PPA_Trabajo_Práctico_N°3_Pandas.ipynb

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      "source": [
        "# Trabajo Práctico 2 - Pandas"
      ],
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      }
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      "source": [
        "## Ejercicio 1\n",
        "Al finalizar el cursado, el profesor menciona que necesita un dataframe donde quede\n",
        "registrado el desempeño de sus alumnos. El alumno “A” se ofrece para diseñarlo. El\n",
        "dataframe deberá consistir de 4 columnas, el nombre del estudiante, su número de legajo, y\n",
        "las notas de un primer y segundo examen. Generar este dataframe a partir de un diccionario\n",
        "que contenga datos de 8 estudiantes genéricos."
      ],
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      "cell_type": "code",
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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
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      "cell_type": "code",
      "source": [
        "alumnos = {\n",
        "    'Alumnos' : ['Pepe', 'Maria', 'Rockstar' ] ,\n",
        "    'Legajo'  : [1010  ,  2020  ,  3030      ] ,\n",
        "    'Nota 1'  : [7     ,  8     ,  4         ] ,\n",
        "    'Nota 2'  : [6     ,  8     ,  10        ] ,\n",
        "}\n",
        "\n",
        "print(type(alumnos))\n",
        "print(alumnos)"
      ],
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          "text": [
            "<class 'dict'>\n",
            "{'Alumnos': ['Pepe', 'Maria', 'Rockstar'], 'Legajo': [1010, 2020, 3030], 'Nota 1': [7, 8, 4], 'Nota 2': [6, 8, 10]}\n"
          ]
        }
      ]
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      "cell_type": "code",
      "source": [
        "registro = pd.DataFrame.from_dict(alumnos)\n",
        "registro.head(3) # por defecto muestra las primeras 5 filas"
      ],
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              "    Alumnos  Legajo  Nota 1  Nota 2\n",
              "0      Pepe    1010       7       6\n",
              "1     Maria    2020       8       8\n",
              "2  Rockstar    3030       4      10"
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              "      <th>Alumnos</th>\n",
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              "      <th>0</th>\n",
              "      <td>Pepe</td>\n",
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              "      <td>Rockstar</td>\n",
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      "source": [
        "# Esto no va\n",
        "# columnas = ['Alumno', 'Legajo', 'Nota 1', 'Nota 2']\n",
        "# registro = pd.DataFrame(columns = columnas)\n",
        "# registro.head()"
      ],
      "metadata": {
        "id": "T6wfBz8557i_"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 2\n",
        "A partir del dataframe diseñado en el ejercicio anterior, agregar una columna con la nota\n",
        "promedio entre los dos exámenes, y luego ordenarlo desde el mayor al menor promedio.\n",
        "Generar otros dos dataframes con los alumnos cuyo promedio supere el 60, y otro con los\n",
        "que no."
      ],
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        "id": "ZSWinCkPiuLa"
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    {
      "cell_type": "code",
      "source": [
        "# agregar una columna con la nota promedio\n",
        "registro['promedio'] = (registro['Nota 1'] + registro['Nota 2'])/2\n",
        "registro.head()"
      ],
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              "    Alumnos  Legajo  Nota 1  Nota 2  promedio\n",
              "0      Pepe    1010       7       6       6.5\n",
              "1     Maria    2020       8       8       8.0\n",
              "2  Rockstar    3030       4      10       7.0"
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      "source": [
        "registro.sort_values(by='promedio', ascending=False)"
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              "    Alumnos  Legajo  Nota 1  Nota 2  promedio\n",
              "1     Maria    2020       8       8       8.0\n",
              "2  Rockstar    3030       4      10       7.0\n",
              "0      Pepe    1010       7       6       6.5"
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              "      <th>2</th>\n",
              "      <td>Rockstar</td>\n",
              "      <td>3030</td>\n",
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              "          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",
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              "        }\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ordenarlo desde el mayor al menor promedio\n",
        "# registro.sort_values(by='promedio', ascending=False, ignore_index=True)\n",
        "registro.sort_values(by='promedio', ascending=False, ignore_index=True, inplace=True)\n",
        "registro.head()"
      ],
      "metadata": {
        "id": "WNAyckuu_bdJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "58b2edb8-f02b-4b07-abd3-419744555ec7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Alumnos  Legajo  Nota 1  Nota 2  promedio\n",
              "0     Maria    2020       8       8       8.0\n",
              "1  Rockstar    3030       4      10       7.0\n",
              "2      Pepe    1010       7       6       6.5"
            ],
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              "\n",
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              "      <th></th>\n",
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              "      <th>promedio</th>\n",
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              "      <td>8</td>\n",
              "      <td>8.0</td>\n",
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              "      <td>Rockstar</td>\n",
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              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pepe</td>\n",
              "      <td>1010</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>6.5</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "    }\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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-9d08cdc6-da32-440f-aa4d-e0d2740b04fa button.colab-df-convert');\n",
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              "\n",
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              "            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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(registro['promedio'] > 7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jmxQ3sKDBM4",
        "outputId": "319d4a4f-9f77-40d6-c055-0b40757bf9cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0     True\n",
            "1    False\n",
            "2    False\n",
            "Name: promedio, dtype: bool\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generar otros dos dataframes con los alumnos cuyo promedio supere el 60, y otro con los que no.\n",
        "registro_buenosPromedios = registro.loc[registro.promedio > 6.8]\n",
        "registro_malosPromedios =  registro.loc[registro.promedio < 6.8]"
      ],
      "metadata": {
        "id": "Et1LrwlZBj83"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "registro_buenosPromedios.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "bhVfYdnAE7zb",
        "outputId": "bd22b5d0-e05f-47f3-a679-6da4132b6f30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Alumnos  Legajo  Nota 1  Nota 2  promedio\n",
