readme/readme_.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Logo Optimus](https://raw.githubusercontent.com/ironmussa/Optimus/master/images/optimus-logo.png)](https://hioptimus.com) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![PyPI version](https://badge.fury.io/py/optimuspyspark.svg)](https://badge.fury.io/py/optimuspyspark) [![Build Status](https://travis-ci.org/ironmussa/Optimus.svg?branch=master)](https://travis-ci.org/ironmussa/Optimus) [![Documentation Status](https://readthedocs.org/projects/optimus-ironmussa/badge/?version=latest)](http://optimus-ironmussa.readthedocs.io/en/latest/?badge=latest)  [![built_by iron](https://img.shields.io/badge/built_by-iron-FF69A4.svg)](http://ironmussa.com) [![Updates](https://pyup.io/repos/github/ironmussa/Optimus/shield.svg)](https://pyup.io/repos/github/ironmussa/Optimus/)  [![GitHub release](https://img.shields.io/github/release/ironmussa/optimus.svg)](https://github.com/ironmussa/Optimus/) \n",
    "[![Codacy Badge](https://api.codacy.com/project/badge/Grade/02b3ba0fe2b64d6297c6b8320f8b15a7)](https://www.codacy.com/app/argenisleon/Optimus?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=ironmussa/Optimus&amp;utm_campaign=Badge_Grade)\n",
    "[![Coverage Status](https://coveralls.io/repos/github/ironmussa/Optimus/badge.svg?branch=master)](https://coveralls.io/github/ironmussa/Optimus?branch=master) [![Mentioned in Awesome Data Science](https://awesome.re/mentioned-badge.svg)](https://github.com/bulutyazilim/awesome-datascience)![Discord](https://img.shields.io/discord/579030865468719104.svg)\n",
    "\n",
    "[![Downloads](https://pepy.tech/badge/optimuspyspark)](https://pepy.tech/project/optimuspyspark)\n",
    "[![Downloads](https://pepy.tech/badge/optimuspyspark/month)](https://pepy.tech/project/optimuspyspark/month)\n",
    "[![Downloads](https://pepy.tech/badge/optimuspyspark/week)](https://pepy.tech/project/optimuspyspark/week)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.7.6\n"
     ]
    }
   ],
   "source": [
    "!python --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.4.5'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To launch a live notebook server to test optimus using binder or Colab, click on one of the following badges:\n",
    "\n",
    "[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/ironmussa/Optimus/master)\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ironmussa/Optimus/blob/master/examples/10_min_from_spark_to_pandas_with_optimus.ipynb)\n",
    "\n",
    "PyOptimus is the missing framework to profile, clean, process and plot small and big data. PyOptimus run over pandas, Dask, cuDF, Dask-cuDF, Spark, Vaex so you can use any of this librearies withou changing your code.\n",
    "\n",
    "## Installation (pip):  \n",
    "  \n",
    "In your terminal just type  `pip install pyoptimus`\n",
    "\n",
    "### Requirements\n",
    "* Python>=3.8  \n",
    "\n",
    "For the engines\n",
    "* RAPIDS >= 0.19\n",
    "* Dask >= 2021.2.0\n",
    "* Vaex >= 4.1\n",
    "* Apache Spark >= 21.8 \n",
    "* Ibis(WIP)\n",
    "\n",
    "## Why PyOptimus\n",
    "\n",
    "Why so many engines\n",
    "Every engine has\n",
    "\n",
    "## Examples \n",
    "\n",
    "You can go to the 10 minutes to Optimus [notebook](https://github.com/ironmussa/Optimus/blob/master/examples/10_min_from_spark_to_pandas_with_optimus.ipynb) where you can find the basic to start working. \n",
    "\n",
    "Also you can go to the [examples](examples/) folder to found specific notebooks about data cleaning, data wrangling, profiling and how to create ML.\n",
    "\n",
    "Besides check the [Cheat Sheet](https://htmlpreview.github.io/?https://github.com/ironmussa/Optimus/blob/master/docs/cheatsheet/optimus_cheat_sheet.html) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Documentation\n",
    "  \n",
    "[![Documentation](https://media.readthedocs.com/corporate/img/header-logo.png)](http://docs.hioptimus.com/en/latest/)  \n",
    "  \n",
    "## Feedback \n",
    "Feedback is what drive Optimus future, so please take a couple of minutes to help shape the Optimus' Roadmap:  http://bit.ly/optimus_survey  \n",
    "\n",
    "Also if you want to a suggestion or feature request use https://github.com/ironmussa/optimus/issues\n",
    " \n",
    "## Start Optimus"
   ]
  },
  {
   "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": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\argenisleon\\Anaconda3\\lib\\site-packages\\socks.py:58: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Callable\n",
      "\n",
      "    You are using PySparkling of version 2.4.10, but your PySpark is of\n",
      "    version 2.3.1. Please make sure Spark and PySparkling versions are compatible. \n",
      "INFO:optimus:Operative System:Windows\n",
      "INFO:optimus:Just check that Spark and all necessary environments vars are present...\n",
      "INFO:optimus:-----\n",
      "INFO:optimus:SPARK_HOME=C:\\opt\\spark\\spark-2.3.1-bin-hadoop2.7\n",
      "INFO:optimus:HADOOP_HOME=C:\\opt\\hadoop-2.7.7\n",
      "INFO:optimus:PYSPARK_PYTHON=C:\\Users\\argenisleon\\Anaconda3\\python.exe\n",
      "INFO:optimus:PYSPARK_DRIVER_PYTHON=jupyter\n",
      "INFO:optimus:PYSPARK_SUBMIT_ARGS=--jars \"file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/RedshiftJDBC42-1.2.16.1027.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/mysql-connector-java-8.0.16.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/ojdbc8.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/postgresql-42.2.5.jar\" --driver-class-path \"C:/Users/argenisleon/Documents/Optimus/optimus/jars/RedshiftJDBC42-1.2.16.1027.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/mysql-connector-java-8.0.16.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/ojdbc8.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/postgresql-42.2.5.jar\" --conf \"spark.sql.catalogImplementation=hive\" pyspark-shell\n",
      "INFO:optimus:JAVA_HOME=C:\\java\n",
      "INFO:optimus:Pyarrow Installed\n",
      "INFO:optimus:-----\n",
      "INFO:optimus:Starting or getting SparkSession and SparkContext...\n",
      "INFO:optimus:Spark Version:2.3.1\n",
      "INFO:optimus:\n",
      "                             ____        __  _                     \n",
      "                            / __ \\____  / /_(_)___ ___  __  _______\n",
      "                           / / / / __ \\/ __/ / __ `__ \\/ / / / ___/\n",
      "                          / /_/ / /_/ / /_/ / / / / / / /_/ (__  ) \n",
      "                          \\____/ .___/\\__/_/_/ /_/ /_/\\__,_/____/  \n",
      "                              /_/                                  \n",
      "                              \n",
      "INFO:optimus:Transform and Roll out...\n",
      "INFO:optimus:Optimus successfully imported. Have fun :).\n",
      "INFO:optimus:Config.ini not found\n"
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   ],
   "source": [
    "from optimus import Optimus\n",
    "op= Optimus(verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You also can use an already created Spark session:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:\n",
      "                             ____        __  _                     \n",
      "                            / __ \\____  / /_(_)___ ___  __  _______\n",
      "                           / / / / __ \\/ __/ / __ `__ \\/ / / / ___/\n",
      "                          / /_/ / /_/ / /_/ / / / / / / /_/ (__  ) \n",
      "                          \\____/ .___/\\__/_/_/ /_/ /_/\\__,_/____/  \n",
      "                              /_/                                  \n",
      "                              \n",
      "INFO:optimus:Transform and Roll out...\n",
      "INFO:optimus:Optimus successfully imported. Have fun :).\n",
      "INFO:optimus:Config.ini not found\n"
     ]
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       "    }\n",
       "\n",
       "    .info_items {\n",
       "        margin: 10px 0;\n",
       "        font-size: 0.8em;\n",
       "    }\n",
       "\n",
       "    .optimus_table td {\n",
       "        border: 0px;\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",
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       "\n",
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       "        border-bottom: 1px solid black;\n",
       "    }\n",
       "    .optimus_table{\n",
       "        font-size: 12px;\n",
       "    }\n",
       "\n",
       "    .optimus_table tbody{\n",
       "        font-family: monospace;\n",
       "        border-bottom: 1px solid #cccccc;\n",
       "    }\n",
       "\n",
       "    /* Profiler */\n",
       "        .main{\n",
       "        width:100%;\n",
       "        overflow:auto;\n",
       "        border-bottom:1px solid #eeeeee;\n",
       "        padding: 10px 0;\n",
       "    }\n",
       "    .panel_profiler{\n",
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       "        font-family:monospace;\n",
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       "    .title_profiler{\n",
       "        padding:20px;\n",
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       "    .info{\n",
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       "    .main td, main th{\n",
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       "    .panel_profiler td {\n",
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       "    }\n",
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       "\n",
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    }
   ],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "from optimus import Optimus\n",
    "\n",
    "spark = SparkSession.builder.appName('optimus').getOrCreate()\n",
    "op= Optimus(spark)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data\n",
    "Now Optimus can load data in csv, json, parquet, avro, excel from a local file or URL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Downloading foo.json from https://raw.githubusercontent.com/ironmussa/Optimus/master/examples/data/foo.json\n",
      "INFO:optimus:Downloaded 2596 bytes\n",
      "INFO:optimus:Creating DataFrame for foo.json. Please wait...\n",
      "INFO:optimus:Successfully created DataFrame for 'foo.json'\n"
     ]
    }
   ],
   "source": [
    "#csv\n",
    "df = op.load.csv(\"../examples/data/foo.csv\")\n",
    "\n",
    "#json\n",
    "# Use a local file\n",
    "df = op.load.json(\"../examples/data/foo.json\")\n",
    "\n",
    "# Use a url\n",
    "df = op.load.json(\"https://raw.githubusercontent.com/ironmussa/Optimus/master/examples/data/foo.json\")\n",
    "\n",
    "# parquet\n",
    "df = op.load.parquet(\"../examples/data/foo.parquet\")\n",
    "\n",
    "# avro\n",
    "# df = op.load.avro(\"../examples/data/foo.avro\").table(5)\n",
    "\n",
    "# excel \n",
    "df = op.load.excel(\"../examples/data/titanic3.xls\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also you can load data from oracle, redshift, mysql and postgres. See ***Database connection***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:`pclass`,`survived`,`name`,`sex`,`age`,`sibsp`,`parch`,`ticket`,`fare`,`cabin`,`embarked`,`boat`,`body`,`home_dest` column(s) was not processed because is/are not date,array,vector,binary,null\n",
      "INFO:optimus:`pclass`,`survived`,`name`,`sex`,`age`,`sibsp`,`parch`,`ticket`,`fare`,`cabin`,`embarked`,`boat`,`body`,`home_dest` column(s) was not processed because is/are not null\n"
     ]
    }
   ],
   "source": [
    "#csv\n",
    "df.save.csv(\"data/foo.csv\")\n",
    "\n",
    "# json\n",
    "df.save.json(\"data/foo.json\")\n",
    "\n",
    "# parquet\n",
    "df.save.parquet(\"data/foo.parquet\")\n",
    "\n",
    "# avro\n",
    "#df.save.avro(\"examples/data/foo.avro\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also you can save data to oracle, redshift, mysql and postgres. See ***Database connection***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling Spark jars, packages and repositories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With optimus is easy to loading jars, packages and repos. You can init optimus/spark like "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Operative System:Windows\n",
      "INFO:optimus:Just check that Spark and all necessary environments vars are present...\n",
      "INFO:optimus:-----\n",
      "INFO:optimus:SPARK_HOME=C:\\opt\\spark\\spark-2.3.1-bin-hadoop2.7\n",
      "INFO:optimus:HADOOP_HOME=C:\\opt\\hadoop-2.7.7\n",
      "INFO:optimus:PYSPARK_PYTHON=C:\\Users\\argenisleon\\Anaconda3\\python.exe\n",
      "INFO:optimus:PYSPARK_DRIVER_PYTHON=jupyter\n",
      "INFO:optimus:PYSPARK_SUBMIT_ARGS=--repositories myrepo --packages org.apache.spark:spark-avro_2.12:2.4.3 --jars \"my.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/RedshiftJDBC42-1.2.16.1027.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/mysql-connector-java-8.0.16.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/ojdbc8.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/postgresql-42.2.5.jar\" --driver-class-path \"this_is_a_jar_class_path.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/RedshiftJDBC42-1.2.16.1027.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/mysql-connector-java-8.0.16.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/ojdbc8.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/postgresql-42.2.5.jar\" --conf \"spark.sql.catalogImplementation=hive\" pyspark-shell\n",
      "INFO:optimus:JAVA_HOME=C:\\java\n",
      "INFO:optimus:Pyarrow Installed\n",
      "INFO:optimus:-----\n",
      "INFO:optimus:Starting or getting SparkSession and SparkContext...\n",
      "INFO:optimus:Spark Version:2.3.1\n",
      "INFO:optimus:\n",
      "                             ____        __  _                     \n",
      "                            / __ \\____  / /_(_)___ ___  __  _______\n",
      "                           / / / / __ \\/ __/ / __ `__ \\/ / / / ___/\n",
      "                          / /_/ / /_/ / /_/ / / / / / / /_/ (__  ) \n",
      "                          \\____/ .___/\\__/_/_/ /_/ /_/\\__,_/____/  \n",
      "                              /_/                                  \n",
      "                              \n",
      "INFO:optimus:Transform and Roll out...\n",
      "INFO:optimus:Optimus successfully imported. Have fun :).\n",
      "INFO:optimus:Config.ini not found\n"
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       "    }\n",
       "\n",
       "    .optimus_table thead {\n",
       "        border-bottom: 1px solid black;\n",
       "    }\n",
       "    .optimus_table{\n",
       "        font-size: 12px;\n",
       "    }\n",
       "\n",
       "    .optimus_table tbody{\n",
       "        font-family: monospace;\n",
       "        border-bottom: 1px solid #cccccc;\n",
       "    }\n",
       "\n",
       "    /* Profiler */\n",
       "        .main{\n",
       "        width:100%;\n",
       "        overflow:auto;\n",
       "        border-bottom:1px solid #eeeeee;\n",
       "        padding: 10px 0;\n",
       "    }\n",
       "    .panel_profiler{\n",
       "        margin-right:2%;\n",
       "        float:left;\n",
       "        padding-bottom:2%;\n",
       "    }\n",
       "    .panel_profiler tbody{\n",
       "        font-family:monospace;\n",
       "    }\n",
       "    .title_profiler{\n",
       "        padding:20px;\n",
       "        background-color: #eeeeee\n",
       "    }\n",
       "    .info{\n",
       "        overflow: auto\n",
       "    }\n",
       "    .main td, main th{\n",
       "        padding:0em\n",
       "    }\n",
       "    .panel_profiler td {\n",
       "        padding:0.2em\n",
       "    }\n",
       "    .none, .true{\n",
       "        color:#0000ff\n",
       "    }\n",
       "    .optimus_table th {\n",
       "        font-family:sans-serif;\n",
       "    }\n",
       "\n",
       "    .info_items{\n",
       "        font-family:sans-serif;\n",
       "        font-size:10px;\n",
       "    }\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op= Optimus(repositories = \"myrepo\", packages=\"org.apache.spark:spark-avro_2.12:2.4.3\", jars=\"my.jar\", driver_class_path=\"this_is_a_jar_class_path.jar\", verbose= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "Also you can create a dataframe from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.types import *\n",
    "from datetime import date, datetime\n",
    "\n",
    "df = op.create.df(\n",
    "    [\n",
    "        (\"names\", \"str\", True), \n",
    "        (\"height(ft)\",\"int\", True), \n",
    "        (\"function\", \"str\", True), \n",
    "        (\"rank\", \"int\", True), \n",
    "        (\"age\",\"int\",True),\n",
    "        (\"weight(t)\",\"float\",True),\n",
    "        (\"japanese name\", ArrayType(StringType()), True),\n",
    "        (\"last position seen\", \"str\", True),\n",
    "        (\"date arrival\", \"str\", True),\n",
    "        (\"last date seen\", \"str\", True),\n",
    "        (\"attributes\", ArrayType(FloatType()), True),\n",
    "        (\"DateType\"),\n",
    "        (\"Tiemstamp\"),\n",
    "        (\"Cybertronian\", \"bool\", True), \n",
    "        (\"NullType\", \"null\", True),\n",
    "    ],\n",
    "    [\n",
    "        (\"Optim'us\", 28, \"Leader\", 10, 5000000, 4.3, [\"Inochi\", \"Convoy\"], \"19.442735,-99.201111\", \"1980/04/10\",\n",
    "         \"2016/09/10\", [8.5344, 4300.0], date(2016, 9, 10), datetime(2014, 6, 24), True,\n",
    "         None),\n",
    "        (\"bumbl#ebéé  \", 17, \"Espionage\", 7, 5000000, 2.0, [\"Bumble\", \"Goldback\"], \"10.642707,-71.612534\", \"1980/04/10\",\n",
    "         \"2015/08/10\", [5.334, 2000.0], date(2015, 8, 10), datetime(2014, 6, 24), True,\n",
    "         None),\n",
    "        (\"ironhide&\", 26, \"Security\", 7, 5000000, 4.0, [\"Roadbuster\"], \"37.789563,-122.400356\", \"1980/04/10\",\n",
    "         \"2014/07/10\", [7.9248, 4000.0], date(2014, 6, 24), datetime(2014, 6, 24), True,\n",
    "         None),\n",
    "        (\"Jazz\", 13, \"First Lieutenant\", 8, 5000000, 1.80, [\"Meister\"], \"33.670666,-117.841553\", \"1980/04/10\",\n",
    "         \"2013/06/10\", [3.9624, 1800.0], date(2013, 6, 24), datetime(2014, 6, 24), True, None),\n",
    "        (\"Megatron\", None, \"None\", 10, 5000000, 5.70, [\"Megatron\"], None, \"1980/04/10\", \"2012/05/10\", [None, 5700.0],\n",
    "         date(2012, 5, 10), datetime(2014, 6, 24), True, None),\n",
    "        (\"Metroplex_)^$\", 300, \"Battle Station\", 8, 5000000, None, [\"Metroflex\"], None, \"1980/04/10\", \"2011/04/10\",\n",
    "         [91.44, None], date(2011, 4, 10), datetime(2014, 6, 24), True, None),\n",
    "\n",
    "    ], infer_schema = True).ext.h_repartition(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With .table() you have a beautifull way to show your data. You have extra information like column number, column data type and marked white spaces \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.table_image(\"images/table.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also you can create a dataframe from a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/pandas.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pdf = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c',3:'d'},\n",
    "                    'B': {0: 1, 1: 3, 2: 5,3:7},\n",
    "                       'C': {0: 2, 1: 4, 2: 6,3:None},\n",
    "                       'D': {0:'1980/04/10',1:'1980/04/10',2:'1980/04/10',3:'1980/04/10'},\n",
    "                       })\n",
    "\n",
    "s_pdf = op.create.df(pdf=pdf)\n",
    "s_pdf.table_image(\"images/pandas.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning and Processing\n",
    "  \n",
    "Optimus V2 was created to make data cleaning a breeze. The API was designed to be super easy to newcomers and very familiar for people that comes from Pandas.\n",
    "Optimus expands the Spark DataFrame functionality adding .rows and .cols attributes.\n",
    "\n",
    "For example you can load data from a url, transform and apply some predefined cleaning functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'names' with function _lower\n",
      "INFO:optimus:Using 'column_exp' to process column 'function' with function _lower\n",
      "INFO:optimus:Using 'column_exp' to process column 'date arrival' with function _date_transform\n",
      "INFO:optimus:Using 'column_exp' to process column 'date arrival' with function _years_between\n",
      "INFO:optimus:Using 'column_exp' to process column 'from arrival' with function _cast_to\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'names' with function _remove_accents\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'names' with function multiple_replace\n",
      "INFO:optimus:`japanese name`,`attributes`,`tiemstamp`,`nulltype` column(s) was not processed because is/are not byte,short,big,int,double,float,string,date,bool\n",
      "INFO:optimus:Using 'column_exp' to process column 'names' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'height(ft)' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'function' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'rank' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'weight(t)' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'last position seen' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'date arrival' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'last date seen' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'datetype' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'cybertronian' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'new_age' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'from arrival' with function _trim\n"
     ]
    }
   ],
   "source": [
    "# This is a custom function\n",
    "def func(value, arg):\n",
    "    return \"this was a number\"\n",
    "    \n",
    "new_df = df\\\n",
    "    .rows.sort(\"rank\",\"desc\")\\\n",
    "    .withColumn('new_age', df.age)\\\n",
    "    .cols.lower([\"names\",\"function\"])\\\n",
    "    .cols.date_transform(\"date arrival\", \"yyyy/MM/dd\", \"dd-MM-YYYY\")\\\n",
    "    .cols.years_between(\"date arrival\", \"dd-MM-YYYY\", output_cols = \"from arrival\")\\\n",
    "    .cols.remove_accents(\"names\")\\\n",
    "    .cols.remove_special_chars(\"names\")\\\n",
    "    .rows.drop(df[\"rank\"]>8)\\\n",
    "    .cols.rename(str.lower)\\\n",
    "    .cols.trim(\"*\")\\\n",
    "    .cols.unnest(\"japanese name\", output_cols=\"other names\")\\\n",
