06.Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv(\"daily-min-temperatures.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of             Date  Temp\n",
       "0     1981-01-01  20.7\n",
       "1     1981-01-02  17.9\n",
       "2     1981-01-03  18.8\n",
       "3     1981-01-04  14.6\n",
       "4     1981-01-05  15.8\n",
       "5     1981-01-06  15.8\n",
       "6     1981-01-07  15.8\n",
       "7     1981-01-08  17.4\n",
       "8     1981-01-09  21.8\n",
       "9     1981-01-10  20.0\n",
       "10    1981-01-11  16.2\n",
       "11    1981-01-12  13.3\n",
       "12    1981-01-13  16.7\n",
       "13    1981-01-14  21.5\n",
       "14    1981-01-15  25.0\n",
       "15    1981-01-16  20.7\n",
       "16    1981-01-17  20.6\n",
       "17    1981-01-18  24.8\n",
       "18    1981-01-19  17.7\n",
       "19    1981-01-20  15.5\n",
       "20    1981-01-21  18.2\n",
       "21    1981-01-22  12.1\n",
       "22    1981-01-23  14.4\n",
       "23    1981-01-24  16.0\n",
       "24    1981-01-25  16.5\n",
       "25    1981-01-26  18.7\n",
       "26    1981-01-27  19.4\n",
       "27    1981-01-28  17.2\n",
       "28    1981-01-29  15.5\n",
       "29    1981-01-30  15.1\n",
       "...          ...   ...\n",
       "3620  1990-12-02  13.2\n",
       "3621  1990-12-03  16.2\n",
       "3622  1990-12-04  17.3\n",
       "3623  1990-12-05  20.5\n",
       "3624  1990-12-06  20.2\n",
       "3625  1990-12-07  19.4\n",
       "3626  1990-12-08  15.5\n",
       "3627  1990-12-09  14.1\n",
       "3628  1990-12-10  11.0\n",
       "3629  1990-12-11  11.1\n",
       "3630  1990-12-12  14.0\n",
       "3631  1990-12-13  11.4\n",
       "3632  1990-12-14  12.5\n",
       "3633  1990-12-15  13.4\n",
       "3634  1990-12-16  13.6\n",
       "3635  1990-12-17  13.9\n",
       "3636  1990-12-18  17.2\n",
       "3637  1990-12-19  14.7\n",
       "3638  1990-12-20  15.4\n",
       "3639  1990-12-21  13.1\n",
       "3640  1990-12-22  13.2\n",
       "3641  1990-12-23  13.9\n",
       "3642  1990-12-24  10.0\n",
       "3643  1990-12-25  12.9\n",
       "3644  1990-12-26  14.6\n",
       "3645  1990-12-27  14.0\n",
       "3646  1990-12-28  13.6\n",
       "3647  1990-12-29  13.5\n",
       "3648  1990-12-30  15.7\n",
       "3649  1990-12-31  13.0\n",
       "\n",
       "[3650 rows x 2 columns]>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Date']=pd.to_datetime(dataset['Date'],infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1981-01-01\n",
       "1      1981-01-02\n",
       "2      1981-01-03\n",
       "3      1981-01-04\n",
       "4      1981-01-05\n",
       "5      1981-01-06\n",
       "6      1981-01-07\n",
       "7      1981-01-08\n",
       "8      1981-01-09\n",
       "9      1981-01-10\n",
       "10     1981-01-11\n",
       "11     1981-01-12\n",
       "12     1981-01-13\n",
       "13     1981-01-14\n",
       "14     1981-01-15\n",
       "15     1981-01-16\n",
       "16     1981-01-17\n",
       "17     1981-01-18\n",
       "18     1981-01-19\n",
       "19     1981-01-20\n",
       "20     1981-01-21\n",
       "21     1981-01-22\n",
       "22     1981-01-23\n",
       "23     1981-01-24\n",
       "24     1981-01-25\n",
       "25     1981-01-26\n",
       "26     1981-01-27\n",
       "27     1981-01-28\n",
       "28     1981-01-29\n",
       "29     1981-01-30\n",
       "          ...    \n",
       "3620   1990-12-02\n",
       "3621   1990-12-03\n",
       "3622   1990-12-04\n",
       "3623   1990-12-05\n",
       "3624   1990-12-06\n",
       "3625   1990-12-07\n",
       "3626   1990-12-08\n",
       "3627   1990-12-09\n",
       "3628   1990-12-10\n",
       "3629   1990-12-11\n",
       "3630   1990-12-12\n",
       "3631   1990-12-13\n",
       "3632   1990-12-14\n",
       "3633   1990-12-15\n",
       "3634   1990-12-16\n",
       "3635   1990-12-17\n",
       "3636   1990-12-18\n",
       "3637   1990-12-19\n",
       "3638   1990-12-20\n",
       "3639   1990-12-21\n",
       "3640   1990-12-22\n",
       "3641   1990-12-23\n",
       "3642   1990-12-24\n",
       "3643   1990-12-25\n",
       "3644   1990-12-26\n",
       "3645   1990-12-27\n",
       "3646   1990-12-28\n",
       "3647   1990-12-29\n",
       "3648   1990-12-30\n",
       "3649   1990-12-31\n",
       "Name: Date, Length: 3650, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "IndexDataset=dataset.set_index(['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ba6f599b0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7c7bHhaUTFFhuld6XZez/cNVCd+3I3sEfCl3wouhTk9bi0jHzhclbZhIjmyhoUJWWUUuD/+/TN2Hl7iPuN0ggQdzMoncbq+dWIXWyXo6NhFyBIiM3zCJ/nBLA21wJXyh0GUbqSa1KjOXMeA46y0s/boj6WUKUk9e3SPOi1VPT8JFFt5WMJF1uInqgixBXnhiK4bfG63H5v39HywiuQDus3n3Ecl3N08X+z1oqk0AsirpFUUkZ/vVr9O3pIojmclm95wjaNKohuDUhZHuBeIs3WrHfLnd/zYDFZUUpZdrNWUmWhUMY3Ydr9jirWJ93rBAj3v0dXZrWtvR9dxeMRa+ViDd8fGGhy+LzRX9Y/q6TW0cqvK444qIqT6lGMrtK9aL/3Tjh+J9vVrsuz3gFysqopWvCetks7xSN8AGPdAD6BqlVFt1009bu15rD32p4+Ue+G8SsotLn2oC3gjAez8wVwhXTgeOtRVHA2YzpiW/X8GsIIxsqFciQ95Bp+dQ0vGphJ+rinYds9XVdGTMtinK9HN55gFt9qPDmkYmrsDFHTFEWXyh0sw7G++nHbAlxaoiMwsF22+7kEfONAJeGGQVFpTgYViBD9pLA+AWxZ4LXf7gIU1hK4NnYKWrlPQUboh4mvlDoZgx3kNr1/gkr8FWUvNF24eVyCeexr1eZfMoHu4ucbmTmo9Sa68EpxRFqnAlfFA2TZ/XhvC3PeiitfodYInj0/iA7j4/CPr5X6JscRHtMXZNTvltQR4axZjZw/oiSuraEo1lp91BujPMWT07D2zO3WJPvoAGRfio6bDFcWnEpxfHC4ph7H6xGigBnrlElH7qFa6fnoueh0B+YIDe3iVcQ1cV8odBFDjdWK1G0lffGz9Gr/djFrkJza8PJ+IXWF6F54oU49HlbD2BLrrlxwvIMt9qfjbdUr5MbaZduLPKOF2Lc/J3lcp0YXH5HRq/yhUIXCetNeH36JmaZFaJcLOrKw6eKsWr3EYz413xbllsk7Co0t6bi4elN3SCSu8gD+hz3fbEC93y+wvQ7H8/fie9srkFUjnKJfPMW7ai8uY1l6/8DE1bipR83YLvNndYKPiiFzokvFvPxxdtxaDw/ZT1W7znqeAVd1EahYsm5toHIFuuxwuKY1rFXeNxiWKWuuFn99UDFbJ9WcyUdPRV6KBcLzo8jk5yjBZgaI8uiqIRsSqGHIcNak1H+66e1OVinFVqwu3mEtblOZjFOWZZ9CHM250UcVjd+tBhD3p7ritzsAycrb1YS2MnCFQnrs3viUl4Gi/e5sHV9W9/v/dovuH/CCszfKqd0pBFf7BQVqWRl1Oo26sdo1doj4uDC3PtFaHqfPToLN3602NZvWTd96JV6nJKcaN8Oufr9hQCA1c9XLjqtX0Y3imFfMmY+TpwuwdXnnXVGHncpEdAXRcMtdMY+IzpRl8wIG5b+BQB7j/CvvWsXZaGH4QV/ql1EW/is4njt/mMdcAAw6M3fon62vtKGI+e4mWzKjPJcLmH92W3vmpdCHWdtyGX63dq9R5E5aqq0Yu1O8IVCL40QOxyJfUcKHMsK33QiAi8NAivISKvKiwMn7Edu+JnKLhd3LXReBlFxKcV7c7bjdAn7gv+0tWzVg/KPh/rInM32CrVXmoFKMA59odAPnbQW/XDMYXWfOZvz0Hf0L46OEQ8wW+icngM+nERVQKgLsZLLhe04op/hszbm4u/TN+GjeeyFa5yGo9o95c8XRXeXPj1pHY6cct9Y9IVCFxUnfNf4ZULk8MINF4EVpNvnfvSLGRCxThNtY5HbPnT9a8cLS5A5aiqTLCMnHbisvNZLflq333UZvlDoVsPqRBfCkA3LbtGS0jKMnbvdkVzZFvrRguIKm1cU0Qm/Rqw+dLv3bv8xPgWmndxhx93Dh+okpkInhIwjhOQRQtYZ3nuBELKXELJK+3+4m40sVQOXG98s34NXpzkLH5SdYOuNGVvw0o8bsExyvVU7GBXrur3uz6yixaGzznaTbCbB56ULnQx95/rcfxrdioX+KYChEd5/m1LaRfvfWUmTGJT6ZJPCzR8vRssn7U0zy8oot3A+KziZwup88NsOpt/xHiDFJfI3KnmdcIXI6qY7edp8cXL3oVPIHDXVU1v9vZDSwYiIx0NMhU4pnQsgetFLAVi10GUHX8zbesDSlHZb3onynWXjft+JQoFlt2RsYnINH52KSN1SVkbLd3ny8tfnx4gOWhdmlPA6Xdb2nyoqibl7MxZOh8pUxigbJzjZWPQAIeQWAMsAPE4pjTj/JYSMBDASAJo1a8YkSGSRWhFc/FYoFnrB9mb4auluya1RiEBkD5618Uz8Na8HibRQVcb2vz6dX/I6P8G6KPoegFYAugDIAfBmtC9SSsdSSrtTSrunp6czCeOZKtZLfLF4l/CCy+HD8ryXZzIfa2POMZRIzM/iJx/n+n3i3GrGHCy8elcsF3q4nFipgK3C2v5jAhK92UXEM5FJoVNKcymlpZTSMgAfAujBt1kVsexysXHMJTulepE8A+tGqq25xzHsn/PwpsUc5oB8l5hMLvvX71Lk8ooEsmuh/2PWVi5yN7P65Dn0NSuH4LGZkSdMCp0QkmH48woA66J9lwd3XdCC+zGv/WAh92PK4ptlu/GyxfqnvJRqnrabbrWkOo1AfD8grHLfF+Ypea2SlGh+sd26Fb9tsbdbUzR28yC5jZWwxS8BLATQhhCyhxByJ4DXCSFrCSFrAFwE4FE3G9m1WR03Dx+haLC/+GLxLtP0pvuPFnLf8u6FAAKlz2PDK+pkSPtGXI4jCh7uOCsGQ56NeHsRLsKYi6KU0usjvP2xC21xDKvFNvydefju3j6uW3yUUvxnkfiqPL1emw0glFmR9ynauWa8I2xkROwcOVWE2tVShMuVTSwL3Wvw6BpWFLDXosZ8sVNUBCJ8YQt3HMRzk9e7LkfhHo99ba3AhEzcUDLeUluxEdVer12XQCn0CYt3I3PUVKZSZjzDsh6duCri+6c9sBFGpkUhc3bAi0MSsnEq7MPFQveatrZAoBT6uN9DfuSco/atbZs7m02ZtHJvxPd92D+iIqMQSDgyrqcfBzkXfHbePPzVRVZCcm2IKbaYBtwJgVLoOiw3MyGBuF4H0Qt5xHk14U4tM6Wda+2B01e4hJsjZ+eBk7Z/w6OvfbaA73pXkYAZui8V+u19M/HQoNZRP2exHkUoWy8oNF4+fBGdMxYyrqcHbqEUWPP38OCVqdZCco3oYbVOMG7Q4oFS6FF4/tIOSK8ePdKApfOJGKhOp4H1Tc7ZD/C+xsv/OIzvV4jP/Dh51V7sP8onPayfYK0A5BSrm6kLikrx2cJsFJeW4ZdNeVxkx1LCdvp0vepVnDXGAr4oEh0RE/OMZbOLWRw3L5z66ds2qon52/xX59At9DTA/c5JR30BgwUILWw//NUqtKyfhl/+Z4AQmW5CiPU9BZv3H8fwjhmxv8iZWFkTS8soHvpyJfKOF2Jp9mHMZKwlGomxc7fjgYHRvQF2ggwa1nS/j/rGQv/09vMr/G12GVn8eQt3HGT4lT1ucLirLFbfuX7sIkfHZ8ELbqSSGGsfp4r4FWrWrUVeBRxkklEr1db3v1oavcSam8zZnG9a/WjP4VOYujYHS7ND+QHnbeVn9BwrNO87dvq/iM14vlHoA9o0qPC3mc87XivZxHoo5XPwK/qR28Yt5X5Mmc+xbXknMHujcyv0mu5NbX0/99iZ/rNg2wGhCcfM8EuSNhFayTcKPRyzJ2NQ1bnTGPI7x/NXbHaQFQO/JJtfIraNOaE0ETLj+S9+67fyKCNZ3PDRYmS9M9/xcVqlp+Gxwec4OoYXZolW8ESBC69idnFOaAVqv1wiZ4roFk3rVLVdCsxIbgDcBJEwi2rSc8/zxi9KJBa8JrOsl4MCeGhQayx6chCfhnAm1mzfznn3O4ctfbgd/KvQTa6knhL2o3nyQq1489qVHfHsJe2R4EChuzE1tWOpuqUDzcbctjw+ebnDCYI+Z8kZHs3V4/S50MimP98rqFwunLCinCKFO+09UoC7/yN3usrCsHMbITU5EalJ3rpl8bpeYWUgy7o2bqoY3q6eRMkK8ccHLzD9PNZ99pY697NCt3AlIw2o0T9tws/r7S0o/fO6Lnj2kvb48i+9bP2OJ7pl/s09fSz/5nRJKXYfOuVWkwAARwuK8dG8HcgcNTV2zK5LvV+G2jQ7l/Nenon7vljuidw9ZvDYTEcpxZjZW5nzro+9pbvjNjihVtVk08/9ZrD4Ng7dioXEo7pbl6a1MaJLE+cHcog++No0qmH5N49/vRo/GgrluhFqRwjBmF+2AQBOni5BSpLZ5id3NLqMQWemDA+eLMK0tfvx5jUCG8QAr/xFdqpWhdOifhqfRjDiMY+JY3yr0K10xlgbEvyE1cFHKS1/2P222f1qL8ZNXEEbHGZYybq4SMDeBiew3q/Jq84kn/PCEHOz38V0uXisz/tKob96Rcfy3VbWXC5nXl/4+i9o07AmUpP96WWyOj0+WVSK6lW02+qxzuaay8WBUnnh0vZ44b/2c4VY4VQR31wg4Rw6WYS6aezpIFhdLg9/FTk9tF0m3WfdfegGjWrGXoj1m8vFV9rthp7NMKhdQwDWFkWNN2P3oQLM4rAZQxZeyNQYC79s8DByW1/+9WpF0e3lmRX+/mHlXhw9VWz5wckjQsOJuut0Vu0Kf0+4qyfTcVjOY0CbdEx5oC+TvDDpHI7BD18pdCOsfdHp87aKpCgTFn+n8K5mEEgpxTM/rMW6vaHdhN8t38M1x4YRrxpRIl1+O/JP4JGJq/DwxJWWfxPep5INZeYeNslmyovw/tkuoyaX41jhgrPro4EFC91v+Fihx76NSYneOb0jp5xVukm0qNE/nrcTmaOmothKcn4XOXCiCJ8v2oUr31sAAHj8G/dKt5VRijs/XYohb7uziSgamaOmosDErSLyOaOnerWTBTKBELQ0LEpOvv9MCF+NVGveWCcuifAhLHISymN28tq0jdyLrzvFOxrPJlb0265Dp5DNkBzfCK9O1uWlmbG/ZNoOaw15e1Yo4sBt/21EDGP7lnFLAEDIg4UCmL0pD1tyQ5uIlv9xuHxmYIdYIWyROHDiNKavy4lY/f2wx8vVlVGKiXf3liY/vE+zuuycjFErz6NZG3Ij1hz+YK73Ni76VqGfn1nX0vcGvDHHkRyvTuejIdPVbnQx6DlPAJhmyuMtFwCuem8BLhljP8/IwLYNYn8pjILiUtzz+QrcGCGT5vNTxBUEZ1GGXy3djfQaVdBWC4Vl6Ttch4fAvptoQ9Zdny3DpQz9SQa+VegNBfm/wjus19cmjbpN9LbkvRGsGJYH4iMXV/bf3tizGV67smPE7/OKRGC5WvoMJNK5ex1RY8gqsWbdmaOm4uuluwGEqhi1fnoaALaHmVUXps7Bk0VYu+coMkdNxV+/XWNbnih8q9BFkWLnUe4lOJpOTw1va+l7l4yZzzX3uJFh52bgz1HSvUbbQGZ7lyzDrdbr/hYUl+K75Xukh7lt2n8ceces+XX1sEEnTeZ5ulYMkHfnhDaxfThvp6MawCzGzjfLQw+Tict2M8t1m/hT6Db7gFm1Eq/DYqB3a1a70nu1q1mPdT5d7Nxn3rZR5GiHaOcTTakMeXuuLbkslp7u7qE0tPC7ZCe/VL12MGacfOlHa3H1qcmJFf6WPfu0Ij7SvWZpt10LHQBKeGw9d5m4UOhO6j/2F5Dy0g1YCmXf0LMZxtzQDdmjs8rfq5qcaOsh6FQpZI/Osr1ZJlp4oN0ivyxtLw2TzboY/cTQNky/Y+WvQ63NumJht58lm8x4LW0W5DT1jJQULNIMcJ9Bd8iefVkhLhR6r9dmczuWqM0zn9/JtslCh1L7HoRXr+iIJrWrVjyO9p8XiDZNdhrvfYe2uYgAOKdhdQDWy7P5YZBH4t4BrcpfO7m/XF0uljYL8pF1tnafjfz96k6V3ptqyIV03FCObs5mPkWoeeNrhf5MVjvbv1mw3R9Fli9oXb/8dUsJCYz0FAmPXOysmowInA7y2/tmAgCuPb8pfri/L2Y91g+zHutv6beVojIZn/cEBKOG8bGaWRFhrJjdK7vpPADgndlbY97/8HDUeU+S94uYAAAdEklEQVRchG7N6sQWFoYx0d1tn8it/hWNmAqdEDKOEJJHCFlneK8uIWQmIWSr9q/9q8OBuy5safs3h0/ZT+pvpEmdqrG/5JDre1Sc+rFUl6dwFuUy6b6+uOuCFri7X0tPhG6alhw0tK+Mwc/ZtG41ZI/OwvmZdVEtJQlnN6iBtCrWNtaUhsljr9xDcU//VrG/KIGaFjcZOYUlJfZbFjI9zni0X4W/m9atZqtdTqhTzf7eBidYsdA/BTA07L1RAGZTSlsDmK39HRe8eU1n12X0alnPdRmxaJdRE89c0h6EkPIJuZWNN06svGkPXcj0O6PL5b3ftjPLZyFcofuRWA9sUeGvVvpOpIXJWC6jhjVT8d6N3TDutu74+ZGKyt3qjlhWEhPEOkFiSqOUzgUQvnQ/AsB47fV4AJdzbpdncbsDPDG0jSfyrxvRB/xRCyXLJi5jr+PavnEouqV1g5B/8/2buiHFQvqGpyatLX+9cLvYlLXhi6JeK0lmh+hRRNEVpp3Z2+rnhpiqXiuXLu8421b7YR0zMLBtw0r1BOxEcPkB1sdHQ0ppDgBo