Aula_5_Pandas.ipynb

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      "name": "Aula_5Pandas",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/arturgmrs/integraschoolpython/blob/master/Aula_5Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40Ls76ak-9HF",
        "colab_type": "text"
      },
      "source": [
        "#Introdução à Programação Python\n",
        "Prof. Carlos Artur Guimarães\n",
        "\n",
        "Material adaptado: Tópicos de Tecnologia Educacional  2019-1 PPGITE/IMD \n",
        "UFRN Prof.s Isabel Nunes e Luiz Affonso Guedes\n",
        "\n",
        "# Aula 5_Pandas\n",
        "\n",
        "\n",
        "*   Importando\n",
        "*   Carregando\n",
        "*   Visualizando Dataset \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFsb7YAMrfDx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Importando o pacote pandas\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhoo4t-GxdLS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Informando os dados\n",
        "data = [1,2,3,4,5,6,7,8,9,10]\n",
        "#Informando o DataFrame\n",
        "df = pd.DataFrame(data)\n",
        "#Imprimindo o DataFrame\n",
        "print (df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "slGkTOIHsJ2_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "#Criando DataFrame\n",
        "data = [['Aldenor', 18],['Bruno',21],['Tereza',23],['Priscila',20]]\n",
        "#Definindo colunas \n",
        "df = pd.DataFrame (data,columns=['Aluno','Idade'])\n",
        "#imprimindo DataFrame\n",
        "print (df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yq3frFh-tXpq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "data = [['Aldenor', 10, 2, 4, 5],['Bruno',9, 8, 8, 9],['Tereza',8,8,9,10],['Priscila',7,7,8,9]]\n",
        "df = pd.DataFrame (data,columns=['Aluno','Nota 1','Nota2','Nota 3', 'Nota 4'])\n",
        "print (df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_CdR2CpuIQB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "c9bc85a4-9395-4017-f287-999ca5e8a247"
      },
      "source": [
        "import pandas as pd\n",
        "d = {'one' : pd.Series([1,2,3], index=['a','b','c']),\n",
        "     'two' : pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9], index=['a','b','c','d','e','f','g','h','i'])\n",
        "     }\n",
        "\n",
        "df = pd.DataFrame(d)\n",
        "print (df)\n",
        "print (df.sum())\n",
        "\n",
        "#Informações sobre o DataFrame\n",
        "\n",
        "#df.index  \n",
        "#descrição do index\n",
        "#df.columns \n",
        "#colunas do DataFrame\n",
        "#df.count(df) \n",
        "#contagem dados não nulos"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   one  two\n",
            "a  1.0    1\n",
            "b  2.0    2\n",
            "c  3.0    3\n",
            "d  NaN    4\n",
            "e  NaN    5\n",
            "f  NaN    6\n",
            "g  NaN    7\n",
            "h  NaN    8\n",
            "i  NaN    9\n",
            "one     6.0\n",
            "two    45.0\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u5xNkV0p2hi1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "eb1a35b1-a5e9-4565-fb46-aba1a19d72d9"
      },
      "source": [
        "#importando dataset\n",
        "import pandas as pd\n",
        "df =pd.read_csv('titanic.csv')\n",
        "df"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>class</th>\n",
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              "      <th>survived</th>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1311</th>\n",
              "      <td>1312</td>\n",
              "      <td>3rd class</td>\n",
              "      <td>child</td>\n",
              "      <td>women</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1312</th>\n",
              "      <td>1313</td>\n",
              "      <td>3rd class</td>\n",
              "      <td>child</td>\n",
              "      <td>women</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1313</th>\n",
              "      <td>1314</td>\n",
              "      <td>3rd class</td>\n",
              "      <td>child</td>\n",
              "      <td>women</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1314</th>\n",
              "      <td>1315</td>\n",
              "      <td>3rd class</td>\n",
              "      <td>child</td>\n",
              "      <td>women</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1315</th>\n",
              "      <td>1316</td>\n",
              "      <td>3rd class</td>\n",
              "      <td>child</td>\n",
              "      <td>women</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1316 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Unnamed: 0      class     age    sex survived\n",
              "0              1  1st class  adults    man      yes\n",
              "1              2  1st class  adults    man      yes\n",
              "2              3  1st class  adults    man      yes\n",
              "3              4  1st class  adults    man      yes\n",
              "4              5  1st class  adults    man      yes\n",
              "...          ...        ...     ...    ...      ...\n",
              "1311        1312  3rd class   child  women       no\n",
              "1312        1313  3rd class   child  women       no\n",
              "1313        1314  3rd class   child  women       no\n",
