{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# datos SOI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "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>YEAR</th>\n",
       "      <th>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
       "      <th>JUN</th>\n",
       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.2</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2016</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2019</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2020</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    YEAR  JAN  FEB  MAR  APR  MAY  JUN  JUL  AUG  SEP  OCT  NOV  DEC\n",
       "0   1960  0.4  0.4  0.6  0.8  0.5  0.4  0.5  0.7  0.5  0.4  0.5  0.3\n",
       "1   1961  0.5 -0.4  0.0 -0.2  0.4  0.2  0.2  0.2  0.0  0.1  0.6  1.3\n",
       "2   1962  1.1  0.6  0.0  0.5  0.7  0.6  0.5  0.4  0.7  0.6  0.5  0.5\n",
       "3   1963  0.6  0.9  0.8  0.8  0.2 -0.1 -0.2 -0.2 -0.6 -0.9 -1.1 -0.8\n",
       "4   1964 -0.5  0.2  0.7  0.8  0.7  0.5  1.0  1.1  1.4  0.9  0.4 -0.2\n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...\n",
       "56  2016 -1.6 -1.4 -1.1 -0.3 -0.1  0.5  0.6  0.8  0.5  0.3  0.0  0.1\n",
       "57  2017  0.1  0.3  0.2  0.3 -0.1  0.2  0.3  0.6  0.7  0.8  0.6  0.6\n",
       "58  2018  0.2  0.7  0.5  0.8  0.3  0.2 -0.1 -0.3 -0.3 -0.2  0.4  0.3\n",
       "59  2019 -0.1 -0.6 -0.5 -0.2 -0.3 -0.4 -0.3 -0.6 -0.6 -0.8 -0.6 -0.4\n",
       "60  2020 -0.2  0.0  0.0  0.2  0.1  0.1  0.4  0.8  0.8  0.7  1.0  1.5\n",
       "\n",
       "[61 rows x 13 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ios nino1.2 nino 3.4\n",
    "%reset -sf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "file=\"nino.xlsx\"\n",
    "soi=pd.read_excel(file, \"SOI\")\n",
    "nino=pd.read_excel(file, \"NINO\")\n",
    "soi\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.rename of     YEAR  JAN  FEB  MAR  APR  MAY  JUN  JUL  AUG  SEP  OCT  NOV  DEC\n",
       "0   1960  0.4  0.4  0.6  0.8  0.5  0.4  0.5  0.7  0.5  0.4  0.5  0.3\n",
       "1   1961  0.5 -0.4  0.0 -0.2  0.4  0.2  0.2  0.2  0.0  0.1  0.6  1.3\n",
       "2   1962  1.1  0.6  0.0  0.5  0.7  0.6  0.5  0.4  0.7  0.6  0.5  0.5\n",
       "3   1963  0.6  0.9  0.8  0.8  0.2 -0.1 -0.2 -0.2 -0.6 -0.9 -1.1 -0.8\n",
       "4   1964 -0.5  0.2  0.7  0.8  0.7  0.5  1.0  1.1  1.4  0.9  0.4 -0.2\n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...\n",
       "56  2016 -1.6 -1.4 -1.1 -0.3 -0.1  0.5  0.6  0.8  0.5  0.3  0.0  0.1\n",
       "57  2017  0.1  0.3  0.2  0.3 -0.1  0.2  0.3  0.6  0.7  0.8  0.6  0.6\n",
       "58  2018  0.2  0.7  0.5  0.8  0.3  0.2 -0.1 -0.3 -0.3 -0.2  0.4  0.3\n",
       "59  2019 -0.1 -0.6 -0.5 -0.2 -0.3 -0.4 -0.3 -0.6 -0.6 -0.8 -0.6 -0.4\n",
       "60  2020 -0.2  0.0  0.0  0.2  0.1  0.1  0.4  0.8  0.8  0.7  1.0  1.5\n",
       "\n",
       "[61 rows x 13 columns]>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soi.rename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FIXED\n",
    "soi_data=soi.set_index('YEAR').stack().reset_index().rename(columns={'level_1':'MON', 0:'SOI'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MON</th>\n",
       "      <th>SOI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960</td>\n",
       "      <td>JAN</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960</td>\n",
       "      <td>FEB</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1960</td>\n",
       "      <td>MAR</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1960</td>\n",
       "      <td>APR</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1960</td>\n",
       "      <td>MAY</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>2020</td>\n",
       "      <td>AUG</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>2020</td>\n",
       "      <td>SEP</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>2020</td>\n",
       "      <td>OCT</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2020</td>\n",
       "      <td>NOV</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>2020</td>\n",
       "      <td>DEC</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>732 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     YEAR  MON  SOI\n",
       "0    1960  JAN  0.4\n",
       "1    1960  FEB  0.4\n",
