Exploring Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring data: Analyzing Police Activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\"> \n",
    "\n",
    "- Quick recap\n",
    " - Python data structures\n",
    " - Pandas\n",
    " - Importing data  \n",
    " - Importing data from statistical software packages\n",
    " - Cleaning data\n",
    " - Data Visualization\n",
    "- Exploratory analysis\n",
    "- Q&A\n",
    "    \n",
    "    \n",
    "</font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "import pandas as pd\n",
    "import re\n",
    "import numpy as np\n",
    "from numpy import NaN\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import zscore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing  and examining the data for analysis\n",
    "\n",
    "Before beginning your analysis, it is critical that you first examine and clean the dataset, to make working with it a more efficient process. You will practice fixing data types, handling missing values, and dropping columns and rows while learning about the Stanford Open Policing Project dataset with a dataset of traffic stops in Rhode Island.\n",
    "\n",
    "- THE STANFORDOPEN POLICING PROJECT [https://openpolicing.stanford.edu/]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>county_name</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_race</th>\n",
       "      <th>violation_raw</th>\n",
       "      <th>violation</th>\n",
       "      <th>search_conducted</th>\n",
       "      <th>search_type</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th>district</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RI</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>12:55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Equipment/Inspection Violation</td>\n",
       "      <td>Equipment</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RI</td>\n",
       "      <td>2005-01-23</td>\n",
       "      <td>23:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RI</td>\n",
       "      <td>2005-02-17</td>\n",
       "      <td>04:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RI</td>\n",
       "      <td>2005-02-20</td>\n",
       "      <td>17:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Call for Service</td>\n",
       "      <td>Other</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arrest Driver</td>\n",
       "      <td>True</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RI</td>\n",
       "      <td>2005-02-24</td>\n",
       "      <td>01:20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state   stop_date stop_time  county_name driver_gender driver_race  \\\n",
       "0    RI  2005-01-04     12:55          NaN             M       White   \n",
       "1    RI  2005-01-23     23:15          NaN             M       White   \n",
       "2    RI  2005-02-17     04:15          NaN             M       White   \n",
       "3    RI  2005-02-20     17:15          NaN             M       White   \n",
       "4    RI  2005-02-24     01:20          NaN             F       White   \n",
       "\n",
       "                    violation_raw  violation  search_conducted search_type  \\\n",
       "0  Equipment/Inspection Violation  Equipment             False         NaN   \n",
       "1                        Speeding   Speeding             False         NaN   \n",
       "2                        Speeding   Speeding             False         NaN   \n",
       "3                Call for Service      Other             False         NaN   \n",
       "4                        Speeding   Speeding             False         NaN   \n",
       "\n",
       "    stop_outcome is_arrested stop_duration  drugs_related_stop district  \n",
       "0       Citation       False      0-15 Min               False  Zone X4  \n",
       "1       Citation       False      0-15 Min               False  Zone K3  \n",
       "2       Citation       False      0-15 Min               False  Zone X4  \n",
       "3  Arrest Driver        True     16-30 Min               False  Zone X1  \n",
       "4       Citation       False      0-15 Min               False  Zone X3  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the pandas library as pd\n",
    "import pandas as pd\n",
    "\n",
    "# Read 'police.csv' into a DataFrame named ri\n",
    "ri = pd.read_csv('data/police.csv')\n",
    "\n",
    "# Examine the head of the dataFrame\n",
    "ri.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 91741 entries, 0 to 91740\n",
      "Data columns (total 15 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   state               91741 non-null  object \n",
      " 1   stop_date           91741 non-null  object \n",
      " 2   stop_time           91741 non-null  object \n",
      " 3   county_name         0 non-null      float64\n",
      " 4   driver_gender       86536 non-null  object \n",
      " 5   driver_race         86539 non-null  object \n",
      " 6   violation_raw       86539 non-null  object \n",
      " 7   violation           86539 non-null  object \n",
      " 8   search_conducted    91741 non-null  bool   \n",
