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Purchasing power_Jupyter_HaitengSUN

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{
"cells": [
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" 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>Unnamed: 0</th>\n",
" <th>Year</th>\n",
" <th>Wheat</th>\n",
" <th>Wages</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1565</td>\n",
" <td>41.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1570</td>\n",
" <td>45.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1575</td>\n",
" <td>42.0</td>\n",
" <td>5.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1580</td>\n",
" <td>49.0</td>\n",
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" <th>4</th>\n",
" <td>5</td>\n",
" <td>1585</td>\n",
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" <td>6</td>\n",
" <td>1590</td>\n",
" <td>47.0</td>\n",
" <td>5.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>1595</td>\n",
" <td>64.0</td>\n",
" <td>5.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>1600</td>\n",
" <td>27.0</td>\n",
" <td>5.61</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1605</td>\n",
" <td>33.0</td>\n",
" <td>5.69</td>\n",
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" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1610</td>\n",
" <td>32.0</td>\n",
" <td>5.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>1615</td>\n",
" <td>33.0</td>\n",
" <td>5.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>1620</td>\n",
" <td>35.0</td>\n",
" <td>6.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>1625</td>\n",
" <td>33.0</td>\n",
" <td>6.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>1630</td>\n",
" <td>45.0</td>\n",
" <td>6.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>1635</td>\n",
" <td>33.0</td>\n",
" <td>6.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>1640</td>\n",
" <td>39.0</td>\n",
" <td>6.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>1645</td>\n",
" <td>53.0</td>\n",
" <td>6.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>1650</td>\n",
" <td>42.0</td>\n",
" <td>6.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>1655</td>\n",
" <td>40.5</td>\n",
" <td>6.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>1660</td>\n",
" <td>46.5</td>\n",
" <td>6.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>1665</td>\n",
" <td>32.0</td>\n",
" <td>6.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>1670</td>\n",
" <td>37.0</td>\n",
" <td>6.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>1675</td>\n",
" <td>43.0</td>\n",
" <td>7.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>1680</td>\n",
" <td>35.0</td>\n",
" <td>7.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>1685</td>\n",
" <td>27.0</td>\n",
" <td>7.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>1690</td>\n",
" <td>40.0</td>\n",
" <td>8.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>1695</td>\n",
" <td>50.0</td>\n",
" <td>8.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>1700</td>\n",
" <td>30.0</td>\n",
" <td>9.00</td>\n",
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" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>1705</td>\n",
" <td>32.0</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>1710</td>\n",
" <td>44.0</td>\n",
" <td>11.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>31</td>\n",
" <td>1715</td>\n",
" <td>33.0</td>\n",
" <td>11.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>32</td>\n",
" <td>1720</td>\n",
" <td>29.0</td>\n",
" <td>12.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>33</td>\n",
" <td>1725</td>\n",
" <td>39.0</td>\n",
" <td>13.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>34</td>\n",
" <td>1730</td>\n",
" <td>26.0</td>\n",
" <td>13.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>35</td>\n",
" <td>1735</td>\n",
" <td>32.0</td>\n",
" <td>13.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>36</td>\n",
" <td>1740</td>\n",
" <td>27.0</td>\n",
" <td>14.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>37</td>\n",
" <td>1745</td>\n",
" <td>27.5</td>\n",
" <td>14.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>38</td>\n",
" <td>1750</td>\n",
" <td>31.0</td>\n",
" <td>15.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>39</td>\n",
" <td>1755</td>\n",
" <td>35.5</td>\n",
" <td>15.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>40</td>\n",
