phil-project.ipynb

{
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  {
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>...</th>\n",
       "      <th>x47</th>\n",
       "      <th>x48</th>\n",
       "      <th>x49</th>\n",
       "      <th>x50</th>\n",
       "      <th>x51</th>\n",
       "      <th>x52</th>\n",
       "      <th>x53</th>\n",
       "      <th>x54</th>\n",
       "      <th>x55</th>\n",
       "      <th>data_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>4.90</td>\n",
       "      <td>22.04</td>\n",
       "      <td>4.28</td>\n",
       "      <td>1.84</td>\n",
       "      <td>3.67</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.48</td>\n",
       "      <td>...</td>\n",
       "      <td>36.26</td>\n",
       "      <td>223.87</td>\n",
       "      <td>133.38</td>\n",
       "      <td>596.16</td>\n",
       "      <td>325.82</td>\n",
       "      <td>2619.46</td>\n",
       "      <td>1068.90</td>\n",
       "      <td>586.35</td>\n",
       "      <td>7.96</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>China</td>\n",
       "      <td>5.59</td>\n",
       "      <td>40.51</td>\n",
       "      <td>5.59</td>\n",
       "      <td>1.12</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.52</td>\n",
       "      <td>...</td>\n",
       "      <td>121.19</td>\n",
       "      <td>1080.07</td>\n",
       "      <td>564.30</td>\n",
       "      <td>2972.57</td>\n",
       "      <td>1532.23</td>\n",
       "      <td>17333.09</td>\n",
       "      <td>9174.88</td>\n",
       "      <td>1382.83</td>\n",
       "      <td>5.01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>2.54</td>\n",
       "      <td>22.25</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>...</td>\n",
       "      <td>80.32</td>\n",
       "      <td>482.85</td>\n",
       "      <td>270.15</td>\n",
       "      <td>1117.69</td>\n",
       "      <td>584.37</td>\n",
       "      <td>2694.05</td>\n",
       "      <td>1269.44</td>\n",
       "      <td>483.19</td>\n",
       "      <td>9.15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Manila</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>3.54</td>\n",
       "      <td>27.40</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.93</td>\n",
       "      <td>...</td>\n",
       "      <td>61.82</td>\n",
       "      <td>559.52</td>\n",
       "      <td>281.78</td>\n",
       "      <td>1754.40</td>\n",
       "      <td>684.81</td>\n",
       "      <td>3536.04</td>\n",
       "      <td>2596.44</td>\n",
       "      <td>419.02</td>\n",
       "      <td>7.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>7.16</td>\n",
       "      <td>52.77</td>\n",
       "      <td>6.03</td>\n",
       "      <td>3.02</td>\n",
       "      <td>4.52</td>\n",
       "      <td>3.86</td>\n",
       "      <td>1.46</td>\n",
       "      <td>...</td>\n",
       "      <td>108.30</td>\n",
       "      <td>809.83</td>\n",
       "      <td>583.60</td>\n",
       "      <td>2621.05</td>\n",
       "      <td>1683.74</td>\n",
       "      <td>21847.94</td>\n",
       "      <td>10832.90</td>\n",
       "      <td>2672.23</td>\n",
       "      <td>3.47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   No.      city      country  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "0    0     Delhi        India  4.90       22.04            4.28   \n",
       "1    1  Shanghai        China  5.59       40.51            5.59   \n",
       "2    2   Jakarta    Indonesia  2.54       22.25            3.50   \n",
       "3    3    Manila  Philippines  3.54       27.40            3.54   \n",
       "4    4     Seoul  South Korea  7.16       52.77            6.03   \n",
       "\n",
       "   Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  Cappuccino  \\\n",
       "0                      1.84                       3.67        1.78   \n",
       "1                      1.12                       4.19        3.96   \n",
       "2                      2.02                       3.18        2.19   \n",
       "3                      1.24                       1.90        2.91   \n",
       "4                      3.02                       4.52        3.86   \n",
       "\n",
       "   Coke/Pepsi  ...     x47      x48     x49      x50      x51       x52  \\\n",
       "0        0.48  ...   36.26   223.87  133.38   596.16   325.82   2619.46   \n",
       "1        0.52  ...  121.19  1080.07  564.30  2972.57  1532.23  17333.09   \n",
       "2        0.59  ...   80.32   482.85  270.15  1117.69   584.37   2694.05   \n",
       "3        0.93  ...   61.82   559.52  281.78  1754.40   684.81   3536.04   \n",
       "4        1.46  ...  108.30   809.83  583.60  2621.05  1683.74  21847.94   \n",
       "\n",
       "        x53      x54   x55  data_quality  \n",
       "0   1068.90   586.35  7.96             1  \n",
       "1   9174.88  1382.83  5.01             1  \n",
       "2   1269.44   483.19  9.15             1  \n",
       "3   2596.44   419.02  7.80             1  \n",
       "4  10832.90  2672.23  3.47             1  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 594,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cost_of_living = pd.read_csv('data/cost-of-living.csv', header=0)\n",
    "\n",
    "cost_of_living.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
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       "      <th>x47</th>\n",
       "      <th>x48</th>\n",
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       "      <th>x51</th>\n",
       "      <th>x52</th>\n",
       "      <th>x53</th>\n",
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       "      <th>x55</th>\n",
       "      <th>data_quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4869</th>\n",
       "      <td>4869</td>\n",
       "      <td>Peterborough</td>\n",
       "      <td>Australia</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>...</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>0</td>\n",
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       "    <tr>\n",
       "      <th>4870</th>\n",
       "      <td>4870</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>Australia</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>...</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4871</th>\n",
       "      <td>4871</td>\n",
       "      <td>Ixtapa Zihuatanejo</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>5.19</td>\n",
       "      <td>31.13</td>\n",
       "      <td>12.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.62</td>\n",
       "      <td>...</td>\n",
       "      <td>103.78</td>\n",
       "      <td>415.11</td>\n",
       "      <td>259.44</td>\n",
       "      <td>518.89</td>\n",
       "      <td>415.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4872</th>\n",
       "      <td>4872</td>\n",
       "      <td>Iqaluit</td>\n",
       "      <td>Canada</td>\n",
       "      <td>29.78</td>\n",
       "      <td>74.61</td>\n",
       "      <td>13.77</td>\n",
       "      <td>6.70</td>\n",
       "      <td>8.93</td>\n",
       "      <td>3.72</td>\n",
       "      <td>3.54</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2978.11</td>\n",
       "      <td>2978.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.53</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4873</th>\n",
       "      <td>4873</td>\n",
       "      <td>Neiafu</td>\n",
       "      <td>Tonga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.53</td>\n",
       "      <td>10.55</td>\n",
       "      <td>10.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.11</td>\n",
       "      <td>...</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       No.                city    country   Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "4869  4869        Peterborough  Australia    NaN         NaN             NaN   \n",
       "4870  4870          Georgetown  Australia    NaN         NaN             NaN   \n",
       "4871  4871  Ixtapa Zihuatanejo     Mexico   5.19       31.13           12.97   \n",
       "4872  4872             Iqaluit     Canada  29.78       74.61           13.77   \n",
       "4873  4873              Neiafu      Tonga    NaN       29.53           10.55   \n",
       "\n",
       "      Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  Cappuccino  \\\n",
       "4869                       NaN                        NaN         NaN   \n",
       "4870                       NaN                        NaN         NaN   \n",
       "4871                      0.99                        NaN        1.82   \n",
       "4872                      6.70                       8.93        3.72   \n",
       "4873                     10.55                        NaN         NaN   \n",
       "\n",
       "      Coke/Pepsi  ...     x47     x48     x49      x50      x51  x52  x53  \\\n",
       "4869         NaN  ...     NaN     NaN     NaN      NaN      NaN  NaN  NaN   \n",
       "4870         NaN  ...     NaN     NaN     NaN      NaN      NaN  NaN  NaN   \n",
       "4871        0.62  ...  103.78  415.11  259.44   518.89   415.11  NaN  NaN   \n",
       "4872        3.54  ...     NaN     NaN     NaN  2978.11  2978.11  NaN  NaN   \n",
       "4873        2.11  ...     NaN     NaN     NaN      NaN      NaN  NaN  NaN   \n",
       "\n",
       "      x54   x55  data_quality  \n",
       "4869  NaN   NaN             0  \n",
       "4870  NaN   NaN             0  \n",
       "4871  NaN   NaN             0  \n",
       "4872  NaN  6.53             0  \n",
       "4873  NaN   NaN             0  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 595,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_of_living.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4874, 59)"
      ]
     },
     "execution_count": 596,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_of_living.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>4.90</td>\n",
       "      <td>22.04</td>\n",
       "      <td>4.28</td>\n",
       "      <td>1.84</td>\n",
       "      <td>3.67</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.48</td>\n",
       "      <td>...</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.36</td>\n",
       "      <td>9.79</td>\n",
       "      <td>1.53</td>\n",
       "      <td>2.51</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>China</td>\n",
       "      <td>5.59</td>\n",
       "      <td>40.51</td>\n",
       "      <td>5.59</td>\n",
       "      <td>1.12</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.52</td>\n",
       "      <td>...</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.62</td>\n",
       "      <td>13.97</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.93</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>2.54</td>\n",
       "      <td>22.25</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>...</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.42</td>\n",
       "      <td>20.66</td>\n",
       "      <td>2.06</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Manila</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>3.54</td>\n",
       "      <td>27.40</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.93</td>\n",
       "      <td>...</td>\n",
       "      <td>2.95</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.65</td>\n",
       "      <td>8.84</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.55</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>7.16</td>\n",
       "      <td>52.77</td>\n",
       "      <td>6.03</td>\n",
       "      <td>3.02</td>\n",
       "      <td>4.52</td>\n",
       "      <td>3.86</td>\n",
       "      <td>1.46</td>\n",
       "      <td>...</td>\n",
       "      <td>6.40</td>\n",
       "      <td>6.36</td>\n",
       "      <td>3.86</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.06</td>\n",
       "      <td>15.08</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.49</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   No.      city      country  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "0    0     Delhi        India  4.90       22.04            4.28   \n",
       "1    1  Shanghai        China  5.59       40.51            5.59   \n",
       "2    2   Jakarta    Indonesia  2.54       22.25            3.50   \n",
       "3    3    Manila  Philippines  3.54       27.40            3.54   \n",
       "4    4     Seoul  South Korea  7.16       52.77            6.03   \n",
       "\n",
       "   Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  Cappuccino  \\\n",
       "0                      1.84                       3.67        1.78   \n",
       "1                      1.12                       4.19        3.96   \n",
       "2                      2.02                       3.18        2.19   \n",
       "3                      1.24                       1.90        2.91   \n",
       "4                      3.02                       4.52        3.86   \n",
       "\n",
       "   Coke/Pepsi  ...  Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "0        0.48  ...     1.01    0.60    0.36   0.41     0.70   \n",
       "1        0.52  ...     2.11    1.47    0.82   1.02     0.82   \n",
       "2        0.59  ...     2.08    1.24    1.35   2.10     1.13   \n",
       "3        0.93  ...     2.95    1.42    1.76   1.75     1.36   \n",
       "4        1.46  ...     6.40    6.36    3.86   2.81     2.40   \n",
       "\n",
       "   Water_Market(1.5L)  Wine_Market  Domestic_Beer_Market(0.5L)  \\\n",
       "0                0.36         9.79                        1.53   \n",
       "1                0.62        13.97                        0.91   \n",
       "2                0.42        20.66                        2.06   \n",
       "3                0.65         8.84                        1.15   \n",
       "4                1.06        15.08                        2.10   \n",
       "\n",
       "   Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "0                         2.51        4.28  \n",
       "1                         1.93        3.49  \n",
       "2                         3.18        2.22  \n",
       "3                         1.55        2.65  \n",
       "4                         2.49        3.39  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 597,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping columns that I'm not going to use.\n",
    "to_drop = ['x28', 'x29', 'x30', 'x31', 'x32', 'x33', 'x34', 'x35', 'x36', 'x37', 'x38', 'x39', 'x40', 'x41', 'x42', 'x43', 'x44', 'x45', 'x46', 'x47', 'x48', 'x49', 'x50', 'x51', 'x52', 'x53', 'x54', 'x55', 'data_quality']\n",
    "\n",
    "cost_of_living.drop(to_drop, inplace=True, axis=1)\n",
    "\n",
    "cost_of_living.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "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>No.</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>4.90</td>\n",
       "      <td>22.04</td>\n",
       "      <td>4.28</td>\n",
       "      <td>1.84</td>\n",
       "      <td>3.67</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.48</td>\n",
       "      <td>...</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.36</td>\n",
       "      <td>9.79</td>\n",
       "      <td>1.53</td>\n",
       "      <td>2.51</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>China</td>\n",
       "      <td>5.59</td>\n",
       "      <td>40.51</td>\n",
       "      <td>5.59</td>\n",
       "      <td>1.12</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.52</td>\n",
       "      <td>...</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.62</td>\n",
       "      <td>13.97</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.93</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Jakarta</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>2.54</td>\n",
       "      <td>22.25</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>...</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.42</td>\n",
       "      <td>20.66</td>\n",
       "      <td>2.06</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Manila</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>3.54</td>\n",
       "      <td>27.40</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.93</td>\n",
       "      <td>...</td>\n",
       "      <td>2.95</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.65</td>\n",
       "      <td>8.84</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.55</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>7.16</td>\n",
       "      <td>52.77</td>\n",
       "      <td>6.03</td>\n",
       "      <td>3.02</td>\n",
       "      <td>4.52</td>\n",
       "      <td>3.86</td>\n",
       "      <td>1.46</td>\n",
       "      <td>...</td>\n",
       "      <td>6.40</td>\n",
       "      <td>6.36</td>\n",
       "      <td>3.86</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.06</td>\n",
       "      <td>15.08</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.49</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4869</th>\n",
       "      <td>4869</td>\n",
       "      <td>Peterborough</td>\n",
       "      <td>Australia</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>...</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>4870</th>\n",
       "      <td>4870</td>\n",
       "      <td>Georgetown</td>\n",
       "      <td>Australia</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>...</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>4871</th>\n",
       "      <td>4871</td>\n",
       "      <td>Ixtapa Zihuatanejo</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>5.19</td>\n",
       "      <td>31.13</td>\n",
       "      <td>12.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.62</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4872</th>\n",
       "      <td>4872</td>\n",
       "      <td>Iqaluit</td>\n",
       "      <td>Canada</td>\n",
       "      <td>29.78</td>\n",
       "      <td>74.61</td>\n",
       "      <td>13.77</td>\n",
       "      <td>6.70</td>\n",
       "      <td>8.93</td>\n",
       "      <td>3.72</td>\n",
       "      <td>3.54</td>\n",
       "      <td>...</td>\n",
       "      <td>6.45</td>\n",
       "      <td>3.86</td>\n",
       "      <td>3.61</td>\n",
       "      <td>3.86</td>\n",
       "      <td>3.36</td>\n",
       "      <td>4.03</td>\n",
       "      <td>14.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4873</th>\n",
       "      <td>4873</td>\n",
       "      <td>Neiafu</td>\n",
       "      <td>Tonga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.53</td>\n",
       "      <td>10.55</td>\n",
       "      <td>10.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.11</td>\n",
       "      <td>...</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4874 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       No.                city      country   Meal  Meal_For_2  \\\n",
       "0        0               Delhi        India   4.90       22.04   \n",
       "1        1            Shanghai        China   5.59       40.51   \n",
       "2        2             Jakarta    Indonesia   2.54       22.25   \n",
       "3        3              Manila  Philippines   3.54       27.40   \n",
       "4        4               Seoul  South Korea   7.16       52.77   \n",
       "...    ...                 ...          ...    ...         ...   \n",
       "4869  4869        Peterborough    Australia    NaN         NaN   \n",
       "4870  4870          Georgetown    Australia    NaN         NaN   \n",
