{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# _Dataviz Code Session_: Quantidades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objetivos da DCS\n",
    "\n",
    "- Aplicar técnicas de dataviz para plotagem e manipulação de representações visuais de quantidades.\n",
    "- Elaborar RV para dados acerca de consumidores da Netflix (Fonte: [kaggle](https://www.kaggle.com/datasets/arnavsmayan/netflix-userbase-dataset))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ferramentas utilizadas\n",
    "\n",
    "- Módulos Python\n",
    "    - `pandas`\n",
    "    - `numpy`\n",
    "    - `matplotlib`\n",
    "    - `seaborn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aplicação do modelo referencial\n",
    "\n",
    "- Vide {numref}`Capítulo %s <cap:estetica>`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('../etc/gcpeixoto-datavis.mplstyle') # style sheet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dados de entrada pré-processados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Carregamento de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Subscription Type</th>\n",
       "      <th>Monthly Revenue</th>\n",
       "      <th>Join Date</th>\n",
       "      <th>Last Payment Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Device</th>\n",
       "      <th>Plan Duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Basic</td>\n",
       "      <td>10</td>\n",
       "      <td>15-01-22</td>\n",
       "      <td>10-06-23</td>\n",
       "      <td>United States</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>1 Month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Premium</td>\n",
       "      <td>15</td>\n",
       "      <td>05-09-21</td>\n",
       "      <td>22-06-23</td>\n",
       "      <td>Canada</td>\n",
       "      <td>35</td>\n",
       "      <td>Female</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>1 Month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Standard</td>\n",
       "      <td>12</td>\n",
       "      <td>28-02-23</td>\n",
       "      <td>27-06-23</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>Smart TV</td>\n",
       "      <td>1 Month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Standard</td>\n",
       "      <td>12</td>\n",
       "      <td>10-07-22</td>\n",
       "      <td>26-06-23</td>\n",
       "      <td>Australia</td>\n",
       "      <td>51</td>\n",
       "      <td>Female</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>1 Month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Basic</td>\n",
       "      <td>10</td>\n",
       "      <td>01-05-23</td>\n",
       "      <td>28-06-23</td>\n",
       "      <td>Germany</td>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>Smartphone</td>\n",
       "      <td>1 Month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496</th>\n",
       "      <td>Premium</td>\n",
       "      <td>14</td>\n",
       "      <td>25-07-22</td>\n",
       "      <td>12-07-23</td>\n",
       "      <td>Spain</td>\n",
       "      <td>28</td>\n",
       "      <td>Female</td>\n",
       "      <td>Smart TV</td>\n",
