02-cam.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "5798e1d6",
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   "source": [
    "### Coeficientes de atracción mutua\n",
    "\n",
    "Se levantan los coeficientes de atracción mutua del 1996 calculados por Macadar y Domínguez (2008), se calculan los mismos con datos del 2011 y se comparan ambas matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f303866",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\user\\anaconda3\\envs\\tesis\\lib\\site-packages\\pyproj\\__init__.py:89: UserWarning: pyproj unable to set database path.\n",
      "  _pyproj_global_context_initialize()\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "\n",
    "from functions.cargar_data import *\n",
    "from functions.func_demograficas import *\n",
    "from functions.graficas import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d8597ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# setea tipo de fuente y tamaño\n",
    "size, font, font_legend = size_font()\n",
    "color_spines = 'grey'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53bf423f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(193567, 15)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cod</th>\n",
       "      <th>depto_destino</th>\n",
       "      <th>loc_destino</th>\n",
       "      <th>SECC</th>\n",
       "      <th>SEGM</th>\n",
       "      <th>VIVIENDA</th>\n",
       "      <th>TIPVIV</th>\n",
       "      <th>HOGARVIV</th>\n",
       "      <th>PARENTESCO</th>\n",
       "      <th>sexo</th>\n",
       "      <th>edad</th>\n",
       "      <th>HACE5AQUI</th>\n",
       "      <th>depto_origen</th>\n",
       "      <th>loc_origen</th>\n",
       "      <th>personas_mig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1301</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36375</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>146352</td>\n",
       "      <td>2</td>\n",
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      "text/plain": [
       "    cod  depto_destino  loc_destino  SECC  SEGM  VIVIENDA  TIPVIV  HOGARVIV  \\\n",
       "0  1301              1           20     1     1     36375       2         1   \n",
       "1   601              1           20     1     1    146352       2         1   \n",
       "\n",
       "   PARENTESCO  sexo  edad  HACE5AQUI  depto_origen  loc_origen  personas_mig  \n",
       "0           8     2    24        NaN            13       522.0             1  \n",
       "1          10     1    51        NaN             6       220.0             1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carga datos del Censo INE 1996\n",
    "# censo 96\n",
    "censo96 = cargar_censo_96()\n",
    "\n",
    "# crea tabla de flujos\n",
    "dict_rename= {'HAC5DEPC': 'depto_origen',\n",
    "              'DPTO':'depto_destino',\n",
    "              'LOC': 'loc_destino',\n",
    "              'HAC5LOC': 'loc_origen',\n",
    "              'SEXO': 'sexo',\n",
    "              'EDAD': 'edad'}\n",
    "\n",
    "censo96.rename(dict_rename, axis=1, inplace=True)\n",
    "\n",
    "flujos_deptos96 = censo96.loc[(censo96.HACE5AQUI.isnull()) & (censo96.depto_origen <= 19)]\n",
    "\n",
    "flujos_deptos96 = flujos_deptos96.loc[flujos_deptos96.depto_destino != flujos_deptos96.depto_origen]\\\n",
    "                  .reset_index(drop=True)\n",
    "\n",
    "flujos_deptos96['personas_mig'] = 1\n",
    "flujos_deptos96['depto_origen'] = flujos_deptos96['depto_origen'].astype(int)\n",
    "\n",
    "# inserta el dato de díada codificada\n",
    "flujos_deptos96.insert(0, 'cod', codif_diada_depto(flujos_deptos96))\n",
    "\n",
    "print(flujos_deptos96.shape)\n",
    "flujos_deptos96.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7a00cce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "depto_origen       1     2      3     4     5     6     7     8     9    10  \\\n",
      "depto_destino                                                                 \n",
      "1                  0  3985  14802  3279  2597  2461   969  1958  1427  2272   \n",
      "2               1277     0    159    40    56    38     7     5    18    59   \n",
      "3              41077   705      0   795   330   738   183  1037   733   691   \n",
      "4               1774    27    311     0    67    77    19    64    78   144   \n",
      "5               2196   103    288    68     0    54   159    63    19   198   \n",
      "6               1333    45    264   124    37     0   122   483    29    67   \n",
      "7                569    14    117    12    91   181     0    87     8    54   \n",
      "8               1477    47    679    72    75   461   119     0   248   134   \n",
      "9               1373    22    491   232    25    93    17   181     0   603   \n",
      "10              4891   313   1387   839   487   338   252   404  1569     0   \n",
      "11              1541   262    189    52    93    79    41    40    21   128   \n",
      "12              1032    91    141    28    82    85    89    63     9    78   \n",
      "13              2353   210    352   173    61    53    17    52    10    80   \n",
      "14              1853    36    369   110    58    53    21    57   226   619   \n",
      "15              2040  1161    200    28   118    31    14    22    60   169   \n",
