LSTM_tf_WeeklyPrecipitationData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8da86ec5",
   "metadata": {
    "id": "8da86ec5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import Sequential, layers, callbacks\n",
    "from tensorflow.keras.layers import Dense, LSTM, Dropout, GRU, Bidirectional\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "44dd90be",
   "metadata": {
    "id": "44dd90be"
   },
   "outputs": [],
   "source": [
    "# Read Data\n",
    "data = pd.read_csv('Data/requiredMunicipalities_temperature_precipitation_elevation_DengueCasesWeek.csv')\n",
    "# Remove extra column\n",
    "data = data.drop('Unnamed: 0', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "d601a8bf",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "id": "d601a8bf",
    "outputId": "62b39a2d-49fd-4c4c-a169-b98c0cd45a87"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Municipality</th>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
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       "      <th>...</th>\n",
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       "      <th>2019/w35</th>\n",
       "      <th>2019/w36</th>\n",
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       "      <th>2019/w42</th>\n",
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       "      <td>161.073792</td>\n",
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       "      <td>243.856186</td>\n",
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       "      <td>320.049988</td>\n",
       "      <td>218.923187</td>\n",
       "      <td>147.733185</td>\n",
       "      <td>165.971741</td>\n",
       "      <td>250.059143</td>\n",
       "      <td>199.013580</td>\n",
       "      <td>310.712250</td>\n",
       "      <td>218.631714</td>\n",
       "      <td>169.890945</td>\n",
       "      <td>317.373627</td>\n",
       "      <td>223.847336</td>\n",
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       "      <td>114.181557</td>\n",
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       "      <th>1</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>amaga</td>\n",
       "      <td>62.527489</td>\n",
       "      <td>46.315376</td>\n",
       "      <td>166.522003</td>\n",
       "      <td>288.957397</td>\n",
       "      <td>350.666260</td>\n",
       "      <td>143.757507</td>\n",
       "      <td>350.168793</td>\n",
       "      <td>210.302658</td>\n",
       "      <td>214.617142</td>\n",
       "      <td>283.321747</td>\n",
       "      <td>279.687347</td>\n",
       "      <td>198.276825</td>\n",
       "      <td>82.823952</td>\n",
       "      <td>139.362946</td>\n",
       "      <td>122.149208</td>\n",
       "      <td>246.294891</td>\n",
       "      <td>1056.371826</td>\n",
       "      <td>233.371964</td>\n",
       "      <td>343.933350</td>\n",
       "      <td>306.676270</td>\n",
       "      <td>213.928513</td>\n",
       "      <td>248.733658</td>\n",
       "      <td>302.210754</td>\n",
       "      <td>232.676270</td>\n",
       "      <td>126.071480</td>\n",
       "      <td>128.074234</td>\n",
       "      <td>205.597870</td>\n",
       "      <td>165.801498</td>\n",
       "      <td>234.811264</td>\n",
       "      <td>206.055435</td>\n",
       "      <td>127.413780</td>\n",
       "      <td>236.348267</td>\n",
       "      <td>192.895981</td>\n",
       "      <td>290.498383</td>\n",
       "      <td>194.566284</td>\n",
       "      <td>140.502640</td>\n",
       "      <td>14.430646</td>\n",
       "      <td>75.684792</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>amalfi</td>\n",
       "      <td>76.737572</td>\n",
       "      <td>62.784908</td>\n",
       "      <td>198.478897</td>\n",
       "      <td>322.928314</td>\n",
       "      <td>381.405701</td>\n",
       "      <td>191.377609</td>\n",
       "      <td>387.475983</td>\n",
       "      <td>408.289520</td>\n",
       "      <td>306.408356</td>\n",
       "      <td>385.371216</td>\n",
       "      <td>336.976227</td>\n",
       "      <td>175.587463</td>\n",
       "      <td>98.434227</td>\n",
       "      <td>97.761215</td>\n",
       "      <td>183.319458</td>\n",
       "      <td>269.138306</td>\n",
       "      <td>851.275208</td>\n",
       "      <td>241.106689</td>\n",
       "      <td>458.701965</td>\n",
       "      <td>496.593597</td>\n",
       "      <td>265.008057</td>\n",
       "      <td>411.743866</td>\n",
       "      <td>385.076141</td>\n",
       "      <td>232.894592</td>\n",
       "      <td>154.217178</td>\n",
       "      <td>130.631485</td>\n",
       "      <td>256.707947</td>\n",
       "      <td>247.239822</td>\n",
       "      <td>362.843414</td>\n",
       "      <td>241.188202</td>\n",
       "      <td>230.991714</td>\n",
       "      <td>357.351288</td>\n",
       "      <td>269.511658</td>\n",
       "      <td>352.920532</td>\n",
       "      <td>263.026733</td>\n",
       "      <td>123.605858</td>\n",
       "      <td>47.158634</td>\n",
       "      <td>60.811092</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>andes</td>\n",
       "      <td>115.193504</td>\n",
       "      <td>61.572941</td>\n",
       "      <td>205.126846</td>\n",
       "      <td>308.268433</td>\n",
       "      <td>332.884583</td>\n",
       "      <td>116.664536</td>\n",
       "      <td>310.750671</td>\n",
       "      <td>189.581451</td>\n",
       "      <td>194.997574</td>\n",
       "      <td>333.450806</td>\n",
       "      <td>295.312958</td>\n",
       "      <td>256.798615</td>\n",
       "      <td>151.755310</td>\n",
       "      <td>209.275269</td>\n",
       "      <td>157.795776</td>\n",
       "      <td>267.709290</td>\n",
       "      <td>1036.946411</td>\n",
       "      <td>197.896515</td>\n",
       "      <td>295.078308</td>\n",
       "      <td>279.076172</td>\n",
       "      <td>196.854523</td>\n",
       "      <td>268.346375</td>\n",
       "      <td>321.546906</td>\n",
       "      <td>290.615112</td>\n",
       "      <td>221.921097</td>\n",
       "      <td>174.336456</td>\n",
       "      <td>255.471024</td>\n",
       "      <td>165.742081</td>\n",
       "      <td>205.896408</td>\n",
       "      <td>170.882233</td>\n",
       "      <td>101.799400</td>\n",
       "      <td>211.234085</td>\n",
       "      <td>172.778656</td>\n",
       "      <td>333.647278</td>\n",
       "      <td>199.595963</td>\n",
       "      <td>186.578568</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>angelopolis</td>\n",
       "      <td>69.933151</td>\n",
       "      <td>43.129227</td>\n",
       "      <td>178.370377</td>\n",
       "      <td>278.665588</td>\n",
       "      <td>326.885010</td>\n",
       "      <td>144.349655</td>\n",
       "      <td>360.914429</td>\n",
       "      <td>234.040939</td>\n",
       "      <td>214.994553</td>\n",
       "      <td>291.776550</td>\n",
       "      <td>281.021088</td>\n",
       "      <td>189.771225</td>\n",
       "      <td>91.823616</td>\n",
       "      <td>128.047577</td>\n",
       "      <td>130.140381</td>\n",
       "      <td>235.732956</td>\n",
       "      <td>995.114075</td>\n",
       "      <td>233.658310</td>\n",
       "      <td>351.166199</td>\n",
       "      <td>344.687408</td>\n",
       "      <td>211.449844</td>\n",
       "      <td>260.028564</td>\n",
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       "      <td>224.315628</td>\n",
       "      <td>140.098541</td>\n",
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       "      <td>219.942856</td>\n",
       "      <td>160.306305</td>\n",
       "      <td>218.278122</td>\n",
       "      <td>207.616272</td>\n",
       "      <td>129.507202</td>\n",
       "      <td>265.811707</td>\n",
       "      <td>193.182587</td>\n",
       "      <td>302.892700</td>\n",
       "      <td>195.555344</td>\n",
       "      <td>134.629974</td>\n",
       "      <td>17.000061</td>\n",
       "      <td>69.415474</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1019 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Municipality  PRECIPITATION_jan_07  ...  2019/w50  2019/w51  2019/w52\n",
       "0  antioquia   alejandria             66.638855  ...         0         0         0\n",
       "1  antioquia        amaga             62.527489  ...         1         0         0\n",
       "2  antioquia       amalfi             76.737572  ...         0         1         0\n",
       "3  antioquia        andes            115.193504  ...         0         0         0\n",
       "4  antioquia  angelopolis             69.933151  ...         0         0         0\n",
       "\n",
       "[5 rows x 1019 columns]"
      ]
     },
     "execution_count": 236,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11df9062",
   "metadata": {
    "id": "11df9062"
   },
   "source": [
    "## Columns with temperature, precipitation, and Dengue Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "9d039cfa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "id": "9d039cfa",
    "outputId": "1f34df7b-a304-4055-c86c-36520e7cb91e"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
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       "      <th>TEMPERATURE_jul_07</th>\n",
       "      <th>TEMPERATURE_aug_07</th>\n",
       "      <th>TEMPERATURE_sep_07</th>\n",
       "      <th>TEMPERATURE_oct_07</th>\n",
       "      <th>TEMPERATURE_nov_07</th>\n",
       "      <th>TEMPERATURE_dec_07</th>\n",
       "      <th>TEMPERATURE_jan_08</th>\n",
       "      <th>TEMPERATURE_feb_08</th>\n",
       "      <th>TEMPERATURE_mar_08</th>\n",
       "      <th>TEMPERATURE_apr_08</th>\n",
       "      <th>TEMPERATURE_may_08</th>\n",
       "      <th>TEMPERATURE_jun_08</th>\n",
       "      <th>TEMPERATURE_jul_08</th>\n",
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       "      <th>TEMPERATURE_sep_08</th>\n",
       "      <th>TEMPERATURE_oct_08</th>\n",
       "      <th>TEMPERATURE_nov_08</th>\n",
       "      <th>TEMPERATURE_dec_08</th>\n",
       "      <th>TEMPERATURE_jan_09</th>\n",
       "      <th>TEMPERATURE_feb_09</th>\n",
       "      <th>TEMPERATURE_mar_09</th>\n",
       "      <th>TEMPERATURE_apr_09</th>\n",
       "      <th>TEMPERATURE_may_09</th>\n",
       "      <th>TEMPERATURE_jun_09</th>\n",
       "      <th>TEMPERATURE_jul_09</th>\n",
       "      <th>TEMPERATURE_aug_09</th>\n",
       "      <th>TEMPERATURE_sep_09</th>\n",
       "      <th>TEMPERATURE_oct_09</th>\n",
       "      <th>TEMPERATURE_nov_09</th>\n",
       "      <th>TEMPERATURE_dec_09</th>\n",
       "      <th>TEMPERATURE_jan_10</th>\n",
       "      <th>TEMPERATURE_feb_10</th>\n",
       "      <th>TEMPERATURE_mar_10</th>\n",
       "      <th>TEMPERATURE_apr_10</th>\n",
       "      <th>...</th>\n",
       "      <th>TEMPERATURE_sep_15</th>\n",
       "      <th>TEMPERATURE_oct_15</th>\n",
       "      <th>TEMPERATURE_nov_15</th>\n",
       "      <th>TEMPERATURE_dec_15</th>\n",
       "      <th>TEMPERATURE_jan_16</th>\n",
       "      <th>TEMPERATURE_feb_16</th>\n",
       "      <th>TEMPERATURE_mar_16</th>\n",
       "      <th>TEMPERATURE_apr_16</th>\n",
       "      <th>TEMPERATURE_may_16</th>\n",
       "      <th>TEMPERATURE_jun_16</th>\n",
       "      <th>TEMPERATURE_jul_16</th>\n",
       "      <th>TEMPERATURE_aug_16</th>\n",
       "      <th>TEMPERATURE_sep_16</th>\n",
       "      <th>TEMPERATURE_oct_16</th>\n",
       "      <th>TEMPERATURE_nov_16</th>\n",
       "      <th>TEMPERATURE_dec_16</th>\n",
       "      <th>TEMPERATURE_jan_17</th>\n",
       "      <th>TEMPERATURE_feb_17</th>\n",
       "      <th>TEMPERATURE_mar_17</th>\n",
       "      <th>TEMPERATURE_apr_17</th>\n",
       "      <th>TEMPERATURE_may_17</th>\n",
       "      <th>TEMPERATURE_jun_17</th>\n",
       "      <th>TEMPERATURE_jul_17</th>\n",
       "      <th>TEMPERATURE_aug_17</th>\n",
       "      <th>TEMPERATURE_sep_17</th>\n",
       "      <th>TEMPERATURE_oct_17</th>\n",
       "      <th>TEMPERATURE_nov_17</th>\n",
       "      <th>TEMPERATURE_dec_17</th>\n",
       "      <th>TEMPERATURE_jan_18</th>\n",
       "      <th>TEMPERATURE_feb_18</th>\n",
       "      <th>TEMPERATURE_mar_18</th>\n",
       "      <th>TEMPERATURE_apr_18</th>\n",
       "      <th>TEMPERATURE_may_18</th>\n",
       "      <th>TEMPERATURE_jun_18</th>\n",
       "      <th>TEMPERATURE_jul_18</th>\n",
       "      <th>TEMPERATURE_aug_18</th>\n",
       "      <th>TEMPERATURE_sep_18</th>\n",
       "      <th>TEMPERATURE_oct_18</th>\n",
       "      <th>TEMPERATURE_nov_18</th>\n",
       "      <th>TEMPERATURE_dec_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.606707</td>\n",
       "      <td>20.672624</td>\n",
       "      <td>21.210392</td>\n",
       "      <td>21.113854</td>\n",
       "      <td>21.331241</td>\n",
       "      <td>21.114738</td>\n",
       "      <td>21.333450</td>\n",
       "      <td>20.634999</td>\n",
       "      <td>20.499266</td>\n",
       "      <td>19.855212</td>\n",
       "      <td>20.018971</td>\n",
       "      <td>19.881327</td>\n",
       "      <td>20.107835</td>\n",
       "      <td>20.358041</td>\n",
       "      <td>20.713429</td>\n",
       "      <td>21.015158</td>\n",
       "      <td>20.705113</td>\n",
       "      <td>20.802585</td>\n",
       "      <td>20.796298</td>\n",
       "      <td>20.581962</td>\n",
       "      <td>20.694927</td>\n",
       "      <td>19.958251</td>\n",
       "      <td>19.779387</td>\n",
       "      <td>19.572386</td>\n",
       "      <td>20.102366</td>\n",
       "      <td>20.214117</td>\n",
       "      <td>20.041468</td>\n",
       "      <td>21.004741</td>\n",
       "      <td>21.138620</td>\n",
       "      <td>21.067168</td>\n",
       "      <td>21.312791</td>\n",
       "      <td>21.638038</td>\n",
       "      <td>21.722964</td>\n",
       "      <td>21.078910</td>\n",
       "      <td>21.115932</td>\n",
       "      <td>21.366743</td>\n",
       "      <td>21.531707</td>\n",
       "      <td>21.776617</td>\n",
       "      <td>22.899976</td>\n",
       "      <td>21.884080</td>\n",
       "      <td>...</td>\n",
       "      <td>22.043798</td>\n",
       "      <td>21.431949</td>\n",
       "      <td>21.015933</td>\n",
       "      <td>21.747211</td>\n",
       "      <td>22.035873</td>\n",
       "      <td>22.390246</td>\n",
       "      <td>21.706051</td>\n",
       "      <td>22.384080</td>\n",
       "      <td>22.221606</td>\n",
       "      <td>22.105623</td>\n",
       "      <td>21.924335</td>\n",
       "      <td>22.346545</td>\n",
       "      <td>21.615760</td>\n",
       "      <td>21.264327</td>\n",
       "      <td>20.604387</td>\n",
       "      <td>20.678288</td>\n",
       "      <td>20.418252</td>\n",
       "      <td>20.966983</td>\n",
       "      <td>20.989558</td>\n",
       "      <td>21.796407</td>\n",
       "      <td>21.574037</td>\n",
       "      <td>21.342168</td>\n",
       "      <td>21.841958</td>\n",
       "      <td>20.969809</td>\n",
       "      <td>21.722964</td>\n",
       "      <td>20.936289</td>\n",
       "      <td>20.707426</td>\n",
       "      <td>20.480198</td>\n",
       "      <td>20.606707</td>\n",
       "      <td>20.862208</td>\n",
       "      <td>21.489558</td>\n",
       "      <td>20.985991</td>\n",
       "      <td>21.025772</td>\n",
       "      <td>21.377411</td>\n",
       "      <td>21.652374</td>\n",
       "      <td>21.425886</td>\n",
       "      <td>21.230342</td>\n",
       "      <td>20.725872</td>\n",
       "      <td>20.687894</td>\n",
       "      <td>20.574121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.814870</td>\n",
       "      <td>20.851651</td>\n",
       "      <td>21.328261</td>\n",
       "      <td>21.144019</td>\n",
       "      <td>21.174519</td>\n",
       "      <td>20.900712</td>\n",
       "      <td>21.281823</td>\n",
       "      <td>20.447760</td>\n",
       "      <td>20.407709</td>\n",
       "      <td>19.706597</td>\n",
       "      <td>19.881884</td>\n",
       "      <td>19.933408</td>\n",
       "      <td>20.301328</td>\n",
       "      <td>20.503733</td>\n",
       "      <td>20.764200</td>\n",
       "      <td>20.907040</td>\n",
       "      <td>20.594570</td>\n",
       "      <td>20.621024</td>\n",
       "      <td>20.658124</td>\n",
       "      <td>20.467551</td>\n",
       "      <td>20.453541</td>\n",
       "      <td>19.829513</td>\n",
       "      <td>19.751676</td>\n",
       "      <td>19.713877</td>\n",
       "      <td>20.314869</td>\n",
       "      <td>20.309984</td>\n",
       "      <td>20.135293</td>\n",
       "      <td>20.976572</td>\n",
       "      <td>20.998216</td>\n",
       "      <td>20.884566</td>\n",
       "      <td>21.298489</td>\n",
       "      <td>21.563906</td>\n",
       "      <td>21.517864</td>\n",
       "      <td>20.825346</td>\n",
       "      <td>21.028499</td>\n",
       "      <td>21.400334</td>\n",
       "      <td>21.741953</td>\n",
       "      <td>21.786807</td>\n",
       "      <td>23.000918</td>\n",
       "      <td>22.004436</td>\n",
       "      <td>...</td>\n",
       "      <td>21.783229</td>\n",
       "      <td>21.329513</td>\n",
       "      <td>20.931364</td>\n",
       "      <td>21.872991</td>\n",
       "      <td>22.110703</td>\n",
       "      <td>22.463369</td>\n",
       "      <td>21.939199</td>\n",
       "      <td>22.347405</td>\n",
       "      <td>22.057070</td>\n",
       "      <td>21.804097</td>\n",
       "      <td>21.787292</td>\n",
       "      <td>22.174323</td>\n",
       "      <td>21.303802</td>\n",
       "      <td>21.022482</td>\n",
       "      <td>20.484749</td>\n",
       "      <td>20.706845</td>\n",
       "      <td>20.525287</td>\n",
       "      <td>21.036807</td>\n",
       "      <td>21.055085</td>\n",
       "      <td>21.771884</td>\n",
       "      <td>21.404466</td>\n",
       "      <td>21.121024</td>\n",
       "      <td>21.760989</td>\n",
       "      <td>20.817552</td>\n",