/9rfYudT3B6wLerx8ZdTSi3l7ObNq9M2OT5GnbQUZI/OwtBzM9A9M7Y3b/2+YzG/44TOZ9WK+ln4A+RWLeUBDzo0ZktWJZL/sZGjp1YM9wOzu8r/kyRuuO4cI4SMBDASAJo1a+a2ON9j5Xnx8uXn4tkf1pl+Z95Wfou/XolykYbJTfnPwmzXxNarXsW1Yxsx3t0Zj/bD0YLiCoVLzO7+1LU5Jp/aww8pou1CCPDdvX1QLSUx9pc5wGqh5xJCMgBA+zdqDA+ldCyltDultHt6uj9juo3oBTbswJIoyow/tW8Y8zuPTORThAAQYwFd2TWym+m2PpkA7JXe442ZmjnpYhK0eg6KV7AQCtusUTlPkqDnuVV9fvZT01xrw9KnL+Z+zPOa12FODWwXVgt9CoBbAYzW/p3MrUUe4f+u7hRxwataShJqpibhWGEJLm7XALM2xo5HtZcoyntWiojxXDWKBTOkQ6MKG51i4YaR52XDkVKKnKOFyHrHveRRPO+/mT/eqjszfGGUZ/vSa4iZFbmFlbDFLwEsBNCGELKHEHInQop8MCFkK4DB2t+B4pruTXFdj8guIr1PWumAVhYS7eJ1B4jMXOx+86ee2yRkuentHtDmzCw2UhqGcI4VlOBpw6IwCyI3Rw22MLsMEqJtgZgWOqX0+igfDeLcFt+gh8lZSQdx8rS9ZFWetAZtDvgPBIcOyiQlKQFFJewPsPBL+/5N5+HwqSIUlZShWd1qeHayeQre+yYsx+/bYkf2/PxIP/zpH+a5bViiXOzyzvVdsedwwZm6txzw625dN/D1TlFZ6DM+K4s4SS5kaxTdf3VxVqMuDpyQV9hB9AKuU99oWflsL/RvanIiMmpVRfN6aZZmgFaUOQDTRbnGWrqHlMTI3+F5RaskJaJVevWoqXuzR2dheMdGHCXGF2K2gAWM0nIL3XzA/bhmn+0txuHKOomlmKjGha3rc4l2oWFKJxYsmew6mYQG2sGNh53Z2bDsTI2E2w9ps92RY67vit+25KNZvcjfEW1A2A0N3pZ3wpG8mY/2Q77HSsmxohQ6A48PPgev/bTJ1Poe9d0afLV0N7I6Ztg6tnEhdvkzFzuqi8p7E5TVXaAfz99p+9jXRsl17nWcplT1grugdrUUT21ms2sOOM2r0rphDbRu6E4UlWgXqnK5ROCbe8zrLN7dvxWyR2eZWuhfaZVV9h21V8nGuKBYr3oVpjqXOtPW7mf+rZGaVUMPhjouhtHx2rAlWj/ystC9TLztQ3h4EL8aCBm13M/9ZERZ6BGwWq/UDZrWFdsBrDCicxMUFJWhVXoa5m7Jl90cU6wqn2/u6Y0CDjHkm3OPO/q9Bwz0mHjd5cKbuy5sgX/O3srlWB/d2p3LcayiLHSXsdM1Zz/eH+c1j/0wEW0xJSQQ3NCzWQUXUwuOKX3v7m8/a2Y0Fu2wVjGoZf009ItRvOTBgWfzaJIpIu/lkqf9EZgmO9DL6QPFGMteX9BuXx2l0B3w16FtY/rI7HSOVumVk+57CaOlllaF31bmJ4fZz2vvFCv35fEhbWxtarKCcQaW1TEDn93RE3/q0BA39WrOVU4kGtRgKwotehLxpMUatl7l0k6NpclWCt0BjWql4v2bzpPdDGEYB7YfXAVmyLICjaXP3r2xGxrVSsUHN3d3tFbiOoLvdUatqty2yvc9234qah59Q3TaBh3lQ3eIjIRCspRpBQs9xd9dx85t4+nTTXAQhsoDQoAbe1pPkndp58aYvo5fAi6rfHtPbxwtKEaf0b84Os5Ht5xv+zc8bvev/zuAyxqNXfw9Kj2A5PEpFD3E7vzMOq5smIoH7unfCk98u0aa/J2v2XMhpQv2AeukVUmyXDXKjGg5gtymZmoyaqaKn3X53uUy7rbuGNFFos8qxuN8+R+HBbXEfXQDXVShbDeRdQ6ypuJOkOleG9qBfdcoqzvUz/3b9wp9YNuGXCMu7CIjRreuJKVg3DHqdx+6j8esEIz5ivx6q4eey/Yw8GQ+JYv4XqEDkDK10SmTkFgwNVnONFJ3ufi5w+sE4RzcRH9gJyQQqbtZ1X2yRyB86Lf0bo7EBILnp5hnprPCtzF2iYbjV+uFBaPLhcfMZPL9fVFQLH7hyC48dQqlodDF3Yfs7SB2wn0DWtn+TXnSME5tuLB1fU5HUpgRCAs9KTEBt2qVbZzS3eYuUT/UfYzGi5d1iJnmwEjrhqE4+RtsREmY0blpbfRqaT+sjAd2opOac6rzqjPtoQvx+6iBXI9pRh2GQsj6A5sQwsVoGXuz2B2TTvDzrCAQCl2H9yYQK+ipR/3IOQ1r4Kw61tvfoEYqskdn4dLOjX3vQ7eTxTIzShZCFqqmJKJGajKaWOw3r1/VybFMltkU7/USWdEm8UagFLrCHkadxlIr1c/YUeh392+FFy/rwEVun1b2ZiTXni8nCyU1LIo6xa4b0wirtTzrsX7sMn28Yq4UehxDCGHuvD430G3lbE9J4ufSY9mktOhJZzlYWCzsMz5058rNrhvTKfWrp+DsBuzpcJXLJc4ZfWVHx8f461Dx+SvKKI271Kg6LIr1szt6SJnJNKoVcnW9/efOwmSWR7kQubM3tgeKjzWyQ5RC50BPDgt79zJEIujUZCxkUVLqQJnH4XOg3znpWPzUxRjZj192SBGw3KoyemZR9Lt7++DNa8Q9TGTj58eBUugGruzKVrVFdge4gDEkjII6cLnEoUaXjN1yhk64qG2D8n/PqlMNlzOODcdIGFyy87E7QSl0A2/9uQvT7/x6/50U2/nb5c7dTE5IdpBLZuzN/syQyRo+yeJD79K0NrJHZ6FL09oA5BstdpA9HnmXfrRDXCv0GhyS/wBAdU7HYYU1rIw68KG3aeSsBuP1Pdhi2XnkQhniID8IEAr39BM8ZlOylaRInJxqAgHuu4jdfeqUuFbokx/oi14tna/A15OUkQ4A7rygBfNvZcaSsyqIXx4fgHlPXMS3MTa5qpt3CiqLghCCHx+8wPbvljwlvkqS02ePk4fXHX1boEqSvJj7uFbodaql4KuR7DGyRuzENfPk2UvaMyvmMsruQ5dFrWrJaFrX/kaf1OQE3NK7OZfNZ37zsfJ6cJ/dwH5FrQY12aok6TDFuEi8PdUluluAOFfoPHN6y1wiZJ1SpyS5e/uTEwlapcvLhGnkiq5N8NKIc2U3QyEAp0aKkwe2k2g1HsS3Qk/wxuk7cZsA7BaYUz94LOpUS8EtvTMjfuZ4WmzzCDLcS6xRU15FhuXrp9lQjxZ1pbpbgIBkW2TFI/ocz17S3vZvRl/ZEVtyTwBgmx1MeaAvGtRIRWkZxYA26bhvAP8K9wmEuKcE/DPOA4Nf3HPSngEeiOSNa4WuW+jv39QNLerb9w8a6dOqHuZtPWD7d6zFKq4zRIk4sT4TEwg+vb0H+wEkUSUxAUUlEpLR28Ej+o9XPnM7inLiyF6OwmLLZTL8RlrNXQ9odEc2KiEkmxCylhCyihCyjFejRKHn8xh6boZj98O/b+zG9DuRa6lXGFwAfs+WmGozex9r9ZpYNDfJxJjVMQOLnhyEr+/ms/AuG6sphzNqpaJny3robTMRmd/xwpji4XS4iFLahVLqiYTHz2S1kyK3WgrbZIePj9BaT2JNEcAKBUXVKNWVRE+LB7Rp4Mpx22dEzoefPToLg9o1RKNaqejRQmxyKp2OTWoB4KdoIt0yY3TXMO2hafaQs0utqvKqkfkRj3iR+XFZZzkFo1n1Ew8LXR+wDWqYx8MbHx4ijAlKgSu7nSVAkveZcFdPTBzZS5i8D24+D/3PSed6zPCHcJ9W9XD1eWfur174xE4my1iMGmY/ad15zcWlSDDihZzvThU6BTCDELKcEDIy0hcIISMJIcsIIcvy8/MdirOAR/yWVrFTOScaNOzfaIi2iin4Dm4/0+fs+lySuFnFaJXzeniHzyYTEwheHHEmT7ye7I1n9FhalSTcZjF1cQ8tTe//XeO8KEgsbu3d3HUZLDidg/ellO4jhDQAMJMQsolSOtf4BUrpWABjAaB79+6uG4ayVuJZlSUPhV6irT4lxjhWhUHO0eHXo0VdLNl5yFReONWrqKm0m1RJTsDNvZvjty35uM6FIhndmtXG85e2rxCmp/dDJ3l2nPC1g0Ia4fypQ0P8vD630vvpNapg6dMXAwDGL/yDmzxeOHqUUkr3af/mAZgEQHq4hI/CVgHwmR4WaoWWO51Vy/R7RiXO88kaPdlVZCmjhrXFIxe35tgCc5xUr4mFF/vb08PbYcA56WhYMxX/ffACx7s1IzHhL70qFZFI11x+5zYx74esiJzsfRClBqrXJ5zMCp0QkkYIqaG/BjAEwDpeDWMl2vWu5rJ/i2VxM6tTBl6/2vn0UM8t8rfLzXdClrq0DF87ShFifVPRgDYVfbn39G+F1CiLpW7gpHpNLLwQ2RDOX/q1dH1DTqTDd2laG5Pv74sHB7rzsOYxm3WzDVd4YCOZEwu9IYD5hJDVAJYAmEopnc6nWexEuuATR/bCorAkQS3ry9+S3j6jJhfF9ufzmyF7dFZMS6ysgsvFsVhT0lIS8dCg0MD+9PYe3At4//M6tlTHCj5Ec212blrbtXWTBMHm8YgulQMs6kQxXgBvBAAwK3RK6Q5KaWft/w6U0ld4NoyVSA/QxATiiad7OKKbdHa6s81TXqJPq/r47A7pHr64RcZwEu3u+Od1XZE9Oguf3HZ++XvjDK+9SODCFiNZDgkJ3ty0LHq6bjVawC14L871OycdtavJWVxd8tSgCml8RYXLLnl6EH5+JPKaQGpygrCQPRnjKdaiv1vo1ZuAUH1XLxM4hR6ppyUpCx1A6MHWtVltIbIiPatGX9WJu+tl1XNDcLk2Nf5Th4Zcj21Gg5qpaFq3Wvk9LBP0dG5QIxXNoqQP3vjSUHzLMdLDDBlJs7w4hr1G4BR6NJdL+Pudm4pRbOH864au5a/v6Ossy6IzPLia5xAZA95Li6KEEGGKNlxKeoxNbTyI5kNvWrcqpj5kv/hGEAmeQo/wXuNaVSsN9teulFMT85JOZ6bmIiM9dIJo40TSqfUlVpFyG68ZqrMe648ZUdxAPIm22Pr6VZ3RobE7oZJ+I3DZFiNZKHXSUlBceiYzX8v6aa4o0wtb10dJKcXCHQejfu4V3LYsZViuxltfu1oyDpw4LUy2hwx1IRivNUslIxYi6fOVzw5GHQ51ZmPx0KDWKCgqifq5ceYtk+Ap9Cjvi5iO/+fOnhi/IDuqQtcfNmOu74oWksImz21SCyt2HYkaO+5njEvf4+/ogWd/WIe/XNhSjHCBGt0LFroXfOh9WtUToswB4LHB55h+bpx5yyR4Cj1KPxMV8mS2pV5vwqWSEogBwDNZ7XFlt7OEWVUiMeajblwrVWiImchc2N6M2XIflRcoNoFT6NEscVEWhden3ilJCejCaUH4qm5nxUw3IJIaEvPDiHQxecFCl0H42I7X62BG4BZFzbjLYe1ORUXevLYzbo0S2y7SYtWVaa9WZ/KOi3YJeCnaJaiE31J1zSsTOIVuNo4f1LaiX9Odf/Y5HbNOJsuiuDzCFma3kTHY4iVOWdYGG9l41UKvXsU7jo7gKXQT/2KtqsnY9sow3NNf0EJZGLL639t/7oLtrw6XJN19vGCoidpYBITisbe9MkyYPK8Q7kP3ylrC6ueHyG5COcFT6DHucVJigqvTcS8ol3AIIa4vKI253hthW6KRNe1PSpQzdKc+dAHeurazFNnhw9YrFrqXFmu9M1fgRPilnfGo+xsejPAsHOEnZBYEjtdrrueSufD1X4XJ7NC4lvBNPPr9jVdXkx2Cp9ANN71L09po7aHwPBmxu6KIU51avvgr4/SbRsnpElQquVwCPJ5YCZ7LxfD6h/v7euqmi2yJ7HzhcarfFS4SPpa9M7K9Q/AUuofv8tBzGwmTNaKL2OopNVLlT/ZkPLzjdWYig/C76+WxLosAKnSCzk1re26R7quRvVwNl5RNanIitvxNTuSFTJ2qyx7YtgGa1K4qsSXBRb/GCWHaSrY+v7tfS9zYs5nkVlREvlnlApPv7ytNdjSLLSUpcM/OSsg+R5kDvG5aCn4fNRCZo6ZKbEUwKdFqJyaHRfbIdqc+ObydVPmRCL6W8QiyrQnhiDSbJZroyuXiPmWaQk+qFIeuCCeQFroXkW1NiEbk1n8voN/drI4ZaN+4ptS2BI1wC716lSScOF2Cqini6wl4HaXQBRFf6lws+i5NOVv/Kz643r2xm4Q2BLugR6luoWsKvX+bdJydXh23982U2CpvohQ6Z8It01bpadief1LYivy8Jy4qt2jihTMKXbxs3eUicwY29aEL0LCmt4sXO2Fg2waYtHIvOjWphblb8kEAPBojP3m8ohS6y+gDXVTeCdmbTQa3b4iZG3KFyizzgFKVOQMLevm1Szs3xuD2DTFD61fxZa7YQy2KcibaIlm8uNDfuFp8no/yreESTHSlXMSQmpx4ZgamLnpUlEJ3mTjR45UQGf2h+1hl5kiKlwe2TPRZrsjMln5DKXSXOVVUCgCuFKX2JJpiE6ngdJdLggSNnpocGkJeyrgXVPRLrPR5dJQP3SUubtcQvVrWxQdzdwDwxtZ4EeiKXGTEicwol79d3hGt0qujX+t04bLjDf32Kgs9OspC54ze1Vqmp+GuC1uWuwPixYLTz1Lk+Q7pEMqR07J+mjCZOnXTUvD4kDZSZgfxhr7ordR5dBwpdELIUELIZkLINkLIKF6NCgL68C4pLQMAJIcnoggouvtDZO7qm3o2w/oX/yQ9wkfhLj1b1EW9tBTcN6CV7KZ4FmY/ACEkEcC7AAYD2ANgKSFkCqV0A6/G+ZHw2aAeE56YGB8WnB5xItJiJYQgzUN1HRXuULtaCpY/O1h2MzyNk1HQA8A2SukOACCEfAVgBIC4VujlaPrs6vPOwmcL/0CVOEjOBUC6i2nWY/2RHCcPT4UiHCcKvQmA3Ya/9wDo6aw5/kdXJrqL5flLO2DUsLaVMsUFlZpVk9GkdlU8nSUnE93ZHqpQpVCIxolCj2QGVVqvIISMBDASAJo181buYDe4qVdz5B8/jfsuCvn5EhMIqqXIcQd8ctv5KCwuFSozOTEBv48aKFSmQqEI4UTT7AFgrNhwFoB94V+ilI4FMBYAunfvHvgF6tTkRM/kSb6obQPZTVAoFAJx4gdYCqA1IaQFISQFwHUApvBplkKhUCjswmyhU0pLCCEPAPgZQCKAcZTS9dxaplAoFApbOHLuUkqnAZjGqS0KhUKhcEB8hF4oFApFHKAUukKhUAQEpdAVCoUiICiFrlAoFAFBKXSFQqEICIQKzC1MCMkH8Afjz+sDOMCxOX6QHW9yZcpW5xwfsv16zs0ppTGT7gtV6E4ghCyjlHaPJ9nxJlembHXO8SE76OesXC4KhUIREJRCVygUioDgJ4U+Ng5lx5tcmbLVOceH7ECfs2986AqFQqEwx08WukKhUCjMoJRK+x/AOAB5ANYZ3usMYCGAtQD+C6Cm9n4ygPHa+xsBPGl2HLflIpQL/lftvfUAHhYoOxXAEgCrNdkvirrW2ueJAFYC+FHgPc7W3l8FYJng/lUbwLcANmmf9RZwj9to56r/fwzAIwLP+VGtb60D8CWAVEFyH9ZkrrdyvgyyUwB8or2/GsAAw2/O097fBuAdaB4MAXJfQaj62wkr52vaJqcHcCQc6AegW9gFWQqgv/b6DgAva69vAPCV9roaQgM8M9px3JYLIANAN+39GgC2AGgvSDYBUN0wOBYD6CXiWmvvPQZgAqwpdF73OBtAfUn9azyAu7TXKQBqi7rW2vuJAPYjFIsson81AbATQFXts68B3CZA7rkIKfNqCGWCnQWgNedzvh/AJ9rrBgCWA0jQ/l4CoDdC4+snAMMEye2FkD5xrNClulwopXMBHAp7uw2AudrrmQCu0r8OII0QkgSgKoAihKyWaMdxVS6lNIdSukI73nGELI0mgmRTSukJ7TvJ2v+miyG8rjUh5CwAWQA+inWuPOWywEM2IaQmQgP3Y+2YRZTSI27LDfvtIADbKaUxN+VxlJ0EoKr2WTVEqEbmgtx2ABZRSk9RSksA/AbgCs7n3B7AbO13eQCOAOhOCMlAyJpeSENa9jMAl7stV/t7EaU0J9Z5WsGLPvR1AC7TXl+DM2XuvgVwEkAOgF0A3qCUWlbibsolhGQC6IqQpSxENiEkkRCyCqEp30xKKYtslnP+B4AnAJQxyHMilwKYQQhZrtWpFSW7JYB8AJ8QQlYSQj4ihKQJkGvkOoTcHqzYkk0p3QvgDe29HABHKaUz3Jarfb8fIaQeIaQagOGoWOaSh+zVAEYQQpIIIS0QcrM0RcgY22P4/R5YMNA4yOWKFxX6HQDuJ4QsR8iVUaS93wNAKYDGAFoAeJwQ0lK2XEJIdQDfIeTvY7UmbcumlJZSSrsgVMu1ByHkXLflEkIuAZBHKV3OcpKscrXP+lJKuwEYpv22nyDZSQhNq9+jlHZFSBGNEiAXAKCVd7wMwDcMMplkE0LqABihvdcYIWv6JrflUko3Avg7QpbtdISUYAmDXDPZ4xBS1ssQMk4WaDIsFb13QS5X5JSjN4FSugnAEAAghJyD0PQeCPndplNKiwHkEUJ+R2jKskOWXEJIMkLK/AtK6fciZRt+e4QQMgfAUISsAzfldgVwGSFkOEILszUJIZ9TSm0NdpbzpZTu036bRwiZhJBSmFvp4PxlzwWwxzAD+hYMCt3BPR4GYAWlNNeuTAeyKYCdlNJ87TffA+gD4HOX5e6glH4Mzb1FCHkVFa1mx7I1V86j+vcIIQsAbAVwGCHjSCdi0XsX5HLFcxY6IaSB9m8CgGcAvK99tAvAQBIiDaGFhE2y5BJCCEIdbyOl9C3BstMJIbW131QFcDEYroVduZTSJymlZ1FKMxFyA/xiV5kznm8aIaSG9ps0hAaMrYeXg3PeD2A3IaSN9r1BADa4Ldfw0+vhzN3CInsXgF6EkGpaPx+E0BqR23KNv2kG4Eownns02do5pWmvBwMooZRu0HzYxwkhvbRzvgXAZLflspybKU5XVZ38j9DNygFQjNCT+E6Ewpa2aP+PxpnNT9URmnauR2hA/a/ZcdyWC+AChCyZNTgTWjZcxDkD6IRQ2OAahBTbc6KuteF4A2AtyoXH+bZEaPqth2k+Lbh/dUFoqrwGwA8A6giSWw3AQQC1JIypFxFSsusA/AdAFUFy52nvrQYwyIVzzgSwGaEH1CwYIocQmiWsA7AdwL/03wiQ+7r2+zLt3xdYdaraKapQKBQBwXMuF4VCoVCwoRS6QqFQBASl0BUKhSIgKIWuUCgUAUEpdIVCoQgISqErFApFQFAKXaFQKAKCUugKhUIREP4fuDs3AJYUeSwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ba6f4ed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(IndexDataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmean=IndexDataset.rolling(window=12).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-06</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-07</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-08</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-09</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-10</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-11</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-12</th>\n",
       "      <td>17.341667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-13</th>\n",
       "      <td>17.008333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-14</th>\n",
       "      <td>17.308333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-15</th>\n",
       "      <td>17.825000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-16</th>\n",
       "      <td>18.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-17</th>\n",
       "      <td>18.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-18</th>\n",
       "      <td>19.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-19</th>\n",
       "      <td>19.641667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-20</th>\n",
       "      <td>19.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-21</th>\n",
       "      <td>19.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-22</th>\n",
       "      <td>18.525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-23</th>\n",
       "      <td>18.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-24</th>\n",
       "      <td>18.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-25</th>\n",
       "      <td>18.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-26</th>\n",
