Analysing the data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "seatbelt_df = pd.read_excel('RSA Data.xlsx', sheet_name = 0)\n",
    "casualty_df = pd.read_excel('RSA Data.xlsx', sheet_name = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Seat belt</th>\n",
       "      <th>Casualty</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>Participant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Injured</td>\n",
       "      <td>1730</td>\n",
       "      <td>1658</td>\n",
       "      <td>1520</td>\n",
       "      <td>2097</td>\n",
       "      <td>2404</td>\n",
       "      <td>1890</td>\n",
       "      <td>1638</td>\n",
       "      <td>1699</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Killed</td>\n",
       "      <td>55</td>\n",
       "      <td>49</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Uninjured</td>\n",
       "      <td>1293</td>\n",
       "      <td>1157</td>\n",
       "      <td>1055</td>\n",
       "      <td>1599</td>\n",
       "      <td>1947</td>\n",
       "      <td>1639</td>\n",
       "      <td>1495</td>\n",
       "      <td>1568</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Injured</td>\n",
       "      <td>184</td>\n",
       "      <td>144</td>\n",
       "      <td>70</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Killed</td>\n",
       "      <td>38</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Seat belt   Casualty  2005  2006  2007  2008  2009  2010  2011  2012  \\\n",
       "0       Yes    Injured  1730  1658  1520  2097  2404  1890  1638  1699   \n",
       "1       Yes     Killed    55    49    42    44    41    39    35    22   \n",
       "2       Yes  Uninjured  1293  1157  1055  1599  1947  1639  1495  1568   \n",
       "3        No    Injured   184   144    70   103   103   103    84    88   \n",
       "4        No     Killed    38    37    14    12    21    13    12    14   \n",
       "\n",
       "   Participant  \n",
       "0  Car drivers  \n",
       "1  Car drivers  \n",
       "2  Car drivers  \n",
       "3  Car drivers  \n",
       "4  Car drivers  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seatbelt_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Participant</th>\n",
       "      <th>Casualty</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
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       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0 - 5 years</td>\n",
       "      <td>Male</td>\n",
       "      <td>Pedestrians</td>\n",
       "      <td>Injured</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>35</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0 - 5 years</td>\n",
       "      <td>Male</td>\n",
       "      <td>Pedestrians</td>\n",
       "      <td>Killed</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0 - 5 years</td>\n",
       "      <td>Male</td>\n",
       "      <td>Pedal cyclists</td>\n",
       "      <td>Injured</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0 - 5 years</td>\n",
       "      <td>Male</td>\n",
       "      <td>Pedal cyclists</td>\n",
       "      <td>Killed</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0 - 5 years</td>\n",
       "      <td>Male</td>\n",
       "      <td>Motor cyclists</td>\n",
       "      <td>Injured</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Age   Sex     Participant Casualty  2005  2006  2007  2008  2009  \\\n",
       "0  0 - 5 years  Male     Pedestrians  Injured    36    32    28    54    35   \n",
       "1  0 - 5 years  Male     Pedestrians   Killed     3     1     0     1     3   \n",
       "2  0 - 5 years  Male  Pedal cyclists  Injured     0     0     2     0     2   \n",
       "3  0 - 5 years  Male  Pedal cyclists   Killed     0     0     0     0     0   \n",
       "4  0 - 5 years  Male  Motor cyclists  Injured     0     0     0     0     0   \n",
       "\n",
       "   2010  2011  2012  \n",
       "0    41    39    40  \n",
       "1     3     0     0  \n",
       "2     5     3     1  \n",
       "3     0     1     0  \n",
       "4     0     0     0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casualty_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "seatbelt_df_new = pd.read_excel('Seatbelt_RSA_Data_after_OpenRefine.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Seat belt</th>\n",
       "      <th>Casualty</th>\n",
       "      <th>2005.0</th>\n",
       "      <th>2006.0</th>\n",
       "      <th>2007.0</th>\n",
       "      <th>2008.0</th>\n",
       "      <th>2009.0</th>\n",
       "      <th>2010.0</th>\n",
       "      <th>2011.0</th>\n",
       "      <th>2012.0</th>\n",
       "      <th>Participant</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Injured</td>\n",
       "      <td>1730</td>\n",
       "      <td>1658</td>\n",
       "      <td>1520</td>\n",
       "      <td>2097</td>\n",
       "      <td>2404</td>\n",
       "      <td>1890</td>\n",
       "      <td>1638</td>\n",
       "      <td>1699</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Killed</td>\n",
       "      <td>55</td>\n",
       "      <td>49</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Uninjured</td>\n",
       "      <td>1293</td>\n",
       "      <td>1157</td>\n",
       "      <td>1055</td>\n",
       "      <td>1599</td>\n",
       "      <td>1947</td>\n",
       "      <td>1639</td>\n",
       "      <td>1495</td>\n",
       "      <td>1568</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Injured</td>\n",
       "      <td>184</td>\n",
       "      <td>144</td>\n",
       "      <td>70</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Killed</td>\n",
       "      <td>38</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No</td>\n",
       "      <td>Uninjured</td>\n",
       "      <td>81</td>\n",
       "      <td>60</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Injured</td>\n",
       "      <td>1377</td>\n",
       "      <td>1241</td>\n",
       "      <td>917</td>\n",
       "      <td>1354</td>\n",
       "      <td>1393</td>\n",
       "      <td>1225</td>\n",
       "      <td>972</td>\n",
       "      <td>1193</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Killed</td>\n",
       "      <td>37</td>\n",
       "      <td>44</td>\n",
       "      <td>32</td>\n",
       "      <td>43</td>\n",
       "      <td>44</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>24</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Uninjured</td>\n",
       "      <td>1252</td>\n",
       "      <td>1005</td>\n",
       "      <td>778</td>\n",
       "      <td>1195</td>\n",
       "      <td>1236</td>\n",
       "      <td>1245</td>\n",
       "      <td>1141</td>\n",
       "      <td>1323</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Injured</td>\n",
       "      <td>962</td>\n",
       "      <td>716</td>\n",
       "      <td>888</td>\n",
       "      <td>663</td>\n",
       "      <td>292</td>\n",
       "      <td>288</td>\n",
       "      <td>291</td>\n",
       "      <td>280</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Seat belt   Casualty 2005.0 2006.0 2007.0 2008.0 2009.0 2010.0 2011.0  \\\n",
       "0       Yes    Injured   1730   1658   1520   2097   2404   1890   1638   \n",
       "1       Yes     Killed     55     49     42     44     41     39     35   \n",
       "2       Yes  Uninjured   1293   1157   1055   1599   1947   1639   1495   \n",
       "3        No    Injured    184    144     70    103    103    103     84   \n",
       "4        No     Killed     38     37     14     12     21     13     12   \n",
       "5        No  Uninjured     81     60     32     30     39     26     26   \n",
       "6   Unknown    Injured   1377   1241    917   1354   1393   1225    972   \n",
       "7   Unknown     Killed     37     44     32     43     44     26     14   \n",
       "8   Unknown  Uninjured   1252   1005    778   1195   1236   1245   1141   \n",
       "9   Unknown    Injured    962    716    888    663    292    288    291   \n",
       "\n",
       "  2012.0  Participant  \n",
       "0   1699  Car drivers  \n",
       "1     22  Car drivers  \n",
       "2   1568  Car drivers  \n",
       "3     88  Car drivers  \n",
       "4     14  Car drivers  \n",
       "5     29  Car drivers  \n",
       "6   1193  Car drivers  \n",
       "7     24  Car drivers  \n",
       "8   1323  Car drivers  \n",
       "9    280  Car drivers  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seatbelt_df_new.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Seat belt', 'Casualty', '2005.0', '2006.0', '2007.0', '2008.0',\n",
       "       '2009.0', '2010.0', '2011.0', '2012.0', 'Participant'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seatbelt_df_new.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Seat belt</th>\n",
       "      <th>Casualty</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>Participant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Injured</td>\n",
       "      <td>1730</td>\n",
       "      <td>1658</td>\n",
       "      <td>1520</td>\n",
       "      <td>2097</td>\n",
       "      <td>2404</td>\n",
       "      <td>1890</td>\n",
       "      <td>1638</td>\n",
       "      <td>1699</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Killed</td>\n",
       "      <td>55</td>\n",
       "      <td>49</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Uninjured</td>\n",
       "      <td>1293</td>\n",
       "      <td>1157</td>\n",
       "      <td>1055</td>\n",
       "      <td>1599</td>\n",
       "      <td>1947</td>\n",
       "      <td>1639</td>\n",
       "      <td>1495</td>\n",
       "      <td>1568</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>All</td>\n",
       "      <td>3078</td>\n",
       "      <td>2864</td>\n",
       "      <td>2617</td>\n",
       "      <td>3740</td>\n",
       "      <td>4392</td>\n",
       "      <td>3568</td>\n",
       "      <td>3168</td>\n",
       "      <td>3289</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Injured</td>\n",
       "      <td>184</td>\n",
       "      <td>144</td>\n",
       "      <td>70</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Seat belt   Casualty  2005  2006  2007  2008  2009  2010  2011  2012  \\\n",
       "0       Yes    Injured  1730  1658  1520  2097  2404  1890  1638  1699   \n",
       "1       Yes     Killed    55    49    42    44    41    39    35    22   \n",
       "2       Yes  Uninjured  1293  1157  1055  1599  1947  1639  1495  1568   \n",
       "3       Yes        All  3078  2864  2617  3740  4392  3568  3168  3289   \n",
       "4        No    Injured   184   144    70   103   103   103    84    88   \n",
       "\n",
       "   Participant  \n",
       "0  Car drivers  \n",
       "1  Car drivers  \n",
       "2  Car drivers  \n",
       "3  Car drivers  \n",
       "4  Car drivers  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seatbelt_cleaned_df = pd.read_excel('Seatbelt_RSA_Data_after_my_excel_cleaning.xls')\n",
    "seatbelt_cleaned_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Seat belt</th>\n",
       "      <th>Casualty</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>Participant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Injured</td>\n",
       "      <td>1730</td>\n",
       "      <td>1658</td>\n",
       "      <td>1520</td>\n",
       "      <td>2097</td>\n",
       "      <td>2404</td>\n",
       "      <td>1890</td>\n",
       "      <td>1638</td>\n",
       "      <td>1699</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Killed</td>\n",
       "      <td>55</td>\n",
       "      <td>49</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Uninjured</td>\n",
       "      <td>1293</td>\n",
       "      <td>1157</td>\n",
       "      <td>1055</td>\n",
       "      <td>1599</td>\n",
       "      <td>1947</td>\n",
       "      <td>1639</td>\n",
       "      <td>1495</td>\n",
       "      <td>1568</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>All</td>\n",
