clustering_por_dias_demanda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from procesadoDatosDemandaString import procesar_datos_string \n",
    "from sklearn.cluster import KMeans\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archivo donde están los datos de la demanda (sin extensión): demanda\n"
     ]
    }
   ],
   "source": [
    "datos = procesar_datos_string()\n",
    "datos\n",
    "labels = list(datos.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "datos = datos.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "datos = normalize(datos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(datos[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_list = []\n",
    "maximo = (0,0)\n",
    "for x in range(2,20):\n",
    "    clusterer = KMeans(n_clusters=x, random_state=1)\n",
    "    cluster_labels = clusterer.fit_predict(datos)\n",
    "    score = silhouette_score(datos, cluster_labels)\n",
    "    score_list.append(score)\n",
    "    if score > maximo[1]:\n",
    "        maximo = (x, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El maximo se obtiene en el nodo 2 con una puntuacion de 0.46844713925624687\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAWfklEQVR4nO3df5QlZX3n8feHGTHEEIUFlQA6qEQXzarsiGb9rWhAXTDxFxhX4y/UFX97dom6HGXPnoAuJnHDrqKSaFaDqIlO4igaRZLoARkQBFRkxFFHWB0UATWCI9/9o6rx2j7dUz1M9b09836dc0/fqnqq7rdr7txPV9Wt50lVIUnSfLtNuwBJ0mwyICRJTQaEJKnJgJAkNRkQkqSm1dMuYKn22WefWrNmzbTLkKQV5cILL7y2qvZdyjorLiDWrFnDhg0bpl2GJK0oSb651HU8xSRJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWpacXdS3xZrTvjYdq236eQn7uBKJGn2eQQhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTaMGRJIjklyRZGOSExZp99QklWTtmPVIkoYbLSCSrAJOA44EDgGOTXJIo92ewMuB88eqRZK0dGMeQRwGbKyqq6rqZuBM4OhGu/8OvBn46Yi1SJKWaMyA2B/49sT05n7erZI8EDiwqv5hsQ0lOS7JhiQbtmzZsuMrlST9ijEDIo15devCZDfgT4HXbGtDVXV6Va2tqrX77rvvDixRkrSQMQNiM3DgxPQBwNUT03sC9wM+m2QT8BBgnReqJWk2jBkQFwAHJzkoye7AMcC6uYVVdX1V7VNVa6pqDXAecFRVbRixJknSQKMFRFVtBY4Hzga+ApxVVZcnOSnJUWO9riRpx1g95saraj2wft68Exdo+6gxa5EkLY13UkuSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqSmUceD2BmtOeFj27XeppOfuIMrkaRxeQQhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaBt0HkeTuwMFV9Y9J9gBWV9WN45a28/JeCkkrwTaPIJK8EPgQ8I5+1gHAR8YsSpI0fUNOMb0UeChwA0BVXQncecyiJEnTNyQgbqqqm+cmkqwGarySJEmzYEhAnJvkdcAeSR4HfBD4+3HLkiRN25CL1CcAzwcuBV4ErAfeNWZRWj7bc8Hci+XSrmHRgEiyCnhPVT0LeOfylCRJmgWLnmKqqp8D+ybZfZnqkSTNiCGnmDYBn0uyDvjx3MyqeutYRUmSpm9IQFzdP3YD9hy3HC2F1w8kjWmbAVFVbwJIsmc3WT8avSppSrzLXfqFIXdS3y/JF4HLgMuTXJjkvuOXJkmapiH3QZwOvLqq7l5Vdwdew8BvNCU5IskVSTYmOaGx/MVJLk1ycZJ/SXLI0sqXJI1lyDWIO1TVOXMTVfXZJHfY1kr9V2RPAx4HbAYuSLKuqr480ez9VfX2vv