      " 9   search_type         3307 non-null   object \n",
      " 10  stop_outcome        86539 non-null  object \n",
      " 11  is_arrested         86539 non-null  object \n",
      " 12  stop_duration       86539 non-null  object \n",
      " 13  drugs_related_stop  91741 non-null  bool   \n",
      " 14  district            91741 non-null  object \n",
      "dtypes: bool(2), float64(1), object(12)\n",
      "memory usage: 9.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# Examine the structure  of the dataFrame\n",
    "ri.info()\n",
    "\n",
    "# Examine the shape of the DataFrame\n",
    "#print(ri.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state                     0\n",
      "stop_date                 0\n",
      "stop_time                 0\n",
      "county_name           91741\n",
      "driver_gender          5205\n",
      "driver_race            5202\n",
      "violation_raw          5202\n",
      "violation              5202\n",
      "search_conducted          0\n",
      "search_type           88434\n",
      "stop_outcome           5202\n",
      "is_arrested            5202\n",
      "stop_duration          5202\n",
      "drugs_related_stop        0\n",
      "district                  0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count the number of missing values in each column\n",
    "print(ri.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping columns\n",
    "Often, a DataFrame will contain columns that are not useful to your analysis. Such columns should be dropped from the DataFrame, to make it easier for you to focus on the remaining columns.\n",
    "\n",
    "You'll drop the county_name column because it only contains missing values, and you'll drop the state column because all of the traffic stops took place in one state (Rhode Island). Thus, these columns can be dropped because they contain no useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(91741, 13)\n"
     ]
    }
   ],
   "source": [
    "# Drop the 'county_name' and 'state' columns\n",
    "ri.drop(['county_name', 'state'], axis='columns', inplace=True)\n",
    "\n",
    "# Examine the shape of the DataFrame (again)\n",
    "print(ri.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping rows\n",
    "When you know that a specific column will be critical to your analysis, and only a small fraction of rows are missing a value in that column, it often makes sense to remove those rows from the dataset.\n",
    "\n",
    "The driver_gender column will be critical to many of your analyses. Because only a small fraction of rows are missing driver_gender, we'll drop those rows from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop_date                 0\n",
      "stop_time                 0\n",
      "driver_gender             0\n",
      "driver_race               0\n",
      "violation_raw             0\n",
      "violation                 0\n",
      "search_conducted          0\n",
      "search_type           83229\n",
      "stop_outcome              0\n",
      "is_arrested               0\n",
      "stop_duration             0\n",
      "drugs_related_stop        0\n",
      "district                  0\n",
      "dtype: int64\n",
      "(86536, 13)\n"
     ]
    }
   ],
   "source": [
    "# Drop all rows that are missing 'driver_gender'\n",
    "ri.dropna(subset=['driver_gender'], inplace=True)\n",
    "\n",
    "# Count the number of missing values in each column (again)\n",
    "print(ri.isnull().sum())\n",
    "\n",
    "# Examine the shape of the DataFrame\n",
    "print(ri.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fixing a data type\n",
    "The **is_arrested** column currently has the object data type. We'll change the data type to bool, which is the most suitable type for a column containing True and False values.\n",
    "\n",
    "Fixing the data type will enable us to use mathematical operations on the is_arrested column that would not be possible otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object\n"
     ]
    }
   ],
   "source": [
    "# Examine the head of the 'is_arrested' column\n",
    "#print(ri.is_arrested.head())\n",
    "\n",
    "# Check the data type of 'is_arrested'\n",
    "print(ri.is_arrested.dtype)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bool\n"
     ]
    }
   ],
   "source": [
    "# Change the data type of 'is_arrested' to 'bool'\n",
    "ri['is_arrested'] = ri.is_arrested.astype('bool')\n",
    "\n",
    "# Check the data type of 'is_arrested' (again)\n",
    "print(ri.is_arrested.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining object columns\n",
    "Currently, the date and time of each traffic stop are stored in separate object columns: stop_date and stop_time.\n",
    "\n",
    "You'll combine these two columns into a single column, and then convert it to datetime format. This will enable convenient date-based attributes that we'll use later in the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop_date                     object\n",
      "stop_time                     object\n",