      "16              4254   123    878    78   410   119   154   208    47   143   \n",
      "17              1599    33    174    34   731    84   154    39    24   109   \n",
      "18              1841    95    213   162    36   360    57    74    22    72   \n",
      "19              1324    21    242   960    36    75     6    79   338   248   \n",
      "Total          73804  7293  21256  7086  5390  5380  2400  4916  4886  5868   \n",
      "\n",
      "depto_origen     11    12    13    14    15    16    17    18    19   Total  \n",
      "depto_destino                                                                \n",
      "1              3000  1785  3881  1935  4043  2421  2865  4007  1669   59356  \n",
      "2               162    47   176     9   794    12    35    48    18    2960  \n",
      "3               447   298   781   534   562  1145   454   855   313   51678  \n",
      "4                35    16   238    74    36    39    21   373   647    4040  \n",
      "5               132   102    82    58   130   438  1156    60    51    5357  \n",
      "6                57    98    81    38    25    50    68   411   102    3434  \n",
      "7                36    72    27    25    13   126   139    71     4    1646  \n",
      "8                59    33    66    84    37   216    75   129   129    4140  \n",
      "9                41    14    37   213    21    43    35    34   371    3846  \n",
      "10              458   246   202  1320   480   252   600   418   877   15333  \n",
      "11                0   660   202    56   933    45   160   480    25    5007  \n",
      "12              738     0    42    31    84    45   525   129    18    3310  \n",
      "13              126    48     0    33   246    51    38   761    39    4703  \n",
      "14               51    39    32     0    39    79    49    61   214    3966  \n",
      "15              590   134   206    42     0    59    84   289    23    5270  \n",
      "16              123    88   128    48    81     0   229    94    44    7249  \n",
      "17              170   336    24    29    67   129     0    93    19    3848  \n",
      "18              329   159   644    26   256    39    50     0    51    4486  \n",
      "19               10    15    56   341    22    37    46    82     0    3938  \n",
      "Total          6564  4190  6905  4896  7869  5226  6629  8395  4614  193567  \n"
     ]
    }
   ],
   "source": [
    "# genera tabla pivot con los flujos de departamento a departamento\n",
    "matrix = pd.crosstab(index = flujos_deptos96.depto_destino,\n",
    "                     columns=flujos_deptos96.depto_origen,\n",
    "                     margins=True,\n",
    "                     margins_name='Total')\n",
    "\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e524dad2",
   "metadata": {},
   "source": [
    "#### Se levanta la matriz del censo 1996 elaborada por Macadar y Domínguez, se verifica que con esa matriz de valores absolutos de obtiene la misma matriz de CAM prsentada por los autores."
   ]
  },
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   "cell_type": "code",
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   "id": "2c8851ae",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "[[ 4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [34  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 4  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 3  1  1  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 3  1  2  1  1  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 1  0  1  0  2  4  0  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 3  0  4  1  1  9  2  0  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 2  0  2  2  0  1  0  3  0  0  0  0  0  0  0  0  0  0]\n",
      " [ 5  2  4  5  3  2  2  3 13  0  0  0  0  0  0  0  0  0]\n",
      " [ 3  3  1  0  1  1  1  1  0  3  0  0  0  0  0  0  0  0]\n",
      " [ 2  1  1  0  1  2  2  1  0  2  9  0  0  0  0  0  0  0]\n",
      " [ 5  2  2  2  1  1  0  1  0  1  2  1  0  0  0  0  0  0]\n",
      " [ 3  0  2  1  1  1  0  1  3 11  1  1  0  0  0  0  0  0]\n",
      " [ 4 11  1  0  1  0  0  0  0  3  7  1  2  0  0  0  0  0]\n",
      " [ 5  1  4  1  4  1  3  3  0  2  1  1  1  1  1  0  0  0]\n",
      " [ 3  0  1  0 10  1  3  1  0  4  2  7  0  0  1  2  0  0]\n",
      " [ 4  1  2  3  1  6  1  1  0  3  4  2  8  1  3  1  1  0]\n",
      " [ 2  0  1 13  1  2  0  2  7  7  0  0  1  5  0  1  1  1]]\n"
     ]
    }
   ],
   "source": [
    "# levanta la matriz elaborada por Macadar y Dominguez (2008)\n",
    "matrix96 = pd.read_csv('tablas/matriz_deptos_96.csv', index_col='DEPID')\n",
    "matrix96\n",
    "\n",
    "# desapila la matriz\n",
    "grupo_pob = matrix96.unstack().dropna().reset_index()\n",
    "grupo_pob.columns = ['depto_origen', 'depto_destino', 'Mij']\n",
    "grupo_pob = grupo_pob.astype(int)\n",
    "\n",
    "# agrega Mji\n",
    "Mji = list(grupo_pob.sort_values(by=['depto_destino', 'depto_origen'])['Mij'])\n",
    "grupo_pob['Mji'] = Mji\n",
    "\n",
    "# agrega Pij y Pji\n",
    "pobl96 = recuperar_poblacion_1996_5años()\n",
    "grupo_pob = grupo_pob.merge(pobl96, left_on='depto_origen', right_on='DPTO')\n",
    "grupo_pob = grupo_pob.merge(pobl96, left_on='depto_destino', right_on='DPTO')\n",
    "grupo_pob.drop(['DPTO_x', 'DPTO_y'], axis=1, inplace=True)\n",