       "4871  4871  Ixtapa Zihuatanejo       Mexico   5.19       31.13   \n",
       "4872  4872             Iqaluit       Canada  29.78       74.61   \n",
       "4873  4873              Neiafu        Tonga    NaN       29.53   \n",
       "\n",
       "      McDonalds_Meal  Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "0               4.28                      1.84                       3.67   \n",
       "1               5.59                      1.12                       4.19   \n",
       "2               3.50                      2.02                       3.18   \n",
       "3               3.54                      1.24                       1.90   \n",
       "4               6.03                      3.02                       4.52   \n",
       "...              ...                       ...                        ...   \n",
       "4869             NaN                       NaN                        NaN   \n",
       "4870             NaN                       NaN                        NaN   \n",
       "4871           12.97                      0.99                        NaN   \n",
       "4872           13.77                      6.70                       8.93   \n",
       "4873           10.55                     10.55                        NaN   \n",
       "\n",
       "      Cappuccino  Coke/Pepsi  ...  Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "0           1.78        0.48  ...     1.01    0.60    0.36   0.41     0.70   \n",
       "1           3.96        0.52  ...     2.11    1.47    0.82   1.02     0.82   \n",
       "2           2.19        0.59  ...     2.08    1.24    1.35   2.10     1.13   \n",
       "3           2.91        0.93  ...     2.95    1.42    1.76   1.75     1.36   \n",
       "4           3.86        1.46  ...     6.40    6.36    3.86   2.81     2.40   \n",
       "...          ...         ...  ...      ...     ...     ...    ...      ...   \n",
       "4869         NaN         NaN  ...      NaN     NaN     NaN    NaN      NaN   \n",
       "4870         NaN         NaN  ...      NaN     NaN     NaN    NaN      NaN   \n",
       "4871        1.82        0.62  ...      NaN     NaN     NaN    NaN      NaN   \n",
       "4872        3.72        3.54  ...     6.45    3.86    3.61   3.86     3.36   \n",
       "4873         NaN        2.11  ...     2.53    2.53    2.11    NaN      NaN   \n",
       "\n",
       "      Water_Market(1.5L)  Wine_Market  Domestic_Beer_Market(0.5L)  \\\n",
       "0                   0.36         9.79                        1.53   \n",
       "1                   0.62        13.97                        0.91   \n",
       "2                   0.42        20.66                        2.06   \n",
       "3                   0.65         8.84                        1.15   \n",
       "4                   1.06        15.08                        2.10   \n",
       "...                  ...          ...                         ...   \n",
       "4869                 NaN          NaN                         NaN   \n",
       "4870                 NaN          NaN                         NaN   \n",
       "4871                0.78          NaN                         NaN   \n",
       "4872                4.03        14.89                         NaN   \n",
       "4873                 NaN        12.66                         NaN   \n",
       "\n",
       "      Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "0                            2.51        4.28  \n",
       "1                            1.93        3.49  \n",
       "2                            3.18        2.22  \n",
       "3                            1.55        2.65  \n",
       "4                            2.49        3.39  \n",
       "...                           ...         ...  \n",
       "4869                          NaN         NaN  \n",
       "4870                          NaN         NaN  \n",
       "4871                          NaN         NaN  \n",
       "4872                          NaN       14.89  \n",
       "4873                          NaN         NaN  \n",
       "\n",
       "[4874 rows x 30 columns]"
      ]
     },
     "execution_count": 598,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_of_living.duplicated()\n",
    "cost_of_living.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No.                            4874\n",
       "city                           4874\n",
       "country                        4874\n",
       "Meal                           4449\n",
       "Meal_For_2                     4432\n",
       "McDonalds_Meal                 4552\n",
       "Domestic_Beer_Rest(0.5L)       4385\n",
       "Imported_Beer_Rest(0.33L)      4445\n",
       "Cappuccino                     4540\n",
       "Coke/Pepsi                     4438\n",
       "Water_Rest(0.33L)              4571\n",
       "Milk                           4494\n",
       "Bread                          4457\n",
       "Rice                           4538\n",
       "Eggs                           4369\n",
       "Cheese                         4393\n",
       "Chicken_Fillets                4318\n",
       "Beef                           4312\n",
       "Apple                          4509\n",
       "Bananas                        4499\n",
       "Oranges                        4439\n",
       "Tomato                         4410\n",
       "Potato                         4444\n",
       "Onion                          4447\n",
       "Lettuce                        4379\n",
       "Water_Market(1.5L)             4490\n",
       "Wine_Market                    4414\n",
       "Domestic_Beer_Market(0.5L)     4149\n",
       "Imported_Beer_Market(0.33L)    4223\n",
       "Cigarettes                     4389\n",
       "dtype: int64"
      ]
     },
     "execution_count": 599,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_of_living.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4874, 30)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        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>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4874.000000</td>\n",
       "      <td>4449.000000</td>\n",
       "      <td>4432.000000</td>\n",
       "      <td>4552.000000</td>\n",
       "      <td>4385.000000</td>\n",
       "      <td>4445.000000</td>\n",
       "      <td>4540.000000</td>\n",
       "      <td>4438.000000</td>\n",
       "      <td>4571.000000</td>\n",
       "      <td>4494.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4439.000000</td>\n",
       "      <td>4410.000000</td>\n",
       "      <td>4444.000000</td>\n",
       "      <td>4447.000000</td>\n",
       "      <td>4379.000000</td>\n",
       "      <td>4490.000000</td>\n",
       "      <td>4414.000000</td>\n",
       "      <td>4149.000000</td>\n",
       "      <td>4223.000000</td>\n",
       "      <td>4389.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2436.500000</td>\n",
       "      <td>10.321643</td>\n",
       "      <td>43.213592</td>\n",
       "      <td>6.875426</td>\n",
       "      <td>3.376732</td>\n",
       "      <td>3.937287</td>\n",
       "      <td>2.742822</td>\n",
       "      <td>1.513053</td>\n",
       "      <td>1.110768</td>\n",
       "      <td>1.139455</td>\n",
       "      <td>...</td>\n",
       "      <td>2.351935</td>\n",
       "      <td>2.547399</td>\n",
       "      <td>1.568906</td>\n",
       "      <td>1.516267</td>\n",
       "      <td>1.219847</td>\n",
       "      <td>0.986062</td>\n",
       "      <td>8.645111</td>\n",
       "      <td>1.657585</td>\n",
       "      <td>2.308425</td>\n",
       "      <td>6.253698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1407.146936</td>\n",
       "      <td>7.165507</td>\n",
       "      <td>25.365827</td>\n",
       "      <td>2.669017</td>\n",
       "      <td>2.080606</td>\n",
       "      <td>2.088773</td>\n",
       "      <td>1.353734</td>\n",
       "      <td>0.905525</td>\n",
       "      <td>0.749957</td>\n",
       "      <td>0.513458</td>\n",
       "      <td>...</td>\n",
       "      <td>1.694907</td>\n",
       "      <td>1.810451</td>\n",
       "      <td>1.233074</td>\n",
       "      <td>1.113866</td>\n",
       "      <td>0.729826</td>\n",
       "      <td>0.679048</td>\n",
       "      <td>4.620889</td>\n",
       "      <td>0.912849</td>\n",
       "      <td>1.192877</td>\n",
       "      <td>4.431934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>1.060000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.310000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1218.250000</td>\n",
       "      <td>4.310000</td>\n",
       "      <td>22.350000</td>\n",
       "      <td>4.910000</td>\n",
       "      <td>1.590000</td>\n",
       "      <td>2.220000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>1.490000</td>\n",
       "      <td>3.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2436.500000</td>\n",
       "      <td>9.830000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.960000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.640000</td>\n",
       "      <td>2.630000</td>\n",
       "      <td>1.450000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.870000</td>\n",
       "      <td>2.120000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>1.190000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>7.810000</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>5.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3654.750000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>3.722500</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>1.570000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.050000</td>\n",
       "      <td>3.560000</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>1.640000</td>\n",
       "      <td>1.390000</td>\n",
       "      <td>11.430000</td>\n",
       "      <td>2.140000</td>\n",
       "      <td>2.890000</td>\n",
       "      <td>8.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4873.000000</td>\n",
       "      <td>56.980000</td>\n",
       "      <td>211.350000</td>\n",
       "      <td>21.810000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.770000</td>\n",
       "      <td>6.810000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.080000</td>\n",
       "      <td>17.640000</td>\n",
       "      <td>15.870000</td>\n",
       "      <td>12.220000</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>51.940000</td>\n",
       "      <td>7.670000</td>\n",
       "      <td>15.670000</td>\n",
       "      <td>56.980000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               No.         Meal   Meal_For_2  McDonalds_Meal  \\\n",
       "count  4874.000000  4449.000000  4432.000000     4552.000000   \n",
       "mean   2436.500000    10.321643    43.213592        6.875426   \n",
       "std    1407.146936     7.165507    25.365827        2.669017   \n",
       "min       0.000000     0.450000     3.250000        1.060000   \n",
       "25%    1218.250000     4.310000    22.350000        4.910000   \n",
       "50%    2436.500000     9.830000    40.000000        6.960000   \n",
       "75%    3654.750000    15.000000    60.000000        8.500000   \n",
       "max    4873.000000    56.980000   211.350000       21.810000   \n",
       "\n",
       "       Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)   Cappuccino  \\\n",
       "count               4385.000000                4445.000000  4540.000000   \n",
       "mean                   3.376732                   3.937287     2.742822   \n",
       "std                    2.080606                   2.088773     1.353734   \n",
       "min                    0.320000                   0.310000     0.220000   \n",
       "25%                    1.590000                   2.220000     1.560000   \n",
       "50%                    3.000000                   3.640000     2.630000   \n",
       "75%                    5.000000                   5.190000     3.722500   \n",
       "max                   15.000000                  17.500000    10.000000   \n",
       "\n",
       "        Coke/Pepsi  Water_Rest(0.33L)         Milk  ...      Oranges  \\\n",
       "count  4438.000000        4571.000000  4494.000000  ...  4439.000000   \n",
       "mean      1.513053           1.110768     1.139455  ...     2.351935   \n",
       "std       0.905525           0.749957     0.513458  ...     1.694907   \n",
       "min       0.110000           0.070000     0.260000  ...     0.200000   \n",
       "25%       0.750000           0.470000     0.820000  ...     1.200000   \n",
       "50%       1.450000           1.040000     1.040000  ...     1.870000   \n",
       "75%       2.080000           1.570000     1.300000  ...     3.050000   \n",
       "max       8.000000           5.770000     6.810000  ...    15.080000   \n",
       "\n",
       "            Tomato       Potato        Onion      Lettuce  Water_Market(1.5L)  \\\n",
       "count  4410.000000  4444.000000  4447.000000  4379.000000         4490.000000   \n",
       "mean      2.547399     1.568906     1.516267     1.219847            0.986062   \n",
       "std       1.810451     1.233074     1.113866     0.729826            0.679048   \n",
       "min       0.120000     0.110000     0.110000     0.080000            0.110000   \n",
       "25%       1.180000     0.730000     0.740000     0.710000            0.490000   \n",
       "50%       2.120000     1.200000     1.190000     1.040000            0.750000   \n",
       "75%       3.560000     2.080000     2.080000     1.640000            1.390000   \n",
       "max      17.640000    15.870000    12.220000     9.250000            5.000000   \n",
       "\n",
       "       Wine_Market  Domestic_Beer_Market(0.5L)  Imported_Beer_Market(0.33L)  \\\n",
       "count  4414.000000                 4149.000000                  4223.000000   \n",
       "mean      8.645111                    1.657585                     2.308425   \n",
       "std       4.620889                    0.912849                     1.192877   \n",
       "min       0.360000                    0.250000                     0.350000   \n",
       "25%       5.190000                    0.990000                     1.490000   \n",
       "50%       7.810000                    1.430000                     2.100000   \n",
       "75%      11.430000                    2.140000                     2.890000   \n",
       "max      51.940000                    7.670000                    15.670000   \n",
       "\n",
       "        Cigarettes  \n",
       "count  4389.000000  \n",
       "mean      6.253698  \n",
       "std       4.431934  \n",
       "min       0.450000  \n",
       "25%       3.040000  \n",
       "50%       5.190000  \n",
       "75%       8.310000  \n",
       "max      56.980000  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 600,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(cost_of_living.shape)\n",
    "cost_of_living.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
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       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <th>India</th>\n",
       "      <td>0</td>\n",
       "      <td>4.90</td>\n",
       "      <td>22.04</td>\n",
       "      <td>4.28</td>\n",
       "      <td>1.84</td>\n",
       "      <td>3.67</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.73</td>\n",
       "      <td>...</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.36</td>\n",
       "      <td>9.79</td>\n",
       "      <td>1.53</td>\n",
       "      <td>2.51</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanghai</th>\n",
       "      <th>China</th>\n",
       "      <td>1</td>\n",
       "      <td>5.59</td>\n",
       "      <td>40.51</td>\n",
       "      <td>5.59</td>\n",
       "      <td>1.12</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.68</td>\n",
       "      <td>...</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.62</td>\n",
       "      <td>13.97</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.93</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jakarta</th>\n",
       "      <th>Indonesia</th>\n",
       "      <td>2</td>\n",
       "      <td>2.54</td>\n",
       "      <td>22.25</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.28</td>\n",
       "      <td>...</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.42</td>\n",
       "      <td>20.66</td>\n",
       "      <td>2.06</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manila</th>\n",
       "      <th>Philippines</th>\n",
       "      <td>3</td>\n",
       "      <td>3.54</td>\n",
       "      <td>27.40</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.63</td>\n",
       "      <td>...</td>\n",
       "      <td>2.95</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.65</td>\n",
       "      <td>8.84</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.55</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seoul</th>\n",
       "      <th>South Korea</th>\n",
       "      <td>4</td>\n",
       "      <td>7.16</td>\n",
       "      <td>52.77</td>\n",
       "      <td>6.03</td>\n",
       "      <td>3.02</td>\n",
       "      <td>4.52</td>\n",
       "      <td>3.86</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.78</td>\n",
       "      <td>2.13</td>\n",
       "      <td>...</td>\n",
       "      <td>6.40</td>\n",
       "      <td>6.36</td>\n",
       "      <td>3.86</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.06</td>\n",
       "      <td>15.08</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.49</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peterborough</th>\n",
       "      <th>Australia</th>\n",
       "      <td>4869</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>...</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>Georgetown</th>\n",
       "      <th>Australia</th>\n",
       "      <td>4870</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>...</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>Ixtapa Zihuatanejo</th>\n",
       "      <th>Mexico</th>\n",
       "      <td>4871</td>\n",
       "      <td>5.19</td>\n",
       "      <td>31.13</td>\n",
       "      <td>12.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.82</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iqaluit</th>\n",
       "      <th>Canada</th>\n",
       "      <td>4872</td>\n",
       "      <td>29.78</td>\n",
       "      <td>74.61</td>\n",
       "      <td>13.77</td>\n",
       "      <td>6.70</td>\n",
       "      <td>8.93</td>\n",
       "      <td>3.72</td>\n",
       "      <td>3.54</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.54</td>\n",
       "      <td>...</td>\n",
       "      <td>6.45</td>\n",
       "      <td>3.86</td>\n",
       "      <td>3.61</td>\n",
       "      <td>3.86</td>\n",
       "      <td>3.36</td>\n",
       "      <td>4.03</td>\n",
       "      <td>14.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neiafu</th>\n",
       "      <th>Tonga</th>\n",
       "      <td>4873</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.53</td>\n",
       "      <td>10.55</td>\n",
       "      <td>10.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.56</td>\n",
       "      <td>...</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4874 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 No.   Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city               country                                                \n",
       "Delhi              India           0   4.90       22.04            4.28   \n",
