agregacao_operacao.ipynb

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  "metadata": {
    "colab": {
      "name": "agregacao_operacao.ipynb",
      "provenance": [],
      "mount_file_id": "1noMOFYF1lTRZYFAbJ6nuspmwh4WiXsIR",
      "authorship_tag": "ABX9TyNZUsAO/aOjc3EbQ3w6pnyo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anicelysantos/book-python-para-analise-de-dados/blob/main/agregacao_operacao.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5jjY432FHnLW"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RfgthO9THGSS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48ebfd6b-8b18-45c2-d815-78603c7672ee"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import statsmodels.api as sm"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxMZ7U44HuFQ"
      },
      "source": [
        "# Funcionamento do GroupBy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "5V4Y2iwIHsaq",
        "outputId": "40d7f750-d30d-4f56-beed-617b3339f66c"
      },
      "source": [
        "df = pd.DataFrame({'key1':['a','a','b','b','a'], 'key':['one','two','one','two','one'], 'data1': np.random.randn(5), 'data2': np.random.randn(5)})\n",
        "df"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>key1</th>\n",
              "      <th>key</th>\n",
              "      <th>data1</th>\n",
              "      <th>data2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a</td>\n",
              "      <td>one</td>\n",
              "      <td>-0.087628</td>\n",
              "      <td>-0.781517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a</td>\n",
              "      <td>two</td>\n",
              "      <td>-1.033337</td>\n",
              "      <td>-1.538188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b</td>\n",
              "      <td>one</td>\n",
              "      <td>-1.680211</td>\n",
              "      <td>-0.884163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b</td>\n",
              "      <td>two</td>\n",
              "      <td>-1.965759</td>\n",
              "      <td>-0.567086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a</td>\n",
              "      <td>one</td>\n",
              "      <td>-0.328473</td>\n",
              "      <td>-0.429713</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  key1  key     data1     data2\n",
              "0    a  one -0.087628 -0.781517\n",
              "1    a  two -1.033337 -1.538188\n",
              "2    b  one -1.680211 -0.884163\n",
              "3    b  two -1.965759 -0.567086\n",
              "4    a  one -0.328473 -0.429713"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-zKLOgxIy-i",
        "outputId": "a325e47f-7269-47bf-d8ae-8b85dd4630f9"
      },
      "source": [
        "grouped = df['data1'].groupby(df['key1'])\n",
        "grouped"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.SeriesGroupBy object at 0x7fa9bb7d6f50>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_oV0DEhCJLLw",
        "outputId": "32f924d2-5ab9-48f5-efbf-ec6bd1fac071"
      },
      "source": [
        "grouped.mean()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key1\n",
              "a   -0.483146\n",
              "b   -1.822985\n",
              "Name: data1, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8qNfqWaJUYo",
        "outputId": "5f873ce6-2e16-4691-82b6-e66d415222ba"
      },
      "source": [
        "means = df['data1'].groupby([df['key1'], df['key']]).mean()\n",
        "means"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key1  key\n",
              "a     one   -0.208050\n",
              "      two   -1.033337\n",
              "b     one   -1.680211\n",
              "      two   -1.965759\n",
              "Name: data1, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
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          "height": 0
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        "id": "VGn2FL5UJnTI",
        "outputId": "27444d7d-61f7-457d-a41b-f6042c4ef68d"
      },
      "source": [
        "means.unstack()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>key1</th>\n",
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              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>-0.208050</td>\n",
              "      <td>-1.033337</td>\n",
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              "    <tr>\n",
              "      <th>b</th>\n",
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            ],
            "text/plain": [
              "key        one       two\n",
              "key1                    \n",
              "a    -0.208050 -1.033337\n",
              "b    -1.680211 -1.965759"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "id": "NyP9_3lgKJI3",
        "outputId": "8f99093d-fff9-4ef1-e291-da59a0f3f4dc"
      },
      "source": [
        "states = np.array(['Ohio', 'California', 'California', 'Ohio', 'Ohio'])\n",
        "years = np.array([2005, 2005, 2006, 2005, 2006])\n",
        "df['data1'].groupby([states, years]).mean()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "California  2005   -1.033337\n",
              "            2006   -1.680211\n",
              "Ohio        2005   -1.026693\n",
              "            2006   -0.328473\n",
              "Name: data1, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
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      "metadata": {
        "colab": {
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          "height": 0
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        "id": "qqmv_S46K6Wq",
        "outputId": "e4073618-0372-4ea3-a46b-7b32e5d7a927"
      },
      "source": [
        "df.groupby('key1').mean()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>key1</th>\n",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>-0.483146</td>\n",
              "      <td>-0.916473</td>\n",
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              "    <tr>\n",
              "      <th>b</th>\n",
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            ],
            "text/plain": [
              "         data1     data2\n",
              "key1                    \n",
              "a    -0.483146 -0.916473\n",
              "b    -1.822985 -0.725624"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xGIKLGxeLPA_",
        "outputId": "5847540d-3420-4710-e095-28f652b6a440"
      },
      "source": [
        "df.groupby(['key1', 'key']).mean()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>data1</th>\n",
              "      <th>data2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>key1</th>\n",
              "      <th>key</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
              "      <th>one</th>\n",
              "      <td>-0.208050</td>\n",
              "      <td>-0.605615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>-1.033337</td>\n",
              "      <td>-1.538188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
              "      <th>one</th>\n",
              "      <td>-1.680211</td>\n",
              "      <td>-0.884163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>-1.965759</td>\n",
              "      <td>-0.567086</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             data1     data2\n",
              "key1 key                    \n",
              "a    one -0.208050 -0.605615\n",
              "     two -1.033337 -1.538188\n",
              "b    one -1.680211 -0.884163\n",
              "     two -1.965759 -0.567086"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hUxXkjRoLa-o",
        "outputId": "c484fe48-f5ef-4db2-a0bd-a288272bb0ce"
      },
      "source": [
        "df.groupby(['key1', 'key']).size()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key1  key\n",
              "a     one    2\n",
              "      two    1\n",
              "b     one    1\n",
              "      two    1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8IstEpqZMFgE"
      },
      "source": [
        "**Iterando por grupos**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UdwDqUJsL_Qf",
        "outputId": "0f27877b-2d34-49f0-b19a-844e8477d374"
      },
      "source": [
        "for nome, grupo in df.groupby('key1'):\n",
        "  print(nome)\n",
        "  print(grupo)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a\n",
            "  key1  key     data1     data2\n",
            "0    a  one -0.087628 -0.781517\n",
            "1    a  two -1.033337 -1.538188\n",
            "4    a  one -0.328473 -0.429713\n",
            "b\n",
            "  key1  key     data1     data2\n",
            "2    b  one -1.680211 -0.884163\n",
            "3    b  two -1.965759 -0.567086\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-n1UWr8MaM6",
        "outputId": "2f951c24-bb21-46bf-faa2-14b7e63ff8c1"
      },
      "source": [
        "for (k1,k2), grupo in df.groupby(['key1','key']):\n",
        "  print((k1,k2))\n",
        "  print(grupo)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('a', 'one')\n",
            "  key1  key     data1     data2\n",
            "0    a  one -0.087628 -0.781517\n",
            "4    a  one -0.328473 -0.429713\n",
            "('a', 'two')\n",
            "  key1  key     data1     data2\n",
            "1    a  two -1.033337 -1.538188\n",
            "('b', 'one')\n",
            "  key1  key     data1     data2\n",
            "2    b  one -1.680211 -0.884163\n",
            "('b', 'two')\n",
            "  key1  key     data1     data2\n",
            "3    b  two -1.965759 -0.567086\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "QQ21uSpNMtEH",
        "outputId": "4fb108d0-5e43-4be8-c3ba-e9349bf1c141"
      },
      "source": [
        "#gerando dicionario com uma parte dos dados\n",
        "\n",
        "parte = dict(list(df.groupby('key1')))\n",
        "parte['b']"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>key1</th>\n",
              "      <th>key</th>\n",
              "      <th>data1</th>\n",
              "      <th>data2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b</td>\n",
              "      <td>one</td>\n",
              "      <td>-1.680211</td>\n",
              "      <td>-0.884163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b</td>\n",
              "      <td>two</td>\n",
              "      <td>-1.965759</td>\n",
              "      <td>-0.567086</td>\n",
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            ],
            "text/plain": [
              "  key1  key     data1     data2\n",
              "2    b  one -1.680211 -0.884163\n",
              "3    b  two -1.965759 -0.567086"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jk-lq2umNIzu",
        "outputId": "19c6532b-db3e-4027-b3c8-9a41b02e5274"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key1      object\n",
              "key       object\n",
              "data1    float64\n",
              "data2    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9nFWZy8qNV_f",
        "outputId": "59e164a0-7fa7-4894-b345-9da965a3ed79"
      },
      "source": [
        "grouped = df.groupby(df.dtypes, axis=1)\n",
        "for dtype, grupo in grouped:\n",
        "  print(dtype)\n",
        "  print(grupo)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "float64\n",
            "      data1     data2\n",
            "0 -0.087628 -0.781517\n",
            "1 -1.033337 -1.538188\n",
            "2 -1.680211 -0.884163\n",
            "3 -1.965759 -0.567086\n",
            "4 -0.328473 -0.429713\n",
            "object\n",
            "  key1  key\n",
            "0    a  one\n",
            "1    a  two\n",
            "2    b  one\n",
            "3    b  two\n",
            "4    a  one\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "koShy4SKN6Nw"
      },
      "source": [
        "**Selecionando uma coluna ou um subconjunto de colunas**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XF-CzIYtNkg3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "bf92ef17-f8de-4ec6-f468-3bd608072a74"
      },
      "source": [
        "df.groupby(['key1', 'key'])[['data2']].mean()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>data2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>key1</th>\n",
              "      <th>key</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
              "      <th>one</th>\n",
              "      <td>-0.605615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>-1.538188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
              "      <th>one</th>\n",
              "      <td>-0.884163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>-0.567086</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             data2\n",
              "key1 key          \n",
              "a    one -0.605615\n",
              "     two -1.538188\n",
              "b    one -0.884163\n",
              "     two -0.567086"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "id3BVx0QQU5t",
        "outputId": "02977dfd-b15d-4eca-e948-c7619411210c"
      },
      "source": [
        "s_grouped = df.groupby(['key1', 'key'])['data2']\n",
        "s_grouped"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.SeriesGroupBy object at 0x7fa9bb7b9d10>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4TJ1_RZ7Ql3c",
        "outputId": "e91941ec-23fc-41ea-f430-33d459761e02"
      },
      "source": [
        "s_grouped.mean()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key1  key\n",
              "a     one   -0.605615\n",
              "      two   -1.538188\n",
              "b     one   -0.884163\n",
              "      two   -0.567086\n",
              "Name: data2, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h7DK7KuzQt7V"
      },
      "source": [
        "**Agrupando com dicionário e séries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PF4c-i9jQpK8"
      },
      "source": [
        "people = pd.DataFrame(np.random.randn(5,5), columns=['a','b','c','d','e'], index=['Joe', 'Steve','Wes','Jim','Travis'])"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "duwWBJ7YSyG1",
        "outputId": "6790a60e-1e2c-40f9-b521-3a92f2d37859"
      },
      "source": [
        "people.iloc[2:3, [1,2]] = np.nan\n",
        "people"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>e</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Joe</th>\n",
