Smog.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from matplotlib.dates import WeekdayLocator\n",
    "from matplotlib.dates import DayLocator, HourLocator, DateFormatter, drange\n",
    "import datetime\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CZAS</th>\n",
       "      <th>Bochnia</th>\n",
       "      <th>Brzeszcze</th>\n",
       "      <th>Gorlice</th>\n",
       "      <th>Krakow-Kurdwanow</th>\n",
       "      <th>Krakow-Piastow</th>\n",
       "      <th>Krakow-Wadow</th>\n",
       "      <th>Krakow-Telimeny</th>\n",
       "      <th>Krakow-Zloty Rog</th>\n",
       "      <th>Muszyna</th>\n",
       "      <th>Niepolomice</th>\n",
       "      <th>Nowa Huta</th>\n",
       "      <th>Nowy Sacz</th>\n",
       "      <th>Nowy Targ</th>\n",
       "      <th>Rabka-Zdroj</th>\n",
       "      <th>Tarnow</th>\n",
       "      <th>Trzebinia</th>\n",
       "      <th>Tuchow</th>\n",
       "      <th>Zakopane</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>47</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>43</td>\n",
       "      <td>25</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>39</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "      <td>34</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>58</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>49</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>66</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>47</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>70</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>43</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CZAS  Bochnia  Brzeszcze  Gorlice  Krakow-Kurdwanow  Krakow-Piastow  \\\n",
       "0     1       27          0       20                15              18   \n",
       "1     2       27          0       22                35              43   \n",
       "2     3       31          0       31                34              38   \n",
       "3     4       53          0       55                58              55   \n",
       "4     5       35          0       38                47              56   \n",
       "\n",
       "   Krakow-Wadow  Krakow-Telimeny  Krakow-Zloty Rog  Muszyna  Niepolomice  \\\n",
       "0             0                0                22        0           22   \n",
       "1             0                0                48        0           36   \n",
       "2             0                0                44        0           34   \n",
       "3             0                0                62        0           51   \n",
       "4             0                0                59        0           43   \n",
       "\n",
       "   Nowa Huta  Nowy Sacz  Nowy Targ  Rabka-Zdroj  Tarnow  Trzebinia  Tuchow  \\\n",
       "0         23         22          0            0      19         25      27   \n",
       "1         47         36          0            0      25         43      25   \n",
       "2         39         38          0            0      29         38      34   \n",
       "3         49         63          0            0       0         69      66   \n",
       "4         70         34          0            0       0         46      43   \n",
       "\n",
       "   Zakopane  \n",
       "0        26  \n",
       "1        39  \n",
       "2        55  \n",
       "3        36  \n",
       "4        19  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_smog = pd.read_csv('./data/SMOG1.csv', sep = ';')\n",
    "big_smog.columns=['CZAS', 'Bochnia', 'Brzeszcze', 'Gorlice', 'Krakow-Kurdwanow', 'Krakow-Piastow',\n",
    "                  'Krakow-Wadow', 'Krakow-Telimeny', 'Krakow-Zloty Rog', 'Muszyna', 'Niepolomice',\n",
    "                  'Nowa Huta', 'Nowy Sacz', 'Nowy Targ', 'Rabka-Zdroj', 'Tarnow',  'Trzebinia',\n",
    "                  'Tuchow', 'Zakopane']\n",
    "big_smog.replace(to_replace = '-', value = 0, inplace = True)\n",
    "big_smog = big_smog[big_smog.columns].astype(int)\n",
    "big_smog.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CZAS                int64\n",
       "Bochnia             int64\n",
       "Brzeszcze           int64\n",
       "Gorlice             int64\n",
       "Krakow-Kurdwanow    int64\n",
       "Krakow-Piastow      int64\n",
       "Krakow-Wadow        int64\n",
       "Krakow-Telimeny     int64\n",
       "Krakow-Zloty Rog    int64\n",
       "Muszyna             int64\n",
       "Niepolomice         int64\n",
       "Nowa Huta           int64\n",
       "Nowy Sacz           int64\n",
       "Nowy Targ           int64\n",
       "Rabka-Zdroj         int64\n",
       "Tarnow              int64\n",
       "Trzebinia           int64\n",
       "Tuchow              int64\n",
       "Zakopane            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_smog.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 19)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_smog.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CZAS</th>\n",
       "      <th>Bochnia</th>\n",
       "      <th>Krakow-Kurdwanow</th>\n",
       "      <th>Nowy Sacz</th>\n",
       "      <th>Tarnow</th>\n",
       "      <th>Trzebinia</th>\n",
       "      <th>Zakopane</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>24</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>50</td>\n",
       "      <td>48</td>\n",
       "      <td>16</td>\n",
