DANE_Proyeccion_Poblacion_2032.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "NDiaz_614182002_Parcial1_Simulacion.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "beqxA1EG7K2c"
      },
      "source": [
        "import numpy as np\n",
        "import scipy as sp\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import io\n",
        "from google.colab import files"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "KIEJnXDb7ePx",
        "outputId": "ca4da09d-b15d-46ea-9018-7200fc72c534"
      },
      "source": [
        "uploaded = files.upload()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-6c13a981-6fc2-4b8e-926a-e684f292b653\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-6c13a981-6fc2-4b8e-926a-e684f292b653\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving ProyeccionesDANE.csv to ProyeccionesDANE.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTp7NlJN7pfB",
        "outputId": "ecddfd0f-3a30-4865-c8e1-30405285bf2e"
      },
      "source": [
        "tabla = pd.read_csv(io.BytesIO(uploaded['ProyeccionesDANE.csv']))\n",
        "print(tabla)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Año  Población\n",
            "0   1993   35032308\n",
            "1   1994   35629174\n",
            "2   1995   36229830\n",
            "3   1996   36830574\n",
            "4   1997   37426532\n",
            "5   1998   38012359\n",
            "6   1999   38585016\n",
            "7   2000   39140080\n",
            "8   2001   39674811\n",
            "9   2002   40190679\n",
            "10  2003   40693254\n",
            "11  2004   41188093\n",
            "12  2005   41671878\n",
            "13  2006   42170126\n",
            "14  2007   42658630\n",
            "15  2008   43134017\n",
            "16  2009   43608630\n",
            "17  2010   44086292\n",
            "18  2011   44553416\n",
            "19  2012   45001571\n",
            "20  2013   45434942\n",
            "21  2014   45866010\n",
            "22  2015   46313898\n",
            "23  2016   46830116\n",
            "24  2017   47419200\n",
            "25  2018   48258494\n",
            "26  2019   49395678\n",
            "27  2020   50372424\n",
            "28  2021   51049498\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "FMkN1aXR77Q3",
        "outputId": "d129004b-70c7-46b2-f268-5135292d844e"
      },
      "source": [
        "plt.figure(figsize = (7,5))\n",
        "plt.plot(tabla['Año'], tabla['Población'],'.')\n",
        "plt.title('Datos originales',)\n",
        "plt.xlabel('Año')\n",
        "plt.ylabel('Población')\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9vZ6109q7yHz"
      },
      "source": [
        "Haremos una interpolación cúbica para estimar la proyección mensual de población de los años mostrados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yFGpXotJ7pcF"
      },
      "source": [
        "from scipy import interpolate"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Up9Nv-J7pZN"
      },
      "source": [
        "y = np.array(tabla['Población'])\n",
        "x = np.array(tabla['Año'])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJmF_nQi-lML"
      },
      "source": [
        "sprep = interpolate.splrep(x, y, s=0)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6B56ara7pWS",
        "outputId": "a409a34e-25b3-4dfb-8c08-c017ccbba9d9"
      },
      "source": [
        "x,y"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n",
              "        2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n",
              "        2015, 2016, 2017, 2018, 2019, 2020, 2021]),\n",
              " array([35032308, 35629174, 36229830, 36830574, 37426532, 38012359,\n",
              "        38585016, 39140080, 39674811, 40190679, 40693254, 41188093,\n",
              "        41671878, 42170126, 42658630, 43134017, 43608630, 44086292,\n",
              "        44553416, 45001571, 45434942, 45866010, 46313898, 46830116,\n",
              "        47419200, 48258494, 49395678, 50372424, 51049498]))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmuxT_Yx8kBQ"
      },
      "source": [
        "Como son $28$ años y queremos datos mensuales, haremos una división de $336 (28\\cdot12)$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nU_AfYok9TTq"
      },
      "source": [
        "M = 336 #valores intermedios\n",
        "xint1 = np.linspace(x[0], x[28], M)\n",
        "yint1 = interpolate.splev(xint1, sprep, der=0)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1tlbC9LpUAGg",
        "outputId": "d1b92ff2-13fc-49c7-bd66-3e84dcca88b0"
      },
      "source": [
        "xint1,yint1"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1993.        , 1993.08358209, 1993.16716418, 1993.25074627,\n",
              "        1993.33432836, 1993.41791045, 1993.50149254, 1993.58507463,\n",
              "        1993.66865672, 1993.75223881, 1993.8358209 , 1993.91940299,\n",
              "        1994.00298507, 1994.08656716, 1994.17014925, 1994.25373134,\n",
              "        1994.33731343, 1994.42089552, 1994.50447761, 1994.5880597 ,\n",
              "        1994.67164179, 1994.75522388, 1994.83880597, 1994.92238806,\n",
              "        1995.00597015, 1995.08955224, 1995.17313433, 1995.25671642,\n",
              "        1995.34029851, 1995.4238806 , 1995.50746269, 1995.59104478,\n",
              "        1995.67462687, 1995.75820896, 1995.84179104, 1995.92537313,\n",
              "        1996.00895522, 1996.09253731, 1996.1761194 , 1996.25970149,\n",
              "        1996.34328358, 1996.42686567, 1996.51044776, 1996.59402985,\n",
              "        1996.67761194, 1996.76119403, 1996.84477612, 1996.92835821,\n",
              "        1997.0119403 , 1997.09552239, 1997.17910448, 1997.26268657,\n",
              "        1997.34626866, 1997.42985075, 1997.51343284, 1997.59701493,\n",
              "        1997.68059701, 1997.7641791 , 1997.84776119, 1997.93134328,\n",
              "        1998.01492537, 1998.09850746, 1998.18208955, 1998.26567164,\n",
              "        1998.34925373, 1998.43283582, 1998.51641791, 1998.6       ,\n",
              "        1998.68358209, 1998.76716418, 1998.85074627, 1998.93432836,\n",
              "        1999.01791045, 1999.10149254, 1999.18507463, 1999.26865672,\n",
              "        1999.35223881, 1999.4358209 , 1999.51940299, 1999.60298507,\n",
              "        1999.68656716, 1999.77014925, 1999.85373134, 1999.93731343,\n",
              "        2000.02089552, 2000.10447761, 2000.1880597 , 2000.27164179,\n",
              "        2000.35522388, 2000.43880597, 2000.52238806, 2000.60597015,\n",
              "        2000.68955224, 2000.77313433, 2000.85671642, 2000.94029851,\n",
              "        2001.0238806 , 2001.10746269, 2001.19104478, 2001.27462687,\n",
              "        2001.35820896, 2001.44179104, 2001.52537313, 2001.60895522,\n",
              "        2001.69253731, 2001.7761194 , 2001.85970149, 2001.94328358,\n",
              "        2002.02686567, 2002.11044776, 2002.19402985, 2002.27761194,\n",
              "        2002.36119403, 2002.44477612, 2002.52835821, 2002.6119403 ,\n",
              "        2002.69552239, 2002.77910448, 2002.86268657, 2002.94626866,\n",
              "        2003.02985075, 2003.11343284, 2003.19701493, 2003.28059701,\n",
              "        2003.3641791 , 2003.44776119, 2003.53134328, 2003.61492537,\n",
              "        2003.69850746, 2003.78208955, 2003.86567164, 2003.94925373,\n",
              "        2004.03283582, 2004.11641791, 2004.2       , 2004.28358209,\n",
              "        2004.36716418, 2004.45074627, 2004.53432836, 2004.61791045,\n",
              "        2004.70149254, 2004.78507463, 2004.86865672, 2004.95223881,\n",
              "        2005.0358209 , 2005.11940299, 2005.20298507, 2005.28656716,\n",
              "        2005.37014925, 2005.45373134, 2005.53731343, 2005.62089552,\n",
              "        2005.70447761, 2005.7880597 , 2005.87164179, 2005.95522388,\n",
              "        2006.03880597, 2006.12238806, 2006.20597015, 2006.28955224,\n",
              "        2006.37313433, 2006.45671642, 2006.54029851, 2006.6238806 ,\n",
              "        2006.70746269, 2006.79104478, 2006.87462687, 2006.95820896,\n",
              "        2007.04179104, 2007.12537313, 2007.20895522, 2007.29253731,\n",
              "        2007.3761194 , 2007.45970149, 2007.54328358, 2007.62686567,\n",
              "        2007.71044776, 2007.79402985, 2007.87761194, 2007.96119403,\n",
              "        2008.04477612, 2008.12835821, 2008.2119403 , 2008.29552239,\n",
              "        2008.37910448, 2008.46268657, 2008.54626866, 2008.62985075,\n",
              "        2008.71343284, 2008.79701493, 2008.88059701, 2008.9641791 ,\n",
              "        2009.04776119, 2009.13134328, 2009.21492537, 2009.29850746,\n",
              "        2009.38208955, 2009.46567164, 2009.54925373, 2009.63283582,\n",
              "        2009.71641791, 2009.8       , 2009.88358209, 2009.96716418,\n",
              "        2010.05074627, 2010.13432836, 2010.21791045, 2010.30149254,\n",
              "        2010.38507463, 2010.46865672, 2010.55223881, 2010.6358209 ,\n",
              "        2010.71940299, 2010.80298507, 2010.88656716, 2010.97014925,\n",
              "        2011.05373134, 2011.13731343, 2011.22089552, 2011.30447761,\n",
              "        2011.3880597 , 2011.47164179, 2011.55522388, 2011.63880597,\n",
              "        2011.72238806, 2011.80597015, 2011.88955224, 2011.97313433,\n",
              "        2012.05671642, 2012.14029851, 2012.2238806 , 2012.30746269,\n",
              "        2012.39104478, 2012.47462687, 2012.55820896, 2012.64179104,\n",
              "        2012.72537313, 2012.80895522, 2012.89253731, 2012.9761194 ,\n",
              "        2013.05970149, 2013.14328358, 2013.22686567, 2013.31044776,\n",
              "        2013.39402985, 2013.47761194, 2013.56119403, 2013.64477612,\n",
              "        2013.72835821, 2013.8119403 , 2013.89552239, 2013.97910448,\n",
              "        2014.06268657, 2014.14626866, 2014.22985075, 2014.31343284,\n",
              "        2014.39701493, 2014.48059701, 2014.5641791 , 2014.64776119,\n",
              "        2014.73134328, 2014.81492537, 2014.89850746, 2014.98208955,\n",
              "        2015.06567164, 2015.14925373, 2015.23283582, 2015.31641791,\n",
              "        2015.4       , 2015.48358209, 2015.56716418, 2015.65074627,\n",
              "        2015.73432836, 2015.81791045, 2015.90149254, 2015.98507463,\n",
              "        2016.06865672, 2016.15223881, 2016.2358209 , 2016.31940299,\n",
              "        2016.40298507, 2016.48656716, 2016.57014925, 2016.65373134,\n",
              "        2016.73731343, 2016.82089552, 2016.90447761, 2016.9880597 ,\n",
              "        2017.07164179, 2017.15522388, 2017.23880597, 2017.32238806,\n",