       "      <td>18.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-27</th>\n",
       "      <td>17.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-28</th>\n",
       "      <td>17.591667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-29</th>\n",
       "      <td>17.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-30</th>\n",
       "      <td>16.358333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-02</th>\n",
       "      <td>12.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-03</th>\n",
       "      <td>12.775000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-04</th>\n",
       "      <td>13.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-05</th>\n",
       "      <td>14.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-06</th>\n",
       "      <td>14.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-07</th>\n",
       "      <td>15.325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-08</th>\n",
       "      <td>15.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-09</th>\n",
       "      <td>15.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-10</th>\n",
       "      <td>15.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-11</th>\n",
       "      <td>15.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-12</th>\n",
       "      <td>15.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-13</th>\n",
       "      <td>15.325000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-14</th>\n",
       "      <td>15.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-15</th>\n",
       "      <td>15.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-16</th>\n",
       "      <td>14.725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-17</th>\n",
       "      <td>14.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-18</th>\n",
       "      <td>13.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td>13.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>13.525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>13.441667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-22</th>\n",
       "      <td>13.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-23</th>\n",
       "      <td>13.858333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>13.525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>13.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-26</th>\n",
       "      <td>13.825000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-27</th>\n",
       "      <td>13.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-28</th>\n",
       "      <td>13.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-29</th>\n",
       "      <td>13.841667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-30</th>\n",
       "      <td>13.716667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>13.575000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3650 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Temp\n",
       "Date                 \n",
       "1981-01-01        NaN\n",
       "1981-01-02        NaN\n",
       "1981-01-03        NaN\n",
       "1981-01-04        NaN\n",
       "1981-01-05        NaN\n",
       "1981-01-06        NaN\n",
       "1981-01-07        NaN\n",
       "1981-01-08        NaN\n",
       "1981-01-09        NaN\n",
       "1981-01-10        NaN\n",
       "1981-01-11        NaN\n",
       "1981-01-12  17.341667\n",
       "1981-01-13  17.008333\n",
       "1981-01-14  17.308333\n",
       "1981-01-15  17.825000\n",
       "1981-01-16  18.333333\n",
       "1981-01-17  18.733333\n",
       "1981-01-18  19.483333\n",
       "1981-01-19  19.641667\n",
       "1981-01-20  19.483333\n",
       "1981-01-21  19.183333\n",
       "1981-01-22  18.525000\n",
       "1981-01-23  18.375000\n",
       "1981-01-24  18.600000\n",
       "1981-01-25  18.583333\n",
       "1981-01-26  18.350000\n",
       "1981-01-27  17.883333\n",
       "1981-01-28  17.591667\n",
       "1981-01-29  17.166667\n",
       "1981-01-30  16.358333\n",
       "...               ...\n",
       "1990-12-02  12.675000\n",
       "1990-12-03  12.775000\n",
       "1990-12-04  13.200000\n",
       "1990-12-05  14.033333\n",
       "1990-12-06  14.791667\n",
       "1990-12-07  15.325000\n",
       "1990-12-08  15.541667\n",
       "1990-12-09  15.983333\n",
       "1990-12-10  15.675000\n",
       "1990-12-11  15.375000\n",
       "1990-12-12  15.483333\n",
       "1990-12-13  15.325000\n",
       "1990-12-14  15.266667\n",
       "1990-12-15  15.033333\n",
       "1990-12-16  14.725000\n",
       "1990-12-17  14.175000\n",
       "1990-12-18  13.925000\n",
       "1990-12-19  13.533333\n",
       "1990-12-20  13.525000\n",
       "1990-12-21  13.441667\n",
       "1990-12-22  13.625000\n",
       "1990-12-23  13.858333\n",
       "1990-12-24  13.525000\n",
       "1990-12-25  13.650000\n",
       "1990-12-26  13.825000\n",
       "1990-12-27  13.875000\n",
       "1990-12-28  13.875000\n",
       "1990-12-29  13.841667\n",
       "1990-12-30  13.716667\n",
       "1990-12-31  13.575000\n",
       "\n",
       "[3650 rows x 1 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "rstd=IndexDataset.rolling(window=12).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-06</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-07</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-08</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-09</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-10</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-11</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-12</th>\n",
       "      <td>2.575217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-13</th>\n",
       "      <td>2.350032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-14</th>\n",
       "      <td>2.680725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-15</th>\n",
       "      <td>3.474354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-16</th>\n",
       "      <td>3.405165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-17</th>\n",
       "      <td>3.362178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-18</th>\n",
       "      <td>3.640638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-19</th>\n",
       "      <td>3.504661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-20</th>\n",
       "      <td>3.654843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-21</th>\n",
       "      <td>3.594651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-22</th>\n",
       "      <td>4.116955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-23</th>\n",
       "      <td>4.240310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-24</th>\n",
       "      <td>4.012027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-25</th>\n",
       "      <td>4.021043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-26</th>\n",
       "      <td>3.916283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-27</th>\n",
       "      <td>3.343605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-28</th>\n",
       "      <td>3.226160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-29</th>\n",
       "      <td>3.128268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-30</th>\n",
       "      <td>2.040703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-02</th>\n",
       "      <td>1.831107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-03</th>\n",
       "      <td>1.995051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-04</th>\n",
       "      <td>2.369503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-05</th>\n",
       "      <td>3.006458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-06</th>\n",
       "      <td>3.329608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-07</th>\n",
       "      <td>3.523460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-08</th>\n",
       "      <td>3.439730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-09</th>\n",
       "      <td>2.770570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-10</th>\n",
       "      <td>3.111306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-11</th>\n",
       "      <td>3.376153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-12</th>\n",
       "      <td>3.302570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-13</th>\n",
       "      <td>3.458619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-14</th>\n",
       "      <td>3.503332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-15</th>\n",
       "      <td>3.528670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-16</th>\n",
       "      <td>3.473831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-17</th>\n",
       "      <td>2.960996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-18</th>\n",
       "      <td>2.496224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td>1.842100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>1.832596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>1.826800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-22</th>\n",
       "      <td>1.662487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-23</th>\n",
       "      <td>1.460049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>1.833588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>1.723369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-26</th>\n",
       "      <td>1.702471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-27</th>\n",
       "      <td>1.697659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-28</th>\n",
       "      <td>1.697659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-29</th>\n",
       "      <td>1.701047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-30</th>\n",
       "      <td>1.471445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>1.449843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3650 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Temp\n",
       "Date                \n",
       "1981-01-01       NaN\n",
       "1981-01-02       NaN\n",
       "1981-01-03       NaN\n",
       "1981-01-04       NaN\n",
       "1981-01-05       NaN\n",
       "1981-01-06       NaN\n",
       "1981-01-07       NaN\n",
       "1981-01-08       NaN\n",
       "1981-01-09       NaN\n",
       "1981-01-10       NaN\n",
       "1981-01-11       NaN\n",
       "1981-01-12  2.575217\n",
       "1981-01-13  2.350032\n",
       "1981-01-14  2.680725\n",
       "1981-01-15  3.474354\n",
       "1981-01-16  3.405165\n",
       "1981-01-17  3.362178\n",
       "1981-01-18  3.640638\n",
       "1981-01-19  3.504661\n",
       "1981-01-20  3.654843\n",
       "1981-01-21  3.594651\n",
       "1981-01-22  4.116955\n",
       "1981-01-23  4.240310\n",
       "1981-01-24  4.012027\n",
       "1981-01-25  4.021043\n",
       "1981-01-26  3.916283\n",
       "1981-01-27  3.343605\n",
       "1981-01-28  3.226160\n",
       "1981-01-29  3.128268\n",
       "1981-01-30  2.040703\n",
       "...              ...\n",
       "1990-12-02  1.831107\n",
       "1990-12-03  1.995051\n",
       "1990-12-04  2.369503\n",
       "1990-12-05  3.006458\n",
