mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-12-18 17:20:16 +00:00
5917 lines
916 KiB
Plaintext
5917 lines
916 KiB
Plaintext
|
{
|
|||
|
|
"cells": [
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "1eecdb4a-89ca-4a1e-9c4c-7c44b2e628a1",
|
|||
|
|
"_uuid": "110a8132a8179a9bed2fc8f1096592dc791f1661"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"# 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"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 1,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
|
|||
|
|
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"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"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 2,
|
|||
|
|
"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": 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": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABYAAAAQcCAYAAAAsgj+iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3XdcU1ffAPBfbkICgYAMWWEECJmEIQiCuCdVqhVxVqtWHDxU3LtqXcVZS52PvmqhKipaFdxWZWhFURTIBBRFtoBhhRGS9w8anoAkoKK29Xz/8SO5uffce84994zfucEplUpAEARBEARBEARBEARBEARB/n2wT50ABEEQBEEQBEEQBEEQBEEQ5MNAA8AIgiAIgiAIgiAIgiAIgiD/UmgAGEEQBEEQBEEQBEEQBEEQ5F8KDQAjCIIgCIIgCIIgCIIgCIL8S6EBYARBEARBEARBEARBEARBkH8pNACMIAiCIAiCIAiCIAiCIAjyL0X41An4WB4+fGhOIBAOAYALoIFvBEEQBEEQBEEQBEEQBEH+eRQAkCmXy2d6enqWdOYLn80AMIFAOGRpacnu3r17BYZhyk+dHgRBEARBEARBEARBEARBkLehUChwpaWlnKKiokMA8GVnvvM5RcK6dO/evRIN/iIIgiAIgiAIgiAIgiAI8k+EYZiye/fuUmh+y0HnvvMB0/N3g6HBXwRBEARBEARBEARBEARB/sn+GuPs9Lju5zQA/LcQFRXVDYfDeaalpemq/jZ79mwbOp3OnT17to2m78XHx1MGDBhA74o0JCYmkqdNm2ar6XOxWEzcv3+/SVccC/l08Hi8J4vF4jCZTA6Hw2Ffv35d/0MfMzc3V2f48OGObf8uFouJurq6PdhsNsfR0ZHL4/HYv/zyi+mHTs/bEIvFRGdnZ+6nTsfHoiofdDqdy2QyOevWrbNoamr61MnSav369eZVVVUtz61+/frRX716he/MdyMjI02NjY3dWCwWx8nJibtjxw6zrk4fmUz26GgbKpXK6+z+vL29mTQazYXJZHJcXFzYd+/e1Xu/FL5J27OFSqXyCgsLCQAAHh4erPc9VkFBAcHV1ZXFZrM5V65cMVD/zNvbm5mYmEgGaL4X7e3tXc6cOWP4vsd8V3l5eYTAwEAHGxsbHpfLZbu7u7OioqK6afvO25RH5J+jM/f124iPj6dQKBR31fNw0aJFVl25f4DW9662bTq7v6CgIBqVSuUxmUwOjUZz+eqrr2jPnj3Tef+Ufjjx8fEU9XbP1q1bu+/evbvT7Q7VM9LZ2Zk7cODAD3JvL1y40HrNmjUWbf+u3h7pqM2uDQ6H8wwJCWnpW6xZs8Zi4cKF1u+e4vZ19T2CACxbtsySTqdzGQwGh8VicW7evKm1DT9//nzrc+fOUbRt0/aeUBcZGWk6depUu/dJM0DXtBWQf66ioiI8i8XisFgsjpmZmZu5ubkri8XiUCgUdycnp3fuY8lkMpyfnx+DxWJxDh48aNyVaX4X27dvNxsxYkRLf7e8vByztbV1EYlExA997FGjRjlER0drbY92dj9UKpWnGiuIi4vTWn+8i8zMTBKLxeK095mnpydT1a/x9/d3rqioeK9xyerqalyvXr0YLBaLc+TIkVZlZNSoUQ5kMtmjsrKy5RhTpkyxw+Fwnh+y3a5+jm/rxx9/7L5v374PMh6HBoA/spiYGJMePXpUR0dHt2TosWPHumdkZAgOHDjw8mOkoW/fvrVHjx7N0/R5VlYW6eTJk2gA+B+ORCIpRCKRQCwWCzZs2JC/cuXKNyYY5HJ5lx6TRqM1Xrly5Wl7n9na2tYLhULB06dP+SdPnszZs2ePxc8//9zpzlhjY2PXJRRpKR/Z2dn8mzdvSq5du2a0ePHiLu8Yvg2FQgHaBqEPHDhgUV1d3fLcSkhIyDYzM+v0qHVgYGCFSCQSJCYmijdu3EjNy8v7278HPyoq6qlYLBaEhISULF68WOMk4YeWlpYmet99xMfHU+h0ep1QKBQMHz68ur1tcnJydIYNG8bYvHlzXlBQUGVn9tvVdYNCoYDAwEB6nz59ql++fJnB5/OFp06depqXl6e1Yf+25RH5fHl5eVULhULB48ePhbGxsaZJSUnkT52mjmzcuPGlWCwWPH36NNPd3b12wIABzLq6OtynTJO2e//mzZuUpKSklommpUuXloaFhZV1dt+qZ2RWVha/W7du8m3btnV/z+S+k47a7NoQiUTlpUuXjDuaDED+Xm7cuKF/9erVbhkZGQKJRCK4deuWxNHRsUHbd3bt2lUwevToKm3btL0nPoSuaCsg/1yWlpZNIpFIIBKJBFOnTi2dM2dOsUgkEqSmpgow7N2Hne7evUtubGzEiUQiQUhISEVnvtPVfVz1/S1cuPBVYWEhUTXpsnjxYuqkSZNesVgsrffp301ERESeSCQSREREvAwPD3/vCaB3lZycnGVsbKx4n33cuXNHH4fDgUgkEkyfPv2NMmJjY1MfExNjBNCclykpKQZmZmZ/28GFFStWlM6dO7f8Q+wbDQB/RFKpFEtNTTU4cuRI7u+//24MADBw4EC6TCbDPDw82AcPHjTm8/kkNzc3louLC3v+/PnW6rPqNTU1+OHDhzs6ODhwv/zySweFovk+Wbx4sZWLiwvb2dmZO3HiRHvV3729vZlz586l8ng8No1Gc1FFXKlHfF28eNFANVPHZrM5FRUV2KpVq6ipqakGLBaL88MPP5iLxWKip6cnk8PhsNUjSePj4yne3t7M9tKE/L1IpVK8kZGRHKA533x8fBiBgYEOTCaT2zbyVT1CRFMZksvlMHv2bBsXFxc2g8HgbNu2zQyg81G0HA6nYevWrXn79++3AAC4desW2cPDg8VmszkeHh6sJ0+ekACaIxICAgIcBw4cSO/Tpw+jbbTi1KlT7SIjI00BAEJDQ6lOTk5cBoPBmTVrlg0AwOHDh42dnZ25TCaT4+XlxVSlsb3yrE5bme/Zsyfziy++cKTRaC6hoaHUffv2mfB4PDaDweDw+XzSu+XQp0WlUuWHDh3KPXLkiLlCodCYv509f4lEQvT19WUwGAyOr68vIysriwjQHFU5ZMgQJyaTyWEymZzr16/ri8VioqOjI/frr7+243K5nJycHOLkyZPtXFxc2HQ6nbtgwQJrAICNGzeal5SU6PTr14/h4+PD+CvdLVFuu3fvNmUwGBwmk8kZPXq0Q0fna2dnV5+dnU2srKzEgoODaS4uLmw2m8357bffugEA1NbW4saOHUtjMBgcNpvdMjMeGRlpOmjQIKc+ffo402g0F03Re99//72F6vqpzgEAwNjYWA4A8Pz5cx0vLy+mKsKsbURsW3379q0pLi5uGYA8e/asobu7O4vD4bADAgIcpVIppromqnuWx+OxMzMzSQDNEXzqM+Lqz5aqqir8kCFDnJycnLiTJk2ya28QXn371atXW6iudWhoKLXttu3l/927d/XWrl1rc+vWLSMWi8Wprq5+Y+AoPz9fZ+jQoYw1a9bkT548WdpRPqjXDdqu+eDBg524XC6bTqdzt2/f3mHkd1xcHEVHR0e5dOnSUtXfGAxGw6pVq0raRkkNGDCAHh8fT1Fd+8LCQoKqTE+YMMGeTqdze/fu7aw6Xz6fT+rTp48zl8tle3p6MlWrgY4fP26kio728/Nj/BMmJz5nmvKruLgYP3jwYCcGg8Fxc3NjpaSkaI38MDQ0VPB4vFqxWEzSVO8qFAqYPXu2jbOzM5fBYLREP8XHx1O8vLyYHd27e/fuNeHxeGwWi8WZNGmSvaoTq6qLKisrsf79+9OZTCbH2dmZ21F0FYZhsHbt2hIzM7PG2NhYIwDt9VFYWBjV3d2d5eLiwk5OTib7+/s729raumzdurW7tvMDaL+u8fb2ZoaFhVF79uzJ3Lhxo0V7eSEWi4lRUVHd9+/fb8FisThXrlwxUI+2zczMJPn5+TFUK6Q6enb36tWrJj8/v6X+ba+uEYvFRAcHB+6YMWNoDAaDM3z4cEfVihX1Z1ViYiLZ29ubqdpXeno6uVevXgx7e3uX9lamqLd7pFI
|
|||
|
|
"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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
|
|||
|
|
"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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
|
|||
|
|
"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": {
|
|||
|
|
"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>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",
|
|||
|
|
" <th>United States of America</th>\n",
|
|||
|
|
" <th>Uruguay</th>\n",
|
|||
|
|
" <th>Uzbekistan</th>\n",
|
|||
|
|
" <th>Vanuatu</th>\n",
|
|||
|
|
" <th>Venezuela (Bolivarian Republic of)</th>\n",
|
|||
|
|
" <th>Viet Nam</th>\n",
|
|||
|
|
" <th>Yemen</th>\n",
|
|||
|
|
" <th>Zambia</th>\n",
|
|||
|
|
" <th>Zimbabwe</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <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",
|
|||
|
|
" <td>22542.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <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",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"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": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Y1961</th>\n",
|
|||
|
|
" <th>Y1962</th>\n",
|
|||
|
|
" <th>Y1963</th>\n",
|
|||
|
|
" <th>Y1964</th>\n",
|
|||
|
|
" <th>Y1965</th>\n",
|
|||
|
|
" <th>Y1966</th>\n",
|
|||
|
|
" <th>Y1967</th>\n",
|
|||
|
|
" <th>Y1968</th>\n",
|
|||
|
|
" <th>Y1969</th>\n",
|
|||
|
|
" <th>Y1970</th>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <th>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>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": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Y1961</th>\n",
|
|||
|
|
" <th>Y1962</th>\n",
|
|||
|
|
" <th>Y1963</th>\n",
|
|||
|
|
" <th>Y1964</th>\n",
|
|||
|
|
" <th>Y1965</th>\n",
|
|||
|
|
" <th>Y1966</th>\n",
|
|||
|
|
" <th>Y1967</th>\n",
|
|||
|
|
" <th>Y1968</th>\n",
|
|||
|
|
" <th>Y1969</th>\n",
|
|||
|
|
" <th>Y1970</th>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <th>Y2006</th>\n",
|
|||
|
|
" <th>Y2007</th>\n",
|
|||
|
|
" <th>Y2008</th>\n",
|
|||
|
|