      "driver_gender                 object\n",
      "driver_race                   object\n",
      "violation_raw                 object\n",
      "violation                     object\n",
      "search_conducted                bool\n",
      "search_type                   object\n",
      "stop_outcome                  object\n",
      "is_arrested                     bool\n",
      "stop_duration                 object\n",
      "drugs_related_stop              bool\n",
      "district                      object\n",
      "stop_datetime         datetime64[ns]\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Concatenate 'stop_date' and 'stop_time' (separated by a space)\n",
    "combined = ri.stop_date.str.cat(ri.stop_time, sep=' ')\n",
    "\n",
    "# Convert 'combined' to datetime format\n",
    "ri['stop_datetime'] = pd.to_datetime(combined)\n",
    "\n",
    "# Examine the data types of the DataFrame\n",
    "print(ri.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_race</th>\n",
       "      <th>violation_raw</th>\n",
       "      <th>violation</th>\n",
       "      <th>search_conducted</th>\n",
       "      <th>search_type</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th>district</th>\n",
       "      <th>stop_datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>12:55</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Equipment/Inspection Violation</td>\n",
       "      <td>Equipment</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>2005-01-04 12:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-01-23</td>\n",
       "      <td>23:15</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K3</td>\n",
       "      <td>2005-01-23 23:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-02-17</td>\n",
       "      <td>04:15</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "      <td>2005-02-17 04:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-02-20</td>\n",
       "      <td>17:15</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Call for Service</td>\n",
       "      <td>Other</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arrest Driver</td>\n",
       "      <td>True</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X1</td>\n",
       "      <td>2005-02-20 17:15:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-02-24</td>\n",
       "      <td>01:20</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X3</td>\n",
       "      <td>2005-02-24 01:20:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    stop_date stop_time driver_gender driver_race  \\\n",
       "0  2005-01-04     12:55             M       White   \n",
       "1  2005-01-23     23:15             M       White   \n",
       "2  2005-02-17     04:15             M       White   \n",
       "3  2005-02-20     17:15             M       White   \n",
       "4  2005-02-24     01:20             F       White   \n",
       "\n",
       "                    violation_raw  violation  search_conducted search_type  \\\n",
       "0  Equipment/Inspection Violation  Equipment             False         NaN   \n",
       "1                        Speeding   Speeding             False         NaN   \n",
       "2                        Speeding   Speeding             False         NaN   \n",
       "3                Call for Service      Other             False         NaN   \n",
       "4                        Speeding   Speeding             False         NaN   \n",
       "\n",
       "    stop_outcome  is_arrested stop_duration  drugs_related_stop district  \\\n",
       "0       Citation        False      0-15 Min               False  Zone X4   \n",
       "1       Citation        False      0-15 Min               False  Zone K3   \n",
       "2       Citation        False      0-15 Min               False  Zone X4   \n",
       "3  Arrest Driver         True     16-30 Min               False  Zone X1   \n",
       "4       Citation        False      0-15 Min               False  Zone X3   \n",
       "\n",
       "        stop_datetime  \n",
       "0 2005-01-04 12:55:00  \n",
       "1 2005-01-23 23:15:00  \n",
       "2 2005-02-17 04:15:00  \n",
       "3 2005-02-20 17:15:00  \n",
       "4 2005-02-24 01:20:00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ri.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting the index\n",
    "\n",
    "Set the stop_datetime column as the DataFrame's index. By replacing the default index with a DatetimeIndex, you'll make it easier to analyze the dataset by date and time, which will come in handy later in the course!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2005-01-04 12:55:00', '2005-01-23 23:15:00',\n",
      "               '2005-02-17 04:15:00', '2005-02-20 17:15:00',\n",
      "               '2005-02-24 01:20:00', '2005-03-14 10:00:00',\n",
      "               '2005-03-29 21:55:00', '2005-04-04 21:25:00',\n",
      "               '2005-07-14 11:20:00', '2005-07-14 19:55:00',\n",
      "               ...\n",
      "               '2015-12-31 13:23:00', '2015-12-31 18:59:00',\n",
      "               '2015-12-31 19:13:00', '2015-12-31 20:20:00',\n",