    "grupo_pob.rename({'poblacion_x': 'Pi', 'poblacion_y': 'Pj'}, inplace=True, axis=1)\n",
    "\n",
    "grupo_pob.head()\n",
    "\n",
    "grupo_pob['cam_round'] = get_cam(grupo_pob)\n",
    "\n",
    "atr1996 = pd.pivot_table(grupo_pob,\n",
    "                        values='cam_round',\n",
    "                        index ='depto_origen',\n",
    "                        columns='depto_destino',\n",
    "                        fill_value=0).iloc[1:19, 0:18]\n",
    "\n",
    "g96 = grupo_pob\n",
    "\n",
    "atr1996_ar = np.tril(np.array(atr1996))\n",
    "print(atr1996_ar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b86409f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Son iguales ambos arrays?:  True\n"
     ]
    }
   ],
   "source": [
    "# lee matriz de CAM 1996\n",
    "atr1996 = pd.read_csv('tablas/atractividad_1996.csv', index_col='deptos').fillna(0)\n",
    "\n",
    "# convierte a numpy array \n",
    "atr1996_ar_v2 = atr1996.values\n",
    "\n",
    "comparison = atr1996_ar == atr1996_ar_v2\n",
    "\n",
    "print('Son iguales ambos arrays?: ', comparison.all())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "14c7439e",
   "metadata": {},
   "outputs": [],
   "source": [
    "del atr1996_ar_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "433b5dcf",
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         1     2    3     4     5    6    7    8     9    10   11   12   13  \\\n",
       "deptos                                                                        \n",
       "2        4   NaN  NaN   NaN   NaN  NaN  NaN  NaN   NaN   NaN  NaN  NaN  NaN   \n",
       "3       34   2.0  NaN   NaN   NaN  NaN  NaN  NaN   NaN   NaN  NaN  NaN  NaN   \n",
       "4        4   0.0  2.0   NaN   NaN  NaN  NaN  NaN   NaN   NaN  NaN  NaN  NaN   \n",
       "5        3   1.0  1.0   1.0   NaN  NaN  NaN  NaN   NaN   NaN  NaN  NaN  NaN   \n",
       "6        3   1.0  2.0   1.0   1.0  NaN  NaN  NaN   NaN   NaN  NaN  NaN  NaN   \n",
       "7        1   0.0  1.0   0.0   2.0  4.0  NaN  NaN   NaN   NaN  NaN  NaN  NaN   \n",
       "8        3   0.0  4.0   1.0   1.0  9.0  2.0  NaN   NaN   NaN  NaN  NaN  NaN   \n",
       "9        2   0.0  2.0   2.0   0.0  1.0  0.0  3.0   NaN   NaN  NaN  NaN  NaN   \n",
       "10       5   2.0  4.0   5.0   3.0  2.0  2.0  3.0  13.0   NaN  NaN  NaN  NaN   \n",
       "11       3   3.0  1.0   0.0   1.0  1.0  1.0  1.0   0.0   3.0  NaN  NaN  NaN   \n",
       "12       2   1.0  1.0   0.0   1.0  2.0  2.0  1.0   0.0   2.0  9.0  NaN  NaN   \n",
       "13       5   2.0  2.0   2.0   1.0  1.0  0.0  1.0   0.0   1.0  2.0  1.0  NaN   \n",
       "14       3   0.0  2.0   1.0   1.0  1.0  0.0  1.0   3.0  11.0  1.0  1.0  0.0   \n",
       "15       4  11.0  1.0   0.0   1.0  0.0  0.0  0.0   0.0   3.0  7.0  1.0  2.0   \n",
       "16       5   1.0  4.0   1.0   4.0  1.0  3.0  3.0   0.0   2.0  1.0  1.0  1.0   \n",
       "17       3   0.0  1.0   0.0  10.0  1.0  3.0  1.0   0.0   4.0  2.0  7.0  0.0   \n",
       "18       4   1.0  2.0   3.0   1.0  6.0  1.0  1.0   0.0   3.0  4.0  2.0  8.0   \n",
       "19       2   0.0  1.0  13.0   1.0  2.0  0.0  2.0   7.0   7.0  0.0  0.0  1.0   \n",
       "\n",
       "         14   15   16   17   18  \n",
       "deptos                           \n",
       "2       NaN  NaN  NaN  NaN  NaN  \n",
       "3       NaN  NaN  NaN  NaN  NaN  \n",
       "4       NaN  NaN  NaN  NaN  NaN  \n",
       "5       NaN  NaN  NaN  NaN  NaN  \n",
       "6       NaN  NaN  NaN  NaN  NaN  \n",
       "7       NaN  NaN  NaN  NaN  NaN  \n",
       "8       NaN  NaN  NaN  NaN  NaN  \n",
       "9       NaN  NaN  NaN  NaN  NaN  \n",
       "10      NaN  NaN  NaN  NaN  NaN  \n",
       "11      NaN  NaN  NaN  NaN  NaN  \n",
       "12      NaN  NaN  NaN  NaN  NaN  \n",
       "13      NaN  NaN  NaN  NaN  NaN  \n",
       "14      NaN  NaN  NaN  NaN  NaN  \n",
       "15      0.0  NaN  NaN  NaN  NaN  \n",
       "16      1.0  1.0  NaN  NaN  NaN  \n",
       "17      0.0  1.0  2.0  NaN  NaN  \n",
       "18      1.0  3.0  1.0  1.0  NaN  \n",
       "19      5.0  0.0  1.0  1.0  1.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# recarga data\n",
    "atr1996 = pd.read_csv('tablas/atractividad_1996.csv', index_col='deptos')\n",
    "atr1996"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "56cdba83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depto_origen</th>\n",
       "      <th>depto_destino</th>\n",
       "      <th>LOC</th>\n",
       "      <th>SECC</th>\n",
       "      <th>SEGM</th>\n",
       "      <th>VIVID</th>\n",
       "      <th>TIPO_VIVIE</th>\n",
       "      <th>HOGCOD</th>\n",
       "      <th>HOGID</th>\n",
       "      <th>PERPH02</th>\n",
       "      <th>...</th>\n",
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       "      <th>PERED03_2</th>\n",
       "      <th>PERED04_R</th>\n",
       "      <th>PERED05_R</th>\n",
       "      <th>PERED06_R</th>\n",
       "      <th>CODIGO_CAR</th>\n",
       "      <th>PERED08</th>\n",
       "      <th>NIVELEDU_R</th>\n",
       "      <th>loc_origen</th>\n",