              "      <td>-0.938876</td>\n",
              "      <td>0.291435</td>\n",
              "      <td>1.262568</td>\n",
              "      <td>0.360246</td>\n",
              "      <td>-0.875104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Steve</th>\n",
              "      <td>0.646470</td>\n",
              "      <td>0.515857</td>\n",
              "      <td>-0.843998</td>\n",
              "      <td>1.431758</td>\n",
              "      <td>-0.717924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wes</th>\n",
              "      <td>-1.114920</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-1.846784</td>\n",
              "      <td>0.113512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jim</th>\n",
              "      <td>-0.271455</td>\n",
              "      <td>-0.350433</td>\n",
              "      <td>-1.612754</td>\n",
              "      <td>0.308810</td>\n",
              "      <td>1.113253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Travis</th>\n",
              "      <td>0.253249</td>\n",
              "      <td>0.810306</td>\n",
              "      <td>-1.319260</td>\n",
              "      <td>-1.248249</td>\n",
              "      <td>1.589779</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               a         b         c         d         e\n",
              "Joe    -0.938876  0.291435  1.262568  0.360246 -0.875104\n",
              "Steve   0.646470  0.515857 -0.843998  1.431758 -0.717924\n",
              "Wes    -1.114920       NaN       NaN -1.846784  0.113512\n",
              "Jim    -0.271455 -0.350433 -1.612754  0.308810  1.113253\n",
              "Travis  0.253249  0.810306 -1.319260 -1.248249  1.589779"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhBC6xW7S-gl"
      },
      "source": [
        "#somar as colunas por grupo\n",
        "mapping = {'a': 'red', 'b':'red', 'c':'blue', 'd':'blue','e':'red','f':'orange'}"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "PIs_AYBcTb7M",
        "outputId": "58f5efd5-f7af-465c-d919-5f1c960b1cf5"
      },
      "source": [
        "by_columns = people.groupby(mapping, axis=1)\n",
        "by_columns.sum()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>blue</th>\n",
              "      <th>red</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Joe</th>\n",
              "      <td>1.622813</td>\n",
              "      <td>-1.522545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Steve</th>\n",
              "      <td>0.587759</td>\n",
              "      <td>0.444403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wes</th>\n",
              "      <td>-1.846784</td>\n",
              "      <td>-1.001408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jim</th>\n",
              "      <td>-1.303944</td>\n",
              "      <td>0.491365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Travis</th>\n",
              "      <td>-2.567509</td>\n",
              "      <td>2.653334</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            blue       red\n",
              "Joe     1.622813 -1.522545\n",
              "Steve   0.587759  0.444403\n",
              "Wes    -1.846784 -1.001408\n",
              "Jim    -1.303944  0.491365\n",
              "Travis -2.567509  2.653334"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZwoCWBWJTsAl",
        "outputId": "317cc964-2b78-49d6-c2f4-20bd5b397521"
      },
      "source": [
        "map_series = pd.Series(mapping)\n",
        "map_series"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a       red\n",
              "b       red\n",
              "c      blue\n",
              "d      blue\n",
              "e       red\n",
              "f    orange\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "yVbFIU0tT9C9",
        "outputId": "3ccddaf9-4f62-4093-a3cb-e20208f61af9"
      },
      "source": [
        "people.groupby(map_series, axis=1).count()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>blue</th>\n",
              "      <th>red</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Joe</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Steve</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wes</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jim</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Travis</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        blue  red\n",
              "Joe        2    3\n",
              "Steve      2    3\n",
              "Wes        1    2\n",
              "Jim        2    3\n",
              "Travis     2    3"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NL_UdJdCU6gL"
      },
      "source": [
        "**Agrupando com funções**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "7K2VloOaUGFH",
        "outputId": "260fb03b-71a8-461f-f431-fefd8d01ef88"
      },
      "source": [
        "#agrupando por tamanho de string (contando string do índice)\n",
        "people.groupby(len).sum()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>e</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-2.325251</td>\n",
              "      <td>-0.058998</td>\n",
              "      <td>-0.350186</td>\n",
              "      <td>-1.177729</td>\n",
              "      <td>0.351662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.646470</td>\n",
              "      <td>0.515857</td>\n",
              "      <td>-0.843998</td>\n",
              "      <td>1.431758</td>\n",
              "      <td>-0.717924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.253249</td>\n",
              "      <td>0.810306</td>\n",
              "      <td>-1.319260</td>\n",
              "      <td>-1.248249</td>\n",
              "      <td>1.589779</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          a         b         c         d         e\n",
              "3 -2.325251 -0.058998 -0.350186 -1.177729  0.351662\n",
              "5  0.646470  0.515857 -0.843998  1.431758 -0.717924\n",
              "6  0.253249  0.810306 -1.319260 -1.248249  1.589779"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "_uaIKzbnVQhl",
        "outputId": "1a891759-8f69-41c0-a0ea-8615b970d21b"
      },
      "source": [
        "key_list = ['one', 'one', 'one','two','two']\n",
        "people.groupby([len, key_list]).min()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>e</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
              "      <th>one</th>\n",
              "      <td>-1.114920</td>\n",
              "      <td>0.291435</td>\n",
              "      <td>1.262568</td>\n",
              "      <td>-1.846784</td>\n",
              "      <td>-0.875104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>two</th>\n",
              "      <td>-0.271455</td>\n",
              "      <td>-0.350433</td>\n",
              "      <td>-1.612754</td>\n",
              "      <td>0.308810</td>\n",
              "      <td>1.113253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <th>one</th>\n",
              "      <td>0.646470</td>\n",
              "      <td>0.515857</td>\n",
              "      <td>-0.843998</td>\n",
              "      <td>1.431758</td>\n",
              "      <td>-0.717924</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <th>two</th>\n",
              "      <td>0.253249</td>\n",
              "      <td>0.810306</td>\n",
              "      <td>-1.319260</td>\n",
              "      <td>-1.248249</td>\n",
              "      <td>1.589779</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              a         b         c         d         e\n",
              "3 one -1.114920  0.291435  1.262568 -1.846784 -0.875104\n",
              "  two -0.271455 -0.350433 -1.612754  0.308810  1.113253\n",
              "5 one  0.646470  0.515857 -0.843998  1.431758 -0.717924\n",
              "6 two  0.253249  0.810306 -1.319260 -1.248249  1.589779"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WXXahtgV3wc"
      },
      "source": [
        "**Agrupando por níveis de índice**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "bwpSHlgiVnrM",
        "outputId": "1653d4e5-77fb-49d7-8a48-a6a110ef17c1"
      },
      "source": [
        "columns = pd.MultiIndex.from_arrays([['US','US','US','JP','JP'], [1,3,5,1,3]], names=['cty','tenor'])\n",
        "hier_df = pd.DataFrame(np.random.randn(4,5), columns=columns)\n",
        "hier_df"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>cty</th>\n",
              "      <th colspan=\"3\" halign=\"left\">US</th>\n",
              "      <th colspan=\"2\" halign=\"left\">JP</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tenor</th>\n",
              "      <th>1</th>\n",
              "      <th>3</th>\n",
              "      <th>5</th>\n",
              "      <th>1</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.812185</td>\n",
              "      <td>-0.076178</td>\n",
              "      <td>0.530321</td>\n",
              "      <td>0.798239</td>\n",
              "      <td>-0.775088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.221232</td>\n",
              "      <td>0.494571</td>\n",
              "      <td>0.377136</td>\n",
              "      <td>-0.240049</td>\n",
              "      <td>0.396134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.194545</td>\n",
              "      <td>0.935806</td>\n",
              "      <td>0.350637</td>\n",
              "      <td>0.627208</td>\n",
              "      <td>-1.401658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.152571</td>\n",
              "      <td>0.676929</td>\n",
              "      <td>-0.575866</td>\n",
              "      <td>-0.126042</td>\n",
              "      <td>0.808398</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "cty          US                            JP          \n",
              "tenor         1         3         5         1         3\n",
              "0      2.812185 -0.076178  0.530321  0.798239 -0.775088\n",
              "1      1.221232  0.494571  0.377136 -0.240049  0.396134\n",
              "2      0.194545  0.935806  0.350637  0.627208 -1.401658\n",
              "3      0.152571  0.676929 -0.575866 -0.126042  0.808398"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "gnlnKEPrWao1",
        "outputId": "aa1b97d4-e306-4250-a4f1-d3064cab0d58"
      },
      "source": [
        "hier_df.groupby(level = 'cty', axis=1).count()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>cty</th>\n",
              "      <th>JP</th>\n",
              "      <th>US</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "cty  JP  US\n",
              "0     2   3\n",
              "1     2   3\n",
              "2     2   3\n",
              "3     2   3"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVeL875yXEw9"
      },
      "source": [
        "# Agregação de dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TpW8-G_8W4rk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "048dc611-9e88-48f5-c9b0-2390460105ed"
      },
      "source": [
        "grouped = df.groupby('key1')\n",
        "grouped['data1'].quantile(0.9)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "key1\n",
              "a   -0.135797\n",
              "b   -1.708766\n",
              "Name: data1, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xEpN31tgKLRQ",
        "outputId": "b42530f7-f8db-47c3-9d7e-2944a6d13a68"
      },
      "source": [
        "#Para usar uma função própria, utilize agg e passe a função entre parenteses\n",
        "def peak_to_peak(arr):\n",
        "  return arr.max() - arr.min()\n",
        "grouped.agg(peak_to_peak)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>data1</th>\n",
              "      <th>data2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>key1</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>0.945709</td>\n",
              "      <td>1.108475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>0.285548</td>\n",
              "      <td>0.317078</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         data1     data2\n",
              "key1                    \n",
              "a     0.945709  1.108475\n",
              "b     0.285548  0.317078"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "B5MnIhFPKunE",
        "outputId": "1d2943fc-c11d-4a5b-c296-012c01e6c5f4"
      },
      "source": [
        "grouped.describe()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">data1</th>\n",
              "      <th colspan=\"8\" halign=\"left\">data2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>key1</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>3.0</td>\n",
              "      <td>-0.483146</td>\n",
              "      <td>0.491461</td>\n",
              "      <td>-1.033337</td>\n",
              "      <td>-0.680905</td>\n",
              "      <td>-0.328473</td>\n",
              "      <td>-0.208050</td>\n",
              "      <td>-0.087628</td>\n",
              "      <td>3.0</td>\n",
              "      <td>-0.916473</td>\n",
              "      <td>0.566426</td>\n",
              "      <td>-1.538188</td>\n",
              "      <td>-1.159852</td>\n",
              "      <td>-0.781517</td>\n",
              "      <td>-0.605615</td>\n",
              "      <td>-0.429713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.822985</td>\n",
              "      <td>0.201913</td>\n",
              "      <td>-1.965759</td>\n",
              "      <td>-1.894372</td>\n",
              "      <td>-1.822985</td>\n",
              "      <td>-1.751598</td>\n",
              "      <td>-1.680211</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.725624</td>\n",
              "      <td>0.224208</td>\n",
              "      <td>-0.884163</td>\n",
              "      <td>-0.804894</td>\n",
              "      <td>-0.725624</td>\n",
              "      <td>-0.646355</td>\n",
              "      <td>-0.567086</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     data1                      ...     data2                    \n",
              "     count      mean       std  ...       50%       75%       max\n",
              "key1                            ...                              \n",
              "a      3.0 -0.483146  0.491461  ... -0.781517 -0.605615 -0.429713\n",
