web_plot_price_distribution_lastest_time_stamp_all.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Snow_Covered_Island',\n",
       " 'Fabled_Passage',\n",
       " 'Once_Upon_a_Time',\n",
       " 'Murderous_Rider_Swift_End',\n",
       " 'Questing_Beast',\n",
       " 'Oko_Thief_of_Crowns']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card_names = [\n",
    "    'Snow Covered Island', \n",
    "    'Fabled Passage', \n",
    "    'Once Upon a Time', \n",
    "    'Murderous Rider // Swift End', \n",
    "    'Questing Beast', \n",
    "    'Oko, Thief of Crowns'\n",
    "]\n",
    "file_names = [card_name.replace('/', '').replace(',', '').replace('  ', '_').replace(' ', '_') for card_name in card_names]\n",
    "index = 5\n",
    "file_name = file_names[index]\n",
    "card_name = card_names[index]\n",
    "file_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "loading the pickled data to plot, produce it in a format ready to be consumed by fusion\n",
    "'''\n",
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt \n",
    "import numpy as np \n",
    "import matplotlib \n",
    "from sqlalchemy import create_engine\n",
    "import pickle\n",
    "from scipy.stats import zscore\n",
    "\n",
    "%matplotlib inline \n",
    "idx = pd.IndexSlice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./pickles/df.pickle', 'rb') as handle:\n",
    "    df = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#javascript code\n",
    "var flask_data = JSON.parse('{{ data_price_distribution_lastest_time_stamp | tojson | safe}}');\n",
    "var dataSource = {\n",
    "    chart: {\n",
    "        caption: \"Item Amount Distribution\",\n",
    "        subcaption: \"From latest timestamp\",\n",
    "        xaxisname: \"Price\",\n",
    "        yaxisname: \"Amount\",\n",
    "        numbersuffix: \"units\",\n",
    "        theme: \"fusion\"\n",
    "    },\n",
    "    data: flask_data\n",
    "};\n",
    "FusionCharts.ready(function() {\n",
    "    var chart = new FusionCharts({\n",
    "        type: 'column2d',\n",
    "        renderAt: 'chart-container-price_distribution_lastest_time_stamp',\n",
    "        width: '100%',//100% 700\n",
    "        height: '700%',//300% 400\n",
    "        dataFormat: 'json',\n",
    "        dataSource\n",
    "    });\n",
    "    chart.render();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# web_plot_price_distribution_lastest_time_stamp_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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      "shape (10673, 1)\n",
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       "      <td>1016</td>\n",
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       "    <tr>\n",
       "      <td>1017</td>\n",
       "      <td>300.00</td>\n",
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       "    <tr>\n",
       "      <td>1018</td>\n",
       "      <td>300.00</td>\n",
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       "    <tr>\n",
       "      <td>1019</td>\n",
       "      <td>300.00</td>\n",
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       "<p>1020 rows × 1 columns</p>\n",
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       "      item_price\n",
       "0          34.50\n",
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       "3          35.00\n",
       "4          35.50\n",
       "...          ...\n",
       "1015      225.00\n",
       "1016      299.99\n",
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       "1018      300.00\n",
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       "\n",
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     "text": [
      "---\n",
      "shape (994, 1)\n",
      "mean 52.893179074446685\n",
      "median 46.995000000000005\n",
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       "     item_price\n",
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       "4         35.50\n",
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     "output_type": "stream",
     "text": [
      "---\n"
     ]
    }
   ],
   "source": [
    "for card_name, file_name in zip(card_names, file_names):\n",
    "\n",
    "\n",
    "    df_ = df.loc[idx[str(df.index[-1][0]), card_name], ['item_price', 'item_amount']].\\\n",
    "        reset_index(drop=True)\n",
    "    df_repeated = pd.DataFrame(np.repeat(df_.item_price.values, df_.item_amount.values), columns=['item_price'])\n",
    "    df_amount = df_.groupby('item_price').sum().reset_index()\n",
    "    \n",
    "    df_filtered = df_repeated\n",
    "    for i in range(5):\n",
    "        df_filtered = df_filtered[(zscore(df_filtered.item_price) < 3)]\n",
    "        print('shape', df_filtered.shape)\n",
    "        print('mean', df_filtered.item_price.mean())\n",
    "        print('median', df_filtered.item_price.median())\n",
    "        print('std', df_filtered.item_price.std())\n",
    "        display(df_filtered)\n",
    "        print('---')\n",
    "\n",
    "\n",
    "    df_ = df_amount.loc[df_amount.item_price.isin(df_filtered.item_price)]\n",
    "    \n",
    "    plot_constant = 25\n",
    "    start = 0.0\n",
    "    stop = df_.item_price.max()\n",
    "    step = round(df_.item_price.max()/plot_constant, 2)\n",
    "    item_price_ = np.arange(start, stop, step)\n",
    "    df__ = pd.DataFrame([(item_price_[i], 0) for i in range(len(item_price_))], columns=['item_price', 'item_amount'])\n",
    "    df_dummy = df__.append(df_).sort_values(by=['item_price']).reset_index(drop=True)\n",
    "\n",
    "    '''\n",
    "    let's try to plot with the interpolated points\n",
    "    Now it runs!! and we have the data that is relevant to us\n",
    "    '''\n",
    "    df_plot = df_dummy.loc[df_dummy.item_price <= df_filtered.item_price.max()]\n",
    "\n",
    "    '''\n",
    "    Finally, lets bin the data so that we can actually see the bars\n",
    "\n",
    "    TODO: come up with a way to set the bin span automatically\n",
    "    '''\n",
    "\n",
    "    df_plot_ = df_plot.set_index('item_price')\n",
    "\n",
    "    # the range of each bin\n",
    "    bin_span = round(df_plot_.index.max()/plot_constant, 2)\n",
    "\n",
    "    df_plot_binned = df_plot_.\\\n",
    "        groupby(pd.cut(df_plot_.index, np.arange(0, df_plot_.index.max() + bin_span, bin_span))).sum()\n",
    "\n",
    "    df_pickle = df_plot_binned.reset_index().copy()\n",
    "    df_pickle = df_pickle.rename(columns={'index':'label', 'item_amount':'value'})\n",
    "    df_pickle = df_pickle.astype({'label':str, 'value':str})\n",
    "    df_pickle.loc[:, 'label'] = df_pickle.loc[:, 'label'].str[1:-1].to_frame()\n",
    "    list_pickle = list(df_pickle.T.to_dict().values())\n",
    "\n",
    "    with open('./app/app/pickles/' + file_name + '_price_distribution_lastest_time_stamp.pickle', 'wb') as handle:\n",
    "        pickle.dump(list_pickle, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  }
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