{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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>YR</th>\n",
       "      <th>MON</th>\n",
       "      <th>NINO1+2</th>\n",
       "      <th>ANOM</th>\n",
       "      <th>NINO3</th>\n",
       "      <th>ANOM.1</th>\n",
       "      <th>NINO4</th>\n",
       "      <th>ANOM.2</th>\n",
       "      <th>NINO3.4</th>\n",
       "      <th>ANOM.3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>24.23</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>25.31</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>27.62</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>26.27</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960</td>\n",
       "      <td>2</td>\n",
       "      <td>25.68</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>25.93</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>27.44</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>26.29</td>\n",
       "      <td>-0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1960</td>\n",
       "      <td>3</td>\n",
       "      <td>26.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>26.87</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>27.75</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>26.98</td>\n",
       "      <td>-0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1960</td>\n",
       "      <td>4</td>\n",
       "      <td>24.43</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>27.15</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>28.01</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>27.49</td>\n",
       "      <td>-0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960</td>\n",
       "      <td>5</td>\n",
       "      <td>23.33</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>26.71</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>28.42</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>27.68</td>\n",
       "      <td>-0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>19.96</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>24.50</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>28.47</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>26.26</td>\n",
       "      <td>-0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>19.50</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>23.91</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>28.21</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>25.89</td>\n",
       "      <td>-0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>20.42</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>23.88</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>27.96</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>25.46</td>\n",
       "      <td>-1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>21.07</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>23.90</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>27.80</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>25.28</td>\n",
       "      <td>-1.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>22.21</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>24.41</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>27.54</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>25.44</td>\n",
       "      <td>-1.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>732 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       YR  MON  NINO1+2  ANOM  NINO3  ANOM.1  NINO4  ANOM.2  NINO3.4  ANOM.3\n",
       "0    1960    1    24.23 -0.33  25.31   -0.34  27.62   -0.70    26.27   -0.28\n",
       "1    1960    2    25.68 -0.42  25.93   -0.47  27.44   -0.75    26.29   -0.46\n",
       "2    1960    3    26.24 -0.25  26.87   -0.33  27.75   -0.57    26.98   -0.30\n",
       "3    1960    4    24.43 -1.11  27.15   -0.43  28.01   -0.62    27.49   -0.33\n",
       "4    1960    5    23.33 -1.09  26.71   -0.54  28.42   -0.50    27.68   -0.25\n",
       "..    ...  ...      ...   ...    ...     ...    ...     ...      ...     ...\n",
       "727  2020    8    19.96 -1.05  24.50   -0.61  28.47   -0.31    26.26   -0.59\n",
       "728  2020    9    19.50 -1.22  23.91   -0.99  28.21   -0.55    25.89   -0.83\n",
       "729  2020   10    20.42 -0.60  23.88   -1.10  27.96   -0.80    25.46   -1.26\n",
       "730  2020   11    21.07 -0.58  23.90   -1.21  27.80   -0.90    25.28   -1.42\n",
       "731  2020   12    22.21 -0.60  24.41   -0.82  27.54   -1.00    25.44   -1.15\n",
       "\n",
       "[732 rows x 10 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%reset -sf\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "file=\"nino.xlsx\"\n",
    "nino=pd.read_excel(file, \"NINO\")\n",
    "nino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YR</th>\n",
       "      <th>MON</th>\n",
       "      <th>NINO1+2</th>\n",
       "      <th>ANOM</th>\n",
       "      <th>NINO3</th>\n",
       "      <th>ANOM.1</th>\n",
       "      <th>NINO4</th>\n",
       "      <th>ANOM.2</th>\n",
       "      <th>NINO3.4</th>\n",
       "      <th>ANOM.3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FECHA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-31</th>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>24.23</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>25.31</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>27.62</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>26.27</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-02-29</th>\n",
       "      <td>1960</td>\n",
       "      <td>2</td>\n",
       "      <td>25.68</td>\n",
       "      <td>-0.42</td>\n",
       "      <td>25.93</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>27.44</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>26.29</td>\n",
       "      <td>-0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-31</th>\n",
       "      <td>1960</td>\n",
       "      <td>3</td>\n",
       "      <td>26.24</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>26.87</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>27.75</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>26.98</td>\n",
       "      <td>-0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-30</th>\n",
       "      <td>1960</td>\n",
       "      <td>4</td>\n",
       "      <td>24.43</td>\n",
       "      <td>-1.11</td>\n",
       "      <td>27.15</td>\n",
       "      <td>-0.43</td>\n",
       "      <td>28.01</td>\n",
       "      <td>-0.62</td>\n",
       "      <td>27.49</td>\n",
       "      <td>-0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-31</th>\n",
       "      <td>1960</td>\n",
       "      <td>5</td>\n",
       "      <td>23.33</td>\n",
       "      <td>-1.09</td>\n",
       "      <td>26.71</td>\n",
       "      <td>-0.54</td>\n",
       "      <td>28.42</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>27.68</td>\n",
       "      <td>-0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>19.96</td>\n",
       "      <td>-1.05</td>\n",
       "      <td>24.50</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>28.47</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>26.26</td>\n",
       "      <td>-0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>19.50</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>23.91</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>28.21</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>25.89</td>\n",
       "      <td>-0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>20.42</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>23.88</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>27.96</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>25.46</td>\n",
       "      <td>-1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>21.07</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>23.90</td>\n",
       "      <td>-1.21</td>\n",
       "      <td>27.80</td>\n",
       "      <td>-0.90</td>\n",
       "      <td>25.28</td>\n",
       "      <td>-1.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>22.21</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>24.41</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>27.54</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>25.44</td>\n",
       "      <td>-1.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>732 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              YR  MON  NINO1+2  ANOM  NINO3  ANOM.1  NINO4  ANOM.2  NINO3.4  \\\n",
       "FECHA                                                                         \n",
       "1960-01-31  1960    1    24.23 -0.33  25.31   -0.34  27.62   -0.70    26.27   \n",
       "1960-02-29  1960    2    25.68 -0.42  25.93   -0.47  27.44   -0.75    26.29   \n",
       "1960-03-31  1960    3    26.24 -0.25  26.87   -0.33  27.75   -0.57    26.98   \n",
       "1960-04-30  1960    4    24.43 -1.11  27.15   -0.43  28.01   -0.62    27.49   \n",
       "1960-05-31  1960    5    23.33 -1.09  26.71   -0.54  28.42   -0.50    27.68   \n",
       "...          ...  ...      ...   ...    ...     ...    ...     ...      ...   \n",
       "2020-08-31  2020    8    19.96 -1.05  24.50   -0.61  28.47   -0.31    26.26   \n",
       "2020-09-30  2020    9    19.50 -1.22  23.91   -0.99  28.21   -0.55    25.89   \n",
       "2020-10-31  2020   10    20.42 -0.60  23.88   -1.10  27.96   -0.80    25.46   \n",
       "2020-11-30  2020   11    21.07 -0.58  23.90   -1.21  27.80   -0.90    25.28   \n",
       "2020-12-31  2020   12    22.21 -0.60  24.41   -0.82  27.54   -1.00    25.44   \n",
       "\n",
       "            ANOM.3  \n",
       "FECHA               \n",
       "1960-01-31   -0.28  \n",
       "1960-02-29   -0.46  \n",
       "1960-03-31   -0.30  \n",
       "1960-04-30   -0.33  \n",
       "1960-05-31   -0.25  \n",
       "...            ...  \n",
       "2020-08-31   -0.59  \n",
       "2020-09-30   -0.83  \n",
       "2020-10-31   -1.26  \n",
       "2020-11-30   -1.42  \n",
       "2020-12-31   -1.15  \n",
       "\n",
       "[732 rows x 10 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date1 = \"1960-01-01\"\n",
    "date2 = \"2020-12-31\"\n",
    "mydates = pd.date_range(date1,date2,freq=\"M\")\n",
    "nino[\"FECHA\"]=mydates\n",
    "nino_idx=nino.set_index(\"FECHA\")\n",
    "nino_idx\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "pearson_corr=[]\n",
    "sprm_corr=[]\n",
    "tauk_corr=[]\n",
    "\n",
    "for i in np.arange(1990,2021,1):\n",
    "    n12=nino_idx[\"ANOM\"].loc[\"1960-01-01\": str(i)+\"-12-31\"]\n",
    "    n34=nino_idx[\"ANOM.3\"].loc[\"1960-01-01\": str(i)+\"-12-31\"]\n",
    "    \n",
    "    r, p = stats.pearsonr(n12,n34)\n",
    "    pearson_corr.append(r)\n",
    "    \n",
    "    r,p = stats.spearmanr(n12,n34)\n",
    "    sprm_corr.append(r)\n",
    "    \n",
    "    r,p = stats.kendalltau(n12,n34)\n",
    "    tauk_corr.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'pearson corr evolution 1960:1990 al 2020')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(1990,2021,1),pearson_corr,marker=\"*\")\n",