       "      <td>21.520729</td>\n",
       "      <td>20.829513</td>\n",
       "      <td>20.635009</td>\n",
       "      <td>20.580022</td>\n",
       "      <td>20.688046</td>\n",
       "      <td>20.980035</td>\n",
       "      <td>21.648314</td>\n",
       "      <td>21.020582</td>\n",
       "      <td>20.880769</td>\n",
       "      <td>21.149931</td>\n",
       "      <td>21.560988</td>\n",
       "      <td>21.380573</td>\n",
       "      <td>21.026458</td>\n",
       "      <td>20.558420</td>\n",
       "      <td>20.625113</td>\n",
       "      <td>20.717262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.520126</td>\n",
       "      <td>21.536331</td>\n",
       "      <td>22.240293</td>\n",
       "      <td>22.223073</td>\n",
       "      <td>22.306279</td>\n",
       "      <td>22.010652</td>\n",
       "      <td>22.411260</td>\n",
       "      <td>21.424171</td>\n",
       "      <td>21.347958</td>\n",
       "      <td>20.695219</td>\n",
       "      <td>20.888090</td>\n",
       "      <td>20.842101</td>\n",
       "      <td>20.980630</td>\n",
       "      <td>21.211158</td>\n",
       "      <td>21.652705</td>\n",
       "      <td>22.070816</td>\n",
       "      <td>21.622859</td>\n",
       "      <td>21.718899</td>\n",
       "      <td>21.888344</td>\n",
       "      <td>21.426429</td>\n",
       "      <td>21.504380</td>\n",
       "      <td>20.797476</td>\n",
       "      <td>20.733924</td>\n",
       "      <td>20.622917</td>\n",
       "      <td>21.055803</td>\n",
       "      <td>21.123918</td>\n",
       "      <td>21.113209</td>\n",
       "      <td>22.056146</td>\n",
       "      <td>22.108190</td>\n",
       "      <td>22.018898</td>\n",
       "      <td>22.455184</td>\n",
       "      <td>22.472261</td>\n",
       "      <td>22.491968</td>\n",
       "      <td>21.874559</td>\n",
       "      <td>21.973420</td>\n",
       "      <td>22.260592</td>\n",
       "      <td>22.405456</td>\n",
       "      <td>22.569839</td>\n",
       "      <td>23.879789</td>\n",
       "      <td>22.968560</td>\n",
       "      <td>...</td>\n",
       "      <td>22.781116</td>\n",
       "      <td>22.251642</td>\n",
       "      <td>21.861008</td>\n",
       "      <td>22.658334</td>\n",
       "      <td>22.834796</td>\n",
       "      <td>23.209248</td>\n",
       "      <td>22.640292</td>\n",
       "      <td>23.456146</td>\n",
       "      <td>23.145776</td>\n",
       "      <td>23.000148</td>\n",
       "      <td>23.009350</td>\n",
       "      <td>23.124171</td>\n",
       "      <td>22.400214</td>\n",
       "      <td>22.072302</td>\n",
       "      <td>21.461006</td>\n",
       "      <td>21.583508</td>\n",
       "      <td>21.307714</td>\n",
       "      <td>21.790498</td>\n",
       "      <td>21.940292</td>\n",
       "      <td>22.970817</td>\n",
       "      <td>22.518693</td>\n",
       "      <td>22.277231</td>\n",
       "      <td>22.882267</td>\n",
       "      <td>21.871915</td>\n",
       "      <td>22.491968</td>\n",
       "      <td>21.772303</td>\n",
       "      <td>21.565174</td>\n",
       "      <td>21.471095</td>\n",
       "      <td>21.507713</td>\n",
       "      <td>21.721662</td>\n",
       "      <td>22.440292</td>\n",
       "      <td>22.133230</td>\n",
       "      <td>21.910445</td>\n",
       "      <td>22.177232</td>\n",
       "      <td>22.709351</td>\n",
       "      <td>22.234674</td>\n",
       "      <td>22.014884</td>\n",
       "      <td>21.497476</td>\n",
       "      <td>21.550504</td>\n",
       "      <td>21.471094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.030849</td>\n",
       "      <td>16.976915</td>\n",
       "      <td>17.263741</td>\n",
       "      <td>17.063804</td>\n",
       "      <td>17.199818</td>\n",
       "      <td>17.035077</td>\n",
       "      <td>17.151788</td>\n",
       "      <td>16.501156</td>\n",
       "      <td>16.793839</td>\n",
       "      <td>15.996531</td>\n",
       "      <td>16.310998</td>\n",
       "      <td>16.359078</td>\n",
       "      <td>16.474338</td>\n",
       "      <td>16.587331</td>\n",
       "      <td>16.769470</td>\n",
       "      <td>16.832033</td>\n",
       "      <td>16.569870</td>\n",
       "      <td>16.730910</td>\n",
       "      <td>16.592412</td>\n",
       "      <td>16.449594</td>\n",
       "      <td>16.780557</td>\n",
       "      <td>16.174917</td>\n",
       "      <td>16.220894</td>\n",
       "      <td>16.194495</td>\n",
       "      <td>16.556109</td>\n",
       "      <td>16.383165</td>\n",
       "      <td>16.060355</td>\n",
       "      <td>16.969532</td>\n",
       "      <td>17.013100</td>\n",
       "      <td>17.096014</td>\n",
       "      <td>17.276268</td>\n",
       "      <td>17.673552</td>\n",
       "      <td>17.880557</td>\n",
       "      <td>17.139500</td>\n",
       "      <td>17.537821</td>\n",
       "      <td>17.873401</td>\n",
       "      <td>17.914442</td>\n",
       "      <td>17.896446</td>\n",
       "      <td>19.008272</td>\n",
       "      <td>18.001304</td>\n",
       "      <td>...</td>\n",
       "      <td>18.189931</td>\n",
       "      <td>17.604604</td>\n",
       "      <td>17.454228</td>\n",
       "      <td>18.360380</td>\n",
       "      <td>18.301421</td>\n",
       "      <td>18.532643</td>\n",
       "      <td>17.969470</td>\n",
       "      <td>18.237241</td>\n",
       "      <td>18.113099</td>\n",
       "      <td>18.005389</td>\n",
       "      <td>17.733298</td>\n",
       "      <td>18.195688</td>\n",
       "      <td>17.638109</td>\n",
       "      <td>17.287416</td>\n",
       "      <td>16.956310</td>\n",
       "      <td>17.149443</td>\n",
       "      <td>16.735276</td>\n",
       "      <td>17.113113</td>\n",
       "      <td>17.056189</td>\n",
       "      <td>17.684115</td>\n",
       "      <td>17.413099</td>\n",
       "      <td>17.249660</td>\n",
       "      <td>17.683299</td>\n",
       "      <td>16.873551</td>\n",
       "      <td>17.865973</td>\n",
       "      <td>17.137417</td>\n",
       "      <td>17.056310</td>\n",
       "      <td>16.995796</td>\n",
       "      <td>16.838922</td>\n",
       "      <td>17.105560</td>\n",
       "      <td>17.669470</td>\n",
       "      <td>16.954950</td>\n",
       "      <td>16.968828</td>\n",
       "      <td>17.341847</td>\n",
       "      <td>17.520799</td>\n",
       "      <td>17.428500</td>\n",
       "      <td>17.365973</td>\n",
       "      <td>16.974917</td>\n",
       "      <td>17.126363</td>\n",
       "      <td>17.195797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.095986</td>\n",
       "      <td>20.092206</td>\n",
       "      <td>20.527178</td>\n",
       "      <td>20.310996</td>\n",
       "      <td>20.335141</td>\n",
       "      <td>20.198780</td>\n",
       "      <td>20.571788</td>\n",
       "      <td>19.733009</td>\n",
       "      <td>19.727717</td>\n",
       "      <td>19.003105</td>\n",
       "      <td>19.240788</td>\n",
       "      <td>19.254847</td>\n",
       "      <td>19.602584</td>\n",
       "      <td>19.781789</td>\n",
       "      <td>19.946796</td>\n",
       "      <td>20.066030</td>\n",
       "      <td>19.749116</td>\n",
       "      <td>19.931243</td>\n",
       "      <td>19.937847</td>\n",
       "      <td>19.764606</td>\n",
       "      <td>19.769384</td>\n",
       "      <td>19.117688</td>\n",
       "      <td>19.122385</td>\n",
       "      <td>19.029239</td>\n",
       "      <td>19.595987</td>\n",
       "      <td>19.583873</td>\n",
       "      <td>19.348619</td>\n",
       "      <td>20.129138</td>\n",
       "      <td>20.169082</td>\n",
       "      <td>20.174647</td>\n",
       "      <td>20.584289</td>\n",
       "      <td>20.844640</td>\n",
       "      <td>20.832144</td>\n",
       "      <td>20.109355</td>\n",
       "      <td>20.389311</td>\n",
       "      <td>20.699726</td>\n",
       "      <td>21.031403</td>\n",
       "      <td>21.050799</td>\n",
       "      <td>22.173272</td>\n",
       "      <td>21.171412</td>\n",
       "      <td>...</td>\n",
       "      <td>21.086919</td>\n",
       "      <td>20.617689</td>\n",
       "      <td>20.289920</td>\n",
       "      <td>21.181930</td>\n",
       "      <td>21.387652</td>\n",
       "      <td>21.735349</td>\n",
       "      <td>21.138463</td>\n",
       "      <td>21.512470</td>\n",
       "      <td>21.203282</td>\n",
       "      <td>21.100254</td>\n",
       "      <td>21.054514</td>\n",
       "      <td>21.467557</td>\n",
       "      <td>20.601763</td>\n",
       "      <td>20.308747</td>\n",
       "      <td>19.841396</td>\n",
       "      <td>20.020298</td>\n",
       "      <td>19.818903</td>\n",
       "      <td>20.300800</td>\n",
       "      <td>20.244105</td>\n",
       "      <td>20.965770</td>\n",
       "      <td>20.571165</td>\n",
       "      <td>20.431244</td>\n",
       "      <td>21.038454</td>\n",
       "      <td>20.114606</td>\n",
       "      <td>20.832752</td>\n",
       "      <td>20.117689</td>\n",
       "      <td>20.009885</td>\n",
       "      <td>19.903371</td>\n",
       "      <td>19.991559</td>\n",
       "      <td>20.247848</td>\n",
       "      <td>20.841154</td>\n",
       "      <td>20.195543</td>\n",
       "      <td>20.037224</td>\n",
       "      <td>20.429421</td>\n",
       "      <td>20.838454</td>\n",
       "      <td>20.657140</td>\n",
       "      <td>20.333967</td>\n",
       "      <td>19.815866</td>\n",
       "      <td>19.987836</td>\n",
       "      <td>20.030714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   TEMPERATURE_jan_07  ...  TEMPERATURE_dec_18\n",
       "0           20.606707  ...           20.574121\n",
       "1           20.814870  ...           20.717262\n",
       "2           21.520126  ...           21.471094\n",
       "3           17.030849  ...           17.195797\n",
       "4           20.095986  ...           20.030714\n",
       "\n",
       "[5 rows x 144 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take just temperature data\n",
    "x_temperature = data[data.columns[147:291]]\n",
    "x_temperature.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "861616e6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "id": "861616e6",
    "outputId": "d52219fe-0bb0-43d6-906a-5dacaa2f39a5"
   },
   "outputs": [
    {
     "data": {
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       "      <th>PRECIPITATION_feb_08</th>\n",
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       "      <th>PRECIPITATION_jan_09</th>\n",
       "      <th>PRECIPITATION_feb_09</th>\n",
       "      <th>PRECIPITATION_mar_09</th>\n",
       "      <th>PRECIPITATION_apr_09</th>\n",
       "      <th>PRECIPITATION_may_09</th>\n",
       "      <th>PRECIPITATION_jun_09</th>\n",
       "      <th>PRECIPITATION_jul_09</th>\n",
       "      <th>PRECIPITATION_aug_09</th>\n",
       "      <th>PRECIPITATION_sep_09</th>\n",
       "      <th>PRECIPITATION_oct_09</th>\n",
       "      <th>PRECIPITATION_nov_09</th>\n",
       "      <th>PRECIPITATION_dec_09</th>\n",
       "      <th>PRECIPITATION_jan_10</th>\n",
       "      <th>PRECIPITATION_feb_10</th>\n",
       "      <th>PRECIPITATION_mar_10</th>\n",
       "      <th>PRECIPITATION_apr_10</th>\n",
       "      <th>...</th>\n",
       "      <th>PRECIPITATION_sep_15</th>\n",
       "      <th>PRECIPITATION_oct_15</th>\n",
       "      <th>PRECIPITATION_nov_15</th>\n",
       "      <th>PRECIPITATION_dec_15</th>\n",
       "      <th>PRECIPITATION_jan_16</th>\n",
       "      <th>PRECIPITATION_feb_16</th>\n",
       "      <th>PRECIPITATION_mar_16</th>\n",
       "      <th>PRECIPITATION_apr_16</th>\n",
       "      <th>PRECIPITATION_may_16</th>\n",
       "      <th>PRECIPITATION_jun_16</th>\n",
       "      <th>PRECIPITATION_jul_16</th>\n",
       "      <th>PRECIPITATION_aug_16</th>\n",
       "      <th>PRECIPITATION_sep_16</th>\n",
       "      <th>PRECIPITATION_oct_16</th>\n",
       "      <th>PRECIPITATION_nov_16</th>\n",
       "      <th>PRECIPITATION_dec_16</th>\n",
       "      <th>PRECIPITATION_jan_17</th>\n",
       "      <th>PRECIPITATION_feb_17</th>\n",
       "      <th>PRECIPITATION_mar_17</th>\n",
       "      <th>PRECIPITATION_apr_17</th>\n",
       "      <th>PRECIPITATION_may_17</th>\n",
       "      <th>PRECIPITATION_jun_17</th>\n",
       "      <th>PRECIPITATION_jul_17</th>\n",
       "      <th>PRECIPITATION_aug_17</th>\n",
       "      <th>PRECIPITATION_sep_17</th>\n",
       "      <th>PRECIPITATION_oct_17</th>\n",
       "      <th>PRECIPITATION_nov_17</th>\n",
       "      <th>PRECIPITATION_dec_17</th>\n",
       "      <th>PRECIPITATION_jan_18</th>\n",
       "      <th>PRECIPITATION_feb_18</th>\n",
       "      <th>PRECIPITATION_mar_18</th>\n",
       "      <th>PRECIPITATION_apr_18</th>\n",
       "      <th>PRECIPITATION_may_18</th>\n",
       "      <th>PRECIPITATION_jun_18</th>\n",
       "      <th>PRECIPITATION_jul_18</th>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66.638855</td>\n",
       "      <td>66.628891</td>\n",
       "      <td>199.928375</td>\n",
       "      <td>286.881470</td>\n",
       "      <td>345.874695</td>\n",
       "      <td>161.073792</td>\n",
       "      <td>339.436737</td>\n",
       "      <td>324.082489</td>\n",
       "      <td>243.856186</td>\n",
       "      <td>361.769562</td>\n",
       "      <td>285.962006</td>\n",
       "      <td>171.435532</td>\n",
       "      <td>91.634064</td>\n",
       "      <td>140.104874</td>\n",
       "      <td>141.978943</td>\n",
       "      <td>252.502441</td>\n",
       "      <td>922.231750</td>\n",
       "      <td>236.358551</td>\n",
       "      <td>399.940521</td>\n",
       "      <td>425.720917</td>\n",
       "      <td>235.005325</td>\n",
       "      <td>367.640900</td>\n",
       "      <td>320.049988</td>\n",
       "      <td>218.923187</td>\n",
       "      <td>147.733185</td>\n",
       "      <td>165.971741</td>\n",
       "      <td>250.059143</td>\n",
       "      <td>199.013580</td>\n",
       "      <td>310.712250</td>\n",
       "      <td>218.631714</td>\n",
       "      <td>169.890945</td>\n",
       "      <td>317.373627</td>\n",
       "      <td>223.847336</td>\n",
       "      <td>349.601868</td>\n",
       "      <td>208.563354</td>\n",
       "      <td>114.181557</td>\n",
       "      <td>23.847799</td>\n",
       "      <td>82.361053</td>\n",
       "      <td>140.205505</td>\n",
       "      <td>343.347748</td>\n",
       "      <td>...</td>\n",
       "      <td>181.159897</td>\n",
       "      <td>217.798950</td>\n",
       "      <td>199.683243</td>\n",
       "      <td>50.143784</td>\n",
       "      <td>74.901917</td>\n",
       "      <td>56.408119</td>\n",
       "      <td>123.612137</td>\n",
       "      <td>273.012634</td>\n",
       "      <td>231.279205</td>\n",
       "      <td>115.304100</td>\n",
       "      <td>193.332321</td>\n",
       "      <td>298.988617</td>\n",
       "      <td>267.838226</td>\n",
       "      <td>266.072052</td>\n",
       "      <td>145.723358</td>\n",
       "      <td>138.651657</td>\n",
       "      <td>107.291565</td>\n",
       "      <td>136.565781</td>\n",
       "      <td>105.653427</td>\n",
       "      <td>238.820831</td>\n",
       "      <td>199.921234</td>\n",
       "      <td>181.151321</td>\n",
       "      <td>115.703644</td>\n",
       "      <td>395.903473</td>\n",
       "      <td>244.643143</td>\n",
       "      <td>321.093597</td>\n",
       "      <td>252.829453</td>\n",
       "      <td>137.453506</td>\n",
       "      <td>140.216995</td>\n",
       "      <td>86.449814</td>\n",
       "      <td>147.506775</td>\n",
       "      <td>292.958588</td>\n",
       "      <td>436.878143</td>\n",
       "      <td>225.225449</td>\n",
       "      <td>195.619019</td>\n",
       "      <td>256.421906</td>\n",
       "      <td>302.413086</td>\n",
       "      <td>353.701385</td>\n",
       "      <td>277.470184</td>\n",
       "      <td>61.464046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62.527489</td>\n",
       "      <td>46.315376</td>\n",
       "      <td>166.522003</td>\n",
       "      <td>288.957397</td>\n",
       "      <td>350.666260</td>\n",
       "      <td>143.757507</td>\n",
       "      <td>350.168793</td>\n",
       "      <td>210.302658</td>\n",
       "      <td>214.617142</td>\n",
       "      <td>283.321747</td>\n",
       "      <td>279.687347</td>\n",
       "      <td>198.276825</td>\n",
       "      <td>82.823952</td>\n",
       "      <td>139.362946</td>\n",
       "      <td>122.149208</td>\n",
       "      <td>246.294891</td>\n",
       "      <td>1056.371826</td>\n",
       "      <td>233.371964</td>\n",
       "      <td>343.933350</td>\n",
       "      <td>306.676270</td>\n",
       "      <td>213.928513</td>\n",
       "      <td>248.733658</td>\n",
       "      <td>302.210754</td>\n",
       "      <td>232.676270</td>\n",
       "      <td>126.071480</td>\n",
       "      <td>128.074234</td>\n",
       "      <td>205.597870</td>\n",
       "      <td>165.801498</td>\n",
       "      <td>234.811264</td>\n",
       "      <td>206.055435</td>\n",
       "      <td>127.413780</td>\n",
       "      <td>236.348267</td>\n",
       "      <td>192.895981</td>\n",
       "      <td>290.498383</td>\n",
       "      <td>194.566284</td>\n",
       "      <td>140.502640</td>\n",
       "      <td>14.430646</td>\n",
       "      <td>75.684792</td>\n",
       "      <td>126.910789</td>\n",
       "      <td>332.311279</td>\n",
       "      <td>...</td>\n",
       "      <td>162.664734</td>\n",
       "      <td>184.293777</td>\n",
       "      <td>188.470245</td>\n",
       "      <td>52.162560</td>\n",
       "      <td>64.502235</td>\n",
       "      <td>46.120049</td>\n",
       "      <td>112.637077</td>\n",
       "      <td>241.846619</td>\n",
       "      <td>239.185791</td>\n",
       "      <td>107.121597</td>\n",
       "      <td>155.130295</td>\n",
