entendendo_as_estruturas_de_dados.ipynb

{
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    {
      "cell_type": "markdown",
      "id": "5EMOxv8jvla-",
      "metadata": {
        "id": "5EMOxv8jvla-"
      },
      "source": [
        "# Exemplos para um melhor entendimento das estruturas contidas no código do MeasureSoftGram Core\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "GWulSzaFvyaK",
      "metadata": {
        "id": "GWulSzaFvyaK"
      },
      "source": [
        "### O dicionário abaixo é uma replicação do formato do json proveniente do SonarQube (e, consequentemente, o formato esperado pelos dataframes neste repositório)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "tRDo91QgvxFh",
      "metadata": {
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        "    {\n",
        "      \"id\": \"AX9GDsKlZuVL7NjXSAZ5\",\n",
        "      \"key\": \"fga-eps-mds_2021-2-MeasureSoftGram-CLI:tests\",\n",
        "      \"name\": \"tests\",\n",
        "      \"qualifier\": \"DIR\",\n",
        "      \"path\": \"tests\",\n",
        "      \"measures\": [\n",
        "        {\n",
        "          \"metric\": \"test_execution_time\",\n",
        "          \"value\": \"534\"\n",
        "        },\n",
        "        {\n",
        "          \"metric\": \"test_failures\",\n",
        "          \"value\": \"0\",\n",
        "          \"bestValue\": \"true\"\n",
        "        },\n",
        "        {\n",
        "          \"metric\": \"test_errors\",\n",
        "          \"value\": \"0\",\n",
        "          \"bestValue\": \"true\"\n",
        "        },\n",
        "        {\n",
        "          \"metric\": \"security_rating\",\n",
        "          \"value\": \"1.0\",\n",
        "          \"bestValue\": \"true\"\n",
        "        },\n",
        "        {\n",
        "          \"metric\": \"tests\",\n",
        "          \"value\": \"22\"\n",
        "        }\n",
        "      ]\n",
        "    },\n",
        "    {\n",
        "      \"id\": \"AX-gLz5MkGDeAMJwS_WK\",\n",
        "      \"key\": \"fga-eps-mds_2021-2-MeasureSoftGram-CLI:tests/unit\",\n",
        "      \"name\": \"unit\",\n",
        "      \"qualifier\": \"DIR\",\n",
        "      \"path\": \"tests/unit\",\n",
        "      \"measures\": [\n",
        "        {\n",
        "          \"metric\": \"test_execution_time\",\n",
        "          \"value\": \"25\"\n",
        "        },\n",
        "        {\n",
        "          \"metric\": \"test_failures\",\n",
        "          \"value\": \"0\",\n",
        "          \"bestValue\": \"true\"\n",
        "        },\n",
        "        {\n",
        "          \"metric\": \"test_errors\",\n",
        "          \"value\": \"0\",\n",
        "          \"bestValue\": \"true\"\n",
        "        },\n",
        "        {\n",
        "          \"metric\": \"security_rating\",\n",
        "          \"value\": \"1.0\",\n",
        "          \"bestValue\": \"true\"\n",
        "        },\n",
        "        {\n",
        "          \"metric\": \"tests\",\n",
        "          \"value\": \"21\"\n",
        "        }\n",
        "      ]\n",
        "    },\n",
        "    {\n",
        "      \"id\": \"AX-gLz5MkGDeAMJwS_WG\",\n",
        "      \"key\": \"fga-eps-mds_2021-2-MeasureSoftGram-CLI:tests/utils\",\n",
        "      \"name\": \"utils\",\n",
        "      \"qualifier\": \"DIR\",\n",
        "      \"path\": \"tests/utils\",\n",
        "      \"measures\": [\n",
        "        {\n",
        "          \"metric\": \"security_rating\",\n",
        "          \"value\": \"1.0\",\n",
        "          \"bestValue\": \"true\"\n",
        "        }\n",
        "      ]\n",
        "    }\n",
        "  ]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "CAUAHS9TwAc-",
      "metadata": {
        "id": "CAUAHS9TwAc-"
      },
      "source": [
        "### Uma vez apresentado o formato do dado proveniente do SonarQube, é apresentado agora o formato de dado esperado para montar a pré-configuração de um repositório."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "4nIJZv3uv_TQ",
      "metadata": {
        "id": "4nIJZv3uv_TQ"
      },
      "outputs": [],
      "source": [
        "pre_config = {\n",
        "  \"pre_config_name\": \"pre-config-name\",\n",
        "  \"characteristics\": [\n",
        "    {\n",
        "      \"name\": \"reliability\",\n",
        "      \"weight\": 50.0,\n",
        "      \"subcharacteristics\": [\n",
        "        {\n",
        "          \"name\": \"testing_status\",\n",
        "          \"weight\": 100.0,\n",
        "          \"measures\": [\n",
        "            {\n",
        "              \"name\": \"passed_tests\",\n",
        "              \"weight\": 33.33\n",
        "            },\n",
        "            {\n",
        "              \"name\": \"test_builds\",\n",
        "              \"weight\": 33.33\n",
        "            },\n",
        "            {\n",
        "              \"name\": \"test_coverage\",\n",
        "              \"weight\": 33.33\n",
        "            }\n",
        "          ]\n",
        "        }\n",
        "      ]\n",
        "    },\n",
        "    {\n",
        "      \"name\": \"maintainability\",\n",
        "      \"weight\": 50.0,\n",
        "      \"subcharacteristics\": [\n",
        "        {\n",