       "1990-12-06  3.329608\n",
       "1990-12-07  3.523460\n",
       "1990-12-08  3.439730\n",
       "1990-12-09  2.770570\n",
       "1990-12-10  3.111306\n",
       "1990-12-11  3.376153\n",
       "1990-12-12  3.302570\n",
       "1990-12-13  3.458619\n",
       "1990-12-14  3.503332\n",
       "1990-12-15  3.528670\n",
       "1990-12-16  3.473831\n",
       "1990-12-17  2.960996\n",
       "1990-12-18  2.496224\n",
       "1990-12-19  1.842100\n",
       "1990-12-20  1.832596\n",
       "1990-12-21  1.826800\n",
       "1990-12-22  1.662487\n",
       "1990-12-23  1.460049\n",
       "1990-12-24  1.833588\n",
       "1990-12-25  1.723369\n",
       "1990-12-26  1.702471\n",
       "1990-12-27  1.697659\n",
       "1990-12-28  1.697659\n",
       "1990-12-29  1.701047\n",
       "1990-12-30  1.471445\n",
       "1990-12-31  1.449843\n",
       "\n",
       "[3650 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rstd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ba4e7e320>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ba6f59860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rmean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ba4d86a20>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ba4e2d550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rstd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfset=adfuller(IndexDataset[\"Temp\"],autolag='AIC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-4.444804924611698,\n",
       " 0.000247082630036101,\n",
       " 20,\n",
       " 3629,\n",
       " {'1%': -3.4321532327220154,\n",
       "  '10%': -2.56719413172842,\n",
       "  '5%': -2.862336767636517},\n",
       " 16642.822304301197)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfout=pd.Series(dfset[0:4],index=['Test statistic','p-value','#Lags used','Number of observations used'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Test statistic                   -4.444805\n",
       "p-value                           0.000247\n",
       "#Lags used                       20.000000\n",
       "Number of observations used    3629.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in dfset[4].items():\n",
    "    dfout[key]=value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Test statistic                   -4.444805\n",
       "p-value                           0.000247\n",
       "#Lags used                       20.000000\n",
       "Number of observations used    3629.000000\n",
       "1%                               -3.432153\n",
       "5%                               -2.862337\n",
       "10%                              -2.567194\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "IndDataLog=np.log(IndexDataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5b90b984a8>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b90e55400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(IndDataLog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "movAv=IndDataLog.rolling(window=12).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-06</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-07</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-08</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-09</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-10</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-11</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-12</th>\n",
       "      <td>2.843061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-13</th>\n",
       "      <td>2.825167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-14</th>\n",
       "      <td>2.840438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-15</th>\n",
       "      <td>2.864190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-16</th>\n",
       "      <td>2.893282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-17</th>\n",
       "      <td>2.915389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-18</th>\n",
       "      <td>2.952959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-19</th>\n",
       "      <td>2.962422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-20</th>\n",
       "      <td>2.952786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-21</th>\n",
       "      <td>2.937745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-22</th>\n",
       "      <td>2.895868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-23</th>\n",
       "      <td>2.886052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-24</th>\n",
       "      <td>2.901455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-25</th>\n",
       "      <td>2.900451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-26</th>\n",
       "      <td>2.888823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-27</th>\n",
       "      <td>2.867690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-28</th>\n",
       "      <td>2.852254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-29</th>\n",
       "      <td>2.828550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-30</th>\n",
       "      <td>2.787204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-02</th>\n",
       "      <td>2.529168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-03</th>\n",
       "      <td>2.535581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-04</th>\n",
       "      <td>2.564687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-05</th>\n",
       "      <td>2.620441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-06</th>\n",
       "      <td>2.670336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-07</th>\n",
       "      <td>2.703696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-08</th>\n",
       "      <td>2.718997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-09</th>\n",
       "      <td>2.758283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-10</th>\n",
       "      <td>2.734120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-11</th>\n",
       "      <td>2.710711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-12</th>\n",
       "      <td>2.718833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-13</th>\n",
       "      <td>2.705987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-14</th>\n",
       "      <td>2.701446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-15</th>\n",
       "      <td>2.685633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-16</th>\n",
       "      <td>2.665580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-17</th>\n",
       "      <td>2.633202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-18</th>\n",
       "      <td>2.619804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td>2.596685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>2.596146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>2.590016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-22</th>\n",
       "      <td>2.605209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-23</th>\n",
       "      <td>2.623955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>2.595915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>2.606216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-26</th>\n",
       "      <td>2.619157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-27</th>\n",
       "      <td>2.622808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-28</th>\n",
       "      <td>2.622808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-29</th>\n",
       "      <td>2.620374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-30</th>\n",
       "      <td>2.612770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>2.602529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3650 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Temp\n",
       "Date                \n",
       "1981-01-01       NaN\n",
       "1981-01-02       NaN\n",
       "1981-01-03       NaN\n",
       "1981-01-04       NaN\n",
       "1981-01-05       NaN\n",
       "1981-01-06       NaN\n",
       "1981-01-07       NaN\n",
       "1981-01-08       NaN\n",
       "1981-01-09       NaN\n",
       "1981-01-10       NaN\n",
       "1981-01-11       NaN\n",
       "1981-01-12  2.843061\n",
       "1981-01-13  2.825167\n",
       "1981-01-14  2.840438\n",
       "1981-01-15  2.864190\n",
       "1981-01-16  2.893282\n",
       "1981-01-17  2.915389\n",
       "1981-01-18  2.952959\n",
       "1981-01-19  2.962422\n",
       "1981-01-20  2.952786\n",
       "1981-01-21  2.937745\n",
       "1981-01-22  2.895868\n",
       "1981-01-23  2.886052\n",
       "1981-01-24  2.901455\n",
       "1981-01-25  2.900451\n",
       "1981-01-26  2.888823\n",
       "1981-01-27  2.867690\n",
       "1981-01-28  2.852254\n",
       "1981-01-29  2.828550\n",
       "1981-01-30  2.787204\n",
       "...              ...\n",
       "1990-12-02  2.529168\n",
       "1990-12-03  2.535581\n",
       "1990-12-04  2.564687\n",
       "1990-12-05  2.620441\n",
       "1990-12-06  2.670336\n",
       "1990-12-07  2.703696\n",
       "1990-12-08  2.718997\n",
       "1990-12-09  2.758283\n",
       "1990-12-10  2.734120\n",
       "1990-12-11  2.710711\n",
       "1990-12-12  2.718833\n",
       "1990-12-13  2.705987\n",
       "1990-12-14  2.701446\n",
       "1990-12-15  2.685633\n",
       "1990-12-16  2.665580\n",
       "1990-12-17  2.633202\n",
       "1990-12-18  2.619804\n",
       "1990-12-19  2.596685\n",
       "1990-12-20  2.596146\n",
       "1990-12-21  2.590016\n",
       "1990-12-22  2.605209\n",
       "1990-12-23  2.623955\n",
       "1990-12-24  2.595915\n",
       "1990-12-25  2.606216\n",
       "1990-12-26  2.619157\n",
       "1990-12-27  2.622808\n",
       "1990-12-28  2.622808\n",
       "1990-12-29  2.620374\n",
       "1990-12-30  2.612770\n",
       "1990-12-31  2.602529\n",
       "\n",
       "[3650 rows x 1 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movAv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-06</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-07</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-08</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-09</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-10</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-11</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-12</th>\n",
       "      <td>0.148022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-13</th>\n",
       "      <td>0.135828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-14</th>\n",
       "      <td>0.152429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-15</th>\n",
       "      <td>0.186669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-16</th>\n",
       "      <td>0.182689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-17</th>\n",
       "      <td>0.181140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-18</th>\n",
       "      <td>0.192387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-19</th>\n",
       "      <td>0.184672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-20</th>\n",
       "      <td>0.193508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-21</th>\n",
       "      <td>0.189533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-22</th>\n",
