sampling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "import gc\n",
    "from sksurv.preprocessing import OneHotEncoder\n",
    "from sksurv.linear_model import CoxPHSurvivalAnalysis\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.decomposition import NMF, PCA\n",
    "from sklearn.externals import joblib\n",
    "from tempfile import mkdtemp\n",
    "from shutil import rmtree\n",
    "from sklearn.externals.joblib import Memory\n",
    "import random\n",
    "# graphics TP1\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib_venn import venn2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import up sound alert dependencies\n",
    "from IPython.display import Audio, display\n",
    "\n",
    "def allDone():\n",
    "    display(Audio(url='https://sound.peal.io/ps/audios/000/000/537/original/woo_vu_luvub_dub_dub.wav', autoplay=True))\n",
    "## Insert whatever audio file you want above"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sampling\n",
    "---\n",
    "\n",
    "## objetivo\n",
    "\n",
    "Escoger suficientes device IDs para que auctions sean 100,000 registros.\n",
    "\n",
    "## Hipotesis\n",
    "\n",
    "Un sample de device IDs obteniendo todos sus registros relacionados en los diferentes datasets al device ID conservan las propiedades de la distribucion de los datos.\n",
    "\n",
    "## Pasos\n",
    "\n",
    "1. Procesar con el TP1 los nuevos datos\n",
    "2. Hacer un archivo con todos los device IDs de Auctions\n",
    "3. Seleccionar al azar device IDs del archivo y buscar todos sus registros en auctions hasta que sean 100,000 registros\n",
    "4. Procesar el sample como el TP1 para ver que la muestra sea valida. Es decir los graficos tienen que ser parecidos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Procesar con el TP1 los nuevos datos\n",
    "\n",
    "1. ¿Qué franja horaria contuvo la mayor cantidad de subastas?\n",
    "2. Una vez que la etapa de subasta terminó y se realizó la impresión en el dispositivo, ¿dónde nuestros consumidores realizaron clicks en la pantalla? ¿por qué ahí? La concentración de clicks en la pantalla al aparecer una publicidad tienen un por qué, y es lo que intentaremos definir.\n",
    "3. ¿Qué franja horaria contuvo la mayor cantidad de events?\n",
    "4. ¿Cuáles clicks generaron conversiones en events?\n",
    "5. ¿En qué franja horaria se produjo la mayor cantidad de instalaciones?¶\n",
    "6. ¿Cuáles fueron las 4 aplicaciones más instaladas?\n",
    "7. ¿Estar conectado a internet vía wifi tuvo peso a la hora de definir si el consumidor instaló ?\n",
    "8. ¿Cuáles fueron las Apps más instaladas con wifi?\n",
    "9. Si el tipo de conexión en installs tuvo gran importancia ¿tendrá un peso similar la conexión vía wifi en los eventos?\n",
    "10. ¿Cuáles clicks generaron conversiones en installs?\n",
    "11. ¿Cuáles fueron las distribuciones de los eventos de la muestra ?¿Y de los installs?\n",
    "12. ¿Cuáles fueron los tiempos de conversión ?\n",
    "13. ¿Cuál fue la distribución de los eventos de Jammp ? Los más frecuentes.\n",
    "14. Eventos por usuario.\n",
    "15. Eventos por aplicación.\n",
    "16. ¿Se intersectarán los clicks e installs según el usuario? ¿Existirá una proporción?\n",
    "17. ¿Existirá una relación entre los clientes y las aplicaciones ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_str_float(ds, col):\n",
    "    ds[col] = ds[col].str.replace(r'[^0-9\\.]','')\n",
    "    ds[col] = np.where(ds[col]=='',np.nan,ds[col])\n",
    "    ds[col] = ds[col].astype('float32')\n",
    "    return ds[col].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source src=\"https://sound.peal.io/ps/audios/000/000/537/original/woo_vu_luvub_dub_dub.wav\" type=\"audio/x-wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setup done\n"
     ]
    }
   ],
   "source": [
    "# clicks\n",
    "clicks_df = pd.read_csv('data/clicks.csv', low_memory=False,dtype={'advertiser_id':'int32','action_id':'float32','source_id':'int32','country_code':'category',\\\n",
    "                                                 'latitude':'float32','longitude':'float32','carrier_id':'float32','os_minor':'category',\\\n",
    "                                                  'os_major':'category','specs_brand':'category','timeToClick':'float32','ref_type':'category'\\\n",
    "                                                                  ,'ref_hash':'object'})\n",
    "\n",
    "clicks_df['touchX'] = fix_str_float(clicks_df,'touchX')\n",
    "clicks_df['touchY'] = fix_str_float(clicks_df,'touchY')\n",
    "clicks_df['created'] = pd.to_datetime(clicks_df['created'])\n",
    "#events\n",
    "events_df = pd.read_csv('data/events.csv', low_memory=False, dtype={'event_id':'int32','ref_type':'category','application_id':'category',\\\n",
    "                                                                                            'attributed':'bool','device_countrycode':'category','device_city':'category',\\\n",
    "                                                                                            'trans_id':'category','carrier':'category','device_os':'category',\\\n",
    "                                                                                            'connection_type':'category'})\n",
    "events_df['date'] = pd.to_datetime(events_df['date'])\n",
    "events_df['wifi'].astype('bool', inplace=True)\n",
    "events_df.drop(columns=['device_countrycode','session_user_agent','ip_address','device_language'], inplace=True)\n",
    "# installs\n",
    "installs_df = pd.read_csv('data/installs.csv', low_memory=False, dtype={'ref_type':'category','application_id':'category',\\\n",
    "                                                      'device_brand':'category','ref_hash':'object','wifi':'category'})\n",
    "installs_df['kind'] = installs_df['kind'].str.lower()\n",
    "installs_df['kind'] = installs_df['kind'].astype('category')\n",
    "installs_df.drop(columns=['session_user_agent','ip_address','device_language','device_model'], inplace=True)\n",
    "installs_df['created'] = pd.to_datetime(installs_df['created'])\n",
    "installs_df.drop(['device_countrycode'], axis=1, inplace=True)\n",
    "# auctions\n",
    "auctions_df = pd.read_csv('data/auctions.csv', low_memory=False, dtype={'country':'category','platform':'category',\\\n",
    "                                                                        'ref_type_id':'category','source_id':'category','device_id':'object'})\n",
    "\n",
    "auctions_df['date'] = pd.to_datetime(auctions_df['date'])\n",
    "allDone()\n",
    "print('setup done')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. ¿Qué franja horaria contuvo la mayor cantidad de subastas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source src=\"https://sound.peal.io/ps/audios/000/000/537/original/woo_vu_luvub_dub_dub.wav\" type=\"audio/x-wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "auctions_df.drop_duplicates(inplace=True)\n",
    "auctions_df['hora'] = auctions_df['date'].apply(lambda x: ('%02d-%02d' % (x.hour, (x.hour+1))))\n",
    "auctions_df['dia'] = auctions_df['date'].dt.day_name()+' '+auctions_df['date'].dt.day.map(str)\n",
    "pivot_auctions = auctions_df.groupby(['dia','hora']).size().reset_index()\n",
    "pivot_auctions.columns = ['dia','hora', 'count']\n",
    "pivot_auctions = pivot_auctions.pivot_table(index='hora', columns='dia', values='count',aggfunc='sum', fill_value=0, dropna=False)\n",
    "pivot_auctions = pivot_auctions[['Thursday 18', 'Friday 19', 'Saturday 20', 'Sunday 21', 'Monday 22', 'Tuesday 23', 'Wednesday 24', 'Thursday 25', 'Friday 26']]\n",
    "allDone()\n",
    "#display(pivot_auctions)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2880x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (40,5))\n",
    "plt.title('Auctions per hour')\n",
    "sns.heatmap(pivot_auctions.T, square = True, linewidth=1, cmap = 'Reds');\n",
    "plt.xticks(rotation = 0);\n",
    "plt.xlabel('Time');\n",
    "plt.ylabel('Day', rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "367583"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_auctions.max().max() #275362"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. ¿Qué franja horaria contuvo la mayor cantidad de events?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_df['hora'] = events_df['date'].apply(lambda x: ('%02d-%02d' % (x.hour, (x.hour+1))))\n",
    "events_df['dia'] = events_df['date'].dt.day_name()+' '+events_df['date'].dt.day.map(str)\n",