" <th>Y2009</th>\n",
|
|||
|
|
" <th>Y2010</th>\n",
|
|||
|
|
" <th>Y2011</th>\n",
|
|||
|
|
" <th>Y2012</th>\n",
|
|||
|
|
" <th>Y2013</th>\n",
|
|||
|
|
" <th>Mean_Produce</th>\n",
|
|||
|
|
" <th>Rank</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Afghanistan</th>\n",
|
|||
|
|
" <td>9481.0</td>\n",
|
|||
|
|
" <td>9414.0</td>\n",
|
|||
|
|
" <td>9194.0</td>\n",
|
|||
|
|
" <td>10170.0</td>\n",
|
|||
|
|
" <td>10473.0</td>\n",
|
|||
|
|
" <td>10169.0</td>\n",
|
|||
|
|
" <td>11289.0</td>\n",
|
|||
|
|
" <td>11508.0</td>\n",
|
|||
|
|
" <td>11815.0</td>\n",
|
|||
|
|
" <td>10454.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>18317.0</td>\n",
|
|||
|
|
" <td>19248.0</td>\n",
|
|||
|
|
" <td>19381.0</td>\n",
|
|||
|
|
" <td>20661.0</td>\n",
|
|||
|
|
" <td>21030.0</td>\n",
|
|||
|
|
" <td>21100.0</td>\n",
|
|||
|
|
" <td>22706.0</td>\n",
|
|||
|
|
" <td>23007.0</td>\n",
|
|||
|
|
" <td>13003.056604</td>\n",
|
|||
|
|
" <td>69.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Albania</th>\n",
|
|||
|
|
" <td>1706.0</td>\n",
|
|||
|
|
" <td>1749.0</td>\n",
|
|||
|
|
" <td>1767.0</td>\n",
|
|||
|
|
" <td>1889.0</td>\n",
|
|||
|
|
" <td>1884.0</td>\n",
|
|||
|
|
" <td>1995.0</td>\n",
|
|||
|
|
" <td>2046.0</td>\n",
|
|||
|
|
" <td>2169.0</td>\n",
|
|||
|
|
" <td>2230.0</td>\n",
|
|||
|
|
" <td>2395.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>6911.0</td>\n",
|
|||
|
|
" <td>6744.0</td>\n",
|
|||
|
|
" <td>7168.0</td>\n",
|
|||
|
|
" <td>7316.0</td>\n",
|
|||
|
|
" <td>7907.0</td>\n",
|
|||
|
|
" <td>8114.0</td>\n",
|
|||
|
|
" <td>8221.0</td>\n",
|
|||
|
|
" <td>8271.0</td>\n",
|
|||
|
|
" <td>4475.509434</td>\n",
|
|||
|
|
" <td>104.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Algeria</th>\n",
|
|||
|
|
" <td>7488.0</td>\n",
|
|||
|
|
" <td>7235.0</td>\n",
|
|||
|
|
" <td>6861.0</td>\n",
|
|||
|
|
" <td>7255.0</td>\n",
|
|||
|
|
" <td>7509.0</td>\n",
|
|||
|
|
" <td>7536.0</td>\n",
|
|||
|
|
" <td>7986.0</td>\n",
|
|||
|
|
" <td>8839.0</td>\n",
|
|||
|
|
" <td>9003.0</td>\n",
|
|||
|
|
" <td>9355.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>51067.0</td>\n",
|
|||
|
|
" <td>49933.0</td>\n",
|
|||
|
|
" <td>50916.0</td>\n",
|
|||
|
|
" <td>57505.0</td>\n",
|
|||
|
|
" <td>60071.0</td>\n",
|
|||
|
|
" <td>65852.0</td>\n",
|
|||
|
|
" <td>69365.0</td>\n",
|
|||
|
|
" <td>72161.0</td>\n",
|
|||
|
|
" <td>28879.490566</td>\n",
|
|||
|
|
" <td>38.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Angola</th>\n",
|
|||
|
|
" <td>4834.0</td>\n",
|
|||
|
|
" <td>4775.0</td>\n",
|
|||
|
|
" <td>5240.0</td>\n",
|
|||
|
|
" <td>5286.0</td>\n",
|
|||
|
|
" <td>5527.0</td>\n",
|
|||
|
|
" <td>5677.0</td>\n",
|
|||
|
|
" <td>5833.0</td>\n",
|
|||
|
|
" <td>5685.0</td>\n",
|
|||
|
|
" <td>6219.0</td>\n",
|
|||
|
|
" <td>6460.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>28247.0</td>\n",
|
|||
|
|
" <td>29877.0</td>\n",
|
|||
|
|
" <td>32053.0</td>\n",
|
|||
|
|
" <td>36985.0</td>\n",
|
|||
|
|
" <td>38400.0</td>\n",
|
|||
|
|
" <td>40573.0</td>\n",
|
|||
|
|
" <td>38064.0</td>\n",
|
|||
|
|
" <td>48639.0</td>\n",
|
|||
|
|
" <td>13321.056604</td>\n",
|
|||
|
|
" <td>68.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Antigua and Barbuda</th>\n",
|
|||
|
|
" <td>92.0</td>\n",
|
|||
|
|
" <td>94.0</td>\n",
|
|||
|
|
" <td>105.0</td>\n",
|
|||
|
|
" <td>95.0</td>\n",
|
|||
|
|
" <td>84.0</td>\n",
|
|||
|
|
" <td>73.0</td>\n",
|
|||
|
|
" <td>64.0</td>\n",
|
|||
|
|
" <td>59.0</td>\n",
|
|||
|
|
" <td>68.0</td>\n",
|
|||
|
|
" <td>77.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>110.0</td>\n",
|
|||
|
|
" <td>122.0</td>\n",
|
|||
|
|
" <td>115.0</td>\n",
|
|||
|
|
" <td>114.0</td>\n",
|
|||
|
|
" <td>115.0</td>\n",
|
|||
|
|
" <td>118.0</td>\n",
|
|||
|
|
" <td>113.0</td>\n",
|
|||
|
|
" <td>119.0</td>\n",
|
|||
|
|
" <td>83.886792</td>\n",
|
|||
|
|
" <td>172.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>5 rows × 55 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 \\\n",
|
|||
|
|
"Afghanistan 9481.0 9414.0 9194.0 10170.0 10473.0 10169.0 \n",
|
|||
|
|
"Albania 1706.0 1749.0 1767.0 1889.0 1884.0 1995.0 \n",
|
|||
|
|
"Algeria 7488.0 7235.0 6861.0 7255.0 7509.0 7536.0 \n",
|
|||
|
|
"Angola 4834.0 4775.0 5240.0 5286.0 5527.0 5677.0 \n",
|
|||
|
|
"Antigua and Barbuda 92.0 94.0 105.0 95.0 84.0 73.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Y1967 Y1968 Y1969 Y1970 ... Y2006 \\\n",
|
|||
|
|
"Afghanistan 11289.0 11508.0 11815.0 10454.0 ... 18317.0 \n",
|
|||
|
|
"Albania 2046.0 2169.0 2230.0 2395.0 ... 6911.0 \n",
|
|||
|
|
"Algeria 7986.0 8839.0 9003.0 9355.0 ... 51067.0 \n",
|
|||
|
|
"Angola 5833.0 5685.0 6219.0 6460.0 ... 28247.0 \n",
|
|||
|
|
"Antigua and Barbuda 64.0 59.0 68.0 77.0 ... 110.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 \\\n",
|
|||
|
|
"Afghanistan 19248.0 19381.0 20661.0 21030.0 21100.0 22706.0 \n",
|
|||
|
|
"Albania 6744.0 7168.0 7316.0 7907.0 8114.0 8221.0 \n",
|
|||
|
|
"Algeria 49933.0 50916.0 57505.0 60071.0 65852.0 69365.0 \n",
|
|||
|
|
"Angola 29877.0 32053.0 36985.0 38400.0 40573.0 38064.0 \n",
|
|||
|
|
"Antigua and Barbuda 122.0 115.0 114.0 115.0 118.0 113.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Y2013 Mean_Produce Rank \n",
|
|||
|
|
"Afghanistan 23007.0 13003.056604 69.0 \n",
|
|||
|
|
"Albania 8271.0 4475.509434 104.0 \n",
|
|||
|
|
"Algeria 72161.0 28879.490566 38.0 \n",
|
|||
|
|
"Angola 48639.0 13321.056604 68.0 \n",
|
|||
|
|
"Antigua and Barbuda 119.0 83.886792 172.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[5 rows x 55 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"mean_produce = []\n",
|
|||
|
|
"for i in range(174):\n",
|
|||
|
|
" mean_produce.append(new_df.iloc[i,:].values.mean())\n",
|
|||
|
|
"new_df['Mean_Produce'] = mean_produce\n",
|
|||
|
|
"\n",
|
|||
|
|
"new_df['Rank'] = new_df['Mean_Produce'].rank(ascending=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
"new_df.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "6f7c4fb7-1475-439f-9929-4cf4b29d8de7",
|
|||
|
|
"_uuid": "da6c9c98eaff45edba1179103ae539bbfbe9753b"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Now, we create another dataframe with items and their total production each year from 1961 to 2013"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "bfd692bc-dce4-4870-9ab9-9775cf69a87f",
|
|||
|
|
"_uuid": "9e11017d381f175eee714643bc5fa763600aaa0b"
|
|||
|
|
},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"item_list = list(df['Item'].unique())\n",
|
|||
|
|
"\n",
|
|||
|
|
"item_df = pd.DataFrame()\n",
|
|||
|
|
"item_df['Item_Name'] = item_list\n",
|
|||
|
|
"\n",
|
|||
|
|
"for yr in year_list:\n",
|
|||
|
|
" item_produce = []\n",
|
|||
|
|
" for it in item_list:\n",
|
|||
|
|
" item_produce.append(df[yr][df['Item']==it].sum())\n",
|
|||
|
|
" item_df[yr] = item_produce\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "3b7ed0c2-6140-4285-861c-d0cd2324a1f5",
|
|||
|
|