              "0     Maria    2020       8       8       8.0\n",
              "1  Rockstar    3030       4      10       7.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa28001b-5044-446d-83db-4e7eb33483d9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>Alumnos</th>\n",
              "      <th>Legajo</th>\n",
              "      <th>Nota 1</th>\n",
              "      <th>Nota 2</th>\n",
              "      <th>promedio</th>\n",
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              "      <th>0</th>\n",
              "      <td>Maria</td>\n",
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              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Rockstar</td>\n",
              "      <td>3030</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa28001b-5044-446d-83db-4e7eb33483d9')\"\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",
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              "    }\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-aa28001b-5044-446d-83db-4e7eb33483d9 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-aa28001b-5044-446d-83db-4e7eb33483d9');\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": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "registro_malosPromedios.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "jKifUHiJE_L5",
        "outputId": "bc881416-8801-424b-daa5-03aa58149d49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Alumnos  Legajo  Nota 1  Nota 2  promedio\n",
              "2    Pepe    1010       7       6       6.5"
            ],
            "text/html": [
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              "      <td>Pepe</td>\n",
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              "      <td>6.5</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\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",
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              "      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-b18141f4-75fd-43ac-af6f-e13de7ca8a25 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-b18141f4-75fd-43ac-af6f-e13de7ca8a25');\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": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 3\n",
        "Suponer que en la siguiente materia se incorporan 3 nuevos estudiantes, y se retiran los\n",
        "alumnos que ocupan la posición 1, 6 y 7 en la lista. Escribir el código que permita hacer estos\n",
        "cambios en las filas del dataframe y reindexar la enumeración acorde. Usar el dataframe\n",
        "original (sin promedios).\n"
      ],
      "metadata": {
        "id": "uGYuSfLSFVDI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def agregar_alumno(registro, alumno, legajo, nota1, nota2):\n",
        "  nuevo_alumno = {\n",
        "      'Alumnos' :  alumno        ,\n",
        "      'Legajo'  :  legajo        ,\n",
        "      'Nota 1'  :  nota1         ,\n",
        "      'Nota 2'  :  nota2         ,\n",
        "      'promedio':  (nota1 + nota2)/2             \n",
        "  }\n",
        "\n",
        "  return registro.append(nuevo_alumno, ignore_index=True)"
      ],
      "metadata": {
        "id": "ZccAYpUXFVWQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nuevo_registro = agregar_alumno(registro, 'juan', 10101010, 5, 4)\n",
        "nuevo_registro.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "lJOm8L7jIpU5",
        "outputId": "f3106319-87f5-4b4a-cee0-53b49b33b418"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Alumnos    Legajo  Nota 1  Nota 2  promedio\n",
              "0     Maria      2020       8       8       8.0\n",
              "1  Rockstar      3030       4      10       7.0\n",
              "2      Pepe      1010       7       6       6.5\n",
              "3      juan  10101010       5       4       4.5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-19d92444-f27b-4b01-89c7-e7a9612a3efa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Alumnos</th>\n",
              "      <th>Legajo</th>\n",
              "      <th>Nota 1</th>\n",
              "      <th>Nota 2</th>\n",
              "      <th>promedio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Maria</td>\n",
              "      <td>2020</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Rockstar</td>\n",
              "      <td>3030</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pepe</td>\n",
              "      <td>1010</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>juan</td>\n",
              "      <td>10101010</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-19d92444-f27b-4b01-89c7-e7a9612a3efa')\"\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",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </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",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\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-19d92444-f27b-4b01-89c7-e7a9612a3efa 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-19d92444-f27b-4b01-89c7-e7a9612a3efa');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nuevos_alumnos = {\n",
        "    'Alumnos'     : 'Pepe2',\n",
        "    'Alumnos'     : 'Maria2',\n",
        "    'Alumnos'     : 'Rockstar2'\n",
        "}\n"
      ],
      "metadata": {
        "id": "FugirJs9LpUC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "registro.append(nuevos_alumnos, ignore_index=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Rvx5OQC1Lhzy",
        "outputId": "a24c87f1-4066-4805-f062-9b6b4e33dc82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Alumnos  Legajo  Nota 1  Nota 2  promedio\n",
              "0      Maria  2020.0     8.0     8.0       8.0\n",
              "1   Rockstar  3030.0     4.0    10.0       7.0\n",
              "2       Pepe  1010.0     7.0     6.0       6.5\n",
              "3  Rockstar2     NaN     NaN     NaN       NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e732199-8bb2-4c95-9a96-9311805adea5\">\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",
              "        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>Alumnos</th>\n",
              "      <th>Legajo</th>\n",
              "      <th>Nota 1</th>\n",
              "      <th>Nota 2</th>\n",
              "      <th>promedio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Maria</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Rockstar</td>\n",
              "      <td>3030.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pepe</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Rockstar2</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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e732199-8bb2-4c95-9a96-9311805adea5')\"\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-6e732199-8bb2-4c95-9a96-9311805adea5 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-6e732199-8bb2-4c95-9a96-9311805adea5');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 4\n",
        "El archivo <a href=\"https://drive.google.com/file/d/1Mw8YvU9KG0LZ5YEpzsl8-vB0N_kG7ZSa/view?usp=sharing\">“nacimientos_muertes_incrementos.csv”</a> contiene información de los nacimientos,\n",
        "las muertes y el incremento natural desde 1952 hasta 2021, generar un dataframe con sus\n",
        "datos. Del mismo, eliminar la columna con los incrementos, y generar otros tres dataframes\n",
        "con los 10 años con más nacimientos, los 10 años con menos muertes, y los años con\n",
        "menos de 50000 nacimientos. Obtener también el valor medio de nacimientos y muertes en\n",
        "todo el dataset."
