productos(mas vistos-comprados).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as npy\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('events.csv',low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1011288 entries, 0 to 1011287\n",
      "Data columns (total 23 columns):\n",
      "timestamp                   1011288 non-null object\n",
      "event                       1011288 non-null object\n",
      "person                      1011288 non-null object\n",
      "url                         82756 non-null object\n",
      "sku                         563838 non-null object\n",
      "model                       564284 non-null object\n",
      "condition                   563836 non-null object\n",
      "storage                     563836 non-null object\n",
      "color                       563836 non-null object\n",
      "skus                        221699 non-null object\n",
      "search_term                 48967 non-null object\n",
      "staticpage                  3598 non-null object\n",
      "campaign_source             82796 non-null object\n",
      "search_engine               50957 non-null object\n",
      "channel                     87378 non-null object\n",
      "new_vs_returning            87378 non-null object\n",
      "city                        87378 non-null object\n",
      "region                      87378 non-null object\n",
      "country                     87378 non-null object\n",
      "device_type                 87378 non-null object\n",
      "screen_resolution           87378 non-null object\n",
      "operating_system_version    87378 non-null object\n",
      "browser_version             87378 non-null object\n",
      "dtypes: object(23)\n",
      "memory usage: 177.5+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 98635 entries, 12 to 1011165\n",
      "Data columns (total 23 columns):\n",
      "timestamp                   98635 non-null object\n",
      "event                       98635 non-null object\n",
      "person                      98635 non-null object\n",
      "url                         0 non-null object\n",
      "sku                         0 non-null object\n",
      "model                       0 non-null object\n",
      "condition                   0 non-null object\n",
      "storage                     0 non-null object\n",
      "color                       0 non-null object\n",
      "skus                        98324 non-null object\n",
      "search_term                 0 non-null object\n",
      "staticpage                  0 non-null object\n",
      "campaign_source             0 non-null object\n",
      "search_engine               0 non-null object\n",
      "channel                     0 non-null object\n",
      "new_vs_returning            0 non-null object\n",
      "city                        0 non-null object\n",
      "region                      0 non-null object\n",
      "country                     0 non-null object\n",
      "device_type                 0 non-null object\n",
      "screen_resolution           0 non-null object\n",
      "operating_system_version    0 non-null object\n",
      "browser_version             0 non-null object\n",
      "dtypes: object(23)\n",
      "memory usage: 18.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.loc[df['event']=='brand listing'].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl = df['model']\n",
    "stg = df['storage']\n",
    "clr = df['color']\n",
    "cnd = df['condition']\n",
    "spc = ' '\n",
    "df['nombreproducto'] = mdl+spc+stg+spc+clr+spc+cnd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1011288 entries, 0 to 1011287\n",
      "Data columns (total 24 columns):\n",
      "timestamp                   1011288 non-null object\n",
      "event                       1011288 non-null object\n",
      "person                      1011288 non-null object\n",
      "url                         82756 non-null object\n",
      "sku                         563838 non-null object\n",
      "model                       564284 non-null object\n",
      "condition                   563836 non-null object\n",
      "storage                     563836 non-null object\n",
      "color                       563836 non-null object\n",
      "skus                        221699 non-null object\n",
      "search_term                 48967 non-null object\n",
      "staticpage                  3598 non-null object\n",
      "campaign_source             82796 non-null object\n",
      "search_engine               50957 non-null object\n",
      "channel                     87378 non-null object\n",
      "new_vs_returning            87378 non-null object\n",
      "city                        87378 non-null object\n",
      "region                      87378 non-null object\n",
      "country                     87378 non-null object\n",
      "device_type                 87378 non-null object\n",
      "screen_resolution           87378 non-null object\n",
      "operating_system_version    87378 non-null object\n",
      "browser_version             87378 non-null object\n",
      "nombreproducto              563836 non-null object\n",
      "dtypes: object(24)\n",
      "memory usage: 185.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2018-05-31 23:38:05</td>\n",
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       "      <th>1</th>\n",
       "      <td>2018-05-31 23:38:05</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-05-31 23:38:09</td>\n",
