Food Wastage Analysis From 1961-2013 Fao
H
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"# About the dataset\n",
"\n",
"Context\n",
"Our world population is expected to grow from 7.3 billion today to 9.7 billion in the year 2050. Finding solutions for feeding the growing world population has become a hot topic for food and agriculture organizations, entrepreneurs and philanthropists. These solutions range from changing the way we grow our food to changing the way we eat. To make things harder, the world's climate is changing and it is both affecting and affected by the way we grow our food – agriculture. This dataset provides an insight on our worldwide food production - focusing on a comparison between food produced for human consumption and feed produced for animals.\n",
"\n",
"Content\n",
"The Food and Agriculture Organization of the United Nations provides free access to food and agriculture data for over 245 countries and territories, from the year 1961 to the most recent update (depends on the dataset). One dataset from the FAO's database is the Food Balance Sheets. It presents a comprehensive picture of the pattern of a country's food supply during a specified reference period, the last time an update was loaded to the FAO database was in 2013. The food balance sheet shows for each food item the sources of supply and its utilization. This chunk of the dataset is focused on two utilizations of each food item available:\n",
"\n",
"Food - refers to the total amount of the food item available as human food during the reference period.\n",
"Feed - refers to the quantity of the food item available for feeding to the livestock and poultry during the reference period.\n",
"Dataset's attributes:\n",
"\n",
"Area code - Country name abbreviation\n",
"Area - County name\n",
"Item - Food item\n",
"Element - Food or Feed\n",
"Latitude - geographic coordinate that specifies the north–south position of a point on the Earth's surface\n",
"Longitude - geographic coordinate that specifies the east-west position of a point on the Earth's surface\n",
"Production per year - Amount of food item produced in 1000 tonnes\n",
"\n",
"This is a simple exploratory notebook that heavily expolits pandas and seaborn"
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"source": [
"# Importing libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"# importing data\n",
"df = pd.read_csv(\"FAO.csv\", encoding = \"ISO-8859-1\")\n",
"pd.options.mode.chained_assignment = None\n",
"from sklearn.linear_model import LinearRegression"
]
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Area Abbreviation</th>\n",
" <th>Area Code</th>\n",
" <th>Area</th>\n",
" <th>Item Code</th>\n",
" <th>Item</th>\n",
" <th>Element Code</th>\n",
" <th>Element</th>\n",
" <th>Unit</th>\n",
" <th>latitude</th>\n",
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" <th>...</th>\n",
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" <th>Y2008</th>\n",
" <th>Y2009</th>\n",
" <th>Y2010</th>\n",
" <th>Y2011</th>\n",
" <th>Y2012</th>\n",
" <th>Y2013</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2511</td>\n",
" <td>Wheat and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>3249.0</td>\n",
" <td>3486.0</td>\n",
" <td>3704.0</td>\n",
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" <td>4252.0</td>\n",
" <td>4538.0</td>\n",
" <td>4605.0</td>\n",
" <td>4711.0</td>\n",
" <td>4810</td>\n",
" <td>4895</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2805</td>\n",
" <td>Rice (Milled Equivalent)</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>419.0</td>\n",
" <td>445.0</td>\n",
" <td>546.0</td>\n",
" <td>455.0</td>\n",
" <td>490.0</td>\n",
" <td>415.0</td>\n",
" <td>442.0</td>\n",
" <td>476.0</td>\n",
" <td>425</td>\n",
" <td>422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2513</td>\n",
" <td>Barley and products</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>58.0</td>\n",
" <td>236.0</td>\n",
" <td>262.0</td>\n",
" <td>263.0</td>\n",
" <td>230.0</td>\n",
" <td>379.0</td>\n",
" <td>315.0</td>\n",
" <td>203.0</td>\n",
" <td>367</td>\n",
" <td>360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2513</td>\n",
" <td>Barley and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>185.0</td>\n",
" <td>43.0</td>\n",
" <td>44.0</td>\n",
" <td>48.0</td>\n",
" <td>62.0</td>\n",
" <td>55.0</td>\n",
" <td>60.0</td>\n",
" <td>72.0</td>\n",
" <td>78</td>\n",
" <td>89</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2514</td>\n",
" <td>Maize and products</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>120.0</td>\n",
" <td>208.0</td>\n",
" <td>233.0</td>\n",
" <td>249.0</td>\n",
" <td>247.0</td>\n",
" <td>195.0</td>\n",
" <td>178.0</td>\n",
" <td>191.0</td>\n",
" <td>200</td>\n",
" <td>200</td>\n",
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" <th>5</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2514</td>\n",
" <td>Maize and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>231.0</td>\n",
" <td>67.0</td>\n",
" <td>82.0</td>\n",
" <td>67.0</td>\n",
" <td>69.0</td>\n",
" <td>71.0</td>\n",
" <td>82.0</td>\n",
" <td>73.0</td>\n",
" <td>77</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2517</td>\n",
" <td>Millet and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>15.0</td>\n",
" <td>21.0</td>\n",
" <td>11.0</td>\n",
" <td>19.0</td>\n",
" <td>21.0</td>\n",
" <td>18.0</td>\n",
" <td>14.0</td>\n",
" <td>14.0</td>\n",
" <td>14</td>\n",
" <td>12</td>\n",
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" <tr>\n",
" <th>7</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2520</td>\n",
" <td>Cereals, Other</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2531</td>\n",
" <td>Potatoes and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>276.0</td>\n",
" <td>294.0</td>\n",
" <td>294.0</td>\n",
" <td>260.0</td>\n",
" <td>242.0</td>\n",
" <td>250.0</td>\n",
" <td>192.0</td>\n",
" <td>169.0</td>\n",
" <td>196</td>\n",
" <td>230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2536</td>\n",
" <td>Sugar cane</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>50.0</td>\n",
" <td>29.0</td>\n",
" <td>61.0</td>\n",
" <td>65.0</td>\n",
" <td>54.0</td>\n",
" <td>114.0</td>\n",
" <td>83.0</td>\n",
" <td>83.0</td>\n",
" <td>69</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2537</td>\n",
" <td>Sugar beet</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>11</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2542</td>\n",
" <td>Sugar (Raw Equivalent)</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>124.0</td>\n",
" <td>152.0</td>\n",
" <td>169.0</td>\n",
" <td>192.0</td>\n",
" <td>217.0</td>\n",
" <td>231.0</td>\n",
" <td>240.0</td>\n",
" <td>240.0</td>\n",
" <td>250</td>\n",
" <td>255</td>\n",
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" <tr>\n",
" <th>12</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2543</td>\n",
" <td>Sweeteners, Other</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>9.0</td>\n",
" <td>15.0</td>\n",
" <td>12.0</td>\n",
" <td>6.0</td>\n",
" <td>11.0</td>\n",
" <td>2.0</td>\n",
" <td>9.0</td>\n",
" <td>21.0</td>\n",
" <td>24</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2745</td>\n",
" <td>Honey</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2549</td>\n",
" <td>Pulses, Other and products</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2549</td>\n",
" <td>Pulses, Other and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>17.0</td>\n",
" <td>35.0</td>\n",
" <td>37.0</td>\n",
" <td>40.0</td>\n",
" <td>54.0</td>\n",
" <td>80.0</td>\n",
" <td>66.0</td>\n",
" <td>81.0</td>\n",
" <td>63</td>\n",
" <td>74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2551</td>\n",
" <td>Nuts and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>11.0</td>\n",
" <td>13.0</td>\n",
" <td>24.0</td>\n",
" <td>34.0</td>\n",
" <td>42.0</td>\n",
" <td>28.0</td>\n",
" <td>66.0</td>\n",
" <td>71.0</td>\n",
" <td>70</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2560</td>\n",
" <td>Coconuts - Incl Copra</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2561</td>\n",
" <td>Sesame seed</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>16.0</td>\n",
" <td>16.0</td>\n",
" <td>13.0</td>\n",
" <td>16.0</td>\n",
" <td>16.0</td>\n",
" <td>16.0</td>\n",
" <td>19.0</td>\n",
" <td>17.0</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2563</td>\n",
" <td>Olives (including preserved)</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2571</td>\n",
" <td>Soyabean Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>6.0</td>\n",
" <td>35.0</td>\n",
" <td>18.0</td>\n",
" <td>21.0</td>\n",
" <td>11.0</td>\n",
" <td>6.0</td>\n",
" <td>15.0</td>\n",
" <td>16.0</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2572</td>\n",
" <td>Groundnut Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2573</td>\n",
" <td>Sunflowerseed Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>9.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>15.0</td>\n",
" <td>16.0</td>\n",
" <td>17</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2574</td>\n",
" <td>Rape and Mustard Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2575</td>\n",
" <td>Cottonseed Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2577</td>\n",
" <td>Palm Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>71.0</td>\n",
" <td>69.0</td>\n",
" <td>56.0</td>\n",
" <td>51.0</td>\n",
" <td>36.0</td>\n",
" <td>53.0</td>\n",
" <td>59.0</td>\n",
" <td>51.0</td>\n",
" <td>61</td>\n",
" <td>64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2579</td>\n",
" <td>Sesameseed Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2580</td>\n",
" <td>Olive Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2586</td>\n",
" <td>Oilcrops Oil, Other</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2601</td>\n",
" <td>Tomatoes and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</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>21447</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2765</td>\n",
" <td>Crustaceans</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21448</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2766</td>\n",
" <td>Cephalopods</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21449</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2767</td>\n",
" <td>Molluscs, Other</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21450</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2775</td>\n",
" <td>Aquatic Plants</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21451</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2680</td>\n",
" <td>Infant food</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21452</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2905</td>\n",
" <td>Cereals - Excluding Beer</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>75.0</td>\n",
" <td>54.0</td>\n",
" <td>75.0</td>\n",
" <td>55.0</td>\n",
" <td>63.0</td>\n",
" <td>62.0</td>\n",
" <td>55.0</td>\n",
" <td>55.0</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21453</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2905</td>\n",
" <td>Cereals - Excluding Beer</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>1844.0</td>\n",
" <td>1842.0</td>\n",
" <td>1944.0</td>\n",
" <td>1962.0</td>\n",
" <td>1918.0</td>\n",
" <td>1980.0</td>\n",
" <td>2011.0</td>\n",
" <td>2094.0</td>\n",
" <td>2071</td>\n",
" <td>2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21454</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2907</td>\n",
" <td>Starchy Roots</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>223.0</td>\n",
" <td>236.0</td>\n",
" <td>238.0</td>\n",
" <td>228.0</td>\n",
" <td>245.0</td>\n",
" <td>258.0</td>\n",
" <td>258.0</td>\n",
" <td>269.0</td>\n",
" <td>272</td>\n",
" <td>276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21455</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2908</td>\n",
" <td>Sugar Crops</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21456</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2909</td>\n",
" <td>Sugar & Sweeteners</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>335.0</td>\n",
" <td>313.0</td>\n",
" <td>339.0</td>\n",
" <td>302.0</td>\n",
" <td>285.0</td>\n",
" <td>287.0</td>\n",
" <td>314.0</td>\n",
" <td>336.0</td>\n",
" <td>396</td>\n",
" <td>416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21457</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2911</td>\n",
" <td>Pulses</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>63.0</td>\n",
" <td>59.0</td>\n",
" <td>61.0</td>\n",
" <td>57.0</td>\n",
" <td>69.0</td>\n",
" <td>78.0</td>\n",
" <td>68.0</td>\n",
" <td>56.0</td>\n",
" <td>52</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21458</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2912</td>\n",
" <td>Treenuts</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21459</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2913</td>\n",
" <td>Oilcrops</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>36.0</td>\n",
" <td>46.0</td>\n",
" <td>41.0</td>\n",
" <td>33.0</td>\n",
" <td>31.0</td>\n",
" <td>19.0</td>\n",
" <td>24.0</td>\n",
" <td>17.0</td>\n",
" <td>27</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21460</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2913</td>\n",
" <td>Oilcrops</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>60.0</td>\n",
" <td>59.0</td>\n",
" <td>61.0</td>\n",
" <td>62.0</td>\n",
" <td>48.0</td>\n",
" <td>44.0</td>\n",
" <td>41.0</td>\n",
" <td>40.0</td>\n",
" <td>38</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21461</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2914</td>\n",
" <td>Vegetable Oils</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>111.0</td>\n",
" <td>114.0</td>\n",
" <td>112.0</td>\n",
" <td>114.0</td>\n",
" <td>134.0</td>\n",
" <td>135.0</td>\n",
" <td>137.0</td>\n",
" <td>147.0</td>\n",
" <td>159</td>\n",
" <td>160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21462</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2918</td>\n",
" <td>Vegetables</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>161.0</td>\n",
" <td>166.0</td>\n",
" <td>208.0</td>\n",
" <td>185.0</td>\n",
" <td>137.0</td>\n",
" <td>179.0</td>\n",
" <td>215.0</td>\n",
" <td>217.0</td>\n",
" <td>227</td>\n",
" <td>227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21463</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2919</td>\n",
" <td>Fruits - Excluding Wine</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>191.0</td>\n",
" <td>134.0</td>\n",
" <td>167.0</td>\n",
" <td>177.0</td>\n",
" <td>185.0</td>\n",
" <td>184.0</td>\n",
" <td>211.0</td>\n",
" <td>230.0</td>\n",
" <td>246</td>\n",
" <td>217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21464</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2922</td>\n",
" <td>Stimulants</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>21.0</td>\n",
" <td>14.0</td>\n",
" <td>24.0</td>\n",
" <td>16.0</td>\n",
" <td>11.0</td>\n",
" <td>23.0</td>\n",
" <td>11.0</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21465</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2923</td>\n",
" <td>Spices</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>11.0</td>\n",
" <td>7.0</td>\n",
" <td>12.0</td>\n",
" <td>16.0</td>\n",
" <td>16.0</td>\n",
" <td>14.0</td>\n",
" <td>11.0</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21466</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2924</td>\n",
" <td>Alcoholic Beverages</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>294.0</td>\n",
" <td>290.0</td>\n",
" <td>316.0</td>\n",
" <td>355.0</td>\n",
" <td>398.0</td>\n",
" <td>437.0</td>\n",
" <td>448.0</td>\n",
" <td>476.0</td>\n",
" <td>525</td>\n",
" <td>516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21467</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2943</td>\n",
" <td>Meat</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>222.0</td>\n",
" <td>228.0</td>\n",
" <td>233.0</td>\n",
" <td>238.0</td>\n",
" <td>242.0</td>\n",
" <td>265.0</td>\n",
" <td>262.0</td>\n",
" <td>277.0</td>\n",
" <td>280</td>\n",
" <td>258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21468</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2945</td>\n",
" <td>Offals</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>20.0</td>\n",
" <td>20.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21469</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2946</td>\n",
" <td>Animal fats</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>26.0</td>\n",
" <td>26.0</td>\n",
" <td>29.0</td>\n",
" <td>29.0</td>\n",
" <td>27.0</td>\n",
" <td>31.0</td>\n",
" <td>30.0</td>\n",
" <td>25.0</td>\n",
" <td>26</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21470</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2949</td>\n",
" <td>Eggs</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>15.0</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>21.0</td>\n",
" <td>22.0</td>\n",
" <td>27.0</td>\n",
" <td>27.0</td>\n",
" <td>24.0</td>\n",
" <td>24</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21471</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2948</td>\n",
" <td>Milk - Excluding Butter</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>23.0</td>\n",
" <td>25.0</td>\n",
" <td>25.0</td>\n",
" <td>30</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21472</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2948</td>\n",
" <td>Milk - Excluding Butter</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>373.0</td>\n",
" <td>357.0</td>\n",
" <td>359.0</td>\n",
" <td>356.0</td>\n",
" <td>341.0</td>\n",
" <td>385.0</td>\n",
" <td>418.0</td>\n",
" <td>457.0</td>\n",
" <td>426</td>\n",
" <td>451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21473</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2960</td>\n",
" <td>Fish, Seafood</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>9.0</td>\n",
" <td>6.0</td>\n",
" <td>9.0</td>\n",
" <td>5.0</td>\n",
" <td>15.0</td>\n",
" <td>15.0</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21474</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2960</td>\n",
" <td>Fish, Seafood</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>18.0</td>\n",
" <td>14.0</td>\n",
" <td>17.0</td>\n",
" <td>14.0</td>\n",
" <td>15.0</td>\n",
" <td>18.0</td>\n",
" <td>29.0</td>\n",
" <td>40.0</td>\n",
" <td>40</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21475</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2961</td>\n",
" <td>Aquatic Products, Other</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21476</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2928</td>\n",
" <td>Miscellaneous</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>21477 rows × 63 columns</p>\n",
"</div>"
],
"text/plain": [
" Area Abbreviation Area Code Area Item Code \\\n",
"0 AFG 2 Afghanistan 2511 \n",
"1 AFG 2 Afghanistan 2805 \n",
"2 AFG 2 Afghanistan 2513 \n",
"3 AFG 2 Afghanistan 2513 \n",
"4 AFG 2 Afghanistan 2514 \n",
"5 AFG 2 Afghanistan 2514 \n",
"6 AFG 2 Afghanistan 2517 \n",
"7 AFG 2 Afghanistan 2520 \n",
"8 AFG 2 Afghanistan 2531 \n",
"9 AFG 2 Afghanistan 2536 \n",
"10 AFG 2 Afghanistan 2537 \n",
"11 AFG 2 Afghanistan 2542 \n",
"12 AFG 2 Afghanistan 2543 \n",
"13 AFG 2 Afghanistan 2745 \n",
"14 AFG 2 Afghanistan 2549 \n",
"15 AFG 2 Afghanistan 2549 \n",
"16 AFG 2 Afghanistan 2551 \n",
"17 AFG 2 Afghanistan 2560 \n",
"18 AFG 2 Afghanistan 2561 \n",
"19 AFG 2 Afghanistan 2563 \n",
"20 AFG 2 Afghanistan 2571 \n",
"21 AFG 2 Afghanistan 2572 \n",
"22 AFG 2 Afghanistan 2573 \n",
"23 AFG 2 Afghanistan 2574 \n",
"24 AFG 2 Afghanistan 2575 \n",
"25 AFG 2 Afghanistan 2577 \n",
"26 AFG 2 Afghanistan 2579 \n",
"27 AFG 2 Afghanistan 2580 \n",
"28 AFG 2 Afghanistan 2586 \n",
"29 AFG 2 Afghanistan 2601 \n",
"... ... ... ... ... \n",
"21447 ZWE 181 Zimbabwe 2765 \n",
"21448 ZWE 181 Zimbabwe 2766 \n",
"21449 ZWE 181 Zimbabwe 2767 \n",
"21450 ZWE 181 Zimbabwe 2775 \n",
"21451 ZWE 181 Zimbabwe 2680 \n",
"21452 ZWE 181 Zimbabwe 2905 \n",
"21453 ZWE 181 Zimbabwe 2905 \n",
"21454 ZWE 181 Zimbabwe 2907 \n",
"21455 ZWE 181 Zimbabwe 2908 \n",
"21456 ZWE 181 Zimbabwe 2909 \n",
"21457 ZWE 181 Zimbabwe 2911 \n",
"21458 ZWE 181 Zimbabwe 2912 \n",
"21459 ZWE 181 Zimbabwe 2913 \n",
"21460 ZWE 181 Zimbabwe 2913 \n",
"21461 ZWE 181 Zimbabwe 2914 \n",
"21462 ZWE 181 Zimbabwe 2918 \n",
"21463 ZWE 181 Zimbabwe 2919 \n",
"21464 ZWE 181 Zimbabwe 2922 \n",
"21465 ZWE 181 Zimbabwe 2923 \n",
"21466 ZWE 181 Zimbabwe 2924 \n",
"21467 ZWE 181 Zimbabwe 2943 \n",
"21468 ZWE 181 Zimbabwe 2945 \n",
"21469 ZWE 181 Zimbabwe 2946 \n",
"21470 ZWE 181 Zimbabwe 2949 \n",
"21471 ZWE 181 Zimbabwe 2948 \n",
"21472 ZWE 181 Zimbabwe 2948 \n",
"21473 ZWE 181 Zimbabwe 2960 \n",
"21474 ZWE 181 Zimbabwe 2960 \n",
"21475 ZWE 181 Zimbabwe 2961 \n",
"21476 ZWE 181 Zimbabwe 2928 \n",
"\n",
" Item Element Code Element Unit \\\n",
"0 Wheat and products 5142 Food 1000 tonnes \n",
"1 Rice (Milled Equivalent) 5142 Food 1000 tonnes \n",
"2 Barley and products 5521 Feed 1000 tonnes \n",
"3 Barley and products 5142 Food 1000 tonnes \n",
"4 Maize and products 5521 Feed 1000 tonnes \n",
"5 Maize and products 5142 Food 1000 tonnes \n",
"6 Millet and products 5142 Food 1000 tonnes \n",
"7 Cereals, Other 5142 Food 1000 tonnes \n",
"8 Potatoes and products 5142 Food 1000 tonnes \n",
"9 Sugar cane 5521 Feed 1000 tonnes \n",
"10 Sugar beet 5521 Feed 1000 tonnes \n",
"11 Sugar (Raw Equivalent) 5142 Food 1000 tonnes \n",
"12 Sweeteners, Other 5142 Food 1000 tonnes \n",
"13 Honey 5142 Food 1000 tonnes \n",
"14 Pulses, Other and products 5521 Feed 1000 tonnes \n",
"15 Pulses, Other and products 5142 Food 1000 tonnes \n",
"16 Nuts and products 5142 Food 1000 tonnes \n",
"17 Coconuts - Incl Copra 5142 Food 1000 tonnes \n",
"18 Sesame seed 5142 Food 1000 tonnes \n",
"19 Olives (including preserved) 5142 Food 1000 tonnes \n",
"20 Soyabean Oil 5142 Food 1000 tonnes \n",
"21 Groundnut Oil 5142 Food 1000 tonnes \n",
"22 Sunflowerseed Oil 5142 Food 1000 tonnes \n",
"23 Rape and Mustard Oil 5142 Food 1000 tonnes \n",
"24 Cottonseed Oil 5142 Food 1000 tonnes \n",
"25 Palm Oil 5142 Food 1000 tonnes \n",
"26 Sesameseed Oil 5142 Food 1000 tonnes \n",
"27 Olive Oil 5142 Food 1000 tonnes \n",
"28 Oilcrops Oil, Other 5142 Food 1000 tonnes \n",
"29 Tomatoes and products 5142 Food 1000 tonnes \n",
"... ... ... ... ... \n",
"21447 Crustaceans 5142 Food 1000 tonnes \n",
"21448 Cephalopods 5142 Food 1000 tonnes \n",
"21449 Molluscs, Other 5142 Food 1000 tonnes \n",
"21450 Aquatic Plants 5142 Food 1000 tonnes \n",
"21451 Infant food 5142 Food 1000 tonnes \n",
"21452 Cereals - Excluding Beer 5521 Feed 1000 tonnes \n",
"21453 Cereals - Excluding Beer 5142 Food 1000 tonnes \n",
"21454 Starchy Roots 5142 Food 1000 tonnes \n",
"21455 Sugar Crops 5142 Food 1000 tonnes \n",
"21456 Sugar & Sweeteners 5142 Food 1000 tonnes \n",
"21457 Pulses 5142 Food 1000 tonnes \n",
"21458 Treenuts 5142 Food 1000 tonnes \n",
"21459 Oilcrops 5521 Feed 1000 tonnes \n",
"21460 Oilcrops 5142 Food 1000 tonnes \n",
"21461 Vegetable Oils 5142 Food 1000 tonnes \n",
"21462 Vegetables 5142 Food 1000 tonnes \n",
"21463 Fruits - Excluding Wine 5142 Food 1000 tonnes \n",
"21464 Stimulants 5142 Food 1000 tonnes \n",
"21465 Spices 5142 Food 1000 tonnes \n",
"21466 Alcoholic Beverages 5142 Food 1000 tonnes \n",
"21467 Meat 5142 Food 1000 tonnes \n",
"21468 Offals 5142 Food 1000 tonnes \n",
"21469 Animal fats 5142 Food 1000 tonnes \n",
"21470 Eggs 5142 Food 1000 tonnes \n",
"21471 Milk - Excluding Butter 5521 Feed 1000 tonnes \n",
"21472 Milk - Excluding Butter 5142 Food 1000 tonnes \n",
"21473 Fish, Seafood 5521 Feed 1000 tonnes \n",
"21474 Fish, Seafood 5142 Food 1000 tonnes \n",
"21475 Aquatic Products, Other 5142 Food 1000 tonnes \n",
"21476 Miscellaneous 5142 Food 1000 tonnes \n",
"\n",
" latitude longitude ... Y2004 Y2005 Y2006 Y2007 Y2008 \\\n",
"0 33.94 67.71 ... 3249.0 3486.0 3704.0 4164.0 4252.0 \n",
"1 33.94 67.71 ... 419.0 445.0 546.0 455.0 490.0 \n",
"2 33.94 67.71 ... 58.0 236.0 262.0 263.0 230.0 \n",
"3 33.94 67.71 ... 185.0 43.0 44.0 48.0 62.0 \n",
"4 33.94 67.71 ... 120.0 208.0 233.0 249.0 247.0 \n",
"5 33.94 67.71 ... 231.0 67.0 82.0 67.0 69.0 \n",
"6 33.94 67.71 ... 15.0 21.0 11.0 19.0 21.0 \n",
"7 33.94 67.71 ... 2.0 1.0 1.0 0.0 0.0 \n",
"8 33.94 67.71 ... 276.0 294.0 294.0 260.0 242.0 \n",
"9 33.94 67.71 ... 50.0 29.0 61.0 65.0 54.0 \n",
"10 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n",
"11 33.94 67.71 ... 124.0 152.0 169.0 192.0 217.0 \n",
"12 33.94 67.71 ... 9.0 15.0 12.0 6.0 11.0 \n",
"13 33.94 67.71 ... 3.0 3.0 3.0 3.0 3.0 \n",
"14 33.94 67.71 ... 3.0 2.0 3.0 3.0 3.0 \n",
"15 33.94 67.71 ... 17.0 35.0 37.0 40.0 54.0 \n",
"16 33.94 67.71 ... 11.0 13.0 24.0 34.0 42.0 \n",
"17 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n",
"18 33.94 67.71 ... 16.0 16.0 13.0 16.0 16.0 \n",
"19 33.94 67.71 ... 1.0 1.0 0.0 0.0 2.0 \n",
"20 33.94 67.71 ... 6.0 35.0 18.0 21.0 11.0 \n",
"21 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n",
"22 33.94 67.71 ... 4.0 6.0 5.0 9.0 3.0 \n",
"23 33.94 67.71 ... 0.0 1.0 3.0 5.0 6.0 \n",
"24 33.94 67.71 ... 2.0 3.0 3.0 3.0 3.0 \n",
"25 33.94 67.71 ... 71.0 69.0 56.0 51.0 36.0 \n",
