{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Iceberg Principle\n",
    "\n",
    "An important principle in data cleaning is the iceberg principle: if you see *anything* in your data that doesn't make sense to you, even if it doesn't seem like a major problem, you **absolutely must** figure out why it's happening, because small things that don't make sense are often signals of big, underlying problems that are hard to detect. \n",
    "\n",
    "To illustrate, suppose we're interested in understanding how the composition of arrests in California have been changing over time. In particular, we're interested in the share of arrests that are felonies (serious crimes), as opposed to misdomeaners (minor crimes). We start with data on total felony and misdomeaner arrests in California from 2001 to 2010:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>felony_arrests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001</td>\n",
       "      <td>472677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002</td>\n",
       "      <td>487364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>507081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>522781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005</td>\n",
       "      <td>538166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006</td>\n",
       "      <td>534460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2007</td>\n",
       "      <td>523276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008</td>\n",
       "      <td>499628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009</td>\n",
       "      <td>466441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010</td>\n",
       "      <td>448552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  felony_arrests\n",
       "0  2001          472677\n",
       "1  2002          487364\n",
       "2  2003          507081\n",
       "3  2004          522781\n",
       "4  2005          538166\n",
       "5  2006          534460\n",
       "6  2007          523276\n",
       "7  2008          499628\n",
       "8  2009          466441\n",
       "9  2010          448552"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "felonies = pd.read_csv(\n",
    "    \"https://raw.githubusercontent.com/nickeubank\"\n",
    "    \"/practicaldatascience/dbc2199fcf7e6302cdd1ea2955dc378605b52659\"\n",
    "    \"/Example_Data/ca_felonies_2001_2010.csv\"\n",
    ")\n",
    "felonies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<seaborn._core.plot.Plot at 0x17e876b50>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "image/png": {
       "height": 378.25,
       "width": 509.15
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "misdomeaners = pd.read_csv(\n",
    "    \"https://github.com/nickeubank/practicaldatascience/raw\"\n",
    "    \"/dbc2199fcf7e6302cdd1ea2955dc378605b52659/Example_Data\"\n",
    "    \"/ca_misdomeaners_2001_2010.csv\"\n",
    ")\n",
    "merged = pd.merge(felonies, misdomeaners, on=\"year\", validate=\"1:1\")\n",
    "merged[\"share_felonies\"] = merged[\"felony_arrests\"] / (\n",
    "    merged[\"felony_arrests\"] + merged[\"misdomeaner_arrests\"]\n",
    ")\n",
    "\n",
    "import seaborn.objects as so\n",
    "from matplotlib import style\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
    "\n",
    "(\n",
    "    so.Plot(merged, x=\"year\", y=\"share_felonies\")\n",
    "    .add(so.Dots())\n",
    "    .add(so.Lines())\n",
    "    .label(title=\"Felonies as a Share of Arrests\")\n",
    "    .theme({**style.library[\"seaborn-v0_8-whitegrid\"]})\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cool! The share of arrests that were felonies were about flat in the early 2000s, then fall sharply in the late 2000s. Everything looks great, right?! Let's publish a paper!\n",
    "\n",
    "But wait... didn't we say our data was form 2001 to 2010? Then why isn't there a point being plotted for 2001? Something seems...🐟 🐠 🐡. (Fishy, get it?!)\n",
    "\n",
    "Let's look more closely at our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>felony_arrests</th>\n",
       "      <th>misdomeaner_arrests</th>\n",
       "      <th>share_felonies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001</td>\n",
       "      <td>472677</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002</td>\n",
       "      <td>487364</td>\n",
       "      <td>907990.0</td>\n",
       "      <td>0.349276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>507081</td>\n",
       "      <td>903249.0</td>\n",
       "      <td>0.359548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>522781</td>\n",
       "      <td>931782.0</td>\n",
       "      <td>0.359408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005</td>\n",
       "      <td>538166</td>\n",
       "      <td>945562.0</td>\n",
       "      <td>0.362712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006</td>\n",
       "      <td>534460</td>\n",
       "      <td>939046.0</td>\n",
       "      <td>0.362713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2007</td>\n",
       "      <td>523276</td>\n",
       "      <td>968408.0</td>\n",
       "      <td>0.350795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008</td>\n",
       "      <td>499628</td>\n",
       "      <td>992588.0</td>\n",
       "      <td>0.334823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009</td>\n",
       "      <td>466441</td>\n",
       "      <td>1010038.0</td>\n",
       "      <td>0.315914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010</td>\n",
       "      <td>448552</td>\n",
       "      <td>970221.0</td>\n",
       "      <td>0.316155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  felony_arrests  misdomeaner_arrests  share_felonies\n",
       "0  2001          472677                  NaN             NaN\n",
       "1  2002          487364             907990.0        0.349276\n",
       "2  2003          507081             903249.0        0.359548\n",
       "3  2004          522781             931782.0        0.359408\n",
       "4  2005          538166             945562.0        0.362712\n",
       "5  2006          534460             939046.0        0.362713\n",
       "6  2007          523276             968408.0        0.350795\n",
       "7  2008          499628             992588.0        0.334823\n",
       "8  2009          466441            1010038.0        0.315914\n",
