Introduction to Exploratory Data Analysis in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Data Science?\n",
    "\n",
    "The hot takes via [Twitter](https://twitter.com/mcmoots/status/429318287864770560):\n",
    "\n",
    "- \"A data scientist is a statistician who lives in San Francisco.\" \n",
    "- \"A data scientist is a business analyst who lives in New York.\"\n",
    "- \"Data Science is statistics on a Mac.\"\n",
    "\n",
    "A more idealistic view:\n",
    "\n",
    "![Venn Diagram of Data Science](https://raw.githubusercontent.com/jakevdp/PythonDataScienceHandbook/8a34a4f653bdbdc01415a94dc20d4e9b97438965/notebooks/figures/Data_Science_VD.png)\n",
    "\n",
    "\"...think of data science not as a new domain of knowledge to learn, but a new set of skills that you can apply within your current area of expertise.\" - [Jake VanderPlas](http://vanderplas.com/), Director of Open Software at the University of Washington's eScience Institute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## So what's the plan?\n",
    "\n",
    "We're going to combine a touch of stats, a hint of hacking, and a pinch of substantive knowledge about policing to analyze a real data set, collected by researchers at the [Stanford Open Policing Project](https://openpolicing.stanford.edu/).\n",
    "\n",
    "On a typical day in the United States, police officers make more than **50,000 traffic stops**. Stanford has collected over 200 million records from dozens of state and local police departments across the country in an effort to create a centralized repository detailing interactions between civilians and police. \n",
    "\n",
    "We're going to look at a subset of this data: **Maryland in 2013-2014, excluding the City of Baltimore.**\n",
    "\n",
    "Why just a subset? It keeps the size manageable, plus we avoid the sparse sections of the MD dataset. From Stanford:\n",
    "\n",
    "> The [Maryland] data is very messy. It comes from three different time periods: 2007, 2009-2012, 2013-2014. They all have different column and slightly different conventions of how things are recorded. We attempted to standardize the fields as much as possible.\n",
    "Time resolution of the data varies by year. Prior to 2013, data is reported annually. From 2013 onward, data is reported daily. So stop dates prior to 2013 are not precise to the nearest day and are just reported as Jan 1.\n",
    "Counties were mapped by running the police departments in the Agency field through Google's geocoder, but this does not work for state patrol stops, for which we have no county information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import packages\n",
    "See all the \"as ...\" contructs? They're just aliasing the package names.\n",
    "\n",
    "That way we can call methods like plt.plot() instead of matplotlib.pyplot.plot().\n",
    "\n",
    "The aliases used here are conventional in the Python data community."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "# Above line is not necessary in latest version of Notebook, but in older versions\n",
    "# ensures that plots are displayed inline, not in a separate window\n",
    "\n",
    "import numpy as np # imports a fast numerical programming library\n",
    "import matplotlib as mpl # this actually imports matplotlib\n",
    "import matplotlib.pyplot as plt # sets up plotting under plt\n",
    "import pandas as pd #lets us handle data as dataframes\n",
    "import seaborn as sns #sets up styles and gives us more plotting options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boilerplate that sets some display options. The defaults are fine too!\n",
    "pd.set_option('display.width', 100)\n",
    "pd.set_option('display.max_columns', 30)\n",
    "pd.set_option('display.max_rows', 25)\n",
    "pd.set_option('display.notebook_repr_html', True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy Version: 1.16.2, Pandas Version: 0.24.1\n"
     ]
    }
   ],
   "source": [
    "# This is an f-string. If it doesn't work, you're running a Python version below 3.6. Consider upgrading!\n",
    "print(f\"Numpy Version: {np.__version__}, Pandas Version: {pd.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Import data\n",
    "\n",
    "Why `low_memory = False`? Inferring datatypes is memory intensive and we're only specifying some of the dtypes during import. This suppresses a warning about memory use when we load data without specifying all dtypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import zipfile\n",
    "with zipfile.ZipFile(\"md_traffic.zip\",\"r\") as zip_ref:\n",
    "    zip_ref.extractall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "md = pd.read_csv('md_traffic.csv', low_memory = False, dtype={\n",
    "    'subject_age': 'float64',\n",
    "    'subject_race': 'category',\n",
    "    'subject_sex': 'category',\n",
    "    'arrest_made': 'bool',\n",
    "    'outcome': 'category',\n",
    "    'search_conducted': 'bool',\n",
    "    'reason_for_arrest': 'category',\n",
    "    })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Aside: Did you know you can measure execution time of small code snippets using the `%timeit` command?\n",
    "\n",
    "Notice that `timeit` executes the code several times and takes an average of the runs.\n",
    "Read more about it in the [official documentation](https://docs.python.org/3.7/library/timeit.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "256 ms ± 29.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "2.13 ms ± 181 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "%timeit array = [random.randint(0, 10) for i in range(100000)]\n",
    "%timeit values = np.random.randint(0, 10, 100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Aside #2: Did you know you can access a function's signature & docstring by adding a \"?\" after a command. E.g. `pd.read_csv?`\n",
    "It's the equivalent of `print(pd.read_csv.__doc__)` but `?` is a shortcut in the IPython environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'read_csv' is a Crime Against Nature masquerading as a function, but it gets the job done.\n",
    "pd.read_csv?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Basic Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(841853, 23)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at the shape of this data.\n",
    "md.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QUESTION: Can this CSV file be opened in Excel? What about Apple's Numbers App?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2312586, 2312587, 2312588, 2312589, 2312590, 2312591, 2312592, 2312593, 2312594,\n",
       "            2312595,\n",
       "            ...\n",
       "            3237323, 3237324, 3237325, 3237326, 3237327, 3237328, 3237329, 3237330, 3237331,\n",
       "            3237332],\n",
       "           dtype='int64', name='raw_row_number', length=841853)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the index. We see a RangeIndex because we did not specify a column to be an index when reading the csv.\n",
    "md.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'time', 'location', 'subject_age', 'subject_race', 'subject_sex', 'department_name',\n",
       "       'disposition', 'violation', 'arrest_made', 'citation_issued', 'warning_issued', 'outcome',\n",
       "       'contraband_found', 'contraband_drugs', 'contraband_weapons', 'search_conducted',\n",
       "       'search_person', 'search_vehicle', 'reason_for_arrest', 'reason_for_search',\n",
       "       'reason_for_stop'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the columns.\n",
    "md.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "raw_row_number           int64\n",
       "date                    object\n",
       "time                    object\n",
       "location                object\n",
       "subject_age            float64\n",
       "subject_race          category\n",
       "subject_sex           category\n",
       "department_name         object\n",
       "disposition             object\n",
       "violation               object\n",
       "arrest_made               bool\n",
       "citation_issued         object\n",
       "warning_issued          object\n",
       "outcome               category\n",
       "contraband_found        object\n",
       "contraband_drugs        object\n",
       "contraband_weapons      object\n",
       "search_conducted          bool\n",
       "search_person           object\n",
       "search_vehicle          object\n",
       "reason_for_arrest     category\n",
       "reason_for_search       object\n",
       "reason_for_stop         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 841853 entries, 0 to 841852\n",
      "Data columns (total 23 columns):\n",
      "raw_row_number        841853 non-null int64\n",
      "date                  841853 non-null object\n",
      "time                  841820 non-null object\n",
      "location              841849 non-null object\n",
      "subject_age           839810 non-null float64\n",
      "subject_race          841788 non-null category\n",
      "subject_sex           841574 non-null category\n",
      "department_name       841853 non-null object\n",
      "disposition           27757 non-null object\n",
      "violation             2759 non-null object\n",
      "arrest_made           841853 non-null bool\n",
      "citation_issued       841778 non-null object\n",
      "warning_issued        841778 non-null object\n",
      "outcome               745774 non-null category\n",
      "contraband_found      28455 non-null object\n",
      "contraband_drugs      28455 non-null object\n",
      "contraband_weapons    28455 non-null object\n",
      "search_conducted      841853 non-null bool\n",
      "search_person         28403 non-null object\n",
      "search_vehicle        28403 non-null object\n",
      "reason_for_arrest     11663 non-null category\n",
      "reason_for_search     28403 non-null object\n",
      "reason_for_stop       839777 non-null object\n",
      "dtypes: bool(2), category(4), float64(1), int64(1), object(15)\n",
      "memory usage: 114.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# I prefer .info() to .dtypes as the former also tells us how many null points we're dealing with\n",
    "md.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>warning_issued</th>\n",
       "      <th>outcome</th>\n",
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       "      <th>contraband_drugs</th>\n",
       "      <th>contraband_weapons</th>\n",
       "      <th>search_conducted</th>\n",
       "      <th>search_person</th>\n",
       "      <th>search_vehicle</th>\n",
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       "      <th>reason_for_search</th>\n",
       "      <th>reason_for_stop</th>\n",
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       "      <th>0</th>\n",
       "      <td>2312586</td>\n",
       "      <td>1/2/13</td>\n",
       "      <td>19:12:00</td>\n",
       "      <td>SRT40@W BEL AIR AVE</td>\n",
       "      <td>61.778218</td>\n",
       "      <td>white</td>\n",
       "      <td>male</td>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>citation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21-202(h1)</td>\n",
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       "      <td>2312587</td>\n",
       "      <td>1/2/13</td>\n",
       "      <td>6:15:00</td>\n",
       "      <td>SRT40@JAMES ST</td>\n",
       "      <td>37.974822</td>\n",
       "      <td>white</td>\n",
       "      <td>female</td>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22-203(b)</td>\n",
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       "      <th>2</th>\n",
       "      <td>2312588</td>\n",
       "      <td>1/2/13</td>\n",
       "      <td>21:20:00</td>\n",
       "      <td>1-95 MILE MARKER 80.5</td>\n",
       "      <td>53.112731</td>\n",
       "      <td>white</td>\n",
       "      <td>female</td>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>warning</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21-301(a)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2312589</td>\n",
       "      <td>1/4/13</td>\n",
       "      <td>18:20:00</td>\n",
       "      <td>SRT 40@FRANKLIN ST</td>\n",
       "      <td>49.829976</td>\n",
       "      <td>white</td>\n",
       "      <td>male</td>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>citation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16-303(c)</td>\n",
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       "   raw_row_number    date      time               location  subject_age subject_race subject_sex  \\\n",
       "0         2312586  1/2/13  19:12:00    SRT40@W BEL AIR AVE    61.778218        white        male   \n",
       "1         2312587  1/2/13   6:15:00         SRT40@JAMES ST    37.974822        white      female   \n",
       "2         2312588  1/2/13  21:20:00  1-95 MILE MARKER 80.5    53.112731        white      female   \n",
       "3         2312589  1/4/13  18:20:00     SRT 40@FRANKLIN ST    49.829976        white        male   \n",
       "\n",
       "              department_name disposition violation  arrest_made citation_issued warning_issued  \\\n",
       "0  Aberdeen Police Department         NaN       NaN        False            True          False   \n",
       "1  Aberdeen Police Department         NaN       NaN        False           False          False   \n",
       "2  Aberdeen Police Department         NaN       NaN        False           False           True   \n",
       "3  Aberdeen Police Department         NaN       NaN        False            True          False   \n",
       "\n",
       "    outcome contraband_found contraband_drugs contraband_weapons  search_conducted search_person  \\\n",
       "0  citation              NaN              NaN                NaN             False           NaN   \n",
       "1       NaN              NaN              NaN                NaN             False           NaN   \n",
       "2   warning              NaN              NaN                NaN             False           NaN   \n",
       "3  citation              NaN              NaN                NaN             False           NaN   \n",
       "\n",
       "  search_vehicle reason_for_arrest reason_for_search reason_for_stop  \n",
       "0            NaN               NaN               NaN      21-202(h1)  \n",
       "1            NaN               NaN               NaN       22-203(b)  \n",
       "2            NaN               NaN               NaN       21-301(a)  \n",
       "3            NaN               NaN               NaN       16-303(c)  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Peak at the data\n",
    "md.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What can we discern just from this look at the data? What about all the NaNs?\n",
    "\n",
    "We may want to set `raw_row_number` as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Verify_integrity is set to false by default. Setting to True ensures a unique index.\n",
    "md.set_index(\"raw_row_number\", verify_integrity=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>raw_row_number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2312586</th>\n",
       "      <td>1/2/13</td>\n",
       "      <td>19:12:00</td>\n",
       "      <td>SRT40@W BEL AIR AVE</td>\n",
       "      <td>61.778218</td>\n",
       "      <td>white</td>\n",
       "      <td>male</td>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>citation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21-202(h1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312587</th>\n",
       "      <td>1/2/13</td>\n",
       "      <td>6:15:00</td>\n",
       "      <td>SRT40@JAMES ST</td>\n",
       "      <td>37.974822</td>\n",
       "      <td>white</td>\n",
       "      <td>female</td>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22-203(b)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312588</th>\n",
       "      <td>1/2/13</td>\n",
       "      <td>21:20:00</td>\n",
       "      <td>1-95 MILE MARKER 80.5</td>\n",
       "      <td>53.112731</td>\n",
       "      <td>white</td>\n",
       "      <td>female</td>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>warning</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21-301(a)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312589</th>\n",
       "      <td>1/4/13</td>\n",
       "      <td>18:20:00</td>\n",
       "      <td>SRT 40@FRANKLIN ST</td>\n",
       "      <td>49.829976</td>\n",
       "      <td>white</td>\n",
       "      <td>male</td>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>citation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16-303(c)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312590</th>\n",
       "      <td>1/4/13</td>\n",
       "      <td>4:20:00</td>\n",
       "      <td>SRT40@MARKET ST</td>\n",
       "      <td>58.361306</td>\n",
       "      <td>white</td>\n",
       "      <td>male</td>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22-204(f)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date      time               location  subject_age subject_race subject_sex  \\\n",
       "raw_row_number                                                                                  \n",
       "2312586         1/2/13  19:12:00    SRT40@W BEL AIR AVE    61.778218        white        male   \n",
       "2312587         1/2/13   6:15:00         SRT40@JAMES ST    37.974822        white      female   \n",
       "2312588         1/2/13  21:20:00  1-95 MILE MARKER 80.5    53.112731        white      female   \n",
       "2312589         1/4/13  18:20:00     SRT 40@FRANKLIN ST    49.829976        white        male   \n",
       "2312590         1/4/13   4:20:00        SRT40@MARKET ST    58.361306        white        male   \n",
