web_plot_price_distribution_lastest_time_stamp_Fabled_Passage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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",
    "card_names_file = [card_name.replace('/', '').replace(',', '').replace('  ', '_').replace(' ', '_') for card_name in card_names]\n",
    "card_names_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "card_name = card_names[1]\n",
    "card_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./pickles/df.pickle', 'rb') as handle:\n",
    "    df = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\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>seller_name</th>\n",
       "      <th>seller_sales</th>\n",
       "      <th>seller_available_items</th>\n",
       "      <th>item_price</th>\n",
       "      <th>item_amount</th>\n",
       "      <th>item_location</th>\n",
       "      <th>item_condition</th>\n",
       "      <th>item_language</th>\n",
       "      <th>item_is_playset</th>\n",
       "      <th>item_is_foil</th>\n",
       "      <th>avg_sell_price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ts</th>\n",
       "      <th>card_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <td>2019-10-10 16:30:00</td>\n",
       "      <td>Snow Covered Island</td>\n",
       "      <td>toycastle</td>\n",
       "      <td>3357</td>\n",
       "      <td>119435</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>NM</td>\n",
       "      <td>English</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        seller_name  seller_sales  \\\n",
       "ts                  card_name                                       \n",
       "2019-10-10 16:30:00 Snow Covered Island   toycastle          3357   \n",
       "\n",
       "                                         seller_available_items  item_price  \\\n",
       "ts                  card_name                                                 \n",
       "2019-10-10 16:30:00 Snow Covered Island                  119435         0.0   \n",
       "\n",
       "                                         item_amount item_location  \\\n",
       "ts                  card_name                                        \n",
       "2019-10-10 16:30:00 Snow Covered Island            1                 \n",
       "\n",
       "                                        item_condition item_language  \\\n",
       "ts                  card_name                                          \n",
       "2019-10-10 16:30:00 Snow Covered Island             NM       English   \n",
       "\n",
       "                                         item_is_playset  item_is_foil  \\\n",
       "ts                  card_name                                            \n",
       "2019-10-10 16:30:00 Snow Covered Island            False         False   \n",
       "\n",
       "                                         avg_sell_price  \n",
       "ts                  card_name                            \n",
       "2019-10-10 16:30:00 Snow Covered Island             0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>item_price</th>\n",
       "      <th>item_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13.95</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>14.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>14.79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>14.80</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>99.00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>99.99</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>100.00</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>199.99</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>138</td>\n",
       "      <td>300.00</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>139 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     item_price  item_amount\n",
       "0         13.95            1\n",
       "1         14.00            2\n",
       "2         14.50            2\n",
       "3         14.79            1\n",
       "4         14.80            2\n",
       "..          ...          ...\n",
       "134       99.00            8\n",
       "135       99.99            8\n",
       "136      100.00            8\n",
       "137      199.99            8\n",
       "138      300.00           12\n",
       "\n",
       "[139 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pickle = df.loc[idx[str(df.index[-1][0]), card_name], ['item_price', 'item_amount']].\\\n",
    "    reset_index(drop=True).groupby('item_price').sum().reset_index()\n",
    "df_pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nmaking the pickles\\n\\nfor card_name in df.index.levels[1]:\\n    # save the dfs \\n    file_name = card_name.replace('/', '').replace(',', '').replace('  ', '_').replace(' ', '_')\\n    df_pickle = df.loc[idx[str(df.index[-1][0]), card_name], ['item_price', 'item_amount']].        reset_index(drop=True).groupby('item_price').sum().reset_index()\\n    df_pickle = df_pickle.rename(columns={'item_price':'label', 'item_amount':'value'})\\n    df_pickle = df_pickle.astype({'label':str, 'value':str})\\n    list_pickle = list(df_pickle.T.to_dict().values())\\n    print(list_pickle)\\n    with open('./pickles/' + file_name + '_price_distribution_lastest_time_stamp.pickle', 'wb') as handle:\\n        pickle.dump(list_pickle, handle, protocol=pickle.HIGHEST_PROTOCOL)    \\n\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "making the pickles\n",
