examples/profiler-test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "inputHidden": false,
    "outputHidden": false
   },
   "source": [
    "# Profiler performance\n",
    "\n",
    "We use the part of the instacart data that you can find here https://www.instacart.com/datasets/grocery-shopping-2017\n",
    "\n",
    "Specically order_products__prior.csv a 4 columns, 33.2 Million rows csv file.\n",
    "\n",
    "It took 355.58 seconds to process all the data set in a Windows 10, \n",
    "Instacart data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"..\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Just check that Spark and all necessary environments vars are present...\n",
      "-----\n",
      "SPARK_HOME=C:\\opt\\spark\\spark-2.3.1-bin-hadoop2.7\n",
      "HADOOP_HOME=C:\\opt\\spark\\spark-2.3.1-bin-hadoop2.7\n",
      "You don't have PYSPARK_PYTHON set\n",
      "You don't have PYSPARK_DRIVER_PYTHON set\n",
      "JAVA_HOME=C:\\Program Files\\Java\\jdk1.8.0_181\n",
      "Pyarrow Installed\n",
      "-----\n",
      "Starting or getting SparkSession and SparkContext...\n",
      "\n",
      "                             ____        __  _                     \n",
      "                            / __ \\____  / /_(_)___ ___  __  _______\n",
      "                           / / / / __ \\/ __/ / __ `__ \\/ / / / ___/\n",
      "                          / /_/ / /_/ / /_/ / / / / / / /_/ (__  ) \n",
      "                          \\____/ .___/\\__/_/_/ /_/ /_/\\__,_/____/  \n",
      "                              /_/                                  \n",
      "                              \n",
      "Transform and Roll out...\n",
      "Setting checkpoint folder local. If you are in a cluster initialize Optimus with master='your_ip' as param\n",
      "Deleting previous folder if exists...\n",
      "Creating the checkpoint directory...\n",
      "Optimus successfully imported. Have fun :).\n"
     ]
    }
   ],
   "source": [
    "# Create optimus\n",
    "from optimus import Optimus\n",
    "op = Optimus(master=\"local[*]\", app_name = \"optimus\" ,verbose =True, checkpoint= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benchmark "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = op.load.csv(\"C:\\\\Users\\\\argenisleon\\\\Desktop\\\\order_products__prior.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "<div class=\"info_items\">Viewing 100 of 32.4 million rows / 4 columns</div>\n",
       "<div class=\"info_items\">8 partition(s)</div>\n",
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       "    <thead>\n",
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       "        <td>\n",
       "            47766\n",
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       "        <td>\n",
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       "        <td>\n",
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       "        <td>\n",
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       "        <td>\n",
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       "        <td>\n",
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       "        <td>\n",
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       "        <td>\n",
       "            11\n",
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       "        <td>\n",
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       "        <td>\n",
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       "        <td>\n",
       "            1\n",
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       "    \n",
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       "        <td>\n",
       "            11\n",
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       "        <td>\n",
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       "        <td>\n",
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       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            11\n",
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       "        \n",
       "        <td>\n",
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       "        \n",
       "        <td>\n",
       "            4\n",
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       "        \n",
       "        <td>\n",
       "            1\n",
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       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            11\n",
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       "        \n",
       "        <td>\n",
       "            31506\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            5\n",
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       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            12\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            30597\n",
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       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            12\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            15221\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            2\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            12\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            43772\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            3\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "\n",
       "<div class=\"info_items\">Viewing 100 of 32.4 million rows / 4 columns</div>\n",
