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matplotlib_line_graphs
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "matplotlib_line_plots",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/vivek081166/48ac2a3573e61586d3fbbeb5dfba0bcc/matplotlib_line_graphs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"colab_type": "text",
"id": "EjcNRwu_fhPQ"
},
"cell_type": "markdown",
"source": [
"### Line Graph"
]
},
{
"metadata": {
"colab_type": "code",
"outputId": "908732cc-757d-46d8-ac75-5bdb5ba51cfe",
"id": "WC74dGyrfhZ8",
"colab": {}
},
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"x = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"y1 = [1, 3, 5, 3, 1, 3, 5, 3, 1]\n",
"y2 = [2, 4, 6, 4, 2, 4, 6, 4, 2]\n",
"plt.plot(x, y1, label=\"line L\")\n",
"plt.plot(x, y2, label=\"line H\")\n",
"plt.plot()\n",
"\n",
"plt.xlabel(\"x axis\")\n",
"plt.ylabel(\"y axis\")\n",
"plt.title(\"Line Graph Example\")\n",
"plt.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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kHcNcKbkTM6TyMVeqLRzYKmCGtOOYKyV3YIa045gr1Q4ObIUxQ9o5zJWSOzBD\n2nHMlWoHB7bCmCHtPOZKqSsu1FYyQ9pJzJVqgyID2+l0Yt68ebj77ruVWE7TmCHtPOZKqSs25mUw\nQ9pJzJVqgyIDe/Xq1QgPD1diKU0rulDCDGkXMFdKnVXvqMfXeRnMkHYBc6XqEz6wS0tLkZGRgYUL\nF4peSvM+P7IZADOkXaFWrjSjeCfq7MyV6tWukr2orLvIDGkXqJkrLaoQ/9q5Hggf2M8//zweeugh\n3T8l1FUVdZXIPLWHGdIuUitXWtVQjYyTWcLXI/drypBaPZkh7SK1cqWf/7BZkfW0TujA3rp1K4KC\ngjBy5EjTHziQWbQDdqedGVI3UCtXuuGHNOZKdciVIU0YGMcMaReplSvNPLWHuVIAniK/+L59+5Ce\nno6MjAzU1dWhqqoKDz30EP70pz+1eT2bTfw/KiXVNtRiW8kuBPj4Y2bkFPh4ij1a+/yFWuzKLUU/\nW3dMnTBY0afklLrvbpg0GGu/OYr9+edxwySx59PaEICEweORfmIHTtafwLj+1wpdT01G+7cHABn7\ntwMAZo6YClug2NvncEpI21cEL08rFk4fjqsCfIWu15xS992iacPxh3f2IONAKe6/Sfy/hTnXTMc/\n9n6I7PPZWBI1R/h6WiZ0YK9YsQIrVqwAAOzZswdvv/12u8MaAMrKjPWb1NbCHaiqr8aNo27AhZ/q\nAIhN7n2ScRx2h4SUmP44d065o5tttgDF7rsJI/rgk/Q8fLLlGMYOCxL+S0m8rXFgrzv4NYb4DBW6\