"_uuid": "cb4641df5ce90f516f88c536e8a6c6870c5b4f65"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Item_Name</th>\n",
|
|||
|
|
" <th>Y1961</th>\n",
|
|||
|
|
" <th>Y1962</th>\n",
|
|||
|
|
" <th>Y1963</th>\n",
|
|||
|
|
" <th>Y1964</th>\n",
|
|||
|
|
" <th>Y1965</th>\n",
|
|||
|
|
" <th>Y1966</th>\n",
|
|||
|
|
" <th>Y1967</th>\n",
|
|||
|
|
" <th>Y1968</th>\n",
|
|||
|
|
" <th>Y1969</th>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <th>Y2004</th>\n",
|
|||
|
|
" <th>Y2005</th>\n",
|
|||
|
|
" <th>Y2006</th>\n",
|
|||
|
|
" <th>Y2007</th>\n",
|
|||
|
|
" <th>Y2008</th>\n",
|
|||
|
|
" <th>Y2009</th>\n",
|
|||
|
|
" <th>Y2010</th>\n",
|
|||
|
|
" <th>Y2011</th>\n",
|
|||
|
|
" <th>Y2012</th>\n",
|
|||
|
|
" <th>Y2013</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>Wheat and products</td>\n",
|
|||
|
|
" <td>138829.0</td>\n",
|
|||
|
|
" <td>144643.0</td>\n",
|
|||
|
|
" <td>147325.0</td>\n",
|
|||
|
|
" <td>156273.0</td>\n",
|
|||
|
|
" <td>168822.0</td>\n",
|
|||
|
|
" <td>169832.0</td>\n",
|
|||
|
|
" <td>171469.0</td>\n",
|
|||
|
|
" <td>179530.0</td>\n",
|
|||
|
|
" <td>189658.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>527394.0</td>\n",
|
|||
|
|
" <td>532263.0</td>\n",
|
|||
|
|
" <td>537279.0</td>\n",
|
|||
|
|
" <td>529271.0</td>\n",
|
|||
|
|
" <td>562239.0</td>\n",
|
|||
|
|
" <td>557245.0</td>\n",
|
|||
|
|
" <td>549926.0</td>\n",
|
|||
|
|
" <td>578179.0</td>\n",
|
|||
|
|
" <td>576597</td>\n",
|
|||
|
|
" <td>587492</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>Rice (Milled Equivalent)</td>\n",
|
|||
|
|
" <td>122700.0</td>\n",
|
|||
|
|
" <td>131842.0</td>\n",
|
|||
|
|
" <td>139507.0</td>\n",
|
|||
|
|
" <td>148304.0</td>\n",
|
|||
|
|
" <td>150056.0</td>\n",
|
|||
|
|
" <td>155583.0</td>\n",
|
|||
|
|
" <td>158587.0</td>\n",
|
|||
|
|
" <td>164614.0</td>\n",
|
|||
|
|
" <td>167922.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>361107.0</td>\n",
|
|||
|
|
" <td>366025.0</td>\n",
|
|||
|
|
" <td>372629.0</td>\n",
|
|||
|
|
" <td>378698.0</td>\n",
|
|||
|
|
" <td>389708.0</td>\n",
|
|||
|
|
" <td>394221.0</td>\n",
|
|||
|
|
" <td>398559.0</td>\n",
|
|||
|
|
" <td>404152.0</td>\n",
|
|||
|
|
" <td>406787</td>\n",
|
|||
|
|
" <td>410880</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>Barley and products</td>\n",
|
|||
|
|
" <td>46180.0</td>\n",
|
|||
|
|
" <td>48915.0</td>\n",
|
|||
|
|
" <td>51642.0</td>\n",
|
|||
|
|
" <td>54184.0</td>\n",
|
|||
|
|
" <td>54945.0</td>\n",
|
|||
|
|
" <td>55463.0</td>\n",
|
|||
|
|
" <td>56424.0</td>\n",
|
|||
|
|
" <td>60455.0</td>\n",
|
|||
|
|
" <td>65501.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>102055.0</td>\n",
|
|||
|
|
" <td>97185.0</td>\n",
|
|||
|
|
" <td>100981.0</td>\n",
|
|||
|
|
" <td>93310.0</td>\n",
|
|||
|
|
" <td>98209.0</td>\n",
|
|||
|
|
" <td>99135.0</td>\n",
|
|||
|
|
" <td>92563.0</td>\n",
|
|||
|
|
" <td>92570.0</td>\n",
|
|||
|
|
" <td>88766</td>\n",
|
|||
|
|
" <td>99452</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>Maize and products</td>\n",
|
|||
|
|
" <td>168039.0</td>\n",
|
|||
|
|
" <td>168305.0</td>\n",
|
|||
|
|
" <td>172905.0</td>\n",
|
|||
|
|
" <td>175468.0</td>\n",
|
|||
|
|
" <td>190304.0</td>\n",
|
|||
|
|
" <td>200860.0</td>\n",
|
|||
|
|
" <td>213050.0</td>\n",
|
|||
|
|
" <td>215613.0</td>\n",
|
|||
|
|
" <td>221953.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>545024.0</td>\n",
|
|||
|
|
" <td>549036.0</td>\n",
|
|||
|
|
" <td>543280.0</td>\n",
|
|||
|
|
" <td>573892.0</td>\n",
|
|||
|
|
" <td>592231.0</td>\n",
|
|||
|
|
" <td>557940.0</td>\n",
|
|||
|
|
" <td>584337.0</td>\n",
|
|||
|
|
" <td>603297.0</td>\n",
|
|||
|
|
" <td>608730</td>\n",
|
|||
|
|
" <td>671300</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>Millet and products</td>\n",
|
|||
|
|
" <td>19075.0</td>\n",
|
|||
|
|
" <td>19019.0</td>\n",
|
|||
|
|
" <td>19740.0</td>\n",
|
|||
|
|
" <td>20353.0</td>\n",
|
|||
|
|
" <td>18377.0</td>\n",
|
|||
|
|
" <td>20860.0</td>\n",
|
|||
|
|
" <td>22997.0</td>\n",
|
|||
|
|
" <td>21785.0</td>\n",
|
|||
|
|
" <td>23966.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>25789.0</td>\n",
|
|||
|
|
" <td>25496.0</td>\n",
|
|||
|
|
" <td>25997.0</td>\n",
|
|||
|
|
" <td>26750.0</td>\n",
|
|||
|
|
" <td>26373.0</td>\n",
|
|||
|
|
" <td>24575.0</td>\n",
|
|||
|
|
" <td>27039.0</td>\n",
|
|||
|
|
" <td>25740.0</td>\n",
|
|||
|
|
" <td>26105</td>\n",
|
|||
|
|
" <td>26346</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>5 rows × 54 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Item_Name Y1961 Y1962 Y1963 Y1964 Y1965 \\\n",
|
|||
|
|
"0 Wheat and products 138829.0 144643.0 147325.0 156273.0 168822.0 \n",
|
|||
|
|
"1 Rice (Milled Equivalent) 122700.0 131842.0 139507.0 148304.0 150056.0 \n",
|
|||
|
|
"2 Barley and products 46180.0 48915.0 51642.0 54184.0 54945.0 \n",
|
|||
|
|
"3 Maize and products 168039.0 168305.0 172905.0 175468.0 190304.0 \n",
|
|||
|
|
"4 Millet and products 19075.0 19019.0 19740.0 20353.0 18377.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Y1966 Y1967 Y1968 Y1969 ... Y2004 Y2005 \\\n",
|
|||
|
|
"0 169832.0 171469.0 179530.0 189658.0 ... 527394.0 532263.0 \n",
|
|||
|
|
"1 155583.0 158587.0 164614.0 167922.0 ... 361107.0 366025.0 \n",
|
|||
|
|
"2 55463.0 56424.0 60455.0 65501.0 ... 102055.0 97185.0 \n",
|
|||
|
|
"3 200860.0 213050.0 215613.0 221953.0 ... 545024.0 549036.0 \n",
|
|||
|
|
"4 20860.0 22997.0 21785.0 23966.0 ... 25789.0 25496.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Y2006 Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013 \n",
|
|||
|
|
"0 537279.0 529271.0 562239.0 557245.0 549926.0 578179.0 576597 587492 \n",
|
|||
|
|
"1 372629.0 378698.0 389708.0 394221.0 398559.0 404152.0 406787 410880 \n",
|
|||
|
|
"2 100981.0 93310.0 98209.0 99135.0 92563.0 92570.0 88766 99452 \n",
|
|||
|
|
"3 543280.0 573892.0 592231.0 557940.0 584337.0 603297.0 608730 671300 \n",
|
|||
|
|
"4 25997.0 26750.0 26373.0 24575.0 27039.0 25740.0 26105 26346 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[5 rows x 54 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"item_df.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "3fa01e1f-bedd-431b-90c3-8d7d70545f34",
|
|||
|
|
"_uuid": "56a647293f1c1aba7c184f249021e008a4d5a8f2"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"# Some more feature engineering\n",
|
|||
|
|
"\n",
|
|||
|
|
"This time, we will use the new features to get some good conclusions.\n",
|
|||
|
|
"\n",
|
|||
|
|
"# 1. Total amount of item produced from 1961 to 2013\n",
|
|||
|
|
"# 2. Providing a rank to the items to know the most produced item"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "3a6bb102-6749-4818-860d-59aaad6de07f",
|
|||
|
|
"_uuid": "9e816786e7a161227ae72d164b25c0029e01e5b4",
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Item_Name</th>\n",
|
|||
|
|
" <th>Y1961</th>\n",
|
|||
|
|
" <th>Y1962</th>\n",
|
|||
|
|
" <th>Y1963</th>\n",
|
|||