      ],
      "metadata": {
        "id": "PItA-IG6OFSi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "ruta = '/content/'\n",
        "elemento = 'nacimientos_muertes_incrementos.csv'\n",
        "df = pd.read_csv(ruta + elemento)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QYq-ZOZbONa6",
        "outputId": "0a315a56-1275-4b6a-e7eb-fdbe289843f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   December year  Live births  Deaths  Natural increase\n",
              "0           1952        51846   18897             32949\n",
              "1           1953        51888   18354             33534\n",
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    {
      "cell_type": "markdown",
      "source": [
        "**Eliminar la columna con los incrementos**"
      ],
      "metadata": {
        "id": "4X4t_xCAgcbw"
      }
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    {
      "cell_type": "code",
      "source": [
        "# eliminar la columna con los incrementos.\n",
        "df.drop(columns = 'Natural increase', inplace=True)\n",
        "df.head()"
      ],
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            "text/plain": [
              "   December year  Live births  Deaths\n",
              "0           1952        51846   18897\n",
              "1           1953        51888   18354\n",
              "2           1954        54057   18876\n",
              "3           1955        55596   19224\n",
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              "            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",
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              "  "
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          "metadata": {},
          "execution_count": 3
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Testeando***"
      ],
      "metadata": {
        "id": "XoUBBWgCh4TO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_años = pd.DataFrame(df['December year'])\n",
        "df_años.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WXIKUberQylR",
        "outputId": "f4f49652-d78b-4e56-a1dc-588e695ab5e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   December year\n",
              "0           1952\n",
              "1           1953\n",
              "2           1954\n",
              "3           1955\n",
              "4           1956"
            ],
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    {
      "cell_type": "code",
      "source": [
        "df_nacim = pd.DataFrame(df['Live births'])\n",
        "df_nacim.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Live births\n",
              "0        51846\n",
              "1        51888\n",
              "2        54057\n",
              "3        55596\n",
              "4        56532"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Live births</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>51846</td>\n",
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              "      <td>55596</td>\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "promedio_nac = df_nacim.mean()\n",
        "print(promedio_nac)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmEHi6dGRKqF",
        "outputId": "df4e3d03-6aa6-48b2-b340-8075fd584c81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Live births    58133.914286\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DataFrame de los 10 años con mas nacimientos**"
      ],
      "metadata": {
        "id": "_7a_G5iIgo6H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# generar dataframe con los 10 años con más nacimientos.\n",
        "df_top = pd.read_csv(ruta + elemento)\n",
        "df_top.drop(columns = 'Natural increase', inplace=True)\n",
        "df_top.drop(columns = 'Deaths', inplace=True)\n",
        "df_top.sort_values(by='Live births', ascending=False, ignore_index=True, inplace=True)\n",
        "df_top.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "HyCYHooZpVnN",
        "outputId": "bd2b19d3-6d3f-4ba2-fc8b-272773cc8537"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   December year  Live births\n",
              "0           1961        65391\n",
              "1           1962        65013\n",
              "2           1963        64527\n",
              "3           1971        64458\n",
              "4           2008        64341\n",
              "5           2007        64044\n",
              "6           2010        63897\n",
              "7           1972        63216\n",
              "8           1960        62778\n",
              "9           2009        62541"
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              "                                                     [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",
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DataFrame de los 10 años con menos muertes**"
      ],
      "metadata": {
        "id": "Gm9PVKi1gxOH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# los 10 años con menos muertes.\n",
        "df_death = pd.read_csv(ruta + elemento)\n",
        "df_death.drop(columns = 'Natural increase', inplace=True)\n",
        "df_death.drop(columns = 'Live births', inplace=True)\n",
        "df_death.sort_values(by='Deaths', ascending=True, ignore_index=True, inplace=True)\n",
        "df_death.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "IwjiSPIdgSQf",
        "outputId": "1d2cb643-f068-4816-9b83-6c38783778d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   December year  Deaths\n",
              "0           1953   18354\n",
              "1           1954   18876\n",
              "2           1952   18897\n",
              "3           1955   19224\n",
              "4           1956   19698\n",
              "5           1958   20301\n",
              "6           1957   20862\n",
              "7           1960   20892\n",
              "8           1959   21126\n",
              "9           1961   21780"
            ],
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a405dfbf-8414-4284-9455-ade62f8796fd');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DataFrame de los años con menos de 50000 nacimientos**"
      ],
      "metadata": {
        "id": "t7DH1iiyiI8O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# y los años con menos de 50000 nacimientos\n",
        "df_births = df['Live births'] < 50000\n",
        "filtered_births_df = df[df_births]\n",
        "filtered_births_df.head()\n",
        "# aparentemente hay uno solo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "96yxKBN_iHbv",
        "outputId": "ab3387cc-44bb-42fa-a0bf-c9451aef7202"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    December year  Live births  Deaths\n",
              "30           1982        49938   25533"
            ],
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              "      <th>December year</th>\n",
              "      <th>Live births</th>\n",
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              "      <td>49938</td>\n",