       "      <td>1 Month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2497</th>\n",
       "      <td>Basic</td>\n",
       "      <td>15</td>\n",
       "      <td>04-08-22</td>\n",
       "      <td>14-07-23</td>\n",
       "      <td>Spain</td>\n",
       "      <td>33</td>\n",
       "      <td>Female</td>\n",
       "      <td>Smart TV</td>\n",
       "      <td>1 Month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2498</th>\n",
       "      <td>Standard</td>\n",
       "      <td>12</td>\n",
       "      <td>09-08-22</td>\n",
       "      <td>15-07-23</td>\n",
       "      <td>United States</td>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>1 Month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2499</th>\n",
       "      <td>Standard</td>\n",
       "      <td>13</td>\n",
       "      <td>12-08-22</td>\n",
       "      <td>12-07-23</td>\n",
       "      <td>Canada</td>\n",
       "      <td>48</td>\n",
       "      <td>Female</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>1 Month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>Basic</td>\n",
       "      <td>15</td>\n",
       "      <td>13-08-22</td>\n",
       "      <td>12-07-23</td>\n",
       "      <td>United States</td>\n",
       "      <td>35</td>\n",
       "      <td>Female</td>\n",
       "      <td>Smart TV</td>\n",
       "      <td>1 Month</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2500 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Subscription Type  Monthly Revenue Join Date Last Payment Date  \\\n",
       "User ID                                                                  \n",
       "1                   Basic               10  15-01-22          10-06-23   \n",
       "2                 Premium               15  05-09-21          22-06-23   \n",
       "3                Standard               12  28-02-23          27-06-23   \n",
       "4                Standard               12  10-07-22          26-06-23   \n",
       "5                   Basic               10  01-05-23          28-06-23   \n",
       "...                   ...              ...       ...               ...   \n",
       "2496              Premium               14  25-07-22          12-07-23   \n",
       "2497                Basic               15  04-08-22          14-07-23   \n",
       "2498             Standard               12  09-08-22          15-07-23   \n",
       "2499             Standard               13  12-08-22          12-07-23   \n",
       "2500                Basic               15  13-08-22          12-07-23   \n",
       "\n",
       "                Country  Age  Gender      Device Plan Duration  \n",
       "User ID                                                         \n",
       "1         United States   28    Male  Smartphone       1 Month  \n",
       "2                Canada   35  Female      Tablet       1 Month  \n",
       "3        United Kingdom   42    Male    Smart TV       1 Month  \n",
       "4             Australia   51  Female      Laptop       1 Month  \n",
       "5               Germany   33    Male  Smartphone       1 Month  \n",
