{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import des données puis affichage pour en vérifier la bonne présence" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0YearWheatWages
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1819165540.56.60
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2829170532.010.00
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" ], "text/plain": [ " Unnamed: 0 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": [ "raw_data = pd.read_csv(data_url)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On se débarasse des années qui manquent des données, afin de ne pas les traiter" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Unnamed: 0YearWheatWages
5051181578.0NaN
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" ], "text/plain": [ " Unnamed: 0 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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On enlève ces données, puis on se débarasse de la colonne 'Unnamed 0' qui ne sert qu'à donner l'indice de la donnée, mais qui nous est sans utilitée" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearWheatWages
0156541.05.00
1157045.05.05
2157542.05.08
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4158541.55.15
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10161533.05.94
11162035.06.01
12162533.06.12
13163045.06.22
14163533.06.30
15164039.06.37
16164553.06.45
17165042.06.50
18165540.56.60
19166046.56.75
20166532.06.80
21167037.06.90
22167543.07.00
23168035.07.30
24168527.07.60
25169040.08.00
26169550.08.50
27170030.09.00
28170532.010.00
29171044.011.00
30171533.011.75
31172029.012.50
32172539.013.00
33173026.013.30
34173532.013.60
35174027.014.00
36174527.514.50
37175031.015.00
38175535.515.70
39176031.016.50
40176543.017.60
41177047.018.50
42177544.019.50
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45179047.525.50
46179576.027.50
47180079.028.50
48180581.029.50
49181099.030.00
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" ], "text/plain": [ " Year Wheat Wages\n", "0 1565 41.0 5.00\n", "1 1570 45.0 5.05\n", "2 1575 42.0 5.08\n", "3 1580 49.0 5.12\n", "4 1585 41.5 5.15\n", "5 1590 47.0 5.25\n", "6 1595 64.0 5.54\n", "7 1600 27.0 5.61\n", "8 1605 33.0 5.69\n", "9 1610 32.0 5.78\n", "10 1615 33.0 5.94\n", "11 1620 35.0 6.01\n", "12 1625 33.0 6.12\n", "13 1630 45.0 6.22\n", "14 1635 33.0 6.30\n", "15 1640 39.0 6.37\n", "16 1645 53.0 6.45\n", "17 1650 42.0 6.50\n", "18 1655 40.5 6.60\n", "19 1660 46.5 6.75\n", "20 1665 32.0 6.80\n", "21 1670 37.0 6.90\n", "22 1675 43.0 7.00\n", "23 1680 35.0 7.30\n", "24 1685 27.0 7.60\n", "25 1690 40.0 8.00\n", "26 1695 50.0 8.50\n", "27 1700 30.0 9.00\n", "28 1705 32.0 10.00\n", "29 1710 44.0 11.00\n", "30 1715 33.0 11.75\n", "31 1720 29.0 12.50\n", "32 1725 39.0 13.00\n", "33 1730 26.0 13.30\n", "34 1735 32.0 13.60\n", "35 1740 27.0 14.00\n", "36 1745 27.5 14.50\n", "37 1750 31.0 15.00\n", "38 1755 35.5 15.70\n", "39 1760 31.0 16.50\n", "40 1765 43.0 17.60\n", "41 1770 47.0 18.50\n", "42 1775 44.0 19.50\n", "43 1780 46.0 21.00\n", "44 1785 42.0 23.00\n", "45 1790 47.5 25.50\n", "46 1795 76.0 27.50\n", "47 1800 79.0 28.50\n", "48 1805 81.0 29.50\n", "49 1810 99.0 30.00" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data = data.drop(columns=\"Unnamed: 0\")\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A priori les données sont déjà triés, mais par précaution on s'assure que les années sont bien dans l'ordre croissant" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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WheatWages
Year
156541.05.00
157045.05.05
157542.05.08
158049.05.12
158541.55.15
159047.05.25
159564.05.54
160027.05.61
160533.05.69
161032.05.78
161533.05.94
162035.06.01
162533.06.12
163045.06.22
163533.06.30
164039.06.37
164553.06.45
165042.06.50
165540.56.60
166046.56.75
166532.06.80
167037.06.90
167543.07.00
168035.07.30
168527.07.60
169040.08.00
169550.08.50
170030.09.00
170532.010.00
171044.011.00
171533.011.75
172029.012.50
172539.013.00
173026.013.30
173532.013.60
174027.014.00
174527.514.50
175031.015.00
175535.515.70