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       "      <th>3</th>\n",
       "      <td>2018-05-31 23:38:40</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-05-29 13:29:25</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "             timestamp\n",
       "0  2018-05-31 23:38:05\n",
       "1  2018-05-31 23:38:05\n",
       "2  2018-05-31 23:38:09\n",
       "3  2018-05-31 23:38:40\n",
       "4  2018-05-29 13:29:25"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timestamp'].head().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['time'] = pd.to_datetime(df['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>2018-05-31 23:38:05</td>\n",
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       "      <td>2018-05-29 13:29:25</td>\n",
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      ],
      "text/plain": [
       "                 time\n",
       "0 2018-05-31 23:38:05\n",
       "1 2018-05-31 23:38:05\n",
       "2 2018-05-31 23:38:09\n",
       "3 2018-05-31 23:38:40\n",
       "4 2018-05-29 13:29:25"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['time'].head().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['month'] = df['time'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    474949\n",
       "6    281882\n",
       "4    130008\n",
       "3     77293\n",
       "2     27886\n",
       "1     19270\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['month'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = df['time'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['hour'] = df['time'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1011288 entries, 0 to 1011287\n",
      "Data columns (total 28 columns):\n",
      "timestamp                   1011288 non-null object\n",
      "event                       1011288 non-null object\n",
      "person                      1011288 non-null object\n",
      "url                         82756 non-null object\n",
      "sku                         563838 non-null object\n",
      "model                       564284 non-null object\n",
      "condition                   563836 non-null object\n",
      "storage                     563836 non-null object\n",
      "color                       563836 non-null object\n",
      "skus                        221699 non-null object\n",
      "search_term                 48967 non-null object\n",
      "staticpage                  3598 non-null object\n",
      "campaign_source             82796 non-null object\n",
      "search_engine               50957 non-null object\n",
      "channel                     87378 non-null object\n",
      "new_vs_returning            87378 non-null object\n",
      "city                        87378 non-null object\n",
      "region                      87378 non-null object\n",
      "country                     87378 non-null object\n",
      "device_type                 87378 non-null object\n",
      "screen_resolution           87378 non-null object\n",
      "operating_system_version    87378 non-null object\n",
      "browser_version             87378 non-null object\n",
      "nombreproducto              563836 non-null object\n",
      "time                        1011288 non-null datetime64[ns]\n",
      "month                       1011288 non-null int64\n",
      "year                        1011288 non-null int64\n",
      "hour                        1011288 non-null int64\n",
      "dtypes: datetime64[ns](1), int64(3), object(24)\n",
      "memory usage: 216.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018    1011288\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_conversions = df[df['event']=='conversion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Samsung Galaxy J5         88\n",
       "iPhone 5s                 84\n",
       "iPhone 6                  71\n",
       "Samsung Galaxy S6 Flat    39\n",
       "iPhone 6S                 37\n",
       "Name: model, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_conversions['model'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                   False\n",
       "event                       False\n",
       "person                      False\n",
       "url                          True\n",
       "sku                         False\n",
       "model                       False\n",
       "condition                   False\n",
       "storage                     False\n",
       "color                       False\n",
       "skus                         True\n",
       "search_term                  True\n",
       "staticpage                   True\n",
       "campaign_source              True\n",
       "search_engine                True\n",
       "channel                      True\n",
       "new_vs_returning             True\n",
       "city                         True\n",
       "region                       True\n",
       "country                      True\n",
       "device_type                  True\n",
       "screen_resolution            True\n",
       "operating_system_version     True\n",
       "browser_version              True\n",
       "nombreproducto              False\n",
       "time                        False\n",
       "month                       False\n",
       "year                        False\n",
       "hour                        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_conversions.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1172, 28)\n",
