{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Opening Zeppelin hourly data from 2010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "filenam1 = '/home/2daa7756-2d5725-2d4dfb-2db0ff-2d5e0a6858a009/shared-ns1000k/inputs//Aerosol_sizedist_obs/Zeppelin_2010_hourly.csv'\n",
    "filenam2 = '/home/2daa7756-2d5725-2d4dfb-2db0ff-2d5e0a6858a009/shared-ns1000k/inputs//Aerosol_sizedist_obs/Zeppelin_2011_hourly.csv'\n",
    "flist=[filenam1, filenam2]\n",
    "ldf = []\n",
    "for f in flist:\n",
    "    ldf.append(pd.read_csv(f, parse_dates=[['0','0.1','0.2','0.3','0.4']],date_parser = mydateparser)) \n",
    "data = pd.concat(ldf, axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydateparser = lambda x: pd.datetime.strptime(x, \"%Y %m %d %H %M\")\n",
    "data = pd.read_csv(filenam, parse_dates=[['0','0.1','0.2','0.3','0.4']],date_parser = mydateparser)\n",
    "#data.head()\n",
    "#data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.rename(columns={'0_0.1_0.2_0.3_0.4':'date'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.set_index('date')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['0.6'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-124-dd6d76ed93db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#remove last column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'0.6'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4100\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4101\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4103\u001b[0m         )\n\u001b[1;32m   4104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3912\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3913\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3914\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3916\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3944\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3945\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3946\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3947\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   5338\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5339\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5340\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{} not found in axis\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5341\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5342\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['0.6'] not found in axis\""
     ]
    }
   ],
   "source": [
    " #remove last column\n",
    " data.drop(labels='0.6', axis=1, inplace=True)   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2be952e10>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize=[20,5])\n",
    "\n",
    "#set projection for plotting\n",
    "\n",
    "ax = plt.subplot(1,1,1)\n",
    "sns.heatmap(data.T.iloc[::-1], vmin=0, vmax=200, cmap='jet')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2010-01-01 00:00:00      9.2141\n",
       "2010-01-01 01:00:00      7.4015\n",
       "2010-01-01 02:00:00      8.2293\n",
       "2010-01-01 03:00:00      6.0217\n",
       "2010-01-01 04:00:00      9.8351\n",
       "                         ...   \n",
       "2010-12-31 19:00:00   -999.0000\n",
       "2010-12-31 20:00:00   -999.0000\n",
       "2010-12-31 21:00:00   -999.0000\n",
       "2010-12-31 22:00:00   -999.0000\n",
       "2010-12-31 23:00:00     15.7530\n",
       "Name: 20, Length: 8760, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Vaihdetaan oikeesta datasta kaikki -999 arvot NaN\n",
    "data = data.replace(-999,np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>0.5</th>\n",
       "      <th>20</th>\n",
       "      <th>22.44</th>\n",
       "      <th>25.179</th>\n",
       "      <th>28.251</th>\n",
       "      <th>31.698</th>\n",
       "      <th>35.566</th>\n",
       "      <th>39.905</th>\n",
       "      <th>44.774</th>\n",
       "      <th>50.238</th>\n",
       "      <th>...</th>\n",
       "      <th>178.25</th>\n",
       "      <th>200</th>\n",
       "      <th>224.4</th>\n",
       "      <th>251.79</th>\n",
       "      <th>282.51</th>\n",
       "      <th>316.98</th>\n",
       "      <th>355.66</th>\n",
       "      <th>399.05</th>\n",
       "      <th>447.74</th>\n",
       "      <th>502.38</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2010-01-01 00:00:00</td>\n",
       "      <td>127.010</td>\n",
       "      <td>9.2141</td>\n",
       "      <td>13.3240</td>\n",
       "      <td>18.261</td>\n",
       "      <td>23.245</td>\n",
       "      <td>30.351</td>\n",