              "b      2.0 -1.822985  0.201913  ... -0.725624 -0.646355 -0.567086\n",
              "\n",
              "[2 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q8mdGzScLY9h"
      },
      "source": [
        "**Aplicação de função nas colunas e aplicação de várias funções**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXDSwvNvLQZW"
      },
      "source": [
        "tips = pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/tips.csv')"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dGXF-nYxL2WT",
        "outputId": "1bc91b07-1b33-4bc7-aa15-c5251dfd919f"
      },
      "source": [
        "#Acrescenta a porcentagem de gorjeta sobre o total da conta\n",
        "tips['tip_pct'] = tips['tip'] / tips['total_bill']\n",
        "tips[:6]"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "      <th>tip_pct</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.059447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "      <td>0.160542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "      <td>0.166587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.139780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "      <td>0.146808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>25.29</td>\n",
              "      <td>4.71</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "      <td>0.186240</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   total_bill   tip smoker  day    time  size   tip_pct\n",
              "0       16.99  1.01     No  Sun  Dinner     2  0.059447\n",
              "1       10.34  1.66     No  Sun  Dinner     3  0.160542\n",
              "2       21.01  3.50     No  Sun  Dinner     3  0.166587\n",
              "3       23.68  3.31     No  Sun  Dinner     2  0.139780\n",
              "4       24.59  3.61     No  Sun  Dinner     4  0.146808\n",
              "5       25.29  4.71     No  Sun  Dinner     4  0.186240"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HrEglL-MFNv",
        "outputId": "0a87df6b-fa77-4fb8-b491-ce10f945c69a"
      },
      "source": [
        "grouped = tips.groupby(['day','smoker'])\n",
        "grouped_pct = grouped['tip_pct']\n",
        "#É possivel passar uma função descritiva como string\n",
        "grouped_pct.agg('mean')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "day   smoker\n",
              "Fri   No        0.151650\n",
              "      Yes       0.174783\n",
              "Sat   No        0.158048\n",
              "      Yes       0.147906\n",
              "Sun   No        0.160113\n",
              "      Yes       0.187250\n",
              "Thur  No        0.160298\n",
              "      Yes       0.163863\n",
              "Name: tip_pct, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "mfObzeigMoVT",
        "outputId": "f612c852-eade-46e0-fe97-9f9b07f42ea7"
      },
      "source": [
        "grouped_pct.agg(['mean','std', peak_to_peak])"
      ],
      "execution_count": 35,
      "outputs": [
        {
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              "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
              "      <th>No</th>\n",
              "      <td>0.151650</td>\n",
              "      <td>0.028123</td>\n",
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              "      <td>0.174783</td>\n",
              "      <td>0.051293</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
              "      <th>No</th>\n",
              "      <td>0.158048</td>\n",
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              "      <td>0.061375</td>\n",
              "      <td>0.290095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
              "      <th>No</th>\n",
              "      <td>0.160113</td>\n",
              "      <td>0.042347</td>\n",
              "      <td>0.193226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.187250</td>\n",
              "      <td>0.154134</td>\n",
              "      <td>0.644685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
              "      <th>No</th>\n",
              "      <td>0.160298</td>\n",
              "      <td>0.038774</td>\n",
              "      <td>0.193350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.163863</td>\n",
              "      <td>0.039389</td>\n",
              "      <td>0.151240</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 mean       std  peak_to_peak\n",
              "day  smoker                                  \n",
              "Fri  No      0.151650  0.028123      0.067349\n",
              "     Yes     0.174783  0.051293      0.159925\n",
              "Sat  No      0.158048  0.039767      0.235193\n",
              "     Yes     0.147906  0.061375      0.290095\n",
              "Sun  No      0.160113  0.042347      0.193226\n",
              "     Yes     0.187250  0.154134      0.644685\n",
              "Thur No      0.160298  0.038774      0.193350\n",
              "     Yes     0.163863  0.039389      0.151240"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "PVotR-vCNvTo",
        "outputId": "f56cb43a-b278-4b56-abe9-da72f222a452"
      },
      "source": [
        "grouped_pct.agg([('foo', 'mean'), ('bar', np.std)])"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <tbody>\n",
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              "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
              "      <th>No</th>\n",
              "      <td>0.151650</td>\n",
              "      <td>0.028123</td>\n",
              "    </tr>\n",
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              "      <th>Yes</th>\n",
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              "      <td>0.051293</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
              "      <th>No</th>\n",
              "      <td>0.158048</td>\n",
              "      <td>0.039767</td>\n",
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              "      <th>Yes</th>\n",
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              "      <td>0.061375</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
              "      <th>No</th>\n",
              "      <td>0.160113</td>\n",
              "      <td>0.042347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.187250</td>\n",
              "      <td>0.154134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
              "      <th>No</th>\n",
              "      <td>0.160298</td>\n",
              "      <td>0.038774</td>\n",
              "    </tr>\n",
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              "      <th>Yes</th>\n",
              "      <td>0.163863</td>\n",
              "      <td>0.039389</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  foo       bar\n",
              "day  smoker                    \n",
              "Fri  No      0.151650  0.028123\n",
              "     Yes     0.174783  0.051293\n",
              "Sat  No      0.158048  0.039767\n",
              "     Yes     0.147906  0.061375\n",
              "Sun  No      0.160113  0.042347\n",
              "     Yes     0.187250  0.154134\n",
              "Thur No      0.160298  0.038774\n",
              "     Yes     0.163863  0.039389"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "X8q43jNCOq7N",
        "outputId": "1a25cb74-c208-4212-e921-01490f020d65"
      },
      "source": [
        "functions = ['count', 'mean', 'max']\n",
        "result = grouped['tip_pct', 'total_bill'].agg(functions)\n",
        "result"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th colspan=\"3\" halign=\"left\">total_bill</th>\n",
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              "      <td>4</td>\n",
              "      <td>0.151650</td>\n",
              "      <td>0.187735</td>\n",
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              "      <td>18.420000</td>\n",
              "      <td>22.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>15</td>\n",
              "      <td>0.174783</td>\n",
              "      <td>0.263480</td>\n",
              "      <td>15</td>\n",
              "      <td>16.813333</td>\n",
              "      <td>40.17</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
              "      <th>No</th>\n",
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              "      <td>0.291990</td>\n",
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              "      <td>19.661778</td>\n",
              "      <td>48.33</td>\n",
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              "      <th>Yes</th>\n",
              "      <td>42</td>\n",
              "      <td>0.147906</td>\n",
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              "      <td>21.276667</td>\n",
              "      <td>50.81</td>\n",
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              "      <th>No</th>\n",
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              "      <td>45.35</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
              "      <th>No</th>\n",
              "      <td>45</td>\n",
              "      <td>0.160298</td>\n",
              "      <td>0.266312</td>\n",
              "      <td>45</td>\n",
              "      <td>17.113111</td>\n",
              "      <td>41.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>17</td>\n",
              "      <td>0.163863</td>\n",
              "      <td>0.241255</td>\n",
              "      <td>17</td>\n",
              "      <td>19.190588</td>\n",
              "      <td>43.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            tip_pct                     total_bill                  \n",
              "              count      mean       max      count       mean    max\n",
              "day  smoker                                                         \n",
              "Fri  No           4  0.151650  0.187735          4  18.420000  22.75\n",
              "     Yes         15  0.174783  0.263480         15  16.813333  40.17\n",
              "Sat  No          45  0.158048  0.291990         45  19.661778  48.33\n",
              "     Yes         42  0.147906  0.325733         42  21.276667  50.81\n",
              "Sun  No          57  0.160113  0.252672         57  20.506667  48.17\n",
              "     Yes         19  0.187250  0.710345         19  24.120000  45.35\n",
              "Thur No          45  0.160298  0.266312         45  17.113111  41.19\n",
              "     Yes         17  0.163863  0.241255         17  19.190588  43.11"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "y3i0qFj1PDy8",
        "outputId": "c405e4a3-9be9-4c8d-c26e-64b7a41df8a1"
      },
      "source": [
        "result['tip_pct']"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
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              "      <td>0.252672</td>\n",
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              "      <th>Yes</th>\n",
              "      <td>19</td>\n",
              "      <td>0.187250</td>\n",
              "      <td>0.710345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
              "      <th>No</th>\n",
              "      <td>45</td>\n",
              "      <td>0.160298</td>\n",
              "      <td>0.266312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>17</td>\n",
              "      <td>0.163863</td>\n",
              "      <td>0.241255</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count      mean       max\n",
              "day  smoker                           \n",
              "Fri  No          4  0.151650  0.187735\n",
              "     Yes        15  0.174783  0.263480\n",
              "Sat  No         45  0.158048  0.291990\n",
              "     Yes        42  0.147906  0.325733\n",
              "Sun  No         57  0.160113  0.252672\n",
              "     Yes        19  0.187250  0.710345\n",
              "Thur No         45  0.160298  0.266312\n",
              "     Yes        17  0.163863  0.241255"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "HqPL0Hd8POYR",
        "outputId": "2764e8ec-6559-4d82-f4d2-312ed992310b"
      },
      "source": [
        "#personalizar o nome das colunas\n",
        "ftuples = [('Durchschnitt', 'mean'), ('Abweichung', np.var)]\n",
        "grouped['tip_pct', 'total_bill'].agg(ftuples)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
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              "      <td>16.813333</td>\n",
              "      <td>82.562438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
              "      <th>No</th>\n",
              "      <td>0.158048</td>\n",
              "      <td>0.001581</td>\n",
              "      <td>19.661778</td>\n",
              "      <td>79.908965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.147906</td>\n",
              "      <td>0.003767</td>\n",
              "      <td>21.276667</td>\n",
              "      <td>101.387535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
              "      <th>No</th>\n",
              "      <td>0.160113</td>\n",
              "      <td>0.001793</td>\n",
              "      <td>20.506667</td>\n",
              "      <td>66.099980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.187250</td>\n",
              "      <td>0.023757</td>\n",
              "      <td>24.120000</td>\n",
              "      <td>109.046044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
              "      <th>No</th>\n",
              "      <td>0.160298</td>\n",
              "      <td>0.001503</td>\n",
              "      <td>17.113111</td>\n",
              "      <td>59.625081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.163863</td>\n",
              "      <td>0.001551</td>\n",
              "      <td>19.190588</td>\n",
              "      <td>69.808518</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 tip_pct              total_bill            \n",
              "            Durchschnitt Abweichung Durchschnitt  Abweichung\n",
              "day  smoker                                                 \n",
              "Fri  No         0.151650   0.000791    18.420000   25.596333\n",
              "     Yes        0.174783   0.002631    16.813333   82.562438\n",
              "Sat  No         0.158048   0.001581    19.661778   79.908965\n",
