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mooc-rr
Commit 4709a735 authored by 521adc37f04e8509ebf5ce131815aa0a's avatar 521adc37f04e8509ebf5ce131815aa0a
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Graphique avec 3 axes Y

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module3/exo3/exercice.ipynb
View file @ 4709a735
......@@ -9,7 +9,7 @@
},
{
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"outputs": [],
"source": [
......@@ -21,7 +21,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
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"metadata": {},
"outputs": [
{
......@@ -484,7 +484,7 @@
"52 53 1821 54.0 NaN"
]
},
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
......@@ -496,7 +496,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"metadata": {},
"outputs": [
{
......@@ -559,7 +559,7 @@
"52 53 1821 54.0 NaN"
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},
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"metadata": {},
"output_type": "execute_result"
}
......@@ -572,7 +572,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"outputs": [
{
......@@ -1011,7 +1011,7 @@
"49 50 1810 99.0 30.00"
]
},
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
......@@ -1032,7 +1032,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 6,
"metadata": {},
"outputs": [
{
......@@ -1041,7 +1041,7 @@
"<BarContainer object of 50 artists>"
]
},
"execution_count": 5,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
......@@ -1071,16 +1071,16 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PolyCollection at 0x7f8ee177f4e0>"
"<matplotlib.collections.PolyCollection at 0x7f7af6d8aeb8>"
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},
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"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
......@@ -1116,16 +1116,16 @@
},
{
"cell_type": "code",
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"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PolyCollection at 0x7f8ee1748518>"
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"metadata": {},
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},
......@@ -1165,7 +1165,7 @@
},
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"outputs": [
{
......@@ -1221,7 +1221,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 10,
"metadata": {},
"outputs": [
{
......@@ -1249,7 +1249,6 @@
" <th>Year</th>\n",
" <th>Wheat</th>\n",
" <th>Wages</th>\n",
" <th>purchase_power</th>\n",
" <th>Purchase_Power</th>\n",
" </tr>\n",
" </thead>\n",
......@@ -1261,7 +1260,6 @@
" <td>41.0</td>\n",
" <td>5.00</td>\n",
" <td>0.121951</td>\n",
" <td>0.121951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
......@@ -1270,7 +1268,6 @@
" <td>45.0</td>\n",
" <td>5.05</td>\n",
" <td>0.112222</td>\n",
" <td>0.112222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
......@@ -1279,7 +1276,6 @@
" <td>42.0</td>\n",
" <td>5.08</td>\n",
" <td>0.120952</td>\n",
" <td>0.120952</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
......@@ -1288,7 +1284,6 @@
" <td>49.0</td>\n",
" <td>5.12</td>\n",
" <td>0.104490</td>\n",
" <td>0.104490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
......@@ -1297,7 +1292,6 @@
" <td>41.5</td>\n",
" <td>5.15</td>\n",
" <td>0.124096</td>\n",
" <td>0.124096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
......@@ -1306,7 +1300,6 @@
" <td>47.0</td>\n",
" <td>5.25</td>\n",
" <td>0.111702</td>\n",
" <td>0.111702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
......@@ -1315,7 +1308,6 @@
" <td>64.0</td>\n",
" <td>5.54</td>\n",
" <td>0.086563</td>\n",
" <td>0.086563</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
......@@ -1324,7 +1316,6 @@
" <td>27.0</td>\n",
" <td>5.61</td>\n",
" <td>0.207778</td>\n",
