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Commit ccdc887c authored by b2401a7c91fe141142099513ca859d76's avatar b2401a7c91fe141142099513ca859d76
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Etape 3

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module3/exo3/exercice.ipynb
View file @ ccdc887c
...@@ -1250,1145 +1250,133 @@ ...@@ -1250,1145 +1250,133 @@
"source": [ "source": [
"### Représentation graphique du pouvoir d'achat au cours du temps\n", "### Représentation graphique du pouvoir d'achat au cours du temps\n",
"\n", "\n",
"Les salaires sont données en shillings par semaine, et les données sont espacées de 5 ans. La première etapes est donc de calculer le salaire sur 5 ans. Il est ensuite ajouté au tableau de données." "Le pouvoir d'achat est définie comme la quantité de blé qu'un ouvrier peut acheter avec son salaire hebdomadaire."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 22,
"metadata": {}, "metadata": {
"outputs": [ "scrolled": false
{ },
"data": { "outputs": [
"text/html": [ {
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\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",
" <th>WagesFor5Year</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",
" <td>1300.0</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",
" <td>1313.0</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",
" <td>1320.8</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",
" <td>1331.2</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",
" <td>1339.0</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",
" <td>1365.0</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",
" <td>1440.4</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",
" <td>1458.6</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",
" <td>1479.4</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",
" <td>1502.8</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",
" <td>1544.4</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",
" <td>1562.6</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",
" <td>1591.2</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",
" <td>1617.2</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",
" <td>1638.0</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",
" <td>1656.2</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",
" <td>1677.0</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",
" <td>1690.0</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",
" <td>1716.0</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",
" <td>1755.0</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",
" <td>1768.0</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",
" <td>1794.0</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",
" <td>1820.0</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",
" <td>1898.0</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",
" <td>1976.0</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",
" <td>2080.0</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",
" <td>2210.0</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",
" <td>2340.0</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",
" <td>2600.0</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",
" <td>2860.0</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",
" <td>3055.0</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",
" <td>3250.0</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",
" <td>3380.0</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",
" <td>3458.0</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",
" <td>3536.0</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",
" <td>3640.0</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",
" <td>3770.0</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",
" <td>3900.0</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",
" <td>4082.0</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",
" <td>4290.0</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",
" <td>4576.0</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",
" <td>4810.0</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",
" <td>5070.0</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",
" <td>5460.0</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",
" <td>5980.0</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",
" <td>6630.0</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",
" <td>7150.0</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",
" <td>7410.0</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",
" <td>7670.0</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",
" <td>7800.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Year Wheat Wages WagesFor5Year\n",
"0 1 1565 41.0 5.00 1300.0\n",
"1 2 1570 45.0 5.05 1313.0\n",
"2 3 1575 42.0 5.08 1320.8\n",
"3 4 1580 49.0 5.12 1331.2\n",
