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Exercice 24 octobre 2023

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module3/exo3/exercice.ipynb
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{ {
"cells": [], "cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle# "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"url=\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\"\n",
"c=pd.read_csv(url)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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>rownames</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",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1570</td>\n",
" <td>45.0</td>\n",
" <td>5.05</td>\n",
" </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",
" <td>5.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>1585</td>\n",
" <td>41.5</td>\n",
" <td>5.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <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",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1605</td>\n",
" <td>33.0</td>\n",
" <td>5.69</td>\n",
" </tr>\n",
" <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",
" </tr>\n",
" <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",
" <td>NaN</td>\n",
" </tr>\n",
" <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": [
" rownames 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": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c1 = pd.DataFrame(c,\n",
" columns=['rownames','Year','Wheat','Wages'])\n",
"c1"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([18., 8., 16., 5., 0., 1., 1., 3., 0., 1.]),\n",
" array([26. , 33.3, 40.6, 47.9, 55.2, 62.5, 69.8, 77.1, 84.4, 91.7, 99. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(c1['Wheat'])"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"x=c1['Year']\n",
"y1=c1['Wheat']\n",
"y2=c1['Wages']"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PolyCollection at 0x7f85561e5128>"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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5sRrXG9nzIknqGGpryX/pS8yp7MUV+5+RdTUdUkR0oi64/DGldE9x9YyIWL/4+frAzMb2Y3iRJHUMv/41uX/+k4sO/BLzu3TPupoOJ+q6WH4HTEop/azeR2OA04vzpwP3NbYvh40kSe3fO++Qv/B7PLXFQP6y7b5ZV9NRfQo4FRgfEa8U110AXA7cERFnAu8Bn21sR4YXSVL7NnMm+SFDWFyA7x3yFZ/pkpGU0tPAihr/4FXZl8NGkqT2a9488oceSu1b73DGsd+jaq11s65IzcDwIklqn6qrKRx+OGnCREYcfQEvbLRD1hWpmRheJEntz5IlFI4+Gp57nnOO/BZPbr571hWpGRleJEntS20tafiJlI0bx7eHfo2HtvlU1hWpmRleJEntR6FA+sIXiHtHc/HBI7hrx8FZV6QWYHiRJLUPKZHOPZe49VZ+ut8p/H7gUVlXpBZieJEktQ/f/S5x7bX8ZtCxXL33CVlXoxZkeJEklb4rroDLLuNPOw/hsgM+77Nc2rkmhZeIGBIRr0XE5IgYuYJtDoiIVyJiYkQ82bxlSpK0AtdcAyNHcu92+3PhoT6EriNo9Am7EZEDrgEOAaqAFyJiTErpP/W26QVcCwxJKb0XEY2+zlqSpDV21VVw3nk8uuWenHf4NyiU5bKuSK2gKT0vg4DJKaW3UkpLgduBYcttcxJwT0rpPYCUUqNvhJQkabWlBJdcAuedxwPb7sdXjv4OtTnfeNNRNCW8bAi8X2+5qriuvq2B3hHxRES8FBGnNbSjiBgRES9GxIu1tbWrV7EkqWNLCc4/Hy6+mDt3GMzXjjzP4NLBNOW/dkODh6mB/exO3YuVugLPRsRzKaXXP/allEYBowAqKyuX34ckSStXKJDOOYe47jpu2fUILjrky6Tw3pOOpinhpQrYqN5yf2BqA9vMTilVA9UR8RSwM/A6kiQ1h9pa0he/SNx8M9cPOpbLvauow2pKXH0B2CoiNouICmA4MGa5be4D9ouI8ojoBuwJTGreUiVJHdbSpaQTTyJuvpmf7XuywaWDa7TnJaVUGxHnAA8DOeDGlNLEiDir+Pn1KaVJEfEQ8G+gANyQUprQkoVLkjqIxYspHH88ZX/5Cz868AvcMOjYrCtSxpp0hVNKaSwwdrl11y+3fCVwZfOVJknq8BYupDDsaHj8r1xw2Nn8aZehWVekNsDLsyVJbVNNDYXDDoNnn+Obh3+D0TsclHVFaiMML5KktqdQIJ1yKjz7LOce+W3+MmC/rCtSG+L9ZZKktmfkSGL0PfzowC8aXPQJhhdJUtsyahRceSW37HoENw48Kutq1AY5bCRJajseeYT01a/y5OYDuWTwCG+HVoMML5KktmHCBPLHf5bJ/Tbm7KO+Td6XLGoFHDaSJGVv+nTyhx/OXDpxxrHfp7pzt6wrUhtmeJEkZaumhvyRR7Jsxiw+f+z3mNZz7awrUhtneJEkZad4S3S89BLnfuY8Jqy3ZdYVqQQYXiRJ2al3S/SjW+2VdTUqEYYXSVI2vCVaq8m7jSRJre+OOyh89as85S3RWg32vEiSWk9K8OMfwwkn8M/1t/GWaK0We14kSa1jyRLSiBHELbcwersDOH/o11la3inrqlSCDC+SpJY3ezaFY46h7OmnuWrfk/n1PsMdKtJqM7xIklrWa6+RP/xw8u9V8c0jv8X92+2fdUUqcYYXSVLL+etfyR97HPNr4czhP+blDQdkXZHaAS/YlSS1jBtuoHDYYbxVsRZHnnqVwUXNxvAiSWpehQJ8+9vwpS/x9EY7cezJP6FqrXWzrkrtiMNGkqTmM2cOhdNOo2zsWG7Z9QguGTzCW6HV7AwvkqTm8fTT5IcPpzBjJhcdcha37nqEdxSpRThsJElaM4UCXHYZhQMOYMqiAsecfCW37vYZg4tajD0vkqTVN2MGhVNOoWzcOB4Y8GkuOOwcFnbulnVVaufseZEkrZ6//pX8zjuz7MmnOH/IuXztyG8ZXLRSEXFjRMyMiAn11l0cEVMi4pXidHhj+zG8SJJWTT4PF11EGjyYd/KdOerUn/HnnQ9zmEhN8XtgSAPrf55S2qU4jW1sJw4bSZKabsoUCiedRNlTT3HnjoO5aPBZLKroknVVKhEppaciYtM13Y89L5KkxtXUwE9+Qn6HHVjy3At844j/x7cP/z+Di5rLORHx7+KwUu/GNja8SJJWbNkyGDWK/JZbwvnn81TfLTnitJ8zeoeDsq5MbU95RLxYbxrRxO9dB2wB7AJMA65q9EBrUKQkqb0qFODOO8lfeCG5yZN5pf8Arjjp6/xjox2yrkxtV21KaeCqfimlNOOj+Yj4LfBAY98xvEiS/icleOQR8iNHknvlFd5cZ1OuOO57PLbFIC/IVYuIiPVTStOKi8cAE1a2PRheJEkfee45CiNHUvbkk0zvtS5XfuabjBnwaQo+3l/NJCJuAw4A+kVEFXARcEBE7AIk4B3gy43tx/AiSR3dxImkC75LjLmPed178YvBX+a2XYawLNcp68rUzqSUTmxg9e9WdT+GF0nqqN55h3TxxXDLLdR07sZ1+53CjQOHUVPRNevKpJUyvEhSRzNzJvz4xxSuu47aFNy0xzFct9fxzOvaM+vKpCYxvEhSRzF/Plx1FfmfXgWLF/PnHQfzq31OZHrPfllXJq0Sw4sktXeLF8O115L/8Y/JzZ3Lg9vsy8/2O4W3+vbPujJptRheJKm9ev99uO468qNGkZszh2c2240rjvw+E9bbMuvKpDVieJGk9iQlePJJ0q9+DffdS0owbstB3HjoeTy/8Y5ZVyc1C8OLJLUH1dXwhz+Q//WvyU2cyPxuPfnTHsfyx12HUrXWullXJzUrw4sklbI33qgbGvrdjeTmf8hr623BTUO/zpgBn2ZJp85ZVye1CMOLJJWSlGD8eBg9mvzdd5MbP57aXI6xW3+Km4/8DC9tOMDH+KvdM7xIUltXKMDzz8M995C/5x5yb71FIYJ/brgdDx54JvcP+DQze/TNukqp1RheJKktWrYMnniirodl9Ghy06dTmyvn7xvvxEOHncO4LfdkVvfeWVcpZcLwIkltRU0NPPII6Z57KNx/P7l581hc0YW/brobD+1xCk9sPpD5XbpnXaWUOcOLJGVp3jx44AHS6NGksQ9StngRC7r24JEtBvHwQXvz1Ka7euGttBzDiyS1tg8+gDvuoHD33fD445TV1jK7Z1/GbncQD2+1N//YaAdqc/7xLK2I/3dIUmuproZf/pL8FT8hN/9D3u+7IQ/uPoyHtt6Hf62/FSnKsq5QKgmGF0lqaUuXwg03kL/kEnIzZ/L4loP4+TEnMXHdLbytWVoNhhdJaimFAtx+O/kLLyT39tu8vNH2XHbyt3i5/4CsK5NKmuFFkppbSjB2LPnvfIfc+PG8vu7mXHH8xTyx+e72tEjNwPAiSc3pueconHceZX//O1P6rM+VR36LBwbs5/UsUjMyvEhSc0gJfvYz0re/zdzKXvz80K/y550O9a4hqQX4f5UkranqatKZXyT+fDsPbbMP5w39P6o7d8u6KqndMrxI0pp46y3yRx9NTJjAT/Y/nev2PN7rWqQWZniRpNX18MPkhw+nekmec46/mKc23z3riqQOwSvIJGlVpQSXXUYaOpTJFb054rSfG1ykVtSk8BIRQyLitYiYHBEjV7LdHhGRj4jjm69ESWpDFiwgHf9ZuOAC7t92P44++Ure77Ve1lVJHUqjw0YRkQOuAQ4BqoAXImJMSuk/DWx3BfBwSxQqSZl74w3yw4YRr77Gjw48k9/tcbTXt0gZaErPyyBgckrprZTSUuB2YFgD250L3A3MbMb6JKlteOAB8gP3YMG7Uzj5hB/yu0HHGFykjDQlvGwIvF9vuaq47r8iYkPgGOD6le0oIkZExIsR8WJtbe2q1ipJra9QgB/8AI48kte69eOI037Bs5vsnHVVUofWlLuNGvqnRVpu+RfA+SmlfKzkXyIppVHAKIDKysrl9yFJbcuHH1I49VTK7r+fu3c4iAsOPZslnTpnXZXU4TUlvFQBG9Vb7g9MXW6bgcDtxeDSDzg8ImpTSvc2S5WS1NomTSI/bBi8+RYXDf4yN+/2GYeJpDaiKeHlBWCriNgMmAIMB06qv0FKabOP5iPi98ADBhdJJWv0aPKnnsaHUc5XTvgRz2+8Y9YVSaqn0WteUkq1wDnU3UU0CbgjpTQxIs6KiLNaukBJajX5PFx4IRx7LBPW2oDDT/2FwUVqg5r0hN2U0lhg7HLrGrw4N6V0xpqXJUmt7IMPKJx0EmUPPcTtOx3KRYecxZLyiqyrktQAXw8gSZMmkf/MZ0jvvseFh53Nn3Ye4vUtUhtmeJHUsU2eTP7AA5lXvZQvDb+Ml/sPyLoiSY0wvEjquKqqyB98MAsWLOKEEy9ncr+Ns65IUhMYXiR1TDNnkh88mMUzZnPK8EsNLlIJ8a3SkjqeefPIH3ootW+9wxnHfZ8J622ZdUWSVoHhRVLHUl1N4fDDSRMmMuLoC3hhox2yrkjSKjK8SOo4liyhcPTR8NzznPuZ83hy892zrkjSajC8SOoYamtJw0+kbNw4vj30azy47b5ZVyRpNRleJLV/hQLpC18g7h3NxQeP4K4dB2ddkdQhRcSNETEzIibUW9cnIh6NiDeKP3s3th/Di6T2LSXSuecSt97KT/c7hd8PPCrriqSO7PfAkOXWjQQeSyltBTxWXF4pw4uk9u273yWuvZbfDDqWq/c+IetqpA4tpfQUMHe51cOAm4vzNwNHN7Yfn/Miqf264gq47DL+tPMQLjvg8z7yX2pZ5RHxYr3lUSmlUU343roppWkAKaVpEbFOowda3QolqU279loYOZL7BuzPhYd+xeAitbzalNLA1jiQw0aS2p8//AHOPptxWw7im0d8g0JZLuuKJK3YjIhYH6D4c2ZjXzC8SGpf7r2Xwhln8OwmO3H2sJHU5uxgltq4McDpxfnTgfsa+4LhRVL7MW4chRNO4N/rbskXj7mQJeUVWVckqZ6IuA14FtgmIqoi4kzgcuCQiHgDOKS4vFL+k0RS+/DMMxSOGsbkXhtw+vEXU925W9YVSVpOSunEFXx08Krsx/AiqfS98gr5oYczpWsvTv7sD/mwa4+sK5LUghw2klTaXnuN/CGHMCsqOPFzP2JW90YfzimpxNnzIql0vfsu+YMP5sPFtZx44hVMWavRx0NIagfseZFUmp54gvxBB1Ez90NO/uwPeLvPhllXJKmVGF4klZaXX6Zw2GFw4IHMmT2f0467mEnrbJ51VZJakcNGkkrD66+TLvwececdLOjWk18f+AVu3fUIlnTqnHVlklqZ4UVS2zZlCumSS+DGG1mcq2DUPsO5YdAxLOhcmXVlkjJieJHUNs2dC5dfTuFXv6ZQW8utuxzONXt/jtmV3k0kdXSGF0ltS3U1/PKX5K/4CbFgPqO3P5Cff+okqnqtl3VlktoIw4uktmHpUvjtb8n/4AfkZs7kr1vuyZXHn8rra2+adWWS2hjDi6RsFQpw223kL7yQ3Dvv8NJGO3D5Kd/i5Q0HZF2ZpDbK8CIpGynB2LHkv/MdcuPH8/q6m3P5Zy/hyc12g4isq5PUhhleJLW+p5+mcP75lD3zDFP6bMCVR36LBwbsRwofPSWpcYYXSa2jUIAHH6Twi19QNm4cc3r05eeHnc0dOx5Cbc4/iiQ1nX9iSGpZH3wAN91E/uqryb39NnN69uWGA87g5t0+w+JOXbKuTlIJMrxIahnjx8PVV5O/9Q/kFtXwz/7bc9NR5/Pw1nvb0yJpjfgniKTmU1sL991H4de/puzJJ1nSqYLRAw7g1t2OYOK6W2RdnaR2wvAiac3U1sLTT8Po0eTvuovc1KlM77UuNx9wBn/e6VDmde2ZdYWS2hnDi6RVt3gxjBtXF1juu4/cnDks6VTBU5vsyh3HfoHHttiDQlku6yoltVOGF0lNs2ABjB1LuuceCn8ZS656IdVdKnl084E8tN8+PLXZbtRUdM26SkkdgOFF0orNmgVjxpDuGU0a9yhlS5fyQfdePLTlvjy89d48s8lOLMt1yrpKSR2M4UXSx733HoweTeGe0cTTfyMKBab2WpcHdxrKQ9vsw8sbbOuQkKT8DJIWAAAOMklEQVRMGV4kwaRJddev3H03uZdfBuDNtTdh7F6f45Gt92biOpv7yH5JbYbhRWrPUoJ582DKlLqpqup/81OmkK+qgqoqcnPmADB+g214aP8zeHjrvXm7z4YZFy9JDTO8SKWqthamT28wlKQpU8i/9z5lU6dStnjRJ776QWUvpnfvw9TufZnRfw8m7bYpj2y1FzN69MvgF5GkVWN4kdqClOqCyKuv1g3hTJ0K8+fX3eEzfz5pwQLShx9S+LBuXSxYQNnCBURKH9vNsvJOzOrRl6mVfZjWfQOm77gj07v3ZUaPvkzv0Zfp3fsys3tflpZ7ka2k0mV4kVpTPg9vv10XUCZNgldfpfCf/5AmvUpu/of/2yzKqOnSjZqKbizo3JUFnYpT575U9+3PwvW7saBzJTN69GF6975M79GP6T36MrdrT69NkdTuGV6k5rJo0ceGbupfZ1KoqqJQNYWyGdMpq63971fm9OjDa737M3mLfXmzb38m992IN/v0Z3qPvoYQSVoBw4v0kZTqhmlmzqx7vsnMmf+b5s797xAOCxaQ5s+n8OGHpPl168qqF1K2dOkndlnTuRszevRlSmUfpvfehukb78O7vdavCyl9+zO/S/cMflFJKm2GF7VPKUF1NcyeDXPmrHBKc+ZQmDWLNGMmZbNnUbZkSYO7W1zRherO3VjYuRsLOnVlfqeuVFd0Y0H3flT36crCim4s6NyNGd3/d23JjB59Wdi5Wyv/4pLU/hleVDoKhbowMmUKTJv28d6R4nx+xgzSzFmUzZpF2ZLFK9zVwi7dmdetB3M792BWt7WY23tb5vTfk9nd1mJut7WY063Xf+fndluLJeUVrfiLSpJWxvCitqG2ti6QvPde3W2/9W79LVRNoVBVRdm0qZQtW/aJry4tr+CDyl7M6rYWs7r2ZG6vrZi9wR7M7daTuV17Mq9rTz7o2oMPuvRkXtcezOvag7xPiJWkkmV4UctbvLhumGb2bHj//bqAUpwK775H4d13yU2fRuTzH/9aRRdmFG/7nd59M2bsNrB4Z01fZlb2YXZlL+Z0W4vqiq5e3CpJHYjhRatv4UL4z39g4sS623+L15ekOXMozJ5Nmj2HmDuX3KKaT3x1WXknZvTsx/vd+zG199ZM2fhTTO25NtN6rs3UHv2Y0aMv8ztXGkokSZ9geFHjFi2qe3jahAkwcSJp4kTy/x5P+Xvv/neTQpSxoGt35nXtyZwu3ZnbpQfzem/DBxv04IOudcM1c7v2ZFqPfkztuQ6zK9ciRVmGv5QkqVQZXjqyZctgxoy6a02mT//fz+J8Ydo0ClOnkZtSRRQKdV/JlfNO3/5M6rsJr++3H6/324TX+23Me73W803DkqRWYXhpT5Yu/d9twLNnf/JZJcVbgvMzZhCzZpKbN6/B3XzYrSczu/dhetdezOq5Be9t/Cle77cJr/XbhHd7r09tztNGkpQd/xYqJW+/DfffD6+/3vC1JdULG/xaIYL53Xoyp9tazOrSk9nd1mHO5lsxt+tazOzeh1mVvZnZvTezKnszu7IXy3K+90aS1HYZXtqyQgFeegnGjCF/773kJkwAYH