"26 33.94 67.71 ... 1.0 1.0 1.0 2.0 2.0 \n",
"27 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n",
"28 33.94 67.71 ... 0.0 1.0 0.0 0.0 3.0 \n",
"29 33.94 67.71 ... 2.0 2.0 8.0 1.0 0.0 \n",
"... ... ... ... ... ... ... ... ... \n",
"21447 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21448 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21449 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21450 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21451 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21452 -19.02 29.15 ... 75.0 54.0 75.0 55.0 63.0 \n",
"21453 -19.02 29.15 ... 1844.0 1842.0 1944.0 1962.0 1918.0 \n",
"21454 -19.02 29.15 ... 223.0 236.0 238.0 228.0 245.0 \n",
"21455 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21456 -19.02 29.15 ... 335.0 313.0 339.0 302.0 285.0 \n",
"21457 -19.02 29.15 ... 63.0 59.0 61.0 57.0 69.0 \n",
"21458 -19.02 29.15 ... 1.0 2.0 1.0 2.0 2.0 \n",
"21459 -19.02 29.15 ... 36.0 46.0 41.0 33.0 31.0 \n",
"21460 -19.02 29.15 ... 60.0 59.0 61.0 62.0 48.0 \n",
"21461 -19.02 29.15 ... 111.0 114.0 112.0 114.0 134.0 \n",
"21462 -19.02 29.15 ... 161.0 166.0 208.0 185.0 137.0 \n",
"21463 -19.02 29.15 ... 191.0 134.0 167.0 177.0 185.0 \n",
"21464 -19.02 29.15 ... 7.0 21.0 14.0 24.0 16.0 \n",
"21465 -19.02 29.15 ... 7.0 11.0 7.0 12.0 16.0 \n",
"21466 -19.02 29.15 ... 294.0 290.0 316.0 355.0 398.0 \n",
"21467 -19.02 29.15 ... 222.0 228.0 233.0 238.0 242.0 \n",
"21468 -19.02 29.15 ... 20.0 20.0 21.0 21.0 21.0 \n",
"21469 -19.02 29.15 ... 26.0 26.0 29.0 29.0 27.0 \n",
"21470 -19.02 29.15 ... 15.0 18.0 18.0 21.0 22.0 \n",
"21471 -19.02 29.15 ... 21.0 21.0 21.0 21.0 21.0 \n",
"21472 -19.02 29.15 ... 373.0 357.0 359.0 356.0 341.0 \n",
"21473 -19.02 29.15 ... 5.0 4.0 9.0 6.0 9.0 \n",
"21474 -19.02 29.15 ... 18.0 14.0 17.0 14.0 15.0 \n",
"21475 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21476 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" Y2009 Y2010 Y2011 Y2012 Y2013 \n",
"0 4538.0 4605.0 4711.0 4810 4895 \n",
"1 415.0 442.0 476.0 425 422 \n",
"2 379.0 315.0 203.0 367 360 \n",
"3 55.0 60.0 72.0 78 89 \n",
"4 195.0 178.0 191.0 200 200 \n",
"5 71.0 82.0 73.0 77 76 \n",
"6 18.0 14.0 14.0 14 12 \n",
"7 0.0 0.0 0.0 0 0 \n",
"8 250.0 192.0 169.0 196 230 \n",
"9 114.0 83.0 83.0 69 81 \n",
"10 0.0 0.0 0.0 0 0 \n",
"11 231.0 240.0 240.0 250 255 \n",
"12 2.0 9.0 21.0 24 16 \n",
"13 3.0 3.0 2.0 2 2 \n",
"14 5.0 4.0 5.0 4 4 \n",
"15 80.0 66.0 81.0 63 74 \n",
"16 28.0 66.0 71.0 70 44 \n",
"17 0.0 0.0 0.0 0 0 \n",
"18 16.0 19.0 17.0 16 16 \n",
"19 3.0 2.0 2.0 2 2 \n",
"20 6.0 15.0 16.0 16 16 \n",
"21 0.0 0.0 0.0 0 0 \n",
"22 8.0 15.0 16.0 17 23 \n",
"23 6.0 1.0 2.0 2 2 \n",
"24 4.0 3.0 3.0 3 4 \n",
"25 53.0 59.0 51.0 61 64 \n",
"26 1.0 1.0 2.0 1 1 \n",
"27 1.0 1.0 1.0 1 1 \n",
"28 1.0 2.0 2.0 2 2 \n",
"29 0.0 0.0 0.0 0 0 \n",
"... ... ... ... ... ... \n",
"21447 0.0 0.0 0.0 0 0 \n",
"21448 0.0 0.0 0.0 0 0 \n",
"21449 0.0 1.0 0.0 0 0 \n",
"21450 0.0 0.0 0.0 0 0 \n",
"21451 0.0 0.0 0.0 0 0 \n",
"21452 62.0 55.0 55.0 55 55 \n",
"21453 1980.0 2011.0 2094.0 2071 2016 \n",
"21454 258.0 258.0 269.0 272 276 \n",
"21455 0.0 0.0 0.0 0 0 \n",
"21456 287.0 314.0 336.0 396 416 \n",
"21457 78.0 68.0 56.0 52 55 \n",
"21458 3.0 4.0 2.0 4 3 \n",
"21459 19.0 24.0 17.0 27 30 \n",
"21460 44.0 41.0 40.0 38 38 \n",
"21461 135.0 137.0 147.0 159 160 \n",
"21462 179.0 215.0 217.0 227 227 \n",
"21463 184.0 211.0 230.0 246 217 \n",
"21464 11.0 23.0 11.0 10 10 \n",
"21465 16.0 14.0 11.0 12 12 \n",
"21466 437.0 448.0 476.0 525 516 \n",
"21467 265.0 262.0 277.0 280 258 \n",
"21468 21.0 21.0 21.0 22 22 \n",
"21469 31.0 30.0 25.0 26 20 \n",
"21470 27.0 27.0 24.0 24 25 \n",
"21471 23.0 25.0 25.0 30 31 \n",
"21472 385.0 418.0 457.0 426 451 \n",
"21473 5.0 15.0 15.0 15 15 \n",
"21474 18.0 29.0 40.0 40 40 \n",
"21475 0.0 0.0 0.0 0 0 \n",
"21476 0.0 0.0 0.0 0 0 \n",
"\n",
"[21477 rows x 63 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "731a952c-b292-46e3-be7a-4afffe2b4ff1",
"_uuid": "5d165c279ce22afc0a874e32931d7b0ebb0717f9"
},
"source": [
"Let's see what the data looks like..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "25c3f986-fd14-4a3f-baff-02571ad665eb",
"_uuid": "5a7da58320ab35ab1bcf83a62209afbe40b672fe"
},
"source": [
"# Plot for annual produce of different countries with quantity in y-axis and years in x-axis"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Area Abbreviation</th>\n",
" <th>Area Code</th>\n",
" <th>Area</th>\n",
" <th>Item Code</th>\n",
" <th>Item</th>\n",
" <th>Element Code</th>\n",
" <th>Element</th>\n",
" <th>Unit</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>...</th>\n",
" <th>Y2004</th>\n",
" <th>Y2005</th>\n",
" <th>Y2006</th>\n",
" <th>Y2007</th>\n",
" <th>Y2008</th>\n",
" <th>Y2009</th>\n",
" <th>Y2010</th>\n",
" <th>Y2011</th>\n",
" <th>Y2012</th>\n",
" <th>Y2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2511</td>\n",
" <td>Wheat and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>3249.0</td>\n",
" <td>3486.0</td>\n",
" <td>3704.0</td>\n",
" <td>4164.0</td>\n",
" <td>4252.0</td>\n",
" <td>4538.0</td>\n",
" <td>4605.0</td>\n",
" <td>4711.0</td>\n",
" <td>4810</td>\n",
" <td>4895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2805</td>\n",
" <td>Rice (Milled Equivalent)</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>419.0</td>\n",
" <td>445.0</td>\n",
" <td>546.0</td>\n",
" <td>455.0</td>\n",
" <td>490.0</td>\n",
" <td>415.0</td>\n",
" <td>442.0</td>\n",
" <td>476.0</td>\n",
" <td>425</td>\n",
" <td>422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2513</td>\n",
" <td>Barley and products</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>58.0</td>\n",
" <td>236.0</td>\n",
" <td>262.0</td>\n",
" <td>263.0</td>\n",
" <td>230.0</td>\n",
" <td>379.0</td>\n",
" <td>315.0</td>\n",
" <td>203.0</td>\n",
" <td>367</td>\n",
" <td>360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2513</td>\n",
" <td>Barley and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>185.0</td>\n",
" <td>43.0</td>\n",
" <td>44.0</td>\n",
" <td>48.0</td>\n",
" <td>62.0</td>\n",
" <td>55.0</td>\n",
" <td>60.0</td>\n",
" <td>72.0</td>\n",
" <td>78</td>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2514</td>\n",
" <td>Maize and products</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>120.0</td>\n",
" <td>208.0</td>\n",
" <td>233.0</td>\n",
" <td>249.0</td>\n",
" <td>247.0</td>\n",
" <td>195.0</td>\n",
" <td>178.0</td>\n",
" <td>191.0</td>\n",
" <td>200</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2514</td>\n",
" <td>Maize and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>231.0</td>\n",
" <td>67.0</td>\n",
" <td>82.0</td>\n",
" <td>67.0</td>\n",
" <td>69.0</td>\n",
" <td>71.0</td>\n",
" <td>82.0</td>\n",
" <td>73.0</td>\n",
" <td>77</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2517</td>\n",
" <td>Millet and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>15.0</td>\n",
" <td>21.0</td>\n",
" <td>11.0</td>\n",
" <td>19.0</td>\n",
" <td>21.0</td>\n",
" <td>18.0</td>\n",
" <td>14.0</td>\n",
" <td>14.0</td>\n",
" <td>14</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2520</td>\n",
" <td>Cereals, Other</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2531</td>\n",
" <td>Potatoes and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>276.0</td>\n",
" <td>294.0</td>\n",
" <td>294.0</td>\n",
" <td>260.0</td>\n",
" <td>242.0</td>\n",
" <td>250.0</td>\n",
" <td>192.0</td>\n",
" <td>169.0</td>\n",
" <td>196</td>\n",
" <td>230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2536</td>\n",
" <td>Sugar cane</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>50.0</td>\n",
" <td>29.0</td>\n",
" <td>61.0</td>\n",
" <td>65.0</td>\n",
" <td>54.0</td>\n",
" <td>114.0</td>\n",
" <td>83.0</td>\n",
" <td>83.0</td>\n",
" <td>69</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2537</td>\n",
" <td>Sugar beet</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2542</td>\n",
" <td>Sugar (Raw Equivalent)</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>124.0</td>\n",
" <td>152.0</td>\n",
" <td>169.0</td>\n",
" <td>192.0</td>\n",
" <td>217.0</td>\n",
" <td>231.0</td>\n",
" <td>240.0</td>\n",
" <td>240.0</td>\n",
" <td>250</td>\n",
" <td>255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2543</td>\n",
" <td>Sweeteners, Other</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>9.0</td>\n",
" <td>15.0</td>\n",
" <td>12.0</td>\n",
" <td>6.0</td>\n",
" <td>11.0</td>\n",
" <td>2.0</td>\n",
" <td>9.0</td>\n",
" <td>21.0</td>\n",
" <td>24</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2745</td>\n",
" <td>Honey</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2549</td>\n",
" <td>Pulses, Other and products</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2549</td>\n",
" <td>Pulses, Other and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>17.0</td>\n",
" <td>35.0</td>\n",
" <td>37.0</td>\n",
" <td>40.0</td>\n",
" <td>54.0</td>\n",
" <td>80.0</td>\n",
" <td>66.0</td>\n",
" <td>81.0</td>\n",
" <td>63</td>\n",
" <td>74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2551</td>\n",
" <td>Nuts and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>11.0</td>\n",
" <td>13.0</td>\n",
" <td>24.0</td>\n",
" <td>34.0</td>\n",
" <td>42.0</td>\n",
" <td>28.0</td>\n",
" <td>66.0</td>\n",
" <td>71.0</td>\n",
" <td>70</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2560</td>\n",
" <td>Coconuts - Incl Copra</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2561</td>\n",
" <td>Sesame seed</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>16.0</td>\n",
" <td>16.0</td>\n",
" <td>13.0</td>\n",
" <td>16.0</td>\n",
" <td>16.0</td>\n",
" <td>16.0</td>\n",
" <td>19.0</td>\n",
" <td>17.0</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2563</td>\n",
" <td>Olives (including preserved)</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2571</td>\n",
" <td>Soyabean Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>6.0</td>\n",
" <td>35.0</td>\n",
" <td>18.0</td>\n",
" <td>21.0</td>\n",
" <td>11.0</td>\n",
" <td>6.0</td>\n",
" <td>15.0</td>\n",
" <td>16.0</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2572</td>\n",
" <td>Groundnut Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2573</td>\n",
" <td>Sunflowerseed Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>9.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>15.0</td>\n",
" <td>16.0</td>\n",
" <td>17</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2574</td>\n",
" <td>Rape and Mustard Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2575</td>\n",
" <td>Cottonseed Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2577</td>\n",
" <td>Palm Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>71.0</td>\n",
" <td>69.0</td>\n",
" <td>56.0</td>\n",
" <td>51.0</td>\n",
" <td>36.0</td>\n",
" <td>53.0</td>\n",
" <td>59.0</td>\n",
" <td>51.0</td>\n",
" <td>61</td>\n",
" <td>64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2579</td>\n",
" <td>Sesameseed Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2580</td>\n",
" <td>Olive Oil</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2586</td>\n",
" <td>Oilcrops Oil, Other</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>AFG</td>\n",
" <td>2</td>\n",
" <td>Afghanistan</td>\n",
" <td>2601</td>\n",
" <td>Tomatoes and products</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>33.94</td>\n",
" <td>67.71</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</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>21447</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2765</td>\n",
" <td>Crustaceans</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21448</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2766</td>\n",
" <td>Cephalopods</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21449</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2767</td>\n",
" <td>Molluscs, Other</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21450</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2775</td>\n",
" <td>Aquatic Plants</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21451</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2680</td>\n",
" <td>Infant food</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21452</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2905</td>\n",
" <td>Cereals - Excluding Beer</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>75.0</td>\n",
" <td>54.0</td>\n",
" <td>75.0</td>\n",
" <td>55.0</td>\n",
" <td>63.0</td>\n",
" <td>62.0</td>\n",
" <td>55.0</td>\n",
" <td>55.0</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21453</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2905</td>\n",
" <td>Cereals - Excluding Beer</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>1844.0</td>\n",
" <td>1842.0</td>\n",
" <td>1944.0</td>\n",
" <td>1962.0</td>\n",
" <td>1918.0</td>\n",
" <td>1980.0</td>\n",
" <td>2011.0</td>\n",
" <td>2094.0</td>\n",
" <td>2071</td>\n",
" <td>2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21454</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2907</td>\n",
" <td>Starchy Roots</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>223.0</td>\n",
" <td>236.0</td>\n",
" <td>238.0</td>\n",
" <td>228.0</td>\n",
" <td>245.0</td>\n",
" <td>258.0</td>\n",
" <td>258.0</td>\n",
" <td>269.0</td>\n",
" <td>272</td>\n",
" <td>276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21455</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2908</td>\n",
" <td>Sugar Crops</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21456</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2909</td>\n",
" <td>Sugar & Sweeteners</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>335.0</td>\n",
" <td>313.0</td>\n",
" <td>339.0</td>\n",
" <td>302.0</td>\n",
" <td>285.0</td>\n",
" <td>287.0</td>\n",
" <td>314.0</td>\n",
" <td>336.0</td>\n",
" <td>396</td>\n",
" <td>416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21457</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2911</td>\n",
" <td>Pulses</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>63.0</td>\n",
" <td>59.0</td>\n",
" <td>61.0</td>\n",
" <td>57.0</td>\n",
" <td>69.0</td>\n",
" <td>78.0</td>\n",
" <td>68.0</td>\n",
" <td>56.0</td>\n",
" <td>52</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21458</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2912</td>\n",
" <td>Treenuts</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21459</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2913</td>\n",
" <td>Oilcrops</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>36.0</td>\n",
" <td>46.0</td>\n",
" <td>41.0</td>\n",
" <td>33.0</td>\n",
" <td>31.0</td>\n",
" <td>19.0</td>\n",
" <td>24.0</td>\n",
" <td>17.0</td>\n",
" <td>27</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21460</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2913</td>\n",
" <td>Oilcrops</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>60.0</td>\n",
" <td>59.0</td>\n",
" <td>61.0</td>\n",
" <td>62.0</td>\n",
" <td>48.0</td>\n",
" <td>44.0</td>\n",
" <td>41.0</td>\n",
" <td>40.0</td>\n",
" <td>38</td>\n",
" <td>38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21461</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2914</td>\n",
" <td>Vegetable Oils</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>111.0</td>\n",
" <td>114.0</td>\n",
" <td>112.0</td>\n",
" <td>114.0</td>\n",
" <td>134.0</td>\n",
" <td>135.0</td>\n",
" <td>137.0</td>\n",
" <td>147.0</td>\n",
" <td>159</td>\n",
" <td>160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21462</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2918</td>\n",
" <td>Vegetables</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>161.0</td>\n",
" <td>166.0</td>\n",
" <td>208.0</td>\n",
" <td>185.0</td>\n",
" <td>137.0</td>\n",
" <td>179.0</td>\n",
" <td>215.0</td>\n",
" <td>217.0</td>\n",
" <td>227</td>\n",
" <td>227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21463</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2919</td>\n",
" <td>Fruits - Excluding Wine</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>191.0</td>\n",
" <td>134.0</td>\n",
" <td>167.0</td>\n",
" <td>177.0</td>\n",
" <td>185.0</td>\n",
" <td>184.0</td>\n",
" <td>211.0</td>\n",
" <td>230.0</td>\n",
" <td>246</td>\n",
" <td>217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21464</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2922</td>\n",
" <td>Stimulants</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>21.0</td>\n",
" <td>14.0</td>\n",
" <td>24.0</td>\n",
" <td>16.0</td>\n",
" <td>11.0</td>\n",
" <td>23.0</td>\n",
" <td>11.0</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21465</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2923</td>\n",
" <td>Spices</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>11.0</td>\n",
" <td>7.0</td>\n",
" <td>12.0</td>\n",
" <td>16.0</td>\n",
" <td>16.0</td>\n",
" <td>14.0</td>\n",
" <td>11.0</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21466</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2924</td>\n",
" <td>Alcoholic Beverages</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>294.0</td>\n",
" <td>290.0</td>\n",
" <td>316.0</td>\n",
" <td>355.0</td>\n",
" <td>398.0</td>\n",
" <td>437.0</td>\n",
" <td>448.0</td>\n",
" <td>476.0</td>\n",
" <td>525</td>\n",
" <td>516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21467</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2943</td>\n",
" <td>Meat</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>222.0</td>\n",
" <td>228.0</td>\n",
" <td>233.0</td>\n",
" <td>238.0</td>\n",
" <td>242.0</td>\n",
" <td>265.0</td>\n",
" <td>262.0</td>\n",
" <td>277.0</td>\n",
" <td>280</td>\n",
" <td>258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21468</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2945</td>\n",
" <td>Offals</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>20.0</td>\n",
" <td>20.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21469</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2946</td>\n",
" <td>Animal fats</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>26.0</td>\n",
" <td>26.0</td>\n",
" <td>29.0</td>\n",
" <td>29.0</td>\n",
" <td>27.0</td>\n",
" <td>31.0</td>\n",
" <td>30.0</td>\n",
" <td>25.0</td>\n",
" <td>26</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21470</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2949</td>\n",
" <td>Eggs</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>15.0</td>\n",
" <td>18.0</td>\n",
" <td>18.0</td>\n",
" <td>21.0</td>\n",
" <td>22.0</td>\n",
" <td>27.0</td>\n",
" <td>27.0</td>\n",
" <td>24.0</td>\n",
" <td>24</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21471</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2948</td>\n",
" <td>Milk - Excluding Butter</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>21.0</td>\n",
" <td>23.0</td>\n",
" <td>25.0</td>\n",
" <td>25.0</td>\n",
" <td>30</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21472</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2948</td>\n",
" <td>Milk - Excluding Butter</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>373.0</td>\n",
" <td>357.0</td>\n",
" <td>359.0</td>\n",
" <td>356.0</td>\n",
" <td>341.0</td>\n",
" <td>385.0</td>\n",
" <td>418.0</td>\n",
" <td>457.0</td>\n",
" <td>426</td>\n",
" <td>451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21473</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2960</td>\n",
" <td>Fish, Seafood</td>\n",
" <td>5521</td>\n",
" <td>Feed</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>9.0</td>\n",
" <td>6.0</td>\n",
" <td>9.0</td>\n",
" <td>5.0</td>\n",
" <td>15.0</td>\n",
" <td>15.0</td>\n",
" <td>15</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21474</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2960</td>\n",
" <td>Fish, Seafood</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>18.0</td>\n",
" <td>14.0</td>\n",
" <td>17.0</td>\n",
" <td>14.0</td>\n",
" <td>15.0</td>\n",
" <td>18.0</td>\n",
" <td>29.0</td>\n",
" <td>40.0</td>\n",
" <td>40</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21475</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2961</td>\n",
" <td>Aquatic Products, Other</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21476</th>\n",
" <td>ZWE</td>\n",
" <td>181</td>\n",
" <td>Zimbabwe</td>\n",
" <td>2928</td>\n",
" <td>Miscellaneous</td>\n",
" <td>5142</td>\n",
" <td>Food</td>\n",
" <td>1000 tonnes</td>\n",
" <td>-19.02</td>\n",
" <td>29.15</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>21477 rows × 63 columns</p>\n",
"</div>"
],
"text/plain": [
" Area Abbreviation Area Code Area Item Code \\\n",
"0 AFG 2 Afghanistan 2511 \n",
"1 AFG 2 Afghanistan 2805 \n",
"2 AFG 2 Afghanistan 2513 \n",
"3 AFG 2 Afghanistan 2513 \n",
"4 AFG 2 Afghanistan 2514 \n",
"5 AFG 2 Afghanistan 2514 \n",
"6 AFG 2 Afghanistan 2517 \n",
"7 AFG 2 Afghanistan 2520 \n",
"8 AFG 2 Afghanistan 2531 \n",
"9 AFG 2 Afghanistan 2536 \n",
"10 AFG 2 Afghanistan 2537 \n",
"11 AFG 2 Afghanistan 2542 \n",
"12 AFG 2 Afghanistan 2543 \n",
"13 AFG 2 Afghanistan 2745 \n",
"14 AFG 2 Afghanistan 2549 \n",
"15 AFG 2 Afghanistan 2549 \n",
"16 AFG 2 Afghanistan 2551 \n",
"17 AFG 2 Afghanistan 2560 \n",
"18 AFG 2 Afghanistan 2561 \n",
"19 AFG 2 Afghanistan 2563 \n",
"20 AFG 2 Afghanistan 2571 \n",
"21 AFG 2 Afghanistan 2572 \n",
"22 AFG 2 Afghanistan 2573 \n",
"23 AFG 2 Afghanistan 2574 \n",
"24 AFG 2 Afghanistan 2575 \n",
"25 AFG 2 Afghanistan 2577 \n",
"26 AFG 2 Afghanistan 2579 \n",
"27 AFG 2 Afghanistan 2580 \n",
"28 AFG 2 Afghanistan 2586 \n",
"29 AFG 2 Afghanistan 2601 \n",
"... ... ... ... ... \n",
"21447 ZWE 181 Zimbabwe 2765 \n",
"21448 ZWE 181 Zimbabwe 2766 \n",
"21449 ZWE 181 Zimbabwe 2767 \n",
"21450 ZWE 181 Zimbabwe 2775 \n",
"21451 ZWE 181 Zimbabwe 2680 \n",
"21452 ZWE 181 Zimbabwe 2905 \n",
"21453 ZWE 181 Zimbabwe 2905 \n",
"21454 ZWE 181 Zimbabwe 2907 \n",
"21455 ZWE 181 Zimbabwe 2908 \n",
"21456 ZWE 181 Zimbabwe 2909 \n",
"21457 ZWE 181 Zimbabwe 2911 \n",
"21458 ZWE 181 Zimbabwe 2912 \n",
"21459 ZWE 181 Zimbabwe 2913 \n",
"21460 ZWE 181 Zimbabwe 2913 \n",
"21461 ZWE 181 Zimbabwe 2914 \n",
"21462 ZWE 181 Zimbabwe 2918 \n",
"21463 ZWE 181 Zimbabwe 2919 \n",
"21464 ZWE 181 Zimbabwe 2922 \n",
"21465 ZWE 181 Zimbabwe 2923 \n",
"21466 ZWE 181 Zimbabwe 2924 \n",
"21467 ZWE 181 Zimbabwe 2943 \n",
"21468 ZWE 181 Zimbabwe 2945 \n",
"21469 ZWE 181 Zimbabwe 2946 \n",
"21470 ZWE 181 Zimbabwe 2949 \n",
"21471 ZWE 181 Zimbabwe 2948 \n",
"21472 ZWE 181 Zimbabwe 2948 \n",
"21473 ZWE 181 Zimbabwe 2960 \n",
"21474 ZWE 181 Zimbabwe 2960 \n",
"21475 ZWE 181 Zimbabwe 2961 \n",
"21476 ZWE 181 Zimbabwe 2928 \n",
"\n",
" Item Element Code Element Unit \\\n",
"0 Wheat and products 5142 Food 1000 tonnes \n",
"1 Rice (Milled Equivalent) 5142 Food 1000 tonnes \n",
"2 Barley and products 5521 Feed 1000 tonnes \n",
"3 Barley and products 5142 Food 1000 tonnes \n",
"4 Maize and products 5521 Feed 1000 tonnes \n",
"5 Maize and products 5142 Food 1000 tonnes \n",
"6 Millet and products 5142 Food 1000 tonnes \n",
"7 Cereals, Other 5142 Food 1000 tonnes \n",
"8 Potatoes and products 5142 Food 1000 tonnes \n",
"9 Sugar cane 5521 Feed 1000 tonnes \n",
"10 Sugar beet 5521 Feed 1000 tonnes \n",
"11 Sugar (Raw Equivalent) 5142 Food 1000 tonnes \n",
"12 Sweeteners, Other 5142 Food 1000 tonnes \n",
"13 Honey 5142 Food 1000 tonnes \n",
"14 Pulses, Other and products 5521 Feed 1000 tonnes \n",
"15 Pulses, Other and products 5142 Food 1000 tonnes \n",
"16 Nuts and products 5142 Food 1000 tonnes \n",
"17 Coconuts - Incl Copra 5142 Food 1000 tonnes \n",
"18 Sesame seed 5142 Food 1000 tonnes \n",
"19 Olives (including preserved) 5142 Food 1000 tonnes \n",
"20 Soyabean Oil 5142 Food 1000 tonnes \n",
"21 Groundnut Oil 5142 Food 1000 tonnes \n",
"22 Sunflowerseed Oil 5142 Food 1000 tonnes \n",
"23 Rape and Mustard Oil 5142 Food 1000 tonnes \n",
"24 Cottonseed Oil 5142 Food 1000 tonnes \n",
"25 Palm Oil 5142 Food 1000 tonnes \n",
"26 Sesameseed Oil 5142 Food 1000 tonnes \n",
"27 Olive Oil 5142 Food 1000 tonnes \n",
"28 Oilcrops Oil, Other 5142 Food 1000 tonnes \n",
"29 Tomatoes and products 5142 Food 1000 tonnes \n",
"... ... ... ... ... \n",
"21447 Crustaceans 5142 Food 1000 tonnes \n",
"21448 Cephalopods 5142 Food 1000 tonnes \n",
"21449 Molluscs, Other 5142 Food 1000 tonnes \n",
"21450 Aquatic Plants 5142 Food 1000 tonnes \n",
"21451 Infant food 5142 Food 1000 tonnes \n",
"21452 Cereals - Excluding Beer 5521 Feed 1000 tonnes \n",
"21453 Cereals - Excluding Beer 5142 Food 1000 tonnes \n",
"21454 Starchy Roots 5142 Food 1000 tonnes \n",
"21455 Sugar Crops 5142 Food 1000 tonnes \n",
"21456 Sugar & Sweeteners 5142 Food 1000 tonnes \n",
"21457 Pulses 5142 Food 1000 tonnes \n",
"21458 Treenuts 5142 Food 1000 tonnes \n",
"21459 Oilcrops 5521 Feed 1000 tonnes \n",
"21460 Oilcrops 5142 Food 1000 tonnes \n",
"21461 Vegetable Oils 5142 Food 1000 tonnes \n",
"21462 Vegetables 5142 Food 1000 tonnes \n",
"21463 Fruits - Excluding Wine 5142 Food 1000 tonnes \n",
"21464 Stimulants 5142 Food 1000 tonnes \n",
"21465 Spices 5142 Food 1000 tonnes \n",
"21466 Alcoholic Beverages 5142 Food 1000 tonnes \n",
"21467 Meat 5142 Food 1000 tonnes \n",
"21468 Offals 5142 Food 1000 tonnes \n",
"21469 Animal fats 5142 Food 1000 tonnes \n",
