EjercicioPM2_5.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EjercicioPM2.5.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MeZ9EXheH1JE"
      },
      "source": [
        "# **IMPORTACIÓN DE DATOS**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nw5weKc3VcCx"
      },
      "source": [
        "## **I. IMPORTAR LIBRERIAS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0EzQSuKZY-C"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.dates as mdates"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDdlQ01DMmhM"
      },
      "source": [
        "import seaborn as sns\n",
        "from seaborn import load_dataset"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qUafKh3oVh7E"
      },
      "source": [
        "## **II.CARGAR DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XNdigT_KZjwX",
        "outputId": "82cb7c36-3ac8-4280-cc3f-0ffd33e99681"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tiypcEzLZtlh"
      },
      "source": [
        "dataset= pd.read_csv(\"gdrive/MyDrive/DATA/PM2.5/PRSA_data_2010.1.1-2014.12.31.csv\", engine='python')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nIvxorvtVonc"
      },
      "source": [
        "## **III.DESPLEGAR INFORMACION DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AKPaujD-Z2XR"
      },
      "source": [
        "dataset.info()\n",
        "print(\"\\n\")\n",
        "print(\"Database size:\",dataset.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDS8dBro_Cfg"
      },
      "source": [
        "print(dataset.dtypes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzFD61ZV_KI7"
      },
      "source": [
        "dataset.describe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8PBEWvYJaDH"
      },
      "source": [
        "dataset.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XAP9LoNAKIWI"
      },
      "source": [
        "dataset.tail()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U7uzgXOKH-sm"
      },
      "source": [
        "# **ACTIVIDADES**\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zs0wh3W5UiDD"
      },
      "source": [
        "## 1. Consideraciones sobre índices\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eT9U7LcXLNSm"
      },
      "source": [
        "Teniendo en cuenta la información del dataset, se considera pertintente para el análisis mantener las variables de: \n",
        "\n",
        "| Columna   | descripción                  |\n",
        "| :----:    |            :----:            |\n",
        "|year:      | Año de registro              |\n",
        "|month:     | Mes de registro              |  \n",
        "|day:       | Día de registro              |  \n",
        "|hour:      | Hora de registro             |   \n",
        "|pm2.5:     | Concentración de PM2.5       |\n",
        "|TEMP:      | Temperatura (â„ƒ)            |   \n",
        "|cbwd:      | Direccion Combinada Viento   |     \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Los anteriores índices se hallan pertinentes pues, con ellos se pueden establecer \"períodos críticos\" de pm2.5 concentrado en la ciudad. e.g.: &nbsp;\n",
        "\n",
        "**a) Incremento del indice pm2.5 en horas pico.**&nbsp;\n",
        "\n",
        "**b) Relación de la concentración de material particulado con fenómenos climáticos estacionales (pm2.5 vs. TEMP).** &nbsp;\n",
        "\n",
        "\n",
        "Al carecer de coordenadas geográficas exactas, se puede pronosticar **c)qué zona de la ciudad podría verse más afectada por la acumulación del mp2.5**, teniendo en cuenta la dirección combinada del viento (cbwd).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zhGjU0FwK2ku"
      },
      "source": [
        "### **Seleccionar subconjunto de columnas**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m7fqijhHLHWe",
        "outputId": "b1417fc5-d2eb-423e-9f8e-c49b52ae0d6f"
      },
      "source": [
        "dataset.columns"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['No', 'year', 'month', 'day', 'hour', 'pm2.5', 'DEWP', 'TEMP', 'PRES',\n",
              "       'cbwd', 'Iws', 'Is', 'Ir'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qne98A-JgYCk"
      },
      "source": [
        "### *Drop* Variables no usables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67TXp1aEgjX8",
        "outputId": "2c51171a-1ad4-4ef1-d2ed-c21b601ac64d"
      },
      "source": [
        "no_use = dataset.drop(['No','DEWP','PRES','Iws','Is','Ir'], axis=1, inplace=False)\n",
        "print (no_use)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       year  month  day  hour  pm2.5  TEMP cbwd\n",
            "0      2010      1    1     0    NaN -11.0   NW\n",
            "1      2010      1    1     1    NaN -12.0   NW\n",
            "2      2010      1    1     2    NaN -11.0   NW\n",
            "3      2010      1    1     3    NaN -14.0   NW\n",
            "4      2010      1    1     4    NaN -12.0   NW\n",
            "...     ...    ...  ...   ...    ...   ...  ...\n",
            "43819  2014     12   31    19    8.0  -2.0   NW\n",
            "43820  2014     12   31    20   10.0  -3.0   NW\n",
            "43821  2014     12   31    21   10.0  -3.0   NW\n",
            "43822  2014     12   31    22    8.0  -4.0   NW\n",
            "43823  2014     12   31    23   12.0  -3.0   NW\n",
            "\n",
            "[43824 rows x 7 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ry6QeRAT3wy"
      },
      "source": [
        "### Convertir ***Fecha*** en única variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Am1QXd2ULmFQ",
        "outputId": "8ec2d274-9e11-4d95-f217-739e97d72733"
      },
      "source": [
        "date = no_use[[\"year\",\"month\",\"day\",\"hour\"]]\n",
        "print(date)\n",
        "print (type(date))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       year  month  day  hour\n",
            "0      2010      1    1     0\n",
            "1      2010      1    1     1\n",
            "2      2010      1    1     2\n",
            "3      2010      1    1     3\n",
            "4      2010      1    1     4\n",
            "...     ...    ...  ...   ...\n",
            "43819  2014     12   31    19\n",
            "43820  2014     12   31    20\n",
            "43821  2014     12   31    21\n",
            "43822  2014     12   31    22\n",
            "43823  2014     12   31    23\n",
            "\n",
            "[43824 rows x 4 columns]\n",
            "<class 'pandas.core.frame.DataFrame'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYndcKHZM60k"
      },
      "source": [
        "no_use[\"date\"]=pd.to_datetime(date)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNI_lyeVSY35"
      },
      "source": [
        "#no_use[\"date\"]=no_use[\"date\"].astype(np.int64)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "poMqcVdvTbki"
      },
      "source": [
        "#no_use.head()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5kgXyKADQg-p"
      },
      "source": [
        "## 2. Eliminar registros NA en pm2.5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NL5qF0UAHhmw"
      },
      "source": [
        "not_na = no_use[no_use[\"pm2.5\"].notna()]\n",
        "print (not_na.shape)\n",
        "print (dataset.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtCglj9sPRmF"
      },
      "source": [
        "no_use.dropna(subset=['pm2.5'], axis=0, inplace=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g787yVmAS3Km"
      },
      "source": [
        "## 3.a. Variables x Año"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TxxMykNaoRo"
      },
      "source": [
        "diez = (not_na['year']==2010)\n",
        "once = (not_na['year']==2011)\n",
        "doce = (not_na['year']==2012)\n",
        "trece = (not_na['year']==2013)\n",
