{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sujet 2 : le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": [ "# Voir les lignes avec des données manquantes\n", "\n", "data[data.isnull().any(axis = 1)]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Supprimer la ligne qui ne contient pas de données valables\n", "# Copier les données\n", "my_data = data.dropna().copy()\n", "my_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Représentation graphique du prix du blé " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(my_data[\"Unnamed: 0\"], my_data[\"Wheat\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Représentation grahique des salaires" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(my_data[\"Unnamed: 0\"], my_data[\"Wages\"], \"r--\")\n", "\n", " \n", "y1 = my_data[\"Wages\"]\n", "x = my_data[\"Unnamed: 0\"]\n", " \n", "plt.fill_between(x, y1, color='#539ecd')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Superposition des deux graphiques" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "\n", "p = plt.bar(my_data[\"Unnamed: 0\"], my_data[\"Wheat\"]), plt.plot(my_data[\"Unnamed: 0\"], my_data[\"Wages\"], \"r--\")\n", "\n", "\n", "x = my_data[\"Unnamed: 0\"]\n", "y2 = my_data[\"Wages\"]\n", "\n", "\n", "plt.fill_between(x, y2, color='#539ecd')\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Utlisaion de 2 axes d'ordonnées" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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CEdmLIhoVxVZWpwMPBJ/7qupygGC9U9GkchynKtm4EW64AQ49FI49Nocdr1pVVspKVZer6uvB53XAXKA/EY0KEfYSYWjc9tEiTBXhJyK0iyJT0ZSViHQERgMPRTxuvIi8JiKvNTY25kc4x3GqkjvugGXL4Oc/z7HHLuYGLB3ax56jwTI+2Y4iMhgYDrxMdKNicnAsIgwApgG9gAuBGyMJHGXnHHMs8Lqqrgi2V4hIP1VdLiL9gJVhB6nqRGAiWCLbwojqOE6lUldnWSmWLDEFte++cPjhOT5J6bkBG1X1oHQ7iUh34BHgElX9TKJr8GHA68HnU4GXVTlOhCOB3wM/ybSjYroBz6DZBQjwODAu+DwO08CO4zh5I5b/b/FiyzXb1GSlQOrqcniSLVtg7dpSU1ZpEZEOmKKqU9W/BM0rAmOCVEZFHO2AzcHnUcDfg8/zgb5R5CmKshKRrsDRwF/imm8CjhaRecF3NxVDNsdxqoew/H+bNmWZ/y8Za9bYurTcgCkRM6EmA3NV9Za4r6IaFW8B54twGKas/hG09wdWR5GpKG5AVW0Aeie0fYxdjOM4TkHIaf6/ZJRn9opDgTOB2SIyK2i7CjMiHhSR84AlmGsvFT8GHgMuB6aoMjtoHw28EkUgLxHiOE7VUltrLsCw9pxRhtkrVHUGkGyAKmOjQpXnRdgR2F6V+CQPv8Pm2GZMZm5AkV0zanMcxykjwib9Zp3/LxlVnsRWla1AOxH+Q4ROQdsi1bTjXS3IdMzqkZC2h6OcyHEcp5R4/32YNMly/g0cmIP8f8koTzdgThBhOxEewgIxXsTGqhDhbhGui9JXajegyJ7A3kAPRE6K+2Z7oHOUEzmO45QKGzbASSdZhopnn4Vd8+knillWvXun3q8y+R9gF+BAYEZc+1+BCZC5wko3ZjUUOB7oCXwzrn0d8L1MT+I4jlNs4udTdeliUYD/+EeeFRWYZdWzJ3TokOcTlSSjgRNVmSVC/LzYuVgS84xJraxUpwHTEDkE1Zcii+k4jlMCxOZTxcLUGxpMd6yOFDydJaWXvaKQ7AB8HNK+HbA1SkeimkESCJHOwHmYS7DZ/ad6bpST5Zpu3brphg0biimC4zhlwODB4VF/gwbBokV5PvlXv2ra8cUX83yizBGRBlXtlv/z8BzwmCq3