"21470 Eggs 5142 Food 1000 tonnes \n",
"21471 Milk - Excluding Butter 5521 Feed 1000 tonnes \n",
"21472 Milk - Excluding Butter 5142 Food 1000 tonnes \n",
"21473 Fish, Seafood 5521 Feed 1000 tonnes \n",
"21474 Fish, Seafood 5142 Food 1000 tonnes \n",
"21475 Aquatic Products, Other 5142 Food 1000 tonnes \n",
"21476 Miscellaneous 5142 Food 1000 tonnes \n",
"\n",
" latitude longitude ... Y2004 Y2005 Y2006 Y2007 Y2008 \\\n",
"0 33.94 67.71 ... 3249.0 3486.0 3704.0 4164.0 4252.0 \n",
"1 33.94 67.71 ... 419.0 445.0 546.0 455.0 490.0 \n",
"2 33.94 67.71 ... 58.0 236.0 262.0 263.0 230.0 \n",
"3 33.94 67.71 ... 185.0 43.0 44.0 48.0 62.0 \n",
"4 33.94 67.71 ... 120.0 208.0 233.0 249.0 247.0 \n",
"5 33.94 67.71 ... 231.0 67.0 82.0 67.0 69.0 \n",
"6 33.94 67.71 ... 15.0 21.0 11.0 19.0 21.0 \n",
"7 33.94 67.71 ... 2.0 1.0 1.0 0.0 0.0 \n",
"8 33.94 67.71 ... 276.0 294.0 294.0 260.0 242.0 \n",
"9 33.94 67.71 ... 50.0 29.0 61.0 65.0 54.0 \n",
"10 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n",
"11 33.94 67.71 ... 124.0 152.0 169.0 192.0 217.0 \n",
"12 33.94 67.71 ... 9.0 15.0 12.0 6.0 11.0 \n",
"13 33.94 67.71 ... 3.0 3.0 3.0 3.0 3.0 \n",
"14 33.94 67.71 ... 3.0 2.0 3.0 3.0 3.0 \n",
"15 33.94 67.71 ... 17.0 35.0 37.0 40.0 54.0 \n",
"16 33.94 67.71 ... 11.0 13.0 24.0 34.0 42.0 \n",
"17 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n",
"18 33.94 67.71 ... 16.0 16.0 13.0 16.0 16.0 \n",
"19 33.94 67.71 ... 1.0 1.0 0.0 0.0 2.0 \n",
"20 33.94 67.71 ... 6.0 35.0 18.0 21.0 11.0 \n",
"21 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n",
"22 33.94 67.71 ... 4.0 6.0 5.0 9.0 3.0 \n",
"23 33.94 67.71 ... 0.0 1.0 3.0 5.0 6.0 \n",
"24 33.94 67.71 ... 2.0 3.0 3.0 3.0 3.0 \n",
"25 33.94 67.71 ... 71.0 69.0 56.0 51.0 36.0 \n",
"26 33.94 67.71 ... 1.0 1.0 1.0 2.0 2.0 \n",
"27 33.94 67.71 ... 0.0 0.0 0.0 0.0 0.0 \n",
"28 33.94 67.71 ... 0.0 1.0 0.0 0.0 3.0 \n",
"29 33.94 67.71 ... 2.0 2.0 8.0 1.0 0.0 \n",
"... ... ... ... ... ... ... ... ... \n",
"21447 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21448 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21449 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21450 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21451 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21452 -19.02 29.15 ... 75.0 54.0 75.0 55.0 63.0 \n",
"21453 -19.02 29.15 ... 1844.0 1842.0 1944.0 1962.0 1918.0 \n",
"21454 -19.02 29.15 ... 223.0 236.0 238.0 228.0 245.0 \n",
"21455 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21456 -19.02 29.15 ... 335.0 313.0 339.0 302.0 285.0 \n",
"21457 -19.02 29.15 ... 63.0 59.0 61.0 57.0 69.0 \n",
"21458 -19.02 29.15 ... 1.0 2.0 1.0 2.0 2.0 \n",
"21459 -19.02 29.15 ... 36.0 46.0 41.0 33.0 31.0 \n",
"21460 -19.02 29.15 ... 60.0 59.0 61.0 62.0 48.0 \n",
"21461 -19.02 29.15 ... 111.0 114.0 112.0 114.0 134.0 \n",
"21462 -19.02 29.15 ... 161.0 166.0 208.0 185.0 137.0 \n",
"21463 -19.02 29.15 ... 191.0 134.0 167.0 177.0 185.0 \n",
"21464 -19.02 29.15 ... 7.0 21.0 14.0 24.0 16.0 \n",
"21465 -19.02 29.15 ... 7.0 11.0 7.0 12.0 16.0 \n",
"21466 -19.02 29.15 ... 294.0 290.0 316.0 355.0 398.0 \n",
"21467 -19.02 29.15 ... 222.0 228.0 233.0 238.0 242.0 \n",
"21468 -19.02 29.15 ... 20.0 20.0 21.0 21.0 21.0 \n",
"21469 -19.02 29.15 ... 26.0 26.0 29.0 29.0 27.0 \n",
"21470 -19.02 29.15 ... 15.0 18.0 18.0 21.0 22.0 \n",
"21471 -19.02 29.15 ... 21.0 21.0 21.0 21.0 21.0 \n",
"21472 -19.02 29.15 ... 373.0 357.0 359.0 356.0 341.0 \n",
"21473 -19.02 29.15 ... 5.0 4.0 9.0 6.0 9.0 \n",
"21474 -19.02 29.15 ... 18.0 14.0 17.0 14.0 15.0 \n",
"21475 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"21476 -19.02 29.15 ... 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" Y2009 Y2010 Y2011 Y2012 Y2013 \n",
"0 4538.0 4605.0 4711.0 4810 4895 \n",
"1 415.0 442.0 476.0 425 422 \n",
"2 379.0 315.0 203.0 367 360 \n",
"3 55.0 60.0 72.0 78 89 \n",
"4 195.0 178.0 191.0 200 200 \n",
"5 71.0 82.0 73.0 77 76 \n",
"6 18.0 14.0 14.0 14 12 \n",
"7 0.0 0.0 0.0 0 0 \n",
"8 250.0 192.0 169.0 196 230 \n",
"9 114.0 83.0 83.0 69 81 \n",
"10 0.0 0.0 0.0 0 0 \n",
"11 231.0 240.0 240.0 250 255 \n",
"12 2.0 9.0 21.0 24 16 \n",
"13 3.0 3.0 2.0 2 2 \n",
"14 5.0 4.0 5.0 4 4 \n",
"15 80.0 66.0 81.0 63 74 \n",
"16 28.0 66.0 71.0 70 44 \n",
"17 0.0 0.0 0.0 0 0 \n",
"18 16.0 19.0 17.0 16 16 \n",
"19 3.0 2.0 2.0 2 2 \n",
"20 6.0 15.0 16.0 16 16 \n",
"21 0.0 0.0 0.0 0 0 \n",
"22 8.0 15.0 16.0 17 23 \n",
"23 6.0 1.0 2.0 2 2 \n",
"24 4.0 3.0 3.0 3 4 \n",
"25 53.0 59.0 51.0 61 64 \n",
"26 1.0 1.0 2.0 1 1 \n",
"27 1.0 1.0 1.0 1 1 \n",
"28 1.0 2.0 2.0 2 2 \n",
"29 0.0 0.0 0.0 0 0 \n",
"... ... ... ... ... ... \n",
"21447 0.0 0.0 0.0 0 0 \n",
"21448 0.0 0.0 0.0 0 0 \n",
"21449 0.0 1.0 0.0 0 0 \n",
"21450 0.0 0.0 0.0 0 0 \n",
"21451 0.0 0.0 0.0 0 0 \n",
"21452 62.0 55.0 55.0 55 55 \n",
"21453 1980.0 2011.0 2094.0 2071 2016 \n",
"21454 258.0 258.0 269.0 272 276 \n",
"21455 0.0 0.0 0.0 0 0 \n",
"21456 287.0 314.0 336.0 396 416 \n",
"21457 78.0 68.0 56.0 52 55 \n",
"21458 3.0 4.0 2.0 4 3 \n",
"21459 19.0 24.0 17.0 27 30 \n",
"21460 44.0 41.0 40.0 38 38 \n",
"21461 135.0 137.0 147.0 159 160 \n",
"21462 179.0 215.0 217.0 227 227 \n",
"21463 184.0 211.0 230.0 246 217 \n",
"21464 11.0 23.0 11.0 10 10 \n",
"21465 16.0 14.0 11.0 12 12 \n",
"21466 437.0 448.0 476.0 525 516 \n",
"21467 265.0 262.0 277.0 280 258 \n",
"21468 21.0 21.0 21.0 22 22 \n",
"21469 31.0 30.0 25.0 26 20 \n",
"21470 27.0 27.0 24.0 24 25 \n",
"21471 23.0 25.0 25.0 30 31 \n",
"21472 385.0 418.0 457.0 426 451 \n",
"21473 5.0 15.0 15.0 15 15 \n",
"21474 18.0 29.0 40.0 40 40 \n",
"21475 0.0 0.0 0.0 0 0 \n",
"21476 0.0 0.0 0.0 0 0 \n",
"\n",
"[21477 rows x 63 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "347e620f-b0e4-448e-81c7-e164f560c5a3",
"_uuid": "0acdd759950f5df3298224b0804562973663a11d",
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1728x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"area_list = list(df['Area'].unique())\n",
"year_list = list(df.iloc[:,10:].columns)\n",
"\n",
"plt.figure(figsize=(24,12))\n",
"for ar in area_list:\n",
" yearly_produce = []\n",
" for yr in year_list:\n",
" yearly_produce.append(df[yr][df['Area'] == ar].sum())\n",
" plt.plot(yearly_produce, label=ar)\n",
"plt.xticks(np.arange(53), tuple(year_list), rotation=60)\n",
"plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=8, mode=\"expand\", borderaxespad=0.)\n",
"plt.savefig('p.png')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 1728x864 with 0 Axes>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 1728x864 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(24,12))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2ebe07e3-739b-4f39-8736-a512426c05bf",
"_uuid": "70900ec0ff5e248cd382ee53b5927cb671efa80e",
"collapsed": true
},
"source": [
"Clearly, China, India and US stand out here. So, these are the countries with most food and feed production.\n",
"\n",
"Now, let's have a close look at their food and feed data\n",
"\n",
"# Food and feed plot for the whole dataset"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "ec0c911d-e154-4f8a-a79f-ced4896d5115",
"_uuid": "683dc56125b3a4c66b1e140098ec91490cbbe96f",
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
" warnings.warn(msg)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot(\"Element\", data=df, kind=\"count\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "189c74af-e6e4-4ddd-a73c-3725f3aa8124",
"_uuid": "bfd404fb5dbb48c3e3bd1dcd45fb27a5fb475a00"
},
"source": [
"So, there is a huge difference in food and feed production. Now, we have obvious assumptions about the following plots after looking at this huge difference.\n",
"\n",
"# Food and feed plot for the largest producers(India, USA, China)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "0bf44e4e-d4c4-4f74-ae9f-82f52139d182",
"_uuid": "be1bc3d49c8cee62f48a09ada0db3170adcedc17"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
" warnings.warn(msg)\n",
"/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
" warnings.warn(msg, UserWarning)\n"
]
},
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x1a218d2550>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhAAAAI4CAYAAAA7/9DSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAHzNJREFUeJzt3Xm4ZHdd5/HPlwRIICAEGoQETJwJS4TI0jBsg0GQCTqYoEFBkERxoj4qiAKi8CjgOIriILtGliSIECQsEX0gGIgge2chGzuBEMhAI2sUUOA3f9TpUOnc213fTt9btzuv1/PUc6tOnarzu/dWV7/vOafOqTFGAAA6rrPsAQAAex4BAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACAtn2XPYBr4qijjhpvfvOblz0MAK49atkD2Cj26DUQX/ziF5c9BAC4VtqjAwIAWA4BAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANr2XfYAgPV36TPvvOwhrJnb/v4Fyx4CXCtYAwEAtAkIAKBNQAAAbfaB2MvYtg3AerAGAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQ4kBcCKHJiOHbEGAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2tYsIKrqZVX1haq6cG7agVX11qr62PT1ptP0qqrnVdXHq+r8qrrbWo0LALjm1nINxElJjtpu2lOSnDnGOCzJmdPtJHlIksOmywlJXryG4wIArqE1C4gxxjuSfGm7yUcnOXm6fnKSY+amnzJm3pvkJlV1q7UaGwBwzaz3PhC3HGNcniTT11tM0w9K8pm5+S6bpl1NVZ1QVVuqasvWrVvXdLAAwMo2yk6UtcK0sdKMY4wTxxibxxibN23atMbDAgBWst4B8fltmyamr1+Ypl+W5DZz8x2c5HPrPDYAYEHrHRCnJzluun5ckjfOTX/M9GmMeyX56rZNHQDAxrPvWj1xVb0qyZFJbl5VlyX5gyR/kuQ1VfXYJJcmefg0+z8m+fEkH0/y70l+Ya3GBQBcc2sWEGOMR65y1wNXmHck+bW1GgsAsHttlJ0oAYA9iIAAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANC2lICoqidU1UVVdWFVvaqq9quqQ6vqfVX1sao6taqut4yxAQA7t+4BUVUHJXlcks1jjDsl2SfJI5I8K8lzxhiHJflykseu99gAgMUsaxPGvkn2r6p9k9wgyeVJfjTJa6f7T05yzJLGBgDsxLoHxBjjs0meneTSzMLhq0nOTvKVMca3p9kuS3LQSo+vqhOqaktVbdm6det6DBkA2M4yNmHcNMnRSQ5NcuskN0zykBVmHSs9foxx4hhj8xhj86ZNm9ZuoADAqpaxCeNBSS4ZY2wdY/xnktcluU+Sm0ybNJLk4CSfW8LYAIAFLCMgLk1yr6q6QVVVkgcmuTjJ25McO81zXJI3LmFsAMAClrEPxPsy21nynCQXTGM4McnvJPmtqvp4kpsleel6jw0AWMy+O59l9xtj/EGSP9hu8ieT3HMJwwEAmhyJEgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANqWEhBVdZOqem1VfbiqPlRV966qA6vqrVX1senrTZcxNgBg55a1BuK5Sd48xrhDkh9O8qEkT0ly5hjjsCRnTrcBgA1o3QOiqm6c5P5JXpokY4z/GGN8JcnRSU6eZjs5yTHrPTYAYDHLWAPxg0m2Jnl5VZ1bVS+pqhsmueUY4/Ikmb7eYqUHV9UJVbWlqrZs3bp1/UYNAFxpGQGxb5K7JXnxGOOuSf4tjc0VY4wTxxibxxibN23atFZjBAB2YBkBcVmSy8YY75tuvzazoPh8Vd0qSaavX1jC2ACABax7QIwx/l+Sz1TV7adJD0xycZLTkxw3TTsuyRvXe2wAwGL2XWSmqjpzjPHAnU1r+I0kr6yq6yX5ZJJfyCxmXlNVj01yaZKH7+JzAwBrbIcBUVX7JblBkptPx2Wo6a4bJ7n1ri50jHFeks0r3LWrQQIArKOdrYH45SS/mVksnJ3vBcTXkrxwDccFAGxgOwyIMcZzkzy3qn5jjPH8dRoTALDBLbQPxBjj+VV1nySHzD9mjHHKGo0LANjAFvoURlW9Ismzk9wvyT2my0r7MAAAc6rqO1V13tzlKdP0s6pqKf+XVtXxVbXL+zImC66ByCwWDh9jjGuyMAC4FvrGGOMuyx7Edo5PcmGSz+3qEyx6HIgLk3z/ri4EAFhdVT24qt5TVedU1d9V1QHT9E9V1f+Z7ttSVXerqrdU1Seq6lfmHv+kqvpAVZ1fVc+Yph0ynfH6r6vqoqo6o6r2r6pjM1sx8Mppjcj+uzLmRQPi5kkungZ9+rbLriwQAK5l9t9uE8bPzt9ZVTdP8rQkDxpj3C3JliS/NTfLZ8YY907yziQnJTk2yb2SPHN6/IOTHJbknknukuTuVXX/6bGHJXnhGOOHknwlyU+PMV47LeNRY4y7jDG+sSvf1KKbMJ6+K08OAOx0E8a9khye5F1VlSTXS/Keufu3/cF+QZIDxhhfT/L1qvpmVd0kyYOny7nTfAdkFg6XJrlkOvZSMjscwyHX/NuZWfRTGP+8uxYIAFxFJXnrGOORq9z/renrd+eub7u97/T4Px5j/NVVnrTqkO3m/06SXdpcsZJFP4Xx9ar62nT55rRH6dd21yAA4FrsvUnuW1X/NUmq6gZVdbvG49+S5Bfn9ps4qKpusZPHfD3JjXZptJNF10BcZSFVdUxm21oAgB3bv6rOm7v95jHGU7bdGGNsrarjk7yqqq4/TX5ako8u8uRjjDOq6o5J3jNtArkiyaMzW+OwmpOS/GVVfSPJvXdlP4hF94G4ijHGG7Z9jhUAWN0YY59Vph85d/1tmR1jaft5Dpm7flJm//GvdN9zkzx3hcXcaW6eZ89dPy3JaYuMfzWLno3zp+ZuXiezj384JgQAXEstugbioXPXv53kU0mO3u2jAQD2CIvuA/ELaz0QAGDPseinMA6uqtdX1Req6vNVdVpVHbzWgwMANqZFj0T58swOZHHrJAcl+ftpGgBwLbRoQGwaY7x8jPHt6XJSkk1rOC4AYANbNCC+WFWPrqp9psujk/zrWg4MANixFU4VfshueM6nV9UTdzbfop/C+MUkL0jynMw+vvnuJHasBIDJ3Z90ym49vMHZf/aYWmC2pZ0qfNE1EH+Y5LgxxqYxxi0yC4qnr9moAIBdMm0p+LO503v/8tx9Vzvt9zT9qVX1kar6pyS3X2Q5i66BOGKM8eVtN8YYX6qquy76zQAAa2L+MNmXjDEeluSxSb46xrjHdGjsd1XVGZmdoXPbab8ryenTab//Lckjktw1sy44J7Mzd+7QogFxnaq66baIqKoDG48FANbGSpswHpzkiKo6drr9fZmFw2qn/b5RktePMf49Sarq9Cxg0Qj48yTvrqrXZrYPxM8k+aMFHwsArJ9K8htjjLdcZWLV/8jKp/3+zezC6SkW2gdijHFKkp9O8vkkW5P81BjjFd2FAQBr7i1JfrWqrpskVXW7qrphVj/t9zuSPKyq9q+qG+Wqp69Y1cKbIcYYFye5uPlNAADr6yVJDklyTs3O7701yTGrnfZ7jHFOVZ2a5Lwkn07yzkUWYj8GANgNFvzY5W41xjhghWnfTfJ702X7+1Y87fcY44/S3DVh0Y9xAgBcSUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAGAPtUan8z6rqjbvbD7HgQCA3eDSZ955t57O+7a/f8FecTpvAGAPUFX7VdXLq+qCqjq3qh6wk+n7V9Wrp1N8n5pk/0WWYw0EAOy5Vjqd968lyRjjzlV1hyRnVNXtdjD9V5P8+xjjiKo6IrPTee+UgACAPddKmzDul+T5STLG+HBVfTrJ7XYw/f5JnjdNP7+qzl9kwTZhAMDeZbV9J3a0T8XanM4bANhjvCPJo5LZqbyT3DbJRxacfqckRyyyEAEBAHuXFyXZp6ouSHJqkuPHGN/awfQXJzlg2nTx5CTvX2Qh9oEAgN1gwY9d7larnM77m0mOb0z/RpJHdJdtDQQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2pYWEFW1T1WdW1Vvmm4fWlXvq6qPVdWpVXW9ZY0NANixZa6BeHySD83dflaS54wxDkvy5SSPXcqoAICdWkpAVNXBSX4iyUum25XkR5O8dprl5CTHLGNsAMDOLWsNxF8keXKS7063b5bkK2OMb0+3L0ty0EoPrKoTqmpLVW3ZunXr2o8UALiadQ+IqvqfSb4wxjh7fvIKs46VHj/GOHGMsXmMsXnTpk1rMkYAYMf2XcIy75vkJ6vqx5Psl+TGma2RuElV7TuthTg4yeeWMDYAYAHrvgZijPG7Y4yDxxiHJHlEkreNMR6V5O1Jjp1mOy7JG9d7bADAYjbScSB+J8lvVdXHM9sn4qVLHg8AsIplbMK40hjjrCRnTdc/meSe67Hcuz/plPVYzFK8/kbLHgEA1wYbaQ0EALCHEBAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgLalno0TYE/n7L5cW1kDAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABA277LHgBsVHd/0inLHsKaef2Nlj0CYE9nDQQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQNu6B0RV3aaq3l5VH6qqi6rq8dP0A6vqrVX1senrTdd7bADAYpaxBuLbSX57jHHHJPdK8mtVdXiSpyQ5c4xxWJIzp9sAwAa07gExxrh8jHHOdP3rST6U5KAkRyc5eZrt5CTHrPfYAIDFLHUfiKo6JMldk7wvyS3HGJcns8hIcotVHnNCVW2pqi1bt25dr6ECAHOWFhBVdUCS05L85hjja4s+boxx4hhj8xhj86ZNm9ZugADAqpYSEFV13czi4ZVjjNdNkz9fVbea7r9Vki8sY2wAwM4t41MYleSlST40xvi/c3ednuS46fpxSd643mMDABaz7xKWed8kP5/kgqo6b5r2e0n+JMlrquqxSS5N8vAljA0AWMC6B8QY41+S1Cp3P3A9xwIA7BpHogQA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIA2AQEAtAkIAKBNQAAAbQICAGgTEABAm4AAANoEBADQJiAAgDYBAQC0CQgAoE1AAABtAgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACgTUAAAG0CAgBoExAAQJuAAADaBAQA0CYgAIC2DRUQVXVUVX2kqj5eVU9Z9ngAgJVtmICoqn2SvDDJQ5IcnuSRVXX4ckcFAKxkwwREknsm+fgY45NjjP9I8uokRy95TADACmqMsewxJEmq6tgkR40xfmm6/fNJ/tsY49e3m++EJCdMN2+f5CPrOtCN7+ZJvrjsQbDheZ2wCK+Tq/viGOOoZQ9iI9h32QOYUytMu1rdjDFOTHLi2g9nz1RVW8YYm5c9DjY2rxMW4XXCjmykTRiXJbnN3O2Dk3xuSWMBAHZgIwXEB5IcVlWHVtX1kjwiyelLHhMAsIINswljjPHtqvr1JG9Jsk+Sl40xLlrysPZENu+wCK8TFuF1wqo2zE6UAMCeYyNtwgAA9hACAgBoExBNVfX9VfXqqvpEVV1cVf9YVberqiOr6k2rPOYlG+2omlX1kzs7XHhVHVJVF+6m5a3682H3qKormvNf+TtZ5PVwbbbSv4WqenpVPXEnj9tcVc+brh9ZVffZhWV/qqpuvsL0X6yqC6rq/Kq6sKqOnqYfX1W3XuB5F5rvmqiqV03je8Iq93+wql61xmPYcO+/e4sNsxPlnqCqKsnrk5w8xnjENO0uSW65o8dtOzjWRjLGOD0+5cLE62FtjDG2JNky3TwyyRVJ3n1Nn7eqDk7y1CR3G2N8taoOSLJpuvv4JBdm5x+DX3S+XR3j9ye5zxjjB1a5/46Z/RF7/6q64Rjj39ZgDPtsxPffvYU1ED0PSPKfY4y/3DZhjHHeGOOd080Dquq1VfXhqnrlFBypqrOqavN0/Yqq+qOpvN9bVbecpj+0qt5XVedW1T9tm76a6a+Zf66q11TVR6vqT6rqUVX1/umvkv+yo+ed/vp4wXT9pKp6XlW9u6o+OR0VdPvlHVJV76yqc6bLfebGcdYq3/dR07R/SfJT1+gnz8J25Xey3euh9Vrkyn/jz5r+/X20qv77NP3IqnpTVR2S5FeSPKGqzquq/15Vm6rqtKr6wHS57/SYm1XVGdPP/6+y8kH2bpHk65kFScYYV4wxLpn+7W5O8sppOftX1e9Pz39hVZ1YMyvNd/fpPeXsqnpLVd1qGs/jara29fyqevUK3/t+VfXy6X3n3Kp6wHTXGUluse37XeF7+Lkkr5jm+8ntfpbPqap3VNWHquoeVfW6qvpYVf3vufkePf28z6uqv6rZ+ZS2vcc+s6rel+TeddX336Om968PVtWZ07R7Tu99505fb7/Ar5wkGWO4LHhJ8rgkz1nlviOTfDWzA2BdJ8l7ktxvuu+sJJun6yPJQ6frf5rkadP1m+Z7n4r5pSR/vpOxHJnkK0luleT6ST6b5BnTfY9P8hc7et7M/vp4wXT9pCR/N4378MzOSZIkhyS5cLp+gyT7TdcPS7JlR993kv2SfGaat5K8Jsmblv073JsvSa7Y1d/Jdq+H1mvx2nCZ/7cwN+3pSZ44XT9r7t/Wjyf5p7nfxZu2n3+6/bdz7xG3TfKh6frzkvz+dP0npveMm2+37H0y+8j7pUlenuk9ZW4sm+duHzh3/RX53vvPlfMluW5ma0Y2Tbd/NrOP0iezNRTXn67fZIWfzW8nefl0/Q7TmPZb6We23eM+muQHkjw4yenbjf9Z0/XHT8vf9j53WZKbJbljkr9Pct1pvhclecx0fST5me1/HpmtoflMkkPnfy5Jbpxk3+n6g5KctuzX255ysQlj93r/GOOyJKmq8zL7B/Qv283zH0m27QtwdpIfm64fnOTUqfqvl+SSBZb3gTHG5dPyPpFZySfJBZmtLek87xvGGN9NcvEqf3FeN8kLarbJ5jtJbjd330rf9xVJLhljfGya/jf53jlMWHvX5HeyK6/Fvd1qn3efn/666evZmf28d+ZBSQ6fVg4lyY2r6kZJ7p9p7dAY4x+q6stXW+gY36mqo5LcI8kDkzynqu4+xnj6Cst5QFU9ObM/Ag5MclFm//nOu32SOyV56zSefZJcPt13fmZrKt6Q5A0rPP/9kjx/GteHq+rTmb0/fG21b7yq7pFk6xjj01V1WZKXVdVNxxjbvtdtm9MuSHLR3PvcJzM7YvH9ktw9yQem8e6f5AvTY76T5LQVFnuvJO8YY1wyjfVL0/TvS3JyVR2W2e/zuquNm6uyCaPnosxetKv51tz172TlfUz+c0ypu908z8/sL8A7J/nlzAp+Z+aX992529/dheedf66VVpk+Icnnk/xwZjV/vVUeO/89OcjI8lyT38muvBb3dv+a2ZqZeQfmqiea2vYzX+3f/vauk+TeY4y7TJeDxhhfn+7b6e9pzLx/jPHHmR2596e3n6eq9svsr/Njp9/nX2fl32dl9h/1trHceYzx4Om+n0jywsze+86uqu2/t5XeL3bmkUnuUFWfSvKJzNYCzI9//r1s+/e5fadlnjw33tvPxdM3xxjfWeV7XOnn+odJ3j7GuFOSh8brfWECoudtSa5fVf9r24Rp+9yP7Ibn/r7MNkMkyXFzz3/Pqjpldz/vLj7P5dNaip/P7C+UHflwkkNr2hcjszcMlmvR38nues3sNcYYVyS5vKoemCRVdWCSo3L1NYw78vUkN5q7fUaSK882PK3dS5J3JHnUNO0huXq4pKpuXVV3m5t0lySfXmE52/4z/GLNdrSc379pfr6PJNlUVfeenv+6VfVDVXWdJLcZY7w9yZOT3CTJAdsNZ368t8tsc8yqZ0menvPhSY4YYxwyxjgkydHpvUecmeTYqrrF9JwHVtWKO2vOeU+SH6mqQ7c9Zpo+/3o/vjGGaz0B0TCtOXhYkh+r2cc4L8psu+bu2Iv56Un+rqremav+VXPbJN9Yg+ftelGS46rqvZmtntzhHtNjjG9mtnr8H2q2w96ndzQ/a6/xO3l6ds9rZm/zmCRPmzYJvS2zfY4+0Xj83yd52NxOhY9LsnnaOfHizHayTJJnZPbJhHMy2z/g0hWe67pJnl2zHWLPy2yfhcdP952U5C+n6d/KbK3DBZltfvjA3HPMz7dPZnHxrKr6YJLzktxnmv43VXVBknMz2wfsK9uN5UVJ9pnmOTXJ8WOMb2V190/y2THGZ+emvSOzzTm32sHjrjTGuDjJ05KcUVXnJ3lrZvtJ7OgxWzN7/b9u+h5Pne760yR/XFXvys7/MGKOQ1lvcFX1Z0leMcY4f9ljAYBtBAQA0GYTBgDQJiAAgDYBAQC0CQgAoE1AwF6mqh5WVaOq7rDssQB7LwEBe59HZnaAo0dsf8e2Ew4BXFMCAvYi09EG75vksZkComZnhHx7Vf1tZgcU2tGZDF9cVVuq6qKqesayvg9g4xMQsHc5JsmbxxgfTfKlucMd3zPJU8cYh1fVHTM7cuF9xxjbTo72qGm+p44xNic5IrPD/h6xzuMH9hACAvYuj0zy6un6q/O98wu8f9tZCDM7e+O2MxmeN93+wem+n5kOoXxukh/K7PTuAFfjdN6wl6iqmyX50SR3qqqR2XH9R5J/zFXPXbLtTIa/u93jD03yxCT3GGN8uapOijMTAquwBgL2HscmOWWM8QPTWQ5vk+SSJPfbbr7VzmR448xC46tVdcskD1nHsQN7GAEBe49HJnn9dtNOS/Jz8xNWO5PhGOODmW26uCjJy5K8a81HDOyxnEwLAGizBgIAaBMQAECbgAAA2gQEANAmIACANgEBALQJCACg7f8DZCwYK+UFz1AAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 521.175x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot(\"Area\", data=df[(df['Area'] == \"India\") | (df['Area'] == \"China, mainland\") | (df['Area'] == \"United States of America\")], kind=\"count\", hue=\"Element\", size=8, aspect=.8)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "94c19dc8-b1e7-4b61-b81f-422c27184c4e",
"_uuid": "0d1cfc7acc74847dbc5813b9b3bd0eb9db450985"
},
"source": [
"Though, there is a huge difference between feed and food production, these countries' total production and their ranks depend on feed production."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "9dba87b4-fa51-43ef-95ae-f31396c20146",
"_uuid": "43e0f00abf706ab1782ebb78cefc38aca17316e6"
},
"source": [
"Now, we create a dataframe with countries as index and their annual produce as columns from 1961 to 2013."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "c4a5f859-0384-4c8e-b894-3f747aec8cf9",
"_uuid": "84dd7a2b601479728dd172d3100951553c2daff5",
"scrolled": true
},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Afghanistan</th>\n",
" <th>Albania</th>\n",
" <th>Algeria</th>\n",
" <th>Angola</th>\n",
" <th>Antigua and Barbuda</th>\n",
" <th>Argentina</th>\n",
" <th>Armenia</th>\n",
" <th>Australia</th>\n",
" <th>Austria</th>\n",
" <th>Azerbaijan</th>\n",
" <th>...</th>\n",
" <th>United Republic of Tanzania</th>\n",
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" <th>Zambia</th>\n",
" <th>Zimbabwe</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9481.0</td>\n",
" <td>1706.0</td>\n",
" <td>7488.0</td>\n",
" <td>4834.0</td>\n",
" <td>92.0</td>\n",
" <td>43402.0</td>\n",
" <td>0.0</td>\n",
" <td>25795.0</td>\n",
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" <td>12367.0</td>\n",
" <td>559347.0</td>\n",
" <td>4631.0</td>\n",
" <td>0.0</td>\n",
" <td>97.0</td>\n",
" <td>9523.0</td>\n",
" <td>23856.0</td>\n",
" <td>2982.0</td>\n",
" <td>2976.0</td>\n",
" <td>3260.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9414.0</td>\n",
" <td>1749.0</td>\n",
" <td>7235.0</td>\n",
" <td>4775.0</td>\n",
" <td>94.0</td>\n",
" <td>40784.0</td>\n",
" <td>0.0</td>\n",
" <td>27618.0</td>\n",
" <td>22627.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>12810.0</td>\n",
" <td>556319.0</td>\n",
" <td>4448.0</td>\n",
" <td>0.0</td>\n",
" <td>101.0</td>\n",
" <td>9369.0</td>\n",
" <td>25220.0</td>\n",
" <td>3038.0</td>\n",
" <td>3057.0</td>\n",
" <td>3503.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>9194.0</td>\n",
" <td>1767.0</td>\n",
" <td>6861.0</td>\n",
" <td>5240.0</td>\n",
" <td>105.0</td>\n",
" <td>40219.0</td>\n",
" <td>0.0</td>\n",
" <td>28902.0</td>\n",
" <td>23637.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>13109.0</td>\n",
" <td>552630.0</td>\n",
" <td>4682.0</td>\n",
" <td>0.0</td>\n",
" <td>103.0</td>\n",
" <td>9788.0</td>\n",
" <td>26053.0</td>\n",
" <td>3147.0</td>\n",
" <td>3069.0</td>\n",
" <td>3479.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10170.0</td>\n",
" <td>1889.0</td>\n",
" <td>7255.0</td>\n",
" <td>5286.0</td>\n",
" <td>95.0</td>\n",
" <td>41638.0</td>\n",
" <td>0.0</td>\n",
" <td>29107.0</td>\n",
" <td>24099.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>12965.0</td>\n",
" <td>555677.0</td>\n",
" <td>4723.0</td>\n",
" <td>0.0</td>\n",
" <td>102.0</td>\n",
" <td>10539.0</td>\n",
" <td>26377.0</td>\n",
" <td>3224.0</td>\n",
" <td>3121.0</td>\n",
" <td>3738.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10473.0</td>\n",
" <td>1884.0</td>\n",
" <td>7509.0</td>\n",
" <td>5527.0</td>\n",
" <td>84.0</td>\n",
" <td>44936.0</td>\n",
" <td>0.0</td>\n",
" <td>28961.0</td>\n",
" <td>22664.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>13742.0</td>\n",
" <td>589288.0</td>\n",
" <td>4581.0</td>\n",
" <td>0.0</td>\n",
" <td>107.0</td>\n",
" <td>10641.0</td>\n",
" <td>26961.0</td>\n",
" <td>3328.0</td>\n",
" <td>3236.0</td>\n",
" <td>3940.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 174 columns</p>\n",
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"text/plain": [
" Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina \\\n",
"0 9481.0 1706.0 7488.0 4834.0 92.0 43402.0 \n",
"1 9414.0 1749.0 7235.0 4775.0 94.0 40784.0 \n",
"2 9194.0 1767.0 6861.0 5240.0 105.0 40219.0 \n",
"3 10170.0 1889.0 7255.0 5286.0 95.0 41638.0 \n",
"4 10473.0 1884.0 7509.0 5527.0 84.0 44936.0 \n",
"\n",
" Armenia Australia Austria Azerbaijan ... \\\n",
"0 0.0 25795.0 22542.0 0.0 ... \n",
"1 0.0 27618.0 22627.0 0.0 ... \n",
"2 0.0 28902.0 23637.0 0.0 ... \n",
"3 0.0 29107.0 24099.0 0.0 ... \n",
"4 0.0 28961.0 22664.0 0.0 ... \n",
"\n",
" United Republic of Tanzania United States of America Uruguay Uzbekistan \\\n",
"0 12367.0 559347.0 4631.0 0.0 \n",
"1 12810.0 556319.0 4448.0 0.0 \n",
"2 13109.0 552630.0 4682.0 0.0 \n",
"3 12965.0 555677.0 4723.0 0.0 \n",
"4 13742.0 589288.0 4581.0 0.0 \n",
"\n",
" Vanuatu Venezuela (Bolivarian Republic of) Viet Nam Yemen Zambia \\\n",
"0 97.0 9523.0 23856.0 2982.0 2976.0 \n",
"1 101.0 9369.0 25220.0 3038.0 3057.0 \n",
"2 103.0 9788.0 26053.0 3147.0 3069.0 \n",
"3 102.0 10539.0 26377.0 3224.0 3121.0 \n",
"4 107.0 10641.0 26961.0 3328.0 3236.0 \n",
"\n",
" Zimbabwe \n",
"0 3260.0 \n",
"1 3503.0 \n",
"2 3479.0 \n",
"3 3738.0 \n",
"4 3940.0 \n",
"\n",
"[5 rows x 174 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_df_dict = {}\n",
"for ar in area_list:\n",
" yearly_produce = []\n",
" for yr in year_list:\n",
" yearly_produce.append(df[yr][df['Area']==ar].sum())\n",
" new_df_dict[ar] = yearly_produce\n",
"new_df = pd.DataFrame(new_df_dict)\n",
"\n",
"new_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "15fbe29c-5cea-4ac3-9b95-f92acd89b336",
"_uuid": "ea48f75e9824a0c4c1a5f19cbd63e59a6cb44fe1"
},
"source": [
"Now, this is not perfect so we transpose this dataframe and add column names."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "145f751e-4f5b-4811-a68c-9d20b3c36e10",
"_uuid": "28e765d82bb4ebec3be49200a30fc4e0eabb24d7"
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Y1961</th>\n",
" <th>Y1962</th>\n",
" <th>Y1963</th>\n",
" <th>Y1964</th>\n",
" <th>Y1965</th>\n",
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" <th>Y2009</th>\n",
" <th>Y2010</th>\n",
" <th>Y2011</th>\n",
" <th>Y2012</th>\n",
" <th>Y2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>9481.0</td>\n",
" <td>9414.0</td>\n",
" <td>9194.0</td>\n",
" <td>10170.0</td>\n",
" <td>10473.0</td>\n",
" <td>10169.0</td>\n",
" <td>11289.0</td>\n",
" <td>11508.0</td>\n",
" <td>11815.0</td>\n",
" <td>10454.0</td>\n",
" <td>...</td>\n",
" <td>16542.0</td>\n",
" <td>17658.0</td>\n",
" <td>18317.0</td>\n",
" <td>19248.0</td>\n",
" <td>19381.0</td>\n",
" <td>20661.0</td>\n",
" <td>21030.0</td>\n",
" <td>21100.0</td>\n",
" <td>22706.0</td>\n",
" <td>23007.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>1706.0</td>\n",
" <td>1749.0</td>\n",
" <td>1767.0</td>\n",
" <td>1889.0</td>\n",
" <td>1884.0</td>\n",
" <td>1995.0</td>\n",
" <td>2046.0</td>\n",
" <td>2169.0</td>\n",
" <td>2230.0</td>\n",
" <td>2395.0</td>\n",
" <td>...</td>\n",
" <td>6637.0</td>\n",
" <td>6719.0</td>\n",
" <td>6911.0</td>\n",
" <td>6744.0</td>\n",
" <td>7168.0</td>\n",
" <td>7316.0</td>\n",
" <td>7907.0</td>\n",
" <td>8114.0</td>\n",
" <td>8221.0</td>\n",
" <td>8271.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>7488.0</td>\n",
" <td>7235.0</td>\n",
" <td>6861.0</td>\n",
" <td>7255.0</td>\n",
" <td>7509.0</td>\n",
" <td>7536.0</td>\n",
" <td>7986.0</td>\n",
" <td>8839.0</td>\n",
" <td>9003.0</td>\n",
" <td>9355.0</td>\n",
" <td>...</td>\n",
" <td>48619.0</td>\n",
" <td>49562.0</td>\n",
" <td>51067.0</td>\n",
" <td>49933.0</td>\n",
" <td>50916.0</td>\n",
" <td>57505.0</td>\n",
" <td>60071.0</td>\n",
" <td>65852.0</td>\n",
" <td>69365.0</td>\n",
" <td>72161.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Angola</th>\n",
" <td>4834.0</td>\n",
" <td>4775.0</td>\n",
" <td>5240.0</td>\n",
" <td>5286.0</td>\n",
" <td>5527.0</td>\n",
" <td>5677.0</td>\n",
" <td>5833.0</td>\n",
" <td>5685.0</td>\n",
" <td>6219.0</td>\n",
" <td>6460.0</td>\n",
" <td>...</td>\n",
