tp1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copia_de_tp1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FhALkD1jaUU7"
      },
      "source": [
        "## Inicializacion de entorno"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6PfgWSKI1Q4g"
      },
      "source": [
        " # importacion general de librerias y de visualizacion (matplotlib y seaborn)\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from pandas_profiling import ProfileReport\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "plt.style.use('default') # haciendo los graficos un poco mas bonitos en matplotlib\n",
        "#plt.rcParams['figure.figsize'] = (20, 10)\n",
        "\n",
        "sns.set(style=\"whitegrid\") # seteando tipo de grid en seaborn\n",
        "\n",
        "pd.options.display.float_format = '{:20,.2f}'.format # suprimimos la notacion cientifica en los outputs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TC_QTP0t6ZhA",
        "outputId": "07f349bb-9a95-465e-9eb9-483fe8fafed1"
      },
      "source": [
        "!pip install PyDrive\n",
        "!pip install pandas-profiling==2.7.1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: PyDrive in /usr/local/lib/python3.7/dist-packages (1.3.1)\n",
            "Requirement already satisfied: google-api-python-client>=1.2 in /usr/local/lib/python3.7/dist-packages (from PyDrive) (1.12.8)\n",
            "Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.7/dist-packages (from PyDrive) (3.13)\n",
            "Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from PyDrive) (4.1.3)\n",
            "Requirement already satisfied: httplib2<1dev,>=0.15.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.2->PyDrive) (0.17.4)\n",
            "Requirement already satisfied: google-auth>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.2->PyDrive) (1.30.0)\n",
            "Requirement already satisfied: six<2dev,>=1.13.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.2->PyDrive) (1.15.0)\n",
            "Requirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.2->PyDrive) (0.0.4)\n",
            "Requirement already satisfied: google-api-core<2dev,>=1.21.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.2->PyDrive) (1.26.3)\n",
            "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.2->PyDrive) (3.0.1)\n",
            "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.7/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.4.8)\n",
            "Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.7/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.2.8)\n",
            "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from oauth2client>=4.0.0->PyDrive) (4.7.2)\n",
            "Requirement already satisfied: setuptools>=40.3.0 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.16.0->google-api-python-client>=1.2->PyDrive) (56.1.0)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth>=1.16.0->google-api-python-client>=1.2->PyDrive) (4.2.2)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (1.53.0)\n",
            "Requirement already satisfied: packaging>=14.3 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (20.9)\n",
            "Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (2.23.0)\n",
            "Requirement already satisfied: protobuf>=3.12.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (3.12.4)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (2018.9)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=14.3->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (2.4.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core<2dev,>=1.21.0->google-api-python-client>=1.2->PyDrive) (2.10)\n",
            "Requirement already satisfied: pandas-profiling==2.7.1 in /usr/local/lib/python3.7/dist-packages (2.7.1)\n",
            "Requirement already satisfied: jinja2>=2.11.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (2.11.3)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (1.4.1)\n",
            "Requirement already satisfied: phik>=0.9.10 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (0.11.2)\n",
            "Requirement already satisfied: ipywidgets>=7.5.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (7.6.3)\n",
            "Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (1.1.5)\n",
            "Requirement already satisfied: visions[type_image_path]==0.4.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (0.4.1)\n",
            "Requirement already satisfied: missingno>=0.4.2 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (0.4.2)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (1.19.5)\n",
            "Requirement already satisfied: tangled-up-in-unicode>=0.0.4 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (0.1.0)\n",
            "Requirement already satisfied: confuse>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (1.4.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (1.0.1)\n",
            "Requirement already satisfied: astropy>=4.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (4.2.1)\n",
            "Requirement already satisfied: tqdm>=4.43.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (4.60.0)\n",
            "Requirement already satisfied: htmlmin>=0.1.12 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (0.1.12)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (2.23.0)\n",
            "Requirement already satisfied: matplotlib>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==2.7.1) (3.2.2)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2>=2.11.1->pandas-profiling==2.7.1) (2.0.0)\n",
            "Requirement already satisfied: ipython>=4.0.0; python_version >= \"3.3\" in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.1->pandas-profiling==2.7.1) (5.5.0)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.1->pandas-profiling==2.7.1) (5.1.3)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.1->pandas-profiling==2.7.1) (1.0.0)\n",
            "Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.1->pandas-profiling==2.7.1) (3.5.1)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.1->pandas-profiling==2.7.1) (5.0.5)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.5.1->pandas-profiling==2.7.1) (4.10.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->pandas-profiling==2.7.1) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->pandas-profiling==2.7.1) (2018.9)\n",
            "Requirement already satisfied: networkx>=2.4 in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.4.1->pandas-profiling==2.7.1) (2.5.1)\n",
            "Requirement already satisfied: attrs>=19.3.0 in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.4.1->pandas-profiling==2.7.1) (21.2.0)\n",
            "Requirement already satisfied: imagehash; extra == \"type_image_path\" in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.4.1->pandas-profiling==2.7.1) (4.2.0)\n",
            "Requirement already satisfied: Pillow; extra == \"type_image_path\" in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.4.1->pandas-profiling==2.7.1) (7.1.2)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (from missingno>=0.4.2->pandas-profiling==2.7.1) (0.11.1)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from confuse>=1.0.0->pandas-profiling==2.7.1) (3.13)\n",
            "Requirement already satisfied: pyerfa in /usr/local/lib/python3.7/dist-packages (from astropy>=4.0->pandas-profiling==2.7.1) (1.7.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23.0->pandas-profiling==2.7.1) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23.0->pandas-profiling==2.7.1) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23.0->pandas-profiling==2.7.1) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23.0->pandas-profiling==2.7.1) (1.24.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==2.7.1) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==2.7.1) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==2.7.1) (0.10.0)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (0.7.5)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (0.8.1)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (2.6.1)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (4.8.0)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (56.1.0)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (1.0.18)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (4.4.2)\n",
            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (2.6.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (0.2.0)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (4.7.1)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (5.3.1)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (5.3.5)\n",
            "Requirement already satisfied: tornado>=4.0 in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (5.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->pandas-profiling==2.7.1) (1.15.0)\n",
            "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.7/dist-packages (from imagehash; extra == \"type_image_path\"->visions[type_image_path]==0.4.1->pandas-profiling==2.7.1) (1.1.1)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (0.2.5)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (5.6.1)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (1.5.0)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (0.9.5)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (22.0.3)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (0.7.1)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (3.3.0)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (0.3)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (1.4.3)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (0.8.4)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (0.4.4)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (0.5.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.5.1->pandas-profiling==2.7.1) (20.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NV3MHSBhW8j3"
      },
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Acdvm5sMXW5v"
      },
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0HNjcfOanUx"
      },
      "source": [
        "##Carga y optimizacion de datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZDJlHQfssms_"
      },
      "source": [
        "Descargo archivos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uv6woxKuW8xK"
      },
      "source": [
        "id='1nt1ABcWS_-jC_CH2HF3Hrcgbso244DWO'\n",
        "downloaded = drive.CreateFile({'id': id})\n",
        "downloaded.GetContentFile('train_labels.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IxeOWtFYW9Fs"
      },
      "source": [
        "id='1CjCIE18Tc7iI5J4dTnzXY1dKQzqMnalR'\n",
        "downloaded = drive.CreateFile({'id': id})\n",
        "downloaded.GetContentFile('train_values.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kC92Jj9HsaRw"
      },
      "source": [
        "Asignamos tipos de datos en la lectura de archivos para luego hacer un merge óptimo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHjVpkyk9675"
      },
      "source": [
        "train_labels = pd.read_csv('train_labels.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RrpgvSCRnqN"
      },
      "source": [
        "train_values = pd.read_csv('train_values.csv', dtype= {'land_surface_condition':'category','foundation_type':'category','roof_type':'category','ground_floor_type':'category' \\\n",
        ", 'other_floor_type':'category', 'position':'category','plan_configuration':'category','damage_grade':'category','legal_ownership_status':'category','has_secondary_use':'int8'\\\n",
        ",'has_superstructure_adobe_mud': 'int8'\\\n",
        ", 'has_superstructure_mud_mortar_stone': 'int8','has_superstructure_stone_flag': 'int8','has_superstructure_cement_mortar_stone': 'int8','has_superstructure_mud_mortar_brick': 'int8'\\\n",
        ", 'has_superstructure_cement_mortar_brick': 'int8', 'has_superstructure_timber': 'int8', 'has_superstructure_bamboo': 'int8', 'has_superstructure_rc_non_engineered': 'int8', 'has_superstructure_rc_engineered': 'int8'\\\n",
        ", 'has_superstructure_other': 'int8', 'has_secondary_use': 'int8', 'has_secondary_use_agriculture': 'int8', 'has_secondary_use_hotel': 'int8', 'has_secondary_use_rental': 'int8', 'has_secondary_use_institution': 'int8'\\\n",
        ", 'has_secondary_use_school': 'int8', 'has_secondary_use_industry': 'int8', 'has_secondary_use_health_post': 'int8', 'has_secondary_use_gov_office': 'int8', 'has_secondary_use_use_police': 'int8', 'has_secondary_use_other': 'int8'})\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vOsyvd1RvAQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7089a625-b832-48b3-a673-aea7326e5a1c"
      },
      "source": [
        "full = train_values.merge(train_labels, how='inner')\n",
        "full.memory_usage().sum() / (1024*1024) # Logramos reducir de 81MB --> 29,3MB"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.327795028686523"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0FMYRLFZ_Om",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "af7fc16f-fd8c-4dd6-8fe3-c0af4d2ae18e"
      },
      "source": [
        "full.sample(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>building_id</th>\n",
              "      <th>geo_level_1_id</th>\n",
              "      <th>geo_level_2_id</th>\n",
              "      <th>geo_level_3_id</th>\n",
              "      <th>count_floors_pre_eq</th>\n",
              "      <th>age</th>\n",
              "      <th>area_percentage</th>\n",
              "      <th>height_percentage</th>\n",
              "      <th>land_surface_condition</th>\n",
              "      <th>foundation_type</th>\n",
              "      <th>roof_type</th>\n",
              "      <th>ground_floor_type</th>\n",
              "      <th>other_floor_type</th>\n",
              "      <th>position</th>\n",
              "      <th>plan_configuration</th>\n",
              "      <th>has_superstructure_adobe_mud</th>\n",
              "      <th>has_superstructure_mud_mortar_stone</th>\n",
              "      <th>has_superstructure_stone_flag</th>\n",
              "      <th>has_superstructure_cement_mortar_stone</th>\n",
              "      <th>has_superstructure_mud_mortar_brick</th>\n",
              "      <th>has_superstructure_cement_mortar_brick</th>\n",
              "      <th>has_superstructure_timber</th>\n",
              "      <th>has_superstructure_bamboo</th>\n",
              "      <th>has_superstructure_rc_non_engineered</th>\n",
              "      <th>has_superstructure_rc_engineered</th>\n",
              "      <th>has_superstructure_other</th>\n",
              "      <th>legal_ownership_status</th>\n",
              "      <th>count_families</th>\n",
              "      <th>has_secondary_use</th>\n",
              "      <th>has_secondary_use_agriculture</th>\n",
              "      <th>has_secondary_use_hotel</th>\n",
              "      <th>has_secondary_use_rental</th>\n",
              "      <th>has_secondary_use_institution</th>\n",
              "      <th>has_secondary_use_school</th>\n",
              "      <th>has_secondary_use_industry</th>\n",
              "      <th>has_secondary_use_health_post</th>\n",
              "      <th>has_secondary_use_gov_office</th>\n",
              "      <th>has_secondary_use_use_police</th>\n",
              "      <th>has_secondary_use_other</th>\n",
              "      <th>damage_grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>219085</th>\n",
              "      <td>740501</td>\n",
              "      <td>20</td>\n",
              "      <td>788</td>\n",
              "      <td>9253</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>q</td>\n",
              "      <td>f</td>\n",
              "      <td>q</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51107</th>\n",
              "      <td>1024624</td>\n",
              "      <td>9</td>\n",
              "      <td>445</td>\n",
              "      <td>7154</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>5</td>\n",
              "      <td>t</td>\n",
              "      <td>u</td>\n",
              "      <td>q</td>\n",
              "      <td>v</td>\n",
              "      <td>x</td>\n",
              "      <td>t</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104707</th>\n",
              "      <td>906182</td>\n",
              "      <td>10</td>\n",
              "      <td>1074</td>\n",
              "      <td>2382</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>t</td>\n",
              "      <td>u</td>\n",
              "      <td>n</td>\n",
              "      <td>x</td>\n",
              "      <td>q</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109969</th>\n",
              "      <td>25998</td>\n",
              "      <td>17</td>\n",
              "      <td>303</td>\n",
              "      <td>9995</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>n</td>\n",
              "      <td>r</td>\n",
              "      <td>q</td>\n",
              "      <td>f</td>\n",
              "      <td>x</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118095</th>\n",
              "      <td>528953</td>\n",
              "      <td>3</td>\n",
              "      <td>1387</td>\n",
              "      <td>925</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>t</td>\n",
              "      <td>i</td>\n",
              "      <td>x</td>\n",
              "      <td>v</td>\n",
              "      <td>s</td>\n",
              "      <td>t</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6240</th>\n",
              "      <td>153145</td>\n",
              "      <td>6</td>\n",
              "      <td>1170</td>\n",
              "      <td>3236</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>n</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>q</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179202</th>\n",