" <td>0.207778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
......@@ -1333,7 +1324,6 @@
" <td>33.0</td>\n",
" <td>5.69</td>\n",
" <td>0.172424</td>\n",
" <td>0.172424</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
......@@ -1342,7 +1332,6 @@
" <td>32.0</td>\n",
" <td>5.78</td>\n",
" <td>0.180625</td>\n",
" <td>0.180625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
......@@ -1351,7 +1340,6 @@
" <td>33.0</td>\n",
" <td>5.94</td>\n",
" <td>0.180000</td>\n",
" <td>0.180000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
......@@ -1360,7 +1348,6 @@
" <td>35.0</td>\n",
" <td>6.01</td>\n",
" <td>0.171714</td>\n",
" <td>0.171714</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
......@@ -1369,7 +1356,6 @@
" <td>33.0</td>\n",
" <td>6.12</td>\n",
" <td>0.185455</td>\n",
" <td>0.185455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
......@@ -1378,7 +1364,6 @@
" <td>45.0</td>\n",
" <td>6.22</td>\n",
" <td>0.138222</td>\n",
" <td>0.138222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
......@@ -1387,7 +1372,6 @@
" <td>33.0</td>\n",
" <td>6.30</td>\n",
" <td>0.190909</td>\n",
" <td>0.190909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
......@@ -1396,7 +1380,6 @@
" <td>39.0</td>\n",
" <td>6.37</td>\n",
" <td>0.163333</td>\n",
" <td>0.163333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
......@@ -1405,7 +1388,6 @@
" <td>53.0</td>\n",
" <td>6.45</td>\n",
" <td>0.121698</td>\n",
" <td>0.121698</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
......@@ -1414,7 +1396,6 @@
" <td>42.0</td>\n",
" <td>6.50</td>\n",
" <td>0.154762</td>\n",
" <td>0.154762</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
......@@ -1423,7 +1404,6 @@
" <td>40.5</td>\n",
" <td>6.60</td>\n",
" <td>0.162963</td>\n",
" <td>0.162963</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
......@@ -1432,7 +1412,6 @@
" <td>46.5</td>\n",
" <td>6.75</td>\n",
" <td>0.145161</td>\n",
" <td>0.145161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
......@@ -1441,7 +1420,6 @@
" <td>32.0</td>\n",
" <td>6.80</td>\n",
" <td>0.212500</td>\n",
" <td>0.212500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
......@@ -1450,7 +1428,6 @@
" <td>37.0</td>\n",
" <td>6.90</td>\n",
" <td>0.186486</td>\n",
" <td>0.186486</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
......@@ -1459,7 +1436,6 @@
" <td>43.0</td>\n",
" <td>7.00</td>\n",
" <td>0.162791</td>\n",
" <td>0.162791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
......@@ -1468,7 +1444,6 @@
" <td>35.0</td>\n",
" <td>7.30</td>\n",
" <td>0.208571</td>\n",
" <td>0.208571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
......@@ -1477,7 +1452,6 @@
" <td>27.0</td>\n",
" <td>7.60</td>\n",
" <td>0.281481</td>\n",
" <td>0.281481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
......@@ -1486,7 +1460,6 @@
" <td>40.0</td>\n",
" <td>8.00</td>\n",
" <td>0.200000</td>\n",
" <td>0.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
......@@ -1495,7 +1468,6 @@
" <td>50.0</td>\n",
" <td>8.50</td>\n",
" <td>0.170000</td>\n",
" <td>0.170000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
......@@ -1504,7 +1476,6 @@
" <td>30.0</td>\n",
" <td>9.00</td>\n",
" <td>0.300000</td>\n",
" <td>0.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
......@@ -1513,7 +1484,6 @@
" <td>32.0</td>\n",
" <td>10.00</td>\n",
" <td>0.312500</td>\n",
" <td>0.312500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
......@@ -1522,7 +1492,6 @@
" <td>44.0</td>\n",
" <td>11.00</td>\n",
" <td>0.250000</td>\n",
" <td>0.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
......@@ -1531,7 +1500,6 @@
" <td>33.0</td>\n",
" <td>11.75</td>\n",
" <td>0.356061</td>\n",
" <td>0.356061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
......@@ -1540,7 +1508,6 @@
" <td>29.0</td>\n",