       "      <td>39.4690</td>\n",
       "      <td>47.835</td>\n",
       "      <td>47.8340</td>\n",
       "      <td>42.7440</td>\n",
       "      <td>...</td>\n",
       "      <td>112.370</td>\n",
       "      <td>115.400</td>\n",
       "      <td>168.740</td>\n",
       "      <td>298.220</td>\n",
       "      <td>358.390</td>\n",
       "      <td>247.520</td>\n",
       "      <td>125.5700</td>\n",
       "      <td>54.1360</td>\n",
       "      <td>22.3210</td>\n",
       "      <td>9.6607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-01-01 01:00:00</td>\n",
       "      <td>110.150</td>\n",
       "      <td>7.4015</td>\n",
       "      <td>12.8350</td>\n",
       "      <td>18.326</td>\n",
       "      <td>23.133</td>\n",
       "      <td>26.412</td>\n",
       "      <td>31.9880</td>\n",
       "      <td>38.002</td>\n",
       "      <td>38.1110</td>\n",
       "      <td>38.6730</td>\n",
       "      <td>...</td>\n",
       "      <td>107.270</td>\n",
       "      <td>101.910</td>\n",
       "      <td>140.920</td>\n",
       "      <td>244.340</td>\n",
       "      <td>294.450</td>\n",
       "      <td>218.560</td>\n",
       "      <td>115.0300</td>\n",
       "      <td>49.6960</td>\n",
       "      <td>20.9440</td>\n",
       "      <td>9.4524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-01-01 02:00:00</td>\n",
       "      <td>98.864</td>\n",
       "      <td>8.2293</td>\n",
       "      <td>10.1860</td>\n",
       "      <td>14.526</td>\n",
       "      <td>19.346</td>\n",
       "      <td>23.288</td>\n",
       "      <td>30.8280</td>\n",
       "      <td>32.256</td>\n",
       "      <td>33.3850</td>\n",
       "      <td>34.3440</td>\n",
       "      <td>...</td>\n",
       "      <td>90.091</td>\n",
       "      <td>88.761</td>\n",
       "      <td>121.910</td>\n",
       "      <td>207.210</td>\n",
       "      <td>267.870</td>\n",
       "      <td>196.080</td>\n",
       "      <td>101.8700</td>\n",
       "      <td>45.4790</td>\n",
       "      <td>20.8550</td>\n",
       "      <td>9.9242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-01-01 03:00:00</td>\n",
       "      <td>103.960</td>\n",
       "      <td>6.0217</td>\n",
       "      <td>10.0140</td>\n",
       "      <td>14.795</td>\n",
       "      <td>21.470</td>\n",
       "      <td>26.444</td>\n",
       "      <td>28.6210</td>\n",
       "      <td>31.333</td>\n",
       "      <td>31.4070</td>\n",
       "      <td>35.1930</td>\n",
       "      <td>...</td>\n",
       "      <td>99.499</td>\n",
       "      <td>93.949</td>\n",
       "      <td>133.170</td>\n",
       "      <td>231.840</td>\n",
       "      <td>290.350</td>\n",
       "      <td>209.660</td>\n",
       "      <td>110.8000</td>\n",
       "      <td>50.5580</td>\n",
       "      <td>23.2000</td>\n",
       "      <td>11.1620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010-01-01 04:00:00</td>\n",
       "      <td>109.060</td>\n",
       "      <td>9.8351</td>\n",
       "      <td>12.5260</td>\n",
       "      <td>15.748</td>\n",
       "      <td>19.991</td>\n",
       "      <td>24.453</td>\n",
       "      <td>30.3280</td>\n",
       "      <td>33.429</td>\n",
       "      <td>36.5330</td>\n",
       "      <td>36.4650</td>\n",
       "      <td>...</td>\n",
       "      <td>102.890</td>\n",
       "      <td>106.060</td>\n",
       "      <td>142.540</td>\n",
       "      <td>243.290</td>\n",
       "      <td>307.780</td>\n",
       "      <td>215.960</td>\n",
       "      <td>115.7900</td>\n",
       "      <td>54.9650</td>\n",
       "      <td>24.6220</td>\n",
       "      <td>12.0210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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",
       "      <td>2011-12-31 19:00:00</td>\n",
       "      <td>33.237</td>\n",
       "      <td>4.5237</td>\n",
       "      <td>8.2171</td>\n",
       "      <td>12.178</td>\n",
       "      <td>13.667</td>\n",
       "      <td>15.023</td>\n",
       "      <td>15.4340</td>\n",
       "      <td>10.998</td>\n",
       "      <td>9.2830</td>\n",
       "      <td>8.3625</td>\n",
       "      <td>...</td>\n",
       "      <td>66.587</td>\n",
       "      <td>74.784</td>\n",
       "      <td>73.260</td>\n",
       "      <td>58.339</td>\n",
       "      <td>35.515</td>\n",
       "      <td>19.784</td>\n",
       "      <td>12.6390</td>\n",
       "      <td>5.4085</td>\n",
       "      <td>3.2198</td>\n",
       "      <td>4.0654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-12-31 20:00:00</td>\n",