       "<div class=\"info_items\">8 partition(s)</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing column 'order_id'...\n",
      "_count_data_types() executed in 18.69 sec\n",
      "count_data_types() executed in 18.69 sec\n",
      "cast_columns() executed in 0.01 sec\n",
      "_exprs() executed in 16.04 sec\n",
      "general_stats() executed in 16.05 sec\n",
      "------------------------------\n",
      "Processing column 'order_id'...\n",
      "frequency() executed in 23.65 sec\n",
      "stats_by_column() executed in 8.83 sec\n",
      "percentile() executed in 12.21 sec\n",
      "extra_numeric_stats() executed in 37.45 sec\n",
      "bucketizer() executed in 0.29 sec\n",
      "hist() executed in 14.6 sec\n",
      "dataset_info() executed in 22.43 sec\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
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       "    <h1>Overview</h1>\n",
       "</div>\n",
       "<div class=\"main\">\n",
       "    <div class=\"panel_profiler\">\n",
       "        <h2>Dataset info</h2>\n",
       "        <table>\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <td>Number of columns</td>\n",
       "                <td>4</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Number of rows</td>\n",
       "                <td>32434489</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Total Missing (%)</td>\n",
       "                <td>0.0%</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Total size in memory</td>\n",
       "                <td>188.4 MB</td>\n",
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       "    <div class=\"panel_profiler\">\n",
       "        <h2>Column types</h2>\n",
       "        <table>\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <td>String</td>\n",
       "                <td>0</td>\n",
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       "                <td>Numeric</td>\n",
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       "</div><style>\n",
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       "    <div class=\"info\">\n",
       "\n",
       "        \n",
       "\n",
       "        <div class=\"panel_profiler\">\n",
       "            <div>\n",
       "                <h2>order_id</h2>\n",
       "                <div>numeric</div>\n",
       "            </div>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>Unique</td>\n",
       "                    <td> 3025302</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Unique (%)</td>\n",
       "                    <td> 9.327</td>\n",
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       "                <tr>\n",
       "                    <td>Missing</td>\n",
       "                    <td>0.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Missing (%)</td>\n",
       "                    <td>0</td>\n",
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       "            <div>\n",
       "                <h3>\n",
       "                    Datatypes\n",
       "                </h3>\n",
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       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        String\n",
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       "                    <td>\n",
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       "                    <td>\n",
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       "                        32434489\n",
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       "                    <td>\n",
       "                        0\n",
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       "                    <td>\n",
       "                        Missing\n",
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       "                        0\n",
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       "\n",
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       "            </table>\n",
       "            \n",
       "            <div>\n",
       "                <h3>\n",
       "                    Basic Stats\n",
       "                </h3>\n",
       "\n",
       "            </div>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>Mean</td>\n",
       "                    <td>1710748.5189427834</td>\n",
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       "                <tr>\n",
       "                    <td>Minimum</td>\n",
       "                    <td>2</td>\n",
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       "                    <td>Maximum</td>\n",
       "                    <td>3421083</td>\n",
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       "                <tr>\n",
       "                    <td>Zeros(%)</td>\n",
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       "\n",
       "                </tbody>\n",
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       "            \n",
       "\n",
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       "            <h3>Frequency</h3>\n",
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       "                <tbody>\n",
       "                <th>Value</th>\n",
       "                <th>Count</th>\n",
       "                <th>Frequency (%)</th>\n",
       "                \n",
       "                <tr>\n",
       "                    <td>1564244</td>\n",
       "                    <td>145</td>\n",
       "                    <td>0.0%</td>\n",