        "catorce = (not_na['year']==2014)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "WCq0vfppcSyJ",
        "outputId": "6dcd33da-3f90-4d29-c29b-3f64c0e23779"
      },
      "source": [
        "not_na[diez].describe()"
      ],
      "execution_count": 23,
      "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",
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              "    }\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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>pm2.5</th>\n",
              "      <th>TEMP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8091.0</td>\n",
              "      <td>8091.000000</td>\n",
              "      <td>8091.000000</td>\n",
              "      <td>8091.000000</td>\n",
              "      <td>8091.000000</td>\n",
              "      <td>8091.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>6.494747</td>\n",
              "      <td>15.590656</td>\n",
              "      <td>11.493511</td>\n",
              "      <td>104.045730</td>\n",
              "      <td>11.313805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.480238</td>\n",
              "      <td>8.680500</td>\n",
              "      <td>6.935037</td>\n",
              "      <td>92.281185</td>\n",
              "      <td>12.684701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-19.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>147.000000</td>\n",
              "      <td>23.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>980.000000</td>\n",
              "      <td>41.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         year        month          day         hour        pm2.5         TEMP\n",
              "count  8091.0  8091.000000  8091.000000  8091.000000  8091.000000  8091.000000\n",
              "mean   2010.0     6.494747    15.590656    11.493511   104.045730    11.313805\n",
              "std       0.0     3.480238     8.680500     6.935037    92.281185    12.684701\n",
              "min    2010.0     1.000000     1.000000     0.000000     1.000000   -19.000000\n",
              "25%    2010.0     3.000000     8.000000     5.000000    35.000000     0.000000\n",
              "50%    2010.0     6.000000    16.000000    12.000000    79.000000    11.000000\n",
              "75%    2010.0    10.000000    23.000000    18.000000   147.000000    23.000000\n",
              "max    2010.0    12.000000    31.000000    23.000000   980.000000    41.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tJrjq8RMdBLz",
        "outputId": "ef4c340f-844e-4173-eb0c-fc8c1ab5f51e"
      },
      "source": [
        "not_na[once].describe()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>pm2.5</th>\n",
              "      <th>TEMP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8032.0</td>\n",
              "      <td>8032.000000</td>\n",
              "      <td>8032.000000</td>\n",
              "      <td>8032.000000</td>\n",
              "      <td>8032.000000</td>\n",
              "      <td>8032.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2011.0</td>\n",
              "      <td>6.601096</td>\n",
              "      <td>15.966633</td>\n",
              "      <td>11.509836</td>\n",
              "      <td>99.071340</td>\n",
              "      <td>12.407869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.477195</td>\n",
              "      <td>8.739076</td>\n",
              "      <td>6.910975</td>\n",
              "      <td>92.967298</td>\n",
              "      <td>12.095837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2011.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2011.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2011.0</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2011.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>139.000000</td>\n",
              "      <td>23.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2011.0</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>595.000000</td>\n",
              "      <td>36.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         year        month          day         hour        pm2.5         TEMP\n",
              "count  8032.0  8032.000000  8032.000000  8032.000000  8032.000000  8032.000000\n",
              "mean   2011.0     6.601096    15.966633    11.509836    99.071340    12.407869\n",
              "std       0.0     3.477195     8.739076     6.910975    92.967298    12.095837\n",
              "min    2011.0     1.000000     1.000000     0.000000     2.000000   -15.000000\n",
              "25%    2011.0     4.000000     9.000000     6.000000    27.000000     2.000000\n",
              "50%    2011.0     7.000000    16.000000    12.000000    71.000000    14.000000\n",
              "75%    2011.0    10.000000    24.000000    17.000000   139.000000    23.000000\n",
              "max    2011.0    12.000000    31.000000    23.000000   595.000000    36.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "hhYFM8pedBi4",
        "outputId": "cf412c82-70b6-4d15-e24d-60a42074c7d8"
      },
      "source": [
        "not_na[doce].describe()"
      ],
      "execution_count": 25,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "        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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>pm2.5</th>\n",
              "      <th>TEMP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8295.0</td>\n",
              "      <td>8295.000000</td>\n",
              "      <td>8295.000000</td>\n",
              "      <td>8295.000000</td>\n",
              "      <td>8295.000000</td>\n",
              "      <td>8295.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2012.0</td>\n",
              "      <td>6.452200</td>\n",
              "      <td>15.471248</td>\n",
              "      <td>11.506088</td>\n",
              "      <td>90.545871</td>\n",
              "      <td>12.109946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.425475</td>\n",
              "      <td>8.871717</td>\n",
              "      <td>6.927641</td>\n",
              "      <td>81.745087</td>\n",
              "      <td>12.339970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2012.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-17.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2012.0</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2012.0</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>14.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2012.0</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>23.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2012.0</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>994.000000</td>\n",
              "      <td>38.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         year        month          day         hour        pm2.5         TEMP\n",
              "count  8295.0  8295.000000  8295.000000  8295.000000  8295.000000  8295.000000\n",
              "mean   2012.0     6.452200    15.471248    11.506088    90.545871    12.109946\n",
              "std       0.0     3.425475     8.871717     6.927641    81.745087    12.339970\n",
              "min    2012.0     1.000000     1.000000     0.000000     0.000000   -17.000000\n",
              "25%    2012.0     3.000000     8.000000     5.000000    25.000000     1.000000\n",
              "50%    2012.0     6.000000    15.000000    12.000000    69.000000    14.000000\n",
              "75%    2012.0     9.000000    23.000000    18.000000   132.000000    23.000000\n",
              "max    2012.0    12.000000    31.000000    23.000000   994.000000    38.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Z5Ueh-H8dBzZ",
        "outputId": "50b66377-0493-4da2-91b1-9acc46973f01"
      },
      "source": [
        "not_na[trece].describe()"