              "        2017.40597015, 2017.48955224, 2017.57313433, 2017.65671642,\n",
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              "        50958063.03769857, 51004894.05717155, 51049498.        ]))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "0wqdFkD4-pxZ",
        "outputId": "d86403f5-fd1b-4cb0-b390-9120e8445d73"
      },
      "source": [
        "plt.figure(figsize = (7,5))\n",
        "plt.plot(tabla['Año'], tabla['Población'],'.') #datos originales en puntos azules\n",
        "plt.plot(xint1,yint1, 'orange') #datos con interpolación cúbica en naranja\n",
        "plt.title('Interpolación cúbica',)\n",
        "plt.xlabel('Año')\n",
        "plt.ylabel('Población')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3XkQ7jn_cjg"
      },
      "source": [
        "Con base en dicha interpolación, veremos en cuál mes de cuál año se presentó el cambio más drástico en la población."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RpNEYsNx_qvi"
      },
      "source": [
        "diff_pob = np.diff(yint1) #hallamos las primeras diferencias"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "sJZQC5vU_qhP",
        "outputId": "dccc7001-b1b0-4036-d05c-21f09c702806"
      },
      "source": [
        "plt.figure(figsize = (10,8))\n",
        "plt.title('Differencias poblacionales')\n",
        "plt.plot(diff_pob)\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7LG7rMK6TKPc",
        "outputId": "ea5341c7-52c6-47ac-f2f4-2085c8444c8f"
      },
      "source": [
        "diff_pob"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([49657.15885042, 49705.87837825, 49752.56925431, 49797.23147858,\n",
              "       49839.86505105, 49880.46997172, 49919.04624061, 49955.59385769,\n",
              "       49990.11282301, 50022.6031365 , 50053.06479823, 50081.49780814,\n",
              "       50107.90216627, 50132.27787262, 50154.62492716, 50174.94332992,\n",
              "       50193.23308086, 50209.49418004, 50223.72662741, 50235.930423  ,\n",
              "       50246.1055668 , 50254.25205878, 50260.36989899, 50264.45903896,\n",
              "       50266.35614736, 50265.53334098, 50261.88417096, 50255.40863736,\n",
              "       50246.10674015, 50233.97847933, 50219.02385491, 50201.24286689,\n",
              "       50180.63551527, 50157.20180002, 50130.94172119, 50101.85509115,\n",
              "       50069.73566929, 50033.98329908, 49994.48941643, 49951.25402126,\n",
              "       49904.27711364, 49853.55869352, 49799.09876095, 49740.89731586,\n",
              "       49678.95435832, 49613.26988828, 49543.84390578, 49470.67777532,\n",
              "       49394.46467781, 49317.02353085, 49238.64907275, 49159.34130358,\n",
              "       49079.10022325, 48997.92583182, 48915.81812929, 48832.7771156 ,\n",
              "       48748.80279083, 48663.89515494, 48578.05420791, 48491.27782506,\n",
              "       48402.96366084, 48311.68420196, 48217.23263805, 48119.60896914,\n",
              "       48018.81319521, 47914.84531626, 47807.70533232, 47697.39324336,\n",
              "       47583.90904938, 47467.25275037, 47347.42434638, 47224.42596643,\n",
              "       47098.63650487, 46970.86823863, 46841.22612262, 46709.71015685,\n",
              "       46576.32034133, 46441.05667602, 46303.91916097, 46164.90779614,\n",
              "       46024.02258155, 45881.2635172 , 45736.63060309, 45590.13150083,\n",
              "       45442.69325007, 45296.11630085, 45150.60751325, 45006.16688724,\n",
              "       44862.79442281, 44720.49011999, 44579.25397877, 44439.08599914,\n",
              "       44299.98618111, 44161.95452467, 44024.99102982, 43889.10223907,\n",
              "       43754.85816629, 43623.26308279, 43494.41921488, 43368.32656253,\n",
              "       43244.98512579, 43124.3949046 , 43006.55589902, 42891.468109  ,\n",
              "       42779.13153458, 42669.54617572, 42562.71203245, 42458.65506162,\n",
              "       42359.07499222, 42266.69166839, 42181.74931246, 42104.24792442,\n",
              "       42034.18750428, 41971.56805201, 41916.38956766, 41868.6520512 ,\n",
              "       41828.35550261, 41795.49992193, 41770.08530913, 41751.95611571,\n",
              "       41733.19927702, 41702.30295762, 41658.35999861, 41601.37039991,\n",
              "       41531.33416153, 41448.25128351, 41352.12176581, 41242.94560847,\n",
              "       41120.72281145, 40985.45337474, 40837.13729838, 40676.24112679,\n",
              "       40521.69128036, 40398.54059821, 40308.5111936 , 40251.60306646,\n",
              "       40227.81621687, 40237.15064477, 40279.60635018, 40355.18333311,\n",
              "       40463.88159355, 40605.70113149, 40780.64194696, 40987.87536763,\n",
              "       41200.02451635, 41384.09595925, 41538.1254361 , 41662.11294692,\n",
              "       41756.05849168, 41819.9620704 , 41853.82368306, 41857.64332967,\n",
              "       41831.42101023, 41775.15672477, 41688.85047323, 41573.04072206,\n",
              "       41442.46556912, 41313.30278078, 41186.37954144, 41061.69585114,\n",
              "       40939.25170981, 40819.04711752, 40701.08207422, 40585.35657993,\n",
              "       40471.87063467, 40360.62423839, 40251.61739115, 40144.99649332,\n",
              "       40044.12875462, 39952.3813847 , 39869.90078402, 39796.68695255,\n",
              "       39732.73989031, 39678.05959729, 39632.6460735 , 39596.49931893,\n",
              "       39569.61933358, 39552.00611744, 39543.65967053, 39544.4433957 ,\n",
              "       39551.68577667, 39562.95303687, 39578.15625662, 39597.29543593,\n",
              "       39620.37057476, 39647.38167317, 39678.32873113, 39713.21174863,\n",
              "       39752.03072572, 39794.78566232, 39841.47655848, 39891.61974364,\n",
              "       39937.09106362, 39971.14935982, 39993.59058373, 40004.41473534,\n",
              "       40003.62181466, 39991.21182168, 39967.1847564 , 39931.54061881,\n",
              "       39884.27940894, 39825.40112678, 39754.90577231, 39673.03761446,\n",
              "       39583.3502117 , 39488.52813566, 39388.63756218, 39283.67849126,\n",
              "       39173.65092293, 39058.55485713, 38938.39029393, 38813.15723328,\n",
              "       38682.8556752 , 38547.48561968, 38407.04706671, 38261.86866467,\n",
              "       38116.1209561 , 37972.67071014, 37831.57427936, 37692.83166375,\n",
              "       37556.44286333, 37422.40787809, 37290.72670799, 37161.39935313,\n",
              "       37034.42581341, 36909.80608888, 36787.54017953, 36668.04267026,\n",
              "       36555.93069164, 36454.08965551, 36362.56362556, 36281.35260175,\n",
              "       36210.4565841 , 36149.8755726 , 36099.60956725, 36059.65856803,\n",
              "       36030.02257501, 36010.7015881 , 36001.69560737, 36002.3849298 ,\n",
              "       36006.68159325, 36011.1301339 , 36015.69089076, 36020.36386378,\n",
              "       36025.14905303, 36030.04645846, 36035.05608009, 36040.17791792,\n",
              "       36045.41197193, 36050.75824224, 36056.21672858, 36065.73334167,\n",
              "       36113.65211716, 36217.65657987, 36377.89287458, 36594.36100131,\n",
              "       36867.06096005, 37195.99275082, 37581.1563736 , 38022.55182841,\n",
              "       38520.17911523, 39074.03823407, 39684.12918492, 40342.30211959,\n",
              "       40985.48296854, 41584.25022861, 42138.43857607, 42648.04801093,\n",
              "       43113.07853318, 43533.53014281, 43909.40283983, 44240.69662425,\n",
              "       44527.41149605, 44769.54745524, 44967.10450182, 45136.94622391,\n",
              "       45395.47393003, 45791.80366949, 46326.10869362, 46998.38900239,\n",
              "       47808.64459579, 48756.87547388, 49843.08163657, 51067.26308394,\n",
              "       52429.41981596, 53929.55183262, 55567.65913393, 57341.22394206,\n",
              "       59234.70830403, 61242.19077939, 63363.65971175, 65599.11510116,\n",
              "       67948.55694752, 70411.98525094, 72989.40001136, 75680.8012288 ,\n",
              "       78486.18890323, 81405.56303471, 84438.92362316, 87522.10783171,\n",
              "       90300.41528488, 92651.9550714 , 94576.61631784, 96074.3990243 ,\n",
              "       97145.30319068, 97789.32881703, 98006.47590333, 97796.74444961,\n",
              "       97160.13445584, 96096.64592201, 94606.27884816, 92744.37130599,\n",
              "       90785.34673287, 88814.11393151, 86830.69809001, 84835.09920839,\n",
              "       82827.3172866 , 80807.35232466, 78775.20432256, 76730.87328032,\n",
              "       74674.35919792, 72605.66207539, 70524.78191268, 68431.71870983,\n",
              "       66326.47246681, 64209.04318368, 62079.43086036, 59937.63549691,\n",
              "       57783.6570933 , 55617.49564953, 53439.15116562, 51248.62364157,\n",
              "       49045.91307735, 46831.01947297, 44603.94282845])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0nnft8oVO6t",
        "outputId": "a0fe5fb5-86e3-4cb1-81d5-7f43b2675654"
      },
      "source": [
        "max_dif = max(diff_pob) #buscamos el valor máximo entre las diferencias\n",
        "max_dif"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "98006.47590333223"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ScK1XP_WW9qL",
        "outputId": "fea96a6e-72ee-4b98-bfb9-23cb18e06553"
      },
      "source": [
        "np.where(diff_pob == max_dif) #hallamos la posición de la máxima diferencia"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([306]),)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhB9zprKZpTy",
        "outputId": "ae56132a-218d-47a9-eca5-7c7fb486c74a"
      },
      "source": [
        "int(1993 + (306/12)) #dividimos por 12 y sumamos al año 0 para hallar el año, usando la función piso"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2018"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RdvAN5Q1ZpJx",
        "outputId": "2a9f4522-83be-4e4e-ed1b-dee2faa256a5"
      },
      "source": [
        "306%12 #aplicamos función módulo para hallar el mes"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOsrP-4WZzLw"
      },
      "source": [
        "El cambio poblacional más fuerte ocurrió en el mes de junio del año 2018."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f9FPD3xm_qBl"
      },
      "source": [
        "Ahora haremos varios ajustes de curva polinomial para elegir el que mejor representa los datos poblacionales."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHInWIcE-2FK"
      },
      "source": [
        "tabla_predicciones = tabla.copy()"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wO4aEJY_-1_y"
      },
      "source": [
        "y2 = np.array(tabla_predicciones['Población'])\n",
        "x2 = np.array(tabla_predicciones['Año'])"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shcqjWH0AKL6",
        "outputId": "844aa233-c6c6-4db8-df05-d30f50a9aa7c"
      },
      "source": [
        "x2,y2"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n",
              "        2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n",
              "        2015, 2016, 2017, 2018, 2019, 2020, 2021]),\n",
              " array([35032308, 35629174, 36229830, 36830574, 37426532, 38012359,\n",
              "        38585016, 39140080, 39674811, 40190679, 40693254, 41188093,\n",
              "        41671878, 42170126, 42658630, 43134017, 43608630, 44086292,\n",
              "        44553416, 45001571, 45434942, 45866010, 46313898, 46830116,\n",
              "        47419200, 48258494, 49395678, 50372424, 51049498]))"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sSKFnGqiANPg"
      },
      "source": [
        "from numpy.polynomial.polynomial import polyval"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47l33WE2AvH-"
      },
      "source": [
        "#### Ajuste grado 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7yYgFKsAWhl"
      },
      "source": [
        "grad = 1\n",
        "c = np.polyfit(x2, y2, grad)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ENUuW49WAeXq",
        "outputId": "a400c927-34f4-49e8-ba00-45960bb71135"
      },
      "source": [
        "c"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 5.29747365e+05, -1.02056650e+09])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "XSx7yHirAfOi",
        "outputId": "0321438c-d024-4928-f803-4fdcac539863"
      },
      "source": [
        "tabla_predicciones['Ajuste grado 1'] = np.polyval(c, tabla_predicciones['Año'])\n",
        "\n",
        "plt.figure(figsize = (7,5))\n",
        "plt.title('Regresión lineal simple')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Población'],'.')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Ajuste grado 1'])\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "puzi7TW7Ax0r"
      },
      "source": [
        "### Ajuste grado 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHenaXN-AnWK"
      },
      "source": [
        "grad = 2\n",
        "c = np.polyfit(x2, y2, grad)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49AAq-5NA0Ig",
        "outputId": "2e3a1428-28b5-429a-d119-42d68255d3c5"
      },
      "source": [
        "c"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.65412731e+03, -6.10991966e+06,  5.64222358e+09])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "AaoXtqGOAz3W",
        "outputId": "60bd3efd-e159-44b9-a643-3d42b421a215"
      },
      "source": [
        "tabla_predicciones['Ajuste grado 2'] = np.polyval(c, tabla_predicciones['Año'])\n",
        "\n",
        "plt.figure(figsize = (7,5))\n",
        "plt.title('Ajuste grado 2')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Población'],'.')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Ajuste grado 2'])\n",
        "plt.show()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGskRg7nBR58"
      },
      "source": [
        "### Ajuste grado 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2N-Pw8EOAz0g"
      },
      "source": [
        "grad = 3\n",
        "c = np.polyfit(x2, y2, grad)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "duLW-1kLAzxv",
        "outputId": "f616b021-9b51-4a9c-b0f7-f2adbd50066b"
      },
      "source": [
        "c"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 6.42371964e+02, -3.86606747e+06,  7.75632651e+09, -5.18731962e+12])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "QF5TQ70QBXuB",
        "outputId": "2b3ecf7d-494b-4869-d3fb-a69ac76c3d83"
      },
      "source": [
        "tabla_predicciones['Ajuste grado 3'] = np.polyval(c, tabla_predicciones['Año'])\n",
        "\n",
        "plt.figure(figsize = (7,5))\n",
        "plt.title('Ajuste grado 3')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Población'],'.')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Ajuste grado 3'])\n",
        "plt.show()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mTJscAOZBhIS"
      },
      "source": [
        "### Ajuste grado 4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kM8gtKQCBhwY"
      },
      "source": [
        "grad = 4\n",
        "c = np.polyfit(x2, y2, grad)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9frCt8SGBhtK",
        "outputId": "483eb894-4a56-48da-c894-42ab0a59843b"
      },
      "source": [
        "c"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 4.21427259e+01, -3.37679431e+05,  1.01464417e+09, -1.35499014e+12,\n",
              "        6.78555505e+14])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "vUfaUy2wBhp_",
        "outputId": "3164d05c-f8ac-4dc7-f8cf-83107eb8f0dd"
      },
      "source": [
        "tabla_predicciones['Ajuste grado 4'] = np.polyval(c, tabla_predicciones['Año'])\n",
        "\n",
        "plt.figure(figsize = (7,5))\n",
        "plt.title('Ajuste grado 4')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Población'],'.')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Ajuste grado 4'])\n",
        "plt.show()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LhZWISDRtWQ9jLoMF78OhZ8Fpj0H1vXZqVpVvSlsWCisRkWjJnA9vDArOU/W+H466VNdPRYnCSkQkGuaOhXcvD26XNOQ9aKOb+kSTwkpEZE/k/gaTb4cvn4aWR8DZL0G95mFXVekorEREymrVHHj7Ysicy6qOF/FO40s4MqsWyfWK/1QpHYWViEhpucPMp2HybVCzPj/2fIHTJtYkJ28J1adl6CLfcqCLgkVESiM7M3js/MS/wwEnwOVfMCnnUF3kW840shIRKakfPgwmUeRkQ59/wxEXgxnd2ibqIt9yprASESlO7m/BIb8vnwku8h0wEpod/PtqXeRb/hRWIiK7s2oOvDUUVs+DbsPhxNugWs2dmuki3/KlsBIRKYo7zBwRTEuvWR/Oe/v3e/tJxVNYiYgUtnEVjL0cFk6BA3tD/yegdpOwq6rSFFYiItu5w/dvkzv+eix3Ez93u5tWJ1+pWybFAE1dFxEB2PAzvDYQ3h7K3C0N6bPlbnp91p70n34NuzJBIysRqerc4esXYdKtsC2XGW2v4YJ5yeR5AokeXDOliRPhK/HIyswSzewbMxu/i/Vnm9lcM5tjZq9Gr0QRkXKybgm81A/euxr27QyXfUat468mKSmJREPXTMWQ0oysrgbmATvd9crM2gM3Ace4e5aZNYtSfSIi0Ze/Lbhd0sd3gyVC34eh6wWQkEByY3TNVAwqUViZWUvgVOBe4NoimlwCPOHuWQDunhm1CkVEoilzPoy7ApZ/Be17BUFVv+UOTXTNVOwp6cjqEeAGoO4u1h8IYGafAYnAHe4+cc/LExGJkm25MOMRmP4AVK8Df/pv8CRfzfSLC8WGlZn1BTLdPd3Muu9mO+2B7kBLYLqZHeruO0yjMbNhwDCAVq1a7UHZIiIlk56RxcJZMzht6b3stW4eHHIGnPIg1GkadmlSCiWZYHEM0M/MlgKvAz3M7JVCbZYD49w9192XAD8QhNcO3P0Zd09x95SmTfWNIiLl65uFy/n22SsYkH4+2WtXsrDH03DWCwqqOFRsWLn7Te7e0t3bAOcCH7v7eYWavUswqsLMmhAcFlwc3VJFRErIHb5/h/ZvnsDQhPG8te04Ts55gA+3pYRdmZRRma+zMrO7gDR3Hwd8CPQys7nANuB6d9cDXUSk4q35ET64DhZPI6FRR85dfQVfbWunaehxztw9lB2npKR4WlpaKPsWkUooZxNM/zd8/h+othf0uAWOGEr6sg2ahh5HzCzd3XcaAusOFiIS39xh/niYeBOsXwadB0LPu6BOcLmnpqFXDgorEYkb6RlZO46S1i6CCX+HhZOhWUe4cAK0PjrsMqUcKKxEJC6kZ2QxaGQqOXn51E3KY0LXNJp/PwISa8DJ98GRl0BitbDLlHKisBKRuJC6eC05efmcYOnckfASzWetDi7q7Xk31Ns37PKknCmsRCQudG+4lsOq388fbRYLvQULer9Kh26nhl2WVBCFlYjEtk1rYOo/OST9BfJq7MWMltew17GX0bXt3mFXJhVIYSUisSkvB758Gj55EHKyIeUikrrfxLG1da1UVaSwEpHY4g7z34fJt8K6xdDuJOh1LzQ7KOzKJEQKKxEJzU5T0VfOhg//AUs/hSYdYNDb0P6ksMuUGKCwEpFQFJyK3jxpA+8eNJUmP74JtRpCn39D8oWQqF9REtB3goiEInXxWsjbwl8SJnB5wlhqLcyFPwyH464LAkukAIWViFS8/Hz6+HROr34vLWwNUzyFfc54kE6HdQ27MolRCisRqVgLP4Ipt7P/L9+xufEhvNvybvZLPoVOun+f7IbCSkQqxs/fwpTbYfE0aNAK/jSSvToN4PSEkjwDVqo6hZWIlK+spfDxPfDd6OBc1Mn3wRFDIalG2JVJHFFYiUhU7DQNffM6mP4gfDUSLAGOvQaO+SvUahB2qRKHFFYisscKTkOvl5TL+0d+T4vvnw7uPNHlz9D9H1C/RdhlShxTWInIHktdvJa8vDwGJEzn2oS32Dd9HRzYG068HfbuGHZ5UgkorERkz7hzcmIavarfTXtbziw/gA2nPEmHo04JuzKpRBRWIlJ2S6bDlDtptyKNLfXbMrHlv2h65Dl0aNMo7MqkklFYiUjprfgaProLFk+Fei3gtMeo2WUQvXV7JCknJf7OMrNEIA1Y4e59d9FmAPAWcIS7p0WnRBGJGat/gKn3wNyxUKsR9LoHjrgYqtUKuzKp5ErzZ9DVwDygXlErzaxupM3MKNQlIiHaaRr6+uUw7V/w7ShIqgXH/x3+cAXULPLXgUjUlSiszKwlcCpwL3DtLprdDdwPXB+d0kQkDAWnoTdLymbsYV+y9/yXAYcjL4U//g3qNA27TKliSjqyegS4Aahb1Eoz6wrs5+7vm5nCSiSOpS5eS7W8TQxLmMAlCe9Te85W6DIQut8Y3CZJJATFhpWZ9QUy3T3dzLoXsT4B+D/gghJsaxgwDKBVK33Ti8Sc3C30+20MA6s/SiPbyCQ/ghZn/JNDOh8ZdmVSxZm7776B2X3AYCAPqElwzuoddz8vsr4+sAjIjnzKPsA6oN/uJlmkpKR4WprmYIjEhG25wfmoTx6ADSvY0PxYPtz7Etp2OT44ZyVSQcws3d1TCi8vdmTl7jcBN0U20h24bntQRdavB5oU2NG0SBslkUisy8+HOe/A1H/CukXQ8gg4YwT19j+Os8KuTaSAMl8UYWZ3AWnuPi6K9YhIRXCHHz6Ej++GVd9Ds0Ng4OvBLZLMwq5OZCelCit3nwZMi7y/bRdtuu9pUSISXTtMRc//Prigd/mX0KgtDHgWDvkT6LlSEsN0ublIJbd9KvpB236kS7U3wb6Dus3htEehyyBIrBZ2iSLFUliJVHILvvuKR3iI3tW/Yp3XYUbbazh24N911wmJKworkcoqKwOm3cfA2W+wKaEGj+QN4GU7lWeO76GgkrijsBKpbLIzgyf0pj0PloB1u5yFbS6i2s/OM9tvnyQSZxRWIpXFb7/C549B6lOQtxW6DobjboD6LegCdOkQdoEiZaewEol3OZth5gj47BHYsh46nQkn/AMaHxB2ZSJRo7ASiVd5OfD1i8Ehv+xV0P5kOPFW2OfQsCsTiTqFlUicSV+yhnUzR3Hc8v9SI3sZtD4Gzn4JWnULuzSRcqOwEokX7iyc8RZ1p9xJsi1jjreheq8XaH/06brrhFR6CiuRePBTKky+nXbLUlni+zA89yo+9CO5Judg2iuopApQWInEslVzg1sj/TAB6uxDxh/u5bQZrfnNE6iWlEC3to3DrlCkQiisRGLRrz/B1Ptg1mtQox6ceBsc9RdaV6/NiwcVeuS8SBWgsBKJJZvWwqcPwVf/BQyOvgKOvRb2avR7k+TWDRVSUuUorERiwdbs4GLezx+DnGzo8mfofhPUbxl2ZSIxQWElEqL0JZlkfzaSo1c8S7Xf1sBBfaHHrdDsoLBLE4kpCiuRMLizaPprNP74TpLtF77yg6nb92kOOuKksCsTiUkKK5GK9tNMmHQLByz/kh+8BRfmXs9078K12W3QeEqkaAorkYqyZiF8dAfMew/q7MPSY/7FGdP3Y4ubpqGLFENhJVLeslfDJ/dD+vOQVBNOuBn+MJw21Wvz0oGahi5SEgorkfKSsxlSn4AZj0LuZki+ALrfCHWa/d5E09BFSkZhJRJt+dvg21dh6r2wcWUww++kO6BJ+7ArE4lbCiuRKPrxszE0/PwemmxaCC1S4MznoPXRYZclEvcSStrQzBLN7BszG1/EumvNbK6ZzTazj8ysdXTLFIlxmfNY/9/TaD/5ArKzN3L1tr+S3nO0gkokSkozsroamAfUK2LdN0CKu282s8uAB4BzolCfSGzbtBam/RPSnqdG4l7ck3ceL+b1It+SOHDJOpLbNCp+GyJSrBKNrMysJXAqMLKo9e4+1d03Rz5MBXSPGKnc8nLg88fhscMh7Xk4Yijzz/qEV6wv+ZakqegiUVbSkdUjwA1A3RK0HQpMKHNFIrHMHea/D5NvhXWLoV1P6HUPNDuILsCoixtpKrpIOSg2rMysL5Dp7ulm1r2YtucBKcDxu1g/DBgG0KpVq1IXKxKqlbPhw3/A0k+h6UEw6G1ov+PtkTQVXaR8lGRkdQzQz8z6ADWBemb2irufV7CRmZ0E3Awc7+5bi9qQuz8DPAOQkpLie1S5SEXZuAo+vhu+eQVqNYQ+/4bkCyFRk2lFKkqxP23ufhNwE0BkZHVdEUF1OPA00NvdM8uhTpGKl/sbfPEEzHgY8rbCH4bDcddDrQZhVyZS5ZT5T0MzuwtIc/dxwINAHWC0mQH85O79olOiSAVzh/nj2fr+jdTIXk5Wq1407P8vaHxA2JWJVFmlCit3nwZMi7y/rcByPddAKofVC2DC32HxVDJ8P+7MvZn0JYcyKrsRyZrcJxIaHXQXAdiyHj55AGaOgOq1md7ueobOOYxcTyTR80ldvFYTJ0RCpLCSqi0/H2a9BlPugE2roev5cOJt1F6TSOKCVPLz8nXNlEgMUFhJ1bUiHT64AVakQcsj4M9vQIuuACTXhlEXd9M1UyIxQmElVU/2avjozmAqeu2mcPoIOOwcSNjxhi66ZkokdiispOrYlgtfjYSp90HuJjj6CjjuBqhZ1O0uRSSWKKykSliQ+gFNpt9C482L4IAe0Pt+aHpg2GWJSAmV+BEhInEpO5O1L19Ah4kD2bxpA5dvu470Pz6roBKJMxpZSeWUvw3SnoOP7qZBziYezzudx/P6k2s1OESP7hCJOworqXxWfA3vXws/fwP7H8e8w2/n8dGryTVNQxeJVworqTx++zW44exXz0KdZjDgWeg0gE5mjKqfpWnoInFMYSXxzx1mvwmTbobNa+HIYdDjZqhZ//cmmoYuEt8UVhLfVi+A9/8WPGOqRTIMeguadwm7KhGJMoWVxKeczTD9geDR8tX3gr4PQ9chkJAYdmUiUg4UVhJ/fpzM1rFXUyN7BWvaDaDJ6fdDnaZhVyUi5UjXWUn8yM6Ety6CUWeybCOck3Mrxy44i/S1+ptLpLLTT7nEPvfgPn6TboHczcxsfSlDfjiaLV5Nj+8QqSIUVhLb1iyE8X8NJlC0OhpOe5SkzU1hcSqJenyHSJWhsJLYlJcDnz0K0x+EajXhtMfg8MGQkEAyenyHSFWjsJLYs+xLGHcVrJ4Hh5wR3HS27t47NNF1UyJVi8JKYseWDfDRXcFjPOq1gIFvQIfeYVclIjFAYSWxYd54+OB6yP4Ful0GJ9wMNeqEXZWIxAiFlYRr4yqy3rqKhhkT2dzoYPa6+JXgThQiIgWU+DorM0s0s2/MbHwR62qY2RtmttDMZppZm2gWKZWQO8x6nbz/HMFeSz/igbxzOXL1zaTntQ27MhGJQaUZWV0NzAOKegb4UCDL3duZ2bnA/cA5UahPKqP1y2H8NfDjJFbX68x52YNZlN+cREPXTIlIkUo0sjKzlsCpwMhdNOkPvBh5/xZwopnZnpcnlYo7pD0PT3SDpTOg9/38fMY7rEhsSaKha6ZEZJdKOrJ6BLgBqLuL9S2AZQDunmdm64HGwJqCjcxsGDAMoFWrVmWpV+LVuiXw3lWwZDq0+SP0+w802l/XTIlIiRQbVmbWF8h093Qz674nO3P3Z4BnAFJSUnxPtiVxIj8fvnwGProTLBH6PgLJF0CBgbeumRKR4pRkZHUM0M/M+gA1gXpm9oq7n1egzQpgP2C5mSUB9YG1Ua9W4suaH2HsFbAsFdr1hNMegfotw65KROJQsWHl7jcBNwFERlbXFQoqgHHAEOAL4EzgY3fXyKkKSs/IYuaiVfTbPIaW3zwM1WrBGU/DYefsMJoSESmNMl9nZWZ3AWnuPg54FnjZzBYC64Bzo1SfxJH0jCzuGDmae+0pWiYsJqvVyTQ86z873SpJRKS0ShVW7j4NmBZ5f1uB5VuAs6JZmMSZ/G1snfYQbyU8RTa1uDL3Kg5qcz7DFVQiEgW6g4XsuTU/wruXcfTyr/iQI7k150I2JDXkggOahF2ZiFQSCispu/x8mDkimOmXVBMGPEuTuj0YsmSdpqGLSFQprKRs1i2BscMh4zNofzKc9ijU25dkILlNo7CrE5FKRmElpeMOac/BpFshIRH6P22hdQsAAA8kSURBVAFdBmmmn4iUK4WVlNz65cF1U4unQtsTgrtQNNgv7KpEpApQWEnx3OHbUTDxJsjfBqf+H6RcpNGUiFQYhZXs1qx5C6g3+W/sv+5TaH1McNiv0f5hlyUiVUyJn2clVc/iqS/R+vUT2HdtKv/MP5/0E15WUIlIKDSykp39lgUfXE/b70bzrR/AtbmXkUFz6i/JIrmNHuEhIhVPYSU7WjQV3r0cslex4vBrGZSWzBZMz5oSkVAprCSQsxmm3AFfPg1NDoRzR9GiRVde6pKlZ02JSOgUVgIrvoYxl8KaH+Cov8BJdwR3S0fPmhKR2KCwqsq25cGM/4NP7ofazWDwGDigR9hViYjsRGFVVa1ZGIymVqRBpzPh1H9DLY2gRCQ2KayqkPSMLFIXraFv7gRap90HidXhzOeg04CwSxMR2S2FVRWRnpHFNSM/4C57mtYJs1jf/I/UP/cZqNc87NJERIqlsKoiMmeOZmzCndQkh9tzL6BZuysYrqASkTihsKrstm6EiTdyytxX+J62/DX3cpYntmSUHowoInFEYVWZLfsK3rkEfs2AP17H1raXcsbSDbpmSkTijsKqMtqWB9MfDF71WsAF70Pro4MHI+7fLOzqRERKTWFV2axdBO8MC6akH3Yu9HkAatYPuyoRkT1SbFiZWU1gOlAj0v4td7+9UJtWwItAAyARuNHdP4h+ubJL7vDNKzDh75CYpCnpIlKplGRktRXo4e7ZZlYNmGFmE9w9tUCbW4A33f0pM+sIfAC0iX65UqTN6+C9q2Dee9Dmj3DGCKjfMuyqRESiptiwcncHsiMfVou8vHAzoF7kfX3g52gVKEVLzwhuMNuzxhwO/PwG2LwWet4Nf7gCEvSYMhGpXEp0zsrMEoF0oB3whLvPLNTkDmCSmV0J1AZOimaRsqP0jCwuHDmda/xVDkyayG8N2lPrktGw72FhlyYiUi5K9Ce4u29z9y5AS+BIM+tUqMlA4AV3bwn0AV42s522bWbDzCzNzNJWr169p7VXWT/MTmW03cyFSRN5Me9kXuz0goJKRCq1Uh0vcvdfgalA70KrhgJvRtp8AdQEdrrq1N2fcfcUd09p2rRp2SquytwhdQTnfHs+jWwDF+XewH12IUe0bxF2ZSIi5aokswGbArnu/quZ1QJ6AvcXavYTcCLwgpkdTBBWGjpF08ZVMPZyWDiFhAN783PXe0heaQzXBb4iUgWU5JzVvsCLkfNWCQSz/sab2V1AmruPA/4G/NfMriGYbHFBZGKGRMOCiTB2OORkw6kPQcpQOpvR+aCwCxMRqRglmQ04Gzi8iOW3FXg/FzgmuqUJOZth8q3w1UjY+1AYMBKaKaFEpOrRHSxi1crZ8PbFsGZBMB39xNsgqUbYVYmIhEJhFWvy8yH1SfjoTqjVSI+aFxFBYRUz0jOymD1vPgN+uod6P8+ADqdCv/9A7cZhlyYiEjqFVQxIz8jiuZH/4Z6Ep0kil4xj7qN1z8vALOzSRERigu7LE7acTdSceA1PJD7Ecm9Cv9x7GV+tl4JKRKQAjazC9PM38PbFdFy7iP/mn8ZDuWdBUnW6tdWhPxGRghRWYcjfBp8/Bh/fA7WbYUPG0TXhUK5cvFZP8RURKYLCqqKtXw5j/gJLP4WO/aHvI7BXo+ApvgopEZEiKawq0pwx8N7VwWPn+z8JXf6sc1MiIiWgsKoIWzcGT/D9dhS0SIY//RcaHxB2VSIicUNhVd6WfQXvXAy//gTHXQ/H/x0Sq4VdlYhIXFFYlYP0jCxmLlpF/41v0OLbR6Fec7jgfWh9dNiliYjEJYVVlKVnZHHDyHHcb4/TIuEH1rXtR6Oz/gO1GoRdmohI3FJYRZM76754iXcT7gPgmtzLabffUIYrqERE9ojCKlo2r4P3r6Xn/DGk04Frcy5jVdI+jNIFviIie0xhFQ2Lp8GYy2BTJvS4Ffa7gLOX/qoLfEVEokRhtSfytsJHd8EXj0Pj9jBwCjQ/PLjAd/8mYVcnIlJpKKzKatUcePsSyJwDR1wMPe+G6nuFXZWISKWksCqt/HyY+RRMuQNq1oc/j4YDe4VdlYhIpaawKo31K+Ddy2DJJ9ChD5z2GNRpGnZVIiKVnsKqGOkZWaQuXktvS+WA1JthWw6c9ih0HaL7+omIVBCF1W6kZ2QxbOTH/IPnOSDxUzY16Uztgc/rvn4iIhWs2CcFm1lNM/vSzGaZ2Rwzu3MX7c42s7mRNq9Gv9SKtyx9ImMTbuD0hBk8mvcnXjr4aQWViEgISjKy2gr0cPdsM6sGzDCzCe6eur2BmbUHbgKOcfcsM2tWTvVWjNzfYMqdnD77KZayD2fn3sGcxA6MardP2JWJiFRJxYaVuzuQHfmwWuTlhZpdAjzh7lmRz8mMZpEVank6jLkU1v4IR17Kug5/pcdPm/mHLvAVEQlNic5ZmVkikA60IwilmYWaHBhp9xmQCNzh7hOjWWi525YLnzwAnz4EdfeB88dC2+50BbrqyJ+ISKhKFFbuvg3oYmYNgDFm1sndvy+0nfZAd6AlMN3MDnX3Xwtux8yGAcMAWrVqFYXyoyRzXjCaWjkLOg+E3v/SXdJFRGJIsRMsCoqEz1Sgd6FVy4Fx7p7r7kuAHwjCq/DnP+PuKe6e0rRpDFyflL8NPnsMnj4+uIbqnFFwxggFlYhIjCnJbMCmkREVZlYL6AnML9TsXYJRFWbWhOCw4OKoVhpt65bAC31h8q3QvidcngoH9w27KhERKUJJDgPuC7wYOW+VALzp7uPN7C4gzd3HAR8CvcxsLrANuN7d15Zb1Xsgfek6Nnw2kuOWPExiYhKcPgI6n6sLfEVEYpgFk/0qXkpKiqelpVXoPmfPnce6Ny6ju33D596JOmc/zWGHdKrQGkREZNfMLN3dUwovL9U5q7jlDl+/RIe3T+Qo5nB77hDOz7mRTzNrhl2ZiIiUQOW/3VJWBrx3FSyeRs4+RzFgxUAW5TejWlIC3fQUXxGRuFB5wyo/H9Kehcm3B+ejTn2IuskXcd+y9aQuXqun+IqIxJHKGVZrF8HYK+Cnz+GAHsFd0hsE13Ult26okBIRiTOVK6zyt0Hqk/DxPZBYA/o/AV0GaaafiEicqzxhlTkfxg6HFWlw4CnQ92Got2/YVYmISBTEf1hty4XPHgnu61e9Dgx4FjoN0GhKRKQSieuwmvv1DJp+fC1NsxfAIWfAKQ/qMfMiIpVQ3F5n9c3CZbQYezZs/IUrt11L+pEPK6hERCqpuB1Zfb5sKw/nXsWs/P3JtjoctHitZvmJiFRScTuy6ta2MV8mdibb6ugCXxGRSi5uR1bJrRsy6uJuusBXRKQKiNuwAl3gKyJSVcTtYUAREak6FFYiIhLzFFYiIhLzFFYiIhLzFFYiIhLzFFYiIhLzFFYiIhLzFFYiIhLzFFYiIhLzzN3D2bHZaiAjlJ2XXhNgTdhFhKgq9199r7qqcv/D7Htrd9/pERqhhVU8MbM0d08Ju46wVOX+q+9Vs+9Qtfsfi33XYUAREYl5CisREYl5CquSeSbsAkJWlfuvvlddVbn/Mdd3nbMSEZGYp5GViIjEvCobVmb2nJllmtn3BZZ1NrMvzOw7M3vPzOpFllc3s+cjy2eZWfcCnzPNzBaY2beRV7MQulMqZrafmU01s7lmNsfMro4sb2Rmk83sx8i/DSPLzcweM7OFZjbbzLoW2NaQSPsfzWxIWH0qqSj3fVuBr/u4sPpUGmXo/0GRn4mtZnZdoW31jnzvLzSzG8PoT2lEue9LI78PvjWztDD6Uxpl6PugyPf7d2b2uZl1LrCtcL7u7l4lX8BxQFfg+wLLvgKOj7y/CLg78n448HzkfTMgHUiIfDwNSAm7P6Xs+75A18j7usAPQEfgAeDGyPIbgfsj7/sAEwADugEzI8sbAYsj/zaMvG8Ydv8qou+Rddlh96cC+t8MOAK4F7iuwHYSgUVAW6A6MAvoGHb/KqLvkXVLgSZh96kc+3709p9l4JQCP/Ohfd2r7MjK3acD6wotPhCYHnk/GRgQed8R+DjyeZnAr0BMXYNQGu6+0t2/jrzfCMwDWgD9gRcjzV4ETo+87w+85IFUoIGZ7QucDEx293XunkXwf9a7ArtSalHse1wqbf/dPdPdvwJyC23qSGChuy929xzg9cg2YlYU+x53ytD3zyM/0wCpQMvI+9C+7lU2rHZhDv/7jz8L2C/yfhbQz8ySzGx/ILnAOoDnI4cDbjUzq7hy95yZtQEOB2YCe7v7ysiqX4C9I+9bAMsKfNryyLJdLY8Le9h3gJpmlmZmqWZ2OnGmhP3flarwtd8dByaZWbqZDSuXIstJGfo+lODoAoT4dU+qiJ3EkYuAx8zsVmAckBNZ/hxwMJBGcIuoz4FtkXWD3H2FmdUF3gYGAy9VaNVlZGZ1CGr+q7tvKJiz7u5mVmmnikap760jX/u2wMdm9p27LyqnkqNKX/s97vuxka99M2Cymc2PHK2JaaXtu5mdQBBWx1ZooUXQyKoAd5/v7r3cPRl4jeDYLO6e5+7XuHsXd+8PNCA45ou7r4j8uxF4lWCYHPPMrBrBN+0od38nsnjV9kNckX8zI8tXsONIsmVk2a6Wx7Qo9b3g134xwbnLw8u9+CgoZf93pSp87XepwNc+ExhDHPzcl7bvZnYYMBLo7+5rI4tD+7orrAqI/JWEmSUAtwAjIh/vZWa1I+97AnnuPjdyWLBJZHk1oC/wfZEbjyGRQ5XPAvPc/f8KrBoHbJ/RNwQYW2D5+RboBqyPHDr4EOhlZg0js4h6RZbFrGj1PdLnGpFtNgGOAeZWSCf2QBn6vytfAe3NbH8zqw6cG9lGzIpW382sduRICpHfC72I8Z/70vbdzFoB7wCD3f2HAu3D+7pXxCyOWHwRjJxWEpw8XU4w1L2aYMT0A/Av/nfRdBtgAcFJySkEh38AahPMDJxNcL7rUSAx7L6VoO/HEhxznw18G3n1ARoDHwE/RvrZKNLegCcIRprfUWD2I8Gh04WR14Vh962i+k4wW+o7gvOZ3wFDw+5bOfV/n8jPxwaCiUXLgXqRdX0iPyuLgJvD7ltF9Z1gJtysyGtOJe37SCCrQNu0AtsK5euuO1iIiEjM02FAERGJeQorERGJeQorERGJeQorERGJeQorERGJeQorERGJeQorERGJeQorERGJef8PzrsjPXS3eaUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZN9Gjf_fCXh4"
      },
      "source": [
        "### Ajuste grado 5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wF3q9nzsBhkA"
      },
      "source": [
        "grad = 5\n",
        "c = np.polyfit(x2, y2, grad)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VIl3CbJ9Bctn",
        "outputId": "22879200-38ca-47cd-f9d1-ec614a19fb70"
      },
      "source": [
        "c"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.59483984e+00, -1.59620785e+04,  6.39029091e+07, -1.27914762e+11,\n",
              "        1.28023465e+14, -5.12527729e+16])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "eo1uN2aoCbzH",
        "outputId": "1278d36c-41a8-492a-8993-518b3d6437ed"
      },
      "source": [
        "tabla_predicciones['Ajuste grado 5'] = np.polyval(c, tabla_predicciones['Año'])\n",
        "\n",
        "plt.figure(figsize = (7,5))\n",
        "plt.title('Ajuste grado 5')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Población'],'.')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Ajuste grado 5'])\n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLwZ0YmFCoUt"
      },
      "source": [
        "### Ajuste grado 6"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xfa7G5oFCg1B",
        "outputId": "f52524e8-3eb5-4cdb-edee-fec78213bd7d"
      },
      "source": [
        "grad = 6\n",
        "c = np.polyfit(x2, y2, grad)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2882: RankWarning: Polyfit may be poorly conditioned\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHNWHEcYCtbu",
        "outputId": "36541be8-a8bb-4d72-8fec-4d53dadddb33"
      },
      "source": [
        "c"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3.94314200e-04, -3.15599610e+00,  7.88769891e+03,  4.78550364e+04,\n",
              "       -3.17476284e+10,  5.07812505e+13, -2.54022438e+16])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "2YJB_8kjCtY7",
        "outputId": "ce5177ab-8561-4071-eb02-ee87bcef3b91"
      },
      "source": [
        "tabla_predicciones['Ajuste grado 6'] = np.polyval(c, tabla_predicciones['Año'])\n",
        "\n",
        "plt.figure(figsize = (7,5))\n",
        "plt.title('Ajuste grado 6')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Población'],'.')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Ajuste grado 6'])\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "UuvhukW_CtVL",
        "outputId": "5f53747c-52a2-444e-8ec0-b6522f09d8b4"
      },
      "source": [
        "display(tabla_predicciones)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "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>Año</th>\n",
              "      <th>Población</th>\n",
              "      <th>Ajuste grado 1</th>\n",
              "      <th>Ajuste grado 2</th>\n",
              "      <th>Ajuste grado 3</th>\n",
              "      <th>Ajuste grado 4</th>\n",
              "      <th>Ajuste grado 5</th>\n",
              "      <th>Ajuste grado 6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1993</td>\n",
              "      <td>35032308</td>\n",
              "      <td>3.522000e+07</td>\n",
              "      <td>3.542842e+07</td>\n",
              "      <td>3.479710e+07</td>\n",
              "      <td>3.509294e+07</td>\n",
              "      <td>35018104.0</td>\n",
              "      <td>35018624.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1994</td>\n",
              "      <td>35629174</td>\n",
              "      <td>3.574975e+07</td>\n",
              "      <td>3.591351e+07</td>\n",
              "      <td>3.555275e+07</td>\n",
              "      <td>3.563728e+07</td>\n",
              "      <td>35642464.0</td>\n",
              "      <td>35642636.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1995</td>\n",
              "      <td>36229830</td>\n",
              "      <td>3.627950e+07</td>\n",
              "      <td>3.640190e+07</td>\n",
              "      <td>3.626161e+07</td>\n",
              "      <td>3.620526e+07</td>\n",
              "      <td>36249032.0</td>\n",
              "      <td>36249012.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1996</td>\n",
              "      <td>36830574</td>\n",
              "      <td>3.680925e+07</td>\n",
              "      <td>3.689361e+07</td>\n",
              "      <td>3.692752e+07</td>\n",
              "      <td>3.678809e+07</td>\n",
              "      <td>36841704.0</td>\n",
              "      <td>36841620.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1997</td>\n",
              "      <td>37426532</td>\n",
              "      <td>3.733899e+07</td>\n",
              "      <td>3.738862e+07</td>\n",
              "      <td>3.755435e+07</td>\n",
              "      <td>3.737800e+07</td>\n",
              "      <td>37423112.0</td>\n",
              "      <td>37423152.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1998</td>\n",
              "      <td>38012359</td>\n",
              "      <td>3.786874e+07</td>\n",
              "      <td>3.788694e+07</td>\n",
              "      <td>3.814594e+07</td>\n",
              "      <td>3.796822e+07</td>\n",
              "      <td>37995048.0</td>\n",
              "      <td>37995124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1999</td>\n",
              "      <td>38585016</td>\n",
              "      <td>3.839849e+07</td>\n",
              "      <td>3.838856e+07</td>\n",
              "      <td>3.870615e+07</td>\n",
              "      <td>3.855299e+07</td>\n",
              "      <td>38558024.0</td>\n",
              "      <td>38558180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2000</td>\n",
