{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> Analyse de la concentration de CO2, <center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "from scipy import interpolate\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"last_expr\"    \n",
    "# ‘all’|’last’|’last_expr’|’none’\n",
    "#https://jupyter-console.readthedocs.io/en/4.0.1/config_options.html\n",
    "pd.options.display.max_rows = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous récupérons les données les plus récentes sur le site en pointant sur un fichier au format .csv, si cette récupération est possible nous enregistrons une copie de ce fichier. Si pour une raison quelconque nous n'arrivons pas a faire ce téléchargement, nous travaillons sur le dernières données téléchargé."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(756, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .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></th>\n",
       "      <th></th>\n",
       "      <th>Excel</th>\n",
       "      <th></th>\n",
       "      <th>[ppm]</th>\n",
       "      <th>[ppm]</th>\n",
       "      <th>[ppm]</th>\n",
       "      <th>[ppm]</th>\n",
       "      <th>[ppm]</th>\n",
       "      <th>[ppm].1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>1</td>\n",
       "      <td>21200</td>\n",
       "      <td>1958.0411</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>2</td>\n",
       "      <td>21231</td>\n",
       "      <td>1958.1260</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>21259</td>\n",
       "      <td>1958.2027</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.44</td>\n",
       "      <td>316.19</td>\n",
       "      <td>314.91</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>21290</td>\n",
       "      <td>1958.2877</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "      <td>317.30</td>\n",
       "      <td>314.99</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>21320</td>\n",
       "      <td>1958.3699</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "      <td>317.86</td>\n",
       "      <td>315.06</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Excel                 [ppm]      [ppm]         [ppm]  \\\n",
       "0  1958    1     21200   1958.0411    -99.99      -99.99       -99.99   \n",
       "1  1958    2     21231   1958.1260    -99.99      -99.99       -99.99   \n",
       "2  1958    3     21259   1958.2027    315.70      314.44       316.19   \n",
       "3  1958    4     21290   1958.2877    317.45      315.16       317.30   \n",
       "4  1958    5     21320   1958.3699    317.51      314.71       317.86   \n",
       "\n",
       "       [ppm]         [ppm]      [ppm].1  \n",
       "0     -99.99        -99.99       -99.99  \n",
       "1     -99.99        -99.99       -99.99  \n",
       "2     314.91        315.70       314.44  \n",
       "3     314.99        317.45       315.16  \n",
       "4     315.06        317.51       314.71  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    raw_data = pd.read_csv(\"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/\"\n",
    "                           \"in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\",skiprows=56)\n",
    "except OSError as err:\n",
    "    print(\"Erreur lors du téléchargement : {0}\".format(err))\n",
    "    print(\"Nous téléchargeons les dernières données enregistrer sur notre PC\")\n",
    "    raw_data = pd.read_csv(\"monthly_in_situ_co2_mlo\")\n",
    "except:\n",
    "    print(\"Unexpected error:\", sys.exc_info()[0])\n",
    "    raise\n",
    "else:\n",
    "    raw_data.to_csv('monthly_in_situ_co2_mlo',index=True)\n",
    "\n",
    "print(raw_data.shape)\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous voyons que les noms des colonnes ne sont pas très représentatives, nous modifions  les noms des colonnes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['    ', '   ', '   Excel', '          ', '   [ppm]', '    [ppm] ',\n",
      "       '      [ppm]', '    [ppm]', '       [ppm]', '    [ppm].1'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "raw_data_1 = raw_data.copy()\n",
    "print(raw_data.columns)\n",
    "raw_data_1.columns = ['Yr','Mn','Date 1','Date 2','s1','s2','s3','s4','s5','s6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mn</th>\n",
       "      <th>Date 1</th>\n",
       "      <th>Date 2</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958</td>\n",
       "      <td>1</td>\n",
       "      <td>21200</td>\n",
       "      <td>1958.0411</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958</td>\n",
       "      <td>2</td>\n",
       "      <td>21231</td>\n",
       "      <td>1958.1260</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "      <td>-99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>21259</td>\n",
       "      <td>1958.2027</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.44</td>\n",
       "      <td>316.19</td>\n",
       "      <td>314.91</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>21290</td>\n",
       "      <td>1958.2877</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "      <td>317.30</td>\n",
       "      <td>314.99</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>21320</td>\n",
       "      <td>1958.3699</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "      <td>317.86</td>\n",
       "      <td>315.06</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Yr  Mn  Date 1     Date 2      s1      s2      s3      s4      s5      s6\n",
       "0  1958   1   21200  1958.0411  -99.99  -99.99  -99.99  -99.99  -99.99  -99.99\n",
       "1  1958   2   21231  1958.1260  -99.99  -99.99  -99.99  -99.99  -99.99  -99.99\n",
       "2  1958   3   21259  1958.2027  315.70  314.44  316.19  314.91  315.70  314.44\n",
       "3  1958   4   21290  1958.2877  317.45  315.16  317.30  314.99  317.45  315.16\n",
       "4  1958   5   21320  1958.3699  317.51  314.71  317.86  315.06  317.51  314.71"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données vide sont représentés par la valeur -99.99, nous remplaçons cette valeur par une valeur plus adéquate NaN dans une autre DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = datetime.date.today()\n",
    "data = raw_data_1.copy()\n",
    "data = data.drop(data[(data.Yr == date.year) & (data.Mn > date.month)].index)\n",
    "data = data.replace(-99.99,np.NaN);\n",
    "d = data[(data.Yr == date.year)].index\n",
    "i,k = d[0], d[-1]\n",
    "\n",
    "while k>=i:\n",
    "    if data.loc[k].isnull().any():\n",
    "        data = data.drop(k)\n",
    "    else:\n",
    "        break\n",
    "    k = k - 1\n",
    "    \n",
    "annee, mois  = data.Yr[0], data.Mn[0]\n",
    "d = data[(data.Yr == annee)].index\n",
    "i,k = d[0], d[-1]\n",
    "\n",
    "while i<=k:\n",
    "    if data.loc[i].isnull().any():\n",
    "        data = data.drop(i)\n",
    "    else:\n",
    "        break\n",
    "    i = i + 1\n",
    "InteractiveShell.ast_node_interactivity = \"all\"     \n",
    "data.head(3)\n",
    "data.tail(3)\n",
    "InteractiveShell.ast_node_interactivity = \"last_expr\"  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On visualise les lignes dont une donnée colonne est manquante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mn</th>\n",
       "      <th>Date 1</th>\n",
       "      <th>Date 2</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1958</td>\n",
       "      <td>6</td>\n",