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              "                                                     [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",
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            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Obtener también el valor medio de nacimientos en todo el dataset**"
      ],
      "metadata": {
        "id": "u3tSAfeCCwkj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# valor medio de nacimientos\n",
        "births_mean_df = df['Live births'].mean()\n",
        "print( f'Promedio: {births_mean_df} ')\n",
        "births_df = pd.DataFrame(df['Live births'])\n",
        "births_df.head(7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "WT7acll2Cx9w",
        "outputId": "f85a70e6-81bb-43c9-cebe-d66160a24837"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Promedio: 58133.91428571429 \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Live births\n",
              "0        51846\n",
              "1        51888\n",
              "2        54057\n",
              "3        55596\n",
              "4        56532\n",
              "5        58425\n",
              "6        60558"
            ],
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              "      <td>58425</td>\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Obtener también el valor medio de muertes en todo el dataset**"
      ],
      "metadata": {
        "id": "jFVq2tJHCybT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#valor medio de muertes\n",
        "deaths_mean_df = df['Deaths'].mean()\n",
        "print( f'Promedio: {deaths_mean_df} ')\n",
        "\n",
        "# Ubicar en la tabla por index\n",
        "df.sort_values(by='Deaths', ascending=True, ignore_index=True, inplace=True)\n",
        "death_mean = pd.DataFrame(df.loc[30:35, 'Deaths']) \n",
        "# print(x1, \"\\n\")\n",
        "\n",
        "# head() ahora esta limitado a los valores de df.loc (o df.ix)\n",
        "death_mean.head(7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "c0-GdMdrCy9E",
        "outputId": "d00daff0-fc6f-47e1-b81b-44bf946a51e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Promedio: 26217.557142857142 \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Deaths\n",
              "30   25962\n",
              "31   25992\n",
              "32   26205\n",
              "33   26391\n",
              "34   26532\n",
              "35   26658"
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aee2a766-6134-48f2-9925-dc322419df90');\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": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# info mas detallada sobre el df especificado:\n",
        "print(df['Deaths'].describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C5bTx2-jGqAz",
        "outputId": "7de0d2a9-7a7c-4e7a-c433-478f00c0263e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count       70.000000\n",
            "mean     26217.557143\n",
            "std       3725.131289\n",
            "min      18354.000000\n",
            "25%      24348.000000\n",
            "50%      26595.000000\n",
            "75%      28214.250000\n",
            "max      34932.000000\n",
            "Name: Deaths, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 5 \n",
        "Del portal <a href=\"https://www.datos.gob.ar/\">datos.gob.ar</a> obtener el dataset de los precios vigentes del <a href=\"https://datos.gob.ar/dataset/energia-precios-surtidor---resolucion-3142016/archivo/energia_80ac25de\n",
        "-a44a-4445-9215-090cf55cfda5\">combustible<sup>1</sup></a>. Analizar la información y responder a las siguientes preguntas:<br>\n",
        "a. ¿Dónde se encuentran los precios más altos? ¿Y los más bajos?<br>\n",
        "b. ¿Cuál es el valor promedio de cada combustible en Argentina?<br>\n",
        "c. ¿Qué empresa vende el GNC a menor precio? ¿Dónde se ubica?<br>\n",
        "d. ¿Cuál es el valor promedio de la Nafta súper en cada provincia?"
      ],
      "metadata": {
        "id": "9OTB-bXRQF8u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "ruta = '/content/'\n",
        "elemento = 'precios-en-surtidor-resolucin-3142016.csv'\n",
        "df = pd.read_csv(ruta + elemento)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "5HcJ3MKE_bde",
        "outputId": "7e870a1a-8754-4603-c996-c909c4b49bbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  indice_tiempo  idempresa           cuit               empresa  \\\n",
              "0       2022-09       1376  33-64337382-9  10 DE SETIEMBRE S.A.   \n",
              "1       2022-09       1376  33-64337382-9  10 DE SETIEMBRE S.A.   \n",
              "2       2022-09       1376  33-64337382-9  10 DE SETIEMBRE S.A.   \n",
              "3       2022-09       1376  33-64337382-9  10 DE SETIEMBRE S.A.   \n",
              "4       2022-09       1376  33-64337382-9  10 DE SETIEMBRE S.A.   \n",
              "\n",
              "         direccion          localidad     provincia    region  idproducto  \\\n",
              "0  Av. Mosconi 299  LOMAS DEL MIRADOR  BUENOS AIRES  PAMPEANA          19   \n",
              "1  Av. Mosconi 299  LOMAS DEL MIRADOR  BUENOS AIRES  PAMPEANA          19   \n",
              "2  Av. Mosconi 299  LOMAS DEL MIRADOR  BUENOS AIRES  PAMPEANA          21   \n",
              "3  Av. Mosconi 299  LOMAS DEL MIRADOR  BUENOS AIRES  PAMPEANA          21   \n",
              "4  Av. Mosconi 299  LOMAS DEL MIRADOR  BUENOS AIRES  PAMPEANA           6   \n",
              "\n",
              "          producto  idtipohorario tipohorario  precio       fecha_vigencia  \\\n",
              "0  Gas Oil Grado 2              2      Diurno  156.80  2022-09-13 14:17:00   \n",
              "1  Gas Oil Grado 2              3    Nocturno  156.80  2022-09-13 14:17:00   \n",
              "2  Gas Oil Grado 3              2      Diurno  219.03  2022-09-05 17:29:00   \n",
              "3  Gas Oil Grado 3              3    Nocturno  219.03  2022-09-05 17:29:00   \n",
              "4              GNC              2      Diurno   63.90  2022-09-05 17:30:00   \n",
              "\n",
              "   idempresabandera empresabandera    latitud   longitud  \\\n",
              "0                28           PUMA -34.658476 -58.529443   \n",
              "1                28           PUMA -34.658476 -58.529443   \n",
              "2                28           PUMA -34.658476 -58.529443   \n",
              "3                28           PUMA -34.658476 -58.529443   \n",
              "4                28           PUMA -34.658476 -58.529443   \n",
              "\n",
              "                                             geojson  \n",
              "0  {\"type\":\"Point\",\"coordinates\":[-58.529443,-34....  \n",
              "1  {\"type\":\"Point\",\"coordinates\":[-58.529443,-34....  \n",
              "2  {\"type\":\"Point\",\"coordinates\":[-58.529443,-34....  \n",