    "    .cols.unnest(\"last position seen\",separator=\",\", output_cols=\"pos\")\\\n",
    "    .cols.drop([\"last position seen\", \"japanese name\",\"date arrival\", \"cybertronian\", \"nulltype\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You transform this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table1.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.table_image(\"images/table1.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Into this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table2.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df.table_image(\"images/table2.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that you can use Optimus functions and Spark functions(`.WithColumn()`) and all the df function availables in a Spark Dataframe at the same time. To know about all the Optimus functionality please go to this [notebooks](examples/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling column output\n",
    "\n",
    "With Optimus you can handle how the output column from a transformation in going to be handled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import functions as F\n",
    "\n",
    "def func(col_name, attr):\n",
    "    return F.upper(F.col(col_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a **string** is passed to **input_cols** and **output_cols** is not defined the result from the operation is going to be saved in the same input column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'names' with function func\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/column_output_1.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_df = df.cols.apply(input_cols=\"names\", output_cols=None,func=func)\n",
    "output_df.table_image(\"images/column_output_1.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a **string** is passed to **input_cols** and a **string** is passed to **output_cols** the output is going to be saved in the output column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'names' with function func\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/column_output_2.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_df = df.cols.apply(input_cols=\"names\", output_cols=\"names_up\",func=func)\n",
    "output_df.table_image(\"images/column_output_2.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a **list** is passed to **input_cols** and a **string** is passed to **out_cols** Optimus will concatenate the list with every element in the list to create a new column name with the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'names' with function func\n",
      "INFO:optimus:Using 'column_exp' to process column 'function' with function func\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/column_output_3.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_df = df.cols.apply(input_cols=[\"names\",\"function\"], output_cols=\"_up\",func=func)\n",
    "output_df.table_image(\"images/column_output_3.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a **list** is passed to **input_cols** and a **list** is passed in **out_cols** Optimus will output every input column in the respective output column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'names' with function func\n",
      "INFO:optimus:Using 'column_exp' to process column 'function' with function func\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/column_output_4.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_df = df.cols.apply(input_cols=[\"names\",\"function\"], output_cols=[\"names_up\",\"function_up\"],func=func)\n",
    "output_df.table_image(\"images/column_output_4.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "### Custom functions\n",
    "Spark has multiple ways to transform your data like rdd, Column Expression, udf and pandas udf. In Optimus we created the `apply()` and `apply_expr` which handles all the implementation complexity.\n",
    "\n",
    "Here you apply a function to the \"billingid\" column. Sum 1 and 2 to the current column value. All powered by Pandas UDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'pandas_udf' to process column 'height(ft)' with function func\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table3.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def func(value, args):\n",
    "    return value + args[0] + args[1]\n",
    "\n",
    "df.cols.apply(\"height(ft)\",func,\"int\", [1,2]).table_image(\"images/table3.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to apply a Column Expression use `apply_expr()` like this. In this case we pass an argument 10 to divide the actual column value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'height(ft)' with function func\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table4.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pyspark.sql import functions as F\n",
    "\n",
    "def func(col_name, args):\n",
    "    return F.col(col_name)/20\n",
    "\n",
    "df.cols.apply(\"height(ft)\", func=func, args=20).table_image(\"images/table4.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the table output back to ascii if you wish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "op.output(\"ascii\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To return to HTML just:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "op.output(\"html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data profiling\n",
    "\n",
    "Optimus comes with a powerful and unique data profiler. Besides basic and advance stats like min, max, kurtosis, mad etc, \n",
    "it also let you know what type of data has every column. For example if a string column have string, integer, float, bool, date Optimus can give you an unique overview about your data. \n",
    "Just run `df.profile(\"*\")` to profile all the columns. For more info about the profiler please go to this [notebook](./examples/profiler.ipynb).\n",
    "\n",
    "Let's load a \"big\" dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Downloading Meteorite_Landings.csv from https://raw.githubusercontent.com/ironmussa/Optimus/master/examples/data/Meteorite_Landings.csv\n",
      "INFO:optimus:Downloaded 4978151 bytes\n",
      "INFO:optimus:Creating DataFrame for Meteorite_Landings.csv. Please wait...\n",
      "INFO:optimus:Successfully created DataFrame for 'Meteorite_Landings.csv'\n"
     ]
    }
   ],
   "source": [
    "df = op.load.csv(\"https://raw.githubusercontent.com/ironmussa/Optimus/master/examples/data/Meteorite_Landings.csv\").ext.h_repartition()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numeric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Processing column 'mass (g)'...\n",
      "INFO:optimus:_count_data_types() executed in 2.31 sec\n",
      "INFO:optimus:count_data_types() executed in 2.31 sec\n",
      "INFO:optimus:cast_columns() executed in 0.0 sec\n",
      "INFO:optimus:agg_exprs() executed in 3.29 sec\n",
      "INFO:optimus:general_stats() executed in 3.3 sec\n",
      "INFO:optimus:------------------------------\n",
      "INFO:optimus:Processing column 'mass (g)'...\n",
      "INFO:optimus:frequency() executed in 4.77 sec\n",
      "INFO:optimus:stats_by_column() executed in 0.0 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n",
      "INFO:optimus:percentile() executed in 0.39 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n",
      "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n",
      "INFO:optimus:extra_numeric_stats() executed in 0.9 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.47 sec\n",
      "INFO:optimus:hist() executed in 2.54 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Including 'nan' as Null in processing 'name'\n",
      "Including 'nan' as Null in processing 'nametype'\n",
      "Including 'nan' as Null in processing 'recclass'\n",
      "Including 'nan' as Null in processing 'fall'\n",
      "Including 'nan' as Null in processing 'year'\n",
      "Including 'nan' as Null in processing 'GeoLocation'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:dataset_info() executed in 2.28 sec\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\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>10</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Number of rows</td>\n",
       "                <td>45716</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Total Missing (%)</td>\n",
       "                <td>0.49%</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Total size in memory</td>\n",
       "                <td>88.2 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",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Numeric</td>\n",
       "                <td>1</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Date</td>\n",
       "                <td>0</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Bool</td>\n",
       "                <td>0</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Array</td>\n",
       "                <td>0</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Not available</td>\n",
       "                <td>0</td>\n",
       "\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "</div><link rel=\"stylesheet\" type=\"text/css\" href=\"optimus/styles/styles.css\">\n",
       "\n",
       "\n",
       "<div class=\"main\">\n",
       "    <div class=\"info\">\n",
       "\n",
       "        \n",
       "\n",
       "        <div class=\"panel_profiler\">\n",
       "            <div>\n",
       "                <h2>mass (g)</h2>\n",
       "                <span>numeric</span>\n",
       "            </div>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>Unique</td>\n",
       "                    <td> 12497</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Unique (%)</td>\n",
       "                    <td> 27.336</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",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Integer\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Float\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Bool\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Date\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Missing\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Null\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        131\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>13278.07855</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Minimum</td>\n",
       "                    <td>0.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Maximum</td>\n",
       "                    <td>60000000.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Zeros(%)</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "\n",
       "                </tbody>\n",
       "            </table>\n",
       "            \n",
       "\n",
       "        </div>\n",
       "        <div class=\"panel_profiler\">\n",
       "            <h3>Frequency</h3>\n",
       "            <table>\n",
       "\n",
       "                <tr>\n",
       "                    <th>Value</th>\n",
       "                    <th>Count</th>\n",
       "                    <th>Frequency (%)</th>\n",
       "                </tr>\n",
       "                \n",
       "                <tr>\n",
       "                    <td>1.3</td>\n",
       "                    <td>171</td>\n",
       "                    <td>0.374%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>1.2</td>\n",
       "                    <td>140</td>\n",
       "                    <td>0.306%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>1.4</td>\n",
       "                    <td>138</td>\n",
       "                    <td>0.302%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>None</td>\n",
       "                    <td>131</td>\n",
       "                    <td>0.287%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2.1</td>\n",
       "                    <td>130</td>\n",
       "                    <td>0.284%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2.4</td>\n",
       "                    <td>126</td>\n",
       "                    <td>0.276%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>1.6</td>\n",
       "                    <td>120</td>\n",
       "                    <td>0.262%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>0.5</td>\n",
       "                    <td>119</td>\n",
       "                    <td>0.26%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>1.1</td>\n",
       "                    <td>116</td>\n",
       "                    <td>0.254%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>3.8</td>\n",
       "                    <td>114</td>\n",
       "                    <td>0.249%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>\"Missing\"</td>\n",
       "                    <td>0</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\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>0.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>5-th percentile</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Q1</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Median</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Q3</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>95-th percentile</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Maximum</td>\n",
       "                    <td>60000000.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Range</td>\n",
       "                    <td>60000000.0</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>574988.87641</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Coef of variation</td>\n",
       "                    <td>43.30362</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Kurtosis</td>\n",
       "                    <td>6796.17061</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Mean</td>\n",
       "                    <td>13278.07855</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>MAD</td>\n",
       "                    <td>0.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Skewness</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Sum</td>\n",
       "                    <td>605281210.638</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Variance</td>\n",
       "                    <td>330612207995.7785</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>\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 10 of 45716 rows / 10 columns</div>\n",
       "<div class=\"info_items\">32 partition(s)</div>\n",
       "\n",
       "<table class=\"optimus_table\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">name</div>\n",
       "            <div class=\"data_type\">1 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">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\">nametype</div>\n",
       "            <div class=\"data_type\">3 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">recclass</div>\n",
       "            <div class=\"data_type\">4 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">mass (g)</div>\n",
       "            <div class=\"data_type\">5 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">fall</div>\n",
       "            <div class=\"data_type\">6 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">year</div>\n",
       "            <div class=\"data_type\">7 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">reclat</div>\n",
       "            <div class=\"data_type\">8 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">reclong</div>\n",
       "            <div class=\"data_type\">9 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">GeoLocation</div>\n",
       "            <div class=\"data_type\">10 (string)</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",
       "            <div class=\" \"\n",
       "                 title='Acfer&#8901;232'>Acfer&#8901;232\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='240'>240\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H5'>H5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='725.0'>725.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1991&#8901;12:00:00&#8901;AM'>01/01/1991&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='27.73944'>27.73944\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='4.32833'>4.32833\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(27.739440,&#8901;4.328330)'>(27.739440,&#8901;4.328330)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Asuka&#8901;87197'>Asuka&#8901;87197\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='2554'>2554\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H4'>H4\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='124.99'>124.99\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1987&#8901;12:00:00&#8901;AM'>01/01/1987&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-72.0'>-72.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='26.0'>26.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-72.000000,&#8901;26.000000)'>(-72.000000,&#8901;26.000000)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Gladstone&#8901;(iron)'>Gladstone&#8901;(iron)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='10920'>10920\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Iron,&#8901;IAB-MG'>Iron,&#8901;IAB-MG\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='736600.0'>736600.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1915&#8901;12:00:00&#8901;AM'>01/01/1915&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-23.9'>-23.9\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='151.3'>151.3\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-23.900000,&#8901;151.300000)'>(-23.900000,&#8901;151.300000)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Nullarbor&#8901;015'>Nullarbor&#8901;015\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='17955'>17955\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L6'>L6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='3986.0'>3986.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1980&#8901;12:00:00&#8901;AM'>01/01/1980&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Lewis&#8901;Cliff&#8901;86533'>Lewis&#8901;Cliff&#8901;86533\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='13461'>13461\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H5'>H5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='15.7'>15.7\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1986&#8901;12:00:00&#8901;AM'>01/01/1986&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-84.26756'>-84.26756\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='161.3631'>161.3631\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-84.267560,&#8901;161.363100)'>(-84.267560,&#8901;161.363100)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Grove&#8901;Mountains&#8901;053589'>Grove&#8901;Mountains&#8901;053589\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='48447'>48447\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L5'>L5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1.4'>1.4\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/2006&#8901;12:00:00&#8901;AM'>01/01/2006&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-72.7825'>-72.7825\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='75.300278'>75.300278\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-72.782500,&#8901;75.300278)'>(-72.782500,&#8901;75.300278)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Sayh&#8901;al&#8901;Uhaymir&#8901;108'>Sayh&#8901;al&#8901;Uhaymir&#8901;108\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='23300'>23300\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H6'>H6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='16.0'>16.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/2001&#8901;12:00:00&#8901;AM'>01/01/2001&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='21.06667'>21.06667\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='57.31667'>57.31667\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(21.066670,&#8901;57.316670)'>(21.066670,&#8901;57.316670)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Northwest&#8901;Africa&#8901;3088'>Northwest&#8901;Africa&#8901;3088\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='31218'>31218\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L6'>L6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='171.0'>171.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/2003&#8901;12:00:00&#8901;AM'>01/01/2003&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Reckling&#8901;Peak&#8901;92423'>Reckling&#8901;Peak&#8901;92423\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='22432'>22432\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L6'>L6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='3.8'>3.8\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1992&#8901;12:00:00&#8901;AM'>01/01/1992&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-76.22029'>-76.22029\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='158.37967'>158.37967\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-76.220290,&#8901;158.379670)'>(-76.220290,&#8901;158.379670)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Sweetwater'>Sweetwater\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='23770'>23770\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H5'>H5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1760.0'>1760.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1961&#8901;12:00:00&#8901;AM'>01/01/1961&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='32.55'>32.55\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-100.41667'>-100.41667\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(32.550000,&#8901;-100.416670)'>(32.550000,&#8901;-100.416670)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 10 of 45716 rows / 10 columns</div>\n",
       "<div class=\"info_items\">32 partition(s)</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:run() executed in 19.68 sec\n"
     ]
    }
   ],
   "source": [
    "op.profiler.run(df, \"mass (g)\", infer=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Warning: Failed to load file:///C:/Users/ARGENI~1/AppData/Local/Temp/optimus/styles/styles.css (ignore)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/profiler_numeric.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op.profiler.to_image(output_path=\"images/profiler_numeric.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Processing column 'name'...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Including 'nan' as Null in processing 'name'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:_count_data_types() executed in 1.43 sec\n",