       "      <td>213.129028</td>\n",
       "      <td>251.061081</td>\n",
       "      <td>219.268570</td>\n",
       "      <td>129.375320</td>\n",
       "      <td>158.177811</td>\n",
       "      <td>85.948349</td>\n",
       "      <td>112.548958</td>\n",
       "      <td>100.372108</td>\n",
       "      <td>211.559204</td>\n",
       "      <td>194.846909</td>\n",
       "      <td>174.252808</td>\n",
       "      <td>94.737900</td>\n",
       "      <td>293.564240</td>\n",
       "      <td>222.163391</td>\n",
       "      <td>281.282043</td>\n",
       "      <td>253.455612</td>\n",
       "      <td>165.416397</td>\n",
       "      <td>123.736755</td>\n",
       "      <td>91.315475</td>\n",
       "      <td>117.747261</td>\n",
       "      <td>256.695129</td>\n",
       "      <td>421.436859</td>\n",
       "      <td>195.391815</td>\n",
       "      <td>162.015167</td>\n",
       "      <td>182.192062</td>\n",
       "      <td>274.654327</td>\n",
       "      <td>259.078125</td>\n",
       "      <td>282.538574</td>\n",
       "      <td>68.923813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>76.737572</td>\n",
       "      <td>62.784908</td>\n",
       "      <td>198.478897</td>\n",
       "      <td>322.928314</td>\n",
       "      <td>381.405701</td>\n",
       "      <td>191.377609</td>\n",
       "      <td>387.475983</td>\n",
       "      <td>408.289520</td>\n",
       "      <td>306.408356</td>\n",
       "      <td>385.371216</td>\n",
       "      <td>336.976227</td>\n",
       "      <td>175.587463</td>\n",
       "      <td>98.434227</td>\n",
       "      <td>97.761215</td>\n",
       "      <td>183.319458</td>\n",
       "      <td>269.138306</td>\n",
       "      <td>851.275208</td>\n",
       "      <td>241.106689</td>\n",
       "      <td>458.701965</td>\n",
       "      <td>496.593597</td>\n",
       "      <td>265.008057</td>\n",
       "      <td>411.743866</td>\n",
       "      <td>385.076141</td>\n",
       "      <td>232.894592</td>\n",
       "      <td>154.217178</td>\n",
       "      <td>130.631485</td>\n",
       "      <td>256.707947</td>\n",
       "      <td>247.239822</td>\n",
       "      <td>362.843414</td>\n",
       "      <td>241.188202</td>\n",
       "      <td>230.991714</td>\n",
       "      <td>357.351288</td>\n",
       "      <td>269.511658</td>\n",
       "      <td>352.920532</td>\n",
       "      <td>263.026733</td>\n",
       "      <td>123.605858</td>\n",
       "      <td>47.158634</td>\n",
       "      <td>60.811092</td>\n",
       "      <td>141.417007</td>\n",
       "      <td>354.992554</td>\n",
       "      <td>...</td>\n",
       "      <td>203.632858</td>\n",
       "      <td>199.490601</td>\n",
       "      <td>226.388794</td>\n",
       "      <td>53.587704</td>\n",
       "      <td>66.496948</td>\n",
       "      <td>44.471161</td>\n",
       "      <td>116.046555</td>\n",
       "      <td>301.919067</td>\n",
       "      <td>234.341919</td>\n",
       "      <td>128.027725</td>\n",
       "      <td>252.298584</td>\n",
       "      <td>334.904449</td>\n",
       "      <td>274.561401</td>\n",
       "      <td>259.554382</td>\n",
       "      <td>205.305801</td>\n",
       "      <td>155.967529</td>\n",
       "      <td>126.792686</td>\n",
       "      <td>109.448967</td>\n",
       "      <td>109.425827</td>\n",
       "      <td>276.338501</td>\n",
       "      <td>217.335678</td>\n",
       "      <td>220.939377</td>\n",
       "      <td>163.253906</td>\n",
       "      <td>449.562988</td>\n",
       "      <td>309.590302</td>\n",
       "      <td>313.149048</td>\n",
       "      <td>292.131226</td>\n",
       "      <td>152.722412</td>\n",
       "      <td>150.041901</td>\n",
       "      <td>63.466251</td>\n",
       "      <td>164.738312</td>\n",
       "      <td>349.761841</td>\n",
       "      <td>433.156830</td>\n",
       "      <td>264.976685</td>\n",
       "      <td>236.360321</td>\n",
       "      <td>286.805847</td>\n",
       "      <td>330.032654</td>\n",
       "      <td>388.256378</td>\n",
       "      <td>315.904602</td>\n",
       "      <td>71.508133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>115.193504</td>\n",
       "      <td>61.572941</td>\n",
       "      <td>205.126846</td>\n",
       "      <td>308.268433</td>\n",
       "      <td>332.884583</td>\n",
       "      <td>116.664536</td>\n",
       "      <td>310.750671</td>\n",
       "      <td>189.581451</td>\n",
       "      <td>194.997574</td>\n",
       "      <td>333.450806</td>\n",
       "      <td>295.312958</td>\n",
       "      <td>256.798615</td>\n",
       "      <td>151.755310</td>\n",
       "      <td>209.275269</td>\n",
       "      <td>157.795776</td>\n",
       "      <td>267.709290</td>\n",
       "      <td>1036.946411</td>\n",
       "      <td>197.896515</td>\n",
       "      <td>295.078308</td>\n",
       "      <td>279.076172</td>\n",
       "      <td>196.854523</td>\n",
       "      <td>268.346375</td>\n",
       "      <td>321.546906</td>\n",
       "      <td>290.615112</td>\n",
       "      <td>221.921097</td>\n",
       "      <td>174.336456</td>\n",
       "      <td>255.471024</td>\n",
       "      <td>165.742081</td>\n",
       "      <td>205.896408</td>\n",
       "      <td>170.882233</td>\n",
       "      <td>101.799400</td>\n",
       "      <td>211.234085</td>\n",
       "      <td>172.778656</td>\n",
       "      <td>333.647278</td>\n",
       "      <td>199.595963</td>\n",
       "      <td>186.578568</td>\n",
       "      <td>19.903400</td>\n",
       "      <td>109.078812</td>\n",
       "      <td>158.369400</td>\n",
       "      <td>351.985199</td>\n",
       "      <td>...</td>\n",
       "      <td>142.607208</td>\n",
       "      <td>209.282303</td>\n",
       "      <td>197.310959</td>\n",
       "      <td>65.190559</td>\n",
       "      <td>112.807556</td>\n",
       "      <td>64.530846</td>\n",
       "      <td>146.569138</td>\n",
       "      <td>253.571014</td>\n",
       "      <td>234.301941</td>\n",
       "      <td>87.279274</td>\n",
       "      <td>131.327820</td>\n",
       "      <td>185.737961</td>\n",
       "      <td>230.088211</td>\n",
       "      <td>252.056702</td>\n",
       "      <td>132.401459</td>\n",
       "      <td>200.021576</td>\n",
       "      <td>146.587601</td>\n",
       "      <td>157.605408</td>\n",
       "      <td>124.956078</td>\n",
       "      <td>225.231049</td>\n",
       "      <td>182.764694</td>\n",
       "      <td>147.023468</td>\n",
       "      <td>80.475418</td>\n",
       "      <td>257.026276</td>\n",
       "      <td>188.383926</td>\n",
       "      <td>332.124237</td>\n",
       "      <td>272.192108</td>\n",
       "      <td>214.027145</td>\n",
       "      <td>212.678162</td>\n",
       "      <td>135.298386</td>\n",
       "      <td>144.462082</td>\n",
       "      <td>262.342285</td>\n",
       "      <td>394.904175</td>\n",
       "      <td>158.639008</td>\n",
       "      <td>142.633423</td>\n",
       "      <td>161.990524</td>\n",
       "      <td>246.869614</td>\n",
       "      <td>293.866364</td>\n",
       "      <td>298.362244</td>\n",
       "      <td>85.697762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.933151</td>\n",
       "      <td>43.129227</td>\n",
       "      <td>178.370377</td>\n",
       "      <td>278.665588</td>\n",
       "      <td>326.885010</td>\n",
       "      <td>144.349655</td>\n",
       "      <td>360.914429</td>\n",
       "      <td>234.040939</td>\n",
       "      <td>214.994553</td>\n",
       "      <td>291.776550</td>\n",
       "      <td>281.021088</td>\n",
       "      <td>189.771225</td>\n",
       "      <td>91.823616</td>\n",
       "      <td>128.047577</td>\n",
       "      <td>130.140381</td>\n",
       "      <td>235.732956</td>\n",
       "      <td>995.114075</td>\n",
       "      <td>233.658310</td>\n",
       "      <td>351.166199</td>\n",
       "      <td>344.687408</td>\n",
       "      <td>211.449844</td>\n",
       "      <td>260.028564</td>\n",
       "      <td>300.990601</td>\n",
       "      <td>224.315628</td>\n",
       "      <td>140.098541</td>\n",
       "      <td>117.915977</td>\n",
       "      <td>219.942856</td>\n",
       "      <td>160.306305</td>\n",
       "      <td>218.278122</td>\n",
       "      <td>207.616272</td>\n",
       "      <td>129.507202</td>\n",
       "      <td>265.811707</td>\n",
       "      <td>193.182587</td>\n",
       "      <td>302.892700</td>\n",
       "      <td>195.555344</td>\n",
       "      <td>134.629974</td>\n",
       "      <td>17.000061</td>\n",
       "      <td>69.415474</td>\n",
       "      <td>136.164429</td>\n",
       "      <td>319.636108</td>\n",
       "      <td>...</td>\n",
       "      <td>163.831360</td>\n",
       "      <td>193.256241</td>\n",
       "      <td>187.649033</td>\n",
       "      <td>49.812702</td>\n",
       "      <td>70.626762</td>\n",
       "      <td>42.494144</td>\n",
       "      <td>119.361565</td>\n",
       "      <td>231.938950</td>\n",
       "      <td>222.828598</td>\n",
       "      <td>108.463943</td>\n",
       "      <td>157.481506</td>\n",
       "      <td>240.612320</td>\n",
       "      <td>248.664597</td>\n",
       "      <td>228.070648</td>\n",
       "      <td>129.521362</td>\n",
       "      <td>152.530487</td>\n",
       "      <td>95.867424</td>\n",
       "      <td>103.086517</td>\n",
       "      <td>107.754158</td>\n",
       "      <td>203.016510</td>\n",
       "      <td>181.114044</td>\n",
       "      <td>175.366592</td>\n",
       "      <td>96.899628</td>\n",
       "      <td>333.343781</td>\n",
       "      <td>227.171509</td>\n",
       "      <td>293.806519</td>\n",
       "      <td>253.489655</td>\n",
       "      <td>158.847275</td>\n",
       "      <td>137.964737</td>\n",
       "      <td>84.714432</td>\n",
       "      <td>126.783783</td>\n",
       "      <td>247.130173</td>\n",
       "      <td>390.748199</td>\n",
       "      <td>197.610321</td>\n",
       "      <td>163.057632</td>\n",
       "      <td>205.655136</td>\n",
       "      <td>273.291779</td>\n",
       "      <td>268.090820</td>\n",
       "      <td>284.551331</td>\n",
       "      <td>67.718147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRECIPITATION_jan_07  ...  PRECIPITATION_dec_18\n",
       "0             66.638855  ...             61.464046\n",
       "1             62.527489  ...             68.923813\n",
       "2             76.737572  ...             71.508133\n",
       "3            115.193504  ...             85.697762\n",
       "4             69.933151  ...             67.718147\n",
       "\n",
       "[5 rows x 144 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take just precipitation data\n",
    "x_precipitation = data[data.columns[2:146]]\n",
    "x_precipitation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "e6780ce6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 253
    },
    "id": "e6780ce6",
    "outputId": "f772b679-5f48-4bc9-b2e7-84535915cb38"
   },
   "outputs": [
    {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 624 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   2007/w01  2007/w02  2007/w03  ...  2018/w50  2018/w51  2018/w52\n",
       "0         0         0         0  ...         0         0         0\n",
       "1         0         0         0  ...         0         0         0\n",
       "2         0         0         0  ...         0         0         0\n",
       "3         0         0         0  ...         0         0         0\n",
       "4         0         0         0  ...         0         0         0\n",
       "\n",
       "[5 rows x 624 columns]"
      ]
     },
     "execution_count": 239,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take Columns of weekly cases (week 1 2007 - week 52 2018)\n",
    "y_data = data[data.columns[343:-52]]\n",
    "y_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f9e3bf",
   "metadata": {
    "id": "51f9e3bf"
   },
   "source": [
    "# Data Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ba3f94b",
   "metadata": {
    "id": "4ba3f94b"
   },
   "source": [
    "# Temperature, Percipitation and Dengue Cases weekly 2007-2018 for Medellin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_lx9a0BFaoOW",
   "metadata": {
    "id": "_lx9a0BFaoOW"
   },
   "source": [
    "Temperature, Percipitation, Cases for Medellin from original data  (sivigila - cases) (google earth engine - precipitation and worldclim - temperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "246419ad",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 129
    },
    "id": "246419ad",
    "outputId": "c96a528d-f683-4cf4-9e11-be81eefa5778"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Municipality</th>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <th>PRECIPITATION_jun_07</th>\n",
       "      <th>PRECIPITATION_jul_07</th>\n",
       "      <th>PRECIPITATION_aug_07</th>\n",
       "      <th>PRECIPITATION_sep_07</th>\n",
       "      <th>PRECIPITATION_oct_07</th>\n",
       "      <th>PRECIPITATION_nov_07</th>\n",
       "      <th>PRECIPITATION_dec_07</th>\n",
       "      <th>PRECIPITATION_jan_08</th>\n",
       "      <th>PRECIPITATION_feb_08</th>\n",
       "      <th>PRECIPITATION_mar_08</th>\n",
       "      <th>PRECIPITATION_apr_08</th>\n",
       "      <th>PRECIPITATION_may_08</th>\n",
       "      <th>PRECIPITATION_jun_08</th>\n",
       "      <th>PRECIPITATION_jul_08</th>\n",
       "      <th>PRECIPITATION_aug_08</th>\n",
       "      <th>PRECIPITATION_sep_08</th>\n",
       "      <th>PRECIPITATION_oct_08</th>\n",
       "      <th>PRECIPITATION_nov_08</th>\n",
       "      <th>PRECIPITATION_dec_08</th>\n",
       "      <th>PRECIPITATION_jan_09</th>\n",
       "      <th>PRECIPITATION_feb_09</th>\n",
       "      <th>PRECIPITATION_mar_09</th>\n",
       "      <th>PRECIPITATION_apr_09</th>\n",
       "      <th>PRECIPITATION_may_09</th>\n",
       "      <th>PRECIPITATION_jun_09</th>\n",
       "      <th>PRECIPITATION_jul_09</th>\n",
       "      <th>PRECIPITATION_aug_09</th>\n",
       "      <th>PRECIPITATION_sep_09</th>\n",
       "      <th>PRECIPITATION_oct_09</th>\n",
       "      <th>PRECIPITATION_nov_09</th>\n",
       "      <th>PRECIPITATION_dec_09</th>\n",
       "      <th>PRECIPITATION_jan_10</th>\n",
       "      <th>PRECIPITATION_feb_10</th>\n",
       "      <th>...</th>\n",
       "      <th>2019/w13</th>\n",
       "      <th>2019/w14</th>\n",
       "      <th>2019/w15</th>\n",
       "      <th>2019/w16</th>\n",
       "      <th>2019/w17</th>\n",
       "      <th>2019/w18</th>\n",
       "      <th>2019/w19</th>\n",
       "      <th>2019/w20</th>\n",
       "      <th>2019/w21</th>\n",
       "      <th>2019/w22</th>\n",
       "      <th>2019/w23</th>\n",
       "      <th>2019/w24</th>\n",
       "      <th>2019/w25</th>\n",
       "      <th>2019/w26</th>\n",
       "      <th>2019/w27</th>\n",
       "      <th>2019/w28</th>\n",
       "      <th>2019/w29</th>\n",
       "      <th>2019/w30</th>\n",
       "      <th>2019/w31</th>\n",
       "      <th>2019/w32</th>\n",
       "      <th>2019/w33</th>\n",
       "      <th>2019/w34</th>\n",
       "      <th>2019/w35</th>\n",
       "      <th>2019/w36</th>\n",
       "      <th>2019/w37</th>\n",
       "      <th>2019/w38</th>\n",
       "      <th>2019/w39</th>\n",
       "      <th>2019/w40</th>\n",
       "      <th>2019/w41</th>\n",
       "      <th>2019/w42</th>\n",
       "      <th>2019/w43</th>\n",
       "      <th>2019/w44</th>\n",
       "      <th>2019/w45</th>\n",
       "      <th>2019/w46</th>\n",
       "      <th>2019/w47</th>\n",
       "      <th>2019/w48</th>\n",
       "      <th>2019/w49</th>\n",
       "      <th>2019/w50</th>\n",
       "      <th>2019/w51</th>\n",
       "      <th>2019/w52</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>antioquia</td>\n",
       "      <td>medellin</td>\n",
       "      <td>65.4114</td>\n",
       "      <td>49.307404</td>\n",
       "      <td>195.452469</td>\n",
       "      <td>291.927124</td>\n",
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       "      <td>140.433243</td>\n",
       "      <td>246.781662</td>\n",
       "      <td>1033.192017</td>\n",
       "      <td>248.036789</td>\n",
       "      <td>390.182495</td>\n",
       "      <td>332.141724</td>\n",
       "      <td>226.306137</td>\n",
       "      <td>302.420685</td>\n",
       "      <td>295.516663</td>\n",
       "      <td>223.920044</td>\n",
       "      <td>133.955582</td>\n",
       "      <td>130.186417</td>\n",
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       "      <td>250.477356</td>\n",
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       "      <td>255.542786</td>\n",
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       "      <td>331.874115</td>\n",
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       "      <td>128.476959</td>\n",
       "      <td>18.438709</td>\n",
       "      <td>72.643066</td>\n",
       "      <td>...</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
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       "      <td>28</td>\n",
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       "      <td>28</td>\n",
       "      <td>30</td>\n",
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       "      <td>45</td>\n",
       "      <td>35</td>\n",
       "      <td>29</td>\n",
       "      <td>26</td>\n",
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       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 1019 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        State Municipality  PRECIPITATION_jan_07  ...  2019/w50  2019/w51  2019/w52\n",
       "62  antioquia     medellin               65.4114  ...        30        14         5\n",
       "\n",
       "[1 rows x 1019 columns]"
      ]
     },
     "execution_count": 240,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take just the row of Medellin from original dataframe\n",