nllqUvP+UcqLiJI6eO4GI3iPRPzBE+O3LOXIGpeeqkTgmFPbaBpTVNghdz0XJ+25wn+7o28sPW/YW\nYsa4MPT0F3tMwCj/CAT4+OPrYxmYZItX5BgEpcn9ZYvnYQvWPEN63VDxB5vpPUMqlxq50ujQSOZK\ndeaXDKn4f3t6z5DKpXyu1AvXDU1krhQKDuy4uDi8+eabSi2nGc0zpIG+yrx+pvcMqVxK50pnD58G\ngLlSvWiZIR0sfD0jZEjlUjpXmjo0kblS8BG2UMyQiqV0rnSkbShzpTrCDKk4SudKA30DmCsFB7ZQ\nzJCKx1wptaay/iIzpIIxV6o8DmyBmCEVj7lSak1m0U5mSAVjrlR5HNiClFadZoZUAcyV0qXqHfXI\nLM5ihlQBzJUqiwNbkDTX0anMkAqnZq603sFcqdbsKtmLiw1VzJAqQM1caUnVaeHraQ0HtgAVdZXY\nU7qPGVJZBe8EAAAgAElEQVSFqJkr3VWyV/h6JF9ThtTiwQypQtTKlaab8FE2B7YAmUU7YJcczJAq\nSK1caXphJnOlGuLKkMYFRzNDqhC1cqV7SveZLlfKge1mtfY6ZBZnwd+rO8aHxAhfr/xiHbIOl6Jv\nLz+MHhYkfD2t8vHyQNK1/VBVa8f2g+JPM+nhE4C44BiU1ZzDgbLDwtcjeVznyKcocKCn0ylhU3YB\nPD2sSI7pL3w9LbtOweNIrBYrUgYkwC45kFG0Q/h6WsKB7Wa7SnJQba9BQr8J8PYQf2pV2t4iOJwS\nUuPCYDXJ0alXkhLTH54eVmzKLoDTKf4AmJQBjefWm/UAGK05UXES+RdOIaL3SAR37yt8vX1Hy1BW\nXov4yGD06G6O0yivZPSwIPTt5Yesw6Uovyg2vQwA44LHwt+rO7YVZ6HWLn49reDAdqPmGdKE/hOF\nr2eWDKlcauRKI3qPZK5UI5ghVY8audKE/hNNlyvlwHaj5hnSAG9/4euZKUMql9K5UtdwYK5UXcyQ\nqk/pXGlCvwmmy5VyYLsJM6TaoHSudGjPwcyVagAzpOpTOlca4O1vulxpuwM7Pz8fdXWNrxFs27YN\nb731FioqxP8g1BtmSLWDuVJzYYZUO5grFavdgf3AAw/AarWisLAQTz31FAoLC/Hwww8rsTddYYZU\nO5grNRdmSLWDuVKx2h3YVqsVXl5eyMjIwJIlS/Dss8+ipET80x16wgyptjBXah7MkGoPc6XitDuw\n6+rqcPbsWWzZsgXjx48HAFM89dARzJBqj1q50sziLOZKFcQMqfYwVypOuwP7lltuwXXXXQc/Pz9E\nRkaisLAQAQHiC0J6wQypNqmVK73YUMVcqUKYIdUu5krFaHdgL168GDk5OXjttdcAAKGhoXjnnXeE\nb0wvmCHVLuZKjY0ZUu1irlSMK06YvXsbHyVkZGS0+G/79u3IyspSbINaxgyptjFXamzMkGobc6Xu\n53mlT6xfvx4xMTH4xz/+cdnnLBYLEhOVeb1Wy1wZ0hmDpjJDqlEpMf3x1e4CbMouQNK1/YS/7pgy\nYDJ2luzB5oJMjOkTKXQtM1MrQ5o4JtT0GVK5mudK5ycOQU9/sccYjAsei89PbMK24ixMH5ikyDEN\nSrviwH7uuecAAO+9955im9ETZkj1wZUrzfjuR+w7WoaxI/oIXc+VKz107nucqDipyFkDZsQMqfa5\ncqWrN/6AzTlFuHFKuND1XLnSL/O/wa6SHEwJE39cg9LafdF17dq1Lf7sdDrx0ksvCduQXjBDqh/M\nlRoLM6T6wVype7U7sL/66issX74cVVVVOH36NH7961/j9GljHzrfHmZI9YW5UmNhhlQ/mCt1r3YH\n9jvvvIOhQ4di/vz5WLJkCRYuXIg//vGPSuxNs5gh1R+lc6UpAxKYKxWAGVL9Ya7Ufdod2E6nExcv\nXoSnpyckSYKPj/FeyO8oZkj1R+lc6RhbBHOlAjBDqj/MlbpPuwN7yZIlqKysxLp167B69Wq88847\nePLJJ5XYmyYxQ6pPzJXqHzOk+sVcqXu0O7B/9atf4bnnnoOPjw/CwsLwwQcfwNfXV4m9aRIzpPrF\nXKm+MUOqX8yVuke7A3vOnDkt/uzt7Y1Vq1YJ25CWMUOqb8yV6hczpPrHXGnXtTuwKysr8de//hV3\n3HEHli5d2vSfGbkypCkDmCHVK+ZK9emXDGkMM6Q6pUautE+3IEPlStudOqtWrYKHhwdOnjyJRYsW\nwcPDA1FRUUrsTVOaZ0jHBTNDqleq5krP5gpfz6h+yZCKP42SGVJxlM6VJg+YbKhcabsD+9SpU3jg\ngQfg6+uLmTNn4u9//ztycnKU2JumuDKkCf0mMEOqcykx/eHpYcWm7AI4neIPgHENGYZUOketDGl8\nZDAzpG7WPFdafrFO+HrjgsfC36s7thVnodYufj3R2h3Y3t6N37BeXl4oLy+Hl5cXzp8/L3xjWsIM\nqbG4cqVl5bXYd1T8aSauXGn+hVM4UXFS+HpGwwypcbhypQ6nhM054o8jceVKq+012FWi/wea7Q7s\nQYMGoby8HLNmzcLixYuxcOFCjBo1Som9aQYzpMajWq7UQAfAKIEZUuNhrrTzrvjmHy5/+ctfAAC3\n3XYbIiMjUVlZiYQE8cEQrWCG1JhcudJvj53FsaIKXB3WU+h6TbnSssM4XV2GvgoU8oyAGVLjceVK\nP92Wj+0HSjBN8DMZrlzp9uJd+K7sEGL6jha6nkgdOtR57NixSEpKgoeHeR71MUNqXMyVahszpMbF\nXGnniD83SeeYITUu5kq1jRlS42KutHM4sNvADKmxMVeqXcyQGh9zpR3X7sCuq9P/ofCdlVbQ+NQl\nM6TGpXSudHzIWOZKZWCG1PjUzJWW6jRX2u7ATk5OxosvvohTp04psR/NaMyQ7mWG1OCUzpX6evow\nV9oOZkjNQ61caZpOH2W3O7D/85//IDAwELfeeituv/12bNmyRdcv2svFDKl5MFeqLcyQmgdzpR3T\n7iTq3bs3/vu//xubN2/GokWL8Pvf/x4pKSl4++23Dft0OTOk5sJcqbYwQ2ouzJXKJ+uhY01NDT76\n6CO8/vrrGDBgAJYvX44TJ07gjjvuEL0/VTBDaj7MlWoDM6Tmw1ypfO0O7GeffRbTpk1Dbm4u/vrX\nv2L16tWYNWsWnnvuOZw5c0aJPSqKGVJzYq5UG5ghNR/mSuVrd2CHhITgyy+/xDPPPINhw4a1+Nzq\n1auFbUwtzJCaF3Ol6mKG1LyYK5Wn3YF9++23IzAwsNXP9enTx+0bUhMzpObmypXml1zAsaIK4etd\nmis1O2ZIzcuVK62us2P7AfHHkbhypedqf8J3ZYeEr+cuDKc0wwwpMVeqDmZIibnS9nFgN8MMKTFX\nqg5mSIm50vZxYP/MlSEd0oMZUjNjrlR5zTOkE0Jjha/HDKl2KZ8r1ddxJBzYP3NlSFMUODoVYIZU\ny5grVVbzDKmPQqdRMkOqTcrnSgfqKlfKgQ1mSKkl5kqVwwwpXYq50ivjwAYzpHQ55kqVwQwpXYq5\n0isz/cBmhpRaw1ypMpTMkDqYIdUN5kpbJ3Rg19fXY+HChZg7dy5mzZqF119/XeRyncIMKV0Jc6Vi\nKZ0h3XWohBlSnWCutHVCB7a3tzdWr16NTz/9FJ9++ikyMzNx4MABkUt2iNIZ0uraBmZIdYS5UrGU\nzpCu35LHDKlOMFfaOuFPiXfr1g1A46Ntu118cq4jlM