    "plt.title(\"pearson corr evolution 1960:1990 al 2020\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'spearman corr evolution 1960:1990 al 2020')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(1990,2021,1),sprm_corr,marker=\"o\")\n",
    "plt.title(\"spearman corr evolution 1960:1990 al 2020\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Tau-Kendall corr evolution 1960:1990 al 2021')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(1990,2021,1),tauk_corr,marker=\"o\")\n",
    "plt.title(\"Tau-Kendall corr evolution 1960:1990 al 2021\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# solo eneros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>YR</th>\n",
       "      <th>MON</th>\n",
       "      <th>NINO1+2</th>\n",
       "      <th>ANOM</th>\n",
       "      <th>NINO3</th>\n",
       "      <th>ANOM.1</th>\n",
       "      <th>NINO4</th>\n",
       "      <th>ANOM.2</th>\n",
       "      <th>NINO3.4</th>\n",
       "      <th>ANOM.3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FECHA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-01-31</th>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>24.23</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>25.31</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>27.62</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>26.27</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961-01-31</th>\n",
       "      <td>1961</td>\n",
       "      <td>1</td>\n",
       "      <td>24.49</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>25.11</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>27.97</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>26.23</td>\n",
       "      <td>-0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-31</th>\n",
       "      <td>1962</td>\n",
       "      <td>1</td>\n",
       "      <td>23.83</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>25.09</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>27.65</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>25.96</td>\n",
       "      <td>-0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-31</th>\n",
       "      <td>1963</td>\n",
       "      <td>1</td>\n",
       "      <td>23.72</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>24.85</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>27.45</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>25.77</td>\n",
       "      <td>-0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-01-31</th>\n",
       "      <td>1964</td>\n",
       "      <td>1</td>\n",
       "      <td>23.92</td>\n",
       "      <td>-0.65</td>\n",
       "      <td>26.02</td>\n",
       "      <td>0.36</td>\n",
       "      <td>28.41</td>\n",
       "      <td>0.09</td>\n",
       "      <td>27.34</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>26.36</td>\n",
       "      <td>1.79</td>\n",
       "      <td>28.25</td>\n",
       "      <td>2.59</td>\n",
       "      <td>29.54</td>\n",
       "      <td>1.22</td>\n",
       "      <td>29.11</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>25.50</td>\n",
       "      <td>0.93</td>\n",
       "      <td>25.58</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>28.16</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>26.12</td>\n",
       "      <td>-0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>23.44</td>\n",
       "      <td>-1.12</td>\n",
       "      <td>24.48</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>27.88</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>25.57</td>\n",
       "      <td>-0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>25.40</td>\n",
       "      <td>0.83</td>\n",
       "      <td>26.26</td>\n",
       "      <td>0.60</td>\n",
       "      <td>29.08</td>\n",
       "      <td>0.76</td>\n",
       "      <td>27.19</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>24.20</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>25.88</td>\n",
       "      <td>0.22</td>\n",
       "      <td>29.17</td>\n",
       "      <td>0.85</td>\n",
       "      <td>27.15</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              YR  MON  NINO1+2  ANOM  NINO3  ANOM.1  NINO4  ANOM.2  NINO3.4  \\\n",
       "FECHA                                                                         \n",
       "1960-01-31  1960    1    24.23 -0.33  25.31   -0.34  27.62   -0.70    26.27   \n",
       "1961-01-31  1961    1    24.49 -0.08  25.11   -0.55  27.97   -0.35    26.23   \n",
       "1962-01-31  1962    1    23.83 -0.74  25.09   -0.57  27.65   -0.66    25.96   \n",
       "1963-01-31  1963    1    23.72 -0.85  24.85   -0.81  27.45   -0.86    25.77   \n",
       "1964-01-31  1964    1    23.92 -0.65  26.02    0.36  28.41    0.09    27.34   \n",
       "...          ...  ...      ...   ...    ...     ...    ...     ...      ...   \n",
       "2016-01-31  2016    1    26.36  1.79  28.25    2.59  29.54    1.22    29.11   \n",
       "2017-01-31  2017    1    25.50  0.93  25.58   -0.08  28.16   -0.16    26.12   \n",
       "2018-01-31  2018    1    23.44 -1.12  24.48   -1.18  27.88   -0.44    25.57   \n",
       "2019-01-31  2019    1    25.40  0.83  26.26    0.60  29.08    0.76    27.19   \n",
       "2020-01-31  2020    1    24.20 -0.37  25.88    0.22  29.17    0.85    27.15   \n",
       "\n",
       "            ANOM.3  \n",
       "FECHA               \n",
       "1960-01-31   -0.28  \n",
       "1961-01-31   -0.31  \n",
       "1962-01-31   -0.59  \n",
       "1963-01-31   -0.78  \n",
       "1964-01-31    0.79  \n",
       "...            ...  \n",
       "2016-01-31    2.57  \n",
       "2017-01-31   -0.43  \n",
       "2018-01-31   -0.98  \n",
       "2019-01-31    0.65  \n",
       "2020-01-31    0.60  \n",
       "\n",
       "[61 rows x 10 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only with enero\n",
    "nino_ene=nino_idx.loc[nino_idx.index.month==1]\n",
    "nino_ene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'pearson corr evolution, enero, 1960-1990:2020')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pearson_corr=[]\n",
    "sprm_corr=[]\n",
    "tauk_corr=[]\n",
    "\n",
    "for i in np.arange(1990,2021,1):\n",
    "    n12=nino_ene[\"ANOM\"].loc[\"1960-01-31\": str(i)+\"-01-31\"]\n",
    "    n34=nino_ene[\"ANOM.3\"].loc[\"1960-01-31\": str(i)+\"-01-31\"]\n",
    "    \n",
    "    r, p = stats.pearsonr(n12,n34)\n",
    "    pearson_corr.append(r)\n",
    "    \n",
    "    r,p = stats.spearmanr(n12,n34)\n",
    "    sprm_corr.append(r)\n",
    "    \n",
    "    r,p = stats.kendalltau(n12,n34)\n",
    "    tauk_corr.append(r)\n",
    "\n",
    "plt.plot(np.arange(1990,2021,1),pearson_corr, marker=\"o\")\n",
    "plt.title(\"pearson corr evolution, enero, 1960-1990:2020\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'spearman corr evolution, Enero 1960-1990:2020')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.arange(1990,2021,1),sprm_corr,marker=\"o\")\n",
    "plt.title(\"spearman corr evolution, Enero 1960-1990:2020\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ejercicio: hacer runmean(mediamovil) de 3 de nino12,y hacer evolucion de correlaciones 90-2020, nino1+2 y el IOS, enero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -0.333333\n",
       "1     -0.593333\n",
       "2     -0.816667\n",
       "3     -1.203333\n",
       "4     -1.273333\n",
       "         ...   \n",
       "725   -1.010000\n",
       "726   -1.183333\n",
       "727   -0.956667\n",
       "728   -0.800000\n",
       "729   -0.593333\n",
       "Name: ANOM, Length: 730, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nino12_r=nino[\"ANOM\"].rolling(3,center=True).mean()\n",
    "nino12_r =nino12_r.iloc[1:]\n",
    "nino12_r =nino12_r.iloc[:-1]\n",
    "nino12_r =nino12_r.reset_index(drop=True)\n",
    "nino12_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>ANOM</th>\n",
       "      <th>SOI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.593333</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.816667</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.203333</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.273333</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>-1.010000</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>-1.183333</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>-0.956667</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>-0.800000</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>-0.593333</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>730 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ANOM  SOI\n",
       "0   -0.333333  0.4\n",
       "1   -0.593333  0.6\n",
       "2   -0.816667  0.8\n",
       "3   -1.203333  0.5\n",
       "4   -1.273333  0.4\n",
       "..        ...  ...\n",
       "725 -1.010000  0.4\n",
       "726 -1.183333  0.8\n",
       "727 -0.956667  0.8\n",
       "728 -0.800000  0.7\n",
       "729 -0.593333  1.0\n",
       "\n",
       "[730 rows x 2 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#soi\n",
    "soi=pd.read_excel(file,\"SOI\")\n",
    "\n",
    "soi_data=soi.set_index(\"YEAR\").stack().reset_index().rename(columns={\"level_1\":\"MON\",0:\"SOI\"})\n",
    "soi_ad=soi_data.SOI.iloc[1:]\n",
    "soi_ad=soi_ad.iloc[:-1]\n",
    "soi_ad=soi_ad.reset_index(drop=True)\n",
    "\n",
    "#unimos columnas\n",
    "\n",
    "newdata= pd.concat([nino12_r, soi_ad],axis=1)\n",
    "newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>ANOM</th>\n",
       "      <th>SOI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FECHA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1960-02-29</th>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-31</th>\n",
       "      <td>-0.593333</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-30</th>\n",
       "      <td>-0.816667</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-31</th>\n",
       "      <td>-1.203333</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-30</th>\n",
       "      <td>-1.273333</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-31</th>\n",
       "      <td>-1.010000</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-08-31</th>\n",
       "      <td>-1.183333</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-09-30</th>\n",
       "      <td>-0.956667</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-31</th>\n",
       "      <td>-0.800000</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-30</th>\n",
       "      <td>-0.593333</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>730 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                ANOM  SOI\n",
       "FECHA                    \n",
       "1960-02-29 -0.333333  0.4\n",
       "1960-03-31 -0.593333  0.6\n",
       "1960-04-30 -0.816667  0.8\n",
       "1960-05-31 -1.203333  0.5\n",
       "1960-06-30 -1.273333  0.4\n",
       "...              ...  ...\n",
       "2020-07-31 -1.010000  0.4\n",
       "2020-08-31 -1.183333  0.8\n",
       "2020-09-30 -0.956667  0.8\n",
       "2020-10-31 -0.800000  0.7\n",
