Analise de dados.ipynb

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        "# importação das bibliotecas\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
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        "# leitura dos arquivos\n",
        "relacionados = pd.read_csv('https://raw.githubusercontent.com/xandeea/Atividade-An-lise-de-dados/main/dados/AssuntosRelacionados.csv', encoding=\"ISO-8859-1\", sep=\";\")\n",
        "geo = pd.read_csv('https://raw.githubusercontent.com/xandeea/Atividade-Analise-de-dados/main/dados/Geo.csv', encoding=\"ISO-8859-1\", sep=\";\", skiprows=2)\n",
        "linha_tempo = pd.read_csv('https://raw.githubusercontent.com/xandeea/Atividade-Analise-de-dados/main/dados/LinhaTempo.csv', encoding=\"ISO-8859-1\", sep=\";\")"
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        "# arrumando nome das colunas\n",
        "geo.columns=['País', 'Valores']\n",
        "\n",
        "# colocando a primeira letra em maiúsculo\n",
        "geo['País'] = geo['País'].str.capitalize()\n",
        "\n",
        "# trocando os valores NaN por 0\n",
        "geo['Valores'] = geo['Valores'].fillna(0)\n",
        "\n",
        "# criando uma coluna para dividir os países que tem e que não tem informação\n",
        "geo['Possui valor'] = np.where(geo['Valores'] != 0, 1, 0)\n",
        "geo.head()"
      ],
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              "        País  Valores  Possui valor\n",
              "0      Índia    100.0             1\n",
              "1     Ruanda      0.0             0\n",
              "2  Singapura     87.0             1\n",
              "3   Zimbábue      0.0             0\n",
              "4    Etiópia      0.0             0"
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        "# arrumando nome das colunas\n",
        "linha_tempo.columns=['Data','Score']\n",
        "\n",
        "# separado a coluna data em duas novas colunas de ano e mês\n",
        "linha_tempo[['Ano','Mês']] = linha_tempo['Data'].str.split('-', expand=True)\n",
        "\n",
        "# transformando o tipo da coluna data\n",
        "linha_tempo['Data'] = pd.to_datetime(linha_tempo[\"Data\"], format = \"%Y-%m\")\n",
        "\n",
        "linha_tempo.head()"
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              "        Data  Score   Ano Mês\n",
              "0 2004-01-01     11  2004  01\n",
              "1 2004-02-01     10  2004  02\n",
              "2 2004-03-01     10  2004  03\n",
              "3 2004-04-01      9  2004  04\n",
              "4 2004-05-01      6  2004  05"
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        "# dropando uma linha sem informações\n",
        "relacionados.drop(index = relacionados.index[25], inplace=True)\n",
        "\n",
        "#colocando a primeira letra em maiúsculo\n",
        "relacionados['Termo'] = relacionados['Termo'].str.capitalize()\n",
        "\n",
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              "              Termo Score\n",
              "0             Dados   100\n",
              "1           Ciência   100\n",
              "2  Ciência de dados   100\n",
              "3             Curso    12\n",
              "4           Emprego     9"
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-188ae88f-1232-400d-ac4f-ac2c3d66a446 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-188ae88f-1232-400d-ac4f-ac2c3d66a446');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# separando a tabela anterior em 2 tabelas, as que possuem score e as que possuem aumento repentino\n",
        "relacionados_valor = relacionados.query(\"Score != 'Aumento repentino'\")\n",
        "aumento_repentino = relacionados.query(\"Score == 'Aumento repentino'\")\n",
        "\n",
        "# transformando o tipo da coluna score\n",
        "relacionados_valor[\"Score\"] = pd.to_numeric(relacionados_valor[\"Score\"])"
      ],
      "metadata": {
        "id": "9U8Fv1bxn3ZD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Análise dos dados**"
      ],
      "metadata": {
        "id": "742JhtRAMMx5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Dos 250 países que a tabela possui, apenas 24% dos países apresentaram pesquisas suficientes de Data Science no Google (61 países)*"
      ],
      "metadata": {
        "id": "4Hq8AViLQV8Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.countplot(data=geo,x='Possui valor', palette=\"deep\")\n",
        "ax.set_title('Países que tiveram ou não pesquisas sobre Data Science no Google', fontsize=18)\n",
        "ax.set_xlabel('Número de pesquisas', fontsize=14)\n",
        "ax.set_ylabel('Quantidade de países', fontsize=14)\n",
        "ax.set_xticklabels([\"Não houve muitas pesquisas\", \"Houve muitas pesquisas\"], fontsize=12)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "1CvrInN1ji69",
        "outputId": "68c8229d-22f8-4176-9745-dcf75b7d7b62"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Abaixo os 10 países que apresentaram maior proporção de pesquisas*"
      ],
      "metadata": {
        "id": "INXYebQ9Y_tI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.barplot(data=geo, x='País',y='Valores', palette=\"deep\", order=geo.sort_values('Valores', ascending=False)['País'][:10])\n",
        "ax.figure.set_size_inches(12,6)\n",
        "ax.set_title('10 países com maior popularidade de consulta por Data Science no Google', fontsize=18)\n",