       "Shanghai           China           1   5.59       40.51            5.59   \n",
       "Jakarta            Indonesia       2   2.54       22.25            3.50   \n",
       "Manila             Philippines     3   3.54       27.40            3.54   \n",
       "Seoul              South Korea     4   7.16       52.77            6.03   \n",
       "...                              ...    ...         ...             ...   \n",
       "Peterborough       Australia    4869    NaN         NaN             NaN   \n",
       "Georgetown         Australia    4870    NaN         NaN             NaN   \n",
       "Ixtapa Zihuatanejo Mexico       4871   5.19       31.13           12.97   \n",
       "Iqaluit            Canada       4872  29.78       74.61           13.77   \n",
       "Neiafu             Tonga        4873    NaN       29.53           10.55   \n",
       "\n",
       "                                Domestic_Beer_Rest(0.5L)  \\\n",
       "city               country                                 \n",
       "Delhi              India                            1.84   \n",
       "Shanghai           China                            1.12   \n",
       "Jakarta            Indonesia                        2.02   \n",
       "Manila             Philippines                      1.24   \n",
       "Seoul              South Korea                      3.02   \n",
       "...                                                  ...   \n",
       "Peterborough       Australia                         NaN   \n",
       "Georgetown         Australia                         NaN   \n",
       "Ixtapa Zihuatanejo Mexico                           0.99   \n",
       "Iqaluit            Canada                           6.70   \n",
       "Neiafu             Tonga                           10.55   \n",
       "\n",
       "                                Imported_Beer_Rest(0.33L)  Cappuccino  \\\n",
       "city               country                                              \n",
       "Delhi              India                             3.67        1.78   \n",
       "Shanghai           China                             4.19        3.96   \n",
       "Jakarta            Indonesia                         3.18        2.19   \n",
       "Manila             Philippines                       1.90        2.91   \n",
       "Seoul              South Korea                       4.52        3.86   \n",
       "...                                                   ...         ...   \n",
       "Peterborough       Australia                          NaN         NaN   \n",
       "Georgetown         Australia                          NaN         NaN   \n",
       "Ixtapa Zihuatanejo Mexico                             NaN        1.82   \n",
       "Iqaluit            Canada                            8.93        3.72   \n",
       "Neiafu             Tonga                              NaN         NaN   \n",
       "\n",
       "                                Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city               country                                           ...   \n",
       "Delhi              India              0.48               0.19  0.73  ...   \n",
       "Shanghai           China              0.52               0.32  2.68  ...   \n",
       "Jakarta            Indonesia          0.59               0.27  1.28  ...   \n",
       "Manila             Philippines        0.93               0.51  1.63  ...   \n",
       "Seoul              South Korea        1.46               0.78  2.13  ...   \n",
       "...                                    ...                ...   ...  ...   \n",
       "Peterborough       Australia           NaN                NaN   NaN  ...   \n",
       "Georgetown         Australia           NaN                NaN   NaN  ...   \n",
       "Ixtapa Zihuatanejo Mexico             0.62               0.42  1.82  ...   \n",
       "Iqaluit            Canada             3.54               4.10  2.54  ...   \n",
       "Neiafu             Tonga              2.11               2.11  1.56  ...   \n",
       "\n",
       "                                Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city               country                                                \n",
       "Delhi              India           1.01    0.60    0.36   0.41     0.70   \n",
       "Shanghai           China           2.11    1.47    0.82   1.02     0.82   \n",
       "Jakarta            Indonesia       2.08    1.24    1.35   2.10     1.13   \n",
       "Manila             Philippines     2.95    1.42    1.76   1.75     1.36   \n",
       "Seoul              South Korea     6.40    6.36    3.86   2.81     2.40   \n",
       "...                                 ...     ...     ...    ...      ...   \n",
       "Peterborough       Australia        NaN     NaN     NaN    NaN      NaN   \n",
       "Georgetown         Australia        NaN     NaN     NaN    NaN      NaN   \n",
       "Ixtapa Zihuatanejo Mexico           NaN     NaN     NaN    NaN      NaN   \n",
       "Iqaluit            Canada          6.45    3.86    3.61   3.86     3.36   \n",
       "Neiafu             Tonga           2.53    2.53    2.11    NaN      NaN   \n",
       "\n",
       "                                Water_Market(1.5L)  Wine_Market  \\\n",
       "city               country                                        \n",
       "Delhi              India                      0.36         9.79   \n",
       "Shanghai           China                      0.62        13.97   \n",
       "Jakarta            Indonesia                  0.42        20.66   \n",
       "Manila             Philippines                0.65         8.84   \n",
       "Seoul              South Korea                1.06        15.08   \n",
       "...                                            ...          ...   \n",
       "Peterborough       Australia                   NaN          NaN   \n",
       "Georgetown         Australia                   NaN          NaN   \n",
       "Ixtapa Zihuatanejo Mexico                     0.78          NaN   \n",
       "Iqaluit            Canada                     4.03        14.89   \n",
       "Neiafu             Tonga                       NaN        12.66   \n",
       "\n",
       "                                Domestic_Beer_Market(0.5L)  \\\n",
       "city               country                                   \n",
       "Delhi              India                              1.53   \n",
       "Shanghai           China                              0.91   \n",
       "Jakarta            Indonesia                          2.06   \n",
       "Manila             Philippines                        1.15   \n",
       "Seoul              South Korea                        2.10   \n",
       "...                                                    ...   \n",
       "Peterborough       Australia                           NaN   \n",
       "Georgetown         Australia                           NaN   \n",
       "Ixtapa Zihuatanejo Mexico                              NaN   \n",
       "Iqaluit            Canada                              NaN   \n",
       "Neiafu             Tonga                               NaN   \n",
       "\n",
       "                                Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city               country                                               \n",
       "Delhi              India                               2.51        4.28  \n",
       "Shanghai           China                               1.93        3.49  \n",
       "Jakarta            Indonesia                           3.18        2.22  \n",
       "Manila             Philippines                         1.55        2.65  \n",
       "Seoul              South Korea                         2.49        3.39  \n",
       "...                                                     ...         ...  \n",
       "Peterborough       Australia                            NaN         NaN  \n",
       "Georgetown         Australia                            NaN         NaN  \n",
       "Ixtapa Zihuatanejo Mexico                               NaN         NaN  \n",
       "Iqaluit            Canada                               NaN       14.89  \n",
       "Neiafu             Tonga                                NaN         NaN  \n",
       "\n",
       "[4874 rows x 28 columns]"
      ]
     },
     "execution_count": 601,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_of_living.set_index(['city', 'country'], inplace=True)\n",
    "cost_of_living"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.32\n"
     ]
    }
   ],
   "source": [
    "# Find average of the meal column\n",
    "meal_len = cost_of_living.Meal.count()\n",
    "meal_sum = cost_of_living.Meal.sum()\n",
    "meal_average = round(meal_sum/meal_len, 2)\n",
    "print(meal_average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.98\n",
      "0.45\n"
     ]
    }
   ],
   "source": [
    "# Finding the max and min values in the Meal column\n",
    "max_meal = cost_of_living.Meal.max()\n",
    "min_meal = cost_of_living.Meal.min()\n",
    "print(max_meal)\n",
    "print(min_meal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "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>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Turkmenabat</th>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>1016</td>\n",
       "      <td>56.98</td>\n",
       "      <td>113.96</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.70</td>\n",
       "      <td>14.25</td>\n",
       "      <td>7.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.85</td>\n",
       "      <td>4.27</td>\n",
       "      <td>...</td>\n",
       "      <td>6.27</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.25</td>\n",
       "      <td>28.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dasoguz</th>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>1540</td>\n",
       "      <td>56.98</td>\n",
       "      <td>170.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.70</td>\n",
       "      <td>7.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.85</td>\n",
       "      <td>...</td>\n",
       "      <td>7.83</td>\n",
       "      <td>3.42</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.71</td>\n",
       "      <td>8.55</td>\n",
       "      <td>2.85</td>\n",
       "      <td>5.70</td>\n",
       "      <td>56.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           No.   Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city        country                                                 \n",
       "Turkmenabat Turkmenistan  1016  56.98      113.96             NaN   \n",
       "Dasoguz     Turkmenistan  1540  56.98      170.94             NaN   \n",
       "\n",
       "                          Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city        country                                                             \n",
       "Turkmenabat Turkmenistan                      5.70                      14.25   \n",
       "Dasoguz     Turkmenistan                     14.25                        NaN   \n",
       "\n",
       "                          Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  \\\n",
       "city        country                                                         \n",
       "Turkmenabat Turkmenistan        7.12         NaN               0.85  4.27   \n",
       "Dasoguz     Turkmenistan        5.70        7.12                NaN  2.85   \n",
       "\n",
       "                          ...  Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city        country       ...                                            \n",
       "Turkmenabat Turkmenistan  ...     6.27    1.99    1.14   0.85      NaN   \n",
       "Dasoguz     Turkmenistan  ...     7.83    3.42    2.85   0.85     0.85   \n",
       "\n",
       "                          Water_Market(1.5L)  Wine_Market  \\\n",
       "city        country                                         \n",
       "Turkmenabat Turkmenistan                1.14          NaN   \n",
       "Dasoguz     Turkmenistan                0.71         8.55   \n",
       "\n",
       "                          Domestic_Beer_Market(0.5L)  \\\n",
       "city        country                                    \n",
       "Turkmenabat Turkmenistan                         NaN   \n",
       "Dasoguz     Turkmenistan                        2.85   \n",
       "\n",
       "                          Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city        country                                                \n",
       "Turkmenabat Turkmenistan                        14.25       28.49  \n",
       "Dasoguz     Turkmenistan                         5.70       56.98  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 604,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the Meal column to find the row with the max value\n",
    "cost_of_living[cost_of_living['Meal'] == 56.98]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "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>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sargodha</th>\n",
       "      <th>Pakistan</th>\n",
       "      <td>712</td>\n",
       "      <td>0.45</td>\n",
       "      <td>17.82</td>\n",
       "      <td>3.56</td>\n",
       "      <td>2.45</td>\n",
       "      <td>4.01</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mardan</th>\n",
       "      <th>Pakistan</th>\n",
       "      <td>1251</td>\n",
       "      <td>0.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.46</td>\n",
       "      <td>2.45</td>\n",
       "      <td>6.68</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city     country                                            \n",
       "Sargodha Pakistan   712  0.45       17.82            3.56   \n",
       "Mardan   Pakistan  1251  0.45         NaN            4.46   \n",
       "\n",
       "                   Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city     country                                                         \n",
       "Sargodha Pakistan                      2.45                       4.01   \n",
       "Mardan   Pakistan                      2.45                       6.68   \n",
       "\n",
       "                   Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city     country                                                    ...   \n",
       "Sargodha Pakistan        0.67        0.16               0.11  0.45  ...   \n",
       "Mardan   Pakistan        0.67        0.16               0.11  0.45  ...   \n",
       "\n",
       "                   Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city     country                                             \n",
       "Sargodha Pakistan     0.42    0.67    0.26   0.22     0.27   \n",
       "Mardan   Pakistan     0.42    0.62    0.26   0.22     0.13   \n",
       "\n",
       "                   Water_Market(1.5L)  Wine_Market  \\\n",
       "city     country                                     \n",
       "Sargodha Pakistan                0.23         0.45   \n",
       "Mardan   Pakistan                0.23         1.78   \n",
       "\n",
       "                   Domestic_Beer_Market(0.5L)  Imported_Beer_Market(0.33L)  \\\n",
       "city     country                                                             \n",
       "Sargodha Pakistan                        0.83                         1.04   \n",
       "Mardan   Pakistan                        0.62                         0.62   \n",
       "\n",
       "                   Cigarettes  \n",
       "city     country               \n",
       "Sargodha Pakistan        0.80  \n",
       "Mardan   Pakistan        1.11  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 605,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering the Meal column to find the row with the min value\n",
    "cost_of_living[cost_of_living['Meal'] == 0.45]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        country \n",
       "Sargodha    Pakistan    0.45\n",
       "Mardan      Pakistan    0.45\n",
       "Shekhupura  Pakistan    0.53\n",
       "Bhavnagar   India       0.61\n",
       "Erode       India       0.61\n",
       "Name: Meal, dtype: float64"
      ]
     },
     "execution_count": 606,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting Meal column to find top 5 least expensive places\n",
    "cost_of_living.Meal.sort_values().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [],
   "source": [
    "bottom_5_meal_values = [0.45, 0.45, 0.53, 0.61, 0.61]\n",
    "bottom_5_meal_location = ['Sargodha, Pakistan', 'Mardan, Pakistan', 'Shekhpura, Pakistan', 'Bhavnagar, India', 'Erode, India']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar graph\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "plt.title('Top 5 Least Expensive Meals Worldwide')\n",
    "\n",
    "bar_width = 0.7\n",
    "\n",
    "plt.bar(bottom_5_meal_location, bottom_5_meal_values, width=bar_width)\n",
    "plt.bar('Average', meal_average, width=bar_width)\n",
    "plt.xticks(rotation=45)\n",
    " \n",
    "\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('Price in USD')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city         country      \n",
       "Dasoguz      Turkmenistan     56.98\n",
       "Turkmenabat  Turkmenistan     56.98\n",
       "Lorain       United States    55.00\n",
       "Cranford     United States    50.00\n",
       "Summit       United States    50.00\n",
       "Name: Meal, dtype: float64"
      ]
     },
     "execution_count": 609,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting Meal column to find top 5 most expensive places\n",
    "cost_of_living.Meal.sort_values(ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_5_meal_values = [56.98, 56.98, 55.00, 50.00, 50.00]\n",
    "top_5_meal_locations = ['Dasoguz, Turkmenistan', ' Turkmenabat, Turkmenistan', 'Lorain, United States', 'Cranford, United States', 'Summit,United States']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar graph\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "plt.title('Top 5 Most Expensive Meals Worldwide')\n",
    "\n",
    "bar_width = 0.7\n",
    "\n",
    "plt.bar(top_5_meal_locations, top_5_meal_values, width=bar_width)\n",
    "plt.bar('Average', meal_average, width=bar_width)\n",
    "plt.xticks(rotation=45)\n",
    " \n",
    "\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('Price in USD')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "211.35\n",
      "3.25\n"
     ]
    }
   ],
   "source": [
    "# Find max and min values for meal for 2 column\n",
    "max_meal_for_2 = cost_of_living.Meal_For_2.max()\n",
    "min_meal_for_2 = cost_of_living.Meal_For_2.min()\n",
    "print(max_meal_for_2)\n",
    "print(min_meal_for_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mendrisio</th>\n",
       "      <th>Switzerland</th>\n",
       "      <td>4621</td>\n",
       "      <td>25.89</td>\n",
       "      <td>211.35</td>\n",
       "      <td>15.85</td>\n",
       "      <td>4.76</td>\n",
       "      <td>5.02</td>\n",
       "      <td>4.07</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1.56</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.76</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.74</td>\n",
       "      <td>10.57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        No.   Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city      country                                                \n",
