From 6c373e8af1012bae2ee72ca7da366f2b55811903 Mon Sep 17 00:00:00 2001
From: Spencer Dixon <spencerlloyddixon@gmail.com>
Date: Mon, 26 Mar 2018 10:44:37 +0100
Subject: [PATCH] Added reason for export graph

---
 ...Learning about Trade Data-checkpoint.ipynb | 313 ++++++++++++------
 2. Learning about Trade Data.ipynb            | 313 ++++++++++++------
 2 files changed, 410 insertions(+), 216 deletions(-)

diff --git a/.ipynb_checkpoints/2. Learning about Trade Data-checkpoint.ipynb b/.ipynb_checkpoints/2. Learning about Trade Data-checkpoint.ipynb
index 8e14745..fff7aaa 100644
--- a/.ipynb_checkpoints/2. Learning about Trade Data-checkpoint.ipynb	
+++ b/.ipynb_checkpoints/2. Learning about Trade Data-checkpoint.ipynb	
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -1507,7 +1507,7 @@
        "[49369 rows x 16 columns]"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1532,7 +1532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -2995,7 +2995,7 @@
        "[49369 rows x 16 columns]"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3017,7 +3017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -3040,7 +3040,7 @@
        "Name: Class, dtype: int64"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3052,16 +3052,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x119e5c5f8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x107eeb828>"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3089,7 +3089,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -3108,7 +3108,7 @@
        "Name: Importer, dtype: int64"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3120,16 +3120,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x119f7efd0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x108b5ac50>"
       ]
      },
-     "execution_count": 213,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3150,23 +3150,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "T    24978\n",
+       "P     7356\n",
+       "B     2236\n",
+       "S     1992\n",
+       "Z     1561\n",
+       "Q     1072\n",
+       "M      963\n",
+       "E      584\n",
+       "H      132\n",
+       "N       59\n",
+       "G       54\n",
+       "L       51\n",
+       "Name: Purpose, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "purpose_distribution = dataframe['Purpose'].value_counts()\n",
+    "purpose_distribution"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11015ab00>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "### Top Reasons for Exports"
+    "purpose_distribution.head(10).plot.bar(figsize=(10, 10), title=\"Top 10 Export Purposes\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3175,7 +3222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3191,7 +3238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -3486,7 +3533,7 @@
        "42  2017      2"
       ]
      },
-     "execution_count": 205,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3507,7 +3554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -3538,223 +3585,259 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>1976</td>\n",
+       "      <td>431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1977</td>\n",
+       "      <td>891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1978</td>\n",
+       "      <td>823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
        "      <td>1979</td>\n",
        "      <td>1000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>4</th>\n",
        "      <td>1980</td>\n",
        "      <td>1090</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>5</th>\n",
        "      <td>1981</td>\n",
        "      <td>1484</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>6</th>\n",
        "      <td>1982</td>\n",
        "      <td>1213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>7</th>\n",