              "      <td>39140080</td>\n",
              "      <td>3.892823e+07</td>\n",
              "      <td>3.889350e+07</td>\n",
              "      <td>3.923884e+07</td>\n",
              "      <td>3.912758e+07</td>\n",
              "      <td>39112064.0</td>\n",
              "      <td>39112324.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2001</td>\n",
              "      <td>39674811</td>\n",
              "      <td>3.945798e+07</td>\n",
              "      <td>3.940174e+07</td>\n",
              "      <td>3.974785e+07</td>\n",
              "      <td>3.968825e+07</td>\n",
              "      <td>39656736.0</td>\n",
              "      <td>39656988.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2002</td>\n",
              "      <td>40190679</td>\n",
              "      <td>3.998773e+07</td>\n",
              "      <td>3.991329e+07</td>\n",
              "      <td>4.023705e+07</td>\n",
              "      <td>4.023228e+07</td>\n",
              "      <td>40191072.0</td>\n",
              "      <td>40191280.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2003</td>\n",
              "      <td>40693254</td>\n",
              "      <td>4.051748e+07</td>\n",
              "      <td>4.042815e+07</td>\n",
              "      <td>4.071028e+07</td>\n",
              "      <td>4.075797e+07</td>\n",
              "      <td>40714024.0</td>\n",
              "      <td>40714216.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2004</td>\n",
              "      <td>41188093</td>\n",
              "      <td>4.104722e+07</td>\n",
              "      <td>4.094632e+07</td>\n",
              "      <td>4.117141e+07</td>\n",
              "      <td>4.126461e+07</td>\n",
              "      <td>41224712.0</td>\n",
              "      <td>41224876.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2005</td>\n",
              "      <td>41671878</td>\n",
              "      <td>4.157697e+07</td>\n",
              "      <td>4.146780e+07</td>\n",
              "      <td>4.162428e+07</td>\n",
              "      <td>4.175252e+07</td>\n",
              "      <td>41722344.0</td>\n",
              "      <td>41722552.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2006</td>\n",
              "      <td>42170126</td>\n",
              "      <td>4.210672e+07</td>\n",
              "      <td>4.199258e+07</td>\n",
              "      <td>4.207275e+07</td>\n",
              "      <td>4.222302e+07</td>\n",
              "      <td>42206856.0</td>\n",
              "      <td>42206920.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2007</td>\n",
              "      <td>42658630</td>\n",
              "      <td>4.263647e+07</td>\n",
              "      <td>4.252068e+07</td>\n",
              "      <td>4.252068e+07</td>\n",
              "      <td>4.267846e+07</td>\n",
              "      <td>42678384.0</td>\n",
              "      <td>42678480.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2008</td>\n",
              "      <td>43134017</td>\n",
              "      <td>4.316621e+07</td>\n",
              "      <td>4.305208e+07</td>\n",
              "      <td>4.297191e+07</td>\n",
              "      <td>4.312218e+07</td>\n",
              "      <td>43138248.0</td>\n",
              "      <td>43138308.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2009</td>\n",
              "      <td>43608630</td>\n",
              "      <td>4.369596e+07</td>\n",
              "      <td>4.358679e+07</td>\n",
              "      <td>4.343031e+07</td>\n",
              "      <td>4.355854e+07</td>\n",
              "      <td>43588560.0</td>\n",
              "      <td>43588584.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2010</td>\n",
              "      <td>44086292</td>\n",
              "      <td>4.422571e+07</td>\n",
              "      <td>4.412481e+07</td>\n",
              "      <td>4.389972e+07</td>\n",
              "      <td>4.399292e+07</td>\n",
              "      <td>44032680.0</td>\n",
              "      <td>44032752.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2011</td>\n",
              "      <td>44553416</td>\n",
              "      <td>4.475546e+07</td>\n",
              "      <td>4.466613e+07</td>\n",
              "      <td>4.438400e+07</td>\n",
              "      <td>4.443168e+07</td>\n",
              "      <td>44475504.0</td>\n",
              "      <td>44475632.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2012</td>\n",
              "      <td>45001571</td>\n",
              "      <td>4.528520e+07</td>\n",
              "      <td>4.521077e+07</td>\n",
              "      <td>4.488701e+07</td>\n",
              "      <td>4.488224e+07</td>\n",
              "      <td>44923392.0</td>\n",
              "      <td>44923528.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2013</td>\n",
              "      <td>45434942</td>\n",
              "      <td>4.581495e+07</td>\n",
              "      <td>4.575871e+07</td>\n",
              "      <td>4.541260e+07</td>\n",
              "      <td>4.535300e+07</td>\n",
              "      <td>45384392.0</td>\n",
              "      <td>45384568.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2014</td>\n",
              "      <td>45866010</td>\n",
              "      <td>4.634470e+07</td>\n",
              "      <td>4.630996e+07</td>\n",
              "      <td>4.596462e+07</td>\n",
              "      <td>4.585336e+07</td>\n",
              "      <td>45868784.0</td>\n",
              "      <td>45868928.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2015</td>\n",
              "      <td>46313898</td>\n",
              "      <td>4.687445e+07</td>\n",
              "      <td>4.686452e+07</td>\n",
              "      <td>4.654693e+07</td>\n",
              "      <td>4.639377e+07</td>\n",
              "      <td>46388688.0</td>\n",
              "      <td>46388872.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2016</td>\n",
              "      <td>46830116</td>\n",
              "      <td>4.740419e+07</td>\n",
              "      <td>4.742239e+07</td>\n",
              "      <td>4.716338e+07</td>\n",
              "      <td>4.698566e+07</td>\n",
              "      <td>46958752.0</td>\n",
              "      <td>46958956.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2017</td>\n",
              "      <td>47419200</td>\n",
              "      <td>4.793394e+07</td>\n",
              "      <td>4.798356e+07</td>\n",
              "      <td>4.781783e+07</td>\n",
              "      <td>4.764148e+07</td>\n",
              "      <td>47596312.0</td>\n",
              "      <td>47596412.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2018</td>\n",
              "      <td>48258494</td>\n",
              "      <td>4.846369e+07</td>\n",
              "      <td>4.854805e+07</td>\n",
              "      <td>4.851413e+07</td>\n",
              "      <td>4.837470e+07</td>\n",
              "      <td>48321048.0</td>\n",
              "      <td>48321160.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2019</td>\n",
              "      <td>49395678</td>\n",
              "      <td>4.899343e+07</td>\n",
              "      <td>4.911584e+07</td>\n",
              "      <td>4.925613e+07</td>\n",
              "      <td>4.919978e+07</td>\n",
              "      <td>49155896.0</td>\n",
              "      <td>49155988.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2020</td>\n",
              "      <td>50372424</td>\n",
              "      <td>4.952318e+07</td>\n",
              "      <td>4.968694e+07</td>\n",
              "      <td>5.004770e+07</td>\n",
              "      <td>5.013222e+07</td>\n",
              "      <td>50126856.0</td>\n",
              "      <td>50126920.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2021</td>\n",
              "      <td>51049498</td>\n",
              "      <td>5.005293e+07</td>\n",
              "      <td>5.026135e+07</td>\n",
              "      <td>5.089267e+07</td>\n",
              "      <td>5.118851e+07</td>\n",
              "      <td>51263096.0</td>\n",
              "      <td>51263256.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Año  Población  ...  Ajuste grado 5  Ajuste grado 6\n",
              "0   1993   35032308  ...      35018104.0      35018624.0\n",
              "1   1994   35629174  ...      35642464.0      35642636.0\n",
              "2   1995   36229830  ...      36249032.0      36249012.0\n",
              "3   1996   36830574  ...      36841704.0      36841620.0\n",
              "4   1997   37426532  ...      37423112.0      37423152.0\n",
              "5   1998   38012359  ...      37995048.0      37995124.0\n",
              "6   1999   38585016  ...      38558024.0      38558180.0\n",
              "7   2000   39140080  ...      39112064.0      39112324.0\n",
              "8   2001   39674811  ...      39656736.0      39656988.0\n",
              "9   2002   40190679  ...      40191072.0      40191280.0\n",
              "10  2003   40693254  ...      40714024.0      40714216.0\n",
              "11  2004   41188093  ...      41224712.0      41224876.0\n",
              "12  2005   41671878  ...      41722344.0      41722552.0\n",
              "13  2006   42170126  ...      42206856.0      42206920.0\n",
              "14  2007   42658630  ...      42678384.0      42678480.0\n",
              "15  2008   43134017  ...      43138248.0      43138308.0\n",
              "16  2009   43608630  ...      43588560.0      43588584.0\n",
              "17  2010   44086292  ...      44032680.0      44032752.0\n",
              "18  2011   44553416  ...      44475504.0      44475632.0\n",
              "19  2012   45001571  ...      44923392.0      44923528.0\n",
              "20  2013   45434942  ...      45384392.0      45384568.0\n",
              "21  2014   45866010  ...      45868784.0      45868928.0\n",
              "22  2015   46313898  ...      46388688.0      46388872.0\n",
              "23  2016   46830116  ...      46958752.0      46958956.0\n",
              "24  2017   47419200  ...      47596312.0      47596412.0\n",
              "25  2018   48258494  ...      48321048.0      48321160.0\n",
              "26  2019   49395678  ...      49155896.0      49155988.0\n",
              "27  2020   50372424  ...      50126856.0      50126920.0\n",
              "28  2021   51049498  ...      51263096.0      51263256.0\n",
              "\n",
              "[29 rows x 8 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "syHeUYRfDHtO"
      },
      "source": [
        "### Comparación ajustes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "cWZj7sHrCtRx",
        "outputId": "737cd9a6-270c-4a24-987c-50b7d5c7d444"
      },
      "source": [
        "plt.figure(figsize = (7,5))\n",
        "plt.title('Comparación ajustes')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Población'],'.')\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Ajuste grado 4'])\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Ajuste grado 5'])\n",