    "pivot_events = events_df.groupby(['dia','hora']).size().reset_index()\n",
    "pivot_events.columns = ['dia','hora', 'count']\n",
    "pivot_events = pivot_events.pivot_table(index='hora', columns='dia', values='count',aggfunc='sum', fill_value=0, dropna=False)\n",
    "pivot_events = pivot_events[['Thursday 18', 'Friday 19', 'Saturday 20', 'Sunday 21', 'Monday 22', 'Tuesday 23', 'Wednesday 24', 'Thursday 25', 'Friday 26']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (40,5))\n",
    "plt.title('Events per hour')\n",
    "sns.heatmap(pivot_events.T, square = True, linewidth=1, cmap = 'Blues');\n",
    "plt.xticks(rotation = 0);\n",
    "plt.xlabel('Time');\n",
    "plt.ylabel('Day', rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. ¿En qué franja horaria se produjo la mayor cantidad de instalaciones?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "installs_df['hora'] = installs_df['created'].apply(lambda x: ('%02d-%02d' % (x.hour, (x.hour+1))))\n",
    "installs_df['dia'] = installs_df['created'].dt.day_name()+' '+installs_df['created'].dt.day.map(str)\n",
    "pivot_installs = installs_df.groupby(['dia','hora']).size().reset_index()\n",
    "pivot_installs.columns = ['dia','hora', 'count']\n",
    "pivot_installs = pivot_installs.pivot_table(index='hora', columns='dia', values='count',aggfunc='sum', fill_value=0, dropna=False)\n",
    "pivot_installs = pivot_installs[['Thursday 18', 'Friday 19', 'Saturday 20', 'Sunday 21', 'Monday 22', 'Tuesday 23', 'Wednesday 24', 'Thursday 25', 'Friday 26']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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myZbAusDawF3ju01JkiRJWjRY+e5Pj1bVJs3rtV3tZ1TVfSP0fzFwPEBVzQBmdJ37tySXApcBzwOeW1UFHAe8JcmKdJL6n49jfocBKzUfELyrGXvW8E5JpjRV94unTZs2juElSZIkabD4zPfC5ZE5nKvhDUnWA94PbFFV9yc5BliqOX008GPgr8BJVfVPyfOogar+QqdaTjr/i7iheQ3vNw0YyrrrkEO/O9YQkiRJkjRQQn8n0+M1KJXv8ToH2AOg2Xl8o6Z9eToJ+4PNM9m7Dl3QbNh2O/Bh4JjxBEuyYpIlm7dvB85pEnJJkiRJ0ggGrfK9qCbfXwcmJpkBfAC4EKCqrqCzJPxq4Cg6u5d3+w5wS1X9bqRBk7w7ya10nueekeSI5tRzgKuTXEsnoX/PfL4fSZIkSRoobrjWh6pq4ghtx9BVoW52Id+wOX4U2H2UsfaeQ6gXAt+awzy+DHx5hPbzgA3mMK4kSZIkqcuEfs+mx2kgku82JLmEzpL09y3ouUiSJEnSoOv3ZeTjZfI9RlW1+YKegyRJkiQtKgYt+V5Un/mWJEmSJKk1Vr4lSZIkSX1nwArfJt+SJEmSpP4zaMvOTb4lSZIkSX3H5FuSJEmSpB4z+ZYkSZIkqcdMviVJkiRJ6rEBy71JVS3oOWhk/mIkSZKkJ2fA0rdFy5of327cOdHtH/5V3/7OrXxLkiRJkvqOy87VmtUPeVHrMe86+NcA5N3PbzVufflKAJb94ORW4z7yqYsBWPewHVqNe9MBZwGw08l7tRr3zNcdC8CHzjuo1bgAn9j6UAB+c+cvW437wqftCMAtj/yp1bhPX/YZANz32N2txl35KU8F4OHHH2w17sQlVgDgr7NnthoXYKnFlgFg5qyHW427zOITAbjvsXtajbvyU1YDYMZ9F7cad6OVO/98/tGNJ7Ya9zWT3gjA56/4TKtxAd678f4ATDnrv1qNO22HLwLwgmP/rdW45+/1fQA2+tqrWo074/+dCsA6n3xJq3FvPvBsAJ75+Ze1GhfgD+/9BQAbfG6XVuNe977TAHjOl17eatxr3vMzACYdtmOrcW88oN3/5tD8N8HkW5IkSZKk3hqw3JsJC3oCkiRJkiQNl2TcrzGMuVSSC5NckeTqJIc07d9J8vskVyU5KskSTXuSfDnJ9UlmJNmsa6y9klzXvOa6pNXkW5IkSZLUdzIPf8bgMWCHqtoY2AR4WZIXAN8Bng08H1gaeHvTf1dgg+Y1Bfg6QJKVgYOBrYAtgYOTrDSnwCbfkiRJkqS+04vKd3UMbRSzRPOqqvpZc66AC4G1mz6vBr7dnDofWDHJGsAuwBlVdV9V3Q+cAcxxEwmTb0mSJElS35mX5DvJlCQXd72mjDDuYkkuB+6mk0Bf0HVuCWBP4BdN01rALV2X39q0jdY+KjdckyRJkiT1nXnZcK2qpgHT5tJnNrBJkhWBU5JsWFVXNae/BpxTVb8emsZIQ8yhfVRWviVJkiRJfacXy867VdUDwHSa5eJJDgZWA97b1e1W4Old79cGbp9D+6hMviVJkiRJi4QkqzUVb5IsDewEXJvk7XSe435TVf2965JTgbc2u56/AHiwqu4ATgNemmSlZqO1lzZto3LZuSRJkiSp74y3kj1GawDHJlmMTjH6+1X1kySzgJuA85q4P6yqjwI/A14OXA/MBPYBqKr7knwMuKgZ96NVdd+cApt8S5IkSZL6Ti+S76qaAWw6QvuIuXGz+/l/jnLuKOCoscbuq2XnSQ5qvuh8RpLLk2w1l/57J1lzPsSdnmTyfBhn3F/MLkmSJEn6Z8n4X/2sb5LvJFsDrwA2q6qN6Ky9v2XOV7E3MK7kO0kvq/3j+mJ2SZIkSdLIer3hWtv6Jvmms/b+3qp6DKCq7q2q2wGS/E+Si5qK8rSmkvx6YDLwnaZKvnSSG5Os2lwzOcn05nhqc93pwLebvic0VegT6STKNH2/3nwf3NVJDmnadkxySlefnZP8cPgNzMMXs0uSJEmSRmDy3TunA09P8ockX0uyXde5r1TVFlW1IZ1E+RVV9QPgYmCPqtqkqh6dy/ibA6+uqjcD+wEzmwr7oc25IQdV1WRgI2C7JBsBZwHPSbJa02cf4OjRAo3ji9klSZIkSSMw+e6RqnqYThI8BbgHODHJ3s3plyS5IMmVwA7A8+YhxKldCfqLgeObuDOAGV39/i3JpcBlTZznNpXs44C3NNvSbw38fA6xxvrF7P8gyZSm6n7xtGlz/F54SZIkSRpog/bMd1/tdl5Vs+l8yfn0JtHeK8kJdJLZyVV1S5KpwFKjDDGLJz5QGN7nkeHhhl+cZD3g/cAWVXV/kmO6xjka+DHwV+Ckqpo10gTyxBezv6OreUxfwF5V04ChrLs+cshxI4WQJEmSpIHX75Xs8eqbyneSZyXZoKtpEzrfszaU/N6bZCLw+q4+DwHLdb2/kSeWkL9uDuHOAfZo4m5IZ4k5wPJ0kvQHk6xOZ6M0AJrnz28HPgwcM8o9jPeL2SVJkiRJIxi0Zef9VPmeCBzeLOueRedLzKdU1QNJvgVcSSe5vqjrmmOAbyR5lM5S8EOAI5N8CLhgDrG+DhydZAZwOZ3N0aiqK5JcBlwN/Ak4d9h13wFWq6rfjTLuNxjHF7NLkiRJkkbW78n0ePVN8l1VlwDbjHLuw3QqzsPbTwZO7mr6NfDMEfpNHfb+UWD3UWLtPYdpvhD41mgn5+WL2SVJkiRJ/2zAcu/+Sb77XZJL6CxJf9+CnoskSZIkDTor34uoqtp87r0kSZIkSfOFybckSZIkSb1l5VuSJEmSpB4bsNzb5FuSJEmS1H+sfEuSJEmS1GODlnxPWNATkCRJkiRp0Fn5liRJkiT1nUGrfJt8S5IkSZL6zoDl3qSqFvQcNDJ/MZIkSdKTM2Dp26Jlq2PeMO6c6IK9T+rb37mVb0mSJElS33HZuVqz0kEvaD3m/YeeD8DEA7ZoNe7Dh10EwGL/tXGrcWd/8QoAnvWFl7Ua9/f//QsAtjlu91bj/nbPEwB4/7kfbDUuwGe3/RQAZ97201bj7rTWbgBc+8CMVuM+e8WNALj3r3e2GnfVpZ4GwF8ev7/VuMsvsRIAj8z6S6txAZZdfPkm9kMtx10OgAf/9udW466w5CoA/OHBq1qN+8wVNgTgpzf/sNW4u63zrwB85rJPtRoXYP9NO/+s/M/p72017le3/zwAu56yT6txf/7aowF47pd3azXu797d+ffCGh97catx7/jIOUD7/80DT/x3z7IfnNxq3Ec+dTEAq3xkm1bj/vljvwVgtYO3bTXuPYec22o8zX8m35IkSZIk9ZjJtyRJkiRJPTZgubfJtyRJkiSp/1j5liRJkiSpx0y+JUmSJEnqMZNvSZIkSZJ6bMByb5NvSZIkSVL/sfItSZIkSVKvmXxLkiRJktRbg1b5nrCgJzAeSQ5KcnWSGUkuT7LVfBp3apL3z4dx3pvkd838fplk3a5zv0jyQJKfPNk4kiRJkjToJmT8r3620FS+k2wNvALYrKoeS7IqsOQCntZwlwGTq2pmkv2ATwNvbM59BlgGeMeCmpwkSZIkLSysfC84awD3VtVjAFV1b1XdDpDkxiYZJ8nkJNOb46lJjkoyPcmfkrx7aLCmiv77JGcCz+pq//ckFyW5IsnJSZZJslySG5Is0fRZvom5RPcEq+rsqprZvD0fWLvr3C+Bh+b/j0WSJEmS1O8WpuT7dODpSf6Q5GtJthvjdc8GdgG2BA5OskSSzYHdgU2BfwW26Or/w6raoqo2Bq4B9q2qh4DpwG5Nn92Bk6vq8TnE3Rf4+RjnKEmSJEnqMiEZ96ufLTTJd1U9DGwOTAHuAU5MsvcYLv1pVT1WVfcCdwOrAy8CTqmqmVX1F+DUrv4bJvl1kiuBPYDnNe1HAPs0x/sAR48WMMlbgMl0lpqPWZIpSS5OcvG0adPGc6kkSZIkDZQk4371s4XmmW+AqppNpwI9vUmO9wKOAWbxxAcJSw277LGu49k8cc81SphjgNdU1RVNcr99E/vcJJOaivtiVXXVSBcn2Qk4CNhuaIn8WFXVNGAo664PHnTUeC6XJEmSpIGx0FSKx2ihuZ8kz0qyQVfTJsBNzfGNdKriAK8bw3DnAK9NsnSS5YBXdp1bDrijeZ57j2HXfRv4HqNUvZNsCnwTeFVV3T2GeUiSJEmSRjBoy84Xpsr3RODwJCvSqXRfT2cJOsAhwJFJPgRcMLeBqurSJCcCl9NJ4H/ddfojzRg3AVfSScaHfAf4OJ0EfCSfaeZ5UrPk4eaqehVAkl/Tef58YpJb6TxLftrc5ipJkiRJi6J+X0Y+XgtN8l1VlwDbjHLu18AzR2ifOuz9hl3HhwKHjnDN14GvjzKNFwI/qKoHRpnHTqNcR1W9aLRzkiRJkqR/1O+V7PFaaJLvBS3J4cCuwMsX9FwkSZIkadBZ+V5EVdW7FvQcJEmSJGlRsdBsUDZGJt+SJEmSpL7jsnNJkiRJknrMZeeSJEmSJPWYlW9JkiRJknpssFJvk29JkiRJUh+y8i1JkiRJUo8NWvI9aLu3S5IkSZIGQJJxv8Yw5tOTnJ3kmiRXJ3nPsPPvT1JJVm3eJ8mXk1yfZEaSzbr67pXkuua111xjV9U8/BjUAn8xkiRJ0pMzWKXTRcxbT3/nuHOib7/0K3P8nSdZA1ijqi5NshxwCfCaqvpdkqcDRwDPBjavqnuTvBx4F/ByYCvgS1W1VZKVgYuByXRyt0uaa+4fLbaVb0mSJEnSIqGq7qiqS5vjh4BrgLWa018APsA/FkJfDXy7Os4HVmwS+F2AM6rqvibhPgN42Zxi+8x3H8u7n996zPrylQAsvf/mrcZ99DOXALD4f2/catxZX7gCgPU+tVOrcW/44JkAbHfCHq3G/dXu3wHgM5d9qtW4APtv+kEAfn3nma3GfdHTOr/bmx6+vtW4605cH4AH/nZvq3FXXHJVAGbOerjVuMssPhGAR2c/0mpcgKUXWxaAB/92X6txV1hyZQBun3lTq3HXXGZdAM67a3qrcbdefXsAjrz2G63G3ffZ/wHA1AunthoXYOqWnZhv+vl+rcb93q5fB2DLo1/fatwL9/kBAGsdun2rcW87aDoA//KZnVuN+8f9zwDav1944p43+Nwurca97n2nAbDmx7drNe7tH/4VsOB+x1p49XrZQpJJwKbABUleBdxWVVcMW76+FnBL1/tbm7bR2kdl8i1JkiRJ6jvzsuFakinAlK6maVU1bYR+E4GTgf8CZgEHAS8dacgR2moO7aMy+ZYkSZIk9Z15Sb6bRPufku1uSZagk3h/p6p+mOT5wHrAUNV7beDSJFvSqWg/vevytYHbm/bth7VPn1Ncn/mWJEmSJPWdHu12HuBI4Jqq+jxAVV1ZVU+tqklVNYlOYr1ZVd0JnAq8tdn1/AXAg1V1B3Aa8NIkKyVZiU7V/LQ5xZ5r5TvJbOBKYAk65fhjgS9W1d/nemeSJEmSJM2DHn3P97bAnsCVSS5v2j5UVT8bpf/P6Ox0fj0wE9gHoKruS/Ix4KKm30erao4b0Ixl2fmjVbUJQJKnAt8FVgAOHsO1kiRJkiSNWy9S76r6zdyGbqrfQ8cF/Oco/Y4Cjhpr7HEtO6+qu+k8vP7Opuw+Kcmvk1zavLYBSHJcklcPXZfkO83ucZIkSZIkzdWEZNyvfjbuZ76r6k/NdU8F7gZ2rqrNgDcCX266HUFTjk+yArANnXK9JEmSJElzNWjJ97zudj50V0sAX0myCTAbeCZAVf0qyVebZer/CpxcVbOe9GwlSZIkSYuEsWygtjAZd/Kd5Bl0Eu276Tz3fRewMZ1q+F+7uh4H7AHsDrztSc9UkiRJkrTIGLSv5hrX/SRZDfgG8JXmwfMVgDuanc/3BBbr6n4MnS8sp6qufjKTTFJJjut6v3iSe5L85MmM2zXe1CTvnw/jvDfJ75LMSPLLJOs27ZskOS/J1c25Nz75WUuSJEnS4OrFV40tSGOpfC/dbME+9FVjxwGfb859DTg5yRuAs4FHhi6qqruSXAP8aD7M8xFgwyRLV9WjwM7AbfNh3PntMmByVc1Msh/waTrPws8E3lpV1yVZE7gkyWlV9cCCnKwkSZIk9at+f4Z7vOaafFfh4H4yAAAgAElEQVTVYnM4dx2wUVfTgUMHSZYBNgC+92Qm2OXnwG7AD4A3NeO+qIm1Mp0t3p9BJ9GdUlUzkkwF1mna16Hz/eRfbq45CHgrcAtwD3BJ0/7vdHZ0X5LOd7kNVfRnAM+sqseTLN+836CqHu/6eZzdNd/zgbc07X/o6nN7kruB1QCTb0mSJEkawaAl3z1ZRp9kJ+Ba4PCqenA+DXsCsHuSpegk/Bd0nTsEuKyqNgI+BHy769yzgV2ALYGDkyyRZHM6z6JvSmdDuC26+v+wqraoqo2Ba4B9q+ohYDqd5J/m2pO7E+8R7EvnA4N/kGRLOon9H8d015IkSZK0CFoUl52PW1WdSafSPD/HnJFkEp2q9/CvLXsh8Lqm31lJVmm+4gzgp1X1GPBYU3FenU7F/JSqmgmQ5NSusTZM8nFgRWAicFrTfgTwATrL6PcB/n20uSZ5CzAZ2G5Y+xp0lu3v1TwnP/y6KXSq7nzzm98c9WchSZIkSYNuAv2dTI9XT5LvHjoV+CywPbBKV/tIv5Vq/n6sq202T9xzMbJjgNdU1RVJ9m5iUVXnJpmUZDtgsaq6aqSLm6r/QcB2TdI/1L488FPgw1V1/kjXVtU0YNrQ23e8+/BRpihJkiRJg63fK9njtbDt3n4U8NGqunJY+zl0vtaMJNsD91bVX+YwzjnAa5MsnWQ54JVd55YD7kiyxNCYXb5N51nzo0caNMmmwDeBV1XV3V3tSwKnAN+uqpPmfIuSJEmSpEGzUFW+q+pW4EsjnJoKHJ1kBp0N1/aayziXJjkRuBy4Cfh11+mP0Hme/CbgSjrJ+JDvAB9n9E3kPkNnqfpJzac0N1fVq4B/A14MrNJU0wH2rqrL5zRPSZIkSVpUDdqGawtF8l1VE0dom05nEzSq6j7g1SP0mTrs/YZdx4cCh45wzdeBr48ylRcCPxjtK8KqaqdR2o8Hjh9lTEmSJEnSMPGZ70VTksOBXYGXL+i5SJIkSdKgG7Rnvk2+x6iq3rWg5yBJkiRJiwqXnUuSJEmS1GNZ6PYHnzOTb0mSJElS37HyLUmSJElSj/nMtyRJkiRJPeZu55IkSZIk9ZjLziVJkiRJ6jGXnUuSJEmS1GMTBmy381TVgp6DRuYvRpIkSXpyBqt0uog57NJPjjsnOmCzA/v2d27lW5IkSZLUd1x2rtZMPGCL1mM+fNhFACy9/+atxn30M5cAsOwHJ7ca95FPXQzAmh/frtW4t3/4Vws07ofOO6jVuACf2PpQAH5955mtxn3R03YC4MaHr2s17qSJGwDwwN/+3GrcFZdcBYBHZj3UatxlF18OgIcff7DVuAATl1gBgAf/dl+rcVdYcmUAbp95c6tx11xmHQAuufe8VuNuvurWAHz3+mNajfvm9fcG4APnHtBqXIBPb3sYADudvFercc983bEAbDrtta3GvWzKKQBs9q12417675246x62Q6txbzrgLACe9YWXtRoX4Pf//QsANvjcLq3Gve59pwGwzidf0mrcmw88G4D1P/vSVuNe//7TW42n+W/CgC1cMPmWJEmSJPUdK9+SJEmSJPXYoH3V2GBtHydJkiRJUh+y8i1JkiRJ6jvxmW9JkiRJknprQgZrobbJtyRJkiSp77jhmiRJkiRJPeayc0mSJEmSemzQdjs3+ZYkSZIk9Z1Bq3z35RPsSVZJcnnzujPJbV3vl+xBvN8k2WQ+jHNCkt8nuSrJEUkWb9r/NcmMZv4XJdnmyc9akiRJkgbXhGTcr37Wl5XvqvozsAlAkqnAw1X12QU6qbH5NvAmIMCJwD7At4DTgVOqqpJs1vTbcIHNUpIkSZL6XAZst/OF6m6SrJ/k8q73ByT5cHO8QZLTklyS5Jwkz2zad28q0VckObtpWybJSU01+gRgqa4xpyW5OMnVSf6nadslyUldfXZN8v3h86uqn1XH34ELgbWb9oerqppuywI1/FpJkiRJ0hMyD3/6WV9WvufRNODtVfXHJNsCXwFeChwMbF9VdyVZsen7TuD+qtooyabAxV3jHFBV9zVLxs9O8gPgDODLSVZpqvL7AEePNpFmafwewH5dba8HDgVWBV4+n+5ZkiRJkgZSvy8jH6+FqvI9miapfgFwclMZ/yqwZnP6XODbSd7OE/f7YuB4gKq6DLi6a7g3JbkUuBR4DvDcppL9XeDNSVYGNqezlHw03wDOrKrzhhqq6gdV9Szg9cDHRrmPKU3V/eJp06aN/QcgSZIkSQMmybhf/Wxhq3zP4h8/MFiqaQtwb1WNtGnavwNbAa8ArkiyUdP+T0u/k2wAvAfYsqoeSHI8TyxJPwo4uTk+sapmjzTBJB8DVgDePtL5qjo7ybeTrFhVDww7N41OBR+g3nvAt0YaQpIkSZIG3oQ+X0Y+Xgtb5ftOYM0kKyVZCtgNoKruB+5I8lqAJBOSbNxc84yqOh/4CHA/sBZwDp1l4TT9ntf0XR54CPhLkjWAXYYCV9UtwL3AAcAxI00uyX8A2wN7NNXyofb103wMk2RyM94DI40hSZIkSbLyvUBV1V+TfAK4CPgT8Luu07sDX292R1+SzrLyK4AvJFmPTnX89Kq6KsmfgGOTzKCzvHzome9LmzGvasY/d9gUvgssX1V/GD63JIvRec78RuD85hd/UlUdCvwbsEeSx4GZwBufzM9BkiRJkgbdoO123vfJd1VNHfb+88DnR+j3J7oq1V3trxqhbSbwhlFC7jmH6byQzleHjTTP2Yzy86yqTwCfmMO4kiRJkqQug7bsvO+T737RbOR2P/DuBT0XSZIkSRp0/b6MfLxMvsdolM3cJEmSJEk90O/f2z1eg7WIXpIkSZKkPmTlW5IkSZLUd1x2LkmSJElSjw3ahmsuO5ckSZIk9Z1kwrhfcx8zRyW5O8lVw9rfleT3Sa5O8umu9gOTXN+c26Wr/WVN2/VJDhjL/Vj5liRJkiT1nR5tuHYM8BXg2/8XJ3kJ8Gpgo6p6LMlTm/bnArsDzwPWBM5M8szmsq8COwO3AhclObWqfjenwCbfkiRJkqS+04tnvqvqnCSThjXvBxxWVY81fe5u2l8NnNC035DkemDL5tz1VfWnZp4nNH3nmHy77FySJEmS1HcyD3/m0TOBFyW5IMmvkmzRtK8F3NLV79ambbT2Od9PVc3rBNVb/mIkSZKkJ2ewduxaxJx8w/fGnRO9/hlvfgcwpatpWlVN6+7TVL5/UlUbNu+vAs4C3gNsAZwIPIPO8vTzqur4pt+RwM/oFLF3qaq3N+17AltW1bvmNDeXnUuSJEmS+s687HbeJNrT5trxH90K/LA6lekLk/wdWLVpf3pXv7WB25vj0dpHZfLdx1Y7eNvWY95zyLkALPvBya3GfeRTFwMw8YAt5tJz/nr4sIsA+JfP7Nxq3D/ufwYAm057batxL5tyCgCHXfrJVuMCHLDZgQCcduuPW427y9qvBODOR2+ZS8/562lLd/55/ODf/txq3BWWXAWAmbMebjXuMotPXCBxu2P/5fH7W427/BIrAXD7zJtbjbvmMusAcPE957Yad/JqnX8nHX/dUa3GfcsGbwPgA+eOaSPZ+erT2x4GwJt+vl+rcb+369cB2OKo17ca96K3/QCATb75mlbjXv6OHwEw6bAdW4174wG/BGCDz+0yl57z33XvOw2ALY9u93d84T6d3/FWx7yh1bgX7H0SANt/f89W407/t+Najaf5r8Xv+f4RsAMwvdlQbUngXuBU4LtJPk9nw7UNgAvprKjYIMl6wG10NmV789yCmHxLkiRJkvpOerBFWZLvAdsDqya5FTgYOAo4qll+/jdgr6YKfnWS79PZSG0W8J9VNbsZ553AacBiwFFVdfXcYpt8S5IkSZL6To92O3/TKKfeMkr/Q4FDR2j/GZ3nv8fM5FuSJEmS1Hd69D3fC4zJtyRJkiSp70xo75nvVph8S5IkSZL6jpVvSZIkSZJ6rMXdzlth8i1JkiRJ6ju92O18QRqsu5EkSZIkqQ9Z+ZYkSZIk9Z1BW3bek8p3ki8k+a+u96clOaLr/eeSvHcc401N8v75Pc+u8fdO8pX5MM4eSWY0r98m2XjY+cWSXJbkJ082liRJkiQNsglk3K9+1qtl578FtgFIMgFYFXhe1/ltgHN7FHtBugHYrqo2Aj4GTBt2/j3ANa3PSpIkSZIWMknG/epnvUq+z6VJvukk3VcBDyVZKclTgOcAlwEk2T/JRU21+JChAZIclOT3Sc4EntXVPj3Jp5JcmOQPSV7UtC+W5DNdY72jaV8jyTlJLk9yVVf/fZrrfwVs2zX+K5Nc0FSoz0yyepIJSa5LslrTZ0KS65Os2n3TVfXbqrq/eXs+sHbXuGsDuwFHIEmSJEmao8zDn37Wk2e+q+r2JLOSrEMnCT8PWAvYGngQmFFVf0vyUmADYEsgwKlJXgw8AuwObNrM8VLgku55V9WWSV4OHAzsBOwLPFhVWzQJ/rlJTgf+FTitqg5NshiwTJI1gEOAzZv5nE3zYQDwG+AFVVVJ3g58oKrel+R4YA/gi028K6rq3jn8GPYFft71/ovAB4DlxvOzlCRJkqRFUb9XsserlxuuDVW/twE+Tyf53oZOsvvbps9Lm9dQ4juRTjK+HHBKVc0ESHLqsLF/2Px9CTCpa6yNkry+eb9CM9ZFwFFJlgB+VFWXJ9kRmF5V9zTjnwg8s7lubeDEJkFfks5ScoCjgP+lk0S/DTh6tBtP8hI6yfcLm/evAO6uqkuSbD+H66YAUwC++c1vjtZNkiRJkgaeXzU2dkPPfT+fzrLz8+lUvruf9w7wyarapHmtX1VHNudqDmM/1vw9myc+QAjwrq6x1quq06vqHODFwG3AcUneOpfxDwe+UlXPB94BLAVQVbcAdyXZAdiKf6xq/58kG9FZWv7qqvpz07wt8KokNwInADs0lfR/UFXTqmpyVU2eMmXKHG5fkiRJkgbbhGTcr37Wy+T7XOAVwH1VNbuq7gNWpJOAn9f0OQ14W5KJAEnWSvJU4BzgtUmWTrIc8MoxxDsN2K+pcJPkmUmWTbIunarzt4Ajgc2AC4Dtk6zS9H9D1zgr0EnUAfYaFuMI4Hjg+1U1e/gEmmX2PwT2rKo/DLVX1YFVtXZVTaKznP6sqnrLGO5JkiRJkhZJPvM9dlfS2eX8u8PaJg49K11Vpyd5DnBes57/YeAtVXVpsxT8cuAm4NdjiHcEnSXol6Yz2D3Aa4Dtgf2TPN6M/9aquiPJVDofAtxB55nyxZpxpgInJbmNTrV+va4Yp9JZbj7akvP/AVYBvtbcz6yqmjyGuUuSJEmSuvjM9xg1leHlh7XtPUK/LwFfGqH9UODQEdq37zq+l+aZ76r6O/Ch5tXt2OY1fJwRk+iq+l86z3aPZGM6G61dO9LJqno78PZRrh3qMx2YPqc+kiRJkrSo6/dK9nj1svI9UJIcAOxHZ8dzSZIkSVIPWfleRFXVYcBhC3oekiRJkrQomDBgu52bfEuSJEmS+o6Vb0mSJEmSesxnviVJkiRJ6jEr35IkSZIk9digVb4H6wl2SZIkSZL6kJVvSZIkSVLfGbTKt8m3JEmSJKn/DNgz36mqBT0HjcxfjCRJkvTkDFb2toi55N7zxp0Tbb7q1n37O7fyLUmSJEnqO+52rtY87/DdWo959bt+CsA6n3xJq3FvPvBsACYdtmOrcW884JdA+z/roZ/z9ie+pdW40994PACHXfrJVuMCHLDZgQCce9dZrcbddvUdALht5o2txl1rmUkA3PfYPa3GXfkpqwHwyKyHWo277OLLAfDX2TNbjQuw1GLLAPCXx+9vNe7yS6wEwF2P3tpq3NWXXhuAS+49r9W4m6+6NQAn/vG4VuO+8V/2BOBLMz7falyA92z0XgD2OeNdrcY9eufDAdjmuN1bjfvbPU8AYPKRr2s17sX7ngzAGh97catx7/jIOQCsdNALWo0LcP+h5wPwtI+2e893/s85CzTuhPds1Grcv39pRqvxNP/5zLckSZIkST1m8i1JkiRJUo+57FySJEmSpB6z8i1JkiRJUo+ZfEuSJEmS1GMuO5ckSZIkqcesfEuSJEmS1GNWviVJkiRJ6jEr35IkSZIk9ZjJtyRJkiRJPeay8x5Isgrwy+bt04DZwD3AJOD2qnpuj+PvDUyuqnc+yXH2AD7YvH0Y2K+qrmjO3Qg8ROfeZlXV5CcTS5IkSZIGmZXvHqiqPwObACSZCjxcVZ9NMgn4ybyOm2Txqpo1P+Y4RjcA21XV/Ul2BaYBW3Wdf0lV3dvifCRJkiRJfWDCgp7AGCyW5FtJrk5yepKlAZJMTzK5OV61qSyTZO8kJyX5MXB6kjWSnJPk8iRXJXlR02+fJH9I8itg26FgSV6Z5IIklyU5M8nqSSYkuS7Jak2fCUmuT7Jq90Sr6rdVdX/z9nxg7R7/bCRJkiRpIGUe/vSzhSH53gD4alU9D3gAeN0Yrtka2KuqdgDeDJxWVZsAGwOXJ1kDOIRO0r0z0L2s/TfAC6pqU+AE4ANV9XfgeGCPps9OwBVzqWLvC/y8633R+TDgkiRTxnAPkiRJkrTISjLuVz/ri2Xnc3FDVV3eHF9C5znwuTmjqu5rji8CjkqyBPCjqro8yY7A9Kq6ByDJicAzm/5rAyc2CfqSdJaSAxwF/C/wReBtwNGjBU/yEjrJ9wu7mretqtuTPBU4I8m1VXXOsOumAFMAvvnNb47hNiVJkiRpUPV3Mj1eC0Pl+7Gu49k88YHBLJ6Y/1LDrnlk6KBJcF8M3AYcl+StQ6dGiXc48JWqej7wjqGxq+oW4K4kO9B5jvvnI12cZCPgCODVzbPsQ/O4vfn7buAUYMvh11bVtKqaXFWTp0yxOC5JkiRp0TVole+FIfkezY3A5s3x60frlGRd4O6q+hZwJLAZcAGwfZJVmor4G7ouWYFOog6w17DhjqCz/Pz7VTV7hFjrAD8E9qyqP3S1L5tkuaFj4KXAVWO8T0mSJEla5AzaM98Lw7Lz0XwW+H6SPYGz5tBve2D/JI/T+fqvt1bVHc2u6ucBdwCXAos1/acCJyW5jc6maet1jXUqneXmoy05/x9gFeBrzacuQ18ptjpwStO2OPDdqvrFeG5WkiRJkhYl/Z5Mj1ffJd9VNbXr+EZgw673n+06vhbYqOvSDzftxwDHdPU7Fjh2hDgjJtFV9b90nu0eycZ0Nlq7dpS5vx14+wjtf2qulSRJkiSNQb8vIx+vvku++1WSA4D9eGLHc0mSJElSjwxa5Xthfua7VVV1WFWtW1W/WdBzkSRJkqRB16tnvpP8d5Krk1yV5HtJlkqyXpILklyX5MQkSzZ9n9K8v745P2le78fkW5IkSZLUd3qx23mStYB3A5OrakM6e3/tDnwK+EJVbQDcT+ero2n+vr+q1ge+0PSbJybfkiRJkqS+08PdzhcHlk6yOLAMnU24dwB+0Jw/FnhNc/xqnthD7AfAjpnHh9FNviVJkiRJfacXle+quo3ON2fdTCfpfhC4BHigqmY13W4F1mqO1wJuaa6d1fRfZV7ux+RbkiRJktR35qXynWRKkou7XlP+YcxkJTrV7PWANYFlgV1HCF//N43Rz42Lu51LkiRJkvrQ+Fd3V9U0YNocuuwE3FBV9/D/27v3eM/meo/jr3czDBMaQi4j45JKQrmUDh2lhDqVqORWoVCkC5WjRznKORgd6UaFGTlEGZ1uFLmfjluYGTNuDU2aiBxJ7uFz/vh+92OW36zrNnvvn9+8n4/H7zG/vdb67ve6/T57fX/rMoCk84A3AJMkjc9ntycDd+fpFwBrAQvyZeovAh7oPGP4zLeZmZmZmZn1IQ3j1cJdwOslTcz3bm8H3AxcCuyap/kg8JP8/qf5Z/L4SyLCZ77NzMzMzMxsMAzzuWa1IuIaSecCNwBPATeSzpT/Ajhb0lfysFNzk1OBMyTNI53x3m242Rpmp91GnjeMmZmZmdlzs/h7bzZq/vzYHzv3iVZbdq2+3eY+821mZmZmZmZ9qG/70cPizncf0y7rjnpmzLgzZb918ujmXrQg5e740tHNveAuACZ85jWjmvvEV28EYMox241q7vzPXwzAv151xKjmAvz7VkcDcPGfzh/V3O3W3AmAPzw8b1Rz115ufQD+8vg9o5q7yjKrA/Dgk/ePau6kpVcG4JGn/j6quQAvHL88AH994i+jmrvihFUA+NOj80c1d82JUwC48s+/HtXcbVZ7CwBnzzu9YcrFa7f10212U288dlRzAQ57zecAOOSKQ0c198Q3Hg/ANmftPqq5V+5+FgAbffMdo5o756CfA/DS/3jTqObedfilAKz0ha1GNRfgga9cBcCyh202qrmPTb0egPWP335Uc+cdeiEAEz87usv76HHXj2qeLX6D1fV259vMzMzMzMz60mB1v935NjMzMzMzs74zEg9cG0v+r8bMzMzMzMzMRpjPfJuZmZmZmVnfkS87NzMzMzMzMxtZg9b59mXnZmZmZmZmZiPMZ77NzMzMzMys7/iBa2ZmZmZmZmbWic98m5mZmZmZWd/xPd99SNLTkmYWXlNKpllD0rkV7S+TtPlimI8zJd0maY6k0yQtVRi3bZ63uZIuf65ZZmZmZmZmg03DePWvQTnz/VhEbFo1UtL4iLgb2HWE5+NMYM/8/ixgP+AkSZOAbwM7RMRdklYd4fkwMzMzMzN7XuvvrnR3A3Hmu4ykD0n6kaSfARdKmiJpTh63rKSzJc2WdA6wbKHdSZJ+m89Q/1setp2kHxemeauk83ozI+L8yIBrgcl51O7AeRFxV57uvpFabjMzMzMzs0EgqfOrnw3Kme9lJc3M738fETvn91sBG0fEAz2Xoh8IPBoRG0vaGLihMO6IPP044OI8/hLgW5JWiYi/AB8GplXNTL7cfC/gkDxoA2ApSZcBywMnRsT3n8PympmZmZmZDbj+7kx3NSid76rLzi+KiAdKhr8R+DpARMyWNLsw7n2SPkpaN6sDG+ZpzgD2lDSN1Knfu2Z+vg1cERFX5p/HA5sB25HOsl8l6eqIuL3YKOd+FOA73/lO/RKbmZmZmZkNsMHqeg9O57vKIzXjoneApHWAQ4EtIuKvkqYDy+TR04CfAY8DP4qIp8p+qaQvAasA+xcGLwDuj4hHgEckXQFsAjyr8x0R3wW+O/Tj/r86pn7pzMzMzMzMBtZgdb8H9p7vBlcAewBI2gjYOA9fgdRh/5uklwA7DjXID2y7G/gCML3sl0raD3gb8IGIeKYw6ifANpLGS5oIvA64ZXEukJmZmZmZmfWvQT/zXeUkYFq+3Hwm6eFoRMQsSTcCc4E7gd/0tDsTWCUibq74vScDfyBdVg7pIWtHRcQtkn4JzAaeAU6JiDmLe6HMzMzMzMwGRb8/QK2rgeh8R8RyJcOmUzhDHRHzgY3y+8eA3Sp+14dqorYGvlczH5XrMyKmAlNrfreZmZmZmZkNqIHofI8GSdeTLkn/zFjPi5mZmZmZ2aDTgN3z7c53SxGx2VjPg5mZmZmZ2ZLDnW8zMzMzMzOzETVYXW93vs3MzMzMzKwP+YFrZmZmZmZmZiPOnW8zMzMzMzOzETVYXW93vs3MzMzMzKwvDVb3251vMzMzMzMz6zuDds/3C8Z6BszMzMzMzMwGnSJirOfBynnDmJmZmZk9N4N16nQJ8/jTj3buEy0zbmLfbnOf+e5fGu5L0v7Ppf3zMdu5g527JC6zcwc/27mDnbskLrNzBz/7eZprz2PLjJuorq+xnuc67nwPpo8ugdnOHezcscx27mDnjmW2cwc7dyyznTvYuWOZvaTlmi1W7nybmZmZmZmZjTB3vs3MzMzMzMxGmDvfg+m7S2C2cwc7dyyznTvYuWOZ7dzBzh3LbOcOdu5YZi9puWaLlZ92bmZmZmZmZjbCfObbzMzMzMzMbIS58/08IGkHSbdJmifp83nYOpKukfQ7SedIWrqi7WaSbsptvy5Jefh7Jc2V9IykzTvkHpR/Dkkr18xz6fxJ+rSkmyXNlnSxpLVb5p6Zh82RdJqkpTrmHpDXw0xJ/yNpw5a5p0qalef3XEnLdVnPhfGHVq2zstzCuG9IerhmPVdt3yMl/Skv70xJO1W0L1vm6ZJ+X2i7aUXbyn1Q0vvydp4r6ayWuZJ0tKTbJd0i6RNdciWdUJjn2yU92DL3ykK7uyX9d8fcl0q6VNKNeT9ZZF1X5G4n6YbCPrl+RW7VNt5E0lV53M8krdAy9805d46k0yWNr8gt/azn7fT1PG62pNeWtD1N0n2S5hSGrSTporz+LpK0YsfcV+TlfULSoRVty3Iba13ddJLeKun6vJ6vl/TmlrlTJd2a19GPJU3qmLtlYb+cJWnnDsv85Zw7U9KFktaoaFtbzyVtIelpSbu2yS2Mq6x3dbmStpX0t8Jyf7Hl8ratd5X7gqSN8/41N2/rZdosr6SD82dsrqTjuuRK2qMwzzPz+E172pYt7zmFNvMlzeyYu5TSZ/8mpVp7eEX7suxNJV2ds38racuKtlXbeMX8eZgt6VpJG7XMbax3ebrKz52kw/M83SbpbSVt11Kq5bfk9XZIHt62dpUep6ihdtXktq1dpcushtpVk9u2dlXl1taumty2dav2eFA1dcusL0SEX338AsYBdwDrAksDs4ANgR8Cu+VpTgYOrGh/LbAV6f85vADYMQ9/JfBy4DJg8w65rwGmAPOBlWvmu3T+gDcBE/P7A4FzWubuxML/r/EHNctblbtCYZp3Ar9smVts95/A57us5zxuLeBXwB9611lVbh63OXAG8HDNeq7avkcChw5z35oO7Npi36xa1y8DbgRWzD+v2jL3w8D3gReUtWvK7ZnmYOC0tuu6MM0MYO+Oy/vdwvsNgfktl/d24JV5mo8B0ztu4+uAf87v9wG+3DL3j8AGeZqjgH0rcks/66TP4gV5fl4PXFPS9o3Aa4E5hWHHkT8/wOeBYzvmrgpsARxNxb5dkVtb65qmy/DzjUgAAA2GSURBVPOzRn6/EfCnlrnbA+Pz+2Nrlrcqd2Kh/erAfUM/t8gu1q1PACd3WdeF/ecS4HxK6kFZbh5eWe9abONtgZ9XbaOa5T2yap9oua7HA7OBTfLPLwbGtch9E/BrYMLQPtolt2eaVwN3tl3PhfFfBb7YcXl3B84u7GfzgSkt1/WFLKxBOwGXddzGU4Ev5fevAC5umVtb75o+d6TaNwuYAKxDqo2923h14LX5/fKkGr0h7WtX6XEKDbWrJrdt7apa5traVZPbtnZV5dbWrprctnWr8niQhrrll1/98PKZ7/63JTAvIu6MiCeBs4F3AW8Gzs3TnA68u7ehpNVJxeyqiAhSx+bdABFxS0Tc1jU3Im6MiPl1MyxJVfMXEZdGxKN5+NXA5Ja550dG6oz0tmvKfagw6QuB3ocdVOU+VPjdy5a0q13P2QnAZ8vaVuVKGkc6SPlsSZu2uU2q9q1Gdesa+AjwrYj4K0BE3Ncy90DgqIh4pqJdU27RB0h/lFsvr6Tl8+9e5Mx3Q24AQ2dhXgTc3TK3qV3TNn45cEV+fxGwS4vcXYAnIuL2mnZpoao/6+8Cvp8/jlcDk/J8FtteATxQ0u70/L5qu1XmRsR9EXEd8I+ydlW5LWpd7XR5foa2zVxgGUkTWuReGBFP5R/Lal1T7qOF9stQXj+qspvqXXHZ5peNI32BNYN04NwqN6urd21ya9XktmlbtS9sD8yOiFl5uv+LiKdb5B4IHBMRT+RpqtZVm32wrGbVLm+uS+8ra9eQG8ALla56WRZ4EnhokYnKsxvrVm5btY03BC7O09wKTJH0kha5TfVuqG3V5+5dpC8cnoiI3wPzSDWy2PaeiLghv/87cAuwJu1rV+lxSlPtqsrtULtKl7mpdtXktq1dVbm1tasmt23dqjserK1bZv3Ane/+tybpTNWQBXnYg4XiNjSsrO2CkrbPJbeNF7ecv31JZ9Ba5+bLi/YCftk1V9LHJd1B+ha795LmylxJ04A/k76l/0ZJbuV6lvRO0rfNs0ra1eUeBPw0Iu6paFebmx2UL+E6reIyubp1fXRue0JvRyOrW9cbABtI+o3S5Yk7tMxdD3i/0qWMF0h6WcdcAJRuZViH9O132+UF2Jl0FmaRg9CG3COBPSUtIH3bfnDL3P2A83O7vYBjSnLrtvEc0lUcAO8lnXFsyl0NWKpwCeOuJe2aDLc2vGRof87/rtoxtx/sAtw41NnqYB8WrXWNJL1O0lzgJuCAwv7Xpu3Rkv4I7AEscvl2Q9s1SZ+Hkzu2a6p3bWyVL1W9QNKrOrRrqnd1NgBC0q+Ubsmo/NKzpN02SrejXC5pi465Re+nohNdYxvg3oj4Xcd25wKPAPcAdwHHR0TbLzQ+CUzN+9bxQOkl6zVmAe+BdHkysDYVnbseTfWuTPFz16luSZpCOnN8DR1rV8NxSq2e3OGoqjW1tasmt23tetZ0bWtXb26XutW7nodbt8xGmzvf/U8lw8aVDCv7hrCsbeXZiNFsK2lP0mXVUzu2/TZwRURc2TU3Ir4VEesBnwO+0LZtRHwYWIP07ez72+ZKmggcQf0fkLK2E0gHF2Ud/cbc/O9JpM7spqQDrK92aH846YuGLYCVSOurS/Z40qXn25LO5pzSc89YVdsJwOMRsTnwPeC0jrlDdgPO7T1z1aJt6ZmnFm0/QLpkfDLpcrgzJBVra1XbTwE75XbTSLc1dMndB/i4pOtJl+492aLtM6T1c4Kka4G/A607dC3maWDljuCxwP4d2x1BWsdnds2MiGsi4lWkz+Lh6rkPuaHtERGxVs49qGP014DPlXyGKrWsd01uANaOiE1I9a/0+Qsl2tS7OuOBrUkH/FsDO0varmW7FUm3XxwG/DCfje5E0uuARyNikfvnG9TVrDpbAk+T/q6tA3xG0rot2x4IfCrvW58CTu2YfQywotJ96geTblFqU4Oa6t2zlHzuWtctpWe7zAA+WfFlbJO645RKzzW3qtY01a6q3La1q2y6NrWrLLdj3epdz53rltlYcOe7/y3g2d/wTiZ9Uz1JCx+UNBm4W9I4LXzIxVG57eSetqWXiLXMrWybzxjMlHQKcH/Z/BWmfQvpIO2dJd/CVuZK+hKwCvDp4eQWnM2il43VLm8u5ucAu3RYz+uRDmxmSZqfh98gabWG3PnA+sC83G6i0kNiWm/fiLg3Ip6OdAn39+i5vK5umfMlYZG3zbShth3W9QLgJxHxj3x5322kznhtbh4+Iw/7MbBxx9whu1F+QFq3b704L+cvhkZ2yN2XdD84EXEV6TK74oOmynLvI91fOnSW4RzgDR238a0RsX1EbJaX9442yxvpEvZtImJL0mWcvytZ3jqdakPBvcqXp+d/7+uYu1hJmpZzz28x7WTSPrl3RPSu57p2HwTeAeyRL5HslDskIm4hnalc5OFULZxFvkS3w7reHDg7159dgW9LarqlpbLetc2NiIci4uH8/nzSVRqVD/YstCutdx3W9QLg8oi4P9JtUeeT7jlusgA4L9fLa0lfbq08jG1cVbMq5Vr0HlLtGBrWNnd30nNP/hHpUvnfkLZ5Gx8Ezsvvf0T534dKeRt/OCI2BfYm/U3/fVNoVb0rW+ayzx0t61Y+mzoDODMihpazde0qO05poyK3atq2y9xYu6py29auqtwhVbWrxfLW1q2K9TycumU2+qIPbjz3q/pF+mb9TtJBzdBDk15F+qNXfPjTxyraX0f6Vn7oYU079Yy/jPIHrpXmFsbPp/6Ba6XzR7q86A7gZR2Xdz/gf4FlG9ZXVe7LCtP8C/Dblrnr5/EiXWJ3/HDWc9U6a1rPeZq6B66V5gKrF6b5FPnhOi2XefXCMn+NdE9jl3W9A3B6fr8y6VK/F7fIPQbYJ0+zLXBdl9z888vzelaXfRo4YGieh7FvXQB8KL9/JemATg25m5A69EMPPtsXmNFxG6+a/30B6V7wfVpu36F2E0j3Xr65Ybnn8+wHJr2dZz9w7dqKdlN49gOTpvLshxYd1yW3MPxIah6u1ZtbGH4ZNQ8tqpoOmJTX3S4N7XqXdwfgZmCVpsyK3HVY+NCitfN+VfUAs97sYr07mHQlSOd1ncdNp+LBRVXruul31uxbq5E/O6RO3V2Uf5Z7l7ex3jWs6xVJZ90n5s/Nr4G3t8g9gPScCkiXoP+xbH7r9kHS53cBsG6XfTrvX5cPc9/6HOmLVZHurb0Z2LjlvnULsG1+vx1wfcdtPAlYOr//COn5EW1ya+tdz3pZ5HNHqn3FB67dyaIPXFP+3V/rGd6qdtFwnEJF7arKrdtvWi5zbe2qWd5Wtasmt7Z21eS2qltN6zlPMx0/cM2vPn2N+Qz41WIjpUtZbyd1Wo/Iw9YlPWhiHqlTMKGi7eake6XuAL7JwgObnUl/8J8A7gV+1TL3E7ndU7mgnlKRWzp/pIOae4GZ+fXTlrlP5Z+H2lU93bUq90TSA0dmApfS08EtyyX9kf8N6Z6lOaTLoFbosp57pplPeWdikeXtGV/X+a7avmfk+Z4N/JTCwWmLdX1JYZn/C1iu47oW6RLqm/Pv2a1l7iTSmeebgKvITx5um5vHHUnFlwV165p0cLNDw+ewank3zPvJrLx/bd9yeXfOyzor55cefNds40Py77yd9MVF2T5XljuVdAB9G+lyv6rlLf2s5+37rfw7b6L8y7sfkC7//Uf+HfuS7pu/mHSm/WJgpY65q+XhDwEP5vcrtMhtrHWF7bHIdKRbVB5hYe2ZyaJP8C/LnUfqjA21qXpyb1XuXiysWTcA765oX5Y9I+8zs4GfkR5m1Hpd90wznfKnnS+S26beNWzjg/IyzyI9wOkNLZe3bb2r3BeAPXP2HEo6VxW5S5Nq5Jy8jUq/yGrI3Ra4uuZzWLqe83Y5oKFmVe1by5Fq2FxSnT6sw761NXB93kbXAJt13MZbkWrAraQz6Cu2zG2sd7lt5eeO9Hf9DlLt27Gk7dakS9FnF9rvRPvaVXqcQkPtqsltW7tKl5mG2lWT27Z2VeXW1q6a3LZ1q/F4EHe+/erj19BBnJmZmZmZmZmNEN/zbWZmZmZmZjbC3Pk2MzMzMzMzG2HufJuZmZmZmZmNMHe+zczMzMzMzEaYO99mZmZmZmZmI2z8WM+AmZlZP5A09F8KQfrvgZ4G/pJ/fjQi3jAmM2ZmZmYDwf/VmJmZWQ9JRwIPR8TxYz0vZmZmNhh82bmZmVkDSQ/nf7eVdLmkH0q6XdIxkvaQdK2kmyStl6dbRdIMSdfl1z+N7RKYmZnZWHPn28zMrJtNgEOAVwN7ARtExJbAKcDBeZoTgRMiYgtglzzOzMzMlmC+59vMzKyb6yLiHgBJdwAX5uE3AW/K798CbChpqM0KkpaPiL+P6pyamZlZ33Dn28zMrJsnCu+fKfz8DAv/rr4A2CoiHhvNGTMzM7P+5cvOzczMFr8LgYOGfpC06RjOi5mZmfUBd77NzMwWv08Am0uaLelm4ICxniEzMzMbW/6vxszMzMzMzMxGmM98m5mZmZmZmY0wd77NzMzMzMzMRpg732ZmZmZmZmYjzJ1vMzMzMzMzsxHmzreZmZmZmZnZCHPn28zMzMzMzGyEufNtZmZmZmZmNsLc+TYzMzMzMzMbYf8P4PcejNrK2VkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2880x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (40,5));\n",
    "plt.title('Installs per hour');\n",
    "sns.heatmap(pivot_installs.T, square = True, linewidth=1, cmap = 'Greens');\n",
    "plt.xticks(rotation = 0);\n",
    "plt.xlabel('Time');\n",
    "plt.ylabel('Day', rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 11. ¿Cuáles fueron las distribuciones de los eventos de la muestra ?¿Y de los installs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (7,5));\n",
    "device_freq = events_df['ref_hash'].value_counts().to_frame().reset_index().reset_index()\n",
    "device_freq.columns = ['x', 'ref_hash', 'count']\n",
    "\n",
    "ax = sns.regplot(x=device_freq['x'], y=device_freq['count'], fit_reg=False);\n",
    "ax.set_xlabel('Quantity of devices');\n",
    "ax.set_ylabel('Events per device');\n",
    "ax.set_title('Distribution of events per device');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = device_freq.plot(x='x',y='count',loglog=True, legend=False);\n",
    "ax.set_xlabel('Log scale of: Quantity of Devices');\n",
    "ax.set_ylabel('log scale of: Quantyty of Events');\n",
    "ax.set_title('Distribution of events per device');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Hacer un archivo con todos los device IDs de Auctions\n",
    "\n",
    "Los IDs no pueden permanecer al target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_percentage = 0.5/100; # 0.5% -> auctions, 50% -> convertions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.005"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IDs con auctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "competencia_ids = pd.read_csv('data/target_competencia_ids.csv')\n",
    "competencia_ids['ref_hash'] = competencia_ids['ref_hash'].str.replace('_sc', '')\n",
    "competencia_ids['ref_hash'] = competencia_ids['ref_hash'].str.replace('_st', '')\n",
    "competencia_ids.drop_duplicates(inplace=True)\n",
    "pd.DataFrame(auctions_df.loc[~auctions_df['device_id'].isin(competencia_ids['ref_hash'])]['device_id'].unique(), columns=['device_id'], dtype='object').to_csv('data/auctions_ids.csv', index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### IDs con conversiones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "competencia_ids = pd.read_csv('data/target_competencia_ids.csv')\n",
