ID_sin_cob.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1t_tlS4Nz9-wVl24PFpfVSpZiuXoBA35G",
      "authorship_tag": "ABX9TyNE3fWZTtnuYo+qlv59nPVx",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JorgeICS/Primeos_Analisis_Geopandas/blob/main/ID_sin_cob.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install geopandas pandas matplotlib"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9e7636b-7a36-4489-f376-b27213b9849a",
        "id": "VT6wsa-HEx0s"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: geopandas in /usr/local/lib/python3.10/dist-packages (0.14.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.1.4)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: fiona>=1.8.21 in /usr/local/lib/python3.10/dist-packages (from geopandas) (1.9.6)\n",
            "Requirement already satisfied: numpy>=1.22 in /usr/local/lib/python3.10/dist-packages (from geopandas) (1.26.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from geopandas) (24.1)\n",
            "Requirement already satisfied: pyproj>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from geopandas) (3.6.1)\n",
            "Requirement already satisfied: shapely>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from geopandas) (2.0.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n",
            "Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.21->geopandas) (23.2.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.21->geopandas) (2024.7.4)\n",
            "Requirement already satisfied: click~=8.0 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.21->geopandas) (8.1.7)\n",
            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.21->geopandas) (1.1.1)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.21->geopandas) (0.7.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from fiona>=1.8.21->geopandas) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import geopandas as gpd\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "o4JmSH1eEx0u"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Juntar Archivos sin cobertura"
      ],
      "metadata": {
        "id": "3SeR-l8jFE1V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lista de nombres de los estados en orden alfabético\n",
        "estados = [\n",
        "    \"Aguascalientes\", \"Baja California\", \"Baja California Sur\", \"Campeche\",\n",
        "    \"Chiapas\", \"Chihuahua\", \"Coahuila de Zaragoza\", \"Colima\", \"Ciudad de México\",\n",
        "    \"Durango\", \"Guanajuato\", \"Guerrero\", \"Hidalgo\", \"Jalisco\", \"México\",\n",
        "    \"Michoacán de Ocampo\", \"Morelos\", \"Nayarit\", \"Nuevo León\", \"Oaxaca\", \"Puebla\", \"Querétaro\",\n",
        "    \"Quintana Roo\", \"San Luis Potosí\", \"Sinaloa\", \"Sonora\", \"Tabasco\", \"Tamaulipas\",\n",
        "    \"Tlaxcala\", \"Veracruz de Ignacio de la Llave\", \"Yucatán\", \"Zacatecas\"\n",
        "]"
      ],
      "metadata": {
        "id": "OMd8EQgIxTFN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Ruta de la carpeta principal\n",
        "base_path = '/content/drive/MyDrive/Carreteras/sin_bam/'\n",
        "dataframes_estados = []\n"
      ],
      "metadata": {
        "id": "JyVshiMgE7yz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for estado in estados:\n",
        "    file_path = f'{base_path}/{estado}_total_carr2023_sincob1t24.shp'\n",
        "    df_estado = gpd.read_file(file_path)\n",
        "    df_estado['Estado'] = estado\n",
        "    dataframes_estados.append(df_estado)\n",
        "\n",
        "# Concatenar todos los DataFrames en uno solo\n",
        "sin_cobertura_todos = pd.concat(dataframes_estados, ignore_index=True)"
      ],
      "metadata": {
        "id": "3PxbqHN_ycgP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sin_cobertura_todos.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sz_3zrxvFugo",
        "outputId": "ac4f78e0-0ca8-444c-df6d-0633561e2ba6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14823, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sin_cobertura_todos.to_file('/content/drive/MyDrive/Carreteras/sin_bam/sin_cobertura_todos.shp')"
      ],
      "metadata": {
        "id": "ziULMz1jFzOX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Juntar archivos totales por Estado"
      ],
      "metadata": {
        "id": "FLRlBF6gFOHR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_path = '/content/drive/MyDrive/Carreteras_federales_porEstado'\n",
        "dataframes_estados = []"
      ],
      "metadata": {
        "id": "rDCApCQaUfQ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for estado in estados:\n",
        "    file_path = f'{base_path}/{estado}_carrFed2023.shp'\n",
        "    df_estado = gpd.read_file(file_path)\n",
        "    df_estado['Estado'] = estado\n",
        "    dataframes_estados.append(df_estado)\n",
        "\n",
        "# Concatenar todos los DataFrames en uno solo\n",
        "cf_totales_x_estado = pd.concat(dataframes_estados, ignore_index=True)"
      ],
      "metadata": {
        "id": "KvSLoPNdUidO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cf_totales_x_estado.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yiQcOz2aVkez",
        "outputId": "c3ee2a87-2514-4412-ee25-8174bc7b398d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(116085, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_totales_x_estado.to_file('/content/drive/MyDrive/Carreteras_federales_porEstado/cf_totales_x_estado.shp')"
      ],
      "metadata": {
        "id": "g7iHEWjXGXdn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Restablecer el índice temporalmente\n",
        "df_reset = cf_totales_x_estado.reset_index(drop=True)\n",
        "\n",
        "# Eliminar duplicados considerando todas las columnas menos el índice original\n",
        "df_sin_duplicados = df_reset.drop_duplicates()\n",
        "\n",
        "# Restaurar el índice original\n",
        "df_sin_duplicados.index = cf_totales_x_estado.index[df_sin_duplicados.index]"
      ],
      "metadata": {
        "id": "Ym34qH-5H74H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_sin_duplicados.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0nSoSHTMIdGd",
        "outputId": "ffb151da-db71-498f-cd1f-3d0d36eae451"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(116085, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_totales_x_estado.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QxfjE_yIIhHb",
        "outputId": "36a3e364-7b90-440b-bdae-dcce37d3072f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(116085, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_totales_x_estado=gpd.read_file('/content/drive/MyDrive/Carreteras_federales_porEstado/cf_totales_x_estado.shp')"
      ],
      "metadata": {
        "id": "-jQ3aI3GCtUw"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sin_cobertura_todos=gpd.read_file('/content/drive/MyDrive/Carreteras/sin_bam/sin_cobertura_todos.shp')"
      ],
      "metadata": {
        "id": "7AoSFZJoC60h"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sin_ID=cf_totales_x_estado.loc[cf_totales_x_estado['NOMBRE']=='N/D']"
      ],
      "metadata": {
        "id": "6qDEudlHKFjt"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cf_con_ID=cf_totales_x_estado.loc[cf_totales_x_estado['NOMBRE']!='N/D']"
      ],
      "metadata": {
        "id": "K-zxp0x_LOB7"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columnas_originales=sin_cobertura_todos.columns\n",
        "nuevos_ID= pd.DataFrame(columns=columnas_originales)\n",
        "nuevos_ID.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OsNsmwLVMUsf",
        "outputId": "e146c153-3d16-43bc-af0a-651caa71cb12"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#CAMBIO BUENO\n",
        "columna1 = 'UNION_INI'\n",
        "columna2 = 'UNION_FIN'\n",
        "columna3 = 'CODIGO'\n",
        "# Columnas cf_con_ID\n",
        "columna4 = 'NOMBRE'\n",
        "\n",
        "while len(sin_ID) > 0:\n",
        "\n",
        "  for idx, row in sin_ID.iterrows():\n",
        "\n",
        "    if (row[columna1] in cf_con_ID[columna2].values) and (row[columna2] in cf_con_ID[columna1].values):\n",
        "      nombre_nuevo = cf_con_ID.loc[cf_con_ID[columna1] == row[columna2], 'NOMBRE'].values[0]\n",
        "      sin_ID.at[idx, 'NOMBRE'] = nombre_nuevo\n",
        "      añadir = sin_ID.loc[sin_ID['NOMBRE'].str.len() > 3]\n",
        "      nuevos_ID = pd.concat([añadir, nuevos_ID], ignore_index=False)\n",
        "      cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "      sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]\n",
        "\n",
        "    elif (row[columna1] in cf_con_ID[columna2].values) and (row[columna2] not in cf_con_ID[columna1].values) and (row[columna3] in cf_con_ID[columna3].values):\n",
        "      nombre_nuevo = cf_con_ID.loc[cf_con_ID[columna2] == row[columna1], 'NOMBRE'].values[0]\n",
        "      sin_ID.at[idx, 'NOMBRE'] = nombre_nuevo\n",
        "      añadir = sin_ID.loc[sin_ID['NOMBRE'].str.len() > 3]\n",
        "      nuevos_ID = pd.concat([añadir, nuevos_ID], ignore_index=False)\n",
        "      cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "      sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]\n",
        "\n",
        "    elif (row[columna1] not in cf_con_ID[columna2].values) and (row[columna2] in cf_con_ID[columna1].values) and (row[columna3] in cf_con_ID[columna3].values):\n",
        "      nombre_nuevo = cf_con_ID.loc[cf_con_ID[columna1] == row[columna2], 'NOMBRE'].values[0]\n",
        "      sin_ID.at[idx, 'NOMBRE'] = nombre_nuevo\n",
        "      añadir = sin_ID.loc[sin_ID['NOMBRE'].str.len() > 3]\n",
        "      nuevos_ID = pd.concat([añadir, nuevos_ID], ignore_index=False)\n",
        "      cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "      sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]\n",
        "\n",
        "    elif (row[columna1] in cf_con_ID[columna1].values) and (row[columna2] in cf_con_ID[columna2].values):\n",
        "      nombre_nuevo = cf_con_ID.loc[cf_con_ID[columna1] == row[columna1], 'NOMBRE'].values[0]\n",
        "      nombre_nuevo2 = cf_con_ID.loc[cf_con_ID[columna2] == row[columna2], 'NOMBRE'].values[0]\n",
        "      if nombre_nuevo == nombre_nuevo2:\n",
        "        sin_ID.at[idx, 'NOMBRE'] = nombre_nuevo\n",
        "        añadir = sin_ID.loc[sin_ID['NOMBRE'].str.len() > 3]\n",
        "        nuevos_ID = pd.concat([añadir, nuevos_ID], ignore_index=False)\n",
        "        cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "        sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]\n",
        "\n",
        "      elif nombre_nuevo != nombre_nuevo2:\n",
        "        sin_ID.at[idx, 'conex_ini'] = nombre_nuevo\n",
        "        sin_ID.at[idx, 'conex_fin'] = nombre_nuevo2\n",
        "      else:\n",
        "        sin_ID.at[idx, 'NOMBRE'] = sin_ID.at[idx, 'NOMBRE']\n",
        "\n",
        "    elif (row[columna1] in cf_con_ID[columna1].values) and (row[columna2] not in cf_con_ID[columna2].values) and (row[columna3] in cf_con_ID[columna3].values):\n",
        "      nombre_nuevo = cf_con_ID.loc[cf_con_ID[columna1] == row[columna1], 'NOMBRE'].values[0]\n",
        "      sin_ID.at[idx, 'NOMBRE'] = nombre_nuevo\n",
        "      añadir = sin_ID.loc[sin_ID['NOMBRE'].str.len() > 3]\n",
        "      nuevos_ID = pd.concat([añadir, nuevos_ID], ignore_index=False)\n",
        "      cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "      sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]\n",
        "\n",
        "    elif (row[columna1] not in cf_con_ID[columna1].values) and (row[columna2] in cf_con_ID[columna2].values) and (row[columna3] in cf_con_ID[columna3].values):\n",
        "      nombre_nuevo = cf_con_ID.loc[cf_con_ID[columna2] == row[columna2], 'NOMBRE'].values[0]\n",
        "      sin_ID.at[idx, 'NOMBRE'] = nombre_nuevo\n",
        "      añadir = sin_ID.loc[sin_ID['NOMBRE'].str.len() > 3]\n",
        "      nuevos_ID = pd.concat([añadir, nuevos_ID], ignore_index=False)\n",
        "      cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "      sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]\n",
        "\n",
        "    else:\n",
        "      sin_ID.at[idx, 'NOMBRE'] = sin_ID.at[idx, 'NOMBRE']\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kDJUjlaQM-Fu",
        "outputId": "0ad4b667-9feb-4966-8c07-3ae4ad4f9fe7"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-2a410bbf71af>:24: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
            "  nuevos_ID = pd.concat([añadir, nuevos_ID], ignore_index=False)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nuevos_ID.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_Do9_hQNLFZ",
        "outputId": "487d39cb-e5ee-49f2-a271-ce31c6e70592"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(698, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sin_ID.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2IDRw-2NOEz",
        "outputId": "792ed63e-3d87-4fa3-fab7-a5e66f075643"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(306, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sin_ID=sin_cobertura_todos.loc[sin_cobertura_todos['NOMBRE']=='N/D']"
      ],
      "metadata": {
        "id": "E5QZE3ziOPsB"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sin_cobertura_nuevos_ID=sin_cobertura_todos.loc[sin_cobertura_todos['NOMBRE']!='N/D']"
      ],
      "metadata": {
        "id": "Trqgk6po1Ohp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columnas_originales=sin_cobertura_todos.columns\n",
        "nuevos_ID2= pd.DataFrame(columns=columnas_originales)\n",
        "nuevos_ID2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YsmLCkweO0Im",
        "outputId": "d06eb4e5-cb9b-47a9-bcb3-63885ff8c270"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sin_ID.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aqd_rfFGPU4C",
        "outputId": "142bb810-3484-45c9-9e88-6d36b216b684"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(141, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#CAMBIO BUENO\n",
        "columna1 = 'UNION_INI'\n",
        "columna2 = 'UNION_FIN'\n",
        "columna3 = 'CODIGO'\n",
        "# Columnas cf_con_ID\n",
        "columna4 = 'NOMBRE'\n",
        "\n",
        "while len(sin_ID) > 0:\n",
        "\n",
        "  for idx, row in sin_ID.iterrows():\n",
        "\n",
        "    if (row[columna1] in cf_con_ID[columna2].values) and (row[columna2] in cf_con_ID[columna1].values):\n",
        "      nombre_nuevo = cf_con_ID.loc[cf_con_ID[columna1] == row[columna2], 'NOMBRE'].values[0]\n",
        "      sin_ID.at[idx, 'NOMBRE'] = nombre_nuevo\n",
        "      añadir = sin_ID.loc[sin_ID['NOMBRE'].str.len() > 3]\n",
        "      nuevos_ID2 = pd.concat([añadir, nuevos_ID2], ignore_index=False)\n",
        "      cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "      sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]\n",
        "\n",
        "    elif (row[columna1] in cf_con_ID[columna2].values) and (row[columna2] not in cf_con_ID[columna1].values) and (row[columna3] in cf_con_ID[columna3].values):\n",
        "      nombre_nuevo = cf_con_ID.loc[cf_con_ID[columna2] == row[columna1], 'NOMBRE'].values[0]\n",
        "      sin_ID.at[idx, 'NOMBRE'] = nombre_nuevo\n",
        "      añadir = sin_ID.loc[sin_ID['NOMBRE'].str.len() > 3]\n",
        "      nuevos_ID2 = pd.concat([añadir, nuevos_ID2], ignore_index=False)\n",
        "      cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "      sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]\n",
        "\n",
        "    elif (row[columna1] not in cf_con_ID[columna2].values) and (row[columna2] in cf_con_ID[columna1].values) and (row[columna3] in cf_con_ID[columna3].values):\n",
        "      nombre_nuevo = cf_con_ID.loc[cf_con_ID[columna1] == row[columna2], 'NOMBRE'].values[0]\n",
        "      sin_ID.at[idx, 'NOMBRE'] = nombre_nuevo\n",
        "      añadir = sin_ID.loc[sin_ID['NOMBRE'].str.len() > 3]\n",
        "      nuevos_ID2 = pd.concat([añadir, nuevos_ID2], ignore_index=False)\n",
        "      cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "      sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]\n",
        "\n",
        "    elif (row[columna1] in cf_con_ID[columna1].values) and (row[columna2] in cf_con_ID[columna2].values):\n",
        "      nombre_nuevo = cf_con_ID.loc[cf_con_ID[columna1] == row[columna1], 'NOMBRE'].values[0]\n",
