carga_de_dados.ipynb

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      "name": "carga_de_dados.ipynb",
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      "mount_file_id": "1s9NZN9aXm9A_BM_mEFgFTpupwTH77eGe",
      "authorship_tag": "ABX9TyMhyZvcyvm6r4eHsoMZeE9D",
      "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/carga_de_dados.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s-dOdCfNItUq"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3IZ01f_LJETu"
      },
      "source": [
        "# **Lendo e escrevendo dados em formato-texto**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "hm6g9N03LuYQ",
        "outputId": "fa3be01b-3759-4a33-85f4-f28876c192c9"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex1.csv')\n",
        "df"
      ],
      "execution_count": null,
      "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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              "    .dataframe thead th {\n",
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              "<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>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>hello</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a   b   c   d message\n",
              "0  1   2   3   4   hello\n",
              "1  5   6   7   8   world\n",
              "2  9  10  11  12     foo"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "MOH54rsFL0CO",
        "outputId": "f0c6c4f8-4dfb-4c92-d673-12fdef60bf8f"
      },
      "source": [
        "pd.read_table('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex1.csv', sep=',')"
      ],
      "execution_count": null,
      "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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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
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              "<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>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>hello</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a   b   c   d message\n",
              "0  1   2   3   4   hello\n",
              "1  5   6   7   8   world\n",
              "2  9  10  11  12     foo"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "bIQq9Xc8L9nW",
        "outputId": "bc10c971-5e25-4e63-ef69-b0a6db7ddf27"
      },
      "source": [
        "#arquivos sem cabeçalho\n",
        "pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex2.csv', header=None)"
      ],
      "execution_count": null,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>hello</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   0   1   2   3      4\n",
              "0  1   2   3   4  hello\n",
              "1  5   6   7   8  world\n",
              "2  9  10  11  12    foo"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "562rWJT-MVsG",
        "outputId": "675a3e39-8ce4-4db3-d800-93f87aa47333"
      },
      "source": [
        "#definir cabeçalho\n",
        "pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex2.csv', names=['a','b','c','d','message'])"
      ],
      "execution_count": null,
      "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>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>hello</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a   b   c   d message\n",
              "0  1   2   3   4   hello\n",
              "1  5   6   7   8   world\n",
              "2  9  10  11  12     foo"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "lCuuCjgfMqWu",
        "outputId": "7c341b91-483e-4e80-d5f8-25174c5c35c6"
      },
      "source": [
        "names = ['a','b','c','d','message']\n",
        "pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex2.csv', names=names, index_col='message')"
      ],
      "execution_count": null,
      "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",
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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>a</th>\n",
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              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>message</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>hello</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>world</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>foo</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         a   b   c   d\n",
              "message               \n",
              "hello    1   2   3   4\n",
              "world    5   6   7   8\n",
              "foo      9  10  11  12"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "id": "erx5KioCM6Vv",
        "outputId": "9e641f39-412b-44a6-a282-e103f2b9d721"
      },
      "source": [
        "parsed = pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/csv_mindex.csv', index_col=['key1','key2'])\n",
        "parsed"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\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>value1</th>\n",
              "      <th>value2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>key1</th>\n",
              "      <th>key2</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">one</th>\n",
              "      <th>a</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">two</th>\n",
              "      <th>a</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>15</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           value1  value2\n",
              "key1 key2                \n",
              "one  a          1       2\n",
              "     b          3       4\n",
              "     c          5       6\n",
              "     d          7       8\n",
              "two  a          9      10\n",
              "     b         11      12\n",
              "     c         13      14\n",
              "     d         15      16"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172
        },
        "id": "yd93reqzNhJm",
        "outputId": "a33345fc-e0f2-44e1-ac40-9bede61f6d22"
      },
      "source": [
        "#arquivo sem delimitador\n",
        "result = pd.read_table('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex3.txt', sep='\\s+')\n",
        "result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>aaa</th>\n",
              "      <td>-0.264438</td>\n",
              "      <td>-1.026059</td>\n",
