air_traffic_covid.ipynb

{
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  "metadata": {
    "colab": {
      "name": "air_traffic_covid.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxxXGy_k2a3T",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "from google.colab import drive\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "plt.rcParams['figure.figsize'] = [10, 5]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OLW53Pqd5LSz",
        "colab_type": "code",
        "outputId": "24f9bb1b-5d25-4e12-fe14-cdaea44e4b1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "air_traffic = pd.read_csv('air_traffic.csv')\n",
        "air_traffic.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Country Name</th>\n",
              "      <th>Country Code</th>\n",
              "      <th>Indicator Name</th>\n",
              "      <th>Indicator Code</th>\n",
              "      <th>1960</th>\n",
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              "      <th>2015</th>\n",
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              "      <th>2017</th>\n",
              "      <th>2018</th>\n",
              "      <th>2019</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aruba</td>\n",
              "      <td>ABW</td>\n",
              "      <td>Air transport, passengers carried</td>\n",
              "      <td>IS.AIR.PSGR</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>274280.00</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>AFG</td>\n",
              "      <td>Air transport, passengers carried</td>\n",
              "      <td>IS.AIR.PSGR</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Angola</td>\n",
              "      <td>AGO</td>\n",
              "      <td>Air transport, passengers carried</td>\n",
              "      <td>IS.AIR.PSGR</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Albania</td>\n",
              "      <td>ALB</td>\n",
              "      <td>Air transport, passengers carried</td>\n",
              "      <td>IS.AIR.PSGR</td>\n",
              "      <td>NaN</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Andorra</td>\n",
              "      <td>AND</td>\n",
              "      <td>Air transport, passengers carried</td>\n",
              "      <td>IS.AIR.PSGR</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Country Name Country Code  ...        2018 2019\n",
              "0        Aruba          ABW  ...   274280.00  NaN\n",
              "1  Afghanistan          AFG  ...  1722612.61  NaN\n",
              "2       Angola          AGO  ...  1516628.00  NaN\n",
              "3      Albania          ALB  ...   303137.00  NaN\n",
              "4      Andorra          AND  ...         NaN  NaN\n",
              "\n",
              "[5 rows x 64 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_sQSDAi9TVb",
        "colab_type": "text"
      },
      "source": [
        "Graficamos la serie de tiempo de Argentina para analizar su comportamiento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mo19GqDz8ffw",
        "colab_type": "code",
        "outputId": "868c04bf-4b0e-45ec-dc89-6bd01c90141c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        }
      },
      "source": [
        "arg_traffic_series = []\n",
        "for index, row in air_traffic.iterrows():\n",
        "  if row['Country Code'] == 'ARG':\n",
        "    arg_traffic_series = row[4:len(row)-1]\n",
        "    break\n",
        "\n",
        "plt.plot(arg_traffic_series)\n",
        "plt.title('Trafico aéreo argentino a lo largo del tiempo')\n",
        "plt.xlabel('Años')\n",
        "plt.ylabel('Pasajeros transportados')\n",
        "plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)\n",
        "plt.show()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "INkNAWvo9YAM",
        "colab_type": "text"
      },
      "source": [
        "Vemos que en los ultimos años el incremento es exponencial por lo que tomamos un promedio de los ultimos 10 años.\n",
        "\n",
        "Armamos un intervalo de confianza para la media usando t de student"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "buJpM7KbFxIq",
        "colab_type": "code",
        "outputId": "6c9ccdb1-3f3b-44da-bd21-006048a64267",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "traffic = []\n",
        "\n",
        "for index, row in air_traffic.iterrows():\n",
        "  if row['Country Code'] == 'ARG':\n",
        "    for i in range(53,len(row)-2): # 2009-2018\n",
        "      traffic.append(float(row[i]))\n",
        "    break\n",
        "\n",
        "arg_avg = np.average(traffic)\n",
        "arg_std = np.std(traffic)\n",
        "t_student = 2.69 # 95% n=10 => n-1=9\n",
        "\n",
        "interval_lower_limit = arg_avg - (t_student * arg_std / np.sqrt(10))\n",
        "interval_upper_limit = arg_avg + (t_student * arg_std / np.sqrt(10))\n",
        "print('Intervalo:', interval_lower_limit, '-', interval_upper_limit)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Intervalo: 8572721.163889524 - 14278409.845371924\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oz5lE5F1HJzg",
        "colab_type": "text"
      },
      "source": [
        "Filtramos los paises que no tienen datos en el período de tiempo 2009-2019 y nos quedamos con aquellos cuya media caiga dentro del rango de pasajeros de Argentina"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZ-B5d7c7pT1",
        "colab_type": "code",
        "outputId": "5a9f71bc-5d9d-47b2-e43b-381771cf0e32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "air_traffic['avg'] = pd.Series(dtype='float64')\n",
        "\n",
        "for index, row in air_traffic.iterrows():\n",