" <td>25541.0</td>\n",
" <td>26696.0</td>\n",
" <td>28247.0</td>\n",
" <td>29877.0</td>\n",
" <td>32053.0</td>\n",
" <td>36985.0</td>\n",
" <td>38400.0</td>\n",
" <td>40573.0</td>\n",
" <td>38064.0</td>\n",
" <td>48639.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Antigua and Barbuda</th>\n",
" <td>92.0</td>\n",
" <td>94.0</td>\n",
" <td>105.0</td>\n",
" <td>95.0</td>\n",
" <td>84.0</td>\n",
" <td>73.0</td>\n",
" <td>64.0</td>\n",
" <td>59.0</td>\n",
" <td>68.0</td>\n",
" <td>77.0</td>\n",
" <td>...</td>\n",
" <td>92.0</td>\n",
" <td>115.0</td>\n",
" <td>110.0</td>\n",
" <td>122.0</td>\n",
" <td>115.0</td>\n",
" <td>114.0</td>\n",
" <td>115.0</td>\n",
" <td>118.0</td>\n",
" <td>113.0</td>\n",
" <td>119.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 53 columns</p>\n",
"</div>"
],
"text/plain": [
" Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 \\\n",
"Afghanistan 9481.0 9414.0 9194.0 10170.0 10473.0 10169.0 \n",
"Albania 1706.0 1749.0 1767.0 1889.0 1884.0 1995.0 \n",
"Algeria 7488.0 7235.0 6861.0 7255.0 7509.0 7536.0 \n",
"Angola 4834.0 4775.0 5240.0 5286.0 5527.0 5677.0 \n",
"Antigua and Barbuda 92.0 94.0 105.0 95.0 84.0 73.0 \n",
"\n",
" Y1967 Y1968 Y1969 Y1970 ... Y2004 \\\n",
"Afghanistan 11289.0 11508.0 11815.0 10454.0 ... 16542.0 \n",
"Albania 2046.0 2169.0 2230.0 2395.0 ... 6637.0 \n",
"Algeria 7986.0 8839.0 9003.0 9355.0 ... 48619.0 \n",
"Angola 5833.0 5685.0 6219.0 6460.0 ... 25541.0 \n",
"Antigua and Barbuda 64.0 59.0 68.0 77.0 ... 92.0 \n",
"\n",
" Y2005 Y2006 Y2007 Y2008 Y2009 Y2010 \\\n",
"Afghanistan 17658.0 18317.0 19248.0 19381.0 20661.0 21030.0 \n",
"Albania 6719.0 6911.0 6744.0 7168.0 7316.0 7907.0 \n",
"Algeria 49562.0 51067.0 49933.0 50916.0 57505.0 60071.0 \n",
"Angola 26696.0 28247.0 29877.0 32053.0 36985.0 38400.0 \n",
"Antigua and Barbuda 115.0 110.0 122.0 115.0 114.0 115.0 \n",
"\n",
" Y2011 Y2012 Y2013 \n",
"Afghanistan 21100.0 22706.0 23007.0 \n",
"Albania 8114.0 8221.0 8271.0 \n",
"Algeria 65852.0 69365.0 72161.0 \n",
"Angola 40573.0 38064.0 48639.0 \n",
"Antigua and Barbuda 118.0 113.0 119.0 \n",
"\n",
"[5 rows x 53 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_df = pd.DataFrame.transpose(new_df)\n",
"new_df.columns = year_list\n",
"\n",
"new_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "57929d23-e3d7-4955-92d1-6fa388eb774d",
"_uuid": "605f908af9ff88120fce2a2b59160816fcdcfa67"
},
"source": [
"Perfect! Now, we will do some feature engineering.\n",
"\n",
"# First, a new column which indicates mean produce of each state over the given years. Second, a ranking column which ranks countries on the basis of mean produce."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "ab91a322-0cb9-4edf-b5a2-cde82a237824",
"_uuid": "979f875019abef3ed85af75e000fe59d1de5a381"
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Y1961</th>\n",
" <th>Y1962</th>\n",
" <th>Y1963</th>\n",
" <th>Y1964</th>\n",
" <th>Y1965</th>\n",
" <th>Y1966</th>\n",
" <th>Y1967</th>\n",
" <th>Y1968</th>\n",
" <th>Y1969</th>\n",
" <th>Y1970</th>\n",
" <th>...</th>\n",
" <th>Y2006</th>\n",
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" <th>Y2008</th>\n",
" <th>Y2009</th>\n",
" <th>Y2010</th>\n",
" <th>Y2011</th>\n",
" <th>Y2012</th>\n",
" <th>Y2013</th>\n",
" <th>Mean_Produce</th>\n",
" <th>Rank</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>9481.0</td>\n",
" <td>9414.0</td>\n",
" <td>9194.0</td>\n",
" <td>10170.0</td>\n",
" <td>10473.0</td>\n",
" <td>10169.0</td>\n",
" <td>11289.0</td>\n",
" <td>11508.0</td>\n",
" <td>11815.0</td>\n",
" <td>10454.0</td>\n",
" <td>...</td>\n",
" <td>18317.0</td>\n",
" <td>19248.0</td>\n",
" <td>19381.0</td>\n",
" <td>20661.0</td>\n",
" <td>21030.0</td>\n",
" <td>21100.0</td>\n",
" <td>22706.0</td>\n",
" <td>23007.0</td>\n",
" <td>13003.056604</td>\n",
" <td>69.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>1706.0</td>\n",
" <td>1749.0</td>\n",
" <td>1767.0</td>\n",
" <td>1889.0</td>\n",
" <td>1884.0</td>\n",
" <td>1995.0</td>\n",
" <td>2046.0</td>\n",
" <td>2169.0</td>\n",
" <td>2230.0</td>\n",
" <td>2395.0</td>\n",
" <td>...</td>\n",
" <td>6911.0</td>\n",
" <td>6744.0</td>\n",
" <td>7168.0</td>\n",
" <td>7316.0</td>\n",
" <td>7907.0</td>\n",
" <td>8114.0</td>\n",
" <td>8221.0</td>\n",
" <td>8271.0</td>\n",
" <td>4475.509434</td>\n",
" <td>104.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>7488.0</td>\n",
" <td>7235.0</td>\n",
" <td>6861.0</td>\n",
" <td>7255.0</td>\n",
" <td>7509.0</td>\n",
" <td>7536.0</td>\n",
" <td>7986.0</td>\n",
" <td>8839.0</td>\n",
" <td>9003.0</td>\n",
" <td>9355.0</td>\n",
" <td>...</td>\n",
" <td>51067.0</td>\n",
" <td>49933.0</td>\n",
" <td>50916.0</td>\n",
" <td>57505.0</td>\n",
" <td>60071.0</td>\n",
" <td>65852.0</td>\n",
" <td>69365.0</td>\n",
" <td>72161.0</td>\n",
" <td>28879.490566</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Angola</th>\n",
" <td>4834.0</td>\n",
" <td>4775.0</td>\n",
" <td>5240.0</td>\n",
" <td>5286.0</td>\n",
" <td>5527.0</td>\n",
" <td>5677.0</td>\n",
" <td>5833.0</td>\n",
" <td>5685.0</td>\n",
" <td>6219.0</td>\n",
" <td>6460.0</td>\n",
" <td>...</td>\n",
" <td>28247.0</td>\n",
" <td>29877.0</td>\n",
" <td>32053.0</td>\n",
" <td>36985.0</td>\n",
" <td>38400.0</td>\n",
" <td>40573.0</td>\n",
" <td>38064.0</td>\n",
" <td>48639.0</td>\n",
" <td>13321.056604</td>\n",
" <td>68.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Antigua and Barbuda</th>\n",
" <td>92.0</td>\n",
" <td>94.0</td>\n",
" <td>105.0</td>\n",
" <td>95.0</td>\n",
" <td>84.0</td>\n",
" <td>73.0</td>\n",
" <td>64.0</td>\n",
" <td>59.0</td>\n",
" <td>68.0</td>\n",
" <td>77.0</td>\n",
" <td>...</td>\n",
" <td>110.0</td>\n",
" <td>122.0</td>\n",
" <td>115.0</td>\n",
" <td>114.0</td>\n",
" <td>115.0</td>\n",
" <td>118.0</td>\n",
" <td>113.0</td>\n",
" <td>119.0</td>\n",
" <td>83.886792</td>\n",
" <td>172.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 55 columns</p>\n",
"</div>"
],
"text/plain": [
" Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 \\\n",
"Afghanistan 9481.0 9414.0 9194.0 10170.0 10473.0 10169.0 \n",
"Albania 1706.0 1749.0 1767.0 1889.0 1884.0 1995.0 \n",
"Algeria 7488.0 7235.0 6861.0 7255.0 7509.0 7536.0 \n",
"Angola 4834.0 4775.0 5240.0 5286.0 5527.0 5677.0 \n",
"Antigua and Barbuda 92.0 94.0 105.0 95.0 84.0 73.0 \n",
"\n",
" Y1967 Y1968 Y1969 Y1970 ... Y2006 \\\n",
"Afghanistan 11289.0 11508.0 11815.0 10454.0 ... 18317.0 \n",
"Albania 2046.0 2169.0 2230.0 2395.0 ... 6911.0 \n",
"Algeria 7986.0 8839.0 9003.0 9355.0 ... 51067.0 \n",
"Angola 5833.0 5685.0 6219.0 6460.0 ... 28247.0 \n",
"Antigua and Barbuda 64.0 59.0 68.0 77.0 ... 110.0 \n",
"\n",
" Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 \\\n",
"Afghanistan 19248.0 19381.0 20661.0 21030.0 21100.0 22706.0 \n",
"Albania 6744.0 7168.0 7316.0 7907.0 8114.0 8221.0 \n",
"Algeria 49933.0 50916.0 57505.0 60071.0 65852.0 69365.0 \n",
"Angola 29877.0 32053.0 36985.0 38400.0 40573.0 38064.0 \n",
"Antigua and Barbuda 122.0 115.0 114.0 115.0 118.0 113.0 \n",
"\n",
" Y2013 Mean_Produce Rank \n",
"Afghanistan 23007.0 13003.056604 69.0 \n",
"Albania 8271.0 4475.509434 104.0 \n",
"Algeria 72161.0 28879.490566 38.0 \n",
"Angola 48639.0 13321.056604 68.0 \n",
"Antigua and Barbuda 119.0 83.886792 172.0 \n",
"\n",
"[5 rows x 55 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_produce = []\n",
"for i in range(174):\n",
" mean_produce.append(new_df.iloc[i,:].values.mean())\n",
"new_df['Mean_Produce'] = mean_produce\n",
"\n",
"new_df['Rank'] = new_df['Mean_Produce'].rank(ascending=False)\n",
"\n",
"new_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6f7c4fb7-1475-439f-9929-4cf4b29d8de7",
"_uuid": "da6c9c98eaff45edba1179103ae539bbfbe9753b"
},
"source": [
"Now, we create another dataframe with items and their total production each year from 1961 to 2013"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "bfd692bc-dce4-4870-9ab9-9775cf69a87f",
"_uuid": "9e11017d381f175eee714643bc5fa763600aaa0b"
},
"outputs": [],
"source": [
"item_list = list(df['Item'].unique())\n",
"\n",
"item_df = pd.DataFrame()\n",
"item_df['Item_Name'] = item_list\n",
"\n",
"for yr in year_list:\n",
" item_produce = []\n",
" for it in item_list:\n",
" item_produce.append(df[yr][df['Item']==it].sum())\n",
" item_df[yr] = item_produce\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "3b7ed0c2-6140-4285-861c-d0cd2324a1f5",
"_uuid": "cb4641df5ce90f516f88c536e8a6c6870c5b4f65"
},
"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>Item_Name</th>\n",
" <th>Y1961</th>\n",
" <th>Y1962</th>\n",
" <th>Y1963</th>\n",
" <th>Y1964</th>\n",
" <th>Y1965</th>\n",
" <th>Y1966</th>\n",
" <th>Y1967</th>\n",
" <th>Y1968</th>\n",
" <th>Y1969</th>\n",
" <th>...</th>\n",
" <th>Y2004</th>\n",
" <th>Y2005</th>\n",
" <th>Y2006</th>\n",
" <th>Y2007</th>\n",
" <th>Y2008</th>\n",
" <th>Y2009</th>\n",
" <th>Y2010</th>\n",
" <th>Y2011</th>\n",
" <th>Y2012</th>\n",
" <th>Y2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Wheat and products</td>\n",
" <td>138829.0</td>\n",
" <td>144643.0</td>\n",
" <td>147325.0</td>\n",
" <td>156273.0</td>\n",
" <td>168822.0</td>\n",
" <td>169832.0</td>\n",
" <td>171469.0</td>\n",
" <td>179530.0</td>\n",
" <td>189658.0</td>\n",
" <td>...</td>\n",
" <td>527394.0</td>\n",
" <td>532263.0</td>\n",
" <td>537279.0</td>\n",
" <td>529271.0</td>\n",
" <td>562239.0</td>\n",
" <td>557245.0</td>\n",
" <td>549926.0</td>\n",
" <td>578179.0</td>\n",
" <td>576597</td>\n",
" <td>587492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Rice (Milled Equivalent)</td>\n",
" <td>122700.0</td>\n",
" <td>131842.0</td>\n",
" <td>139507.0</td>\n",
" <td>148304.0</td>\n",
" <td>150056.0</td>\n",
" <td>155583.0</td>\n",
" <td>158587.0</td>\n",
" <td>164614.0</td>\n",
" <td>167922.0</td>\n",
" <td>...</td>\n",
" <td>361107.0</td>\n",
" <td>366025.0</td>\n",
" <td>372629.0</td>\n",
" <td>378698.0</td>\n",
" <td>389708.0</td>\n",
" <td>394221.0</td>\n",
" <td>398559.0</td>\n",
" <td>404152.0</td>\n",
" <td>406787</td>\n",
" <td>410880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Barley and products</td>\n",
" <td>46180.0</td>\n",
" <td>48915.0</td>\n",
" <td>51642.0</td>\n",
" <td>54184.0</td>\n",
" <td>54945.0</td>\n",
" <td>55463.0</td>\n",
" <td>56424.0</td>\n",
" <td>60455.0</td>\n",
" <td>65501.0</td>\n",
" <td>...</td>\n",
" <td>102055.0</td>\n",
" <td>97185.0</td>\n",
" <td>100981.0</td>\n",
" <td>93310.0</td>\n",
" <td>98209.0</td>\n",
" <td>99135.0</td>\n",
" <td>92563.0</td>\n",
" <td>92570.0</td>\n",
" <td>88766</td>\n",
" <td>99452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Maize and products</td>\n",
" <td>168039.0</td>\n",
" <td>168305.0</td>\n",
" <td>172905.0</td>\n",
" <td>175468.0</td>\n",
" <td>190304.0</td>\n",
" <td>200860.0</td>\n",
" <td>213050.0</td>\n",
" <td>215613.0</td>\n",
" <td>221953.0</td>\n",
" <td>...</td>\n",
" <td>545024.0</td>\n",
" <td>549036.0</td>\n",
" <td>543280.0</td>\n",
" <td>573892.0</td>\n",
" <td>592231.0</td>\n",
" <td>557940.0</td>\n",
" <td>584337.0</td>\n",
" <td>603297.0</td>\n",
" <td>608730</td>\n",
" <td>671300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Millet and products</td>\n",
" <td>19075.0</td>\n",
" <td>19019.0</td>\n",
" <td>19740.0</td>\n",
" <td>20353.0</td>\n",
" <td>18377.0</td>\n",
" <td>20860.0</td>\n",
" <td>22997.0</td>\n",
" <td>21785.0</td>\n",
" <td>23966.0</td>\n",
" <td>...</td>\n",
" <td>25789.0</td>\n",
" <td>25496.0</td>\n",
" <td>25997.0</td>\n",
" <td>26750.0</td>\n",
" <td>26373.0</td>\n",
" <td>24575.0</td>\n",
" <td>27039.0</td>\n",
" <td>25740.0</td>\n",
" <td>26105</td>\n",
" <td>26346</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 54 columns</p>\n",
"</div>"
],
"text/plain": [
" Item_Name Y1961 Y1962 Y1963 Y1964 Y1965 \\\n",
"0 Wheat and products 138829.0 144643.0 147325.0 156273.0 168822.0 \n",
"1 Rice (Milled Equivalent) 122700.0 131842.0 139507.0 148304.0 150056.0 \n",
"2 Barley and products 46180.0 48915.0 51642.0 54184.0 54945.0 \n",
"3 Maize and products 168039.0 168305.0 172905.0 175468.0 190304.0 \n",
"4 Millet and products 19075.0 19019.0 19740.0 20353.0 18377.0 \n",
"\n",
" Y1966 Y1967 Y1968 Y1969 ... Y2004 Y2005 \\\n",
"0 169832.0 171469.0 179530.0 189658.0 ... 527394.0 532263.0 \n",
"1 155583.0 158587.0 164614.0 167922.0 ... 361107.0 366025.0 \n",
"2 55463.0 56424.0 60455.0 65501.0 ... 102055.0 97185.0 \n",
"3 200860.0 213050.0 215613.0 221953.0 ... 545024.0 549036.0 \n",
"4 20860.0 22997.0 21785.0 23966.0 ... 25789.0 25496.0 \n",
"\n",
" Y2006 Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013 \n",
"0 537279.0 529271.0 562239.0 557245.0 549926.0 578179.0 576597 587492 \n",
"1 372629.0 378698.0 389708.0 394221.0 398559.0 404152.0 406787 410880 \n",
"2 100981.0 93310.0 98209.0 99135.0 92563.0 92570.0 88766 99452 \n",
"3 543280.0 573892.0 592231.0 557940.0 584337.0 603297.0 608730 671300 \n",
"4 25997.0 26750.0 26373.0 24575.0 27039.0 25740.0 26105 26346 \n",
"\n",
"[5 rows x 54 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"item_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "3fa01e1f-bedd-431b-90c3-8d7d70545f34",
"_uuid": "56a647293f1c1aba7c184f249021e008a4d5a8f2"
},
"source": [
"# Some more feature engineering\n",
"\n",
"This time, we will use the new features to get some good conclusions.\n",
"\n",
"# 1. Total amount of item produced from 1961 to 2013\n",
"# 2. Providing a rank to the items to know the most produced item"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "3a6bb102-6749-4818-860d-59aaad6de07f",
"_uuid": "9e816786e7a161227ae72d164b25c0029e01e5b4",
"scrolled": true
},
"outputs": [
{
"data": {
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Item_Name</th>\n",
" <th>Y1961</th>\n",
" <th>Y1962</th>\n",
" <th>Y1963</th>\n",
" <th>Y1964</th>\n",
" <th>Y1965</th>\n",
" <th>Y1966</th>\n",
" <th>Y1967</th>\n",
" <th>Y1968</th>\n",
" <th>Y1969</th>\n",
" <th>...</th>\n",
" <th>Y2006</th>\n",
" <th>Y2007</th>\n",
" <th>Y2008</th>\n",
" <th>Y2009</th>\n",
" <th>Y2010</th>\n",
" <th>Y2011</th>\n",
" <th>Y2012</th>\n",
" <th>Y2013</th>\n",
" <th>Sum</th>\n",
" <th>Production_Rank</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Wheat and products</td>\n",
" <td>138829.0</td>\n",
" <td>144643.0</td>\n",
" <td>147325.0</td>\n",
" <td>156273.0</td>\n",
" <td>168822.0</td>\n",
" <td>169832.0</td>\n",
" <td>171469.0</td>\n",
" <td>179530.0</td>\n",
" <td>189658.0</td>\n",
" <td>...</td>\n",
" <td>537279.0</td>\n",
" <td>529271.0</td>\n",
" <td>562239.0</td>\n",
" <td>557245.0</td>\n",
" <td>549926.0</td>\n",
" <td>578179.0</td>\n",
" <td>576597</td>\n",
" <td>587492</td>\n",
" <td>19194671.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Rice (Milled Equivalent)</td>\n",
" <td>122700.0</td>\n",
" <td>131842.0</td>\n",
" <td>139507.0</td>\n",
" <td>148304.0</td>\n",
" <td>150056.0</td>\n",
" <td>155583.0</td>\n",
" <td>158587.0</td>\n",
" <td>164614.0</td>\n",
" <td>167922.0</td>\n",
" <td>...</td>\n",
" <td>372629.0</td>\n",
" <td>378698.0</td>\n",
" <td>389708.0</td>\n",
" <td>394221.0</td>\n",
" <td>398559.0</td>\n",
" <td>404152.0</td>\n",
" <td>406787</td>\n",
" <td>410880</td>\n",
" <td>14475448.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Barley and products</td>\n",
" <td>46180.0</td>\n",
" <td>48915.0</td>\n",
" <td>51642.0</td>\n",
" <td>54184.0</td>\n",
" <td>54945.0</td>\n",
" <td>55463.0</td>\n",
" <td>56424.0</td>\n",
" <td>60455.0</td>\n",
" <td>65501.0</td>\n",
" <td>...</td>\n",
" <td>100981.0</td>\n",
" <td>93310.0</td>\n",
" <td>98209.0</td>\n",
" <td>99135.0</td>\n",
" <td>92563.0</td>\n",
" <td>92570.0</td>\n",
" <td>88766</td>\n",
" <td>99452</td>\n",
" <td>4442742.0</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Maize and products</td>\n",
" <td>168039.0</td>\n",
" <td>168305.0</td>\n",
" <td>172905.0</td>\n",
" <td>175468.0</td>\n",
" <td>190304.0</td>\n",
" <td>200860.0</td>\n",
" <td>213050.0</td>\n",
" <td>215613.0</td>\n",
" <td>221953.0</td>\n",
" <td>...</td>\n",
" <td>543280.0</td>\n",
" <td>573892.0</td>\n",
" <td>592231.0</td>\n",
" <td>557940.0</td>\n",
" <td>584337.0</td>\n",
" <td>603297.0</td>\n",
" <td>608730</td>\n",
" <td>671300</td>\n",
" <td>19960640.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Millet and products</td>\n",
" <td>19075.0</td>\n",
" <td>19019.0</td>\n",
" <td>19740.0</td>\n",
" <td>20353.0</td>\n",
" <td>18377.0</td>\n",
" <td>20860.0</td>\n",
" <td>22997.0</td>\n",
" <td>21785.0</td>\n",
" <td>23966.0</td>\n",
" <td>...</td>\n",
" <td>25997.0</td>\n",
" <td>26750.0</td>\n",
" <td>26373.0</td>\n",
" <td>24575.0</td>\n",
" <td>27039.0</td>\n",
" <td>25740.0</td>\n",
" <td>26105</td>\n",
" <td>26346</td>\n",
" <td>1225400.0</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 56 columns</p>\n",
"</div>"
],
"text/plain": [
" Item_Name Y1961 Y1962 Y1963 Y1964 Y1965 \\\n",
"0 Wheat and products 138829.0 144643.0 147325.0 156273.0 168822.0 \n",
"1 Rice (Milled Equivalent) 122700.0 131842.0 139507.0 148304.0 150056.0 \n",
"2 Barley and products 46180.0 48915.0 51642.0 54184.0 54945.0 \n",
"3 Maize and products 168039.0 168305.0 172905.0 175468.0 190304.0 \n",
"4 Millet and products 19075.0 19019.0 19740.0 20353.0 18377.0 \n",
"\n",
" Y1966 Y1967 Y1968 Y1969 ... Y2006 \\\n",
"0 169832.0 171469.0 179530.0 189658.0 ... 537279.0 \n",
"1 155583.0 158587.0 164614.0 167922.0 ... 372629.0 \n",
"2 55463.0 56424.0 60455.0 65501.0 ... 100981.0 \n",
"3 200860.0 213050.0 215613.0 221953.0 ... 543280.0 \n",
"4 20860.0 22997.0 21785.0 23966.0 ... 25997.0 \n",
"\n",
" Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013 \\\n",
"0 529271.0 562239.0 557245.0 549926.0 578179.0 576597 587492 \n",
"1 378698.0 389708.0 394221.0 398559.0 404152.0 406787 410880 \n",
"2 93310.0 98209.0 99135.0 92563.0 92570.0 88766 99452 \n",
"3 573892.0 592231.0 557940.0 584337.0 603297.0 608730 671300 \n",
"4 26750.0 26373.0 24575.0 27039.0 25740.0 26105 26346 \n",
"\n",
" Sum Production_Rank \n",
"0 19194671.0 6.0 \n",
"1 14475448.0 8.0 \n",
"2 4442742.0 20.0 \n",
"3 19960640.0 5.0 \n",
"4 1225400.0 38.0 \n",
"\n",
"[5 rows x 56 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum_col = []\n",
"for i in range(115):\n",
" sum_col.append(item_df.iloc[i,1:].values.sum())\n",
"item_df['Sum'] = sum_col\n",
"item_df['Production_Rank'] = item_df['Sum'].rank(ascending=False)\n",
"\n",
"item_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "7e20740c-565b-4969-a52e-d986e462b750",
"_uuid": "f483c9add5f6af9af9162b5425f6d65eb1c5f4aa"
},
"source": [
"# Now, we find the most produced food items in the last half-century"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "3130fe83-404c-4b3c-addc-560b2e2f32bf",
"_uuid": "0403e9ab2e13587588e3a30d64b8b6638571d3d5"
},
"outputs": [
{
"data": {
"text/plain": [
"56 Cereals - Excluding Beer\n",
"65 Fruits - Excluding Wine\n",
"3 Maize and products\n",
"53 Milk - Excluding Butter\n",
"6 Potatoes and products\n",
"1 Rice (Milled Equivalent)\n",
"57 Starchy Roots\n",
"64 Vegetables\n",
"27 Vegetables, Other\n",
"0 Wheat and products\n",
"Name: Item_Name, dtype: object"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"item_df['Item_Name'][item_df['Production_Rank'] < 11.0].sort_values()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "b6212fed-588b-426e-9271-6d857cd6aacb",
"_uuid": "e2c83f4c851b755ea6cf19f1bca168e705bd4edd"
},
"source": [
"So, cereals, fruits and maize are the most produced items in the last 50 years\n",
"\n",
"# Food and feed plot for most produced items "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "493f9940-1762-4718-acb4-fba5c4c73f4b",
"_uuid": "f8454c5200bdeb3995b9a0ada3deb5ca1c31f181"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
" warnings.warn(msg)\n",
"/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
" warnings.warn(msg, UserWarning)\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1212.38x1440 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot(\"Item\", data=df[(df['Item']=='Wheat and products') | (df['Item']=='Rice (Milled Equivalent)') | (df['Item']=='Maize and products') | (df['Item']=='Potatoes and products') | (df['Item']=='Vegetables, Other') | (df['Item']=='Milk - Excluding Butter') | (df['Item']=='Cereals - Excluding Beer') | (df['Item']=='Starchy Roots') | (df['Item']=='Vegetables') | (df['Item']=='Fruits - Excluding Wine')], kind=\"count\", hue=\"Element\", size=20, aspect=.8)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "45dda825-49a0-41ab-9ebd-eaa609aac986",
"_uuid": "ce5b2d38ff24ea08da632c4e2773dbd0bd026b9d",
"collapsed": true
},
"source": [
"# Now, we plot a heatmap of correlation of produce in difference years"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "b1bab0ec-6615-452c-8d06-a81d4f2ae252",
"_uuid": "a2ed2aae2364810ce640648cf50880adcf2cdcc4"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a23b4b128>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"year_df = df.iloc[:,10:]\n",
"fig, ax = plt.subplots(figsize=(16,10))\n",
"sns.heatmap(year_df.corr(), ax=ax)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "43e1af94-ba07-4b95-8da3-1d774db940cd",
"_uuid": "70d2b0a7db9b8a5535b3c5b3c2eb927b904bf6d3"
},
"source": [
"So, we gather that a given year's production is more similar to its immediate previous and immediate following years."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "58cde27d-5ddc-4ebe-a8e1-80a8257f44c1",
"_uuid": "6f48b52c09ea6a207644044cace5a88c983bf316"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
"image/png": 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AIUTqxQzNgZZAl7nTda1Brl65kV11bdgULDt7FotPmozdpijK9pDmGi4Dr4mXsHeqlHoMOB0Yp5SqwlyN/HPgCaXUt4B9wFesw58HzgV2A37gGwBa60al1G3Ae9ZxK7TWHQtf/hNzpbQXeMH6oZ/XEEIMgUjMoKafcNixHcRf3zWnD59QnM1tFw6+rvJYmOMjhEiN3qbHVDb6uXZVGXW+EE67Yvl5czht+jgcNnMz/tFQHWUwEhYQtdaX9XHXmb0cq4Hv9fE8DwMP99K+AZjXS3tDb68hhDh6kZhBdXOQqNF7OIwZmntf3cWzR1FX2aYU4zPdgw6UQggxEL2NgOyq9XH96s00ByJ4nXZ+etFcTpySi9NuY0L22Jz/LD2wEGJAwlHzymFf4TAcNfjZC9tYv/MgAGfPLeKHZ80cVF1lh81GYbYbt0NWKgshhl5vIyCbq1q48enNtFu7LPx88XxmF2XhtqqjDLY2/GghAVEIcVjhqEF1S4CY0ftmAYFwjOVry3nfqqt8yYLJXLmoZFCbx8pKZSFEIvU2AvJ2RQM/eWYroahBfoaLuxaXMm1cOmkuB4VZY3szfgmIQoh+haIxalqCfYbDlkCEG57azPaaQ3WVv3rK4LZdSXM5KMh0y2IUIURC9DYC8vr2On72wnZihmZijodfLDmeomwPGR4H4zPGdjgECYhCiH4EIzFqW/sOh/W+ENetLmNvgx+bgu9/fibnlU4Y1GtkeZ2My3APxekKIUQPvX3JfWbTAX79yi40UDI+nbsWl5KX7iLb6yRf+iNAAqIQog/BiNmpGrr3cFjZ6Oe61WXUtpor/m469zgWDbJ0Xn6Gm2yvrFQWQiRGb19yH3t3H7//x0cAzJmQxR0XzyPT45SdE7qRgCiE6OFw4XBnrY9l1oo/j9PGbRfO4xPH5A74+W1KUZDlHlN7igkhkqt7P6a15vf/+IiV75lbcC04JpefXDiXNJdjzFRHGQzpnYUQXQTC5jfuvsJh97rKHSv+BkpWKgshEq17PxYzNL9+ZRfPbTa34Fo0cxw3nnMcbqedQvmy2iv5JyKE6BQIx6hpDaL7CIfxdZXHZ7i5a8n8QZXOczvtFGa6ZaWyECJh/OEota2hzn4sHDW444XtvLGzHoBz5xfxg8/PxOWwUZjlweOUL6u9kYAohAB6dqrd/X1LDb94ySydV5zr5a4lpRQOonReuttcqTzWVwYKIRKnPRSlzneoHwtEYty6tpz3Pja34Lp0wWSuWFSC024fk9VRBkMCohCiR6fa3ZMbKrn/jQoAZhZm8POL55OT5hrw88vKQCFEorWFotTH9WO+YIQbn95C+YFW4NAWXE67WTrPKSMZ/ZKAKMQY171TjdezrnIOt104d8Bl8JRS5Ge4yJLJ30KIBPIFI9T7Qp23G9vDXLe6jIr6dhRw1ZkzuPCEiWO+OspgSEAUYgxrC0Wpaw32el/3Sd2nTc/n5i8NvK6yTSkKszx4XTK/RwiROK3BCAfjwmFNS5BrV5WxvzmA3aZYdvZszjyuAK/LTmGmRzbkHyAJiEKMUd2/ccfrXlf5nHlFXPOFgddVdtrNyd8yv0cIkUgtgQgNbYf6sb0N7Vy7qoyDbWFcDhu3nj+HU0vyyXA7GC9zoAdFAqIQY1D3b9zxAuEYy9ds4f19zcDg6yrLEI4QIhma/WEa28Odt7fXtLJs9WZag1HSXXZu//I8SifnyBzoIyQBUYgxpvs37u73xddVvuIz01i6cOB1leVbuhAiGZrawzT5D4XDjZXN3PT0FgKRGDleJ3cuns+Mwkzy0l2DWlAnDpGAKMQY0uKP0NDeezis94W4blUZexvNusrXfGEm584feF3lnDQXeenSEQshEquxPUxzXDiM35+1INPNXUtKmZKXxrhMtyyQOwoSEIUYI7oPx8Tb1+jnulVl1PkGX1dZVioLIZLlYFuI1kCk8/bLW2u58+/bMTRMzvVy95JSirK9FGS6B7zbguid/NMTYgzoPhwTL76ustdp57aL5nLSlIHVVZaVykKIZKn3hfAFD4XDpz7Yz29e3w3A9IIM7lw8n/x0N0XZUh1lKEhAFGKU6z4cE29jZTM/PsK6yrJSWQiRLHW+IG3BKGDuz/qnt/fyyD/3AjB/Uha3f3k+OV6X1HkfQhIQhRjFGtpCtMQNx8R7c9dBbnvuUF3lu5eUMiU/bUDPKyuVhRDJoLWm3heiLWSGQ0NrfrtuD099sB+AU6blccv5c8j0OKU6yhCTgCjEKNV9rk68F7bU8MsjrKssK5WFEMmgtabOF6LdCocxQ/OLl3bwYnktAGfMLmDZ2bNI9zjlC2sCSEAUYhTqPlcn3hMbKvmdVVd5VmEmd1w8b8DbQMhKZSFEMmitqW0N4Q+b4TAcNbjtua28tbsBgAuOn8hVZ04n3e2Q6igJIgFRiFEmfq5OPK01f3jzIx6z6iqfOMWsq5zmOnw3oJRiXIaLTFmpLIRIMMPQ1PqCBMIxAPzhKMvXlPOBtXn/106ZwjdPm0qmxymjGQkkAVGIUaSvcNi9rvJnZozjpnOPG9ACE7vNXKksqwKFEIlmGJqa1iDBiBkOu2/e/53PlnDJgmKyvE7GSXWUhJKAKMQo0H0id7xw1OBnz29j/S6zrvK584r4wQDrKstKZSFEssSscBiywuHBNnPz/o8bum7en5vmIlemuiScBEQhRph12+t4YH0FlU1+inPTuOIz05gzKbtzIne87kMzS08u5j8+M21AQzIep51CmfgthEiCmKGpbgkQjhoA7G8OcN2qMqpbgjhsipu+dByfnTleqqMkkVwWEGIEWbe9juVry6nzBcnxOqltDXDTmi28vq2ux7Et/gg/fKKsMxxe8ZlpXLGoZEDhMMPjYEK2hEMhROJFYwYHmg+Fw4r6Nq5euZHqliAeh43bvzyP02cVUJjlkXCYRCkJiEqpHyilypVSW5RSjymlPEqpaUqpd5RSu5RSjyulXNaxbuv2buv+qXHPc4PVvkMp9cW49rOttt1KqWXJf4dCJMYD6ytw2lXnwhKXw45dKVa+V9nluLrWIFc/vpEdtT5sCn501kyWLpwyoNfITXNRkOmRid9CiISLxgyqW4JEYmY43HqglR88sYnG9jAZbgd3f6WUU6blU5TlkdJ5SZb0gKiUmgRcBSzQWs8D7MBS4E7gHq31DKAJ+Jb1kG8BTVrr6cA91nEopeZYj5sLnA38VillV0rZgf8FzgHmAJdZxwox4lU2+fE67WitiRoaw9B4nDZqWgOdx+xr9HPVyo3sa/TjtCtuOX8u586fcNjnVkpRkOWRuT1CiKSIdAuHGz5u5EdPbsIXjJKb5uSeS4+ndHIOE3KknGcqpGqI2QF4lVIOIA2oBs4AVln3PwpcZP19oXUb6/4zlXlp40JgpdY6pLX+CNgNLLR+dmutK7TWYWCldawQI15xbhr+cJRIzAyHAMGIQVGWFzDrKl+9ciN1vhBep507Lp7PZ2aMO+zz2m2KCdkeMuQbuhAiCcJRg+rmQ+Fw/c56bnx6C8GoQVGWh/uWnsjsoiwm5nildF6KJD0gaq33A78A9mEGwxbgfaBZa90xy74KmGT9PQmotB4btY7Pj2/v9pi+2oUY8f7jM9MIRgz84SgaTSASI2polp5czIf7mvjB45toCUTI9jr51SXHc9KU3MM+p9NuY2KOV7axEUIkRThqUNMSJGqY4fCFzdWseHYrUUNzTH4a9y49gWnj05mY45XSeSmUiiHmXMwretOAiUA65nBwd7rjIX3cN9j23s7lCqXUBqXUhvr6+sOduhApFTM0syZkcdUZM8hPd+MLRslPd3P1GTMIxQyWPbWZQCRGQaabey89gVlFmYd9Tq/LLp3wCCX9lxiJQtEY1S2BznD45IZK7n5pJ4aG2UWZ/PrSE5iSn8bEbK8skkuxVIwnfR74SGtdD6CUegr4FJCjlHJYVwknAwes46uAYqDKGpLOBhrj2jvEP6av9i601g8CDwIsWLCg1xApxHAQvwXEwpI8Fpbkdd4XX1d5Sl4ady2eT8EA6ipneByMz5AqBCOV9F9ipAlGYtS0BDG0RmvNw299zF/e2Qccquw0PtNDgVRHGRZScdlgH3CqUirNmkt4JrAVeB1YYh1zObDG+nutdRvr/te01tpqX2qtcp4GzADeBd4DZlirol2YC1nWJuF9CZEQMUN32QIi3sr3Krn7RTMczirM5N5LTxhQOMxLl5XKQojkiQ+Hhtbc9+ruznB42rH53PHl+RRmeSnMkn5puEj6FUSt9TtKqVXAB0AU+BDzW/BzwEql1E+ttoeshzwE/EkptRvzyuFS63nKlVJPYIbLKPA9rXUMQCn1X8CLmCukH9Zalyfr/QkxlLpvAdFBa83v//FR5/Y2J03JYcUA6iorpRif6ZbFKEKIpAmEY9S0BtFa86/dDfzipR00BSKA2XfdesFcxmW4ZQeFYSYlnxJa61uAW7o1V2CuQO5+bBD4Sh/Pcztwey/tzwPPH/2ZCpE6fYXDmKG55+WdPL+lBhh4XWWpqSyESDZ/OEptawitNW/uPMjtL2wjZI2GZLjtHGgOsLPGx4zjDz9nWiSXzEwXYhjqvj9Yh3DUYMWzWzvD4bnzi1h+3pzDhkNZqSyESLb20KFw2BaKcueL2zvDYX66iwnZHrxOO3+2hprF8CLjTEIMM5GYuT9Yxyq/Dv5wlJvXlPPhIOsqe112CjM92GRFoBAiSdpCUep9Zjhs9oe5fvVm2sMxAMZnuMhNd+G02XA7oKrJn+KzFb2RgCjEMNJ9f7AOLf4Iy57azI5aHwBXLCph6cnFvT1FF5keJ+MyXDLpWwiRNL5ghHpfCDDLfl63ejP7Gs0QmJfmNMOh3YZNKfzhKJNz01J5uqIPEhCFSIF12+t4YH0FlU1+inPTuHJRCZ+cnk9NS5CY0XXHkrrWINeuKqOyKYBNwQ+/MJNzBlA6Lz/dTXaaFLYXQiRPazDCQSscVjb6uXZVGXW+EE67YumCKby6vZZozMBlt3GwLUhje4Rmf5jLHnybKxeVcPrsghS/A9FB5iAKkWTrttexfG05db4gOV4ndb4gN6/ZwpoP9vcIh/sazLrKlU2BzrrKhwuHSpmLUSQcCiGSqcV/KBzu6lb28+cXz+fK00u47cJ5FGZ5qWk1w2FumpMJ2V7qfEGWry1n3fa6FL8L0UECohBJ9sD6Cpx2RZrLgVIKj9OOUvDXdyu7HLejxsfVj5sdbJrL7GAPV1fZYbMxIdtDumxjI4RIomZ/mIZ2MxyWVTVzzRObaA5EyPI4+OUlpXzy2HFMzPZy5pxCHrviVGYUZDI518t4az/WNJcDp13xwPqKFL8T0UE+RYRIssomPzle8+qeoTWRmIHbYaOmNdB5zAf7mrj5b+UEIjGyvU7uXDyfmYX9bwPhctgoyvLgkLJ5QogkamwP0+wPA/B2RQO3PrOVcNRgXIaLu5aUMndido/qKPH9YAev0y4LVoYRCYhCJFlxbhp1viAep93cxkZDMGJQlOUFYP2uem5/bhuRmKYg081dS0qZktf/JO40l4OCTLesVBZCJFVDW4gWa9Pr17bXcccL24kZmkk5Xu5eUsqMwkzGZ7p7PK6jH4zf3D8QicmClWFELjUIkWRXLiohFDXwBSNorQlEYkQNzdKTi3l+czUrntlKJKaZkpfGfUtPOGw4zPI6KcqWbWyEEMl1MC4crt10gNuf20bM0JSMT+fepScwe0JWr+EQzH4wEtP4w1G0Nn9HYporF5Uk8y2IfkhAFCLJTinJ578+N528NDe+YJT8dDdXnzGDioNt/OKlnWZd5aKB1VXOT3czLqP3DlgIIRKl3heiNWB+yf3rO/v49Su70MCcCVncc8nxzCjIJK+f0nmnzy5gxQVzKcj00BKIUJDpYcUFc2UV8zAiQ8xCJFFH2amF0/JYOC0PMOsqP7i+gsc3VAEDq6tss2oqy2IUIUSy1fmCtAWjPfquk6fm8pML5jElP21A9d5Pn10ggXAYk08XIZKkPRSlzqos0CFmaH718k5esErnLZoxjhsPU1fZYbNRmO3G7ZCyeUKI5NFaU+cL0R6KmjXhX9nJ85utvmvmOG7+0hwm5Xr7/XIrRg75tyhEEsSXneoQjhozQO4uAAAgAElEQVT89LltvLn7IABfmj+B739+BvZ+5hLKSmUhRCporaltDeEPRwlHDe54YTtv7KwHzJrwPzprltR7H2UkIAqRYPFlpzq0h8y6yhsrzbrKly0s5tuf7r+usqxUFkKkgtaamtYggXCMQCTGLWvK2bC3CYBLF0zmu6dPZ0KOt9+RDzHySEAUIoF6C4fN/jDLntrMzto2AL7z2RIuWdB/XeUsr1MWowghks4wzHAYjMTwBSPc8NQWtla3AvDtT0/j8k9NZUK2jGqMRhIQhUiQ+JqkHWqtuspVHXWVz5rFOfOK+n2e/Aw32V4pmyeESC7D0FS3BglFYjS2h7ludRkV9e0o4OrPz+CSBcUUZnn6nRYjRi4JiEIkQEsgQkNb13C4t6Gd61Ztpr7NLFx/85fm8Ol+SufZlKIgyy0TvoUQSRczNNUtAcJRg5oW84vt/uYAdpvihnNmc17pRAqz3P1OixEjm3zyCDHEWvyRzpqkHbbXtLJs9WZag1HSXHZuu3AuJ07J7fM5ZKWyECJVXttay29e383+lgC5XhcHWgK0BqO4HDZuPX8On59TSHlVCz94/CMqm/wU56Zx5aIS2bJmlJGAKMQQavaHaWwPd2n7YG8TP16zhWDEGFBdZbfTTmGmW+b0CCGS7tWttdy8Zgt2m8JlV+ys82FocDts3Ll4Pp+ZMZ7NVS3c8sxWnHZFjtdJnS/I8rXlrAAJiaOIfAIJMUSa2nuGw/W76rnh6c0EIwYFmW7uXXpCv+Ew3e1gokz4FkKkQDRm8JvXd2O3KbTWVDUHMTTYFByTl8bnZhWSn+HmgfUVOO2KNJcDpczfTrvigfUVqX4LYgjJFUQhBmnd9joeWF/RZWiltDiHZn/XcPhcWTX3vGKWzjsmL407F8/vt3RettdJvqxUFkKkQCRmUN0c5ECLuYCupiWEBhw2xaQcD+3hKNlp5mK5yiY/Od0Wznmddqqa/Ck4c5EocplCiEFYt72O5WvLqfMFO4dWbvrbFl6yKqF0eOzdffzy5UN1lX/dT11lpRT5GW4Jh0KIlAhHzXAYNQzcdjvVVjh02hXFeV5sNsWUvPTO44tz0whEYl2eIxCJMTk3LclnLhJJAqIQg9B9aMVlt2FTsPK9SsDcUPZ3b+zh9//4CACXXeGy2dhR4+v1+WxKUZgl29gIIVIjFI1R3RIgahg89UEV+6yrgE676gx8MQOuXFTS+ZgrF5UQiWn8YbMesz8cJRLTXY4RI58ERCEGobLJj9cqJRWJGcQMjcdpo6Y1QMzQ3P3iTp6wCtd7nTaK87w0B8Lc+9ou3q1o7PJcDpuNCTke2cZGCJESwUiMmpYg0ZjBo//8mN+8vgeAafnpHFeURSQaoyjLy4oL5nZZfHL67AJWXDCXgkwPLYEIBZmeHseIke+IPpmUUsu11iuG+mSEGO6Kc9Oo8wVx2m0YhllX2VyA4uHWZ8p5a3cDAOkuOxOzPSil8DrN4ZeV71WysCQPMFcqF8kGsyKOMjeU+wqggVXAGcCFwHbgd1prI4WnJ0aZznBoGPx23R6e+mA/AKeW5PHTC+cxdVx6v4vlTp9dIIFwlDvSK4jfHtKzEGKEuHJRCcGIQXsoikYTiMQIxwzaQtHOcJjmsjMhu+sGsh1XGeHQSmUJh6Kb/wUuAf4N+BPwHWADsAi4J4XnJUaZQNgMh5GYwd0v7ugMh2fOLuDOxaVMG58hOymIvq8gKqVa+7oL8CbmdIQYvrTWzJmYxX9/bjor36ukpjVAfrqb5kCYioPtgFlX+e09jTS0h4ifVhiMGBRleclJc5GX7krROxDD3Ge01vOVUk6gBpigtQ4rpf4KfJjicxOjRCAco8Yqn3fbc1s7v9heePxErj9nNkVZHmzy5VXQ/xBzM3Cy1rq2+x1KqcrEnZIQw4/WmjpfiPZQlIUleSwsyaOmNch1q8rY3xzEpuBHZ83i7HlFTM1L597XdhGIxPA4bQQjBlFDc8WiaRIORX+iAFrriFLqPa112LodVUrF+n+oEIfXHopa/ViEm9eU8+G+ZgC+dsoUrjpzOgWZHimdJzr1dw35j8Axfdz316N5UaVUjlJqlVJqu1Jqm1Lqk0qpPKXUy0qpXdbvXOtYpZS6Tym1WylVppQ6Ke55LreO36WUujyu/RNKqc3WY+5T8l+8OApaa2pbzXDYYW9DO1c/tpGqpgBOu+InF8zl7HlFACwsyePqM2aQn+7GF4ySn+Hm1vPncG7pxFS9BTEy1CilMgC01md3NCqlioBwn48SYgDarHDY7A/zoyfLOsPhdz5bwjVfmElhllfCoeiizyuIWusf93Pf9Uf5uvcCf9daL1FKuYA04EbgVa31z5VSy4BlwPXAOcAM6+cU4H7gFKVUHnALsABzUvf7Sqm1Wusm65grgLeB54GzgReO8pzFGKS1pqY1SCB86ALOtupWbnjqUF3ln140jxOKc7o8ruMqo9NuozDLg8uRmPk8vW3aLRPHRyat9Tl93OUDzkvmuYjRxReMUO8LcbAtxHWryvi4wY9NwQ+/MJOvnXoMOWkysiF66vNTSynlir/yppT6nFLqh0qpvjqxAVFKZWFOun4IQGsd1lo3Y67We9Q67FHgIuvvC4E/atPbQI5SagLwReBlrXWjFQpfBs627svSWv9La60xr4R2PJcQA2YYPcPhB3ub+OGTm2gNRsnxOvnVJcf3CIcd3E47E3O8CQ2H3TftXr62nHXb6xLyeiKx+upzgUVaa/mXKo5IqxUO9zcHuOqxjXzc4MdhU9x83hz+7VNTJRyKPvX3yfUekAOglLoWuB1zcco1SqmfH8VrlgD1wP8ppT5USv1BKZUOFGqtqwGs3x2XQSYB8XMeq6y2/tqremnvQSl1hVJqg1JqQ319/VG8JTHa9BYO39h5qK5yYVb/dZUzkrBSWeqhjjr99bl3dD9Y+i9xOC2BCAd9ISrq27h65UZqWoN4HDbuuHg+l55cTJZHNugXfesvINqtK3MAlwJnaq1/ijnke+5RvKYDOAm4X2t9ItCOOZzcl94+YfURtPds1PpBrfUCrfWC8ePH93/WYswwDE11a5BgXCmpZ8uqWfHMViIxzTH5ady39ESK83ovK5WT5qIgK/GTveM37e4g9VBHtP763C91P1j6L9GfFn+EhrYQ5Qda+P7jm2hsD5PhdvDN06by5IYqzrpnPZc9+LaMOIg+9RcQW5VS86y/DwIdhWQdh3nc4VQBVVrrd6zbqzADY601PIz1uy7u+OK4x08GDhymfXIv7UIcVszQHGgJELLCodaav76zj1+9vBMNHDfBrKs8PrNn3WSlFOMz3UlbqSz1UEedRPW5Yoxpag/T0B7ivY8bufbJMtpCUfLSXfzHZ6bxTFk1De0hmZYiDqu/Tuc7wF+UUn/EDGsblFIPA28CPzvSF9Ra1wCVSqlZVtOZwFZgLdCxEvlyYI3191rg69Zq5lOBFmsI+kXgLKVUrrXi+SzgRes+n1LqVGs+z9fjnkuIPsUMTXVLgHDULFhh1lWu4A9vmnWVP3FMLr9YcnyvdZPtNkVRlofMJA7ZSD3UUSchfa4YWxrbwzT5w7yxs56bnt5CMGowIdvDb796Ev/c04DLYZNpKWJA+lvF3LGlzFnATGAT5tW5a6xFJUfjvzE7QhdQAXwDM6w+oZT6FrAPs+QUmKuQzwV2A37rWLTWjUqp2zDn7QCs0Fp3FLv9T+ARzPk7LyArmMVhRGMG1VZlATDD4i9e2sGL5eY2oJ+dOZ4bzpnd64KTRK9U7svpswtYgTkXsarJz2RZxTyiJbjPFWNAQ1uIlkCE5zdX86uXd2JoOCY/jXuXnsj8Sdnsbw6Q0+0LrkxLEX3ptxaz1jpGAgKW1noj5vY03Z3Zy7Ea+F4fz/Mw8HAv7RuAeT0fIURP3cNhOGpw27NbeWuPWWHgvNIJXH3mjF4XnHicdgpTWFNZ6qGOLonqc8XoV+8L4QtGeGJDJb97w7wiOKsok3svNRfT2Wyqs5Z8muvQR79MSxF96W+bmwyl1AqlVLlSqkUpVa+Uelsp9e9JPD8hEqp7OGwPRbl+dVlnOPzaKVP4wed7D4cZHgcTpKayGCLS54ojVecL0hoI89CbH3WGwxOn5PC7r53ErKLMztJ5Mi1FDEZ/VxD/AjyNud/gJUA6sBL4sVJqptb6xiScnxAJE4kZnQXrAZr8YZat3syuujYA/vOzJXxlQXGvj81Nc5ErZfPE0JI+VwyK1pp6X4jWYIT/eXU3azaZ6zFPm57PXUtKmZTT9cqgTEsRg6HMEdxe7lBqk9b6+Ljb72mtT1ZK2YCtWuvZyTrJZFiwYIHesGFDqk9DJEk4aobDqGGGw466ylVNAWwKrvviLM6aW9TjcUopxmW4kroYRSSOUup9rXVv012S7mj6XOm/xp6O+vAt/jB3/n0Hr1orkb84t5DbvzyPcRmewzyDGOkS3X/1N6u+XSn1aeskzgcaAbTWBr3vNSjEiNA9HH7c0M5Vj33Ypa5yb+HQblNMyE7uSmUxpkifKwakoz58Y1uI5WvLO8Ph4pMmcefiUgmHYkj0N8T8HeAPSqmZwBbgmwBKqfHA/ybh3MQolqoawqFojJqWIDHDvHIeX1c53aqrfHwvpfOcdhtF2R6cdtmOTiSM9LnisAxDU+sLUu8L8eO/baGsqgWAb5w2lR9+YSYZ8gVWDJF+t7kBFvbSXg/cl8iTEqNbRw1hp1112ax1BSQ0JHYPhxs+bmT52nKCEYPcNCc/v3g+M3opnZfqlcpibJA+VxxORwnQ6pYA16/ezG5rvvRVZ0znu5+bjqdbZSUhjsYRXQ5RSn1jqE9EjB2pqCEcjHQNh2/srOfGp7d0qavcWziUlcpiOJA+V8SsEqD7Gtq5euVGdte1YVNw07nH8b0zJByKoXek42U/GdKzEGNKsmsIdw+Hz5YdYMUzW4kamqlWXeXe9gHLS3dRkJn4mspCDID0uWNYR5WnXbU+rlq5sXO+9E8vmse/nzYVt0PCoRh6fQ4xK6XK+roLKEzM6YixIJmbtXaEQ0NrtNY89m5lZ+m84yZk8rMvz+9ROq+jpnKGu9995IUYUtLnit507NW69UAL16/eTHMggtdp587F8/lS6UQZ3RAJ098nYCHmflxN3doV8M+EnZEY9a5cVMLyteX4w1G8TjuBSKzfzVrve2Unf3jzI9rDMdJddr796Wlc9fmZh32dQDhGTWsQrTWG1jzwRgVPvl8FwIJjcvnJBXPxurp+87bbFIVZHhmuEakgfe4odaR9WEc4fH9vIzc9vYX2cIwsj4N7Lj2Bz80q6NwAW4hE6C8gPgtkWGXxulBKrUvYGYlRbzCbtd73yk7ufW03NgUOm3ml8d7XdgP028H6w1FqW0NorXvUVT595nhuOHd2jxXJslJZpJj0uaPQkfZhHRv5/2NXPbc+s5Vw1GBchovfXHYip5Tky9QXkXD9BcSJwP7e7tBafzUxpyPGioHWEP7Dmx9ZHasZ2mwKoobBH978qM/ONT4chiIxbntuG/+0Suedf/wErjqjZ+k8r8tOYaZHvpGLVJI+dxQ6kj6sY6/Wl7bWcMcL24kZmkk5Xu7/fydROrnnNlxCJEJ/l0r+D3hRKXWTUko2VhIp0R6O0T2z2ZTZ3uvxoUPhsC0U5fqnNneGw6+dMoXvn9kzHGZ6nBRlSTgUKSd97ig02D6sIxyu/qCK25/bRszQlIxP55FvnCzhUCRVf/sgPqGUeg5YDmxQSv0JMOLu/1USzk+Mcekuc45ifAdraLO9u7ZQlHqfGQ6b/OEu+4R99/RjWfKJyT0ek5fuIidNaiqL1JM+d3QaTB8Wisaobg7wp7f38tCbHwMwZ0IWD/zbJyjOG/pFfEL053CTrSJAO+AGMrv9CJFw3/70NAxtDskY2rB+m+3xfMEIddaClJrWYJd9wpadPatHOFRKUZDlkXAohhvpc0eZgfZhwYgZDu9ft6czHJ48NZdHvnmyhEOREv1tc3M28CtgLXCS1joxm9QJ0Y+OOTr9rQBsDUY46AsB8NHBdq5fXcbBtjAuh43l5x3Hp44d1+U5ZaWyGI6kzx2dBtKHBSMx9jcF+OVLO3h+Sw1gLqa7d+mJZKfJbAORGkpr3fsdSv0D+I7Wujy5p5QaCxYs0Bs2bEj1aYhBaglEaGgzw2GPuspfnsfx3ebsyEplEU8p9b7WekGqzwOOrs+V/mvkCoRj7Gv0c/vzW1m/8yAA55VO4M7FpaTLXqyiH4nuv/qbg/iZRL2oEEOhxR+hod0Mh93rKt+5uJTpBRldjpeVymI4kz537PGHo3x80M/yNVvYsNfc/vKyhcXcev5c3DLCIVJMvp6IEanZH6axPQzAuh31/Oz5bUQNTVGWh7uWzO9RlSXT42Rchkv2DhNCDAvtoSh76ttYtnozW6tbAbjysyVce9YsHDLCIYYBCYhiSKzbXscD6yuobPJT3M/G10MhPhw+W3aAe17ehQam5qdx15JSxmW4uxyfn+6WeTxCiGGjLRRlR00r164qo6K+HQVc+8VZfOezx8oIhxg2JCCKo7Zuex3L15bjtCtyvE7qfEGWry1nBQx5SGxqD9PkD6O15q/v7ovbCsKsq5wVV1dZKUVBplvm8Qghhg1fMMLmqhauXVXG/uYAdpviJxfM5WunTJERDjGsyCenOGoPrK/AaVekucz/nNJcDvzhKA+srxjSgNjYHqbZH8bQmt+9sYdV75tFJ06emsutF8zFGzdnx2GzUZDllpXKQoghd6QjJq3BCBs+buTaVWU0tIVxO2zcubiUi06clISzFmJwJCCKo1bZ5CfH23UI1+u0U9U0dLt0NLSFaAlEiMYMfvHSTl7aatZV/tys8Sw7p2tdZZfDRlGWR+bxCCGG3JGOmLT4I7y1p55lqw/ttHDfZSdy5nGFyTt5IQZBPkHFUSvOTSMQ6Vo2KhCJ9VgocqQOWuEwFIlxy9qtneHw/OMncOO5x3UJh2kuBxOzvRIOhRAJET9iopT522lXPLC+os/HNPvDvLKthh8+UUZrMEqO18lDl58s4VAMa/IpKo7alYtKiMQ0/nAUrc3fkZjmykUlR/3c9b4QrYEIbaEo163ezL8qzLrK/3Zqz7rKWV4nRdmyjY0QInEqm/xdprNA/yMmje1hntl0gGVPbSYQiVGY6ebP3z6FU4/NT8bpCnHEZIhZHLXTZxewAvObdVWTn8lDtIq5zhekLRilsT3MstWb2V3fd13l/Aw32V5ZqSyESKzi3DTqfMHOOdfQ94hJQ1uIJzdUcteLOzA0FOd5eeQbJ3PseKmcKIY/CYhiSJw+u2DIFqRoran3hWgLRalpCXau9rMpuO7s2Zw159CwjE0pCrLcXTprIYRIlCsXlbB8bTn+cBSv004gEut1xORgW4hH3vqI37y+B4CZhRk88o2FTMzxpuK0hRi0lA0xK6XsSqkPlVLPWrenKaXeUUrtUko9rpRyWe1u6/Zu6/6pcc9xg9W+Qyn1xbj2s6223UqpZcl+b+LIaa2ps8LhRwfb+e+VH7K/OYDLYWPFhXO7hEOHzcaEHI+EQyFE0pw+u4AVF8ylINNDSyBCQaaHFRfM7fIFua41yP+8uqszHB4/OZvH/uNUCYdiREnlJ+vVwDYgy7p9J3CP1nqlUup3wLeA+63fTVrr6UqppdZxlyql5gBLgbnAROAVpVRH9fP/Bb4AVAHvKaXWaq23JuuNiSPTEQ7bQ1G2Hmjlhqc347NW+93+5XmUxtVVlpXKQohU6W/EpKY1wF0v7OCpD81tuD51bD4P/tsnyPDIFBgxsqTk01UpNRn4EvAH67YCzgBWWYc8Clxk/X2hdRvr/jOt4y8EVmqtQ1rrj4DdwELrZ7fWukJrHQZWWseKYUxrTW2rGQ7f+7iRHz25CV8wSm6ak3suPaFLOEx3y0plIcTworXmQLOfW9aUd4bDs+YU8vC/nyzhUIxIqfqE/TVwHWBYt/OBZq111LpdBXTsHDoJqASw7m+xju9s7/aYvtp7UEpdoZTaoJTaUF9ff7TvSRwhrTU1rUH84SjrdtRx09NbCEYNirI83Lf0RKYXZHQem+11UpglK5WFkP5r+NBas6/Rz7VPlvFiubkN1+KTJnH/106SzfrFiJX0gKiUOg+o01q/H9/cy6H6MPcNtr1no9YPaq0XaK0XjB8/vp+zFoliGJrqliCBcIw1Gw9w27PbiBqaaePSue+yE5iUe2jOTn6Gm/xudZaFGKuk/xoetNbsqW/j6pUbeWuPuQ3XN0+byt1LSrHLKIcYwVIxB/E04AKl1LmAB3MO4q+BHKWUw7pKOBk4YB1fBRQDVUopB5ANNMa1d4h/TF/tYhgxDE11a5BgOMpf3tnHw299DMCcCVnccfE8Mq1hGVmpLIQYjgxDs6PWx/cf38iOGh8A13xhJledOSPFZybE0Uv61xut9Q1a68la66mYi0xe01p/DXgdWGIddjmwxvp7rXUb6/7XtNbaal9qrXKeBswA3gXeA2ZYq6Jd1musTcJbE4MQs8JhIBzlt+v2dIbDk6fmcvdXSjvDoaxUFkIMR4ahKdvfwnf+/D47anzYFNx6/lxKJ2Vz2YNv8+k7X+OyB99m3fa6VJ+qEEdkOH3qXg+sVEr9FPgQeMhqfwj4k1JqN+aVw6UAWutypdQTwFYgCnxPax0DUEr9F/AiYAce1lqXJ/WdiH7FDE11SwB/KMrdL+3k5T7qKruddgoz3bIYRQiRcOu21/HA+goqm/wUH2az/5ih2bC3kasf20hNaxCnXXHX4lJy01xHVKdZiOFImRfjxIIFC/SGDRtSfRqjXkc49AUi/OTZrbxd0QjABcdP5L/PmN5ZOi/d7aAg0425YF2IxFBKva+1XpDq8zha0n8dnXXb6zqDXfzm1933NwSzD3tzdz0/eHwTje1hPE4bv7nsRD4/p4jLHny7R5UVfzhKQaaHx644NdlvS4xyie6/5NKMSJpozOBAc4DG9jDXrd7cGQ6/fuoxXH3moXDYsVJZwqEQIhkeWF+B065IczlQyvzttCseWF/R5bhozOCl8hq+95cPaWwPk+F28Mg3FvL5OUXA4Os0CzGcDachZjGKRWMG1S1BaluDXL+6jD317QD81+eO5eKTzLrKSinyM1xkyZ5hQogkqmzyk9Otlnv3YBeNGazZeICbnt5MMGqQl+7i0W8sZP7k7M5jBlOnWYjhTq4gioSLWOGwstHP1Ss3sqe+HZuCG86Z3RkObUpRlOWRcCiESLri3DQCkViXtvhgF4kZ/PXdfVy/uoxg1GBCtocnrzy1SzgEs05zJKbxh6Nobf7urU6zECOBBESRUJGYQXVzkJ21vi51lX960Ty+YNVVdtptTMzx4nXJhrJCiOTrL9iFowa/X1/BrWvLO/doXf2fn+LYgswezzOQOs1CjBQyxCwSJhw1qGkJsqmqiRuf3mLWVXbb+dlF8zu/ebuddoqyPJ3zD4UQItlOn13ACsy5iFVNfiZbq5g/OT2fe17ewf1vmHMR50zI4s/fWkhePxv291enWYiRRAKiSIiOcPivioPcsqacYNQgN83JXYtLOdYqnZfhdjBeVioLIYaB7sEuEI6y4pmt/OWdfYC5R+v/SV1lMYZIQBRDLhSNUdMS5JWttdzxwnaihmZCtoe7Fpd2ls7LSXORl+5K8ZkKIUTPPRAv/+QxvLClhjWbzCJcp88az+/+3yekrrIYUyQgiiHVEQ6f+mA/9726Cw2UjEvnzsXzyc8wrxaOy3B1VkoRQohUit8DMcfrpLrFz/ef2EgwYgBw/vETuOeSE2TDfjHmSEAUQ2Ld9jruX7eHjxvaUMpGTWsQ6FpX2W5TFGR6ZDGKEGLYiN8DMRozqPeFO8Ph/ztlCisunIdN5kiLMUgCojhq67bX8eM1W1BoQlGD5kAYgJmFGdz9lVK8TjtOu43CLA8uh3wLF0IMHx17IEaiBnsb2wlY4TDT4+C2i+bJHGkxZklAFEftt+v2oNA0+6P4QlEA0px2PA47Xqcdj9NOoaxUFkIMQ8W5aexv8lPrCxGKmuEwL93JrMIsCYdiTJPLOeKoBMIxPm5oo7E90hkOs71OJua4qfMFyXA7mJAt4VAIMTydN38C+1uCneFwfIaLDLdTNrcWY55cQRRHzB+OsqeujfZQjPawWYUgL81JfrqLYNRgcm4aBVmeFJ+lEEL0blNVM/e+touYoVGYVw6nF2Ry5aIS2ctQjHkSEMURaQ9F2V7TynWryjrDYY7XSX6Gi2DEQAP/9bnpqT1JIYTow9sVDVz5p/dpCURIc9n5/dcXcNr0cak+LSGGDQmIYtDaQlHKKpv50apNHGgOYrcplpw0iR01bdS2BijOS+e7px8r38CFEMPSa9tq+e/HPqQ9HCPb6+TRb5zMCVNyU31aQgwrEhDFoLSForxT0cB1q8poaA/jdti45fw5nFqSP2QrlbtvWivDPUKIobJ2435+tKqMcNSgINPNX759CjMKe9ZVFmKsk0UqYsB8wQivbavl6pUbaWgPk+62c9fiUk4tycfjtDMxxzsk4XD52nLqfEFyvE7qfEGWry1n3fa6IXoXQoix6q/v7OUHT2wiHDUozvXy1Hc/JeFQiD5IQBQD0hqM8FxZNdeuKqMtFCUv3cWvLzmB+ZOzyfAM3Url+E1rlTJ/O+2KB9ZXDMG7EEKMVb97Yw83Pb2FmKGZWZjBU989jcm5aak+LSGGLRliFofVEojw5IZK7nhhO7GOuspLSpmU4yU3zUXuENZU7ti0Np7XaaeqyT9kryGEGDu01tz94g5+u24PACcU5/DoNxeS7ZVyn0L0RwKi6FeLP8LDb1Vw36u7u9RVHpfpYXymmwz30P4nVJybRp0vSJrr0PMGIjH5pi+EGDStNTev2cKf394HwGnT8/n91xd06V+EEL2TIWbRp6b2EPe8soN7rXA4b2IW91x6PAVZHiZke4Y8HAJcuVfJpCMAACAASURBVKiESEzjD0fR2vwdiWnZtFYIMSjRmMHVKzd2hsOz5xbxf/++UMKhEAMk/6eIHu57ZScPrN9De9jobFs4LY9bz59DpsdJUbYHpz0x3y1On13ACsy5iFVNfibLKmYhxCDc98pOHly/h7a4/uuSBZO54+JSqegkxCBIQBRd3PfKTu55ZVeP9uMKMshNd1GQmfiyeafPLpBAKIQYtL76r0lS7lOIQZMhZtHFA+vNidzaum1TYFeweuN+irKkkxVCDF8Prt+D5lD/5bApnHbFQ299nMKzEmJkkoAoOn10sI32sNHZudptCodNYbdBIGKglIRDIcTwVNsS7DKsbIZDG+j/z96dx9dV33f+f33u1dVmybtlGy/YBoMNNGxmN8TFJHG6AJ1mgaSBsBSaSSd0Op1C5pdJprTJI2nnkQy0nQwECJCmcUiaTtyUhMF2HGPAYEOAYGxsYxvkBUu2ZGu9++f3xz0SukKWtdyru+j95KGH7v2ec8/5ythHn/M93+/n47RFkyz/xnpufHCzcqqKDJECRAFg+6E2bntsa+/7iiA4DJnhGBMqwwXsnYjIie070sl/+PZzve8jQXCYSjuJdOZJiBLviwyPAkTh1++0cttjW9jT3IkZZMYJHfc0KXfSDrcvX1jgXoqIvN/2Q2187P88x4Fj3cFNLWCQ9jTxVGZEcdqESiXeFxmmMQ8QzWyemf3SzLab2TYzuyton2pmT5vZruD7lKDdzOx+M9ttZq+Z2QV9jnVzsP8uM7u5T/uFZvab4DP3m56NntBzbx3htse2cvBYlKqKEF+9/hxuvWIBNZEwKTdqImHuuvp0vnDNGYXuqohIlpf2tfDJB57nSEecuqoKfnDHpfzZysXURMIk02AGM+oizJpU0/sZJd4XGZpCrGJOAv/F3V82s3rgJTN7GvgssM7dv25m9wD3AHcDHwUWB1+XAN8GLjGzqcBXgGVk5iS/ZGZr3L012OcOYDPwJLAK+PkY/oxFz91Zt/0wf/bDV+mIJamrquBrf3AOFy2cyg0Xzee///7Zhe6iiMgJPbOzmTu+9xLdiRRTaiN877ZLOGfOJC5aMLX3hvbGBzfT1B7N+pwS74sMzZiPILr7IXd/OXjdDmwH5gDXAY8Fuz0GXB+8vg543DM2A5PNbDbwEeBpd28JgsKngVXBtonu/ry7O/B4n2MJmeDwX399gM//86976yp/65Pnctlp05k1sZqQViqLSIFs2NHEjQ9uHnRRyZO/OcStj22hO5Fi9qRq/uVzl3POnEnv20+J90VGrqBzEM1sAXA+8AIw090PQSaIBHoS4c0BGvt8bH/QNlj7/gHahUxw+L3n3+a//vg1Ysk0sydVc/8N53HxgmnMqK/SSmURKZgNO5r48pptNLVHT7io5IdbGvlP//xrEiln0fQJ/OQ/Xs6iGXUDHm/FkgbuvfZsGuqrOd6doKG+mnuvPVt5VkWGoGCJss2sDvgX4M/cvW2QwGSgDT6C9oH6cAeZR9HMnz//ZF0uee7OP6zfzTef3pmpqzxjAn/3sQ+wZNZEJpygbN6GHU08sHEPja1dzFNVE5GiUU7Xr57rzMvvtGLArEnVmBmptHPoWBe3PLaFiuB3RCKduZzPn1rLj/7kMqbVVQ16bCXeFxmZggSIZhYhExx+391/EjQfNrPZ7n4oeEzcc8u4H5jX5+NzgYNB+4p+7RuC9rkD7P8+7v4g8CDAsmXLBgwiS839a3fy0Ka9dMZTTKgMc/vyhXxg7mS+vWE32w610xFLApm6yt/4ww9wWkMd1ZGBU9j03M1HwpZ1N38v6IIrUmDlcv3qe51Ju2PAwWNRptSmONIRIxX8ZAl/70cMG3TGEnz6O5vpiKd08yqSB4VYxWzAw8B2d/9mn01rgJ6VyDcDP+3TflOwmvlS4HjwCPop4MNmNiVY8fxh4KlgW7uZXRqc66Y+xypr96/dyX3rd9OdSFERykzG/tbaXfzJ97awZV9rb3AIsGz+ZM6cVX/C4BAy9ZAj4UxqCKWIEJF86Hud8bQTTznxVJrD7e8Fh/2lHFo7E+xr6VJ+Q5E8KcQcxCuAzwBXm9krwdfvAF8HPmRmu4APBe8hswp5D7Ab+A7wHwHcvQX4a2BL8HVv0AbwOeCh4DNvMQ5WMG/Y0cR963eTSjuptJNOQ0UohAPRFKT77f+9F95h064jgx6zsbWLmn4BpFJEiEguNbZ2kUyl2fFuG8lhjIM6kEq7bl5F8mTMHzG7+yYGnicIsHKA/R34/AmO9QjwyADtW4FzRtHNktLziCYVzM1JO6TdSaRTJ/xM2jN37oM9kpk3pZam9ii1le/9NVGKCBHJpbrKMLubO0mmh/eU3IHK8HtjHLp5FcktVVIpAz2PaIaSnabvLie7mCpFhIjkS086m7eODD847DG9zwIV3byK5FbBVjHL6PWs/HtxXwtVYcOME6zXfk/P5kjYTnoxXbGkgXvJBKD7W7uYq4ngIpIDfRemAFSEINl/HswQxJIp3CvoTqR08yqSYwoQS1TPBTaeTGVG9xJDvwMPG0ysiQzpYqoUESKSa30XplSGQyRTjoe8d5rMYEKWqa1cETY6YymOdyd08yqSBwoQS9CGHU18YfWv6YgmT5j4sb++g4tmUF0R4us/386Xfvq6UkSIyJi5f+1ONu85ipMJ9qorQsRT6RM+/Oi5doUNIuEQ7nCsO8Epk6qpqAnxzN1Xj13nRcYRBYhFrn+i6ssWTeXhZ/fSFn0vZc3J7rkrw4YDiZRTGTZmT6rmwLEoEGPO5GrlNxSRMXH/2p18c+2u3vdph67E+58tV0eM8+dN5c6rFvHAxj38+p3WzM2wBVNp0nC4Pcb586aMXedFxhktUiliA5Wd+tbaXRzvTp78w4FwKLOiOZlywgazJ9VwpCNOOGSEzTjSEVeKCBHJu/+8+uWs4HAwnqb3xnXn4TZmTqzCg+wMPf9pzqFIfmkEsYj1nafTHk3wTkvXSUcLe4Qt8/gmFApRX11BezTJrIlVTKyJcPB4N+FgyXM8lbl7V4oIEcm1nicgr+4/Rlf8xGm3+ounnNrKit7MCRXhEKdMrqa5PUY8lSZsxmkzJuiJh0geKUAsYo2tmSoBPcHhcDJBpDzzZek0NZEwixvqaWqPApncYcm0g7+XR0wpIkQkl/quVB5OcAjvTZupiYSprAiRSDmRsLFw+oTeFct3r1qS+06LSC89Yi5i86bU0p1I0dweG1Zw2KNncndjazezJlb25jScXleZqbjizvS6SuU3FJGc6llId+BYF+8ejw778z0L77oTKRY31HPvtWfTUF/N8e4EDfXV3Hvt2Ro9FMkzjSAWofvX7uTv1+9igLnbgzID7xtIBhGiAet2NHP/Def35jRc3FCHu9MZT9FQX61VzCKSEz0jh13xFBUhI5Yc3ughZK5ZfW9clW5LZOwpQCwy/Vf5DYf7wO/DIeiMp3SRFZG8e2DjHjqiCZJpH1Hya4BIRUg3riIFpgCxyDy0aW9OjmNkRhTDZmBQGwnn5LgiIoPZdvB4Vhqu4ZpYFeb+Gy9QYChSYAoQi8xoLqyQWbmcdqgIajOnPfN1+/KFOeqhiMjANuxoGtU1bO7kav7m+t9ScChSBBQgFokNO5r4+s+3j/o4FSEjlXZqImE64ykmVIa5fflCPjB3Mjc+uLk34bYe3YhILm3Y0cR//fGrw/7crIlVVFaEtfBEpMgoQCwCPZO6Dx7rHvWx0g6LG+r4xX/+4PuOHwlbb8JtVU4RkZHqX+Gpp+LJkY74sI5TXQELp9fphlWkCClALJD71+7koU17aQ/qKeeCAZNrI9zz0aVZ7X0TbgO9CWgf2LhHF2URGZa+N5yxRJLn9xzl+T1Hh32cPzhvNt+64YI89FBEckEBYgHcv3Yn963fjac9Z8EhwJJZ9dy9asn7gr6ehNt9qXKKiIxEzw1nc1uUttjwU9gAXLZomoJDkSKnRNkF8NCmveDOCDNAZDGDabUVLJ1VT3ssMyq4YUdT1j49Cbf7UuUUERmJxtYukqn0iIPD+qqwkvKLlAAFiAXQEUuSGsXQoZHJbfjoZy/iuzdfxITqSuKpdNb8wr5B4p1XLeqtouLuqpwiIiNWX1XB2y0jmy9dGTL+XilsREqCAsQxdv/anSMqm9dXJGwsnlHHiiUNWfMLzTLfI2HjgY17evdfsaRBpapEZFQ27Gjio/9rI9vfbR/xMR68aZmuOyIlQnMQx8hIy+f1Vxk2Zk2q6V2IMtT5haqiIiIj0ZOCa2dTx6hubv/8msW6BomUEAWIY+DGB57j+b2toz5OOASLpk/gno8u7b3QzptSS1N7tHeFMmh+oYjkRs+K5QOtXaMKDpfOqucL15yRu46JSN7pEXOe3b92Z06Cw3lTanj4pov4xX/+YNZduOYXiki+PLBxD23d8VHNma4KGx2x0VWIEpGxpxHEPMtFbeV5U2p45u6rB9y2YkkD95K5kO9v7WKuqqSISI7sPNzGse6RB3cGpBw90RApQQoQ82y0tZUB/vq6cwbdrvmFIpIP7aMIDiGThiscMj3REClBesScRzc+8Nyoj1FbGVbwJyJj7v61O4mPMuVCJBzi8ytO0zVMpARpBDFPcrUw5U905y0iY+z+tTv55tpdI/58fVWYc+ZM1nQXkRJWtgGima0C7gPCwEPu/vWxOveGHU2jDg5DBhMqQ1r5JyJjbqTBYSQE37npIgWFImWgLANEMwsD/wh8CNgPbDGzNe7+xlic/7OPbhnxZ0MGZ58yia54kob66hz2SkTk5Bbd8+8j/qyCQ5HyUa5zEC8Gdrv7HnePA6uB6wrcpyGprggpVY2IFMxIc/kvnVWv4FCkjJTlCCIwB2js834/cEmB+jIkBlRWhKitqqChvlpzd0SkZETCxt2rlhS6GyKSQ+UaINoAbe9bjmdmdwB3AMyfPz/ffTohA86cWZdVIUVEZDDFcv0C+E+/fbquXSJlplwDxP3AvD7v5wIH++/k7g8CDwIsW7ZsdPkcMsfjW0/vHNZnls6q5+5VS3RxFZFhyfX1C+CVxmPD2t+A6ogW04mUo3INELcAi81sIXAAuAH4VD5PmEo7X1nzOv+0+Z2T7hsC5k6t5d5rz1ZgKCJF4dndR/jjx7cOad8QEA4baYfPffC0/HZMRAqiLBepuHsS+FPgKWA78IS7b8vX+eLJNHet/nVvcPgfzp9zwn3rq8JcsmiagkMRKRpPbXuXW767ha54ijmTa0643x+cN5uJ1RVYyKiJhLnr6tM1eihSpsp1BBF3fxJ4Mt/n6Yon+dw/vcyvdjYDcOsVC/nS7y7lm588L9+nFhEZtR+/tJ+//PGrpB0WzZjAP912CacMEiSKyPhQtgHiWDjWFefWR7fw8juZeTv/9SNn8h9XnIbZQGtkRESKyyOb9nLvzzLpYX9rziQeveUiptVVFbhXIlIMFCCO0OG2KJ95+AV2Hu7ADP76unP4o0tPLXS3REROyt25b90u/ldQMeWShVN56OZl1FdHCtwzESkWChBHYN+RTv7ooRfYf6ybSNj41ifP4/c+cEqhuyUiclLptHPvz97g0ef2AXDN0gb+4VMXUB0JF7ZjIlJUFCAO07aDx7n5kRc50hGnJhLmgc9cyFVnzCh0t0RETiqZSvOX//IaP3n5AADXn3cKf/fxc4mEy3K9ooiMggLEYUim0vzpP/+aIx1xJtdEeOSWi7hg/pRCd0tEZEj+4Ze7e4PDmy47lf/x+2cTCmnOtIi8n24bh6EiHOLvbzyf0xvqeOJPLlNwKCIl5bblC/nA3El84erT+atrFRyKyIlpBHGYzpkziaf+7CrCurCKSImpr47wxJ2Xab6hiJyURhBHQMGhiJQqBYciMhQKEEVEREQkiwJEEREREcmiAFFEREREsihAFBEREZEsChBFREREJIsCRBERERHJogBRRERERLIoQBQRERGRLObuhe5DUTCzZuDtPBx6OnAkD8dVH0qvD4U+v/rw/j6c6u4zCtyXUdP1S31QH8ZVH8bk+qUAMc/MbKu7L1Mf1IdCn199KK4+lIJi+HNSH9QH9aEw59cjZhERERHJogBRRERERLIoQMy/BwvdAdSHHoXuQ6HPD+pDj2LoQykohj8n9SFDfchQH8bo/JqDKCXPzAx4Bviqu/88aPsEcCtwEPg9oMndz+nzmXOB/wPUAfuAT7t7W7DtA8ADwEQgDVzk7lEzuxH4b4AHx/0jdy/0ZGkRKWG6fkmxUoAoZcHMzgF+BJwPhIFXgFXAHKADeLzfBXYL8Bfu/iszuxVY6O7/3cwqgJeBz7j7q2Y2DTgGGJmL6lnufsTM/hbocvf/MXY/pYiUI12/pBjpEbOUBXd/Hfg34G7gK2QuqG+5+0agZYCPnAlsDF4/Dfxh8PrDwGvu/mpw3KPuniJzgTVgQnDHP5HMBVdEZFR0/ZJiVFHoDojk0F+RuXuOAydLAfA6cC3wU+DjwLyg/QzAzewpYAaw2t3/1t0TZvY54DdAJ7AL+HzufwQRGad0/ZKiohFEKRvu3gn8EPieu8dOsvutwOfN7CWgnsxFGTI3TcuBTwff/8DMVppZBPgcmUdApwCvAV/M/U8hIuORrl9SbDSCKOUmHXwNyt13kHkcg5mdAfxusGk/8Kueydtm9iRwAdAWfO6toP0J4J5cd15ExjVdv6RoaARRxiUzawi+h4AvkVkRCPAU8AEzqw0mfH8QeAM4AJxlZj1ljT4EbB/bXouI6PolY0MBopQ1M/sB8DxwppntN7Pbgk03mtlOYAeZydrfBXD3VuCbwBYyKwlfdvd/d/eDZOYIbTSz14DzgK+N7U8jIuOJrl9SSEpzIyIiIiJZNIIoIiIiIlkUIIqIiIhIFgWIIiIiIpJFAaKIiIiIZFGAKCIiIiJZFCCKiIiISBYFiCIiIiKSRQGiiIiIiGRRgCgiIiIiWRQgioiIiEgWBYgiIiIikkUBooiIiIhkUYAoIiIiIlkUIIqIiIhIFgWIIiIiIpJFAaKIiIiIZFGAKCIiIiJZFCCKiIiISBYFiCIiIiKSRQGiiIiIiGRRgCgiIiIiWRQgioiIiEgWBYgiIiIikkUBooiIiIhkUYAoIiIiIlkUIIqIiIhIFgWIIiIiIpJFAaKIiIiIZFGAKCIiIiJZFCCKiIiISBYFiCIiIiKSRQGiiIiIiGRRgCgiIiIiWRQgioiIiEgWBYgiIiIikkUBooiIiIhkqSh0B4rF9OnTfcGCBYXuhoiMoZdeeumIu88odD9GS9cvkfEn39cvBYiBBQsWsHXr1kJ3Q0TGkJm9Xeg+5IKuXyLjT76vX3rELCIiIiJZFCCKiIiISJa8Bohmts/MfmNmr5jZ1qBtqpk9bWa7gu9TgnYzs/vNbLeZvWZmF/Q5zs3B/rvM7OY+7RcGx98dfNYGO4eIiIiInNxYjCD+truf5+7Lgvf3AOvcfTGwLngP8FFgcfB1B/BtyAR7wFeAS4CLga/0Cfi+Hezb87lVJzmHiIiIiJxEIR4xXwc8Frx+DLi+T/vjnrEZmGxms4GPAE+7e4u7twJPA6uCbRPd/Xl3d+Dxfsca6BwiIiIichL5DhAd+H9m9pKZ3RG0zXT3QwDB94agfQ7Q2Oez+4O2wdr3D9A+2DlERERE5CTynebmCnc/aGYNwNNmtmOQfW2ANh9B+5AFQesdAPPnzx/OR0VECkrXLxHJp7yOILr7weB7E/CvZOYQHg4eDxN8bwp23w/M6/PxucDBk7TPHaCdQc7Rv38Puvsyd182Y0bJ58oVkXFE16/C2bCjiRsf3Mzyb6znxgc3s2HHgL9iREpa3gJEM5tgZvU9r4EPA68Da4Celcg3Az8NXq8BbgpWM18KHA8eDz8FfNjMpgSLUz4MPBVsazezS4PVyzf1O9ZA5xARERmxDTua+PKabTS1R5lcE6GpPcqX12xTkChlJ5+PmGcC/xpknqkA/tndf2FmW4AnzOw24B3g48H+TwK/A+wGuoBbANy9xcz+GtgS7Hevu7cErz8HPArUAD8PvgC+foJziIiIjNgDG/cQCRu1lZlfn7WVFXTFkzywcQ8rlmi6u5SPvAWI7r4HOHeA9qPAygHaHfj8CY71CPDIAO1bgXOGeg4REZHRaGztYnJNJKutJhJmf2tXgXokkh+qpCIiIjJE86bU0p1IZbV1J1LMnVJboB6J5IcCRBERkSG686pFJFJOVzyJe+Z7IuXcedWiQndNJKcUIIqIiAzRiiUN3Hvt2TTUV3O8O0FDfTX3Xnu25h9K2cl3HkQREZGysmJJgwJCKXsaQRQRERGRLAoQRURERCSLAkQRERERyaIAUURERESyKEAUERERkSwKEEVEREQkiwJEEREREcmiAFFEREREsihAFBEREZEsChBFREREJIsCRBERERHJogBRRERERLIoQBQRERGRLAoQRURERCSLAkQRERERyaIAUURERESyKEAUERERkSwKEEVEREQkiwJEEREREclSUegOiIiIFNqGHU08sHEPja1dzJtSy51XLWLFkoZCd0ukYDSCKCIi49qGHU18ec02mtqjTK6J0NQe5ctrtrFhR1OhuyZSMAoQRURkXHtg4x4iYaO2sgKzzPdI2Hhg455Cd02kYBQgiojIuNbY2kVNJJzVVhMJs7+1q0A9Eik8BYgiIjKuzZtSS3cildXWnUgxd0ptgXokMjh3z/s5FCCKiMi4dudVi0iknK54EvfM90TKufOqRYXumsj7xJNpDh6P5v08ChBFRGRcW7GkgXuvPZuG+mqOdydoqK/m3mvP1ipmKTodsSQHj3WTSKbzfi6luRERkXFvxZIGBYRStNydo51x2roTAITM8n5OBYgiIiIiRSqRStPUHiPWb55svilAFBERESlCXfEkze0xUun8L0rpL+9zEM0sbGa/NrOfBe8XmtkLZrbLzH5oZpVBe1XwfnewfUGfY3wxaH/TzD7Sp31V0LbbzO7p0z7gOURERERKQWtnnHePRwsSHMLYLFK5C9je5/03gG+5+2KgFbgtaL8NaHX304FvBfthZmcBNwBnA6uA/x0EnWHgH4GPAmcBNwb7DnYOERERkaKVSjuHjnfT2hUvaD/yGiCa2Vzgd4GHgvcGXA38ONjlMeD64PV1wXuC7SuD/a8DVrt7zN33AruBi4Ov3e6+x93jwGrgupOcQ0RERKQoRRMpDrR20x0f2/mGA8n3COL/Av4S6FmPPQ045u7J4P1+YE7weg7QCBBsPx7s39ve7zMnah/sHCIiIiJF53hXgkPHoyTT+U9hMxR5CxDN7PeAJnd/qW/zALv6Sbblqn2gPt5hZlvNbGtzc/NAu4iIFCVdv0TKQzrtNLVFOdoZG5MKKUOVzxHEK4BrzWwfmce/V5MZUZxsZj2rp+cCB4PX+4F5AMH2SUBL3/Z+nzlR+5FBzpHF3R9092XuvmzGjBkj/0lFRMaYrl8ipS+WTHHgWDcdseTJdx5jeQsQ3f2L7j7X3ReQWWSy3t0/DfwS+Fiw283AT4PXa4L3BNvXeyaUXgPcEKxyXggsBl4EtgCLgxXLlcE51gSfOdE5RERERAquPZrg4LEoiVRxPFLurxCl9u4G/tzMdpOZL/hw0P4wMC1o/3PgHgB33wY8AbwB/AL4vLungjmGfwo8RWaV9BPBvoOdQ0RERKRg3J3m9hjN7cX1SLm/MUmU7e4bgA3B6z1kViD33ycKfPwEn/8q8NUB2p8EnhygfcBziIiIiBRKoaqijIQqqYiIiIjkWSGrooyEAkQRERGRPGrpjHOswImvh0sBooiIiEgepNJOU3u0KBJfD5cCRBEREZEciyZSNLXFiibx9XApQBQRERHJoeNdCVq64kW9SvlkFCCKiIiI5EA67TR3xOgswsTXw6UAUURERGSUYsnMI+ViTXw9XAoQRUREREahPZrgSEdpP1LuTwGiiIiIyAi4Zx4pd0RL/5FyfwoQRURERIYpkUpzuC1KPFkej5T7U4AoIiIiMgydsUxVlHQZPVLuTwGiiIiIyBC4Oy2dcY53JwrdlbxTgCgiIiJyEslUmqb2GNFE6VVFGQkFiCIiIiKD6I6naGqPkkqX7yPl/hQgioiIiJzAsa44LZ3xQndjzClAFBEREeknlXaa22N0xcsvhc1QKEAUERER6SOaSNHcXj5VUUZCAaKIiIhIoC2a4GiZVUUZCQWIIiIiMu6Vc1WUkVCAKCIiIuNaPJmmqb18q6KMhAJEERERGbfGQ1WUkVCAKCIiIuPOeKqKMhIKEEVERGRcGW9VUUZCAaKIiIiMG+OxKspIKEAUERGRcaG1M05r1/irijISChBFRESkrI33qigjoQBRREREypaqooyMAkQREREpS8e7E7R0qirKSChAFBERkbKSTjtHOmJ0xPRIeaQUIIqIiEjZiCfTHG6L6pHyKClAFBERkbLQEUtyRFVRckIBooiIiJQ0d+doZ5w2VUXJGQWIIiIiUrKSqTSH22PEVBUlpxQgioiISEnqiidpbo+pKkoehPJ1YDOrNrMXzexVM9tmZn8VtC80sxfMbJeZ/dDMKoP2quD97mD7gj7H+mLQ/qaZfaRP+6qgbbeZ3dOnfcBziIiISHlo7Yzz7nGVzMuXvAWIQAy42t3PBc4DVpnZpcA3gG+5+2KgFbgt2P82oNXdTwe+FeyHmZ0F3ACcDawC/reZhc0sDPwj8FHgLODGYF8GOYeIiIiUsFTaOXS8WyXz8ixvAaJndARvI8GXA1cDPw7aHwOuD15fF7wn2L7SzCxoX+3uMXffC+wGLg6+drv7HnePA6uB64LPnOgcIiIiUqKiiRQHWrvpjmu+Yb7lcwSRYKTvFaAJeBp4Czjm7j2ZK/cDc4LXc4BGgGD7cWBa3/Z+nzlR+7RBztG/f3eY2VYz29rc3DyaH1VEZEzp+iXjzfHuBIeOR0mmld9wLOQ1QHT3lLufB8wlM+K3dKDdgu92gm25w9llqwAAIABJREFUah+ofw+6+zJ3XzZjxoyBdhERKUq6fsl4kU47TW1RjnbEVDJvDI3JKmZ3P2ZmG4BLgclmVhGM8M0FDga77QfmAfvNrAKYBLT0ae/R9zMDtR8Z5BwiIiJSIlQVZWAHjnXn/Rz5XMU8w8wmB69rgGuA7cAvgY8Fu90M/DR4vSZ4T7B9vWduFdYANwSrnBcCi4EXgS3A4mDFciWZhSxrgs+c6BwiIiJSAtqjCQ4e61Zw2MeRjhjfWruTmx55Me/nyucI4mzgsWC1cQh4wt1/ZmZvAKvN7G+AXwMPB/s/DHzPzHaTGTm8AcDdt5nZE8AbQBL4vLunAMzsT4GngDDwiLtvC4519wnOISIiIkXM3TnSEac9qqooPdqjCVZvaeQnLx8glhybgDlvAaK7vwacP0D7HjLzEfu3R4GPn+BYXwW+OkD7k8CTQz2HiIiIFK9EKk2TqqL0iiZS/OTlA6ze0khHLLP2dnJNhM9cdip/8Y38nluVVERERKTgVBXlPclUmn//zbv80+a3OdqZyfdYWxnmk8vm8YcXzqGuKsJf5LkPChBFRESkoFo64xxT4mvS7vxyRxOPPLuPQ8ejAETCxvXnzeFTF89nUm1kzPqiAFFEREQKIpV2mtqj4z7xtbvzwt4WHtq0lz3NnQCEDFadM4ubLj2VhonVY94nBYgiIiIy5qKJFE1tsXGf+Po3+4/z0KY9/OZAW2/bB8+YwS1XLGD+1NqC9UsBooiIiIyp410JWrri4zrx9VtNHTz87F4272npbbvw1CncvnwhZ86qL2DPMhQgioiIyJhIp53mjhidseTJdy5TB4518+iz+1i/o6m3zNuSWfXcfuVCLpg/paB960sBooiIiORdLJl5pDxeE18f7YjxT5vf4We/OdS7UvvUqbXcunwhy0+fhtlAlYILRwGiiIiI5FV7NMGRjvH5SHmgJNcN9VV89vIFfOismYRDxRUY9lCAKCIiInkxnquiRBMp/vXXB/jBi+8luZ5UE+HTl8zn2nNPobIib9WOc0IBooiIiORcIpXmcFuU+BiVhisWyVSaJ19/l+89n53k+hPL5vKxC+dSW1kaoVdp9FJERERKRmcsUxUlPY4eKWeSXDfz3ef2cvBYYZNc58IJA0QzuwTY7u5tZlYD3ANcALwBfM3dj49RH0VExgUzuxhwd99iZmcBq4AdQd15kZIw3qqi9CS5fnjTXt7qm+T67FncdFlhklznwmAjiI8A5wav7wO6gG8AK4HvAv8hv10TERk/zOwrwEeBCjN7GrgE2ADcY2bnu/tXC9k/kZNJptI0tceIJsZPVZTXDxznO8/s5TcH3hszu2rxdG69YiHzpxUuyXUuDBYghty9J1HRMne/IHi9ycxeyXO/RETGm48B5wFVwLvA3OAJzt8BLwAKEKVojbeqKG81d/Dwpn5JrudP5rYrF7Jk1sQC9ix3BgsQXzezW9z9u8CrZrbM3bea2RnA+FuOJCKSX0l3TwFdZvaWu7cBuHu3mY2P37pSko51xWnpHB+PlA8e6+bR5/axbvt7Sa7PnFXPHy9fyAWnFk+S61wYLEC8HbjPzL4EHAGeN7NGoDHYJiIiuRM3s1p37wIu7Gk0s0mAAkQpOuOpKkpLZ5zvPf/2+5Jc37J8AVeePr3oklznwgkDxGARymfNrB5YFOy7390Pj1XnRETGkavcPQbg7n0Dwghwc2G6JDKw8VIVpSOaZPWWd/jJyweI9klyffNlp/Lhs2cVbZLrXBg0zY2ZzQfa3P1VM1sAXGlmO9z99bHonIjIeNETHA7QfsTMomPdH5ETaYsmOFrmVVGiiRT/99cH+MGWRtqjpZfkOhcGS3NzD3AnEDOz/wn8BfAs8Fdm9rC7f3OM+igiMt69AcwvdCdkfHPPPFLuiJbvI+VkKs3PX3+Xxze/zdGOzLzKmkgmyfXHl5VOkutcGOwn/QxwFlAL7AMWuXuzmU0gs6JOAaKISI6Y2Z+faBNQN5Z9Eemv3KuipN3Z8GYz3312HweOdQOZJNfXnnsKn75kPpNrKwvcw7E3WICYClbPxYFu4CiAu3eW42RMEZEC+xrwd8BAwzPl/zxLilY5V0Vxd17c18LDz+xjd3MHkEly/eGzZnHz5acys0STXOfCYAHiy2b2z8AEYB3wmJn9AriazOMOERHJnZeB/+vuL/XfYGbKHCFjzt1p6YxzvLs8M9u9fuA4D23ay2v7s5Nc33LFAk6dNqGAPSsOJ0tz83HAgR+Tyep/I/Am8I/575qIyLhyC9Bygm3LxrIjIuVcFWVPcwcPb9rH83uO9rZdMH8yt5dRkutcGCzNTRL4QZ+mZ4MvERHJMXd/c5BtSi8mY6Y7nqKpPdqb769cDJjkemY9t1+5kAvLLMl1Lgy2irkO+EvgD4G5QBx4C/i2uz82Nt0TERkfgoTYXwSuB2YEzU3AT4Gvu/uxQvVNxo9yrIrS0hnne5vf5t9fO0QyCHrnTanhtisXlm2S61wY7BHz94F/BT4CfILMXMTVwJfM7Ex3/29j0D8RkfHiCWA9sMLd3wUws1lkkmT/CPhQAfsmZS6VdprbY3TFyyeFTUc0yQ+3NvIvL+0fd0muc2GwAHGBuz8avP6mmW1x9782s1vILFJRgCgiJSeRStMeTVKEvxoWuPs3+jYEgeI3zOzWAvVJxoFoIkVze/lURTlRkutPXTKf68ZJkutcGCxA7DSz5e6+ycx+n2DytLunTeOxIlJiuuJJ2rqTvSMkE2siBe7R+7xtZn8JPNYz59DMZgKfBRoL2TEpX+VUFeVESa4/fmEmyfWEqvGT5DoXBvvT+hPgITM7A3gduBXAzGagVcwiUgJSaac9mqA9miyF0ZFPAvcAvzKzhqDtMLCGzDQfkZwpp6ooaXd+9WYzj/RLcv37557CH43TJNe5MNgq5teAiwdobwbuz2enRERGozueoj2aoDOeKpmREXdvBe4OvkTyJp5M09Re+lVR3J0t+1p5aNNedjdlJ7m+6fJTmVWmSa7NjNrKcN7PM6LxVjO7xd2/m+vOiIiMVDrttMeStHUnSmG0cEBmtgSYA2x2984+7avc/ReF65mUi45YkiNFUhXlxT0trN7SyKG2bmZPrOGGi+Zx8aKpQ/rstoPHeeiZvbzaJ8n1lYunc2sZJ7mOhEPUV1dQXx0ZkwU2I30g/1eAAkQRKbhoIkV7NElHLFkyo4UDMbMvAJ8HtgMPm9ld7v7TYPPXAAWIMmLFVhXlxT0t3Ld+FxUhY2J1BUc7Y9y3fhd3sXjQIHHvkU4e3rSX5956L8n1+fMnc/vyhSydXX5Jrs2MCVVhJlZHqI7kf9Swr8HyIL52ok3AzPx0R0Tk5Nwzo4Xt0SSx8qn08MfAhe7eYWYLgB+b2QJ3vw+KcdG1lIpkKs3h9lhR/VtZvaWRipBREwQ9NZEw3YkUq7c0DhggHjrezaPPvc3aNw6PiyTXlRUh6qsj1FVVFCwdz2AjiDPJ5EBs7dduwHMnO7CZzQMeB2YBaeBBd7/PzKYCPwQWAPuAT7h7a7Ay+j7gd4Au4LPu/nJwrJuBLwWH/pueRN1mdiHwKFADPAnc5e5+onOcrM8iUtziyTRt0QQd0WRRPCLLsbC7dwC4+z4zW0EmSDwVBYgyQsVaFeVQWzcTq7NDkOpIiHfburPaWjrj/NPmt/lZ/yTXyxdy5eLySnIdMmNCVQX11RVjPlo4kMECxJ8Bde7+Sv8NZrZhCMdOAv/F3V82s3rgJTN7mkzKhnXu/nUzu4fMqr27gY8Ci4OvS4BvA5cEwd5XyNQi9eA4a4KA79vAHcBmMgHiKuDnwTEHOoeIlBh3pzOeoq07UZZ1Yft418zO67nmBiOJvwc8AvxWYbsmpai1M05rV3FWRZk9sYajnbHeEUSAaCLNrIk1QGau5A+3NPIvL+8nmsjMKZ5RV8VNl53KqnPKK8l1VSRMfXUFdZUVhIro5xosQDwFODDQBnf/1MkO7O6HgEPB63Yz205m8vV1wIpgt8eADWSCt+uAxz0ziWizmU02s9nBvk+7ewtAEGSuCoLUie7+fND+OJkSVT8f5BwiUiJ6Elq3RxNFN/qRJ2kga9mluyeBm8zsgcJ0SUpRKVRFueGiedy3fhfdiRTVkRDRRJpk2vnDC+aweksjq198h7YgBc/E6go+dcl8rj9vTtkkuQ6H3hstrKoo/GjhQAYLEL8LPGVmjwF/6+4jntkazKc5H3gBmBkEj7j7oT75vuaQnQx2f9A2WPv+AdoZ5Bz9+3UHmRFI5s+fP8KfTkRyqTOYW1jMv9zy5EHg8YGuue7+bP+ddf0amg07mnhg4x4aW7uYN6WWO69axIolA/5KKAvRRIqmthjJdHGv5L940VTuYjGrtzTybls3M+urOW1GHfet38WRIMl1dSTEJy6cV1ZJrqt7RgurKor+8fhgeRCfMLN/B74MbDWz75G5w+3Z/s2hnMDM6oB/Af7M3dsG+QMZaIOPoH3I3P1BMhdlli1bNi6GKESKUbJ3tDCZ919s8WSaF/a2sHFXc17PM1zDvebq+nVyG3Y08eU124iEjck1EZrao3x5zTbuhbIMEo93J2jpLJ2qKBcvmsqyhVPYuDOT5PrVVzIPLXuSXH/6kvlMKYMk1+GQ9S44KaUR0JOF5AmgE6gC6ulzsRoKM4uQCQ6/7+4/CZoPm9nsYGRvNtAUtO8H5vX5+FzgYNC+ol/7hqB97gD7D3YOkbJS6qMj3fEUbdEEXXlOaJ1257X9x1m7/TAbdx6hI1a0o5OjuuZKtgc27iESNmorM7/qaisr6IoneWDjnpL6d3Iy6bRzpCNWzH+v38fd2fp2K995JjvJ9YfOmsnNly1g1qTST3JdW5l5hFxbGS760cKBDJbmZhXwTTJlni5w967hHDhYlfwwsL3fne8a4Gbg68H3n/Zp/1MzW01mkcrxIMB7CviamfWsY/8w8EV3bzGzdjO7lMyj65uAvz/JOUTKRqmOjqTSTkc0SVs0vwmt3Z09zZ2s3X6YdTuaeh9bQeYX0SULp/F23s4+fKO95sr7NbZ2Mblfze2aSJj9reXzRxtPpjncFs3pv6XRJLAeijcOtvHQpj280vhekusrTp/GrVcsZOH00k5yXRHKJLOuq64gEi6d0cKBDDaC+P8BH3f3bSM89hXAZ4DfmFnPSuj/RiZoe8LMbgPeAT4ebHuSTIqb3WTS3NwCEASCfw1sCfa7t2fBCvA53ktz8/Pgi0HOIVI2Sm10JJrIjBZ2xvI7Wvju8Sjrdhxm7fYm3j6aHQgsmVXPNUsbWHFmAwumT2D1nXnrxkiM9por/cybUktTe7T33whAdyLF3Cm1BexV7uSjKspIE1gPxd4jnTyyaS/P9klyfd68yfzxlaWd5Lqn9F1mtLA85krC4HMQrxzNgd19EyfO3bVygP2dTBWBgY71CJlUD/3btwLnDNB+dKBziJSTUhgdSaedjnim/F0+674e70qwYWcTa7c3se1gW9a2uVNqWLmkgZVLG4o6MBjtNVfe786rFvHlNdvoiid7EzEnUs6dVy0qdNdGxd052hmnLQ9VUYabwHoo3j0e5dHn9vF0nyTXZ8ys4/blmSTXpfj4Fd4rfVdXVUFFiY8WDqR8Ql2RcaaYR0diyaD8XR4TWncnUjy3+wjrdjSxZV9rViqcqRMq+e0zZ3DN0pmcMbOuZH8ByeisWNLAvWRG2/e3djG3BOfp9pfvqihDTWA9FC2dcb7/wjv826sHe5Ncz51Sw61XLOSDZ5RmkmszY0JlmPrqCDWVxZmeJlcUIIqUqGIbHXF3OoIUNflKaJ1MpXnpnVbWbW9i0+4jvQl0AWorw1y5eDorlzRw/vwpZZVIV0ZuxZKGkg4I++qKJ2luj+U1L+jJElgPRUcsyRNbG/nxS9lJrm++/FQ+cnZpJrmOhENMrI5QV1240ndjTQGiSIkqltGRRCpNW3eCjlgyL7+43J3th9pZu/0wG95s5lifx2oVIeOShVNZuXQmly2aSlURlKcSyYeWzjjHxqAqyokSWN9w0byTfjaWSPF/XznID/oluf70JfO5rgSTXBdb6buxpgBRpIQVcnSkM5ZZidwdz89o4dtHO1m3o4l125s4dDyate3cuZNYubSBqxbPYGK/eZgi5SSVdprao3n7d9Zf/wTWs4awijmVdn7++rs8/vy+skhyXayl78Zaaf1fE5GCyndC6+b2GL98M7PYpCc3Wo9FMyZwzZIGrl7SQMPE0s+RJnIyhaqKcvGiqUNakJJ2Z+POIzzy7F72t2bmKFaEMkmu/+jS0kpyHTKjrrq4S9+NNQWIInJS+Uxo3RFL8szOZtbuaOKVd45llUOaObEqWIE8s+Tzo4kMx/GuBC1dxVkVpSfJ9cOb9rLzcOZGzsgkuf7s5aWV5LqUSt+NNQWIIjKgfCa0jifTbN57lHXbm9i85yiJ1Hu/BCdWV/DBM2dwzZKZnD1nIiFdtGUcKfaqKJkk13t5pfFYb9sVp03j1uWlk+Q6HDLqqiqor46U3LzIsaQAUUSy5CuhdSrtvLb/GOu2N/GrXc10xt6bU1VVEeLy06ZxzdKZLFswpeQrEIiMRCyZeaSczwpDI7X3SCePPLuXZ3f3TXI9iduWL+TsUyYVsGdDVxOkp5lQoqXvxpoCRBEhnXbaY0nao7lNaO3u7G7qYO32Jta/2cTRfuXulp06hauXzmT56dPGrAJBKKh6UFdiE+elvLVHExzpKL5Hyu+2RXnsuX38v23vJble3FDH7VcuZFkJJLmuCIV65xbqxnN4dIUUGcdiyRRt3Uk6Y7lNaH3wWHfvCuR3WrIru5w1u56rl8xkxZkzmDphbCaxR8IhairDTKisoDoSKvpfajJ+uDtHOuK0R3NfFWU0WrvifH/zO/zbawd7p4D0JLm+6ozpRT31o1xL3401/cmJjDM9Ca3bosmcVmM41hXnl282s257E28cyi53N29KDdcsncnVSxuYM3noCXdf3NPC6i2NHGrrZvYQ0m30VRUJM6EyTE1lWKsSpSglUmma8lgVZSQ6Ykl+tLWRH/VJcj29rpKbL1vAqnOKO8l1uZe+G2sKEEXGiXgyTXs0twmtu+Mpnn3rCGu3N7F1Xwt9DzttQiW/vSRT7m5xw/DL3b24p4X71u+iImRMrK7gaGeM+9bv4i4WDxgk9owa1FSGqY2E9QtCitpYVEUZjlgixU9fPcg/v5Cd5PrGi+dz/XmnFG0S+vFU+m6sKUAUKWPuTmc8RXsOE1onU2m2vp0pd/fs7iNE+8xZnFAZ5srFM1i5tIHz5k0e1WjD6i2NVISst+RXTznB1VsaewPEilDw6LgqTE1EE8/L2YYdTTywcQ+NrV3MK/GaymNVFWUoUmnnF6+/y+PPv01zRwzIJLn+2IVz+cSyeUU7V3c8lr4ba8X5f15ERiWZStMWTdKRo4TW7s62g22s297Ehp3NHO9T7i4SNi5eOJVrls7k0oW5K3d3qK2bidXZl6jqSIjDbd1Mrq2ktjI8LstfjUcbdjTx5TXbiISNyTURmtqjfHnNNu6FkgoSx7oqymDcnY27jvDIpr009kty/elL5o/Z/ODhMDMmVIWZWB3Rv/0xoABRpIx0xZO0dSfpiucmh9rbRzszK5B3ZJe7M+DceZNYuWQmV50xnfrq3Je7mz2xhqOdMWoqw4TMCJkRTSRZML2uKH95Sf48sHEPkbD1LjioraygK57kgY17SiZALFRVlP5KMcl1ZUWI+uoI9VXju/TdWFOAKFLiUmmnPZqgPZrMSf605vYY64MVyLubs8vdnT6jjpVLM+XuZtRXjfpcJxIOGZ+9fAH/8+k3SabS1FZW0J1IkUzDnVctytt5pTg1tnYxuV/N7ZpImP2tXSf4RHEplqoo2w+18Z1nspNcX37aNG4rwiTXITMmVFUwsUal7wpFAaJIiYomUrR1J+jMQfm7jmiSjbuaWbu9iVcbs8vdzZpYzcqlDaxc2sCCafn7JRIJh6itDFMbpKI5ddoEJtdGeGDjHva3djG3xOedycjNm1JLU3s0K2VJdyLF3Cm1BezVyaXTTnNHjM4CV0UZKMn1uXMncfuVxZfkWqXviocCRJES0pPQuq179OXv4sk0m/ccZe32Jl7Ym13ublJNhBVnZBabnH3KxLxdqKsj4d6gcKCSVyuWNCggFO68ahFfXrONrniyd7FSIuVFPZpcDFVRepJcP/3G4d4MA6c31PHHRZbkulxL35X6wioFiCIlIJpI0R5N0hFLjmq0MJV2Xm08xtrtTTyzq5nOPpPlqytCXHH6dFYubWDZqVPykiYmZJZJQxMEhVp9KEOxYkkD90LJjCYXuipKa1ec77/wDv/2anaS61suX8AHz5xRNEmuy7n0XTksrFKAKFKk3HvK340uobW7s6upg3UnKne3YCrXLG3gitOm5yWPWEUoRG1VJihUKhoZqVIYTS50VZTOWJIfbd3Pj17aT3dwzZhWV8nNl53KqrNnFUVu0PFS+q4cFlYpQBQpMvFkmrZogo7o6MrfHTjWzfrtTazdfrg3jUWPs2ZPZOXSBlacOYMptblfEVwVySSrrq1SFRMZHxKpNIfbojmtZT5U8WS6N8l1Twqq+iDJ9R8USZLr2sqKoPTd+LhJLPWFVaAAUaQo9CS0butOEB3FaGFrV5wNbzazbvth3jjUnrVt/tTa3hXIwyl3NxRmmYTWtVWqYlLOSn1OVb50xjJVUXJZz3woUmnnqW2ZJNdN7UGS64oQf3jhXD65bB511YX9FR8Jh4K5heOv9F2pLqzqSwGiSAElUmnao0nao4kRl9zqjqfYtPsI67YfZuvbrdnl7uoqufrMBq5Z2sDpIyh3N5hwKDOfcEJlBTWRsPKTlblymFOVa+5OS2c8K3H8WJ13oCTXv/eB2fzRpacWNE/oyUrfjZebjFJcWNWfAkSRAhhtQutkKs2Wfa2s3X6Y5946SqxfuburghXI584dXbm7/iLhEBOqKlTFZBwqhzlVuZRMpWlqj41qxH8kXnq7lYee2cubhzNPCAxYubSBW65YwOxJuX0yMBxDKX03nm4ySm1h1UAUIIqMkWQqTUcsExiOpJpC2p1tB9pYu+Mwv3qzmbboe8FlJGxcumgaK5c2cOnCaTlLFWFmVEdC1EYqqK0Kl/WkchlcOcypypVCVEXZfqiNhzft5eV33ktyfdmiady2fAGLZtSNWT/6Gm7pu/F2k1EKC6sGowBRJA/6PkY5ZVINn7p4HufOnzKitBd7j3Sybvth1u1o4nBbrLc9U+5uMh9a2sCVi2fkbL5RyCyThqaqglo9OpZAOcypyoVjXXFaOuMn3zFH9h3t5JFN+9i0+0hv2wfmTuL25Qs5Z05hklyPtPSdbjJKiwJEkRzbsKOJ//7T1wmHjNpImEPHu/nGU29y19WLuXjR1CEdo6ktyvpgsclbzZ1Z204JaqXGU2lwmDahatTBYSQc6p1PWB0JjYtVhjI85TCnajTSaaepPZazOucnM2CS6xl13HblAi5eMHXM/432lL6rr64Y8fQS3WSUFgWIIjkUTaT4+/W7AagMHsf2/DJdvaVx0ACxPZrgVzszi01e2388q9zd7EnVXL2kgZl1VfxgayMVIWNKVYSjnTHuW7+Luxh68NmjKhJmQmWYmkqlopGTK4c5VSM1llVRjgVJrtf0SXI9Z3INt1yxgBUFSHJd1VP6rnJ4o4UDGe83GaVGAaLIKKXTTkc8U/4unkyz/1gXE/uN6FVHQrzb1v2+z8aTaZ7fc5S12w/z4t6W95e7O3MG1yxt4KzZmXJ3f/7DV6kIZVLKwNCDT8jMF6oNAkKlopGRKPU5VSPRFk1wdAyqonTGkvzopf38aGvhk1yHQ++NFuby5nE832SUIgWIIiMUSwbl7/oltJ49sYajnbHeIA4gmkgza2JmhWEq7bzSeIy12w+zadeR7HJ3kRDLg3J3F85/f7m7Q23dQw4+IVO1oKYyzIQqVTGR0jbW6VHcneaOGB3R/D5SLqYk12NR+m483mSUKgWIIsPg7nQE5e9OlN7ihovmcd/6XXQnUlRHQkQTaRKpNMtPn8Y//nI3v3yzOWuSezhkXLRgCiuXNHD56dOzAsv+ThZ8QmYCeW2lUtFI+Rjr9CjxZJqm9pFVRXlxTwurtzRyqK2b2RNruOGieQOO7qfSzv974zCPPbevoEmux0vpOxk+BYgiQ5BIpWnrTtARS540ofXFi6ZyF4tZvaWR/ce6qAiFSKadf9jwVtZ+55wSlLs7o4FJtZETHC3bQMFnMu3cdNmpTKurorZSqWikdN2/dicPbdpLZzzFhMowty9fyBeuOWNM06OMpirKi3tauG/9LipCxsTqigHnCLs7z+w+wiOb9vFOS2b1bkXI+N0PzOYzY5jkeryVvpPhU4AoMojOYLRwOCsXWzrj7D/WRTSZ4khHdjqMU4NydyuXNowoqW1P8PnDrY0cbosyd0otf/LBRVy9dOawjyVSTO5fu5P71u8mZFARyqxuvS9Y8DUW6VFyURVl9ZbGQecIv/x2K9/ZtJc3381Ocv3ZyxdwSo7LXw6kIhSivnp8lr6T4ctbgGhmjwC/BzS5+zlB21Tgh8ACYB/wCXdvtczty33A7wBdwGfd/eXgMzcDXwoO+zfu/ljQfiHwKFADPAnc5e5+onPk6+eU8pPsLX839ITWXfEkm3YfZd32w7zUr9zd9LpKrl7SwDVLZ3LajAkjvluPhEPUVoa5/oI53HDJ/BEdQ6RYPbRpbxAcZgKXkEEyneahTXs5+5RJeU2PkquqKCeaI9zY2slf/OjVgiS57lmclhkt1JiQDF0+/7Y8CvwD8HiftnuAde7+dTO7J3h/N/BRYHHwdQnwbeCSINj7CrAMcOAlM1sTBHzfBu4ANpMJEFcBPx/kHFJEirEeZ3c8RVs0QVc8NaQVi4lUmi37Wli3ven95e6qwnzwjBlcs3QmH5iUqPkRAAAgAElEQVQ7acSpKaojQa3jynDOqqOIFKPOeIr+f8VDlmnPZ3qU7niKpvboiGuh99V/jnA8meZwe5TuRJqjnZng8LfmTOKPr8x/kutIuGe0MJLTcpsyfuQtQHT3jWa2oF/zdcCK4PVjwAYywdt1wOOe+a282cwmm9nsYN+n3b0FwMyeBlaZ2QZgors/H7Q/DlxPJkA80TmkSBRTPc5U2umIJmmLJoaU4yztzusHjrNuR9OA5e4uO20a1yyZycULp44ooAuZZdLQVIaprTxxTVORcjOhMhP49f0rn/ZMe77So+S6KkrPHOH2WILOWCrr+nDajAncfuXCvCa5Hm7pO5HBjPV480x3PwTg7ofMrOdf9xygsc9++4O2wdr3D9A+2Dnex8zuIDMKyfz5emQ3VoqhHmc0kRkt7IwNbbRwT3MHa7c3sX5HU++KQ8jMITp//mRWLp3JlYunU1c1/H9SFaEQtVWZoFCpaGSoyu36dfvyhdy3fjfJdJqQZYLDtGfaIbfpUVJppzkPVVHOmFXHadMn8OxbR3sT3U+bUMnnVpyW1yTXPaXv6qrG7qayGJ8CSW4Vy4SEgf5G+wjah8XdHwQeBFi2bFl+s6BKr5NNOM/XhSeddtpjSdqjiSGlrzjcFmX9jibWbW9iz5HscndnzKxj5ZIGfntJA9Prqobdl6pIJll1bZWqmMjIlNv16wvXnAEw4CrmXIomUjS357YqSlc8yY+27ueJvkmuJ1Ry02Wn8tFz8pPkOhel70aqmJ4CSf6MdYB42MxmByN7s4GmoH0/MK/PfnOBg0H7in7tG4L2uQPsP9g5JI+GE9QNVo8zHxeeWDJFW3eSzljypKkr2roTbNzVzNrtTby2/3jWtlMmV7NySQMrl8xk/rThTY43y6xsrK1SFRORE/nCNWfkPCDs63h3gpbO3FVFiSfTrHn1IN/vl+T6hovm8Qfnz8lL4JbL0ncjVQxPgST/xjpAXAPcDHw9+P7TPu1/amarySxSOR4EeE8BXzOzKcF+Hwa+6O4tZtZuZpfy/7d359FxlWeex79v7Vq9SvKGseWAF3ZjNschBDsbZMiQpHugMyQQOOmhcyZJ98mZJJ30TJbunGwni3uYgbRDQmgawjBpshImNmExO8ZgDJaNLRkj2ZZsWbtU+zt/3FulKlkllZZSlaTf55w6Lt17de+tK+nxc+/7vs8LzwOfAP55lGNIgYw1qRupw/lkBZ5UQevucJzIKCMTI7GEO91dGy80nSKe0Vl9Xrmfq1bXsmVtLWsWVY2p+dfrcfoTVgR8lPm9RQvmIjPRWG5Kk0nLyd4IvZHhm5TzLW6dkqvI9UfWL+WGS5ZPepHrQk19N15TUXZIiq+QZW7ux3n6t9AY04wzGvnbwIPGmFuBI8BfuJv/AafEzUGcMje3ALiJ4DeBF93tvpEasALczmCZm0fcFyMcQwpkrEndSB3Ov/rrvRMKPNF4kp7w6AWtE0nLy0c62LGvjafePJluFkodb9NZC9m8ppaLz5w3pj49fq+HiqBmMZHZZar7o43lpjQaT9LaHc7ZpJxPceuUVJHrn+08zFtukWuvx/Ch8xbzny9fzoJxdDcZSSj1tDDoK6m+ySO1AsnMUchRzDfmWLV5mG0t8Jkc+7kbuHuY5S8B5w6zvH24Y0i2yQzo47mbzNXhfDyBx1pLv1uiZiCa+2mhtZb9rT1s39fGnxva6OgfLIibmu5uy9o6Nq5akHdyZ4wh5PdQ7vdRHtQsJjL7FKM/Wr43pb2ROCdHmRVltOLWKS+/1cG2nU00DCly/cmNK1g6SpHre585zIO7nP6JZX4vf3nxMm7auGLYbb0eQ2XQKU9TqqWtCll2SEpHqQxSkSk02QF9Mu8mxxJ48i1o3dzRnx6B3NwxkLXuvKXVXL2mjqvOrsl7ujuPW3i2POijXE3HMssVoz/aaDel1lra+6J05zErSq7i1se7nVix/3gP255qZFdGkevL6+dz66aVrMqjyPW9zxzmnufewmPA63H6RN/z3FsAWUnidJr6rlBlh6S0KEGchSY7oE/m3WQ+gac/6iSFfTn6E4Ez3d1jDW3saGhLT2uVsmJBOVvW1nH1mloWzQnldV6pWUzKAz5Cfk/JB3CRqVKo/miZrRxVQZ/Trzia4Ix55VS6NROHuymNJ5K09kRG7XucMrS4NUA4lmRuWYCv/eZ1nnzzZHr5eIpcP7ir2U0O3aeBBkgmeXBXM7dsqneakEO+adf6MJllh6Q0KUGchSY7oE/23eRwgSeRtPSEY/SE4zn7EvVF4uw8eJLt+9rYfSR7uruaymB6DuT6hflNdxf0e6kIeCkLqBSNSC6T2YKQSgoPtHbTG0kwv8JPwOvhzbZeAJbODdHWE6Z7IJaua5Z5U3rzxjNp6RwY06woqeLWA7EEIb+H3kiczv44zbEBGlqdm8uJFLkeiCUYmvt5jLN8rNUQRKaSEsRZqBAdjAt1NxmOJegeiNGXY/q7WCLJC02n2L6vjWcb27PqG1aFfFx5Vg1b1tZyXh7T3aXmLE2NPNYsJiKjm6wWhMyuL+FYkqS1tPfG3KdvBiyc7I2m5y4OeD3MLQ+kb0o/fulyzqqrGvOUeZfWz+dznMW/PvcWje19DEQT6eRzydwQt2xcwXvW1I67yHWZ30skngAzWMA3iaEyqJtOKW1KEGehUu9gnCpo3T0w/PR3SWt5raWLHfvaeOLACXoyprMK+DxcXj+f966t45IVo0935/N4nIQwqFlMRMZjrC0IuQbIZXZ9iSaSeI3BApF4kqDf+TuOuvGgzO+layDGI5+/csKzovRH4zS0dtPY3ke/O8htfkWAmy4/k2vPm1iRa2MMH79sOdt2NmGsHXaGGJFSpQRxFirVDsajFbQ+dKKXHcNMd+cxcNHyeWxeU8u7zlpIxSjT3QV8Hqc2YRFL0WiaKplJ8m1BGGmAXGbXl4DXQzxhMW5uZi1gneUw2OIRjiVo646MOEgtl2g8yW/3HOW+547Q6Q5mqQw6Ra4/sn5iRa79Xg/VIT+VIR9/f+06KoO+gs8QIzLZzGRVlJ/uNmzYYF966aVin8asM1pB6+PdYR7b5ww2aRoy3d3quio2r63lPatrRqw/li5FE/BRESj+LCaZ/0lmPsH9xnXnKEmcYsaYXdbaDcU+j4maLvHrxp88d1r3lv5onNoqZ7BYal33QIyjXc4oYg+QcP+bWjo3hM/rIZawfPH9q1m3dM6YZ0VJJC1/eqOVn2cUuQ6mi1yfQVUov2oGQxVz6juZnQodv/QEUYoiVdC6J3z608KugRhPHDjBjn2tvNbSnbXOABcsm8Pn33s2y+fn7jOZmsWkPFB6pWg0TZVMF5P9pHukAXLf/PC56a4vVSEfC+IBOvpjVIV81FQGsdbSF01QUxnkhkvOYO2S6jElh9Zanj7Yzk+fbuKt9sEi19eet5ibJlDkuhSmvhMpBCWIMqLJ/A8iFeB7hiloHY4leOZQO9v3tfLi4Y7TOpobwOMBLOxp6eKJhrbTCs2mStFUBEv7Dl7TVMl0UIgC2CMNkBva9WXlwkq+PSTejDYrSi67jzhFrvcdGyxyffWaWm5+5+hFrofjMYbKUOlMfSdSCEoQJafJ+g8ili5oHctK/FLT3W3f18bOYaa7e9dZC3niwAniiWRWs3DCrSF208YVhPzedH/CUp11YChNUyXTwXifdI90UznaALmR+jL2hGO090ZHnBVlqP3He9i2s4ldb3Wkl122cj63bVrJqtrRi1wPVapT34kUghJEyWmiTaH90TjdA/Gs0YXWWhqOO9PdPb4/e7o7n8dw6cr5bFlby+X1znR32/e15qwhduaCiqKUopnoU9VSH0UuAuN70j3aTeV4BsiNZVaUlCOn+rn76SaePJBZ5Lqa2zbVc96y/Itcw/SY+k6kEJQgSk5j+Q8ilTQdOdXH4jll/KcNZ3Dxinnp9UdO9fPYvja2N7RytDOc9b3nLa1my9o6rjy7hjnDHC8ST5B5s56qIVas5HCiT1VLdRS5SKZ8nnQPvVnq6IuMelM5lpqpsUSStjHMinKiJ8I9zx7mj3uPpwvl19dUcNumlVy2cmxFrssCXqpCfiqmwdR3IoWgBFFyyrcp9PGGNr766714DZQHvLR2h/nB9gPcsnEFHQMxduxr5UBrb9b31C+scEYgr6llUfXp090F/V7K/V5u3bSCOx5vJFEiNcQma4CJpqmSUvZ4QxsdfREOt/fh93ioqw6mRw+nnnQPd7N0uL2PZUP69I23f21/NM6Jnkheha+7+mP82wtHePiVFmLukOfFc0Lc8s4VXD2GItc+jyfdt3C6TX0nMtmUIM4S42kWHa0pNJG09Ibj/HjHmxgg6POSSFqi8SSdAzG+9UhD1v5qq4JcvaaWLWtr07MhpBjjlHwpDzqJYarP4d+9bw0+j6dkaohpgInMdJmJ37K5ZbT2RHi7Y4CQz0tZwMNdTzYCw98s+T0eWnsiVJcF0vtL3VSOJQad6ovS2R8d9Vz7o3Ee2tXMgy81n1bk+przFuWd5KXK02TeDIvMdvprmAVSAT+WSNDVH+NY1wAvH+ngM1etGjHRytUUevmqBbT1hOmLONPftXT24/UYjnZFnWUZ+6gO+Xj32TVsXlvLuUuzp7vzepz/XMoDXspHaMb57JazS6aorAaYyEw3NPEzxtDcMUASS2XQx+63O7j1Fy/hMbBkTvbT/7rqIM2d4dNuKq+on59X14xE0tLWEz6tysFQ0XiS3+05yr8OU+T6+vVLKcujioHf60kPOCl2bVSRUqQEcRa468lGYokE7b0xjHECYyJpuePxQ5y/bO6ITxJTTaHJpKU36kx/d7RzgKS17GnuYvu+Vtp7YyQyRhYanNF+i6pD3HnT+qy7eL/XQ0XQSQpLuRRNLhpgIjPd0Kfkzaf6iVuIJy1vnRpIL08AzR0DLMNQ7W7fHY5hreXQiT6MgaVzQvzjfzwvr64Z+cyKkkhatu9zily3dg8Wub7+oqXceOnoRa6NMVS4fQvLAtMv/ohMJSWIM8xwzThvd/TT3hMh5k5XZQx4jTM7wWh956LxJN3hGL3hOIlkksYTfWzf18pjDSc40RvJ2jbk8zCnzLkbT1onmQr4vM4sJn4f5UHvtO/XowEmMtNlPiU/3jVAfIQugAkLx7oGqAr5aOnsp6M/jtdA0GdIWjjWHWFPc+eoXTO6+mOc6o/mLHydq8j1Nect4hOXnzlqkevMqe+KMbhNZDpSgjgDpJLCA63ddPTFcPNAjnYOsLelkzkhH9GMm3LrDvQIehm271yqoHX3QIxwLMHxrjA7GlrZsa+Nw+3Z269ZVMWWtbXMCfn5/WvHOd49QE1ViJuvOJMt5ywquVlMJoMGmMhMlvmUvL0vdz9Aj3FaC2IJS9dAjO5wIj2QLJKRVd75RCMXnDF32K4ZS+eW0dodpi8SH+YIjlfe7mTbU4284Ra5BqfI9S0bV7B0Xu4i18YYKoJeqkP+adlaIVJsShCnucwO5V39MTIbZ5IWeiIJeiKD/XlSqVoqiczsO5dZ0PpUb5THD7SxfV8brx/Nnu5u2bwyNq+pZfPa2vT3+70ePnLxMsoDPkJ+j8pCiExTmU/JD53oy7mdz53ayGcMT33xauq//HuGG3DcH0twRf18Hnq5JatrRjSe5KPrl+VMDg+09vDTnU28eHiwyLXB6ff4vrV1OZPDgM9DVchPVVBT34lMhBLEaS6zb89ITUGpO3vrvvfgNA/99ZX19EXi9ITjtPdFeOZgOzsaTp/ubn5FgPesrmHL2jrOrqvEGEPQ76XCne9YBWRFZo7UU/LVX32ESHz4PoEGJ4a8Y6Fzk2iMcZonhrFtZxO3bVrJs42naO7oZ9GcEB9dvyxdK/WFxlM88OLbHOseYF5ZAL/Pw2stXVn78Bondh3vDvOdRxv44vvXcGn9fMCZ+q4i6KO6TFPfjWay59eWmUsJ4jQ3XN+e4fg9HpJYEkmL12PwAMsXVLB8QTm/ebWFHfva2HnwJOHY4H8G5QFnurvNa2q5aPk8fO5cx2UBZ3o79eURmbm2bj+QMzn0GDAe8Fk40jHAqr//w4j1CvsicR56uYWv/4d1nLtsLj3hwVlRvvX7N9jecCL9dWrwCUDI7yHuzrvs9bg3oUlLXzTOAy++zZWrazT13RgUYn5tmbmUIE5zlQEvB0/0jhicDZDEYtz3i6uD9EWTLKwM8pH/9Uy6TAQ4091dtnI+m9fWcUX9fCqCzmi/iqCXMr9mFBCZLe58ojHnOo+BWCyJ03ll9FlOEhZauwb4pz/s4yef2JBefu8zh7OSwxRjnMEy4Vic9t4oXq/JWpdMWk72hlkyN3cfRDndZBX6l9lBCeI0MrRp4Ir6+bT3RYnGkozQukzQC+HE4BYtnWESFp5tbE8vO3/ZHLasreXKs2pYWBWkIuCjbJqWohGRiXm8oY3+Eae3MyRGjDqniyctjSf7eKHxFJfWz2cgmuAXz7817LbWQjSeoDLop60nSiJuMVh8HoMBfF4PZ8yvGNPxRYX+ZWyUIE4TwzUN3PH4IcoDHjweM+ITxPCQOJ/KFVfVVLB5bR2b19SyfEE55QEfFQGvisaKzHLf+WPDiOvjeUx/N1TCgt8Ddz1xkO89Gqe9Pzbi9uUBHx39UTxAEqf/dCxp8RqoDvpVe3QcVOhfxkIJYoka+rSwsz+abhroCcc41jlAJGHTfYR8HkPS2vQowjkhH33RxLCB3ODULLz/05c7M5nMwFI0IjJ+jSdzj16eiHgSmjKKbY/EZ2BeuZ/qkJ/j3WGibqwL+Dx8/2MXqEl0HFToX8ZCCWKJyEwIKwNe2vuiVJf5008LD7f3s2xuiJ5wjLdP9ZMYkvcNTQS7wrnrivm9EE1aaqtCObcRkdlrpBaJich3rwsrA/THk8wt82PM4Ewt1jo1F5Ucjo8K/ctYKEEsAanm455wlM7+eDqInuh1itQanMB6rCuMhdOSw1xSs6WkGHdHFme6qVy2bj/Atp1N9EUTVAS83LZpZcnMhSwihWfG2L9w8o4L1WU+vv+xC7jryUY1hxaACv1LvpQgFlHqqeHLRzqIJ5I5E7/U4miemaHHgMeteVhTGUgnmqkSZUkLt21aOez3bt1+gB8/dhCPAZ/HCcg/fuwggJJEkdkidTc5hfwew4YV87OeaKk5VKR4NBqhSB5vaOMLD73K7rc7iMRzJ4djZXACrTVOx26vx1BT6SfVxbA84OVzV78jZ7K3bWeTmxx68BiP+6+zXERmh1xzIhdSXVWA+z99eTo5vGpNLd+47hxqq0J0DcSorQrxjevO0dMvkSmiJ4gFltm3EGs51RcbpXzExKRH+nkM1UEvHf0xqoJeLlu5IK++Jn3RBEMnRfEYZ7mIzA456mMXVHNXhK3bD2TdvKo5VKR4lCAW0NbtB9j62JtTHmyTFhaW+ambU0Z/NE5tVYj7P315Xt9bEXCacjIHNSctI/ZZFJGZ48a7ninase94/BDnL5urpFCkBChBLJCt2w/ww+1vFqmrN3QOxCkPxqgM+sZUBPW2TSv58WMHiSeT6fmbR+qzKCIzy7NNHUU7diJpuevJRvY0d2qgnEiRKUEsgMcb2oqaHAJEE0kOt/djgGXz8p+OKhWEFZxFZKoFfR72tnTywuFTGignUmQzNkE0xnwA+DHgBbZZa789Vcf+7AO7i5ocZrJAc8fAaX17RvLZLWcrEIvIlPN7DT2RRHqgHDh9oOPJJNt2NuUVl1SmS2RyzMhRzMYYL3AH8EFgHXCjMWbdVB2/e4Qi1YU03GQofo/B5zUahSwiJa9zIE4iaYknLOFYgkg8QTyRzHugXKpM10AskfX0cev2A1Nw9iIzy4xMEIFLgYPW2kZrbRR4APhwkc9p0hnj1jw0UBX0snJh9uT1TnLo0ShkESl5Bqe4PzgtHxan/3MsaYklbF4D5VSmS2TyzNQEcSnwdsbXze6yggrHEjzy2rGC7NsAi6qDLJ9fzvUXLqY65MNjDJVBH5/ffBb/fON6aqtCGAZrIfq8zo9Xo5BFpNgyWzjK/F6GNniE/F6GzvCX2ibfgXJ90cRpLSm6QRYZn5naB3GYxtbTuwUaYz4NfBpg+fLl4zpQIml5vrGdh19p4ZG9x+kpQPNy0OdxnxBWjljL8Ko1tekmFgwkbVKjkEVmqMmIX5maTvbxg/+3f8L7yaWmMkhbTyT9tTGDszulWFKtIoZ40mJxgnlVyJdXP0KV6RKZPDM1QWwGzsj4ehlwdOhG1tqfAD8B2LBhQ97jSqy1vH60m4d3t/DbV4/SmhH0vB4z4Ynuy/webn/3qnF1rNYoZJHZYbzxa6jjXWF++KcDPLSrmUSBZlCprQxQGfIRjifoHohnldECpykrmbQY973XYwj6PNTXVKZrueZDZbpEJs9MTRBfBM4yxqwEWoAbgL+a6E6PtPfz8Cst/PvuFppO9mWtW798Lh++cCnXnr+YDf+4Pe99Br1w102XTGphWI1CFpHRdPRF2LrjIPe9cISoW81/yZwQR7vCE9pvdciXvilNzSTV3NFPbVWIf7h2XVaNwzK/oSLoo6M/hvE4Bf47+p2BKouqg/RH42Oaf1k3yCKTxxRjzs2pYIy5BvgRTpmbu621/zTS9hs2bLAvvfTSactP9kb47atHeXh3C682d2Wtq6+p4PoLl/LhC5eyfEF51roVX/r9qOd4/YWL+eEN60fdTkQKwxizy1q7odjnMVG54tdwOvoj/MuTTdzz7GH6Ik7fvHnlfv7Lu1dx8ztXEPR584pfa+oq+OPfXjWBsx6UmUhWBLwYY+iNxFk2rzyvKUJFZqNCx68ZmyCOVWaA7YvE+ePe4/xqdzPPHTqV1exSUxXkuguWcP1FSzlnSTXGDNfdUUSmg9mSICaTlo7+KPc9f4SfPd1ER38McPrm/dVly7n9qlXMrwhO1emKyCQodPyaqU3MY2aBR18/zsO7W/jz/jbCscEJlCuDPj5wziI+sn4pl9UvwDtcwUERkRITjiXo7I/xm1db+NnThznmNh8HfB6uv3AJt26q5x21lXgU00RkCCWIrn1Hu/nre3elv/Z7DVeeXcPH1i/jPWtqCfk1Ck5ESl8iaemNxOkeiPLkgZP8dGcTjW6faY+Ba85bzCeuWMHquirmlPuLfLYiUqqUILoS1hlBd/GKeVx/4VI+dMES5pQpeIrI9DAQTdATidEXSfDq2x38y1NNvH60O73+PatruHnjClYurKS2OqibXhEZkRJEV111iKe/eDVL5pUV+1RERMYknrQc6xrgYFsv23Y28ULTqfS6S1bM47ZNKzmrroqygJfaqpC6yYjIqJQgumqrgkoORWRaisQTfPN3b/Dn/SfSy9YtruK2d9Vz4RlzAZhXHmBeRaBYpygi04wSRBGRaa7xRB8DbnJ45oJybtu0ko2rFmCMwesx1FQFKQ8o3ItI/hQxRERmgEXVIW7eeCab19alm5CDfi91VcH0vOwiIvlSgigiMs3VVYf4+S2XEPANJoLVZX4WVARUq1VExkUJoojINDevPJBODj3GsLAqSGVQ4V1Exk8RRERkhvB7PdRVh7KeJIqIjIcSRBGRGaAy6GNhZVCzoojIpFCCKCIyzfk8htrqULFPQ0RmELVDiIhMcxqHIiKTTQmiiIiIiGRRgigiIiIiWZQgioiIiEgWJYgiIiIikkUJooiIiIhkUYIoIiIiIlmUIIqIiIhIFiWIIiIiIpJFCaKIiIiIZFGCKCIiIiJZjLW22OdQEowxJ4C3CrDrhcDJAuxX5zD9zqHYx9c5nH4OZ1pra4p8LhOm+KVz0DnMqnOYkvilBLHAjDEvWWs36Bx0DsU+vs6htM5hOiiF66Rz0DnoHIpzfDUxi4iIiEgWJYgiIiIikkUJYuH9pNgngM4hpdjnUOzjg84hpRTOYTooheukc3DoHBw6hyk6vvogioiIiEgWPUEUERERkWzWWr0K8AI+AOwHDgJfmoT9nQH8GdgHvA58zl0+H/gT8Kb77zx3uQG2usffA6zP2Ncn3e3fBD6Zsfxi4DX3e7biPmEe5ly8wG7gd+7XK4Hn3f39Egi4y4Pu1wfd9Ssy9vFld/l+4P1juW7AXOAhoMG9HldM5XUA/tb9GewF7gdCU3ENgLuBNmBvxrKCf+6MY3QBUeCNjO/5nvtz2AP8OzB3Ap8vn2vYhlPeYW/mz8Td7guABRYW8BpkXeeZ/Mr1cxrnvhS/Brcpavxyt5nyGEbx49ebQAtwYsg5KIbl+rstdhCaiS+cAHQIqAcCwKvAugnuc3HqlwOoAg4A64DvZvwBfgn4jvv+GuAR9xfscuD5jF+SRvffee771B/lCzjByrjf+8Ec5/J3wL8xGGAfBG5w398J3O6+/xvgTvf9DcAv3ffr3GsSdP+gDrnXLK/rBtwD3Oa+D+AE3Cm5DsBSoAkoy/jsN0/FNQCuBNaTHdwK/rlTx3CPvxU4kXH89wE+9/13Mo4/ns+XzzX8GvAoQ4IrTgLyKE4twIWFugZDr/NMfY30c1L8mr7xq5gxjCLHr4zP9fMh56AYluvvttiBaCa+3B/Ooxlffxn48iQf49fAe3HuYha7yxYD+933dwE3Zmy/311/I3BXxvK73GWLgYaM5VnbZSxfBuwArgZ+5/4Snsz4A0t/dveX/Qr3vc/dzgy9Hqnt8rluQDVOcBv6VG9KrgNOcH3b/cP0udfg/VN1DYAVZAe3gn/uIce4BIjk+J28Hrgvx3mP+PnG+Ht0itOD60PABcBhBoNroa5B+jrP1Ndov4eTsH/Fr+zlU3YdKGIMo/jxazFOcnfa0zt3vWJYxkt9EAsj9QeY0uwumxTGmBXARTiPsuustccA3H9rRzmHkZY353HOPwL+G5B0v14AdFpr48N8X/pY7voud/uxnlumepwmgp8ZY3YbY7YZYyqm6jpYa1uA7wNHgGPuZ9o1xdcg01R87vQxcK69L5lyKPIAAAWzSURBVMe5fArnjnU8xx/L71EPzl08AMaY64AWa+2rQ86nINdgyHWeqQoWwxS/ihe/3P2XUgyb0vjl/rswx7mAYlgWJYiFYYZZZidlx8ZUAv8X+Ly1tnsc5zDW5ZnH/hDQZq3dlcdxCnIOOMnJeuB/W2svAvpwHpfnMqnnYIyZB3wYp8lhCVABfHCE7ynENcjHlB7XGPMVIA7cV4DjD7cuddxy4CvAfx9u9SSew2xTkGuh+FXc+AXTJoZN+TEVw06nBLEwmnH6E6QsA45OdKfGGD9OcL3PWvsrd3GrMWaxu34xTgfYkc5hpOXLRjnndwLXGWMOAw/gNNP8CJhrjPEN833pY7nr5+A8Wh/ruWVqBpqttc+7Xz+EE3Cn6jpsAZqstSestTHgV8DGKb4Gmabic6ePAdTgBNE0Y8wngQ8BH7du+8U4jn+S/K9hFZBw163C+Y/uVff3chnwsjFmUaGuwZDrPFNNegxT/Ervs5jxC0orhk1p/HL/PW0OZcWwHEZrg9ZrXP1rfDidRlcy2In1nAnu0wC/AH40ZPn3yO54+l33/bVkd259wV0+H6cPzDz31QTMd9e96G6b6tx6zQjncxWDnbz/D9kdc//Gff8Zsjs3P+i+P4fszr+NOI/b87puwFPAavf919xrMCXXAbgMZ/Rfubv+HuC/TtU14PQ+PAX/3EOO8R2yB6l8AHgDqBnyMxrz5xvDNfwdufsQHWaw/06hrkH6Os/U12i/h+PYn+LX4LGLFr/c9UWLYRQ/fn3J/WyZ56AYluvvpNiBaKa+cEYfHcDpEPuVSdjfJpxHxXuAV9zXNTj9HnbgDF3fkfFLYoA73OO/BmzI2NencIbAHwRuyVi+AafswSHgf8LwZSLcba9iMMDW44ycOuj+gQTd5SH364Pu+vqM7/+Ke5z9ZI8SHvW6ARcCL7nX4mH3D2TKrgPwdZyyCHuBe3ECSMGvAU45imNADOdO8dap+NwZx+gBIkOOfxCnL0zqd/LOCXy+fK5hO86db/ochvxsDpNdImKyr0HWdZ7Jr1w/p3HuS/FrcJuixi93mymPYRQ/fr0JHHdfimF5xDDNpCIiIiIiWdQHUURERESyKEEUERERkSxKEEVEREQkixJEEREREcmiBFFEREREsihBlGnPOHYaYz6YsewvjTF/NMbcbYxpM8bsHfI9FxhjnjXGvGaM+a0xpjpj3fnuutfd9SF3+Y3u13vcfY80ZZOIyKgUv6RUqcyNzAjGmHNx6kxdhFPM9BWcAqhLgV7gF9baczO2fxH4grX2CWPMp4CV1tp/cKvcvwzcZK191RizAOjEqUd1FFhnrT1pjPku0G+t/drUfUoRmYkUv6QU6QmizAjW2r3Ab4EvAv8DJ6AestY+iTMt1FCrgSfd938CPuq+fx+wx7qTpltr2621CZwAa4AKY4wBqpmE6RNFRBS/pBT5Rt9EZNr4Os7dcxSnmvxI9gLXAb8G/oLBeS3PBqwx5lGceYcfsNZ+11obM8bcjlPNvg+nGv1nJv8jiMgspfglJUVPEGXGsNb2Ab8E7rXWRkbZ/FPAZ4wxu3AmTo+6y30404J93P33emPMZmOMH7gdpwloCc40WV+e/E8hIrOR4peUGj1BlJkm6b5GZK1twGmOwRhzNs6k6ODMjfmEtfaku+4PwHqg2/2+Q+7yB3EmPBcRmSyKX1Iy9ARRZiVjTK37rwf4KnCnu+pR4HxjTLnb4fvdwBtAC7DOGFPjbvdeYN/UnrWIiOKXTA0liDKjGWPuB54FVhtjmo0xt7qrbjTGHAAacDpr/wzAWtsB/AB4EWck4cvW2t9ba4/i9BF60hizB7gQ+NbUfhoRmU0Uv6SYVOZGRERERLLoCaKIiIiIZFGCKCIiIiJZlCCKiIiISBYliCIiIiKSRQmiiIiIiGRRgigiIiIiWZQgioiIiEgWJYgiIiIikuX/A9xa8uDiN6tfAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x720 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10,10))\n",
"ax1.set(xlabel='Y1968', ylabel='Y1961')\n",
"ax2.set(xlabel='Y1968', ylabel='Y1963')\n",
"ax3.set(xlabel='Y1968', ylabel='Y1986')\n",
"ax4.set(xlabel='Y1968', ylabel='Y2013')\n",
"sns.jointplot(x=\"Y1968\", y=\"Y1961\", data=df, kind=\"reg\", ax=ax1)\n",
"sns.jointplot(x=\"Y1968\", y=\"Y1963\", data=df, kind=\"reg\", ax=ax2)\n",
"sns.jointplot(x=\"Y1968\", y=\"Y1986\", data=df, kind=\"reg\", ax=ax3)\n",
"sns.jointplot(x=\"Y1968\", y=\"Y2013\", data=df, kind=\"reg\", ax=ax4)\n",
"plt.close(2)\n",
"plt.close(3)\n",
"plt.close(4)\n",
"plt.close(5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "8a297a06-977f-4ff7-a9ad-c7e8804930a8",
"_uuid": "6b738ce8b15a764fab90fac96f9534f94c14342e"
},
"source": [
"# Heatmap of production of food items over years\n",
"\n",
"This will detect the items whose production has drastically increased over the years"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "588cebd9-e97c-460d-8ed5-e663ac293711",
"_uuid": "16ce47d43a3038874a74d8bbb9a2e26f6ee54437"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x1728 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_item_df = item_df.drop([\"Item_Name\",\"Sum\",\"Production_Rank\"], axis = 1)\n",
"fig, ax = plt.subplots(figsize=(12,24))\n",
"sns.heatmap(new_item_df,ax=ax)\n",
"ax.set_yticklabels(item_df.Item_Name.values[::-1])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "825620f9-7ab5-4fe2-9529-c4f1a300138e",
"_uuid": "5c42595537332ea71089d8c3dc041d3bf7d41b55"
},
"source": [
"There is considerable growth in production of Palmkernel oil, Meat/Aquatic animals, ricebran oil, cottonseed, seafood, offals, roots, poultry meat, mutton, bear, cocoa, coffee and soyabean oil.\n",
"There has been exceptional growth in production of onions, cream, sugar crops, treenuts, butter/ghee and to some extent starchy roots."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "80428f51-2fd4-468d-9530-9279215b4218",
"_uuid": "4c9bb27cd76099c5348243a99448c509ef0c5ded"
},
"source": [
"Now, we look at clustering."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a3f1db3a-1b82-4e42-8e7d-f1a26915693b",
"_uuid": "da167de5a5b92e164fc6993b32ebbfab4ef9a6e3",
"collapsed": true
},
"source": [
"# What is clustering?\n",
"Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "136315a0-b37d-4d89-bd0d-037727062c34",
"_uuid": "04ab802ec92eaf6a27706f2008933dcf3865855a"
},
"source": [
"# Today, we will form clusters to classify countries based on productivity scale"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "27ba0b5d-c57e-485d-9588-017e16fe1904",