       "      <td>3078</td>\n",
       "      <td>2864</td>\n",
       "      <td>2617</td>\n",
       "      <td>3740</td>\n",
       "      <td>4392</td>\n",
       "      <td>3568</td>\n",
       "      <td>3168</td>\n",
       "      <td>3289</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Injured</td>\n",
       "      <td>184</td>\n",
       "      <td>144</td>\n",
       "      <td>70</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No</td>\n",
       "      <td>Killed</td>\n",
       "      <td>38</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>No</td>\n",
       "      <td>Uninjured</td>\n",
       "      <td>81</td>\n",
       "      <td>60</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>No</td>\n",
       "      <td>All</td>\n",
       "      <td>303</td>\n",
       "      <td>241</td>\n",
       "      <td>116</td>\n",
       "      <td>145</td>\n",
       "      <td>163</td>\n",
       "      <td>142</td>\n",
       "      <td>122</td>\n",
       "      <td>131</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Injured</td>\n",
       "      <td>2339</td>\n",
       "      <td>1957</td>\n",
       "      <td>1805</td>\n",
       "      <td>2017</td>\n",
       "      <td>1685</td>\n",
       "      <td>1513</td>\n",
       "      <td>1263</td>\n",
       "      <td>1473</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Killed</td>\n",
       "      <td>51</td>\n",
       "      <td>61</td>\n",
       "      <td>54</td>\n",
       "      <td>53</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Uninjured</td>\n",
       "      <td>1824</td>\n",
       "      <td>1641</td>\n",
       "      <td>1785</td>\n",
       "      <td>1863</td>\n",
       "      <td>1552</td>\n",
       "      <td>1495</td>\n",
       "      <td>1392</td>\n",
       "      <td>1574</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>All</td>\n",
       "      <td>4214</td>\n",
       "      <td>3659</td>\n",
       "      <td>3644</td>\n",
       "      <td>3933</td>\n",
       "      <td>3282</td>\n",
       "      <td>3036</td>\n",
       "      <td>2673</td>\n",
       "      <td>3075</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>All</td>\n",
       "      <td>Injured</td>\n",
       "      <td>4253</td>\n",
       "      <td>3759</td>\n",
       "      <td>3395</td>\n",
       "      <td>4217</td>\n",
       "      <td>4192</td>\n",
       "      <td>3506</td>\n",
       "      <td>2985</td>\n",
       "      <td>3260</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>All</td>\n",
       "      <td>Killed</td>\n",
       "      <td>144</td>\n",
       "      <td>147</td>\n",
       "      <td>110</td>\n",
       "      <td>109</td>\n",
       "      <td>107</td>\n",
       "      <td>80</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>All</td>\n",
       "      <td>Uninjured</td>\n",
       "      <td>3198</td>\n",
       "      <td>2858</td>\n",
       "      <td>2872</td>\n",
       "      <td>3492</td>\n",
       "      <td>3538</td>\n",
       "      <td>3160</td>\n",
       "      <td>2913</td>\n",
       "      <td>3171</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>All</td>\n",
       "      <td>All</td>\n",
       "      <td>15190</td>\n",
       "      <td>13528</td>\n",
       "      <td>12754</td>\n",
       "      <td>15636</td>\n",
       "      <td>15674</td>\n",
       "      <td>13492</td>\n",
       "      <td>11926</td>\n",
       "      <td>12990</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Injured</td>\n",
       "      <td>461</td>\n",
       "      <td>504</td>\n",
       "      <td>554</td>\n",
       "      <td>791</td>\n",
       "      <td>898</td>\n",
       "      <td>761</td>\n",
       "      <td>611</td>\n",
       "      <td>653</td>\n",
       "      <td>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Killed</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Uninjured</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>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Yes</td>\n",
       "      <td>K&amp;I</td>\n",
       "      <td>476</td>\n",
       "      <td>520</td>\n",
       "      <td>571</td>\n",
       "      <td>808</td>\n",
       "      <td>906</td>\n",
       "      <td>770</td>\n",
       "      <td>622</td>\n",
       "      <td>657</td>\n",
       "      <td>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>No</td>\n",
       "      <td>Injured</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>33</td>\n",
       "      <td>47</td>\n",
       "      <td>58</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>No</td>\n",
       "      <td>Killed</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>No</td>\n",
       "      <td>Uninjured</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>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>No</td>\n",
       "      <td>K&amp;I</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>39</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "      <td>48</td>\n",
       "      <td>43</td>\n",
       "      <td>30</td>\n",
       "      <td>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Injured</td>\n",
       "      <td>676</td>\n",
       "      <td>627</td>\n",
       "      <td>591</td>\n",
       "      <td>622</td>\n",
       "      <td>568</td>\n",
       "      <td>451</td>\n",
       "      <td>367</td>\n",
       "      <td>453</td>\n",
       "      <td>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Killed</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>Uninjured</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>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>K&amp;I</td>\n",
       "      <td>706</td>\n",
       "      <td>653</td>\n",
       "      <td>605</td>\n",
       "      <td>635</td>\n",
       "      <td>579</td>\n",
       "      <td>455</td>\n",
       "      <td>374</td>\n",
       "      <td>464</td>\n",
       "      <td>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>All</td>\n",
       "      <td>Injured</td>\n",
       "      <td>1181</td>\n",
       "      <td>1176</td>\n",
       "      <td>1178</td>\n",
       "      <td>1460</td>\n",
       "      <td>1524</td>\n",
       "      <td>1251</td>\n",
       "      <td>1020</td>\n",
       "      <td>1136</td>\n",
       "      <td>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>All</td>\n",
       "      <td>Killed</td>\n",
       "      <td>50</td>\n",
       "      <td>46</td>\n",
       "      <td>37</td>\n",
       "      <td>33</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>All</td>\n",
       "      <td>Uninjured</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>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>All</td>\n",
       "      <td>K&amp;I</td>\n",
       "      <td>1231</td>\n",
       "      <td>1173</td>\n",
       "      <td>1215</td>\n",
       "      <td>1493</td>\n",
       "      <td>1545</td>\n",
       "      <td>1273</td>\n",
       "      <td>1039</td>\n",
       "      <td>1151</td>\n",
       "      <td>Front seat passengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>All</td>\n",
       "      <td>K&amp;I</td>\n",
       "      <td>4573</td>\n",
       "      <td>4178</td>\n",
       "      <td>4197</td>\n",
       "      <td>5094</td>\n",
       "      <td>5190</td>\n",
       "      <td>4513</td>\n",
       "      <td>4017</td>\n",
       "      <td>4386</td>\n",
       "      <td>All</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Seat belt   Casualty   2005   2006   2007   2008   2009   2010   2011  \\\n",
       "0        Yes    Injured   1730   1658   1520   2097   2404   1890   1638   \n",
       "1        Yes     Killed     55     49     42     44     41     39     35   \n",
       "2        Yes  Uninjured   1293   1157   1055   1599   1947   1639   1495   \n",
       "3        Yes        All   3078   2864   2617   3740   4392   3568   3168   \n",
       "4         No    Injured    184    144     70    103    103    103     84   \n",
       "5         No     Killed     38     37     14     12     21     13     12   \n",
       "6         No  Uninjured     81     60     32     30     39     26     26   \n",
       "7         No        All    303    241    116    145    163    142    122   \n",
       "8    Unknown    Injured   2339   1957   1805   2017   1685   1513   1263   \n",
       "9    Unknown     Killed     51     61     54     53     45     28     18   \n",
       "10   Unknown  Uninjured   1824   1641   1785   1863   1552   1495   1392   \n",
       "11   Unknown        All   4214   3659   3644   3933   3282   3036   2673   \n",
       "12       All    Injured   4253   3759   3395   4217   4192   3506   2985   \n",
       "13       All     Killed    144    147    110    109    107     80     65   \n",
       "14       All  Uninjured   3198   2858   2872   3492   3538   3160   2913   \n",
       "15       All        All  15190  13528  12754  15636  15674  13492  11926   \n",
       "16       NaN        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "17       Yes    Injured    461    504    554    791    898    761    611   \n",
       "18       Yes     Killed     15     16     17     17      8      9     11   \n",
       "19       Yes  Uninjured     ..     ..     ..     ..     ..     ..     ..   \n",
       "20       Yes        K&I    476    520    571    808    906    770    622   \n",
       "21        No    Injured     44     45     33     47     58     39     42   \n",
       "22        No     Killed      5      4      6      3      2      9      1   \n",
       "23        No  Uninjured     ..     ..     ..     ..     ..     ..     ..   \n",
       "24        No        K&I     49     49     39     50     60     48     43   \n",
       "25   Unknown    Injured    676    627    591    622    568    451    367   \n",
       "26   Unknown     Killed     30     26     14     13     11      4      7   \n",
       "27   Unknown  Uninjured     ..     ..     ..     ..     ..     ..     ..   \n",
       "28   Unknown        K&I    706    653    605    635    579    455    374   \n",
       "29       All    Injured   1181   1176   1178   1460   1524   1251   1020   \n",
       "30       All     Killed     50     46     37     33     21     22     19   \n",
       "31       All  Uninjured     ..     ..     ..     ..     ..     ..     ..   \n",
       "32       All        K&I   1231   1173   1215   1493   1545   1273   1039   \n",
       "33       All        K&I   4573   4178   4197   5094   5190   4513   4017   \n",
       "\n",
       "     2012            Participant  \n",
       "0    1699            Car drivers  \n",
       "1      22            Car drivers  \n",
       "2    1568            Car drivers  \n",
       "3    3289            Car drivers  \n",
       "4      88            Car drivers  \n",
       "5      14            Car drivers  \n",
       "6      29            Car drivers  \n",
       "7     131            Car drivers  \n",
       "8    1473            Car drivers  \n",
       "9      28            Car drivers  \n",
       "10   1574            Car drivers  \n",
       "11   3075            Car drivers  \n",
       "12   3260            Car drivers  \n",
       "13     64            Car drivers  \n",
       "14   3171            Car drivers  \n",
       "15  12990            Car drivers  \n",
       "16    NaN                    NaN  \n",
       "17    653  Front seat passengers  \n",
       "18      4  Front seat passengers  \n",
       "19     ..  Front seat passengers  \n",
       "20    657  Front seat passengers  \n",
       "21     30  Front seat passengers  \n",
       "22      0  Front seat passengers  \n",
       "23     ..  Front seat passengers  \n",
       "24     30  Front seat passengers  \n",
       "25    453  Front seat passengers  \n",
       "26     11  Front seat passengers  \n",
       "27     ..  Front seat passengers  \n",
       "28    464  Front seat passengers  \n",
       "29   1136  Front seat passengers  \n",
