{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nino12=nino[\"ANOM\"]\n",
    "nino34=nino[\"ANOM.3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "fig1 = plt.figure()\n",
    "\n",
    "plot_acf(nino12,lags=10)\n",
    "\n",
    "plt.savefig(\"autocorr_nino12.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "fig1 = plt.figure()\n",
    "\n",
    "plot_acf(nino34,lags=10)\n",
    "\n",
    "plt.savefig(\"autocorr_nino34.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2675a0c92c8>"
      ]
     },
     "execution_count": 14,
     "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": [
    "########### con pandas\n",
    "from pandas.plotting import autocorrelation_plot\n",
    "\n",
    "autocorrelation_plot(nino34,label=\"nino34\").set_xlim([0, 15])\n",
    "autocorrelation_plot(nino12,label=\"nino12\").set_xlim([0, 15])\n",
    "\n",
    "plt.grid()\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}