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Nouveaux graphiques et améliorations

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module3/exo3/exercice.ipynb
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{
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"## Ajustement des données pour observer l'évolution du pouvoir d'achat\n",
"\n",
"On souhaite représenter le pouvoir d'achat au cours du temps, défini comme la quantité de blé qu'un ouvrier peut acheter avec son salaire hebdomadaire. \n",
"On crée une nouvelle colonne au tableau : la colonne Power qui représente le pouvoir d'achat de l'année, la quantité de quart de boisseaux de blé qu'un ouvrier peut acheter par semaine."
]
},
{
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"source": []
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: middle;\n",
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"\n",
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"<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>Power</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>0.121951</td>\n",
" </tr>\n",
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" <th>1</th>\n",
" <td>2</td>\n",
" <td>1570</td>\n",
" <td>45.0</td>\n",
" <td>5.05</td>\n",
" <td>0.112222</td>\n",
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" <td>3</td>\n",
" <td>1575</td>\n",
" <td>42.0</td>\n",
" <td>5.08</td>\n",
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" <td>0.124096</td>\n",
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" <td>6</td>\n",
" <td>1590</td>\n",
" <td>47.0</td>\n",
" <td>5.25</td>\n",
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" <tr>\n",
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" <td>0.207778</td>\n",
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" <td>9</td>\n",
" <td>1605</td>\n",
" <td>33.0</td>\n",
" <td>5.69</td>\n",
" <td>0.172424</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>32.0</td>\n",
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" <td>13</td>\n",
" <td>1625</td>\n",
" <td>33.0</td>\n",
" <td>6.12</td>\n",
" <td>0.185455</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",
" <td>6.22</td>\n",
" <td>0.138222</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>0.190909</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",
" <td>0.163333</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>0.121698</td>\n",
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" <tr>\n",
" <th>17</th>\n",
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" <td>1650</td>\n",
" <td>42.0</td>\n",
" <td>6.50</td>\n",
" <td>0.154762</td>\n",
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" <tr>\n",
" <th>18</th>\n",
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" <td>1655</td>\n",
" <td>40.5</td>\n",
" <td>6.60</td>\n",
" <td>0.162963</td>\n",
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" <td>20</td>\n",
" <td>1660</td>\n",
" <td>46.5</td>\n",
" <td>6.75</td>\n",
" <td>0.145161</td>\n",
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" <th>20</th>\n",
" <td>21</td>\n",
" <td>1665</td>\n",
" <td>32.0</td>\n",
" <td>6.80</td>\n",
" <td>0.212500</td>\n",
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" <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>0.186486</td>\n",
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" <td>0.208571</td>\n",
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" <td>7.60</td>\n",
" <td>0.281481</td>\n",
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" <td>8.00</td>\n",
" <td>0.200000</td>\n",
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" <td>9.00</td>\n",
" <td>0.300000</td>\n",
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" <td>1705</td>\n",
" <td>32.0</td>\n",
" <td>10.00</td>\n",
" <td>0.312500</td>\n",
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" <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>0.250000</td>\n",
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" <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>0.356061</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>0.431034</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>0.333333</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>0.511538</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",
" <td>0.425000</td>\n",
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" <td>36</td>\n",
" <td>1740</td>\n",
" <td>27.0</td>\n",
" <td>14.00</td>\n",
" <td>0.518519</td>\n",
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" <td>37</td>\n",
" <td>1745</td>\n",
" <td>27.5</td>\n",
" <td>14.50</td>\n",
" <td>0.527273</td>\n",
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" <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>0.483871</td>\n",