              "     Yes        0.147906   0.003767    21.276667  101.387535\n",
              "Sun  No         0.160113   0.001793    20.506667   66.099980\n",
              "     Yes        0.187250   0.023757    24.120000  109.046044\n",
              "Thur No         0.160298   0.001503    17.113111   59.625081\n",
              "     Yes        0.163863   0.001551    19.190588   69.808518"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "3he26al5Pk9E",
        "outputId": "c5aef0dc-9832-48ae-c550-969ebacbf013"
      },
      "source": [
        "grouped.agg({'tip': np.max, 'size':'sum'})"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>tip</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <th>smoker</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
              "      <th>No</th>\n",
              "      <td>3.50</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>4.73</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
              "      <th>No</th>\n",
              "      <td>9.00</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>10.00</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
              "      <th>No</th>\n",
              "      <td>6.00</td>\n",
              "      <td>167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>6.50</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
              "      <th>No</th>\n",
              "      <td>6.70</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>5.00</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               tip  size\n",
              "day  smoker             \n",
              "Fri  No       3.50     9\n",
              "     Yes      4.73    31\n",
              "Sat  No       9.00   115\n",
              "     Yes     10.00   104\n",
              "Sun  No       6.00   167\n",
              "     Yes      6.50    49\n",
              "Thur No       6.70   112\n",
              "     Yes      5.00    40"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "fMSJfX24P-U5",
        "outputId": "916b7b2d-c84c-4de7-d7a6-48550c295201"
      },
      "source": [
        "grouped.agg({'tip_pct':['min','max','mean','std'], 'size': 'sum'})"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead tr th {\n",
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              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"4\" halign=\"left\">tip_pct</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>sum</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <th>smoker</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
              "      <th>No</th>\n",
              "      <td>0.120385</td>\n",
              "      <td>0.187735</td>\n",
              "      <td>0.151650</td>\n",
              "      <td>0.028123</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.103555</td>\n",
              "      <td>0.263480</td>\n",
              "      <td>0.174783</td>\n",
              "      <td>0.051293</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
              "      <th>No</th>\n",
              "      <td>0.056797</td>\n",
              "      <td>0.291990</td>\n",
              "      <td>0.158048</td>\n",
              "      <td>0.039767</td>\n",
              "      <td>115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.035638</td>\n",
              "      <td>0.325733</td>\n",
              "      <td>0.147906</td>\n",
              "      <td>0.061375</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
              "      <th>No</th>\n",
              "      <td>0.059447</td>\n",
              "      <td>0.252672</td>\n",
              "      <td>0.160113</td>\n",
              "      <td>0.042347</td>\n",
              "      <td>167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.065660</td>\n",
              "      <td>0.710345</td>\n",
              "      <td>0.187250</td>\n",
              "      <td>0.154134</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
              "      <th>No</th>\n",
              "      <td>0.072961</td>\n",
              "      <td>0.266312</td>\n",
              "      <td>0.160298</td>\n",
              "      <td>0.038774</td>\n",
              "      <td>112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.090014</td>\n",
              "      <td>0.241255</td>\n",
              "      <td>0.163863</td>\n",
              "      <td>0.039389</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              tip_pct                               size\n",
              "                  min       max      mean       std  sum\n",
              "day  smoker                                             \n",
              "Fri  No      0.120385  0.187735  0.151650  0.028123    9\n",
              "     Yes     0.103555  0.263480  0.174783  0.051293   31\n",
              "Sat  No      0.056797  0.291990  0.158048  0.039767  115\n",
              "     Yes     0.035638  0.325733  0.147906  0.061375  104\n",
              "Sun  No      0.059447  0.252672  0.160113  0.042347  167\n",
              "     Yes     0.065660  0.710345  0.187250  0.154134   49\n",
              "Thur No      0.072961  0.266312  0.160298  0.038774  112\n",
              "     Yes     0.090014  0.241255  0.163863  0.039389   40"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbI-BHdOQdI9"
      },
      "source": [
        "**Devolvendo dados agregados sem índice de linha**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "oGG9jw_UQSUg",
        "outputId": "bb323920-b9e5-4ab3-808f-683a047ac738"
      },
      "source": [
        "tips.groupby(['day','smoker'], as_index=False).mean()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>day</th>\n",
              "      <th>smoker</th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>size</th>\n",
              "      <th>tip_pct</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Fri</td>\n",
              "      <td>No</td>\n",
              "      <td>18.420000</td>\n",
              "      <td>2.812500</td>\n",
              "      <td>2.250000</td>\n",
              "      <td>0.151650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fri</td>\n",
              "      <td>Yes</td>\n",
              "      <td>16.813333</td>\n",
              "      <td>2.714000</td>\n",
              "      <td>2.066667</td>\n",
              "      <td>0.174783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sat</td>\n",
              "      <td>No</td>\n",
              "      <td>19.661778</td>\n",
              "      <td>3.102889</td>\n",
              "      <td>2.555556</td>\n",
              "      <td>0.158048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Sat</td>\n",
              "      <td>Yes</td>\n",
              "      <td>21.276667</td>\n",
              "      <td>2.875476</td>\n",
              "      <td>2.476190</td>\n",
              "      <td>0.147906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Sun</td>\n",
              "      <td>No</td>\n",
              "      <td>20.506667</td>\n",
              "      <td>3.167895</td>\n",
              "      <td>2.929825</td>\n",
              "      <td>0.160113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Sun</td>\n",
              "      <td>Yes</td>\n",
              "      <td>24.120000</td>\n",
              "      <td>3.516842</td>\n",
              "      <td>2.578947</td>\n",
              "      <td>0.187250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Thur</td>\n",
              "      <td>No</td>\n",
              "      <td>17.113111</td>\n",
              "      <td>2.673778</td>\n",
              "      <td>2.488889</td>\n",
              "      <td>0.160298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Thur</td>\n",
              "      <td>Yes</td>\n",
              "      <td>19.190588</td>\n",
              "      <td>3.030000</td>\n",
              "      <td>2.352941</td>\n",
              "      <td>0.163863</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    day smoker  total_bill       tip      size   tip_pct\n",
              "0   Fri     No   18.420000  2.812500  2.250000  0.151650\n",
              "1   Fri    Yes   16.813333  2.714000  2.066667  0.174783\n",
              "2   Sat     No   19.661778  3.102889  2.555556  0.158048\n",
              "3   Sat    Yes   21.276667  2.875476  2.476190  0.147906\n",
              "4   Sun     No   20.506667  3.167895  2.929825  0.160113\n",
              "5   Sun    Yes   24.120000  3.516842  2.578947  0.187250\n",
              "6  Thur     No   17.113111  2.673778  2.488889  0.160298\n",
              "7  Thur    Yes   19.190588  3.030000  2.352941  0.163863"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0NsjGndQQ_Kk"
      },
      "source": [
        "# Método apply: separar-aplicar-combinar genérico"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fac0_Htw31d-"
      },
      "source": [
        "Usando a base de dados de gorjetas vamos selecionar os cinco primeiros valores de `tip_pct`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "hSyiYsGi3_6K",
        "outputId": "952f8f7e-837b-4980-84f3-2e48bb87a658"
      },
      "source": [
        "#Essa função seleciona as linhas com os maiores valores em uma coluna\n",
        "def top (df, n=5, column = 'tip_pct'):\n",
        "  return df.sort_values(by=column)[-n:]\n",
        "\n",
        "top(tips, n=6)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "      <th>tip_pct</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>14.31</td>\n",
              "      <td>4.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.279525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>23.17</td>\n",
              "      <td>6.50</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "      <td>0.280535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>11.61</td>\n",
              "      <td>3.39</td>\n",
              "      <td>No</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.291990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>3.07</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>1</td>\n",
              "      <td>0.325733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>9.60</td>\n",
              "      <td>4.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.416667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>7.25</td>\n",
              "      <td>5.15</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.710345</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     total_bill   tip smoker  day    time  size   tip_pct\n",
              "109       14.31  4.00    Yes  Sat  Dinner     2  0.279525\n",
              "183       23.17  6.50    Yes  Sun  Dinner     4  0.280535\n",
              "232       11.61  3.39     No  Sat  Dinner     2  0.291990\n",
              "67         3.07  1.00    Yes  Sat  Dinner     1  0.325733\n",
              "178        9.60  4.00    Yes  Sun  Dinner     2  0.416667\n",
              "172        7.25  5.15    Yes  Sun  Dinner     2  0.710345"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Fj7z05ZM4euu",
        "outputId": "a89c029e-d1d6-4abc-b932-ad50cc8dcb28"
      },
      "source": [
        "tips.groupby('smoker').apply(top)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "      <th>tip_pct</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoker</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">No</th>\n",
              "      <th>88</th>\n",
              "      <td>24.71</td>\n",
              "      <td>5.85</td>\n",
              "      <td>No</td>\n",
              "      <td>Thur</td>\n",
              "      <td>Lunch</td>\n",
              "      <td>2</td>\n",
              "      <td>0.236746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>20.69</td>\n",
              "      <td>5.00</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>5</td>\n",
              "      <td>0.241663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>10.29</td>\n",
              "      <td>2.60</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.252672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>7.51</td>\n",
              "      <td>2.00</td>\n",
              "      <td>No</td>\n",
              "      <td>Thur</td>\n",
              "      <td>Lunch</td>\n",
              "      <td>2</td>\n",
              "      <td>0.266312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>11.61</td>\n",
              "      <td>3.39</td>\n",
              "      <td>No</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.291990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Yes</th>\n",
              "      <th>109</th>\n",
              "      <td>14.31</td>\n",
              "      <td>4.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.279525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>23.17</td>\n",
              "      <td>6.50</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "      <td>0.280535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>3.07</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>1</td>\n",
              "      <td>0.325733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>9.60</td>\n",
              "      <td>4.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.416667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>7.25</td>\n",
              "      <td>5.15</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.710345</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            total_bill   tip smoker   day    time  size   tip_pct\n",
              "smoker                                                           \n",
              "No     88        24.71  5.85     No  Thur   Lunch     2  0.236746\n",
              "       185       20.69  5.00     No   Sun  Dinner     5  0.241663\n",
              "       51        10.29  2.60     No   Sun  Dinner     2  0.252672\n",
              "       149        7.51  2.00     No  Thur   Lunch     2  0.266312\n",
              "       232       11.61  3.39     No   Sat  Dinner     2  0.291990\n",
              "Yes    109       14.31  4.00    Yes   Sat  Dinner     2  0.279525\n",
              "       183       23.17  6.50    Yes   Sun  Dinner     4  0.280535\n",
              "       67         3.07  1.00    Yes   Sat  Dinner     1  0.325733\n",