       "...                 ...  ...     ...         ...           ...  \n",
       "2496              Spain   28  Female    Smart TV       1 Month  \n",
       "2497              Spain   33  Female    Smart TV       1 Month  \n",
       "2498      United States   38    Male      Laptop       1 Month  \n",
       "2499             Canada   48  Female      Tablet       1 Month  \n",
       "2500      United States   35  Female    Smart TV       1 Month  \n",
       "\n",
       "[2500 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../data/netflix-data.csv')\n",
    "df = df.set_index('User ID')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualização de quantidades com histogramas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotagem com `pandas`\n",
    "\n",
    "- O módulo `pandas` possui métodos para plotagem básica que provêm do `matplotlib` e são um _wrapper_ de `plt.plot()`.\n",
    "- Esses métodos são diretamente aplicáveis a `Series` e `DataFrames`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Histogramas para objetos `DataFrame`\n",
    "    - Visualizar distribuições para todas as variáveis possíveis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(8,2),\n",
    "        bins=6,\n",
    "        grid=False,\n",
    "        xlabelsize=14,\n",
    "        ylabelsize=10,\n",
    "        xrot=60,\n",
    "        yrot=-20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Histogramas para objetos `Series`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhMAAADjCAYAAAA/rpYUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAdiElEQVR4nO3df1BVdeL/8deVC5cCIRxFIgUMMZN+uGl+8yerpTbNfMzULBdNSpxtZ2fbsB1XF5dfa5pY6kx/5KYpppFb2a9t18lpFDFSq924qdHO1cQfsGqiQFyE+HG+fxh3vd3Lj+sB+eHz4TDDfZ/3ObzPe97qi/N+n3MshmEYAgAAuEq9OrsBAACgeyNMAAAAUwgTAADAFMIEAAAwhTABAABMIUwAAABTCBMAAMAUwgQAADClx4eJ6h8vyX7qG1X/eKmzmwIAQI9k7ewGdDTH2eOatGaWPnpmq+4YMLSzmwMAQLfS2z+o1To9/soEAADoWIQJAABgCmECAACYQpgAAACmECYAAIApPf5uDgBAz2L56Q88GT/9udYIEwCAbsMii4KsgbJYuLDujWE0yllfc80DBWECANBtWGSRxdJLp344qNqGHzq7OV2Kza+3Bvb+f7LIQpgAAKA1tQ0/qKahvLObgZ9wnQgAAJhCmAAAAKYQJgAAgCmECQAAYAphAgAAmEKYAAAAphAmAACAKYQJAABgCmECAACYQpgAAACmECYAAIApvJsDQKfiddLN66zXSQO+IkwA6DS8TrplnfU6acBXhAkAnYbXSTevM18nDfiKMAGg0/E6aaB7I0xcJeZ5vWOOFwCuP4SJq8A8b/OY4wWA64/pMJGdna0//vGPkqT9+/frvvvu86hTWVmpjIwM7dixQ2fOnFFERIRmzpypjIwMhYSEeD1ubm6u1q1bpyNHjiggIECjR49WVlaWRo4cabbJpjHP6x1zvABwfTIVJoqKipSWlqagoCA5nU6vdZxOpxISElRYWKjJkydrzpw5stvtWrt2rfbs2aNPP/1UQUFBbvusWLFCqampioqK0tNPP62qqipt375dY8eO1ccff6xf/vKXZprdbpjnBQDAxEOrGhoaNH/+fN1999165JFHmq2XnZ2twsJCLV68WLt27dILL7ygnTt3Ki0tTYWFhcrOznar73A4lJ6eriFDhujrr7/WSy+9pL/+9a/67LPPZLValZycrPr6+qttNgAAaGdXHSZWrVolu92uTZs2yc/Pz2sdwzC0ceNGBQcHKy0tzW3b0qVLFRYWptdee02G8b9L4ps3b1Z9fb1SU1MVGhrqKo+Pj9cTTzyhY8eOaffu3VfbbAAA0M6uKkwcPnxYmZmZWrZsmeLj45ut53A4VFpaqrFjx3pMZQQGBmrChAkqKSnR0aNHXeV5eXmSpClTpngcb+rUqZKkvXv3Xk2zAQBAB/B5zUR9fb2SkpJ0++23a8mSJS3WdTgckqS4uDiv25vKHQ6H2/fBwcGKiIhosX5zamtrVVtb6/rsdFa12EbgWuF2Yk+96A+gR/A5TKxYsUJ2u10HDx6Uv79/i3UrKiokyW264kpNd3I01Wv6Pjw8vM31f27lypXKzMx0ffbrF6jQxEEtthPoaNxODKAn8ylM2O12LV++XH/4wx90zz33dFSbTFm6dKkWLVrk+ny49Fv931+TOq9BgLiduDnB/v0VEXRnZzcDgEk+hYn58+crNjZWGRkZbarfdEWiuSsJlZWVbvWavvel/s/