       "      <td>21351</td>\n",
       "      <td>1958.4548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>317.24</td>\n",
       "      <td>315.14</td>\n",
       "      <td>317.24</td>\n",
       "      <td>315.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1958</td>\n",
       "      <td>10</td>\n",
       "      <td>21473</td>\n",
       "      <td>1958.7890</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312.44</td>\n",
       "      <td>315.40</td>\n",
       "      <td>312.44</td>\n",
       "      <td>315.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1964</td>\n",
       "      <td>2</td>\n",
       "      <td>23422</td>\n",
       "      <td>1964.1257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.01</td>\n",
       "      <td>319.36</td>\n",
       "      <td>320.01</td>\n",
       "      <td>319.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1964</td>\n",
       "      <td>3</td>\n",
       "      <td>23451</td>\n",
       "      <td>1964.2049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.74</td>\n",
       "      <td>319.41</td>\n",
       "      <td>320.74</td>\n",
       "      <td>319.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1964</td>\n",
       "      <td>4</td>\n",
       "      <td>23482</td>\n",
       "      <td>1964.2896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321.83</td>\n",
       "      <td>319.45</td>\n",
       "      <td>321.83</td>\n",
       "      <td>319.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Yr  Mn  Date 1     Date 2  s1  s2      s3      s4      s5      s6\n",
       "5   1958   6   21351  1958.4548 NaN NaN  317.24  315.14  317.24  315.14\n",
       "9   1958  10   21473  1958.7890 NaN NaN  312.44  315.40  312.44  315.40\n",
       "73  1964   2   23422  1964.1257 NaN NaN  320.01  319.36  320.01  319.36\n",
       "74  1964   3   23451  1964.2049 NaN NaN  320.74  319.41  320.74  319.41\n",
       "75  1964   4   23482  1964.2896 NaN NaN  321.83  319.45  321.83  319.45"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mn</th>\n",
       "      <th>Date 1</th>\n",
       "      <th>Date 2</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
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       "      <th>s6</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>21259</td>\n",
       "      <td>1958.2027</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.44</td>\n",
       "      <td>316.19</td>\n",
       "      <td>314.91</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>21290</td>\n",
       "      <td>1958.2877</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "      <td>317.30</td>\n",
       "      <td>314.99</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>21320</td>\n",
       "      <td>1958.3699</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "      <td>317.86</td>\n",
       "      <td>315.06</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1958</td>\n",
       "      <td>6</td>\n",
       "      <td>21351</td>\n",
       "      <td>1958.4548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>317.24</td>\n",
       "      <td>315.14</td>\n",
       "      <td>317.24</td>\n",
       "      <td>315.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "      <td>21381</td>\n",
       "      <td>1958.5370</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.19</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.22</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Yr  Mn  Date 1     Date 2      s1      s2      s3      s4      s5      s6\n",
       "2  1958   3   21259  1958.2027  315.70  314.44  316.19  314.91  315.70  314.44\n",
       "3  1958   4   21290  1958.2877  317.45  315.16  317.30  314.99  317.45  315.16\n",
       "4  1958   5   21320  1958.3699  317.51  314.71  317.86  315.06  317.51  314.71\n",
       "5  1958   6   21351  1958.4548     NaN     NaN  317.24  315.14  317.24  315.14\n",
       "6  1958   7   21381  1958.5370  315.86  315.19  315.86  315.22  315.86  315.19"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On ajoute un index 'périod' à la DataFrame, cet index représente la période de mesure. \n",
    "Cette date est mise dans au format compréhensible par pandas. On visualise toutes les lignes qui seront supprimées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(745, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mn</th>\n",
       "      <th>Date 1</th>\n",
       "      <th>Date 2</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1958-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1958-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>21259.0</td>\n",
       "      <td>1958.2027</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.44</td>\n",
       "      <td>316.19</td>\n",
       "      <td>314.91</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.44</td>\n",
       "      <td>1958-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>21290.0</td>\n",
       "      <td>1958.2877</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "      <td>317.30</td>\n",
       "      <td>314.99</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "      <td>1958-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21320.0</td>\n",
       "      <td>1958.3699</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "      <td>317.86</td>\n",
       "      <td>315.06</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "      <td>1958-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>43723.0</td>\n",
       "      <td>2019.7068</td>\n",
       "      <td>408.55</td>\n",
       "      <td>412.06</td>\n",
       "      <td>408.52</td>\n",
       "      <td>412.05</td>\n",
       "      <td>408.55</td>\n",
       "      <td>412.06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>43753.0</td>\n",
       "      <td>2019.7890</td>\n",
       "      <td>408.43</td>\n",
       "      <td>412.07</td>\n",
       "      <td>408.67</td>\n",
       "      <td>412.29</td>\n",
       "      <td>408.43</td>\n",
       "      <td>412.07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>43784.0</td>\n",
       "      <td>2019.8740</td>\n",
       "      <td>410.28</td>\n",
       "      <td>412.56</td>\n",
       "      <td>410.29</td>\n",
       "      <td>412.54</td>\n",
       "      <td>410.28</td>\n",
       "      <td>412.56</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>43814.0</td>\n",
       "      <td>2019.9562</td>\n",
       "      <td>411.85</td>\n",
       "      <td>412.78</td>\n",
       "      <td>411.88</td>\n",
       "      <td>412.79</td>\n",
       "      <td>411.85</td>\n",
       "      <td>412.78</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>744</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43845.0</td>\n",
       "      <td>2020.0410</td>\n",
       "      <td>413.37</td>\n",
       "      <td>413.33</td>\n",
       "      <td>413.11</td>\n",
       "      <td>413.05</td>\n",
       "      <td>413.37</td>\n",
       "      <td>413.33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>745 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Yr    Mn   Date 1     Date 2      s1      s2      s3      s4      s5  \\\n",
       "0       NaN   NaN      NaN        NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "1       NaN   NaN      NaN        NaN     NaN     NaN     NaN     NaN     NaN   \n",
       "2    1958.0   3.0  21259.0  1958.2027  315.70  314.44  316.19  314.91  315.70   \n",