       "2    1960  MAR  0.6\n",
       "3    1960  APR  0.8\n",
       "4    1960  MAY  0.5\n",
       "..    ...  ...  ...\n",
       "727  2020  AUG  0.8\n",
       "728  2020  SEP  0.8\n",
       "729  2020  OCT  0.7\n",
       "730  2020  NOV  1.0\n",
       "731  2020  DEC  1.5\n",
       "\n",
       "[732 rows x 3 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soi_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NINO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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",
       "  </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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -0.33\n",
       "1     -0.42\n",
       "2     -0.25\n",
       "3     -1.11\n",
       "4     -1.09\n",
       "       ... \n",
       "727   -1.05\n",
       "728   -1.22\n",
       "729   -0.60\n",
       "730   -0.58\n",
       "731   -0.60\n",
       "Name: ANOM, Length: 732, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nino[\"ANOM\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaN\n",
       "1     -0.333333\n",
       "2     -0.593333\n",
       "3     -0.816667\n",
       "4     -1.203333\n",
       "         ...   \n",
       "727   -1.183333\n",
       "728   -0.956667\n",
       "729   -0.800000\n",
       "730   -0.593333\n",
       "731         NaN\n",
       "Name: ANOM, Length: 732, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#running mean 3 mon\n",
    "nino12_r=nino[\"ANOM\"].rolling(3,center=True).mean()\n",
    "nino12_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 43,
     "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": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "730 732\n"
     ]
    }
   ],
   "source": [
    "print(len(nino12_r),len(soi_data.SOI))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.4\n",
       "1      0.4\n",
       "2      0.6\n",
       "3      0.8\n",
       "4      0.5\n",
       "      ... \n",
       "727    0.8\n",
       "728    0.8\n",
       "729    0.7\n",
       "730    1.0\n",
       "731    1.5\n",
       "Name: SOI, Length: 732, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soi_data.SOI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.4\n",
       "1      0.6\n",
       "2      0.8\n",
       "3      0.5\n",
       "4      0.4\n",
       "      ... \n",
       "725    0.4\n",
       "726    0.8\n",
       "727    0.8\n",
       "728    0.7\n",
       "729    1.0\n",
       "Name: SOI, Length: 730, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing SOI first and last row\n",
    "soi_data.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",
    "soi_ad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250a28348c8>"
      ]
     },
     "execution_count": 47,
     "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": [
    "soi_ad.plot(marker='x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250a34f3288>"
      ]
     },
     "execution_count": 48,
     "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": [
    "nino12_r.plot(marker='x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#UNIR COLUMNAS EN NUEVO DATAFRAME\n",
    "newdata = pd.concat([nino12_r, soi_ad],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# SCATTER PLOT IOS NINO1+2\n",
    "ax=newdata.plot.scatter(x=\"SOI\",y=\"ANOM\",marker=\"x\")\n",
    "ax.axvline(0,color=\"k\")\n",
    "ax.axhline(0,color=\"k\")\n",
    "#ax.axvline(-2,color=\"r\",linestyle=\"dashed\")\n",
    "ax.axvline(-1.5,color=\"r\",linestyle=\"dashed\")\n",
    "#ax.axvline(-1,color=\"y\",linestyle=\"dashed\")\n",
    "\n",
    "\n",
    "ax.axhline(1,color=\"r\",linestyle=\"dashed\")\n",
    "ax.set_title('SOI VS ANOM1+2')\n",
    "plt.savefig(\"SOI_ANOM1+2.png\",dpi =300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numero de eventos donde SOI <= -1.5: 17\n",
      "#eventos donde ANOM12>=1 ! SOI <= -1.5: 13\n",
      "prob. de el objetivo: 76.47058823529412\n"
     ]
    }
   ],
   "source": [
    "#PROB. DE NINO 1+2 >=1, WHEN IOS <=-1.5\n",
    "soi_neg=newdata[(newdata[\"SOI\"] <= -1.5)]\n",
    "print(\"numero de eventos donde SOI <= -1.5:\", len(soi_neg))\n",
    "\n",
    "target=newdata[(newdata[\"SOI\"] <= -1.5) & (newdata[\"ANOM\"] >= 1)]\n",
    "print(\"#eventos donde ANOM12>=1 ! SOI <= -1.5:\", len(target))\n",
