Project_Assignment_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Import Successful\n"
     ]
    }
   ],
   "source": [
    "# Importing the necessary libraries \n",
    "import os \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "print(\"Import Successful\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Python_Edureka\\\\projects\\\\Project_2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the working directory to where the dataset resides\n",
    "os.chdir(\"F:\\Python_Edureka\\projects\\Project_2\")\n",
    "\n",
    "# Verify the set working directory\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1)Read the data fileand check for any missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>year</th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>436</td>\n",
       "      <td>429</td>\n",
       "      <td>422</td>\n",
       "      <td>415</td>\n",
       "      <td>407</td>\n",
       "      <td>397</td>\n",
       "      <td>397</td>\n",
       "      <td>387</td>\n",
       "      <td>374</td>\n",
       "      <td>373</td>\n",
       "      <td>346</td>\n",
       "      <td>326</td>\n",
       "      <td>304</td>\n",
       "      <td>308</td>\n",
       "      <td>283</td>\n",
       "      <td>267</td>\n",
       "      <td>251</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>50</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>53</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>American Samoa</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year            1990  1991  1992  1993  1994  1995  1996  1997  1998  1999  \\\n",
       "country                                                                      \n",
       "Afghanistan      436   429   422   415   407   397   397   387   374   373   \n",
       "Albania           42    40    41    42    42    43    42    44    43    42   \n",
       "Algeria           45    44    44    43    43    42    43    44    45    46   \n",
       "American Samoa    42    14     4    18    17    22     0    25    12     8   \n",
       "Andorra           39    37    35    33    32    30    28    23    24    22   \n",
       "\n",
       "year            2000  2001  2002  2003  2004  2005  2006  2007  \n",
       "country                                                         \n",
       "Afghanistan      346   326   304   308   283   267   251   238  \n",
       "Albania           40    34    32    32    29    29    26    22  \n",
       "Algeria           48    49    50    51    52    53    55    56  \n",
       "American Samoa     8     6     5     6     9    11     9     5  \n",
       "Andorra           20    20    21    18    19    18    17    19  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the data file into a Pandas Dataframe\n",
    "food_data = pd.read_csv(\"Project_Data_1.csv\", index_col = 0, thousands = \",\")\n",
    "data = food_data.copy()\n",
    "food_data.index.names = [\"country\"]\n",
    "food_data.columns.names = [\"year\"]\n",
    "\n",
    "# Display the first five rows of the data\n",
    "food_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values in the data \n",
    "food_data.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.03110452,  1.01641938,  1.02196785, ...,  0.45473638,\n",
       "         0.38936941,  0.33411829],\n",
       "       [-0.66846748, -0.68394081, -0.67693899, ..., -0.67479082,\n",
       "        -0.67927409, -0.68603309],\n",
       "       [-0.65552658, -0.66645639, -0.66356177, ..., -0.56088892,\n",
       "        -0.54153782, -0.52545371],\n",
       "       ...,\n",
       "       [ 0.29347302,  0.28207359,  0.31297523, ..., -0.16223226,\n",
       "        -0.16157569, -0.1759574 ],\n",
       "       [ 1.03110452,  1.13443924,  1.34302111, ...,  1.33747613,\n",
       "         1.20153847,  1.03783382],\n",
       "       [ 0.91463639,  0.96396611,  0.99075433, ...,  2.41479829,\n",
       "         2.51715736,  2.58222966]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_be_scaled = food_data.iloc[:, 0:].values\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "food_data = scaler.fit_transform(to_be_scaled)\n",
    "food_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.92039771 0.05704903]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components = 2)\n",
    "principal_components = pca.fit_transform(food_data)\n",
    "print(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pca1</th>\n",
       "      <th>pca2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales of Wheat in tons</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.498735</td>\n",
       "      <td>0.975856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>-2.936103</td>\n",
       "      <td>0.012171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>-2.725665</td>\n",
       "      <td>-0.185039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>American Samoa</td>\n",
       "      <td>-3.445596</td>\n",