       "30     15  Front seat passengers  \n",
       "31     ..  Front seat passengers  \n",
       "32   1151  Front seat passengers  \n",
       "33   4386                    All  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seatbelt_cleaned_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_wearing_seatbelt = seatbelt_cleaned_df[(seatbelt_cleaned_df['Seat belt'] == 'Yes') & (seatbelt_cleaned_df['Casualty'] == 'All')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Seat belt Casualty 2005 2006 2007 2008 2009 2010 2011 2012  Participant\n",
      "1       Yes   Killed   55   49   42   44   41   39   35   22  Car drivers\n"
     ]
    }
   ],
   "source": [
    "'''killed_wearing_seatbelt = '''\n",
    "print(seatbelt_cleaned_df[(seatbelt_cleaned_df['Seat belt'] == 'Yes') & (seatbelt_cleaned_df['Casualty'] == 'Killed') & (seatbelt_cleaned_df['Participant'] == 'Car drivers')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "prop_killed_wearing_seatbelt = killed_wearing_seatbelt/total_wearing_seatbelt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Seat belt Casualty 2005 2006 2007 2008 2009 2010 2011 2012 Participant\n",
      "1        NaN      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN         NaN\n",
      "3        NaN      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN         NaN\n",
      "18       NaN      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN         NaN\n"
     ]
    }
   ],
   "source": [
    "print(prop_killed_wearing_seatbelt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Participant</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pedestrians</td>\n",
       "      <td>36</td>\n",
       "      <td>32</td>\n",
       "      <td>28</td>\n",
       "      <td>54</td>\n",
       "      <td>35</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pedestrians</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pedal cyclists</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pedal cyclists</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Motor cyclists</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Participant  2005  2006  2007  2008  2009  2010  2011  2012\n",
       "0     Pedestrians    36    32    28    54    35    41    39    40\n",
       "1     Pedestrians     3     1     0     1     3     3     0     0\n",
       "2  Pedal cyclists     0     0     2     0     2     5     3     1\n",
       "3  Pedal cyclists     0     0     0     0     0     0     1     0\n",
       "4  Motor cyclists     0     0     0     0     0     0     0     0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_df = pd.concat([casualty_df['Participant'], casualty_df[2005], casualty_df[2006], casualty_df[2007], casualty_df[2008], casualty_df[2009], casualty_df[2010], casualty_df[2011], casualty_df[2012]], axis=1)\n",
    "concat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_df = concat_df.groupby(by='Participant').sum()\n",
    "df = grouped_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Participant  2005  2006  2007  2008  2009  2010  2011  2012\n",
      "0       Car drivers  3668  3673  3353  4164  4171  3420  2911  3212\n",
      "1    Car passengers  1895  1870  1923  2499  2675  2219  1863  2071\n",
      "2    Motor cyclists   541   492   390   497   445   388   329   348\n",
      "3  Other road users   924   914   761   786   697   694   637   560\n",
      "4    Pedal cyclists   197   198   260   332   361   386   387   621\n",
      "5       Pedestrians   982   946   929  1140  1093   950   964  1025\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Participant', 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6 entries, 0 to 5\n",
      "Data columns (total 9 columns):\n",
      "Participant    6 non-null object\n",
      "2005           6 non-null int64\n",
      "2006           6 non-null int64\n",
      "2007           6 non-null int64\n",
      "2008           6 non-null int64\n",
      "2009           6 non-null int64\n",
      "2010           6 non-null int64\n",
      "2011           6 non-null int64\n",
      "2012           6 non-null int64\n",
      "dtypes: int64(8), object(1)\n",
      "memory usage: 512.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Participant</th>\n",
       "      <th>Car drivers</th>\n",
       "      <th>Car passengers</th>\n",
       "      <th>Motor cyclists</th>\n",
       "      <th>Other road users</th>\n",
       "      <th>Pedal cyclists</th>\n",
       "      <th>Pedestrians</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Participant</th>\n",
       "      <td>Car drivers</td>\n",
       "      <td>Car passengers</td>\n",
       "      <td>Motor cyclists</td>\n",
       "      <td>Other road users</td>\n",
       "      <td>Pedal cyclists</td>\n",
       "      <td>Pedestrians</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>3668</td>\n",
       "      <td>1895</td>\n",
       "      <td>541</td>\n",
       "      <td>924</td>\n",
       "      <td>197</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>3673</td>\n",
       "      <td>1870</td>\n",
       "      <td>492</td>\n",
       "      <td>914</td>\n",
       "      <td>198</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>3353</td>\n",
       "      <td>1923</td>\n",
       "      <td>390</td>\n",
       "      <td>761</td>\n",
       "      <td>260</td>\n",
       "      <td>929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>4164</td>\n",
       "      <td>2499</td>\n",
       "      <td>497</td>\n",
       "      <td>786</td>\n",
       "      <td>332</td>\n",
       "      <td>1140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Participant  Car drivers  Car passengers  Motor cyclists  Other road users  \\\n",
       "Participant  Car drivers  Car passengers  Motor cyclists  Other road users   \n",
       "2005                3668            1895             541               924   \n",
       "2006                3673            1870             492               914   \n",
       "2007                3353            1923             390               761   \n",
       "2008                4164            2499             497               786   \n",
       "\n",
       "Participant  Pedal cyclists  Pedestrians  \n",
       "Participant  Pedal cyclists  Pedestrians  \n",
       "2005                    197          982  \n",
       "2006                    198          946  \n",
       "2007                    260          929  \n",
       "2008                    332         1140  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transpose = df.transpose()\n",
    "df_transpose.columns = df_transpose.iloc[0]\n",
    "df_transpose.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\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>Participant</th>\n",
       "      <th>Car drivers</th>\n",
       "      <th>Car passengers</th>\n",
       "      <th>Motor cyclists</th>\n",
       "      <th>Other road users</th>\n",
       "      <th>Pedal cyclists</th>\n",
       "      <th>Pedestrians</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>3668</td>\n",
       "      <td>1895</td>\n",
       "      <td>541</td>\n",
       "      <td>924</td>\n",
       "      <td>197</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>3673</td>\n",
       "      <td>1870</td>\n",
       "      <td>492</td>\n",
       "      <td>914</td>\n",
       "      <td>198</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>3353</td>\n",
       "      <td>1923</td>\n",
       "      <td>390</td>\n",
       "      <td>761</td>\n",
       "      <td>260</td>\n",
       "      <td>929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>4164</td>\n",
       "      <td>2499</td>\n",
       "      <td>497</td>\n",
       "      <td>786</td>\n",
       "      <td>332</td>\n",
       "      <td>1140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>4171</td>\n",
       "      <td>2675</td>\n",
       "      <td>445</td>\n",
       "      <td>697</td>\n",
       "      <td>361</td>\n",
       "      <td>1093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Participant Car drivers Car passengers Motor cyclists Other road users  \\\n",
       "2005               3668           1895            541              924   \n",
       "2006               3673           1870            492              914   \n",
       "2007               3353           1923            390              761   \n",
       "2008               4164           2499            497              786   \n",
       "2009               4171           2675            445              697   \n",
       "\n",
       "Participant Pedal cyclists Pedestrians  \n",
       "2005                   197         982  \n",
       "2006                   198         946  \n",
       "2007                   260         929  \n",
       "2008                   332        1140  \n",
       "2009                   361        1093  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df_transpose.drop(index='Participant')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Car drivers', 'Car passengers', 'Motor cyclists', 'Other road users',\n",
       "       'Pedal cyclists', 'Pedestrians'],\n",
       "      dtype='object', name='Participant')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Participant</th>\n",
       "      <th>index</th>\n",
       "      <th>Car drivers</th>\n",
       "      <th>Car passengers</th>\n",
       "      <th>Motor cyclists</th>\n",
       "      <th>Other road users</th>\n",
       "      <th>Pedal cyclists</th>\n",
       "      <th>Pedestrians</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>3668</td>\n",
       "      <td>1895</td>\n",
       "      <td>541</td>\n",
       "      <td>924</td>\n",
       "      <td>197</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>3673</td>\n",
       "      <td>1870</td>\n",
       "      <td>492</td>\n",
       "      <td>914</td>\n",
       "      <td>198</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007</td>\n",
       "      <td>3353</td>\n",
       "      <td>1923</td>\n",
       "      <td>390</td>\n",
       "      <td>761</td>\n",
       "      <td>260</td>\n",
       "      <td>929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>4164</td>\n",
       "      <td>2499</td>\n",
       "      <td>497</td>\n",
       "      <td>786</td>\n",
       "      <td>332</td>\n",
       "      <td>1140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>4171</td>\n",
       "      <td>2675</td>\n",
       "      <td>445</td>\n",
       "      <td>697</td>\n",
       "      <td>361</td>\n",
       "      <td>1093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Participant  index Car drivers Car passengers Motor cyclists Other road users  \\\n",
       "0             2005        3668           1895            541              924   \n",
       "1             2006        3673           1870            492              914   \n",
       "2             2007        3353           1923            390              761   \n",
       "3             2008        4164           2499            497              786   \n",
       "4             2009        4171           2675            445              697   \n",
       "\n",
       "Participant Pedal cyclists Pedestrians  \n",
       "0                      197         982  \n",
       "1                      198         946  \n",
       "2                      260         929  \n",
       "3                      332        1140  \n",
       "4                      361        1093  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df1.reset_index(level=0)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Participant</th>\n",
       "      <th>Year</th>\n",
       "      <th>Car drivers</th>\n",
       "      <th>Car passengers</th>\n",
       "      <th>Motor cyclists</th>\n",
       "      <th>Other road users</th>\n",
       "      <th>Pedal cyclists</th>\n",
       "      <th>Pedestrians</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005</td>\n",
