8+fBj16tVD3bp1JW/xqDtRzp8/L+m8cXNzEztuAKBBgwYwNjZGbGxsvvk9efIkAGDYsGGSdB8fH2zYsAEnT55k5w0RERER0XuCnTdERERERERa2NnZiW/Q5FSmTBm0b98eBw4cQFpaGgwMDPDzzz8jPT1dHDINAO7fv4+7d+/C1dVV63KePn0q+b9atWoa05iZmUGlUuWb37i4OOjq6ko6fwCgUqVKMDU1RVxcXL7LICIiIiKikoGdN0RERERERIXg5eWFHTt24NSpU2jXrh0OHz6MunXrokGDBuI0WVlZUCgUmDZtmtZlVK1aVfK/np6e1ukEQZCdLx0dHdnTEhERERFRycTOGyIiIiIiokJwdnZGpUqVcOjQITg5OeHcuXMYNWqUZJqaNWvi5s2bcHV1LbJOldyWU6NGDWRlZeH+/fuoV6+emP7kyROoVCrUqFGjSNZPRERERERvn25xZ4CIiIiIiOh9pKuri44dO+L48eP48ccfkZGRIRkyDQA6deqER48e4fvvv9eYPzU1FSkpKQVer6GhIQAgKSlJkt66dWsAwKZNmyTpGzdulPxOREREREQlH9+8ISIiIiIi0uLUqVO4d++eRrqTkxM++ugjAK87ZzZv3ozQ0FAoFArJGy8A4O3tjZ9++gmzZ8/G+fPn4eTkhMzMTNy7dw+HDx/G+vXrc/2uTm4aNWoEAFixYgU6d+4MfX19eHh4oEGDBujevTt27NgBlUoFZ2dnXL16FXv27EG7du3QrFmzQu4JIiIiIiJ619h5Q0REREREpEVoaKjW9ODgYLHzxsnJCdWqVcO///6r8dYN8PrtnIiICHzzzTfYt28fjhw5AkNDQ1hZWWHQoEGoU6dOgfNlZ2eH8ePHY/v27Th9+jSysrJw9OhRGBkZYcGCBbCyssKePXsQGRmJihUrYuTIkRg3blyB10NERERERMVHRyjIly+JiIiIiIiIiIiIiIjoreI3b4iIiIiIiIiIiIiIiEoQdt4QERERERERERERERGVIOy8ISIiIiIiIiIiIiIiKkHYeUNERERERERERERERFSCsPOGiIiIiIiIiIiIiIioBGHnDRERERERERERERERUQnCzhsiIiIiIiIiIiIiIqIShJ03REREREREREREREREJQg7b4iIiIiIiIiIiIiIiEoQdt4QERERERERERERERGVIOy8ISIiIiIiIiIiIiIiKkHYeUNERERERERERERERFSCsPOGiIiIiIiIiIiIiIioBPk/m3FOpss1vNoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1970x927 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(19.7,9.27)})\n",
    "ax = sns.barplot(x=eventosPopulares.index, y=eventosPopulares.event)\n",
    "ax.set_title('Eventos más populares')\n",
    "ax.set(xlabel='Evento', ylabel='Cantidad')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Ejercico 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            NaN\n",
       "1            NaN\n",
       "2           2694\n",
       "3           2694\n",
       "4          15338\n",
       "5            NaN\n",
       "6            NaN\n",
       "7          15338\n",
       "8            NaN\n",
       "9            NaN\n",
       "           ...  \n",
       "1011278      NaN\n",
       "1011279      NaN\n",
       "1011280      NaN\n",
       "1011281     8513\n",
       "1011282     8512\n",
       "1011283     8512\n",
       "1011284      NaN\n",
       "1011285      NaN\n",
       "1011286     8527\n",
       "1011287     8527\n",
       "Name: sku, Length: 1011288, dtype: object"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_rows', 20)\n",
    "eventos['sku']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "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>cantidad eventos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6371.0</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6370.0</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7631.0</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2692.0</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3371.0</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cantidad eventos\n",
       "6371.0                29\n",
       "6370.0                18\n",
       "7631.0                17\n",
       "2692.0                16\n",
       "3371.0                14"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sku_mayores = eventos.loc[eventos['event'] == 'conversion', 'sku'].to_frame()\n",
    "sku_mayores = pd.DataFrame({'sku':sku_mayores['sku'], 'hola':sku_mayores.index})\n",
    "sku_mayores = sku_mayores.set_index('sku').index.value_counts().head(5).to_frame()\n",
    "sku_mayores.rename(columns={'sku':'cantidad eventos'}, \n",
    "                 inplace=True)\n",
    "sku_mayores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Número de Sku'), Text(0.5,0,'Cantidad de compras')]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1970x927 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(19.7,9.27)})\n",
    "ax = sns.barplot(x=sku_mayores.index, y=sku_mayores['cantidad eventos'])\n",
    "ax.set_title('Sku con mayores cantidad de compras')\n",
    "ax.set(xlabel='Cantidad de compras', ylabel='Número de Sku')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Ejercicio 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cantidad personas'], dtype='object')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "personActive = eventos['person'].value_counts().head(10).to_frame()\n",
    "personActive.rename(columns={'person':'cantidad personas'}, \n",
    "                 inplace=True)\n",
    "personActive.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Cantidad de compras'), Text(0.5,0,'Usuario')]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1970x927 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(19.7,9.27)})\n",
    "ax = sns.barplot(x=personActive.index, y = personActive['cantidad personas'])\n",
    "ax.set_title('10 usuarios con más compras')\n",
    "ax.set(xlabel='Usuario', ylabel='Cantidad de compras')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

## gistfile1.txt
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importacion general de librerias y de visualizacion (matplotlib y seaborn)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import cufflinks as cf\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('default') # haciendo los graficos un poco mas bonitos en matplotlib\n",