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       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>790903</td>\n",
       "                    <td>137</td>\n",
       "                    <td>0.0%</td>\n",
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       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>61355</td>\n",
       "                    <td>127</td>\n",
       "                    <td>0.0%</td>\n",
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       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2970392</td>\n",
       "                    <td>121</td>\n",
       "                    <td>0.0%</td>\n",
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       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2069920</td>\n",
       "                    <td>116</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>3308010</td>\n",
       "                    <td>115</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2753324</td>\n",
       "                    <td>114</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2499774</td>\n",
       "                    <td>112</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2621625</td>\n",
       "                    <td>109</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>77151</td>\n",
       "                    <td>109</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>\"Missing\"</td>\n",
       "                    <td>0</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "        \n",
       "        <div class=\"panel_profiler\">\n",
       "\n",
       "\n",
       "            <h3>Quantile statistics</h3>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>Minimum</td>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>5-th percentile</td>\n",
       "                    <td>2.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Q1</td>\n",
       "                    <td>2.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Median</td>\n",
       "                    <td>2.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Q3</td>\n",
       "                    <td>2.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>95-th percentile</td>\n",
       "                    <td>2.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Maximum</td>\n",
       "                    <td>3421083</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Range</td>\n",
       "                    <td>3421081</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Interquartile range</td>\n",
       "                    <td>0.0</td>\n",
       "                </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"panel_profiler\">\n",
       "            <h3>Descriptive statistics</h3>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>Standard deviation</td>\n",
       "                    <td>987300.6964529774</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Coef of variation</td>\n",
       "                    <td>0.57712</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Kurtosis</td>\n",
       "                    <td>-1.199128348852751</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Mean</td>\n",
       "                    <td>1710748.5189427834</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>MAD</td>\n",
       "                    <td>0.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Skewness</td>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Sum</td>\n",
       "                    <td>55487254019416</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Variance</td>\n",
       "                    <td>974762665216.534</td>\n",
       "                </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </div>\n",
       "        \n",
       "    </div>\n",
       "    <table>\n",
       "        \n",
       "        <tr>\n",
       "\n",
       "            <td>\n",
       "\n",
       "                <div>\n",
       "                    <img 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7VqNGjVJxcbEyMjIkSRkZGdq4caMkqbi4WOnp6bLZbJo+fbqam5tVX1+vrVu3yu12KywsTKGhoXK73SopKVF9fb2OHTumGTNmyGazKT09vd1cnfUAAAAAgL7KH8CKiop09913S5IOHjwoh8MhSXI4HDp06JAkqa6uTlFRUf6DIyMjVVdX12U9MjKyQ72rHgAAAADQVwVJUktLi95++23dddddXQ4+9/2t89lstkuuX4r8/HwlJCQoISFBDQ0Nl3QsAAAAAPQmQdLZ73bdcMMNGjFihCRpxIgRqq+vlyTV19crPDxc0tkrWDU1Nf6Da2trFRER0WW9tra2Q72rHhfKyspSZWWlKisrNXz48G574gAAAABgtSBJWrdunf/jh5Lk8Xj8dzL0er2aP3++v15YWChjjMrLyxUcHCyHw6GkpCSVlpaqqalJTU1NKi0tVVJSkhwOhwYPHqzy8nIZY1RYWNhurs56AAAAAEBfZf/666/1xz/+Ub/5zW/8xWXLlik1NVUFBQUaOXKk1q9fL0lKTk7W5s2b5XQ6NXDgQK1Zs0aSFBYWphUrVigxMVGS9MQTTygsLEyStHr1ai1cuFAnT57UvHnzNG/evC57AAAAAEBfZR84cKAaGxvbFYcOHaqysrIOg202m1atWtXpRJmZmcrMzOxQT0hI0J49ezrUA/UAAAAAgL4q6NuHAAAAAAC6AwEMAAAAACxCAAMAAAAAixDAAAAAAMAiBDAAAAAAsAgBDAAAAAAsQgADAAAAAIsQwAAAAADAIgQwAAAAALAIAQwAAAAALEIAAwAAAACLEMAAAAAAwCIEMAAAAACwCAEMAAAAACxCAAMAAAAAixDAAAAAAMAiBDAAAAAAsEhQc3OzUlJSNH78eMXExOjDDz/UkSNH5Ha75XK55Ha71dTUJEkyxignJ0dOp1Px8fHauXOnfyKv1yuXyyWXyyWv1+uv79ixQ3FxcXI6ncrJyZExRpIC9gAAAACAvipo8eLFuvnmm/Xpp5/q448/VkxMjPLy8jRnzhz5fD7NmTNHeXl5kqQtW7bI5/PJ5/MpPz9f2dnZks6GqdzcXG3fvl0VFRXKzc31B6rs7Gzl5+f7jyspKZGkgD0AAAAAoK8K+uCDD7Ro0SJJUv/+/RUSEqLi4mJlZGRIkjIyMrRx40ZJUnFxsdLT02Wz2TR9+nQ1Nzervr5eW7duldvtVlhYmEJDQ+V2u1VSUqL6+nodO3ZMM2bMkM1mU3p6eru5OusBAAAAAH1V0PDhw/Xzn/9ckydP1n333acTJ07o4MGDcjgckiSHw6FDhw5Jkurq6hQVFeU/ODIyUnV1dV3WIyMjO9QlBewBAAAAAH1V0M6dO5Wdna1du3bpRz/6UZcfBTz3/a3z2Wy2S65fivz8fCUkJCghIUENDQ2XdCwAAAAA9CZBkZGRmjZtmiQpJSVFO3fu1IgRI1RfXy9Jqq+vV3h4uKSzV7Bqamr8B9fW1ioiIqLLem1tbYe6pIA9LpSVlaXKykpVVlZq+PDh3fjUAQAAAMBaQVFRUfq///s/SVJZWZkmTJggj8fjv5Oh1+vV/PnzJUkej0eFhYUyxqi8vFzBwcFyOBxKSkpSaWmpmpqa1NTUpNLSUiUlJcnhcGjw4MEqLy+XMUaFhYXt5uqsBwAAAAD0VfaXX35Z99xzj1paWhQdHa01a9bozJkzSk1NVUFBgUaOHKn169dLkpKTk7V582Y5nU4NHDhQa9askSSFhYVpxYoVSkxMlCQ98cQTCgsLkyStXr1aCxcu1MmTJzVv3jzNmzdPkrRs2bJOewAAAABAX2WfNGmSKisrOzxQVlbWoWaz2bRq1apOJ8rMzFRmZmaHekJCgvbs2dOhPnTo0E57AAAAAEBfFdTTCwAAAACAHwoCGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFCGAAAAAAYBECGAAAAABYhAAGAAAAABYhgAEAAACARYJGjx6tuLg4TZo0SQkJCZKkI0eOyO12y+Vyye12q6mpSZJkjFFOTo6cTqfi4+O1c+dO/0Rer1cul0sul0ter9df37Fjh+Li4uR0OpWTkyNjTJc9AAAAAKCvCpKkP/3pT9q9e7cqKyslSXl5eZozZ458Pp/mzJmjvLw8SdKWLVvk8/nk8/mUn5+v7OxsSWfDVG5urrZv366Kigrl5ub6A1V2drby8/P9x5WUlHTZAwAAAAD6qk4/glhcXKyMjAxJUkZGhjZu3Oivp6eny2azafr06WpublZ9fb22bt0qt9utsLAwhYaGyu12q6SkRPX19Tp27JhmzJghm82m9PT0dnN11gMAAAAA+qogm82muXPnasqUKcrPz5ckHTx4UA6HQ5LkcDh06NAhSVJdXZ2ioqL8B0dGRqqurq7LemRkZId6Vz0AAAAAoK+y/+Uvf1FERIQOHTokt9ut8ePHBxx87vtb57PZbJdcvxT5+fn+YNjQ0HBJxwIAAABAbxIUEREhSQoPD9ftt9+uiooKjRgxQvX19ZKk+vp6hYeHSzp7BaumpsZ/cG1trSIiIrqs19bWdqhLCtjjQllZWaqsrFRlZaWGDx/ejU8dAAAAAKwV9NVXX0mSTpw4odLSUk2cOFEej8d/J0Ov16v58+dLkjwejwoLC2WMUXl5uYKDg+VwOJSUlKTS0lI1NTWpqalJpaWlSkpKksPh0ODBg1VeXi5jjAoLC9vN1VkPAAAAAOir7DfeeKMkqbW1VX/3d3+nm2++WYmJiUpNTVVBQYFGjhyp9evXS5KSk5O1efNmOZ1ODRw4UGvWrJEkhYWFacWKFUpMTJQkPfHEEwoLC5MkrV69WgsXLtTJkyc1b948zZs3T5K0bNmyTnsAAAAAQF9l//jjjzsUhw4dqrKysg51m82mVatWdTpRZmamMjMzO9QTEhK0Z8+ei+4BAAAAAH1Vp7ehBwAAAAB0PwIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFCGAAAAAAYBECGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFgiSpra1NkydP1i233CJJqq6u1rRp0+RyubRgwQK1tLRIkk6dOqUFCxbI6XRq2rRp2r9/v3+ilStXyul0aty4cdq6dau/XlJSonHjxsnpdCovL89fD9QDAAAAAPqqIEl68cUXFRMT4y8uXbpUS5Yskc/nU2hoqAoKCiRJBQUFCg0N1eeff64lS5Zo6dKlkqSqqioVFRVp7969Kikp0YMPPqi2tja1tbXpoYce0pYtW1RVVaV169apqqqqyx4AAAAA0FcF1dbWatOmTbrvvvskScYYvffee0pJSZEkZWRkaOPGjZKk4uJiZWRkSJJSUlJUVlYmY4yKi4uVlpamAQMGaMyYMXI6naqoqFBFRYWcTqeio6PVv39/paWlqbi4uMseAAAAANBXBT3yyCP61a9+paCgs18Ha2xsVEhIiOx2uyQpMjJSdXV1kqS6ujpFRUVJkux2u4KDg9XY2Niufv4xgepd9QAAAACAviooPDxcU6ZM8ReMMR0G2Wy2Lh/rrnpn8vPzlZCQoISEBDU0NAR+JgAAAADQy9nffvttbd68Wd98842OHTumRx55RM3NzWptbZXdbldtba0iIiIknb1SVVNTo8jISLW2turo0aMKCwvz1885/5jO6sOGDQvY40JZWVnKysqSJCUkJFyu1wEAAAAALrug2tpa7d+/X0VFRZo9e7beeOMNzZo1Sxs2bJAkeb1ezZ8/X5Lk8Xjk9XolSRs2bNDs2bNls9nk8XhUVFSkU6dOqbq6Wj6fT1OnTlViYqJ8Pp+qq6vV0tKioqIieTwe2Wy2gD0AAAAAoK/q9P8D9uyzz+rXv/61nE6nGhsbtWjRIknSokWL1NjYKKfTqV//+tf+28rHxsYqNTVVEyZM0M0336xVq1apX79+stvteuWVV5SUlKSYmBilpqYqNja2yx4AAAAA0FfZz/1l5syZmjlzpiQpOjpaFRUVHQZfffXVWr9+facTLV++XMuXL+9QT05OVnJycod6oB4AAAAA0Fd1egUMAAAAAND9CGAAAAAAYBECGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFCGAAAAAAYBECGAAAAABYhAAGAAAAABYJmjp1qq6//nrFxsbqySeflCRVV1dr2rRpcrlcWrBggVpaWiRJp06d0oIFC+R0OjVt2jTt37/fP9HKlSvldDo1btw4bd261V8vKSnRuHHj5HQ6lZeX568H6gEAAAAAfVXQe++9p48//li7d+9WSUmJysvLtXTpUi1ZskQ+n0+hoaEqKCiQJBUUFCg0NFSff/65lixZoqVLl0qSqqqqVFRUpL1796qkpEQPPvig2tra1NbWpoceekhbtmxRVVWV1q1bp6qqKkkK2AMAAAAA+qqgQYMGSZJOnz6t06dPy2az6b333lNKSookKSMjQxs3bpQkFRcXKyMjQ5KUkpKisrIyGWNUXFystLQ0DRgwQGPGjJHT6VRFRYUqKirkdDoVHR2t/v37Ky0tTcXFxTLGBOwBAAAAAH1VUFtbmyZNmqTw8HC53W6NHTtWISEhstvtkqTIyEjV1dVJkurq6hQVFSVJstvtCg4OVmNjY7v6+ccEqjc2NgbsAQAAAAB9VVC/fv20e/du1dbWqqKiQp988kmHQTabTZJkjOn0se6qdyY/P18JCQlKSEhQQ0PDtz4hAAAAAOit/HdBDAkJ0cyZM1VeXq7m5ma1trZKkmpraxURESHp7JWqmpoaSVJra6uOHj2qsLCwdvXzjwlUHzZsWMAeF8rKylJlZaUqKys1fPjwbn76AAAAAGCdoObmZknSyZMn9e677yomJkazZs3Shg0bJEler1fz58+XJHk8Hnm9XknShg0bNHv2bNlsNnk8HhUVFenUqVOqrq6Wz+fT1KlTlZiYKJ/Pp+rqarW0tKioqEgej0c2my1gDwAAAADoq+yzZs1SW1ubzpw5o9TUVN1yyy2aMGGC0tLS9Pjjj2vy5MlatGiRJGnRokW699575XQ6FRYWpqKiIklSbGysUlNTNWHCBNntdq1atUr9+vWTJL3yyitKSkpSW1ubMjMzFRsbK0l69tlnO+0BAAAAAH2VfdeuXR2K0dHRqqio6FC/+uqrtX79+k4nWr58uZYvX96hnpycrOTk5IvuAQAAAAB9VdC3DwEAAAAAdAcCGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFCGAAAAAAYBECGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGCRoFmzZikmJkaxsbF68cUXJUlHjhyR2+2Wy+WS2+1WU1OTJMkYo5ycHDmdTsXHx2vnzp3+ibxer1wul1wul7xer7++Y8cOxcXFyel0KicnR8aYLnsAAAAAQF8V9Nxzz+mTTz5ReXm5Vq1apaqqKuXl5WnOnDny+XyaM2eO8vLyJElbtmyRz+eTz+dTfn6+srOzJZ0NU7m5udq+fbsqKiqUm5vrD1TZ2dnKz8/3H1dSUiJJAXsAAAAAQF8VdMMNN0iSBg8erJiYGNXV1am4uFgZGRmSpIyMDG3cuFGSVFxcrPT0dNlsNk2fPl3Nzc2qr6/X1q1b5Xa7FRYWptDQULndbpWUlKi+vl7Hjh3TjBkzZLPZlJ6e3m6uznoAAAAAQF/l/w7Y/v37tWvXLk2bNk0HDx6Uw+GQJDkcDh06dEiSVFdXp6ioKP/BkZGRqqur67IeGRnZoS4pYA8AAAAA6KvsknT8+HHdeeedeuGFFzRkyJCAg899f+t8NpvtkuuXIj8/X/n5+ZKkhoaGSzoWAAAAAHqToNOnT+vOO+/UPffcozvuuEOSNGLECNXX10uS6uutxBSAAAAQzElEQVTrFR4eLunsFayamhr/wbW1tYqIiOiyXltb26HeVY8LZWVlqbKyUpWVlRo+fHg3PnUAAAAAsFbQokWLFBMTo0cffdRf9Hg8/jsZer1ezZ8/318vLCyUMUbl5eUKDg6Ww+FQUlKSSktL1dTUpKamJpWWliopKUkOh0ODBw9WeXm5jDEqLCxsN1dnPQAAAACgr7K//vrriouL06RJkyRJzzzzjJYtW6bU1FQVFBRo5MiRWr9+vSQpOTlZmzdvltPp1MCBA7VmzRpJUlhYmFasWKHExERJ0hNPPKGwsDBJ0urVq7Vw4UKdPHlS8+bN07x58yQpYA8AAAAA6KvsnX1PS5LKyso61Gw2m1atWtXp+MzMTGVmZnaoJyQkaM+ePR3qQ4cO7bQHAAAAAPRVQd8+BAAAAADQHQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFCGAAAAAAYBECGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWCcrMzFR4eLgmTpzoLx45ckRut1sul0tut1tNTU2SJGOMcnJy5HQ6FR8fr507d/qP8Xq9crlccrlc8nq9/vqOHTsUFxcnp9OpnJwcGWO67AEAAAAAfVXQwoULVVJS0q6Yl5enOXPmyOfzac6cOcrLy5MkbdmyRT6fTz6fT/n5+crOzpZ0Nkzl5uZq+/btqqioUG5urj9QZWdnKz8/33/cuV6BegAAAABAXxV00003KSwsrF2xuLhYGRkZkqSMjAxt3LjRX09PT5fNZtP06dPV3Nys+vp6bd26VW63W2FhYQoNDZXb7VZJSYnq6+t17NgxzZgxQzabTenp6e3m6qwHAAAAAPRVnX4H7ODBg3I4HJIkh8OhQ4cOSZLq6uoUFRXlHxcZGam6urou65GRkR3qXfUAAAAAgL7KfimDz31/63w2m+2S65ciPz9f+fn5kqSGhoZLOhYAAAAAepNOr4CNGDFC9fX1kqT6+nqFh4dLOnsFq6amxj+utrZWERERXdZra2s71LvqcaGsrCxVVlaqsrJSw4cP/z7PFQAAAAB6VKcBzOPx+O9k6PV6NX/+fH+9sLBQxhiVl5crODhYDodDSUlJKi0tVVNTk5qamlRaWqqkpCQ5HA4NHjxY5eXlMsaosLCw3Vyd9QAAAACAvsp+99136/3339fhw4cVGRmp3NxcLVu2TKmpqSooKNDIkSO1fv16SVJycrI2b94sp9OpgQMHas2aNZKksLAwrVixQomJiZKkJ554wn9jj9WrV2vhwoU6efKk5s2bp3nz5klSwB4AAAAA0FfZ161b1+kDZWVlHWo2m02rVq3qdHxmZqYyMzM71BMSErRnz54O9aFDh3baAwAAAAD6qk4/gggAAAAA6H4EMAAAAACwCAEMAAAAACxCAAMAAAAAixDAAAAAAMAiBDAAAAAAsAgBDAAAAAAsQgADAAAAAIsQwAAAAADAIgQwAAAAALAIAQwAAAAALEIAAwAAAACLEMAAAAAAwCIEMAAAAACwCAEMAAAAACxCAAMAAAAAi/R4ACspKdG4cePkdDqVl5fX08sBAAAAgMumRwNYW1ubHnroIW3ZskVVVVVat26dqqqqenJJAAAAAHDZ9GgAq6iokNPpVHR0tPr376+0tDQVFxf35JIAAAAA4LLp0QBWV1enqKgo/8+RkZGqq6vrwRUBAAAAwOVjM8aYnmq+fv16bd26Va+99pok6fXXX1dFRYVefvll/5j8/Hzl5+dLkj799FONHz++R9bamYaGBg0fPrynl4GLxH5dedizKwv7dWVhv64s7NeVhf26snTnfu3fv1+HDx/ucoy9Wzp9R5GRkaqpqfH/XFtbq4iIiHZjsrKylJWVZfXSLkpCQoIqKyt7ehm4SOzXlYc9u7KwX1cW9uvKwn5dWdivK4vV+9WjH0FMTEyUz+dTdXW1WlpaVFRUJI/H05NLAgAAAIDLpkevgNntdr3yyitKSkpSW1ubMjMzFRsb25NLAgAAAIDLpt9TTz31VE8uwOVy6Re/+IUWL16sm266qSeX8p1MmTKlp5eAS8B+XXnYsysL+3VlYb+uLOzXlYX9urJYuV89ehMOAAAAAPgh6dHvgAEAAADADwkB7DsqKSnRuHHj5HQ6lZeX19PL6fNGjx6tuLg4TZo0SQkJCZKkI0eOyO12y+Vyye12q6mpSZJkjFFOTo6cTqfi4+O1c+dO/zxer1cul0sul0ter9df37Fjh+Li4uR0OpWTk6NzF4YD9UBHmZmZCg8P18SJE/21ntyjrnqg8/166qmndO2112rSpEmaNGmSNm/e7H9s5cqVcjqdGjdunLZu3eqvB3ovrK6u1rRp0+RyubRgwQK1tLRIkk6dOqUFCxbI6XRq2rRp2r9//7f2gFRTU6NZs2YpJiZGsbGxevHFFyVxjvVWgfaLc6x3+uabbzR16lRdf/31io2N1ZNPPimpe1/j7tzHH7pA+7Vw4UKNGTPGf37t3r1bUi99PzS4ZK2trSY6Otrs27fPnDp1ysTHx5u9e/f29LL6tFGjRpmGhoZ2tX/8x380K1euNMYYs3LlSvNP//RPxhhjNm3aZG6++WZz5swZ8+GHH5qpU6caY4xpbGw0Y8aMMY2NjebIkSNmzJgx5siRI8YYYxITE822bdvMmTNnzM0332w2b97cZQ909Oc//9ns2LHDxMbG+ms9uUeBeuCszvbrySefNP/6r//aYezevXtNfHy8+eabb8wXX3xhoqOjTWtra5fvhXfddZdZt26dMcaYBx54wLz66qvGGGNWrVplHnjgAWOMMevWrTOpqald9sBZBw4cMDt27DDGGHPs2DHjcrnM3r17Ocd6qUD7xTnWO505c8Z89dVXxhhjWlpazNSpU82HH37Yba9xd+4jAu9XRkaGWb9+fYfxvfH9kAD2HWzbts3MnTvX//MzzzxjnnnmmR5cUd/XWQC77rrrzIEDB4wxZ/+xu+6664wxxmRlZZk333yzw7g333zTZGVl+evnxh04cMCMGzfOXz9/XKAe6Fx1dXW7/6DvyT0K1AP/34X7Feg/Di98j5s7d67Ztm1bwPfCM2fOmKFDh5rTp08bY9q/Z5471hhjTp8+bYYOHWrOnDkTsAc65/F4TGlpKefYFeLcfnGO9X4nTpwwkydPNuXl5d32GnfnPqK98/crUADrje+HfATxO6irq1NUVJT/58jISNXV1fXgivo+m82muXPnasqUKcrPz5ckHTx4UA6HQ5LkcDh06NAhSYH3p6t6ZGRkh3pXPXBxenKPOE+/m1deeUXx8fHKzMz0f7TiUversbFRISEhstvt7eoXzmW32xUcHKzGxkb26xLs379fu3bt0rRp0zjHrgDn75fEOdZbtbW1adKkSQoPD5fb7dbYsWO77TXuzn3EWRfu17nza/ny5YqPj9eSJUt06tQpSb3z/ZAA9h2YTm4cabPZemAlPxx/+ctftHPnTm3ZskWrVq3SBx98EHBsoP251DouHyv2iH29dNnZ2dq3b592794th8Ohf/iHf5DUvfvFefj9HD9+XHfeeadeeOEFDRkyJOA4zrHe4cL94hzrvfr166fdu3ertrZWFRUV+uSTTzqM+a6vcXfuI866cL/27NmjlStX6tNPP9VHH32kI0eO6Nlnn5XUO98PCWDfQWRkpGpqavw/19bWKiIiogdX1Pede33Dw8N1++23q6KiQiNGjFB9fb0kqb6+XuHh4ZIC709X9dra2g51SQF74OL05B5xnl66ESNGqF+/fgoKCtL999+viooKSZe+X8OGDVNzc7NaW1vb1S+cq7W1VUePHlVYWBj7dRFOnz6tO++8U/fcc4/uuOMOSZxjvVmg/eIc691CQkI0c+ZMlZeXd9tr3J37iPbO7VdJSYkcDodsNpsGDBign//859/5/LLi/ZAA9h0kJibK5/OpurpaLS0tKioqksfj6ell9VknTpzQV1995f97aWmpJk6cKI/H479jjdfr1fz58yVJHo9HhYWFMsaovLxcwcHBcjgcSkpKUmlpqZqamtTU1KTS0lIlJSXJ4XBo8ODBKi8vlzFGhYWF7ebqrAcuTk/uUaAeCOzcPyqS9N///d/+OyR6PB4VFRXp1KlTqq6uls/n09SpUwO+F9psNs2aNUsbNmyQ1HFfzu3Xhg0bNHv2bNlstoA9cJYxRosWLVJMTIweffRRf51zrHcKtF+cY71TQ0ODmpubJUknT57Uu+++q5iYmG57jbtzH9H5fo0fP95/fhljtHHjxnbnV697Pwz47TB0adOmTcblcpno6Gjz9NNP9/Ry+rR9+/aZ+Ph4Ex8fbyZMmOB/vQ8fPmxmz55tnE6nmT17tmlsbDTGnL07zoMPPmiio6PNxIkTzUcffeSfq6CgwIwdO9aMHTvW/Md//Ie//tFHH5nY2FgTHR1tHnroIf8XXQP1QEdpaWnmxz/+sbHb7ebaa681r732Wo/uUVc90Pl+/exnPzMTJ040cXFx5tZbb233BeKnn37aREdHm+uuu85/NyhjAr8X7tu3zyQmJpqxY8ealJQU88033xhjjDl58qRJSUkxY8eONYmJiWbfvn3f2gPG/M///I+RZOLi4sz1119vrr/+erNp0ybOsV4q0H5xjvVOH3/8sZk0aZKJi4szsbGxJjc31xjTva9xd+7jD12g/Zo1a5aZOHGiiY2NNffcc4//Tom98f3QZkwnH1oEAAAAAHQ7PoIIAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFCGAAAAAAYBECGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFCGAAAAAAYBECGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFCGAAAAAAYBECGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFCGAAAAAAYBECGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFCGAAAAAAYBECGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFCGAAAAAAYBECGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFiEAAYAAAAAFiGAAQAAAIBFCGAAAAAAYBECGAAAAABYhAAGAAAAABYhgAEAAACARQhgAAAAAGARAhgAAAAAWIQABgAAAAAWIYABAAAAgEUIYAAAAABgEQIYAAAAAFjk/wHT/Xx6WR+bOgAAAABJRU5ErkJggg==\">\n",
       "                </div>\n",
       "            </td>\n",
       "        </tr>\n",
       "        \n",
       "        \n",
       "        <tr>\n",
       "            <td>\n",
       "                <div>\n",
       "                    <img 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\">\n",
       "                </div>\n",
       "\n",
       "            </td>\n",
       "\n",
       "        </tr>\n",
       "        \n",
       "        \n",
       "        \n",
       "        \n",
       "        \n",
       "        \n",
       "\n",
       "    </table>\n",
       "</div><style>\n",
       "    .data_type {\n",
       "        font-size: 0.8em;\n",
       "        font-weight: normal;\n",
       "    }\n",
       "\n",
       "    .column_name {\n",
       "        font-size: 1.2em;\n",
       "    }\n",
       "\n",
       "    .info_items {\n",
       "        margin: 10px 0;\n",
       "        font-size: 0.8em;\n",
       "    }\n",
       "\n",
       "    .optimus_table tr:nth-child(even) {\n",
       "        background-color: #f2f2f2 !important;\n",
       "    }\n",
       "\n",
       "    .optimus_table tr:nth-child(odd) {\n",
       "        background-color: #ffffff !important;\n",
       "    }\n",
       "\n",
       "    .optimus_table thead {\n",
       "        border-bottom: 1px solid black;\n",
       "    }\n",
       "    .optimus_table{\n",
       "        font-size: 12px;\n",
       "    }\n",
       "\n",
       "</style>\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 10 of 32.4 million rows / 4 columns</div>\n",
       "<div class=\"info_items\">8 partition(s)</div>\n",
       "\n",
       "<table class=\"optimus_table\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">order_id</div>\n",
       "            <div class=\"data_type\">1 (int)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">product_id</div>\n",
       "            <div class=\"data_type\">2 (int)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">add_to_cart_order</div>\n",
       "            <div class=\"data_type\">3 (int)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">reordered</div>\n",
       "            <div class=\"data_type\">4 (int)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "    </tr>\n",
       "\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            2\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            33120\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            2\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            28985\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            2\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            2\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            9327\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            3\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            0\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            2\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            45918\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            4\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            2\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            30035\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            5\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            0\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            2\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            17794\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            6\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            2\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            40141\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            7\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            2\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1819\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            8\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            2\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            43668\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            9\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            0\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            3\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            33754\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            1\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "\n",
       "<div class=\"info_items\">Viewing 10 of 32.4 million rows / 4 columns</div>\n",
       "<div class=\"info_items\">8 partition(s)</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "run() executed in 186.8 sec\n"
     ]
    }
   ],
   "source": [
    "op.profiler.run(df, \"order_id\", infer=False, relative_error=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing column 'order_id'...\n",
      "_count_data_types() executed in 21.72 sec\n",
      "count_data_types() executed in 21.72 sec\n",
      "cast_columns() executed in 0.01 sec\n",
      "_exprs() executed in 17.72 sec\n",
      "general_stats() executed in 17.73 sec\n",
      "------------------------------\n",
      "Processing column 'order_id'...\n",
      "frequency() executed in 25.8 sec\n",
      "stats_by_column() executed in 9.99 sec\n",
      "percentile() executed in 13.46 sec\n",
      "extra_numeric_stats() executed in 39.63 sec\n",
      "bucketizer() executed in 0.3 sec\n",
      "hist() executed in 14.25 sec\n",
      "dataset_info() executed in 22.55 sec\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
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       "\n",
       "\n",
       "</style>\n",
       "\n",
       "<div class=\"title_profiler\">\n",
       "    <h1>Overview</h1>\n",
       "</div>\n",
       "<div class=\"main\">\n",
       "    <div class=\"panel_profiler\">\n",
       "        <h2>Dataset info</h2>\n",
       "        <table>\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <td>Number of columns</td>\n",
       "                <td>4</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Number of rows</td>\n",
       "                <td>32434489</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Total Missing (%)</td>\n",
       "                <td>0.0%</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Total size in memory</td>\n",
       "                <td>8.3 MB</td>\n",
       "\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"panel_profiler\">\n",