    "\n",
    "ptarget=len(target)/len(soi_neg)*100\n",
    "print(\"prob. de el objetivo:\", ptarget)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EJER. SOI VS NINO3.4, DISPERSION, Y PROB.SOI <= -1.5, NINO3.4>= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
       "  </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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -0.346667\n",
       "1     -0.363333\n",
       "2     -0.293333\n",
       "3     -0.356667\n",
       "4     -0.383333\n",
       "         ...   \n",
       "725   -0.410000\n",
       "726   -0.573333\n",
       "727   -0.893333\n",
       "728   -1.170000\n",
       "729   -1.276667\n",
       "Name: ANOM.3, Length: 730, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3 runmean para nino3.4\n",
    "\n",
    "nino34_r=nino[\"ANOM.3\"].rolling(3,center=True).mean()\n",
    "\n",
    "nino34_r = nino34_r.iloc[1:]\n",
    "nino34_r = nino34_r.iloc[:-1]\n",
    "nino34_r=nino34_r.reset_index(drop=True)\n",
    "nino34_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       ANOM.3  SOI\n",
      "0   -0.346667  0.4\n",
      "1   -0.363333  0.6\n",
      "2   -0.293333  0.8\n",
      "3   -0.356667  0.5\n",
      "4   -0.383333  0.4\n",
      "..        ...  ...\n",
      "725 -0.410000  0.4\n",
      "726 -0.573333  0.8\n",
      "727 -0.893333  0.8\n",
      "728 -1.170000  0.7\n",
      "729 -1.276667  1.0\n",
      "\n",
      "[730 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SOI VS ANOM3.4')"
      ]
     },
     "execution_count": 20,
     "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": [
    "#UNIR COLUMNAS EN NUEVO DATAFRAME\n",
    "newdata = pd.concat([nino34_r, soi_ad],axis=1)\n",
    "print(newdata)\n",
    "# SCATTER PLOT IOS NINO3.4\n",
    "ax=newdata.plot.scatter(x=\"SOI\",y=\"ANOM.3\",marker=\"x\")\n",
    "ax.set_ylabel('ANOM 3.4')\n",
    "\n",
    "ax.axvline(0,color=\"k\")\n",
    "ax.axhline(0,color=\"k\")\n",
    "#ax.axvline(-2,color=\"r\",linestyle=\"dashed\")\n",
    "ax.axvline(-1.5,color=\"ORANGE\",linestyle=\"dashed\")\n",
    "\n",
    "ax.axhline(1,color=\"r\",linestyle=\"dashed\")\n",
    "ax.set_title('SOI VS ANOM3.4')\n",
    "\n",
    "#plt.savefig(\"ithaca\"+'_pp_vs_tmin_scatterplot.png',dpi =300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numero de eventos donde SOI <= -1.5: 17\n",
      "#eventos donde ANOM3.4 >= 1 ! SOI <= 1.5: 16\n",
      "prob. de el objetivo: 94.11764705882352\n"
     ]
    }
   ],
   "source": [
    "#PROB. DE NINO 3.4 >= 1, WHEN IOS <= -1.5\n",
    "soi_neg=newdata[(newdata[\"SOI\"] <= -1.5)]\n",
    "print(\"numero de eventos donde SOI <= -1.5:\", len(soi_neg))\n",
    "\n",
    "target=newdata[(newdata[\"SOI\"] <= -1.5) & (newdata[\"ANOM.3\"] >= 1 )]\n",
    "print(\"#eventos donde ANOM3.4 >= 1 ! SOI <= 1.5:\", len(target))\n",
    "\n",
    "ptarget=len(target)/len(soi_neg)*100\n",
    "print(\"prob. de el objetivo:\", ptarget)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>NINO3.4</th>\n",
       "      <th>SOI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.346667</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.363333</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.293333</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.356667</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.383333</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>-0.410000</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>-0.573333</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>-0.893333</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>-1.170000</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>-1.276667</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>730 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      NINO3.4  SOI\n",
       "0   -0.346667  0.4\n",
       "1   -0.363333  0.6\n",
       "2   -0.293333  0.8\n",
       "3   -0.356667  0.5\n",
       "4   -0.383333  0.4\n",
       "..        ...  ...\n",
       "725 -0.410000  0.4\n",
       "726 -0.573333  0.8\n",
       "727 -0.893333  0.8\n",
       "728 -1.170000  0.7\n",
       "729 -1.276667  1.0\n",
       "\n",
       "[730 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata.rename(columns={'ANOM.3': 'NINO3.4'},inplace=True)\n",