ibAO2E+VhSLcBQxS5bhM+8o0dP1+4B3g68ANwFjMjHMcxyl5CjKfKhmrVpm2rE6uAv4pwt6Yvrk0+DwSiJTUKtNowD1Q/RmwAdUpwDeAfaOcyElDXZ39QdfU2Dqn+V4cp7rp3z+8PafzqZKxenXVugFVeRH4EtARS7E0ClgGHKK6LWdgRmRqWW0J1p8gsg/wEVbR18kFiQ71xYttG3IcQ+s41UdDA3Tq1Lo95/OpwlAtu/IguSbIWjEu7Y5pyNSymohVhPwZlhtqDvDLtp7cCQhLUNbQkOMEZY5TfWzdCt/5jhVTvOQSG6PK23yqMD77zBLZVqllJUJtkmVgkNki874yCrAoUSomwKKmxt7AEhGxNNCO42RMfIh69+6wbh3cdhtcfHERhJk/H/bYA+67D8a12bjIGQUMsGgCUimZz7BSIVeokrJAYabplvoiMhmRJ4PtvbBEhk4uSOY4L4hD3XEqh8SSH+vWQfv2RTRsqjh7RcAZwFLgp1g1jaODz0uAc7FJwWdiXruUZOoGvA/4JzYTGeA94JIIAjupmDChtVO9IA51x6kswjzqjY1F9KjHJnJVqRsQOB/4kSq/UOWZYPkFcBlwriq/AS7ClFpKMlVWfVB9EDCflGojESd0OSkYOxZOP715u2AOdcepLIoaoh6GW1b/AdvKgsTzFjAi+PwSMCBdR5kqqw2I9CbmexQ5GPg0w2OdTIjlDdtzT5ul6IrKcSIzcGB4e9E86mVYHiTHLAbGh7R/D3MFAuwIrEnXUaah65diUYC7IzIz6Dwkub6TNfPn2/qTT4orh+OUMQcd1NqKKqpHffVqc/F3714kAYrOZcAjIhwHvIoZPCOA3YGTg31GAA+m6ygzZaX6OiJfwRLbCvAuqlvSHOVEYcECW7uycpysmDkTHnsMDj0Uli41pVVba4qqaI6K2BwrSVbHsLJR5W8iDAEuoFl/PA7crWqWlSp3ZtJX5qHrIl/CJgI3KzjVP0SQO64r6QlMAvbBNO25wLvAn4NzLAK+paprk3QBVFDouipst52NDKvCxo3Q2SuwOE6mfPop7L8/tGsH9fWw/fbFlijgm980zVlfX2xJWlCo0PVckpllJXI/ZrbNojmwQoGslBXwG+AfqnqKiHQEumI5pKar6k0iciVwJfDjLPsvL1autAI7w4bB3Ln2n+fKynEy5oILTCfMmFFCigqqPntFDBF2AWqxtEvbUOX5TPvIdMzqIGAvcjCDWES2xxIYng2gqpuBzSIyBjgi2G0K8BzVoqxi41UHHmjK6pNPoG/f4srkOCVObPJvLJv6ySfDwQcXV6ZWrFoFu+9ebCmKRqCk/og98xVzA8brkXaZ9pVpNOBbwM6ZdpqG3YBVwO9FpF5EJolIN6Cvqi4HCNY75eh8pU9svOrAA23t41aOk5L4yb8xnnyyBPM/V3ES24DbMG/cXkADcBhwKla145goHaUra/8EpgW3A+Yg8grw+bbvVUdHOVncOQ8EfqiqL4vIbzCXX0aIyHiCUMiOHTum2btMmD/fBmAPOMC2P/VZAY6TilTpNEtm1sfnn1tuwOp2A34F+IYq7wSVglepMlOEz4H/Bp7OtKN0bsBngn3qac683laWAktV9eVg+2FMWa0QkX6qulxE+gErww5W1YnARLAAixzJVFwWLIABA5pdf25ZOU5KSm7ybxgfBwVyq9uy6gLE6jGvwTxm72HJ0PeL0lE6N2B/4ETgIeB6rPhid2A2qv+KcqIYqvoR8IGIDA2aRmGCP05zGvlxwLRs+i9L5s+H3XaDnj1t25WV4yRl06bwkh9QYuk0PXsFWNHePYPPs4D/FGEQcCHwYZSOUltWqpcDYBF7B2FFtM4F7kHkE1T3iiR2Mz8E6oJIwAXAOZjifFAsQe4SzK9ZHcyfD8cd58rKcdLw+ecWSLFpE3TsCJs3N39Xcuk0XVmBRX7H4h1uAP6B5QH8nIg1rjKNBuwCbA/0CJZlhOd7yghVnYUpv0RGZdtn2dLQAB99ZJZV166WItqVleNsI77kR+fONg3xd7+Dbt2a24s++TeMKk5iK8IRwIuqbAt5UeV1EQZjltYS1W3uwYxIF2AxEdgbWAe8DLwI3EKaybpOBGKRgLvvbkEWPXq4snKcgMQi2hs3QocOpqjGji0x5ZRIdVtWzwCbRHgJeDbYflmVBohWzj5GujGrWqATVsb+Qyw4wp+kuSReWYG5Al1ZOQ4QHvW3ZUuZFNFevdpeQHv1KrYkxeALwMWY7jgfmAF8IsKTIvyXCAeJECkHVboxq2MQEcy6+hKWlHAfRNYAL6F6bRYX4cQTmxC82262dmXlONsoi6i/ZKxaZYqqXcbzXisGVd4H3gfuARBhT+BILJT9MuAmrHJHxpo8/ZiVZa14C5FPgs4/BY4HRgKurNrKggXm+ou9ffXs6fOsHAdYscJcfvFBFDFKKuovGZ5qaRvBPKs1WPj6p8DpWGR5xqR2A4pchMifEPkAeB5TUu8CJxFBIzopiIWtx7Iyu2XlOMybB4ccYnmdy7aIdhlnrxCRe0VkpYi8Fdd2nYh8KCKzguW41H3QW4STRfitCHOwKO+LgI+BbwE7RJEp3ZjVYGzS7khUd0P1TFTvRPUNVJuinMhJwoIFLXOHubJyqpC6Ohg8GGpqoF8/yzy2bp0lpp082Ypni5RZEe3