       "      <th>loc_destino</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>102001001000501</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>55131</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3732</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>102001001003001</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>55152</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>17220</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>102001001003601</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>33101</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>18220</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>102001001004601</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33101</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2220</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>1</td>\n",
       "      <td>102001001009301</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>22101</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12320</td>\n",
       "      <td>1020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   depto_origen  depto_destino  LOC  SECC  SEGM  VIVID  TIPO_VIVIE  \\\n",
       "0             3              1   20     1     1      5           1   \n",
       "1            17              1   20     1     1     30           1   \n",
       "2            18              1   20     1     1     36           1   \n",
       "3             2              1   20     1     1     46           1   \n",
       "4            12              1   20     1     1     93           1   \n",
       "\n",
       "            HOGCOD  HOGID  PERPH02  ...  PERED03_1  PERED03_2  PERED04_R  \\\n",
       "0  102001001000501      1        1  ...         11          0          2   \n",
       "1  102001001003001      1        2  ...          0          0          0   \n",
       "2  102001001003601      1        1  ...          6          0          2   \n",
       "3  102001001004601      1        1  ...          6          0          2   \n",
       "4  102001001009301      1        2  ...          4          0          2   \n",
       "\n",
       "  PERED05_R  PERED06_R  CODIGO_CAR  PERED08  NIVELEDU_R  loc_origen  \\\n",
       "0         3          4       55131        1           9        3732   \n",
       "1         2          4       55152        1           9       17220   \n",
       "2         2          0       33101        1           5       18220   \n",
       "3         1          0       33101        1           5        2220   \n",
       "4         1          0       22101        1           4       12320   \n",
       "\n",
       "   loc_destino  \n",
       "0         1020  \n",
       "1         1020  \n",
       "2         1020  \n",
       "3         1020  \n",
       "4         1020  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_migr = cargar_migrantes_internos()\n",
    "p_migr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e3452f17",
   "metadata": {},
   "outputs": [
    {
     "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>DPTO</th>\n",
       "      <th>poblacion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1236133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>67863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>484060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DPTO  poblacion\n",
       "0     1    1236133\n",
       "1     2      67863\n",
       "2     3     484060"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# recupera población con 5 o más años de edad\n",
    "pobl = recuperar_poblacion_2011_5años()\n",
    "pobl.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e742f999",
   "metadata": {},
   "outputs": [
    {
     "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>depto_origen</th>\n",
       "      <th>depto_destino</th>\n",
       "      <th>Mij</th>\n",
       "      <th>Mji</th>\n",
       "      <th>Pi</th>\n",
       "      <th>Pj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>914</td>\n",
       "      <td>2395</td>\n",
       "      <td>1236133</td>\n",
       "      <td>67863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "      <td>536</td>\n",
       "      <td>484060</td>\n",
       "      <td>67863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>78717</td>\n",
       "      <td>67863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>167</td>\n",
       "      <td>115155</td>\n",
       "      <td>67863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>53162</td>\n",
       "      <td>67863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   depto_origen  depto_destino  Mij   Mji       Pi     Pj\n",
       "0             1              2  914  2395  1236133  67863\n",
       "1             3              2   74   536   484060  67863\n",
       "2             4              2   15    20    78717  67863\n",
       "3             5              2   28   167   115155  67863\n",
       "4             6              2   32    33    53162  67863"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# genera un sólo dataframe solo para deptos\n",
    "flujos_deptos = p_migr.loc[p_migr.depto_origen != p_migr.depto_destino, ['depto_origen', 'depto_destino']]\n",
    "grupo = flujos_deptos.groupby(by=['depto_origen', 'depto_destino']).size()\n",
    "grupo = pd.DataFrame(grupo, columns=['Mij']).reset_index()\n",
    "\n",
    "# genera un sólo dataframe solo para deptos\n",
    "flujos_deptos = p_migr.loc[p_migr.depto_origen != p_migr.depto_destino, ['depto_origen', 'depto_destino']]\n",