              "3  {\"type\":\"Point\",\"coordinates\":[-58.529443,-34....  \n",
              "4  {\"type\":\"Point\",\"coordinates\":[-58.529443,-34....  "
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>indice_tiempo</th>\n",
              "      <th>idempresa</th>\n",
              "      <th>cuit</th>\n",
              "      <th>empresa</th>\n",
              "      <th>direccion</th>\n",
              "      <th>localidad</th>\n",
              "      <th>provincia</th>\n",
              "      <th>region</th>\n",
              "      <th>idproducto</th>\n",
              "      <th>producto</th>\n",
              "      <th>idtipohorario</th>\n",
              "      <th>tipohorario</th>\n",
              "      <th>precio</th>\n",
              "      <th>fecha_vigencia</th>\n",
              "      <th>idempresabandera</th>\n",
              "      <th>empresabandera</th>\n",
              "      <th>latitud</th>\n",
              "      <th>longitud</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2022-09</td>\n",
              "      <td>1376</td>\n",
              "      <td>33-64337382-9</td>\n",
              "      <td>10 DE SETIEMBRE S.A.</td>\n",
              "      <td>Av. Mosconi 299</td>\n",
              "      <td>LOMAS DEL MIRADOR</td>\n",
              "      <td>BUENOS AIRES</td>\n",
              "      <td>PAMPEANA</td>\n",
              "      <td>19</td>\n",
              "      <td>Gas Oil Grado 2</td>\n",
              "      <td>2</td>\n",
              "      <td>Diurno</td>\n",
              "      <td>156.80</td>\n",
              "      <td>2022-09-13 14:17:00</td>\n",
              "      <td>28</td>\n",
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              "      <td>{\"type\":\"Point\",\"coordinates\":[-58.529443,-34....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-09</td>\n",
              "      <td>1376</td>\n",
              "      <td>33-64337382-9</td>\n",
              "      <td>10 DE SETIEMBRE S.A.</td>\n",
              "      <td>Av. Mosconi 299</td>\n",
              "      <td>LOMAS DEL MIRADOR</td>\n",
              "      <td>BUENOS AIRES</td>\n",
              "      <td>PAMPEANA</td>\n",
              "      <td>19</td>\n",
              "      <td>Gas Oil Grado 2</td>\n",
              "      <td>3</td>\n",
              "      <td>Nocturno</td>\n",
              "      <td>156.80</td>\n",
              "      <td>2022-09-13 14:17:00</td>\n",
              "      <td>28</td>\n",
              "      <td>PUMA</td>\n",
              "      <td>-34.658476</td>\n",
              "      <td>-58.529443</td>\n",
              "      <td>{\"type\":\"Point\",\"coordinates\":[-58.529443,-34....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-09</td>\n",
              "      <td>1376</td>\n",
              "      <td>33-64337382-9</td>\n",
              "      <td>10 DE SETIEMBRE S.A.</td>\n",
              "      <td>Av. Mosconi 299</td>\n",
              "      <td>LOMAS DEL MIRADOR</td>\n",
              "      <td>BUENOS AIRES</td>\n",
              "      <td>PAMPEANA</td>\n",
              "      <td>21</td>\n",
              "      <td>Gas Oil Grado 3</td>\n",
              "      <td>2</td>\n",
              "      <td>Diurno</td>\n",
              "      <td>219.03</td>\n",
              "      <td>2022-09-05 17:29:00</td>\n",
              "      <td>28</td>\n",
              "      <td>PUMA</td>\n",
              "      <td>-34.658476</td>\n",
              "      <td>-58.529443</td>\n",
              "      <td>{\"type\":\"Point\",\"coordinates\":[-58.529443,-34....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-09</td>\n",
              "      <td>1376</td>\n",
              "      <td>33-64337382-9</td>\n",
              "      <td>10 DE SETIEMBRE S.A.</td>\n",
              "      <td>Av. Mosconi 299</td>\n",
              "      <td>LOMAS DEL MIRADOR</td>\n",
              "      <td>BUENOS AIRES</td>\n",
              "      <td>PAMPEANA</td>\n",
              "      <td>21</td>\n",
              "      <td>Gas Oil Grado 3</td>\n",
              "      <td>3</td>\n",
              "      <td>Nocturno</td>\n",
              "      <td>219.03</td>\n",
              "      <td>2022-09-05 17:29:00</td>\n",
              "      <td>28</td>\n",
              "      <td>PUMA</td>\n",
              "      <td>-34.658476</td>\n",
              "      <td>-58.529443</td>\n",
              "      <td>{\"type\":\"Point\",\"coordinates\":[-58.529443,-34....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-09</td>\n",
              "      <td>1376</td>\n",
              "      <td>33-64337382-9</td>\n",
              "      <td>10 DE SETIEMBRE S.A.</td>\n",
              "      <td>Av. Mosconi 299</td>\n",
              "      <td>LOMAS DEL MIRADOR</td>\n",
              "      <td>BUENOS AIRES</td>\n",
              "      <td>PAMPEANA</td>\n",
              "      <td>6</td>\n",
              "      <td>GNC</td>\n",
              "      <td>2</td>\n",
              "      <td>Diurno</td>\n",
              "      <td>63.90</td>\n",
              "      <td>2022-09-05 17:30:00</td>\n",
              "      <td>28</td>\n",
              "      <td>PUMA</td>\n",
              "      <td>-34.658476</td>\n",
              "      <td>-58.529443</td>\n",
              "      <td>{\"type\":\"Point\",\"coordinates\":[-58.529443,-34....</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13dfa354-9a18-434e-80c3-94fb692bee5c')\"\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-13dfa354-9a18-434e-80c3-94fb692bee5c 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-13dfa354-9a18-434e-80c3-94fb692bee5c');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**a. Precios mas altos:**"
      ],
      "metadata": {
        "id": "vACxUmVMD01e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# precio mas alto\n",
        "for x in df['precio']:\n",
        "    if x == df['precio'].max():\n",
        "       print(f' {x} es el precio mas alto. ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "odR1O2lQQX0n",
        "outputId": "dec51ed1-79f5-4e42-c140-c5231eb90390"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " 17260.0 es el precio mas alto. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# precios mas altos y la empresa\n",
        "df_top_precios = pd.DataFrame(df[['precio','empresa']])\n",
        "df_top_precios.sort_values(by='precio', ascending=False, ignore_index=True, inplace=True)\n",
        "df_top_precios.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "D0nLediBBPbG",
        "outputId": "ec27095e-9f5a-4a3b-d42c-eb1de94c60a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    precio                         empresa\n",
              "0  17260.0                SERVICIOS LER SA\n",
              "1  11490.0         JULIO CESAR SANGIULIANO\n",
              "2   7399.0                         ALOS SA\n",
              "3    299.0  SUC.MANUEL GARCIA Y OTROS S.H.\n",
              "4    299.0  SUC.MANUEL GARCIA Y OTROS S.H."