    "newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EJER. PROB. NINO 1+2>0, cuando NINO 3.4 >0?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     -0.346667\n",
       "1     -0.363333\n",
       "2     -0.293333\n",
       "3     -0.356667\n",
       "4     -0.383333\n",
       "         ...   \n",
       "725   -0.410000\n",
       "726   -0.573333\n",
       "727   -0.893333\n",
       "728   -1.170000\n",
       "729   -1.276667\n",
       "Name: ANOM.3, Length: 730, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nino34_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nino12_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>NINO1+2</th>\n",
       "      <th>NINO3.4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.346667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.593333</td>\n",
       "      <td>-0.363333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.816667</td>\n",
       "      <td>-0.293333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.203333</td>\n",
       "      <td>-0.356667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.273333</td>\n",
       "      <td>-0.383333</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>-0.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>-1.183333</td>\n",
       "      <td>-0.573333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>-0.956667</td>\n",
       "      <td>-0.893333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>-0.800000</td>\n",
       "      <td>-1.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>-0.593333</td>\n",
       "      <td>-1.276667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>730 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      NINO1+2   NINO3.4\n",
       "0   -0.333333 -0.346667\n",
       "1   -0.593333 -0.363333\n",
       "2   -0.816667 -0.293333\n",
       "3   -1.203333 -0.356667\n",
       "4   -1.273333 -0.383333\n",
       "..        ...       ...\n",
       "725 -1.010000 -0.410000\n",
       "726 -1.183333 -0.573333\n",
       "727 -0.956667 -0.893333\n",
       "728 -0.800000 -1.170000\n",
       "729 -0.593333 -1.276667\n",
       "\n",
       "[730 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unir nino1+2 y 3.4 ,runmean\n",
    "newdata = pd.concat([nino12_r, nino34_r],axis=1)\n",
    "newdata.rename(columns={'ANOM.3': 'NINO3.4',\"ANOM\":\"NINO1+2\"},inplace=True)\n",
    "newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# SCATTER PLOT IOS NINO1+2\n",
    "ax=newdata.plot.scatter(x=\"NINO3.4\",y=\"NINO1+2\",marker=\"x\")\n",
    "ax.set_xlabel('ANOM 3.4')\n",
    "ax.set_ylabel('ANOM 1+2')\n",
    "\n",
    "\n",
    "ax.axvline(0,color=\"k\")\n",
    "ax.axhline(0,color=\"k\")\n",
    "\n",
    "ax.axvline(1,color=\"r\")\n",
    "ax.axhline(1,color=\"r\")\n",
    "ax.set_title('ANOM3.4 VS ANOM1+2')\n",
    "\n",
    "plt.savefig(\"nino1+2_nino3.4.png\",dpi =300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numero de eventos donde NINO3.4 >= 1: 66\n",
      "#eventos donde ANOM12>= 1 ! ANOM3.4 >= 1: 40\n",
      "prob. de el objetivo: 60.60606060606061\n"
     ]
    }
   ],
   "source": [
    "#PROB. DE NINO1+2>= 1, WHEN ANOM3.4 >= 1\n",
    "nino34_pos=newdata[(newdata[\"NINO3.4\"] >= 1)]\n",
    "print(\"numero de eventos donde NINO3.4 >= 1:\", len(nino34_pos))\n",
    "\n",
    "target=newdata[(newdata[\"NINO1+2\"] >= 1) & (newdata[\"NINO3.4\"] >= 1 )]\n",
    "print(\"#eventos donde ANOM12>= 1 ! ANOM3.4 >= 1:\", len(target))\n",
    "\n",
    "ptarget=len(target)/len(nino34_pos)\n",
    "print(\"prob. de el objetivo:\", ptarget*100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# time series -extra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>NINO1+2</th>\n",
       "      <th>NINO3.4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.346667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.593333</td>\n",
       "      <td>-0.363333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.816667</td>\n",
       "      <td>-0.293333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.203333</td>\n",
       "      <td>-0.356667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.273333</td>\n",