    "data_medellin = data[data.Municipality=='medellin']\n",
    "data_medellin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83882d60",
   "metadata": {
    "id": "83882d60"
   },
   "source": [
    "### PRECIPITATION DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "5f68a43c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 129
    },
    "id": "5f68a43c",
    "outputId": "4cbe6dfc-0b5b-462d-d97d-a0182cdac197"
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <th>PRECIPITATION_jun_07</th>\n",
       "      <th>PRECIPITATION_jul_07</th>\n",
       "      <th>PRECIPITATION_aug_07</th>\n",
       "      <th>PRECIPITATION_sep_07</th>\n",
       "      <th>PRECIPITATION_oct_07</th>\n",
       "      <th>PRECIPITATION_nov_07</th>\n",
       "      <th>PRECIPITATION_dec_07</th>\n",
       "      <th>PRECIPITATION_jan_08</th>\n",
       "      <th>PRECIPITATION_feb_08</th>\n",
       "      <th>PRECIPITATION_mar_08</th>\n",
       "      <th>PRECIPITATION_apr_08</th>\n",
       "      <th>PRECIPITATION_may_08</th>\n",
       "      <th>PRECIPITATION_jun_08</th>\n",
       "      <th>PRECIPITATION_jul_08</th>\n",
       "      <th>PRECIPITATION_aug_08</th>\n",
       "      <th>PRECIPITATION_sep_08</th>\n",
       "      <th>PRECIPITATION_oct_08</th>\n",
       "      <th>PRECIPITATION_nov_08</th>\n",
       "      <th>PRECIPITATION_dec_08</th>\n",
       "      <th>PRECIPITATION_jan_09</th>\n",
       "      <th>PRECIPITATION_feb_09</th>\n",
       "      <th>PRECIPITATION_mar_09</th>\n",
       "      <th>PRECIPITATION_apr_09</th>\n",
       "      <th>PRECIPITATION_may_09</th>\n",
       "      <th>PRECIPITATION_jun_09</th>\n",
       "      <th>PRECIPITATION_jul_09</th>\n",
       "      <th>PRECIPITATION_aug_09</th>\n",
       "      <th>PRECIPITATION_sep_09</th>\n",
       "      <th>PRECIPITATION_oct_09</th>\n",
       "      <th>PRECIPITATION_nov_09</th>\n",
       "      <th>PRECIPITATION_dec_09</th>\n",
       "      <th>PRECIPITATION_jan_10</th>\n",
       "      <th>PRECIPITATION_feb_10</th>\n",
       "      <th>PRECIPITATION_mar_10</th>\n",
       "      <th>PRECIPITATION_apr_10</th>\n",
       "      <th>...</th>\n",
       "      <th>PRECIPITATION_sep_15</th>\n",
       "      <th>PRECIPITATION_oct_15</th>\n",
       "      <th>PRECIPITATION_nov_15</th>\n",
       "      <th>PRECIPITATION_dec_15</th>\n",
       "      <th>PRECIPITATION_jan_16</th>\n",
       "      <th>PRECIPITATION_feb_16</th>\n",
       "      <th>PRECIPITATION_mar_16</th>\n",
       "      <th>PRECIPITATION_apr_16</th>\n",
       "      <th>PRECIPITATION_may_16</th>\n",
       "      <th>PRECIPITATION_jun_16</th>\n",
       "      <th>PRECIPITATION_jul_16</th>\n",
       "      <th>PRECIPITATION_aug_16</th>\n",
       "      <th>PRECIPITATION_sep_16</th>\n",
       "      <th>PRECIPITATION_oct_16</th>\n",
       "      <th>PRECIPITATION_nov_16</th>\n",
       "      <th>PRECIPITATION_dec_16</th>\n",
       "      <th>PRECIPITATION_jan_17</th>\n",
       "      <th>PRECIPITATION_feb_17</th>\n",
       "      <th>PRECIPITATION_mar_17</th>\n",
       "      <th>PRECIPITATION_apr_17</th>\n",
       "      <th>PRECIPITATION_may_17</th>\n",
       "      <th>PRECIPITATION_jun_17</th>\n",
       "      <th>PRECIPITATION_jul_17</th>\n",
       "      <th>PRECIPITATION_aug_17</th>\n",
       "      <th>PRECIPITATION_sep_17</th>\n",
       "      <th>PRECIPITATION_oct_17</th>\n",
       "      <th>PRECIPITATION_nov_17</th>\n",
       "      <th>PRECIPITATION_dec_17</th>\n",
       "      <th>PRECIPITATION_jan_18</th>\n",
       "      <th>PRECIPITATION_feb_18</th>\n",
       "      <th>PRECIPITATION_mar_18</th>\n",
       "      <th>PRECIPITATION_apr_18</th>\n",
       "      <th>PRECIPITATION_may_18</th>\n",
       "      <th>PRECIPITATION_jun_18</th>\n",
       "      <th>PRECIPITATION_jul_18</th>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>65.4114</td>\n",
       "      <td>49.307404</td>\n",
       "      <td>195.452469</td>\n",
       "      <td>291.927124</td>\n",
       "      <td>345.532501</td>\n",
       "      <td>157.963028</td>\n",
       "      <td>386.515533</td>\n",
       "      <td>230.007416</td>\n",
       "      <td>234.679382</td>\n",
       "      <td>320.92984</td>\n",
       "      <td>275.321289</td>\n",
       "      <td>184.041061</td>\n",
       "      <td>86.091049</td>\n",
       "      <td>131.370422</td>\n",
       "      <td>140.433243</td>\n",
       "      <td>246.781662</td>\n",
       "      <td>1033.192017</td>\n",
       "      <td>248.036789</td>\n",
       "      <td>390.182495</td>\n",
       "      <td>332.141724</td>\n",
       "      <td>226.306137</td>\n",
       "      <td>302.420685</td>\n",
       "      <td>295.516663</td>\n",
       "      <td>223.920044</td>\n",
       "      <td>133.955582</td>\n",
       "      <td>130.186417</td>\n",
       "      <td>241.387421</td>\n",
       "      <td>177.008194</td>\n",
       "      <td>250.477356</td>\n",
       "      <td>224.566727</td>\n",
       "      <td>148.674896</td>\n",
       "      <td>255.542786</td>\n",
       "      <td>211.922562</td>\n",
       "      <td>331.874115</td>\n",
       "      <td>194.040817</td>\n",
       "      <td>128.476959</td>\n",
       "      <td>18.438709</td>\n",
       "      <td>72.643066</td>\n",
       "      <td>146.273071</td>\n",
       "      <td>337.406647</td>\n",
       "      <td>...</td>\n",
       "      <td>179.241959</td>\n",
       "      <td>213.469482</td>\n",
       "      <td>184.867233</td>\n",
       "      <td>49.636089</td>\n",
       "      <td>67.450592</td>\n",
       "      <td>46.293457</td>\n",
       "      <td>126.497833</td>\n",
       "      <td>249.590607</td>\n",
       "      <td>233.860794</td>\n",
       "      <td>118.415253</td>\n",
       "      <td>177.197144</td>\n",
       "      <td>235.356659</td>\n",
       "      <td>266.614624</td>\n",
       "      <td>252.005188</td>\n",
       "      <td>127.503548</td>\n",
       "      <td>149.539246</td>\n",
       "      <td>93.142609</td>\n",
       "      <td>111.025299</td>\n",
       "      <td>113.966263</td>\n",
       "      <td>218.512985</td>\n",
       "      <td>192.741974</td>\n",
       "      <td>185.905487</td>\n",
       "      <td>109.116791</td>\n",
       "      <td>323.284515</td>\n",
       "      <td>248.888855</td>\n",
       "      <td>318.701874</td>\n",
       "      <td>245.842239</td>\n",
       "      <td>152.662018</td>\n",
       "      <td>131.176727</td>\n",
       "      <td>86.131554</td>\n",
       "      <td>140.323318</td>\n",
       "      <td>267.799438</td>\n",
       "      <td>418.379303</td>\n",
       "      <td>219.436752</td>\n",
       "      <td>180.841248</td>\n",
       "      <td>200.803131</td>\n",
       "      <td>294.888611</td>\n",
       "      <td>302.151306</td>\n",
       "      <td>276.914551</td>\n",
       "      <td>67.528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PRECIPITATION_jan_07  ...  PRECIPITATION_dec_18\n",
       "62               65.4114  ...                67.528\n",
       "\n",
       "[1 rows x 144 columns]"
      ]
     },
     "execution_count": 241,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the columns of precipitation for Medellin\n",
    "x_precipitation_medellin = data_medellin[data_medellin.columns[2:146]]\n",
    "x_precipitation_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "84641f80",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "84641f80",
    "outputId": "29ac5b33-3a86-4951-93d9-9a9bcd4fac75"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
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       "  <tbody>\n",
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       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
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       "      <th>PRECIPITATION_mar_07</th>\n",
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       "      <th>PRECIPITATION_may_07</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <td>200.803131</td>\n",
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       "    <tr>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <td>294.888611</td>\n",
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       "    <tr>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <td>302.151306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <td>276.914551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "      <td>67.528000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin\n",
       "PRECIPITATION_jan_07               65.411400\n",
       "PRECIPITATION_feb_07               49.307404\n",
       "PRECIPITATION_mar_07              195.452469\n",
       "PRECIPITATION_apr_07              291.927124\n",
       "PRECIPITATION_may_07              345.532501\n",
       "...                                      ...\n",
       "PRECIPITATION_aug_18              200.803131\n",
       "PRECIPITATION_sep_18              294.888611\n",
       "PRECIPITATION_oct_18              302.151306\n",
       "PRECIPITATION_nov_18              276.914551\n",
       "PRECIPITATION_dec_18               67.528000\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 242,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the row to a Column of precipitation for Medellin\n",
    "x_precipitation_medellin_transposed = x_precipitation_medellin.T\n",
    "x_precipitation_medellin_transposed = x_precipitation_medellin_transposed.rename(columns= {62:'percipitation_medellin'})  ##integer\n",
    "x_precipitation_medellin_transposed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb2d34a",
   "metadata": {
    "id": "2bb2d34a"
   },
   "source": [
    "## Get date of precipitation from index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "71deca79",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "71deca79",
    "outputId": "74d803e5-359c-4dbd-d889-a38f16de33e7"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_aug_18</th>\n",
       "      <td>200.803131</td>\n",
       "      <td>[PRECIPITATION, aug, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <td>294.888611</td>\n",
       "      <td>[PRECIPITATION, sep, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <td>302.151306</td>\n",
       "      <td>[PRECIPITATION, oct, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <td>276.914551</td>\n",
       "      <td>[PRECIPITATION, nov, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "      <td>67.528000</td>\n",
       "      <td>[PRECIPITATION, dec, 18]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin                      DATE\n",
       "PRECIPITATION_jan_07               65.411400  [PRECIPITATION, jan, 07]\n",
       "PRECIPITATION_feb_07               49.307404  [PRECIPITATION, feb, 07]\n",
       "PRECIPITATION_mar_07              195.452469  [PRECIPITATION, mar, 07]\n",
       "PRECIPITATION_apr_07              291.927124  [PRECIPITATION, apr, 07]\n",
       "PRECIPITATION_may_07              345.532501  [PRECIPITATION, may, 07]\n",
       "...                                      ...                       ...\n",
       "PRECIPITATION_aug_18              200.803131  [PRECIPITATION, aug, 18]\n",
       "PRECIPITATION_sep_18              294.888611  [PRECIPITATION, sep, 18]\n",
       "PRECIPITATION_oct_18              302.151306  [PRECIPITATION, oct, 18]\n",
       "PRECIPITATION_nov_18              276.914551  [PRECIPITATION, nov, 18]\n",
       "PRECIPITATION_dec_18               67.528000  [PRECIPITATION, dec, 18]\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 243,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a new column of date\n",
    "x_precipitation_medellin_transposed['DATE']=x_precipitation_medellin_transposed.index.str.split(\"_\")\n",
    "x_precipitation_medellin_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "b013aad8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "b013aad8",
    "outputId": "2723bcc0-aa2c-40fc-e5b2-fa204112156d"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin                      DATE  YEAR\n",
       "PRECIPITATION_jan_07               65.411400  [PRECIPITATION, jan, 07]  2007\n",
       "PRECIPITATION_feb_07               49.307404  [PRECIPITATION, feb, 07]  2007\n",
       "PRECIPITATION_mar_07              195.452469  [PRECIPITATION, mar, 07]  2007\n",
       "PRECIPITATION_apr_07              291.927124  [PRECIPITATION, apr, 07]  2007\n",
       "PRECIPITATION_may_07              345.532501  [PRECIPITATION, may, 07]  2007"
      ]
     },
     "execution_count": 244,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get year from date column\n",
    "x_precipitation_medellin_transposed['YEAR'] = x_precipitation_medellin_transposed['DATE'].apply(lambda x: \"20\" + x[2])  \n",
    "x_precipitation_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "3061f76c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "3061f76c",
    "outputId": "91762edb-0620-4f65-e767-34094bd67878"
   },
   "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>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin  ... MONTH\n",
       "PRECIPITATION_jan_07               65.411400  ...   jan\n",
       "PRECIPITATION_feb_07               49.307404  ...   feb\n",
       "PRECIPITATION_mar_07              195.452469  ...   mar\n",
       "PRECIPITATION_apr_07              291.927124  ...   apr\n",
       "PRECIPITATION_may_07              345.532501  ...   may\n",
       "\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 245,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get year from date column\n",
    "x_precipitation_medellin_transposed['MONTH'] = x_precipitation_medellin_transposed['DATE'].apply(lambda x: x[1])  \n",
    "x_precipitation_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "7856c75e",
   "metadata": {
    "id": "7856c75e"
   },
   "outputs": [],
   "source": [
    "## function to change month as int to numeric\n",
    "def month_as_int(month):\n",
    "  if month == 'jan':\n",
    "    return '01'\n",
    "  if month == 'feb':\n",
    "    return '02' \n",
    "  if month == 'mar':\n",
    "    return '03'\n",
    "  if month == 'apr':\n",
    "    return '04' \n",
    "  if month == 'may':\n",
    "    return '05'\n",
    "  if month == 'jun':\n",
    "    return '06' \n",
    "  if month == 'jul':\n",
    "    return '07'\n",
    "  if month == 'aug':\n",
    "    return '08' \n",
    "  if month == 'sep':\n",
    "    return '09'\n",
    "  if month == 'oct':\n",
    "    return '10' \n",
    "  if month == 'nov':\n",
    "    return '11'   \n",
    "  if month == 'dec':\n",
    "    return '12'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "137703de",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "137703de",
    "outputId": "fbef418d-f72c-48a5-958b-0a6ebcb147c9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>may</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>PRECIPITATION_aug_18</th>\n",
       "      <td>200.803131</td>\n",
       "      <td>[PRECIPITATION, aug, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_sep_18</th>\n",
       "      <td>294.888611</td>\n",
       "      <td>[PRECIPITATION, sep, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_oct_18</th>\n",
       "      <td>302.151306</td>\n",
       "      <td>[PRECIPITATION, oct, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>oct</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_nov_18</th>\n",
       "      <td>276.914551</td>\n",
       "      <td>[PRECIPITATION, nov, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>nov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_dec_18</th>\n",
       "      <td>67.528000</td>\n",
       "      <td>[PRECIPITATION, dec, 18]</td>\n",
       "      <td>2018</td>\n",
       "      <td>dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin  ... MONTH\n",
       "PRECIPITATION_jan_07               65.411400  ...   jan\n",
       "PRECIPITATION_feb_07               49.307404  ...   feb\n",
       "PRECIPITATION_mar_07              195.452469  ...   mar\n",
       "PRECIPITATION_apr_07              291.927124  ...   apr\n",
       "PRECIPITATION_may_07              345.532501  ...   may\n",
       "...                                      ...  ...   ...\n",
       "PRECIPITATION_aug_18              200.803131  ...   aug\n",
       "PRECIPITATION_sep_18              294.888611  ...   sep\n",
       "PRECIPITATION_oct_18              302.151306  ...   oct\n",
       "PRECIPITATION_nov_18              276.914551  ...   nov\n",
       "PRECIPITATION_dec_18               67.528000  ...   dec\n",
       "\n",
       "[144 rows x 4 columns]"
      ]
     },
     "execution_count": 247,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_precipitation_medellin_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "fbffca04",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "fbffca04",
    "outputId": "3cd9ac1c-b716-4689-e594-6319ac835a8d"
   },
   "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>percipitation_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_jan_07</th>\n",
       "      <td>65.411400</td>\n",
       "      <td>[PRECIPITATION, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_feb_07</th>\n",
       "      <td>49.307404</td>\n",
       "      <td>[PRECIPITATION, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_mar_07</th>\n",
       "      <td>195.452469</td>\n",
       "      <td>[PRECIPITATION, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_apr_07</th>\n",
       "      <td>291.927124</td>\n",
       "      <td>[PRECIPITATION, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRECIPITATION_may_07</th>\n",
       "      <td>345.532501</td>\n",
       "      <td>[PRECIPITATION, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      percipitation_medellin  ... MONTH\n",
       "PRECIPITATION_jan_07               65.411400  ...    01\n",
       "PRECIPITATION_feb_07               49.307404  ...    02\n",
       "PRECIPITATION_mar_07              195.452469  ...    03\n",