       "Mendrisio Switzerland  4621  25.89      211.35           15.85   \n",
       "\n",
       "                       Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city      country                                                            \n",
       "Mendrisio Switzerland                      4.76                       5.02   \n",
       "\n",
       "                       Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city      country                                                       ...   \n",
       "Mendrisio Switzerland        4.07        3.96               3.17  1.56  ...   \n",
       "\n",
       "                       Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city      country                                                \n",
       "Mendrisio Switzerland      NaN    4.76    2.11   2.11      NaN   \n",
       "\n",
       "                       Water_Market(1.5L)  Wine_Market  \\\n",
       "city      country                                        \n",
       "Mendrisio Switzerland                1.74        10.57   \n",
       "\n",
       "                       Domestic_Beer_Market(0.5L)  \\\n",
       "city      country                                   \n",
       "Mendrisio Switzerland                         NaN   \n",
       "\n",
       "                       Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city      country                                               \n",
       "Mendrisio Switzerland                          NaN         NaN  \n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "execution_count": 613,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter to find max value in meal for 2 column\n",
    "cost_of_living[cost_of_living.Meal_For_2 == 211.35]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "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></th>\n",
       "      <th>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Moratuwa</th>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>1632</td>\n",
       "      <td>1.36</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.73</td>\n",
       "      <td>...</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.19</td>\n",
       "      <td>9.49</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.81</td>\n",
       "      <td>4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nuwara Eliya</th>\n",
       "      <th>Sri Lanka</th>\n",
       "      <td>3118</td>\n",
       "      <td>1.36</td>\n",
       "      <td>3.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.95</td>\n",
       "      <td>1.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.73</td>\n",
       "      <td>...</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.19</td>\n",
       "      <td>9.49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city         country                                             \n",
       "Moratuwa     Sri Lanka  1632  1.36        3.25            1.98   \n",
       "Nuwara Eliya Sri Lanka  3118  1.36        3.25             NaN   \n",
       "\n",
       "                        Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city         country                                                          \n",
       "Moratuwa     Sri Lanka                      0.95                       1.08   \n",
       "Nuwara Eliya Sri Lanka                      0.95                       1.08   \n",
       "\n",
       "                        Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city         country                                                     ...   \n",
       "Moratuwa     Sri Lanka        0.27        0.19               0.14  0.73  ...   \n",
       "Nuwara Eliya Sri Lanka         NaN        0.19               0.14  0.73  ...   \n",
       "\n",
       "                        Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city         country                                              \n",
       "Moratuwa     Sri Lanka     1.63    0.49    0.47   0.43     0.62   \n",
       "Nuwara Eliya Sri Lanka     1.63    0.49    0.47   0.43      NaN   \n",
       "\n",
       "                        Water_Market(1.5L)  Wine_Market  \\\n",
       "city         country                                      \n",
       "Moratuwa     Sri Lanka                0.19         9.49   \n",
       "Nuwara Eliya Sri Lanka                0.19         9.49   \n",
       "\n",
       "                        Domestic_Beer_Market(0.5L)  \\\n",
       "city         country                                 \n",
       "Moratuwa     Sri Lanka                        1.08   \n",
       "Nuwara Eliya Sri Lanka                         NaN   \n",
       "\n",
       "                        Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city         country                                             \n",
       "Moratuwa     Sri Lanka                         0.81        4.88  \n",
       "Nuwara Eliya Sri Lanka                          NaN        4.88  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter to find min value in meal for 2 column\n",
    "cost_of_living[cost_of_living.Meal_For_2 == 3.25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city          country   \n",
       "Moratuwa      Sri Lanka     3.25\n",
       "Nuwara Eliya  Sri Lanka     3.25\n",
       "Kurunegala    Sri Lanka     3.36\n",
       "Tangail       Bangladesh    3.75\n",
       "Negombo       Sri Lanka     4.07\n",
       "Tamale        Ghana         4.11\n",
       "Abbottabad    Pakistan      4.46\n",
       "Shekhupura    Pakistan      4.46\n",
       "Sahiwal       Pakistan      4.46\n",
       "Comilla       Bangladesh    4.68\n",
       "Name: Meal_For_2, dtype: float64"
      ]
     },
     "execution_count": 615,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort meal for 2 column to find top 10 least expensive places\n",
    "cost_of_living.Meal_For_2.sort_values().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        country      \n",
       "Mendrisio   Switzerland      211.35\n",
       "East Ridge  United States    175.00\n",
       "Dasoguz     Turkmenistan     170.94\n",
       "The Valley  Anguilla         170.00\n",
       "Ojai        United States    162.31\n",
       "Monaco      Monaco           155.80\n",
       "Hamilton    Bermuda          150.00\n",
       "Harrison    United States    150.00\n",
       "Or Yehuda   Israel           145.65\n",
       "Ashgabat    Turkmenistan     142.45\n",
       "Name: Meal_For_2, dtype: float64"
      ]
     },
     "execution_count": 616,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort meal for 2 column to find 10 most expensive places\n",
    "cost_of_living.Meal_For_2.sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.88\n"
     ]
    }
   ],
   "source": [
    "# Find average of the McDonalds column\n",
    "mcdonalds_len = cost_of_living.McDonalds_Meal.count()\n",
    "mcdonalds_sum = cost_of_living.McDonalds_Meal.sum()\n",
    "mcdonalds_average = round(mcdonalds_sum/mcdonalds_len, 2)\n",
    "print(mcdonalds_average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.81\n",
      "1.06\n"
     ]
    }
   ],
   "source": [
    "# Find the min and max values in the McDonald's meal column\n",
    "max_mcdonalds = cost_of_living.McDonalds_Meal.max()\n",
    "min_mcdonalds = cost_of_living.McDonalds_Meal.min()\n",
    "print(max_mcdonalds)\n",
    "print(min_mcdonalds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
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       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pago Pago</th>\n",
       "      <th>American Samoa</th>\n",
       "      <td>548</td>\n",
       "      <td>23.48</td>\n",
       "      <td>6.57</td>\n",
       "      <td>21.81</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.17</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.04</td>\n",
       "      <td>...</td>\n",
       "      <td>3.12</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          No.   Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city      country                                                  \n",
       "Pago Pago American Samoa  548  23.48        6.57           21.81   \n",
       "\n",
       "                          Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city      country                                                               \n",
       "Pago Pago American Samoa                      1.88                       1.88   \n",
       "\n",
       "                          Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  \\\n",
       "city      country                                                           \n",
       "Pago Pago American Samoa        0.99        1.17               2.08  1.04   \n",
       "\n",
       "                          ...  Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city      country         ...                                            \n",
       "Pago Pago American Samoa  ...     3.12     NaN     NaN    NaN      NaN   \n",
       "\n",
       "                          Water_Market(1.5L)  Wine_Market  \\\n",
       "city      country                                           \n",
       "Pago Pago American Samoa                 NaN          NaN   \n",
       "\n",
       "                          Domestic_Beer_Market(0.5L)  \\\n",
       "city      country                                      \n",
       "Pago Pago American Samoa                         NaN   \n",
       "\n",
       "                          Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city      country                                                  \n",
       "Pago Pago American Samoa                          NaN         NaN  \n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "execution_count": 619,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter to find the max value in the McDonalds column\n",
    "cost_of_living[cost_of_living.McDonalds_Meal == 21.81]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "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>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tacloban</th>\n",
       "      <th>Philippines</th>\n",
       "      <td>1435</td>\n",
       "      <td>0.97</td>\n",
       "      <td>25.9</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.62</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.48</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.41</td>\n",
       "      <td>...</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.65</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.72</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city     country                                               \n",
       "Tacloban Philippines  1435  0.97        25.9            1.06   \n",
       "\n",
       "                      Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city     country                                                            \n",
       "Tacloban Philippines                      0.62                       2.39   \n",
       "\n",
       "                      Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city     country                                                       ...   \n",
       "Tacloban Philippines        2.48        0.35               0.22  1.41  ...   \n",
       "\n",
       "                      Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city     country                                                \n",
       "Tacloban Philippines     1.06    1.41    1.65   1.06     1.06   \n",
       "\n",
       "                      Water_Market(1.5L)  Wine_Market  \\\n",
       "city     country                                        \n",
       "Tacloban Philippines                0.52         6.72   \n",
       "\n",
       "                      Domestic_Beer_Market(0.5L)  Imported_Beer_Market(0.33L)  \\\n",
       "city     country                                                                \n",
       "Tacloban Philippines                        0.85                         1.68   \n",
       "\n",
       "                      Cigarettes  \n",
       "city     country                  \n",
       "Tacloban Philippines        1.77  \n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "execution_count": 620,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter to find the min value in the McDonalds column\n",
    "cost_of_living[cost_of_living.McDonalds_Meal == 1.06]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city             country    \n",
       "Tacloban         Philippines    1.06\n",
       "Tuguegarao       Philippines    1.33\n",
       "Sievierodonetsk  Ukraine        1.35\n",
       "Biskra           Algeria        1.44\n",
       "Ain Defla        Algeria        1.44\n",
       "Name: McDonalds_Meal, dtype: float64"
      ]
     },
     "execution_count": 621,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the top 5 least expensive places for McDonalds Meal\n",
    "cost_of_living['McDonalds_Meal'].sort_values().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcdonalds_least_values = [1.06, 1.33, 1.35, 1.44, 1.44]\n",
    "mcdonalds_least_locations = ['Tacloban, Philippines', 'Tuguegarao, Philippines', 'Sievierodonetsk, Ukraine', 'Biskra, Algeria', 'Ain Defla, Algeria']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar graph\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "plt.title('Top 5 Least Expensive Mcdonalds Meals Worldwide')\n",
    "\n",
    "bar_width = 0.7\n",
    "\n",
    "plt.bar(mcdonalds_least_locations, mcdonalds_least_values, width=bar_width)\n",
    "plt.bar('Average', mcdonalds_average, width=bar_width)\n",
    "plt.xticks(rotation=45)\n",
    " \n",
    "\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('Price in USD')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       country          \n",
       "Pago Pago  American Samoa       21.81\n",
       "Malabo     Equatorial Guinea    19.79\n",
       "Aigle      Switzerland          19.02\n",
       "Eilat      Israel               18.35\n",
       "Bat Yam    Israel               18.21\n",
       "Name: McDonalds_Meal, dtype: float64"
      ]
     },
     "execution_count": 624,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the top 5 most expensive places for McDonalds Meal\n",
    "cost_of_living['McDonalds_Meal'].sort_values(ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [],
   "source": [
    "mcdonalds_most_values = [21.81, 19.79, 19.02, 18.35, 18.21]\n",
    "mcdonalds_most_locations = ['Pago Pago, American Somoa', 'Malabo, Equatorial Guinea', 'Aigle, Switzerland', 'Eilat, Israel', 'Bat Yam, Israel']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar graph\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "plt.title('Top 5 Least Expensive Mcdonalds Meals Worldwide')\n",
    "\n",
    "bar_width = 0.7\n",
    "\n",
    "plt.bar(mcdonalds_most_locations, mcdonalds_most_values, width=bar_width)\n",
    "plt.bar('Average', mcdonalds_average, width=bar_width)\n",
    "plt.xticks(rotation=45)\n",
    " \n",
    "\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('Price in USD')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n",
      "0.22\n"
     ]
    }
   ],
   "source": [
    "# Find max and min value in the cappuccino column\n",
    "max_capp = cost_of_living.Cappuccino.max()\n",
    "min_capp = cost_of_living.Cappuccino.min()\n",
    "print(max_capp)\n",
    "print(min_capp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "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>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gallup</th>\n",
       "      <th>United States</th>\n",
       "      <td>4457</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>...</td>\n",
       "      <td>6.61</td>\n",
       "      <td>6.61</td>\n",
       "      <td>5.51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city   country                                                 \n",
       "Gallup United States  4457   NaN        60.0            15.0   \n",
       "\n",
       "                      Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city   country                                                              \n",
       "Gallup United States                       NaN                        NaN   \n",
       "\n",
       "                      Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city   country                                                         ...   \n",
       "Gallup United States        10.0         7.0                4.0  1.59  ...   \n",
       "\n",
       "                      Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city   country                                                  \n",
       "Gallup United States     6.61    6.61    5.51    NaN      1.5   \n",
       "\n",
       "                      Water_Market(1.5L)  Wine_Market  \\\n",
       "city   country                                          \n",
       "Gallup United States                 NaN         13.0   \n",
       "\n",
       "                      Domestic_Beer_Market(0.5L)  Imported_Beer_Market(0.33L)  \\\n",
       "city   country                                                                  \n",
       "Gallup United States                        4.29                          5.0   \n",
       "\n",
       "                      Cigarettes  \n",
       "city   country                    \n",
       "Gallup United States         NaN  \n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "execution_count": 628,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find max value in cappuccino column\n",
    "cost_of_living[cost_of_living.Cappuccino == 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Medea</th>\n",
       "      <th>Algeria</th>\n",
       "      <td>2113</td>\n",
       "      <td>1.99</td>\n",
       "      <td>13.45</td>\n",
       "      <td>2.71</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.22</td>\n",
       "      <td>7.22</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.44</td>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laghouat</th>\n",
       "      <th>Algeria</th>\n",
       "      <td>2167</td>\n",
       "      <td>1.08</td>\n",
       "      <td>10.83</td>\n",
       "      <td>3.07</td>\n",
       "      <td>1.62</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.60</td>\n",
       "      <td>...</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.22</td>\n",
       "      <td>3.61</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city     country                                           \n",
       "Medea    Algeria  2113  1.99       13.45            2.71   \n",
       "Laghouat Algeria  2167  1.08       10.83            3.07   \n",
       "\n",
       "                  Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city     country                                                        \n",
       "Medea    Algeria                      1.58                       1.76   \n",
       "Laghouat Algeria                      1.62                       1.08   \n",
       "\n",
       "                  Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city     country                                                   ...   \n",
       "Medea    Algeria        0.22        0.34               0.16  0.45  ...   \n",
       "Laghouat Algeria        0.22        0.34               0.14  0.60  ...   \n",
       "\n",
       "                  Oranges  Tomato  Potato  Onion  Lettuce  Water_Market(1.5L)  \\\n",
       "city     country                                                                \n",