       "3    1958.0   4.0  21290.0  1958.2877  317.45  315.16  317.30  314.99  317.45   \n",
       "4    1958.0   5.0  21320.0  1958.3699  317.51  314.71  317.86  315.06  317.51   \n",
       "..      ...   ...      ...        ...     ...     ...     ...     ...     ...   \n",
       "740  2019.0   9.0  43723.0  2019.7068  408.55  412.06  408.52  412.05  408.55   \n",
       "741  2019.0  10.0  43753.0  2019.7890  408.43  412.07  408.67  412.29  408.43   \n",
       "742  2019.0  11.0  43784.0  2019.8740  410.28  412.56  410.29  412.54  410.28   \n",
       "743  2019.0  12.0  43814.0  2019.9562  411.85  412.78  411.88  412.79  411.85   \n",
       "744  2020.0   1.0  43845.0  2020.0410  413.37  413.33  413.11  413.05  413.37   \n",
       "\n",
       "         s6           0  \n",
       "0       NaN  1958-03-01  \n",
       "1       NaN  1958-04-01  \n",
       "2    314.44  1958-05-01  \n",
       "3    315.16  1958-07-01  \n",
       "4    314.71  1958-08-01  \n",
       "..      ...         ...  \n",
       "740  412.06         NaN  \n",
       "741  412.07         NaN  \n",
       "742  412.56         NaN  \n",
       "743  412.78         NaN  \n",
       "744  413.33         NaN  \n",
       "\n",
       "[745 rows x 11 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.dropna().copy()\n",
    "period = [datetime.date(y,m,1) for y,m in zip(data['Yr'],data['Mn'])]\n",
    "period = pd.Series(period)\n",
    "newdf = pd.concat([data,period],axis=1)\n",
    "print(newdf.shape)\n",
    "#data['period'] = pd.Series([datetime.date(y,m,1) for y,m in zip(data['Yr'],data['Mn'])])\n",
    "\n",
    "#data1 = data1.set_index('period')   \n",
    "#data\n",
    "newdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Représentation graphique de la concentration  de CO2 de 1958 à nos jours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1['s1'].plot(title = 'concentration de CO2',);\n",
    "data1['s1'].plot(figsize=(15, 10),).grid(linestyle='--', linewidth=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous allons approximé la concentration de CO2 avec une droite, puis faire la différence pour \n",
    "n'obtenir que les variations de la concentration de CO2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(data1['Date 2'], data1['s1'])\n",
    "def predict(x):\n",
    "    return slope*x+intercept\n",
    "\n",
    "data_lineaire = data1.copy()\n",
    "data_lineaire['reg_lineaire'] = predict(data_lineaire['Date 2'])\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax1.grid(linestyle='--', linewidth=1)\n",
    "ax2.grid(linestyle='--', linewidth=1)\n",
    "#plot(figsize=(8, 5))  .plot(figsize=(15, 10), grid=True).grid(linestyle='--', linewidth=1);\n",
    "\n",
    "ax1.set(title = 'Concentration CO2',xlabel='Période',ylabel='Concentration (ppm)')\n",
    "ax1.plot(data_lineaire['s1'])\n",
    "#data1['s1'].plot()\n",
    "ax1.plot(data_lineaire['reg_lineaire']) \n",
    "data_lineaire['co2'] = data_lineaire['reg_lineaire']-data_lineaire['s1']\n",
    "ax2.set(title = 'Variation de la concentration CO2',xlabel='Période',ylabel='Concentration (ppm)')\n",
    "ax2.plot(data_lineaire['co2']); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le résultat n'est pas satisfaisant ...\n",
    "Nous pouvons faire une optimisation avec une fonction de la forme $a*(x-b)^2+c$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "def func(x,a,b,c):\n",
    "    return a*(x-b)**(2)+c\n",
    "\n",
    "data_cube = data1.copy()\n",
    "\n",
    "x = data_cube['Date 2']\n",
    "y = data_cube['s1']\n",
    "popt, pcov = curve_fit(func,x,y)\n",
    "\n",
    "def fa(x):\n",
    "    return popt[0]*(x- popt[1])**(2)+popt[2]\n",
    "\n",
    "data_cube['reg_cube'] = fa(data_cube['Date 2'])\n",
    "\n",
    "fig = plt.figure(figsize=(18, 5))\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax2 = fig.add_subplot(122)\n",
    "ax1.grid(linestyle='--', linewidth=1)\n",
    "ax2.grid(linestyle='--', linewidth=1)\n",
    "\n",
    "ax1.set(title = 'Concentration CO2',xlabel='Période',ylabel='Concentration (ppm)')\n",
    "ax1.plot(data_cube['s1'])\n",
    "ax1.plot(data_cube['reg_cube']) \n",
    "data_cube['co2'] = data_cube['reg_cube']-data_cube['s1']\n",
    "ax2.set(title = 'Variation de la concentration CO2',xlabel='Période',ylabel='Concentration (ppm)')\n",
    "ax2.plot(data_cube['co2']); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous recherchons l'oscillation lente, pour cela nous allons opéré en 2 etapes :\n",
    "- Recherche de la fréquence lente, par FFT. Pour cela nous devons faire une interpolation pour les quelque points manquant. Ce qui nous donnera une gamme de fréquence possible (échantillonne de la FFT)\n",
    "- Pour la gamme de fréquence spécifiées, nous  estimons tous les paramètres pour une approximation sinusoïdale (moyenne, amplitude et phase) par les moindres carrés ordinaires \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mn</th>\n",
       "      <th>Date 1</th>\n",
       "      <th>Date 2</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-08-01</th>\n",
       "      <td>1958</td>\n",
       "      <td>6</td>\n",
       "      <td>21351</td>\n",
       "      <td>1958.4548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>317.24</td>\n",
       "      <td>315.14</td>\n",
       "      <td>317.24</td>\n",
       "      <td>315.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-12-01</th>\n",
       "      <td>1958</td>\n",
       "      <td>10</td>\n",
       "      <td>21473</td>\n",
       "      <td>1958.7890</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312.44</td>\n",
       "      <td>315.40</td>\n",
       "      <td>312.44</td>\n",
       "      <td>315.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-04-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>2</td>\n",
       "      <td>23422</td>\n",
       "      <td>1964.1257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.01</td>\n",
       "      <td>319.36</td>\n",
       "      <td>320.01</td>\n",
       "      <td>319.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-05-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>3</td>\n",
       "      <td>23451</td>\n",
       "      <td>1964.2049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>320.74</td>\n",
       "      <td>319.41</td>\n",
       "      <td>320.74</td>\n",
       "      <td>319.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-06-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>4</td>\n",
       "      <td>23482</td>\n",
       "      <td>1964.2896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321.83</td>\n",
       "      <td>319.45</td>\n",
       "      <td>321.83</td>\n",
       "      <td>319.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Yr  Mn  Date 1     Date 2  s1  s2      s3      s4      s5  \\\n",
       "period                                                                    \n",
       "1958-08-01  1958   6   21351  1958.4548 NaN NaN  317.24  315.14  317.24   \n",
       "1958-12-01  1958  10   21473  1958.7890 NaN NaN  312.44  315.40  312.44   \n",
       "1964-04-01  1964   2   23422  1964.1257 NaN NaN  320.01  319.36  320.01   \n",
       "1964-05-01  1964   3   23451  1964.2049 NaN NaN  320.74  319.41  320.74   \n",
       "1964-06-01  1964   4   23482  1964.2896 NaN NaN  321.83  319.45  321.83   \n",
       "\n",
       "                s6  \n",
       "period              \n",
       "1958-08-01  315.14  \n",