      ],
      "execution_count": 26,
      "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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>pm2.5</th>\n",
              "      <th>TEMP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8678.0</td>\n",
              "      <td>8678.000000</td>\n",
              "      <td>8678.000000</td>\n",
              "      <td>8678.000000</td>\n",
              "      <td>8678.000000</td>\n",
              "      <td>8678.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2013.0</td>\n",
              "      <td>6.517516</td>\n",
              "      <td>15.674464</td>\n",
              "      <td>11.497580</td>\n",
              "      <td>101.712376</td>\n",
              "      <td>12.383614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.446639</td>\n",
              "      <td>8.797737</td>\n",
              "      <td>6.927949</td>\n",
              "      <td>98.070477</td>\n",
              "      <td>12.033222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2013.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-17.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2013.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2013.0</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>71.500000</td>\n",
              "      <td>13.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2013.0</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>23.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2013.0</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>38.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         year        month          day         hour        pm2.5         TEMP\n",
              "count  8678.0  8678.000000  8678.000000  8678.000000  8678.000000  8678.000000\n",
              "mean   2013.0     6.517516    15.674464    11.497580   101.712376    12.383614\n",
              "std       0.0     3.446639     8.797737     6.927949    98.070477    12.033222\n",
              "min    2013.0     1.000000     1.000000     0.000000     2.000000   -17.000000\n",
              "25%    2013.0     4.000000     8.000000     5.000000    32.000000     2.000000\n",
              "50%    2013.0     7.000000    16.000000    11.000000    71.500000    13.000000\n",
              "75%    2013.0    10.000000    23.000000    18.000000   138.000000    23.000000\n",
              "max    2013.0    12.000000    31.000000    23.000000   886.000000    38.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "1lsXwrZPdG_Q",
        "outputId": "5be7f7e4-9cf0-414c-ffab-f5d34b6b7e59"
      },
      "source": [
        "not_na[catorce].describe()"
      ],
      "execution_count": 27,
      "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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>pm2.5</th>\n",
              "      <th>TEMP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8661.0</td>\n",
              "      <td>8661.000000</td>\n",
              "      <td>8661.000000</td>\n",
              "      <td>8661.000000</td>\n",
              "      <td>8661.000000</td>\n",
              "      <td>8661.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>6.505715</td>\n",
              "      <td>15.729708</td>\n",
              "      <td>11.504676</td>\n",
              "      <td>97.734557</td>\n",
              "      <td>13.709156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3.442517</td>\n",
              "      <td>8.825550</td>\n",
              "      <td>6.923977</td>\n",
              "      <td>93.531528</td>\n",
              "      <td>11.616259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-13.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>133.000000</td>\n",
              "      <td>23.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>671.000000</td>\n",
              "      <td>42.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         year        month          day         hour        pm2.5         TEMP\n",
              "count  8661.0  8661.000000  8661.000000  8661.000000  8661.000000  8661.000000\n",
              "mean   2014.0     6.505715    15.729708    11.504676    97.734557    13.709156\n",
              "std       0.0     3.442517     8.825550     6.923977    93.531528    11.616259\n",
              "min    2014.0     1.000000     1.000000     0.000000     2.000000   -13.000000\n",
              "25%    2014.0     4.000000     8.000000     6.000000    28.000000     3.000000\n",
              "50%    2014.0     7.000000    16.000000    12.000000    72.000000    15.000000\n",
              "75%    2014.0     9.000000    23.000000    18.000000   133.000000    23.000000\n",
              "max    2014.0    12.000000    31.000000    23.000000   671.000000    42.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iFbsrtQ4i5EM"
      },
      "source": [
        "## 3.b. Gráfico Variación **pm2.5** *v.s.* **año**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vUOr4iFe2jd"
      },
      "source": [
        "X=not_na['year']\n",
        "Y=not_na['pm2.5'].values\n",
        "Z=not_na['TEMP'].values"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "jGgduJd3yup-",
        "outputId": "f9ac40db-7293-4cda-94a8-5429f32ab2bc"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.boxplot(x=not_na[\"year\"], y=not_na[\"pm2.5\"])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jc3MDlQY66xo"
      },
      "source": [
        "> Podemos inferir que la Cantidad Anual Concentrada de pm2.5 en la ciudad de Beijing oscila entre los 90 y 105 $ug/m^3$ promedio registro. Siendo el año 2012 el caso con menos unidades concentradas promedio (90.545871\t $ug/m^3$)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--CdRICM_h6V"
      },
      "source": [
        "## 4. Gráfico de disperción **TEMP** *v.s.* **PM2.5**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GdBMIluyC71G"
      },
      "source": [
        "colores=['blue','red','orange']\n",
        "tamanios=[30,60]"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "BsesEbPQC8tv",
        "outputId": "27f7f113-53d1-451a-fe63-ebc90c28e19b"
      },
      "source": [
        "asignar=[]\n",
        "lista1=[]\n",
        "lista2=[]\n",
        "for index, row in not_na.iterrows():\n",
        "    if(row['pm2.5'] >500) :\n",
        "        asignar.append(colores[0])\n",
        "        lista1.append\n",
        "    else:\n",
        "        asignar.append(colores[1])\n",
        "        lista2.append\n",
        "\n",
        "plt.scatter(Z, Y, c=asignar, s=tamanios[0])\n",
        "plt.title('Relación TEMP-PM2.5')\n",
        "plt.xlabel('Temperatura (Cº)')\n",
        "plt.ylabel('PM2.5')\n",