1RwFuBI5byC0hzdqbTQzvT76KVa8gRxFVJ/luSNf3jDcA3Bqx3GLCxqq7qu+o4Ezh6skFV3TAxeQfswkOSZsaQI4jnAW8C/raf/ifguQPW2x/49sT0ZuDB8xsleSnwamB34DGtDSU5DjgO4G53u9uAl5amx7/+tbMY8i2m64CXb8e209pcY/unAacleSbwBuA5jTan010LYe3atR5l7IT8UJVmz5BvMX0qyZ0mpvdKcvaAbW8GDpyYPoDufoqFnAk8ecB2JUnLYMgppn2q6odzE1V1XZIh40FcAByc5CDgO8AxwDMnGyQ5uB9fAuCJwJVoxfGGvZ3XLB3Z7Yj3me/VpRkSELckuVtVfQtuHX50m6d5qmprkuOBs4FVwBlVdXmSk4ANVbUOOD7J4cDPgOtonF6StPL5wbwyDQmI1wP/kuTcfvoR9BeMt6Wq1tN1Dz4578SJ568YWKckaZkNuUj9iSSHAg/pZ72qqq4dtyxJt9UsnR7SyrRgQPSnkn5YVddX1bVJfkx3Efm3k/zF5DCkkrSr2JWCd7EjiLOA3weuT/IAuqFG/wS4P/C/gReMX54k7XxWSsgsFhB7VNXc11KfRXeR+dQkuwEXj1+aJGmaFrsPYvJGt8cAnwaoqltGrUiSNBMWO4L4TJKzgGuAvYDPACTZD/D6gyTt5BYLiFcCzwD2Ax5WVT/r59+V7quvkqSd2IIBUVVF1/3F/PlfHLUiSdJMGNLdtyRpF2RASJKaBgVEkj2S3HvsYiRJs2NId9//ke6+h0/00w9Ism7swiRJ0zXkCOKNdMOH/hCgqi4G1oxXkiRpFgwJiK1Vdf3olUiSZsqQ7r4v64cDXZXkYLrhRz8/blmSpGkbcgTxMuC+wE3A+4HrAcdxkKSd3JAjiCdW1euZuHs6ydPoeneVJO2khhxB/PHAeZKknchiAwYdCTwB2D/J2yYW/SawdezCJEnTtdgppquBDcBRwIUT828EXjVmUZKk6Vuss75LgEuS3KWq3jO5LMkrgD8fuzhJ0vQMuQZxTGPeH+3gOiRJM2axaxDHAs8EDprXtcaewPfHLkySNF2LXYP4PN1ocvsAp07MvxH40phFSZKmb7FrEN8Evgn8bpK7AwdX1T8m2QPYgy4oJEk7qSG9ub4Q+BDwjn7WAcBHxixKkjR9Qy5SvxR4KHADQFVdCdx5zKIkSdM3JCBuqqqb5yaSrAZqvJIkSbNgSECcm+R1wB5JHkfXB9Pfj1uWJGnahgTECcAW4FLgRcB64A1jFiVJmr5t9uZaVbcA7+wfkqRdxDYDIsk3aFxzqKp7jFKRJGkmDBkPYu3E818DngbsPU45kqRZsc1rEFX1/YnHd6rqz4DHLENtkqQpGnKK6dCJyd3ojij2HK0iSdJMGHKKabIfpq3AJuDpQzae5Ai6bsFXAe+qqpPnLX818IJ+u1uA5/VdfEiSpmzIt5gevT0bTrIKOA14HLAZuCDJuqr68kSzLwJrq+onSV4CvBl4xva8niRpxxrSF9Mdk7w1yYb+cWqSOw7Y9mHAxqq6qr8T+0zg6MkGVXVOVf2knzyPrp8nSdIMGHKj3Bl0Pbc+vX/cAPzlgPX2B749Mb25n7eQ5wMfby1IctxcQG3ZsmXAS0uSbqsh1yDuWVVPmZh+U5KLB6yXxrxmH05JnkV38fuRreVVdTpwOsDatWvtB0qSlsGQI4h/TfKwuYkkDwX+dcB6m4EDJ6YPAK6e3yjJ4cDrgaOq6qYB25UkLYMhRxAvBt7bX3cI8AOGjUl9AXBwkoOA79CNbf3MyQZJHkg3zsQRVfW9JdQtSRrZkG8xXQLcP8lv9tM3DNlwVW1NcjxwNt3XXM+oqsuTnARsqKp1wFuA3wA+mATgW1V11Pb9KpKkHWnIjXK3B54CrAFW9x/kVNVJ21q3qtbT9f46Oe/EieeHL61cSdJyGXKK6aPA9cCFgNcIJGkXMSQgDqiqI0avRJI0U4Z8i+nzSX5n9EokSTNlwSOIJJfS3bewGnhukqvoTjEFqKr6d8tToiRpGhY7xfSkZatCkjRzFguIG5etCknSzFksIC6kO8W0UJcZDjkqSTuxBQOiqg5azkIkSbNlsYvU96mqr84bUe5WVXXReGVJkqZtsVNMrwFeyC+PKDencFxqSdqpLXaK6YX9z+0aUU6StLIteKNckgcluevE9LOTfDTJ25LsvTzlSZKmZbE7qd8B3AyQ5BHAycB76fplOn380iRJ07TYNYhVVfWD/vkzgNOr6sPAhweOKCdJWsEWO4JYlWQuQB4LfGZi2ZBO/iRJK9hiH/R/A5yb5Fq6IUb/GSDJvehOM0mSdmKLfYvpfyT5NLAf8Mmqqn7RbsDLlqM4SdL0LHqqqKrOa8z72njlSJJmxZDxICRJuyADQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkplEDIskRSa5IsjHJCY3lj0hyUZKtSZ46Zi2SpKUZLSCSrAJOA44EDgGOTXLIvGbfAv4IeP9YdUiSts+iY1LfRocBG6vqKoAkZwJHA1+ea1BVm/plt4xYhyRpO4x5iml/4NsT05v7eZKkFWDMgEhjXm3XhpLjkmxIsmHLli23sSxJ0hBjBsRm4MCJ6QOAq7dnQ1V1elWtraq1++677w4pTpK0uDED4gLg4CQHJdkdOAZYN+LrSZJ2oNECoqq2AscDZwNfAc6qqsuTnJTkKIAkD0qyGXga8I4kl49VjyRpacb8FhNVtR5YP2/eiRPPL6A79SRJmjHeSS1JajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNRkQkqQmA0KS1GRASJKaDAhJUpMBIUlqMiAkSU0GhCSpyYCQJDUZEJKkJgNCktRkQEiSmgwISVKTASFJajIgJElNBoQkqcmAkCQ1GRCSpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnJgJAkNY0aEEmOSHJFko1JTmgsv32SD/TLz0+yZsx6JEnDjRYQSVYBpwFHAocAxyY5ZF6z5wPXVdW9gD8FThmrHknS0ox5BHEYsLGqrqqqm4EzgaPntTkaeE///EPAY5NkxJokSQOlqsbZcPJU4IiqekE//Z+AB1fV8RNtLuvbbO6nv963uXbeto4Djusn7w1cMUrR27YPcO02W03fSqkTrHUsK6XWlVInrPxa715V+y5lI6t3XD2/onUkMD+NhrShqk4HTt8RRd0WSTZU1dpp17EtK6VOsNaxrJRaV0qdsGvWOuYpps3AgRPTBwBXL9QmyWrgjsAPRqxJkjTQmAFxAXBwkoOS7A4cA6yb12Yd8Jz++VOBz9RY57wkSUsy2immqtqa5HjgbGAVcEZVXZ7kJGBDVa0D3g38dZKNdEcOx4xVzw4y9dNcA62UOsFax7JSal0pdcIuWOtoF6klSSubd1JLkpoMCElSkwExIcmBSc5J8pUklyd5RaPNo5Jcn+Ti/nHiNGrta9mU5NK+jg2N5Unytr4rky8lOXRKdd57Yn9dnOSGJK+c12Zq+zXJGUm+19+XMzdv7ySfSnJl/3OvBdZ9Tt/myiTPabVZhlrfkuSr/b/x3yW50wLrLvp+WYY635jkOxP/xk9YYN1Fu+hZplo/MFHnpiQXL7Dusu3T/vWan1GjvV+rykf/APYDDu2f7wl8DThkXptHAf8w7Vr7WjYB+yyy/AnAx+nuN3kIcP4M1LwK+H90N+3MxH4FHgEcClw2Me/NwAn98xOAUxrr7Q1c1f/cq3++1xRqfTywun9+SqvWIe+XZajzjcBrB7w/vg7cA9gduGT+/8HlqHXe8lOBE6e9T/vXa35GjfV+9QhiQlVdU1UX9c9vBL4C7D/dqm6To4H3Vuc84E5J9ptyTY8Fvl5V35xyHbeqqn/iV++/mewG5j3Akxur/h7wqar6QVVdB3wKOGK0QmnXWlWfrKqt/eR5dPccTdUC+3SIIV307FCL1dp3/fN04G/GrGGoRT6jRnm/GhAL6HuWfSBwfmPx7ya5JMnHk9x3WQv7ZQV8MsmFfXck8+0PfHtiejPTD7xjWPg/26zsV4C7VNU10P2nBO7caDOL+/d5dEeNLdt6vyyH4/tTYWcscBpk1vbpw4HvVtWVCyyf2j6d9xk1yvvVgGhI8hvAh4FXVtUN8xZfRHd65P7A/wI+stz1TXhoVR1K12PuS5M8Yt7yQV2ZLJf+hsmjgA82Fs/Sfh1q1vbv64GtwPsWaLKt98vY/g9wT+ABwDV0p27mm6l9ChzL4kcPU9mn2/iMWnC1xrxF960BMU+S29Ht+PdV1d/OX15VN1TVj/rn64HbJdlnmcucq+Xq/uf3gL+jOzyfNKS7k+V0JHBRVX13/oJZ2q+9786djut/fq/RZmb2b3/B8UnAH1Z/wnm+Ae+XUVXVd6vq51V1C/DOBV5/lvbpauAPgA8s1GYa+3SBz6hR3q8GxIT+fOO7ga9U1VsXaHPXvh1JDqPbh99fvipvreMOSface053ofKyec3WAc/uv830EOD6ucPQKVnwr7FZ2a8TJruBeQ7w0Uabs4HHJ9mrP13y+H7eskpyBPBfgaOq6icLtBnyfhnVvOtfv7/A6w/pome5HA58tfrepuebxj5d5DNqnPfrcl19XwkP4GF0h1xfAi7uH08AXgy8uG9zPHA53bcrzgP+w5RqvUdfwyV9Pa/v50/WGrpBm74OXAqsneK+/XW6D/w7Tsybif1KF1rXAD+j+yvr+cC/AT4NXNn/3LtvuxZ418S6zwM29o/nTqnWjXTnlufes2/v2/4WsH6x98sy1/nX/fvwS3QfaPvNr7OffgLdt3O+PnadC9Xaz/+ruffnRNup7dP+NRf6jBrl/WpXG5KkJk8xSZKaDAhJUpMBIUlqMiAkSU0GhCSpyYDQzElSSU6dmH5tkjfuoG3/VZKn7ohtbeN1ntb3uHnOmHUlWZPkmUuvUNo2A0Kz6CbgD6Z8J/WvSLJqCc2fD/znqnr0WPX01gBLCogl/h7ahRkQmkVb6cbUfdX8BfP/0k7yo/7no5Kcm+SsJF9LcnKSP0zyhb6//ntObObwJP/ct3tSv/6qdOMqXNB3Jveiie2ek+T9dDd5za/n2H77lyU5pZ93It0NTW9P8pbGOv+lX+eSJCc3lm+aC8cka5N8tn/+yPxijIIv9nfxngw8vJ/3qqG/R38X8Mf6Gi5L8owh/zDatayedgHSAk