       "      <td>0.227308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-23</th>\n",
       "      <td>0.234945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-24</th>\n",
       "      <td>0.219139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-25</th>\n",
       "      <td>0.219596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-26</th>\n",
       "      <td>0.213525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-27</th>\n",
       "      <td>0.189034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-28</th>\n",
       "      <td>0.181995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-29</th>\n",
       "      <td>0.175829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-30</th>\n",
       "      <td>0.130166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-02</th>\n",
       "      <td>0.154787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-03</th>\n",
       "      <td>0.164181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-04</th>\n",
       "      <td>0.186957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-05</th>\n",
       "      <td>0.215190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-06</th>\n",
       "      <td>0.230085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-07</th>\n",
       "      <td>0.242123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-08</th>\n",
       "      <td>0.237788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-09</th>\n",
       "      <td>0.168565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-10</th>\n",
       "      <td>0.197822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-11</th>\n",
       "      <td>0.219254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-12</th>\n",
       "      <td>0.214166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-13</th>\n",
       "      <td>0.226982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-14</th>\n",
       "      <td>0.230248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-15</th>\n",
       "      <td>0.230504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-16</th>\n",
       "      <td>0.225244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-17</th>\n",
       "      <td>0.195570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-18</th>\n",
       "      <td>0.171795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td>0.136021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>0.135408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>0.134600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-22</th>\n",
       "      <td>0.120493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-23</th>\n",
       "      <td>0.103085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>0.138337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>0.129471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-26</th>\n",
       "      <td>0.128451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-27</th>\n",
       "      <td>0.128333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-28</th>\n",
       "      <td>0.128333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-29</th>\n",
       "      <td>0.128422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-30</th>\n",
       "      <td>0.116021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>0.114201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3650 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Temp\n",
       "Date                \n",
       "1981-01-01       NaN\n",
       "1981-01-02       NaN\n",
       "1981-01-03       NaN\n",
       "1981-01-04       NaN\n",
       "1981-01-05       NaN\n",
       "1981-01-06       NaN\n",
       "1981-01-07       NaN\n",
       "1981-01-08       NaN\n",
       "1981-01-09       NaN\n",
       "1981-01-10       NaN\n",
       "1981-01-11       NaN\n",
       "1981-01-12  0.148022\n",
       "1981-01-13  0.135828\n",
       "1981-01-14  0.152429\n",
       "1981-01-15  0.186669\n",
       "1981-01-16  0.182689\n",
       "1981-01-17  0.181140\n",
       "1981-01-18  0.192387\n",
       "1981-01-19  0.184672\n",
       "1981-01-20  0.193508\n",
       "1981-01-21  0.189533\n",
       "1981-01-22  0.227308\n",
       "1981-01-23  0.234945\n",
       "1981-01-24  0.219139\n",
       "1981-01-25  0.219596\n",
       "1981-01-26  0.213525\n",
       "1981-01-27  0.189034\n",
       "1981-01-28  0.181995\n",
       "1981-01-29  0.175829\n",
       "1981-01-30  0.130166\n",
       "...              ...\n",
       "1990-12-02  0.154787\n",
       "1990-12-03  0.164181\n",
       "1990-12-04  0.186957\n",
       "1990-12-05  0.215190\n",
       "1990-12-06  0.230085\n",
       "1990-12-07  0.242123\n",
       "1990-12-08  0.237788\n",
       "1990-12-09  0.168565\n",
       "1990-12-10  0.197822\n",
       "1990-12-11  0.219254\n",
       "1990-12-12  0.214166\n",
       "1990-12-13  0.226982\n",
       "1990-12-14  0.230248\n",
       "1990-12-15  0.230504\n",
       "1990-12-16  0.225244\n",
       "1990-12-17  0.195570\n",
       "1990-12-18  0.171795\n",
       "1990-12-19  0.136021\n",
       "1990-12-20  0.135408\n",
       "1990-12-21  0.134600\n",
       "1990-12-22  0.120493\n",
       "1990-12-23  0.103085\n",
       "1990-12-24  0.138337\n",
       "1990-12-25  0.129471\n",
       "1990-12-26  0.128451\n",
       "1990-12-27  0.128333\n",
       "1990-12-28  0.128333\n",
       "1990-12-29  0.128422\n",
       "1990-12-30  0.116021\n",
       "1990-12-31  0.114201\n",
       "\n",
       "[3650 rows x 1 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movSTD=IndDataLog.rolling(window=12).std()\n",
    "movSTD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5b90b91c18>]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b90bcfc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(movAv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5b90b41828>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T9i2vLgYARAOwHOM+d9/j6/1tz0nbK7dzOKRbsI47x4lbpguAzdLrKvU9mWMAHENEXxLRHCIabfZDRDSBiBYQ0YLq6urMepwGuTrB5fcF2hD1v/yt1oxmuQcqusE1DYBHkG7CJX+d40TczWwv4/EsAtAHwEgA4wFMJqJWSV8SYpIQolIIUVlRUZFuX9MmF8Q9F4aMDTH/j0NiQtX/lpPe8auWvIXM5MI1kD/wwXSKE3GvAtBNet0VwFaTbd4WQjQIITYAWA1F7ANFtlhzCb9v9oUb9/rbIILLUDXH39GKkVOObutL+4VAkHkMjQ0n4j4fQB8i6kVEJQDGAZhi2OYtAGcCABG1g+KmWe9mRzMhJyz3gNqVIzRalhXH//arP9oN53u0TIB5BSwx3mN2jCMxwQJvQkpxF0JEANwEYCqAlQBeEUIsJ6K7ieh8dbOpAHYT0QoAnwL4rRBit1eddkouiLs53l+IO2pq43/XNcR8d48YM1QLo+SCORwK6R5m5/K+D1bh/qmr/e9MjuMkWgZCiPcBvG94b6L0twBws/ovZwha3J/5cgOWb02OlNh3uAG1DVE0LQ571nZYMpmDsWpUn3sALQeFboEQ6QVru/c8P/tb/G70sUF3I6fI6wzVoFfGeXrWRry6sCrp/Q+WbceIv3lc70ba9SDERQTkcw+ynrvczux1u6X3/eNwfQQ9b3sPUxYbp8XyAytDJRr08CwHyWtx3yOt4/nAx2t8b9/uettxwNt6N7Kg68TN51T8RFXI/C8/IHP55LlS+/51QCtU9q9PvvGtTT+xOpKH66Oo2stF2mTyWtxvfOGr+N+PTP8Gh+sjvrYfbJkBObY9CAs6mAxV06Qxv9q2aMjPh4sWU1/qocsvSOyO5R1vLfOvI42AvBZ3Iw9N89easbsQi8Peip7QuWX8f8gkQiF9bzqJoEfsfh5/TeCa5Ku42zyqy0ryc58zJa/F/fJh3XWvD9X5bLkHKCqyoMjd8Go08frCKmzanRgWB5XEFKjP3apwmI/XgTaBn6+Wu92pbFlabP1hAZLX4m5cnDkXrEi/MEZueLnr9ZEYbnl1MS6bNFtqVPmPciAUMmiCcM/lq7jbHcnmTVncZfJa3I2WlP+rEeWGpHndDW1EtOtgYpLYOKEaKH4lMQXsc5ev99I8dVHYHcuinLjYcoe8Fveg44tzxS2ji5zxoE9aWxGpofW7DgIIYkLV2Xt+tQ34X6wN8H5OJxcJs7jryHNxN1juAdU0D4Ikt4yH+65putbmiq0HMPHt5QByI4kpCHGVCcLIKG/iKD+x0WF3T+XC2gE1tQ3YnCPr5ua1uBsvA79Lr9ppitehiXLTXouL8SH6yoJEhWjyueRvLpaE9do99/WmvVi0eZ9uP9s1a+Jpm0FhdyhzwXIf8bfPcPr9HicoOiQ/H+8qRovN78U77G5pr69DvaB4Ky5G8Zq6fHv871ywpvwiqJK/Fz02CwCw9t4x3jaUA9gdylwQdzlxMmjy2nI3ljGP+FwCWL6pjZM9Xoue3i2T+Ls+GsNXm9wtAWwcGWzbnyhalguusCBHDcr7fpU/8L9Nv7Hbr1wyJJZv3R90F/Jb3I03uv/L7iXaDxnEPeK5I1Y/oapFCk18ezkufmyWq35Bu6qHOWBMBe6WCaL9oIMJvMLecvetGykZ+8gXQXchv8XdeIH7Xn5Aat8oci2aeusRs6wto1IXce9BZ2ckJqypICeX/becZfwKibUareUVdvNYOTF9nzvktbgbL/Av1/pbYl5uPmwYMnZrU+Zt27ryA2afu3f367Jhjb/r9/0W5IRqwBmqunpCgY9XvMFuv/J1nzMlz8Vdf7J/NLxHYO0b3TIxoRQz+3yNNwuF6wuHJYdCumm5W8XUAzniB/VNXC3eD8Dn70eb7y3Zhu/8/bOcWYwk6NFKrlXizOtoGeNw2Hs/tx6d5W4Q90N1kXgZ4o33jXW9bXky2eyid3P+IaYbJegbS5T8da05W3JDZvT45RaSr28/2rz1tcU4VB/FofqIb6n/drsV9DPm7x/5X1bcjvy23A2vX5i7yd/2dT53vbiv3XnQ27ZTDNHrIzHc/PIi/OX9lVm3pbfcjeJubrlv238EPW97z/XIHTP8Kxxm1b4/TFmUWKDDjza1ypO1Df4FKtiJe5BumXkb9gTWthV5Le5BPsl31tTqJnDtYnCve2a+6+3rfO6xZNd3XSSGN77egkkzsl/HXO9z139mtdfzNyqi/uTMDVm3L2Nmsfo3arDwuft0IbYplxZC96HJpkWKfNRF/EsOtNutIN0yX/tgpKSLI3EnotFEtJqI1hLRbTbbXUJEgogq3eti5hityN7tyn1pd9v+Ixh673Q0SHH1xglVmemrdrreB3nXX1mwGb9/c6nu83o3fe7STyVFhljst1ax0811bldsPYDtB2qT3g86zt0vIyNVhJTbpLLcN+465Po6xnbupiBj+1uXlQTWthUpxZ2IwgAeBTAGQH8A44mov8l2zQH8AsBc42eBYTjXRWoxpa837cWrUoq828hJPBrFRfYTi25fmPLNPXfDHkxbqX+AuCruUt+N8xpxn7vhO5rmZyN81z4zH5c+kSgzfO4jMzFnffLwWEDgqqfmYdKMdZk3lgV+uQvufS/hYvPjgaI9oGsbki33ukgUI//+GW6SVkNzg1y13HMhO9aIE8t9KIC1Qoj1Qoh6AC8BuMBku3sA3A8gWdkCImlCVbWkL3psFn772hLP2jU7zWXF9nPXNS4tJLLw2z2YtmJHygt9z2H30qTlthoMDw0rn7v2fjaW3SerdmLextS+TiGAz9dU4y/vr8q4LScEHS2zZd8R0zb3Hqr3JCqrSbEiH3dNWZ70mWbNT12+w9U2c3VC9YjJAy5onIh7FwCymVulvheHiAYD6CaEeNfFvmWNUdwbDPUIvPKFFoWSD2uq+tqTXfI9f//x2fjxfxekTJypc/FilNtqiJpb7ka09z9fU42a2oas+7D/SPa/kS1BlB+IWlzD8mhhwnMLcNVT81w5zjJNVJ/7gm+T/c3yQ9vddnMzzt1t95MbOBF3s9szfhSJKATgQQC3pPwhoglEtICIFlRXexPfLWOsbx01CM+ug3WWN0c21EeThTPVyjhuzwek2is3syb14q6/yI1iryE3v3FX9qUQHrZZHzfoEDkv27cUFfn4qssfHqpz17psUmR9Tcv9mu9gdOUU22iZAM9z0DH2ZjgR9yoA3aTXXQFslV43B3A8gM+IaCOAkwFMMZtUFUJMEkJUCiEqKyoqMu+1Q2I6cQ+hwXCXDf3LdFz99DzX260zmWBKtXiv27k+qS42+fNsbz75sBrFRrNajf2RffNXuXAOBndvZfmZX1aVZbRMAJa73KZmWBysc9dyt7tm5Tmdmlp/yn4EOaGag9ruSNznA+hDRL2IqATAOABTtA+FEPuFEO2EED2FED0BzAFwvhBigSc9TovEIT9cH0V1TR12GKIpZn6zy/VWPzGJfknllvnlS4vwq5e+dq0PqcLTDko+/h9Ik5KZIGzcMlZW6zuLE/aB12VS3Zw8tiWAOHerSqeyzmnifsBlkT26fbOk9+ojMWzZdwSHpbUTXK1jZPE+UbACa3ywNPe4dpQTUoq7ECIC4CYAUwGsBPCKEGI5Ed1NROd73cFsMBOWNTtqPG938hfJ/nMnCxa/tWhrym2ccv+Hq20/P+ziwiWy9ZhkuVvccu8t3eZa+wAQMdZ3lvDjnAN2E6reyY5xHsmsL01Vw8JtC9qsUNftbyzFqfd9ontgu+n6tDqUISLPRkhH6qPYtv9I6g1VhvVq44m7N10cxbkLId4XQhwjhDhKCHGv+t5EIcQUk21H5obVbj4cjsYEju/Swve+yMuejTq2veftbdx9yPbzL9cmRiwj+2bnIrN3y6j/e2xXWfn2AWB3wAsoeOktsLLcv9q0Fz1vew/f7KhBqRrV4vaEqnx/aa69d5coBsp/Z2+Mf+bmkn9W1xHBu+P8+zeXYvhfP7FdyU1uu/pgne+lTszI6wxV0yXXROqwxGwZcUyyWPbv3AIXDuqMj399Bo4yGc66TaqFSVZtT1iz2SZg2LllrHpx3oBOWbVpxO+FWMywstC99LlbjVi+3rQPAPDn91bGY7C9LBPwgydmY+eB2rgLRg6BdHPkYme5Gz9a+O3eJDdsJrz59RYAwFYb611+6HRpVdp4LPfGitlN9fL8zSn939lyUs/WSe8VhwkPjRuMPh2au14pMRoTWLntgO49OzeFkX2H67O6AeXrOGKw3K2Eze2LP5399YogMlRTPdQ+X1MdzyB2O/TXeG73HWnAT07vBUC/hqurbhmrDyi5P99/fBaG/WV61m1qIZ/bTZIT4/1Sm37q6kr0ad8c0ZgwTe7yk7wWd7Ob7cPl29G0OGTYTtmwPhLDh8u2Z30xmn1d9k9qMd5uZbU9M2sjxjw8Ewu/TUS9pGPJfrq6Oh5nv/DbPfjn9PRKl8o3VZ0q7jeMPArfH9IVx3duafodt4etdm4ZDa8XSAkiiSnVQ21477bx8xN1uSNGMT1YF4H2bJfrKrl5rq2MEAI8m1HVJkft3FraLg7v3S5+f7sxasiG/BZ3h2dbS8J4aNoaXP/8QsxZn/miHp+s2oFDKVZ80m6yEsO6YMaHjlPueXcFAGBddcLPbryh7r9kgO1vvP5VFQAlCeofH6dXulS+yd9drEyUzvxmF/5x6cD42rHGe9J1y91BuGMrj+p/7Kypxay11lFXnk6opnioHd+lRfzYu/1ANf7cwdpIfM5FnrD3w0VhdMu4ecy1eH6jW2vht3vjgq9pDRFQ2bMNAPfzCtIl+HgdD7EyaozXmnbxbdiliOO+w5lNPK3YegDXPpN6Lrkhoop7UUiXtpztMmE7bSyFPiZ+/j7tm+EbtfRwNveC/N2+HZV2fnhyDwDJsdCRaAzn/fMLnc8/W27430K8v3R7yu027TmMfYfr0aqsBPM37sHug/U4uXebrEX/ksdnY9Oew7jm1J6mn5sd2j2HFFdYW8l9kYodB2rx65cX4S8XnYCe7cpx+xtLsWxL8kLMRSGKC3kkJuLCk41bpqa2Ialmu1FAD9ZFTEcSfkwuEun3z8lIziklqltGvldnrd2FyyfPxeDurfDmDafq7oEStY5U0K7CvLbcZYtywhm9AShhSsaLUhN3zRee6QTYjG+cZd1q1o1d4k0m/P2jNejQwlwsyMTPL7uFshmyy8dLO4ZWywjuO9KQJOxty0sQiwnc