        "plt.plot(lista1, label = \"Niveles Peligrosos PM2.5\", marker = 'o')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4ffo0/Pz8kJqaqj3foUMHrYrL398fjDGkpqaiUqVKed7LmhSLMJk7dy7mzp1rVF63bl1s3rxZsE1Bz5V29uwBVAZmEZUK2LGDhIyzc+Hv0bAhCYysLP3ySpW4ICkoh2Lv48CZ+9rXKpVK60Si4fq9FOQo9fPYZ+WosOr/LmDf6Xsmr/12yxroHFTDon40btwYjRo1wqZNmzBixAjBOlWqVMEbb7yBo0ePokuXLkhJScHp06fx5Zdf4v79+4iOjsYPP/wANzc3XLp0CZMnT8bhw4f1rmFY7+bNm3jvvfdw+PBh/Pbbb+jcuTPGjRsHABZ5DZ06dQpZWVmoVasWpkyZgvfffx8tWrRAdnY23n33Xbz55pto166dyfYLFy5Eq1at8P777+PRo0fo2bMn2rdvrz1/9epV/PLLL3BxccGRI0ewc+dO/P7773B1dcWMGTMQHR2NadOmYdWqVfj8888REBAAlUqFzNdPdp988gn69++Pfv364datWxgyZAj27NmDuLg4ZGRkYPfu3RaPNSkpCefOncOAAQO0ZWFhYRg6dCimTp2Kbdu2ITIyEj/++KNg++3bt6NGjRomBcndu3cRGRkJqVSKwYMHC65SC0KZNsCXRDIySO1kCGO0arCGMOnShQTK5cu5qyAXF+Crrwp/bY5pDAWJufKC8uGHH2L48OHo27evyTqRkZHYtm0bunTpgpiYGHTu3BkuLi44duwY7t+/jyFDhgCgjZlKpRLPnz/Xa29YD4C2XosWLbBs2TJkZmaiVatWaN26tcl+TJ48GY6OjnBzc8Pq1ashlUpx5swZva0AGRkZSEhIyFOYnD59WvtAW7VqVbRp00bvfFhYGFxcXAAAJ0+eRLdu3eDm5gYA6N+/PxYvXgwAaN26NZYsWYLQ0FAEBwfDz88P6enpuHr1Kvr06QMAqFevHho0aIALFy6gfv36SEhIwIIFC9CyZUu89dZbJvu4fv16bN68GRKJBGPGjEHbtm2151xcXNCsWTMcOHAAcXFxWLRokaAwOXPmDFauXIkNGzYI3qNRo0Y4cuQI3N3d8eDBA4wcORK+vr569yooXJiUMLp2zXUH1iAWAwEBgIeHde4hFgN//w2sWgVs3gxUrAhMmwZ07myd65dFOgfprx4yMjLg6uqqV2fUwv14lpJp2BQ+5Z2x5P32RuUFpU6dOujYsaPJJ1sACA0NxZIlS5CSkoJt27Zh9uzZ2nMdOnRAVFQUAP1xJCQk6F1Dt54uXbt2RbNmzXD8+HF89913+OOPP7B8+XLBfmhsJhrS09MhEomwZcsWq7pdawSJOWbPno3r16/j1KlTmDJlCkaOHIlu3bqZrF+9enXExMTg1KlTOHr0KP7zn/9g165dggEZNTYTU0RGRmLKlCnaVYUhFy9exKxZsxAdHY06deoIXkMjIDV9CwkJwblz56wiTPhuqhKGjw/www+0AnF3p6NSJeC336x7HxcXYOZMIC6OVGtckBQ9w99pAEcHfdWXo4MEw99pYPV7ffDBB/jtt99MupI6OzujS5cuWLFiBdLT0xEUFAQAaNeuHY4dO4abN29q6166dMmofV717t27Bx8fH/Tu3RsTJ07E5cuXLe63m5sbAgMDsX79em1ZYmIinj17lme7li1bYtu2bdr6p06dMlm3TZs22LNnD9LT08EYw5YtW7ST7e3bt+Hv748RI0YgPDwcly9fhpubGxo0aKC9fkJCAq5du4ZmzZrhyZMnkEgkCAkJwaxZs/DixQs9W0d+aNWqFcaNG6e32tNw6dIlzJo1C6tWrUKjRo1MXuPp06dgr1UbqampOH78OOrXr1+g/hjCVyYlkMGDgbAw4NAhoFw5oFMnwEYOKBwrojGyF6U3l4ZKlSohIiLCpDoEoCfhIUOGYMqUKdqyWrVqYdmyZZgzZw4UCgWysrIQFBSEJk2a6LU1rJeTk4PmzZujSZMm2LNnD3bt2qXNdKq76rGE5cuXY8mSJejZsycAiiO1aNEi+Pj4mGwzZ84cTJ8+Hbt27UK1atXQpEkTvad0XTp27Ijr169j4MCBAMjONGHCBADAV199hXv37kEikcDDw0PrMbZ8+XLMmzcPGzduhFQqRVRUFLy8vHDkyBF89Vo/rFarMXbsWPj6+uZrvBpEIhFGjRoleG7BggVQKBSYN2+etiwqKgr+/v5YuXIlKlasiEGDBmH//v3YtGkTpFIpVCoVevXqhZCQkAL1x6h/jAlp4Es3WVlZ2kQy5uL/x8XFITAwsJh6VnTwcRQ/V69eRYMGwqsKITVXSaSkjEOhUEAqlUIqleLp06fo27cvNm7ciDp16pSYMZjD2uMQ+v7mNXfy51kOh1PquXv3LmbMmAHGGJRKJSZNmmTSrsApGFyYcDicUk/9+vWxY8cOW3ejVMMN8BwOh8MpNFyYcDhFSBk0SXJKAQX53nJhwuEUEU5OTkhOTuYChVOiYIwhOTlZMK5XXnCbCYdTRFSrVg0PHz4U3AORnZ1tFBurJFIaxlEaxgBYdxxOTk6oVq1avtpwYcLhFBEODg6oXbu24Lm4uDg0bdq0mHtkfUrDOErDGADbj4OruTgcDodTaLgw4eSJWi0cWJLD4XB04cKEI8iNG0BwMIWc9/AAZs0yDn3P4XA4GrjNhGOEXA60bw88f06rkvR0iiAMAEuW2LZvHA7HPuErE44RO3cCCoW+eksuB1av5iovDocjDBcmHCOePRNO2yuXc1UXh8MRhgsTjhGhoZRXXheRCGjZkoe653A4wnBhUga5eRNYswb49VdKA2yIvz8wfTol4HJ0BNzcgPLlgTwS83E4nDIOf84sY3z5JbBgAf0vlQIffAAcOwbUrUtZFb28gAYNgM8+AwYMAPbvB7y9gchIoBSkfOBwOEUEFyZliNu3SZAoFLllIhHQqxeQlET/K5VAw4aUqrdBAzo4HA7HHFzNVYY4cAAQG3zijAG3bgFpacCrV2Rkv3gRGD266PuTlET3qVoVCAgAXqfQ5nA4JRC+MilDeHsDEon5ejk5wO7dtEopKoO7QgG0aAEkJtJ9Hj8Ghg4F1q0Dhg0rmntyOJyig69MyhA9epBRXXd1IpPZxkNr61YgJYUEiQa5HJgzp/j7wuFwCg8XJmUIJyfgn3+Adu1IoLi4AGPGUMgUXWQyICKiaIXM7dskPAxJTCy6e3I4nKKDq7nKGG+8ARw9SpsPxWIyukdEAAMH0ipBqQSCgoDoaGDDBmDzZsDXF5gyhewa1qJNGxJm6en65c2aWe8eHA6n+ODCpIyiazsJDSVj+OXLtJ+kVi0gLAw4fpz2oYjFJFQ2bwa6dbPO/Tt3pkCSR47QPWQyOqKjrXN9DodTvHBhwgFAqq7mzen/Y8dyBQlAYejlcmDiRODOHevcTySiGGDbtgG7dgHVqwNjxwI1aljn+hwOp3jhNhOOEefO6RvGNdy7Jxyzy5DHj4HBg8l7zM8P2LhRuJ5EAvTtC/z3v8DChVyQcDglGS5MyhhPnwLDh9NEX7s2qZUMIwE3aGBslAfIdmLOKK9QUAyvzZuBFy8odMvEiflTX714AcTE0I58HqWYwykZ2IUw+fvvv9GrVy9EREQgPDwc+/fvBwDcuXMHAwYMQNeuXTFgwADcvXtX2yavcxxhlEqgbVvg999pwr57l2JwGeYoCQmhFYWTU26ZiwsQFWUcANKQ7duBly+NXX41IVzMsXYtbWIcMgTo2JGcAVJSLGvL4XBsh82FCWMM06dPR1RUFHbs2IGoqCjMmDEDarUa8+fPx+DBg7Fv3z4MHjwY8+bN07bL6xxHmH37aGWiq6rKyKB4XWp1bplYTIbxWbOAxo2BTp1oX4glmwnv39cP16Lh+XPzba9fB6ZOpfavXlHf4uOByZPNt+VwOLbF5sIEAMRiMdLS0gAAaWlpqFixIlJSUnDlyhX06NEDANCjRw9cuXIFL168QHJysslzHNM8eCBsC5HLgaws/TI3N2DePPLwOnQI6NrVsnt06ECRhg2xxK142zbj/mVnkyDjcDj2jc29uUQiEb7++mu8//77cHFxQUZGBtavX4/ExET4+vpC8tqHVSKRoGLFikhMTARjzOQ5Ly8vi+8dHx9vUb24uLj8D8wOKV/+X6jV9QHox1SpWlWBK1f+tco9HByAdu1q49ixcsjMlEAmU0MqZZgy5Qbi4gR2Kerw9GlFiERVYfiMIxYrERd3Ufu6tHwecXFxSE2V4JdffBEb6446dTIxYkQSatbMMt/YjigNn0dpGANg43EwG5OTk8NGjBjBYmNjGWOMxcbGso4dO7LTp0+zbt266dV95513WHx8PLt8+bLJc5agUChYbGwsUygUZutq+lXS0Yxj/HjGXF0ZE4kYc3Sk/48ds/w6ajVjd+8ylpKSd509exibNImxRYsYe/zYsms/fMiYiwtjZHanw9mZsY8/Nh5HSSc2NpalpDBWtSp9DgBjEgljbm6MXbpk695ZTmn4PErDGBgrnnHkNXfafGVy9epVPH36FIGBgQCAwMBAODs7w9HREUlJSVCpVJBIJFCpVHj69CkqV64MxpjJc5y8iY4G+venPR4VKpBnV/XqlrU9fhwYNIjsH2o17ZzfuJHifekiEtGmx7Cw/PWtalXgjz+oT5oUwZGRwOLF+btOSeGHH8gRQqNiVKnITjRnDn0+HE5JwuY2k0qVKuHJkye4ffs2ACAhIQHJycmoWbMmGjRogJiYGABATEwMGjRoAC8vL3h7e5s8x8kbkYgM6v/5D01algqSlBQSDg8eAJmZNAHu3ElhVqxJWBjF5zp/Hnj0iLJBCtlgSgOnT9N7qQtjNHYOp6Rh85WJj48PPvvsM0yZMgWi136nixcvhqenJz777DPMnDkT0dHR8PDwwNKlS7Xt8jrHsT5btxrv+VAogJ9+Ar791rzLcH6QSCiGWGmnRQvaT6MrUEQiik+WnU2bRCtXJmcIDsfesbkwAYDw8HCEh4cbldetWxebN28WbJPXOY71UShIDWOIUklCxprCpKzw3nvA11/T+5qdTULUyYmETIUK9L4qlZRaeelS/h5z7Bubq7k4JYOePY3LpFJyGTbM3sixDE9P4MIF4MMPSYAMHUqRmpcupcyX6ekkxL/5Bvj+e1v3lsPJGz4NcCyiRg1gzRp6cvbwINVL7dp8kissPj4kPM6cIWeG/fuN7ShyObB6tU26x+FYDBcmHMjlwKJFpKsPCaEJTYjRo8ko/t//Anv2ANeukU6fYz0yM4XjkQlFFeBw7Am7sJlwbIdaTd5dly7lTlgnT1KMrOHDjet7eQG9ehVvH8sSQ4cCO3bkhv8HyPVa6LPgcOwJvjIp4/z9N3Dliv6Tr1wOTJtGxt9//6WQ8pziISwMGD8+V53o7EwBL6dNs3XPOJy84SuTMs7Vq8Lxup49IxWWQkHng4OBLVsAd/fi72NZQiQCli8no/y5c0C9ekDDhrbuFYdjHi5MyjgBAfopfHXRjfR75Ag9Mf/6q3G9e/eAv/4iY3JYmHAuFE7+qFaNDg6npMDVXGWctm2Bdu0oXwlAT8aafOy6ZGVRwivdUPUAeSLVr09h4ocOpfzxPLUMh1P24CuTMo5IRLuwf/qJkmZVqkQhVlauNK6rCb+o4fp1Snqla2/JyADGjAEOHiz6vnM4HPuBCxMOHBzI7Xf0aHqdmEg7sw3rhIXpq8T27DFeqahUZNTnu+I5nLIFV3NxjKhcmfaSuLiQR5GrKxmBf/hBv165csI54Z2dS5cg0YSM4XA4puHChCNIv36U4nfbNgo9f/48Gdh16d3bWJi4uAATJhRfP4uSEyeARo3IfuTtTZGWuVDhcIThwoRjEldXoHNnoGlT4ZVGuXKk0mrUiISKkxMwalTpyD/y4AEQGkp7cBijEPxz5wI//mjrnnE49gm3mXAKRUAAEB9PgQmdnEqPW/CGDUBOjn6ZXA58+SUJTA6How9fmXAKRXIyhUhv3Bho04bynpQGnj6lsPCGpKQUf184nJIAX5lwCkxODgmQe/do4r1/Hxg2DEhNLflP7+Hh5ISgGyNLKgV69LBdnzgce4avTDgFJiaG3Ih1n+DlckoHLIRcDhw9SvtT7J3QUKBPH3IocHSkMDLVq9MmTQ6HYwxfmXAKzK1b+k/uGk9PfxIAACAASURBVJ48Md5nsmkTZRaUSGhFExhIwqhcueLrb34QiWhlMnky8M8/QM2aQPfupccmxOFYGy5MOAXG0VHYVVYm0xck9+7RhkjdpE9nzgCTJgE//1z0/SwMgYF0cDicvOFqLk6BycoSdhnOztYXMtu2Ge+Uz86mKMQcDqd0wIUJp8D4+9NeFEOqVNEXMioVCR5DDAUMh8MpuXBhwikw3boJCxNDA7yzs3B7U6HvORxOyYMLE06BuXcPSEoyLv/uO/3XCQnC7XVtKBwOp2TDhQmnwJgKm3Lhgr7N5MaN4ukPh8OxHVyYcArMy5emz/GAiBxO2YILE06BGTlSuLx8eUCs883q1Uu4npub9fvE4XBsAxcmnALTrRvQsaN+mURi7PI7fDhQtap+mUgEfPtt0faPw+EUH1yYcAqMSAQcOgTs2AH07QtMnQrcuUPxur7+Gmjdmsrj4igdsG7uE5kMqFDBdn3ncDjWhe+A5xQKsZiCIoaH02u1GnjzTeDqVbKbnD5NmxZlMspYqCErKzdUyfHjgK8v0KpV6crQyOGUJbgw4ViVLVsooZQuajWgUBjXvXEjd4OjSATUqUOBIPmKhcMpediFmisrKwvz589HaGgoevbsiU8//RQAcOfOHQwYMABdu3bFgAEDcPfuXW2bvM5xCo9CQWlq27YFIiNpBWEJv/9u+T0Yo6CP2dm0Url6lULYczickoddrEyWLVsGR0dH7Nu3DyKRCM+fPwcAzJ8/H4MHD0ZERAR27NiBefPm4aeffjJ7jlM4GANCQoBz53I3Fu7fD3z1FbBvH3DgAODpCUybRqoqXdWUl5fp6zo55a5QpFJ9tZeGffusNw4Oh1N82HxlkpGRge3bt2PKlCkQvZ6VKlSogOTkZFy5cgU9Xmcj6tGjB65cuYIXL17keY5TeP7+G7h4UX+HulwOTJwI7NpFYecfPQJmzzbO7zFtmrDdo3lzCrPSvDklmGrRQvjefH8Kh1MysfnK5MGDB/D09MSaNWtw+vRpuLq6YsqUKXBycoKvry8krwM4SSQSVKxYEYmJiWCMmTznldejMcciLl82zn8OGAdm1OREb9EC2L2bPLaGDQOiooCZM3NzmlSqBPz5J/2dO5fafvEFcPKk8T2kNv9GcjicgmDzn65KpcKDBw/QsGFDzJgxAxcvXsT48eOxcuXKIr93fHy8RfXi4uKKuCfFg6XjcHR0h1hcF4BhJEYGQH/Z8fIlQ48eaigUYshkDJ99xvDFF3fQvHlFnD/vBhcXNfr3f4yHD5/h0aPcds+f+wKoanQ9kUiN1q3TcP68O8qVU2LkyET06/dcb7VT1j4PeyevcSgUIjx5IkOlStlwcrLfZWdZ+CyKHGZjkpOTWcOGDZlardaWvfPOO+zixYssMDCQKZVKxhhjSqWSBQYGsuTkZPb8+XOT5yxBoVCw2NhYplAozNaNjY0twKjsj/yMQ61mrF07xlxcGKP1BWNOTozJZLmvNYdIZFwmkTAmFue+dnVlbNky/XssXcqYg4NxW8NrurgwtmaN8ThUKsZ27mTsgw/o2k+fWuNdKj7Kwvdq8WL6/Nzc6G9UVDF2LB+Uhc/CWuQ1d9rcZuLl5YVWrVrh+PHjAMhLKzk5GbVq1UKDBg0QExMDAIiJiUGDBg3g5eUFb29vk+c4hUckAg4epECObdpQOJSDB4GBA/XDpIjFwjYOlUpfJZaRQeowXSIijEPQSyR06F5TLgcWLdKvp1ZT+0GDgNWrgU8/Bd54w9glmWM7duygz00uB9LT6e+CBaQO5ZRSilyUWcD9+/fZ0KFDWY8ePVivXr3Y4cOHGWOM3bp1i/Xt25eFhoayvn37soSEBG2bvM6Zg69M8k9ODmPOzsIrCUsOkYixLl1odePjw9jy5YytXEmrE83h7S3c1tFRfxx799LTruH1O3Uy7velS7TKcnBgrHJlxtauLdTbYDVK+/eqSxfhz7Jbt2LuoAWU9s/CmuQ1d9rcZgIA1atXx88CycDr1q2LzZs3C7bJ6xzH+nz5ZeHyj4jFwF9/0f/PnpEhvnZtep2TQ6uh9HR992GAytu21b/WsWNUVxfNbntdnj8H2rcHXr2i14mJFPLFwYFy0luLnBzgjz/IC87PD3j3XcDb23rXLy4yM4HffgPOnAGaNiVnCnf3gl1LKLOm5h6c0oldCBOO/cEYsHcvqSt8fYU9r/LC1ZVUG87OdC3DHfAKBW1S1L1fVhZNXhJJbltHR2DtWv221aqRcDL0LitfXv/1zz/Thkhd5HJS31kiTDRC4tAhoG5dYNQoSgg2YQJw/jxQsSIwfz6wcSN5wGVkUJ8XLwZOnABiY0nA1K9PEZbtWcC8ekVeeY8e0ThcXOgB4ty5gkUkePddaiuX55a5upqONM0p+eRbmKjVaiQkJIAxhrp162rdczmli+HDKaZWRgbF1crP/g+xmDY5HjhAE+6hQ8aRhE2RlkYhVQ4dIlfigQOBcuX06/j4COePN3yKfvxYOIzL6z2xeZKTA3TuTEIjI4NWTIsX00ZLzQSZmAh88AGNV/MknplJ/7dpQ4JMI2C+/BI4ezZ3NWZvREcD9+/nvl9yOY116VJg2bL8X+/dd4HDh+lzd3Sk96RfP2DIEGv2mmNPmDXAa0KbAMCjR48QERGB/v37Y8CAAejRowfu379fpB3kFD+xscDWrTQRAjQp5uToG981GO4LkUrJON62LT21T5hgrJIyx86dFJZl82Z6ujVEqAygyVCX0FDjHPUSCQkJc2zblitIAJpkX73Sf9IG6H0xVOmo1UBKSm7bzEx6PXu2+fvair17jQVvdjY9EBQEiYRWhv/+S3+vXAF+/FH4O8QpHZj9aP/880/t/0uXLkXLli0RFxeH2NhYtG/fHlFRUUXaQU7xc+IEeWQZolYDAwaQOqlWLeC//wU2bKAnbw8PSnbVqJFxnhJfX8vvLZEAq1YB166RjaVHD1K13boFfP898M8/HqhRg1ZLhhjeJyQE6NmTBIpYTP3z9qbw+OY4dChXGFgDtRo4ckT4XEYGCchnz6x3v/zi52fsXScSAfXqFe66derQZ2CvKzKO9TCr5mI6+o1z585h7969EL9+vPjwww8RGhpadL3jFCtqNU0gNWvSZG34xF2+PLBpk3G4lIgIEkAVKwIBAcbn+/enp1ND1ZREYiy0pFL9+8rlpGfPzNQ81daBp6exLQQAGjTQfy0SkUH5r7/I9uHnB4wda7xaEaJePRKSlhqMRaJcVaDQuABS2xmyZg0wYwaNOzsbGDoUWLfOeGLPL2p1/lYBn3xC75WuAHV2tu/VFMe+sOjr9uDBA9y/fx9isRhOTk7acmdnZ8gN1/2cEsetW5Qx0cGB7A7//EN2CQeH3DouLsC8ecJxtzw8gLAwirslEtFEumUL2V1mzgT8/SlRlmY1IRKRHl3nqwTAWJBoSEkhFYxcDsjlEjx5IjyOEyeMy379FejdmybK2bOBESNMexrpMnIkTaaG+2qEcHWlp+8KFeg9MGXcN7TpnDxJgkQuJxWaQkH9XLPGfP9MsX8/7bmRSCi8/y+/WNbOz49sVZ070+qtXTtScTVvXvC+cMoWZlcmmZmZCA0N1a5QLly4gKCgIADAjRs34JsfHQbHblCpcg3H7dqRUVqtpifT6GgyoGZlUWBHLy8SCsOHA7dv0yRToQLQvTtNgAsXAtu3kwCaPZva//VXbnyvr78mnfz162SLqFaNjuXL9fskFEVYCCHjO0C2mcxM8qyqXJkm6bFj9VcXu3eTUBw2jFyMq1cnQWho+/H2JhfZ6dNpkq1enYSmkKpKLCZVnAZTYfRv3tR//cMPxisfuZxWJlOmCF8jLy5fpnQBug4C48bRZxUWZr598+a57tscTn4xK0yuXbtmurFUis8++8ya/eFYkbQ02oW8ZQsgk9XHggVAs2bk4nriBK0MOnemCU13gpbLyd01LU3/aXzxYgrQKBLR5OvsTE/l9++TcEpIAPr0IaGg6/2VlUWCKCqKBFP58vQkbqlNwpTayBClkuwimrH4+hqrwzIzySazejW9lkpJCJ48SWo6XerWJfWYhjlzaNVm2BcfH7LnHDhAbsBVqgj3r1o1/ddqtekIAgVh1SrjVZcmGKclwoTDKQyF2mdSr1491CushY5TJKjVwFtvkTcNTTCuePddEg5yOZ2Xyyl/iNCTvkJBk7NGNXXjBq1AdD1+0tOBp0/12wlFGwaABw+AMWNIgDg45C89b34mV92xJCUJ1zH0WsrMpM2MAvtmceECCZCaNUlF9vXX+h5dzs70/k6cSIJLIjGtDjMUniNGAP/7n/H1Ro0yPb68SEwUfq8MP6O8YIzGIZPxFMqc/FFoR73169dbox8cK3P4MAkAQ2N2err+hJuTYzwBiUSk8tD1mNq71/gpOr+5RzSTqSa7oqWIRGR3kUoBJycVPDzyd19dhAzbSiUQE0Ori379gFmzaLU1ahSpAKdNo/0R77wD/PQTebJJpWQDCQ2l/Sya8ahUpgXq9ev6rzt2JLWgkxPtpXFyItXh1KkFG1ufPsbOBY6OpPqyhO3bgRo1yD5WubLl9hYOB7CCMDl79qw1+sGxMjdvmrYtCKHZbe7mRlkUN27UP1++fOE9jArDtWs0aR86dAGvTXYFQiwWdiuWy4EPPySV4IoVpK76/XcqVyhI5ffgAeVluX0bePGCHAOePrVcqAqtWObMAR4+pIn82jXaW6Pr+JAfhg6lzZJOTnQvR0fySpsxw3zbM2dIYD58SN+bpCSytxw8WLC+cMoehRYm3333nTX6wbEyLVtaXlcsBiZNIqNzjRpkJG/USL9Oz562i6vk5kb2Bl9fIDg4IF9qG0NycowN/VIprX50N2lmZhqPNyeHHBJEotywL48fW35vT0/hcm9vUknWrGn5tYQQiWj1o1CQQMjKohWWJQ8VX38t7Axg6CTB4ZiC70ctpQQE0IY/jdpDLGaQyYSfjtVqCpnx+DHtVH7rLcBQe3n0qLErr2mEZi/2+tB9bR4nJ1LN+T06iG0sHAuUs3E9PgtSGOqSVBbfw3ByVSotcxcGjAVCuXKASHC8xiQnW3aPgvL++7R60iUtjVRn5khKEl5hFUZwc8oWZoWJWq3Gd999hwkTJmDFihVITU3VOz927Ngi6xyncGzaROqqfv2A3r2f4aefaHL2xzV8iP9gBDbCGcIuVR98AHpUPXoU+PdfJCQAyhxTQiIXB2ShGh5iHcbhIariPJqhDzSBuXQtuiIADK5IRzCOwB/kNSiVAk2akNBzdaWNiLdYLfyFtxGOXZiFpVDACe9hHZwhhxMy4Y5XcIPcxD2EsEyQGRqgXVyAGR9lkSvYO+8AH3+MwW0SUB4pkCB3uSNFFkQwtoQXdX77X38VLj91ynzb/v1pfLo4O1M5h2MR5uLXR0VFscjISPbjjz+y999/n3Xs2JHdvHlTez4gIMCK0fKLh7Kcz2RT4y+YHM4sE47sFVzZUkxjIqiM8k5EYCtTO7toUyy+qtuUVcddo3qVJU8YoGZueMWkyGJj8C27jRosBxJtpQw4sZH4njXFefYxlrOh+Ik5IZ2NwnqWDmeWBhcmhyP7B23ZlzMoW6ZCQdkU19f8nKkNbqp+fVxCY7YKk9gW9GZBOGUil4rawjLGpFL9DI9ffcVY/fqUNdLTk7Fli7OZumYtvVSQGWJXdg1+bBg2shq4yzriEHsbewWvL5MZfx7WxFS+GZHIfNusLMZCQhir7ZzIujr8xfxcHrBWrRjLyMi7XWn4fZSGMTBm+3wmZoVJx44dWVJSkvb15s2bWbt27djly5cZY4w1a9bMil0tHsqsMLl1i6mdnPRmmj0IZS5I15t8KuIJy4ZUr54aYCfRirniFU28yGIuSGeH8BZLQTmWAzHLgpRlix2YSmBGk0PGMuDMFJCxV3BjySgnKCTYG28w9ugR5XjdsYMpHFyM6mnr6hxTEZWH4MgtF0HFpFAITrqjRjFWsyZjwcGMHTrEGEtPZ2zlSqbs+g5TT57C2PTpFmUCW4ppzAlyo1Pt2xt8Hlamd2/hLjVsaEFjtZqpP/yIqRwcmcKpHFPKnJh61CiS6HlQGn4fpWEMjNlemJjdZ5KWlqaXDrdv377w9PTE2LFjsWrVKoi4M3rJ4eBBo8/rbRxEXdzCZTSBRi00Dt9CCn0rtQhAK5zGjxiJbYhEZTzBeKzDG7ilU0stbC4B4IRsrdLJEdmC1UQAuaFVraotk0FYWWVYtgDzsQ4TkAFXnbMMH+MrvIQnduMd1MADTMVyjMSPUMJRpzVDz9ZP8cOoW4DfP7Tdvdk7QKsOwJ07kMjlwKGDFm94GY0NWCabgxyVs7aJiwuwZIlFzQvMxo20cTItLbdMKgX27LGg8datEH23HqKcLDjmvDYg/f47hX+2ZiYxTqnFrDCpWbMmLl68iMDAQG1ZSEgIZDIZJk6ciOz8bBjg2BYfH6O4IRKocdSxK5pUeIwHj0QQi4GQyleBR8bNRQD64Q/0wx/GJ/OJpZ4fZFkxbf3Q4IpM3ERd9ME2lMcLvIAX+oq24WO23KhtdTzAWKxHPN6EJ1IxEauxIG4JECohVygnp9xIlxoXJ1ObRwTwRjLO9V2C+TcG43C8D96omIrPVpRDm/YmtsYXhidPgIsXgTfegHudOkj89wXOv/cNnM4chcK/GRp99yHK16hq/jo//GC8q1Iup803XJhwLMCsMBk+fDhu3rypJ0wAIDg4GF9//TXWGqbB49gv3bvTI3JGRq5Lk4sLPIdH4n72e2C7dgGenhC1aQP8ZN1bF2b9amnbyniKE2iXW+DiCiH/gtY4g0toBhXEkGjWSDmvD0ATUbIQPQaq/7YUG5xXAYpMINEBGOkE1D9p7HNtiidPaPt93boUuTE+nkL7xsZS2eLFFL5g1Sp6QFAqgbAwuMbGon1yMgnFc0eBDt9TfHtzMeANg5NpsPfkd9nZlADn+nUgMJB2kfKkKbahyJVsdkiZtZkwxlhCAmOdOpFV2c2NsWnTGKtblzEHB33rs4FthR+FPEQixnr0MP48hJg9mzFHR8bKlaPP4e236bPSvZ5MxphEYnwPqb6ti0kkjI0YYf4L8uef9LnrtnV1ZezXXy37XtmClBTG/PzovdF8n9u3J2+CfFDqfuNFSKFsJpbscG/RooVVBBunGKheHRg8mJ7efHxoJ+DTp/pqHLmcduW1bEmhdV1daSv1hQu263dJhzFaVZhj/37gP/8hFZtm88uhQ8b2GiH1MmPGOzJVKopiaY5u3Si2y8KFpOLLzqaNK4MGmW9bnNy6RauQJk0oVn9CQu57k55OW/l/+okCwXGKFbPCZNiwYfD29oaDgwMYY0bnRSIRDh8+XBR941gbxmgr+7FjJDBEIgqLK/C5IiODVCk7d1JWpzNnuDApLIbZu4RYt854K3pBwwgD9Bm/+aZxeWws8PHHZG/x86Ot7nPm0AajhAQKQFa+fMHva22UShJsf/6Za88SCiednU35gbkwKXbMCpMuXbrg4sWL6NSpE3r16oWmTZsWR784RcHJkxQCV2MPYMy0YdnZGWjfvvj6VhrRneycnUk4m+PWLfN1zKGxoYjFdF/DNBE3blAES83qJjaWchwfP06r0YCAwvfB2kRHUywbXacIUzx/Xjx94uhh1lL1zTffICYmBvXq1cPChQsRFhaGtWvXIjExsTj6xxFCqaR4J506URrBI0dIKKxbR2V9+tDqw5ALF0z/EDXbn2UyihBozQToVkRgDVV4TBmZC2PI9fDIbS+RUIAxS4JveXsX/J4ArUQ0qi61mgzSPj4U7bF9ewrCNn68sZpMqaRye2XNGstj3phKKMMpUizKZ+Lp6YkhQ4ZgyJAhuH37Nn799VeEhITg559/RnOe17P4iYwkPbpmhbF3L3n83LqlX7Z+PYWC1aBSCUf9E4tJ3bVzJ2WI2rOH1Fp2SJHsalKp9JO4a3ByIuGrKS9fnkIFG3Xqda809RwcSLhrVn0qFbWbPZtUMIbcvw/ExZFdavBgCmGTn5DPuhiOYds28vrS9Of0adMpLS9fFi6/dYvONWxIuQBMcfMmeZ01bkzfR2uSnyBh1r43xyIsTo7FGMM///yD7du349SpUwgPD0f16tWLsm9lk1evKBmJuzsQHGz81BwXB/z9t77ramYmcOmSfj1NTPVBg/SfkIUmTcaA776jeOMeHvbvDloUCNmNDN2DU1KE9fSGbXUFiQalkozrhu0++YRUOFIpCZDGjQsuSEyhO468ciOr1SQ4Ll0i+46/PzByJPB//0cr1pwcci/ftEnflVilolSa27blCtJevSjbmLW+S25uwMuXltX9v/8zjlTKKXLMCpPr169j+/bt2LNnD+rWrYtevXph0aJFcLI8hCzHUrZto6QUUilNNB4eJFh0s1kaCo28ePmStkOXK0evGzXK3WdiyPbtNJm8emV6zwGHJk6Nt5NIRILaUgN5pUr6r/fvJ/WNrsrJlitCsZiM8YzR2OrVAx49oj0rmvSUu3fTRkZdldh//wvs2KEft3/nTiovaNpIQ/r1oz01lgjaV6+sc09OvjCrFI6IiMDhw4fRv39/hIWFISsrCzExMdiyZYv24FiB1FRSScnl9GNIS6M8rAMH6tdr2NDya7q60gpHQ3AwJYF3ds4tc3SkiUP3R5rX0ysHmDwZaNqUbFOVK1vWxsUFmDdPv2zlyvylnCxqlMrcVRZjpLYyXJ3J5caZ0zZuNH5AycgwrgcAd+8CEyYALVqQ59jDh5b1bfZsCrOjse0ZppTk2Byzj6CaPSSnXsexNnQPFolE6Nu3bxF0rYyxf7/xikCtJl31ixeAJj5ay5aUTu/EidwfupMTGScN1S3e3vpGZJGIVFlr1lDcJR8fuu5vvwn3SfMELhbnbmUr64jFQIcO9NnUqUOrP6EJ0dmZBPe5czQJLloERETo1zl/vnj6bG1cXWml8vAhRAqF6YndsPzOHfIUy8ggwXXxIn33Ll3Si8cmiI8PcPUqqdguXQJat9a3B+pSFtW0doBZYfLtt99i7dq1uHnzJho2bIjx48dDJpT3lFM4nJ2NE2ho0M3jKhKRr/26dZTAonx52kewdq3x0+G9e1Sm+6NOTiZBdPUqTYL165vuk+ap2do6/JKMWk2TWHo6PSWbUgmGhpLu/t492hjq4UH2rg8+AM6fR6MKFWg1WtJwdaWHFHd3QK1GM5GIHEJcXPRXMS4ur5Pi6LB4ca4gAci2kp4OfPUV5Uq25N66+0dGjBBeReuuxjnFhllh8sUXXyA+Ph4dOnTA/v378fLlS3z66afF0beyRWio8cQkkwFduxr/OGQyUrVMnkyvN28Gvv3W+Jpisf41c3JoVfP4Men55fKiT/9XGklPp795xe86fpw845RKeq8HDaLP6XVbp4cPTT882BPOzvSkr1mVdutG43iNGAC2bDFehTg7A82bk/fauXNAUBBw9qzx5J+dTeWGZGTQ6vniRVqN9+tHKlldevQgW58hkyble5gcK2AuFku7du20+UweP37MOnXqZOVoL7msXr2a+fn5sevXrzPGGDt//jzr2bMnCw0NZSNHjmTPnz/X1s3rnDnsNjbXxYuM1aunTUjF3nmHsdRU8+3S043jNAGMBQZSXpDmzVlqy5aMzZ/PmLu77eNUlcVDKhX+jOz9CAyk2F516jA2aBBjVapY1s7RkTEvL4rvBdDfcuWMY4fJZIxNnar/fX7+nBLLaNq6uTHWqBF9z3V59ozuoXu9evUYk8vz9bPjsbksp1DJsQwzKbZo0cJ6PdMhPj6ejR49mnXq1Ildv36dqVQqFhISws6ePcsYY+ybb75hM2fOZIyxPM9Zgt0KE8YYU6sZe/CAflCWcuKEcGBGiUQ//Z5Mph/QMa9DJKLgebaezPhhu0PzUFPQ9joZKRlA13Jyyv0OOjgw5u1NydB0+eQT4/s6OzO2fLl+vX//NRZOzs6MPXmSr58cFyaWk9fcadabS6VS4dSpUzh58iROnjwJpVKp9/qkJUHkzJCdnY3PP/8cn+mEfYiPj4ejoyOCgoIAAAMHDsTevXvNnivxiERAtWr52wn977/Cu7VVKv0d79nZlufl8PSkvQZiMakXPD0t7w/HMuxRzeXkRGotmYzicxXG24wx/dfZ2eSePnYsGdDff5/UWIY71vfuNb5vZqZxlq+RI43VZpmZtHfn1CkK+PjvvwXvPydfmLWZeHt7Y/bs2drXnp6eeq9FIhH++uuvQnVi5cqVCA8PR7Vq1bRliYmJqKLzJfPy8oJarUZqamqe5zzL4qTXtKnlE5NIRMIhK4smDLVaWMCkpOTu9taNYMspHK/jZjGxGCI3N/t7b9VqeggRiyk6rzURi8m1fc2avOvVrk076XWRSCiPiy4XLwq3/9//aM+WSERj6d2bBAvPc1KkmBUmhw4dKtIOnD9/HvHx8fjkk0+K9D5CxBt+YU0QFxdXxD0pJCIR6jVrBre4OEgUCjCRCGqJBGKl0ij8CAOgksnonFIJpZsbpC9fCtaz5zIhrNnW0nb5vQc0n4laDaazuU6TUVL3dXHDAIg0