       "      <td>3668</td>\n",
       "      <td>1895</td>\n",
       "      <td>541</td>\n",
       "      <td>924</td>\n",
       "      <td>197</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>3673</td>\n",
       "      <td>1870</td>\n",
       "      <td>492</td>\n",
       "      <td>914</td>\n",
       "      <td>198</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007</td>\n",
       "      <td>3353</td>\n",
       "      <td>1923</td>\n",
       "      <td>390</td>\n",
       "      <td>761</td>\n",
       "      <td>260</td>\n",
       "      <td>929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>4164</td>\n",
       "      <td>2499</td>\n",
       "      <td>497</td>\n",
       "      <td>786</td>\n",
       "      <td>332</td>\n",
       "      <td>1140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>4171</td>\n",
       "      <td>2675</td>\n",
       "      <td>445</td>\n",
       "      <td>697</td>\n",
       "      <td>361</td>\n",
       "      <td>1093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Participant  Year Car drivers Car passengers Motor cyclists Other road users  \\\n",
       "0            2005        3668           1895            541              924   \n",
       "1            2006        3673           1870            492              914   \n",
       "2            2007        3353           1923            390              761   \n",
       "3            2008        4164           2499            497              786   \n",
       "4            2009        4171           2675            445              697   \n",
       "\n",
       "Participant Pedal cyclists Pedestrians  \n",
       "0                      197         982  \n",
       "1                      198         946  \n",
       "2                      260         929  \n",
       "3                      332        1140  \n",
       "4                      361        1093  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df1.rename(index=str, columns={'index': 'Year'})\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16b7d0c44e0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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04YVxR1LYqpeLvPRSePNNleIWoPxMAGvXhr7NoqJwa9Hiq/c1H7dpk952jWmr6/VPPglleSecADNnQteucf8JZldFRVjy8dRT445ERo6EMWPCxeCrroo7Gmli6gJKinnzwkLoe+4ZzgTato07ouxwh732Ct9z6tS4oxEIFUFvvBHGZTTTZcF8oAVhck2vXmFahNdeCwOjNm6MO6LsqKwMPzSq/kmOsjJ49114/vm4I5EmpgSQJAMHwm23wVNPwQ9/mJ+TdaVSodvrlFPijkSqDRkSzjg1JqDgKAEkzahRoU/2jjvg17+OO5rM2rw5JIDjjoMOHeKORqptt10ox3344XA9SgqGEkASXXNN6Aa69NIvZ8vMBy+9BFVVmvsnicrLYd06eOyxuCORJqQEkETNmsFf/wqHHhoW8MiXGu2KCmjdGk4+Oe5IpLbDDw9Tcmi5yIJSZwIws9ZmNsvM5pnZAjO7Imo3M7vGzP7PzBaa2YU12n9nZovNbL6ZHVRjX+Vm9mZ0K8/e18oDrVuHUtZu3WDwYFi8OO6IGmfTpnA2c/zxsOOOcUcjtTVrFi4G/+1v8P77cUcjTSSdM4D1QD937wX0BgaaWV/gv4BuQE933weoiLYfBPSIbqOA2wHMrCMwFjgE6AOMNTN1BG/LTjvB5MnhYvDxx8PKlXFH1HDPPQcffaTqnyQ7++zwb+3+++OORJpInQnAg3XR06Lo5sD3gCvdfXO03fJom8HAvdH7Xgbam1kX4DhgmruvcvfVwDRgYGa/Th7q0SOcCbz3XqjW+PzzuCNqmFQKtt8+DHaTZNprLzjssFANlI8VaPIf0roGYGbNzWwusJzwIz4T2BM4w8wqzewpM6seR94VWFrj7VVR29bapS6HHRb6Zl98Ec45J1TT5JING+CRR0Lf/3bbxR2NbEtZGbz+OsyZE3ck0gTSSgDuvsndewMlQB8z2x9oBXwejTb7E3B3tLltaRfbaP8KMxsVJZXKFStWpBNeYTjjjDC/UUUF/OIXcUdTP888A6tWqfonF5x+OrRqpTEBBaJeVUDuvgaYQei6qQLGRy89ChwQPa4iXBuoVgJ8sI322p9xp7uXuntpcXFxfcLLf5deCv/933DttfDnP8cdTfoqKqBduzDQTZKtfftQdPDQQ2GqaMlr6VQBFZtZ++hxG+BY4A3gMaBftNmRwP9FjycCZVE1UF9grbsvA6YAA8ysQ3Txd0DUJukygz/8IQyk+u53c2MunfXrQ235kCHhyFKSr7wc/vnPMCJd8lo6ZwBdgOlmNh+YTbgG8CRwPXCqmb0KXAecH20/GVgCLCZ0DX0fwN1XAVdF+5hNuIC8KoPfpTAUFYUFPPbbD047LUytnGRTpoTZWVX9kzsGDAhrNagbKO9pNtBcVVUVFpFp1ixMIb3rrnFHtGVnnhnOVJYtC8lLcsOPfwy//334e+vUKe5opJ40G2i+KymBSZNgzRo48cQwjD9pPv0UJk6EoUP1459ryspC9VYqFXckkkVKALmsd+/QHTR/fuhiSdoU0pMmhcnF1P2Te3r1CjdNDZHXlABy3aBB4cLwpElw0UXJGsCTSoW+5COPjDsSaYiyMpg1KywWI3lJCSAffOc7cMklYS2Bm2+OO5rgX/8KSWnYsLD8o+SeM88Mf3e6GJy3lADyxfXXh6qgn/wExo+ve/tsmzgxTFuhwV+5q3PnUHJ83325N/pc0qIEkC+aNQtHan37wllnwcsvxxtPRUW4UH3oofHGIY1TVhYqzqZPjzsSyQIlgHzSpk2YOG7XXcO8O0uWxBPH6tWh/v/007XIeK47+eQwilvdQHlJ/zvzTXFxGMG5aVOYQnpVDGPtHn00lBCq+if3tWkTEvn48cksNZZGUQLIR3vvHaZfePvtsPj6+vVN+/mpFHzta1Ba5zgUyQXl5aGcd8KEuCORDFMCyFff/nZYVvL55+Hcc5uuPHTFijD75xlnhLmLJPcdeijsuafGBOQhJYB8NmJEWGD+wQdh7Nim+czx40P3k6p/8odZuBg8fXpYmEjyhhJAvrvsMjjvPLjqKvjLX7L/eRUV0LMnHHBA3dtK7tBykXlJCSDfmcHtt0P//jBqVFj0O1s++CB0Oan7J//ssQcccYSWi8wzSgCFoKgIHn44HJmfeiq89lp2Pufhh8OPg7p/8lNZGSxaBLNnxx2JZIgSQKFo1y5MzVC9MPuyZZn/jFQqdP3ss0/m9y3xGzYMWrfWxeA8ogRQSHbbDZ58ElauzPwU0u++Cy+9pNr/fLbjjqGsuKKi6UuLJSuUAArNQQeFI/W5c8NkX5s2ZWa/48aFe3X/5Lfy8jC4cNKkuCORDFACKEQnnBBWe3riCbj44sxc1KuogIMPDgPAJH8deyx06aKpIfKEEkCh+v73w7J/t94Kv/1t4/b15pvwj3/o6L8QNG8eJhucNCkM+pOcpgRQyG68MVQFjR4d5u9pqOplA08/PTNxSbKVlYXV5yoq4o5EGkkJoJA1axbmeu/TB0aODKs/NUQqBYcdBt26ZTY+Sab994cDD1Q1UB5QAih0bdqExVs6d4aTTgoTyNXHggVhXIGqfwpLeTnMmRP+/iVnKQEI7LwzTJ4cpnA+/vgwn3+6UqlwJnHaadmLT5JnxAho0SJMOCg5q84EYGatzWyWmc0zswVmdkWt139vZutqPG9lZikzW2xmM82se43XLovaF5nZcZn8ItJIPXuGKaSXLIGhQ+GLL+p+j3voBz7qqHAGIYVj553DmIA77oDly+OORhoonTOA9UA/d+8F9AYGmllfADMrBdrX2v48YLW77wXcDNwQbbsvMBzYDxgI3GZmWi08SY44Au6+G2bMgPPPr7s8dO7cUAGk6p/CdPXV8NlncOWVcUciDVRnAvCg+gi/KLp59ON9E3BprbcMBqqvDj0CHGNmFrVXuPt6d38bWAz0ycB3kEwaOTLMHHrffXDFFdvetqIidAMMHdo0sUmy7L13mGDwj38MBwKSc9K6BmBmzc1sLrAcmObuM4EfABPdvfakMl2BpQDuvhFYC3Sq2R6pitpqf9YoM6s0s8oVqjOOx+WXwznnhASwtUoP99D/f+yxsNNOTRufJMfYsdCqFfzsZ3FHIg2QVgJw903u3hsoAfqY2RHAMOD3W9h8S/MA+zbaa3/Wne5e6u6lxcXF6YQnmWYWjuqOOSZ0BT377H9uM3NmmP9H1T+FbZdd4JJL4JFHwr8JySn1qgJy9zXADOBoYC9gsZm9A2xnZoujzaqAbgBm1gJoB6yq2R4pAT5oROySTUVF4T/1178eunhef/2rr6dS0LIlDBkST3ySHD/+8ZeJQGsF5JR0qoCKzax99LgNcCwwx907u3t3d+8OfBpd9AWYCJRHj08DnnV3j9qHR1VCewA9gAaOPJIm0b59GPLfpk0oD/3ww9C+eXOY/G3QoDDNtBS2HXaAX/0KXnghzDYrOSOdM4AuwHQzmw/MJlwD2Nbf8l1Ap+iMYDQwBsDdFwDjgNeBp4EL3D1DU1FK1uy+e/hPvWJFGCj2ySfw4oth9S9V/0i1884LF4V/+tMwTYTkBPMEn7KVlpZ6ZWVl3GEIhJlDhwwJSaBz5zAb5PLl4ehPBGDChDC31J/+FK4dSWzMbI67l9a1nUYCS3pOOinMGvr44+EC8Ykn6sdfvuqUU+Bb3wqVQZ98Enc0kgYlAEnfD34AP/pReDxyZLyxSPKYwU03he7BW26JOxpJg7qApH42b4ZXX4VeveKORJLqlFPgmWfgrbdApdyxUBeQZEezZvrxl2277jr49NMwolwSTQlARDKrZ89QFXT77bB4cd3bS2yUAEQk8371qzBQ8PLL445EtkEJQEQyr0uXMEJ43DiYPTvuaGQrlABEJDsuuSRcBNYUEYmlBCAi2dG2bRgT8NxzYcU5SRwlABHJnlGjYK+9whQRmzTzS9IoAYhI9hQVhbLQBQu2vraExEYJQESy69RT4ZBD4Je/DOMDJDGUAEQku8zgxhvh/ffhd7+LOxqpQQlARLLviCPChILXXQf//Gfc0UhECUBEmsb118O6dXDNNXFHIhElABFpGvvuC+eeC3/4AyxZEnc0ghKAiDSlK66AFi3g5z+POxJBCUBEmtKuu8Lo0fDQQzBnTtzRFDwlABFpWpdeCjvtFO41RUSslABEpGntuGMYE/DsszBlStzRFDQlABFpet/5Duy5ZzgL0BQRsVECEJGm17JlKAd99VW4//64oylYdSYAM2ttZrPMbJ6ZLTCzK6L2B8xskZm9ZmZ3m1lR1G5m9jszW2xm883soBr7KjezN6Nbefa+logk3rBhcPDB8ItfwGefxR1Nsrz/Pnz+edY/Jp0zgPVAP3fvBfQGBppZX+ABoCfwDaANcH60/SCgR3QbBdwOYGYdgbHAIUAfYKyZdcjcVxGRnNKsWZgiYulS+P3v444mOZYvh6OPhjPPzPpH1ZkAPFgXPS2Kbu7uk6PXHJgFlETbDAbujV56GWhvZl2A44Bp7r7K3VcD04CBmf5CIpJDjjoKTjgBrr0WVq6MO5r4ffwxDBwIVVXwk59k/ePSugZgZs3NbC6wnPAjPrPGa0XA2cDTUVNXYGmNt1dFbVtrF5FCdv318K9/hSRQyD7/HE4+OVwXGT8eDj006x+ZVgJw903u3ptwlN/HzPav8fJtwPPu/kL03La0i220f4WZjTKzSjOrXLFiRTrhiUgu239/KC+HW2+Fd96JO5p4bNwII0bA88/DvffCoEFN8rH1qgJy9zXADKKuGzMbCxQDo2tsVgV0q/G8BPhgG+21P+NOdy9199Li4uL6hCciuerKK8M1gUKcIsI9rJz22GNhuuwRI5rso9OpAio2s/bR4zbAscAbZnY+oV9/hLtvrvGWiUBZVA3UF1jr7suAKcAAM+sQXfwdELWJSKErKYGLL4YHHoBXXok7mqb105/CX/4S1k/+wQ+a9KPTOQPoAkw3s/nAbMI1gCeBO4BdgJfMbK6Z/TLafjKwBFgM/An4PoC7rwKuivYxG7gyahMRgTFjoFOn8INYKG68EW66CS64ICSAJmae4Lk4SktLvbKyMu4wRKSp3HIL/OhHYYqIAQPijia77roLzj8fhg8PZz7NMjcu18zmuHtpXdtpJLCIJMf3vgfdu4cpIjZvrnPznPXoo6Hf/7jj4J57MvrjXx9KACKSHK1ahXLQefPCUXE+mj49HPX36RPKPVu2jC0UJQARSZYzzoBvfjNUBDXBdAhNas4cGDwYevSASZNg++1jDUcJQESSpXqKiPfeC8tH5otFi8Io344dwzWOjh3jjkgJQEQSqF+/8GN5zTWwenXc0TReVVW4qG0G06ZB12RMgqAEICLJdMMNsGYNXHdd3JE0zsqV4cd/zZpw5N+jR9wR/ZsSgIgk0wEHQFlZGB373ntxR9Mw69aFye6WLIGJE+HAA+OO6CuUAEQkua68Mtz/4hfxxtEQ69fD0KEwezakUnDkkXFH9B+UAEQkuXbbDS66CO67L5SG5opNm8LZy7Rp8Oc/h8qfBFICEJFkGzMG2rfPnSki3MOcPuPGwa9/DeecE3dEW6UEICLJ1qFDGBMwZQr87W9xR1O3sWPhjjtCwvrxj+OOZpuUAEQk+S64AHbfPflTRPz2t3DVVXDeeTlRvaQEICLJ16oVXH11mCq6oiLuaLbs/vvDlNZDh4YzANvSGljJogQgIrnhzDOhd2+4/PJQYZMkkyaFvv5+/cIcRi1axB1RWpQARCQ3VE8R8c47cNttcUfzpRdfhNNOg169wqperVvHHVHalABEJHf07x9G1V59dRhZG7f58+HEE0O56lNPQdu2cUdUL0oAIpJbbrghzA90/fXxxrFkSZjPv23bUO+fg2uYKwGISG7p3RtGjgwVN0uXxhPDhx+Gs5EvvoCpU8MZQA5SAhCR3HP11aEc9Je/rHvbTFuzJhz5f/QRTJ4M++zT9DFkiBKAiOSe3XeHH/4wLKf46qtN97mffgonnQQLF4ZlHQ85pOk+OwuUAEQkN/3sZ9CuXZgqoils2BBWK/vf/w2lnv37N83nZpESgIjkpo4dQxKYPDmss5tNmzfDuefCk0/C7bfDsGHZ/bwmUmcCMLPWZjbLzOaZ2QIzuyJq38Ou6W7xAAAIkklEQVTMZprZm2aWMrOWUXur6Pni6PXuNfZ1WdS+yMyOy9aXEpEC8cMfQrdu2Z0iwh1Gjw4jfa++Gr7znex8TgzSOQNYD/Rz915Ab2CgmfUFbgBudvcewGrgvGj784DV7r4XcHO0HWa2LzAc2A8YCNxmZs0z+WVEpMC0bh1+lCsrw+yb2XDttaHi6OKLwxlHHqkzAXiwLnpaFN0c6Ac8ErXfAwyJHg+OnhO9foyZWdRe4e7r3f1tYDHQJyPfQkQK18iRYfWwyy8PZZmZ9Mc/hplIzzoLfvObnJjfpz7SugZgZs3NbC6wHJgGvAWscfeN0SZVQPUqx12BpQDR62uBTjXbt/AeEZGGad48TBGxZEmYhC1THn4Yvve9sKTj3XeHqSjyTFrfyN03uXtvoIRw1L6lwleP7reUIn0b7V9hZqPMrNLMKlesWJFOeCJS6AYMgGOOCUtIrl3b+P1NmxbOLA47LHQtFRU1fp8JVK+U5u5rgBlAX6C9mVVPeVcCfBA9rgK6AUSvtwNW1Wzfwntqfsad7l7q7qXFOTi0WkRiYBamiFi5MpwNNMasWXDKKWGA1xNPwHbbZSbGBEqnCqjYzNpHj9sAxwILgenAadFm5cDj0eOJ0XOi1591d4/ah0dVQnsAPYBZmfoiIlLgvvnNMGX0zTfD++83bB8LF8KgQbDLLmEFsvbtMxtjwqRzBtAFmG5m84HZwDR3fxL4KTDazBYT+vjvira/C+gUtY8GxgC4+wJgHPA68DRwgbtvyuSXEZECd/XVYUH2sWPr/9533w2Du1q2DF1AnTtnPr6EsXBwnkylpaVeWVkZdxgikktGjw5lm/Pnw377pfeeFSvg8MPD/D7PPx+qinKYmc1x99K6tsu/y9oiUtguvzxM0ZzuFBH/+lfo9nnvvTDSN8d//OtDCUBE8kunTnDZZeHH/Lnntr3t55/DkCEwdy488kg4CyggSgAikn8uvBBKSsIUEVvr5t64MVw0fvZZ+OtfQ71/gVECEJH806ZNGBMwa1Y4sq/NHb773TCl829/G0b6FiAlABHJT2VlsP/+oTuo9hQRP/sZ3HVXmObhwgvjiS8BlABEJD81bx4Gh731Ftx555ftv/51WE/4u98NZwkFTAlARPLXoEFw9NHhh/7jj0Nf/yWXwOmnw6235t3kbvXVou5NRERylFmYGuLgg8MiLs88EwZ73XdfOEMocDoDEJH8VloalnKcOjVMFzFhQhjtKzoDEJEC8JvfhJXDxoyBHXaIO5rEUAIQkfzXtSvcdFPcUSSOuoBERAqUEoCISIFSAhARKVBKACIiBUoJQESkQCkBiIgUKCUAEZECpQQgIlKgEr0msJmtAN5txC52Av6ZoXCyLZdihdyKV7FmTy7Fm0uxQuPi3d3di+vaKNEJoLHMrDKdhZGTIJdihdyKV7FmTy7Fm0uxQtPEqy4gEZECpQQgIlKg8j0B3Fn3JomRS7FCbsWrWLMnl+LNpVihCeLN62sAIiKydfl+BiAiIluRUwnAzLqZ2XQzW2hmC8zsoqi9o5lNM7M3o/sOUbuZ2e/MbLGZzTezg2rsa5OZzY1uExMe625mNjXa1+tm1j2p8ZrZ0TX+XOea2edmNiSJsUav3RjtY2G0TcYXic1wvDeY2WvR7YwExNrTzF4ys/Vm9pNa+xpoZoui7zEm4bHebWbLzey1TMeZ6Xi3tp8GcfecuQFdgIOix22B/wP2BW4ExkTtY4AbosfHA08BBvQFZtbY17ocinUG0D96vAOwXZLjrbHPjsCqTMebqViBQ4H/BZpHt5eAo5L6ZwucAEwjLOS0PVAJ7BhzrDsDBwPXAD+psZ/mwFvA14CWwDxg3yTGGr12BHAQ8Fqm//6z8Ge7xf00KKZsfdmmuAGPA/2BRUCXGn84i6LHfwRG1Ni+5nZZTQCZijX6B/JiLv3Z1mgbBTyQ1FiBbwFzgDbAdoQf1H0SHO8lwM9rtN8FnB5nrDW2+1WtH6lvAVNqPL8MuCyJsdZo704WE0Cm4629n4bEkFNdQDVF3SAHAjOBXdx9GUB0v3O0WVdgaY23VUVtAK3NrNLMXs50F0WGY90bWGNmE8zsFTO7ycyaJzjemoYDDyU1Vnd/CZgOLItuU9x9YVLjJRxFDzKz7cxsJ+BooFvMsW5NOv8+MqaRsTa5TMVbaz/1lpNrApvZDsB44GJ3/3gb3bZbeqG67Gk3d//AzL4GPGtmr7r7WwmMtQXwbcJf8ntACvgvwtFfxmXozxYz6wJ8A5iS8SC//IxGxWpmewH7ACVR2zQzO8Ldn898tI2P192nmtnBwN+BFYQuq40xx7rVXWyhLSslhxmItUllKt7a+2nIPnLuDMDMighf+gF3nxA1fxT94FT/8CyP2qv46hFSCfABgLtX3y8h9LEfmNBYq4BX3H2Ju28EHiP0VWZcpv5sI6cDj7r7hgTHegrwsruvc/d1hH73vgmOF3e/xt17u3t/wo/smzHHujV1/ftIUqxNJlPxbmU/9ZZTCcBCqrwLWOju/1PjpYlAefS4nNAnVt1eZkFfYK27LzOzDmbWKtrnTsBhwOtJjBWYDXQws+qJnfplOtYMx1ttBFnq/slgrO8BR5pZi+g/1JFAxruAMvjvtrmZdYr2eQBwADA15li3ZjbQw8z2MLOWhO7AjFbbZTDWJpGpeLexn/prqgsembgBhxNOI+cDc6Pb8UAn4BnC0dAzQMdoewP+QKhGeBUojdoPjZ7Pi+7PS2qs0Wv9o/28CvwVaJnweLsD7wPNEv7voDnhgutCQlL9n4TH2zqK83XgZaB3AmLtTDja/xhYEz3eMXrteEKFylvA5QmP9SHCdaANUXsSfhO2GO/W9tOQmDQSWESkQOVUF5CIiGSOEoCISIFSAhARKVBKACIiBUoJQESkQCkBiIgUKCUAEZECpQQgIlKg/h9btd+s6dly9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(new_df['Year'], new_df['Car drivers'], color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.YTick at 0x16b7d397eb8>,\n",
       "  <matplotlib.axis.YTick at 0x16b7d3977b8>,\n",
       "  <matplotlib.axis.YTick at 0x16b7d394710>,\n",
       "  <matplotlib.axis.YTick at 0x16b7d3b8630>,\n",
       "  <matplotlib.axis.YTick at 0x16b7d3b8b00>,\n",
       "  <matplotlib.axis.YTick at 0x16b7d3b8d30>,\n",
       "  <matplotlib.axis.YTick at 0x16b7d3c0198>],\n",