    "competencia_ids['ref_hash'] = competencia_ids['ref_hash'].str.replace('_sc', '')\n",
    "competencia_ids['ref_hash'] = competencia_ids['ref_hash'].str.replace('_st', '')\n",
    "competencia_ids.drop_duplicates(inplace=True)\n",
    "convertions = installs_df[['ref_hash']].append(events_df[['ref_hash']], ignore_index=True)\n",
    "pd.DataFrame(convertions.loc[~convertions['ref_hash'].isin(competencia_ids['ref_hash'])]['ref_hash'].unique(), columns=['ref_hash'], dtype='object').to_csv('data/convertions_ids.csv', index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Seleccionar al azar device IDs del archivo y buscar todos sus registros en auctions hasta que sean 100,000 registros\n",
    "\n",
    "Al menos 100,000 registros por ventana\n",
    "\n",
    "#### Sample IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "auctions_ids = pd.read_csv('data/auctions_ids.csv', dtype={'device_id':'object'})\n",
    "sample_size = auctions_ids.shape[0]\n",
    "\n",
    "auctions_ids_sample = auctions_ids['device_id'].sample(n=int(sample_size*sample_percentage), random_state=3)\n",
    "#save sample ids\n",
    "auctions_ids_sample.to_frame().to_csv('data/auctions_ids_sample.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2356"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(sample_size*sample_percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "convertions_ids = pd.read_csv('data/convertions_ids.csv', dtype={'ref_hash':'object'})\n",
    "sample_size = convertions_ids.shape[0]\n",
    "convertions_ids_sample = convertions_ids['ref_hash'].sample(n=int(sample_size*sample_percentage), random_state=3)\n",
    "#save sample ids\n",
    "convertions_ids_sample.to_frame().to_csv('data/convertions_ids_sample.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3760"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(sample_size*sample_percentage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Auctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47409528, 4)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(241085, 4)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#auctions sample\n",
    "auctions_sample = auctions_df.loc[auctions_df['device_id'].isin(auctions_ids_sample)].copy()\n",
    "auctions_sample.to_csv('data/auctions_sample.csv', index=False)\n",
    "display(auctions_df.shape)\n",
    "auctions_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2356"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = sum(1 for line in open('data/auctions_ids_sample.csv'))-1  # Calculate number of rows in file\n",
    "n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Installs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "installs_sample = installs_df.loc[installs_df['ref_hash'].isin(auctions_ids_sample)].copy()\n",
    "installs_sample.to_csv('data/installs_sample.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_sample = events_df.loc[events_df['ref_hash'].isin(auctions_ids_sample)].copy()\n",
    "events_sample.to_csv('data/events_sample.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Procesar el sample como el TP1 para ver que la muestra sea valida. Es decir los graficos tienen que ser parecidos\n",
    "\n",
    "#### Auctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "auctions_sample['hora'] = auctions_sample['date'].apply(lambda x: ('%02d-%02d' % (x.hour, (x.hour+1))))\n",
    "auctions_sample['dia'] = auctions_sample['date'].dt.day_name()+' '+auctions_sample['date'].dt.day.map(str)\n",
    "pivot_auctions = auctions_sample.groupby(['dia','hora']).size().reset_index()\n",
    "pivot_auctions.columns = ['dia','hora', 'count']\n",
    "pivot_auctions = pivot_auctions.pivot_table(index='hora', columns='dia', values='count',aggfunc='sum', fill_value=0, dropna=False)\n",
    "pivot_auctions = pivot_auctions[['Thursday 18', 'Friday 19', 'Saturday 20', 'Sunday 21', 'Monday 22', 'Tuesday 23', 'Wednesday 24', 'Thursday 25', 'Friday 26']]\n",
    "#display(pivot_auctions)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_auctions = pivot_auctions.max().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(np.inf, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (40,5))\n",
    "plt.title('Sample Auctions per hour')\n",
    "sns.heatmap(pivot_auctions.T/max_auctions, square = True, linewidth=1, cmap = 'Reds');\n",
    "plt.xticks(rotation = 0);\n",
    "plt.xlabel('Time');\n",
    "plt.ylabel('Day', rotation=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>dia</th>\n",
       "      <th>hora</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.720353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0.625736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>0.664424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>0.651388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>0.660219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>0.451220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>0.423465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>0.277544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>0.190076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>0.145500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>0.187973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>0.118587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>0.162321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>0.337679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>0.433137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>0.484020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>0.485702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>0.485702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>0.634987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>0.549201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>0.582002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>18</td>\n",
       "      <td>21</td>\n",
       "      <td>0.735913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>0.590412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>0.565601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0.489066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>0.603869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>0.685029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>0.653490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>0.666526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>25</td>\n",
       "      <td>19</td>\n",
       "      <td>0.473507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>0.492431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>0.528595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>25</td>\n",
       "      <td>22</td>\n",
       "      <td>0.610597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>0.610597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0.514718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0.415475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>0.595038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>0.927670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>0.899075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>0.746426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.598823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>0.348192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>0.230866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>0.084525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>26</td>\n",
       "      <td>10</td>\n",
       "      <td>0.071909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>26</td>\n",
       "      <td>11</td>\n",
       "      <td>0.124895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>0.229605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>26</td>\n",
       "      <td>13</td>\n",
       "      <td>0.269554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>0.292262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>26</td>\n",
       "      <td>15</td>\n",
       "      <td>0.380151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>0.424306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>26</td>\n",
       "      <td>17</td>\n",
       "      <td>0.525652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>0.701850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>0.635828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>0.478553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>26</td>\n",