              "       178        9.60  4.00    Yes   Sun  Dinner     2  0.416667\n",
              "       172        7.25  5.15    Yes   Sun  Dinner     2  0.710345"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Wp5ery9i4jiy",
        "outputId": "5dbbe546-44df-4073-db2b-ace6917970d8"
      },
      "source": [
        "tips.groupby(['smoker', 'day']).apply(top, n=1, column='total_bill')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "      <th>tip_pct</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">No</th>\n",
              "      <th>Fri</th>\n",
              "      <th>94</th>\n",
              "      <td>22.75</td>\n",
              "      <td>3.25</td>\n",
              "      <td>No</td>\n",
              "      <td>Fri</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sat</th>\n",
              "      <th>212</th>\n",
              "      <td>48.33</td>\n",
              "      <td>9.00</td>\n",
              "      <td>No</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "      <td>0.186220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sun</th>\n",
              "      <th>156</th>\n",
              "      <td>48.17</td>\n",
              "      <td>5.00</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>6</td>\n",
              "      <td>0.103799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thur</th>\n",
              "      <th>142</th>\n",
              "      <td>41.19</td>\n",
              "      <td>5.00</td>\n",
              "      <td>No</td>\n",
              "      <td>Thur</td>\n",
              "      <td>Lunch</td>\n",
              "      <td>5</td>\n",
              "      <td>0.121389</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Yes</th>\n",
              "      <th>Fri</th>\n",
              "      <th>95</th>\n",
              "      <td>40.17</td>\n",
              "      <td>4.73</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Fri</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "      <td>0.117750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sat</th>\n",
              "      <th>170</th>\n",
              "      <td>50.81</td>\n",
              "      <td>10.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "      <td>0.196812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sun</th>\n",
              "      <th>182</th>\n",
              "      <td>45.35</td>\n",
              "      <td>3.50</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "      <td>0.077178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thur</th>\n",
              "      <th>197</th>\n",
              "      <td>43.11</td>\n",
              "      <td>5.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Thur</td>\n",
              "      <td>Lunch</td>\n",
              "      <td>4</td>\n",
              "      <td>0.115982</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 total_bill    tip smoker   day    time  size   tip_pct\n",
              "smoker day                                                             \n",
              "No     Fri  94        22.75   3.25     No   Fri  Dinner     2  0.142857\n",
              "       Sat  212       48.33   9.00     No   Sat  Dinner     4  0.186220\n",
              "       Sun  156       48.17   5.00     No   Sun  Dinner     6  0.103799\n",
              "       Thur 142       41.19   5.00     No  Thur   Lunch     5  0.121389\n",
              "Yes    Fri  95        40.17   4.73    Yes   Fri  Dinner     4  0.117750\n",
              "       Sat  170       50.81  10.00    Yes   Sat  Dinner     3  0.196812\n",
              "       Sun  182       45.35   3.50    Yes   Sun  Dinner     3  0.077178\n",
              "       Thur 197       43.11   5.00    Yes  Thur   Lunch     4  0.115982"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "W7wFwCnX44Qz",
        "outputId": "eada4f29-5d49-4d7c-84a4-90bc624bbef7"
      },
      "source": [
        "result = tips.groupby('smoker')['tip_pct'].describe()\n",
        "result"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoker</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>No</th>\n",
              "      <td>151.0</td>\n",
              "      <td>0.159328</td>\n",
              "      <td>0.039910</td>\n",
              "      <td>0.056797</td>\n",
              "      <td>0.136906</td>\n",
              "      <td>0.155625</td>\n",
              "      <td>0.185014</td>\n",
              "      <td>0.291990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>93.0</td>\n",
              "      <td>0.163196</td>\n",
              "      <td>0.085119</td>\n",
              "      <td>0.035638</td>\n",
              "      <td>0.106771</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.195059</td>\n",
              "      <td>0.710345</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        count      mean       std  ...       50%       75%       max\n",
              "smoker                             ...                              \n",
              "No      151.0  0.159328  0.039910  ...  0.155625  0.185014  0.291990\n",
              "Yes      93.0  0.163196  0.085119  ...  0.153846  0.195059  0.710345\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VbJ3W1Q5Jth",
        "outputId": "117ca318-25a3-4fc7-e45a-280df64b23dd"
      },
      "source": [
        "result.unstack('smoker')"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       smoker\n",
              "count  No        151.000000\n",
              "       Yes        93.000000\n",
              "mean   No          0.159328\n",
              "       Yes         0.163196\n",
              "std    No          0.039910\n",
              "       Yes         0.085119\n",
              "min    No          0.056797\n",
              "       Yes         0.035638\n",
              "25%    No          0.136906\n",
              "       Yes         0.106771\n",
              "50%    No          0.155625\n",
              "       Yes         0.153846\n",
              "75%    No          0.185014\n",
              "       Yes         0.195059\n",
              "max    No          0.291990\n",
              "       Yes         0.710345\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "80W8HXeg5USC",
        "outputId": "9e39c736-e362-4186-d25a-f61d5b5770fe"
      },
      "source": [
        "#mesma coisa da célula anterior\n",
        "f = lambda x: x.describe()\n",
        "grouped.apply(f)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>size</th>\n",
              "      <th>tip_pct</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <th>smoker</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Fri</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">No</th>\n",
              "      <th>count</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>18.420000</td>\n",
              "      <td>2.812500</td>\n",
              "      <td>2.25</td>\n",
              "      <td>0.151650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.059282</td>\n",
              "      <td>0.898494</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.028123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>12.460000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.120385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>15.100000</td>\n",
              "      <td>2.625000</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.137239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Thur</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">Yes</th>\n",
              "      <th>min</th>\n",
              "      <td>10.340000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.090014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>13.510000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.148038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>16.470000</td>\n",
              "      <td>2.560000</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.153846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>19.810000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.194837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>43.110000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.00</td>\n",
              "      <td>0.241255</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>64 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   total_bill       tip  size   tip_pct\n",
              "day  smoker                                            \n",
              "Fri  No     count    4.000000  4.000000  4.00  4.000000\n",
              "            mean    18.420000  2.812500  2.25  0.151650\n",
              "            std      5.059282  0.898494  0.50  0.028123\n",
              "            min     12.460000  1.500000  2.00  0.120385\n",
              "            25%     15.100000  2.625000  2.00  0.137239\n",
              "...                       ...       ...   ...       ...\n",
              "Thur Yes    min     10.340000  2.000000  2.00  0.090014\n",
              "            25%     13.510000  2.000000  2.00  0.148038\n",
              "            50%     16.470000  2.560000  2.00  0.153846\n",
              "            75%     19.810000  4.000000  2.00  0.194837\n",
              "            max     43.110000  5.000000  4.00  0.241255\n",
              "\n",
              "[64 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bfTv2cW65qp1"
      },
      "source": [
        "**Suprimindo as chaves de grupo**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "MZVICJlw5g7Z",
        "outputId": "14da0091-e867-4290-cdba-fcf6871d31da"
      },
      "source": [
        "#ocultar os índices hierarquicos\n",
        "tips.groupby('smoker', group_keys=False).apply(top)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "      <th>tip_pct</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>24.71</td>\n",
              "      <td>5.85</td>\n",
              "      <td>No</td>\n",
              "      <td>Thur</td>\n",
              "      <td>Lunch</td>\n",
              "      <td>2</td>\n",
              "      <td>0.236746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>185</th>\n",
              "      <td>20.69</td>\n",
              "      <td>5.00</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>5</td>\n",
              "      <td>0.241663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>10.29</td>\n",
              "      <td>2.60</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.252672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>7.51</td>\n",
              "      <td>2.00</td>\n",
              "      <td>No</td>\n",
              "      <td>Thur</td>\n",
              "      <td>Lunch</td>\n",
              "      <td>2</td>\n",
              "      <td>0.266312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>11.61</td>\n",
              "      <td>3.39</td>\n",
              "      <td>No</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.291990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>14.31</td>\n",
              "      <td>4.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.279525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>23.17</td>\n",
              "      <td>6.50</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "      <td>0.280535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>3.07</td>\n",
              "      <td>1.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sat</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>1</td>\n",
              "      <td>0.325733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>9.60</td>\n",
              "      <td>4.00</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.416667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>7.25</td>\n",
              "      <td>5.15</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "      <td>0.710345</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     total_bill   tip smoker   day    time  size   tip_pct\n",
              "88        24.71  5.85     No  Thur   Lunch     2  0.236746\n",
              "185       20.69  5.00     No   Sun  Dinner     5  0.241663\n",
              "51        10.29  2.60     No   Sun  Dinner     2  0.252672\n",
              "149        7.51  2.00     No  Thur   Lunch     2  0.266312\n",
              "232       11.61  3.39     No   Sat  Dinner     2  0.291990\n",
              "109       14.31  4.00    Yes   Sat  Dinner     2  0.279525\n",
              "183       23.17  6.50    Yes   Sun  Dinner     4  0.280535\n",
              "67         3.07  1.00    Yes   Sat  Dinner     1  0.325733\n",
              "178        9.60  4.00    Yes   Sun  Dinner     2  0.416667\n",
              "172        7.25  5.15    Yes   Sun  Dinner     2  0.710345"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLhExPLs6ZP5"
      },
      "source": [
        "**Análise de quantis e de buckets**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KpkUuYvO6EDT"
      },
      "source": [
        "frame = pd.DataFrame({'data1': np.random.randn(1000), 'data2':np.random.randn(1000)})"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SH3ssIz96tt7"
      },
      "source": [
        "quartiles = pd.cut(frame.data1, 4)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TKQVxrIN6yY5",
        "outputId": "d90f2bde-28ab-42e0-e5b6-48f16ba62735"
      },
      "source": [
        "quartiles[:10]"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     (0.0286, 1.841]\n",
              "1    (-1.783, 0.0286]\n",
              "2     (0.0286, 1.841]\n",
              "3    (-1.783, 0.0286]\n",
              "4    (-1.783, 0.0286]\n",
              "5     (0.0286, 1.841]\n",
              "6    (-1.783, 0.0286]\n",
              "7     (0.0286, 1.841]\n",
              "8    (-1.783, 0.0286]\n",
              "9     (0.0286, 1.841]\n",
              "Name: data1, dtype: category\n",
              "Categories (4, interval[float64]): [(-3.603, -1.783] < (-1.783, 0.0286] < (0.0286, 1.841] <\n",
              "                                    (1.841, 3.653]]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxiZ35nJ60Yp"
      },
      "source": [
        "def get_stats(group):\n",
        "  return {'min': group.min(), 'max': group.max(), 'count': group.count(), 'mean':group.mean()}"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9fGUsZl7G_h"
      },
      "source": [