" <td>1760</td>\n",
" <td>31.0</td>\n",
" <td>16.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>41</td>\n",
" <td>1765</td>\n",
" <td>43.0</td>\n",
" <td>17.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>42</td>\n",
" <td>1770</td>\n",
" <td>47.0</td>\n",
" <td>18.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>43</td>\n",
" <td>1775</td>\n",
" <td>44.0</td>\n",
" <td>19.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>44</td>\n",
" <td>1780</td>\n",
" <td>46.0</td>\n",
" <td>21.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>45</td>\n",
" <td>1785</td>\n",
" <td>42.0</td>\n",
" <td>23.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>46</td>\n",
" <td>1790</td>\n",
" <td>47.5</td>\n",
" <td>25.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>47</td>\n",
" <td>1795</td>\n",
" <td>76.0</td>\n",
" <td>27.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>48</td>\n",
" <td>1800</td>\n",
" <td>79.0</td>\n",
" <td>28.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>49</td>\n",
" <td>1805</td>\n",
" <td>81.0</td>\n",
" <td>29.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>50</td>\n",
" <td>1810</td>\n",
" <td>99.0</td>\n",
" <td>30.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>51</td>\n",
" <td>1815</td>\n",
" <td>78.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>52</td>\n",
" <td>1820</td>\n",
" <td>54.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>53</td>\n",
" <td>1821</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Year Wheat Wages\n",
"0 1 1565 41.0 5.00\n",
"1 2 1570 45.0 5.05\n",
"2 3 1575 42.0 5.08\n",
"3 4 1580 49.0 5.12\n",
"4 5 1585 41.5 5.15\n",
"5 6 1590 47.0 5.25\n",
"6 7 1595 64.0 5.54\n",
"7 8 1600 27.0 5.61\n",
"8 9 1605 33.0 5.69\n",
"9 10 1610 32.0 5.78\n",
"10 11 1615 33.0 5.94\n",
"11 12 1620 35.0 6.01\n",
"12 13 1625 33.0 6.12\n",
"13 14 1630 45.0 6.22\n",
"14 15 1635 33.0 6.30\n",
"15 16 1640 39.0 6.37\n",
"16 17 1645 53.0 6.45\n",
"17 18 1650 42.0 6.50\n",
"18 19 1655 40.5 6.60\n",
"19 20 1660 46.5 6.75\n",
"20 21 1665 32.0 6.80\n",
"21 22 1670 37.0 6.90\n",
"22 23 1675 43.0 7.00\n",
"23 24 1680 35.0 7.30\n",
"24 25 1685 27.0 7.60\n",
"25 26 1690 40.0 8.00\n",
"26 27 1695 50.0 8.50\n",
"27 28 1700 30.0 9.00\n",
"28 29 1705 32.0 10.00\n",
"29 30 1710 44.0 11.00\n",
"30 31 1715 33.0 11.75\n",
"31 32 1720 29.0 12.50\n",
"32 33 1725 39.0 13.00\n",
"33 34 1730 26.0 13.30\n",
"34 35 1735 32.0 13.60\n",
"35 36 1740 27.0 14.00\n",
"36 37 1745 27.5 14.50\n",
"37 38 1750 31.0 15.00\n",
"38 39 1755 35.5 15.70\n",
"39 40 1760 31.0 16.50\n",
"40 41 1765 43.0 17.60\n",
"41 42 1770 47.0 18.50\n",
"42 43 1775 44.0 19.50\n",
"43 44 1780 46.0 21.00\n",
"44 45 1785 42.0 23.00\n",
"45 46 1790 47.5 25.50\n",
"46 47 1795 76.0 27.50\n",
"47 48 1800 79.0 28.50\n",
"48 49 1805 81.0 29.50\n",
"49 50 1810 99.0 30.00\n",
"50 51 1815 78.0 NaN\n",
"51 52 1820 54.0 NaN\n",
"52 53 1821 54.0 NaN"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_url, skiprows=0)\n",
"raw_data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>Year</th>\n",
" <th>Wheat</th>\n",
" <th>Wages</th>\n",
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" <tr>\n",
" <th>50</th>\n",
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"text/plain": [
" Unnamed: 0 Year Wheat Wages\n",
"50 51 1815 78.0 NaN\n",
"51 52 1820 54.0 NaN\n",
"52 53 1821 54.0 NaN"
]
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"execution_count": 13,
"metadata": {},
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],
"source": [
" raw_data[raw_data.isnull().any(axis=1)]"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_axes([0,0,1,1])\n",
"Year = raw_data['Year']\n",
"Wheat = raw_data['Wheat']\n",
"Wage = raw_data['Wages']\n",
"x0 = [1565, 1810]\n",
"\n",
"ax.bar(Year,Wheat,color = 'gray')\n",
"\n",
"sorted_data = raw_data.set_index('Year').sort_index()\n",
"sorted_data['Wages'].plot(color = 'r')\n",
"plt.fill_between(Year, 0, Wage, facecolor='b', alpha=0.3)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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vVd1HVc9V1XWqulRVe6hq++h5WWXqds4VEG5FbpGJtsZdpPxnVI8AtibkpPopel6AyNeZaIgxxtS4IUMQVeYd/adst6S++BrYIxMVJduMErYEWgPbAm0ImYG3yURDjDGmRq1Zgz7yKIsOOoHVO2Tk71NTsf7AIO+9I+zpuFGqKOdc7F5d3P28HiNMk/81YabJ+4QFZu+h+l3ckxljTK0xYgSyZDEzru+T7ZbUJ/+NnkcRdhIpJtH7hnEritvzagU8jAUrY0xdUFSEDrqfFbt3Yum+XRN9NVfSPdVS3TJVUdxFymdm6oTGGJN1b7yBTP2OGTeOqBUTNeoL59z7maor6ZiXMcbkPB00iHXb7MSCI87IdlPqHe/9vsBlwO6Evb3yvfcnA987576KW0/8DBvGGFMX5OUh777LjBOvq+8ZNWqc9/5Y4HNCJvnuQNPoo92BRPdiLXgZY+qXQYMoaLYFc461zHZZcAdwo3PuFGB9Svl7QKJV4hUHL5EGiHRApHmSio0xptaZOxd9/nm+P+ZiCppvle3W1EcdSb+H4zLCxMDY4vS8FJgAtE1SsTHG1DoPPwzArBOvy3JD6q3lhFuGpXUG5iWpqOLgpaqEXS63TVKxMcbUKitWoIMHs+DwP7Bmu19luzX11b+Be733OxE6Ro2890cD9xF2Kokt7pjXzcC9iByA2LxSY0wOGjIEWbGCGSfbouQs6gfMAr4nJPydDIwFPgLuSlJR3KnyI4EmhHQeBYis2+hT1Xq9b7YxppYrKEAfeJBlHY/kp/YHZ7s19ZZzbgPwJ+/9X4BOhA7UV865aUnrihu8rk5asTHG1BrDhiFz5zDjtoez3RIDOOdmeO8XRq9XVaaOuBk2hlemcmOMybply9C+t7K8wxEsPLRXtltT73nvrwduJJq44b1fQMiV+6BzTsv7bqr4GTZE2gDnEhaT3Y7qEkS6AAtQnZWg7cYYU3P69YMfl/PNXx61VFBZ5r0fCFwK3AuMi4oPA/5CmNF+c9y64maVPxB4hzDQ1jE68RLgGGBP4Oy4JzTGmBrz5Zfo448z6/fXsnLX/bLdGgMXAxc7515MKRvrvZ8KPEGC4BV3tuF9wEOodgJSJ2uMBrrEqUBEdhaRd0VkiohMEpHrovJWIjJGRKZFz1vHbbwxxpSpqAi98irWt9yOqWdnd4dis5FvyihLlPEp7sEHAunGvfIJm1LGUQD0UdW9gd8AV4lIB6Av8I6qtif07vrGrM8YY8o2bBjy2Xgmnz/QsmnUHk8DV6UpvwL4V5KK4o55rQHS9Yj2ImxOWSFVzScEO1R1pYhMIQzYnUTY6BJCgHwPuCVmu4wxZlPLlqE338LyDkcwr9u52W6NKbE5cLb3/jjg06jsUGAH4Bnv/S/TQZ1z15ZXUdzg9QrgEDk9eq+ItAPuAV6K3+5Awnc7AeOBNlFgQ1XzRWS7Mr5zKWGgj8aNGyc9pTGmPrn9dli+jG//8ohN0qhd9gLyotfFaU5+iB57pxxX4azDuMHrJkIyxcVAM8Jq6DbAx4QV07GJSAtCwLteVVfETdihqoOBwQDNmzePPZ3SGFPP5OWh//gHs35/DSt23T/brTEpnHM1vpPyCuAIRLoTEig2APJQfTvJyURkM0LgekZVR0XFC0WkbdTrakvM25DGGLOJaJLGhq22ZepZNkmjLku2k7LqWEIeqsQkdLGGAFNU9f6Uj14FzgcGRM+vVKZ+Y4zh1luR8Z8y6frhFLRome3WmGoUf2qiyMmIfIDIkujxISKnJDhXF8Ii5+4iMiF6HE8IWseIyDTCurEBSS7AGGMAePBBGDiQWcdfZZM06oG4i5T7AH8jTHMcFpUeBvwbkdtRva+iKlT1I6CsAa4esdphjDHpPPcc3HAD+YefxsRLHrJJGvVAkgkbV6P6ZErZUEQ+A/5KWMRsjDE175130PPOY9k+R5F34who2DDbLTI1IG7wagG8m6b83egzY4ypeV99hZ5yCqt2/DWf3fYKRY2bZLtFphzRxpNrnXPjo/cXEFJGTQL6JMkwH3fM62XgD2nKTyNMuDCV5L3NiDKmUmbNQnv+jrVNWvKpe9MmaOSGB4HtAbz3vybkM/yGMAx1b5KK4gav6UBfREYj0j96vAncCkxF5MZfHsYYU91++AE99jgK1qzn0/6jWdt6x2y3yMSzO/Bt9Po0YIxz7krgEuDEJBXFvW14AbCckEF+z5Ty5cCFKe+VsC+LMcZUjx9+QLt2o2j+Asb3f4tVO+9d8XdMbaFA8aBkD+A/0esfgNZJKoq7SHnXJJUaY0y1KA5cc+by6e2vs3zvw7PdIpPM58Dt3vsxwJFEKf+AdoQAFluiFPTGGJM1pQLXsn2OynaLTHLXAwcAjwB3OedmROWnA58kqShZhg1jjMkGC1w1RkRaAv8E9iHc5rsImAo8T+ghzQbOUNXlSet2zk0E0u0KehNQmKQuC17GmNrNAldNewh4U1X/ICKNCcnYbyPsuzhARPoS9l3M2NZVzrm1Sb9jwcsYU3stX452626Bq4aIyJbAUYRJeqjqemC9iGRk30Xv/SzSb3eiwFrCzPYhzrkKl2DZmJcxpnYqKED/+Ed0+nTG93vNAldmNBKRL1Iel5b6fDfC1ldPichXIvJPEWlOqX0XgbT7LsbwFNAKmAaMiB7TorJXCbcOR3nvz6zwQmKdTqQDUIjq1Oj9MYQM8JOAgagmuldpjDEVuvlmZMwYvr76nyzdt2u2W1NXFKjqQeV83oiw7dU1qjpeRB4i3CLMlN2AAc65jRKwe+9vBjo450713t9G6NU9V15FcXteQwg7H4PIToRtS1oBVwF3Jmq6McZUZNgweOABZp54LXOP7Z3t1tQn84B5qjo+ev8iIZgtjPZbpIr7Lp4a1VnaqOgzCHs+tq+oorjBa29Ktm4+HRiP6vGELU7OilmHMcZUbNw49LLLWLJ/DyZfNCjbralXVPUHYK6I/Doq6gFMpmTfRajavourCeu7Sjsy+gzCIuY1FVUUd8JGQ2B99LoH8Hr0egbQJmYdxhhTvnnz0FNOZU3rnfji/55HG9qcsiy4Bngmmmk4k5BFqQEwUkR6A3MInZjKeAh4zHt/EGHBsgKHECaI3BEd0xOYUFFFcf/PmAhcgchrhOB1a1S+I7AkdrONMaYsa9agJ59M4YpVjL/3bTZsmShbkMkQVZ0ApBsXq/K+i865u6MZh9dSctfuO+Ai59zz0ft/AI9VVFfc4HULIbP8TcBwVIsTK/YCPovbcGOMSauwEC66CPLyyLvtZVbt0jHbLTLVxDn3HOVMxnDOVXjLEOLnNvwAkW2BLdl4VfUTlNynNMaY5NasQc8+G3n5Zb47724WHtor2y0yNcB735JS8y6cc8vifj/+DeUwHX55qbLZsb9vjDGlLV2K9uoF48Yx8eIHmdXrumy3yFQj7/2vgMeBbsBmKR8JG2ecr1DcdV7vUvGq6OGo5qU5xhhjNjV7NnpcT3T2bPJuHkl+l3T73Zo65imgJSFf4gLSx5VY4va8pgBnA/mEGSIABxN2xHwZOAK4EpGeqL5T2cYYY+qJCRPQnr+j4Oe1fNb/LcueUX8cAvwmStBbJXGD11pgGKrXb1QqMghQVA8krMS+E7DgZYwp25gx6GmnsbZJSz4d8LZNzqhfZgGbZ6KiuIuUzwceTVP+BCU7KQ8GOmSiUcaYOkgVBg1Cf/c7VrZux0cDx1ngqn+uA+723u9R1Yri9rwE6EhIoJiqQ/QZwAagqKoNMsbUQStXhqnwL77ID4edyoTrnqKg2ZbZblXO8N7jnPvlOYe9Quh5TfXerwMKUj90zsX+nyJu8BoODEGkPRuvir4FGBYdczRhMXNaIjIU+D2wSFX3icr6A5cQshgD3Kaqr6evwRiTkyZPRk85FWZMZ/KF9zLz5D4gUvH3TF10daYqihu8bgIWAjcQJmkA/ADcC9wXvR8NvFFOHcMIWz8/Xar8AVW9b9PDTV1TB/7VaJJ6/nm0d282bNacL+54h6X7HJ3tFpkscs4Nz1RdcRcpFwIDgAGEzcpAdUWpY+aUX4V+ICLtKtFGY0yuWbYMbr8dHnuM5Xsfzhc3v8C61jtku1UmC7z3rYoXH3vvW5V3bJJFysk3o1RdsUngqpqrReQbERkqIluXdZCIXFq8gVpBQUFZhxljsmndOnjgAXSPPdDHH2dmr+v55M53LXDVb4u998WbVy4hDBOVfhSXxxZ3kXIr4C5CYsbtKB30VCs78voPQiZhjZ4HERavbUJVBxNmNNK8efNKL2wzxlQDVXjxRfSWvsismSzufByTL7iXle32zXbLTPZ1B4p7VN0yVWncMa/izSgHU8VV0alUdWHxaxF5EngtE/UaY2rQuHFonz7IuHGsbLcvk/u/yeLOx2W7VaaWcM69n+51VcUNXj2AYyjZXTMjRKStquZHb0+hnNmKxphaZsYMuPVWeOEF1rfanilX/5O5PS6AhrHT05l6oKJxrlTVkZh3EbAqbqXpiMizQFdgGxGZBzigq4gcQOjJzQYuq8o5jKkt6vTMymXL4M470UceoajhZkw/qz8zTu5DYdMW2W6ZqZ2WUPHdumpKzAt/Bv6KyPmoViqIqepZaYqHVKYuY0wWrFsHjz2G3nEH/Pgjc357EVPP/qtNxjAVydg4V6q4wasf0A5YhMj3hGwaJVT3y2yzar86/S9rY1ItXw6DB6MP/x1ZMJ/FnY4NkzF2rXd/7E0lZHKcK1Xc4PVidZzcGFOLTZ8ODz2EDh2KrF7N0v17MP2yoSzudGy2W2ZySHbHvFR93AqNMTlswwZ45x30iSfglVfQho2Yf9TZzDzpBlbsun+2W2dyU1bHvIwxddWGDTB2LIwcif7nZWT5MjZs2ZrZp/+Z2cdfybpWbbPdwqzIy/N07mxDAxlQw2NeIiuA3VBdgshKyouclV+kXG/YGJmpVdIErIJmW/DDISexoMvpLO50LEWNm2S7leWy4JIbsjHmdQ2wMnqdsUzAxpgsqQMBy+Se6sptWHbwUh2e9rUxWWa92IS++QYefhgd9Z+SgHVwLxYccYYFLFMTFnvv2zrnFlH2+JeNeRljIgsWQL9+6LBhFDZpXtLD6nxcvQxYdpsxa2o4t2FF41ypbMzLmNpj1Sq49170vvvQ9RuY1esGpv2xHxtalLlpgzHVJhu5DevVOJfdiiqb/bfJEYWF8NRTaL/bkYU/sOCIM/juvLtZvf1u2W6ZMRvx3u9Amh1KnHN5ceuIN+ZljKndVqxAzzoLef11lu99OJP7jGL5Xodlu1XGbMR73wkYAexFGOdKlWjMK/lmlMaYGuN9jPwAM2eivzkMRo/mm8sf5eMBH9XrwJWXZzkVarHBwFzgSGA3YNeUR6JbBDbmZUwu++AD9JRTKdhQxOf932Lp/t2z3SJjytMB6OSc+19VK7IxL2Ny1ZAh6BVX8HOb3fis3//j5x3aZ7tF1cJmCdYp3wLbA9UYvGzMy5jaqbAQbrkFBg1iyQHH8MXNIylo0TLbrTImrVILk28DBnrv+xEC2UY7lCRZpGxjXhWINeZgqsT+GyewYgXaqxcMGsSsE65mvHvdAlcV2RhZtVsCLI4eY4BDgLeABSnlxcfEZrkNM8ymlZtqM3MmemIvmPod31z+GN8ff0W2W2TqIBFpCHwBzFfV34tIK+B5wp6Os4EzVHV5giprfDPKOpnb0IKLyUkffhgmZqwv5Iv+o1myf49st8jUXdcBU4DiTklf4B1VHSAifaP3t8StrOYT81puQ5Nl9g+NyNCh6OWXs3q7XRl/12t1dmKGyT4R2Qk4AbgLuDEqPgnoGr0eDrxHguCVynt/NLDWOTc+en8BcDEwCejjnFsVt67kY14iLRFptdHDmHqqWsfriorCc+/eLNmnKx/e+6kFLlNVjUTki5THpaU+fxC4GShKKWujqvkA0fN2VTj/g4TZhnjvfw08AXwDHAbcm6SieMFL5FeIvIHIWmApVRhkM8bEoArXXQfArOOvYvxfXrfchCYTClT1oJTH4OIPROT3wCJV/bIaz787YZYhwGnAGOfclcAlwIlJKorb83oKaANcBPQgZAnuThiIs1WRZiM2ezAD7rkHHnkEgImXP4I2rNoGEDajzsTQBeglIrPv/7xqAAAapUlEQVSB54DuIjICWCgibQGi50VVOEdqCqgewJvR6x+A1kkqivsn4hDgN6hOTFK5MaYShg2DW29l3tFnZ7slph5R1VuBWwFEpCtwk6qeIyL3AucDA6LnV6pwms+B2733YwgpoopvW7YjBLDY4va8ZgGbJ6nYGFMJr7+OXnwxiw/4LROufSrbrTEGQtA6RkSmAcdE7yvreuAA4BHgLufcjKj8dOCTJBXF7XldB9yNyJWoTk9ygmIiMhQovqe6T1RW1fUDxtQd48ejp5/Oil335/O+o9DNGme7RaaeUtX3CLMKUdWlhFt8Veacmwjsl+ajm4DCJHWV3fMSWYnIimix8suEqZJTEVn9S3nJ53EMA3qWKiteP9AeeCd6b0xG5cQY3P/+hx5/Amu22p5Pb3+dwmZbZLtFxtQY59xa59yGio8sUWOJeVX1AxFpV6o4Y+sHjMlZ+fnoscexoagB49xo1m/dJtstMiZjvPevA2c5536KefxLwJXOuYXlHZftxLwbrR8QkTLXD0TrES4FaNzYbqeYOuKnn9Cev6No0WI+vfM9Vu+wR7ZbZEymHQds772Pu9HkMUDzig6KN+Yl0gEoRHVq9P4YwqyTScBAVBPdq6yMaD3CYIDmzZvH22fMmNps3Tr0lFPQSZP4/C//5af2B2W7RcZUBwEmZ7rSuBM2hgAPEca8diJMlXwPuIqQ/+rWSp5/oYi0jXpdVV0/YEzuKCqC885D3n2Xr274F4s7HZvtFtUY25+r3qlMYt75FR0QN3jtDeRFr08HxqN6PCLdCAuYKxu8XiVz6wdMTJYzsGw18t9GFW64AUaOZNKF9zK/2znVez5jsqjmE/NurCGwPnrdA3g9ej2DkHmjQiLyLGFyxjYiMg9whKA1UkR6A3MIgdGYum3gQHj4YWacdAMzT+6T7dYYU6O89/sClxFSRV3knMv33p8MfO+c+ypuPXGD10TgCkReIwSv4p7WjoT8hhVS1bPK+Mj2djD1x/Dh0Lcv8488k8kX3gci2W6RMTXGe38s4Y7bG4TUgk2jj3YHLgBOjltX3AwbtxASJ74HPItqcWLFXsBncU9mTL21YAFcfDF64YUs2b8HE64fBg1sI3NT79wB3OicO4WSu3kQYsshSSqK96dH9QNgW2AbVC9K+eQJwLZzrUFJFtwmXZxb3cfXSz//DP37o+3bUzT8aWb2uoHPbnuZos2qlm3NEu2aHNWRkmGnVMuARNtrxU9VHabDLy9VNjvJyYypV4YMQfvdjvyQT36X05ly3t2sbrt7tltlTDYtJww3zS5V3hmYl6Si8tJDPYZIi9g1idyPSKKU9sZkUq3pCb71Vni++GJ+3OpXfHTPx3x5y8hyA1dt60nVtvaYOuPfwL3e+50I26M0inZXvg94OklF5d02vIySwbQ4Lga2SnJyY+qUiRPRnj3huOMA+OLm5/lo4Ccs3/vwLDfMmFqjH2GXku+BFoTFy2OBj4C7klRU3m1DAWYiEjebRYXpPIypk/Lz4S9/QYcOpaDZlvzvokHASvKPOCPbLcuqpIuRbfFy3Rcl3/2T9/52wq3CBsBXzrlpSesqL3hdWIm2lZtI0Zg6ZfFiALR9e3TdemadeB3TzujHhi1agd12M2YT3vvGQAPn3ExgZkp5E6DIObe+zC+Xku3EvMbknilT4MEH0aefhr59yd+/J1POGxArqa71Lkw99wLwPnB/qfLLCUksMrzOS6QDIr9OeX8MIiMQuRWRuJmCjcldqvD22+gJJ0CHDhQOe5o5R58HwJd9X7Rs8MbE0wV4K035GCDR4HDcVZJDgE4AKYl5WxES896Z5ITG5JTJk8F7dO8OcMwxrB/3Jd+d/VfeHjKHb656Itut24TNEjS1XDOgIE15EZBoB9ZsJ+Y1pvaZPBleeAF9fiQyZTIqwrKORzL32puZf/TZVV5gbEw99g1wFiG3baqzCWkIY6uxxLzG1GpRwAKgY8dfAtaCyx4h/7BTWdeqbXbbZ0zdcAfwsvd+D8IUeQgx5XTglCQV1VhiXmNqnTQ9LJzjWwtYxlQL59x/vfcnEtZ7PRwVfwX0cs69kaSuuMHrFuBl4CZguCXmrX6251YVrV8P8+fDvCjjzMCBMHcuzJ2LzpuHzplLg8WLSnpYl/6d/MNPg9mDmX3CVZtUZ7MEA/vvYKrKOfcm8GZV64kXvFQ/QGRbYEtUU/MbPgGsrmojjInl55/DNHWAoUNhxQr46afwvGIF7LQTevDBITAtSlly2L8/3HILBc23Ys22O7Om1U6s6dSJFbsesGkPa3ZNXpAxprKSJeYVaYjIocAEVNdZYl6TcevWhe1D5s4N72+5BSZNomjSZOT72YhqCEa9e//ylcKmzSlotiVccxmLi7ZhzQEHsGabnVnbeifWbLMz6Ce8/twKCpslmsxUL1hPylQ37/0KYDfn3BLv/UpCTsO0nHNbxq03XvAS2QIYCpwWnbg9IXXU48APqPaPe0JTjxQVwbJlsHAhLFoUyp58ctMe069+VWaPqej+B/h5p1+zYudDWHn4BazapSMwkbf/OZuCZltS0HQLtGH0v3GeZ3z/NLfN8z6xwGVM9lwDrIxeX52pSuP2vO4BdiDkovoopfw1QjLF/plqkKnFiopg+fIQjBZGQeaRR2DpUliypORxxBEUtd0BWbIYKUhZ0tG/P1x6KQAqQmGzLUKP6aqLWVzYmjUHHPBLb2ntNjuBjuP1kT+jjTbbuB15E1mz3a9q5pqNMVXinBsO4L1vBCwGxjvnlla13rjBqxdwCqoTSiXqnQLsVtVGmFpgfbQS4v33wy27efNKnjt3pmiHHZHFizYNRtdcA8CGLbZm/ZbbsH6LbQCYu+/xrG/ZhnVbbce6lm1Y17INFHzAmKFz2dBsKwqbNC/ZSTjPM96nGb/NG7dp4DLG5CTnXIH3fhSwF1BjwWvrMk62BVBY1UaYarRu3cY9o6XRz3jbbTB7Nvr99+js75H8BeAcdO36y1c3bLF1GDPq3Jl5HXv+EoTWRUGJwg8Z/fRCNmzRquTWHUCe55tr/rlpW/I+CD0qY0x99TWwBxmYGhU3eH1O6H09GL0v