        "      nombre_nuevo2 = cf_con_ID.loc[cf_con_ID[columna2] == row[columna2], 'NOMBRE'].values[0]\n",
        "      if nombre_nuevo == nombre_nuevo2:\n",
        "        sin_ID.at[idx, 'NOMBRE'] = nombre_nuevo\n",
        "        añadir = sin_ID.loc[sin_ID['NOMBRE'].str.len() > 3]\n",
        "        nuevos_ID2 = pd.concat([añadir, nuevos_ID2], ignore_index=False)\n",
        "        cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "        sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]\n",
        "\n",
        "      elif nombre_nuevo != nombre_nuevo2:\n",
        "        sin_ID.at[idx, 'conex_ini'] = nombre_nuevo\n",
        "        sin_ID.at[idx, 'conex_fin'] = nombre_nuevo2\n",
        "      else:\n",
        "        sin_ID.at[idx, 'NOMBRE'] = sin_ID.at[idx, 'NOMBRE']\n",
        "\n",
        "    elif (row[columna1] in cf_con_ID[columna1].values) and (row[columna2] not in cf_con_ID[columna2].values) and (row[columna3] in cf_con_ID[columna3].values):\n",
        "      nombre_nuevo = cf_con_ID.loc[cf_con_ID[columna1] == row[columna1], 'NOMBRE'].values[0]\n",
        "      sin_ID.at[idx, 'NOMBRE'] = nombre_nuevo\n",
        "      añadir = sin_ID.loc[sin_ID['NOMBRE'].str.len() > 3]\n",
        "      nuevos_ID2 = pd.concat([añadir, nuevos_ID2], ignore_index=False)\n",
        "      cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "      sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]\n",
        "\n",
        "    elif (row[columna1] not in cf_con_ID[columna1].values) and (row[columna2] in cf_con_ID[columna2].values) and (row[columna3] in cf_con_ID[columna3].values):\n",
        "      nombre_nuevo = cf_con_ID.loc[cf_con_ID[columna2] == row[columna2], 'NOMBRE'].values[0]\n",
        "      sin_ID.at[idx, 'NOMBRE'] = nombre_nuevo\n",
        "      añadir = sin_ID.loc[sin_ID['NOMBRE'].str.len() > 3]\n",
        "      nuevos_ID2 = pd.concat([añadir, nuevos_ID2], ignore_index=False)\n",
        "      cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "      sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]\n",
        "\n",
        "    else:\n",
        "      sin_ID.at[idx, 'NOMBRE'] = sin_ID.at[idx, 'NOMBRE']\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "LNjHMyYTPOuW",
        "outputId": "0f253d45-48e4-49d9-e373-d93819d14c76"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-91121b73c647>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m       \u001b[0msin_ID\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'NOMBRE'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msin_ID\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'NOMBRE'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2487\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2488\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2490\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2438\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2439\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2440\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2442\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, index, col, takeable)\u001b[0m\n\u001b[1;32m   4003\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4005\u001b[0;31m         \u001b[0mseries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4006\u001b[0m         \u001b[0mengine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4007\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   4413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4414\u001b[0m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4415\u001b[0;31m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ixs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4417\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_ixs\u001b[0;34m(self, i, axis)\u001b[0m\n\u001b[1;32m   3802\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0mcol_mgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_col_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcol_mgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m             \u001b[0;31m# this is a cached value, mark it so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_box_col_values\u001b[0;34m(self, values, loc)\u001b[0m\n\u001b[1;32m   4390\u001b[0m         \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4391\u001b[0m         \u001b[0;31m# We get index=self.index bc values is a SingleDataManager\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4392\u001b[0;31m         \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor_sliced_from_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4393\u001b[0m         \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4394\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/geopandas/geodataframe.py\u001b[0m in \u001b[0;36m_constructor_sliced_from_mgr\u001b[0;34m(self, mgr, axes)\u001b[0m\n\u001b[1;32m   1648\u001b[0m         \u001b[0mis_row_proxy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1650\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGeometryDtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_row_proxy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1651\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mGeoSeries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1652\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_from_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mproperties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.CachedProperty.__get__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mdtype\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    581\u001b[0m     \u001b[0;31m# Array-Like Methods\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 583\u001b[0;31m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    584\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDtypeObj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nuevos_ID2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4tS0uM8Pjvt",
        "outputId": "97d17481-85d8-4393-81ef-15acd0572784"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(122, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nuevos_ID2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 930
        },
        "id": "E95PNsJWRxzq",
        "outputId": "dc59fa6f-5c89-4b62-9c26-6849f57e8583"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ID_RED  TIPO_VIAL                                    NOMBRE CODIGO  \\\n",
              "8760  1111699  Carretera                       Guadalajara - Tepic    N/D   \n",
              "8762  1111773  Carretera                       Guadalajara - Tepic    N/D   \n",
              "7416   862875  Carretera                        Jiquilpan - Colima    N/D   \n",
              "8755   515157  Carretera                       Guadalajara - Tepic    N/D   \n",
              "8757   862875  Carretera                        Jiquilpan - Colima    N/D   \n",
              "...       ...        ...                                       ...    ...   \n",
              "777    647971  Carretera  San José del Cabo - Aeropuerto Los Cabos      1   \n",
              "776    647972  Carretera  San José del Cabo - Aeropuerto Los Cabos      1   \n",
              "773   2566412  Carretera  San José del Cabo - Aeropuerto Los Cabos      1   \n",
              "772   2566405  Carretera  San José del Cabo - Aeropuerto Los Cabos      1   \n",
              "454    428160  Carretera     Ramal a Observatorio San Pedro Mártir    N/D   \n",
              "\n",
              "           COND_PAV   RECUBRI CARRILES     ESTATUS     CONDICION NIVEL  ...  \\\n",
              "8760  Con pavimento   Asfalto        2  Habilitado  En operación     0  ...   \n",
              "8762  Con pavimento   Asfalto        2  Habilitado  En operación     0  ...   \n",
              "7416  Con pavimento   Asfalto        2  Habilitado  En operación     0  ...   \n",
              "8755  Con pavimento   Asfalto        2  Habilitado  En operación     0  ...   \n",
              "8757  Con pavimento   Asfalto        2  Habilitado  En operación     0  ...   \n",
              "...             ...       ...      ...         ...           ...   ...  ...   \n",
              "777   Con pavimento  Concreto        2  Habilitado  En operación     1  ...   \n",
              "776   Con pavimento  Concreto        2  Habilitado  En operación     1  ...   \n",
              "773   Con pavimento  Concreto        2  Habilitado  En operación     0  ...   \n",
              "772   Con pavimento  Concreto        2  Habilitado  En operación     0  ...   \n",
              "454   Con pavimento   Asfalto        2  Habilitado  En operación     0  ...   \n",
              "\n",
              "         LONGITUD ANCHO            FECHA_ACT    CALIREPR               Estado  \\\n",
              "8760   485.118422   7.0  2022-06-15 20:40:51  Aproximada              Nayarit   \n",
              "8762   309.999791   7.0  2022-06-15 20:54:08  Aproximada              Nayarit   \n",
              "7416   924.197189   7.0  2017-09-15 13:46:44    Definida              Jalisco   \n",
              "8755  1054.557478   7.0  2022-06-15 21:07:39  Aproximada              Nayarit   \n",
              "8757   924.197189   7.0  2017-09-15 13:46:44    Definida              Nayarit   \n",
              "...           ...   ...                  ...         ...                  ...   \n",
              "777    634.213673   7.0  2021-06-12 11:11:01  Aproximada  Baja California Sur   \n",
              "776    632.237655   7.0  2021-06-11 10:26:20  Aproximada  Baja California Sur   \n",
              "773    158.804246   7.0  2023-07-05 13:18:24  Aproximada  Baja California Sur   \n",
              "772    267.645030   7.0  2022-08-02 17:53:52  Aproximada  Baja California Sur   \n",
              "454    177.798696   6.0  2012-12-03 11:10:47    Definida      Baja California   \n",
              "\n",
              "                                               geometry  \\\n",
              "8760  MULTILINESTRING ((2286720.867 1029406.231, 228...   \n",
              "8762  LINESTRING (2286644.782 1029588.785, 2286643.0...   \n",
              "7416  LINESTRING (2282301.976 1022197.173, 2282300.5...   \n",
              "8755  MULTILINESTRING ((2287036.286 1030525.666, 228...   \n",
              "8757  MULTILINESTRING ((2282266.106 1022268.873, 228...   \n",
              "...                                                 ...   \n",
              "777   MULTILINESTRING ((1710869.373 1248153.027, 171...   \n",
              "776   MULTILINESTRING ((1710814.666 1248746.436, 171...   \n",
              "773   MULTILINESTRING ((1710916.554 1247993.202, 171...   \n",
              "772   MULTILINESTRING ((1710878.092 1248102.305, 171...   \n",
              "454   LINESTRING (1202946.447 2164585.362, 1202967.9...   \n",
              "\n",
              "                    inicio entre1                  inicio entre2  \\\n",
              "8760        Durango - Villa Unión  Lagos de Moreno - Guadalajara   \n",
              "8762  Querétaro - San Luis Potosí            Guadalajara - Tepic   \n",
              "7416            Mérida - Progreso             Jiquilpan - Colima   \n",
              "8755  Querétaro - San Luis Potosí            Guadalajara - Tepic   \n",
              "8757            Mérida - Progreso             Jiquilpan - Colima   \n",
              "...                           ...                            ...   \n",
              "777                           NaN                            NaN   \n",
              "776                           NaN                            NaN   \n",
              "773                           NaN                            NaN   \n",
              "772                           NaN                            NaN   \n",
              "454                           NaN                            NaN   \n",
              "\n",
              "                       final entre1                       final entre2  \n",
              "8760  Lagos de Moreno - Guadalajara  Libramiento Noroeste de Monterrey  \n",
              "8762  Lagos de Moreno - Guadalajara  Libramiento Noroeste de Monterrey  \n",
              "7416              Mérida - Progreso                 Jiquilpan - Colima  \n",
              "8755    Querétaro - San Luis Potosí                Guadalajara - Tepic  \n",
              "8757              Mérida - Progreso                 Jiquilpan - Colima  \n",
              "...                             ...                                ...  \n",
              "777                             NaN                                NaN  \n",
              "776                             NaN                                NaN  \n",
              "773                             NaN                                NaN  \n",
              "772                             NaN                                NaN  \n",
              "454                             NaN                                NaN  \n",
              "\n",
              "[129 rows x 28 columns]"
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID_RED</th>\n",
              "      <th>TIPO_VIAL</th>\n",
              "      <th>NOMBRE</th>\n",
              "      <th>CODIGO</th>\n",
              "      <th>COND_PAV</th>\n",
              "      <th>RECUBRI</th>\n",
              "      <th>CARRILES</th>\n",
              "      <th>ESTATUS</th>\n",
              "      <th>CONDICION</th>\n",
              "      <th>NIVEL</th>\n",
              "      <th>...</th>\n",
              "      <th>LONGITUD</th>\n",
              "      <th>ANCHO</th>\n",
              "      <th>FECHA_ACT</th>\n",
              "      <th>CALIREPR</th>\n",
              "      <th>Estado</th>\n",
              "      <th>geometry</th>\n",
              "      <th>inicio entre1</th>\n",
              "      <th>inicio entre2</th>\n",
              "      <th>final entre1</th>\n",
              "      <th>final entre2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8760</th>\n",
              "      <td>1111699</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>485.118422</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-15 20:40:51</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>MULTILINESTRING ((2286720.867 1029406.231, 228...</td>\n",
              "      <td>Durango - Villa Unión</td>\n",
              "      <td>Lagos de Moreno - Guadalajara</td>\n",
              "      <td>Lagos de Moreno - Guadalajara</td>\n",
              "      <td>Libramiento Noroeste de Monterrey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8762</th>\n",
              "      <td>1111773</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>309.999791</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-15 20:54:08</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2286644.782 1029588.785, 2286643.0...</td>\n",
              "      <td>Querétaro - San Luis Potosí</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>Lagos de Moreno - Guadalajara</td>\n",
              "      <td>Libramiento Noroeste de Monterrey</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7416</th>\n",
              "      <td>862875</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>Jiquilpan - Colima</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>924.197189</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2017-09-15 13:46:44</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Jalisco</td>\n",
              "      <td>LINESTRING (2282301.976 1022197.173, 2282300.5...</td>\n",
              "      <td>Mérida - Progreso</td>\n",
              "      <td>Jiquilpan - Colima</td>\n",
              "      <td>Mérida - Progreso</td>\n",
              "      <td>Jiquilpan - Colima</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8755</th>\n",
              "      <td>515157</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1054.557478</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-15 21:07:39</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>MULTILINESTRING ((2287036.286 1030525.666, 228...</td>\n",
              "      <td>Querétaro - San Luis Potosí</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>Querétaro - San Luis Potosí</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8757</th>\n",