K4KcHJPjsvS9MixXA7jatSsUZn5PLn36wG//fkh0hKzKwQFXQ0ORpdO2qYMDpAKCmOXkQKTzgKTauhV333wTqSEhJu9p979xC7HlOGy+1fns2bNISEhAly5dAABPnjzB6NGjMWzYMDx+/Fhb78WLFxCLxfD09ETlypVNnssPjRs3hqOhh4gBcXFxRlkm7ZK//6ZNYps2QeTtDUmtWuRuaeDWK2IMUp1JzEE3YbhuPTsvE8KabfMzmRfkHoXpa1Fh6XsgCgwk9ZFUCnV2NsQuLoJuzobtxTIZGqWnU0ZEXbZvp3D49+5RXK1Vq0jVNW0aRThu0gSSFSvQ2DBvUs+etO/EzH0lmZmoe/o0BbvUJSUF2LABz44ehU9kJG0QdnSk2GgJCeRFUiIe9wAAIABJREFU1rixiXfB/iiOuSorK8v0Q3iRW2zyia4BvkuXLiYN8KbOWYJdG+CtxZ493HOLH9Y/nJxyDeSOjkzl4MDYu+9a3j44WP97euSIcYZHFxfGzp0z/x1/6y3L7/vuu/pt799nzMcn10HF1ZU8xgICyHvM1ZXODR9OTjElAFsb4G2+MjGFWCxGVFQU5s+fj6ysLFStWhXLli0ze65U8vgxRX51d6dAe7rhUEzx9tsU6kOhyLWJaPJcmEMoGCSHA9D356uvtN8PMUABHQvKl18a79dRKGhVbe66+UmDYRhJeN48iiyhiauWkZFrI9L9jfzxB9lcDKMXcIywO2Gia6Np3rw5du3aJVgvr3Oliu+/p53EEgkZEB0cKH+J4fI7PR147z0KxOfqSrGMTpygpf2OHVA4OsLJw4N2vgvh5wfcvk33aNSo5Ib64BQtQg8j+ckEaWgEFxIIajUFmDSEMcrTc/UqbYisXdtyJwHDjZGHDxv3W2hsGRkUyYALE7PYnTDh6JCURIJEE7FVw+DB+tGDGaM4Uc+e0etXr6jdxYsUqbZ8eShlMv2wLIY8fpz7Y9Jch8OxNk+e6L+OjASuXdP/jru40GpAF7kc6NKFvLw0dsD8RLf28NB/Xbs2BZ00h0RS+IRlZQQuTOyZgweFfzDXr5PxUJOj+6efhAXA999r3YDdgLyFiSZECGB5JFdO6Ucstm5sNsNrffwxhWO5c4f2iDg706rbMIf7ihX0cKS7byo/+3R8fPRfL1hAe1E01xOJcnPLG/Z3xAjL71OG4Y7X9ky5csK+8SIRbS7TkJf7tu6Pw9INixyOBmsH+VSrKUf7qVNks3Bzo9hdv/4KLFxIKqXjx/W/3wDtHTFMOZ0fu961a/qvO3SggKmtWyOnfHmyRb72KNVDJhOOHcYxggsTeyY01DiasJMT0L+/vhG+Xbvi7xuHUxCSkoDq1YGwMAo7P2cOqZLCw4GZM4F33qEHqPPnKZBjrVpA377GwiU/yGTCeYA6dQJOnsSlAweAXbuE0xZnZeX9sMbRwoWJPSOTkcGxZUtSd8lkwIABxhGCx4zJzabI4dgzaWlkH3n5kv6uXElZGhmj14zRpN62La0c7t0jj6rLlwu2g10T8WHQIODzzymh2erV+mpdDboZTTU4OpJDS+fOQI0alH7AEltLWaTIHZPtkBK5zyQ9nbGsLNPnk5MZCwujfQCenox17277PQn84IclR5MmjFWsSIFFq1ZlrGnTwl1PJqMgk5pgpSNGUHRhTTBUFxfG3niDsbQ0xpjOb/zYMf09L2Ix7TnRDZYqFtO1nj4t+t98PrH1PhO+MikpuLrSysQUXl4UCC8zk4zzXbsWX984nMJw+TLw9GmuS7CpmFuWkp2dK1rUatqvkpqa6zEml5OTyQ8/6Ldr35523gcHU/DJ3r1JPaZrq1Grqf369YXrYymEe3OVVm7etHUPOBzLYKxory/kRJCZSbaQKVP0yzt0oH1cGnSCz2pRKIwDUXK4zaTUYhhhlcPh5OLoSOmuzdGmjbGtxsUFeOutIulWSYYLk9JKZGTeajEOpyxhuCdFKgUmTgRUKojT002vjpYsoTBGmv1ejo7kYTZ8eJF2tyTChUlJQeMFo+HiRWDCBODTT8lf35AaNSjukZMT4OgIlZMTPVFxOByK9rBiBeDlhaYhIaTOiokxrufpSYdYnBuzrlYt/qAmABcm9s6NG0CLFhTSoVw5YNgweipq1gxYt442evn4GGehA2ifSu3agCa/Q/Pmxd9/DsceMFx5ZGVRwMpXryBWKimcUN++5AyQkAAcOEBOAfPm0TmNUT87m2wq27bZZhx2DDfA2wq1mnb7/vILCYmJE8m3XpfsbDIIPnuW+2PYvFk45EOfPvrRVzMzgY4dadXCGD01nD5dlCPicEoWQgImLIx+M46OpAlwdDSOHJGRQTlY+vYtvr6WALgwsRUjRtDTTUYGLZ+3bweio/XjAO3fT0JB90tvKsVrZia5O2q8T/78M/dpSgMPp8Lh5I0m6Z5GpWyYix4g+4lh3noOFybFxtWrwM6dZLdo1ox29Wr81xmjVcVHH9EOW42x7+XL/LlNzp5NfvLu7uSFkp/Q4BwOxxih359SKRzHq4zDhUlx8J//UAyinBwSFJoNVYbI5aSn1Tz1vP22cY4FU4mrpFIKlqdWk1rswQO+EuFwigKxmGyUfGOwHtwAX9Q8eUIrhsxMEgwKBamqhJbPOTlAhQq5rytWJCGk69bo4EBeWrreJN7etArR3ZzFBQmHk38M0zQIhblXqynKBEcPLkyKmmPHLFdViUTkAqwhK4sC4em2Z4xCdmdmkufJvXvk3VXUu4g5nLKARELu9O7uFMKofn3heq1a0S74nTtz7SxlHC5Mihqp1LTR3BCJhA4N+/ZRTCFdcnKArVtJmFSoQJ5gmiRZHA6ncCgUFJm7bl1g8mRhDQJAmxlbtSJX/Tp1SINQxuE2k6ImK8uybHUODuTK6+mZW/bvv8J5qZVKoGlT8t5iDGjd2rp95nDKMr/8Qmrjq1dNC5PHj/V/0ytXAiEhlCOljMJXJkVN1arCiX2kUv0d6VWq0JdYF3d309dNSMi1vRw7Zp2+cjicXC/IrCzT6mPDh0O5HNi0qWj7ZedwYVLUtG8P1Kypb9jT6GR1n3qePQMWLdJv27q1ZWEbuL2Ew7Ethqm0yyBcmBQ1IhGFX4iMpFS75ctTnoScHH0VllwOfPedfu6EoCCKTqqbolfXpsLhcOwDJydg9Ghb98KmcGFSHPj4AP/7HwmMFy+Anj2FXQ4ZMza479pFrsBBQaSTHTq0ePrM4XBMU706CRA3N3KCWb+e7JhlGC5MbEG7dsL7QLy9gUqV9MtkMvIqOXuWgs81blw8feRwOKaZMgXYsgX44APg+HGKXHH5MjB4MBAYCMyYATx/buteFivcm8sWVK8OzJwJREWRK6JUSsfGjcIrFl1eviyWLnI4nDyYPj3XCL9kCdk3L1zIjel14QKlC752DfDwsF0/ixEuTGzF/PlAt26Uh7p8ecpNUqMGPeVs3UpfwOHDKYS8LkKuwhwOp3gx9OY6dcr4/JMnlCZi+vTi65cN4cLEVpw/D/TvT7G41Grg5EmgSRNgwwayrTg40Mrlf/8Drl8HfvuNdLM1ati65xwOxxIYo4CuXJhwigyFgqKO6sb3OXECOHo01803O5uOvn1p06PGy4tneONwSg5lKEYeFyZFwT//0IrCzQ14913A31///MGDxuHhTX3pDEOxmNqRy+Fw7A9x2fFx4sLE2sydSyHnMzNpT8jKlbQzNiIit45h0ioOh1M6efHC1j0oNmwuNlNSUvDee++ha9eu6NmzJyZNmoQXrz+ACxcuIDw8HF27dsWoUaOQnJysbZfXOZvx8CHllZbLSVgolSRUxozRX4mEhOR6feji6Ki/U94wHDaHwylZvPmmcPmzZ8ClS8LzQAnF5sJEJBJhzJgx2LdvH3bt2oXq1atj+fLlUKvVmDZtGubNm4d9+/YhKCgIy5cvB4A8z9mU06eFbRoZGZTvffx44JNPKICjkAuwu7u+ustccEgOh2PfLFhATjYXLtCDZU4OpeauXp1CLVWsCPz0k617aR2YnbF37142YsQIdvHiRda9e3dteXJyMmvWrBljjOV5zhIUCgWLjY1lCoXCbN3Y2FjLO3/2LGOurpo8irmHRMKYoyP9LxIx5uDAmFhsXI8f/OBH6TkcHBgLCqLfvocHY25ujPXqxZizs349Z2fGLl+2fJ6xxlxVQPKaO22+MtFFrVZj06ZN6Ny5MxITE1FFk74WgJeXF9RqNVJTU/M8Z1MCA2mHuqNjbpmTE60wNIZ0xujphK86OJzSTU4OJbLLygJevQLS04EdO/Tj7wFkQy0FqxO7MsB/8cUXcHFxwdChQ3HgwIEiv198fLxF9eLi4iy+pjgqClXWroXXgQNQy2TI8PdH+cOHYajUYoDFZUwshvi18FHLZEBODkSMaeuy138Lcw97LuNwSiIMgMjgoZHp/G61ZWo1nj56hIf5mGdMkZ+5ytrYjTBZunQp7t27h3Xr1kEsFqNy5cp4rJMO88WLFxCLxfD09MzzXH5o3LgxHHVXEQLExcUhMDAwf4MJDtb+67hoEXD4sFEVoQlTsEwkgigsjNyJZTKIe/cmt2Mdl2FTk6/F97DzMg6nJGLy+22QLE/k7AzfDz+Eb37nGQMKNFflk6ysLJMP4Xah5lqxYgXi4+PxzTffQPbagN24cWMoFArExsYCAH7//XeEhYWZPWd3vP124XzNGQN276alcHo6LYctTQPM4XDsj4oVKa2EhwepwT//HGjRwta9KjQ2X5ncvHkT3377LWrVqoWBAwcCAKpVq4ZvvvkGUVFRmD9/PrKyslC1alUsW7YMACAWi02esztatKDw8WfPkmAAcnOSGG5c5HA4pZ+AAKBWLeDKFcpzNG6crXtkFWwuTN544w1cv35d8Fzz5s2xa9eufJ+zK0QiYNAgEiYaGCOBwoUJh1P22LePooRnZwOxsaRtOHmyxIdKsgs1V6nm2TPg449zVyUA6UvLUMweDoejg1qdGxYpI4MCuW7ebNs+WQEuTIqaDRv0BQmHw+HokpFBQV5LOFyYFDUPHti6BxwOx14QCpHk4gI0bFj8fbEyXJhYG7mcsihWrw74+VGCHFPohlQxl2GRw+GUTKRS+n07OFBGRheX3HMSCeDqSonwSjg2N8CXKhgD3nqLjGoa1VZCgnBdqRRo04bymPx/e/ceFlW1/gH8ywx3VBCUm2ZeUh+VSoXEe0e8QB4kuyhUYoWlJzU9lSZe8pZyRJ9SMx81tUNHPdKxEo+SKBc1UzEwUjn2UD8ETGZEEZU7DDPv748Vw2wYcGDQmcH38zz70VmzL2ux9+x3r7X3XsvaGujUCcjPf2hZZYw9JLWjo9a+Eb9sGZCQAOTmAmPGAB9/LEZbtXAcTFpTero0kAD6u02Ry4ElS0QncCqVeA/luec4mDDW1pWViSCSnCzeG3N2rmuVUKnE97ppFoSbuVpTfLxhN9vValEjAUTVVy4XzWKMsbYvK0vURNzdgR49RO8WK1bUpfXsCaSkmDqXzcY1k9bUpYvh8yYliSpvSopoM+WOHxl7NJw9W/dqQF4eMHGiaOqu7QAyNxeYNEkMVdG9u6ly2WwcTFrTlCnAO+8Y/jLisGGiJmNjw12kMPaoqP+OmUrVMK2mBvjqK3FO+eMP0YuGm9vDy2MLcDNXa3JxEQeAjY240rCxaXq0xOpqcRCVl/Pb8IyxOtXVwM6dojum0FCga1cxHLgZ42BijJIS4N13RTtnt25AdDQQFiauJHbuBPbuBa5eNXUuGWPmTC4XF5+6rK3FCI3l5cC9e2J432XLgIwM0+TRANzM1VJEwIQJYufWNlGtXi0CyeefA2+8UTdvTAwQEVF3X4T75WKM1fL2FvdN8/PFOaJ2qt/0XV4OxMaKjiLNENdMWiojA7h8WbrDy8uB3btFjUXX66+LGsoHH4iA88orDzevjDHzpVAAffuKG/BlZaJ7+saeCr19++HmrRm4ZtJS167VdSWvSyYDCguB9u3r0hISxI00mUwcJLWdvDHGmFothvOtVVTU+LzGjI30gHEwaakhQ/Q/geXgIO6f1CopAV5+WVxxMMZYS8nlgKenqXPRKPMNc+bO2xtYvFj0s2NlJW6YOTiIZi7dGktysv4aDGOMNYetLRAebupcNIprJsZYsQIIDAT+8x8RVKZPF5076rrPGPOMMaaXk5N436T2FYOdO4HevU2dq0ZxMDHW0KFiaszYsQ1HUJPJ+I13xljTevcG1q8Xw/s+/7zZvw3PzVwPQnq6aAJbs0Y8qZGcLA4EW1txhdGrl6lzyBgzdwoF8OKLwPLlQL9+omNYM8Y1k9a2dq2YKitFDSQqSjwSfPNmXQ3l5k3T5pExZv5u3ZI+Irx+vehdfMgQ0+WpCRxMWpNCAaxcWTd+gVotnh1fs8ak2WKMWaD675pUVIix4s00mHAzV2tKSqoLJIwx1pqsrYF27Uydi0ZxzcRYd+4AiYmAvT1w5oypc8MYa6tsbPjR4DYrLg549dW6TtrqdyPNGGOt5a9/FQNnmSlu5mqpkhLgtddEO2ZJiZgqK02dK8ZYW3XkCHDunKlz0SiumTTH7dvAP/8pXibq3Fn//RF+h4Qx9iBUVIgeNk6dEh3HTpgATJ7csPt6EzGPXFiCjRvFI761T1hYWenv2VOjEY8A13bm2Nh8jDHWXDEx4oJVpQL27ROjtR47ZhZdNnEzlyGKioD335cGhaYChG6vwBxIGGOtRa2uuzdbXg788AMwcybQuTMGDxkC+PsDv/wCfPqpaD2Ry0UPHZcuifFSVq0CZswAvvmm1cdU4pqJIbZtM3UOGGOsIZVKNL0TwQoAfvpJBBS5XDSLAcD588Dw4aKVRKUSvZ1//TWwaxfw/fet1q0910wMYW9v6hwwxph+9Vs/qqvrAkmtsjKgtLRu2IyyMvEqQ2Jiq2WDg4kh3nlHRHVDOTiIf62sGnbyyBhj5qC0tFXfjbPoYJKTk4PQ0FAEBgYiNDQUubm5D2ZDjo6iWqh7k8vBAXj66YbzvveeuEkWHAyEhoq34kNDH0y+GGOspZycgB49Wm99ZMHCw8MpLi6OiIji4uIoPDzcoOUqKyspPT2dKisr7ztvenp63YeqKqL4eKITJ4jUaiKNhmjLFqL+/Yn8/Ij++9/GV3TzJtFbbxEtWkT0+edEonLKE0888fTwJ2trIk9PotJSg86Zhpw70aw1mZHCwkLy9fWlmpoaIiKqqakhX19fun379n2XbXEwaS3nzhHZ2pr+gOKJJ54enenJJ4mefprIw4MoPJzo+vVmn7qaOnda7NNcSqUSHh4ekP/Z9CSXy+Hu7g6lUglXV1eD1pGZmWnQfBcuXGhxPvWytkbfPn3gmJUF2Z+P+WlsbWFVXY36d2YIkKTRn//eL82YZY1Zn775HsU0fVrz72foNpvjQayzpQwp74PYPzBiu/qWvV8aoXF6t2FlBSsiSZqhefnfsmWo0h1L6cYNMbWW1rjQNoXLly/TxIkTJWnPPfccZWZm3ndZk9dMiIhKSogWLCB67DGivn2Jtm0j+vDDhlcTM2dKP8vlRKGhzb4q0Zj6qogn3h88ETk6Eq1cWdcy0bkz0Q8/iHPC998TrVhBdPUq0eTJ+pePiCBq106cB3r3Jpo9m0gmk85ja0vk6yvmkcuJbGyItm9vldMWN3PVYxbBpDH/939EGzYQ7dhBdPeuSCspIdq6lejAAXGvhojo3j2R9s03Iu3bb6UHlZcXkVJJtHcvkZsblXbrRpSXJ5Y9fZpoxAiiV14hKi4WaQcPEg0YQBQYKJbLyBDrqF3fyy8TVVcTffklkbc3kb8/UUEB0dmz0gO5f39xb0g3zcpK/Ajqp2VliQO9Nu2tt4i