"_uuid": "659afdada04e8854765b5e7208394915b30f859a"
},
"source": [
"For this, we will use k-means clustering algorithm.\n",
"# K-means clustering\n",
"(Source [Wikipedia](https://en.wikipedia.org/wiki/K-means_clustering#Standard_algorithm) )\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "7aeb3175-33bd-4f49-903a-57d43380e90e",
"_uuid": "6b0b4881e623ed3c133b68b98e6fb6755e18fd78"
},
"source": [
"This is the data we will use."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "a5b99ea8-975f-4467-9895-bffe1db876eb",
"_uuid": "57aba4000bfc422e848b14ad24b02a570d6c0554"
},
"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>Y1961</th>\n",
" <th>Y1962</th>\n",
" <th>Y1963</th>\n",
" <th>Y1964</th>\n",
" <th>Y1965</th>\n",
" <th>Y1966</th>\n",
" <th>Y1967</th>\n",
" <th>Y1968</th>\n",
" <th>Y1969</th>\n",
" <th>Y1970</th>\n",
" <th>...</th>\n",
" <th>Y2006</th>\n",
" <th>Y2007</th>\n",
" <th>Y2008</th>\n",
" <th>Y2009</th>\n",
" <th>Y2010</th>\n",
" <th>Y2011</th>\n",
" <th>Y2012</th>\n",
" <th>Y2013</th>\n",
" <th>Mean_Produce</th>\n",
" <th>Rank</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Afghanistan</th>\n",
" <td>9481.0</td>\n",
" <td>9414.0</td>\n",
" <td>9194.0</td>\n",
" <td>10170.0</td>\n",
" <td>10473.0</td>\n",
" <td>10169.0</td>\n",
" <td>11289.0</td>\n",
" <td>11508.0</td>\n",
" <td>11815.0</td>\n",
" <td>10454.0</td>\n",
" <td>...</td>\n",
" <td>18317.0</td>\n",
" <td>19248.0</td>\n",
" <td>19381.0</td>\n",
" <td>20661.0</td>\n",
" <td>21030.0</td>\n",
" <td>21100.0</td>\n",
" <td>22706.0</td>\n",
" <td>23007.0</td>\n",
" <td>13003.056604</td>\n",
" <td>69.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albania</th>\n",
" <td>1706.0</td>\n",
" <td>1749.0</td>\n",
" <td>1767.0</td>\n",
" <td>1889.0</td>\n",
" <td>1884.0</td>\n",
" <td>1995.0</td>\n",
" <td>2046.0</td>\n",
" <td>2169.0</td>\n",
" <td>2230.0</td>\n",
" <td>2395.0</td>\n",
" <td>...</td>\n",
" <td>6911.0</td>\n",
" <td>6744.0</td>\n",
" <td>7168.0</td>\n",
" <td>7316.0</td>\n",
" <td>7907.0</td>\n",
" <td>8114.0</td>\n",
" <td>8221.0</td>\n",
" <td>8271.0</td>\n",
" <td>4475.509434</td>\n",
" <td>104.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Algeria</th>\n",
" <td>7488.0</td>\n",
" <td>7235.0</td>\n",
" <td>6861.0</td>\n",
" <td>7255.0</td>\n",
" <td>7509.0</td>\n",
" <td>7536.0</td>\n",
" <td>7986.0</td>\n",
" <td>8839.0</td>\n",
" <td>9003.0</td>\n",
" <td>9355.0</td>\n",
" <td>...</td>\n",
" <td>51067.0</td>\n",
" <td>49933.0</td>\n",
" <td>50916.0</td>\n",
" <td>57505.0</td>\n",
" <td>60071.0</td>\n",
" <td>65852.0</td>\n",
" <td>69365.0</td>\n",
" <td>72161.0</td>\n",
" <td>28879.490566</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Angola</th>\n",
" <td>4834.0</td>\n",
" <td>4775.0</td>\n",
" <td>5240.0</td>\n",
" <td>5286.0</td>\n",
" <td>5527.0</td>\n",
" <td>5677.0</td>\n",
" <td>5833.0</td>\n",
" <td>5685.0</td>\n",
" <td>6219.0</td>\n",
" <td>6460.0</td>\n",
" <td>...</td>\n",
" <td>28247.0</td>\n",
" <td>29877.0</td>\n",
" <td>32053.0</td>\n",
" <td>36985.0</td>\n",
" <td>38400.0</td>\n",
" <td>40573.0</td>\n",
" <td>38064.0</td>\n",
" <td>48639.0</td>\n",
" <td>13321.056604</td>\n",
" <td>68.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Antigua and Barbuda</th>\n",
" <td>92.0</td>\n",
" <td>94.0</td>\n",
" <td>105.0</td>\n",
" <td>95.0</td>\n",
" <td>84.0</td>\n",
" <td>73.0</td>\n",
" <td>64.0</td>\n",
" <td>59.0</td>\n",
" <td>68.0</td>\n",
" <td>77.0</td>\n",
" <td>...</td>\n",
" <td>110.0</td>\n",
" <td>122.0</td>\n",
" <td>115.0</td>\n",
" <td>114.0</td>\n",
" <td>115.0</td>\n",
" <td>118.0</td>\n",
" <td>113.0</td>\n",
" <td>119.0</td>\n",
" <td>83.886792</td>\n",
" <td>172.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 55 columns</p>\n",
"</div>"
],
"text/plain": [
" Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 \\\n",
"Afghanistan 9481.0 9414.0 9194.0 10170.0 10473.0 10169.0 \n",
"Albania 1706.0 1749.0 1767.0 1889.0 1884.0 1995.0 \n",
"Algeria 7488.0 7235.0 6861.0 7255.0 7509.0 7536.0 \n",
"Angola 4834.0 4775.0 5240.0 5286.0 5527.0 5677.0 \n",
"Antigua and Barbuda 92.0 94.0 105.0 95.0 84.0 73.0 \n",
"\n",
" Y1967 Y1968 Y1969 Y1970 ... Y2006 \\\n",
"Afghanistan 11289.0 11508.0 11815.0 10454.0 ... 18317.0 \n",
"Albania 2046.0 2169.0 2230.0 2395.0 ... 6911.0 \n",
"Algeria 7986.0 8839.0 9003.0 9355.0 ... 51067.0 \n",
"Angola 5833.0 5685.0 6219.0 6460.0 ... 28247.0 \n",
"Antigua and Barbuda 64.0 59.0 68.0 77.0 ... 110.0 \n",
"\n",
" Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 \\\n",
"Afghanistan 19248.0 19381.0 20661.0 21030.0 21100.0 22706.0 \n",
"Albania 6744.0 7168.0 7316.0 7907.0 8114.0 8221.0 \n",
"Algeria 49933.0 50916.0 57505.0 60071.0 65852.0 69365.0 \n",
"Angola 29877.0 32053.0 36985.0 38400.0 40573.0 38064.0 \n",
"Antigua and Barbuda 122.0 115.0 114.0 115.0 118.0 113.0 \n",
"\n",
" Y2013 Mean_Produce Rank \n",
"Afghanistan 23007.0 13003.056604 69.0 \n",
"Albania 8271.0 4475.509434 104.0 \n",
"Algeria 72161.0 28879.490566 38.0 \n",
"Angola 48639.0 13321.056604 68.0 \n",
"Antigua and Barbuda 119.0 83.886792 172.0 \n",
"\n",
"[5 rows x 55 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "66964df2-892d-4e55-a4b1-f94d10e4c7dd",
"_uuid": "19bdd89a3ad9df962959ad6b996946f6f3916d58"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
"For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
" after removing the cwd from sys.path.\n"
]
}
],
"source": [
"X = new_df.iloc[:,:-2].values\n",
"\n",
"X = pd.DataFrame(X)\n",
"X = X.convert_objects(convert_numeric=True)\n",
"X.columns = year_list"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "461e5bcc-0101-4ea1-ae13-20600f883929",
"_uuid": "0d3e50235c9505ebc255053d4a5aae547fc17d8d"
},
"source": [
"# Elbow method to select number of clusters\n",
"This method looks at the percentage of variance explained as a function of the number of clusters: One should choose a number of clusters so that adding another cluster doesn't give much better modeling of the data. More precisely, if one plots the percentage of variance explained by the clusters against the number of clusters, the first clusters will add much information (explain a lot of variance), but at some point the marginal gain will drop, giving an angle in the graph. The number of clusters is chosen at this point, hence the \"elbow criterion\". This \"elbow\" cannot always be unambiguously identified. Percentage of variance explained is the ratio of the between-group variance to the total variance, also known as an F-test. A slight variation of this method plots the curvature of the within group variance.\n",
"# Basically, number of clusters = the x-axis value of the point that is the corner of the \"elbow\"(the plot looks often looks like an elbow)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "06271223-bd32-48ac-a373-6c1e6bbf7c7b",
"_uuid": "c57d7277510a8c11fdc3d311e4d8a22539617ed9"
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.cluster import KMeans\n",
"wcss = []\n",
"for i in range(1,11):\n",
" kmeans = KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\n",
" kmeans.fit(X)\n",
" wcss.append(kmeans.inertia_)\n",
"plt.plot(range(1,11),wcss)\n",
"plt.title('The Elbow Method')\n",
"plt.xlabel('Number of clusters')\n",
"plt.ylabel('WCSS')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ad4bc40a-9540-497d-95e3-3fee6088ea95",
"_uuid": "6450dd1c3d7a8114931dc358d2f09a0424b52fd7"
},
"source": [
"As the elbow corner coincides with x=2, we will have to form **2 clusters**. Personally, I would have liked to select 3 to 4 clusters. But trust me, only selecting 2 clusters can lead to best results.\n",
"Now, we apply k-means algorithm."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "eed3f672-e089-4dbb-aad8-b9618967abf3",
"_uuid": "d92d758ee7213ddcd84e9b8b2f61c9e260ed6ba2"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
" after removing the cwd from sys.path.\n"
]
}
],
"source": [
"kmeans = KMeans(n_clusters=2,init='k-means++',max_iter=300,n_init=10,random_state=0) \n",
"y_kmeans = kmeans.fit_predict(X)\n",
"\n",
"X = X.as_matrix(columns=None)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ef07bd6d-679d-4375-b7b3-abeca3421e37",
"_uuid": "6f93a4bd3f17427f4b2dbe08af8e015a1e4a2f89"
},
"source": [
"Now, let's visualize the results."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "5a7fe139-13df-453b-8c16-891929bc595e",
"_uuid": "a57e0a38f4c0f0385be75fd9f71d4a2d8213aea3"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0,1],s=100,c='red',label='Others')\n",
"plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1,1],s=100,c='blue',label='China(mainland),USA,India')\n",
"plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s=300,c='yellow',label='Centroids')\n",
"plt.title('Clusters of countries by Productivity')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "923d4536-2bce-4b99-b98a-33b801a56a8b",
"_uuid": "fe531e8c41eec0eb5dc52a9890871841f5d27211"
},
"source": [
"So, the blue cluster represents China(Mainland), USA and India while the red cluster represents all the other countries.\n",
"This result was highly probable. Just take a look at the plot of cell 3 above. See how China, USA and India stand out. That has been observed here in clustering too.\n",
"\n",
"You should try this algorithm for 3 or 4 clusters. Looking at the distribution, you will realise why 2 clusters is the best choice for the given data"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6dee7acb-0f08-4ae1-85b4-f4704026694a",
"_uuid": "179a1ede21ae330664a0b7c63e36574acdc0428c"
},
"source": [
"This is not the end! More is yet to come."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Now, lets try to predict the production using regression for 2020. We will predict the production for USA,India and Pakistan.**\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"ename": "ValueError",
"evalue": "Expected 2D array, got scalar array instead:\narray=2020.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-24-da7cfa1c86d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2020\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mArea\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'India'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mElement\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'Food'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Y1961'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 212\u001b[0m \"\"\"\n\u001b[0;32m--> 213\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0m_preprocess_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 197\u001b[0m return safe_sparse_dot(X, self.coef_.T,\n\u001b[1;32m 198\u001b[0m dense_output=True) + self.intercept_\n",
"\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m 543\u001b[0m \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[1;32m 546\u001b[0m \u001b[0;31m# If input is 1D raise error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 547\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Expected 2D array, got scalar array instead:\narray=2020.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
]
}
],
"source": [
"india_list=[]\n",
"year_list = list(df.iloc[:,10:].columns)\n",
"for i in year_list:\n",
" x=df[(df.Area=='India') & (df.Element=='Food')][i].mean()\n",
" india_list.append(x) \n",
"\n",
"reset=[]\n",
"for i in year_list:\n",
" reset.append(int(i[1:]))\n",
"\n",
"\n",
"reset=np.array(reset)\n",
"reset=reset.reshape(-1,1)\n",
"\n",
"\n",
"india_list=np.array(india_list)\n",
"india_list=india_list.reshape(-1,1)\n",
"\n",
"\n",
"reg = LinearRegression()\n",
"reg.fit(reset,india_list)\n",
"predictions = reg.predict(reset)\n",
"plt.title(\"India\")\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Production\")\n",
"plt.scatter(reset,india_list)\n",
"plt.plot(reset,predictions)\n",
"plt.show()\n",
"print(reg.predict(2020))\n",
"\n",
"df[(df.Area=='India') & (df.Element=='Food')]['Y1961'].mean()\n",
"\n",
"df[(df.Area=='Pakistan') & (df.Element=='Food')]\n",
"\n",
"Pak_list=[]\n",
"year_list = list(df.iloc[:,10:].columns)\n",
"for i in year_list:\n",
" yx=df[(df.Area=='Pakistan') & (df.Element=='Food')][i].mean()\n",
" Pak_list.append(yx) \n",
"\n",
"Pak_list=np.array(Pak_list)\n",
"Pak_list=Pak_list.reshape(-1,1)\n",
"Pak_list\n",
"reg = LinearRegression()\n",
"reg.fit(reset,Pak_list)\n",
"predictions = reg.predict(reset)\n",
"plt.title(\"Pakistan\")\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Production\")\n",
"plt.scatter(reset,Pak_list)\n",
"plt.plot(reset,predictions)\n",
"plt.show()\n",
"print(reg.predict(2020))\n",
"\n",
"\n",
"\n",
"usa_list=[]\n",
"year_list = list(df.iloc[:,10:].columns)\n",
"for i in year_list:\n",
" xu=df[(df.Area=='United States of America') & (df.Element=='Food')][i].mean()\n",
" usa_list.append(xu)\n",
"\n",
"usa_list=np.array(usa_list)\n",
"usa_list=india_list.reshape(-1,1)\n",
"\n",
"\n",
"reg = LinearRegression()\n",
"reg.fit(reset,usa_list)\n",
"predictions = reg.predict(reset)\n",
"plt.title(\"USA\")\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Production\")\n",
"plt.scatter(reset,usa_list)\n",
"plt.plot(reset,predictions)\n",
"plt.show()\n",
"print(reg.predict(2020))"
]
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이 알고리즘에 대해
About the dataset
Context Our world population is expected to grow from 7.3 billion today to 9.7 billion in the year 2050. Finding solutions for feeding the growing world population has become a hot topic for food and agriculture organizations, entrepreneurs and philanthropists. These solutions range from changing the way we grow our food to changing the way we eat. To make things harder, the world's climate is changing and it is both affecting and affected by the way we grow our food – agriculture. This dataset provides an insight on our worldwide food production - focusing on a comparison between food produced for human consumption and feed produced for animals.
Content The Food and Agriculture Organization of the United Nations provides free access to food and agriculture data for over 245 countries and territories, from the year 1961 to the most recent update (depends on the dataset). One dataset from the FAO's database is the Food Balance Sheets. It presents a comprehensive picture of the pattern of a country's food supply during a specified reference period, the last time an update was loaded to the FAO database was in 2013. The food balance sheet shows for each food item the sources of supply and its utilization. This chunk of the dataset is focused on two utilizations of each food item available:
Food - refers to the total amount of the food item available as human food during the reference period. Feed - refers to the quantity of the food item available for feeding to the livestock and poultry during the reference period. Dataset's attributes:
Area code - Country name abbreviation Area - County name Item - Food item Element - Food or Feed Latitude - geographic coordinate that specifies the north–south position of a point on the Earth's surface Longitude - geographic coordinate that specifies the east-west position of a point on the Earth's surface Production per year - Amount of food item produced in 1000 tonnes
This is a simple exploratory notebook that heavily expolits pandas and seaborn
# Importing libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# importing data
df = pd.read_csv("FAO.csv", encoding = "ISO-8859-1")
pd.options.mode.chained_assignment = None
from sklearn.linear_model import LinearRegressiondf| Area Abbreviation | Area Code | Area | Item Code | Item | Element Code | Element | Unit | latitude | longitude | ... | Y2004 | Y2005 | Y2006 | Y2007 | Y2008 | Y2009 | Y2010 | Y2011 | Y2012 | Y2013 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AFG | 2 | Afghanistan | 2511 | Wheat and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 3249.0 | 3486.0 | 3704.0 | 4164.0 | 4252.0 | 4538.0 | 4605.0 | 4711.0 | 4810 | 4895 |
| 1 | AFG | 2 | Afghanistan | 2805 | Rice (Milled Equivalent) | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 419.0 | 445.0 | 546.0 | 455.0 | 490.0 | 415.0 | 442.0 | 476.0 | 425 | 422 |
| 2 | AFG | 2 | Afghanistan | 2513 | Barley and products | 5521 | Feed | 1000 tonnes | 33.94 | 67.71 | ... | 58.0 | 236.0 | 262.0 | 263.0 | 230.0 | 379.0 | 315.0 | 203.0 | 367 | 360 |
| 3 | AFG | 2 | Afghanistan | 2513 | Barley and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 185.0 | 43.0 | 44.0 | 48.0 | 62.0 | 55.0 | 60.0 | 72.0 | 78 | 89 |
| 4 | AFG | 2 | Afghanistan | 2514 | Maize and products | 5521 | Feed | 1000 tonnes | 33.94 | 67.71 | ... | 120.0 | 208.0 | 233.0 | 249.0 | 247.0 | 195.0 | 178.0 | 191.0 | 200 | 200 |
| 5 | AFG | 2 | Afghanistan | 2514 | Maize and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 231.0 | 67.0 | 82.0 | 67.0 | 69.0 | 71.0 | 82.0 | 73.0 | 77 | 76 |
| 6 | AFG | 2 | Afghanistan | 2517 | Millet and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 15.0 | 21.0 | 11.0 | 19.0 | 21.0 | 18.0 | 14.0 | 14.0 | 14 | 12 |
| 7 | AFG | 2 | Afghanistan | 2520 | Cereals, Other | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 8 | AFG | 2 | Afghanistan | 2531 | Potatoes and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 276.0 | 294.0 | 294.0 | 260.0 | 242.0 | 250.0 | 192.0 | 169.0 | 196 | 230 |
| 9 | AFG | 2 | Afghanistan | 2536 | Sugar cane | 5521 | Feed | 1000 tonnes | 33.94 | 67.71 | ... | 50.0 | 29.0 | 61.0 | 65.0 | 54.0 | 114.0 | 83.0 | 83.0 | 69 | 81 |
| 10 | AFG | 2 | Afghanistan | 2537 | Sugar beet | 5521 | Feed | 1000 tonnes | 33.94 | 67.71 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 11 | AFG | 2 | Afghanistan | 2542 | Sugar (Raw Equivalent) | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 124.0 | 152.0 | 169.0 | 192.0 | 217.0 | 231.0 | 240.0 | 240.0 | 250 | 255 |
| 12 | AFG | 2 | Afghanistan | 2543 | Sweeteners, Other | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 9.0 | 15.0 | 12.0 | 6.0 | 11.0 | 2.0 | 9.0 | 21.0 | 24 | 16 |
| 13 | AFG | 2 | Afghanistan | 2745 | Honey | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2 | 2 |
| 14 | AFG | 2 | Afghanistan | 2549 | Pulses, Other and products | 5521 | Feed | 1000 tonnes | 33.94 | 67.71 | ... | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 5.0 | 4.0 | 5.0 | 4 | 4 |
| 15 | AFG | 2 | Afghanistan | 2549 | Pulses, Other and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 17.0 | 35.0 | 37.0 | 40.0 | 54.0 | 80.0 | 66.0 | 81.0 | 63 | 74 |
| 16 | AFG | 2 | Afghanistan | 2551 | Nuts and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 11.0 | 13.0 | 24.0 | 34.0 | 42.0 | 28.0 | 66.0 | 71.0 | 70 | 44 |
| 17 | AFG | 2 | Afghanistan | 2560 | Coconuts - Incl Copra | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 18 | AFG | 2 | Afghanistan | 2561 | Sesame seed | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 16.0 | 16.0 | 13.0 | 16.0 | 16.0 | 16.0 | 19.0 | 17.0 | 16 | 16 |
| 19 | AFG | 2 | Afghanistan | 2563 | Olives (including preserved) | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 1.0 | 1.0 | 0.0 | 0.0 | 2.0 | 3.0 | 2.0 | 2.0 | 2 | 2 |
| 20 | AFG | 2 | Afghanistan | 2571 | Soyabean Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 6.0 | 35.0 | 18.0 | 21.0 | 11.0 | 6.0 | 15.0 | 16.0 | 16 | 16 |
| 21 | AFG | 2 | Afghanistan | 2572 | Groundnut Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 22 | AFG | 2 | Afghanistan | 2573 | Sunflowerseed Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 4.0 | 6.0 | 5.0 | 9.0 | 3.0 | 8.0 | 15.0 | 16.0 | 17 | 23 |
| 23 | AFG | 2 | Afghanistan | 2574 | Rape and Mustard Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 0.0 | 1.0 | 3.0 | 5.0 | 6.0 | 6.0 | 1.0 | 2.0 | 2 | 2 |
| 24 | AFG | 2 | Afghanistan | 2575 | Cottonseed Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 3.0 | 3.0 | 3 | 4 |
| 25 | AFG | 2 | Afghanistan | 2577 | Palm Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 71.0 | 69.0 | 56.0 | 51.0 | 36.0 | 53.0 | 59.0 | 51.0 | 61 | 64 |
| 26 | AFG | 2 | Afghanistan | 2579 | Sesameseed Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1 | 1 |
| 27 | AFG | 2 | Afghanistan | 2580 | Olive Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1 | 1 |
| 28 | AFG | 2 | Afghanistan | 2586 | Oilcrops Oil, Other | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 3.0 | 1.0 | 2.0 | 2.0 | 2 | 2 |
| 29 | AFG | 2 | Afghanistan | 2601 | Tomatoes and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 2.0 | 2.0 | 8.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 21447 | ZWE | 181 | Zimbabwe | 2765 | Crustaceans | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 21448 | ZWE | 181 | Zimbabwe | 2766 | Cephalopods | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 21449 | ZWE | 181 | Zimbabwe | 2767 | Molluscs, Other | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0 | 0 |
| 21450 | ZWE | 181 | Zimbabwe | 2775 | Aquatic Plants | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 21451 | ZWE | 181 | Zimbabwe | 2680 | Infant food | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 21452 | ZWE | 181 | Zimbabwe | 2905 | Cereals - Excluding Beer | 5521 | Feed | 1000 tonnes | -19.02 | 29.15 | ... | 75.0 | 54.0 | 75.0 | 55.0 | 63.0 | 62.0 | 55.0 | 55.0 | 55 | 55 |
| 21453 | ZWE | 181 | Zimbabwe | 2905 | Cereals - Excluding Beer | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 1844.0 | 1842.0 | 1944.0 | 1962.0 | 1918.0 | 1980.0 | 2011.0 | 2094.0 | 2071 | 2016 |
| 21454 | ZWE | 181 | Zimbabwe | 2907 | Starchy Roots | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 223.0 | 236.0 | 238.0 | 228.0 | 245.0 | 258.0 | 258.0 | 269.0 | 272 | 276 |
| 21455 | ZWE | 181 | Zimbabwe | 2908 | Sugar Crops | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 21456 | ZWE | 181 | Zimbabwe | 2909 | Sugar & Sweeteners | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 335.0 | 313.0 | 339.0 | 302.0 | 285.0 | 287.0 | 314.0 | 336.0 | 396 | 416 |
| 21457 | ZWE | 181 | Zimbabwe | 2911 | Pulses | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 63.0 | 59.0 | 61.0 | 57.0 | 69.0 | 78.0 | 68.0 | 56.0 | 52 | 55 |
| 21458 | ZWE | 181 | Zimbabwe | 2912 | Treenuts | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 3.0 | 4.0 | 2.0 | 4 | 3 |
| 21459 | ZWE | 181 | Zimbabwe | 2913 | Oilcrops | 5521 | Feed | 1000 tonnes | -19.02 | 29.15 | ... | 36.0 | 46.0 | 41.0 | 33.0 | 31.0 | 19.0 | 24.0 | 17.0 | 27 | 30 |
| 21460 | ZWE | 181 | Zimbabwe | 2913 | Oilcrops | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 60.0 | 59.0 | 61.0 | 62.0 | 48.0 | 44.0 | 41.0 | 40.0 | 38 | 38 |
| 21461 | ZWE | 181 | Zimbabwe | 2914 | Vegetable Oils | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 111.0 | 114.0 | 112.0 | 114.0 | 134.0 | 135.0 | 137.0 | 147.0 | 159 | 160 |
| 21462 | ZWE | 181 | Zimbabwe | 2918 | Vegetables | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 161.0 | 166.0 | 208.0 | 185.0 | 137.0 | 179.0 | 215.0 | 217.0 | 227 | 227 |
| 21463 | ZWE | 181 | Zimbabwe | 2919 | Fruits - Excluding Wine | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 191.0 | 134.0 | 167.0 | 177.0 | 185.0 | 184.0 | 211.0 | 230.0 | 246 | 217 |
| 21464 | ZWE | 181 | Zimbabwe | 2922 | Stimulants | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 7.0 | 21.0 | 14.0 | 24.0 | 16.0 | 11.0 | 23.0 | 11.0 | 10 | 10 |
| 21465 | ZWE | 181 | Zimbabwe | 2923 | Spices | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 7.0 | 11.0 | 7.0 | 12.0 | 16.0 | 16.0 | 14.0 | 11.0 | 12 | 12 |
| 21466 | ZWE | 181 | Zimbabwe | 2924 | Alcoholic Beverages | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 294.0 | 290.0 | 316.0 | 355.0 | 398.0 | 437.0 | 448.0 | 476.0 | 525 | 516 |
| 21467 | ZWE | 181 | Zimbabwe | 2943 | Meat | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 222.0 | 228.0 | 233.0 | 238.0 | 242.0 | 265.0 | 262.0 | 277.0 | 280 | 258 |
| 21468 | ZWE | 181 | Zimbabwe | 2945 | Offals | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 20.0 | 20.0 | 21.0 | 21.0 | 21.0 | 21.0 | 21.0 | 21.0 | 22 | 22 |
| 21469 | ZWE | 181 | Zimbabwe | 2946 | Animal fats | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 26.0 | 26.0 | 29.0 | 29.0 | 27.0 | 31.0 | 30.0 | 25.0 | 26 | 20 |
| 21470 | ZWE | 181 | Zimbabwe | 2949 | Eggs | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 15.0 | 18.0 | 18.0 | 21.0 | 22.0 | 27.0 | 27.0 | 24.0 | 24 | 25 |
| 21471 | ZWE | 181 | Zimbabwe | 2948 | Milk - Excluding Butter | 5521 | Feed | 1000 tonnes | -19.02 | 29.15 | ... | 21.0 | 21.0 | 21.0 | 21.0 | 21.0 | 23.0 | 25.0 | 25.0 | 30 | 31 |
| 21472 | ZWE | 181 | Zimbabwe | 2948 | Milk - Excluding Butter | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 373.0 | 357.0 | 359.0 | 356.0 | 341.0 | 385.0 | 418.0 | 457.0 | 426 | 451 |
| 21473 | ZWE | 181 | Zimbabwe | 2960 | Fish, Seafood | 5521 | Feed | 1000 tonnes | -19.02 | 29.15 | ... | 5.0 | 4.0 | 9.0 | 6.0 | 9.0 | 5.0 | 15.0 | 15.0 | 15 | 15 |
| 21474 | ZWE | 181 | Zimbabwe | 2960 | Fish, Seafood | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 18.0 | 14.0 | 17.0 | 14.0 | 15.0 | 18.0 | 29.0 | 40.0 | 40 | 40 |
| 21475 | ZWE | 181 | Zimbabwe | 2961 | Aquatic Products, Other | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 21476 | ZWE | 181 | Zimbabwe | 2928 | Miscellaneous | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
21477 rows × 63 columns
Let's see what the data looks like...