7XHv99RsncLj34oOcWzFt31+JtwXXXmHzQtQdzis8r+aBrD4d0JEntRpPCS0QMAX4J5IAbUkqXL/f5ycD5xcWFwFdSSv9qzkI7jCVL4PHH4b77yN93H7lp0yhEGS9utD2PHPRFHt1yT97rvX7WVUqSlJlGw0tE5IBrgEOAKuCFiBiTUvpPvc3eBvZPKX0QEUOBUcCeLVFwu7BkCUyd+t+X9n30MLb01lsUxj1Grnohiyq68vimuzJut+H8dYs9mNe1Z9ZVS5K0RiLiHWABkAdqU0oDV2c/Tel5GQRMTim9VTzw7cAw4L/hJaX0TL3tnwP6r04xbdLSpXXvyFl+qqn53/yiRXUPYlu8+OPz9aY0fz6FqipS1RTK5875xGEWV3Rheo9+PLPlvjyy1Z48u8nOPpJektQeHZhSmr0mO2hKeNkQeL/echUr71U5E3hwTYpaI2+9VXf770dvAC6+BfhjP+fPr3vA2uLFpMWLSTWLSIsWkeqFjViyhLLFiz7x1NemWJYrZ1l5BUvKK1haXsHiThVUl3dmWmUfpm+8B9O3q3tK7H9f4tejnw9kkySpiZoSXhr6GzU1uGHEgdSFl31X8PkIYARARUUL9Sp8+cswblyDHy3pVEF150qqK7pS3akLNeUVLM5VsKS8E0vKe7Kkcz+WVHZicXlnlpR3YmmuEzWdurCoUxdqOnVmUafOxfm6aVFFZ2o6dWFxecXHvuPFsZIkNSgBj0REAn6TUhq1OjtpSnipAjaqt9wfmLr8RhGxE3ADMDSl9MlxEaBY5CiAysrKBgPQGrvkEs7s+2k+KO9KdeeuLKzoxsKKrlRXdPX5JJIktZzyiHix3vKoBsLJp1JKUyNiHeDRiHg1pfTUKh+oCdu8AGwVEZsBU4DhwEn1N4iIjYF7gFNTSq+vahHNap99+NtfPmRpvpBpGZIkdTCNXoCbUppa/DkzIkZTd13tKoeXRl8uk1KqBc4BHgYmAXeklCZGxFkRcVZxs+8DfYFrI+KV5ZKXJEnq4CKiMiJ6fDQPHApMWJ19NWkcJaU0Fhi73Lrr681/Efji6hQgSZI6hHWB0VF3c0o58KeU0kOrsyMvApEkSS2u+MiVnZtjX40OG0mSJLUlhhdJklRSDC+SJKmkGF4kSVJJMbxIkqSSYniRJEklxfAiSZJKiuFFkiSVFMOLJEkqKYYXSZJUUgwvkiSppBheJElSSTG8SJKkkmJ4kSRJJcXwIkmSSorhRZIklRTDiyRJKimGF0mSVFIML5IkqaQYXiRJUkkxvEiSpJJieJEkSSXF8CJJkkqK4UWSJJUUw4skSSophhdJklRSDC+SJKmkGF4kSVJJMbxIkqSSYniRJEklxfAiSZJKiuFFkiSVFMOLJEkqKYYXSZJUUgwvkiSppBheJElSSTG8SJKkkmJ4kSRJJcXwIkmSSorhRZIklRTDiyRJKimGF0mS1CoiYkhEvBYRkyNi5Orux/AiSZJaXETkgGuAocB2wIkRsd3q7Ku8OQtrKzrlAnOZJEkflyvL9O/GQcDklNJbABFxOzAM+M+q7qhdhpeJPxiSdQmSJHU05RHxYr3lUSmlUfWWNwTer7dcBey5WgdanS9JkiQtpzalNHAln0cD69LqHMixFUmS1BqqgI3qLfcHpq7OjgwvkiSpNbwAbBURm0VEBTAcGLM6O3LYSJIktbiUUm1EnAM8DOSAG1NKE1dnX5HSag03rbHKyspUXV2dybElSVLzioialFJlaxzLYSNJklRSDC+SJKmkGF4kSVJJMbxIkqSSYniRJEklpUnhpbG3QEadXxU//3dE7Nb8pUqSJDUhvDTxLZBDga2K0wjgumauU5IkCWhaz8t/3wKZUloKfPQWyPqGAbekOs8BvSJi/WauVZIkqUnhpaG3QG64GttIkiStsaa8HqApb4Fs0psiI2IEdcNKACkiFjXh+Fo95UBt1kV0