       "2020-11-30 -0.593333  1.0\n",
       "\n",
       "[730 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date1 = \"1960-02-01\"\n",
    "date2 = \"2020-11-30\"\n",
    "\n",
    "mydates = pd.date_range(date1,date2,freq=\"M\")\n",
    "newdata[\"FECHA\"]=mydates\n",
    "newdata = newdata.set_index(\"FECHA\")\n",
    "newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>ANOM</th>\n",
       "      <th>SOI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FECHA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1961-01-31</th>\n",
       "      <td>0.026667</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-01-31</th>\n",
       "      <td>-0.783333</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963-01-31</th>\n",
       "      <td>-0.920000</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-01-31</th>\n",
       "      <td>-0.743333</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-01-31</th>\n",
       "      <td>-0.780000</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966-01-31</th>\n",
       "      <td>0.033333</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-01-31</th>\n",
       "      <td>-0.746667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-01-31</th>\n",
       "      <td>-1.623333</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-31</th>\n",
       "      <td>-0.263333</td>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-31</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-31</th>\n",
       "      <td>-1.423333</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-31</th>\n",
       "      <td>-0.306667</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-31</th>\n",
       "      <td>1.143333</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-31</th>\n",
       "      <td>-1.290000</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-31</th>\n",
       "      <td>-1.263333</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-01-31</th>\n",
       "      <td>-1.213333</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01-31</th>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-01-31</th>\n",
       "      <td>-0.453333</td>\n",
       "      <td>-1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-31</th>\n",
       "      <td>-0.110000</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-31</th>\n",
       "      <td>-0.250000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-31</th>\n",
       "      <td>-1.023333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-31</th>\n",
       "      <td>-0.410000</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-01-31</th>\n",
       "      <td>2.613333</td>\n",
       "      <td>-3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-01-31</th>\n",
       "      <td>-0.353333</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-31</th>\n",
       "      <td>-0.853333</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-31</th>\n",
       "      <td>-0.160000</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-01-31</th>\n",
       "      <td>0.853333</td>\n",
       "      <td>-1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-31</th>\n",
       "      <td>0.126667</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-01-31</th>\n",
       "      <td>-0.370000</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-31</th>\n",
       "      <td>-0.346667</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-31</th>\n",
       "      <td>-0.473333</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-31</th>\n",
       "      <td>0.636667</td>\n",
       "      <td>-1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-31</th>\n",
       "      <td>0.063333</td>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-31</th>\n",
       "      <td>-0.133333</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-31</th>\n",
       "      <td>0.563333</td>\n",
       "      <td>-0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-31</th>\n",
       "      <td>-0.646667</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-31</th>\n",
       "      <td>-0.750000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-31</th>\n",
       "      <td>3.480000</td>\n",
       "      <td>-1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-31</th>\n",
       "      <td>-0.500000</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-31</th>\n",
       "      <td>-0.673333</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-31</th>\n",
       "      <td>-0.690000</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-31</th>\n",
       "      <td>-0.623333</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-31</th>\n",
       "      <td>0.370000</td>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-31</th>\n",
       "      <td>-0.020000</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-31</th>\n",
       "      <td>-0.200000</td>\n",
       "      <td>-1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-31</th>\n",
       "      <td>-0.356667</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-31</th>\n",
       "      <td>0.493333</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-31</th>\n",
       "      <td>-0.640000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-31</th>\n",
       "      <td>-0.433333</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>-1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-31</th>\n",
       "      <td>-0.513333</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-31</th>\n",
       "      <td>-0.166667</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>-0.753333</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-31</th>\n",
       "      <td>-0.353333</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>-0.093333</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>1.766667</td>\n",
       "      <td>-1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>0.906667</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>-1.170000</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-31</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>-0.130000</td>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ANOM  SOI\n",
       "FECHA                    \n",
       "1961-01-31  0.026667  0.5\n",
       "1962-01-31 -0.783333  1.1\n",
       "1963-01-31 -0.920000  0.6\n",
       "1964-01-31 -0.743333 -0.5\n",
       "1965-01-31 -0.780000 -0.1\n",
       "1966-01-31  0.033333 -0.4\n",
       "1967-01-31 -0.746667  1.0\n",
       "1968-01-31 -1.623333  0.4\n",
       "1969-01-31 -0.263333 -0.6\n",
       "1970-01-31  0.200000 -0.6\n",
       "1971-01-31 -1.423333  1.4\n",
       "1972-01-31 -0.306667  0.6\n",
       "1973-01-31  1.143333 -1.0\n",
       "1974-01-31 -1.290000  2.1\n",
       "1975-01-31 -1.263333  0.2\n",
       "1976-01-31 -1.213333  1.7\n",
       "1977-01-31  0.080000  0.2\n",
       "1978-01-31 -0.453333 -1.4\n",
       "1979-01-31 -0.110000  0.2\n",
       "1980-01-31 -0.250000  0.0\n",
       "1981-01-31 -1.023333  0.0\n",
       "1982-01-31 -0.410000  0.7\n",
       "1983-01-31  2.613333 -3.1\n",
       "1984-01-31 -0.353333  0.4\n",
       "1985-01-31 -0.853333  0.3\n",
       "1986-01-31 -0.160000  0.1\n",
       "1987-01-31  0.853333 -1.1\n",
       "1988-01-31  0.126667 -0.3\n",
       "1989-01-31 -0.370000  1.3\n",
       "1990-01-31 -0.346667 -0.8\n",
       "1991-01-31 -0.473333  0.2\n",
       "1992-01-31  0.636667 -1.9\n",
       "1993-01-31  0.063333 -0.7\n",
       "1994-01-31 -0.133333  0.1\n",
       "1995-01-31  0.563333 -0.6\n",
       "1996-01-31 -0.646667  0.3\n",
       "1997-01-31 -0.750000  1.0\n",
       "1998-01-31  3.480000 -1.9\n",
       "1999-01-31 -0.500000  1.4\n",
       "2000-01-31 -0.673333  1.3\n",
       "2001-01-31 -0.690000  1.2\n",
       "2002-01-31 -0.623333  0.2\n",
       "2003-01-31  0.370000 -0.7\n",
       "2004-01-31 -0.020000  0.3\n",
       "2005-01-31 -0.200000 -1.2\n",
       "2006-01-31 -0.356667  0.6\n",
       "2007-01-31  0.493333 -0.4\n",
       "2008-01-31 -0.640000  2.0\n",
       "2009-01-31 -0.433333  1.5\n",
       "2010-01-31  0.333333 -1.1\n",
       "2011-01-31 -0.513333  2.6\n",
       "2012-01-31 -0.166667  1.4\n",
       "2013-01-31 -0.753333 -0.3\n",
       "2014-01-31 -0.353333  0.5\n",
       "2015-01-31 -0.093333 -0.4\n",
       "2016-01-31  1.766667 -1.6\n",
       "2017-01-31  0.906667  0.1\n",
       "2018-01-31 -1.170000  0.2\n",
       "2019-01-31  0.800000 -0.1\n",
       "2020-01-31 -0.130000 -0.2"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#solo enero \n",
    "nino_ene_2=newdata.loc[newdata.index.month==1]\n",
    "nino_ene_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'pearson corr evolution, enero, 1960-1990:2020')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LBMaYHmFIvwwqqurb9B/cN50nPv8RmluU5hZl/qNrOVrTtr48PzuD1Qs/vK1pxpJXKfNz0I9UvXooKqrq+MoTG9hwoJI7Z45i4XXjSU1O6tKDeygsERhjeoRJeX3bJILM1GQWXTeBwkG9z/a7d06g+vLxraZdMHtcl9art8e7kXpA7zTqGpwYfvuZqXzs/KFdvvxQWSIwxkTdoap63tx1jKkjsjlcfabdqpJg68s93x/8xw7Kq+rplZ7MA/PO6/IzcN9G6mOnGhBg4XXjemQSAEsExpge4Jcvf0BLC/z65ikMz8nqcPxgq1Q84/37w+9w7NSZbqmG8dforcDjaw5w9+WFXb78zgj36aPGGBOWDw7X8Pf1pdw2fWRQSaAzZhTmsuNQDUdq2rZBRFqgxuhoNVIHwxKBMSaqfvbiDnqlp/DVj3bd2fLMwlwA3tp1vMuW4TGwT7rf/tFopA6WJQJjTNS8vec4q3Yc4ctXFNK/V1qXLWdSXl+ys1J5c9exLlsGQF1DM0nStn93NVJ3liUCY0xUqCqLX9jO0H4ZfHZGQZcuKylJmDEml9W7jqHq//6CcKkq963YyuGaM9x9+WjyszMRnDuFF9/Y9Y3U4bDGYmNMVDy/pYLNB6tY+snzyUhN7vLlzSjM5fktFew5dpoxA3t3PEGInlxXytMbDvL1WWP51tXnsOi6CRFfRlexEoExpts1NLWwtLiE8UP6cOPUYd2yTE87weouqB7aVl7Ffzy7jZmFuXxj1tiIz7+rWSIwxnS7ZWsPsP94Ld+7bjzJ/irVu8CIAVkMz8nkjZ2RTQTV9Y18+YkNZGel8qubJ3fb+kSSJQJjTLeqqW/k16t2Mn30AK44Z2C3Lntm4UDe3n2cpuaWjkcOgqqy4O+bOXiyjt9+Ziq5vf1fMdTTWSIwxnSrh17fw4nTDSyaMx6R7j17nlmYS82ZJt4rq4rI/B55cy/F2w6z8NrxFBXkRGSe0WCJwBjTbQ5X1/P7N/bw8QvyOH9Ydrcvf/qYAYjA6ghUD63ff4IlL+7gmomD+fyloyIQXfTYVUPGmG7zq1c+oLlFWXBNdK6pz+mVxqS8vry56xhf60SjrvfD5EQgOyuVpZ+6oNtLNpFmicAY06W8D54KXDY2lxEDuuZREsGYUZjLH97cy+kzTfRKD/4Q6PswOVU4faaZf+440qPvEQiGVQ0ZY7qM5+BZ5iYBgLX7TrBiY1nUYppZmEtjs7J234mQpvP3MLkz7mWwsc4SgTGmy/g7eNY3RvfgeWFBDmkpSSG3E8Tiw+SCZYnAGNNleuLBMyM1mQsL+of83KFAD43ryQ+TC5YlAmNMl+mpB0/PY6n9vfIykC9eMbpNv57+MLlgWSIwxnSZBbPHtbnTticcPM8+lnp38KWCA8drARjUJz1mHiYXLLtqyBjTZWYU5oIqWWnJ1DU0B3ytZHeblNePfpmpvLnzGHMndxzLkep6Hl+znxun5vOLT0/u+gC7mSUCY0yX+dOafbQAz31tZpc88bOzkpOEGYUDzj6WuqP7AP77X7tpalG+fmXsPVAuGFY1ZIzpEnUNzfzp7f1cNWFwj0oCHjMKcymvqmfvsdPtjneoqp4n3jnATVPzKcjt1U3RdS9LBMaYLvHUhoOcrG3kC5e2bWTtCYJ9LPV/vbaLlhbla3FaGgBLBMaYLtDcojzyxh4uGJ7NhQX9ox2OXyNyshjWv/3HUpdV1vHXtaV8qmg4w3Oidzd0V7NEYIyJuFe2H2bf8VruunR0j30Oj4gwszCXNXsCP5b6t//cBcBXryzsztC6nSUCY0zE/f71PQzrn8nsSYOjHUq7ZhTmUlPfxBY/j6UuPVHLk++WcvNFw8mPg5vG2hNWIhCRHBF5WUR2un/blAFFZJyIbPL6VIvIN4Od3hgTWzYcOMm6/Se5c+YoUpJ79rnmjHbaCX7z6i6SkoQvXxHfpQEIv0SwEFilqmOBVe73VlS1RFUnq+pkYBpQCzwT7PTGmNjy8Bt76JuRwqeLhkc7lA55P5ba2/7jp3lqw0FuvXgEQ/plRCm67hNuIpgLPOZ2PwbM62D8WcBuVd3fyemNMT3YgeO1/GPrIW79yMiQHvEcTTMLc1m//yS1DU1n+/3nql2kJgtfumJMFCPrPuEmgsGqWgHg/h3Uwfg3A8s6M72I3CUi60Rk3dGjR8MM2xjTFf6wei/JScIdlxREO5SgzfA8lnqv81jqPUdP8czGg9z2kZEM6hP/pQEIIhGIyCsistXPZ24oCxKRNOAG4O+dCVRVH1LVIlUtGjiwe194bYzpWGVtA397t5QbLshncN/YOYBeWJBDWnLS2XaC/1y1k/SUZO6+PDFKAxDEIyZU9apAw0TksIgMVdUKERkKHGlnVtcBG1T1sFe/UKY3xvRgT7xzgLrGZr5wWWy9vzczLZmigv68ues4u47U8Ozmcu6+bAy5vdOjHVq3CbdqaCUw3+2eDzzbzri30LpaKNTpjTE91JmmZv741j4uO2cg44f0jXY4Ieuflcr2imqu+sXroDC8f3xfLuor3ESwBLhaRHYCV7vfEZE8EXnBM5KIZLnDlwczvTEmtjy7qZyjNWf4wqWxVRoA53War2z/sDJCgZ88vz2qr9PsbmE166vqcZwrgXz7lwNzvL7XAgOCnd4YEztUlYff2MP4IX3OPr8nliwtLuFMU+s7i+sam1laXBL1x2V3l9i4vssY0+Os2FjG0uISytzXTt568fAe+ziJ9vTE12l2t559258xpkdasbGMRcu3nE0CAE9vKIvJ6pSe+jrN7mSJwBgTsqXFJdQ1NrfqV9/YwtLikihF1HkLZo8jMzW5Vb+e8DrN7mRVQ8aYkMVTdYqnHWBpcQnllXU95nWa3ckSgTEmZHnZma2qhbz7x6J5U/IT6sDvy6qGjDEhc6pTWh8+Eq06JZ5YIjDGhGzelHy+dfWHB/387EwW33heQp9VxzKrGjLGdMpg9/HML3z9Uibmxd7dxOZDViIwxnTKtrIq0pKTGDu4d7RDMWGyRGCM6ZSt5VWMH9qH1B7+FjLTMduDxpiQqSpby6qZlNcv2qGYCLBEYIwJ2cGTdVTVNXJuvrUNxANLBMaYkG0tqwLgXCsRxAVLBMaYkG0tryIlSRg3pE+0QzERYInAGBOyrWXVjB3chwyfZ/SY2GSJwBgTEqehuIpz7d6BuGGJwBgTksPVZzh+uoFz8619IF5YIjDGhORsQ7FdMRQ3LBEYY0KypayKJIEJQy0RxAtLBMaYkGwrr2LMwN5kpdmjyuKFJQJjTEi2llVb+0CcsURgjAna0ZozHKquZ5JdMRRXLBEYY4K2rdzTUGwlgnhiicAYEzTPFUNWIogvlgiMMUHbWlbNqNxe9MlIjXYoJoIsERhjgra1vMpKA3HIEoExJiiVtQ0cPFln7QNxyBKBMSYo28qrAXv0dDyyRGCMCcoWayiOW5YIjDFB2VpWxbD+mfTvlRbtUEyEWSIwxgRlW3m1VQvFKUsExpgO1dQ3svfYaXviaJyyRGCM6dD7bkPxJLtiKC5ZIjDGdGiLvaw+rlkiMMZ0aFt5NUP6ZjCwT3q0QzFdIKxEICI5IvKyiOx0//b3M844Ednk9akWkW+6w5aKyA4ReU9EnhGR7HDiMcZ0ja1lVdY+EMfCLREsBFap6lhglfu9FVUtUdXJqjoZmAbUAs+4g18GzlXV84EPgEVhxmOMibDahiZ2Hz3FJKsWilvhJoK5wGNu92PAvA7GnwXsVtX9AKr6kqo2ucPeBoaFGY8xJsK2V9TQovbo6XgWbiIYrKoVAO7fQR2MfzOwLMCwzwEvBppQRO4SkXUisu7o0aOdCtYYEzp7WX386/CloyLyCjDEz6B7Q1mQiKQBN+Cn+kdE7gWagCcCTa+qDwEPARQVFWkoyzbGdN7Wsipye6cxpG9GtEMxXaTDRKCqVwUaJiKHRWSoqlaIyFDgSDuzug7YoKqHfeYxH7gemKWqdoA3pofZWl7NpLx+iEi0QzFdJNyqoZXAfLd7PvBsO+Pegk+1kIhcC3wPuEFVa8OMxRgTYfWNzew8XGPVQnEu3ESwBLhaRHYCV7vfEZE8EXnBM5KIZLnDl/tM/xugD/Cye2np78KMxxgTQR8crqGpRe1GsjjXYdVQe1T1OM6VQL79y4E5Xt9rgQF+xisMZ/nGmK61tcx9B4FdMRTX7M5iY0xAW8qq6JeZyrD+mdEOxXQhSwTGmIC2lTt3FFtDcXyzRGCM8auxuYUdFTXWPpAALBEYY/zaefgUDc0t9ujpBBBWY7ExJnas2FjG0uISyivryMvOZMHsccybkh9w/K3lnkdP26Wj8c4SgTEJYMXGMhYt30JdYzMAZZV1LFq+BSBgMthaVkXv9BQKBvTqtjhNdFjVkDEJYGlxydkk4FHX2MzS4pKA02wtq2JiXl+SkqyhON5ZIjAmAZRX1vntX1ZZR1VtY5v+zS3K+xX2svpEYYnAmAQwpF/gB8ZdvPgVFi3fQsmhGsCpRpq+eBX1jS08s/EgKzaWdVeYJkqsjcCYBFA4sBcVVfWt+mWmJvPVKwspPVHL8g0HWbb2AGMG9qL0RC0Nzc7zH0/WNnbYlmBinyUCY+LcloNVrN59nEsLB7DnWK3fq4a+d+14/raulKX/KKHZ5yHAnrYESwTxyxKBMXGsuUW5b8UWcnql85tbp9EvM9XveP17pfHFy8fwsxd3+B0eqI3BxAdrIzAmji1be4DNB6v4wfUTAiYBb3nZ/p8pFKi/iQ+WCIyJU8dOneHBf+xg+ugB3HBBXlDTLJg9jszU5Fb9MlOTWTB7XFeEaHoIqxoyJk799IXt1DU28+N5k4J+aJynHSCUO5BN7LNEYEwcenvPcZZvKOPLV4yhcFCfkKadNyXfDvwJxqqGjIkzjc0t/GDFVvKzM/nalWOjHY6JAVYiMCbOPPLmXnYeOcXDtxeRmZbc8QQm4VmJwJg4UlZZx69f2clVEwZz1cTB0Q7HxAhLBMbEkR89tw1F+T8fnxjtUEwMsaohY2Kc5z0DZe5NX9efP4ThOVlRjsrEEisRGBPDPO8ZKPO68/eV7UfsQXEmJJYIjIlh/t4zUN/Y0u57BozxZYnAmBgW6BlA9mwgEwpLBMbEMHs2kIkESwTGxLBvXz0W34dH2LOBTKgsERgTw7LSU1Egp1caAuRnZ7L4xvPsEREmJHb5qDEx7PE1+8jrl8Hr3/0oKcl2Xmc6x345xsSonYdreGv3cf59+khLAiYs9usxJkY9vmY/aSlJ/FvR8GiHYmKcJQJjYlB1fSNPbzjIx8/PY0Dv9GiHY2KcJQJjYtDy9QepbWhm/iUjox2KiQOWCIyJMS0tyuNr9jN5eDbnD8uOdjgmDlgiMCbGvLnrGHuOnbbSgIkYSwTGxJjH1+xjQK805pw3NNqhmDgRViIQkRwReVlEdrp/+/sZZ5yIbPL6VIvIN33G+Y6IqIjkhhOPMfGu9EQtq3Yc4ZaLRpCeYm8fM5ERbolgIbBKVccCq9zvrahqiapOVtXJwDSgFnjGM1xEhgNXAwfCjMWYuPfnt/eTJMKtHxkR7VBMHAk3EcwFHnO7HwPmdTD+LGC3qu736vdL4LuAhhmLMXGtrqGZv75byuxJgxnazx4qZyIn3EQwWFUrANy/gzoY/2ZgmeeLiNwAlKnq5o4WJCJ3icg6EVl39OjRcGI2JiY9t7mcqrpGbp9eEO1QTJzp8FlDIvIKMMTPoHtDWZCIpAE3AIvc71nuPK4JZnpVfQh4CKCoqMhKDyahqCp/fGsf4wb34eJROdEOx8SZDhOBql4VaJiIHBaRoapaISJDgSPtzOo6YIOqHna/jwFGAZtFBGAYsEFELlLVQ0GvgTEJYMOBk7xfUc0DnzgX9//FmIgJt2poJTDf7Z4PPNvOuLfgVS2kqltUdZCqFqhqAXAQmGpJwJi2HntrP30yUpg32R4vbSIv3MdQLwGeFJE7ca76+RSAiOQBD6vqHPd7Fs6VQXeHuTxjYtqKjWUsLS6hvLKOvOxMFswe1+G7A45U1/PClgpun15Ar3R7cryJvLB+Vap6HOdKIN/+5cAcr++1wIAO5lUQTizG9HQrNpaxaPmWsy+bL6usY9HyLQDtJoNla0tpalFum253EpuuYacXxkRAMGf6S4tLziYBj7rGZpYWl/hNBCs2lvFg8Q7KK+tJT0lic2klo3J7del6mMRkicCYMAU606+ua2B4Ti+2lVfxfkU1ZZV1fqcvq6zjByu2csHwbCYP78fo3N6s3Fzeap5nmlqCKj0Y0xmiGntXYhYVFem6deuiHYYxAMxY8mrAg7zHyAFZHK6up76xpc2w9JQkUpOTOHWmCYA+6Sk0NLdwpqntuPnZmaxeeGVkAjcJR0TWq2qRb38rERgTpvJ2ksCTd09n/NA+9M1IbVNyAMhMTWbxjedxwwV57Dl2ik2lVWwureRPb+/3O7/2lmVMZ1kiMCZMedmZfksE+dmZXOR185enSidQW0LhoD4UDurDJ6cN49UdR/zOMy/bHi1hIs8SgTFhWjB7HN99ajMNzR9Ws2amJrNg9rg2486bkh9UHf+C2eP8lh78zdOYcNn7CIwJ07wp+YwZ2JskAcEpCSy+8bywGnXnTcln8Y3nkZ+dGbF5GhOIlQiMCdOhqnpKDtfw5SsK+U4Ez9iDLT0YEy4rERgTpqfWl9Ki8Omi4dEOxZhOsURgTBhaWpQn1x1k+ugBjBiQFe1wjOkUSwTGhOHtvcc5cKKWf7vQSgMmdlkiMCYMT75bSp+MFK49198rO4yJDZYIjOmkqrpGXtx6iHmT88lItRfJm9hlicCYTlq5qYwzTS1WLWRiniUCYzrpb+tKmTi0L+fm94t2KMaExRKBMZ2wrbyKrWXVVhowccESgTGd8OS7paSlJNmrI01csERgTIjqG5tZsamcaycNoV9WarTDMSZslgiMCVHxtkNU1TVatZCJG5YIjAnRk+tKGZ6TyfTR7b6G25iYYYnAmBCUnqhl9a7jfGracJKSJNrhGBMRlgiMCcHf15UiAp+cNizaoRgTMZYIjAlSc4vy9/UHuWzsQHtTmIkrlgiMCdIbO49SUVVvjcQm7lgiMCZIT64rJadXGldNGBztUIyJKHtDmYkrKzaWBXw5fDiOnzrDy+8f5vbpBaSl2PmTiS+WCExMCOYAv2JjWasXvpdV1rFo+RaAsJPBMxvLaGxWqxYycckSgYmqcA/wV00czKGqeg5X1/PD57adHcejrrGZpcUlnU4ETnw7KKusJzVZeL+8mnMG9+nUvIzpqSwRmKjxd4BfuPw9jp8+w8WjBlBV10hVXSP3r/R/gL/nb5vQIJZTXlkXkfgamzViJQxjehJLBCZqlry4o80Bvr6xhR//7/agplfge9eOZ0i/dAb3zeCbf93EkZozbcbLSE0+W+IIxeIXt0e8hGFMT2SJwHSr5hbl9Q+O8pe1BzhUXR9wvP+5bRr9MlPpl5nKHY+u5XB12wN8fnYmX7pizNnv358zodUZPEBKktDQ1MyV//c1vnR5IXdfPrrDt4mt33+Ch9/Y63eZ0PkShjE9VcIkgmCvJumqq04Sib9teNGoHP72bil/X1dKeVU9ub3T6J2ewqkzTW2mz8/OZPakD98BvOi6tgf4zNRkFswe12o6z37yXfa0kf1Z8uIOfvnKBzy5rpR7PzaBM43N/PylD86O9+2rx5KemsLDb+5h44FK+makBIzPbiYz8UZUg6ll7VmKiop03bp1QY/vW9cLzoFk8Y3ntTrIBzueCczfNkwSaFEQgUvHDuSWC4cza8JgXthSEfT2jkSCXrP7OD98bhs7DtWcjclDcKqaRg7I4s6Zo7hp6jBefv+w/R5MXBGR9apa1KZ/IiSCGUtepcxPcb53ejK3Ty8gJTmJ1CTh92/sobre/xnq6oVXhhVzogi0rfukp/DCNy5leE5Wq/7dXQJrblGm/vhlquoa2wzL6ZXGu/deRbLXw+SshGjiSaBEEFbVkIjkAH8DCoB9wKdV9aTPOOPccTxGA/+hqr9yh38N+CrQBDyvqt8NJyZ/AtXpnjrTzO/f2ENjc/vJ0OqEgxd4Wze1SQLgVOd054E1OUmo9pMEAE6ebmiVBKD74zMmGsK9RXIhsEpVxwKr3O+tqGqJqk5W1cnANKAWeAZARD4KzAXOV9VJwM/DjMevQHW6+dmZ7HxgDnsXz2HnA9eR1y8jpOlNW4G2VU/ahrEQozHdKdxEMBd4zO1+DJjXwfizgN2qut/9/iVgiaqeAVDVI2HG49eC2ePI9LlSxLuxUURITU7iu9eObzNeRkpSm0ZJE9h3rjkH8XlMv7+G3Wjq6PdgTKIJNxEMVtUKAPfvoA7GvxlY5vX9HOBSEXlHRP4lIheGGY9f86bks/jG88jPzkRwSgL+Gvx8xwO4YtwgqxoIQe+MVFQhOzO13W0dTcH+HoxJFB02FovIK8AQP4PuBR5T1WyvcU+qav8A80kDyoFJqnrY7bcVeBX4BnAhTlvCaPUTlIjcBdwFMGLEiGn79+/3HSXi7nh0LdvKq1n9vSvtQWNBaG5Rrv3V6zS3KC/dcxkpybbNjOlJAjUWd/ifqqpXqeq5fj7PAodFZKi7gKFAe1U71wEbPEnAdRBYro61QAuQGyCOh1S1SFWLBg4c2FHYEXHHJQUcrTnDC1squmV5se7pDQfZeeQUC2aPsyRgTAwJ9791JTDf7Z4PPNvOuLfQuloIYAVwJYCInAOkAcfCjCliLhs7kNEDe/Ho6r3E4mW23am+sZlfvvwBFwzP5tpz/RUgjTE9VbiJYAlwtYjsBK52vyMieSLygmckEclyhy/3mf4PwGi3iuivwHx/1ULRkpQk3HFJAZsPVrGxtDLa4fRoj6/ZR0VVPQuvHY/4thYbY3q0sO4jUNXjOFcC+fYvB+Z4fa8FBvgZrwH493Bi6Go3TR3G0n+U8OjqfUwd4bf5I+FV1TXy23/u5vJzBjJ9TJvdbIzp4awitwO90lP49IXDeXFLBYeqAj8kLZH97l+7qa5v5HvXjo92KMaYTrBEEIT50wtoVuXPb3f9lUqx5lBVPY+u3svcC/KYmNc32uEYYzrBEkEQRgzI4qoJg/nL2gPU+zyfPtH9etUHNLco377GbsYyJlZZIgjSZy8p4MTpBlZuLo92KD3GriOneHLdQW69eKTf5wgZY2KDJYIgTR8zgHGD+/Do6n12Kanr58UlZKQk8dUrC6MdijEmDJYIgiQi3DGjgO0V1azdeyLa4UTdhgMn+ce2Q9x12Rhye6dHOxxjTBgsEYRg3uR8srNSeXT1vmiHElWqys9e3EFu7zQ+f+moaIdjjAlTwryqMhIy05K5+cIRPPT6bg6erGVY/8SqF/e8pMXz4pmbpubTK91+QsbEOisRhOi26SMREf60JrEuJfW8gtL77WPPb6lgxcayKEZljIkESwQhcl6sPphlaw9Q29D2tZbxamlxSat39wLUN7awtLgkShEZYyLFEkEnfHbGKKrrm3gmgc6GA72C0l7jaUzss0TQCUUj+3Nufl/+mECXkuZl22s8jYlXlgg6QUQ4L78fO4+cYtSiF5ix5NW4ryu/dGzbd0DY6x2NiQ92yUcnrNhY1qpaqKyyjkXLtwCE9bpDz1U55ZV15GVnsmD2uB7x+sSyyjqef6+CMQN7UdfYTEVlfY+KzxgTHksEnbC0uIT6xpZW/eoam1lavKPTB0bPVTmeBtlIJZdwqSoLn36PZlX++NmL7FESxsQhqxrqhEANpGWV9fzm1Z0cPFkb0vwamlr4yfPvt7kqx0ku0b0qZ9naUt7YeYxFcyZYEjAmTlmJoBPysjNbXU/vkZacxM9f+oCfv/QB00cP4Map+Vx33lBeef9wqyqfb189ljGD+vDW7uO8tfsY6/adbJMEPMoq66hvbCYjNbmrV6uN0hO1PPD8+8woHMCtF43o9uUbY7qHxOJVL0VFRbpu3bqoLd+3GgechtPFN57HtJH9eWZjGcs3HGTf8VpSkkBVaA6wnccO6s0lYwbw3HvlnDjd6HecPhkpfGJKPjdfOIKJeX27pS2hpUX590feYXNpJcX3XJZwd1EbE49EZL2qFvn2txJBJ3gOuoEOxl+fNZavXVnIhgOV3PbIO9Q2tD3b75+VSvE9lzGoj3NZ5pQR/f0klyQ+N3MUZSfr+Ou7pTy+Zj/D+2dyqLqexmYnsbTXlhBOwnhi7QHe2n2cxTeeZ0nAmDhniaCT5k3Jb/egKiJMG9mfOj9JAKCytvFsEvDMDwInl/trG1ixsYyfPL+dppbWpYu6xmZ++Nw2RuX2YkROFtlZqTy7qbzTjc8Hjtey+IXtXDo2l5svHN7BljDGxDpLBF0sUHuCvxux2ksu2Vlp3DFjFD987n2/w0/WNjL3t6sB6J2eQn1js9+EsbS4pN1E0NKiLHhqM8ki/Oym8xGRgOMaY+KDXTXUxRbMHkemT0NvODdiBbqTd1CfdB66bRr3fWwCn5w2rE0S8CirrKOpucXvMIDH1+zjnb0n+MH1E+2uYWMShJUIulhHVT6hWjB7nN+G6u/PmcA1k4ac7ffy+4f9lkQAPrJ4Fdefn8cnpuRz/rB+PLup/Gx8CkwY0odPFQ3rVHzGmNhjVw3FoGAagf1d2ZSRmsRnLhpBRVU9q3YcoaGphYG90zhZ29iqBJGRksSSm863u4aNiTN21VAc6aih2jMOBC6JVNU18o+tFfzg2W1tqpHqm1o6bEswxsQPSwRxrL2E0S8zlX+7cAQLn97id7g9XtqYxGGNxQkuUIOwNRQbkzgsESS4SF/VZIyJPVY1lOAifVWTMSb2WCIwQTU+G2Pil1UNGWNMgrNEYIwxCc4SgTHGJDhLBMYYk+AsERhjTIKLyWcNichRYH8nJ88FjkUwnGiydel54mU9wNalpwpnXUaq6kDfnjGZCMIhIuv8PXQpFtm69Dzxsh5g69JTdcW6WNWQMcYkOEsExhiT4BIxETwU7QAiyNal54mX9QBbl54q4uuScG0ExhhjWkvEEoExxhgvlgiMMSbBxUUiEJE/iMgREdnq1e8CEVkjIltE5DkR6ev2TxORR93+m0XkCq9pprn9d4nIf4qIxOh6vCYiJSKyyf0M6s71cGMYLiL/FJHtIrJNRL7h9s8RkZdFZKf7t7/XNIvcbV8iIrO9+kdtv0R4PaK6X0JdFxEZ4I5/SkR+4zOvaP+vRHJdYm2/XC0i693tv15ErvSaV+f2i6rG/Ae4DJgKbPXq9y5wudv9OeDHbvdXgEfd7kHAeiDJ/b4WmA4I8CJwXYyux2tAUZT3yVBgqtvdB/gAmAg8CCx0+y8EfuZ2TwQ2A+nAKGA3kBzt/RLh9YjqfunEuvQCZgJfBH7jM69o/69Ecl1ibb9MAfLc7nOBsnD3S1yUCFT1deCET+9xwOtu98vATW73RGCVO90RoBIoEpGhQF9VXaPOFn0cmNe1kbcWifXo+iiDo6oVqrrB7a4BtgP5wFzgMXe0x/hwG88F/qqqZ1R1L7ALuCja+yVS69Fd8bYn1HVR1dOq+iZQ7z2faO8TN7aIrEtP0Il12aiq5W7/bUCGiKSHs1/iIhEEsBW4we3+FDDc7d4MzBWRFBEZBUxzh+UDB72mP+j2i7ZQ18PjUbeY+4PuLrb7EpECnLOYd4DBqloBzj8ATmkGnG1d6jWZZ/v3mP0S5np49Ij9EuS6BNJj9gmEvS4esbpfbgI2quoZwtgv8ZwIPgd8RUTW4xS3Gtz+f8DZQOuAXwFvAU04RSlfPeHa2lDXA+BWVT0PuNT93NadAXsTkd7A08A3VbW6vVH99NN2+nerCKwH9JD9EsK6BJyFn35R+V+JwLpAjO4XEZkE/Ay429PLz2hB7Ze4TQSqukNVr1HVacAynLpaVLVJVe9R1cmqOhfIBnbiHFSHec1iGFBOlHViPVDVMvdvDfAXolQ1ISKpOD/sJ1R1udv7sFuE9VQxHHH7H6R1icaz/aO+XyK0Hj1iv4S4LoFEfZ9AxNYlJveLiAwDngFuV9Xdbu9O75e4TQSeln8RSQLuA37nfs8SkV5u99VAk6q+7xa9akTkI27R8Hbg2ehE/6FQ18OtKsp1+6cC1+NUL3V33AI8AmxX1V94DVoJzHe75/PhNl4J3OzWdY4CxgJro71fIrUePWG/dGJd/Ir2PoHIrUss7hcRyQaeBxap6mrPyGHtl860cve0D86ZcgXQiJMV7wS+gdP6/gGwhA/voi4ASnAaZF7BeSyrZz5FOD+C3cBvPNPE0nrgXB2xHngPpyHp17hXrXTzuszEKZa+B2xyP3OAATiN3Dvdvzle09zrbvsSvK52iOZ+idR69IT90sl12YdzAcMp9zc5Mdr7JJLrEov7BeeE8LTXuJuAQeHsF3vEhDHGJLi4rRoyxhgTHEsExhiT4CwRGGNMgrNEYIwxCc4SgTHGJDhLBMYYk+AsERhjTIL7/0onGYFKWg+WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pearson_corr=[]\n",
    "sprm_corr=[]\n",
    "tauk_corr=[]\n",
    "\n",
    "for i in np.arange(1990,2021,1):\n",
    "    n12=nino_ene_2[\"ANOM\"].loc[\"1960-01-31\": str(i)+\"-01-31\"]\n",
    "    soi=nino_ene_2[\"SOI\"].loc[\"1960-01-31\": str(i)+\"-01-31\"]\n",
    "    \n",
    "    r, p = stats.pearsonr(n12,soi)\n",
    "    pearson_corr.append(r)\n",
    "    \n",
    "    r,p = stats.spearmanr(n12,soi)\n",
    "    sprm_corr.append(r)\n",
    "    \n",
    "    r,p = stats.kendalltau(n12,soi)\n",
    "    tauk_corr.append(r)\n",
    "\n",
    "plt.plot(np.arange(1990,2021,1),pearson_corr, marker=\"o\")\n",
    "plt.title(\"pearson corr evolution, enero, 1960-1990:2020\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HEATMAPS correlacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>ANOM</th>\n",
       "      <th>ANOM.1</th>\n",
       "      <th>ANOM.2</th>\n",
       "      <th>ANOM.3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.34</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.55</td>\n",