       "\n",
       "                           department_name disposition violation  arrest_made citation_issued  \\\n",
       "raw_row_number                                                                                  \n",
       "2312586         Aberdeen Police Department         NaN       NaN        False            True   \n",
       "2312587         Aberdeen Police Department         NaN       NaN        False           False   \n",
       "2312588         Aberdeen Police Department         NaN       NaN        False           False   \n",
       "2312589         Aberdeen Police Department         NaN       NaN        False            True   \n",
       "2312590         Aberdeen Police Department         NaN       NaN        False           False   \n",
       "\n",
       "               warning_issued   outcome contraband_found contraband_drugs contraband_weapons  \\\n",
       "raw_row_number                                                                                 \n",
       "2312586                 False  citation              NaN              NaN                NaN   \n",
       "2312587                 False       NaN              NaN              NaN                NaN   \n",
       "2312588                  True   warning              NaN              NaN                NaN   \n",
       "2312589                 False  citation              NaN              NaN                NaN   \n",
       "2312590                 False       NaN              NaN              NaN                NaN   \n",
       "\n",
       "                search_conducted search_person search_vehicle reason_for_arrest reason_for_search  \\\n",
       "raw_row_number                                                                                      \n",
       "2312586                    False           NaN            NaN               NaN               NaN   \n",
       "2312587                    False           NaN            NaN               NaN               NaN   \n",
       "2312588                    False           NaN            NaN               NaN               NaN   \n",
       "2312589                    False           NaN            NaN               NaN               NaN   \n",
       "2312590                    False           NaN            NaN               NaN               NaN   \n",
       "\n",
       "               reason_for_stop  \n",
       "raw_row_number                  \n",
       "2312586             21-202(h1)  \n",
       "2312587              22-203(b)  \n",
       "2312588              21-301(a)  \n",
       "2312589              16-303(c)  \n",
       "2312590              22-204(f)  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md.head()\n",
    "# What did the first driver get cited for?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access individual columns through dot-attribute notation (`md.subject_sex`) or through\n",
    "getitem dictionary-style notation (`md['subject_sex']`).\n",
    "\n",
    "QUESTION: Why might the latter be preferable even as the former is somewhat more natural to write?\n",
    "\n",
    "The return value is a Pandas Series; basically a one-column DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "raw_row_number\n",
       "2312586      male\n",
       "2312587    female\n",
       "2312588    female\n",
       "2312589      male\n",
       "2312590      male\n",
       "Name: subject_sex, dtype: category\n",
       "Categories (2, object): [female, male]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Attribute notation\n",
    "md.subject_sex.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "raw_row_number\n",
       "2312586    citation\n",
       "2312587         NaN\n",
       "2312588     warning\n",
       "2312589    citation\n",
       "2312590         NaN\n",
       "Name: outcome, dtype: category\n",
       "Categories (3, object): [arrest, citation, warning]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getitem syntax (uses __.getitem__)\n",
    "md['outcome'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department_name</th>\n",
       "      <th>citation_issued</th>\n",
       "      <th>reason_for_stop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raw_row_number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2312586</th>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>True</td>\n",
       "      <td>21-202(h1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312587</th>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>False</td>\n",
       "      <td>22-203(b)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312588</th>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>False</td>\n",
       "      <td>21-301(a)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312589</th>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>True</td>\n",
       "      <td>16-303(c)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312590</th>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>False</td>\n",
       "      <td>22-204(f)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           department_name citation_issued reason_for_stop\n",
       "raw_row_number                                                            \n",
       "2312586         Aberdeen Police Department            True      21-202(h1)\n",
       "2312587         Aberdeen Police Department           False       22-203(b)\n",
       "2312588         Aberdeen Police Department           False       21-301(a)\n",
       "2312589         Aberdeen Police Department            True       16-303(c)\n",
       "2312590         Aberdeen Police Department           False       22-204(f)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access multiple columns with a comma\n",
    "md[['department_name', 'citation_issued', 'reason_for_stop']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can index the rows by using the **loc** and **iloc** accessors.\n",
    "\n",
    "`loc` does *label-based* indexing.\n",
    "\n",
    "`iloc` performs *integer-based* indexing.\n",
    "\n",
    "You can use a comma separated list to access multiple fields at the same time.\n",
    "\n",
    "Both support the standard Python slicing operations `[start:end:step]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
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       "      <th>subject_age</th>\n",
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       "      <th>search_vehicle</th>\n",
       "      <th>reason_for_arrest</th>\n",
       "      <th>reason_for_search</th>\n",
       "      <th>reason_for_stop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raw_row_number</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3237323</th>\n",
       "      <td>3/31/14</td>\n",
       "      <td>12:01:00</td>\n",
       "      <td>BALTIMORE ST / NORTH POINT BLVD</td>\n",
       "      <td>29.703594</td>\n",
       "      <td>black</td>\n",
       "      <td>male</td>\n",
       "      <td>Baltimore County Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>warning</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13-401(h)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312594</th>\n",
       "      <td>1/5/13</td>\n",
       "      <td>17:53:00</td>\n",
       "      <td>E BEL AIR AVE@POST RD</td>\n",
       "      <td>31.381933</td>\n",
       "      <td>white</td>\n",
       "      <td>female</td>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>citation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21-707(a)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   date      time                         location  subject_age subject_race  \\\n",
       "raw_row_number                                                                                 \n",
       "3237323         3/31/14  12:01:00  BALTIMORE ST / NORTH POINT BLVD    29.703594        black   \n",
       "2312594          1/5/13  17:53:00            E BEL AIR AVE@POST RD    31.381933        white   \n",
       "\n",
       "               subject_sex                     department_name disposition violation  arrest_made  \\\n",
       "raw_row_number                                                                                      \n",
       "3237323               male  Baltimore County Police Department         NaN       NaN        False   \n",
       "2312594             female          Aberdeen Police Department         NaN       NaN        False   \n",
       "\n",
       "               citation_issued warning_issued   outcome contraband_found contraband_drugs  \\\n",
       "raw_row_number                                                                              \n",
       "3237323                  False           True   warning              NaN              NaN   \n",
       "2312594                   True          False  citation              NaN              NaN   \n",
       "\n",
       "               contraband_weapons  search_conducted search_person search_vehicle  \\\n",
       "raw_row_number                                                                     \n",
       "3237323                       NaN             False           NaN            NaN   \n",
       "2312594                       NaN             False           NaN            NaN   \n",
       "\n",
       "               reason_for_arrest reason_for_search reason_for_stop  \n",
       "raw_row_number                                                      \n",
       "3237323                      NaN               NaN       13-401(h)  \n",
       "2312594                      NaN               NaN       21-707(a)  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the rows for index IDs 3237323 and 2312594\n",
    "md.loc[[3237323,2312594]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2316687</th>\n",
       "      <td>8/21/13</td>\n",
       "      <td>14:44:00</td>\n",
       "      <td>SPA RD @ SILOPANNA</td>\n",
       "      <td>49.085264</td>\n",
       "      <td>white</td>\n",
       "      <td>female</td>\n",
       "      <td>Annapolis Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>citation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21-801.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2316697</th>\n",
       "      <td>8/21/13</td>\n",
       "      <td>15:25:00</td>\n",
       "      <td>WEST @ S SOUTHWOOD</td>\n",
       "      <td>49.301559</td>\n",
       "      <td>white</td>\n",
       "      <td>male</td>\n",
       "      <td>Annapolis Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>citation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22-412.3(b)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2316707</th>\n",
       "      <td>8/21/13</td>\n",
       "      <td>16:29:00</td>\n",
       "      <td>BYWATER RD @ FOREST DR</td>\n",
       "      <td>24.047070</td>\n",
       "      <td>asian/pacific islander</td>\n",
       "      <td>male</td>\n",
       "      <td>Annapolis Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>citation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13-703(g)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2316717</th>\n",
       "      <td>8/22/13</td>\n",
       "      <td>1:23:00</td>\n",
       "      <td>YAWL RD @ CUTTER COURT</td>\n",
       "      <td>21.541882</td>\n",
       "      <td>black</td>\n",
       "      <td>male</td>\n",
       "      <td>Annapolis Police Department</td>\n",
       "      <td>contra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>arrest</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>stop</td>\n",
       "      <td>prob</td>\n",
       "      <td>16-303(c)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2316727</th>\n",
       "      <td>8/22/13</td>\n",
       "      <td>12:46:00</td>\n",
       "      <td>FOREST DRIVE AND PAROLE</td>\n",
       "      <td>51.316661</td>\n",
       "      <td>white</td>\n",
       "      <td>female</td>\n",
       "      <td>Annapolis Police Department</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>citation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21-502(a2)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   date      time                 location  subject_age            subject_race  \\\n",
       "raw_row_number                                                                                    \n",
       "2316687         8/21/13  14:44:00       SPA RD @ SILOPANNA    49.085264                   white   \n",
       "2316697         8/21/13  15:25:00       WEST @ S SOUTHWOOD    49.301559                   white   \n",
       "2316707         8/21/13  16:29:00   BYWATER RD @ FOREST DR    24.047070  asian/pacific islander   \n",
       "2316717         8/22/13   1:23:00   YAWL RD @ CUTTER COURT    21.541882                   black   \n",
       "2316727         8/22/13  12:46:00  FOREST DRIVE AND PAROLE    51.316661                   white   \n",
       "\n",
       "               subject_sex              department_name disposition violation  arrest_made  \\\n",
       "raw_row_number                                                                               \n",
       "2316687             female  Annapolis Police Department         NaN       NaN        False   \n",
       "2316697               male  Annapolis Police Department         NaN       NaN        False   \n",
       "2316707               male  Annapolis Police Department         NaN       NaN        False   \n",
       "2316717               male  Annapolis Police Department      contra       NaN         True   \n",
       "2316727             female  Annapolis Police Department         NaN       NaN        False   \n",
       "\n",
       "               citation_issued warning_issued   outcome contraband_found contraband_drugs  \\\n",
       "raw_row_number                                                                              \n",
       "2316687                   True          False  citation              NaN              NaN   \n",
       "2316697                   True          False  citation              NaN              NaN   \n",
       "2316707                   True          False  citation              NaN              NaN   \n",
       "2316717                  False          False    arrest            False            False   \n",
       "2316727                   True          False  citation              NaN              NaN   \n",
       "\n",
       "               contraband_weapons  search_conducted search_person search_vehicle  \\\n",
       "raw_row_number                                                                     \n",
       "2316687                       NaN             False           NaN            NaN   \n",
       "2316697                       NaN             False           NaN            NaN   \n",
       "2316707                       NaN             False           NaN            NaN   \n",
       "2316717                     False              True          True           True   \n",
       "2316727                       NaN             False           NaN            NaN   \n",
       "\n",
       "               reason_for_arrest reason_for_search reason_for_stop  \n",
       "raw_row_number                                                      \n",
       "2316687                      NaN               NaN        21-801.1  \n",
       "2316697                      NaN               NaN     22-412.3(b)  \n",
       "2316707                      NaN               NaN       13-703(g)  \n",
       "2316717                     stop              prob       16-303(c)  \n",
       "2316727                      NaN               NaN      21-502(a2)  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get every tenth row starting from 4100, until 4150.\n",
    "md.iloc[4100:4150:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>date</th>\n",
       "      <th>time</th>\n",
       "      <th>location</th>\n",
       "      <th>subject_age</th>\n",
       "      <th>subject_race</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raw_row_number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2312586</th>\n",
       "      <td>1/2/13</td>\n",
       "      <td>19:12:00</td>\n",
       "      <td>SRT40@W BEL AIR AVE</td>\n",
       "      <td>61.778218</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312587</th>\n",
       "      <td>1/2/13</td>\n",
       "      <td>6:15:00</td>\n",
       "      <td>SRT40@JAMES ST</td>\n",
       "      <td>37.974822</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312588</th>\n",
       "      <td>1/2/13</td>\n",
       "      <td>21:20:00</td>\n",
       "      <td>1-95 MILE MARKER 80.5</td>\n",
       "      <td>53.112731</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312589</th>\n",
       "      <td>1/4/13</td>\n",
       "      <td>18:20:00</td>\n",
       "      <td>SRT 40@FRANKLIN ST</td>\n",
       "      <td>49.829976</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312590</th>\n",
       "      <td>1/4/13</td>\n",
       "      <td>4:20:00</td>\n",
       "      <td>SRT40@MARKET ST</td>\n",
       "      <td>58.361306</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date      time               location  subject_age subject_race\n",
       "raw_row_number                                                                   \n",
       "2312586         1/2/13  19:12:00    SRT40@W BEL AIR AVE    61.778218        white\n",
       "2312587         1/2/13   6:15:00         SRT40@JAMES ST    37.974822        white\n",
       "2312588         1/2/13  21:20:00  1-95 MILE MARKER 80.5    53.112731        white\n",
       "2312589         1/4/13  18:20:00     SRT 40@FRANKLIN ST    49.829976        white\n",
       "2312590         1/4/13   4:20:00        SRT40@MARKET ST    58.361306        white"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the first 5 rows and the first 5 columns\n",