              "      <td>862875</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>Jiquilpan - Colima</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>924.197189</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2017-09-15 13:46:44</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>MULTILINESTRING ((2282266.106 1022268.873, 228...</td>\n",
              "      <td>Mérida - Progreso</td>\n",
              "      <td>Jiquilpan - Colima</td>\n",
              "      <td>Mérida - Progreso</td>\n",
              "      <td>Jiquilpan - Colima</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>777</th>\n",
              "      <td>647971</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>San José del Cabo - Aeropuerto Los Cabos</td>\n",
              "      <td>1</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Concreto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>634.213673</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2021-06-12 11:11:01</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Baja California Sur</td>\n",
              "      <td>MULTILINESTRING ((1710869.373 1248153.027, 171...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>776</th>\n",
              "      <td>647972</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>San José del Cabo - Aeropuerto Los Cabos</td>\n",
              "      <td>1</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Concreto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>632.237655</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2021-06-11 10:26:20</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Baja California Sur</td>\n",
              "      <td>MULTILINESTRING ((1710814.666 1248746.436, 171...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>773</th>\n",
              "      <td>2566412</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>San José del Cabo - Aeropuerto Los Cabos</td>\n",
              "      <td>1</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Concreto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>158.804246</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2023-07-05 13:18:24</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Baja California Sur</td>\n",
              "      <td>MULTILINESTRING ((1710916.554 1247993.202, 171...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>772</th>\n",
              "      <td>2566405</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>San José del Cabo - Aeropuerto Los Cabos</td>\n",
              "      <td>1</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Concreto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>267.645030</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-08-02 17:53:52</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Baja California Sur</td>\n",
              "      <td>MULTILINESTRING ((1710878.092 1248102.305, 171...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>454</th>\n",
              "      <td>428160</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>Ramal a Observatorio San Pedro Mártir</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>177.798696</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2012-12-03 11:10:47</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Baja California</td>\n",
              "      <td>LINESTRING (1202946.447 2164585.362, 1202967.9...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>129 rows × 28 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe2ced0b-c8c1-4f40-be5b-241084baa723')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-fe2ced0b-c8c1-4f40-be5b-241084baa723 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-fe2ced0b-c8c1-4f40-be5b-241084baa723');\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",
              "\n",
              "\n",
              "<div id=\"df-74cedf4b-587d-48c2-8a92-732b00d97219\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-74cedf4b-587d-48c2-8a92-732b00d97219')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-74cedf4b-587d-48c2-8a92-732b00d97219 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a0e60d97-3a1d-4cce-b570-682316125181\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
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              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('nuevos_ID2')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a0e60d97-3a1d-4cce-b570-682316125181 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('nuevos_ID2');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "nuevos_ID2"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sin_ID.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDQv_I1WPpMr",
        "outputId": "035f84ef-2070-4d03-d8ec-a249a3e217b9"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sin_ID.columns\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5oKnq0WZWtQa",
        "outputId": "90b18978-53d9-4ae5-b27b-af79bdc455fd"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID_RED', 'TIPO_VIAL', 'NOMBRE', 'CODIGO', 'COND_PAV', 'RECUBRI',\n",
              "       'CARRILES', 'ESTATUS', 'CONDICION', 'NIVEL', 'PEAJE', 'ADMINISTRA',\n",
              "       'JURISDI', 'CIRCULA', 'ESCALA_VIS', 'VELOCIDAD', 'UNION_INI',\n",
              "       'UNION_FIN', 'LONGITUD', 'ANCHO', 'FECHA_ACT', 'CALIREPR', 'Estado',\n",
              "       'geometry'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sin_ID.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "vdyP__z7Yuhk",
        "outputId": "8c9e741a-b236-4b5d-96b7-4948ebb0f414"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      ID_RED  TIPO_VIAL NOMBRE CODIGO       COND_PAV  RECUBRI CARRILES  \\\n",
              "6611   12925  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "6613   15817  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "6614  679292  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "6616   15816  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "7416  862875  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "\n",
              "         ESTATUS     CONDICION  NIVEL  ... ESCALA_VIS VELOCIDAD UNION_INI  \\\n",
              "6611  Habilitado  En operación      0  ...          2        80   1490838   \n",
              "6613  Habilitado  En operación      0  ...          2        80   1491773   \n",
              "6614  Habilitado  En operación      0  ...          2        80   1491772   \n",
              "6616  Habilitado  En operación      0  ...          2        80   1490959   \n",
              "7416  Habilitado  En operación      0  ...          3        60   1723391   \n",
              "\n",
              "     UNION_FIN     LONGITUD ANCHO            FECHA_ACT  CALIREPR  \\\n",
              "6611   1490839   703.185436   6.5  2020-10-12 12:53:13  Definida   \n",
              "6613   1490838  1840.262975   6.5  2020-10-12 14:22:17  Definida   \n",
              "6614   1491773   748.410712   6.5  2020-10-12 11:04:56  Definida   \n",
              "6616   1491772   960.034091   6.5  2020-10-12 12:14:40  Definida   \n",
              "7416   1723392   924.197189   7.0  2017-09-15 13:46:44  Definida   \n",
              "\n",
              "                                               geometry    Estado  \n",
              "6611  LINESTRING (2536546.932 641003.224, 2536547.95...  Guerrero  \n",
              "6613  MULTILINESTRING ((2534695.090 641762.787, 2534...  Guerrero  \n",
              "6614  LINESTRING (2534349.965 642392.000, 2534350.29...  Guerrero  \n",
              "6616  LINESTRING (2534249.447 643305.797, 2534272.82...  Guerrero  \n",
              "7416  LINESTRING (2282301.976 1022197.173, 2282300.5...   Jalisco  \n",
              "\n",
              "[5 rows x 24 columns]"
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID_RED</th>\n",
              "      <th>TIPO_VIAL</th>\n",
              "      <th>NOMBRE</th>\n",
              "      <th>CODIGO</th>\n",
              "      <th>COND_PAV</th>\n",
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              "      <th>CARRILES</th>\n",
              "      <th>ESTATUS</th>\n",
              "      <th>CONDICION</th>\n",
              "      <th>NIVEL</th>\n",
              "      <th>...</th>\n",
              "      <th>ESCALA_VIS</th>\n",
              "      <th>VELOCIDAD</th>\n",
              "      <th>UNION_INI</th>\n",
              "      <th>UNION_FIN</th>\n",
              "      <th>LONGITUD</th>\n",
              "      <th>ANCHO</th>\n",
              "      <th>FECHA_ACT</th>\n",
              "      <th>CALIREPR</th>\n",
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              "      <th>6611</th>\n",
              "      <td>12925</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
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              "      <td>1490838</td>\n",
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              "      <td>703.185436</td>\n",
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              "      <td>2020-10-12 12:53:13</td>\n",
              "      <td>Definida</td>\n",
              "      <td>LINESTRING (2536546.932 641003.224, 2536547.95...</td>\n",
              "      <td>Guerrero</td>\n",
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              "    <tr>\n",
              "      <th>6613</th>\n",
              "      <td>15817</td>\n",
              "      <td>Carretera</td>\n",
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              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
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              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
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              "      <td>1491773</td>\n",
              "      <td>1490838</td>\n",
              "      <td>1840.262975</td>\n",
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              "      <td>2020-10-12 14:22:17</td>\n",
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              "      <th>6614</th>\n",
              "      <td>679292</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>80</td>\n",
              "      <td>1491772</td>\n",
              "      <td>1491773</td>\n",
              "      <td>748.410712</td>\n",
              "      <td>6.5</td>\n",
              "      <td>2020-10-12 11:04:56</td>\n",
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              "      <td>LINESTRING (2534349.965 642392.000, 2534350.29...</td>\n",
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              "    <tr>\n",
              "      <th>6616</th>\n",
              "      <td>15816</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
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              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
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              "      <td>2</td>\n",
              "      <td>80</td>\n",
              "      <td>1490959</td>\n",
              "      <td>1491772</td>\n",
              "      <td>960.034091</td>\n",
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              "    <tr>\n",
              "      <th>7416</th>\n",
              "      <td>862875</td>\n",
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              "      <td>N/D</td>\n",
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              "      <td>Jalisco</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ab7bdaaf-8861-4eac-a8c7-8a0b65716aca 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-ab7bdaaf-8861-4eac-a8c7-8a0b65716aca');\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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              "</svg>\n",
              "  </button>\n",
              "\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6b8e3219-a07f-4c47-810e-6f0f838c7226 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sin_ID"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_con_ID_sorted_fin = cf_con_ID.sort_values(by='UNION_FIN')"
      ],
      "metadata": {
        "id": "g1T9WhcdomLH"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cf_con_ID_sorted_ini = cf_con_ID.sort_values(by='UNION_INI')"
      ],
      "metadata": {
        "id": "0D5uq4Ekpo4R"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Prueba pares consecutivos BUENO\n",
        "\n",
        "# while len(sin_ID) > 0:\n",
        "\n",
        "for idx, row in sin_ID.iterrows():\n",
        "# CASO UNION_INI < UNION_FIN\n",
        "    if row[columna1] <= row[columna2]:\n",
        "    # INICIOS CON FINALES\n",
        "        if any((row[columna1] > cf_con_ID_sorted_fin[columna2].iloc[i]) and (row[columna1] <= cf_con_ID_sorted_fin[columna2].iloc[i + 1])\n",
        "               for i in range(len(cf_con_ID_sorted_fin) - 1)):\n",
        "            # Identificar el índice del límite inferior\n",
        "            for i in range(len(cf_con_ID_sorted_fin) - 1):\n",
        "                if (cf_con_ID_sorted_fin[columna2].iloc[i] <= row[columna1] <= cf_con_ID_sorted_fin[columna2].iloc[i + 1]):\n",
        "                    lim_inf = cf_con_ID_sorted_fin['NOMBRE'].iloc[i]\n",
        "            # Identificar el índice del límite superior\n",
        "                    lim_sup = cf_con_ID_sorted_fin['NOMBRE'].iloc[i + 1]\n",
        "                    # Acotar limites\n",
        "                    if lim_inf == lim_sup and len(lim_inf) > 3:\n",
        "                        sin_ID.at[idx, 'inicio entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'inicio entre2'] = lim_inf\n",
        "                    elif lim_inf == lim_sup and len(lim_inf) == 3:\n",
        "                        sin_ID.at[idx, 'inicio entre1'] = 'nd'\n",
        "                        sin_ID.at[idx, 'inicio entre2'] = 'nd'\n",
        "                    else:\n",
        "                        sin_ID.at[idx, 'inicio entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'inicio entre2'] = lim_sup\n",
        "                    break\n",
        "\n",
        "      # FINALES CON INICIOS\n",
        "        if any((row[columna2] > cf_con_ID_sorted_ini[columna1].iloc[i]) and (row[columna2] <= cf_con_ID_sorted_ini[columna1].iloc[i + 1])\n",
        "               for i in range(len(cf_con_ID_sorted_ini) - 1)):\n",
        "            # Identificar el índice del límite inferior\n",
        "            for i in range(len(cf_con_ID_sorted_ini) - 1):\n",
        "                if (cf_con_ID_sorted_ini[columna1].iloc[i] <= row[columna2] <= cf_con_ID_sorted_ini[columna1].iloc[i + 1]):\n",
        "                    lim_inf = cf_con_ID_sorted_ini['NOMBRE'].iloc[i]\n",
        "            # Identificar el índice del límite superior\n",
        "                    lim_sup = cf_con_ID_sorted_ini['NOMBRE'].iloc[i + 1]\n",
        "                    # Acotar limites\n",
        "                    if lim_inf == lim_sup and len(lim_inf) > 3:\n",
        "                        sin_ID.at[idx, 'final entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'final entre2'] = lim_inf\n",
        "                    elif lim_inf == lim_sup and len(lim_inf) == 3:\n",
        "                        sin_ID.at[idx, 'final entre1'] = 'nd'\n",
        "                        sin_ID.at[idx, 'final entre2'] = 'nd'\n",
        "                    else:\n",
        "                        sin_ID.at[idx, 'final entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'final entre2'] = lim_sup\n",
        "\n",
        "                    break\n",
        "\n",
        "    # CASO UNION_INI > UNION_FIN\n",
        "    if row[columna1] >= row[columna2]:\n",
        "        # INICIOS CON FINALES\n",
        "        if any((row[columna2] > cf_con_ID_sorted_fin[columna2].iloc[i]) and (row[columna2] <= cf_con_ID_sorted_fin[columna2].iloc[i + 1])\n",
        "               for i in range(len(cf_con_ID_sorted_fin) - 1)):\n",
        "            # Identificar el índice del límite inferior\n",
        "            for i in range(len(cf_con_ID_sorted_fin) - 1):\n",
        "                if (cf_con_ID_sorted_fin[columna2].iloc[i] <= row[columna2] <= cf_con_ID_sorted_fin[columna2].iloc[i + 1]):\n",
        "                    lim_inf = cf_con_ID_sorted_fin['NOMBRE'].iloc[i]\n",
        "                    # Identificar el índice del límite superior\n",
        "                    lim_sup = cf_con_ID_sorted_fin['NOMBRE'].iloc[i + 1]\n",
        "                    # Acotar limites\n",
        "                    if lim_inf == lim_sup and len(lim_inf) > 3:\n",