              "      <td>-0.619500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bbb</th>\n",
              "      <td>0.927272</td>\n",
              "      <td>0.302904</td>\n",
              "      <td>-0.032399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ccc</th>\n",
              "      <td>-0.264273</td>\n",
              "      <td>-0.386314</td>\n",
              "      <td>-0.217601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ddd</th>\n",
              "      <td>-0.871858</td>\n",
              "      <td>-0.348382</td>\n",
              "      <td>1.100491</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            A         B         C\n",
              "aaa -0.264438 -1.026059 -0.619500\n",
              "bbb  0.927272  0.302904 -0.032399\n",
              "ccc -0.264273 -0.386314 -0.217601\n",
              "ddd -0.871858 -0.348382  1.100491"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "sAx1nX9fOIbY",
        "outputId": "e99145d7-c855-4458-f1ad-51fbb5ecd993"
      },
      "source": [
        "pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex4.csv', skiprows=[0,2,3])"
      ],
      "execution_count": null,
      "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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              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>hello</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a   b   c   d message\n",
              "0  1   2   3   4   hello\n",
              "1  5   6   7   8   world\n",
              "2  9  10  11  12     foo"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "GrnilPJvOlWI",
        "outputId": "5dd4b65b-3fcf-4247-9715-9ba04040737a"
      },
      "source": [
        "result = pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex5.csv')\n",
        "result "
      ],
      "execution_count": null,
      "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",
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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>something</th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>one</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>two</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>three</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  something  a   b     c   d message\n",
              "0       one  1   2   3.0   4     NaN\n",
              "1       two  5   6   NaN   8   world\n",
              "2     three  9  10  11.0  12     foo"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "9VB6GhFxPTuQ",
        "outputId": "e1549ff8-532a-40a3-8679-d88882513e73"
      },
      "source": [
        "pd.isnull(result)"
      ],
      "execution_count": null,
      "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>something</th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   something      a      b      c      d  message\n",
              "0      False  False  False  False  False     True\n",
              "1      False  False  False   True  False    False\n",
              "2      False  False  False  False  False    False"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "h0MlZQihPrC9",
        "outputId": "d9ea3326-8d90-4ec7-aa76-71522249066b"
      },
      "source": [
        "result = pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex5.csv', na_values=['NULL'])\n",
        "result"
      ],
      "execution_count": null,
      "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>something</th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>one</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>two</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>three</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  something  a   b     c   d message\n",
              "0       one  1   2   3.0   4     NaN\n",
              "1       two  5   6   NaN   8   world\n",
              "2     three  9  10  11.0  12     foo"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "Zrg32K1kP7Wu",
        "outputId": "60b7013b-35a8-43b9-a93e-817f951910d1"
      },
      "source": [
        "sentinels = {'message': ['foo','NA'], 'something': ['two']}\n",
        "pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex5.csv', na_values=sentinels)"
      ],
      "execution_count": null,
      "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",
              "        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>something</th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>one</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>three</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  something  a   b     c   d message\n",
              "0       one  1   2   3.0   4     NaN\n",
              "1       NaN  5   6   NaN   8   world\n",
              "2     three  9  10  11.0  12     NaN"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WQWN3tSRJt0"
      },
      "source": [
        "**Lendo arquivos-texto em partes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "SMsRwuyEQVGd",
        "outputId": "ef57b3e4-6594-4365-b0c4-11e58a40e748"
      },
      "source": [
        "#otimizando pra exibição ser mais compacta\n",
        "pd.options.display.max_rows=10\n",
        "\n",
        "result = pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex6.csv')\n",
        "result"
      ],
      "execution_count": null,
      "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",
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              "\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>one</th>\n",
              "      <th>two</th>\n",
              "      <th>three</th>\n",
              "      <th>four</th>\n",
              "      <th>key</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.467976</td>\n",
              "      <td>-0.038649</td>\n",
              "      <td>-0.295344</td>\n",
              "      <td>-1.824726</td>\n",
              "      <td>L</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.358893</td>\n",
              "      <td>1.404453</td>\n",
              "      <td>0.704965</td>\n",
              "      <td>-0.200638</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.501840</td>\n",
              "      <td>0.659254</td>\n",
              "      <td>-0.421691</td>\n",
              "      <td>-0.057688</td>\n",