              "1314        1315  3rd class   child  women       no\n",
              "1315        1316  3rd class   child  women       no\n",
              "\n",
              "[1316 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbNbSFgtjKt3",
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          "height": 162
        },
        "outputId": "1b875d5c-9e0a-44b4-c023-b2b6d06aebe3"
      },
      "source": [
        "df = [\"age\"].plot.hist()"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-138-7a0ac6d93c56>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"age\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'plot'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hMkT2n3mr7Bq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "outputId": "e3e8a216-5083-46a7-c364-108b032d430f"
      },
      "source": [
        "#importando dataset\n",
        "import pandas as pd\n",
        "#leitura do csv\n",
        "df =pd.read_csv('titanic.csv')\n",
        "#leitura do csv e indexando uma coluna como principal\n",
        "df.set_index(['class','sex'])\n",
        "#df\n",
        "#Selecionando uma única coluna\n",
        "#df.sex"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>age</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">1st class</th>\n",
              "      <th>man</th>\n",
              "      <td>1</td>\n",
              "      <td>adults</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>man</th>\n",
              "      <td>2</td>\n",
              "      <td>adults</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>man</th>\n",
              "      <td>3</td>\n",
              "      <td>adults</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>man</th>\n",
              "      <td>4</td>\n",
              "      <td>adults</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>man</th>\n",
              "      <td>5</td>\n",
              "      <td>adults</td>\n",
              "      <td>yes</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">3rd class</th>\n",
              "      <th>women</th>\n",
              "      <td>1312</td>\n",
              "      <td>child</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>women</th>\n",
              "      <td>1313</td>\n",
              "      <td>child</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>women</th>\n",
              "      <td>1314</td>\n",
              "      <td>child</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>women</th>\n",
              "      <td>1315</td>\n",
              "      <td>child</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>women</th>\n",
              "      <td>1316</td>\n",
              "      <td>child</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1316 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Unnamed: 0     age survived\n",
              "class     sex                               \n",
              "1st class man             1  adults      yes\n",
              "          man             2  adults      yes\n",
              "          man             3  adults      yes\n",
              "          man             4  adults      yes\n",
              "          man             5  adults      yes\n",
              "...                     ...     ...      ...\n",
              "3rd class women        1312   child       no\n",
              "          women        1313   child       no\n",
              "          women        1314   child       no\n",
              "          women        1315   child       no\n",
              "          women        1316   child       no\n",
              "\n",
              "[1316 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9--BacfN9czE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# arquivo do GitHub - Livro Handbook of data Science\n",
        "Estados_USA = pd.read_csv('https://raw.githubusercontent.com/jakevdp/PythonDataScienceHandbook/master/notebooks/data/state-population.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puVlvCwt9fAK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "aa500321-855b-4b56-d761-ac7fbd68090c"
      },
      "source": [
        "Estados_USA"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state/region</th>\n",
              "      <th>ages</th>\n",
              "      <th>year</th>\n",
              "      <th>population</th>\n",
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              "      <th>0</th>\n",
              "      <td>AL</td>\n",
              "      <td>under18</td>\n",
              "      <td>2012</td>\n",
              "      <td>1117489.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AL</td>\n",
              "      <td>total</td>\n",
              "      <td>2012</td>\n",
              "      <td>4817528.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AL</td>\n",
              "      <td>under18</td>\n",
              "      <td>2010</td>\n",
              "      <td>1130966.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AL</td>\n",
              "      <td>total</td>\n",
              "      <td>2010</td>\n",
              "      <td>4785570.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AL</td>\n",
              "      <td>under18</td>\n",
              "      <td>2011</td>\n",
              "      <td>1125763.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2539</th>\n",
              "      <td>USA</td>\n",
              "      <td>total</td>\n",