       "      <td>-0.35</td>\n",
       "      <td>-0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>-0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.85</td>\n",
       "      <td>-0.81</td>\n",
       "      <td>-0.86</td>\n",
       "      <td>-0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.65</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1.79</td>\n",
       "      <td>2.59</td>\n",
       "      <td>1.22</td>\n",
       "      <td>2.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.93</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>-1.12</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-0.44</td>\n",
       "      <td>-0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>-0.37</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    ANOM  ANOM.1  ANOM.2  ANOM.3\n",
       "0  -0.33   -0.34   -0.70   -0.28\n",
       "1  -0.08   -0.55   -0.35   -0.31\n",
       "2  -0.74   -0.57   -0.66   -0.59\n",
       "3  -0.85   -0.81   -0.86   -0.78\n",
       "4  -0.65    0.36    0.09    0.79\n",
       "..   ...     ...     ...     ...\n",
       "56  1.79    2.59    1.22    2.57\n",
       "57  0.93   -0.08   -0.16   -0.43\n",
       "58 -1.12   -1.18   -0.44   -0.98\n",
       "59  0.83    0.60    0.76    0.65\n",
       "60 -0.37    0.22    0.85    0.60\n",
       "\n",
       "[61 rows x 4 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%reset -sf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "file=\"nino.xlsx\"\n",
    "nino=pd.read_excel(file, \"NINO\")\n",
    "\n",
    "date1 = '1960-01-01'\n",
    "date2 = '2020-12-31'\n",
    "mydates = pd.date_range(date1, date2,freq=\"M\")\n",
    "\n",
    "nino[\"FECHA\"]=mydates\n",
    "nino_idx=nino.set_index('FECHA')\n",
    "# solo enero !!!\n",
    "nino_ene=nino_idx.loc[nino_idx.index.month==1]\n",
    "nino_ene_data=nino_ene[[\"ANOM\",\"ANOM.1\",\"ANOM.2\",\"ANOM.3\"]]\n",
    "nino_ene_data.reset_index(drop=True, inplace=True)\n",
    "nino_ene_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------\n",
      "covarianza\n",
      "            ANOM    ANOM.1    ANOM.2    ANOM.3\n",
      "ANOM    0.825999  0.856376  0.408259  0.767271\n",
      "ANOM.1  0.856376  1.177675  0.700323  1.172162\n",
      "ANOM.2  0.408259  0.700323  0.659904  0.810671\n",
      "ANOM.3  0.767271  1.172162  0.810671  1.248094\n",
      "-------------------------------\n",
      "            ANOM    ANOM.1    ANOM.2    ANOM.3\n",
      "ANOM    1.000000  0.868284  0.552976  0.755675\n",
      "ANOM.1  0.868284  1.000000  0.794412  0.966832\n",
      "ANOM.2  0.552976  0.794412  1.000000  0.893265\n",
      "ANOM.3  0.755675  0.966832  0.893265  1.000000\n",
      "-------------------------------\n",
      "            ANOM    ANOM.1    ANOM.2    ANOM.3\n",
      "ANOM    1.000000  0.628226  0.442984  0.536479\n",
      "ANOM.1  0.628226  1.000000  0.650960  0.853071\n",
      "ANOM.2  0.442984  0.650960  1.000000  0.742200\n",
      "ANOM.3  0.536479  0.853071  0.742200  1.000000\n"
     ]
    }
   ],
   "source": [
    "covr=nino_ene_data.cov()\n",
    "corrm_p = nino_ene_data.corr(method=\"pearson\")\n",
    "\n",
    "corrm_k = nino_ene_data.corr(method=\"kendall\")\n",
    "\n",
    "print(\"-------------------------------\")\n",
    "print(\"covarianza\")\n",
    "print(covr)\n",
    "print(\"-------------------------------\")\n",
    "print(corrm_p)\n",
    "print(\"-------------------------------\")\n",
    "print(corrm_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "sn.heatmap(corrm_p, annot=True, vmin =-1, vmax=1, cmap=\"coolwarm\")\n",
    "plt.title(\"pearson\")\n",
    "plt.show()\n",
    "\n",
    "sn.heatmap(corrm_k, annot=True, vmin =-1, vmax=1, cmap=\"coolwarm\")\n",
    "plt.title(\"tau kendall\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y los p values???\n",
    "\n",
    "from itertools import product\n",
    "from scipy.stats import pearsonr\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "corr_prs = pd.DataFrame(index=nino_ene_data.columns,columns=nino_ene_data.columns, dtype=np.float64)\n",
    "pvals_prs = corr_prs.copy()\n",
    "\n",
    "corr_spr = corr_prs.copy()\n",
    "pvals_spr =corr_prs.copy()\n",
    "\n",
    "for i, j in product(nino_ene_data.columns, nino_ene_data.columns):\n",
    "    corr_prs.loc[i,j], pvals_prs.loc[i,j] = pearsonr(nino_ene_data[i], nino_ene_data[j])\n",
    "    corr_spr.loc[i,j], pvals_spr.loc[i,j] = spearmanr(nino_ene_data[i], nino_ene_data[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            ANOM    ANOM.1    ANOM.2    ANOM.3\n",
      "ANOM    1.000000  0.868284  0.552976  0.755675\n",
      "ANOM.1  0.868284  1.000000  0.794412  0.966832\n",
      "ANOM.2  0.552976  0.794412  1.000000  0.893265\n",
      "ANOM.3  0.755675  0.966832  0.893265  1.000000\n",
      "                ANOM        ANOM.1        ANOM.2        ANOM.3\n",
      "ANOM    0.000000e+00  1.290075e-19  3.812211e-06  1.937674e-12\n",
      "ANOM.1  1.290075e-19  0.000000e+00  2.161313e-14  1.137837e-36\n",
      "ANOM.2  3.812211e-06  2.161313e-14  0.000000e+00  3.755311e-22\n",
      "ANOM.3  1.937674e-12  1.137837e-36  3.755311e-22  0.000000e+00\n"
     ]
    }
   ],
   "source": [
    "print(corr_prs)\n",
    "print(pvals_prs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(corr_spr, annot=True,cmap=\"YlOrBr\",vmin=0.8,vmax=1)\n",
    "\n",
    "plt.title(\"spearman\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(pvals_spr, annot=True,cmap=\"magma\",vmin=0,vmax=0.05)\n",
    "\n",
    "plt.title(\"pvals spearman\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ejercicio con datos huanuco, enero, heatmap pvalyes y correlacion pearson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Index</th>\n",
       "      <th>St_109003_h</th>\n",
       "      <th>St_109027_h</th>\n",
       "      <th>St_110025_h</th>\n",
       "      <th>St_109028_h</th>\n",
       "      <th>St_100020_h</th>\n",
       "      <th>St_103044_R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964-01-31</td>\n",
       "      <td>25.049537</td>\n",
       "      <td>221.5</td>\n",
       "      <td>34.516593</td>\n",
       "      <td>191.481491</td>\n",
       "      <td>25.049537</td>\n",
       "      <td>231.758166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1964-02-29</td>\n",
       "      <td>45.525474</td>\n",
       "      <td>252.5</td>\n",
       "      <td>73.440489</td>\n",
       "      <td>222.631588</td>\n",
       "      <td>47.424215</td>\n",
       "      <td>346.234380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1964-03-31</td>\n",
       "      <td>65.686331</td>\n",
       "      <td>264.5</td>\n",
       "      <td>118.104350</td>\n",
       "      <td>264.071606</td>\n",
       "      <td>73.440489</td>\n",
       "      <td>402.428793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1964-04-30</td>\n",
       "      <td>26.040000</td>\n",
       "      <td>218.5</td>\n",
       "      <td>48.402449</td>\n",
       "      <td>191.481491</td>\n",
       "      <td>32.448268</td>\n",
       "      <td>248.635037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964-05-31</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>288.0</td>\n",
       "      <td>39.447304</td>\n",
       "      <td>261.434099</td>\n",
       "      <td>33.466919</td>\n",
       "      <td>216.022275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>205.0</td>\n",
       "      <td>12.800000</td>\n",
       "      <td>179.500000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>143.026887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>79.3</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>95.300000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>136.002613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>96.100000</td>\n",
       "      <td>483.1</td>\n",
       "      <td>124.800000</td>\n",
       "      <td>387.400000</td>\n",
       "      <td>105.300000</td>\n",
       "      <td>272.144238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>47.600000</td>\n",
       "      <td>513.2</td>\n",
       "      <td>87.200000</td>\n",
       "      <td>492.700000</td>\n",
       "      <td>36.100000</td>\n",
       "      <td>264.071606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>44.604208</td>\n",
       "      <td>432.0</td>\n",
       "      <td>83.600000</td>\n",
       "      <td>309.300000</td>\n",
       "      <td>55.200000</td>\n",
       "      <td>390.505671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>660 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Index  St_109003_h  St_109027_h  St_110025_h  St_109028_h  \\\n",
       "0   1964-01-31    25.049537        221.5    34.516593   191.481491   \n",
       "1   1964-02-29    45.525474        252.5    73.440489   222.631588   \n",
       "2   1964-03-31    65.686331        264.5   118.104350   264.071606   \n",
       "3   1964-04-30    26.040000        218.5    48.402449   191.481491   \n",
       "4   1964-05-31    14.250000        288.0    39.447304   261.434099   \n",
       "..         ...          ...          ...          ...          ...   \n",
       "655 2018-08-31     9.800000        205.0    12.800000   179.500000   \n",
       "656 2018-09-30    18.100000         79.3    22.500000    95.300000   \n",
       "657 2018-10-31    96.100000        483.1   124.800000   387.400000   \n",
       "658 2018-11-30    47.600000        513.2    87.200000   492.700000   \n",
       "659 2018-12-31    44.604208        432.0    83.600000   309.300000   \n",
       "\n",
       "     St_100020_h  St_103044_R  \n",
       "0      25.049537   231.758166  \n",
       "1      47.424215   346.234380  \n",
       "2      73.440489   402.428793  \n",
       "3      32.448268   248.635037  \n",
       "4      33.466919   216.022275  \n",
       "..           ...          ...  \n",
       "655    10.800000   143.026887  \n",
       "656    15.100000   136.002613  \n",
       "657   105.300000   272.144238  \n",
       "658    36.100000   264.071606  \n",
       "659    55.200000   390.505671  \n",
       "\n",
       "[660 rows x 7 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file=\"datos_huanuco_mensuales_pp.xlsx\"\n",
    "huanuco=pd.read_excel(file)\n",
    "huanuco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>St_109003_h</th>\n",
       "      <th>St_109027_h</th>\n",
       "      <th>St_110025_h</th>\n",
       "      <th>St_109028_h</th>\n",
       "      <th>St_100020_h</th>\n",
       "      <th>St_103044_R</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FECHA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1964-01-31</th>\n",
       "      <td>1964-01-31</td>\n",
       "      <td>25.049537</td>\n",
       "      <td>221.5</td>\n",
       "      <td>34.516593</td>\n",
       "      <td>191.481491</td>\n",
       "      <td>25.049537</td>\n",
       "      <td>231.758166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-02-29</th>\n",
       "      <td>1964-02-29</td>\n",