              "      <td>G</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.204886</td>\n",
              "      <td>1.074134</td>\n",
              "      <td>1.388361</td>\n",
              "      <td>-0.982404</td>\n",
              "      <td>R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.354628</td>\n",
              "      <td>-0.133116</td>\n",
              "      <td>0.283763</td>\n",
              "      <td>-0.837063</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>2.311896</td>\n",
              "      <td>-0.417070</td>\n",
              "      <td>-1.409599</td>\n",
              "      <td>-0.515821</td>\n",
              "      <td>L</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>-0.479893</td>\n",
              "      <td>-0.650419</td>\n",
              "      <td>0.745152</td>\n",
              "      <td>-0.646038</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>0.523331</td>\n",
              "      <td>0.787112</td>\n",
              "      <td>0.486066</td>\n",
              "      <td>1.093156</td>\n",
              "      <td>K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>-0.362559</td>\n",
              "      <td>0.598894</td>\n",
              "      <td>-1.843201</td>\n",
              "      <td>0.887292</td>\n",
              "      <td>G</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>-0.096376</td>\n",
              "      <td>-1.012999</td>\n",
              "      <td>-0.657431</td>\n",
              "      <td>-0.573315</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           one       two     three      four key\n",
              "0     0.467976 -0.038649 -0.295344 -1.824726   L\n",
              "1    -0.358893  1.404453  0.704965 -0.200638   B\n",
              "2    -0.501840  0.659254 -0.421691 -0.057688   G\n",
              "3     0.204886  1.074134  1.388361 -0.982404   R\n",
              "4     0.354628 -0.133116  0.283763 -0.837063   Q\n",
              "...        ...       ...       ...       ...  ..\n",
              "9995  2.311896 -0.417070 -1.409599 -0.515821   L\n",
              "9996 -0.479893 -0.650419  0.745152 -0.646038   E\n",
              "9997  0.523331  0.787112  0.486066  1.093156   K\n",
              "9998 -0.362559  0.598894 -1.843201  0.887292   G\n",
              "9999 -0.096376 -1.012999 -0.657431 -0.573315   0\n",
              "\n",
              "[10000 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "8Cba8Z9aR0k-",
        "outputId": "8873d59a-2f21-45bb-f1e4-36e93474dde4"
      },
      "source": [
        "#ler apenas uma quantidade de linhas específicas\n",
        "pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex6.csv', nrows=5)"
      ],
      "execution_count": null,
      "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",
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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",
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              "      <th></th>\n",
              "      <th>one</th>\n",
              "      <th>two</th>\n",
              "      <th>three</th>\n",
              "      <th>four</th>\n",
              "      <th>key</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.467976</td>\n",
              "      <td>-0.038649</td>\n",
              "      <td>-0.295344</td>\n",
              "      <td>-1.824726</td>\n",
              "      <td>L</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.358893</td>\n",
              "      <td>1.404453</td>\n",
              "      <td>0.704965</td>\n",
              "      <td>-0.200638</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.501840</td>\n",
              "      <td>0.659254</td>\n",
              "      <td>-0.421691</td>\n",
              "      <td>-0.057688</td>\n",
              "      <td>G</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.204886</td>\n",
              "      <td>1.074134</td>\n",
              "      <td>1.388361</td>\n",
              "      <td>-0.982404</td>\n",
              "      <td>R</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.354628</td>\n",
              "      <td>-0.133116</td>\n",
              "      <td>0.283763</td>\n",
              "      <td>-0.837063</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        one       two     three      four key\n",
              "0  0.467976 -0.038649 -0.295344 -1.824726   L\n",
              "1 -0.358893  1.404453  0.704965 -0.200638   B\n",
              "2 -0.501840  0.659254 -0.421691 -0.057688   G\n",
              "3  0.204886  1.074134  1.388361 -0.982404   R\n",
              "4  0.354628 -0.133116  0.283763 -0.837063   Q"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRB-a8usSOHf",
        "outputId": "d3370757-0365-4616-ab06-e78d336476ba"
      },
      "source": [
        "#ler arquivos em partes\n",
        "chunker = pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex6.csv', chunksize=1000)\n",
        "chunker"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.parsers.TextFileReader at 0x7f5d6ce073d0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6z0PIkDScw9",
        "outputId": "8c52db50-7b58-4022-b5d5-a7d255573d3f"
      },
      "source": [
        "chunker = pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex6.csv', chunksize=1000)\n",
        "tot = pd.Series([])\n",
        "for piece in chunker:\n",
        "  tot = tot.add(piece['key'].value_counts(), fill_value=0)\n",
        "tot = tot.sort_values(ascending=False)\n",
        "\n",
        "tot[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "E    368.0\n",
              "X    364.0\n",
              "L    346.0\n",
              "O    343.0\n",
              "Q    340.0\n",
              "M    338.0\n",
              "J    337.0\n",
              "F    335.0\n",
              "K    334.0\n",
              "H    330.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z80hUNPkTWbi"
      },
      "source": [
        "**Salvando dados em formato-texto**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "UdfPJWfpTHzu",
        "outputId": "17298793-2009-489b-d551-c8e7a3207bb0"
      },
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex5.csv')\n",
        "data"
      ],
      "execution_count": null,