       "Medea    Algeria     0.58    0.29    0.36   0.11      NaN                0.22   \n",
       "Laghouat Algeria     1.26    0.54    0.49   0.22     0.58                0.22   \n",
       "\n",
       "                  Wine_Market  Domestic_Beer_Market(0.5L)  \\\n",
       "city     country                                            \n",
       "Medea    Algeria         7.22                        1.08   \n",
       "Laghouat Algeria         3.61                        1.44   \n",
       "\n",
       "                  Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city     country                                           \n",
       "Medea    Algeria                         1.44        2.17  \n",
       "Laghouat Algeria                         0.90        1.52  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 629,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost_of_living[cost_of_living.Cappuccino == 0.22]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                country  \n",
       "Laghouat            Algeria      0.22\n",
       "Medea               Algeria      0.22\n",
       "Bordj Bou Arreridj  Algeria      0.23\n",
       "Mostaganem          Algeria      0.25\n",
       "Ratnapura           Sri Lanka    0.27\n",
       "Moratuwa            Sri Lanka    0.27\n",
       "Puttalam            Sri Lanka    0.27\n",
       "Batticaloa          Sri Lanka    0.27\n",
       "El Bayadh           Algeria      0.29\n",
       "Setif               Algeria      0.30\n",
       "Name: Cappuccino, dtype: float64"
      ]
     },
     "execution_count": 630,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the top 10 least expensive places that sell cappuccino\n",
    "cost_of_living.Cappuccino.sort_values().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city          country      \n",
       "Gallup        United States    10.00\n",
       "Plainedge     United States     8.00\n",
       "Perrysburg    United States     8.00\n",
       "Menominee     United States     8.00\n",
       "Topeka        United States     7.67\n",
       "Turkmenabat   Turkmenistan      7.12\n",
       "Commack       United States     7.00\n",
       "Spanish Fork  United States     7.00\n",
       "League City   United States     7.00\n",
       "Thomasville   United States     7.00\n",
       "Name: Cappuccino, dtype: float64"
      ]
     },
     "execution_count": 631,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the top 10 most expensive places that sell cappuccino\n",
    "cost_of_living.Cappuccino.sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.81\n",
      "0.26\n"
     ]
    }
   ],
   "source": [
    "# Find max and min values of the Milk column\n",
    "max_milk = cost_of_living.Milk.max()\n",
    "min_milk = cost_of_living.Milk.min()\n",
    "print(max_milk)\n",
    "print(min_milk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "      <th></th>\n",
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       "      <th>No.</th>\n",
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       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
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       "      <th>Potato</th>\n",
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       "      <th>Water_Market(1.5L)</th>\n",
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       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
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       "      <th>city</th>\n",
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       "    <tr>\n",
       "      <th>Beirut</th>\n",
       "      <th>Lebanon</th>\n",
       "      <td>477</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.82</td>\n",
       "      <td>3.51</td>\n",
       "      <td>2.99</td>\n",
       "      <td>1.07</td>\n",
       "      <td>6.81</td>\n",
       "      <td>...</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.53</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.61</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.19</td>\n",
       "      <td>3.33</td>\n",
       "      <td>5.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city   country                                          \n",
       "Beirut Lebanon  477  10.0        60.0             6.1   \n",
       "\n",
       "                Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city   country                                                        \n",
       "Beirut Lebanon                       3.0                       4.82   \n",
       "\n",
       "                Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  Oranges  \\\n",
       "city   country                                                   ...            \n",
       "Beirut Lebanon        3.51        2.99               1.07  6.81  ...     1.38   \n",
       "\n",
       "                Tomato  Potato  Onion  Lettuce  Water_Market(1.5L)  \\\n",
       "city   country                                                       \n",
       "Beirut Lebanon    1.81    1.53   1.01     0.83                0.61   \n",
       "\n",
       "                Wine_Market  Domestic_Beer_Market(0.5L)  \\\n",
       "city   country                                            \n",
       "Beirut Lebanon          7.0                        1.19   \n",
       "\n",
       "                Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city   country                                           \n",
       "Beirut Lebanon                         3.33        5.79  \n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "execution_count": 633,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find max Milk value in dataframe\n",
    "cost_of_living[cost_of_living.Milk == 6.81]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
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       "      <th>Pyongyang</th>\n",
       "      <th>North Korea</th>\n",
       "      <td>181</td>\n",
       "      <td>4.27</td>\n",
       "      <td>20.49</td>\n",
       "      <td>3.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.99</td>\n",
       "      <td>0.26</td>\n",
       "      <td>...</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city      country                                              \n",
       "Pyongyang North Korea  181  4.27       20.49            3.13   \n",
       "\n",
       "                       Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city      country                                                            \n",
       "Pyongyang North Korea                       NaN                        NaN   \n",
       "\n",
       "                       Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city      country                                                       ...   \n",
       "Pyongyang North Korea        1.81        1.68               1.99  0.26  ...   \n",
       "\n",
       "                       Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city      country                                                \n",
       "Pyongyang North Korea      2.2     2.2     2.2    2.2      1.0   \n",
       "\n",
       "                       Water_Market(1.5L)  Wine_Market  \\\n",
       "city      country                                        \n",
       "Pyongyang North Korea                 0.3          6.0   \n",
       "\n",
       "                       Domestic_Beer_Market(0.5L)  \\\n",
       "city      country                                   \n",
       "Pyongyang North Korea                         NaN   \n",
       "\n",
       "                       Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city      country                                               \n",
       "Pyongyang North Korea                          NaN       18.88  \n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "execution_count": 634,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find min Milk value in dataframe\n",
    "cost_of_living[cost_of_living.Milk == 0.26]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            country    \n",
       "Pyongyang       North Korea    0.26\n",
       "Kars            Turkey         0.32\n",
       "Sidi Bel Abbes  Algeria        0.32\n",
       "Mpigi           Uganda         0.32\n",
       "Fort Portal     Uganda         0.32\n",
       "Nallacheruvu    India          0.34\n",
       "Jendouba        Tunisia        0.34\n",
       "Sakiet ez Zit   Tunisia        0.35\n",
       "Toba Tek Singh  Pakistan       0.36\n",
       "Gojra           Pakistan       0.36\n",
       "Name: Milk, dtype: float64"
      ]
     },
     "execution_count": 635,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 least expensive places that sell milk\n",
    "cost_of_living.Milk.sort_values().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city           country                 \n",
       "Beirut         Lebanon                     6.81\n",
       "Yaren          Nauru                       6.71\n",
       "Grand Turk     Turks And Caicos Islands    6.00\n",
       "Vreed-en-Hoop  Guyana                      5.73\n",
       "Roseau         Dominica                    5.00\n",
       "Williams Lake  Canada                      4.83\n",
       "As Sib         Oman                        4.67\n",
       "Turkmenabat    Turkmenistan                4.27\n",
       "Hamilton       Bermuda                     4.24\n",
       "Ashgabat       Turkmenistan                4.20\n",
       "Name: Milk, dtype: float64"
      ]
     },
     "execution_count": 636,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 most expensive places that sell milk\n",
    "cost_of_living.Milk.sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48.47\n",
      "0.9\n"
     ]
    }
   ],
   "source": [
    "# Find max and min values in cheese column\n",
    "max_cheese = cost_of_living.Cheese.max()\n",
    "min_cheese = cost_of_living.Cheese.min()\n",
    "print(max_cheese)\n",
    "print(min_cheese)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bac Ninh</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>847</td>\n",
       "      <td>1.89</td>\n",
       "      <td>12.12</td>\n",
       "      <td>3.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.21</td>\n",
       "      <td>...</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.48</td>\n",
       "      <td>6.06</td>\n",
       "      <td>0.61</td>\n",
       "      <td>2.02</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hai Duong</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>870</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>1.21</td>\n",
       "      <td>...</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.48</td>\n",
       "      <td>6.06</td>\n",
       "      <td>0.61</td>\n",
       "      <td>2.02</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bac Giang</th>\n",
       "      <th>Vietnam</th>\n",
       "      <td>2853</td>\n",
       "      <td>2.83</td>\n",
       "      <td>16.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.21</td>\n",
       "      <td>...</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.61</td>\n",
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       "      <td>6.06</td>\n",
       "      <td>0.61</td>\n",
       "      <td>2.02</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city      country                                           \n",
       "Bac Ninh  Vietnam   847  1.89       12.12            3.23   \n",
       "Hai Duong Vietnam   870   NaN         NaN             NaN   \n",
       "Bac Giang Vietnam  2853  2.83       16.16             NaN   \n",
       "\n",
       "                   Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city      country                                                        \n",
       "Bac Ninh  Vietnam                       NaN                       1.21   \n",
       "Hai Duong Vietnam                       NaN                        NaN   \n",
       "Bac Giang Vietnam                      0.32                       0.97   \n",
       "\n",
       "                   Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city      country                                                   ...   \n",
       "Bac Ninh  Vietnam        1.41        0.48               0.36  1.21  ...   \n",
       "Hai Duong Vietnam         NaN         NaN                NaN  1.21  ...   \n",
       "Bac Giang Vietnam        1.01        0.36               0.16  1.21  ...   \n",
       "\n",
       "                   Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city      country                                            \n",
       "Bac Ninh  Vietnam     1.62     0.4    0.81   1.01     0.61   \n",
       "Hai Duong Vietnam     1.62     0.4    0.81   1.01     0.61   \n",
       "Bac Giang Vietnam     1.62     0.4    0.81   1.01     0.61   \n",
       "\n",
       "                   Water_Market(1.5L)  Wine_Market  \\\n",
       "city      country                                    \n",
       "Bac Ninh  Vietnam                0.48         6.06   \n",
       "Hai Duong Vietnam                0.48         6.06   \n",
       "Bac Giang Vietnam                0.48         6.06   \n",
       "\n",
       "                   Domestic_Beer_Market(0.5L)  Imported_Beer_Market(0.33L)  \\\n",
       "city      country                                                            \n",
       "Bac Ninh  Vietnam                        0.61                         2.02   \n",
       "Hai Duong Vietnam                        0.61                         2.02   \n",
       "Bac Giang Vietnam                        0.61                         2.02   \n",
       "\n",
       "                   Cigarettes  \n",
       "city      country              \n",
       "Bac Ninh  Vietnam        0.48  \n",
       "Hai Duong Vietnam        0.48  \n",
       "Bac Giang Vietnam        0.48  \n",
       "\n",
       "[3 rows x 28 columns]"
      ]
     },
     "execution_count": 638,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find max value in dataframe\n",
    "cost_of_living[cost_of_living.Cheese == 48.47]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
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       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>Ikeja</th>\n",
       "      <th>Nigeria</th>\n",
       "      <td>611</td>\n",
       "      <td>1.13</td>\n",
       "      <td>11.26</td>\n",
       "      <td>3.38</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.79</td>\n",
       "      <td>...</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.68</td>\n",
       "      <td>3.38</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.89</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city  country                                          \n",
       "Ikeja Nigeria  611  1.13       11.26            3.38   \n",
       "\n",
       "               Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city  country                                                        \n",
       "Ikeja Nigeria                      0.79                       0.96   \n",
       "\n",
       "               Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  Oranges  \\\n",
       "city  country                                                   ...            \n",
       "Ikeja Nigeria        0.45        0.23               0.15  0.79  ...     0.45   \n",
       "\n",
       "               Tomato  Potato  Onion  Lettuce  Water_Market(1.5L)  \\\n",
       "city  country                                                       \n",
       "Ikeja Nigeria    0.68    3.38   2.03     0.68                0.34   \n",
       "\n",
       "               Wine_Market  Domestic_Beer_Market(0.5L)  \\\n",
       "city  country                                            \n",
       "Ikeja Nigeria         1.89                        0.39   \n",
       "\n",
       "               Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city  country                                           \n",
       "Ikeja Nigeria                         0.62        0.68  \n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "execution_count": 639,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find min value in dataframe\n",
    "cost_of_living[cost_of_living.Cheese == 0.9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city     country \n",
       "Ikeja    Nigeria     0.90\n",
       "Bello    Colombia    1.22\n",
       "Cartago  Colombia    1.22\n",
       "Luxor    Egypt       1.28\n",
       "Abnub    Egypt       1.28\n",
       "Popayan  Colombia    1.32\n",
       "Makurdi  Nigeria     1.35\n",
       "Minna    Nigeria     1.35\n",
       "Ilorin   Nigeria     1.35\n",
       "Titel    Serbia      1.40\n",
       "Name: Cheese, dtype: float64"
      ]
     },
     "execution_count": 640,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 least expensive cities that sell cheese\n",
    "cost_of_living.Cheese.sort_values().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city        country    \n",
       "Bac Ninh    Vietnam        48.47\n",
       "Hai Duong   Vietnam        48.47\n",
       "Bac Giang   Vietnam        48.47\n",
       "Utsunomiya  Japan          43.31\n",
       "Modling     Austria        41.55\n",
       "Keelung     Taiwan         39.57\n",
       "Solothurn   Switzerland    38.04\n",
       "Aomori      Japan          37.54\n",
       "Bastia      France         37.39\n",
       "Schwyz      Switzerland    36.99\n",
       "Name: Cheese, dtype: float64"
      ]
     },
     "execution_count": 641,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 most expensive cities that sell cheese\n",
    "cost_of_living.Cheese.sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44.09\n",
      "1.17\n"
     ]
    }
   ],
   "source": [
    "# Find max and min values in chicken fillet column\n",
    "max_chicken = cost_of_living.Chicken_Fillets.max()\n",
    "min_chicken = cost_of_living.Chicken_Fillets.min()\n",
    "print(max_chicken)\n",
    "print(min_chicken)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>South Miami Heights</th>\n",
       "      <th>United States</th>\n",
       "      <td>3039</td>\n",
       "      <td>50.0</td>\n",
       "      <td>25.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
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       "      <td>9.5</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.29</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city                country                                                 \n",
       "South Miami Heights United States  3039  50.0        25.0            11.5   \n",
       "Gallup              United States  4457   NaN        60.0            15.0   \n",
       "\n",
       "                                   Domestic_Beer_Rest(0.5L)  \\\n",
       "city                country                                   \n",
       "South Miami Heights United States                      10.0   \n",
       "Gallup              United States                       NaN   \n",
       "\n",
       "                                   Imported_Beer_Rest(0.33L)  Cappuccino  \\\n",
       "city                country                                                \n",
       "South Miami Heights United States                       15.0         6.0   \n",
       "Gallup              United States                        NaN        10.0   \n",
       "\n",
       "                                   Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city                country                                             ...   \n",