       "1958-12-01  315.40  \n",
       "1964-04-01  319.36  \n",
       "1964-05-01  319.41  \n",
       "1964-06-01  319.45  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data.copy()\n",
    "\n",
    "data2['period'] = pd.Series([datetime.date(y,m,1) for y,m in zip(data2['Yr'],data2['Mn'])])\n",
    "data2 = data2.set_index('period')   \n",
    "data2.head()\n",
    "d = data2[data2.isnull().any(axis=1)]\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mn</th>\n",
       "      <th>Date 1</th>\n",
       "      <th>Date 2</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-05-01</th>\n",
       "      <td>1958</td>\n",
       "      <td>3</td>\n",
       "      <td>21259</td>\n",
       "      <td>1958.2027</td>\n",
       "      <td>315.700</td>\n",
       "      <td>314.44</td>\n",
       "      <td>316.19</td>\n",
       "      <td>314.91</td>\n",
       "      <td>315.70</td>\n",
       "      <td>314.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-06-01</th>\n",
       "      <td>1958</td>\n",
       "      <td>4</td>\n",
       "      <td>21290</td>\n",
       "      <td>1958.2877</td>\n",
       "      <td>317.450</td>\n",
       "      <td>315.16</td>\n",
       "      <td>317.30</td>\n",
       "      <td>314.99</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-07-01</th>\n",
       "      <td>1958</td>\n",
       "      <td>5</td>\n",
       "      <td>21320</td>\n",
       "      <td>1958.3699</td>\n",
       "      <td>317.510</td>\n",
       "      <td>314.71</td>\n",
       "      <td>317.86</td>\n",
       "      <td>315.06</td>\n",
       "      <td>317.51</td>\n",
       "      <td>314.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-08-01</th>\n",
       "      <td>1958</td>\n",
       "      <td>6</td>\n",
       "      <td>21351</td>\n",
       "      <td>1958.4548</td>\n",
       "      <td>316.685</td>\n",
       "      <td>314.95</td>\n",
       "      <td>317.24</td>\n",
       "      <td>315.14</td>\n",
       "      <td>317.24</td>\n",
       "      <td>315.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-09-01</th>\n",
       "      <td>1958</td>\n",
       "      <td>7</td>\n",
       "      <td>21381</td>\n",
       "      <td>1958.5370</td>\n",
       "      <td>315.860</td>\n",
       "      <td>315.19</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.22</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-10-01</th>\n",
       "      <td>1958</td>\n",
       "      <td>8</td>\n",
       "      <td>21412</td>\n",
       "      <td>1958.6219</td>\n",
       "      <td>314.930</td>\n",
       "      <td>316.19</td>\n",
       "      <td>314.00</td>\n",
       "      <td>315.29</td>\n",
       "      <td>314.93</td>\n",
       "      <td>316.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-11-01</th>\n",
       "      <td>1958</td>\n",
       "      <td>9</td>\n",
       "      <td>21443</td>\n",
       "      <td>1958.7068</td>\n",
       "      <td>313.210</td>\n",
       "      <td>316.08</td>\n",
       "      <td>312.46</td>\n",
       "      <td>315.35</td>\n",
       "      <td>313.21</td>\n",
       "      <td>316.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-12-01</th>\n",
       "      <td>1958</td>\n",
       "      <td>10</td>\n",
       "      <td>21473</td>\n",
       "      <td>1958.7890</td>\n",
       "      <td>313.270</td>\n",
       "      <td>315.64</td>\n",
       "      <td>312.44</td>\n",
       "      <td>315.40</td>\n",
       "      <td>312.44</td>\n",
       "      <td>315.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>1958</td>\n",
       "      <td>11</td>\n",
       "      <td>21504</td>\n",
       "      <td>1958.8740</td>\n",
       "      <td>313.330</td>\n",
       "      <td>315.20</td>\n",
       "      <td>313.62</td>\n",
       "      <td>315.46</td>\n",
       "      <td>313.33</td>\n",
       "      <td>315.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-02-01</th>\n",
       "      <td>1958</td>\n",
       "      <td>12</td>\n",
       "      <td>21534</td>\n",
       "      <td>1958.9562</td>\n",
       "      <td>314.670</td>\n",
       "      <td>315.43</td>\n",
       "      <td>314.77</td>\n",
       "      <td>315.51</td>\n",
       "      <td>314.67</td>\n",
       "      <td>315.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-03-01</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>21565</td>\n",
       "      <td>1959.0411</td>\n",
       "      <td>315.580</td>\n",
       "      <td>315.54</td>\n",
       "      <td>315.62</td>\n",
       "      <td>315.57</td>\n",
       "      <td>315.58</td>\n",
       "      <td>315.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-01</th>\n",
       "      <td>1959</td>\n",
       "      <td>2</td>\n",
       "      <td>21596</td>\n",
       "      <td>1959.1260</td>\n",
       "      <td>316.490</td>\n",
       "      <td>315.86</td>\n",
       "      <td>316.27</td>\n",
       "      <td>315.63</td>\n",
       "      <td>316.49</td>\n",
       "      <td>315.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-05-01</th>\n",
       "      <td>1959</td>\n",
       "      <td>3</td>\n",
       "      <td>21624</td>\n",
       "      <td>1959.2027</td>\n",
       "      <td>316.650</td>\n",
       "      <td>315.38</td>\n",
       "      <td>316.98</td>\n",
       "      <td>315.69</td>\n",
       "      <td>316.65</td>\n",
       "      <td>315.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-06-01</th>\n",
       "      <td>1959</td>\n",
       "      <td>4</td>\n",
       "      <td>21655</td>\n",
       "      <td>1959.2877</td>\n",
       "      <td>317.720</td>\n",
       "      <td>315.42</td>\n",
       "      <td>318.09</td>\n",
       "      <td>315.77</td>\n",
       "      <td>317.72</td>\n",
       "      <td>315.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-07-01</th>\n",
       "      <td>1959</td>\n",
       "      <td>5</td>\n",
       "      <td>21685</td>\n",
       "      <td>1959.3699</td>\n",
       "      <td>318.290</td>\n",
       "      <td>315.49</td>\n",
       "      <td>318.65</td>\n",
       "      <td>315.85</td>\n",
       "      <td>318.29</td>\n",
       "      <td>315.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-08-01</th>\n",
       "      <td>1959</td>\n",
       "      <td>6</td>\n",
       "      <td>21716</td>\n",
       "      <td>1959.4548</td>\n",
       "      <td>318.150</td>\n",
       "      <td>316.03</td>\n",
       "      <td>318.04</td>\n",
       "      <td>315.94</td>\n",
       "      <td>318.15</td>\n",
       "      <td>316.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-09-01</th>\n",
       "      <td>1959</td>\n",
       "      <td>7</td>\n",
       "      <td>21746</td>\n",
       "      <td>1959.5370</td>\n",
       "      <td>316.540</td>\n",
       "      <td>315.86</td>\n",
       "      <td>316.67</td>\n",
       "      <td>316.03</td>\n",
       "      <td>316.54</td>\n",
       "      <td>315.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-10-01</th>\n",
       "      <td>1959</td>\n",
       "      <td>8</td>\n",
       "      <td>21777</td>\n",
       "      <td>1959.6219</td>\n",
       "      <td>314.800</td>\n",
       "      <td>316.06</td>\n",
       "      <td>314.83</td>\n",
       "      <td>316.12</td>\n",
       "      <td>314.80</td>\n",
       "      <td>316.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-11-01</th>\n",
       "      <td>1959</td>\n",