       "      <td>58</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>41</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CZAS  Bochnia  Krakow-Kurdwanow  Nowy Sacz  Tarnow  Trzebinia  Zakopane\n",
       "0     1       25                15         18      18         23        25\n",
       "1     2       26                 0         30      24         39        37\n",
       "2     3       28                 0         31      28         31        51\n",
       "3     4       45                50         48      16         58        31\n",
       "4     5       31                41         24       0         39        17"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_smog = pd.read_csv('./data/SMOG2.csv', sep = ';')\n",
    "small_smog.rename(columns=lambda x: x.strip(), inplace=True)\n",
    "#small_smog = small_smog[small_smog.columns].apply(lambda x: x.astype(int) if x != '-' else x == np.nan, axis = 1)\n",
    "\n",
    "small_smog.replace(to_replace = '-', value = 0, inplace = True)\n",
    "small_smog = small_smog[small_smog.columns].astype(int)\n",
    "small_smog.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CZAS                int64\n",
       "Bochnia             int64\n",
       "Krakow-Kurdwanow    int64\n",
       "Nowy Sacz           int64\n",
       "Tarnow              int64\n",
       "Trzebinia           int64\n",
       "Zakopane            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_smog.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 7)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_smog.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>sn_day_avg</th>\n",
       "      <th>sn_day_max</th>\n",
       "      <th>sn_day_min</th>\n",
       "      <th>sm_day_avg</th>\n",
       "      <th>sm_day_max</th>\n",
       "      <th>sm_day_min</th>\n",
       "      <th>sx_day_avg</th>\n",
       "      <th>sx_day_max</th>\n",
       "      <th>sx_day_min</th>\n",
       "      <th>...</th>\n",
       "      <th>rc_day_min</th>\n",
       "      <th>rd_day_avg</th>\n",
       "      <th>rd_day_max</th>\n",
       "      <th>rd_day_min</th>\n",
       "      <th>ri_day_avg</th>\n",
       "      <th>ri_day_max</th>\n",
       "      <th>ri_day_min</th>\n",
       "      <th>rp_day_avg</th>\n",
       "      <th>rp_day_max</th>\n",
       "      <th>rp_day_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>0.95</td>\n",
       "      <td>4.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>6.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.33</td>\n",
       "      <td>9.7</td>\n",
       "      <td>1.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19476.88</td>\n",
       "      <td>26130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>0.74</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.96</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3.06</td>\n",
       "      <td>9.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1125.67</td>\n",
       "      <td>26130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-03</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.64</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1329.96</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>0.45</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.27</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.09</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.58</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-05</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>4.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2.88</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  sn_day_avg  sn_day_max  sn_day_min  sm_day_avg  sm_day_max  \\\n",
       "0 2016-03-01        0.95         4.4         0.0        2.65         6.4   \n",
       "1 2016-03-02        0.74         3.9         0.0        1.96         6.5   \n",
       "2 2016-03-03        0.53         2.9         0.0        1.59         4.3   \n",
       "3 2016-03-04        0.45         2.2         0.0        1.27         3.3   \n",
       "4 2016-03-05        0.52         2.8         0.0        1.68         4.1   \n",
       "\n",
       "   sm_day_min  sx_day_avg  sx_day_max  sx_day_min     ...      rc_day_min  \\\n",
       "0         0.5        4.33         9.7         1.1     ...             0.0   \n",
       "1         0.1        3.06         9.7         0.3     ...             0.0   \n",
       "2         0.2        2.64         5.8         0.6     ...             0.0   \n",
       "3         0.2        2.09         5.1         0.5     ...             0.0   \n",
       "4         0.1        2.88         6.5         0.4     ...             0.0   \n",
       "\n",
       "   rd_day_avg  rd_day_max  rd_day_min  ri_day_avg  ri_day_max  ri_day_min  \\\n",
       "0    19476.88     26130.0         0.0        0.23         2.9         0.0   \n",
       "1     1125.67     26130.0         0.0        0.00         0.0         0.0   \n",
       "2     1329.96      2020.0         0.0        0.01         0.8         0.0   \n",
       "3       85.58      2020.0         0.0        0.00         0.0         0.0   \n",
       "4        0.00         0.0         0.0        0.00         0.0         0.0   \n",
       "\n",
       "   rp_day_avg  rp_day_max  rp_day_min  \n",
       "0        2.95         4.3         0.0  \n",
       "1        0.14         3.3         0.0  \n",