       "South Miami Heights United States         3.0                1.5  1.19  ...   \n",
       "Gallup              United States         7.0                4.0  1.59  ...   \n",
       "\n",
       "                                   Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city                country                                                  \n",
       "South Miami Heights United States    12.11    5.51    7.72   3.64      1.5   \n",
       "Gallup              United States     6.61    6.61    5.51    NaN      1.5   \n",
       "\n",
       "                                   Water_Market(1.5L)  Wine_Market  \\\n",
       "city                country                                          \n",
       "South Miami Heights United States                 3.0         17.0   \n",
       "Gallup              United States                 NaN         13.0   \n",
       "\n",
       "                                   Domestic_Beer_Market(0.5L)  \\\n",
       "city                country                                     \n",
       "South Miami Heights United States                         NaN   \n",
       "Gallup              United States                        4.29   \n",
       "\n",
       "                                   Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city                country                                                 \n",
       "South Miami Heights United States                          1.5         9.5  \n",
       "Gallup              United States                          5.0         NaN  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 643,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find max chicken value in the dataframe\n",
    "cost_of_living[cost_of_living.Chicken_Fillets == 44.09]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Narsingdi</th>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>1749</td>\n",
       "      <td>1.4</td>\n",
       "      <td>5.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.34</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.66</td>\n",
       "      <td>...</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.54</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city      country                                              \n",
       "Narsingdi Bangladesh  1749   1.4        5.62             NaN   \n",
       "\n",
       "                      Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city      country                                                           \n",
       "Narsingdi Bangladesh                       NaN                        NaN   \n",
       "\n",
       "                      Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city      country                                                      ...   \n",
       "Narsingdi Bangladesh        2.34        0.33               0.07  0.66  ...   \n",
       "\n",
       "                      Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city      country                                               \n",
       "Narsingdi Bangladesh     1.26    0.66    0.28   0.54      NaN   \n",
       "\n",
       "                      Water_Market(1.5L)  Wine_Market  \\\n",
       "city      country                                       \n",
       "Narsingdi Bangladesh                0.23          NaN   \n",
       "\n",
       "                      Domestic_Beer_Market(0.5L)  Imported_Beer_Market(0.33L)  \\\n",
       "city      country                                                               \n",
       "Narsingdi Bangladesh                         NaN                          NaN   \n",
       "\n",
       "                      Cigarettes  \n",
       "city      country                 \n",
       "Narsingdi Bangladesh        3.04  \n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "execution_count": 644,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find min chicken value in the dataframe\n",
    "cost_of_living[cost_of_living.Chicken_Fillets == 1.17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city             country   \n",
       "Narsingdi        Bangladesh    1.17\n",
       "Pabna            Bangladesh    1.22\n",
       "Puri             India         1.22\n",
       "Tataouine        Tunisia       1.24\n",
       "Kairouan         Tunisia       1.24\n",
       "Saint Ann's Bay  Jamaica       1.30\n",
       "Mandeville       Jamaica       1.30\n",
       "Andimeshk        Iran          1.30\n",
       "Malayer          Iran          1.30\n",
       "Tangail          Bangladesh    1.31\n",
       "Name: Chicken_Fillets, dtype: float64"
      ]
     },
     "execution_count": 645,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 least expensive cities that sell chicken\n",
    "cost_of_living.Chicken_Fillets.sort_values().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                 country      \n",
       "South Miami Heights  United States    44.09\n",
       "Gallup               United States    44.09\n",
       "Meilen               Switzerland      41.21\n",
       "Affoltern am Albis   Switzerland      36.99\n",
       "Opfikon              Switzerland      35.93\n",
       "Vernier              Switzerland      35.75\n",
       "Saratoga Springs     United States    35.25\n",
       "Allschwil            Switzerland      34.87\n",
       "Webb City            United States    34.02\n",
       "San Dimas            United States    33.07\n",
       "Name: Chicken_Fillets, dtype: float64"
      ]
     },
     "execution_count": 646,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 most expensive cities that sell chicken\n",
    "cost_of_living.Chicken_Fillets.sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.81\n",
      "0.05\n"
     ]
    }
   ],
   "source": [
    "# Find max and min values of bread column\n",
    "max_bread = cost_of_living.Bread.max()\n",
    "min_bread = cost_of_living.Bread.min()\n",
    "print(max_bread)\n",
    "print(min_bread)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
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       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
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       "      <th>city</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Plantation</th>\n",
       "      <th>United States</th>\n",
       "      <td>3518</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>1.26</td>\n",
       "      <td>...</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>Vandenberg Village</th>\n",
       "      <th>United States</th>\n",
       "      <td>4794</td>\n",
       "      <td>15.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.50</td>\n",
       "      <td>...</td>\n",
       "      <td>3.31</td>\n",
       "      <td>2.65</td>\n",
       "      <td>3.68</td>\n",
       "      <td>3.04</td>\n",
       "      <td>2.53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city               country                                                 \n",
       "Plantation         United States  3518   NaN         NaN             NaN   \n",
       "Vandenberg Village United States  4794  15.0        40.0             9.0   \n",
       "\n",
       "                                  Domestic_Beer_Rest(0.5L)  \\\n",
       "city               country                                   \n",
       "Plantation         United States                       NaN   \n",
       "Vandenberg Village United States                       7.0   \n",
       "\n",
       "                                  Imported_Beer_Rest(0.33L)  Cappuccino  \\\n",
       "city               country                                                \n",
       "Plantation         United States                        NaN         NaN   \n",
       "Vandenberg Village United States                        5.0         5.0   \n",
       "\n",
       "                                  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city               country                                             ...   \n",
       "Plantation         United States         NaN                NaN  1.26  ...   \n",
       "Vandenberg Village United States         NaN               0.99  1.50  ...   \n",
       "\n",
       "                                  Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city               country                                                  \n",
       "Plantation         United States      NaN     NaN     NaN    NaN      NaN   \n",
       "Vandenberg Village United States     3.31    2.65    3.68   3.04     2.53   \n",
       "\n",
       "                                  Water_Market(1.5L)  Wine_Market  \\\n",
       "city               country                                          \n",
       "Plantation         United States                 NaN          NaN   \n",
       "Vandenberg Village United States                 NaN          NaN   \n",
       "\n",
       "                                  Domestic_Beer_Market(0.5L)  \\\n",
       "city               country                                     \n",
       "Plantation         United States                         NaN   \n",
       "Vandenberg Village United States                         NaN   \n",
       "\n",
       "                                  Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city               country                                                 \n",
       "Plantation         United States                          NaN         NaN  \n",
       "Vandenberg Village United States                          NaN         NaN  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 648,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find max bread value in dataframe\n",
    "cost_of_living[cost_of_living.Bread == 8.81]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Djemmal</th>\n",
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       "      <td>1.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.96</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.39</td>\n",
       "      <td>...</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.54</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.77</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
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       "<p>1 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city    country                                           \n",
       "Djemmal Tunisia  3689  1.97         NaN            2.96   \n",
       "\n",
       "                 Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city    country                                                        \n",
       "Djemmal Tunisia                      0.99                       0.99   \n",
       "\n",
       "                 Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city    country                                                   ...   \n",
       "Djemmal Tunisia        0.49        0.37               0.22  0.39  ...   \n",
       "\n",
       "                 Oranges  Tomato  Potato  Onion  Lettuce  Water_Market(1.5L)  \\\n",
       "city    country                                                                \n",
       "Djemmal Tunisia     0.93    0.31    0.31   0.31     0.31                0.31   \n",
       "\n",
       "                 Wine_Market  Domestic_Beer_Market(0.5L)  \\\n",
       "city    country                                            \n",
       "Djemmal Tunisia         1.54                        0.62   \n",
       "\n",
       "                 Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city    country                                           \n",
       "Djemmal Tunisia                         0.77        2.01  \n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "execution_count": 649,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find min bread value in dataframe\n",
    "cost_of_living[cost_of_living.Bread == 0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city       country\n",
       "Djemmal    Tunisia    0.05\n",
       "Mascara    Algeria    0.07\n",
       "Adrar      Algeria    0.07\n",
       "Tindouf    Algeria    0.07\n",
       "Laghouat   Algeria    0.08\n",
       "Gafsa      Tunisia    0.08\n",
       "Jendouba   Tunisia    0.08\n",
       "Nabeul     Tunisia    0.08\n",
       "Hammamet   Tunisia    0.08\n",
       "Kasserine  Tunisia    0.08\n",
       "Name: Bread, dtype: float64"
      ]
     },
     "execution_count": 650,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the top 10 least expensive places that sell bread\n",
    "cost_of_living.Bread.sort_values().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                country      \n",
       "Plantation          United States    8.81\n",
       "Vandenberg Village  United States    8.81\n",
       "Moses Lake          United States    7.71\n",
       "California          United States    7.71\n",
       "Odawara             Japan            7.22\n",
       "Royal Oak           United States    7.17\n",
       "Hamilton            Bermuda          6.85\n",
       "Klamath Falls       United States    6.61\n",
       "Bethel Park         United States    6.61\n",
       "New Haven           United States    6.61\n",
       "Name: Bread, dtype: float64"
      ]
     },
     "execution_count": 651,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the top 10 most expensive places that sell bread\n",
    "cost_of_living.Bread.sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.65\n",
      "0.1\n"
     ]
    }
   ],
   "source": [
    "# Find max and min values in eggs column\n",
    "max_eggs = cost_of_living.Eggs.max()\n",
    "min_eggs = cost_of_living.Eggs.min()\n",
    "print(max_eggs)\n",
    "print(min_eggs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
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       "      <th>Water_Rest(0.33L)</th>\n",
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       "    </tr>\n",
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       "      <th>city</th>\n",
       "      <th>country</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Solothurn</th>\n",
       "      <th>Switzerland</th>\n",
       "      <td>3227</td>\n",
       "      <td>22.72</td>\n",
       "      <td>92.47</td>\n",
       "      <td>17.44</td>\n",
       "      <td>6.34</td>\n",
       "      <td>6.34</td>\n",
       "      <td>5.55</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.43</td>\n",
       "      <td>1.93</td>\n",
       "      <td>...</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1.37</td>\n",
       "      <td>2.64</td>\n",
       "      <td>1.02</td>\n",
       "      <td>11.62</td>\n",
       "      <td>2.33</td>\n",
       "      <td>2.11</td>\n",
       "      <td>9.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        No.   Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city      country                                                \n",
       "Solothurn Switzerland  3227  22.72       92.47           17.44   \n",
       "\n",
       "                       Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city      country                                                            \n",
       "Solothurn Switzerland                      6.34                       6.34   \n",
       "\n",
       "                       Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city      country                                                       ...   \n",
       "Solothurn Switzerland        5.55        3.43               3.43  1.93  ...   \n",
       "\n",
       "                       Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city      country                                                \n",
       "Solothurn Switzerland      4.7     3.7    1.27   1.37     2.64   \n",
       "\n",
       "                       Water_Market(1.5L)  Wine_Market  \\\n",
       "city      country                                        \n",
       "Solothurn Switzerland                1.02        11.62   \n",
       "\n",
       "                       Domestic_Beer_Market(0.5L)  \\\n",
       "city      country                                   \n",
       "Solothurn Switzerland                        2.33   \n",
       "\n",
       "                       Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city      country                                               \n",
       "Solothurn Switzerland                         2.11        9.51  \n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "execution_count": 653,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find max eggs value in dataframe\n",
    "cost_of_living[cost_of_living.Eggs == 9.65]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
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       "      <th>Water_Market(1.5L)</th>\n",
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       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
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       "      <th>city</th>\n",
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       "      <th>Konotop</th>\n",
       "      <th>Ukraine</th>\n",
       "      <td>2475</td>\n",
       "      <td>3.79</td>\n",
       "      <td>8.12</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.54</td>\n",
       "      <td>...</td>\n",
       "      <td>0.68</td>\n",
       "      <td>2.07</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.44</td>\n",
       "      <td>4.87</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.81</td>\n",
       "      <td>1.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city    country                                           \n",
       "Konotop Ukraine  2475  3.79        8.12            2.92   \n",
       "\n",
       "                 Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city    country                                                        \n",
       "Konotop Ukraine                      0.72                       1.08   \n",
       "\n",
       "                 Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city    country                                                   ...   \n",
       "Konotop Ukraine        0.68        0.51               0.27  0.54  ...   \n",
       "\n",
       "                 Oranges  Tomato  Potato  Onion  Lettuce  Water_Market(1.5L)  \\\n",
       "city    country                                                                \n",
       "Konotop Ukraine     0.68    2.07    0.34   0.41     0.54                0.44   \n",
       "\n",
       "                 Wine_Market  Domestic_Beer_Market(0.5L)  \\\n",
       "city    country                                            \n",
       "Konotop Ukraine         4.87                        0.81   \n",
       "\n",
       "                 Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city    country                                           \n",
       "Konotop Ukraine                         0.81        1.89  \n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "execution_count": 654,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find min eggs value in dataframe\n",
    "cost_of_living[cost_of_living.Eggs == 0.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city          country  \n",
       "Konotop       Ukraine      0.10\n",
       "Gafsa         Tunisia      0.37\n",
       "Pithapuram    India        0.51\n",
       "Kutahya       Turkey       0.54\n",
       "Sanandaj      Iran         0.57\n",