       " <a list of 7 Text yticklabel objects>)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ct8BzzjlXTVE2Ye4K9BKRnkADoKmIPKGqZyWuJCLdgGuBw1R1dYTxOOecy7GMhYyIbIMVGK2BVcB0YKKqpq0CU9WrgauDbRwOXF5JAbMf8ADQQ1UXVecFOJd31q2D1avhl19sqrhf3dtdd4Vf/zruV+VctaQsZETkCOAqoDnWCmwRdkZyErCLiAwD7lDV5VXZoYgMxAqpF7DqscbAsyIC8LWq9qrOC3EutNGjYdas7BQAld2uD3MJsgpE4OCDoW3b7G7XuRxIdybTE7hQVb9OXiAidYDjgaOB4Zl2oqrjgfHB/RsS5nerWrjObaY5c6Bnz03n168P9erZVHG/sttGjTadn+k51V23fn2YP9/OZJ54Aq65Jvfvl3ObKWUho6pXpFm2FhgZSUTORemZZ+x28mTYZRc7kNepY2cL+WiXXeDQQ2HoULj66vyN07kUMrYuE5GLRaSpmIdF5CMR6Z6L4JzLurIyOOAA2HdfaNwY6tbN/wN3v37w6acwYULckThXZWGaMJ8XXHfpDrQEzgVujzQq56IwaxZ89BGcdlrckVTNqadCw4Z2NuNcgQlTyFT8zesJPKqqUxLmOVc4ysvttm/feOOoqqZN4eST4emnrWGBcwUkTCEzSURexQqZMSLShHA9+J3LL+Xl0LUr7LBD3JFUXWkpLFkCL78cdyTOVUmYQuZ8rCnzAUGesXpYlZlzhWPGDJg+HU4/Pe5IqqdbN2jVCh57LO5InKuSlIWMiLQVkbZAK1X9SFWXAqjq96o6NWcROpcN5eVQq5Zd3yhEtWvDWWfBqFHg+ftcAUl3JvNYMN2Vo1ici4aqtSo7/HDYbru4o6m+0lJYu9auzThXIFIWMqp6RDAV6F8/5wKTJ1vLskJrVZasfXvYf39vZeYKStrcZSLyK+BEYHtAgfnAC6qaahAy5/JPWZl1uDzllLgj2XylpXDJJXaNae+9447GuYzSXZO5EijDmitPAD4M7j8tIlflJjznNpOqXY/p1g1atIg7ms13xhlWYPrZjCsQ6a7JnI+1KLtdVZ8IptuBA4NlzuW/Dz6wfGWF2qos2TbbwLHHWi6zdevijsa5jNIVMuux9P7JWuH9ZFyhKC+3ZJMnnRR3JNnTr58lznz99bgjcS6jdNdkLgFeF5FZwNxgXltgV+APUQfm3GZbv94SYvboAVtuGXc02XP88bDVVtZnprunEXT5LV0W5ldEZHesemx77HrMPOBDVfXzdJf/3n7b/vEXS1VZhfr17TUNGQLLl1vaGefyVKYe/5owrQtuvarMFYayMkssecIJcUeSfaWlsGoVDM84nJNzsUrXuqw7MAu4CctbdhxwMzDLU/27vLd2LQwbZlVLjRvHHU32deoEu+/uaWZc3kt3TeYuoJuqzk6cKSI7AaOAPSOMy7nNM26cpV8p9A6YqYjY2cx118Hs2dCuXdwROVepdNVldbBrMMm+AepGE45zWVJebmcwlQ21XCzOPttuH3883jicSyNdIfMI8KGIXCkivw6mK4EPgIdzE55z1fDLL3at4sQT7ZpMsWrbFo44wjpmqsYdjXOVSpe77DbgTKxV2UFAl+D+mcEy5/LT2LGwdGnxtSqrTL9+8Pnn8N57cUfiXKXS5i5T1ZnATBFpbg91SW7Ccm4zlJVBs2Y1ow/JKafA735nZzNdusQdjXObyDSeTJmILMKqyCaIyKJgXrtsBiEitUXkYxF5KZvbdTXQqlXw/PN28K1XL+5ootekCfTubdegfv457mic20S6azLlwHPYoGW7qepuWEqZkVjizGy6GPDMzm7zjR4NK1YUb6uyypSWWvXgiy/GHYlzm0hXyLRQ1fLE3v2quk5Vy4CtsxWAiLTB+uD8O1vbdDVYebllWz7yyLgjyZ0jjoA2bbzPjMtL6QqZSSJyn4h0EpHWwdRJRO4DPs5iDP8ABpAmk4CI9BeRiSIycbEPPetSWbnS/s2feqqlw68pKoZmfuUVWLgw7mic20i6QqYUmIb18h8DvIr1/p8OnJ2NnYvI8cAiVZ2Ubj1VfVBVS1S1pGXLltnYtStGL71k12RqQquyZKWllvr/qafijsS5jYjG2L5eRG7DCqy1QAOgKTBCVc9K9ZySkhKdOHFijiJ0BeWkk2DCBJg71/7d1zQHHghr1sDH2axocMVCRCapakmu95spQWalROSGbOxcVa9W1Taq2g44HXgjXQHjXErLltlF/759a2YBA3Y2M3kyTJ0adyTO/U+1ChnggqxG4dzmev556+lfk1qVJTv9dKhb14dmdnklXT+Z5SmmFVQ+YuZmUdXxqnp8trfraoiyMthxR+jcOe5I4tOiBRx3nA3NvHZt3NE4B6Q/k1kK7KaqTZOmJsCCHMXnXGbff2+pZPr2tezENVm/ftbCbOzYuCNxDkhfyAwFdkyxzJuwuPwxYoT9c6+JrcqS9ewJW2/tfWZc3kiXIPM6VZ2QYtmV0YXkXBWVl8Ouu8J++8UdSfzq1YMzzoCRIy0LgHMxq+6Ff+fyw8KFNkDZ6ad7VVmF0lJYvdpGBnUuZl7IuMI2bBisX1+zW5UlKymBPff0KjOXF7yQcYWtrAz23hvat487kvxRMTTz22/DF1/EHY2r4UIVMkEq/tZB+v+2ItI26sCcy2jePDuQ+lnMps46ywobH5rZxSxjISMifwQWAmOBl4PJx31x8XvmGbv1QmZTbdrAUUf50MwudmHOZC4G9lDVvVV1n2DqEHVgzmVUXm4tynbfPe5I8lO/fvDVV3a251xMwhQyc4FlUQfiXJV8+aUlw/S+MamdfDI0buxpZlyswgy68SUwXkReBlZXzFTVOyOLyrlMKqrK+vaNN458tsUWNrbOM8/A3XdDw4ZxR+RqoDBnMl9j12PqAU0SJufiU1ZmecratYs7kvxWWgrLl1sCUedikPFMRlVvzkUgzoX2yScwZQr8/e9xR5L/DjsM2ra1PjNetehikLKQEZEXgZTNUlS1VyQROZdJebk1z+3TJ+5I8l+tWnD22XDbbbBgAbRqFXdEroZJdybzfzmLwrmwVK2q7JBDYPvt446mMJx9Ntx6Kzz5JFx+edzRuBomZSGjqm/mMhDnQpk2zarL/vSnuCMpHHvsYdevHnsMLrvMc7y5nEo3aNmLInKCiNStZNnOIjJQRM6LNjznkpSXWxVQ795xR1JYSkth+nQbntm5HErXuuxC4BDgExH5UERGicgbIvIl8AAwSVUfyUmUzsGGqrKjjoJttok7msJy2mk2DID3mXE5lq667FtgADBARNoBrYBVwGeq+lNOonMu0aRJ1gnzmmvijqTwNG8OJ5xg12UGDYK6m1RQOBeJUAkyVXW2qr6nqpO9gHGxKSuzg+PJJ8cdSWHq1w8WL4YxY+KOxNUgnurfFYb1663nevfu9q/cVV2PHtCypVeZuZzyQsYVhvfeg7lzPePy5qhbF379a+v9v2RJ3NG4GiKyQkZEGojIBBGZIiIzRGSTzAHB2DTjRORjEZkqIj2jiscVuPJyqF8fTjwx7kgKW2kp/PLLhtxvzkVMNMVYEyIyjfQ9/tOm+xcRAbZQ1ZVBM+i3gYtV9f2EdR4EPlbVf4nIXsAoVW2XbrslJSU6ceLEdKu4YrNunY2PctBBMGJE3NEUNlXo0AGaNIF33407GpdDIjJJVUtyvd90Pf6PD25/H9xWDLF3JpDx4r9a6bUyeFg3mJILLQWaBve3BOZn2q6rgd56C7791nNvZUPF0MwDBsCsWbDbbnFH5IpcyuoyVZ2jqnOArqo6QFWnBdNVwDFhNh4M2zwZWASMVdUPkla5CThLROYBo4A/pthOfxGZKCITFy9eHGbXrpiUlUGjRnDccXFHUhzOPNM6tHoDAJcDYa7JbCEiB1c8EJEuwBZhNq6q61S1I9AGOFBE2ietcgYwRFXbAD2Bx0Vkk5hU9UFVLVHVkpYtW4bZtSsWa9bA8OHQq5eNj+I2X+vWcPTR8Pjj1mrPuQiFKWTOB+4Vkdki8hVwH1CldDKquhQYD/SoZNvPBOu8BzQAWlRl267Ivf46fP+9tyrLtn79YM4cq4p0LkIZCxlVnaSq+wIdgI6q2lFVP8r0PBFpKSLNgvsNgW7AJ0mrfQ0cFayzJ1bIeH2Y26C8HJo2tT4eLntOPNEu/nuVmYtYxkJGRLYVkYeBclVdJiJ7icj5IbbdChgnIlOBD7FrMi8FiTUrxqK5DLhQRKYATwPnaKrmbq7mWb0annsOTjoJGjSIO5ri0qiRjcfz7LPw449xR+OKWJjqsiHAGKB18Pgz4JJMT1LVqaq6n6p2UNX2qjowmH+Dqr4Q3J+pql1Vdd/gDOnV6r0MV5TGjIFly7xVWVT69YOVK2HkyLgjcUUsTCHTQlWfAdYDqOpaYF2kUTkHVlXWvDl06xZ3JMXp4IOhXTsbZ8a5iIQpZH4Uka0J+riISGdgWaRROffTT5b+pHdvzxgclVq1rM/Ma6/BN9/EHY0rUmEKmUuBF4BdROQdYCgp+rM4lzWjRtm1Am9VFq2zz7YsAE88EXckrkilLWSCPisNgMOALsBvgL1VdWoOYnM1WVkZbLstHH543JEUt113ha5drZWZt7lxEUhbyKjqeuAOVV2rqjNUdbqqrslRbK6mWrECXn4ZTj0VateOO5riV1oKM2faoHDOZVmY6rJXRaR3kPDSuei98AL8/LO3KsuVvn0tw7X3mXERCHtN5llgtYgsF5EVIrI84rhcTVZeDttvD126xB1JzdCsmXXOfOopGwbAuSwK0+O/iarWUtV6qto0eNw00/Ocq5YlS+CVV+yCfy0fUy9n+vWz9D2jR8cdiSsy6VL9AyAih1Y2X1U96ZHLvpEjLSmmtyrLre7draHF0KE+MJzLqoyFDHBFwv0GwIHAJODISCJyNVtZGey0ExxwQNyR1Cx16tgQAPfcY2c0W28dd0SuSISpLjshYToaaA8sjD40V+MsXmxZl087zQbXcrlVWmpnkeXlcUfiikh1Kr3nYQWNc9k1fLgNteytyuKx7742eZoZl0Vhrsncw4Zhk2sBHYEpUQblaqjycthjDxuD3sWjtBQuuww++QR+9au4o3FFIMw1mYkJ99cCT6vqOxHFU5hU7R/4mjU2rV278W2meZUtV4WePWtO3fj8+fDmm3DDDV5VFqdf/xoGDLAGAH/9a9zRuCKQsZBR1eI4d371VRg2rGoH+rCFw9q10cTcvj28/TZsuWU0288nw4ZZweqtyuK13XZwzDE2NPNf/uLNyN1mS1nIiMgzqtpXRKaxobqsggI/AP9Q1eejDDBrPv8cXnrJWtHUrbvhNvF+nTrW87lx48zrRT1v+nTrid2nj6VYKfZMxGVlVk22555xR+L69bPCftw4OOqouKNxBU5SDUQpIq1UdYGI7JjiuS2AJ1U1pxW3JSUlOnHixMwrFoNHH4XzzoPzz4eHHireaqQ5c2xck1tvhWuuiTsat2oVtGpl/WW8EUDREJFJqlqS6/2mPJNR1QXB7ZwUq8wRkTMjicqZc8+FL7+0aotddoGrr447omg884zdelVZfmjY0M6in3oK7r3Xzuydq6aMFa4icoqIzBKRZcm5y1TV07ZGbeBAuxh7zTXw9NNxRxON8nIoKbGC1OWHfv1sPJ8RI+KOxBW4MFf1BgG9VHVLz10WAxF45BE49FA45xz4z3/ijii7Pv/cUsx735j80qWLFfpeXeY2U5hCZqGq/jfySFxq9evDc89ZupWTToLPPos7ouyp6F3et2+8cbiNiVifmXHj4Ouv447GFbAwhcxEESkXkTOCqrNTROSUyCNzG2ve3IYkrl3b+s8sXhx3RNlRVmYjM+6wQ9yRuGQ+NLPLgjCFTFPgJ6A7cEIwHR9lUC6FnXe2Ab2++cZa/qxaFXdEm2fGDGuq7Rf889NOO1k1rQ/N7DZDmASZ51YynZfpeSLSQEQmiMgUEZkhIjenWK+viMwM1nmqOi+iRunc2f5Zvv++VWesXx93RNVXXm6d/fr0iTsSl0ppKXz6KUyYEHckrkClLGREZEBwe4+I3J08hdj2auBIVd0Xy3fWQ0Q6J+1jN+BqoKuq7g1cUu1XUpP07g2DB1sv+UJt1qxqhcxhh1kvc5ef+vSBBg18aGZXbenSylRc7K9Wz0fggOEAAAAdlElEQVS1Xp4rg4d1gyn5nPtC4F5VXRI8Z1F19lUjXXqp9aEZNMiqNS66KO6IqmbyZGvAcNllcUfi0mnaFE4+2ZrP33mnNUJxrgrSdcZ8MbitdhtGEamNDXC2K1aYfJC0yu7Beu8AtYGbVPWVSrbTH+gP0LZt2+qGU1xE4K67rLf8738Pbdtag4BCUV5ujRhO8TYkea9fPytkXn7ZPy9XZZFmv1PVdaraEWgDHCgiyePQ1AF2Aw4HzgD+LSLNKtnOg6paoqolLVu2jDLkwlKnjrXO2ndfu3g+eXLcEYVTUVV29NHQokXc0bhMunWzNDNeZeaqIScpVlV1KTAe6JG0aB7wvKquUdWvgE+xQseF1bixJf5s1gyOOw7mzYs7oswmTIDZs71VWaGoXRvOOsvOZIql6bzLmbSFjIjUFpE/V2fDItKy4qxERBoC3YBPklYbCRwRrNMCqz77sjr7q9Fat7Y+NCtWWEGzfHncEaVXXg716lnHUlcYSkttSIuysrgjcQUmbSGjquuAE6u57VbAOBGZCnwIjFXVl0RkoIj0CtYZA3wvIjOBccAVqvp9NfdXs+2zj7U2mzHDes+vWRN3RJVbv94SYvboYWdfrjC0bw/77+9pZlyVhakue0dE/ikih4jI/hVTpiep6lRV3U9VO6hqe1UdGMy/QVVfCO6rql6qqnup6j6q6n+TNkf37nD//TBmjDUGyMcOdO+8Y51Jvaqs8JSWWp65GTPijsQVkDCFTBdgb2AgcEcw/V+UQbnNcMEFlrH5oYeseXO+KSuzVPK9emVe1+WXM86wxibeAMBVQcpBy/JVjRq0rLrWr7cLtU8/bQf1fDlrWLsWtt/eUpU8+2zc0bjq6NXLzma+/toaBLiCEdegZWHGk9lSRO4UkYnBdIeI1IBB5wtYrVo2qubBB1sfh3feiTsiM348LFrkaf0LWWkpzJ8Pr78edySuQISpLnsEWAH0DablwKNRBuWyoH59GDnSOmmeeCLMmhV3RNaqrHHjwuo06jZ2wgnWYMOrzFxIYQqZXVT1RlX9MphuBnaOOjCXBVtvbU2bRezA/t138cXyyy8wfLgVeA0bxheH2zz169uZ6IgR+d9U3uWFMIXMKhE5uOKBiHQFCjzHfA2y667w/PMwd64d4H/+OZ44XnsNlizJn+tDrvr69bNhJoYPjzsSVwDCFDIXAfeKyGwRmQ38E/hNpFG57OrSBR5/HN5914ZwjmN4gLIyq2bp3j33+3bZ1akT7Lab95lxoYQZT2ZKkK6/A9Ah6PsyNfrQXFb16WNNmsvL4dprc7vvn3+260Mnn+xZfIuBiJ3NvPmmpQdyLo3QuctUdbmqeiVsIbv8chsS4PbbrR9NrowebSlvvFVZ8TjrLLt9/PF443B5LycJMl2eEIF77oFjj4Xf/tYyA+RCebllWz7yyNzsz0Vvxx3hiCN8aGaXkRcyNU2dOnbQb9/eqtCmTIl2fz/+CC++CKeeavt2xaO0FD7/HN57L+5IXB5L+asXkbSjE6nqiOyH43KiSRNL296pk2Vt/uAD64kfhZdegp9+8lZlxah3b8uRN3SoNS5xrhLpzmROCKbzgYeBM4Pp38BZ0YfmIrX99lbQLFsGxx9v10yiUFZmA14dckg023fxadLERsosL4+vabzLeykLGVU9V1XPBRTYS1V7q2pvLFmmKwb77ms5xKZNszONtWuzu/1ly+yif58+nueqWPXrB0uXWpWoc5UIc02mnaouSHi8EBtczBWDHj3gvvusMPjjH7N7Eff552H1am9VVsyOOMLOij3NjEshzJXY8SIyBngaO6s5HRtgzBWL/v3hq6+safPOO8MVV2Rnu+Xlljutc+fsbM/ln9q14eyzYfBgWLgQtt027ohcngnTGfMPwP3AvkBH4EFV/WPUgbkcu/VWqzIbMCA7afi//x5efdW2KbL523P5q7QU1q2zoSWcSxK2CfNHwMuq+mdgjIg0iTAmF4datWDIEGsldPbZloJmczz3nF3j8VZlxW/PPeGAAzzNjKtUmPFkLgSGAQ8Es7YHRkYZlItJgwZ2HWWHHSyZ5hdfVH9bZWWWnHP/jCN1u2JQWgqTJ8NUzzjlNhbmTOb3QFdsHBlUdRawTZRBuRi1aGHDA6ja8ADff1/1bSxcCOPGeVVZTXL66VC3rjcAcJsIU8isVtVfKh6ISB2sAYArVrvtZmc0c+bASSdVvQ/EsGGW6dlbldUcLVpYx94nn8x+U3hX0MIUMm+KyDVAQxE5GngW8Ebxxa5rV6tjf/ttOPfcqg0PUF4Oe+1lqWtczVFaCt9+m7uceK4ghClkrgIWA9OwcWRGAddlepKINBCRCSIyRURmiMjNadY9VURURErCBu5y4LTTrFlzWRlcf32458ybB//5j5/F1ETHHWd9Zm65xZNmuv/J2E9GVdcDDwVTVawGjlTVlSJSF3hbREar6vuJKwUt1f4EfFDF7btcGDDAGgD89a/Wh+b889OvX9H82VuV1Tz16lkBc955NmrmqafGHZHLA6Ip/nGIyDTSXHtR1Q6hdyLSCHgb+K2qfpC07B/Aa8DlwOWqOjHdtkpKSnTixLSruGxbswZOOMGGUB41Kv3olp062foffZS7+Fz+WLcOOna063gzZ1pjAJcXRGSSqua8tihdddnxbEiSWdmUkYjUFpHJwCJgbCUFzH7ADqr6Uobt9BeRiSIycfHixWF27bKpbl145hnYe2/7dzptWuXrffUVTJjgZzE1We3aVsX6+efw4INxR+PyQLoEmXPSTWE2rqrrVLUj0AY4UET+dyVYRGoBfwcuC7GdB1W1RFVLWrZsGWbXLtuaNrWszU2aWN37/PmbrlNebrdeyNRsPXvC4YfDzTdHl93bFYyUhYyIrBCR5ZVMK0SkSsMwq+pSYDzQI2F2E6A9lhttNtAZeMEv/uexNm2soFmyxIYHWLly4+Xl5VZd1q5dLOG5PCECgwbB4sWW08zVaOnOZJqoatNKpiaq2jTThkWkpYg0C+43BLoBnyRsf5mqtlDVdqraDngf6JXpmoyLWceOVnU2daq1IKvoE/Hpp9bj21uVObA0M6edBnfcAQsWZF7fFa10ZzJNg9vmlU0htt0KGCciU4EPsWsyL4nIQBHplZ3wXSyOPRbuvdfOai6+2JqrlpfbP9g+feKOzuWLW2+1RiA33RR3JC5G6ZowP4Vd/J+EtTJLzA+iwM7pNqyqU4H9Kpl/Q4r1D88Qq8snv/mNNW0ePNiaNpeV2eiXUQ3j7ArPLrvARRfZeEV//jP86ldxR+RikLIJc9oniYhW54lZ4E2Y88j69VYlMmyYPb73Xvjd7+KNyeWXxYutsDnqKMvM7WKTj02YARCRgUmPawFPRBaRKxy1allCxIMOso54vXvHHZHLNy1bwpVXwsiR8M47cUfjYhAmrUxbEbkaQETqY2n+Z0UalSscDRvC2LHWEMBHRXSV+fOfoXVrG3HV083UOGEKmXOBfYKC5kVgnKreFGlUrrBssQXssUfcUbh81aiR9Zl57z2vMquB0qWVSRxtqi42aNk7wMMAqhpL3hC/JuNcAVq7Fjp0sLQz06d7upkYxHVNJl3rsjuSHi8B9grmK3BkVEE554pMnTrwt79Br17w73/Db38bd0QuR6rVuixOfibjXIFShcMOg88+s9xmjRvHHVGNkrety5xzLitErF/VwoWWCcDVCF7IOOdyp1Mny+Q9eLCNoumKnhcyzrnc+utfYfVqGDgw87qu4IXpjNlIRK4XkYeCx7uJyPHRh+acK0q77WZpiR580BKruqIW5kzmUWwo5YOCx/OAv0QWkXOu+N1wg3XkveaauCNxEQtTyOyiqoOANQCquoqNk2U651zVbLMNDBgAI0ZYJ01XtMIUMr8E48EogIjsgp3ZOOdc9V16KWy3naebKXJhCpkbgVeAHUTkSeB1YECkUTnnit8WW9hYM++8Ay+8EHc0LiKhOmOKyNbY8MgCvK+q30UdWCreGdO5IrJ2LbRvb31opk2zzAAuEnnXGVNE9q+YgB2BBcB8LCvz/qme55xzodWpA7ffDp98Ao88Enc0LgLpEmSOS/M8VdVYcpf5mYxzRUbVRlX94gtLN7PFFnFHVJTyLkGmqh6Ry0CcczWUCAwaBF27wp13wvXXxx2Ry6IwnTEbiMilIjJCRIaLyCUi0iAXwTnnaoguXeCUU6ywWbQo7mhcFoVpXTYU2Bu4B/gnlu7/8SiDcs7VQH/9K6xa5elmikyYQmYPVT1fVccFU39g96gDc87VMHvsARdeCA88ALN8hPdiEaaQ+VhEOlc8EJFO2AiZaQXVbBNEZIqIzBCRmytZ51IRmSkiU0XkdRHZsWrhO+eKyo03Qv36cO21cUeSf778siA7rYYpZDoB74rIbBGZDbwHHCYi00RkaprnrQaOVNV9gY5Aj8TCKvAxUKKqHYBhwKAqvwLnXPHYbju4/HJ49ln44IO4o8kf775r/YnuuSfuSKosTM+nHtXZsFrb6JXBw7rBpEnrJDaTfh84qzr7cs4Vkcsug/vvt9xm48db67OabNo0OO44aNMGTj897miqLOOZjKrOAZoBJwRTM1WdUzGle66I1BaRycAiYKyqpvtrcj4wOnzozrmi1KSJVZu99Ra89FLc0cTrq6/gmGOs79DYsZZYtMCEacJ8MfAksE0wPSEifwyzcVVdp6odgTbAgSLSPsU+zgJKgMEplvcXkYkiMnHx4sVhdu2cK2QXXAC77w5XXWWpZ2qihQvh6KNtgLdXX4UdC/OSdZhrMucDnVT1BlW9ActhdmFVdqKqS4HxVFL1JiLdgGuBXqpaaXZnVX1QVUtUtaRly5ZV2bVzrhDVrQu33QYzZ8KQIXFHk3tLl9oZzIIF8PLLsNdecUdUbWEKGQHWJTxeR4jxZESkpYg0C+43BLoBnyStsx/wAFbAeA8s59wGJ58MBx1kVWc//RR3NLmzahX06mUF7IgR0Dm5vVRhCTsy5gcicpOI3IRdoH84xPNaAeOCFmgfYtdkXhKRgSLSK1hnMNAYeFZEJouI5/t2zpmKdDPz58M//hF3NLmxdi2cdhq8/TY8/ridzRS4sKn+9wcOxs5g3lLVj6MOLBVPkOlcDXPSSfDGG5ZAs5iry9evh/POg8ceg/vug9/+Nqubz7tU/0kaAStU9S5gnojsFGFMzjm3wW23wY8/wl/+Enck0VG1/kGPPWZpdbJcwMQpTOuyG4ErgauDWXWBJ6IMyjnn/mfPPa212b/+ZWczxej22+Hvf4c//Qmuuy7uaLIqzJnMyUAv4EcAVZ0PNIkyKOec28hNN1mLs2JMN/PQQ3DNNXDmmVbQFFnn0zCFzC9B730FEBEfUcg5l1utWlkmgPJy+PDDuKPJnmHD4KKLoGdPePRRqBX2CkbhCPOKnhGRB4BmInIh8BrwULRhOedckiuusAv/AwYUZKLITbz2mp29HHSQ5WqrWzfuiCIRJq3M/2HJK4djKf5vUNXCy9LmnCtsTZrADTdYPrPRBZ6B6sMPrdXcHnvAiy9Co0ZxRxSZsOdm04D/AG8F951zLvf694ddd4Urr4R16zKvn48++QSOPdbykI0ZA1ttFXdEkQrTuuwCYAJwCnAq8L6InBd1YM45t4l69WwEzenTYejQuKOpuq+/tnxkdepYPrJWreKOKHIZO2OKyKdAF1X9Pni8NfCuqu6Rg/g24Z0xnavhVC3Vyjff2AiaDRvGHVE4330HBx8M334Lb74J++6b093nc2fMecCKhMcrgLnRhOOccxmIwODBVsjcdVfc0YSzYoW1IJszx67B5LiAiVOYQuYbNuQuuxHLXfZ5MHTypdGG55xzlTj0UDjhBMsG8N13cUeT3urVluzzo4+sFdkhh8QdUU6FKWS+AEayYVTL54EFWIdM75TpnIvH7bfDypVw661xR5LaunXWTPn11+GRR+D44+OOKOcyDr+sqjfnIhDnnKuSvfaCc8+Fe++1dCw75VlKRVXLQTZ8uPXkLy2NO6JYFF/3UudczXHzzdZSKx/TzVx3naWMufZauOSSuKOJjRcyzrnCtf328Oc/w9NPw6RJcUezwZ13WlPr3/wGbrkl7mhilbKQEZG/Bbd9cheOc85V0YAB0KJF/qSbGTrU8qydeqpV5RVZwsuqSncm01NE6rIhxb9zzuWfLbeE66+3gc3GjIk3lhdftIHHunWDJ56A2rXjjScPpCtkXgG+AzqIyHIRWZF4m6P4nHMus4sugp13jjfdzFtvQd++sP/+8NxzUL9+PHHkmZSFjKpeoapbAi+ralNVbZJ4m8MYnXMuvYp0M1On2hlErk2ebP122rWDUaOgcePcx5CnMqaVARCRbYEDgocfqOriSKNKw9PKOOcqtX49dOoECxfCZ59Bgwa52e/nn0PXrnbm8s47sMMOudlvFeVtWpngwv8EoA/QF5ggIqdGHZhzzlVJrVowaBDMnQv35Gg0kvnzLeHl+vWW8DJPC5g4hUmQOQU4WlUXBY9bAq+paizJd/xMxjmX1nHHwbvvwhdfQPPm0e1nyRJLbzN7NowbByU5P0mokrw9kwFqVRQwge9DPs8553Lv9tth2TK7RhOVH3+0FDGffQYjR+Z9AROnMIXFKyIyRkTOEZFzgJeBUdkKQEQaiMgEEZkiIjNExNPYOOeqb5994JxzrMps9uzsb3/NGujTB95/3zqBHnVU9vdRRMIMv3wF8ADQAdgXeFBVr8xiDKuBI4Pqt45ADxHpnMXtO+dqmoED7RrN9ddnd7vr11sBNno03H8/nHJKdrdfhEJVe6nqCFW9VFX/rKrPZTMANSuDh3WDKQ+67TrnClabNpYv7Mkn4eOPs7NNVbj4YnjqKRti4MILs7PdIpcX11ZEpLaITAYWAWNV9YOk5f1FZKKITFy8OLbW0865QnLllbDVVnabDbfcAv/8p6WMydY2a4C8KGRUdZ2qdgTaAAeKSPuk5Q+qaomqlrRs2TKeIJ1zhaVZM8uEPHasNS/eHPfdBzfeaFVlgwfX+HxkVZEXhUwFVV0KjAd6xByKc64Y/O531gv/yivtekp1lJXBH/4AvXpZ6n4vYKqkWoWMiNyUrQBEpKWINAvuNwS6AZ9ka/vOuRqsfn0bOXPyZLuWUlWvvAJnn21DJpeV2dg1rkqqeyaTzYEbWgHjRGQq8CF2TealLG7fOVeTnX66Ja287jr4+efwz3vvPejdG9q3hxdegIYNo4uxiFWrkFHVF7MVgKpOVdX9VLWDqrZX1YHZ2rZzzv0v3cycOTa+SxgzZljmgNat7Wxmyy2jjbGIhcld1kZEnhORxSKyUESGi0ibXATnnHNZcdRR0KOHVZ0tWZJ+3dmzoXt3S7D56quw7bY5CbFYhTmTeRR4AavW2h54MZjnnHOF429/g6VLrY9LKgsXWsLLn36yAmannXIXX5EKU8i0VNVHVXVtMA0BvB2xc66wdOhgF/Hvvhu+/nrT5cuWwbHHWmblUaPsWozbbGEKme9E5Kygw2RtETkLS5LpnHOF5ZZb7DY53czPP8OJJ8K0aTB8OBx0UO5jK1JhCpnzsHFkvgUWAKcG85xzrrC0bQt/+hM8/jhMmWLz1q61FmhvvQVDh9q1G5c1YRJkfq2qvVS1papuo6onqeqcXATnnHNZd/XVlg3gqqssH1n//vD881aNdsYZcUdXdFL2LBKRG9I8T1X1lgjicc65aG21FVx7LVx+uY0JM2qUpYz5wx/ijqwopTuT+bGSCeB8wLPDOecK1+9/b1Vno0bZ/RtvjDuiopXyTEZV76i4LyJNgIuBc4Ey4I5Uz3POubzXoIGlmRk/3qrPPB9ZZNIm4hGR5sClwJnAY8D+qpqhJ5NzzhWArl1tcpFKd01mMHAK8CCwT8LAYs4551wo6a7JXAa0Bq4D5ovI8mBaISLLcxOec865QpbumkxejTXjnHOu8HhB4pxzLjJeyDjnnIuMFzLOOeci44WMc865yHgh45xzLjKiqnHHUCUishioboLOFsB3WQwnaoUUbyHFCoUVbyHFCoUVbyHFCpsX746qmvOxwAqukNkcIjJRVUvijiOsQoq3kGKFwoq3kGKFwoq3kGKFwosXvLrMOedchLyQcc45F5maVsg8GHcAVVRI8RZSrFBY8RZSrFBY8RZSrFB48dasazLOOedyq6adyTjnnMshL2Scc85FpqALGRHZQUTGich/RWSGiFwczG8uImNFZFZwu1UwX0TkbhH5XESmisj+CdtaJyKTg+mFAoi3rYi8Gmxrpoi0y8dYReSIhPd1soj8LCInZTPWbMYbLBsUbOO/wTpZHTYxy7H+TUSmB9Np2YxzM+L9lYi8JyKrReTypG31EJFPg9dyVZ7H+oiILBKR6dmOM9vxptpOXlDVgp2AVthonQBNgM+AvYBBwFXB/KuAvwX3ewKjAQE6Ax8kbGtlgcU7Hjg6uN8YaJSvsSZssznwQ7ZjzWa8QBfgHaB2ML0HHJ6nsR4HjMWG7NgCmAg0zYP3dhvgAOBW4PKE7dQGvgB2BuoBU4C98jHWYNmhwP7A9Gy/pxG8t5VuJ6q4q/Qa4w4gyx/Y88DRwKdAq4Q3/9Pg/gPAGQnrJ64XeSGTrXiDL+HbhRBr0jb6A0/mc7zAQcAkoCHQCDtw75mnsV4BXJcw/2Ggb9zvbcJ6NyUdCA8CxiQ8vhq4Oh9jTZjfjggLmWzHm7ydXMWdbiro6rJEQXXRfsAHwLaqugAguN0mWG17YG7C0+YF8wAaiMhEEXk/iuqcLMe7O7BUREaIyMciMlhEaudprIlOB56OKs4KmxOvqr4HjAMWBNMYVf1vPsaKnQkcKyKNRKQFcASwQ1SxViHeVMJ8R7JmM2PNuWzFm7Sd2KUcGbOQiEhjYDhwiaouT1OFXtmCijbcbVV1vojsDLwhItNU9YsIws1GvHWAQ7Av0tdAOXAO9k8232Kt2E4rYB9gTLZj3CiIzYxXRHYF9gTaBPPGisihqvpWvsWqqq+KyAHAu8BirGpvbbbj/F8Q4eNNuYlK5kXShyILseZUtuJN3k4WQ6y2gj+TEZG62Jv6pKqOCGYvDA5qFQe3RcH8eWz8T68NMB9AVStuv8Sud+yXx/HOAz5W1S9VdS0wEqs7zsdYK/QFnlPVNdmOM8vxngy8r6orVXUldi2kc57GiqreqqodVfVo7CA+K9uxViPeVDJ9R/Ip1pzJVrwpthO7gi5kxIr7h4H/quqdCYteAPoF9/th9ZMV80vFdAaWqeoCEdlKROoH22wBdAVm5mu8wIfAViJSkVH1yGzHm8VYK5xBhFVlWYz3a+AwEakT/GgPA7JaXZbF721tEdk62GYHoAPwajZjrWa8qXwI7CYiO4lIPaz6NKstObMYa05kK94024lf3BeFNmcCDsZOt6cCk4OpJ7A18Dr2r+51oHmwvgD3Yi1cpgElwfwuweMpwe35+RxvsOzoYDvTgCFAvTyOtR3wDVCrAL4LtbEL7f/FCu478zjWBkGMM4H3gY558t5uh521LAeWBvebBst6Yi2fvgCuzfNYn8auy60J5mf9uJCteFNtJ6rfW1UmTyvjnHMuMgVdXeaccy6/eSHjnHMuMl7IOOeci4wXMs455yLjhYxzzrnIeCHjXBpB35S3ReTYhHl9ReSVOONyrlB4E2bnMhCR9sCzWBaI2lgfhB66GWmHRKSOWrYG54qaFzLOhSAig4AfsZT6K1T1FhHpB/weS1v/LvAHVV0vIg9iaX4aAuWqOjDYxjyso2cP4B+q+mwML8W5nCqKBJnO5cDNwEfAL0BJcHZzMtBFVdcGBcvpwFPYOCA/iEgdYJyIDFPVirQ/P6pq1zhegHNx8ELGuRBU9UcRKcfGHVotIt2wwaMmBhlzG7Ihjf0ZInI+9vtqjY3/U1HIlOc2cufi5YWMc+GtDyawfGKPqOr1iSuIyG7AxcCBqrpURJ7AcoxV+DEnkTqXJ7x1mXPV8xrQN8jajYhsLSJtsWSFK4DlQYr2Y2KM0bnY+ZmMc9WgqtNE5GbgNRGphWXqvQgbrnkmMB34Engnviidi5+3LnPOORcZry5zzjkXGS9knHPORcYLGeecc5HxQsY551xkvJBxzjkXGS9knHPORcYLGeecc5H5/36APy++vdatAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(new_df['Year'], new_df['Car drivers'], color='red')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel(\"No. of people killed or injured (In 1000's)\")\n",
    "plt.title('Number of car drivers killed or injured between 2005 and 2012\\n')\n",
    "plt.yticks([3000, 3200, 3400, 3600, 3800, 4000, 4200], [3, 3.2, 3.4, 3.6, 3.8, 4, 4.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16b7d354b38>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Owner\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:107: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16b7e6f0780>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_transpose.drop(index='Participant').plot()\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel(\"Number of casualties\")\n",
    "plt.title('Number of casualties between 2005 and 2012 by their mode of transport\\n')\n",
    "ax = plt.subplot(111)\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "#plt.yticks([3000, 3200, 3400, 3600, 3800, 4000, 4200], [3, 3.2, 3.4, 3.6, 3.8, 4, 4.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "seatbelt = pd.read_excel('RSA Data.xlsx', sheet_name = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Seat belt</th>\n",
       "      <th>Casualty</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>Participant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Injured</td>\n",
       "      <td>1730</td>\n",
       "      <td>1658</td>\n",
       "      <td>1520</td>\n",
       "      <td>2097</td>\n",
       "      <td>2404</td>\n",
       "      <td>1890</td>\n",
       "      <td>1638</td>\n",
       "      <td>1699</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Killed</td>\n",
       "      <td>55</td>\n",
       "      <td>49</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Uninjured</td>\n",
       "      <td>1293</td>\n",
       "      <td>1157</td>\n",
       "      <td>1055</td>\n",
       "      <td>1599</td>\n",
       "      <td>1947</td>\n",
       "      <td>1639</td>\n",
       "      <td>1495</td>\n",
       "      <td>1568</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Injured</td>\n",
       "      <td>184</td>\n",
       "      <td>144</td>\n",
       "      <td>70</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Killed</td>\n",
       "      <td>38</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Seat belt   Casualty  2005  2006  2007  2008  2009  2010  2011  2012  \\\n",
       "0       Yes    Injured  1730  1658  1520  2097  2404  1890  1638  1699   \n",
       "1       Yes     Killed    55    49    42    44    41    39    35    22   \n",
       "2       Yes  Uninjured  1293  1157  1055  1599  1947  1639  1495  1568   \n",
       "3        No    Injured   184   144    70   103   103   103    84    88   \n",
       "4        No     Killed    38    37    14    12    21    13    12    14   \n",
       "\n",
       "   Participant  \n",
       "0  Car drivers  \n",
       "1  Car drivers  \n",
       "2  Car drivers  \n",
       "3  Car drivers  \n",
       "4  Car drivers  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seatbelt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "seatbelt.replace(to_replace='Not stated', value='Unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "seat_df = seatbelt.groupby(by=[ 'Participant', 'Seat belt', 'Casualty']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Participant</th>\n",
       "      <th>Seat belt</th>\n",
       "      <th>Casualty</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 rowspan=\"5\" valign=\"top\">Car drivers</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">No</th>\n",
       "      <th>Injured</th>\n",
       "      <td>184</td>\n",
       "      <td>144</td>\n",
       "      <td>70</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Killed</th>\n",
       "      <td>38</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uninjured</th>\n",
       "      <td>81</td>\n",
       "      <td>60</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Unknown</th>\n",
       "      <th>Injured</th>\n",
       "      <td>2339</td>\n",
       "      <td>1957</td>\n",
       "      <td>1805</td>\n",
       "      <td>2017</td>\n",
       "      <td>1685</td>\n",
       "      <td>1513</td>\n",
       "      <td>1263</td>\n",
       "      <td>1473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Killed</th>\n",
       "      <td>51</td>\n",
       "      <td>61</td>\n",
       "      <td>54</td>\n",
       "      <td>53</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 2005  2006  2007  2008  2009  2010  2011  \\\n",
       "Participant Seat belt Casualty                                              \n",
       "Car drivers No        Injured     184   144    70   103   103   103    84   \n",
       "                      Killed       38    37    14    12    21    13    12   \n",
       "                      Uninjured    81    60    32    30    39    26    26   \n",
       "            Unknown   Injured    2339  1957  1805  2017  1685  1513  1263   \n",
       "                      Killed       51    61    54    53    45    28    18   \n",
       "\n",
       "                                 2012  \n",
       "Participant Seat belt Casualty         \n",
       "Car drivers No        Injured      88  \n",
       "                      Killed       14  \n",
       "                      Uninjured    29  \n",
       "            Unknown   Injured    1473  \n",
       "                      Killed       28  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Participant</th>\n",
       "      <th>Seat belt</th>\n",
       "      <th>Casualty</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Car drivers</td>\n",
       "      <td>No</td>\n",
       "      <td>Injured</td>\n",
       "      <td>184</td>\n",
       "      <td>144</td>\n",
       "      <td>70</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Car drivers</td>\n",
       "      <td>No</td>\n",
       "      <td>Killed</td>\n",
       "      <td>38</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Car drivers</td>\n",
       "      <td>No</td>\n",
       "      <td>Uninjured</td>\n",
       "      <td>81</td>\n",
       "      <td>60</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>39</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Car drivers</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Injured</td>\n",
       "      <td>2339</td>\n",
       "      <td>1957</td>\n",
       "      <td>1805</td>\n",
       "      <td>2017</td>\n",
       "      <td>1685</td>\n",
       "      <td>1513</td>\n",
       "      <td>1263</td>\n",
       "      <td>1473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Car drivers</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Killed</td>\n",
       "      <td>51</td>\n",
       "      <td>61</td>\n",
       "      <td>54</td>\n",
       "      <td>53</td>\n",
       "      <td>45</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Participant Seat belt   Casualty  2005  2006  2007  2008  2009  2010  2011  \\\n",
       "0  Car drivers        No    Injured   184   144    70   103   103   103    84   \n",
       "1  Car drivers        No     Killed    38    37    14    12    21    13    12   \n",
       "2  Car drivers        No  Uninjured    81    60    32    30    39    26    26   \n",
       "3  Car drivers   Unknown    Injured  2339  1957  1805  2017  1685  1513  1263   \n",
       "4  Car drivers   Unknown     Killed    51    61    54    53    45    28    18   \n",
       "\n",
       "   2012  \n",
       "0    88  \n",
       "1    14  \n",
       "2    29  \n",
       "3  1473  \n",
       "4    28  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seatbelt_dataframe = seat_df.reset_index()\n",
    "seatbelt_dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Car drivers', 'No', 'All', 0                                     1023\n",
       " 1                                      198\n",