       "      <td>9</td>\n",
       "      <td>21808</td>\n",
       "      <td>1959.7068</td>\n",
       "      <td>313.840</td>\n",
       "      <td>316.72</td>\n",
       "      <td>313.32</td>\n",
       "      <td>316.22</td>\n",
       "      <td>313.84</td>\n",
       "      <td>316.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-12-01</th>\n",
       "      <td>1959</td>\n",
       "      <td>10</td>\n",
       "      <td>21838</td>\n",
       "      <td>1959.7890</td>\n",
       "      <td>313.330</td>\n",
       "      <td>316.32</td>\n",
       "      <td>313.33</td>\n",
       "      <td>316.30</td>\n",
       "      <td>313.33</td>\n",
       "      <td>316.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-01-01</th>\n",
       "      <td>1959</td>\n",
       "      <td>11</td>\n",
       "      <td>21869</td>\n",
       "      <td>1959.8740</td>\n",
       "      <td>314.810</td>\n",
       "      <td>316.68</td>\n",
       "      <td>314.54</td>\n",
       "      <td>316.39</td>\n",
       "      <td>314.81</td>\n",
       "      <td>316.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-02-01</th>\n",
       "      <td>1959</td>\n",
       "      <td>12</td>\n",
       "      <td>21899</td>\n",
       "      <td>1959.9562</td>\n",
       "      <td>315.580</td>\n",
       "      <td>316.35</td>\n",
       "      <td>315.72</td>\n",
       "      <td>316.47</td>\n",
       "      <td>315.58</td>\n",
       "      <td>316.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-03-01</th>\n",
       "      <td>1960</td>\n",
       "      <td>1</td>\n",
       "      <td>21930</td>\n",
       "      <td>1960.0410</td>\n",
       "      <td>316.430</td>\n",
       "      <td>316.39</td>\n",
       "      <td>316.61</td>\n",
       "      <td>316.56</td>\n",
       "      <td>316.43</td>\n",
       "      <td>316.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-04-01</th>\n",
       "      <td>1960</td>\n",
       "      <td>2</td>\n",
       "      <td>21961</td>\n",
       "      <td>1960.1257</td>\n",
       "      <td>316.980</td>\n",
       "      <td>316.35</td>\n",
       "      <td>317.27</td>\n",
       "      <td>316.64</td>\n",
       "      <td>316.98</td>\n",
       "      <td>316.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-05-01</th>\n",
       "      <td>1960</td>\n",
       "      <td>3</td>\n",
       "      <td>21990</td>\n",
       "      <td>1960.2049</td>\n",
       "      <td>317.580</td>\n",
       "      <td>316.28</td>\n",
       "      <td>318.03</td>\n",
       "      <td>316.71</td>\n",
       "      <td>317.58</td>\n",
       "      <td>316.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-06-01</th>\n",
       "      <td>1960</td>\n",
       "      <td>4</td>\n",
       "      <td>22021</td>\n",
       "      <td>1960.2896</td>\n",
       "      <td>319.030</td>\n",
       "      <td>316.70</td>\n",
       "      <td>319.14</td>\n",
       "      <td>316.79</td>\n",
       "      <td>319.03</td>\n",
       "      <td>316.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-07-01</th>\n",
       "      <td>1960</td>\n",
       "      <td>5</td>\n",
       "      <td>22051</td>\n",
       "      <td>1960.3716</td>\n",
       "      <td>320.040</td>\n",
       "      <td>317.22</td>\n",
       "      <td>319.67</td>\n",
       "      <td>316.86</td>\n",
       "      <td>320.04</td>\n",
       "      <td>317.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-08-01</th>\n",
       "      <td>1960</td>\n",
       "      <td>6</td>\n",
       "      <td>22082</td>\n",
       "      <td>1960.4563</td>\n",
       "      <td>319.590</td>\n",
       "      <td>317.48</td>\n",
       "      <td>319.01</td>\n",
       "      <td>316.93</td>\n",
       "      <td>319.59</td>\n",
       "      <td>317.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-09-01</th>\n",
       "      <td>1960</td>\n",
       "      <td>7</td>\n",
       "      <td>22112</td>\n",
       "      <td>1960.5383</td>\n",
       "      <td>318.180</td>\n",
       "      <td>317.53</td>\n",
       "      <td>317.60</td>\n",
       "      <td>316.98</td>\n",
       "      <td>318.18</td>\n",
       "      <td>317.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960-10-01</th>\n",
       "      <td>1960</td>\n",
       "      <td>8</td>\n",
       "      <td>22143</td>\n",
       "      <td>1960.6230</td>\n",
       "      <td>315.900</td>\n",
       "      <td>317.20</td>\n",
       "      <td>315.69</td>\n",
       "      <td>317.01</td>\n",
       "      <td>315.90</td>\n",
       "      <td>317.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>42962</td>\n",
       "      <td>2017.6219</td>\n",
       "      <td>405.240</td>\n",
       "      <td>406.77</td>\n",
       "      <td>405.20</td>\n",
       "      <td>406.76</td>\n",
       "      <td>405.24</td>\n",
       "      <td>406.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01</th>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>42993</td>\n",
       "      <td>2017.7068</td>\n",
       "      <td>403.270</td>\n",
       "      <td>406.76</td>\n",
       "      <td>403.41</td>\n",
       "      <td>406.92</td>\n",
       "      <td>403.27</td>\n",
       "      <td>406.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-01</th>\n",
       "      <td>2017</td>\n",
       "      <td>10</td>\n",
       "      <td>43023</td>\n",
       "      <td>2017.7890</td>\n",
       "      <td>403.640</td>\n",
       "      <td>407.25</td>\n",
       "      <td>403.47</td>\n",
       "      <td>407.07</td>\n",
       "      <td>403.64</td>\n",
       "      <td>407.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>43054</td>\n",
       "      <td>2017.8740</td>\n",
       "      <td>405.170</td>\n",
       "      <td>407.43</td>\n",
       "      <td>404.98</td>\n",
       "      <td>407.22</td>\n",
       "      <td>405.17</td>\n",
       "      <td>407.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>43084</td>\n",
       "      <td>2017.9562</td>\n",
       "      <td>406.750</td>\n",
       "      <td>407.68</td>\n",
       "      <td>406.46</td>\n",
       "      <td>407.37</td>\n",
       "      <td>406.75</td>\n",
       "      <td>407.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-01</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>43115</td>\n",
       "      <td>2018.0411</td>\n",
       "      <td>408.050</td>\n",
       "      <td>408.00</td>\n",
       "      <td>407.58</td>\n",
       "      <td>407.52</td>\n",
       "      <td>408.05</td>\n",
       "      <td>408.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-01</th>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>43146</td>\n",
       "      <td>2018.1260</td>\n",
       "      <td>408.340</td>\n",
       "      <td>407.59</td>\n",
       "      <td>408.44</td>\n",
       "      <td>407.68</td>\n",
       "      <td>408.34</td>\n",
       "      <td>407.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01</th>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>43174</td>\n",
       "      <td>2018.2027</td>\n",
       "      <td>409.250</td>\n",
       "      <td>407.71</td>\n",
       "      <td>409.39</td>\n",
       "      <td>407.82</td>\n",
       "      <td>409.25</td>\n",
       "      <td>407.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-01</th>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>43205</td>\n",