       "      <td>0.005623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>-3.181315</td>\n",
       "      <td>0.033922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            pca1      pca2\n",
       "Sales of Wheat in tons                    \n",
       "Afghanistan             3.498735  0.975856\n",
       "Albania                -2.936103  0.012171\n",
       "Algeria                -2.725665 -0.185039\n",
       "American Samoa         -3.445596  0.005623\n",
       "Andorra                -3.181315  0.033922"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = pd.DataFrame(principal_components, columns = [\"pca1\", \"pca2\"])\n",
    "new_data.index = data.index\n",
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 3641.966555746667, 2: 927.1243171671338, 3: 551.956791265397, 4: 429.3482312069803, 5: 332.3892108100146, 6: 258.6845648183441, 7: 218.42479802348674, 8: 171.33594077985504, 9: 140.90901671607713}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "sse = {}\n",
    "for k in range(1,10):\n",
    "    kmeans = KMeans(n_clusters = k, max_iter = 1000).fit(new_data)\n",
    "    sse[k] = kmeans.inertia_\n",
    "print(sse)\n",
    "plt.figure(figsize = (15, 6))\n",
    "plt.plot(list(sse.keys()), list(sse.values()))\n",
    "plt.xlabel(\"Number of Clusters\")\n",
    "plt.ylabel(\"SSE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km = KMeans(n_clusters = 3)\n",
    "km.fit(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.47393975  0.05330768]\n",
      " [ 3.94539763 -0.35411163]\n",
      " [ 9.47222647  0.6368249 ]]\n"
     ]
    }
   ],
   "source": [
    "centers = km.cluster_centers_\n",
    "print(centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 2 1 0 1 0 0 0 0 1 1 2 0 0 1 0\n",
      " 1 1 0 1 0 0 1 0 0 0 0 0 0 1 2 1 0 2 0 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0\n",
      " 0 0 0 1 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 2 0 0 1 0 0 1 1 0 0 0 1 1 0 0\n",
      " 2 0 2 0 0 0 0 1 0 0 1 1 2 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 1 2 0 0 0 0\n",
      " 0 0 0 0 1 0 0 0 0 0 1 0 1 0 2 0 0 0 1 1 2 0 0 1 0 2 0 0 0 0 1 0 2 2 0 0 0\n",
      " 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 0 2 1]\n"
     ]
    }
   ],
   "source": [
    "new_labels = km.labels_\n",
    "print(new_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "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>pca1</th>\n",
       "      <th>pca2</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales of Wheat in tons</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.498735</td>\n",
       "      <td>0.975856</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>-2.936103</td>\n",
       "      <td>0.012171</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>-2.725665</td>\n",
       "      <td>-0.185039</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>American Samoa</td>\n",
       "      <td>-3.445596</td>\n",
       "      <td>0.005623</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>-3.181315</td>\n",
       "      <td>0.033922</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            pca1      pca2  labels\n",
       "Sales of Wheat in tons                            \n",
       "Afghanistan             3.498735  0.975856       1\n",
       "Albania                -2.936103  0.012171       0\n",
       "Algeria                -2.725665 -0.185039       0\n",
       "American Samoa         -3.445596  0.005623       0\n",
       "Andorra                -3.181315  0.033922       0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data[\"labels\"] = new_labels\n",
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data.sort_values([\"labels\", \"pca1\", \"pca2\"])\n",
    "new_data.to_csv(\"output.csv\", index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1080x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vwhf6inZgAKCW+VPz2dzazNrNtTRNk7Wba9nc2sz8qfm2SwNgyAlf6Csr11cyNTa1Z007MABwEOaOz2Xh9EKmO9O5unE1053pLJxesL0ZgOrMfKGvzByZyeraajrjnVtr2oEBgIMyd3xO2AJAz+l8oa9oBwYAAGDYCF/oK9qBAQAAGDa2HdF3tAMDAAAwTHS+AAAAAFQkfAEAAACoSPgCAAAAUJHwBQAAAKAi4QsAAABARcIXAAAAgIqELwAAAAAVCV8AAAAAKhK+AAAAAFQkfAEAAACoSPgCAAAAUJHwBQAAAKAi4QsAAABARcIXAAAAgIqELwAAAAAVCV8AAAAAKhpvuwAAGCVLl5ayeH4xK9dXMnNkJvOn5jN3fK7tsgAAqEjnCwB9benSUp76/FN5/Bcfz1OffypLl5baLum+LV1aytmXzmZ1bTVHJ49mdW01Z186O9D/TAAA3J3wBYC+NWxhxeL5xUyMTaQz3kkpJZ3xTibGJrJ4frHt0gAAqEj4AkDfGrawYuX6SqbGpvasTY1NZeX6SksVAQDQC2a+ANC3Vq6v5Ojk0T1rgxxWzByZyeraajrjnVtr61vrmTkys+fjzIUBABguOl8A6FszR2ayvrW+Z+12YcWgmD81n82tzazdXEvTNFm7uZbNrc3Mn5q/9THDttUKAADhCwB9bD9hxSCZOz6XhdMLme5M5+rG1Ux3prNwemFPV8uwbbUCAMC2IwD62NzxuSxkYai24Mwdn7tj/cO21QoAAOELAH3ubmHFsNnvXBgA9s8sLaBtth0BQB8Ztq1WAG0zSwvoB8IXAOgj+5kLA8D+maUF9APbjgCgz4zaViuAmszSAvqBzhcAAGBozRyZyfrW+p41s7SAXhO+AAAAQ8ssLaAfCF8AAIChZZYW0A/MfAEAAIaaWVpA23S+AAAAAFQkfAEAAACoSPgCAAAAUJGZLwAAMCSWLi1l8fxiVq6vZObITOZPzZt1AtAHdL4AAMAQWLq0lLMvnc3q2mqOTh7N6tpqzr50NkuXltouDWDkCV8AAGAILJ5fzMTYRDrjnZRS0hnvZGJsIovnF9suDWDkCV8AAGAIrFxfydTY1J61qbGprFxfaakiAHYIXwAAYAjMHJnJ+tb6nrX1rfXMHJlpqSIAdghfAABgCMyfms/m1mbWbq6laZqs3VzL5tZm5k/Nt10awMgTvgAAwBCYOz6XhdMLme5M5+rG1Ux3prNwesFpRwB9wFHTAAAwJOaOzwlbAPqQ8AUA6FtLl5ayeH4xK9dXMnNkJvOn5t1YAgADx7YjAKAvLV1aytmXzmZ1bTVHJ49mdW01Z186m6VLS22XBgBwT4QvAEBfWjy/mImxiXTGOymlpDPeycTYRBbPL7ZdGgDAPRG+AAB9aeX6SqbGpvasTY1NZeX6SksVAQDcH+ELANCXZo7MZH1rfc/a+tZ6Zo7MtFQRAMD9Eb4AwJBburSUpz7/VB7/xcfz1OefGpiZKfOn5rO5tZm1m2tpmiZrN9eyubWZ+VPzbZcGAHBPhC8AMMQGeWjt3PG5LJxeyHRnOlc3rma6M52F0wtOOwIA3qaUcv0u7/+2Uspv3+PnXCyl/NUHq6zLUdMAMMR2D61Ncuu6eH5xIEKMueNzA1EnAMCd3HfnSynlzEEWAgAcPENrAYBRUko5Ukr5lVLKvyil/FYp5ft3vXu8lPLzpZTfLKX8L6WUw9uv+a5Syj8vpfx6KeXzpZRvvc3n/alSyu9sv/bZe63rQbYd/ewDvBYA6AFDawGAEbOe5AeapvkLSb43yd8qpZTt931HkuebpvnzSa4m+eullIkkfzvJX22a5ruS/FySn9z9CUsp35zkB5Kc2n7tf3mvRd1x21Ep5TPv9K4k/9K9fjEAoLfmT83n7Etnk3Q7Xta31g2tBQCGWUlytpTywSRvJplJ8q7t973WNM3/vf33/2OS/yzJ55L8uSTntjOasSR/+JbPeTXdUOfvlVL+aZLP3mtRd5v5MpfkP0ry1sE1JckH7vWLAQC9NXd8LgtZyOL5xaxcX8nMkZnMn5o3RwUAGFb/YZLpJN/VNM1mKeX3k+zswW7e8rFNuvnG+aZpvvudPmHTNDdLKR9I8peT/GCSH07yl+6lqLuFL19KcqNpmn/+1neUUn73Xr4QANAOQ2sBgBHyTUkubwcv35vkxK73vbuU8t1N0/xakieT/F9JfjfJ9M769jakk03TnN95USnlSJLDTdP8cinlS0l+716LumP40jTNh+/wvg/e6xcDAAAAqOgfJPnfSynLSX4jycu73veVJB8ppTyX5P9J8neaptnYPk76vy2lfFO6Ocknk5zf9bqHkvxSKWUq3U6Zv3GvRZWmeWvXTf+ZnZ1tlpeX2y4DAAAAqKPc/UMG175OOyql/MVSypdLKddLKRullK1SytXaxQEAAAAMurvNfNnxM+kOlfnHSWaT/MdJ/pVaRQ2VC+eSL34quXIxOXYieezp5OSZtqsCAAAAemRfnS9J0jTN7yUZa5pmq2ma/z7d87K5kwvnkheeSa69nkw93L2+8Ex3HQAAABgJ+w1fbpRSJpP8Rinlvyql/I0k31ixruHwxU8lhyaTycNJKd3rocnuOgAAADAS9hu+/LXtj/3hJH+a5NEk/06toobGlYvJRGfv2kQnufJqO/UAAAAAPbffmS9/lGSjaZr1JP9FKWUsyTfUK2tIHDvR3Wo0efjra5trybF3t1cTAAAA0FP77Xz5lSS7EoR0kvyfB1/OkHns6eTNjWTjRtI03eubG911RteFc8niE8kn39+9mgEEQB9YurSUpz7/VB7/xcfz1OefytKlpbZLAoC+U0p5vJTyu6WU3yul/Oi+X9c0zX4++W80TfOdd1urZXZ2tlleXu7Flzp4t047erXb8eK0o9G2M4T50GR3C9rmWjeQ+/Czfi4AaM3SpaWcfelsJsYmMjU2lfWt9WxubWbh9ELmjs+1XR4Ao6Ec9Cf8th/9p48n+XiS9yT5apJP/P5P/Vufu9/Pt70L6EKSM0kuJflykiebpvmdu712v50vf1pK+Qu7vuBskrX7qHX0nDyTzH82+ZHf7F7dYI82Q5gB6EOL5xczMTaRzngnpZR0xjuZGJvI4vnFtksDgPuyHbx8Osm3Jvnj7eunt9fv1weS/F7TNK80TbOR5B8m+f79vHC/M19+JMk/LqX8QZImyZ9J8u/fT6Uw0q5c7B47vpshzAC0bOX6So5OHt2zNjU2lZXrKy1VBAAP7ONJvpbkxvbbN3at32/3y0yS13a9fSnJ6f28cL+dL7+V5O+mW/gfJXkuyfl7KBBIukOYN9/SNGYIMwAtmzkyk/Wt9T1r61vrmTky01JFAPDA3pOvBy47bmyv36/bbY26+yyX7D98+ftJviPJTyb520m+Pcn/sM/XAjsMYQagD82fms/m1mbWbq6laZqs3VzL5tZm5k/Nt10aANyvr2bvwUHZfvurD/A5LyV5dNfbx5P8wX5euN/w5TuapvlPmqb5Z9t/fTTJyXssEjh5pjtc96F3JetXulfDdgFo2dzxuSycXsh0ZzpXN65mujNt2C4Ag+4TSb4hXw9gDm+//YkH+JxfTvLtpZT3lFImk/xgks/s54X7Pe1oMcnfbZrmS9tvn07ykaZp/vp9l3wPBvq0IwAAAOBu+v60oyQppXxfkk8mGUvyc03T/OS+XrfP8OUr6W472pkK+u4kX0nyZpKmaZo/fz9F75fwBQAAAIbagYcv/WS/px09yFFMAAAAACNrX+FL0zQXaxcCAAAAMIz2O3AXAAAAgPsgfAEAAACoSPgCAAAAUJHwBQAAAKAi4QsAAADAPpRSfq6UcrmU8tv38rr9HjUNAAAAMDh+/JseT/LxJO9J8tUkn8iP/8nnHvCzLib5mSR//15epPMFAAAAGC7d4OXTSb41yR9vXz+9vX7fmqZ5cfvz3RPhCwAAADBsPp7ka0lubL99Y/vtj7dRjG1HADAKLpxLvvip5MrF5NiJ5LGnk5Nn2q4KAKCW9+TtHSo3ttd7TucLAAy7C+eSF55Jrr2eTD3cvb7wTHcdAGA4fTXJ4besHd5e7znhCwAMuy9+Kjk0mUweTkrpXg9NdtcBAIbTJ5J8Q74ewBzefvsTbRQjfAGAYXflYjLR2bs20UmuvNpOPQAAtXVPNfqhJH+Y5Ju3rz/0oKcdlVJ+IcmvJfmOUsqlUspT+3mdmS8AMOyOnehuNZrc1Xm7uZYce3d7NQEA1NYNWh70aOk9mqZ58n5ep/MFAIbdY08nb24kGzeSpule39zorgMAUJ3wBQ7ahXPJ4hPJJ9/fvRpoCbTt5Jnkw88mD70rWb/SvX74WacdAQD0SGmapu0a7mp2drZZXl5uuwy4u50TRQ5NducpbK51ny67yQEAALiT0nYBNel8gYPkRBEAAADeQvgCB8mJIgAAALyF8AUO0rET3a1GuzlRBAAAYKQJX+AgOVEEAABgaJVSHi2l/LNSyldKKedLKfu62RuvXRiMlJNnkjzbnfFy5dVux8tjTxu2CwAA0GPv//n3P57k40nek+SrST7xWx/5rc894Ke9meQ/b5rmX5RSHkry66WUc03T/M6dXuS0IwAAAKBtB3ra0Xbw8ukkX0tyI8nhJN+Q5IcOIIC5pZTyS0l+pmmac3f6ONuOAAAAgGHz8Xw9eMn29Wvb6weilPJtSf71JC/d7WOFLwAAAMCweU++HrzsuLG9/sBKKUeS/GKSH2ma5urdPl74AgAAAAybr6a71Wi3w9vrD6SUMpFu8PIPmqb5X/fzGuHLsLpwLll8Ivnk+7vXC3fcfgYAAADD5BPpznjZCWB2Zr584kE+aSmlJPnZJF9pmua/3u/rhC/D6MK55IVnkmuvJ1MPd68vPCOAAQCGSy8fNnmwBTBQtofq/lCSP0zyzdvXgxi2+28k+WtJ/lIp5Te2//q+u73IaUfDaPGJbuAyuavDauNG8tC7kvnPtlfXsLhwbvso6YvJsROOkgaANuw8bDo0mUx0ks215M2N5MPPHvz/l3v5tQBG14GedtRvdL4MoysXu78Y7DbRSa682k49w6Qfu4o8iQNgFH3xU90wZPJwUkr3emiyuz7IXwuAoSR8GUbHTnSfyOy2uZYce3c79QyTGr98PUh40o9hEAD0Qi8fNnmwBcADEr4Mo8ee7rbCbtxImqZ7fXOju86DOehfvh40PPEkDoBR1cuHTR5sAfCAhC/D6OSZ7h7kh96VrF/pXvtxT/Igbpc56F++HjQ88SQOgFHVy4dNHmwB8IDG2y6ASk6e6b+wZbfdg+t2d3ykD0Oi3R57ulvnRvYO3LvfX76uXOz+8+92L+HJsRNvH67sSRwAo+DkmSTPbg/Bf7X7/75aQ/B7+bUAGErCF9qxu+Mj6V43ttf7+ReZg/7l60HDk4MOgwBgkPTyYVO/P9gCoK8JX2jHg3Z8tOkgf/l60PDEkzgAAIC+J3yhHbbLdB1EeOJJHAAAQF8TvtAO22W+TngCAP3twrntByUXuw+QdJkCcI+cdkQ7BuVEJgBgtO0cEnDt9b2HBAzCKY0A9A2dL7RHxwcA0O8G9ZAAAPpKK+FLKeX3k1xLspXkZtM0s23UAQAcENsyGFaDfEgAAH2jzW1H39s0zXcKXgBgwNmWwTA7dqI7m263UTwkAIAHYuYLAPBgdm/LKKV7PTTZXYdB99jT3UMBNm4kTdO9juohAQDct7bClybJ/1FK+fVSykdv9wGllI+WUpZLKcurq6s9Lg9o1YVzyeITySff3716eg797crF7jaM3WzLYFg4JACAA1Capun9Fy3lzzRN8wellEeSnEvynzZN8+I7ffzs7GyzvLzcuwKB9uxsXzg0ufcYcr/oQv9afKK71WhnIGnS7Q546F3J/GfbqwsAGCSl7QJqaqXzpWmaP9i+Xk7yT5J8oI06gD5k+wIMHtsyAADuqOfhSynlG0spD+38fZJ/M8lv97oOoE/ZvgCDx7YMAIA7auOo6Xcl+SellJ2v/z81TfO5FuoA+tGxE2/fvuBUCeh/J88IWwAA3kHPw5emaV5J8q/1+usCA+Kxp7szXzayd+aL7Qv1XDjX3dZ15WI3/HrsaTfRAABwgBw1DfQX2xd6