       "      <td>21</td>\n",
       "      <td>0.540791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>0.656434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "      <td>0.669050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     dia  hora     count\n",
       "0     18     0  0.720353\n",
       "1     18     1  0.625736\n",
       "2     18     2  0.664424\n",
       "3     18     3  0.651388\n",
       "4     18     4  0.660219\n",
       "5     18     5  0.451220\n",
       "6     18     6  0.423465\n",
       "7     18     7  0.277544\n",
       "8     18     8  0.190076\n",
       "9     18     9  0.145500\n",
       "10    18    10  0.187973\n",
       "11    18    11  0.118587\n",
       "12    18    12  0.162321\n",
       "13    18    13  0.337679\n",
       "14    18    14  0.433137\n",
       "15    18    15  0.484020\n",
       "16    18    16  0.485702\n",
       "17    18    17  0.485702\n",
       "18    18    18  0.634987\n",
       "19    18    19  0.549201\n",
       "20    18    20  0.582002\n",
       "21    18    21  0.735913\n",
       "22    18    22  0.590412\n",
       "23    18    23  0.565601\n",
       "24    19     0  0.489066\n",
       "25    19     1  0.500841\n",
       "26    19     2  0.603869\n",
       "27    19     3  0.685029\n",
       "28    19     4  1.000000\n",
       "29    19     5  0.653490\n",
       "..   ...   ...       ...\n",
       "186   25    18  0.666526\n",
       "187   25    19  0.473507\n",
       "188   25    20  0.492431\n",
       "189   25    21  0.528595\n",
       "190   25    22  0.610597\n",
       "191   25    23  0.610597\n",
       "192   26     0  0.514718\n",
       "193   26     1  0.415475\n",
       "194   26     2  0.595038\n",
       "195   26     3  0.927670\n",
       "196   26     4  0.899075\n",
       "197   26     5  0.746426\n",
       "198   26     6  0.598823\n",
       "199   26     7  0.348192\n",
       "200   26     8  0.230866\n",
       "201   26     9  0.084525\n",
       "202   26    10  0.071909\n",
       "203   26    11  0.124895\n",
       "204   26    12  0.229605\n",
       "205   26    13  0.269554\n",
       "206   26    14  0.292262\n",
       "207   26    15  0.380151\n",
       "208   26    16  0.424306\n",
       "209   26    17  0.525652\n",
       "210   26    18  0.701850\n",
       "211   26    19  0.635828\n",
       "212   26    20  0.478553\n",
       "213   26    21  0.540791\n",
       "214   26    22  0.656434\n",
       "215   26    23  0.669050\n",
       "\n",
       "[216 rows x 3 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auctions_sample['hora'] = auctions_sample['date'].dt.hour\n",
    "auctions_sample['dia'] = auctions_sample['date'].dt.day\n",
    "pivot_auctions = auctions_sample.groupby(['dia','hora']).size().reset_index()\n",
    "pivot_auctions.columns = ['dia','hora', 'count']\n",
    "pivot_auctions['count'] = pivot_auctions['count']/max_auctions\n",
    "pivot_auctions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>dia</th>\n",
       "      <th>hora</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>00-01</td>\n",
       "      <td>1713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>01-02</td>\n",
       "      <td>1488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>02-03</td>\n",
       "      <td>1580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>03-04</td>\n",
       "      <td>1549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>04-05</td>\n",
       "      <td>1570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>05-06</td>\n",
       "      <td>1073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>06-07</td>\n",
       "      <td>1007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>07-08</td>\n",
       "      <td>660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>08-09</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>09-10</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>10-11</td>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>11-12</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>12-13</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>13-14</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>14-15</td>\n",
       "      <td>1030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>15-16</td>\n",
       "      <td>1151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>16-17</td>\n",
       "      <td>1155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>17-18</td>\n",
       "      <td>1155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>18-19</td>\n",
       "      <td>1510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>19-20</td>\n",
       "      <td>1306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>20-21</td>\n",
       "      <td>1384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>21-22</td>\n",
       "      <td>1750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>22-23</td>\n",
       "      <td>1404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Thursday 18</td>\n",
       "      <td>23-24</td>\n",
       "      <td>1345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Friday 19</td>\n",
       "      <td>00-01</td>\n",
       "      <td>1163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Friday 19</td>\n",
       "      <td>01-02</td>\n",
       "      <td>1191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Friday 19</td>\n",
       "      <td>02-03</td>\n",
       "      <td>1436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Friday 19</td>\n",
       "      <td>03-04</td>\n",
       "      <td>1629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Friday 19</td>\n",
       "      <td>04-05</td>\n",
       "      <td>2378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Friday 19</td>\n",
       "      <td>05-06</td>\n",
       "      <td>1554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Thursday 25</td>\n",
       "      <td>18-19</td>\n",
       "      <td>1585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Thursday 25</td>\n",
       "      <td>19-20</td>\n",
       "      <td>1126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>Thursday 25</td>\n",
       "      <td>20-21</td>\n",
       "      <td>1171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Thursday 25</td>\n",
       "      <td>21-22</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Thursday 25</td>\n",
       "      <td>22-23</td>\n",
       "      <td>1452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>Thursday 25</td>\n",
       "      <td>23-24</td>\n",
       "      <td>1452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>00-01</td>\n",
       "      <td>1224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>01-02</td>\n",
       "      <td>988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>02-03</td>\n",
       "      <td>1415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>03-04</td>\n",
       "      <td>2206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>04-05</td>\n",
       "      <td>2138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>05-06</td>\n",
       "      <td>1775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>06-07</td>\n",
       "      <td>1424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>07-08</td>\n",
       "      <td>828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>08-09</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>09-10</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>10-11</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>11-12</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>12-13</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>13-14</td>\n",
       "      <td>641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>14-15</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>15-16</td>\n",
       "      <td>904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>16-17</td>\n",
       "      <td>1009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>17-18</td>\n",
       "      <td>1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>18-19</td>\n",
       "      <td>1669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>19-20</td>\n",
       "      <td>1512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>20-21</td>\n",
       "      <td>1138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>21-22</td>\n",