4DvpTkzUtY5/7Av6XruvkqujtID0s3qMrLgLlBitYAj6TrOO6cJPcCnk3XFcmDktwe+FyST/btDwPuV1XfmHyxJL9FN/7Cvweuo+vV88lVdVKSx9CNfbBh3jpH0nXF/OCq+kmSvZfw+70WeGlVfS5dZ20/pev7/7VVNRd0xw35PZI8Bbi6qp7Yr3fHJdShXYRHEJpJ1fVQ+V7g5UtY7YLq+su/ia6bhrkPxkvpQmHOWVV1S3VdOF8F3IeuX5pnpxs57Hy6rgsO7tt/YX449B4EfLaqtlQ3HsP76AafWczhwF9W32dSVS1lzITPAW9N8nLgTvWLMSAmDf09LqU7kjolycOr6vol1KFdhAGhWfZndOfy7zAxbyv9+7bvuGz3iWU3TTy/ZWL6Fn75aHl+/zJF12/Vy6rqAf3joKqaC5gfL1Bfq/vkbUnj9ee79XcEfu3WIqtOBl4A7AGcl+Q+C2x/m79HVX2N7sjnUuBPMsWhczW7DAjNrP6v67PoQmLOJroPNuhG0brddmz6aUl2669L3AO4gq5Xy5ek60qZJL/d99C5mPOBRybZp7/weyxw7jbW+STwvCS/3r9O6xTTJn7xOz5lbmaSe1bVpVV1CrCB7sjnRrqhJ+cM+j3602M/qar/C/xPuiE3pV/iNQjNulPpenqd807go0m+QNdr5UJ/3S/mCroP8rvQ9db50yTvojsNdVF/ZLKF9rCNt6qqa5L8MXAO3V/u66uq1c3y5DqfSPIAYEOSm4H1wOvmNXsT8O4kr+OXRzR8ZZJHAz8Hvkw3ctwtwNYkl9D1PvrnA3+P3wHekuQWul5MX7JY3do12ZurJKnJU0ySpCYDQpLUZEBIkpoMCElSkwEhSWoyICRJTQaEJKnp/wOH+stvAcPyCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"Silhouette Score\")\n",
    "plt.bar([x for x in range(2, 20, 1)], score_list)\n",
    "print(\"El maximo se obtiene en el nodo {} con una puntuacion de {}\".format(maximo[0], maximo[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusterer = KMeans(n_clusters=6, random_state=1)\n",
    "cluster_labels = clusterer.fit_predict(datos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': 89, '1': 282, '2': 106, '3': 146, '4': 211, '5': 261}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dia_cluster = {}\n",
    "for (i, j) in enumerate(labels):\n",
    "    dia_cluster[j] = cluster_labels[i]\n",
    "dia_cluster\n",
    "\n",
    "dic_cluster = {\"0\":0, \"1\":0, \"2\":0, \"3\":0, \"4\":0, \"5\":0}\n",
    "for j in cluster_labels:\n",
    "    if j == 0:\n",
    "        dic_cluster[\"0\"]+=1\n",
    "    elif j == 1:\n",
    "        dic_cluster[\"1\"]+=1\n",
    "    elif j == 2:\n",
    "        dic_cluster[\"2\"]+=1\n",
    "    elif j == 3:\n",
    "        dic_cluster[\"3\"]+=1\n",
    "    elif j == 4:\n",
    "        dic_cluster[\"4\"]+=1\n",
    "    elif j == 5:\n",
    "        dic_cluster[\"5\"]+=1\n",
    "\n",
    "dic_cluster\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "festivos_2017 = ['20170101',\n",
    "                '20170106',\n",
    "                '20170202',\n",
    "                '20170228',\n",
    "                '20170301',\n",
    "                '20170319',\n",
    "                '20170326',\n",
    "                '20170409',\n",
    "                '20170410',\n",
    "                '20170411',\n",
    "                '20170412',\n",
    "                '20170413',\n",