7n1dBnxS1UaYDFi9Oqxb+vZb+OYb9Jtv4KijoEmTTY/t35+igfeydtudWb1dO1bvfQxrjgq34cbd8TZrWu/E2m12Cr0jgDzP19cO2bSevA9Z33K7arwoY0wd0x8Y5L13wJfAz6kfOueWxa0obvC6DRiNSMfoOzdGrw8Bjop7MlMJGzaEyQ6LFpWMM/3tb2EPqQUL0Px8dP4CZN5cpKgIgMImzVi5S0cAvvvTHeF2XsqDpS/x3xfXQsNSOZXzPEv271GTV2eMqV/+Gz2PYuNZhxK9j53oPe46r08QOZywSHkGIctGHnBYyoJlk9TUqeH5/vtLbuktWYIuWQLdu6OtWiPLS/1DpH9/+POf2dCiJeta7cCarXdg7e5Hsfrw3VjRbj9WttuXn9vsFgJTnmfaH/ttet6lL20auIwxpvp1p5yp8kkkWef1LXB+Jk5ab6nC11/DqFHoiy8hUyaHYNSnD9qwYUnvKJr0MPuwM1nXsg3rUyc9rBnDf19YTdHmTbN7LcYYk5Bz7r1M1RV3ndcuZXyiwFpUF1elESIym7AOoBAoUNWDqlJfrbB+fcli2yhFke6+BzJrJtqgAcs6HsWCSy8HlvHGv5dT0HwrECn5fp5n4uWPblpv3hgLXMaYnOK9bwbcS9hscjPgbeBa51ylc+PG7XnNpryunsgKwtYoN6Oabq+WOLqpas0n+S0qglWrwutp02DtWlizpuQZ4JlnQmqiVatKnps1g4sv3vjYtWvh6KMp2r4tsmhhyAZRrH9/Fm+9J/nH9+WHQ08qmeiQ5ylo0bJmr9kYY9IQkZ2Bp4HtCXtsDVbVh0SkFfA80I4QD87QjVMFVsQDFwDPAGsJ26L8g5BNvlLiBq+zgIHA48D4qOxQ4FLC7JGWhCzBK9l0n5baIz8fgKKO+8BPPyErV8DKlSUph/bcc9Pv9O8P55yzUVHRZo3hz7ex7uU3KNy8KYWbNaGwcXgGmLvfCazdZucwa2/b8MySF9JnfzDGmNqjAOijqnkSMit9KSJjCIHnHVUdICJ9gb6EhO1xnQr0ds49B+C9HwF87L1v6Jyr1HKruMHrCuAGVEellI1FZCpwHapHI7KIEF0rE7wUeEvCAugnVHVw6QNE5FJCsKRx48aVOAWwedhE8IeWe1HQdsuQXqjZlmxothWwirwb/kVRSjAqatwE1oxh7D+mUtCkBYVNmlPYpHlY05Tneeup+Zueo6w1TrZxjDGmllPVfCA/er1SRKYQtr46CegaHTYceI9kwWtn4MPiN865z7z3BYTMTXMr09a4wetQIN2swonAwdHrcUBlV6B2UdUFIrIdMEZEvlPVD1IPiALaYIDmzZtXbrZKq1YAfNn3xU0/y/PM73ZOmvIx/Lxjmh6ZMcbknkYi8kXK+8HpOgsAItIO6ES429YmCmyoan70d3USqRsaFysgyaTBUuJ+8XtCr+f/SpVfAsyJXm8LxF5glkpVF0TPi0TkP4T1Yx+U/y1jjDEJxZoQJyItgJeA61V1haROJqscAUZ479ellDUBnvTe/7KtlnOuV9wK4wavPsBLiBxPyLahhB7X7oRM80TvR8Y9cTERaQ40iLqozYFjgb8mrccYY0zVichmhMD1jJYMFS0UkbZRr6stsChhtcPTlI2oSjvjLlL+LyLtgSuBXxOi6KvA46jOiY55rJJtaAP8J4rsjYB/q2qVd9k0xhiTjIS/iIcAU1T1/pSPXiWs8x0QPb+SpF7n3IUZa2QkySLlucCtmW6Aqs4E9s90vcYYYxLrApwLfCsiE6Ky2whBa6SI9CYMFVV6inumJBssE9kB2AXYeLpfqckVxhhjco+qfkS4s5ZOrUp8GjfDxg7AvwlJeJWSJIrFLFGeMcaYGtMg5nEPElI3dQBWA0cSuo1TgJ7V0zRjjDEmvbi3DY8GTkD1u2gn5cWofozIOuAOYEy1tdAYY0yd4L3fBZjrnNNS5QLs7Jybk/6bm4rb82pKSY6IZUDxArXJwH5xT2aMMaZem0VYE1xaq+iz2OIGr++AvaLXE4DLEfkVcBWQJkeSMcYYs4nS8yWKtSAk7I0t7m3DhwhZhiEsIH6TkKx3HbbHlzHGmHJ47x+OXipwd2pWDcKEv0MIHaPY4i5SfibldR4h59VewByysY2JMcaYXLJv9CzA3myc53A9kAfcl6TC8oOXSFfgE1Q3Tqioujo6mTHGGFMu51w3AO/9U8B1zrkVVa2zop7XWGAtIuOAd6P341Gt1P4rxhhj6q9MpomqKHjtCXQj7ONyBWG862dEPiIEsneBL1Gt3BYlxhhj6hXvfTfCnIlNsjU557rHraf82Yaq01F9EtU/obojYZHyzcBPhEzz44GlyZpujDGmPvLeXwC8AWxB6BQtBrYGOhOWXsUWd6p8oPodIVX+fwhZhVcRpjgaY4wxFbkJuNo5dxawAbjVOdeJsD3KqiQVVRy8RFojchoijyAymZBR+FpCj+sMQtQ0xhhjKrIb8Hb0eh0lnZ9HgAuSVFTRbMOvCeNeXwDvA9cBH0ezDY0xxpgklhJuGUJIcLEP8A3QmpDJKbaKJmy0B5YDM4EZwHQLXMYYYyrpQ+BY4FtgJPCw9/4YwnYriXLkVhS8tgIOJcw4PBd4FJHFwHu/PFQT5aMyxhhTb10NNIle3w0UEDbAHAncmaSi8oOX6gbgo+hxByKbA4cRZolcADyGyEJU2yU5qTHGmPrHObcs5XURcE9l60q2kzIUpTyKN6XcubInN8YYU79479sQ7uTtDtzunFvive8CLHDOxb6TV9GEjUaEhIndosdhhC7fHMIC5SHRszHGGFMu7/2BwDuE7U86AvcStts6hjA58Oy4dVU0Vf5HwgDbFUA+cA2wB6q7onoRqv9CdV7ySzDGGFMP3Qc8FK3tWpdSPpow9hVbRbcN+wBjUZ2WrH3GGGPMJg4EeqcpzwfaJKmoogkbTySpzBhjjCnHGtInttgLWJSkomTpoYwxxpjKewVw3vvNo/fqvW9HmHX4UpKKakXwEpGeIjJVRKaLSN9st8cYY0y1uAloRUjI24ywDGs6YX5FvyQVJZ0qn3Ei0hB4lDDbZB7wuYi8qqqJMgwbY4yp3aJNKI/w3ncnZJJvAOQ5594u/5ubynrwIkzFn66qMwFE5DngJBKmx0+iceO6WV6b2pIr5bWpLbleXpvakgvlIumPrQ+cc2MJe0JWWm0IXjsCc1PezyOkpNqIiFwKXArQOMmfnjSOO27Tsk8/ze3ydGpbG2tbeTq1rY25Up5ObWtjbSuvr7z3hxJyGW5HqaEr59y1ceupDcEr3b8/NtmZWVUHA4MBmjdvbjs3G2NMjvHe3wQMJIxzLWDjv+sT/b1eG4LXPDZOMbUT4aKMMcbULdcB1zrnHqlqRbVhtuHnQHsR2VVEGgNnAq9muU3GGGMyb0vg9UxUlPXgpaoFhDT5o4EpwEhVnZTdVhljjKkGzwI9M1FRbbhtiKq+ToaisTHGmNrDe39jytu5och3IeygvCH1WOfc/XHrrRXByxhjTJ11Tan3q4DDo0cqBSx4GWOMSUZEegIPAQ2Bf6rqgKrW6ZzbtcoNSyPrY17GGGOyLyXb0e+ADsBZItIhE3V777t67zfLRF3FrOdljDEGqjfb0Vhgrfd+HGED47HAeOdcYWUrFNXcW+8rIkWE1Pp1WSOgINuNqCH16Vqhfl2vXWvt0hTIS3k/OEoAgYj8AeipqhdH788FDlXVq6t6Uu/9HkA3oGv0aAv8TEjMO5YQ0L50zsUOSDnZ81LVOn+7U0S+UNWDst2OmlCfrhXq1/XateaUWNmOKsM5N52QVeNJAO/9XoRgdjRh0+MBwE+EjPOx1PkgYIwxJpYay3bknPuOsH/Xfwh7fK0CWiSpIyd7XsYYYzLul2xHwHxCtqOzM1W597414ZZhN6A7sBvwJfA+cAbhFmJsFrxqr8HZbkANqk/XCvXreu1ac4SqFohIcbajhsDQTGU78t5/DewJfEEIVtcBHzvnVle2zpycsGGMMSZ3eO9XE3ZLHgO8B7znnJtVlToteBljjKlW0RqvQwm3DLsBvwEWEwUyKhHMLHgZY4ypUd77zYHDKBkDOwRY6JxrF7cOm21YQ0RkqIgsEpGJpcqvEZGpIjJJRAamlN8qItOjz45LKT9QRL6NPntYpHZuJp7kekWknYisEZEJ0ePxlONr/fWmu1YReT7lemaLyISUz3L2t01yrbn+u0KZ13uAiHwaXdMXInJIymc5+9vWsKKUhxKm6e9c7jdKsQkbNWcY8AjwdHGBiHQjrGDfT1XXich2UXkHwkyfjsAOwNsisqeqFgL/AC4FPiVk4u8JvFGD1xHXMGJeb2SGqh6Qpp5cuN5hlLpWVf1j8WsRGURYw1IXftthxLzWSC7/rpDmegk7AXtVfUNEjo/ed60Dv2218d43IvSuim8bHgY0AeYQFigPiZ5js+BVQ1T1AxFpV6r4CmCAqq6LjlkUlZ8EPBeVzxKR6cAhIjIb2FJVxwGIyNPAydTCPwQJrzctEWlLDlxvGdcKQPQv7DMIU4Mhx3/bhNeaVq78rlDm9SphU0WArShZC5XTv201+5GQ3SOfEKSuAd6tyqQNC17ZtSdwpIjcBawFblLVz4EdCf9CKzYvKtsQvS5dnivKul6AXUXkK2AF0E9VPyRcWy5fL8CRwEJVnRa9r6u/LWx6rVA3f9frgdEich9h6KV4a4+6/NtWVR9grHNuWoVHxmTBK7saAVsTZt4cDIwUkd0oO01LtaVvqSFlXW8+sIuqLhWRA4GXRaQjuX+9AGcRdo8tVld/W9j0Wuvq73oFcIOqviQiZxBuef2Wuv3bVolz7olM12nBK7vmAaM0TPn8TELC4W0oO03LvOh16fJckfZ6VXUxUHwr8UsRmUHopeX09YpII+BU4MCU4jr526a71uj2WZ37XYHzCYtsAV4A/hm9rpO/bW1lsw2z62Wi8QER2RNoDCwBXgXOFJHNJaRqaQ98pqr5wEoR+U00vnAeIS9Yrkh7vSKyrYS9hIh6Yu2BmXXgen8LfKeqqbeM6upvu8m11uHfdQEhoSyE/5+Lb4XV1d+2dlJVe9TAg3A7JZ+S+9+9CX95jwAmErYp6J5y/J+BGcBU4Hcp5QdFx88gzIKSbF9bVa8XOA2YBHwdlZ+YS9eb7lqj8mHA5WmOz9nfNsm15vrvWs7/x0cQcvJ9DYwHDqwLv22uPWyRsjHGmJxjtw2NMcbkHAtexhhjco4FL2OMMTnHgpcxxpicY8HLGGNMzrHgZYwxJudY8DLGGJNzLHgZU0uIyGbZboMxucKClzGliMh5IrJURDYvVf6MiLwavT5RRL4UkbUiMktE7hKRxinHniMin4vIymgzwxdEZMeUz7uKiIrI8SLymYisB47DGBOLBS9jNvUC4c/GScUFIrIVcAowJNoh9xlCmp+OwEXAH4C/pdTRGHDA/sDvCQmXUzOuF7sH6AfsRUg1ZIyJwdJDGZOGiDwC7KGqPaP3VxCC0U7AWGCMqt6RcvzJhLyNW2iaP1QishcwBdhZVeeJSFfCpnx/UNWXqvt6jKlrbEsUY9J7EsgTkZ00ZEq/CBiuqgXR3lSHiMgtKcc3IOwUuz2QLyKdCcHuAKAVJXs67cLGGxN+Uc3XYUydZMHLmDRU9WsRyQMuEJGXCVnBz4k+bgB4wu3F0haLSHNgNPA2cC6wiHDb8EPC7cRUP1dD842p8yx4GVO2J4GbCYHnY1WdGpXnAXup6vR0XxKR/aPv3Kaqs6KyU2ugvcbUGzZhw5iyPUu4DXgFYav3Yn8FzhaRv4rIPiKyl4j8QUQGRp/PIewgfLWI7CYiJwB3YIzJGAtexpRBVVcCI4H10XNx+WjgBKAb8Fn06EsIWqjqYsJW8ScDkwljXzfWZNuNqetstqEx5RCRN4B5qnpJtttijClhY17GpCEirYDfAscS1moZY2oRC17GpJdHmOJ+m6pOzHZjjDEbs9uGxhhjco5N2DDGGJNzLHgZY4zJORa8jDHG5BwLXsYYY3KOBS9jjDE55/8DQsFYqYQi7aQAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# create figure and axis objects with subplots()\n",
"fig,ax = plt.subplots()\n",
"# make a plot\n",
"ax.plot(Year,Wage, color=\"red\")\n",
"plt.fill_between(Year, 0, Wage, facecolor='b', alpha=0.3)\n",
"# set x-axis label\n",
"ax.set_xlabel(\"year\",fontsize=14)\n",
"# set y-axis label\n",
"ax.set_ylabel(\"Wages[shillings per week]\",color=\"red\",fontsize=14)\n",
"\n",
"# twin object for two different y-axis on the sample plot\n",
"ax2=ax.twinx()\n",
"# make a plot with different y-axis using second axis object\n",
"ax2.bar(Year,Wheat,color = 'gray')\n",
"ax2.set_ylabel(\"Wheat Price[shillings per quarter]\",color=\"gray\",fontsize=14)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"ename": "AttributeError",
"evalue": "module 'matplotlib.pyplot' has no attribute 'set_xlabel'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-95-cc86f6b26a16>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# set x-axis label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"year\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;31m# set y-axis label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Purchase Power\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"red\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'set_xlabel'"
]
}
],
"source": [
"fig3 = plt.figure()\n",
"ax = fig.add_axes([0,0,1,1])\n",
"\n",
"Purch_power = Wage / Wheat\n",
"\n",
"plt.plot(Year, Purch_power)\n",
"plt.show()\n",
"\n",
"# set x-axis label\n",
"plt.set_xlabel(\"year\",fontsize=14)\n",
"# set y-axis label\n",
"plt.set_ylabel(\"Purchase Power\",color=\"red\",fontsize=14)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
module3/exo3/exercice.ipynb
View file @ 0a8aa943
{ {
"cells": [], "cells": [
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <td>1595</td>\n",
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" <td>1610</td>\n",
" <td>32.0</td>\n",
" <td>5.78</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>1615</td>\n",
" <td>33.0</td>\n",
" <td>5.94</td>\n",
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" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>1620</td>\n",
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" <th>12</th>\n",
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" <td>1625</td>\n",
" <td>33.0</td>\n",
" <td>6.12</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>1630</td>\n",
" <td>45.0</td>\n",
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" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>1635</td>\n",
" <td>33.0</td>\n",
" <td>6.30</td>\n",
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" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>1640</td>\n",
" <td>39.0</td>\n",
" <td>6.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>1645</td>\n",
