aula_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f27a7957",
   "metadata": {
    "id": "ieK1eCIN2oE8"
   },
   "source": [
    "# Aula 1 - Leitura de arquivos com o Polars"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6501aa0a",
   "metadata": {
    "id": "hjDmt9oey93e"
   },
   "source": [
    "https://excelbianalytics.com/wp/downloads-18-sample-csv-files-data-sets-for-testing-sales/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "460f6262",
   "metadata": {
    "id": "3ZHydavC2yLe"
   },
   "source": [
    "## Vídeo 1.2 - Colocando um arquivo em dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9500111a",
   "metadata": {
    "id": "16ksz_-yxmMF"
   },
   "outputs": [],
   "source": [
    "#url ='/content/drive/MyDrive/Alura/Cursos/4403 - Polars: manipulando tabelas/Projeto/dadocurso.zip'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87547a69",
   "metadata": {
    "id": "9NuilQ-3ATrm"
   },
   "outputs": [],
   "source": [
    "#import zipfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "efccc7d0",
   "metadata": {
    "id": "NT25J22CAb3L"
   },
   "outputs": [],
   "source": [
    "# Diretório onde o arquivo será extraído\n",
    "#extract_path = '/content/drive/MyDrive/Alura/Cursos/4403 - Polars: manipulando tabelas/Projeto/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3611e845",
   "metadata": {
    "id": "OFv9TTytAehF"
   },
   "outputs": [],
   "source": [
    "# Extraia o conteúdo do arquivo ZIP\n",
    "#with zipfile.ZipFile(url, 'r') as zip_ref:\n",
    "#    zip_ref.extractall(extract_path)\n",
    "\n",
    "#print(\"Arquivo descompactado com sucesso!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "29d8b217",
   "metadata": {
    "id": "Hv1xZJCh2HGJ"
   },
   "outputs": [],
   "source": [
    "import polars as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2927bc6",
   "metadata": {
    "id": "DzzUO3k2CL4u"
   },
   "outputs": [],
   "source": [
    "caminho_csv = '/content/drive/MyDrive/Alura/Cursos/4403 - Polars: manipulando tabelas/Projeto/dadocurso.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "54397530",
   "metadata": {
    "id": "H08OZa13B9My"
   },
   "outputs": [],
   "source": [
    "df = pl.read_csv(caminho_csv)\n",
    "df_original = df.clone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "82a4093a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "zO2-krn9CT0b",
    "outputId": "58d6f89a-5746-417d-e011-4a2d9d857542"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 14)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>str</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Australia and Oceania&quot;</td><td>&quot;Palau&quot;</td><td>&quot;Office Supplies&quot;</td><td>&quot;Online&quot;</td><td>&quot;H&quot;</td><td>&quot;3/6/2016&quot;</td><td>517073523</td><td>&quot;3/26/2016&quot;</td><td>2401</td><td>651.21</td><td>524.96</td><td>1.2508e6</td><td>1.2604e6</td><td>-9584.792</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Poland&quot;</td><td>&quot;Beverages&quot;</td><td>&quot;Online&quot;</td><td>&quot;L&quot;</td><td>&quot;4/18/2010&quot;</td><td>380507028</td><td>&quot;5/26/2010&quot;</td><td>9340</td><td>47.45</td><td>31.79</td><td>354546.4</td><td>296918.6</td><td>57627.8</td></tr><tr><td>&quot;North America&quot;</td><td>&quot;Canada&quot;</td><td>&quot;Cereal&quot;</td><td>&quot;Online&quot;</td><td>&quot;M&quot;</td><td>&quot;1/8/2015&quot;</td><td>504055583</td><td>&quot;1/31/2015&quot;</td><td>103</td><td>205.7</td><td>117.11</td><td>16949.68</td><td>12062.33</td><td>4887.35</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Belarus&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Online&quot;</td><td>&quot;C&quot;</td><td>&quot;1/19/2014&quot;</td><td>954955518</td><td>&quot;2/27/2014&quot;</td><td>1414</td><td>152.58</td><td>97.44</td><td>172598.496</td><td>137780.16</td><td>34818.336</td></tr><tr><td>&quot;Middle East and North Africa&quot;</td><td>&quot;Oman&quot;</td><td>&quot;Cereal&quot;</td><td>&quot;Offline&quot;</td><td>&quot;H&quot;</td><td>&quot;4/26/2019&quot;</td><td>970755660</td><td>&quot;6/2/2019&quot;</td><td>7027</td><td>205.7</td><td>117.11</td><td>1.1564e6</td><td>822931.97</td><td>333431.15</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 14)\n",
       "┌────────────┬─────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ Região     ┆ País    ┆ Tipo de   ┆ Canal de  ┆ … ┆ Custo     ┆ Receita   ┆ Custo     ┆ Lucro     │\n",
       "│ ---        ┆ ---     ┆ Item      ┆ Venda     ┆   ┆ Unitário  ┆ Total     ┆ Total     ┆ Total     │\n",
       "│ str        ┆ str     ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---       │\n",
       "│            ┆         ┆ str       ┆ str       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64       │\n",
       "╞════════════╪═════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ Australia  ┆ Palau   ┆ Office    ┆ Online    ┆ … ┆ 524.96    ┆ 1.2508e6  ┆ 1.2604e6  ┆ -9584.792 │\n",
       "│ and        ┆         ┆ Supplies  ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ Oceania    ┆         ┆           ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ Europe     ┆ Poland  ┆ Beverages ┆ Online    ┆ … ┆ 31.79     ┆ 354546.4  ┆ 296918.6  ┆ 57627.8   │\n",
       "│ North      ┆ Canada  ┆ Cereal    ┆ Online    ┆ … ┆ 117.11    ┆ 16949.68  ┆ 12062.33  ┆ 4887.35   │\n",
       "│ America    ┆         ┆           ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ Europe     ┆ Belarus ┆ Snacks    ┆ Online    ┆ … ┆ 97.44     ┆ 172598.49 ┆ 137780.16 ┆ 34818.336 │\n",
       "│            ┆         ┆           ┆           ┆   ┆           ┆ 6         ┆           ┆           │\n",
       "│ Middle     ┆ Oman    ┆ Cereal    ┆ Offline   ┆ … ┆ 117.11    ┆ 1.1564e6  ┆ 822931.97 ┆ 333431.15 │\n",
       "│ East and   ┆         ┆           ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ North      ┆         ┆           ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ Africa     ┆         ┆           ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "└────────────┴─────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2c3deae",
   "metadata": {
    "id": "DWMZafSz3osN"
   },
   "source": [
    "## Vídeo 1.3 - Analisando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8711f139",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hq-ITEtiMEqe",
    "outputId": "4634d250-1543-4fd4-a9c0-8c85234783f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[String,\n",
       " String,\n",
       " String,\n",
       " String,\n",
       " String,\n",
       " String,\n",
       " Int64,\n",
       " String,\n",
       " Int64,\n",
       " Float64,\n",
       " Float64,\n",
       " Float64,\n",
       " Float64,\n",
       " Float64]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bdd2dd04",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "rmwS4EUg2QOE",
    "outputId": "8babf3e6-4186-4f94-84b0-bb9bbb0e53db"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (9, 15)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>statistic</th><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>f64</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;count&quot;</td><td>&quot;5000000&quot;</td><td>&quot;5000000&quot;</td><td>&quot;5000000&quot;</td><td>&quot;5000000&quot;</td><td>&quot;5000000&quot;</td><td>&quot;5000000&quot;</td><td>5e6</td><td>&quot;5000000&quot;</td><td>5e6</td><td>5e6</td><td>5e6</td><td>5e6</td><td>5e6</td><td>5e6</td></tr><tr><td>&quot;null_count&quot;</td><td>&quot;0&quot;</td><td>&quot;0&quot;</td><td>&quot;0&quot;</td><td>&quot;0&quot;</td><td>&quot;0&quot;</td><td>&quot;0&quot;</td><td>0.0</td><td>&quot;0&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>&quot;mean&quot;</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>5.4989e8</td><td>null</td><td>4999.991061</td><td>266.190616</td><td>187.655991</td><td>1.1439e6</td><td>938378.122412</td><td>205508.425247</td></tr><tr><td>&quot;std&quot;</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>2.5978e8</td><td>null</td><td>2886.787007</td><td>217.015273</td><td>175.701379</td><td>1.3158e6</td><td>1.1501e6</td><td>451094.254368</td></tr><tr><td>&quot;min&quot;</td><td>&quot;Asia&quot;</td><td>&quot;Afghanistan&quot;</td><td>&quot;Baby Food&quot;</td><td>&quot;Offline&quot;</td><td>&quot;C&quot;</td><td>&quot;1/1/2010&quot;</td><td>1.00000321e8</td><td>&quot;1/1/2010&quot;</td><td>1.0</td><td>9.33</td><td>6.92</td><td>7.464</td><td>6.92</td><td>-271780.0</td></tr><tr><td>&quot;25%&quot;</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>3.24965131e8</td><td>null</td><td>2500.0</td><td>109.28</td><td>56.67</td><td>231366.2</td><td>161925.12</td><td>5065.57</td></tr><tr><td>&quot;50%&quot;</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>5.49764287e8</td><td>null</td><td>4999.0</td><td>205.7</td><td>117.11</td><td>658009.728</td><td>467712.0</td><td>80190.0</td></tr><tr><td>&quot;75%&quot;</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>null</td><td>7.74837243e8</td><td>null</td><td>7500.0</td><td>437.2</td><td>364.69</td><td>1.5568e6</td><td>1.1974e6</td><td>258412.7</td></tr><tr><td>&quot;max&quot;</td><td>&quot;Sub-Saharan Africa&quot;</td><td>&quot;Zimbabwe&quot;</td><td>&quot;Vegetables&quot;</td><td>&quot;Online&quot;</td><td>&quot;M&quot;</td><td>&quot;9/9/2020&quot;</td><td>9.99999892e8</td><td>&quot;9/9/2020&quot;</td><td>10000.0</td><td>668.27</td><td>524.96</td><td>1.002405e7</td><td>5.2496e6</td><td>4.99865e6</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (9, 15)\n",
       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
       "│ statistic ┆ Região    ┆ País      ┆ Tipo de   ┆ … ┆ Custo     ┆ Receita   ┆ Custo     ┆ Lucro    │\n",
       "│ ---       ┆ ---       ┆ ---       ┆ Item      ┆   ┆ Unitário  ┆ Total     ┆ Total     ┆ Total    │\n",
       "│ str       ┆ str       ┆ str       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---      │\n",
       "│           ┆           ┆           ┆ str       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64      │\n",
       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
       "│ count     ┆ 5000000   ┆ 5000000   ┆ 5000000   ┆ … ┆ 5e6       ┆ 5e6       ┆ 5e6       ┆ 5e6      │\n",
       "│ null_coun ┆ 0         ┆ 0         ┆ 0         ┆ … ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0      │\n",
       "│ t         ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "│ mean      ┆ null      ┆ null      ┆ null      ┆ … ┆ 187.65599 ┆ 1.1439e6  ┆ 938378.12 ┆ 205508.4 │\n",
       "│           ┆           ┆           ┆           ┆   ┆ 1         ┆           ┆ 2412      ┆ 25247    │\n",
       "│ std       ┆ null      ┆ null      ┆ null      ┆ … ┆ 175.70137 ┆ 1.3158e6  ┆ 1.1501e6  ┆ 451094.2 │\n",
       "│           ┆           ┆           ┆           ┆   ┆ 9         ┆           ┆           ┆ 54368    │\n",
       "│ min       ┆ Asia      ┆ Afghanist ┆ Baby Food ┆ … ┆ 6.92      ┆ 7.464     ┆ 6.92      ┆ -271780. │\n",
       "│           ┆           ┆ an        ┆           ┆   ┆           ┆           ┆           ┆ 0        │\n",
       "│ 25%       ┆ null      ┆ null      ┆ null      ┆ … ┆ 56.67     ┆ 231366.2  ┆ 161925.12 ┆ 5065.57  │\n",
       "│ 50%       ┆ null      ┆ null      ┆ null      ┆ … ┆ 117.11    ┆ 658009.72 ┆ 467712.0  ┆ 80190.0  │\n",
       "│           ┆           ┆           ┆           ┆   ┆           ┆ 8         ┆           ┆          │\n",
       "│ 75%       ┆ null      ┆ null      ┆ null      ┆ … ┆ 364.69    ┆ 1.5568e6  ┆ 1.1974e6  ┆ 258412.7 │\n",
       "│ max       ┆ Sub-Sahar ┆ Zimbabwe  ┆ Vegetable ┆ … ┆ 524.96    ┆ 1.002405e ┆ 5.2496e6  ┆ 4.99865e │\n",
       "│           ┆ an Africa ┆           ┆ s         ┆   ┆           ┆ 7         ┆           ┆ 6        │\n",
       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8fed7113",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "id": "Di2t2VtJSZK3",
    "outputId": "adf127ba-4d63-4d3b-e39e-db61a4817656"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (9, 8)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>statistic</th><th>ID do Pedido</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;count&quot;</td><td>5e6</td><td>5e6</td><td>5e6</td><td>5e6</td><td>5e6</td><td>5e6</td><td>5e6</td></tr><tr><td>&quot;null_count&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>&quot;mean&quot;</td><td>5.4989e8</td><td>4999.991061</td><td>266.190616</td><td>187.655991</td><td>1.1439e6</td><td>938378.122412</td><td>205508.425247</td></tr><tr><td>&quot;std&quot;</td><td>2.5978e8</td><td>2886.787007</td><td>217.015273</td><td>175.701379</td><td>1.3158e6</td><td>1.1501e6</td><td>451094.254368</td></tr><tr><td>&quot;min&quot;</td><td>1.00000321e8</td><td>1.0</td><td>9.33</td><td>6.92</td><td>7.464</td><td>6.92</td><td>-271780.0</td></tr><tr><td>&quot;25%&quot;</td><td>3.24965131e8</td><td>2500.0</td><td>109.28</td><td>56.67</td><td>231366.2</td><td>161925.12</td><td>5065.57</td></tr><tr><td>&quot;50%&quot;</td><td>5.49764287e8</td><td>4999.0</td><td>205.7</td><td>117.11</td><td>658009.728</td><td>467712.0</td><td>80190.0</td></tr><tr><td>&quot;75%&quot;</td><td>7.74837243e8</td><td>7500.0</td><td>437.2</td><td>364.69</td><td>1.5568e6</td><td>1.1974e6</td><td>258412.7</td></tr><tr><td>&quot;max&quot;</td><td>9.99999892e8</td><td>10000.0</td><td>668.27</td><td>524.96</td><td>1.002405e7</td><td>5.2496e6</td><td>4.99865e6</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (9, 8)\n",
       "┌────────────┬────────────┬────────────┬───────────┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ statistic  ┆ ID do      ┆ Unidades   ┆ Preço     ┆ Custo     ┆ Receita   ┆ Custo     ┆ Lucro     │\n",
       "│ ---        ┆ Pedido     ┆ Vendidas   ┆ Unitário  ┆ Unitário  ┆ Total     ┆ Total     ┆ Total     │\n",
       "│ str        ┆ ---        ┆ ---        ┆ ---       ┆ ---       ┆ ---       ┆ ---       ┆ ---       │\n",
       "│            ┆ f64        ┆ f64        ┆ f64       ┆ f64       ┆ f64       ┆ f64       ┆ f64       │\n",