ytqzuIzwd0q2qekCw/D3ZwSK8gcU4XILFOVwM9FLlUFWuUuWfqkQqmZFuzOrSKJ05Edm6FRYuhDFjmttcWTlVRmLE30cfmWK67joYOdKWslBOiaxeDV/6UrGlyApVfV5EBrehiyHAWmwe7Xzg/SASMGsyTWTr5INly8whn2hZNTSEO+odpwIJi/hThdtvL448OaGpyZRV+VpWyfiBiLwZuAlTufF6YK6+94EzgbdFWCzCFBHOEWHXqCd2ZVVMEiMBwYItwIMsnKqhrCP+kvHpp+Y5KV1l1V5EXotbxmdwzF1YXcMDgOXAr5PtqMoWVWao8t+qHIWNT40DFmLloeaIsCiKwK6siklMWSVaVuCuQKciiR+bqq2Fr37VrKgwyiLiLxmxCcGlG2DRqKoHxS0T0x2gqitUdatavMI9WER4pjTFLbG6VgOjCJxpuiUnHyxYYOmVBsbds5iycsvKqTASx6Y++MCW/faz6L+NG5v3LZuIv2RUYPaKWFWMYPNErM5hkn1pjymzI4PlEKAzFhH4LDA5WGeMW1bFZP58C29qH/fOUG2WVfyr9uDBJVg9z8kVYWNTYO9l99xTphF/yYjlBSxTZSUiDwAvAUNFZGmQYPyXIjJbRN7EFNCPUnTxCfAClr1iOZa8fA9VdlXlXFXuV2VpFJncsiomiWHrUF3KKvFVe/Fi24Yyf1I5iTQ1tazqG8+SJWWQ5y8qpe8GTImqnhHSPDlCF5cBz6gyL0ciuWVVVGITguOpJmWVqtyrU9bEG8z9+8NeKYoJlfXYVDKqOOM6gCq/y6WiAresiscnn8CaNdVtWVVkGJiTaDAvW2brI4+El19u+X5S9mNTyVi1yi6ua9diS1IxuGVVLGLZ1hMtq+7d7XW0GpRVslfqinzVrh6uuip8bGrBAhuLqqixqWSUd/aKksSVVbFILA0So5pqWk2YUMaJ3xxo6e4bNAh++MPUBvPYsbBokY1hLVpUoYoKKnVCcFFxZVUswiYEx6iWlEtjx8IppzRv9+xZwa/alUfM3bd4sc2VWrIEfvvb5BUxqspgXrWqaser8kVRlJWILApCIGeJyGtBWy8ReVpE5gXrSBl5y44FC+zNa7vtWn9XTWVCunSx32G33WDUKFdUJUrYDIMrrwx39/Xo0XqopuoMZresck4xLasjg8y9BwXbVwLTVXUIMD3YrlzCIgFjVItlBTB3LgwbZtlKX3212NIUjjKaX5ZoQS1eDOPGwdIks2TWrq2isalkuGWVc0rJDTgGmBJ8ngKcUERZ8s/8+a3Hq2JUi7JShTlzLK55xAjzI61YUWyp8k/Y03/8+JJVWGEzDLZubS7BlkhtbRWNTYWxcSNs2OCWVY4plrJS4CkR+XdcAsW+sVQewXqnIsmWf7ZssQdztVtWK1bYa3hMWUF1WFclOr8s0dj7/e/h/vuTT+ZVdXdfKGWevaJUKZayOlRVDwSOBS4UkcMzPVBExscyBTc2NuZPwkzI1pWzeLG9cla7ZTVnjq332suq7dXUVIeyKsH5ZWHG3rnnwllntcwGFk/MvVfV7r4wyjx7RalSFGWlqsuC9UrgUSzh4QoR6QeWMBFYmeTYibFMwe2T/RcVgra4cpLNsYrRowesXw/FVsb5Jl5ZdesGe+8Nr7xSXJkKQQnOL0uWt69vX7jvvuQWVFW7+5LhllVeKLiyEpFuIrJd7DPwNSx77+NYvROC9bRCyxaJtrhywkqDxFMtmdfnzLFr3Xln244FWSSrGVEplNj8ssbG5K6+lStNAbkFFQG3rPJCMSyrvsAMEXkDeAX4m6r+A7gJOFpE5gFHB9ulS1tcOQsWQOfO0K9f+PfVknIpFlwRG6kfMQI+/the0SuZsWPhqKOat3feuaBP/3jv9S67wJAhyfeNGXtuQUWgAsuDlAIFV1aqukBV9w+WvVV1QtD+saqOUtUhwXpNoWWLRFtcOfPnw6672tMijGqyrOIznMaCLKrBFbh2bfN9vuOOgiqqeO/18uWmfI4+2oMlckJdHVx7rX0ePrxkIzzLkVIKXS8vJkxoPfKcyX93XR389a82vyhZUEY1WFarVtkSr6z23dfcY5UeZLFxI/z733DyybZdwMCKZGNT771XBa6+fM9ti70JfPaZbS9ZUtJTEsoOVS3bpWvXrlo0mppUe/VS7dJF1V5SVW++OfUxU6eqdu3avD/Y9tSpLfebNcu+e+SR/MlfbP71L7vGf/yjZfvBB6sedlh2fU6dqjpokKqIrRN/11Lh+eft2qdNs7+fSy8t2KlFWv75xRZBdMUgAAAgAElEQVSRgolQHDL932sLgwaF/7iDBuXuHDkC2KAl8AyPsrhllS2vvWYlPu66CxYuzOyYTIMyqsGyio8EjGfkSHj9dZt1GoVymmg7c6atv/QlGDiwYJbVkiVVnLevEHPbSnBKQiXhyipbHn7Y3ICjR5tLYb/94PHHUx+T6R9zjx62rnRl1b07DBjQsn3ECJv9P3dutP5KdKJtKC++CF/4gkWL1dbCBx/k/ZTvvw+HHQYdOpRUIGLhyFaRRHEdluCUhErClVU2qJqy+upXYYcg3+7o0TBjhkWzJWPgwPD2xD/m7be3gYNKV1bxkYAxsg2yKJe3WlVTVoceatsDB+ZNWcU/Z4cOtT/NGTNg8uQKH5sKIxtFEtVav/761m1V8