        "      <td>1983</td>\n",
        "      <td>1032</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>8</th>\n",
        "      <td>1984</td>\n",
        "      <td>895</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>9</th>\n",
        "      <td>1985</td>\n",
        "      <td>996</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>10</th>\n",
        "      <td>1986</td>\n",
        "      <td>1076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
+       "      <th>11</th>\n",
        "      <td>1987</td>\n",
        "      <td>862</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>12</th>\n",
        "      <td>1988</td>\n",
        "      <td>1576</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>13</th>\n",
        "      <td>1989</td>\n",
        "      <td>1203</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
+       "      <th>14</th>\n",
        "      <td>1990</td>\n",
        "      <td>1142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>15</th>\n",
        "      <td>1991</td>\n",
        "      <td>1032</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>16</th>\n",
        "      <td>1992</td>\n",
        "      <td>1335</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>17</th>\n",
        "      <td>1993</td>\n",
        "      <td>1441</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>18</th>\n",
        "      <td>1994</td>\n",
        "      <td>1268</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>19</th>\n",
        "      <td>1995</td>\n",
        "      <td>1386</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
+       "      <th>20</th>\n",
        "      <td>1996</td>\n",
        "      <td>1241</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>21</th>\n",
        "      <td>1997</td>\n",
        "      <td>1181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>22</th>\n",
        "      <td>1998</td>\n",
        "      <td>1477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
+       "      <th>23</th>\n",
        "      <td>1999</td>\n",
        "      <td>1458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>24</th>\n",
        "      <td>2000</td>\n",
        "      <td>1555</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>22</th>\n",
+       "      <th>25</th>\n",
        "      <td>2001</td>\n",
        "      <td>1084</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
+       "      <th>26</th>\n",
        "      <td>2002</td>\n",
        "      <td>1234</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>24</th>\n",
+       "      <th>27</th>\n",
        "      <td>2003</td>\n",
        "      <td>1327</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25</th>\n",
+       "      <th>28</th>\n",
        "      <td>2004</td>\n",
        "      <td>1390</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>29</th>\n",
        "      <td>2005</td>\n",
        "      <td>1405</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
+       "      <th>30</th>\n",
        "      <td>2006</td>\n",
        "      <td>1080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28</th>\n",
+       "      <th>31</th>\n",
        "      <td>2007</td>\n",
        "      <td>1094</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
+       "      <th>32</th>\n",
        "      <td>2008</td>\n",
        "      <td>1177</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
+       "      <th>33</th>\n",
        "      <td>2009</td>\n",
        "      <td>1408</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>31</th>\n",
+       "      <th>34</th>\n",
        "      <td>2010</td>\n",
        "      <td>1222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>32</th>\n",
+       "      <th>35</th>\n",
        "      <td>2011</td>\n",