      "INFO:optimus:count_data_types() executed in 1.43 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'name' with function _cast_to\n",
      "INFO:optimus:cast_columns() executed in 0.02 sec\n",
      "INFO:optimus:agg_exprs() executed in 1.94 sec\n",
      "INFO:optimus:general_stats() executed in 1.95 sec\n",
      "INFO:optimus:------------------------------\n",
      "INFO:optimus:Processing column 'name'...\n",
      "INFO:optimus:frequency() executed in 4.21 sec\n",
      "INFO:optimus:stats_by_column() executed in 0.0 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'name_len' with function func_col_exp\n",
      "INFO:optimus:Using 'column_exp' to process column 'name_len' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.35 sec\n",
      "INFO:optimus:hist() executed in 3.02 sec\n",
      "INFO:optimus:hist_string() executed in 5.39 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Including 'nan' as Null in processing 'name'\n",
      "Including 'nan' as Null in processing 'nametype'\n",
      "Including 'nan' as Null in processing 'recclass'\n",
      "Including 'nan' as Null in processing 'fall'\n",
      "Including 'nan' as Null in processing 'year'\n",
      "Including 'nan' as Null in processing 'GeoLocation'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:dataset_info() executed in 1.78 sec\n"
     ]
    },
    {
     "data": {
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       "\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>10</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Number of rows</td>\n",
       "                <td>45716</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Total Missing (%)</td>\n",
       "                <td>0.49%</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Total size in memory</td>\n",
       "                <td>92.1 MB</td>\n",
       "\n",
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       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "    <div class=\"panel_profiler\">\n",
       "        <h2>Column types</h2>\n",
       "        <table>\n",
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       "            <tr>\n",
       "                <td>String</td>\n",
       "                <td>1</td>\n",
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       "\n",
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       "        \n",
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       "        <div class=\"panel_profiler\">\n",
       "            <div>\n",
       "                <h2>name</h2>\n",
       "                <span>categorical</span>\n",
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       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>Unique</td>\n",
       "                    <td> 45515</td>\n",
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       "                <tr>\n",
       "                    <td>Unique (%)</td>\n",
       "                    <td> 99.56</td>\n",
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       "                <tr>\n",
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       "                    <td>0.0</td>\n",
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       "                <tr>\n",
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       "                <h3>\n",
       "                    Datatypes\n",
       "                </h3>\n",
       "            </div>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>\n",
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       "                    <td>\n",
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       "                <tr>\n",
       "                    <td>\n",
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       "                    <td>\n",
       "                        0\n",
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       "                <tr>\n",
       "                    <td>\n",
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       "                    <td>\n",
       "                        0\n",
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       "                    <td>\n",
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       "\n",
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       "            \n",
       "\n",
       "        </div>\n",
       "        <div class=\"panel_profiler\">\n",
       "            <h3>Frequency</h3>\n",
       "            <table>\n",
       "\n",
       "                <tr>\n",
       "                    <th>Value</th>\n",
       "                    <th>Count</th>\n",
       "                    <th>Frequency (%)</th>\n",
       "                </tr>\n",
       "                \n",
       "                <tr>\n",
       "                    <td>Święcany</td>\n",
       "                    <td>1</td>\n",
       "                    <td>0.002%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>Łowicz</td>\n",
       "                    <td>1</td>\n",
       "                    <td>0.002%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>Österplana 064</td>\n",
       "                    <td>1</td>\n",
       "                    <td>0.002%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>Österplana 063</td>\n",
       "                    <td>1</td>\n",
       "                    <td>0.002%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>Österplana 062</td>\n",
       "                    <td>1</td>\n",
       "                    <td>0.002%</td>\n",
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       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>Österplana 061</td>\n",
       "                    <td>1</td>\n",
       "                    <td>0.002%</td>\n",
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       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>Österplana 060</td>\n",
       "                    <td>1</td>\n",
       "                    <td>0.002%</td>\n",
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       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>Österplana 059</td>\n",
       "                    <td>1</td>\n",
       "                    <td>0.002%</td>\n",
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       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>Österplana 058</td>\n",
       "                    <td>1</td>\n",
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       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>Österplana 057</td>\n",
       "                    <td>1</td>\n",
       "                    <td>0.002%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>\"Missing\"</td>\n",
       "                    <td>0</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
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       "        \n",
       "\n",
       "        \n",
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       "\n",
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\">\n",
       "                </div>\n",
       "            </td>\n",
       "        </tr>\n",
       "        \n",
       "        \n",
       "        <tr>\n",
       "            <td>\n",
       "                <div>\n",
       "                    <img 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AAGxCAAMAAAAAmxDAAAAAAMAmBDAAAAAAsAkBDAAAAABsQgADAAAAAJsQwAAAAADAJgQwAAAAALAJAQwAAAAAbEIAAwAAAACbEMAAAAAAwCYEMAAAAACwCQEMAAAAAGxCAMtBy5cvV82aNeXr66uxY8e6XOa7775TnTp1VLduXXXt2tXmDgEAAADYyT23G7hbpaamKjg4WL/++qu8vLzk5+engIAA1alTx1omMjJSH3zwgf73v/+pRIkSiouLy8WOAQAAAOQ03gHLIVu2bJGvr6+qVq0qDw8PBQUFadGiRemWmTp1qoKDg1WiRAlJUpkyZXKjVQAAAAA2IYDlkNjYWHl7e1uXvby8FBsbm26ZiIgIRUREqGXLlmrevLmWL19ud5sAAAAAbMQpiDnEGJPhOofDke5ySkqKIiMjtXbtWh05ckQPPvigdu/ereLFi6dbbsqUKZoyZYokKT4+PueaBgAAAJCjeAcsh3h5eSkmJsa6fOTIEVWoUCHDMh06dFDevHnl4+OjmjVrKjIyMsN9vfjiiwoNDVVoaKhKly6d470DAAAAyBkEsBzi5+enyMhIHTp0SElJSZo3b54CAgLSLdOxY0etWbNGkpSQkKCIiAhVrVo1N9oFAAAAYAMCWA5xd3fX5MmT1bZtW9WuXVuBgYGqW7euRowYocWLF0uS2rZtK09PT9WpU0ePPPKIPv74Y3l6euZy5wAAAAByCp8By0H+/v7y9/dPd92oUaOs/zscDo0fP17jx4+3uzUAAAAAuYB3wAAAAADAJgQwAAAAALAJAQwAAAAAbEIAAwAAAACbEMAAAAAAwCYEMAAAAACwCQEMAAAAAGxCAAMAAAAAmxDAAAAAAMAmBDAAAAAAsAkBDAAAAABsQgADAAAAAJsQwAAAAADAJgQwAAAAALAJAQwAAAAAbEIAAwAAAACbEMAAAAAAwCYEMAAAAACwCQEMAAAAAGxCAAMAAAAAmxDAAAAAAMAmBDAAAAAAsAkBDAAAAABsQgADAAAAAJsQwAAAAADAJgQwAAAAALAJAQwAAAAAbEIAAwAAAACbEMAAAAAAwCYEMAAAAACwCQEMAAAAAGxCAAMAAAAAmxDAAAAAAMAmBDAAAAAAsAkBDAAAAABsQgADAAAAAJsQwAAAAADAJgQwAAAAALAJAQwAAAAAbEIAAwAAAACbEMAAAAAAwCYEMAAAAACwCQEsBy1fvlw1a9aUr6+vxo4dm+ly33//vRwOh0JDQ23sDgAAAIDdCGA5JDU1VcHBwVq2bJnCwsIUEhKisLCwDMudP39eEydOVLNmzXKhSwAAAAB2IoDlkC1btsjX11dVq1aVh4eHgoKCtGjRogzLDR8+XEOHDlX+/PlzoUsAAAAAdiKA5ZDY2Fh5e3tbl728vBQbG5tume3btysmJkZPPPGE3e0BAAAAyAXuud3A3coYk+E6h8Nh/f/q1asaOHCgZs6cecP7mjJliqZMmSJJio+Pv209AgAAALAX74DlEC8vL8XExFiXjxw5ogoVKliXz58/r927d+vhhx9WlSpVtGnTJgUEBLj8Io4XX3xRoaGhCg0NVenSpW3pHwAAAMDtRwDLIX5+foqMjNShQ4eUlJSkefPmKSAgwLq9WLFiSkhIUFRUlKKiotS8eXMtXrxYTZs2zcWuAQAAAOQkAlgOcXd31+TJk9W2bVvVrl1bgYGBqlu3rkaMGKHFixfndnsAAAAAcgGfActB/v7+8vf3T3fdqFGjXC67du1aGzoCAAAAkJt4BwwAAAAAbEIAAwAAAACbEMAAAAAAwCYEMAAAAACwCQEMAAAAAGxCAAMAAAAAmxDAAAAAAMAmBDAAAAAAsAkBDAAAAABsQgADAAAAAJsQwAAAAADAJgQwAAAAALAJAQwAAAAAbEIAAwAAAACbEMAAAAAAwCYEMAAAAACwCQEMAAAAAGxCAAMAAAAAmxDAAAAAAMAmBDAAAAAAsAkBDAAAAABsQgADAAAAAJsQwAAAAADAJgQwAAAAALAJAQwAAAAAbEIAAwAAAACbEMAAAAAAwCYEMAAAAACwCQEMAAAAAGxCAAMAAAAAmxDAAAAAAMAmBDAAAAAAsAkBDAAAAABsQgADAAAAAJsQwAAAAADAJgQwAAAAALAJAQwAAAAAbEIAAwAAAACbEMAAAAAAwCYEMAAAAACwCQEMAAAAAGxCAAMAAAAAmxDAAAAAAMAmBLActHz5ctWsWVO+vr4aO3ZshtvHjx+vOnXqqEGDBnrssccUHR2dC10CAAAAsAsBLIekpqYqODhYy5YtU1hYmEJCQhQWFpZumUaNGik0NFQ7d+5Up06dNHTo0FzqFgAAAIAdCGA5ZMuWLfL19VXVqlXl4eGhoKAgLVq0KN0yjzzyiAoWLChJat68uY4cOZIbrQIAAACwCQEsh8TGxsrb29u67OXlpdjY2EyXnzZtmtq3b29HawAAAAByiXtuN3C3MsZkuM7hcLhcds6cOQoNDdVvv/3m8vYpU6ZoypQpkqT4+Pjb1yQAAAAAW/EOWA7x8vJSTEyMdfnIkSOqUKFChuVWrlyp999/X4sXL1a+fPlc3teLL76o0NBQhYaGqnTp0jnWMwAAAICcRQDLIX5+foqMjNShQ4eUlJSkefPmKSAgIN0y27dv17///W8tXrxYZcqUyaVOAQAAANiFAJZD3N3dNXnyZLVt21a1a9dWYGCg6tatqxEjRmjx4sWSpCFDhujChQt69tln1bBhwwwBDQAAAMDdhc+A5SB/f3/5+/unu27UqFHW/1euXGl3SwAAAAByEe+AAQAAAIBNCGAAAAAAYBMCGAAAAADYhAAGAAAAADYhgAEAAACATQhgAAAAAGATAhgAAAAA2IQABgAAAAA2IYABAAAAgE0IYAAAAABgEwIYAAAAANiEAAYAAAAANiGAAQAAAIBNCGAAAAAAYBMCGAAAAADYhAAGAAAAADYhgAEAAACATQhgAAAAAGATAhgAAAAA2IQABgAAAAA2IYABAAAAgE0IYAAAAABgEwIYAAAAANiEAAYAAAAANiGAAQAAAIBNCGAAAAAAYBMCGAAAAADYhAAGAAAAADYhgAEAAACATQhgAAAAAGATAhgAAAAA2IQABgAAAAA2IYABAAAAgE0IYAAAAABgEwIYAAAAANiEAAYAAAAANiGAAQAAAIBNCGAAAAAAYBMCGAAAAADYhAAGAAAAADYhgAEAAACATQhgAAAAAGATAhgAAAAA2IQAloOWL1+umjVrytfXV2PHjs1w+5UrV9S5c2f5+vqqWbNmioqKsr9JAAAAALYhgOWQ1NRUBQcHa9myZQoLC1NISIjCwsLSLTNt2jSVKFFC+/fv18CBA/X666/nUrcAAAAA7EAAyyFbtmyRr6+vqlatKg8PDwUFBWnRokXpllm0aJF69uwpSerUqZNWrVolY0xutAsAAADABgSwHBIbGytvb2/rspeXl2JjYzNdxt3dXcWKFdPJkydt7RMAAACAfRyGt1xyxPz587VixQp9/fXXkqRvvvlGW7Zs0aRJk6xl6tatqxUrVsjLy0uSVK1aNW3ZskWenp7p7mvKlCmaMmWKJGnv3r2qVauWTWuRNfHx8SpduvQ9U5ubY9P3vVGbm2Pfi33fi+ucm2Pfi33fi+ucm2Pfi+ucU6KiopSQkJDbbdx9DHLEhg0bTJs2bazLY8aMMWPGjEm3TJs2bcyGDRuMMcYkJycbT09Pc/XqVVv7vB2aNGlyT9Xm5tj0fW/U5ubY92Lf9+I65+bY92Lf9+I65+bY9+I6487CKYg5xM/PT5GRkTp06JCSkpI0b948BQQEpFsmICBAs2bNkiR9//33evTRR+VwOHKjXQAAAAA2cM/tBu5W7u7umjx5stq2bavU1FQ9//zzqlu3rkaMGKGmTZsqICBAL7zwgnr06CFfX1+VLFlS8+bNy+22AQAAAOQgt5EjR47M7SbuVtWrV1f//v31yiuv6J///Kck6ZFHHlHNmjUl/RXSAgMDNWDAAP3rX/9SiRIlcrPdW9KkSZN7qjY3x6bve6M2N8e+F/u+F9c5N8e+F/u+F9c5N8e+F9cZdw6+hAMAAAAAbMJnwAAAAADAJgQw5Bjnm6u8yQoAAAD8hQCGHGGMsb7R8dSpU7ncDXLK1atXrf/fbNBOu3za+7lZBPy7W9q5cbPz5HbNMdzdmGPIabeyr8TdiQCG2y5t+JoxY4aCgoJ07ty5LNU6n6SuXLly009SrnZ+Wd0h3uqO89peb6b32933zayLc9mUlBSlpqbe9Nh58uTRgQMHtHfvXjkcjpuqdTgcOnz4sM6cOaM8ebL+VOQc4+TJk7p06dJN/3QDc+zm+rldc8wYY/V8s3Ps0KFDio+PV548ebK83s45duzYMZ0/fz5bc+zcuXNKTk7Oct219c7HVHZqb3RdVmtvxq0cGDLHmGNZcSfOsVvZTzqXze6+EncvAhiuKztPls4D4smTJ2vMmDE6d+5clu/H+STVrl07rVmzJss7JucT3N69e/X2229ryJAhOnLkSJZ2iM7aiIgIjR07Vh988IGOHTuWpXGd9Q6HQ8ePH9eePXsk/bUNsrLOzrHDwsL07rvv6r333tPhw4dvqu/w8HCNGTNG48aNU0xMTJYPApz1e/bs0b///W917txZ+/fvv6mxIyMjdf/99ysgICDLByDGGKtvf39//fTTT1nqN+24e/fuVe/evRUREZHlWifm2M3PsYiICH3++eeaN2+edZCalQOItGMPGjRIffv2veltFhkZKT8/P3Xu3FmJiYlZCtzOORYWFqZnnnlGq1atumHNteOGh4erX79+Onz4cJZrnZx9d+7cWVFRUTc9dkREhD7++GNNmDBBJ06cuKnttX//fn3xxReaPXu2EhISbmpsh8OhkydP6vjx41muSzv2vn379Omnn2rOnDlZDjPMMebYzfR9K3MsO/vKW9lPpq3Pzr4SdzdmAK7L4XBo06ZNevfdd2+4bNonwR9//FEzZ85UaGiomjVrpsjIyCyPeeHCBYWFhen333/X+vXrlZSUdMMa547wueeeU+nSpZWcnKyuXbtm6dUq5xNz165ddenSJR09elStWrXSyZMns9Svc+yGDRtqwoQJ+t///icpawfIefLk0ZEjR/T000+rQIECOn/+vDp06KCVK1fqypUrN6w9ePCgunfvrmLFiiklJUXt27fX8uXLs/SqqDOI9OzZUw899JCqV6+ubt26KSUlRdL1w7dzm3Xv3l3Dhw9Xx44dtXbtWkk3fmXQ4XDo/Pnzeu655zRo0CB17979hr2mHTcsLEw9e/bUs88+q/vuuy/d7Vl9VZE5lvU5FhkZqa5du+rUqVPat2+fGjVqpD179mTpANlZ36tXL/n6+ip//vwKDg6+4bsUzoPbvXv3qnv37nrxxRfVoEEDHTx48Lp1Tg6HQ6dPn1ZQUJD+9a9/qWPHjtdd/tqew8PD1atXL7Vu3VrVqlVLd3tW59jevXu1cOFCzZw5U1FRUVkKy2kfVxcvXlR4eLiGDh2apXd1nHMkKChI4eHh2rBhg3r16pXlsw+cB5l169bVl19+aW3rrNYeOnRIHTt21NGjR7VlyxY1a9ZM4eHhN5yjzDHmWFZrb2WOZXdfeSv7SWd9dveVuLvxO2DIlPNUwlOnTmnp0qV67LHH5OHh4fLVwbSnHR4/flwnTpzQoEGDVLp0aX355ZeqVauWatSoodWrVytPnjwqXrx4pmMmJSVp48aNyp8/v3bv3q0qVaooX758unz5sgoUKOCy7uLFixoyZIieeeYZ9evXT/Xq1dP27dtVqFAhFSpUSEWLFrVefXNVO3DgQAUGBuq1116Tv7+/9uzZo+LFi2fYMbqSmpqqiRMnqnTp0qpTp4527NihggULytvbO0uvpK5bt05HjhzRxIkT1bp1a+XNm1czZ85U+fLlVa1atUz7lqR58+bJw8NDI0eO1D/+8Q/t3LlTX331lRo2bKjKlStft1aS5s6dKzc3N7399ttq1aqVVq5cqdOnT8vDw0OlSpXK9FW6kydPql+/furZs6deeuklrV69WuvWrVOnTp2ytM6XLl3S2rVrNXbsWEnSyJEjtWjRIuXJk0dFihRRoUKFXNZduXJFwcHBSkxM1MSJEyVJISEh2rx5s8qUKaNixYrq594GAAAgAElEQVTdcJ0l2T7HLly4oNdee03PPvtstuZYSkqKJk2apFKlSmVrjv3+++/ZnmNTpkyRt7e3Ro0apYcffljr16/XhAkT5O/vL09Pzxtu76+++koVKlTQW2+9pYYNG2rLli1KSUlRoUKFVLx4cZf1ztO6nn/+eb300ksaOHCgZs2apa1bt6pjx45ZWue4uDht2rRJH374oSTps88+07Jly1SiRInrzu0LFy6oT58+Kl68uDU/f/75Z0VGRqpEiRIqXLhwluaYJEVFRenkyZPavHmzWrVqpdTUVDkcjkzHPnPmjPr3769u3bpp4MCBKlGihA4cOKCKFSvK3d1dhQoVynTsM2fOqG/fvurdu7fefPNNNW/eXJs2bVLt2rVVpkyZG/Z6+fJlffjhhypVqpT1bmu5cuWy/PuUS5Ys0ZUrV/Tll1+qffv2unz5skaNGqUHH3xQZcqUYY6lwRyzf47dyr4yJCQkW/tJSUpISFBwcHC295W4exHAkCnnk0NKSopCQkJUuXJllweLacPXJ598oiVLlqh79+4qW7asJOnQoUOqV6+e/vzzT7377rt69tlnVaxYMUn/d0pCUlKS3Nzc5HA4VKxYMcXFxWnAgAFKSEjQd999p3HjxumBBx5QxYoVrXHT1hYoUED169dXmzZtJEkdO3bU2bNnlZycrHfeeUePPvqoSpcunaE2OTlZ+fLlU4ECBdSxY0e5u7vL4XBo8eLFOnfunB566CGX2ybt2Hnz5lWtWrXUpUsXlStXTrt27dLevXuVP39+VapUKcN2ctZeuXLF2uGtWbNGlStXVoUKFdS4cWNdvnxZH3zwgVq3bq2SJUtmGNd5X0ePHlVMTIzq16+vokWL6sSJE4qPj1dISIg6dOigokWLuuzbWX/mzBlt27ZNx48fV//+/VWwYEG5u7vr66+/lo+Pj3x8fDLUOudE48aN1a5dO0lSixYtNG7cOOXPn18NGzbMdHs55c+fX/Pnz1dKSoo+/PBD5cuXT2XLltXy5cvl7u6u+vXrZ9he0l8/Xl65cmUdO3ZM69ev18SJE3X8+HFFRUVp7Nixat26dbq/s6uxjTEqXrz4Tc0x6a/Hw3333XfTc8w5ZqFChbI1x6S/XkWtWbPmTc8x5+WCBQve9Bxz2rVrlxITE/XII49I+uuAIj4+Xl988YW6deumggULXnd779+/XzExMTLGqH///kpOTtbp06c1evRoPfbYY5luswsXLqhhw4Z64oknJEkPPPCAdaDu6nno2nELFy6sRYsWqVy5cho8eLD1SveMGTNUtWpVValSxWVtUlKSPDw8lJKSooiICL333nsKCwvTrl27NHz4cAUGBlrPX67qnePkzZtXhw8f1tixY7V+/XrNmzdPkyZN0sMPPyxPT0+XtZcvX1a9evX0+OOPS5K6d++uo0ePKi4uToMHD9YzzzyTbuy0tZcuXZKHh4eCgoLk5uamggUL6ptvvpGbm5v8/PwybC9XfVeqVEkvv/yyypQpo8WLF+v06dMqW7ZsuvmR2fZOTk7Wjh07dP/996tw4cL6xz/+obNnz2rgwIHq0qWLihQpYi2bdp8hSXv27NG5c+f02GOPSbrxHLu2/uDBg9ZpeDeaY2lrExMT1ahRI2t732iOXTtu0aJF9eOPP6p8+fI3nGNpa5OTk+Xh4aGkpCRFRkZmaY6lrXf+P0+ePIqJibnhHEtbm5SUdFNzLG3t5cuXlS9fPj377LNyd3fP0hy7tu/KlSurX79+Klu27A3n2LXbOzU1Vdu2bVOzZs1ueo4dO3ZM0dHRatCgwQ33ldfWnjlzRqGhoTpx4sQN95OutlnTpk2zvK/EvYMABpf27Nmjnj17qkmTJqpatapKly6tCRMmqG3bthl2hs4nmpCQEM2YMUOTJk2ywpckbd++XQMGDNAff/yh6dOnq3r16pLSn9P9+eefq3r16taT4Jw5c1ShQgU1adJEb7/9tjw9PRUQEKDSpUtbp6U4aydPnqzatWtbO7uDBw/Ky8tLH374oVq1aqXo6GjNnz9fgYGB1sGps3bSpElq0KCB6tSpo/z581vrk5CQoNTUVLVo0ULbt2+3QpqrvmvUqKGKFSvKzc1Nnp6e8vLy0u7du7V3715Vq1ZNUVFRunTpkkqWLOmytlixYtq4caPi4uJUvXp1FS5cWI0aNdLhw4e1Zs0aa0eZ9lzyTZs2qUaNGipYsKC+/fZbbd68WT/++KNWrlypVatW6fDhw4qLi1PTpk2tv4Or+sKFC+vs2bM6duyY4uLitHTpUrVq1Urnzp3TlClT1LVrV7m5uaWr3bBhgxo0aKBy5cpJ+utgIn/+/DLG6NChQ3rsscfSHZxdO66Pj4/c3Nx04cIF7dixQ+fPn9d//vMftWzZUikpKfr666/VuXNn5c2bN13txo0bVaNGDZUvX17ly5fXtGnTVKVKFX399dd66qmndOzYMX3//ffpXlm8dmxfX1/r1cr//ve/Kl++/A3nmHPs2rVrq1SpUjc1x5zbq06dOqpSpcpNz7G0612kSBG5u7tneY4517l69erKkyePNm3apPj4ePn6+mY6x5y9HThwQPHx8SpVqpQKFCigDz74QHv37tVPP/2kVatWafXq1dq/f78KFChgPZ4zqy9cuLC2bdumffv26ezZs1qxYoUef/xxnThxQnPmzFHnzp3ThdwDBw7oxIkT8vHxscJlSkqKChQooAMHDigxMVEtWrTIEACctQkJCfL09FRSUpL27NmjXbt2ycPDQ5MnT1br1q117tw5TZ06VV26dJGbm1u62ri4OFWoUEGVKlVSYmKi/vOf/6hu3bqaPn26OnXqpOjoaG3ZssUK4a7Gdh7sFyhQQHPmzJGfn5/8/Pz01ltvqUiRIurfv788PDwy1MbFxcnLy0tlypRRnjx5tG3bNpUrV06TJk1Su3btdODAAW3cuNE6kLu21hkaChQooNTUVOuzOiVKlFCjRo0UFhYmDw8Paw5e+7cqW7asihUrJnd3d1WoUEFeXl5asmSJTp48qaZNm2rPnj3Kmzev9fx/7fY2xigkJESXL19Ws2bNJEktW7ZUdHS0oqOj1aJFi3Rz+/jx4zpz5oyuXr0qHx8fjRo1SuHh4TecY9fWp6amqnr16tq8ebPCw8OvO8fS1p46dUoeHh6qVatWlubYteOePXtWxYsX1+7du7Vz587rzrFrx3Vzc1OzZs107tw5ffXVVzecY9eOfeHCBRUtWjRLc+zasSVZ2/NGc8zV9rr//vvl7u6epTmWtv706dNKSkpS1apV5XA4bjjHrl3nS5cuqWLFipo1a5YSExPVvHnzG86xhIQE6zN95cuX19y5c7Vx40YtWLAg033ltbWS5OXlpVOnTik2Nlbx8fGZ7ievrb948WK6F8muXLmiAgUKZLqvxL2Fz4DBkvZc5ho1asjPz08fffSRevTooYiICHl5eVmfWbl69ap27dqlFStWWDWHDh1Sz549VaFCBSUmJsoYY+1cfXx8NHfuXNWtW9da3nluddu2bVWpUiV5eXlZt3Xs2FE///yzAgMD9eqrr+qJJ57QlClTdObMGZe15cuXt2qrVq2qTp06WZcfeOABVapUKd27CWlrS5UqZe2s0r5SZ4zRH3/8oaeeekq7du3KtG/nOybO7VejRg1169ZNZcqU0dChQ9W0aVPryyLS1np7e6tixYoqXLiwBgwYoK1bt+rrr7+2xvLz88uwE923b5+efvppa0daqVIlffbZZwoKClK7du0UEhIiSS7f+XJVX7ZsWb388st64403VK9ePevD4C1atFCZMmWUkpKSodb5N3Cuc968ea1+v/32W61bt84KOa7GdS7ftm1bubu7a/fu3Vbf9erVU5EiRXT58uUMtadPn5Ykubm5qWHDhpo+fbo+//xzq5cmTZrIy8srwwFT2rHTfmA7ICBAS5cudTnHMhs7K3Mss+2VL1++LM2xzMZ23vf15pirdXY4HNZB2ebNmzVt2jSXc8zp4sWL6t69uxo2bKidO3eqXr16CgkJUaNGjVS/fn3Nnj3b6ufChQu6Vtr6Xbt2qXr16vroo480ZMgQ66BJkh588MF0L9SkrW3cuLHCwsIk/fWqt7u7uzw8PNSmTRuNHz9ef/75Z4bTfpy19913n/bs2aP8+fOrV69eOnLkiEJDQ/X7779L+uuArVy5cuk+++GsbdSokXbv3q3ixYurXbt2Gj9+vHWqqyTVqlUr3avsrsYOCwuz/sYtWrTQrFmz1L17dw0ePFh169bVoEGDrAO7a8cOCwuTu7u7JKlx48Z6/vnnreUaNGiQ4V2RtLV79uxR4cKF0736njdvXrm5uWn79u0KCgrSvn37Mv1bObeZ8zHv5+dnfUFEcHCwWrZsqR07drhc5127dqlixYoaPXq0vv76a02YMMGat76+vtZnhNJ+GcJTTz2l119/XQMHDlSePHn07bffqnHjxqpXr16mc8xV/aBBg3Tu3Dl9/PHHev31163wJ6WfY9fWvvnmm3rzzTe1bds2SbruHLu29o033tDIkSN14MAB9e7dW7Gxsdq6davLOeZq3MGDBys8PFzt27fXp59+qgkTJmQ6x1yN/c4772jr1q2S/nrumTlzpss55mrst956y1rn682xzPp21jq3TWZzzFXfw4YNs+qNMZnOMVd/5zfffFMnT57UmDFjNH36dH322WfXnWPOL0fp1auXhgwZouPHj+uLL75QUFCQ/P39Xe4rXdUOHTpUe/fuVb9+/fTGG2/ovvvuc7mfdFXfu3dvDRs2TL/99puk/9sHuNpX4h5kgDRWr15tPvroIxMaGmqMMebs2bNmzZo1pl27dqZ8+fKmY8eOxhhjkpKSzOLFi01cXJw5fPiwMcaY999/37z00kvp7m/FihVm69at5tixYy7HGzdunHnrrbeMMcakpqaaAwcOmP3795tTp04ZX19fM2bMGOu2vXv3Xrf24MGDZvfu3emW2bhxo2nUqJH5+eefb1i7Z88e6/bZs2cbX19f06RJE7N06dIb9n1tvTHGTJ061RQpUsT89NNP162NjIw0J06cMGfPnjX9+vUz3bp1Mz169DBVq1Y1P/74o1UXHR1t6tatayZMmGDVnj9/3ly5ciXd/a9evdrUqVPHrFmzJt31ruoTExPN+fPnTWpqqunRo4cZMGCAGTFihGnQoIFZsGBBprVXr161atP64IMPTOfOnc2lS5euO+6FCxeMMcYkJiaacePGmeeff9507drV1K5d2/zwww/XHffixYsmMTEx3bgbNmwwDRs2zPB3zqzv5ORkExsba6pVq2Y++OADq6+0cyyz2nPnzqUbw9Ucy6zWud7GGPPNN99kOseyUm+M6zmW2Twxxpjjx4+bvn37mu7du7ucY04ffPCBadOmjSlevLjZsGFDhtvXr19vatWqZTZu3JjhtmvrN23aZIwx5tixY6Zly5bm7bffNpMnTzYNGzY0ixYtyrS2RIkSZvPmzdb6O73xxhvmzTffzDDvM+s7KirKBAcHm1deecX069fP1K5d+4br7FyvlJQU6/YtW7aYhg0bmhUrVlx3ndP2vW3bNuPp6WlGjx5tjDEmOTnZbN++PUu1aW3dutU0atTI5djXq500aZJp2LChady4cYbnoevVp93en332mSlYsKBZvHhxprVpt/eOHTtMu3btTN++fc3AgQONr69vurEPHDhg/Pz8zH//+19z6tQp07t375uaY67qnX2fO3fOtGzZ0gwbNszlHLu29vnnnzdbtmzJMLarOeZqXGdv8fHxJjg42AwYMMDlHHNV63xcpJXZHHPVt3Od9+3bZzw9Pc17771njMk4x7K6zq7mWFZqrzfHsjq2qzl2vXmyf/9+065dO/PSSy+5nGMHDx409evXNz/88IOJiIgw77//vvnoo48yjOtqX+mqdty4ccYYY06dOmW6du1qBg4c6HI/6ap+zJgxVn1qaqq1nKt9Je4tBDBYO9vNmzeb+vXrWwFg/PjxJj4+3hjz1xPPrl27TPfu3c2uXbus2ujoaBMUFGQWLlxozpw5Y3x8fMzbb79tDh48aGbNmmV8fHysgOZKSEiIdXDy6KOPmi5dupj69eubjz/+OEN/N6rt2rWradiwoXnttdeMMX8dlD/66KPWDjjt/dyodsuWLaZChQqZHmzdqP7q1avmhRdeMCEhIdZl5/iZrfP7779vjDEmIiLCfPPNN9bO3Vm3aNEi8+ijj5qNGzeahIQE06tXL9O5c2fTu3dvM2PGDGPMXwe5Xbt2Nd99912GnjOr79mzp1myZIlJSEgwkydPNsOGDTO//vprlseeOXOmNcYff/yRIYhmVvvcc8+Z77//3hhjzPnz582vv/5qdu7cedPj7tixw7Rt29YsXLgww985s/oePXqY5cuXW8u5mmNZGXv9+vUu51hW/labN282FStWdDnHsjJ2amqq6dOnT4Y55qo2MDDQPPfcc1af+/btcznHkpOTjTHGTJ8+3WzcuNHMmTPHlCxZ0ixZssRMnz7dqu3QoYPLOZZZ/aJFi8yyZctMRESEGTx4sOnfv79ZtmzZDcf29PQ0P/30k7XNnNtmx44dWR534cKFJjEx0ezdu9fMmzcvQ8i43jo7x92wYYP5xz/+4TIwZla/cOFCs3DhQnP69Ol0492o9tp1/uWXX0zz5s0zjH29Wuff6ocffjAlS5a0Hs83O/a5c+dMUFCQ+fbbb611uHr16nXX+eeffzYnT540v/76q/nss8+sg1vn+v/www9m1qxZVh/169c3HTp0MMOGDbOu379/f6ZzLLP6N9980/zyyy8mJibGDB061Lz88ssZ5tj1xp49e7Z1vas5llntG2+8YZYsWWKM+eu529Ucy8q4mzdvznSO3Wjsy5cvpxsvK7Vpx165cqXLOZaVv9WCBQtMiRIlXM6xrIx98eJFl3Pseuu8fPlyk5iYaFatWuVyjs2fP9+8/vrrVu2qVavMAw88YM6dO2euXr1qUlNTzdGjR01QUFCGOeaqtmXLlubMmTPGGGMiIyPNpEmTzFtvvZVhP3m9eucLd85lQ0NDM+wrcW8hgMEY89erX4888oj5448/jDF/PXEOGjTIfPLJJyYuLs5a7umnn7Zepfrzzz/NsWPHzNdff2169+5t1qxZY06ePGkCAgLM888/b1q1apXuHSnnqz9pX1X+5ZdfTKtWrcysWbOsUHL06FHj4+NjPSGnpqZmufbYsWOmWrVqZvHixebs2bNm//79JjU11Vy9ejXLtQsWLDBxcXFm27Zt6cbMav38+fONMX8d4DjHdh6wXK/Wx8cn3aumznVOWztnzhwr7A0ePNjs2LHDfP/996ZDhw5m3759JiUlxdpROPvNSv0TTzxhoqOjsz122h3JzdQGBASYsLCwbI8bGRlpzpw5Yw4dOnTT6xwQEGDNz7RzLKtjHzhwwJw6dSrdHMtqbUREhDl27Jj1eLuZvjt06GBts7RzLCkp6Ya1Tz75pNm3b1+G7Z12bhtjzLp160yfPn2MMcaMGDHCOBwO693t8+fPW+9ou3psZFb/r3/9K906unpc3mjs7PT973//O9u1znGPHTtmzfGsrPPw4cPTjZ2SkpLp89iNxo6NjTU7d+7M1vbav3+/+f3337Pct7O+b9++xpi/znQ4e/asy/mdWe2LL77ocntfW5uSkmLefPNNM2DAALN3716zcOFC4+/vb/bu3WsSExMzzLEb1S9YsMC0bdvWxMTEWAe5mfWd2djOF4Bupu8FCxaYdu3a3fB57HrjRkREmLi4uAxzLCtjt2/f3npRNLPnseuNHRkZaY4fP55ujt1M31FRURnmWFbrd+/ebVJTU9PNsbTPY9fb3hEREdfd3kePHjWpqakmOTnZnDhxwvj7+1tB1XkmQWZz21Wt852qtO9iZWfss2fPGsAYAhj+v/Xr15sCBQqYYcOGWdctXLjQ9O3b14wdO9ZcunTJREdHm2bNmpnw8HBz/vx507RpU/PVV18ZY4yZMWOG6dmzZ7pXwdI+0TifpMLCwszgwYPN8OHDrZ3NW2+9ZQoVKmQmT55sLf/+++9br8Zlp9Z5WsCt1OZG35nVDhs2zNrBL1y40IwaNcqqu3jxogkMDHR5YJ2d+put7dy5c7Zrb2XcwMDAdKcMZqdv5048O2OHh4ffcu3faXuPGDHC/Pnnn+bs2bPm1KlTZsCAASYqKsrcf//9plOnTsbDw8MKjNmtd76okZ1a52lVdtc6T8fOzbFzo++c+Fu9/fbbZseOHdYpsUePHk03P7t06eIyyNxMfWaPy79zrasXsOweOzu1rl5gzc7f+nav886dO9OdHn/p0iXz0EMPmeTkZLNu3TrTr18/c+rUqZuu/e2330y/fv1MQkKC9U5adsZOSEgwAJ/+u0eZ//9FBImJiUpMTFTLli31008/afHixfryyy8lSR06dFCrVq0UEBBgfZPP8uXLlZSUpMKFCyskJEQhISHasGGDgoKC9Oijj2rq1KmaP3++JKX7IHGePHl09OhRPfbYY/Lx8dGJEyf01VdfacyYMXr//fc1YMAATZs2TVu3btWSJUs0e/Zs67dBslPr/AreW6nNjb4zq42Pj9dXX32l9957T61bt9bw4cOtHg8cOKDIyEjrg8C3Wn+ztREREdmuvZVxIyMj032RQnb6Tk5OzvbYzi/EuJXav9P2Pn78uGbOnKnx48crX758cnNzk4+Pj55++mnNnz9f48aNU0JCQqZ9Z6U+Pj4+27VxcXG5Upv2x7Jza+zc6Dsn/lZxcXGaOXOmPvroI508eVLly5e39kUHDx7U3r17r/vYyEq98znhTqo1ab4AK7fGzk5tWrfyt77d6zxjxgx99NFH1pd0XLlyRWXKlNHs2bP18ssv64knnlCJEiVuurZ///564okn5Onpaf0MQHbGTnuMgXtYrsQ+5Crn6RkLFy40AQEBpm3bttaXAKxbt874+fmZTz/9NN3yKSkpJjk52ezcudM4HA7zwgsvmB9//NEsXLjQfPLJJyY5OdkcP37czJ07N92rVWnt3LnT9OzZ07rP1atXm4EDB1qffZo0aZJ57733zJNPPpnhyxRyq/bv1rfznTTn+eQLFiwwDRo0cPkB+VupvxNr6fv21L722mvmnXfeMStXrjS//PJLhl7Tft7hVurvxFr6vj21Q4cOTTc/586da+rXr5/lx0ZW6+/EWvq+PbVDhgwxw4cPt07Jf+CBB0ylSpUyfOb2VmpvRz3uXQSwe9SyZctMkyZNzJEjR8wLL7xgSpQoYebOnWuM+eubgerXr2+io6PTne/s/P+IESPMG2+8YTp16mTatWtnOnTokOHzUq44P9vl/LIEY4xZs2aNeeWVV9J9M5LzG+7SHjzkVu3fse/XXnvNrF+/3hhjzOTJk63gltW+s1J/J9bS9+2pXb16tXn99dfNunXrrOsye1zfSv2dWEvft6f22vk5fvx464sssvPYyKz+Tqyl79tXO3jwYKs2ODjY+lx5VnrOSu3tqMe9ix9ivketW7dOvXv3VkREhJYuXarXXntNgwYNUtmyZfXUU08pKChI5cqVk8PhkDFGv//+u5588kk99NBD1mkDo0eP1okTJ7RgwQKtW7dOL7zwgvLkyePyRwWvXr2qokWLqkyZMlq8eLE8PT1VuXJleXt7a/Xq1dq+fbvat28v6a/fY3E4HOl+zyk3av+ufa9cuVK7du1S+/btdf/996t69erpfvvnVuvvxFr6vn21lSpV0ooVK7Rnzx7rB1ld/VbNrdTfibX0fftqr52fLVq0UI0aNbL92HBVfyfW0vftrf3111+1Y8cO+fv7y9/fX3Xr1r1ttbejHvc2Atg9wvnA37Ztm9zc3PTAAw+oSJEieuuttzR27Fg98cQT2rp1q3788Ud17txZJUuWtJ4oHA6HKleuLIfDoaVLl0qSZs2apdTUVA0ZMkT+/v7q1q2bihcvnumTi/N6T09PnT17VkuXLpW7u7tq1aolh8OhP/74Q23btlXevHkz3Edu1f6d+w4NDVWbNm1chsZbrb8Ta+n79tbmyZNHoaGh1vx05Vbq78Ra+r69tQ6HQ1u3br2lx8b16u/EWvq+/bV//PGHWrdube1nb1ft7ajHPc6299qQ65YvX258fHzMli1brLfvg4ODzfjx483KlStNz549M/yQ59SpU82rr75qhg4dao4ePWpiY2PN3LlzTfPmzU2pUqXMypUrszR22tMFYmJizDfffGOqV69u+vbta0qVKuXyt09yu5a+75xa+qZv1pm+7/Ra+r5zam9HPe5tDmPSfP0O7lqHDx/W008/rfHjx+uf//yn9Y1Cs2fPVmhoqFasWKFPP/1Ujz/+uFUzbdo0TZ06VUOGDNEff/yhGTNmaO3atapZs6Z27Nih9957TxMmTFDFihUzjGdcvNWenJyc7lXSqKgoRUVFqWjRomrcuLFVk1u19H3n1N6p60zfd07tnbrO9H3n1N6p63yn9p2b6wxciwB2j4iPj9fAgQM1Z84cSdLly5eVP39+nTp1SiVLltSRI0fk5eWl1NRUubm5SZKGDh2qZs2a6ZlnnpEkffrpp/rhhx+0ePFilSxZMsMTj5PzCefnn3/Wpk2bdOnSJY0ePVr58+e3aq5everyswq5VUvfd04tfdM360zfd3otfd85tbejHsjA4K7kfGvc+YOAV65cMY0bNzZjx461llmzZo3p37+/uXLlyv9r79xjav7/OP48KdVqJrpha1gZYpGxaEMXZyz0R9hCaxnmth1aOmILm2yF8Y9hiNrKJbftNJeEQ3OwtORe1mojcqnltONEnZ7fP6j9XMp3nO/vnDevx3+denzej9c/be/zOef9+eqjh1VVVSTJ1NRUpqend79usVi4ZMmS7qfI90ZxcTHDw8NZXl7OSZMmMSIiovukv/99YrwzudKtjivd0i0zO9/af2P332XRez0AAAfASURBVDizqt2OnFkQvkW26n8g/PJOzcWLFzF//nxkZGQgLy8PZ86cwZEjR6DT6XDw4EGsXbsW0dHR6Nu3LzIzMxEeHg4AuHbtGgBg9erVyM/PR3Z2NiwWC86cOYMHDx6gra2t1/WtVisMBgPy8vLw4sULeHt7IzAwEOPHj4fVaoWrq6vTudKtjivd0i0zS7fqrnSr49rDF4TvcPQOUPh9vn0WB/n5gcrjxo1jeXk5dTodp0+fTpKsr69nWloat27d2v1QwC4/Li6OYWFhTEhI4Pv370mSNTU1nDVrFhMTExkZGcmHDx/+q4bm5mY+e/aMEydO5OvXr0mSvr6+HDduHDs7O7/6e0e50q2OK93SLTNLtzN0/40zq9rtyJkF4WfIlv0PQKPR4NWrV6itrUVkZCQ0Gg1aWlqwe/dutLa2wmQyoaioCADg7u6OHTt2fOXzyx2z4uJiJCcn48aNG+jXrx8AICQkBHFxcbDZbNizZw/8/f17bCgrK0NTUxMGDBiAqVOnoq2tDaNGjYLFYsGdO3ewcuVKaLXaHx7l6ghXutVxpVu6ZWbpVt2VbnVce/iC0CuO3gEK9qGwsJBPnjyh2Wym1WqlwWCgv78/w8PD2dLSQpIsKSnhmjVrfujbbDaS5JUrV+jt7U2tVtt9XV9fXz569KjX9cvKyjh48GCmpqZy7Nix3LdvH0ly6dKlTElJ4aBBg7qPrP/2XSJHudKtjivd0i0zS7fqrnSr49rDF4TekA3YH4TZbOaiRYt47tw5tre3U6/XMzY2ls3NzSwtLWVwcDB1Oh1NJhPr6+t/eI3m5mbevn2bM2bMoJeXF4cPH959KEdP3L17l3q9niUlJSRJk8nE4OBgnjx5kiRZXV3NiooKp3KlWx1XuqVbZpZu1V3pVse1hy8IP0M2YH8It27d4qdPn3jgwAGmpKSwuLiYdXV13LRpE6dNm8aZM2cyPz+fBoOBWq2Wr169+uk1ly1bxsrKyh5/33XXTK/Xc8SIETx69Cg/ffpE8vM/q4CAAO7du9epXOlWx5Vu6ZaZpVt1V7rVce3hC8K/pc+WLVu2OPpjkMKvwy/f39q2bRtOnTqFrKwsNDQ04OrVqwgICMDy5cuRlJSEefPmYcKECfD390dCQgL8/Px6vKbNZoOLiwvmzJmDwMDAHtc0m83w8PBAbGwsWltbYTQaERYWhoEDByIoKAhTpkzBgAEDMHToUIe70q2OK93SLTNLtzN0/40zq9rtyJkF4VeQDZjiVFVVITAwEBEREaiuroZGo8GCBQtQV1eH4uJiuLu7IyQkBK6urtBoNPDw8ICnp2ev1+ztQYKdXx40eP78eWRmZqKmpgYuLi5YvHgxHj9+jFOnTmHkyJHw8/NDUFAQhg4d2v2PzVGudKvjqjqzdKvjqjqzdKvjqjqzqt2OnFkQfplfum8m/F/p6Oj47rXOzk42NzfTz8+PCQkJPHfuHDds2MCdO3d2/01OTg7v37//W2t3fbG067Y8SZaWlnLMmDGsrKykVqulVqtlQUEBSXL9+vWMj49na2urw1zpVsdVdWbpVsdVdWbpVsdVdWZVux05syDYCzmG3smx2WxYuHAh9uzZA1dXV7S2tmL48OFoaWmBj48PUlNTUV5eDo1Gg8rKSty7dw+enp5YtWoV1q9f/9vrazQaNDY24vLly4iKisKQIUNw584dFBQUoLGxEW/fvkViYiIKCwvRp08f5OTk4NmzZ/D29gYAh7nSrY6r6szSrY6r6szSrY6r6syqdjtyZkGwC47eAQo/p7y8nJcuXeLo0aNZVFTEp0+fcu7cuczNzWVDQwMjIyNpNBppMpk4YsQITp48mY2NjXY7FvXy5cucO3cuDx06RLPZzPb2dra0tHD27Nl88+YNSTIiIoLJyclsaGhwCle61XGlW7plZulW3ZVudVx7+ILwu8gdMAX4+PEjdDod0tPTMW/ePLS1tSErKwvJycno27cvoqOjkZubi7y8PBw7dgxubm4ICAiw2/qxsbFob29HQUEBbDYb4uPj4eXlhdraWlRUVCAkJAQ+Pj5IS0vD4MGDncKVbnVc6ZZumVm6VXelWx3XHr4g/DaO3gEKPdN1Bys3N5fr1q3rfq2iooIGg4G7du3i2rVruXnzZrq5ufH8+fP/yfpdGI1GJiYmcv/+/ezo6ODp06cZGhrKsLAwnj171ilc6VbHlW7plpmlW3VXutVx7eELgr3QkKSjN4FC71y/fh0bN25EZmYmTpw4AavViqqqKmi1Wrx8+RLbt29HRkYGVqxYgZiYGLuuXVJSgpKSEgwbNgzx8fF49+4dsrOzERUVhaSkJFgsFnz48AFBQUHfnQrkKFe61XGlW7plZul2hu6/cWZVux05syDYi57PGxechokTJ2L+/PnQ6/Uwm81YtWoVbt68iZSUFHh7eyM4OBhFRUWIiYmBPfbTXdd49OgRtm7div79++P58+eYPXs2PDw8kJaWhgsXLuDo0aPw8fFBUFAQgM9fbHWUK93quKrOLN3quKrOLN3quKrOrGq3I2cWhP+EX7hrJjiIpqamr342Go2cMmUKGxoa7HbgRhcmk4mhoaE8fvx492uHDx9mXFwcOzo6aDAYWFlZ6VSudKvjSrd0y8zSrbor3eq49vAFwZ78AwGbQXasAaajAAAAAElFTkSuQmCC\">\n",
       "                </div>\n",
       "\n",
       "            </td>\n",
       "\n",
       "        </tr>\n",
       "        \n",
       "        \n",
       "        \n",
       "        \n",
       "        \n",
       "        \n",
       "\n",
       "    </table>\n",
       "</div>\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 10 of 45716 rows / 10 columns</div>\n",
       "<div class=\"info_items\">32 partition(s)</div>\n",
       "\n",
       "<table class=\"optimus_table\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">name</div>\n",
       "            <div class=\"data_type\">1 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">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\">nametype</div>\n",
       "            <div class=\"data_type\">3 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">recclass</div>\n",
       "            <div class=\"data_type\">4 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">mass (g)</div>\n",
       "            <div class=\"data_type\">5 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">fall</div>\n",
       "            <div class=\"data_type\">6 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">year</div>\n",
       "            <div class=\"data_type\">7 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">reclat</div>\n",
       "            <div class=\"data_type\">8 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">reclong</div>\n",
       "            <div class=\"data_type\">9 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">GeoLocation</div>\n",
       "            <div class=\"data_type\">10 (string)</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",
       "            <div class=\" \"\n",
       "                 title='Acfer&#8901;232'>Acfer&#8901;232\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='240'>240\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H5'>H5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='725.0'>725.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1991&#8901;12:00:00&#8901;AM'>01/01/1991&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='27.73944'>27.73944\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='4.32833'>4.32833\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(27.739440,&#8901;4.328330)'>(27.739440,&#8901;4.328330)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Asuka&#8901;87197'>Asuka&#8901;87197\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='2554'>2554\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H4'>H4\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='124.99'>124.99\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1987&#8901;12:00:00&#8901;AM'>01/01/1987&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-72.0'>-72.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='26.0'>26.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-72.000000,&#8901;26.000000)'>(-72.000000,&#8901;26.000000)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Gladstone&#8901;(iron)'>Gladstone&#8901;(iron)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='10920'>10920\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Iron,&#8901;IAB-MG'>Iron,&#8901;IAB-MG\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='736600.0'>736600.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1915&#8901;12:00:00&#8901;AM'>01/01/1915&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-23.9'>-23.9\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='151.3'>151.3\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-23.900000,&#8901;151.300000)'>(-23.900000,&#8901;151.300000)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Nullarbor&#8901;015'>Nullarbor&#8901;015\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='17955'>17955\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L6'>L6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='3986.0'>3986.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1980&#8901;12:00:00&#8901;AM'>01/01/1980&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Lewis&#8901;Cliff&#8901;86533'>Lewis&#8901;Cliff&#8901;86533\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='13461'>13461\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H5'>H5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='15.7'>15.7\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1986&#8901;12:00:00&#8901;AM'>01/01/1986&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-84.26756'>-84.26756\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='161.3631'>161.3631\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-84.267560,&#8901;161.363100)'>(-84.267560,&#8901;161.363100)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Grove&#8901;Mountains&#8901;053589'>Grove&#8901;Mountains&#8901;053589\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='48447'>48447\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L5'>L5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1.4'>1.4\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/2006&#8901;12:00:00&#8901;AM'>01/01/2006&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-72.7825'>-72.7825\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='75.300278'>75.300278\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-72.782500,&#8901;75.300278)'>(-72.782500,&#8901;75.300278)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Sayh&#8901;al&#8901;Uhaymir&#8901;108'>Sayh&#8901;al&#8901;Uhaymir&#8901;108\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='23300'>23300\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H6'>H6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='16.0'>16.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/2001&#8901;12:00:00&#8901;AM'>01/01/2001&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='21.06667'>21.06667\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='57.31667'>57.31667\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(21.066670,&#8901;57.316670)'>(21.066670,&#8901;57.316670)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Northwest&#8901;Africa&#8901;3088'>Northwest&#8901;Africa&#8901;3088\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='31218'>31218\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L6'>L6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='171.0'>171.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/2003&#8901;12:00:00&#8901;AM'>01/01/2003&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Reckling&#8901;Peak&#8901;92423'>Reckling&#8901;Peak&#8901;92423\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='22432'>22432\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L6'>L6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='3.8'>3.8\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1992&#8901;12:00:00&#8901;AM'>01/01/1992&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-76.22029'>-76.22029\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='158.37967'>158.37967\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-76.220290,&#8901;158.379670)'>(-76.220290,&#8901;158.379670)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Sweetwater'>Sweetwater\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='23770'>23770\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H5'>H5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1760.0'>1760.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1961&#8901;12:00:00&#8901;AM'>01/01/1961&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='32.55'>32.55\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-100.41667'>-100.41667\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(32.550000,&#8901;-100.416670)'>(32.550000,&#8901;-100.416670)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 10 of 45716 rows / 10 columns</div>\n",
       "<div class=\"info_items\">32 partition(s)</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:run() executed in 17.73 sec\n"
     ]
    }
   ],
   "source": [
    "op.profiler.run(df, \"name\", infer=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "expected string or bytes-like object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-63-1304007aee00>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_image\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_path\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"images/profiler.png\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Documents\\Optimus\\optimus\\profiler\\profiler.py\u001b[0m in \u001b[0;36mto_image\u001b[1;34m(self, output_path)\u001b[0m\n\u001b[0;32m    267\u001b[0m         \"\"\"\n\u001b[0;32m    268\u001b[0m         \u001b[0mcss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mabsolute_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"/css/styles.css\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 269\u001b[1;33m         \u001b[0mimgkit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_string\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhtml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutput_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcss\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    270\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    271\u001b[0m         \u001b[0mprint_html\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"<img src='\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0moutput_path\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"'>\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\imgkit\\api.py\u001b[0m in \u001b[0;36mfrom_string\u001b[1;34m(string, output_path, options, toc, cover, css, config, cover_first)\u001b[0m\n\u001b[0;32m     87\u001b[0m     \"\"\"\n\u001b[0;32m     88\u001b[0m     rtn = IMGKit(string, 'string', options=options, toc=toc, cover=cover, css=css,\n\u001b[1;32m---> 89\u001b[1;33m                  config=config, cover_first=cover_first)\n\u001b[0m\u001b[0;32m     90\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mrtn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_img\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\imgkit\\imgkit.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, url_or_file, source_type, options, toc, cover, css, config, cover_first)\u001b[0m\n\u001b[0;32m     42\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msource\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_find_options_in_meta\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl_or_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\imgkit\\imgkit.py\u001b[0m in \u001b[0;36m_find_options_in_meta\u001b[1;34m(self, content)\u001b[0m\n\u001b[0;32m    199\u001b[0m         \u001b[0mfound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    200\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 201\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfindall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'<meta [^>]*>'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    202\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'name=[\"\\']%s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmeta_tag_prefix\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    203\u001b[0m                 name = re.findall('name=[\"\\']%s([^\"\\']*)' %\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\re.py\u001b[0m in \u001b[0;36mfindall\u001b[1;34m(pattern, string, flags)\u001b[0m\n\u001b[0;32m    221\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    222\u001b[0m     Empty matches are included in the result.\"\"\"\n\u001b[1;32m--> 223\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_compile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfindall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    224\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    225\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfinditer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstring\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: expected string or bytes-like object"
     ]
    }
   ],
   "source": [
    "op.profiler.to_image(output_path=\"images/profiler.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing Dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "For dates data types Optimus can give you extra information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Processing column 'year'...\n",
      "INFO:optimus:_count_data_types() executed in 23.75 sec\n",
      "INFO:optimus:count_data_types() executed in 23.75 sec\n",
      "INFO:optimus:cast_columns() executed in 0.0 sec\n",
      "INFO:optimus:agg_exprs() executed in 1.59 sec\n",
      "INFO:optimus:general_stats() executed in 1.6 sec\n",
      "INFO:optimus:------------------------------\n",
      "INFO:optimus:Processing column 'year'...\n",
      "INFO:optimus:frequency() executed in 3.26 sec\n",
      "INFO:optimus:stats_by_column() executed in 0.0 sec\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'year' with function func_infer_date\n",
      "INFO:optimus:Using 'column_exp' to process column 'year_0' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.81 sec\n",
      "INFO:optimus:hist() executed in 3.44 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'year_1' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.12 sec\n",
      "INFO:optimus:hist() executed in 1.61 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'year_2' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.1 sec\n",
      "INFO:optimus:hist() executed in 1.76 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'year_3' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.22 sec\n",
      "INFO:optimus:hist() executed in 1.7 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'year_4' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.52 sec\n",
      "INFO:optimus:hist() executed in 1.97 sec\n",
      "INFO:optimus:hist_date() executed in 62.45 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Including 'nan' as Null in processing 'name'\n",
      "Including 'nan' as Null in processing 'nametype'\n",
      "Including 'nan' as Null in processing 'recclass'\n",
      "Including 'nan' as Null in processing 'fall'\n",
      "Including 'nan' as Null in processing 'year'\n",
      "Including 'nan' as Null in processing 'GeoLocation'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:dataset_info() executed in 1.79 sec\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\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>10</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Number of rows</td>\n",
       "                <td>45716</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Total Missing (%)</td>\n",
       "                <td>0.49%</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Total size in memory</td>\n",
       "                <td>97.6 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",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Numeric</td>\n",
       "                <td>0</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Date</td>\n",
       "                <td>1</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Bool</td>\n",
       "                <td>0</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Array</td>\n",
       "                <td>0</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Not available</td>\n",
       "                <td>0</td>\n",
       "\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "</div><link rel=\"stylesheet\" type=\"text/css\" href=\"optimus/styles/styles.css\">\n",
       "\n",
       "\n",
       "<div class=\"main\">\n",
       "    <div class=\"info\">\n",
       "\n",
       "        \n",
       "\n",
       "        <div class=\"panel_profiler\">\n",
       "            <div>\n",
       "                <h2>year</h2>\n",
       "                <span>date</span>\n",
       "            </div>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>Unique</td>\n",
       "                    <td> 265</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Unique (%)</td>\n",
       "                    <td> 0.58</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",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Integer\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Float\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Bool\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Date\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        45428\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Missing\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Null\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        288\n",
       "                    </td>\n",
       "\n",
       "                </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "            \n",
       "\n",
       "        </div>\n",
       "        <div class=\"panel_profiler\">\n",
       "            <h3>Frequency</h3>\n",
       "            <table>\n",
       "\n",
       "                <tr>\n",
       "                    <th>Value</th>\n",
       "                    <th>Count</th>\n",
       "                    <th>Frequency (%)</th>\n",
       "                </tr>\n",
       "                \n",
       "                <tr>\n",
       "                    <td>01/01/2003 12:00:00 AM</td>\n",
       "                    <td>3323</td>\n",
       "                    <td>7.269%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>01/01/1979 12:00:00 AM</td>\n",
       "                    <td>3046</td>\n",
       "                    <td>6.663%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>01/01/1998 12:00:00 AM</td>\n",
       "                    <td>2697</td>\n",
       "                    <td>5.899%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>01/01/2006 12:00:00 AM</td>\n",
       "                    <td>2456</td>\n",
       "                    <td>5.372%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>01/01/1988 12:00:00 AM</td>\n",
       "                    <td>2296</td>\n",
       "                    <td>5.022%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>01/01/2002 12:00:00 AM</td>\n",