    "md.iloc[:5, :5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>subject_sex</th>\n",
       "      <th>warning_issued</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>raw_row_number</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2312586</th>\n",
       "      <td>male</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312587</th>\n",
       "      <td>female</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2312588</th>\n",
       "      <td>female</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               subject_sex warning_issued\n",
       "raw_row_number                           \n",
       "2312586               male          False\n",
       "2312587             female          False\n",
       "2312588             female           True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the all the rows up to index 2312588 and the columns named 'subject_sex' and 'warning_issued'\n",
    "md.loc[:2312588, ['subject_sex', 'warning_issued']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Dealing with Data Types and NaN Cleaning \n",
    "![Office Space Meme](https://media.makeameme.org/created/i-was-told-592f12.jpg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might be worried about the large number of NaNs in the dataset. Another way to quantify our null fields is by summing the result of applying `isnull()` to the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                       0\n",
       "time                      33\n",
       "location                   4\n",
       "subject_age             2043\n",
       "subject_race              65\n",
       "subject_sex              279\n",
       "department_name            0\n",
       "disposition           814096\n",
       "violation             839094\n",
       "arrest_made                0\n",
       "citation_issued           75\n",
       "warning_issued            75\n",
       "outcome                96079\n",
       "contraband_found      813398\n",
       "contraband_drugs      813398\n",
       "contraband_weapons    813398\n",
       "search_conducted           0\n",
       "search_person         813450\n",
       "search_vehicle        813450\n",
       "reason_for_arrest     830190\n",
       "reason_for_search     813450\n",
       "reason_for_stop         2076\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Why does this work? Think about what True and False are equivalent to.\n",
    "md.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But are the null fields really a problem? It depends. If `reason_for_search` is ALWAYS null, we have a problem, but if it's only null when `search_conducted` is False, we're in business."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "raw_row_number\n",
       "2312586    False\n",
       "2312587    False\n",
       "2312588    False\n",
       "2312589    False\n",
       "2312590    False\n",
       "2312591    False\n",
       "2312592    False\n",
       "2312593    False\n",
       "2312594    False\n",
       "2312595    False\n",
       "2312596    False\n",
       "2312597    False\n",
       "           ...  \n",
       "3237321    False\n",
       "3237322    False\n",
       "3237323    False\n",
       "3237324    False\n",
       "3237325    False\n",
       "3237326    False\n",
       "3237327    False\n",
       "3237328    False\n",
       "3237329    False\n",
       "3237330    False\n",
       "3237331    False\n",
       "3237332    False\n",
       "Name: search_conducted, Length: 841853, dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md.search_conducted == True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                      0\n",
       "time                      0\n",
       "location                  1\n",
       "subject_age             103\n",
       "subject_race              1\n",
       "subject_sex               2\n",
       "department_name           0\n",
       "disposition             702\n",
       "violation             25795\n",
       "arrest_made               0\n",
       "citation_issued           2\n",
       "warning_issued            2\n",
       "outcome                 824\n",
       "contraband_found          4\n",
       "contraband_drugs          4\n",
       "contraband_weapons        4\n",
       "search_conducted          0\n",
       "search_person            56\n",
       "search_vehicle           56\n",
       "reason_for_arrest     17702\n",
       "reason_for_search        56\n",
       "reason_for_stop          31\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md[md.search_conducted == True].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like `reason_for_search` is null only 56 times when a search has been conducted. An enterprising journalist might want to look at these 56 cases to see if there's anything fishy or if it's just a bookkeeping error (likely!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we hadn't done data conversions at import, we could do so now:\n",
    "\n",
    "Convert column \"subject_age\" to float64 dtype and \"subject_race\" to categorical type\n",
    "`md = md.astype({\"subject_age\": 'float', \"subject_race\": 'category'})`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "raw_row_number\n",
       "2312586      srt40@w bel air ave\n",
       "2312587           srt40@james st\n",
       "2312588    1-95 mile marker 80.5\n",
       "2312589       srt 40@franklin st\n",
       "2312590          srt40@market st\n",
       "Name: location, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#may want to use string methods to normalize string objects\n",
    "md.location.str.lower().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Exploring the Data More Deeply\n",
    "\n",
    "Here, the memes stop and we have to be more sober.\n",
    "\n",
    "Let's look at the kinds of values we find in various columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      540049\n",
       "female    301525\n",
       "Name: subject_sex, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also normalize this\n",
    "md.subject_sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.222497698481334"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can apply functions to columns\n",
    "md.subject_age.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### QUESTION: Who is the youngest person in this data set? What were they cited for? Does the citation pass the smell test?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Your Code Here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load solution/youngest.py\n",
    "print(md['subject_age'].min())\n",
    "#It's a 10-year-old boy, cited for driving without a license.\n",
    "md[(md['subject_age']) == md['subject_age'].min()]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are dozens of ways to plot age data. Let's look at two. For more on what KDE means, see [Kernel Density Estimation](https://en.wikipedia.org/wiki/Kernel_density_estimation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn is very pretty!\n",
    "sns.distplot(md.subject_age[md.subject_age.isnull()==False], kde= True, bins = 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MG0lSd4aNJKk7w0aS1J1hI0nqzrCRJHVn2EiSujNsJEndGTaSpO4MG0lSd4aNJKk7w0aS1N0B4+7AJFiw4vpxd0GS9mpe2UiSujNsJEndGTaSpO7GEjZJNia5I8l3kqxvtcOTrEtyb/s+rNWT5JIkG5LcnuSEoe0sa+3vTbJsqH5i2/6Gtm72/FFKkqaM88rm96vq+Kpa1OZXADdU1ULghjYPcCqwsH2WA5fCIJyAC4DXAicBF0wFVGtzztB6S/ofjiRpJnvTbbSlwKo2vQo4fah+ZQ3cBBya5CjgzcC6qtpaVY8C64AlbdlLquqmqirgyqFtSZLGYFxDnwv4P0kK+GxVXQYcWVUPtOUPAke26XnA/UPrbmq12eqbpqlL+6RxDq3feNFpY9u3Jsu4wub1VbU5ya8D65J8d3hhVVULoq6SLGdwa45jjjmm9+4kab81lttoVbW5fT8MfIXBM5eH2i0w2vfDrflm4Oih1ee32mz1+dPUp+vHZVW1qKoWzZ07d1cPS5I0gz0eNklelOTXpqaBU4A7gdXA1IiyZcB1bXo1cGYblbYYeKzdblsLnJLksDYw4BRgbVu2LcniNgrtzKFtSZLGYBy30Y4EvtJGIx8A/HVV/V2SW4Crk5wN/AB4R2u/BngLsAF4AjgLoKq2JvkocEtr95Gq2tqm3wNcARwEfK19JEljssfDpqruA141Tf0R4ORp6gWcO8O2VgIrp6mvB16xy52VJO0We9PQZ0nShDJsJEndGTaSpO4MG0lSd4aNJKk7w0aS1J1hI0nqzrCRJHVn2EiSujNsJEndGTaSpO4MG0lSd4aNJKk7w0aS1J1hI0nqzrCRJHVn2EiSujNsJEndGTaSpO4MG0lSd4aNJKk7w0aS1N0B4+6ApL3XghXXj2W/Gy86bSz7VT9e2UiSujNsJEndGTaSpO4MG0lSd4aNJKk7w0aS1J1hI0nqzrCRJHU3sWGTZEmS7yXZkGTFuPsjSfuziXyDQJI5wKeBNwGbgFuSrK6qu8fbM0mjGNebC8C3F/QyqVc2JwEbquq+qnoSuApYOuY+SdJ+a1LDZh5w/9D8plaTJI3BRN5GG1WS5cDyNvuTJN8bZ3920hHAP467E3vY/njM4HHvUfnYnt7jr9jXzvdvjNJoUsNmM3D00Pz8VnuWqroMuGxPdWp3SrK+qhaNux970v54zOBxj7sfe9qkHvek3ka7BViY5NgkBwJnAKvH3CdJ2m9N5JVNVT2V5DxgLTAHWFlVd425W5K035rIsAGoqjXAmnH3o6N98vbfLtofjxk87v3NRB53qmrcfZAkTbhJfWYjSdqLGDZ7uSRHJ7kxyd1J7kryvlY/PMm6JPe278PG3dceksxJcluS/93mj01yc3sN0d+0ASATJcmhSa5J8t0k9yT5l5N+vpP8p/bf951JvpTkhZN4rpOsTPJwkjuHatOe2wxc0o7/9iQnjK/nu86w2fs9BXygqo4DFgPnJjkOWAHcUFULgRva/CR6H3DP0PzHgIur6uXAo8DZY+lVX58C/q6qfht4FYPjn9jznWQe8F5gUVW9gsGgnjOYzHN9BbBku9pM5/ZUYGH7LAcu3UN97MKw2ctV1QNV9e02/TiDf3jmMXj9zqrWbBVw+nh62E+S+cBpwOfbfIA3Ate0JhN33EkOAf4VcDlAVT1ZVT9m8s/3AcBBSQ4ADgYeYALPdVV9E9i6XXmmc7sUuLIGbgIOTXLUnunp7mfY7EOSLABeDdwMHFlVD7RFDwJHjqlbPf0F8F+BX7T5lwI/rqqn2vwkvoboWGAL8Fft9uHnk7yICT7fVbUZ+DjwQwYh8xhwK5N/rqfMdG4n6rVbhs0+IsmLgS8D76+qbcPLajCkcKKGFSZ5K/BwVd067r7sYQcAJwCXVtWrgZ+y3S2zSTvf7RnFUgZB+8+BF/Grt5r2C5N2bocZNvuAJM9nEDRfrKprW/mhqUvq9v3wuPrXyeuAP0yykcFbu9/I4FnGoe1WC8zwGqJ93CZgU1Xd3OavYRA+k3y+/w3w/araUlX/D7iWwfmf9HM9ZaZzO9Jrt/YVhs1erj2nuBy4p6o+ObRoNbCsTS8DrtvTfeupqj5UVfOragGDh8XfqKo/Bm4E3t6aTeJxPwjcn+S3Wulk4G4m+3z/EFic5OD23/vUMU/0uR4y07ldDZzZRqUtBh4but22z/GPOvdySV4P/F/gDp55dnE+g+c2VwPHAD8A3lFV2z94nAhJ3gD8l6p6a5KXMbjSORy4DfiTqvr5OPu3uyU5nsGgiAOB+4CzGPyP4cSe7yT/HXgng9GXtwH/nsHziYk610m+BLyBwZudHwIuAL7KNOe2Be//YHBL8QngrKpaP45+7w6GjSSpO2+jSZK6M2wkSd0ZNpKk7gwbSVJ3ho0kqTvDRpLUnWEjSerOsJEkdff/AXDbnZIDoqUxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pandas (which uses Matplotlib under the hood) is less pretty by default, but very convenient\n",
    "md.subject_age.plot(kind='hist');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RendererRegistry.enable('notebook')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import altair as alt\n",
    "alt.renderers.enable('notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building Blocks of Visualization:\n",
    "1. Data\n",
    "2. Transformation\n",
    "3. Marks\n",
    "4. Encoding – mapping from\n",
    "fields to mark properties\n",
    "5. Scale – functions that map data\n",
    "to visual scales\n",
    "6. Guides – visualization of scales\n",
    "(axes, legends, etc.)\n",
    "\n",
    "#### Imperative\n",
    "- Specify How something\n",
    "should be done.\n",
    "- Specification &\n",
    "Execution intertwined.\n",
    "- “Put a red circle here\n",
    "and a blue circle here”\n",
    "\n",
    "####  Declarative\n",
    "- Specify What should be\n",
    "done.\n",
    "- Separates Specification\n",
    "from Execution\n",
    "- “Map <x> to a position,\n",
    "and <y> to a color”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "ename": "MaxRowsError",
     "evalue": "The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMaxRowsError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/altair/vegalite/v3/api.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    357\u001b[0m         \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m         \u001b[0moriginal_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 359\u001b[0;31m         \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_prepare_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moriginal_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/altair/vegalite/v3/api.py\u001b[0m in \u001b[0;36m_prepare_data\u001b[0;34m(data, context)\u001b[0m\n\u001b[1;32m     82\u001b[0m     \u001b[0;31m# convert dataframes to dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_transformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0;31m# convert string input to a URLData\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(data, *funcs)\u001b[0m\n\u001b[1;32m    550\u001b[0m     \"\"\"\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    284\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_curry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/altair/vegalite/data.py\u001b[0m in \u001b[0;36mdefault_data_transformer\u001b[0;34m(data, max_rows)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mcurry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdefault_data_transformer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit_rows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(data, *funcs)\u001b[0m\n\u001b[1;32m    550\u001b[0m     \"\"\"\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    281\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 283\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    284\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_curry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/altair/utils/data.py\u001b[0m in \u001b[0;36mlimit_rows\u001b[0;34m(data, max_rows)\u001b[0m\n\u001b[1;32m     70\u001b[0m                            \u001b[0;34m'than the maximum allowed ({}). '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m                            \u001b[0;34m'For information on how to plot larger datasets '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m                            'in Altair, see the documentation'.format(max_rows))\n\u001b[0m\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMaxRowsError\u001b[0m: The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Chart({\n",
       "  data:                    date      time                             location  subject_age  \\\n",
       "  raw_row_number                                                                        \n",
       "  2312586          1/2/13  19:12:00                  SRT40@W BEL AIR AVE    61.778218   \n",
       "  2312587          1/2/13   6:15:00                       SRT40@JAMES ST    37.974822   \n",
       "  2312588          1/2/13  21:20:00                1-95 MILE MARKER 80.5    53.112731   \n",
       "  2312589          1/4/13  18:20:00                   SRT 40@FRANKLIN ST    49.829976   \n",
       "  2312590          1/4/13   4:20:00                      SRT40@MARKET ST    58.361306   \n",
       "  2312591          1/4/13  18:20:00                   SRT 40@FRANKLIN ST    49.829976   \n",
       "  2312592          1/4/13   4:20:00                      SRT40@MARKET ST    58.361306   \n",
       "  2312593          1/4/13  18:15:00                    SRT40@FRANKLIN ST    53.728761   \n",
       "  2312594          1/5/13  17:53:00                E BEL AIR AVE@POST RD    31.381933   \n",
       "  2312595          1/5/13  18:52:00                        SRT22@POST RD    41.178178   \n",
       "  2312596          1/5/13   0:13:00            SRT40@OLD PHILADELPHIA RD    25.210682   \n",
       "  2312597          1/6/13  16:55:00              W BEAL AIR AVE@PARKE ST    34.248526   \n",
       "  ...                 ...       ...                                  ...          ...   \n",
       "  3237321         3/31/14  11:51:00           PULASKI HWY AND TODDS LANE    43.094715   \n",
       "  3237322         3/31/14  11:55:00                       322 @ MARLBORO    63.486675   \n",
       "  3237323         3/31/14  12:01:00      BALTIMORE ST / NORTH POINT BLVD    29.703594   \n",
       "  3237324         3/31/14  12:01:00    NORTH POINT BLVD / E BALTIMORE ST    50.377558   \n",
       "  3237325         3/31/14  12:02:00              TRAPPE RD / WALLFORD DR    30.155349   \n",
       "  3237326         3/31/14  12:05:00     LANSDOWNE RD / HAMMONDS FERRY RD    24.307172   \n",
       "  3237327         3/31/14  12:29:00  ROSSVILLE BLVD @ FRANKLIN SQUARE DR    52.012091   \n",
       "  3237328         3/31/14  12:39:00                WASHINGTON BLVD & 695    24.556322   \n",
       "  3237329         3/31/14  12:40:00                 YORK RD & WILLOW AVE    87.572075   \n",
       "  3237330         3/31/14  12:43:00           WISTERIA DR @ TEAKWOOD CIR    33.443580   \n",
       "  3237331         3/31/14  12:49:00                11809 REISTERSTOWN RD    52.504915   \n",
       "  3237332         3/31/14  12:56:00           N/B 301 N/O TRUMPS HILL RD    71.963246   \n",
       "  \n",
       "                   subject_race subject_sex                     department_name disposition  \\\n",
       "  raw_row_number                                                                              \n",
       "  2312586                 white        male          Aberdeen Police Department         NaN   \n",
       "  2312587                 white      female          Aberdeen Police Department         NaN   \n",
       "  2312588                 white      female          Aberdeen Police Department         NaN   \n",
       "  2312589                 white        male          Aberdeen Police Department         NaN   \n",
       "  2312590                 white        male          Aberdeen Police Department         NaN   \n",
       "  2312591                 white        male          Aberdeen Police Department         NaN   \n",
       "  2312592                 white        male          Aberdeen Police Department         NaN   \n",
       "  2312593                 white        male          Aberdeen Police Department         NaN   \n",
       "  2312594                 white      female          Aberdeen Police Department         NaN   \n",
       "  2312595                 white      female          Aberdeen Police Department         NaN   \n",
       "  2312596                 black        male          Aberdeen Police Department         NaN   \n",
       "  2312597         other/unknown        male          Aberdeen Police Department         NaN   \n",
       "  ...                       ...         ...                                 ...         ...   \n",
       "  3237321                 black        male  Baltimore County Police Department         NaN   \n",