SZQGFxZxZPpW9SsWRZ+Hl/eYswYi+v9+9+T9z9uXOu2sD/mmhpTWKWqrHIxUJ0YCRhjyBCzLKMGWZTLW+2775rWiFdWy5fD5s05PU3ic7apyTyrc+dWaRj65ZeHt196afJjolrrnTvbescdq+xNoEAUe9CsLUtOAyyiDMBedZVqu3aqq1Y1t23dqrrLLqonn5z8HKefbgPqAwemDwIYNEj1rLPackX5IRcD1WvW2HG//GX496NGqX7xi9Hkuuuu1gPbuR5AzwWTJplsc+e23F64MKenKaOx/sJw5pn2P7vLLva/t8suqp06qR5yiOrnn4cfE/YDpopGOeww1d12s2dBiUMZBlgUXYC2LDlVVpn+dzc1qQ4ZYg/URL7/fdVu3VQ3bmz93fLlqh06qF5ySWby7L+/6ujRUa8i/+TiKThzph3zxBPh3//kJ6rt24f/jsn4zW+sz3btmuUpNUWlqnruuRZFGnug/fOfJu/zz+f0NFUb9RfGyy/bxV95Zcv2Bx+09sT/yaYm1WuuSa6swv7W33zTvvvVr/J2GbmkHJWVuwFjZDrm8dZbMG9eSxdgjDFjLDjguedaf3fPPZZt/YILMpOnVJPZ5mJsKFkkYIwRIywH0BtvZN7nvffCF78IF11kBR0XLChN98vMmRYFGJsQHhvHbMvYWoJb9skrnkWTZKwqNa9o3lGFSy6xLCFXXdXyu1NPhYsvhttuM9ddTY257g4/HG64wdaJM6W7dAkfg7rzTnMDnnNO/q6lynFlFSPTMY+HHzZ/9Ikntt73yCMtIeu0hLSGjY3wu9/B176WOrdNPKWqrHIxNjRnjv3TDxoU/n3UIIv6elNs554Le+5pk25LLbACLMHpu+82j1dBs7LKNsiiro66c/6XwYufo0Yb2XHxKxz/q8MY1GcdXbq03LUqx/ofeABeegl+/vPwqtzDh5uSWr3aFNuSJRaFcvLJ9tIZP1Ma4IADWr8Effqp1VI5/XTo3Tvvl1S1FNu0a8uSUzfgFVe0Nvc7dWrtStprL9WvfCV5PyedZP7wpqbmtocftv4eeyxzecaNU62tjXIFhSEXY1bHHKM6fHjy75uaVPv1s3GGTPjBD+xerVnTPOH2ySczl6dQTJtmsv3rXy3bd9hB9YILsupyau8falfWt7gdNTTqPb2uKJs50nlj/XrVAQNUDzww+ThSFLf2T39q382c2bL9//0/a3/11VxfQd6gDN2ARRegLUvOlNXmzarDhqnutJMpCBEbM9luO9WlS5v3mzPHfrLbb0/e13332T6vvdbcduSR1m9jY+YyXXSR6vbbR7+WQnDPPc3/1N26RX8K1taqjh2bep/Ro1X33DN9Xxs32sP+jDNse+VKk+uWW6LJFE++nvI//rGNWzY0tGzff3/V44/PqstBLAx/1rKw7fKWK7H7F/sxfvaz5PtGGdxbt85eREeMaFZ+TU32dzpyZF4uJV+Uo7JyNyDA3XdbTO/Eic2FEd9809annWZjTQCPPGLrk05K3tc3vmFuhdgE4blz4dln4T//M3n6gDB69rQcY1EzORSCmCtzu+1srCTK2NC6deZqSTZeFWPkSHjnnfTJfKdNs6SwsbGCHXc0V8w772QuUzz5zIQxc6YVmUz0z7VhrtUSwt2vydornvj7F+PXv05+/6K4tbt3h5tusmkVU6da2zPP2N/ahRe2TW4nPcXWlm1ZcmJZrV5tb+ajRrV03amqPvCAvWVdfrlt77+/6qGHpu/zsMNsX1VzUXXsqLpiRTS5brnFzr1mTbTjCsGtt5ps3/ueRd8lWgqpeOUVO/bRR1PvF4uSmz499X5f+1prq/XLX84+v2C+Yr43bTJXZVgewPPPtwjBLNil54ZwcXuva5u85UrU+xfVrb11q1lR/fqZpXXiiaq9e0eLXC0BcMuqDLn+ent7v/XW1tkUTj/d3phuvtkyVbzxhgUHpHvLHj3a9n37bZgyxaKOdtopmlylXCakvt6iq445xiy/2bMzPzZdJGCMWE2rUaOSTzxesgSefhrOPrul1brnntlbVvnKhPH66/D55xYJmEhtreWZ3LAhUpfr14N06wq0DP3r2rGRCb/p3gZhy5io9y9qZcmaGoseXL7c/gcefdQ8LzGvi5M3qltZzZljIaff/76VpwhjxAj7A41F5q1dm94tFFN6++xjbq899oguWykns62vtyiq4cObtzNlzhzo2BF22y35PnV18KMfNW8nc8VNmWLvwmef3bJ92DArP5Iq9VUy8pUJ48UXbR0fCRgji4hAVfjud2H5cuXKzrcxqMMyhCYG9dnAxHvbl2TUfkHI5v5FTemxYIG9HMVeLj77rHSTJlcSxTbt2rJk7QacOtVcR7GB1LvuSr5vvt0KyXjmGTv22WejHZdvNm40199VV5nbtGdPmwydKccfr7rvvqn3yeQ337rVsgUcdVTr4//2N9t/xozM5YoxdWrzxOK23L9ETjzR5A0jVi7lqacy7u7Xv7ZDfnHlJ7otoKRz54KWGylJpk41d2uu7188FZAehDJ0AxZdgLYsWSmrqMokaiqAXP0hv/66ZjS2U2hefdXkeugh2z7iiGiRULvtpnraaan3yeQ3f/ZZawu7bwsW2Hf33JO5XDHWrbOHXbduzeedPDl6P/E0NVmkabJQ/IUL7TyTJiXtIj5AsW9fW594omrT40/oNsV88ME2XlftnH56899LPmL2KyA9SDplBdwLrATeimvrBTwNzAvWO6TqI9dL0dyAItJOROpF5K/Bdi8ReVpE5gXrHfJy4qjJKaO6FXI15lGqZUJiLr+YC/CAAyxysrEx/bENDVb7K914VbLfVhW+/W0bVzjySHO3hiWAra21bALZjFs9/LCNLf3znzYeBhZh2Bbmz4eVK8PHqwD697drSeIGTAxQXLHC2o87DmRWvR27//7NtcAyuReVjIi5VvOVqbdckia3jfuAYxLargSmq+oQYHqwXTCKOWZ1MRBftKgwP0RUZTJhQuuUK6lSAeTqD7lUx6zq6y0j/K672vbw4bBpk2VmSMe779rTNp2yCvvNYxkvHnig+V6pwg9+0HqsoF07q4kRtSYWWNqmL3zBFMthh1lGkiefjN5PjLo6OPhg+3zDDeHjGh062GB9kr/BsPcrVbjxRux+DBliYdUjRtiO2Vx3JREbU80XUZ8JZYiqPg+sSWgeA0wJPk8BTiikTEVRViIyAPgGMCmuuTA/RFRlEjVaKFd/yNtvb+tSU1azZpk1FcttF3sozJqV/thMIwHDfvN77rEndCLJrOJsIgLnzYMXXrA5WyJWpXDUKCv7EnbudMRMoligx/LlyQfiUxRhTPl+Ff9gHjnS1lFrgVUSGzbYS1E+lVXUZ0Jp0l5EXotbxmdwTF9VXQ4QrCOGOLeRQvoc43yfDwNfBI4A/hq0fZKwz9p0/RRkzCobcpUBYbvtMs/SXggaG+23uuii5rbNm22M57LL0h9/1VWWGSRZSYZ0RBkruO46a48yB+yqq1RralQ//LC57e677Rxz5kSXN8r45SmnqA4dGq2bAY324aabbMetW1V79IgW8FJpvPii/SZRUptVIWQQYAEMpuWYVeRndC6XgltWInI8sFJV/53l8eNjbwON2fjmC/FWlKvqdqWWzHbePLNk4t9aO3SwsP904et1dTaXrbHR3GzZhPlGsYr33NOe6fPmZdb31q1w331w7LGwyy7N7ccea+tsXIFRXM4DB1p7iAX3ve+13r1rV5hwVmA5xu5HTQ0cdFD2llUuimoWm8QxVSeXrBCRfgDBemUhT14MN+ChwGgRWQT8CThKRKaS4Q+hqhNV9SBVPah9+/bZSVAupVJLTVklexAMH27fJXOVxdxhGzfadrYpjKK4WIcNs3Wm4zdPPQXLllnm9nhqa2HvvVNXgE5GFOVaW2u/z5qWwwQffwyTJtmc9AEDEt6vev/Tdoq/HyNH2iTtTZuiyZrPNFOFpL4eevVqnrvm5JLHgXHB53HAtBT75p5CmnEhZuYRNLsBfwVcGXy+EvhluuNzmnW9FDnsMAsNLxX+678sddTmzS3b77zTXC+LFoUfl8t5KZm6WBsabJ/rrsus31NOUe3TJ9xFefnlloB2XcQURlOn2u+Vics5lpm/vn5bU2Oj6te/bl28/HJI/9/5jmr//i3b/vIX6+ell6LJWgFzh1RV9aCDwgujOi0gfej6A8ByYAuwFDgP6I0Fv80L1r1S9ZHrpZQyWNwEHC0i84Cjg+3qphQtq332MddfPOkyWeQyhVGmVnGXLhaxmIlltXq1JcQ980zLrpHIscdaSp3p06PLOmqUfU7ncg4sgbr7t27zxPXubRH0t9/eHDvRgvp6C3aJJ9sgi3ylmSokW7aYVZn4mziRUdUzVLWfqnZQ1QGqOllVP1bVUao6JFgnRgvmlaIqK1V9TlWPDz4X9YcoSXr0KB1lpZo8JHi//ezpmkxZFWteSqYRgXV19qBLdAHG+PKXLTQ8m3GrxkbLtJ5OudbWUscZjL99v22euE8/tSj8bt1C9t+40a4t8X707w/9+llm8ChUwtyhd96xOXI+XlWRlJJl5SRSSpbV0qU2gBL2IOja1eY1JVNWYeUTCjEvZdgwC2NOVmalrs6snUsuMYvqjTfC9+vYEb76VVNWycblkvHmm8nzTsaz005czS9o2NLSat26Ncl89dmz7cuw+zFiRHTLasKE1qVLym3ukAdXVDSurEqZnj3t9bqpqdiSpH8QxIIswli92txgrSIE8hzYsueeFmgQ5sqKBRTEvtu8OXVAwXHH2b6xuWKZsGqVpZvYb7/0+9bUsITwoIBQT1yq+zFyJLz3XrQXnbFjW4YddupUfnOH6utN4Q4dWmxJnDzgyqqU6dnT3uTXry+2JPYgEEn+4B0+3Kyv1atbtm/ZYtnRR4+2Sa+FjMBMFREYNe1WNiHssdIpIZZVYpT4dddBewm3AEM9cfX19vcxeHDr70aMsPW/I84Oqamxh/1ZZ1lhzW9/O9rxxaa+3v4+oxQ5dcoGV1alTCmlXKqvt/lR3ZPUSYoNaidaV//4h1kXycaD8smee9o6bNwqakDBgAEWXBIlhD2JsgqLEr/+eugoW+jE5y32TeqJiwVXJNZgA5trBdFdgTNnmqI78EB76YglISwHVC2LirsAKxZXVqVMqSmrVA+CZBGB994Lffs2WyaFpHdvS0IbZlllE1Bw3HEwY4bVKMuE2bOhTx+7/jjCjDqAXt02M1nOY1CtpvaWNjbaWFiy+9Grl9VQixJk0dBg9+7QQ5uVa5Simvkkk8nKCxeay9yVVcXiyqqUKRVl9fHHZnGkehD07m3h1/HKasUK+Otfza2UGO5eKJJFBF53Xeu2dAEFUUPYZ8+2B3+C9ZPMeFu6rgdjtY5FLy5L7S19910bi0t1P0aMiKasXn3VlGCpKatMJyt7cEXF48qqlCmUskr35hpLUpvuQZAYZDF1qj0Azzknl9JGY9iwcMsqlqprp50yD/pYssT2PfHE9OmImprgrbeo63j2tp920CA477zkh9TuGGT4SFcxOJMH88iRNoa4fHnqvmLMnGnrQw4xa3TnnUtDWWU6tlhfb2NVmUReOmWJK6tSphA1rTJ5c42irN57zzJfq5oL8OCDmwMdisGee5plGB/40dQEN99s8n70UWZBH3V1cP75zaHr6dIRLVhAXcMYxj97xrafdskS+0n69bNyW/F07QoTLg1kTDcRt77eOoiNyYURC7LI1LqaOdPuU69etr3vvuZqLDaZji3W15v8iT+sUzG4siplCmFZZfLmWl9vAQZ9+qTua/hweyq/+aY9JOfMKU5gRTxhEYFPPGGutCuuCA9QCCNq9ODs2VzNz2nY3Nr92b695ftrlUv5P4OyMOksq1mzTJmkyo05fLhZGpkEWTQ1wUsvmQswxr772v1LNketUAwYEN6eOLaY7xpWTtFxZVXKxCyrTz/N3zkWLw5vj39zzfRBEB9kce+9FgZ92mltl7EthEUE/upX5sY75ZTM+0nzhp/oSb13Sg2LGRR6yAcfJMka1aOHRVumsqxSZRKJp2tXi17MxLJ65x1Yu7ZlJeN997VxsfffT398Pjn88NZtiWOLK1aYu9OVVUXjyqqU6dDBcu3kw7JShZ/+NPn3MSuqoSE8rU8YAweaG+nFF62i76mnNheRLBa1taY0Y5bViy+ay+vSS1NbJmH9JGkP86SeN200EG61JQ04FElZhBEwRbZ2bWb572JBFumybsTGqxItKyjuuNWGDfD001asc1Cc4r/xxpYuWw+uqApcWZU6yVIuZVN7KP6Y7t3t7fSII1qX3aipsewLZ58Nu+9ur/933ZX+HCI2MF9XB599ZllYi11ioqbGMhrELKtf/coUalT3ZFh5ki5dYMKEJKHowvbt1kcvGj1wYGplFeXBrGqKrV271H8jM2daUMWQIc1te+1lv10xldUdd8DKlVYletEi+PBDy6yRKFPsN/EEtpVNIVO853qp+BIhqqp776160kkt27Kpdhx2TIcO1p5YdmPyZNXhw1uXi8jkHO3bRzumEJxxhurgwarvvGPX+LOfZdXN1PNf0EHtPlBhqw5iod7/9T/o9OnhlTVAVWiKXjT6u99V3Wmn5N9fc41VM96wIY2wU1U7d87sXgwZojpmTOv2oUNVTzwxjcB54rPPVHv3Vj3mmJbtl1yi2q6d6nvvNbedeqrqrrsWVr4yhwwqBZfaUnQB2rJUhbI69FDVo45q2ZZN7aHa2mjHRN0/W7kKwfXXm7YYO9Ye4CtWRO4iTNcLTbaWJJfdZ310WW+4wQ7etCn8+29+U3XYsPT9ZHovVqyw9v/5n9Z9nHKK6u67R72C3HDjjSZXYiGvjz5S7dLFannFGDKkeEq1TClHZeVuwFInrExIsgH4xYstzCzePXj33ebjj5peKJkrKtXgf6nWRFq92h7VdXU2TvX00yl3D/Ow/uQnrV19itCnjzJ5ckgBYzYw4cdZBMbEKtwuXRou1BNP2H1O517N9F68+KKt48erYuy7LyxYYGNHycjGHZ2OTz6xqQXf/GbrQl59+8IPfgB//KONQ65bB/Pm+XhVNVBsbdmWpSosq29/W3W33Vq2JXtrTrUkuoTSWT3ZWEmlaFmlcIeFuejCLKhEz2YL60qatp1mW1/bfaxT259lpX6j8r//ax0/80zLa4jq9s30Xlx+uZUi3rixdR+xqsOhZYqzlCsTrr3W+nr99fDvV61S7d5d9bTTVF94wfZ94om2nbPKoAwtq6IL0JalKpTVBReo9urVsi3Z2FCPHuEPqH79oj9YcjUuVsAxq9DxoUGDdCpn6CAWbhtrmsoZOrX3D1uJ2rmz6nbbhf+ESV19PT9pLcjRR6seeGB2F/Hee9bxlCnNbdm8BITdi86dW9+LL31J9ZBDwvuYN8+OmzQp/PtcvpzE3zwRK0+fiquusnN1727r/v2LPzZaRriyKvBSFcrqqqtsQLmpqblt82bV7bc33338kznZE1XEjos62h85OiDLYyKQrPtkevJ8fqtdWd+ivQObtDMbIhunrfqXBp069PrWQu68s+q4cdldYEODdX7jjc1t6e5rJj9WTY2NP8X/HW3caFbV5ZeHH791q130xReHf5+tXGFyZqJY4/nd78JvkCusjHBllckJoTPwCvAG8DZwfdDeC3gamBesd0jXV1Uoq1/+0m7TunXNbdOmWdvjj7fctxTdcGlIpXwycdF17ap6++2qO+6YTMk0RWxP/hO2kmn0n+1FYu3a5gtaudIOuPnm7H+UPn1Ux4+3z59+Gt2FG8bvf2/HxD/MZ8ywtkcfTX7cyJGtA3xi5OrvrVJczmWEK6tMTmgzJbsHnzsALwMHA78ErgzarwT+J11fVaGsJk602/TBB81tY8bY2/uWLS33LbIbLhVRlM/554e377BD5solW2XVu3eEnzA2XvLQQ81tsVj2p57K/ofq0MH66N9fdcAA+9E6dmzbfd26VfWLX7Q+1wdRirEXoVTRkeedZ8oz3iKLETu+rX9v2