    "Mji = list(flujos_deptos.groupby(by=['depto_destino', 'depto_origen']).size())\n",
    "grupo['Mji'] = Mji\n",
    "\n",
    "grupo_pob = grupo.merge(pobl, left_on='depto_origen', right_on='DPTO')\n",
    "grupo_pob = grupo_pob.merge(pobl, left_on='depto_destino', right_on='DPTO')\n",
    "grupo_pob.drop(['DPTO_x', 'DPTO_y'], axis=1, inplace=True)\n",
    "grupo_pob.rename({'poblacion_x': 'Pi', 'poblacion_y': 'Pj'}, inplace=True, axis=1)\n",
    "grupo_pob.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "74baefb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calcula cam\n",
    "grupo_pob['cam_round'] = get_cam(grupo_pob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5b069268",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3. nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      " [26.  1. nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      " [ 2.  0.  1. nan nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      " [ 4.  1.  1.  0. nan nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      " [ 2.  1.  1.  1.  1. nan nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      " [ 1.  0.  0.  0.  1.  3. nan nan nan nan nan nan nan nan nan nan nan nan]\n",
      " [ 2.  0.  3.  1.  1.  6.  2. nan nan nan nan nan nan nan nan nan nan nan]\n",
      " [ 2.  0.  2.  1.  0.  1.  0.  3. nan nan nan nan nan nan nan nan nan nan]\n",
      " [ 4.  2.  3.  4.  2.  1.  1.  2.  6. nan nan nan nan nan nan nan nan nan]\n",
      " [ 2.  2.  1.  1.  1.  1.  0.  1.  0.  2. nan nan nan nan nan nan nan nan]\n",
      " [ 2.  1.  1.  0.  2.  1.  2.  1.  0.  2.  7. nan nan nan nan nan nan nan]\n",
      " [ 3.  2.  2.  2.  1.  1.  0.  1.  0.  1.  1.  1. nan nan nan nan nan nan]\n",
      " [ 2.  0.  1.  1.  0.  1.  0.  1.  2.  7.  0.  1.  0. nan nan nan nan nan]\n",
      " [ 3.  6.  1.  0.  1.  0.  0.  0.  0.  2.  5.  2.  1.  0. nan nan nan nan]\n",
      " [ 5.  1.  3.  0.  3.  1.  2.  4.  1.  1.  1.  1.  1.  1.  1. nan nan nan]\n",
      " [ 2.  0.  1.  0.  8.  1.  2.  1.  0.  2.  2.  5.  0.  0.  1.  1. nan nan]\n",
      " [ 3.  1.  1.  2.  1.  4.  1.  1.  1.  2.  3.  2.  5.  0.  2.  1.  1. nan]\n",
      " [ 1.  0.  1.  7.  0.  2.  0.  1.  4.  5.  0.  0.  1.  3.  0.  0.  0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "# genera matriz de atracción mutua\n",
    "atr2011 = pd.pivot_table(grupo_pob,\n",
    "                         values='cam_round',\n",
    "                         index ='depto_origen',\n",
    "                         columns='depto_destino',\n",
    "                         fill_value=0).iloc[1:19, 0:18]\n",
    "\n",
    "# convierte en array y reemplaza el triángulo superior por ceros\n",
    "atr2011_ar = np.tril(np.array(atr2011, dtype=float))\n",
    "atr2011_ar[np.triu_indices(atr2011_ar.shape[0], 1)] = np.nan\n",
    "print(atr2011_ar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0756de8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cam(data, output_name, font=font, colors='RdBu'):\n",
    "    \"Plotea la grilla de CAM\"\n",
    "    nomdep = cargar_nombres()\n",
    "    fig = plt.figure(figsize=(5, 5))\n",
    "\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(data, cmap=colors,  vmin=0, vmax=30)\n",
    "\n",
    "    ax.grid(color='silver', linestyle='--', linewidth=0.5)\n",
    "\n",
    "    plt.xticks(np.arange(0, 18, step=1), labels=nomdep[0:18], rotation=90, **font, color=color_spines)\n",
    "    plt.yticks(np.arange(0, 18, step=1), labels=nomdep[1:19], **font, color=color_spines)\n",
    "\n",
    "    ax.tick_params(axis=u'both', which=u'both',length=0)\n",
    "\n",
    "    # color bar\n",
    "    cbar = fig.colorbar(cax, fraction=0.045, drawedges=False)\n",
    "    cbar.outline.set_visible(False)\n",
    "    cbar.ax.tick_params(size=3, color='w', labelsize=size, labelcolor=color_spines)\n",
    "    for l in cbar.ax.yaxis.get_ticklabels():\n",
    "        l.set_family(font['fontname'])\n",
    "        \n",
    "    hide_spines(ax, todos=True)\n",
    "\n",
    "    save_chart(output_name, metadatos_figs)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6303be33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se guardó la gráfica en mapas_graficas/003_eda_coef_1996.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cam(atr1996, '003_eda_coef_1996.pdf', font, colors='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79d5075e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se guardó la gráfica en mapas_graficas/003_eda_coef_2011.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cam(atr2011_ar, '003_eda_coef_2011.pdf', font, colors='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e8aae1b",
   "metadata": {},
   "outputs": [
    {
     "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-8.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      1    2    3    4    5    6    7    8    9   10   11   12   13   14   15  \\\n",
       "2  -1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "3  -8.0 -1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "4  -2.0  0.0 -1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "5   1.0  0.0  0.0 -1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "6  -1.0  0.0 -1.0  0.0  0.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "7   0.0  0.0 -1.0  0.0 -1.0 -1.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "8  -1.0  0.0 -1.0  0.0  0.0 -3.0  0.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "9   0.0  0.0  0.0 -1.0  0.0  0.0  0.0  0.0  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "10 -1.0  0.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -7.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "11 -1.0 -1.0  0.0  1.0  0.0  0.0 -1.0  0.0  0.0 -1.0  NaN  NaN  NaN  NaN  NaN   \n",