        "      <td>1305</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>33</th>\n",
+       "      <th>36</th>\n",
        "      <td>2012</td>\n",
        "      <td>1423</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>34</th>\n",
+       "      <th>37</th>\n",
        "      <td>2013</td>\n",
        "      <td>1479</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>1328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>1153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1162</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "    Year  Count\n",
-       "0   1979   1000\n",
-       "1   1980   1090\n",
-       "2   1981   1484\n",
-       "3   1982   1213\n",
-       "4   1983   1032\n",
-       "5   1984    895\n",
-       "6   1985    996\n",
-       "7   1986   1076\n",
-       "8   1987    862\n",
-       "9   1988   1576\n",
-       "10  1989   1203\n",
-       "11  1990   1142\n",
-       "12  1991   1032\n",
-       "13  1992   1335\n",
-       "14  1993   1441\n",
-       "15  1994   1268\n",
-       "16  1995   1386\n",
-       "17  1996   1241\n",
-       "18  1997   1181\n",
-       "19  1998   1477\n",
-       "20  1999   1458\n",
-       "21  2000   1555\n",
-       "22  2001   1084\n",
-       "23  2002   1234\n",
-       "24  2003   1327\n",
-       "25  2004   1390\n",
-       "26  2005   1405\n",
-       "27  2006   1080\n",
-       "28  2007   1094\n",
-       "29  2008   1177\n",
-       "30  2009   1408\n",
-       "31  2010   1222\n",
-       "32  2011   1305\n",
-       "33  2012   1423\n",
-       "34  2013   1479"
+       "0   1976    431\n",
+       "1   1977    891\n",
+       "2   1978    823\n",
+       "3   1979   1000\n",
+       "4   1980   1090\n",
+       "5   1981   1484\n",
+       "6   1982   1213\n",
+       "7   1983   1032\n",
+       "8   1984    895\n",
+       "9   1985    996\n",
+       "10  1986   1076\n",
+       "11  1987    862\n",
+       "12  1988   1576\n",
+       "13  1989   1203\n",
+       "14  1990   1142\n",
+       "15  1991   1032\n",
+       "16  1992   1335\n",
+       "17  1993   1441\n",
+       "18  1994   1268\n",
+       "19  1995   1386\n",
+       "20  1996   1241\n",
+       "21  1997   1181\n",
+       "22  1998   1477\n",
+       "23  1999   1458\n",
+       "24  2000   1555\n",
+       "25  2001   1084\n",
+       "26  2002   1234\n",
+       "27  2003   1327\n",
+       "28  2004   1390\n",
+       "29  2005   1405\n",
+       "30  2006   1080\n",
+       "31  2007   1094\n",
+       "32  2008   1177\n",
+       "33  2009   1408\n",
+       "34  2010   1222\n",
+       "35  2011   1305\n",
+       "36  2012   1423\n",
+       "37  2013   1479\n",
+       "38  2014   1328\n",
+       "39  2015   1153\n",
+       "40  2016   1162"
       ]
      },
-     "execution_count": 224,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3775,22 +3858,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x119f16588>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x109a65f60>"
       ]
      },
-     "execution_count": 225,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3805,7 +3888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 251,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3815,7 +3898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 267,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -3824,7 +3907,7 @@
        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
       ]
      },
-     "execution_count": 267,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3836,7 +3919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 268,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3845,12 +3928,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 270,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3868,12 +3951,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 271,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3903,6 +3986,20 @@
