2_qgrid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install jupyterlab ipympl\n",
    "import qgrid\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lrcno\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "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>id</th>\n",
       "      <th>seq</th>\n",
       "      <th>topWait</th>\n",
       "      <th>hospTime</th>\n",
       "      <th>hospCoord</th>\n",
       "      <th>hospUrlB5</th>\n",
       "      <th>hospUrlEn</th>\n",
       "      <th>hospUrlGb</th>\n",
       "      <th>pastHours</th>\n",
       "      <th>hospNameB5</th>\n",
       "      <th>hospNameEn</th>\n",
       "      <th>hospNameGb</th>\n",
       "      <th>hospTimeEn</th>\n",
       "      <th>topWaitTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>QMH 21/2/2019 9:00am</td>\n",
       "      <td>HK-02</td>\n",
       "      <td>&gt; 2</td>\n",
       "      <td>2019-02-21T09:00:09.29</td>\n",
       "      <td>POINT(114.131259 22.270695)</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>3</td>\n",
       "      <td>瑪麗醫院</td>\n",
       "      <td>Queen Mary Hospital</td>\n",
       "      <td>玛丽医院</td>\n",
       "      <td>21/2/2019 9:00am</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AHN 21/2/2019 10:00am</td>\n",
       "      <td>NT-01</td>\n",
       "      <td>&gt; 1</td>\n",
       "      <td>2019-02-21T10:00:01.783</td>\n",
       "      <td>POINT(114.17472 22.458575)</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>3</td>\n",
       "      <td>雅麗氏何妙齡那打素醫院</td>\n",
       "      <td>Alice Ho Miu Ling Nethersole Hospital</td>\n",
       "      <td>雅丽氏何妙龄那打素医院</td>\n",
       "      <td>21/2/2019 10:00am</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>QMH 21/2/2019 11:00am</td>\n",
       "      <td>HK-02</td>\n",
       "      <td>&gt; 1</td>\n",
       "      <td>2019-02-21T11:00:11.893</td>\n",
       "      <td>POINT(114.131259 22.270695)</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>3</td>\n",
       "      <td>瑪麗醫院</td>\n",
       "      <td>Queen Mary Hospital</td>\n",
       "      <td>玛丽医院</td>\n",
       "      <td>21/2/2019 11:00am</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AHN 21/2/2019 12:00pm</td>\n",
       "      <td>NT-01</td>\n",
       "      <td>&gt; 1</td>\n",
       "      <td>2019-02-21T12:00:02.256</td>\n",
       "      <td>POINT(114.17472 22.458575)</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>3</td>\n",
       "      <td>雅麗氏何妙齡那打素醫院</td>\n",
       "      <td>Alice Ho Miu Ling Nethersole Hospital</td>\n",
       "      <td>雅丽氏何妙龄那打素医院</td>\n",
       "      <td>21/2/2019 12:00pm</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PYN 21/2/2019 12:00pm</td>\n",
       "      <td>HK-01</td>\n",
       "      <td>&gt; 3</td>\n",
       "      <td>2019-02-21T12:00:01.646</td>\n",
       "      <td>POINT(114.235707 22.269419)</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>http://www.ha.org.hk/visitor/ha_visitor_index....</td>\n",
       "      <td>3</td>\n",
       "      <td>東區尤德夫人那打素醫院</td>\n",
       "      <td>Pamela Youde Nethersole Eastern Hospital</td>\n",
       "      <td>东区尤德夫人那打素医院</td>\n",
       "      <td>21/2/2019 12:00pm</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id    seq topWait                 hospTime  \\\n",
       "0   QMH 21/2/2019 9:00am  HK-02     > 2   2019-02-21T09:00:09.29   \n",
       "1  AHN 21/2/2019 10:00am  NT-01     > 1  2019-02-21T10:00:01.783   \n",
       "2  QMH 21/2/2019 11:00am  HK-02     > 1  2019-02-21T11:00:11.893   \n",
       "3  AHN 21/2/2019 12:00pm  NT-01     > 1  2019-02-21T12:00:02.256   \n",
       "4  PYN 21/2/2019 12:00pm  HK-01     > 3  2019-02-21T12:00:01.646   \n",
       "\n",
       "                     hospCoord  \\\n",
       "0  POINT(114.131259 22.270695)   \n",
       "1   POINT(114.17472 22.458575)   \n",
       "2  POINT(114.131259 22.270695)   \n",
       "3   POINT(114.17472 22.458575)   \n",
       "4  POINT(114.235707 22.269419)   \n",
       "\n",
       "                                           hospUrlB5  \\\n",
       "0  http://www.ha.org.hk/visitor/ha_visitor_index....   \n",
       "1  http://www.ha.org.hk/visitor/ha_visitor_index....   \n",
       "2  http://www.ha.org.hk/visitor/ha_visitor_index....   \n",
       "3  http://www.ha.org.hk/visitor/ha_visitor_index....   \n",
       "4  http://www.ha.org.hk/visitor/ha_visitor_index....   \n",
       "\n",
       "                                           hospUrlEn  \\\n",
       "0  http://www.ha.org.hk/visitor/ha_visitor_index....   \n",
       "1  http://www.ha.org.hk/visitor/ha_visitor_index....   \n",