       "  3237322                 white      female                              TALBOT         NaN   \n",
       "  3237323                 black        male  Baltimore County Police Department         NaN   \n",
       "  3237324                 white        male  Baltimore County Police Department         NaN   \n",
       "  3237325                 white      female  Baltimore County Police Department         NaN   \n",
       "  3237326                 white        male  Baltimore County Police Department         NaN   \n",
       "  3237327              hispanic      female  Baltimore County Police Department         NaN   \n",
       "  3237328                 black        male  Baltimore County Police Department         NaN   \n",
       "  3237329                 white      female  Baltimore County Police Department         NaN   \n",
       "  3237330                 black        male                          MONTGOMERY         NaN   \n",
       "  3237331                 white        male  Baltimore County Police Department         NaN   \n",
       "  3237332                 black        male                                 MSP         NaN   \n",
       "  \n",
       "                 violation  arrest_made citation_issued warning_issued   outcome contraband_found  \\\n",
       "  raw_row_number                                                                                    \n",
       "  2312586              NaN        False            True          False  citation              NaN   \n",
       "  2312587              NaN        False           False          False       NaN              NaN   \n",
       "  2312588              NaN        False           False           True   warning              NaN   \n",
       "  2312589              NaN        False            True          False  citation              NaN   \n",
       "  2312590              NaN        False           False          False       NaN              NaN   \n",
       "  2312591              NaN        False           False           True   warning              NaN   \n",
       "  2312592              NaN        False           False           True   warning              NaN   \n",
       "  2312593              NaN        False           False           True   warning              NaN   \n",
       "  2312594              NaN        False            True          False  citation              NaN   \n",
       "  2312595              NaN        False            True          False  citation              NaN   \n",
       "  2312596              NaN        False           False           True   warning              NaN   \n",
       "  2312597              NaN        False            True          False  citation              NaN   \n",
       "  ...                  ...          ...             ...            ...       ...              ...   \n",
       "  3237321              NaN        False           False           True   warning              NaN   \n",
       "  3237322              NaN        False            True          False  citation              NaN   \n",
       "  3237323              NaN        False           False           True   warning              NaN   \n",
       "  3237324              NaN        False           False           True   warning              NaN   \n",
       "  3237325              NaN        False           False           True   warning              NaN   \n",
       "  3237326              NaN        False           False           True   warning              NaN   \n",
       "  3237327              NaN        False           False           True   warning              NaN   \n",
       "  3237328              NaN        False           False           True   warning              NaN   \n",
       "  3237329              NaN        False           False           True   warning              NaN   \n",
       "  3237330              NaN        False           False           True   warning              NaN   \n",
       "  3237331              NaN        False           False           True   warning              NaN   \n",
       "  3237332              NaN        False           False          False       NaN              NaN   \n",
       "  \n",
       "                 contraband_drugs contraband_weapons  search_conducted search_person search_vehicle  \\\n",
       "  raw_row_number                                                                                      \n",
       "  2312586                     NaN                NaN             False           NaN            NaN   \n",
       "  2312587                     NaN                NaN             False           NaN            NaN   \n",
       "  2312588                     NaN                NaN             False           NaN            NaN   \n",
       "  2312589                     NaN                NaN             False           NaN            NaN   \n",
       "  2312590                     NaN                NaN             False           NaN            NaN   \n",
       "  2312591                     NaN                NaN             False           NaN            NaN   \n",
       "  2312592                     NaN                NaN             False           NaN            NaN   \n",
       "  2312593                     NaN                NaN             False           NaN            NaN   \n",
       "  2312594                     NaN                NaN             False           NaN            NaN   \n",
       "  2312595                     NaN                NaN             False           NaN            NaN   \n",
       "  2312596                     NaN                NaN             False           NaN            NaN   \n",
       "  2312597                     NaN                NaN             False           NaN            NaN   \n",
       "  ...                         ...                ...               ...           ...            ...   \n",
       "  3237321                     NaN                NaN             False           NaN            NaN   \n",
       "  3237322                     NaN                NaN             False           NaN            NaN   \n",
       "  3237323                     NaN                NaN             False           NaN            NaN   \n",
       "  3237324                     NaN                NaN             False           NaN            NaN   \n",
       "  3237325                     NaN                NaN             False           NaN            NaN   \n",
       "  3237326                     NaN                NaN             False           NaN            NaN   \n",
       "  3237327                     NaN                NaN             False           NaN            NaN   \n",
       "  3237328                     NaN                NaN             False           NaN            NaN   \n",
       "  3237329                     NaN                NaN             False           NaN            NaN   \n",
       "  3237330                     NaN                NaN             False           NaN            NaN   \n",
       "  3237331                     NaN                NaN             False           NaN            NaN   \n",
       "  3237332                     NaN                NaN             False           NaN            NaN   \n",
       "  \n",
       "                 reason_for_arrest reason_for_search reason_for_stop  \n",
       "  raw_row_number                                                      \n",
       "  2312586                      NaN               NaN      21-202(h1)  \n",
       "  2312587                      NaN               NaN       22-203(b)  \n",
       "  2312588                      NaN               NaN       21-301(a)  \n",
       "  2312589                      NaN               NaN       16-303(c)  \n",
       "  2312590                      NaN               NaN       22-204(f)  \n",
       "  2312591                      NaN               NaN       21-301(a)  \n",
       "  2312592                      NaN               NaN       13-409(b)  \n",
       "  2312593                      NaN               NaN      21-405(e1)  \n",
       "  2312594                      NaN               NaN       21-707(a)  \n",
       "  2312595                      NaN               NaN      21-201(a1)  \n",
       "  2312596                      NaN               NaN      21-201(a1)  \n",
       "  2312597                      NaN               NaN     22-412.3(b)  \n",
       "  ...                          ...               ...             ...  \n",
       "  3237321                      NaN               NaN      21-405(e1)  \n",
       "  3237322                      NaN               NaN   21-1124.2(d2)  \n",
       "  3237323                      NaN               NaN       13-401(h)  \n",
       "  3237324                      NaN               NaN       13-401(h)  \n",
       "  3237325                      NaN               NaN       21-310(a)  \n",
       "  3237326                      NaN               NaN      13-411(c2)  \n",
       "  3237327                      NaN               NaN       21-309(b)  \n",
       "  3237328                      NaN               NaN       13-411(a)  \n",
       "  3237329                      NaN               NaN      21-201(a1)  \n",
       "  3237330                      NaN               NaN        21-801.1  \n",
       "  3237331                      NaN               NaN      21-201(a1)  \n",
       "  3237332                      NaN               NaN             61*  \n",
       "  \n",
       "  [841853 rows x 22 columns],\n",
       "  mark: 'point'\n",
       "})"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(md).mark_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Acceleration</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Displacement</th>\n",
       "      <th>Horsepower</th>\n",
       "      <th>Miles_per_Gallon</th>\n",
       "      <th>Name</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Weight_in_lbs</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "      <td>USA</td>\n",
       "      <td>3504</td>\n",
       "      <td>1970-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.5</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>buick skylark 320</td>\n",
       "      <td>USA</td>\n",
       "      <td>3693</td>\n",
       "      <td>1970-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>plymouth satellite</td>\n",
       "      <td>USA</td>\n",
       "      <td>3436</td>\n",
       "      <td>1970-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>amc rebel sst</td>\n",
       "      <td>USA</td>\n",
       "      <td>3433</td>\n",
       "      <td>1970-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.5</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>ford torino</td>\n",
       "      <td>USA</td>\n",
       "      <td>3449</td>\n",
       "      <td>1970-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Acceleration  Cylinders  Displacement  Horsepower  Miles_per_Gallon                       Name  \\\n",
       "0          12.0          8         307.0       130.0              18.0  chevrolet chevelle malibu   \n",
       "1          11.5          8         350.0       165.0              15.0          buick skylark 320   \n",
       "2          11.0          8         318.0       150.0              18.0         plymouth satellite   \n",
       "3          12.0          8         304.0       150.0              16.0              amc rebel sst   \n",
       "4          10.5          8         302.0       140.0              17.0                ford torino   \n",
       "\n",
       "  Origin  Weight_in_lbs       Year  \n",
       "0    USA           3504 1970-01-01  \n",
       "1    USA           3693 1970-01-01  \n",
       "2    USA           3436 1970-01-01  \n",
       "3    USA           3433 1970-01-01  \n",
       "4    USA           3449 1970-01-01  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from vega_datasets import data\n",
    "cars = data.cars()\n",
    "\n",
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "chart = alt.Chart(cars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'config': {'view': {'width': 400, 'height': 300}, 'mark': {'tooltip': None}},\n",
       " 'data': {'name': 'data-36a712fbaefa4d20aa0b32e160cfd83a'},\n",
       " 'mark': 'point',\n",
       " '$schema': 'https://vega.github.io/schema/vega-lite/v3.2.1.json',\n",
       " 'datasets': {'data-36a712fbaefa4d20aa0b32e160cfd83a': [{'Acceleration': 12.0,\n",
       "    'Cylinders': 8,\n",
       "    'Displacement': 307.0,\n",
       "    'Horsepower': 130.0,\n",
       "    'Miles_per_Gallon': 18.0,\n",
       "    'Name': 'chevrolet chevelle malibu',\n",
       "    'Origin': 'USA',\n",
       "    'Weight_in_lbs': 3504,\n",
       "    'Year': '1970-01-01T00:00:00'}]}}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars1 = cars.iloc[:1]\n",
    "alt.Chart(cars1).mark_point().to_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"When you pass a pandas dataframe to an Altair chart, the result is that the data is converted to JSON and stored in the chart specification. This specification is then embedded in the output of your notebook, and if you make a few dozen charts this way with a large enough dataset, it can significantly slow down your machine.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>petalLength</th>\n",
       "      <th>petalWidth</th>\n",
       "      <th>sepalLength</th>\n",
       "      <th>sepalWidth</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   petalLength  petalWidth  sepalLength  sepalWidth species\n",
       "0          1.4         0.2          5.1         3.5  setosa\n",
       "1          1.4         0.2          4.9         3.0  setosa\n",
       "2          1.3         0.2          4.7         3.2  setosa\n",
       "3          1.5         0.2          4.6         3.1  setosa\n",
       "4          1.4         0.2          5.0         3.6  setosa"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = data.iris()\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}, \"mark\": {\"tooltip\": null}}, \"data\": {\"url\": \"altair-data-4044aea27ef3f83da930a874ed26bc9e.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"point\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v3.2.1.json\"};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"4cdb5f16-43a6-42db-a816-dc7b9a298785\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x11e137668>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#4cdb5f16-43a6-42db-a816-dc7b9a298785"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAB4AAAAeCAYAAAA7MK6iAAAAqUlEQVRIie3Wyw3DIAwG4OzEFKaDsACxZ2EB8BTpIjAAQxh6QqpyqKIW0he/5CufbYHEsszMnJmU0pZSqh1rOwrXzoMcO2/C/w0r47K2XmDlAisXbb0o4/JQWBmXNXG97EoT10f4yzBgkAYBBgEM0hoBDDIOJi57pDUDxOUH4btVa+tFW3/Oqt92uRoOGASICxAXwDD+OT2bCU/4I+Cuf64Y47XnIDPfkRs7PaHuKkTBEwAAAABJRU5ErkJggg=="
     },
     "metadata": {
      "jupyter-vega": "#4cdb5f16-43a6-42db-a816-dc7b9a298785"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(iris).mark_point()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}, \"mark\": {\"tooltip\": null}}, \"data\": {\"url\": \"altair-data-4044aea27ef3f83da930a874ed26bc9e.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"point\", \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"petalLength\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v3.2.1.json\"};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"4dd6d347-a844-4fd4-8e82-acd393e85627\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x11e12df28>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#4dd6d347-a844-4fd4-8e82-acd393e85627"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {
      "jupyter-vega": "#4dd6d347-a844-4fd4-8e82-acd393e85627"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(iris).mark_point().encode(\n",
    "    x='petalLength'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}, \"mark\": {\"tooltip\": null}}, \"data\": {\"url\": \"altair-data-4044aea27ef3f83da930a874ed26bc9e.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"point\", \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"petalLength\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"petalWidth\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v3.2.1.json\"};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"96e14af0-3150-4c97-9c6f-6def40623000\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x11e12d7b8>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#96e14af0-3150-4c97-9c6f-6def40623000"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#96e14af0-3150-4c97-9c6f-6def40623000"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alt.Chart(iris).mark_point().encode(\n",
    "    x='petalLength',\n",
    "    y='petalWidth'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}, \"mark\": {\"tooltip\": null}}, \"data\": {\"url\": \"altair-data-4044aea27ef3f83da930a874ed26bc9e.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"point\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"species\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"petalLength\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"petalWidth\"}}, \"selection\": {\"selector001\": {\"type\": \"interval\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v3.2.1.json\"};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"27dab416-0633-4f27-882c-31aa817ba4ae\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x11e142978>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#27dab416-0633-4f27-882c-31aa817ba4ae"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#27dab416-0633-4f27-882c-31aa817ba4ae"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interval = alt.selection_interval()\n",
    "\n",
    "alt.Chart(iris).mark_point().encode(\n",
    "    x='petalLength',\n",