      "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>something</th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>one</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>two</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>three</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11.0</td>\n",
              "      <td>12</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  something  a   b     c   d message\n",
              "0       one  1   2   3.0   4     NaN\n",
              "1       two  5   6   NaN   8   world\n",
              "2     three  9  10  11.0  12     foo"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEG3AjzoTlLH"
      },
      "source": [
        "#salvando com separador vígula\n",
        "data.to_csv('/content/drive/MyDrive/dados_pandas/out.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACuuGVBoT1Ce"
      },
      "source": [
        "#salvando com separador barra\n",
        "data.to_csv('/content/drive/MyDrive/dados_pandas/out_barra.csv', sep='|', na_rep='NULL')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mxz4R_WHVFKu"
      },
      "source": [
        "#Salvar sem índice e sem cabeçalho\n",
        "data.to_csv('/content/drive/MyDrive/dados_pandas/out_si_sh.csv', index=False, header=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPELzYTBVP4E"
      },
      "source": [
        "#Salvando um outro conjunto de colunas\n",
        "data.to_csv('/content/drive/MyDrive/dados_pandas/out_column.csv', index=False, columns=['a','b','c'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "QN7pXLv6Yidp",
        "outputId": "448ec74b-27e4-4b8e-a77e-31546b4366e1"
      },
      "source": [
        "pd.read_csv('/content/drive/MyDrive/dados_pandas/out_column.csv')"
      ],
      "execution_count": null,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a   b     c\n",
              "0  1   2   3.0\n",
              "1  5   6   NaN\n",
              "2  9  10  11.0"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlbHrOXfYrfo"
      },
      "source": [
        "#Salvando uma Series\n",
        "\n",
        "dates = pd.date_range('1/1/2000', periods=7)\n",
        "ts = pd.Series(np.arange(7), index=dates)\n",
        "ts.to_csv('/content/drive/MyDrive/dados_pandas/tseries.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0oSKfhCjZfwH"
      },
      "source": [
        "**Trabalhando com formatos delimitados**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dx_yzrAAZUK7",
        "outputId": "0a1492a9-1564-4775-a42b-73128786b188"
      },
      "source": [
        "import csv\n",
        "f = open('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex7.csv')\n",
        "reader = csv.reader(f)\n",
        "\n",
        "for line in reader:\n",
        "  print(line)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['a', 'b', 'c']\n",
            "['1', '2', '3']\n",
            "['1', '2', '3']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSewwPrHZ1Fg",
        "outputId": "d478408a-8603-41b4-d97f-b28de766cee9"
      },
      "source": [
        "#lendo o arquivo em uma lista de linhas\n",
        "with open('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex7.csv') as f:\n",
        "  lines = list(csv.reader(f))\n",
        "\n",
        "#separando as linhas do cabeçalho\n",
        "header, values = lines[0], lines[1:]\n",
        "\n",
        "#transpondo as linhas para as colunas\n",
        "\n",
        "data_dict = {h:v for h, v in zip(header, zip(*values))}\n",
        "\n",
        "data_dict"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'a': ('1', '1'), 'b': ('2', '2'), 'c': ('3', '3')}"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skjc_jh1a5jm"
      },
      "source": [
        "#mudando o delimitador\n",
        "class my_dialect(csv.Dialect):\n",
        "  lineterminator = '\\n'\n",
        "  delimiter = ';'\n",
        "  quotechar='\"'\n",
        "  quoting = csv.QUOTE_MINIMAL\n",
        "reader = csv.reader(f, dialect=my_dialect)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-u57o5rEciRm"
      },
      "source": [
        "#mudando o delimitador de um jeito mais simples\n",
        "reader = csv.reader(f, delimiter='|')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gC-f4zuudTxq"
      },
      "source": [
        "#escrever arquivos com delimitadores manualmente\n",
        "with open('mydata.csv', 'w') as f:\n",
        "  writer = csv.writer(f, dialect=my_dialect)\n",
        "  writer.writerow(('one', 'two', 'three'))\n",
        "  writer.writerow(('1','2','3'))\n",
        "  writer.writerow(('4','5','6'))\n",
        "  writer.writerow(('7','8','9'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wcJtnHFhLrOV"
      },
      "source": [
        "# **Dados JSON**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9DywKAoqLtFE"
      },
      "source": [
        "import json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7MAuvZsMEau"
      },
      "source": [
        "obj = \"\"\"\n",
        "{\"name\": \"Wes\",\n",
        "\"places_lived\": [\"United States\", \"Spain\", \"Germany\"],\n",
        "\"pet\": null,\n",
        "\"siblings\": [{\"name\":\"Scott\", \"age\":30, \"pets\":[\"Zeus\", \"Zuko\"]},\n",
        "{\"name\":\"Katie\",\"age\":38,\n",
        "\"pets\":[\"Sixes\",\"Stache\",\"Cisco\"]}]}\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R700BLSLMKI7",
        "outputId": "47d58916-86d0-41f9-e1ac-ea4628c8a467"
      },
      "source": [
        "result = json.loads(obj)\n",
        "result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'name': 'Wes',\n",
              " 'pet': None,\n",
              " 'places_lived': ['United States', 'Spain', 'Germany'],\n",
              " 'siblings': [{'age': 30, 'name': 'Scott', 'pets': ['Zeus', 'Zuko']},\n",
              "  {'age': 38, 'name': 'Katie', 'pets': ['Sixes', 'Stache', 'Cisco']}]}"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2U8CqUmkM9py"
      },
      "source": [
        "#converte um objeto python em Json\n",
        "asjson = json.dumps(result)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "OeA6BCgcNOSo",
        "outputId": "b7da5ae9-9dcf-4e80-c61f-5133257b4ddd"
      },
      "source": [
        "#json para dataframe a partir de uma coluna especifica do dicionario\n",
        "siblings = pd.DataFrame(result['siblings'], columns = ['name','age'])\n",
        "siblings"
      ],