       "        <h2>Column types</h2>\n",
       "        <table>\n",
       "            <tbody>\n",
       "            <tr>\n",
       "                <td>String</td>\n",
       "                <td>0</td>\n",
       "\n",
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       "            <tr>\n",
       "                <td>Numeric</td>\n",
       "                <td>1</td>\n",
       "\n",
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       "                <td>Date</td>\n",
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       "                <td>Not available</td>\n",
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       "</div><style>\n",
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       "\n",
       "\n",
       "</style>\n",
       "\n",
       "<div class=\"main\">\n",
       "    <div class=\"info\">\n",
       "\n",
       "        \n",
       "\n",
       "        <div class=\"panel_profiler\">\n",
       "            <div>\n",
       "                <h2>order_id</h2>\n",
       "                <div>numeric</div>\n",
       "            </div>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>Unique</td>\n",
       "                    <td> 3025302</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Unique (%)</td>\n",
       "                    <td> 9.327</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Missing</td>\n",
       "                    <td>0.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Missing (%)</td>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "            <div>\n",
       "                <h3>\n",
       "                    Datatypes\n",
       "                </h3>\n",
       "            </div>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        String\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
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       "                    <td>\n",
       "                        Integer\n",
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       "                    <td>\n",
       "                        32434489\n",
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       "                    <td>\n",
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       "                        0\n",
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       "                    <td>\n",
       "                        Date\n",
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       "                    <td>\n",
       "                        0\n",
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       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Missing\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
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       "                    <td>\n",
       "                        Null\n",
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       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "\n",
       "                </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "            \n",
       "            <div>\n",
       "                <h3>\n",
       "                    Basic Stats\n",
       "                </h3>\n",
       "\n",
       "            </div>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>Mean</td>\n",
       "                    <td>1710748.5189427834</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Minimum</td>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Maximum</td>\n",
       "                    <td>3421083</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Zeros(%)</td>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "\n",
       "                </tbody>\n",
       "            </table>\n",
       "            \n",
       "\n",
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       "        <div class=\"panel_profiler\">\n",
       "            <h3>Frequency</h3>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <th>Value</th>\n",
       "                <th>Count</th>\n",
       "                <th>Frequency (%)</th>\n",
       "                \n",
       "                <tr>\n",
       "                    <td>1564244</td>\n",
       "                    <td>145</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>790903</td>\n",
       "                    <td>137</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>61355</td>\n",
       "                    <td>127</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2970392</td>\n",
       "                    <td>121</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2069920</td>\n",
       "                    <td>116</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>3308010</td>\n",
       "                    <td>115</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2753324</td>\n",
       "                    <td>114</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2499774</td>\n",
       "                    <td>112</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2621625</td>\n",
       "                    <td>109</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>77151</td>\n",
       "                    <td>109</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>\"Missing\"</td>\n",
       "                    <td>0</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
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       "        \n",
       "\n",
       "        \n",
       "        <div class=\"panel_profiler\">\n",