ZbDbZbDbX56CK4Da1FbgWuJ3Ync2vd2c3AUA78PnKhHR58aQ3o0ePliS99957mj59eqtrHLytqYiLi9P+/ftdD7dqrT66nsvz4FzOvxJrAwD0ZD6FiQULFngtz8/Pl8Ph0LRp09SvXz/FxMRIuvyffmRkpAoKCuR0Ot3u6KipqVF+fr4iIyM1ePBgV3lCQoL279+vXbt26YknnnD7OR9//LGrDroeq8UmwzB0g7/3sAkA6Jl8ChMbN270Wp6UlCSHw6GlS5e6PU7bYrEoOTlZWVlZysrK0qpVq1zbVq5cqYsXL+p3v/udLJb//db25JNP6sUXX9Tzzz+vhx9+2DWlceTIEb3++uuKjY3VpEmTfDpJXBt+vQJksVhYF+AFawMA9GQd/qKvxYsX68MPP1R2dra++uorjRgxQna7XTt37tTw4cO1ePFit/pDhgxRRkaGli1bprvuukuzZs2S0+nUm2++qbq6Om3YsEFWK+8n68pYF+CJtQEAerIOn9gOCgpSXl6eUlJS9O233+qll17S4cOHlZKSory8PI+HWUlSamqqtm3bpvDwcL3yyivavn27xowZo4KCAk2cOLGjmwwAAHzQLr/i5+TkKCcnp9ntoaGhWrNmjdasWdPmYyYmJioxMbEdWgcAADoS8wUA0IVxd5Q77ozqmggTANAFcXcUuhPCBAB0Qdwd5R13RnVNhAkA6MK4O8odd0Z1TUzEAQAAUwgTAADAFMIEAAAwhTABAABMIUwAAABTCBMAAMAUwgQAADCFMAEAAEwhTAAAAFMIEwAAwBTCBAAAMIUwAQAATCFMAAAAUwgTAADAFMIEAAAwhTABAABMIUwAAABTCBMAAMAUwgQAADCFMAEAAEwhTAAAAFMIEwAAwBTCBAAAMIUwAQAATCFMAAAAUwgTAADAFMIEAAAwhTABAABMIUwAAABTCBMAAMAUwgQAADCFMAEAAEwhTAAAAFMIEwAAwBTCBAAAMIUwAQAATCFMAAAAUwgTAADAFMIEAAAwhTABAABMIUwAAABTfAoTJSUlWrdunaZMmaKoqCgFBAQoIiJCM2fO1MGDB73uU1lZqUWLFik6Olo2m03R0dFatGiRKisrm/05ubm5GjVqlIKCghQWFqaHHnpIX375pW9nBgAArgmfwsTLL7+slJQUfffdd5o8ebKee+45jRs3Th988IHGjBmjt956y62+0+lUQkKC1q5dq9tuu00pKSkaNmyY1q5dq4SEBDmdTo+fsWLFCiUmJurs2bN6+umnNXv2bBUUFGjs2LHKy8szdbIAAKD9WX2pPGrUKOXn52v8+PFu5fv27dP999+v3/zmN3r44Ydls9kkSdnZ2SosLNTixYu1atUqV/309HRlZWUpOztbmZmZrnKHw6H09HQNGTJEn3/+uUJDQyVJzzzzjEaNGqXk5GR9++23slp9ajYAAOhAPl2ZmDFjhkeQkKTx48dr4sSJunDhgg4dOiRJMgxDGzduVHBwsNLS0tzqL126VGFhYXrttddkGIarfPPmzaqvr1dqaqorSEhSfHy8nnjiCR07dky7d+/26QQBAEDHarcFmP7+/pLkumrgcDhUWlqqsWPHKigoyK1uYGCgJkyYoJKSEh09etRV3jSNMWXKFI/jT506VZK0d+/e9moyAABoB+0SJk6ePKlPPvlEERERuvPOOyVdDhOSFBcX53WfpvKmek3fBwcHKyIiok31vamtrVVlZaXry+ms8v2EAABAm5kOE3V1dZo3b55qa2uVnZ0tPz8/SVJFRYUkuU1XXCkkJMStXtP3vtT3ZuXKlQoNDXV9TZiQ4NsJAQAAn5gKE42NjXrqqaeUn5+vhQsXat68ee3Vrqu2dOlSVVRUuL7y85kWAQCgI131bRGGYWjhwoXatm2b5s6dq/Xr17ttb7rC0NyVhKbnTFx5JSI0NNSn+t7YbDbX3SSSFFQR3MqZAAAAM67qykRjY6MWLFigTZs2ac6cOcrJyVGvXu6Ham2Ng7c1FXFxcaqqqtKZM2faVB8AAHQ+n8NEY2OjkpOTtXnzZj322GPaunWra53EleLi4hQZGamCggKPh1PV1NQoPz9fkZGRGjx4sKs8IeHy+oZdu3Z5HO/jjz92qwMAALoGn8JE0xWJzZs369FHH9W2bdu8BglJslgsSk5OVlVVlbKysty2rVy5UhcvXlRycrIsFour/Mknn5TVatXzzz/vNt1x5MgRvf7664qNjdWkSZN8aTIAAOhgPq2ZyMrKUk5OjoKDgzVkyBAtX77co8706dM1fPhwSdLixYv14YcfKjs7W1999ZVGjBghu92unTt3avjw4Vq8eLHbvkOGDFFGRoaWLVumu+66S7NmzZLT6dSbb76puro6bdiwgadfAgDQxfj0P3NxcbEkqaqqSs8//7zXOjExMa4wERQUpLy8PGVmZuqdd95RXl6eIiIilJKSovT0dI+HWUlSamqqYmJitG7dOr3yyisKCAjQmDFjlJWVpXvvvde3swMAAB3OpzCRk5OjnJwcn35AaGio1qxZozVr1rR5n8TERCUmJvr0cwAAQOdot8dpAwCA6xNhAgAAmEKYAAAAphAmAACAKYQJAABgCmECAACYQpgAAACmECYAAIAphAkAAGAKYQIAAJhCmAAAAKYQJgAAgCmECQAAYAphAgAAmEKYAAAAphAmAACAKYQJAABgCmECAACYQpgAAACmECYAAIAphAkAAGAKYQIAAJhCmAAAAKYQJgAAgCmECQAAYAphAgAAmEKYAAAAphAmAACAKYQJAABgCmECAACYQpgAAACmECYAAIAphAkAAGAKYQIAAJhCmAAAAKYQJgAAgCmECQAAYAphAgAAmEKYAAAAphAmAACAKYQJAABgCmECAACYQpgAAACmECYAAIAphAkAAGAKYQIAAJjSZcPEF198oYceekhhYWEKCgrSqFGjlJub29nNAgAAP2Pt7AZ4k5eXp6lTpyogIECPP/64QkND9e677yoxMVHFxcX605/+1NlNBAAAP+lyVybq6+uVnJwsi8Wi/Px8bdiwQS+++KLsdrvi4+OVnp4uh8PR2c0EAAA/6XJhYvfu3Tp27Jh+9atf6Re/+IWrvHfv3vrzn/+s+vp6bd68uRNbCAAArtTlwkReXp4kacqUKR7bmsr27