    "\n",
    "for card_name in df.index.levels[1]:\n",
    "    # save the dfs \n",
    "    file_name = card_name.replace('/', '').replace(',', '').replace('  ', '_').replace(' ', '_')\n",
    "    df_pickle = df.loc[idx[str(df.index[-1][0]), card_name], ['item_price', 'item_amount']].\\\n",
    "        reset_index(drop=True).groupby('item_price').sum().reset_index()\n",
    "    df_pickle = df_pickle.rename(columns={'item_price':'label', 'item_amount':'value'})\n",
    "    df_pickle = df_pickle.astype({'label':str, 'value':str})\n",
    "    list_pickle = list(df_pickle.T.to_dict().values())\n",
    "    print(list_pickle)\n",
    "    with open('./pickles/' + file_name + '_price_distribution_lastest_time_stamp.pickle', 'wb') as handle:\n",
    "        pickle.dump(list_pickle, handle, protocol=pickle.HIGHEST_PROTOCOL)    \n",
    "'''"
   ]
  },
  {
   "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": [
    "# Removing unnecessary points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'df_repeated'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(979, 1)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>14.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>14.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>14.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>14.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>14.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_price\n",
       "0       13.95\n",
       "1       14.00\n",
       "2       14.00\n",
       "3       14.50\n",
       "4       14.50\n",
       "5       14.79\n",
       "6       14.80\n",
       "7       14.80\n",
       "8       14.90\n",
       "9       14.98"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'df_amount'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(139, 2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>item_price</th>\n",
       "      <th>item_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>13.95</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>14.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>14.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>14.79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>14.80</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>14.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>14.98</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>14.99</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>15.00</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>15.11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_price  item_amount\n",
       "0       13.95            1\n",
       "1       14.00            2\n",
       "2       14.50            2\n",
       "3       14.79            1\n",
       "4       14.80            2\n",
       "5       14.90            1\n",
       "6       14.98            1\n",
       "7       14.99            4\n",
       "8       15.00           21\n",
       "9       15.11            1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"\\nstart = 0.0\\nstop = df_.item_price.max()\\nstep = 0.01\\nitem_price_ = np.arange(start, stop, step)\\ndf__ = pd.DataFrame([(item_price_[i], 0) for i in range(len(item_price_))], columns=['item_price', 'item_amount'])\\ndf_dummy = df__.append(df_amount).sort_values(by=['item_price']).reset_index(drop=True)\\ndisplay('df_dummy', df_dummy.shape)\\ndisplay(df_dummy.head(10))\\n\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "display('df_repeated', df_repeated.shape)\n",
    "display(df_repeated.head(10))\n",
    "\n",
    "df_amount = df_.groupby('item_price').sum().reset_index()\n",
    "display('df_amount', df_amount.shape)\n",
    "display(df_amount.head(10))\n",
    "\n",
    "'''\n",
    "Not gonna make or use dummy data now, gonna remove listings with highest prices first \n",
    "'''\n",
    "\n",
    "'''\n",
    "start = 0.0\n",
    "stop = df_.item_price.max()\n",
    "step = 0.01\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_amount).sort_values(by=['item_price']).reset_index(drop=True)\n",
    "display('df_dummy', df_dummy.shape)\n",
    "display(df_dummy.head(10))\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139, 2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# based on filtering done to df_repeated, filter values in df_amount to display the bar plot\n",
    "df_amount.loc[df_amount.item_price.isin(df_repeated.item_price)].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_repeated.item_price.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nNot gonna make or use dummy data now, gonna remove listings with highest prices first \\n'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# based on filtering done to df_repeated, filter values in df_dummy to display the bar plot\n",
    "# note this will include the rows where the item_amount is 0\n",
    "# also note that the row count is correct because there is no dummy row for the most expensive listing\n",
    "'''\n",
    "Not gonna make or use dummy data now, gonna remove listings with highest prices first \n",
    "'''\n",
    "#df_dummy.loc[df_dummy.item_price <= df_repeated.item_price.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape (139, 2)\n",
      "mean 28.297410071942448\n",
      "median 20.75\n",
      "std 31.282222211448143\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'price distribution at latest timestamp')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "I can plot the amount of offers at each price that is represented in the data\n",