       "2        0.54         0.8         0.0  \n",
       "3        0.03         0.8         0.0  \n",
       "4        0.00         0.0         0.0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_climate = pd.read_csv('./data/generatedMeteoData.csv', usecols=[*range(0, 43)])\n",
    "df_climate['time'] = pd.to_datetime(df_climate['time'].apply(lambda x: x[0:10]))\n",
    "df_climate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 43)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_climate.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['time', 'sn_day_avg', 'sn_day_max', 'sn_day_min', 'sm_day_avg',\n",
       "       'sm_day_max', 'sm_day_min', 'sx_day_avg', 'sx_day_max', 'sx_day_min',\n",
       "       'dn_day_avg', 'dn_day_max', 'dn_day_min', 'dm_day_avg', 'dm_day_max',\n",
       "       'dm_day_min', 'dx_day_avg', 'dx_day_max', 'dx_day_min', 'ta_day_avg',\n",
       "       'ta_day_max', 'ta_day_min', 'pa_day_avg', 'pa_day_max', 'pa_day_min',\n",
       "       'tp_day_avg', 'tp_day_max', 'tp_day_min', 'ua_day_avg', 'ua_day_max',\n",
       "       'ua_day_min', 'rc_day_avg', 'rc_day_max', 'rc_day_min', 'rd_day_avg',\n",
       "       'rd_day_max', 'rd_day_min', 'ri_day_avg', 'ri_day_max', 'ri_day_min',\n",
       "       'rp_day_avg', 'rp_day_max', 'rp_day_min'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_climate.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>sm_day_avg</th>\n",
       "      <th>ta_day_avg</th>\n",
       "      <th>rc_day_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.49</td>\n",
       "      <td>4.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-02</td>\n",
       "      <td>1.96</td>\n",
       "      <td>3.59</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-03</td>\n",
       "      <td>1.59</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-04</td>\n",
       "      <td>1.27</td>\n",
       "      <td>5.31</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-05</td>\n",
       "      <td>1.68</td>\n",
       "      <td>5.78</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time  sm_day_avg  ta_day_avg  rc_day_avg\n",
       "0 2016-03-01        2.65        2.49        4.54\n",
       "1 2016-03-02        1.96        3.59        0.25\n",
       "2 2016-03-03        1.59        5.70        0.12\n",
       "3 2016-03-04        1.27        5.31        0.01\n",
       "4 2016-03-05        1.68        5.78        0.00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp1 = df_climate.loc[:,['time', 'sm_day_avg', 'ta_day_avg', 'rc_day_avg']]\n",
    "df_temp1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LJizA5aLtjnP/dsPNffV5Hpfb9EoOPw+CyxBQCPxLVV+spVzP4563i4GFIvIGbn7tVbj7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\nJAng8+eLFIehWBMdHU1CQgLlypWjRo0a+R6vSpUqjBs3ziWV6EopWrZsy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RwAACAASURB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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1101d0cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1a, ax2a, ax3a) = plt.subplots(3,1, sharex = True, figsize = (10, 8))\n",
    "fig.autofmt_xdate()\n",
    "plt.subplots_adjust(left=None, bottom=None, right=None, \n",
    "                    top=None, wspace=None, hspace=0.001)\n",
    "\n",
    "ax1a.plot(df_temp1['time'], big_smog['Krakow-Zloty Rog'], color = 'black', \n",
    "          linewidth=2, alpha = 0.7, label = 'rog PM10')\n",
    "ax1a.set_yticks(np.arange(0, 301, 100))\n",
    "ax1a.tick_params('y', colors='black')\n",
    "ax1b = ax1a.twinx()\n",
    "ax1b.plot(df_temp1['time'], df_temp1['rc_day_avg'], color = 'green', linewidth=2, alpha = 1, label = 'Rain')\n",
    "ax1b.set_yticks(np.arange(0, 3.1, 0.5))\n",
    "ax1b.set_ylabel('Percipitation ($mm$)', color='green')\n",
    "ax1b.tick_params('y', colors='green')\n",
    "ax1b.set_ylim([0, 3.0])\n",
    "\n",
    "ax2a.plot(df_temp1['time'], big_smog['Krakow-Zloty Rog'], color = 'black',\n",
    "          linewidth=2, alpha = 0.7, label = 'rog PM10')\n",
    "ax2a.set_ylabel('PM$_{10}$ Concentrations ($\\mu g/m^3$)', color='black', fontsize=15)\n",
    "ax2a.set_yticks(np.arange(0, 301, 100))\n",
    "ax2a.tick_params('y', colors='black')\n",
    "ax2b = ax2a.twinx()\n",
    "ax2b.plot(df_temp1['time'], df_temp1['sm_day_avg'], color = 'blue', linewidth=2, alpha = 0.6, label = 'Wind')\n",
    "ax2b.set_yticks(np.arange(0, 7, 1))\n",
    "ax2b.set_ylabel('Wind Speed ($m/s$)', color='blue')\n",
    "ax2b.tick_params('y', colors='blue')\n",
    "ax2b.set_ylim([0, 7])\n",
    "\n",
    "ax3a.plot(df_temp1['time'], big_smog['Krakow-Zloty Rog'], color = 'black',\n",
    "          linewidth=2, alpha = 0.7, label = 'rog PM10')\n",
    "ax3a.set_yticks(np.arange(0, 301, 100))\n",
    "ax3a.tick_params('y', colors='black')\n",
    "ax3b = ax3a.twinx()\n",
    "ax3b.plot(df_temp1['time'], df_temp1['ta_day_avg'], color = 'red', linewidth=2, alpha = 1, label = 'Temp')\n",
    "ax3b.set_yticks(np.arange(-20, 15, 5))\n",
    "ax3b.set_xlabel('days')\n",
    "ax3b.set_ylabel('Temperature ($^\\circ$C)', color='red')\n",
    "ax3b.tick_params('y', colors='red')\n",
    "ax3b.set_ylim([-20, 15])\n",
    "\n",
    "ax3a.set_xlim([datetime.date(2017, 1, 1), datetime.date(2017, 2, 28)])\n",
    "ax1a.set_title('The Influence of Weather Conditions\\n on Smog (PM$_{10}$) in Kraków', fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}