t17LZsEAABa0OXWTDRNYcTFxXlsCwsLU9++fVuc5qitrVVtba3rc1l5mSTp6LnidmujRRbdYA3U2eoy/dhY3W7H7e5utJ7RhfJv6Bcv6Bvv6Jfm0Tfe0S/NC+jVIOcP3+hSfY0MGe123ButNyiu/yDdGHBDs3W6XJioqKiQJIWGhnrdHhISotOnTze7/8qVK5WZmen67D8kRL0fukXPbv9z+zYUAIDrxO5F7+jugcOa3d7lwoRZS5cu1aJFi1yfLzjL9dmJfym2f7QCrYFyOqs0YUKC8vP3KigouBNb2nPQp+2L/mx/9Gn7oj/bX1fv07j+g1rc3uXCRNMViaYrFD9XWVnZ7FULSbLZbLLZbK7PISEhirk5ym3/hu9rdEfkUIWEhLRTq69v9Gn7oj/bH33avujP9tfd+7TLLcBsWivhbV3ExYsXdf78ea/rKQAAQOfocmEiISFBkrRr1y6PbU1lTXUAAEDn63Jh4v7779ett96q3NxcFRYWusp/+OEH/eUvf5HValVSUtJVH99msyk9Pd1tKgTm0Kfti/5sf/Rp+6I/219371OLYRjtd/9IO9mzZ4+mTp0qm82mOXPmKCQkRO+++66OHz+u5cuXKzU1tbObCAAAftIlw4Qkff7550pPT9f+/fv1448/Kj4+Xs8++6wSExM7u2kAAOAKXTZMAACA7qHLrZkAAADdC2ECAACY0mPCRElJidatW6cpU6YoKipKAQEBioiI0MyZM3Xw4EGP+hkZGbJYLF6/AgMDO+EMup7y8nI988wzGj16tCIiImSz2XTLLbdo0qRJ2rFjh7zNkFVWVmrRokWKjo6WzWZTdHS0Fi1apMrKyk44g67F1/5kjPouOzvb1UcHDhzwWocx6pvW+pRx2rqYmJhm++jpp5/2qN8dx2iXewLm1Xr55Ze1atUqxcbGavLkyQoPD5fD4dD777+v999/X2+++aZmz57tsd/8+fMVExPjVma19phuMeX8+fPatGmT7rvvPk2fPl19+vTRuXPn9Pe//12zZs3SwoUL9eqrr7rqO51OJSQkqLCwUJMnT9acOXNkt9u1du1a7dmzR59++qmCgoI68Yw6l6/92YQx2jZFRUVKS0tTUFCQnE6n1zqMUd+0pU+bME5bFhoaqmeffdajfOTIkW6fu+0YNXqIHTt2GPn5+R7l+fn5hr+/v9GnTx+jpqbGVZ6enm5IMvbs2XMNW9m91NfXG3V1dR7llZWVxrBhwwxJxuHDh13laWlphiRj8eLFbvWbytPS0jq8zV2Zr/3JGG27+vp649577zVGjRplzJ0715Bk7N+/36MeY7Tt2tqnjNPWRUdHG9HR0W2q213HaI+Z5pgxY4bGjx/vUT5+/HhNnDhRFy5c0KFDhzqhZd2Xn5+f198sevfuralTp0qSjh49KkkyDEMbN25UcHCw0tLS3OovXbpUYWFheu2117xOjVwvfOlP+GbVqlWy2+3atGmT/Pz8vNZhjPqmLX2K9tWdx+h1cQ3K399fkvdLbvv27dPnn38uPz8/DR06VA888EC3fQLZtVJTU6Pdu3fLYrFo2LDLr6R1OBwqLS3V1KlTPS7BBQYGasKECfrggw909OhR3q3yM97680qM0ZYdPnxYmZmZWrZsmeLj45utxxhtu7b26ZUYpy2rra3Vli1bVFJSorCwMI0ZM0Z33323W53uPEZ7fJg4efKkPvnkE0VEROjOO+/02P7z9HfzzTdry5Ytmjx58rVqYpdXXl6udevWqbGxUefOndM///lPnTp1Sunp6R4vZmtugF9Zr6v9JbjW2tKfV2KMNq++vl5JSUm6/fbbtWTJkhbrMkbbxpc+vRLjtGVnzpzxeBXEgw8+qK1bt6pv376SuvcY7dFhoq6uTvPmzVNtba2ys7PdLtUNHz5cW7ZsUUJCgvr376/Tp09r+/btWrFihaZNm6YDBw54pMbrVXl5uTIzM12f/f39tXr1aj333HOusqZXxjf3evimV+o292r560lb+lNijLbFihUrZLfbdfDgQdcVyOYwRtvGlz6VGKdt8dRTTykhIUHx8fGy2Wz65ptvlJmZqZ07d2ratGkqKCiQxWLp3mO0MxdsdKSGhgbXoqGFCxe2eb9XX33VkGTMmjWrA1vXPdXX1xvHjx83Vq5caQQEBBiPPPKIa0HhG2+8YUgyUlNTve6blZVlSDJyc3OvZZO7tJb6syWM0csKCwsNf39/Y8mSJW7l8+fP97pYkDHaOl/7tCWM05Y1NDQY48aNMyQZH330kWEY3XuM9pgFmFcyDEMLFy7Utm3bNHfuXK1fv77N+86fP19Wq1UFBQUd2MLuyc/PTzExMVqyZImWL1+u9957Txs2bJD0vyTdXGJuuj+6ucR9PWqpP1vCGL1s/vz5io2NVUZGRpvqM0Zb52uftnYsxmnzevXqpSeffFKSXH3UncdojwsTjY2NWrBggTZt2qQ5c+YoJydHvXq1/TQDAgLUu3dvVVdXd2Aru78pU6ZIkvLy8iTJY+3Ez7U2F3i9+3l/toQxepndbte3336rwMBAt4cAbdmyRZI0evRoWSwWvf/++5IYo23ha5+2hHHauqa1Ek191J3HaI9aM9HY2Kjk5GRt3rxZjz32mLZu3erzLU0Oh0MXL1687uf4WlNaWirpf3fIxMXFKTIyUgUFBXI6nW4rkWtqapSfn6/IyEgNHjy4U9rb1f28P1vCGL1swYIFXsvz8/PlcDg0bdo09evXz/UgJcZo63zt05YwTlvX9HTmHjFGO3uepb00NDQYSUlJhiTj0UcfbXHuubKy0rDb7R7lFy5cMMaPH29IMl544YWObG638NVXXxnl5eUe5WVlZcbw4cMNScbWrVtd5d31YSvXii/9yRi9ei3N7zNGr05zfco4bd2RI0eMixcvepTv27fPCAwMNGw2m3HixAlXeXcdoz3mFeQZGRnKzMxUcHCwfv/733v9DW/69OkaPny4iouLNWjQII0cOVJ33nmnwsPDVVJSop07d6qsrEyTJ0/WRx99pICAgE44k67j2Wef1caNGzVx4kRFR0crKChIJ06c0D/+8Q9VVVVp5syZeuutt1zTSE6nU+PGjXM9BnbEiBGy2+3auXOnhg8f3nUfA3uN+NKfjNGrl5SUpC1btmj//v2677773