        "grouped = frame.data2.groupby(quartiles)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Sqpuqy4v7MUT",
        "outputId": "794bf653-1c8b-433b-a86d-4cf171259c42"
      },
      "source": [
        "grouped.apply(get_stats).unstack()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data1</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(-3.603, -1.783]</th>\n",
              "      <td>-2.171576</td>\n",
              "      <td>1.757315</td>\n",
              "      <td>55.0</td>\n",
              "      <td>-0.060771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(-1.783, 0.0286]</th>\n",
              "      <td>-3.504567</td>\n",
              "      <td>2.517869</td>\n",
              "      <td>473.0</td>\n",
              "      <td>0.047023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(0.0286, 1.841]</th>\n",
              "      <td>-2.848933</td>\n",
              "      <td>2.621860</td>\n",
              "      <td>449.0</td>\n",
              "      <td>-0.028949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(1.841, 3.653]</th>\n",
              "      <td>-1.964970</td>\n",
              "      <td>1.350980</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.066947</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       min       max  count      mean\n",
              "data1                                                \n",
              "(-3.603, -1.783] -2.171576  1.757315   55.0 -0.060771\n",
              "(-1.783, 0.0286] -3.504567  2.517869  473.0  0.047023\n",
              "(0.0286, 1.841]  -2.848933  2.621860  449.0 -0.028949\n",
              "(1.841, 3.653]   -1.964970  1.350980   23.0  0.066947"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mfPfSW1Z7RbQ"
      },
      "source": [
        "#Obter só o número dos quantis\n",
        "\n",
        "grouping = pd.qcut(frame.data1, 10, labels = False)"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBDDGZT57miw"
      },
      "source": [
        "grouped = frame.data2.groupby(grouping)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "wGzu2EdZ7rLx",
        "outputId": "4a6a25bf-c956-4e87-c0f8-2d929f438832"
      },
      "source": [
        "grouped.apply(get_stats).unstack()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>data1</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-2.171576</td>\n",
              "      <td>2.178041</td>\n",
              "      <td>100.0</td>\n",
              "      <td>-0.039485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-2.026593</td>\n",
              "      <td>2.517869</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.111008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.069152</td>\n",
              "      <td>2.248274</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.198256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-3.504567</td>\n",
              "      <td>2.141294</td>\n",
              "      <td>100.0</td>\n",
              "      <td>-0.010792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-3.221085</td>\n",
              "      <td>2.355536</td>\n",
              "      <td>100.0</td>\n",
              "      <td>-0.055707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-2.848933</td>\n",
              "      <td>2.141268</td>\n",
              "      <td>100.0</td>\n",
              "      <td>-0.038064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-2.366739</td>\n",
              "      <td>1.891016</td>\n",
              "      <td>100.0</td>\n",
              "      <td>-0.070531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-2.335437</td>\n",
              "      <td>2.152606</td>\n",
              "      <td>100.0</td>\n",
              "      <td>-0.033606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-2.683618</td>\n",
              "      <td>2.621860</td>\n",
              "      <td>100.0</td>\n",
              "      <td>-0.002978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-2.230404</td>\n",
              "      <td>2.166489</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.016308</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            min       max  count      mean\n",
              "data1                                     \n",
              "0     -2.171576  2.178041  100.0 -0.039485\n",
              "1     -2.026593  2.517869  100.0  0.111008\n",
              "2     -2.069152  2.248274  100.0  0.198256\n",
              "3     -3.504567  2.141294  100.0 -0.010792\n",
              "4     -3.221085  2.355536  100.0 -0.055707\n",
              "5     -2.848933  2.141268  100.0 -0.038064\n",
              "6     -2.366739  1.891016  100.0 -0.070531\n",
              "7     -2.335437  2.152606  100.0 -0.033606\n",
              "8     -2.683618  2.621860  100.0 -0.002978\n",
              "9     -2.230404  2.166489  100.0  0.016308"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rb7JkTpC79y9"
      },
      "source": [
        "**Preenchendo valores ausentes com valores específicos de grupo**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xx1TEH5W7vy4",
        "outputId": "cc284276-d43e-469d-b69c-8911a5a39d7b"
      },
      "source": [
        "s = pd.Series(np.random.randn(6))\n",
        "s[::2] = np.nan\n",
        "s"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         NaN\n",
              "1   -0.944933\n",
              "2         NaN\n",
              "3    0.906374\n",
              "4         NaN\n",
              "5   -0.633858\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08UILxAc8QiL",
        "outputId": "83a8b7d4-7021-4906-df89-1ba941d1a334"
      },
      "source": [
        "#preenchendo os valores ausentes com a media dos números\n",
        "s.fillna(s.mean())"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   -0.224139\n",
              "1   -0.944933\n",
              "2   -0.224139\n",
              "3    0.906374\n",
              "4   -0.224139\n",
              "5   -0.633858\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9KynOssL8UqB"
      },
      "source": [
        "#preenchendo de acordo com o grupo\n",
        "states = ['Ohio', 'New Your', 'Vermont', 'Florida', 'Oregon', 'Nevada', 'California', 'Idaho']"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BujdwXco87Tb"
      },
      "source": [
        "group_key = ['East'] * 4 + ['West'] * 4"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8e-H5eAr87yQ",
        "outputId": "3ec50e54-4bf2-428f-f60a-43bc364e7438"
      },
      "source": [
        "data = pd.Series(np.random.randn(8), index=states)\n",
        "data"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ohio         -0.324559\n",
              "New Your      0.026962\n",
              "Vermont      -0.200493\n",
              "Florida      -1.449512\n",
              "Oregon       -1.440504\n",
              "Nevada        0.527384\n",
              "California    0.007224\n",
              "Idaho         1.804063\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tk1b-bvy9CTR",
        "outputId": "9c421c3f-973c-4fb3-a303-52bcb2d865c1"
      },
      "source": [
        "#Definindo alguns valores como ausentes\n",
        "data[['Vermont', 'Nevada', 'Idaho']] = np.nan\n",
        "data"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ohio         -0.324559\n",
              "New Your      0.026962\n",
              "Vermont            NaN\n",
              "Florida      -1.449512\n",
              "Oregon       -1.440504\n",
              "Nevada             NaN\n",
              "California    0.007224\n",
              "Idaho              NaN\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BrnBxZcg9QHo"
      },
      "source": [
        "#Preenchendo usando as medias dos grupos\n",
        "fill_mean = lambda g: g.fillna(g.mean())"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5lTndT3r9fSY",
        "outputId": "45550ea8-988c-4248-cd35-053305ceb196"
      },
      "source": [
        "data.groupby(group_key).apply(fill_mean)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ohio         -0.324559\n",
              "New Your      0.026962\n",
              "Vermont      -0.582370\n",
              "Florida      -1.449512\n",
              "Oregon       -1.440504\n",
              "Nevada       -0.716640\n",
              "California    0.007224\n",
              "Idaho        -0.716640\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asAx987X9lSx"
      },
      "source": [
        "#Preencher com valores pré-definidos\n",
        "fill_values = {'East': 0.5, 'West': -1}\n"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BVX9nb690og"
      },
      "source": [
        "fill_func = lambda g: g.fillna(fill_values[g.name])"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NpT1BS_B97AI",
        "outputId": "74f71a60-7654-42b9-f341-426bd5cd9e80"
      },
      "source": [
        "data.groupby(group_key).apply(fill_func)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ohio         -0.324559\n",
              "New Your      0.026962\n",
              "Vermont       0.500000\n",
              "Florida      -1.449512\n",
              "Oregon       -1.440504\n",
              "Nevada       -1.000000\n",
              "California    0.007224\n",
              "Idaho        -1.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0S6EYdMNAUXv"
      },
      "source": [
        "**Amostragem aleatória e permutação**<br>\n",
        "\n",
        "Uma maneira de construir um baralho em inglês"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XA--upZ7-AUw"
      },
      "source": [
        "#Copas (Hearts), Espada (Spades), Paus(Clubs), Ouro (Diamonds)\n",
        "suits = ['H', 'S', 'C', 'D']\n",
        "card_val=(list(range(1,11)) + [10] * 3) * 4\n",
        "base_names = ['A'] + list(range(2,11)) + ['J', 'K', 'Q']\n",
        "cards = []\n",
        "for suit in ['H', 'S', 'C', 'D']:\n",
        "  cards.extend(str(num) + suit for num in base_names)\n",
        "deck = pd.Series(card_val, index=cards)"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXyJqSeQBQ7j",
        "outputId": "37e98771-3fc2-4a20-c2c3-1a81cede7b3f"
      },
      "source": [
        "deck[:13]"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AH      1\n",
              "2H      2\n",
              "3H      3\n",
              "4H      4\n",
              "5H      5\n",
              "6H      6\n",
              "7H      7\n",
              "8H      8\n",
              "9H      9\n",
              "10H    10\n",
              "JH     10\n",
              "KH     10\n",
              "QH     10\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yOnAhHitBhQY",
        "outputId": "1f4a78f8-20e2-4b56-9f36-b8a6e4bb3662"
      },
      "source": [
        "#sortear uma mão de cinco baralhos\n",
        "\n",
        "def draw(deck, n=5):\n",
        "  return deck.sample(n)\n",
        "\n",
        "draw(deck)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KC    10\n",
              "JD    10\n",
              "AC     1\n",
              "5H     5\n",
              "QC    10\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5xtGnfGBwAf",
        "outputId": "d42912e6-c7bd-403b-ec3d-623765238f5f"
      },
      "source": [
        "#Duas cartas aleatórias de cada naipe\n",
        "get_suit = lambda card: card[-1] #a ultima letra é o naipe\n",
        "deck.groupby(get_suit).apply(draw, n=2)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C  QC    10\n",
              "   JC    10\n",
              "D  5D     5\n",
              "   KD    10\n",
              "H  5H     5\n",
              "   JH    10\n",
              "S  7S     7\n",
              "   5S     5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lZYReuhuCFf_",
        "outputId": "db164bf8-fce1-4b4a-c300-f38820c709b1"
      },
      "source": [
        "#Outra forma de fazer a mesma coisa da célula anterior\n",
        "deck.groupby(get_suit, group_keys=False).apply(draw, n=2)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KC     10\n",
              "4C      4\n",
              "3D      3\n",
              "8D      8\n",
              "5H      5\n",
              "10H    10\n",
              "QS     10\n",
              "7S      7\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vckdZH7KCa9m"
      },
      "source": [
        "**Média ponderada de grupos e correlação**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4HeRYKeCWjg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "20f9563f-90c7-4442-c341-4a0cc7bd20c8"
      },
      "source": [
        "df = pd.DataFrame({'category': ['a','a','a','a','b','b','b','b'], 'data': np.random.randn(8), 'weights': np.random.rand(8)})\n",
        "df"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>category</th>\n",
              "      <th>data</th>\n",
              "      <th>weights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a</td>\n",
              "      <td>0.657151</td>\n",
              "      <td>0.709631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>a</td>\n",
              "      <td>1.326900</td>\n",
              "      <td>0.577720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>a</td>\n",
              "      <td>0.793720</td>\n",
              "      <td>0.894361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>a</td>\n",
              "      <td>1.018203</td>\n",
              "      <td>0.606043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b</td>\n",
              "      <td>-0.853526</td>\n",
              "      <td>0.460248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>b</td>\n",
              "      <td>-0.081822</td>\n",
              "      <td>0.582106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>b</td>\n",
              "      <td>-0.484096</td>\n",
              "      <td>0.844015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>b</td>\n",
              "      <td>0.837172</td>\n",
              "      <td>0.435769</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  category      data   weights\n",
              "0        a  0.657151  0.709631\n",
              "1        a  1.326900  0.577720\n",
              "2        a  0.793720  0.894361\n",
              "3        a  1.018203  0.606043\n",
              "4        b -0.853526  0.460248\n",
