Ayudantías/Ayudantía 2/solucion_C2E1.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Cargamos el dataframe"
      ],
      "metadata": {
        "id": "BN7JDRz-2q1q"
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      "source": [
        "df = pd.read_csv('data_E1.csv')\n",
        "df.head()"
      ],
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              "   Year  Month  Day     O3  PM2.5 Environmental_risk\n",
              "0  2008      1    1  29.63    NaN                NaN\n",
              "1  2008      1    2  21.46    NaN                NaN\n",
              "2  2008      1    3  24.25    NaN                NaN\n",
              "3  2008      1    4  29.04    NaN                NaN\n",
              "4  2008      1    5  30.17    NaN                NaN"
            ],
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      "outputs": [
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          "data": {
            "text/plain": [
              "              Year        Month          Day           O3        PM2.5\n",
              "count  2984.000000  2984.000000  2984.000000  2878.000000  2725.000000\n",
              "mean   2011.592493     6.419236    15.715818    16.313767    28.814594\n",
              "std       2.357040     3.487660     8.804066     7.995680    17.479388\n",
              "min    2008.000000     1.000000     1.000000     1.000000     5.000000\n",
              "25%    2010.000000     3.000000     8.000000     9.392500    16.170000\n",
              "50%    2012.000000     6.000000    16.000000    16.960000    23.210000\n",
              "75%    2014.000000     9.000000    23.000000    22.357500    37.210000\n",
              "max    2016.000000    12.000000    31.000000    56.300000   126.200000"
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              "      <th>count</th>\n",
              "      <td>2984.000000</td>\n",
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              "      <td>2011.592493</td>\n",
              "      <td>6.419236</td>\n",
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              "      <td>16.313767</td>\n",
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              "      <th>min</th>\n",
              "      <td>2008.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <th>50%</th>\n",
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              "      <td>6.000000</td>\n",
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              "      <td>23.210000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>2014.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>22.357500</td>\n",
              "      <td>37.210000</td>\n",
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              "      <th>max</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>56.300000</td>\n",
              "      <td>126.200000</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e16b92ad-383f-4bd8-9829-b9b1ca1c75c3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e16b92ad-383f-4bd8-9829-b9b1ca1c75c3')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e16b92ad-383f-4bd8-9829-b9b1ca1c75c3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Misión 1"
      ],
      "metadata": {
        "id": "qpuuABOjEc2W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Buscamos los datos extremos"
      ],
      "metadata": {
        "id": "rYqEqVG_3xfo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.boxplot(column = 'O3')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "S5-J6Yqh3z1e",
        "outputId": "2031e5d6-c844-4499-a3ff-326f4221d0b3"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(column='O3', bins=30)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "dPysSK6H_86l",
        "outputId": "47bd6e12-8de6-49a7-e051-fa34b00a23ae"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.boxplot(column = 'PM2.5')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "hIb8PO-5BNHK",
        "outputId": "a1697d0e-a717-463a-e1a2-e5fa230c0799"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(column='PM2.5')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "W2UstE0rBPdd",
        "outputId": "36ad2333-1dc2-4a6b-8d42-6ba192bfdad6"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Los corregimos\n",
        "df_filtrado = df[df['O3'] < 40]\n",
        "df_filtrado.boxplot(column = 'O3')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "x6LmyD75A_RX",
        "outputId": "9ec0abc6-c1a6-45fa-8e40-7c9cfc4d0ce3"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_filtrado['PM2.5'] = np.log(df['PM2.5'])\n",
        "df_filtrado['PM2.5'].hist()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "aUYxysA1CA4u",
        "outputId": "809c25d3-7c6a-4dd5-c155-b512d99c3191"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-35-24be8082a522>:1: 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",