       "                    <td>2078</td>\n",
       "                    <td>4.545%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>01/01/2004 12:00:00 AM</td>\n",
       "                    <td>1940</td>\n",
       "                    <td>4.244%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>01/01/2000 12:00:00 AM</td>\n",
       "                    <td>1792</td>\n",
       "                    <td>3.92%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>01/01/1997 12:00:00 AM</td>\n",
       "                    <td>1696</td>\n",
       "                    <td>3.71%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>01/01/1999 12:00:00 AM</td>\n",
       "                    <td>1691</td>\n",
       "                    <td>3.699%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>\"Missing\"</td>\n",
       "                    <td>0</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\n",
       "            </table>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "        \n",
       "    </div>\n",
       "    <table>\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",
       "        <tr>\n",
       "            <td>\n",
       "                <div>\n",
       "                    <img 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\">\n",
       "                </div>\n",
       "\n",
       "            </td>\n",
       "\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",
       "        <tr>\n",
       "            <td>\n",
       "                <div>\n",
       "                    <img 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8/Kz1uro6vfnmm+rXr19graysTF6vV16vVwUFBZo/f74k6eDBg1q6dKm2bt2qqqoqLV26VI2NjZKk+fPnq6CgIPC6c+0LAAAAQOggwC5g7NixcjgcZ60vXLhQTz75pGw2W2CttLRUc+fOlc1m05gxY9TU1KSGhgZt3LhRqampcjgcioiIUGpqqsrLy9XQ0KDDhw8rJSVFNptNc+fOVUlJicnDAwAAAGAYAXaRXn/9dV111VW65ppr2qzX19crJiYm8Njlcqm+vv6C6y6X66x1AAAAAKGL3wN2EY4fP66f/exnqqioOOu5c31+y2azXfT6uRQUFKigoECStG/fvosdGwAAAMC3BFfALsLu3btVU1Oja665RrGxsfL5fBo5cqS+/PJLuVwu1dXVBbb1+XyKjo6+4LrP5ztr/Vzy8vLk8Xjk8XgUGRnZcQcIAAAAoEMRYBdh6NCh2rt3r2pra1VbWyuXy6Xq6mpdeeWVSk9P18qVK2VZlrZs2aI+ffrI6XQqLS1NFRUVamxsVGNjoyoqKpSWlian06nevXtry5YtsixLK1eu1LRp04J9iAAAAAA6EAF2ATNnzlRKSop27twpl8ulwsLC8247depUxcXFye1266677tILL7wgSXI4HHrkkUeUnJys5ORkLV68OPDFHi+++KLmzZsnt9utq6++WlOmTDFyXAAAAACCw2bxy6cuKUlJSfJ4PMb3G/vgeuP7DCW1+TcFewQAAICLEqyfO0MdV8AAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBACDAAAAAAMIcAAAAAAwBAC7AJycnIUFRWlIUOGBNZ+/OMfa9CgQRo2bJhuvfVWNTU1BZ5btmyZ3G63Bg4cqI0bNwbWy8vLNXDgQLndbuXn5wfWa2pqNHr0aMXHx2vGjBlqbm42c2AAAAAAgoIAu4A77rhD5eXlbdZSU1P10Ucf6Y9//KO+853vaNmyZZKkHTt2qLi4WNu3b1d5ebnuvvtu+f1++f1+/eAHP1BZWZl27Nih1atXa8eOHZKkRYsWaeHChfJ6vYqIiFBhYaHxYwQAAABgDgF2AWPHjpXD4WizNnnyZNntdknSmDFj5PP5JEmlpaXKyspS165dNWDAALndblVVVamqqkput1txcXHq0qWLsrKyVFpaKsuy9PbbbysjI0OSlJ2drZKSErMHCAAAAMAoAuxr+NWvfqUpU6ZIkurr6xUTExN4zuVyqb6+/rzrBw4cUHh4eCDm/rIOAAAAIHTZgz3ApepnP/uZ7Ha7Zs2aJUmyLOusbWw2m06dOnXO9fNtfy4FBQUqKCiQJO3bt+/rjA0AAAAgiAiwdigqKtK6detUWVkZiCaXy6W6urrANj6fT9HR0ZJ0zvUrrrhCTU1Nam1tld1ub7P9mfLy8pSXlydJSkpK6qjDAgAAANDBuAXxIpWXl+uJJ57Q66+/rh49egTW09PTVVxcrJMnT6qmpkZer1fXXnutkpOT5fV6VVNTo+bmZhUXFys9PV02m00TJkzQ2rVrJZ2OumnTpgXrsAAAAAAYQIBdwMyZM5WSkqKdO3fK5XKpsLBQP/zhD3XkyBGlpqZq+PDh+v73vy9JSkxMVGZmpgYPHqwbb7xRy5cvV+fOnWW32/X8888rLS1NCQkJyszMVGJioiTpiSee0C9+8Qu53W4dOHBAubm5wTxcAAAAAB3MZp3rw0j41kpKSpLH4zG+39gH1xvfZyipzb8p2CMAAABclGD93BnquAIGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAF2ATk5OYqKitKQIUMCawcPHlRqaqri4+OVmpqqxsZGSZJlWVqwYIHcbreGDRum6urqwGuKiooUHx+v+Ph4FRUVBdY/+OADDR06VG63WwsWLJBlWeYODgAAAIBxBNgF3HHHHSovL2+zlp+fr0mTJsnr9WrSpEnKz8+XJJWVlcnr9crr9aqgoEDz58+XdDrYli5dqq1bt6qqqkpLly4NRNv8+fNVUFAQeN2Z+wIAAAAQWgiwCxg7dqwcDkebtdLSUmVnZ0uSsrOzVVJSElifO3eubDabxowZo6amJjU0NGjjxo1KTU2Vw+FQRESEUlNTVV5eroaGBh0+fFgpKSmy2WyaO3du4L0AAAAAhCYC7CLt2bNHTqdTkuR0OrV3715JUn19vWJiYgLbuVwu1dfXX3Dd5XKdtQ4AAAAgdNmDPUCoONfnt2w220Wvn0tBQYEKCgokSfv27fuakwIAAAAIFq6AXaS+ffuqoaFBktTQ0KCoqChJp69g1dXVBbbz+XyKjo6+4LrP5ztr/Vzy8vLk8Xjk8XgUGRnZEYcFAAAAwAAC7CKlp6cHvsmwqKhI06ZNC6yvXLlSlmVpy5Yt6tOnj5xOp9LS0lRRUaHGxkY1NjaqoqJCaWlpcjqd6t27t7Zs2SLLsrRy5crAewEAAAAITdyCeAEzZ87UO++8o/3798vlcmnp0qV68MEHlZmZqcLCQvXr109r1qyRJE2dOlUbNmyQ2+1Wjx49tGLFCkmSw+HQI488ouTkZEnS4sWLA1/s8eKLL+qOO+7QiRMnNGXKFE2ZMiU4BwoAAADACJvFL5+6pCQlJcnj8Rjfb+yD643vM5TU5t8U7BEAAAAuSrB+7gx13IIIAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYO301FNPKTExUUOGDNHMmTP11VdfqaamRqNHj1Z8fLxmzJih5uZmSdLJkyc1Y8YMud1ujR49WrW1tYH3WbZsmdxutwYOHKiNGzcG6WgAAAAAmECAtUN9fb2effZZeTweffTRR/L7/SouLtaiRYu0cOFCeb1eRUREqLCwUJJUWFioiIgI7dq1SwsXLtSiRYskSTt27FBxcbG2b9+u8vJy3X333fL7/cE8NAAAAAAdiABrp9bWVp04cUKtra06fvy4nE6n3n77bWVkZEiSsrOzVVJSIkkqLS1Vdna2JCkjI0OVlZWyLEulpaXKyspS165dNWDAALndblVVVQXtmAAAAAB0LAKsHa666io98MAD6tevn5xOp/r06aNRo0YpPDxcdrtdkuRyuVRfXy/p9BWzmJgYSZLdblefPn104MCBNutnvgYAAABA6CHA2qGxsVGlpaWqqanRF198oWPHjqmsrOys7Ww2myTJsqxzPne+9TMVFBQoKSlJSUlJ2rdv3zdwBAAAAACCgQBrh7feeksDBgxQZGSkLrvsMt12221677331NTUpNbWVkmSz+dTdHS0pNNXturq6iSdvnXx0KFDcjgcbdbPfM1fy8vLk8fjkcfjUWRkpIEjBAAAANAR7MEe4FLUr18/bdmyRcePH1f37t1VWVmppKQkTZgwQWvXrlVWVpaKioo0bdo0SVJ6erqKioqUkpKitWvXauLEibLZbEpPT9ftt9+u++67T1988YW8Xq+uvfbaIB8dEJpiH1wf7BEuWbX5NwV7BAAAQgYB1g6jR49WRkaGRo4cKbvdrhEjRigvL0833XSTsrKy9PDDD2vEiBHKzc2VJOXm5mrOnDlyu91yOBwqLi6WJCUmJiozM1ODBw+W3W7X8uXL1blz52AeGgAAAIAOZLPO9UEkfGslJSXJ4/EY3y9XD74eriAEH3+H24+/vwDwjylYP3eGOj4DBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACG8DX0wLcE39LXfnxLHwAAuFRwBQwAAAAADCHAAAAAAMAQAgwAAAAADCHAAAAAAMAQAgwAAAAADCHAAAAAAMAQAgwAAAAADCHAAAAAAMAQAgwAAAAADCHAAAAAAMAQAgwAAAAADCHAAAAAAMAQAgwAAAAADLEHewAAwKUv9sH1wR7hklWbf1OwRwAAGMQVMAAAAAAwhAADAAAAAEMIMAAAAAAwhAADAAAAAEMIMAAAAAAwhAADAAAAAEMIMAAAAAAwhAADAAAAAEMIsHZqampSRkaGBg0apISEBL3//vs6ePCgUlNTFR8fr9TUVDU2NkqSLMvSggUL5Ha7NWzYMFVXVwfep6ioSPHx8YqPj1dRUVGwDgcAAACAAQRYO91zzz268cYb9fHHH+sPf/iDEhISlJ+fr0mTJsnr9WrSpEnKz8+XJJWVlcnr9crr9aqgoEDz58+XJB08eFBLly7V1q1bVVVVpaVLlwaiDQAAAEDoIcDa4fDhw9q0aZNyc3MlSV26dFF4eLhKS0uVnZ0tScrOzlZJSYkkqbS0VHPnzpXNZtOYMWPU1NSkhoYGbdy4UampqXI4HIqIiFBqaqrKy8uDdlwAAAAAOhYB1g6ffvqpIiMjdeedd2rEiBGaN2+ejh07pj179sjpdEqSnE6n9u7dK0mqr69XTExM4PUul0v19fXnXQcAAAAQmgiwdmhtbVV1dbXmz5+vbdu2qWfPnoHbDc/Fsqyz1mw223nXz1RQUKCkpCQlJSVp3759X294AAAAAEFDgLWDy+WSy+XS6NGjJUkZGRmqrq5W37591dDQIElqaGhQVFRUYPu6urrA630+n6Kjo8+7fqa8vDx5PB55PB5FRkZ25KEBAAAA6EAEWDtceeWViomJ0c6dOyVJlZWVGjx4sNLT0wPfZFhUVKRp06ZJktLT07Vy5UpZlqUtW7aoT58+cjqdSktLU0VFhRobG9XY2KiKigqlpaUF7bgAAAAAdCx7sAe4VD333HOaNWuWmpubFRcXpxUrVujUqVPKzMxUYWGh+vXrpzVr1kiSpk6dqg0bNsjtdqtHjx5asWKFJMnhcOiRRx5RcnKyJGnx4sVyOBxBOyYAAAAAHYsAa6fhw4fL4/GctV5ZWXnWms1m0/Lly8/5Pjk5OcrJyfnG5wMAAADw7cMtiAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIYQYAAAAABgCAEGAAAAAIbYgz0AAAC4sNgH1wd7hEtabf5NwR4BAAK4AgYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhtiDPQAAAECwxT64PtgjXLJq828K9gjAJYUrYAAAAABgCAEGAAAAAIYQYO3k9/s1YsQI3XzzzZKkmpoajR49WvHx8ZoxY4aam5slSSdPntSMGTPkdrs1evRo1dbWBt5j2bJlcrvdGjhwoDZu3BiMwwAAAABgEAHWTs8884wSEhICjxctWqSFCxfK6/UqIiJChYWFkqTCwkJFRERo165dWrhwoRYtWiRJ2rFjh4qLi7V9+3aVl5fr7rvvlt/vD8qxAAAAADCDAGsHn8+n9evXa968eZJizONvAAANJ0lEQVQky7L09ttvKyMjQ5KUnZ2tkpISSVJpaamys7MlSRkZGaqsrJRlWSotLVVWVpa6du2qAQMGyO12q6qqKjgHBAAAAMAIAqwd7r33Xj355JPq1On06Ttw4IDCw8Nlt5/+UkmXy6X6+npJUn19vWJiYiRJdrtdffr00YEDB9qsn/kaAAAAAKGJALtI69atU1RUlEaNGhVYsyzrrO1sNtsFn7vQa85UUFCgpKQkJSUlad++fe0dHQAAAECQ8XvALtLmzZv1+uuva8OGDfrqq690+PBh3XvvvWpqalJra6vsdrt8Pp+io6Mlnb6yVVdXJ5fLpdbWVh06dEgOhyOw/hd//Zoz5eXlKS8vT5KUlJTU8QcJAAAAoENwBewiLVu2TD6fT7W1tSouLtbEiRP16quvasKECVq7dq0kqaioSNOmTZMkpaenq6ioSJK0du1aTZw4UTabTenp6SouLtbJkydVU1Mjr9era6+9NmjHBQAAAKDjcQXsG/LEE08oKytLDz/8sEaMGKHc3FxJUm5urubMmSO32y2Hw6Hi4mJJUmJiojIzMzV48GDZ7XYtX75cnTt3DuYhAAAAAOhgBNjXMH78eI0fP16SFBcXd85vMezWrZvWrFlzztc/9NBDeuihhzpyRAAAAADfItyCCAAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgBBgAAAACGEGAAAAAAYAgB1g51dXWaMGGCEhISlJiYqGeeeUaSdPDgQaWmpio+Pl6pqalqbGyUJFmWpQULFsjtdmvYsGGqrq4OvFdRUZHi4+MVHx+voqKioBwPAAAAADMIsHaw2+36z//8T/35z3/Wli1btHz5cu3YsUP5+fmaNGmSvF6vJk2apPz8fElSWVmZvF6vvF6vCgoKNH/+fEmng23p0qXaunWrqqqqtHTp0kC0AQAAAAg9BFg7OJ1OjRw5UpLUu3dvJSQkqL6+XqWlpcrOzpYkZWdnq6SkRJJUWlqquXPnymazacyYMWpqalJDQ4M2btyo1NRUORwORUREKDU1VeXl5UE7LgAAAAAdiwD7mmpra7Vt2zaNHj1ae/bskdPplHQ60vbu3StJqq+vV0xMTOA1LpdL9fX1510HAAAAEJrswR7gUnb06FFNnz5dTz/9tMLCws67nWVZZ63ZbLbzrp+poKBABQUFkqR9+/Z9jYkBAAAABBNXwNqppaVF06dP16xZs3TbbbdJkvr27auGhgZJUkNDg6KioiSdvrJVV1cXeK3P51N0dPR518+Ul5cnj8cjj8ejyMjIjjwsAAAAAB2IAGsHy7KUm5urhIQE3XfffYH19PT0wDcZFhUVadq0aYH1lStXyrIsbdmyRX369JHT6VRaWpoqKirU2NioxsZGVVRUKC0tLSjHBAAAAKDjcQtiO2zevFmvvPKKhg4dquHDh0uSHn/8cT344IPKzMxUYWGh+vXrpzVr1kiSpk6dqg0bNsjtdqtHjx5asWKFJMnhcOiRRx5RcnKyJGnx4sVyOBzBOSgAAAAAHY4Aa4cbbrjhnJ/fkqTKysqz1mw2m5YvX37O7XNycpSTk/ONzgcAAADg24lbEAEAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIMAAAAAAwhwAAAAADAEAIsyMrLyzVw4EC53W7l5+cHexwAAAAAHYgACyK/368f/OAHKisr044dO7R69Wrt2LEj2GMBAAAA6CAEWBBVVVXJ7XYrLi5OXbp0UVZWlkpLS4M9FgAAAIAOQoAFUX19vWJiYgKPXS6X6uvrgzgRAAAAgI5kD/YA/8gsyzprzWaznbVWUFCggoICSdLHH3+spKSkDp9t3759ioyMDDy+osP3GNqSkpa0eXzm+ZU4x18H57djnXl+Jf4b8U3i/HY8/hvRsf6e84tvlqlzXFtb2+H7+EdEgAWRy+VSXV1d4LHP51N0dPRZ2+Xl5SkvL8/kaEpKSpLH4zG6z38knN+OxfnteJzjjsX57Vic347F+e14nONLG7cgBlFycrK8Xq9qamrU3Nys4uJipaenB3ssAAAAAB2EK2BBZLfb9fzzzystLU1+v185OTlKTEwM9lgAAAAAOkjnRx999NFgD/GPLD4+Xj/60Y90zz33aOzYscEep41Ro0YFe4SQxvntWJzfjsc57lic347F+e1YnN+Oxzm+dNmsc30TBAAAAADgG8dnwAAAAADAEAIMZykvL9fAgQPldruVn58f7HFCSk5OjqKiojRkyJBgjxKS6urqNGHCBCUkJCgxMVHPPPNMsEcKKV999ZWuvfZaXXPNNUpMTNSSJWd/fTq+Pr/frxEjRujmm28O9ighKTY2VkOHDtXw4cON/FqXfzRNTU3KyMjQoEGDlJCQoPfffz/YI4WMnTt3avjw4YF/wsLC9PTTTwd7LLQDtyCiDb/fr+985zt688035XK5lJycrNWrV2vw4MHBHi0kbNq0Sb169dLcuXP10UcfBXuckNPQ0KCGhgaNHDlSR44c0ahRo1RSUsLf32+IZVk6duyYevXqpZaWFt1www165plnNGbMmGCPFlJ+8YtfyOPx6PDhw1q3bl2wxwk5sbGx8ng8uuIKfvNXR8jOztY//dM/ad68eWpubtbx48cVHh4e7LFCjt/v11VXXaWtW7eqf//+wR4HF4krYGijqqpKbrdbcXFx6tKli7KyslRaWhrssULG2LFj5XA4gj1GyHI6nRo5cqQkqXfv3kpISFB9fX2QpwodNptNvXr1kiS1tLSopaXlnL88Hu3n8/m0fv16zZs3L9ijABft8OHD2rRpk3JzcyVJXbp0Ib46SGVlpa6++mri6xJFgKGN+vp6xcTEBB67XC5+gMUlqba2Vtu2bdPo0aODPUpI8fv9Gj58uKKiopSamsr5/Ybde++9evLJJ9WpE//z3FFsNpsmT56sUaNGqaCgINjjhJRPP/1UkZGRuvPOOzVixAjNmzdPx44dC/ZYIam4uFgzZ84M9hhoJ/4LjzbOdUcq/w83LjVHjx7V9OnT9fTTTyssLCzY44SUzp0768MPP5TP51NVVRW30n6D1q1bp6ioKL5auoNt3rxZ1dXVKisr0/Lly7Vp06ZgjxQyWltbVV1drfnz52vbtm3q2bMnnyXvAM3NzXr99df1L//yL8EeBe1EgKENl8ulurq6wGOfz6fo6OggTgRcnJaWFk2fPl2zZs3SbbfdFuxxQlZ4eLjGjx+v8vLyYI8SMjZv3qzXX39dsbGxysrK0ttvv63Zs2cHe6yQ85f/TYuKitKtt96qqqqqIE8UOlwul1wuV+DKeEZGhqqrq4M8VegpKyvTyJEj1bdv32CPgnYiwNBGcnKyvF6vampq1NzcrOLiYqWnpwd7LODvYlmWcnNzlZCQoPvuuy/Y44Scffv2qampSZJ04sQJvfXWWxo0aFCQpwody5Ytk8/nU21trYqLizVx4kStWrUq2GOFlGPHjunIkSOBf6+oqOBbab9BV155pWJiYrRz505Jpz+nxJcgffNWr17N7YeXOHuwB8C3i91u1/PPP6+0tDT5/X7l5OQoMTEx2GOFjJkzZ+qdd97R/v375XK5tHTp0sCHlfH1bd68Wa+88krgK6Yl6fHHH9fUqVODPFloaGhoUHZ2tvx+v06dOqXMzEy+Kh2XlD179ujWW2+VdPp2udtvv1033nhjkKcKLc8995xmzZql5uZmxcXFacWKFcEeKaQcP35cb775pn75y18GexR8DXwNPQAAAAAYwi2IAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGAIAQYAAAAAhhBgAAAAAGDI/wORGCBgclDPlAAAAABJRU5ErkJggg==\">\n",
       "                </div>\n",
       "\n",
       "            </td>\n",
       "\n",
       "        </tr>\n",
       "        \n",
       "        \n",
       "        <tr>\n",
       "            <td>\n",
       "                <div>\n",
       "                    <img src=\"data:image/jpeg;base64,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\">\n",
       "                </div>\n",
       "\n",
       "            </td>\n",
       "\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",
       "    </table>\n",
       "</div>\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 10 of 45716 rows / 10 columns</div>\n",
       "<div class=\"info_items\">32 partition(s)</div>\n",
       "\n",
       "<table class=\"optimus_table\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">name</div>\n",
       "            <div class=\"data_type\">1 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">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\">nametype</div>\n",
       "            <div class=\"data_type\">3 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">recclass</div>\n",
       "            <div class=\"data_type\">4 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">mass (g)</div>\n",
       "            <div class=\"data_type\">5 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">fall</div>\n",
       "            <div class=\"data_type\">6 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">year</div>\n",
       "            <div class=\"data_type\">7 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">reclat</div>\n",
       "            <div class=\"data_type\">8 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">reclong</div>\n",
       "            <div class=\"data_type\">9 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">GeoLocation</div>\n",
       "            <div class=\"data_type\">10 (string)</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",
       "            <div class=\" \"\n",
       "                 title='Acfer&#8901;232'>Acfer&#8901;232\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='240'>240\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H5'>H5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='725.0'>725.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1991&#8901;12:00:00&#8901;AM'>01/01/1991&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='27.73944'>27.73944\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='4.32833'>4.32833\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(27.739440,&#8901;4.328330)'>(27.739440,&#8901;4.328330)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Asuka&#8901;87197'>Asuka&#8901;87197\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='2554'>2554\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H4'>H4\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='124.99'>124.99\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1987&#8901;12:00:00&#8901;AM'>01/01/1987&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-72.0'>-72.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='26.0'>26.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-72.000000,&#8901;26.000000)'>(-72.000000,&#8901;26.000000)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Gladstone&#8901;(iron)'>Gladstone&#8901;(iron)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='10920'>10920\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Iron,&#8901;IAB-MG'>Iron,&#8901;IAB-MG\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='736600.0'>736600.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1915&#8901;12:00:00&#8901;AM'>01/01/1915&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-23.9'>-23.9\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='151.3'>151.3\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-23.900000,&#8901;151.300000)'>(-23.900000,&#8901;151.300000)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Nullarbor&#8901;015'>Nullarbor&#8901;015\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='17955'>17955\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L6'>L6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='3986.0'>3986.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1980&#8901;12:00:00&#8901;AM'>01/01/1980&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Lewis&#8901;Cliff&#8901;86533'>Lewis&#8901;Cliff&#8901;86533\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='13461'>13461\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H5'>H5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='15.7'>15.7\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1986&#8901;12:00:00&#8901;AM'>01/01/1986&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-84.26756'>-84.26756\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='161.3631'>161.3631\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-84.267560,&#8901;161.363100)'>(-84.267560,&#8901;161.363100)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Grove&#8901;Mountains&#8901;053589'>Grove&#8901;Mountains&#8901;053589\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='48447'>48447\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L5'>L5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1.4'>1.4\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/2006&#8901;12:00:00&#8901;AM'>01/01/2006&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-72.7825'>-72.7825\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='75.300278'>75.300278\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-72.782500,&#8901;75.300278)'>(-72.782500,&#8901;75.300278)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Sayh&#8901;al&#8901;Uhaymir&#8901;108'>Sayh&#8901;al&#8901;Uhaymir&#8901;108\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='23300'>23300\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H6'>H6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='16.0'>16.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/2001&#8901;12:00:00&#8901;AM'>01/01/2001&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='21.06667'>21.06667\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='57.31667'>57.31667\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(21.066670,&#8901;57.316670)'>(21.066670,&#8901;57.316670)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Northwest&#8901;Africa&#8901;3088'>Northwest&#8901;Africa&#8901;3088\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='31218'>31218\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L6'>L6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='171.0'>171.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/2003&#8901;12:00:00&#8901;AM'>01/01/2003&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Reckling&#8901;Peak&#8901;92423'>Reckling&#8901;Peak&#8901;92423\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='22432'>22432\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L6'>L6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='3.8'>3.8\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1992&#8901;12:00:00&#8901;AM'>01/01/1992&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-76.22029'>-76.22029\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='158.37967'>158.37967\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-76.220290,&#8901;158.379670)'>(-76.220290,&#8901;158.379670)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Sweetwater'>Sweetwater\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='23770'>23770\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H5'>H5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1760.0'>1760.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1961&#8901;12:00:00&#8901;AM'>01/01/1961&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='32.55'>32.55\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-100.41667'>-100.41667\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(32.550000,&#8901;-100.416670)'>(32.550000,&#8901;-100.416670)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 10 of 45716 rows / 10 columns</div>\n",
       "<div class=\"info_items\">32 partition(s)</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:run() executed in 96.23 sec\n"
     ]
    }
   ],
   "source": [
    "op.profiler.run(df, \"year\", infer=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Warning: Failed to load file:///C:/Users/ARGENI~1/AppData/Local/Temp/optimus/styles/styles.css (ignore)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/profiler1.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "op.profiler.to_image(output_path=\"images/profiler1.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Profiler Speed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With **relative_error** and **approx_count** params you can control how some operations are caculated so you can speedup the profiling in case is needed.\n",
    "\n",
    "relative_error: Relative Error for quantile discretizer calculation. 1 is Faster, 0 Slower\n",
    "\n",
    "approx_count: Use ```approx_count_distinct``` or ```countDistinct```. ```approx_count_distinct``` is faster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Processing column 'mass (g)'...\n",
      "INFO:optimus:_count_data_types() executed in 1.33 sec\n",
      "INFO:optimus:count_data_types() executed in 1.33 sec\n",
      "INFO:optimus:cast_columns() executed in 0.0 sec\n",
      "INFO:optimus:agg_exprs() executed in 1.64 sec\n",
      "INFO:optimus:general_stats() executed in 1.65 sec\n",
      "INFO:optimus:------------------------------\n",
      "INFO:optimus:Processing column 'mass (g)'...\n",
      "INFO:optimus:frequency() executed in 3.56 sec\n",
      "INFO:optimus:stats_by_column() executed in 0.0 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n",
      "INFO:optimus:percentile() executed in 0.23 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n",
      "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _cast_to\n",
      "INFO:optimus:extra_numeric_stats() executed in 0.58 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'mass (g)' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.3 sec\n",
      "INFO:optimus:hist() executed in 2.04 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Including 'nan' as Null in processing 'name'\n",
      "Including 'nan' as Null in processing 'nametype'\n",
      "Including 'nan' as Null in processing 'recclass'\n",
      "Including 'nan' as Null in processing 'fall'\n",
      "Including 'nan' as Null in processing 'year'\n",
      "Including 'nan' as Null in processing 'GeoLocation'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:dataset_info() executed in 1.75 sec\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\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>10</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Number of rows</td>\n",
       "                <td>45716</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Total Missing (%)</td>\n",
       "                <td>0.49%</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Total size in memory</td>\n",
       "                <td>98.2 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",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Numeric</td>\n",
       "                <td>1</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Date</td>\n",
       "                <td>0</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Bool</td>\n",
       "                <td>0</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Array</td>\n",
       "                <td>0</td>\n",
       "\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td>Not available</td>\n",
       "                <td>0</td>\n",
       "\n",
       "            </tr>\n",
       "            </tbody>\n",