       "      <td>45.525474</td>\n",
       "      <td>252.5</td>\n",
       "      <td>73.440489</td>\n",
       "      <td>222.631588</td>\n",
       "      <td>47.424215</td>\n",
       "      <td>346.234380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-03-31</th>\n",
       "      <td>1964-03-31</td>\n",
       "      <td>65.686331</td>\n",
       "      <td>264.5</td>\n",
       "      <td>118.104350</td>\n",
       "      <td>264.071606</td>\n",
       "      <td>73.440489</td>\n",
       "      <td>402.428793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-04-30</th>\n",
       "      <td>1964-04-30</td>\n",
       "      <td>26.040000</td>\n",
       "      <td>218.5</td>\n",
       "      <td>48.402449</td>\n",
       "      <td>191.481491</td>\n",
       "      <td>32.448268</td>\n",
       "      <td>248.635037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-05-31</th>\n",
       "      <td>1964-05-31</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>288.0</td>\n",
       "      <td>39.447304</td>\n",
       "      <td>261.434099</td>\n",
       "      <td>33.466919</td>\n",
       "      <td>216.022275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31</th>\n",
       "      <td>2018-08-31</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>205.0</td>\n",
       "      <td>12.800000</td>\n",
       "      <td>179.500000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>143.026887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>2018-09-30</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>79.3</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>95.300000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>136.002613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>2018-10-31</td>\n",
       "      <td>96.100000</td>\n",
       "      <td>483.1</td>\n",
       "      <td>124.800000</td>\n",
       "      <td>387.400000</td>\n",
       "      <td>105.300000</td>\n",
       "      <td>272.144238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>47.600000</td>\n",
       "      <td>513.2</td>\n",
       "      <td>87.200000</td>\n",
       "      <td>492.700000</td>\n",
       "      <td>36.100000</td>\n",
       "      <td>264.071606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>44.604208</td>\n",
       "      <td>432.0</td>\n",
       "      <td>83.600000</td>\n",
       "      <td>309.300000</td>\n",
       "      <td>55.200000</td>\n",
       "      <td>390.505671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>660 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Index  St_109003_h  St_109027_h  St_110025_h  St_109028_h  \\\n",
       "FECHA                                                                       \n",
       "1964-01-31 1964-01-31    25.049537        221.5    34.516593   191.481491   \n",
       "1964-02-29 1964-02-29    45.525474        252.5    73.440489   222.631588   \n",
       "1964-03-31 1964-03-31    65.686331        264.5   118.104350   264.071606   \n",
       "1964-04-30 1964-04-30    26.040000        218.5    48.402449   191.481491   \n",
       "1964-05-31 1964-05-31    14.250000        288.0    39.447304   261.434099   \n",
       "...               ...          ...          ...          ...          ...   \n",
       "2018-08-31 2018-08-31     9.800000        205.0    12.800000   179.500000   \n",
       "2018-09-30 2018-09-30    18.100000         79.3    22.500000    95.300000   \n",
       "2018-10-31 2018-10-31    96.100000        483.1   124.800000   387.400000   \n",
       "2018-11-30 2018-11-30    47.600000        513.2    87.200000   492.700000   \n",
       "2018-12-31 2018-12-31    44.604208        432.0    83.600000   309.300000   \n",
       "\n",
       "            St_100020_h  St_103044_R  \n",
       "FECHA                                 \n",
       "1964-01-31    25.049537   231.758166  \n",
       "1964-02-29    47.424215   346.234380  \n",
       "1964-03-31    73.440489   402.428793  \n",
       "1964-04-30    32.448268   248.635037  \n",
       "1964-05-31    33.466919   216.022275  \n",
       "...                 ...          ...  \n",
       "2018-08-31    10.800000   143.026887  \n",
       "2018-09-30    15.100000   136.002613  \n",
       "2018-10-31   105.300000   272.144238  \n",
       "2018-11-30    36.100000   264.071606  \n",
       "2018-12-31    55.200000   390.505671  \n",
       "\n",
       "[660 rows x 7 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date1 = \"1964-01-31\"\n",
    "date2= \"2018-12-31\"\n",
    "mydates=pd.date_range(date1, date2, freq =\"M\")\n",
    "huanuco[\"FECHA\"]=mydates\n",
    "huanuco_idx=huanuco.set_index(\"FECHA\")\n",
    "huanuco_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>St_109003_h</th>\n",
       "      <th>St_109027_h</th>\n",
       "      <th>St_110025_h</th>\n",
       "      <th>St_109028_h</th>\n",
       "      <th>St_100020_h</th>\n",
       "      <th>St_103044_R</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FECHA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1964-01-31</th>\n",
       "      <td>25.049537</td>\n",
       "      <td>221.5</td>\n",
       "      <td>34.516593</td>\n",
       "      <td>191.481491</td>\n",
       "      <td>25.049537</td>\n",
       "      <td>231.758166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-02-29</th>\n",
       "      <td>45.525474</td>\n",
       "      <td>252.5</td>\n",
       "      <td>73.440489</td>\n",
       "      <td>222.631588</td>\n",
       "      <td>47.424215</td>\n",
       "      <td>346.234380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-03-31</th>\n",
       "      <td>65.686331</td>\n",
       "      <td>264.5</td>\n",
       "      <td>118.104350</td>\n",
       "      <td>264.071606</td>\n",
       "      <td>73.440489</td>\n",
       "      <td>402.428793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-04-30</th>\n",
       "      <td>26.040000</td>\n",
       "      <td>218.5</td>\n",
       "      <td>48.402449</td>\n",
       "      <td>191.481491</td>\n",
       "      <td>32.448268</td>\n",
       "      <td>248.635037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-05-31</th>\n",
       "      <td>14.250000</td>\n",
       "      <td>288.0</td>\n",
       "      <td>39.447304</td>\n",
       "      <td>261.434099</td>\n",
       "      <td>33.466919</td>\n",
       "      <td>216.022275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-31</th>\n",
       "      <td>9.800000</td>\n",
       "      <td>205.0</td>\n",
       "      <td>12.800000</td>\n",
       "      <td>179.500000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>143.026887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>18.100000</td>\n",
       "      <td>79.3</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>95.300000</td>\n",
       "      <td>15.100000</td>\n",
       "      <td>136.002613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>96.100000</td>\n",
       "      <td>483.1</td>\n",
       "      <td>124.800000</td>\n",
       "      <td>387.400000</td>\n",
       "      <td>105.300000</td>\n",
       "      <td>272.144238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-30</th>\n",
       "      <td>47.600000</td>\n",
       "      <td>513.2</td>\n",
       "      <td>87.200000</td>\n",
       "      <td>492.700000</td>\n",
       "      <td>36.100000</td>\n",
       "      <td>264.071606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>44.604208</td>\n",
       "      <td>432.0</td>\n",
       "      <td>83.600000</td>\n",
       "      <td>309.300000</td>\n",
       "      <td>55.200000</td>\n",
       "      <td>390.505671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>660 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            St_109003_h  St_109027_h  St_110025_h  St_109028_h  St_100020_h  \\\n",
       "FECHA                                                                         \n",
       "1964-01-31    25.049537        221.5    34.516593   191.481491    25.049537   \n",
       "1964-02-29    45.525474        252.5    73.440489   222.631588    47.424215   \n",
       "1964-03-31    65.686331        264.5   118.104350   264.071606    73.440489   \n",
       "1964-04-30    26.040000        218.5    48.402449   191.481491    32.448268   \n",
       "1964-05-31    14.250000        288.0    39.447304   261.434099    33.466919   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2018-08-31     9.800000        205.0    12.800000   179.500000    10.800000   \n",
       "2018-09-30    18.100000         79.3    22.500000    95.300000    15.100000   \n",
       "2018-10-31    96.100000        483.1   124.800000   387.400000   105.300000   \n",
       "2018-11-30    47.600000        513.2    87.200000   492.700000    36.100000   \n",
       "2018-12-31    44.604208        432.0    83.600000   309.300000    55.200000   \n",
       "\n",
       "            St_103044_R  \n",
       "FECHA                    \n",
       "1964-01-31   231.758166  \n",
       "1964-02-29   346.234380  \n",
       "1964-03-31   402.428793  \n",
       "1964-04-30   248.635037  \n",
       "1964-05-31   216.022275  \n",
       "...                 ...  \n",
       "2018-08-31   143.026887  \n",
       "2018-09-30   136.002613  \n",
       "2018-10-31   272.144238  \n",
       "2018-11-30   264.071606  \n",
       "2018-12-31   390.505671  \n",
       "\n",
       "[660 rows x 6 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huanuco_idx=huanuco_idx.drop(columns=[\"Index\"])\n",
    "huanuco_idx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sacar solo mes de enero #spearman, heatmap de pvalues y correlacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
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       "        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>St_109003_h</th>\n",
       "      <th>St_109027_h</th>\n",
       "      <th>St_110025_h</th>\n",
       "      <th>St_109028_h</th>\n",
       "      <th>St_100020_h</th>\n",
       "      <th>St_103044_R</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FECHA</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1964-01-31</th>\n",
       "      <td>25.049537</td>\n",
       "      <td>221.50</td>\n",
       "      <td>34.516593</td>\n",
       "      <td>191.481491</td>\n",
       "      <td>25.049537</td>\n",
       "      <td>231.758166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-01-31</th>\n",
       "      <td>30.930000</td>\n",
       "      <td>211.00</td>\n",
       "      <td>47.910887</td>\n",
       "      <td>203.383882</td>\n",
       "      <td>33.813317</td>\n",
       "      <td>238.846707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966-01-31</th>\n",
       "      <td>73.400000</td>\n",
       "      <td>446.01</td>\n",
       "      <td>93.632408</td>\n",
       "      <td>367.706155</td>\n",
       "      <td>64.365853</td>\n",
       "      <td>346.234380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967-01-31</th>\n",
       "      <td>59.330000</td>\n",
       "      <td>399.00</td>\n",