    "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,
diff --git a/2. Learning about Trade Data.ipynb b/2. Learning about Trade Data.ipynb
index 8e14745..fff7aaa 100644
--- a/2. Learning about Trade Data.ipynb	
+++ b/2. Learning about Trade Data.ipynb	
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -1507,7 +1507,7 @@
        "[49369 rows x 16 columns]"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1532,7 +1532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -2995,7 +2995,7 @@
        "[49369 rows x 16 columns]"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3017,7 +3017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -3040,7 +3040,7 @@
        "Name: Class, dtype: int64"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3052,16 +3052,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x119e5c5f8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x107eeb828>"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3089,7 +3089,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -3108,7 +3108,7 @@
        "Name: Importer, dtype: int64"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3120,16 +3120,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x119f7efd0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x108b5ac50>"
       ]
      },
-     "execution_count": 213,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -3150,23 +3150,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "T    24978\n",
+       "P     7356\n",
+       "B     2236\n",
+       "S     1992\n",
+       "Z     1561\n",
+       "Q     1072\n",
+       "M      963\n",
+       "E      584\n",
+       "H      132\n",
+       "N       59\n",
+       "G       54\n",
+       "L       51\n",
+       "Name: Purpose, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "purpose_distribution = dataframe['Purpose'].value_counts()\n",
+    "purpose_distribution"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x11015ab00>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "### Top Reasons for Exports"
+    "purpose_distribution.head(10).plot.bar(figsize=(10, 10), title=\"Top 10 Export Purposes\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3175,7 +3222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3191,7 +3238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -3486,7 +3533,7 @@
        "42  2017      2"
       ]
      },
-     "execution_count": 205,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3507,7 +3554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -3538,223 +3585,259 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>1976</td>\n",
+       "      <td>431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1977</td>\n",
+       "      <td>891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1978</td>\n",
+       "      <td>823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
        "      <td>1979</td>\n",
        "      <td>1000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>4</th>\n",
        "      <td>1980</td>\n",
        "      <td>1090</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <th>5</th>\n",
        "      <td>1981</td>\n",
        "      <td>1484</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
+       "      <th>6</th>\n",
        "      <td>1982</td>\n",
        "      <td>1213</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
+       "      <th>7</th>\n",
        "      <td>1983</td>\n",
        "      <td>1032</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
+       "      <th>8</th>\n",
        "      <td>1984</td>\n",
        "      <td>895</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
+       "      <th>9</th>\n",
        "      <td>1985</td>\n",