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" <tr>\n",
" <th>38</th>\n",
" <td>39</td>\n",
" <td>1755</td>\n",
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" <td>0.442254</td>\n",
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" <th>39</th>\n",
" <td>40</td>\n",
" <td>1760</td>\n",
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" <td>16.50</td>\n",
" <td>0.532258</td>\n",
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" <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>0.409302</td>\n",
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" <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>0.393617</td>\n",
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" <th>42</th>\n",
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" <td>19.50</td>\n",
" <td>0.443182</td>\n",
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" <th>43</th>\n",
" <td>44</td>\n",
" <td>1780</td>\n",
" <td>46.0</td>\n",
" <td>21.00</td>\n",
" <td>0.456522</td>\n",
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" <td>45</td>\n",
" <td>1785</td>\n",
" <td>42.0</td>\n",
" <td>23.00</td>\n",
" <td>0.547619</td>\n",
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" <th>45</th>\n",
" <td>46</td>\n",
" <td>1790</td>\n",
" <td>47.5</td>\n",
" <td>25.50</td>\n",
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" <td>47</td>\n",
" <td>1795</td>\n",
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" <td>1800</td>\n",
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" <td>0.360759</td>\n",
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" <tr>\n",
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" <td>49</td>\n",
" <td>1805</td>\n",
" <td>81.0</td>\n",
" <td>29.50</td>\n",
" <td>0.364198</td>\n",
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" <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>0.303030</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>1821</td>\n",
" <td>54.0</td>\n",
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"text/plain": [
" Unnamed: 0 Year Wheat Wages Power\n",
"0 1 1565 41.0 5.00 0.121951\n",
"1 2 1570 45.0 5.05 0.112222\n",
"2 3 1575 42.0 5.08 0.120952\n",
"3 4 1580 49.0 5.12 0.104490\n",
"4 5 1585 41.5 5.15 0.124096\n",
"5 6 1590 47.0 5.25 0.111702\n",
"6 7 1595 64.0 5.54 0.086563\n",
"7 8 1600 27.0 5.61 0.207778\n",
"8 9 1605 33.0 5.69 0.172424\n",
"9 10 1610 32.0 5.78 0.180625\n",
"10 11 1615 33.0 5.94 0.180000\n",
"11 12 1620 35.0 6.01 0.171714\n",
"12 13 1625 33.0 6.12 0.185455\n",
"13 14 1630 45.0 6.22 0.138222\n",
"14 15 1635 33.0 6.30 0.190909\n",
"15 16 1640 39.0 6.37 0.163333\n",
"16 17 1645 53.0 6.45 0.121698\n",
"17 18 1650 42.0 6.50 0.154762\n",
"18 19 1655 40.5 6.60 0.162963\n",
"19 20 1660 46.5 6.75 0.145161\n",
"20 21 1665 32.0 6.80 0.212500\n",
"21 22 1670 37.0 6.90 0.186486\n",
"22 23 1675 43.0 7.00 0.162791\n",
"23 24 1680 35.0 7.30 0.208571\n",
"24 25 1685 27.0 7.60 0.281481\n",
"25 26 1690 40.0 8.00 0.200000\n",
"26 27 1695 50.0 8.50 0.170000\n",
"27 28 1700 30.0 9.00 0.300000\n",
"28 29 1705 32.0 10.00 0.312500\n",
"29 30 1710 44.0 11.00 0.250000\n",
"30 31 1715 33.0 11.75 0.356061\n",
"31 32 1720 29.0 12.50 0.431034\n",
"32 33 1725 39.0 13.00 0.333333\n",
"33 34 1730 26.0 13.30 0.511538\n",
"34 35 1735 32.0 13.60 0.425000\n",
"35 36 1740 27.0 14.00 0.518519\n",
"36 37 1745 27.5 14.50 0.527273\n",
"37 38 1750 31.0 15.00 0.483871\n",
"38 39 1755 35.5 15.70 0.442254\n",
"39 40 1760 31.0 16.50 0.532258\n",
"40 41 1765 43.0 17.60 0.409302\n",
"41 42 1770 47.0 18.50 0.393617\n",
"42 43 1775 44.0 19.50 0.443182\n",
"43 44 1780 46.0 21.00 0.456522\n",
"44 45 1785 42.0 23.00 0.547619\n",
"45 46 1790 47.5 25.50 0.536842\n",
"46 47 1795 76.0 27.50 0.361842\n",
"47 48 1800 79.0 28.50 0.360759\n",
"48 49 1805 81.0 29.50 0.364198\n",
"49 50 1810 99.0 30.00 0.303030\n",
"50 51 1815 78.0 NaN NaN\n",
"51 52 1820 54.0 NaN NaN\n",
"52 53 1821 54.0 NaN NaN"
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"source": [
"data['Power'] = data['Wages']/data['Wheat']\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On représente maintenant l'évolution de ce pouvoir d'achat dans le temps"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'Buying power')"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(data['Year'],data['Power'], color='green', marker = 'o')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Buying power')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dans un autre graphique, montrez les deux quantités (prix du blé, salaire) sur deux axes différents, sans l'axe du temps. Trouvez une autre façon d'indiquer la progression du temps dans ce graphique. Quelle représentation des données vous paraît la plus claire ? "
]
}
],
"metadata": {
......
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