|
|
" <th>Y1964</th>\n",
|
|||
|
|
" <th>Y1965</th>\n",
|
|||
|
|
" <th>Y1966</th>\n",
|
|||
|
|
" <th>Y1967</th>\n",
|
|||
|
|
" <th>Y1968</th>\n",
|
|||
|
|
" <th>Y1969</th>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <th>Y2006</th>\n",
|
|||
|
|
" <th>Y2007</th>\n",
|
|||
|
|
" <th>Y2008</th>\n",
|
|||
|
|
" <th>Y2009</th>\n",
|
|||
|
|
" <th>Y2010</th>\n",
|
|||
|
|
" <th>Y2011</th>\n",
|
|||
|
|
" <th>Y2012</th>\n",
|
|||
|
|
" <th>Y2013</th>\n",
|
|||
|
|
" <th>Sum</th>\n",
|
|||
|
|
" <th>Production_Rank</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>Wheat and products</td>\n",
|
|||
|
|
" <td>138829.0</td>\n",
|
|||
|
|
" <td>144643.0</td>\n",
|
|||
|
|
" <td>147325.0</td>\n",
|
|||
|
|
" <td>156273.0</td>\n",
|
|||
|
|
" <td>168822.0</td>\n",
|
|||
|
|
" <td>169832.0</td>\n",
|
|||
|
|
" <td>171469.0</td>\n",
|
|||
|
|
" <td>179530.0</td>\n",
|
|||
|
|
" <td>189658.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>537279.0</td>\n",
|
|||
|
|
" <td>529271.0</td>\n",
|
|||
|
|
" <td>562239.0</td>\n",
|
|||
|
|
" <td>557245.0</td>\n",
|
|||
|
|
" <td>549926.0</td>\n",
|
|||
|
|
" <td>578179.0</td>\n",
|
|||
|
|
" <td>576597</td>\n",
|
|||
|
|
" <td>587492</td>\n",
|
|||
|
|
" <td>19194671.0</td>\n",
|
|||
|
|
" <td>6.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>Rice (Milled Equivalent)</td>\n",
|
|||
|
|
" <td>122700.0</td>\n",
|
|||
|
|
" <td>131842.0</td>\n",
|
|||
|
|
" <td>139507.0</td>\n",
|
|||
|
|
" <td>148304.0</td>\n",
|
|||
|
|
" <td>150056.0</td>\n",
|
|||
|
|
" <td>155583.0</td>\n",
|
|||
|
|
" <td>158587.0</td>\n",
|
|||
|
|
" <td>164614.0</td>\n",
|
|||
|
|
" <td>167922.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>372629.0</td>\n",
|
|||
|
|
" <td>378698.0</td>\n",
|
|||
|
|
" <td>389708.0</td>\n",
|
|||
|
|
" <td>394221.0</td>\n",
|
|||
|
|
" <td>398559.0</td>\n",
|
|||
|
|
" <td>404152.0</td>\n",
|
|||
|
|
" <td>406787</td>\n",
|
|||
|
|
" <td>410880</td>\n",
|
|||
|
|
" <td>14475448.0</td>\n",
|
|||
|
|
" <td>8.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>Barley and products</td>\n",
|
|||
|
|
" <td>46180.0</td>\n",
|
|||
|
|
" <td>48915.0</td>\n",
|
|||
|
|
" <td>51642.0</td>\n",
|
|||
|
|
" <td>54184.0</td>\n",
|
|||
|
|
" <td>54945.0</td>\n",
|
|||
|
|
" <td>55463.0</td>\n",
|
|||
|
|
" <td>56424.0</td>\n",
|
|||
|
|
" <td>60455.0</td>\n",
|
|||
|
|
" <td>65501.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>100981.0</td>\n",
|
|||
|
|
" <td>93310.0</td>\n",
|
|||
|
|
" <td>98209.0</td>\n",
|
|||
|
|
" <td>99135.0</td>\n",
|
|||
|
|
" <td>92563.0</td>\n",
|
|||
|
|
" <td>92570.0</td>\n",
|
|||
|
|
" <td>88766</td>\n",
|
|||
|
|
" <td>99452</td>\n",
|
|||
|
|
" <td>4442742.0</td>\n",
|
|||
|
|
" <td>20.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>Maize and products</td>\n",
|
|||
|
|
" <td>168039.0</td>\n",
|
|||
|
|
" <td>168305.0</td>\n",
|
|||
|
|
" <td>172905.0</td>\n",
|
|||
|
|
" <td>175468.0</td>\n",
|
|||
|
|
" <td>190304.0</td>\n",
|
|||
|
|
" <td>200860.0</td>\n",
|
|||
|
|
" <td>213050.0</td>\n",
|
|||
|
|
" <td>215613.0</td>\n",
|
|||
|
|
" <td>221953.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>543280.0</td>\n",
|
|||
|
|
" <td>573892.0</td>\n",
|
|||
|
|
" <td>592231.0</td>\n",
|
|||
|
|
" <td>557940.0</td>\n",
|
|||
|
|
" <td>584337.0</td>\n",
|
|||
|
|
" <td>603297.0</td>\n",
|
|||
|
|
" <td>608730</td>\n",
|
|||
|
|
" <td>671300</td>\n",
|
|||
|
|
" <td>19960640.0</td>\n",
|
|||
|
|
" <td>5.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>Millet and products</td>\n",
|
|||
|
|
" <td>19075.0</td>\n",
|
|||
|
|
" <td>19019.0</td>\n",
|
|||
|
|
" <td>19740.0</td>\n",
|
|||
|
|
" <td>20353.0</td>\n",
|
|||
|
|
" <td>18377.0</td>\n",
|
|||
|
|
" <td>20860.0</td>\n",
|
|||
|
|
" <td>22997.0</td>\n",
|
|||
|
|
" <td>21785.0</td>\n",
|
|||
|
|
" <td>23966.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>25997.0</td>\n",
|
|||
|
|
" <td>26750.0</td>\n",
|
|||
|
|
" <td>26373.0</td>\n",
|
|||
|
|
" <td>24575.0</td>\n",
|
|||
|
|
" <td>27039.0</td>\n",
|
|||
|
|
" <td>25740.0</td>\n",
|
|||
|
|
" <td>26105</td>\n",
|
|||
|
|
" <td>26346</td>\n",
|
|||
|
|
" <td>1225400.0</td>\n",
|
|||
|
|
" <td>38.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>5 rows × 56 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Item_Name Y1961 Y1962 Y1963 Y1964 Y1965 \\\n",
|
|||
|
|
"0 Wheat and products 138829.0 144643.0 147325.0 156273.0 168822.0 \n",
|
|||
|
|
"1 Rice (Milled Equivalent) 122700.0 131842.0 139507.0 148304.0 150056.0 \n",
|
|||
|
|
"2 Barley and products 46180.0 48915.0 51642.0 54184.0 54945.0 \n",
|
|||
|
|
"3 Maize and products 168039.0 168305.0 172905.0 175468.0 190304.0 \n",
|
|||
|
|
"4 Millet and products 19075.0 19019.0 19740.0 20353.0 18377.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Y1966 Y1967 Y1968 Y1969 ... Y2006 \\\n",
|
|||
|
|
"0 169832.0 171469.0 179530.0 189658.0 ... 537279.0 \n",
|
|||
|
|
"1 155583.0 158587.0 164614.0 167922.0 ... 372629.0 \n",
|
|||
|
|
"2 55463.0 56424.0 60455.0 65501.0 ... 100981.0 \n",
|
|||
|
|
"3 200860.0 213050.0 215613.0 221953.0 ... 543280.0 \n",
|
|||
|
|
"4 20860.0 22997.0 21785.0 23966.0 ... 25997.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 Y2013 \\\n",
|
|||
|
|
"0 529271.0 562239.0 557245.0 549926.0 578179.0 576597 587492 \n",
|
|||
|
|
"1 378698.0 389708.0 394221.0 398559.0 404152.0 406787 410880 \n",
|
|||
|
|
"2 93310.0 98209.0 99135.0 92563.0 92570.0 88766 99452 \n",
|
|||
|
|
"3 573892.0 592231.0 557940.0 584337.0 603297.0 608730 671300 \n",
|
|||
|
|
"4 26750.0 26373.0 24575.0 27039.0 25740.0 26105 26346 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Sum Production_Rank \n",
|
|||
|
|
"0 19194671.0 6.0 \n",
|
|||
|
|
"1 14475448.0 8.0 \n",
|
|||
|
|
"2 4442742.0 20.0 \n",
|
|||
|
|
"3 19960640.0 5.0 \n",
|
|||
|
|
"4 1225400.0 38.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[5 rows x 56 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"sum_col = []\n",
|
|||
|
|
"for i in range(115):\n",
|
|||
|
|
" sum_col.append(item_df.iloc[i,1:].values.sum())\n",
|
|||
|
|
"item_df['Sum'] = sum_col\n",
|
|||
|
|
"item_df['Production_Rank'] = item_df['Sum'].rank(ascending=False)\n",
|
|||
|
|
"\n",
|
|||
|
|
"item_df.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "7e20740c-565b-4969-a52e-d986e462b750",
|
|||
|
|
"_uuid": "f483c9add5f6af9af9162b5425f6d65eb1c5f4aa"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"# Now, we find the most produced food items in the last half-century"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "3130fe83-404c-4b3c-addc-560b2e2f32bf",
|
|||
|
|
"_uuid": "0403e9ab2e13587588e3a30d64b8b6638571d3d5"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"56 Cereals - Excluding Beer\n",
|
|||
|
|
"65 Fruits - Excluding Wine\n",
|
|||
|
|
"3 Maize and products\n",
|
|||
|
|
"53 Milk - Excluding Butter\n",
|
|||
|
|
"6 Potatoes and products\n",
|
|||
|
|
"1 Rice (Milled Equivalent)\n",