"4 5 1585 41.5 5.15 1339.0\n",
"5 6 1590 47.0 5.25 1365.0\n",
"6 7 1595 64.0 5.54 1440.4\n",
"7 8 1600 27.0 5.61 1458.6\n",
"8 9 1605 33.0 5.69 1479.4\n",
"9 10 1610 32.0 5.78 1502.8\n",
"10 11 1615 33.0 5.94 1544.4\n",
"11 12 1620 35.0 6.01 1562.6\n",
"12 13 1625 33.0 6.12 1591.2\n",
"13 14 1630 45.0 6.22 1617.2\n",
"14 15 1635 33.0 6.30 1638.0\n",
"15 16 1640 39.0 6.37 1656.2\n",
"16 17 1645 53.0 6.45 1677.0\n",
"17 18 1650 42.0 6.50 1690.0\n",
"18 19 1655 40.5 6.60 1716.0\n",
"19 20 1660 46.5 6.75 1755.0\n",
"20 21 1665 32.0 6.80 1768.0\n",
"21 22 1670 37.0 6.90 1794.0\n",
"22 23 1675 43.0 7.00 1820.0\n",
"23 24 1680 35.0 7.30 1898.0\n",
"24 25 1685 27.0 7.60 1976.0\n",
"25 26 1690 40.0 8.00 2080.0\n",
"26 27 1695 50.0 8.50 2210.0\n",
"27 28 1700 30.0 9.00 2340.0\n",
"28 29 1705 32.0 10.00 2600.0\n",
"29 30 1710 44.0 11.00 2860.0\n",
"30 31 1715 33.0 11.75 3055.0\n",
"31 32 1720 29.0 12.50 3250.0\n",
"32 33 1725 39.0 13.00 3380.0\n",
"33 34 1730 26.0 13.30 3458.0\n",
"34 35 1735 32.0 13.60 3536.0\n",
"35 36 1740 27.0 14.00 3640.0\n",
"36 37 1745 27.5 14.50 3770.0\n",
"37 38 1750 31.0 15.00 3900.0\n",
"38 39 1755 35.5 15.70 4082.0\n",
"39 40 1760 31.0 16.50 4290.0\n",
"40 41 1765 43.0 17.60 4576.0\n",
"41 42 1770 47.0 18.50 4810.0\n",
"42 43 1775 44.0 19.50 5070.0\n",
"43 44 1780 46.0 21.00 5460.0\n",
"44 45 1785 42.0 23.00 5980.0\n",
"45 46 1790 47.5 25.50 6630.0\n",
"46 47 1795 76.0 27.50 7150.0\n",
"47 48 1800 79.0 28.50 7410.0\n",
"48 49 1805 81.0 29.50 7670.0\n",
"49 50 1810 99.0 30.00 7800.0"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Wages5year = (data['Wages']*52*5)\n",
"data.insert(4, \"Wages for 5 year\", Wages5year, True)\n",
"\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"purchasingPower = data['WagesFor5Year']/data['Wheat']\n",
"data.insert(4, \"Purchasing power\", purchasingPower, True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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",
" <th>Purchasing power</th>\n",
" <th>WagesFor5Year</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",
" <td>31.707317</td>\n",
" <td>1300.0</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",
" <td>29.177778</td>\n",
" <td>1313.0</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",
" <td>31.447619</td>\n",
" <td>1320.8</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",
" <td>27.167347</td>\n",
" <td>1331.2</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",
" <td>32.265060</td>\n",
" <td>1339.0</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",
" <td>29.042553</td>\n",
" <td>1365.0</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",
" <td>22.506250</td>\n",
" <td>1440.4</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",
" <td>54.022222</td>\n",
" <td>1458.6</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",
" <td>44.830303</td>\n",
" <td>1479.4</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",
" <td>46.962500</td>\n",
" <td>1502.8</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",
" <td>46.800000</td>\n",
" <td>1544.4</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",
" <td>44.645714</td>\n",
" <td>1562.6</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",
" <td>48.218182</td>\n",
" <td>1591.2</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",
" <td>35.937778</td>\n",
" <td>1617.2</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",
" <td>49.636364</td>\n",
" <td>1638.0</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",
" <td>42.466667</td>\n",
" <td>1656.2</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",
" <td>31.641509</td>\n",
" <td>1677.0</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",
" <td>40.238095</td>\n",
" <td>1690.0</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",
" <td>42.370370</td>\n",
" <td>1716.0</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",
" <td>37.741935</td>\n",
" <td>1755.0</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",
" <td>55.250000</td>\n",
" <td>1768.0</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",
" <td>48.486486</td>\n",
" <td>1794.0</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",
" <td>42.325581</td>\n",
" <td>1820.0</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",
" <td>54.228571</td>\n",
" <td>1898.0</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",
" <td>73.185185</td>\n",
" <td>1976.0</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",
" <td>52.000000</td>\n",
" <td>2080.0</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",
" <td>44.200000</td>\n",
" <td>2210.0</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",
" <td>78.000000</td>\n",
" <td>2340.0</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",
" <td>81.250000</td>\n",
" <td>2600.0</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",
" <td>65.000000</td>\n",
