snippsat/gist:086de8a25298a19641f49499a3d2a91e

## gistfile1.txt
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_clipboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Time_golden</th>\n",
       "      <th>Golden</th>\n",
       "      <th>is_golden</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-20</th>\n",
       "      <td>10:24:30</td>\n",
       "      <td>98.6</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-20</th>\n",
       "      <td>11:10:30</td>\n",
       "      <td>97.0</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-20</th>\n",
       "      <td>13:13:30</td>\n",
       "      <td>96.0</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-21</th>\n",
       "      <td>13:43:16</td>\n",
       "      <td>95.0</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-23</th>\n",
       "      <td>10:37:11</td>\n",
       "      <td>94.6</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-23</th>\n",
       "      <td>18:43:19</td>\n",
       "      <td>93.0</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-24</th>\n",
       "      <td>22:19:43</td>\n",
       "      <td>92.0</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-25</th>\n",
       "      <td>09:23:45</td>\n",
       "      <td>90.0</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-26</th>\n",
       "      <td>11:42:51</td>\n",
       "      <td>89.0</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-27</th>\n",
       "      <td>20:32:51</td>\n",
       "      <td>87.3</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-27</th>\n",
       "      <td>23:42:51</td>\n",
       "      <td>86.0</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-28</th>\n",
       "      <td>00:52:23</td>\n",
       "      <td>84.0</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <th>2019</th>\n",
       "      <th>-03-28</th>\n",
       "      <td>03:40:40</td>\n",
       "      <td>82.3</td>\n",
       "      <td>golden</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Time_golden  Golden is_golden\n",
       "0  2019 -03-20    10:24:30    98.6    golden\n",
       "1  2019 -03-20    11:10:30    97.0    golden\n",
       "2  2019 -03-20    13:13:30    96.0    golden\n",