       "        </table>\n",
       "    </div>\n",
       "</div><link rel=\"stylesheet\" type=\"text/css\" href=\"optimus/styles/styles.css\">\n",
       "\n",
       "\n",
       "<div class=\"main\">\n",
       "    <div class=\"info\">\n",
       "\n",
       "        \n",
       "\n",
       "        <div class=\"panel_profiler\">\n",
       "            <div>\n",
       "                <h2>mass (g)</h2>\n",
       "                <span>numeric</span>\n",
       "            </div>\n",
       "            <table>\n",
       "                <tbody>\n",
       "                <tr>\n",
       "                    <td>Unique</td>\n",
       "                    <td> 12497</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Unique (%)</td>\n",
       "                    <td> 27.336</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",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Integer\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Float\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Bool\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Date\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Missing\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        0\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>\n",
       "                        Null\n",
       "                    </td>\n",
       "                    <td>\n",
       "                        131\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>13278.07855</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Minimum</td>\n",
       "                    <td>0.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Maximum</td>\n",
       "                    <td>60000000.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Zeros(%)</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "\n",
       "                </tbody>\n",
       "            </table>\n",
       "            \n",
       "\n",
       "        </div>\n",
       "        <div class=\"panel_profiler\">\n",
       "            <h3>Frequency</h3>\n",
       "            <table>\n",
       "\n",
       "                <tr>\n",
       "                    <th>Value</th>\n",
       "                    <th>Count</th>\n",
       "                    <th>Frequency (%)</th>\n",
       "                </tr>\n",
       "                \n",
       "                <tr>\n",
       "                    <td>1.3</td>\n",
       "                    <td>171</td>\n",
       "                    <td>0.374%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>1.2</td>\n",
       "                    <td>140</td>\n",
       "                    <td>0.306%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>1.4</td>\n",
       "                    <td>138</td>\n",
       "                    <td>0.302%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>None</td>\n",
       "                    <td>131</td>\n",
       "                    <td>0.287%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2.1</td>\n",
       "                    <td>130</td>\n",
       "                    <td>0.284%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>2.4</td>\n",
       "                    <td>126</td>\n",
       "                    <td>0.276%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>1.6</td>\n",
       "                    <td>120</td>\n",
       "                    <td>0.262%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>0.5</td>\n",
       "                    <td>119</td>\n",
       "                    <td>0.26%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>1.1</td>\n",
       "                    <td>116</td>\n",
       "                    <td>0.254%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>3.8</td>\n",
       "                    <td>114</td>\n",
       "                    <td>0.249%</td>\n",
       "                </tr>\n",
       "\n",
       "                \n",
       "                <tr>\n",
       "                    <td>\"Missing\"</td>\n",
       "                    <td>0</td>\n",
       "                    <td>0.0%</td>\n",
       "                </tr>\n",
       "\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>0.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>5-th percentile</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Q1</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Median</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Q3</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>95-th percentile</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Maximum</td>\n",
       "                    <td>60000000.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Range</td>\n",
       "                    <td>60000000.0</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>574988.87641</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Coef of variation</td>\n",
       "                    <td>43.30362</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Kurtosis</td>\n",
       "                    <td>6796.17061</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Mean</td>\n",
       "                    <td>13278.07855</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>MAD</td>\n",
       "                    <td>0.0</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Skewness</td>\n",
       "                    <td></td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Sum</td>\n",
       "                    <td>605281210.638</td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td>Variance</td>\n",
       "                    <td>330612207995.7785</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>\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 10 of 45716 rows / 10 columns</div>\n",
       "<div class=\"info_items\">32 partition(s)</div>\n",
       "\n",
       "<table class=\"optimus_table\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">name</div>\n",
       "            <div class=\"data_type\">1 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">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\">nametype</div>\n",
       "            <div class=\"data_type\">3 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">recclass</div>\n",
       "            <div class=\"data_type\">4 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">mass (g)</div>\n",
       "            <div class=\"data_type\">5 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">fall</div>\n",
       "            <div class=\"data_type\">6 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">year</div>\n",
       "            <div class=\"data_type\">7 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">reclat</div>\n",
       "            <div class=\"data_type\">8 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">reclong</div>\n",
       "            <div class=\"data_type\">9 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">GeoLocation</div>\n",
       "            <div class=\"data_type\">10 (string)</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",
       "            <div class=\" \"\n",
       "                 title='Acfer&#8901;232'>Acfer&#8901;232\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='240'>240\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H5'>H5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='725.0'>725.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1991&#8901;12:00:00&#8901;AM'>01/01/1991&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='27.73944'>27.73944\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='4.32833'>4.32833\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(27.739440,&#8901;4.328330)'>(27.739440,&#8901;4.328330)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Asuka&#8901;87197'>Asuka&#8901;87197\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='2554'>2554\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H4'>H4\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='124.99'>124.99\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1987&#8901;12:00:00&#8901;AM'>01/01/1987&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-72.0'>-72.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='26.0'>26.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-72.000000,&#8901;26.000000)'>(-72.000000,&#8901;26.000000)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Gladstone&#8901;(iron)'>Gladstone&#8901;(iron)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='10920'>10920\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Iron,&#8901;IAB-MG'>Iron,&#8901;IAB-MG\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='736600.0'>736600.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1915&#8901;12:00:00&#8901;AM'>01/01/1915&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-23.9'>-23.9\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='151.3'>151.3\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-23.900000,&#8901;151.300000)'>(-23.900000,&#8901;151.300000)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Nullarbor&#8901;015'>Nullarbor&#8901;015\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='17955'>17955\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L6'>L6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='3986.0'>3986.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1980&#8901;12:00:00&#8901;AM'>01/01/1980&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Lewis&#8901;Cliff&#8901;86533'>Lewis&#8901;Cliff&#8901;86533\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='13461'>13461\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H5'>H5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='15.7'>15.7\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1986&#8901;12:00:00&#8901;AM'>01/01/1986&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-84.26756'>-84.26756\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='161.3631'>161.3631\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-84.267560,&#8901;161.363100)'>(-84.267560,&#8901;161.363100)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Grove&#8901;Mountains&#8901;053589'>Grove&#8901;Mountains&#8901;053589\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='48447'>48447\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L5'>L5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1.4'>1.4\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/2006&#8901;12:00:00&#8901;AM'>01/01/2006&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-72.7825'>-72.7825\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='75.300278'>75.300278\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-72.782500,&#8901;75.300278)'>(-72.782500,&#8901;75.300278)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Sayh&#8901;al&#8901;Uhaymir&#8901;108'>Sayh&#8901;al&#8901;Uhaymir&#8901;108\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='23300'>23300\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H6'>H6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='16.0'>16.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/2001&#8901;12:00:00&#8901;AM'>01/01/2001&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='21.06667'>21.06667\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='57.31667'>57.31667\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(21.066670,&#8901;57.316670)'>(21.066670,&#8901;57.316670)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Northwest&#8901;Africa&#8901;3088'>Northwest&#8901;Africa&#8901;3088\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='31218'>31218\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L6'>L6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='171.0'>171.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/2003&#8901;12:00:00&#8901;AM'>01/01/2003&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\"none \"\n",
       "                 title='None'>None\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Reckling&#8901;Peak&#8901;92423'>Reckling&#8901;Peak&#8901;92423\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='22432'>22432\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='L6'>L6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='3.8'>3.8\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1992&#8901;12:00:00&#8901;AM'>01/01/1992&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-76.22029'>-76.22029\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='158.37967'>158.37967\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(-76.220290,&#8901;158.379670)'>(-76.220290,&#8901;158.379670)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Sweetwater'>Sweetwater\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='23770'>23770\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Valid'>Valid\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='H5'>H5\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1760.0'>1760.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Found'>Found\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='01/01/1961&#8901;12:00:00&#8901;AM'>01/01/1961&#8901;12:00:00&#8901;AM\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='32.55'>32.55\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='-100.41667'>-100.41667\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='(32.550000,&#8901;-100.416670)'>(32.550000,&#8901;-100.416670)\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 10 of 45716 rows / 10 columns</div>\n",
       "<div class=\"info_items\">32 partition(s)</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:run() executed in 13.74 sec\n"
     ]
    }
   ],
   "source": [
    "op.profiler.run(df, \"mass (g)\", infer=False, relative_error =1, approx_count=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots\n",
    "Besides histograms and frequency plots you also have scatter plots and box plots. All powered by Apache by pyspark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = op.load.excel(\"../examples/data/titanic3.xls\")\n",
    "df = df.rows.drop_na([\"age\",\"fare\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can output to the notebook or as an image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'fare' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.11 sec\n",
      "INFO:optimus:hist() executed in 5.16 sec\n",
      "INFO:optimus:hist() executed in 9.98 sec\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/hist.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Output and image\n",
    "df.plot.hist(\"fare\", output_format=\"image\", output_path=\"images/hist.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src='images/frequency.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.frequency(\"age\")\n",
    "df.plot.frequency(\"age\", output_format=\"image\", output_path=\"images/frequency.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'fare' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.29 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.38 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'fare' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.29 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _bucketizer\n",
      "INFO:optimus:bucketizer() executed in 0.3 sec\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/scatter.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter([\"fare\", \"age\"], buckets=30)\n",
    "df.plot.scatter([\"fare\", \"age\"], buckets=30, output_format=\"image\", output_path=\"images/scatter.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _cast_to\n",
      "INFO:optimus:percentile() executed in 6.89 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _cast_to\n",
      "INFO:optimus:percentile() executed in 3.92 sec\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/box.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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h0NU1knwuyd4kDydZ29g2lOR/k9yT5OYk/9jY3p/kM0m+3vhzRWenl6bmp1zUNZKcV0r5YZKzga8Dvw3cC7wWeBb4CvBfpZTrk2wHtpVS9iS5GPhiKWVZx4aXmuBNotVN3pfkbY3fLwLeBXy1lPJDgCSfBn618fybgVclOX7suUleWkp5di4HlqbDoKsrJHkTk5F+QynluST3AN8ETnXW3dPY9/m5mVCaOdfQ1S1eBjzdiPkrgdcD5wBvTLIoyTzg90/Y/0vA9ccfJHn1nE4rtcCgq1t8AZiX5BvA3wD/ATwF/B1wP/DvwCPAjxr7vw8YTPKNJI8A6+Z+ZGl6fFNUXS3JS0op/9c4Q78d+EQp5fZOzyW1wjN0dbsPJXkQeAjYD3yuw/NILfMMXZIq4Rm6JFXCoEtSJQy6JFXCoEtSJQy6JFXi/wHno0/sqV6NfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEICAYAAABPgw/pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAD11JREFUeJzt3X+s3XV9x/Hn695eUqhiS7kaVn5ctrJZ6TI1N0ZLN20x2SzL0EyDbFNm7tI0hsrWLbPu/qHL1qUms4506yZ4XUhmSxVFiDQq02JScMTbwRxw56gUC1j1GkFZoXrbfvbHPSVFW++5595zT/s5z0fS3Hu+5/vtefefZ7/5nHO+35RSkCSd+Xo6PYAkaXYYdEmqhEGXpEoYdEmqhEGXpEoYdEmqhEGXpEoYdEmqhEGXpEoYdFUvycYk30rybJJHkrytsb03yUeS/CDJ/iTXJylJ5jWef1mSkSQHkzyV5G+T9Hb2XyOd2rxODyDNgW8Bvwl8F3gH8G9JlgJXA28BXg0cAj79M8fdAnwPWAosAD4PPAF8bG7GlqYnXstF3SbJg8AHgRuAnaWUjzW2vxm4G+gDFgMHgIWllOcbz18LrC2lrOrI4NIUPENX9ZK8G9gADDQ2vQQ4H/glJs+4jzvx90uYDPvBJMe39fzMPtJpxaCrakkuAW4GrgS+Vko52jhDD3AQuPCE3S864fcngJ8A55dSjszVvNJM+KaoarcAKMA4QJL3AMsbz30KuCHJkiQLgfcfP6iUchD4EvCRJOcm6UnyK0neOLfjS80z6KpaKeUR4CPA15h8g/PXgXsbT9/MZLS/ATwA7AKOAEcbz78bOAt4BHgauA24YK5ml6bLN0WlhiRvAf6llHJJp2eRWuEZurpWkrOTrEkyL8kSJj/5cnun55Ja5Rm6ulaSc4CvAq8EngfuAm4opfy4o4NJLTLoklQJl1wkqRJz+jn0888/vwwMDMzlS0rSGW/v3r0/KKX0T7XfnAZ9YGCA0dHRuXxJSTrjJfl2M/u55CJJlTDoklQJgy5JlTDoklQJgy5JlWgq6En+LMnDSR5KsiPJ/CSXJrk/yaNJdiY5q93DSrNtx44dLF++nN7eXpYvX86OHTs6PZLUsimD3rjGxfuAwVLKcqAXeCfwYeCjpZTLmLwS3VA7B5Vm244dOxgeHmbr1q0cPnyYrVu3Mjw8bNR1xmp2yWUecHbj5rnnMHljgNVMXk4UJu+9+NbZH09qn02bNjEyMsKqVavo6+tj1apVjIyMsGnTpk6PJrVkyqCXUp4C/p7J+yseBH4E7AWeOeFOLk8CS052fJK1SUaTjI6Pj8/O1NIsGBsbY+XKlS/atnLlSsbGxjo0kTQzzSy5LGLy7uiXMnkPxgVM3in9Z530Kl+llJtKKYOllMH+/im/uSrNmWXLlrFnz54XbduzZw/Lli3r0ETSzDSz5PJmYH8pZbyUMgF8FlgBLGwswcDkfRm/06YZpbYYHh5maGiI3bt3MzExwe7duxkaGmJ4eLjTo0ktaeZaLgeA1zeuHf08kzfbHQV2A28HbgWuA+5o15BSO1x77bUArF+/nrGxMZYtW8amTZte2C6daZq6HnqSvwauYfJ+iw8Af8LkmvmtwHmNbX9USvnJL/p7BgcHixfnkqTpSbK3lDI41X5NXW2xlPJBJm/PdaLHgNe1MJskqQ38pqi6ml8sUk3m9Hro0unk+BeLRkZGWLlyJXv27GFoaPL7ca6j60w0p/cUdQ1dp5Ply5ezdetWVq1a9cK23bt3s379eh566KEOTia9WLNr6AZdXau3t5fDhw/T19f3wraJiQnmz5/P0aNHOziZ9GLNBt01dHUtv1ik2riGrq41PDzMNddcw4IFCzhw4AAXX3wxhw4d4sYbb+z0aFJLPEOXgLlcepTaxaCra23atImdO3eyf/9+jh07xv79+9m5c6dXW9QZyzdF1bV8U1RnCt8Ulabgm6KqjUFX1/Jqi6qNn3JR1/Jqi6qNZ+jqavfddx/79u3j2LFj7Nu3j/vuu6/TI0ktM+jqWuvXr2fbtm0sWrSInp4eFi1axLZt21i/fn2nR5Na4qdc1LX6+vro7e3l2LFjTExM0NfXR09PD0ePHmViYqLT40kv8FMu0hSOHDnCxMQEmzdv5tChQ2zevJmJiQmOHDky9cHSacigq6utWbOGDRs2cM4557BhwwbWrFnT6ZGklhl0dbVdu3axZcsWnnvuObZs2cKuXbs6PZLUMtfQ1bVcQ9eZwjV0aQrr1q1jYmKCxYsX09PTw+LFi5mYmGDdunWdHk1qiV8sUtfaunUrADfffDPHjh3j6aef5r3vfe8L26UzjWfo6morVqxg6dKl9PT0sHTpUlasWNHpkaSWeYauruVNolUb3xRV1/Im0TpTeJNoaQpeD11nCj/lIk3B66GrNq6hq0pJmtpv9erVMzree5HqdOIZuqpUSmnqz/bt27n88sshPVx++eVs37696WONuU43rqFLwMDGu3h881WdHkM6KdfQJanLGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqkRTQU+yMMltSf4nyViSNyQ5L8ndSR5t/FzU7mElSafW7Bn6jcAXSimvBH4DGAM2Al8upVwGfLnxWJLUIVMGPcm5wG8BIwCllJ+WUp4BrgZuaex2C/DWdg0pSZpaM2fovwyMA/+a5IEkH0+yAHhFKeUgQOPny092cJK1SUaTjI6Pj8/a4JKkF2sm6POA1wL/XEp5DXCIaSyvlFJuKqUMllIG+/v7WxxTkjSVZoL+JPBkKeX+xuPbmAz895JcAND4+f32jChJasaUQS+lfBd4IsmvNTZdCTwC3Alc19h2HXBHWyaUJDWl2TsWrQc+meQs4DHgPUz+Z/CpJEPAAeAd7RlRktSMpoJeSnkQONnF1a+c3XEkSa3ym6KSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVAmDLkmVMOiSVImmg56kN8kDST7feHxpkvuTPJpkZ5Kz2jemJGkq0zlDvwEYO+Hxh4GPllIuA54GhmZzMEnS9DQV9CQXAlcBH288DrAauK2xyy3AW9sxoCSpOc2eof8D8JfAscbjxcAzpZQjjcdPAktOdmCStUlGk4yOj4/PaFhJ0qlNGfQkvwt8v5Sy98TNJ9m1nOz4UspNpZTBUspgf39/i2NKkqYyr4l9rgB+L8kaYD5wLpNn7AuTzGucpV8IfKd9Y6qbXbH5Kzz1zPNtf52BjXe19e9fsvBs7t24uq2voe42ZdBLKR8APgCQ5E3AX5RS/jDJp4G3A7cC1wF3tHFOdbGnnnmexzdf1ekxZqzd/2FIM/kc+vuBDUn2MbmmPjI7I0mSWtHMkssLSin3APc0fn8MeN3sjyRJaoXfFJWkShh0SaqEQZekShh0SaqEQZekShh0SaqEQZekShh0SaqEQZekShh0SaqEQZekShh0SaqEQZekShh0SaqEQZekShh0SaqEQZekShh0SaqEQZekShh0SaqEQZekShh0SarEvE4PIE3l8fl/AB/q9BQz9/h8gB91egxVzKDrtDdweDuPb76q02PM2MDGu3i800Ooai65SFIlDLokVcKgS1IlDLokVcKgS1IlDLokVcKgS1IlDLokVcKgS1IlDLokVcKgS1IlDLokVcKgS1Ilpgx6kouS7E4yluThJDc0tp+X5O4kjzZ+Lmr/uJKkU2nm8rlHgD8vpfxnkpcCe5PcDfwx8OVSyuYkG4GNwPvbN6q61ZKFZzOw8a5OjzFjSxae3ekRVLkpg15KOQgcbPz+bJIxYAlwNfCmxm63APdg0NUG925c3fbXGNh4VxXXXFd3m9YaepIB4DXA/cArGrE/Hv2Xn+KYtUlGk4yOj4/PbFpJ0ik1HfQkLwE+A/xpKeXHzR5XSrmplDJYShns7+9vZUZJUhOaCnqSPiZj/slSymcbm7+X5ILG8xcA32/PiJKkZjTzKZcAI8BYKWXLCU/dCVzX+P064I7ZH0+S1KxmPuVyBfAu4L+TPNjY9lfAZuBTSYaAA8A72jOiJKkZzXzKZQ+QUzx95eyOI0lqld8UlaRKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKGHRJqoRBl6RKzCjoSX4nyTeT7EuycbaGkiRNX8tBT9IL/BPwFuBVwLVJXjVbg0mSpmcmZ+ivA/aVUh4rpfwUuBW4enbGkiRN10yCvgR44oTHTza2vUiStUlGk4yOj4/P4OUkSb/ITIKek2wrP7ehlJtKKYOllMH+/v4ZvJwk6ReZSdCfBC464fGFwHdmNo4kqVUzCfrXgcuSXJrkLOCdwJ2zM5YkabrmtXpgKeVIkuuBLwK9wCdKKQ/P2mSSpGlpOegApZRdwK5ZmkWSNAN+U1SSKmHQJakSBl2SKmHQJakSBl2SKmHQJakSBl2SKmHQJakSM/pikXS6Sk527bgpjvnw9F+nlJ+7Hp3UMQZdVTK06kYuuUhSJQy6JFXCoEtSJQy6JFXCoEtSJQy6JFXCoEtSJQy6JFUic/kFjCTjwLfn7AWl5p0P/KDTQ0incEkppX+qneY06NLpKsloKWWw03NIM+GSiyRVwqBLUiUMujTppk4PIM2Ua+iSVAnP0CWpEgZdkiph0CWpEgZdkiph0NU1knwuyd4kDydZ29g2lOR/k9yT5OYk/9jY3p/kM0m+3vhzRWenl6bmp1zUNZKcV0r5YZKzga8Dvw3cC7wWeBb4CvBfpZTrk2wHtpVS9iS5GPhiKWVZx4aXmuBNotVN3pfkbY3fLwLeBXy1lPJDgCSfBn618fybgVclOX7suUleWkp5di4HlqbDoKsrJHkTk5F+QynluST3AN8ETnXW3dPY9/m5mVCaOdfQ1S1eBjzdiPkrgdcD5wBvTLIoyTzg90/Y/0vA9ccfJHn1nE4rtcCgq1t8AZiX5BvA3wD/ATwF/B1wP/DvwCPAjxr7vw8YTPKNJI8A6+Z+ZGl6fFNUXS3JS0op/9c4Q78d+EQp5fZOzyW1wjN0dbsPJXkQeAjYD3yuw/NILfMMXZIq4Rm6JFXCoEtSJQy6JFXCoEtSJQy6JFXi/wHno0/sqV6NfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.box(\"age\")\n",
    "df.plot.box(\"age\", output_format=\"image\", output_path=\"images/box.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:`name`,`sex`,`ticket`,`cabin`,`embarked`,`boat`,`home_dest` column(s) was not processed because is/are not byte,short,big,int,double,float\n",
      "INFO:optimus:Using 'column_exp' to process column 'pclass' with function _cast_to\n",
      "INFO:optimus:Casting pclass to float...\n",
      "INFO:optimus:Using 'column_exp' to process column 'survived' with function _cast_to\n",
      "INFO:optimus:Casting survived to float...\n",
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _cast_to\n",
      "INFO:optimus:Casting age to float...\n",
      "INFO:optimus:Using 'column_exp' to process column 'sibsp' with function _cast_to\n",
      "INFO:optimus:Casting sibsp to float...\n",
      "INFO:optimus:Using 'column_exp' to process column 'parch' with function _cast_to\n",
      "INFO:optimus:Casting parch to float...\n",
      "INFO:optimus:Using 'column_exp' to process column 'fare' with function _cast_to\n",
      "INFO:optimus:Casting fare to float...\n",
      "INFO:optimus:Using 'column_exp' to process column 'body' with function _cast_to\n",