    "                '20170414',\n",
    "                '20170415',\n",
    "                '20170416',\n",
    "                '20170417',\n",
    "                '20170501',\n",
    "                '20170515',\n",
    "                '20170605',\n",
    "                '20170615',\n",
    "                '20170813',\n",
    "                '20170815',\n",
    "                '20171012',\n",
    "                '20171101',\n",
    "                '20171206',\n",
    "                '20171208',\n",
    "                '20171225']\n",
    "\n",
    "festivos_2018 = ['20180101',\n",
    "                '20180106',\n",
    "                '20180202',\n",
    "                '20180211',\n",
    "                '20180228',\n",
    "                '20180301',\n",
    "                '20180319',\n",
    "                '20180409',\n",
    "                '20180410',\n",
    "                '20180411',\n",
    "                '20180412',\n",
    "                '20180413',\n",
    "                '20180414',\n",
    "                '20180415',\n",
    "                '20180416',\n",
    "                '20180417',\n",
    "                '20180501',\n",
    "                '20180515',\n",
    "                '20180605',\n",
    "                '20180615',\n",
    "                '20180813',\n",
    "                '20180815',\n",
    "                '20181012',\n",
    "                '20181101',\n",
    "                '20181206',\n",
    "                '20181208',\n",
    "                '20181225']\n",
    "\n",
    "festivos_2019 = ['20190101',\n",
    "                '20190106',\n",
    "                '20190202',\n",
    "                '20190211',\n",
    "                '20190228',\n",
    "                '20190301',\n",
    "                '20190319',\n",
    "                '20190414',\n",
    "                '20190415',\n",
    "                '20190416',\n",
    "                '20190417',\n",
    "                '20190418',\n",
    "                '20190419',\n",
    "                '20190420',\n",
    "                '20190421',\n",
    "                '20190422',\n",
    "                '20190501',\n",
    "                '20190515',\n",
    "                '20190620',\n",
    "                '20190725',\n",
    "                '20190815',\n",
    "                '20191012',\n",
    "                '20191101',\n",
    "                '20191206',\n",
    "                '20191208',\n",
    "                '20191225']\n",
    "\n",
    "festivos = festivos_2017 + festivos_2018 + festivos_2019\n",