    "#plt.rcParams['figure.figsize'] = (20, 10)\n",
    "\n",
    "sns.set(style=\"whitegrid\") # seteando tipo de grid en seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vista previa de los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/flox/.local/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2785: DtypeWarning:\n",
      "\n",
      "Columns (4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "eventos = pd.read_csv(\"events.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['timestamp', 'event', 'person', 'url', 'sku', 'model', 'condition',\n",
       "       'storage', 'color', 'skus', 'search_term', 'staticpage',\n",
       "       'campaign_source', 'search_engine', 'channel', 'new_vs_returning',\n",
       "       'city', 'region', 'country', 'device_type', 'screen_resolution',\n",
       "       'operating_system_version', 'browser_version'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventos.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            NaN\n",
       "1            NaN\n",
       "2           2694\n",
       "3           2694\n",
       "4          15338\n",
       "5            NaN\n",
       "6            NaN\n",
       "7          15338\n",
       "8            NaN\n",
       "9            NaN\n",
       "           ...  \n",
       "1011278      NaN\n",
       "1011279      NaN\n",
       "1011280      NaN\n",
       "1011281     8513\n",
       "1011282     8512\n",
       "1011283     8512\n",
       "1011284      NaN\n",
       "1011285      NaN\n",
       "1011286     8527\n",
       "1011287     8527\n",
       "Name: sku, Length: 1011288, dtype: object"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_rows', 20)\n",
    "eventos['sku']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23259624"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventos.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventos.columns.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iPhone 6                                     50916\n",
       "iPhone 5s                                    44878\n",
       "iPhone 6S                                    39296\n",
       "iPhone 7                                     27556\n",
       "Samsung Galaxy S7 Edge                       23926\n",
       "Samsung Galaxy S7                            19337\n",
       "iPhone 7 Plus                                18792\n",
       "Samsung Galaxy S8                            16959\n",
       "iPhone SE                                    16531\n",
       "Samsung Galaxy J5                            13044\n",
       "                                             ...  \n",
       "iPad Mini 2 Wi-Fi + 4G                           4\n",
       "Motorola Moto G1 3G                              4\n",
       "LG L80 Dual                                      3\n",
       "Samsung Galaxy Tab A 2016 10.1  W-Fi + 4G        3\n",
       "iPad 2 Wi-Fi                                     3\n",
       "Quantum Muv Up                                   2\n",
       "Quantum GO 3G                                    2\n",
       "Samsung Galaxy Young 2 Duos TV                   1\n",
       "Blackberry Blackberry Z30                        1\n",
       "Motorola RAZR I                                  1\n",
       "Name: model, Length: 202, dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_rows', 20)\n",
    "eventos['model'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Windows 7           19675\n",
       "Windows 10          17110\n",
       "Android 7            9964\n",
       "Android 6.0.1        7805\n",
       "Android 6            6871\n",
       "Android 5.1.1        4123\n",
       "Android 7.1.1        3325\n",
       "Windows 8.1          2781\n",
       "Android 5.0.2        1640\n",
       "Android 4.4.2        1402\n",
       "                    ...  \n",
       "Android 6.3             1\n",
       "Mac OS X 10.10.3        1\n",
       "iOS 9.1                 1\n",
       "Mac OS X 10.11.1        1\n",
       "iOS 8.1                 1\n",
       "iOS 11.4.1              1\n",
       "Mac OS X 10.6.4         1\n",
       "Mac OS X 10.10.4        1\n",
       "Mac OS X 10.10.1        1\n",
       "iOS 9.2.1               1\n",
       "Name: operating_system_version, Length: 121, dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_rows', 20)\n",
    "eventos['operating_system_version'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Ejercicio 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "viewed product       528931\n",
       "brand listing         98635\n",
       "visited site          87378\n",
       "ad campaign hit       82827\n",
       "generic listing       67534\n",
       "searched products     56073\n",
       "search engine hit     50957\n",
       "checkout              33735\n",
       "staticpage             3598\n",
       "conversion             1172\n",
       "Name: event, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_rows', 20)\n",
    "eventos['event'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "eventosPopulares = eventos['event'].value_counts().head(10).to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "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>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>viewed product</th>\n",