       "      <td>2018.2877</td>\n",
       "      <td>410.300</td>\n",
       "      <td>407.51</td>\n",
       "      <td>410.82</td>\n",
       "      <td>408.00</td>\n",
       "      <td>410.30</td>\n",
       "      <td>407.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-07-01</th>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>43235</td>\n",
       "      <td>2018.3699</td>\n",
       "      <td>411.300</td>\n",
       "      <td>407.90</td>\n",
       "      <td>411.60</td>\n",
       "      <td>408.20</td>\n",
       "      <td>411.30</td>\n",
       "      <td>407.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>43266</td>\n",
       "      <td>2018.4548</td>\n",
       "      <td>410.880</td>\n",
       "      <td>408.30</td>\n",
       "      <td>410.97</td>\n",
       "      <td>408.42</td>\n",
       "      <td>410.88</td>\n",
       "      <td>408.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>43296</td>\n",
       "      <td>2018.5370</td>\n",
       "      <td>408.900</td>\n",
       "      <td>408.08</td>\n",
       "      <td>409.43</td>\n",
       "      <td>408.65</td>\n",
       "      <td>408.90</td>\n",
       "      <td>408.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>43327</td>\n",
       "      <td>2018.6219</td>\n",
       "      <td>407.100</td>\n",
       "      <td>408.63</td>\n",
       "      <td>407.33</td>\n",
       "      <td>408.91</td>\n",
       "      <td>407.10</td>\n",
       "      <td>408.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>2018</td>\n",
       "      <td>9</td>\n",
       "      <td>43358</td>\n",
       "      <td>2018.7068</td>\n",
       "      <td>405.590</td>\n",
       "      <td>409.09</td>\n",
       "      <td>405.66</td>\n",
       "      <td>409.18</td>\n",
       "      <td>405.59</td>\n",
       "      <td>409.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>2018</td>\n",
       "      <td>10</td>\n",
       "      <td>43388</td>\n",
       "      <td>2018.7890</td>\n",
       "      <td>405.990</td>\n",
       "      <td>409.62</td>\n",
       "      <td>405.83</td>\n",
       "      <td>409.44</td>\n",
       "      <td>405.99</td>\n",
       "      <td>409.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>2018</td>\n",
       "      <td>11</td>\n",
       "      <td>43419</td>\n",
       "      <td>2018.8740</td>\n",
       "      <td>408.120</td>\n",
       "      <td>410.39</td>\n",
       "      <td>407.47</td>\n",
       "      <td>409.72</td>\n",
       "      <td>408.12</td>\n",
       "      <td>410.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-01</th>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>43449</td>\n",
       "      <td>2018.9562</td>\n",
       "      <td>409.230</td>\n",
       "      <td>410.16</td>\n",
       "      <td>409.07</td>\n",
       "      <td>409.97</td>\n",
       "      <td>409.23</td>\n",
       "      <td>410.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>43480</td>\n",
       "      <td>2019.0411</td>\n",
       "      <td>410.920</td>\n",
       "      <td>410.87</td>\n",
       "      <td>410.29</td>\n",
       "      <td>410.23</td>\n",
       "      <td>410.92</td>\n",
       "      <td>410.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-01</th>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>43511</td>\n",
       "      <td>2019.1260</td>\n",
       "      <td>411.660</td>\n",
       "      <td>410.90</td>\n",
       "      <td>411.24</td>\n",
       "      <td>410.47</td>\n",
       "      <td>411.66</td>\n",
       "      <td>410.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-01</th>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>43539</td>\n",
       "      <td>2019.2027</td>\n",
       "      <td>412.000</td>\n",
       "      <td>410.45</td>\n",
       "      <td>412.25</td>\n",
       "      <td>410.68</td>\n",
       "      <td>412.00</td>\n",
       "      <td>410.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-01</th>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>43570</td>\n",
       "      <td>2019.2877</td>\n",
       "      <td>413.520</td>\n",
       "      <td>410.72</td>\n",
       "      <td>413.73</td>\n",
       "      <td>410.91</td>\n",
       "      <td>413.52</td>\n",
       "      <td>410.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07-01</th>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>43600</td>\n",
       "      <td>2019.3699</td>\n",
       "      <td>414.830</td>\n",
       "      <td>411.42</td>\n",
       "      <td>414.54</td>\n",
       "      <td>411.13</td>\n",
       "      <td>414.83</td>\n",
       "      <td>411.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>43631</td>\n",
       "      <td>2019.4548</td>\n",
       "      <td>413.960</td>\n",
       "      <td>411.38</td>\n",
       "      <td>413.91</td>\n",
       "      <td>411.35</td>\n",
       "      <td>413.96</td>\n",
       "      <td>411.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-09-01</th>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>43661</td>\n",
       "      <td>2019.5370</td>\n",
       "      <td>411.850</td>\n",
       "      <td>411.03</td>\n",
       "      <td>412.36</td>\n",
       "      <td>411.57</td>\n",
       "      <td>411.85</td>\n",
       "      <td>411.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-10-01</th>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>43692</td>\n",
       "      <td>2019.6219</td>\n",
       "      <td>410.080</td>\n",
       "      <td>411.62</td>\n",
       "      <td>410.23</td>\n",
       "      <td>411.81</td>\n",
       "      <td>410.08</td>\n",
       "      <td>411.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>43723</td>\n",
       "      <td>2019.7068</td>\n",
       "      <td>408.550</td>\n",
       "      <td>412.06</td>\n",
       "      <td>408.52</td>\n",
       "      <td>412.05</td>\n",
       "      <td>408.55</td>\n",
       "      <td>412.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>43753</td>\n",
       "      <td>2019.7890</td>\n",
       "      <td>408.430</td>\n",
       "      <td>412.07</td>\n",
       "      <td>408.67</td>\n",
       "      <td>412.29</td>\n",
       "      <td>408.43</td>\n",
       "      <td>412.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>43784</td>\n",
       "      <td>2019.8740</td>\n",
       "      <td>410.280</td>\n",
       "      <td>412.56</td>\n",
       "      <td>410.29</td>\n",
       "      <td>412.54</td>\n",
       "      <td>410.28</td>\n",
       "      <td>412.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>43814</td>\n",
       "      <td>2019.9562</td>\n",
       "      <td>411.850</td>\n",
       "      <td>412.78</td>\n",
       "      <td>411.88</td>\n",
       "      <td>412.79</td>\n",
       "      <td>411.85</td>\n",
       "      <td>412.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>43845</td>\n",
       "      <td>2020.0410</td>\n",
       "      <td>413.370</td>\n",
       "      <td>413.33</td>\n",
       "      <td>413.11</td>\n",
       "      <td>413.05</td>\n",
       "      <td>413.37</td>\n",
       "      <td>413.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>743 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Yr  Mn  Date 1     Date 2       s1      s2      s3      s4  \\\n",
       "period                                                                     \n",
       "1958-05-01  1958   3   21259  1958.2027  315.700  314.44  316.19  314.91   \n",