      "               '2015-12-31 20:50:00', '2015-12-31 21:21:00',\n",
      "               '2015-12-31 21:59:00', '2015-12-31 22:04:00',\n",
      "               '2015-12-31 22:09:00', '2015-12-31 22:47:00'],\n",
      "              dtype='datetime64[ns]', name='stop_datetime', length=86536, freq=None)\n"
     ]
    }
   ],
   "source": [
    "# Set 'stop_datetime' as the index\n",
    "ri.set_index('stop_datetime', inplace=True)\n",
    "\n",
    "# Examine the index\n",
    "print(ri.index)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['stop_date', 'stop_time', 'driver_gender', 'driver_race',\n",
      "       'violation_raw', 'violation', 'search_conducted', 'search_type',\n",
      "       'stop_outcome', 'is_arrested', 'stop_duration', 'drugs_related_stop',\n",
      "       'district'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Examine the columns\n",
    "print(ri.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "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>stop_date</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>driver_gender</th>\n",
       "      <th>driver_race</th>\n",
       "      <th>violation_raw</th>\n",
       "      <th>violation</th>\n",
       "      <th>search_conducted</th>\n",
       "      <th>search_type</th>\n",
       "      <th>stop_outcome</th>\n",
       "      <th>is_arrested</th>\n",
       "      <th>stop_duration</th>\n",
       "      <th>drugs_related_stop</th>\n",
       "      <th>district</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_datetime</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>2005-01-04 12:55:00</th>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>12:55</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Equipment/Inspection Violation</td>\n",
       "      <td>Equipment</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-23 23:15:00</th>\n",
       "      <td>2005-01-23</td>\n",
       "      <td>23:15</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone K3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-17 04:15:00</th>\n",
       "      <td>2005-02-17</td>\n",
       "      <td>04:15</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-20 17:15:00</th>\n",
       "      <td>2005-02-20</td>\n",
       "      <td>17:15</td>\n",
       "      <td>M</td>\n",
       "      <td>White</td>\n",
       "      <td>Call for Service</td>\n",
       "      <td>Other</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Arrest Driver</td>\n",
       "      <td>True</td>\n",
       "      <td>16-30 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-24 01:20:00</th>\n",
       "      <td>2005-02-24</td>\n",
       "      <td>01:20</td>\n",
       "      <td>F</td>\n",
       "      <td>White</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>Speeding</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Citation</td>\n",
       "      <td>False</td>\n",
       "      <td>0-15 Min</td>\n",
       "      <td>False</td>\n",
       "      <td>Zone X3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      stop_date stop_time driver_gender driver_race  \\\n",
       "stop_datetime                                                         \n",
       "2005-01-04 12:55:00  2005-01-04     12:55             M       White   \n",
       "2005-01-23 23:15:00  2005-01-23     23:15             M       White   \n",
       "2005-02-17 04:15:00  2005-02-17     04:15             M       White   \n",
       "2005-02-20 17:15:00  2005-02-20     17:15             M       White   \n",
       "2005-02-24 01:20:00  2005-02-24     01:20             F       White   \n",
       "\n",
       "                                      violation_raw  violation  \\\n",
       "stop_datetime                                                    \n",
       "2005-01-04 12:55:00  Equipment/Inspection Violation  Equipment   \n",
       "2005-01-23 23:15:00                        Speeding   Speeding   \n",
       "2005-02-17 04:15:00                        Speeding   Speeding   \n",
       "2005-02-20 17:15:00                Call for Service      Other   \n",
       "2005-02-24 01:20:00                        Speeding   Speeding   \n",
       "\n",
       "                     search_conducted search_type   stop_outcome  is_arrested  \\\n",
       "stop_datetime                                                                   \n",
       "2005-01-04 12:55:00             False         NaN       Citation        False   \n",
       "2005-01-23 23:15:00             False         NaN       Citation        False   \n",
       "2005-02-17 04:15:00             False         NaN       Citation        False   \n",
       "2005-02-20 17:15:00             False         NaN  Arrest Driver         True   \n",
       "2005-02-24 01:20:00             False         NaN       Citation        False   \n",
       "\n",
       "                    stop_duration  drugs_related_stop district  \n",
       "stop_datetime                                                   \n",
       "2005-01-04 12:55:00      0-15 Min               False  Zone X4  \n",