       "2  http://www.ha.org.hk/visitor/ha_visitor_index....   \n",
       "3  http://www.ha.org.hk/visitor/ha_visitor_index....   \n",
       "4  http://www.ha.org.hk/visitor/ha_visitor_index....   \n",
       "\n",
       "                                           hospUrlGb  pastHours   hospNameB5  \\\n",
       "0  http://www.ha.org.hk/visitor/ha_visitor_index....          3         瑪麗醫院   \n",
       "1  http://www.ha.org.hk/visitor/ha_visitor_index....          3  雅麗氏何妙齡那打素醫院   \n",
       "2  http://www.ha.org.hk/visitor/ha_visitor_index....          3         瑪麗醫院   \n",
       "3  http://www.ha.org.hk/visitor/ha_visitor_index....          3  雅麗氏何妙齡那打素醫院   \n",
       "4  http://www.ha.org.hk/visitor/ha_visitor_index....          3  東區尤德夫人那打素醫院   \n",
       "\n",
       "                                 hospNameEn   hospNameGb         hospTimeEn  \\\n",
       "0                       Queen Mary Hospital         玛丽医院   21/2/2019 9:00am   \n",
       "1     Alice Ho Miu Ling Nethersole Hospital  雅丽氏何妙龄那打素医院  21/2/2019 10:00am   \n",
       "2                       Queen Mary Hospital         玛丽医院  21/2/2019 11:00am   \n",
       "3     Alice Ho Miu Ling Nethersole Hospital  雅丽氏何妙龄那打素医院  21/2/2019 12:00pm   \n",
       "4  Pamela Youde Nethersole Eastern Hospital  东区尤德夫人那打素医院  21/2/2019 12:00pm   \n",
       "\n",
       "   topWaitTime  \n",
       "0          2.0  \n",
       "1          1.0  \n",
       "2          1.0  \n",
       "3          1.0  \n",
       "4          3.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA = Path('data')\n",
    "\n",
    "df = pd.read_csv(DATA/'ae_data.csv',encoding='utf8')\n",
    "df['topWaitTime'] = df['topWait'].copy()\n",
    "df['topWaitTime']  = df['topWaitTime'].str.replace('> ', '')\n",
    "df['topWaitTime'][df['topWaitTime']== '< 1'] = 0.5\n",
    "df['topWaitTime'] = df['topWaitTime'].fillna(0)\n",
    "df['topWaitTime'] = df['topWaitTime'].astype(float)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "widget = qgrid.show_grid(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# After upgrading to 1.0.3\n",
    "def handle_json_updated(event, qgrid_widget):\n",
    "    # exclude 'viewport_changed' events since that doesn't change the DataFrame\n",
    "    if (event['triggered_by'] != 'viewport_changed'):\n",
    "        print(qgrid_widget.get_changed_df())\n",
    "    (sns.barplot(y='hospNameEn',\n",
    "             x='topWaitTime',\n",
    "             data=widget.get_changed_df(),\n",
    "             orient='h',\n",
    "             color='red'))\n",
    "    print(123)\n",
    "    \n",
    "    \n",
    "widget.on('filter_changed', handle_json_updated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70288b1cb3a84bb99cbedd4d4d73574a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = (sns.barplot(y='hospNameEn',\n",
    "             x='topWaitTime',\n",
    "             data=widget.get_changed_df(),\n",
    "             orient='h',\n",
    "             color='red'))\n",
    "# fig = fig.set_title('Average waiting Time of Accident & Emergency waiting Time in HK Hospital')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d75ee049b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97458ce2c6a24504bae5d958263a91f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QgridWidget(grid_options={'fullWidthRows': True, 'syncColumnCellResize': True, 'forceFitColumns': True, 'defau…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import qgrid\n",
    "randn = np.random.randn\n",
    "df_types = pd.DataFrame({\n",
    "    'A' : 1.,\n",
    "    'B' : pd.Series(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
    "               '2013-01-05', '2013-01-06', '2013-01-07', '2013-01-08', '2013-01-09'],index=list(range(9)),dtype='datetime64[ns]'),\n",
    "    'C' : pd.Series(randn(9),index=list(range(9)),dtype='float32'),\n",
    "    'D' : np.array([3] * 9,dtype='int32'),\n",
    "    'E' : pd.Categorical([\"washington\", \"adams\", \"washington\", \"madison\", \"lincoln\",\"jefferson\", \"hamilton\", \"roosevelt\", \"kennedy\"]),\n",
    "    'F' : [\"foo\", \"bar\", \"buzz\", \"bippity\",\"boppity\", \"foo\", \"foo\", \"bar\", \"zoo\"] })\n",
    "df_types['G'] = df_types['F'] == 'foo'\n",
    "qgrid_widget = qgrid.show_grid(df_types, show_toolbar=True)\n",