LYxRq9Oc33KOG1dRkaGsrOzdf/99ysmJkY2m02HDx/Wrl271KtXL61fv17Jycmu+t12jHZ2mmkvTcm5pa/NmzcbhmEYFRUVxm9/+1tjxIgRRt++fQ2r1WqEhoYa48aNM9avX2/U19d37sl0Efv27TOSkpKMoUOHGiEhIYbVajXCw8ONBx980MjNzTUaGxs99ikvLzdSUlKMgQMHGv7+/sbAgQONlJQUr7+RX2986U/G6NVr7c4DxqjvmutTxmnr8vLyjNmzZxuDBw82evfubfj7+xsDBgwwHn/8cePgwYNe9+mOY7THXJkAAACdo8fdzQEAAK4twgQAADCFMAEAAEwhTAAAAFMIEwAAwBTCBAAAMIUwAQAATCFMAAAAUwgTANpNQUGBVq9erZMnT3bI8U+ePKnVq1fzWmugiyFMAGhWRUWFVq9erbfffruzmwKgCyNMAAAAUwgTAADAFM/3dANAKyorK7V3714VFxeroaFB/fv317hx47zWbWhokN1u17Fjx1RWVqbq6mrZbDbdcsstGj16tPr37++xT11dnT777DMVFRWpurpaYWFhuueeexQWFtZsm8rLy3XgwAEVFxerurpagYGBiomJ0dixYxUaGtpu5w7AE2ECgE+qqqr0xhtvqKqqSjExMerfv7/Kysr09ttva+DAgR71L126pN27d2vAgAEaNGiQAgMDVVFRoaNHj+r48eN6/PHHdfPNN7vqG4ah9957TydOnFDfvn11++2369KlS9qzZ4+ioqK8tqm0tFTvvPOO6urqFBsbq5tuukmVlZUqKirS8ePHlZiYqJtuuqmjugS47hEmAPgkPz9fVVVVGjdunEaPHu0qt9vt2rVrl0f9wMBA/frXv1bv3r3dys+fP69t27Zp3759mj17tqv8yJEjOnHihAYNGqQZM2aoV6/Ls7EjRozQ1q1bPY7f0NCgjz76SIZhaN68eQoPD3dtO336tLZv367du3drxowZps8dgHesmQDQZg0NDfrPf/6jG2+8Uffee6/btrvuusvrNITVavUIEpLUt29fRUVF6fTp02poaHCVHzlyRJI0fvx4V5CQpH79+mnYsGEexzl27JgqKio0atQotyAhSQMGDNDgwYP13Xffqba21reTBdBmXJkA0GYXLlxQfX29oqKiZLW6//NhsVh0yy236OLFix77nT17Vl988YVOnz4tp9OpxsZGt+2XLl1ScHCwJOncuXPy9/f3upZiwIABOnTokFvZf//7X0lSWVmZ1+dPOJ1OGYahixcvKiIiwrcTBtAmhAkAbdb02/2NN97odbu38pKSEv3tb3+TJMXExCgsLEz+/v6yWCxyOBz6/vvv3a5M1NbWKiQkxOvxg4KCPMpqamokSUVFRS22va6ursXtAK4eYQJAm9lsNklSdXW11+3eyg8cOKCGhgbNmTNHAwYMcNtWWlqq77//3uNnNHd8p9PpURYQECBJmjFjhmJjY1s/CQDtjjUTANqsT58+slqtOnPmjOrr6922GYahkpISj33Ky8sVGBjoESTq6up07tw5j/rh4eGqq6vT2bNnPbadPn3ao6zpTpDS0lKfzgVA+yFMAGgzPz8/3XbbbaqurtYXX3zhtu3rr7/2ul4iJCRENTU1On/+vKussbFReXl5Xq9ANC2y3Ldvn9vaiu+//17ffPONR/3BgwcrJCREX375pU6dOuWxvaGhwWsIAdB+mOYA4JMJEyboxIkT+vTTT1VSUqLw8HCVlZXp+PHjiomJUXFxsVv9e+65R8XFxcrNzdVtt90mq9WqU6dOqaqqSgMHDvQIAHfccYfr+RBbtmzRrbfeqpqaGhUVFSkmJkbHjh1zq2+1WjVt2jTt2LFD27dvV1RUlPr16yfp8sO1Tp8+rRtuuEELFizo0H4BrmdcmQDgk+DgYCUmJmro0KEqLS3Vv//9b9XU1OjRRx9VZGSkR/3Y2FhNmzZNN910k4qKilRUVKQ+ffpo7ty5XhdaWiwWPfLIIxo1apRqamr0r3/9SyUlJZo4caJGjhzptU0333yz5s+frxEjRuiHH35QYWGhDh06pAsXLiguLk4PPPBAu/cDgP+xGIZhdHYjAABA98WVCQAAYAphAgAAmEKYAAAAphAmAACAKYQJAABgCmECAACYQpgAAACmECYAAIAphAkAAGAKYQIAAJhCmAAAAKYQJgAAgCmECQAAYMr/BzW9iclHjgnvAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "df['Age'].hist(figsize=(6,2),\n",
    "               bins=6, \n",
    "               grid=False,\n",
    "               color='#d3ecab',\n",
    "               edgecolor= 'w',\n",
    "               xlabelsize=14,\n",
    "               ylabelsize=14)\n",
    "\n",
    "ax.set_xlabel('Idade');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Histograma gerado por meio de `plot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iloc[10:40]['Age'].plot(kind='bar',\n",
    "                           title='Histograma (faixa de usuários)',\n",
    "                           figsize=(8,2),\n",
    "                           xlabel='Usuários',\n",
    "                           ylabel='Idade',\n",
    "                          grid=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotagem com `matplotlib`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Histogramas gerados com `hist` e suas opções."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,3))\n",