a2fA8bXXk4vWddIAACAASURBVKmHu9cXnnHCFO1w0tlo8n0HYAS0ctrRvXLaESNHJwK94pQa+oWTzkaT7zsAX+e0I6CHdCLQSwYc0y+cdDaafN95JzqigCEjfIF+4xdReunYie6T5t0MOKYNwx4EupG8vWH/vnN/PIgChpDwBfqNX0Tppcee7rb4b9xImqZ7NeCYNgxzEOhG8p0N8/ed++dBFDCEhC9t8hSM2/GLKL1kwDH9YpiDQDeS72yYv+/cPw+igCHU86Om2bZ7wNzup2Bx0zPyHLVMr508488d2nfyTJJnt4eNv9oNnIdl2PiVi93/1+/mRrJrmL/v3L9jJ94+DN6DKGDACV/asvspWNK9bmyv+4VjtPlFFBhVwxoEupG8s2H9vnP/PIgChpDwpS2egnEnfhEFGB5uJOHeeBAFDCHhS1s8BQOA0eBGEu6dB1HAkBG+tMVTMODCue2bsYvdQNbNGAwvN5IAMNKcdtQWJ4zAaHP0LA/CaXkAAAOlNE3Tdg13NTs72ywvL7ddBsDBWXzi7VsPN250g9j5z7ZXF/1v92l5uzsnBfgAwGArbRdQk84XgDZcudi9cd7N0G32Y/dpeaV0r4cmu+sAAPQl4QtAG46d6HYs7GboNvshuAMAGDjCF4A2PPZ0d6vIxo2kabpXQ7fZD8EdAMDAEb4AtMHQbe6X4K6/GH4MAOyDgbsAMGhuHVP+arfjxTHl7TD8GAAO0lAP3B1vuwAA4B6dPOPmvh/sHn6cdK8b2+u+PwDALsIXGEa3nopf7M6H8FQc4OBduZhMPbx3zfBjAOA2zHyBYbPTBn/t9e5NwbXXu2+bQwBwsAw/BgD2SfgCw2Z3G3wp3euhye46AAfH8GMAYJ+ELzBsrlzstr3vpg2efufEGAaRU8sAgH0y8wWGzbET3a1GOwMgE23w9LfdJ8bs3iqXB7yJNfuI+3GvPzeGHwMA+6DzBYaNNngGTY2tcmYfcT/83AAAlQhfYNhog2fQ1NgqZ/YR98PPDQBQiW1HMIy0wTNIamyVcwQw98PPDQBQic4XANpVY6ucI4C5H35uAIBKhC8AtKvGVjmzj7gffm6gPU69A4ZcaZqm7RruanZ2tlleXm67DAAGya1Ta17tdi447Yj98HMDvbf71LuJTrfj7M0NM+tg9JS2C6hJ+AIAALRn8Ym3z/7auNHthJz/bHt1Ab021OGLgbsAAG91qwPmYncWjA4YqMewa2AEmPkCALDbzhaIa693bwivvd592wwKqMOwa2AECF8AAHb74qe6sycmDyeldK+HJrvrwMEz7BoYAcIXAIDdrlzsbnnYrV+2QDgRhmFU49Q7gD5j5gsAwG7HTrx9+Gc/bIHYfSLM7u1QcZPKEDh5xs8xMNR0vgDAINIBUU+/boGwHQoABpbwBQAGjYGwdfXrFoh+3g4FANyRbUcAMGh2d0Ak3evG9nrbAcGw6MctEP26HQoAuCudLwAwaHRA9L8a28L6dTsUAHBXwhcAGDTHTnQ7HnbTAdE/am0L69ftUADAXdl2BACD5rGnuzfzG+l2vGyu6YDoJzW3hfXjdigA4K6ELzCsLpzr/qJ/5WL3KfljT/uFHYbFyTNJnt3+b/zVbseL/8b7x5WL3Y6X3R50W5g/0wFgoAlfYBjttLwfmtzb8h7t6TA0dED0r4MejOvP9N4SdAFQgZkvMIx2t7yX0r0emuyuA1DXQQ/G9Wd67zjGHYBKhC8wjJyEAtCegx6M68/03hF0AVCJbUcwjA665R2Ae3OQ28L8md47Neb1AEB0vsBwOuiWd2D/LpxLFp9IPvn+7tV2BR6UP9N7xzHuAFQifIFhdNAt78D+mBdBDf5M7x1BFwCVlKZp2q7hrmZnZ5vl5eW2ywCAO1t84u3bQzZudG+W5z/bXl3A/t067cgx7gA9VtouoCYzX4DR4ghRajIvAgafY9wBqMC2I2B02BJCbeZFAABwG8IXYHQ4QpTazIsAAOA2hC/A6LhysbsFZLe2toQ4EWc4GYwKAMBtmPkCjI5jJ94+DLWNLSE7258OTe7d/hQ36UPBvAgAAN5C5wswOvplS4jtTwAAMFKEL8Do6JctIf20/QkAAKjOtiNgtPTDlpB+2f4EAAD0hM4X4PYMhK2nX7Y/AQAAPSF8Ad5uZyDstdf3DoQVwByMftn+BAAA9IRtR8Db7R4Im3SvG9vrAoKD0Q/bnwAAgJ7Q+QK8nYGwAAAAB0b4ArzdsRPdAbC7GQgLAABwX4QvwNsZCAsAAHBghC/A2xkICwAAcGAM3AVuz0BYAACAAyF8AYBRcuFc9+SyKxe7850ee1rQCgBQmW1H0E8unEsWn0g++f7u9cK5tisChsmFc8kLzyTXXk+mHu5eX3jGnzUAAJUJX6BfuCkCavvip5JDk8nk4aSU7vXQZHcdAIBqhC/QL9wUAbVduZhMdPauTXSSK6+2Uw8AwIgQvkC/cFME1HbsRLK5tndtcy059u526gEAGBHCF+gXboqA2h57OnlzI9m4kTRN9/rmRncdAIBqhC/QL+7lpshgXuB+nDyTfPjZ5KF3JetXutcPP+u0IwCAykrTNG3XcFezs7PN8vJy22VAfbeOgH212/FyuyNgdwbzHprsbkvaXOuGNG6gAACAwVXaLqCm8bYLAHY5eebuAcruwbxJ97qxvS58AQbBraD5YnfL5e2CZgCAIWLbEQwag3mBQbbTvXft9WTq4e71hWdsnwQAhprwBQaNwbzAINvdvVdK93posrs+SszuAoCRInyBQeO0EmCQ6d7T/VODMAuAPid8gUHjtBJgkOne0/1z0IRZAAwAA3dhEO1nMC9AP3rs6e6N8Ub2ntg2St17Vy52Q4LdRq375yAZRA/AAND5AgD0ju493T8HzVY2AAaAzhcAoLdGvXtP98/BOnaiu9Vop/MlEWYB0Hd0vgAA9JLun4NlED0AA6A0TdN2DXc1OzvbLC8vt10GADCoLpzrzgC5crHbKfHY08KOYXLr+/tqt+PF9xdgEJW2C6hJ+AIADLed03AOTe7d5qPbBAD6yVCHL7YdAQDDrZ+Pdr5wLll8Ivnk+7tXxyMDwFASvgAAw61fT8PZ6ci59nr36Olrr3ffFsAAwNARvgAAw61fj3bu544cAOBACV8AgOHWr6fh9GtHDgBw4IQvAMBw69ejnfu1IwcAOHDjbRcAAFDdyTPthy1v9djT3RkvG9l7ClPbHTkAwIHT+QIA0IZ+7cgBAA6czhcAgLb0Y0cOAHDgdL4AAAAAVCR8AQAAAKhI+AIAAABQkZkvAAA1XTiXfPFTyZWL3eOlH3vanBcAGDE6XwAAarlwrnuc9LXXk6mHu9cXnumuAwAjQ/gCAFDLFz+VHJpMJg8npXSvhya76wDAyBC+AADUcuViMtHZuzbRSa682k49AEArzHwBAKjl2InuVqPJw19f21xLjr27vZoOilk2ALBvOl8AAGp57OnkzY1k40bSNN3rmxvd9UFmlg0A3BPhCwBALSfPJB9+NnnoXcn6le71w88OfoeIWTYAcE9sOwIAqOnkmcEPW97qysVux8tuZtkAwDvS+QIAwL05dqI7u2a3YZllAwAVCF8AALg3wzrLBgAqEb4AAHBvhnWWDQBUYuYLAAD3bhhn2QBAJTpfAAAAACoSvgAAAABUJHwBAAAAqEj4AgAAAFCR8AUAAACgIuELAAAAQEXCFwAAAICKhC8AAAAAFY23XQAA9JMvvHw5z734Sl5740YeffhwPvbB9+ZD73uk7bIAABhgOl8AYNsXXr6cH/vM+Vy+tp5jnYlcvraeH/vM+Xzh5cttlwYAwAATvgDAtudefCUTYyWHJ8dTSvc6MVby3IuvtF0aAAADTPgCANtee+NGOhNje9Y6E2O59MaNlioCAGAYCF8AYNujDx/O2ubWnrW1za0cf/hwSxUBADAMhC8AsO1jH3xvNrea3Ni4mabpXje3mnzsg+9tuzR65AsvX86Tz38p3/PTv5onn/+SeT8AwIEQvgDAtg+975H8xF85lUcemsqfrG3mkYem8hN/5ZTTjkaEgcsAQC2OmgaAXT70vkeELSNq98DlJDk8OZ4bGzfz3Iuv3PfPhKPLAYBE5wsAQJKDH7iskwYA2CF8AQDIwQ9cdnQ5ALBD+AIAkIMfuOzocgBgh/AFACAHP3B5dyfN1bXNvLJ6PS//v9fyJ2ubth4BwIgpTdO0XcNdzc7ONsvLy22XAQwwQy+BXtuZ+bJxcyv/359udBeb5FsemszE2JiTtABgr9J2ATXpfAGGnqGXQBt2OmlubGzlzabJ5NihzDzcybccmTL7BQBGjPAFGHqGXgJt+dD7HsnRzkT+1X/5aN47fSQPTU0kMfsFAEaN8AUYeoZeAm066FOUAIDBI3wBhp4bH6BNB32K0m5fePlynnz+S/men/7VPPn8l2ynBIA+JXwBhl7NGx+AuznoU5R2mGcFAIOjldOOSimPJ/lUkrEkf69pmp+608c77Qh4UDunHV1640aOO+0IGAJPPv+lXL62nsOT47fWbmzczCMPTeUXPvoXW6wMAO7LUJ92NH73DzlYpZSxJJ9OcibJpSRfLqV8pmma3+l1LcDo+ND7HhG2AEPltTdu5FhnYs+aeVYA0J/a2Hb0gSS/1zTNK03TbCT5h0m+v4U6AAAGlnlWADA42ghfZpK8tuvtS9tre5RSPlpKWS6lLK+urvasOACAXrufwbnmWQHA4GgjfLndPq63DZ5pmub5pmlmm6aZnZ6e7kFZAAC9d7+Dc2sN8gUADl7PZ76k2+ny6K63jyf5gxbqAABo3XMvvpKJsXJrcO7hyfHc2LiZ51585a5BinlWADAY2ghfvpzk20sp70mykuQHk/wHLdQBANC6Ow3O3Tmp7bU3buRRJ7UBwMDq+bajpmluJvnhJJ9P8pUk/6hpmvO9rgMAoB+80+Dcb5wcu6/tSABA/2lj5kuapvnlpmlONk3zZ5um+ck2agAA6AfvNDi3lHJrO1Ip3evEWMlzL77SdskAwD1qJXwBAKDrnQbnXvvazXQmxvZ87M52JABgsLQx8wUAgF1uNzj30RcP5/K19VuDeJPudqTjDx/udXkAwAPS+QIAd/GFly/nyee/lO/56V/Nk89/ycwNeuKdtiN97IPvbbs0AOAeCV8A4A6+8PJlQ09pxTttR3LaEQAMHtuOAOAOnnvxlVtDT5Pk8OR4bmzczHMvvuImmOputx3poDjGGgB6R/gCwEBo60bxtTdu5FhnYs+aoacMup2Oromxsqej6ycSAQwAVGDbEQB9r82tP48+fDhrm1t71gw9ZdDt7uhyjDUA1Cd8AaDvtXmjaOgpw+i1N244xhoAekj4AkDfa/NG0dBThpGOLgDoLTNfAOh7jz58OJevrd8aepv09kax5tBTaMPHPvje/NhnzufGxs10JsaytrmlowsAKtL5AkDfs/UHDpaOLgDordI0Tds13NXs7GyzvLzcdhkAtGjntKNLb9zIccfiHhjHDQMAfaK0XUBNwhcAGFG7jxvevfVEBwQA0IKhDl9sOwKAEeW4YQCA3hC+AMCIctwwAEBvCF8AYEQ5bhgAoDeELwAwopwiBQDQG8IXABhRjhsGAOiN8bYLAADa86H3PSJsAQCoTPgCwFD6wsuX89yLr+S1N27k0YcP52MffG9fhQz9Xh8AAAfHtiMAhs4XXr6cH/vM+Vy+tp5jnYlcvraeH/vM+Xzh5cttl5ak/+sDAOBg6XwBYOg89+IrmRgrOTzZ/d/c4cnx3Ni4medefCUfet8jrXed3K2+QdP2v08AgH4nfAFg6Lz2xo0c60zsWetMjOXSGzdudZ1MjJU9XSc/kfQsMLhTfYOm9r9PwQ4AMAyELwD0nQe94X704cO5fG39VmdJkqxtbuX4w4f7ouvkTvUNmpr/Pu812Hnrz813v/eb82uv/LHgBgBonZkvAPSVg5iH8rEPvjebW01ubNxM03Svm1v/f3v3Hxv3fd93/PX+fu94x58SbYnyDymzWcuVIy+xPSVwWkPVPGOwm0JBhwyNsAQN1iEq0NZpt3V2kdXDVAyYkGE/0gaYhKy/UwebuwJGsrpN4QhKO2uxasdBjDCyK/+QrFjUD0oieUfe3ff73h93Rx2pI3ki+b2fz4dB3/F4vPvwvl9buhff7/fHdXDvuM5M5dSfDhfdv9lVJyutr9Mk+XrWBjvTcyW9f3VO567k9eTXXr3hfFh63rx1cUb/7cU39falGebqAACAliN8AQC0ldo33Gbly3RoOnL89KL7HZuY1IGjJ/TI4Rd14OiJRW+q9+0a06H9uzU2nNXVfFFjw1kd2r9b+3aNacfogPLFaNFjNbvqZKX1dZokX89qsHMtX9S5q3mVIlcYSLOF0g1BytLzZnqupMCka/nSiucRAABAM9B2BABoK43MQ2mkHWXfrrG6YcbBveN65vnXlSuU1J8OlS9GLak6WW59nSbJ17PannVxZl6BTEFgil3KpoKFIKX6Gi49bwpRrMDKl1WdOlcHAAB0PipfAABtpZFKikarY+rppqqTdpDk61ltz5ovxZK5Yne5S1uHMzcEKUvPm74wUOzly6pOnasDAAA6H5UvAIC20kglxXp3C+qWqpN2kdTruW/XmA5JevJrr2q2UFI2FWjrcEbD2bRyhdKiIGXpeTOcTenCTEEj/Sm5e8sqnAAAACQqXwAAbaaRSop2mNuC5ti3a0xf+tSDunPzgG7blNVQJlV3QPHS8+buLUP6/KP36K5bh6hwAgAALWfu3uo1rGrPnj1+8uTJVi8DANAmame+1FbH8Oa6e1W3kT47ldN2to0GAKAbWasXkCTCFwBAR+LNOAAAQFchfGk1whcAwEqqQcyZqZx2EMQAAAB0oq4OX5j5AgDoaNUWpMnpuUXbTh+bmGz10gAAAABJhC8AgA63nm2nAQAAgGZgq2kAQEdb77bT7YLWKQAAgO5F+AIA6Gg7Rgc0OT2ngb7rf6R12rbTtbs31bZOHZLaJoAhHAIAAFg72o4AAB3t4N5xFSNXrlCSe/myGLkO7h1v9dIa1u6tU8zVAQAAWB8qXwAAHam2EmM4k5K762q+2JHbTrd761RtOCRJA30p5QolHTl+uqNe50ZQ4QMAAJJA+AIA6DhL23TyxUjFyPVbn7i/I98ot3vrVLuHQxulE9q/AABAZ6LtCADQcdq9TedmtXvr1I7RAeWL0aLbNiIcOjYxqQNHT+iRwy/qwNETLW9j6rbzCgAAtA8qXwAAHafbKjH27RrTIZXf/J+dyq27derYxKQOvzCh0xdnJUl33zqgp5+4b82Pd3DvuJ55/nXlCiX1p8OFSqP1hENf+qtT+vKxv1MUuzKpQKUobnmVSbedVwAAoH0QvgAAOk67t+msxb5dYxsSOhybmNSvP/eapnJFBVa+7c0Ls/rXz72m//TJD6/pOZIIh7587O8UuysVmEqR69JsQbcO9rV0jkw3nlcAAKA9EL4AABrSToNIk6jE6BZHjp/W9FxJYWAKrJy+WOyamV/fgNzVwqFGzo/qfV55d0rzpVh9ocnMZCYplqbnSi2tMuG8AgAASSF8AQCsqt0GkW50JUanaCTgODOVUymOlQqvj3Uzk6LYEws2Gjk/au8Tu0uSCpGrT64wKAcw86W4pVUmvXpeAQCA5BG+AABW1Y5bDW9Um06naDQA2zE6oIvT83Ivhy6S5C6FgSUWbFTPjyh2vXVxVoUoVmimwy9MLKyt9hzqCwO5xyrFrmIUKwgCRV4OYVpdZdJr5xUAAGgOdjsCAKzqzFRO/elw0W3dNIi03XbdqefI8dMqlCK9f3VOPzw/rfevzqlQim7Yiefg3nENZ1OKYlcUx+UPdw1lUokFG2emcipFsc5dmVMpLocosbtOTc4svJa159CWoYzMpFQguVQOYMz0S/t+jOADAAB0JcIXAMCqktpquB1UK0omp+cWVZS0WwBz6vw1XZotqBS5Qrs+pPaN89cW3W/frjF98ZMf1s6xoco8FdM9WwfXPGy3ETtGB3T+2rzMpMBM1X9qt2muPYdG+tO6Y1O/UmGgTCrQR++6VUc+/Q/05GP3JrI+AACAVqPtCACwqm4eRNqOLVX1FKPynJSgsoWRmRTHrkLl9lrLtc4kNTT54N5x/cIfvqzQTK5ym1Ms1x3D2YXqqKXnUCo0jQ1ndWj/bu3bNbZQfdQOA50BAAA2GpUvAIBV7ds1pkP7d2tsOKur+eKiN82drpktVetpb+qr9OjE7nJ5eWitV25v8LmTqvDZt2tMO7cOKQhMkbtSoS1UtlSro1Y6h6rbY7/67pTev5rXq+9O6defe63tqo8AAADWisoXAEBDunUQ6Y7RAb11cUbTcyUVolh9YaDhbEp3bxna0OdZ745RO8eG9falGV3LX1/nyGBad916fZ0rVbYkXeHz9BP3Lfx8y1VHLXcOHX5hQlO5osLAlAoDuUtTueKigb0AAACdjMoXAEBP+9j4LbowU1AhihWYVIhiXZgp6GPjt2zo89SGH2bly9qZKLXqVcgc3DuudBjqtk1Z/fi2Yd22Kat0GC6EG6tVtiRd4bOe6qjTF2cV1MyLCcwUWPl2AACAbkDlCwCgp710+rLGhvt0LV/SfCkut/NI+spfv6UPbd+8YZUXZ6Zy2tyfXnRbKYr1yrtTeuTwiwuVKpLqV8js361D+3fryPHTOjuV0/abrGzZMTqgyem5ha9LGz80udHqqKUVOrG7bMNWAQAA0H4IXwAAPe3MVE63DmaUSYU6d2VOYWCSXLlCdFNtQatZGn5cyxf13pU5pYLFIctgX7hsiPLs5x5edi31wp3aypZmDU1ebahvvfYrd1fsksllVhnY69I9WwYaflwAAIB2RtsRAKCnVbdAvjB9fatkuSmTCpZtC1qLg3vHVYxcuUJJ7q7z03OSpNs2ZRe1IZ2+OLum9qDVtgNvxtDkRob61mu/2jKUkZlkgRS5ywJp80BaTz9xX8OPCwAA0M6ofAEA9LRqRchcKVIqMMVxeZvkLUPZjZ+JIi20DblLd27Oajh7vVqlGrrki9FNtwc1UtmS9NDkRob61qvQ2TKUUSmKtXPbyJpaqlqNqhwAALAawhcAQE+rhiJPfu1V5QqRMinTlqGsRvrTyhVKic1EOXD0hCYr1S9V+WKku28dUK4Y33R7UG2488bktAqleFHlTjPCgNVan6Qb26+k8s+9c9uInv3cw2t+3FZZ7y5WAACgN9B2BADoeft2jelLn3pQd2zu122bshrOlisrkpiJUrW0Dan6fE8/cd+a24P27RrTwb3jGuhLaetwRrdv6m9qi85qrU/S8j/3Sq9zI4/bKjezixUAAOhdhC8AAKg5M1Eafb5qiLJ9dEBnpnI6cvx0w+HJWsOAettb36xGgpW1vM5rCWyaJektvAEAQHcwr2yp2c727NnjJ0+ebPUyAABoitpWltrWo0bCoEcOv6jN/WmZXd+82d11NV/Ut596dMOfr95jLbcd9nok9bjrVW0fq22jyhVKGhvOLttGBQAA6rLV79K5mPkCAECbWc+A2eVmqqzUorORA22XG+q73qG0SQ8LXqtmbeENAAA6G+ELAABtZj0DZhsNA2rDkAvT87ptJLOm51vuMWsDlpsdSttJuwct3cWqnapyAABA+yB8AQCgzayleqWqkTBgaRhycXpe712Zk2QaqYQ+NzvQdqWApVpZE8Wuty7OqhDFCs10+IWJG0KKTtw9qF2rcgAAQPsgfAEAoM2st5VltTBgaZvRbZuyOjuV1/npOQ1nU4uer9EqlJVal85M5RSa9KOr8zKTwsAUx65TkzM6NjG56PE2sgUKAACgXbDbEQAAbSbpnZeW7tAznE3rzs1ZuWvR80nSM8+/rsnpuUVVKPV2Qlpp158dowP60dU5FeNYhVL5I3bV3YWJ3YMAAEA3ovIFAIA2tFr1ynrmotRra0qFgR76wOiiHXoOHD3RcBXK0se8li/q/PSc3CWPYxWi67srukuxu0qx65V3pxZVv6yn5QoAAKBdUfkCAEAbOzYxqQNHT+iRwy/qwNETOjYxuTAXpZGKlHoO7h1XMXLlCiW5ly/rtTXdTBVK9TEvTM/p1PlpvXM5p/lirJFsqIu5Yt11mCQzLVp7o2sDAADoJIQvAAC0qeVClsMvTCxUpJiVL+u18Cyn0bamHaMDyhejRbctV4Wyb9eYPvnQnZrKFTVfiiWVZ7tcyZfDk3p/4XBJI5nFa0+65QoAAKAVaDsCAKAJ1tImtNzw2dMXZ7VzbGjRfetVpKz0nI3s0HOzg39fOn1Z20f79e7lnMLAZCoP1o08lte5v0maLUQaG8kuWju7BwEAgG5D+AIAQMLWun3ymamcNle2fq6qtgHli9GKc1FWek5JDQVBjWxbXW+9fWGgUuwyK7cVSeUql8rVhSAmFUiFKGamCwAA6HrmXu93Ue1lz549fvLkyVYvAwCANTlw9MQNQ2RzhZLGhrOLBtw2+n3pwJQrxkqHtlCRcjVf1NahjKbnS9oxOqCp2XkVY6/7vZdmC5qeK6kUx0oFgYazKX3m4b+nl05fXtMA36XrjWLXuStzMpPcXUFgKkWxAjNFLsWxKzQpCEyBmcZGaC0CAAALv6fpSlS+AACQsOUqWFbbPnm5tp/f/PgHJV2vSBnsC2UqV5FUq1zevjSr7Zv7dS1f1MWZeRWiWH1hoHwhUhCYwsCUCgO5S5dnC/rtb72pD9wycFOVOUvbmj42fouee+U9pUPT7ZsyOn9tXiV3jd8yqJ/++7cvfK0UxTo/Pa9i5PqxrYN66vFdBC8AAKCrEb4AAJCwtW6fXNv288b5aypErr5UoCPHT+vg3vGFqpkDR08sqnIZ6EspHQQ6dyUvmSmQKTTTXDFSrHLlSeyu0MoBTORSFHlDW0pX1Wtreu6V9/TJh+7US6cv6+xUTg9+YHRRBc2Htm9eCIwe3DG6puoaAACATkTbEQAACasN8p7iMwAADcZJREFUKmorWBpttVn6/Zdm53V5tqihTKh7t43o1Plrun1Tv8yuV+teyxf0zuW8QivPWImX+eM+HZiKscsk3X/npoXb3V3vX81r57aRuq1Ia22lAgAAWAZtRwAAYO1udnDtUrW7Hk3PFXVhuqBS7CqUYr16ZkqlKFY6DJRNh7owXW4xCs0UmBSt8juWYiWVSS3ZC/rizLym56MbtrmutiKttZUKAACgFxG+AADQBGvZPrk6U+U7b19WJiwPpn3/6pxKlcDEJXksuUuT0/MKKoGLSSrFvlDtYlLdrZ6rTFI2HS6aLTOVK+qWwfSyrUhrbaUCAADoRYQvAAC0odpWo2wqUCGKde7KnIpRvBCmBJUdg0KZipErlTJFsasvDLRlKKN3LperUOoFL4GVW5ECk7aNZLS5v0+bB/oWKnOu5ou6dTCz6HuqlS3HJiZ1JVfQ25dySoembcMZpcJAxch1cO944q8NAABApyF8AQCgDdW2Gm0Zyujc1bxcvihIiV2aL0ULYcw9W4cW5r5cyxdXfPx0ZaejOzZnNZRJ6Wq+qD//1b0LX6830yVfjDTYFy6EQts3Z3X+2rzOXslr59Yh/ebHP8gAXQAAgDoIXwAAaEO1M1VGKpcXZ+ZVjKKF+5jKLUexpExoyhcjlaLyoNz5OsNeApXvK0mpwLR1OKPhbFoXZ+Y0Ox/pkcMvLgzWXW6b674wWAiFymvrU65Q0uhghuAFAABgGcHqdwEAAM22Y3RA+eL1oGWkP63bNmXVnw4U2uI5LqFJW4czupov6r0reRWWBC9Wc2UkE2rrUJ9u21SueLk4M6fJ6YIG+sJFg3Ul6dD+3Robzupqvqix4awO7d+t6flyGFOLQbsAAAAro/IFAIA2tFzlyWAmpaGM69JsUe6SmXTLYFpmpq1DGc3MlRYG8lYDGlc5oEmFgb504CFJ13demp2PtHWoT1uHs5IWD9Z99nMP31DNsuM4g3YBAABuFpUvAAC0oX27xupWnmwZ7NNUrqR0ECibCpQOAk3lShrsCzU9X9I9Y0PKVPaNXtp4NL5lUFI5eDlTGazblyoP5621UiXLwb3jKkauXKEk9/Ilg3YBAABWRuULAABtqt721IdfmChfMV3vJ3LJzBa2f47jWEtFLl2otBSlQ1toMZqeKykdzmvLUHbhvitVsuzbNaZDul45s70yI4Z5LwAAAMsjfAEAoINMz5d05+asLs4UVIhi9YWBbhvJaGa+pKce36Vnnn9dxSXZi6m8pfSl2aI2DfQttAwN9KU0OpDW5dmiBvpSi9qbVqpkqRcKAQAAYHmELwAAdIBjE5M6cvy0LkzPy90VmCl2KVeI9M7lvCTps7//sm4dSC3MeglMSgWBwsAUxbGiyG8YlrtlKKNSFGtsOEslCwAAQEIIXwAAaHPHJiYX2oVGsqEuzBR140SXsku50sL12KVCFEtReeBufzpQvhjdMCx357YRPfu5h5P+MQAAAHoWA3cBAGhzR46fVjq0yk5EsVK2+vcsFbn0+O5tDMsFAABoAcIXAADa1LGJSR04ekLfefuy3r86p2v5ogpRLLPG0hdbcn3i/Zm6OyjRYgQAAJAs2o4AAGhDta1GKZNmC5FmL9ff/nklNRsi6dTkjCTRYgQAANBkVL4AANCGqq1GUewqxfXnuzTErl+kQ9OR46c3ZH0AAABoHJUvAAC0mWMTk3rl3SmVYle0juDFF/4lpQLTtuGMzk7dfPUMAAAA1ofwBQCANlJtN3JfX/BSZZIyqUDbRrJKhaax4ez6FwkAAICbQvgCAEAbqbYbrSd26QsDuccqxtL41kH1p0PlixE7GwEAALQI4QsAAG3kzFROoUnFaO3xSxiYSrFpIB1obDirs1M5bR8d0MG94+xsBAAA0AKELwAAtJEdowN69d2pNX+/SSrFsWKXfvGnxvXkY/du3OIAAACwJux2BABAGzm4d1zFOL7p70sFJjPJTOpPh/r8o/cQvAAAALQJKl8AAGgj+3aNaefWIU2cn2n4e0zSPVsH9fQT99FWBAAA0IaofAEAoM08/cR9SofW0H13jPbr9z77Eb3waz9F8AIAANCmCF8AAGgz+3aN6Vf+4T2r3u9fPrZT337qUUIXAACANkf4AgBAG3rysXv1sw/cXvdroZWDF2a6AAAAdAZmvgAA0Kb+y6ce0t1bTukrf/2WZguRBvtC/YtH7iZ0AQAA6DDm7q1ew6r27NnjJ0+ebPUyAAAAAABAMhobeNehaDsCAAAAAABIEOELAAAAAABAgghfAAAAAAAAEkT4AgAAAAAAkCDCFwAAAAAAgAQRvgAAAAAAACSI8AUAAAAAACBBhC8AAAAAAAAJInwBAAAAAABIEOELAAAAAABAgghfAAAAAAAAEkT4AgAAAAAAkCDCFwAAAAAAgAQRvgAAAAAAACSI8AUAAAAAACBBhC8AAAAAAAAJInwBAAAAAABIEOELAAAAAABAgghfAAAAAAAAEkT4AgAAAAAAkCDCFwAAAAAAgAQRvgAAAAAAACSI8AUAAAAAACBBhC8AAAAAAAAJInwBAAAAAABIEOELAAAAAABAgszdW72GVZnZBUnvtHodXWSLpIutXgSajuPeezjmvYdj3ps47r2HY96bOO69p9eO+UV3f7zVi0hKR4Qv2FhmdtLd97R6HWgujnvv4Zj3Ho55b+K49x6OeW/iuPcejnl3oe0IAAAAAAAgQYQvAAAAAAAACSJ86U1HW70AtATHvfdwzHsPx7w3cdx7D8e8N3Hcew/HvIsw8wUAAAAAACBBVL4AAAAAAAAkiPAFAAAAAAAgQYQvPcrMfsvMvmdm3zWzvzSzO1q9JiTLzL5oZhOV4/5nZra51WtC8szsn5rZ62YWmxlbFXYxM3vczH5oZm+a2dOtXg+SZ2a/a2aTZvb9Vq8FzWFmO8zsW2b2g8r/2z/f6jU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      "text/plain": [
       "<Figure size 1122.38x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 6))\n",
    "ax = sns.lmplot(x = \"pca2\", y = \"pca1\", hue = \"labels\", data = new_data, fit_reg = False, height = 10, aspect = 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales of Wheat in tons</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>436</td>\n",
       "      <td>429</td>\n",
       "      <td>422</td>\n",
       "      <td>415</td>\n",
       "      <td>407</td>\n",
       "      <td>397</td>\n",
       "      <td>397</td>\n",
       "      <td>387</td>\n",
       "      <td>374</td>\n",
       "      <td>373</td>\n",
       "      <td>346</td>\n",
       "      <td>326</td>\n",
       "      <td>304</td>\n",
       "      <td>308</td>\n",
       "      <td>283</td>\n",
       "      <td>267</td>\n",
       "      <td>251</td>\n",