       "╞════════════╪════════════╪════════════╪═══════════╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ count      ┆ 5e6        ┆ 5e6        ┆ 5e6       ┆ 5e6       ┆ 5e6       ┆ 5e6       ┆ 5e6       │\n",
       "│ null_count ┆ 0.0        ┆ 0.0        ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       ┆ 0.0       │\n",
       "│ mean       ┆ 5.4989e8   ┆ 4999.99106 ┆ 266.19061 ┆ 187.65599 ┆ 1.1439e6  ┆ 938378.12 ┆ 205508.42 │\n",
       "│            ┆            ┆ 1          ┆ 6         ┆ 1         ┆           ┆ 2412      ┆ 5247      │\n",
       "│ std        ┆ 2.5978e8   ┆ 2886.78700 ┆ 217.01527 ┆ 175.70137 ┆ 1.3158e6  ┆ 1.1501e6  ┆ 451094.25 │\n",
       "│            ┆            ┆ 7          ┆ 3         ┆ 9         ┆           ┆           ┆ 4368      │\n",
       "│ min        ┆ 1.00000321 ┆ 1.0        ┆ 9.33      ┆ 6.92      ┆ 7.464     ┆ 6.92      ┆ -271780.0 │\n",
       "│            ┆ e8         ┆            ┆           ┆           ┆           ┆           ┆           │\n",
       "│ 25%        ┆ 3.24965131 ┆ 2500.0     ┆ 109.28    ┆ 56.67     ┆ 231366.2  ┆ 161925.12 ┆ 5065.57   │\n",
       "│            ┆ e8         ┆            ┆           ┆           ┆           ┆           ┆           │\n",
       "│ 50%        ┆ 5.49764287 ┆ 4999.0     ┆ 205.7     ┆ 117.11    ┆ 658009.72 ┆ 467712.0  ┆ 80190.0   │\n",
       "│            ┆ e8         ┆            ┆           ┆           ┆ 8         ┆           ┆           │\n",
       "│ 75%        ┆ 7.74837243 ┆ 7500.0     ┆ 437.2     ┆ 364.69    ┆ 1.5568e6  ┆ 1.1974e6  ┆ 258412.7  │\n",
       "│            ┆ e8         ┆            ┆           ┆           ┆           ┆           ┆           │\n",
       "│ max        ┆ 9.99999892 ┆ 10000.0    ┆ 668.27    ┆ 524.96    ┆ 1.002405e ┆ 5.2496e6  ┆ 4.99865e6 │\n",
       "│            ┆ e8         ┆            ┆           ┆           ┆ 7         ┆           ┆           │\n",
       "└────────────┴────────────┴────────────┴───────────┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select(pl.col(pl.Int64, pl.Float64)).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e4a8d857",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 147
    },
    "id": "69a_5P4mMHTH",
    "outputId": "e57b08cc-e059-440a-afc5-a00d773fbf28"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (1, 14)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th></tr><tr><td>u32</td><td>u32</td><td>u32</td><td>u32</td><td>u32</td><td>u32</td><td>u32</td><td>u32</td><td>u32</td><td>u32</td><td>u32</td><td>u32</td><td>u32</td><td>u32</td></tr></thead><tbody><tr><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (1, 14)\n",
       "┌────────┬──────┬─────────┬──────────┬───┬──────────┬─────────┬─────────────┬─────────────┐\n",
       "│ Região ┆ País ┆ Tipo de ┆ Canal de ┆ … ┆ Custo    ┆ Receita ┆ Custo Total ┆ Lucro Total │\n",
       "│ ---    ┆ ---  ┆ Item    ┆ Venda    ┆   ┆ Unitário ┆ Total   ┆ ---         ┆ ---         │\n",
       "│ u32    ┆ u32  ┆ ---     ┆ ---      ┆   ┆ ---      ┆ ---     ┆ u32         ┆ u32         │\n",
       "│        ┆      ┆ u32     ┆ u32      ┆   ┆ u32      ┆ u32     ┆             ┆             │\n",
       "╞════════╪══════╪═════════╪══════════╪═══╪══════════╪═════════╪═════════════╪═════════════╡\n",
       "│ 0      ┆ 0    ┆ 0       ┆ 0        ┆ … ┆ 0        ┆ 0       ┆ 0           ┆ 0           │\n",
       "└────────┴──────┴─────────┴──────────┴───┴──────────┴─────────┴─────────────┴─────────────┘"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.null_count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d42bde8",
   "metadata": {
    "id": "eo1p-veRRkmW"
   },
   "outputs": [],
   "source": [
    "nan_counts = {\n",
    "    col: (\n",
    "        # Contar NaN apenas para colunas float\n",
    "        (df[col].is_nan().sum() if df[col].dtype in (pl.Int64, pl.Float64) else 0)\n",
    "        # Contar null para todas as colunas\n",
    "        + df[col].is_null().sum()\n",
    "    )\n",
    "    for col in df.columns\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4c9427b2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FMbxkBnsRly9",
    "outputId": "64fb46de-c1e3-458e-aff8-fa88b72afe36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Região': 0,\n",
       " 'País': 0,\n",
       " 'Tipo de Item': 0,\n",
       " 'Canal de Venda': 0,\n",
       " 'Prioridade do Pedido': 0,\n",
       " 'Data do Pedido': 0,\n",
       " 'ID do Pedido': 0,\n",
       " 'Data de Envio': 0,\n",
       " 'Unidades Vendidas': 0,\n",
       " 'Preço Unitário': 0,\n",
       " 'Custo Unitário': 0,\n",
       " 'Receita Total': 0,\n",
       " 'Custo Total': 0,\n",
       " 'Lucro Total': 0}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e6e49a0",
   "metadata": {
    "id": "c78RmtZq32Gw"
   },
   "source": [
    "## Vídeo - 1.4 - Seleção de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5ecb0c19",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 318
    },
    "id": "5iI2ZaKC36ph",
    "outputId": "bd68d5c2-8442-4809-d9d3-bf8cbea8dfe5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (7,)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th></tr><tr><td>str</td></tr></thead><tbody><tr><td>&quot;Asia&quot;</td></tr><tr><td>&quot;Central America and the Caribb…</td></tr><tr><td>&quot;Europe&quot;</td></tr><tr><td>&quot;Middle East and North Africa&quot;</td></tr><tr><td>&quot;Australia and Oceania&quot;</td></tr><tr><td>&quot;North America&quot;</td></tr><tr><td>&quot;Sub-Saharan Africa&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (7,)\n",
       "Series: 'Região' [str]\n",
       "[\n",
       "\t\"Asia\"\n",
       "\t\"Central America and the Caribb…\n",
       "\t\"Europe\"\n",
       "\t\"Middle East and North Africa\"\n",
       "\t\"Australia and Oceania\"\n",
       "\t\"North America\"\n",
       "\t\"Sub-Saharan Africa\"\n",
       "]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Região'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c885c165",
   "metadata": {
    "id": "VezmAQdUCHFf"
   },
   "outputs": [],
   "source": [
    "df_regions = df.filter((pl.col(\"Região\") == \"Europe\") | (pl.col(\"Região\") == \"Asia\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9ffb3529",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hsL7f3AJCMt3",
    "outputId": "295d423c-5f53-4b84-f401-bfade46a594d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2026538, 14)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_regions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d3cd5a62",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 344
    },
    "id": "3XEc03pMCryd",
    "outputId": "b29b8d14-d070-45a9-ef55-27b32348d797"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 14)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>str</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Europe&quot;</td><td>&quot;Poland&quot;</td><td>&quot;Beverages&quot;</td><td>&quot;Online&quot;</td><td>&quot;L&quot;</td><td>&quot;4/18/2010&quot;</td><td>380507028</td><td>&quot;5/26/2010&quot;</td><td>9340</td><td>47.45</td><td>31.79</td><td>354546.4</td><td>296918.6</td><td>57627.8</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Belarus&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Online&quot;</td><td>&quot;C&quot;</td><td>&quot;1/19/2014&quot;</td><td>954955518</td><td>&quot;2/27/2014&quot;</td><td>1414</td><td>152.58</td><td>97.44</td><td>172598.496</td><td>137780.16</td><td>34818.336</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Montenegro&quot;</td><td>&quot;Personal Care&quot;</td><td>&quot;Online&quot;</td><td>&quot;H&quot;</td><td>&quot;11/24/2012&quot;</td><td>598814380</td><td>&quot;12/25/2012&quot;</td><td>1337</td><td>81.73</td><td>56.67</td><td>87418.408</td><td>75767.79</td><td>11650.618</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Portugal&quot;</td><td>&quot;Cosmetics&quot;</td><td>&quot;Online&quot;</td><td>&quot;M&quot;</td><td>&quot;11/12/2012&quot;</td><td>390570247</td><td>&quot;11/13/2012&quot;</td><td>7270</td><td>437.2</td><td>263.33</td><td>2.5428e6</td><td>1914409.1</td><td>628346.1</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>&quot;Personal Care&quot;</td><td>&quot;Offline&quot;</td><td>&quot;H&quot;</td><td>&quot;12/25/2010&quot;</td><td>842548644</td><td>&quot;1/24/2011&quot;</td><td>7722</td><td>81.73</td><td>56.67</td><td>504895.248</td><td>437605.74</td><td>67289.508</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 14)\n",
       "┌────────┬────────────┬────────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ Região ┆ País       ┆ Tipo de    ┆ Canal de  ┆ … ┆ Custo     ┆ Receita   ┆ Custo     ┆ Lucro     │\n",
       "│ ---    ┆ ---        ┆ Item       ┆ Venda     ┆   ┆ Unitário  ┆ Total     ┆ Total     ┆ Total     │\n",
       "│ str    ┆ str        ┆ ---        ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---       │\n",
       "│        ┆            ┆ str        ┆ str       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64       │\n",
       "╞════════╪════════════╪════════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ Europe ┆ Poland     ┆ Beverages  ┆ Online    ┆ … ┆ 31.79     ┆ 354546.4  ┆ 296918.6  ┆ 57627.8   │\n",
       "│ Europe ┆ Belarus    ┆ Snacks     ┆ Online    ┆ … ┆ 97.44     ┆ 172598.49 ┆ 137780.16 ┆ 34818.336 │\n",
       "│        ┆            ┆            ┆           ┆   ┆           ┆ 6         ┆           ┆           │\n",
       "│ Europe ┆ Montenegro ┆ Personal   ┆ Online    ┆ … ┆ 56.67     ┆ 87418.408 ┆ 75767.79  ┆ 11650.618 │\n",
       "│        ┆            ┆ Care       ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ Europe ┆ Portugal   ┆ Cosmetics  ┆ Online    ┆ … ┆ 263.33    ┆ 2.5428e6  ┆ 1914409.1 ┆ 628346.1  │\n",
       "│ Europe ┆ Germany    ┆ Personal   ┆ Offline   ┆ … ┆ 56.67     ┆ 504895.24 ┆ 437605.74 ┆ 67289.508 │\n",
       "│        ┆            ┆ Care       ┆           ┆   ┆           ┆ 8         ┆           ┆           │\n",
       "└────────┴────────────┴────────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_regions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ac3659cd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 324
    },
    "id": "IRy-h014Eqa3",
    "outputId": "44400dbd-0cf6-40c4-8ff1-49716d44f3c1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 14)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>i64</td><td>str</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>&quot;Personal Care&quot;</td><td>&quot;Offline&quot;</td><td>&quot;H&quot;</td><td>&quot;12/25/2010&quot;</td><td>842548644</td><td>&quot;1/24/2011&quot;</td><td>7722</td><td>81.73</td><td>56.67</td><td>504895.248</td><td>437605.74</td><td>67289.508</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>&quot;Clothes&quot;</td><td>&quot;Online&quot;</td><td>&quot;L&quot;</td><td>&quot;7/1/2011&quot;</td><td>650888288</td><td>&quot;8/18/2011&quot;</td><td>1163</td><td>109.28</td><td>35.84</td><td>101674.112</td><td>41681.92</td><td>59992.192</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>&quot;Meat&quot;</td><td>&quot;Online&quot;</td><td>&quot;H&quot;</td><td>&quot;9/9/2013&quot;</td><td>444275796</td><td>&quot;10/3/2013&quot;</td><td>9936</td><td>421.89</td><td>364.69</td><td>3.3535e6</td><td>3.6236e6</td><td>-270040.608</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>&quot;Cereal&quot;</td><td>&quot;Online&quot;</td><td>&quot;M&quot;</td><td>&quot;4/10/2011&quot;</td><td>847790753</td><td>&quot;5/22/2011&quot;</td><td>8979</td><td>205.7</td><td>117.11</td><td>1.4776e6</td><td>1.0515e6</td><td>426053.55</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>&quot;Baby Food&quot;</td><td>&quot;Offline&quot;</td><td>&quot;H&quot;</td><td>&quot;6/2/2011&quot;</td><td>817252624</td><td>&quot;6/22/2011&quot;</td><td>2067</td><td>255.28</td><td>159.42</td><td>422131.008</td><td>329521.14</td><td>92609.868</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 14)\n",
       "┌────────┬─────────┬────────────┬────────────┬───┬────────────┬────────────┬───────────┬───────────┐\n",
       "│ Região ┆ País    ┆ Tipo de    ┆ Canal de   ┆ … ┆ Custo      ┆ Receita    ┆ Custo     ┆ Lucro     │\n",
       "│ ---    ┆ ---     ┆ Item       ┆ Venda      ┆   ┆ Unitário   ┆ Total      ┆ Total     ┆ Total     │\n",
       "│ str    ┆ str     ┆ ---        ┆ ---        ┆   ┆ ---        ┆ ---        ┆ ---       ┆ ---       │\n",
       "│        ┆         ┆ str        ┆ str        ┆   ┆ f64        ┆ f64        ┆ f64       ┆ f64       │\n",
       "╞════════╪═════════╪════════════╪════════════╪═══╪════════════╪════════════╪═══════════╪═══════════╡\n",
       "│ Europe ┆ Germany ┆ Personal   ┆ Offline    ┆ … ┆ 56.67      ┆ 504895.248 ┆ 437605.74 ┆ 67289.508 │\n",
       "│        ┆         ┆ Care       ┆            ┆   ┆            ┆            ┆           ┆           │\n",
       "│ Europe ┆ Germany ┆ Clothes    ┆ Online     ┆ … ┆ 35.84      ┆ 101674.112 ┆ 41681.92  ┆ 59992.192 │\n",
       "│ Europe ┆ Germany ┆ Meat       ┆ Online     ┆ … ┆ 364.69     ┆ 3.3535e6   ┆ 3.6236e6  ┆ -270040.6 │\n",
       "│        ┆         ┆            ┆            ┆   ┆            ┆            ┆           ┆ 08        │\n",
       "│ Europe ┆ Germany ┆ Cereal     ┆ Online     ┆ … ┆ 117.11     ┆ 1.4776e6   ┆ 1.0515e6  ┆ 426053.55 │\n",
       "│ Europe ┆ Germany ┆ Baby Food  ┆ Offline    ┆ … ┆ 159.42     ┆ 422131.008 ┆ 329521.14 ┆ 92609.868 │\n",
       "└────────┴─────────┴────────────┴────────────┴───┴────────────┴────────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.filter(df[\"País\"] == \"Germany\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8ff3938",
   "metadata": {
    "id": "SIW1Kn3a4E9l"
   },
   "source": [
    "# Aula 2 - Expressões"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "151309c6",
   "metadata": {
    "id": "VUJmDu3f4M2B"
   },
   "source": [
    "## Vídeo 2.1 - Modificando o tipo das colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4d596078",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "id": "wHsjXU-64LoQ",