       "12  0.0  0.0  0.0  0.0  1.0 -1.0  0.0  0.0  0.0  0.0 -2.0  NaN  NaN  NaN  NaN   \n",
       "13 -2.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 -1.0  0.0  NaN  NaN  NaN   \n",
       "14 -1.0  0.0 -1.0  0.0 -1.0  0.0  0.0  0.0 -1.0 -4.0 -1.0  0.0  0.0  NaN  NaN   \n",
       "15 -1.0 -5.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0 -1.0 -2.0  1.0 -1.0  0.0  NaN   \n",
       "16  0.0  0.0 -1.0 -1.0 -1.0  0.0 -1.0  1.0  1.0 -1.0  0.0  0.0  0.0  0.0  0.0   \n",
       "17 -1.0  0.0  0.0  0.0 -2.0  0.0 -1.0  0.0  0.0 -2.0  0.0 -2.0  0.0  0.0  0.0   \n",
       "18 -1.0  0.0 -1.0 -1.0  0.0 -2.0  0.0  0.0  1.0 -1.0 -1.0  0.0 -3.0 -1.0 -1.0   \n",
       "19 -1.0  0.0  0.0 -6.0 -1.0  0.0  0.0 -1.0 -3.0 -2.0  0.0  0.0  0.0 -2.0  0.0   \n",
       "\n",
       "     16   17   18  \n",
       "2   NaN  NaN  NaN  \n",
       "3   NaN  NaN  NaN  \n",
       "4   NaN  NaN  NaN  \n",
       "5   NaN  NaN  NaN  \n",
       "6   NaN  NaN  NaN  \n",
       "7   NaN  NaN  NaN  \n",
       "8   NaN  NaN  NaN  \n",
       "9   NaN  NaN  NaN  \n",
       "10  NaN  NaN  NaN  \n",
       "11  NaN  NaN  NaN  \n",
       "12  NaN  NaN  NaN  \n",
       "13  NaN  NaN  NaN  \n",
       "14  NaN  NaN  NaN  \n",
       "15  NaN  NaN  NaN  \n",
       "16  NaN  NaN  NaN  \n",
       "17 -1.0  NaN  NaN  \n",
       "18  0.0  0.0  NaN  \n",
       "19 -1.0 -1.0  0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dif = atr2011_ar - atr1996_ar\n",
    "\n",
    "# diferencias a df\n",
    "cols = list(atr1996.columns)\n",
    "idx  = list(atr1996.index)\n",
    "dif_df = pd.DataFrame(dif, columns = cols, index=idx)\n",
    "dif_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1cb6132",
   "metadata": {},
   "source": [
    "Grafica diferencias de CAM entre 1996 y 2011"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9138e9f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cam_diverging(data, output_name, font=font):\n",
    "    \"Plotea la grilla de CAM\"\n",
    "    nomdep = cargar_nombres()\n",
    "    divnorm = colors.TwoSlopeNorm(vmin=-8, vcenter=0, vmax=1)\n",
    "\n",
    "    # a 2D array with linearly increasing values on the diagonal\n",
    "    fig = plt.figure(figsize=(5, 5))\n",
    "\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(data, cmap='RdBu', norm=divnorm)\n",
    "\n",
    "    ax.grid(color='silver', linestyle='--', linewidth=0.5)\n",
    "\n",
    "    plt.xticks(np.arange(0, 18, step=1), labels=nomdep[0:18], rotation=90, **font, color=color_spines)\n",
    "    plt.yticks(np.arange(0, 18, step=1), labels=nomdep[1:19], **font, color=color_spines)\n",
    "\n",
    "    ax.tick_params(axis=u'both', which=u'both',length=0)\n",
    "\n",
    "    # color bar\n",
    "    cbar = fig.colorbar(cax, fraction=0.045, drawedges=False)\n",
    "    cbar.outline.set_visible(False)\n",
    "    cbar.ax.tick_params(size=3, color='w', labelsize=size, labelcolor=color_spines)\n",
    "    for l in cbar.ax.yaxis.get_ticklabels():\n",
    "        l.set_family(font['fontname'])\n",
    "\n",
    "    hide_spines(ax, todos=True)\n",
    "\n",
    "    save_chart(output_name, metadatos_figs)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bef27fd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se guardó la gráfica en mapas_graficas/003_eda_delta_coef_1996_2011.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cam_diverging(dif, '003_eda_delta_coef_1996_2011.pdf', font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d40371c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Menor atractivididad: 71. Estables: 93. Mayor atractividad 7\n"
     ]
    }
   ],
   "source": [
    "print('Menor atractivididad: {}. Estables: {}. Mayor atractividad {}'\\\n",
    "      .format(len(dif[dif<0]), len(dif[dif==0]), len(dif[dif>0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e1b3df8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# análisis numérico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0e683087",
   "metadata": {},
   "outputs": [
    {
     "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>11_depto_origen</th>\n",
       "      <th>11_depto_destino</th>\n",
       "      <th>11_Mij</th>\n",
       "      <th>11_Mji</th>\n",
       "      <th>11_Pi</th>\n",
       "      <th>11_Pj</th>\n",
       "      <th>11_cam_round</th>\n",
       "      <th>11_cod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>914</td>\n",
       "      <td>2395</td>\n",
       "      <td>1236133</td>\n",
       "      <td>67863</td>\n",
       "      <td>3</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>33127</td>\n",
       "      <td>11162</td>\n",
       "      <td>1236133</td>\n",
       "      <td>484060</td>\n",
       "      <td>26</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    11_depto_origen  11_depto_destino  11_Mij  11_Mji    11_Pi   11_Pj  \\\n",
       "0                 1                 2     914    2395  1236133   67863   \n",