Plot for annual produce of different countries with quantity in y-axis and years in x-axis
df| Area Abbreviation | Area Code | Area | Item Code | Item | Element Code | Element | Unit | latitude | longitude | ... | Y2004 | Y2005 | Y2006 | Y2007 | Y2008 | Y2009 | Y2010 | Y2011 | Y2012 | Y2013 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | AFG | 2 | Afghanistan | 2511 | Wheat and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 3249.0 | 3486.0 | 3704.0 | 4164.0 | 4252.0 | 4538.0 | 4605.0 | 4711.0 | 4810 | 4895 |
| 1 | AFG | 2 | Afghanistan | 2805 | Rice (Milled Equivalent) | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 419.0 | 445.0 | 546.0 | 455.0 | 490.0 | 415.0 | 442.0 | 476.0 | 425 | 422 |
| 2 | AFG | 2 | Afghanistan | 2513 | Barley and products | 5521 | Feed | 1000 tonnes | 33.94 | 67.71 | ... | 58.0 | 236.0 | 262.0 | 263.0 | 230.0 | 379.0 | 315.0 | 203.0 | 367 | 360 |
| 3 | AFG | 2 | Afghanistan | 2513 | Barley and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 185.0 | 43.0 | 44.0 | 48.0 | 62.0 | 55.0 | 60.0 | 72.0 | 78 | 89 |
| 4 | AFG | 2 | Afghanistan | 2514 | Maize and products | 5521 | Feed | 1000 tonnes | 33.94 | 67.71 | ... | 120.0 | 208.0 | 233.0 | 249.0 | 247.0 | 195.0 | 178.0 | 191.0 | 200 | 200 |
| 5 | AFG | 2 | Afghanistan | 2514 | Maize and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 231.0 | 67.0 | 82.0 | 67.0 | 69.0 | 71.0 | 82.0 | 73.0 | 77 | 76 |
| 6 | AFG | 2 | Afghanistan | 2517 | Millet and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 15.0 | 21.0 | 11.0 | 19.0 | 21.0 | 18.0 | 14.0 | 14.0 | 14 | 12 |
| 7 | AFG | 2 | Afghanistan | 2520 | Cereals, Other | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 2.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 8 | AFG | 2 | Afghanistan | 2531 | Potatoes and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 276.0 | 294.0 | 294.0 | 260.0 | 242.0 | 250.0 | 192.0 | 169.0 | 196 | 230 |
| 9 | AFG | 2 | Afghanistan | 2536 | Sugar cane | 5521 | Feed | 1000 tonnes | 33.94 | 67.71 | ... | 50.0 | 29.0 | 61.0 | 65.0 | 54.0 | 114.0 | 83.0 | 83.0 | 69 | 81 |
| 10 | AFG | 2 | Afghanistan | 2537 | Sugar beet | 5521 | Feed | 1000 tonnes | 33.94 | 67.71 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 11 | AFG | 2 | Afghanistan | 2542 | Sugar (Raw Equivalent) | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 124.0 | 152.0 | 169.0 | 192.0 | 217.0 | 231.0 | 240.0 | 240.0 | 250 | 255 |
| 12 | AFG | 2 | Afghanistan | 2543 | Sweeteners, Other | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 9.0 | 15.0 | 12.0 | 6.0 | 11.0 | 2.0 | 9.0 | 21.0 | 24 | 16 |
| 13 | AFG | 2 | Afghanistan | 2745 | Honey | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2 | 2 |
| 14 | AFG | 2 | Afghanistan | 2549 | Pulses, Other and products | 5521 | Feed | 1000 tonnes | 33.94 | 67.71 | ... | 3.0 | 2.0 | 3.0 | 3.0 | 3.0 | 5.0 | 4.0 | 5.0 | 4 | 4 |
| 15 | AFG | 2 | Afghanistan | 2549 | Pulses, Other and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 17.0 | 35.0 | 37.0 | 40.0 | 54.0 | 80.0 | 66.0 | 81.0 | 63 | 74 |
| 16 | AFG | 2 | Afghanistan | 2551 | Nuts and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 11.0 | 13.0 | 24.0 | 34.0 | 42.0 | 28.0 | 66.0 | 71.0 | 70 | 44 |
| 17 | AFG | 2 | Afghanistan | 2560 | Coconuts - Incl Copra | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 18 | AFG | 2 | Afghanistan | 2561 | Sesame seed | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 16.0 | 16.0 | 13.0 | 16.0 | 16.0 | 16.0 | 19.0 | 17.0 | 16 | 16 |
| 19 | AFG | 2 | Afghanistan | 2563 | Olives (including preserved) | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 1.0 | 1.0 | 0.0 | 0.0 | 2.0 | 3.0 | 2.0 | 2.0 | 2 | 2 |
| 20 | AFG | 2 | Afghanistan | 2571 | Soyabean Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 6.0 | 35.0 | 18.0 | 21.0 | 11.0 | 6.0 | 15.0 | 16.0 | 16 | 16 |
| 21 | AFG | 2 | Afghanistan | 2572 | Groundnut Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 22 | AFG | 2 | Afghanistan | 2573 | Sunflowerseed Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 4.0 | 6.0 | 5.0 | 9.0 | 3.0 | 8.0 | 15.0 | 16.0 | 17 | 23 |
| 23 | AFG | 2 | Afghanistan | 2574 | Rape and Mustard Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 0.0 | 1.0 | 3.0 | 5.0 | 6.0 | 6.0 | 1.0 | 2.0 | 2 | 2 |
| 24 | AFG | 2 | Afghanistan | 2575 | Cottonseed Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 2.0 | 3.0 | 3.0 | 3.0 | 3.0 | 4.0 | 3.0 | 3.0 | 3 | 4 |
| 25 | AFG | 2 | Afghanistan | 2577 | Palm Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 71.0 | 69.0 | 56.0 | 51.0 | 36.0 | 53.0 | 59.0 | 51.0 | 61 | 64 |
| 26 | AFG | 2 | Afghanistan | 2579 | Sesameseed Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 1.0 | 1.0 | 1.0 | 2.0 | 2.0 | 1.0 | 1.0 | 2.0 | 1 | 1 |
| 27 | AFG | 2 | Afghanistan | 2580 | Olive Oil | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | 1 | 1 |
| 28 | AFG | 2 | Afghanistan | 2586 | Oilcrops Oil, Other | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 0.0 | 1.0 | 0.0 | 0.0 | 3.0 | 1.0 | 2.0 | 2.0 | 2 | 2 |
| 29 | AFG | 2 | Afghanistan | 2601 | Tomatoes and products | 5142 | Food | 1000 tonnes | 33.94 | 67.71 | ... | 2.0 | 2.0 | 8.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 21447 | ZWE | 181 | Zimbabwe | 2765 | Crustaceans | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 21448 | ZWE | 181 | Zimbabwe | 2766 | Cephalopods | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 21449 | ZWE | 181 | Zimbabwe | 2767 | Molluscs, Other | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0 | 0 |
| 21450 | ZWE | 181 | Zimbabwe | 2775 | Aquatic Plants | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 21451 | ZWE | 181 | Zimbabwe | 2680 | Infant food | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 21452 | ZWE | 181 | Zimbabwe | 2905 | Cereals - Excluding Beer | 5521 | Feed | 1000 tonnes | -19.02 | 29.15 | ... | 75.0 | 54.0 | 75.0 | 55.0 | 63.0 | 62.0 | 55.0 | 55.0 | 55 | 55 |
| 21453 | ZWE | 181 | Zimbabwe | 2905 | Cereals - Excluding Beer | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 1844.0 | 1842.0 | 1944.0 | 1962.0 | 1918.0 | 1980.0 | 2011.0 | 2094.0 | 2071 | 2016 |
| 21454 | ZWE | 181 | Zimbabwe | 2907 | Starchy Roots | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 223.0 | 236.0 | 238.0 | 228.0 | 245.0 | 258.0 | 258.0 | 269.0 | 272 | 276 |
| 21455 | ZWE | 181 | Zimbabwe | 2908 | Sugar Crops | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 21456 | ZWE | 181 | Zimbabwe | 2909 | Sugar & Sweeteners | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 335.0 | 313.0 | 339.0 | 302.0 | 285.0 | 287.0 | 314.0 | 336.0 | 396 | 416 |
| 21457 | ZWE | 181 | Zimbabwe | 2911 | Pulses | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 63.0 | 59.0 | 61.0 | 57.0 | 69.0 | 78.0 | 68.0 | 56.0 | 52 | 55 |
| 21458 | ZWE | 181 | Zimbabwe | 2912 | Treenuts | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 1.0 | 2.0 | 1.0 | 2.0 | 2.0 | 3.0 | 4.0 | 2.0 | 4 | 3 |
| 21459 | ZWE | 181 | Zimbabwe | 2913 | Oilcrops | 5521 | Feed | 1000 tonnes | -19.02 | 29.15 | ... | 36.0 | 46.0 | 41.0 | 33.0 | 31.0 | 19.0 | 24.0 | 17.0 | 27 | 30 |
| 21460 | ZWE | 181 | Zimbabwe | 2913 | Oilcrops | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 60.0 | 59.0 | 61.0 | 62.0 | 48.0 | 44.0 | 41.0 | 40.0 | 38 | 38 |
| 21461 | ZWE | 181 | Zimbabwe | 2914 | Vegetable Oils | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 111.0 | 114.0 | 112.0 | 114.0 | 134.0 | 135.0 | 137.0 | 147.0 | 159 | 160 |
| 21462 | ZWE | 181 | Zimbabwe | 2918 | Vegetables | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 161.0 | 166.0 | 208.0 | 185.0 | 137.0 | 179.0 | 215.0 | 217.0 | 227 | 227 |
| 21463 | ZWE | 181 | Zimbabwe | 2919 | Fruits - Excluding Wine | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 191.0 | 134.0 | 167.0 | 177.0 | 185.0 | 184.0 | 211.0 | 230.0 | 246 | 217 |
| 21464 | ZWE | 181 | Zimbabwe | 2922 | Stimulants | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 7.0 | 21.0 | 14.0 | 24.0 | 16.0 | 11.0 | 23.0 | 11.0 | 10 | 10 |
| 21465 | ZWE | 181 | Zimbabwe | 2923 | Spices | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 7.0 | 11.0 | 7.0 | 12.0 | 16.0 | 16.0 | 14.0 | 11.0 | 12 | 12 |
| 21466 | ZWE | 181 | Zimbabwe | 2924 | Alcoholic Beverages | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 294.0 | 290.0 | 316.0 | 355.0 | 398.0 | 437.0 | 448.0 | 476.0 | 525 | 516 |
| 21467 | ZWE | 181 | Zimbabwe | 2943 | Meat | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 222.0 | 228.0 | 233.0 | 238.0 | 242.0 | 265.0 | 262.0 | 277.0 | 280 | 258 |
| 21468 | ZWE | 181 | Zimbabwe | 2945 | Offals | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 20.0 | 20.0 | 21.0 | 21.0 | 21.0 | 21.0 | 21.0 | 21.0 | 22 | 22 |
| 21469 | ZWE | 181 | Zimbabwe | 2946 | Animal fats | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 26.0 | 26.0 | 29.0 | 29.0 | 27.0 | 31.0 | 30.0 | 25.0 | 26 | 20 |
| 21470 | ZWE | 181 | Zimbabwe | 2949 | Eggs | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 15.0 | 18.0 | 18.0 | 21.0 | 22.0 | 27.0 | 27.0 | 24.0 | 24 | 25 |
| 21471 | ZWE | 181 | Zimbabwe | 2948 | Milk - Excluding Butter | 5521 | Feed | 1000 tonnes | -19.02 | 29.15 | ... | 21.0 | 21.0 | 21.0 | 21.0 | 21.0 | 23.0 | 25.0 | 25.0 | 30 | 31 |
| 21472 | ZWE | 181 | Zimbabwe | 2948 | Milk - Excluding Butter | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 373.0 | 357.0 | 359.0 | 356.0 | 341.0 | 385.0 | 418.0 | 457.0 | 426 | 451 |
| 21473 | ZWE | 181 | Zimbabwe | 2960 | Fish, Seafood | 5521 | Feed | 1000 tonnes | -19.02 | 29.15 | ... | 5.0 | 4.0 | 9.0 | 6.0 | 9.0 | 5.0 | 15.0 | 15.0 | 15 | 15 |
| 21474 | ZWE | 181 | Zimbabwe | 2960 | Fish, Seafood | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 18.0 | 14.0 | 17.0 | 14.0 | 15.0 | 18.0 | 29.0 | 40.0 | 40 | 40 |
| 21475 | ZWE | 181 | Zimbabwe | 2961 | Aquatic Products, Other | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
| 21476 | ZWE | 181 | Zimbabwe | 2928 | Miscellaneous | 5142 | Food | 1000 tonnes | -19.02 | 29.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0 | 0 |
21477 rows × 63 columns
area_list = list(df['Area'].unique())
year_list = list(df.iloc[:,10:].columns)
plt.figure(figsize=(24,12))
for ar in area_list:
yearly_produce = []
for yr in year_list:
yearly_produce.append(df[yr][df['Area'] == ar].sum())
plt.plot(yearly_produce, label=ar)
plt.xticks(np.arange(53), tuple(year_list), rotation=60)
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=8, mode="expand", borderaxespad=0.)
plt.savefig('p.png')
plt.show()
plt.figure(figsize=(24,12))<Figure size 1728x864 with 0 Axes><Figure size 1728x864 with 0 Axes>Clearly, China, India and US stand out here. So, these are the countries with most food and feed production.
Now, let's have a close look at their food and feed data
Food and feed plot for the whole dataset
sns.factorplot("Element", data=df, kind="count")
plt.show()/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.
warnings.warn(msg)
So, there is a huge difference in food and feed production. Now, we have obvious assumptions about the following plots after looking at this huge difference.
Food and feed plot for the largest producers(India, USA, China)
sns.factorplot("Area", data=df[(df['Area'] == "India") | (df['Area'] == "China, mainland") | (df['Area'] == "United States of America")], kind="count", hue="Element", size=8, aspect=.8)/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.
warnings.warn(msg)
/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.
warnings.warn(msg, UserWarning)
<seaborn.axisgrid.FacetGrid at 0x1a218d2550>
Though, there is a huge difference between feed and food production, these countries' total production and their ranks depend on feed production.
Now, we create a dataframe with countries as index and their annual produce as columns from 1961 to 2013.
new_df_dict = {}
for ar in area_list:
yearly_produce = []
for yr in year_list:
yearly_produce.append(df[yr][df['Area']==ar].sum())
new_df_dict[ar] = yearly_produce
new_df = pd.DataFrame(new_df_dict)
new_df.head()| Afghanistan | Albania | Algeria | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | Austria | Azerbaijan | ... | United Republic of Tanzania | United States of America | Uruguay | Uzbekistan | Vanuatu | Venezuela (Bolivarian Republic of) | Viet Nam | Yemen | Zambia | Zimbabwe | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 9481.0 | 1706.0 | 7488.0 | 4834.0 | 92.0 | 43402.0 | 0.0 | 25795.0 | 22542.0 | 0.0 | ... | 12367.0 | 559347.0 | 4631.0 | 0.0 | 97.0 | 9523.0 | 23856.0 | 2982.0 | 2976.0 | 3260.0 |
| 1 | 9414.0 | 1749.0 | 7235.0 | 4775.0 | 94.0 | 40784.0 | 0.0 | 27618.0 | 22627.0 | 0.0 | ... | 12810.0 | 556319.0 | 4448.0 | 0.0 | 101.0 | 9369.0 | 25220.0 | 3038.0 | 3057.0 | 3503.0 |
| 2 | 9194.0 | 1767.0 | 6861.0 | 5240.0 | 105.0 | 40219.0 | 0.0 | 28902.0 | 23637.0 | 0.0 | ... | 13109.0 | 552630.0 | 4682.0 | 0.0 | 103.0 | 9788.0 | 26053.0 | 3147.0 | 3069.0 | 3479.0 |
| 3 | 10170.0 | 1889.0 | 7255.0 | 5286.0 | 95.0 | 41638.0 | 0.0 | 29107.0 | 24099.0 | 0.0 | ... | 12965.0 | 555677.0 | 4723.0 | 0.0 | 102.0 | 10539.0 | 26377.0 | 3224.0 | 3121.0 | 3738.0 |
| 4 | 10473.0 | 1884.0 | 7509.0 | 5527.0 | 84.0 | 44936.0 | 0.0 | 28961.0 | 22664.0 | 0.0 | ... | 13742.0 | 589288.0 | 4581.0 | 0.0 | 107.0 | 10641.0 | 26961.0 | 3328.0 | 3236.0 | 3940.0 |
5 rows × 174 columns
Now, this is not perfect so we transpose this dataframe and add column names.
new_df = pd.DataFrame.transpose(new_df)
new_df.columns = year_list
new_df.head()| Y1961 | Y1962 | Y1963 | Y1964 | Y1965 | Y1966 | Y1967 | Y1968 | Y1969 | Y1970 | ... | Y2004 | Y2005 | Y2006 | Y2007 | Y2008 | Y2009 | Y2010 | Y2011 | Y2012 | Y2013 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Afghanistan | 9481.0 | 9414.0 | 9194.0 | 10170.0 | 10473.0 | 10169.0 | 11289.0 | 11508.0 | 11815.0 | 10454.0 | ... | 16542.0 | 17658.0 | 18317.0 | 19248.0 | 19381.0 | 20661.0 | 21030.0 | 21100.0 | 22706.0 | 23007.0 |
| Albania | 1706.0 | 1749.0 | 1767.0 | 1889.0 | 1884.0 | 1995.0 | 2046.0 | 2169.0 | 2230.0 | 2395.0 | ... | 6637.0 | 6719.0 | 6911.0 | 6744.0 | 7168.0 | 7316.0 | 7907.0 | 8114.0 | 8221.0 | 8271.0 |
| Algeria | 7488.0 | 7235.0 | 6861.0 | 7255.0 | 7509.0 | 7536.0 | 7986.0 | 8839.0 | 9003.0 | 9355.0 | ... | 48619.0 | 49562.0 | 51067.0 | 49933.0 | 50916.0 | 57505.0 | 60071.0 | 65852.0 | 69365.0 | 72161.0 |
| Angola | 4834.0 | 4775.0 | 5240.0 | 5286.0 | 5527.0 | 5677.0 | 5833.0 | 5685.0 | 6219.0 | 6460.0 | ... | 25541.0 | 26696.0 | 28247.0 | 29877.0 | 32053.0 | 36985.0 | 38400.0 | 40573.0 | 38064.0 | 48639.0 |
| Antigua and Barbuda | 92.0 | 94.0 | 105.0 | 95.0 | 84.0 | 73.0 | 64.0 | 59.0 | 68.0 | 77.0 | ... | 92.0 | 115.0 | 110.0 | 122.0 | 115.0 | 114.0 | 115.0 | 118.0 | 113.0 | 119.0 |
5 rows × 53 columns
Perfect! Now, we will do some feature engineering.
First, a new column which indicates mean produce of each state over the given years. Second, a ranking column which ranks countries on the basis of mean produce.
mean_produce = []
for i in range(174):
mean_produce.append(new_df.iloc[i,:].values.mean())
new_df['Mean_Produce'] = mean_produce
new_df['Rank'] = new_df['Mean_Produce'].rank(ascending=False)
new_df.head()| Y1961 | Y1962 | Y1963 | Y1964 | Y1965 | Y1966 | Y1967 | Y1968 | Y1969 | Y1970 | ... | Y2006 | Y2007 | Y2008 | Y2009 | Y2010 | Y2011 | Y2012 | Y2013 | Mean_Produce | Rank | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Afghanistan | 9481.0 | 9414.0 | 9194.0 | 10170.0 | 10473.0 | 10169.0 | 11289.0 | 11508.0 | 11815.0 | 10454.0 | ... | 18317.0 | 19248.0 | 19381.0 | 20661.0 | 21030.0 | 21100.0 | 22706.0 | 23007.0 | 13003.056604 | 69.0 |
| Albania | 1706.0 | 1749.0 | 1767.0 | 1889.0 | 1884.0 | 1995.0 | 2046.0 | 2169.0 | 2230.0 | 2395.0 | ... | 6911.0 | 6744.0 | 7168.0 | 7316.0 | 7907.0 | 8114.0 | 8221.0 | 8271.0 | 4475.509434 | 104.0 |
| Algeria | 7488.0 | 7235.0 | 6861.0 | 7255.0 | 7509.0 | 7536.0 | 7986.0 | 8839.0 | 9003.0 | 9355.0 | ... | 51067.0 | 49933.0 | 50916.0 | 57505.0 | 60071.0 | 65852.0 | 69365.0 | 72161.0 | 28879.490566 | 38.0 |
| Angola | 4834.0 | 4775.0 | 5240.0 | 5286.0 | 5527.0 | 5677.0 | 5833.0 | 5685.0 | 6219.0 | 6460.0 | ... | 28247.0 | 29877.0 | 32053.0 | 36985.0 | 38400.0 | 40573.0 | 38064.0 | 48639.0 | 13321.056604 | 68.0 |
| Antigua and Barbuda | 92.0 | 94.0 | 105.0 | 95.0 | 84.0 | 73.0 | 64.0 | 59.0 | 68.0 | 77.0 | ... | 110.0 | 122.0 | 115.0 | 114.0 | 115.0 | 118.0 | 113.0 | 119.0 | 83.886792 | 172.0 |
5 rows × 55 columns
Now, we create another dataframe with items and their total production each year from 1961 to 2013
item_list = list(df['Item'].unique())
item_df = pd.DataFrame()
item_df['Item_Name'] = item_list
for yr in year_list:
item_produce = []
for it in item_list:
item_produce.append(df[yr][df['Item']==it].sum())
item_df[yr] = item_produce
item_df.head()| Item_Name | Y1961 | Y1962 | Y1963 | Y1964 | Y1965 | Y1966 | Y1967 | Y1968 | Y1969 | ... | Y2004 | Y2005 | Y2006 | Y2007 | Y2008 | Y2009 | Y2010 | Y2011 | Y2012 | Y2013 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Wheat and products | 138829.0 | 144643.0 | 147325.0 | 156273.0 | 168822.0 | 169832.0 | 171469.0 | 179530.0 | 189658.0 | ... | 527394.0 | 532263.0 | 537279.0 | 529271.0 | 562239.0 | 557245.0 | 549926.0 | 578179.0 | 576597 | 587492 |
| 1 | Rice (Milled Equivalent) | 122700.0 | 131842.0 | 139507.0 | 148304.0 | 150056.0 | 155583.0 | 158587.0 | 164614.0 | 167922.0 | ... | 361107.0 | 366025.0 | 372629.0 | 378698.0 | 389708.0 | 394221.0 | 398559.0 | 404152.0 | 406787 | 410880 |
| 2 | Barley and products | 46180.0 | 48915.0 | 51642.0 | 54184.0 | 54945.0 | 55463.0 | 56424.0 | 60455.0 | 65501.0 | ... | 102055.0 | 97185.0 | 100981.0 | 93310.0 | 98209.0 | 99135.0 | 92563.0 | 92570.0 | 88766 | 99452 |
| 3 | Maize and products | 168039.0 | 168305.0 | 172905.0 | 175468.0 | 190304.0 | 200860.0 | 213050.0 | 215613.0 | 221953.0 | ... | 545024.0 | 549036.0 | 543280.0 | 573892.0 | 592231.0 | 557940.0 | 584337.0 | 603297.0 | 608730 | 671300 |
| 4 | Millet and products | 19075.0 | 19019.0 | 19740.0 | 20353.0 | 18377.0 | 20860.0 | 22997.0 | 21785.0 | 23966.0 | ... | 25789.0 | 25496.0 | 25997.0 | 26750.0 | 26373.0 | 24575.0 | 27039.0 | 25740.0 | 26105 | 26346 |
5 rows × 54 columns
Some more feature engineering
This time, we will use the new features to get some good conclusions.
1. Total amount of item produced from 1961 to 2013
2. Providing a rank to the items to know the most produced item
sum_col = []
for i in range(115):
sum_col.append(item_df.iloc[i,1:].values.sum())
item_df['Sum'] = sum_col
item_df['Production_Rank'] = item_df['Sum'].rank(ascending=False)
item_df.head()| Item_Name | Y1961 | Y1962 | Y1963 | Y1964 | Y1965 | Y1966 | Y1967 | Y1968 | Y1969 | ... | Y2006 | Y2007 | Y2008 | Y2009 | Y2010 | Y2011 | Y2012 | Y2013 | Sum | Production_Rank | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Wheat and products | 138829.0 | 144643.0 | 147325.0 | 156273.0 | 168822.0 | 169832.0 | 171469.0 | 179530.0 | 189658.0 | ... | 537279.0 | 529271.0 | 562239.0 | 557245.0 | 549926.0 | 578179.0 | 576597 | 587492 | 19194671.0 | 6.0 |
| 1 | Rice (Milled Equivalent) | 122700.0 | 131842.0 | 139507.0 | 148304.0 | 150056.0 | 155583.0 | 158587.0 | 164614.0 | 167922.0 | ... | 372629.0 | 378698.0 | 389708.0 | 394221.0 | 398559.0 | 404152.0 | 406787 | 410880 | 14475448.0 | 8.0 |
| 2 | Barley and products | 46180.0 | 48915.0 | 51642.0 | 54184.0 | 54945.0 | 55463.0 | 56424.0 | 60455.0 | 65501.0 | ... | 100981.0 | 93310.0 | 98209.0 | 99135.0 | 92563.0 | 92570.0 | 88766 | 99452 | 4442742.0 | 20.0 |
| 3 | Maize and products | 168039.0 | 168305.0 | 172905.0 | 175468.0 | 190304.0 | 200860.0 | 213050.0 | 215613.0 | 221953.0 | ... | 543280.0 | 573892.0 | 592231.0 | 557940.0 | 584337.0 | 603297.0 | 608730 | 671300 | 19960640.0 | 5.0 |
| 4 | Millet and products | 19075.0 | 19019.0 | 19740.0 | 20353.0 | 18377.0 | 20860.0 | 22997.0 | 21785.0 | 23966.0 | ... | 25997.0 | 26750.0 | 26373.0 | 24575.0 | 27039.0 | 25740.0 | 26105 | 26346 | 1225400.0 | 38.0 |
5 rows × 56 columns
Now, we find the most produced food items in the last half-century
item_df['Item_Name'][item_df['Production_Rank'] < 11.0].sort_values()56 Cereals - Excluding Beer
65 Fruits - Excluding Wine
3 Maize and products
53 Milk - Excluding Butter
6 Potatoes and products
1 Rice (Milled Equivalent)
57 Starchy Roots
64 Vegetables
27 Vegetables, Other
0 Wheat and products
Name: Item_Name, dtype: objectSo, cereals, fruits and maize are the most produced items in the last 50 years
Food and feed plot for most produced items
sns.factorplot("Item", data=df[(df['Item']=='Wheat and products') | (df['Item']=='Rice (Milled Equivalent)') | (df['Item']=='Maize and products') | (df['Item']=='Potatoes and products') | (df['Item']=='Vegetables, Other') | (df['Item']=='Milk - Excluding Butter') | (df['Item']=='Cereals - Excluding Beer') | (df['Item']=='Starchy Roots') | (df['Item']=='Vegetables') | (df['Item']=='Fruits - Excluding Wine')], kind="count", hue="Element", size=20, aspect=.8)
plt.show()/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.
warnings.warn(msg)
/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.
warnings.warn(msg, UserWarning)
Now, we plot a heatmap of correlation of produce in difference years
year_df = df.iloc[:,10:]
fig, ax = plt.subplots(figsize=(16,10))
sns.heatmap(year_df.corr(), ax=ax)<matplotlib.axes._subplots.AxesSubplot at 0x1a23b4b128>
So, we gather that a given year's production is more similar to its immediate previous and immediate following years.
f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10,10))
ax1.set(xlabel='Y1968', ylabel='Y1961')
ax2.set(xlabel='Y1968', ylabel='Y1963')
ax3.set(xlabel='Y1968', ylabel='Y1986')
ax4.set(xlabel='Y1968', ylabel='Y2013')
sns.jointplot(x="Y1968", y="Y1961", data=df, kind="reg", ax=ax1)
sns.jointplot(x="Y1968", y="Y1963", data=df, kind="reg", ax=ax2)
sns.jointplot(x="Y1968", y="Y1986", data=df, kind="reg", ax=ax3)
sns.jointplot(x="Y1968", y="Y2013", data=df, kind="reg", ax=ax4)
plt.close(2)
plt.close(3)
plt.close(4)
plt.close(5)/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Heatmap of production of food items over years
This will detect the items whose production has drastically increased over the years
new_item_df = item_df.drop(["Item_Name","Sum","Production_Rank"], axis = 1)
fig, ax = plt.subplots(figsize=(12,24))
sns.heatmap(new_item_df,ax=ax)
ax.set_yticklabels(item_df.Item_Name.values[::-1])
plt.show()
There is considerable growth in production of Palmkernel oil, Meat/Aquatic animals, ricebran oil, cottonseed, seafood, offals, roots, poultry meat, mutton, bear, cocoa, coffee and soyabean oil. There has been exceptional growth in production of onions, cream, sugar crops, treenuts, butter/ghee and to some extent starchy roots.
Now, we look at clustering.
What is clustering?
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics.
Today, we will form clusters to classify countries based on productivity scale
This is the data we will use.
new_df.head()| Y1961 | Y1962 | Y1963 | Y1964 | Y1965 | Y1966 | Y1967 | Y1968 | Y1969 | Y1970 | ... | Y2006 | Y2007 | Y2008 | Y2009 | Y2010 | Y2011 | Y2012 | Y2013 | Mean_Produce | Rank | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Afghanistan | 9481.0 | 9414.0 | 9194.0 | 10170.0 | 10473.0 | 10169.0 | 11289.0 | 11508.0 | 11815.0 | 10454.0 | ... | 18317.0 | 19248.0 | 19381.0 | 20661.0 | 21030.0 | 21100.0 | 22706.0 | 23007.0 | 13003.056604 | 69.0 |
| Albania | 1706.0 | 1749.0 | 1767.0 | 1889.0 | 1884.0 | 1995.0 | 2046.0 | 2169.0 | 2230.0 | 2395.0 | ... | 6911.0 | 6744.0 | 7168.0 | 7316.0 | 7907.0 | 8114.0 | 8221.0 | 8271.0 | 4475.509434 | 104.0 |
| Algeria | 7488.0 | 7235.0 | 6861.0 | 7255.0 | 7509.0 | 7536.0 | 7986.0 | 8839.0 | 9003.0 | 9355.0 | ... | 51067.0 | 49933.0 | 50916.0 | 57505.0 | 60071.0 | 65852.0 | 69365.0 | 72161.0 | 28879.490566 | 38.0 |
| Angola | 4834.0 | 4775.0 | 5240.0 | 5286.0 | 5527.0 | 5677.0 | 5833.0 | 5685.0 | 6219.0 | 6460.0 | ... | 28247.0 | 29877.0 | 32053.0 | 36985.0 | 38400.0 | 40573.0 | 38064.0 | 48639.0 | 13321.056604 | 68.0 |
| Antigua and Barbuda | 92.0 | 94.0 | 105.0 | 95.0 | 84.0 | 73.0 | 64.0 | 59.0 | 68.0 | 77.0 | ... | 110.0 | 122.0 | 115.0 | 114.0 | 115.0 | 118.0 | 113.0 | 119.0 | 83.886792 | 172.0 |
5 rows × 55 columns
X = new_df.iloc[:,:-2].values
X = pd.DataFrame(X)
X = X.convert_objects(convert_numeric=True)
X.columns = year_list/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use DataFrame.infer_objects()
For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
after removing the cwd from sys.path.
Elbow method to select number of clusters
This method looks at the percentage of variance explained as a function of the number of clusters: One should choose a number of clusters so that adding another cluster doesn't give much better modeling of the data. More precisely, if one plots the percentage of variance explained by the clusters against the number of clusters, the first clusters will add much information (explain a lot of variance), but at some point the marginal gain will drop, giving an angle in the graph. The number of clusters is chosen at this point, hence the "elbow criterion". This "elbow" cannot always be unambiguously identified. Percentage of variance explained is the ratio of the between-group variance to the total variance, also known as an F-test. A slight variation of this method plots the curvature of the within group variance.
Basically, number of clusters = the x-axis value of the point that is the corner of the "elbow"(the plot looks often looks like an elbow)
from sklearn.cluster import KMeans
wcss = []
for i in range(1,11):
kmeans = KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)
kmeans.fit(X)
wcss.append(kmeans.inertia_)
plt.plot(range(1,11),wcss)
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()
As the elbow corner coincides with x=2, we will have to form 2 clusters. Personally, I would have liked to select 3 to 4 clusters. But trust me, only selecting 2 clusters can lead to best results. Now, we apply k-means algorithm.
kmeans = KMeans(n_clusters=2,init='k-means++',max_iter=300,n_init=10,random_state=0)
y_kmeans = kmeans.fit_predict(X)
X = X.as_matrix(columns=None)/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
after removing the cwd from sys.path.
Now, let's visualize the results.
plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0,1],s=100,c='red',label='Others')
plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1,1],s=100,c='blue',label='China(mainland),USA,India')
plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s=300,c='yellow',label='Centroids')
plt.title('Clusters of countries by Productivity')
plt.legend()
plt.show()
So, the blue cluster represents China(Mainland), USA and India while the red cluster represents all the other countries. This result was highly probable. Just take a look at the plot of cell 3 above. See how China, USA and India stand out. That has been observed here in clustering too.
You should try this algorithm for 3 or 4 clusters. Looking at the distribution, you will realise why 2 clusters is the best choice for the given data
This is not the end! More is yet to come.
Now, lets try to predict the production using regression for 2020. We will predict the production for USA,India and Pakistan.
india_list=[]
year_list = list(df.iloc[:,10:].columns)
for i in year_list:
x=df[(df.Area=='India') & (df.Element=='Food')][i].mean()
india_list.append(x)
reset=[]
for i in year_list:
reset.append(int(i[1:]))
reset=np.array(reset)
reset=reset.reshape(-1,1)
india_list=np.array(india_list)
india_list=india_list.reshape(-1,1)
reg = LinearRegression()
reg.fit(reset,india_list)
predictions = reg.predict(reset)
plt.title("India")
plt.xlabel("Year")
plt.ylabel("Production")
plt.scatter(reset,india_list)
plt.plot(reset,predictions)
plt.show()
print(reg.predict(2020))
df[(df.Area=='India') & (df.Element=='Food')]['Y1961'].mean()
df[(df.Area=='Pakistan') & (df.Element=='Food')]
Pak_list=[]
year_list = list(df.iloc[:,10:].columns)
for i in year_list:
yx=df[(df.Area=='Pakistan') & (df.Element=='Food')][i].mean()
Pak_list.append(yx)
Pak_list=np.array(Pak_list)
Pak_list=Pak_list.reshape(-1,1)
Pak_list
reg = LinearRegression()
reg.fit(reset,Pak_list)
predictions = reg.predict(reset)
plt.title("Pakistan")
plt.xlabel("Year")
plt.ylabel("Production")
plt.scatter(reset,Pak_list)
plt.plot(reset,predictions)
plt.show()
print(reg.predict(2020))
usa_list=[]
year_list = list(df.iloc[:,10:].columns)
for i in year_list:
xu=df[(df.Area=='United States of America') & (df.Element=='Food')][i].mean()
usa_list.append(xu)
usa_list=np.array(usa_list)
usa_list=india_list.reshape(-1,1)
reg = LinearRegression()
reg.fit(reset,usa_list)
predictions = reg.predict(reset)
plt.title("USA")
plt.xlabel("Year")
plt.ylabel("Production")
plt.scatter(reset,usa_list)
plt.plot(reset,predictions)
plt.show()
print(reg.predict(2020))
<span style="color:rgb(187,0,0)">---------------------------------------------------------------------------</span>
<span style="color:rgb(187,0,0)">ValueError</span> Traceback (most recent call last)
<span style="color:rgb(0,187,0)">&lt;ipython-input-24-da7cfa1c86d1&gt;</span> in <span style="color:rgb(0,187,187)">&lt;module&gt;</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 27</span> plt<span style="color:rgb(0,0,187)">.</span>plot<span style="color:rgb(0,0,187)">(</span>reset<span style="color:rgb(0,0,187)">,</span>predictions<span style="color:rgb(0,0,187)">)</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 28</span> plt<span style="color:rgb(0,0,187)">.</span>show<span style="color:rgb(0,0,187)">(</span><span style="color:rgb(0,0,187)">)</span>
<span style="color:rgb(0,187,0)">---&gt; 29</span><span style="color:rgb(187,0,0)"> </span>print<span style="color:rgb(0,0,187)">(</span>reg<span style="color:rgb(0,0,187)">.</span>predict<span style="color:rgb(0,0,187)">(</span><span style="color:rgb(0,187,187)">2020</span><span style="color:rgb(0,0,187)">)</span><span style="color:rgb(0,0,187)">)</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 30</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 31</span> df<span style="color:rgb(0,0,187)">[</span><span style="color:rgb(0,0,187)">(</span>df<span style="color:rgb(0,0,187)">.</span>Area<span style="color:rgb(0,0,187)">==</span><span style="color:rgb(0,0,187)">'India'</span><span style="color:rgb(0,0,187)">)</span> <span style="color:rgb(0,0,187)">&</span> <span style="color:rgb(0,0,187)">(</span>df<span style="color:rgb(0,0,187)">.</span>Element<span style="color:rgb(0,0,187)">==</span><span style="color:rgb(0,0,187)">'Food'</span><span style="color:rgb(0,0,187)">)</span><span style="color:rgb(0,0,187)">]</span><span style="color:rgb(0,0,187)">[</span><span style="color:rgb(0,0,187)">'Y1961'</span><span style="color:rgb(0,0,187)">]</span><span style="color:rgb(0,0,187)">.</span>mean<span style="color:rgb(0,0,187)">(</span><span style="color:rgb(0,0,187)">)</span>
<span style="color:rgb(0,187,0)">/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py</span> in <span style="color:rgb(0,187,187)">predict</span><span style="color:rgb(0,0,187)">(self, X)</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 211</span> Returns predicted values<span style="color:rgb(0,0,187)">.</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 212</span> """
<span style="color:rgb(0,187,0)">--&gt; 213</span><span style="color:rgb(187,0,0)"> </span><span style="color:rgb(0,187,0)">return</span> self<span style="color:rgb(0,0,187)">.</span>_decision_function<span style="color:rgb(0,0,187)">(</span>X<span style="color:rgb(0,0,187)">)</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 214</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 215</span> _preprocess_data <span style="color:rgb(0,0,187)">=</span> staticmethod<span style="color:rgb(0,0,187)">(</span>_preprocess_data<span style="color:rgb(0,0,187)">)</span>
<span style="color:rgb(0,187,0)">/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py</span> in <span style="color:rgb(0,187,187)">_decision_function</span><span style="color:rgb(0,0,187)">(self, X)</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 194</span> check_is_fitted<span style="color:rgb(0,0,187)">(</span>self<span style="color:rgb(0,0,187)">,</span> <span style="color:rgb(0,0,187)">"coef_"</span><span style="color:rgb(0,0,187)">)</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 195</span>
<span style="color:rgb(0,187,0)">--&gt; 196</span><span style="color:rgb(187,0,0)"> </span>X <span style="color:rgb(0,0,187)">=</span> check_array<span style="color:rgb(0,0,187)">(</span>X<span style="color:rgb(0,0,187)">,</span> accept_sparse<span style="color:rgb(0,0,187)">=</span><span style="color:rgb(0,0,187)">[</span><span style="color:rgb(0,0,187)">'csr'</span><span style="color:rgb(0,0,187)">,</span> <span style="color:rgb(0,0,187)">'csc'</span><span style="color:rgb(0,0,187)">,</span> <span style="color:rgb(0,0,187)">'coo'</span><span style="color:rgb(0,0,187)">]</span><span style="color:rgb(0,0,187)">)</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 197</span> return safe_sparse_dot(X, self.coef_.T,
<span style="font-weight:bold;color:rgb(0,187,0)"> 198</span> dense_output=True) + self.intercept_
<span style="color:rgb(0,187,0)">/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py</span> in <span style="color:rgb(0,187,187)">check_array</span><span style="color:rgb(0,0,187)">(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 543</span> <span style="color:rgb(0,0,187)">"Reshape your data either using array.reshape(-1, 1) if "</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 544</span> <span style="color:rgb(0,0,187)">"your data has a single feature or array.reshape(1, -1) "</span>
<span style="color:rgb(0,187,0)">--&gt; 545</span><span style="color:rgb(187,0,0)"> "if it contains a single sample.".format(array))
</span><span style="font-weight:bold;color:rgb(0,187,0)"> 546</span> <span style="color:rgb(187,0,0)"># If input is 1D raise error</span>
<span style="font-weight:bold;color:rgb(0,187,0)"> 547</span> <span style="color:rgb(0,187,0)">if</span> array<span style="color:rgb(0,0,187)">.</span>ndim <span style="color:rgb(0,0,187)">==</span> <span style="color:rgb(0,187,187)">1</span><span style="color:rgb(0,0,187)">:</span>
<span style="color:rgb(187,0,0)">ValueError</span>: Expected 2D array, got scalar array instead:
array=2020.
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.