      "execution_count": null,
      "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>name</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Scott</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Katie</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    name  age\n",
              "0  Scott   30\n",
              "1  Katie   38"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "8wasERvFNn3a",
        "outputId": "4e40804f-1ef0-4f7c-a9cf-05b23b8a4b90"
      },
      "source": [
        "#converte json para dataframe automaticamente\n",
        "data = pd.read_json('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/example.json')\n",
        "data"
      ],
      "execution_count": null,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSz9J1pjc6Fr",
        "outputId": "cbaa3d80-25f0-42cf-807b-aff468112a60"
      },
      "source": [
        "#exportar dados do pandas pra um JSON\n",
        "print(data.to_json())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{\"a\":{\"0\":1,\"1\":4,\"2\":7},\"b\":{\"0\":2,\"1\":5,\"2\":8},\"c\":{\"0\":3,\"1\":6,\"2\":9}}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1RHc5MqrdLCw",
        "outputId": "9c10d38b-bcd1-45cd-e985-76255f003bb4"
      },
      "source": [
        "print(data.to_json(orient='records'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[{\"a\":1,\"b\":2,\"c\":3},{\"a\":4,\"b\":5,\"c\":6},{\"a\":7,\"b\":8,\"c\":9}]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfMrGqFOdZh8"
      },
      "source": [
        "# **XML e HTML: web scraping**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YNi1FLDdexd",
        "outputId": "0aaf4f47-186b-453d-ae4d-0b30adb8cd05"
      },
      "source": [
        "!pip install beautifulsoup4 html5lib"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.7/dist-packages (4.6.3)\n",
            "Requirement already satisfied: html5lib in /usr/local/lib/python3.7/dist-packages (1.0.1)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from html5lib) (1.15.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from html5lib) (0.5.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u0tSwIFfdvgL",
        "outputId": "b475efbf-239b-48cf-8f91-bf9c70b4ab5c"
      },
      "source": [
        "!pip install lxml"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (4.2.6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u-NrHny6d3dC"
      },
      "source": [
        "tables = pd.read_html('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/fdic_failed_bank_list.html')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0y9KJYIuen8o",
        "outputId": "783c5d8d-e70d-4c36-9f84-b861964af77b"
      },
      "source": [
        "len(tables)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nt1dmbbUeoq7"
      },
      "source": [
        "failures = tables[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "O3aW6zxpercp",
        "outputId": "18c713ee-ec55-4c3e-a1be-fcd501b82f9c"
      },
      "source": [
        "failures.head()"
      ],
      "execution_count": null,
      "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>Bank Name</th>\n",
              "      <th>City</th>\n",
              "      <th>ST</th>\n",
              "      <th>CERT</th>\n",
              "      <th>Acquiring Institution</th>\n",
              "      <th>Closing Date</th>\n",
              "      <th>Updated Date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Allied Bank</td>\n",
              "      <td>Mulberry</td>\n",
              "      <td>AR</td>\n",
              "      <td>91</td>\n",
              "      <td>Today's Bank</td>\n",
              "      <td>September 23, 2016</td>\n",
              "      <td>November 17, 2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The Woodbury Banking Company</td>\n",
              "      <td>Woodbury</td>\n",
              "      <td>GA</td>\n",
              "      <td>11297</td>\n",
              "      <td>United Bank</td>\n",
              "      <td>August 19, 2016</td>\n",
              "      <td>November 17, 2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>First CornerStone Bank</td>\n",
              "      <td>King of Prussia</td>\n",
              "      <td>PA</td>\n",
              "      <td>35312</td>\n",
              "      <td>First-Citizens Bank &amp; Trust Company</td>\n",
              "      <td>May 6, 2016</td>\n",
              "      <td>September 6, 2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Trust Company Bank</td>\n",
              "      <td>Memphis</td>\n",
              "      <td>TN</td>\n",
              "      <td>9956</td>\n",
              "      <td>The Bank of Fayette County</td>\n",
              "      <td>April 29, 2016</td>\n",
              "      <td>September 6, 2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>North Milwaukee State Bank</td>\n",
              "      <td>Milwaukee</td>\n",
              "      <td>WI</td>\n",
              "      <td>20364</td>\n",
              "      <td>First-Citizens Bank &amp; Trust Company</td>\n",
              "      <td>March 11, 2016</td>\n",
              "      <td>June 16, 2016</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Bank Name  ...       Updated Date\n",
              "0                   Allied Bank  ...  November 17, 2016\n",
              "1  The Woodbury Banking Company  ...  November 17, 2016\n",
              "2        First CornerStone Bank  ...  September 6, 2016\n",
              "3            Trust Company Bank  ...  September 6, 2016\n",
              "4    North Milwaukee State Bank  ...      June 16, 2016\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YoRSS0b_eunp"
      },
      "source": [
        "#Mudar o formato de data de string para data e colocar dentro de um dataframe\n",
        "close_timestamps = pd.to_datetime(failures['Closing Date'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "odvO6eNIfKFB",
        "outputId": "344cc847-25a4-4219-abb2-f49696c5289c"
      },
      "source": [
        "#Contar quantos bancos faliram por ano\n",
        "close_timestamps.dt.year.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2010    157\n",
              "2009    140\n",
              "2011     92\n",
              "2012     51\n",
              "2008     25\n",
              "2013     24\n",
              "2014     18\n",