++EI6n7c30ebN2s8agKhHD6JDh6TzjRolfqS6ae7uRDt3StMGDxZl0U1r145o/nxpWpcu4gepm2ZtTRQcLE3r0IFozJiG63N2bngiiIvT/l8NEEVHE40d2zDPmzZJ06ZPJ9q9W5o2axaRnV3DbdjbN/w76zspDR0q/dy7t2hD101zcmq4Prlc8lkbFDt2lM7XqRORq2vDNENOuNbW4qSqm/bVV0Q9e0rThg9v+LePiBDHgm7aa6/p305MDJGVlSiDnR1RWhrRb7+J48vWlmjkSPE7+/jjumVkMnFv9LHHpOt66SWipCSi9u3FPL16ESkU4rf7/vtEEyaIY7H293s/5eXiPqyVlZjkcqKoKPFdTY1YL5FY3/LlRO3akUYmExemZ8+K7377jejkybrfeCtok8GEiGjatGmSG/DTpk0zaDmzDibGqKwk+vFHIj21sxaXo7ycSKW6/3xlZeIg110uMZHof/9rej5D1/dnWvr58wbNZ1Da1avioQpdBQXS8ublieB+6pR0vtJS6YmhqIgoIUGsk0g8nJGQQPTmm6LWWV0tWVyyP27fFvPm5lKzaDREhw8TzZkjToy15btyhWjtWqLU1Lp5MzOJFi8m+uknaVkTEqRt52q1SNctW36+WP8ff9Tld8YMoscfp5vBwUSFhSJdqRTrUyrrllUopGkqFVFkpAhmH30ktnPrlqilDx1KtHQp0Z07jZdZX/5Uqvun1dSIC7Rp00Qw0Gi0s7b4t5GTI8pWVNSy5Q2RnS2OvXv3mp6vpoZ+Pn36weXjT02dO62IiFqv0ezhys7ORmRkJIqLi9GhQwdER0ejpwFdNFdVVSEzMxM+Pj6ws7Nrct4LFy7A19e3tbJsMlwO88LlMB9toQzAwylHU+dOi70BDwC9evXCgQMHTJ0Nxhh75Fn0S4uMMcbMAwcTxhhjRuNgwhhjzGgWfc+kpWqfOajWHXekCVW1PW1aOC6HeeFymI+2UAbgwZej9pyp77kti36aq6VKSkrw22+/mTobjDFmkfr06YP27dtL0h7JYKLRaFBWVgYbGxtYNadrecYYe4QREVQqFZycnCCrN6jWIxlMGGOMtS6+Ac8YY8xoHEwYY4wZjYMJY4wxo3EwYYwxZjQOJowxxozGwYQxxpjROJgwxhgzGgcTPVatWoWgoCCEhIQgLCwMly9f1n5XWFiIiIgIBAYGIiQkBBcvXjRhTpt26NAhTJo0Cf3798fevXsl31VUVODvf/87xo8fj6CgIJw4ccJEuTRMTk4OQkNDERgYiNDQUOTm5po6SwaJjo5GQEAA+vbtK+l1wZLKc+fOHbz99tsIDAzEpEmTMHfuXBQVFQEAfvnlF4SEhCAwMBARERG4ffu2iXPbtNmzZyMkJASTJ0/Gq6++il9//RWAZe0PXZ9//rnk2DLp/njgQ3NZoJSUFKr+c2S8lJQUGjt2rPa7yMhI2rp1KxERpaWl0fjx40mjM2qbOcnKyqLff/+dFi5cSHv27JF8t2XLFlq6dCkREeXk5NDw4cOptLTUFNk0SHh4uGRUzfDwcBPnyDBpaWmkUChozJgxlJWVpU23pPLcuXOHUnVGbVy3bh0tXryY1Go1jRs3jtLS0oiIaOvWrRQZGWmqbBqkWGcI28TERJo8eTIRWdb+qJWZmUkzZszQHlum3h9cM9FjzJgxsLGxAQAMHDgQN27cgEajAQAkJCQgLCwMAODn5wdbW1tJzcWc9OnTB0888USDbg8A4OjRowgNDQUAdO/eHT4+Pvjhhx8edhYNcvv2bVy5cgXBwcEAgODgYFy5ckV7dWzO/Pz84OXlJUmztPK4uLjA399f+3ngwIFQKBTIzMyEnZ0d/Pz8AABhYWFISEgwVTYNotufVGlpKaysrCxufwCiw8XVq1dj5cqV2jRT749Hstfg5ti3bx/+8pe/QCaT4c6dOyAiuLq6ar/38vLCjRs38NRTT5kwl82nUCjQpUsX7efacpgjpVIJDw8PyOVyAIBcLoe7uzuUSqVkX1gKSy6PRqPB/v37ERAQAKVSCW9vb+13rq6u0Gg0uHv3LlxcXEyYy6YtXboUZ86cARFh165dFrk/Nm/ejJCQEHTt2lWbZur98UgGkxdeeAEKhULvd2fPntUeVPHx8Th8+DD27dv3MLNnMEPLwVhr+fjjj+Ho6Ihp06YhMTHR1NlpkbVr1wIA4uLisH79esyfP9/EOWqejIwMZGZmYsGCBabOisQjGUwOHjx433kSExOxceNGxMTEoFOnTgCAjh07AgCKioq0VyxKpRKenp4PLrNNMKQcjfH29kZ+fr6kHLpNGebEy8sLBQUFUKvVkMvlUKvVuHnzZoPmI0thqeWJjo5GXl4etm/fDplMBi8vL8nFTFFREWQymVnXSnRNnjwZy5cvh6enp0Xtj7S0NGRnZ2Ps2LEAgBs3bmDGjBkIDw836f7geyZ6nDhxAv/4xz+we/duSTUSAIKCghAbGwsASE9PR2VlJXx8fEyRTaMEBQXh66+/BgDk5ubi8uXLGDVqlIlzpZ+bmxv69euHI0eOAACOHDmCfv36mW0TxP1YYnk+/fRTZGZmYuvWrbC1tQUA+Pj4oLKyEunp6QCA2NhYBAUFmTKbTSorK4NSqdR+TklJgbOzs8Xtj5kzZ+LHH39ESkoKUlJS4Onpid27d+Ott94y6f7gLuj1GDp0KGxsbCQHU0xMDDp27Ihbt25h4cKFUCgUsLOzw6pVqzB48GAT5rZxR44cwfr161FcXAwbGxs4ODjgyy+/xBNPPIHy8nJERkbi119/hUwmw8KFCzFu3DhTZ7lR2dnZiIyMRHFxMTp06IDo6Gj07NnT1Nm6rzVr1uD48eMoLCxEx44d4eLigvj4eIsqz++//47g4GB0794d9vb2AICuXbti69at+Pnnn7FixQpUVVWhS5cu2LBhg7Ymb24KCwsxe/ZsVFRUQCaTwdnZGYsWLcKAAQMsan/UFxAQgO3bt6NPnz4m3R8cTBhjjBmNm7kYY4wZjYMJY4wxo3EwYYwxZjQOJowxxozGwYQxxpjROJgwxrTCwsJw5coVo9axZ88ebNiwoZVyxCzFI/kGPGtbBg0apP1/RUUFbG1ttV3JrFq1CiEhIabKWosFBARgzZo1GD58+EPbZkpKCpycnNC/f39tWk5ODjZu3Ijz58+jpqYG3t7eePHFFzF9+nTcu3cPS5Ys0b45HhUVBVdXV0ydOhXjx49HREQE3NzcHlr+mWlxzYRZvIyMDO3k7e2N7du3az+bYyCpqakxy23Exsbi+eef136+du0apk6dCi8vLxw+fBgXLlzA5s2bkZmZibKyMuzYsQMTJkzAzp07ERQUhB07dgAA7OzsMHr0aMTFxbVaeZj542DC2iyNRoMvvvgC48aNg7+/P+bPn4+7d+8CAK5fv46+ffvi22+/xbPPPotnnnkG+/fvx6VLlzBp0iT4+flh9erV2nV99913CAsLw+rVq+Hr64ugoCCcO3dO+32VzMNfAAAF1UlEQVRJSQmWLFmCkSNHYtSoUdi4cSPUarVk2aioKPj7+2PLli24du0apk+fDn9/f/j7++ODDz5AcXExAGh7WPjb3/6GQYMGYefOnTh//jxGjx4tKV9AQADOnj0LANiyZQvmzZuHBQsWYPDgwTh48CAuXbqE0NBQ+Pn5YeTIkVi9ejWqq6v1/q2qq6uRmpqKIUOGaNM+++wzDBo0CIsXL4a7uzsAoGfPnvjkk0/QoUMHZGdn4+mnnwYAPPnkk7h69ap22SFDhuDkyZMt2m/MMnEwYW3Wnj17kJSUhL179+L06dNwdnaWBAgAuHjxIo4fP46NGzciKioK27dvR0xMDOLj43H06FH89NNP2nkvXbqEbt26ITU1FfPmzcPcuXO1wSkyMhLW1tY4fvw44uLicObMGRw4cECy7GOPPYYzZ87gnXfeARFh1qxZOH36NI4ePYobN25gy5YtAIANGzZIalhvv/22QeVNTk5GUFAQ0tPTMWnSJMhkMixevBipqamIjY3FuXPn8O9//1vvsnl5eZDJZJJOS8+dO4fAwMBGt9erVy/tSKMXL16UdD/Sq1cvZGVlGZRv1jZwMGFtVmxsLN577z14enrC1tYWc+fOxbFjxyRNQHPmzIGdnR1GjhwJR0dHBAcHw83NDR4eHvDz85PcjHZ1dcXrr78OGxsbTJw4ET169MDJkydRWFiIU6dOYcmSJXB0dISbmxveeOMNxMfHa5d1d3dHeHg4rK2tYW9vj8cffxwjRoyAra0tXF1d8eabbyItLc2o8g4cOBDjxo2DTCaDvb09fHx8MHDgQFhbW6Nr164IDQ1tdBvFxcVwcnKSpN29exedO3dudHuzZs1CQkICIiIikJCQgFmzZmm/c3JyQklJiVHlYZaFb8CzNkuhUGDOnDmSkSZlMplkXGzdG8R2dnYNPpeXl2s/e3h4wMrKSvvZ29sbN2/ehEKhQE1NDUaOHKn9TqPRSLowrz9MQWFhIdauXYv09HSUlZWBiNChQwejylt/Gzk5OVi3bh0yMzNRUVEBtVqNAQMG6F3W2dkZZWVlkjQXFxfcunWr0e0lJyejoqICAKBSqZCcnIwpU6YAED306o5qyNo+DiaszfL09ERUVBR8fX0bfHf9+vVmr6+goABEpA0oSqUSAQEB2ppPamoqrK31/6R0gxAgunS3srLC4cOH4eLigqSkpAZNcLocHBxQWVmp/axWqxsMK1t/GytXrkT//v3xySefoF27doiJicGxY8f0rr9bt24gIhQUFMDDwwMAMGzYMBw/fhwvvfSS3mWmTJmiDR71ZWdno2/fvo2Wh7U93MzF2qxXXnkFmzZtQn5+PgAxWFBSUlKL11dUVIR//etfUKlUOHr0KLKzs/Hss8/C3d0dI0aMwLp161BaWgqNRoNr165J7rfUV1ZWBkdHR7Rv3x4FBQXYtWuX5PtOnTrhjz/+0H7u0aMHqqqqcPLkSahUKmzbtq3Rm+m623BycoKTkxOys7Oxf//+Rue1tbXFsGHDJHmeN28eMjIyEB0dra2h5OXlYcGCBdqHBRqTlpbW4IEB1rZxMGFt1vTp0xEQEICIiAgMGjQIU6dOxaVLl1q8vqeeegp5eXkYOnQoNm3ahM8++0w7+ub69euhUqkwceJEPPPMM5g3b16TTURz587FlStX4Ofnh5kzZ2LChAmS72fOnIlt27bBz88Pu3fvRvv27bFixQosW7YMo0ePhoODw31H+Fy0aBGOHDmCwYMH46OPPsLEiRObnD8sLAyHDh3Sfu7WrRtiY2ORn5+P4OBg+Pr64t1334WPj0+D+yu6qqqqcOrUKbzwwgtNbo+1LTyeCWMG+O6773DgwIEmr+7bgrCwMCxfvlzy4mJz7dmzB0qlEh9++GEr5oyZO75nwhjTqh2S2hjh4eGtkBNmabiZizHGmNG4mYsxxpjRuGbCGGPMaBxMGGOMGY2DCWOMMaNxMGGMMWY0DiaMMcaMxsGEMcaY0f4fU0nn8208YMwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "agtkLVx2F0ZQ"
      },
      "source": [
        ">En la ciudad de Beijing, existe una tendencia a concentrar unidades de PM2.5 mayores al permitido por el Indice de Calidad de Aire de los Estados Unidos (U.S. AQI) en temperaturas $-0^{\\circ}$C "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQ_kBbBAH40V"
      },
      "source": [
        "## 5. Cálculo PM2.5 > 120"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsqt6vxOpd5-"
      },
      "source": [
        "elevated=not_na[not_na['pm2.5'] > 120]"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "X8e0HEfKptLj",
        "outputId": "8d813a92-82d1-4474-a1c8-411b4b7b9ea1"
      },
      "source": [
        "elevated"
      ],
      "execution_count": 45,
      "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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>pm2.5</th>\n",
              "      <th>TEMP</th>\n",
              "      <th>cbwd</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>2010-01-02 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>148.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>2010-01-02 01:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>159.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>2010-01-02 02:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>181.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>2010-01-02 03:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>138.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>SE</td>\n",
              "      <td>2010-01-02 04:00:00</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43763</th>\n",
              "      <td>2014</td>\n",
              "      <td>12</td>\n",
              "      <td>29</td>\n",
              "      <td>11</td>\n",
              "      <td>134.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>cv</td>\n",
              "      <td>2014-12-29 11:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43773</th>\n",
              "      <td>2014</td>\n",
              "      <td>12</td>\n",
              "      <td>29</td>\n",
              "      <td>21</td>\n",
              "      <td>128.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>cv</td>\n",
              "      <td>2014-12-29 21:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43774</th>\n",