176031.016.50
176543.017.60
177047.018.50
177544.019.50
178046.021.00
178542.023.00
179047.525.50
179576.027.50
180079.028.50
180581.029.50
181099.030.00
\n", "
" ], "text/plain": [ " Wheat Wages\n", "Year \n", "1565 41.0 5.00\n", "1570 45.0 5.05\n", "1575 42.0 5.08\n", "1580 49.0 5.12\n", "1585 41.5 5.15\n", "1590 47.0 5.25\n", "1595 64.0 5.54\n", "1600 27.0 5.61\n", "1605 33.0 5.69\n", "1610 32.0 5.78\n", "1615 33.0 5.94\n", "1620 35.0 6.01\n", "1625 33.0 6.12\n", "1630 45.0 6.22\n", "1635 33.0 6.30\n", "1640 39.0 6.37\n", "1645 53.0 6.45\n", "1650 42.0 6.50\n", "1655 40.5 6.60\n", "1660 46.5 6.75\n", "1665 32.0 6.80\n", "1670 37.0 6.90\n", "1675 43.0 7.00\n", "1680 35.0 7.30\n", "1685 27.0 7.60\n", "1690 40.0 8.00\n", "1695 50.0 8.50\n", "1700 30.0 9.00\n", "1705 32.0 10.00\n", "1710 44.0 11.00\n", "1715 33.0 11.75\n", "1720 29.0 12.50\n", "1725 39.0 13.00\n", "1730 26.0 13.30\n", "1735 32.0 13.60\n", "1740 27.0 14.00\n", "1745 27.5 14.50\n", "1750 31.0 15.00\n", "1755 35.5 15.70\n", "1760 31.0 16.50\n", "1765 43.0 17.60\n", "1770 47.0 18.50\n", "1775 44.0 19.50\n", "1780 46.0 21.00\n", "1785 42.0 23.00\n", "1790 47.5 25.50\n", "1795 76.0 27.50\n", "1800 79.0 28.50\n", "1805 81.0 29.50\n", "1810 99.0 30.00" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted_data = data.set_index('Year').sort_index()\n", "sorted_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Affichage des données dans une format similaire à PlayFair" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(sorted_data.Wheat)\n", "plt.plot(sorted_data[\"Wages\"], color=\"red\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Affichage des données d'une manière plus correcte, en séparant les axes des ordonnées des deux données" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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A7rcf3w1wfoPBYOh85s2zut2efHKHpvnHxxt5fUUeN540kltOGY0EMbEixDwHrMRKhADIB97Ah1isN04ipynAOFUNSpSiqt+IyJtYa5Au4DusSCgJmCsi12A5sBnBuJ/BYDAEnYYGK4X8tNOs5oIB8nHOLp5ato1LjhrCrT8bE0QDw4IRqnqhiFwMoKo14ofndeKc1gIDsLsiBgNV/Qvwl2an67CiKIPBYAhvFiyA4mK4/PKAp9ixt5rb3vieQwem8uezxgXRuLChXkTi2Z/pPQLrc94RTpxTP2CdiCz3nlhVz/HTUIPBYOgZ/Oc/kJ0Np58e0PDahkZ++fJKBHjiksOIi+6W2nnt8RfgI2CwiLyMpXR+pdPBTpzTnQGZZTAYDD2RvDz46CP44x8hKjB50rveW0dOQTlPXz6FwendOl28TVR1oYisAo7GyvS+SVX3OB3vJJV8aQfsMxgMhp7Fs89aWnrXXBPQ8LdX5fPq8h1cf+IIfjouM8jGhQ8ichywWlXft8uF/igij6hqrpPxbeYr2k2hEJEKESn3OipEpDw45hsMBkM3orHRck6nnALDHIsdNLFxVwV/mreWI4elc9vPRneCgWHFk1jqEBOB3wG5+JHl3aZzUtWf2I/JqpridSSrakpHrTYYDIZux8KFsGMHXHut30Mr61z88uWVJMZG8djFk4mK7P61TO3gsrO8pwOPquojdEazQVtOyFv2fIc/VhoMBkO35+mnoV+/gDrevvrNDrbtruLV646mf0rg6efdiAoRuQO4FDhBRCIBx7IX7bpuETlHRDYDPwJLsboZfhiYrQaDwdBNKSqCd9+10sdjY/0eXlBWQ1JsFMeM6NsJxoUlF2JleF+jqruAgVg6e45wElfeg5VtsUlVh2HVIn0RgKEGg8HQfZkzB1yugJb0AEqq6umT2L318vykAnhEVT8TkdHAJOBVp4OdOKcGVd0LRIhIhKousW9iMBgMvQNVa0nvJz+Bgw8OaIqSqnrSE/2PuLoxy4BYu4ffIuAqrOa1jnDinPaJSJJ9o5dF5BEs2SGDwWDoHSxbBps3Bxw1ge2cEnpV5CSqWg2cD/xLVc/DatnuCCfOaTqWQOtvsap9twJnB2CowWAwdE+efhpSUmBG4JKfvTByEhE5BrgEeN8+51gKw0kRbpV9lxTgvUAsNBgMhm5LaSm8+SZcdRUkBKbmoKqUVNXTN6lX9aC7GbgDmKeqOSIyHFjidLCTlhm/AO7Gip7cWDIUCgwPyFyDwWDoTrz8MtTWwnXXBTxFdX0jdS43fRJ6j3Oy1YWWer3eBvzG6XgndU63AeP90UQyGAyGHoGqJfJ6+OEweXLA05RU1QPQN7HnOycReVhVbxaR97AVyb1xKhruxDltBar9tM9gMBi6P4sXw5o18O9/d2gaj3Pq0wucE/Ci/fhARyZx4pzuAL4UkW84sGWG4/DMYDAYuh2qlvL44MFwxRUdmqqk2nJO6b3AOanqSvtxqYjEAB4RwY2q2uB0HifO6SlgMfAD1p6TwWAw9Hzmz4fly+GZZzrU7RagpLL3LOt5EJGpwBwsVSHB6ut0haouczLeiXNyqeotAVtoMBgM3Y3GRvjf/4XRozvU7dZDL1vW8/Ag8DNV3Qhgq0S8ChzuZLCTOqclIjJLRLJEJN1zBGqtiIwRkdVeR7mI3GzPu1BENtuPfQK9h8FgMHSI116DtWvhnnsCbijoTUl1PVERQkpcx+fqRkR7HBOAqm7CD+FXsRTNfVwg8mMrp1VVO5xKbqvU7gSOAn4NlKjq/SJyO9BHVf/ga3xiYqJWVVV11AyDwWDYT0MDjB1rFd2uXAkRHW9t8Yc317B4YzHf/umnQTCw44hItaom+nh/MFbvpQFY2zmzVfUROzB5HRiKtVw3U1VL25jjWaxsPU+CxCVAlKpe5cRGJ0W4/nfUcs7JwFZVzRWR6cBU+/wc4FPAp3MyGAyGoPPss7BtG7z/flAcE1iRUzfbb3IBt6rqKhFJBlaKyELgSmCRVxBxO21/Tv8SK+j4Ddae0zLgCacGOCnCTQBuAYao6iwRGQWMUdUFTm/ig4vYr1KbqaqFAKpaaPePMhgMhq6jpgbuvhuOOw5OPz1o01rSRd3HOdmfxZ7P4woRWY/V8sJxEKGqdSLyGJboqxsrW6/eqQ1OvhY8B9QDx9qv84F7nd6gLewUw3OAN/wcN0tEVojICpfL6M8aDIYg8vjjUFAAf/0riARtWqtdRlg5pyjP56h9zGrrQhEZCkwGvqFZEAG0GUSIyJlYdbKPAI8BW0TEscd3sjs3QlUvFJGLbYNqRILyWzsdWKWqRfbrIhHJsqOmLKC4tUGqOhuYDdaeUxDsMBgMBigvh//7Pzj1VDjhhKBOXVIVdst6LlWd0t5FdkeKt4CbVbXcz4/+B4FpqrrFnmsElgCso2a1TiKnehGJx5ahsG9Q53uIIy7mwMZT8wFPpdsVwLtBuIfBYDA446GHoKQE7rsvqNM2NLopq2noVst6ACISjeWYXlbVt+3TRXbwgK8gwqbY45hstrVz/QE4iZz+gtUqY7CIvAwch7UpFjD2PtYpwC+8Tt8PzBWRa4AdQODa9AaDweAPu3fDgw/CBRdYOnpBZF+1JYrQnZyTvTr2DLBeVR/yessTRNxPG0GEiJxvP80RkQ+AuVjBzQzgW6c2OMnWWygiq7BatQtwU0dFYO0GVH2bnduLlb1nMBgMXYfbbckT1dVZdU1BxlOA252cE1YQchnwg4ists/9EWdBhHe/vyLgRPv5bsBx/aqTbL3jgNWq+r6IXAr8UUQeUdVcpzcxGAyGsOWuu+DDD+GJJwJuwe6LvVXWLkh6N2qXoaqfYwUjreEziHBax9QeTvacngSqRWQi8DsgF6s4y2AwGLo3CxZYqeNXXAHXX98ptyitspf1elejwQ7jxDm51JKRmA48qqqPAMmda5bBYDB0Mlu2wKWXWn2annwyqKnj3pR4IqfutawXcpwkRFSIyB3ApcAJtuSQY30kg8FgCDuqquD88y0FiLfegvj4TrtViR059aYuuMHASeR0IVbq+DWqugurSvgfnWqVwWAwdBaqMGuWJez66qswrDMV2qzIKSUuiujI4EghdRdEJFNEnhGRD+3X4+xECke0+6+lqrtU9SFV/cx+vUNVzZ6TwWDonvzrX/DKK9Ze06mndvrt9nYz6aIg8jzwMZBtv94E3Ox0cO9y5QaDoXfz2Wdw661w9tlWl9suoLS61zqnfqo6F7tJraq6gEang41zMhgMvYNNm+Dcc2H4cHjhhaApjrfH3sp60hNju+ReYUaViPRlv7rQ0UCZ08E+fzsiEikiL3XMPoPBYAgxxcWWynhkJHzwAaSlddmtrcipV+aQ3YKlKDFCRL7AKkG60elgn9l6qtooIhkiEuOP1LnBYDCEDdXVcM45ltr4kiUwYkSX3VpV7XYZvS9ysntBnQiMwSro3aiqDU7HO0kl3w58ISLzgaa2s830lgxB4NFFmzlqWDpHDe/b/sUGg6F9GhutWqbly62U8aOP7tLbV9S5aGjUXhU5eWnrNWe0iOAlIusTJ86pwD4iMMW3nUa9y83Dn2zioiOHGOdkMASL226DefPg4YfhvPO6/PalTbp6vSpy8mjr9cfqA7jYfj0NqzlhcJyTqt4VgHEGP9m5rwa3QnmN46jXYDD44pFHLKd0881w000hMWGv7ZzCrJdTp+LR1hORBcA4T3NCu8XG407ncSL8mgH8HhgPxHkZcJKfNht8kLvXWjEtrzXdfQ2GDvPOO/Db31rR0gMPhMwMT+QUZl1wu4qhHsdkUwSMdjrYSS7ly8AGYBhwF9YelOOeHAZn7CipBkzkZDB0mB9/hMsvhyOOgJdesjL0QkRvjJy8+FREPhaRK0XkCqwuuEucDnbinPqq6jNAg6ouVdWrsXo7GYLIjr2Wc6qoNc7JYAgYlwsuucQScX39dUhICKk5Jb04clLVG4B/AxOBScBsVQ1OKrmN59OyUETOxEqOGOSvoQbf5HoiJ7OsZzAEzr33wldfWfJEQ4eG2hpKq+qJiYogMSZ00VsoUdV5wLxAxjpxTveKSCpwK/AvIAX4bSA3M7RNnlnWMxg6xhdfWJ1sL7sMLr441NYA1rJe38QYpJPacfRknGTrLbCflmGlAnYYEUkDngYOwZK2uBrYCLwODMXa15qpqqXBuF+4o6rsKKlGBOpcbmobGomL7p3ftAyGgCgrs5bzhg6Fxx4LtTVNlFbVm1YZAdLunpOIDBeR90Rkj4gUi8i7IjK8g/d9BPhIVcdirUeuB24HFqnqKGCR/bpXsKeynur6RkZkJAFQYZb2DAb/+NWvID8fXn4ZUlJCbU0Te6vq6dtLO+CKSIv8/dbOtYWThIhXgLnAACzp8zeAV53eoDkikgKcADwDoKr1qroPq9PuHPuyOcC5gd6ju7GjxEojPyTb+qMqN0kRBoNzXnrJ2mP6y1+6XAGiPUp6b7sMgCtaOXel08FOnJOo6ouq6rKPl7BVZgNkOLAbeE5EvhORp0UkEcj05MTbj/07cI9uhSeN/JCBqYDZdzIYHPPjj1bU9JOfdFkLDH/ojct6InKxiLwHDBeR+V7HEmCv03mcJEQsEZHbgdewnNKFwPsikg6gqiV+2h4FHAbcqKrfiMgj+LGEJyKzgFkAMTE945eeu9fabxqXZUVOZlnPYHBAdbWV+CACL74Y0nqm1qhzNVJR5+qNNU5fAoVAP+BBr/MVwBqnkzhxThfaj79odv5qLGfl7/5TPpCvqt/Yr9/Eck5FIpKlqoW2zEVxa4NVdTYwGyAxMbEjEVzYsKOkmqyUOPolW/pbZlnPYGiHmhpLafzbb2Hu3LBIG2/Ovmrr77i31Tipaq6I5ANVqro00HmcZOsNC3TyNubbJSJ5IjJGVTcCJwPr7OMK4H778d1g3jec2bG3msHpCaTEWcrF5TUmcjIY2qS21moauHgxPP88XHBBqC1qlb2VvVcdwm63VC0iqarquMGgN04ip87gRuBlEYkBtgFXYe1/zRWRa4AdwIwQ2dbl5JZUM21MBinx1q/DRE4GQxvU1VnO6L//hWeesWSKwpSSJkXy3uecbGqBH0RkIQe2W/qNk8EhcU6quhqY0spbJ3e1LaGmpr6R3RV1DElPID46kqgIMQkRBkNr1NfDzJlWJ9unnoKrrw61RT4pqe71zul9+wiIUEVOBhtPpt6QvomICMlxUSZyMhia09AAF10E8+fD44/DrFmhtqhdSirrgN7rnFR1TvtXtY2TItzj7FRvRORSEXlIRA7qyE0N+/E4p4PSLYHKlPhos+dkMHjT0GB1s503z+rR9KtfhdoiR5RUNyACab0sldyDiIwSkTdFZJ2IbPMcTsc7qXN6EqgWkYlYfZ1ygRcCtNfQDE8fpyEe5xQXbSIng8FDZSWcfbaVkffAA/AbR9sVYUFJVR1p8dFERvRaXb3nsPyHC0v67gXgRaeDnTgnl6oqloLDI6r6CKZde9DIK6kmOS6KtAQrUy8lPsrUORkMAEVFMHUqfPIJ/Oc/cOutobbIL3q5OgRAvKouwhJyyFXVOwHHTWqdOKcKEbkDuBSr+DYSiA7IVEMLckuqGZKe0KRanBIXbRIiDIbNm+GYY2D9enj3Xbj22lBb5Dfd2TmJyLO2lupar3N3ishOEVltH2e0M02tiEQAm0XkBhE5Dz+Uf5w4pwuBOuAaVd0FDAT+4fQGBt/sKKnmoL77G6KZZT1Dr2f5cjj2WKiogCVL4MwzQ21RQHRn5wQ8D5zWyvl/quok+/ignTluBhKA3wCHA5fRut5eqzgpwt0FPOT1egdmzykoNLqV/JIaThmX2XQuJT7KJEQYei8LFsCFF8KAAfDRRzBqVKgtCpiSqgYOP6h7OidVXSYiQzs4x7f200qsWla/aNc5iUgFLYVey4AVwK2q6jj7wnAgReW11De6OSg9selcSlw0NQ2N1LvcxEQ5CWwNhh5AXR3cf7/VLHDSJHj/fcjMbH9cmOJ2K6XV3TpyaosbRORy9n/+t9lzzxZ6bSExp6qO9p2c1Dk9hNWa/RVAgIuw2mdsBJ4Fpjq5kaEluXvtGqf0/ct6yXHWr6SitoG+SbEhsctg6FK++AKuu85suyIhAAAgAElEQVTaX7r4Ypg9G5KSQm1Vh6ioddHoVtITw/ZvOEpEVni9nm3rlvriSeAeLIdzD5aoq69K6Nu8nscBF2Bl7jkz0ME1p6nqUV6vZ4vI16p6t4iEn0Z9N8LTx+mAPad4W1+v1mWck6FnU1YGd9wBTz4JQ4ZY0dIZ7e2xdw/2VnkKcMM2d8ylqq2p9LSJqhZ5novIf4AFPi5HVVc2O/WFiDgWgnWybuQWkZkiEmEfM73v7/RGhpbsKKkmKkLISo1rOucRf60wSRGGnsw778C4cZYM0c03Q05Oj3FM4K2r13O+YNrdIjycB6xt61r7+nSvo5+InIq16uYIJ5HTJVht1Z/AckZfA5eKSDxwg9MbGVqSu7eagX3iiYrc/x2hKXIySRGGnkhBAdx4I7z9NkyYYDmpI44ItVVBx+OcuqsiuYi8irVl089uf/EXYKqITMLyA9tp2UapOSvtawVrOe9H4BqnNjjJ1tsGnN3G2587vZGhJXl2jZM3Rpnc0CNxu61C2j/8wUp++Otf4bbbIDpsl706hMc5dddeTqp6cSunn/Fzjg61W3KSrZcBXAcM9b5eVcNbErgbkFtSzZmHZh1wbn9Pp97jnG5/aw2D+sRzw0ndN23Y4IMNGyyh1s8+g5NOgn//u1uniDuhSZG8l+rqAYjI+b7eV9W3fb3vZFnvXeAz4BOg0blpBl+U1TSwr7rhgGQI8E6I6B3OSVV57/sCstKMc+px1Ndb6eH33QeJifDss3DllVZb9R5OSWU98dGRxMeEV+v4LuYa4Fhgsf16GvApVimSAh12Tgmq+ocOGGhohbySlmnkAIkxkURI79lzKiyrpaq+ka27K6mobSA5rmcu8/QqGhvhtdfgrrssGaKLLoKHH+7WdUv+0s3VIYKFAuNUtRCaEioeV1VHBblOsvUWONBQMvhJUx8nrwJcwO7p1HskjDYXVwKgCj/sDKibsyFccLst9fBDD7VaXMTHW40BX321VzkmsJb1+ib1euc01OOYbIqA0U4HO3FON2E5qBoRKReRChEp99dKw4E0FeA2W9YDj4RRL3FORRVNz9fkG+fULVG1su8mTrSkh0TgjTfgu+/g9NNDbV1IKKmqp08v3m+y+VREPhaRK0XkCqyuuEucDnaSrRf09hgish2owNrDcqnqFBFJB17HSrzYDsz0JY3R3dlRUk3fxBiSYlv+ClLiontN24wtxZX0TYwhMTaK7/P2hdocgz+UlsIrr1iKDmvWwOjR1uuZMyGyV++1UFJVz8iM7q1y0VFU1aNEfoJ9araqznM6vk3nJCJjVXWDiBzWxo1X+WdqC6ap6h6v17cDi1T1fhG53X7dY/e6dpRUMTi9ZdQEvUuZfEtxJSP6J5GZEseq3B77XaTn0NgIixdbyQ3z5llp4ZMmwfPPwyWXQJSTbeyeT0lVfbdNIw8mtjNy7JC88fU/6VasFPIHW7snfjSNcsh09uv0zcHK6uixzil3bzWHH9Sn1fdS4qPYvqe6iy3qelSVzcWVnDUhi2H9Ennv+wJ2V9SRkdxzqup7DNu2WQ7o+echLw/S06308KuugsmTQ21dWFHb0Eh1faNJiOggbTonVb3OfpzWCfdV4L8iosBTtuBgpmfzTFULRcRxU6ruRkOjm4J9NZw3eWCr7/eWyGl3ZR1lNQ2M6p/EuOxUANbk7+Pkg3vH5vkLX22notbFr6eNDLUprVNdDW+9Bc89Z/VVEoFTT4UHH4RzzoFY8yWiNbq7OkS44GtZr0MFVO1wnKoW2A5ooYhscDpQRGYBswBiYrrnL39naQ1ubZlG7iElvnd0w91SZGXqjcpM5pCBKUQIfJ9f1muc0zOf/4gq4eWcVK1mf88+a6WDl5fDiBFw771w+eUweHCoLQx7urs6RDCwO6bPUdVLA53D17JeW5JF4KCAyheqWmA/FovIPOBIoEhEsuyoKQsobmPsbGA2QGJiYsiFZ91upcHtJjbK+QbwjjZqnDwkx0VRVd+Iq9F9gO5eT8OTRj6qfxIJMVGMzkzuNUkRuyvqyN1bTUxkBG63EhERBoWp27fDL39pNflLSIAZM+Dqq+H443tF4WywMJETqGqjiGSISIyq1gcyh69lPb87FzpBRBKBCFWtsJ//DLgbmI/Vwvd++/Hdzrh/sHngvxtZsKaQJbdNJdLhB0yu7ZwO6pvY6vv7lcldPfrb1+biClLiopr2mCYOSuO/63ahqkgP/zBcmVsCQH2jmz1VdfRPjmtnRCficsGjj8Kf/2w5oQcfhGuvhZSU0NnUjTGRUxPbsdpkzAeqPCdV9aE2R3jha1nvFl8Dnd6gFTKBefaHTxTwiqp+JCLfAnNF5BpgBzAjwPm7DLdbeWtVPkXldazO29dmgkNz8kqqiY2KoH8bG//eEkY9+T/45qJKRmUmNzmiCYNTeX1FHvmlNW1mMvYUVmzfn5lYuK82dM5p9WrLEa1cCWeeCU88YfVWMgTMXhM5eSiwjwjA75IkX8t6Qa9vgiaV84mtnN8LnNwZ9+wsvssrpajcaiq2cF2RY+eUu9dKI29rKSelqRtuz6512lJcySnj9u8vTRyUBsDqvH093jmt3FFKSlwU5bUuCstqmDg4rWsNqK625IUefBD69rX2l2bONMt3QaC0qp7ICGlaAemtqOpdHRnva1mvQxP3Bj78YRcxkREcnJ3CJ+uLuP30sY7G7Sip4SAfH777ezr13KSIvZV17K2qZ2T//YWKYwYkExMVwZr8fZw9MTuE1nUutQ2NrN1ZxvmTB/H6ijx27qvtWgP27IHTTrOipauvhn/8w0oNNwSFvVX19EmIDo99xBBid7T4PTAeq007AKrqqAypzd12Efm9/fgvEXm0+dFBu7s9qsqHa3dx/Kh+nDspmy3Flfy4p8rRuB172y7ABa+2GT04nXxL8f5MPQ/RkRGMz07h+7yeLWO0Jr+Mhkblp+MyiYuOoHBfTdfdfNcumDoV1q6Fd9+FZ54xjinIFOyroW8P6oDbAV4GNgDDgLuw9qC+dTrYVyrYevtxBVZHw+ZHr2ZNfhk799Vw+qFZ/NROff5kXVG74/ZW1VNV39iiVYY3TQ0He7AyuXemnjcTB6WxtqCMRnfIEzE7jRV2MsThB/UhOzWegrIuck47dliZd9u3W4Ks55zTNfcNI1ZsL+GBjzd22v+vwrIaPt+yh6ljMjpl/m5GX1V9BmhQ1aV2D8CjnQ72taz3nv04p+M29jw+WFtIVIRwysGZpCZEM3ZAMgvXF3HdCcN9jvs4ZxeAzz2G5F4SOSXGRJKVemAiwMTBqTz/5Xa2FFcyZoD/254rtpcweUgfx5mToWBVbinD+yWSnhhDdlo8BV2xrLdlC5x8MpSVwX//C8ce2/n3DDNqGxq56bXV7NxXQ2SE8NtTHAtkO+bV5Xm4VbnkqIOCPnc3xPMBVigiZ2IlRwxyOrjdIhoRGS0is0XkvyKy2HMEaGyPQFX58IddHDeyH6kJliM5ZVwmK7aXUFrVdkq/qvLiV7mMy0phsi/nFBuFSM/ec9pcXMFIr0w9DxPspIhA6p2WbtrNz//9FZ+sbz+CDRWqysrc0qbkmazUOAo7O3Jatw5OOAGqqixdvF7omAD+s2wbO/fVcMTQPjy6eDPLNu0O6vz1LjevLt/B1NEZrXYb6IXcKyKpWFJ4twFPA791OthJhecbwHfA/wK/8zp6LTkF5ewoqeaMQwc0nTtlXCZuhSUbW60dBmBFbikbdlVw+TEH+azjiYgQkmKtTK6eyuaiyhZLegDD+iaSHBfF9/n+O6e53+YBsKGwop0rQ8fW3VWUVjcwZajtnNLiKa6oo97l7pwbrl4NJ55oKT8sXQqHtarj3OPZVVbLE59u5bTxA3jh6qMYk5nMTa99R0E7+32fb97Dcfcv5qO1u9q9x8c5u9hdUcflxwwNktXdG1VdoKplqrpWVaep6uGqOt/peCfOyaWqT6rqclVd6Tk6YHO358O1hURGCKeM2++cDslOJTMlloU+9p1e+CqX5LgozpnUfiZaT9bXK6tuoLiirlXnFBEhTBiU6ndvp5Kqev67zvoA2bq7Mih2dgYrm/abrCSEgWlxqEJReScs7X39NUybZqk9fPYZjB8f/Ht0E/7+0QYa3cofzziY+JhInrjkMBoalRteWdXmF4O5K/K48rnl7NxXw53zc6iu9/1l8cWvchmSnsCJo81+E4CIDBeR90Rkj4gUi8i7IuJ738MLX9l66XaPpfdE5FcikuU5Z5/vkeSVVFPnamzzfVXlgx92cfTw9ANUhyMihJMPzmTppt3UNrQcX1xRy0drC5lx+GASYtpvK2Dp64Vv5FTnanSUndgaW3Zbkc3IVpwTWEkR6wvLW/13bIt3V++koVEZnB7