    "outputId": "6bbfffbd-1b72-461f-c1e6-93a53300a441"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 14)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>date</td><td>i64</td><td>date</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Europe&quot;</td><td>&quot;Poland&quot;</td><td>&quot;Beverages&quot;</td><td>&quot;Online&quot;</td><td>&quot;L&quot;</td><td>2010-04-18</td><td>380507028</td><td>2010-05-26</td><td>9340</td><td>47.45</td><td>31.79</td><td>354546.4</td><td>296918.6</td><td>57627.8</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Belarus&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Online&quot;</td><td>&quot;C&quot;</td><td>2014-01-19</td><td>954955518</td><td>2014-02-27</td><td>1414</td><td>152.58</td><td>97.44</td><td>172598.496</td><td>137780.16</td><td>34818.336</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Montenegro&quot;</td><td>&quot;Personal Care&quot;</td><td>&quot;Online&quot;</td><td>&quot;H&quot;</td><td>2012-11-24</td><td>598814380</td><td>2012-12-25</td><td>1337</td><td>81.73</td><td>56.67</td><td>87418.408</td><td>75767.79</td><td>11650.618</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Portugal&quot;</td><td>&quot;Cosmetics&quot;</td><td>&quot;Online&quot;</td><td>&quot;M&quot;</td><td>2012-11-12</td><td>390570247</td><td>2012-11-13</td><td>7270</td><td>437.2</td><td>263.33</td><td>2.5428e6</td><td>1914409.1</td><td>628346.1</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>&quot;Personal Care&quot;</td><td>&quot;Offline&quot;</td><td>&quot;H&quot;</td><td>2010-12-25</td><td>842548644</td><td>2011-01-24</td><td>7722</td><td>81.73</td><td>56.67</td><td>504895.248</td><td>437605.74</td><td>67289.508</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 14)\n",
       "┌────────┬────────────┬────────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ Região ┆ País       ┆ Tipo de    ┆ Canal de  ┆ … ┆ Custo     ┆ Receita   ┆ Custo     ┆ Lucro     │\n",
       "│ ---    ┆ ---        ┆ Item       ┆ Venda     ┆   ┆ Unitário  ┆ Total     ┆ Total     ┆ Total     │\n",
       "│ str    ┆ str        ┆ ---        ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---       │\n",
       "│        ┆            ┆ str        ┆ str       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ f64       │\n",
       "╞════════╪════════════╪════════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ Europe ┆ Poland     ┆ Beverages  ┆ Online    ┆ … ┆ 31.79     ┆ 354546.4  ┆ 296918.6  ┆ 57627.8   │\n",
       "│ Europe ┆ Belarus    ┆ Snacks     ┆ Online    ┆ … ┆ 97.44     ┆ 172598.49 ┆ 137780.16 ┆ 34818.336 │\n",
       "│        ┆            ┆            ┆           ┆   ┆           ┆ 6         ┆           ┆           │\n",
       "│ Europe ┆ Montenegro ┆ Personal   ┆ Online    ┆ … ┆ 56.67     ┆ 87418.408 ┆ 75767.79  ┆ 11650.618 │\n",
       "│        ┆            ┆ Care       ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ Europe ┆ Portugal   ┆ Cosmetics  ┆ Online    ┆ … ┆ 263.33    ┆ 2.5428e6  ┆ 1914409.1 ┆ 628346.1  │\n",
       "│ Europe ┆ Germany    ┆ Personal   ┆ Offline   ┆ … ┆ 56.67     ┆ 504895.24 ┆ 437605.74 ┆ 67289.508 │\n",
       "│        ┆            ┆ Care       ┆           ┆   ┆           ┆ 8         ┆           ┆           │\n",
       "└────────┴────────────┴────────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converter colunas de data para o formato datetime\n",
    "df = df_regions.with_columns(\n",
    "    pl.col(\"Data do Pedido\").str.strptime(pl.Date, \"%m/%d/%Y\"),\n",
    "    pl.col(\"Data de Envio\").str.strptime(pl.Date, \"%m/%d/%Y\")\n",
    ")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0f2a86c5",
   "metadata": {
    "id": "pgJ3qR51KgLJ"
   },
   "outputs": [],
   "source": [
    "df = df.with_columns(\n",
    "    (pl.col(\"Data de Envio\") - pl.col(\"Data do Pedido\")).alias(\"Duração do envio\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "67f45100",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "id": "iXFlUsThLMYL",
    "outputId": "421c7844-7fa5-4485-fef9-d341690486d1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 15)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th><th>Duração do envio</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>date</td><td>i64</td><td>date</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>duration[ms]</td></tr></thead><tbody><tr><td>&quot;Europe&quot;</td><td>&quot;Poland&quot;</td><td>&quot;Beverages&quot;</td><td>&quot;Online&quot;</td><td>&quot;L&quot;</td><td>2010-04-18</td><td>380507028</td><td>2010-05-26</td><td>9340</td><td>47.45</td><td>31.79</td><td>354546.4</td><td>296918.6</td><td>57627.8</td><td>38d</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Belarus&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Online&quot;</td><td>&quot;C&quot;</td><td>2014-01-19</td><td>954955518</td><td>2014-02-27</td><td>1414</td><td>152.58</td><td>97.44</td><td>172598.496</td><td>137780.16</td><td>34818.336</td><td>39d</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Montenegro&quot;</td><td>&quot;Personal Care&quot;</td><td>&quot;Online&quot;</td><td>&quot;H&quot;</td><td>2012-11-24</td><td>598814380</td><td>2012-12-25</td><td>1337</td><td>81.73</td><td>56.67</td><td>87418.408</td><td>75767.79</td><td>11650.618</td><td>31d</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Portugal&quot;</td><td>&quot;Cosmetics&quot;</td><td>&quot;Online&quot;</td><td>&quot;M&quot;</td><td>2012-11-12</td><td>390570247</td><td>2012-11-13</td><td>7270</td><td>437.2</td><td>263.33</td><td>2.5428e6</td><td>1914409.1</td><td>628346.1</td><td>1d</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>&quot;Personal Care&quot;</td><td>&quot;Offline&quot;</td><td>&quot;H&quot;</td><td>2010-12-25</td><td>842548644</td><td>2011-01-24</td><td>7722</td><td>81.73</td><td>56.67</td><td>504895.248</td><td>437605.74</td><td>67289.508</td><td>30d</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 15)\n",
       "┌────────┬────────────┬────────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ Região ┆ País       ┆ Tipo de    ┆ Canal de  ┆ … ┆ Receita   ┆ Custo     ┆ Lucro     ┆ Duração   │\n",
       "│ ---    ┆ ---        ┆ Item       ┆ Venda     ┆   ┆ Total     ┆ Total     ┆ Total     ┆ do envio  │\n",
       "│ str    ┆ str        ┆ ---        ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---       │\n",
       "│        ┆            ┆ str        ┆ str       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ duration[ │\n",
       "│        ┆            ┆            ┆           ┆   ┆           ┆           ┆           ┆ ms]       │\n",
       "╞════════╪════════════╪════════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ Europe ┆ Poland     ┆ Beverages  ┆ Online    ┆ … ┆ 354546.4  ┆ 296918.6  ┆ 57627.8   ┆ 38d       │\n",
       "│ Europe ┆ Belarus    ┆ Snacks     ┆ Online    ┆ … ┆ 172598.49 ┆ 137780.16 ┆ 34818.336 ┆ 39d       │\n",
       "│        ┆            ┆            ┆           ┆   ┆ 6         ┆           ┆           ┆           │\n",
       "│ Europe ┆ Montenegro ┆ Personal   ┆ Online    ┆ … ┆ 87418.408 ┆ 75767.79  ┆ 11650.618 ┆ 31d       │\n",
       "│        ┆            ┆ Care       ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ Europe ┆ Portugal   ┆ Cosmetics  ┆ Online    ┆ … ┆ 2.5428e6  ┆ 1914409.1 ┆ 628346.1  ┆ 1d        │\n",
       "│ Europe ┆ Germany    ┆ Personal   ┆ Offline   ┆ … ┆ 504895.24 ┆ 437605.74 ┆ 67289.508 ┆ 30d       │\n",
       "│        ┆            ┆ Care       ┆           ┆   ┆ 8         ┆           ┆           ┆           │\n",
       "└────────┴────────────┴────────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2bcab2f1",
   "metadata": {
    "id": "J2XCdqNnMStO"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5231f67e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "gnsIkwlbMGNq",
    "outputId": "9fbc245a-2e0b-494a-de8b-dc5dfb963727"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filtrar a classe desejada (exemplo: \"H\")\n",
    "filtered_data = df.filter(pl.col(\"Prioridade do Pedido\") == \"H\").to_pandas()\n",
    "filtered_data[\"Duração do envio (dias)\"] = filtered_data[\"Duração do envio\"].dt.days\n",
    "# Plotar histograma\n",
    "plt.hist(filtered_data[\"Duração do envio (dias)\"], bins=50, edgecolor=\"black\")\n",
    "plt.title(\"Distribuição de Duração do envio (Prioridade do Pedido: H)\")\n",
    "plt.xlabel(\"Duração do envio (dias)\")\n",
    "plt.ylabel(\"Frequência\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4b2f47bb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "A_YivSqHPlD0",
    "outputId": "e8c1e759-c060-47ed-8d6f-fff16e0a0e49"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "filtered_data = df.filter(pl.col(\"Prioridade do Pedido\") == \"L\").to_pandas()\n",
    "filtered_data[\"Duração do envio (dias)\"] = filtered_data[\"Duração do envio\"].dt.days\n",
    "# Plotar histograma\n",
    "plt.hist(filtered_data[\"Duração do envio (dias)\"], bins=50, edgecolor=\"black\")\n",
    "plt.title(\"Distribuição de Duração do envio (Prioridade do Pedido: L)\")\n",
    "plt.xlabel(\"Duração do envio (dias)\")\n",
    "plt.ylabel(\"Frequência\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae5b900",
   "metadata": {
    "id": "nJzNLMhA4Tig"
   },
   "source": [
    "## Vídeo 2.2 - Modificação de valores em colunas e linhas específicas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "38bdb552",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "id": "CZkrrxxgVJD9",
    "outputId": "dc55c671-3990-4879-b94d-e76ac0b16f13"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 15)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th><th>Duração do envio</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>date</td><td>i64</td><td>date</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>duration[ms]</td></tr></thead><tbody><tr><td>&quot;Europe&quot;</td><td>&quot;Poland&quot;</td><td>&quot;Beverages&quot;</td><td>&quot;Online&quot;</td><td>&quot;L&quot;</td><td>2010-04-18</td><td>380507028</td><td>2010-05-26</td><td>9340</td><td>47.45</td><td>31.79</td><td>354546.4</td><td>296918.6</td><td>57627.8</td><td>38d</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Belarus&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Online&quot;</td><td>&quot;C&quot;</td><td>2014-01-19</td><td>954955518</td><td>2014-02-27</td><td>1414</td><td>152.58</td><td>97.44</td><td>172598.496</td><td>137780.16</td><td>34818.336</td><td>39d</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Montenegro&quot;</td><td>&quot;Personal Care&quot;</td><td>&quot;Online&quot;</td><td>&quot;H&quot;</td><td>2012-11-24</td><td>598814380</td><td>2012-12-25</td><td>1337</td><td>81.73</td><td>56.67</td><td>87418.408</td><td>75767.79</td><td>11650.618</td><td>31d</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Portugal&quot;</td><td>&quot;Cosmetics&quot;</td><td>&quot;Online&quot;</td><td>&quot;M&quot;</td><td>2012-11-12</td><td>390570247</td><td>2012-11-13</td><td>7270</td><td>437.2</td><td>263.33</td><td>2.5428e6</td><td>1914409.1</td><td>628346.1</td><td>1d</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>&quot;Personal Care&quot;</td><td>&quot;Offline&quot;</td><td>&quot;H&quot;</td><td>2010-12-25</td><td>842548644</td><td>2011-01-24</td><td>7722</td><td>81.73</td><td>56.67</td><td>504895.248</td><td>437605.74</td><td>67289.508</td><td>30d</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 15)\n",
       "┌────────┬────────────┬────────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ Região ┆ País       ┆ Tipo de    ┆ Canal de  ┆ … ┆ Receita   ┆ Custo     ┆ Lucro     ┆ Duração   │\n",
       "│ ---    ┆ ---        ┆ Item       ┆ Venda     ┆   ┆ Total     ┆ Total     ┆ Total     ┆ do envio  │\n",
       "│ str    ┆ str        ┆ ---        ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---       │\n",
       "│        ┆            ┆ str        ┆ str       ┆   ┆ f64       ┆ f64       ┆ f64       ┆ duration[ │\n",
       "│        ┆            ┆            ┆           ┆   ┆           ┆           ┆           ┆ ms]       │\n",
       "╞════════╪════════════╪════════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ Europe ┆ Poland     ┆ Beverages  ┆ Online    ┆ … ┆ 354546.4  ┆ 296918.6  ┆ 57627.8   ┆ 38d       │\n",
       "│ Europe ┆ Belarus    ┆ Snacks     ┆ Online    ┆ … ┆ 172598.49 ┆ 137780.16 ┆ 34818.336 ┆ 39d       │\n",
       "│        ┆            ┆            ┆           ┆   ┆ 6         ┆           ┆           ┆           │\n",
       "│ Europe ┆ Montenegro ┆ Personal   ┆ Online    ┆ … ┆ 87418.408 ┆ 75767.79  ┆ 11650.618 ┆ 31d       │\n",
       "│        ┆            ┆ Care       ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ Europe ┆ Portugal   ┆ Cosmetics  ┆ Online    ┆ … ┆ 2.5428e6  ┆ 1914409.1 ┆ 628346.1  ┆ 1d        │\n",
       "│ Europe ┆ Germany    ┆ Personal   ┆ Offline   ┆ … ┆ 504895.24 ┆ 437605.74 ┆ 67289.508 ┆ 30d       │\n",
       "│        ┆            ┆ Care       ┆           ┆   ┆ 8         ┆           ┆           ┆           │\n",
       "└────────┴────────────┴────────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8ba2450d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "m3vXKitSWJqx",
    "outputId": "43b3ce46-390b-47f7-a9d0-525ab05f9972"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (4,)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Prioridade do Pedido</th></tr><tr><td>str</td></tr></thead><tbody><tr><td>&quot;L&quot;</td></tr><tr><td>&quot;H&quot;</td></tr><tr><td>&quot;M&quot;</td></tr><tr><td>&quot;C&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (4,)\n",
       "Series: 'Prioridade do Pedido' [str]\n",
       "[\n",
       "\t\"L\"\n",
       "\t\"H\"\n",
       "\t\"M\"\n",
       "\t\"C\"\n",
       "]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Prioridade do Pedido\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "72dea0ed",
   "metadata": {
    "id": "oquvs5pcR46a"
   },
   "outputs": [],
   "source": [
    "df = df.with_columns(\n",