      "INFO:optimus:Casting body to float...\n",
      "object of type <class 'float'> cannot be safely interpreted as an integer.\n",
      "INFO:optimus:`name`,`sex`,`ticket`,`cabin`,`embarked`,`boat`,`home_dest` column(s) was not processed because is/are not byte,short,big,int,double,float\n",
      "INFO:optimus:Using 'column_exp' to process column 'pclass' with function _cast_to\n",
      "INFO:optimus:Casting pclass to float...\n",
      "INFO:optimus:Using 'column_exp' to process column 'survived' with function _cast_to\n",
      "INFO:optimus:Casting survived to float...\n",
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _cast_to\n",
      "INFO:optimus:Casting age to float...\n",
      "INFO:optimus:Using 'column_exp' to process column 'sibsp' with function _cast_to\n",
      "INFO:optimus:Casting sibsp to float...\n",
      "INFO:optimus:Using 'column_exp' to process column 'parch' with function _cast_to\n",
      "INFO:optimus:Casting parch to float...\n",
      "INFO:optimus:Using 'column_exp' to process column 'fare' with function _cast_to\n",
      "INFO:optimus:Casting fare to float...\n",
      "INFO:optimus:Using 'column_exp' to process column 'body' with function _cast_to\n",
      "INFO:optimus:Casting body to float...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/correlation.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.correlation(\"*\")\n",
    "df.plot.correlation(\"*\", output_format=\"image\", output_path=\"images/correlation.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using other plotting libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimus has a tiny API so you can use any plotting library. For example, you can use ```df.cols.scatter()```, ```df.cols.frequency()```, ```df.cols.boxplot()``` or ```df.cols.hist()``` to output a JSON that you can process to adapt the data to any plotting library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the ouliers using tukey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _cast_to\n",
      "INFO:optimus:percentile() executed in 4.02 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table5.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.outliers.tukey(\"age\").select().table_image(\"images/table5.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove the outliers using tukey"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _cast_to\n",
      "INFO:optimus:percentile() executed in 4.1 sec\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table6.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.outliers.tukey(\"age\").drop().table_image(\"images/table6.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _cast_to\n",
      "INFO:optimus:percentile() executed in 3.94 sec\n",
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _cast_to\n",
      "INFO:optimus:percentile() executed in 4.09 sec\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'count_outliers': 1045,\n",
       " 'count_non_outliers': 1036,\n",
       " 'lower_bound': -6.0,\n",
       " 'upper_bound': 66.0,\n",
       " 'iqr1': 21.0,\n",
       " 'iqr3': 39.0}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.outliers.tukey(\"age\").info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can also use z_score, modified_z_score or mad\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _z_score\n",
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _cast_to\n",
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _cast_to\n",
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _cast_to\n",
      "INFO:optimus:Using 'column_exp' to process column 'age' with function _cast_to\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DataFrame[pclass: bigint, survived: bigint, name: string, sex: string, age: double, sibsp: bigint, parch: bigint, ticket: string, fare: double, cabin: string, embarked: string, boat: string, body: double, home_dest: string]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.outliers.z_score(\"age\", threshold=2).drop()\n",
    "df.outliers.modified_z_score(\"age\", threshold = 2).drop()\n",
    "df.outliers.mad(\"age\", threshold = 2).drop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Database connection\n",
    "Optimus have handy tools to connect to databases and extract informacion. Optimus can handle **redshift**, **postgres**, **oracle** and **mysql**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Operative System:Windows\n",
      "INFO:optimus:Just check that Spark and all necessary environments vars are present...\n",
      "INFO:optimus:-----\n",
      "INFO:optimus:SPARK_HOME=C:\\opt\\spark\\spark-2.3.1-bin-hadoop2.7\n",
      "INFO:optimus:HADOOP_HOME=C:\\opt\\hadoop-2.7.7\n",
      "INFO:optimus:PYSPARK_PYTHON=C:\\Users\\argenisleon\\Anaconda3\\python.exe\n",
      "INFO:optimus:PYSPARK_DRIVER_PYTHON=jupyter\n",
      "INFO:optimus:PYSPARK_SUBMIT_ARGS=--jars \"file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/RedshiftJDBC42-1.2.16.1027.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/mysql-connector-java-8.0.16.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/ojdbc8.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/postgresql-42.2.5.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/RedshiftJDBC42-1.2.16.1027.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/mysql-connector-java-8.0.16.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/ojdbc8.jar,file:///C:/Users/argenisleon/Documents/Optimus/optimus/jars/postgresql-42.2.5.jar\" --driver-class-path \"C:/Users/argenisleon/Documents/Optimus/optimus/jars/RedshiftJDBC42-1.2.16.1027.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/mysql-connector-java-8.0.16.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/ojdbc8.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/postgresql-42.2.5.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/RedshiftJDBC42-1.2.16.1027.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/mysql-connector-java-8.0.16.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/ojdbc8.jar;C:/Users/argenisleon/Documents/Optimus/optimus/jars/postgresql-42.2.5.jar\" --conf \"spark.sql.catalogImplementation=hive\" pyspark-shell\n",
      "INFO:optimus:JAVA_HOME=C:\\java\n",
      "INFO:optimus:Pyarrow Installed\n",
      "INFO:optimus:-----\n",
      "INFO:optimus:Starting or getting SparkSession and SparkContext...\n",
      "INFO:optimus:Spark Version:2.3.1\n",
      "INFO:optimus:\n",
      "                             ____        __  _                     \n",
      "                            / __ \\____  / /_(_)___ ___  __  _______\n",
      "                           / / / / __ \\/ __/ / __ `__ \\/ / / / ___/\n",
      "                          / /_/ / /_/ / /_/ / / / / / / /_/ (__  ) \n",
      "                          \\____/ .___/\\__/_/_/ /_/ /_/\\__,_/____/  \n",
      "                              /_/                                  \n",
      "                              \n",
      "INFO:optimus:Transform and Roll out...\n",
      "INFO:optimus:Optimus successfully imported. Have fun :).\n",
      "INFO:optimus:Config.ini not found\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style> /* Tables*/\n",
       "\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 td {\n",
       "        border: 0px;\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",
       "    .optimus_table tbody{\n",
       "        font-family: monospace;\n",
       "        border-bottom: 1px solid #cccccc;\n",
       "    }\n",
       "\n",
       "    /* Profiler */\n",
       "        .main{\n",
       "        width:100%;\n",
       "        overflow:auto;\n",
       "        border-bottom:1px solid #eeeeee;\n",
       "        padding: 10px 0;\n",
       "    }\n",
       "    .panel_profiler{\n",
       "        margin-right:2%;\n",
       "        float:left;\n",
       "        padding-bottom:2%;\n",
       "    }\n",
       "    .panel_profiler tbody{\n",
       "        font-family:monospace;\n",
       "    }\n",
       "    .title_profiler{\n",
       "        padding:20px;\n",
       "        background-color: #eeeeee\n",
       "    }\n",
       "    .info{\n",
       "        overflow: auto\n",
       "    }\n",
       "    .main td, main th{\n",
       "        padding:0em\n",
       "    }\n",
       "    .panel_profiler td {\n",
       "        padding:0.2em\n",
       "    }\n",
       "    .none, .true{\n",
       "        color:#0000ff\n",
       "    }\n",
       "    .optimus_table th {\n",
       "        font-family:sans-serif;\n",
       "    }\n",
       "\n",
       "    .info_items{\n",
       "        font-family:sans-serif;\n",
       "        font-size:10px;\n",
       "    }\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append(\"..\")\n",
    "\n",
    "from optimus import Optimus\n",
    "op= Optimus(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:jdbc:redshift://yopter.ccyy9l6xansm.us-east-1.redshift.amazonaws.com:5439/yopterdwh?currentSchema=public\n",
      "INFO:optimus:(\n",
      "            SELECT relname as table_name,cast (reltuples as integer) AS count \n",
      "            FROM pg_class C LEFT JOIN pg_namespace N ON (N.oid = C.relnamespace) \n",
      "            WHERE nspname IN ('public') AND relkind='r' ORDER BY reltuples DESC) AS t\n",
      "INFO:optimus:jdbc:redshift://yopter.ccyy9l6xansm.us-east-1.redshift.amazonaws.com:5439/yopterdwh?currentSchema=public\n"
     ]
    },
    {
     "ename": "Py4JJavaError",
     "evalue": "An error occurred while calling o6140.load.\n: java.sql.SQLException: No suitable driver\r\n\tat java.sql.DriverManager.getDriver(DriverManager.java:315)\r\n\tat org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions$$anonfun$7.apply(JDBCOptions.scala:85)\r\n\tat org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions$$anonfun$7.apply(JDBCOptions.scala:85)\r\n\tat scala.Option.getOrElse(Option.scala:121)\r\n\tat org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:84)\r\n\tat org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:35)\r\n\tat org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:34)\r\n\tat org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:340)\r\n\tat org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:239)\r\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:227)\r\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:164)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\r\n\tat java.lang.reflect.Method.invoke(Method.java:498)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPy4JJavaError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-f35140062f08>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;31m# Show all tables names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mdb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"all\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Documents\\Optimus\\optimus\\io\\jdbc.py\u001b[0m in \u001b[0;36mtables\u001b[1;34m(self, schema, database, limit)\u001b[0m\n\u001b[0;32m    123\u001b[0m                     FROM user_tables ORDER BY table_name\"\"\"\n\u001b[0;32m    124\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 125\u001b[1;33m         \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    126\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    127\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\Optimus\\optimus\\io\\jdbc.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self, query, limit)\u001b[0m\n\u001b[0;32m    230\u001b[0m             \u001b[0mconf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moption\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"driver\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdriver_option\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    231\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 232\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mconf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    233\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    234\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdf_to_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"overwrite\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pyspark\\sql\\readwriter.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(self, path, format, schema, **options)\u001b[0m\n\u001b[0;32m    170\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jreader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_spark\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPythonUtils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoSeq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 172\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jreader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    174\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0msince\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\py4j\\java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m   1255\u001b[0m         \u001b[0manswer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1256\u001b[0m         return_value = get_return_value(\n\u001b[1;32m-> 1257\u001b[1;33m             answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[0;32m   1258\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1259\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pyspark\\sql\\utils.py\u001b[0m in \u001b[0;36mdeco\u001b[1;34m(*a, **kw)\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoString\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\py4j\\protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[1;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[0;32m    326\u001b[0m                 raise Py4JJavaError(\n\u001b[0;32m    327\u001b[0m                     \u001b[1;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 328\u001b[1;33m                     format(target_id, \".\", name), value)\n\u001b[0m\u001b[0;32m    329\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    330\u001b[0m                 raise Py4JError(\n",
      "\u001b[1;31mPy4JJavaError\u001b[0m: An error occurred while calling o6140.load.\n: java.sql.SQLException: No suitable driver\r\n\tat java.sql.DriverManager.getDriver(DriverManager.java:315)\r\n\tat org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions$$anonfun$7.apply(JDBCOptions.scala:85)\r\n\tat org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions$$anonfun$7.apply(JDBCOptions.scala:85)\r\n\tat scala.Option.getOrElse(Option.scala:121)\r\n\tat org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:84)\r\n\tat org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:35)\r\n\tat org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:34)\r\n\tat org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:340)\r\n\tat org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:239)\r\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:227)\r\n\tat org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:164)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\r\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\r\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\r\n\tat java.lang.reflect.Method.invoke(Method.java:498)\r\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\r\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\r\n\tat py4j.Gateway.invoke(Gateway.java:282)\r\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\r\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\r\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\r\n\tat java.lang.Thread.run(Thread.java:748)\r\n"
     ]
    }
   ],
   "source": [
    "# This import is only to hide the credentials\n",
    "from credentials import *\n",
    "\n",
    "# For others databases use in db_type accepts 'oracle','mysql','redshift','postgres'\n",
    "\n",
    "db =  op.connect(\n",
    "    db_type=DB_TYPE,\n",
    "    host=HOST,\n",
    "    database= DATABASE,\n",
    "    user= USER,\n",
    "    password = PASSWORD,\n",
    "    port=PORT)\n",
    "    \n",
    "# Show all tables names\n",
    "db.tables(limit=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Show a summary of every table\n",
    "db.table.show(\"*\",20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Get a table as dataframe\n",
    "df_ = db.table_to_df(\"places_interest\").table()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Create new table in the database\n",
    "db.df_to_table(df, \"new_table\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data enrichment\n",
    "\n",
    "You can connect to any external API to enrich your data using Optimus. Optimus uses MongoDB to download the data and then merge it with the Spark Dataframe. You need to install MongoDB\n",
    "\n",
    "Let's load a tiny dataset we can enrich"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = op.load.json(\"https://raw.githubusercontent.com/ironmussa/Optimus/master/examples/data/foo.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "def func_request(params):\n",
    "    # You can use here whatever header or auth info you need to send. \n",
    "    # For more information see the requests library\n",
    "    \n",
    "    url= \"https://jsonplaceholder.typicode.com/todos/\" + str(params[\"id\"])\n",
    "    return requests.get(url)\n",
    "\n",
    "def func_response(response):\n",
    "    # Here you can parse de response\n",
    "    return response[\"title\"]\n",
    "\n",
    "\n",
    "e = op.enrich(host=\"localhost\", port=27017, db_name=\"jazz\")\n",
    "\n",
    "df_result = e.run(df, func_request, func_response, calls= 60, period = 60, max_tries = 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_result.table(\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_result.table_image(\"images/table7.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering Strings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimus implements some funciton to cluster Strings. We get graet inspiration from OpenRefine\n",
    "\n",
    "Here a quote from its site:\n",
    "\n",
    "\"In OpenRefine, clustering refers to the operation of \"finding groups of different values that might be alternative representations of the same thing\". For example, the two strings \"New York\" and \"new york\" are very likely to refer to the same concept and just have capitalization differences. Likewise, \"Gödel\" and \"Godel\" probably refer to the same person.\"\n",
    "\n",
    "For more informacion see this:\n",
    "https://github.com/OpenRefine/OpenRefine/wiki/Clustering-In-Depth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keycolision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = op.read.csv(\"../examples/data/random.csv\",header=True, sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from optimus.ml import keycollision as keyCol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _lower\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function multiple_replace\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _remove_accents\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _split_sort_remove_join\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 5 of 5 rows / 4 columns</div>\n",
       "<div class=\"info_items\">200 partition(s)</div>\n",
       "\n",
       "<table class=\"optimus_table\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_CLUSTER_SIZE</div>\n",
       "            <div class=\"data_type\">1 (int)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                not nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_CLUSTER</div>\n",
       "            <div class=\"data_type\">2 (array&lt;string&gt;)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_COUNT</div>\n",
       "            <div class=\"data_type\">3 (bigint)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_RECOMMENDED</div>\n",
       "            <div class=\"data_type\">4 (string)</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",
       "            <div class=\" \"\n",
       "                 title='1'>1\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='['Estado&#8901;de&#8901;México']'>['Estado&#8901;de&#8901;México']\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='810'>810\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Estado&#8901;de&#8901;México'>Estado&#8901;de&#8901;México\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='2'>2\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='['México&#8901;D.F.',&#8901;'Mexico&#8901;D.F.']'>['México&#8901;D.F.',&#8901;'Mexico&#8901;D.F.']\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='2495'>2495\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Mexico&#8901;D.F.'>Mexico&#8901;D.F.\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1'>1\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='['D.F.']'>['D.F.']\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='66'>66\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='D.F.'>D.F.\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1'>1\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='['Distriro&#8901;Federal']'>['Distriro&#8901;Federal']\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='259'>259\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Distriro&#8901;Federal'>Distriro&#8901;Federal\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='3'>3\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='['Distrito&#8901;Federal',&#8901;'DISTRITO&#8901;FEDERAL',&#8901;'distrito&#8901;federal']'>['Distrito&#8901;Federal',&#8901;'DISTRITO&#8901;FEDERAL',&#8901;'distrito&#8901;federal']\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='11930'>11930\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Distrito&#8901;Federal'>Distrito&#8901;Federal\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 5 of 5 rows / 4 columns</div>\n",
       "<div class=\"info_items\">200 partition(s)</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table8.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_kc = keyCol.fingerprint_cluster(df, 'STATE')\n",
    "df_kc.table()\n",
    "df_kc.table_image(\"images/table8.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _lower\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function multiple_replace\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _remove_accents\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _split_sort_remove_join\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'STATE_CLUSTER_SIZE': 1,\n",
       "  'STATE_CLUSTER': ['Estado de México'],\n",
       "  'STATE_COUNT': 810,\n",
       "  'STATE_RECOMMENDED': 'Estado de México'},\n",
       " {'STATE_CLUSTER_SIZE': 2,\n",