       "      <td>36.031</td>\n",
       "      <td>7.5069</td>\n",
       "      <td>11.4000</td>\n",
       "      <td>15.513</td>\n",
       "      <td>16.042</td>\n",
       "      <td>16.272</td>\n",
       "      <td>15.7030</td>\n",
       "      <td>12.395</td>\n",
       "      <td>11.8270</td>\n",
       "      <td>12.5400</td>\n",
       "      <td>...</td>\n",
       "      <td>65.547</td>\n",
       "      <td>67.752</td>\n",
       "      <td>62.612</td>\n",
       "      <td>48.068</td>\n",
       "      <td>31.817</td>\n",
       "      <td>18.368</td>\n",
       "      <td>8.8337</td>\n",
       "      <td>7.7784</td>\n",
       "      <td>6.9098</td>\n",
       "      <td>5.9238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-12-31 21:00:00</td>\n",
       "      <td>38.550</td>\n",
       "      <td>4.4545</td>\n",
       "      <td>9.5881</td>\n",
       "      <td>15.145</td>\n",
       "      <td>18.117</td>\n",
       "      <td>17.796</td>\n",
       "      <td>13.6140</td>\n",
       "      <td>11.582</td>\n",
       "      <td>9.2801</td>\n",
       "      <td>8.4581</td>\n",
       "      <td>...</td>\n",
       "      <td>81.728</td>\n",
       "      <td>91.344</td>\n",
       "      <td>84.708</td>\n",
       "      <td>53.480</td>\n",
       "      <td>37.032</td>\n",
       "      <td>22.753</td>\n",
       "      <td>10.9250</td>\n",
       "      <td>9.4918</td>\n",
       "      <td>7.4926</td>\n",
       "      <td>5.1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-12-31 22:00:00</td>\n",
       "      <td>38.347</td>\n",
       "      <td>4.6390</td>\n",
       "      <td>8.8948</td>\n",
       "      <td>13.311</td>\n",
       "      <td>12.494</td>\n",
       "      <td>11.177</td>\n",
       "      <td>9.7762</td>\n",
       "      <td>11.722</td>\n",
       "      <td>10.7130</td>\n",
       "      <td>9.8151</td>\n",
       "      <td>...</td>\n",
       "      <td>72.779</td>\n",
       "      <td>84.278</td>\n",
       "      <td>82.020</td>\n",
       "      <td>57.439</td>\n",
       "      <td>37.294</td>\n",
       "      <td>20.893</td>\n",
       "      <td>9.2273</td>\n",
       "      <td>6.8520</td>\n",
       "      <td>7.2738</td>\n",
       "      <td>8.6812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2011-12-31 23:00:00</td>\n",
       "      <td>46.968</td>\n",
       "      <td>11.0710</td>\n",
       "      <td>12.0850</td>\n",
       "      <td>13.415</td>\n",
       "      <td>18.010</td>\n",
       "      <td>20.073</td>\n",
       "      <td>18.3820</td>\n",
       "      <td>13.677</td>\n",
       "      <td>13.1990</td>\n",
       "      <td>14.4220</td>\n",
       "      <td>...</td>\n",
       "      <td>91.955</td>\n",
       "      <td>91.564</td>\n",
       "      <td>84.006</td>\n",
       "      <td>65.885</td>\n",
       "      <td>39.213</td>\n",
       "      <td>21.230</td>\n",
       "      <td>13.9460</td>\n",
       "      <td>10.3320</td>\n",
       "      <td>8.6885</td>\n",
       "      <td>8.1243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14702 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0.5       20    22.44  25.179  28.251  31.698  \\\n",
       "date                                                                     \n",
       "2010-01-01 00:00:00  127.010   9.2141  13.3240  18.261  23.245  30.351   \n",
       "2010-01-01 01:00:00  110.150   7.4015  12.8350  18.326  23.133  26.412   \n",
       "2010-01-01 02:00:00   98.864   8.2293  10.1860  14.526  19.346  23.288   \n",
       "2010-01-01 03:00:00  103.960   6.0217  10.0140  14.795  21.470  26.444   \n",
       "2010-01-01 04:00:00  109.060   9.8351  12.5260  15.748  19.991  24.453   \n",
       "...                      ...      ...      ...     ...     ...     ...   \n",
       "2011-12-31 19:00:00   33.237   4.5237   8.2171  12.178  13.667  15.023   \n",
       "2011-12-31 20:00:00   36.031   7.5069  11.4000  15.513  16.042  16.272   \n",
       "2011-12-31 21:00:00   38.550   4.4545   9.5881  15.145  18.117  17.796   \n",
       "2011-12-31 22:00:00   38.347   4.6390   8.8948  13.311  12.494  11.177   \n",
       "2011-12-31 23:00:00   46.968  11.0710  12.0850  13.415  18.010  20.073   \n",
       "\n",
       "                      35.566  39.905   44.774   50.238  ...   178.25      200  \\\n",
       "date                                                    ...                     \n",
       "2010-01-01 00:00:00  39.4690  47.835  47.8340  42.7440  ...  112.370  115.400   \n",