       "      <td>238</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Albania</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Algeria</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>50</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>53</td>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>American Samoa</td>\n",
       "      <td>42</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Andorra</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>32</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        1990  1991  1992  1993  1994  1995  1996  1997  1998  \\\n",
       "Sales of Wheat in tons                                                         \n",
       "Afghanistan              436   429   422   415   407   397   397   387   374   \n",
       "Albania                   42    40    41    42    42    43    42    44    43   \n",
       "Algeria                   45    44    44    43    43    42    43    44    45   \n",
       "American Samoa            42    14     4    18    17    22     0    25    12   \n",
       "Andorra                   39    37    35    33    32    30    28    23    24   \n",
       "\n",
       "                        1999  2000  2001  2002  2003  2004  2005  2006  2007  \\\n",
       "Sales of Wheat in tons                                                         \n",
       "Afghanistan              373   346   326   304   308   283   267   251   238   \n",
       "Albania                   42    40    34    32    32    29    29    26    22   \n",
       "Algeria                   46    48    49    50    51    52    53    55    56   \n",
       "American Samoa             8     8     6     5     6     9    11     9     5   \n",
       "Andorra                   22    20    20    21    18    19    18    17    19   \n",
       "\n",
       "                        clusters  \n",
       "Sales of Wheat in tons            \n",
       "Afghanistan                    1  \n",
       "Albania                        0  \n",
       "Algeria                        0  \n",
       "American Samoa                 0  \n",
       "Andorra                        0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"clusters\"] = km.labels_\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales of Wheat in tons</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Guam</td>\n",
       "      <td>103</td>\n",
       "      <td>101</td>\n",
       "      <td>96</td>\n",
       "      <td>110</td>\n",
       "      <td>146</td>\n",
       "      <td>93</td>\n",
       "      <td>91</td>\n",
       "      <td>89</td>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>44</td>\n",
       "      <td>47</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>95</td>\n",
       "      <td>87</td>\n",
       "      <td>85</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "      <td>94</td>\n",
       "      <td>109</td>\n",
       "      <td>137</td>\n",
       "      <td>163</td>\n",
       "      <td>134</td>\n",
       "      <td>141</td>\n",
       "      <td>148</td>\n",
       "      <td>150</td>\n",
       "      <td>155</td>\n",
       "      <td>152</td>\n",
       "      <td>147</td>\n",
       "      <td>144</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Switzerland</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sweden</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kuwait</td>\n",
       "      <td>89</td>\n",
       "      <td>84</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>72</td>\n",
       "      <td>68</td>\n",
       "      <td>66</td>\n",
       "      <td>64</td>\n",
       "      <td>61</td>\n",
       "      <td>35</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        1990  1991  1992  1993  1994  1995  1996  1997  1998  \\\n",
       "Sales of Wheat in tons                                                         \n",
       "Guam                     103   101    96   110   146    93    91    89    87   \n",
       "Kazakhstan                95    87    85    84    85    94   109   137   163   \n",
       "Switzerland               14    13    12    11    10    10     9     8     8   \n",
       "Sweden                     5     5     6     6     5     5     5     4     4   \n",
       "Kuwait                    89    84    80    75    72    68    66    64    61   \n",
       "\n",
       "                        1999  2000  2001  2002  2003  2004  2005  2006  2007  \\\n",
       "Sales of Wheat in tons                                                         \n",
       "Guam                      86    44    45    44    47    41    42    39    36   \n",
       "Kazakhstan               134   141   148   150   155   152   147   144   139   \n",
       "Switzerland                8     7     6     6     6     6     5     5     5   \n",
       "Sweden                     4     4     4     4     4     4     4     4     5   \n",
       "Kuwait                    35    33    33    30    29    29    30    25    25   \n",
       "\n",
       "                        clusters  \n",
       "Sales of Wheat in tons            \n",
       "Guam                           0  \n",