       "      <td>528931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand listing</th>\n",
       "      <td>98635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visited site</th>\n",
       "      <td>87378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ad campaign hit</th>\n",
       "      <td>82827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generic listing</th>\n",
       "      <td>67534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>searched products</th>\n",
       "      <td>56073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>search engine hit</th>\n",
       "      <td>50957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>checkout</th>\n",
       "      <td>33735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>staticpage</th>\n",
       "      <td>3598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conversion</th>\n",
       "      <td>1172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    event\n",
       "viewed product     528931\n",
       "brand listing       98635\n",
       "visited site        87378\n",
       "ad campaign hit     82827\n",
       "generic listing     67534\n",
       "searched products   56073\n",
       "search engine hit   50957\n",
       "checkout            33735\n",
       "staticpage           3598\n",
       "conversion           1172"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eventosPopulares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Cantidad'), Text(0.5,0,'Evento')]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1970x927 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(19.7,9.27)})\n",
    "ax = sns.barplot(x=eventosPopulares.index, y=eventosPopulares.event)\n",
    "ax.set_title('Eventos más populares')\n",
    "ax.set(xlabel='Evento', ylabel='Cantidad')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Ejercico 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            NaN\n",
       "1            NaN\n",
       "2           2694\n",
       "3           2694\n",
       "4          15338\n",
       "5            NaN\n",
       "6            NaN\n",
       "7          15338\n",
       "8            NaN\n",
       "9            NaN\n",
       "           ...  \n",
       "1011278      NaN\n",
       "1011279      NaN\n",
       "1011280      NaN\n",
       "1011281     8513\n",
       "1011282     8512\n",
       "1011283     8512\n",
       "1011284      NaN\n",
       "1011285      NaN\n",
       "1011286     8527\n",
       "1011287     8527\n",
       "Name: sku, Length: 1011288, dtype: object"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('max_rows', 20)\n",
    "eventos['sku']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "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>cantidad eventos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6371.0</th>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6370.0</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7631.0</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2692.0</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3371.0</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cantidad eventos\n",
       "6371.0                29\n",
       "6370.0                18\n",
       "7631.0                17\n",
       "2692.0                16\n",
       "3371.0                14"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sku_mayores = eventos.loc[eventos['event'] == 'conversion', 'sku'].to_frame()\n",
    "sku_mayores = pd.DataFrame({'sku':sku_mayores['sku'], 'hola':sku_mayores.index})\n",
    "sku_mayores = sku_mayores.set_index('sku').index.value_counts().head(5).to_frame()\n",
    "sku_mayores.rename(columns={'sku':'cantidad eventos'}, \n",
    "                 inplace=True)\n",
    "sku_mayores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Número de Sku'), Text(0.5,0,'Cantidad de compras')]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1970x927 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(19.7,9.27)})\n",
    "ax = sns.barplot(x=sku_mayores.index, y=sku_mayores['cantidad eventos'])\n",
    "ax.set_title('Sku con mayores cantidad de compras')\n",
    "ax.set(xlabel='Cantidad de compras', ylabel='Número de Sku')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3) Ejercicio 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cantidad personas'], dtype='object')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "personActive = eventos['person'].value_counts().head(10).to_frame()\n",
    "personActive.rename(columns={'person':'cantidad personas'}, \n",
    "                 inplace=True)\n",
    "personActive.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,'Cantidad de compras'), Text(0.5,0,'Usuario')]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1970x927 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(19.7,9.27)})\n",
    "ax = sns.barplot(x=personActive.index, y = personActive['cantidad personas'])\n",
    "ax.set_title('10 usuarios con más compras')\n",
    "ax.set(xlabel='Usuario', ylabel='Cantidad de compras')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}