MLZ5Nmz31mebt75SaPOurXWgpoSXprwFsklvikwpjQJGrWKNWg0R8WIjryZXM7PNs2G7tz7bvPXZ5h/XlGGjprwFcgxwWvGuo72AD1NK05q5VkmSpMZ7Xlb0FsiIOKv4+fXAWOBwYDJQA3y+5UqWJEkdWVOGjUgpjaUuoNRfd329+QSc3bylaQ05PNf6bPNs2O6tzzZvfbZ5PVGXOyRJkkqDrweQJEklxfBSQiLixoiYGRETllt/bvH1DRMj4if11n+n+MqG1yLisHrrd4+I8cXPfhURDd3qLlatzSNi04hYFBGvFKfr621vmzdRQ20eEX+u167vRMQr9T7zPF9Dq9LmnufNYwVtvktEPFds1xcjYlC9zzzP60spOZXIBHwa2A2YUG/dgcA4oHNxeZ3iz+2AfwGdgc2AN4Fc8bN/AHtT93yeB4GhWf9ubXVaxTbftP52y+3HNl+DNl/u86uA7xfnPc9bv809z1uozYFHPmoz6m6CeaI473m+3GTPSwlJKT0FzF1u9VeAy1NKS4rbzCyuHwbcnlJaklJ6m7o7wQYVX9vQM6X0bKo7828Bjm6d36D0rGKbN8g2XzUraHOg7iWwwOeA24qrPM+bwSq2eYNs81WzgjZPQM/i/Fr873lpnufLMbyUvq2B/SLi+Yh4MiL2KK5f0SsbNizOL79eTbeiNgfYLCL+WVy/X3Gdbd589gNmpJTeKC57nre85dscPM9byv8BV0bE+8BPge8U13ueL6dJt0qrTSsHegN7AXsAd0TE5qz4lQ1NepWDVmpFbT4N2DilNCcidgfujYjtsc2b04l8vAfA87zlLd/mnuct5yvAN1JKd0fE54DfAYPxPP8Ew0vpqwLuKXYZ/iMiCkA/VvzKhqri/PLr1XQNtnlKaRbw0VDSSxHxJnW9NLZ5M4iIcuBYYPd6qz3PW1BDbV4cLvU8bxmnA18vzt8J3FCc9zxfjsNGpe9e4CCAiNgaqABmU/fKhuER0TkiNgO2Av6R6l7bsCAi9iqOZZ8G3JdN6SWrwTaPiLUjIldcvzl1bf6Wbd5sBgOvppTqd5N7nresT7S553mLmgrsX5w/CPhoqM7zfHlZXzHs1PSJuq7bacAy6hL3mdT9xfkHYALwMnBQve2/S91V6a9R7wp0YGBx+zeBqyk+rNBpzdocOA6YSN1dAS8DR9rmzdPmxfW/B85qYHvP81Zsc8/zlmtzYF/gpWLbPg/sXm97z/N6k0/YlSRJJcVhI0mSVFIML5IkqaQYXiRJUkkxvEiSpJJieJEkSSXF8CJJkkqK4UWSJJUUw4skSSop/x/NvSdA5VmakwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 648x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax1 = plt.subplots(figsize=(9, 6))\n",
"\n",
"# Instantiate a second axes that shares the same x-axis\n",
"ax2 = ax1.twinx() \n",
"plt.plot(x,y2, color='r')\n",
"plt.fill_between(x, y2) \n",
"plt.bar(x,y1,width = 5, color='black')\n",
"plt.xlabel('year')\n",
"ax1.set_ylabel('Wheat')\n",
"ax2.set_ylabel('Wages')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3",
...@@ -16,10 +615,9 @@ ...@@ -16,10 +615,9 @@
"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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