        "          \"name\": \"modifiability\",\n",
        "          \"weight\": 100.0,\n",
        "          \"measures\": [\n",
        "            {\n",
        "              \"name\": \"non_complex_file_density\",\n",
        "              \"weight\": 50.0\n",
        "            },\n",
        "            {\n",
        "              \"name\": \"commented_file_density\",\n",
        "              \"weight\": 30.0\n",
        "            },\n",
        "            {\n",
        "              \"name\": \"duplication_absense\",\n",
        "              \"weight\": 20.0\n",
        "            }\n",
        "          ]\n",
        "        }\n",
        "      ]\n",
        "    }\n",
        "  ],\n",
        "  \"measures\": [\n",
        "      \"non_complex_file_density\",\n",
        "      \"commented_file_density\",\n",
        "      \"duplication_absense\",\n",
        "      \"passed_tests\",\n",
        "      \"test_builds\",\n",
        "      \"test_coverage\",\n",
        "  ]\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "WgnLKkMBwQg7",
      "metadata": {
        "id": "WgnLKkMBwQg7"
      },
      "source": [
        "### Agora que foram definidos os formatos de entrada e da pré-configuração, é preciso agora explicar como é o processo de montagem dos dataframes esperados pelo código.\n",
        "\n",
        "Abaixo, você consegue visualizar como são escolhidas as colunas, a partir das medidas, relativas a um projeto."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "A8I9WnohwP4z",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "A8I9WnohwP4z",
        "outputId": "4c7592cc-33dd-474a-ad8e-a476f8a2ff72"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>non_complex_file_density</th>\n",
              "      <th>commented_file_density</th>\n",
              "      <th>duplication_absense</th>\n",
              "      <th>passed_tests</th>\n",
              "      <th>test_builds</th>\n",
              "      <th>test_coverage</th>\n",
              "      <th>qualifier</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [non_complex_file_density, commented_file_density, duplication_absense, passed_tests, test_builds, test_coverage, qualifier]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "metrics = pre_config[\"measures\"]\n",
        "df_columns = metrics + [\"qualifier\"]\n",
        "df = pd.DataFrame(columns=df_columns)\n",
        "display(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "BxB4DCGnxXJ1",
      "metadata": {
        "id": "BxB4DCGnxXJ1"
      },
      "source": [
        "### Uma vez definidas as colunas, é preciso popular os dados "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "62KXdn1Kwxgt",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "62KXdn1Kwxgt",
        "outputId": "7e3c1a71-ad61-4818-c757-35f1add35545"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Número de linhas do dataframe: 17\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>non_complex_file_density</th>\n",
              "      <th>commented_file_density</th>\n",
              "      <th>duplication_absense</th>\n",
              "      <th>passed_tests</th>\n",
              "      <th>test_builds</th>\n",
              "      <th>test_coverage</th>\n",
              "      <th>qualifier</th>\n",
              "      <th>complexity</th>\n",
              "      <th>functions</th>\n",
              "      <th>ncloc</th>\n",
              "      <th>security_rating</th>\n",
              "      <th>files</th>\n",
              "      <th>test_errors</th>\n",
              "      <th>duplicated_lines_density</th>\n",
              "      <th>test_failures</th>\n",
              "      <th>coverage</th>\n",
              "      <th>comment_lines_density</th>\n",
              "      <th>test_execution_time</th>\n",
              "      <th>tests</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>measuresoftgram/__init__.py</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FIL</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>tests/__init__.py</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>UTS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tests/integration/__init__.py</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>UTS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tests/unit/__init__.py</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>UTS</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>measuresoftgram/cli.py</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>FIL</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>57.1</td>\n",
              "      <td>3.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              non_complex_file_density commented_file_density  \\\n",
              "measuresoftgram/__init__.py                        NaN                    NaN   \n",
              "tests/__init__.py                                  NaN                    NaN   \n",
              "tests/integration/__init__.py                      NaN                    NaN   \n",
              "tests/unit/__init__.py                             NaN                    NaN   \n",
              "measuresoftgram/cli.py                             NaN                    NaN   \n",
              "\n",
              "                              duplication_absense passed_tests test_builds  \\\n",
              "measuresoftgram/__init__.py                   NaN          NaN         NaN   \n",