       "PRECIPITATION_apr_07              291.927124  ...    04\n",
       "PRECIPITATION_may_07              345.532501  ...    05\n",
       "\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change the month from jan to 01 and apply to every row in the df\n",
    "x_precipitation_medellin_transposed['MONTH'] = x_precipitation_medellin_transposed['MONTH'].apply(lambda x: month_as_int(month=x.strip()))\n",
    "x_precipitation_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "562ce574",
   "metadata": {
    "id": "562ce574"
   },
   "outputs": [],
   "source": [
    "x_precipitation_medellin_transposed['MONTH']=x_precipitation_medellin_transposed['MONTH'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90626348",
   "metadata": {
    "id": "90626348"
   },
   "source": [
    "### TEMPERATURE DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "fa53cc76",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 129
    },
    "id": "fa53cc76",
    "outputId": "b203b122-3e4e-4297-be69-afac3b289ef2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>TEMPERATURE_jan_07</th>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <th>TEMPERATURE_jun_07</th>\n",
       "      <th>TEMPERATURE_jul_07</th>\n",
       "      <th>TEMPERATURE_aug_07</th>\n",
       "      <th>TEMPERATURE_sep_07</th>\n",
       "      <th>TEMPERATURE_oct_07</th>\n",
       "      <th>TEMPERATURE_nov_07</th>\n",
       "      <th>TEMPERATURE_dec_07</th>\n",
       "      <th>TEMPERATURE_jan_08</th>\n",
       "      <th>TEMPERATURE_feb_08</th>\n",
       "      <th>TEMPERATURE_mar_08</th>\n",
       "      <th>TEMPERATURE_apr_08</th>\n",
       "      <th>TEMPERATURE_may_08</th>\n",
       "      <th>TEMPERATURE_jun_08</th>\n",
       "      <th>TEMPERATURE_jul_08</th>\n",
       "      <th>TEMPERATURE_aug_08</th>\n",
       "      <th>TEMPERATURE_sep_08</th>\n",
       "      <th>TEMPERATURE_oct_08</th>\n",
       "      <th>TEMPERATURE_nov_08</th>\n",
       "      <th>TEMPERATURE_dec_08</th>\n",
       "      <th>TEMPERATURE_jan_09</th>\n",
       "      <th>TEMPERATURE_feb_09</th>\n",
       "      <th>TEMPERATURE_mar_09</th>\n",
       "      <th>TEMPERATURE_apr_09</th>\n",
       "      <th>TEMPERATURE_may_09</th>\n",
       "      <th>TEMPERATURE_jun_09</th>\n",
       "      <th>TEMPERATURE_jul_09</th>\n",
       "      <th>TEMPERATURE_aug_09</th>\n",
       "      <th>TEMPERATURE_sep_09</th>\n",
       "      <th>TEMPERATURE_oct_09</th>\n",
       "      <th>TEMPERATURE_nov_09</th>\n",
       "      <th>TEMPERATURE_dec_09</th>\n",
       "      <th>TEMPERATURE_jan_10</th>\n",
       "      <th>TEMPERATURE_feb_10</th>\n",
       "      <th>TEMPERATURE_mar_10</th>\n",
       "      <th>TEMPERATURE_apr_10</th>\n",
       "      <th>...</th>\n",
       "      <th>TEMPERATURE_sep_15</th>\n",
       "      <th>TEMPERATURE_oct_15</th>\n",
       "      <th>TEMPERATURE_nov_15</th>\n",
       "      <th>TEMPERATURE_dec_15</th>\n",
       "      <th>TEMPERATURE_jan_16</th>\n",
       "      <th>TEMPERATURE_feb_16</th>\n",
       "      <th>TEMPERATURE_mar_16</th>\n",
       "      <th>TEMPERATURE_apr_16</th>\n",
       "      <th>TEMPERATURE_may_16</th>\n",
       "      <th>TEMPERATURE_jun_16</th>\n",
       "      <th>TEMPERATURE_jul_16</th>\n",
       "      <th>TEMPERATURE_aug_16</th>\n",
       "      <th>TEMPERATURE_sep_16</th>\n",
       "      <th>TEMPERATURE_oct_16</th>\n",
       "      <th>TEMPERATURE_nov_16</th>\n",
       "      <th>TEMPERATURE_dec_16</th>\n",
       "      <th>TEMPERATURE_jan_17</th>\n",
       "      <th>TEMPERATURE_feb_17</th>\n",
       "      <th>TEMPERATURE_mar_17</th>\n",
       "      <th>TEMPERATURE_apr_17</th>\n",
       "      <th>TEMPERATURE_may_17</th>\n",
       "      <th>TEMPERATURE_jun_17</th>\n",
       "      <th>TEMPERATURE_jul_17</th>\n",
       "      <th>TEMPERATURE_aug_17</th>\n",
       "      <th>TEMPERATURE_sep_17</th>\n",
       "      <th>TEMPERATURE_oct_17</th>\n",
       "      <th>TEMPERATURE_nov_17</th>\n",
       "      <th>TEMPERATURE_dec_17</th>\n",
       "      <th>TEMPERATURE_jan_18</th>\n",
       "      <th>TEMPERATURE_feb_18</th>\n",
       "      <th>TEMPERATURE_mar_18</th>\n",
       "      <th>TEMPERATURE_apr_18</th>\n",
       "      <th>TEMPERATURE_may_18</th>\n",
       "      <th>TEMPERATURE_jun_18</th>\n",
       "      <th>TEMPERATURE_jul_18</th>\n",
       "      <th>TEMPERATURE_aug_18</th>\n",
       "      <th>TEMPERATURE_sep_18</th>\n",
       "      <th>TEMPERATURE_oct_18</th>\n",
       "      <th>TEMPERATURE_nov_18</th>\n",
       "      <th>TEMPERATURE_dec_18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>21.425911</td>\n",
       "      <td>21.237032</td>\n",
       "      <td>21.322522</td>\n",
       "      <td>21.243176</td>\n",
       "      <td>21.686978</td>\n",
       "      <td>20.866394</td>\n",
       "      <td>20.705536</td>\n",
       "      <td>19.954406</td>\n",
       "      <td>20.165262</td>\n",
       "      <td>20.200329</td>\n",
       "      <td>20.545569</td>\n",
       "      <td>20.715391</td>\n",
       "      <td>20.868446</td>\n",
       "      <td>21.03434</td>\n",
       "      <td>20.722521</td>\n",
       "      <td>20.963748</td>\n",
       "      <td>21.05486</td>\n",
       "      <td>20.869431</td>\n",
       "      <td>20.800153</td>\n",
       "      <td>20.054407</td>\n",
       "      <td>20.020644</td>\n",
       "      <td>19.925068</td>\n",
       "      <td>20.516054</td>\n",
       "      <td>20.529974</td>\n",
       "      <td>20.278254</td>\n",
       "      <td>21.067674</td>\n",
       "      <td>21.157071</td>\n",
       "      <td>21.179807</td>\n",
       "      <td>21.67361</td>\n",
       "      <td>21.932799</td>\n",
       "      <td>21.84807</td>\n",
       "      <td>21.098417</td>\n",
       "      <td>21.300679</td>\n",
       "      <td>21.629496</td>\n",
       "      <td>21.9572</td>\n",
       "      <td>22.008793</td>\n",
       "      <td>23.055945</td>\n",
       "      <td>22.064983</td>\n",
       "      <td>...</td>\n",
       "      <td>22.125761</td>\n",
       "      <td>21.55024</td>\n",
       "      <td>21.200679</td>\n",
       "      <td>22.083401</td>\n",
       "      <td>22.35477</td>\n",
       "      <td>22.693342</td>\n",
       "      <td>22.010981</td>\n",
       "      <td>22.454306</td>\n",
       "      <td>22.202557</td>\n",
       "      <td>22.16045</td>\n",
       "      <td>22.171787</td>\n",
       "      <td>22.600335</td>\n",
       "      <td>21.645379</td>\n",
       "      <td>21.301369</td>\n",
       "      <td>20.765263</td>\n",
       "      <td>20.966389</td>\n",
       "      <td>20.775342</td>\n",
       "      <td>21.256711</td>\n",
       "      <td>21.156814</td>\n",
       "      <td>21.926354</td>\n",
       "      <td>21.574605</td>\n",
       "      <td>21.464356</td>\n",
       "      <td>22.151562</td>\n",
       "      <td>21.220647</td>\n",
       "      <td>21.84807</td>\n",
       "      <td>21.055275</td>\n",
       "      <td>20.936096</td>\n",
       "      <td>20.836353</td>\n",
       "      <td>20.979769</td>\n",
       "      <td>21.180842</td>\n",
       "      <td>21.726171</td>\n",
       "      <td>21.124271</td>\n",
       "      <td>21.037365</td>\n",
       "      <td>21.457151</td>\n",
       "      <td>21.952168</td>\n",
       "      <td>21.719432</td>\n",
       "      <td>21.347202</td>\n",
       "      <td>20.748416</td>\n",
       "      <td>20.886703</td>\n",
       "      <td>20.936353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    TEMPERATURE_jan_07  ...  TEMPERATURE_dec_18\n",
       "62           21.009283  ...           20.936353\n",
       "\n",
       "[1 rows x 144 columns]"
      ]
     },
     "execution_count": 250,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the columns of temperature for Medellin\n",
    "x_temperature_medellin = data_medellin[data_medellin.columns[147:291]]\n",
    "x_temperature_medellin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "c45e3b87",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "c45e3b87",
    "outputId": "06cb3085-eefe-4f5f-b3e7-fe2340d878a2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>temperature_medellin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_aug_18</th>\n",
       "      <td>21.719432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_sep_18</th>\n",
       "      <td>21.347202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_oct_18</th>\n",
       "      <td>20.748416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_nov_18</th>\n",
       "      <td>20.886703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_dec_18</th>\n",
       "      <td>20.936353</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin\n",
       "TEMPERATURE_jan_07             21.009283\n",
       "TEMPERATURE_feb_07             20.982925\n",
       "TEMPERATURE_mar_07             21.425911\n",
       "TEMPERATURE_apr_07             21.237032\n",
       "TEMPERATURE_may_07             21.322522\n",
       "...                                  ...\n",
       "TEMPERATURE_aug_18             21.719432\n",
       "TEMPERATURE_sep_18             21.347202\n",
       "TEMPERATURE_oct_18             20.748416\n",
       "TEMPERATURE_nov_18             20.886703\n",
       "TEMPERATURE_dec_18             20.936353\n",
       "\n",
       "[144 rows x 1 columns]"
      ]
     },
     "execution_count": 251,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose the row to a Column of temperaure for Medellin\n",
    "x_temperature_medellin_transposed = x_temperature_medellin.T\n",
    "x_temperature_medellin_transposed = x_temperature_medellin_transposed.rename(columns= {62:'temperature_medellin'})  ##integer\n",
    "x_temperature_medellin_transposed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "izqABr2bdUgB",
   "metadata": {
    "id": "izqABr2bdUgB"
   },
   "source": [
    "## Get date of temperature from index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "7ca81f40",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "7ca81f40",
    "outputId": "88e92187-33b0-4496-92ac-a820ac5be910"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "      <td>[TEMPERATURE, mar, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "      <td>[TEMPERATURE, apr, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "      <td>[TEMPERATURE, may, 07]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_aug_18</th>\n",
       "      <td>21.719432</td>\n",
       "      <td>[TEMPERATURE, aug, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_sep_18</th>\n",
       "      <td>21.347202</td>\n",
       "      <td>[TEMPERATURE, sep, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_oct_18</th>\n",
       "      <td>20.748416</td>\n",
       "      <td>[TEMPERATURE, oct, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_nov_18</th>\n",
       "      <td>20.886703</td>\n",
       "      <td>[TEMPERATURE, nov, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_dec_18</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin                    DATE\n",
       "TEMPERATURE_jan_07             21.009283  [TEMPERATURE, jan, 07]\n",
       "TEMPERATURE_feb_07             20.982925  [TEMPERATURE, feb, 07]\n",
       "TEMPERATURE_mar_07             21.425911  [TEMPERATURE, mar, 07]\n",
       "TEMPERATURE_apr_07             21.237032  [TEMPERATURE, apr, 07]\n",
       "TEMPERATURE_may_07             21.322522  [TEMPERATURE, may, 07]\n",
       "...                                  ...                     ...\n",
       "TEMPERATURE_aug_18             21.719432  [TEMPERATURE, aug, 18]\n",
       "TEMPERATURE_sep_18             21.347202  [TEMPERATURE, sep, 18]\n",
       "TEMPERATURE_oct_18             20.748416  [TEMPERATURE, oct, 18]\n",
       "TEMPERATURE_nov_18             20.886703  [TEMPERATURE, nov, 18]\n",
       "TEMPERATURE_dec_18             20.936353  [TEMPERATURE, dec, 18]\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 252,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new column of date\n",
    "x_temperature_medellin_transposed['DATE']=x_temperature_medellin_transposed.index.str.split(\"_\")\n",
    "x_temperature_medellin_transposed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "4f15a154",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "4f15a154",
    "outputId": "788857bf-20f7-48c5-c6dc-037d40834c99"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "      <td>[TEMPERATURE, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "      <td>[TEMPERATURE, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "      <td>[TEMPERATURE, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin                    DATE  YEAR\n",
       "TEMPERATURE_jan_07             21.009283  [TEMPERATURE, jan, 07]  2007\n",
       "TEMPERATURE_feb_07             20.982925  [TEMPERATURE, feb, 07]  2007\n",
       "TEMPERATURE_mar_07             21.425911  [TEMPERATURE, mar, 07]  2007\n",
       "TEMPERATURE_apr_07             21.237032  [TEMPERATURE, apr, 07]  2007\n",
       "TEMPERATURE_may_07             21.322522  [TEMPERATURE, may, 07]  2007"
      ]
     },
     "execution_count": 253,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get year from date column\n",
    "x_temperature_medellin_transposed['YEAR'] = x_temperature_medellin_transposed['DATE'].apply(lambda x: \"20\" + x[2])  \n",
    "x_temperature_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "93a97f67",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "93a97f67",
    "outputId": "caab4303-b1ad-4756-e3bd-8dca47708236"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "      <td>[TEMPERATURE, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>mar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "      <td>[TEMPERATURE, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>apr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "      <td>[TEMPERATURE, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>may</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin                    DATE  YEAR MONTH\n",
       "TEMPERATURE_jan_07             21.009283  [TEMPERATURE, jan, 07]  2007   jan\n",
       "TEMPERATURE_feb_07             20.982925  [TEMPERATURE, feb, 07]  2007   feb\n",
       "TEMPERATURE_mar_07             21.425911  [TEMPERATURE, mar, 07]  2007   mar\n",
       "TEMPERATURE_apr_07             21.237032  [TEMPERATURE, apr, 07]  2007   apr\n",
       "TEMPERATURE_may_07             21.322522  [TEMPERATURE, may, 07]  2007   may"
      ]
     },
     "execution_count": 254,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get month from date column\n",
    "x_temperature_medellin_transposed['MONTH'] = x_temperature_medellin_transposed['DATE'].apply(lambda x: x[1])  \n",
    "x_temperature_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "5a057643",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "5a057643",
    "outputId": "0e654d3a-05c2-4299-843e-1543c1cda478"
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>TEMPERATURE_jan_07</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_feb_07</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_mar_07</th>\n",
       "      <td>21.425911</td>\n",
       "      <td>[TEMPERATURE, mar, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_apr_07</th>\n",
       "      <td>21.237032</td>\n",
       "      <td>[TEMPERATURE, apr, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEMPERATURE_may_07</th>\n",
       "      <td>21.322522</td>\n",
       "      <td>[TEMPERATURE, may, 07]</td>\n",
       "      <td>2007</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    temperature_medellin                    DATE  YEAR MONTH\n",
       "TEMPERATURE_jan_07             21.009283  [TEMPERATURE, jan, 07]  2007    01\n",
       "TEMPERATURE_feb_07             20.982925  [TEMPERATURE, feb, 07]  2007    02\n",
       "TEMPERATURE_mar_07             21.425911  [TEMPERATURE, mar, 07]  2007    03\n",
       "TEMPERATURE_apr_07             21.237032  [TEMPERATURE, apr, 07]  2007    04\n",
       "TEMPERATURE_may_07             21.322522  [TEMPERATURE, may, 07]  2007    05"
      ]
     },
     "execution_count": 255,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change the month from jan to 01 and apply to every row in the df\n",
    "x_temperature_medellin_transposed['MONTH'] = x_temperature_medellin_transposed['MONTH'].apply(lambda x: month_as_int(month=x.strip()))\n",
    "x_temperature_medellin_transposed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "cc537bef",
   "metadata": {
    "id": "cc537bef"
   },
   "outputs": [],
   "source": [
    "x_temperature_medellin_transposed['MONTH'] = x_temperature_medellin_transposed['MONTH'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6278d6b",
   "metadata": {
    "id": "a6278d6b"
   },
   "source": [
    "## Dengue Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "07225e1f",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "07225e1f",
    "outputId": "0022028c-df10-4c5f-a589-08d1c2c0f8e4"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>cases_medellin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007/w01</th>\n",
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       "      <td>0</td>\n",
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       "      <th>2007/w03</th>\n",
       "      <td>0</td>\n",
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       "      <th>2007/w04</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2007/w05</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2018/w48</th>\n",
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       "      <td>12</td>\n",
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       "      <td>27</td>\n",
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       "      <th>2018/w51</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w52</th>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>624 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin\n",
       "2007/w01               1\n",