       "Matale        Sri Lanka    0.59\n",
       "Malayer       Iran         0.60\n",
       "Andimeshk     Iran         0.60\n",
       "Moratuwa      Sri Lanka    0.65\n",
       "Nuwara Eliya  Sri Lanka    0.65\n",
       "Name: Eggs, dtype: float64"
      ]
     },
     "execution_count": 655,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 least expensive cities that sell eggs\n",
    "cost_of_living.Eggs.sort_values().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                country      \n",
       "Solothurn           Switzerland      9.65\n",
       "Lachen              Switzerland      8.45\n",
       "Bellinzona          Switzerland      8.11\n",
       "Madinat Hamad       Bahrain          7.96\n",
       "Fribourg            Switzerland      7.78\n",
       "Affoltern am Albis  Switzerland      7.61\n",
       "Aigle               Switzerland      7.61\n",
       "Schaan              Liechtenstein    7.37\n",
       "Triesen             Liechtenstein    7.37\n",
       "Ruggell             Liechtenstein    7.37\n",
       "Name: Eggs, dtype: float64"
      ]
     },
     "execution_count": 656,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 most expensive cities that sell eggs\n",
    "cost_of_living.Eggs.sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.11\n",
      "1.43\n"
     ]
    }
   ],
   "source": [
    "# Find max and min values in Beef column\n",
    "max_beef = cost_of_living.Beef.max()\n",
    "min_beef = cost_of_living.Beef.min()\n",
    "print(max_beef)\n",
    "print(min_beef)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Aarau</th>\n",
       "      <th>Switzerland</th>\n",
       "      <td>3179</td>\n",
       "      <td>23.25</td>\n",
       "      <td>84.54</td>\n",
       "      <td>15.85</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.28</td>\n",
       "      <td>4.57</td>\n",
       "      <td>3.59</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1.87</td>\n",
       "      <td>...</td>\n",
       "      <td>5.23</td>\n",
       "      <td>1.96</td>\n",
       "      <td>2.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.15</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.64</td>\n",
       "      <td>8.98</td>\n",
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      ],
      "text/plain": [
       "                    No.   Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city  country                                                \n",
       "Aarau Switzerland  3179  23.25       84.54           15.85   \n",
       "\n",
       "                   Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city  country                                                            \n",
       "Aarau Switzerland                      6.08                       5.28   \n",
       "\n",
       "                   Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city  country                                                       ...   \n",
       "Aarau Switzerland        4.57        3.59               3.59  1.87  ...   \n",
       "\n",
       "                   Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city  country                                                \n",
       "Aarau Switzerland     5.23    1.96    2.75    NaN      NaN   \n",
       "\n",
       "                   Water_Market(1.5L)  Wine_Market  \\\n",
       "city  country                                        \n",
       "Aarau Switzerland                 NaN        12.15   \n",
       "\n",
       "                   Domestic_Beer_Market(0.5L)  Imported_Beer_Market(0.33L)  \\\n",
       "city  country                                                                \n",
       "Aarau Switzerland                         1.8                         2.64   \n",
       "\n",
       "                   Cigarettes  \n",
       "city  country                  \n",
       "Aarau Switzerland        8.98  \n",
       "\n",
       "[1 rows x 28 columns]"
      ]
     },
     "execution_count": 658,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find max beef value in dataframe\n",
    "cost_of_living[cost_of_living.Beef == 95.11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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></th>\n",
       "      <th>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Soledad</th>\n",
       "      <th>Colombia</th>\n",
       "      <td>670</td>\n",
       "      <td>3.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.08</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.16</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.71</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sincelejo</th>\n",
       "      <th>Colombia</th>\n",
       "      <td>1313</td>\n",
       "      <td>3.06</td>\n",
       "      <td>16.3</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.16</td>\n",
       "      <td>2.55</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city      country                                            \n",
       "Soledad   Colombia   670  3.06         NaN            4.08   \n",
       "Sincelejo Colombia  1313  3.06        16.3            4.08   \n",
       "\n",
       "                    Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city      country                                                         \n",
       "Soledad   Colombia                      3.06                       0.82   \n",
       "Sincelejo Colombia                      1.02                       0.82   \n",
       "\n",
       "                    Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city      country                                                    ...   \n",
       "Soledad   Colombia        1.63        0.61                NaN   NaN  ...   \n",
       "Sincelejo Colombia        0.98        0.53                0.4   0.7  ...   \n",
       "\n",
       "                    Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city      country                                             \n",
       "Soledad   Colombia      NaN     NaN    0.41   0.22      0.2   \n",
       "Sincelejo Colombia     0.98    0.91    0.41   0.22      0.2   \n",
       "\n",
       "                    Water_Market(1.5L)  Wine_Market  \\\n",
       "city      country                                     \n",
       "Soledad   Colombia                0.16         2.55   \n",
       "Sincelejo Colombia                0.16         2.55   \n",
       "\n",
       "                    Domestic_Beer_Market(0.5L)  Imported_Beer_Market(0.33L)  \\\n",
       "city      country                                                             \n",
       "Soledad   Colombia                        0.56                         0.71   \n",
       "Sincelejo Colombia                        0.56                         0.71   \n",
       "\n",
       "                    Cigarettes  \n",
       "city      country               \n",
       "Soledad   Colombia         NaN  \n",
       "Sincelejo Colombia        1.63  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 659,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find min beef value in dataframe\n",
    "cost_of_living[cost_of_living.Beef == 1.43]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 660,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            country \n",
       "Soledad         Colombia    1.43\n",
       "Sincelejo       Colombia    1.43\n",
       "Mbeya           Tanzania    1.72\n",
       "Kumasi          Ghana       1.76\n",
       "Sambalpur       India       1.84\n",
       "Krishnapur      India       1.84\n",
       "Mandi Burewala  Pakistan    2.00\n",
       "Loja            Ecuador     2.00\n",
       "Osogbo          Nigeria     2.10\n",
       "Ondo            Nigeria     2.10\n",
       "Name: Beef, dtype: float64"
      ]
     },
     "execution_count": 660,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 least expensive places that sell beef\n",
    "cost_of_living.Beef.sort_values().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city                country    \n",
       "Aarau               Switzerland    95.11\n",
       "Schwyz              Switzerland    89.83\n",
       "Lucerne             Switzerland    72.21\n",
       "Beirut              Lebanon        68.01\n",
       "Dubendorf           Switzerland    66.22\n",
       "Basel               Switzerland    66.12\n",
       "Aigle               Switzerland    63.41\n",
       "Affoltern am Albis  Switzerland    63.41\n",
       "Glarus              Switzerland    62.35\n",
       "Lausanne            Switzerland    59.31\n",
       "Name: Beef, dtype: float64"
      ]
     },
     "execution_count": 661,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 most expensive cities that sell beef\n",
    "cost_of_living.Beef.sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n",
      "0.11\n"
     ]
    }
   ],
   "source": [
    "# Find max and min values in market water 1.5L \n",
    "max_water = cost_of_living[\"Water_Market(1.5L)\"].max()\n",
    "min_water = cost_of_living[\"Water_Market(1.5L)\"].min()\n",
    "print(max_water)\n",
    "print(min_water)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>No.</th>\n",
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       "      <th>city</th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>East Orange</th>\n",
       "      <th>United States</th>\n",
       "      <td>2789</td>\n",
       "      <td>25.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.27</td>\n",
       "      <td>...</td>\n",
       "      <td>11.02</td>\n",
       "      <td>10.56</td>\n",
       "      <td>2.20</td>\n",
       "      <td>8.36</td>\n",
       "      <td>1.99</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.99</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Brunswick</th>\n",
       "      <th>United States</th>\n",
       "      <td>4075</td>\n",
       "      <td>22.5</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>11.02</td>\n",
       "      <td>13.23</td>\n",
       "      <td>6.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrightsville</th>\n",
       "      <th>United States</th>\n",
       "      <td>4851</td>\n",
       "      <td>25.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.39</td>\n",
       "      <td>1.27</td>\n",
       "      <td>...</td>\n",
       "      <td>11.02</td>\n",
       "      <td>10.56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.36</td>\n",
       "      <td>1.99</td>\n",
       "      <td>5.0</td>\n",
       "      <td>18.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.99</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city            country                                                 \n",
       "East Orange     United States  2789  25.0        45.0            9.00   \n",
       "North Brunswick United States  4075  22.5        60.0            6.99   \n",
       "Wrightsville    United States  4851  25.0        45.0           11.00   \n",
       "\n",
       "                               Domestic_Beer_Rest(0.5L)  \\\n",
       "city            country                                   \n",
       "East Orange     United States                       5.0   \n",
       "North Brunswick United States                       NaN   \n",
       "Wrightsville    United States                       5.0   \n",
       "\n",
       "                               Imported_Beer_Rest(0.33L)  Cappuccino  \\\n",
       "city            country                                                \n",
       "East Orange     United States                        6.0         3.5   \n",
       "North Brunswick United States                        3.0         2.0   \n",
       "Wrightsville    United States                        8.0         3.5   \n",
       "\n",
       "                               Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city            country                                             ...   \n",
       "East Orange     United States        1.95               1.39  1.27  ...   \n",
       "North Brunswick United States        1.50               1.99   NaN  ...   \n",
       "Wrightsville    United States        1.95               1.39  1.27  ...   \n",
       "\n",
       "                               Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city            country                                                  \n",
       "East Orange     United States    11.02   10.56    2.20   8.36     1.99   \n",
       "North Brunswick United States    11.02   13.23    6.61    NaN     4.00   \n",
       "Wrightsville    United States    11.02   10.56     NaN   8.36     1.99   \n",
       "\n",
       "                               Water_Market(1.5L)  Wine_Market  \\\n",
       "city            country                                          \n",
       "East Orange     United States                 5.0        18.99   \n",
       "North Brunswick United States                 5.0         8.00   \n",
       "Wrightsville    United States                 5.0        18.99   \n",
       "\n",
       "                               Domestic_Beer_Market(0.5L)  \\\n",
       "city            country                                     \n",
       "East Orange     United States                         NaN   \n",
       "North Brunswick United States                         NaN   \n",
       "Wrightsville    United States                         NaN   \n",
       "\n",
       "                               Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city            country                                                 \n",
       "East Orange     United States                         2.99        11.0  \n",
       "North Brunswick United States                         2.00         9.0  \n",
       "Wrightsville    United States                         2.99        11.0  \n",
       "\n",
       "[3 rows x 28 columns]"
      ]
     },
     "execution_count": 663,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find max value of water in the dataframe\n",
    "cost_of_living[cost_of_living['Water_Market(1.5L)'] == 5.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
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       "      <th>city</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aksaray</th>\n",
       "      <th>Turkey</th>\n",
       "      <td>2739</td>\n",
       "      <td>1.54</td>\n",
       "      <td>10.73</td>\n",
       "      <td>2.95</td>\n",
       "      <td>1.5</td>\n",
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       "      <td>1.35</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.71</td>\n",
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       "      <td>0.47</td>\n",
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       "      <td>0.53</td>\n",
       "      <td>0.11</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.33</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gardez</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>2743</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>0.67</td>\n",
       "      <td>...</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      No.  Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city    country                                               \n",
       "Aksaray Turkey       2739  1.54       10.73            2.95   \n",
       "Gardez  Afghanistan  2743   NaN         NaN             NaN   \n",
       "\n",
       "                     Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city    country                                                            \n",
       "Aksaray Turkey                            1.5                       2.15   \n",
       "Gardez  Afghanistan                       NaN                        NaN   \n",
       "\n",
       "                     Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  ...  \\\n",
       "city    country                                                       ...   \n",
       "Aksaray Turkey             1.35        0.38               0.11  0.71  ...   \n",
       "Gardez  Afghanistan         NaN         NaN                NaN  0.67  ...   \n",
       "\n",
       "                     Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city    country                                                \n",
       "Aksaray Turkey          0.47    0.74    0.39   0.27     0.53   \n",
       "Gardez  Afghanistan     0.89    0.44    0.33   0.33      NaN   \n",
       "\n",
       "                     Water_Market(1.5L)  Wine_Market  \\\n",
       "city    country                                        \n",
       "Aksaray Turkey                     0.11          5.9   \n",
       "Gardez  Afghanistan                0.11          NaN   \n",
       "\n",
       "                     Domestic_Beer_Market(0.5L)  Imported_Beer_Market(0.33L)  \\\n",
       "city    country                                                                \n",
       "Aksaray Turkey                             1.33                         1.33   \n",
       "Gardez  Afghanistan                         NaN                          NaN   \n",
       "\n",
       "                     Cigarettes  \n",
       "city    country                  \n",
       "Aksaray Turkey              1.5  \n",
       "Gardez  Afghanistan         NaN  \n",
       "\n",
       "[2 rows x 28 columns]"
      ]
     },
     "execution_count": 664,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find min value of water in the dataframe\n",
    "cost_of_living[cost_of_living['Water_Market(1.5L)'] == 0.11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city            country    \n",
       "Gardez          Afghanistan    0.11\n",
       "Aksaray         Turkey         0.11\n",
       "Agri            Turkey         0.13\n",
       "Nysa            Poland         0.13\n",
       "Hassi Messaoud  Algeria        0.14\n",
       "Andimeshk       Iran           0.15\n",
       "Jendouba        Tunisia        0.15\n",
       "Al Bayda'       Libya          0.15\n",
       "Malayer         Iran           0.15\n",
       "Tulsipur        Nepal          0.15\n",
       "Name: Water_Market(1.5L), dtype: float64"
      ]
     },
     "execution_count": 665,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 least expensive places that sell water in the market at 1.5L\n",
    "cost_of_living[\"Water_Market(1.5L)\"].sort_values().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 666,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city             country      \n",
       "North Brunswick  United States    5.00\n",
       "East Orange      United States    5.00\n",
       "Wrightsville     United States    5.00\n",
       "Cairns           Australia        4.69\n",
       "Port Douglas     Australia        4.69\n",
       "Ocean Springs    United States    4.50\n",
       "Clemson          United States    4.16\n",
       "Thornton         United States    4.12\n",
       "Iqaluit          Canada           4.03\n",
       "Margaret River   Australia        4.02\n",
       "Name: Water_Market(1.5L), dtype: float64"
      ]
     },
     "execution_count": 666,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find top 10 most expensive places that sell water in the market at 1.5L\n",
    "cost_of_living[\"Water_Market(1.5L)\"].sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 667,
   "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>No.</th>\n",
       "      <th>Meal</th>\n",
       "      <th>Meal_For_2</th>\n",
       "      <th>McDonalds_Meal</th>\n",