              "tests/__init__.py                             NaN          NaN         NaN   \n",
              "tests/integration/__init__.py                 NaN          NaN         NaN   \n",
              "tests/unit/__init__.py                        NaN          NaN         NaN   \n",
              "measuresoftgram/cli.py                        NaN          NaN         NaN   \n",
              "\n",
              "                              test_coverage qualifier complexity functions  \\\n",
              "measuresoftgram/__init__.py             NaN       FIL          0         0   \n",
              "tests/__init__.py                       NaN       UTS        NaN       NaN   \n",
              "tests/integration/__init__.py           NaN       UTS        NaN       NaN   \n",
              "tests/unit/__init__.py                  NaN       UTS        NaN       NaN   \n",
              "measuresoftgram/cli.py                  NaN       FIL          6         4   \n",
              "\n",
              "                              ncloc security_rating files test_errors  \\\n",
              "measuresoftgram/__init__.py       0             1.0     1           0   \n",
              "tests/__init__.py               NaN             1.0   NaN           0   \n",
              "tests/integration/__init__.py   NaN             1.0   NaN           0   \n",
              "tests/unit/__init__.py          NaN             1.0   NaN           0   \n",
              "measuresoftgram/cli.py           50             1.0     1           0   \n",
              "\n",
              "                              duplicated_lines_density test_failures coverage  \\\n",
              "measuresoftgram/__init__.py                        0.0             0      NaN   \n",
              "tests/__init__.py                                  0.0             0      NaN   \n",
              "tests/integration/__init__.py                      0.0             0      NaN   \n",
              "tests/unit/__init__.py                             0.0             0      NaN   \n",
              "measuresoftgram/cli.py                             0.0             0     57.1   \n",
              "\n",
              "                              comment_lines_density test_execution_time tests  \n",
              "measuresoftgram/__init__.py                     NaN                 NaN   NaN  \n",
              "tests/__init__.py                               NaN                 NaN   NaN  \n",
              "tests/integration/__init__.py                   NaN                 NaN   NaN  \n",
              "tests/unit/__init__.py                          NaN                 NaN   NaN  \n",
              "measuresoftgram/cli.py                          3.8                 NaN   NaN  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for component in components:\n",
        "    df.at[component[\"path\"], \"qualifier\"] = component[\"qualifier\"]\n",
        "    for metric in component[\"measures\"]:\n",
        "        df.at[component[\"path\"], metric[\"metric\"]] = metric[\"value\"]\n",
        "\n",
        "print(f'Número de linhas do dataframe: {df.shape[0]}\\n\\n')\n",
        "display(df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "pr-FwCmVyldn",
      "metadata": {
        "id": "pr-FwCmVyldn"
      },
      "source": [
        "### Uma vez compreendido o processo de popular o dataframe, agora vamos entender sobre o processo de filtrar o dataframe a partir do tipo de componente analisado pelo sonar (se é um diretório ou se é um arquivo, por exemplo)\n",
        "\n",
        "Abaixo você pode visualizar um exemplo de dataframe filtado para componentes de diretório."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "7vRy-KhixrZ3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "7vRy-KhixrZ3",
        "outputId": "94b1d8df-5e1e-47eb-8ea1-bddd8855c307"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Número de componentes diretório: 5\n",
            "\n",
            "\n"
          ]
        },
        {
          "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>non_complex_file_density</th>\n",
              "      <th>commented_file_density</th>\n",
              "      <th>duplication_absense</th>\n",
              "      <th>passed_tests</th>\n",
              "      <th>test_builds</th>\n",
              "      <th>test_coverage</th>\n",
              "      <th>qualifier</th>\n",
              "      <th>complexity</th>\n",
              "      <th>functions</th>\n",
              "      <th>ncloc</th>\n",
              "      <th>security_rating</th>\n",
              "      <th>files</th>\n",
              "      <th>test_errors</th>\n",
              "      <th>duplicated_lines_density</th>\n",
              "      <th>test_failures</th>\n",
              "      <th>coverage</th>\n",
              "      <th>comment_lines_density</th>\n",
              "      <th>test_execution_time</th>\n",
              "      <th>tests</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>tests/integration</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DIR</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>509</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>measuresoftgram</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DIR</td>\n",
              "      <td>64</td>\n",