       "      <td>-0.383333</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>-0.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>-1.183333</td>\n",
       "      <td>-0.573333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>-0.956667</td>\n",
       "      <td>-0.893333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>-0.800000</td>\n",
       "      <td>-1.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>-0.593333</td>\n",
       "      <td>-1.276667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>730 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      NINO1+2   NINO3.4\n",
       "0   -0.333333 -0.346667\n",
       "1   -0.593333 -0.363333\n",
       "2   -0.816667 -0.293333\n",
       "3   -1.203333 -0.356667\n",
       "4   -1.273333 -0.383333\n",
       "..        ...       ...\n",
       "725 -1.010000 -0.410000\n",
       "726 -1.183333 -0.573333\n",
       "727 -0.956667 -0.893333\n",
       "728 -0.800000 -1.170000\n",
       "729 -0.593333 -1.276667\n",
       "\n",
       "[730 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
       "  </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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nino"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "730\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['02-1960', '03-1960', '04-1960', '05-1960', '06-1960', '07-1960',\n",
       "       '08-1960', '09-1960', '10-1960', '11-1960',\n",
       "       ...\n",
       "       '02-2020', '03-2020', '04-2020', '05-2020', '06-2020', '07-2020',\n",
       "       '08-2020', '09-2020', '10-2020', '11-2020'],\n",
       "      dtype='object', length=730)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date1 = '1960-02-15'\n",
    "date2 = '2020-12-15'\n",
    "mydates = pd.date_range(date1, date2,freq=\"M\").strftime('%m-%Y')\n",
    "print(len(mydates))\n",
    "mydates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>NINO1+2</th>\n",
       "      <th>NINO3.4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>02-1960</th>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.346667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-1960</th>\n",
       "      <td>-0.593333</td>\n",
       "      <td>-0.363333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04-1960</th>\n",
       "      <td>-0.816667</td>\n",
       "      <td>-0.293333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-1960</th>\n",
       "      <td>-1.203333</td>\n",
       "      <td>-0.356667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06-1960</th>\n",
       "      <td>-1.273333</td>\n",
       "      <td>-0.383333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-2020</th>\n",
       "      <td>-1.010000</td>\n",
       "      <td>-0.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-2020</th>\n",
       "      <td>-1.183333</td>\n",
       "      <td>-0.573333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09-2020</th>\n",
       "      <td>-0.956667</td>\n",
       "      <td>-0.893333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-2020</th>\n",
       "      <td>-0.800000</td>\n",
       "      <td>-1.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11-2020</th>\n",
       "      <td>-0.593333</td>\n",
       "      <td>-1.276667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>730 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          NINO1+2   NINO3.4\n",
       "02-1960 -0.333333 -0.346667\n",
       "03-1960 -0.593333 -0.363333\n",
       "04-1960 -0.816667 -0.293333\n",
       "05-1960 -1.203333 -0.356667\n",
       "06-1960 -1.273333 -0.383333\n",
       "...           ...       ...\n",
       "07-2020 -1.010000 -0.410000\n",
       "08-2020 -1.183333 -0.573333\n",
       "09-2020 -0.956667 -0.893333\n",
       "10-2020 -0.800000 -1.170000\n",
       "11-2020 -0.593333 -1.276667\n",
       "\n",
       "[730 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_idx=newdata.set_index(mydates) \n",
    "data_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250a39f0508>"
      ]
     },
     "execution_count": 32,
     "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": [
    "data_idx.plot(marker='x')"
   ]
  },
  {
   "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": "3.7.6"
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