|
|||
|
|
"57 Starchy Roots\n",
|
|||
|
|
"64 Vegetables\n",
|
|||
|
|
"27 Vegetables, Other\n",
|
|||
|
|
"0 Wheat and products\n",
|
|||
|
|
"Name: Item_Name, dtype: object"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"item_df['Item_Name'][item_df['Production_Rank'] < 11.0].sort_values()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "b6212fed-588b-426e-9271-6d857cd6aacb",
|
|||
|
|
"_uuid": "e2c83f4c851b755ea6cf19f1bca168e705bd4edd"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"So, cereals, fruits and maize are the most produced items in the last 50 years\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Food and feed plot for most produced items "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "493f9940-1762-4718-acb4-fba5c4c73f4b",
|
|||
|
|
"_uuid": "f8454c5200bdeb3995b9a0ada3deb5ca1c31f181"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
|
|||
|
|
" warnings.warn(msg)\n",
|
|||
|
|
"/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
|
|||
|
|
" warnings.warn(msg, UserWarning)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1212.38x1440 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"sns.factorplot(\"Item\", data=df[(df['Item']=='Wheat and products') | (df['Item']=='Rice (Milled Equivalent)') | (df['Item']=='Maize and products') | (df['Item']=='Potatoes and products') | (df['Item']=='Vegetables, Other') | (df['Item']=='Milk - Excluding Butter') | (df['Item']=='Cereals - Excluding Beer') | (df['Item']=='Starchy Roots') | (df['Item']=='Vegetables') | (df['Item']=='Fruits - Excluding Wine')], kind=\"count\", hue=\"Element\", size=20, aspect=.8)\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "45dda825-49a0-41ab-9ebd-eaa609aac986",
|
|||
|
|
"_uuid": "ce5b2d38ff24ea08da632c4e2773dbd0bd026b9d",
|
|||
|
|
"collapsed": true
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"# Now, we plot a heatmap of correlation of produce in difference years"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "b1bab0ec-6615-452c-8d06-a81d4f2ae252",
|
|||
|
|
"_uuid": "a2ed2aae2364810ce640648cf50880adcf2cdcc4"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a23b4b128>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA2QAAAJYCAYAAAANJyWqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3X+QXOV95/v3p+eHhMaSk9goldjcJa615LKNpGDWcCsOZkOFQNjF67gQTCjuboktXXnlW3XvJgRSgQ0kUTZFvCorlZQpRRYs4BVyvCaghUireA0yjlKLUIQYsCxbFInHSiyDsQ36wcx0f+8f5wg37Z7pPl8PTc/o86rqmp7znO95nnP6nNPzzDnn+ygiMDMzMzMzs96rvdkNMDMzMzMzO1O5Q2ZmZmZmZvYmcYfMzMzMzMzsTeIOmZmZmZmZ2ZvEHTIzMzMzM7M3iTtkZmZmZmZmbxJ3yMzMzMzMzN4k7pCZmZmZmZm9SdwhMzMzMzMze5O4Q2ZmZmZmZvYmGXyzG5A1+cJzUTmoPlk5JCYnKscAxInvV47R4HCurqlcG1PUmz58vPxCT+opKmvk4iZfrV7Vi/+Yq2swcahOTeXqymyPRnIbnjpROSRe/E6qKg0NVa/r5KlUXfGd76biUnVNVj+vZbZFUVn1z3nqH15MVaXhgeoxIwuqVzSRO05isl45ZuqF6ueMrFe+mft6b0xVP8dPTeS+F+qN6nEnT+b23RdPnVU55hVV3wcBjg1WX6/sN+tA9b+EeCVZ2T8MJPZ5Eg0EvhPV/645Tu5Yfql+snLMK43qx/IrU7nvk+Fa7lh+6p/+RqnAN1Hqb/ukobe/q6+2z4yHpQqPS7qiadpqSTslbZV0TNJYS8xKSXslPS1ph6QlTWUryrJnyvKF5fQNkr4p6ZXZXkEzMzMzM7N+NWOHLCICWAdslLRQ0giwAVgP3A1c3iZsC3BzRJwHPADcCCBpELgPWBcR7wMuAU7/a3cH8MEfd2XMzMzMzGwOatR79+ozHa+DRsSYpB3ATcAIcE9EHAGOSDq3TchyYE/5fjewC7gVuAw4GBFPlct97T6WiPhbAKmvrh6amZmZmZm9obq9MfV2YD8wAVzQYd4x4CrgQeBq4Jxy+jIgJO0Czgbuj4g7KrfYzMzMzMzml+wz/fNAV492RsRxYDtwb0R0epJxDbBe0pPAYopOHBSdvw8B15U/Pyrp0iqNlbRW0j5J+7bcs61KqJmZmZmZWd+pkrqlUb5mFBGHKG5PRNIy4MqyaBx4LCJeKMseAc4HvthtAyJiM7AZepuJxczMzMzM7I0w6znMJS0tf9aAW4A7y6JdwApJi8oEHx8Gnp3t+s3MzMzMbI5pNHr36jPpDpmkbcBeYLmkcUk3lEWjkg4Dh4CjwF0AEfESsBF4AjgA7I+Ih8tl3SFpHFhULuu2bLvMzMzMzMzmChWZ7eeeyW9/rXrDBxIDSiYGkwaIU8erB9Vyg1Cm0ndmH5zs0cDQAPHKSz2ri3r1ASWjkRho/HvHKscAaKj6gLcxUX2wy7QeDgzN93IDDacG1z6V24bxYqKNjeS5uJ44/mu9y2hb/2Zyn88MDH1WYmBoIE4ljuVXc98N9RcT+/xU9X3j5LeqVwMw9Wr1c/zkqdx3V6NefT88cWI4Vde3Ty6qHHM8OTD0Pw1V34ZKHv6Zb+T0wNC16t+Tk+mBoasPvHwicgNDf7de/Zh8pZ4b5DkzOPRQ8m/DQ8eemHOpyyeOPtOzTsnwz76vr7ZP7/66Nqug3ztjZjY/ZDpjWanOmJnNC5nOmJ05ZuyQqfC4pCuapq2WtFPSVknHJI21xKyUtFfS05J2SFrSVLaiLHumLF9YPlP2sKRD5fQ/mv3VNDMzMzOzvuVnyNqL4n7GdcDGsvM0AmwA1gN3A5e3CdsC3BwR5wEPADcClIk87gPWRcT7gEuA0/+a/GREvAf4eeAXmjuAZmZmZmZm81XHhyoiYkzSDuAmYAS4JyKOAEckndsmZDmwp3y/myK74q0UqfAPRsRT5XJPP2RxAvhSOW1C0n7gndkVMjMzMzOzOcYDQ3d0O/DrwBXAHR3mHQOuKt9fDZxTvl8GhKRdkvZL+q3WQEk/AfxrKoxNZmZmZmZmNld11SGLiOPAduDeiI6pb9YA6yU9CSwGJsrpg8CHgOvKnx+VdOnpoPKWxm3An0TEc+0WLGmtpH2S9m25d3s3TTczMzMzs37XqPfu1Weq5IFulK8ZRcQhitsTkbQMuLIsGgcei4gXyrJHgPP54dWwzcDXI+JTMyx7czlfLu29mZmZmZlZH5n1tPeSlpY/a8AtwJ1l0S5gRZlVcRD4MPBsOe8fAG8F/t/Zbo+ZmZmZmfW5aPTu1WfSHTJJ24C9wHJJ45JuKItGJR0GDgFHgbsAIuIlYCPwBHAA2B8RD0t6J/A7wHuB/ZIOSPr36TUyMzMzMzObI7q+ZTEibmv5fXSa+TYBm6Ypu48i9X3ztHGgr0bLNjMzMzOzHurD8cF6pcozZH0lJic6z9Qi1esbGMpEobMWVw+qT6Xqih5eei3uRK0mMus1fFb1mKzk9tNkp/w2bYy8NVVXZj/UwpFcXZn1ykp8zumHRwcTp7tTJ1JVpc412S+iTFxyn49G9a1fO5ncnwYGKodowXD1ehZW/y4B0KlT1YMmkuf4evXPa+gHue1eG+zd49mNevUjZWgy9zB+7WQqLCVzdNWS/5ZOHJJk0xlk1quRPGPXE7VlYiDXxuhRDEAjnDLhTDDjX9cqPN48ULOk1ZJ2Stoq6ZiksZaYlZL2Snpa0g5JS5rKVpRlz5TlC8vpOyU9VU6/U1L1b2IzMzMzM5uTIho9e/WbGTtkERHAOmCjpIWSRoANwHrgbuDyNmFbgJsj4jzgAeBGeC2t/X3Auoh4H3AJMFnGrI6IlcD7gbMpxi8zMzMzMzOb1zrewxMRY5J2ADcBI8A9EXEEOCLp3DYhy4E95fvdFNkVb6VIhX8wIp4ql/tiUx0/aGrPMD/GXUlmZmZmZmZzRbcPVdwO7KcY5PmCDvOOAVcBD1Jc6TqnnL4MCEm7KK6C3R8Rd5wOKqd/EPgr4PPdroCZmZmZmc1xZ3BSj64yNETEcWA7cG9EdHpSeA2wXtKTwGKKThwUnb8PAdeVPz8q6dKmOn4F+BlgAfBL7RYsaa2kfZL2bfms+2xmZmZmZja3VUk71qCLJDsRcYji9kQkLQOuLIvGgcci4oWy7BHgfOCLTbGnJD0EfITidsfWZW8GNgNMjD/t2xrNzMzMzOaDPky20SvpgaGnI2lp+bMG3ALcWRbtAlZIWlQm+Pgw8Kykt0j6mTJmEPhVikGlzczMzMzM5rV0h0zSNmAvsFzSuKQbyqJRSYcpOlVHgbsAIuIlYCPwBHAA2B8RD1MkCnlI0kHgKeAYP+zEmZmZmZnZfNeo9+7VZ7q+ZTEibmv5fXSa+TYBm6Ypu48i9X3ztG8D/6LbdpiZmZmZmc0XVZ4h6ytx4vvVgxYsqhyisxZXrweglhjbuj6Vq6uHWWkic021Ptl5nla1Wb+bdnrZzZdp48BQqioNLqgcE5H8D5AS65W973sgcQqa6pRXqL3UNhwcTtWVOiaz2zBTV/KcoXpinzp5MlUXg4l9Y6j68aV0+6qf43UqcS4EmKz+3TCwaKLzTG1Vfzx7cCq3PzXqqhwzcDK57/ZwNJ1ePgVTq74JaSRiACKxDevJ7Z7ZhsXQudXVE+feRqKuevI7eSDznTxX+RkyMzMzMzMz67UZO2QqPC7piqZpqyXtlLRV0jFJYy0xKyXtlfS0pB2SljSVrSjLninLF7bEPtS6PDMzMzMzm+cajd69+syMHbIorv+uAzZKWihpBNgArAfuBi5vE7YFuDkizgMeAG6E1zIo3gesi4j3AZcAr92/IenXgFd+zPUxMzMzMzObMzrepB8RY5J2ADdRZES8JyKOAEckndsmZDmwp3y/myLd/a0UY5MdjIinyuW+eDpA0luA/wisBT6XXRkzMzMzM5uDzuBnyLp9avp2YD8wAVzQYd4x4CrgQeBq4Jxy+jIgJO0Czgb
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1152x720 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"year_df = df.iloc[:,10:]\n",
|
|||
|
|
"fig, ax = plt.subplots(figsize=(16,10))\n",
|
|||
|
|
"sns.heatmap(year_df.corr(), ax=ax)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "43e1af94-ba07-4b95-8da3-1d774db940cd",
|
|||
|
|
"_uuid": "70d2b0a7db9b8a5535b3c5b3c2eb927b904bf6d3"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"So, we gather that a given year's production is more similar to its immediate previous and immediate following years."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "58cde27d-5ddc-4ebe-a8e1-80a8257f44c1",
|
|||
|
|
"_uuid": "6f48b52c09ea6a207644044cace5a88c983bf316"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
|
|||
|
|