              "      <td>2010</td>\n",
              "      <td>309326295.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2540</th>\n",
              "      <td>USA</td>\n",
              "      <td>under18</td>\n",
              "      <td>2011</td>\n",
              "      <td>73902222.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2541</th>\n",
              "      <td>USA</td>\n",
              "      <td>total</td>\n",
              "      <td>2011</td>\n",
              "      <td>311582564.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2542</th>\n",
              "      <td>USA</td>\n",
              "      <td>under18</td>\n",
              "      <td>2012</td>\n",
              "      <td>73708179.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2543</th>\n",
              "      <td>USA</td>\n",
              "      <td>total</td>\n",
              "      <td>2012</td>\n",
              "      <td>313873685.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2544 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     state/region     ages  year   population\n",
              "0              AL  under18  2012    1117489.0\n",
              "1              AL    total  2012    4817528.0\n",
              "2              AL  under18  2010    1130966.0\n",
              "3              AL    total  2010    4785570.0\n",
              "4              AL  under18  2011    1125763.0\n",
              "...           ...      ...   ...          ...\n",
              "2539          USA    total  2010  309326295.0\n",
              "2540          USA  under18  2011   73902222.0\n",
              "2541          USA    total  2011  311582564.0\n",
              "2542          USA  under18  2012   73708179.0\n",
              "2543          USA    total  2012  313873685.0\n",
              "\n",
              "[2544 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0UYzRY_p-EFA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Exemplo 2 de importação de um site www\n",
        "x1 = pd.read_csv('https://raw.githubusercontent.com/jakevdp/PythonDataScienceHandbook/master/notebooks/data/state-population.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9yNI5T7-Iy-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "3f48b746-30fa-444d-f5e2-ec2332bcf448"
      },
      "source": [
        "x1"
      ],
      "execution_count": 96,
      "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>state/region</th>\n",
              "      <th>ages</th>\n",
              "      <th>year</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AL</td>\n",
              "      <td>under18</td>\n",
              "      <td>2012</td>\n",
              "      <td>1117489.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AL</td>\n",
              "      <td>total</td>\n",
              "      <td>2012</td>\n",
              "      <td>4817528.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AL</td>\n",
              "      <td>under18</td>\n",
              "      <td>2010</td>\n",
              "      <td>1130966.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AL</td>\n",
              "      <td>total</td>\n",
              "      <td>2010</td>\n",
              "      <td>4785570.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AL</td>\n",
              "      <td>under18</td>\n",
              "      <td>2011</td>\n",
              "      <td>1125763.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2539</th>\n",
              "      <td>USA</td>\n",
              "      <td>total</td>\n",
              "      <td>2010</td>\n",
              "      <td>309326295.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2540</th>\n",
              "      <td>USA</td>\n",
              "      <td>under18</td>\n",
              "      <td>2011</td>\n",
              "      <td>73902222.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2541</th>\n",
              "      <td>USA</td>\n",
              "      <td>total</td>\n",
              "      <td>2011</td>\n",
              "      <td>311582564.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2542</th>\n",
              "      <td>USA</td>\n",
              "      <td>under18</td>\n",
              "      <td>2012</td>\n",
              "      <td>73708179.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2543</th>\n",
              "      <td>USA</td>\n",
              "      <td>total</td>\n",
              "      <td>2012</td>\n",
              "      <td>313873685.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2544 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     state/region     ages  year   population\n",
              "0              AL  under18  2012    1117489.0\n",
              "1              AL    total  2012    4817528.0\n",
              "2              AL  under18  2010    1130966.0\n",
              "3              AL    total  2010    4785570.0\n",
              "4              AL  under18  2011    1125763.0\n",
              "...           ...      ...   ...          ...\n",
              "2539          USA    total  2010  309326295.0\n",
              "2540          USA  under18  2011   73902222.0\n",
              "2541          USA    total  2011  311582564.0\n",
              "2542          USA  under18  2012   73708179.0\n",
              "2543          USA    total  2012  313873685.0\n",
              "\n",