              "      <td>17</td>\n",
              "      <td>331</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>54.9</td>\n",
              "      <td>0.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tests</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DIR</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>534</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tests/unit</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DIR</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tests/utils</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DIR</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  non_complex_file_density commented_file_density  \\\n",
              "tests/integration                      NaN                    NaN   \n",
              "measuresoftgram                        NaN                    NaN   \n",
              "tests                                  NaN                    NaN   \n",
              "tests/unit                             NaN                    NaN   \n",
              "tests/utils                            NaN                    NaN   \n",
              "\n",
              "                  duplication_absense passed_tests test_builds test_coverage  \\\n",
              "tests/integration                 NaN          NaN         NaN           NaN   \n",
              "measuresoftgram                   NaN          NaN         NaN           NaN   \n",
              "tests                             NaN          NaN         NaN           NaN   \n",
              "tests/unit                        NaN          NaN         NaN           NaN   \n",
              "tests/utils                       NaN          NaN         NaN           NaN   \n",
              "\n",
              "                  qualifier complexity functions ncloc security_rating files  \\\n",
              "tests/integration       DIR        NaN       NaN   NaN             1.0   NaN   \n",
              "measuresoftgram         DIR         64        17   331             1.0     5   \n",
              "tests                   DIR        NaN       NaN   NaN             1.0   NaN   \n",
              "tests/unit              DIR        NaN       NaN   NaN             1.0   NaN   \n",
              "tests/utils             DIR        NaN       NaN   NaN             1.0   NaN   \n",
              "\n",
              "                  test_errors duplicated_lines_density test_failures coverage  \\\n",
              "tests/integration           0                      NaN             0      NaN   \n",
              "measuresoftgram           NaN                      0.0           NaN     54.9   \n",
              "tests                       0                      NaN             0      NaN   \n",
              "tests/unit                  0                      NaN             0      NaN   \n",
              "tests/utils               NaN                      NaN           NaN      NaN   \n",
              "\n",
              "                  comment_lines_density test_execution_time tests  \n",
              "tests/integration                   NaN                 509     1  \n",
              "measuresoftgram                     0.6                 NaN   NaN  \n",
              "tests                               NaN                 534    22  \n",
              "tests/unit                          NaN                  25    21  \n",
              "tests/utils                         NaN                 NaN   NaN  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dirs = df[df[\"qualifier\"] == \"DIR\"]\n",
        "print(f'Número de componentes diretório: {dirs.shape[0]}\\n\\n')\n",
        "display(dirs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "83DWy9_Oy-NB",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83DWy9_Oy-NB",
        "outputId": "8ac2449a-f4a2-4b41-e46c-739aa7882fe3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tests/integration      1\n",
              "measuresoftgram      NaN\n",
              "tests                 22\n",
              "tests/unit            21\n",
              "tests/utils          NaN\n",
              "Name: tests, dtype: object"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dirs[\"tests\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "W0N8Gj2U3Db4",
      "metadata": {
        "id": "W0N8Gj2U3Db4"
      },
      "source": [
        "### Agora vamos entender o funcionamento do retorno do idxmax, também utilizado no projeto. O idxmax é uma função no panda para retornar o índice do valor máximo de uma determinada coluna.\n",
        "\n",
        "Abaixo, você primeiro pode verificar a coluna \"tests\", disponível no dataframe.\n",
        "\n",
        "Logo abaixo dessa coluna, você pode verificar o resultado do idxmax a partir das seguintes operações:   \n",
        "1. Separação da coluna \"tests\" \n",
        "2. Casting dos valores da pd.Series para float\n",
        "3. Chamada da função idxmax para retornar os valores máximos\n",
        "4. Aplicação do resultado das linhas anteriores para a linha \"tests\" do dataframe."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "W-j5lgAmz9NJ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 971
        },
        "id": "W-j5lgAmz9NJ",
        "outputId": "1ece328b-840f-427c-9175-42b7cde39f4f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Coluna tests:\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "tests/integration      1\n",