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>precio</th>\n",
              "      <th>empresa</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17260.0</td>\n",
              "      <td>SERVICIOS LER SA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11490.0</td>\n",
              "      <td>JULIO CESAR SANGIULIANO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7399.0</td>\n",
              "      <td>ALOS SA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>299.0</td>\n",
              "      <td>SUC.MANUEL GARCIA Y OTROS S.H.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>299.0</td>\n",
              "      <td>SUC.MANUEL GARCIA Y OTROS S.H.</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**¿Y los más bajos?**"
      ],
      "metadata": {
        "id": "_HP8R1keDtW9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# precio mas bajo\n",
        "for x in df['precio']:\n",
        "    if x == df['precio'].min():\n",
        "       print(f' {x} es el precio mas bajo.')\n",
        "# esta repetido?"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4OThaHgkDuO4",
        "outputId": "e2db68eb-9d72-4d74-fe1e-d42160b11799"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " 7.0 es el precio mas bajo.\n",
            " 7.0 es el precio mas bajo.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# precios mas bajos y la empresa\n",
        "df_top_precios = pd.DataFrame(df[['precio','empresa']])\n",
        "df_top_precios.sort_values(by='precio', ascending=True, ignore_index=True, inplace=True)\n",
        "df_top_precios.head()"
      ],
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          "height": 206
        },
        "id": "ew91LGB_EfPt",
        "outputId": "e06d11bc-fc79-4e45-db76-17bad63147fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   precio                                      empresa\n",
              "0   7.000              MONTES G-GARGIULO ANTONIO  S.H.\n",
              "1   7.000              MONTES G-GARGIULO ANTONIO  S.H.\n",
              "2   7.777                  H Y G L CONSTRUCCIONES S.A.\n",
              "3   7.777                  H Y G L CONSTRUCCIONES S.A.\n",
              "4   8.290  COMBUSTIBLES BOULEVARD SETENTA Y DOS S.R.L."
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              "\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "**b. ¿Cuál es el valor promedio de cada combustible en Argentina?**"
      ],
      "metadata": {
        "id": "5cr6gwsfEyor"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# de cada tipo de combustible ?\n",
        "\n",
        "# valor promedio de todos los combustibles\n",
        "mean_df = df['precio'].mean()\n",
        "print( f'Promedio: {mean_df} ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I2yv6d6rE1BG",
        "outputId": "ceca5d7b-f2de-4b98-e4e5-409f8a0ece27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Promedio: 156.0681668973977 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**c. ¿Qué empresa vende el GNC a menor precio? ¿Dónde se ubica?**"
      ],
      "metadata": {
        "id": "njx9QoWDXHRc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GNC a menor precio (empresa y ubicacion)\n",
        "df_top_precios = pd.DataFrame(df[['precio','producto','empresa','region','provincia','localidad','direccion']])\n",
        "df_top_precios.sort_values(by='precio', ascending=True, ignore_index=True, inplace=True)\n",
        "# Con esto comprobamos que falta filtrar por GNC:\n",
        "# df_top_precios = pd.DataFrame(df_top_precios.loc[34220:, 'producto']) \n",
        "df_top_precios.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "B5p-r0AKXI0C",
        "outputId": "c55b1e97-10ca-4847-d087-abd5ad04ab21"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   precio producto                                      empresa    region  \\\n",
              "0   7.000      GNC              MONTES G-GARGIULO ANTONIO  S.H.  PAMPEANA   \n",
              "1   7.000      GNC              MONTES G-GARGIULO ANTONIO  S.H.  PAMPEANA   \n",
              "2   7.777      GNC                  H Y G L CONSTRUCCIONES S.A.  PAMPEANA   \n",
              "3   7.777      GNC                  H Y G L CONSTRUCCIONES S.A.  PAMPEANA   \n",
              "4   8.290      GNC  COMBUSTIBLES BOULEVARD SETENTA Y DOS S.R.L.  PAMPEANA   \n",
              "\n",
              "      provincia localidad               direccion  \n",
              "0  BUENOS AIRES  LA PLATA                60 Y 150  \n",
              "1  BUENOS AIRES  LA PLATA                60 Y 150  \n",
              "2  BUENOS AIRES   CASEROS  Avenida Bme Mitre 4708  \n",
              "3  BUENOS AIRES   CASEROS  Avenida Bme Mitre 4708  \n",
              "4  BUENOS AIRES  LA PLATA                 72 1194  "
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              "        buttonEl.style.display =\n",
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              "\n",
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              "          const element = document.querySelector('#df-7a0b3bd6-da01-49f0-9c6d-d84924c86dde');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Asi seria una version correcta filtrada por GNC:\n",
        "df_GNC = df.loc[df['producto'] == 'GNC']\n",
        "df_GNC.head()\n",
        "\n",
        "df_top_GNC = pd.DataFrame(df_GNC[['precio','producto','empresa','region','provincia','localidad','direccion']])\n",
        "df_top_GNC.sort_values(by='precio', ascending=True, ignore_index=True, inplace=True)\n",
        "# para asegurar que solo haya productos == GNC:\n",
        "# df_top_GNC = pd.DataFrame(df_GNC.loc[34220:, 'producto']) \n",
        "df_top_GNC.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "UmdaM8Gd-i4v",
        "outputId": "1b728820-2984-4c4f-97dc-13ec2fc769f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   precio producto                                      empresa    region  \\\n",
              "0   7.000      GNC              MONTES G-GARGIULO ANTONIO  S.H.  PAMPEANA   \n",
              "1   7.000      GNC              MONTES G-GARGIULO ANTONIO  S.H.  PAMPEANA   \n",
              "2   7.777      GNC                  H Y G L CONSTRUCCIONES S.A.  PAMPEANA   \n",
              "3   7.777      GNC                  H Y G L CONSTRUCCIONES S.A.  PAMPEANA   \n",