              "[2544 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-h11rrcx-MSM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "1c2ecb0c-2c50-43af-8439-24c4d7dbffab"
      },
      "source": [
        "x1['population']\n",
        "x1.population.head()"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1117489.0\n",
              "1    4817528.0\n",
              "2    1130966.0\n",
              "3    4785570.0\n",
              "4    1125763.0\n",
              "Name: population, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYYFUX6u-T44",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "a1d1ffca-d587-4be5-a9b9-d5276aa0026a"
      },
      "source": [
        "x1[['year','population']]"
      ],
      "execution_count": 116,
      "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>year</th>\n",
              "      <th>population</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2012</td>\n",
              "      <td>1117489.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012</td>\n",
              "      <td>4817528.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010</td>\n",
              "      <td>1130966.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010</td>\n",
              "      <td>4785570.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011</td>\n",
              "      <td>1125763.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2539</th>\n",
              "      <td>2010</td>\n",
              "      <td>309326295.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2540</th>\n",
              "      <td>2011</td>\n",
              "      <td>73902222.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2541</th>\n",
              "      <td>2011</td>\n",
              "      <td>311582564.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2542</th>\n",
              "      <td>2012</td>\n",
              "      <td>73708179.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2543</th>\n",
              "      <td>2012</td>\n",
              "      <td>313873685.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2544 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      year   population\n",
              "0     2012    1117489.0\n",
              "1     2012    4817528.0\n",
              "2     2010    1130966.0\n",
              "3     2010    4785570.0\n",
              "4     2011    1125763.0\n",
              "...    ...          ...\n",
              "2539  2010  309326295.0\n",
              "2540  2011   73902222.0\n",
              "2541  2011  311582564.0\n",
              "2542  2012   73708179.0\n",
              "2543  2012  313873685.0\n",
              "\n",
              "[2544 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r8fb7WQo-ZY0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kiz876i9-cAh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "outputId": "77b37e37-3179-498d-ce3e-c6303ead1e3d"
      },
      "source": [
        "%matplotlib inline  \n",
        "x1['population'].plot()\n",
        "plt.show()"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sl0m1yhj-VYh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "fd5ad5c3-b93a-4a11-9504-dfcb1427a8cd"
      },
      "source": [
        "x1['state/region'].head()"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    AL\n",
              "1    AL\n",
              "2    AL\n",
              "3    AL\n",
              "4    AL\n",
              "Name: state/region, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoeYm_ZV-wnU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Carregando os arquivos csv\n",
        "#Código \n",
        "pop = pd.read_csv('https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-population.csv')\n",
        "areas = pd.read_csv('https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-areas.csv')\n",
        "abbrevs = pd.read_csv('https://raw.githubusercontent.com/jakevdp/data-USstates/master/state-abbrevs.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_sJiC76f-yGp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "453e8878-bb13-4212-d851-a69140bf402d"
      },
      "source": [
        "# Verificando os conteúdos dos arquivos\n",
        "\n",
        "print(pop.head())\n",
        "print()\n",
        "print(areas.head())\n",
        "print()\n",
        "print(abbrevs.head())"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  state/region     ages  year  population\n",
            "0           AL  under18  2012   1117489.0\n",
            "1           AL    total  2012   4817528.0\n",
            "2           AL  under18  2010   1130966.0\n",
            "3           AL    total  2010   4785570.0\n",
            "4           AL  under18  2011   1125763.0\n",
            "\n",
            "        state  area (sq. mi)\n",
            "0     Alabama          52423\n",
            "1      Alaska         656425\n",
            "2     Arizona         114006\n",
            "3    Arkansas          53182\n",
            "4  California         163707\n",
            "\n",
            "        state abbreviation\n",
            "0     Alabama           AL\n",
            "1      Alaska           AK\n",
            "2     Arizona           AZ\n",
            "3    Arkansas           AR\n",
            "4  California           CA\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "luQTzlKfbhHp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#kagle ipea dataset"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}