    "ax.hist(x=df['Age'],\n",
    "        density=True,\n",
    "        histtype='bar',\n",
    "        align='mid',\n",
    "        rwidth=0.9,\n",
    "        color='#99ba00',\n",
    "        bins=8,\n",
    "        alpha=0.5);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,3))\n",
    "ax.hist(x=df[['Age','Monthly Revenue']],\n",
    "        density=True,\n",
    "        histtype='step',\n",
    "        align='mid',\n",
    "        rwidth=0.1,\n",
    "        linewidth=10,\n",
    "        color=['#74bbff','#de45fb'],\n",
    "        bins=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotagem com `seaborn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Opções de plotagem com `histplot`\n",
    "    - Plotar diversidade de casos com alteração das variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, a = plt.subplots(figsize=(8,3))\n",
    "sb.set_theme(style='darkgrid')\n",
    "\n",
    "hp = sb.histplot(data=df,\n",
    "            x='Age', # 'y' para barra horizontal\n",
    "            hue='Gender',\n",
    "            bins=10,\n",
    "            #binwidth=1, # opção com bins\n",
    "            #binrange=(32,40), # extensão\n",
    "            cumulative=False, # cumulativa\n",
    "            stat='percent', # 'count' | 'frequency' | 'probability' | 'density'\n",
    "            multiple='stack',\n",
    "            palette='muted',\n",
    "            kde=True, # density \n",
    "            kde_kws={'bw_method':'silverman', # método de binning: 'scott' | 'silverman'\n",
    "                     'bw_adjust':0.5},  # ajuste da largura de banda: quanto maior, mais suave\n",
    "            element='bars', # 'bars' | 'step' | 'poly'\n",
    "            linewidth=1,\n",
    "            edgecolor='w',\n",
    "            log_scale=True,\n",
    "            ax=a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualização de quantidades com mapa de calor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, a = plt.subplots(figsize=(4,6))\n",
    "dfpt = pd.pivot_table(df, index='Country', columns='Device', values='Age')\n",
    "\n",
    "# escolha 'mask' para exibir mapa de calor com máscara\n",
    "test = 'mask'\n",
    "\n",
    "if test == 'full':\n",
    "     mask = ~np.ones(dfpt.shape,dtype=bool)   \n",
    "else:\n",
    "     mask = ~np.ones(dfpt.shape,dtype=bool)\n",
    "     indices = np.argwhere(dfpt.values > 37)    \n",
    "     mask[indices[:,0], indices[:,1]] = True\n",
    "\n",
    "g = sb.heatmap(data=dfpt,\n",
    "                 cmap=sb.color_palette('mako'),\n",
    "                 annot=False,\n",
    "                 linewidths=10,\n",
    "                 linecolor='white',\n",
    "                 square=True,\n",
    "                 cbar=False,\n",
    "                 xticklabels=False,\n",
    "                 yticklabels=False,\n",
    "                 mask=mask,\n",
    "               ax=a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(38.361702127659576)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[ (df['Country'] == 'Australia') & (df['Device'] == 'Laptop')]\n",
