From 05ac7799506d2ff4464edbafeca89c4d36dcfce2 Mon Sep 17 00:00:00 2001 From: 6fe417a114f34bcda6e186e0c8a18f64 <6fe417a114f34bcda6e186e0c8a18f64@app-learninglab.inria.fr> Date: Mon, 23 Oct 2023 11:18:18 +0000 Subject: [PATCH] no commit message --- module3/exo3/exercice.ipynb | 1326 ++++++++++++++++++++++++++++++++--- 1 file changed, 1233 insertions(+), 93 deletions(-) diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index eb4b1d8..277315b 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -7,31 +7,1059 @@ "# le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle# " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Données de Playfair" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rownamesYearWheatWages
01156541.05.00
12157045.05.05
23157542.05.08
34158049.05.12
45158541.55.15
56159047.05.25
67159564.05.54
78160027.05.61
89160533.05.69
910161032.05.78
1011161533.05.94
1112162035.06.01
1213162533.06.12
1314163045.06.22
1415163533.06.30
1516164039.06.37
1617164553.06.45
1718165042.06.50
1819165540.56.60
1920166046.56.75
2021166532.06.80
2122167037.06.90
2223167543.07.00
2324168035.07.30
2425168527.07.60
2526169040.08.00
2627169550.08.50
2728170030.09.00
2829170532.010.00
2930171044.011.00
3031171533.011.75
3132172029.012.50
3233172539.013.00
3334173026.013.30
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rownamesYearWheatWages
01156541.05.00
12157045.05.05
23157542.05.08
34158049.05.12
45158541.55.15
56159047.05.25
67159564.05.54
78160027.05.61
89160533.05.69
910161032.05.78
1011161533.05.94
1112162035.06.01
1213162533.06.12
1314163045.06.22
1415163533.06.30
1516164039.06.37
1617164553.06.45
1718165042.06.50
1819165540.56.60
1920166046.56.75
2021166532.06.80
2122167037.06.90
2223167543.07.00
2324168035.07.30
2425168527.07.60
2526169040.08.00
2627169550.08.50
2728170030.09.00
2829170532.010.00
2930171044.011.00
3031171533.011.75
3132172029.012.50
3233172539.013.00
3334173026.013.30
3435173532.013.60
3536174027.014.00
3637174527.514.50
3738175031.015.00
3839175535.515.70
3940176031.016.50
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4142177047.018.50
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4445178542.023.00
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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": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c1 = pd.DataFrame(c,\n", + " columns=['rownames','Year','Wheat','Wages'])\n", + "c1" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import numpy as np\n", - "import pandas as pd" + "x=c1['Year']\n", + "y1=c1['Wheat']\n", + "y2=c1['Wages']" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0,0.5,'Wages')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax1 = plt.subplots(figsize=(9, 6))\n", + "plt.plot(x,y2, color='r')\n", + "plt.fill_between(x, y2) \n", + "plt.bar(x,y1,width = 5, color='black')\n", + "ax2 = ax1.twinx() \n", + "plt.xlabel('year')\n", + "ax1.set_ylabel('Wheat')\n", + "ax2.set_ylabel('Wages')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Changement