        "plt.plot(tabla_predicciones['Año'], tabla_predicciones['Ajuste grado 6'])\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3VEMpnIjCtOp"
      },
      "source": [
        "x_zoomin = np.linspace(2018, 2021, 29)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "dcljNy0MDaI0",
        "outputId": "a4905189-6f62-4489-910c-194ff8c50590"
      },
      "source": [
        "plt.figure(figsize = (15,12))\n",
        "plt.title('Comparación ajustes (zoom-in 2018, 2021)')\n",
        "plt.plot(x_zoomin, tabla_predicciones['Población'],'o')\n",
        "plt.plot(x_zoomin, tabla_predicciones['Ajuste grado 4'])\n",
        "plt.plot(x_zoomin, tabla_predicciones['Ajuste grado 5'])\n",
        "plt.plot(x_zoomin, tabla_predicciones['Ajuste grado 6'])\n",
        "plt.show()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u-MnpC2HEYDr"
      },
      "source": [
        "Está gráfica con acercamiento a los años $2018-2021$ tampoco nos muestra diferencias significativas entre los ajustes de grado 4, 5 y 6.<br/>\n",
        "\n",
        "Implementaremos una función que halle el error entre los datos originales y los arrojados por los ajustes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tTpgtSIHDhkd"
      },
      "source": [
        "def error(n, m):\n",
        "  err = []\n",
        "  err = [0 for i in range(len(n))]\n",
        "  for i in range(len(n)):\n",
        "    err[i] = n[i] - m[i]\n",
        "  return err"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JV9UkVQNEsFG"
      },
      "source": [
        "poblacion = tabla_predicciones['Población']\n",
        "p4 = tabla_predicciones['Ajuste grado 4']\n",
        "p5 = tabla_predicciones['Ajuste grado 5']\n",
        "p6 = tabla_predicciones['Ajuste grado 6']"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2pZVrMQE3tw"
      },
      "source": [
        "tabla_predicciones['Error p_4'] = error(poblacion,p4)  \n",
        "tabla_predicciones['Error p_5'] = error(poblacion,p5)\n",
        "tabla_predicciones['Error p_6'] = error(poblacion,p6)"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 996
        },
        "id": "qDMicF89FAhK",
        "outputId": "8438076b-a249-4f36-e449-0d275f56b901"
      },
      "source": [
        "display(tabla_predicciones)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "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>Año</th>\n",
              "      <th>Población</th>\n",
              "      <th>Ajuste grado 1</th>\n",
              "      <th>Ajuste grado 2</th>\n",
              "      <th>Ajuste grado 3</th>\n",
              "      <th>Ajuste grado 4</th>\n",
              "      <th>Ajuste grado 5</th>\n",
              "      <th>Ajuste grado 6</th>\n",
              "      <th>Error p_4</th>\n",
              "      <th>Error p_5</th>\n",
              "      <th>Error p_6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1993</td>\n",
              "      <td>35032308</td>\n",
              "      <td>3.522000e+07</td>\n",
              "      <td>3.542842e+07</td>\n",
              "      <td>3.479710e+07</td>\n",
              "      <td>3.509294e+07</td>\n",
              "      <td>35018104.0</td>\n",
              "      <td>35018624.0</td>\n",
              "      <td>-60633.000</td>\n",
              "      <td>14204.0</td>\n",
              "      <td>13684.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1994</td>\n",
              "      <td>35629174</td>\n",
              "      <td>3.574975e+07</td>\n",
              "      <td>3.591351e+07</td>\n",
              "      <td>3.555275e+07</td>\n",
              "      <td>3.563728e+07</td>\n",
              "      <td>35642464.0</td>\n",
              "      <td>35642636.0</td>\n",
              "      <td>-8104.625</td>\n",
              "      <td>-13290.0</td>\n",
              "      <td>-13462.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1995</td>\n",
              "      <td>36229830</td>\n",
              "      <td>3.627950e+07</td>\n",
              "      <td>3.640190e+07</td>\n",
              "      <td>3.626161e+07</td>\n",
              "      <td>3.620526e+07</td>\n",
              "      <td>36249032.0</td>\n",
              "      <td>36249012.0</td>\n",
              "      <td>24572.250</td>\n",
              "      <td>-19202.0</td>\n",
              "      <td>-19182.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1996</td>\n",
              "      <td>36830574</td>\n",
              "      <td>3.680925e+07</td>\n",
              "      <td>3.689361e+07</td>\n",
              "      <td>3.692752e+07</td>\n",
              "      <td>3.678809e+07</td>\n",
              "      <td>36841704.0</td>\n",
              "      <td>36841620.0</td>\n",
              "      <td>42483.625</td>\n",
              "      <td>-11130.0</td>\n",
              "      <td>-11046.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1997</td>\n",
              "      <td>37426532</td>\n",
              "      <td>3.733899e+07</td>\n",
              "      <td>3.738862e+07</td>\n",
              "      <td>3.755435e+07</td>\n",
              "      <td>3.737800e+07</td>\n",
              "      <td>37423112.0</td>\n",
              "      <td>37423152.0</td>\n",
              "      <td>48533.250</td>\n",
              "      <td>3420.0</td>\n",
              "      <td>3380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1998</td>\n",
              "      <td>38012359</td>\n",
              "      <td>3.786874e+07</td>\n",
              "      <td>3.788694e+07</td>\n",
              "      <td>3.814594e+07</td>\n",
              "      <td>3.796822e+07</td>\n",
              "      <td>37995048.0</td>\n",
              "      <td>37995124.0</td>\n",
              "      <td>44141.375</td>\n",
              "      <td>17311.0</td>\n",
              "      <td>17235.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1999</td>\n",
              "      <td>38585016</td>\n",
              "      <td>3.839849e+07</td>\n",
              "      <td>3.838856e+07</td>\n",
              "      <td>3.870615e+07</td>\n",
              "      <td>3.855299e+07</td>\n",
              "      <td>38558024.0</td>\n",
              "      <td>38558180.0</td>\n",
              "      <td>32023.750</td>\n",
              "      <td>26992.0</td>\n",
              "      <td>26836.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2000</td>\n",
              "      <td>39140080</td>\n",
              "      <td>3.892823e+07</td>\n",
              "      <td>3.889350e+07</td>\n",
              "      <td>3.923884e+07</td>\n",
              "      <td>3.912758e+07</td>\n",
              "      <td>39112064.0</td>\n",
              "      <td>39112324.0</td>\n",
              "      <td>12499.750</td>\n",
              "      <td>28016.0</td>\n",
              "      <td>27756.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2001</td>\n",
              "      <td>39674811</td>\n",
              "      <td>3.945798e+07</td>\n",
              "      <td>3.940174e+07</td>\n",
              "      <td>3.974785e+07</td>\n",
              "      <td>3.968825e+07</td>\n",
              "      <td>39656736.0</td>\n",
              "      <td>39656988.0</td>\n",
              "      <td>-13439.250</td>\n",
              "      <td>18075.0</td>\n",
              "      <td>17823.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2002</td>\n",
              "      <td>40190679</td>\n",
              "      <td>3.998773e+07</td>\n",
              "      <td>3.991329e+07</td>\n",
              "      <td>4.023705e+07</td>\n",
              "      <td>4.023228e+07</td>\n",
              "      <td>40191072.0</td>\n",
              "      <td>40191280.0</td>\n",
              "      <td>-41602.125</td>\n",
              "      <td>-393.0</td>\n",
              "      <td>-601.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2003</td>\n",
              "      <td>40693254</td>\n",
              "      <td>4.051748e+07</td>\n",
              "      <td>4.042815e+07</td>\n",
              "      <td>4.071028e+07</td>\n",
              "      <td>4.075797e+07</td>\n",
              "      <td>40714024.0</td>\n",
              "      <td>40714216.0</td>\n",
              "      <td>-64711.125</td>\n",
              "      <td>-20770.0</td>\n",
              "      <td>-20962.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2004</td>\n",
              "      <td>41188093</td>\n",
              "      <td>4.104722e+07</td>\n",
              "      <td>4.094632e+07</td>\n",
              "      <td>4.117141e+07</td>\n",
              "      <td>4.126461e+07</td>\n",
              "      <td>41224712.0</td>\n",
              "      <td>41224876.0</td>\n",
              "      <td>-76512.375</td>\n",
              "      <td>-36619.0</td>\n",
              "      <td>-36783.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2005</td>\n",
              "      <td>41671878</td>\n",
              "      <td>4.157697e+07</td>\n",
              "      <td>4.146780e+07</td>\n",
              "      <td>4.162428e+07</td>\n",
              "      <td>4.175252e+07</td>\n",
              "      <td>41722344.0</td>\n",
              "      <td>41722552.0</td>\n",
              "      <td>-80637.375</td>\n",
              "      <td>-50466.0</td>\n",
              "      <td>-50674.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2006</td>\n",
              "      <td>42170126</td>\n",
              "      <td>4.210672e+07</td>\n",
              "      <td>4.199258e+07</td>\n",
              "      <td>4.207275e+07</td>\n",
              "      <td>4.222302e+07</td>\n",
              "      <td>42206856.0</td>\n",
              "      <td>42206920.0</td>\n",
              "      <td>-52895.125</td>\n",
              "      <td>-36730.0</td>\n",
              "      <td>-36794.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2007</td>\n",
              "      <td>42658630</td>\n",
              "      <td>4.263647e+07</td>\n",
              "      <td>4.252068e+07</td>\n",
              "      <td>4.252068e+07</td>\n",
              "      <td>4.267846e+07</td>\n",
              "      <td>42678384.0</td>\n",
              "      <td>42678480.0</td>\n",
              "      <td>-19829.875</td>\n",
              "      <td>-19754.0</td>\n",
              "      <td>-19850.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2008</td>\n",
              "      <td>43134017</td>\n",
              "      <td>4.316621e+07</td>\n",
              "      <td>4.305208e+07</td>\n",
              "      <td>4.297191e+07</td>\n",
              "      <td>4.312218e+07</td>\n",
              "      <td>43138248.0</td>\n",
              "      <td>43138308.0</td>\n",
              "      <td>11837.125</td>\n",
              "      <td>-4231.0</td>\n",
              "      <td>-4291.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2009</td>\n",