    "qgrid_widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Cannot change to a different GUI toolkit: nbagg. Using ipympl instead.\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'C:\\\\Users\\\\lrcno\\\\AppData\\\\Roaming\\\\Python\\\\Python36\\\\site-packages\\\\ipympl\\\\static\\\\package.json'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-cfb48c372658>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'nbagg'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[1;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[0;32m   2285\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'local_ns'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2286\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2287\u001b[1;33m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2288\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2289\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<C:\\Users\\lrcno\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\decorator.py:decorator-gen-108>\u001b[0m in \u001b[0;36mmatplotlib\u001b[1;34m(self, line)\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(f, *a, **k)\u001b[0m\n\u001b[0;32m    185\u001b[0m     \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    186\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 187\u001b[1;33m         \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\magics\\pylab.py\u001b[0m in \u001b[0;36mmatplotlib\u001b[1;34m(self, line)\u001b[0m\n\u001b[0;32m     97\u001b[0m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Available matplotlib backends: %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mbackends_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 99\u001b[1;33m             \u001b[0mgui\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbackend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menable_matplotlib\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    100\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_show_matplotlib_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgui\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36menable_matplotlib\u001b[1;34m(self, gui)\u001b[0m\n\u001b[0;32m   3353\u001b[0m                 \u001b[0mgui\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbackend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind_gui_and_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpylab_gui_select\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3354\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3355\u001b[1;33m         \u001b[0mpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mactivate_matplotlib\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3356\u001b[0m         \u001b[0mpt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfigure_inline_support\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3357\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\pylabtools.py\u001b[0m in \u001b[0;36mactivate_matplotlib\u001b[1;34m(backend)\u001b[0m\n\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m     \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 314\u001b[1;33m     \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mswitch_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    315\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    316\u001b[0m     \u001b[1;31m# This must be imported last in the matplotlib series, after\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mswitch_backend\u001b[1;34m(newbackend)\u001b[0m\n\u001b[0;32m    205\u001b[0m         else \"matplotlib.backends.backend_{}\".format(newbackend.lower()))\n\u001b[0;32m    206\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 207\u001b[1;33m     \u001b[0mbackend_mod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbackend_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    208\u001b[0m     Backend = type(\n\u001b[0;32m    209\u001b[0m         \"Backend\", (matplotlib.backends._Backend,), vars(backend_mod))\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\importlib\\__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m    124\u001b[0m                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    125\u001b[0m             \u001b[0mlevel\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 126\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    127\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    128\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[1;34m(name, package, level)\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_load_unlocked\u001b[1;34m(spec)\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mexec_module\u001b[1;34m(self, module)\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_call_with_frames_removed\u001b[1;34m(f, *args, **kwds)\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\ipympl\\backend_nbagg.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m \u001b[0mhere\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdirname\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m__file__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhere\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'static'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'package.json'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfid\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    120\u001b[0m     \u001b[0mjs_version\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'version'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\lrcno\\\\AppData\\\\Roaming\\\\Python\\\\Python36\\\\site-packages\\\\ipympl\\\\static\\\\package.json'"
