snippsat/gist:62fbba46b3a3506179cf549262a082dd

## gistfile1.txt
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_clipboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg = df['Time_golden']\n",
    "yg = df['Golden']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2019  -03-20    10:24:30\n",
       "1   2019  -03-20    11:10:30\n",
       "2   2019  -03-20    13:13:30\n",
       "3   2019  -03-21    13:43:16\n",
       "4   2019  -03-23    10:37:11\n",
       "5   2019  -03-23    18:43:19\n",
       "6   2019  -03-24    22:19:43\n",
       "7   2019  -03-25    09:23:45\n",
       "8   2019  -03-26    11:42:51\n",
       "9   2019  -03-27    20:32:51\n",
       "10  2019  -03-27    23:42:51\n",
       "11  2019  -03-28    00:52:23\n",
       "12  2019  -03-28    03:40:40\n",
       "Name: Time_golden, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2019  -03-20   2019-03-30 10:24:30\n",
       "1   2019  -03-20   2019-03-30 11:10:30\n",
       "2   2019  -03-20   2019-03-30 13:13:30\n",
       "3   2019  -03-21   2019-03-30 13:43:16\n",
       "4   2019  -03-23   2019-03-30 10:37:11\n",
       "5   2019  -03-23   2019-03-30 18:43:19\n",
       "6   2019  -03-24   2019-03-30 22:19:43\n",
       "7   2019  -03-25   2019-03-30 09:23:45\n",
       "8   2019  -03-26   2019-03-30 11:42:51\n",
       "9   2019  -03-27   2019-03-30 20:32:51\n",
       "10  2019  -03-27   2019-03-30 23:42:51\n",
       "11  2019  -03-28   2019-03-30 00:52:23\n",
       "12  2019  -03-28   2019-03-30 03:40:40\n",
       "Name: Time_golden, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Need to convert from dtype: object to datetime object\n",
    "df['Time_golden'] = df['Time_golden'].apply(pd.to_datetime)\n",
    "xg = df['Time_golden']\n",
    "yg = df['Golden']\n",
    "xg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1eaa522c9e8>"
      ]
     },
     "execution_count": 67,
     "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": [
    "# Need to use values\n",
    "plt.scatter(xg.values, yg.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1eaa52637f0>]"
      ]
     },
     "execution_count": 68,
     "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": [
    "# Line make more sense to,maybe need to do something to get scatter to work\n",
    "plt.xticks(rotation=45)\n",
    "plt.plot(xg.values, yg.values)"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}