      "timestamp                      0\n",
      "event                          0\n",
      "person                         0\n",
      "url                         1172\n",
      "sku                            0\n",
      "model                          0\n",
      "condition                      0\n",
      "storage                        0\n",
      "color                          0\n",
      "skus                        1172\n",
      "search_term                 1172\n",
      "staticpage                  1172\n",
      "campaign_source             1172\n",
      "search_engine               1172\n",
      "channel                     1172\n",
      "new_vs_returning            1172\n",
      "city                        1172\n",
      "region                      1172\n",
      "country                     1172\n",
      "device_type                 1172\n",
      "screen_resolution           1172\n",
      "operating_system_version    1172\n",
      "browser_version             1172\n",
      "nombreproducto                 0\n",
      "time                           0\n",
      "month                          0\n",
      "year                           0\n",
      "hour                           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df_conversions.shape)\n",
    "print(df_conversions.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8883946cc0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "fig_size[0]=10\n",
    "fig_size[1]=7\n",
    "plt.rcParams[\"figure.figsize\"] = fig_size\n",
    "\n",
    "plot = df_conversions['month'].value_counts().sort_index().plot.bar()\n",
    "plot.set_title('Cantidad de ventas en funcion del mes\\n',size=18)\n",
    "plot.set_xlabel('Mes',size=16)\n",
    "plot.set_ylabel('Cant. de ventas',size=16)\n",
    "plt.xticks([0,1,2,3,4,5],['Enero','Febrero','Marzo','Abril','Mayo','Junio'])\n",
    "plot.grid(True)\n",
    "plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_viewed = df[df['event']=='viewed product']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Productos más vistos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>nombreproducto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>iPhone 6 16GB Cinza espacial Bom</th>\n",
       "      <td>4163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iPhone 6 16GB Prateado Bom</th>\n",
       "      <td>3574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iPhone 6 16GB Dourado Bom</th>\n",
       "      <td>3566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samsung Galaxy S7 32GB Preto Bom</th>\n",
       "      <td>3421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samsung Galaxy S8 64GB Preto Bom</th>\n",
       "      <td>2866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  nombreproducto\n",
       "iPhone 6 16GB Cinza espacial Bom            4163\n",
       "iPhone 6 16GB Prateado Bom                  3574\n",
       "iPhone 6 16GB Dourado Bom                   3566\n",
       "Samsung Galaxy S7 32GB Preto Bom            3421\n",
       "Samsung Galaxy S8 64GB Preto Bom            2866"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_viewed['nombreproducto'].value_counts().head().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vistos = df_viewed['nombreproducto'].value_counts().head()\n",
    "f,a = plt.subplots(figsize=(10,5))\n",
    "g = sns.barplot(data=vistos.to_frame(),y=vistos.index,x='nombreproducto',palette='Reds_r')\n",
    "plt.title('Los 5 productos más vistos\\n',size=22)\n",
    "plt.xlabel('Vistas',size=16)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Productos más comprados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>nombreproducto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Samsung Galaxy J5 16GB Dourado Bom</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samsung Galaxy J5 16GB Dourado Muito Bom</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Motorola Moto G3 4G 16GB Preto Bom</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iPhone 5s 16GB Cinza espacial Bom</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samsung Galaxy S6 Flat 32GB Dourado Bom</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          nombreproducto\n",
       "Samsung Galaxy J5 16GB Dourado Bom                    30\n",
       "Samsung Galaxy J5 16GB Dourado Muito Bom              18\n",
       "Motorola Moto G3 4G 16GB Preto Bom                    17\n",
       "iPhone 5s 16GB Cinza espacial Bom                     16\n",
       "Samsung Galaxy S6 Flat 32GB Dourado Bom               14"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_conversions['nombreproducto'].value_counts().head().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comprados = df_conversions['nombreproducto'].value_counts().head().to_frame()\n",
    "f,a = plt.subplots(figsize=(10,5))\n",
    "h = sns.barplot(ax=a,data=comprados,y=comprados.index,x='nombreproducto',palette='Blues_r')\n",
    "h.set_title('Top 5 celulares más comprados\\n',size=18)\n",
    "h.set_xlabel('Compras')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>nombreproducto</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nombreproducto\n",
       "1            3573\n",
       "0               1"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verificacion de que cada sku tiene un unico 'model+storage+color+condition'\n",
    "porsku = df.groupby('sku')\n",