       "18                1                 3   33127   11162  1236133  484060   \n",
       "\n",
       "    11_cam_round  11_cod  \n",
       "0              3     102  \n",
       "18            26     103  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['depto_origen', 'depto_destino']\n",
    "grupo_pob['cod'] = codif_diada_depto(grupo_pob)\n",
    "g11 = grupo_pob.sort_values(cols).add_prefix('11_')\n",
    "\n",
    "g11.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b061adb1",
   "metadata": {},
   "outputs": [
    {
     "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>depto_origen</th>\n",
       "      <th>depto_destino</th>\n",
       "      <th>Mij</th>\n",
       "      <th>Mji</th>\n",
       "      <th>Pi</th>\n",
       "      <th>Pj</th>\n",
       "      <th>cam_round</th>\n",
       "      <th>cod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1191</td>\n",
       "      <td>3775</td>\n",
       "      <td>1187111</td>\n",
       "      <td>63587</td>\n",
       "      <td>4</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>39551</td>\n",
       "      <td>13806</td>\n",
       "      <td>1187111</td>\n",
       "      <td>384620</td>\n",
       "      <td>34</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    depto_origen  depto_destino    Mij    Mji       Pi      Pj  cam_round  cod\n",
       "0              1              2   1191   3775  1187111   63587          4  102\n",
       "18             1              3  39551  13806  1187111  384620         34  103"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g96 = g96.sort_values(cols)\n",
    "g96['cod'] = codif_diada_depto(g96)\n",
    "g96.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "31e6991c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ambos = pd.concat([g96, g11], axis=1)\n",
    "aumentan = ambos.loc[ambos.cam_round < ambos['11_cam_round']]\n",
    "se_mantienen = ambos.loc[ambos.cam_round == ambos['11_cam_round']]\n",
    "reducen = ambos.loc[ambos.cam_round > ambos['11_cam_round']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fa1c2048",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aumentan su coef.: 14\n",
      "Mantienen su coef.: 186\n",
      "Reducen su coef.: 142\n"
     ]
    }
   ],
   "source": [
    "print(f'Aumentan su coef.: {aumentan.shape[0]}')\n",
    "print(f'Mantienen su coef.: {se_mantienen.shape[0]}')\n",
    "print(f'Reducen su coef.: {reducen.shape[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a1c283d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aumentan.shape[0] + se_mantienen.shape[0] + reducen.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e3af5e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>depto_origen</th>\n",
       "      <th>depto_destino</th>\n",
       "      <th>Mij</th>\n",
       "      <th>Mji</th>\n",
       "      <th>Pi</th>\n",
       "      <th>Pj</th>\n",
       "      <th>cam_round</th>\n",
       "      <th>cod</th>\n",
       "      <th>11_depto_origen</th>\n",
       "      <th>11_depto_destino</th>\n",
       "      <th>11_Mij</th>\n",
       "      <th>11_Mji</th>\n",
       "      <th>11_Pi</th>\n",
       "      <th>11_Pj</th>\n",
       "      <th>11_cam_round</th>\n",
       "      <th>11_cod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1956</td>\n",
       "      <td>2447</td>\n",
       "      <td>1187111</td>\n",
       "      <td>104493</td>\n",
       "      <td>3</td>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2100</td>\n",
       "      <td>2690</td>\n",
       "      <td>1236133</td>\n",
       "      <td>115155</td>\n",
       "      <td>4</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "      <td>70939</td>\n",
       "      <td>96318</td>\n",
       "      <td>0</td>\n",
       "      <td>411</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>61</td>\n",
       "      <td>35</td>\n",
       "      <td>78717</td>\n",
       "      <td>104855</td>\n",
       "      <td>1</td>\n",
       "      <td>411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     depto_origen  depto_destino   Mij   Mji       Pi      Pj  cam_round  cod  \\\n",
       "54              1              5  1956  2447  1187111  104493          3  105   \n",
       "165             4             11    50    20    70939   96318          0  411   \n",
       "\n",
       "     11_depto_origen  11_depto_destino  11_Mij  11_Mji    11_Pi   11_Pj  \\\n",
       "54                 1                 5    2100    2690  1236133  115155   \n",
       "165                4                11      61      35    78717  104855   \n",
       "\n",
       "     11_cam_round  11_cod  \n",
       "54              4     105  \n",
       "165             1     411  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aumentan.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6780573f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\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>depto_origen</th>\n",
       "      <th>depto_destino</th>\n",
       "      <th>CAM 1996</th>\n",
       "      <th>CAM 2011</th>\n",
       "      <th>migrantes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Colonia</td>\n",
       "      <td>Montevideo</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Montevideo</td>\n",
       "      <td>Colonia</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>Florida</td>\n",