8+4Kxwt3yzeyUdjN4q81Bt39MbbsO4IfPDEb1z+/EIPu/hgzsqy7sknt84Ej5iM2+fh8tWkvahuiGHLPxzjxz9PSauftRVswa91uvKwWvXtx3iYsNRH3lqUJEY7GRPz6ztSCXld9ECfc9RFeMRTbM14yh+oiqI8kt3HnW8vQ87b3MmrbiNVlWlIUQm1Ejq1P/H0gyyghbYT9n5nr4wUHL5+s1EfUJq41iBLlR4IuSZDX4i5fBwO6Kr7fEX0rki6QhmgM66oPxi+CTMW9yMaHLruItJN+dv+OmHnrmfH33Zhnbeognl5Dq5IJJFt16QxroyYWk/FQaKJtZj1GYgIrth3Ak19swI0OqwjaPYxSWajG2ttzN7iztu6mPebhp1p3qmvqcPFjs3DF5MwKp2rJWIs377PdR/k6isREvP2dNXUZ1V7XHl7vLtHnJhjvk/pozHNBs3K1dmzRFDsO1MVfyy6RbCc2Nct9ffUhy4KD2v1CIBSrDwOzB52f5Le4SxefdsAjUZFcUCwqMOofn8ezVc3KB1jxxldVuGLyHADJF78V2k1aHKYkC3f+xj1YsfWA2dccYVlIykQMw1KBM+MxScfIM4uWMY4UJjy3UPnc5IdjscQar06H03ZWaKolBBdv1ltbB9NM7rns37Px96mJJDEtQU1bgMSIduy1YfpCk0JbTtCO3ax1u3Hfh+YVLmfd9h3dsX9h7qZ4+098vg7Xq+chHZqrcerGEY7xFNRHYpiy2DoRr7YhimdnbcT0lZlXirR6pjdrUoRDatb1/324Ck99uSH+WbYumuKwtdWlCb/WL6KEW4Ytdw/R15bR/GDC9KKUqU3Durn5lcX4cq1i+S0yiIaMfFFqE7bGSoYxIfD/KO1pAAAgAElEQVSDJ2bj3EdmOm7fKNpWUTJmWlgsmddFhsnddIbwujj3uItLe0d/Y5hZUVEh4u4cpyMGW3FPke7+H0MFzmdnf+uoTUDp39wNe/CvT9fG37NzAynfUf7PRFhlZGv9pXnmS0Z2blWaNHMju24+XZ2+C8qqQFiS5W447prwaew5VI8/TlmO657NPMfRzi2jienjn63TrTDmZLLdDuOltrMmMbfVRL1vtE0ICUNy6z7nWa1ekNfiLgsFgZSJpmgMAkDvikQVxltfW6z7XjqWu8YX3+zClSd3d7TtVjVe1rjuoixYTkXOWJdFG3YbI3HMpvVkn7vRpZSOa0q++KvUIbyZjx8AbnohuX5ONCbSnu+wc8t4aTG9/tWWpPdSRWW9t3Qb1u6sweKqZP94OsijnvImRTiqwrySqPHQmz0H56zfjZ897+xho1uP12ZJRaO4G1/bGT9OsTrSxeGQ5UM92+vBeC8+8VniwVGcZLlT3Od+2xv61c/8Jq/FXb6oiRQfcyQmIIRAq9Ji/PF7yoJShwxpxZn4JZds2ee4HKkW8njhYF1ZfN3w0WmxpVrDdtr3jK4Js77JPvficEh3Eacn7olt31Cz+cymH4RQfOtG6iKx+ENqzQ77hcNH/u1TPDtro63POd1CYenkGyzfmizQToIiznpgRjpdMkV++JeWhFFiYVGnirr65UtfY9ykOfhg2Xas2p7aBSgfatmYMIpeXSQWd+GYcd8H2S2W0hCN4VFpxCRTUhRCfVTEI97038vOLWO81OQomJK45Z4YscrLOayvtr+evSSvxD0SjcVv+h0HanWWgvZEramNYNmW/SAijDnePAX+45Xpr2laWx+1dRXIn3RrrfjZj6qwHs47tTaMNeqtHgpmXZN97uEQ4UEpvj2VWyYWE7hi8hws2LjH9EGgWeLGyT0r/vGRfaEzrU8bdx/GH6cs1/XPaKmmezP3aGse2WNG8ogou9yZdMIT5f1aX33I8rupJubflgrUVe1J7TqQz2/1wcSkpfEw10djaG8RrQUkJmYz5Z3FW03FG1DOS30kiqq9yW1ka7kbr+//Sm48bflM7VQQkS4D3Y3RSqbklbgf/YcPcPnkOaipbcD6av1FQFAs1RfnbcLeww04cKRBZ7nKGCfcrJAtl6PaN7MVL3nbH6sp2p1bNbXc3mmGqeaeOO3odsrrNCZUZVdMcZjiIXZAamv0xfmb8OXa3bjkidmmowKzEgt2D4yYbrhvvp08opLdMsa2Uk2oGkknSMnst7NJmHltYZXj7xtFxqo4XDqjrmYOsnbl/snX5W5J6AFlxORlSLvdCKs4TKhtiKG6pi7ps2wtd7vrNu7C1bmAE9z8ymK8trAqq/YzJW/EXbNi5qzfgxP/PC3pQghJvjBASUgoNrHC0kG2ksOqP1/myasqceGgzknfu+yk7th439ikpBAZx5a7ut9Niuz3xezylMsQF4VCulCyVHHvrUoTiT9mYqLprZ2rp215Cbq2LlXak24gq/Uo5bkQ+eGTbQRpOrV+2pYnJzxlUxhs/a5D6HX7+zjrgc9Tbmt8sFiJjvb+RQa3nxlOrjO5GbnNWev0IaR1kainRdLsVn4qLQljy74juPmVxUmfZeJmlbHbpZ3qw0Qgcc0b55ueNCkB7gd5I+5yYkx9JJbk81WSCxJvCmE+xAaA1mXWoitzSFrwoq4hlmS5j+rXIe76SPeSd2p9xsXdonRBdzXUUo5/1x4E5U0kcQ9TXGiB1IKljXp6tC0zFRlNMGWhNm7WtDgcfwjLx84qNFEWItnv6SQD8uTebSw/S0fc25Qn3A7ag0tuvk15CR69fIjjss5LqpRR4tqdqX2zRkvZ6hxpx6O7RSKZjJP5Cdn9Y3es6yKxpGvhimHmQQaZlB+2K8/RzibT165Ur5HD9RFMXa5PinPywBIiYWR0a1Oq+8zrmkZW5I24/+CJWbrXZjWkZTfMln1HLN0yxhAuK+T6GbWRqKkrJdPEJMduGfVmamph1Txw6UD8+4cnoqfkV66Lx9kn9rMoRGjfPHGDpPIFa6JQFCJzt4z603I9DuON36qsOP5d+SN5lSgZeaSUbmb3iz852fKzdM6R/FDR9idmGJKPHdDJtob5xvvGxv82Wr8y9ZGYrmaNXEZXadeij+q106I0tZHixI1iVfWzXTP9KKZq75Gk68bqwfn5mmocqY+m5ZO2uy/tKr1ajQTNuOOtZfjpcwt1uSZW4v6rs/oon6slHjSL3TjCSLXEplfkjbiPNFhKptEhBnPeKqPUqbDKBcLMLHd9h6w/evTyIWhlGC04dctoF14Ti8zU5k2Lcc5xHVFWYvKwk0cyhs9SuWUS4h6ynVCV49qNbqtoTMSzguWSxcstkrjkY+K0XII2OiMiPHDpQN1nx3RQJrRXba9xXAlT9t9q51vuitMHxcq7R6NLq1Lbbe77YBUunzzXtH6MHdoDyEkhOuMDNxoTWG0oDyGf36iNFT9vwx7debljbD9dTLjM4boo+k38EBc++qVlPPjyrfvx8nwpnt/mlIdtDnw6q45tUhcTlw0Mq9tau3++XLdLZ7kbmb1+N16ctwmLN+/DvsP+LRzTKMW9tiGalEJuvFHMBMeYNGQVi+1UWHVumUgMkVjCHaS5drQW7GKhxw7ohJcnDNe9l7ZbxsKq0XbRzOoJhSi+aEZMGGrNpLLc1f4VhSl+8X9HesBqPyVb7sabbNX2GjwybnDSb282iXiQ2zT2r5mNlfzZb0di+i0jAChuMo3fntMXH/16BIb2Utw1TithyteG9rf+WlOTsVL8TmlJWCe+5SbWnRamqIUfaiU0UqEdGydzSsbJ3Ce/WI9zHpqhs6jl/Xv9qy048Z6PcagukhSppbSd+LtJcRi7Dyp9v0sNO060k/BDW5XGveBfX+J3ry+NjwbsLkk7F1Q65QfMkumsLHfN5frDJ+chImVZJ7cfw+1vLMUFj36Jix+fZb6RBzRKcb/zrWW46LFZ2CI98Y2+QzNx2uuw2uOB2kjKCIZ3Fm+Nr9ACAB+t2I5IVGBgt1b4y0Un4LPfKDVjEhOL9m0aLfdJM9Y7iqLQrCfZp64tmACkjuF+eNxgHNe5BWIxgcHdE4uMpGpadsuYlTDWHpzyZNYXhrK4vSvKMbBbcvG0nm3Nk3MaIuY3nJUbB1B8sVrIqfwA1Gp0p1NS/6Pl23Up+NoIz+zmtxKEs6QHjCy+xlwLpW9K516ctwl3vLXUcdZwJC7uqXfOOAJauU2x2uU5ALnZ17+qwu5D9diw65Dp+q3yfjcJh+LltIuLQrpj3WDi3rLaDy1j3M5Asjsyu2rqcO0z852V9dDuV+ktq/7JI9+9h+odVXVdX31IV+veSxqluGvp1PIQxxjfnemK651aKuGJj0w3T5bQ+PmLX+P5OYkh49eb9iESi6E4FMLlw7qjZdxyd6YeRivr7UVb8cmq1PH2cbeMJFwh6aKzG66u3XkQ4RChrCSMmBC6iadUQqKJ9tb9tfERjPyAovh2ifOg+aH/+L3+eHjcIEz91RmmrjGrB4uV5W6H/PPyw0ez9p1Opr63ZBsmPLdQ5yPXxEcXuZMYqiXRvU0ZHho3yLINY8lmTZw/WLYdz8/ZZLnPxvkE7fg5sdyNP0mG/5Vtktutrqkz7Y9O3A1uoeFHtU1qB0g9Ia6N/uw2s5v0fGHeJnyyaqejsh5av6xcUTLyffbqwirHLrkvvrGu/e8mjsSdiEYT0WoiWktEt5l8fjMRrSCiJUQ0nYh6uN/VBJpgbN2XuBmMlvt2w41iddxvH3Ns/O+N943FNrU0wIPTzIfp3+yowcPTkv2zvSvKMWf9HksfYyrM3CZ2FqmGWbRMmMh0mPro5UPw/i9Oj7/Wyh8QEeoiMTw3J5GcoVl0m/cc1l3cn63eiRtf+AoL1CJZ1TV12H+kAUUh0pWaNfO5a+fo+C4tccGgLigOh0wnta0sNPkcm91w//vxsKT3ZPEOhQh/Uxct0VwcdkunAcpC4vd/uMq0WqXm29aXubDehxd+MkznQjLGZG/dX6ublzBW+LQSMFk0ZYpCIQwyGRnpftMqL0L+26TdA7UNuvkH7XqTz0tJOITvDeys/gbwr/FD4ovGyL84btIc24f1l2t34ewHP8ezszYCAB42eUDaCf+ugwkjMFV5Z+1ykefdrNy0RsPE5dUzsyaluBNRGMCjAMYA6A9gPBH1N2z2NYBKIcQAAK8BuN/tjspohZrkWXCjj/oXLybXMJHRIkO0Ou8afTs0j/99x1v62hBCCHz3wRlJwn9Sz9bxpKmNu839xansTLMhtByXbzWU27JXcU3J0TIN0VjcHSMPu8cO6IT+nVvEX2vzFFv2HkmqVBiLCbwwdxNOv/9T9Lvzw3ja99VPz8d7S7bhoxWJyI09B+vRurxEJ9RmCxX85lUlBlkeZcj7qGFlhDXYWO4VzZvgVDWRS8Z4w11yYlcs/uPZOLq9cp7XW2Q8alwxeS4e+2yd6WevLqjC/iMN8VpBgOyz1W+78b6x6Npa/8A17uaURVtx9B8+iE+gGh/46a7sc+BIQ3zS2IqX52/GNnXhZyFEvHyEjFlkkrG2u/bQqpHCWIkSYYACQOvyEpyvir0xydBYI0nm5y9+jTU7DsZHsi1M8kPkB5CdK/JHT83TvY7GhOkD7nB9FPsO1+OLb3ZZhouGjOLucJTu1+LZTiz3oQDWCiHWCyHqAbwE4AJ5AyHEp0IITdXmAOjqbjf1aKFF3+46hNvfWIKGaAz1aSYqTFMn2YyTqr3aJfy9stsFsI5FrtprncLt1Ocuuwze/flpABJW70fLt6P/xKmmmbO3qIIpW3n/mblBZ0Ub0dwnrdWEnC0m0QoxkciArI/G8Lep1uUBDtQ2oFVpsc4NoF2/vzrrmKS8ATkm2bQGjUU7dtEyPxtxlOl3jOeXiGyPTTo88PEaXPO0XixChvPdpVUpzu7fAWYYBW3WOmW4ft4/vwCQHLVldp7scDLym71+N4b/9RMs2rzPkJSX2MZsxKBVQtUwqxkUjSVPUFq5itKJezezkHW1mxyGMsdiAsP/Oh03/C8xKtNGI9pCLlc+ORcHLPIuMrXc082gzhQnR6ELAPkxXaW+Z8V1AD4w+4CIJhDRAiJaUF2d+eo3s1W/5z8+XoMX523G9/75RVIMcCrMnv4A0LGlviSAPHQ2y34DnKU3pzrxsghVqKMKbSJJm4g0Rgitk4oSydbwFcO64/Erh+DW0X3Ru13y5OQz1wwFYF4KVbOsojGRZF1Z0RAVKAqHdDeuZp20LC3GHWP1A71ik4lXGauJ5HpdnLt+G6e5CXZkUkbgK8NKPJo1p7ll7hjbD5N+VOnot1oblvtzEuVxsU0W6uUWCURmTJ65Xvfw1Nf6T/19s0zshmgiekw7X1ZG9Xf+oc/QXbPDehlGM8tcfgA5XbBm35EG7Kypw4dS0tK3FiNv034YDQeH30u3sF2mOLkjzPpserqJ6EoAlQD+Zva5EGKSEKJSCFFZUVHhvJcGdhp8lW6sxznupG4AgN+NPlb3vuyu2Gbhn62Qkn+uGp79dIOWkLFkszI8N2Zxbth1CNv312KUdEPIPvcRx1SgU8tS3DDyaFPx1Po7oGuyP1bLrjtQ24BpDhdVqG2IojhMOteSbuk9w1XmJIrDSCwmcOfby+OvjUPbbJdSA5B1SV4gMYqTF29wShtDaYPpDibUr7S53poWhx2vjXu4PqozUlJNqBrpI7kzfzZSGUX1rijHiepi0X07Ku5AInJ0/sc8bD35aVq8TbbcU4i79tA0cx3WWTxQh/VqgzdvOCX+evmfzkl6yMj32ps3nIJpN59h+lt+We5O8mKrAHSTXncFkLTcChGdBeAPAEYIIZKr97iEWWGgbJEzBktLwvj+kK54/Sul2M/1zy/E1af0xMxvqtG1dSl2HUxuX87Uk33amaKNKjTB1oZ/0ZjA019uwJ/eWYEfn9ZL9x05Ky5VBEiXVqV49+enxWvaTzyvP+5+dwUAxJOdxk2a47i/h+sjKArpa/fooncM/bEamr//i9Nx7iMzTQVpyZb9umNvdMsYM3Tfuek0zF6fXlTChY9+qbsWAOXcyhNyADCoW6uUmZV/uuA4NGtahDMdliEAEm4nu/DMHm3LsONAbTyCxGm0z1XDe+CjFTssDZSYEHjjqyrda7O/rZCzYS8c1AUTTu+N1uUlGNC1FSp7tEZnKQ8lHCLb0e72/bW2fmnjwjLGPqZK3hr2l+lo16wEz11nMgFvcfBP79NOFypc3qQoWdylv+VtjeSS5T4fQB8i6kVEJQDGAZgib0BEgwH8G8D5Qoj06+WmQTblO7VEEbPYahnj5OUzszZiXfUhfNfCdyr7VMMGMzXdkf5AKVFFm0Cara7c9NGKHfjTO4oITzYUI5KjMEzmKJM4vkvLuJDLN4ZdHQwrt8Xh+iiKwiFdSVdtwhJIvpiNETLnHNcBl1Z2jc+lmInJhY9+qXv96gJ9pb0zjtFPpp7QtSUmnGHuh7dizPEdk94z0xgntYc6tSzFA5cOsi12ZUSb3LVzg/xoeE88cGkiWkTWF7tCYcd1bonvD7GeCisJh/Dn91bGX8uTt3ZCq7n95HmMcCgxnwNAJ+yAfhJ99Z9HJ/3myX+dbtme1lcj8iVjV5APUJIZ11UfwkxpQXttH60mY80MkqQSxg5HaX4tv5dSBoQQEQA3AZgKYCWAV4QQy4nobiI6X93sbwCaAXiViBYR0RSLn8uadA9MfMUlAl6/4RT84MSueO364bbfsYpDnbLIfH3Ii6Wbxjjk1HyvTs774oln4+WfJvqmDfGXbVEmq+zW3pTbTacQFqAPB2zfwroM8YPTvjEV/yOqW2ZYL/PiXMbaHsWGp8+/f1iJ+y8ZaDr5/PGKHfjOPz5L+s03DVEd8sMkHU7u3QYl4RDalJckuUUA86JTcgiumxgn7I2JbYByno/tmNhX7VxvvG8sHrwsOURQluV5G/ckfa5hLF/x+zeVSDEhRHxit7KH3ho9tmPzeMlgszBYK+SJ3iZFYVwwSAuXdGYJycbBWf3aY/zQ7rrQ00evGOLod2asSdznL89XphVTuYyev24YPvq14m4xXteN0ecOIcT7QohjhBBHCSHuVd+bKISYov59lhCigxBikPrvfPtfzJx0xP17AzvrimEd27EF/vaDgSmTO6wqLK62mOSRCwNZPfmd6G3LsuIkf+E7NgsOy8gXWrriLls6rWwiSR6Z/o1p5MChughCRBhzglLKwPgAMC5baDX0JUPq/n9mrMdP/rvA8cRuJrw0YTjW3DsGxWFKik4RQpgWnSpr4m4hKLN5msP1EeyTMqq1JfWKQiH0rmiGPmo4sFXxuyRIXx7CiFkJgIXf7kWv29/HS2p9F7NJ65+ecRTaNSvRFaZzurKVthLa0WoG8Un3Tnck8PIc1+SrTsJfLz4BXVop7d8xtp9pzZ73fnFa8ntLEwvaH6xTjrXVxLyWE3Jan3Y4Rp1fMO6l1XUNJJIjgRwT91wincL7VhULU2GMWtDo1a5cd5I05ImUXYY5gWzLW/88Rby+xsm9E4ks6SwbBwDXj0zE+rd0WO5YZtfBesxURzsf/PJ0TL9lpO5zp/W0tcP47KyNGHDXVNz7/kr7L7hIUSiUlClp9LVrmMVFd7QZ8VjxwS9Px/2XDMAPh/dM+sw4StSuMU3MtTkHswzfa0/thZN6Jvt8z7VYeaxdsyZJ6/kCwKX/ng0AmK8mrBnrF0VjAmMHdMKCO76bluWucZI62aqtQ7rrYB2e/nJjyu9pBpDsnjmtTzu8ecMpuPbUXqbfOa6zfV2ed5coQm9VbbTGJKxUuwq03d1nU95kiOSDr8sVt0yu8ZZJkoUV6VqwqdhzqD5ek8SKcJYLgGRCS0OMebq7LfuFzUJE5ZuobXmJbTXDfp1a6CwrQF88zAlLt+y3jC32CmV9XX0/f/Wy/sF68RDFp20WjfHstUPTbrNfpxa4tLKb6fmyuna1c3HrOX3RpCgUt1hlJn6vP169Xons0ET+6PbNLEekViVpj1eDA7SRmHHEK09qt5IMIqfGhfZ7ckG55+d+a7V5HO3nz+qvH4kM7t7a1nrWFqe5YWTyXMySqv2Yu363ZS7B70b3tfxdq3UhZP5y0Qm4/5IBaNakSFcjyUsanbjbXTjGgv3FYXI1JXj/kQbLCoQr7j4Hf734BIw/qZvp517y6OV6H6NdPRkrNMEuNQjX/33/BKy8JzHpVVMbMa2dPX6odUz1iL7Owl4zOVcXDe6Ch0x8zelSFKIky92YqDNUtTR/aOJGOaqiHL3aleOXo/qk3baZK+LW15foXmuHRrPcRx/fCav/PMa2jjkAXFrZDV/87kwM6d7asnKo1dqmmh9ee9DWGB64VnMszsVd2U6ObnFikJWVFGHazWfoJpaN/Gh4D0xW8wu0h9PbN52K6beM0M2RyVymRoidN6BTPDRa48Qe1ou92Ll5y0vCKCkKoWVZMS6t7IaDdRE8Pyf1A8wNGp24n9jDPMToJ6f3wnPXDUXb8hLcdObRAJRhpNurflnNxJeVFGH80O5JYVrxoVuai8HdMbaf7vVYtTTvb8/piy9+d6buM+M8hJ31YsXD4wZh4nn9k6y7y07qrrtZ66Mx00zdvjZp7if1bIO3bjw1ZR+sSjDL3Him3uq6YeRRuNDBcnKpKAqF4kldNbUNpglenVqVYuN9Y3HegOSEnXCI8OlvRuLX3z0m7baPbt8cT199kuXnssvHrFyDHUQUL3tgjNw5uXcbfPir082+BiC5+N4ZxyQe0tNuPgN3nX+c6fecjpg1UfzJ6Qm3oJMVqQDlmNnFs999wfE4q38HvHXjqZh2s5KNfkyH5jiqoplpYp9MOES41IGRdv0ZR+HCQZ1xmbqtWSbytFtGxDPONeqjsbQWRs+URifuo/p1wKzbvpP0fklRCP06tcDCO7+L60cehbEndMLPJSsqHXHtaXPynSwo7AY/li54AHj4skF46upKXD/iKHRuae4W0YbgmVw4lT3b4NrTeqGspCg+TJcno8cPTVzsWharTIcUPmcnQ1cnZ8goHMYH2dzfj8LMW/UPPyes3lGDqct34EBtA0646yPc9sbSpG3kUdsVw7rjnguPj7928mCywyoeXpnDGIFvVNErz2Iy1zhZ+Ptz++HYjsl5GVo0jjGhp015wrA5un1zyzDPdN0yxeGQbU1+wHqUkIpB3VolRYCFQoQHLxto8Q2l7EP75tbL9mm0LCvGQ+MGx0dTZiHWnVqWxidgZYxL+XlBoxN3QImbvebUnrr3ZIumWZMiPHrFENt1Fe144NKBmPyjSjx9TbI1VeYwtVlDJEz3rCgKh/CdYzsgHKJky1x9qQmf01WKrNDipbUKfoASX61xQpeWCJEy9NUoS3FzaqFqdvd9JvpodEF1aNEU3RysHWrFgLs+AgC8trAK/Tu10NVfl6OA7r3oBPzwZE+LnwJQCtmVNymK1+g3E2OnyKLbs21ZvACfkSvV/TJmflsFGiS14/BEypE+s29PNthkbvrO0fjpiN74/bnH2m7nlIsGW8f8N0Rj6Nq6DF/d+V38bvSxpj56GS0aLNUi9TLp1gnKhGBWbnWBief1x/ih3fH2oi149NN1lunnmchc86bFOMswxGrWpAgH6yK6oeCr1w93vMpLJtp+4aDOeGvR1pRWjWZlaDdvuuuLGrl19LE4tmNzjJCG4V2kxbNLS8JY/1clk/O/sxX/odFXb6SturD0necZC4omkK3yFk2LHE2qphsZlA4rth3QFZJrm6Gx4JRLK7viFSk565NbRsQf5H8Y2x9/GGt97JzSr1MLVPZorRt1jD6uY7y+yujjOlpOsJrlAZjh1HMk5zuUmywDeVLP1vjN2X3x0+cXol+nFji9T+YlS+y4/5IBuPW1xByH5uZsU14SL6Vgh2ZMOFmQXGO3TRVMt2i04k5EOKZD83hlPbswpGzo26E5Vu+oQbc2ZVi57YDuIaKFctmhRWBkIkIPjRuMu84/znQNyDduOAX7jzSgbXlJPMzLrMxvJrQsLU4Kz5PdKid0SYSVLZ54Nv4371vLuRCNji2bYvmfzrFdLFg7Qid0aYkbzzwK1z+vr6F+38UnJK23mcn8Qjos2bIPT15ViTU7DjoWt0y5/5KB2H+kIV4Er3eFfbneTHjfJN77h8N7xMX9gcsGYq5JWGQ45LyapmO3TJGUeGfxnWG922LRxLMd/V66DOvVBnM37MGY4zvqxD3V4iFGrhjWHd3blGFUGqUm7LKF3aLRirvGnef1R6uykvhK5EayvfVfuX44Vmw9gPH/UWbSO1n4u63QrNFUKdFWtCorgUm0my5u1tiWF5M1ckSA7F9vWVaMG0Ye7eg3ylOMQCqaN8HE8/rjvIGd4otkd21dimtO7YXr1Fo6xoWs2zh0FaTiquE98Ozs5CiGzXuOYFS/Drr1V73knguOx9Kq/bbRR9lgNjegRdx0btkUZSVFptcPIbmMgBVOVoACkieHh/duGy+14QdyNrhMuvXWmxaHLUuTGLl1dF8s33LA0iXmJo1e3MtKipIqOcpkK3MtS4t1K91cfUpPnNm3wnFltzvP648urUptswPdIu6WcTtECHprLFX4XaYQEa5VRXxtWJlA7F3RLC7sAHDewM74x8dr8OGvTkeXVqWu9eWs/h3i4t6jbVm89KscIWLG6z8bjnU73cugbd+iKWbdPsq133OC5lLThP/Mvolr9ez+HfDRih2IxETKh/P5AztjyuKtjsXdmOqv1XS6dXRf3P+h9foBXmPMVHYTp4aQGzTKCdVMcCvevbxJGMN6t3Xs/2tTXoLfnNPXU9+whtaE1yu92LlW3GJIj9a4aHAX3Cv5hgElS3jjfWNxbMcWGY+GzJATrYb1aoMZvz0TLZoW4a7v2fu5T+zRxlHYXC6jTQRqhnQoRJj3h1F47xenJV2391xwHHxSjlUAAAi2SURBVJ648kTT33nwskFYeXdyITAj2uSwcRTx23OOReeWTdG/U/aVVdPlk1tG4JHxgwEg46Uyc41Gb7n7xRNXnojHPltrOvGTK/xs5NGYvW43Kh3MBWSDU8ssG5oWh00LYXnFKdLorFmTYnRvW4Yld53jW/tB0qllKU7o0hK//m7Ctdm+eVO0b9406VyblUrQCIfI0UjKanL4tD7tMOv2UZjro2tGo3dFM3RvU4YPT+iYdjXRXCV3lcolrjutF+Zt2GMaa5oOo4/viNEmJWFziRN7tMZyB5YTk0x5kyLc/N1j8MDHa2zDNfOR0pIw3vl58kQr4G00khXawDPdxL9sKQqH8NgV5qOSxkjei/s5x3VMWoCByYynrznJdB3XfCFe7a/AxN2OHmq1x0xq52RKd7XNcUMbt7sraCiTdSPdoLKyUixYsCCQthnGjIZoDA9+vAbjh3bPKhEqn6iLRDFjzS6c1a991lm46RCJxkxXXGIAIloohEi5MG/eW+4M45TicAi32kReFSJNipyH+bkJC3v28BFkGIbJQ1jcGYZh8hAWd4ZhmDzEkbgT0WgiWk1Ea4noNpPPmxDRy+rnc4mop9sdZRiGYZyTUtyJKAzgUQBjAPQHMJ6IjBkI1wHYK4Q4GsCDAP7P7Y4yDMMwznFiuQ8FsFYIsV4IUQ/gJQAXGLa5AMCz6t+vARhFfsZNMQzDMDqciHsXAJul11Xqe6bbCCEiAPYDaAuGYRgmEJyIu5kFbsx8crINiGgCES0gogXV1dVO+scwDMNkgJMkpioAch5wVwBbLbapIqIiAC0BJFX8F0JMAjAJAIiomogyWQa8HYBdGXzPDQqxbd7nwmib97nxtO1ofUcn4j4fQB8i6gVgC4BxAC43bDMFwFUAZgO4BMAnIkVdAyFERmtmEdECJ6m3XlCIbfM+F0bbvM/513ZKcRdCRIjoJgBTAYQBPCWEWE5EdwNYIISYAuBJAM8R0VooFvs4LzvNMAzD2OOotowQ4n0A7xvemyj9XQvgB+52jWEYhsmUxpihOonbLoh2g2yb97kw2s7rfQ6s5C/DMAzjHY3RcmcYhmFSIYQI/B+ApwDsBLBMem8glOibpQDeAdBCfb8YSjbsUgArAdxu9ztetwslBPRT9b3lAH7pY9tNAcwDsFht+09+Hm/18zCArwG86+N53qi+vwjKpL6f11grKFnYq9TPhvtwnvuq+6r9OwDgVz7u86/V62sZgBcBNPWp3V+qbS53sr8ZtF0C4Gn1/cUARkrfOVF9fy2AR6B6OXxo914oCaEHneyvbZ+y/QE3/gE4A8AQw4GZD2CE+ve1AO5R/74cwEvq32VQbvSeVr/jdbsAOgEYor7fHMAaAP19apsANJNukrkATvbreKvv3QzgBTgTd7fO80YA7QK6xp4F8GP17xIArfw61ur7YQDbAfTw6RrrAmADgFL1s1cAXO1Du8dDEfYyKIEf0wD0cXmfbwTwtPp3ewALAYTU1/MADIdyj30AYIxP7Z4MRVOyFveccMsIIWYgOempL4AZ6t8fA/i+tjmAcjVZqhRAPRRLxup3PG1XCLFNCPGV+ns1UKwPY3kGr9oWQoiD6jbF6r+UkyhuHW8i6gpgLIDJqdp0s91McKNtImoB5SZ+Uv3NeiGE7aKyHuzzKADrhBApEwBdbLsIQKn6WRmSkxi9aLcfgDlCiMNCKWnyOYCLXN7n/gCmq9/bCWAfgEoi6gTFyp4tFMX9L4ALvW5XfT1HCLEt1X46ISfE3YJlAM5X//4BElmyrwE4BGAbgE0A/i6EcCzoXrarljoeDMWC9qVtIgoT0SIoQ8KPhRC+tQ3gIQC3Aohl2Gam7QoAHxHRQiKa4GPbvQFUA3iaiL4moslEVO5DuzLjoLhGMiWttoUQWwD8XX1vG4D9QoiPvG5X3f4MImpLRGUAzoU+U96NthcDuICIitQkzRPVz7pAybrXMKun5UW7rpLL4n4tgBuJaCEUd0e9+v5QAFEAnQH0AnALEfUOul0iagbgdSi+wUwtzLTbFkJEhRCDoJSFGEpEx/vRNhGdB2CnEGJhhu1l1K762alCiCFQylDfSERn+NR2EZSh9+NCiMFQRClpfQMP2gUAEFEJFLF4NYM2M2qbiFpDqfraS/2snIiu9LpdIcRKKKXDPwbwIRRBjGTQrl3bT0ER7gVQDJVZahuOamV50K6r5OwC2UKIVQDOBgAiOgbK8B9QfHQfCiEaAOwkoi+hDGnWB9UuERVDEfb/CSHe8LNt6bv7iOgzAKOhWAxetz0YwPlEdC6Uid0WRPS8ECKtGz+TfRZCbFW/u5OI3oQiEDOSftz9tmcAqJJGR68hA3HP4jyPAfCVEGJHum1m0bYAsEEIUa1+5w0ApwB43uN21wshnoTqAiOiv0BvTWfdturu+bW2HRHNAvANgL1QjCUNs3paXrTrKjlruRNRe/X/EIA7ADyhfrQJwHdIoRzKBMSqoNpV69Y/CWClEOIBn9uuIKJW6ndKAZyFDI9Fum0LIW4XQnQVQvSE4ir4JF1hz3Cfy4moufqdcig3T9oPswz3eTuAzUTUV91uFIAVXrcrfXU8snPJZNL2JgAnE1GZeq2PgjKv5HW78ne6A7gYGe67VdvqPpWrf38XQEQIsUL1edcQ0cnqPv8IwNtet5vJvtmS7YysG/+gnLRtABqgPJ2vgxIGtUb9dx8SCVfNoAxLl0O5sX5r9ztetwvgNCjWzRIkQtXO9WOfAQyAEoa4BIrATfTzeEu/NxLOomXc2OfeUIboWvjnH3y+xgZBGU4vAfAWgNY+tVsGYDeAlgHcV3+CIrjLADwHoIlP7c5U31sMYJQH+9wTwGooD6tpkCKQoIwelgFYB+Bf2nd8aPd+9fsx9f+7MtFUIQRnqDIMw+QjOeuWYRiGYTKHxZ1hGCYPYXFnGIbJQ1jcGYZh8hAWd4ZhmDyExZ1hGCYPYXFnGIbJQ1jcGYZh8pD/ByjMn1Sc5WijAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b90b502b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(movSTD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "DLMV=IndDataLog-movAv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-06</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-07</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-08</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-09</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-10</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-11</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-12</th>\n",
       "      <td>-0.255297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-13</th>\n",
       "      <td>-0.009758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-14</th>\n",
       "      <td>0.227615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-15</th>\n",
       "      <td>0.354686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-16</th>\n",
       "      <td>0.136851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-17</th>\n",
       "      <td>0.109902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-18</th>\n",
       "      <td>0.257885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-19</th>\n",
       "      <td>-0.088857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-20</th>\n",
       "      <td>-0.211946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-21</th>\n",
       "      <td>-0.036323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-22</th>\n",
       "      <td>-0.402662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-23</th>\n",
       "      <td>-0.218824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-24</th>\n",
       "      <td>-0.128866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-25</th>\n",
       "      <td>-0.097090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-26</th>\n",
       "      <td>0.039700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-27</th>\n",
       "      <td>0.097584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-28</th>\n",
       "      <td>-0.007345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-29</th>\n",
       "      <td>-0.087710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-30</th>\n",
       "      <td>-0.072509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-02</th>\n",
       "      <td>0.051049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-03</th>\n",
       "      <td>0.249430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-04</th>\n",
       "      <td>0.286019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-05</th>\n",
       "      <td>0.399984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-06</th>\n",
       "      <td>0.335347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-07</th>\n",
       "      <td>0.261577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-08</th>\n",
       "      <td>0.021843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-09</th>\n",
       "      <td>-0.112108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-10</th>\n",
       "      <td>-0.336225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-11</th>\n",
       "      <td>-0.303766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-12</th>\n",
       "      <td>-0.079775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-13</th>\n",
       "      <td>-0.272373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-14</th>\n",
       "      <td>-0.175717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-15</th>\n",
       "      <td>-0.090378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-16</th>\n",
       "      <td>-0.055510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-17</th>\n",
       "      <td>-0.001313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-18</th>\n",
       "      <td>0.225105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td>0.091162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>0.138221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>-0.017404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-22</th>\n",
       "      <td>-0.024992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-23</th>\n",
       "      <td>0.007934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>-0.293330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>-0.048989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-26</th>\n",
       "      <td>0.061864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-27</th>\n",
       "      <td>0.016250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-28</th>\n",
       "      <td>-0.012738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-29</th>\n",
       "      <td>-0.017685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-30</th>\n",
       "      <td>0.140890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>-0.037579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3650 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Temp\n",
       "Date                \n",
       "1981-01-01       NaN\n",
       "1981-01-02       NaN\n",
       "1981-01-03       NaN\n",
       "1981-01-04       NaN\n",
       "1981-01-05       NaN\n",
       "1981-01-06       NaN\n",
       "1981-01-07       NaN\n",
       "1981-01-08       NaN\n",
       "1981-01-09       NaN\n",
       "1981-01-10       NaN\n",
       "1981-01-11       NaN\n",
       "1981-01-12 -0.255297\n",
       "1981-01-13 -0.009758\n",
       "1981-01-14  0.227615\n",
       "1981-01-15  0.354686\n",
       "1981-01-16  0.136851\n",
       "1981-01-17  0.109902\n",
       "1981-01-18  0.257885\n",
       "1981-01-19 -0.088857\n",
       "1981-01-20 -0.211946\n",
       "1981-01-21 -0.036323\n",
       "1981-01-22 -0.402662\n",
       "1981-01-23 -0.218824\n",
       "1981-01-24 -0.128866\n",
       "1981-01-25 -0.097090\n",
       "1981-01-26  0.039700\n",
       "1981-01-27  0.097584\n",
       "1981-01-28 -0.007345\n",
       "1981-01-29 -0.087710\n",
       "1981-01-30 -0.072509\n",
       "...              ...\n",
       "1990-12-02  0.051049\n",
       "1990-12-03  0.249430\n",
       "1990-12-04  0.286019\n",
       "1990-12-05  0.399984\n",
       "1990-12-06  0.335347\n",
       "1990-12-07  0.261577\n",
       "1990-12-08  0.021843\n",
       "1990-12-09 -0.112108\n",
       "1990-12-10 -0.336225\n",
       "1990-12-11 -0.303766\n",
       "1990-12-12 -0.079775\n",
       "1990-12-13 -0.272373\n",
       "1990-12-14 -0.175717\n",
       "1990-12-15 -0.090378\n",
       "1990-12-16 -0.055510\n",
       "1990-12-17 -0.001313\n",
       "1990-12-18  0.225105\n",
       "1990-12-19  0.091162\n",
       "1990-12-20  0.138221\n",
       "1990-12-21 -0.017404\n",
       "1990-12-22 -0.024992\n",
       "1990-12-23  0.007934\n",
       "1990-12-24 -0.293330\n",
       "1990-12-25 -0.048989\n",
       "1990-12-26  0.061864\n",
       "1990-12-27  0.016250\n",
       "1990-12-28 -0.012738\n",
       "1990-12-29 -0.017685\n",
       "1990-12-30  0.140890\n",
       "1990-12-31 -0.037579\n",
       "\n",
       "[3650 rows x 1 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DLMV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-02</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-03</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-04</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-05</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Temp\n",
       "Date            \n",
       "1981-01-01   NaN\n",
       "1981-01-02   NaN\n",
       "1981-01-03   NaN\n",
       "1981-01-04   NaN\n",