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 720x720 with 4 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10,10))\n",
|
|||
|
|
"ax1.set(xlabel='Y1968', ylabel='Y1961')\n",
|
|||
|
|
"ax2.set(xlabel='Y1968', ylabel='Y1963')\n",
|
|||
|
|
"ax3.set(xlabel='Y1968', ylabel='Y1986')\n",
|
|||
|
|
"ax4.set(xlabel='Y1968', ylabel='Y2013')\n",
|
|||
|
|
"sns.jointplot(x=\"Y1968\", y=\"Y1961\", data=df, kind=\"reg\", ax=ax1)\n",
|
|||
|
|
"sns.jointplot(x=\"Y1968\", y=\"Y1963\", data=df, kind=\"reg\", ax=ax2)\n",
|
|||
|
|
"sns.jointplot(x=\"Y1968\", y=\"Y1986\", data=df, kind=\"reg\", ax=ax3)\n",
|
|||
|
|
"sns.jointplot(x=\"Y1968\", y=\"Y2013\", data=df, kind=\"reg\", ax=ax4)\n",
|
|||
|
|
"plt.close(2)\n",
|
|||
|
|
"plt.close(3)\n",
|
|||
|
|
"plt.close(4)\n",
|
|||
|
|
"plt.close(5)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "8a297a06-977f-4ff7-a9ad-c7e8804930a8",
|
|||
|
|
"_uuid": "6b738ce8b15a764fab90fac96f9534f94c14342e"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"# Heatmap of production of food items over years\n",
|
|||
|
|
"\n",
|
|||
|
|
"This will detect the items whose production has drastically increased over the years"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "588cebd9-e97c-460d-8ed5-e663ac293711",
|
|||
|
|
"_uuid": "16ce47d43a3038874a74d8bbb9a2e26f6ee54437"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 864x1728 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"new_item_df = item_df.drop([\"Item_Name\",\"Sum\",\"Production_Rank\"], axis = 1)\n",
|
|||
|
|
"fig, ax = plt.subplots(figsize=(12,24))\n",
|
|||
|
|
"sns.heatmap(new_item_df,ax=ax)\n",
|
|||
|
|
"ax.set_yticklabels(item_df.Item_Name.values[::-1])\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "825620f9-7ab5-4fe2-9529-c4f1a300138e",
|
|||
|
|
"_uuid": "5c42595537332ea71089d8c3dc041d3bf7d41b55"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"There is considerable growth in production of Palmkernel oil, Meat/Aquatic animals, ricebran oil, cottonseed, seafood, offals, roots, poultry meat, mutton, bear, cocoa, coffee and soyabean oil.\n",
|
|||
|
|
"There has been exceptional growth in production of onions, cream, sugar crops, treenuts, butter/ghee and to some extent starchy roots."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "80428f51-2fd4-468d-9530-9279215b4218",
|
|||
|
|
"_uuid": "4c9bb27cd76099c5348243a99448c509ef0c5ded"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Now, we look at clustering."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "a3f1db3a-1b82-4e42-8e7d-f1a26915693b",
|
|||
|
|
"_uuid": "da167de5a5b92e164fc6993b32ebbfab4ef9a6e3",
|
|||
|
|
"collapsed": true
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"# What is clustering?\n",
|
|||
|
|
"Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression, and computer graphics."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "136315a0-b37d-4d89-bd0d-037727062c34",
|
|||
|
|
"_uuid": "04ab802ec92eaf6a27706f2008933dcf3865855a"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"# Today, we will form clusters to classify countries based on productivity scale"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "27ba0b5d-c57e-485d-9588-017e16fe1904",
|
|||
|
|
"_uuid": "659afdada04e8854765b5e7208394915b30f859a"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"For this, we will use k-means clustering algorithm.\n",
|
|||
|
|
"# K-means clustering\n",
|
|||
|
|
"(Source [Wikipedia](https://en.wikipedia.org/wiki/K-means_clustering#Standard_algorithm) )\n",
|
|||
|
|
""
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "7aeb3175-33bd-4f49-903a-57d43380e90e",
|
|||
|
|
"_uuid": "6b0b4881e623ed3c133b68b98e6fb6755e18fd78"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"This is the data we will use."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "a5b99ea8-975f-4467-9895-bffe1db876eb",
|
|||
|
|
"_uuid": "57aba4000bfc422e848b14ad24b02a570d6c0554"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Y1961</th>\n",
|
|||
|
|
" <th>Y1962</th>\n",
|
|||
|
|
" <th>Y1963</th>\n",
|
|||
|
|
" <th>Y1964</th>\n",
|
|||
|
|
" <th>Y1965</th>\n",
|
|||
|
|
" <th>Y1966</th>\n",
|
|||
|
|
" <th>Y1967</th>\n",
|
|||
|
|
" <th>Y1968</th>\n",
|
|||
|
|
" <th>Y1969</th>\n",
|
|||
|
|
" <th>Y1970</th>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <th>Y2006</th>\n",
|
|||
|
|
" <th>Y2007</th>\n",
|
|||
|
|
" <th>Y2008</th>\n",
|
|||
|
|
" <th>Y2009</th>\n",
|
|||
|
|
" <th>Y2010</th>\n",
|
|||
|
|
" <th>Y2011</th>\n",
|
|||
|
|
" <th>Y2012</th>\n",
|
|||
|
|
" <th>Y2013</th>\n",
|
|||
|
|
" <th>Mean_Produce</th>\n",
|
|||
|
|
" <th>Rank</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Afghanistan</th>\n",
|
|||
|
|
" <td>9481.0</td>\n",
|
|||
|
|
" <td>9414.0</td>\n",
|
|||
|
|
" <td>9194.0</td>\n",
|
|||
|
|
" <td>10170.0</td>\n",
|
|||
|
|
" <td>10473.0</td>\n",
|
|||
|
|
" <td>10169.0</td>\n",
|
|||
|
|
" <td>11289.0</td>\n",
|
|||
|
|
" <td>11508.0</td>\n",
|
|||
|
|
" <td>11815.0</td>\n",
|
|||
|
|
" <td>10454.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>18317.0</td>\n",
|
|||
|
|
" <td>19248.0</td>\n",
|
|||
|
|
" <td>19381.0</td>\n",
|
|||
|
|
" <td>20661.0</td>\n",
|
|||
|
|
" <td>21030.0</td>\n",
|
|||
|
|
" <td>21100.0</td>\n",
|
|||
|
|
" <td>22706.0</td>\n",
|
|||
|
|
" <td>23007.0</td>\n",
|
|||
|
|
" <td>13003.056604</td>\n",
|
|||
|
|
" <td>69.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Albania</th>\n",
|
|||
|
|
" <td>1706.0</td>\n",
|
|||
|
|
" <td>1749.0</td>\n",
|
|||
|
|
" <td>1767.0</td>\n",
|
|||
|
|
" <td>1889.0</td>\n",
|
|||
|
|
" <td>1884.0</td>\n",
|
|||
|
|
" <td>1995.0</td>\n",
|
|||
|
|
" <td>2046.0</td>\n",
|
|||
|
|
" <td>2169.0</td>\n",
|
|||
|
|
" <td>2230.0</td>\n",
|
|||
|
|
" <td>2395.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>6911.0</td>\n",
|
|||
|
|
" <td>6744.0</td>\n",
|
|||
|
|
" <td>7168.0</td>\n",
|
|||
|
|
" <td>7316.0</td>\n",
|
|||
|
|
" <td>7907.0</td>\n",
|
|||
|
|
" <td>8114.0</td>\n",
|
|||
|
|
" <td>8221.0</td>\n",
|
|||
|
|
" <td>8271.0</td>\n",
|
|||
|
|
" <td>4475.509434</td>\n",
|
|||
|
|
" <td>104.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Algeria</th>\n",
|
|||
|
|
" <td>7488.0</td>\n",
|
|||
|
|
" <td>7235.0</td>\n",
|
|||
|
|
" <td>6861.0</td>\n",
|
|||
|
|
" <td>7255.0</td>\n",
|
|||
|
|
" <td>7509.0</td>\n",
|
|||
|
|
" <td>7536.0</td>\n",
|
|||
|
|
" <td>7986.0</td>\n",
|
|||
|
|
" <td>8839.0</td>\n",
|
|||
|
|
" <td>9003.0</td>\n",
|
|||
|
|
" <td>9355.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>51067.0</td>\n",
|
|||
|
|
" <td>49933.0</td>\n",
|
|||
|
|
" <td>50916.0</td>\n",
|
|||
|
|
" <td>57505.0</td>\n",
|
|||
|
|
" <td>60071.0</td>\n",
|
|||
|
|
" <td>65852.0</td>\n",
|
|||
|
|
" <td>69365.0</td>\n",
|
|||
|
|
" <td>72161.0</td>\n",
|
|||
|
|
" <td>28879.490566</td>\n",
|
|||
|
|
" <td>38.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Angola</th>\n",
|
|||
|
|