    "    y='petalWidth',\n",
    "    color='species'\n",
    ").properties(\n",
    "    selection=interval\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}, \"mark\": {\"tooltip\": null}}, \"hconcat\": [{\"data\": {\"url\": \"altair-data-4044aea27ef3f83da930a874ed26bc9e.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"point\", \"encoding\": {\"color\": {\"value\": \"lightgray\", \"condition\": {\"type\": \"nominal\", \"field\": \"species\", \"selection\": \"selector005\"}}, \"x\": {\"type\": \"quantitative\", \"field\": \"petalLength\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"petalWidth\"}}, \"selection\": {\"selector005\": {\"type\": \"interval\"}}}, {\"data\": {\"url\": \"altair-data-4044aea27ef3f83da930a874ed26bc9e.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"point\", \"encoding\": {\"color\": {\"value\": \"lightgray\", \"condition\": {\"type\": \"nominal\", \"field\": \"species\", \"selection\": \"selector005\"}}, \"x\": {\"type\": \"quantitative\", \"field\": \"sepalLength\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"petalWidth\"}}, \"selection\": {\"selector005\": {\"type\": \"interval\"}}}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v3.2.1.json\"};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"6bfb29d6-e692-4a7d-a708-54ae4167cb50\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x11e148048>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#6bfb29d6-e692-4a7d-a708-54ae4167cb50"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#6bfb29d6-e692-4a7d-a708-54ae4167cb50"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interval = alt.selection_interval()\n",
    "\n",
    "base = alt.Chart(iris).mark_point().encode(\n",
    "    y='petalWidth',\n",
    "    color=alt.condition(interval, 'species', alt.value('lightgray'))\n",
    ").properties(\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "base.encode(x='petalLength') | base.encode(x='sepalLength')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#alt.data_transformers.enable('json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>department_name</th>\n",
       "      <th>subject_age</th>\n",
       "      <th>arrest_made</th>\n",
       "      <th>search_conducted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aberdeen Police Department</td>\n",
       "      <td>38.983794</td>\n",
       "      <td>0.031685</td>\n",
       "      <td>0.047528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Allegany County Sheriff's Office</td>\n",
       "      <td>36.888487</td>\n",
       "      <td>0.028708</td>\n",
       "      <td>0.043062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Annapolis Police Department</td>\n",
       "      <td>37.828852</td>\n",
       "      <td>0.065674</td>\n",
       "      <td>0.090977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anne Arundel County Police Department</td>\n",
       "      <td>37.388815</td>\n",
       "      <td>0.016226</td>\n",
       "      <td>0.040317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anne Arundel County Sheriff's Office</td>\n",
       "      <td>40.379105</td>\n",
       "      <td>0.063492</td>\n",
       "      <td>0.079365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         department_name  subject_age  arrest_made  search_conducted\n",
       "0             Aberdeen Police Department    38.983794     0.031685          0.047528\n",
       "1       Allegany County Sheriff's Office    36.888487     0.028708          0.043062\n",
       "2            Annapolis Police Department    37.828852     0.065674          0.090977\n",
       "3  Anne Arundel County Police Department    37.388815     0.016226          0.040317\n",
       "4   Anne Arundel County Sheriff's Office    40.379105     0.063492          0.079365"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departments = md.groupby('department_name')\n",
    "rates = departments.mean().reset_index()\n",
    "rates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}, \"mark\": {\"tooltip\": null}}, \"hconcat\": [{\"data\": {\"url\": \"altair-data-1052c5c46802bde1565ab85c5f2a8477.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"point\", \"encoding\": {\"color\": {\"value\": \"lightgray\", \"condition\": {\"type\": \"nominal\", \"field\": \"department_name\", \"selection\": \"selector009\"}}, \"tooltip\": {\"type\": \"nominal\", \"field\": \"department_name\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"subject_age\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"arrest_made\"}}, \"selection\": {\"selector009\": {\"type\": \"interval\"}}}, {\"data\": {\"url\": \"altair-data-1052c5c46802bde1565ab85c5f2a8477.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"point\", \"encoding\": {\"color\": {\"value\": \"lightgray\", \"condition\": {\"type\": \"nominal\", \"field\": \"department_name\", \"selection\": \"selector009\"}}, \"tooltip\": {\"type\": \"nominal\", \"field\": \"department_name\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"search_conducted\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"arrest_made\"}}, \"selection\": {\"selector009\": {\"type\": \"interval\"}}}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v3.2.1.json\"};\n",
       "var opt = {};\n",
       "var type = \"vega-lite\";\n",
       "var id = \"c0430537-3895-4071-975a-b66e100dbe46\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
       "  var target = document.createElement(\"div\");\n",
       "  target.id = id;\n",
       "  target.className = \"vega-embed\";\n",
       "\n",
       "  var style = document.createElement(\"style\");\n",
       "  style.textContent = [\n",
       "    \".vega-embed .error p {\",\n",
       "    \"  color: firebrick;\",\n",
       "    \"  font-size: 14px;\",\n",
       "    \"}\",\n",
       "  ].join(\"\\\\n\");\n",
       "\n",
       "  // element is a jQuery wrapped DOM element inside the output area\n",
       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
       "  element[0].appendChild(target);\n",
       "  element[0].appendChild(style);\n",
       "\n",
       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== \"scripterror\") {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ],
      "text/plain": [
       "<vega.vegalite.VegaLite at 0x115afc710>"
      ]
     },
     "metadata": {
      "jupyter-vega": "#c0430537-3895-4071-975a-b66e100dbe46"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega": "#c0430537-3895-4071-975a-b66e100dbe46"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interval = alt.selection_interval()\n",
    "\n",
    "base = alt.Chart(rates).mark_point().encode(\n",
    "    y='arrest_made',\n",
    "    color=alt.condition(interval, 'department_name', alt.value('lightgray')),\n",
    "    tooltip = 'department_name'\n",
    ").properties(\n",
    "    selection=interval\n",
    ")\n",
    "\n",
    "base.encode(x='subject_age') | base.encode(x='search_conducted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Exploring Race and Policing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "white                     426220\n",
       "black                     304582\n",
       "hispanic                   59986\n",
       "other/unknown              27736\n",
       "asian/pacific islander     23264\n",
       "Name: subject_race, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md.subject_race.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maryland State Police                                           208287\n",
      "Baltimore County Police Department                               91602\n",
      "Montgomery County Police Department                              54627\n",
      "Maryland Transportation Authority Police                         52474\n",
      "Anne Arundel County Police Department                            50227\n",
      "MSP                                                              43277\n",
      "Prince George's County Police Department                         40937\n",
      "Howard County Police Department                                  27421\n",
      "Frederick Police Department                                      19792\n",
      "MONTGOMERY                                                       18207\n",
      "Ocean City Police Department                                     13698\n",
      "MDTA                                                              9965\n",
      "PGCOPD                                                            9781\n",
      "Charles County Sheriff's Office                                   9241\n",
      "Harford County Sheriff's Office                                   9240\n",
      "University of Maryland College Park Police                        8194\n",
      "Greenbelt Police Department                                       7023\n",
      "Calvert County Sheriff's Office                                   6204\n",
      "Carroll County Sheriff's Office                                   6082\n",
      "HOCPD                                                             5981\n",
      "Annapolis Police Department                                       5375\n",
      "Laurel Police Department                                          5195\n",
      "Gaithersburg Police Department                                    4822\n",
      "Washington County Sheriff's Office                                4566\n",
      "Maryland National Capital Park Police Montgomery County           4482\n",
      "Rockville Police Department                                       4145\n",
      "Westminster Police Department                                     4044\n",
      "Bowie Police Department                                           4000\n",
      "Saint Mary's County Sheriff's Office                              3827\n",
      "Maryland National Capital Park Police Prince George's County      3099\n",
      "Wicomico County Sheriff's Office                                  3027\n",
      "Cambridge Police Department                                       2839\n",
      "Fruitland Police Department                                       2812\n",
      "FREDERICK                                                         2767\n",
      "Cecil County Sheriff's Office                                     2764\n",
      "Easton Police Department                                          2687\n",
      "Takoma Park Police Department                                     2413\n",
      "Worcester County Sheriff's Office                                 2374\n",
      "CHARLES                                                           2264\n",
      "HARFORD                                                           2192\n",
      "Hyattsville Police Department                                     2166\n",
      "FREDERICKPD                                                       2132\n",
      "Aberdeen Police Department                                        2083\n",
      "Hagerstown Police Department                                      2045\n",
      "Elkton Police Department                                          2045\n",
      "UMCP                                                              1981\n",
      "Cumberland Police Department                                      1774\n",
      "Kent County Sheriff's Office                                      1754\n",
      "GAITHERSBURG                                                      1746\n",
      "WASHINGTON                                                        1746\n",
      "CARROLL                                                           1576\n",
      "New Carrollton Police Department                                  1549\n",
      "STMARYS                                                           1459\n",
      "MCPARK                                                            1398\n",
      "LAUREL                                                            1380\n",
      "Riverdale Police Department                                       1352\n",
      "Cheverly Police Department                                        1297\n",
      "ROCKVILLE                                                         1218\n",
      "OCEANCITY                                                         1213\n",
      "Pocomoke City Police Department                                   1177\n",
      "PGPARK                                                            1166\n",
      "GREENBELT                                                         1137\n",
      "TAKOMA                                                            1099\n",
      "Talbot County Sheriff's Office                                    1078\n",
      "Maryland Transit Administration                                   1064\n",
      "Salisbury Police Department                                       1020\n",
      "Taneytown Police Department                                       1007\n",
      "EASTON                                                             988\n",
      "WICOMICO                                                           952\n",
      "Caroline County Sheriff's Office                                   951\n",
      "Chevy Chase Village Police Department                              900\n",
      "University of Maryland Baltimore Police                            877\n",
      "Chestertown Police Department                                      844\n",
      "University of Maryland Baltimore County Police                     830\n",
      "Queen Anne's County sheriff's Office                               825\n",
      "Somerset County Sheriff's Office                                   782\n",
      "WESTMINSTER                                                        773\n",
      "Dorchester County Sheriff's Office                                 751\n",
      "Sykesville Police Department                                       751\n",
      "Garrett County Sheriff's Office                                    740\n",
      "Edmonston Police Department                                        735\n",
      "FRUITLAND                                                          722\n",
      "Morningside Police Department                                      710\n",
      "Centreville Police Department                                      681\n",
      "Natural Resources Police                                           680\n",
      "Bel Air Police Department                                          648\n",
      "CHEVERLY                                                           621\n",
      "KENT                                                               621\n",
      "La Plata Police Department                                         614\n",
      "Seat Pleasant Police Department                                    598\n",
      "HAGERSTOWN                                                         591\n",
      "Mount Rainier Police Department                                    567\n",
      "CALVERT                                                            537\n",
      "HYATTSVILLE                                                        536\n",
      "Berlin Police Department                                           528\n",
      "Brunswick Police Department                                        522\n",
      "Thurmont Police Department                                         512\n",
      "Frostburg City Police Department                                   506\n",
      "Berwyn Heights Police Department                                   476\n",
      "Landover Police Department                                         447\n",
      "Cottage City Police Department                                     435\n",
      "CUMBERLAND                                                         433\n",
      "ELKTON                                                             423\n",
      "RIVERDALE                                                          422\n",
      "Princess Anne Police Department                                    417\n",
      "BOWIE                                                              415\n",
      "Federalsburg Police Department                                     399\n",
      "CAROLINE                                                           394\n",
      "WORCESTER                                                          388\n",
      "CHESTERTOWN                                                        373\n",
      "LAPLATA                                                            366\n",
      "POCOMOKE                                                           344\n",
      "Crofton Police Department                                          341\n",
      "SALISBURY                                                          339\n",
      "Snow Hill Police Department                                        331\n",
      "Towson University Police Department                                316\n",
      "EDMONSTON                                                          316\n",
      "CAMBRIDGE                                                          295\n",
      "Ocean Pines Police Department                                      293\n",
      "Glenarden Police Department                                        287\n",
      "TALBOT                                                             279\n",
      "Ridgley Police Department                                          278\n",
      "GARRETT                                                            272\n",
      "Hurlock Police Department                                          270\n",
      "Rising Sun Police Department                                       263\n",
      "Coppin State University Police                                     256\n",
      "Boonsboro Police Department                                        242\n",
      "PRINCESSANNE                                                       236\n",