     ]
    }
   ],
   "source": [
    "%matplotlib nbagg\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "n = 50\n",
    "\n",
    "qgrid_df = qgrid_widget.get_changed_df()\n",
    "x = qgrid_df.index\n",
    "y = qgrid_df['C']\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fit = np.polyfit(x, y, deg=1)\n",
    "line, = ax.plot(x, fit[0] * x + fit[1], color='red')\n",
    "scatter, = ax.plot(x,y,ms=8,color='b',marker='o',ls='')\n",
    "\n",
    "def handle_filter_changed(event, widget):\n",
    "    qgrid_df = qgrid_widget.get_changed_df()\n",
    "    x = qgrid_df.index\n",
    "    y = qgrid_df['C']\n",
    "    fit = np.polyfit(x, y, deg=1)\n",
    "    line.set_data(x, fit[0] * x + fit[1])\n",
    "    fig.canvas.draw()\n",
    "    scatter.set_data(x, y)\n",
    "    fig.canvas.draw()\n",
    "\n",
    "qgrid_widget.on('filter_changed', handle_filter_changed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: ipympl in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (0.2.1)\n",
      "Requirement already satisfied: ipykernel>=4.7 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from ipympl) (5.1.0)\n",
      "Requirement already satisfied: ipywidgets>=7.0.0 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from ipympl) (7.4.2)\n",
      "Requirement already satisfied: six in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from ipympl) (1.12.0)\n",
      "Requirement already satisfied: matplotlib>=2.0.0 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from ipympl) (3.0.3)\n",
      "Requirement already satisfied: ipython>=5.0.0 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from ipykernel>=4.7->ipympl) (7.2.0)\n",
      "Requirement already satisfied: tornado>=4.2 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from ipykernel>=4.7->ipympl) (5.1.1)\n",
      "Requirement already satisfied: jupyter-client in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from ipykernel>=4.7->ipympl) (5.2.4)\n",
      "Requirement already satisfied: traitlets>=4.1.0 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from ipykernel>=4.7->ipympl) (4.3.2)\n",
      "Requirement already satisfied: widgetsnbextension~=3.4.0 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from ipywidgets>=7.0.0->ipympl) (3.4.2)\n",
      "Requirement already satisfied: nbformat>=4.2.0 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from ipywidgets>=7.0.0->ipympl) (4.4.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from matplotlib>=2.0.0->ipympl) (2.7.5)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from matplotlib>=2.0.0->ipympl) (1.0.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from matplotlib>=2.0.0->ipympl) (2.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from matplotlib>=2.0.0->ipympl) (0.10.0)\n",
      "Requirement already satisfied: numpy>=1.10.0 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from matplotlib>=2.0.0->ipympl) (1.16.1)\n",
      "Requirement already satisfied: jedi>=0.10 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from ipython>=5.0.0->ipykernel>=4.7->ipympl) (0.13.2)\n",
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      "Requirement already satisfied: entrypoints>=0.2.2 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets>=7.0.0->ipympl) (0.3)\n",
      "Requirement already satisfied: bleach in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets>=7.0.0->ipympl) (3.1.0)\n",
      "Requirement already satisfied: pandocfilters>=1.4.1 in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets>=7.0.0->ipympl) (1.4.2)\n",
      "Requirement already satisfied: testpath in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets>=7.0.0->ipympl) (0.4.2)\n",
      "Requirement already satisfied: defusedxml in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets>=7.0.0->ipympl) (0.5.0)\n",
      "Requirement already satisfied: webencodings in c:\\users\\lrcno\\appdata\\local\\continuum\\anaconda3\\lib\\site-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.4.0->ipywidgets>=7.0.0->ipympl) (0.5.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install ipympl"
   ]
  },
  {
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}