    "    pl.col(\"Prioridade do Pedido\").replace({\n",
    "        \"H\": \"High\",\n",
    "        \"C\": \"Critical\",\n",
    "        \"M\": \"Medium\",\n",
    "        \"L\": \"Low\"\n",
    "    }).alias(\"Prioridade do Pedido\")\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1660b868",
   "metadata": {
    "id": "YBMsl6vT4Z_6"
   },
   "outputs": [],
   "source": [
    "# Criar uma coluna de margem de lucro percentual\n",
    "df = df.with_columns(\n",
    "    (df[\"Lucro Total\"] / df[\"Custo Total\"] * 100).alias(\"Margem de Lucro (%)\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7713df0d",
   "metadata": {
    "id": "PCARs64wT8FI"
   },
   "outputs": [],
   "source": [
    "df = df.with_columns(\n",
    "    pl.when(pl.col(\"Unidades Vendidas\") > 5000)\n",
    "    .then(pl.lit(\"Online\"))  # Use pl.lit to indicate a literal string value\n",
    "    .otherwise(pl.col(\"Canal de Venda\"))\n",
    "    .alias(\"Tipo de venda\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "53eedd78",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "id": "Qve1vrwHqDGM",
    "outputId": "74c9ea09-0be5-4917-e959-12b5f707efae"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 17)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th><th>Duração do envio</th><th>Margem de Lucro (%)</th><th>Tipo de venda</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>date</td><td>i64</td><td>date</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>duration[ms]</td><td>f64</td><td>str</td></tr></thead><tbody><tr><td>&quot;Europe&quot;</td><td>&quot;Poland&quot;</td><td>&quot;Beverages&quot;</td><td>&quot;Online&quot;</td><td>&quot;Low&quot;</td><td>2010-04-18</td><td>380507028</td><td>2010-05-26</td><td>9340</td><td>47.45</td><td>31.79</td><td>354546.4</td><td>296918.6</td><td>57627.8</td><td>38d</td><td>19.408619</td><td>&quot;Online&quot;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Belarus&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2014-01-19</td><td>954955518</td><td>2014-02-27</td><td>1414</td><td>152.58</td><td>97.44</td><td>172598.496</td><td>137780.16</td><td>34818.336</td><td>39d</td><td>25.270936</td><td>&quot;Online&quot;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Montenegro&quot;</td><td>&quot;Personal Care&quot;</td><td>&quot;Online&quot;</td><td>&quot;High&quot;</td><td>2012-11-24</td><td>598814380</td><td>2012-12-25</td><td>1337</td><td>81.73</td><td>56.67</td><td>87418.408</td><td>75767.79</td><td>11650.618</td><td>31d</td><td>15.376743</td><td>&quot;Online&quot;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Portugal&quot;</td><td>&quot;Cosmetics&quot;</td><td>&quot;Online&quot;</td><td>&quot;Medium&quot;</td><td>2012-11-12</td><td>390570247</td><td>2012-11-13</td><td>7270</td><td>437.2</td><td>263.33</td><td>2.5428e6</td><td>1914409.1</td><td>628346.1</td><td>1d</td><td>32.821934</td><td>&quot;Online&quot;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>&quot;Personal Care&quot;</td><td>&quot;Offline&quot;</td><td>&quot;High&quot;</td><td>2010-12-25</td><td>842548644</td><td>2011-01-24</td><td>7722</td><td>81.73</td><td>56.67</td><td>504895.248</td><td>437605.74</td><td>67289.508</td><td>30d</td><td>15.376743</td><td>&quot;Online&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 17)\n",
       "┌────────┬────────────┬────────────┬───────────┬───┬───────────┬───────────┬───────────┬───────────┐\n",
       "│ Região ┆ País       ┆ Tipo de    ┆ Canal de  ┆ … ┆ Lucro     ┆ Duração   ┆ Margem de ┆ Tipo de   │\n",
       "│ ---    ┆ ---        ┆ Item       ┆ Venda     ┆   ┆ Total     ┆ do envio  ┆ Lucro (%) ┆ venda     │\n",
       "│ str    ┆ str        ┆ ---        ┆ ---       ┆   ┆ ---       ┆ ---       ┆ ---       ┆ ---       │\n",
       "│        ┆            ┆ str        ┆ str       ┆   ┆ f64       ┆ duration[ ┆ f64       ┆ str       │\n",
       "│        ┆            ┆            ┆           ┆   ┆           ┆ ms]       ┆           ┆           │\n",
       "╞════════╪════════════╪════════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪═══════════╡\n",
       "│ Europe ┆ Poland     ┆ Beverages  ┆ Online    ┆ … ┆ 57627.8   ┆ 38d       ┆ 19.408619 ┆ Online    │\n",
       "│ Europe ┆ Belarus    ┆ Snacks     ┆ Online    ┆ … ┆ 34818.336 ┆ 39d       ┆ 25.270936 ┆ Online    │\n",
       "│ Europe ┆ Montenegro ┆ Personal   ┆ Online    ┆ … ┆ 11650.618 ┆ 31d       ┆ 15.376743 ┆ Online    │\n",
       "│        ┆            ┆ Care       ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "│ Europe ┆ Portugal   ┆ Cosmetics  ┆ Online    ┆ … ┆ 628346.1  ┆ 1d        ┆ 32.821934 ┆ Online    │\n",
       "│ Europe ┆ Germany    ┆ Personal   ┆ Offline   ┆ … ┆ 67289.508 ┆ 30d       ┆ 15.376743 ┆ Online    │\n",
       "│        ┆            ┆ Care       ┆           ┆   ┆           ┆           ┆           ┆           │\n",
       "└────────┴────────────┴────────────┴───────────┴───┴───────────┴───────────┴───────────┴───────────┘"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec2c49c9",
   "metadata": {
    "id": "RuHOkQb44pxy"
   },
   "source": [
    "## Vídeo 2.3 - Analisando os atrasos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "55e77902",
   "metadata": {
    "id": "j5JrzmXw4v8z"
   },
   "outputs": [],
   "source": [
    "#Como selecionar dados com base em condições"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8868e9d9",
   "metadata": {
    "id": "8dbUBCPutDbR"
   },
   "outputs": [],
   "source": [
    "# Filtrar pedidos críticos e ordenar pela duração de entrega\n",
    "df_critical = df.filter(pl.col(\"Prioridade do Pedido\") == \"Critical\").sort(\"Duração do envio\", descending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7f620c33",
   "metadata": {
    "id": "u-cKvq2TtUSY"
   },
   "outputs": [],
   "source": [
    "# Extrair mês e ano da data do pedido\n",
    "df_critical = df_critical.with_columns(\n",
    "    pl.col(\"Data do Pedido\").dt.month().alias(\"Mês Pedido\"),\n",
    "    pl.col(\"Data do Pedido\").dt.year().alias(\"Ano Pedido\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "33148010",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 671
    },
    "id": "L95gYjSCunmM",
    "outputId": "4bf54457-dfbe-4317-8bb0-47abd934d8da"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (506_312, 19)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th><th>Duração do envio</th><th>Margem de Lucro (%)</th><th>Tipo de venda</th><th>Mês Pedido</th><th>Ano Pedido</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>date</td><td>i64</td><td>date</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>duration[ms]</td><td>f64</td><td>str</td><td>i8</td><td>i32</td></tr></thead><tbody><tr><td>&quot;Europe&quot;</td><td>&quot;Czech Republic&quot;</td><td>&quot;Beverages&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2019-05-21</td><td>173127639</td><td>2019-07-10</td><td>1170</td><td>47.45</td><td>31.79</td><td>44413.2</td><td>37194.3</td><td>7218.9</td><td>50d</td><td>19.408619</td><td>&quot;Offline&quot;</td><td>5</td><td>2019</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Monaco&quot;</td><td>&quot;Meat&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2014-07-11</td><td>935839736</td><td>2014-08-30</td><td>4629</td><td>421.89</td><td>364.69</td><td>1.5623e6</td><td>1.6882e6</td><td>-125806.962</td><td>50d</td><td>-7.452357</td><td>&quot;Online&quot;</td><td>7</td><td>2014</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Iceland&quot;</td><td>&quot;Meat&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2017-08-27</td><td>918327271</td><td>2017-10-16</td><td>2302</td><td>421.89</td><td>364.69</td><td>776952.624</td><td>839516.38</td><td>-62563.756</td><td>50d</td><td>-7.452357</td><td>&quot;Online&quot;</td><td>8</td><td>2017</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;Myanmar&quot;</td><td>&quot;Household&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2015-11-05</td><td>769704139</td><td>2015-12-25</td><td>1414</td><td>668.27</td><td>502.54</td><td>755947.024</td><td>710591.56</td><td>45355.464</td><td>50d</td><td>6.382776</td><td>&quot;Online&quot;</td><td>11</td><td>2015</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;Malaysia&quot;</td><td>&quot;Cosmetics&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2013-09-22</td><td>213717401</td><td>2013-11-11</td><td>2583</td><td>437.2</td><td>263.33</td><td>903430.08</td><td>680181.39</td><td>223248.69</td><td>50d</td><td>32.821934</td><td>&quot;Online&quot;</td><td>9</td><td>2013</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Russia&quot;</td><td>&quot;Fruits&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2015-01-10</td><td>719973838</td><td>2015-01-10</td><td>5113</td><td>9.33</td><td>6.92</td><td>38163.432</td><td>35381.96</td><td>2781.472</td><td>0ms</td><td>7.861272</td><td>&quot;Online&quot;</td><td>1</td><td>2015</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Ukraine&quot;</td><td>&quot;Beverages&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2013-11-12</td><td>944166600</td><td>2013-11-12</td><td>8733</td><td>47.45</td><td>31.79</td><td>331504.68</td><td>277622.07</td><td>53882.61</td><td>0ms</td><td>19.408619</td><td>&quot;Online&quot;</td><td>11</td><td>2013</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Andorra&quot;</td><td>&quot;Baby Food&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2012-04-12</td><td>317978942</td><td>2012-04-12</td><td>5890</td><td>255.28</td><td>159.42</td><td>1.2029e6</td><td>938983.8</td><td>263895.56</td><td>0ms</td><td>28.104378</td><td>&quot;Online&quot;</td><td>4</td><td>2012</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>&quot;Household&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2012-08-05</td><td>953894650</td><td>2012-08-05</td><td>7480</td><td>668.27</td><td>502.54</td><td>3.9989e6</td><td>3758999.2</td><td>239928.48</td><td>0ms</td><td>6.382776</td><td>&quot;Online&quot;</td><td>8</td><td>2012</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Vatican City&quot;</td><td>&quot;Vegetables&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2012-03-03</td><td>932866561</td><td>2012-03-03</td><td>4801</td><td>154.06</td><td>90.93</td><td>591713.648</td><td>436554.93</td><td>155158.718</td><td>0ms</td><td>35.541625</td><td>&quot;Offline&quot;</td><td>3</td><td>2012</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (506_312, 19)\n",
       "┌────────┬──────────┬──────────────┬──────────┬───┬─────────────────┬─────────┬────────┬────────┐\n",
       "│ Região ┆ País     ┆ Tipo de Item ┆ Canal de ┆ … ┆ Margem de Lucro ┆ Tipo de ┆ Mês    ┆ Ano    │\n",
       "│ ---    ┆ ---      ┆ ---          ┆ Venda    ┆   ┆ (%)             ┆ venda   ┆ Pedido ┆ Pedido │\n",
       "│ str    ┆ str      ┆ str          ┆ ---      ┆   ┆ ---             ┆ ---     ┆ ---    ┆ ---    │\n",
       "│        ┆          ┆              ┆ str      ┆   ┆ f64             ┆ str     ┆ i8     ┆ i32    │\n",
       "╞════════╪══════════╪══════════════╪══════════╪═══╪═════════════════╪═════════╪════════╪════════╡\n",
       "│ Europe ┆ Czech    ┆ Beverages    ┆ Offline  ┆ … ┆ 19.408619       ┆ Offline ┆ 5      ┆ 2019   │\n",
       "│        ┆ Republic ┆              ┆          ┆   ┆                 ┆         ┆        ┆        │\n",
       "│ Europe ┆ Monaco   ┆ Meat         ┆ Online   ┆ … ┆ -7.452357       ┆ Online  ┆ 7      ┆ 2014   │\n",
       "│ Europe ┆ Iceland  ┆ Meat         ┆ Online   ┆ … ┆ -7.452357       ┆ Online  ┆ 8      ┆ 2017   │\n",
       "│ Asia   ┆ Myanmar  ┆ Household    ┆ Online   ┆ … ┆ 6.382776        ┆ Online  ┆ 11     ┆ 2015   │\n",
       "│ Asia   ┆ Malaysia ┆ Cosmetics    ┆ Online   ┆ … ┆ 32.821934       ┆ Online  ┆ 9      ┆ 2013   │\n",
       "│ …      ┆ …        ┆ …            ┆ …        ┆ … ┆ …               ┆ …       ┆ …      ┆ …      │\n",
       "│ Europe ┆ Russia   ┆ Fruits       ┆ Offline  ┆ … ┆ 7.861272        ┆ Online  ┆ 1      ┆ 2015   │\n",
       "│ Europe ┆ Ukraine  ┆ Beverages    ┆ Offline  ┆ … ┆ 19.408619       ┆ Online  ┆ 11     ┆ 2013   │\n",
       "│ Europe ┆ Andorra  ┆ Baby Food    ┆ Online   ┆ … ┆ 28.104378       ┆ Online  ┆ 4      ┆ 2012   │\n",
       "│ Europe ┆ Germany  ┆ Household    ┆ Offline  ┆ … ┆ 6.382776        ┆ Online  ┆ 8      ┆ 2012   │\n",
       "│ Europe ┆ Vatican  ┆ Vegetables   ┆ Offline  ┆ … ┆ 35.541625       ┆ Offline ┆ 3      ┆ 2012   │\n",
       "│        ┆ City     ┆              ┆          ┆   ┆                 ┆         ┆        ┆        │\n",
       "└────────┴──────────┴──────────────┴──────────┴───┴─────────────────┴─────────┴────────┴────────┘"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_critical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9e7ef1ec",
   "metadata": {
    "id": "UrVtHGUqzRkW"
   },
   "outputs": [],
   "source": [
    "season_map = {\n",
    "    1: \"Winter\", 2: \"Winter\", 3: \"Spring\",\n",
    "    4: \"Spring\", 5: \"Spring\", 6: \"Summer\",\n",
    "    7: \"Summer\", 8: \"Summer\", 9: \"Fall\",\n",
    "    10: \"Fall\", 11: \"Fall\", 12: \"Winter\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5d328535",
   "metadata": {
    "id": "z6gtXlzMzYXs"
   },
   "outputs": [],
   "source": [
    "# Converter a coluna \"Order Month\" para string e mapear\n",
    "df_critical = df_critical.with_columns(\n",
    "    pl.col(\"Mês Pedido\").cast(pl.Utf8).replace(season_map).alias(\"Estação\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b83760a0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "id": "oVM9tc6a0BnZ",
    "outputId": "584766cc-1410-458a-a016-860ac2d4e8c9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 20)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th><th>Duração do envio</th><th>Margem de Lucro (%)</th><th>Tipo de venda</th><th>Mês Pedido</th><th>Ano Pedido</th><th>Estação</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>date</td><td>i64</td><td>date</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>duration[ms]</td><td>f64</td><td>str</td><td>i8</td><td>i32</td><td>str</td></tr></thead><tbody><tr><td>&quot;Europe&quot;</td><td>&quot;Czech Republic&quot;</td><td>&quot;Beverages&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2019-05-21</td><td>173127639</td><td>2019-07-10</td><td>1170</td><td>47.45</td><td>31.79</td><td>44413.2</td><td>37194.3</td><td>7218.9</td><td>50d</td><td>19.408619</td><td>&quot;Offline&quot;</td><td>5</td><td>2019</td><td>&quot;Spring&quot;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Monaco&quot;</td><td>&quot;Meat&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2014-07-11</td><td>935839736</td><td>2014-08-30</td><td>4629</td><td>421.89</td><td>364.69</td><td>1.5623e6</td><td>1.6882e6</td><td>-125806.962</td><td>50d</td><td>-7.452357</td><td>&quot;Online&quot;</td><td>7</td><td>2014</td><td>&quot;Summer&quot;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Iceland&quot;</td><td>&quot;Meat&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2017-08-27</td><td>918327271</td><td>2017-10-16</td><td>2302</td><td>421.89</td><td>364.69</td><td>776952.624</td><td>839516.38</td><td>-62563.756</td><td>50d</td><td>-7.452357</td><td>&quot;Online&quot;</td><td>8</td><td>2017</td><td>&quot;Summer&quot;</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;Myanmar&quot;</td><td>&quot;Household&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2015-11-05</td><td>769704139</td><td>2015-12-25</td><td>1414</td><td>668.27</td><td>502.54</td><td>755947.024</td><td>710591.56</td><td>45355.464</td><td>50d</td><td>6.382776</td><td>&quot;Online&quot;</td><td>11</td><td>2015</td><td>&quot;Fall&quot;</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;Malaysia&quot;</td><td>&quot;Cosmetics&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2013-09-22</td><td>213717401</td><td>2013-11-11</td><td>2583</td><td>437.2</td><td>263.33</td><td>903430.08</td><td>680181.39</td><td>223248.69</td><td>50d</td><td>32.821934</td><td>&quot;Online&quot;</td><td>9</td><td>2013</td><td>&quot;Fall&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 20)\n",
       "┌────────┬──────────┬──────────────┬──────────┬───┬─────────┬────────┬────────────┬─────────┐\n",
       "│ Região ┆ País     ┆ Tipo de Item ┆ Canal de ┆ … ┆ Tipo de ┆ Mês    ┆ Ano Pedido ┆ Estação │\n",
       "│ ---    ┆ ---      ┆ ---          ┆ Venda    ┆   ┆ venda   ┆ Pedido ┆ ---        ┆ ---     │\n",
       "│ str    ┆ str      ┆ str          ┆ ---      ┆   ┆ ---     ┆ ---    ┆ i32        ┆ str     │\n",
       "│        ┆          ┆              ┆ str      ┆   ┆ str     ┆ i8     ┆            ┆         │\n",
       "╞════════╪══════════╪══════════════╪══════════╪═══╪═════════╪════════╪════════════╪═════════╡\n",
       "│ Europe ┆ Czech    ┆ Beverages    ┆ Offline  ┆ … ┆ Offline ┆ 5      ┆ 2019       ┆ Spring  │\n",
       "│        ┆ Republic ┆              ┆          ┆   ┆         ┆        ┆            ┆         │\n",
       "│ Europe ┆ Monaco   ┆ Meat         ┆ Online   ┆ … ┆ Online  ┆ 7      ┆ 2014       ┆ Summer  │\n",
       "│ Europe ┆ Iceland  ┆ Meat         ┆ Online   ┆ … ┆ Online  ┆ 8      ┆ 2017       ┆ Summer  │\n",
       "│ Asia   ┆ Myanmar  ┆ Household    ┆ Online   ┆ … ┆ Online  ┆ 11     ┆ 2015       ┆ Fall    │\n",
       "│ Asia   ┆ Malaysia ┆ Cosmetics    ┆ Online   ┆ … ┆ Online  ┆ 9      ┆ 2013       ┆ Fall    │\n",
       "└────────┴──────────┴──────────────┴──────────┴───┴─────────┴────────┴────────────┴─────────┘"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_critical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6ca907cf",
   "metadata": {
    "id": "4nuiR7rn1oXM"
   },
   "outputs": [],
   "source": [
    "\n",
    "# Converter para Pandas (se necessário)\n",
    "df_critical_pd = df_critical.to_pandas()\n",
    "\n",
    "# Converter Shipping Duration para dias (caso não tenha sido feito antes)\n",
    "df_critical_pd[\"Dias de envio\"] = df_critical_pd[\"Duração do envio\"].dt.days\n",
    "\n",
    "# Lista de dados para o boxplot (usando Shipping Days)\n",
    "data = [\n",
    "    df_critical_pd[df_critical_pd[\"Estação\"] == season][\"Dias de envio\"]\n",
    "    for season in df_critical_pd[\"Estação\"].unique()\n",
    "]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bd99a63e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 542
    },
    "id": "jimpELvX4gz_",
    "outputId": "6e9e1aba-7f74-45a8-b53e-cb22d77337b4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-727ad8b8ec6e>:11: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n",
      "  bp = plt.boxplot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Definindo cores mais vivas\n",
    "cores = {\n",
    "    'box': '#00A6FB',  # Azul vibrante\n",
    "    'mediana': '#FF1E1E',  # Vermelho vibrante\n",
    "    'whiskers': '#000000',  # Preto para os whiskers\n",
    "    'caps': '#000000',  # Preto para as caps\n",
    "    'fliers': '#FF4500'  # Laranja vibrante para outliers\n",
    "}\n",
    "\n",
    "# Criando o boxplot\n",
    "bp = plt.boxplot(\n",
    "    data,\n",
    "    vert=False,\n",
    "    patch_artist=True,\n",
    "    labels=df_critical_pd[\"Estação\"].unique(),\n",
    "    boxprops=dict(facecolor=cores['box'], alpha=0.8),\n",
    "    medianprops=dict(color=cores['mediana'], linewidth=1.5),\n",
    "    whiskerprops=dict(color=cores['whiskers'], linewidth=1.5),\n",
    "    capprops=dict(color=cores['caps'], linewidth=1.5),\n",
    "    flierprops=dict(marker='o', markerfacecolor=cores['fliers'], markersize=8)\n",
    ")\n",
    "\n",
    "plt.title(\"Distribuição de Dias para Entrega por Estação (Pedidos Críticos)\",\n",
    "          fontsize=12, pad=15)\n",
    "plt.xlabel(\"Dias de Entrega\", fontsize=10)\n",
    "plt.ylabel(\"Estação do Ano\", fontsize=10)\n",
    "plt.grid(axis=\"x\", linestyle=\"--\", alpha=0.7)\n",
    "\n",
    "# Ajustando o layout\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ef34d9",
   "metadata": {
    "id": "LJrOnkIM44QH"
   },
   "source": [
    "# Aula 3 - Transformações e operações"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48714f57",
   "metadata": {
    "id": "kpM8LP_X5Zjl"
   },
   "source": [
    "## Vídeo 3.1 - Joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "37fd7919",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9ihUVrx6cfgp",
    "outputId": "ecd01400-124d-4e41-f7a6-51e783b4030b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Household\n",
      "Cereal\n",
      "Office Supplies\n",
      "Cosmetics\n",
      "Clothes\n",
      "Personal Care\n",
      "Meat\n",
      "Vegetables\n",
      "Fruits\n",
      "Beverages\n",
      "Baby Food\n",
      "Snacks\n"
     ]
    }
   ],
   "source": [
    "itens_unicos = df['Tipo de Item'].unique()\n",
    "\n",
    "# Loop for para imprimir item por item\n",
    "for item in itens_unicos:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0985d2a8",
   "metadata": {
    "id": "2Zv7USx74zDQ"
   },
   "outputs": [],
   "source": [
    "data = {'Tipo de Item': ['Snacks', 'Cereal', 'Office Supplies', 'Household', 'Personal Care',\n",
    "                      'Clothes', 'Cosmetics', 'Vegetables', 'Meat', 'Baby Food', 'Beverages', 'Fruits']}\n",
    "df_simples = pl.DataFrame(data)\n",
    "\n",
    "# Obtendo os valores únicos da coluna 'Item Type'\n",
    "itens_unicos = df_simples['Tipo de Item'].unique().to_list()\n",
    "\n",
    "# Criando o DataFrame delivery_goals baseado no Item Type\n",
    "delivery_goals = pl.DataFrame({\n",
    "    \"Tipo de Item\": itens_unicos,\n",
    "    \"Dias de envio alvo\": [5, 7, 10, 8, 6, 9, 7, 3, 4, 5, 6, 2]  # Exemplo de dias de entrega\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "69b369c9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "Th5DBKRycQjh",
    "outputId": "de856da1-f1eb-401b-e092-a8c45ffdf672"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (12, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Tipo de Item</th><th>Dias de envio alvo</th></tr><tr><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Fruits&quot;</td><td>5</td></tr><tr><td>&quot;Personal Care&quot;</td><td>7</td></tr><tr><td>&quot;Baby Food&quot;</td><td>10</td></tr><tr><td>&quot;Beverages&quot;</td><td>8</td></tr><tr><td>&quot;Snacks&quot;</td><td>6</td></tr><tr><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Office Supplies&quot;</td><td>3</td></tr><tr><td>&quot;Clothes&quot;</td><td>4</td></tr><tr><td>&quot;Cosmetics&quot;</td><td>5</td></tr><tr><td>&quot;Vegetables&quot;</td><td>6</td></tr><tr><td>&quot;Household&quot;</td><td>2</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (12, 2)\n",
       "┌─────────────────┬────────────────────┐\n",
       "│ Tipo de Item    ┆ Dias de envio alvo │\n",
       "│ ---             ┆ ---                │\n",
       "│ str             ┆ i64                │\n",
       "╞═════════════════╪════════════════════╡\n",
       "│ Fruits          ┆ 5                  │\n",
       "│ Personal Care   ┆ 7                  │\n",
       "│ Baby Food       ┆ 10                 │\n",
       "│ Beverages       ┆ 8                  │\n",
       "│ Snacks          ┆ 6                  │\n",
       "│ …               ┆ …                  │\n",
       "│ Office Supplies ┆ 3                  │\n",
       "│ Clothes         ┆ 4                  │\n",
       "│ Cosmetics       ┆ 5                  │\n",
       "│ Vegetables      ┆ 6                  │\n",
       "│ Household       ┆ 2                  │\n",
       "└─────────────────┴────────────────────┘"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "53a5f754",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 689
    },
    "id": "8WBNRU7KbkBM",
    "outputId": "094bb78a-6c56-487a-f70a-c10b530f5702"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (506_312, 21)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th><th>Duração do envio</th><th>Margem de Lucro (%)</th><th>Tipo de venda</th><th>Mês Pedido</th><th>Ano Pedido</th><th>Estação</th><th>Dias de envio alvo</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>date</td><td>i64</td><td>date</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>duration[ms]</td><td>f64</td><td>str</td><td>i8</td><td>i32</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Europe&quot;</td><td>&quot;Czech Republic&quot;</td><td>&quot;Beverages&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2019-05-21</td><td>173127639</td><td>2019-07-10</td><td>1170</td><td>47.45</td><td>31.79</td><td>44413.2</td><td>37194.3</td><td>7218.9</td><td>50d</td><td>19.408619</td><td>&quot;Offline&quot;</td><td>5</td><td>2019</td><td>&quot;Spring&quot;</td><td>8</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Monaco&quot;</td><td>&quot;Meat&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2014-07-11</td><td>935839736</td><td>2014-08-30</td><td>4629</td><td>421.89</td><td>364.69</td><td>1.5623e6</td><td>1.6882e6</td><td>-125806.962</td><td>50d</td><td>-7.452357</td><td>&quot;Online&quot;</td><td>7</td><td>2014</td><td>&quot;Summer&quot;</td><td>7</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Iceland&quot;</td><td>&quot;Meat&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2017-08-27</td><td>918327271</td><td>2017-10-16</td><td>2302</td><td>421.89</td><td>364.69</td><td>776952.624</td><td>839516.38</td><td>-62563.756</td><td>50d</td><td>-7.452357</td><td>&quot;Online&quot;</td><td>8</td><td>2017</td><td>&quot;Summer&quot;</td><td>7</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;Myanmar&quot;</td><td>&quot;Household&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2015-11-05</td><td>769704139</td><td>2015-12-25</td><td>1414</td><td>668.27</td><td>502.54</td><td>755947.024</td><td>710591.56</td><td>45355.464</td><td>50d</td><td>6.382776</td><td>&quot;Online&quot;</td><td>11</td><td>2015</td><td>&quot;Fall&quot;</td><td>2</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;Malaysia&quot;</td><td>&quot;Cosmetics&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2013-09-22</td><td>213717401</td><td>2013-11-11</td><td>2583</td><td>437.2</td><td>263.33</td><td>903430.08</td><td>680181.39</td><td>223248.69</td><td>50d</td><td>32.821934</td><td>&quot;Online&quot;</td><td>9</td><td>2013</td><td>&quot;Fall&quot;</td><td>5</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Russia&quot;</td><td>&quot;Fruits&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2015-01-10</td><td>719973838</td><td>2015-01-10</td><td>5113</td><td>9.33</td><td>6.92</td><td>38163.432</td><td>35381.96</td><td>2781.472</td><td>0ms</td><td>7.861272</td><td>&quot;Online&quot;</td><td>1</td><td>2015</td><td>&quot;Winter&quot;</td><td>5</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Ukraine&quot;</td><td>&quot;Beverages&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2013-11-12</td><td>944166600</td><td>2013-11-12</td><td>8733</td><td>47.45</td><td>31.79</td><td>331504.68</td><td>277622.07</td><td>53882.61</td><td>0ms</td><td>19.408619</td><td>&quot;Online&quot;</td><td>11</td><td>2013</td><td>&quot;Fall&quot;</td><td>8</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Andorra&quot;</td><td>&quot;Baby Food&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2012-04-12</td><td>317978942</td><td>2012-04-12</td><td>5890</td><td>255.28</td><td>159.42</td><td>1.2029e6</td><td>938983.8</td><td>263895.56</td><td>0ms</td><td>28.104378</td><td>&quot;Online&quot;</td><td>4</td><td>2012</td><td>&quot;Spring&quot;</td><td>10</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>&quot;Household&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2012-08-05</td><td>953894650</td><td>2012-08-05</td><td>7480</td><td>668.27</td><td>502.54</td><td>3.9989e6</td><td>3758999.2</td><td>239928.48</td><td>0ms</td><td>6.382776</td><td>&quot;Online&quot;</td><td>8</td><td>2012</td><td>&quot;Summer&quot;</td><td>2</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Vatican City&quot;</td><td>&quot;Vegetables&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2012-03-03</td><td>932866561</td><td>2012-03-03</td><td>4801</td><td>154.06</td><td>90.93</td><td>591713.648</td><td>436554.93</td><td>155158.718</td><td>0ms</td><td>35.541625</td><td>&quot;Offline&quot;</td><td>3</td><td>2012</td><td>&quot;Spring&quot;</td><td>6</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (506_312, 21)\n",
