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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yNcnd7fOAVk+SC5JMJ7ktyauG9rOxtb87ycZJHY8kjcukz7h/parWVdX6tnwucENVrQVuaMsAbwLWtmkTcBEMgh44D3g1cBxw3lzYS9JKNeng3tnJwKVt/lLglKH6ZTXwDWD/JIcAJwFbq+rRqvoRsBXYMO5OS9I4TTK4C7g+yS1JNrXai6tqO0D7PLjVDwUeGNp2ptUWqj9Fkk1JppJMzc7O7ubDkKTx2muC3/2aqnowycHA1iTfXaRt5qnVIvWnFqouBi4GWL9+/dPWS1JPJnbGXVUPts+HgasZjFE/1IZAaJ8Pt+YzwGFDm68BHlykLkkr1kSCO8nPJ3nR3DxwInAHsAWYuzNkI/CVNr8FOKPdXXI88OM2lHIdcGKSA9pFyRNbTZJWrEkNlbwYuDrJXB++WFXXJrkZuDLJmcD9wNtb+2uANwPTwOPAuwGq6tEkHwVubu0+UlWPju8wJGn8JhLcVXUv8I/nqf8QOGGeegFnL7CvzcDm3d1HSVqultvtgJKkXTC4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzkzyRQrSHu3+j/zSpLugEXjpv7195N/hGbckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUmbEHd5LDkvxZku8kuTPJv2j1Dyf5QZJtbXrz0DYfSjKd5K4kJw3VN7TadJJzx30skjQJk3hZ8A7gX1bVrUleBNySZGtb96mq+sRw4yRHAacBrwBeAnwtyZFt9YXAG4EZ4OYkW6rq22M5CkmakLEHd1VtB7a3+ceSfAc4dJFNTgauqKongO8lmQaOa+umq+pegCRXtLYGt6QVbaJj3EkOB14J/EUrnZPktiSbkxzQaocCDwxtNtNqC9UlaUWbWHAneSFwFfD+qvoJcBHwMmAdgzPyT841nWfzWqQ+33dtSjKVZGp2dvY5912SJmkiwZ3k+QxC+wtV9ScAVfVQVT1ZVX8HfJafDYfMAIcNbb4GeHCR+tNU1cVVtb6q1q9evXr3Howkjdkk7ioJ8DngO1X1n4fqhww1OxW4o81vAU5Lsk+SI4C1wDeBm4G1SY5IsjeDC5hbxnEMkjRJk7ir5DXAbwG3J9nWav8aOD3JOgbDHfcB7wWoqjuTXMngouMO4OyqehIgyTnAdcAqYHNV3TnOA5GkSZjEXSX/g/nHp69ZZJvzgfPnqV+z2HaStBL5y0lJ6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1JnTG4JakzBrckdcbglqTOGNyS1BmDW5I6Y3BLUmcMbknqjMEtSZ0xuCWpMwa3JHXG4JakzhjcktQZg1uSOmNwS1JnDG5J6ozBLUmdMbglqTMGtyR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSepM98GdZEOSu5JMJzl30v2RpFHrOriTrAIuBN4EHAWcnuSoyfZKkkar6+AGjgOmq+reqvpb4Arg5An3SZJGaq9Jd+A5OhR4YGh5Bnj1zo2SbAI2tcW/TnLXGPrWu4OARybdiXHIJzZOugt7gj3m74nz8ly2vraqNuyqUe/BPd+/UD2tUHUxcPHou7NyJJmqqvWT7odWBv+edq/eh0pmgMOGltcAD06oL5I0Fr0H983A2iRHJNkbOA3YMuE+SdJIdT1UUlU7kpwDXAesAjZX1Z0T7tZK4dCSdif/nnajVD1tSFiStIz1PlQiSXscg1uSOmNw70GSPJlk29B0+G7Y558n8TavPdCI/p4+nORfPfferWxdX5zUM/bTqlo36U5oxfDvaUI8497DJfm5JJ9PcnuSbyX5lV3U901yRZLbknwJ2HeiB6BlJcmqJB9PcnP7G3nv0LrfGar/u6H677YHxX0NePlEOt4Zz7j3LPsm2dbmv1dVpwJnA1TVLyX5h8D1SY5cpH4W8HhVHZPkGODW8R+Glon5/p7OBH5cVf8kyT7A/0xyPbC2Tccx+MXzliSvA/6Gwe8vXskgj24FbhnzcXTH4N6zzPdf238GfBqgqr6b5PvAkYvUXwdc0Oq3JbltXJ3XsjPf39OJwDFJ3taW/x6DwD6xTd9q9Re2+ouAq6vqcYAk/oBuCQxuLfREnMWelOPN/1pIgN+uquueUkxOAv5jVf2Xnervx7+nZ8wxbt0I/CZAGwp5KXDXEutHA8eMv8taxq4DzkryfBj87ST5+VZ/T5IXtvqhSQ5m8Pd0art28iLg1yfV8Z54xq0/AD6T5HZgB/CuqnoiyUL1i4DPtyGSbcA3J9ZzLUd/CBwO3JokwCxwSlVdn+QfATcNyvw18M6qurVd5N4GfB/475Ppdl/8ybskdcahEknqjMEtSZ0xuCWpMwa3JHXG4Jakzhjc2qPM80S7c1t9Yk85TPKuJC+ZxHerT97HrT3Ncnyi3buAO/BF11oiz7ilnSQ5MclNSW5N8sdDv/a7L8l/aOumkrwqyXVJ7knyvqHtn/YUvCSHJ/lOks8muTPJ9e3Xgm8D1gNfaP8D8GmL2iWDW3uafXcaKvmN4ZVJDgL+DfCrVfUqYAr4wFCTB6rqlxn8wu8S4G3A8cBH2vYn8rOn4K0Djm1PwaPVL6yqVwB/Bfzzqvpy+47frKp1VfXTkRy1VhSHSrSn2dVQyfHAUQweRwqwN3DT0Pq5p9fdDrywqh4DHkvyf5Psz8JPwbufwaNP5x6DeguDn4ZLz5jBLT1VgK1VdfoC659on383ND+3vFfbfr6n4B2+U/sn8SUUepYcKpGe6hvAa5L8A4AkL2hPR1yqhZ6Ct5jHGDyXWloSz7i1pxl+awvAtVV17txCVc0meRdweXuDCwzGvP9yKTtf6Cl4DM6wF3IJgycx/hT4Zce5tSs+HVCSOuNQiSR1xuCWpM4Y3JLUGYNbkjpjcEtSZwxuSeqMwS1Jnfl/+L4Y6b2CQ0EAAAAASUVORK5CYII=\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": "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\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",
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" 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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" <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"