        "ax.set_xlabel('Países', fontsize=14)\n",
        "ax.set_ylabel('Popularidade de pesquisa', fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "WgHIy6S5MRp0",
        "outputId": "50ec8aa4-e2bd-40cb-f70a-4a6470f6ffaa"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Destes, apenas Singapura e Estados Unidos fazem parte dos 10 países mais tecnológicos do mundo¹**\n",
        "\n",
        "¹ [Países mais tecnológicos](https://www.gov.br/inpi/pt-br/central-de-conteudo/noticias/brasil-ocupa-o-57deg-lugar-entre-os-132-paises-mais-inovadores)"
      ],
      "metadata": {
        "id": "OKzSq3-Jh0Wv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Abaixo a quantidade de pesquisas com o passar do anos e também as médias por ano/mês*"
      ],
      "metadata": {
        "id": "aiSzNmuylOME"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.lineplot(data=linha_tempo,y='Score',x='Data')\n",
        "ax.figure.set_size_inches(16,6)\n",
        "ax.set_title('Interesse de pesquisa por Data Science no Google por ano', fontsize=18)\n",
        "ax.set_xlabel('Anos', fontsize=14)\n",
        "ax.set_ylabel('Interesse de pesquisa', fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "S4psCb-lg7ZD",
        "outputId": "600d544b-b626-448e-9592-61cdd9694b26"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ],
            "image/png": 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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pesquisas_por_ano = pd.DataFrame(linha_tempo.groupby('Ano')['Score'].mean()).sort_values(by='Score', ascending=False)\n",
        "pesquisas_por_ano.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "AqCcLSt8m8Xg",
        "outputId": "d7ef2616-7635-4d37-e9d8-0361fefacd39"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Score\n",
              "Ano            \n",
              "2020  87.416667\n",
              "2022  87.000000\n",
              "2021  81.833333\n",
              "2019  80.083333\n",
              "2018  59.416667\n",
              "2017  43.500000\n",
              "2016  23.833333\n",
              "2015  17.750000\n",
              "2014  12.083333\n",
              "2004   8.250000"
            ],
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-efef9506-e448-4b0b-ad4f-6d9beca66bfe button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-efef9506-e448-4b0b-ad4f-6d9beca66bfe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pesquisas_por_mes = pd.DataFrame(linha_tempo.groupby('Mês')['Score'].mean()).sort_values(by='Score', ascending=False)\n",
        "pesquisas_por_mes.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "zfQ0Qs9crmpy",
        "outputId": "3c64d84e-6285-41bf-8be8-47d69e29b77d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Score\n",
              "Mês           \n",
              "09   29.611111\n",
              "02   28.736842\n",
              "01   28.526316\n",
              "10   28.000000\n",
              "03   27.789474\n",
              "04   27.315789\n",
              "11   27.055556\n",
              "08   26.444444\n",
              "07   25.277778\n",
              "12   25.111111"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b76d5dce-2c1e-4476-917e-903166af06aa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    .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>Score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mês</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>09</th>\n",
              "      <td>29.611111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>02</th>\n",
              "      <td>28.736842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>01</th>\n",
              "      <td>28.526316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>28.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>03</th>\n",
              "      <td>27.789474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>04</th>\n",
              "      <td>27.315789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>27.055556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>08</th>\n",
              "      <td>26.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>07</th>\n",
              "      <td>25.277778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>25.111111</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b76d5dce-2c1e-4476-917e-903166af06aa')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b76d5dce-2c1e-4476-917e-903166af06aa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b76d5dce-2c1e-4476-917e-903166af06aa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pode-se observar que houve um aumento bem considerável nos últimos anos, com duas subidas consideráveis, tanto em 2017 como mais recentemente em 2019 e vem mantendo um alto nível de pesquisa desde então**\n",
        "\n",
        "**Em relação aos meses não possui nenhuma discrepância muito grande**"
      ],
      "metadata": {
        "id": "Ol34IoCTlas8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## *Abaixo os 10 termos relacionados com Data Science mais pesquisados e logo abaixo os temas que vem tendo um aumento nas pesquisas do Google*"
      ],
      "metadata": {
        "id": "OlMu82GmofT-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.barplot(data=relacionados_valor, x='Termo',y='Score', palette=\"deep\", order=relacionados_valor.sort_values('Score', ascending=False)['Termo'][:10])\n",
        "ax.figure.set_size_inches(20,6)\n",
        "ax.set_title('10 termos relacionados com Data Science também pesquisados no Google', fontsize=18)\n",
        "ax.set_xlabel('Termos pesquisados', fontsize=14)\n",
        "ax.set_ylabel('Popularidade de pesquisa', fontsize=14)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "81p3QsfV0Obx",
        "outputId": "4203ed95-dedb-4f4f-ce7a-6965c75653e5"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aumento_repentino"
      ],
      "metadata": {
        "id": "SMQM1l8H6GZQ",
        "outputId": "56aa5c0d-8c8d-4e2f-d3d4-bc296723eb57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       Termo              Score\n",
              "26                                    Python  Aumento repentino\n",
              "27                    Aprendizado de máquina  Aumento repentino\n",
              "28                                   Máquina  Aumento repentino\n",
              "29                                  Big data  Aumento repentino\n",
              "30                                  Coursera  Aumento repentino\n",
              "31                                         R  Aumento repentino\n",
              "32                              Certificação  Aumento repentino\n",
              "33                                Engenheiro  Aumento repentino\n",
              "34                Universidade da califórnia  Aumento repentino\n",
              "35                 Certificação profissional  Aumento repentino\n",
              "36                               Programador  Aumento repentino\n",
              "37                                 Bangalore  Aumento repentino\n",
              "38                    Master in data science  Aumento repentino\n",
              "39                               Bacharelato  Aumento repentino\n",
              "40                        Business analytics  Aumento repentino\n",
              "41         Master of business administration  Aumento repentino\n",
              "42                                     Udemy  Aumento repentino\n",
              "43                                    Github  Aumento repentino\n",
              "44                      Universidade harvard  Aumento repentino\n",
              "45                                     Quora  Aumento repentino\n",
              "46                       R para data science  Aumento repentino\n",
              "47                              Profissional  Aumento repentino\n",
              "48                  Inteligência empresarial  Aumento repentino\n",
              "49  Instituto de tecnologia de massachusetts  Aumento repentino\n",
              "50                                       Edx  Aumento repentino"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6517fb36-dd40-48ae-a602-24cb45f9f24c\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Termo</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Python</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Aprendizado de máquina</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Máquina</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Big data</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Coursera</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>R</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Certificação</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Engenheiro</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Universidade da califórnia</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Certificação profissional</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Programador</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Bangalore</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Master in data science</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Bacharelato</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Business analytics</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Master of business administration</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Udemy</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Github</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Universidade harvard</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Quora</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>R para data science</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Profissional</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Inteligência empresarial</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Instituto de tecnologia de massachusetts</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Edx</td>\n",
              "      <td>Aumento repentino</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6517fb36-dd40-48ae-a602-24cb45f9f24c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6517fb36-dd40-48ae-a602-24cb45f9f24c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6517fb36-dd40-48ae-a602-24cb45f9f24c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dos 10 termos mais pesquisados, muitos deles são palavras mais gerais, provavelmente buscando entender mais sobre a área. Enquanto no aumento repentino já possuem mais termos relacionados a cursos/universidades/especializações, provavelmente buscando entrarem na área**"
      ],
      "metadata": {
        "id": "N-Wc2xJSpP8_"
      }
    }
  ]
}