        "  if np.isnan(row['2009']) or np.isnan(row['2010']) or np.isnan(row['2011']) or np.isnan(row['2012']) or np.isnan(row['2013']) or np.isnan(row['2014']) or np.isnan(row['2015']) or np.isnan(row['2016']) or np.isnan(row['2017']) or np.isnan(row['2018']):\n",
        "    air_traffic = air_traffic.drop(index)\n",
        "  else:\n",
        "    traffic = []\n",
        "    for i in range(53,len(row)-2): # 2009-2018\n",
        "      traffic.append(float(row[i]))\n",
        "    \n",
        "    avg = np.average(traffic)\n",
        "    if avg >= interval_lower_limit and avg <= interval_upper_limit:\n",
        "      air_traffic.at[index, 'avg'] = avg\n",
        "    else:\n",
        "      air_traffic = air_traffic.drop(index)\n",
        "\n",
        "print(air_traffic)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "         Country Name Country Code  ... 2019           avg\n",
            "7           Argentina          ARG  ...  NaN  1.209120e+07\n",
            "12            Austria          AUT  ...  NaN  1.394201e+07\n",
            "15            Belgium          BEL  ...  NaN  1.019254e+07\n",
            "37              Chile          CHL  ...  NaN  1.379146e+07\n",
            "65   Egypt, Arab Rep.          EGY  ...  NaN  1.004390e+07\n",
            "73            Finland          FIN  ...  NaN  1.101736e+07\n",
            "87             Greece          GRC  ...  NaN  1.103206e+07\n",
            "183            Panama          PAN  ...  NaN  9.963203e+06\n",
            "184              Peru          PER  ...  NaN  1.189864e+07\n",
            "192          Portugal          PRT  ...  NaN  1.262984e+07\n",
            "\n",
            "[10 rows x 65 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J25tBS_fH6GX",
        "colab_type": "text"
      },
      "source": [
        "Descartamos los paises de la Unión Europea, ya que tienen un mayor numero de casos debido a la cercanía geográfica con los paises de mayor foco de contagio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yZdFCvFCISoA",
        "colab_type": "code",
        "outputId": "8cd0d49d-b569-4fdb-ad28-76a8190df47f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "european_union = ['AUT', 'BEL', 'FIN', 'GRC', 'PRT']\n",
        "\n",
        "for index, row in air_traffic.iterrows():\n",
        "  if row['Country Code'] in european_union:\n",
        "    air_traffic = air_traffic.drop(index)\n",
        "\n",
        "print(air_traffic)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "         Country Name Country Code  ... 2019           avg\n",
            "7           Argentina          ARG  ...  NaN  1.209120e+07\n",
            "37              Chile          CHL  ...  NaN  1.379146e+07\n",
            "65   Egypt, Arab Rep.          EGY  ...  NaN  1.004390e+07\n",
            "178       New Zealand          NZL  ...  NaN  1.443425e+07\n",
            "183            Panama          PAN  ...  NaN  9.963203e+06\n",
            "184              Peru          PER  ...  NaN  1.189864e+07\n",
            "\n",
            "[6 rows x 65 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Boe1B67NM1S4",
        "colab_type": "text"
      },
      "source": [
        "Calculamos las estadisticas por pais"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0AqQ4iRJALa",
        "colab_type": "code",
        "outputId": "7fab5aae-30dc-4b4c-9846-ca15dd496665",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "cases = {'ARG': {'population': 43007500, 'cases': 3780, 'deaths': 182}, 'CHL': {'population': 17969000, 'cases': 12858, 'deaths': 181}, 'EGY': {'population': 92443000, 'cases': 4319, 'deaths': 307}, 'NZL': {'population': 4615000, 'cases': 1470, 'deaths': 18}, 'PAN': {'population': 3968000, 'cases': 5538, 'deaths': 159}, 'PER': {'population': 30471000, 'cases': 25331, 'deaths': 700}}\n",
        "results = pd.DataFrame(index=cases.keys(), columns=['population', 'cases', 'cases_population', 'deaths', 'deaths_cases'])\n",
        "\n",
        "for index, row in air_traffic.iterrows():\n",
        "  results.at[row['Country Code'], 'population'] = cases[row['Country Code']]['population']\n",
        "  results.at[row['Country Code'], 'cases'] = cases[row['Country Code']]['cases']\n",
        "  results.at[row['Country Code'], 'deaths'] = cases[row['Country Code']]['deaths']\n",
        "\n",
        "results['cases_population'] = results['cases'] / results['population']\n",
        "results['deaths_cases'] = results['deaths'] / results['cases']\n",
        "\n",
        "print(results)\n",
        "print('Media de casos por habitante:', results['cases_population'].mean())\n",
        "print('Media de decesos por caso:', results['deaths_cases'].mean())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    population  cases cases_population deaths deaths_cases\n",
            "ARG   43007500   3780      8.78916e-05    182    0.0481481\n",
            "CHL   17969000  12858      0.000715566    181    0.0140768\n",
            "EGY   92443000   4319      4.67207e-05    307    0.0710813\n",
            "NZL    4615000   1470      0.000318527     18    0.0122449\n",
            "PAN    3968000   5538       0.00139567    159    0.0287107\n",
            "PER   30471000  25331      0.000831315    700    0.0276341\n",
            "Media de casos por habitante: 0.0005659474854192629\n",
            "Media de decesos por caso: 0.033649334055142324\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RSALrIBeS4A0",
        "colab_type": "text"
      },
      "source": [
        "Fuentes de datos:\n",
        "\n",
        "*   https://population.un.org/wpp/\n",
        "*   https://data.worldbank.org/indicator/is.air.psgr?name_desc=false\n",
        "*   https://time.com/5815492/flight-attendants-coronavirus/\n",
        "\n"
      ]
    }
  ]
}