       "9  2010          448552             970221.0        0.316155"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Huh... ok weird. Missing data for 2001 in `misdomeaner_arrests`. Was that in the original data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>misdomeaner_arrests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002.0</td>\n",
       "      <td>907990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003.0</td>\n",
       "      <td>903249.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004.0</td>\n",
       "      <td>931782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005.0</td>\n",
       "      <td>945562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>939046.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2007.0</td>\n",
       "      <td>968408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>992588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>1010038.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>970221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>918279.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  misdomeaner_arrests\n",
       "0   2001.0                  NaN\n",
       "1   2002.0             907990.0\n",
       "2   2003.0             903249.0\n",
       "3   2004.0             931782.0\n",
       "4   2005.0             945562.0\n",
       "5   2006.0             939046.0\n",
       "6   2007.0             968408.0\n",
       "7   2008.0             992588.0\n",
       "8   2009.0            1010038.0\n",
       "9   2010.0             970221.0\n",
       "10     NaN             918279.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "misdomeaners"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uh... well that's bad. Not only is there no data for 2001, but there's data for a row with no year?! That *clearly* can't be right. \n",
    "\n",
    "We better go check the source data for this table. Here's what I see when I go back to the CA Bureau of Justice Crime Statistics:\n",
    "\n",
    "![Crime source data](images/source_crime_data.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmm... so the original data says 907,990 is the entry for 2001, but in our table it says that's the value for 2002. You know what I think happened? When the data was transcribed, it was offset one row by accident:\n",
    "\n",
    "![data spreadsheet](images/misdomeaner_spreadsheet.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which means... the problem isn't just that I'm missing data from 2001. It means that **all** of my data is wrong, not just 2001! The real result is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>misdomeaner_arrests</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001.0</td>\n",
       "      <td>907990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002.0</td>\n",
       "      <td>903249.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003.0</td>\n",
       "      <td>931782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004.0</td>\n",
       "      <td>945562.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2005.0</td>\n",
       "      <td>939046.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2006.0</td>\n",
       "      <td>968408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2007.0</td>\n",
       "      <td>992588.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>1010038.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2009.0</td>\n",
       "      <td>970221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2010.0</td>\n",
       "      <td>918279.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      year  misdomeaner_arrests\n",
       "1   2001.0             907990.0\n",
       "2   2002.0             903249.0\n",
       "3   2003.0             931782.0\n",
       "4   2004.0             945562.0\n",
       "5   2005.0             939046.0\n",
       "6   2006.0             968408.0\n",
       "7   2007.0             992588.0\n",
       "8   2008.0            1010038.0\n",
       "9   2009.0             970221.0\n",
       "10  2010.0             918279.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "misdomeaners[\"year\"] = misdomeaners[\"year\"] - 1\n",
    "misdomeaners.loc[misdomeaners[\"year\"].isnull(), \"year\"] = 2010\n",
    "misdomeaners = misdomeaners[misdomeaners[\"year\"] != 2000]\n",
    "misdomeaners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<seaborn._core.plot.Plot at 0x17e8b6690>"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "image/png": {
       "height": 378.25,
       "width": 509.15
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This time I'll do an outer merge and check it!\n",
    "merged = pd.merge(\n",
    "    felonies, misdomeaners, on=\"year\", validate=\"1:1\", how=\"outer\", indicator=True\n",
    ")\n",
    "assert (merged._merge == \"both\").all()\n",
    "merged[\"share_felonies\"] = merged[\"felony_arrests\"] / (\n",
    "    merged[\"felony_arrests\"] + merged[\"misdomeaner_arrests\"]\n",
    ")\n",
    "\n",
    "(\n",
    "    so.Plot(merged, x=\"year\", y=\"share_felonies\")\n",
    "    .add(so.Dots())\n",
    "    .add(so.Lines())\n",
    "    .label(title=\"Felonies as a Share of Arrests\")\n",
    "    .theme({**style.library[\"seaborn-v0_8-whitegrid\"]})\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phew. OK, so the real answer is that there was a big increase in felonies as a share of total arrests in the early 2000s, then an even steeper decline than we saw before. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And that, my friends, is the iceberg principle: often times very small anomylies in your data are the only warning you will get about much bigger problems, so you always want to keep an eye out for things you don't expect, and if you see something weird, *make sure you figure out why it's happening*, because while sometimes small problems are just small problems, other times your just seeing the tip of an iceberg:\n",
    "\n",
    "![iceberg](images/iceberg.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And lest you think this is a contrived example: it's not. It is a simplified verison of a problem I encountered in my own research just 2 months ago, in which a single outlier observation lead me to realize that our data entry team had made an off-by-one error during data entry that had corrupted several years of data. I cannot tell you how many times over the years I've had this kind of thing occur -- I was one observation short in a regression, or I had one outlier, and when I dug into I discovered a much, much bigger problem. So *please* heed my warning: if you see something that seems off, no matter how small, figure out what's causing it!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}