       "Kazakhstan                     0  \n",
       "Switzerland                    0  \n",
       "Sweden                         0  \n",
       "Kuwait                         0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values([\"clusters\"]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Largest Importer and constantly increasing\n",
    "data.loc[\"Sierra Leone\"]\n",
    "X = data.loc[\"Sierra Leone\"].index[0:18]\n",
    "Y = data.loc[\"Sierra Leone\"].values[0:18]\n",
    "plt.figure(figsize = (15, 6))\n",
    "ax= plt.bar(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BKGoAAACDUdQAAAAGo6gBAAAMRlEDAAAYjKIGAAAwGEUNAABgMIoaAADAYBQ1AACAwShqAAAAg1HUAAAABqOoAQAADEZRAwAAGIyiBgAAMBhFDQAAYDCKGgAAwGAUNQAAgMEoagAAAINR1AAAAAajqAEAAAxGUQMAABiMogYAADAYRQ0AAGAwihoAAMBgFDUAAIDBKGoAAACDUdQAAAAGo6gBAAAMRlEDAAAYjKIGAAAwmA0Vtap6alXdWlX7q+qyNZY/uKrePC2/tqp2zh0UAABgq1i3qFXVSUleneRpSR6X5JKqetyqYS9M8tnufmySX0ryyrmDAgAAbBUb2aJ2fpL93X1bd/9Nkt9KctGqMRclecM0/ZYkT66qmi8mAADA1rGRonZ6kttXXD8wzVtzTHffm+SuJI+cIyAAAMBWU939wAOqnpXku7r7RdP15yQ5v7v/7Yox+6YxB6brH5vGfHrVunYn2T1d/UdJbp3rHzKY7Uk+dbxDbJCsy7FZsm6WnImsy7JZsm6WnImsy7JZsm6WnImsy7JZsm6WnMnmynq4zu7uHWst2LaBGx9IcuaK62ckueN+xhyoqm1JvirJZ1avqLuvTHLlRhJvZlW1t7t3He8cGyHrcmyWrJslZyLrsmyWrJslZyLrsmyWrJslZyLrsmyWrJslZ7K5ss5pI7s+fiDJOVX1NVV1SpKLk+xZNWZPkkun6Wcm+aNeb1MdAAAAa1p3i1p331tVL0nyriQnJbmqu/dV1c8k2dvde5K8NsmvV9X+LLakXbzM0AAAACeyjez6mO6+OsnVq+a9fMX0F5I8a95om9pm2r1T1uXYLFk3S85E1mXZLFk3S85E1mXZLFk3S85E1mXZLFk3S85kc2WdzbonEwEAAODY2sgxagAAABxDitoGVdVVVfXJqvrQinmPr6o/q6oPVtXbq+orp/mnVNXrpvk3VtWFK27zxGn+/qp61dx/GHzGnP+pqm6vqnvmzDd31qp6SFW9s6o+XFX7quoVo2adlv3BNG9fVV1RVSeNmnXFbfesXNdoOavqmqq6tapumC6PGjjrKVV1ZVV9ZHrOft+IWavqYSsezxuq6lNV9csjZp2WXTLNv2n6Hds+aM5nTxn3VdXPz5lxxX2cWVV/XFW3TPfz0mn+I6rq3VX10ennqdP8qsV70f4p23kr1nXpNP6jVXXp/d3nIFn/oKo+V1XvGDVnVZ07PWf2TfOfPXDWs6vquun3f19V/eCoWVes7yur6hNVdfnIWavq7+q+19bVJ+cbKedZVfU/p3XdXFU7R8xaVd9eX/x+9YWq+t45sx5X3e2ygUuSb01yXpIPrZj3gSTfNk2/IMnPTtMvTvK6afpRSa5L8qDp+p8neVKSSvL7SZ42aM4LkpyW5J6RH9MkD0ny7dP8U5K8d+7HdObH9Sunn5XkrUkuHjXrNO9fJvnNlesaLWeSa5LsWtbzdOasP53k56bpByXZPmrWVeu8Lsm3jpg1i2OtP3nosUzy80l+asCcj0zyf5LsmJa9IcmTl/CYnpbkvGn6YUk+kuRx0+Ny2TT/siSvnKafnsV7UWXxun/tNP8RSW6bfp46TZ86YtZp2ZOT/Isk7xj4Mf3aJOdM049JcmeShw+a9ZQkD56mH5rk40keM2LWFev7lSzery4f9TkwLVvm56o5c16T5CkrngMPGTXrinU+IouTGs6a9XhejnuAzXRJsjNf/EZ9d+47zu/MJDdP069O8gMrxr0nyfnTk/LDK+ZfkuTXRsu5al1Le0GZO+s0/1eS/JvRsyY5Ocnbkzx71KzTC/P7phfO2YvajDmvyZKL2oxZb0/yFZsh64p550y5a8Ss0+/SwSRnZ/EGfkWS3QPm/CdJ/nDF/Ock+a/H4LnwP5I8JcmtSU6b5p2W5NZp+teSXLJi/K3T8i96f1o9bqSsK65fmCUUtblzrph/Y6biNnLW3Pclw6xFbc6sSZ6Y5LeSPC9LKGozZ13q56o5cmbxvv++Y5Vzxufq7iS/cSxzL/ti18ej86Ek3zNNPyv3/WHwG5NcVFXbquprsngBOTPJ6Vn8cfBDDkzzRst5PB1x1qp6eBbfqL5n5KxV9a4stgB8PslbBs76s0n+S5K/PEYZjzRnkrxu2uXhP1bNuzvxXFmn52eS/GxVXV9Vv1NVjx4x66rbXpLkzT29Cx4Dh5W1u/82yQ8l+WCSO7L4gPHa0XIm2Z/k66pqZ1VtS/K9WfLr7bSr0hOSXJvk0d19Z5JMPw/tInx6FkX8kEPvS/c3f8Ssx8xcOavq/Cy2Wn1s1KzTrmk3Tctf2d13jJi1qh6UxXvVf1hWvrmyTtNfVlV7q+r9y9xF7yhzfm2Sz1XV26rqL6rqF2oJh2rMlHWli5O8aVk5jwdF7ei8IMmLq+q6LDbb/s00/6osnkB7k/xykj9Ncm8W3/audiw+/BxuzuPpiLJOH3zelORV3X3byFm7+7uy+JbowUm+Y8SsVXVuksd29+8eo3xHlHNa9v3d/Q1JvmW6PGfQrNuSnJHkf3X3eUn+LMl/HjTrSsf6je9wn6snZ1HUnpDF7mQ3Jfmx0XJ292ennG/OYhftj2eJr7dV9dAsdq9+WXff/UBD15jXDzB/djNkPSbmyllVpyX59STP7+6/nzfl/7+Po87a3bd39zcmeWySS5f1xdIMWX84ydXdffsay2c103PgrO7eleRfJ/nlqvqHM8ecI+e2LN5PfzSLvQH+QRZbK2c38+/VN2Txd59PGBv6O2qsrbs/nOQ7k6SqvjbJM6b59yb594fGVdWfJvloks9m8UHtkDOy+AZ4tJzHzVFkvTLJR7t79hMeLCFruvsLtTiI+KIk7x4w67cleWJVfTyL14lHVdU13X3hYDnT3Z+Yfn6+qn4zi93M3rjMnEeY9dNZbJ08VH5/J8kLl53zCLMeuv74JNu6+7pjkfMIs547Lf/YNP+3sziuYbSc6e63Z7HLc6pqd5K/W0a2qby+NYtdgN42zf6/VXVad985faD55DT/QL54y96h96UDWexKuHL+NYNmXbq5ctbipDPvTPIT3f3+kbMe0t13VNW+LD64z7oXyExZn5TkW6rqh7PYZf+Uqrqnu2d9HZjrcT20ZbK7b6uqa7L4kmm2Lasz5Tw5yV8c+uK7qn4vi+PCZt1bYebn6r9K8rvTXhYnDFvUjkJNZ5ebNrv/RBbHRhw6E+FXTNNPyeLb1JunTbifr6oLpt2znpvFPrlD5Vx2ngdyJFmr6ueSfFWSl42ctaoeOr3oHNoC+PQkHx4xa3e/prsf0907k3xzko8su6QdSc5a7F62fZp/cpLvzmKXtKU7gse0s/iQfuG0iicnOSa/b0fxGnBJjvFuJEeQ9RNJHldVO6ZVPCXJLQPmXHmbU7PYCvDfl5CrsvgwdUt3/+KKRXuSXDpNX5r73nv2JHluLVyQ5K7pvepdSb6zqk6d8n5nZv6mesasSzVXzqo6JYsvat7Y3b8zeNYzqurLp3WemuSbsjgmaLis3f393X3W9H71o1k8vnOXtLke11Or6sHTOrdn8bjO9j4w4+/UB5KcuuJ19TvmzDlz1kOO+fvVMdEDHCi3GS5Z/OffmeRvs2j1L0zy0izOUvORJK/IfQeW78ziBe2WJH+Y5OwV69mVxQfJjyW5/NBtBsz589Pt/376+VMjPqZZfKPS0/wbpsuLBs366Cxe/G5Ksi/Jr2axtWK4rKvWtzPLOevjHI/pV2RxVr1Dj+mvJDlpxKzTsrOT/MmU9z1Z7AIzZNZp+W1Jvm7ujEt4XH9wmn9TFmX4kYPmfFMWH3ZuzhLO+Drdxzdn8Zp4U+57TXx6FieEeE8WW/fek+QR0/jK4gQoH8viOL9dK9b1giyOrdufxW56I2d9bxYnlfmr6f/ou0bLmeQHpufQDSsu5474mGbxhcdNWRxzeVOWc4Ke2f7/V6zzeVnOWR/nelz/2XT9xunnC0fMueo58MEkr09yysBZd2bxpd2XnLV4s18OvbEAAAAwCLs+AgAADEZRAwAAGIyiBgAAMBhFDQAAYDCKGgAAwGAUNQAAgMEoagAAAINR1AAAAAbz/wCM/kXYuzc2ZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Most consistent\n",
    "data.loc[\"Monaco\"]\n",
    "X = data.loc[\"Monaco\"].index[0:18]\n",
    "Y = data.loc[\"Monaco\"].values[0:18]\n",
    "plt.figure(figsize = (15, 6))\n",
    "ax= plt.bar(X, Y)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}