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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 XIX siècle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[William Playfair](https://fr.wikipedia.org/wiki/William_Playfair) était un des pionniers de la présentation graphique des données. Il est notamment considéré comme l'inventeur de l'histogramme. \n",
"Un de ses graphes célèbres, tiré de son livre [\"A Letter on Our Agricultural Distresses, Their Causes and Remedies\"](https://books.google.fr/books/about/A_Letter_on_Our_Agricultural_Distresses.html?id=aQZGAQAAMAAJ), montre [l'évolution du prix du blé et du salaire moyen entre 1565 et 1821](https://fr.wikipedia.org/wiki/William_Playfair#/media/File:Chart_Showing_at_One_View_the_Price_of_the_Quarter_of_Wheat,_and_Wages_of_Labour_by_the_Week,_from_1565_to_1821.png). Playfair n'a pas publié les données numériques brutes qu'il a utilisées, car à son époque la réplicabilité n'était pas encore considérée comme essentielle. Des [valeurs obtenues par numérisation du graphe](https://vincentarelbundock.github.io/Rdatasets/doc/HistData/Wheat.html) sont aujourd'hui téléchargeables, la [version en format CSV](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv) étant la plus pratique.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Quelques remarques pour la compréhension des données :\n",
"* Jusqu'en 1971, la livre sterling était divisée en 20 shillings, et un shilling en 12 pences.\n",
"* Le prix du blé est donné en shillings pour un quart de boisseau de blé. Un quart de boisseau équivaut 15 livres britanniques ou 6,8 kg.\n",
"* Les salaires sont donnés en shillings par semaine."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reproduction du graphe de Playfair à partir des données numériques"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Il s'agit de:\n",
"* Représenter, comme Playfair, le prix du blé par des barres et les salaires par une surface bleue délimitée par une courbe rouge. \n",
"* Superposer les deux de la même façon dans un seul graphique. \n",
"Le style du graphique pourra rester différent par rapport à l'original, mais l'impression globale devrait être la même."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pour créer un graphique représentant le prix du blé par des barres et les salaires par une surface bleue délimitée par une courbe rouge, vous pouvez utiliser la bibliothèque `pandas` pour manipuler les données et `matplotlib` pour créer le graphique."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Chargement des données depuis l'URL"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\""
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
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],
"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": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_url)\n",
"raw_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y a-t-il des points manquants dans ce jeux de données ? Oui, les années 1815, 1820 et 1821 n'ont pas de valeurs associées pour les salaires."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" rownames Year Wheat Wages\n",
"50 51 1815 78.0 NaN\n",
"51 52 1820 54.0 NaN\n",
"52 53 1821 54.0 NaN"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous éliminons ces points, ce qui n'a pas d'impact fort sur notre analyse."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
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" <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": [
" 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"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous définissons les années d'observation comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite. Ensuite, nous trions les points par période, dans le sens chronologique."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"sorted_data = data.set_index('Year').sort_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous vérifions la cohérence des données. Entre la fin d'une période et le début de la période qui suit, la différence temporelle doit être 5."
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
"Year = sorted_data.index\n",
"for Y1, Y2 in zip(Year[:-1], Year[1:]):\n",
" delta = Y2 - Y1\n",
" if delta != 5:\n",
" print(Y1, Y2)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f66fdb45b70>"
]
},
"execution_count": 78,
"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": [
"sorted_data['Wheat'].plot.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Création d'un graphique avec deux axes y"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax1 = plt.subplots(figsize=(10, 6))"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "'5' is not a valid estimator for `bins`",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-68-bd261631604f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Wheat'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'5'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tan'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0medgecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, normed, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m 3135\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3136\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3137\u001b[0;31m stacked=stacked, normed=normed, data=data, **kwargs)\n\u001b[0m\u001b[1;32m 3138\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3139\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1865\u001b[0m \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1866\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1867\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1869\u001b[0m inner.__doc__ = _add_data_doc(inner.__doc__,\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 6637\u001b[0m \u001b[0;31m# this will automatically overwrite bins,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6638\u001b[0m \u001b[0;31m# so that each histogram uses the same bins\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6639\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhist_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6640\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# causes problems later if it's an int\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6641\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmlast\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(a, bins, range, normed, weights, density)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ravel_and_check_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m \u001b[0mbin_edges\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muniform_bins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_bin_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0;31m# Histogram is an integer or a float array depending on the weights.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36m_get_bin_edges\u001b[0;34m(a, bins, range, weights)\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbin_name\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_hist_bin_selectors\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 319\u001b[0m raise ValueError(\n\u001b[0;32m--> 320\u001b[0;31m \"{!r} is not a valid estimator for `bins`\".format(bin_name))\n\u001b[0m\u001b[1;32m 321\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 322\u001b[0m raise TypeError(\"Automated estimation of the number of \"\n",
"\u001b[0;31mValueError\u001b[0m: '5' is not a valid estimator for `bins`"
]
},
{
"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(df['Wheat'], bins='5', color='tan', edgecolor='black')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Barres pour le prix du blé"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"ax1.bar(df['Year'], df['Wheat'], color='tan', edgecolor='black', width=1, label='Wheat')\n",
"ax1.set_xlabel('Année')\n",
"ax1.set_ylabel('Wheat', color='brown')\n",
"ax1.tick_params('y', colors='brown')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Création d'un deuxième axe y pour les salaires"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"ax2 = ax1.twinx()\n",
"ax2.plot(df['Year'], df['Wages'], color='red', marker='o', label='Wages')\n",
"ax2.fill_between(df['Year'], 0, df['Wages'], color='lightblue')\n",
"ax2.set_ylabel('Wages', color='red')\n",
"ax2.tick_params('y', colors='red')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Titre du graphique"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'Prix du blé et Salaires au fil du temps (1565-1821)')"
]
},
"execution_count": 55,
"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.title('Prix du blé et Salaires au fil du temps (1565-1821)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Légende"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f66fdf0d860>"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fig.tight_layout()\n",
"fig.legend(loc='upper left')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Afficher le graphique"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"for i, row in df.iterrows():\n",
" ax1.text(row['Year'], row['Wheat'], f\"{row['Wheat']:.1f}\", ha='center', va='bottom', color='black')\n",
" ax2.text(row['Year'], row['Wages'], f\"{row['Wages']:.1f}\", ha='center', va='top', color='black')"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
......@@ -16,10 +1381,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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