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Commit 338300a4 authored by 521adc37f04e8509ebf5ce131815aa0a's avatar 521adc37f04e8509ebf5ce131815aa0a
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Représentation graphique du prix du blé et des salaires

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module3/exo3/exercice.ipynb
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......@@ -570,6 +570,545 @@
"data[data.isnull().any(axis = 1)]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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>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",
" <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",
" </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"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Supprimer la ligne qui ne contient pas de données valables\n",
"# Copier les données\n",
"my_data = data.dropna().copy()\n",
"my_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Représentation graphique du prix du blé "
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([18., 8., 16., 3., 0., 1., 1., 2., 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": 41,
"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(my_data[\"Wheat\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Représentation grahique des salaires"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PolyCollection at 0x7f5bf1a1bcf8>"
]
},
"execution_count": 65,
"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.plot(my_data[\"Wages\"], my_data[\"Unnamed: 0\"], \"r--\")\n",
"\n",
" \n",
"x = my_data[\"Wages\"]\n",
"y1 = my_data[\"Unnamed: 0\"]\n",
" \n",
"plt.fill_between(x, y1, color='#539ecd')"
]
},
{
"cell_type": "code",
"execution_count": null,
......
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