    "df2['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Device</th>\n",
       "      <th>Laptop</th>\n",
       "      <th>Smart TV</th>\n",
       "      <th>Smartphone</th>\n",
       "      <th>Tablet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>38.361702</td>\n",
       "      <td>39.157895</td>\n",
       "      <td>38.709091</td>\n",
       "      <td>37.186047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>39.227273</td>\n",
       "      <td>37.475000</td>\n",
       "      <td>38.272727</td>\n",
       "      <td>38.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <td>38.437500</td>\n",
       "      <td>38.397436</td>\n",
       "      <td>39.462500</td>\n",
       "      <td>38.473684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>40.942308</td>\n",
       "      <td>38.767442</td>\n",
       "      <td>36.914894</td>\n",
       "      <td>39.658537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>39.349206</td>\n",
       "      <td>38.619048</td>\n",
       "      <td>38.527778</td>\n",
       "      <td>39.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>39.280000</td>\n",
       "      <td>37.826087</td>\n",
       "      <td>38.319149</td>\n",
       "      <td>38.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mexico</th>\n",
       "      <td>38.636364</td>\n",
       "      <td>39.414634</td>\n",
       "      <td>38.934783</td>\n",
       "      <td>38.442308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>39.224299</td>\n",
       "      <td>38.444444</td>\n",
       "      <td>38.294118</td>\n",
       "      <td>39.241379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>40.025000</td>\n",
       "      <td>40.259259</td>\n",
       "      <td>38.088889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>38.727273</td>\n",
       "      <td>39.060345</td>\n",
       "      <td>39.030303</td>\n",
       "      <td>38.913043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Device             Laptop   Smart TV  Smartphone     Tablet\n",
       "Country                                                    \n",
       "Australia       38.361702  39.157895   38.709091  37.186047\n",
       "Brazil          39.227273  37.475000   38.272727  38.272727\n",
       "Canada          38.437500  38.397436   39.462500  38.473684\n",
       "France          40.942308  38.767442   36.914894  39.658537\n",
       "Germany         39.349206  38.619048   38.527778  39.428571\n",
       "Italy           39.280000  37.826087   38.319149  38.750000\n",
       "Mexico          38.636364  39.414634   38.934783  38.442308\n",
       "Spain           39.224299  38.444444   38.294118  39.241379\n",
       "United Kingdom  38.250000  40.025000   40.259259  38.088889\n",
       "United States   38.727273  39.060345   39.030303  38.913043"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfpt"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dataviz",
   "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": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}