            "  df_filtrado['PM2.5'] = np.log(df['PM2.5'])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Buscamos los valores nulos"
      ],
      "metadata": {
        "id": "c94Bo4CI4hlG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.apply(lambda x: sum(x.isnull()),axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2pNZCVL4g_w",
        "outputId": "bec5a24f-99dd-4efd-8444-7cc43f435347"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                    0\n",
              "Month                   0\n",
              "Day                     0\n",
              "O3                    106\n",
              "PM2.5                 259\n",
              "Environmental_risk    328\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Datos arreglados\n",
        "df_arreglados = df_filtrado.copy()\n",
        "df_arreglados['O3'] = df['O3'].fillna(df['O3'].mean())\n",
        "df_arreglados['PM2.5'] = df['PM2.5'].fillna(df['PM2.5'].mean())\n",
        "print(df_arreglados.shape)"
      ],
      "metadata": {
        "id": "ciwKQK7L29iW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f7698a3-4d02-4b5e-86ee-bbb1d3a080fb"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2873, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Datos eliminados\n",
        "df_eliminados = df_filtrado.dropna()\n",
        "print(df_eliminados.shape)"
      ],
      "metadata": {
        "id": "ayd6FqLD2_W4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a615f49-9638-48a6-ef6d-47ceb2c95b6c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2651, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Misión 2"
      ],
      "metadata": {
        "id": "7YQRkVJtDweM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "indicadores = {\n",
        "    # 03\n",
        "    'Media O3': [df_arreglados['O3'].mean()],\n",
        "    'Desviación Estándar O3': [df_arreglados['O3'].std()],\n",
        "    'Máximo O3': [df_arreglados['O3'].max()],\n",
        "    'Mínimo O3': [df_arreglados['O3'].min()],\n",
        "    'Kurtosis O3': [df_arreglados['O3'].kurtosis()],\n",
        "    # PM 2.5\n",
        "    'Media PM2.5': [df_arreglados['PM2.5'].mean()],\n",
        "    'Desviación Estándar PM2.5': [df_arreglados['PM2.5'].std()],\n",
        "    'Máximo PM2.5': [df_arreglados['PM2.5'].max()],\n",
        "    'Mínimo PM2.5': [df_arreglados['PM2.5'].min()],\n",
        "    'Kurtosis PM2.5': [df_arreglados['PM2.5'].kurtosis()]\n",
        "}\n",
        "\n",
        "correlation = df_arreglados['O3'].corr(df_arreglados['PM2.5'])\n",
        "\n",
        "indicators_df = pd.DataFrame.from_dict(indicadores)\n",
        "\n",
        "indicators_df['Correlación'] = correlation\n",
        "\n",
        "indicators_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "nwhCB9wbDyyf",
        "outputId": "f6fc4967-3d9c-469a-c936-a8ad06dc1318"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Media O3  Desviación Estándar O3  Máximo O3  Mínimo O3  Kurtosis O3  \\\n",
              "0  16.250821                7.857782      39.65        1.0    -0.944356   \n",
              "\n",
              "   Media PM2.5  Desviación Estándar PM2.5  Máximo PM2.5  Mínimo PM2.5  \\\n",
              "0    28.950613                  16.868082         126.2           5.0   \n",
              "\n",
              "   Kurtosis PM2.5  Correlación  \n",
              "0        2.354072    -0.478362  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d7abfa8-e4c9-4163-ac08-1c06e9d8a4b4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Media O3</th>\n",
              "      <th>Desviación Estándar O3</th>\n",
              "      <th>Máximo O3</th>\n",
              "      <th>Mínimo O3</th>\n",
              "      <th>Kurtosis O3</th>\n",
              "      <th>Media PM2.5</th>\n",
              "      <th>Desviación Estándar PM2.5</th>\n",
              "      <th>Máximo PM2.5</th>\n",
              "      <th>Mínimo PM2.5</th>\n",
              "      <th>Kurtosis PM2.5</th>\n",
              "      <th>Correlación</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.250821</td>\n",
              "      <td>7.857782</td>\n",
              "      <td>39.65</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.944356</td>\n",
              "      <td>28.950613</td>\n",
              "      <td>16.868082</td>\n",
              "      <td>126.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.354072</td>\n",
              "      <td>-0.478362</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d7abfa8-e4c9-4163-ac08-1c06e9d8a4b4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7d7abfa8-e4c9-4163-ac08-1c06e9d8a4b4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7d7abfa8-e4c9-4163-ac08-1c06e9d8a4b4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "indicadores = {\n",
        "    # 03\n",
        "    'Media O3': [df_eliminados['O3'].mean()],\n",