       "3  2019 -03-21    13:43:16    95.0    golden\n",
       "4  2019 -03-23    10:37:11    94.6    golden\n",
       "5  2019 -03-23    18:43:19    93.0    golden\n",
       "6  2019 -03-24    22:19:43    92.0    golden\n",
       "7  2019 -03-25    09:23:45    90.0    golden\n",
       "8  2019 -03-26    11:42:51    89.0    golden\n",
       "9  2019 -03-27    20:32:51    87.3    golden\n",
       "10 2019 -03-27    23:42:51    86.0    golden\n",
       "11 2019 -03-28    00:52:23    84.0    golden\n",
       "12 2019 -03-28    03:40:40    82.3    golden"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg = df['Time_golden']\n",
    "yg = df['Golden']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x18f9b650160>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pdCGEqAhS6EIIURGk0IUQoiJIoQshREWQQhdCiIoghS6EEBVBCl0IISqCFLoQQlSEpha4SKsSnQo48CRwirv/JR27Om2vW1oqhRCiYOYvXMKcexbz/CvLmDB6JOfNmDroV0nqs4VuZhOBs4Bud58GDAWOTce6gdGlplAIIQpm/sIlXDDvSZa8sgwHlryyjAvmPcn8hUv6O2lt0azJZRgw0syGAaOA581sKDAH+HhZiRNCiDKYc89ili1/faV9y5a/zpx7FvdTioqhT4Xu7kuAq4DngD8A/+vu9wIfBv7F3f/Q2//N7HQzW2BmC5YuXVpEmoUQoi2ef2VZS/sHC82YXMYAM4HNgAnAOmZ2InA0cHVf/3f3ue7e7e7dXV1d7aZXCCHaZsLokS3tHyw0Y3I5APiduy919+XAPOBSYAvgN2b2DDDKzH5TXjKFEKI4zpsxlZHDh660b+TwoZw3Y2o/pagYmvFyeQ7Y3cxGAcuA/YEvuPvfWudm9id336KkNAohRKHUvFmq5uXSp0J390fN7A7gp8AKYCEwt+yECSFEmRy508RBr8AbacoP3d0vAS7p5bh80IUQop/RTFEhhKgIUuhCCFERpNCFEKIiSKELIURFkEIXQoiKIIUuhBAVQQpdCCEqghS6EEJUBCl0IYSoCFLoQghREaTQhRCiIjQVy0UIIUQ+Orl2qRS6EEKURG3t0tpyd7W1S4FSlLpMLkIIURKdXrtUCl0IIUqi02uXNqXQzexcM/uFmS0ys1vNbISZ3WJmi9O+G8xseCkpFEKIQUqn1y5tZpHoicBZQLe7TwOGAscCtwBbA9sBI4FTS0mhEEIMUjq9dmmzg6LDgJFmthwYBTzv7vfWDprZY8AmJaRPCCEGLZ1eu7SZNUWXmNlVxGLRy4B7G5T5cOAE4Oye/m9mpwOnA0yaNKmINAshxKChk2uXNmNyGQPMBDYDJgDrmNnxmVOuAX7o7j/q6f/uPtfdu929u6urq4g0CyGE6IFmBkUPAH7n7kvdfTkwD9gTwMwuAbqAj5aXRCGEEM3QjA39OWB3MxtFmFz2BxaY2anADGB/d3+jxDQKIYRogmZs6I+a2R3AT4EVwEJgLvBn4FngJ2YGMM/dP1NiWoUQQvRCU14u7n4JcEme/wohhOgM5u6dE2a2lGjV52FD4MUCk9Ofcqoio1NyqiKjU3KqIqNTcgZDXia7e59eJR1V6O1gZgvcvbsKcqoio1NyqiKjU3KqIqNTcqqUF8VyEUKIiiCFLoQQFWEwKfS5FZJTFRmdklMVGZ2SUxUZnZJTmbwMGhu6EEKI3hlMLXQhhBC9IIUuhBAVQQpdCCEqghR6DizFOqh9C1E0ZlaJZzPzrJSWn07I6A/y5KdSNwDAzNZq2C5D6Y4C8DSiXJZi70RezGw7M5tc9HV7kDO077PaljG8