              "      <td>43608630</td>\n",
              "      <td>4.369596e+07</td>\n",
              "      <td>4.358679e+07</td>\n",
              "      <td>4.343031e+07</td>\n",
              "      <td>4.355854e+07</td>\n",
              "      <td>43588560.0</td>\n",
              "      <td>43588584.0</td>\n",
              "      <td>50089.125</td>\n",
              "      <td>20070.0</td>\n",
              "      <td>20046.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2010</td>\n",
              "      <td>44086292</td>\n",
              "      <td>4.422571e+07</td>\n",
              "      <td>4.412481e+07</td>\n",
              "      <td>4.389972e+07</td>\n",
              "      <td>4.399292e+07</td>\n",
              "      <td>44032680.0</td>\n",
              "      <td>44032752.0</td>\n",
              "      <td>93377.000</td>\n",
              "      <td>53612.0</td>\n",
              "      <td>53540.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2011</td>\n",
              "      <td>44553416</td>\n",
              "      <td>4.475546e+07</td>\n",
              "      <td>4.466613e+07</td>\n",
              "      <td>4.438400e+07</td>\n",
              "      <td>4.443168e+07</td>\n",
              "      <td>44475504.0</td>\n",
              "      <td>44475632.0</td>\n",
              "      <td>121731.750</td>\n",
              "      <td>77912.0</td>\n",
              "      <td>77784.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2012</td>\n",
              "      <td>45001571</td>\n",
              "      <td>4.528520e+07</td>\n",
              "      <td>4.521077e+07</td>\n",
              "      <td>4.488701e+07</td>\n",
              "      <td>4.488224e+07</td>\n",
              "      <td>44923392.0</td>\n",
              "      <td>44923528.0</td>\n",
              "      <td>119327.250</td>\n",
              "      <td>78179.0</td>\n",
              "      <td>78043.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2013</td>\n",
              "      <td>45434942</td>\n",
              "      <td>4.581495e+07</td>\n",
              "      <td>4.575871e+07</td>\n",
              "      <td>4.541260e+07</td>\n",
              "      <td>4.535300e+07</td>\n",
              "      <td>45384392.0</td>\n",
              "      <td>45384568.0</td>\n",
              "      <td>81943.750</td>\n",
              "      <td>50550.0</td>\n",
              "      <td>50374.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2014</td>\n",
              "      <td>45866010</td>\n",
              "      <td>4.634470e+07</td>\n",
              "      <td>4.630996e+07</td>\n",
              "      <td>4.596462e+07</td>\n",
              "      <td>4.585336e+07</td>\n",
              "      <td>45868784.0</td>\n",
              "      <td>45868928.0</td>\n",
              "      <td>12645.250</td>\n",
              "      <td>-2774.0</td>\n",
              "      <td>-2918.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2015</td>\n",
              "      <td>46313898</td>\n",
              "      <td>4.687445e+07</td>\n",
              "      <td>4.686452e+07</td>\n",
              "      <td>4.654693e+07</td>\n",
              "      <td>4.639377e+07</td>\n",
              "      <td>46388688.0</td>\n",
              "      <td>46388872.0</td>\n",
              "      <td>-79874.750</td>\n",
              "      <td>-74790.0</td>\n",
              "      <td>-74974.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2016</td>\n",
              "      <td>46830116</td>\n",
              "      <td>4.740419e+07</td>\n",
              "      <td>4.742239e+07</td>\n",
              "      <td>4.716338e+07</td>\n",
              "      <td>4.698566e+07</td>\n",
              "      <td>46958752.0</td>\n",
              "      <td>46958956.0</td>\n",
              "      <td>-155545.375</td>\n",
              "      <td>-128636.0</td>\n",
              "      <td>-128840.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2017</td>\n",
              "      <td>47419200</td>\n",
              "      <td>4.793394e+07</td>\n",
              "      <td>4.798356e+07</td>\n",
              "      <td>4.781783e+07</td>\n",
              "      <td>4.764148e+07</td>\n",
              "      <td>47596312.0</td>\n",
              "      <td>47596412.0</td>\n",
              "      <td>-222282.500</td>\n",
              "      <td>-177112.0</td>\n",
              "      <td>-177212.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2018</td>\n",
              "      <td>48258494</td>\n",
              "      <td>4.846369e+07</td>\n",
              "      <td>4.854805e+07</td>\n",
              "      <td>4.851413e+07</td>\n",
              "      <td>4.837470e+07</td>\n",
              "      <td>48321048.0</td>\n",
              "      <td>48321160.0</td>\n",
              "      <td>-116204.500</td>\n",
              "      <td>-62554.0</td>\n",
              "      <td>-62666.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2019</td>\n",
              "      <td>49395678</td>\n",
              "      <td>4.899343e+07</td>\n",
              "      <td>4.911584e+07</td>\n",
              "      <td>4.925613e+07</td>\n",
              "      <td>4.919978e+07</td>\n",
              "      <td>49155896.0</td>\n",
              "      <td>49155988.0</td>\n",
              "      <td>195894.750</td>\n",
              "      <td>239782.0</td>\n",
              "      <td>239690.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2020</td>\n",
              "      <td>50372424</td>\n",
              "      <td>4.952318e+07</td>\n",
              "      <td>4.968694e+07</td>\n",
              "      <td>5.004770e+07</td>\n",
              "      <td>5.013222e+07</td>\n",
              "      <td>50126856.0</td>\n",
              "      <td>50126920.0</td>\n",
              "      <td>240201.125</td>\n",
              "      <td>245568.0</td>\n",
              "      <td>245504.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2021</td>\n",
              "      <td>51049498</td>\n",
              "      <td>5.005293e+07</td>\n",
              "      <td>5.026135e+07</td>\n",
              "      <td>5.089267e+07</td>\n",
              "      <td>5.118851e+07</td>\n",
              "      <td>51263096.0</td>\n",
              "      <td>51263256.0</td>\n",
              "      <td>-139016.625</td>\n",
              "      <td>-213598.0</td>\n",
              "      <td>-213758.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Año  Población  Ajuste grado 1  ...   Error p_4  Error p_5  Error p_6\n",
              "0   1993   35032308    3.522000e+07  ...  -60633.000    14204.0    13684.0\n",
              "1   1994   35629174    3.574975e+07  ...   -8104.625   -13290.0   -13462.0\n",
              "2   1995   36229830    3.627950e+07  ...   24572.250   -19202.0   -19182.0\n",
              "3   1996   36830574    3.680925e+07  ...   42483.625   -11130.0   -11046.0\n",
              "4   1997   37426532    3.733899e+07  ...   48533.250     3420.0     3380.0\n",
              "5   1998   38012359    3.786874e+07  ...   44141.375    17311.0    17235.0\n",
              "6   1999   38585016    3.839849e+07  ...   32023.750    26992.0    26836.0\n",
              "7   2000   39140080    3.892823e+07  ...   12499.750    28016.0    27756.0\n",
              "8   2001   39674811    3.945798e+07  ...  -13439.250    18075.0    17823.0\n",
              "9   2002   40190679    3.998773e+07  ...  -41602.125     -393.0     -601.0\n",
              "10  2003   40693254    4.051748e+07  ...  -64711.125   -20770.0   -20962.0\n",
              "11  2004   41188093    4.104722e+07  ...  -76512.375   -36619.0   -36783.0\n",
              "12  2005   41671878    4.157697e+07  ...  -80637.375   -50466.0   -50674.0\n",
              "13  2006   42170126    4.210672e+07  ...  -52895.125   -36730.0   -36794.0\n",
              "14  2007   42658630    4.263647e+07  ...  -19829.875   -19754.0   -19850.0\n",
              "15  2008   43134017    4.316621e+07  ...   11837.125    -4231.0    -4291.0\n",
              "16  2009   43608630    4.369596e+07  ...   50089.125    20070.0    20046.0\n",
              "17  2010   44086292    4.422571e+07  ...   93377.000    53612.0    53540.0\n",
              "18  2011   44553416    4.475546e+07  ...  121731.750    77912.0    77784.0\n",
              "19  2012   45001571    4.528520e+07  ...  119327.250    78179.0    78043.0\n",
              "20  2013   45434942    4.581495e+07  ...   81943.750    50550.0    50374.0\n",
              "21  2014   45866010    4.634470e+07  ...   12645.250    -2774.0    -2918.0\n",
              "22  2015   46313898    4.687445e+07  ...  -79874.750   -74790.0   -74974.0\n",
              "23  2016   46830116    4.740419e+07  ... -155545.375  -128636.0  -128840.0\n",
              "24  2017   47419200    4.793394e+07  ... -222282.500  -177112.0  -177212.0\n",
              "25  2018   48258494    4.846369e+07  ... -116204.500   -62554.0   -62666.0\n",
              "26  2019   49395678    4.899343e+07  ...  195894.750   239782.0   239690.0\n",
              "27  2020   50372424    4.952318e+07  ...  240201.125   245568.0   245504.0\n",
              "28  2021   51049498    5.005293e+07  ... -139016.625  -213598.0  -213758.0\n",
              "\n",
              "[29 rows x 11 columns]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "liXVCOAdGoLq"
      },
      "source": [
        "### Eligiendo el mejor ajuste"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MtET70EoGrJD"
      },
      "source": [
        "Sacamos el promedio del error de los ajustes 4, 5 y 6."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSkhzJ3lFGRG",
        "outputId": "f8e8f17a-c875-4813-b181-50d558c34e61"
      },
      "source": [
        "np.mean(p4)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42636466.12068965"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U4kSP9nzF_I3",
        "outputId": "78a7cfae-2246-41ea-c134-925cf263181f"
      },
      "source": [
        "np.mean(p5)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42636409.93103448"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DK_nkE3nGPKH",
        "outputId": "ace8f5bf-dacb-4ff7-ea8e-f302c1aacfd9"
      },
      "source": [
        "np.mean(p6)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42636546.48275862"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0-YaEkREGRM7",
        "outputId": "b7e9a578-9df3-4a98-a5d8-4bc745d3d442"
      },
      "source": [
        "np.mean(p5)<np.mean(p4)<np.mean(p6)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-TJ5UtXSGv9q"
      },
      "source": [
        "Vemos que el ajuste que mejor representa la población es el ajuste de grado 5."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PfAa0txJG7p3"
      },
      "source": [
        "Ahora estimaremos la población de cada\n",