       "2007/w02               0\n",
       "2007/w03               0\n",
       "2007/w04               0\n",
       "2007/w05               0\n",
       "...                  ...\n",
       "2018/w48              28\n",
       "2018/w49              12\n",
       "2018/w50              27\n",
       "2018/w51              17\n",
       "2018/w52              17\n",
       "\n",
       "[624 rows x 1 columns]"
      ]
     },
     "execution_count": 257,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take columns of Dengue cases from Medellin data\n",
    "x_cases_medellin = data_medellin[data_medellin.columns[343:-52]]\n",
    "# convert to Row\n",
    "x_cases_transposed_medellin = x_cases_medellin.T\n",
    "# Rename Column\n",
    "x_cases_transposed_medellin = x_cases_transposed_medellin.rename(columns= {62:'cases_medellin'})  ##integer\n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9lEuYI_xerCc",
   "metadata": {
    "id": "9lEuYI_xerCc"
   },
   "source": [
    "## Date of Dengue Cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "bbbdafe2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "bbbdafe2",
    "outputId": "13bbea17-55dc-4734-a0c7-19c6c38628e7"
   },
   "outputs": [
    {
     "data": {
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       "      <th>2007/w05</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>2018/w48</th>\n",
       "      <td>28</td>\n",
       "      <td>[2018, w48]</td>\n",
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       "    <tr>\n",
       "      <th>2018/w49</th>\n",
       "      <td>12</td>\n",
       "      <td>[2018, w49]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w50</th>\n",
       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
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       "    <tr>\n",
       "      <th>2018/w51</th>\n",
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       "      <td>[2018, w51]</td>\n",
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       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin         DATA\n",
       "2007/w01               1  [2007, w01]\n",
       "2007/w02               0  [2007, w02]\n",
       "2007/w03               0  [2007, w03]\n",
       "2007/w04               0  [2007, w04]\n",
       "2007/w05               0  [2007, w05]\n",
       "...                  ...          ...\n",
       "2018/w48              28  [2018, w48]\n",
       "2018/w49              12  [2018, w49]\n",
       "2018/w50              27  [2018, w50]\n",
       "2018/w51              17  [2018, w51]\n",
       "2018/w52              17  [2018, w52]\n",
       "\n",
       "[624 rows x 2 columns]"
      ]
     },
     "execution_count": 258,
     "metadata": {
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     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new Column\n",
    "x_cases_transposed_medellin['DATA'] = x_cases_transposed_medellin.index.str.split('/')  #index column split function though works on string only -> yyyy week\n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "9f29230d",
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    "colab": {
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     "height": 419
    },
    "id": "9f29230d",
    "outputId": "be87d8bd-c5f9-4d7a-a57f-822072c8bbc3"
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   "outputs": [
    {
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2018/w48</th>\n",
       "      <td>28</td>\n",
       "      <td>[2018, w48]</td>\n",
       "      <td>2018</td>\n",
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       "      <th>2018/w49</th>\n",
       "      <td>12</td>\n",
       "      <td>[2018, w49]</td>\n",
       "      <td>2018</td>\n",
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       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
       "      <td>2018</td>\n",
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       "      <td>[2018, w51]</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>2018/w52</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>624 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin         DATA  YEAR\n",
       "2007/w01               1  [2007, w01]  2007\n",
       "2007/w02               0  [2007, w02]  2007\n",
       "2007/w03               0  [2007, w03]  2007\n",
       "2007/w04               0  [2007, w04]  2007\n",
       "2007/w05               0  [2007, w05]  2007\n",
       "...                  ...          ...   ...\n",
       "2018/w48              28  [2018, w48]  2018\n",
       "2018/w49              12  [2018, w49]  2018\n",
       "2018/w50              27  [2018, w50]  2018\n",
       "2018/w51              17  [2018, w51]  2018\n",
       "2018/w52              17  [2018, w52]  2018\n",
       "\n",
       "[624 rows x 3 columns]"
      ]
     },
     "execution_count": 259,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take year\n",
    "x_cases_transposed_medellin['YEAR'] = x_cases_transposed_medellin['DATA'].apply(lambda x: x[0])\n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "6192112f",
   "metadata": {
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    },
    "id": "6192112f",
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   },
   "outputs": [
    {
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       "      <td>201849</td>\n",
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       "    <tr>\n",
       "      <th>2018/w50</th>\n",
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       "      <td>[2018, w50]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201850</td>\n",
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       "    <tr>\n",
       "      <th>2018/w51</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w51]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201851</td>\n",
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       "    <tr>\n",
       "      <th>2018/w52</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201852</td>\n",
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       "<p>624 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin         DATA  YEAR YearWeek\n",
       "2007/w01               1  [2007, w01]  2007   200701\n",
       "2007/w02               0  [2007, w02]  2007   200702\n",
       "2007/w03               0  [2007, w03]  2007   200703\n",
       "2007/w04               0  [2007, w04]  2007   200704\n",
       "2007/w05               0  [2007, w05]  2007   200705\n",
       "...                  ...          ...   ...      ...\n",
       "2018/w48              28  [2018, w48]  2018   201848\n",
       "2018/w49              12  [2018, w49]  2018   201849\n",
       "2018/w50              27  [2018, w50]  2018   201850\n",
       "2018/w51              17  [2018, w51]  2018   201851\n",
       "2018/w52              17  [2018, w52]  2018   201852\n",
       "\n",
       "[624 rows x 4 columns]"
      ]
     },
     "execution_count": 260,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to format year + week \n",
    "x_cases_transposed_medellin['YearWeek'] = x_cases_transposed_medellin['DATA'].apply(lambda x: x[0] + x[1].split('w')[1])  ##concate year and week index 0 and 1 in a list and apply to all rows\n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
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      "text/plain": [
       "          cases_medellin         DATA  YEAR  YearWeek\n",
       "2007/w01               1  [2007, w01]  2007    200701\n",
       "2007/w02               0  [2007, w02]  2007    200702\n",
       "2007/w03               0  [2007, w03]  2007    200703\n",
       "2007/w04               0  [2007, w04]  2007    200704\n",
       "2007/w05               0  [2007, w05]  2007    200705"
      ]
     },
     "execution_count": 261,
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     },
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    }
   ],
   "source": [
    "#YearWeekInt --> changed to int\n",
    "x_cases_transposed_medellin['YearWeek'] = x_cases_transposed_medellin['YearWeek'].astype(int)\n",
    "x_cases_transposed_medellin.head()  #integer"
   ]
  },
  {
   "cell_type": "code",
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       "      <td>2018</td>\n",
       "      <td>201848</td>\n",
       "      <td>2018-12-01</td>\n",
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       "      <th>2018/w49</th>\n",
       "      <td>12</td>\n",
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       "      <td>2018</td>\n",
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       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201850</td>\n",
       "      <td>2018-12-15</td>\n",
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       "      <td>17</td>\n",
       "      <td>[2018, w51]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201851</td>\n",
       "      <td>2018-12-22</td>\n",
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       "    <tr>\n",
       "      <th>2018/w52</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201852</td>\n",
       "      <td>2018-12-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin         DATA  YEAR  YearWeek LastDayWeek\n",
       "2007/w01               1  [2007, w01]  2007    200701  2007-01-06\n",
       "2007/w02               0  [2007, w02]  2007    200702  2007-01-13\n",
       "2007/w03               0  [2007, w03]  2007    200703  2007-01-20\n",
       "2007/w04               0  [2007, w04]  2007    200704  2007-01-27\n",
       "2007/w05               0  [2007, w05]  2007    200705  2007-02-03\n",
       "...                  ...          ...   ...       ...         ...\n",
       "2018/w48              28  [2018, w48]  2018    201848  2018-12-01\n",
       "2018/w49              12  [2018, w49]  2018    201849  2018-12-08\n",
       "2018/w50              27  [2018, w50]  2018    201850  2018-12-15\n",
       "2018/w51              17  [2018, w51]  2018    201851  2018-12-22\n",
       "2018/w52              17  [2018, w52]  2018    201852  2018-12-29\n",
       "\n",
       "[624 rows x 5 columns]"
      ]
     },
     "execution_count": 262,
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   ],
   "source": [
    "x_cases_transposed_medellin['LastDayWeek'] = pd.to_datetime((x_cases_transposed_medellin['YearWeek']-1).astype(str) + \"6\" , format=\"%Y%U%w\")   # 200701 + 6 -> yearweeknumber + Lastday of week \n",
    "##-1 -> our data weeknumber e.g. Yearweek 200701 yearweek starts from 01 as week 1 and datatime library starts from 00 as week 1 -> 1 -52 weeks data -> 0-51 weeks datelibrary\n",
    "## %Uweek number 0 51 = 1 52  \n",
    "##%w last day of week    This is taken from epiweek (1st epi week of 2007 starts 31-Dec-2016 ends 6-Jan-2017) \n",
    "##Library takes in yearweeknumber + weekday  -> the date form the last day of the week \n",
    "x_cases_transposed_medellin"
   ]
  },
  {
   "cell_type": "code",
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    {
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       "      <th>2018/w48</th>\n",
       "      <td>28</td>\n",
       "      <td>[2018, w48]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201848</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>2018/w49</th>\n",
       "      <td>12</td>\n",
       "      <td>[2018, w49]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201849</td>\n",
       "      <td>2018-12-08</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>2018/w50</th>\n",
       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201850</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w51</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w51]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201851</td>\n",
       "      <td>2018-12-22</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/w52</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201852</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cases_medellin         DATA  YEAR  YearWeek LastDayWeek  MONTH\n",
       "2007/w01               1  [2007, w01]  2007    200701  2007-01-06      1\n",
       "2007/w02               0  [2007, w02]  2007    200702  2007-01-13      1\n",
       "2007/w03               0  [2007, w03]  2007    200703  2007-01-20      1\n",
       "2007/w04               0  [2007, w04]  2007    200704  2007-01-27      1\n",
       "2007/w05               0  [2007, w05]  2007    200705  2007-02-03      2\n",
       "...                  ...          ...   ...       ...         ...    ...\n",
       "2018/w48              28  [2018, w48]  2018    201848  2018-12-01     12\n",
       "2018/w49              12  [2018, w49]  2018    201849  2018-12-08     12\n",
       "2018/w50              27  [2018, w50]  2018    201850  2018-12-15     12\n",
       "2018/w51              17  [2018, w51]  2018    201851  2018-12-22     12\n",
       "2018/w52              17  [2018, w52]  2018    201852  2018-12-29     12\n",
       "\n",
       "[624 rows x 6 columns]"
      ]
     },
     "execution_count": 263,
     "metadata": {
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     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take month to append and duplicate temperature\n",
    "x_cases_transposed_medellin['MONTH'] = pd.DatetimeIndex(x_cases_transposed_medellin['LastDayWeek']).month  \n",
    "x_cases_transposed_medellin    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3cd0749",
   "metadata": {
    "id": "b3cd0749"
   },
   "source": [
    "### JOIN TEMPERATURE AND CASES AND PRECIPITATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "342a0e49",
   "metadata": {
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    "id": "342a0e49",
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   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>cases_medellin</th>\n",
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       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
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       "      <th>1</th>\n",
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       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w05]</td>\n",
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       "      <td>200705</td>\n",
       "      <td>2007-02-03</td>\n",
       "      <td>2</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
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       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
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       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>12</td>\n",
       "      <td>[2018, w49]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201849</td>\n",
       "      <td>2018-12-08</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201850</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w51]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201851</td>\n",
       "      <td>2018-12-22</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201852</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cases_medellin         DATA  ... temperature_medellin                    DATE\n",
       "0                 1  [2007, w01]  ...            21.009283  [TEMPERATURE, jan, 07]\n",
       "1                 0  [2007, w02]  ...            21.009283  [TEMPERATURE, jan, 07]\n",
       "2                 0  [2007, w03]  ...            21.009283  [TEMPERATURE, jan, 07]\n",
       "3                 0  [2007, w04]  ...            21.009283  [TEMPERATURE, jan, 07]\n",
       "4                 0  [2007, w05]  ...            20.982925  [TEMPERATURE, feb, 07]\n",
       "..              ...          ...  ...                  ...                     ...\n",
       "619              28  [2018, w48]  ...            20.936353  [TEMPERATURE, dec, 18]\n",
       "620              12  [2018, w49]  ...            20.936353  [TEMPERATURE, dec, 18]\n",
       "621              27  [2018, w50]  ...            20.936353  [TEMPERATURE, dec, 18]\n",
       "622              17  [2018, w51]  ...            20.936353  [TEMPERATURE, dec, 18]\n",
       "623              17  [2018, w52]  ...            20.936353  [TEMPERATURE, dec, 18]\n",
       "\n",
       "[624 rows x 8 columns]"
      ]
     },
     "execution_count": 264,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_cases_temp = pd.merge(x_cases_transposed_medellin,x_temperature_medellin_transposed, on=['MONTH','YEAR'],how ='left')  ##month and year pair match , to avoid duplicate for each month or year it matches with\n",
    "merge_cases_temp    ##624  # 143 -> 144 1 month is missing -> DEC!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2zF1M4cmxTRO",
   "metadata": {
    "id": "2zF1M4cmxTRO"
   },
   "source": [
    "# Load new weekly precipitation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "e83e9b84",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "e83e9b84",
    "outputId": "0f695465-7ed3-4dcf-be91-48dc90f1f9b5"
   },
   "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>LastDayWeek</th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>percipitation_medellin_rural</th>\n",
       "      <th>percipitation_medellin_urbano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-01-06</td>\n",
       "      <td>13.096688</td>\n",
       "      <td>14.097693</td>\n",
       "      <td>12.628933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-01-13</td>\n",
       "      <td>8.437044</td>\n",
       "      <td>6.910390</td>\n",
       "      <td>9.067214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-01-20</td>\n",
       "      <td>19.373165</td>\n",
       "      <td>21.616968</td>\n",
       "      <td>18.406665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-01-27</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-02-03</td>\n",
       "      <td>3.725206</td>\n",
       "      <td>3.144225</td>\n",
       "      <td>3.901021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  LastDayWeek  ...  percipitation_medellin_urbano\n",
       "0  2007-01-06  ...                      12.628933\n",