       "┌────────┬─────────────┬─────────────┬────────────┬───┬────────┬────────────┬─────────┬────────────┐\n",
       "│ Região ┆ País        ┆ Tipo de     ┆ Canal de   ┆ … ┆ Mês    ┆ Ano Pedido ┆ Estação ┆ Dias de    │\n",
       "│ ---    ┆ ---         ┆ Item        ┆ Venda      ┆   ┆ Pedido ┆ ---        ┆ ---     ┆ envio alvo │\n",
       "│ str    ┆ str         ┆ ---         ┆ ---        ┆   ┆ ---    ┆ i32        ┆ str     ┆ ---        │\n",
       "│        ┆             ┆ str         ┆ str        ┆   ┆ i8     ┆            ┆         ┆ i64        │\n",
       "╞════════╪═════════════╪═════════════╪════════════╪═══╪════════╪════════════╪═════════╪════════════╡\n",
       "│ Europe ┆ Czech       ┆ Beverages   ┆ Offline    ┆ … ┆ 5      ┆ 2019       ┆ Spring  ┆ 8          │\n",
       "│        ┆ Republic    ┆             ┆            ┆   ┆        ┆            ┆         ┆            │\n",
       "│ Europe ┆ Monaco      ┆ Meat        ┆ Online     ┆ … ┆ 7      ┆ 2014       ┆ Summer  ┆ 7          │\n",
       "│ Europe ┆ Iceland     ┆ Meat        ┆ Online     ┆ … ┆ 8      ┆ 2017       ┆ Summer  ┆ 7          │\n",
       "│ Asia   ┆ Myanmar     ┆ Household   ┆ Online     ┆ … ┆ 11     ┆ 2015       ┆ Fall    ┆ 2          │\n",
       "│ Asia   ┆ Malaysia    ┆ Cosmetics   ┆ Online     ┆ … ┆ 9      ┆ 2013       ┆ Fall    ┆ 5          │\n",
       "│ …      ┆ …           ┆ …           ┆ …          ┆ … ┆ …      ┆ …          ┆ …       ┆ …          │\n",
       "│ Europe ┆ Russia      ┆ Fruits      ┆ Offline    ┆ … ┆ 1      ┆ 2015       ┆ Winter  ┆ 5          │\n",
       "│ Europe ┆ Ukraine     ┆ Beverages   ┆ Offline    ┆ … ┆ 11     ┆ 2013       ┆ Fall    ┆ 8          │\n",
       "│ Europe ┆ Andorra     ┆ Baby Food   ┆ Online     ┆ … ┆ 4      ┆ 2012       ┆ Spring  ┆ 10         │\n",
       "│ Europe ┆ Germany     ┆ Household   ┆ Offline    ┆ … ┆ 8      ┆ 2012       ┆ Summer  ┆ 2          │\n",
       "│ Europe ┆ Vatican     ┆ Vegetables  ┆ Offline    ┆ … ┆ 3      ┆ 2012       ┆ Spring  ┆ 6          │\n",
       "│        ┆ City        ┆             ┆            ┆   ┆        ┆            ┆         ┆            │\n",
       "└────────┴─────────────┴─────────────┴────────────┴───┴────────┴────────────┴─────────┴────────────┘"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_joined = df_critical.join(delivery_goals, on=\"Tipo de Item\", how=\"left\")\n",
    "df_joined"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16628707",
   "metadata": {
    "id": "JArbc4X2oOde"
   },
   "source": [
    "## Vídeo 3.2 - Produto cartesiano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9d714e62",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JANehvlEc5YJ",
    "outputId": "322d112f-f20d-4c6d-8b9b-1f5baf8caab4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape: (6, 2)\n",
      "┌───────┬────────────────┐\n",
      "│ name  ┆ monopoly_token │\n",
      "│ ---   ┆ ---            │\n",
      "│ str   ┆ str            │\n",
      "╞═══════╪════════════════╡\n",
      "│ Alice ┆ hat            │\n",
      "│ Alice ┆ shoe           │\n",
      "│ Alice ┆ boat           │\n",
      "│ Bob   ┆ hat            │\n",
      "│ Bob   ┆ shoe           │\n",
      "│ Bob   ┆ boat           │\n",
      "└───────┴────────────────┘\n"
     ]
    }
   ],
   "source": [
    "# DataFrame 1: Jogadores\n",
    "players = pl.DataFrame({\"name\": [\"Alice\", \"Bob\"]})\n",
    "\n",
    "# DataFrame 2: Tokens do jogo\n",
    "tokens = pl.DataFrame({\"monopoly_token\": [\"hat\", \"shoe\", \"boat\"]})\n",
    "\n",
    "# Produto cartesiano\n",
    "result = players.join(tokens, how=\"cross\")\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9f860bda",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "doLWzU9JqxTM",
    "outputId": "a11b4415-e6ef-4be5-a448-02e1f8c74347"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape: (36, 2)\n",
      "┌──────────────┬───────────────────┐\n",
      "│ Tipo de Item ┆ Método de entrega │\n",
      "│ ---          ┆ ---               │\n",
      "│ str          ┆ str               │\n",
      "╞══════════════╪═══════════════════╡\n",
      "│ Household    ┆ Standard          │\n",
      "│ Household    ┆ Express           │\n",
      "│ Household    ┆ Same Day          │\n",
      "│ Meat         ┆ Standard          │\n",
      "│ Meat         ┆ Express           │\n",
      "│ …            ┆ …                 │\n",
      "│ Baby Food    ┆ Express           │\n",
      "│ Baby Food    ┆ Same Day          │\n",
      "│ Vegetables   ┆ Standard          │\n",
      "│ Vegetables   ┆ Express           │\n",
      "│ Vegetables   ┆ Same Day          │\n",
      "└──────────────┴───────────────────┘\n"
     ]
    }
   ],
   "source": [
    "# DataFrame original com os tipos de itens\n",
    "item_types = df_critical.select(\"Tipo de Item\").unique()\n",
    "\n",
    "# DataFrame com categorias de entrega\n",
    "delivery_methods = pl.DataFrame({\"Método de entrega\": [\"Standard\", \"Express\", \"Same Day\"]})\n",
    "\n",
    "# Produto cartesiano entre os itens e os métodos de entrega\n",
    "cartesian_result = item_types.join(delivery_methods, how=\"cross\")\n",
    "\n",
    "print(cartesian_result)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b22eee81",
   "metadata": {
    "id": "0FvPXjk7sMa3"
   },
   "source": [
    "## Vídeo 3.3 - Concatenação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0b38d507",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "rDV-hUj4sEHA",
    "outputId": "5b2ce39f-a553-4620-82b6-f8537d07012e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (4, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Vendas</th></tr><tr><td>str</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Europe&quot;</td><td>&quot;Germany&quot;</td><td>5000</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;France&quot;</td><td>7000</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;China&quot;</td><td>8000</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;Japan&quot;</td><td>6000</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (4, 3)\n",
       "┌────────┬─────────┬────────┐\n",
       "│ Região ┆ País    ┆ Vendas │\n",
       "│ ---    ┆ ---     ┆ ---    │\n",
       "│ str    ┆ str     ┆ i64    │\n",
       "╞════════╪═════════╪════════╡\n",
       "│ Europe ┆ Germany ┆ 5000   │\n",
       "│ Europe ┆ France  ┆ 7000   │\n",
       "│ Asia   ┆ China   ┆ 8000   │\n",
       "│ Asia   ┆ Japan   ┆ 6000   │\n",
       "└────────┴─────────┴────────┘"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame 1: Vendas da Europa\n",
    "sales_europe = pl.DataFrame({\n",
    "    \"Região\": [\"Europe\", \"Europe\"],\n",
    "    \"País\": [\"Germany\", \"France\"],\n",
    "    \"Vendas\": [5000, 7000]\n",
    "})\n",
    "\n",
    "# DataFrame 2: Vendas da Ásia\n",
    "sales_asia = pl.DataFrame({\n",
    "    \"Região\": [\"Asia\", \"Asia\"],\n",
    "    \"País\": [\"China\", \"Japan\"],\n",
    "    \"Vendas\": [8000, 6000]\n",
    "})\n",
    "\n",
    "# Concatenação vertical\n",
    "sales_combined = pl.concat([sales_europe, sales_asia], how=\"vertical\")\n",
    "\n",
    "sales_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4ae91cbd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "id": "2hUTMT47wOG_",
    "outputId": "7d1c0a1d-c772-4fb4-b285-f507c1232a76"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (4, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>País</th><th>Vendas</th><th>Lucro</th></tr><tr><td>str</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Germany&quot;</td><td>5000</td><td>1200</td></tr><tr><td>&quot;France&quot;</td><td>7000</td><td>1500</td></tr><tr><td>&quot;China&quot;</td><td>8000</td><td>2000</td></tr><tr><td>&quot;Japan&quot;</td><td>6000</td><td>1800</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (4, 3)\n",
       "┌─────────┬────────┬───────┐\n",
       "│ País    ┆ Vendas ┆ Lucro │\n",
       "│ ---     ┆ ---    ┆ ---   │\n",
       "│ str     ┆ i64    ┆ i64   │\n",
       "╞═════════╪════════╪═══════╡\n",
       "│ Germany ┆ 5000   ┆ 1200  │\n",
       "│ France  ┆ 7000   ┆ 1500  │\n",
       "│ China   ┆ 8000   ┆ 2000  │\n",
       "│ Japan   ┆ 6000   ┆ 1800  │\n",
       "└─────────┴────────┴───────┘"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame 1: Informações de vendas\n",
    "sales_info = pl.DataFrame({\n",
    "    \"País\": [\"Germany\", \"France\", \"China\", \"Japan\"],\n",
    "    \"Vendas\": [5000, 7000, 8000, 6000]\n",
    "})\n",
    "\n",
    "# DataFrame 2: Informações de lucro\n",
    "profit_info = pl.DataFrame({\n",
    "    \"Lucro\": [1200, 1500, 2000, 1800]\n",
    "})\n",
    "\n",
    "# Concatenação horizontal\n",
    "combined_info = pl.concat([sales_info, profit_info], how=\"horizontal\")\n",
    "combined_info\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a14905f8",
   "metadata": {
    "id": "nLpGf_FhwaBo"
   },
   "outputs": [],
   "source": [
    "# DataFrame de metas de entrega\n",
    "delivery_goals = pl.DataFrame({\n",
    "    \"Tipo de Item\": [\"Snacks\", \"Cereal\", \"Office Supplies\"],\n",
    "    \"Dias de envio alvo\": [5, 7, 10]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ed92a79d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 192
    },
    "id": "8z_Ok8Kg0Yez",
    "outputId": "5941d398-4264-49b9-8c9a-6a8657436e04"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (3, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Tipo de Item</th><th>Dias de envio alvo</th></tr><tr><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Snacks&quot;</td><td>5</td></tr><tr><td>&quot;Cereal&quot;</td><td>7</td></tr><tr><td>&quot;Office Supplies&quot;</td><td>10</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (3, 2)\n",
       "┌─────────────────┬────────────────────┐\n",
       "│ Tipo de Item    ┆ Dias de envio alvo │\n",
       "│ ---             ┆ ---                │\n",
       "│ str             ┆ i64                │\n",
       "╞═════════════════╪════════════════════╡\n",
       "│ Snacks          ┆ 5                  │\n",
       "│ Cereal          ┆ 7                  │\n",
       "│ Office Supplies ┆ 10                 │\n",
       "└─────────────────┴────────────────────┘"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivery_goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "defe91a7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 192
    },
    "id": "0a_YEEHz1asw",
    "outputId": "169ba7af-7039-4ffc-9aa1-2c780adb5f22"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (3, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Dias de envio alvo</th></tr><tr><td>i64</td></tr></thead><tbody><tr><td>5</td></tr><tr><td>7</td></tr><tr><td>10</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (3, 1)\n",
       "┌────────────────────┐\n",
       "│ Dias de envio alvo │\n",
       "│ ---                │\n",
       "│ i64                │\n",
       "╞════════════════════╡\n",
       "│ 5                  │\n",
       "│ 7                  │\n",
       "│ 10                 │\n",
       "└────────────────────┘"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecionar apenas a coluna \"Target Shipping Days\" do delivery_goals\n",
    "delivery_goals = delivery_goals.select(\"Dias de envio alvo\")\n",
    "delivery_goals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4cff5a32",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 671
    },
    "id": "B2VCDGeO0Z_U",
    "outputId": "98fb8811-ecce-4029-bdac-fae109a81f9e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (126_597, 20)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th><th>Duração do envio</th><th>Margem de Lucro (%)</th><th>Tipo de venda</th><th>Mês Pedido</th><th>Ano Pedido</th><th>Estação</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>date</td><td>i64</td><td>date</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>duration[ms]</td><td>f64</td><td>str</td><td>i8</td><td>i32</td><td>str</td></tr></thead><tbody><tr><td>&quot;Europe&quot;</td><td>&quot;Vatican City&quot;</td><td>&quot;Office Supplies&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2014-03-20</td><td>975229156</td><td>2014-05-09</td><td>834</td><td>651.21</td><td>524.96</td><td>434487.312</td><td>437816.64</td><td>-3329.328</td><td>50d</td><td>-0.760439</td><td>&quot;Offline&quot;</td><td>3</td><td>2014</td><td>&quot;Spring&quot;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Latvia&quot;</td><td>&quot;Office Supplies&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2017-03-31</td><td>518085038</td><td>2017-05-20</td><td>1731</td><td>651.21</td><td>524.96</td><td>901795.608</td><td>908705.76</td><td>-6910.152</td><td>50d</td><td>-0.760439</td><td>&quot;Offline&quot;</td><td>3</td><td>2017</td><td>&quot;Spring&quot;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Bulgaria&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2016-06-15</td><td>394126212</td><td>2016-08-04</td><td>9559</td><td>152.58</td><td>97.44</td><td>1.1668e6</td><td>931428.96</td><td>235380.816</td><td>50d</td><td>25.270936</td><td>&quot;Online&quot;</td><td>6</td><td>2016</td><td>&quot;Summer&quot;</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;Singapore&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2015-12-08</td><td>609007632</td><td>2016-01-27</td><td>7747</td><td>152.58</td><td>97.44</td><td>945629.808</td><td>754867.68</td><td>190762.128</td><td>50d</td><td>25.270936</td><td>&quot;Online&quot;</td><td>12</td><td>2015</td><td>&quot;Winter&quot;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Belarus&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2011-10-26</td><td>903968656</td><td>2011-12-15</td><td>3268</td><td>152.58</td><td>97.44</td><td>398905.152</td><td>318433.92</td><td>80471.232</td><td>50d</td><td>25.270936</td><td>&quot;Online&quot;</td><td>10</td><td>2011</td><td>&quot;Fall&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;France&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2016-03-21</td><td>194693028</td><td>2016-03-21</td><td>2848</td><td>152.58</td><td>97.44</td><td>347638.272</td><td>277509.12</td><td>70129.152</td><td>0ms</td><td>25.270936</td><td>&quot;Offline&quot;</td><td>3</td><td>2016</td><td>&quot;Spring&quot;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Greece&quot;</td><td>&quot;Office Supplies&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2015-07-02</td><td>810981833</td><td>2015-07-02</td><td>6401</td><td>651.21</td><td>524.96</td><td>3.3347e6</td><td>3.3603e6</td><td>-25552.792</td><td>0ms</td><td>-0.760439</td><td>&quot;Online&quot;</td><td>7</td><td>2015</td><td>&quot;Summer&quot;</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;Philippines&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2013-10-27</td><td>649255788</td><td>2013-10-27</td><td>8070</td><td>152.58</td><td>97.44</td><td>985056.48</td><td>786340.8</td><td>198715.68</td><td>0ms</td><td>25.270936</td><td>&quot;Online&quot;</td><td>10</td><td>2013</td><td>&quot;Fall&quot;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Norway&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2013-07-20</td><td>682427632</td><td>2013-07-20</td><td>4356</td><td>152.58</td><td>97.44</td><td>531710.784</td><td>424448.64</td><td>107262.144</td><td>0ms</td><td>25.270936</td><td>&quot;Offline&quot;</td><td>7</td><td>2013</td><td>&quot;Summer&quot;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Slovenia&quot;</td><td>&quot;Office Supplies&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2010-05-08</td><td>186315953</td><td>2010-05-08</td><td>3886</td><td>651.21</td><td>524.96</td><td>2.0245e6</td><td>2.0400e6</td><td>-15512.912</td><td>0ms</td><td>-0.760439</td><td>&quot;Online&quot;</td><td>5</td><td>2010</td><td>&quot;Spring&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (126_597, 20)\n",