" <td>12.50</td>\n",
" <td>0.431034</td>\n",
" <td>0.431034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
......@@ -1549,7 +1516,6 @@
" <td>39.0</td>\n",
" <td>13.00</td>\n",
" <td>0.333333</td>\n",
" <td>0.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
......@@ -1558,7 +1524,6 @@
" <td>26.0</td>\n",
" <td>13.30</td>\n",
" <td>0.511538</td>\n",
" <td>0.511538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
......@@ -1567,7 +1532,6 @@
" <td>32.0</td>\n",
" <td>13.60</td>\n",
" <td>0.425000</td>\n",
" <td>0.425000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
......@@ -1576,7 +1540,6 @@
" <td>27.0</td>\n",
" <td>14.00</td>\n",
" <td>0.518519</td>\n",
" <td>0.518519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
......@@ -1585,7 +1548,6 @@
" <td>27.5</td>\n",
" <td>14.50</td>\n",
" <td>0.527273</td>\n",
" <td>0.527273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
......@@ -1594,7 +1556,6 @@
" <td>31.0</td>\n",
" <td>15.00</td>\n",
" <td>0.483871</td>\n",
" <td>0.483871</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
......@@ -1603,7 +1564,6 @@
" <td>35.5</td>\n",
" <td>15.70</td>\n",
" <td>0.442254</td>\n",
" <td>0.442254</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
......@@ -1612,7 +1572,6 @@
" <td>31.0</td>\n",
" <td>16.50</td>\n",
" <td>0.532258</td>\n",
" <td>0.532258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
......@@ -1621,7 +1580,6 @@
" <td>43.0</td>\n",
" <td>17.60</td>\n",
" <td>0.409302</td>\n",
" <td>0.409302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
......@@ -1630,7 +1588,6 @@
" <td>47.0</td>\n",
" <td>18.50</td>\n",
" <td>0.393617</td>\n",
" <td>0.393617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
......@@ -1639,7 +1596,6 @@
" <td>44.0</td>\n",
" <td>19.50</td>\n",
" <td>0.443182</td>\n",
" <td>0.443182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
......@@ -1648,7 +1604,6 @@
" <td>46.0</td>\n",
" <td>21.00</td>\n",
" <td>0.456522</td>\n",
" <td>0.456522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
......@@ -1657,7 +1612,6 @@
" <td>42.0</td>\n",
" <td>23.00</td>\n",
" <td>0.547619</td>\n",
" <td>0.547619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
......@@ -1666,7 +1620,6 @@
" <td>47.5</td>\n",
" <td>25.50</td>\n",
" <td>0.536842</td>\n",
" <td>0.536842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
......@@ -1675,7 +1628,6 @@
" <td>76.0</td>\n",
" <td>27.50</td>\n",
" <td>0.361842</td>\n",
" <td>0.361842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
......@@ -1684,7 +1636,6 @@
" <td>79.0</td>\n",
" <td>28.50</td>\n",
" <td>0.360759</td>\n",
" <td>0.360759</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
......@@ -1693,7 +1644,6 @@
" <td>81.0</td>\n",
" <td>29.50</td>\n",
" <td>0.364198</td>\n",
" <td>0.364198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
......@@ -1702,67 +1652,66 @@
" <td>99.0</td>\n",
" <td>30.00</td>\n",
" <td>0.303030</td>\n",
" <td>0.303030</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Year Wheat Wages purchase_power Purchase_Power\n",
"0 1 1565 41.0 5.00 0.121951 0.121951\n",
"1 2 1570 45.0 5.05 0.112222 0.112222\n",
"2 3 1575 42.0 5.08 0.120952 0.120952\n",
"3 4 1580 49.0 5.12 0.104490 0.104490\n",
"4 5 1585 41.5 5.15 0.124096 0.124096\n",
"5 6 1590 47.0 5.25 0.111702 0.111702\n",
"6 7 1595 64.0 5.54 0.086563 0.086563\n",
"7 8 1600 27.0 5.61 0.207778 0.207778\n",
"8 9 1605 33.0 5.69 0.172424 0.172424\n",
"9 10 1610 32.0 5.78 0.180625 0.180625\n",
"10 11 1615 33.0 5.94 0.180000 0.180000\n",
"11 12 1620 35.0 6.01 0.171714 0.171714\n",
"12 13 1625 33.0 6.12 0.185455 0.185455\n",
"13 14 1630 45.0 6.22 0.138222 0.138222\n",
"14 15 1635 33.0 6.30 0.190909 0.190909\n",
"15 16 1640 39.0 6.37 0.163333 0.163333\n",
"16 17 1645 53.0 6.45 0.121698 0.121698\n",