        "                        sin_ID.at[idx, 'inicio entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'inicio entre2'] = lim_inf\n",
        "                    elif lim_inf == lim_sup and len(lim_inf) == 3:\n",
        "                        sin_ID.at[idx, 'inicio entre1'] = 'nd'\n",
        "                        sin_ID.at[idx, 'inicio entre2'] = 'nd'\n",
        "                    else:\n",
        "                        sin_ID.at[idx, 'inicio entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'inicio entre2'] = lim_sup\n",
        "                    break\n",
        "        # FINALES CON INICIOS\n",
        "        if any((row[columna1] > cf_con_ID_sorted_ini[columna1].iloc[i]) and (row[columna1] <= cf_con_ID_sorted_ini[columna1].iloc[i + 1])\n",
        "               for i in range(len(cf_con_ID_sorted_ini) - 1)):\n",
        "            # Identificar el índice del límite inferior\n",
        "            for i in range(len(cf_con_ID_sorted_ini) - 1):\n",
        "                if (cf_con_ID_sorted_ini[columna1].iloc[i] <= row[columna1] <= cf_con_ID_sorted_ini[columna1].iloc[i + 1]):\n",
        "                    lim_inf = cf_con_ID_sorted_ini['NOMBRE'].iloc[i]\n",
        "                    # Identificar el índice del límite superior\n",
        "                    lim_sup = cf_con_ID_sorted_ini['NOMBRE'].iloc[i + 1]\n",
        "                    # Acotar limites\n",
        "                    if lim_inf == lim_sup and len(lim_inf) > 3:\n",
        "                        sin_ID.at[idx, 'final entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'final entre2'] = lim_inf\n",
        "                    elif lim_inf == lim_sup and len(lim_inf) == 3:\n",
        "                        sin_ID.at[idx, 'final entre1'] = 'nd'\n",
        "                        sin_ID.at[idx, 'final entre2'] = 'nd'\n",
        "                    else:\n",
        "                        sin_ID.at[idx, 'final entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'final entre2'] = lim_sup\n",
        "                    break\n",
        "    else:\n",
        "        sin_ID.at[idx, 'NOMBRE'] = sin_ID.at[idx, 'NOMBRE']\n"
      ],
      "metadata": {
        "id": "OaTdrdcUr-Q3"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sin_ID.loc[(sin_ID['inicio entre1'].str.len()>3)|(sin_ID['inicio entre2'].str.len()>3)|(sin_ID['final entre1'].str.len()>3)|(sin_ID['final entre2'].str.len()>3)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 947
        },
        "id": "lJML-3qWMUDP",
        "outputId": "4a54ff51-a162-4407-cd3a-80765aee6ed3"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID_RED  TIPO_VIAL NOMBRE CODIGO       COND_PAV  RECUBRI CARRILES  \\\n",
              "6611     12925  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "6613     15817  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "6614    679292  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "6616     15816  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "7416    862875  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "...        ...        ...    ...    ...            ...      ...      ...   \n",
              "14160  2372643  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "14204   303274  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "14205   303275  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "14206   303271  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "14207   303269  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "\n",
              "          ESTATUS     CONDICION  NIVEL  ...     LONGITUD ANCHO  \\\n",
              "6611   Habilitado  En operación      0  ...   703.185436   6.5   \n",
              "6613   Habilitado  En operación      0  ...  1840.262975   6.5   \n",
              "6614   Habilitado  En operación      0  ...   748.410712   6.5   \n",
              "6616   Habilitado  En operación      0  ...   960.034091   6.5   \n",
              "7416   Habilitado  En operación      0  ...   924.197189   7.0   \n",
              "...           ...           ...    ...  ...          ...   ...   \n",
              "14160  Habilitado  En operación      0  ...  1966.361967  12.0   \n",
              "14204  Habilitado  En operación      0  ...   326.714545   7.0   \n",
              "14205  Habilitado  En operación      0  ...   440.359407   7.0   \n",
              "14206  Habilitado  En operación      0  ...   983.731124   7.0   \n",
              "14207  Habilitado  En operación      0  ...  3756.127414   7.0   \n",
              "\n",
              "                 FECHA_ACT  CALIREPR    Estado  \\\n",
              "6611   2020-10-12 12:53:13  Definida  Guerrero   \n",
              "6613   2020-10-12 14:22:17  Definida  Guerrero   \n",
              "6614   2020-10-12 11:04:56  Definida  Guerrero   \n",
              "6616   2020-10-12 12:14:40  Definida  Guerrero   \n",
              "7416   2017-09-15 13:46:44  Definida   Jalisco   \n",
              "...                    ...       ...       ...   \n",
              "14160  2021-06-09 10:46:03  Definida   Yucatán   \n",
              "14204  2021-06-09 16:03:03  Definida   Yucatán   \n",
              "14205  2021-06-09 10:59:38  Definida   Yucatán   \n",
              "14206  2021-06-09 10:20:41  Definida   Yucatán   \n",
              "14207  2021-06-09 11:57:40  Definida   Yucatán   \n",
              "\n",
              "                                                geometry  \\\n",
              "6611   LINESTRING (2536546.932 641003.224, 2536547.95...   \n",
              "6613   MULTILINESTRING ((2534695.090 641762.787, 2534...   \n",
              "6614   LINESTRING (2534349.965 642392.000, 2534350.29...   \n",
              "6616   LINESTRING (2534249.447 643305.797, 2534272.82...   \n",
              "7416   LINESTRING (2282301.976 1022197.173, 2282300.5...   \n",
              "...                                                  ...   \n",
              "14160  LINESTRING (3772410.307 1085598.937, 3772410.3...   \n",
              "14204  MULTILINESTRING ((3772090.538 1087269.856, 377...   \n",
              "14205  MULTILINESTRING ((3772152.680 1087078.125, 377...   \n",
              "14206  LINESTRING (3771999.616 1087853.377, 3771999.6...   \n",
              "14207  MULTILINESTRING ((3770809.372 1091927.615, 377...   \n",
              "\n",
              "                           inicio entre1                   inicio entre2  \\\n",
              "6611      Las Cruces - Pinotepa Nacional  Las Cruces - Pinotepa Nacional   \n",
              "6613      Las Cruces - Pinotepa Nacional  Las Cruces - Pinotepa Nacional   \n",
              "6614               Zihuatanejo - La Mira           Zihuatanejo - La Mira   \n",
              "6616      Las Cruces - Pinotepa Nacional           Cuernavaca - Acapulco   \n",
              "7416                   Mérida - Progreso              Jiquilpan - Colima   \n",
              "...                                  ...                             ...   \n",
              "14160  Naica - Santa Gertrudis - Valerio                 Alazán - Canoas   \n",
              "14204  Naica - Santa Gertrudis - Valerio                 Alazán - Canoas   \n",
              "14205  Naica - Santa Gertrudis - Valerio                 Alazán - Canoas   \n",
              "14206  Naica - Santa Gertrudis - Valerio                 Alazán - Canoas   \n",
              "14207            Libramiento de Progreso                 Mérida - Cancún   \n",
              "\n",
              "                            final entre1                    final entre2  \n",
              "6611      Las Cruces - Pinotepa Nacional  Las Cruces - Pinotepa Nacional  \n",
              "6613               Zihuatanejo - La Mira           Zihuatanejo - La Mira  \n",
              "6614               Zihuatanejo - La Mira           Zihuatanejo - La Mira  \n",
              "6616               Zihuatanejo - La Mira           Zihuatanejo - La Mira  \n",
              "7416                   Mérida - Progreso              Jiquilpan - Colima  \n",
              "...                                  ...                             ...  \n",
              "14160  Naica - Santa Gertrudis - Valerio                 Alazán - Canoas  \n",
              "14204            Libramiento de Progreso                 Mérida - Cancún  \n",
              "14205            Libramiento de Progreso                 Mérida - Cancún  \n",
              "14206            Libramiento de Progreso                 Mérida - Cancún  \n",
              "14207            Libramiento de Progreso                 Mérida - Cancún  \n",
              "\n",
              "[64 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f559af27-e92f-420c-8199-158cf01828b2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\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>ID_RED</th>\n",
              "      <th>TIPO_VIAL</th>\n",
              "      <th>NOMBRE</th>\n",
              "      <th>CODIGO</th>\n",
              "      <th>COND_PAV</th>\n",
              "      <th>RECUBRI</th>\n",
              "      <th>CARRILES</th>\n",
              "      <th>ESTATUS</th>\n",
              "      <th>CONDICION</th>\n",
              "      <th>NIVEL</th>\n",
              "      <th>...</th>\n",
              "      <th>LONGITUD</th>\n",
              "      <th>ANCHO</th>\n",
              "      <th>FECHA_ACT</th>\n",
              "      <th>CALIREPR</th>\n",
              "      <th>Estado</th>\n",
              "      <th>geometry</th>\n",
              "      <th>inicio entre1</th>\n",
              "      <th>inicio entre2</th>\n",
              "      <th>final entre1</th>\n",
              "      <th>final entre2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6611</th>\n",
              "      <td>12925</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>703.185436</td>\n",
              "      <td>6.5</td>\n",
              "      <td>2020-10-12 12:53:13</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Guerrero</td>\n",
              "      <td>LINESTRING (2536546.932 641003.224, 2536547.95...</td>\n",
              "      <td>Las Cruces - Pinotepa Nacional</td>\n",
              "      <td>Las Cruces - Pinotepa Nacional</td>\n",
              "      <td>Las Cruces - Pinotepa Nacional</td>\n",
              "      <td>Las Cruces - Pinotepa Nacional</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6613</th>\n",
              "      <td>15817</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1840.262975</td>\n",
              "      <td>6.5</td>\n",
              "      <td>2020-10-12 14:22:17</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Guerrero</td>\n",
              "      <td>MULTILINESTRING ((2534695.090 641762.787, 2534...</td>\n",
              "      <td>Las Cruces - Pinotepa Nacional</td>\n",
              "      <td>Las Cruces - Pinotepa Nacional</td>\n",
              "      <td>Zihuatanejo - La Mira</td>\n",
              "      <td>Zihuatanejo - La Mira</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6614</th>\n",
              "      <td>679292</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>748.410712</td>\n",
              "      <td>6.5</td>\n",
              "      <td>2020-10-12 11:04:56</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Guerrero</td>\n",
              "      <td>LINESTRING (2534349.965 642392.000, 2534350.29...</td>\n",
              "      <td>Zihuatanejo - La Mira</td>\n",
              "      <td>Zihuatanejo - La Mira</td>\n",
              "      <td>Zihuatanejo - La Mira</td>\n",
              "      <td>Zihuatanejo - La Mira</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6616</th>\n",
              "      <td>15816</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>960.034091</td>\n",
              "      <td>6.5</td>\n",
              "      <td>2020-10-12 12:14:40</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Guerrero</td>\n",
              "      <td>LINESTRING (2534249.447 643305.797, 2534272.82...</td>\n",
              "      <td>Las Cruces - Pinotepa Nacional</td>\n",
              "      <td>Cuernavaca - Acapulco</td>\n",
              "      <td>Zihuatanejo - La Mira</td>\n",
              "      <td>Zihuatanejo - La Mira</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7416</th>\n",
              "      <td>862875</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
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              "      <td>924.197189</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2017-09-15 13:46:44</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Jalisco</td>\n",
              "      <td>LINESTRING (2282301.976 1022197.173, 2282300.5...</td>\n",
              "      <td>Mérida - Progreso</td>\n",
              "      <td>Jiquilpan - Colima</td>\n",
              "      <td>Mérida - Progreso</td>\n",
              "      <td>Jiquilpan - Colima</td>\n",
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              "    <tr>\n",
              "      <th>14160</th>\n",
              "      <td>2372643</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1966.361967</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2021-06-09 10:46:03</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Yucatán</td>\n",
              "      <td>LINESTRING (3772410.307 1085598.937, 3772410.3...</td>\n",
              "      <td>Naica - Santa Gertrudis - Valerio</td>\n",
              "      <td>Alazán - Canoas</td>\n",
              "      <td>Naica - Santa Gertrudis - Valerio</td>\n",
              "      <td>Alazán - Canoas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14204</th>\n",
              "      <td>303274</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>326.714545</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2021-06-09 16:03:03</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Yucatán</td>\n",
              "      <td>MULTILINESTRING ((3772090.538 1087269.856, 377...</td>\n",
              "      <td>Naica - Santa Gertrudis - Valerio</td>\n",
              "      <td>Alazán - Canoas</td>\n",
              "      <td>Libramiento de Progreso</td>\n",
              "      <td>Mérida - Cancún</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14205</th>\n",
              "      <td>303275</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>440.359407</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2021-06-09 10:59:38</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Yucatán</td>\n",
              "      <td>MULTILINESTRING ((3772152.680 1087078.125, 377...</td>\n",
              "      <td>Naica - Santa Gertrudis - Valerio</td>\n",
              "      <td>Alazán - Canoas</td>\n",
              "      <td>Libramiento de Progreso</td>\n",
              "      <td>Mérida - Cancún</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14206</th>\n",
              "      <td>303271</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>983.731124</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2021-06-09 10:20:41</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Yucatán</td>\n",
              "      <td>LINESTRING (3771999.616 1087853.377, 3771999.6...</td>\n",
              "      <td>Naica - Santa Gertrudis - Valerio</td>\n",
              "      <td>Alazán - Canoas</td>\n",
              "      <td>Libramiento de Progreso</td>\n",
              "      <td>Mérida - Cancún</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14207</th>\n",
              "      <td>303269</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3756.127414</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2021-06-09 11:57:40</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Yucatán</td>\n",
              "      <td>MULTILINESTRING ((3770809.372 1091927.615, 377...</td>\n",
              "      <td>Libramiento de Progreso</td>\n",
              "      <td>Mérida - Cancún</td>\n",
              "      <td>Libramiento de Progreso</td>\n",
              "      <td>Mérida - Cancún</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>64 rows × 28 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f559af27-e92f-420c-8199-158cf01828b2')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-f559af27-e92f-420c-8199-158cf01828b2 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-f559af27-e92f-420c-8199-158cf01828b2');\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",