" <td>53.0</td>\n",
" <td>6.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>1650</td>\n",
" <td>42.0</td>\n",
" <td>6.50</td>\n",
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" <td>19</td>\n",
" <td>1655</td>\n",
" <td>40.5</td>\n",
" <td>6.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>1660</td>\n",
" <td>46.5</td>\n",
" <td>6.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>1665</td>\n",
" <td>32.0</td>\n",
" <td>6.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>1670</td>\n",
" <td>37.0</td>\n",
" <td>6.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>1675</td>\n",
" <td>43.0</td>\n",
" <td>7.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>1680</td>\n",
" <td>35.0</td>\n",
" <td>7.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>1685</td>\n",
" <td>27.0</td>\n",
" <td>7.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>1690</td>\n",
" <td>40.0</td>\n",
" <td>8.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>1695</td>\n",
" <td>50.0</td>\n",
" <td>8.50</td>\n",
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" <td>1705</td>\n",
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" <td>10.00</td>\n",
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" <th>29</th>\n",
" <td>30</td>\n",
" <td>1710</td>\n",
" <td>44.0</td>\n",
" <td>11.00</td>\n",
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" <th>30</th>\n",
" <td>31</td>\n",
" <td>1715</td>\n",
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" <td>11.75</td>\n",
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" <th>31</th>\n",
" <td>32</td>\n",
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" <td>12.50</td>\n",
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" <tr>\n",
" <th>32</th>\n",
" <td>33</td>\n",
" <td>1725</td>\n",
" <td>39.0</td>\n",
" <td>13.00</td>\n",
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" <tr>\n",
" <th>33</th>\n",
" <td>34</td>\n",
" <td>1730</td>\n",
" <td>26.0</td>\n",
" <td>13.30</td>\n",
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" <tr>\n",
" <th>34</th>\n",
" <td>35</td>\n",
" <td>1735</td>\n",
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" <td>13.60</td>\n",
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" <th>35</th>\n",
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" <td>1740</td>\n",
" <td>27.0</td>\n",
" <td>14.00</td>\n",
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" <tr>\n",
" <th>36</th>\n",
" <td>37</td>\n",
" <td>1745</td>\n",
" <td>27.5</td>\n",
" <td>14.50</td>\n",
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" <tr>\n",
" <th>37</th>\n",
" <td>38</td>\n",
" <td>1750</td>\n",
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" <td>15.00</td>\n",
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" <th>38</th>\n",
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" <td>1755</td>\n",
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" <td>15.70</td>\n",
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" <tr>\n",
" <th>39</th>\n",
" <td>40</td>\n",
" <td>1760</td>\n",
" <td>31.0</td>\n",
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" <th>40</th>\n",
" <td>41</td>\n",
" <td>1765</td>\n",
" <td>43.0</td>\n",
" <td>17.60</td>\n",
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" <tr>\n",
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" <tr>\n",
" <th>42</th>\n",
" <td>43</td>\n",
" <td>1775</td>\n",
" <td>44.0</td>\n",
" <td>19.50</td>\n",
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" <tr>\n",
" <th>43</th>\n",
" <td>44</td>\n",
" <td>1780</td>\n",
" <td>46.0</td>\n",
" <td>21.00</td>\n",
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" <tr>\n",
" <th>44</th>\n",
" <td>45</td>\n",
" <td>1785</td>\n",
" <td>42.0</td>\n",
" <td>23.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>46</td>\n",
" <td>1790</td>\n",
" <td>47.5</td>\n",
" <td>25.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>47</td>\n",
" <td>1795</td>\n",
" <td>76.0</td>\n",
" <td>27.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
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" <tr>\n",
" <th>48</th>\n",
" <td>49</td>\n",
" <td>1805</td>\n",
" <td>81.0</td>\n",
" <td>29.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>50</td>\n",
" <td>1810</td>\n",
" <td>99.0</td>\n",
" <td>30.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>51</td>\n",
" <td>1815</td>\n",