        "    'Desviación Estándar O3': [df_eliminados['O3'].std()],\n",
        "    'Máximo O3': [df_eliminados['O3'].max()],\n",
        "    'Mínimo O3': [df_eliminados['O3'].min()],\n",
        "    'Kurtosis O3': [df_eliminados['O3'].kurtosis()],\n",
        "    # PM 2.5\n",
        "    'Media PM2.5': [df_eliminados['PM2.5'].mean()],\n",
        "    'Desviación Estándar PM2.5': [df_eliminados['PM2.5'].std()],\n",
        "    'Máximo PM2.5': [df_eliminados['PM2.5'].max()],\n",
        "    'Mínimo PM2.5': [df_eliminados['PM2.5'].min()],\n",
        "    'Kurtosis PM2.5': [df_eliminados['PM2.5'].kurtosis()]\n",
        "}\n",
        "\n",
        "correlation = df_eliminados['O3'].corr(df_eliminados['PM2.5'])\n",
        "\n",
        "indicators_df = pd.DataFrame.from_dict(indicadores)\n",
        "\n",
        "indicators_df['Correlación'] = correlation\n",
        "\n",
        "indicators_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "s42ugMNmExrq",
        "outputId": "eeed3852-5801-46c6-ac24-cae4ae5da609"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Media O3  Desviación Estándar O3  Máximo O3  Mínimo O3  Kurtosis O3  \\\n",
              "0  15.892422                7.890672      39.65        1.0    -0.944448   \n",
              "\n",
              "   Media PM2.5  Desviación Estándar PM2.5  Máximo PM2.5  Mínimo PM2.5  \\\n",
              "0     3.201269                   0.571391      4.837868      1.609438   \n",
              "\n",
              "   Kurtosis PM2.5  Correlación  \n",
              "0       -0.535191    -0.448351  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4e60fabd-01dd-4e51-b7bb-1c9c3e254cc8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Media O3</th>\n",
              "      <th>Desviación Estándar O3</th>\n",
              "      <th>Máximo O3</th>\n",
              "      <th>Mínimo O3</th>\n",
              "      <th>Kurtosis O3</th>\n",
              "      <th>Media PM2.5</th>\n",
              "      <th>Desviación Estándar PM2.5</th>\n",
              "      <th>Máximo PM2.5</th>\n",
              "      <th>Mínimo PM2.5</th>\n",
              "      <th>Kurtosis PM2.5</th>\n",
              "      <th>Correlación</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15.892422</td>\n",
              "      <td>7.890672</td>\n",
              "      <td>39.65</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.944448</td>\n",
              "      <td>3.201269</td>\n",
              "      <td>0.571391</td>\n",
              "      <td>4.837868</td>\n",
              "      <td>1.609438</td>\n",
              "      <td>-0.535191</td>\n",
              "      <td>-0.448351</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e60fabd-01dd-4e51-b7bb-1c9c3e254cc8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4e60fabd-01dd-4e51-b7bb-1c9c3e254cc8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4e60fabd-01dd-4e51-b7bb-1c9c3e254cc8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Misión 3"
      ],
      "metadata": {
        "id": "AeD4f9SxEPC9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df_arreglados['PM2.5'], edgecolor='black')\n",
        "plt.title('Histograma de PM2.5')\n",
        "plt.xlabel('PM2.5')\n",
        "plt.ylabel('Frecuencia')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "0h7i6UJ1EQZg",
        "outputId": "cbe202fa-7fd1-486a-9fc8-15be90a98a31"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='Month', y='O3', data=df_arreglados)\n",
        "plt.title('Boxplot de O3 por Mes')\n",
        "plt.xlabel('Mes')\n",
        "plt.ylabel('O3')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "NZpxXFOuJR_H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "a611122a-59d6-4695-ed9a-9fedb29ec6ae"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "promedios_por_ano = df_arreglados.groupby(\"Year\")[[\"O3\", \"PM2.5\"]].mean()\n",
        "\n",
        "print(promedios_por_ano)\n",
        "plt.plot(promedios_por_ano.index, promedios_por_ano[\"O3\"], marker='o', label=\"O3\")\n",
        "plt.plot(promedios_por_ano.index, promedios_por_ano[\"PM2.5\"], marker='o', label=\"PM2.5\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "yG1A_Rr6JUUG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "ae75523b-2d9e-4d16-a7bd-c40ceb7f294a"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             O3      PM2.5\n",
            "Year                      \n",
            "2008  16.624617  30.599163\n",
            "2009  18.190083  27.881949\n",
            "2010  18.168437  29.200577\n",
            "2011  15.833573  31.288212\n",
            "2012  15.631971  24.742286\n",
            "2013  14.958796  27.431398\n",
            "2014  14.807611  30.006810\n",
            "2015  14.870671  32.322061\n",
            "2016  21.121667  15.639444\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df_eliminados['PM2.5'], edgecolor='black')\n",