              "4   8.290      GNC  COMBUSTIBLES BOULEVARD SETENTA Y DOS S.R.L.  PAMPEANA   \n",
              "\n",
              "      provincia localidad               direccion  \n",
              "0  BUENOS AIRES  LA PLATA                60 Y 150  \n",
              "1  BUENOS AIRES  LA PLATA                60 Y 150  \n",
              "2  BUENOS AIRES   CASEROS  Avenida Bme Mitre 4708  \n",
              "3  BUENOS AIRES   CASEROS  Avenida Bme Mitre 4708  \n",
              "4  BUENOS AIRES  LA PLATA                 72 1194  "
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**d. Valor promedio de la Nafta súper en cada provincia**"
      ],
      "metadata": {
        "id": "4Rt3JRX2XlAV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_nafta = df.loc[df['producto'] == 'Nafta (súper) entre 92 y 95 Ron']          # Filtrado por nafta super\n",
        "df_nafta.head()\n",
        "\n",
        "mean_nafta_df = df_nafta['precio'].mean()                                       # Promedio de la nafta super\n",
        "print( f'Promedio: {mean_nafta_df} ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwFFA40pXmHc",
        "outputId": "ae9b15bf-2528-4435-b27e-960c3741dc86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Promedio: 140.18595917325848 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_nafta_df = pd.DataFrame(df_nafta[['precio','producto','provincia']])       # DataFrame de precio, producto y provincia con el filtro de \"df_nafta\"\n",
        "mean_nafta_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7Cz7R2yPXCyZ",
        "outputId": "53564a10-432a-4033-b8f3-ea6d1ac5f9c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    precio                         producto     provincia\n",
              "8    147.4  Nafta (súper) entre 92 y 95 Ron  BUENOS AIRES\n",
              "9    147.4  Nafta (súper) entre 92 y 95 Ron  BUENOS AIRES\n",
              "16   109.2  Nafta (súper) entre 92 y 95 Ron        CHUBUT\n",
              "17   109.2  Nafta (súper) entre 92 y 95 Ron        CHUBUT\n",
              "24   155.6  Nafta (súper) entre 92 y 95 Ron  BUENOS AIRES"
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "provincia = mean_nafta_df.loc[df['provincia'] == 'ENTRE RIOS']                # Cambiando este parametro vemos el promedio de las diferentes provincias\n",
        "# buenosAires = mean_nafta_df.loc[df['producto'] == 'Nafta (súper) entre 92 y 95 Ron']\n",
        "df_buenosAires = pd.DataFrame(provincia[['precio','producto','provincia']])   # DataFrame de precio, producto y provincia con ambos filtros de \"provincia\" y \"df_nafta\"\n",
        "df_buenosAires.head()                                                         # No entiendo bien si realmente esta tomando el promedio y por que ¡?"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "tx0osDwOYHMe",
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      "execution_count": null,
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            "text/plain": [
              "     precio                         producto   provincia\n",
              "74    145.9  Nafta (súper) entre 92 y 95 Ron  ENTRE RIOS\n",
              "75    145.9  Nafta (súper) entre 92 y 95 Ron  ENTRE RIOS\n",
              "118   150.1  Nafta (súper) entre 92 y 95 Ron  ENTRE RIOS\n",
              "119   150.1  Nafta (súper) entre 92 y 95 Ron  ENTRE RIOS\n",
              "614   145.9  Nafta (súper) entre 92 y 95 Ron  ENTRE RIOS"
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              "      <th>provincia</th>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 6\n",
        "El <a href=\"https://datos.gob.ar/dataset/produccion-porcentaje-mujeres-por-provincia-sector-actividad/archivo/produccion_07874c24-6f5d-45e1-bae0-dd0ec001500b\">dataset<sup>2</sup></a>, también obtenido de <a href=\"https://www.datos.gob.ar/\">datos.gob.ar</a> contiene el porcentaje de puestos ocupados por mujeres en cada provincia, mensualmente, desde el año 2013 hasta el 2021. Analizar la información y obtener los siguientes datos:<br>\n",
        "a. Año con el mayor porcentaje de mujeres ocupadas.<br>\n",
        "b. Provincia con mayor porcentaje de mujeres ocupadas a lo largo de todos los años.<br>\n",
        "&nbsp;&nbsp;&nbsp;&nbsp;¿Cuál es la provincia que le sigue en la lista?<br>\n",
        "c. Mes del año que mayor ocupación posee, en promedio.<br>\n",
        "d. ¿En qué casos la ocupación superó el 50% de ocupación? ¿Y el 40%"
      ],
      "metadata": {
        "id": "sX3_Ybb5iwVO"
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "ruta = '/content/'\n",
        "elemento = 'share_mujer_todos.csv'\n",
        "df = pd.read_csv(ruta + elemento)\n",
        "df.head()"
      ],
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      "execution_count": null,
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        {
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            "text/plain": [
              "        fecha        zona_prov  share_mujer\n",
              "0  2013-01-01     BUENOS AIRES     0.396839\n",
              "1  2013-01-01  CAPITAL FEDERAL     0.405973\n",
              "2  2013-01-01        CATAMARCA     0.387500\n",
              "3  2013-01-01            CHACO     0.396491\n",
              "4  2013-01-01           CHUBUT     0.363042"
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              "\n",
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    {
      "cell_type": "markdown",
      "source": [
        "**a. Año con el mayor porcentaje de mujeres ocupadas.**"
      ],
      "metadata": {
        "id": "_iHRIPYfmKFN"
      }
    },
    {
      "cell_type": "code",
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        "mayorPorcentaje = pd.read_csv(ruta + elemento)\n",
        "mayorPorcentaje.sort_values(by='share_mujer', ascending=False, ignore_index=True, inplace=True)\n",
        "mayorPorcentaje.head()"
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            "text/plain": [
              "        fecha zona_prov  share_mujer\n",
              "0  2020-03-01     CHACO     0.445126\n",
              "1  2019-12-01     CHACO     0.445041\n",
              "2  2021-09-01  LA PAMPA     0.444698\n",
              "3  2020-02-01     CHACO     0.444633\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2020 es el año con mayor porcentaje de mujeres ocupadas."