              "      <td>392494</td>\n",
              "      <td>17</td>\n",
              "      <td>1149</td>\n",
              "      <td>5807</td>\n",
              "      <td>3</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>q</td>\n",
              "      <td>t</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43236</th>\n",
              "      <td>162699</td>\n",
              "      <td>8</td>\n",
              "      <td>172</td>\n",
              "      <td>7334</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>x</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126872</th>\n",
              "      <td>372190</td>\n",
              "      <td>10</td>\n",
              "      <td>1140</td>\n",
              "      <td>2887</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>q</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212494</th>\n",
              "      <td>722818</td>\n",
              "      <td>20</td>\n",
              "      <td>943</td>\n",
              "      <td>6700</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>j</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        building_id  geo_level_1_id  ...  has_secondary_use_other  damage_grade\n",
              "219085       740501              20  ...                        0             2\n",
              "51107       1024624               9  ...                        0             1\n",
              "104707       906182              10  ...                        0             3\n",
              "109969        25998              17  ...                        0             3\n",
              "118095       528953               3  ...                        0             2\n",
              "6240         153145               6  ...                        0             2\n",
              "179202       392494              17  ...                        0             3\n",
              "43236        162699               8  ...                        0             3\n",
              "126872       372190              10  ...                        0             3\n",
              "212494       722818              20  ...                        0             1\n",
              "\n",
              "[10 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDN0eVwBem3M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0784734-7f97-44fc-b4b7-a59efed6bd71"
      },
      "source": [
        "full.info() # no hay elementos nulos en todo el dataset."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 260601 entries, 0 to 260600\n",
            "Data columns (total 40 columns):\n",
            " #   Column                                  Non-Null Count   Dtype   \n",
            "---  ------                                  --------------   -----   \n",
            " 0   building_id                             260601 non-null  int64   \n",
            " 1   geo_level_1_id                          260601 non-null  int64   \n",
            " 2   geo_level_2_id                          260601 non-null  int64   \n",
            " 3   geo_level_3_id                          260601 non-null  int64   \n",
            " 4   count_floors_pre_eq                     260601 non-null  int64   \n",
            " 5   age                                     260601 non-null  int64   \n",
            " 6   area_percentage                         260601 non-null  int64   \n",
            " 7   height_percentage                       260601 non-null  int64   \n",
            " 8   land_surface_condition                  260601 non-null  category\n",
            " 9   foundation_type                         260601 non-null  category\n",
            " 10  roof_type                               260601 non-null  category\n",
            " 11  ground_floor_type                       260601 non-null  category\n",
            " 12  other_floor_type                        260601 non-null  category\n",
            " 13  position                                260601 non-null  category\n",
            " 14  plan_configuration                      260601 non-null  category\n",
            " 15  has_superstructure_adobe_mud            260601 non-null  int8    \n",
            " 16  has_superstructure_mud_mortar_stone     260601 non-null  int8    \n",
            " 17  has_superstructure_stone_flag           260601 non-null  int8    \n",
            " 18  has_superstructure_cement_mortar_stone  260601 non-null  int8    \n",
            " 19  has_superstructure_mud_mortar_brick     260601 non-null  int8    \n",
            " 20  has_superstructure_cement_mortar_brick  260601 non-null  int8    \n",
            " 21  has_superstructure_timber               260601 non-null  int8    \n",
            " 22  has_superstructure_bamboo               260601 non-null  int8    \n",
            " 23  has_superstructure_rc_non_engineered    260601 non-null  int8    \n",
            " 24  has_superstructure_rc_engineered        260601 non-null  int8    \n",
            " 25  has_superstructure_other                260601 non-null  int8    \n",
            " 26  legal_ownership_status                  260601 non-null  category\n",
            " 27  count_families                          260601 non-null  int64   \n",
            " 28  has_secondary_use                       260601 non-null  int8    \n",
            " 29  has_secondary_use_agriculture           260601 non-null  int8    \n",
            " 30  has_secondary_use_hotel                 260601 non-null  int8    \n",
            " 31  has_secondary_use_rental                260601 non-null  int8    \n",
            " 32  has_secondary_use_institution           260601 non-null  int8    \n",
            " 33  has_secondary_use_school                260601 non-null  int8    \n",
            " 34  has_secondary_use_industry              260601 non-null  int8    \n",
            " 35  has_secondary_use_health_post           260601 non-null  int8    \n",
            " 36  has_secondary_use_gov_office            260601 non-null  int8    \n",
            " 37  has_secondary_use_use_police            260601 non-null  int8    \n",
            " 38  has_secondary_use_other                 260601 non-null  int8    \n",
            " 39  damage_grade                            260601 non-null  int64   \n",
            "dtypes: category(8), int64(10), int8(22)\n",
            "memory usage: 29.3 MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBiWbblWbi3C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "01923f97-aac0-4b50-a815-6bd97a2f8187"
      },
      "source": [
        "full.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>building_id</th>\n",
              "      <th>geo_level_1_id</th>\n",
              "      <th>geo_level_2_id</th>\n",
              "      <th>geo_level_3_id</th>\n",
              "      <th>count_floors_pre_eq</th>\n",
              "      <th>age</th>\n",
              "      <th>area_percentage</th>\n",
              "      <th>height_percentage</th>\n",
              "      <th>has_superstructure_adobe_mud</th>\n",
              "      <th>has_superstructure_mud_mortar_stone</th>\n",
              "      <th>has_superstructure_stone_flag</th>\n",
              "      <th>has_superstructure_cement_mortar_stone</th>\n",
              "      <th>has_superstructure_mud_mortar_brick</th>\n",
              "      <th>has_superstructure_cement_mortar_brick</th>\n",
              "      <th>has_superstructure_timber</th>\n",
              "      <th>has_superstructure_bamboo</th>\n",
              "      <th>has_superstructure_rc_non_engineered</th>\n",
              "      <th>has_superstructure_rc_engineered</th>\n",
              "      <th>has_superstructure_other</th>\n",
              "      <th>count_families</th>\n",
              "      <th>has_secondary_use</th>\n",
              "      <th>has_secondary_use_agriculture</th>\n",
              "      <th>has_secondary_use_hotel</th>\n",
              "      <th>has_secondary_use_rental</th>\n",
              "      <th>has_secondary_use_institution</th>\n",
              "      <th>has_secondary_use_school</th>\n",
              "      <th>has_secondary_use_industry</th>\n",
              "      <th>has_secondary_use_health_post</th>\n",
              "      <th>has_secondary_use_gov_office</th>\n",
              "      <th>has_secondary_use_use_police</th>\n",
              "      <th>has_secondary_use_other</th>\n",
              "      <th>damage_grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>525,675.48</td>\n",
              "      <td>13.90</td>\n",
              "      <td>701.07</td>\n",
              "      <td>6,257.88</td>\n",
              "      <td>2.13</td>\n",
              "      <td>26.54</td>\n",
              "      <td>8.02</td>\n",
              "      <td>5.43</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>304,545.00</td>\n",
              "      <td>8.03</td>\n",
              "      <td>412.71</td>\n",
              "      <td>3,646.37</td>\n",
              "      <td>0.73</td>\n",
              "      <td>73.57</td>\n",
              "      <td>4.39</td>\n",
              "      <td>1.92</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>261,190.00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>350.00</td>\n",
              "      <td>3,073.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>525,757.00</td>\n",
              "      <td>12.00</td>\n",
              "      <td>702.00</td>\n",
              "      <td>6,270.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>15.00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>789,762.00</td>\n",
              "      <td>21.00</td>\n",
              "      <td>1,050.00</td>\n",
              "      <td>9,412.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>30.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1,052,934.00</td>\n",
              "      <td>30.00</td>\n",
              "      <td>1,427.00</td>\n",
              "      <td>12,567.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>995.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>32.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               building_id  ...         damage_grade\n",
              "count           260,601.00  ...           260,601.00\n",
              "mean            525,675.48  ...                 2.24\n",
              "std             304,545.00  ...                 0.61\n",
              "min                   4.00  ...                 1.00\n",
              "25%             261,190.00  ...                 2.00\n",
              "50%             525,757.00  ...                 2.00\n",
              "75%             789,762.00  ...                 3.00\n",
              "max           1,052,934.00  ...                 3.00\n",
              "\n",
              "[8 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HlOS9YWgw_iK"
      },
      "source": [
        "## Reporte Perfilado de Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qNC0GyHgxXfZ"
      },
      "source": [
        "#ProfileReport(full)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lqav8tjv22Op"
      },
      "source": [
        "##Exploracion de datos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDKr9JGS3FZP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efa06d9d-e4a4-405b-8889-100ea6564b92"
      },
      "source": [
        "full.building_id.is_unique"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7pIdMTB23O2k",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89bd6ce3-d9f4-46a8-8584-07e323f37ea0"
      },
      "source": [
        "full.damage_grade.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    148259\n",
              "3     87218\n",
              "1     25124\n",
              "Name: damage_grade, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6z0VyOrK-0oN"
      },
      "source": [
        "###Exploracion datos numericos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "phsqcfS44z0l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "a0278c25-efc3-47d6-8fc6-0bc208c6edfc"
      },
      "source": [
        "numerical_columns = ['age','count_floors_pre_eq', 'area_percentage', 'height_percentage']\n",
        "full[numerical_columns].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>age</th>\n",
              "      <th>count_floors_pre_eq</th>\n",
              "      <th>area_percentage</th>\n",
              "      <th>height_percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>26.54</td>\n",
              "      <td>2.13</td>\n",
              "      <td>8.02</td>\n",
              "      <td>5.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>73.57</td>\n",
              "      <td>0.73</td>\n",
              "      <td>4.39</td>\n",
              "      <td>1.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>10.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>15.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>5.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>30.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>6.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>995.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>32.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       age  ...    height_percentage\n",
              "count           260,601.00  ...           260,601.00\n",
              "mean                 26.54  ...                 5.43\n",
              "std                  73.57  ...                 1.92\n",
              "min                   0.00  ...                 2.00\n",
              "25%                  10.00  ...                 4.00\n",
              "50%                  15.00  ...                 5.00\n",
              "75%                  30.00  ...                 6.00\n",
              "max                 995.00  ...                32.00\n",
              "\n",
              "[8 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1S0578R53YwQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "434c87be-e307-4bee-941b-bb6e322459ff"
      },
      "source": [
        "full.count_floors_pre_eq.value_counts().sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    156623\n",
              "3     55617\n",
              "1     40441\n",
              "4      5424\n",
              "5      2246\n",
              "6       209\n",
              "7        39\n",
              "8         1\n",
              "9         1\n",
              "Name: count_floors_pre_eq, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YeJXJL7d_JNC"
      },
      "source": [
        "###Exploracion datos categoricos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TfpsBU83COrR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "f15cadfb-c746-49ee-f3ca-eb814e6bfc72"
      },
      "source": [
        "full.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>building_id</th>\n",
              "      <th>geo_level_1_id</th>\n",
              "      <th>geo_level_2_id</th>\n",
              "      <th>geo_level_3_id</th>\n",
              "      <th>count_floors_pre_eq</th>\n",
              "      <th>age</th>\n",
              "      <th>area_percentage</th>\n",
              "      <th>height_percentage</th>\n",
              "      <th>land_surface_condition</th>\n",
              "      <th>foundation_type</th>\n",
              "      <th>roof_type</th>\n",
              "      <th>ground_floor_type</th>\n",
              "      <th>other_floor_type</th>\n",
              "      <th>position</th>\n",
              "      <th>plan_configuration</th>\n",
              "      <th>has_superstructure_adobe_mud</th>\n",
              "      <th>has_superstructure_mud_mortar_stone</th>\n",
              "      <th>has_superstructure_stone_flag</th>\n",
              "      <th>has_superstructure_cement_mortar_stone</th>\n",
              "      <th>has_superstructure_mud_mortar_brick</th>\n",
              "      <th>has_superstructure_cement_mortar_brick</th>\n",
              "      <th>has_superstructure_timber</th>\n",
              "      <th>has_superstructure_bamboo</th>\n",
              "      <th>has_superstructure_rc_non_engineered</th>\n",
              "      <th>has_superstructure_rc_engineered</th>\n",
              "      <th>has_superstructure_other</th>\n",
              "      <th>legal_ownership_status</th>\n",
              "      <th>count_families</th>\n",
              "      <th>has_secondary_use</th>\n",
              "      <th>has_secondary_use_agriculture</th>\n",
              "      <th>has_secondary_use_hotel</th>\n",
              "      <th>has_secondary_use_rental</th>\n",
              "      <th>has_secondary_use_institution</th>\n",
              "      <th>has_secondary_use_school</th>\n",
              "      <th>has_secondary_use_industry</th>\n",
              "      <th>has_secondary_use_health_post</th>\n",
              "      <th>has_secondary_use_gov_office</th>\n",
              "      <th>has_secondary_use_use_police</th>\n",
              "      <th>has_secondary_use_other</th>\n",
              "      <th>damage_grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>126581</th>\n",
              "      <td>974528</td>\n",
              "      <td>26</td>\n",
              "      <td>886</td>\n",
              "      <td>9266</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>t</td>\n",
              "      <td>w</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>j</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194212</th>\n",
              "      <td>571964</td>\n",
              "      <td>13</td>\n",
              "      <td>213</td>\n",
              "      <td>111</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>q</td>\n",
              "      <td>f</td>\n",
