eda_GSSHOP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "\n",
    "%autoreload 2\n",
    "\n",
    "from util import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine(\"mysql://wspider:wspider00!q@133.186.143.65:3306/wspider\",\n",
    "                       connect_args={'connect_timeout': 10000})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_sql_query(\"SELECT ID, SELL_CLOSE_DT, COLLECT_SITE, SITE_NAME, TOTAL_EVAL_CNT, AVG_POINT FROM MWS_COLT_ITEM WHERE RELEASE_DT > '2018-01-01' AND SITE_NAME IN ('GSSHOP', 'HMALL') LIMIT 1000\", engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.read_sql_query(\"SELECT ITEM_ID, STOCK_ID, REG_DT, STOCK_AMOUNT FROM MWS_COLT_ITEM_IVT WHERE REG_DT >'2018-01-01' LIMIT 100\" % id, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data.SELL_CLOSE_DT.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>SELL_CLOSE_DT</th>\n",
       "      <th>COLLECT_SITE</th>\n",
       "      <th>SITE_NAME</th>\n",
       "      <th>TOTAL_EVAL_CNT</th>\n",
       "      <th>AVG_POINT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6739669</td>\n",
       "      <td>None</td>\n",
       "      <td>www.gsshop.com</td>\n",
       "      <td>GSSHOP</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6739671</td>\n",
       "      <td>None</td>\n",
       "      <td>www.gsshop.com</td>\n",
       "      <td>GSSHOP</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6739672</td>\n",
       "      <td>None</td>\n",
       "      <td>www.gsshop.com</td>\n",
       "      <td>GSSHOP</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6739673</td>\n",
       "      <td>None</td>\n",
       "      <td>www.gsshop.com</td>\n",
       "      <td>GSSHOP</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6739677</td>\n",
       "      <td>None</td>\n",
       "      <td>www.gsshop.com</td>\n",
       "      <td>GSSHOP</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>6740726</td>\n",
       "      <td>None</td>\n",
       "      <td>www.hyundaihmall.com</td>\n",
       "      <td>HMALL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>6740727</td>\n",
       "      <td>None</td>\n",
       "      <td>www.hyundaihmall.com</td>\n",
       "      <td>HMALL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>6740728</td>\n",
       "      <td>None</td>\n",
       "      <td>www.hyundaihmall.com</td>\n",
       "      <td>HMALL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>6740729</td>\n",
       "      <td>None</td>\n",
       "      <td>www.hyundaihmall.com</td>\n",
       "      <td>HMALL</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>6740730</td>\n",
       "      <td>None</td>\n",
       "      <td>www.hyundaihmall.com</td>\n",
       "      <td>HMALL</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>619 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID SELL_CLOSE_DT          COLLECT_SITE SITE_NAME  TOTAL_EVAL_CNT AVG_POINT\n",
       "1    6739669          None        www.gsshop.com    GSSHOP               0       0.0\n",
       "3    6739671          None        www.gsshop.com    GSSHOP               0       0.0\n",
       "4    6739672          None        www.gsshop.com    GSSHOP               0       0.0\n",
       "5    6739673          None        www.gsshop.com    GSSHOP               0       0.0\n",
       "9    6739677          None        www.gsshop.com    GSSHOP               0       0.0\n",
       "..       ...           ...                   ...       ...             ...       ...\n",
       "995  6740726          None  www.hyundaihmall.com     HMALL               0       0.0\n",
       "996  6740727          None  www.hyundaihmall.com     HMALL               0       0.0\n",
       "997  6740728          None  www.hyundaihmall.com     HMALL               0       0.0\n",
       "998  6740729          None  www.hyundaihmall.com     HMALL               0       0.0\n",
       "999  6740730          None  www.hyundaihmall.com     HMALL               1       5.0\n",
       "\n",
       "[619 rows x 6 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = []\n",
    "\n",
    "for id in data.ID.values:\n",
    "    lst.append({\n",
    "       'id': id,\n",
    "        'df': pd.read_sql_query(\"SELECT STOCK_ID, REG_DT, STOCK_AMOUNT FROM MWS_COLT_ITEM_IVT WHERE ITEM_ID = %s\" % id, engine)\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in lst:\n",
    "    tmp_df = item['df']\n",
    "    tmp_df = tmp_df.set_index('REG_DT')\n",
    "    tmp_df = tmp_df.pivot_table(index=tmp_df.index, columns='STOCK_ID', values='STOCK_AMOUNT')\n",
    "    item['df'] = tmp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12abc0210>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12aaa2ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lst[22]['df'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a8d120b1544547ecb2430248096581ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>QgridWidget</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "QgridWidget(grid_options={'enableTextSelectionOnCells': True, 'editable': True, 'explicitInitialization': True, 'minVisibleRows': 8, 'enableColumnReorder': False, 'defaultColumnWidth': 150, 'maxVisibleRows': 15, 'fullWidthRows': True, 'rowHeight': 28, 'autoEdit': False, 'syncColumnCellResize': True, 'forceFitColumns': True}, precision=5)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qgrid.show_grid(lst[25]['df'].resample('1D').first())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "for item in lst:\n",
    "    print(item['df'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>STOCK_ID</th>\n",
       "      <th>28770107001</th>\n",
       "      <th>28770107002</th>\n",
       "      <th>28770107003</th>\n",
       "      <th>28770107004</th>\n",
       "      <th>28770107005</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REG_DT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02 00:00:02</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-06 18:51:36</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11 09:54:42</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16 09:25:23</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18 10:04:27</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22 19:02:43</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26 23:52:23</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01 03:33:24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "STOCK_ID             28770107001  28770107002  28770107003  28770107004  28770107005\n",
       "REG_DT                                                                              \n",
       "2018-01-02 00:00:02            0            1            2            3            0\n",
       "2018-01-06 18:51:36            0            1            2            3            0\n",
       "2018-01-11 09:54:42            0            1            2            3            0\n",
       "2018-01-16 09:25:23            0            1            2            3            0\n",
       "2018-01-18 10:04:27            0            1            2            2            0\n",
       "2018-01-22 19:02:43            0            1            2            2            0\n",
       "2018-01-26 23:52:23            0            1            2            2            0\n",
       "2018-02-01 03:33:24            0            0            2            2            0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tmp_df = lst[0]['df']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_df_5558979_pivot = sample_df_5558979.pivot_table(index=sample_df_5558979.index, columns='STOCK_ID', values='STOCK_AMOUNT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "77d7b298361242f48e0e98fd5599fc66",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>QgridWidget</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "QgridWidget(grid_options={'enableTextSelectionOnCells': True, 'editable': True, 'explicitInitialization': True, 'minVisibleRows': 8, 'enableColumnReorder': False, 'defaultColumnWidth': 150, 'maxVisibleRows': 15, 'fullWidthRows': True, 'rowHeight': 28, 'autoEdit': False, 'syncColumnCellResize': True, 'forceFitColumns': True}, precision=5)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qgrid.show_grid(sample_df_5549142_pivot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
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    "name": "ipython",
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