              "\n",
              "\n",
              "<div id=\"df-896693ed-5e15-4a7e-8047-3f08d9eee6ec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-896693ed-5e15-4a7e-8047-3f08d9eee6ec')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-896693ed-5e15-4a7e-8047-3f08d9eee6ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sin_ID.loc[(sin_ID['inicio entre1'] == sin_ID['final entre1']) & (sin_ID['inicio entre2'] == sin_ID['final entre2'])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6WYzRtTaNZk0",
        "outputId": "3cb5b5b2-1932-404d-ca38-37f525bcdc9d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID_RED  TIPO_VIAL NOMBRE CODIGO       COND_PAV  RECUBRI CARRILES  \\\n",
              "6611     12925  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "6614    679292  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "7416    862875  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8752   1423865  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8755    515157  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8757    862875  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8759   1509077  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8764    515158  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8767   1423872  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8768    515166  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8769    515167  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8771    769860  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8773   1426778  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8775    515171  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8777    515176  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8830   2642722  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8831    515156  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "11016  1416809  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "11021   557775  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "11025   557253  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "11027  1415389  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "11031   558172  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "11042   557785  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "11046   768333  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "14160  2372643  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "14207   303269  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "\n",
              "          ESTATUS     CONDICION  NIVEL  ...     LONGITUD ANCHO  \\\n",
              "6611   Habilitado  En operación      0  ...   703.185436   6.5   \n",
              "6614   Habilitado  En operación      0  ...   748.410712   6.5   \n",
              "7416   Habilitado  En operación      0  ...   924.197189   7.0   \n",
              "8752   Habilitado  En operación      0  ...   336.056896   7.0   \n",
              "8755   Habilitado  En operación      0  ...  1054.557478   7.0   \n",
              "8757   Habilitado  En operación      0  ...   924.197189   7.0   \n",
              "8759   Habilitado  En operación      0  ...   827.439304   7.0   \n",
              "8764   Habilitado  En operación      0  ...   266.346881   7.0   \n",
              "8767   Habilitado  En operación      0  ...   300.536836   7.0   \n",
              "8768   Habilitado  En operación      0  ...   538.500630   7.0   \n",
              "8769   Habilitado  En operación      0  ...   598.361594   7.0   \n",
              "8771   Habilitado  En operación      0  ...   149.157549   7.0   \n",
              "8773   Habilitado  En operación      0  ...   322.215850   7.0   \n",
              "8775   Habilitado  En operación      0  ...   759.109403   7.0   \n",
              "8777   Habilitado  En operación      0  ...    56.357596   7.0   \n",
              "8830   Habilitado  En operación      0  ...   682.063579   7.0   \n",
              "8831   Habilitado  En operación      0  ...  1088.360669   7.0   \n",
              "11016  Habilitado  En operación      0  ...  1870.921019   7.0   \n",
              "11021  Habilitado  En operación      0  ...   596.255068   7.0   \n",
              "11025  Habilitado  En operación      0  ...   543.152140   7.0   \n",
              "11027  Habilitado  En operación      0  ...   765.606179   7.0   \n",
              "11031  Habilitado  En operación      0  ...   489.675669   7.0   \n",
              "11042  Habilitado  En operación      0  ...   318.215844   7.0   \n",
              "11046  Habilitado  En operación      0  ...   416.406724   7.0   \n",
              "14160  Habilitado  En operación      0  ...  1966.361967  12.0   \n",
              "14207  Habilitado  En operación      0  ...  3756.127414   7.0   \n",
              "\n",
              "                 FECHA_ACT    CALIREPR     Estado  \\\n",
              "6611   2020-10-12 12:53:13    Definida   Guerrero   \n",
              "6614   2020-10-12 11:04:56    Definida   Guerrero   \n",
              "7416   2017-09-15 13:46:44    Definida    Jalisco   \n",
              "8752   2022-06-17 08:01:05  Aproximada    Nayarit   \n",
              "8755   2022-06-15 21:07:39  Aproximada    Nayarit   \n",
              "8757   2017-09-15 13:46:44    Definida    Nayarit   \n",
              "8759   2022-06-15 19:35:30  Aproximada    Nayarit   \n",
              "8764   2022-06-15 21:19:03  Aproximada    Nayarit   \n",
              "8767   2022-06-20 20:41:58  Aproximada    Nayarit   \n",
              "8768   2022-06-20 21:06:17  Aproximada    Nayarit   \n",
              "8769   2022-06-21 17:34:08  Aproximada    Nayarit   \n",
              "8771   2022-06-21 17:48:13  Aproximada    Nayarit   \n",
              "8773   2022-06-21 18:42:57  Aproximada    Nayarit   \n",
              "8775   2022-06-21 21:01:31  Aproximada    Nayarit   \n",
              "8777   2022-06-21 21:27:28  Aproximada    Nayarit   \n",
              "8830   2022-06-15 20:13:04  Aproximada    Nayarit   \n",
              "8831   2022-06-15 21:03:08  Aproximada    Nayarit   \n",
              "11016  2018-10-11 12:57:54    Definida  Querétaro   \n",
              "11021  2018-04-12 08:51:01    Definida  Querétaro   \n",
              "11025  2018-04-12 09:31:19    Definida  Querétaro   \n",
              "11027  2018-04-12 09:15:46    Definida  Querétaro   \n",
              "11031  2018-04-11 10:57:28    Definida  Querétaro   \n",
              "11042  2018-04-12 11:18:34    Definida  Querétaro   \n",
              "11046  2018-04-12 11:17:12    Definida  Querétaro   \n",
              "14160  2021-06-09 10:46:03    Definida    Yucatán   \n",
              "14207  2021-06-09 11:57:40    Definida    Yucatán   \n",
              "\n",
              "                                                geometry  \\\n",
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              "\n",
              "                              inicio entre1                   inicio entre2  \\\n",
              "6611         Las Cruces - Pinotepa Nacional  Las Cruces - Pinotepa Nacional   \n",
              "6614                  Zihuatanejo - La Mira           Zihuatanejo - La Mira   \n",
              "7416                      Mérida - Progreso              Jiquilpan - Colima   \n",
              "8752       Ramal a la Laguna de Santa María             Guadalajara - Tepic   \n",
              "8755            Querétaro - San Luis Potosí         Libramiento de Irapuato   \n",
              "8757                      Mérida - Progreso              Jiquilpan - Colima   \n",
              "8759                 Acapulco - Zihuatanejo         Gómez Palacio - Jiménez   \n",
              "8764            Querétaro - San Luis Potosí         Libramiento de Irapuato   \n",
              "8767                    Guadalajara - Tepic             Guadalajara - Tepic   \n",
              "8768                    Guadalajara - Tepic             Guadalajara - Tepic   \n",
              "8769                    Guadalajara - Tepic             Guadalajara - Tepic   \n",
              "8771                Libramiento de Irapuato                 Irapuato - León   \n",
              "8773                 Munguía - Las Adjuntas    Tehuacán - Huajuapan de León   \n",
              "8775                Libramiento de Irapuato                 Irapuato - León   \n",
              "8777                       Tepic - Mazatlán                Tepic - Mazatlán   \n",
              "8830             San Juan del Río - Xilitla            Imuris - Agua Prieta   \n",
              "8831            Querétaro - San Luis Potosí         Libramiento de Irapuato   \n",
              "11016  Monclova - San Pedro de las Colonias          Ciudad Alemán - Sayula   \n",
              "11021            Jalpan de Serra - Rioverde      Jalpan de Serra - Rioverde   \n",
              "11025            San Juan del Río - Xilitla           Los Herrera - La Mora   \n",
              "11027              Monterrey - Nuevo Laredo       Tuxtepec - Ent. Palomares   \n",
              "11031            San Juan del Río - Xilitla      San Juan del Río - Xilitla   \n",
              "11042            Jalpan de Serra - Rioverde      Jalpan de Serra - Rioverde   \n",
              "11046                 San Hipólito - Xalapa                El Sueco - Janos   \n",
              "14160     Naica - Santa Gertrudis - Valerio                 Alazán - Canoas   \n",
              "14207               Libramiento de Progreso                 Mérida - Cancún   \n",
              "\n",
              "                               final entre1                    final entre2  \n",
              "6611         Las Cruces - Pinotepa Nacional  Las Cruces - Pinotepa Nacional  \n",
              "6614                  Zihuatanejo - La Mira           Zihuatanejo - La Mira  \n",
              "7416                      Mérida - Progreso              Jiquilpan - Colima  \n",
              "8752       Ramal a la Laguna de Santa María             Guadalajara - Tepic  \n",
              "8755            Querétaro - San Luis Potosí         Libramiento de Irapuato  \n",
              "8757                      Mérida - Progreso              Jiquilpan - Colima  \n",
              "8759                 Acapulco - Zihuatanejo         Gómez Palacio - Jiménez  \n",
              "8764            Querétaro - San Luis Potosí         Libramiento de Irapuato  \n",
              "8767                    Guadalajara - Tepic             Guadalajara - Tepic  \n",
              "8768                    Guadalajara - Tepic             Guadalajara - Tepic  \n",
              "8769                    Guadalajara - Tepic             Guadalajara - Tepic  \n",
              "8771                Libramiento de Irapuato                 Irapuato - León  \n",
              "8773                 Munguía - Las Adjuntas    Tehuacán - Huajuapan de León  \n",
              "8775                Libramiento de Irapuato                 Irapuato - León  \n",
              "8777                       Tepic - Mazatlán                Tepic - Mazatlán  \n",
              "8830             San Juan del Río - Xilitla            Imuris - Agua Prieta  \n",
              "8831            Querétaro - San Luis Potosí         Libramiento de Irapuato  \n",
              "11016  Monclova - San Pedro de las Colonias          Ciudad Alemán - Sayula  \n",
              "11021            Jalpan de Serra - Rioverde      Jalpan de Serra - Rioverde  \n",
              "11025            San Juan del Río - Xilitla           Los Herrera - La Mora  \n",
              "11027              Monterrey - Nuevo Laredo       Tuxtepec - Ent. Palomares  \n",
              "11031            San Juan del Río - Xilitla      San Juan del Río - Xilitla  \n",
              "11042            Jalpan de Serra - Rioverde      Jalpan de Serra - Rioverde  \n",
              "11046                 San Hipólito - Xalapa                El Sueco - Janos  \n",
              "14160     Naica - Santa Gertrudis - Valerio                 Alazán - Canoas  \n",
              "14207               Libramiento de Progreso                 Mérida - Cancún  \n",
              "\n",
              "[26 rows x 28 columns]"
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              "      <th>ID_RED</th>\n",
              "      <th>TIPO_VIAL</th>\n",
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              "      <td>703.185436</td>\n",
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              "      <td>LINESTRING (2536546.932 641003.224, 2536547.95...</td>\n",
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              "      <td>Las Cruces - Pinotepa Nacional</td>\n",
              "      <td>Las Cruces - Pinotepa Nacional</td>\n",
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              "      <th>6614</th>\n",
              "      <td>679292</td>\n",
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              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
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              "      <td>748.410712</td>\n",
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              "      <td>2020-10-12 11:04:56</td>\n",
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              "      <td>Zihuatanejo - La Mira</td>\n",
              "      <td>Zihuatanejo - La Mira</td>\n",
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              "      <th>7416</th>\n",
              "      <td>862875</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
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              "      <td>Asfalto</td>\n",
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              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>924.197189</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2017-09-15 13:46:44</td>\n",
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              "      <td>Jalisco</td>\n",
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              "      <td>Mérida - Progreso</td>\n",
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              "      <td>Mérida - Progreso</td>\n",
              "      <td>Jiquilpan - Colima</td>\n",
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              "    <tr>\n",
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              "      <td>1423865</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
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              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>336.056896</td>\n",
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              "      <td>Ramal a la Laguna de Santa María</td>\n",
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              "      <td>Ramal a la Laguna de Santa María</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
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              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
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              "      <td>1054.557478</td>\n",
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              "      <td>2022-06-15 21:07:39</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
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              "      <td>Libramiento de Irapuato</td>\n",
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              "      <td>862875</td>\n",
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              "      <td>N/D</td>\n",
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              "      <td>Definida</td>\n",
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              "      <td>Jiquilpan - Colima</td>\n",
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              "    <tr>\n",
              "      <th>8759</th>\n",
              "      <td>1509077</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>827.439304</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-15 19:35:30</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>MULTILINESTRING ((2284485.513 1028455.886, 228...</td>\n",
              "      <td>Acapulco - Zihuatanejo</td>\n",
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              "      <td>Acapulco - Zihuatanejo</td>\n",
              "      <td>Gómez Palacio - Jiménez</td>\n",