      ],
      "metadata": {
        "id": "Yl0tpMigL6s2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**b. Provincia con mayor porcentaje de mujeres ocupadas a lo largo de todos los años.**<br>&nbsp;&nbsp;&nbsp;&nbsp;¿Cuál es la provincia que le sigue en la lista?<br><br>\n"
      ],
      "metadata": {
        "id": "AVKRI2J7vryS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Chaco es la provincia con mayor porcentaje de mujeres ocupadas y La Pampa es la provincia que le sigue."
      ],
      "metadata": {
        "id": "lxV_FI-D12OL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**c. Mes del año que mayor ocupación posee, en promedio.**"
      ],
      "metadata": {
        "id": "UXbU0f3FMHX1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "share_mujer_promedio = df['share_mujer'].mean()\n",
        "print( f'Promedio: {share_mujer_promedio} ')\n",
        "\n",
        "# mesPromedio = df.loc[df['share_mujer'] == share_mujer_promedio]\n",
        "\n",
        "df.sort_values(by='share_mujer', ascending=True, ignore_index=True, inplace=True)\n",
        "mesPromedio = pd.DataFrame(df.loc[990:])\n",
        "mesPromedio.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "OwRaHR6tvtSq",
        "outputId": "8b10aa41-82a0-47cb-f7a5-9702b44add7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Promedio: 0.38930630361356927 \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          fecha zona_prov  share_mujer\n",
              "990  2015-05-01     SALTA     0.389572\n",
              "991  2019-11-01     SALTA     0.389595\n",
              "992  2020-07-01  MISIONES     0.389614\n",
              "993  2017-10-01     SALTA     0.389616\n",
              "994  2016-10-01     SALTA     0.389623"
            ],
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              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "992 2020-07-01 MISIONES ?¿ 0.389614 es el mas cercano al promedio"
      ],
      "metadata": {
        "id": "4WaIdfORysw3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ahora si casi seguro que es este el mas cercano al promedio\n",
        "# si el promedio de toda la ocupacion  de mujeres es \"share_mujer_promedio\"\n",
        "# entonces el mas cercano a ese promedio va a ser la fecha con mas ocupacion en promedio.\n",
        "df['share_mujer'] = df['share_mujer'].astype(str)\n",
        "mesPromedio = df[df['share_mujer'].str.startswith(\"0.38930\")]\n",
        "# mesPromedio.dtypes\n",
        "mesPromedio.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "4iHVPFZ799js",
        "outputId": "c054291f-e1ac-48a7-920e-a27139913d1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          fecha zona_prov share_mujer\n",
              "979  2016-07-01   MENDOZA  0.38930362"
            ],
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              "          const element = document.querySelector('#df-42a84b47-3287-4ad4-bcd7-9c09fc1518ff');\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**d. ¿En qué casos la ocupación superó el 50% de ocupación? ¿Y el 40%**"
      ],
      "metadata": {
        "id": "Jg-HDiMNy-il"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['share_mujer'] = df['share_mujer'].astype(str)\n",
        "ocupacion50 = df[df['share_mujer'].str.startswith(\"0.44\")] # cambiando este parametro al 50% no se encuentra nada.\n",
        "ocupacion50.head(10)\n",
        "# Casos en que supero el 40%:"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "9pu1nBMKy_xO",
        "outputId": "99aab024-c982-4c28-bd41-c4d87063a91f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           fecha zona_prov share_mujer\n",
              "2706  2019-11-01     CHACO  0.44351566\n",
              "2707  2020-01-01     CHACO   0.4440603\n",
              "2708  2020-02-01     CHACO  0.44463295\n",
              "2709  2021-09-01  LA PAMPA  0.44469762\n",
              "2710  2019-12-01     CHACO  0.44504061\n",
              "2711  2020-03-01     CHACO  0.44512558"
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              "    .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-28efe26b-e588-48a7-8728-cf617216ae65 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-28efe26b-e588-48a7-8728-cf617216ae65');\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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Casos en que la ocupacion supero el 50% = 0"
      ],
      "metadata": {
        "id": "sUEU_Gb0ALY8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Otra forma de filtrar busquedas"
      ],
      "metadata": {
        "id": "XqCznCVf9Rbt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "provincias = df.groupby('zona_prov')['share_mujer'].mean()\n",
        "provincias.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3RWjEZv1owN",
        "outputId": "0fba2e54-9370-4370-bbf7-37a188d1887d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "zona_prov\n",
              "BUENOS AIRES       0.405184\n",
              "CAPITAL FEDERAL    0.410664\n",
              "CATAMARCA          0.395907\n",
              "CHACO              0.413318\n",
              "CHUBUT             0.379282\n",
              "Name: share_mujer, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    }
  ]
}