des unités" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "url=\"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\"\n", - "c=pd.read_csv(url)" + "#Wheat price \n", + "Wheat2=c1['Wheat']/6.8\n", + "c1.insert(3, 'Wheat2', Wheat2 )" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# shilling per month\n", + "Wages2=4*c1['Wages']\n", + "c1.insert(5, 'Wages/month', Wages2 )" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -58,7 +1086,9 @@ " rownames\n", " Year\n", " Wheat\n", + " Wheat2\n", " Wages\n", + " Wages/month\n", " \n", " \n", " \n", @@ -67,356 +1097,458 @@ " 1\n", " 1565\n", " 41.0\n", + " 6.029412\n", " 5.00\n", + " 20.00\n", " \n", " \n", " 1\n", " 2\n", " 1570\n", " 45.0\n", + " 6.617647\n", " 5.05\n", + " 20.20\n", " \n", " \n", " 2\n", " 3\n", " 1575\n", " 42.0\n", + " 6.176471\n", " 5.08\n", + " 20.32\n", " \n", " \n", " 3\n", " 4\n", " 1580\n", " 49.0\n", + " 7.205882\n", " 5.12\n", + " 20.48\n", " \n", " \n", " 4\n", " 5\n", " 1585\n", " 41.5\n", + " 6.102941\n", " 5.15\n", + " 20.60\n", " \n", " \n", " 5\n", " 6\n", " 1590\n", " 47.0\n", + " 6.911765\n", " 5.25\n", + " 21.00\n", " \n", " \n", " 6\n", " 7\n", " 1595\n", " 64.0\n", + " 9.411765\n", " 5.54\n", + " 22.16\n", " \n", " \n", " 7\n", " 8\n", " 1600\n", " 27.0\n", + " 3.970588\n", " 5.61\n", + " 22.44\n", " \n", " \n", " 8\n", " 9\n", " 1605\n", " 33.0\n", + " 4.852941\n", " 5.69\n", + " 22.76\n", " \n", " \n", " 9\n", " 10\n", " 1610\n", " 32.0\n", + " 4.705882\n", " 5.78\n", + " 23.12\n", " \n", " \n", " 10\n", " 11\n", " 1615\n", " 33.0\n", + " 4.852941\n", " 5.94\n", + " 23.76\n", " \n", " \n", " 11\n", " 12\n", " 1620\n", " 35.0\n", + " 5.147059\n", " 6.01\n", + " 24.04\n", " \n", " \n", " 12\n", " 13\n", " 1625\n", " 33.0\n", + " 4.852941\n", " 6.12\n", + " 24.48\n", " \n", " \n", " 13\n", " 14\n", " 1630\n", " 45.0\n", + " 6.617647\n", " 6.22\n", + " 24.88\n", " \n", " \n", " 14\n", " 15\n", " 1635\n", " 33.0\n", + " 4.852941\n", " 6.30\n", + " 25.20\n", " \n", " \n", " 15\n", " 16\n", " 1640\n", " 39.0\n", + " 5.735294\n", " 6.37\n", + " 25.48\n", " \n", " \n", " 16\n", " 17\n", " 1645\n", " 53.0\n", + " 7.794118\n", " 6.45\n", + " 25.80\n", " \n", " \n", " 17\n", " 18\n", " 1650\n", " 42.0\n", + " 6.176471\n", " 6.50\n", + " 26.00\n", " \n", " \n", " 18\n", " 19\n", " 1655\n", " 40.5\n", + " 5.955882\n", " 6.60\n", + " 26.40\n", " \n", " \n", " 19\n", " 20\n", " 1660\n", " 46.5\n", + " 6.838235\n", " 6.75\n", + " 27.00\n", " \n", " \n", " 20\n", " 21\n", " 1665\n", " 32.0\n", + " 4.705882\n", " 6.80\n", + " 27.20\n", " \n", " \n", " 21\n", " 22\n", " 1670\n", " 37.0\n", + " 5.441176\n", " 6.90\n", + " 27.60\n", " \n", " \n", " 22\n", " 23\n", " 1675\n", " 43.0\n", + " 6.323529\n", " 7.00\n", + " 28.00\n", " \n", " \n", " 23\n", " 24\n", " 1680\n", " 35.0\n", + " 5.147059\n", " 7.30\n", + " 29.20\n", " \n", " \n", " 24\n", " 25\n", " 1685\n", " 27.0\n", + " 