" <td>4834.0</td>\n",
|
|||
|
|
" <td>4775.0</td>\n",
|
|||
|
|
" <td>5240.0</td>\n",
|
|||
|
|
" <td>5286.0</td>\n",
|
|||
|
|
" <td>5527.0</td>\n",
|
|||
|
|
" <td>5677.0</td>\n",
|
|||
|
|
" <td>5833.0</td>\n",
|
|||
|
|
" <td>5685.0</td>\n",
|
|||
|
|
" <td>6219.0</td>\n",
|
|||
|
|
" <td>6460.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>28247.0</td>\n",
|
|||
|
|
" <td>29877.0</td>\n",
|
|||
|
|
" <td>32053.0</td>\n",
|
|||
|
|
" <td>36985.0</td>\n",
|
|||
|
|
" <td>38400.0</td>\n",
|
|||
|
|
" <td>40573.0</td>\n",
|
|||
|
|
" <td>38064.0</td>\n",
|
|||
|
|
" <td>48639.0</td>\n",
|
|||
|
|
" <td>13321.056604</td>\n",
|
|||
|
|
" <td>68.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>Antigua and Barbuda</th>\n",
|
|||
|
|
" <td>92.0</td>\n",
|
|||
|
|
" <td>94.0</td>\n",
|
|||
|
|
" <td>105.0</td>\n",
|
|||
|
|
" <td>95.0</td>\n",
|
|||
|
|
" <td>84.0</td>\n",
|
|||
|
|
" <td>73.0</td>\n",
|
|||
|
|
" <td>64.0</td>\n",
|
|||
|
|
" <td>59.0</td>\n",
|
|||
|
|
" <td>68.0</td>\n",
|
|||
|
|
" <td>77.0</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>110.0</td>\n",
|
|||
|
|
" <td>122.0</td>\n",
|
|||
|
|
" <td>115.0</td>\n",
|
|||
|
|
" <td>114.0</td>\n",
|
|||
|
|
" <td>115.0</td>\n",
|
|||
|
|
" <td>118.0</td>\n",
|
|||
|
|
" <td>113.0</td>\n",
|
|||
|
|
" <td>119.0</td>\n",
|
|||
|
|
" <td>83.886792</td>\n",
|
|||
|
|
" <td>172.0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>5 rows × 55 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Y1961 Y1962 Y1963 Y1964 Y1965 Y1966 \\\n",
|
|||
|
|
"Afghanistan 9481.0 9414.0 9194.0 10170.0 10473.0 10169.0 \n",
|
|||
|
|
"Albania 1706.0 1749.0 1767.0 1889.0 1884.0 1995.0 \n",
|
|||
|
|
"Algeria 7488.0 7235.0 6861.0 7255.0 7509.0 7536.0 \n",
|
|||
|
|
"Angola 4834.0 4775.0 5240.0 5286.0 5527.0 5677.0 \n",
|
|||
|
|
"Antigua and Barbuda 92.0 94.0 105.0 95.0 84.0 73.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Y1967 Y1968 Y1969 Y1970 ... Y2006 \\\n",
|
|||
|
|
"Afghanistan 11289.0 11508.0 11815.0 10454.0 ... 18317.0 \n",
|
|||
|
|
"Albania 2046.0 2169.0 2230.0 2395.0 ... 6911.0 \n",
|
|||
|
|
"Algeria 7986.0 8839.0 9003.0 9355.0 ... 51067.0 \n",
|
|||
|
|
"Angola 5833.0 5685.0 6219.0 6460.0 ... 28247.0 \n",
|
|||
|
|
"Antigua and Barbuda 64.0 59.0 68.0 77.0 ... 110.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Y2007 Y2008 Y2009 Y2010 Y2011 Y2012 \\\n",
|
|||
|
|
"Afghanistan 19248.0 19381.0 20661.0 21030.0 21100.0 22706.0 \n",
|
|||
|
|
"Albania 6744.0 7168.0 7316.0 7907.0 8114.0 8221.0 \n",
|
|||
|
|
"Algeria 49933.0 50916.0 57505.0 60071.0 65852.0 69365.0 \n",
|
|||
|
|
"Angola 29877.0 32053.0 36985.0 38400.0 40573.0 38064.0 \n",
|
|||
|
|
"Antigua and Barbuda 122.0 115.0 114.0 115.0 118.0 113.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" Y2013 Mean_Produce Rank \n",
|
|||
|
|
"Afghanistan 23007.0 13003.056604 69.0 \n",
|
|||
|
|
"Albania 8271.0 4475.509434 104.0 \n",
|
|||
|
|
"Algeria 72161.0 28879.490566 38.0 \n",
|
|||
|
|
"Angola 48639.0 13321.056604 68.0 \n",
|
|||
|
|
"Antigua and Barbuda 119.0 83.886792 172.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[5 rows x 55 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"new_df.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "66964df2-892d-4e55-a4b1-f94d10e4c7dd",
|
|||
|
|
"_uuid": "19bdd89a3ad9df962959ad6b996946f6f3916d58"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: convert_objects is deprecated. To re-infer data dtypes for object columns, use DataFrame.infer_objects()\n",
|
|||
|
|
"For all other conversions use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
|
|||
|
|
" after removing the cwd from sys.path.\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"X = new_df.iloc[:,:-2].values\n",
|
|||
|
|
"\n",
|
|||
|
|
"X = pd.DataFrame(X)\n",
|
|||
|
|
"X = X.convert_objects(convert_numeric=True)\n",
|
|||
|
|
"X.columns = year_list"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "461e5bcc-0101-4ea1-ae13-20600f883929",
|
|||
|
|
"_uuid": "0d3e50235c9505ebc255053d4a5aae547fc17d8d"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"# Elbow method to select number of clusters\n",
|
|||
|
|
"This method looks at the percentage of variance explained as a function of the number of clusters: One should choose a number of clusters so that adding another cluster doesn't give much better modeling of the data. More precisely, if one plots the percentage of variance explained by the clusters against the number of clusters, the first clusters will add much information (explain a lot of variance), but at some point the marginal gain will drop, giving an angle in the graph. The number of clusters is chosen at this point, hence the \"elbow criterion\". This \"elbow\" cannot always be unambiguously identified. Percentage of variance explained is the ratio of the between-group variance to the total variance, also known as an F-test. A slight variation of this method plots the curvature of the within group variance.\n",
|
|||
|
|
"# Basically, number of clusters = the x-axis value of the point that is the corner of the \"elbow\"(the plot looks often looks like an elbow)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "06271223-bd32-48ac-a373-6c1e6bbf7c7b",
|
|||
|
|
"_uuid": "c57d7277510a8c11fdc3d311e4d8a22539617ed9"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.cluster import KMeans\n",
|
|||
|
|
"wcss = []\n",
|
|||
|
|
"for i in range(1,11):\n",
|
|||
|
|
" kmeans = KMeans(n_clusters=i,init='k-means++',max_iter=300,n_init=10,random_state=0)\n",
|
|||
|
|
" kmeans.fit(X)\n",
|
|||
|
|
" wcss.append(kmeans.inertia_)\n",
|
|||
|
|
"plt.plot(range(1,11),wcss)\n",
|
|||
|
|
"plt.title('The Elbow Method')\n",
|
|||
|
|
"plt.xlabel('Number of clusters')\n",
|
|||
|
|
"plt.ylabel('WCSS')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "ad4bc40a-9540-497d-95e3-3fee6088ea95",
|
|||
|
|
"_uuid": "6450dd1c3d7a8114931dc358d2f09a0424b52fd7"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"As the elbow corner coincides with x=2, we will have to form **2 clusters**. Personally, I would have liked to select 3 to 4 clusters. But trust me, only selecting 2 clusters can lead to best results.\n",
|
|||
|
|
"Now, we apply k-means algorithm."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "eed3f672-e089-4dbb-aad8-b9618967abf3",
|
|||
|
|
"_uuid": "d92d758ee7213ddcd84e9b8b2f61c9e260ed6ba2"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
|
|||
|
|
" after removing the cwd from sys.path.\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"kmeans = KMeans(n_clusters=2,init='k-means++',max_iter=300,n_init=10,random_state=0) \n",
|
|||
|
|
"y_kmeans = kmeans.fit_predict(X)\n",
|
|||
|
|
"\n",
|
|||
|
|
"X = X.as_matrix(columns=None)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "ef07bd6d-679d-4375-b7b3-abeca3421e37",
|
|||
|
|