        "      <td>996</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>10</th>\n",
        "      <td>1986</td>\n",
        "      <td>1076</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>8</th>\n",
+       "      <th>11</th>\n",
        "      <td>1987</td>\n",
        "      <td>862</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
+       "      <th>12</th>\n",
        "      <td>1988</td>\n",
        "      <td>1576</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>13</th>\n",
        "      <td>1989</td>\n",
        "      <td>1203</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
+       "      <th>14</th>\n",
        "      <td>1990</td>\n",
        "      <td>1142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
+       "      <th>15</th>\n",
        "      <td>1991</td>\n",
        "      <td>1032</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>13</th>\n",
+       "      <th>16</th>\n",
        "      <td>1992</td>\n",
        "      <td>1335</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
+       "      <th>17</th>\n",
        "      <td>1993</td>\n",
        "      <td>1441</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>15</th>\n",
+       "      <th>18</th>\n",
        "      <td>1994</td>\n",
        "      <td>1268</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>19</th>\n",
        "      <td>1995</td>\n",
        "      <td>1386</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
+       "      <th>20</th>\n",
        "      <td>1996</td>\n",
        "      <td>1241</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>18</th>\n",
+       "      <th>21</th>\n",
        "      <td>1997</td>\n",
        "      <td>1181</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>19</th>\n",
+       "      <th>22</th>\n",
        "      <td>1998</td>\n",
        "      <td>1477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>20</th>\n",
+       "      <th>23</th>\n",
        "      <td>1999</td>\n",
        "      <td>1458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>21</th>\n",
+       "      <th>24</th>\n",
        "      <td>2000</td>\n",
        "      <td>1555</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>22</th>\n",
+       "      <th>25</th>\n",
        "      <td>2001</td>\n",
        "      <td>1084</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>23</th>\n",
+       "      <th>26</th>\n",
        "      <td>2002</td>\n",
        "      <td>1234</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>24</th>\n",
+       "      <th>27</th>\n",
        "      <td>2003</td>\n",
        "      <td>1327</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25</th>\n",
+       "      <th>28</th>\n",
        "      <td>2004</td>\n",
        "      <td>1390</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26</th>\n",
+       "      <th>29</th>\n",
        "      <td>2005</td>\n",
        "      <td>1405</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>27</th>\n",
+       "      <th>30</th>\n",
        "      <td>2006</td>\n",
        "      <td>1080</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>28</th>\n",
+       "      <th>31</th>\n",
        "      <td>2007</td>\n",
        "      <td>1094</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
+       "      <th>32</th>\n",
        "      <td>2008</td>\n",
        "      <td>1177</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>30</th>\n",
+       "      <th>33</th>\n",
        "      <td>2009</td>\n",
        "      <td>1408</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>31</th>\n",
+       "      <th>34</th>\n",
        "      <td>2010</td>\n",
        "      <td>1222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>32</th>\n",
+       "      <th>35</th>\n",
        "      <td>2011</td>\n",