    "'''\n",
    "print('shape', df_amount.shape)\n",
    "print('mean', df_amount.item_price.mean())\n",
    "print('median', df_amount.item_price.median())\n",
    "print('std', df_amount.item_price.std())\n",
    "df_repeated.boxplot(vert=False, figsize=(15,3))\n",
    "\n",
    "ax = df_amount.set_index('item_price').plot(kind='bar', figsize=(15,3))\n",
    "\n",
    "# fix axis\n",
    "n = 4 # this value should be ajusted according to the number of listings\n",
    "ticks = ax.xaxis.get_ticklocs()\n",
    "ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]  # <---- blearghk!!\n",
    "ax.xaxis.set_ticks(ticks[::n])\n",
    "ax.xaxis.set_ticklabels(ticklabels[::n])\n",
    "plt.xlabel('item_price')\n",
    "plt.ylabel('item_amount')\n",
    "plt.title('price distribution at latest timestamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nbut when i try to interpolate to get the prices that are not represented in the x axis \\nLOL 30253 points, there is no way i can plot this, it doesnt stop running\\n\\n\\nprint(df_dummy.shape)\\nprint(df_dummy.item_price.mean())\\nprint(df_dummy.item_price.mean())\\nprint(df_dummy.item_price.std())\\n\\nax = df_dummy.set_index('item_price').plot(kind='bar', figsize=(15,2))\\n\\n# fix axis\\nn = 4 # this value should be ajusted according to the number of listings\\nticks = ax.xaxis.get_ticklocs()\\nticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]  # <---- blearghk!!\\nax.xaxis.set_ticks(ticks[::n])\\nax.xaxis.set_ticklabels(ticklabels[::n])\\nplt.xlabel('item_price')\\nplt.ylabel('item_amount')\\nplt.title(card_name + ' price distribution at ')\\n\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "but when i try to interpolate to get the prices that are not represented in the x axis \n",
    "LOL 30253 points, there is no way i can plot this, it doesnt stop running\n",
    "\n",
    "\n",
    "print(df_dummy.shape)\n",
    "print(df_dummy.item_price.mean())\n",
    "print(df_dummy.item_price.mean())\n",
    "print(df_dummy.item_price.std())\n",
    "\n",
    "ax = df_dummy.set_index('item_price').plot(kind='bar', figsize=(15,2))\n",
    "\n",
    "# fix axis\n",
    "n = 4 # this value should be ajusted according to the number of listings\n",
    "ticks = ax.xaxis.get_ticklocs()\n",
    "ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]  # <---- blearghk!!\n",
    "ax.xaxis.set_ticks(ticks[::n])\n",
    "ax.xaxis.set_ticklabels(ticklabels[::n])\n",
    "plt.xlabel('item_price')\n",
    "plt.ylabel('item_amount')\n",
    "plt.title(card_name + ' price distribution at ')\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape (959, 1)\n",
      "mean 23.16021897810219\n",
      "median 19.5\n",
      "std 13.869383254572936\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nNot gonna make or use dummy data now, gonna remove listings with highest prices first \\n'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Lets try to trim some of the most expensive cards\n",
    "'''\n",
    "\n",
    "'''\n",
    "In statistics, the standard score is the signed fractional number of standard deviations \n",
    "by which the value of an observation or data point is above the mean value of what \n",
    "is being observed or measured\n",
    "\n",
    "only values that are greater than the mean are to be excluded, \n",
    "because cheap items are desirable in the representation.\n",
    "threrefore, the absolute value of the zscore is not used\n",
    "'''\n",
    "df_repeated_z_iter_1 = df_repeated[(zscore(df_repeated.item_price) < 3)]\n",
    "df_filtered = df_repeated_z_iter_1\n",
    "df_filtered.boxplot(vert=False, figsize=(15,3))\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",
    "\n",
    "'''\n",
    "df_amount bar plot \n",
    "'''\n",
    "# based on filtering done to df_repeated, filter values in df_amount to display the bar plot\n",
    "df_ = df_amount.loc[df_amount.item_price.isin(df_filtered.item_price)]\n",
    "ax = df_.set_index('item_price').plot(kind='bar', figsize=(15,2))\n",
    "\n",
    "# fix axis\n",
    "n = 4 # this value should be ajusted according to the number of listings\n",
    "ticks = ax.xaxis.get_ticklocs()\n",
    "ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]  # <---- blearghk!!\n",
    "ax.xaxis.set_ticks(ticks[::n])\n",
    "ax.xaxis.set_ticklabels(ticklabels[::n])\n",
    "plt.xlabel('item_price')\n",
    "plt.ylabel('item_amount')\n",
    "plt.title('price distribution at ')\n",
    "\n",
    "'''\n",
    "df_dummy point count \n",
    "'''\n",
    "'''\n",
    "Not gonna make or use dummy data now, gonna remove listings with highest prices first \n",
    "'''\n",
    "\n",
    "# based on filtering done to df_repeated, filter values in df_dummy to display the bar plot\n",
    "# note this will include the rows where the item_amount is 0\n",
    "# also note that the row count is correct because there is no dummy row for the most expensive listing\n",
    "#'Ok, %d points, getting better '%df_dummy.loc[df_dummy.item_price <= df_filtered.item_price.max()].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape (934, 1)\n",
      "mean 21.144261241970018\n",
      "median 19.244999999999997\n",