              "      <td>2014</td>\n",
              "      <td>12</td>\n",
              "      <td>29</td>\n",
              "      <td>22</td>\n",
              "      <td>164.0</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>NE</td>\n",
              "      <td>2014-12-29 22:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43775</th>\n",
              "      <td>2014</td>\n",
              "      <td>12</td>\n",
              "      <td>29</td>\n",
              "      <td>23</td>\n",
              "      <td>159.0</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>NW</td>\n",
              "      <td>2014-12-29 23:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43776</th>\n",
              "      <td>2014</td>\n",
              "      <td>12</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>cv</td>\n",
              "      <td>2014-12-30 00:00:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12483 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       year  month  day  hour  pm2.5  TEMP cbwd                date\n",
              "24     2010      1    2     0  129.0  -4.0   SE 2010-01-02 00:00:00\n",
              "25     2010      1    2     1  148.0  -4.0   SE 2010-01-02 01:00:00\n",
              "26     2010      1    2     2  159.0  -5.0   SE 2010-01-02 02:00:00\n",
              "27     2010      1    2     3  181.0  -5.0   SE 2010-01-02 03:00:00\n",
              "28     2010      1    2     4  138.0  -5.0   SE 2010-01-02 04:00:00\n",
              "...     ...    ...  ...   ...    ...   ...  ...                 ...\n",
              "43763  2014     12   29    11  134.0   5.0   cv 2014-12-29 11:00:00\n",
              "43773  2014     12   29    21  128.0   0.0   cv 2014-12-29 21:00:00\n",
              "43774  2014     12   29    22  164.0  -3.0   NE 2014-12-29 22:00:00\n",
              "43775  2014     12   29    23  159.0  -3.0   NW 2014-12-29 23:00:00\n",
              "43776  2014     12   30     0  189.0  -2.0   cv 2014-12-30 00:00:00\n",
              "\n",
              "[12483 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gpFSP5YcqbI9",
        "outputId": "330c911c-104e-4023-e71f-503bfe018dac"
      },
      "source": [
        "elevated.info()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 12483 entries, 24 to 43776\n",
            "Data columns (total 8 columns):\n",
            " #   Column  Non-Null Count  Dtype         \n",
            "---  ------  --------------  -----         \n",
            " 0   year    12483 non-null  int64         \n",
            " 1   month   12483 non-null  int64         \n",
            " 2   day     12483 non-null  int64         \n",
            " 3   hour    12483 non-null  int64         \n",
            " 4   pm2.5   12483 non-null  float64       \n",
            " 5   TEMP    12483 non-null  float64       \n",
            " 6   cbwd    12483 non-null  object        \n",
            " 7   date    12483 non-null  datetime64[ns]\n",
            "dtypes: datetime64[ns](1), float64(2), int64(4), object(1)\n",
            "memory usage: 877.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KA_gqAAdrYc1"
      },
      "source": [
        "elevated.count()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9JxL5SCTqjAZ",
        "outputId": "7830ec2c-b0f1-4f91-8ba2-819691f54c48"
      },
      "source": [
        "print(\"La probabilidad PM2.5 > 120 es de:\", elevated['pm2.5'].count()/not_na['pm2.5'].count()*100, \"%\")"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "La probabilidad PM2.5 > 120 es de: 29.894388964724477 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVFgWLU3sLef"
      },
      "source": [
        "La probabilidad de encontrar en la base de datos un PM2.5 encima de 120 es de **29.894388964724477%**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9eNxyfviFUK"
      },
      "source": [
        "### PRUEBA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLW7GHLxJoXB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "360bf2b8-b87a-4cd8-ed0b-c8d7b9ffeb5c"
      },
      "source": [
        "asignar=[]\n",
        "lista1=[]\n",
        "lista2=[]\n",
        "lista3=[]\n",
        "for index, row in not_na.iterrows():\n",
        "    if(row['pm2.5'] > 500):\n",
        "        asignar.append(colores[1])\n",
        "        lista1.append\n",
        "    else:\n",
        "      if((row[\"pm2.5\"] > 120) & (row[\"pm2.5\"] <= 500)):\n",
        "        asignar.append(colores[2])\n",
        "        lista2.append\n",
        "      else:\n",
        "        asignar.append(colores[0])\n",
        "        lista3.append\n",
        "    \n",
        "plt.scatter(X, Y, c=asignar, s=tamanios[0])\n",
        "plt.xlabel('Anno')\n",
        "plt.autoscale(enable=True, tight= False)\n",
        "plt.ylabel('PM2.5')\n",
        "plt.plot(lista1, label = \"Hazardous\", marker = 'o', color = \"red\")\n",
        "plt.plot(lista2, label = \"Elevated\", marker = 'o', color = \"orange\")\n",
        "plt.plot(lista3, label = \"Normal\", marker = 'o', color = \"blue\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUAPG74eAJF8"
      },
      "source": [
        "## 6. Organización Ascendente de Datos Por Año"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3wdN79T6iCq"
      },
      "source": [
        "by_year = not_na.groupby(['year']).mean()\n",
        "by_year.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "uXhJCp3J4C7-",
        "outputId": "9fd0085c-0508-45d8-a718-2052350f25e9"
      },
      "source": [
        "by_year.sort_values(by=['pm2.5'],ascending=True)"
      ],
      "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>month</th>\n",
              "      <th>day</th>\n",
              "      <th>hour</th>\n",
              "      <th>pm2.5</th>\n",
              "      <th>TEMP</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</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>2012</th>\n",
              "      <td>6.452200</td>\n",
              "      <td>15.471248</td>\n",
              "      <td>11.506088</td>\n",
              "      <td>90.545871</td>\n",
              "      <td>12.109946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>6.505715</td>\n",
              "      <td>15.729708</td>\n",
              "      <td>11.504676</td>\n",
              "      <td>97.734557</td>\n",
              "      <td>13.709156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>6.601096</td>\n",
              "      <td>15.966633</td>\n",
              "      <td>11.509836</td>\n",
              "      <td>99.071340</td>\n",
              "      <td>12.407869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>6.517516</td>\n",
              "      <td>15.674464</td>\n",
              "      <td>11.497580</td>\n",
              "      <td>101.712376</td>\n",
              "      <td>12.383614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>6.494747</td>\n",
              "      <td>15.590656</td>\n",
              "      <td>11.493511</td>\n",
              "      <td>104.045730</td>\n",
              "      <td>11.313805</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         month        day       hour       pm2.5       TEMP\n",
              "year                                                       \n",
              "2012  6.452200  15.471248  11.506088   90.545871  12.109946\n",
              "2014  6.505715  15.729708  11.504676   97.734557  13.709156\n",
              "2011  6.601096  15.966633  11.509836   99.071340  12.407869\n",
              "2013  6.517516  15.674464  11.497580  101.712376  12.383614\n",
              "2010  6.494747  15.590656  11.493511  104.045730  11.313805"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 145
        }
      ]
    }
  ]
}