       "      <td>59.230000</td>\n",
       "      <td>135.800000</td>\n",
       "      <td>38.251906</td>\n",
       "      <td>303.904923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-01-31</th>\n",
       "      <td>78.010000</td>\n",
       "      <td>529.10</td>\n",
       "      <td>51.150000</td>\n",
       "      <td>359.800000</td>\n",
       "      <td>71.966469</td>\n",
       "      <td>256.237556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-31</th>\n",
       "      <td>75.810000</td>\n",
       "      <td>208.81</td>\n",
       "      <td>57.540000</td>\n",
       "      <td>234.700000</td>\n",
       "      <td>26.112639</td>\n",
       "      <td>241.257207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-31</th>\n",
       "      <td>29.410000</td>\n",
       "      <td>284.41</td>\n",
       "      <td>75.730000</td>\n",
       "      <td>454.400000</td>\n",
       "      <td>84.626944</td>\n",
       "      <td>306.969268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-31</th>\n",
       "      <td>99.130000</td>\n",
       "      <td>288.90</td>\n",
       "      <td>189.710000</td>\n",
       "      <td>319.730000</td>\n",
       "      <td>51.984531</td>\n",
       "      <td>264.071606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-31</th>\n",
       "      <td>38.240000</td>\n",
       "      <td>297.31</td>\n",
       "      <td>86.030000</td>\n",
       "      <td>332.110000</td>\n",
       "      <td>61.177923</td>\n",
       "      <td>294.893621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-31</th>\n",
       "      <td>51.520000</td>\n",
       "      <td>515.83</td>\n",
       "      <td>114.210000</td>\n",
       "      <td>435.220000</td>\n",
       "      <td>106.770073</td>\n",
       "      <td>335.972054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-31</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>546.40</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>640.610000</td>\n",
       "      <td>112.295562</td>\n",
       "      <td>300.871068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-31</th>\n",
       "      <td>64.000000</td>\n",
       "      <td>362.40</td>\n",
       "      <td>53.600000</td>\n",
       "      <td>243.600000</td>\n",
       "      <td>61.177923</td>\n",
       "      <td>360.405284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-01-31</th>\n",
       "      <td>66.500000</td>\n",
       "      <td>291.51</td>\n",
       "      <td>80.800000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>67.033484</td>\n",
       "      <td>300.871068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01-31</th>\n",
       "      <td>45.500000</td>\n",
       "      <td>892.50</td>\n",
       "      <td>98.930000</td>\n",
       "      <td>253.900000</td>\n",
       "      <td>82.096285</td>\n",
       "      <td>201.350228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-01-31</th>\n",
       "      <td>53.600000</td>\n",
       "      <td>291.20</td>\n",
       "      <td>108.500000</td>\n",
       "      <td>444.800000</td>\n",
       "      <td>64.365853</td>\n",
       "      <td>216.022275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-31</th>\n",
       "      <td>28.078527</td>\n",
       "      <td>211.70</td>\n",
       "      <td>68.620000</td>\n",
       "      <td>190.700000</td>\n",
       "      <td>26.385125</td>\n",
       "      <td>107.853180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-31</th>\n",
       "      <td>68.900000</td>\n",
       "      <td>427.60</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>208.800000</td>\n",
       "      <td>51.984531</td>\n",
       "      <td>256.237556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-31</th>\n",
       "      <td>63.715452</td>\n",
       "      <td>412.60</td>\n",
       "      <td>20.300000</td>\n",
       "      <td>337.300000</td>\n",
       "      <td>82.931417</td>\n",
       "      <td>243.691932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-31</th>\n",
       "      <td>109.410000</td>\n",
       "      <td>718.30</td>\n",
       "      <td>802.900000</td>\n",
       "      <td>331.000000</td>\n",
       "      <td>60.559242</td>\n",
       "      <td>251.143911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-01-31</th>\n",
       "      <td>43.701184</td>\n",
       "      <td>688.50</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>411.700000</td>\n",
       "      <td>47.424215</td>\n",
       "      <td>353.248980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-01-31</th>\n",
       "      <td>65.686331</td>\n",
       "      <td>681.00</td>\n",
       "      <td>101.514064</td>\n",
       "      <td>479.600000</td>\n",
       "      <td>113.434202</td>\n",
       "      <td>303.904923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-31</th>\n",
       "      <td>39.044847</td>\n",
       "      <td>480.00</td>\n",
       "      <td>47.424215</td>\n",
       "      <td>465.200000</td>\n",
       "      <td>68.407852</td>\n",
       "      <td>56.974311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-31</th>\n",
       "      <td>40.710000</td>\n",
       "      <td>347.60</td>\n",
       "      <td>108.947172</td>\n",
       "      <td>211.500000</td>\n",
       "      <td>56.397457</td>\n",
       "      <td>199.336810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-01-31</th>\n",
       "      <td>35.600000</td>\n",
       "      <td>612.90</td>\n",
       "      <td>108.947172</td>\n",
       "      <td>549.900000</td>\n",
       "      <td>115.745926</td>\n",
       "      <td>346.234380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-31</th>\n",
       "      <td>88.900000</td>\n",
       "      <td>539.30</td>\n",
       "      <td>256.300000</td>\n",
       "      <td>277.800000</td>\n",
       "      <td>116.919242</td>\n",
       "      <td>227.149245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-01-31</th>\n",
       "      <td>56.800000</td>\n",
       "      <td>319.20</td>\n",
       "      <td>140.174964</td>\n",
       "      <td>289.034534</td>\n",
       "      <td>79.400000</td>\n",
       "      <td>310.064411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-31</th>\n",
       "      <td>45.800000</td>\n",
       "      <td>628.10</td>\n",
       "      <td>151.400000</td>\n",
       "      <td>310.064411</td>\n",
       "      <td>64.900000</td>\n",
       "      <td>326.013024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-31</th>\n",
       "      <td>25.700000</td>\n",
       "      <td>512.90</td>\n",
       "      <td>137.310000</td>\n",
       "      <td>261.434099</td>\n",
       "      <td>15.310000</td>\n",
       "      <td>303.904923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-31</th>\n",
       "      <td>43.100000</td>\n",
       "      <td>306.90</td>\n",
       "      <td>23.046754</td>\n",
       "      <td>191.481491</td>\n",
       "      <td>19.720000</td>\n",
       "      <td>266.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-01-31</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>465.10</td>\n",
       "      <td>57.710000</td>\n",
       "      <td>353.248980</td>\n",
       "      <td>84.626944</td>\n",
       "      <td>227.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-31</th>\n",
       "      <td>69.809983</td>\n",
       "      <td>672.00</td>\n",
       "      <td>105.400000</td>\n",
       "      <td>398.414610</td>\n",
       "      <td>71.400000</td>\n",
       "      <td>112.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-31</th>\n",
       "      <td>38.700000</td>\n",
       "      <td>373.80</td>\n",
       "      <td>105.700000</td>\n",
       "      <td>326.013024</td>\n",
       "      <td>58.600000</td>\n",
       "      <td>386.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-31</th>\n",
       "      <td>59.500000</td>\n",
       "      <td>508.90</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>322.759190</td>\n",
       "      <td>51.100000</td>\n",
       "      <td>137.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-31</th>\n",
       "      <td>54.600000</td>\n",
       "      <td>396.30</td>\n",
       "      <td>99.500000</td>\n",
       "      <td>406.320000</td>\n",
       "      <td>88.700000</td>\n",
       "      <td>104.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-31</th>\n",
       "      <td>93.500000</td>\n",
       "      <td>332.60</td>\n",
       "      <td>143.400000</td>\n",
       "      <td>474.220000</td>\n",
       "      <td>94.700000</td>\n",
       "      <td>318.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-31</th>\n",
       "      <td>81.700000</td>\n",
       "      <td>568.10</td>\n",
       "      <td>129.400000</td>\n",
       "      <td>615.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>355.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-31</th>\n",
       "      <td>68.240000</td>\n",
       "      <td>412.20</td>\n",
       "      <td>119.900000</td>\n",
       "      <td>310.400000</td>\n",
       "      <td>79.300000</td>\n",
       "      <td>242.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-31</th>\n",
       "      <td>45.220000</td>\n",
       "      <td>442.11</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>370.300000</td>\n",
       "      <td>115.400000</td>\n",
       "      <td>315.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-31</th>\n",
       "      <td>29.500000</td>\n",
       "      <td>304.71</td>\n",
       "      <td>35.100000</td>\n",
       "      <td>372.810000</td>\n",
       "      <td>18.700000</td>\n",
       "      <td>342.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-31</th>\n",
       "      <td>44.720000</td>\n",
       "      <td>239.61</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>50.100000</td>\n",
       "      <td>301.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-31</th>\n",
       "      <td>33.620000</td>\n",
       "      <td>301.11</td>\n",
       "      <td>40.200000</td>\n",
       "      <td>317.200000</td>\n",
       "      <td>29.600000</td>\n",
       "      <td>97.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-31</th>\n",
       "      <td>32.620000</td>\n",
       "      <td>245.12</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>266.500000</td>\n",
       "      <td>77.600000</td>\n",
       "      <td>173.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-31</th>\n",
       "      <td>81.920000</td>\n",
       "      <td>284.80</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>250.700000</td>\n",
       "      <td>122.500000</td>\n",
       "      <td>284.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-31</th>\n",
       "      <td>33.930000</td>\n",
       "      <td>526.80</td>\n",
       "      <td>93.600000</td>\n",
       "      <td>400.200000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>288.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-31</th>\n",
       "      <td>29.830000</td>\n",