       "      <td>1286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>22-23</td>\n",
       "      <td>1561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Friday 26</td>\n",
       "      <td>23-24</td>\n",
       "      <td>1591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             dia   hora     0\n",
       "0    Thursday 18  00-01  1713\n",
       "1    Thursday 18  01-02  1488\n",
       "2    Thursday 18  02-03  1580\n",
       "3    Thursday 18  03-04  1549\n",
       "4    Thursday 18  04-05  1570\n",
       "5    Thursday 18  05-06  1073\n",
       "6    Thursday 18  06-07  1007\n",
       "7    Thursday 18  07-08   660\n",
       "8    Thursday 18  08-09   452\n",
       "9    Thursday 18  09-10   346\n",
       "10   Thursday 18  10-11   447\n",
       "11   Thursday 18  11-12   282\n",
       "12   Thursday 18  12-13   386\n",
       "13   Thursday 18  13-14   803\n",
       "14   Thursday 18  14-15  1030\n",
       "15   Thursday 18  15-16  1151\n",
       "16   Thursday 18  16-17  1155\n",
       "17   Thursday 18  17-18  1155\n",
       "18   Thursday 18  18-19  1510\n",
       "19   Thursday 18  19-20  1306\n",
       "20   Thursday 18  20-21  1384\n",
       "21   Thursday 18  21-22  1750\n",
       "22   Thursday 18  22-23  1404\n",
       "23   Thursday 18  23-24  1345\n",
       "24     Friday 19  00-01  1163\n",
       "25     Friday 19  01-02  1191\n",
       "26     Friday 19  02-03  1436\n",
       "27     Friday 19  03-04  1629\n",
       "28     Friday 19  04-05  2378\n",
       "29     Friday 19  05-06  1554\n",
       "..           ...    ...   ...\n",
       "186  Thursday 25  18-19  1585\n",
       "187  Thursday 25  19-20  1126\n",
       "188  Thursday 25  20-21  1171\n",
       "189  Thursday 25  21-22  1257\n",
       "190  Thursday 25  22-23  1452\n",
       "191  Thursday 25  23-24  1452\n",
       "192    Friday 26  00-01  1224\n",
       "193    Friday 26  01-02   988\n",
       "194    Friday 26  02-03  1415\n",
       "195    Friday 26  03-04  2206\n",
       "196    Friday 26  04-05  2138\n",
       "197    Friday 26  05-06  1775\n",
       "198    Friday 26  06-07  1424\n",
       "199    Friday 26  07-08   828\n",
       "200    Friday 26  08-09   549\n",
       "201    Friday 26  09-10   201\n",
       "202    Friday 26  10-11   171\n",
       "203    Friday 26  11-12   297\n",
       "204    Friday 26  12-13   546\n",
       "205    Friday 26  13-14   641\n",
       "206    Friday 26  14-15   695\n",
       "207    Friday 26  15-16   904\n",
       "208    Friday 26  16-17  1009\n",
       "209    Friday 26  17-18  1250\n",
       "210    Friday 26  18-19  1669\n",
       "211    Friday 26  19-20  1512\n",
       "212    Friday 26  20-21  1138\n",
       "213    Friday 26  21-22  1286\n",
       "214    Friday 26  22-23  1561\n",
       "215    Friday 26  23-24  1591\n",
       "\n",
       "[216 rows x 3 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "pivot_auctions.unstack().to_frame().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_sample['hora'] = events_sample['date'].apply(lambda x: ('%02d-%02d' % (x.hour, (x.hour+1))))\n",
    "events_sample['dia'] = events_sample['date'].dt.day_name()+' '+events_sample['date'].dt.day.map(str)\n",
    "pivot_events = events_sample.groupby(['dia','hora']).size().reset_index()\n",
    "pivot_events.columns = ['dia','hora', 'count']\n",
    "pivot_events = pivot_events.pivot_table(index='hora', columns='dia', values='count',aggfunc='sum', fill_value=0, dropna=False)\n",
    "pivot_events = pivot_events[['Thursday 18', 'Friday 19', 'Saturday 20', 'Sunday 21', 'Monday 22', 'Tuesday 23', 'Wednesday 24', 'Thursday 25', 'Friday 26']]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (40,5))\n",
    "plt.title('Sample Events per hour')\n",
    "sns.heatmap(pivot_events.T, square = True, linewidth=1, cmap = 'Blues');\n",
    "plt.xticks(rotation = 0);\n",
    "plt.xlabel('Time');\n",
    "plt.ylabel('Day', rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Installs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "installs_sample['hora'] = installs_sample['created'].apply(lambda x: ('%02d-%02d' % (x.hour, (x.hour+1))))\n",
    "installs_sample['dia'] = installs_sample['created'].dt.day_name()+' '+installs_sample['created'].dt.day.map(str)\n",
    "pivot_installs = installs_sample.groupby(['dia','hora']).size().reset_index()\n",
    "pivot_installs.columns = ['dia','hora', 'count']\n",
    "pivot_installs = pivot_installs.pivot_table(index='hora', columns='dia', values='count',aggfunc='sum', fill_value=0, dropna=False)\n",
    "pivot_installs = pivot_installs[['Thursday 18', 'Friday 19', 'Saturday 20', 'Sunday 21', 'Monday 22', 'Tuesday 23', 'Wednesday 24', 'Thursday 25', 'Friday 26']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (40,5));\n",
    "plt.title('Sample Installs per hour');\n",
    "sns.heatmap(pivot_installs.T, square = True, linewidth=1, cmap = 'Greens');\n",
    "plt.xticks(rotation = 0);\n",
    "plt.xlabel('Time');\n",
    "plt.ylabel('Day', rotation=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribución"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RqWZmVjbKBnv2P3V1dVFfX1/pMMzMrJeQNC8i6jqr55lvzMzMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcpwYzczMcsqWGCXdKKlB0sJc2ZWSnpW0ID3OyC27XNIySU9LOi1XPi2VLZN0WbniNTMzg/K2GG8GphUovzYipqTHvQCSDgPOBQ5P63xHUpWkKuDbwOnAYcB5qa6ZmVlZDC7XhiNirqTxJVY/E7gjIrYCKyQtA16fli2LiOUAku5IdZ/q5nDNzMyAypxj/JCkJ1JXa00qGw2sytVZncqKlZuZmZVFTyfG64GDgSnAWuBrqVwF6kYH5QVJukRSvaT69evX726sZmY2APVoYoyIdRHREhGtwHd5pbt0NTA2V3UMsKaD8mLbnxkRdRFRV1tb273Bm5nZgNCjiVHSqNzbdwBtI1ZnAedKGippAjAJ+APwKDBJ0gRJQ8gG6MzqyZjNzGxgKdvgG0m3A1OB/SWtBj4PTJU0haw7dCUwHSAiFkm6i2xQzXbg0ohoSdv5EHA/UAXcGBGLyhWzmZmZIoqesuvT6urqor6+vtJhmJlZLyFpXkTUdVbPM9+YmZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnldJoYJR0g6fuS7kvvD5P0vvKHZmZm1vNKaTHeDNwPHJje/xH4WLkCMjMzq6RSEuP+EXEX0AoQEduBlrJGZWZmViGlJMaXJb0KCABJxwIbO1tJ0o2SGiQtzJV9RdISSU9IukfSvql8vKTNkhakxw25dY6W9KSkZZKuk6Rd/pRmZmYlKiUx/iswCzhY0m+BW4EPl7DezcC0dmWzgddExGvJumQvzy17JiKmpMcHcuXXA5cAk9Kj/TbNzMy6zeDOKkTEfElvBA4FBDwdEc0lrDdX0vh2ZQ/k3j4CnN3RNiSNAvaOiIfT+1uBs4D7Otu/mZlZV5QyKvVSYK+IWBQRC4G9JP1LN+z7n9gxwU2Q9JikhySdmMpGA6tzdVanMjMzs7IopSv1/RHxQtubiGgE3r87O5X0WWA7cFsqWguMi4gjybpufyRpb7IWanvRwXYvkVQvqX79+vW7E6KZmQ1QpSTGQfkBL5KqgCFd3aGkC4G3AudHRABExNaIeD69ngc8AxxC1kIck1t9DLCm2LYjYmZE1EVEXW1tbVdDNDOzAayUxHg/cJekN0t6E3A78Muu7EzSNODTwNsjoilXXpsSLpImkg2yWR4Ra4FNko5NyfkC4Gdd2beZmVkpOh18Q5bIpgMfJOvafAD4XmcrSbodmArsL2k18HmyUahDgdmpEfpIGoF6EnCVpLZrJD8QERvSpj5INsJ1GNk5SQ+8MTOzslHqzex36urqor6+vtJhmJlZLyFpXkTUdVavaItR0l0RcY6kJykw4CVdi2hmZtavdNSV+tH0/NaeCMTMzKw3KJoY08AXgHcCd0XEsz0TkpmZWeWUMip1b+ABSb+RdKmkA8odlJmZWaV0mhgj4gsRcThwKdmtpx6S9GDZIzMzM6uAUlqMbRqAvwDPAyPLE46ZmVlllTJX6gclzQF+BexPNkWcR6SamVm/VMoF/gcBH4uIBeUOxszMrNJKOcd4GdkdNS6Gv07fNqHskZmZmVVAKV2pnyebFq7tpsLVwA/LGZSZmVmllDL45h3A24GXASJiDTCinEGZmZlVSimJcVu6PVQASNqzvCGZmZlVTimJ8S5JM4B9Jb0feBD4bnnDMjMzq4xOR6VGxFclnQK8CBwKXBERs8semZmZWQWUcrkGKRE6GZqZWb/X0W2nNlHgdlNtImLvskRkZmZWQR3dXWMEgKSryKaC+wEg4Hw8KtXMzPqpUgbfnBYR34mITRHxYkRcD/xDuQMzMzOrhFISY4uk8yVVSRok6XygpdyBmZmZVUIpifE9wDnAuvR4VyozMzPrd0q5XGMlcGb5QzEzM6u8Xbkfo5mZWb/nxGhmZpbTYWJMg23O6erGJd0oqUHSwlzZfpJmS1qanmtSuSRdJ2mZpCckHZVb58JUf6mkC7saj5mZWWc6TIwR0Qp8aDe2fzMwrV3ZZcCvImIS8Kv0HuB0YFJ6XAJcD1kiBT4PHAO8Hvh8WzI1MzPrbqV0pc6W9ElJY1Nrb7+UrDoVEXOBDe2KzwRuSa9vAc7Kld8amUfIJi0fBZwGzI6IDRHRSDY1Xftka2Zm1i1KmSv1n9LzpbmyACZ2cZ8HRMRagIhYK2lkKh8NrMrVW53KipWX1ZwlDcyYu5xVjU2MrRnO9JMmMnXyyM5XNDOzPq2UyzUm9EQgZNPN7bT7Dsp33oB0CVk3LOPGjetyIHOWNHDFrEVUV4l9h1XTsGkLV8xaxFXg5Ghm1s912pUqabikz0mamd5PkvTW3djnutRFSnpuSOWrgbG5emOANR2U7yQiZkZEXUTU1dbWdjnAGXOXU10lhg8ZjJQ9V1eJGXOXd3mbZmbWN5RyjvEmYBtwfHq/GvjibuxzFtA2svRC4Ge58gvS6NRjgY2py/V+4FRJNWnQzamprGxWNTYxrLpqh7Jh1VWsbmwq527NzKwXKCUxHhwRXwaaASJiM4W7N3ci6XbgYeBQSaslvQ+4BjhF0lLglPQe4F5gObAM+C7wL2l/G4B/Bx5Nj6tSWdmMrRnO5uYdp4Pd3NzCmJrh5dytmZn1AqUMvtkmaRjpvJ6kg4GtpWw8Is4rsujNBeoGOw7wyS+7EbixlH12h+knTeSKWYto2radYdVVbG5uobklmH5SV8cbmZlZX1FKYvw88EtgrKTbgDcAF5UzqEqbOnkkV5Gda1zd2MQYj0o1MxswShmVOlvSfOBYsi7Uj0bEc2WPrMKmTh7pRGhmNgCV0mIEeCNwAll3ajVwT9kiMjMzq6BSLtf4DvAB4ElgITBd0rfLHZiZmVkllNJifCPwmjQ4Bkm3kCXJfs0z35iZDUylXK7xNJCfRmYs8ER5wukd5ixp4JN3P85jqxpZ9+IWHlvVyCfvfpw5Sxo6X9nMzPq0UhLjq4DFkuZImgM8BdRKmiVpVlmjq5Br7lvMC03NRCtUSUQrvNDUzDX3La50aGZmVmaldKVeUfYoepkVzzcxSDBoUDaPgQTRGqx43jPfmJn1d6VcrvFQTwRiZmbWG5TSlTrgTNx/T1oDWiMIgtYIWiMrNzOz/s2JsYBPT5tMzfBqBGxvaUVAzfBqPj1tcqVDMzOzMtulxJjucPHacgXTW0ydPJKvnH0ER46rYdQ+wzhyXA1fOfsIX65hZjYAdHqOMY1EfXuquwBYL+mhiPjXMsdWUZ4SzsxsYCqlxbhPRLwIvBO4KSKOBk4ub1hmZmaVUUpiHCxpFHAO8PMyx2NmZlZRpSTGLwD3A8si4lFJE4Gl5Q3LzMysMkq5wH9tRPx1wE1ELJf09TLGZGZmVjGltBi/WWKZmZlZn1e0xSjpOOB4snlR8yNQ9waqyh2YmZlZJXTUlToE2CvVGZErfxE4u5xBmZmZVUrRxJjmSH1I0s0R8acejMnMzKxiShl8M1TSTGB8vn5EvKlcQZmZmVVKKYnxx8ANwPeAlvKGY2ZmVlmlJMbtEXF9d+1Q0qHAnbmiiWT3fNwXeD+wPpV/JiLuTetcDryPLDF/JCLu7654zMzM8kpJjP8j6V+Ae4CtbYURsaErO4yIp4EpAJKqgGfTti8Gro2Ir+brSzoMOBc4HDgQeFDSIRHh1quZmXW7UhLjhen5U7myIGvp7a43A89ExJ8kFatzJnBHRGwFVkhaBrweeLgb9m9mZraDThNjREwo4/7PBW7Pvf+QpAuAeuATEdEIjAYeydVZncrMzMy6Xacz30gaLulzaWQqkiZJeuvu7ljSELLbWf04FV0PHEzWzboW+Fpb1QKrR5FtXiKpXlL9+vXrC1UxMzPrUClTwt0EbCObBQeyFtsXu2HfpwPzI2IdQESsi4iWiGgFvkvWXdq2v7G59cYAawptMCJmRkRdRNTV1tZ2Q4hmZjbQlJIYD46ILwPNABGxmcKtuF11Hrlu1HRrqzbvABam17OAcyUNlTQBmAT8oRv2b2ZmtpNSBt9skzSM1H0p6WByo1O7QtJw4BRgeq74y5KmpP2sbFsWEYsk3QU8BWwHLvWIVDMzK5dSEuOVwC+BsZJuA94AXLQ7O42IJuBV7cre20H9q4Grd2efZmZmpShlVOoDkuYBx5J1oX40Ip4re2RmZmYV0GlilDSL7FzgrIh4ufwhmZmZVU4pg2++BpwIPCXpx5LOlrRHmeMyMzOriFK6UttuP1UFvIlsPtMbyW5YbGZm1q+UMviGNCr1bcC7gaOAW8oZlJmZWaWUco7xTuAYspGp3wbmpIvwzczM+p1SWow3Ae/xtYNmZjYQFB18I+nfACLil8A72y37jzLHZWZmVhEdjUo9N/f68nbLppUhll5lzpIGzpv5CCf85685b+YjzFnSUOmQzMysB3SUGFXkdaH3/cqcJQ1cMWsRDZu2sO+waho2beGKWYucHM3MBoCOEmMUeV3ofb8yY+5yqqvE8CGDkbLn6ioxY+7ySodmZmZl1tHgmyMkvUjWOhyWXpPe9+sL/Fc1NrHvsOodyoZVV7G6salCEZmZWU8pmhgjoqonA+lNxtYMp2HTFoYPeeXwbG5uYUzN8ApGZWZmPaGUKeEGnOknTaS5JWjatp2I7Lm5JZh+0sRKh2ZmZmXmxFjA1MkjOfuo0azftJXFf9nE+k1bOfuo0UydPLLSoZmZWZk5MRYwZ0kDd89/ltoRQ3n134ygdsRQ7p7/rEelmpkNAE6MBXhUqpnZwFXSJOIDzarGJqoEy9e/xLaWVoZUDWL/vYZ4VKqZ2QDgFmMBew2p4tkXtrC9JaiS2N4SPPvCFvYcMmAH6pqZDRhOjAVIaWIf5R75cjMz67ecGAvYtHU7o/fdg8GDREtrMHiQGL3vHry0dXulQzMzszLzOcYC2i7wn1i711/LmrZtZ+SIfj3hj5mZ4RZjQb7A38xs4KpYYpS0UtKTkhZIqk9l+0maLWlpeq5J5ZJ0naRlkp6QdFQ5Y5s6eSRXvf1wRo7Yg42bmxk5Yg+uevvhvsDfzGwAqHRX6t9HxHO595cBv4qIayRdlt5/GjgdmJQexwDXp+eymTp5pBOhmdkA1Nu6Us8EbkmvbwHOypXfGplHgH0ljapEgGZm1r9VMjEG8ICkeZIuSWUHRMRagPTc1mQbDazKrbs6lZmZmXWrSnalviEi1kgaCcyWtKSDuoUuINzpZskpwV4CMG7cuO6J0szMBpSKtRgjYk16bgDuAV4PrGvrIk3PbbN2rwbG5lYfA6wpsM2ZEVEXEXW1tbXlDN/MzPqpiiRGSXtKGtH2GjgVWAjMAi5M1S4EfpZezwIuSKNTjwU2tnW5mpmZdadKdaUeANyTplgbDPwoIn4p6VHgLknvA/4MvCvVvxc4A1gGNAEX90SQc5Y0MGPuclY1NjG2ZjjTT5rokapmZv2cInY6Vdcv1NXVRX19fZfXn7OkgStmLaK6SgyrrmJzcwvNLeHrGc3M+ihJ8yKirrN6ve1yjV7D92Q0MxuYKn2Bf6/lezKamQ1MbjEW4XsympkNTE6MRfiejGZmA5MTYxG+J6OZ2cDkxFjE2JrhbN3eukPZ1u2tjKkZXqGIzMysJzgxFnHcxP1Y/9I2trW0MkiwraWV9S9t47iJ+1U6NDMzKyMnxiIeXr6BkSOGMKRqEK0BQ6oGMXLEEB5evqHSoZmZWRn5co0iVjU28ao9h7L/Xnv8tSwifLmGmVk/5xZjEWNrhrO5uWWHss3NLT7HaGbWzzkxFjH9pIls3NzM0oZNLPnLiyxt2MTGzc1MP2lipUMzM7MycldqB7Y1t7BteyutAS2twbZBLZ2vZGZmfZpbjEVcc99imppbqR5JWAYfAAAOR0lEQVQ0iD0GD6J60CCamlu55r7FlQ7NzMzKyC3GIlY838QgwaBB2Uw3EkRrsOJ5D74xM+vP3GI0MzPLcWIsYuL+e9Ia0BpBELRG0BpZuZmZ9V9OjEV8etpkaoZXI2B7SysCaoZX8+lpkysdmpmZlZETYxFTJ4/kK2cfwZHjahi1zzCOHFfDV84+gqmTR1Y6NDMzKyMnxhJEpQMwM7Me41GpRcxZ0sAVsxaxbXsLm7Zs5y8btzD/z41cOvVgPnLyIZUOz8zMysQtxiJmzF3Otu0tNGzaStO2Fra3Btu2t3Ldr5cyZ0lDpcMzM7MycWIsYlVjE40vb6M1XulKDWB7K77I38ysH3NiLGJszXC2thQ+u7hk3Us9HI2ZmfWUHk+MksZK+l9JiyUtkvTRVH6lpGclLUiPM3LrXC5pmaSnJZ3WE3F6snAzs4GpEoNvtgOfiIj5kkYA8yTNTsuujYiv5itLOgw4FzgcOBB4UNIhEVHWGb07uyxjzpIGX7phZtYP9XiLMSLWRsT89HoTsBgY3cEqZwJ3RMTWiFgBLANeX/5IOzZj7vJKh2BmZmVQ0XOMksYDRwK/T0UfkvSEpBsl1aSy0cCq3Gqr6TiRdpvjJtQULBewdN2LPRGCmZn1sIolRkl7AT8BPhYRLwLXAwcDU4C1wNfaqhZYveCoGEmXSKqXVL9+/frdjvH26cdTNWjn3QewrcjAHDMz69sqkhglVZMlxdsi4r8BImJdRLRERCvwXV7pLl0NjM2tPgZYU2i7ETEzIuoioq62trZbYh1UZN6bl7Zs75btm5lZ71KJUakCvg8sjoiv58pH5aq9A1iYXs8CzpU0VNIEYBLwh56Kt7m1cHkr+EJ/M7N+qBItxjcA7wXe1O7SjC9LelLSE8DfAx8HiIhFwF3AU8AvgUvLPSK1VNN/8GilQzAzs27W45drRMT/Ufi84b0drHM1cHXZguqirS2+bMPMrL/xzDed2H+vIR0ud6vRzKx/cWLsxFfPPqLD5Vtb4LoH/9hD0ZiZWbk5MXZi6uSRDK/u+DB989dLeygaMzMrNyfGEnzn/KM7XN7c6hGqZmb9hRNjCaZOHlnwQv+8f77F5xrNzPoDJ8YSffRNf9vh8u3hc41mZv2BE2OJPnLyIQWvMcn7+oM+12hm1tc5Me6Cs6aM6rTOeTN+1wORmJlZuTgx7oJrzz2q01bjwysa+fgd83skHjMz635OjLvo4ydP6rTOPQvWcvRV9/dANGZm1t2cGHfRR04+pNPrGgGeb9rO+Mt+4cs4zMz6GCfGLujsusa8i25+lAmX/cIjVs3M+ggnxi6YOnkkx02oKbl+kI1YHX/ZLzw4x8ysl3Ni7KLbpx/PXkN2/fA9vKKR8Zf9wq1IM7NeyolxNyy86nSGVnVtXbcizcx6J0VEpWMoi7q6uqivr++RfU27dg5L1r3cLdsas89Q/u/yk7tlW2Zm9gpJ8yKirtN6TozdY86SBi66uXzzpR43oYbbpx9ftu2bmfV3Tow9nBjbdGfrsSMiu6byIycfUvZ9mZn1B06MFUqMbXoqQRYztErMeG8dUyePrFgMZma9iRNjhRNjm+se/GOvn1zcSdTMBgInxl6SGPPOm/E7Hl7RWOkwupXPfZpZX+HE2AsTY5u+0IqsBCdZMysnJ8ZenBjbO+FLD7J649ZKh2Hd5B1TRnHtuUdVOgwza6ffJUZJ04BvAFXA9yLimo7q96XEWIhblWZmO5p8wJ788uNTu7x+v0qMkqqAPwKnAKuBR4HzIuKpYuv09cRYSH88R2lmtit2JzmWmhgHd2nrPe/1wLKIWA4g6Q7gTKBoYuyPOjv/VulLRMzMyq0n/sb1lcQ4GliVe78aOKZCsfRau/JflJOomVlhfSUxqkDZTn3Aki4BLgEYN25cuWPq07raFeFzn2bW3/WVxLgaGJt7PwZY075SRMwEZkJ2jrFnQhtYPnLyId02DZ2TrJntqskH7Fn2ffSVwTeDyQbfvBl4lmzwzXsiYlGxdfrj4BvrnTwoyqxn9NSo1D7RYoyI7ZI+BNxPdrnGjR0lRbOe5EkJzPqXPpEYASLiXuDeSsdhZmb926BKB2BmZtabODGamZnlODGamZnlODGamZnlODGamZnlODGamZnlODGamZnl9ImZb7pC0nrgT7u5mf2B57ohnIHOx7F7+Dh2Dx/H7tEXj+NBEVHbWaV+mxi7g6T6UqYPso75OHYPH8fu4ePYPfrzcXRXqpmZWY4To5mZWY4TY8dmVjqAfsLHsXv4OHYPH8fu0W+Po88xmpmZ5bjFaGZmluPEWISkaZKelrRM0mWVjqe3k7RS0pOSFkiqT2X7SZotaWl6rknlknRdOrZPSDqqstFXjqQbJTVIWpgr2+XjJunCVH+ppAsr8VkqpcgxvFLSs+nncYGkM3LLLk/H8GlJp+XKB/TvvKSxkv5X0mJJiyR9NJUPvJ/HiPCj3YPsZsjPABOBIcDjwGGVjqs3P4CVwP7tyr4MXJZeXwb8Z3p9BnAfIOBY4PeVjr+Cx+0k4ChgYVePG7AfsDw916TXNZX+bBU+hlcCnyxQ97D0+zwUmJB+z6v8Ox8Ao4Cj0usRwB/T8RpwP49uMRb2emBZRCyPiG3AHcCZFY6pLzoTuCW9vgU4K1d+a2QeAfaVNKoSAVZaRMwFNrQr3tXjdhowOyI2REQjMBuYVv7oe4cix7CYM4E7ImJrRKwAlpH9vg/43/mIWBsR89PrTcBiYDQD8OfRibGw0cCq3PvVqcyKC+ABSfMkXZLKDoiItZD90gEjU7mPb8d29bj5eBb2odTFd2Nb9x8+hiWRNB44Evg9A/Dn0YmxMBUo8/Ddjr0hIo4CTgculXRSB3V9fLum2HHz8dzZ9cDBwBRgLfC1VO5j2AlJewE/AT4WES92VLVAWb84lk6Mha0GxubejwHWVCiWPiEi1qTnBuAesq6pdW1dpOm5IVX38e3Yrh43H892ImJdRLRERCvwXbKfR/Ax7JCkarKkeFtE/HcqHnA/j06MhT0KTJI0QdIQ4FxgVoVj6rUk7SlpRNtr4FRgIdkxaxuRdiHws/R6FnBBGtV2LLCxravGgF0/bvcDp0qqSV2Gp6ayAavdOet3kP08QnYMz5U0VNIEYBLwB/w7jyQB3wcWR8TXc4sG3s9jpUf/9NYH2YirP5KNVPtspePpzQ+ykXyPp8eituMFvAr4FbA0Pe+XygV8Ox3bJ4G6Sn+GCh6728m6+prJ/tN+X1eOG/BPZANJlgEXV/pz9YJj+IN0jJ4g+wM+Klf/s+kYPg2cnisf0L/zwAlkXZ5PAAvS44yB+PPomW/MzMxy3JVqZmaW48RoZmaW48RoZmaW48RoZmaW48RoZmaW48Ro1glJYyT9LN0pYLmkb0kaWob9TJV0fO79ByRdkF5fJOnAbtrPienuCQskDeug3pWSPtnFfVwl6eSuR2lWOU6MZh1IFz3/N/DTiJhEdkH4MLI7DnS3qcBfE2NE3BARt6a3FwHdkhiB84GvRsSUiNjcTdvcQURcEREPlmPbZuXmxGjWsTcBWyLiJoCIaAE+Tjbjx16pJfettsqSfi5panp9vaT61Dr7Qq7OSklfkDRf2T0sJ6dJmz8AfDy15E5sa7FJOhuoA25Ly94i6Z7c9k6R1DZ9F7nyN0t6LO3jxjTbyz8D5wBXSLqtwDqfVXZPwgeBQ3PlB0v6ZZok/jcp5n3SZxmU6gyXtEpStaSbU9xIep2k30l6XNIfJI2QVCXpK5IeTRN9T091R0mamz7nQkkndvF7M+syJ0azjh0OzMsXRDax8krgbztZ97MRUQe8FnijpNfmlj0X2aTr15PdN3AlcANwbWrJ/Sa3v7uBeuD8iJgC3Au8WlJtqnIxcFN+x5L2AG4G3h0RfwcMBj4YEd8jmwnmUxFxfrt1jiabCu1I4J3A63KLZwIfjoijgU8C34mIjWSzHb0x1XkbcH9ENOe2OQS4E/hoRBwBnAxsJpudZmNEvC7t5/1pirb3pG1MAY4gm33FrEc5MZp1TBS+M0ChOwi0d46k+cBjZAn2sNyythbePGD8rgQU2XRVPwD+UdK+wHFkN4zNOxRYERF/TO9vIbuhb0dOBO6JiKaU/GfBX++2cDzwY0kLgBlkN7WFLOm9O70+N71vH8faiHg0xf5iRGwnmz/zgrS935NNOzaJbM7SiyVdCfxdZPcFNOtRgysdgFkvtwj4h3yBpL2BA8jm2nwNO/6DuUeqM4GsZfW6iGiUdHPbsmRrem6ha7+HNwH/A2wBfpySzQ5hdmGbUPifgEHAC6kV194s4EuS9gOOBn5dII5i/1h8OCJ2mlxa2S3L3gL8QNJXcudZzXqEW4xmHfsVMDw3OrSK7N5+30oDV1YCUyQNkjSWV25vtDfwMrBR0gFk96nszCZgRCnLIrvN1xrgc2Rdpu0tAcZLauvufS/wUCf7nwu8Q9IwZXdLeVva14vACknvgmxAkqQj0rKXyO5O8Q3g5+kcbPs4DpT0urTuCEmDye628EFltzlC0iHK7tJyENAQEd8lu9PDUZ3EbNbtnBjNOpC6Ld8BnC1pKfA80BoRV6cqvwVWkN1d4KvA/LTe42RdqIuAG1O9zvwPWWJaUGDQyc3ADe0usbgNWBURTxWIewvZuccfS3oSaCU7h9nRZ51P1hW6gOyefL/JLT4feJ+ktjuonJlbdifwj+zcjUpEbCPrav1mWnc2Wcv5e8BTwHxJC8m6ZweTjcxdIOkxspb6NzqK2awcfHcNs12g7DrD24F3RsS8zuqXOZZvAY9FxPcrGYdZf+PEaNYHSZpH1lV7SkRs7ay+mZXOidHMzCzH5xjNzMxynBjNzMxynBjNzMxynBjNzMxynBjNzMxynBjNzMxy/j8sGU/ugoWitQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (7,5));\n",
    "device_freq = events_sample['ref_hash'].value_counts().to_frame().reset_index().reset_index()\n",
    "device_freq.columns = ['x', 'ref_hash', 'count']\n",
    "\n",
    "ax = sns.regplot(x=device_freq['x'], y=device_freq['count'], fit_reg=False);\n",
    "ax.set_xlabel('Quantity of devices');\n",
    "ax.set_ylabel('Events per device');\n",
    "ax.set_title('Sample distribution of events per device');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = device_freq.plot(x='x',y='count',loglog=True, legend=False);\n",
    "ax.set_xlabel('Log scale of: Quantity of Devices');\n",
    "ax.set_ylabel('log scale of: Quantyty of Events');\n",
    "ax.set_title('Distribution of events per device');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source src=\"https://sound.peal.io/ps/audios/000/000/537/original/woo_vu_luvub_dub_dub.wav\" type=\"audio/x-wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "allDone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}