       "┌────────┬──────────────┬──────────┬──────────┬───┬─────────┬────────┬────────────┬─────────┐\n",
       "│ Região ┆ País         ┆ Tipo de  ┆ Canal de ┆ … ┆ Tipo de ┆ Mês    ┆ Ano Pedido ┆ Estação │\n",
       "│ ---    ┆ ---          ┆ Item     ┆ Venda    ┆   ┆ venda   ┆ Pedido ┆ ---        ┆ ---     │\n",
       "│ str    ┆ str          ┆ ---      ┆ ---      ┆   ┆ ---     ┆ ---    ┆ i32        ┆ str     │\n",
       "│        ┆              ┆ str      ┆ str      ┆   ┆ str     ┆ i8     ┆            ┆         │\n",
       "╞════════╪══════════════╪══════════╪══════════╪═══╪═════════╪════════╪════════════╪═════════╡\n",
       "│ Europe ┆ Vatican City ┆ Office   ┆ Offline  ┆ … ┆ Offline ┆ 3      ┆ 2014       ┆ Spring  │\n",
       "│        ┆              ┆ Supplies ┆          ┆   ┆         ┆        ┆            ┆         │\n",
       "│ Europe ┆ Latvia       ┆ Office   ┆ Offline  ┆ … ┆ Offline ┆ 3      ┆ 2017       ┆ Spring  │\n",
       "│        ┆              ┆ Supplies ┆          ┆   ┆         ┆        ┆            ┆         │\n",
       "│ Europe ┆ Bulgaria     ┆ Snacks   ┆ Offline  ┆ … ┆ Online  ┆ 6      ┆ 2016       ┆ Summer  │\n",
       "│ Asia   ┆ Singapore    ┆ Snacks   ┆ Online   ┆ … ┆ Online  ┆ 12     ┆ 2015       ┆ Winter  │\n",
       "│ Europe ┆ Belarus      ┆ Snacks   ┆ Online   ┆ … ┆ Online  ┆ 10     ┆ 2011       ┆ Fall    │\n",
       "│ …      ┆ …            ┆ …        ┆ …        ┆ … ┆ …       ┆ …      ┆ …          ┆ …       │\n",
       "│ Europe ┆ France       ┆ Snacks   ┆ Offline  ┆ … ┆ Offline ┆ 3      ┆ 2016       ┆ Spring  │\n",
       "│ Europe ┆ Greece       ┆ Office   ┆ Offline  ┆ … ┆ Online  ┆ 7      ┆ 2015       ┆ Summer  │\n",
       "│        ┆              ┆ Supplies ┆          ┆   ┆         ┆        ┆            ┆         │\n",
       "│ Asia   ┆ Philippines  ┆ Snacks   ┆ Online   ┆ … ┆ Online  ┆ 10     ┆ 2013       ┆ Fall    │\n",
       "│ Europe ┆ Norway       ┆ Snacks   ┆ Offline  ┆ … ┆ Offline ┆ 7      ┆ 2013       ┆ Summer  │\n",
       "│ Europe ┆ Slovenia     ┆ Office   ┆ Online   ┆ … ┆ Online  ┆ 5      ┆ 2010       ┆ Spring  │\n",
       "│        ┆              ┆ Supplies ┆          ┆   ┆         ┆        ┆            ┆         │\n",
       "└────────┴──────────────┴──────────┴──────────┴───┴─────────┴────────┴────────────┴─────────┘"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtrar dados de itens específicos no dataset principal\n",
    "filtered_items = df_critical.filter(pl.col(\"Tipo de Item\").is_in([\"Snacks\", \"Cereal\", \"Office Supplies\"]))\n",
    "filtered_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "42c03f3e",
   "metadata": {
    "id": "LoOYbokn0d3B"
   },
   "outputs": [],
   "source": [
    "# Concatenar horizontalmente para adicionar as metas de entrega\n",
    "combined_data = pl.concat([filtered_items, delivery_goals], how=\"horizontal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b2511583",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 689
    },
    "id": "8rEKG34C0gVD",
    "outputId": "987d23ca-3694-4aaa-c8d0-976145f26461"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (126_597, 21)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>País</th><th>Tipo de Item</th><th>Canal de Venda</th><th>Prioridade do Pedido</th><th>Data do Pedido</th><th>ID do Pedido</th><th>Data de Envio</th><th>Unidades Vendidas</th><th>Preço Unitário</th><th>Custo Unitário</th><th>Receita Total</th><th>Custo Total</th><th>Lucro Total</th><th>Duração do envio</th><th>Margem de Lucro (%)</th><th>Tipo de venda</th><th>Mês Pedido</th><th>Ano Pedido</th><th>Estação</th><th>Dias de envio alvo</th></tr><tr><td>str</td><td>str</td><td>str</td><td>str</td><td>str</td><td>date</td><td>i64</td><td>date</td><td>i64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>duration[ms]</td><td>f64</td><td>str</td><td>i8</td><td>i32</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Europe&quot;</td><td>&quot;Vatican City&quot;</td><td>&quot;Office Supplies&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2014-03-20</td><td>975229156</td><td>2014-05-09</td><td>834</td><td>651.21</td><td>524.96</td><td>434487.312</td><td>437816.64</td><td>-3329.328</td><td>50d</td><td>-0.760439</td><td>&quot;Offline&quot;</td><td>3</td><td>2014</td><td>&quot;Spring&quot;</td><td>5</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Latvia&quot;</td><td>&quot;Office Supplies&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2017-03-31</td><td>518085038</td><td>2017-05-20</td><td>1731</td><td>651.21</td><td>524.96</td><td>901795.608</td><td>908705.76</td><td>-6910.152</td><td>50d</td><td>-0.760439</td><td>&quot;Offline&quot;</td><td>3</td><td>2017</td><td>&quot;Spring&quot;</td><td>7</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Bulgaria&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2016-06-15</td><td>394126212</td><td>2016-08-04</td><td>9559</td><td>152.58</td><td>97.44</td><td>1.1668e6</td><td>931428.96</td><td>235380.816</td><td>50d</td><td>25.270936</td><td>&quot;Online&quot;</td><td>6</td><td>2016</td><td>&quot;Summer&quot;</td><td>10</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;Singapore&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2015-12-08</td><td>609007632</td><td>2016-01-27</td><td>7747</td><td>152.58</td><td>97.44</td><td>945629.808</td><td>754867.68</td><td>190762.128</td><td>50d</td><td>25.270936</td><td>&quot;Online&quot;</td><td>12</td><td>2015</td><td>&quot;Winter&quot;</td><td>null</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Belarus&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2011-10-26</td><td>903968656</td><td>2011-12-15</td><td>3268</td><td>152.58</td><td>97.44</td><td>398905.152</td><td>318433.92</td><td>80471.232</td><td>50d</td><td>25.270936</td><td>&quot;Online&quot;</td><td>10</td><td>2011</td><td>&quot;Fall&quot;</td><td>null</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;France&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2016-03-21</td><td>194693028</td><td>2016-03-21</td><td>2848</td><td>152.58</td><td>97.44</td><td>347638.272</td><td>277509.12</td><td>70129.152</td><td>0ms</td><td>25.270936</td><td>&quot;Offline&quot;</td><td>3</td><td>2016</td><td>&quot;Spring&quot;</td><td>null</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Greece&quot;</td><td>&quot;Office Supplies&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2015-07-02</td><td>810981833</td><td>2015-07-02</td><td>6401</td><td>651.21</td><td>524.96</td><td>3.3347e6</td><td>3.3603e6</td><td>-25552.792</td><td>0ms</td><td>-0.760439</td><td>&quot;Online&quot;</td><td>7</td><td>2015</td><td>&quot;Summer&quot;</td><td>null</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;Philippines&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2013-10-27</td><td>649255788</td><td>2013-10-27</td><td>8070</td><td>152.58</td><td>97.44</td><td>985056.48</td><td>786340.8</td><td>198715.68</td><td>0ms</td><td>25.270936</td><td>&quot;Online&quot;</td><td>10</td><td>2013</td><td>&quot;Fall&quot;</td><td>null</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Norway&quot;</td><td>&quot;Snacks&quot;</td><td>&quot;Offline&quot;</td><td>&quot;Critical&quot;</td><td>2013-07-20</td><td>682427632</td><td>2013-07-20</td><td>4356</td><td>152.58</td><td>97.44</td><td>531710.784</td><td>424448.64</td><td>107262.144</td><td>0ms</td><td>25.270936</td><td>&quot;Offline&quot;</td><td>7</td><td>2013</td><td>&quot;Summer&quot;</td><td>null</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Slovenia&quot;</td><td>&quot;Office Supplies&quot;</td><td>&quot;Online&quot;</td><td>&quot;Critical&quot;</td><td>2010-05-08</td><td>186315953</td><td>2010-05-08</td><td>3886</td><td>651.21</td><td>524.96</td><td>2.0245e6</td><td>2.0400e6</td><td>-15512.912</td><td>0ms</td><td>-0.760439</td><td>&quot;Online&quot;</td><td>5</td><td>2010</td><td>&quot;Spring&quot;</td><td>null</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (126_597, 21)\n",
       "┌────────┬─────────────┬─────────────┬────────────┬───┬────────┬────────────┬─────────┬────────────┐\n",
       "│ Região ┆ País        ┆ Tipo de     ┆ Canal de   ┆ … ┆ Mês    ┆ Ano Pedido ┆ Estação ┆ Dias de    │\n",
       "│ ---    ┆ ---         ┆ Item        ┆ Venda      ┆   ┆ Pedido ┆ ---        ┆ ---     ┆ envio alvo │\n",
       "│ str    ┆ str         ┆ ---         ┆ ---        ┆   ┆ ---    ┆ i32        ┆ str     ┆ ---        │\n",
       "│        ┆             ┆ str         ┆ str        ┆   ┆ i8     ┆            ┆         ┆ i64        │\n",
       "╞════════╪═════════════╪═════════════╪════════════╪═══╪════════╪════════════╪═════════╪════════════╡\n",
       "│ Europe ┆ Vatican     ┆ Office      ┆ Offline    ┆ … ┆ 3      ┆ 2014       ┆ Spring  ┆ 5          │\n",
       "│        ┆ City        ┆ Supplies    ┆            ┆   ┆        ┆            ┆         ┆            │\n",
       "│ Europe ┆ Latvia      ┆ Office      ┆ Offline    ┆ … ┆ 3      ┆ 2017       ┆ Spring  ┆ 7          │\n",
       "│        ┆             ┆ Supplies    ┆            ┆   ┆        ┆            ┆         ┆            │\n",
       "│ Europe ┆ Bulgaria    ┆ Snacks      ┆ Offline    ┆ … ┆ 6      ┆ 2016       ┆ Summer  ┆ 10         │\n",
       "│ Asia   ┆ Singapore   ┆ Snacks      ┆ Online     ┆ … ┆ 12     ┆ 2015       ┆ Winter  ┆ null       │\n",
       "│ Europe ┆ Belarus     ┆ Snacks      ┆ Online     ┆ … ┆ 10     ┆ 2011       ┆ Fall    ┆ null       │\n",
       "│ …      ┆ …           ┆ …           ┆ …          ┆ … ┆ …      ┆ …          ┆ …       ┆ …          │\n",
       "│ Europe ┆ France      ┆ Snacks      ┆ Offline    ┆ … ┆ 3      ┆ 2016       ┆ Spring  ┆ null       │\n",
       "│ Europe ┆ Greece      ┆ Office      ┆ Offline    ┆ … ┆ 7      ┆ 2015       ┆ Summer  ┆ null       │\n",
       "│        ┆             ┆ Supplies    ┆            ┆   ┆        ┆            ┆         ┆            │\n",
       "│ Asia   ┆ Philippines ┆ Snacks      ┆ Online     ┆ … ┆ 10     ┆ 2013       ┆ Fall    ┆ null       │\n",
       "│ Europe ┆ Norway      ┆ Snacks      ┆ Offline    ┆ … ┆ 7      ┆ 2013       ┆ Summer  ┆ null       │\n",
       "│ Europe ┆ Slovenia    ┆ Office      ┆ Online     ┆ … ┆ 5      ┆ 2010       ┆ Spring  ┆ null       │\n",
       "│        ┆             ┆ Supplies    ┆            ┆   ┆        ┆            ┆         ┆            │\n",
       "└────────┴─────────────┴─────────────┴────────────┴───┴────────┴────────────┴─────────┴────────────┘"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "967045dc",
   "metadata": {
    "id": "HaGuSMci190p"
   },
   "source": [
    "# Aula 4 - Pivoteamento"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3225c6e",
   "metadata": {
    "id": "lnf-v-Ew2Clu"
   },
   "source": [
    "## Vídeo 4.1 - Entendendo o pivoteamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e4fe2a4c",
   "metadata": {
    "id": "f2_erth21fkY"
   },
   "outputs": [],
   "source": [
    "# Criando um DataFrame de exemplo\n",
    "df_varejo = pl.DataFrame({\n",
    "    \"Loja\": [\"Loja A\", \"Loja A\", \"Loja B\", \"Loja B\", \"Loja C\"],\n",
    "    \"Produto\": [\"Camiseta\", \"Calça\", \"Camiseta\", \"Tênis\", \"Tênis\"],\n",
    "    \"Unidades Vendidas\": [10, 5, 8, 12, 7]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9604e8ca",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255
    },
    "id": "9-1ku7kyhYN9",
    "outputId": "c55e1b52-6aca-4469-c63e-5ee1f3825497"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (5, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Loja</th><th>Produto</th><th>Unidades Vendidas</th></tr><tr><td>str</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Loja A&quot;</td><td>&quot;Camiseta&quot;</td><td>10</td></tr><tr><td>&quot;Loja A&quot;</td><td>&quot;Calça&quot;</td><td>5</td></tr><tr><td>&quot;Loja B&quot;</td><td>&quot;Camiseta&quot;</td><td>8</td></tr><tr><td>&quot;Loja B&quot;</td><td>&quot;Tênis&quot;</td><td>12</td></tr><tr><td>&quot;Loja C&quot;</td><td>&quot;Tênis&quot;</td><td>7</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (5, 3)\n",
       "┌────────┬──────────┬───────────────────┐\n",
       "│ Loja   ┆ Produto  ┆ Unidades Vendidas │\n",