              "2002     11\n",
              "2015      8\n",
              "2016      5\n",
              "2004      4\n",
              "2001      4\n",
              "2007      3\n",
              "2003      3\n",
              "2000      2\n",
              "Name: Closing Date, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IfqV_Jrufm4y"
      },
      "source": [
        "**Fazendo parse de XML com lxml.objectify**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hb4T09E2fQLR"
      },
      "source": [
        "from lxml import objectify"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IPTObh0pgemg"
      },
      "source": [
        "path = '/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/datasets/mta_perf/Performance_MNR.xml'\n",
        "parsed = objectify.parse(open(path))\n",
        "root = parsed.getroot()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YLDc96lpgyTV"
      },
      "source": [
        "data = []\n",
        "skip_fields = ['PARENT_SEQ','INDICATOR_SEQ','DESIRED_CHANGE', 'DECIMAL_PLACES']\n",
        "\n",
        "for elt in root.INDICATOR:\n",
        "  el_data = {}\n",
        "  for child in elt.getchildren():\n",
        "    if child.tag in skip_fields:\n",
        "      continue\n",
        "    el_data[child.tag] = child.pyval\n",
        "    data.append(el_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DEZpLrShl0G"
      },
      "source": [
        "perf = pd.DataFrame(data)\n",
        "perf.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHZ-POmGhtqo"
      },
      "source": [
        "from io import StringIO"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-tCJaLF8h_2h"
      },
      "source": [
        "tag = '<a href=\"http://wwww.google.com\">Google</a>'\n",
        "root = objectify.parse(StringIO(tag)).getroot()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U18Pb6I2iRFf",
        "outputId": "f6563e03-0734-4687-cb88-b4a49216920b"
      },
      "source": [
        "root"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Element a at 0x7f07ef497140>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "KAJ7lDADiWMN",
        "outputId": "8cbdae63-681a-4a0e-a3a5-1b30d717a953"
      },
      "source": [
        "root.get('href')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'http://wwww.google.com'"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "OWvBxbiCiUL9",
        "outputId": "bdd447c7-4d05-4beb-d4d0-61d28a90afc4"
      },
      "source": [
        "root.text"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Google'"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rigsRSPRiibP"
      },
      "source": [
        "# **Formatos de dados binários**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "UwJfqXlwibs5",
        "outputId": "b7b934e3-b38a-41ba-a78f-600486fb1a1d"
      },
      "source": [
        "frame = pd.read_csv('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex1.csv')\n",
        "frame"
      ],
      "execution_count": null,
      "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>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>hello</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a   b   c   d message\n",
              "0  1   2   3   4   hello\n",
              "1  5   6   7   8   world\n",
              "2  9  10  11  12     foo"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeyiEP4amXMD"
      },
      "source": [
        "frame.to_pickle('/content/drive/MyDrive/dados_pandas/frame_pickle')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "8gM_sgj4mqr4",
        "outputId": "f64d6a87-fbb9-4224-aee7-74d79cce3316"
      },
      "source": [
        "pd.read_pickle('/content/drive/MyDrive/dados_pandas/frame_pickle')"
      ],
      "execution_count": null,
      "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>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>hello</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   a   b   c   d message\n",
              "0  1   2   3   4   hello\n",
              "1  5   6   7   8   world\n",
              "2  9  10  11  12     foo"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cC8LJJqqnPTX"
      },
      "source": [
        "# **Usando o formato HDF5**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "997TBXzhnOy6"
      },
      "source": [
        "frame = pd.DataFrame({'a':np.random.randn(100)})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q5GQyBT5nsTp"
      },
      "source": [
        "store = pd.HDFStore('mydata.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NBnj6e1snzkI"
      },
      "source": [
        "store['obj1'] = frame"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l35Mr_6Rn4W4"
      },
      "source": [
        "store['obj1_col'] = frame['a']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vY4dky1qn9xv",
        "outputId": "cd6cf60d-4457-407a-9284-9a139c5fa2f4"
      },
      "source": [
        "store"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'pandas.io.pytables.HDFStore'>\n",
              "File path: mydata.h5"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "Mpx0OHbEn-bf",
        "outputId": "4ad1af65-1563-494b-d48d-b836f386fec6"
      },
      "source": [
        "store['obj1']"
      ],
      "execution_count": null,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.695694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.514278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.216923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.158399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.933002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>1.467235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>-1.137128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>-1.227166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>-0.007332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>0.644578</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           a\n",
              "0  -0.695694\n",
              "1   0.514278\n",
              "2  -0.216923\n",
              "3  -0.158399\n",
              "4   0.933002\n",
              "..       ...\n",
              "95  1.467235\n",
              "96 -1.137128\n",
              "97 -1.227166\n",
              "98 -0.007332\n",
              "99  0.644578\n",
              "\n",