    "len(festivos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En el cluster 1:  16\n",
      "En el cluster 2:  10\n",
      "En el cluster 3:  16\n",
      "En el cluster 4:  12\n",
      "En el cluster 5:  5\n",
      "En el cluster 6:  21\n"
     ]
    }
   ],
   "source": [
    "# Para dias festivos y no festivos\n",
    "cluster1 = 0\n",
    "cluster2 = 0\n",
    "cluster3 = 0\n",
    "cluster4 = 0\n",
    "cluster5 = 0\n",
    "cluster6 = 0\n",
    "for i in dia_cluster:\n",
    "    if i in festivos and dia_cluster[i]==0:\n",
    "        cluster1+=1\n",
    "    elif i in festivos and dia_cluster[i]==1:\n",
    "        cluster2+=1\n",
    "    elif i in festivos and dia_cluster[i]==2:\n",
    "        cluster3+=1\n",
    "    elif i in festivos and dia_cluster[i]==3:\n",
    "        cluster4+=1\n",
    "    elif i in festivos and dia_cluster[i]==4:\n",
    "        cluster5+=1\n",
    "    elif i in festivos and dia_cluster[i]==5:\n",
    "        cluster6+=1\n",
    "print(\"En el cluster 1: \", cluster1)\n",
    "print(\"En el cluster 2: \", cluster2)\n",
    "print(\"En el cluster 3: \", cluster3)\n",
    "print(\"En el cluster 4: \", cluster4)\n",
    "print(\"En el cluster 5: \", cluster5)\n",
    "print(\"En el cluster 6: \", cluster6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dias de verano\n",
    "\n",
    "junio_2017 = [\"201706\" +str(x).zfill(2) for x in range(21,31)]\n",
    "julio_2017 = [\"201707\" + str(x).zfill(2) for x in range(1,32)]\n",
    "agosto_2017 = [\"201708\" + str(x).zfill(2) for x in range(1,32)]\n",
    "septiembre_2017 = [\"201709\" + str(x).zfill(2) for x in range(1,23)]\n",
    "\n",
    "lista_verano = junio_2017 + julio_2017 + agosto_2017 + septiembre_2017\n",
    "\n",
    "junio_2018 = [\"201806\" +str(x).zfill(2) for x in range(21,31)]\n",
    "julio_2018 = [\"201807\" + str(x).zfill(2) for x in range(1,32)]\n",
    "agosto_2018 = [\"201808\" + str(x).zfill(2) for x in range(1,32)]\n",
    "septiembre_2018 = [\"201809\" + str(x).zfill(2) for x in range(1,24)]\n",
    "\n",
    "lista_verano = lista_verano + junio_2018 + julio_2018 + agosto_2018 + septiembre_2018\n",
    "\n",
    "junio_2019 = [\"201906\" +str(x).zfill(2) for x in range(21,31)]\n",
    "julio_2019 = [\"201907\" + str(x).zfill(2) for x in range(1,32)]\n",
    "agosto_2019 = [\"201908\" + str(x).zfill(2) for x in range(1,32)]\n",
    "septiembre_2019 = [\"201909\" + str(x).zfill(2) for x in range(1,24)]\n",
    "\n",
    "lista_verano = lista_verano + junio_2019 + julio_2019 + agosto_2019 + septiembre_2019\n",
    "\n",