       " 2                                      383\n",
       " 3                                        0\n",
       " 4                                      399\n",
       " 5                                    14767\n",
       " 6                                    16294\n",
       " 7                                      376\n",
       " 8                                    12910\n",
       " 9                                      383\n",
       " 10                                      34\n",
       " 11                      ..................\n",
       " 12                                    4982\n",
       " 13                                     142\n",
       " 14    ....................................\n",
       " 15                                    5737\n",
       " 16                                     113\n",
       " 17                      ..................\n",
       " dtype: object]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seatbelt_dataframe.loc[3] = \n",
    "['Car drivers', 'No', 'All', seatbelt_dataframe[[2005, 2006, 2006, 2007, 2008, 2009, 2010, 2011, 2012]].sum(axis=1)]\n",
    "#seatbelt_dataframe.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Sex</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Male</td>\n",
       "      <td>52</td>\n",
       "      <td>51</td>\n",
       "      <td>69</td>\n",
       "      <td>97</td>\n",
       "      <td>85</td>\n",
       "      <td>68</td>\n",
       "      <td>54</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sex  2005  2006  2007  2008  2009  2010  2011  2012\n",
       "0  Male    36    32    28    54    35    41    39    40\n",
       "1  Male     3     1     0     1     3     3     0     0\n",
       "2  Male     0     0     2     0     2     5     3     1\n",
       "3  Male     0     0     0     0     0     0     1     0\n",
       "4  Male     0     0     0     0     0     0     0     0\n",
       "5  Male     0     0     0     0     0     0     0     0\n",
       "6  Male     1     0     0     0     0     0     0     0\n",
       "7  Male     0     0     0     0     0     0     0     0\n",
       "8  Male    52    51    69    97    85    68    54    83\n",
       "9  Male     0     4     2     0     2     0     1     0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_df = pd.concat([casualty_df['Sex'], casualty_df[2005], casualty_df[2006], casualty_df[2007], casualty_df[2008], casualty_df[2009], casualty_df[2010], casualty_df[2011], casualty_df[2012]], axis=1)\n",
    "sex_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
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       "    <tr>\n",
       "      <th>Sex</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>3233</td>\n",
       "      <td>3365</td>\n",
       "      <td>3242</td>\n",
       "      <td>3983</td>\n",
       "      <td>4018</td>\n",
       "      <td>3486</td>\n",
       "      <td>2992</td>\n",
       "      <td>3304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
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       "      <td>4728</td>\n",
       "      <td>4374</td>\n",
       "      <td>5435</td>\n",
       "      <td>5424</td>\n",
       "      <td>4571</td>\n",
       "      <td>4099</td>\n",
       "      <td>4533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        2005  2006  2007  2008  2009  2010  2011  2012\n",
       "Sex                                                   \n",
       "Female  3233  3365  3242  3983  4018  3486  2992  3304\n",
       "Male    4974  4728  4374  5435  5424  4571  4099  4533"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = sex_df.groupby(by=['Sex']).sum()\n",
    "grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
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       "      <td>3242</td>\n",
       "      <td>3983</td>\n",
       "      <td>4018</td>\n",
       "      <td>3486</td>\n",
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       "      <th>1</th>\n",
       "      <td>Male</td>\n",
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       "      <td>4374</td>\n",
       "      <td>5435</td>\n",
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      ],
      "text/plain": [
       "      Sex  2005  2006  2007  2008  2009  2010  2011  2012\n",
       "0  Female  3233  3365  3242  3983  4018  3486  2992  3304\n",
       "1    Male  4974  4728  4374  5435  5424  4571  4099  4533"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <td>3233</td>\n",
       "      <td>4974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>3365</td>\n",
       "      <td>4728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>3242</td>\n",
       "      <td>4374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>3983</td>\n",
       "      <td>5435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>4018</td>\n",
       "      <td>5424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex   Female  Male\n",
       "2005    3233  4974\n",
       "2006    3365  4728\n",
       "2007    3242  4374\n",
       "2008    3983  5435\n",
       "2009    4018  5424"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans = grouped.transpose()\n",
    "trans.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args, **kwargs)\n",
      "    Plot y versus x as lines and/or markers.\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "        plot([x], y, [fmt], data=None, **kwargs)\n",
      "        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
      "    \n",
      "    The coordinates of the points or line nodes are given by *x*, *y*.\n",
      "    \n",
      "    The optional parameter *fmt* is a convenient way for defining basic\n",
      "    formatting like color, marker and linestyle. It's a shortcut string\n",
      "    notation described in the *Notes* section below.\n",
      "    \n",
      "    >>> plot(x, y)        # plot x and y using default line style and color\n",
      "    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
      "    >>> plot(y)           # plot y using x as index array 0..N-1\n",
      "    >>> plot(y, 'r+')     # ditto, but with red plusses\n",
      "    \n",
      "    You can use `.Line2D` properties as keyword arguments for more\n",
      "    control on the  appearance. Line properties and *fmt* can be mixed.\n",
      "    The following two calls yield identical results:\n",
      "    \n",
      "    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
      "    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
      "            linewidth=2, markersize=12)\n",
      "    \n",
      "    When conflicting with *fmt*, keyword arguments take precedence.\n",
      "    \n",
      "    **Plotting labelled data**\n",
      "    \n",
      "    There's a convenient way for plotting objects with labelled data (i.e.\n",
      "    data that can be accessed by index ``obj['y']``). Instead of giving\n",
      "    the data in *x* and *y*, you can provide the object in the *data*\n",
      "    parameter and just give the labels for *x* and *y*::\n",
      "    \n",
      "    >>> plot('xlabel', 'ylabel', data=obj)\n",
      "    \n",
      "    All indexable objects are supported. This could e.g. be a `dict`, a\n",
      "    `pandas.DataFame` or a structured numpy array.\n",
      "    \n",
      "    \n",
      "    **Plotting multiple sets of data**\n",
      "    \n",
      "    There are various ways to plot multiple sets of data.\n",
      "    \n",
      "    - The most straight forward way is just to call `plot` multiple times.\n",
      "      Example:\n",
      "    \n",
      "      >>> plot(x1, y1, 'bo')\n",
      "      >>> plot(x2, y2, 'go')\n",
      "    \n",
      "    - Alternatively, if your data is already a 2d array, you can pass it\n",
      "      directly to *x*, *y*. A separate data set will be drawn for every\n",
      "      column.\n",
      "    \n",
      "      Example: an array ``a`` where the first column represents the *x*\n",
      "      values and the other columns are the *y* columns::\n",
      "    \n",
      "      >>> plot(a[0], a[1:])\n",
      "    \n",
      "    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
      "      groups::\n",
      "    \n",
      "      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "    \n",
      "      In this case, any additional keyword argument applies to all\n",
      "      datasets. Also this syntax cannot be combined with the *data*\n",
      "      parameter.\n",
      "    \n",
      "    By default, each line is assigned a different style specified by a\n",
      "    'style cycle'. The *fmt* and line property parameters are only\n",
      "    necessary if you want explicit deviations from these defaults.\n",
      "    Alternatively, you can also change the style cycle using the\n",
      "    'axes.prop_cycle' rcParam.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array-like or scalar\n",
      "        The horizontal / vertical coordinates of the data points.\n",
      "        *x* values are optional. If not given, they default to\n",
      "        ``[0, ..., N-1]``.\n",
      "    \n",
      "        Commonly, these parameters are arrays of length N. However,\n",
      "        scalars are supported as well (equivalent to an array with\n",
      "        constant value).\n",
      "    \n",
      "        The parameters can also be 2-dimensional. Then, the columns\n",
      "        represent separate data sets.\n",
      "    \n",
      "    fmt : str, optional\n",
      "        A format string, e.g. 'ro' for red circles. See the *Notes*\n",
      "        section for a full description of the format strings.\n",
      "    \n",
      "        Format strings are just an abbreviation for quickly setting\n",
      "        basic line properties. All of these and more can also be\n",
      "        controlled by keyword arguments.\n",
      "    \n",
      "    data : indexable object, optional\n",
      "        An object with labelled data. If given, provide the label names to\n",
      "        plot in *x* and *y*.\n",
      "    \n",
      "        .. note::\n",
      "            Technically there's a slight ambiguity in calls where the\n",
      "            second label is a valid *fmt*. `plot('n', 'o', data=obj)`\n",
      "            could be `plt(x, y)` or `plt(y, fmt)`. In such cases,\n",
      "            the former interpretation is chosen, but a warning is issued.\n",
      "            You may suppress the warning by adding an empty format string\n",
      "            `plot('n', 'o', '', data=obj)`.\n",
      "    \n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    scalex, scaley : bool, optional, default: True\n",
      "        These parameters determined if the view limits are adapted to\n",
      "        the data limits. The values are passed on to `autoscale_view`.\n",
      "    \n",
      "    **kwargs : `.Line2D` properties, optional\n",
      "        *kwargs* are used to specify properties like a line label (for\n",
      "        auto legends), linewidth, antialiasing, marker face color.\n",
      "        Example::\n",
      "    \n",
      "        >>> plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)\n",
      "        >>> plot([1,2,3], [1,4,9], 'rs',  label='line 2')\n",
      "    \n",
      "        If you make multiple lines with one plot command, the kwargs\n",
      "        apply to all those lines.\n",
      "    \n",
      "        Here is a list of available `.Line2D` properties:\n",
      "    \n",
      "          agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array \n",
      "      alpha: float (0.0 transparent through 1.0 opaque) \n",
      "      animated: bool \n",
      "      antialiased or aa: bool \n",
      "      clip_box: a `.Bbox` instance \n",
      "      clip_on: bool \n",
      "      clip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None] \n",
      "      color or c: any matplotlib color \n",
      "      contains: a callable function \n",