      "MORNINGSIDE                                                        230\n",
      "Hampstead Police Department                                        210\n",
      "Allegany County Sheriff's Office                                   209\n",
      "MTA                                                                205\n",
      "Delmar Police Department                                           186\n",
      "Department of General Services                                     183\n",
      "Montgomery County Sheriff's Office                                 183\n",
      "SEATPLEASANT                                                       182\n",
      "SOMERSET                                                           176\n",
      "TANEYTOWN                                                          174\n",
      "DORCHESTER                                                         172\n",
      "FROSTBURG                                                          168\n",
      "NRP                                                                168\n",
      "COTTAGECITY                                                        166\n",
      "Upper Marlboro Police Department                                   163\n",
      "OCEANPINES                                                         163\n",
      "Baltimore City School Police                                       156\n",
      "St. Michaels Police Department                                     152\n",
      "University Park Police                                             142\n",
      "NCPD                                                               137\n",
      "Bladensburg Police Department                                      136\n",
      "Comptroller of Maryland                                            133\n",
      "CENTREVILLE                                                        133\n",
      "Capitol Heights Police Department                                  133\n",
      "THURMONT                                                           127\n",
      "QACSO                                                              126\n",
      "LANDOVER                                                           123\n",
      "University of Maryland Eastern Shore Police                        113\n",
      "HURLOCK                                                            113\n",
      "DISTHEIGHTS                                                        104\n",
      "District Heights Police Department                                 104\n",
      "Smithsburg Police Department                                       102\n",
      "Greensboro Police Department                                       100\n",
      "University of Baltimore Police Department                           96\n",
      "COMPTROLLER                                                         96\n",
      "DENTON                                                              85\n",
      "FEDERALSBURG                                                        84\n",
      "CHEVYCHASE                                                          84\n",
      "SYKESVILLE                                                          82\n",
      "Rock Hall Police Department                                         81\n",
      "GLENARDEN                                                           77\n",
      "SNOWHILL                                                            71\n",
      "BRUNSWICK                                                           71\n",
      "MTRAINIER                                                           66\n",
      "Colmar Police Department                                            64\n",
      "CROFTON                                                             64\n",
      "Anne Arundel County Sheriff's Office                                63\n",
      "UMPD                                                                62\n",
      "Havre de Grace Police Department                                    57\n",
      "HAMPSTEAD                                                           52\n",
      "DELTA                                                               51\n",
      "CAPITOLHEIGHTS                                                      50\n",
      "MONTSHERIFF                                                         47\n",
      "UMB                                                                 46\n",
      "DELMAR                                                              44\n",
      "UMBC                                                                44\n",
      "Maryland Motor Vehicle Administration                               39\n",
      "Oxford Police Department                                            35\n",
      "ROCKHALL                                                            34\n",
      "COLMAR                                                              30\n",
      "RIDGELY                                                             28\n",
      "STMICHAELS                                                          26\n",
      "Salisbury University Police Department                              26\n",
      "UNIVERSITYPARK                                                      24\n",
      "UMES                                                                21\n",
      "SMITHSBURG                                                          20\n",
      "TOWSONU                                                             17\n",
      "DGS                                                                 15\n",
      "Manchester Police Department                                        10\n",
      "GREENSBORO                                                           9\n",
      "OAKLAND                                                              9\n",
      "MVA                                                                  7\n",
      "UBALTPD                                                              6\n",
      "COPPIN                                                               6\n",
      "Trappe Police Department                                             5\n",
      "MANCHESTER                                                           5\n",
      "HAVREDEGRACE                                                         5\n",
      "Department of Health and Mental Hygiene                              4\n",
      "OXFORD                                                               3\n",
      "TRAPPE                                                               3\n",
      "Oakland Police Department                                            2\n",
      "Name: department_name, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "with pd.option_context('max_rows', 210):\n",
    "    print(md.department_name.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to establish some baseline to determine if the proportion of White, Black, Hispanic, and Asian drivers being pulled over is disproportionate to their share of the population. Since we don't have good data for the whole state, let's compare select counties' traffic stops to their Census populations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "black                     0.491337\n",
       "white                     0.402116\n",
       "hispanic                  0.040090\n",
       "other/unknown             0.036466\n",
       "asian/pacific islander    0.029991\n",
       "Name: subject_race, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcpd = md[md.department_name == 'Baltimore County Police Department']\n",
    "bcpd_by_race = bcpd.subject_race.value_counts(normalize=True)\n",
    "bcpd_by_race"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that Black motorists represent 49% of the people pulled over by the Baltimore County Police Department in 2013 - early 2014. What about Montgomery?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "white                     0.392733\n",
       "black                     0.311352\n",
       "hispanic                  0.175025\n",
       "asian/pacific islander    0.066357\n",
       "other/unknown             0.054533\n",
       "Name: subject_race, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mcpd = md[(md.department_name == 'MONTGOMERY') | (md.department_name == 'Montgomery County Police Department') |(md.department_name == 'Montgomery County Sherrif\\'s Office') ]\n",
    "mcpd_by_race = mcpd.subject_race.value_counts(normalize=True)\n",
    "mcpd_by_race"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### EXERCISE: Create a new data frame for PG County "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "black                     0.754456\n",
       "hispanic                  0.119351\n",
       "white                     0.095039\n",
       "other/unknown             0.016563\n",
       "asian/pacific islander    0.014591\n",
       "Name: subject_race, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %load solution/pg.py\n",
    "pgcopd = md[(md.department_name == 'Prince George\\'s County Police Department') | (md.department_name == 'PGCOPD')]\n",
    "pgcopd.subject_race.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See [Census Data](https://www.census.gov/quickfacts/fact/table/princegeorgescountymaryland,baltimorecountymaryland,montgomerycountymaryland,US/PST045218)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "baltimore_co_census = pd.Series([.572, .292,.055 ,.064,], index=['white', 'black', 'hispanic', 'asian/pacific islander'])\n",
    "moco_census = pd.Series([.438, .197, .196, .156,], index=['white', 'black', 'hispanic', 'asian/pacific islander'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>police_stops</th>\n",
       "      <th>moco_census</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>asian/pacific islander</th>\n",
       "      <td>0.066357</td>\n",
       "      <td>0.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black</th>\n",
       "      <td>0.311352</td>\n",
       "      <td>0.197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic</th>\n",
       "      <td>0.175025</td>\n",
       "      <td>0.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other/unknown</th>\n",
       "      <td>0.054533</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>0.392733</td>\n",
       "      <td>0.438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        police_stops  moco_census\n",
       "asian/pacific islander      0.066357        0.156\n",
       "black                       0.311352        0.197\n",
       "hispanic                    0.175025        0.196\n",
       "other/unknown               0.054533          NaN\n",
       "white                       0.392733        0.438"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moco_benchmark = pd.concat([mcpd_by_race, moco_census], keys=['police_stops', 'moco_census'], axis = 1)\n",
    "moco_benchmark"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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+/PFHVczuuOMO1z0E2IVC89xzzwmco+EfhHAqRYsWdeXJzAEUu7lz56pvzx/+8Ae5evWq/PWvf5WZM2dK7969tV3EsjMSFCWkSPUX087zn7AlQAUobIeWHSMBEnAnsGLFCp3ewcomWFLKli0r5cuX12uw5nimxo0b6yUoMBUqVFBlAZYXI8E6ghhwSFCokA4dOqTfnv/ACgQFac2aNRozDk7OUMC8re6CBQgpf/78rmoQCBd5e/XqpVNXCxculOLFi7vizbkyuh14m7aKjY0VKD5Ihuzx8fFpzt2VPMiAegyZNCP/IYEwIUAFKEwGkt0gARJIn8Bnn32mGQwfHvjEGNYWd38aoxZYRzxTtmw3/2S63zeUDff77mWxFB6WJyhA//73v9Wa42t5vFEXLEVGgqUICtxTTz2lSht8iHr27HmLxckoi3KGL49RB75z587tOjWmyDDFh+RNduMaZGcigXAjcPN/c7j1jP0hARIggf8ROHbsmFpe8EMOa477B1l27NghmKJyT1AykFAWihKUB8NXBte///57+emnn3CoU1r4LleuHL5uSYULF5Z69eoJfHbgh3Tbbbfp+S0ZUy/A+RnJ3ery5ptvyujRo+Uvf/mLzJgxQyZNmqR54NODZCg2J06c0POdO3eq5UZPAvgHTuJQlAyZAqiKRUnAcQS4DN5xQ0KBSIAEgk3AWMmEVVRYleWeunXrpiu7sCcQ/H6MhNVSUDAwrQVrT48ePdJMWcFC89BDD+mGklu2bFELCuryldq2bav1wfkYioxhXfHMD8sUlI7jx4+7bp0/f15Xqz3wwAO6MmvPnj2a5//9v/+neeAwDevPxIkTZe/evfL111+r8/alS5dcdfhzYGzV4O6Q7U89LEMCTiRAC5ATR4UykQAJBJUAFCAoHAkJCbfUC/8aKBzI455eeeUVXd2FJego9/e//939tlpwoABBGYE/DlaJYXdtXwk+P9h/CMnX9BfuwTkaStB3332HU01YYdanTx9VwL799ltVbrBKDUoZEjYtxAfTXlu3bpVnnnlG4LtkTHMhD47dz41r+DaSZx5YuWD9ga8UEwmEGwEGQw23EWV/fBK4838rWoy3Wp8ZTb7hBDmcIIPJmP2uHg7NmPb66quvvE79wIcIlh5Yk6ZMmZLpdqAoQUnB/joIb+GpjLhXhGmu1157TVeZQRmyI2FlXP369aVr164CBYyJBMKNAC1A4Tai7E9EEnB3mI1IAA7uNKayMLWGZeZQejp27Jiu8oOuIA5XdHS0rF692raewWEbVrOHH37YNhnYMAmYSYAKkJl0WXfIEsAuuZ5OsXZ0Br4n2IkYm9H5SrAWeFvG7Ss/r2dMID3rDEp7ThWlVyMUGfjkYCdp+AH169cvvex6D8v0seQdmx/apdzOnz9flbUyZcpkKC8zkEAoEuAUWCiOGmX2i0Bmp32wsqdTp06CqOFmpMzKgba3bdumcZiMJdze5EEYhqlTp2bJTyMrMnhrk9dIgARIINQJ0AIU6iNI+YNOAEoHrC5ImIKAwypW1sAPAtMXcIh9++239T6cRBE00khYhtywYUM5efKkcSlL31h2jL1e4CsCB1fEdqpVq5Yus4ZSBodd3DNiPsE/4/Tp0xqXCtYi7Grsno+WoSzhZ2YSIIEIIsBl8BE02Oxq5ghs375dFSBMP2BpNKJhY1UNlB+sDEKC4rF582apUaOGOsxev35dN7rDSiBMXWQ1RIEhGeIy3XvvvTJ+/Hg5cuSItGjRQuM1wdl2woQJug8NlK6+ffuqYvbII4/oyqJp06ZpkE9YgjzzeYs3ZbTHbxIgARKIVAJUgCJ15NlvnwRgAYIlZ+3atbJy5UrdjwX7vGAvlunTp2s5WGpwjmXNpUqVUn8hbFx38OBBGTt2rM+607uBeEuIFQVlBgmxoLDBHTbQQwgD+GTAjwS+SVhJhIQN/GrWrKnH+fLlk+7du3vNpxn4zy0E6tatq3GuYmJibrln1QVY8JDslAHtQw4s49+0aRNOmUgg7AlQAQr7IWYHs0Lg4sWLgmCQ2LMFe7vAClSxYkVBvCRjyTNiJSHqtzFNhh8NKC7YnXfkyJEZrvDxJQ823MMOxYYDLpQpxH9C3Cf4JSH0AZQtKEiIUo4EaxWsQUhQvBA001s+zcB/biEApTM5OdlW5QPtI9mtAEEOBj295RHhhTAmQAUojAeXXcs6AVhUsOtt//79VRHCfi/YXRehBWDhwY8Uprlg/XFXgOATBGtCtWrVst7o/0pAqYHSAysPlCBMt8G6A+sTVg7BFwjTX4hADn8krA7CudEm8kFuz3x+CxQBBTGe+Ni5N5RTHNINOSJg2NlFElACVID4IJCAGwFYVIwpJVhjENsJP46wqsDfBoEjEU18xIgRrlLYJRfTB3BeDiTBn6hZs2a6VBrt5syZU6pUqSKIOg5lCMvdsUswrEB58uRRBQjTZFCOZs+erZYgb/kCkYllSYAESCBcCXAZfLiOLPt1CwHjDTfYb/vY5A7OyumFN3AXxiw53NvI6NgJMmQkoxX3ncDBCTKAtVPksGLc2QYJgACXwfM5IAE/CWCfoHbt2knZsmUzrfz42RSLkQAJkAAJBJkAp8CCDJTVRQ4BLFfHMnkmEiABEiCB0CNAC1DojRklJgESIAESIAESCJAAFaAAAbI4CZAACZAACZBA6BGgAhR6Y0aJSYAESIAESIAEAiRABShAgCxOAiRAAiRAAiQQegSoAIXemFFiEiABEsiQADbKZCIBEvBNgAqQbza8QwIkQAIuAg0aNNA4bK4LNh1cvXpVdyE/d+6cTwmwaeayZct83ucNEiAB7gPEZ4AESIAEMiSAWGywqJQpUybDvGZn2LVrl+4GXqBAAZ9NQQFCSBcmEiAB3wRoAfLNhndIgARIQAls27bNFftt9erVuvHl3r17NSZbx44dJSEhQRAPDgnx2Vq2bKnH+OfEiRPSsGFDOXnypOtaVg7Onj2rYVY6dOggPXr0kOXLl2u8t/Xr10unTp2kc+fOgnu4jtS1a1cNzTJ8+HCBtWjdunVp8tEylBX6zBvOBLgRYjiPLvtGAiQQFAKIEYfgtwsWLNDNL+fMmSPPPPOMQPlB/DUkKB6bN28WxHQ7duyYXL9+XbJnz67Bc3v16iVFixb1S5ZBgwYJNt0cP368HDlyRMOuvPDCCzJlyhSZMGGClCxZUpWuvn37qmKGmHW5cuWSadOmaaT7qVOn3pKvdevWfsnCQiQQTgSoAIXTaLIv6RJAwNLk5GRXzKN0M5t4MykpSWs3Yi+Z2JTPqiFDdHS0z/u8kZYALECw5Kxdu1ZWrlwpx48fly1btsj58+dl+vTpmhmWGpxD+UDA2sTERDl16pQcPHhQxo4dm7bCTJ7t27dPDhw4oMoMiiD4be7cuaVevXoSGxsr8+fPl8uXL2tbRYoU0Vp37NjhCuibL18+6d69u9d8mRSB2UggbAlQAQrboWXHSIAEgkHg4sWLsn//fmnevLkUL15crUAVK1aU+Ph4tcKgjStXrsju3btd02RxcXGquEyaNElGjhwpUVFRfoly6NAhqVChgqs8lKkcOXLIwoULBX5JPXv2VGUL1p4SJUpoG7BWwRqEBMULyq63fJqB/5BABBOgAhTBgx9pXY+JiRF8gh0NPqscDcuPnXIYMmRV9kjMD4sKHIr79++vilC3bt1k4sSJsnPnTrXw4JkaN26cWn8wTYYEBQg+QXXr1pVq1ar5jQ1KDZQeWHmgRGG6rWbNmmp96tevn/oCwedo0aJF6o8ER22cG23CSgW5a9WqpdeNfH4LxIIkEEYEqACF0WCyKyRAAsEnAIsKlA4kWGPKlSunSjSsKvC3yZs3r9SpU0dGjBjharx8+fLqiPzUU0+5rvlzAH+iZs2aSdu2bbXdnDlzSpUqVaRq1aqqDGG1V8GCBdUKlCdPHl2phmkyKEezZ89WSxCUJs98/sjCMiQQbgSiUlJTuHWK/SEBbwQMq4edlhfI5QQ5nCCDtzGy+ppZHAYOHKjOyq1atcqwS2bJkGHDHhmcIoeHWDwlAdMIcBm8aWhZMQmQQKQR2Lhxo7Rr107Kli2rK7Iirf/sLwmEEgFOgYXSaFFWEiABRxPAcvUlS5Y4WkYKRwIk8H8EaAHik0ACJEACJEACJBBxBKgARdyQs8MkQAIkQAIkQAJUgPgMkAAJkAAJkAAJRBwBKkARN+TsMAmQAAmQAAmQABUgPgMkQAKOJIDQEwj1cOHCBUfKR6FIgARCmwBXgYX2+FF6EghbAt99951GOkewT2z8h92N69evLwjkiRhXTCRAAiQQCAEqQIHQY9mII3Dt2jVp1KiRhkZANG670pAhQzTcwmOPPWaXCJa1+/PPPws+q1evdkU1p0JkGX42RAJhS4AKUNgOLTtmBgH8CFeqVEkDXyJGE8IiWJkQGRzBNRGHCvGmrEy//fabNGzY0BWY0+y2oWx6S94UIoSoQIiId955R7Jnz+6tGK+RAAmQQBoCVIDS4OAJCaRPYO7cudKmTRtB2ADEV4IyYmVC+wkJCYJpIatT/vz5dUoqWzZrXAcTExNl7969PruJyOyGJahevXoaL4vKj09cvEECJOBBgAqQBxCekoAvAj/++KNaXhAJHPHEEBV88ODBUqhQIV9Fgn592LBhWufXX38d9LozqjB37twydOjQjLIF7f4XX3whkydPdtXnTeGJjo523ecBCZAACWSFABWgDGhhugN/+Hfs2JFBzuDexsqXRYsWabTp4NbM2vwlMG/ePJ0CKlCggFSvXl0jcGOVUu/evf2tkuXSIQDl5v7775fatWuLYeEJtsJz+vRpSU5OVoteOqKYeispKUnrN4KRmtpYOpVDjmDzTac53iIB2wlQAbJ9CG4V4MyZMxpQEStdHnnkkVsz8IrlBC5evCiLFy+WPHnySNOmTSUlJUV/OOfMmSM9e/ak34kJIwKlZ926dSbUzCpJgARIQIQKkAOfgnPnzskvv/xiuYOtA1E4RqSlS5dK4cKFZdWqVS6ZME6NGzeWFStWqF+Q6wYPQoZATEyM4IMpTbuSYfmxUwb03ZDDLg5slwSsJmCNN6PVvTKpvaNHj+oKoL59+8qsWbOkQYMGEh8fL6+++qqrRaxCwbSZMTVSs2ZNad++vWBPEyM1adJE82BVDRKm11AGPiVIzZo105U2WGVUuXJlXQKsN/iPbQQw/eW55BxTYV27dpWZM2faJhcbJgESIAES8I8ALUB+cNuyZYts375dypcvL1u3btUfQCwPxiZtUVFRWuPYsWOlWLFiasX5/vvvpV+/frJmzRqdQkEGI5+35u+77z7ZsGGDzsffe++9rjLe8vKaNQTgj+Ut/e1vfxN8rE6vvPKK1U2yPRIgARIIKwK0APkxnHBQnj17tn6wKy3Srl270tRUtmxZWbZsmToyQ6GBtQdTJZlJxtLq2NhY3dcEUy9MJEACJEACJEACwSNABcgPlrDswPqDhI3w4BB75cqVNDVh9YqxXwr8RJDH1xw/7jGRAAmQAAmQAAlYR4AKkB+ssRLISMbGa55KzNWrV40sqvzgxHPa68aNG5rHU3lyFeQBCZAACZAACZCAKQSoAPmB1VOR8VbFf/7zHzGUoC+//FKVHyNsgqFAIdo1kuceQ4blyFCQvNXPayRAAiRAAiRAAv4ToBO0/+zSLXno0CGNWo3wAXv27BHsYtuiRQstg5VdWOH197//XbBKDAqSeypYsKDuK4NQANgHCA6vd9xxh3sWHpMACZAACZAACQRAgBagTMBzt/jg2P0cxT2v4bxHjx5y1113CYJXIl7RP//5T8mVK5e29vTTT0vdunXl+PHjcuTIEXnjjTfS1IHdWHv16iVYZg0lCJvwMaUlcP36dcGHiQRIwD4CsHL/+9//tk8AtkwCARCgBSgDeO7BGEuVKiVY0u6eEAbBWygExIeaNGmSe1bXcYkSJeTDDz90nePAs95BgwYJPky3Evj111+lS5cuusIOFjYmEiABewi8/fbb8tprr+ku6diyg4kEQokALUAmjJanQ7QJTURslf/973+lefPmgm0GqPxE7GPAjjuEAF7S7r77bhk4cKBgZ3QmEgglAlSATBgtzykyE5qIyCphNUtISBBEZcfGkkwk4AQC165d013hMW1tR8JO9NhqAzvO4/PUU09ZJgZWwU6ePFmVnz59+rhWvFomABsigQAIcAosAHjeij755JOCD1PwCMCiNmTIEHn33Xc1ACk2n0SEcCYScAKB1atXayib3bt36+IGY7WnVbJhV/oJEyZIrVq1rGoyTTt/+MMfdBoMoWIgh5UKWBpBeEICWSRAC1AWgTG7tQTOnz8vHTt21D+wycnJ2jjOmUjAKQTmzp2r07KtWrWSGTNmWCoW9hDDKtPp06dLu3btdCrq2LFjlsqAxh588EH1hYQ/0MaNGy1vnw2SgD8EqAD5Q41lLCGAP+zw91m4cKGrPbzlPvroo65zHpCAnQQwHbtz506B8gMF5NNPP5UzZ85YJhL2EkMMwsGDB8uSJUt0W43+/ftb1r57Q2PGjJE6derQH8gdCo8dTSBsp8CwzHzfvn0SExNj6wCcPn1a27dTDsgQFxcnmzZtspVFVhrHH3P8Ucc2Au6pZcuWukeS+7XMHoMDrEh33nlnZouYki8pKUnrtVMOyIDtFpgCIzBv3jxBIGRsWVG9enXBStH58+d7XRkaWEveS5cuXVref/99182ePXvKxIkT5ejRoyqL64YFB4Y/ULNmzQT+QHPmzLllyxALxGATJJBpAmFrAYLyY0yZZJqGCRkhg91yoH3wCJUEPwLso+Sp/GAzSW4NECqjGP5yYn+uxYsXy9atW6Vp06bSpEkTOXnypP7wW7VH1Q8//KCWH3fa8JnLkcOed1vDHwiBn/H/mIkEnEzAnv8lFhCBxQUfXwFILRBBmzDe8u2Uw5DBqj4H2g7eqCtWrCjffvttmqowHYZAtP4mPhM3yYXaM3FTcuccLV26VAoXLiyrVq1yCYWl4FiRBQWgTZs2rutmHSBsDqae4uPj1eIDq0ulSpUkNjbWrCYzrBf+QF9//bX67dWrV0+4P1CGyJjBJgJhawGyiSebDQIB+BFgCgxvk0bCLtqPP/64ccpvErCdAKa/sPLJPWEqrGvXrjJz5kz3y6YdV6hQQYYOHSp9+/bV0DvYlXn8+PGmtZfZiukPlFlSzGcngahUc2mKnQKY1bbxhmun5QV9c4IcTpAhK+OMRxI7PW/btk26d+8u7733nlqEAt1y3ykcnCCHE2TIyjNhVl4ncHCCDOAbbDl++ukngT8QNkqkP5BZTzDrDYRA2E6BBQKFZe0lgNhoK1eu1LdorKz585//LIbjsL2SsXUSIIHMEjD8gbg/UGaJMZ/VBKgAWU2c7aVLAHuIjBs3TleRQPlBwhskPkwkQAKhRYD+QKE1XpEmLX2AIm3EHdxf7J8yYMAA3UsEPgRMJEACoU+A/kChP4bh2gMqQOE6siHWL/j9YO8Q7Pw8ZcoUv/f6CbFuU1wSCHsCxv5AWCHHeGFhP9wh1UEqQCE1XOErLPx+Pv/8cxk7dqxgczcmEiCB8CEAfyAEbeX+QOEzpuHQEypA4TCKId4Hb34/Id4lik8CJOBBAJHqe/furfsDMV6YBxye2kKACpAt2ANr9MaNG4FV4KDSht8PnJxffvllB0lGUUiABIJNAP5AtWvXZrywYINlfX4RoAKUiq1BgwaSmJjoF8BgFrp69ar+ccBcua+EaNPLli3zdTukrht+PwjVESp+P9euXdPnpVevXrayXrNmDVfG2ToCbNwfAvAHwv91+gP5Q49lgk0g4hUgbNYFi0qZMmWCzTbL9e3atUu3s8dusr4SFKDKlSv7uh1S1w2/n9dee83ywI3+glq9erWGGti9e7ccPHjQ32oCKofNPeErFaZ7mAbEhoWdT4D+QM4fo0iRMOIVIOw2DJMsEn7cWrVqJXv37pUhQ4ZIx44dJSEhQd5++229//333wuikRvpxIkTGgkaARD9SWfPnpWnnnpKOnTooME/ly9fLrVq1ZL169dLp06dpHPnznoP15GwxT4img8fPlxgLVq3bl2afKFkGQpVv5+5c+cKYpLhOYEyanVCAM5nn31Wn0+r22Z7JBAsAvQHChZJ1hMQgdS3yLBMqRadFHwySiNGjEhJNcmmfPzxxymPPvpoyqlTp1J69uyZkroTsasorm/atCnl8uXLKTVr1kxJnQbRe08//XTKhx9+6Mrn7SA9OVJ3SNW2US51Ci4l1bKTMnv2bJXj6NGjWt2ePXtS7r//fj1OXUGRkhoPS49Tl4v7zKcZ3P5JTwa3bJYdpvr9pKTG+0pJVSZdLK1oPFAO+/fvT6lRo0ZKquKasnPnzpRUZTXl999/z7LogcjxzDPPpHzyyScpqTtjp6Qq7llu2ygQiAxGHeHw7QQOTpABY2m1HPg7mrrLe0qq/5/+nwqH54l9CC0CEb8TNCxAsOSsXbtWwy8cP35ctmzZovvRTJ8+XZVLWGqwPw0CcpYqVUr9hVIVJZ0CwVSEP2nfvn1y4MABmTZtmha/4447JHfu3ILoyYjkPH/+fElVuLStIkWKaJ4dO3ZIqgKmx/ny5dM4Wd7y+SOPVWVS/3voXiCh5PdjsEHwS0SqxxRl9erV9VkAf6xssSIhnlKOHDkEb88MDRIc4rCo4lk04mAFp9as1WKMpZ0yQGLIER0dnTXhA8ht+AMhXhj2B2K8sABgsqhfBCJaAcJ0QupbvU5pFC9eXBYsWKBBN+Pj49VRD0SvXLki8Pcwpsni4uJUcZk0aZKMHDlSoqKi/AJ/6NAhQSRnozz8SfDjtnDhQoFfUqoVSn9goSCVKFFC29i+fbtGfcYJFC/8wfKWzy+BLCqESNWI84XpIyiToZLwrCxevFjy5MkjTZs2Vf8b/HDijzbGAH/MzU5o/9KlS6oA4bk0jidPnixFixY1u/mwrB//n/EyYmeyUulIr5+QAzysTIY/UKplWyZMmKAuAVa2z7Yim0BEK0CwqMChuH///qoIdevWTSZOnCip0xsCC09MTIzGpYL1x10Bgk9Q3bp1pVq1an4/PVBqoPTAygMl6JVXXlHrDqxP/fr1U18g+BwtWrRI/T3gqI1zo03kg9zwGXLP57dAFhSE3w8UN7ztGXG+LGg2KE0sXbpUChcuLKtWrXLVh5UsjRs31s3d2rRp47pu1kHqNK2r6tQpUkGbeD6Y/CeQOrXtf2GWDAoBWDS/+eYb3R8IFvB77703KPWyEhLIiEBEK0CwqBhTSrDGlCtXTrDCBm/0jzzyiOTNm1fjUqX6Cbk4li9fXh2R4bwcSEr1JRGYftu2bavt5syZU6pUqSJVq1ZVZQgWkoIFC6qVBFYHKECYJoNylOonpJYgKE2e+QKRycyymEZEnK9Q3e8H01+Iau2eMBUGx/SZM2eqMuJ+z4pjw3poRVtsgwTMJID9gfBCOnDgQHVHSG8lrJlysO7IIhAFl6Vw7LIxnw6FJpgJ/0FbtGihq4AyU69ZcmSmbSOP3TLgEXv44YcF/lZffPGFbVNfdnNwynhADqewMJjwmwQw9Y+XQrwk0R+Iz4MVBCJ+GXxmIWP6BtM2ZcuWzbTyk9m6wz2f4fcTSvv9hPuYsH8k4DQChj8Q44U5bWTCV56IngLLyrBiXnrJkiVZKcK8qQRC2e+HA0gCJGAtAfoDWcs70lujBSjSnwAT+x/qfj8momHVJEACPggwXpgPMLwcdAJUgIKOlBWCQCjv98MRJAESsI+AsT8Q44XZNwaR0jIVoEgZaYv7iT09sN8P/X4sBs/mSCAMCNAfKAwGMQS6QAUoBAYp1ESE3w8UH+yQHGr7/YQaa8pLAuFKgPHCwnVkndMvKkDOGYuwkMTw+0mN9SWYy2ciARIgAX8J0B/IX3IslxkCVIAyQ4l5MkXA3e9n6tSploSHyJRgzEQCJBCSBOgPFJLDFjJCUwEKmaFyvqDc78f5Y0QJSSDUCNAfKNRGLHTkpQIUOmPlaEmN/X7o9+PoYaJwJBCSBOgPFJLD5nihqQA5foicL+CZM2c0zhf9fpw/VpSQBEKVAP2BQnXknCs3FSDnjk1ISEa/n5AYJgpJAiFPgP5AIT+EjutA2IbCOH36tCQnJ7uCPtpFPikpSZs2gk/aIQdkiI6ONqXpN954Qz7//HONSl+qVClT2ghWpXwmbpI085m42QqPSCC4BAx/oMcff1yw19hTTz0V3AYyqO3nn3+Wt956SzZs2CCwfONvXkJCgvTs2VOyZTPHnlCpUiXJnTu37Nixw6t0ixYtkiFDhkjnzp3lpZde8pqHF70TMGfEvLfFq2FGAH4/48aNC5n9fuLi4kxTBLMytFBGzVJIMysH2gcPJhIINQJ2+QMdOXJElZ2lS5dKvnz5JD4+Xo4dOyZ4CRw6dKhtGEuUKCHNmjWTKlWq2CZDqDYclTqFkRKqwqcnt2FxOXz4cHrZTL/nBDnMkAFvP02aNJFixYrJZ599xiXvpj9JbIAESMAgcP36dWndurX8+uuvsnbtWilQoIAcP35cb8fGxhrZgvr92GOPyTfffCOPPPKIS+HZu3evdOrUSa5cuaJ/B++6666gtonKMrIABb3BCKqQFqAIGuxgdZV+P8EiyXpIgAT8IeDpD7RixQp9IXvllVf8qS7DMlCuvv76a8mTJ488/fTTrvxQTtDmhx9+KGXKlJEbN27IxIkTpWnTplK9enV54IEH1EXAKPDOO++oQvPRRx/ptFnNmjV16gpT0qjn7rvvlkaNGsmyZcuMIq7vxYsXyx//+Ee1PI0ePVqgBCJhCgxyGNNfRhv/+te/ZODAgVKrVi3585//LF999ZWrrvPnz8s//vEPratu3bry3HPPCTaxjbREBSjSRjwI/TX8fhjnKwgwWQUJkIBfBAx/oOXLl0uXLl1kz549qmxcvnzZr/rSK/T999/r7bJly6oS5J63VatWqkjkyJFDRowYIW+//bZcvHhRlZnExET529/+JlDQkKKiovQbfzuhcBQqVEi2b98umNaD0lO+fHn55Zdf1MIEH1YjXb16VeuGhQkvoHPmzJEpU6YYt9N8G22MHTtW0D6s9PgeNmyYK9/zzz8vS5YsUR8m1IlpPcgZaYkKUKSNeID9hfNfKPn9BNhdFicBEnAoAVhlpk+frs7ImJJHwpSUL8UgkG4gMj1Ser57UFxgdYFSA7eAGTNmqDUI5eCw7Z5g+fn4448Fm8ciQdmZP3++fmBJunTpkvz000+uIlB6kBd1on84nzt3ruu+twMoU1ByPvnkE8mbN68qVr/99pvWu2rVKqldu7benzdvnkCJw/Tevn37vFUVtteoAIXt0Aa/Y9zvJ/hMWSMJkIB/BPr166eKhmdpTAkFO8HpGQlTR77Szp07VTFp0KCBFC5cWLPdd999UqRIEVU6sArVSJh2QjL8lYoXL67WGFwrWrQovtSvSA9S/4FV5/7779dT7LdWsGBBOXnypKRn7br33ns1f/78+dPU+eOPP+r1rVu36tQZps+MKbddu3bpvUj5J2yXwUfKAFrVT8Pv58KFC/oGgjl4JhIgARKwiwAsIL169VJLCHxvjARfl3Xr1knDhg2NSwF/V6xYUes4dOiQWmfgC2SkJ554QqfFqlWrZlzy+m1MTeEby9qRjKXz7vUZ+dwrwd9f9NH4u4tyyGecu+fFMe6512nkQz3Xrl3T7CVLlrxl5dhtt93mWVVYn9MCFNbDG7zOGX4/r776qpQuXTp4FbMmEiABEvCDAH7gZ82apb4xmHYyElZkTZs2zTgNyjf+5sFBGRaX119/3VUntgLBKrQvvvhC7rnnHr0ON4FTp07pMZQxrFTDtFagysXKlSu1Tliafv/9d7UYwe/IV/KmSCFvuXLltAiYwV/p3XfflRo1aqjTNqbmIin5phdJFNjXdAm4+/08+OCD6eblTRIgARKwigB+5LEHD/bAweosWGiQVq9eLSdOnFAH4GDJAgdnLIGHA/KXX36pdW/btk2nvQYNGqQyYNXXp59+qkv0sc8W7sNaM3jwYJcYsMJkNcGHB07M8DGCnxP6/eijj/qsJr024PSMqTkoby1bttTptO+++05gEcJS/0hKtABF0mj70Vf6/fgBjUVIgAQsJdChQwdVPAwrDByS33zzzaDKAKfiBQsWqMMw/i7u3r1bfWiw2qpHjx7aFpayDxgwQOB3A+UH1hZYWZo3b+6SxdMyg3Nv14wCuAcLFJa+G5EF0F63bt2MLFrevQ73YyOT+zU4VLdr104tSQcOHNDl9R988IHkypXLyB4R39wI0eRhvvPOO7UFOzdk9FcGvEVgeSmWaa5Zs4ZTXyY/K6yeBEggMAJYrQXlACufsP/N5s2bffrJBNYSS4cDAVqAwmEUTeoD/X5MAstqSYAETCGAHaGxvPzZZ5+V/fv3y+zZs01ph5WGBwEqQOmMI7zlsaQRKw3sTAh0B/OklQm7nhr7/dDvx0rybIsESCAQAvC5wUaDcO7NaK+cQNph2dAnQAUonTGEIx32SMBc78GDB9PJac4tzM12795dDO9/c1rxXiuWfWKOecyYMd4z8CoJkAAJOJgApsKC7Qfk4O5SND8IUAFKBxreHuC8hl0ysQOn1QntJyQkSIsWLaxuWjfyggXI2D/CcgHYIAmQAAkESKBy5coB1sDi4UyACpCP0cVumdhvAcoPvOWxtNHYbt1HkaBfxrJHLKtkIgESIAESIAESCC4B7gPkgyfio2AnUTjVIapvqVKlNE5L7969fZTgZRIgARIggVAngDAViIkVExNjW1eMsBl2yoDOQw7sZ7Rp0ybbWJjZMC1AXugiku/ixYsFsVKaNm0qTZo00bgr2ADr+vXrXkrwEgmQAAmQQDgQgPLjHondjj6hfbtlQL8hQzgHSKUFyMvTvXTpUvWBQcRcI2F/icaNG8uKFSukTZs2xmV+kwAJkAAJhBEBWF3wCcW924I9DMYecsGu1yn10QLkZSQw/eW5JTimwrp27SozZ870UoKXSIAESIAESIAEQokAd4I2ebQMDZpvEyaDZvUkQAIkEAQC/Jt9E6ITWNyUJvhHtAAFnylrJAESIAESIAEScDgBKkAOHyCKRwIkQAIkQAIkEHwCVICCz5Q1kgAJkAAJkAAJOJwAFSCHDxDFIwESIAESIAESCD4BKkDBZ8oaSYAESIAESIAEHE6ACpDDB4jikQAJkAAJkAAJBJ8AFaDgM2WNJEACJEACJOA4Ajdu3HCcTHYKRAXITvpsmwRIgARIIGQJNGjQQBITE22X/+rVq1K7dm1BxAJfacaMGbJs2TJftyPyOhWgiBx2dpoESIAESCAQAj/99JPAolKmTJlAqglK2V27dmnAbkQs8JWgAFWuXNnX7Yi8TgUoIoednSYBEiABEgiEwLZt29TqgjpWr14trVq1kr1798qQIUOkY8eOkpCQIG+//bY28f3330vLli1dzZ04cUIaNmyoQbZdF7NwcPbsWXnqqaekQ4cO0qNHD1m+fLnUqlVL1q9fL506dZLOnTvrPVxHQhgnRHYfPny4wFq0bt26NPki1TLEYKhZeOiYlQRIgARIgARAYPv27aoALViwQJYsWSJz5syRZ555RpWfV155RSFB8di8ebPUqFFDjh07JtevX5fs2bPLuHHjpFevXlK0aFG/YA4aNEjuvfdeGT9+vBw5ckRatGghL7zwgkyZMkUmTJggJUuWFChdffv2VcXskUcekVy5csm0adM0wvvUqVNvyde6dWu/ZAnlQmGrAEHbTU5OFiOWiV2DlJSUpE3bKQdkiI6OtguBY9qtW7eu7Nu3TyM92ykUnk0kRJy2K0GGuLg42bRpk10isF0SCGkCsADBkrN27VpZuXKlHD9+XLZs2SLnz5+X6dOna99gqcE5lI9SpUqpv9CpU6fk4MGDMnbsWL/6j79hBw4cUGUGFdxxxx2SO3duqVevnsTGxsr8+fPl8uXL2laRIkW0jR07dkjNmjX1OF++fNK9e3ev+TRDBP0TtgpQBI0hu5pJAvjDAaXYTsUDokIGJDvlgAzgwUQCJJB1AhcvXpT9+/dL8+bNpXjx4gIrUMWKFSU+Pl6tMKjxypUrsnv3btc0GV44oLhMmjRJRo4cKVFRUVlvOLXEoUOHpEKFCq7yUKZy5MghCxcuFPgl9ezZU5UtWHtKlCihbcBaBWsQEhQvvBR7y6cZIuifsFWA8OOCj51R2PEcGZYfO+UwZIig59prV/lM3MTCZ+ImCx6RQFYJwKICh+L+/furItStWzeZOHGi7Ny5U2Dhwd8aTHPB+oPVWUhQgOATBEt0tWrVstqkKz+UGig9sPJAicJ0G6w7sD7169dPfYEw/bVo0SL1R4KjNs6NNpEPcsNnyD2fq4EIOghbBSiCxpBdJQESIAESsJAALCrGlBKsMeXKldOXbVhV4G+TN29eqVOnjowYMcIlVfny5dURGc7LgST4EzVr1kzatm2r7ebMmVOqVKkiVatWVWUIq70KFiyoVqA8efLoSjVMk0E5mj17tlqCoDR55gtEplAtG5WSmkJV+PTkNt5w7bS8QD4nyOEEGdIbK6vuOYWDE+RwggxWjTvbIYGsEDDr/8bAgQPVWRmrxTJKZsmQUbue950ih6dcwTrnMvhgkWQ9JEACJEACJOBBYOPGjdKuXTspW7asrsjyuM1TGwlwCsxG+GyaBEiABEggvAlguTqWyTM5jwAtQM4bE0pEAiRAAiRAAiRgMgEqQCYDZvUkQAIkQAIkQALOI0AFyHljQolIgARIgARIgARMJkAFyGTArJ4ESIAESIAESMB5BKgAOW9MKBEJkAAJkAAJkIDJBKgAmQyY1ZOAFQSw2ysTCZAACZBA5glQAfLC6ujRo7qzZvv27eXBBx/UHTc7deokW7du9ZLbvEtYOon9IyBHly5dZNeuXeY1xprTEGjQoIEGE0xz0YaTq1ev6lb6586d89k6dnRdtmyZz/u8QQIkQAIkcCsB7gN0KxO9gi3EEUvFSCtWrNC4Kp9//rlxydRvBLx7/fXXZfHixXL77bfLunXrZMCAARp52NSGWbkGFIRFpUyZMrbTgNKLKNIFChTwKQsUoKlTp/q8zxskQAIkQAK3EqAF6FYmXq+cPn1aihUr5vWeGRdz5colo0ePVuUH9SOQ3a+//irXrl0zoznW6UZg27ZtrgCGq1ev1t1b9+7dqwpwx44dJSEhQYMaogiCCbZs2dJV+sSJE9KwYUM5efKk61pWDs6ePSuIFdShQwfp0aOHLF++XIMWrl+/XmCF7Ny5s97DdaSuXbtqfKHhw4cLrEVQlN3z0TKUFfrMSwIkEEkEaAHyMdqXLl3SqSeESsOPEn7QEO3XqoS3fnyMhOB1TZs2lRw5OGQGE7O+EegQEZwXLFigO7jOmTNHnnnmGYHyg3FAguKxefNmQWDCY8eOyfXr1yV79uwaAbpXr15StGhRv8QbNGiQYOfY8ePHy5EjRzR20AsvvCBTpkyRCRMmSMmSJVXp6tu3rypmCLwIZXnatGmSnJysliDPfK1bt/ZLFhYiARIggXAmwF9TH6PrOQUGqwB+2OCX466Y+CgetMsXL16U5557TmBZ4DRH0LCmWxHGGrzXrl0rK1eulOPHj8uWLVvk/PnzMn36dC0LpRjnUD7wPCQmJsqpU6fk4MGDMnbs2HTr93Vz3759cuDAAVVmkAcRnHPnzi316tWT2NhYmT9/vly+fFnbKlKkiFazY8cOV1TqfPnySffu3b3m89VmpF+vW7eugHtMTIxtKGBdRrJTBrQPOeLi4mTTpk04jdgEDniZMAKB2gEiKSlJm7VTBggAOaKjo+1AYEmbVIAyiRkWAQSz27lzp2UK0M8//yz9+vWT8uXLy6xZs/THNpPiMpufBKBw7t+/X5o3by7FixdXK1DFihUlPj5erTCo9sqVK7J7927XNBl+NKC4TJo0SUaOHClRUVF+tQ6/rwoVKrjKQ5mCxW/hwoXql9SzZ0999mDtKVGihLYBaxWsQUhQvPAHy1s+zcB/biEA5Qc/dnYqH2gfyU4Z0D7kAA8mEogUAlSAfIw0pr7cE36c8JZfpUoV98umHZ85c0YeffRR9Td54oknTGuHFaclAItK5cqVpX///qoIdevWTac+ofjCwoMfqXHjxqn1B0oxEhSgt99+W2BNgK+WvwlKDZQeWHmgRGG6rWbNmmp9giJcq1Ytnf6Cc/6QIUMEjtrwQTLahJUKcnvm81eeSCiH8cTn8OHDtnXXeMu3UwZ03pDDNhAOaZjPxM2BCPdnggrQzbFOc4S3fCw/R4IyhM+oUaMsWxn00Ucf6dTLmjVrBI64SPhRxIqfQoUK6Tn/CT4BWFSgdCDBGlOuXDn9cYRVBf42efPmlTp16siIESNcjcNCB7M5nJcDSfAnatasmW67gHZz5sypCnfVqlVVGcLYFyxYUK1AmKKFAoRpMihHs2fPVksQlCbPfIHIxLIkQAIkEK4EolJ/2NOaOsKkp4bm6pS3KjvlcAoLux8tszgMHDhQnZVbtWqVqS6aJUemGv9fJifIkBV5zcrrBA5OkAF8nSKHWWOd2XqdwMEJMkTCM8Fl8Jn9X8F8JOBBYOPGjbpRJXzDMqv8eFTBUxIgARIgAZsIcArMJvBsNvQJYLk6VgUykQAJkAAJhB4BWoBCb8woMQmQAAmQAAmQQIAEqAAFCJDFSYAESIAESIAEQo8AFaDQGzNKTAIkQAIkQAIkECABKkABAmRxEiABEiABEiCB0CNABSj0xowSkwAJkECGBLBPFBMJkIBvAlSAfLPhHRIgARJwEWjQoIHuBu+6YNPB1atXNQzLuXPnfEqAzTCXLVvm8z5vmEvg2rVrgucF8SPtSK+++qo0btxYN/PFhr6BbtJqRx+saJPL4K2gzDZIgARCmsBPP/2kO2+XKVPG9n7s2rVLdwMvUKCAT1mgADF4sk88pt/A7v2VKlXSmIEIb4Od3a1M2NF+woQJGhbHynZDrS1agEJtxCgvCZCA5QS2bdvmCn6LHzdsfLl3716NydaxY0eN2Yd4cEiIz9ayZUuXjCdOnJCGDRvKyZMnXdeycnD27Fl9g+/QoYP06NFDli9frj9s69evl06dOknnzp0F93AdqWvXrhqaZfjw4QJr0bp169Lko2UoK/T9yzt37lwNqIznBMqolQlhnPbs2SPTp0/XjVqxU/2xY8esFCFk2qICFDJDRUFJgATsIoA3agS/XbBggcyaNUvmzJkjr7/+ujRq1Eg+/vhjWbhwoWzatEk2b94sd911l/7gXL9+XcVF8FxMhRQtWtQv8QcNGqQx4T755BMZOXKkto2AvVOmTNG3/Hnz5snLL78sr732mtaPmHXx8fGCH2H8GMISBGuAkW/s2LF+ycFCmSPw448/CoInQ/lp166dfPrpp4Lg1lYlKNz169eXwYMH60atiG2IIMlMtxII2ykwBKdMTk52xbe5tevWXElKStKGjNgu1rSathXIEB0dnfZiBJ7xmbg56HwmbrLIzBEsQPhhWbt2raxcuVIDFW/ZskXOnz+vb9qoA5YanOfKlUunqBITE+XUqVOCKRB/lY59+/bJgQMHZNq0aSomgt/mzp1b6tWrJ7GxsTJ//ny5fPmy+iYVKVJE8+zYscMV0DdfvnzSvXt3r/k0M/8JOgEomrD4YYqyevXq+ixgnHr37h30trxVWLp0aXn//fddtxDIeeLEiXL06FGVxXWDBxK2ChDHlgRIgASCQeDixYuyf/9+ndIoXry4WoEqVqyoVhZYYZBgadm9e7drmiwuLk4Vl0mTJqnVJioqyi9RDh06JBUqVBCjPJSpHDlyqMUJfkn4cStVqpQqSCVKlNA2YK3q27evHkPxgrLrLZ9fArFQugTwrCxevFjy5MkjTZs2FcQax4s4LIYYg+zZs6dbPhg3f/jhB52ehfXJSJADzw1TWgJhSyQmJkbwsTMKO1Ablh875TBkSDv0kXfGZ+LmmPOZuMkioyNYVDDlhGkEKELdunXTN2pMc8DCg+cK01yw/mCaDAkKEHyC6tatK9WqVcuoCZ/3odRA6YGVB0rQK6+8otYdWJ/69eunvkDwOVq0aJH6I2HpO86NNpEPcteqVUuvG/l8NsgbARFYunSpFC5cWFatWuWqB6v1sCJrxYoV0qZNG9d1sw6yZcsmY8aMUQUdyjGULzhkw2LIlJZA2CpAabvJMxIgARLwjwAsKvCjQII1Bit68EKDN3r42+TNm1fq1KkjI0aMcDVQvnx5dUQOdPlxjRo1pFmzZtK2bVttN2fOnOoPVLVqVVWG4GBbsGBBtQLB6gAFCNNkUI5mz56tliAoTZ75XILyIKgEMP312GOPpakTU2FwTJ85c6YlChCe0aFDh+rY43mA1XL8+PFpZOLJ/xGISjWNpYQjDOMN107LC7g6QQ4nyOCEZ8wpHJwghxNkCOdnAitvWrRooY6wGfXTKWPhFDky4mX2fSdwcIIM4OwUOcwac64CM4ss6yUBEog4Ahs3btSVP2XLls2U8hNxgNhhEnAQAU6BOWgwKAoJkEBoE7j33nt16XFo94LSk0BkEKAFKDLGmb0kARIgARIgARJwI0AFyA0GD0mABEiABEiABCKDABWgyBhn9pIESIAESIAESMCNABUgNxg8JAESIAESIAESiAwCVIAiY5zZSxIgARIgARIgATcCVIDcYPCQBEiABEiABEggMghwGbyXccbumdi187PPPtOdVa9evapRn7G5GQIdWpWwkyt2FsUW+NjdddSoUbrNulXts52bBBBIsHnz5oIYUNg7FJG+EWjyueee012Ab+Y092jJkiUafBPb3WPn3xdeeMEV9sDcllk7CZAACYQXAVqAvIzniy++KIj/AyUIsXMWLFggCEo4bNgwL7nNuYTAih988IFGcf70009VAXrrrbfMacwBtULpRMwcJycoHHgeEOwQY9KjRw+Nv2SVzHgGX3/9dVWAIAcCXg4YMMCq5i1vBy8eFy5csLxdNuhcAoiJdunSJecKSMlCigAtQB7DhcjJsPxs2LBB3/BxMfv4UgAALZ1JREFUGz98I0eOlG3btnnkNu8UsX4QUA/Rg/Gf/sSJE1K6dGnzGrS5ZoQsueeee9SaUb16dQ0iiWjGiHPk1HT69GkpVqyYZeLB+jh69Gi5/fbbtU0EvPz111/l2rVrYRnpef369fLwww8L4mGhr/Xq1dOYWLC8MUUmAVjF8YKK5wGf++67T1q2bKl/oyOTCHsdCAEqQB709uzZowEPPf/I4kcHQQmtTFB+1qxZo4HtcufOLX/729+sbN7ythBZ+z//+Y9+0DiUiypVqohTFCK8ebZv316nwM6ePSsnT57UqOBWgUJkZ3yMhCCXTZs2DUvlx+gjFH/8H8AHCdHR8XJAhcggFHnfmI7G5/PPP5c33nhDXwyNZ4IKUeQ9D4H0mAqQBz34VmA6xikJShc+H3/8sTz++OOuHwKz5QMDvH2DhxXpypUrt3DHjx8+UIqQPBWiTp06WeqTZUyBqTCp/8Ai2KtXLw194K6YGPfN+r548aL6HoHN1KlTzWrmlnoxRYlnEBHJrUhnzpy5pZljx44JPr4Uoo4dO2bpmYUVLzk52RX08ZYGLbgAqzOSEXjSgia9NgE5oqOjvd7zdRGKCF7MrPKNPH78+C2iQG58vClEDRo00Nhs8KPMbOIzcZOUP8/EzdLOP7Lm1835HFwSwtpw4MCBW3wP8B+vT58+gh9qK9KRI0dky5YtrqYSEhLk559/Fm8/Cq5MPLCUQO3atQVBL3fu3GlZu3gGOnfurErIrFmzJH/+/Ja17fSGsvIj5/S+UL7ACeB54DMROMdwroEWII/RjY2NVT+D559/Xv0t8ANz/vx5GTFihK7AsupNB2/3gwcPVuvCbbfdJkuXLpW4uDgpVKiQh8TmnMLygxVoVqWDBw+qg7G79c3T4mO3TxBWf7knOCUnJibqNJ37dbOOofw++uijAmX4iSeeMKsZn/UWKFBALZE+MwT5xhdffCHLly9PU6v7FFj9+vWlTZs2Ll+9NBkzeRITEyP4wAfNrmRYfuyUAX035MgKB1g+sUjEqjRt2jT597//naY5+EZiCgwvr5gCa9GiRUA+QXwmbuL155m4Wdr5R1SAvIzRSy+9JO+995506dJF/Stg9cE0lJUrbuLj46Vfv37StWtXlQHKAGQK51S4cGH17XCKz48nazwH8AFCgjKED7YmKFOmjGdWU84/+ugjgSUS0z+rV6/WNvCGO2PGDMsUY1M6lk6leO4NJ+hgKDzpNMVbIUIg2ApPiHSbYppAICr1j3ja11oTGrGjSkNzdcpblZ1yOIVFes8BLD/wxYCVwazkFA5OkMMJMmQ0zlgGj4/ngoSMymXlvhM4OEEGMHOKHOmNH1bE4icL/nhmJSdwcIIM4OsUOcwaa1qAzCLLerNEAFNuZio/WRKGmR1BAM7WVjlcO6LDFCJDAlgNy0QCwSJAJ+hgkWQ9JEACJEACJEACIUOAClDIDBUFJQESIAESIAESCBYBKkDBIsl6SIAESIAESIAEQoYAFaCQGSoKSgIkQAIkQAIkECwCVICCRZL1kAAJkAAJkAAJhAwBKkAhM1QUlARIgARIgARIIFgEqAAFiyTrIQESIAESIAESCBkC3AcoZIaKgpIACTiJAAKBNm/eXCpWrKib812/fl03bXzuueekTp06loi6ePFi3QnciHl19uxZ3S18/fr1GrrHEiHYiIsAnwkXipA4oAIUEsNEIUmABJxIADsSL1q0yCXaihUrZMiQIRqZ3HXRxIMHH3xQ8EG6du2axorr27cvlR8TmWdUNZ+JjAg55z6nwJwzFpSEBEggxAmcPn1aEL/MjjR58mS5/fbbpWPHjnY0zzZ9EOAz4QOMAy7TAuSAQaAIJEACoUng0qVLGiAX8akw/XTy5EmZOHGi5Z3BjyyC4mJKjMleAnwm7OWfldbDVgHCHwQE1zSCuWUFSjDzJiUlaXV2ygEZoqOjg9mtkKyLz8TNYeMzcZNFIEee0x3btm2TXr16yZIlS6RUqVKBVJ2lsv/617+kadOmUrJkySyVY+bgE+AzEXymZtXIKTCzyLJeEiCBiCNQu3ZtKVu2rOzcudPSvi9fvlwSEhIsbZONZY4An4nMcbIjV9hagGJiYgSfw4cP28HV1aZh+bFTDkMGl1AResBn4ubA85m4ySKQI0x9uadDhw5JYmKiVKlSxf2yqceYejty5Ijgh5bJfgJ8Juwfg8xKELYKUGYBMB8JkAAJ+EvgypUr6gOE8vjhw2fUqFFSpkwZf6vMcjkoXHC8zp49e5bLskDwCfCZCD5Ts2qkAmQWWdZLAiQQ1gTg47N7927b+1i9enXLlt3b3lmHC8BnwuED5CEefYA8gPCUBEiABEiABEgg/AlQAQr/MWYPSYAESIAESIAEPAhQAfIAwlMSIAESIAESIIHwJ0AFKPzHmD0kARIgARIgARLwIEAFyAMIT0mABEiABEiABMKfABWg8B9j9pAESIAESIAESMCDABUgDyA8JQESIAESIAESCH8C3AconTG+du2aNGrUSCpXrixTpkxJJ6e5t9asWSPPPfecbNmyxdyGWLtPAkePHpXmzZtLxYoVdbO769evS758+XRc6tSp47NcsG+8+uqruufLbbfdplUj7ML48eOD3QzrIwESIIGwJ0AFKJ0hXr16tVSqVEk3Ozt48KCUK1cundzm3EIIjbFjx+qPrjktsNbMEvAMcrhixQoZMmSIpZvQbd++XSZMmCC1atXKrNjMRwIkQAIk4IUAp8C8QDEuzZ07V9/6W7VqJTNmzDAuW/Z98eJFefbZZ/VH1rJG2VCmCSC6PEIQWJWwxf6ePXtk+vTp0q5dOxk4cKAcO3bMqubZDgmQAAmEFQFagHwM548//qgRnSdOnKgBVbt16yaDBw+WQoUK+SgR/MsvvviidOnSReLi4oJfOWvMMoFLly5p3CfEe0IAypMnTwqeD6vSiRMnpH79+vocItbUtGnTpH///rJo0SKrRGA7JEACJBA2BKgA+RjKefPmScOGDaVAgQKCWDuI8TJ//nzp3bu3jxLBvTxnzhzJkSOH/uAmJSUFt3LW5hcBzymwbdu2Sa9evWTJkiX6fPhVaRYKlS5dWt5//31XiZ49e6oCBv8kPJ9MWScAK15ycrLceeedWS8cpBLG/287ZUBXIEd0dHSQehW61fCZuDl24f5McArs5li7jjD1tHjxYtm6das0bdpUmjRpom/7UErg/GpFQvvfffedKkB9+vQRw/oAqwOTMwjUrl1b4IS8c+dOSwT64YcfVNlybwzWKCjKTP4RgHXV7h99tG+3DKAHGWht9u85YqnQJMC/nF7GbenSpVK4cGFZtWqV6+65c+ekcePGAsfXNm3auK6bdfDxxx+7qsYbPtrkVIcLiS0HUDbc06FDhyQxMVGqVKniftm042zZssmYMWMkPj5eLT5QyOGkHxsba1qb4V7xpk2bwr2L7F8WCcTExAg+WIBiVzKsgXbKgL4bctjFwex2qQB5IYzpr8ceeyzNHUyFde3aVWbOnGmJApSm8dSTqKgoz0s8t5gAnJDbt2+vrUIZwmfUqFECfxwrUoUKFWTo0KHSt29fuXHjhhQvXpxL4K0AzzZIgATCkkBU6h/xtK+1YdJNQ3N1igZtpxxOYWH3o+UUDk6Qwwky2P08sH0S8EbACf83nCAD2DhFDm/jFIxr9AEKBkXWQQIkQAIkQAIkEFIEqACF1HBRWBIgARIgARIggWAQoAIUDIqsgwRIgARIgARIIKQIUAEKqeGisCRAAiRAAiRAAsEgQAUoGBRZBwmQAAmQAAmQQEgRoAIUUsNFYUmABEiABEiABIJBgApQMCiyDhIgARIgARIggZAiQAUopIaLwpIACZAACZAACQSDAHeCDgZF1kECJEACJBDxBLBDO6IFfPbZZ7pb+9WrV6VRo0YycOBAyZUrl2V8PvzwQ0GonLx580q5cuXkxRdflIIFC1rWfqg0RAtQqIwU5SQBEiABEnA0ASgaO3bsUCUIsRsXLFggiBk4bNgwy+T+5ptvZNq0aTJr1iyNH3n//fdrCB3LBAihhmgBCqHBoqgkQAIkQALOJJCUlKSWnw0bNki+fPlUyDx58sjIkSNl27Ztlgm9Z88eqV+/vhQrVkzb/NOf/qQK0LVr1yRHDv7kuw8ELUDuNHhMAiRAAiRAAn4QgOKBgMWG8mNUcfvtt0uzZs2MU9O/a9SoId9++60cO3ZM21q4cKFA+fn9999NbzvUGghbdfD06dOSnJzsCuZm18DgrQDJCCpnhxyQITo62o6mHdUmn4mbw8Fn4iYLHpFAMAhky5ZN/X6CUVcgdcTHx8sTTzyhH8iUkJAghQoVkpw5cwZSbViWpQUoLIeVnfJGIC4uzhGKIJRRuxVStA8eTCRAAsEhUL16dTlw4IBcuHAhTYXHjx+XPn36yJUrV9JcN+sEL/5169aVTz75RH2QMAWGBCWIKS2BsLUAxcTECD6HDx9O22OLzwzLj51yGDJY3HXHNbdp0ybHyUSBSIAEwoNAbGystG3bVp5//nkZPXq05M+fX86fPy8jRoyQwoULW7YK7MSJE9KjRw9ZtmyZyjBx4kRp06ZNeEAOci/CVgEKMidWRwIkQAIkQALpEnjppZfkvffeky5duqjDMaw+8P8ZMGBAuuWCebNs2bLSu3dv+ctf/iIpKSly9913y/Dhw4PZRNjUFZUKKCVseuPWEcPqYaflBeI4QQ4nyOA2NDwkARIgAccScMLfSyfIgAFyihxmPSz0ATKLLOslARIgARIgARJwLAEqQI4dGgpGAiRAAiRAAiRgFgEqQGaRZb0kQAIkQAIkQAKOJUAFyLFDQ8FIgARIgARIgATMIkAFyCyyrJcESIAESIAESMCxBKgAOXZoKBgJkAAJkAAJkIBZBKgAmUWW9ZIACZAACZAACTiWADdC9DI0R48elebNm0vFihV1I6nr169rgLvnnntO6tSp46WEOZdeffVV+fzzz+W2227TBrDB1fjx481pjLWSAAmQAAmQQAQRoALkY7Dz5MkjixYtct1dsWKFDBkyRBUS10WTD7Zv3y4TJkyQWrVqmdwSqycBEiABEiCByCJABSiT441I4sWKFctk7sCzYQv1PXv2yPTp0yUxMVHKlCmjCliJEiUCr5w1kAAJkAAJkECEE6AC5OMBuHTpkrRv316nwM6ePSsnT54UBJWzKiGgXf369WXw4MGq/EybNk369++fxipllSxshwRIgARIgATCjQAVIB8j6jkFtm3bNunVq5csWbJESpUq5aNU8C6XLl1a3n//fVeFPXv2VAUM/klWtO9qmAckQAIkEEEEYO1PTk52xcGyo+tJSUnarBGLyw4Z0CbkiI6Otqt509vlKrBMIq5du7bACXnnzp2ZLBFYth9++EGVLfdaELc2Rw7qrO5MeEwCJEACwSQQFxdn+48+lA4nKB6QATzCNfHX1MfIQtlwT4cOHVJfnCpVqrhfNu04W7ZsMmbMGImPj1eLz5w5c6RSpUoSGxtrWpusmARIgAQincCmTZsiHUHE9J8KkI+hhhMyfICQoAzhM2rUKPXH8VEkqJcrVKggQ4cOlb59+8qNGzekePHiXAIfVMKsjARIgARIIJIJRKX+sKc1dYQJDWPu9PDhw7b2yAly+CPDb7/9JtiHCJ/s2bPbypCNkwAJkIA/BL7//nupXLmyP0VZJgII0AcoAgbZny7u379fZs2apUvv/SnPMiRAAiRgJ4EZM2bIoEGD7BSBbTucABUghw+QXeLVq1dPnnnmGZk8eTKX3ts1CGyXBEggywTgMoBd+5988kl5+OGHs1yeBSKHABWgyBnrLPcUexC1aNFC/vGPf8hPP/2U5fIsQAIkQAJWEjh37pw89NBDMnbsWIEf5aOPPmpl82wrxAhQAQqxAbNS3KioKLUA5c+fX/dAQkw0JhIgARJwIoFdu3ZpDEcjhFHLli3pv+jEgXKQTFSAHDQYThSlYMGC8vbbbws2gkQsNCYSIAEScBqBTz75RB544AH59ttvVTSsmqX/j9NGyXnyUAFy3pg4TqL77ruP/kCOGxUKRAIkYGxP8vjjjwv2ajNSs2bNLI3daLTL79AiwH2AQmu8bJMW/kCbN29WfyBszviHP/zBNlnYMAmQAAkgXuNf//pX+eijj3SvNINIrly59Lpxzm8S8EWAFiBfZHg9DQH6A6XBwRMSIAGbCWCFF3bIx6ov99SgQQNp2LCh+yUek4BXAlSAvGLhRW8E6A/kjQqvkQAJ2EFg0qRJ0rZt21uafvDBB2+5xgsk4I0AFSBvVHjNJwH6A/lEwxskQAIWEkBcxMcee0zwYlaoUCFtGfESe/fubaEUbCqUCVABCuXRs0l27g9kE3g2SwIk4CKAvcmwR1mrVq3UDwiBqv/85z9L7ty5XXl4QALpEWAssPToBOGeP3G4gtBsmirMkOHs2bPSuHFjKVq0qCxbtoz7baQhzhMSIAEzCWBPstatW8uvv/4qa9eulQIFCsjx48e1SViGmEggMwTCVgGCSTQ5OVlKly6dGQ6m5UlKStK67ZQDMkRHR8uZM2eC2s+NGzdK+/btpXv37rrzalArN6GyunXryr59+yQmJsaE2jNf5enTpzWznXJAhri4ONm0aVPmBWdOEnAIAYS6QKwvbHp47733OkQqihFqBDgFFmoj5iB58YcnlOKFQfmBUmx3ggx2y4H2wYOJBEKNAJSe999/X5599lkqP6E2eA6TN2wtQGZM+/gzdk6Qw0wZsBFZly5ddKfoNWvWOHp/IDM5ZOXZcIIcTpAhK8yYlwRAAH4/2OSwTp06MnfuXMH2HEwk4C8BWoD8JcdySoD7A/FBIAESsIIA/H569eql/j6TJ0+m8mMF9DBvgwpQmA+wFd3DMtR33nmH8cKsgM02SCBCCTz//PP6NwaxCeH0zEQCgRKgAhQoQZZXAvAHwpw83syMaMxEQwIkQALBIEC/n2BQZB2eBKgAeRLhud8EnnrqKWnRooXuzYG5eiYSIAESCJSAsd9Py5YtBX9jmEggWASoAAWLJOvROXlYgPLnz69z9ZizZyIBEiABfwnQ78dfciyXGQJUgDJDiXkyTYD+QJlGxYwkQAIZEKDfTwaAeDsgAlSAAsLHwt4I0B/IGxVeIwESyAoB+v1khRbz+kOACpA/1FgmQwL0B8oQETOQAAn4IEC/Hx9geDmoBKgABRUnKzMIcH8ggwS/SYAEskKAfj9ZocW8gRCgAhQIPZZNl4C7PxDm8pnMI3Djxg3zKmfNJGAhAfr9WAg7wpuiAuTlAcCPyQcffCAJCQka7LNNmzby+uuvy5UrV7zkNu/SDz/8IF27dlUZHnroIdm9e7d5jZlUs+EPhNg9ixcvNqmV4FfboEEDSUxMDH7FWazx6tWrUrt2bTl37pzPkggKuWzZMp/3eYMEQoUA/X5CZaTCQ04qQF7G8cUXX5QdO3bIzJkzdVO/BQsWyKFDh2TYsGFecptz6dKlS9KzZ0/p3bu3ytC/f38NPGpOa+bWavgDIYJzKOwPBBmhBJcpU8ZcMJmofdeuXVKqVKl0d76FAlS5cuVM1MYsJOBcAobfD/YS434/zh2ncJKMCpDHaCYlJclnn30mY8aM0f1scDtPnjwycuRIad68uUdu806/+uor/QH+4x//qI00adJE3nzzTfMaNLHmUPMH2rZtm1pdgGT16tXSqlUr2bt3rwwZMkQ6duyolkFsx4/0/fffCzZoM9KJEyekYcOGcvLkSeNSlr7Pnj2rf/w7dOggPXr0kOXLl0utWrVk/fr10qlTJ+ncubPgHq4jwUJ4+vRpGT58uMBatG7dujT5aBnKEn5mtomAu9/PlClTGOfLpnGItGZzRFqHM+rvnj17pEKFCpIvX740WW+//XaNQpzmooknhw8fFrT5wgsv6I9voUKF5OmnnzaxRXOrNvyB2rdvL5jjf+2118xtMIDat2/frgoQLH9LliyROXPmqPUNys8rr7yiNUPx2Lx5s9SoUUOOHTsm+AOePXt2GTdunG4CWbRoUb8kGDRokGDacPz48XLkyBHdWRvPAH4UJkyYICVLllSlq2/fvqqYPfLII5IrVy6ZNm2aJCcny9SpU2/J17p1a79kYSESsIqA4feDKTDG+bKKOtuhAuTxDGTLlk2nPzwuW3567do1+fLLL2XWrFlSvXp1+eKLL3Q6bO3atZIzZ07L5QlGg4Y/0KhRo6R+/fry4IMPBqPaoNcBCxAsOWC9cuVKOX78uGzZskXOnz8v06dP1/ZgqcE5lA9MUcFf6NSpU3Lw4EEZO3asXzLt27dPDhw4oMoMKrjjjjskd+7cUq9ePYmNjZX58+fL5cuXta0iRYpoG5iqrVmzph5Dae/evbvXfJqB/9xCoG7dugLuMTExt9yz6gIseEh2yoD2IUdcXJxs2rQJp5Ylw+9n6NChqvxb1jAbingCnALzeASgbOBH6MKFC2nu4EewT58+ljlCFytWTMqWLavKDwRp2rSpWhlCwYcmDTiPE6f7A128eFH2798vVapUkS5dugisQFBq4uPjZd68eapcfPjhhzpF2qhRI+0dfjTwzGDadMSIEX6b7+FnBusjpgyR0G6OHDlk4cKFaolq3Lix+oXBN8lQemCtMo6heMFi5S2fVsh/biEA5QeWMzsT2rdbBvQfMoCHlcnd72fw4MFWNs22SEBoAfJ4CPCm3bZtW52mGT16tPoB4U0fP2yFCxfWN36PIqac3n///TpNhCk5/BjjrQzWqdKlS5vSnlWVGv5A+JHu1auXrl7C1JFTEiwqcCiG0zkUoW7dusnEiRNl586dauHBWzqmufBMYHUWEhQg+ATBmlCtWjW/u1KiRAlVemDlASdMt0G5gfWpX79+6gsEnyO8McMfCY7aODfaRD7IDZ8h93x+CxQBBTGe+GDK2a505513atN2ygABDDlUGAv+od+PBZDZRLoEqAB5wfPSSy/Je++9pxYAvIFj+XuzZs1kwIABXnKbcwlTHJABssAqgamWd9991zIFzJxe/V+tTvYHcreowBpTrlw5/XHEijz42+TNm1fq1KmjCrHBqHz58jp9EOjKFfgT4TmDAo52MdUJ5bdq1aqqDGG1F9hhyg2O+VCAME0G5Wj27NkCvyAoTZ75DDn5TQJOIkC/HyeNRmTKEpWSmsKx68bbjFPequyUwyksPJ8z7K0EKxt+sK3wBzKLw8CBA9VZGavFMpPMkiMzbRt5nCCDIYud307g4AQZMAZWygEr5uOPPy7w++HUl53/AyK7bfoARfb429p7/OHDnh+hsj+QJ6yNGzdKu3bt1Fcrs8qPZx08J4FII+Du9xOo1TTS2LG/wSVABSi4PFlbFggY/kD58+dXfyD4BIRSwqo2OB3//e9/DyWxKSsJ2EbA3e9n8uTJLod/2wRiwxFNgApQRA+//Z03/IGw9Bw+AUwkQALhS8Dw+8GiAe73E77jHCo9owIUKiMVxnLCkvLMM89IqMULC+MhYddIIOgE4PcDq8+zzz7L/X6CTpcV+kOACpA/1Fgm6ARC3R8o6EBYIQmEEQH6/YTRYIZRV6gAhdFghnJXQt0fKJTZU3YSMJMA/H4Q1BlTXvT7MZM0684qASpAWSXG/KYRoD+QaWhZcQQSwD5RTkjw+9m6datuFkq/HyeMCGUwCFABMkjw2xEEvPkDYYfjxYsXO0I+ChG5BBo0aKBx2OwmcPXqVd2F/Ny5cz5Fwd5ay5Yt83nfqhv0+7GKNNvxhwB3gvaHGsuYSgD+QIi0jv2Bdu3aJZMmTZKKFStaslmiqR1j5SFLAD4ssKggDpvdCf8nsBt4etYUKEBTp061VVT6/diKn41nggAtQJmAxCzWEoA/0FtvvaUhQIYNGyY///yzbNiwQaOzWysJWyOB/yOAbRqM2G+rV68WbHy5d+9ejcnWsWNHSUhI0Cke5EYctpYtW7rQnThxQho2bCgnT550XcvKwdmzZwUbBnbo0EF69Oghy5cv13hv69evl06dOknnzp31Hq4jde3aVUOzDB8+XGAtWrduXZp8VliG6PeTlRFmXrsI0AJkF3m265MApryefPJJwRukkRCP7YMPPtBI58Y1fpOAVQQQIw4K0IIFC3Tzyzlz5ujWDVB+EH8NCYoHLJeI6Xbs2DGBEoBAvwiei8C/RYsW9UvcQYMG6bLx8ePHy5EjR3T39BdeeEGmTJkiEyZMkJIlS6rShVhwUMwQsw6xA6dNm6YR3mEJ8szXunVrv2TJbCHD7wdTYOlZqjJbH/ORgBkEwlYBOn36tP7nN+LbmAEvM3UmJSVpNjvlgAzR0dGZEdcRefBmi7drz4Q3b7xNFytWzPNWps75TNzEFGrPxE3J7TmCBQjP3tq1a2XlypVy/PhxgaJ+/vx5mT59ugoFSw3OoXxgiioxMVFOnTolBw8elLFjx/ol+L59++TAgQOqzKACBL/NnTu31KtXT2JjY2X+/Ply+fJlbQsBlJF27NghNWvW1ON8+fJJ9+7dvebTDCb8Y/j9wHoLnz4mEnAqgbBVgOLi4gR/POxOTlA8IAN4hEpCaAlEQ4cvEP74G+mXX36RN998U8aMGWNc4jcJmE7g4sWLsn//fmnevLkUL15crUDwSYuPj1crDASAhXL37t2uaTL8f8OzC/+1kSNH+h3y4dChQ1KhQgVXeShTOXLkkIULF6qFtGfPnqpswdpTokQJZQFrFaxBSFC8oOx6y6cZgvyP4feDKUCGiAkyXFYXdAJhqwBt2rQp6LBYoXUEEGQUPyKIGP3NN9+4Gl6xYoWMGjVKpxZcFzN5EBMTI/gcPnw4kyXMyWZYA+2Uw5DBnB6GV62wqFSuXFn69++vilC3bt1k4sSJsnPnTrXw4JnCNBesP4afEJ5dhHuoW7euVKtWzW8gUGqg9MDKA984TLfBugPrU79+/dQXCD5HsLoMGTJEHbVxbrSJfJC7Vq1aOk1m5PNboHQKuvv9YFd3yMtEAk4mELYKkJOhU7bMEcCPDqa9HnvsMX3rRim83c6ePVvN+pmrhblIIDACeOaMKSVYY2CdhPIKqwr8bfLmzSt16tSRESNGuBoqX768OiIHGu0c/kTNmjWTtm3bars5c+aUKlWqSNWqVVUZwmov7J+FKbc8efKoAoRpMihH+H8CSxCUJs98LkGDeEC/nyDCZFWWEIhKSU2WtMRGSMBPAnhEhw4dqivDkpOTBQ6cn332WZZrM6wedlpeILQT5HCCDFkeQBMKmMVh4MCB6qwMp+SMklkyZNSu5/1A5IBlCdZa+P0EqvR5ysVzEjCLAJfBm0WW9QaNAEzpL7/8svpTlC5dWpf1wimViQScRmDjxo2C6duyZcvqiiynyWeGPPT7MYMq67SCABUgKyizjaAQwDLjTz75RDC9AOdSJhJwGgGselqyZEnEOADT78dpTyDlyQoBKkBZocW8thOAUyn8gjCNBadTJhIgAfsIYFUmHK3h8M39fuwbB7bsHwE6QfvHjaVsJID9TrAajIkESMBeAvB1wso37vdj7ziwdf8IUAHyjxtL2UwAO+wykQAJ2EsAq9KaNGlirxBsnQT8JMApMD/BsRgJkAAJkAAJkEDoEqACFLpjR8lJgARIgARIgAT8JEAFyE9wLEYCJEACJEACJBC6BOgDFLpjR8ktJHDjxg2ZOXOmbsCI46tXr0qjRo0ETqAIfmlVwu6+8+bN0zAD2PEXYUEKFy5sVfNshwRIgATChgAtQGEzlOyImQRefPFFjbINJQi73i5YsEAQqBI731qVEGzzgw8+0Mjen376qUYGf+utt6xqnu2QAAmQQFgRoAUorIaTnTGDAKJpI/TGhg0bJF++fNoE4i4hyreVO1Ij/tOqVas0ECyCY544cUKwMzYTCZAACZBA1gnQApR1ZiwRYQT27NkjCIJpKD9G92+//XYNVGmcW/GN5f9r1qyRhg0byubNmyUhIcGKZtkGCZAACYQdAVqAwm5I2aFgE8iWLZtG2Q52vf7Wh+jg+Hz88ccagBIKEZN/BE6fPi0IsGsEAvWvlsBKwcKIZKcMaB9yREdH45CJBCKCAC1AETHM7GQgBKpXry4HDhyQCxcupKnm+PHj0qdPH7ly5Uqa62adHDlyRMMOGPXD+vPzzz/LmTNnjEth9f3NN9+opWvQoEHy0UcfqaISVh1kZ0iABGwlQAuQrfjZeCgQiI2NlbZt28rzzz8vo0ePlvz582scshEjRugKLKtWgcHnZ/DgwRps87bbbpOlS5dKXFycFCpUKBQwZllGWGbWr1+vHxQuXry4wA+qWrVqcs8998gDDzwQsMUiJiZG8EFsObuSYfmxUwb03ZDDLg5slwSsJkAFyGribC8kCbz00kvy3nvvSZcuXSRHjhxq9cE01IABAyzrT3x8vPTr10+6du2qMhQrVkxlskoAOF6PGzdOMCVoRUpMTEzTzC+//CL4fPHFF3rdDIUoTYM8IQESCGsCUSmpKax7yM6RwP8IGG+4TnnTtlMOf1j89ttvOiUVFRVlyTN17do12bt3b4ZtlShRQi1hsA4hKnlW4sT5wyFDgbKYwQkyQGSnyJFFfMxOAn4ToAXIb3QsSAKRRQCr3nbt2mVZp2HpgZXNM5UsWdI1FVa/fn1p3br1LSv0PMvwnARIgAQ8CVAB8iTCcxIgAUcRgIUHfj/4UOFx1NBQGBIIaQJUgEJ6+Cg8CYQvAay+mz9/vrRp04YWnvAdZvaMBGwjQAXINvRsmARIID0CcPL+y1/+kl4W3iMBEiABvwlYs5zDb/FYkARIgARIgARIgASCT4AKUPCZskYSIAESIAESIAGHE6AC5PABongkQAIkQAIkQALBJ0AFKPhMWSMJkAAJkAAJkIDDCdAJ2uEDRPFIgAScSeDGjRsyc+ZM+eyzzzRY7tWrV6VRo0YycOBAsSo8Csh8+OGHMmfOHMmbN6+UK1dOXnzxRSlYsKAzoVEqEnAQAVqAHDQYFIUESCB0CEDR2LFjhypBixYtkgULFsihQ4dk2LBhlnUCAWOnTZsms2bNEshw//33y9ChQy1rnw2RQCgToAUolEePspMACdhCICkpSS0/GzZscO1RlCdPHhk5cqRs27bNMpn27Nmjm0NiywCkP/3pT6oAIYwIYtYxkQAJ+CZAC5BvNrxDAiRAAl4JQPGoUKGCS/kxMiFciLfwHcb9YH/XqFFDvv32Wzl27JhWvXDhQoHy8/vvvwe7KdZHAmFHgK8IYTek7JAvAqdPn5bk5GRX0Edf+cy+DusBkhF80uz2vNUPGaKjo73d4rVMEMiWLZv6/WQiq6lZ4uPj5YknntAPZEpISJBChQpJzpw5TW2XlZNAOBCgBSgcRpF9IAESsJQAwnQcOHBALly4kKbd48ePS58+feTKlStprpt1AoW+bt268sknn6gPEqbAkKAEMZEACaRPgBag9PnwbhgRiImJEXwOHz5sa68My4+dchgy2AoihBuPjY2Vtm3byvPPPy+jR4+W/Pnzy/nz52XEiBFSuHBhy1aBnThxQnr06CHL/n97d8wTxRqFAfgImBCgQoMNCaGAQmJlrTFo6w+gJoGEBCpsNCra2Yu9/AEaLI0FWlkQGwMFsZCGigo1AeT6TcJN7jYqF2aWPc80upGd+d7njOHNMOy8eVOt4dWrV9Wz0y4wraUTqE1AAaqN2oEIEOgkgaWlpVheXo6pqanqhuNy1afc/zM/P19bzNHR0ZiZmamemXZ8fBw3b96MJ0+e1HZ8ByJwkQUu/fpPc3yRA1g7gT8VOLnq0eSVl7LWdlhHO6zhT+d2nl/XDg7tsIZ2OS/Pc9b2TaBVwD1ArSJeEyBAgAABAh0voAB1/IgFJECAAAECBFoFFKBWEa8JECBAgACBjhdQgDp+xAISIECAAAECrQIKUKuI1wQIECBAgEDHCyhAHT9iAQkQIECAAIFWAQWoVcRrAgQIECBAoOMFfBBix49YwLMQ+PnzZ7x+/bp6Anj5+8HBQdy5cycWFhZq+9TfkmNra6v65OHyqcPd3d3VJw9PTEycRUT7IECAQCoBV4BSjVvY0wo8ffo0Pn36VJWg1dXV6rlLX758icePH592l3/9vh8/fsT09HT1yb9lDXNzc/HgwYO/3o83ECBAgECEK0DOAgK/EShPTl9bW4sPHz5EX19f9dW9vb3x/Pnz2NjY+M27z+6f379/HyMjI3Hr1q1qp5OTkzE8PHx2B7AnAgQIJBJQgBINW9TTCXz+/DnGxsb+LT8ne7ly5Ur17KeT1+f9Z3mERznmo0ePYnNzs3ri9+Li4nkf1v4JECDQkQIKUEeOVaizFOjq6opy30/T2+HhYayvr8fKykrcuHEj3r59W/047N27d3H58uWml3chj7+3txf7+/tx8jyuJkKUK4xla3IN5fhlHf39/eWvNgIpBNwDlGLMQv4fgVI2tre349u3b//Zze7ubszOzkZ5Cngd29DQUJSnf5f1lO3u3btxdHQUX79+rePwHXmM8fHxxr/pl9LRDsWjrKF42AhkEXAFKMuk5Ty1wLVr1+L+/fvx8OHD6jewBgYGovwW1rNnz2JwcLC23wK7fft2vHjxIsqP5K5fvx4fP36McnXKfUCnHm1lePp3eycBAhdZQAG6yNOz9toElpaWYnl5OaampqKnp6e66nPv3r2Yn5+vbQ1Xr16t1lDW8v3796p4vXz5srYCVltQByJAgEANApeOf201HMchCDQucHKPRbmZuMmtHdbRDmtocgaOTYAAAfcAOQcIECBAgACBdAIKULqRC0yAAAECBAgoQM4BAgQIECBAIJ2AApRu5AITIECAAAECCpBzgAABAgQIEEgnoAClG7nABAgQIECAgALkHCBAgAABAgTSCShA6UYuMAECBAgQIKAAOQcIECBAgACBdAIKULqRC0yAAAECBAgoQM4BAgQIECBAIJ2AApRu5AITIECAAAECngbvHEgjsLe3F/v7+3HyINCmgu/s7FSHbnIdZQ39/f1NETguAQIEGhdwBajxEVhAXQLj4+Nt8U2/FI+my0c5fvGwESBAIKvApeNfW9bwchMgQIAAAQI5BVwByjl3qQkQIECAQGoBBSj1+IUnQIAAAQI5BRSgnHOXmgABAgQIpBZQgFKPX3gCBAgQIJBTQAHKOXepCRAgQIBAagEFKPX4hSdAgAABAjkFFKCcc5eaAAECBAikFlCAUo9feAIECBAgkFNAAco5d6kJECBAgEBqAQUo9fiFJ0CAAAECOQUUoJxzl5oAAQIECKQWUIBSj194AgQIECCQU0AByjl3qQkQIECAQGoBBSj1+IUnQIAAAQI5BRSgnHOXmgABAgQIpBZQgFKPX3gCBAgQIJBTQAHKOXepCRAgQIBAagEFKPX4hSdAgAABAjkFFKCcc5eaAAECBAikFlCAUo9feAIECBAgkFNAAco5d6kJECBAgEBqAQUo9fiFJ0CAAAECOQUUoJxzl5oAAQIECKQWUIBSj194AgQIECCQU0AByjl3qQkQIECAQGoBBSj1+IUnQIAAAQI5BRSgnHOXmgABAgQIpBZQgFKPX3gCBAgQIJBTQAHKOXepCRAgQIBAagEFKPX4hSdAgAABAjkFFKCcc5eaAAECBAikFvgH3aGFq3NjIGwAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s do the same sort of benchmark comparison for search and frisk rates. We can use the stopped population as our baseline, defining search rate to be the proportion of stopped people who were subsequently searched, and frisk rate as proportion of stopped people who were subsequently personally searched.\n",
    "\n",
    "We'll want to use pandas `GroupBy` functionality to implement the `split-apply-combine` pattern.\n",
    "\n",
    "The idea here is that we **split** the data by some key or set of keys then **apply** a function to each group and then **combine** the outputs back into a single DataFrame.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_race            search_conducted\n",
       "asian/pacific islander  False               0.977235\n",
       "                        True                0.022765\n",
       "black                   False               0.934236\n",
       "                        True                0.065764\n",
       "hispanic                False               0.938800\n",
       "                        True                0.061200\n",
       "other/unknown           False               0.977084\n",
       "                        True                0.022916\n",
       "white                   False               0.956221\n",
       "                        True                0.043779\n",
       "Name: search_conducted, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In MoCo, how often are Hispanic drivers searched after being pulled over? White drivers? Asian drivers?\n",
    "mcpd.groupby('subject_race').search_conducted.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_race            search_person\n",
       "asian/pacific islander  True             0.872727\n",
       "                        False            0.127273\n",
       "black                   True             0.847753\n",
       "                        False            0.152247\n",
       "hispanic                True             0.852564\n",
       "                        False            0.147436\n",
       "other/unknown           True             0.725275\n",
       "                        False            0.274725\n",
       "white                   True             0.794728\n",
       "                        False            0.205272\n",
       "Name: search_person, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How often are drivers frisked during a stop?\n",
    "mcpd[mcpd.search_conducted == True].groupby('subject_race').search_person.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_race            search_conducted\n",
       "asian/pacific islander  False               0.980872\n",
       "                        True                0.019128\n",
       "black                   False               0.958445\n",
       "                        True                0.041555\n",
       "hispanic                False               0.957090\n",
       "                        True                0.042910\n",
       "other/unknown           False               0.980350\n",
       "                        True                0.019650\n",
       "white                   False               0.971289\n",
       "                        True                0.028711\n",
       "Name: search_conducted, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What about statewide?\n",
    "md.groupby('subject_race').search_conducted.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_race            search_person\n",
       "asian/pacific islander  True             0.866516\n",
       "                        False            0.133484\n",
       "black                   True             0.832239\n",
       "                        False            0.167761\n",
       "hispanic                True             0.843701\n",
       "                        False            0.156299\n",
       "other/unknown           True             0.805915\n",
       "                        False            0.194085\n",
       "white                   True             0.861109\n",
       "                        False            0.138891\n",
       "Name: search_person, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md[md.search_conducted == True].groupby('subject_race').search_person.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![https://imgs.xkcd.com/comics/correlation.png](https://imgs.xkcd.com/comics/correlation.png)\n",
    "\n",
    "**\"Correlation doesn't imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing 'look over there'.\"** - [XKCD 552](https://www.explainxkcd.com/wiki/index.php/552:_Correlation)\n",
    "\n",
    "To get closer to causality, we can use the \"outcome test\" in which a lower search  success rate for one group relative to another is seen as evidence of bias against that group, as it suggests a lower evidentiary bar was applied when making search decisions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_race            contraband_found\n",
       "asian/pacific islander  False               0.995506\n",
       "                        True                0.004494\n",
       "black                   False               0.986250\n",
       "                        True                0.013750\n",
       "hispanic                False               0.991841\n",
       "                        True                0.008159\n",
       "other/unknown           False               0.994495\n",
       "                        True                0.005505\n",
       "white                   False               0.987251\n",
       "                        True                0.012749\n",
       "Name: contraband_found, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pay close attention to the figure for Hispanics\n",
    "md[md.search_conducted == True].groupby('subject_race').contraband_found.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subject_race            outcome \n",
       "asian/pacific islander  arrest      0.559908\n",
       "                        citation    0.255760\n",
       "                        warning     0.184332\n",
       "black                   arrest      0.428513\n",
       "                        citation    0.361505\n",
       "                        warning     0.209982\n",
       "hispanic                arrest      0.590873\n",
       "                        citation    0.296032\n",
       "                        warning     0.113095\n",
       "other/unknown           arrest      0.417769\n",
       "                        warning     0.293006\n",
       "                        citation    0.289225\n",
       "white                   arrest      0.541410\n",
       "                        warning     0.238352\n",
       "                        citation    0.220238\n",
       "Name: outcome, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "md[md.search_conducted == True].groupby('subject_race').outcome.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "departments = md.groupby(\"department_name\")\n",
    "rates = departments.mean().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Closing Remarks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attribution And Further Exploration\n",
    "\n",
    "- Jake VanderPlas: [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/): Free, CC License, available as a series of Jupyter Notebooks. Chapter 3 has strong coverage of Pandas.\n",
    "- [dataMontgomery](https://data.montgomerycountymd.gov/): Montgomery County, MD offers excellent datasets on various public services, including law enforcement. For example, its 1.4 million row/35 column dataset on traffic stops has data on vehicle make/model, city of origin of the driver, and exact geolocation of the stop.\n",
    "- [The Stanford Open Policing Project](https://openpolicing.stanford.edu/): See the whole dataset, plus a tutorial on how to work with it (unfortunately in R)\n",
    "- [Kaggle DataSets](https://www.kaggle.com/datasets): Collection of datasets, many amendable to machine learning techniques\n",
    "- [Altair documentation](https://altair-viz.github.io/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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