       "2010-01-01 01:00:00  31.9880  38.002  38.1110  38.6730  ...  107.270  101.910   \n",
       "2010-01-01 02:00:00  30.8280  32.256  33.3850  34.3440  ...   90.091   88.761   \n",
       "2010-01-01 03:00:00  28.6210  31.333  31.4070  35.1930  ...   99.499   93.949   \n",
       "2010-01-01 04:00:00  30.3280  33.429  36.5330  36.4650  ...  102.890  106.060   \n",
       "...                      ...     ...      ...      ...  ...      ...      ...   \n",
       "2011-12-31 19:00:00  15.4340  10.998   9.2830   8.3625  ...   66.587   74.784   \n",
       "2011-12-31 20:00:00  15.7030  12.395  11.8270  12.5400  ...   65.547   67.752   \n",
       "2011-12-31 21:00:00  13.6140  11.582   9.2801   8.4581  ...   81.728   91.344   \n",
       "2011-12-31 22:00:00   9.7762  11.722  10.7130   9.8151  ...   72.779   84.278   \n",
       "2011-12-31 23:00:00  18.3820  13.677  13.1990  14.4220  ...   91.955   91.564   \n",
       "\n",
       "                       224.4   251.79   282.51   316.98    355.66   399.05  \\\n",
       "date                                                                         \n",
       "2010-01-01 00:00:00  168.740  298.220  358.390  247.520  125.5700  54.1360   \n",
       "2010-01-01 01:00:00  140.920  244.340  294.450  218.560  115.0300  49.6960   \n",
       "2010-01-01 02:00:00  121.910  207.210  267.870  196.080  101.8700  45.4790   \n",
       "2010-01-01 03:00:00  133.170  231.840  290.350  209.660  110.8000  50.5580   \n",
       "2010-01-01 04:00:00  142.540  243.290  307.780  215.960  115.7900  54.9650   \n",
       "...                      ...      ...      ...      ...       ...      ...   \n",
       "2011-12-31 19:00:00   73.260   58.339   35.515   19.784   12.6390   5.4085   \n",
       "2011-12-31 20:00:00   62.612   48.068   31.817   18.368    8.8337   7.7784   \n",
       "2011-12-31 21:00:00   84.708   53.480   37.032   22.753   10.9250   9.4918   \n",
       "2011-12-31 22:00:00   82.020   57.439   37.294   20.893    9.2273   6.8520   \n",
       "2011-12-31 23:00:00   84.006   65.885   39.213   21.230   13.9460  10.3320   \n",
       "\n",
       "                      447.74   502.38  \n",
       "date                                   \n",
       "2010-01-01 00:00:00  22.3210   9.6607  \n",
       "2010-01-01 01:00:00  20.9440   9.4524  \n",
       "2010-01-01 02:00:00  20.8550   9.9242  \n",
       "2010-01-01 03:00:00  23.2000  11.1620  \n",
       "2010-01-01 04:00:00  24.6220  12.0210  \n",
       "...                      ...      ...  \n",
       "2011-12-31 19:00:00   3.2198   4.0654  \n",
       "2011-12-31 20:00:00   6.9098   5.9238  \n",
       "2011-12-31 21:00:00   7.4926   5.1997  \n",
       "2011-12-31 22:00:00   7.2738   8.6812  \n",
       "2011-12-31 23:00:00   8.6885   8.1243  \n",
       "\n",
       "[14702 rows x 30 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#valitaan datasta pelkästään tietyt kolumnit (20-50nm) ja summataan ne yhteen\n",
    "small_particle_data = data.iloc[:,1:9].sum(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# take the mean for every row for indexes between 1-9 so (20-50 nm)\n",
    "small_particle_data_mean = data.iloc[:,1:9].mean(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the data\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(2, 1, 1)\n",
    "\n",
    "line, = ax.plot(small_particle_data_mean, color='blue', lw=2)\n",
    "\n",
    "# change the y-axes to log\n",
    "#ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2010-01-01 00:00:00    28.691637\n",
       "2010-01-01 01:00:00    24.526063\n",
       "2010-01-01 02:00:00    21.505537\n",
       "2010-01-01 03:00:00    21.263212\n",
       "2010-01-01 04:00:00    22.855387\n",
       "                         ...    \n",
       "2010-12-31 19:00:00          NaN\n",
       "2010-12-31 20:00:00          NaN\n",
       "2010-12-31 21:00:00          NaN\n",
       "2010-12-31 22:00:00          NaN\n",
       "2010-12-31 23:00:00    33.761125\n",
       "Length: 8760, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_particle_data_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2c2efe748>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets take mean for every month and plot them together\n",
    "small_particle_data_mean.resample('M').mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "_se = small_particle_data_mean.resample('M').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = 'y'\n",
    "M = 'm'\n",