       "2005-01-23 23:15:00      0-15 Min               False  Zone K3  \n",
       "2005-02-17 04:15:00      0-15 Min               False  Zone X4  \n",
       "2005-02-20 17:15:00     16-30 Min               False  Zone X1  \n",
       "2005-02-24 01:20:00      0-15 Min               False  Zone X3  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ri.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the relationship between gender and policing\n",
    "\n",
    "Does the gender of a driver have an impact on police behavior during a traffic stop? You will explore that question while practicing filtering, grouping, method chaining, Boolean math, string methods, and more!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining traffic violations\n",
    "Before comparing the violations being committed by each gender, you should examine the violations committed by all drivers to get a baseline understanding of the data.\n",
    "\n",
    "You'll count the unique values in the violation column, and then separately express those counts as proportions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speeding               48423\n",
      "Moving violation       16224\n",
      "Equipment              10921\n",
      "Other                   4409\n",
      "Registration/plates     3703\n",
      "Seat belt               2856\n",
      "Name: violation, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Count the unique values in 'violation'\n",
    "print(ri.violation.value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speeding               0.559571\n",
      "Moving violation       0.187483\n",
      "Equipment              0.126202\n",
      "Other                  0.050950\n",
      "Registration/plates    0.042791\n",
      "Seat belt              0.033004\n",
      "Name: violation, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Express the counts as proportions\n",
    "print(ri.violation.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing violations by gender\n",
    "The question we're trying to answer is whether male and female drivers tend to commit different types of traffic violations.\n",
    "\n",
    "You'll first create a DataFrame for each gender, and then analyze the violations in each DataFrame separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speeding               0.658114\n",
      "Moving violation       0.138218\n",
      "Equipment              0.105199\n",
      "Registration/plates    0.044418\n",
      "Other                  0.029738\n",
      "Seat belt              0.024312\n",
      "Name: violation, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame of female drivers\n",
    "female = ri[ri.driver_gender == 'F']\n",
    "\n",
    "# Compute the violations by female drivers (as proportions)\n",
    "print(female.violation.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Speeding               0.522243\n",
      "Moving violation       0.206144\n",
      "Equipment              0.134158\n",
      "Other                  0.058985\n",
      "Registration/plates    0.042175\n",
      "Seat belt              0.036296\n",
      "Name: violation, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame of male drivers\n",
    "male = ri[ri.driver_gender == 'M']\n",
    "\n",
    "# Compute the violations by male drivers (as proportions)\n",
    "print(male.violation.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing speeding outcomes by gender\n",
    "When a driver is pulled over for speeding, many people believe that gender has an impact on whether the driver will receive a ticket or a warning. Can you find evidence of this in the dataset?\n",
    "\n",
    "First, you'll create two DataFrames of drivers who were stopped for speeding: one containing females and the other containing males.\n",
    "\n",
    "Then, for each gender, you'll use the stop_outcome column to calculate what percentage of stops resulted in a \"Citation\" (meaning a ticket) versus a \"Warning\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Citation            0.952192\n",
      "Warning             0.040074\n",
      "Arrest Driver       0.005752\n",
      "N/D                 0.000959\n",
      "Arrest Passenger    0.000639\n",
      "No Action           0.000383\n",
      "Name: stop_outcome, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame of female drivers stopped for speeding\n",
    "female_and_speeding = ri[(ri.driver_gender == 'F') & (ri.violation == 'Speeding')]\n",
    "\n",
    "# Compute the stop outcomes for female drivers (as proportions)\n",
    "print(female_and_speeding.stop_outcome.value_counts(normalize=True))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Citation            0.944595\n",
      "Warning             0.036184\n",
      "Arrest Driver       0.015895\n",
      "Arrest Passenger    0.001281\n",
      "No Action           0.001068\n",
      "N/D                 0.000976\n",
      "Name: stop_outcome, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame of male drivers stopped for speeding\n",