        "      <td>1305</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>33</th>\n",
+       "      <th>36</th>\n",
        "      <td>2012</td>\n",
        "      <td>1423</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>34</th>\n",
+       "      <th>37</th>\n",
        "      <td>2013</td>\n",
        "      <td>1479</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>1328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>1153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>1162</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "    Year  Count\n",
-       "0   1979   1000\n",
-       "1   1980   1090\n",
-       "2   1981   1484\n",
-       "3   1982   1213\n",
-       "4   1983   1032\n",
-       "5   1984    895\n",
-       "6   1985    996\n",
-       "7   1986   1076\n",
-       "8   1987    862\n",
-       "9   1988   1576\n",
-       "10  1989   1203\n",
-       "11  1990   1142\n",
-       "12  1991   1032\n",
-       "13  1992   1335\n",
-       "14  1993   1441\n",
-       "15  1994   1268\n",
-       "16  1995   1386\n",
-       "17  1996   1241\n",
-       "18  1997   1181\n",
-       "19  1998   1477\n",
-       "20  1999   1458\n",
-       "21  2000   1555\n",
-       "22  2001   1084\n",
-       "23  2002   1234\n",
-       "24  2003   1327\n",
-       "25  2004   1390\n",
-       "26  2005   1405\n",
-       "27  2006   1080\n",
-       "28  2007   1094\n",
-       "29  2008   1177\n",
-       "30  2009   1408\n",
-       "31  2010   1222\n",
-       "32  2011   1305\n",
-       "33  2012   1423\n",
-       "34  2013   1479"
+       "0   1976    431\n",
+       "1   1977    891\n",
+       "2   1978    823\n",
+       "3   1979   1000\n",
+       "4   1980   1090\n",
+       "5   1981   1484\n",
+       "6   1982   1213\n",
+       "7   1983   1032\n",
+       "8   1984    895\n",
+       "9   1985    996\n",
+       "10  1986   1076\n",
+       "11  1987    862\n",
+       "12  1988   1576\n",
+       "13  1989   1203\n",
+       "14  1990   1142\n",
+       "15  1991   1032\n",
+       "16  1992   1335\n",
+       "17  1993   1441\n",
+       "18  1994   1268\n",
+       "19  1995   1386\n",
+       "20  1996   1241\n",
+       "21  1997   1181\n",
+       "22  1998   1477\n",
+       "23  1999   1458\n",
+       "24  2000   1555\n",
+       "25  2001   1084\n",
+       "26  2002   1234\n",
+       "27  2003   1327\n",
+       "28  2004   1390\n",
+       "29  2005   1405\n",
+       "30  2006   1080\n",
+       "31  2007   1094\n",
+       "32  2008   1177\n",
+       "33  2009   1408\n",
+       "34  2010   1222\n",
+       "35  2011   1305\n",
+       "36  2012   1423\n",
+       "37  2013   1479\n",
+       "38  2014   1328\n",
+       "39  2015   1153\n",
+       "40  2016   1162"
       ]
      },
-     "execution_count": 224,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3775,22 +3858,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x119f16588>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x109a65f60>"
       ]
      },
-     "execution_count": 225,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3805,7 +3888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 251,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3815,7 +3898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 267,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -3824,7 +3907,7 @@
        "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
       ]
      },