VhoubLqqhRXVlFPDl2B14H/AN4F+gXt/YB30x1fFcrqwQftNs2ebdsffWRv81dcEb5/nt1w6U4RRSn16pX8eRNVKfXtG97erl14eyqllPFPuGWLas+equec09x2223W2fLl2f2wiUKB6pVX5ua+xpTrtdfa9pgxqnvskfqYVNdzxRUmT0yhgs2DiopbVgXHlVWmJ4V2wCxgfcyCAj5J2Gdtun6qQlk99ZTdphdesO2bb7btuXPzfuooiifZdx07hj9/c7nEZItipUVSSqn41rcsgCVmeaSyRNJRiAfwaafZWOfixeY7TTe2lsxS3LzZ3hBik4m3bLGJucmCNVIxaVLrN5QSD+Ypd1xZRT059ASeBfbJVFkB44HXgNc6duwY8RaVIS+/bLfpiSfsAbjXXhbBlQW5GB9K5orr0MHG8HOhfLKxhqJeX86IjQXV19v2yJGqRx6ZXV+FcG0tXmyRpJ06Wd+9eqX+QWJjcL/+dcv2xx+39mnTmtt++1trmzEjmkwXXmjH9e1bUsE8lYwrq2wEgGuBy90NmIR337XbdP/9qv/3f/b5nnsidxPF8kgVwp3LJZnyybs1lEuWLzcBJ0ywcaFu3VQvuii7vgphWcWPiWVqkey8s+rZZ7dsO+EEUy6bNze3bdhgN3X06Mzlef55kyFZxKGTF1xZZXJC2BHoGXzuArwAHA/8KiHA4pfp+qoKZRVLh/Pb31qEWNeuqp9+GtmKSPYczGZ8KNnzNNk5shkfKjmllIoDD1T98pdV339fs32ZUNXCuLayUYhHH23BGTFWrDDrLCzkPTahNxM39YYNNma2667NQR9OQXBllZmy2g+oB94E3gKuCdp7A9OD0PXpQK90fVWDspp63+ZgQmuTDpLFOvWwuyJZSR06mNcw39ZQJuNZZaN8onL11eYDvfdeu+j/+7/s+8r3D5WNq/HSS83cjmXk+PWv7Zi3326978qVtu9556WX5fLLrZ/p07O7FidrXFkVeKkkZZXxuFGnRu3dO/nzJqy9XTsbU0/2Xa6soWTXUfHMnGk/0D772IXHz4krNbKxrGLjcu+80zxuevDByfe/4AKLrFm2rPV38X8gkP34ntMmXFm5ssqKfEfRiRQxWq4aaGxs+SOW8o+Vjavxtddsv4ceag74mTgx+f4xd+j226d/++rSpXR/qwrGlVWBl3JUVokKYMqUVNkXki3hE1qTWUmxl+aKGB8qRaZObf3jl3IYddQb3tBgbs5rrlH9/vdNwXz6aer+E3+PLl2SR+343KiC48qqQpVV1Id8lPx12Sil3rI6spXk5JFqmKA6dKgVQtx+e9Wzzkq9b7LfI5Xp7xSUTJQVsAiYHcyJfS3d/vleiq5w2rJkq6xyMd8omWIIa+/USfXMM5O/WCabn9Sbla2SsHZlvU7l224llRLVkPpn5Mjm6+rbN/UfVtQQ00pS6mVCBGXVJ91+hVqKLkBblmyUVTLlM25c63yhHTs2VyAIew5Fc92lXkItot4/DC1v4f/cJUalW1ZR52ZlM4fBKSiurAq8ZKOsonoocrWIpK4UH2oReUqZ8qDS71NUZVy1cxjKhwyV1cIgd+u/gfHp9s/3UnSF05YlG2WVK4soWTBDqiCHrJ5p/s9dHlTyfcrGzVnJv0cFAHweS1sXLK2UEbBLsN4pKOl0eOI+hVwkEKYs6datm27YsCHSMYMHw+LFrdvbtYOtW1u39+4NGzdCQ0NzW9euMG4cTJmSefvEiTB2LNTVwdVXw5IlUFsLEyZYu+OULMn+aQYNgkWLCi2NkwNEpEFVu0XY/zpgvarenD+pUlNTrBMXiwkTTHnE07UrjB8f3v6b35iiGTQIRGw9cSLceWe09phCGjvW/r+bmmztisopeZL900yYUBx5nLwjIt1EZLvYZ+BrWMah4slUbZYVJLdu3OpxnCT4P0dFkc6yEpHdgEeDzfbAH1W1qG8nVamsHMdxqpmobsBSoOrcgI7jOE754crKcRzHKXlcWTmO4zgljysrx3Ecp+RxZeU4juOUPGUdDSgiTcDGNLu1BxoLIE6pUa3XDdV77X7d1UVbrruLqpaVsVLWyioTROQ1VT2o2HIUmmq9bqjea/frri6q7brLSrM6juM41YkrK8dxHKfkqQZlNbHYAhSJar1uqN5r9+uuLqrquit+zMpxHMcpf6rBsnIcx3HKnIpWViJyjIi8KyLvi8iVxZYnX4jIvSKyUkTeimvrJSJPi8i8YL1DMWXMByIyUESeFZG5IvK2iFwctFf0tYtIZxF5RUTeCK77+qC9oq87hoi0E5F6EflrsF3x1y0ii0RktojMEpHXgraKv+54KlZZiUg74A7gWGAv4AwR2au4UuWN+4BjEtquBKar6hBgerBdaTQCl6nqMOBg4MLgHlf6tX8OHKWq+wMHAMeIyMFU/nXHuBiYG7ddLdd9pKoeEBeuXi3XDVSwsgJGAu+r6gJV3Qz8CRhTZJnygqo+D6xJaB4DTAk+TwFOKKhQBUBVl6vq68HnddgDrD8Vfu1BZfL1wWaHYFEq/LoBRMVlgJwAAAW3SURBVGQA8A1gUlxzxV93EqrquitZWfUHPojbXhq0VQt9VXU52EMd2KnI8uQVERkMDAdepgquPXCFzQJWAk+ralVcN3AbcAXQFNdWDdetwFMi8m8RGR+0VcN1b6N9