      "      dash_capstyle: ['butt' | 'round' | 'projecting'] \n",
      "      dash_joinstyle: ['miter' | 'round' | 'bevel'] \n",
      "      dashes: sequence of on/off ink in points \n",
      "      drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] \n",
      "      figure: a `.Figure` instance \n",
      "      fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] \n",
      "      gid: an id string \n",
      "      label: object \n",
      "      linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]\n",
      "      linewidth or lw: float value in points \n",
      "      marker: :mod:`A valid marker style <matplotlib.markers>`\n",
      "      markeredgecolor or mec: any matplotlib color \n",
      "      markeredgewidth or mew: float value in points \n",
      "      markerfacecolor or mfc: any matplotlib color \n",
      "      markerfacecoloralt or mfcalt: any matplotlib color \n",
      "      markersize or ms: float \n",
      "      markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]\n",
      "      path_effects: `.AbstractPathEffect` \n",
      "      picker: float distance in points or callable pick function ``fn(artist, event)`` \n",
      "      pickradius: float distance in points\n",
      "      rasterized: bool or None \n",
      "      sketch_params: (scale: float, length: float, randomness: float) \n",
      "      snap: bool or None \n",
      "      solid_capstyle: ['butt' | 'round' |  'projecting'] \n",
      "      solid_joinstyle: ['miter' | 'round' | 'bevel'] \n",
      "      transform: a :class:`matplotlib.transforms.Transform` instance \n",
      "      url: a url string \n",
      "      visible: bool \n",
      "      xdata: 1D array \n",
      "      ydata: 1D array \n",
      "      zorder: float \n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    lines\n",
      "        A list of `.Line2D` objects representing the plotted data.\n",
      "    \n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scatter : XY scatter plot with markers of variing size and/or color (\n",
      "        sometimes also called bubble chart).\n",
      "    \n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    **Format Strings**\n",
      "    \n",
      "    A format string consists of a part for color, marker and line::\n",
      "    \n",
      "        fmt = '[color][marker][line]'\n",
      "    \n",
      "    Each of them is optional. If not provided, the value from the style\n",
      "    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
      "    the data will be a line without markers.\n",
      "    \n",
      "    **Colors**\n",
      "    \n",
      "    The following color abbreviations are supported:\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        color\n",
      "    =============    ===============================\n",
      "    ``'b'``          blue\n",
      "    ``'g'``          green\n",
      "    ``'r'``          red\n",
      "    ``'c'``          cyan\n",
      "    ``'m'``          magenta\n",
      "    ``'y'``          yellow\n",
      "    ``'k'``          black\n",
      "    ``'w'``          white\n",
      "    =============    ===============================\n",
      "    \n",
      "    If the color is the only part of the format string, you can\n",
      "    additionally use any  `matplotlib.colors` spec, e.g. full names\n",
      "    (``'green'``) or hex strings (``'#008000'``).\n",
      "    \n",
      "    **Markers**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'.'``          point marker\n",
      "    ``','``          pixel marker\n",
      "    ``'o'``          circle marker\n",
      "    ``'v'``          triangle_down marker\n",
      "    ``'^'``          triangle_up marker\n",
      "    ``'<'``          triangle_left marker\n",
      "    ``'>'``          triangle_right marker\n",
      "    ``'1'``          tri_down marker\n",
      "    ``'2'``          tri_up marker\n",
      "    ``'3'``          tri_left marker\n",
      "    ``'4'``          tri_right marker\n",
      "    ``'s'``          square marker\n",
      "    ``'p'``          pentagon marker\n",
      "    ``'*'``          star marker\n",
      "    ``'h'``          hexagon1 marker\n",
      "    ``'H'``          hexagon2 marker\n",
      "    ``'+'``          plus marker\n",
      "    ``'x'``          x marker\n",
      "    ``'D'``          diamond marker\n",
      "    ``'d'``          thin_diamond marker\n",
      "    ``'|'``          vline marker\n",
      "    ``'_'``          hline marker\n",
      "    =============    ===============================\n",
      "    \n",
      "    **Line Styles**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'-'``          solid line style\n",
      "    ``'--'``         dashed line style\n",
      "    ``'-.'``         dash-dot line style\n",
      "    ``':'``          dotted line style\n",
      "    =============    ===============================\n",
      "    \n",
      "    Example format strings::\n",
      "    \n",
      "        'b'    # blue markers with default shape\n",
      "        'ro'   # red circles\n",
      "        'g-'   # green solid line\n",
      "        '--'   # dashed line with default color\n",
      "        'k^:'  # black triangle_up markers connected by a dotted line\n",
      "    \n",
      "    .. note::\n",
      "        In addition to the above described arguments, this function can take a\n",
      "        **data** keyword argument. If such a **data** argument is given, the\n",
      "        following arguments are replaced by **data[<arg>]**:\n",
      "    \n",
      "        * All arguments with the following names: 'x', 'y'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Number of male / female casualties between 2005 and 2012\\n')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(trans)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of casualties')\n",
    "plt.title('Number of male / female casualties between 2005 and 2012\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "seat = pd.read_excel('Seatbelt_RSA_Data_after_my_excel_cleaning.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Seat belt</th>\n",
       "      <th>Casualty</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>Participant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Injured</td>\n",
       "      <td>1730</td>\n",
       "      <td>1658</td>\n",
       "      <td>1520</td>\n",
       "      <td>2097</td>\n",
       "      <td>2404</td>\n",
       "      <td>1890</td>\n",
       "      <td>1638</td>\n",
       "      <td>1699</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Killed</td>\n",
       "      <td>55</td>\n",
       "      <td>49</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>41</td>\n",
       "      <td>39</td>\n",
       "      <td>35</td>\n",
       "      <td>22</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Uninjured</td>\n",
       "      <td>1293</td>\n",
       "      <td>1157</td>\n",
       "      <td>1055</td>\n",
       "      <td>1599</td>\n",
       "      <td>1947</td>\n",
       "      <td>1639</td>\n",
       "      <td>1495</td>\n",
       "      <td>1568</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>All</td>\n",
       "      <td>3078</td>\n",
       "      <td>2864</td>\n",
       "      <td>2617</td>\n",
       "      <td>3740</td>\n",
       "      <td>4392</td>\n",
       "      <td>3568</td>\n",
       "      <td>3168</td>\n",
       "      <td>3289</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Injured</td>\n",
       "      <td>184</td>\n",
       "      <td>144</td>\n",
       "      <td>70</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>103</td>\n",
       "      <td>84</td>\n",
       "      <td>88</td>\n",
       "      <td>Car drivers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Seat belt   Casualty  2005  2006  2007  2008  2009  2010  2011  2012  \\\n",
       "0       Yes    Injured  1730  1658  1520  2097  2404  1890  1638  1699   \n",
       "1       Yes     Killed    55    49    42    44    41    39    35    22   \n",
       "2       Yes  Uninjured  1293  1157  1055  1599  1947  1639  1495  1568   \n",
       "3       Yes        All  3078  2864  2617  3740  4392  3568  3168  3289   \n",
       "4        No    Injured   184   144    70   103   103   103    84    88   \n",
       "\n",
       "   Participant  \n",
       "0  Car drivers  \n",
       "1  Car drivers  \n",
       "2  Car drivers  \n",
       "3  Car drivers  \n",
       "4  Car drivers  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_car_yes = seat.loc[(seat['Seat belt'] == 'Yes') & (seat['Casualty'] == 'All') & (seat['Participant'] == 'Car drivers')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([  'Seat belt',    'Casualty',          2005,          2006,\n",
       "                2007,          2008,          2009,          2010,\n",
       "                2011,          2012, 'Participant'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_car_yes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_car_yes = all_car_yes.drop(['Seat belt', 'Casualty', 'Participant'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1 entries, 3 to 3\n",
      "Data columns (total 8 columns):\n",
      "2005    1 non-null object\n",
      "2006    1 non-null object\n",
      "2007    1 non-null object\n",
      "2008    1 non-null object\n",
      "2009    1 non-null object\n",
      "2010    1 non-null object\n",
      "2011    1 non-null object\n",
      "2012    1 non-null object\n",
      "dtypes: object(8)\n",
      "memory usage: 152.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "all_car_yes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_car_yes = all_car_yes.applymap(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1 entries, 3 to 3\n",
      "Data columns (total 8 columns):\n",
      "2005    1 non-null int64\n",
      "2006    1 non-null int64\n",
      "2007    1 non-null int64\n",
      "2008    1 non-null int64\n",
      "2009    1 non-null int64\n",
      "2010    1 non-null int64\n",
      "2011    1 non-null int64\n",
      "2012    1 non-null int64\n",
      "dtypes: int64(8)\n",
      "memory usage: 152.0 bytes\n"
     ]
    }
   ],
   "source": [
    "all_car_yes.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_car = seat.loc[(seat['Seat belt'] == 'All') & (seat['Casualty'] == 'All') & (seat['Participant'] == 'Car drivers')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_car = all_car.drop(['Seat belt', 'Casualty', 'Participant'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_car = all_car.applymap(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1 entries, 15 to 15\n",
      "Data columns (total 8 columns):\n",
      "2005    1 non-null int64\n",
      "2006    1 non-null int64\n",
      "2007    1 non-null int64\n",
      "2008    1 non-null int64\n",
      "2009    1 non-null int64\n",
      "2010    1 non-null int64\n",
      "2011    1 non-null int64\n",
      "2012    1 non-null int64\n",
      "dtypes: int64(8)\n",
      "memory usage: 152.0 bytes\n"
     ]
    }
   ],
   "source": [
    "all_car.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</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",
       "    </tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "    2005  2006  2007  2008  2009  2010  2011  2012\n",
       "3    NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN\n",
       "15   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_car_yes/all_car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2005    0.202633\n",
       "2006    0.211709\n",
       "2007    0.205191\n",
       "2008    0.239192\n",
       "2009    0.280209\n",
       "2010    0.264453\n",
       "2011    0.265638\n",
       "2012    0.253195\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_car_yes.iloc[0]/all_car.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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