    "var = 'spdm'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'var' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-202-bf7276411ff0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0m_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_se\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0m_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0m_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0m_df\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0m_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'var' is not defined"
     ]
    }
   ],
   "source": [
    "_df = pd.DataFrame(_se,columns=[var])\n",
    "_df[M] = _df.index.month\n",
    "_df[Y] = _df.index.year\n",
    "\n",
    "_df= _df.set_index(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y\n",
       "2010    [AxesSubplot(0.125,0.2;0.775x0.68)]\n",
       "2011    [AxesSubplot(0.125,0.2;0.775x0.68)]\n",
       "dtype: object"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_df.groupby('y').plot(y='spdm',subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe21ef6c6a0>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_df.plot(y='spdm',color='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe21f46e080>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_df.plot(y='spdm',x='m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe21f0b7160>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "_df[_df['y']==2010].plot(x='m',y='spdm',ax=ax, label ='2010' )\n",
    "_df[_df['y']==2011].plot(x='m',y='spdm',ax=ax, label ='2011')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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>m</th>\n",
       "      <th>spdm</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>34.447331</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>65.437523</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>56.889464</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>158.139479</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>557.717221</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>564.419334</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>457.328173</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>297.233322</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>65.094502</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>52.024606</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>23.716328</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>33.761125</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>245.573784</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>170.079196</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>546.593581</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>738.481650</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>351.219401</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>241.201983</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>26.122856</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>11</td>\n",
       "      <td>21.797788</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>43.021865</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     m        spdm     y\n",
       "0    1   34.447331  2010\n",
       "1    2   65.437523  2010\n",
       "2    3   56.889464  2010\n",
       "3    4  158.139479  2010\n",
       "4    5  557.717221  2010\n",
       "5    6  564.419334  2010\n",
       "6    7  457.328173  2010\n",
       "7    8  297.233322  2010\n",
       "8    9   65.094502  2010\n",
       "9   10   52.024606  2010\n",
       "10  11   23.716328  2010\n",
       "11  12   33.761125  2010\n",
       "12   1         NaN  2011\n",
       "13   2         NaN  2011\n",
       "14   3         NaN  2011\n",
       "15   4  245.573784  2011\n",
       "16   5  170.079196  2011\n",
       "17   6  546.593581  2011\n",
       "18   7  738.481650  2011\n",
       "19   8  351.219401  2011\n",
       "20   9  241.201983  2011\n",
       "21  10   26.122856  2011\n",
       "22  11   21.797788  2011\n",
       "23  12   43.021865  2011"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "_df1 = _df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "_df2=_df1.set_index(['m','y']).unstack('y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('spdm', 2010),\n",
       "            ('spdm', 2011)],\n",
       "           names=[None, 'y'])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y\n",
       "2010    [AxesSubplot(0.125,0.2;0.775x0.68)]\n",
       "2011    [AxesSubplot(0.125,0.2;0.775x0.68)]\n",
       "dtype: object"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_gr.plot(y='spdm',subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}