YbvQ+2VmbzezOWb2fjObVuS1G+QcAdxsZkPKbjR0oFGyKYC7v1Giwi1dhpntY2afMLNDzexNZchIcrYys+3NbO08QQ8rpdDNbAZwlZldbGY7mtlwd/ciK62ZHQLcYGafM7N3phtfqIwkpxN5ORy4G9io8eVRoIzpZralu79eplI3swOBvzezs9MDMazI+2VmBwNfBJYCewH7FXHdHuTMAD4JfMPd3/CS3NDMbH8z27Os6ycZRwDPmNkcKEfhdkjGwcA/AusD55DChxdNystDwAXAj83sYDMb29JF3L0SH+CtRJyEWcA/AV9IN2atWkO6ABk7An8EDgM+AfwD8DVgZFEyOpiXKcBjwNvS9vDstQuScQDwBvAqsH3aN7SEst+LiBE0E5gNXAt8pDFPbVx/C+BBYHraPi3J2BHYusB8bAv8P+CAtN0F7Jrkb1CgnOmpXF4E9iq6PJKMCcB84AxgMTAnc6yQOtAhGdsCPwf2SdufAC4FJgMbF3i/1gVuA/ZO2x8Avg6cCIxp+jplFGZ/fIjY7Fem36OAWovqE8CwgmTsDHwp/V6bWCT7S+nhXnuQ5WV94Kvp9xTgJuBy4GLSC6rN66+VKv5BwCnA/2SUeiF5yMg6C7ggUy5fB35AtKYKe7DT90bAr9LDdxXw3SKVInAv8FVgc+DHwLeBh4kX+vg2r22pXM5Idew9wG8yympIweXy9sw9exa4qsjrd1DGTul7ArAEuD09L9cAbylQzq3AxzLbRwM3U3/B91k+hWa8Pz9AN/BbYM+0PRw4hGhFTy1IxjbA74CDajc47fsicGjaV0TLttS8pHS/CfgP4F3A9cSqU+8Bvky0ctt+uIHxQFf6fXZS6jtmjhfVo5kJ3J958GYTL6fPA+u3cd3dgYMb9u0MHJt+jwauAE5pM/1bA1tmtr9PrDfwgbT9NsI0tlObcmo9lrHAuPT7VOBpYN9s/WhDxh6kVma2jFNd+JvCTedtPoBlrFe7R5l9ewOnp99TgRuBQ9osk/G1+52e8cuBXTLHzyTWoBje1PXaSUx/f4DtUmXfNG2fBdwA7JC2RwLfIvPWyyGjm3hTTkvb7wEeoN6qWQv4DHDxIMjLug3bRwI/Ab6VtocQtuEvtiFjbC/HziGU+nhgX+D4IvICbAx8HPglcAtwX3ogvw8cl+PaQwhzx7PEwi4zezn386TeQQ45RvSO/kK09nfKHHtXw7k395aOJmQdTEw9X8UMRZiQnga2SnXivJwyZgCPZxVS2l8zFY4nejc/AX4GbDJAZRxGNBB+BHy6l/OuJSn4nPdrB+BPwDtSnZtINNo+BXRnzrsdeHNT18ybmP7+pIL9FWHDXk50hyYBFxJvzprCPRv4HDm63knGYsKs8n+EzdSADxLd4Fqr/AxC+a5FjlZnh/JyePZaad8IogXwGvCOtO8UQhGu02pekox7gXdn9lmqrLVW1PGE/fYP5Oxt9JKXXYB9Mg/3p4Cj2qhjnwPOJ9bRPaaH48cmpbFVm3X5/iTrokZFlZHzJBETO++z8ghwYMP+oZnfM1Pd+z05zAjEC+M56g2ftciY1qi3Qs8jbPfbDVAZBwK/IBocWxE98lV6YKlMfk6TinY1srYjBtlvBw5P+6YQSn12qg8nEg2LpsxtuSthf36IQcOnqQ/oXQO8nbCfjidaHM8SI9Mv5KygOxL2xZqMK4nW+ZhUkY4gzCLXEXa1bQdwXrZNFfwuwq69UncVOCZV4quIF1geGVslZfA1wmxzdIOMmkI/OG8+VpOXfVZz3oeIl2TLyjaT1suIMYVZwB2EHfuj6dgxwFN5y71B3jXEy3sOYfo6ili7dyhhEnu6jfo1jXiB1gbbNgLekhTHkMx5BxG9p7zlcloq1w3S8/GNdM8+C2yYSct3Sb3OgSYjXfNjwKzMvmOB8zPbw4ke++KCyv6KVOYPEyaijYCdiN769WR66U1dr90E9ccnVcju9HsS4UVxM9HNOjjt345wL5qcU8Y46jbZSYRN8xbgh8Bpaf/mhCJruVvX4bxsmh7YrQjz0BU0KMKUl83z5oUwCR2UZJ1KvOiySn0oYQb5LKmFVWBe9m6QM4HoKeSWk661M/Ch9PszqWw+kzk+sc3r11qUHyAU+DrEIOifgPekY5sAU9qUcy9wJ2GaejjV40WEghxJDLx/ENimTTnvIxpBvyVMhrsB/wpckTmnaY+N/pBBeBSNyWwfQwywZ01UY4FJbeZjaLrvNwJvJhpxP0tlv13mvJacLXInqD8/1FtQw1IBn5u2TyS6SJOLlEXYuM5O24cQdtXtB0teUh5qrpVvJlq2s6mbcgpxiSN1fwn78/sIpf7utO9N2XPKzgswIuf1s63WXQi30aOTErkyKcd3FlXumTp1AWEueo5Qvp+kTQ8KVjZHfI9oqZ+Rtg8FFlD33sntDdSQlw8DF2a2NyNMSqMLvF+lyOhJHtGQuiv9PoXUmCtQzimEeWcqYYb8GTH4mq/+Fpm4TnxoGIEH1mnY/gbtewMMbdi2hu0baLBHDuC8rOKxQL11ez7R3f932nC7XI2MjYH3E4OG3yJ83tfrQF5+TJircnvQEC+k9dPvfwCeB45I27Nos3WWkbMB0UKeQpgJfkfYu9+c8tRVcP1qHGi9CdivoLys35MSAt5LtHCLcIXtlIy1M9ubEGNoxwFPkMMu33D92otiBDG2NJNoIP6WcEh4F/CdvM9K2wXZyQ/1FuAYkv9pw/HjiIGKCQXIGk8PLbFUeRaRvFEKkDO2A3nZgAY7XKpQ9wPP5HlpANsDW2S2x5DGARrOu5Vode7YqoxO5IXosr8N2C2z7wKSuYgYgN01cyxXS5Zo7e9N3Yw3hBi82zVtX0hyh03bbSundJ0ueh7QfS8xbtJyPSZMc1tQf+kNJbyM9mg47+SkBFs2fQ0UGYRL6RvEoHSuSWSE98qmme0RqY5tlLZvIjN4Txs9jbYrTNkfwrb0Y+qeC2sRg0bZyr8B4T3xFPkG9GYAVxPeKrXBo71ZeXBkPcKe9qs8MjJ5uSxVwq3TvqLzsjcxyHIQaSYbYaNttJnvRYwL5HkQZgD/RX0soacyMcKP+095ZHQiL4TZ4QnCw+RG4KZezm3KD7gXOT8jzDffIga/htLDeEWbcvYmeipHkfza0705MnNOrax+nbN+vQN4lLDFf54YZzAaei1Eg+jTOevXQJKxHjGJLK8yf1eS8xjR6zoy7d95dWVPO73LvH/s1Id4Y75BKPURad/YhnOGpoemZRciwm75a8Iu9zGi63P0as7dBdgsZz4OTsrjQuKNfGbaP7LhvHbycjDR6ppN2Hpn9nS/anLJMdhGKNdHUx4eJrVYaTAXZc7P1ZMpOy9EK+m7JNMZMZD6R5JPfkM6cpuKCBvs49Rb4l8jQiIMbzhvJ9ozex2e7teVScalhOlplTELYjJcnrKfTjQ0phHeGFfTg8860Xsb3pPsQSZjh/Q89li3m5CzAeHcsDPRSv9QKpsTG857EwXN0m37AmV/gC2JgYMvAb9K+7L+s3uT8XLIcf1jSNP50/bbgZdIswHTvgNoz595y6T8agN3ZxCufduystlin7x5SRX8yYyMi9JnIqlrl/bvQg+tgyZl7Ea0zHdL2/+cZDSOMewFzGjjfnUiL2sRLa/sBI7ZxEv36sy+88n5Es+UaS0GzNh0/76XlMjFaX8XbUztJ8wGjwG7Z2Q+SEOskVSPZ+WRkf5/JMk5IG3vQcxgHUXdY2d8Kqtc9n+i93DWAJFxcV4Z6RrjiMlJm2e2jyZCO9T8zndMZZ/rpbGKzCIuUtaHeDvuCHw3bX+F8Ml+Jt38MUSXpmX3MeqDEwcC32w49jbCzzT7gExuMy9vyVSUxYQy/ALhclVrveXKS0bGLun7TYRv/B2EKeErxODhKGIi0UY5r78e9ZmrRtj5v5a5l0OIlu8728lHJ/KSrv0xolX+XmIW5bWE6+h19DLjtclrD2vYHkpMdPog8TLZg/Cln0YbrcDM9Y/LXiPVr4MazslVj8n0IkkufYRX1rbEi6NmKqiFExiVQ8abMr/HlSQj23jaoAwZ2XuUfl9KuImOT9sbAucCl6TtEbQZo2cl2UVdqLAExVtso4Z9V6TvqUTr+dnMsTxdrkYvlntZValfCLw3/c5l06Jnr4zdSNPRiRbbHOCENvKyPQ2tVGK0/IPp97ZEsKqaaaHlbj1hjtg8s11rxYwmBm7P6e3+DpS8EC/qIxr2vZ9oIX0u80D/C20MehNuiF8k5hNMyOxfu+G822nDi4mIxLhfw76aCew26gO7e5DzBUtMg78h5WWThmNjgbvT7xMIW3TLg7mECeSXJJ//kmTMIAbOJ5clI/3/AKJVXrOVb0z0/K6krtQnA/9GAQ4PjZ8BFQ89xee+C7jbzD6TjZ9tZncRrdl3AQ+Z2SMp7vHrOWTMN7MNa/vc/SBgqpndklkkYV3ChoanUmhRzkHA1WY2Om0PSdd61N2/lX6/RLQQNk5/azovFmxKTHT6hJntkcnPA+7+j+n3L4ip/ZPS4b+2mI/DiKnv3zCzz6drvpHijb9CtHL3MLOJGfmtlknpeTGzAwh3sK+Y2cmZ61/v7pe7+yfdfbmZHU+0ov7SSh4ycg4hZtzeA6wAbjSzYUnWa5nzZhL+00tzyDAz24jwV59tZodmDtfq738Bf0jldzmQpw4fRrzovkGMY3254ZS/An82s0t4SxEPAAANRElEQVSAvwNucPdlrcohQgb/D7ClmX204djydmWY2TuIAclL3P3ZHk4pKh811gV2NLMj3f2PxP1bDlxrZlsTzgIQE9WKpeg3RN4PMVPul0SrYwox8PbhdGwaEYsi69rT8tuNCLT1DGHDfJA0XThz/G7gm6kAfkl+b5a9gZeJl9ONJDckVu0ZHEN4P2yZR066xjcIW99XSbbaHmT8nBxR54hWzSIiPMH6xODeZg3nTCFaG6u4Xg6wvHycmOy0S7rnJ2eOGfWIlo+R08WSGAS7jZW9SlaKA0PdNS6Xq12DvNlET3I+DWM8RKjlX6TnJk9Mk3WJHkRttvJUwoxzbvo9Jp3zbJKTK55NuvdT0zN5Qir7k4lxgUmEme+ZvDII09zPgXlpeyOiV3Zh+j2M6Gm2lY+MvLcSPYGLiBfgnsTs5u2SzO8TuifX2E+f8su4aI6bMISYLn5cZt8M6rHHR1F3W8u9yAPxYqi52s0hBioblfquhLP/Fq1ev+EaH00F+QXCI6Sm1GvmirbiQVC3W19DzCr8aJJ1MvBuQkEdR7hZ5pVxHPVYzBsTsyVvTvKykQHf386D0KG8DKUe7+MAQqm/r6EOTiBjy80pYycyQaOIwc+zG847jDam2Wfu1xxi0PbdRGv976jPaL6M6CG0EzyqVmfHAv9JmCKuIBoptYHeC8kZm6VB1kWEYt+b6In/lvp4zSfbkZGe+3tT/XqQcHW8k3gRblpkPtK1zifGfk4hGnWPU3cjXY+cs0Cbkl3WhXPchI0zFcgIhf4Qdbtmbv/cBjm1aePDCLvWD6nH7M49wNaDnFq6tyC64DdTH1AalfLYzgBo7aE+guS5QATw+jP16d1dtDGYS/3lsxZhu/4sYf+7mjZC7HY6L/Q8lnEgodQPI1wxTyooLyMatj9Evaf5DtpslTdce1fq8bkvI7rwn8ocz90oaSj/Eaw82eYy4J8KykPN5n8jYUvfl5gC/z16sKm3IectRJCzT2b23Vh0PU6fu4iX0yGESe1faYirX9ZnGAMED1sTZmbu7mb2HPB/HnbNE4GxZna1t2ifrWFmQ939dXdfZmZD3H2FmV0E/D3wdTN7kLDhnevuf86bj3TtN9x9edr1W8KD4gPApWb2LPHyusDdl+SUYZ5qEPEQzzCzPxMvwduAndJ6kT9uIx/maZFad/9rui8vpWOXAbea2Xh3fyGvjE7kpVYeZjbC3f9mF3f3+8zsBGJ+wwpyrBOZqau1aJJvsKqtehgwzMzeTSjCg/LkIyNziNcXDx4CdJvZi0Qr/VpgHzOb6e7fcffftCljOPBaum8/yez/NTAyjaOsKEDG68Skq/cTvadzCZv6IWY2tlbvmrzuKmWS1uP9pZnt7e7/nZH9OGEma4vM9dZy99fM7GbieT8i5WUMMN3M/t3d/1+78nqlE2+NJt9uY9INqW2PJRTh6YQNrO2WDTHY1VMsiB8RFaioLtdKeUn7RiU5L1LANPh0f0YQXbibien1hxG27o/Qhumgt3yk/UcTi3zkXg2ozLwQppO1SG58RBTDO4jeUjbI07HE4GHesZKa10KtN7Y+YQPelHrr9kQirPDDeeX0ILeLek/zy0SLthZr5t3kiDWT0py9Z7W8NM6cfD+hCFs2fRHjLRtQdxlcj3BDnUDYmOdRt9mvk6d+UZ9OXyuT0YS76EYNZX9yykfesp9AvFBr5bA+Mf4zmnAPXUDd13wMDasflfUpXcBqbsZuRNfqrZl9F1F3RxtCdO1XEBNMWl4IoafKQLinvbNh32FEiyPv9PQ9iNmEBzbIaVxM4D3pwc7zIPQk43xg//T7g6y8hNgqSjhnmVzcIHNYeqB/1sb92o8I23paZt8FReUl3aefEN3pr1Efe3lz5pyhqY59Kk95pGscQgx+zSY8KCan/Y1xZnYjZxiHPupxzTR1JCvHocmz+MlhxOD3XGIgdGran10ucARh4rmbfIOsM4ggVF8l/LJrg6rZULE1k2uuiJwpHz9O+bgs6ZC1SfNJMnV4N2JVq1yBtlId+3HKyyVEY21Kw7Mztp285P10TFAmo4cQtqy5hK3p+rR/FRs5YUfNE29iFuFFsBsrh0PtyZa6CzkHjqjHArkyVfTaqj+rhKMl7IMtD4S1KCPXOEOzZUJMirqgTSW4iBi8e4j62pyrTK3Pkxdilu+viYG1bkLZHt9Y9rQfX3xb0uLKxMS3K4gW+OR0vDYmUHuZ5GqdtViP89wvI1rmTxI++hulsllCfSJcradRG1BuOQxCuvaiVD5bp3q2QeY+1QaQc/tlExEq/5NolOxDDKQ+Qn2WZi0f66d854qZToy9/DzlZSZhKsqGKa6NCbQVKTP3feiosGgZfZv6RJr1idCttzectyfxJs3T0pxCuNHdl2R1r+YBOIg2ItoR8RkWkAaLCFv8zMaCJKbB543N3YqMlu9Vi2WyO2HvzDvJah3CN/uwtP1hwnOlcW3IdvJyXi0fafvjwNzMthGucN9J+cwVP4PwZLk+s31EumcPkibfEOEe5uatYy3W49xeE6n85xJhFWoK9mxCqW+VtrdK5+SdOfkRkltrytfzRFjia6h7f2xF9KhaXvow/X8McF2mnI1w3fwJ9RftdkRvKlfMHKJXdxl1S8JmhOfV5SSvtoycS9spl9zl2XGBcZNPaNj3I+Br6fc6RFc419s6PbD7pt+fImb9dVNvBdQq7aW0F6NjV+qhAcamSvqvhC/11Wn/SFIMkoEqo4UyuThvmWSucRPRLd6R8C2+jei63pnOGZHktJQXwja+NdGDmJzZvxvw7cx2LbhbvljT4bnw1oxSOjXtv4xYPefT1Bf0WJ92wqC2Vo+n5Lj+Fikv41I5fLzh+MdTea2dNy9JxjbUw9OOSNc8j+jlnE+8BNcl7Ol5ZEwlTJJdROPnQ5ljQ1J9qsXM2ZwGN+UW5Gyd0rxu2h5KvGwvJLyXPk201tciFH0uOe1+OiMk46NMhIZdRGawhRisvCMVvpGvZZ6VsUHm98WEEnxr2m43QH1WTs0WeybJ7Y0IiP8gYWIpIi+dkFFKmfQg5xxicspjwJWZ/Y9RN7+0ajM/nOgCP0y86LbNHHsr8Gj6fQJpgW3ytQBnEa2xhwhTzjXEQPpXCVv6MCLo2uUF1q9S6nHDPfsK0cN4hvC8qp0zhUzvpg0ZDxEx8WsrI03NnDOBCCuQt8VcK5MHCZ/8rxA9ixMz58wArm2zTGpyHiAG7c8iFPfkzDl7kXoI/fkpX0AU7Kus3FL6LOFdkFUg3yYzeJFTxq2ZfVmPmYuJONRXEPbCvFHtVpGT9jfG6Lge2GsQyCitTHqRM4rw/Dggs+9KUsu2xevvmR602qIR1xDTtmvHt0jlfjTh0ZA3pvVwohW7V9o+huhir+QFQriqzSH/oF7p9biHezaXMOVNILyLLkr37WSixduyrXk15fL1Hs57L/FSySOjsUyOTWVyGxFw7SNp/8nEC3c98r3IG+W8k5hg9RlWfuGeQMwCzSWnqE+5F49u9g8I18ObGirqZ4kBnw8QAxhPkcME0oOMb2aOZZeSeojoJucd2e5NTnZQZBbwH+SLatcfMgovk9XI+Vbm2ElJeeyaji8k37TuPVl5Cn8XMfuvZlrZgJjYkcvNLnPd4elhPTltDyUGxa4kvHKGEt4/vyG/90+n6nFP96wWmGpzosV8DaHMi5Qxv5YPwvTyoVTX8g6w91Qm+1GfZfxDotHzVN58rEbOEGLgdXYq+7UJX/O26lhRn/IFxJt/Xepd+KwCOYropv5T3gdhNTIaIydulZRGW37mvclJBX9mKthS8lKijMLLZDVybskcq7U2v5tXTnqIs8uHbZLKuTbztxaHPlfLvEHWgYQde5+MvOOI2D/rpIe6ZffaTtfjXu5ZbRHvyYT5KPfC4U2UyxbESyN3+INeyuQkQqHX1mtt25bdS9l/gxiMvbXdZ6WoT2eFxQDMnTUFQgwyTC5JxjfT9o6ErbnQQYoe5GxNeAe0Nd26H2WUViYNcr6dtjdPZZPLNt/D9YclZXh/2j6R6HEUshI80ar8MGGimJ7Z/xBtBFdroewLr8c93LPjCU+TQtYzXY2MEwiTUduT0nopk4dpc3H1FuRMpqDVhor41EbKO0YKWzuH6JYNJRYW/n1JMmprN+7r7s8XKaNBzp7EwOF0TyEMBrGMUsqkQc5eRF7eXkLZ30TMnDwIOMXdf17gtccQLbNamOfXCG+Q/dz9v4uSk5HXqXp8E/V7drK7P1myjMLKpVNl0umyz0vHY7m4+4tm9nNiksmBZSiOHmQU/hCsRk6hirYfZRReJmXLSbE7hhOTSoYTM0+fLur6AO7+spldR4RW/gARM/34sh7osutxJ+5Z2TI6VSadLvvcdLpLQNic7gO2H8wyqpSXit2vk+nA4BTJnXSw369O3bMOySi9TDopJ8+n4yYXgMbId4NVRqfkVEVGJ+Q0RG8c9HSo7Eu/Z1Url4FKvyh0IYQQxTOg1hQVQgiRHyl0IYSoCFLoQghREaTQhRCiIkihCyFERZBCF0KIivD/AasHFSPw/3GBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(rotation=45)\n",
    "plt.scatter(xg, yg)"
   ]
  }
 ],
 "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": "3.6.5"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"import pandas as pd\n",
	"import matplotlib.pyplot as plt\n",
	"\n",
	"df = pd.read_clipboard()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/html": [
	"<div>\n",
	"<style scoped>\n",
	" .dataframe tbody tr th:only-of-type {\n",
	" vertical-align: middle;\n",
	" }\n",
	"\n",
	" .dataframe tbody tr th {\n",
	" vertical-align: top;\n",
	" }\n",
	"\n",
	" .dataframe thead th {\n",
	" text-align: right;\n",
	" }\n",
	"</style>\n",
	"<table border=\"1\" class=\"dataframe\">\n",
	" <thead>\n",
	" <tr style=\"text-align: right;\">\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th></th>\n",
	" <th>Time_golden</th>\n",
	" <th>Golden</th>\n",
	" <th>is_golden</th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th>0</th>\n",
	" <th>2019</th>\n",
	" <th>-03-20</th>\n",
	" <td>10:24:30</td>\n",
	" <td>98.6</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>1</th>\n",
	" <th>2019</th>\n",
	" <th>-03-20</th>\n",
	" <td>11:10:30</td>\n",
	" <td>97.0</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>2</th>\n",
	" <th>2019</th>\n",
	" <th>-03-20</th>\n",
	" <td>13:13:30</td>\n",
	" <td>96.0</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>3</th>\n",
	" <th>2019</th>\n",
	" <th>-03-21</th>\n",
	" <td>13:43:16</td>\n",
	" <td>95.0</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>4</th>\n",
	" <th>2019</th>\n",
	" <th>-03-23</th>\n",
	" <td>10:37:11</td>\n",
	" <td>94.6</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>5</th>\n",
	" <th>2019</th>\n",
	" <th>-03-23</th>\n",
	" <td>18:43:19</td>\n",
	" <td>93.0</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>6</th>\n",
	" <th>2019</th>\n",