              "[100 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7whuMGs4oQZp"
      },
      "source": [
        "store.put('obj2', frame, format='table')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "sr1gUItZobZJ",
        "outputId": "29fd86c6-8640-4ddd-efd8-7f0fc0345e11"
      },
      "source": [
        "store.select('obj2', where=['index >= 10 and index <= 15'])"
      ],
      "execution_count": null,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>-2.512878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>-0.131403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1.351557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>-2.106987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.253309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.117143</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           a\n",
              "10 -2.512878\n",
              "11 -0.131403\n",
              "12  1.351557\n",
              "13 -2.106987\n",
              "14  0.253309\n",
              "15  0.117143"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drA2WoHZopq5"
      },
      "source": [
        "store.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZIGVMj0KovH4"
      },
      "source": [
        "\n",
        "frame.to_hdf('mydata.h5', 'obj3', format='table')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "YzzGvZc5pE-w",
        "outputId": "fb92872b-48b6-474b-abd6-8668fa0d7526"
      },
      "source": [
        "pd.read_hdf('mydata.h5', 'obj3',where = ['index < 5'])"
      ],
      "execution_count": null,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.695694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.514278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.216923</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.158399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.933002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          a\n",
              "0 -0.695694\n",
              "1  0.514278\n",
              "2 -0.216923\n",
              "3 -0.158399\n",
              "4  0.933002"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BHCATonPqztZ"
      },
      "source": [
        "# **Lendo arquivos do Microsoft Excel**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4NDmt-0mROv"
      },
      "source": [
        "xlsx = pd.ExcelFile('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex1.xlsx')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "TShkxMhemvcU",
        "outputId": "3d5f2527-a2cc-4a67-c661-e2d2571de7a4"
      },
      "source": [
        "pd.read_excel(xlsx,'Sheet1')"
      ],
      "execution_count": 4,
      "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>Unnamed: 0</th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>hello</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  a   b   c   d message\n",
              "0           0  1   2   3   4   hello\n",
              "1           1  5   6   7   8   world\n",
              "2           2  9  10  11  12     foo"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "h6kbdU6rnGp4",
        "outputId": "958a06b8-9413-4468-a796-fe7b9aac8412"
      },
      "source": [
        "#lendo várias planilhas de um arquivo\n",
        "\n",
        "frame = pd.read_excel('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex1.xlsx', 'Sheet1')\n",
        "frame"
      ],
      "execution_count": 7,
      "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>Unnamed: 0</th>\n",
              "      <th>a</th>\n",
              "      <th>b</th>\n",
              "      <th>c</th>\n",
              "      <th>d</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>hello</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>world</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>foo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  a   b   c   d message\n",
              "0           0  1   2   3   4   hello\n",
              "1           1  5   6   7   8   world\n",
              "2           2  9  10  11  12     foo"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b9EvZENgnRHx"
      },
      "source": [
        "#exportando no formato excel\n",
        "\n",
        "writer = pd.ExcelWriter('/content/drive/MyDrive/dados_pandas/pydata-book-2nd-edition/examples/ex1.xlsx')\n",
        "frame.to_excel(writer, 'Sheet1')\n",
        "writer.save()"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSLVeVFCnsKV"
      },
      "source": [
        "frame.to_excel('/content/drive/MyDrive/dados_pandas/ex2.xlsx')"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5X6-kewoI6l"
      },
      "source": [
        "# **Interagindo com APIs Web**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TpmyAgOYoZNE"
      },
      "source": [
        "import requests"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v8ATK2Rdoa00"
      },
      "source": [
        "#Pegar as issues do repositorio do pandas\n",
        "url = 'https://api.github.com/repos/pandas-dev/pandas/issues'"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PdC2GFJ5ooY6",
        "outputId": "347e7763-392d-443c-dc11-4834f76f8285"
      },
      "source": [
        "resp = requests.get(url)\n",
        "resp"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Response [200]>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DOZabVu2ovPK"
      },
      "source": [
        "data = resp.json()"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "ZeL4g94mo0O6",
        "outputId": "4df99934-c80a-4fcd-a7db-df7edc4c2aa6"
      },
      "source": [
        "data[0]['title']"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'BUG: rolling with Int64'"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KH6iQ0uo3lK"
      },
      "source": [
        "issues = pd.DataFrame(data, columns=['number','title','labels','state'])"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "jQyZxNNypKWh",
        "outputId": "efd2bd35-85e0-48e9-b2ad-523c4159af47"
      },
      "source": [
        "issues.head()"
      ],
      "execution_count": 29,
      "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>number</th>\n",
              "      <th>title</th>\n",
              "      <th>labels</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>43174</td>\n",
              "      <td>BUG: rolling with Int64</td>\n",