3.970588\n", " 7.60\n", + " 30.40\n", " \n", " \n", " 25\n", " 26\n", " 1690\n", " 40.0\n", + " 5.882353\n", " 8.00\n", + " 32.00\n", " \n", " \n", " 26\n", " 27\n", " 1695\n", " 50.0\n", + " 7.352941\n", " 8.50\n", + " 34.00\n", " \n", " \n", " 27\n", " 28\n", " 1700\n", " 30.0\n", + " 4.411765\n", " 9.00\n", + " 36.00\n", " \n", " \n", " 28\n", " 29\n", " 1705\n", " 32.0\n", + " 4.705882\n", " 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" \n", " 39\n", " 40\n", " 1760\n", " 31.0\n", + " 4.558824\n", " 16.50\n", + " 66.00\n", " \n", " \n", " 40\n", " 41\n", " 1765\n", " 43.0\n", + " 6.323529\n", " 17.60\n", + " 70.40\n", " \n", " \n", " 41\n", " 42\n", " 1770\n", " 47.0\n", + " 6.911765\n", " 18.50\n", + " 74.00\n", " \n", " \n", " 42\n", " 43\n", " 1775\n", " 44.0\n", + " 6.470588\n", " 19.50\n", + " 78.00\n", " \n", " \n", " 43\n", " 44\n", " 1780\n", " 46.0\n", + " 6.764706\n", " 21.00\n", + " 84.00\n", " \n", " \n", " 44\n", " 45\n", " 1785\n", " 42.0\n", + " 6.176471\n", " 23.00\n", + " 92.00\n", " \n", " \n", " 45\n", " 46\n", " 1790\n", " 47.5\n", + " 6.985294\n", " 25.50\n", + " 102.00\n", " \n", " \n", " 46\n", " 47\n", " 1795\n", " 76.0\n", + " 11.176471\n", " 27.50\n", + " 110.00\n", " \n", " \n", " 47\n", " 48\n", " 1800\n", " 79.0\n", + " 11.617647\n", " 28.50\n", + " 114.00\n", " \n", " \n", " 48\n", " 49\n", " 1805\n", " 81.0\n", + " 11.911765\n", " 29.50\n", + " 118.00\n", " \n", " \n", " 49\n", " 50\n", " 1810\n", " 99.0\n", + " 14.558824\n", " 30.00\n", + " 120.00\n", " \n", " \n", " 50\n", " 51\n", " 1815\n", " 78.0\n", + " 11.470588\n", + " NaN\n", " NaN\n", " \n", " \n", @@ -424,6 +1556,8 @@ " 52\n", " 1820\n", " 54.0\n", + " 7.941176\n", + " NaN\n", " NaN\n", " \n", " \n", @@ -431,6 +1565,8 @@ " 53\n", " 1821\n", " 54.0\n", + " 7.941176\n", + " NaN\n", " NaN\n", " \n", " \n", @@ -438,95 +1574,91 @@ "" ], "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" + " rownames Year Wheat Wheat2 Wages Wages/month\n", + "0 1 1565 41.0 6.029412 5.00 20.00\n", + "1 2 1570 45.0 6.617647 5.05 20.20\n", + "2 3 1575 42.0 6.176471 5.08 20.32\n", + "3 4 1580 49.0 7.205882 5.12 20.48\n", + "4 5 1585 41.5 6.102941 5.15 20.60\n", + "5 6 1590 47.0 6.911765 5.25 21.00\n", + "6 7 1595 64.0 9.411765 5.54 22.16\n", + "7 8 1600 27.0 3.970588 5.61 22.44\n", + "8 9 1605 33.0 4.852941 5.69 22.76\n", + "9 10 1610 32.0 4.705882 5.78 23.12\n", + "10 11 1615 33.0 4.852941 5.94 23.76\n", + "11 12 1620 35.0 5.147059 6.01 24.04\n", + "12 13 1625 33.0 4.852941 6.12 24.48\n", + "13 14 1630 45.0 6.617647 6.22 24.88\n", + "14 15 1635 33.0 4.852941 6.30 25.20\n", + "15 16 1640 39.0 5.735294 6.37 25.48\n", + "16 17 1645 53.0 7.794118 6.45 25.80\n", + "17 18 1650 42.0 6.176471 6.50 26.00\n", + "18 19 1655 40.5 5.955882 6.60 26.40\n", + "19 20 1660 46.5 6.838235 6.75 27.00\n", + "20 21 1665 32.0 4.705882 6.80 27.20\n", + "21 22 1670 37.0 5.441176 6.90 27.60\n", + "22 23 1675 43.0 6.323529 7.00 28.00\n", + "23 24 1680 35.0 5.147059 