       "  'STATE_CLUSTER': ['México D.F.', 'Mexico D.F.'],\n",
       "  'STATE_COUNT': 2495,\n",
       "  'STATE_RECOMMENDED': 'Mexico D.F.'},\n",
       " {'STATE_CLUSTER_SIZE': 1,\n",
       "  'STATE_CLUSTER': ['D.F.'],\n",
       "  'STATE_COUNT': 66,\n",
       "  'STATE_RECOMMENDED': 'D.F.'},\n",
       " {'STATE_CLUSTER_SIZE': 1,\n",
       "  'STATE_CLUSTER': ['Distriro Federal'],\n",
       "  'STATE_COUNT': 259,\n",
       "  'STATE_RECOMMENDED': 'Distriro Federal'},\n",
       " {'STATE_CLUSTER_SIZE': 3,\n",
       "  'STATE_CLUSTER': ['Distrito Federal',\n",
       "   'DISTRITO FEDERAL',\n",
       "   'distrito federal'],\n",
       "  'STATE_COUNT': 11930,\n",
       "  'STATE_RECOMMENDED': 'Distrito Federal'}]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyCol.fingerprint_cluster(df, \"STATE\").to_json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'STATE_NGRAM' with function _lower\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_NGRAM' with function _remove_white_spaces\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_NGRAM' with function multiple_replace\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_NGRAM' with function _remove_accents\n",
      "INFO:optimus:Using 'column_exp' to process column 'count' with function _cast_to\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE' with function _cast_to\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_NGRAM' with function _cast_to\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_NGRAM' with function func_col_exp\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_NGRAM_FINGERPRINT' with function remote_white_spaces_remove_sort_join\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 1 of 1 rows / 4 columns</div>\n",
       "<div class=\"info_items\">200 partition(s)</div>\n",
       "\n",
       "<table class=\"optimus_table\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_CLUSTER_SIZE</div>\n",
       "            <div class=\"data_type\">1 (int)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                not nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_CLUSTER</div>\n",
       "            <div class=\"data_type\">2 (array&lt;string&gt;)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_COUNT</div>\n",
       "            <div class=\"data_type\">3 (double)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_RECOMMENDED</div>\n",
       "            <div class=\"data_type\">4 (string)</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",
       "            <div class=\" \"\n",
       "                 title='8'>8\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='['Distrito&#8901;Federal',&#8901;'México&#8901;D.F.',&#8901;'DISTRITO&#8901;FEDERAL',&#8901;'Mexico&#8901;D.F.',&#8901;'Distriro&#8901;Federal',&#8901;'D.F.',&#8901;'Estado&#8901;de&#8901;México',&#8901;'distrito&#8901;federal']'>['Distrito&#8901;Federal',&#8901;'México&#8901;D.F.',&#8901;'DISTRITO&#8901;FEDERAL',&#8901;'Mexico&#8901;D.F.',&#8901;'Distr...\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='15560.0'>15560.0\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Mexico&#8901;D.F.'>Mexico&#8901;D.F.\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 1 of 1 rows / 4 columns</div>\n",
       "<div class=\"info_items\">200 partition(s)</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table9.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_kc = keyCol.n_gram_fingerprint_cluster(df, \"STATE\" , 2)\n",
    "df_kc.table()\n",
    "df_kc.table_image(\"images/table9.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'STATE_NGRAM' with function _lower\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_NGRAM' with function _remove_white_spaces\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_NGRAM' with function multiple_replace\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_NGRAM' with function _remove_accents\n",
      "INFO:optimus:Using 'column_exp' to process column 'count' with function _cast_to\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE' with function _cast_to\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_NGRAM' with function _cast_to\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_NGRAM' with function func_col_exp\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_NGRAM_FINGERPRINT' with function remote_white_spaces_remove_sort_join\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'STATE_CLUSTER_SIZE': 8,\n",
       "  'STATE_CLUSTER': ['Distrito Federal',\n",
       "   'México D.F.',\n",
       "   'DISTRITO FEDERAL',\n",
       "   'Mexico D.F.',\n",
       "   'Distriro Federal',\n",
       "   'D.F.',\n",
       "   'Estado de México',\n",
       "   'distrito federal'],\n",
       "  'STATE_COUNT': 15560.0,\n",
       "  'STATE_RECOMMENDED': 'Mexico D.F.'}]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyCol.n_gram_fingerprint_cluster(df, \"STATE\" , 2).to_json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nearest Neighbor Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _lower\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function multiple_replace\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _remove_accents\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _split_sort_remove_join\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table10.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from optimus.ml import distancecluster as dc\n",
    "df_dc = dc.levenshtein_matrix(df,\"STATE\")\n",
    "df_dc.table_image(\"images/table10.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _lower\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function multiple_replace\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _remove_accents\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _split_sort_remove_join\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 5 of 5 rows / 3 columns</div>\n",
       "<div class=\"info_items\">200 partition(s)</div>\n",
       "\n",
       "<table class=\"optimus_table\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_FROM</div>\n",
       "            <div class=\"data_type\">1 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_LEVENSHTEIN_DISTANCE</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\">STATE_TO</div>\n",
       "            <div class=\"data_type\">3 (string)</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",
       "            <div class=\" \"\n",
       "                 title='estadodemexico'>estadodemexico\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='10'>10\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='mexicodf'>mexicodf\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='df'>df\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='6'>6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='mexicodf'>mexicodf\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='distrirofederal'>distrirofederal\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1'>1\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='distritofederal'>distritofederal\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='distritofederal'>distritofederal\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1'>1\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='distrirofederal'>distrirofederal\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='mexicodf'>mexicodf\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='6'>6\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='df'>df\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 5 of 5 rows / 3 columns</div>\n",
       "<div class=\"info_items\">200 partition(s)</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table11.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dc=dc.levenshtein_filter(df,\"STATE\")\n",
    "df_dc.table()\n",
    "df_dc.table_image(\"images/table11.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _lower\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function multiple_replace\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _remove_accents\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _split_sort_remove_join\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _lower\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function multiple_replace\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _remove_accents\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _split_sort_remove_join\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 5 of 5 rows / 4 columns</div>\n",
       "<div class=\"info_items\">1 partition(s)</div>\n",
       "\n",
       "<table class=\"optimus_table\">\n",
       "    <thead>\n",
       "    <tr>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_CLUSTER</div>\n",
       "            <div class=\"data_type\">1 (array&lt;string&gt;)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_CLUSTER_SIZE</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\">STATE_RECOMMENDED</div>\n",
       "            <div class=\"data_type\">3 (string)</div>\n",
       "            <div class=\"data_type\">\n",
       "                \n",
       "                nullable\n",
       "                \n",
       "            </div>\n",
       "        </th>\n",
       "        \n",
       "        <th>\n",
       "            <div class=\"column_name\">STATE_COUNT</div>\n",
       "            <div class=\"data_type\">4 (bigint)</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",
       "            <div class=\" \"\n",
       "                 title='['Estado&#8901;de&#8901;México']'>['Estado&#8901;de&#8901;México']\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1'>1\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Estado&#8901;de&#8901;México'>Estado&#8901;de&#8901;México\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='810'>810\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='['D.F.']'>['D.F.']\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1'>1\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='D.F.'>D.F.\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='66'>66\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='['Distriro&#8901;Federal']'>['Distriro&#8901;Federal']\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='1'>1\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Distriro&#8901;Federal'>Distriro&#8901;Federal\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='259'>259\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='['Distrito&#8901;Federal',&#8901;'DISTRITO&#8901;FEDERAL',&#8901;'distrito&#8901;federal']'>['Distrito&#8901;Federal',&#8901;'DISTRITO&#8901;FEDERAL',&#8901;'distrito&#8901;federal']\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='3'>3\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Distrito&#8901;Federal'>Distrito&#8901;Federal\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='11930'>11930\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='['Mexico&#8901;D.F.',&#8901;'México&#8901;D.F.']'>['Mexico&#8901;D.F.',&#8901;'México&#8901;D.F.']\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='2'>2\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='Mexico&#8901;D.F.'>Mexico&#8901;D.F.\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "        <td>\n",
       "            <div class=\" \"\n",
       "                 title='2495'>2495\n",
       "            </div>\n",
       "        </td>\n",
       "        \n",
       "    </tr>\n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "\n",
       "\n",
       "<div class=\"info_items\">Viewing 5 of 5 rows / 4 columns</div>\n",
       "<div class=\"info_items\">1 partition(s)</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table12.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dc = dc.levenshtein_cluster(df,\"STATE\")\n",
    "df_dc.table()\n",
    "df_dc.table_image(\"images/table12.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _lower\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function multiple_replace\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _remove_accents\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _split_sort_remove_join\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _trim\n",
      "INFO:optimus:Using 'column_exp' to process column 'STATE_FINGERPRINT' with function _lower\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function multiple_replace\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _remove_accents\n",
      "INFO:optimus:Using 'pandas_udf' to process column 'STATE_FINGERPRINT' with function _split_sort_remove_join\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'STATE_CLUSTER': ['Estado de México'],\n",
       "  'STATE_CLUSTER_SIZE': 1,\n",
       "  'STATE_RECOMMENDED': 'Estado de México',\n",
       "  'STATE_COUNT': 810},\n",
       " {'STATE_CLUSTER': ['D.F.'],\n",
       "  'STATE_CLUSTER_SIZE': 1,\n",
       "  'STATE_RECOMMENDED': 'D.F.',\n",
       "  'STATE_COUNT': 66},\n",
       " {'STATE_CLUSTER': ['Distriro Federal'],\n",
       "  'STATE_CLUSTER_SIZE': 1,\n",
       "  'STATE_RECOMMENDED': 'Distriro Federal',\n",
       "  'STATE_COUNT': 259},\n",
       " {'STATE_CLUSTER': ['Distrito Federal',\n",
       "   'DISTRITO FEDERAL',\n",
       "   'distrito federal'],\n",
       "  'STATE_CLUSTER_SIZE': 3,\n",
       "  'STATE_RECOMMENDED': 'Distrito Federal',\n",
       "  'STATE_COUNT': 11930},\n",
       " {'STATE_CLUSTER': ['Mexico D.F.', 'México D.F.'],\n",
       "  'STATE_CLUSTER_SIZE': 2,\n",
       "  'STATE_RECOMMENDED': 'Mexico D.F.',\n",
       "  'STATE_COUNT': 2495}]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc.to_json(df, \"STATE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine Learning \n",
    "\n",
    "Machine Learning is one of the last steps, and the goal for most Data Science WorkFlows.\n",
    "\n",
    "Apache Spark created a library called MLlib where they coded great algorithms for Machine Learning. Now\n",
    "with the ML library we can take advantage of the Dataframe API and its optimization to create Machine Learning Pipelines easily.\n",
    "\n",
    "Even though this task is not extremely hard, it is not easy. The way most Machine Learning models work on Spark\n",
    "are not straightforward, and they need lots of feature engineering to work. That's why we created the feature engineering\n",
    "section inside Optimus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the best \"tree\" models for machine learning is Random Forest. What about creating a RF model with just\n",
    "one line? With Optimus is really easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:optimus:Downloading data_cancer.csv from https://raw.githubusercontent.com/ironmussa/Optimus/master/tests/data_cancer.csv\n",
      "INFO:optimus:Downloaded 125205 bytes\n",
      "INFO:optimus:Creating DataFrame for data_cancer.csv. Please wait...\n",
      "INFO:optimus:Successfully created DataFrame for 'data_cancer.csv'\n"
     ]
    }
   ],
   "source": [
    "df_cancer = op.load.csv(\"https://raw.githubusercontent.com/ironmussa/Optimus/master/tests/data_cancer.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean',\n",
    "           'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean',\n",
    "           'fractal_dimension_mean']\n",
    "\n",
    "df_predict, rf_model = op.ml.random_forest(df_cancer, columns, \"diagnosis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will create a DataFrame with the predictions of the Random Forest model.\n",
    "\n",
    "So lets see the prediction compared with the actual label:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src='images/table13.png'>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_predict.cols.select([\"label\",\"prediction\"]).table_image(\"images/table13.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rf_model variable contains the Random Forest model for analysis.\n",
    " \n",
    "## Contributing to Optimus\n",
    "Contributions go far beyond pull requests and commits. We are very happy to receive any kind of contributions   \n",
    "including:  \n",
    "  \n",
    "* [Documentation](https://github.com/ironmussa/Optimus/tree/master/docs/source) updates, enhancements, designs, or   bugfixes.  \n",
    "* Spelling or grammar fixes.  \n",
    "* README.md corrections or redesigns.  \n",
    "* Adding unit, or functional [tests](https://github.com/ironmussa/Optimus/tree/master/tests)   \n",
    "* Triaging GitHub issues -- especially determining whether an issue still persists or is reproducible.  \n",
    "* [Searching #optimusdata on twitter](https://twitter.com/search?q=optimusdata) and helping someone else who needs help.  \n",
    "* [Blogging, speaking about, or creating tutorials](https://hioptimus.com/category/blog/)   about Optimus and its many features.  \n",
    "* Helping others on [Discord](https://img.shields.io/discord/579030865468719104.svg)    \n",
    "  \n",
    "## Backers  \n",
    "[[Become a backer](https://opencollective.com/optimus#backer)] and get your image on our README on Github with a link to your site.  \n",
    "[![OpenCollective](https://opencollective.com/optimus/backers/badge.svg)](#backers)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sponsors  \n",
    "[[Become a sponsor](https://opencollective.com/optimus#backer)] and get your image on our README on Github with a link to your site.  \n",
    "[![OpenCollective](https://opencollective.com/optimus/sponsors/badge.svg)](#sponsors)  \n",
    "  \n",
    "## Core Team\n",
    "Argenis Leon and Luis Aguirre\n",
    "\n",
    "## License:  \n",
    "  \n",
    "Apache 2.0 © [Iron](https://github.com/ironmussa)  \n",
    "  \n",
    "[![Logo Iron](https://iron-ai.com/wp-content/uploads/2017/08/iron-svg-2.png)](https://ironmussa.com)  \n",
    "  \n",
    "<a href=\"https://twitter.com/optimus_data\"><img src=\"https://www.shareicon.net/data/256x256/2015/09/01/94063_circle_512x512.png\" alt=\"Optimus twitter\" border=\"0\" height=\"60\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Post-process readme script. Always run this if you modify the notebook. \n",
    "\n",
    "This will recreate README.md"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bellow script process the ```readme_.md``` that is ouputed from this notebook and remove the header from jupytext, python comments and convert/add table to images and output ```readme.md```.\n",
    "\n",
    "To make ```table_image()``` function be sure to install imagekit ```pip install imgkit```\n",
    "Also install wkhtmltopdf https://wkhtmltopdf.org/downloads.html. This is responsible to generate the optimus tables as images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shutil import copyfile\n",
    "output_file = \"../README.md\"\n",
    "copyfile(\"readme_.md\", output_file)\n",
    "\n",
    "import sys\n",
    "import fileinput\n",
    "import re\n",
    "\n",
    "pattern = r'\"([A-Za-z0-9_\\./\\\\-]*)\"'\n",
    "\n",
    "jupytext_header = False\n",
    "flag_remove = False\n",
    "\n",
    "remove = [\"load_ext\", \"autoreload\",\"import sys\",\"sys.path.append\"]\n",
    "\n",
    "buffer = None\n",
    "for i, line in enumerate(fileinput.input(output_file, inplace=1)):\n",
    "    done= False\n",
    "    try:\n",
    "        # Remove some helper lines\n",
    "        for r in remove:\n",
    "            if re.search(r, line):\n",
    "                done= True\n",
    "        \n",
    "        #Remove the post process code\n",
    "        if re.search(\"Post-process\", line):\n",
    "            flag_remove = True\n",
    "            \n",
    "        if flag_remove is True:\n",
    "            done = True        \n",
    "            \n",
    "        \n",
    "        # Remove jupytext header\n",
    "        if jupytext_header is True:\n",
    "            done = True\n",
    "            \n",
    "        if  \"---\\n\" == line: \n",
    "            jupytext_header = not jupytext_header      \n",
    "                    \n",
    "        elif done is False:\n",
    "     \n",
    "            # Replace .table_image(...) by table()\n",
    "            chars_table=re.search(\".table_image\", line)\n",
    "            chars_image=re.search(\".to_image\", line)\n",
    "            chars_plot = True if len(re.findall('(.plot.|output_path=)', line))==2 else False\n",
    "            \n",
    "            \n",
    "            \n",
    "            path = \"readme/\"\n",
    "            if chars_table:\n",
    "                print(line[0:int(chars_table.start())]+\".table()\")\n",
    "\n",
    "                m = re.search(r'table_image\\(\"(.*?)\"\\)', line).group(1)\n",
    "                if m:\n",
    "                    buffer = \"![](\"+ path + m + \")\"              \n",
    "            elif chars_image:\n",
    "                m = re.search(r'to_image\\(output_path=\"(.*?)\"\\)', line).group(1)\n",
    "                if m:\n",
    "                    buffer = \"![](\"+ path + m + \")\"  \n",
    "            elif chars_plot:\n",
    "\n",
    "                m = re.search('output_path=\"(.*?)\"', line).group(1)\n",
    "\n",
    "                if m:\n",
    "                    buffer = \"![](\"+ path + m + \")\"  \n",
    "            \n",
    "            else:\n",
    "                sys.stdout.write(line)\n",
    "                \n",
    "            if \"```\\n\"==line and buffer:                \n",
    "                print(buffer)\n",
    "                buffer = None\n",
    "                \n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        \n",
    "fileinput.close()\n",
    "\n",
    "\n",
    "# Remove empyt python cells\n",
    "flag = False\n",
    "for i, line in enumerate(fileinput.input(output_file, inplace=1)):\n",
    "   \n",
    "    if re.search(\"```python\", line):     \n",
    "        flag = True\n",
    "    elif re.search(\"```\", line) and flag is True:\n",
    "        flag=False\n",
    "    elif flag is True:\n",
    "        flag = False\n",
    "        print(\"```python\")\n",
    "        print(line,end=\"\")\n",
    "    else:\n",
    "        print(line, end=\"\")\n",
    "                    \n",
    "        \n",
    "fileinput.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images/profiler.png\n"
     ]
    }
   ],
   "source": [
    "line = 'op.profiler.to_image(output_path=\"images/profiler.png\")\")'\n",
    "m = re.search(r'to_image\\(output_path=\"(.*?)\"\\)', line).group(1)\n",
    "print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,md"
  },
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}