6Qbt5TwAypzjBXKoYaGdIfCn5ihlRHmudK\nq2sbhK+nh1yp8IHtdDoxd+5cxMfHIz4+HlFRUaKXlEWNDOlnmceZIdWZ5rnS3PzzwtczS66UGVJq\nT/Nc6WYFjhjXQ65U+MC2Wq1NT4fv378feXl5opeURY0M6Zmfapgh1SHXD/n1W8V/75ohV6pWhnT4\ngKuYIdUZV670s8zjzJUC8FRqIX9/f8TFxWHbtm0YOnRom5e12cSf3vHPI41HA94YdR1sQWLXkyQJ\nm/fthcUC3JQ6ArYg8U+/q0WJ+05pQUH+GL49H7sPl+LWmdegfx+xt3Fa7wn4/ORG7C7di1vHzkeg\nr3J/p0rcf1sOZaDBacfskVMR3Len8PXWbj0OCcC8KUPRp0/rb2RkBEb8t2cDMG3cQHyxIx9Hf6xE\nwrVij+63IQCxhaOxp/g7nEUprrFdLXS9jhI6sM+fPw8vLy8EBASgtrYWWVlZuPPOO9u9XlmZ2HPh\nSqtOY9+PBzGkx0D0kvoIX+/7Uz/heFEFJkaFwEuShK+nFpstwLC3LeXafvjh1E9Y8/UR3HLdCOHr\nJYZOwkfHPsO6/Ztww5DpwtcDlLn/6h312Hh0K/w8uyEyMEr4ehVV9UjLLoStpy/GR4YY9vvTyP/2\nJkf0xZc78/Hvb45iRL9A4S+hTA6Ox57i7/DxgY24Z7QycR25v2wJfUq8rKwMS5cuxZw5c7Bw4UJM\nmjQJiYnKpD/bonSG1PX62bwpbT+zQNoVfbUNIb27Y8dB5kq7QukMaXqzDKkHM6S61OcqP0yIDGGu\nFIIH9vDhw7F+/Xp89tln2LBhA+655x6Ry8miZoZ0xMBewtcjMaxWC+YkhquSK91daoxcKTOk1Fnz\nf36wY/ZcqelKZ8yQUmelxIapkitNKzBGrpQZUuqs4QN7MVcKkw1sZkipK3y9PZEcrXSuNNowuVIl\nM6TOZhnSFGZIDeGXNwUxb67UVAObGVLqquRopXOlCQD0nytVOkO672hZU4Y0kBlSQxg9lLlS0wxs\npTOkNXV2ZkgNiLnSzlE6Q7pxTwEzpAbDXKmJBrbSGdLtB0qYITUo5ko7Ro0M6YkfLzBDakDNc6U1\ndeJT11rLlZpiYKuRId2UXcgMqUE1z5UeK6oQvp7ec6XMkJK7NM+Vbj8g/jgSreVKTTGw1ciQnrtQ\nywypgbmGwcbd4k8z0XOuVK0M6ZDQQGZIDcqVK92UXWi6XKkpBvbmgsYDdqb+fACPSJIkYePuxtfP\nUvn6mWEN698D4aGB+C7vLErOVQlfb4wtAr19e2F3SQ4q68Wf1uIumUU70eC0IzlsMjys4l8a2pRd\nCAmNp1Eq8WielBfg541JUSE4d6EWOUfEP+PUx8+GKNsonKosRF75CeHrtcXwA7u06jQOnfv+53rN\nIOHrHSkox6nTlYgebkOfq/yEr0fqsFgsTY+yN2WLP83Ew+qB5LDJaHDakVm0U/h67lDvqEdmcRb8\nPLthQmis8PUqquqx42ApbD19EX21+GfSSD2u40g2muw4EsMPbLUypAylGF/01Tb06dmNudIrUDND\namWG1ND6XOWH6OE20+VKDT2w1cyQhvfj62dGZ7VaMD0ujLnSVjBDSqL9ElIxT67U0AObGVISLT4y\nhLnSVjBDSqKFh/YwXa7UsAObGVJSgo+XB3OlrWCGlJRgtlypYQc2M6SkFLVypWkF2syVMkNKSjFb\nrtSQA5sZUlKSWrnSExXazJUyQ0pKMVuu1JADmxlSUhpzpY2YISWlmSlXariBzQwpqYG50kauDOnU\ngYmKhEtcpTlmSM1LjVzp+JBYVXKlhhvYzJCSWsyeK22eIR0dNEr4eiXnqvBdHjOkpHyuNDlskiq5\nUsMNbGZISS1mz5WqkSEFmCEl8+RKDTWwmSElNZk5V8oMKanNDLlSQw1sZkhJbWbNlTJDSmozQ67U\nMAObGVLSAjPmSpkhJa0weq7UMAObGVLSiha50gbj50qZISWtMHqu1BADu+7n18+YISUtaJErVeA0\nE7VzpcyQkpaolSvNVCBXaoiBnfVjNjOkpClmyZUyQ0pao1auNFOBXKnuBzYzpKRFZsmVMkNKWmPk\nXKnuBzYzpKRVyudKGx9lK3WaCTOkpFVGzZXqemAzQ0papnyudEhTrvSMArlSZkhJq4yaK9X1wGaG\nlLROrVxpmuBcKTOkpHVGzJXqemAzQ0paZ9RcKTOkpHVGzJXqdmAzQ0p6YMRcKTOkpBdGy5XqdmAz\nQ0p6YbRcKTOkpBdGy5XqcmAzQ0p6YqRcKTOkpDdGypXqcmAzQ0p6Y5RcKTOkpDdGypXqbmC3zJCO\nFb4eM6TkDkbJlTJDSnpklFyp7gZ2U4a0/0R4e3gJX48ZUnIXvedKXRnSyCBmSElfjJIr1dXAbpEh\n7TdB+HrMkJI76T1X6jryNSWMGVLSF6PkSnU1sJkhJb3Ta66UGVLSOyPkSnUzsJkhJSPQa66UGVLS\nOyPkSnUzsJkhJaPQW66UGVIyCr3nSnUzsJkhJaNQJ1d6VadzpcyQklHoPVeqi4HNDCkZiTq50oTG\nXGlxVoeuywwpGY2ec6W6GNjMkJLRqJYrLdrZoVwpM6RkNHrOlWp+YDNDSkakh1wpM6RkVHrNlWp+\nYDNDSkalVq40vWCbrFwpM6RkVHrNlWp6YDNDSkamVq70TM1ZWblSZkjJyPSYK9X0wGaGlIxOq7lS\nZkjJ6PSYK9XswGaGlMxAq7lSpTOkX+1mhpSUpcdcqWYHNjOkZBZNp5ns0UauVI0MaX4JM6SkPL3l\nSjU5sJkhJTNx5UpP/KiNXCkzpGQWesuVanJgM0NKZqOVXCkzpGQ2esqVanJgM0NKZqOVXCkzpGQ2\nesqVam5gF10oYYaUTEcLudI6OzOkZE56yZUKHdilpaVYunQpZsyYgVmzZmH16tXtXufzH9IAMENK\n5qN2rjTjZBYzpGRKesmVCh3YHh4eePTRR/Hll19izZo1+OCDD3D8+PE2r5N5cjczpGRKauZKs0py\n8PkPacyQkmnpIVcqdGDbbDaMHDkSANC9e3eEh4fjzJkzbV7H7rQzQ0qmpVaudH3eFyi9WMYMKZmW\nmrlSuRR7DbuoqAhHjhxBVFRUm5cL8PFnhpRMS61caYOzAQAzpGRuauVK5fIUuJ8mVVVVWLZsGVat\nWoXu3dsOI8wYloR+wb2E7+mr7EI4nBJuTB6Gvn0Cha/nYrOJf/SiJt6+rls4bQS+2l2AtH1FWDh9\nBDwEv757o8/12F26F9GhkYgcNFToWgCwY/+PKCuvRer4gQgf1Fv4es0Z+fvTyLcNUOb2Te3tj3WZ\nJ7ArtxR3zI9Cr0BfoevNvGoKNp5Kk3154QPbbrdj2bJlmDNnDqZOndru5eeNvA5lZZ1/NxM5aurs\n+GJ7PgL8vBA5sKfw9VxstgDF1lIDb5/7TIwIRsZ3P2LTjhMYO6KP0LV84I9VcSsQ3i9U+O2TJAlr\nv/kBFgAJkcGKfr8Y+fvTyLcNUPb2pcT0x+qNP2Dt10dw45Rw4es9GH2v7MsKf0p81apVGDp0KG65\n5RZZl7daxT9LzwwpaZ3SudLg7n3g59VN+DrMkJLWKZ0rvcq3p+zLCp2Oe/fuxYYNG7Br1y7MnTsX\n8+bNQ2Zm197Au6uYISU9UDpXqhRmSEnrlM6VdoTQp8RjYmLw/fffi1yiw1wZ0qTofsyQkqalxg3A\nt8fOYuPuAlwdJv+3cK1ihpT0Iunafvgy6xQ2ZRciOaYfPBR45lcObexCIcyQkp4onSsVjRlS0gul\nc6VymWpgM0NKeqJ0rlQkZkhJb5TOlcphqoHNDCnpjdK5UlGYISW9UTpXKodpBjYzpKRHSudKRair\nZ4aU9EnpXGl7TDOwmSElvVI6V+pu2w8yQ0r6pHSutD2mGNjMkJKeKZ0rdSdmSEnvlMyVtscUAztt\nbxEcTgmpcWGw8uhU0qHk6P7w9LBiU3YBnE5tHAAjx76jZSgrr0V8ZDACu/M0StKf0UOD0LeXH7IO\nl6L8Yp2qezH8wK6ps2PLvmIE+Hlh4qhgtbdD1CmB3b0RHxmMsvJa7DuqndNM2iJJEr76+TTK6TyN\nknTKarEgNS4MDqeEzTnqHkdi+IHNDCkZhdK50q5ihpSMQulc6ZUYemAzQ0pGordcKTOkZBRayZUa\nemC7MqTxUSHMkJIhuIafVk4zuRJmSMlokq7tB29PKzZlF8LhdKqyB8MObGZIyYiacqXHtJ0rZYaU\njEYLuVLDDmxmSMmIXLlSCdrNlTJDSkaldq7UsAObGVIyKq3nSpkhJaNSO1dqyIHNDCkZmZZzpcyQ\nktGpmSs15MBmhpSMTqu5UmZIyejUzJUabmAzQ0pmoMVcKTOkZBZq5UoNN7CZISWz0FqulBlSMgu1\ncqWGGtjMkJKZaClXygwpmUnzXGmagseRGGpgM0NKZqOVXCkzpGQ2rlzpln3K5UoNM7CZISUz0kqu\nlBlSMhs1cqWGGdjMkJJZqZ0rZYaUzErpXKkhBjYzpGRmaudKm59GyQwpmYnSuVJDDGxmSMnM1MyV\nVlTVY+chZkjJvJTMlRpiYDNDSmanVq6UGVIyOyVzpbof2MyQEqmTK2WGlKiRUrlS3Q9sZkiJGimd\nK2WGlKiRUrlSXQ9sZkiJfqFkrpQZUqKWlMiV6npgM0NK1JJSuVJmSIlaUiJXqtuBzQwp0eWUyJUy\nQ0p0OSVypbod2MyQErVOdK6UGVKi1onOlepyYDNDSnRlonOlzJAStU50rlSXA5sZUqK2icqVMkNK\n1DaRuVLdDWxmSInaJypXygwpUdtE5kp1N7CZISVqn4hcKTOkRPKIypXqbmAzQ0okT/Nc6QU35EqZ\nISWSR1SuVFcDmxlSIvma50q7epoJM6REHSMiV6qrgc0MKVHHuCtXygwpUceIyJXqZmAzQ0rUce7I\nlTJDStQ57s6V6mZgM0NK1DldzZUyQ0rUOe7OlepiYDNDStR5XcmVMkNK1HnuzpXqYmAzQ0rUNZ3N\nlTJDStQ17syVan5gM0NK1HWdzZUyQ0rUNe7MlWp+YDNDSuQeHc2VMkNK5B7uypVqemAzQ0rkPh3N\nlTJDSuQe7sqVanpgM0NK5D4dyZUyQ0rkXu7IlWp6YDNDSuRecnOlzJASuZc7cqWaHdjMkBK5n5xc\nKTOkRGJ0NVeq2YHNDCmRGO3lSpkhJRKjq7lSTQ5sZkiJxGkrV8oMKZFYXcmVanJgM0NKJNaVcqXM\nkBKJ1ZVcqeYGdnVtAzOkRIK1litlhpR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"text/plain": [
"<matplotlib.figure.Figure at 0x55674bad6690>"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
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commented Feb 27, 2019

updated

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