" <td>78.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>52</td>\n",
" <td>1820</td>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>52</th>\n",
" <td>53</td>\n",
" <td>1821</td>\n",
" <td>54.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Year Wheat Wages\n",
"0 1 1565 41.0 5.00\n",
"1 2 1570 45.0 5.05\n",
"2 3 1575 42.0 5.08\n",
"3 4 1580 49.0 5.12\n",
"4 5 1585 41.5 5.15\n",
"5 6 1590 47.0 5.25\n",
"6 7 1595 64.0 5.54\n",
"7 8 1600 27.0 5.61\n",
"8 9 1605 33.0 5.69\n",
"9 10 1610 32.0 5.78\n",
"10 11 1615 33.0 5.94\n",
"11 12 1620 35.0 6.01\n",
"12 13 1625 33.0 6.12\n",
"13 14 1630 45.0 6.22\n",
"14 15 1635 33.0 6.30\n",
"15 16 1640 39.0 6.37\n",
"16 17 1645 53.0 6.45\n",
"17 18 1650 42.0 6.50\n",
"18 19 1655 40.5 6.60\n",
"19 20 1660 46.5 6.75\n",
"20 21 1665 32.0 6.80\n",
"21 22 1670 37.0 6.90\n",
"22 23 1675 43.0 7.00\n",
"23 24 1680 35.0 7.30\n",
"24 25 1685 27.0 7.60\n",
"25 26 1690 40.0 8.00\n",
"26 27 1695 50.0 8.50\n",
"27 28 1700 30.0 9.00\n",
"28 29 1705 32.0 10.00\n",
"29 30 1710 44.0 11.00\n",
"30 31 1715 33.0 11.75\n",
"31 32 1720 29.0 12.50\n",
"32 33 1725 39.0 13.00\n",
"33 34 1730 26.0 13.30\n",
"34 35 1735 32.0 13.60\n",
"35 36 1740 27.0 14.00\n",
"36 37 1745 27.5 14.50\n",
"37 38 1750 31.0 15.00\n",
"38 39 1755 35.5 15.70\n",
"39 40 1760 31.0 16.50\n",
"40 41 1765 43.0 17.60\n",
"41 42 1770 47.0 18.50\n",
"42 43 1775 44.0 19.50\n",
"43 44 1780 46.0 21.00\n",
"44 45 1785 42.0 23.00\n",
"45 46 1790 47.5 25.50\n",
"46 47 1795 76.0 27.50\n",
"47 48 1800 79.0 28.50\n",
"48 49 1805 81.0 29.50\n",
"49 50 1810 99.0 30.00\n",
"50 51 1815 78.0 NaN\n",
"51 52 1820 54.0 NaN\n",
"52 53 1821 54.0 NaN"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_url, skiprows=0)\n",
"raw_data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" Unnamed: 0 Year Wheat Wages\n",
"50 51 1815 78.0 NaN\n",
"51 52 1820 54.0 NaN\n",
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]
},
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"metadata": {},
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}
],
"source": [
" raw_data[raw_data.isnull().any(axis=1)]"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_axes([0,0,1,1])\n",
"Year = raw_data['Year']\n",
"Wheat = raw_data['Wheat']\n",
"Wage = raw_data['Wages']\n",
"x0 = [1565, 1810]\n",
"\n",
"ax.bar(Year,Wheat,color = 'gray')\n",
"\n",
"sorted_data = raw_data.set_index('Year').sort_index()\n",
"sorted_data['Wages'].plot(color = 'r')\n",
"plt.fill_between(Year, 0, Wage, facecolor='b', alpha=0.3)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# create figure and axis objects with subplots()\n",
"fig,ax = plt.subplots()\n",
"# make a plot\n",
"ax.plot(Year,Wage, color=\"red\")\n",
"plt.fill_between(Year, 0, Wage, facecolor='b', alpha=0.3)\n",
"# set x-axis label\n",
"ax.set_xlabel(\"year\",fontsize=14)\n",
"# set y-axis label\n",
"ax.set_ylabel(\"Wages[shillings per week]\",color=\"red\",fontsize=14)\n",
"\n",
"# twin object for two different y-axis on the sample plot\n",
"ax2=ax.twinx()\n",
"# make a plot with different y-axis using second axis object\n",
"ax2.bar(Year,Wheat,color = 'gray')\n",
"ax2.set_ylabel(\"Wheat Price[shillings per quarter]\",color=\"gray\",fontsize=14)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"ename": "AttributeError",
"evalue": "module 'matplotlib.pyplot' has no attribute 'set_xlabel'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-95-cc86f6b26a16>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# set x-axis label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"year\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;31m# set y-axis label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Purchase Power\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"red\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'set_xlabel'"
]
}
],
"source": [
"fig3 = plt.figure()\n",
"ax = fig.add_axes([0,0,1,1])\n",
"\n",
"Purch_power = Wage / Wheat\n",
"\n",
"plt.plot(Year, Purch_power)\n",
"plt.show()\n",
"\n",
"# set x-axis label\n",
"plt.set_xlabel(\"year\",fontsize=14)\n",
"# set y-axis label\n",
"plt.set_ylabel(\"Purchase Power\",color=\"red\",fontsize=14)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
...@@ -16,10 +711,9 @@ ...@@ -16,10 +711,9 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.3" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 2 "nbformat_minor": 2
} }
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