sQXIIxLS5qGPFYiIdAceAS5R1c9Ewm59ZaGqW4EDRKQn8KiI7FNsmfKNiBwPrFTVf4vIEcWWp8AcqqrLRGQn4GkReafYAhWaSraslgID47YHAMuKJEsxWCEi/QCC9coiy5MXRKQDpqjqVPUvQXNVXDuAqn4CPIeNWVb6dR8KjBaRRZhb/ygRmUrlXzequixYr8TKzY+kCq47nkpWVq8CQ0RkVxHpCJwOPF5kmQrJ48C44PM4YFoRZckLYibUZGCuqt4S91VFX7uI7BhYVIhIF+CrwDtU+HWr6k9UdYCqDsb+n59R1e9Q4dctIt1EZLvYZ+BrwFtU+HUnUtGTgkXkOMzH3Q64V1UnFFmkvCAiDwBHAH2AFcC1wGPAg0AtsAQ4VVUTgzDKGhH5MvACMJvmMYyrsHGrir12EdkPG1Bvh71wPqiqN4hIbyr4uuMJ3ICXq+rxlX7dIrIbZk2BDd38UVUnVPp1J1LRyspxHMepDCrZDeg4juNUCK6sHMdxnJLHlZXjOI5T8riychzHcUoeV1aO4zhOyePKynHKDBFZLyJnF1sOxykkrqycskNEnhOR34a0ny0i68OOqXbEuE5ElonIxuA33LvYcjlOpriycpzq4ArgMuCHwAiCBLixzAiOU+q4snIqFhG5T0T+KiIXi8iHIrJWRH4vIl3j9nlORO4UkZ+LyOqgiOXNIlITt893RORVEVkXfP+QiPSP+/4IEVEROTbIir1RRF4QkQEi8pWgSOL6QJbeCTKeIyJzRGSTiLwnIj9KOPcegYybxAqJHp/F7yDAJcBNqvqIqr6FpefZDvh21P4cpxi4snIqncOAfbD8eacBJ2LF++IZixVy/BLwA+zBflrc9x2xFFb7A8djaa0eCDnX9cGx/wHsAPwZuAYYj6XD2hu4LraziHwP+HmwzzDM8vkxcEHwfQ2WZqcGOAQ4Nzi+U/xJA2X2XIrfYFdgZ+CpWIOqbgSeD67ZcUqeSi4R4jgAnwHnq2ojMFdEHgJGAb+I22eOql4TfH4vUCKjCBSSqt4bt+8CETk/6GuAqi6N++5nqvoCgIjcDdwOfDFWIFJEpgCnxO8PXKGqDwfbC0XkJkxZ/RZTsHsBu6rqkqCPS7B8iPEsSfMb7BysVyS0r6C6arw5ZYwrK6fSmRMoqhjLMMsnnjcTtpcRV8hORA7ELKsDgF4010qrxUrRhPUTUwyzE9p2CvrcESth8zsRuStun/Zx/Q8DPowpqoCXaVl4EFU9i8xITAQqIW2OU5K4snLKkc+AHiHtPYFPE9q2JGwrrd3fSfcJSjL8E/hf4EwsMKEPZt10TNGPAqhqYlvs3LH1fwIvhlwLhBcQzYaPgvXOtKyevROtrS3HKUl8zMopR94FDpTWJYEPDL7LJXtiyukqVX1eVd8hB+XDVXUF8CGwu6q+n7gEu80B+otIfBHRkUT/v12IKayjYw0i0hkbz0umKB2npHDLyilH7sICIW4XkXuATcBxwBnAmByfawnwOfADEbkDc839d476vg67hk+AvwMdMIXbX1V/gVlz7wB/EJEfAV2AW7FgkG2IyB8guTtQVVVEbgOuDsqhvwf8FFgP/DFH1+I4ecUtK6fsUNUFwOHAECzC7RWscuypqvr3HJ9rFRbmfQJm6VwLXJqjvidhEX5nAm9grsXxmCWEqjZh0Ys12FjVH4AbMeUZT22wpOKXwC3AHcBrQD/ga6q6LhfX4jj5xosvOo7jOCWPW1aO4zhOyePKynEcxyl5XFk5juM4JY8rK8dxHKfkcWXlOI7jlDyurBzHcZySx5WV4ziOU/K4snIcx3FKHldWjuM4Tsnz/wHMPua8j8QYVgAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create figure and axis objects\n", "fig,ax = plt.subplots()\n", "\n", "# make a plot\n", "ax.plot(my_data[\"Unnamed: 0\"], my_data[\"Wheat\"], color = \"red\", marker = \"o\")\n", "\n", "# set x-axis l# set x-axis label\n", "ax.set_xlabel(\"Unnamed: 0\",fontsize = 14)\n", "# set y-axis l# set x-axis label\n", "ax.set_ylabel(\"Wheat\", color = \"red\")\n", "\n", "# twin object for two different y-axis on the sample plot\n", "ax2 = ax.twinx()\n", "\n", "# make a plot with different y-axis using second axis object\n", "ax2.plot(my_data[\"Unnamed: 0\"],my_data[\"Wages\"] ,color = \"blue\",marker = \"o\")\n", "ax2.set_ylabel(\"Wages\",color = \"blue\", fontsize = 14)\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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 }