              "measuresoftgram      NaN\n",
              "tests                 22\n",
              "tests/unit            21\n",
              "tests/utils          NaN\n",
              "Name: tests, dtype: object"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Linha tests:\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "non_complex_file_density    NaN\n",
              "commented_file_density      NaN\n",
              "duplication_absense         NaN\n",
              "passed_tests                NaN\n",
              "test_builds                 NaN\n",
              "test_coverage               NaN\n",
              "qualifier                   DIR\n",
              "complexity                  NaN\n",
              "functions                   NaN\n",
              "ncloc                       NaN\n",
              "security_rating             1.0\n",
              "files                       NaN\n",
              "test_errors                   0\n",
              "duplicated_lines_density    NaN\n",
              "test_failures                 0\n",
              "coverage                    NaN\n",
              "comment_lines_density       NaN\n",
              "test_execution_time         534\n",
              "tests                        22\n",
              "Name: tests, dtype: object"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Resultado das operações descritas acima:\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "non_complex_file_density    NaN\n",
              "commented_file_density      NaN\n",
              "duplication_absense         NaN\n",
              "passed_tests                NaN\n",
              "test_builds                 NaN\n",
              "test_coverage               NaN\n",
              "qualifier                   DIR\n",
              "complexity                  NaN\n",
              "functions                   NaN\n",
              "ncloc                       NaN\n",
              "security_rating             1.0\n",
              "files                       NaN\n",
              "test_errors                   0\n",
              "duplicated_lines_density    NaN\n",
              "test_failures                 0\n",
              "coverage                    NaN\n",
              "comment_lines_density       NaN\n",
              "test_execution_time         534\n",
              "tests                        22\n",
              "Name: tests, dtype: object"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Coluna tests:\")\n",
        "display(dirs[\"tests\"])\n",
        "print(\"\\n\")\n",
        "\n",
        "print(\"Linha tests:\")\n",
        "display(dirs.loc[\"tests\"])\n",
        "print(\"\\n\")\n",
        "\n",
        "print(\"Resultado das operações descritas acima:\")\n",
        "display(dirs.loc[dirs[\"tests\"].astype(float).idxmax()])\n",
        "print(\"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "V4JulA0j4NR1",
      "metadata": {
        "id": "V4JulA0j4NR1"
      },
      "source": [
        "### Para clarificar um pouco mais, segue um exemplo da utilização do idxmax"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "7QguVLDY1ir1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "7QguVLDY1ir1",
        "outputId": "526c820d-fa8f-4f17-e730-228b3a63ab9f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DATAFRAME:\n"
          ]
        },
        {
          "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>consumption</th>\n",
              "      <th>co2_emissions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pork</th>\n",
              "      <td>10.51</td>\n",
              "      <td>37.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wheat Products</th>\n",
              "      <td>103.11</td>\n",
              "      <td>19.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beef</th>\n",
              "      <td>55.48</td>\n",
              "      <td>1712.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                consumption  co2_emissions\n",
              "Pork                  10.51          37.20\n",
              "Wheat Products       103.11          19.66\n",
              "Beef                  55.48        1712.00"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "RESULTADO:\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "consumption      Wheat Products\n",
              "co2_emissions              Beef\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "example = pd.DataFrame({'consumption': [10.51, 103.11, 55.48],\n",
        "                   'co2_emissions': [37.2, 19.66, 1712]},\n",
        "                   index=['Pork', 'Wheat Products', 'Beef'])\n",
        "print(\"DATAFRAME:\")\n",
        "display(example)\n",
        "print(\"\\n\\n\")\n",
        "\n",
        "print(\"RESULTADO:\")\n",
        "display(example.idxmax())\n",
        "print(\"\\n\\n\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "entendendo-as-estruturas-de-dados.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.10.5 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.5"
    },
    "vscode": {
      "interpreter": {
        "hash": "c42a92b1511fc8394f8ae431370e4a1ba2a939c9cae64ffb83b92b2962e17c5c"
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  "nbformat": 4,
  "nbformat_minor": 5
}