        "plt.title('Histograma de PM2.5')\n",
        "plt.xlabel('PM2.5')\n",
        "plt.ylabel('Frecuencia')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "4090e972-dc17-4d65-e7ff-e0095ebf4801",
        "id": "q81ZC3Jhmzz1"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x='Month', y='O3', data=df_eliminados)\n",
        "plt.title('Boxplot de O3 por Mes')\n",
        "plt.xlabel('Mes')\n",
        "plt.ylabel('O3')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "b801040e-f957-44fa-fdcf-554d81b960a1",
        "id": "x8mtxa43mzz2"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "promedios_por_ano = df_eliminados.groupby(\"Year\")[[\"O3\", \"PM2.5\"]].mean()\n",
        "\n",
        "print(promedios_por_ano)\n",
        "plt.plot(promedios_por_ano.index, promedios_por_ano[\"O3\"], marker='o', label=\"O3\")\n",
        "plt.plot(promedios_por_ano.index, promedios_por_ano[\"PM2.5\"], marker='o', label=\"PM2.5\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "6fd6190f-0422-46af-b27d-9519fbcb818b",
        "id": "p9edc_ucmzz2"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "             O3     PM2.5\n",
            "Year                     \n",
            "2008  14.304812  3.271499\n",
            "2009  18.067059  3.185063\n",
            "2010  18.108790  3.238632\n",
            "2011  15.833000  3.317615\n",
            "2012  15.631971  3.051845\n",
            "2013  14.835705  3.194721\n",
            "2014  13.928069  3.225503\n",
            "2015  14.611642  3.226634\n",
            "2016  21.121667  2.701500\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Misión 4"
      ],
      "metadata": {
        "id": "YEw14Fve9nVc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Environmental_risk'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDz3QovrfGKx",
        "outputId": "29a6ce1e-5eb2-457a-babe-fe394f0a0756"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "medio      1605\n",
              "Bajo        750\n",
              "nulo        134\n",
              "Alto        106\n",
              "extremo      61\n",
              "Name: Environmental_risk, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Implementaremos como ejemplo 4 categorías\n",
        "# Extremo, alto, bajo riesgo\n",
        "\n",
        "def calcular_riesgo(o3, pm25):\n",
        "    if o3 > 22:\n",
        "        if pm25 > 34:\n",
        "            return \"Extremo\"\n",
        "        return \"Alto\"\n",
        "    elif pm25 > 32:\n",
        "        return \"Alto\"\n",
        "    elif o3 < 17 and pm25 < 22:\n",
        "        return \"Bajo\"\n",
        "    else:\n",
        "        return \"medio\""
      ],
      "metadata": {
        "id": "ZW7p1nblauX9"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_arreglados['Environmental_risk'] = df_arreglados.apply(lambda row : calcular_riesgo(row['O3'], row['PM2.5']) if (pd.isnull(row['Environmental_risk']) or row['Environmental_risk'] == \"nulo\") else row['Environmental_risk'], axis = 1)"
      ],
      "metadata": {
        "id": "44uX20qDm-Zx"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "OO4f9HOEnTSG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_arreglados['Environmental_risk'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ioE2o0L0fxGE",
        "outputId": "b6c942bb-5e61-4769-ee56-1e3c4cb94470"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "medio      1798\n",
              "Bajo        777\n",
              "Alto        238\n",
              "extremo      60\n",
              "Name: Environmental_risk, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_eliminados['Environmental_risk'] = df_eliminados.apply(lambda row : calcular_riesgo(row['O3'], row['PM2.5']) if (pd.isnull(row['Environmental_risk']) or row['Environmental_risk'] == \"nulo\") else row['Environmental_risk'], axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SDzTJRRim9i7",
        "outputId": "60f9d243-f723-47df-f93e-5321cdf44ecd"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-51-bd0e8271aa2f>:1: 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",
            "  df_eliminados['Environmental_risk'] = df_eliminados.apply(lambda row : calcular_riesgo(row['O3'], row['PM2.5']) if (pd.isnull(row['Environmental_risk']) or row['Environmental_risk'] == \"nulo\") else row['Environmental_risk'], axis = 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_eliminados['Environmental_risk'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cKThNaTem5iL",
        "outputId": "0284ba6a-19c9-4dca-d54c-b229f2767e62"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "medio      1682\n",
              "Bajo        777\n",
              "Alto        132\n",
              "extremo      60\n",
              "Name: Environmental_risk, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    }
  ]
}