       "1958-06-01  1958   4   21290  1958.2877  317.450  315.16  317.30  314.99   \n",
       "1958-07-01  1958   5   21320  1958.3699  317.510  314.71  317.86  315.06   \n",
       "1958-08-01  1958   6   21351  1958.4548  316.685  314.95  317.24  315.14   \n",
       "1958-09-01  1958   7   21381  1958.5370  315.860  315.19  315.86  315.22   \n",
       "1958-10-01  1958   8   21412  1958.6219  314.930  316.19  314.00  315.29   \n",
       "1958-11-01  1958   9   21443  1958.7068  313.210  316.08  312.46  315.35   \n",
       "1958-12-01  1958  10   21473  1958.7890  313.270  315.64  312.44  315.40   \n",
       "1959-01-01  1958  11   21504  1958.8740  313.330  315.20  313.62  315.46   \n",
       "1959-02-01  1958  12   21534  1958.9562  314.670  315.43  314.77  315.51   \n",
       "1959-03-01  1959   1   21565  1959.0411  315.580  315.54  315.62  315.57   \n",
       "1959-04-01  1959   2   21596  1959.1260  316.490  315.86  316.27  315.63   \n",
       "1959-05-01  1959   3   21624  1959.2027  316.650  315.38  316.98  315.69   \n",
       "1959-06-01  1959   4   21655  1959.2877  317.720  315.42  318.09  315.77   \n",
       "1959-07-01  1959   5   21685  1959.3699  318.290  315.49  318.65  315.85   \n",
       "1959-08-01  1959   6   21716  1959.4548  318.150  316.03  318.04  315.94   \n",
       "1959-09-01  1959   7   21746  1959.5370  316.540  315.86  316.67  316.03   \n",
       "1959-10-01  1959   8   21777  1959.6219  314.800  316.06  314.83  316.12   \n",
       "1959-11-01  1959   9   21808  1959.7068  313.840  316.72  313.32  316.22   \n",
       "1959-12-01  1959  10   21838  1959.7890  313.330  316.32  313.33  316.30   \n",
       "1960-01-01  1959  11   21869  1959.8740  314.810  316.68  314.54  316.39   \n",
       "1960-02-01  1959  12   21899  1959.9562  315.580  316.35  315.72  316.47   \n",
       "1960-03-01  1960   1   21930  1960.0410  316.430  316.39  316.61  316.56   \n",
       "1960-04-01  1960   2   21961  1960.1257  316.980  316.35  317.27  316.64   \n",
       "1960-05-01  1960   3   21990  1960.2049  317.580  316.28  318.03  316.71   \n",
       "1960-06-01  1960   4   22021  1960.2896  319.030  316.70  319.14  316.79   \n",
       "1960-07-01  1960   5   22051  1960.3716  320.040  317.22  319.67  316.86   \n",
       "1960-08-01  1960   6   22082  1960.4563  319.590  317.48  319.01  316.93   \n",
       "1960-09-01  1960   7   22112  1960.5383  318.180  317.53  317.60  316.98   \n",
       "1960-10-01  1960   8   22143  1960.6230  315.900  317.20  315.69  317.01   \n",
       "...          ...  ..     ...        ...      ...     ...     ...     ...   \n",
       "2017-10-01  2017   8   42962  2017.6219  405.240  406.77  405.20  406.76   \n",
       "2017-11-01  2017   9   42993  2017.7068  403.270  406.76  403.41  406.92   \n",
       "2017-12-01  2017  10   43023  2017.7890  403.640  407.25  403.47  407.07   \n",
       "2018-01-01  2017  11   43054  2017.8740  405.170  407.43  404.98  407.22   \n",
       "2018-02-01  2017  12   43084  2017.9562  406.750  407.68  406.46  407.37   \n",
       "2018-03-01  2018   1   43115  2018.0411  408.050  408.00  407.58  407.52   \n",
       "2018-04-01  2018   2   43146  2018.1260  408.340  407.59  408.44  407.68   \n",
       "2018-05-01  2018   3   43174  2018.2027  409.250  407.71  409.39  407.82   \n",
       "2018-06-01  2018   4   43205  2018.2877  410.300  407.51  410.82  408.00   \n",
       "2018-07-01  2018   5   43235  2018.3699  411.300  407.90  411.60  408.20   \n",
       "2018-08-01  2018   6   43266  2018.4548  410.880  408.30  410.97  408.42   \n",
       "2018-09-01  2018   7   43296  2018.5370  408.900  408.08  409.43  408.65   \n",
       "2018-10-01  2018   8   43327  2018.6219  407.100  408.63  407.33  408.91   \n",
       "2018-11-01  2018   9   43358  2018.7068  405.590  409.09  405.66  409.18   \n",
       "2018-12-01  2018  10   43388  2018.7890  405.990  409.62  405.83  409.44   \n",
       "2019-01-01  2018  11   43419  2018.8740  408.120  410.39  407.47  409.72   \n",
       "2019-02-01  2018  12   43449  2018.9562  409.230  410.16  409.07  409.97   \n",
       "2019-03-01  2019   1   43480  2019.0411  410.920  410.87  410.29  410.23   \n",
       "2019-04-01  2019   2   43511  2019.1260  411.660  410.90  411.24  410.47   \n",
       "2019-05-01  2019   3   43539  2019.2027  412.000  410.45  412.25  410.68   \n",
       "2019-06-01  2019   4   43570  2019.2877  413.520  410.72  413.73  410.91   \n",
       "2019-07-01  2019   5   43600  2019.3699  414.830  411.42  414.54  411.13   \n",
       "2019-08-01  2019   6   43631  2019.4548  413.960  411.38  413.91  411.35   \n",
       "2019-09-01  2019   7   43661  2019.5370  411.850  411.03  412.36  411.57   \n",
       "2019-10-01  2019   8   43692  2019.6219  410.080  411.62  410.23  411.81   \n",
       "2019-11-01  2019   9   43723  2019.7068  408.550  412.06  408.52  412.05   \n",
       "2019-12-01  2019  10   43753  2019.7890  408.430  412.07  408.67  412.29   \n",
       "2020-01-01  2019  11   43784  2019.8740  410.280  412.56  410.29  412.54   \n",
       "NaN         2019  12   43814  2019.9562  411.850  412.78  411.88  412.79   \n",
       "NaN         2020   1   43845  2020.0410  413.370  413.33  413.11  413.05   \n",
       "\n",
       "                s5      s6  \n",
       "period                      \n",
       "1958-05-01  315.70  314.44  \n",
       "1958-06-01  317.45  315.16  \n",
       "1958-07-01  317.51  314.71  \n",
       "1958-08-01  317.24  315.14  \n",
       "1958-09-01  315.86  315.19  \n",
       "1958-10-01  314.93  316.19  \n",
       "1958-11-01  313.21  316.08  \n",
       "1958-12-01  312.44  315.40  \n",
       "1959-01-01  313.33  315.20  \n",
       "1959-02-01  314.67  315.43  \n",
       "1959-03-01  315.58  315.54  \n",
       "1959-04-01  316.49  315.86  \n",
       "1959-05-01  316.65  315.38  \n",
       "1959-06-01  317.72  315.42  \n",
       "1959-07-01  318.29  315.49  \n",
       "1959-08-01  318.15  316.03  \n",
       "1959-09-01  316.54  315.86  \n",
       "1959-10-01  314.80  316.06  \n",
       "1959-11-01  313.84  316.72  \n",
       "1959-12-01  313.33  316.32  \n",
       "1960-01-01  314.81  316.68  \n",
       "1960-02-01  315.58  316.35  \n",
       "1960-03-01  316.43  316.39  \n",
       "1960-04-01  316.98  316.35  \n",
       "1960-05-01  317.58  316.28  \n",
       "1960-06-01  319.03  316.70  \n",
       "1960-07-01  320.04  317.22  \n",
       "1960-08-01  319.59  317.48  \n",
       "1960-09-01  318.18  317.53  \n",
       "1960-10-01  315.90  317.20  \n",
       "...            ...     ...  \n",
       "2017-10-01  405.24  406.77  \n",
       "2017-11-01  403.27  406.76  \n",
       "2017-12-01  403.64  407.25  \n",
       "2018-01-01  405.17  407.43  \n",
       "2018-02-01  406.75  407.68  \n",
       "2018-03-01  408.05  408.00  \n",
       "2018-04-01  408.34  407.59  \n",
       "2018-05-01  409.25  407.71  \n",
       "2018-06-01  410.30  407.51  \n",
       "2018-07-01  411.30  407.90  \n",
       "2018-08-01  410.88  408.30  \n",
       "2018-09-01  408.90  408.08  \n",
       "2018-10-01  407.10  408.63  \n",
       "2018-11-01  405.59  409.09  \n",
       "2018-12-01  405.99  409.62  \n",
       "2019-01-01  408.12  410.39  \n",
       "2019-02-01  409.23  410.16  \n",
       "2019-03-01  410.92  410.87  \n",
       "2019-04-01  411.66  410.90  \n",
       "2019-05-01  412.00  410.45  \n",