              "      <td>x</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186117</th>\n",
              "      <td>546540</td>\n",
              "      <td>26</td>\n",
              "      <td>36</td>\n",
              "      <td>5318</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>v</td>\n",
              "      <td>j</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182899</th>\n",
              "      <td>622822</td>\n",
              "      <td>7</td>\n",
              "      <td>1265</td>\n",
              "      <td>9455</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>q</td>\n",
              "      <td>f</td>\n",
              "      <td>q</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>181884</th>\n",
              "      <td>474832</td>\n",
              "      <td>9</td>\n",
              "      <td>713</td>\n",
              "      <td>9605</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>t</td>\n",
              "      <td>i</td>\n",
              "      <td>n</td>\n",
              "      <td>x</td>\n",
              "      <td>s</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        building_id  geo_level_1_id  ...  has_secondary_use_other  damage_grade\n",
              "126581       974528              26  ...                        0             1\n",
              "194212       571964              13  ...                        0             2\n",
              "186117       546540              26  ...                        0             1\n",
              "182899       622822               7  ...                        0             3\n",
              "181884       474832               9  ...                        0             2\n",
              "\n",
              "[5 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noCU2UyIACCg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "640b104c-392e-462d-cc16-736f53c121d6"
      },
      "source": [
        "categorias = full.loc[:,['land_surface_condition','foundation_type','roof_type','ground_floor_type','other_floor_type',\\\n",
        "'position','plan_configuration','legal_ownership_status']]\n",
        "categorias.sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>land_surface_condition</th>\n",
              "      <th>foundation_type</th>\n",
              "      <th>roof_type</th>\n",
              "      <th>ground_floor_type</th>\n",
              "      <th>other_floor_type</th>\n",
              "      <th>position</th>\n",
              "      <th>plan_configuration</th>\n",
              "      <th>legal_ownership_status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>155770</th>\n",
              "      <td>t</td>\n",
              "      <td>u</td>\n",
              "      <td>q</td>\n",
              "      <td>x</td>\n",
              "      <td>s</td>\n",
              "      <td>j</td>\n",
              "      <td>d</td>\n",
              "      <td>v</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36253</th>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>q</td>\n",
              "      <td>f</td>\n",
              "      <td>j</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>v</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258901</th>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>j</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>v</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>180728</th>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>q</td>\n",
              "      <td>t</td>\n",
              "      <td>d</td>\n",
              "      <td>v</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254325</th>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>j</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>v</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       land_surface_condition  ... legal_ownership_status\n",
              "155770                      t  ...                      v\n",
              "36253                       t  ...                      v\n",
              "258901                      t  ...                      v\n",
              "180728                      t  ...                      v\n",
              "254325                      t  ...                      v\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 273
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7F9X3dHJ_nJL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c248fc42-bc62-432f-95cf-42ee276c9817"
      },
      "source": [
        "categorias.land_surface_condition.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "t    216757\n",
              "n     35528\n",
              "o      8316\n",
              "Name: land_surface_condition, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 274
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AA6kv088DXPQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20f0539c-cad6-4d08-ca7b-4075574b5f20"
      },
      "source": [
        "categorias.foundation_type.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "r    219196\n",
              "w     15118\n",
              "u     14260\n",
              "i     10579\n",
              "h      1448\n",
              "Name: foundation_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 275
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7gIqk5GDYA-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "75bcecc7-65b0-42e4-d75a-e4ca8508c871"
      },
      "source": [
        "categorias.roof_type.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "n    182842\n",
              "q     61576\n",
              "x     16183\n",
              "Name: roof_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 276
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhU0MPHGDYkU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "250c95ab-0fb2-41e6-8bf1-aff648fa1e9c"
      },
      "source": [
        "categorias.ground_floor_type.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "f    209619\n",
              "x     24877\n",
              "v     24593\n",
              "z      1004\n",
              "m       508\n",
              "Name: ground_floor_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 277
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FySq-tVODhtN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd8f033d-781b-49b5-e65c-0dc043518c3d"
      },
      "source": [
        "categorias.other_floor_type.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "q    165282\n",
              "x     43448\n",
              "j     39843\n",
              "s     12028\n",
              "Name: other_floor_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdptAhmtDgOy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22b112a2-b3f7-46a3-ef8c-e96aaf4d410c"
      },
      "source": [
        "categorias.position.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "s    202090\n",
              "t     42896\n",
              "j     13282\n",
              "o      2333\n",
              "Name: position, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 279
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0_tuB_GDhTN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0e63a12-310c-4baa-9c1d-ede158900686"
      },
      "source": [
        "categorias.plan_configuration.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "d    250072\n",
              "q      5692\n",
              "u      3649\n",
              "s       346\n",
              "c       325\n",
              "a       252\n",
              "o       159\n",
              "m        46\n",
              "n        38\n",
              "f        22\n",
              "Name: plan_configuration, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 280
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrPQtSakDhh0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17ccf94b-6b91-4f83-819f-3442403ca5c3"
      },
      "source": [
        "categorias.legal_ownership_status.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "v    250939\n",
              "a      5512\n",
              "w      2677\n",
              "r      1473\n",
              "Name: legal_ownership_status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 281
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UaYqeKt_815O"
      },
      "source": [
        "## Distribuciones importantes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EL3wPlxH7Y3q"
      },
      "source": [
        "###Distribucion del grado de daño a edificios"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-vL4LARL8ZvY",
        "outputId": "4263ed21-de2c-4aa7-8790-ee1888d0550b"
      },
      "source": [
        "distribucion_daño = train_labels['damage_grade'].value_counts()\n",
        "distribucion_daño"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    148259\n",
              "3     87218\n",
              "1     25124\n",
              "Name: damage_grade, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 282
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "dv5PB4P47hzY",
        "outputId": "0e5bcb26-08d4-4c3a-f740-7a7012f73fbb"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Distribución del grado de daño para los edificios', {'fontweight':'bold'})\n",
        "\n",
        "distribucion_daño.plot(kind='pie', ax=f.gca(), ylabel='Grado de daño', colors=['#ff944d','red','#16a1ff'], autopct='%1.0f%%')\n",
        "\n",
        "plt.savefig('damage_pie.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1dxHv7Py9W5q"
      },
      "source": [
        "###Distribucion de edad de los edificios"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVtNP1Lt-W5h",
        "outputId": "51a72ec4-8484-4c3e-8ca3-fcecf80a77cd"
      },
      "source": [
        "antiguedad_filtrada2 = train_values[train_values['age']<150] # Filtramos edificios con edades mayores a 150 años ya que consideramos esos datos como ambigüos para nuestro análisis.\n",
        "antiguedad_filtrada2['age'].value_counts()                   # Especial hincapié en edificios con edad '995 años', el cual corresponde a un dato no certero. "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10     38896\n",
              "15     36010\n",
              "5      33697\n",
              "20     32182\n",
              "0      26041\n",
              "25     24366\n",
              "30     18028\n",
              "35     10710\n",
              "40     10559\n",
              "50      7257\n",
              "45      4711\n",
              "60      3612\n",
              "80      3055\n",
              "55      2033\n",
              "70      1975\n",
              "100     1364\n",
              "65      1123\n",
              "90      1085\n",
              "85       847\n",
              "75       512\n",
              "95       414\n",
              "120      180\n",
              "110      100\n",
              "105       89\n",
              "125       37\n",
              "115       21\n",
              "140        9\n",
              "130        9\n",
              "135        5\n",
              "145        3\n",
              "Name: age, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 284
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "B5snx_AU9lBf",
        "outputId": "9405381c-44ce-46f8-d6f9-908be5532620"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.tight_layout()\n",
        "plt.title(\"Distribución de los edificios por antigüedad\", {'fontweight':'bold'})\n",
        "antiguedad_filtrada2['age'].plot(kind='hist', ax=f.gca())\n",
        "plt.ylabel(\"Cantidad de edificios\")\n",
        "plt.xlabel(\"Antigüedad\")\n",
        "plt.savefig(\"edificios_antiguedad.pdf\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RI8fZA04KBny"
      },
      "source": [
        "### Distribucion de los materiales y grado de daño recibidos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eCsI0CLMLyx"
      },
      "source": [
        "Distribucion total de los materiales"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hNdsNmJEKRUr"
      },
      "source": [
        "materials = ['has_superstructure_adobe_mud', 'has_superstructure_mud_mortar_stone', 'has_superstructure_stone_flag',\n",
        "                              'has_superstructure_cement_mortar_stone', 'has_superstructure_mud_mortar_brick', 'has_superstructure_cement_mortar_brick',\n",
        "                              'has_superstructure_timber', 'has_superstructure_bamboo', 'has_superstructure_rc_non_engineered', 'has_superstructure_rc_engineered',\n",
        "                              'has_superstructure_other']                             "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TO1pHe-RKRUr"
      },
      "source": [
        "materials_es = ['Adobe and mud', 'Mud and Stone', 'Stone',\n",
        "                              'Cement and Stone', 'Mud and Brick', 'Cement and Brick',\n",
        "                              'Timber', 'Bamboo', 'RC non enrineered', 'RC engineered',\n",
        "                              'Other']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mpirhbOMKJVR",
        "outputId": "860de812-8d52-4808-bddb-dc3ca3b82167"
      },
      "source": [
        "distribucion_materiales = full.loc[:,materials].mean()\n",
        "distribucion_materiales.index = materials_es\n",
        "distribucion_materiales"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Adobe and mud                       0.09\n",
              "Mud and Stone                       0.76\n",
              "Stone                               0.03\n",
              "Cement and Stone                    0.02\n",
              "Mud and Brick                       0.07\n",
              "Cement and Brick                    0.08\n",
              "Timber                              0.25\n",
              "Bamboo                              0.09\n",
              "RC non enrineered                   0.04\n",
              "RC engineered                       0.02\n",
              "Other                               0.01\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 288
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "WLNjg8UVMXW0",
        "outputId": "bdc962ae-663b-4de8-9545-1a22e1cab52a"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Distribución de los materiales para los edificios', {'fontweight':'bold'})\n",
        "\n",
        "distribucion_materiales.plot(kind='pie', ax=f.gca(), ylabel='Materiales', colors=['#FEF9E7','#FAE5D3','#CD6155','#DC7633','#BA4A00','#873600','#6E2C00','#D5DBDB','#BFC9CA','#5D6D7E','#273746'], autopct='%1.0f%%')\n",
        "\n",
        "plt.savefig('materiales_distribucion.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9N-xvHs2KRUk"
      },
      "source": [
        "Para todos los materiales se puede encontrar una tendencia de acuerdo al daño"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "hABQQKBKKRUk",
        "outputId": "95c49ccb-541b-495e-aaf0-15acbc6fbab7"
      },
      "source": [
        "full.loc[full['has_superstructure_adobe_mud'] & (full['damage_grade']==1)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>building_id</th>\n",
              "      <th>geo_level_1_id</th>\n",
              "      <th>geo_level_2_id</th>\n",
              "      <th>geo_level_3_id</th>\n",
              "      <th>count_floors_pre_eq</th>\n",
              "      <th>age</th>\n",
              "      <th>area_percentage</th>\n",
              "      <th>height_percentage</th>\n",
              "      <th>land_surface_condition</th>\n",
              "      <th>foundation_type</th>\n",
              "      <th>roof_type</th>\n",
              "      <th>ground_floor_type</th>\n",
              "      <th>other_floor_type</th>\n",
              "      <th>position</th>\n",
              "      <th>plan_configuration</th>\n",
              "      <th>has_superstructure_adobe_mud</th>\n",
              "      <th>has_superstructure_mud_mortar_stone</th>\n",
              "      <th>has_superstructure_stone_flag</th>\n",
              "      <th>has_superstructure_cement_mortar_stone</th>\n",
              "      <th>has_superstructure_mud_mortar_brick</th>\n",
              "      <th>has_superstructure_cement_mortar_brick</th>\n",
              "      <th>has_superstructure_timber</th>\n",
              "      <th>has_superstructure_bamboo</th>\n",
              "      <th>has_superstructure_rc_non_engineered</th>\n",
              "      <th>has_superstructure_rc_engineered</th>\n",
              "      <th>has_superstructure_other</th>\n",
              "      <th>legal_ownership_status</th>\n",
              "      <th>count_families</th>\n",
              "      <th>has_secondary_use</th>\n",
              "      <th>has_secondary_use_agriculture</th>\n",
              "      <th>has_secondary_use_hotel</th>\n",
              "      <th>has_secondary_use_rental</th>\n",
              "      <th>has_secondary_use_institution</th>\n",
              "      <th>has_secondary_use_school</th>\n",
              "      <th>has_secondary_use_industry</th>\n",
              "      <th>has_secondary_use_health_post</th>\n",
              "      <th>has_secondary_use_gov_office</th>\n",
              "      <th>has_secondary_use_use_police</th>\n",
              "      <th>has_secondary_use_other</th>\n",
              "      <th>damage_grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>643</th>\n",
              "      <td>306848</td>\n",
              "      <td>5</td>\n",
              "      <td>1418</td>\n",
              "      <td>11670</td>\n",
              "      <td>1</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>j</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>660</th>\n",
              "      <td>1050374</td>\n",
              "      <td>20</td>\n",
              "      <td>173</td>\n",