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              "    <tr>\n",
              "      <th>8764</th>\n",
              "      <td>515158</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>266.346881</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-15 21:19:03</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2287121.508 1031333.880, 2287122.0...</td>\n",
              "      <td>Querétaro - San Luis Potosí</td>\n",
              "      <td>Libramiento de Irapuato</td>\n",
              "      <td>Querétaro - San Luis Potosí</td>\n",
              "      <td>Libramiento de Irapuato</td>\n",
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              "    <tr>\n",
              "      <th>8767</th>\n",
              "      <td>1423872</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>300.536836</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-20 20:41:58</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2291046.058 1032254.100, 2291049.5...</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
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              "    <tr>\n",
              "      <th>8768</th>\n",
              "      <td>515166</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>538.500630</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-20 21:06:17</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2291255.176 1032434.200, 2291260.7...</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8769</th>\n",
              "      <td>515167</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>598.361594</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-21 17:34:08</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2291482.353 1032837.745, 2291481.5...</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8771</th>\n",
              "      <td>769860</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>149.157549</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-21 17:48:13</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2291519.492 1033460.505, 2291535.7...</td>\n",
              "      <td>Libramiento de Irapuato</td>\n",
              "      <td>Irapuato - León</td>\n",
              "      <td>Libramiento de Irapuato</td>\n",
              "      <td>Irapuato - León</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8773</th>\n",
              "      <td>1426778</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>322.215850</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-21 18:42:57</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2291675.563 1033840.149, 2291669.9...</td>\n",
              "      <td>Munguía - Las Adjuntas</td>\n",
              "      <td>Tehuacán - Huajuapan de León</td>\n",
              "      <td>Munguía - Las Adjuntas</td>\n",
              "      <td>Tehuacán - Huajuapan de León</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8775</th>\n",
              "      <td>515171</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>759.109403</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-21 21:01:31</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2292195.682 1034397.324, 2292227.0...</td>\n",
              "      <td>Libramiento de Irapuato</td>\n",
              "      <td>Irapuato - León</td>\n",
              "      <td>Libramiento de Irapuato</td>\n",
              "      <td>Irapuato - León</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8777</th>\n",
              "      <td>515176</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>56.357596</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-21 21:27:28</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2292489.986 1035272.632, 2292489.7...</td>\n",
              "      <td>Tepic - Mazatlán</td>\n",
              "      <td>Tepic - Mazatlán</td>\n",
              "      <td>Tepic - Mazatlán</td>\n",
              "      <td>Tepic - Mazatlán</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8830</th>\n",
              "      <td>2642722</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>682.063579</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-15 20:13:04</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2285103.823 1030208.715, 2285108.8...</td>\n",
              "      <td>San Juan del Río - Xilitla</td>\n",
              "      <td>Imuris - Agua Prieta</td>\n",
              "      <td>San Juan del Río - Xilitla</td>\n",
              "      <td>Imuris - Agua Prieta</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8831</th>\n",
              "      <td>515156</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1088.360669</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-15 21:03:08</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2286516.121 1029868.923, 2286513.5...</td>\n",
              "      <td>Querétaro - San Luis Potosí</td>\n",
              "      <td>Libramiento de Irapuato</td>\n",
              "      <td>Querétaro - San Luis Potosí</td>\n",
              "      <td>Libramiento de Irapuato</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11016</th>\n",
              "      <td>1416809</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1870.921019</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2018-10-11 12:57:54</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Querétaro</td>\n",
              "      <td>MULTILINESTRING ((2751643.452 977863.306, 2751...</td>\n",
              "      <td>Monclova - San Pedro de las Colonias</td>\n",
              "      <td>Ciudad Alemán - Sayula</td>\n",
              "      <td>Monclova - San Pedro de las Colonias</td>\n",
              "      <td>Ciudad Alemán - Sayula</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11021</th>\n",
              "      <td>557775</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>596.255068</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2018-04-12 08:51:01</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Querétaro</td>\n",
              "      <td>LINESTRING (2743019.205 977644.454, 2743045.29...</td>\n",
              "      <td>Jalpan de Serra - Rioverde</td>\n",
              "      <td>Jalpan de Serra - Rioverde</td>\n",
              "      <td>Jalpan de Serra - Rioverde</td>\n",
              "      <td>Jalpan de Serra - Rioverde</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11025</th>\n",
              "      <td>557253</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>543.152140</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2018-04-12 09:31:19</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Querétaro</td>\n",
              "      <td>LINESTRING (2744142.656 978842.045, 2744156.14...</td>\n",
              "      <td>San Juan del Río - Xilitla</td>\n",
              "      <td>Los Herrera - La Mora</td>\n",
              "      <td>San Juan del Río - Xilitla</td>\n",
              "      <td>Los Herrera - La Mora</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11027</th>\n",
              "      <td>1415389</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>765.606179</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2018-04-12 09:15:46</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Querétaro</td>\n",
              "      <td>LINESTRING (2750847.818 978737.722, 2750840.43...</td>\n",
              "      <td>Monterrey - Nuevo Laredo</td>\n",
              "      <td>Tuxtepec - Ent. Palomares</td>\n",
              "      <td>Monterrey - Nuevo Laredo</td>\n",
              "      <td>Tuxtepec - Ent. Palomares</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11031</th>\n",
              "      <td>558172</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>489.675669</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2018-04-11 10:57:28</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Querétaro</td>\n",
              "      <td>LINESTRING (2748650.359 980031.548, 2748667.71...</td>\n",
              "      <td>San Juan del Río - Xilitla</td>\n",
              "      <td>San Juan del Río - Xilitla</td>\n",
              "      <td>San Juan del Río - Xilitla</td>\n",
              "      <td>San Juan del Río - Xilitla</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11042</th>\n",
              "      <td>557785</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>318.215844</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2018-04-12 11:18:34</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Querétaro</td>\n",
              "      <td>LINESTRING (2744692.326 979180.930, 2744673.08...</td>\n",
              "      <td>Jalpan de Serra - Rioverde</td>\n",
              "      <td>Jalpan de Serra - Rioverde</td>\n",
              "      <td>Jalpan de Serra - Rioverde</td>\n",
              "      <td>Jalpan de Serra - Rioverde</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11046</th>\n",
              "      <td>768333</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>416.406724</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2018-04-12 11:17:12</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Querétaro</td>\n",
              "      <td>LINESTRING (2746233.021 979831.517, 2746251.96...</td>\n",
              "      <td>San Hipólito - Xalapa</td>\n",
              "      <td>El Sueco - Janos</td>\n",
              "      <td>San Hipólito - Xalapa</td>\n",
              "      <td>El Sueco - Janos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14160</th>\n",
              "      <td>2372643</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1966.361967</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2021-06-09 10:46:03</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Yucatán</td>\n",
              "      <td>LINESTRING (3772410.307 1085598.937, 3772410.3...</td>\n",
              "      <td>Naica - Santa Gertrudis - Valerio</td>\n",
              "      <td>Alazán - Canoas</td>\n",
              "      <td>Naica - Santa Gertrudis - Valerio</td>\n",
              "      <td>Alazán - Canoas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14207</th>\n",
              "      <td>303269</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3756.127414</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2021-06-09 11:57:40</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Yucatán</td>\n",
              "      <td>MULTILINESTRING ((3770809.372 1091927.615, 377...</td>\n",
              "      <td>Libramiento de Progreso</td>\n",
              "      <td>Mérida - Cancún</td>\n",
              "      <td>Libramiento de Progreso</td>\n",
              "      <td>Mérida - Cancún</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>26 rows × 28 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-624782c1-daab-4256-98a2-c39f447df0f3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
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          },
          "metadata": {},
          "execution_count": 22
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "sin_ID.loc[\n",
        "    (sin_ID['inicio entre1'] == sin_ID['inicio entre2']) &\n",
        "    (sin_ID['inicio entre1'] == sin_ID['final entre1']) &\n",
        "    (sin_ID['inicio entre1'] == sin_ID['final entre2'])\n",
        "]#=sin_ID['inicio entre1']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 96
        },
        "id": "BHeytTQ6hOyM",
        "outputId": "88982d74-8521-42e8-fa39-2434fa0512e6"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty GeoDataFrame\n",
              "Columns: [ID_RED, TIPO_VIAL, NOMBRE, CODIGO, COND_PAV, RECUBRI, CARRILES, ESTATUS, CONDICION, NIVEL, PEAJE, ADMINISTRA, JURISDI, CIRCULA, ESCALA_VIS, VELOCIDAD, UNION_INI, UNION_FIN, LONGITUD, ANCHO, FECHA_ACT, CALIREPR, Estado, geometry, inicio entre1, inicio entre2, final entre1, final entre2]\n",
              "Index: []\n",
              "\n",
              "[0 rows x 28 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID_RED</th>\n",
              "      <th>TIPO_VIAL</th>\n",
              "      <th>NOMBRE</th>\n",
              "      <th>CODIGO</th>\n",
              "      <th>COND_PAV</th>\n",
              "      <th>RECUBRI</th>\n",
              "      <th>CARRILES</th>\n",
              "      <th>ESTATUS</th>\n",
              "      <th>CONDICION</th>\n",
              "      <th>NIVEL</th>\n",
              "      <th>...</th>\n",
              "      <th>LONGITUD</th>\n",
              "      <th>ANCHO</th>\n",
              "      <th>FECHA_ACT</th>\n",
              "      <th>CALIREPR</th>\n",
              "      <th>Estado</th>\n",
              "      <th>geometry</th>\n",
              "      <th>inicio entre1</th>\n",
              "      <th>inicio entre2</th>\n",
              "      <th>final entre1</th>\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-264b73c5-d1b1-44eb-a48e-4836d02fbdbb 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-264b73c5-d1b1-44eb-a48e-4836d02fbdbb');\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",
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              "\n",
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              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
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          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "añadir=sin_ID.loc[sin_ID['NOMBRE'].str.len()>3]"
      ],
      "metadata": {
        "id": "KNI3IbSthiSO"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "añadir=añadir.drop(['inicio entre1','inicio entre2','final entre2','final entre1'], axis=1)"
      ],
      "metadata": {
        "id": "qQWPO4DPiGuE"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nuevos_ID2 = pd.concat([añadir, nuevos_ID2], ignore_index=False)\n",
        "cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]"
      ],
      "metadata": {
        "id": "NHDuxkMai0Ni"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sin_ID.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZShChX37jPSA",
        "outputId": "2910f075-d4de-4118-e4ec-683810002f77"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(55, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nuevos_ID2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "voaPgf-SjRim",
        "outputId": "63c89357-b525-49fd-9252-16bd2c6c5f12"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(86, 24)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sin_ID['NOMBRE'].loc[(sin_ID['inicio entre2']=='Jiquilpan - Colima')|(sin_ID['final entre2']=='Guadalajara - Tepic')]=sin_ID['inicio entre2']"
      ],
      "metadata": {
        "id": "9H69ToEOTj39"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "añadir=sin_ID.loc[sin_ID['NOMBRE'].str.len()>3]"
      ],
      "metadata": {
        "id": "szn4dpZIuQC8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nuevos_ID2 = pd.concat([añadir, nuevos_ID2], ignore_index=False)\n",
        "cf_con_ID = pd.concat([añadir, cf_con_ID], ignore_index=True)\n",
        "sin_ID = sin_ID.loc[sin_ID['NOMBRE'].str.len() == 3]"
      ],
      "metadata": {
        "id": "-Z_3-X6iuZie"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qbVyytryWUtE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cf_con_ID_sorted_fin = cf_con_ID.sort_values(by='UNION_FIN')"
      ],
      "metadata": {
        "id": "7M5VEZqKbuBt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cf_con_ID_sorted_ini = cf_con_ID.sort_values(by='UNION_INI')"
      ],
      "metadata": {
        "id": "mmoFSTYQbuBv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Prueba pares consecutivos BUENO\n",
        "\n",
        "for idx, row in sin_ID.iterrows():\n",
        "    # CASO UNION_INI < UNION_FIN\n",
        "    if row[columna1] <= row[columna2]:\n",
        "        # INICIOS CON INICIOS\n",
        "        if any((row[columna1] > cf_con_ID_sorted_ini[columna1].iloc[i]) and (row[columna1] <= cf_con_ID_sorted_ini[columna1].iloc[i + 1])\n",
        "               for i in range(len(cf_con_ID_sorted_ini) - 1)):\n",
        "            # Identificar el índice del límite inferior\n",
        "            for i in range(len(cf_con_ID_sorted_ini) - 1):\n",
        "                if (cf_con_ID_sorted_ini[columna1].iloc[i] <= row[columna1] <= cf_con_ID_sorted_ini[columna1].iloc[i + 1]):\n",
        "                    lim_inf = cf_con_ID_sorted_ini['NOMBRE'].iloc[i]\n",
        "                    # Identificar el índice del límite superior\n",
        "                    lim_sup = cf_con_ID_sorted_ini['NOMBRE'].iloc[i + 1]\n",