       "1  2007-01-13  ...                       9.067214\n",
       "2  2007-01-20  ...                      18.406665\n",
       "3  2007-01-27  ...                       0.000000\n",
       "4  2007-02-03  ...                       3.901021\n",
       "\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 265,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_prec_weekly = pd.read_csv('Data/Datos_prec_semanal.csv')\n",
    "# Rename extra column\n",
    "data_prec_weekly = data_prec_weekly.rename({'Unnamed: 0': 'LastDayWeek', 'Medellin':'percipitation_medellin', 'MedellinRural':'percipitation_medellin_rural', 'MedellinUrbano':'percipitation_medellin_urbano'}, axis=1)\n",
    "data_prec_weekly.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "SYk_EpqJy0BV",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SYk_EpqJy0BV",
    "outputId": "0641e09b-1a41-4e8c-a081-4551b23f7896"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LastDayWeek                      datetime64[ns]\n",
       "percipitation_medellin                  float64\n",
       "percipitation_medellin_rural            float64\n",
       "percipitation_medellin_urbano           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 266,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LastDayWeek to datetame to be merged with other data\n",
    "data_prec_weekly.LastDayWeek = pd.to_datetime(data_prec_weekly.LastDayWeek)\n",
    "data_prec_weekly.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8xqcCd2shDSY",
   "metadata": {
    "id": "8xqcCd2shDSY"
   },
   "source": [
    "### merge new precipitaton data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "MD5WPT1Ay0NA",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 609
    },
    "id": "MD5WPT1Ay0NA",
    "outputId": "2f3cd831-e369-4390-90d4-873798e61f7e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cases_medellin</th>\n",
       "      <th>DATA</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>YearWeek</th>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>temperature_medellin</th>\n",
       "      <th>DATE</th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>percipitation_medellin_rural</th>\n",
       "      <th>percipitation_medellin_urbano</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[2007, w01]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200701</td>\n",
       "      <td>2007-01-06</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>13.096688</td>\n",
       "      <td>14.097693</td>\n",
       "      <td>12.628933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w02]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200702</td>\n",
       "      <td>2007-01-13</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>8.437044</td>\n",
       "      <td>6.910390</td>\n",
       "      <td>9.067214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w03]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200703</td>\n",
       "      <td>2007-01-20</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>19.373165</td>\n",
       "      <td>21.616968</td>\n",
       "      <td>18.406665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w04]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200704</td>\n",
       "      <td>2007-01-27</td>\n",
       "      <td>1</td>\n",
       "      <td>21.009283</td>\n",
       "      <td>[TEMPERATURE, jan, 07]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>[2007, w05]</td>\n",
       "      <td>2007</td>\n",
       "      <td>200705</td>\n",
       "      <td>2007-02-03</td>\n",
       "      <td>2</td>\n",
       "      <td>20.982925</td>\n",
       "      <td>[TEMPERATURE, feb, 07]</td>\n",
       "      <td>3.725206</td>\n",
       "      <td>3.144225</td>\n",
       "      <td>3.901021</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>28</td>\n",
       "      <td>[2018, w48]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201848</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "      <td>8.916642</td>\n",
       "      <td>8.656351</td>\n",
       "      <td>9.114419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>12</td>\n",
       "      <td>[2018, w49]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201849</td>\n",
       "      <td>2018-12-08</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "      <td>17.006086</td>\n",
       "      <td>16.076869</td>\n",
       "      <td>17.334870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>27</td>\n",
       "      <td>[2018, w50]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201850</td>\n",
       "      <td>2018-12-15</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "      <td>5.673248</td>\n",
       "      <td>5.388778</td>\n",
       "      <td>5.794555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w51]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201851</td>\n",
       "      <td>2018-12-22</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "      <td>2.785103</td>\n",
       "      <td>2.708024</td>\n",
       "      <td>2.810241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>17</td>\n",
       "      <td>[2018, w52]</td>\n",
       "      <td>2018</td>\n",
       "      <td>201852</td>\n",
       "      <td>2018-12-29</td>\n",
       "      <td>12</td>\n",
       "      <td>20.936353</td>\n",
       "      <td>[TEMPERATURE, dec, 18]</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cases_medellin  ... percipitation_medellin_urbano\n",
       "0                 1  ...                     12.628933\n",
       "1                 0  ...                      9.067214\n",
       "2                 0  ...                     18.406665\n",
       "3                 0  ...                      0.000000\n",
       "4                 0  ...                      3.901021\n",
       "..              ...  ...                           ...\n",
       "619              28  ...                      9.114419\n",
       "620              12  ...                     17.334870\n",
       "621              27  ...                      5.794555\n",
       "622              17  ...                      2.810241\n",
       "623              17  ...                      0.000000\n",
       "\n",
       "[624 rows x 11 columns]"
      ]
     },
     "execution_count": 267,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_cases_temp_precip = pd.merge(merge_cases_temp, data_prec_weekly, on=['LastDayWeek'],how ='left')  ##month and year pair match , to avoid duplicate for each month or year it matches with\n",
    "merge_cases_temp_precip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JDVHbhn9zGhb",
   "metadata": {
    "id": "JDVHbhn9zGhb"
   },
   "source": [
    "# Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "zxNDtkPwhKJt",
   "metadata": {
    "id": "zxNDtkPwhKJt"
   },
   "source": [
    "## Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "6d55f16d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "id": "6d55f16d",
    "outputId": "18a3bf93-59fc-45ce-fcbe-a0cd15f8fe00"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dengue cases in time\n",
    "def timeseries (x_axis, y_axis, x_label):\n",
    "    plt.figure(figsize = (12, 8))\n",
    "    plt.plot(x_axis, y_axis, color ='black')\n",
    "    plt.xlabel(x_label) \n",
    "    plt.ylabel('Dengue Cases')\n",
    "\n",
    "timeseries(merge_cases_temp_precip['LastDayWeek'], merge_cases_temp_precip['cases_medellin'], 'cases by Week')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be7476a",
   "metadata": {
    "id": "3be7476a"
   },
   "source": [
    "## DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "b3934515",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "id": "b3934515",
    "outputId": "bb9c693d-39b7-41dd-ec64-878d93da83d9"
   },
   "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>temperature_medellin</th>\n",
       "      <th>percipitation_medellin</th>\n",
       "      <th>cases_medellin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LastDayWeek</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-06</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>13.096688</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-13</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>8.437044</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-20</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>19.373165</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-27</th>\n",
       "      <td>21.009283</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-02-03</th>\n",
       "      <td>20.982925</td>\n",
       "      <td>3.725206</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>8.916642</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-08</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>17.006086</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-15</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>5.673248</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-22</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>2.785103</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-29</th>\n",
       "      <td>20.936353</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>624 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             temperature_medellin  percipitation_medellin  cases_medellin\n",
       "LastDayWeek                                                              \n",
       "2007-01-06              21.009283               13.096688               1\n",
       "2007-01-13              21.009283                8.437044               0\n",
       "2007-01-20              21.009283               19.373165               0\n",
       "2007-01-27              21.009283                0.000000               0\n",
       "2007-02-03              20.982925                3.725206               0\n",
       "...                           ...                     ...             ...\n",
       "2018-12-01              20.936353                8.916642              28\n",
       "2018-12-08              20.936353               17.006086              12\n",
       "2018-12-15              20.936353                5.673248              27\n",
       "2018-12-22              20.936353                2.785103              17\n",
       "2018-12-29              20.936353                0.000000              17\n",
       "\n",
       "[624 rows x 3 columns]"
      ]
     },
     "execution_count": 269,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = merge_cases_temp_precip[['temperature_medellin','percipitation_medellin','cases_medellin']]\n",
    "dataset.index = merge_cases_temp_precip.LastDayWeek\n",
    "dataset  #DF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JeddvRFhkTnY",
   "metadata": {
    "id": "JeddvRFhkTnY"
   },
   "source": [
    "# Prepare data to supervised learning time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "LFsBjJYWhZaQ",
   "metadata": {
    "id": "LFsBjJYWhZaQ"
   },
   "source": [
    "we will use:\n",
    "* data: is the dataframe in our case (Dengue Cases, Precipitation and Temperature)\n",
    "* n_in: is the number of lag weeks in the past (length of window)\n",
    "\n",
    "The heart of this \"series_to_supervised\" function is the <a href=\"https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.shift.html\">shift</a> fuction of pandas\n",
    "\n",
    "This function gets as input the number of periods(in this case the number of weeks represented as rows up or down in the dataframe) to move the columns of a dataframe.\n",
    "E.g. \n",
    "* If we have merge_cases_temp_precip['cases_medellin'].shift(1) all the rows of column cases_medellin will move one row down\n",
    "* If we have merge_cases_temp_precip['cases_medellin'].shift(-1) all the rows of column cases_medellin will move one row up\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "BH5JesTNkW1V",
   "metadata": {
    "id": "BH5JesTNkW1V"
   },
   "outputs": [],
   "source": [
    "# prepare data for lstm\n",
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# convert series to supervised learning\n",
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "    n_vars = 1 if type(data) is list else data.shape[1]\n",
    "    df = DataFrame(data)\n",
    "    cols, names = list(), list()\n",
    "    # input sequence (t-n, ... t-1)\n",
    "    for i in range(n_in, 0, -1):\n",
    "        cols.append(df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # forecast sequence (t, t+1, ... t+n)\n",
    "    for i in range(0, n_out):\n",
    "        cols.append(df.shift(-i))\n",
    "        if i == 0:\n",
    "            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "        else:\n",
    "            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4hM9aJnekTOF",
   "metadata": {
    "id": "4hM9aJnekTOF"
   },
   "source": [
    "### normalize features\n",
    "As we are working with a Neural Network the data values ​​must be normalized to help backpropagation algorithm\n",
    "So we will use the <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">MinMaxScaler</a> from sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "gq1acAcggdew",
   "metadata": {
    "id": "gq1acAcggdew"
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1)) # Scaler between 0 and 1\n",
    "scaled = scaler.fit_transform(dataset) # As we can see data set has 3 Columns (This shape is also important for inverse scaler as we will see in future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "HhBFNwjskQ0u",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "HhBFNwjskQ0u",
    "outputId": "f7c7af93-16a9-4c98-803c-3a22f7126803"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var1(t-2)</th>\n",
       "      <th>var2(t-2)</th>\n",
       "      <th>var3(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "      <th>var2(t-1)</th>\n",
       "      <th>var3(t-1)</th>\n",
       "      <th>var1(t)</th>\n",
       "      <th>var2(t)</th>\n",
       "      <th>var3(t)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.00122</td>\n",
       "      <td>0.364241</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.066375</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.00000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.364241</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
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       "      <td>0.029307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.356053</td>\n",
       "      <td>0.095100</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.356053</td>\n",
       "      <td>0.029307</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.356053</td>\n",
       "      <td>0.095100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.356053</td>\n",
       "      <td>0.011560</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "   var1(t-2)  var2(t-2)  var3(t-2)  ...   var1(t)   var2(t)  var3(t)\n",
       "2   0.364241   0.103033    0.00122  ...  0.364241  0.152411      0.0\n",
       "3   0.364241   0.066375    0.00000  ...  0.364241  0.000000      0.0\n",
       "4   0.364241   0.152411    0.00000  ...  0.356053  0.029307      0.0\n",
       "5   0.364241   0.000000    0.00000  ...  0.356053  0.095100      0.0\n",
       "6   0.356053   0.029307    0.00000  ...  0.356053  0.011560      0.0\n",
       "\n",
       "[5 rows x 9 columns]"
      ]
     },
     "execution_count": 272,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# length of window\n",
    "weeks = 2\n",
    "\n",
    "# frame as supervised learning\n",
    "data = series_to_supervised(scaled, n_in=weeks)\n",
    "DataFrame(data).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Uss0IPaQqnWB",
   "metadata": {
    "id": "Uss0IPaQqnWB"
   },
   "source": [
    "## Features Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "eWFelunOgdml",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eWFelunOgdml",
    "outputId": "9f9e74b6-e038-4875-f5e2-0aab18e2025e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.36424094, 0.10303316, 0.00121951, 0.36424094, 0.0663752 ,\n",
       "        0.        ],\n",
       "       [0.36424094, 0.0663752 , 0.        , 0.36424094, 0.15241093,\n",
       "        0.        ],\n",
       "       [0.36424094, 0.15241093, 0.        , 0.36424094, 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.34158643, 0.07014825, 0.03414634, 0.34158643, 0.13378884,\n",
       "        0.01463415],\n",
       "       [0.34158643, 0.13378884, 0.01463415, 0.34158643, 0.0446321 ,\n",
       "        0.03292683],\n",
       "       [0.34158643, 0.0446321 , 0.03292683, 0.34158643, 0.02191073,\n",
       "        0.02073171]])"
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     },
     "execution_count": 273,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We define the number of features as 3 (Temperature, Precipitation and Dengue Cases)\n",
    "n_features = 3\n",
    "# The features to train the model will be all except the values of the actual week \n",
    "# We can't use the temperature and precipitation in week t because whe need to resample a a 3D Array\n",
    "features_set = DataFrame(data.values[:,:-n_features])\n",
    "# Convert pandas data frame to np.array to reshape as 3D Array\n",
    "features_set = features_set.to_numpy()\n",
    "features_set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ojvAeBMPr3ol",
   "metadata": {
    "id": "ojvAeBMPr3ol"
   },
   "source": [
    "## Labels Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "q4jhxnI5gdqz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "q4jhxnI5gdqz",
    "outputId": "ef920bbb-eeca-46c5-e6a2-cf04a56b6c30"
   },
   "outputs": [
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       "       [0.02073171],\n",
       "       [0.02073171]])"
      ]
     },
     "execution_count": 274,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We will use Dengue cases in last week \n",
    "labels_set = DataFrame(data.values[:,-1])\n",
    "# Convert pandas data frame to np.array\n",
    "labels_set = labels_set.to_numpy()\n",