"_uuid": "6f93a4bd3f17427f4b2dbe08af8e015a1e4a2f89"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Now, let's visualize the results."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "5a7fe139-13df-453b-8c16-891929bc595e",
|
|||
|
|
"_uuid": "a57e0a38f4c0f0385be75fd9f71d4a2d8213aea3"
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0,1],s=100,c='red',label='Others')\n",
|
|||
|
|
"plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1,1],s=100,c='blue',label='China(mainland),USA,India')\n",
|
|||
|
|
"plt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s=300,c='yellow',label='Centroids')\n",
|
|||
|
|
"plt.title('Clusters of countries by Productivity')\n",
|
|||
|
|
"plt.legend()\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "923d4536-2bce-4b99-b98a-33b801a56a8b",
|
|||
|
|
"_uuid": "fe531e8c41eec0eb5dc52a9890871841f5d27211"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"So, the blue cluster represents China(Mainland), USA and India while the red cluster represents all the other countries.\n",
|
|||
|
|
"This result was highly probable. Just take a look at the plot of cell 3 above. See how China, USA and India stand out. That has been observed here in clustering too.\n",
|
|||
|
|
"\n",
|
|||
|
|
"You should try this algorithm for 3 or 4 clusters. Looking at the distribution, you will realise why 2 clusters is the best choice for the given data"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"_cell_guid": "6dee7acb-0f08-4ae1-85b4-f4704026694a",
|
|||
|
|
"_uuid": "179a1ede21ae330664a0b7c63e36574acdc0428c"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"This is not the end! More is yet to come."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"**Now, lets try to predict the production using regression for 2020. We will predict the production for USA,India and Pakistan.**\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"needs_background": "light"
|
|||
|
|
},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"ename": "ValueError",
|
|||
|
|
"evalue": "Expected 2D array, got scalar array instead:\narray=2020.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.",
|
|||
|
|
"output_type": "error",
|
|||
|
|
"traceback": [
|
|||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|||
|
|
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"\u001b[0;32m<ipython-input-24-da7cfa1c86d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2020\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mArea\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'India'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mElement\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'Food'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Y1961'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0mReturns\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 212\u001b[0m \"\"\"\n\u001b[0;32m--> 213\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[0m_preprocess_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstaticmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36m_decision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 197\u001b[0m return safe_sparse_dot(X, self.coef_.T,\n\u001b[1;32m 198\u001b[0m dense_output=True) + self.intercept_\n",
|
|||
|
|
"\u001b[0;32m/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m 543\u001b[0m \u001b[0;34m\"Reshape your data either using array.reshape(-1, 1) if \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 544\u001b[0m \u001b[0;34m\"your data has a single feature or array.reshape(1, -1) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 545\u001b[0;31m \"if it contains a single sample.\".format(array))\n\u001b[0m\u001b[1;32m 546\u001b[0m \u001b[0;31m# If input is 1D raise error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 547\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|||
|
|
"\u001b[0;31mValueError\u001b[0m: Expected 2D array, got scalar array instead:\narray=2020.\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample."
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"india_list=[]\n",
|
|||
|
|
"year_list = list(df.iloc[:,10:].columns)\n",
|
|||
|
|
"for i in year_list:\n",
|
|||
|
|
" x=df[(df.Area=='India') & (df.Element=='Food')][i].mean()\n",
|
|||
|
|
" india_list.append(x) \n",
|
|||
|
|
"\n",
|
|||
|
|
"reset=[]\n",
|
|||
|
|
"for i in year_list:\n",
|
|||
|
|
" reset.append(int(i[1:]))\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"reset=np.array(reset)\n",
|
|||
|
|
"reset=reset.reshape(-1,1)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"india_list=np.array(india_list)\n",
|
|||
|
|
"india_list=india_list.reshape(-1,1)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"reg = LinearRegression()\n",
|
|||
|
|
"reg.fit(reset,india_list)\n",
|
|||
|
|
"predictions = reg.predict(reset)\n",
|
|||
|
|
"plt.title(\"India\")\n",
|
|||
|
|
"plt.xlabel(\"Year\")\n",
|
|||
|
|
"plt.ylabel(\"Production\")\n",
|
|||
|
|
"plt.scatter(reset,india_list)\n",
|
|||
|
|
"plt.plot(reset,predictions)\n",
|
|||
|
|
"plt.show()\n",
|
|||
|
|
"print(reg.predict(2020))\n",
|
|||
|
|
"\n",
|
|||
|
|
"df[(df.Area=='India') & (df.Element=='Food')]['Y1961'].mean()\n",
|
|||
|
|
"\n",
|
|||
|
|
"df[(df.Area=='Pakistan') & (df.Element=='Food')]\n",
|
|||
|
|
"\n",
|
|||
|
|
"Pak_list=[]\n",
|
|||
|
|
"year_list = list(df.iloc[:,10:].columns)\n",
|
|||
|
|
"for i in year_list:\n",
|
|||
|
|
" yx=df[(df.Area=='Pakistan') & (df.Element=='Food')][i].mean()\n",
|
|||
|
|
" Pak_list.append(yx) \n",
|
|||
|
|
"\n",
|
|||
|
|
"Pak_list=np.array(Pak_list)\n",
|
|||
|
|
"Pak_list=Pak_list.reshape(-1,1)\n",
|
|||
|
|
"Pak_list\n",
|
|||
|
|
"reg = LinearRegression()\n",
|
|||
|
|
"reg.fit(reset,Pak_list)\n",
|
|||
|
|
"predictions = reg.predict(reset)\n",
|
|||
|
|
"plt.title(\"Pakistan\")\n",
|
|||
|
|
"plt.xlabel(\"Year\")\n",
|
|||
|
|
"plt.ylabel(\"Production\")\n",
|
|||
|
|
"plt.scatter(reset,Pak_list)\n",
|
|||
|
|
"plt.plot(reset,predictions)\n",
|
|||
|
|
"plt.show()\n",
|
|||
|
|
"print(reg.predict(2020))\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"usa_list=[]\n",
|
|||
|
|
"year_list = list(df.iloc[:,10:].columns)\n",
|
|||
|
|
"for i in year_list:\n",
|
|||
|
|
" xu=df[(df.Area=='United States of America') & (df.Element=='Food')][i].mean()\n",
|
|||
|
|
" usa_list.append(xu)\n",
|
|||
|
|
"\n",
|
|||
|
|
"usa_list=np.array(usa_list)\n",
|
|||
|
|
"usa_list=india_list.reshape(-1,1)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"reg = LinearRegression()\n",
|
|||
|
|
"reg.fit(reset,usa_list)\n",
|
|||
|
|
"predictions = reg.predict(reset)\n",
|
|||
|
|
"plt.title(\"USA\")\n",
|
|||
|
|
"plt.xlabel(\"Year\")\n",
|
|||
|
|
"plt.ylabel(\"Production\")\n",
|
|||
|
|
"plt.scatter(reset,usa_list)\n",
|
|||
|
|
"plt.plot(reset,predictions)\n",
|
|||
|
|
"plt.show()\n",
|
|||
|
|
"print(reg.predict(2020))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "Python 3",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.7.1"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 1
|
|||
|
|
}
|