       "      <td>San José</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>San José</td>\n",
       "      <td>Florida</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Río Negro</td>\n",
       "      <td>Colonia</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>Salto</td>\n",
       "      <td>Río Negro</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Río Negro</td>\n",
       "      <td>Salto</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Colonia</td>\n",
       "      <td>Río Negro</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Cerro Largo</td>\n",
       "      <td>Paysandú</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Tacuarembó</td>\n",
       "      <td>Lavalleja</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Lavalleja</td>\n",
       "      <td>San José</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Lavalleja</td>\n",
       "      <td>Tacuarembó</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>San José</td>\n",
       "      <td>Lavalleja</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Paysandú</td>\n",
       "      <td>Cerro Largo</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    depto_origen depto_destino  CAM 1996  CAM 2011  migrantes\n",
       "327      Colonia    Montevideo         3         4       2690\n",
       "54    Montevideo       Colonia         3         4       2100\n",
       "259      Florida      San José         3         4        321\n",
       "122     San José       Florida         3         4        252\n",
       "64     Río Negro       Colonia         1         2        176\n",
       "193        Salto     Río Negro         1         2        161\n",
       "245    Río Negro         Salto         1         2        133\n",
       "184      Colonia     Río Negro         1         2         88\n",
       "165  Cerro Largo      Paysandú         0         1         61\n",
       "142   Tacuarembó     Lavalleja         0         1         50\n",
       "260    Lavalleja      San José         0         1         45\n",
       "296    Lavalleja    Tacuarembó         0         1         45\n",
       "140     San José     Lavalleja         0         1         44\n",
       "45      Paysandú   Cerro Largo         0         1         35"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aum_ori = decode_depto_pretty(aumentan.depto_origen)\n",
    "aum_des = decode_depto_pretty(aumentan.depto_destino)\n",
    "cod_pop = aumentan.loc[:, ['cod', 'cam_round', '11_cam_round', '11_Mij']]\n",
    "diadas_aumentan = pd.concat([aum_ori, aum_des, cod_pop], axis=1)\n",
    "\n",
    "diadas_aumentan.sort_values('11_Mij', ascending=False, inplace=True)\n",
    "dict_rename = {'11_Mij': 'migrantes', 'cam_round': 'CAM 1996', '11_cam_round': 'CAM 2011'}\n",
    "diadas_aumentan.rename(dict_rename, axis=1, inplace=True)\n",
    "diadas_aumentan.drop('cod',  axis=1, inplace=True)\n",
    "\n",
    "diadas_aumentan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1d35497f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# guarda en markdown\n",
    "da_md = diadas_aumentan.to_markdown(index=False)\n",
    "f = open(\"tablas/diadas_aumentan.md\", \"w\")\n",
    "f.write(da_md)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c3df7eb",
   "metadata": {},
   "source": [
    "#### Exporta tablas a latex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8d9dc616",
   "metadata": {},
   "outputs": [],
   "source": [
    "# decodifica índice\n",
    "atr1996.set_index(decode_depto_short(atr1996.index), inplace=True)\n",
    "\n",
    "# renombra índice\n",
    "atr1996.index.names = ['depto origen']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "02be3054",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_10224\\3887285216.py:6: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  atr1996.to_latex(buf=\"tablas/matriz_atr1996.tex\",\n"
     ]
    }
   ],
   "source": [
    "# setea ancho de columnas\n",
    "ancho = 'p{0.25cm}'\n",
    "colformato='l' + ancho * 20\n",
    "\n",
    "# exporta 1996\n",
    "atr1996.to_latex(buf=\"tablas/matriz_atr1996.tex\",\n",
    "bold_rows=False,\n",
    "na_rep='',\n",
    "float_format=\"{:0.0f}\".format,\n",
    "column_format = colformato,\n",
    "caption= 'Matriz de coeficientes de atracción mutua interdepartamental 1996 (Macadar y Domínguez, 2005)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "591d017e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_10224\\3317957284.py:9: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  atr2011_ar_df.to_latex(buf=\"tablas/matriz_atr2011.tex\",\n"
     ]
    }
   ],
   "source": [
    "# exporta 2011\n",
    "\n",
    "# convierte el erray de 2011 en DF\n",
    "cols = list(atr2011.columns)\n",
    "idx  = decode_depto_short((atr2011.index))\n",
    "atr2011_ar_df = pd.DataFrame(atr2011_ar, columns = cols, index=idx)\n",
    "\n",
    "# exporta el DF a latex\n",
    "atr2011_ar_df.to_latex(buf=\"tablas/matriz_atr2011.tex\",\n",
    "bold_rows=False,\n",
    "na_rep='',\n",
    "float_format=\"{:0.0f}\".format,\n",
    "column_format = colformato,\n",
    "caption= 'Matriz de coeficientes de atracción mutua interdepartamental 2011, elaboración propia en base a INE (2011)')"
   ]
  }
 ],
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