        "                    # Acotar limites\n",
        "                    if lim_inf == lim_sup and len(lim_inf) > 3:\n",
        "                        sin_ID.at[idx, 'inicio entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'inicio entre2'] = lim_inf\n",
        "                    elif lim_inf == lim_sup and len(lim_inf) == 3:\n",
        "                        sin_ID.at[idx, 'inicio entre1'] = 'nd'\n",
        "                        sin_ID.at[idx, 'inicio entre2'] = 'nd'\n",
        "                    else:\n",
        "                        sin_ID.at[idx, 'inicio entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'inicio entre2'] = lim_sup\n",
        "                    break\n",
        "\n",
        "        # FINALES CON FINALES\n",
        "        if any((row[columna2] > cf_con_ID_sorted_fin[columna2].iloc[i]) and (row[columna2] <= cf_con_ID_sorted_fin[columna2].iloc[i + 1])\n",
        "               for i in range(len(cf_con_ID_sorted_fin) - 1)):\n",
        "            # Identificar el índice del límite inferior\n",
        "            for i in range(len(cf_con_ID_sorted_fin) - 1):\n",
        "                if (cf_con_ID_sorted_fin[columna2].iloc[i] <= row[columna2] <= cf_con_ID_sorted_fin[columna2].iloc[i + 1]):\n",
        "                    lim_inf = cf_con_ID_sorted_fin['NOMBRE'].iloc[i]\n",
        "                    # Identificar el índice del límite superior\n",
        "                    lim_sup = cf_con_ID_sorted_fin['NOMBRE'].iloc[i + 1]\n",
        "                    # Acotar limites\n",
        "                    if lim_inf == lim_sup and len(lim_inf) > 3:\n",
        "                        sin_ID.at[idx, 'final entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'final entre2'] = lim_inf\n",
        "                    elif lim_inf == lim_sup and len(lim_inf) == 3:\n",
        "                        sin_ID.at[idx, 'final entre1'] = 'nd'\n",
        "                        sin_ID.at[idx, 'final entre2'] = 'nd'\n",
        "                    else:\n",
        "                        sin_ID.at[idx, 'final entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'final entre2'] = lim_sup\n",
        "                    break\n",
        "    if row[columna1] >= row[columna2]:\n",
        "        # INICIOS CON IMICIOS\n",
        "        if any((row[columna2] > cf_con_ID_sorted_ini[columna1].iloc[i]) and (row[columna2] <= cf_con_ID_sorted_ini[columna1].iloc[i + 1])\n",
        "               for i in range(len(cf_con_ID_sorted_ini) - 1)):\n",
        "            # Identificar el índice del límite inferior\n",
        "            for i in range(len(cf_con_ID_sorted_ini) - 1):\n",
        "                if (cf_con_ID_sorted_ini[columna1].iloc[i] <= row[columna2] <= cf_con_ID_sorted_ini[columna1].iloc[i + 1]):\n",
        "                    lim_inf = cf_con_ID_sorted_ini['NOMBRE'].iloc[i]\n",
        "                    # Identificar el índice del límite superior\n",
        "                    lim_sup = cf_con_ID_sorted_ini['NOMBRE'].iloc[i + 1]\n",
        "                    # Acotar limites\n",
        "                    if lim_inf == lim_sup and len(lim_inf) > 3:\n",
        "                        sin_ID.at[idx, 'inicio entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'inicio entre2'] = lim_inf\n",
        "                    elif lim_inf == lim_sup and len(lim_inf) == 3:\n",
        "                        sin_ID.at[idx, 'inicio entre1'] = 'nd'\n",
        "                        sin_ID.at[idx, 'inicio entre2'] = 'nd'\n",
        "                    else:\n",
        "                        sin_ID.at[idx, 'inicio entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'inicio entre2'] = lim_sup\n",
        "                    break\n",
        "\n",
        "        # FINALES CON FINALES\n",
        "        if any((row[columna1] > cf_con_ID_sorted_fin[columna2].iloc[i]) and (row[columna1] <= cf_con_ID_sorted_fin[columna2].iloc[i + 1])\n",
        "               for i in range(len(cf_con_ID_sorted_fin) - 1)):\n",
        "            # Identificar el índice del límite inferior\n",
        "            for i in range(len(cf_con_ID_sorted_fin) - 1):\n",
        "                if (cf_con_ID_sorted_fin[columna2].iloc[i] <= row[columna1] <= cf_con_ID_sorted_fin[columna2].iloc[i + 1]):\n",
        "                    lim_inf = cf_con_ID_sorted_fin['NOMBRE'].iloc[i]\n",
        "                    # Identificar el índice del límite superior\n",
        "                    lim_sup = cf_con_ID_sorted_fin['NOMBRE'].iloc[i + 1]\n",
        "                    # Acotar limites\n",
        "                    if lim_inf == lim_sup and len(lim_inf) > 3:\n",
        "                        sin_ID.at[idx, 'final entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'final entre2'] = lim_inf\n",
        "                    elif lim_inf == lim_sup and len(lim_inf) == 3:\n",
        "                        sin_ID.at[idx, 'final entre1'] = 'nd'\n",
        "                        sin_ID.at[idx, 'final entre2'] = 'nd'\n",
        "                    else:\n",
        "                        sin_ID.at[idx, 'final entre1'] = lim_inf\n",
        "                        sin_ID.at[idx, 'final entre2'] = lim_sup\n",
        "                    break\n",
        "    else:\n",
        "        sin_ID.at[idx, 'NOMBRE'] = sin_ID.at[idx, 'NOMBRE']\n"
      ],
      "metadata": {
        "id": "HT63WY4EWYZ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sin_ID"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xz6HC857Wd3-",
        "outputId": "c4b21ec2-1c76-4be7-f343-80f0537b996b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID_RED  TIPO_VIAL NOMBRE CODIGO       COND_PAV  RECUBRI CARRILES  \\\n",
              "7590     83252  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8565    962251  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8758   1112130  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8759   1509077  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8761   1111452  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8830   2642722  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "8854    397286  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "14160  2372643  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "14204   303274  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "14205   303275  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "14206   303271  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "14207   303269  Carretera    N/D    N/D  Con pavimento  Asfalto        2   \n",
              "\n",
              "          ESTATUS     CONDICION  NIVEL  ...     LONGITUD ANCHO  \\\n",
              "7590   Habilitado  En operación      0  ...   647.482148   8.0   \n",
              "8565   Habilitado  En operación      1  ...    76.013491   7.0   \n",
              "8758   Habilitado  En operación      0  ...  2100.974485   7.0   \n",
              "8759   Habilitado  En operación      0  ...   827.439304   7.0   \n",
              "8761   Habilitado  En operación      0  ...  2376.053307   7.0   \n",
              "8830   Habilitado  En operación      0  ...   682.063579   7.0   \n",
              "8854   Habilitado  En operación      0  ...   389.560844   6.0   \n",
              "14160  Habilitado  En operación      0  ...  1966.361967  12.0   \n",
              "14204  Habilitado  En operación      0  ...   326.714545   7.0   \n",
              "14205  Habilitado  En operación      0  ...   440.359407   7.0   \n",
              "14206  Habilitado  En operación      0  ...   983.731124   7.0   \n",
              "14207  Habilitado  En operación      0  ...  3756.127414   7.0   \n",
              "\n",
              "                 FECHA_ACT    CALIREPR      Estado  \\\n",
              "7590   2022-06-09 16:40:19    Definida      México   \n",
              "8565   2023-08-22 12:52:16    Definida     Nayarit   \n",
              "8758   2022-06-13 18:19:02  Aproximada     Nayarit   \n",
              "8759   2022-06-15 19:35:30  Aproximada     Nayarit   \n",
              "8761   2022-06-15 19:57:27  Aproximada     Nayarit   \n",
              "8830   2022-06-15 20:13:04  Aproximada     Nayarit   \n",
              "8854   2023-07-21 18:34:14    Definida  Nuevo León   \n",
              "14160  2021-06-09 10:46:03    Definida     Yucatán   \n",
              "14204  2021-06-09 16:03:03    Definida     Yucatán   \n",
              "14205  2021-06-09 10:59:38    Definida     Yucatán   \n",
              "14206  2021-06-09 10:20:41    Definida     Yucatán   \n",
              "14207  2021-06-09 11:57:40    Definida     Yucatán   \n",
              "\n",
              "                                                geometry  \\\n",
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              "8565   LINESTRING (2180669.737 1062489.884, 2180669.8...   \n",
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              "8761   MULTILINESTRING ((2284288.764 1029131.007, 228...   \n",
              "8830   LINESTRING (2285103.823 1030208.715, 2285108.8...   \n",
              "8854   MULTILINESTRING ((2698155.473 1402797.680, 269...   \n",
              "14160  LINESTRING (3772410.307 1085598.937, 3772410.3...   \n",
              "14204  MULTILINESTRING ((3772090.538 1087269.856, 377...   \n",
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              "14206  LINESTRING (3771999.616 1087853.377, 3771999.6...   \n",
              "14207  MULTILINESTRING ((3770809.372 1091927.615, 377...   \n",
              "\n",
              "                           inicio entre1  \\\n",
              "7590               Libramiento Pirámides   \n",
              "8565   Entronque Playa Azul - Manzanillo   \n",
              "8758          San Juan del Río - Xilitla   \n",
              "8759              Acapulco - Zihuatanejo   \n",
              "8761           San Bernardino - Tepexpan   \n",
              "8830          San Juan del Río - Xilitla   \n",
              "8854      Matehuala - Ent. Puerto México   \n",
              "14160  Naica - Santa Gertrudis - Valerio   \n",
              "14204  Naica - Santa Gertrudis - Valerio   \n",
              "14205  Naica - Santa Gertrudis - Valerio   \n",
              "14206  Naica - Santa Gertrudis - Valerio   \n",
              "14207            Libramiento de Progreso   \n",
              "\n",
              "                                inicio entre2  \\\n",
              "7590                     Ecatepec - Pirámides   \n",
              "8565   Entronque Tesistán - Entronque Malpaso   \n",
              "8758                     Imuris - Agua Prieta   \n",
              "8759                  Gómez Palacio - Jiménez   \n",
              "8761                          Peñón - Texcoco   \n",
              "8830                     Imuris - Agua Prieta   \n",
              "8854               Linares - Ent. San Roberto   \n",
              "14160                         Alazán - Canoas   \n",
              "14204                         Alazán - Canoas   \n",
              "14205                         Alazán - Canoas   \n",
              "14206                         Alazán - Canoas   \n",
              "14207                         Mérida - Cancún   \n",
              "\n",
              "                            final entre1                   final entre2  \n",
              "7590                    México - Cuautla          Los Reyes - Zacatepec  \n",
              "8565                 Guadalajara - Tepic     Ensenada - Lázaro Cárdenas  \n",
              "8758              Acapulco - Zihuatanejo        Gómez Palacio - Jiménez  \n",
              "8759              Acapulco - Zihuatanejo        Gómez Palacio - Jiménez  \n",
              "8761              Acapulco - Zihuatanejo        Gómez Palacio - Jiménez  \n",
              "8830          San Juan del Río - Xilitla           Imuris - Agua Prieta  \n",
              "8854          Linares - Ent. San Roberto  Iturbide - Cuevas - Camarones  \n",
              "14160  Naica - Santa Gertrudis - Valerio                Alazán - Canoas  \n",
              "14204            Libramiento de Progreso                Mérida - Cancún  \n",
              "14205            Libramiento de Progreso                Mérida - Cancún  \n",
              "14206            Libramiento de Progreso                Mérida - Cancún  \n",
              "14207            Libramiento de Progreso                Mérida - Cancún  \n",
              "\n",
              "[12 rows x 28 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID_RED</th>\n",
              "      <th>TIPO_VIAL</th>\n",
              "      <th>NOMBRE</th>\n",
              "      <th>CODIGO</th>\n",
              "      <th>COND_PAV</th>\n",
              "      <th>RECUBRI</th>\n",
              "      <th>CARRILES</th>\n",
              "      <th>ESTATUS</th>\n",
              "      <th>CONDICION</th>\n",
              "      <th>NIVEL</th>\n",
              "      <th>...</th>\n",
              "      <th>LONGITUD</th>\n",
              "      <th>ANCHO</th>\n",
              "      <th>FECHA_ACT</th>\n",
              "      <th>CALIREPR</th>\n",
              "      <th>Estado</th>\n",
              "      <th>geometry</th>\n",
              "      <th>inicio entre1</th>\n",
              "      <th>inicio entre2</th>\n",
              "      <th>final entre1</th>\n",
              "      <th>final entre2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7590</th>\n",
              "      <td>83252</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>647.482148</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2022-06-09 16:40:19</td>\n",
              "      <td>Definida</td>\n",
              "      <td>México</td>\n",
              "      <td>LINESTRING (2838717.360 842699.584, 2838741.54...</td>\n",
              "      <td>Libramiento Pirámides</td>\n",
              "      <td>Ecatepec - Pirámides</td>\n",
              "      <td>México - Cuautla</td>\n",
              "      <td>Los Reyes - Zacatepec</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8565</th>\n",
              "      <td>962251</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>76.013491</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2023-08-22 12:52:16</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2180669.737 1062489.884, 2180669.8...</td>\n",
              "      <td>Entronque Playa Azul - Manzanillo</td>\n",
              "      <td>Entronque Tesistán - Entronque Malpaso</td>\n",
              "      <td>Guadalajara - Tepic</td>\n",
              "      <td>Ensenada - Lázaro Cárdenas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8758</th>\n",
              "      <td>1112130</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>2100.974485</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-13 18:19:02</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2283882.322 1027322.904, 2283884.7...</td>\n",
              "      <td>San Juan del Río - Xilitla</td>\n",
              "      <td>Imuris - Agua Prieta</td>\n",
              "      <td>Acapulco - Zihuatanejo</td>\n",
              "      <td>Gómez Palacio - Jiménez</td>\n",
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              "      <th>8759</th>\n",
              "      <td>1509077</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>827.439304</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2022-06-15 19:35:30</td>\n",
              "      <td>Aproximada</td>\n",
              "      <td>Nayarit</td>\n",
              "      <td>MULTILINESTRING ((2284485.513 1028455.886, 228...</td>\n",
              "      <td>Acapulco - Zihuatanejo</td>\n",
              "      <td>Gómez Palacio - Jiménez</td>\n",
              "      <td>Acapulco - Zihuatanejo</td>\n",
              "      <td>Gómez Palacio - Jiménez</td>\n",
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              "    <tr>\n",
              "      <th>8761</th>\n",
              "      <td>1111452</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
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              "      <td>2022-06-15 19:57:27</td>\n",
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              "      <td>MULTILINESTRING ((2284288.764 1029131.007, 228...</td>\n",