    "porsku.agg({'nombreproducto':'nunique'})['nombreproducto'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Productos comprados por más personas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "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>Producto</th>\n",
       "      <th>Personas que compraron</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Samsung Galaxy J5 16GB Dourado Bom</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Samsung Galaxy J5 16GB Dourado Muito Bom</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Samsung Galaxy J5 16GB Preto Bom</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Samsung Galaxy J5 16GB Branco Bom</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>iPhone 5s 16GB Cinza espacial Muito Bom</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Producto  Personas que compraron\n",
       "1        Samsung Galaxy J5 16GB Dourado Bom                      18\n",
       "2  Samsung Galaxy J5 16GB Dourado Muito Bom                      12\n",
       "3          Samsung Galaxy J5 16GB Preto Bom                      10\n",
       "4         Samsung Galaxy J5 16GB Branco Bom                       9\n",
       "5   iPhone 5s 16GB Cinza espacial Muito Bom                       8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ventas_porsku = df_conversions.groupby('nombreproducto')\n",
    "ventas_unicas = ventas_porsku.agg({'person':'nunique'}).sort_values('person',ascending=False).head().reset_index()\n",
    "ventas_unicas.index = ventas_unicas.index + 1\n",
    "ventas_unicas.columns = ['Producto','Personas que compraron']\n",
    "ventas_unicas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,a = plt.subplots(figsize=(10,5))\n",
    "i = sns.barplot(ax=a,data=ventas_unicas,y=ventas_unicas.Producto,x=ventas_unicas['Personas que compraron'],palette='Purples_r')\n",
    "i.set_title('Top 5 productos con más compras (1 por persona)\\n',size=22)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ads = df.loc[df['event']=='ad campaign hit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp                   82827\n",
       "event                       82827\n",
       "person                      82827\n",
       "url                         82756\n",
       "sku                             0\n",
       "model                           0\n",
       "condition                       0\n",
       "storage                         0\n",
       "color                           0\n",
       "skus                            0\n",
       "search_term                     0\n",
       "staticpage                      0\n",
       "campaign_source             82796\n",
       "search_engine                   0\n",
       "channel                         0\n",
       "new_vs_returning                0\n",
       "city                            0\n",
       "region                          0\n",
       "country                         0\n",
       "device_type                     0\n",
       "screen_resolution               0\n",
       "operating_system_version        0\n",
       "browser_version                 0\n",
       "nombreproducto                  0\n",
       "time                        82827\n",
       "month                       82827\n",
       "year                        82827\n",
       "hour                        82827\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ads.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Celulares más vendidos por mes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Modelo</th>\n",
       "      <th>Ventas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Samsung Galaxy S6 Flat 32GB Dourado Bom</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Samsung Galaxy Note 4 32GB Branco Muito Bom</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Motorola Moto G3 4G 16GB Preto Bom</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>iPhone 5s 16GB Cinza espacial Bom</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samsung Galaxy J5 16GB Dourado Bom</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Samsung Galaxy Note 3 Neo Duos 16GB Branco Bom</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Modelo  Ventas\n",
       "Mes                                                        \n",
       "1           Samsung Galaxy S6 Flat 32GB Dourado Bom       7\n",
       "2       Samsung Galaxy Note 4 32GB Branco Muito Bom       8\n",
       "3                Motorola Moto G3 4G 16GB Preto Bom      13\n",
       "4                 iPhone 5s 16GB Cinza espacial Bom       6\n",
       "5                Samsung Galaxy J5 16GB Dourado Bom      17\n",
       "6    Samsung Galaxy Note 3 Neo Duos 16GB Branco Bom       5"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def values(x):\n",
    "    return x.value_counts().values[0]\n",
    "def index(x):\n",
    "    return x.value_counts().index[0]\n",
    "\n",
    "g = df_conversions.groupby('month').agg({'nombreproducto': [index,values]})\n",
    "g.columns = ['Modelo','Ventas']\n",
    "g.index.names = ['Mes']\n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 7 modelos mas vendidos\\n')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=df_conversions['model'].value_counts()\n",
    "fig,ax=plt.subplots(figsize=(10,5))\n",
    "p = x.head(7).to_frame().reset_index()\n",
    "sns.barplot(data=p,x='model',y='index',palette=\"spring\")\n",
    "plt.grid(True)\n",
    "plt.xlabel('Compras',size=15)\n",
    "plt.ylabel(' ')\n",
    "plt.title('Top 7 modelos mas vendidos\\n',size=22)\n"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}