    "male_and_speeding = ri[(ri.driver_gender == 'M') & (ri.violation == 'Speeding')]\n",
    "\n",
    "# Compute the stop outcomes for male drivers (as proportions)\n",
    "print(male_and_speeding.stop_outcome.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the search rate\n",
    "During a traffic stop, the police officer sometimes conducts a search of the vehicle. You'll calculate the percentage of all stops that result in a vehicle search, also known as the search rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bool\n",
      "False    0.961785\n",
      "True     0.038215\n",
      "Name: search_conducted, dtype: float64\n",
      "0.0382153092354627\n"
     ]
    }
   ],
   "source": [
    "# Check the data type of 'search_conducted'\n",
    "print(ri.search_conducted.dtype)\n",
    "\n",
    "# Calculate the search rate by counting the values\n",
    "print(ri.search_conducted.value_counts(normalize=True))\n",
    "\n",
    "# Calculate the search rate by taking the mean\n",
    "print(ri.search_conducted.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing search rates by gender\n",
    "\n",
    "You'll compare the rates at which female and male drivers are searched during a traffic stop. Remember that the vehicle search rate across all stops is about 3.8%.\n",
    "\n",
    "First, you'll filter the DataFrame by gender and calculate the search rate for each group separately. Then, you'll perform the same calculation for both genders at once using a .groupby()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.019180617481282074\n"
     ]
    }
   ],
   "source": [
    "# Calculate the search rate for female drivers\n",
    "print(ri[ri.driver_gender == 'F'].search_conducted.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04542557598546892\n"
     ]
    }
   ],
   "source": [
    "# Calculate the search rate for male drivers\n",
    "print(ri[ri.driver_gender == 'M'].search_conducted.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "driver_gender\n",
      "F    0.019181\n",
      "M    0.045426\n",
      "Name: search_conducted, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the search rate for both groups simultaneously\n",
    "print(ri.groupby('driver_gender').search_conducted.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a second factor to the analysis\n",
    "Even though the search rate for males is much higher than for females, it's possible that the difference is mostly due to a second factor.\n",
    "\n",
    "For example, you might hypothesize that the search rate varies by violation type, and the difference in search rate between males and females is because they tend to commit different violations.\n",
    "\n",
    "You can test this hypothesis by examining the search rate for each combination of gender and violation. If the hypothesis was true, you would find that males and females are searched at about the same rate for each violation. Find out below if that's the case!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "driver_gender  violation          \n",
      "F              Equipment              0.039984\n",
      "               Moving violation       0.039257\n",
      "               Other                  0.041018\n",
      "               Registration/plates    0.054924\n",
      "               Seat belt              0.017301\n",
      "               Speeding               0.008309\n",
      "M              Equipment              0.071496\n",
      "               Moving violation       0.061524\n",
      "               Other                  0.046191\n",
      "               Registration/plates    0.108802\n",
      "               Seat belt              0.035119\n",
      "               Speeding               0.027885\n",
      "Name: search_conducted, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the search rate for each combination of gender and violation\n",
    "print(ri.groupby(['driver_gender', 'violation']).search_conducted.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "violation            driver_gender\n",
      "Equipment            F                0.039984\n",
      "                     M                0.071496\n",
      "Moving violation     F                0.039257\n",
      "                     M                0.061524\n",
      "Other                F                0.041018\n",
      "                     M                0.046191\n",
      "Registration/plates  F                0.054924\n",
      "                     M                0.108802\n",
      "Seat belt            F                0.017301\n",
      "                     M                0.035119\n",
      "Speeding             F                0.008309\n",
      "                     M                0.027885\n",
      "Name: search_conducted, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Reverse the ordering to group by violation before gender\n",
    "print(ri.groupby(['violation', 'driver_gender']).search_conducted.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting protective frisks\n",
    "During a vehicle search, the police officer may pat down the driver to check if they have a weapon. This is known as a \"protective frisk.\"\n",