" <td>2860.0</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",
" <td>92.575758</td>\n",
" <td>3055.0</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",
" <td>112.068966</td>\n",
" <td>3250.0</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",
" <td>86.666667</td>\n",
" <td>3380.0</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",
" <td>133.000000</td>\n",
" <td>3458.0</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",
" <td>110.500000</td>\n",
" <td>3536.0</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",
" <td>134.814815</td>\n",
" <td>3640.0</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",
" <td>137.090909</td>\n",
" <td>3770.0</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",
" <td>125.806452</td>\n",
" <td>3900.0</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",
" <td>114.985915</td>\n",
" <td>4082.0</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",
" <td>138.387097</td>\n",
" <td>4290.0</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",
" <td>106.418605</td>\n",
" <td>4576.0</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",
" <td>102.340426</td>\n",
" <td>4810.0</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",
" <td>115.227273</td>\n",
" <td>5070.0</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",
" <td>118.695652</td>\n",
" <td>5460.0</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",
" <td>142.380952</td>\n",
" <td>5980.0</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",
" <td>139.578947</td>\n",
" <td>6630.0</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",
" <td>94.078947</td>\n",
" <td>7150.0</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",
" <td>93.797468</td>\n",
" <td>7410.0</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",
" <td>94.691358</td>\n",
" <td>7670.0</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",
" <td>78.787879</td>\n",
" <td>7800.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [ "text/plain": [
" Unnamed: 0 Year Wheat Wages Purchasing power WagesFor5Year\n", "<Figure size 432x288 with 1 Axes>"
"0 1 1565 41.0 5.00 31.707317 1300.0\n", ]
"1 2 1570 45.0 5.05 29.177778 1313.0\n", },
"2 3 1575 42.0 5.08 31.447619 1320.8\n", "metadata": {
"3 4 1580 49.0 5.12 27.167347 1331.2\n", "needs_background": "light"
"4 5 1585 41.5 5.15 32.265060 1339.0\n", },
"5 6 1590 47.0 5.25 29.042553 1365.0\n", "output_type": "display_data"
"6 7 1595 64.0 5.54 22.506250 1440.4\n", }
"7 8 1600 27.0 5.61 54.022222 1458.6\n", ],
"8 9 1605 33.0 5.69 44.830303 1479.4\n", "source": [
"9 10 1610 32.0 5.78 46.962500 1502.8\n", "purchasingPower = data['Wages']/data['Wheat']\n",
"10 11 1615 33.0 5.94 46.800000 1544.4\n", "plt.plot( data['Year'],purchasingPower)\n",
"11 12 1620 35.0 6.01 44.645714 1562.6\n", "\n",
"12 13 1625 33.0 6.12 48.218182 1591.2\n", "plt.ylabel('purchasing power')\n",
"13 14 1630 45.0 6.22 35.937778 1617.2\n", "plt.xlabel('Year')\n",
"14 15 1635 33.0 6.30 49.636364 1638.0\n", "\n",
"15 16 1640 39.0 6.37 42.466667 1656.2\n", "plt.show()"
"16 17 1645 53.0 6.45 31.641509 1677.0\n",
"17 18 1650 42.0 6.50 40.238095 1690.0\n",
"18 19 1655 40.5 6.60 42.370370 1716.0\n",
"19 20 1660 46.5 6.75 37.741935 1755.0\n",
"20 21 1665 32.0 6.80 55.250000 1768.0\n",
"21 22 1670 37.0 6.90 48.486486 1794.0\n",
"22 23 1675 43.0 7.00 42.325581 1820.0\n",
"23 24 1680 35.0 7.30 54.228571 1898.0\n",
"24 25 1685 27.0 7.60 73.185185 1976.0\n",
"25 26 1690 40.0 8.00 52.000000 2080.0\n",
"26 27 1695 50.0 8.50 44.200000 2210.0\n",
"27 28 1700 30.0 9.00 78.000000 2340.0\n",
"28 29 1705 32.0 10.00 81.250000 2600.0\n",
"29 30 1710 44.0 11.00 65.000000 2860.0\n",
"30 31 1715 33.0 11.75 92.575758 3055.0\n",
"31 32 1720 29.0 12.50 112.068966 3250.0\n",
"32 33 1725 39.0 13.00 86.666667 3380.0\n",
"33 34 1730 26.0 13.30 133.000000 3458.0\n",
"34 35 1735 32.0 13.60 110.500000 3536.0\n",
"35 36 1740 27.0 14.00 134.814815 3640.0\n",
"36 37 1745 27.5 14.50 137.090909 3770.0\n",
"37 38 1750 31.0 15.00 125.806452 3900.0\n",
"38 39 1755 35.5 15.70 114.985915 4082.0\n",
"39 40 1760 31.0 16.50 138.387097 4290.0\n",
"40 41 1765 43.0 17.60 106.418605 4576.0\n",
"41 42 1770 47.0 18.50 102.340426 4810.0\n",
"42 43 1775 44.0 19.50 115.227273 5070.0\n",
"43 44 1780 46.0 21.00 118.695652 5460.0\n",
"44 45 1785 42.0 23.00 142.380952 5980.0\n",
"45 46 1790 47.5 25.50 139.578947 6630.0\n",
"46 47 1795 76.0 27.50 94.078947 7150.0\n",
"47 48 1800 79.0 28.50 93.797468 7410.0\n",
"48 49 1805 81.0 29.50 94.691358 7670.0\n",
"49 50 1810 99.0 30.00 78.787879 7800.0"
] ]
}, },
"execution_count": 13, {
"cell_type": "markdown",
"metadata": {}, "metadata": {},
"output_type": "execute_result" "source": [
"### Représentation des deux quantités (prix du blé, salaire) sur deux axes différents\n",
"Le prix du blé est sur l'axe y. Le salaire est le temps sont sur l'axe x (Salaire en haut et temps en bas)."