              "      <td>3258</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>t</td>\n",
              "      <td>w</td>\n",
              "      <td>q</td>\n",
              "      <td>f</td>\n",
              "      <td>x</td>\n",
              "      <td>s</td>\n",
              "      <td>u</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1106</th>\n",
              "      <td>453197</td>\n",
              "      <td>13</td>\n",
              "      <td>480</td>\n",
              "      <td>12455</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>q</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1299</th>\n",
              "      <td>87974</td>\n",
              "      <td>13</td>\n",
              "      <td>480</td>\n",
              "      <td>1917</td>\n",
              "      <td>2</td>\n",
              "      <td>30</td>\n",
              "      <td>13</td>\n",
              "      <td>5</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>q</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1442</th>\n",
              "      <td>180268</td>\n",
              "      <td>15</td>\n",
              "      <td>668</td>\n",
              "      <td>3329</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>n</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>j</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "      <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>258083</th>\n",
              "      <td>49776</td>\n",
              "      <td>13</td>\n",
              "      <td>480</td>\n",
              "      <td>7457</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>q</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258370</th>\n",
              "      <td>541206</td>\n",
              "      <td>20</td>\n",
              "      <td>173</td>\n",
              "      <td>1199</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>t</td>\n",
              "      <td>w</td>\n",
              "      <td>q</td>\n",
              "      <td>f</td>\n",
              "      <td>j</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258681</th>\n",
              "      <td>773391</td>\n",
              "      <td>10</td>\n",
              "      <td>310</td>\n",
              "      <td>6799</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>t</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>q</td>\n",
              "      <td>t</td>\n",
              "      <td>d</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259994</th>\n",
              "      <td>711529</td>\n",
              "      <td>24</td>\n",
              "      <td>628</td>\n",
              "      <td>10591</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>o</td>\n",
              "      <td>r</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>q</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260105</th>\n",
              "      <td>811094</td>\n",
              "      <td>1</td>\n",
              "      <td>1169</td>\n",
              "      <td>12544</td>\n",
              "      <td>2</td>\n",
              "      <td>35</td>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>t</td>\n",
              "      <td>u</td>\n",
              "      <td>n</td>\n",
              "      <td>f</td>\n",
              "      <td>q</td>\n",
              "      <td>s</td>\n",
              "      <td>d</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>v</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>594 rows × 40 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        building_id  geo_level_1_id  ...  has_secondary_use_other  damage_grade\n",
              "643          306848               5  ...                        0             1\n",
              "660         1050374              20  ...                        0             1\n",
              "1106         453197              13  ...                        0             1\n",
              "1299          87974              13  ...                        0             1\n",
              "1442         180268              15  ...                        0             1\n",
              "...             ...             ...  ...                      ...           ...\n",
              "258083        49776              13  ...                        0             1\n",
              "258370       541206              20  ...                        0             1\n",
              "258681       773391              10  ...                        0             1\n",
              "259994       711529              24  ...                        0             1\n",
              "260105       811094               1  ...                        0             1\n",
              "\n",
              "[594 rows x 40 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 290
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hx8PBiCTKRUl",
        "outputId": "cba7b574-d3ba-4036-b327-4658eec0f95f"
      },
      "source": [
        "(full['damage_grade']==1).value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    235477\n",
              "True      25124\n",
              "Name: damage_grade, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 291
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CY5xiYKQKRUl"
      },
      "source": [
        "materials_per_damage = full.groupby('damage_grade')[['has_superstructure_adobe_mud', 'has_superstructure_mud_mortar_stone', 'has_superstructure_stone_flag',\n",
        "                              'has_superstructure_cement_mortar_stone', 'has_superstructure_mud_mortar_brick', 'has_superstructure_cement_mortar_brick',\n",
        "                              'has_superstructure_timber', 'has_superstructure_bamboo', 'has_superstructure_rc_non_engineered', 'has_superstructure_rc_engineered',\n",
        "                              'has_superstructure_other']].mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "tys6cHHHKRUq",
        "outputId": "1169a9b4-1ec5-4a0d-840a-0904cb8e7ad4"
      },
      "source": [
        "materials_per_damage"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>has_superstructure_adobe_mud</th>\n",
              "      <th>has_superstructure_mud_mortar_stone</th>\n",
              "      <th>has_superstructure_stone_flag</th>\n",
              "      <th>has_superstructure_cement_mortar_stone</th>\n",
              "      <th>has_superstructure_mud_mortar_brick</th>\n",
              "      <th>has_superstructure_cement_mortar_brick</th>\n",
              "      <th>has_superstructure_timber</th>\n",
              "      <th>has_superstructure_bamboo</th>\n",
              "      <th>has_superstructure_rc_non_engineered</th>\n",
              "      <th>has_superstructure_rc_engineered</th>\n",
              "      <th>has_superstructure_other</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>damage_grade</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.09</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              has_superstructure_adobe_mud  ...  has_superstructure_other\n",
              "damage_grade                                ...                          \n",
              "1                                     0.02  ...                      0.03\n",
              "2                                     0.09  ...                      0.02\n",
              "3                                     0.10  ...                      0.01\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 293
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "w_ktRYfZKRUr",
        "outputId": "c79597ef-5d2f-4f7b-fa2f-68cca6038239"
      },
      "source": [
        "materials_df = pd.DataFrame(index = materials_per_damage.index)\n",
        "\n",
        "for i, material in enumerate(materials):\n",
        "  material_distribution = full.loc[full[material]==1].groupby([material, 'damage_grade'])[['damage_grade']].count()['damage_grade']\n",
        "  material_distribution = material_distribution / material_distribution.sum() *100\n",
        "  materials_df[materials_es[i]] = material_distribution.values\n",
        "\n",
        "materials_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>Adobe and mud</th>\n",
              "      <th>Mud and Stone</th>\n",
              "      <th>Stone</th>\n",
              "      <th>Cement and Stone</th>\n",
              "      <th>Mud and Brick</th>\n",
              "      <th>Cement and Brick</th>\n",
              "      <th>Timber</th>\n",
              "      <th>Bamboo</th>\n",
              "      <th>RC non enrineered</th>\n",
              "      <th>RC engineered</th>\n",
              "      <th>Other</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>damage_grade</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.57</td>\n",
              "      <td>4.41</td>\n",
              "      <td>2.03</td>\n",
              "      <td>17.45</td>\n",
              "      <td>3.51</td>\n",
              "      <td>36.22</td>\n",
              "      <td>11.50</td>\n",
              "      <td>12.81</td>\n",
              "      <td>34.75</td>\n",
              "      <td>64.26</td>\n",
              "      <td>16.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60.18</td>\n",
              "      <td>57.39</td>\n",
              "      <td>50.68</td>\n",
              "      <td>68.35</td>\n",
              "      <td>65.86</td>\n",
              "      <td>58.23</td>\n",
              "      <td>60.48</td>\n",
              "      <td>63.20</td>\n",
              "      <td>52.55</td>\n",
              "      <td>33.92</td>\n",
              "      <td>57.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37.25</td>\n",
              "      <td>38.20</td>\n",
              "      <td>47.29</td>\n",
              "      <td>14.20</td>\n",
              "      <td>30.63</td>\n",
              "      <td>5.55</td>\n",
              "      <td>28.02</td>\n",
              "      <td>23.99</td>\n",
              "      <td>12.70</td>\n",
              "      <td>1.81</td>\n",
              "      <td>25.63</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Adobe and mud  ...                Other\n",
              "damage_grade                       ...                     \n",
              "1                            2.57  ...                16.80\n",
              "2                           60.18  ...                57.57\n",
              "3                           37.25  ...                25.63\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 294
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "xM9tlmtfKRUr",
        "outputId": "0f3f3c73-2c53-41a3-af1f-8ba1e7b1ed9a"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Distribución de daño para cada tipo de superestructura' , {'fontweight':'bold'})\n",
        "\n",
        "materials_df.transpose().sort_values(by=[3]).plot(kind='bar', ax=f.gca(), xlabel='Tipo de superestructura', ylabel='Porcentaje de edificios dañados', color=['#16a1ff', 'orange', 'red'], stacked=True)\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
        "#plt.show()\n",
        "plt.savefig('materials.pdf', bbox_inches='tight')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fe9TdOgLKRUr",
        "outputId": "d9af5f6f-7efd-4408-d251-df2a964478d5"
      },
      "source": [
        "adobe_mud = full.loc[full['has_superstructure_adobe_mud']==1].groupby(['has_superstructure_adobe_mud', 'damage_grade'])[['damage_grade']].count()['damage_grade']\n",
        "adobe_mud = adobe_mud / adobe_mud.sum()\n",
        "adobe_mud.values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.02571317, 0.60179213, 0.3724947 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 296
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cPyWQLTuQbX0"
      },
      "source": [
        "###Distribucion de daño para zonas geograficas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehykYp0cZpN3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c92650f-4478-4f36-f631-24c169d9068d"
      },
      "source": [
        "train_values['geo_level_1_id'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6     24381\n",
              "26    22615\n",
              "10    22079\n",
              "17    21813\n",
              "8     19080\n",
              "7     18994\n",
              "20    17216\n",
              "21    14889\n",
              "4     14568\n",
              "27    12532\n",
              "13     9608\n",
              "11     8220\n",
              "3      7540\n",
              "22     6252\n",
              "25     5624\n",
              "16     4332\n",
              "0      4011\n",
              "9      3958\n",
              "12     3194\n",
              "18     3189\n",
              "1      2701\n",
              "5      2690\n",
              "30     2686\n",
              "15     2320\n",
              "14     1714\n",
              "24     1310\n",
              "23     1121\n",
              "2       931\n",
              "29      396\n",
              "19      372\n",
              "28      265\n",
              "Name: geo_level_1_id, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 297
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BWw5taqrbEFT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "fe322edb-e561-4a62-e7e6-1bc3f7874b31"
      },
      "source": [
        "full[['geo_level_1_id', 'damage_grade']]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>geo_level_1_id</th>\n",
              "      <th>damage_grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>22</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260596</th>\n",
              "      <td>25</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260597</th>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260598</th>\n",
              "      <td>17</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260599</th>\n",
              "      <td>26</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>260600</th>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>260601 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        geo_level_1_id  damage_grade\n",
              "0                    6             3\n",
              "1                    8             2\n",
              "2                   21             3\n",
              "3                   22             2\n",
              "4                   11             3\n",
              "...                ...           ...\n",
              "260596              25             2\n",
              "260597              17             3\n",
              "260598              17             3\n",
              "260599              26             2\n",
              "260600              21             3\n",
              "\n",
              "[260601 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 298
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5t6nNUoC6nK"
      },
      "source": [
        "NECESITAMOS OBTENER EL PROMEDIO PARA CADA CELDA. SERIA EL VALOR ACTUAL DIVIDIDO POR LA SUMATORIA DE LOS VALORES EN ESA FILA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eg4jFrMPYmSn"
      },
      "source": [
        "damage_per_zone = full[['geo_level_1_id', 'damage_grade', 'building_id']].pivot_table(index='damage_grade', columns='geo_level_1_id', aggfunc='count')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wV2BD_mZEhd0"
      },
      "source": [
        "damage_per_zone.columns = damage_per_zone.columns.get_level_values(1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qh1c0uuuL0rw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "9f59e218-41d1-4a1b-b27c-42a2f2dc8eec"
      },
      "source": [
        "damage_per_zone"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>geo_level_1_id</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>damage_grade</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>337.00</td>\n",
              "      <td>411.00</td>\n",
              "      <td>85.00</td>\n",
              "      <td>245.00</td>\n",
              "      <td>521.00</td>\n",
              "      <td>446.00</td>\n",
              "      <td>2,108.00</td>\n",
              "      <td>1,033.00</td>\n",
              "      <td>654.00</td>\n",
              "      <td>561.00</td>\n",
              "      <td>1,211.00</td>\n",
              "      <td>386.00</td>\n",
              "      <td>199.00</td>\n",
              "      <td>1,966.00</td>\n",
              "      <td>191.00</td>\n",
              "      <td>163.00</td>\n",
              "      <td>200.00</td>\n",
              "      <td>285.00</td>\n",
              "      <td>72.00</td>\n",
              "      <td>43.00</td>\n",
              "      <td>3,311.00</td>\n",
              "      <td>322.00</td>\n",
              "      <td>811.00</td>\n",
              "      <td>72.00</td>\n",
              "      <td>270.00</td>\n",
              "      <td>468.00</td>\n",
              "      <td>8,028.00</td>\n",
              "      <td>465.00</td>\n",
              "      <td>nan</td>\n",
              "      <td>8.00</td>\n",
              "      <td>252.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3,075.00</td>\n",
              "      <td>1,985.00</td>\n",
              "      <td>610.00</td>\n",
              "      <td>4,550.00</td>\n",
              "      <td>11,164.00</td>\n",
              "      <td>2,014.00</td>\n",
              "      <td>16,222.00</td>\n",
              "      <td>11,273.00</td>\n",
              "      <td>8,513.00</td>\n",
              "      <td>2,733.00</td>\n",
              "      <td>12,107.00</td>\n",
              "      <td>4,672.00</td>\n",
              "      <td>2,310.00</td>\n",
              "      <td>6,275.00</td>\n",
              "      <td>1,247.00</td>\n",
              "      <td>1,673.00</td>\n",
              "      <td>3,188.00</td>\n",