              "      <td>San Bernardino - Tepexpan</td>\n",
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              "      <td>Acapulco - Zihuatanejo</td>\n",
              "      <td>Gómez Palacio - Jiménez</td>\n",
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              "      <td>Carretera</td>\n",
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              "      <td>N/D</td>\n",
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              "      <td>En operación</td>\n",
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              "      <td>2022-06-15 20:13:04</td>\n",
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              "      <td>Nayarit</td>\n",
              "      <td>LINESTRING (2285103.823 1030208.715, 2285108.8...</td>\n",
              "      <td>San Juan del Río - Xilitla</td>\n",
              "      <td>Imuris - Agua Prieta</td>\n",
              "      <td>San Juan del Río - Xilitla</td>\n",
              "      <td>Imuris - Agua Prieta</td>\n",
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              "    <tr>\n",
              "      <th>8854</th>\n",
              "      <td>397286</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
              "      <td>2</td>\n",
              "      <td>Habilitado</td>\n",
              "      <td>En operación</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>389.560844</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2023-07-21 18:34:14</td>\n",
              "      <td>Definida</td>\n",
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              "      <td>MULTILINESTRING ((2698155.473 1402797.680, 269...</td>\n",
              "      <td>Matehuala - Ent. Puerto México</td>\n",
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              "      <td>Linares - Ent. San Roberto</td>\n",
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              "      <td>N/D</td>\n",
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              "      <td>Asfalto</td>\n",
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              "      <td>12.0</td>\n",
              "      <td>2021-06-09 10:46:03</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Yucatán</td>\n",
              "      <td>LINESTRING (3772410.307 1085598.937, 3772410.3...</td>\n",
              "      <td>Naica - Santa Gertrudis - Valerio</td>\n",
              "      <td>Alazán - Canoas</td>\n",
              "      <td>Naica - Santa Gertrudis - Valerio</td>\n",
              "      <td>Alazán - Canoas</td>\n",
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              "      <td>303274</td>\n",
              "      <td>Carretera</td>\n",
              "      <td>N/D</td>\n",
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              "      <td>0</td>\n",
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              "      <td>326.714545</td>\n",
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              "      <td>2021-06-09 16:03:03</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Yucatán</td>\n",
              "      <td>MULTILINESTRING ((3772090.538 1087269.856, 377...</td>\n",
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              "      <td>7.0</td>\n",
              "      <td>2021-06-09 10:59:38</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Yucatán</td>\n",
              "      <td>MULTILINESTRING ((3772152.680 1087078.125, 377...</td>\n",
              "      <td>Naica - Santa Gertrudis - Valerio</td>\n",
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              "      <td>Libramiento de Progreso</td>\n",
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              "      <td>303271</td>\n",
              "      <td>Carretera</td>\n",
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              "      <td>N/D</td>\n",
              "      <td>Con pavimento</td>\n",
              "      <td>Asfalto</td>\n",
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              "      <td>Habilitado</td>\n",
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              "      <td>0</td>\n",
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              "      <td>983.731124</td>\n",
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              "      <td>2021-06-09 10:20:41</td>\n",
              "      <td>Definida</td>\n",
              "      <td>Yucatán</td>\n",
              "      <td>LINESTRING (3771999.616 1087853.377, 3771999.6...</td>\n",
              "      <td>Naica - Santa Gertrudis - Valerio</td>\n",
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              "      <td>0</td>\n",
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              "      <td>3756.127414</td>\n",
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              "      <td>2021-06-09 11:57:40</td>\n",
              "      <td>Definida</td>\n",
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              "      <td>MULTILINESTRING ((3770809.372 1091927.615, 377...</td>\n",
              "      <td>Libramiento de Progreso</td>\n",
              "      <td>Mérida - Cancún</td>\n",
              "      <td>Libramiento de Progreso</td>\n",
              "      <td>Mérida - Cancún</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12 rows × 28 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-575aff9b-16bc-4d76-b7b4-3598a7a9f70a')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-575aff9b-16bc-4d76-b7b4-3598a7a9f70a 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-575aff9b-16bc-4d76-b7b4-3598a7a9f70a');\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",
              "\n",
              "\n",
              "<div id=\"df-ad47f1c6-6b70-4846-890d-6eb0a418a1c9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ad47f1c6-6b70-4846-890d-6eb0a418a1c9')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ad47f1c6-6b70-4846-890d-6eb0a418a1c9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_56a1e85c-9093-4210-9d42-c66c5e9f7f87\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('sin_ID')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_56a1e85c-9093-4210-9d42-c66c5e9f7f87 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('sin_ID');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sin_ID"
            }
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Gómez_Palacio_Jiménez=cf_con_ID.loc[cf_con_ID['NOMBRE']=='Gómez Palacio - Jiménez']"
      ],
      "metadata": {
        "id": "2PhgnX0Llp_r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cf_con_ID_grouped=cf_con_ID."
      ],
      "metadata": {
        "id": "ieZHKI_qtsY-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nuevos_ID2.plot(color='red')\n",
        "#Gómez_Palacio_Jiménez.plot(ax=axis,color='blue',figsize=(40, 40))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "gdWZx9MIuRJE",
        "outputId": "fef5ff8a-6af9-438c-ec99-067df5380423"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "carreteras_sin_cob=cf_con_ID[['NOMBRE','Estado','geometry','ID_RED']]"
      ],
      "metadata": {
        "id": "UH8LdQyMwM0M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "carreteras_sin_cob_diss=carreteras_sin_cob.dissolve(by='NOMBRE', aggfunc={'ID_RED':'count','Estado':'nunique'})"
      ],
      "metadata": {
        "id": "OwaSLACwxEBu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "carreteras_sin_cob_diss"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "VrMgB6ZkycSo",
        "outputId": "a9ebc2d7-8c98-423c-b5c2-2aa777be8872"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                               geometry  \\\n",
              "NOMBRE                                                                                    \n",
              "4 Caminos - Cercalisa                 MULTILINESTRING ((2351302.071 837544.376, 2351...   \n",
              "A Aguascalientes                      MULTILINESTRING ((2506315.745 1038725.053, 250...   \n",
              "A Cerro Gordo                         MULTILINESTRING ((2720957.081 931821.611, 2721...   \n",
              "A Ent. Maxipista Guadalajara - Tepic  MULTILINESTRING ((2307189.948 984149.533, 2307...   \n",
              "A Guadalajara                         MULTILINESTRING ((2598508.978 1123579.978, 259...   \n",
              "...                                                                                 ...   \n",
              "Zihuatanejo - La Mira                 MULTILINESTRING ((2534249.447 643305.797, 2534...   \n",
              "Zihuatanejo - Playa Azul              MULTILINESTRING ((2486920.713 664948.153, 2487...   \n",
              "Zitácuaro - Ciudad Altamirano         MULTILINESTRING ((2657954.590 805020.136, 2657...   \n",
              "Álamo - Huejutla de Reyes             MULTILINESTRING ((2894580.662 1000464.685, 289...   \n",
              "Ávila Camacho - Tihuatlán             MULTILINESTRING ((2937097.513 949512.408, 2936...   \n",
              "\n",
              "                                      ID_RED  Estado  \n",
              "NOMBRE                                                \n",
              "4 Caminos - Cercalisa                     34       1  \n",
              "A Aguascalientes                          15       1  \n",
              "A Cerro Gordo                             22       1  \n",
              "A Ent. Maxipista Guadalajara - Tepic       5       1  \n",
              "A Guadalajara                              4       1  \n",
              "...                                      ...     ...  \n",
              "Zihuatanejo - La Mira                    248       2  \n",
              "Zihuatanejo - Playa Azul                  52       1  \n",
              "Zitácuaro - Ciudad Altamirano            337       1  \n",
              "Álamo - Huejutla de Reyes                208       2  \n",
              "Ávila Camacho - Tihuatlán                 41       2  \n",
              "\n",
              "[999 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-670a1497-f2f7-4a56-86cb-1700081cc967\" 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>geometry</th>\n",
              "      <th>ID_RED</th>\n",
              "      <th>Estado</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NOMBRE</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4 Caminos - Cercalisa</th>\n",
              "      <td>MULTILINESTRING ((2351302.071 837544.376, 2351...</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A Aguascalientes</th>\n",
              "      <td>MULTILINESTRING ((2506315.745 1038725.053, 250...</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A Cerro Gordo</th>\n",
              "      <td>MULTILINESTRING ((2720957.081 931821.611, 2721...</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A Ent. Maxipista Guadalajara - Tepic</th>\n",
              "      <td>MULTILINESTRING ((2307189.948 984149.533, 2307...</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A Guadalajara</th>\n",
              "      <td>MULTILINESTRING ((2598508.978 1123579.978, 259...</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zihuatanejo - La Mira</th>\n",
              "      <td>MULTILINESTRING ((2534249.447 643305.797, 2534...</td>\n",
              "      <td>248</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zihuatanejo - Playa Azul</th>\n",
              "      <td>MULTILINESTRING ((2486920.713 664948.153, 2487...</td>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zitácuaro - Ciudad Altamirano</th>\n",
              "      <td>MULTILINESTRING ((2657954.590 805020.136, 2657...</td>\n",
              "      <td>337</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Álamo - Huejutla de Reyes</th>\n",
              "      <td>MULTILINESTRING ((2894580.662 1000464.685, 289...</td>\n",
              "      <td>208</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ávila Camacho - Tihuatlán</th>\n",
              "      <td>MULTILINESTRING ((2937097.513 949512.408, 2936...</td>\n",
              "      <td>41</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>999 rows × 3 columns</p>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "carreteras_sin_cob_diss.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "SaS-EubuyFvw",
        "outputId": "5459c075-efa1-4841-cfe3-f73e2bc60718"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "axis=carreteras_sin_cob_diss.loc[carreteras_sin_cob_diss['Nombre']=='Zihuatanejo - La Mira'].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "p8fBRHf0z1ly",
        "outputId": "303686f7-7ac5-483e-e88c-98b7a1165d37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: agregar el index una nueva columna 'Nombre'\n",
        "\n",
        "carreteras_sin_cob_diss['Nombre'] = carreteras_sin_cob_diss.index\n"
      ],
      "metadata": {
        "id": "4utHEB_oy270"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "carreteras_sin_cob_diss"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "e5ZR819Ezywi",
        "outputId": "8d2fdd74-98b9-4aa1-b870-ad5e8d794daa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                               geometry  \\\n",
              "NOMBRE                                                                                    \n",
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              "...                                                                                 ...   \n",
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              "                                      ID_RED  Estado  \\\n",
              "NOMBRE                                                 \n",
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              "...                                      ...     ...   \n",
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              "\n",
              "                                                                    Nombre  \n",
              "NOMBRE                                                                      \n",
              "4 Caminos - Cercalisa                                4 Caminos - Cercalisa  \n",
              "A Aguascalientes                                          A Aguascalientes  \n",
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              "A Guadalajara                                                A Guadalajara  \n",
              "...                                                                    ...  \n",
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              "Zitácuaro - Ciudad Altamirano                Zitácuaro - Ciudad Altamirano  \n",
              "Álamo - Huejutla de Reyes                        Álamo - Huejutla de Reyes  \n",
              "Ávila Camacho - Tihuatlán                        Ávila Camacho - Tihuatlán  \n",
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\"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Nombre\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 999,\n        \"samples\": [\n          \"Libramiento de Cardel\",\n          \"Ramal al Estribo\",\n          \"El Calvario - (Morelia - Zinapecuaro)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sin_cobertura_nuevos_ID=pd.concat([sin_cobertura_nuevos_ID, nuevos_ID2], ignore_index=False)\n"
      ],
      "metadata": {
        "id": "jxPgi1OH1mrt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "axis=nuevos_ID2.plot(color='red',figsize=(40, 40))\n",
        "sin_cobertura_nuevos_ID.plot(ax=axis,color='yellow',alpha=0.5,figsize=(40, 40))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Y6cItbGg13Rj",
        "outputId": "359a237a-90ce-4c78-9844-5a84a4b31713"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 4000x4000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yuc=sin_ID.loc[sin_ID['Estado']=='Nayarit']#.plot(color='red',figsize=(20,20))\n"
      ],
      "metadata": {
        "id": "BpDO_YGV4v7x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "axis=yuc.plot(color='yellow',figsize=(20,20))\n",
        "cf_con_ID.loc[(cf_con_ID['NOMBRE']=='Guadalajara - Tepic')|(cf_con_ID['NOMBRE']=='Jala - Las Caleras')|(cf_con_ID['NOMBRE']=='Jiquilpan - Colima')|(cf_con_ID['NOMBRE']=='Laguna de Tepetiltic')].plot(ax=axis, column='NOMBRE',alpha=0.9,legend=True,figsize=(20,20))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2NtzUclQ5U1Y",
        "outputId": "dbdf25ea-0c38-4cca-9d77-f4d1959414a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f-JtJoy1-X6g"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}