flAkYjqzMLSUtIZrh/Sx1kKzUeMBqVx9UPv0UfvpT6NfPckwjw0i/r4tZnbePt7/byTXHD2tabhuekcTfLpjAqh37uP/DA6U9VZWHFm7i92+u4ZgRfXn2yinsKq/lqaXb2rzHhl3lLN9ewqVHD+n1KeRevALMBQYA2VircK86HewrclqJlal3BdYy3pfsz9RbEaCxYc3W3ZWc9OCnXPfCStxtZPNsLKrgxz1VnH5IVov3ThmXSXV9I19v29vivdeW59HQqFx2jLONUqtAM3wjpxe+zOXUh5dRVu2/jZs9gq/9W094mDAoDZdbWV/ovOHy3BX5HDowlZ+NG8C2PZVt/v5CzYrcUg4f0qfpAyw7zUoIaW95yS8+/NDqQDt0KCxb1qsVH1SVu9/LISM5toXA7pkTsrjquKE8+8WPfPiD1U283uXm1je+59FFm5lx+CCevfIIThqbyZkTsnhq2dY2f08vfpVLbFQEMw43wrheiKq+qKou+3gJS5fVEW06J1UdpqrD7cfmh+PQrDtx3/vrUYVlm3bz2JItrV7zwQ+7rM4B4we0eO+Y4X1JiIlssbTnanTzyjc7OH5UP4b1a11Prznhrky+akcp9S43OYX+1yRtLq4kLjqCgX3iW31/4mCrfYbTpIi1O8tYX1jOzCmDGJGRRG2Dm51dWTvkkJKqerbtruLwofuVRDyRU9DSyd9+G6ZPh3HjrOgpq+WXqN7E/O8LWLVjH787dUyrXafvOP1gJg9J43dvruH7vH1c+dxy3l61k1tOGc3ffz6BaFt4+Y7Tx6JKiygLrKzaed/t5OyJ2T1abiwAlojI7SIyVEQOsmtn33e6+uZrWe8IERng9fpye83w0Z64rLd0024Wbyjm96eN4dxJ2fzzk018vnlPi+s+/KGQI4emt9oQLy46khNGZfDJ+iJU939BWLiuiF3ltVx2tPP00nBv1Z5TYEU16wqcRzcethRXMiIjqc107wEpcfRPjnW87/TGijxioiI4Z+LApqXCcNx3Wml3+j18yH7nlBgbRWp8NIUdSCdXVeZ8uZ2y/zxnSRAdcYSVldevX4dt7s5U17u4/8MNHDIwhZ8f1noGc0xUBI//z2FERwrTH/+Cb7eX8NDMifzm5FEHJC0N6pPArBOGM//7gqZ9Qw9vr8ynur6Ryx2uivQiLgR+ASzBah77S+BqHK6++VrWewqoBxCRE7DUwl8AyrBbVvQUGhrd3LNgHUP7JnDlscO477xDGZmRxE2vfccur72ALcUVbC6u5IxD2/42+tNxmRSV17F25/4P7Re+ymVgWrxffYqS46LCNnIqr21oavuRE6Bzai0ZwoOIMGFQmqOMvdqGRt5ZXcBp4weQmhDNiIzEpnuEGytzS4mOlBY1blatU+CR07Y9VWy4+0FSfnGNlZn38ceQmtpRc7s9Ty3dRmFZLf/vrPE+94Gy0+J59OLJjB2QzJyrjuT8NhzZ9SeOIDMllrvfW9e0bKyqvPh1LhMHpzWVQRgs2lh1c7z65ss5Raqq5yvChcBsVX1LVf8M9Kjd1Ze/zmVLcSV/PONgYqIiSIyN4slLD6OmoZEbXllFQ6OVzfPhD1Y22GmHtFzS83DS2P5ECCy0a222FFfw1ba9/M9RQ/wqDE2Jj6aizhWWSgmeaCkpNoqcAv+W9SrrXOzcV3OAbFFrTBqcytbdVe3uuy1cV0RZTQMzplgfKH2TYumTEM3W3YEla3QmK3NLGJ+dSlz0gb2/slPjKOhAQsTel9/g/z5+jO8POQYWLICkth1/b6FgXw1PLdvKmROyOHJY+ws9x4/K4KObT+DYkW1Hm4mxUfzhtLF8n1/GvO92AvDl1r1s3V3F5X6sihic4dM5iYhnkfZkwLvwtv10s25CaVU9//xkM8eN7HuAQvbI/sncf8EEVuSW8o+PNwLwwdpdTDmoD5kpbbc3SE+MYcpB6U37Ti9+lUtMZAQXHuHfRqlHmbwyDJf2PNHS2ROz2Lq7yq+suq12RNNWpp4Hz7fQte0s7c1dkcfAtHiOHbH/Q2Vk/6Sm+wTKu6t3csMrqw5Ynu0Ida5Gvs8vY0oryvVZaR0oxN20iQl/+g3fDxjFrOl3oHEh7AsVRjz8ySZUrb2iYHLupIFMHJzG3z7aQFWdixe+2k6fhGjOnNC79/Y6A1/O6VVgqYi8C9QAnwGIyEispb0ewSOLNlNR28CfzxrXojD2nInZXH7MQcxeto2nlm5lfWG5z6jJw0/H9Wd9YTmbiip4a9VOzjh0AP2S/BOC9O7pFG7kFJSRkRzLiaMzaHQrG3Y5L3ptS1OvORMGWctSq30s7e3cZ+mYXXD4oAOi0hEZSR3ac1JVHlm0mQVrCtkWYLp8c9buLKfe5W61rUp2Wjz7qhvaraNpQWUlnH8+DRGR/OrcOyhuEPJKwi8RJBR8vnkPPxs/gEF9gqvUEBEh/L+zxlFcUcdf5uewcF0RM48Y3CIaNnQcX9l692HJTjwP/ET3f4WMAG7sfNM6n81FFbz4dS7/c9QQxg5ovevnn848mImDUvk/O0vndB/7TR48xbk3v7aayjoXlwVQMR7OyuTrCsoZn53C+GzLgfiztLe5uIKYyIg2W9R7SEuIYWjfBNb4UCh/a2U+qjDj8AP3CEb2T2JvVb1PKSlfrNpRyjZ7WXDJhrYVP/ya05MMMbQV5+TJ2PMnKUIVrr0WXb+e311wB2kHWyvtvpx5b6G0qp6CsloOye6cTr6HH9SH6ZOyeXNlPgpcanT0WkVEjrO7nSMil4rIQyLi+B/Lp0KEqn6tqvNU1bvF7iZVXRW4yeGBqnLP++tJiInkllPGtHldbFQkj19yGKnx0UwanMbAtNbTn70Z1i+RERmJrCu0PsQPG+L/Rmm4KpPXNjSyubiS8dkpDOoTT0pclF9JEVuKKhmekUhUZPviJIcN6cOnm4p54avtLeqW3G7lzZX5HDuib4v2IyMyOpaxN/fbfBJiIhmSnsDSIOmvrcgtYUh6Qqsdbz3it34t7f3zn/D661T++U4+GnAI500eSGxUBGsC0CTsaXj+P3q+PHUGfzhtLHHREZw8tn+Pb4zZAZ4EqkVkIlZfp1yspDpHOJEv6pEs2VjMsk27uenkUQcoPbTGoD4JvPPr43j8Eue6ZD+196/a09Fri3CNnDYVVdDoVsZnpyIijMtO8cs5bS6ubHe/ycMfTh/LEUPT+X/v5nDR7K8PUKT45scSdpRUM3NKy708z/yBZOxV1blYsKaAsyZk8bNxmXyzrcT/5bZmeMReW9tvAmtZD/woxP30U/j97+G881g+8zoAJg1OY1x2SkCahD0NTyQ/vpMiJ7B+Z+/++if87YIJnXaPUCIiz9qSQ2u9zqWLyEIR2Ww/ttf622WvuE0HHlHVR/Cjw3rInJOIRIrIdyKywH7t7w8eMPUuN/cuWM/wfomORRqH9Ut0FDV5uOzog7j8mIOYPmlgQDY2OacwSyff/63U+sMfl5XKhsJyXI3tC5fW1DeSV1rdpjJEczJT4njh6iP5+88nsGFXOac9vIzZy7bianTzxoo8kmOjWi2Gzk6LJzYqIqDI6YMfCqmqb2TmlMFMHdOf+kY3X25pqfjhD7l7q9lTWc9hbTinAalxiDhc1svPhwsvtOSInn+enMIKRODgrBSrF9ZOZ7+LnkxOQTnZqXGdXhA7ZkAyff3cS+5GPA+c1uzc7cAiVR0FLLJf+6JCRO4ALsUqvo0EHPeuD2XkdBP7GxqC/z94wKzMLSW3pJr/PctKHe8MBvVJ4O7phwS8Udq0rBdm2Xo5BWUkx0Yx2N5oHp+dQp3L7ShxYOvuSlRhVKbzVGcRYeaUwSy85UROGJ3BXz/YwAVPfskHaws5e1I28TEt/30jI4ThGUkBRU5vrMhneL9EDj+oD0cM60NCTCSfburYvpOn+HZKK/tNYHUAzkiKbT9yqq+HGTOguhrmzYOUFHIKyhjWN5HE2CgmDk6lpqGRLWFYgNyV5BSUMa4Tl/R6A6q6DChpdno64OnvNwc4t51pLgTqgGtUdRcwEPiHUxtC4pxEZBBwJlZ/Dw/+/uABc8yIvnx621SmjenfWbfoMB6plXCLnNYVlHNwdkpTUeP4gSlN59tji8NMvdbITIlj9mWH86+LJ5NXWkNtg7vVJT0PIzIS/a51+nFPFcu3l/DzKYMQEWKjIjl2RD+WbNjdoZTyFbmlJMdFMdpHxJidFt+++OvNN1tK4889BwcfDFhRwjg7ip3YgV5YPYXqehfb9lR16pJeDyFKRFZ4HbMcjMlU1UIA+9HnB6iq7lLVh1T1M/v1DlUN+z2nh7E2yLzXH/z6wTvK4PSEgPaCuoqoyAiSYqPCSsKo0a2sL6xgXNb+P/wRGUnEREU4ytjbXFxBVIRwUF9n+oLNERHOnpjNJ7ecyMvXHsUkHw0bR/ZPIq+02q8arDdX5hEhcIGXQsC0sRns3FfTodT0lbklHOYl9toa2WlxvvX15syBJ5+E3/0Ofv5zwGo9kl9a07TxP7SpF1aPqfTwm/WFFah27n5TD8GlqlO8jqCr/ohIhYiUNzvyRGSek9YZXe6cROQsoDjQnlAiMsvj7V2u8Png7gzCTZn8xz1V1DQ0HvCHHx0ZwdgByY6SIjYXVTK0X2KHl1LTE2M4zkclP1hOUxXHbT0a7ey/qWP6H1BkPdWOrj/dGFjWXm1DI1uKK5k4yPcyU1aqJWHUaoT23Xdw/fVWb6a//rXptEd01/P78PTC6s2R0zpPMsRAs6zXCRSJSBaA/djeevdDWB0tBmK1Z78N+A/wGvBsezcLReR0HHCOiGzHMvIkEXkJhz+4qs72ePuoqB4jVNEq4aZMvj8L6sA//PF2xl57S19biisZmdE10jr+Zuwt27ybovI6Zk45sGZqYFo8ozOTfHY49sXmokrcCmOzfH+Tz06Lp7bBzb7mLUj27oXzz7dEXF97Dbz+z69rlpwC1tLexl0VfkWMPYmcgnLSEqLJTjVKGZ3AfKwWStiP77Zz/Wmq+pSqVqhquR2dnaGqrwPtJrx1uXNS1TtUdZCqDgUuAhar6qX4/4P3eMKtG+66gnJiIiNaJDSMy06lrKbBZ5uKitoGckuq/UqG6AjD+iUi4rzW6Y0VeaQnxnDS2JbivFPH9Gf5jyVU1fkfqW/YZTmQMQN8Zyh6PkwPWNprbIRLLoGCAnjrLeh/4Ep3TkE5A1LiDsgY8/TCWudHLyxvPvyhkOPuX0xNffd0bp7awnBesu8OiMirwFfAGBHJF5FrsMS/TxGRzcAp9mtfuEVkpohE2MdMr/fa3cQNpzonf3/wHk9KfFRYFeHmFJQzekBSU48bD55v7r6W9t5fU0ijW5k2tmuSUOKiIxncJ8FR5FRSVc/CdUWcN3lgq0uOU0dn0NCofLGlZQuV9ti4q4LYqAiGtrPPlpXWikrEnXdaCuP/+hcceWSLMVZW2oERmWcfLtClvY9zdrFzXw0bi5xLUoULDY1uNuw6cE/UEBiqerGqZqlqtB1MPKOqe1X1ZFUdZT82z+ZrziXAZVirYEX280tFJB64oT0bQuqcVPVTVT3Lfu7vD97jSQ6jyElVySkoY3xWy7X8gwekECG+ndMbK/MZ2T+JyT6SGIKN04y9d76zWry3lf03ZWg6iTGRfBqAWsTGogpGZbbdu8qDpyNuk0rE/Plw771w9dVw3XUtrq9taGTr7pZZaQNS/euF1ZwVdtr7xl2BRV6hZOvuSupd7k5VhjA4R1W3qerZqtpPVTPs51tUtUZVP29vfM/etOnmpIRRT6fCslpKqxuaUse9iY+JZHhGUtNmdHO2FFeyMreUP54xtkuXW0b2T+LLrXtpdGubzkFVmbsijwmDUttceouJiuC4kf1YutFKKffnZ1hfWMGJozPava5fYizRkWJFTj/8AJddBocfDo8/Dq3cb8Muj1JHy9/HhEFpAUVOReW15JfWNNnd3cjZ2XIPzhA6RCQDuA4YipevUdWrnYwPp2U9QzM8PZ2a68qFgubKEM0Z70PG6I2VeURGCOdNbr2JW2cxIiOJOpfbZ3FrTkE5G3ZVMMNHzRTAtLH92bmvpklV3Ql7K+vYU1nHwVntK2JERAiDEiM59NlHYcoUiI219pnaaIHRVnIKWL2wtu2poszPLzaeYuHEmEg2+qE0Hy7kFJQTFx3B8C5KujG0y7tAKvAJ8L7X4QjjnMKYlLhoVKGqg9puwSCnoAwR2lRvH5+dQmFZLSXNlMAbGt28tXInJ43t32pr+87EScbe69/mERsVwTkTs33ONXWMFf186kfWnucDvr1kCAC++ooXHv8lZ775JJx3nhU9HdS2gHNOQTkpcVEM6tNSUqupF9ZO/5b2VmwvJS46glMPGcDGooqg9bLqKnIKyhg7IMWvpp6GTiVBVf+gqnPtRrVvqepbTgcb5xTGhJOEUU5BOcP6WTI5rTEuq/X2GUs37mZPZZ1PNYfOoj118rLqBt5elc+Zh2aRGu9b8isrNZ4xmcks2eB832mDE+dUXg433ADHHUdSXQ23XXGflTKe2TJr0BuPMkRrS4xNvbD8XNpbmVvChEFpHJKdSklVPbsr67BUW68AABoxSURBVPwaH0pUtSlTzxA2LBCRMwIdbJxTGBNO4q9WD6e2N5rbytibuyKPfkmxTZFHV9InMYa+iTFtRk4vfZNLVX0j1x7fbrE6AFPHZrAit4RKhynlG3dVkJ4YQ0Zb4qDz58O4cfDEE3Djjcz5zwLmZU+isZ1lXFejmw2Fbf8+mnph+aFQXlPfSE5BOVMO6sNY25l2p6W9vJIaKmpdJhkivLgJy0HV2OoQFSLiONPGOKcwpqkbboidU2lVPTv31fj8VtonMYbs1LgDnNPuijoWbyjmgsMGtkg/7yra6opb29DI819u5/hR/VqkY7fF1NH9/Uop31BUwZjM5JbRTWGhJUE0fTqkp8NXX8Ejj9Avqx+NbmV3he+IZdueKupcbp+/jwmD0vzK2Ps+fx8utzJlaJ+mSK87OaeuaJNh8A9VTVbVCFWNV9UU+7XjX5BxTmHM/p5OoV3W8xR0tveHPy479YBlvXe+24nLrcyY0rWJEN6M6N+6Ovk73+1kd0Ud1584wvFcU4b2ISk2ytG+k9utbNpVceCSntsNs2dboq0LFsB998HKlXDUUcD+dHKfGnv4TobwMHFwGoVltRSXO+uu60mGOGxIH/omxZKRHNu0LBlq6l1unlq61Wdn45yCciIjxNn+nqFTEZGx9uNhrR1O5zGp5GFMclzXKZPvrawjMTaq1RYf6xx2Fh2fncKiDUVU17uIj45k7oo8Jg9JY6TD/k2dwYiMREqrGyipqm9qKul2K7M/28b47BSOHdHX8VzRkRH8ZGQ/PnWQUr6jpJqahsb9mXobNsCsWfDZZzB1Kjz1FIwefcAY76aDhw1pW90lZ2c5sVERjMhou7DXo+X3fX4Zp4xrX8pnxfYSRvZPIi3B+jcaOyC5Sd0i1Ly6fAf/9+EGdu6r4e7ph7R6TU5BGSMzkgJuUWMIKrdipZA/2Mp7CpzkZBITOYUxTct6nVyIu3TTbk74+xLO+tfn5JVUt3g/p6CMrNS4djsGj89OQdWqkfk+v4zNxZUhSYTwprWMvU/WF7FtdxW/OHGE33VX08ZmUFhW266CgifqODhJLKWHiROtDLynn4bFi1s4JrCSLgAK22k6uK6wnLEDkn22uh+fnUpkhDiqd3K7lVU79h3QqXdMZjKbiyrb3f/qbGobGnl8yRYiBF5bntemRFZOgUmGCBdU9Tr7cVorhyPHBMY5hTWeyKkz22a8tnwHVz//LQP7xFNcXst5T3zZYiPd6R++Rwl6XUEZc1fkERcdwVkTsjrFbqe0lrE3e9k2BvWJ54xDWnbRbY8TRztTKd/6YxHXf/Mmhx4/Ge66y0oPX78errmm1aJasIquE2MifWoUWkod5e0204uPiWR0ZrKjtu1bd1dSVtNwQKfeMQOSqXO52b7Xv55Ywealr3MprqjjgRkTAXhs8ZYW1+yuqKO4os7x3qGhcxGR830dTucxzimMiY6MICEmslOW9VSVBz7eyO1v/8BxI/vx9q+O4+1fHUtcdAQXPvU1i9YXAVYW19bdlY46i2anxpGWEM3K3FLeW13AGYdmkRznuCtzpzAwLZ646IimyGllbgkrcku59ifDfEYebTEgNY6xA5Lb3neqroaHHuLyS6dx+6fPI0ceCcuXW+nhA3w7QxGxmw627Zx27quhrKbB0QfxxEGprMkva7deySNZ5B05eerZQpkUUV3v4t9Lt3LsiL6cf9ggLjpyMG+syGPH3gOjeyd7cIYu5Wwfx1lOJzHOKczpDGXyOlcjv319NY8t2cJFRwzmmSumkBQbxcj+ybz9q2MZlZnEdS+s4MWvtrNhVzluh83bRITx2SnM/76AijpXyJf0wFJeGN5vf8beU0u3kZYQzcwjArdt2tj+rNheSoX376WsDB55BEaMgFtvZWPmcO7//+2deXhU5bnAf282MglZWJIQkpCFEIIIgmFVExOhilRtXUBtLUq1WrBava1W7+Jt+zz3Xkt7q90UqcViFQuIxYqlj8A1gMjShE2gkBCyQUJCICtkz3f/OGcyk2SSTAhmJpPv9zzfM4fvnDk5L99M3rzL977//ib87W8wc6bT947spSNub5U67LkuJpTq+mYKL3R11dqTVVDJqEA/4kfbYlgTIobjJbg0KeLtPYVU1DXxg1sNF+iTGYl4eQm/+b/cDtdZ/0+05eQeKKWW9jCcKl0EOiHC7bnalcmrLzfzxDtZ7D19kedum8jy9I5xl/Agf/78+Byefu8g//HhsfYKz8768yePDWH3qQvEjgpgdvzIq/bc/SExfDgHiirJO1/H1n+W8VRGIgF+V/7RT08K4/XMPHbnlLOg4qTRNv3996GhAdLSaHznXRZvq+d7qYl9vvfYEP9uaxSC8YvYS4xiu70xtT0pooq40d0nTxwoquT62BEdPgf+vt7EjQ50WQHYusYW3tiRx81JYaTEGp+jiGB/Hpody5o9BSzPSGxXpsdLaogZael1I7VmYBCRf+npvFLql87cR1tObk5PltPOnPM8/nYWnxw712vguqmljU0Hz3L3a7vJLqzk1fun8WRGosOEgAA/H9741gyWzI3leGkNIRZfokK7lslxhFWJLUqJdpueOuPDhnO2qp7fbM/Fz9uLJTfE9et+11PDc/vWMWv+LJg3Dz76CJYuNdx3mZnkTExxqsGgI8aGWqioa6KxxXE/peMl1SSEDcfi13tWWlJEEMN8vNrTxB1RUddIfsUlUmK7ZgcaGXuusZz+uDufysvNPPuVjokjy9LH4+st/Hq7zXo6Xlqj22S4F0G9DKfQlpObE+Tv020ZmVe35XCgqIpPjpcxbmQAj9wQx6IZ0R3iPBfqGlm7r4g/mYHlhLBA/vTobOYk9JxC7e0l/OSuyUyKNDLwnFU06UnhPDAzhm/M7r4u3ECTGG60bN90qIRvzh7H6O4qNvREQwNs2gSrV+O7bRtPKsX+8dcz89WfI3ffDRab8na2waAjIs2mg+eqG4h10APqWEkNs5y0SH29vZh/TQQbss6wPD2RMQ66w2Y7iDdZmRgRzJaj57jc1NIvS7OvVNc3s2rnaeZPCm/vT2UlLGgYD8+N4/e7TvNkxnjGhFjIr7jE3dOjBuz5ND2jlPrJ1biPVk5uTrDF12FPolPltRwoquJHC5KJHRXA6s/y+enm4/xyaw6LZkQzf1IEfz1Uwl8OnaWppY20pDBW3BdH2oQwvJwsjCkiPDhrXJ+eNyTAl5fvndqn93zZjA83fsmL4HSpIgCUggMHYPVqWLsWqqqMYqwvvcTm6bfyvT2VbMlIZZKlo1XpbINBR1j3Op2tqu+inC5eaqK0uqFPKdMvLEhm6/EyVvz9BL+8f1qX89mFlfh5e3FtVNdkgoljglAKcsrquigJe1pa2/D2kqtmKa/+LJ+ahhaemd813R7giZvH887eQl7dlsvDphWs08jdBxF5Xim1QkR+g4OOt0qpp525j1ZObk53br0NWWfw8RLuS4kmLGgYC6dEcuRMFW/tLuCdvYW8tbsAi683i1KiWXpjnEs3wrqauFGB+HoL85IjOgT9O1BdDfn5UFBge83MhCNHjLYV995rNP5LTwcvL2bWNMCe7Xx6spxJnVxKzjYYdIRVOXXe66SU4g+fnQZwqEi6I2ZkAI/dFM9rmXl8a24s0ztt7s0urOTaqGCHm1dtNfZqulVOdY0tpP/8U7578/i+Kf5uqLrcxOrP8lkweUy3co4M9OORG+N4LTOv3UugM/Xcin+ar1n9uYlWTm5OsMWH2oaWDhUJmlvb2HigaxuKqdGhvHL/NF68PZl9+RdJnTC6fcf/UMbfx4t3F11DUnMlfPyxoXzsFVF+vmEV2TN8OEyZAq+/Dg88AKEdfzlHBPtzTWQwmSfPszy9Y+LDiXO1pE24skK3VreefTp5c2sb//rBF2zIPsM906OYE+98VQuA5RmJbMg+w083H+eDZTe0f44amlv54kw1j9wY5/B940YGYPH17jHu9PGREirqmli5I4+H5sT2u0LD73edpq6phWe+MqHH676TmsDbnxfy3v4iRgX6ERE8sO1YNN2jlPrIfF3Tn/sMuHISEX9gJzDM/PnvK6X+U0RGAuswuiYWAIuVUt1HcocIwf6+tLYpLje1treryOylDUV4sD939tKfyGNQyiikevCgMY4ehYoKQ9lUVUFlJVRVMautreP7LBaIizPGnDkQH28bcXFGQdZe3FQZyWGs3HGa6vrm9kyxC3WNnK9tbLc6+oq/rzcjA/04a1pOtQ3NLH/3ALtyK3h63gSenT+hz+6z4cN8eP62iTz3/hE+PFTC1834zNGz1TS1tjlMhgAjDT9pTFCPe53WZ50hyN+HiromPjhwlm/M7psb2J6Ll5p4a3cBX50S2W3fMCuhAX58+6Z4frU9t9vWIRrXIiJJwA/p2gnXqSoRrrCcGoFblFJ1IuILfCYiW4B7gO1KqZdF5AXgBeBHLng+t8K+hJFVOa3PKiYsyDVtKFxGczMUF9usndxcm0I6b1etISHB2OwaEQHJyYbFYx3R0YbiiY+H8PBelU9vpE8M53ef5rH7VAULpxiVMKy/yJOd6H7bHWND/Smtrqe0up6lb/2DU+V1rLhvar/2jd17fTRv7ynk5S0nuHVyBAF+Pu3JEN0pJ4DkiCC2/rPMYS3BU+V1ZBdW8uLtyWw+Usqbu05z/8yYK27297MtJ2hsaeOZ+T1bTVYeTY1n7f6iXpN7NC5jA7ASeBNwnH7aAwOunJSxXd1aS8bXHAr4GpBuzq8BMtHKya6nUwuRIbY2FI+lXlmFA7dBKaPRXkmJMazWjmnptI9z5wxldOaMUdXbiq8vTJ4Md9wB06cb47rrIGjgYmvTY0IJ9vfh0xPl7crJqQaDvRAZYuFwcRV3/+5z6hpb+OPSWdw0YXS/ntXLS3jpzmtYtHIPb+w4zbNfSSKrsJK4UQE9Zi9OHBPEuqxiztc1Eh7UMdtvQ3Yx3l7CPddHEzXCwvfWHmTr8TIWXEFZqF2551mXVcyy9PFOx0eD/X3Z9XwGfoP5e+DZtCilXr/SN7sk5iQi3kA2kAj8Tim1T0QilFKlAEqpUhEJd8WzuRu2brhGUsSmg2dpbVMsSnF99QWHNDUZCqWszPEoLbUppMvdVC7w9YURIwxrZ/RoSE21udusrrfoaOM6F+Lj7UVqUhg7cmxVynttMOgEY0P82Xq8kcgQf95fNrdXF5ezzIwbyR1TI3ljZx6LZ8ZwoLCS9Ik9f83sGw/aK6fm1jY2ZtvingsmjyFmpIVVO/P6rJzqGlt4YeMXJIQF8v15zllNVnQVcvfDDNEAfCQiy4G/YHjMAFBKXXTmPi5RTkqpVmCaiIQCfxERx3XwHSAijwOPA/j5eX6wP8iuG65SivVZxaTEjmivtj2gNDdDUZHNtVZUZFM01nG+m4KoQUGGK23sWEhJgTvvNI6joiAy0jhndb9ZLP12uQ0U6UlhfHyk1GwRHtJ9g8E+MG9SBMWV9fz33VMc7k3qDy8unMTW42U8/d5BLlxqYkZc9y49oEPjwVS7JI8dneKePt5efCc1gZc+PEZWwUVmxDlfHWTF309QUl3PhifmamXjGWRjeMOsX4Ln7M4pwKm0Tpdm6ymlqkQkE1gAlIlIpGk1RQIOK2sqpVYBqwACAwNdW89/AAj2t1lOh4qryC2v4+V7pnx5P7C6Gk6dso28PGMUFHR1rXl5GbGdsWNh3DgjsWDsWEPZWOM+ERGG4rE4V2FisHGzGffLPHmeSWOCyS2r7XdNwbSkMNKSvpx4YlSohSfSEvi1Wd27p3gT0G3jwfVZxYwe3jHuuSglhle25rByx2nedFI57c+/yNt7Cll6Y1yfFJrGfVFKxV+N+7giWy8MaDYVkwWYD/wM+CvwMPCy+frhQD+bO2JNiKhtaGF91hksvt589UrbUNTUwOHDhpJx5HIrLDRiP/ZERhpJBmlpXV1rUVEud625mvAgf66NCibzZDl3TI3kclPrFWfqDRTfTR/Puqxi6ptaSQzr3QJP7pSxV1FnxD2/fVM8vnbxHoufN0vmxvGr7bmcKq/r1bqvb2rl+fcPEzPSwnO3TbxygTRuhYjMBIqVUufMfy8B7gUKgR+7s1svElhjxp28gPVKqc0isgdYLyKPAkXAIhc8m9th7elUVtPAR4f70Ibi0iUjky0ryzZOnux4jY+PYdVYLZyUFEhMtI2EBAjse5WDoUZ6UjivZZ5iX77xnXP3VuHW2okX6hqdqhYyMSKIP+0tpLVN4e0lbDp4lpY2xaKU6C7XLpkby8odefx+52l+dl/PlUJe2ZZDwYXLrH1s9oCWR9J86byBYXQgImkYBsdTwDQMr9d9ztzEFdl6R4DpDuYvAPMG+nncnWE+3vj7erEx+yx1jS0snhFtSzooKTGsoOJiI/5TXGw7LiszMuLAsHBmzICHHjIUUFycoYxCQw3XnKZfZCSH8dtPT/GHXfmIGAVX3Z2eyhF1xr7xYMLoQNb9o5jp40KZ4EDOUcOHsXhGDOv+UcwPbk0iPNhxzOxQcRVv7jrNg7PGcUNi/zIRNW6Ht511dD+wSim1EdgoIoecvYn+c8WdqK01XGtlZVBe3u5uW/HJQYKqKoipr2L86hrHSQcBARATY8R+Fi40asBNn24opUjXdqP1dKbFjCDE4svJslpiRwW070fzFOwbD9Y2tJBbXsf/9BD3fCw1nnf3FfLW5wX8aEFyl/ONLYY7LyLYnxcXdj2vGfR4i4iPUqoFw+B43O6c018Oz/oWDUaam2HLFlizBjZvNqwie3x8uCEglFJLCN4JscjUCUbSgf0YN85IvR4kGW6ehreXkJYUxkeHS5g4CKymvmLfePCzUxX4+3pxRw9xz9hRgdx+bSTv7C3kyYxEAv28Kalu4GBRJQeLqtiTd4GcsjreemRm+z4+jUfxHrBDRCqAemAXgIgkAt03K+uEVk6uQCkjHrRmjVHtuqICwsJg2TKYO9cWA4qIgBEj+M7rn3O4uIrdL9wCIZ6Z9TbYSTeVk7snQ1wJ1saDh4urOFBY6VTc8/G0BD7+opQHVu2hvKaR8lpjm8swHy+mRofwk7smk5GstzK6MyJSANRiVHdoUUrNcOZ9Sqn/EpHtGPkFn5iFF8DIMXjK2Z+vldOXhVJGUkJBQcdK1/n5cOwY5OSAnx/cdRcsWQILFnSb+TY3YRTJY4KI1IrJbbklOZykiOHc3Mum1sFK8pgg/vbFOQCnUuWviwnljqmRHD1bzY2Jo5k+LpTpMSNIjgzqkOGncXsylFIVvV/WEaXUXgdzOX25h9iU2uAjMDBQXbrUtddRn2ls7Fgy59Ilo3qB/aivh7o6Iy5kHTU1xmt313dXbDQhwSi7s3ixUWBUo3FzfrUtl1e25RA7KoDMH6brQquDHBG5rJTqMRXXtJxmXIlyuhoMTcvp8GF48EGbMqqv7/09VgICjGoH9iM83Ei5DgiwDYvFOBcba9sfdBWKjWo0rsCaHr8oJVorpqGDAj4REQW8YRZAGDCGpnIKDjaKhoaG2mq42Y/hwzsqGquyCQgAb11eRTP0SJ0wmm/fGM83Z8e6+lE0VwcfEbFvBrjKgfK5USlVYtY53SoiJ5RSOwfqAbVbT6PRaIYYzrj1Ol3/Y6BOKfWLL++pOqIjkxqNRqPpgIgEikiQ9Ri4FTg6kM8wNN16Go1Go+mJCIyOEWDoibVKqb8P5ANo5aTRaDSaDiilTgPXufIZtFtPo9FoNG6HVk4ajUajcTu0ctJoNBqN26GVk0aj0WjcDq2cNBqNRuN2DOpNuCLShlGSfSjgA7S4+iFcgJZ7aKHlHhgsSim3Nk4GtXIaSohIlrMl6z0JLffQQsutseLWmlOj0Wg0QxOtnDQajUbjdmjlNHgY0HL1boSWe2ih5dYAOuak0Wg0GjdEW04ajUajcTu0cnIRIrJaRMpF5Gin+adE5KSIHBORFXbzL4rIKfPcbXbzKSLyhXnu1zII2pT2RXYRiRORehE5ZI6VdtcPKtkdyS0i6+xkKxCRQ3bnPGLN+yL3EFjvaSKy15QtS0Rm2Z3ziPW+aiil9HDBANKA64GjdnMZwDZgmPnvcPP1GuAwMAyIB/IAb/PcfmAuIMAW4HZXy3aVZY+zv67TfQaV7I7k7nT+f4GXPG3N+yi3R6838In1uYGFQKanrffVGtpychHKaHd8sdP0MuBlpVSjeU25Of814M9KqUalVD5wCpglIpFAsFJqjzI+xW8DXx8YCa6cPsrukMEoezdyA2D+NbwYeM+c8pg176PcDvEguRUQbB6HACXmsces99VCKyf3IglIFZF9IrJDRGaa81FAsd11Z8y5KPO48/xgpDvZAeJF5KA5n2rOeZLsAKlAmVIq1/z3UFhz6Co3ePZ6PwP8XESKgV8AL5rzQ2W9nUY3G3QvfIARwBxgJrBeRBIwzPnOqB7mByPdyV4KjFNKXRCRFGCTiEzGs2QHeJCO1sNQWHPoKrenr/cy4Fml1EYRWQz8AZjP0Flvp9HKyb04A3xgmu/7xagdONqcj7G7LhrDHXDGPO48PxhxKLtS6jxgdfVli0gehpXlMbKLiA9wD5BiN+3xa+5IbtOt68nr/TDwffN4A/Cmeezx691XtFvPvdgE3AIgIkmAH1AB/BV4QESGiUg8MAHYr5QqBWpFZI7pu18CfOiaR+83DmUXkTAR8TbnEzBkP+1hss8HTiil7N03Q2HNu8g9BNa7BLjZPL4FsLozh8J69w1XZ2QM1YHhyigFmjH+OnoU4xfyO8BR4ABwi931/4aRwXMSu2wdYIZ5fR7wW8yN1e48+iI7cC9wDCOT6QBw52CV3ZHc5vwfge86uN4j1rwvcnv6egM3AdmmfPuAFE9b76s1dIUIjUaj0bgd2q2n0Wg0GrdDKyeNRqPRuB1aOWk0Go3G7dDKSaPRaDRuh1ZOGo1Go3E7tHLSaDQajduhlZNGo9Fo3A6tnDQajUbjdvw/u8rxIKAW+G4AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data[\"Year\"], data[\"Wheat\"])\n", "plt.ylabel(\"Shillings par semaine\")\n", "plt.twinx()\n", "plt.plot(data[\"Year\"], data[\"Wages\"], color=\"red\")\n", "plt.ylabel(\"Shillings par quart de boisseau de blé\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Graphe montrant pour chaque année la quantité de blé pouvant être acheté, exprimé sous forme de fraction représentant la portion que pouvait se permettre un ouvrier, une augmentation graduelle est constasté (Suivi d'une chute mais le manque d'une suite de données ne permet pas de conclure)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pouvoir_achat = []\n", "\n", "for value in sorted_data.values:\n", " pouvoir_achat.append(value[1] / value[0])\n", "\n", "plt.plot(pouvoir_achat)" ] } ], "metadata": { "kernelspec": { "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, "nbformat_minor": 2 }