              "      <td>3,913.00</td>\n",
              "      <td>786.00</td>\n",
              "      <td>263.00</td>\n",
              "      <td>11,860.00</td>\n",
              "      <td>5,857.00</td>\n",
              "      <td>4,624.00</td>\n",
              "      <td>768.00</td>\n",
              "      <td>908.00</td>\n",
              "      <td>4,384.00</td>\n",
              "      <td>12,645.00</td>\n",
              "      <td>6,007.00</td>\n",
              "      <td>157.00</td>\n",
              "      <td>349.00</td>\n",
              "      <td>2,127.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>599.00</td>\n",
              "      <td>305.00</td>\n",
              "      <td>236.00</td>\n",
              "      <td>2,745.00</td>\n",
              "      <td>2,883.00</td>\n",
              "      <td>230.00</td>\n",
              "      <td>6,051.00</td>\n",
              "      <td>6,688.00</td>\n",
              "      <td>9,913.00</td>\n",
              "      <td>664.00</td>\n",
              "      <td>8,761.00</td>\n",
              "      <td>3,162.00</td>\n",
              "      <td>685.00</td>\n",
              "      <td>1,367.00</td>\n",
              "      <td>276.00</td>\n",
              "      <td>484.00</td>\n",
              "      <td>944.00</td>\n",
              "      <td>17,615.00</td>\n",
              "      <td>2,331.00</td>\n",
              "      <td>66.00</td>\n",
              "      <td>2,045.00</td>\n",
              "      <td>8,710.00</td>\n",
              "      <td>817.00</td>\n",
              "      <td>281.00</td>\n",
              "      <td>132.00</td>\n",
              "      <td>772.00</td>\n",
              "      <td>1,942.00</td>\n",
              "      <td>6,060.00</td>\n",
              "      <td>108.00</td>\n",
              "      <td>39.00</td>\n",
              "      <td>307.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "geo_level_1_id                   0   ...                   30\n",
              "damage_grade                         ...                     \n",
              "1                            337.00  ...               252.00\n",
              "2                          3,075.00  ...             2,127.00\n",
              "3                            599.00  ...               307.00\n",
              "\n",
              "[3 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 301
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xOdMGb-LEqLr"
      },
      "source": [
        "mean_damage = pd.DataFrame(index = damage_per_zone.index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_nh45-2HfUD"
      },
      "source": [
        "for columna in damage_per_zone.columns:\n",
        "  mean_damage[columna] = damage_per_zone[columna] / damage_per_zone[columna].sum() *100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fF0vnZD0IHKa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "ff367e90-a6a4-43b6-df4f-716018179489"
      },
      "source": [
        "mean_damage"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>damage_grade</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.40</td>\n",
              "      <td>15.22</td>\n",
              "      <td>9.13</td>\n",
              "      <td>3.25</td>\n",
              "      <td>3.58</td>\n",
              "      <td>16.58</td>\n",
              "      <td>8.65</td>\n",
              "      <td>5.44</td>\n",
              "      <td>3.43</td>\n",
              "      <td>14.17</td>\n",
              "      <td>5.48</td>\n",
              "      <td>4.70</td>\n",
              "      <td>6.23</td>\n",
              "      <td>20.46</td>\n",
              "      <td>11.14</td>\n",
              "      <td>7.03</td>\n",
              "      <td>4.62</td>\n",
              "      <td>1.31</td>\n",
              "      <td>2.26</td>\n",
              "      <td>11.56</td>\n",
              "      <td>19.23</td>\n",
              "      <td>2.16</td>\n",
              "      <td>12.97</td>\n",
              "      <td>6.42</td>\n",
              "      <td>20.61</td>\n",
              "      <td>8.32</td>\n",
              "      <td>35.50</td>\n",
              "      <td>3.71</td>\n",
              "      <td>nan</td>\n",
              "      <td>2.02</td>\n",
              "      <td>9.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>76.66</td>\n",
              "      <td>73.49</td>\n",
              "      <td>65.52</td>\n",
              "      <td>60.34</td>\n",
              "      <td>76.63</td>\n",
              "      <td>74.87</td>\n",
              "      <td>66.54</td>\n",
              "      <td>59.35</td>\n",
              "      <td>44.62</td>\n",
              "      <td>69.05</td>\n",
              "      <td>54.83</td>\n",
              "      <td>56.84</td>\n",
              "      <td>72.32</td>\n",
              "      <td>65.31</td>\n",
              "      <td>72.75</td>\n",
              "      <td>72.11</td>\n",
              "      <td>73.59</td>\n",
              "      <td>17.94</td>\n",
              "      <td>24.65</td>\n",
              "      <td>70.70</td>\n",
              "      <td>68.89</td>\n",
              "      <td>39.34</td>\n",
              "      <td>73.96</td>\n",
              "      <td>68.51</td>\n",
              "      <td>69.31</td>\n",
              "      <td>77.95</td>\n",
              "      <td>55.91</td>\n",
              "      <td>47.93</td>\n",
              "      <td>59.25</td>\n",
              "      <td>88.13</td>\n",
              "      <td>79.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14.93</td>\n",
              "      <td>11.29</td>\n",
              "      <td>25.35</td>\n",
              "      <td>36.41</td>\n",
              "      <td>19.79</td>\n",
              "      <td>8.55</td>\n",
              "      <td>24.82</td>\n",
              "      <td>35.21</td>\n",
              "      <td>51.95</td>\n",
              "      <td>16.78</td>\n",
              "      <td>39.68</td>\n",
              "      <td>38.47</td>\n",
              "      <td>21.45</td>\n",
              "      <td>14.23</td>\n",
              "      <td>16.10</td>\n",
              "      <td>20.86</td>\n",
              "      <td>21.79</td>\n",
              "      <td>80.75</td>\n",
              "      <td>73.10</td>\n",
              "      <td>17.74</td>\n",
              "      <td>11.88</td>\n",
              "      <td>58.50</td>\n",
              "      <td>13.07</td>\n",
              "      <td>25.07</td>\n",
              "      <td>10.08</td>\n",
              "      <td>13.73</td>\n",
              "      <td>8.59</td>\n",
              "      <td>48.36</td>\n",
              "      <td>40.75</td>\n",
              "      <td>9.85</td>\n",
              "      <td>11.43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               0   ...                   30\n",
              "damage_grade                       ...                     \n",
              "1                            8.40  ...                 9.38\n",
              "2                           76.66  ...                79.19\n",
              "3                           14.93  ...                11.43\n",
              "\n",
              "[3 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1J_FZZ1J5ey",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "1c02fefb-d119-4d14-80b1-dc4ea8a2a7f4"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Distribución de daño para cada zona geográfica' , {'fontweight':'bold'})\n",
        "\n",
        "mean_damage.transpose().sort_values(by=[3]).plot(kind='bar', ax=f.gca(), xlabel='Zona geográfica', ylabel='Porcentaje de edificios dañados', color=['#16a1ff', 'orange', 'red'], stacked=True)\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
        "#plt.show()\n",
        "plt.savefig('zones_more_affected.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m8bsksLoD-lp"
      },
      "source": [
        "##Relaciones con damage_grade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opnNFJuOEL3i"
      },
      "source": [
        "### Numero de pisos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "849yyh9zH9iL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7ea8bfc-da1c-4740-d57f-8b1c217df9b3"
      },
      "source": [
        "full['count_floors_pre_eq'].value_counts() # Ecuacion mas peligrosa de la historia: descartamos edificios mayores a 3 pisos"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    156623\n",
              "3     55617\n",
              "1     40441\n",
              "4      5424\n",
              "5      2246\n",
              "6       209\n",
              "7        39\n",
              "9         1\n",
              "8         1\n",
              "Name: count_floors_pre_eq, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 306
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CuCu0xKCIPRi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee2f1e4d-fa73-4d89-b410-372c48cb0b63"
      },
      "source": [
        "edificios_menores_seis_pisos = full[(full['count_floors_pre_eq'] < 4)]\n",
        "edificios_menores_seis_pisos['count_floors_pre_eq'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    156623\n",
              "3     55617\n",
              "1     40441\n",
              "Name: count_floors_pre_eq, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 307
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZM0nJ_l5_I1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17ff7f71-75a4-4338-9049-ede15811f6cb"
      },
      "source": [
        "edificios_menores_seis_pisos.groupby('count_floors_pre_eq')['damage_grade'].value_counts().sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count_floors_pre_eq  damage_grade\n",
              "2                    2               93968\n",
              "                     3               50062\n",
              "3                    2               28479\n",
              "                     3               24601\n",
              "1                    2               20798\n",
              "2                    1               12593\n",
              "1                    3               10674\n",
              "                     1                8969\n",
              "3                    1                2537\n",
              "Name: damage_grade, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 308
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fvmYbwOt7b72"
      },
      "source": [
        "Vemos que edificios sufrieron un mayor grado de daño con respecto a su numero de pisos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSWPVApZ7P25",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf716e6c-ea42-48a0-c8fa-9f34eed0b42e"
      },
      "source": [
        "promedio_daño_por_pisos = edificios_menores_seis_pisos.groupby('count_floors_pre_eq')['damage_grade'].mean()\n",
        "promedio_daño_por_pisos #conclucion: ligero incremento de daño en edificios mas altos."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count_floors_pre_eq\n",
              "1                   2.04\n",
              "2                   2.24\n",
              "3                   2.40\n",
              "Name: damage_grade, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 309
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P1j3S-kg9ELx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "dece10f2-0ca7-4fc1-ef87-2a878b89d53e"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Promedio de daño a edificios por cantidad de pisos', {'fontweight':'bold'})\n",
        "\n",
        "promedio_daño_por_pisos.plot(kind='bar', ax=f.gca(), ylabel='Promedio grado de daño', xlabel='Cantidad de pisos')\n",
        "\n",
        "plt.savefig('pisos.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hyg5sSFFDqle"
      },
      "source": [
        "Ahora vamos a evaluar el promedio de pisos para cada grado de daño"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TesCFqcfD1Lp"
      },
      "source": [
        "#means_antiguedad = full[full['age']<150].groupby('damage_grade')[numerical_columns].mean()\n",
        "means = full.groupby('damage_grade')[numerical_columns].mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "wYPflFbUHA3W",
        "outputId": "5cadd5fd-dce5-4eba-af50-8edb7a3a6f57"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Promedio de cantidad de pisos de los edificios para cada grado de daño', {'fontweight':'bold'})\n",
        " \n",
        "means['count_floors_pre_eq'].plot(kind='bar', ax=f.gca(),color=['#16a1ff', '#ff944d', 'red'], ylabel='Cantidad de pisos', xlabel='Grado de daño')\n",
        " \n",
        "plt.savefig('floors.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAArYAAAHKCAYAAAD2CwjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeWAMZ9wH8O8m2bC5D0kQiYQgcYQ66yYVR11VilISR1B1hFJ60GpfR1tXaesmou6KIsRVJe5blIZEDrmIkENCJJHs+0fenXdXdnOJzO76fv4hM7Mzv9mdnf3uM888K5HL5XIQEREREek4A7ELICIiIiKqCAy2RERERKQXGGyJiIiISC8w2BIRERGRXmCwJSIiIiK9wGBLRERERHqBwZaIiIiI9AKDLRERERHpBQZbIiIiItILDLblNGLECDRo0AANGjRAQkICACAhIUGYNmLEiEqrRbFNLy+vStumtli5cqWw/0FBQSUuP3v2bGH5ixcvVmgtFbVubXk9g4KChFpWrlwpai3lVdbjoyzUnQN0kabXubj9O3DgAPr164emTZuiQYMGaNmyJQDAy8tLeExF1UHa6eLFi8LrNXv2bLHLeePE2F8xMkVFbjM1NRX9+/dHu3btsHbtWjx8+BANGzZ84+dLo7IsvHLlSvz6669FppuZmaFevXoYOHAgBg0aBIlEUmEF0tvt6dOn2Lx5MwDA0dERH374ocgVEb3drl+/jpkzZ4K/xk5ExQkJCQEALFq0CCtXrsSSJUvQpk0b1KpV641ut0zBVpOsrCxcv34d169fx7Vr17Bw4cKKWK3Osbe3x9atWwEA5ubmIlejH54+fSp8mWrdunWRYDtw4EC0bdsWAODq6lrp9emzzp07C8dzzZo1Ra6GKts333yDzMxMAIXnNoWTJ08KoXbIkCHo168fjIwKP0p++eUX5OTklGt7PN6I9Mv7778Pb29v2Nvbo2PHjnj48CEcHBze+HbLHWw7deqE8ePHIzc3F4cOHcLu3bsBFF5OGjZsGJo0aaLxsQUFBcjLy0OVKlXKu3mtZGxsLFySo8pRs2ZNfgi+Iba2trC1tRW7DBKJpu4Ejx49Ev7//vvvq5zzijvvl+RtPd5yc3NhYGAgfDmgivP8+XOYmJiIXcZby9raWvi/RCJBjRo1KmW75X4n2draCie0tm3b4vz580K/iatXr6JJkyYqXRfmz5+PR48eYffu3Xj48CECAgLQpk0byOVy7Nq1C3v27EFkZCRevnwJR0dHdO/eHX5+fiotnyNGjMClS5cAAHv27EFAQAD+/vtvmJiYYOjQoZg0aRLu3r2L+fPnIywsDDY2Nhg9ejRGjhypUnteXh7++OMPHDhwANHR0QCAevXq4ZNPPkH//v1Vls3Pz8fvv/+OXbt24enTp/D09MTXX3+t9jlJSEjAe++9B6CwdXHLli3CvKysLKxbtw5Hjx5FYmIiDA0N4ebmhoEDB2LIkCGl6r6RmpqKRYsW4e+//4ZEIoGXl1exfX3Ksp/FCQ4Oxs6dO3Hnzh1kZ2fD3t4eLVq0wNy5c2Fubo7k5GQsX74ct2/fRnJyMrKysmBqagp3d3eMHDkS3bp1E9Z18eJF4fUYMGAAevfujeXLlyMiIgK2trYqr9fs2bOxd+9e4bGXLl0SPmwVz6/yMbZw4UKVFt0//vgDAQEBePToEerXr4/PP/9c4z6uXbsWp0+fxv3795Geng6JRAJHR0d4e3tjwoQJkMlkKsuXZd2aVPbrqe65X7ZsGSIjI2FnZ4eRI0fC19dXWD4oKAhffvklAGDSpEmYPHkygMLjfNmyZbh48SLS0tIgk8lgb2+Ppk2bwsfHB+7u7sI6bt++jTVr1uDq1avIyMiApaUlWrRogXHjxqFx48bCcgUFBVizZg0OHjyIuLg4yOVy2Nraon79+vD29sZHH31U4v4dOnQIv/32G+Li4lC7dm1MnDix2OXv3LmDtWvX4tKlS0hPT4e1tTU6deqEyZMno3r16iVurziHDx/G1q1bER4ejhcvXsDBwQGdO3fGhAkTVFo/09LSsGzZMpw+fRopKSmQSqWwt7dHo0aNMHToULRu3brEbV25cgXr16/HjRs3kJWVBXt7e3h7e2PixImwtLRUWfb8+fNYvHgxIiIi4ODggFGjRhU5thWUz7d///03AAjnNwUfHx8A//9+9PLyQmJiIgDg7t27KsuWdB7RdLwBwP3797F69WqcO3cOT548gampKTw9PTF69Gjhio3Cjh07sGvXLsTExCAvLw/W1taoW7cu2rdvDz8/v2KfS+VzzsaNG3H58mXs2bMHGRkZaNKkCb766is0atRIWP748eP4888/ERERgbS0NOTl5cHOzg7vvvsuPvvsM5VLrsrrXrt2Lc6fP4/g4GA8fvwYx48fh1QqLfV5tDgvXrxAQEAAjhw5gtjYWMjlcuEzderUqQCAy5cvY8uWLbhz5w5SU1Px4sULWFlZoUWLFvj0009V3sMAEB4ejgULFiAsLAxWVlYYPHgwmjdvrnb7ZV23JocPH8avv/6K+/fvC+/nqKgoted75ePun3/+wcKFC3Hu3DlYWlrixIkTb3R/ASAlJQVr1qzByZMn8fDhQ1StWhUeHh4YNmwYevXqVar9jY+Px/z583HhwgXIZDL07t0bQ4cO1bj8s2fPsHHjRhw5cgRxcXEwMjJCo0aNMHbsWHTu3LnCtxkREYE1a9YgPDwcjx8/xrNnz2BhYYEmTZrAz88PrVq1EpZ99b3s7OyMdevWITY2Fo6Ojpg6dSref/99lfVXxHOoUCFfESUSCczMzIS/c3NziyyzevVqxMfHq0yTy+X4/PPPcfDgQZXpMTExWLNmDY4dO4YdO3YUOTkDwLRp0xAXFweg8FvZr7/+ioyMDOzbtw9Pnz4FADx48ADz58+Hm5sb2rVrB6AwHPj5+eH8+fMq67t58ya++OILREREYObMmcL0+fPnC5fHgMJwNXz4cLU1aZKRkYGhQ4cKYUR5mzdv3sSlS5ewdOnSYteRm5uLMWPG4L///hOm7du3D3fu3FG7fFn3U5OvvvoKe/bsUZmWmJiIxMRETJ06Febm5njw4EGRG3MyMjJw8eJFXLx4ET/++CM++OCDIuu+dOkS9u3bh4KCAgDqX6/y2rBhA3766Sfh73///Rd+fn5wdnZWu3xQUBBiYmJUpkVFRSEqKgrXr19HYGBgudetjlivp8LVq1exf/9+5OfnAyh8TRcuXIjc3FyMGzdO4+NevnyJMWPGIDY2VpiWmZmJzMxMREVFoXnz5sKHxN9//42pU6ciLy9PWPbx48c4cuQITpw4gV9++UUISqtWrcKKFStUtvXgwQM8ePAAmZmZJQbbkJAQTJ8+XbhEHhkZiWnTpmlsdTx16hQmTZqkcq569OgR/vzzT5w6dQrbt2+Hk5NTsdvU5Oeff8b69etVpiUkJGDr1q04evSoyrr9/f1x4cIFYbm8vDzExsYiNjYWTk5OJQbb3bt3Y+7cucJ7CCh8LQMCAnDq1Cns3LlTOFddu3YNfn5+wusRHx+P77//vlw3epVVac4jmty8eRO+vr549uyZMC09PR2hoaE4ffo05s6di2HDhgEA/vrrL3z77bcqj3/06BEePXqE6OjoEoOtsh9++EHlnHDlyhWMHDkSf/75p9DtKTQ0FP/884/K45KSkhAUFITQ0FDs379fbSv0Dz/8UOTzsLznUWVZWVn45JNPEB4erjL93r17yM7OFoLt9evXceTIEZVlUlJScPjwYZw6dQp79uxB3bp1ARR+qRgxYoTQLSU5OVm4KVOdsqxbk6NHj8Lf37/I+7k0oXjkyJHCc6s49t/k/sbHx+Pjjz9GSkqKMC0vLw+XLl3CpUuXcPv2bcyYMaPYmtPT0zFixAg8ePAAAJCdnY0tW7YIXyxflZmZiWHDhiEiIkKYlpOTI2xz7ty5GD58eIVuMzIyEsHBwSrTUlNTcerUKZw+fRqbNm3Cu+++W+Rx+/btUznWY2Nj8fnnn8Pd3R116tQBUDHPobLXDraKrgjK387VHQDx8fHo27cv+vbti7S0NDg4OCAkJEQItZaWlpgxYwZsbGywYsUK3L17F9HR0Vi6dCnmzZtXZH3Pnj3D0qVLkZCQIITCLVu2oE6dOli4cCHOnDmD7du3Ayj8Bq8ISoGBgUI4aNasGfz8/JCfn49ly5YhJiYG69evR/fu3dG0aVNERUVh27ZtAAADAwN89tlnaNy4MbZs2YIzZ86U+jlaunSpEGrr16+PKVOmID09HT///DMyMjJw8OBBdOvWrcg3GGVBQUFCCLKyssIXX3wBU1NTLF68WO3yZdlPTY4cOSJ8GBkaGsLHxwfvvvsu0tLSsH//fqGVuVq1avj888/h4uICc3NzGBgY4MGDB/jxxx+RmpqKVatWqT0hJyYm4r333sNHH32EAwcOCMeC4vWaMGECunTpIpyMPTw88M033wAovg9zRkaGSkgaMWIEOnbsiODgYOzfv1/tY4YOHQpra2tYWVlBJpMhKysLO3bswKlTp3Dx4kVcu3YNzZs3L9e61RHj9VQWFxeHPn36oF+/fjh37hwCAgIAFN4gOmjQINjY2Kh9XHR0tBBq27Vrh9GjRyM/Px8JCQk4deoUpFIpgMIvm19//bUQoj7++GN07doVJ0+exLZt25CXl4evv/4abdu2hYmJidAiaGFhgTlz5sDOzg7Jycm4fv060tLSit2X/Px8LFy4UPgQ7N27N/r376+yX8qys7Mxe/Zs5ObmwsjICJMnT0aTJk1w7tw5rF+/HikpKZg3b16RcFoaYWFhwuOqVKkCf39/uLi4ICAgABcvXlRZd1ZWljB6RsOGDTF58mQYGRkhKSkJ586dK/ESanJyMr7//nsUFBTA1NQU06dPh4uLCw4ePCh8UVM+f/7444/C69GuXTuMHDkS4eHham8IVkdxD8GaNWsQGhoKoLAfroeHR7Hvx9KeR9SRy+X48ssvhVDbo0cPDBw4EDdu3MDq1atRUFCABQsWoGvXrqhRowZOnDgBADAyMsKcOXNQu3ZtPH78GP/99x/CwsJKtZ8KDx8+xNdff42aNWti1apVuHXrFrKysrB06VJh1IYOHTqgUaNGsLe3h6mpKXJycnDu3Dls3LgRjx8/xu7duzFhwoQi646Pj8eIESPQuXNnJCUlwdTUFADKdR5VtmzZMiHUWllZ4dNPP0XdunVx//59lQDepEkTzJkzBzVr1oSpqSny8/Nx+/ZtLF68GNnZ2QgICMAPP/wAoLDftCLkNWzYEJ999hkePnyo8VxVlnWrk5+fjwULFgjv5549e+LDDz/E6dOnVa6CavLkyRN8+eWXqFevnnAV+U3u77x584RA1rp1a4waNQpxcXFYunQpcnJysG7dOnh7exd7bt6wYYMQMB0dHTFjxgzk5OTgxx9/VLv8smXLhFDbuXNnDB8+HGlpaVi8eDFSUlKwcOFCeHl5FXvpv6zbdHV1xezZs+Hk5AQzMzPI5XLExsZiwYIFyM3Nxdq1a9UG2/j4eAwaNAje3t4ICAjA+fPnUVBQgN27d2PWrFkV9hwqK3ew3bt3r8plYoXGjRujQ4cORaY3b968yIGh/AROmTIFgwcPBgA4Ozujb9++AApbYr777rsiJz9/f3/07t0bQGFr8PPnzwEAc+fORdu2bdG8eXMh2CpadgGohA9fX19YWVkBAPr27SsElv3796Np06Y4ceKE8Obq3r07Jk2aBABo0aIFOnbsiOzs7OKfJBReYlXcGQgAS5YsQf369QEUfsNSvJmCg4OLDbaKD37FczVw4EAAhUFg1KhRRZYvy35qsm/fPuH/Y8eOxfTp04W/lU+wtWrVgp2dHTZv3oyIiAhkZmaq3DEdGxuLrKwslVZ9oLA7y/Lly2FsbIwmTZoIwVbxerm4uKj0OzM3Ny9VH+azZ8/ixYsXAApPaIow3KFDB1y5cgVJSUlFHtO+fXusWrUKV69exZMnT1RaGQHg1q1baN68ebnWrY4Yr6eymjVr4qeffoKhoSE6d+6Mmzdv4tq1a8jNzUVoaKjGD1Dl18POzg4uLi5wdHSEgYEBPvnkE2He2bNnhUDaqFEjfPfddwAKT8JhYWG4ffs20tLScO7cOXTr1k0IxDKZDM7OzmjQoAFkMlmJH+QAhEu3QGH4+umnn2BkZKSyX8rOnj2L1NRUAIUBT3FMde3aFSEhIUhMTMSZM2eQmpqqMeBrcuDAAeH/w4cPx+jRowEUfhnp3LkzcnNzcebMGaSnp6Nq1aqQSCSQy+WwtrZG7dq1Ubt2bRgZGRV7CVIhJCREaHHu0aOH0Jo1cOBAhISEIDs7GwcPHsS3336LtLQ03LhxA0DhvQDLli2DlZUVunbtiujoaJW6NVHcQ/Dnn38K0+rXr1/ie7K05xF1wsPDce/ePQCFx9uSJUsglUrRuXNnREVF4ciRI8jLy8ORI0fg6+srHJ9SqRS1a9dGkyZNYGZmJnyelIWPj4/QdcfNzQ09evQAUNjan5eXB6lUitatW2P16tXYtGkTHjx4IJwbFG7duqV23X369BHOHQrW1tblOo8qFBQUqLSoLVmyRPgs7tixo8r7s1mzZrh69Sp27tyJ+Pj4Ip9liroLCgpUAvHPP/8MNzc3AIUtnqtXry5SR2nXrcnt27eFwGVnZ4fFixcLr/m///4rHMeafPnll0KWeNP7m56eLjRyGRsbY8WKFUK/0uTkZGzcuBFA4ed7cedm5c+Db7/9VuhK8PLlyyLHifLrLJVKMWrUKEilUpiamsLb21toOAgJCRHOP6+7TaCwwfLy5ctYvXo1oqOj8fz5c5XjU9Pr6u7ujvnz5wMoPMYVDTSKz/mKeg6VVVhvdalUil69euGrr76CoaFhkfldu3YtMk35cqanp6fw//r160MmkyE7OxsZGRlITU0tcjlHeXlLS0sh2CpuXlD+QFJ0TXh1m/7+/mr3JSoqCgBUms+Vb4owNzeHq6urymVkTVJTU5GRkQGg8ENbEWpf3QflutTRVIvyOpSVZT81UV5Hly5dNC4XEBBQ4kgYT58+LXJCbtq0KYyNjQFACGqKZV+H8hh5ys+VoaEhGjVqVCR8JiYmYujQocjKyiq2/vKsWxMxXk9ljRs3Vnmfenp6CgGwuDEGXVxc0LJlS1y5cgX79u3Dvn37ULVqVbi7u8Pb2xsjR46EsbGxyiXcV09Gnp6euH37NgAIyw0aNAg3btxAcnKy0OfcyckJbdu2xahRo4od8UL5ufTw8FAJ38r7paBcW2hoqND6qEwulyM6OrrMwVbTOc3GxgZOTk6IioqCXC5HXFwcPD090bt3bxw4cABnz57F+++/D6lUCjc3N3Tt2hWjR48utiVUeVtBQUFqx+nNzMzEo0eP8PDhQ2Gas7OzyvvN09OzVMG2vEp7HinpsQ0bNhS+AAGF7xvF5WXFch9++CEOHTqE7Oxsob949erV0apVK/j4+JTp5jbl49bFxQWWlpbIyMhATk4OHj16hOrVq2PUqFHFfg5oOpepG6O6vOdRhbS0NKSnpwMoDAjFdeeaPn260LqtaTtAYeun4rPVxMRECHmA5nNVadetifL7+dXXvFmzZiUGW3VZ403t7/3794Vw5+zsrHKzlPKxVt7Pd3XHa1pampAp8vLyVO6LUFbS50FZtgkU9mkursVc0+uq3PdW3ed8RT2Hyl57VASJRAJTU1O4uLigatWqGpev6Ltdld/cBgYGaqeXV2laYsszVq+2je9bmv0sDeWDfezYsejQoQOkUinmzZsnXC5R7gOooNxPubLuCFb3Guzdu1cIte+88w7Gjh0LKysr/PPPP8Jl5dKM2Sn26/s6r2dpazcwMMDatWuxa9cunD17FlFRUUhKSsKNGzdw48YNxMXF4fvvvy/ztj766CM4ODggODgY4eHhiI2NRVxcHOLi4nDixAkcOnQIFhYWb2y/1Kmo94eCuloWLlyIVq1a4eTJk7h37x4SEhIQHh6O8PBw3Lx5Exs2bHjt7So+qMtSly5QV3eHDh2wfft2BAUF4datW4iJicHDhw9x4MABHD9+HAcOHCh33+lXt3ft2jUh1NrZ2WHGjBmoVasWkpOThVZpTecNdZ+H5T2PaqpV0+ualJQkhDwTExPMnDlTCHCKAfnLe76rqHUXt42SVKtW7Y3UVNZaKuJ9Jcb5S902c3NzsWvXLgCFn9VTp05F06ZNYWhoiEmTJiEtLU3jc6j8Oa+u0bOstZRGhYyKUBrqCnRxcRH6nv7777/CN6KIiAjhRbG0tCxzq0lxXFxchBt0jh8/rvYkp9i28jzlZvbMzMwiNxppYmNjAwsLCzx9+hTPnz9HZGQk6tWrB6Dwpgjluorj5OQkbPPWrVvCc6W8DmVl2U9NXFxchG99p06d0nhXqOIysJWVlXAD0/Pnz1WGBSov5S8tpT2pK9+JrPy65efnq71colzn+PHjhW/86vrMlnXdmojxeiq7ffs2CgoKhOdXuf9hcYNny+VymJqaYtSoUUKXidTUVHz00UdISEjAsWPH8P3336u0sL66T8p/K5aTy+Xo1KkTOnXqBKDwcthPP/2EzZs3IyUlBdevX9d4p6/ycxEeHo78/HzhBKquX6VybQMGDMCiRYuKLJOdna1xtIDiuLi44PTp0wAK91NxN29aWppw6U0ikQg3GhoZGWHIkCEYMmQIgMKbf8aOHYvr16/j7NmzxQ5XpHzOeHUUgVf34/Hjx8K0+Ph4YYQKQP1zVJFKex7R9FiF//77Dy9fvhS+BKs7f8rlcrzzzjt45513ABSeMzZv3oxFixYhOzsboaGhJd5Uo3Dz5k2hZVUxWgpQ2Hfa3t4e169fF5bt27ev0K3i1Zuh1VH3efi651Fra2uVVuVz586hffv2GrcDFHZRUNx4p64l1NbWFiYmJnj+/DmeP3+OqKgo4SYrdcdNWdatifL7+b///lN5P5dmPa8+t29yf52dnYXuRHFxcUhLSxNaHMv6+a54j/z777/CuU7d54Hy62xiYoIzZ84IfbQVFEOqVtQ209PThfGpGzRoINxgnJycLLQel1dFPYfKRB04r2/fvsI3qRUrVsDY2BjW1tYqNzP06tWrQlsU+vbtKwSECRMmYOzYsahevbpw1+yJEycwatQofPjhh/Dy8hL6BR89ehS//fYbGjdujD/++KPEVhAFAwMDvP/++9ixYwcAYMaMGZg0aRIyMjJUfjayT58+xa7Hy8tLuGS6YsUKVK1aFSYmJhpHUyjLfmrSr18/oR/O+vXr8fLlS7Rp0wbp6enYv38/5s2bB0dHRzg6OiI2Nhbp6elYu3YtGjRogMDAQOGD4HUot9JFRETg+PHjsLKyKnb82vbt26NKlSrIycnBzZs3MX/+fHTo0AGHDh1S21VAeT1btmyBVCpFWFhYkbu4y7NuTcR4PZUlJiZi1qxZ6NOnDy5cuCBcrjc2NhbCpTrJycnw9fVFr1694ObmBltbWyQkJAh9VhV9Ptu3bw8rKyukp6fj1q1b+P7779G5c2eEhoYKXwCsra2Fy6VTpkyBqakpWrRogerVqxf5oqBupBWFRo0awcHBAcnJyXj06BG++OIL9OvXT2W/lLVr1w42NjZITU3FX3/9BUtLS7Rr1w4FBQVITEzEtWvXcOfOHRw6dKhUz6WyPn36CC1vW7duhYODA2rXro3NmzcL+9ChQwfhkly3bt3QvXt3uLu7w97eHqmpqUJXELlcjtzcXI3BtmfPnliyZIlw44ZEIkGzZs3w4sULJCQk4MKFC8jJycGmTZtQrVo1NG3aFGFhYcjJycH06dMxYsSIcu9nWZT2PKKOh4cH6tati6ioKKSkpGDGjBkYMGAAbt68iWPHjgEo7Aan6P/6P//zP0hJSUG7du1Qo0YNGBoa4sqVK8L6SvqwV7Z582ZUq1YNNWrUUOlb2alTJ0ilUpXzxpEjR9CiRQtkZGRgyZIlpX9ylLzuedTAwAB9+vQRRvH5/PPPMXHiRNSpUwfx8fE4ceIE1q1bp1L3hQsXEBwcDAMDAyxbtkztOrt06SIcIzNnzsTEiRORnJysMlKMQlnWrUmjRo1Qo0YNPHjwQOX9fPr06TIF5PLUVNb9tba2RocOHXD69Gnk5ubC398fvr6+iIuLE248B0r3+a4ImT/88AOeP3+OnJwcjTX27t0b27Ztw/PnzzFmzBiMGDEC1tbWePjwISIjI3H06FEsWLAAbdq0qZBtVqtWTfjci4iIwM6dO2Fra4vff/+91A1OmlTUc6hM1GDbq1cvHDt2DIcOHUJ6enqRDst16tRRudGgIowcORJnzpzB+fPnce/evWLHDa1bty6GDh2KHTt2ID8/X7hJp2rVqsIHaWlMmzYNly5dQnR0NO7cuSPchKbQu3fvEsdpGzhwIHbs2IE7d+4gLS1NGCNO07eYsuynJj179sSAAQOwd+9evHz5EuvXr1e5U1xx6WHw4MHC8FeKk7q1tTVcXV1L3bKtiZmZGRo1aoTbt2/j6dOn+OyzzwBobqECClv5J02aJNQSGBiIwMBAGBgYwMnJqcgwO/369cPq1auRnZ2Ns2fP4uzZswAKb3h8NRiVdd2aiPF6Kqtbty5CQkKKtEpPnDixxCskMTEx+P3339XOU9zQaWJigvnz58Pf3x95eXnYunWryrB5UqkU8+fPF0JbZmYmjh49qvaG1GrVqqm921bB0NAQs2bNEs4VwcHBws0VtWvXxv3791WWNzExwaJFi4ThvgICAoqMnqApaJWkWbNmGDt2LNavX4+cnJwifSbt7OxUhqN68OCBcHPEq5QDsDrVq1fH3LlzMXfuXOTm5qp8UVZQHi7siy++gK+vL/Ly8nDmzBnhhg0XF5cy9V8rq9KeR9SRSCRYtGiRMNxXSEiIys24EokEX331lXD394sXL3DkyJEiQzsBheftV8fhLY6Tk1ORu/dNTEwwbdo0AIV9cBs0aIC7d+8iMTFRODc1b94cT548KfV2FCriPDpt2jRcuXIFd+/eRVpamnDTDvD/x7SDgwO6dOmCkydPIiMjQxiDu3nz5io3WitMnToVoaGhyMrKwu3bt4X9VHfclHXd6hgaGuKrr77ClClTIJfLVd6Nu3oAACAASURBVN7P9evXVxniqjTe5P4ChTdeKYaqunDhgsrwfQDg5+dX4k1PY8aMwf79+5GcnIz4+HjhPgoXFxe1x5LidY6IiBB+9bWsyrJNAwMDDBo0CFu3bkVeXh7mzp0rLGtra1uu411ZRTyHKvW+VjWvSSKRYMmSJZg3bx48PT1hYmICY2NjuLi4YNy4cdi1a1eZxostDWNjY6xfvx7ffPMNPD09YWpqiipVqqBWrVro0qUL5s+fD29vb2H5OXPmYOLEibCzs0OVKlXQvHlzBAQEoHbt2qXeppWVFXbt2oXx48fD1dUVxsbGMDExQZMmTfDdd99hyZIlJbZKGxsbY9OmTejbty/MzMxgZmaGXr16qf0WWZ791GTRokX46aef0Lp1a5ibmwstFX379hVeG19fX/j7+8PR0REymQytW7fG5s2bYWdnV+rnqDhLly5Fx44dy3QsjBs3Dl9//TUcHR1hbGwMDw8P/P7772q7z9SsWRMbNmyAp6cnqlatCmdnZ3z77bcax04ty7o1Eev1VPD09MS6devQpEkTGBsbw9HREbNnz8ann35a7OMUwb5169aws7ODVCpF1apV0aBBA/j7+2POnDnCst26dcOOHTvQo0cP2NrawsjICDY2NujevTu2b9+uEjKGDRuG999/H87OzjAxMYGRkREcHBzQt29fbNu2rcSfqO7duzeWLl2KunXrQiqVwtXVFQsWLNB4N3znzp2xZ88e9O/fH9WrV4dUKoW1tTU8PDwwatQoLF++vNTP5atmzpyJ5cuXo3Xr1jAzM4NUKoWjoyOGDx+OoKAglUut06ZNQ4cOHVC9enUYGxvD2NgYrq6uGDNmDH755ZcSt/XRRx/hjz/+QPfu3VGtWjUYGRmhWrVq8PT0xMSJE1VCdMuWLbF27Vo0atRIqGnGjBnFjltcUUpzHtHE09MTQUFBGDBgABwcHGBkZARLS0t07NgRGzduFC4tA4VXNgYMGABXV1eYm5vD0NAQtra26NatG7Zu3Vqm/rWzZ8/G5MmT4eDgAGNjY7Ro0QKBgYHCpWlDQ0OsXbsW7733HszNzWFjY4ORI0fif/7nf8r1HFXEedTc3Bw7d+7E1KlT4e7ujqpVq0Imk6Fu3boqP+Ly008/YcCAAbC2toaFhQX69++vdoQDoDC8BAYGolWrVjA2NoadnR38/PzU3jlf1nVr0r17dyxfvhxubm6QSqWoW7culixZovJjHMXd0/M6NZV1f52cnBAUFIRPPvkEtWrVglQqhZmZGVq1aoVly5aVavxVa2trbN26FV27doVMJhN+FELTOcDCwkLt6+zi4oIePXpg6dKlaNasWYVuc9asWfDx8YGdnR1MTEzg5eWFgICAMr0OmlTEc6hMIi9LT24i0lmv/vKYur6lRG8z5V8HCwwMLPZSLr05crlcbWPP4MGDhb6ue/fuRcOGDSu7NNIB/HFqIiIi0hpXrlzB9u3bMWDAANSpUweZmZnYuXOnEGpdXV1L/dO89PZhsCUiIiKtUVBQgIMHD6odYcLU1BSLFi1SGTGHSBmDLREREWkNJycn9OvXDzdu3EBKSgry8/NRo0YNtGvXDmPGjCn3WMT0dmAfWyIiIiLSC2zLJyIiIiK9wGBLRERERHqBfWy1xPXr1yGXyyGVSsUuhYiIiEopLy8PEolE+ClnEhdbbLWEXC4v9hd4qPQUP0XK55O0BY9J0kY8LisGP7+1C1tstYSipbZJkyYiV6L7nj9/jvDwcLi5uQk/2UokJh6TpI14XFaMf//9V+wSSAlbbImIiIhILzDYEhEREZFeYLAlIiIiIr3AYEtEREREeoHBloiIiIj0AoMtEREREekFBlsiIiIi0gsMtkRERESkFxhsiYiIiEgvMNgSERERkV5gsCUiIiIivcBgS0RERER6gcGWiIiIiPQCgy0RERER6QUGWyIiIiLSCwy2REREbyGJRAKZTAaJRCJ2KUQVxkjsAoiIiPROfj5gaCh2FcWSyWRo2LCh2GWUTAeeS9IeDLZEREQVzdAQGD4cCA8XuxLd5uEBbN0qdhWkQxhsiYiI3oTwcOD6dbGrIHqrsI8tEREREekFBlsiIiIi0gsMtkRERESkFxhsiYiIiEgvMNgSERERkV5gsCUiIiIivcBgS0RERER6gcGWiIiIiPQCgy0R0RsmkUggk8kgkUjELoWISK/xl8eISPcVFAAG2vs9XSaToWHDhmKXUTpa/lwSERWHwZaIdJ+BAXBsCZAaL3Ylus3GCfD+XOwqiIjKjcGWiPRDajzwOFrsKoiISES83kREREREeoHBloiIiIj0AoMtEREREekFBlsiIiIi0gsMtkRERESkFxhsiYiIiEgvMNgSERERkV5gsCUiIiIivcBgS0RERER6gcGWiIiIiPQCgy0RERER6QUGWyIiIiLSC291sA0JCcGnn36KTp06oVmzZujfvz/+/PNPyOXyYh8nl8uxdu1adOnSBZ6enhgyZAhu3LhRSVUTERERkTpvdbANCAiATCbD7NmzsWrVKnTq1Alz5szBb7/9Vuzj1q1bhxUrVsDX1xdr1qyBnZ0dRo8ejfj4+EqqnIiIiIheZSR2AWJatWoVbGxshL/btm2L9PR0bNq0CRMnToSBQdHcn5OTgzVr1mD06NHw9fUFALRo0QI9e/bEhg0b8N1331VS9URERESk7K1usVUOtQoeHh7IysrC8+fP1T7m2rVryMrKQq9evYRpxsbG8Pb2Rmho6BurlYiIiIiK91YHW3WuXr0KBwcHmJmZqZ0fHR0NAKhTp47K9Lp16yIpKQkvXrx44zUSERERUVFvdVeEV125cgWHDh3CrFmzNC7z9OlTGBsbo0qVKirTLSwsIJfLkZGRgapVq5Zr+3K5XGNLMZVedna2yr+k3yQSCWQymdhl6JXs7OwSb6IlzXhMVjxtPiblcjkkEonYZdD/YbD9Pw8fPsS0adPQpk0bjBw5UpQa8vLyEB4eLsq29VFsbKzYJVAlkMlkaNiwodhl6JWYmBh+MXwNPCYrnrYfk8bGxmKXQP+HwRaFrbB+fn6wsrLCypUr1d40pmBhYYHc3Fzk5OSotNo+ffoUEokElpaW5a5DKpXCzc2t3I+nQtnZ2YiNjYWLiwtbTd4CbCmpeK6urlrbOqYLeExWPG0+Ju/duyd2CaTkrQ+2L168wPjx45GZmYmdO3fC3Ny82OUVfWtjYmLg7u4uTI+OjkbNmjXL3Q0BKDwZmpiYlPvxpEomk/H5JCoHfiEkbaPNxyS/yGiXt/rmsZcvX8Lf3x/R0dFYv349HBwcSnxM8+bNYWZmhpCQEGFaXl4ejh49ik6dOr3JcomIiIioGG91i+28efPwzz//YPbs2cjKylL59bCGDRvC2NgYPj4+SEpKwrFjxwAAVapUwfjx47Fy5UrY2Nigfv362L59O9LT0zFmzBixdoWIiIjorfdWB9uzZ88CABYtWlRk3t9//41atWqhoKAA+fn5KvP8/Pwgl8uxceNGpKamwsPDAxs2bICTk1Ol1E1ERERERb3VwfbEiRMlLrNly5Yi0yQSCcaPH4/x48e/ibKIiIiIqBze6j62RERERKQ/GGyJiIiISC8w2BIRERGRXmCwJSIiIiK9wGBLRERERHqBwZaIiIiI9AKDLRERERHpBQZbIiIiItILDLZEREREpBcYbImIiIhILzDYEhEREZFeYLAlIiIiIr3AYEtEREREeoHBloiIiIj0AoMtEREREekFBlsiIiIi0gsMtkRERESkFxhsSe9IJBLIZDJIJBKxSyEiIqJKZCR2AaR78gsAQy3+SiSTydCwYUOxyyiRtj+PREREuobBlsrM0ACYeBqIyBC7Et1V3xL4vaPYVRAREekXBlsql4gM4N9UsasgIiIi+n+8EEpEREREeoHBloiIiIj0AoMtEREREekFBlsiIiIi0gsMtkRERESkFxhsiYiIiEgvMNgSERERkV5gsCUiIiIivcBgS0RERER6gcGWiIiIiPQCgy0RERER6QUGWyIiIiLSCwy2RERERKQXGGyJiIiISC8w2BIRERGRXmCwJSIiIiK9wGBLRERERHqBwZaIiIiI9AKDLRERERHpBQZbIiIiItILDLZEREREpBcYbImIiIhILzDYEhEREZFeYLAlIiIiIr3AYEtEREREeoHBloiIiIj0AoMtEREREekFBlsiIiIi0gsMtkRERESkFxhsiYiIiEgvMNgSERERkV5gsCUiIiIivcBgS0RERER6wUjsAsorOzsbBw8eRG5uLjp37gxHR0exSyIiIiIiEelEsP3qq69w8+ZNBAcHAwByc3MxePBgREZGAgDMzc2xefNmNGzYUMwyiYiIiEhEOtEV4eLFi/D29hb+Dg4ORmRkJBYvXozg4GBUq1YNv/76q4gVEhEREZHYdCLYPn78WKWrwfHjx9G4cWP06dMHbm5uGDx4MG7evClihUREREQkNp0ItjKZDJmZmQCAly9f4tKlS+jQoYMw39TUVJhPRERERG8nnehj26hRI+zatQtt2rTBiRMn8OzZM3h5eQnz4+LiYGtrK2KFRERERCQ2nQi2/v7+GDt2LAYOHAi5XI4ePXrA09NTmH/s2DE0b95cxAqJiIiISGw6EWybNGmCkJAQXLt2DRYWFmjdurUw7+nTpxg2bJjKNCIiIiJ6++hEsAUAGxsbdOvWrch0CwsL+Pj4iFAREREREWkTnQm2AHDp0iWcPHkSSUlJAICaNWuia9euaNWqlciVEREREZHYdCLY5ubm4vPPP8fx48chl8thYWEBoLAbwqZNm+Dt7Y0lS5ZAKpWKXCkRERERiUUnhvv67bffcOzYMYwaNQpnzpzBpUuXcOnSJZw9exajR4/G0aNH8dtvv4ldJhERERGJSCeC7YEDBzBgwAB88cUXqFatmjDd1tYWM2fOxAcffID9+/eLWCERERERiU0nuiKkpKSoDO/1Kk9PTxw8eLBc675//z42bNiAsLAwREZGok6dOggODi7xcV5eXkhMTCwy/ebNm6hSpUq5aiEiIiKi8tOJYFu9enVcunQJH3/8sdr5ly9fRvXq1cu17sjISJw6dQpNmzZFQUEB5HJ5qR/bo0cPjB49WmWasbFxueogIiIiotejE8H2gw8+wMqVK2Fubg5fX1/Url0bEokEsbGx2Lx5Mw4fPozJkyeXa91eXl7CMGKzZ8/GrVu3Sv3YatWqoVmzZuXaLhERERFVLJ0IthMmTEB8fDx27dqF3bt3w8CgsGuwooV1wIABmDBhQrnWrVgXEREREek2nQi2hoaGWLRoEXx9fREaGir0bXV0dESnTp3g7u4uSl0HDhzArl27IJVK0bJlS8yYMQMNGjQQpRYiIiKit51OBFsFd3d30ULsq7y8vODp6YmaNWsiPj4eq1evxrBhw/DXX3/BycmpXOuUy+V4/vx5BVdasSQSCWQymdhl6I3s7Owy9eumonhMVjwel6+Hx2TF0+ZjUi6XQyKRiF0G/R+dCLZZWVnIzMxEjRo1hGnJycnYsWMHcnNz0aNHj2JHTXgTvvnmG+H/LVu2RPv27dGrVy9s2LAB3333XbnWmZeXh/Dw8Aqq8M2QyWRo2LCh2GXojZiYGGRnZ4tdhk7jMVnxeFy+Hh6TFU/bj0neOK49dCLYzp07FwkJCdi1axeAwqA7ePBgJCcnw8DAAIGBgVi/fj3atGkjWo329vZo0aIFbt++Xe51SKVSuLm5VWBVFY/fSiuWq6ur1rZC6AoekxWPx+Xr4TFZ8bT5mLx3757YJZASnQi2V69exZAhQ4S/9+3bh5SUFOzYsQNubm7w9fXFqlWrRA22FUEikcDExETsMqgS8XIlaSMel6RttPmY5BcZ7aITQwKkpaXBwcFB+PvEiRNo0aIFmjVrBjMzM3zwwQe4c+eOiBUWdo24evUqmjRpImodRERERG8rnWixtbCwwOPHjwEAL168wNWrV1WG9zI0NMSLFy/Kte7s7GycOnUKAJCYmIisrCwcPnwYANC6dWvY2NjAx8cHSUlJOHbsGAAgODgY//zzDzp37gx7e3vEx8dj7dq1MDQ0xKhRo15nV4mIiIionHQi2L7zzjvYtm0b6tSpg9OnTyMnJwfvvfeeMD82NlalRbcsnjx5gqlTp6pMU/wdGBiINm3aoKCgAPn5+cL8WrVq4dGjR1iwYAEyMzNhbm6Od999F1OmTCn3iAhERERE9Hp0ItjOmDEDo0ePFn5dbNSoUahXrx4AID8/H4cPH0bHjh3Lte5atWrh7t27xS6zZcsWlb+bNWtWZBoRERERiUsngm3t2rVx+PBhREVFwczMDLVq1RLmZWdnY86cOVozvi0RERERiUMngi1QOBSWuvBqZmaGbt26iVAREREREWkTrQy2ly9fBgC0atVK5e+SKJYnIiIiorePVgbbESNGQCKRICwsDMbGxsLfmih+zk7bf7WLiIiIiN4crQy2gYGBAP7/J+oUfxMRERERaaKVwbZ169bF/k1ERERE9CqtDLbFefLkCRITEwEAjo6OsLW1FbkiIiIiItIGOhNsz58/j59//rlIP1oPDw/MmDED7dq1E6kyIiIiItIGOhFsjx07hqlTp8LW1hZjx46Fi4sLACAmJgb79u2Dn58fli9fDm9vb3ELJSIiIiLR6ESwXb58OerVq4etW7fCzMxMZd6ECRPw8ccfM9gSERERveUMxC6gNOLj4/Hhhx8WCbVA4Q80DBo0CAkJCSJURkRERETaQieCbZ06dZCamqpx/pMnT4TuCURERET0dtKJYDtz5kzs2LEDx48fLzLv2LFj2LlzJ2bNmiVCZURERESkLXSij+2WLVtgbW2NyZMnw97eHs7OzgCAuLg4PHr0CC4uLggMDFT5IQeJRIJVq1aJVTIRERERVTKdCLYREREAgBo1agCAMI6toaEhatSogZycHGEZheJ+gpeIiIiI9I9OBNsTJ06IXQIRERERaTmd6GNLRERERFQSBlsiIiIi0gsMtkRERESkFxhsiYiIiEgvMNgSERERkV5gsCUiIiIivaATw30pJCcn4/Lly3jy5Al69OiB6tWrIz8/H5mZmTA3N4ehoaHYJRIRERGRSHQi2MrlcixatAhbt27Fy5cvIZFIUL9+fVSvXh3Pnz+Hl5cXpkyZAl9fX7FLJSIiIiKR6ERXhPXr1yMwMBCjR4/Gpk2bIJfLhXnm5ubo3r07jh49KmKFRERERCQ2nQi2u3fvxgcffIDp06fD3d29yPwGDRogNja28gsjIiIiIq2hE8H2wYMHeOeddzTOl8lkyMrKqsSKiIiIiEjb6ESwtbW1xYMHDzTOv337NmrUqFGJFRERERGRttGJYOvt7Y0dO3YgPj5emCaRSAAAZ86cwd69e9GzZ0+xyiMiIiIiLaAToyJMmTIFFy9eRP/+/dGyZUtIJBKsW7cOv/zyC27cuAEPDw9MmDBB7DKJiIiISEQ60WJrbm6OXbt2YezYsUhOTkaVKlVw+fJlZGZm4rPPPsO2bdsgk8nELpOIiIiIRKQTLbYAULVqVUycOBETJ04UuxQiIiIi0kI60WJLRERERFQSrWyx/fLLL8v8GIlEggULFryBaoiIiIhIF2hlsL148WKRaS9evEBqaioAwNLSEgCQkZEBALCxsWEfWyIiIqK3nFYG2xMnTqj8fe/ePYwePRrjx4+Hj48PbGxsAACpqanYvHkz/vrrL6xdu1aMUomIiIhIS+hEH9sffvgBnTp1wrRp04RQCxS21E6bNg0dO3bEDz/8IGKFRERERCQ2nQi2YWFhaNiwocb5Hh4eCAsLq8SKiIiIiEjb6ESwtbS0RGhoqMb5oaGhMDc3r8SKiIiIiEjb6ESwHTJkCE6ePIlPP/0U586dQ0JCAhISEnD27FlMmDABoaGhGDp0qNhlEhEREZGItPLmsVdNnDgRubm52LBhA06ePKkyz9DQEOPGjeMPNxARERG95XQi2AKAv78/Ro4cifPnzyMxMREA4OjoiLZt26rcUEZEREREbyedCbZA4SgIvXv3FrsMIiIiItJCOtHHloiIiIioJAy2RERERKQXGGyJiIiISC8w2BIRERGRXmCwJSIiIiK9wGBLRERERHpBK4f78vLygkQiKdNjJBIJjh8//oYqIiIiIiJtp5XBtnXr1kWC7a1btxAZGQk3Nze4uroCAGJiYnDv3j3Uq1cPjRs3FqNUIiIiItISWhlsFy1apPL38ePHcfz4cWzatAlt27ZVmXf27Fn4+/tj6tSplVkiEREREWkZnehj+8svv+CTTz4pEmoBoH379hg+fDh++eUXESojIiIiIm2hE8H2/v37sLKy0jjfysoKcXFxlVgREREREWkbnQi2zs7OCAoKwrNnz4rMy8rKwp49e+Dk5CRCZURERESkLbSyj+2r/P39MWXKFPTq1QsDBgxA7dq1ARS25O7duxdPnjxhVwQiIiKit5xOBNtu3bph7dq1WLx4MdasWaMyz8PDA/Pnz0fHjh1Fqo6IiIiItIFOBFsA6NChAzp06ICUlBQkJSUBAGrWrAk7OzuRKyMiIiIibaAzwVbBzs6OYZaIiIiIitCpYPvw4UP8999/yMzMhFwuLzL/gw8+EKEqIiIiItIGOhFsc3JyMGvWLBw9ehQFBQWQSCRCsFX+hTIGWyIiIqK3l04M97V06VIcO3YM/v7+2LJlC+RyORYtWoSNGzeiU6dOcHd3x759+8Quk4iIiIhEpBPB9siRI/jwww8xbtw4uLm5AQAcHBzQrl07rFmzBubm5ti6davIVRIRERGRmHQi2D558gSenp4AgKpVqwIAsrOzhfk9evTAsWPHRKmNiIiIiLSDTgTbatWqIS0tDQAgk8lgaWmJmJgYYX5WVhZycnLEKo+IiIiItIBO3Dzm6emJa9euCX937doVGzZsgJ2dHQoKChAQEIBmzZqJWCERERERiU0ngu2IESNw+PBh5ObmwtjYGFOnTsX169fxxRdfAACcnZ3x9ddfi1wlEREREYlJJ4Jty5Yt0bJlS+HvGjVqICQkBBERETAwMECdOnVgZFS+Xbl//z42bNiAsLAwREZGok6dOggODi7xcXK5HOvWrcO2bduQmpoKDw8PfPnll2w5JiIiIhKJTvSxVcfAwADu7u6oX79+uUMtAERGRuLUqVOoXbs26tatW+rHrVu3DitWrICvry/WrFkDOzs7jB49GvHx8eWuhYiIiIjKTytbbC9fvlyux7Vq1arMj/Hy8kK3bt0AALNnz8atW7dKfExOTg7WrFmD0aNHw9fXFwDQokUL9OzZExs2bMB3331X5jqIiIiI6PVoZbAdMWKEyi+KyeVylb81CQ8PL/O2DAzK3mh97do1ZGVloVevXsI0Y2NjeHt7c9gxIiIiIpFoZbANDAxU+Ts3Nxc///wzXrx4gcGDB8PV1RUAEB0djd27d0Mmk2HmzJmVVl90dDQAoE6dOirT69ati82bN+PFixfCeLtlIZfL8fz58wqp8U2RSCSQyWRil6E3srOzhZ+HpvLhMVnxeFy+Hh6TFU+bj8nSNr5R5dDKYNu6dWuVvxcuXAipVIpdu3ahSpUqwnQvLy8MHz4cn3zyCU6fPo327dtXSn1Pnz6FsbGxSi0AYGFhAblcjoyMjHIF27y8vHK1OlcmmUyGhg0bil2G3oiJiVH5sREqOx6TFY/H5evhMVnxtP2YNDY2FrsE+j9aGWxfdeDAAXz66adFgiRQeALp378/Vq9ejdmzZ4tQXcWRSqXCTwZrK34rrViurq5a2wqhK3hMVjwel6+Hx2TF0+Zj8t69e2KXQEp0IthmZ2cjJSVF4/yUlJRK/SZnYWGB3Nxc5OTkqITtp0+fQiKRwNLSslzrlUgkMDExqagySQfwciVpIx6XpG20+ZjkFxntohPDfbVt2xaBgYE4evRokXlHjhxBYGAg2rVrV2n1KPrWKv+sL1DY97ZmzZrl6oZARERERK9HJ1psv/32W4wcORJTp06FnZ0dateuDQCIi4vDo0eP4OzsjDlz5lRaPc2bN4eZmRlCQkLg7u4OoLB/7NGjR9GpU6dKq4OIiIiI/p9OBFsHBwfs378fO3bsQGhoKJKSkgAAbm5uGDNmDAYPHlzuVtLs7GycOnUKAJCYmIisrCwcPnwYQOFNbDY2NvDx8UFSUpIwlFeVKlUwfvx4rFy5EjY2Nqhfvz62b9+O9PR0jBkzpgL2mIiIiIjKSieCLVAYJn18fODj41Oh633y5AmmTp2qMk3xd2BgINq0aYOCggLk5+erLOPn5we5XI6NGzcKP6m7YcMGODk5VWh9RERERFQ6OhNs35RatWrh7t27xS6zZcuWItMkEgnGjx+P8ePHv6nSiIiIiKgMtDLYjhgxAgYGBtiwYQOMjIwwcuTIEh8jkUiwefPmSqiOiIiIiLSRVgZbACgoKBD+X5qx67R1fDsiIiIiqhxaGWxfvfSvrisAEREREZEynRjH9vLly0hNTdU4PzU1FZcvX67EioiIiIhI2+hEsB05ciTOnj2rcf6FCxdK1Q+XiIiIiPSXTgTbkvrP5ubmwtDQsJKqISIiIiJtpJV9bAEgKSkJiYmJwt/R0dFquxs8ffoUO3bsQM2aNSuzPCIiIiLSMlobbIOCgvDrr79CIpFAIpFg9erVWL16dZHl5HI5DA0NMW/ePBGqJCIiIiJtobXBtlevXqhXrx7kcjn8/f0xYsQItGzZUmUZiUQCmUwGDw8PVKtWTaRKiYiIiEgbaG2wrVu3LurWrQsAWLhwIVq2bMmfqyUiIiIijbQ22CobMGCA2CUQERERkZbTiWALAFFRUdizZw8SEhKQkZFRZKQE/qQuERER0dtNJ4LtX3/9ha+++gpGRkZwdXWFhYVFkWX4k7pEREREbzedCLa//vorPDw8sG7dOtjY2IhdDhERERFpIZ34gYZHjx5h4MCBDLVEREREpJFOBNsGfZjOcQAAGcJJREFUDRrg0aNHYpdBRERERFpMJ4Lt7Nmz8eeff+LatWtil0JEREREWkon+tiuW7cO5ubmGD58ONzc3FCjRg0YGKhmcolEglWrVolUIRERERGJTSeCbUREBACgRo0aePbsGe7du1dkGYlEUtllEREREZEW0Ylge+LECbFLICIiIiItpxN9bImIiIiISqITLbbKsrKykJWVhYKCgiLzatasKUJFRERERKQNdCbYbtu2DQEBAYiPj9e4THh4eCVWRERERETaRCe6Imzfvh3ff/89nJ2d4e/vD7lcDh8fH4wbNw7VqlWDu7s75s+fL3aZRERERCQinQi2f/zxBzp06ID169dj8ODBAIDOnTtj2rRpOHToEJ49e4b09HSRqyQiIiIiMelEsI2Li0PXrl0BAFKpFACQl5cHADA3N8egQYOwbds20eojIiIiIvHpRLA1NzdHfn4+AMDMzAwymQwPHz4U5puamuLx48dilUdEREREWkAngm29evVw584d4e+mTZti+/btSE5OxoMHD7Bz5064uLiIVyARERERiU4ngm2/fv0QGRmJ3NxcAMDkyZMRFRWFLl26wMvLCzExMfD39xe5SiIiIiISk04M9zVw4EAMHDhQ+LtFixY4ePAgTpw4AUNDQ7Rv3x6urq4iVkhEREREYtOJYKuOk5MTfHx8xC6DiIiIiLSE1nZFyMnJwdy5c7Fly5ZilwsMDMS3334rjJJARERERG8nrQ22O3fuxN69e9GlS5dil+vSpQuCgoKwe/fuyimMiIiIiLSS1gbbkJAQdO/eHU5OTsUu5+zsjJ49e+LgwYOVVBkRERERaSOtDbYRERFo0aJFqZZ95513cPfu3TdcERERERFpM60Ntnl5ecKvjJVEKpUKQ4ERERER0dtJa4Otvb09IiMjS7VsZGQk7O3t33BFRERERKTNtDbYtmvXDvv27cOTJ0+KXe7JkyfYt28f2rVrV0mVEREREZE20tpg6+fnh5ycHPj4+CAsLEztMmFhYfD19UVOTg7Gjh1byRUSERERkTbR2h9ocHJywvLlyzF9+nQMHToUTk5OqF+/PkxNTfHs2TNERkYiLi4OVatWxdKlS+Hs7Cx2yUREREQkIq0NtkDhGLX79+/HunXrcPLkSRw/flyYZ29vj48++gh+fn4lDglGRERERPpPq4MtANSqVQvz5s0DAGRlZeHZs2cwNTWFmZmZyJURERERkTbR+mCrzMzMjIGW/re9ew+K6rz/OP5ZQIwXwGoA613AFVK0IE4MMiNoTSOpjjaSgraWqBBijVZNmmhqrUmcaKjTTrxkapSo0UairdqJqSgaRytYG02i0djYiDFcIvHGVbmf3x/W/blZwBuwePb9mmGSfc7znOe763H8+PicswAAAPVqtTePAQAAAHeCYAsAAABTINgCAADAFAi2AAAAMAWCLQAAAEyBYAsAAABTINgCAADAFAi2AAAAMAWCLQAAAEyBYAsAAABTINgCAADAFAi2AAAAMAWCLQAAAEyBYAsAAABTINgCAADAFAi2AAAAMAWCLQAAAEyBYAsAAABTINgCAADAFAi2AAAAMAWCLQAAAEyBYAsAAABTINgCAADAFDycXYCznTlzRosWLdInn3yiDh06aOzYsZo1a5Y8PT0bHTdixAjl5+c7tB8/flxt27ZtrnIBAADQAJcOtsXFxUpMTFSfPn20fPlyFRYWasmSJaqoqNCCBQtuOf6xxx7TlClT7NpuFYgBAADQPFw62Kanp6u8vFwrVqxQp06dJEm1tbV6+eWXlZKSIn9//0bHP/jggwoLC2uJUgEAAHALLr3H9sCBA4qMjLSFWkmKjY1VXV2dsrKynFgZAAAA7pRLB9ucnBwFBATYtXl7e8vX11c5OTm3HP/+++8rNDRU4eHhSk5O1hdffNFcpQIAAOAWXHorQklJiby9vR3afXx8VFxc3OjYESNGaODAgerWrZtyc3P15z//WRMnTtT27dvVs2fPu6rHMAxdvXr1rsa2FIvFonbt2jm7DNO4du2aDMNwdhn3Na7Jpsd1eW+4Jptea74mDcOQxWJxdhn4H5cOtvdi/vz5tv8fPHiwoqKiFBsbq7S0NC1cuPCuzlldXa1Tp041UYXNo127dnrooYecXYZpnD17VteuXXN2Gfc1rsmmx3V5b7gmm15rvya5cbz1cOlg6+3trdLSUof24uJi+fj43NG5/Pz8FBERoZMnT951PW3atFFQUNBdj28J/K20afXt27fVrkLcL7gmmx7X5b3hmmx6rfma/PLLL51dAm7i0sE2ICDAYS9taWmpLly44LD3tiVYLBa1b9++xeeF8/DPlWiNuC7R2rTma5K/yLQuLn3z2LBhw5Sdna2SkhJbW0ZGhtzc3BQVFXVH5yosLNTRo0c1YMCApi4TAAAAt8GlV2wTEhK0YcMGTZ8+XSkpKSosLFRqaqoSEhLsnmGbmJiogoICZWZmSpJ27Nihffv2KTo6Wn5+fsrNzdVbb70ld3d3TZ482VlvBwAAwKW5dLD18fHR+vXr9eqrr2r69Onq0KGD4uLiNHv2bLt+dXV1qq2ttb3u0aOHvv32W7322msqLS2Vl5eXHnnkEc2cOfOun4gAAACAe+PSwVaSAgMDtW7dukb7bNiwwe51WFiYQxsAAACcy6X32AIAAMA8CLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATIFgCwAAAFMg2AIAAMAUCLYAAAAwBYItAAAATMHlg+2ZM2c0efJkhYWFKSoqSqmpqaqqqrrlOMMw9NZbbykmJkYDBw5UfHy8Pv300xaoGAAAAPVx6WBbXFysxMREVVdXa/ny5Zo9e7Y2b96sJUuW3HLs6tWrtWzZMj311FNatWqVfH19NWXKFOXm5rZA5QAAAPguD2cX4Ezp6ekqLy/XihUr1KlTJ0lSbW2tXn75ZaWkpMjf37/ecZWVlVq1apWmTJmip556SpIUERGhUaNGKS0tTQsXLmyhdwAAAIAbXHrF9sCBA4qMjLSFWkmKjY1VXV2dsrKyGhz38ccfq6ysTLGxsbY2T09PPfroozpw4ECz1gwAAID6uXSwzcnJUUBAgF2bt7e3fH19lZOT0+g4SQ5jAwMDVVBQoIqKiqYvFgAAAI1y6a0IJSUl8vb2dmj38fFRcXFxo+M8PT3Vtm1bu3Zvb28ZhqHi4mI98MADd1RLdXW1DMPQ8ePH72icM1gsFv3WX6r2dXYl9682btJnn12/CRH3zmKxSH3jpN41zi7l/ubmIX32GddlE7BYLNIf/iBVVzu7lPtbmzat/pqsrq6+/uuNVsGlg21rcuM3xf3ym+PBO8vtaMD98ut9X2jn4+wKTIPrson4+Tm7AtNozdekxWJp1fW5GpcOtt7e3iotLXVoLy4ulo9Pw39Ient7q6qqSpWVlXartiUlJbJYLI2ObUh4ePgdjwEAAMD/c+k9tgEBAQ57aUtLS3XhwgWH/bPfHSdJZ8+etWvPyclRt27d7ngbAgAAAO6dSwfbYcOGKTs7WyUlJba2jIwMubm5KSoqqsFxgwYNUseOHbVz505bW3V1tXbv3q1hw4Y1a80AAACon0tvRUhISNCGDRs0ffp0paSkqLCwUKmpqUpISLB7hm1iYqIKCgqUmZkpSWrbtq1SUlK0fPlyde7cWVarVZs2bVJRUZGmTp3qrLcDAADg0lw62Pr4+Gj9+vV69dVXNX36dHXo0EFxcXGaPXu2Xb+6ujrV1tbatSUnJ8swDL399tu6fPmyQkJClJaWpp49e7bkWwAAAMD/WIzW/AwNAAAA4Da59B5bAAAAmAfBFgAAAKZAsAUAAIApEGwBAABgCgRbAAAAmALBFgAAAKZAsAUAAIApEGwBAABgCgRbAAAAmALBFqZ35coVffTRR84uAy7m6tWrDR6rrq5WQUFBC1YDV3fhwgW7a84wDO3evVurV6/Wrl27VFNT48TqgKbj4ewCgOb273//W7NmzdKpU6ecXQpcwMqVK7Vu3TqVlZXJ399fU6dO1aRJk+z6fP7550pISOCaRLMrKyvTr3/9a2VnZ0uSfvSjH2np0qVKSUnR4cOH5eHhoZqaGoWEhGjjxo3q0KGDkysG7g0rtgDQRP72t79p5cqVio2N1YIFCxQREaHFixdr6tSpKisrc3Z5cEErVqzQyZMn9corr+iNN95QXl6eZs6cqdzcXG3fvl0nTpxQenq6Lly4oLVr1zq7XOCeWQzDMJxdBHA3xowZc1v9ysvL9c0337A6hmY3btw4RUdHa/bs2ba2I0eOaM6cOercubNWr14tX19fHTt2jBVbtIiRI0dq8uTJ+vnPfy5JOnnypMaPH69FixYpLi7O1m/t2rXaunWr3n//fWeVCjQJtiLgvpWTk6OgoCA99NBDjfbLz8/XN99800JVwZWdO3dOQ4cOtWsbPHiwNm/erOTkZMXHx2vNmjVOqg6uqLCwUFar1fa6X79+dv+9ITg4WPn5+S1aG9AcCLa4b/Xr10+9e/fW4sWLG+23a9cubh5Di/D29tbly5cd2rt27ap3331XTz/9tCZOnKhnnnnGCdXBFXXs2FFFRUW21x4eHvL391e7du3s+lVWVsrNjd2JuP9xFeO+NXDgQB0/fvy2+rLjBi3hBz/4gfbs2VPvMS8vL61bt05hYWFasmRJC1cGVxUUFKRjx47ZXru5uWn//v12q7iS9MUXX6hXr14tXR7Q5Ai2uG8lJSVp/vz5t+wXHR2tvXv3tkBFcHVjxoxRfn6+3QrZzdq2bauVK1fqySef1Pe///0Wrg6uKCkpySHE1ufEiROKjY1tgYqA5sXNYwAAADAFVmwBAABgCgRbAAAAmALBFgAAAKZAsAUASXPnztWIESOafZ5JkyY5fMVua5znH//4hyIiIjR58mSdP39eSUlJyszMbMIKAaDp8RxbAE6Vm5urtWvXKisrS+fPn5ckde/eXUOGDFF8fLyCg4OdXKFrSktLU3x8vM6ePavhw4era9euioyMdHZZANAogi0Ap9m3b59mz54td3d3jRkzRsHBwXJzc1NOTo52796tTZs2ae/everevbuzS3U5y5Ytk7+/vzw8PHTp0iV5eXnJ09PT2WUBQKMItgCc4uuvv9acOXPUrVs3rVu3Tn5+fnbHn3/+eb377ru3/Dakq1evqn379s1Zqku6+S8TXbp0cWIlAHD72GMLwCnWrFmjq1evavHixQ6hVrr+1Z+//OUv7b7IYO7cuQoPD9fXX3+t5ORkhYeH6/nnn5ckHTlyRDNnzlRMTIxCQ0MVHR2t1157TRUVFQ7n3rNnj0aPHq0BAwZo9OjRDe4dvXr1qpYsWaLo6GiFhobqscceU1pa2m1/k917772nkSNHauDAgYqLi9ORI0fq7VdVVaVly5bp0UcftdWempqqqqqqJpunqqpKb7zxhp544glFREQoLCxMEydO1L/+9S+7fnl5eerfv7/S0tJs5w0NDdX48ePr/aa/Q4cOaeLEiQoLC9PgwYM1bdo0nTlz5rbqBoCmxootAKfYt2+fevfurR/+8Id3NK6mpkZTp05VRESEXnzxRT3wwAOSpIyMDFVUVGjChAnq1KmTjh8/ro0bN+r8+fNatmyZbfzBgwc1Y8YMBQUF6bnnntOVK1c0b948de3a1W4ewzA0bdo0HT58WHFxcQoJCdE///lPpaamqrCwUC+99FKjdW7ZskULFixQeHi4EhMTlZubq2nTpsnHx8curNfV1WnatGk6evSofvaznykwMFCnT5/W+vXr9dVXX+nNN99sknnKysq0ZcsWjR49Wk8++aTKy8v117/+VUlJSdqyZYtCQkLszrtjxw6Vl5crPj5eFotFa9as0YwZM7Rnzx61adNGkpSdna3k5GT16NFDzz77rCoqKrRx40ZNmDBBW7duVY8ePRqtHQCanAEALay0tNSwWq3Gr371K4djxcXFxqVLl2w/165dsx178cUXDavVaixdutRh3M39bli1apXRv39/Iz8/39Y2duxYIyoqyigpKbG1HTx40LBarcbw4cNtbZmZmYbVajXefPNNu3POmDHD6N+/v3Hu3LkG319VVZURGRlpjB071qisrLS1v/fee4bVajV+8Ytf2Nq2b99uBAcHGx999JHdOTZt2mRYrVbj6NGjTTJPTU2NXR/DuP5ZDx061Jg3b56tLTc317BarcbDDz9sFBUV2dr37NljWK1W48MPP7S1jR071oiMjDSuXLliazt16pQRHBxsvPDCCw3WDQDNha0IAFpcWVmZJNW7N3bSpEmKjIy0/fzlL39x6DNhwgSHthsrt9L1LQSXL19WeHi4DMPQ559/Lkn69ttvderUKf30pz+Vl5eXrX9UVJSCgoLsznfgwAG5u7s7PDJrypQpMgxDBw4caPD9nThxQpcuXVJCQoLdDVffnVe6vtIcGBiogIAAXb582fbzyCOPSJIOHz7cJPO4u7vb+tTV1amoqEg1NTUKDQ21fT43e/zxx+Xj42N7PXjwYEnXn2Ih2X+WnTp1svULDg7W0KFDtX///gbrBoDmwlYEAC2uQ4cOkq4H0O965ZVXVF5erosXL+o3v/mNw3EPDw+HbQOSVFBQoGXLlunDDz9UcXGx3bEbQbqgoECS1Lt3b4fxffv2tQt4+fn58vPzU8eOHe36BQYG2o43pKF52rRpo549e9q1nTt3TmfOnGnwUVqXLl1qknkkadu2bXr77bd19uxZVVdX29rr2zJw8zYGSbaQW1JSYjd33759HcYGBgbq4MGD3NgHoMURbAG0OC8vL/n6+uq///2vw7Ebe27z8vLqHevp6enwpITa2lpNnjxZxcXFSkpKUkBAgNq3b6/CwkLNnTtXdXV1Tf8mmkhdXZ2sVqvmzZtX7/H6Qvzd+Pvf/665c+dq5MiRmjp1qrp06SJ3d3etWrXKtgp7M3d393rPY9zmjXMA4AwEWwBOERMToy1btuj48eMaOHDgPZ3r9OnT+uqrr/T6669r3LhxtvasrCy7ft26dZN0fZX0u86ePWv3unv37jp06JDKysrsVm1zcnJsxxty8zw3r8RWV1crLy/P7ksnevXqpf/85z+KjIyUxWK55Xu923l27dqlnj17asWKFXbz3Hxj3d3M/d3PTbr+GX3ve99jtRZAi2OPLQCnSEpKUrt27fTSSy/p4sWLDsfvZGXwxgruzWMMw9A777xj18/Pz08hISHatm2bSktLbe1ZWVn68ssv7foOGzZMtbW1Dnt8161bJ4vFomHDhjVYT2hoqDp37qz09HS7R3Zt27bN9k/5N8TGxqqwsFCbN292OE9FRUW92zXuZp4bK7A3f0bHjh3Tp59+2uD5G3Pjs9y+fbvdXKdPn1ZWVpaio6Pv6rwAcC9YsQXgFH369NHSpUv13HPPadSoUbZvHjMMQ3l5edqxY4fc3Nxu65/iAwIC1KtXL73++usqLCxUx44dtWvXLodwJ0lz5sxRSkqKJk6cqPHjx6uoqEgbN25Uv3797ELkiBEjNGTIEP3pT39Sfn6++vfvr6ysLO3du1eJiYnq1atXg/W0adNGs2bN0oIFC5SYmKjHH39ceXl52rp1q8Pe17Fjx2rnzp36/e9/r8OHD2vQoEGqra1VTk6OMjIytGbNGg0YMOCe54mJidHu3bs1ffp0xcTEKC8vT+np6QoKCmo0PDfmhRdeUHJysuLj4xUXF2d73JeXl5eeffbZuzonANwL94ULFy50dhEAXFNAQIB+8pOfqLy8XNnZ2dq5c6cOHjyoixcvKioqSosWLdLQoUNt/ffs2aMzZ87omWeesTuPu7u7oqKidOrUKX3wwQf6+OOPFRERoTlz5ig9PV0jR460Pae1d+/eslqtOnTokDIyMnTp0iXNnz9f5eXlys/PV2JioiTJYrHoxz/+sSorK5WZmanMzEzV1NTo6aef1syZM2+5bSA0NFRdunTRwYMHtXv3blVWVmrJkiU6efKkJOmJJ56wzTNq1Ci1b99e2dnZ+uCDD/TJJ5+ooqJC48aN0/Dhwxv9KtvbnSckJESenp7av3+/MjIyVFZWpt/97ncqKSmxe98lJSV65513FBUVpUGDBtnNtWLFCj388MMaMmSIJKlnz54aNGiQjh07ph07dujYsWMaNGiQ/vjHP6pPnz6Nfj4A0BwsBncCAAAAwATYYwsAAABTINgCAADAFAi2AAAAMAWCLQAAAEyBYAsAAABTINgCAADAFAi2AAAAMAWCLQAAAEyBYAsAAABTINgCAADAFAi2AAAAMAWCLQAAAEyBYAsAAABT+D8ggf4ZYJhy7QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mM_2AGc4EcsS"
      },
      "source": [
        "###Condicion de superficie terrestre"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ijC4SD34LZ02",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5c9e9ed-057b-4898-df31-5a8530a3b559"
      },
      "source": [
        "full['land_surface_condition'].value_counts() ## Ecuacion mas peligrosa de la Historia: suficientes datos para poder evaluar"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "t    216757\n",
              "n     35528\n",
              "o      8316\n",
              "Name: land_surface_condition, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 313
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otqO10dlEyh1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8398a1fa-10cc-4381-a95a-334465491a84"
      },
      "source": [
        "daño_por_cond_superficie = full.groupby('land_surface_condition')['damage_grade'].mean().sort_values(ascending=False)\n",
        "daño_por_cond_superficie #conclucion: condicion superficie terrestre no es relevante"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "land_surface_condition\n",
              "o                   2.29\n",
              "n                   2.25\n",
              "t                   2.23\n",
              "Name: damage_grade, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 314
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXb0hW_RGLT2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "e95d0432-366b-44d9-940c-c02d90e0f0bd"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Promedio de daño a edificios por condición de superficie terrestre', {'fontweight':'bold'})\n",
        "\n",
        "daño_por_cond_superficie.plot(kind='bar', ax=f.gca(),color=['#663300', '#009933', '#660066'], ylabel='Promedio grado de daño', xlabel='Condicion superficie terrestre')\n",
        "\n",
        "plt.savefig('superficie.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ILcQA_ZMG2-8"
      },
      "source": [
        "###Tipo de cimientos "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JtPnR2_1HNU1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3aa35e0-858e-4b99-fb1b-1658c36191d9"
      },
      "source": [
        "full['foundation_type'].value_counts() ## Ecuacion mas peligrosa de la Historia: descartamos h"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "r    219196\n",
              "w     15118\n",
              "u     14260\n",
              "i     10579\n",
              "h      1448\n",
              "Name: foundation_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 316
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BM2DZofZLpRo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "101d79f6-c40d-4a01-93d4-8535138d4c0c"
      },
      "source": [
        "tipo_de_cimientos_filtrado = full[(full['foundation_type'] != 'h')]\n",
        "tipo_de_cimientos_filtrado['foundation_type'] = tipo_de_cimientos_filtrado.foundation_type.cat.remove_unused_categories()\n",
        "tipo_de_cimientos_filtrado['foundation_type'].value_counts() \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "r    219196\n",
              "w     15118\n",
              "u     14260\n",
              "i     10579\n",
              "Name: foundation_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 317
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHo7ZDpDG-3O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85424942-84d1-42c3-b805-87f2d45a64be"
      },
      "source": [
        "daño_por_tipo_de_cimientos = tipo_de_cimientos_filtrado.groupby('foundation_type')['damage_grade'].mean().sort_values(ascending=False)\n",
        "daño_por_tipo_de_cimientos #conclucion: tipo de cimiento r sufrio daños considerablementes mayores, podemos ver que el cimiento i fue el mas resistente."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "foundation_type\n",
              "r                   2.33\n",
              "u                   1.88\n",
              "w                   1.81\n",
              "i                   1.45\n",
              "Name: damage_grade, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 318
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARoNuDsKNSww",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "bc9fcc31-daf0-4250-d1b3-3c18993ebd0c"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Promedio de daño a edificios por tipo de cimiento', {'fontweight':'bold'})\n",
        "\n",
        "daño_por_tipo_de_cimientos.plot(kind='bar', ax=f.gca(),color=['#663300', '#009933', '#660066', '#ff944d'], ylabel='Promedio del grado de daño', xlabel='Tipo de cimiento')\n",
        "\n",
        "plt.savefig('cimiento.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "63dJMHutpBBG"
      },
      "source": [
        "### Tipo de techo\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o6EbSOHypLlG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aacf0260-8d5e-49b7-db1f-b9c663ecf45a"
      },
      "source": [
        "full['roof_type'].value_counts() ## Ecuacion mas peligrosa de la Historia: suficientes datos para poder evaluar"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "n    182842\n",
              "q     61576\n",
              "x     16183\n",
              "Name: roof_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 320
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1K5K50Npa87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cacdfd50-a51d-4b7a-d904-81d080d85080"
      },
      "source": [
        "daño_por_tipo_de_techo = full.groupby('roof_type')['damage_grade'].mean().sort_values(ascending=False)\n",
        "daño_por_tipo_de_techo # Conclucion: los tipos de techo x son mas resistentes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "roof_type\n",
              "q                   2.32\n",
              "n                   2.27\n",
              "x                   1.57\n",
              "Name: damage_grade, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 321
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QFxIE0tayOxN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "7a7ca5f7-bff8-4d6a-91d2-24e19eabed54"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Promedio de daño a edificios por tipo de techo', {'fontweight':'bold'})\n",
        "\n",
        "daño_por_tipo_de_techo.plot(kind='bar', ax=f.gca(),color=['#663300', '#009933', '#660066'], ylabel='Promedio del grado de daño', xlabel='Tipo de techo')\n",
        "\n",
        "plt.savefig('techo.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1Vi3NYEp0MP"
      },
      "source": [
        "### Tipo de construccion en planta baja"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFmSYZQiqQId",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1b52f4b-8021-48a2-9dd2-ae5d33d8e133"
      },
      "source": [
        "full['ground_floor_type'].value_counts() # Descartamos z y m"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "f    209619\n",
              "x     24877\n",
              "v     24593\n",
              "z      1004\n",
              "m       508\n",
              "Name: ground_floor_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 323
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MM7X8JHKqhFl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b68fb8cb-9b1a-46f0-c9f3-ae5da0930c5d"
      },
      "source": [
        "tipo_planta_baja = full[(full['ground_floor_type'] != 'z')]\n",
        "tipo_planta_baja = tipo_planta_baja[(tipo_planta_baja['ground_floor_type'] != 'm')]\n",
        "tipo_planta_baja['ground_floor_type'] = tipo_planta_baja.ground_floor_type.cat.remove_unused_categories()\n",
        "tipo_planta_baja['ground_floor_type'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "f    209619\n",
              "x     24877\n",
              "v     24593\n",
              "Name: ground_floor_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 324
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ajPhBAhIsjyk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1ef2975-cfe1-48d9-8082-44f482843ffa"
      },
      "source": [
        "tipo_planta_baja_ordenado = tipo_planta_baja.groupby('ground_floor_type')['damage_grade'].mean().sort_values(ascending=False)\n",
        "tipo_planta_baja_ordenado #Conclucion: el tipo de construccion en planta baja v es el mas resistente"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ground_floor_type\n",
              "f                   2.31\n",
              "x                   2.25\n",
              "v                   1.63\n",
              "Name: damage_grade, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 325
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VwQUIRlwyy9p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "outputId": "990fa0b1-9521-419e-e5dc-b3695cf695ad"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Promedio de daño a edificios por tipo de planta baja', {'fontweight':'bold'})\n",
        "\n",
        "tipo_planta_baja_ordenado.plot(kind='bar', ax=f.gca(),color=['#663300', '#009933', '#660066'], ylabel='Promedio del grado de daño', xlabel='Tipo de planta baja')\n",
        "\n",
        "plt.savefig('planta_baja.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LlV4tLWEvtv4"
      },
      "source": [
        "### Orientacion del edificio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOFNxIXnvXUz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b8c74b1-bf66-4781-e3fb-43058fa65387"
      },
      "source": [
        "full['position'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "s    202090\n",
              "t     42896\n",
              "j     13282\n",
              "o      2333\n",
              "Name: position, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 327
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U7rt5UBMv6ZI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9136372-2e6a-4358-8a00-c27e8437ad57"
      },
      "source": [
        "daño_por_orientacion = full.groupby('position')['damage_grade'].mean().sort_values(ascending=False)\n",
        "daño_por_orientacion # conclucion: no es relevante"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "position\n",
              "t                   2.31\n",
              "s                   2.23\n",
              "o                   2.21\n",
              "j                   2.15\n",
              "Name: damage_grade, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 328
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQqdAV2tzprq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "499d8654-fb87-4f39-b744-e4e1c9c78f65"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Promedio de daño a edificios por orientación', {'fontweight':'bold'})\n",