    "\n",
    "You'll first check to see how many times \"Protective Frisk\" was the only search type. Then, you'll use a string method to locate all instances in which the driver was frisked."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Incident to Arrest                                          1290\n",
      "Probable Cause                                               924\n",
      "Inventory                                                    219\n",
      "Reasonable Suspicion                                         214\n",
      "Protective Frisk                                             164\n",
      "Incident to Arrest,Inventory                                 123\n",
      "Incident to Arrest,Probable Cause                            100\n",
      "Probable Cause,Reasonable Suspicion                           54\n",
      "Probable Cause,Protective Frisk                               35\n",
      "Incident to Arrest,Inventory,Probable Cause                   35\n",
      "Incident to Arrest,Protective Frisk                           33\n",
      "Inventory,Probable Cause                                      25\n",
      "Protective Frisk,Reasonable Suspicion                         19\n",
      "Incident to Arrest,Inventory,Protective Frisk                 18\n",
      "Incident to Arrest,Probable Cause,Protective Frisk            13\n",
      "Inventory,Protective Frisk                                    12\n",
      "Incident to Arrest,Reasonable Suspicion                        8\n",
      "Incident to Arrest,Probable Cause,Reasonable Suspicion         5\n",
      "Probable Cause,Protective Frisk,Reasonable Suspicion           5\n",
      "Incident to Arrest,Inventory,Reasonable Suspicion              4\n",
      "Incident to Arrest,Protective Frisk,Reasonable Suspicion       2\n",
      "Inventory,Reasonable Suspicion                                 2\n",
      "Inventory,Probable Cause,Protective Frisk                      1\n",
      "Inventory,Protective Frisk,Reasonable Suspicion                1\n",
      "Inventory,Probable Cause,Reasonable Suspicion                  1\n",
      "Name: search_type, dtype: int64\n",
      "bool\n",
      "303\n"
     ]
    }
   ],
   "source": [
    "# Count the 'search_type' values\n",
    "print(ri.search_type.value_counts())\n",
    "\n",
    "# Check if 'search_type' contains the string 'Protective Frisk'\n",
    "ri['frisk'] = ri.search_type.str.contains('Protective Frisk', na=False)\n",
    "\n",
    "# Check the data type of 'frisk'\n",
    "print(ri.frisk.dtype)\n",
    "\n",
    "# Take the sum of 'frisk'\n",
    "print(ri.frisk.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing frisk rates by gender\n",
    "\n",
    "You'll compare the rates at which female and male drivers are frisked during a search. Are males frisked more often than females, perhaps because police officers consider them to be higher risk?\n",
    "\n",
    "Before doing any calculations, it's important to filter the DataFrame to only include the relevant subset of data, namely stops in which a search was conducted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09162382824312065\n"
     ]
    }
   ],
   "source": [
    "# Create a DataFrame of stops in which a search was conducted\n",
    "searched = ri[ri.search_conducted == True]\n",
    "\n",
    "# Calculate the overall frisk rate by taking the mean of 'frisk'\n",
    "print(searched.frisk.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "driver_gender\n",
      "F    0.074561\n",
      "M    0.094353\n",
      "Name: frisk, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the frisk rate for each gender\n",
    "print(searched.groupby('driver_gender').frisk.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The frisk rate is higher for males than for females, though we can't conclude that this difference is caused by the driver's gender."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visual exploratory data analysis\n",
    "\n",
    "Are you more likely to get arrested at a certain time of day? Are drug-related stops on the rise? You will answer these and other questions by analyzing the dataset visually, since plots can help you to understand trends in a way that examining the raw data cannot.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the hourly arrest rate\n",
    "When a police officer stops a driver, a small percentage of those stops ends in an arrest. This is known as the arrest rate. You'll find out whether the arrest rate varies by time of day.\n",
    "\n",
    "First, you'll calculate the arrest rate across all stops. Then, you'll calculate the hourly arrest rate by using the hour attribute of the index. The hour ranges from 0 to 23, in which:\n",
    "\n",
    "0 = midnight   \n",
    "12 = noon   \n",
    "23 = 11 PM   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0355690117407784\n"
     ]
    }
   ],
   "source": [
    "# Calculate the overall arrest rate\n",
    "print(ri.is_arrested.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop_datetime\n",