              "5        b -0.081822  0.582106\n",
              "6        b -0.484096  0.844015\n",
              "7        b  0.837172  0.435769"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGOY9tj5Bqbs"
      },
      "source": [
        "grouped = df.groupby('category')"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ig-IL59bCF2I"
      },
      "source": [
        "get_wavg = lambda g: np.average(g['data'], weights=g['weights'])"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GJLJIIzeCRMG",
        "outputId": "4afe4b30-4dcf-4dfa-99f7-037003580240"
      },
      "source": [
        "grouped.apply(get_wavg)"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "category\n",
              "a    0.918251\n",
              "b   -0.208529\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YIL4KA6OCUV2"
      },
      "source": [
        "#Preço das ações\n",
        "close_px = pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/stock_px_2.csv', parse_dates=True, index_col=0)"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVATmKNSC-3U",
        "outputId": "0d4fc955-7d39-4c70-bdc3-2032adae2001"
      },
      "source": [
        "close_px.info()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 2214 entries, 2003-01-02 to 2011-10-14\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   AAPL    2214 non-null   float64\n",
            " 1   MSFT    2214 non-null   float64\n",
            " 2   XOM     2214 non-null   float64\n",
            " 3   SPX     2214 non-null   float64\n",
            "dtypes: float64(4)\n",
            "memory usage: 86.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dpoE_yw6DBRl",
        "outputId": "f80c170a-078f-414d-f575-a0462c5ac014"
      },
      "source": [
        "close_px[-4:]"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>XOM</th>\n",
              "      <th>SPX</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2011-10-11</th>\n",
              "      <td>400.29</td>\n",
              "      <td>27.00</td>\n",
              "      <td>76.27</td>\n",
              "      <td>1195.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-10-12</th>\n",
              "      <td>402.19</td>\n",
              "      <td>26.96</td>\n",
              "      <td>77.16</td>\n",
              "      <td>1207.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-10-13</th>\n",
              "      <td>408.43</td>\n",
              "      <td>27.18</td>\n",
              "      <td>76.37</td>\n",
              "      <td>1203.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011-10-14</th>\n",
              "      <td>422.00</td>\n",
              "      <td>27.27</td>\n",
              "      <td>78.11</td>\n",
              "      <td>1224.58</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              AAPL   MSFT    XOM      SPX\n",
              "2011-10-11  400.29  27.00  76.27  1195.54\n",
              "2011-10-12  402.19  26.96  77.16  1207.25\n",
              "2011-10-13  408.43  27.18  76.37  1203.66\n",
              "2011-10-14  422.00  27.27  78.11  1224.58"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLu4B8rxDJ-E"
      },
      "source": [
        "#Calcula a correlação\n",
        "spx_corr = lambda x: x.corrwith(x['SPX'])"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QQPZJUeyDcD_"
      },
      "source": [
        "#Calcula as mudanças percentuais\n",
        "rets = close_px.pct_change().dropna()"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "oxppst_lDqMm",
        "outputId": "9c9308e1-01de-4e08-a2c1-cff3caf462c4"
      },
      "source": [
        "#agrupar as mudanças percentuais por ano\n",
        "get_year = lambda x: x.year\n",
        "by_year = rets.groupby(get_year)\n",
        "by_year.apply(spx_corr)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>XOM</th>\n",
              "      <th>SPX</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>0.541124</td>\n",
              "      <td>0.745174</td>\n",
              "      <td>0.661265</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>0.374283</td>\n",
              "      <td>0.588531</td>\n",
              "      <td>0.557742</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>0.467540</td>\n",
              "      <td>0.562374</td>\n",
              "      <td>0.631010</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>0.428267</td>\n",
              "      <td>0.406126</td>\n",
              "      <td>0.518514</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>0.508118</td>\n",
              "      <td>0.658770</td>\n",
              "      <td>0.786264</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>0.681434</td>\n",
              "      <td>0.804626</td>\n",
              "      <td>0.828303</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>0.707103</td>\n",
              "      <td>0.654902</td>\n",
              "      <td>0.797921</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>0.710105</td>\n",
              "      <td>0.730118</td>\n",
              "      <td>0.839057</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>0.691931</td>\n",
              "      <td>0.800996</td>\n",
              "      <td>0.859975</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          AAPL      MSFT       XOM  SPX\n",
              "2003  0.541124  0.745174  0.661265  1.0\n",
              "2004  0.374283  0.588531  0.557742  1.0\n",
              "2005  0.467540  0.562374  0.631010  1.0\n",
              "2006  0.428267  0.406126  0.518514  1.0\n",
              "2007  0.508118  0.658770  0.786264  1.0\n",
              "2008  0.681434  0.804626  0.828303  1.0\n",
              "2009  0.707103  0.654902  0.797921  1.0\n",
              "2010  0.710105  0.730118  0.839057  1.0\n",
              "2011  0.691931  0.800996  0.859975  1.0"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GDfOYjjND-Am",
        "outputId": "2bbebed9-7c7b-4c91-fed5-7ae36f1750cf"
      },
      "source": [
        "#Calculo de correlação entre as colunas\n",
        "by_year.apply(lambda g: g['AAPL'].corr(g['MSFT']))"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2003    0.480868\n",
              "2004    0.259024\n",
              "2005    0.300093\n",
              "2006    0.161735\n",
              "2007    0.417738\n",
              "2008    0.611901\n",
              "2009    0.432738\n",
              "2010    0.571946\n",
              "2011    0.581987\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brmPTjI2EYUK"
      },
      "source": [
        "**Regessão linear nos grupos**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPsuiCx7ESZu"
      },
      "source": [
        "#usando a biblioteca statsmodel\n",
        "\n",
        "def regress(data, yvar, xvars):\n",
        "  Y = data[yvar]\n",
        "  X = data[xvars]\n",
        "  \n",
        "  X['intercept'] = 1\n",
        "  result = sm.OLS(Y, X).fit()\n",
        "  return result.params"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dZQNP1s7E_qL",
        "outputId": "a14efba3-effb-48e5-8759-0bd30dc0ae9a"
      },
      "source": [
        "#regressão linear de AAPL\n",
        "\n",
        "by_year.apply(regress, 'AAPL', ['SPX'])"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SPX</th>\n",
              "      <th>intercept</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>1.195406</td>\n",
              "      <td>0.000710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>1.363463</td>\n",
              "      <td>0.004201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>1.766415</td>\n",
              "      <td>0.003246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>1.645496</td>\n",
              "      <td>0.000080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>1.198761</td>\n",
              "      <td>0.003438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>0.968016</td>\n",
              "      <td>-0.001110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>0.879103</td>\n",
              "      <td>0.002954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>1.052608</td>\n",
              "      <td>0.001261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>0.806605</td>\n",
              "      <td>0.001514</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           SPX  intercept\n",
              "2003  1.195406   0.000710\n",
              "2004  1.363463   0.004201\n",
              "2005  1.766415   0.003246\n",
              "2006  1.645496   0.000080\n",
              "2007  1.198761   0.003438\n",
              "2008  0.968016  -0.001110\n",
              "2009  0.879103   0.002954\n",
              "2010  1.052608   0.001261\n",
              "2011  0.806605   0.001514"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "os9xWZhJFhGD"
      },
      "source": [
        "# Tabelas pivôs e tabulação cruzada"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0Wr3ZdBFmli",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "013f243f-a4c6-48d7-a549-36f2504aa39c"
      },
      "source": [
        "#Calcular uma tabela de médias de grupos\n",
        "\n",
        "tips.pivot_table(index=['day', 'smoker'])"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>size</th>\n",
              "      <th>tip</th>\n",
              "      <th>tip_pct</th>\n",
              "      <th>total_bill</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <th>smoker</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Fri</th>\n",
              "      <th>No</th>\n",
              "      <td>2.250000</td>\n",
              "      <td>2.812500</td>\n",
              "      <td>0.151650</td>\n",
              "      <td>18.420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>2.066667</td>\n",
              "      <td>2.714000</td>\n",
              "      <td>0.174783</td>\n",
              "      <td>16.813333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sat</th>\n",
              "      <th>No</th>\n",
              "      <td>2.555556</td>\n",
              "      <td>3.102889</td>\n",
              "      <td>0.158048</td>\n",
              "      <td>19.661778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>2.476190</td>\n",
              "      <td>2.875476</td>\n",
              "      <td>0.147906</td>\n",
              "      <td>21.276667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Sun</th>\n",
              "      <th>No</th>\n",
              "      <td>2.929825</td>\n",
              "      <td>3.167895</td>\n",
              "      <td>0.160113</td>\n",
              "      <td>20.506667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>2.578947</td>\n",
              "      <td>3.516842</td>\n",
              "      <td>0.187250</td>\n",
              "      <td>24.120000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Thur</th>\n",
              "      <th>No</th>\n",
              "      <td>2.488889</td>\n",
              "      <td>2.673778</td>\n",
              "      <td>0.160298</td>\n",
              "      <td>17.113111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>2.352941</td>\n",
              "      <td>3.030000</td>\n",
              "      <td>0.163863</td>\n",
              "      <td>19.190588</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 size       tip   tip_pct  total_bill\n",
              "day  smoker                                          \n",
              "Fri  No      2.250000  2.812500  0.151650   18.420000\n",
              "     Yes     2.066667  2.714000  0.174783   16.813333\n",
              "Sat  No      2.555556  3.102889  0.158048   19.661778\n",
              "     Yes     2.476190  2.875476  0.147906   21.276667\n",
              "Sun  No      2.929825  3.167895  0.160113   20.506667\n",
              "     Yes     2.578947  3.516842  0.187250   24.120000\n",
              "Thur No      2.488889  2.673778  0.160298   17.113111\n",
              "     Yes     2.352941  3.030000  0.163863   19.190588"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "5LSBuQbhWGvD",
        "outputId": "906310e3-e68c-44da-e758-38efb35c3dd7"
      },
      "source": [
        "#agrupar por tip_pct e size de acordo com time\n",
        "tips.pivot_table(['tip_pct', 'size'], index=['time', 'day'], columns='smoker')"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">size</th>\n",
              "      <th colspan=\"2\" halign=\"left\">tip_pct</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>smoker</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th>day</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Dinner</th>\n",
              "      <th>Fri</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.222222</td>\n",
              "      <td>0.139622</td>\n",
              "      <td>0.165347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sat</th>\n",
              "      <td>2.555556</td>\n",
              "      <td>2.476190</td>\n",
              "      <td>0.158048</td>\n",
              "      <td>0.147906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sun</th>\n",
              "      <td>2.929825</td>\n",
              "      <td>2.578947</td>\n",
              "      <td>0.160113</td>\n",
              "      <td>0.187250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thur</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.159744</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Lunch</th>\n",
              "      <th>Fri</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>0.187735</td>\n",
              "      <td>0.188937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thur</th>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.352941</td>\n",
              "      <td>0.160311</td>\n",
              "      <td>0.163863</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 size             tip_pct          \n",
              "smoker             No       Yes        No       Yes\n",
              "time   day                                         \n",
              "Dinner Fri   2.000000  2.222222  0.139622  0.165347\n",
              "       Sat   2.555556  2.476190  0.158048  0.147906\n",
              "       Sun   2.929825  2.578947  0.160113  0.187250\n",
              "       Thur  2.000000       NaN  0.159744       NaN\n",
              "Lunch  Fri   3.000000  1.833333  0.187735  0.188937\n",