"17 18 1650 42.0 6.50 0.154762 0.154762\n",
"18 19 1655 40.5 6.60 0.162963 0.162963\n",
"19 20 1660 46.5 6.75 0.145161 0.145161\n",
"20 21 1665 32.0 6.80 0.212500 0.212500\n",
"21 22 1670 37.0 6.90 0.186486 0.186486\n",
"22 23 1675 43.0 7.00 0.162791 0.162791\n",
"23 24 1680 35.0 7.30 0.208571 0.208571\n",
"24 25 1685 27.0 7.60 0.281481 0.281481\n",
"25 26 1690 40.0 8.00 0.200000 0.200000\n",
"26 27 1695 50.0 8.50 0.170000 0.170000\n",
"27 28 1700 30.0 9.00 0.300000 0.300000\n",
"28 29 1705 32.0 10.00 0.312500 0.312500\n",
"29 30 1710 44.0 11.00 0.250000 0.250000\n",
"30 31 1715 33.0 11.75 0.356061 0.356061\n",
"31 32 1720 29.0 12.50 0.431034 0.431034\n",
"32 33 1725 39.0 13.00 0.333333 0.333333\n",
"33 34 1730 26.0 13.30 0.511538 0.511538\n",
"34 35 1735 32.0 13.60 0.425000 0.425000\n",
"35 36 1740 27.0 14.00 0.518519 0.518519\n",
"36 37 1745 27.5 14.50 0.527273 0.527273\n",
"37 38 1750 31.0 15.00 0.483871 0.483871\n",
"38 39 1755 35.5 15.70 0.442254 0.442254\n",
"39 40 1760 31.0 16.50 0.532258 0.532258\n",
"40 41 1765 43.0 17.60 0.409302 0.409302\n",
"41 42 1770 47.0 18.50 0.393617 0.393617\n",
"42 43 1775 44.0 19.50 0.443182 0.443182\n",
"43 44 1780 46.0 21.00 0.456522 0.456522\n",
"44 45 1785 42.0 23.00 0.547619 0.547619\n",
"45 46 1790 47.5 25.50 0.536842 0.536842\n",
"46 47 1795 76.0 27.50 0.361842 0.361842\n",
"47 48 1800 79.0 28.50 0.360759 0.360759\n",
"48 49 1805 81.0 29.50 0.364198 0.364198\n",
"49 50 1810 99.0 30.00 0.303030 0.303030"
" Unnamed: 0 Year Wheat Wages Purchase_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"
]
},
"execution_count": 13,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
......@@ -1776,7 +1725,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 11,
"metadata": {},
"outputs": [
{
......@@ -2266,13 +2215,13 @@
"49 50 1810 99.0 30.00 0.303030"
]
},
"execution_count": 19,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Effacer une colonne en double\n",
"# delete a duplicate column\n",
"# del my_data[\"purchase_power\"]\n",
"my_data"
]
......@@ -2286,7 +2235,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 12,
"metadata": {},
"outputs": [
{
......@@ -2326,7 +2275,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 13,
"metadata": {},
"outputs": [
{
......@@ -2356,14 +2305,71 @@
"# twin object for two different y-axis on the sample plot\n",
"ax2 = ax.twinx()\n",
"\n",
"\n",
"# make a plot with different y-axis using second axis object\n",
"ax2.plot(my_data[\"Wages\"] ,color = \"blue\",marker = \"o\")\n",
"ax2.plot(my_data[\"Wages\"] ,color = \"blue\", marker = \"o\")\n",
"ax2.set_ylabel(\"Wages\",color = \"blue\", fontsize = 14)\n",
"\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Progression du temps dans la représentation graphique du prix du blé et du salaire"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f7af1813358>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# create figure and axis objects\n",
"fig, ax = plt.subplots()\n",
"\n",
"# twin object for three different y-axis on the sample plot\n",
"ax3 = ax.twinx()\n",
"\n",
"# Set the position of the spine\n",
"rspine = ax3.spines['right']\n",
"rspine.set_position(('axes', 1.15))\n",
"\n",
"# make a plot with different y-axis \n",
"my_data.Wheat.plot(ax = ax, style ='r-', marker = \"o\")\n",
"my_data.Wages.plot(ax = ax, style ='b-', secondary_y = True)\n",
"my_data.Year.plot(ax = ax3, style ='g--')\n",
"\n",
"# add legend\n",
"ax.legend([ax.get_lines()[0], ax.right_ax.get_lines()[0], ax3.get_lines()[0]],\n",
" ['Wheat','Wages','Year'], bbox_to_anchor = (1.75, 1))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
......@@ -2377,18 +2383,6 @@
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
......
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