    "len(lista_verano)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En el cluster 1:  0\n",
      "En el cluster 2:  3\n",
      "En el cluster 3:  53\n",
      "En el cluster 4:  33\n",
      "En el cluster 5:  177\n",
      "En el cluster 6:  18\n"
     ]
    }
   ],
   "source": [
    "# Para dias de verano\n",
    "cluster1 = 0\n",
    "cluster2 = 0\n",
    "cluster3 = 0\n",
    "cluster4 = 0\n",
    "cluster5 = 0\n",
    "cluster6 = 0\n",
    "for i in dia_cluster:\n",
    "    if i in lista_verano and dia_cluster[i]==0:\n",
    "        cluster1+=1\n",
    "    elif i in lista_verano and dia_cluster[i]==1:\n",
    "        cluster2+=1\n",
    "    elif i in lista_verano and dia_cluster[i]==2:\n",
    "        cluster3+=1\n",
    "    elif i in lista_verano and dia_cluster[i]==3:\n",
    "        cluster4+=1\n",
    "    elif i in lista_verano and dia_cluster[i]==4:\n",
    "        cluster5+=1\n",
    "    elif i in lista_verano and dia_cluster[i]==5:\n",
    "        cluster6+=1\n",
    "print(\"En el cluster 1: \", cluster1)\n",
    "print(\"En el cluster 2: \", cluster2)\n",
    "print(\"En el cluster 3: \", cluster3)\n",
    "print(\"En el cluster 4: \", cluster4)\n",
    "print(\"En el cluster 5: \", cluster5)\n",
    "print(\"En el cluster 6: \", cluster6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "189"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dias de invierno\n",
    "\n",
    "diciembre = [\"201712\" +str(x).zfill(2) for x in range(21,32)]\n",
    "enero = [\"201801\" + str(x).zfill(2) for x in range(1,32)]\n",
    "febrero = [\"201802\" + str(x).zfill(2) for x in range(1,29)]\n",
    "marzo = [\"201803\" + str(x).zfill(2) for x in range(1,21)]\n",
    "\n",
    "lista_invierno = diciembre + enero + febrero + marzo\n",
    "\n",
    "diciembre = [\"201812\" +str(x).zfill(2) for x in range(21,32)]\n",
    "enero = [\"201901\" + str(x).zfill(2) for x in range(1,32)]\n",
    "febrero = [\"201902\" + str(x).zfill(2) for x in range(1,29)]\n",
    "marzo = [\"201903\" + str(x).zfill(2) for x in range(1,21)]\n",
    "\n",
    "lista_invierno = lista_invierno + diciembre + enero + febrero + marzo\n",
    "\n",
    "diciembre = [\"201912\" +str(x).zfill(2) for x in range(22,31)]\n",
    "\n",
    "lista_invierno = lista_invierno + diciembre\n",
    "\n",
    "len(lista_invierno)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En el cluster 1:  37\n",
      "En el cluster 2:  100\n",
      "En el cluster 3:  0\n",
      "En el cluster 4:  23\n",
      "En el cluster 5:  0\n",
      "En el cluster 6:  29\n"
     ]
    }
   ],
   "source": [
    "# Para dias de invierno\n",