              "      <td>[{'id': 1045950827, 'node_id': 'MDU6TGFiZWwxMD...</td>\n",
              "      <td>open</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43173</td>\n",
              "      <td>TST: Add testing to df.where() typecasting as ...</td>\n",
              "      <td>[{'id': 127685, 'node_id': 'MDU6TGFiZWwxMjc2OD...</td>\n",
              "      <td>open</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>43172</td>\n",
              "      <td>BUG: Pass index data correctly in groupby.tran...</td>\n",
              "      <td>[{'id': 233160, 'node_id': 'MDU6TGFiZWwyMzMxNj...</td>\n",
              "      <td>open</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>43171</td>\n",
              "      <td>PERF: Rolling._apply</td>\n",
              "      <td>[]</td>\n",
              "      <td>open</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>43170</td>\n",
              "      <td>TST: DataFrame.mad with nullable dtype</td>\n",
              "      <td>[{'id': 127685, 'node_id': 'MDU6TGFiZWwxMjc2OD...</td>\n",
              "      <td>open</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   number  ... state\n",
              "0   43174  ...  open\n",
              "1   43173  ...  open\n",
              "2   43172  ...  open\n",
              "3   43171  ...  open\n",
              "4   43170  ...  open\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jq29K8taqLDl"
      },
      "source": [
        "# **Interagindo com banco de dados**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dgPw_UJsqQ3K"
      },
      "source": [
        "import sqlite3"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cLA95LXAqaZc",
        "outputId": "263ca095-40d0-415a-d45c-07c88384acde"
      },
      "source": [
        "#criando um banco de dados\n",
        "query = \"\"\" CREATE TABLE teste (a VARCHAR(20), b VARCHAR(20), c REAL, d INTEGER);\"\"\"\n",
        "con = sqlite3.connect('mydata.sqlite')\n",
        "con.execute(query)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sqlite3.Cursor at 0x7fb13e5ebf10>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YKONJ4ejq3ej"
      },
      "source": [
        "con.commit()"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NpN4s8bNq6ob",
        "outputId": "ae1c166b-3017-4535-e652-0f8d0ea4646d"
      },
      "source": [
        "#Inserindo dados\n",
        "data =  [('Atlanta','Georgia',1.25,6),('Tallahassee','Florida',2.6,3),('Sacramentoo','California',1.7,5)]\n",
        "stmt = \"INSERT INTO teste VALUES(?,?,?,?)\"\n",
        "con.executemany(stmt,data)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sqlite3.Cursor at 0x7fb1429f4f80>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wIWJyoidrrej"
      },
      "source": [
        "con.commit()"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JPpZ-7vTr8Hr"
      },
      "source": [
        "cursor = con.execute('select * from teste')"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "niKNgv9psHIa",
        "outputId": "e4f24137-248e-4ab5-d381-2ccf5fc94445"
      },
      "source": [
        "rows = cursor.fetchall()\n",
        "rows"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Atlanta', 'Georgia', 1.25, 6),\n",
              " ('Tallahassee', 'Florida', 2.6, 3),\n",
              " ('Sacramentoo', 'California', 1.7, 5)]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H4pzkascsNMD",
        "outputId": "ce4b77e4-d30f-45f1-96ed-283a06f21541"
      },
      "source": [
        "#passar os dados pra um DataFrame\n",
        "cursor.description"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(('a', None, None, None, None, None, None),\n",
              " ('b', None, None, None, None, None, None),\n",
              " ('c', None, None, None, None, None, None),\n",
              " ('d', None, None, None, None, None, None))"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "s_DoYC1hsb8L",
        "outputId": "5a80b19f-0bf0-41ea-d0ef-3671f820384c"
      },
      "source": [
        "pd.DataFrame(rows, columns=[x[0] for x in cursor.description])"
      ],
      "execution_count": 41,
      "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Atlanta</td>\n",
              "      <td>Georgia</td>\n",
              "      <td>1.25</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Tallahassee</td>\n",
              "      <td>Florida</td>\n",
              "      <td>2.60</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sacramentoo</td>\n",
              "      <td>California</td>\n",
              "      <td>1.70</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             a           b     c  d\n",
              "0      Atlanta     Georgia  1.25  6\n",
              "1  Tallahassee     Florida  2.60  3\n",
              "2  Sacramentoo  California  1.70  5"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qY2nK7r0s7Vl"
      },
      "source": [
        "**Usando SQLAlchemy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NlxqODaetKqV"
      },
      "source": [
        "import sqlalchemy as sqla"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xGdQyG_sqXr"
      },
      "source": [
        "db = sqla.create_engine('sqlite:///mydata.sqlite')"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "GKoO2UqntPWK",
        "outputId": "ae6f3d32-5b4b-4d45-dbd1-ae9e187a505d"
      },
      "source": [
        "pd.read_sql('select * from teste', db)"
      ],
      "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",
              "    }\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Atlanta</td>\n",
              "      <td>Georgia</td>\n",
              "      <td>1.25</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Tallahassee</td>\n",
              "      <td>Florida</td>\n",
              "      <td>2.60</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sacramentoo</td>\n",
              "      <td>California</td>\n",
              "      <td>1.70</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             a           b     c  d\n",
              "0      Atlanta     Georgia  1.25  6\n",
              "1  Tallahassee     Florida  2.60  3\n",
              "2  Sacramentoo  California  1.70  5"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tuyyGZqAtURq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}