      "std 6.366872293183135\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nNot gonna make or use dummy data now, gonna remove listings with highest prices first \\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "Lets try to trim some of the most expensive cards\n",
    "'''\n",
    "\n",
    "'''\n",
    "In statistics, the standard score is the signed fractional number of standard deviations \n",
    "by which the value of an observation or data point is above the mean value of what \n",
    "is being observed or measured\n",
    "\n",
    "only values that are greater than the mean are to be excluded, \n",
    "because cheap items are desirable in the representation.\n",
    "threrefore, the absolute value of the zscore is not used\n",
    "'''\n",
    "df_repeated_z_iter_2 = df_repeated_z_iter_1[(zscore(df_repeated_z_iter_1.item_price) < 3)]\n",
    "df_filtered = df_repeated_z_iter_2\n",
    "df_filtered.boxplot(vert=False, figsize=(15,3))\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",
    "\n",
    "'''\n",
    "df_amount bar plot \n",
    "'''\n",
    "# based on filtering done to df_repeated, filter values in df_amount to display the bar plot\n",
    "df_ = df_amount.loc[df_amount.item_price.isin(df_filtered.item_price)]\n",
    "ax = df_.set_index('item_price').plot(kind='bar', figsize=(15,2))\n",
    "\n",
    "# fix axis\n",
    "n = 4 # this value should be ajusted according to the number of listings\n",
    "ticks = ax.xaxis.get_ticklocs()\n",
    "ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]  # <---- blearghk!!\n",
    "ax.xaxis.set_ticks(ticks[::n])\n",
    "ax.xaxis.set_ticklabels(ticklabels[::n])\n",
    "plt.xlabel('item_price')\n",
    "plt.ylabel('item_amount')\n",
    "plt.title('price distribution at ')\n",
    "\n",
    "'''\n",
    "df_dummy point count \n",
    "'''\n",
    "'''\n",
    "Not gonna make or use dummy data now, gonna remove listings with highest prices first \n",
    "'''\n",
    "\n",
    "# based on filtering done to df_repeated, filter values in df_dummy to display the bar plot\n",
    "# note this will include the rows where the item_amount is 0\n",
    "# also note that the row count is correct because there is no dummy row for the most expensive listing\n",
    "#'Ok, %d points, getting better '%df_dummy.loc[df_dummy.item_price <= df_filtered.item_price.max()].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'df_dummy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(197, 2)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>item_price</th>\n",
       "      <th>item_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_price  item_amount\n",
       "0         0.0            0\n",
       "1         1.0            0\n",
       "2         2.0            0\n",
       "3         3.0            0\n",
       "4         4.0            0\n",
       "5         5.0            0\n",
       "6         6.0            0\n",
       "7         7.0            0\n",
       "8         8.0            0\n",
       "9         9.0            0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(197, 2)\n",
      "25.788680203045686\n",
      "13.549113655089569\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'price distribution at ')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_filtered = df_repeated_z_iter_2\n",
    "df_amount_filtered = df_amount.loc[df_amount.item_price.isin(df_filtered.item_price)]\n",
    "\n",
    "start = 0.0\n",
    "stop = df_amount_filtered.item_price.max()\n",
    "step = 1\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_amount_filtered).sort_values(by=['item_price']).reset_index(drop=True)\n",
    "display('df_dummy', df_dummy.shape)\n",
    "display(df_dummy.head(10))\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",
    "ax = df_plot.set_index('item_price').plot(kind='bar', figsize=(15,2))\n",
    "\n",
    "print(df_plot.shape)\n",
    "print(df_plot.item_price.mean())\n",
    "print(df_plot.item_price.std())\n",
    "\n",
    "# fix axis\n",
    "n = 25 # this value should be ajusted according to the number of listings\n",
    "ticks = ax.xaxis.get_ticklocs()\n",
    "ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]  # <---- blearghk!!\n",
    "ax.xaxis.set_ticks(ticks[::n])\n",
    "ax.xaxis.set_ticklabels(ticklabels[::n])\n",
    "plt.xlabel('item_price')\n",
    "plt.ylabel('item_amount')\n",
    "plt.title('price distribution at ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'price distribution at ')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\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()/110, 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",
    "ax = df_plot_binned.plot(kind='bar', figsize=(15,5))\n",
    "\n",
    "# fix axis\n",
    "n = 2 # this value should be ajusted according to the number of listings\n",
    "ticks = ax.xaxis.get_ticklocs()\n",
    "ticklabels = [l.get_text() for l in ax.xaxis.get_ticklabels()]  # <---- blearghk!!\n",
    "ax.xaxis.set_ticks(ticks[::n])\n",
    "ax.xaxis.set_ticklabels(ticklabels[::n])\n",
    "plt.xlabel('item_price')\n",
    "plt.ylabel('item_amount')\n",
    "plt.title('price distribution at ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106.05000000000001"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plot_.index.max()/.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5784545454545454"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plot_.index.max()/110"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}