    "cluster1 = 0\n",
    "cluster2 = 0\n",
    "cluster3 = 0\n",
    "cluster4 = 0\n",
    "cluster5 = 0\n",
    "cluster6 = 0\n",
    "for i in dia_cluster:\n",
    "    if i in lista_invierno and dia_cluster[i]==0:\n",
    "        cluster1+=1\n",
    "    elif i in lista_invierno and dia_cluster[i]==1:\n",
    "        cluster2+=1\n",
    "    elif i in lista_invierno and dia_cluster[i]==2:\n",
    "        cluster3+=1\n",
    "    elif i in lista_invierno and dia_cluster[i]==3:\n",
    "        cluster4+=1\n",
    "    elif i in lista_invierno and dia_cluster[i]==4:\n",
    "        cluster5+=1\n",
    "    elif i in lista_invierno and dia_cluster[i]==5:\n",
    "        cluster6+=1\n",
    "print(\"En el cluster 1: \", cluster1)\n",
    "print(\"En el cluster 2: \", cluster2)\n",
    "print(\"En el cluster 3: \", cluster3)\n",
    "print(\"En el cluster 4: \", cluster4)\n",
    "print(\"En el cluster 5: \", cluster5)\n",
    "print(\"En el cluster 6: \", cluster6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "En el cluster 1:  37\n",
      "En el cluster 2:  103\n",
      "En el cluster 3:  53\n",
      "En el cluster 4:  56\n",
      "En el cluster 5:  177\n",
      "En el cluster 6:  47\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'0': 89, '1': 282, '2': 106, '3': 146, '4': 211, '5': 261}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista_verano_invierno = lista_verano + lista_invierno\n",
    "\n",
    "# Para dias de verano o invierno\n",
    "\n",
    "cluster1 = 0\n",
    "cluster2 = 0\n",
    "cluster3 = 0\n",
    "cluster4 = 0\n",
    "cluster5 = 0\n",
    "cluster6 = 0\n",
    "for i in dia_cluster:\n",
    "    if i in lista_verano_invierno and dia_cluster[i]==0:\n",
    "        cluster1+=1\n",
    "    elif i in lista_verano_invierno and dia_cluster[i]==1:\n",
    "        cluster2+=1\n",
    "    elif i in lista_verano_invierno and dia_cluster[i]==2:\n",
    "        cluster3+=1\n",
    "    elif i in lista_verano_invierno and dia_cluster[i]==3:\n",
    "        cluster4+=1\n",
    "    elif i in lista_verano_invierno and dia_cluster[i]==4:\n",
    "        cluster5+=1\n",
    "    elif i in lista_verano_invierno and dia_cluster[i]==5:\n",
    "        cluster6+=1\n",
    "print(\"En el cluster 1: \", cluster1)\n",
    "print(\"En el cluster 2: \", cluster2)\n",
    "print(\"En el cluster 3: \", cluster3)\n",
    "print(\"En el cluster 4: \", cluster4)\n",
    "print(\"En el cluster 5: \", cluster5)\n",
    "print(\"En el cluster 6: \", cluster6)\n",
    "\n",
    "dic_cluster"
   ]
  }
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