       "      <td>428.81</td>\n",
       "      <td>100.200000</td>\n",
       "      <td>214.800000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>289.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-31</th>\n",
       "      <td>67.250000</td>\n",
       "      <td>481.20</td>\n",
       "      <td>84.910000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>342.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>21.800000</td>\n",
       "      <td>301.90</td>\n",
       "      <td>134.920000</td>\n",
       "      <td>191.700000</td>\n",
       "      <td>26.400000</td>\n",
       "      <td>200.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-31</th>\n",
       "      <td>74.310000</td>\n",
       "      <td>458.10</td>\n",
       "      <td>125.700000</td>\n",
       "      <td>389.700000</td>\n",
       "      <td>80.500000</td>\n",
       "      <td>154.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-31</th>\n",
       "      <td>78.600000</td>\n",
       "      <td>376.50</td>\n",
       "      <td>85.100000</td>\n",
       "      <td>365.800000</td>\n",
       "      <td>78.800000</td>\n",
       "      <td>393.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-31</th>\n",
       "      <td>36.210000</td>\n",
       "      <td>438.20</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>278.500000</td>\n",
       "      <td>64.200000</td>\n",
       "      <td>249.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-31</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>350.70</td>\n",
       "      <td>110.100000</td>\n",
       "      <td>441.800000</td>\n",
       "      <td>69.300000</td>\n",
       "      <td>255.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>75.600000</td>\n",
       "      <td>511.40</td>\n",
       "      <td>97.400000</td>\n",
       "      <td>499.400000</td>\n",
       "      <td>58.600000</td>\n",
       "      <td>302.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31</th>\n",
       "      <td>34.700000</td>\n",
       "      <td>517.00</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>347.100000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>270.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-31</th>\n",
       "      <td>72.100000</td>\n",
       "      <td>385.10</td>\n",
       "      <td>109.600000</td>\n",
       "      <td>427.700000</td>\n",
       "      <td>66.200000</td>\n",
       "      <td>375.154514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>88.500000</td>\n",
       "      <td>388.20</td>\n",
       "      <td>118.800000</td>\n",
       "      <td>350.500000</td>\n",
       "      <td>92.700000</td>\n",
       "      <td>290.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            St_109003_h  St_109027_h  St_110025_h  St_109028_h  St_100020_h  \\\n",
       "FECHA                                                                         \n",
       "1964-01-31    25.049537       221.50    34.516593   191.481491    25.049537   \n",
       "1965-01-31    30.930000       211.00    47.910887   203.383882    33.813317   \n",
       "1966-01-31    73.400000       446.01    93.632408   367.706155    64.365853   \n",
       "1967-01-31    59.330000       399.00    59.230000   135.800000    38.251906   \n",
       "1968-01-31    78.010000       529.10    51.150000   359.800000    71.966469   \n",
       "1969-01-31    75.810000       208.81    57.540000   234.700000    26.112639   \n",
       "1970-01-31    29.410000       284.41    75.730000   454.400000    84.626944   \n",
       "1971-01-31    99.130000       288.90   189.710000   319.730000    51.984531   \n",
       "1972-01-31    38.240000       297.31    86.030000   332.110000    61.177923   \n",
       "1973-01-31    51.520000       515.83   114.210000   435.220000   106.770073   \n",
       "1974-01-31    58.000000       546.40   198.000000   640.610000   112.295562   \n",
       "1975-01-31    64.000000       362.40    53.600000   243.600000    61.177923   \n",
       "1976-01-31    66.500000       291.51    80.800000   296.000000    67.033484   \n",
       "1977-01-31    45.500000       892.50    98.930000   253.900000    82.096285   \n",
       "1978-01-31    53.600000       291.20   108.500000   444.800000    64.365853   \n",
       "1979-01-31    28.078527       211.70    68.620000   190.700000    26.385125   \n",
       "1980-01-31    68.900000       427.60    35.500000   208.800000    51.984531   \n",
       "1981-01-31    63.715452       412.60    20.300000   337.300000    82.931417   \n",
       "1982-01-31   109.410000       718.30   802.900000   331.000000    60.559242   \n",
       "1983-01-31    43.701184       688.50   114.000000   411.700000    47.424215   \n",
       "1984-01-31    65.686331       681.00   101.514064   479.600000   113.434202   \n",
       "1985-01-31    39.044847       480.00    47.424215   465.200000    68.407852   \n",
       "1986-01-31    40.710000       347.60   108.947172   211.500000    56.397457   \n",
       "1987-01-31    35.600000       612.90   108.947172   549.900000   115.745926   \n",
       "1988-01-31    88.900000       539.30   256.300000   277.800000   116.919242   \n",
       "1989-01-31    56.800000       319.20   140.174964   289.034534    79.400000   \n",
       "1990-01-31    45.800000       628.10   151.400000   310.064411    64.900000   \n",
       "1991-01-31    25.700000       512.90   137.310000   261.434099    15.310000   \n",
       "1992-01-31    43.100000       306.90    23.046754   191.481491    19.720000   \n",
       "1993-01-31    17.500000       465.10    57.710000   353.248980    84.626944   \n",
       "1994-01-31    69.809983       672.00   105.400000   398.414610    71.400000   \n",
       "1995-01-31    38.700000       373.80   105.700000   326.013024    58.600000   \n",
       "1996-01-31    59.500000       508.90    86.000000   322.759190    51.100000   \n",
       "1997-01-31    54.600000       396.30    99.500000   406.320000    88.700000   \n",
       "1998-01-31    93.500000       332.60   143.400000   474.220000    94.700000   \n",
       "1999-01-31    81.700000       568.10   129.400000   615.000000    83.000000   \n",
       "2000-01-31    68.240000       412.20   119.900000   310.400000    79.300000   \n",
       "2001-01-31    45.220000       442.11   125.400000   370.300000   115.400000   \n",
       "2002-01-31    29.500000       304.71    35.100000   372.810000    18.700000   \n",
       "2003-01-31    44.720000       239.61    67.100000   277.000000    50.100000   \n",
       "2004-01-31    33.620000       301.11    40.200000   317.200000    29.600000   \n",
       "2005-01-31    32.620000       245.12    71.500000   266.500000    77.600000   \n",
       "2006-01-31    81.920000       284.80   112.000000   250.700000   122.500000   \n",
       "2007-01-31    33.930000       526.80    93.600000   400.200000    45.000000   \n",
       "2008-01-31    29.830000       428.81   100.200000   214.800000    74.000000   \n",
       "2009-01-31    67.250000       481.20    84.910000   320.000000   111.000000   \n",
       "2010-01-31    21.800000       301.90   134.920000   191.700000    26.400000   \n",
       "2011-01-31    74.310000       458.10   125.700000   389.700000    80.500000   \n",
       "2012-01-31    78.600000       376.50    85.100000   365.800000    78.800000   \n",
       "2013-01-31    36.210000       438.20    94.000000   278.500000    64.200000   \n",
       "2014-01-31    72.000000       350.70   110.100000   441.800000    69.300000   \n",
       "2015-01-31    75.600000       511.40    97.400000   499.400000    58.600000   \n",
       "2016-01-31    34.700000       517.00    49.600000   347.100000    25.400000   \n",
       "2017-01-31    72.100000       385.10   109.600000   427.700000    66.200000   \n",
       "2018-01-31    88.500000       388.20   118.800000   350.500000    92.700000   \n",
       "\n",
       "            St_103044_R  \n",
       "FECHA                    \n",
       "1964-01-31   231.758166  \n",
       "1965-01-31   238.846707  \n",
       "1966-01-31   346.234380  \n",
       "1967-01-31   303.904923  \n",
       "1968-01-31   256.237556  \n",
       "1969-01-31   241.257207  \n",
       "1970-01-31   306.969268  \n",
       "1971-01-31   264.071606  \n",
       "1972-01-31   294.893621  \n",
       "1973-01-31   335.972054  \n",
       "1974-01-31   300.871068  \n",
       "1975-01-31   360.405284  \n",
       "1976-01-31   300.871068  \n",
       "1977-01-31   201.350228  \n",
       "1978-01-31   216.022275  \n",
       "1979-01-31   107.853180  \n",
       "1980-01-31   256.237556  \n",
       "1981-01-31   243.691932  \n",
       "1982-01-31   251.143911  \n",
       "1983-01-31   353.248980  \n",
       "1984-01-31   303.904923  \n",
       "1985-01-31    56.974311  \n",
       "1986-01-31   199.336810  \n",
       "1987-01-31   346.234380  \n",
       "1988-01-31   227.149245  \n",
       "1989-01-31   310.064411  \n",
       "1990-01-31   326.013024  \n",
       "1991-01-31   303.904923  \n",
       "1992-01-31   266.000000  \n",
       "1993-01-31   227.000000  \n",
       "1994-01-31   112.000000  \n",
       "1995-01-31   386.000000  \n",
       "1996-01-31   137.000000  \n",
       "1997-01-31   104.600000  \n",
       "1998-01-31   318.800000  \n",
       "1999-01-31   355.400000  \n",
       "2000-01-31   242.000000  \n",
       "2001-01-31   315.800000  \n",
       "2002-01-31   342.300000  \n",
       "2003-01-31   301.200000  \n",
       "2004-01-31    97.800000  \n",
       "2005-01-31   173.400000  \n",
       "2006-01-31   284.800000  \n",
       "2007-01-31   288.900000  \n",
       "2008-01-31   289.500000  \n",
       "2009-01-31   342.300000  \n",
       "2010-01-31   200.600000  \n",
       "2011-01-31   154.200000  \n",
       "2012-01-31   393.100000  \n",
       "2013-01-31   249.300000  \n",
       "2014-01-31   255.300000  \n",
       "2015-01-31   302.000000  \n",
       "2016-01-31   270.400000  \n",
       "2017-01-31   375.154514  \n",
       "2018-01-31   290.300000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "huanuco_ene=huanuco_idx.loc[huanuco_idx.index.month==1]\n",
    "huanuco_ene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "corr_prs = pd.DataFrame(index=huanuco_ene.columns, columns=huanuco_ene.columns, dtype=np.float64)\n",
    "pvals_prs = corr_prs.copy()\n",
    "\n",
    "corr_spr = corr_prs.copy()\n",
    "pvals_spr = corr_prs.copy()\n",
    "\n",
    "\n",
    "for i, j in product(huanuco_ene.columns, huanuco_ene.columns):\n",
    "    corr_prs.loc[i,j], pvals_prs.loc[i,j] = pearsonr(huanuco_ene[i], huanuco_ene[j])\n",
    "    corr_spr.loc[i,j], pvals_spr.loc[i,j] = spearmanr(huanuco_ene[i], huanuco_ene[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(corr_spr, annot=True,cmap=\"coolwarm\",vmin=-1, vmax=1)\n",
    "\n",
    "plt.title(\"spearman\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.heatmap(pvals_spr, annot=True,cmap=\"magma\",vmin=0, vmax=0.05)\n",
    "\n",
    "plt.title(\"pearson\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}