        "año a partir de 2022 y hasta 2032."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fx0GeQj_G7gC"
      },
      "source": [
        "tabla_estimacion = tabla.copy()"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qjlFXuNHMzb"
      },
      "source": [
        "y3 = np.array(tabla_estimacion['Población'])\n",
        "x3 = np.array(tabla_estimacion['Año'])"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JWwBrQ8kII7Q",
        "outputId": "e0b09783-a2fe-438b-e523-6e94056aeb1e"
      },
      "source": [
        "x3,y3"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n",
              "        2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n",
              "        2015, 2016, 2017, 2018, 2019, 2020, 2021]),\n",
              " array([35032308, 35629174, 36229830, 36830574, 37426532, 38012359,\n",
              "        38585016, 39140080, 39674811, 40190679, 40693254, 41188093,\n",
              "        41671878, 42170126, 42658630, 43134017, 43608630, 44086292,\n",
              "        44553416, 45001571, 45434942, 45866010, 46313898, 46830116,\n",
              "        47419200, 48258494, 49395678, 50372424, 51049498]))"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFhwAnIMIKAB"
      },
      "source": [
        "grad = 5\n",
        "c = np.polyfit(x2, y2, grad)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFhwj-COIPOo",
        "outputId": "39699386-6478-4055-bb6c-6ff6a7b5113f"
      },
      "source": [
        "c"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.59483984e+00, -1.59620785e+04,  6.39029091e+07, -1.27914762e+11,\n",
              "        1.28023465e+14, -5.12527729e+16])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0kwRTyIJ0PF"
      },
      "source": [
        "estimacion_x = np.concatenate((np.array(tabla_estimacion['Año']), np.arange(2022,2032,1)))"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9-aS5S-MYEy"
      },
      "source": [
        "b = np.array([0]*10)"
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PRxm2MqMYAP"
      },
      "source": [
        "estimacion_y = np.concatenate((np.array(tabla_predicciones['Ajuste grado 5']), b))"
      ],
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmwGlaTbMX7t",
        "outputId": "cf4f4085-d458-4544-9e27-9c68160ed8a2"
      },
      "source": [
        "estimacion_x,estimacion_y"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003,\n",
              "        2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n",
              "        2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025,\n",
              "        2026, 2027, 2028, 2029, 2030, 2031]),\n",
              " array([35018104., 35642464., 36249032., 36841704., 37423112., 37995048.,\n",
              "        38558024., 39112064., 39656736., 40191072., 40714024., 41224712.,\n",
              "        41722344., 42206856., 42678384., 43138248., 43588560., 44032680.,\n",
              "        44475504., 44923392., 45384392., 45868784., 46388688., 46958752.,\n",
              "        47596312., 48321048., 49155896., 50126856., 51263096.,        0.,\n",
              "               0.,        0.,        0.,        0.,        0.,        0.,\n",
              "               0.,        0.,        0.]))"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LdoPNgBjPpfj"
      },
      "source": [
        "### Esta gráfica nos sirve para ver el crecimiento de la población hasta 2032."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "ssgysSJuIP2P",
        "outputId": "62906090-454e-4dce-9db0-a2ccddf98094"
      },
      "source": [
        "w = np.polyval(c, estimacion_x)\n",
        "\n",
        "plt.figure(figsize = (12,10))\n",
        "plt.title('Población hasta 2032')\n",
        "plt.plot(estimacion_x, w,'.-')\n",
        "plt.show()"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2LZl5-JJ9tA"
      },
      "source": [
        "data = (estimacion_x, w)"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r7a1GiQOKbuK"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "df.to_csv('proyeccion_poblacion.csv')\n"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "b8cu2Px-LEcA",
        "outputId": "ad7b12e3-15a7-40be-dc43-f583e66f728e"
      },
      "source": [
        "tabla = pd.read_csv('proyeccion_poblacion.csv')\n",
        "tabla.transpose()\n"
      ],
      "execution_count": 119,
      "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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              "    .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>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1993.0</td>\n",
              "      <td>35018104.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1994.0</td>\n",
              "      <td>35642464.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1995.0</td>\n",
              "      <td>36249032.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1996.0</td>\n",
              "      <td>36841704.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1997.0</td>\n",
              "      <td>37423112.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1998.0</td>\n",
              "      <td>37995048.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1999.0</td>\n",
              "      <td>38558024.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2000.0</td>\n",
              "      <td>39112064.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2001.0</td>\n",
              "      <td>39656736.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2002.0</td>\n",
              "      <td>40191072.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2003.0</td>\n",
              "      <td>40714024.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2004.0</td>\n",
              "      <td>41224712.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2005.0</td>\n",
              "      <td>41722344.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2006.0</td>\n",
              "      <td>42206856.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2007.0</td>\n",
              "      <td>42678384.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2008.0</td>\n",
              "      <td>43138248.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2009.0</td>\n",
              "      <td>43588560.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2010.0</td>\n",
              "      <td>44032680.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2011.0</td>\n",
              "      <td>44475504.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2012.0</td>\n",
              "      <td>44923392.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2013.0</td>\n",
              "      <td>45384392.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>45868784.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2015.0</td>\n",
              "      <td>46388688.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>2016.0</td>\n",
              "      <td>46958752.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>2017.0</td>\n",
              "      <td>47596312.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>2018.0</td>\n",
              "      <td>48321048.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2019.0</td>\n",
              "      <td>49155896.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>2020.0</td>\n",
              "      <td>50126856.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>2021.0</td>\n",
              "      <td>51263096.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2022.0</td>\n",
              "      <td>52597400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2023.0</td>\n",
              "      <td>54166216.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2024.0</td>\n",
              "      <td>56009896.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2025.0</td>\n",
              "      <td>58172824.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>2026.0</td>\n",
              "      <td>60703704.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>2027.0</td>\n",
              "      <td>63655488.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>2028.0</td>\n",
              "      <td>67086216.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>2029.0</td>\n",
              "      <td>71058184.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>2030.0</td>\n",
              "      <td>75639344.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>2031.0</td>\n",
              "      <td>80902368.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 0           1\n",
              "Unnamed: 0     0.0         1.0\n",
              "0           1993.0  35018104.0\n",
              "1           1994.0  35642464.0\n",
              "2           1995.0  36249032.0\n",
              "3           1996.0  36841704.0\n",
              "4           1997.0  37423112.0\n",
              "5           1998.0  37995048.0\n",
              "6           1999.0  38558024.0\n",
              "7           2000.0  39112064.0\n",
              "8           2001.0  39656736.0\n",
              "9           2002.0  40191072.0\n",
              "10          2003.0  40714024.0\n",
              "11          2004.0  41224712.0\n",
              "12          2005.0  41722344.0\n",
              "13          2006.0  42206856.0\n",
              "14          2007.0  42678384.0\n",
              "15          2008.0  43138248.0\n",
              "16          2009.0  43588560.0\n",
              "17          2010.0  44032680.0\n",
              "18          2011.0  44475504.0\n",
              "19          2012.0  44923392.0\n",
              "20          2013.0  45384392.0\n",
              "21          2014.0  45868784.0\n",
              "22          2015.0  46388688.0\n",
              "23          2016.0  46958752.0\n",
              "24          2017.0  47596312.0\n",
              "25          2018.0  48321048.0\n",
              "26          2019.0  49155896.0\n",
              "27          2020.0  50126856.0\n",
              "28          2021.0  51263096.0\n",
              "29          2022.0  52597400.0\n",
              "30          2023.0  54166216.0\n",
              "31          2024.0  56009896.0\n",
              "32          2025.0  58172824.0\n",
              "33          2026.0  60703704.0\n",
              "34          2027.0  63655488.0\n",
              "35          2028.0  67086216.0\n",
              "36          2029.0  71058184.0\n",
              "37          2030.0  75639344.0\n",
              "38          2031.0  80902368.0"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEZudpzgQJvt"
      },
      "source": [
        "### Referencias\n",
        "DANE.\n",
        "Proyecciones\n",
        "de\n",
        "población.\n",
        "https://www.dane.gov.co/\n",
        "index.php/estadisticas-por-tema/demografia-y-poblacion/\n",
        "proyecciones-de-poblacion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMip0e5JQNhL"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}