    "labels_set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "RlwXfwaLsHza",
   "metadata": {
    "id": "RlwXfwaLsHza"
   },
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "-sb-9LCMgdwr",
   "metadata": {
    "id": "-sb-9LCMgdwr"
   },
   "outputs": [],
   "source": [
    "# We need a sequence so we can't split randomly\n",
    "# To divide into Train (90%) and test (10%) to do that we need to know the 90% of the total dataframe\n",
    "size = features_set.shape[0]\n",
    "split = int(size*(9/10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_2LNWDMusZsl",
   "metadata": {
    "id": "_2LNWDMusZsl"
   },
   "source": [
    "### train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "FJan_zurgd1q",
   "metadata": {
    "id": "FJan_zurgd1q"
   },
   "outputs": [],
   "source": [
    "# We will train with 1st 90% of data and test with last 10%\n",
    "train_X = features_set[:split] ##90% train\n",
    "train_y = labels_set[:split]  ##90% train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t17j8OtzemAG",
   "metadata": {
    "id": "t17j8OtzemAG"
   },
   "source": [
    "### test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "DS6rGhy5gd7B",
   "metadata": {
    "id": "DS6rGhy5gd7B"
   },
   "outputs": [],
   "source": [
    "test_X = features_set[split:] ##10% test\n",
    "test_y = labels_set[split:] ##10% test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "UExWrkq9Ib_J",
   "metadata": {
    "id": "UExWrkq9Ib_J"
   },
   "source": [
    "## Reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "G1xmvWGQgeDB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G1xmvWGQgeDB",
    "outputId": "220bf48e-feda-4160-f5d2-8d78f98f0ef1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(559, 2, 3) (559, 1) (63, 2, 3) (63, 1)\n"
     ]
    }
   ],
   "source": [
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], weeks, n_features))\n",
    "test_X = test_X.reshape((test_X.shape[0], weeks, n_features))\n",
    "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac223af3",
   "metadata": {
    "id": "ac223af3"
   },
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "h5ZtewNE5vXX",
   "metadata": {
    "id": "h5ZtewNE5vXX"
   },
   "source": [
    "### LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "wPD_nuIfgeK9",
   "metadata": {
    "id": "wPD_nuIfgeK9"
   },
   "outputs": [],
   "source": [
    "# design network\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mae', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "CRKoVYEqmcav",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CRKoVYEqmcav",
    "outputId": "d1a043a6-89e1-4758-a055-8996a81afb41"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "8/8 - 3s - loss: 0.0836 - val_loss: 0.0107\n",
      "Epoch 2/50\n",
      "8/8 - 0s - loss: 0.0753 - val_loss: 0.0218\n",
      "Epoch 3/50\n",
      "8/8 - 0s - loss: 0.0750 - val_loss: 0.0118\n",
      "Epoch 4/50\n",
      "8/8 - 0s - loss: 0.0703 - val_loss: 0.0075\n",
      "Epoch 5/50\n",
      "8/8 - 0s - loss: 0.0653 - val_loss: 0.0101\n",
      "Epoch 6/50\n",
      "8/8 - 0s - loss: 0.0625 - val_loss: 0.0115\n",
      "Epoch 7/50\n",
      "8/8 - 0s - loss: 0.0597 - val_loss: 0.0094\n",
      "Epoch 8/50\n",
      "8/8 - 0s - loss: 0.0559 - val_loss: 0.0093\n",
      "Epoch 9/50\n",
      "8/8 - 0s - loss: 0.0520 - val_loss: 0.0099\n",
      "Epoch 10/50\n",
      "8/8 - 0s - loss: 0.0483 - val_loss: 0.0088\n",
      "Epoch 11/50\n",
      "8/8 - 0s - loss: 0.0438 - val_loss: 0.0085\n",
      "Epoch 12/50\n",
      "8/8 - 0s - loss: 0.0390 - val_loss: 0.0082\n",
      "Epoch 13/50\n",
      "8/8 - 0s - loss: 0.0338 - val_loss: 0.0077\n",
      "Epoch 14/50\n",
      "8/8 - 0s - loss: 0.0282 - val_loss: 0.0070\n",
      "Epoch 15/50\n",
      "8/8 - 0s - loss: 0.0228 - val_loss: 0.0064\n",
      "Epoch 16/50\n",
      "8/8 - 0s - loss: 0.0186 - val_loss: 0.0055\n",
      "Epoch 17/50\n",
      "8/8 - 0s - loss: 0.0164 - val_loss: 0.0051\n",
      "Epoch 18/50\n",
      "8/8 - 0s - loss: 0.0158 - val_loss: 0.0050\n",
      "Epoch 19/50\n",
      "8/8 - 0s - loss: 0.0159 - val_loss: 0.0051\n",
      "Epoch 20/50\n",
      "8/8 - 0s - loss: 0.0158 - val_loss: 0.0053\n",
      "Epoch 21/50\n",
      "8/8 - 0s - loss: 0.0157 - val_loss: 0.0050\n",
      "Epoch 22/50\n",
      "8/8 - 0s - loss: 0.0156 - val_loss: 0.0052\n",
      "Epoch 23/50\n",
      "8/8 - 0s - loss: 0.0157 - val_loss: 0.0052\n",
      "Epoch 24/50\n",
      "8/8 - 0s - loss: 0.0156 - val_loss: 0.0053\n",
      "Epoch 25/50\n",
      "8/8 - 0s - loss: 0.0155 - val_loss: 0.0055\n",
      "Epoch 26/50\n",
      "8/8 - 0s - loss: 0.0155 - val_loss: 0.0053\n",
      "Epoch 27/50\n",
      "8/8 - 0s - loss: 0.0154 - val_loss: 0.0052\n",
      "Epoch 28/50\n",
      "8/8 - 0s - loss: 0.0155 - val_loss: 0.0053\n",
      "Epoch 29/50\n",
      "8/8 - 0s - loss: 0.0154 - val_loss: 0.0052\n",
      "Epoch 30/50\n",
      "8/8 - 0s - loss: 0.0153 - val_loss: 0.0057\n",
      "Epoch 31/50\n",
      "8/8 - 0s - loss: 0.0154 - val_loss: 0.0053\n",
      "Epoch 32/50\n",
      "8/8 - 0s - loss: 0.0153 - val_loss: 0.0052\n",
      "Epoch 33/50\n",
      "8/8 - 0s - loss: 0.0153 - val_loss: 0.0052\n",
      "Epoch 34/50\n",
      "8/8 - 0s - loss: 0.0153 - val_loss: 0.0052\n",
      "Epoch 35/50\n",
      "8/8 - 0s - loss: 0.0152 - val_loss: 0.0051\n",
      "Epoch 36/50\n",
      "8/8 - 0s - loss: 0.0152 - val_loss: 0.0051\n",
      "Epoch 37/50\n",
      "8/8 - 0s - loss: 0.0151 - val_loss: 0.0051\n",
      "Epoch 38/50\n",
      "8/8 - 0s - loss: 0.0152 - val_loss: 0.0052\n",
      "Epoch 39/50\n",
      "8/8 - 0s - loss: 0.0152 - val_loss: 0.0052\n",
      "Epoch 40/50\n",
      "8/8 - 0s - loss: 0.0152 - val_loss: 0.0051\n",
      "Epoch 41/50\n",
      "8/8 - 0s - loss: 0.0151 - val_loss: 0.0051\n",
      "Epoch 42/50\n",
      "8/8 - 0s - loss: 0.0151 - val_loss: 0.0052\n",
      "Epoch 43/50\n",
      "8/8 - 0s - loss: 0.0152 - val_loss: 0.0057\n",
      "Epoch 44/50\n",
      "8/8 - 0s - loss: 0.0153 - val_loss: 0.0054\n",
      "Epoch 45/50\n",
      "8/8 - 0s - loss: 0.0151 - val_loss: 0.0051\n",
      "Epoch 46/50\n",
      "8/8 - 0s - loss: 0.0151 - val_loss: 0.0051\n",
      "Epoch 47/50\n",
      "8/8 - 0s - loss: 0.0150 - val_loss: 0.0051\n",
      "Epoch 48/50\n",
      "8/8 - 0s - loss: 0.0150 - val_loss: 0.0054\n",
      "Epoch 49/50\n",
      "8/8 - 0s - loss: 0.0152 - val_loss: 0.0054\n",
      "Epoch 50/50\n",
      "8/8 - 0s - loss: 0.0151 - val_loss: 0.0051\n"
     ]
    }
   ],
   "source": [
    "# fit network\n",
    "history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "9yqmdQXsmcfH",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "9yqmdQXsmcfH",
    "outputId": "43ec900a-53e8-45ef-be72-e55bfad9e159"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "# plot history\n",
    "pyplot.plot(history.history['loss'], label='train')\n",
    "pyplot.plot(history.history['val_loss'], label='test')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "C21mhuRullNL",
   "metadata": {
    "id": "C21mhuRullNL"
   },
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "from numpy import concatenate\n",
    "\n",
    "# make a prediction\n",
    "yhat = model.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "TtordubT422m",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TtordubT422m",
    "outputId": "00e84aae-3658-40e5-fbe9-c9fb970727ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 1)"
      ]
     },
     "execution_count": 283,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "5Ozj32Jwq4VL",
   "metadata": {
    "id": "5Ozj32Jwq4VL"
   },
   "outputs": [],
   "source": [
    "# Convert test data to 2D \n",
    "test_X = test_X.reshape((test_X.shape[0], weeks*n_features))\n",
    "\n",
    "# invert scaling for forecast\n",
    "# As we said Scaler needs 3 columns so we can take those columns from test data and take again the predictions\n",
    "# Concatenate last 2 columns of test data with predicted data (yhat)\n",
    "inv_yhat = concatenate((test_X[:, -(n_features-1):], yhat), axis=1)\n",
    "# Inverse Scaler\n",
    "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
    "# Take predicted data scaled to original Dengue cases\n",
    "inv_yhat = inv_yhat[:,-1]\n",
    "\n",
    "# invert scaling for actual\n",
    "# Same process than for predicted data (yhat)\n",
    "test_y = test_y.reshape((len(test_y), 1))\n",
    "inv_y = concatenate((test_X[:, -(n_features-1):], test_y), axis=1)\n",
    "inv_y = scaler.inverse_transform(inv_y)\n",
    "inv_y = inv_y[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "TdDRHwvNllQR",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TdDRHwvNllQR",
    "outputId": "d98d310f-b0c3-4aa8-e4b6-feaac57f8c7c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE: 4.198\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import  mean_absolute_error\n",
    "\n",
    "# calculate MAE\n",
    "mae = mean_absolute_error(inv_y, inv_yhat)\n",
    "print('Test MAE: %.3f' % mae)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "PJzx3p9psAqW",
   "metadata": {
    "id": "PJzx3p9psAqW"
   },
   "source": [
    "#### Plot predicted vs actual dengue cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "nPOiCVoxsr-1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "id": "nPOiCVoxsr-1",
    "outputId": "ff563ff7-8539-4af3-a186-aefccd14b882"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_predict = inv_yhat  ## predicted target  cases\n",
    "dataY_plot = inv_y  ##  real test-target cases\n",
    "\n",
    "data_predict = data_predict.reshape(len(data_predict), 1)\n",
    "dataY_plot = dataY_plot.reshape(len(dataY_plot), 1)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(dataY_plot, label = 'actual')\n",
    "plt.plot(data_predict, label = 'predicted')\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.suptitle('Time-Series Prediction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "K9YPygMk5zdO",
   "metadata": {
    "id": "K9YPygMk5zdO"
   },
   "source": [
    "### GRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "6bc62cc6",
   "metadata": {
    "id": "6bc62cc6"
   },
   "outputs": [],
   "source": [
    "# design network\n",
    "model = Sequential()\n",
    "model.add(GRU(50, input_shape=(train_X.shape[1], train_X.shape[2])))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mae', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "46i6dieT53R_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "46i6dieT53R_",
    "outputId": "f7f22543-9084-448a-f33f-c391c48efb72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "8/8 - 2s - loss: 0.0965 - val_loss: 0.0540\n",
      "Epoch 2/50\n",
      "8/8 - 0s - loss: 0.1048 - val_loss: 0.0466\n",
      "Epoch 3/50\n",
      "8/8 - 0s - loss: 0.0925 - val_loss: 0.0074\n",
      "Epoch 4/50\n",
      "8/8 - 0s - loss: 0.0779 - val_loss: 0.0066\n",
      "Epoch 5/50\n",
      "8/8 - 0s - loss: 0.0674 - val_loss: 0.0152\n",
      "Epoch 6/50\n",
      "8/8 - 0s - loss: 0.0649 - val_loss: 0.0108\n",
      "Epoch 7/50\n",
      "8/8 - 0s - loss: 0.0594 - val_loss: 0.0110\n",
      "Epoch 8/50\n",
      "8/8 - 0s - loss: 0.0546 - val_loss: 0.0123\n",
      "Epoch 9/50\n",
      "8/8 - 0s - loss: 0.0507 - val_loss: 0.0096\n",
      "Epoch 10/50\n",
      "8/8 - 0s - loss: 0.0455 - val_loss: 0.0091\n",
      "Epoch 11/50\n",
      "8/8 - 0s - loss: 0.0407 - val_loss: 0.0080\n",
      "Epoch 12/50\n",
      "8/8 - 0s - loss: 0.0352 - val_loss: 0.0084\n",
      "Epoch 13/50\n",
      "8/8 - 0s - loss: 0.0301 - val_loss: 0.0070\n",
      "Epoch 14/50\n",
      "8/8 - 0s - loss: 0.0244 - val_loss: 0.0070\n",
      "Epoch 15/50\n",
      "8/8 - 0s - loss: 0.0200 - val_loss: 0.0061\n",
      "Epoch 16/50\n",
      "8/8 - 0s - loss: 0.0170 - val_loss: 0.0053\n",
      "Epoch 17/50\n",
      "8/8 - 0s - loss: 0.0157 - val_loss: 0.0052\n",
      "Epoch 18/50\n",
      "8/8 - 0s - loss: 0.0156 - val_loss: 0.0064\n",
      "Epoch 19/50\n",
      "8/8 - 0s - loss: 0.0162 - val_loss: 0.0095\n",
      "Epoch 20/50\n",
      "8/8 - 0s - loss: 0.0168 - val_loss: 0.0064\n",
      "Epoch 21/50\n",
      "8/8 - 0s - loss: 0.0158 - val_loss: 0.0056\n",
      "Epoch 22/50\n",
      "8/8 - 0s - loss: 0.0154 - val_loss: 0.0054\n",
      "Epoch 23/50\n",
      "8/8 - 0s - loss: 0.0153 - val_loss: 0.0055\n",
      "Epoch 24/50\n",
      "8/8 - 0s - loss: 0.0153 - val_loss: 0.0055\n",
      "Epoch 25/50\n",
      "8/8 - 0s - loss: 0.0153 - val_loss: 0.0057\n",
      "Epoch 26/50\n",
      "8/8 - 0s - loss: 0.0156 - val_loss: 0.0081\n",
      "Epoch 27/50\n",
      "8/8 - 0s - loss: 0.0164 - val_loss: 0.0080\n",
      "Epoch 28/50\n",
      "8/8 - 0s - loss: 0.0161 - val_loss: 0.0055\n",
      "Epoch 29/50\n",
      "8/8 - 0s - loss: 0.0152 - val_loss: 0.0052\n",
      "Epoch 30/50\n",
      "8/8 - 0s - loss: 0.0152 - val_loss: 0.0056\n",
      "Epoch 31/50\n",
      "8/8 - 0s - loss: 0.0153 - val_loss: 0.0060\n",
      "Epoch 32/50\n",
      "8/8 - 0s - loss: 0.0153 - val_loss: 0.0057\n",
      "Epoch 33/50\n",
      "8/8 - 0s - loss: 0.0151 - val_loss: 0.0053\n",
      "Epoch 34/50\n",
      "8/8 - 0s - loss: 0.0150 - val_loss: 0.0053\n",
      "Epoch 35/50\n",
      "8/8 - 0s - loss: 0.0152 - val_loss: 0.0071\n",
      "Epoch 36/50\n",
      "8/8 - 0s - loss: 0.0161 - val_loss: 0.0093\n",
      "Epoch 37/50\n",
      "8/8 - 0s - loss: 0.0165 - val_loss: 0.0064\n",
      "Epoch 38/50\n",
      "8/8 - 0s - loss: 0.0154 - val_loss: 0.0052\n",
      "Epoch 39/50\n",
      "8/8 - 0s - loss: 0.0150 - val_loss: 0.0062\n",
      "Epoch 40/50\n",
      "8/8 - 0s - loss: 0.0154 - val_loss: 0.0066\n",
      "Epoch 41/50\n",
      "8/8 - 0s - loss: 0.0154 - val_loss: 0.0057\n",
      "Epoch 42/50\n",
      "8/8 - 0s - loss: 0.0149 - val_loss: 0.0052\n",
      "Epoch 43/50\n",
      "8/8 - 0s - loss: 0.0150 - val_loss: 0.0063\n",
      "Epoch 44/50\n",
      "8/8 - 0s - loss: 0.0155 - val_loss: 0.0085\n",
      "Epoch 45/50\n",
      "8/8 - 0s - loss: 0.0161 - val_loss: 0.0081\n",
      "Epoch 46/50\n",
      "8/8 - 0s - loss: 0.0158 - val_loss: 0.0058\n",
      "Epoch 47/50\n",
      "8/8 - 0s - loss: 0.0150 - val_loss: 0.0053\n",
      "Epoch 48/50\n",
      "8/8 - 0s - loss: 0.0148 - val_loss: 0.0058\n",
      "Epoch 49/50\n",
      "8/8 - 0s - loss: 0.0151 - val_loss: 0.0062\n",
      "Epoch 50/50\n",
      "8/8 - 0s - loss: 0.0152 - val_loss: 0.0058\n"
     ]
    }
   ],
   "source": [
    "# reshape input to be 3D [samples, timesteps, features]\n",
    "train_X = train_X.reshape((train_X.shape[0], weeks, n_features))\n",
    "test_X = test_X.reshape((test_X.shape[0], weeks, n_features))\n",
    "\n",
    "# fit network\n",
    "history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "nK00v032gIOS",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "nK00v032gIOS",
    "outputId": "11c44ad5-5504-4261-a735-bf83198d7f3c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "# plot history\n",
    "pyplot.plot(history.history['loss'], label='train')\n",
    "pyplot.plot(history.history['val_loss'], label='test')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "Q5TDfiOegQRx",
   "metadata": {
    "id": "Q5TDfiOegQRx"
   },
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "from numpy import concatenate\n",
    "\n",
    "# make a prediction\n",
    "yhat = model.predict(test_X) \n",
    "test_X = test_X.reshape((test_X.shape[0], weeks*n_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "yTHTHYXKhQkq",
   "metadata": {
    "id": "yTHTHYXKhQkq"
   },
   "outputs": [],
   "source": [
    "# invert scaling for forecast\n",
    "inv_yhat = concatenate((test_X[:, -(n_features-1):], yhat), axis=1)\n",
    "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
    "inv_yhat = inv_yhat[:,-1]\n",
    "\n",
    "# invert scaling for actual\n",
    "test_y = test_y.reshape((len(test_y), 1))\n",
    "inv_y = concatenate((test_X[:, -(n_features-1):], test_y), axis=1)\n",
    "inv_y = scaler.inverse_transform(inv_y)\n",
    "inv_y = inv_y[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "e6i5YWdrgNa0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e6i5YWdrgNa0",
    "outputId": "9be79ecd-4926-44cc-ea48-9fe7c09bac33"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MAE: 4.784\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import  mean_absolute_error\n",
    "\n",
    "# calculate MAE\n",
    "mae = mean_absolute_error(inv_y, inv_yhat)\n",
    "print('Test MAE: %.3f' % mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "HHG7JuNk9qS_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "id": "HHG7JuNk9qS_",
    "outputId": "0b8db16a-3b9f-4ce2-e62d-45ce52e09efb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_predict = inv_yhat  ## predicted target  cases\n",
    "dataY_plot = inv_y  ##  real test-target cases\n",
    "\n",
    "data_predict = data_predict.reshape(len(data_predict), 1)\n",
    "dataY_plot = dataY_plot.reshape(len(dataY_plot), 1)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(dataY_plot, label = 'actual')\n",
    "plt.plot(data_predict, label = 'predicted')\n",
    "plt.legend(loc=\"upper left\")\n",
    "\n",
    "plt.suptitle('Time-Series Prediction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "dkIy2vsI-UeR",
   "metadata": {
    "id": "dkIy2vsI-UeR"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "Fv0Rr2s_5lig",
   "metadata": {
    "id": "Fv0Rr2s_5lig"
   },
   "source": [
    "<ol>\n",
    "  <li> <a href=\"https://towardsdatascience.com/predictive-analytics-time-series-forecasting-with-gru-and-bilstm-in-tensorflow-87588c852915\">Predictive Analytics: Time-Series Forecasting with GRU and BiLSTM in TensorFlow</a></li>\n",
    "  <li><a href=\"https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/\">Multivariate Time Series Forecasting with LSTMs in Keras</a></li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "kMiD5Vp27n5W",
   "metadata": {
    "id": "kMiD5Vp27n5W"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "LSTM_tf_WeeklyPrecipitationData.ipynb",
   "provenance": []
  },
  "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}