        "\n",
        "daño_por_orientacion.plot(kind='bar', ax=f.gca(),color=['#663300', '#009933', '#660066', '#ff944d'], ylabel='Promedio del grado de daño', xlabel='Orientación')\n",
        "\n",
        "plt.savefig('orientacion.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pwJRe1xHwFdK"
      },
      "source": [
        "### Configuración de los planos del edificio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5xvKIe3wzLo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "62f244e6-3bc9-4514-e506-53fb963b4961"
      },
      "source": [
        "full['plan_configuration'].value_counts() # Filtro m, n y f."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "d    250072\n",
              "q      5692\n",
              "u      3649\n",
              "s       346\n",
              "c       325\n",
              "a       252\n",
              "o       159\n",
              "m        46\n",
              "n        38\n",
              "f        22\n",
              "Name: plan_configuration, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 330
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KKRi4_UKxeSs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80e55fd5-3ab5-4f72-daa3-9caaecd6205e"
      },
      "source": [
        "daño_por_plan_configuration = full[(full['plan_configuration'] != 'm')]\n",
        "daño_por_plan_configuration = daño_por_plan_configuration[(daño_por_plan_configuration['plan_configuration'] != 'n')]\n",
        "daño_por_plan_configuration = daño_por_plan_configuration[(daño_por_plan_configuration['plan_configuration'] != 'f')]\n",
        "daño_por_plan_configuration['plan_configuration'] = daño_por_plan_configuration.plan_configuration.cat.remove_unused_categories()\n",
        "daño_por_plan_configuration['plan_configuration'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "d    250072\n",
              "q      5692\n",
              "u      3649\n",
              "s       346\n",
              "c       325\n",
              "a       252\n",
              "o       159\n",
              "Name: plan_configuration, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 331
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4eUYVoLw4Xj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03960967-ef98-4655-ecd7-d29dcf7b894c"
      },
      "source": [
        "daño_por_plan_configuration_ordenado = daño_por_plan_configuration.groupby('plan_configuration')['damage_grade'].mean().sort_values(ascending=False)\n",
        "daño_por_plan_configuration_ordenado"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "plan_configuration\n",
              "q                   2.27\n",
              "d                   2.24\n",
              "s                   2.05\n",
              "u                   1.92\n",
              "o                   1.89\n",
              "a                   1.85\n",
              "c                   1.84\n",
              "Name: damage_grade, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 332
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FBIecj-yL3N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "64e184f1-78d5-49ce-def6-5d7c94087e66"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Promedio de daño a edificios por configuracion de planos', {'fontweight':'bold'})\n",
        "\n",
        "daño_por_plan_configuration_ordenado.plot(kind='bar', ax=f.gca(),color=['#663300', '#009933', '#660066', '#ff944d', '#ff99cc', '#6699ff', '#669999'], ylabel='Promedio grado de daño', xlabel='Configuracion de los planos')\n",
        "\n",
        "plt.savefig('plan_configuration.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3VMiyVTyzKN"
      },
      "source": [
        "### Tipo de construccion en otros pisos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P8SrAo-Py6WX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ab00d1c-9d73-424a-9d56-c46661abd0fc"
      },
      "source": [
        "full['other_floor_type'].value_counts() # No es necesario filtrar"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "q    165282\n",
              "x     43448\n",
              "j     39843\n",
              "s     12028\n",
              "Name: other_floor_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 334
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgWSNy5Qy613",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "929bde96-624b-48ca-8ee0-32e0d33d4b2a"
      },
      "source": [
        "daño_por_tipo_de_construccion_otros_pisos = full.groupby('other_floor_type')['damage_grade'].mean().sort_values(ascending=False)\n",
        "daño_por_tipo_de_construccion_otros_pisos # conclucion: cuando construyendo con los pisos q y x hubo un mayor daño."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "other_floor_type\n",
              "q                   2.32\n",
              "x                   2.30\n",
              "j                   2.04\n",
              "s                   1.61\n",
              "Name: damage_grade, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 335
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWCNNRnnzaiH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "e1e33933-c4ec-4e14-9136-f5958055d1e8"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Promedio de daño a edificios por tipo de construccion con otros pisos', {'fontweight':'bold'})\n",
        "\n",
        "daño_por_tipo_de_construccion_otros_pisos.plot(kind='bar', ax=f.gca(),color=['blue'], ylabel='Promedio grado de daño', xlabel='Tipo de construccion con otros pisos')\n",
        "\n",
        "plt.savefig('construccion_otros_pisos.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mg-T0l1S2Z7P"
      },
      "source": [
        "### Altura de los edificios "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7mDomeXFqMW"
      },
      "source": [
        "numerical_columns = ['age','count_floors_pre_eq', 'area_percentage', 'height_percentage']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ek-VZEGRFmbc"
      },
      "source": [
        "#means_antiguedad = full[full['age']<150].groupby('damage_grade')[numerical_columns].mean()\n",
        "#means = full.groupby('damage_grade')[numerical_columns].mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZKAeTc_qt0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "0d509bbe-1b3f-4fb2-e8c4-2473c05782e4"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Promedio de altura normalizada de los edificios para cada grado de daño', {'fontweight':'bold'})\n",
        "\n",
        "means['height_percentage'].plot(kind='bar', ax=f.gca(),color=['#16a1ff', '#ff944d', 'red'], ylabel='Altura', xlabel='Grado de daño')\n",
        "\n",
        "plt.savefig('height.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPvBhYIfRrDl"
      },
      "source": [
        "### Superficie ocupada por los edificios"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDDwhbnPquZE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "outputId": "f566e9bd-577c-4887-d497-93bb84f98fe4"
      },
      "source": [
        "f = plt.figure()\n",
        "plt.title('Promedio de superficie normalizada ocupada por los edificios para cada grado de daño', {'fontweight':'bold'})\n",
        "\n",
        "means['area_percentage'].plot(kind='bar', ax=f.gca(),color=['#16a1ff', '#ff944d', 'red'], ylabel='Superficie normalizada ocupada', xlabel='Grado de daño')\n",
        "\n",
        "plt.savefig('area.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MvyF8IJeQxy8"
      },
      "source": [
        "##Otras comparaciones"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpFWPCfmNhFv"
      },
      "source": [
        "more_affected = mean_damage.transpose().nlargest(5, columns=[3])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bafS5YaN9U_0"
      },
      "source": [
        "more_affected_zones = more_affected.index"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHU1SMKo9mjx"
      },
      "source": [
        "columns = ['geo_level_1_id', 'age', 'has_superstructure_adobe_mud', 'has_superstructure_mud_mortar_stone', 'has_superstructure_stone_flag']\n",
        "\n",
        "means = full.loc[full['geo_level_1_id'].isin(more_affected_zones.to_list())][columns].groupby('geo_level_1_id').mean()\n",
        "\n",
        "means['general_mean_age'] = train_values['age'].mean()\n",
        "means['general_mean_adobe_mud'] = train_values['has_superstructure_adobe_mud'].mean()\n",
        "means['general_mean_stone_mud'] = train_values['has_superstructure_mud_mortar_stone'].mean()\n",
        "means['general_mean_stone'] = train_values['has_superstructure_stone_flag'].mean()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVia3uDqE-dh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "e9487b0f-4eeb-4a1a-8946-410608c994d9"
      },
      "source": [
        "mean_age = means[['age', 'general_mean_age']].rename(columns = {'age': 'Zona en particular', 'general_mean_age':'Nepal'})\n",
        "\n",
        "f = plt.figure()\n",
        "plt.title('Comparación entre las zonas más afectadas y Nepal de la antigüedad promedio de los edificios' , {'fontweight':'bold'})\n",
        "\n",
        "mean_age.plot(kind='bar', ax=f.gca(), ylabel='Antigüedad promedio', xlabel='Zona geográfica', color=['#660066', '#6699ff'])\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
        "plt.savefig('age_comparison.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZfRDgA-tE__B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "5840cba0-c32e-42a2-cf88-a8ed6c76f185"
      },
      "source": [
        "mean_adobe_mud = means[['general_mean_adobe_mud', 'has_superstructure_adobe_mud']].rename(columns = {'general_mean_adobe_mud': 'Nepal', 'has_superstructure_adobe_mud':'Zona en particular'})\n",
        "\n",
        "mean_adobe_mud = mean_adobe_mud.transform(lambda x: x*100)\n",
        "\n",
        "f = plt.figure()\n",
        "plt.title('Comparación entre las zonas más afectadas y Nepal del porcentaje de edificios construidos con adobe y barro' , {'fontweight':'bold'})\n",
        "\n",
        "mean_adobe_mud.plot(kind='bar', ax=f.gca(), ylabel='Porcentaje', xlabel='Zona geográfica', color=['#660066', '#6699ff'])\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
        "plt.savefig('adobe_mud_comparison.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8f4HSt7Foma",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "89e5b573-3986-4aee-d72f-bbad6189520e"
      },
      "source": [
        "mean_stone_mud = means[['general_mean_stone_mud', 'has_superstructure_mud_mortar_stone']].rename(columns = {'general_mean_stone_mud': 'Nepal', 'has_superstructure_mud_mortar_stone':'Zona en particular'})\n",
        "\n",
        "mean_stone_mud = mean_stone_mud.transform(lambda x: x*100)\n",
        "\n",
        "f = plt.figure()\n",
        "plt.title('Comparación entre las zonas más afectadas y Nepal del porcentaje de edificios construidos con adobe y piedra' , {'fontweight':'bold'})\n",
        "\n",
        "mean_stone_mud.plot(kind='bar', ax=f.gca(), ylabel='Porcentaje', xlabel='Zona geográfica', color=['#660066', '#6699ff'])\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
        "plt.savefig('adobe_stone_comparison.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75GqjBk0F1U9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "5f03dbe4-df96-452b-ca7c-c0d0a189f9ac"
      },
      "source": [
        "mean_stone = means[['general_mean_stone', 'has_superstructure_stone_flag']].rename(columns = {'general_mean_stone': 'Nepal', 'has_superstructure_stone_flag':'Zona en particular'})\n",
        "\n",
        "mean_stone = mean_stone.transform(lambda x: x*100)\n",
        "\n",
        "f = plt.figure()\n",
        "plt.title('Comparación entre las zonas más afectadas y Nepal del porcentaje de edificios construidos con piedra' , {'fontweight':'bold'})\n",
        "\n",
        "mean_stone.plot(kind='bar', ax=f.gca(), ylabel='Porcentaje', xlabel='Zona geográfica', color=['#660066', '#6699ff'])\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
        "plt.savefig('stone_comparison.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xTv4eCBrSaQX"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "# Tiene formato de código\n",
        "```\n",
        "\n",
        "## Desvios estandar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-ktq0NECJVc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f88f7d78-fde8-496a-8477-5a1a91f838e7"
      },
      "source": [
        "antiguedad_filtrada = full[numerical_columns]\n",
        "antiguedad_filtrada = antiguedad_filtrada[(full['age'] < 200)]\n",
        "antiguedad_filtrada.std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                                  19.27\n",
              "count_floors_pre_eq                   0.73\n",
              "area_percentage                       4.39\n",
              "height_percentage                     1.92\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 348
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VmyuvY5YGh9S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "09e76cce-71da-4f2d-8662-410794f306ca"
      },
      "source": [
        "full.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>building_id</th>\n",
              "      <th>geo_level_1_id</th>\n",
              "      <th>geo_level_2_id</th>\n",
              "      <th>geo_level_3_id</th>\n",
              "      <th>count_floors_pre_eq</th>\n",
              "      <th>age</th>\n",
              "      <th>area_percentage</th>\n",
              "      <th>height_percentage</th>\n",
              "      <th>has_superstructure_adobe_mud</th>\n",
              "      <th>has_superstructure_mud_mortar_stone</th>\n",
              "      <th>has_superstructure_stone_flag</th>\n",
              "      <th>has_superstructure_cement_mortar_stone</th>\n",
              "      <th>has_superstructure_mud_mortar_brick</th>\n",
              "      <th>has_superstructure_cement_mortar_brick</th>\n",
              "      <th>has_superstructure_timber</th>\n",
              "      <th>has_superstructure_bamboo</th>\n",
              "      <th>has_superstructure_rc_non_engineered</th>\n",
              "      <th>has_superstructure_rc_engineered</th>\n",
              "      <th>has_superstructure_other</th>\n",
              "      <th>count_families</th>\n",
              "      <th>has_secondary_use</th>\n",
              "      <th>has_secondary_use_agriculture</th>\n",
              "      <th>has_secondary_use_hotel</th>\n",
              "      <th>has_secondary_use_rental</th>\n",
              "      <th>has_secondary_use_institution</th>\n",
              "      <th>has_secondary_use_school</th>\n",
              "      <th>has_secondary_use_industry</th>\n",
              "      <th>has_secondary_use_health_post</th>\n",
              "      <th>has_secondary_use_gov_office</th>\n",
              "      <th>has_secondary_use_use_police</th>\n",
              "      <th>has_secondary_use_other</th>\n",
              "      <th>damage_grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "      <td>260,601.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>525,675.48</td>\n",
              "      <td>13.90</td>\n",
              "      <td>701.07</td>\n",
              "      <td>6,257.88</td>\n",
              "      <td>2.13</td>\n",
              "      <td>26.54</td>\n",
              "      <td>8.02</td>\n",
              "      <td>5.43</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>2.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>304,545.00</td>\n",
              "      <td>8.03</td>\n",
              "      <td>412.71</td>\n",
              "      <td>3,646.37</td>\n",
              "      <td>0.73</td>\n",
              "      <td>73.57</td>\n",
              "      <td>4.39</td>\n",
              "      <td>1.92</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>261,190.00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>350.00</td>\n",
              "      <td>3,073.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>525,757.00</td>\n",
              "      <td>12.00</td>\n",
              "      <td>702.00</td>\n",
              "      <td>6,270.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>15.00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>5.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>789,762.00</td>\n",
              "      <td>21.00</td>\n",
              "      <td>1,050.00</td>\n",
              "      <td>9,412.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>30.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>6.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1,052,934.00</td>\n",
              "      <td>30.00</td>\n",
              "      <td>1,427.00</td>\n",
              "      <td>12,567.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>995.00</td>\n",
              "      <td>100.00</td>\n",
              "      <td>32.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>9.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>3.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               building_id  ...         damage_grade\n",
              "count           260,601.00  ...           260,601.00\n",
              "mean            525,675.48  ...                 2.24\n",
              "std             304,545.00  ...                 0.61\n",
              "min                   4.00  ...                 1.00\n",
              "25%             261,190.00  ...                 2.00\n",
              "50%             525,757.00  ...                 2.00\n",
              "75%             789,762.00  ...                 3.00\n",
              "max           1,052,934.00  ...                 3.00\n",
              "\n",
              "[8 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 349
        }
      ]
    }
  ]
}