       "│ ---    ┆ ---      ┆ ---               │\n",
       "│ str    ┆ str      ┆ i64               │\n",
       "╞════════╪══════════╪═══════════════════╡\n",
       "│ Loja A ┆ Camiseta ┆ 10                │\n",
       "│ Loja A ┆ Calça    ┆ 5                 │\n",
       "│ Loja B ┆ Camiseta ┆ 8                 │\n",
       "│ Loja B ┆ Tênis    ┆ 12                │\n",
       "│ Loja C ┆ Tênis    ┆ 7                 │\n",
       "└────────┴──────────┴───────────────────┘"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_varejo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a94a0375",
   "metadata": {
    "id": "yy3bn_OhjFvs"
   },
   "source": [
    "- index: Coluna que será usada como índice (eixo Y).\n",
    "- values: Coluna cujos valores serão agregados.\n",
    "- columns: Coluna que será transformada em colunas (eixo X).\n",
    "- aggregate_function: Função de agregação (ex.: sum, mean, first, etc.).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "527d82ba",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 227
    },
    "id": "uaKDiiOEhZVf",
    "outputId": "f5ca99bd-51ce-43cd-9634-9ec02ede2583"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-57-92a0a2756cdc>:2: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.\n",
      "  df_pivot_varejo = df_varejo.pivot(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (3, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Loja</th><th>Camiseta</th><th>Calça</th><th>Tênis</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Loja A&quot;</td><td>10</td><td>5</td><td>null</td></tr><tr><td>&quot;Loja B&quot;</td><td>8</td><td>null</td><td>12</td></tr><tr><td>&quot;Loja C&quot;</td><td>null</td><td>null</td><td>7</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (3, 4)\n",
       "┌────────┬──────────┬───────┬───────┐\n",
       "│ Loja   ┆ Camiseta ┆ Calça ┆ Tênis │\n",
       "│ ---    ┆ ---      ┆ ---   ┆ ---   │\n",
       "│ str    ┆ i64      ┆ i64   ┆ i64   │\n",
       "╞════════╪══════════╪═══════╪═══════╡\n",
       "│ Loja A ┆ 10       ┆ 5     ┆ null  │\n",
       "│ Loja B ┆ 8        ┆ null  ┆ 12    │\n",
       "│ Loja C ┆ null     ┆ null  ┆ 7     │\n",
       "└────────┴──────────┴───────┴───────┘"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Aplicando o pivoteamento\n",
    "df_pivot_varejo = df_varejo.pivot(\n",
    "    values=\"Unidades Vendidas\",  # Coluna com os valores que queremos agregar\n",
    "    index=\"Loja\",                # Coluna que será usada como índice (eixo Y)\n",
    "    columns=\"Produto\",           # Coluna que será transformada em colunas (eixo X)\n",
    "    aggregate_function=\"sum\"     # Função de agregação (soma das unidades vendidas)\n",
    ")\n",
    "\n",
    "df_pivot_varejo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c58870c",
   "metadata": {
    "id": "4jmAEy3qkgDv"
   },
   "source": [
    "## Vídeo 4.2 - Aplicando o pivoteamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b63d9163",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 474
    },
    "id": "HCfMnSHrjiDf",
    "outputId": "e3ddb467-9648-4042-9c9d-19e6e49e2706"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-59-441281372eb0>:1: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.\n",
      "  df_pivot_course = df_original.pivot(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (7, 13)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>Office Supplies</th><th>Beverages</th><th>Cereal</th><th>Snacks</th><th>Personal Care</th><th>Cosmetics</th><th>Clothes</th><th>Meat</th><th>Fruits</th><th>Household</th><th>Vegetables</th><th>Baby Food</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Australia and Oceania&quot;</td><td>169241150</td><td>168735088</td><td>168916812</td><td>169223792</td><td>169462841</td><td>169200945</td><td>168288204</td><td>168929484</td><td>168826580</td><td>169086144</td><td>169329989</td><td>168771464</td></tr><tr><td>&quot;Europe&quot;</td><td>540723490</td><td>540977816</td><td>540875183</td><td>540562848</td><td>539538344</td><td>539427410</td><td>540488195</td><td>540411960</td><td>540570520</td><td>539572959</td><td>539142982</td><td>540720962</td></tr><tr><td>&quot;North America&quot;</td><td>44949047</td><td>45416824</td><td>44823153</td><td>44782833</td><td>45231598</td><td>45207313</td><td>44699881</td><td>45176168</td><td>44773692</td><td>45514260</td><td>44805713</td><td>45124320</td></tr><tr><td>&quot;Middle East and North Africa&quot;</td><td>259555391</td><td>259124989</td><td>258351714</td><td>258783348</td><td>259351493</td><td>259024641</td><td>259766368</td><td>259076584</td><td>258490855</td><td>259041413</td><td>259190198</td><td>258142174</td></tr><tr><td>&quot;Sub-Saharan Africa&quot;</td><td>540567381</td><td>540357017</td><td>540731460</td><td>540435025</td><td>540693358</td><td>541222488</td><td>540138802</td><td>541255030</td><td>539103276</td><td>540066894</td><td>541443939</td><td>540841322</td></tr><tr><td>&quot;Asia&quot;</td><td>304569763</td><td>303873891</td><td>304061018</td><td>304464586</td><td>303602484</td><td>304272575</td><td>304268880</td><td>303794194</td><td>304762161</td><td>304159324</td><td>304290192</td><td>304234741</td></tr><tr><td>&quot;Central America and the Caribb…</td><td>225052571</td><td>225150841</td><td>225490518</td><td>225382091</td><td>224363740</td><td>225186647</td><td>225666574</td><td>225481928</td><td>225717563</td><td>226049470</td><td>224660005</td><td>225114426</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (7, 13)\n",
       "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬───────────┬──────────┐\n",
       "│ Região    ┆ Office    ┆ Beverages ┆ Cereal    ┆ … ┆ Fruits    ┆ Household ┆ Vegetable ┆ Baby     │\n",
       "│ ---       ┆ Supplies  ┆ ---       ┆ ---       ┆   ┆ ---       ┆ ---       ┆ s         ┆ Food     │\n",
       "│ str       ┆ ---       ┆ i64       ┆ i64       ┆   ┆ i64       ┆ i64       ┆ ---       ┆ ---      │\n",
       "│           ┆ i64       ┆           ┆           ┆   ┆           ┆           ┆ i64       ┆ i64      │\n",
       "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪═══════════╪══════════╡\n",
       "│ Australia ┆ 169241150 ┆ 168735088 ┆ 168916812 ┆ … ┆ 168826580 ┆ 169086144 ┆ 169329989 ┆ 16877146 │\n",
       "│ and       ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 4        │\n",
       "│ Oceania   ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "│ Europe    ┆ 540723490 ┆ 540977816 ┆ 540875183 ┆ … ┆ 540570520 ┆ 539572959 ┆ 539142982 ┆ 54072096 │\n",
       "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 2        │\n",
       "│ North     ┆ 44949047  ┆ 45416824  ┆ 44823153  ┆ … ┆ 44773692  ┆ 45514260  ┆ 44805713  ┆ 45124320 │\n",
       "│ America   ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "│ Middle    ┆ 259555391 ┆ 259124989 ┆ 258351714 ┆ … ┆ 258490855 ┆ 259041413 ┆ 259190198 ┆ 25814217 │\n",
       "│ East and  ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 4        │\n",
       "│ North     ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "│ Africa    ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "│ Sub-Sahar ┆ 540567381 ┆ 540357017 ┆ 540731460 ┆ … ┆ 539103276 ┆ 540066894 ┆ 541443939 ┆ 54084132 │\n",
       "│ an Africa ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 2        │\n",
       "│ Asia      ┆ 304569763 ┆ 303873891 ┆ 304061018 ┆ … ┆ 304762161 ┆ 304159324 ┆ 304290192 ┆ 30423474 │\n",
       "│           ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 1        │\n",
       "│ Central   ┆ 225052571 ┆ 225150841 ┆ 225490518 ┆ … ┆ 225717563 ┆ 226049470 ┆ 224660005 ┆ 22511442 │\n",
       "│ America   ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆ 6        │\n",
       "│ and the   ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "│ Caribb…   ┆           ┆           ┆           ┆   ┆           ┆           ┆           ┆          │\n",
       "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴───────────┴──────────┘"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot_course = df_original.pivot(\n",
    "    values=\"Unidades Vendidas\",\n",
    "    index=\"Região\",\n",
    "    columns=\"Tipo de Item\",\n",
    "    aggregate_function=\"sum\"\n",
    ")\n",
    "df_pivot_course\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9fb687b9",
   "metadata": {
    "id": "MjSjY1HfoPUd"
   },
   "outputs": [],
   "source": [
    "import matplotlib.cm as cm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f14ba506",
   "metadata": {
    "id": "kjT6q9cpqkRP"
   },
   "outputs": [],
   "source": [
    "# Converter para pandas para facilitar a visualização (se necessário)\n",
    "df_pivot_pd = df_pivot_course.to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f07f7726",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 589
    },
    "id": "jWUt2ag3nCWF",
    "outputId": "6435f65f-5988-4ec1-c29f-6c9676f618e1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Garantir que a coluna 'Region' seja usada como rótulo no eixo X\n",
    "df_pivot_pd = df_pivot_pd.set_index(\"Região\")  # Define a coluna 'Region' como índice\n",
    "\n",
    "# Criar uma paleta de cores personalizada usando um colormap\n",
    "num_items = len(df_pivot_pd.columns)  # Número de categorias (tipos de itens)\n",
    "colors = cm.tab20(np.linspace(0, 1, num_items))  # Usando o colormap \"tab20\"\n",
    "\n",
    "# Criar um gráfico de barras empilhadas\n",
    "ax = df_pivot_pd.plot(\n",
    "    kind=\"bar\",\n",
    "    stacked=True,\n",
    "    figsize=(12, 6),\n",
    "    color=colors,\n",
    "    edgecolor=\"black\"\n",
    ")\n",
    "\n",
    "\n",
    "# Ajustar título e rótulos\n",
    "plt.title(\"Receita Total por Tipo de Item e Região\", fontsize=14)\n",
    "plt.xlabel(\"Região\", fontsize=12)\n",
    "plt.ylabel(\"Receita Total\", fontsize=12)\n",
    "# Rotacionar os rótulos do eixo X\n",
    "plt.xticks(rotation=45, ha=\"right\")\n",
    "# Mover a legenda para a direita do gráfico\n",
    "plt.legend(\n",
    "    title=\"Tipo de Item\",\n",
    "    bbox_to_anchor=(1.05, 1),  # Posição fora do gráfico (à direita)\n",
    "    loc=\"upper left\",          # Localização\n",
    "    fontsize=10\n",
    ")\n",
    "\n",
    "# Ajustar layout para evitar cortes\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e684c4c1",
   "metadata": {
    "id": "2_a35rTWrpxk"
   },
   "source": [
    "## Vídeo 4.3 - Despivotando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "9b0e29da",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "9a63XZTGn1Sz",
    "outputId": "4fbf575f-7886-4df5-d64e-f528eccd30fe"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (35, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Região</th><th>Tipo de Item</th><th>Unidades Vendidas</th></tr><tr><td>str</td><td>str</td><td>i64</td></tr></thead><tbody><tr><td>&quot;Australia and Oceania&quot;</td><td>&quot;Office Supplies&quot;</td><td>169241150</td></tr><tr><td>&quot;Europe&quot;</td><td>&quot;Office Supplies&quot;</td><td>540723490</td></tr><tr><td>&quot;North America&quot;</td><td>&quot;Office Supplies&quot;</td><td>44949047</td></tr><tr><td>&quot;Middle East and North Africa&quot;</td><td>&quot;Office Supplies&quot;</td><td>259555391</td></tr><tr><td>&quot;Sub-Saharan Africa&quot;</td><td>&quot;Office Supplies&quot;</td><td>540567381</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;North America&quot;</td><td>&quot;Personal Care&quot;</td><td>45231598</td></tr><tr><td>&quot;Middle East and North Africa&quot;</td><td>&quot;Personal Care&quot;</td><td>259351493</td></tr><tr><td>&quot;Sub-Saharan Africa&quot;</td><td>&quot;Personal Care&quot;</td><td>540693358</td></tr><tr><td>&quot;Asia&quot;</td><td>&quot;Personal Care&quot;</td><td>303602484</td></tr><tr><td>&quot;Central America and the Caribb…</td><td>&quot;Personal Care&quot;</td><td>224363740</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (35, 3)\n",
       "┌─────────────────────────────────┬─────────────────┬───────────────────┐\n",
       "│ Região                          ┆ Tipo de Item    ┆ Unidades Vendidas │\n",
       "│ ---                             ┆ ---             ┆ ---               │\n",
       "│ str                             ┆ str             ┆ i64               │\n",
       "╞═════════════════════════════════╪═════════════════╪═══════════════════╡\n",
       "│ Australia and Oceania           ┆ Office Supplies ┆ 169241150         │\n",
       "│ Europe                          ┆ Office Supplies ┆ 540723490         │\n",
       "│ North America                   ┆ Office Supplies ┆ 44949047          │\n",
       "│ Middle East and North Africa    ┆ Office Supplies ┆ 259555391         │\n",
       "│ Sub-Saharan Africa              ┆ Office Supplies ┆ 540567381         │\n",
       "│ …                               ┆ …               ┆ …                 │\n",
       "│ North America                   ┆ Personal Care   ┆ 45231598          │\n",
       "│ Middle East and North Africa    ┆ Personal Care   ┆ 259351493         │\n",
       "│ Sub-Saharan Africa              ┆ Personal Care   ┆ 540693358         │\n",
       "│ Asia                            ┆ Personal Care   ┆ 303602484         │\n",
       "│ Central America and the Caribb… ┆ Personal Care   ┆ 224363740         │\n",
       "└─────────────────────────────────┴─────────────────┴───────────────────┘"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unpivot_course = df_pivot_course.unpivot(\n",
    "    [\"Office Supplies\",\"Beverages\", \"Cereal\", \"Snacks\", \"Personal Care\"],  # Colunas para transformar em linhas\n",
    "    index=[\"Região\"],                                      # Coluna que permanece fixa\n",
    "    variable_name=\"Tipo de Item\",                             # Nome da nova coluna para categorias\n",
    "    value_name=\"Unidades Vendidas\"                                # Nome da nova coluna para os valores\n",
    ")\n",
    "df_unpivot_course"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 5
}