-     "execution_count": 267,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3836,7 +3919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 268,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3845,12 +3928,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 270,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3868,12 +3951,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 271,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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772dTOw8+GFI7mYulHHFEbFI7HUc4QTi66VGAk4LI2wVhzOwo4APgjpwAsLO7r40efx0Y4+5fM7OJwH8QAsARwPXufoSZ7QY0AVWAA/OBse7+7tb+tgKApEZLS7YD9/HHw/DMzKidkSOLXbst0miw+MrbKCB3n2tmlR3K1uY83ZHQqEM4KrjDQ1SZZ2a7mtlewNHAbHdfHVVuNjABmNb1qogk0KZNIZWTafTffDOM2jnvPPjtb8PFU2JO80FKX687gc1sKnAmsAY4JioeCizPWaw1KuusXCQ93n8fHn44m9oZMiTs4f/qV7FK7XRXQUc4Sb/odSewu9e5+3CgAbgwXxUys1ozazKzplWrVuXrY0WKY+lS+O//hhNPhL33DgnyqqpwOobnn4cf/xg++9mSa/whjGQqK2tfpvkgpSUfo4AagC9Hj1cAw3NeGxaVdVa+GXevd/cqd68qLy/PQ/VE+tGmTSGHf/nlcOCBMG5cuBj6174Gr78Os2bBhRfGNq/fE30e4SRF16sUkJmNcvcl0dPJwEvR4xnAhWY2ndAJvMbd3zCzWcCPzGxwtNyJwOV9qLdIfKxdG1I7M2eG1M6ee4YO3JtvDgGgBPfuu0vzQUpblwHAzKYROnF3N7NW4ApgopntTxgG2gJ8LVr8QcIIoGbCMNCzAdx9dTR09OlouR9kOoRFStJrr2WvgztvXkjjTJoEV14ZhseIlACdDE6kOzZtCg19ZhbuqlVwyimh0T/hBPjEJ4pdQ5F/0sngRPpq7dqQs8+kdvbeOzT4t9wSUjsDNJFeSpu+wdKvYn/qgNdeC5dAPOGEcFnEX/86NPZNTfDcc2GIy/jxavwlEXQEIP0mFhdH6SiT2slMyHr77ZDaOf98uO8+2GmnIlVMpPDUByD9JjanDsikdhob4Y9/DHv6kyaFkTuHH669eyl56gOQ2CnqqQNefTW7l//UU/D5z4dG/4c/1NRVSS0FAOk3/XrqgE2b4Iknso3+6tWhwb/wQjj+eKV2RFAAkH5U8EtJrlnTPrUzbFhI69x2Wzj9glI7Iu0oAEi/KcilJF95JTs2P5Paqa6GH/0Ihg/v+v0iKaZOYIm9hoZs0Bg5fCO/PPMJTlwfzcLNpHYyE7J23LHY1RUpOnUCSyI0NMAl563hqI8e4oc0MmHZQ6yYOpznT53EQUrtiPSJfjkST83NcO21VH71OBZ/NIwp3M5jfI5P83cO9b9T/exVsZuNG/tJbiId6AhA4mHjxvajdt57D045hZ/+4+vM5njW0T61E7erTsVykptIF9QHIMXz3nvZUTsPPRR6hTMTssaOhQED4jN5rAulUk9Jh+72AcTn+FnSYckSuPZaOPbY0ODfeScceSQ8+2y4cMoPftBuNm6pXHUq6dfHVXormZQCksLauBEeeyw7VHPt2nCunYsvDhOyOrbuHRRk6GgBJPn6uEpvJZdSQJJ/774bUjozZ4b7yspsaueww2LVcZsvHRtJCLEtCZdIVHqr9CgFlEc6/O2GxYvhmmvgmGNCy9DQAEcdBQsWwPz58P3vJ3rIZpKvj5v09FaaKQXUBR3+diKT2smM2nn//bCX/5//Cccd12VqJ4mSen3cJKe30i6Zu2N5VFfX/rAewvO6uuLUp6jefRemTQut3JAhobHfaSe4+25obQ27vNXVqWz8k6xUOuKl57oMAGZ2q5mtNLOFOWU/NbOXzGyBmd1nZrvmvHa5mTWb2ctmdlJO+YSorNnMLsv/qhRGEg5/+5TCWrwYfv7zbGrn7rvhC1/IpnauvPKfQzYlmZKc3kq7LjuBzewo4APgDnc/MCo7EfiTu280s58AuPu3zWwMMA0YB+wNPAKMjj5qMXAC0Ao8DZzu7ou29rfj0Alc6h1gPe6c3LAhm9qZORM++CDbgXvssdq7FykBeesEdve5wOoOZQ+7+8bo6TxgWPR4MjDd3de7+2tAMyEYjAOa3f1Vd/8YmB4tG3ulfvjbrRTWu++GPfvTTw+pnUsugZ13zqZ2fvWrEATU+IskSj46gc8B/jd6PJQQEDJaozKA5R3Kj9jSh5lZLVALMCIGvUylMg69M52lqrZveRl+FnXg/v3vIa1TXR3SPXvv3b+VFJGi6FMAMLM6YCOQt4GR7l4P1ENIAeXrc/uilEd3ZEZwbMMGPs/fqKaRSczkEwPXQfMk+Na3lNoRSaleBwAzOwuYBBzn2Y6EFUDuVTiGRWVspVwKZfVqGib+kdfrZ3Lcplm8yj40Us3Z20/n/Js/Tc1XrNg1FJEi6tXQDTObAFwKnOruuRnmGcBpZjbIzEYCo4CnCJ2+o8xspJltB5wWLSv55A4vvQQ//WlI6VRW8rkV9zD8rGOZMHQh46yJ2yuu4PxbDktM469JeiK91+URgJlNA44GdjezVuAK4HJgEDDbzADmufvX3P0FM7sHWERIDV3g7puiz7kQmAUMBG519xcKsD7ps2ED/PWv2VE7H30UOmwvvTSkdnbYgfGEKJw0mqTXc7lXVyu1/izJP50LqBS980646HljIzz8MIwalR2qeeihYbB2CpT6EN3+luTzFUl73R0GqgBQCjKpncxe/rPPholZ1dXhzJp77VXsGhbFgAFh03RkBm1t/V+fuFPATA9dE7jU5aZ2Ghth/fqwl3/ZZaHx32GHYtew6HSOmp5Jwqx2yS8FgDjZUmqnuhp+9zs45JDUpHa6a+rULac0SmWSXn9TwJSOFACKyR1efDF7sZTnngsdt9XVcP31sOeexa5hrJX6JL3+poApHakPoL99/HH71M6GDdkO3GOOge23L3YNJcE0Cigd1AcQJ++8Aw8+GBr82bNh9OjQ4N97Lxx8sFI70m9KeVa75J8CQCFkUjuZUTsLFmRTOzfcoNSOiMSCTuKeLx9/DI88AhddBPvtBxMmhOPs73wH3noL7rsPzjkndo2/ZtKKpJeOAPri7bfbp3Y++cmSSu1oJq1IuqkTuCfcYdGibAfuwoXh+rfV1TBxYjiXfgnRxCCRZFIncL58/DE8+mg2n79pU2jwv/c9OProkh61o4lBIummALAlq1aF1M7MmSG186lPhUb/D3+Agw6KfWqnuzQxSCTdFAAgpHZeeCE7ISs3tXPjjSWX2ukuTQwSSbf0BoD160NqJ9Pot7UlJrXTXZpJK5Ju6QoAmdROY2MYsjlmTJiFO2MGHHhgYlI7PaGJQSLplewA4B7SOZm9/EWL4PjjQ6N/002wxx7FrqGISNEkNwB8//tw223hcXU1XHlluEzioEHFrJWISGwkNwAcfjh8+ctwwAGpTO2IiHSly1NBmNmtZrbSzBbmlP2bmb1gZm1mVtVh+cvNrNnMXjazk3LKJ0RlzWZ2WX5XYwsmTkxtXl9EpDu6cy6g24AJHcoWAl8C5uYWmtkY4DTggOg9N5nZQDMbCPwCOBkYA5weLSsiIkXSZQrI3eeaWWWHshcBbPO968nAdHdfD7xmZs3AuOi1Znd/NXrf9GjZRX2pvIiI9F6+zwY6FFie87w1KuusXETyTGd4le6K3emgzazWzJrMrGnVqlV5/3z9OCTJMmd4bWkJo6AzZ3jV91y2JN8BYAUwPOf5sKiss/LNuHu9u1e5e1V5eXleK6cfh/RV3Hcg6uran9oDwvO6uuLUR+It3wFgBnCamQ0ys5HAKOAp4GlglJmNNLPtCB3FM/L8t7ukH4f0RSnsQOgMr9IT3RkGOg14AtjfzFrN7Fwz+1czawU+AzxgZrMA3P0F4B5C5+5DwAXuvsndNwIXArOAF4F7omX7lX4c0hdx2oHo7EikszO56gyvsiWpuiBMqVwApaFBJ2iLowEDwp5/R2bhXIL9peOV3CCcxbW+Pjzu7DV9h9KjuxeEiV0ncCFNnRp+DLnidvrjUkgzpFVc9q63diRSUxMa+4qKEJgqKtT4S+dSdQQA8d+7LpWjlDTa2p53f36H4nIkIvGlI4BO1NSEhrStLdzHqfEH9VPEWVz2ruNyJCKlL3UBIO704463OOxAlEIqU0qDAkDM6MctXYnLkYiUvuSeDrpE6TKN0h26kpvkgwJADOnHLSL9QSkgEZGUUgAQEUkpBQARkZRSABARSSkFgD6K++mB+yLJ6yYiGgXUJx1PDZA5bw+U/iieJK+biASpOxdQPiX5vD1JXjeRpNO5gPpBks/bk+R1E5FAAaAPknzeniSvm4gECgB9kOTz9iR53UQkUADogySflCvJ6yYigTqBRUQSJm+dwGZ2q5mtNLOFOWW7mdlsM1sS3Q+Oys3MbjCzZjNbYGaH5bxnSrT8EjOb0tsVExGR/OhOCug2YEKHssuAOe4+CpgTPQc4GRgV3WqBX0IIGMAVwBHAOOCKTNAoBE1gEhHpWpcBwN3nAqs7FE8Gbo8e3w58Maf8Dg/mAbua2V7AScBsd1/t7u8Cs9k8qORFqVxUXUFKRIqtt53AQ9z9jejxm8CQ6PFQYHnOcq1RWWfleVdX1/6i3RCe19UV4q/1TqkEKRFJtj6PAvLQi5y3nmQzqzWzJjNrWrVqVY/fXwoTmEohSIlI8vU2ALwVpXaI7ldG5SuA4TnLDYvKOivfjLvXu3uVu1eVl5f3uGKlMIGpFIKUiCRfbwPADCAzkmcKcH9O+ZnRaKDxwJooVTQLONHMBkedvydGZXlXChOYSiFIiUjydWcY6DTgCWB/M2s1s3OBq4ETzGwJcHz0HOBB4FWgGbgZOB/A3VcDVwFPR7cfRGV5VwoTmEohSIlI8mkiWJE0NISc/7JlYc9/6tR4BSkRKV3dnQim6wEUSU2NGnwRKS6dC0hEJKUUAEREUkoBQEQkpRQARERSSgFARCSlFABERFJKAUBEJKUUAEREUkoBQEQkpRQARERSSgFARCSlFABERFJKAUBEJKUUAEREUkoBQEQkpRQARERSSgFARCSlFABERFKqTwHAzC4ys4Vm9oKZXRyV7WZms81sSXQ/OCo3M7vBzJrNbIGZHZaPFRARkd7pdQAwswOB84BxwCHAJDPbD7gMmOPuo4A50XOAk4FR0a0W+GUf6i0iIn3UlyOATwFPuvs6d98IPAp8CZgM3B4tczvwxejxZOAOD+YBu5rZXn34+7HX0ACVlTBgQLhvaCh2jUREsvoSABYCR5rZv5hZGTARGA4Mcfc3omXeBIZEj4cCy3Pe3xqVtWNmtWbWZGZNq1at6kP1iquhAWproaUF3MN9ba2CgIjER68DgLu/CPwEeBh4CHgW2NRhGQe8h59b7+5V7l5VXl7e2+oVXV0drFvXvmzdulAuIhIHfeoEdvdfu/tYdz8KeBdYDLyVSe1E9yujxVcQjhAyhkVlibRsWc/KRUT6W19HAe0R3Y8g5P/vBmYAU6JFpgD3R49nAGdGo4HGA2tyUkWJM2JEz8pFRPpbX+cB/N7MFgGNwAXu/h5wNXCCmS0Bjo+eAzwIvAo0AzcD5/fxb8fa1KlQVta+rKwslIuIxME2fXmzux+5hbJ3gOO2UO7ABX35e6Wkpibc19WFtM+IEaHxz5SLiBRbnwKAbF1NjRp8EYkvnQpCRCSlFABKjCaXiUi+KAVUQjKTyzLzCzKTy0CpJhHpOR0BlBBNLhORfFIAKCGaXCYi+aQAUEI0uUxE8kkBoIRocpmI5JMCQAmpqYH6eqioALNwX1+vDmAR6R2NAioxmlwmIvmiIwARkZRSABARSSkFABGRlFIAEBFJKQUAEZGUsnCa/ngys1VAS4E+fnfg7QJ9dinS9tictsnmtE3ai+v2qHD3Li+qHusAUEhm1uTuVcWuR1xoe2xO22Rz2ibtlfr2UApIRCSlFABERFIqzQGgvtgViBltj81pm2xO26S9kt4eqe0DEBFJuzQfAYiIpFpiAoCZ3WpmK81sYU7ZIWb2hJk9b2aNZrZzVL6tmd0elb9oZpfnvGeCmb1sZs1mdlkx1iVferhNtjOz30Tlz5nZ0TnvGRuVN5vZDWZmRVidPjOz4Wb2ZzNbZGYvmNlFUfluZjbbzJZE94OjcovWt9nMFpjZYTmfNSVafomZTSnWOvVVL7bJJ6Pvz3ozu6TDZ5X8b6cX26Mm+m48b2aPm9khOZ8V/+3h7om4AUcBhwELc8qeBr4QPT4HuCp6fAYwPXpcBiwFKoGBwCvAPsB2wHOprzOBAAADpklEQVTAmGKvWz9tkwuA30SP9wDmAwOi508B4wED/gicXOx16+X22As4LHr8CWAxMAb4L+CyqPwy4CfR44nR+lq0/k9G5bsBr0b3g6PHg4u9fv20TfYADgemApfkfE4ifju92B6fzfzvgZNzviMlsT0ScwTg7nOB1R2KRwNzo8ezgS9nFgd2NLNtgB2Aj4G1wDig2d1fdfePgenA5ELXvVB6uE3GAH+K3rcSeA+oMrO9gJ3dfZ6Hb/YdwBcLXfdCcPc33P2Z6PH7wIvAUML/+PZosdvJrt9k4A4P5gG7RtvjJGC2u69293cJ23FCP65K3vR0m7j7Snd/GtjQ4aMS8dvpxfZ4PPoOAMwDhkWPS2J7JCYAdOIFshv934Dh0ePfAR8CbwDLgJ+5+2rCP3p5zvtbo7Ik6WybPAecambbmNlIYGz02lDCdshIxDYxs0rg08CTwBB3fyN66U1gSPS4s+9DIr8n3dwmnUncNunF9jiXcMQIJbI9kh4AzgHON7P5hMO5j6PyccAmYG9gJPBNM9unOFXsd51tk1sJX9Im4DrgccI2Shwz2wn4PXCxu6/NfS06yknd0Dhtk/Z6uj3M7BhCAPh2v1UyDxJ9RTB3fwk4EcDMRgOnRC+dATzk7huAlWb2GFBFiNjDcz5iGLCi/2pceJ1tE3ffCHwjs5yZPU7If75L9rAWSnybmNm2hB92g7vfGxW/ZWZ7ufsbUYpnZVS+gi1/H1YAR3co/0sh611IPdwmnelsW5Wcnm4PMzsYuIXQN/ZOVFwS2yPRRwBmtkd0PwD4LvA/0UvLgGOj13YkdPC9ROggHWVmI81sO+A0YEZ/17uQOtsmZlYWbQvM7ARgo7svig5715rZ+Gj0z5nA/cWpfd9E9f818KK7X5Pz0gwgM5JnCtn1mwGcGY0GGg+sibbHLOBEMxscjQY5MSorOb3YJp1JxG+np9vDzEYA9wL/x90X5yxfGtuj2L3Q+boB0wg5/Q2EVMa5wEWEvdjFwNVkJ77tBPyWkA9fBHwr53MmRsu/AtQVe736cZtUAi8TOr0eIZxNMPM5VcDCaJvcmHlPqd2AzxMO3RcAz0a3icC/AHOAJdG67xYtb8AvovV+HqjK+axzgObodnax160ft8me0XdpLWGgQCthkEAifju92B63EI6SM8s25XxW7LeHZgKLiKRUolNAIiLSOQUAEZGUUgAQEUkpBQARkZRSABARSSkFABGRlFIAEBFJKQUAEZGU+v9ru7DTxKjwUgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3903,6 +3986,20 @@
    "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,