       "2019-06-01  413.52  410.72  \n",
       "2019-07-01  414.83  411.42  \n",
       "2019-08-01  413.96  411.38  \n",
       "2019-09-01  411.85  411.03  \n",
       "2019-10-01  410.08  411.62  \n",
       "2019-11-01  408.55  412.06  \n",
       "2019-12-01  408.43  412.07  \n",
       "2020-01-01  410.28  412.56  \n",
       "NaN         411.85  412.78  \n",
       "NaN         413.37  413.33  \n",
       "\n",
       "[743 rows x 10 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data2.interpolate(method='linear', limit_direction='forward',limit=3)\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Yr</th>\n",
       "      <th>Mn</th>\n",
       "      <th>Date 1</th>\n",
       "      <th>Date 2</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1964-03-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>1</td>\n",
       "      <td>23391</td>\n",
       "      <td>1964.0410</td>\n",
       "      <td>319.5700</td>\n",
       "      <td>319.5300</td>\n",
       "      <td>319.37</td>\n",
       "      <td>319.32</td>\n",
       "      <td>319.57</td>\n",
       "      <td>319.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-04-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>2</td>\n",
       "      <td>23422</td>\n",
       "      <td>1964.1257</td>\n",
       "      <td>320.2425</td>\n",
       "      <td>319.4975</td>\n",
       "      <td>320.01</td>\n",
       "      <td>319.36</td>\n",
       "      <td>320.01</td>\n",
       "      <td>319.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-05-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>3</td>\n",
       "      <td>23451</td>\n",
       "      <td>1964.2049</td>\n",
       "      <td>320.9150</td>\n",
       "      <td>319.4650</td>\n",
       "      <td>320.74</td>\n",
       "      <td>319.41</td>\n",
       "      <td>320.74</td>\n",
       "      <td>319.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-06-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>4</td>\n",
       "      <td>23482</td>\n",
       "      <td>1964.2896</td>\n",
       "      <td>321.5875</td>\n",
       "      <td>319.4325</td>\n",
       "      <td>321.83</td>\n",
       "      <td>319.45</td>\n",
       "      <td>321.83</td>\n",
       "      <td>319.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-07-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>5</td>\n",
       "      <td>23512</td>\n",
       "      <td>1964.3716</td>\n",
       "      <td>322.2600</td>\n",
       "      <td>319.4000</td>\n",
       "      <td>322.34</td>\n",
       "      <td>319.49</td>\n",
       "      <td>322.26</td>\n",
       "      <td>319.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-08-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>6</td>\n",
       "      <td>23543</td>\n",
       "      <td>1964.4563</td>\n",
       "      <td>321.8900</td>\n",
       "      <td>319.7500</td>\n",
       "      <td>321.64</td>\n",
       "      <td>319.52</td>\n",
       "      <td>321.89</td>\n",
       "      <td>319.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-09-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>7</td>\n",
       "      <td>23573</td>\n",
       "      <td>1964.5383</td>\n",
       "      <td>320.4400</td>\n",
       "      <td>319.7800</td>\n",
       "      <td>320.19</td>\n",
       "      <td>319.55</td>\n",
       "      <td>320.44</td>\n",
       "      <td>319.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-10-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>8</td>\n",
       "      <td>23604</td>\n",
       "      <td>1964.6230</td>\n",
       "      <td>318.6900</td>\n",
       "      <td>320.0000</td>\n",
       "      <td>318.24</td>\n",
       "      <td>319.58</td>\n",
       "      <td>318.69</td>\n",
       "      <td>320.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-11-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>9</td>\n",
       "      <td>23635</td>\n",
       "      <td>1964.7077</td>\n",
       "      <td>316.7100</td>\n",
       "      <td>319.6500</td>\n",
       "      <td>316.64</td>\n",
       "      <td>319.60</td>\n",
       "      <td>316.71</td>\n",
       "      <td>319.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-12-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>10</td>\n",
       "      <td>23665</td>\n",
       "      <td>1964.7896</td>\n",
       "      <td>316.8700</td>\n",
       "      <td>319.9100</td>\n",
       "      <td>316.59</td>\n",
       "      <td>319.62</td>\n",
       "      <td>316.87</td>\n",
       "      <td>319.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-01-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>11</td>\n",
       "      <td>23696</td>\n",
       "      <td>1964.8743</td>\n",
       "      <td>317.6800</td>\n",
       "      <td>319.5800</td>\n",
       "      <td>317.76</td>\n",
       "      <td>319.64</td>\n",
       "      <td>317.68</td>\n",
       "      <td>319.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965-02-01</th>\n",
       "      <td>1964</td>\n",
       "      <td>12</td>\n",
       "      <td>23726</td>\n",
       "      <td>1964.9563</td>\n",
       "      <td>318.7100</td>\n",
       "      <td>319.4900</td>\n",
       "      <td>318.89</td>\n",
       "      <td>319.66</td>\n",
       "      <td>318.71</td>\n",
       "      <td>319.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Yr  Mn  Date 1     Date 2        s1        s2      s3      s4  \\\n",
       "period                                                                        \n",
       "1964-03-01  1964   1   23391  1964.0410  319.5700  319.5300  319.37  319.32   \n",
       "1964-04-01  1964   2   23422  1964.1257  320.2425  319.4975  320.01  319.36   \n",
       "1964-05-01  1964   3   23451  1964.2049  320.9150  319.4650  320.74  319.41   \n",
       "1964-06-01  1964   4   23482  1964.2896  321.5875  319.4325  321.83  319.45   \n",
       "1964-07-01  1964   5   23512  1964.3716  322.2600  319.4000  322.34  319.49   \n",
       "1964-08-01  1964   6   23543  1964.4563  321.8900  319.7500  321.64  319.52   \n",
       "1964-09-01  1964   7   23573  1964.5383  320.4400  319.7800  320.19  319.55   \n",
       "1964-10-01  1964   8   23604  1964.6230  318.6900  320.0000  318.24  319.58   \n",
       "1964-11-01  1964   9   23635  1964.7077  316.7100  319.6500  316.64  319.60   \n",
       "1964-12-01  1964  10   23665  1964.7896  316.8700  319.9100  316.59  319.62   \n",
       "1965-01-01  1964  11   23696  1964.8743  317.6800  319.5800  317.76  319.64   \n",
       "1965-02-01  1964  12   23726  1964.9563  318.7100  319.4900  318.89  319.66   \n",
       "\n",
       "                s5      s6  \n",
       "period                      \n",
       "1964-03-01  319.57  319.53  \n",
       "1964-04-01  320.01  319.36  \n",
       "1964-05-01  320.74  319.41  \n",
       "1964-06-01  321.83  319.45  \n",
       "1964-07-01  322.26  319.40  \n",
       "1964-08-01  321.89  319.75  \n",
       "1964-09-01  320.44  319.78  \n",
       "1964-10-01  318.69  320.00  \n",
       "1964-11-01  316.71  319.65  \n",
       "1964-12-01  316.87  319.91  \n",
       "1965-01-01  317.68  319.58  \n",
       "1965-02-01  318.71  319.49  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = data2[(data2.Yr >= 1964) & (data2.Yr < 1965)]\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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