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
} }
], ],
"source": [ "source": [
"data" "fig, ax1 = plt.subplots()\n",
"ax1.set_xlabel('Year')\n",
"plt.xticks([0,1,2,3,4], data['Year'])\n",
"ax1.set_ylabel('Wheat')\n",
"\n",
"ax2 = ax1.twiny()\n",
"ax2.set_xlabel('Wages')\n",
"\n",
"p1 = plt.plot(data['Wages'], data['Wheat'] )\n",
"plt.axis([5,31,20,101])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quelle représentation des données vous paraît la plus claire ?\n",
"La représentation qui me parait la plus claire reste celle inspiré de Playfair (ci-dessous)."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 123,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"image/png": 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\n",
"text/plain": [ "text/plain": [
"0 0.121951\n", "<Figure size 432x288 with 2 Axes>"
"1 0.112222\n",
"2 0.120952\n",
"3 0.104490\n",
"4 0.124096\n",
"5 0.111702\n",
"6 0.086563\n",
"7 0.207778\n",
"8 0.172424\n",
"9 0.180625\n",
"10 0.180000\n",
"11 0.171714\n",
"12 0.185455\n",
"13 0.138222\n",
"14 0.190909\n",
"15 0.163333\n",
"16 0.121698\n",
"17 0.154762\n",
"18 0.162963\n",
"19 0.145161\n",
"20 0.212500\n",
"21 0.186486\n",
"22 0.162791\n",
"23 0.208571\n",
"24 0.281481\n",
"25 0.200000\n",
"26 0.170000\n",
"27 0.300000\n",
"28 0.312500\n",
"29 0.250000\n",
"30 0.356061\n",
"31 0.431034\n",
"32 0.333333\n",
"33 0.511538\n",
"34 0.425000\n",
"35 0.518519\n",
"36 0.527273\n",
"37 0.483871\n",
"38 0.442254\n",
"39 0.532258\n",
"40 0.409302\n",
"41 0.393617\n",
"42 0.443182\n",
"43 0.456522\n",
"44 0.547619\n",
"45 0.536842\n",
"46 0.361842\n",
"47 0.360759\n",
"48 0.364198\n",
"49 0.303030\n",
"dtype: float64"
] ]
}, },
"execution_count": 14, "metadata": {
"metadata": {}, "needs_background": "light"
"output_type": "execute_result" },
"output_type": "display_data"
} }
], ],
"source": [ "source": [
"purchasingPower = data['Wages']/data['Wheat']\n", "fig, ax1 = plt.subplots()\n",
"purchasingPower" "color = 'tab:orange'\n",
"ax1.set_xlabel('Year')\n",
"ax1.set_ylabel('Shillings par semaine', color=color)\n",
"\n",
"ax2 = ax1.twinx()\n",
"\n",
"color = 'tab:blue'\n",
"ax2.set_ylabel('shillings par quart de boisseau de blé', color=color)\n",
"\n",
"p1 = plt.bar( data['Year'],data['Wheat'] )\n",
"\n",
"p2 = plt.bar( data['Year'],data['Wages'])\n",
"\n",
"\n",
"plt.legend([p1, p2], [\"Wheat\", \"Wages\"])\n",
"plt.show()\n"
] ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {
......
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