       "\n",
       "\n",
       "            <h3>Quantile statistics</h3>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>Minimum</td>\n",
       "                    <td>2</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>5-th percentile</td>\n",
       "                    <td>2.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Q1</td>\n",
       "                    <td>2.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Median</td>\n",
       "                    <td>2.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Q3</td>\n",
       "                    <td>2.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>95-th percentile</td>\n",
       "                    <td>2.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Maximum</td>\n",
       "                    <td>3421083</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Range</td>\n",
       "                    <td>3421081</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Interquartile range</td>\n",
       "                    <td>0.0</td>\n",
       "                </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </div>\n",
       "        <div class=\"panel_profiler\">\n",
       "            <h3>Descriptive statistics</h3>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>Standard deviation</td>\n",
       "                    <td>987300.6964529774</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Coef of variation</td>\n",
       "                    <td>0.57712</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Kurtosis</td>\n",
       "                    <td>-1.199128348852751</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Mean</td>\n",
       "                    <td>1710748.5189427834</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>MAD</td>\n",
       "                    <td>0.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Skewness</td>\n",
       "                    <td>0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Sum</td>\n",
       "                    <td>55487254019416</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Variance</td>\n",
       "                    <td>974762665216.534</td>\n",
       "                </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </div>\n",
       "        \n",
       "    </div>\n",
       "    <table>\n",
       "        \n",
       "        <tr>\n",
       "\n",
       "            <td>\n",
       "\n",
       "                <div>\n",
       "                    <img 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\">\n",
       "                </div>\n",
       "            </td>\n",
       "        </tr>\n",
       "        \n",
       "        \n",
       "        <tr>\n",
       "            <td>\n",
       "                <div>\n",
       "                    <img 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\">\n",
       "                </div>\n",
       "\n",
       "            </td>\n",
       "\n",
       "        </tr>\n",
       "        \n",
       "        \n",
       "        \n",
       "        \n",
       "        \n",
       "        \n",
       "\n",
       "    </table>\n",
       "</div><style>\n",
       "    .data_type {\n",
       "        font-size: 0.8em;\n",
       "        font-weight: normal;\n",
       "    }\n",
       "\n",
       "    .column_name {\n",
       "        font-size: 1.2em;\n",
       "    }\n",
       "\n",
       "    .info_items {\n",
       "        margin: 10px 0;\n",
       "        font-size: 0.8em;\n",
       "    }\n",
       "\n",
       "    .optimus_table tr:nth-child(even) {\n",
       "        background-color: #f2f2f2 !important;\n",
       "    }\n",
       "\n",
       "    .optimus_table tr:nth-child(odd) {\n",
       "        background-color: #ffffff !important;\n",
       "    }\n",
       "\n",
       "    .optimus_table thead {\n",
       "        border-bottom: 1px solid black;\n",
       "    }\n",
       "    .optimus_table{\n",
       "        font-size: 12px;\n",
       "    }\n",
       "\n",
       "</style>\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 10 of 32.4 million rows / 4 columns</div>\n",
       "<div class=\"info_items\">8 partition(s)</div>\n",
       "\n",
       "<table class=\"optimus_table\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">order_id</div>\n",
       "            <div class=\"data_type\">1 (int)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">product_id</div>\n",
       "            <div class=\"data_type\">2 (int)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">add_to_cart_order</div>\n",
       "            <div class=\"data_type\">3 (int)</div>\n",
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       "            <div class=\"data_type\">4 (int)</div>\n",
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       "            1\n",
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       "        <td>\n",
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       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "\n",
       "<div class=\"info_items\">Viewing 10 of 32.4 million rows / 4 columns</div>\n",
       "<div class=\"info_items\">8 partition(s)</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "run() executed in 199.09 sec\n"
     ]
    }
   ],
   "source": [
    "op.profiler.run(df, \"order_id\", infer=True, relative_error=1)"
   ]
  }
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