7.30 29.20\n", + "24 25 1685 27.0 3.970588 7.60 30.40\n", + "25 26 1690 40.0 5.882353 8.00 32.00\n", + "26 27 1695 50.0 7.352941 8.50 34.00\n", + "27 28 1700 30.0 4.411765 9.00 36.00\n", + "28 29 1705 32.0 4.705882 10.00 40.00\n", + "29 30 1710 44.0 6.470588 11.00 44.00\n", + "30 31 1715 33.0 4.852941 11.75 47.00\n", + "31 32 1720 29.0 4.264706 12.50 50.00\n", + "32 33 1725 39.0 5.735294 13.00 52.00\n", + "33 34 1730 26.0 3.823529 13.30 53.20\n", + "34 35 1735 32.0 4.705882 13.60 54.40\n", + "35 36 1740 27.0 3.970588 14.00 56.00\n", + "36 37 1745 27.5 4.044118 14.50 58.00\n", + "37 38 1750 31.0 4.558824 15.00 60.00\n", + "38 39 1755 35.5 5.220588 15.70 62.80\n", + "39 40 1760 31.0 4.558824 16.50 66.00\n", + "40 41 1765 43.0 6.323529 17.60 70.40\n", + "41 42 1770 47.0 6.911765 18.50 74.00\n", + "42 43 1775 44.0 6.470588 19.50 78.00\n", + "43 44 1780 46.0 6.764706 21.00 84.00\n", + "44 45 1785 42.0 6.176471 23.00 92.00\n", + "45 46 1790 47.5 6.985294 25.50 102.00\n", + "46 47 1795 76.0 11.176471 27.50 110.00\n", + "47 48 1800 79.0 11.617647 28.50 114.00\n", + "48 49 1805 81.0 11.911765 29.50 118.00\n", + "49 50 1810 99.0 14.558824 30.00 120.00\n", + "50 51 1815 78.0 11.470588 NaN NaN\n", + "51 52 1820 54.0 7.941176 NaN NaN\n", + "52 53 1821 54.0 7.941176 NaN NaN" ] }, - "execution_count": 25, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "c1 = pd.DataFrame(c,\n", - " columns=['rownames','Year','Wheat','Wages'])\n", "c1" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(array([18., 8., 16., 5., 0., 1., 1., 3., 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", - " )" + "Text(0,0.5,'Wages/Month')" ] }, - "execution_count": 29, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -536,40 +1668,54 @@ } ], "source": [ - "plt.hist(c1['Wheat'])" + "y3=c1['Wheat2']\n", + "y4=c1['Wages/month']\n", + "fig, ax1 = plt.subplots(figsize=(9, 6))\n", + "plt.bar(x,y3,width = 5, color='r')\n", + "plt.plot(x,y4/10)\n", + "\n", + "ax2 = ax1.twinx() \n", + "ax1.set_xlabel('year')\n", + "ax1.set_ylabel('shillings/kg')\n", + "ax2.set_ylabel('Wages/Month')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pouvoir d'achat " ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "x=c1['Year']\n", - "y1=c1['Wheat']\n", - "y2=c1['Wages']" + "p=c1['Wages']/c1['Wheat2']" ] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "Text(0,0.5,'Wages/Wheat')" ] }, - "execution_count": 59, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, "metadata": { @@ -579,16 +1725,10 @@ } ], "source": [ - "fig, ax1 = plt.subplots(figsize=(9, 6))\n", - "\n", - "# Instantiate a second axes that shares the same x-axis\n", - "ax2 = ax1.twinx() \n", - "plt.plot(x,y2, color='r')\n", - "plt.fill_between(x, y2) \n", - "plt.bar(x,y1,width = 5, color='black')\n", - "plt.xlabel('year')\n", - "ax1.set_ylabel('Wheat')\n", - "ax2.set_ylabel('Wages')\n" + "plt.figure()\n", + "plt.plot(x,p)\n", + "plt.xlabel('Years')\n", + "plt.ylabel('Wages/Wheat')" ] }, { -- 2.18.1