              "       Thur  2.500000  2.352941  0.160311  0.163863"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "QRIVWxg9Wi4l",
        "outputId": "481605bc-2ae0-430d-db52-b52dc9a8ba1e"
      },
      "source": [
        "#Expandir a tabela incluindo totais parciais\n",
        "\n",
        "tips.pivot_table(['tip_pct','size'], index=['time','day'], columns='smoker', margins=True)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">size</th>\n",
              "      <th colspan=\"3\" halign=\"left\">tip_pct</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>smoker</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>All</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th>day</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Dinner</th>\n",
              "      <th>Fri</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.222222</td>\n",
              "      <td>2.166667</td>\n",
              "      <td>0.139622</td>\n",
              "      <td>0.165347</td>\n",
              "      <td>0.158916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sat</th>\n",
              "      <td>2.555556</td>\n",
              "      <td>2.476190</td>\n",
              "      <td>2.517241</td>\n",
              "      <td>0.158048</td>\n",
              "      <td>0.147906</td>\n",
              "      <td>0.153152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sun</th>\n",
              "      <td>2.929825</td>\n",
              "      <td>2.578947</td>\n",
              "      <td>2.842105</td>\n",
              "      <td>0.160113</td>\n",
              "      <td>0.187250</td>\n",
              "      <td>0.166897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thur</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.159744</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.159744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Lunch</th>\n",
              "      <th>Fri</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.833333</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.187735</td>\n",
              "      <td>0.188937</td>\n",
              "      <td>0.188765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thur</th>\n",
              "      <td>2.500000</td>\n",
              "      <td>2.352941</td>\n",
              "      <td>2.459016</td>\n",
              "      <td>0.160311</td>\n",
              "      <td>0.163863</td>\n",
              "      <td>0.161301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <th></th>\n",
              "      <td>2.668874</td>\n",
              "      <td>2.408602</td>\n",
              "      <td>2.569672</td>\n",
              "      <td>0.159328</td>\n",
              "      <td>0.163196</td>\n",
              "      <td>0.160803</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 size                       tip_pct                    \n",
              "smoker             No       Yes       All        No       Yes       All\n",
              "time   day                                                             \n",
              "Dinner Fri   2.000000  2.222222  2.166667  0.139622  0.165347  0.158916\n",
              "       Sat   2.555556  2.476190  2.517241  0.158048  0.147906  0.153152\n",
              "       Sun   2.929825  2.578947  2.842105  0.160113  0.187250  0.166897\n",
              "       Thur  2.000000       NaN  2.000000  0.159744       NaN  0.159744\n",
              "Lunch  Fri   3.000000  1.833333  2.000000  0.187735  0.188937  0.188765\n",
              "       Thur  2.500000  2.352941  2.459016  0.160311  0.163863  0.161301\n",
              "All          2.668874  2.408602  2.569672  0.159328  0.163196  0.160803"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "mDwcfZIKW7JU",
        "outputId": "30d61060-b23a-4e3d-ca12-ee2f6aa4ac90"
      },
      "source": [
        "tips.pivot_table('tip_pct', index=['time', 'smoker'], columns='day', aggfunc=len, margins=True)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>day</th>\n",
              "      <th>Fri</th>\n",
              "      <th>Sat</th>\n",
              "      <th>Sun</th>\n",
              "      <th>Thur</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th>smoker</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Dinner</th>\n",
              "      <th>No</th>\n",
              "      <td>3.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>57.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>106.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>9.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Lunch</th>\n",
              "      <th>No</th>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>44.0</td>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>17.0</td>\n",
              "      <td>23.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <th></th>\n",
              "      <td>19.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>244.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "day             Fri   Sat   Sun  Thur    All\n",
              "time   smoker                               \n",
              "Dinner No       3.0  45.0  57.0   1.0  106.0\n",
              "       Yes      9.0  42.0  19.0   NaN   70.0\n",
              "Lunch  No       1.0   NaN   NaN  44.0   45.0\n",
              "       Yes      6.0   NaN   NaN  17.0   23.0\n",
              "All            19.0  87.0  76.0  62.0  244.0"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "id": "wIc2G2j8Xk28",
        "outputId": "c087c63c-bf98-4d1d-db43-356058062e30"
      },
      "source": [
        "tips.pivot_table('tip_pct', index=['time','size','smoker'], columns='day', aggfunc='mean', fill_value=0)"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th rowspan=\"11\" valign=\"top\">Dinner</th>\n",
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              "      <td>0.139622</td>\n",
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              "      <th>Yes</th>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th>Yes</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.152660</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
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              "      <td>0.150096</td>\n",
              "      <td>0.148143</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.117750</td>\n",
              "      <td>0.124515</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.206928</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106572</td>\n",
              "      <td>0.065660</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <th>No</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.103799</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"10\" valign=\"top\">Lunch</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>No</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.181728</td>\n",
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              "      <th>Yes</th>\n",
              "      <td>0.223776</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
              "      <th>No</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.166005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.181969</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.158843</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
              "      <th>No</th>\n",
              "      <td>0.187735</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.084246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Yes</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.204952</td>\n",
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              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
              "      <th>No</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>Yes</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.155410</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <th>No</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.121389</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <th>No</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.173706</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "day                      Fri       Sat       Sun      Thur\n",
              "time   size smoker                                        \n",
              "Dinner 1    No      0.000000  0.137931  0.000000  0.000000\n",
              "            Yes     0.000000  0.325733  0.000000  0.000000\n",
              "       2    No      0.139622  0.162705  0.168859  0.159744\n",
              "            Yes     0.171297  0.148668  0.207893  0.000000\n",
              "       3    No      0.000000  0.154661  0.152663  0.000000\n",
              "            Yes     0.000000  0.144995  0.152660  0.000000\n",
              "       4    No      0.000000  0.150096  0.148143  0.000000\n",
              "            Yes     0.117750  0.124515  0.193370  0.000000\n",
              "       5    No      0.000000  0.000000  0.206928  0.000000\n",
              "            Yes     0.000000  0.106572  0.065660  0.000000\n",
              "       6    No      0.000000  0.000000  0.103799  0.000000\n",
              "Lunch  1    No      0.000000  0.000000  0.000000  0.181728\n",
              "            Yes     0.223776  0.000000  0.000000  0.000000\n",
              "       2    No      0.000000  0.000000  0.000000  0.166005\n",
              "            Yes     0.181969  0.000000  0.000000  0.158843\n",
              "       3    No      0.187735  0.000000  0.000000  0.084246\n",
              "            Yes     0.000000  0.000000  0.000000  0.204952\n",
              "       4    No      0.000000  0.000000  0.000000  0.138919\n",
              "            Yes     0.000000  0.000000  0.000000  0.155410\n",
              "       5    No      0.000000  0.000000  0.000000  0.121389\n",
              "       6    No      0.000000  0.000000  0.000000  0.173706"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GuCF7kh6aOzg"
      },
      "source": [
        "**Tabulações cruzadas: crosstab**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9rteSdbGaTBx"
      },
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/nationality.csv')"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "ddu9j3Fcb2KC",
        "outputId": "96b0e33c-37a2-4cb5-d7bc-5e0142bdf74b"
      },
      "source": [
        "data"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>sample</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Japan</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>USA</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Left- handed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Right-handed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>USA</td>\n",
              "      <td>Right-handed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>USA</td>\n",
              "      <td>Left- handed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Right-handed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>USA</td>\n",
              "      <td>Right-handed</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sample Nationality    Handedness\n",
              "0       1         USA  Right-handed\n",
              "1       2       Japan  Left- handed\n",
              "2       3         USA  Right-handed\n",
              "3       4       Japan  Right-handed\n",
              "4       5       Japan  Left- handed\n",
              "5       6       Japan  Right-handed\n",
              "6       7         USA  Right-handed\n",
              "7       8         USA  Left- handed\n",
              "8       9       Japan  Right-handed\n",
              "9      10         USA  Right-handed"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "tUIalJ5Ib7j4",
        "outputId": "749bef05-4e05-4bbc-b9dd-96e898603759"
      },
      "source": [
        "#Resumir de acordo com a nacionalidade e se são destras ou canhotas\n",
        "pd.crosstab(data.Nationality, data.Handedness, margins=True)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Handedness</th>\n",
              "      <th>Left- handed</th>\n",
              "      <th>Right-handed</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nationality</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USA</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Handedness   Left- handed  Right-handed  All\n",
              "Nationality                                 \n",
              "Japan                   2             3    5\n",
              "USA                     1             4    5\n",
              "All                     3             7   10"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jR6x6wS7cPue",
        "outputId": "2a6f17b1-a474-429e-c0c0-b8ab85500f2c"
      },
      "source": [
        "pd.crosstab([tips.time, tips.day], tips.smoker, margins=True)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>smoker</th>\n",
              "      <th>No</th>\n",
              "      <th>Yes</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <th>day</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Dinner</th>\n",
              "      <th>Fri</th>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sat</th>\n",
              "      <td>45</td>\n",
              "      <td>42</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sun</th>\n",
              "      <td>57</td>\n",
              "      <td>19</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thur</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Lunch</th>\n",
              "      <th>Fri</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thur</th>\n",
              "      <td>44</td>\n",
              "      <td>17</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <th></th>\n",
              "      <td>151</td>\n",
              "      <td>93</td>\n",
              "      <td>244</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "smoker        No  Yes  All\n",
              "time   day                \n",
              "Dinner Fri     3    9   12\n",
              "       Sat    45   42   87\n",
              "       Sun    57   19   76\n",
              "       Thur    1    0    1\n",
              "Lunch  Fri     1    6    7\n",
              "       Thur   44   17   61\n",
              "All          151   93  244"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    }
  ]
}