	" <th>-03-24</th>\n",
	" <td>22:19:43</td>\n",
	" <td>92.0</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>7</th>\n",
	" <th>2019</th>\n",
	" <th>-03-25</th>\n",
	" <td>09:23:45</td>\n",
	" <td>90.0</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>8</th>\n",
	" <th>2019</th>\n",
	" <th>-03-26</th>\n",
	" <td>11:42:51</td>\n",
	" <td>89.0</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>9</th>\n",
	" <th>2019</th>\n",
	" <th>-03-27</th>\n",
	" <td>20:32:51</td>\n",
	" <td>87.3</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>10</th>\n",
	" <th>2019</th>\n",
	" <th>-03-27</th>\n",
	" <td>23:42:51</td>\n",
	" <td>86.0</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>11</th>\n",
	" <th>2019</th>\n",
	" <th>-03-28</th>\n",
	" <td>00:52:23</td>\n",
	" <td>84.0</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>12</th>\n",
	" <th>2019</th>\n",
	" <th>-03-28</th>\n",
	" <td>03:40:40</td>\n",
	" <td>82.3</td>\n",
	" <td>golden</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" Time_golden Golden is_golden\n",
	"0 2019 -03-20 10:24:30 98.6 golden\n",
	"1 2019 -03-20 11:10:30 97.0 golden\n",
	"2 2019 -03-20 13:13:30 96.0 golden\n",
	"3 2019 -03-21 13:43:16 95.0 golden\n",
	"4 2019 -03-23 10:37:11 94.6 golden\n",
	"5 2019 -03-23 18:43:19 93.0 golden\n",
	"6 2019 -03-24 22:19:43 92.0 golden\n",
	"7 2019 -03-25 09:23:45 90.0 golden\n",
	"8 2019 -03-26 11:42:51 89.0 golden\n",
	"9 2019 -03-27 20:32:51 87.3 golden\n",
	"10 2019 -03-27 23:42:51 86.0 golden\n",
	"11 2019 -03-28 00:52:23 84.0 golden\n",
	"12 2019 -03-28 03:40:40 82.3 golden"
	]
	},
	"execution_count": 2,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"df"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [],
	"source": [
	"xg = df['Time_golden']\n",
	"yg = df['Golden']"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.collections.PathCollection at 0x18f9b650160>"
	]
	},
	"execution_count": 6,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.xticks(rotation=45)\n",
	"plt.scatter(xg, yg)"
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3",
	"language": "python",
	"name": "python3"
	},
	"language_info": {
	"codemirror_mode": {
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