       "1981-01-05   NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DLMV.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "DLMV.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1981-01-12</th>\n",
       "      <td>-0.255297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-13</th>\n",
       "      <td>-0.009758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-14</th>\n",
       "      <td>0.227615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-15</th>\n",
       "      <td>0.354686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-16</th>\n",
       "      <td>0.136851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-17</th>\n",
       "      <td>0.109902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-18</th>\n",
       "      <td>0.257885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-19</th>\n",
       "      <td>-0.088857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-20</th>\n",
       "      <td>-0.211946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-21</th>\n",
       "      <td>-0.036323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-22</th>\n",
       "      <td>-0.402662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-23</th>\n",
       "      <td>-0.218824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-24</th>\n",
       "      <td>-0.128866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-25</th>\n",
       "      <td>-0.097090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-26</th>\n",
       "      <td>0.039700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-27</th>\n",
       "      <td>0.097584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-28</th>\n",
       "      <td>-0.007345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-29</th>\n",
       "      <td>-0.087710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-30</th>\n",
       "      <td>-0.072509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-31</th>\n",
       "      <td>-0.041237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-01</th>\n",
       "      <td>-0.046669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-02</th>\n",
       "      <td>0.156632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-03</th>\n",
       "      <td>0.259821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-04</th>\n",
       "      <td>0.137097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-05</th>\n",
       "      <td>-0.047288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-06</th>\n",
       "      <td>-0.036813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-07</th>\n",
       "      <td>-0.156545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-08</th>\n",
       "      <td>0.012028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-09</th>\n",
       "      <td>0.360661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-10</th>\n",
       "      <td>-0.147432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-02</th>\n",
       "      <td>0.051049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-03</th>\n",
       "      <td>0.249430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-04</th>\n",
       "      <td>0.286019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-05</th>\n",
       "      <td>0.399984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-06</th>\n",
       "      <td>0.335347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-07</th>\n",
       "      <td>0.261577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-08</th>\n",
       "      <td>0.021843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-09</th>\n",
       "      <td>-0.112108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-10</th>\n",
       "      <td>-0.336225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-11</th>\n",
       "      <td>-0.303766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-12</th>\n",
       "      <td>-0.079775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-13</th>\n",
       "      <td>-0.272373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-14</th>\n",
       "      <td>-0.175717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-15</th>\n",
       "      <td>-0.090378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-16</th>\n",
       "      <td>-0.055510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-17</th>\n",
       "      <td>-0.001313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-18</th>\n",
       "      <td>0.225105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-19</th>\n",
       "      <td>0.091162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>0.138221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>-0.017404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-22</th>\n",
       "      <td>-0.024992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-23</th>\n",
       "      <td>0.007934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>-0.293330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>-0.048989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-26</th>\n",
       "      <td>0.061864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-27</th>\n",
       "      <td>0.016250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-28</th>\n",
       "      <td>-0.012738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-29</th>\n",
       "      <td>-0.017685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-30</th>\n",
       "      <td>0.140890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-31</th>\n",
       "      <td>-0.037579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3615 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Temp\n",
       "Date                \n",
       "1981-01-12 -0.255297\n",
       "1981-01-13 -0.009758\n",
       "1981-01-14  0.227615\n",
       "1981-01-15  0.354686\n",
       "1981-01-16  0.136851\n",
       "1981-01-17  0.109902\n",
       "1981-01-18  0.257885\n",
       "1981-01-19 -0.088857\n",
       "1981-01-20 -0.211946\n",
       "1981-01-21 -0.036323\n",
       "1981-01-22 -0.402662\n",
       "1981-01-23 -0.218824\n",
       "1981-01-24 -0.128866\n",
       "1981-01-25 -0.097090\n",
       "1981-01-26  0.039700\n",
       "1981-01-27  0.097584\n",
       "1981-01-28 -0.007345\n",
       "1981-01-29 -0.087710\n",
       "1981-01-30 -0.072509\n",
       "1981-01-31 -0.041237\n",
       "1981-02-01 -0.046669\n",
       "1981-02-02  0.156632\n",
       "1981-02-03  0.259821\n",
       "1981-02-04  0.137097\n",
       "1981-02-05 -0.047288\n",
       "1981-02-06 -0.036813\n",
       "1981-02-07 -0.156545\n",
       "1981-02-08  0.012028\n",
       "1981-02-09  0.360661\n",
       "1981-02-10 -0.147432\n",
       "...              ...\n",
       "1990-12-02  0.051049\n",
       "1990-12-03  0.249430\n",
       "1990-12-04  0.286019\n",
       "1990-12-05  0.399984\n",
       "1990-12-06  0.335347\n",
       "1990-12-07  0.261577\n",
       "1990-12-08  0.021843\n",
       "1990-12-09 -0.112108\n",
       "1990-12-10 -0.336225\n",
       "1990-12-11 -0.303766\n",
       "1990-12-12 -0.079775\n",
       "1990-12-13 -0.272373\n",
       "1990-12-14 -0.175717\n",
       "1990-12-15 -0.090378\n",
       "1990-12-16 -0.055510\n",
       "1990-12-17 -0.001313\n",
       "1990-12-18  0.225105\n",
       "1990-12-19  0.091162\n",
       "1990-12-20  0.138221\n",
       "1990-12-21 -0.017404\n",
       "1990-12-22 -0.024992\n",
       "1990-12-23  0.007934\n",
       "1990-12-24 -0.293330\n",
       "1990-12-25 -0.048989\n",
       "1990-12-26  0.061864\n",
       "1990-12-27  0.016250\n",
       "1990-12-28 -0.012738\n",
       "1990-12-29 -0.017685\n",
       "1990-12-30  0.140890\n",
       "1990-12-31 -0.037579\n",
       "\n",
       "[3615 rows x 1 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DLMV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ba119a9e8>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ba11cc710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(DLMV)\n",
    "plt.plot(movAv)\n",
    "plt.plot(movSTD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "def test_stationarity(timeseries):\n",
    "    rolmean = pd.rolling_mean(timeseries, window=12)\n",
    "    rolstd = pd.rolling_std(timeseries, window=12)\n",
    "    orig = plt.plot(timeseries, color='blue',label='Original')\n",
    "    mean = plt.plot(rolmean, color='red', label='Rolling Mean')\n",
    "    std = plt.plot(rolstd, color='black', label = 'Rolling Std')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean &amp; Standard Deviation')\n",
    "    plt.show(block=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=12,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:4: FutureWarning: pd.rolling_std is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=12,center=False).std()\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ba119ae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_stationarity(IndDataLog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ba08f4be0>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ba08c75f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(IndDataLog, color='blue',label='Original')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=12,center=False).mean()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ba0852518>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b90ac0da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pd.rolling_mean(IndDataLog, window=12), color='blue',label='Original')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: FutureWarning: pd.rolling_std is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=12,center=False).std()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ba07ca240>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ba08119b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(pd.rolling_std(IndDataLog, window=12), color='blue',label='Original')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:1: RuntimeWarning: invalid value encountered in log\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:2: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=6,center=False).mean()\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ba0876cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Log = np.log(IndDataLog)\n",
    "moving_avg = pd.rolling_mean(df_Log,6)\n",
    "plt.plot(movSTD)\n",
    "plt.plot(movAv, color=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=12,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/usr/lib/python3/dist-packages/ipykernel_launcher.py:4: FutureWarning: pd.rolling_std is deprecated for DataFrame and will be removed in a future version, replace with \n",
      "\tDataFrame.rolling(window=12,center=False).std()\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ba11a65f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_log_moving_avg_diff = df_Log-moving_avg\n",
    "df_log_moving_avg_diff.dropna(inplace=True)\n",
    "test_stationarity(df_log_moving_avg_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5ba0723e80>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b90b79940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Log_diff = df_Log-df_Log.shift()\n",
    "plt.plot(df_Log_diff)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}