       "      <th>Domestic_Beer_Rest(0.5L)</th>\n",
       "      <th>Imported_Beer_Rest(0.33L)</th>\n",
       "      <th>Cappuccino</th>\n",
       "      <th>Coke/Pepsi</th>\n",
       "      <th>Water_Rest(0.33L)</th>\n",
       "      <th>Milk</th>\n",
       "      <th>...</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Tomato</th>\n",
       "      <th>Potato</th>\n",
       "      <th>Onion</th>\n",
       "      <th>Lettuce</th>\n",
       "      <th>Water_Market(1.5L)</th>\n",
       "      <th>Wine_Market</th>\n",
       "      <th>Domestic_Beer_Market(0.5L)</th>\n",
       "      <th>Imported_Beer_Market(0.33L)</th>\n",
       "      <th>Cigarettes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Delhi</th>\n",
       "      <th>India</th>\n",
       "      <td>0</td>\n",
       "      <td>4.90</td>\n",
       "      <td>22.04</td>\n",
       "      <td>4.28</td>\n",
       "      <td>1.84</td>\n",
       "      <td>3.67</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.73</td>\n",
       "      <td>...</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.36</td>\n",
       "      <td>9.79</td>\n",
       "      <td>1.53</td>\n",
       "      <td>2.51</td>\n",
       "      <td>4.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shanghai</th>\n",
       "      <th>China</th>\n",
       "      <td>1</td>\n",
       "      <td>5.59</td>\n",
       "      <td>40.51</td>\n",
       "      <td>5.59</td>\n",
       "      <td>1.12</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.68</td>\n",
       "      <td>...</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.62</td>\n",
       "      <td>13.97</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.93</td>\n",
       "      <td>3.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jakarta</th>\n",
       "      <th>Indonesia</th>\n",
       "      <td>2</td>\n",
       "      <td>2.54</td>\n",
       "      <td>22.25</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.28</td>\n",
       "      <td>...</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.13</td>\n",
       "      <td>0.42</td>\n",
       "      <td>20.66</td>\n",
       "      <td>2.06</td>\n",
       "      <td>3.18</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manila</th>\n",
       "      <th>Philippines</th>\n",
       "      <td>3</td>\n",
       "      <td>3.54</td>\n",
       "      <td>27.40</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.63</td>\n",
       "      <td>...</td>\n",
       "      <td>2.95</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.76</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.65</td>\n",
       "      <td>8.84</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.55</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seoul</th>\n",
       "      <th>South Korea</th>\n",
       "      <td>4</td>\n",
       "      <td>7.16</td>\n",
       "      <td>52.77</td>\n",
       "      <td>6.03</td>\n",
       "      <td>3.02</td>\n",
       "      <td>4.52</td>\n",
       "      <td>3.86</td>\n",
       "      <td>1.46</td>\n",
       "      <td>0.78</td>\n",
       "      <td>2.13</td>\n",
       "      <td>...</td>\n",
       "      <td>6.40</td>\n",
       "      <td>6.36</td>\n",
       "      <td>3.86</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.06</td>\n",
       "      <td>15.08</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.49</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mackay</th>\n",
       "      <th>Australia</th>\n",
       "      <td>4859</td>\n",
       "      <td>13.41</td>\n",
       "      <td>93.89</td>\n",
       "      <td>7.71</td>\n",
       "      <td>4.53</td>\n",
       "      <td>5.36</td>\n",
       "      <td>4.44</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1.27</td>\n",
       "      <td>...</td>\n",
       "      <td>2.79</td>\n",
       "      <td>3.69</td>\n",
       "      <td>2.24</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.54</td>\n",
       "      <td>10.73</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.91</td>\n",
       "      <td>25.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tirupati</th>\n",
       "      <th>India</th>\n",
       "      <td>4863</td>\n",
       "      <td>2.45</td>\n",
       "      <td>9.18</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.11</td>\n",
       "      <td>3.06</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.58</td>\n",
       "      <td>...</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.36</td>\n",
       "      <td>11.02</td>\n",
       "      <td>1.89</td>\n",
       "      <td>3.06</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Port Douglas</th>\n",
       "      <th>Australia</th>\n",
       "      <td>4864</td>\n",
       "      <td>15.76</td>\n",
       "      <td>66.39</td>\n",
       "      <td>10.06</td>\n",
       "      <td>5.36</td>\n",
       "      <td>4.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.29</td>\n",
       "      <td>...</td>\n",
       "      <td>2.51</td>\n",
       "      <td>8.22</td>\n",
       "      <td>2.14</td>\n",
       "      <td>1.88</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.69</td>\n",
       "      <td>11.40</td>\n",
       "      <td>4.06</td>\n",
       "      <td>3.38</td>\n",
       "      <td>30.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rockhampton</th>\n",
       "      <th>Australia</th>\n",
       "      <td>4865</td>\n",
       "      <td>13.41</td>\n",
       "      <td>63.71</td>\n",
       "      <td>8.05</td>\n",
       "      <td>4.19</td>\n",
       "      <td>3.35</td>\n",
       "      <td>3.35</td>\n",
       "      <td>2.70</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1.14</td>\n",
       "      <td>...</td>\n",
       "      <td>2.79</td>\n",
       "      <td>4.36</td>\n",
       "      <td>2.24</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.61</td>\n",
       "      <td>8.05</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.52</td>\n",
       "      <td>25.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Egilsstadhir</th>\n",
       "      <th>Iceland</th>\n",
       "      <td>4867</td>\n",
       "      <td>17.30</td>\n",
       "      <td>70.61</td>\n",
       "      <td>8.47</td>\n",
       "      <td>4.24</td>\n",
       "      <td>3.53</td>\n",
       "      <td>3.95</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.06</td>\n",
       "      <td>...</td>\n",
       "      <td>2.54</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.12</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.41</td>\n",
       "      <td>2.12</td>\n",
       "      <td>15.18</td>\n",
       "      <td>4.24</td>\n",
       "      <td>3.53</td>\n",
       "      <td>9.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3366 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           No.   Meal  Meal_For_2  McDonalds_Meal  \\\n",
       "city         country                                                \n",
       "Delhi        India           0   4.90       22.04            4.28   \n",
       "Shanghai     China           1   5.59       40.51            5.59   \n",
       "Jakarta      Indonesia       2   2.54       22.25            3.50   \n",
       "Manila       Philippines     3   3.54       27.40            3.54   \n",
       "Seoul        South Korea     4   7.16       52.77            6.03   \n",
       "...                        ...    ...         ...             ...   \n",
       "Mackay       Australia    4859  13.41       93.89            7.71   \n",
       "Tirupati     India        4863   2.45        9.18            4.28   \n",
       "Port Douglas Australia    4864  15.76       66.39           10.06   \n",
       "Rockhampton  Australia    4865  13.41       63.71            8.05   \n",
       "Egilsstadhir Iceland      4867  17.30       70.61            8.47   \n",
       "\n",
       "                          Domestic_Beer_Rest(0.5L)  Imported_Beer_Rest(0.33L)  \\\n",
       "city         country                                                            \n",
       "Delhi        India                            1.84                       3.67   \n",
       "Shanghai     China                            1.12                       4.19   \n",
       "Jakarta      Indonesia                        2.02                       3.18   \n",
       "Manila       Philippines                      1.24                       1.90   \n",
       "Seoul        South Korea                      3.02                       4.52   \n",
       "...                                            ...                        ...   \n",
       "Mackay       Australia                        4.53                       5.36   \n",
       "Tirupati     India                            2.11                       3.06   \n",
       "Port Douglas Australia                        5.36                       4.86   \n",
       "Rockhampton  Australia                        4.19                       3.35   \n",
       "Egilsstadhir Iceland                          4.24                       3.53   \n",
       "\n",
       "                          Cappuccino  Coke/Pepsi  Water_Rest(0.33L)  Milk  \\\n",
       "city         country                                                        \n",
       "Delhi        India              1.78        0.48               0.19  0.73   \n",
       "Shanghai     China              3.96        0.52               0.32  2.68   \n",
       "Jakarta      Indonesia          2.19        0.59               0.27  1.28   \n",
       "Manila       Philippines        2.91        0.93               0.51  1.63   \n",
       "Seoul        South Korea        3.86        1.46               0.78  2.13   \n",
       "...                              ...         ...                ...   ...   \n",
       "Mackay       Australia          4.44        2.46               2.35  1.27   \n",
       "Tirupati     India              2.01        0.46               0.13  0.58   \n",
       "Port Douglas Australia          3.45        2.21               2.28  1.29   \n",
       "Rockhampton  Australia          3.35        2.70               2.35  1.14   \n",
       "Egilsstadhir Iceland            3.95        1.77               1.41  1.06   \n",
       "\n",
       "                          ...  Oranges  Tomato  Potato  Onion  Lettuce  \\\n",
       "city         country      ...                                            \n",
       "Delhi        India        ...     1.01    0.60    0.36   0.41     0.70   \n",
       "Shanghai     China        ...     2.11    1.47    0.82   1.02     0.82   \n",
       "Jakarta      Indonesia    ...     2.08    1.24    1.35   2.10     1.13   \n",
       "Manila       Philippines  ...     2.95    1.42    1.76   1.75     1.36   \n",
       "Seoul        South Korea  ...     6.40    6.36    3.86   2.81     2.40   \n",
       "...                       ...      ...     ...     ...    ...      ...   \n",
       "Mackay       Australia    ...     2.79    3.69    2.24   1.77     1.68   \n",
       "Tirupati     India        ...     0.84    0.43    0.43   0.44     0.37   \n",
       "Port Douglas Australia    ...     2.51    8.22    2.14   1.88     2.50   \n",
       "Rockhampton  Australia    ...     2.79    4.36    2.24   1.68     2.57   \n",
       "Egilsstadhir Iceland      ...     2.54    2.47    2.12   0.71     1.41   \n",
       "\n",
       "                          Water_Market(1.5L)  Wine_Market  \\\n",
       "city         country                                        \n",
       "Delhi        India                      0.36         9.79   \n",
       "Shanghai     China                      0.62        13.97   \n",
       "Jakarta      Indonesia                  0.42        20.66   \n",
       "Manila       Philippines                0.65         8.84   \n",
       "Seoul        South Korea                1.06        15.08   \n",
       "...                                      ...          ...   \n",
       "Mackay       Australia                  0.54        10.73   \n",
       "Tirupati     India                      0.36        11.02   \n",
       "Port Douglas Australia                  4.69        11.40   \n",
       "Rockhampton  Australia                  1.61         8.05   \n",
       "Egilsstadhir Iceland                    2.12        15.18   \n",
       "\n",
       "                          Domestic_Beer_Market(0.5L)  \\\n",
       "city         country                                   \n",
       "Delhi        India                              1.53   \n",
       "Shanghai     China                              0.91   \n",
       "Jakarta      Indonesia                          2.06   \n",
       "Manila       Philippines                        1.15   \n",
       "Seoul        South Korea                        2.10   \n",
       "...                                              ...   \n",
       "Mackay       Australia                          3.46   \n",
       "Tirupati     India                              1.89   \n",
       "Port Douglas Australia                          4.06   \n",
       "Rockhampton  Australia                          3.46   \n",
       "Egilsstadhir Iceland                            4.24   \n",
       "\n",
       "                          Imported_Beer_Market(0.33L)  Cigarettes  \n",
       "city         country                                               \n",
       "Delhi        India                               2.51        4.28  \n",
       "Shanghai     China                               1.93        3.49  \n",
       "Jakarta      Indonesia                           3.18        2.22  \n",
       "Manila       Philippines                         1.55        2.65  \n",
       "Seoul        South Korea                         2.49        3.39  \n",
       "...                                               ...         ...  \n",
       "Mackay       Australia                           3.91       25.48  \n",
       "Tirupati     India                               3.06        3.98  \n",
       "Port Douglas Australia                           3.38       30.18  \n",
       "Rockhampton  Australia                           3.52       25.48  \n",
       "Egilsstadhir Iceland                             3.53        9.89  \n",
       "\n",
       "[3366 rows x 28 columns]"
      ]
     },
     "execution_count": 667,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping all rows with NaN values\n",
    "cost_of_living.dropna(inplace=True)\n",
    "cost_of_living"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 668,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city          country    \n",
       "Delhi         India           79.58\n",
       "Shanghai      China          134.23\n",
       "Jakarta       Indonesia       99.55\n",
       "Manila        Philippines     90.27\n",
       "Seoul         South Korea    208.01\n",
       "                              ...  \n",
       "Mackay        Australia      233.35\n",
       "Tirupati      India           65.01\n",
       "Port Douglas  Australia      239.24\n",
       "Rockhampton   Australia      193.78\n",
       "Egilsstadhir  Iceland        209.78\n",
       "Name: goods_sum, Length: 3366, dtype: float64"
      ]
     },
     "execution_count": 668,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function that will add all the column values together\n",
    "def sum_columns(row):\n",
    "  goods_sum = row.Meal + row.Meal_For_2 + row.McDonalds_Meal + row[\"Domestic_Beer_Rest(0.5L)\"] + row['Imported_Beer_Rest(0.33L)'] + row.Cappuccino + row['Coke/Pepsi'] + row['Water_Rest(0.33L)'] + row.Milk + row.Rice + row.Eggs + row.Cheese + row.Chicken_Fillets + row.Beef + row.Apple + row.Bananas + row.Oranges + row.Tomato + row.Potato + row.Onion + row.Lettuce + row['Water_Market(1.5L)'] + row.Wine_Market + row['Domestic_Beer_Market(0.5L)'] + row['Imported_Beer_Market(0.33L)'] + row.Cigarettes\n",
    "  return goods_sum\n",
    "\n",
    "cost_of_living['goods_sum'] = cost_of_living.apply(sum_columns, axis=1)\n",
    "\n",
    "cost_of_living['goods_sum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "138.75\n"
     ]
    }
   ],
   "source": [
    "# Calculate the average of cost of goods\n",
    "goods_len = cost_of_living.goods_sum.count()\n",
    "goods_sum = cost_of_living.goods_sum.sum()\n",
    "\n",
    "goods_average = round(goods_sum/goods_len, 2)\n",
    "print(goods_average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 669,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city             country \n",
       "Sahiwal          Pakistan    32.70\n",
       "Dera Ghazi Khan  Pakistan    32.88\n",
       "Faisalabad       Pakistan    34.91\n",
       "Bahawalpur       Pakistan    37.60\n",
       "Ho               Ghana       38.44\n",
       "Name: goods_sum, dtype: float64"
      ]
     },
     "execution_count": 669,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorts goods sum column to show least expensive cities\n",
    "cost_of_living.goods_sum.sort_values().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 671,
   "metadata": {},
   "outputs": [],
   "source": [
    "goods_least_values = [32.70, 32.88, 34.91, 37.60, 38.44]\n",
    "goods_least_locations = ['Sahiwal, Pakistan', 'Dera Ghazi Khan, Pakistan', 'Daisalabad, Pakistan', 'Bahawalpur, Pakistan', 'Ho, Ghana']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 674,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar graph\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "plt.title('Top 5 Least Expensive Amount of Goods')\n",
    "\n",
    "bar_width = 0.7\n",
    "\n",
    "plt.bar(goods_least_locations, goods_least_values, width=bar_width)\n",
    "plt.bar('Average', goods_average, width=bar_width)\n",
    "plt.xticks(rotation=45)\n",
    " \n",
    "\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('Price in USD')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 670,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city      country     \n",
       "Hamilton  Bermuda         416.71\n",
       "Meilen    Switzerland     390.09\n",
       "Basel     Switzerland     388.04\n",
       "Kaka      Turkmenistan    385.80\n",
       "Ashgabat  Turkmenistan    385.78\n",
       "Name: goods_sum, dtype: float64"
      ]
     },
     "execution_count": 670,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorts goods sum column to show most expensive cities\n",
    "cost_of_living.goods_sum.sort_values(ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 672,
   "metadata": {},
   "outputs": [],
   "source": [
    "goods_most_values = [416.71, 390.09, 388.04, 385.80, 385.78]\n",
    "goods_most_locations = ['Hamilton, Bermuda', 'Meilen, Switzerland', 'Basel, Switzerland', 'Kaka, Turmenistan', 'Ashgabat, Turmenistan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 676,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create bar graph\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "plt.title('Top 5 Most Expensive Amount of Goods')\n",
    "\n",
    "bar_width = 0.7\n",
    "\n",
    "plt.bar(goods_most_locations, goods_most_values, width=bar_width)\n",
    "plt.bar('Average', goods_average, width=bar_width)\n",
    "plt.xticks(rotation=45)\n",
    " \n",
    "\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('Price in USD')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.15 ('venv': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.15"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "c2c1ad3c9b5cbec2bcb014243000bb8782a29538251d4881f3c14b08fae038fd"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}