      "0     0.051431\n",
      "1     0.064932\n",
      "2     0.060798\n",
      "3     0.060549\n",
      "4     0.048000\n",
      "5     0.042781\n",
      "6     0.013813\n",
      "7     0.013032\n",
      "8     0.021854\n",
      "9     0.025206\n",
      "10    0.028213\n",
      "11    0.028897\n",
      "12    0.037399\n",
      "13    0.030776\n",
      "14    0.030605\n",
      "15    0.030679\n",
      "16    0.035281\n",
      "17    0.040619\n",
      "18    0.038204\n",
      "19    0.032245\n",
      "20    0.038107\n",
      "21    0.064541\n",
      "22    0.048666\n",
      "23    0.047592\n",
      "Name: is_arrested, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the hourly arrest rate\n",
    "print(ri.groupby(ri.index.hour).is_arrested.mean())\n",
    "\n",
    "# Save the hourly arrest rate\n",
    "hourly_arrest_rate = ri.groupby(ri.index.hour).is_arrested.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the hourly arrest rate\n",
    "You'll create a line plot from the hourly_arrest_rate object. A line plot is appropriate in this case because you're showing how a quantity changes over time.\n",
    "\n",
    "This plot should help you to spot some trends that may not have been obvious when examining the raw numbers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C2QGE8zfrTKDIObcewMyeBs4DWoez84DHXPDGqA/MLMPMBgDVwAnAVQDOuXogandZfWVlCU3NjnMmhndKsyOS4n0kxfvITEvs8mss3rSLC+97n1c+LuEr0w/+wyciIrFnTVloMcABVmq2yEhNZNrQfrxZuJ0fnDamp0qLGuFcEDAI2NLq8dbQsY6cMwIoBx42s6Vm9hcz+/xdhVHipeUljMhJY9yAz69aiUZTh/RjWFYqzy7Z6nUpIiISYVp6ao7tf+jfeSeNCbBy2162763tibKiSjjD2YHm79ouHTzYOfHAVOA+59wUgiNptx7wImbXmdkiM1tUXh55y3K3763lgw07OXfiwF5zk72ZccHUPD5Yv4utu9WVQEREPlVYWklqoo+8fimHPO/kULeABYWR97vba+EMZ1uB1nNeeUBxB8/ZCmx1zn0YOv5PgmHtc5xzDzjnpjvnpufk5HRL4d3p5Y9LcI6wr9LsaedPCQ6CPr9E+6mJiMinCksrGZ3bh7h2VmGO7d+HAenJauV0AOEMZwuBUWY2PHRD/yXA7DbnzAausKCjgQrnXIlzrhTYYmYtE9Ff5LP3qkWNOcuLOWJAX/IDB597j0aDM1M5angmzy3dpr3UREQEAOcchWWVjMlt/3eemTFrbIB3i3Z0aCeBWBK2cOacawS+A7wKrAaecc6tMrMbzOyG0GlzgfVAEfAg8O1WL/Fd4AkzWwFMBn4VrlrDZcuuGpZs3tPrRs1aXDg1jw07qlmyeY/XpYiISAQor6pjV3X9IRcDtDZrTICqukYWbdwV5sqiS1j3QXDOzSUYwFofu7/Vxw648SCfuwyYHs76wu3lj0sAOGdC9/bSjBRnTujPz2ev5LklW5k2VEuhRURi3aeLAToWzo7LzyIxPo43C7ZzbH52OEuLKmrfFEYvrShm0uAMhmRF7RZth9QnOYHTj+zPnOXF1DY0eV2OiIh4rCWcdXTkLDUxnqNHZPFmoe47a03hLEzWl1excttezo2Avc3C6cKpeeytbdQNnSIiQkFpJdn+JLL8SR3+nFljclhfXs2mndVhrCy6KJyFyUsrSjCDcyb2zinNFsflZ5PbN4lnF2vPMxGRWFdYWtnhKc0WLVtqqBH6pxTOwsA5x+zlxcwYlkn/9GSvywkrX5zx5SmDWLCmnB1VdV6XIyIiHmlqdqwpq+zwlGaLoVlpjMhJ403td7afwlkYFJZVUrS9inMn9e5RsxYXTs2jqdnx4rK229iJiEis2LyrhrrG5g5to9HWrDEBPli/k5r6xjBUFn0UzsJgzvJi4gzOHN/f61J6xOjcPkwYlM5zauckIhKzCkv3Ah1fDNDayWMD1Dc2817Rzu4uKyopnHUz5xxzlpdwXH422Z24ITLaXTB1EKuK91IQ+uEUEZHYUlBaiVnwP+ydNWNYJv6keK3aDFE462Yfb6tg864azu3lCwHa+tKkgcTHGc+pnZOISEwqLK1kaGYqKYm+Tn9uYnwcx+dnM79gu7rOoHDW7eYsLybBZ5x+ZGxMabbI8idx0pgAzy/dRmOT2nCIiMSawtLOLwZobdbYHEoqaikI7ZUWyxTOulFzs+OlFSWcODqH9NQEr8vpcRdNG0R5ZR3vFu3wuhQREelBtQ1NbNxZzZj+fbv8GrPGhLbU0NSmwll3Wrx5NyUVtTGzSrOtWWMDpKck8KymNkVEYsrasiqaXcfbNh1IoG8y4wf11X5nKJx1qznLi0mKj+OLR+R6XYonkuJ9fGnSQOatKmVvbYPX5YiISA8pLOtc26aDmTUmwOJNu9lTU98dZUUthbNu0tjUzNyPS/jiEQH8SWHtJx/RLpg6iLrGZuauKPG6FBER6SGFpXtJjI9jaObh9ZKeNTZAs4O318b27TEKZ93kww272FFVH3OrNNuaPDiDETlpWrUpIhJDCkorGRXwE+87vFgxKS+DzLTEmJ/aVDjrJnOWF5OW6GNWqEdYrDIzLpyax0cbd7F5Z43X5YiISA843JWaLXxxxkmjc1hQuJ2m5tjdUkPhrBvUNzbzyspSTjuyP8kJnd/fpbc5f8ogzOC5peoYICLS2+2urmd7Zd1hLQZo7aSxAXbXNLB8655ueb1opHDWDd4tKqdiXwPnThrgdSkRYWBGCseOzOK5Jdu0maCISC/Xsi/Z4Wyj0dqJo3LwxVlMT20qnHWDOctLSE9J4Pj8HK9LiRgXTMlj864aFm3a7XUpIiISRi09Nbtr5Cw9NYFpQ/rxpsKZdFVtQxPzVpVyxpH9SYzXH2eLM8b3JzXRx7OLNbUpItKbFZZVkpGaQKBP9/WTPmlsDquK91K2t7bbXjOaKE0cpvkF26mub4rZjWcPJi0pnjPG9+flFSXUNjR5XY6IiIRJYWklo3P7YGbd9ponhxbXLYjRbgEKZ4fppRUlZPsTOXpEptelRJyLpuZRWdfIvE/KvC5FRETCwDnHmrKqbpvSbDEmtw8D05NjdmpT4ewwVNU18kZBGWdNGHDYe7v0RkePyGJgejLPLdHUpohIb7R19z6q6hq7ZRuN1syMk8YGeHftDuoaY2/2RYniMLyxuozahmZNaR5EXJxx/tRBvL2mnO0xet+AiEhvVhhaqdndI2cAJ48JUF3fxKKNsbewTOHsMMxZXkz/vslMG9LP61Ii1gVT82h28OKyYq9LERGRbtbSU3N0bveHs2Pzs0iMj4vJqU2Fsy6qqGngrTXlnDNxAHFx3XcTZG8zMsfP5MEZPLtkq/Y8ExHpZQpKKxmUkUKf5IRuf+3UxHiOGZEVk/udKZx10aurSmlocprS7IALpw6ioLSSVcV7vS5FRES6UWHp3rBMabaYNSaH9Tuq2bijOmzXiEQKZ100Z0UxQzJTmZiX7nUpEe/cSQNJ8JmaoYuI9CL1jc2sL6/u9sUArZ08NheA+TG2pYbCWRfsqKrjvXU7OXfSgG7d16W3ykhN5Itjc3lx2TYampq9LkdERLrB+h1VNDa7sIazIVmpjMxJi7n7zhTOuuCVlaU0NWtKszMunJbHzup63l5T7nUpIiLSDQr399QMXzgDmDUmwIfrd1Fd1xjW60QShbMumLO8mPyAnzFhWJ3SW504OofMtESe1Z5nIiK9QkFpJfFxxohsf1ivc/LYAPVNzby3bmdYrxNJFM46qaRiHws37uLciQM1pdkJifFxfGnSQF7/ZDsVNQ1elyMiIoepsLSSkTn+sPeVnj4sE39SfExNbSqcddLLK0pwDs6ZNMDrUqLOhVPzqG9qZs4K7XkmIhLtCksrwz6lCcH/3H9hVDYLCrfHzJZMCmedNGdFCUcO7MvInPAO4/ZG4wf1ZXSuX+2cRESi3N7aBrbt2dcj4QyC952VVNSyuqSyR67nNYWzTtiyq4blW/ZoIUAXmRkXTM1jyeY9rC+v8rocERHpojVhbNt0ICeNzQFiZ0sNhbNOaJmOO3uCpjS76vwpg4gzeH6p9jwTEYlWLW2bemrkLNAnmQmD0mOmW4DCWSfMWV7ClCEZDM5M9bqUqJXbN5nj8rN5bsk2mptj494BEZHeprC0En9SPIMyUnrsmrPG5LBk8252V9f32DW9onDWQc45fnLmWH5w6mivS4l6F03LY9uefXy4YZfXpYiISBcUlFYyOtffo7sWzBoboNnB22t7/36ZCmcdZGacMDqHL4zK8bqUqHfauP74k+K155mISBRyzoVWavbt0etOyssgKy0xJqY2Fc6kx6Uk+jhrQn9e+biEmvrY2fFZRKQ3KNtbR8W+hh5bDNAiLs44cXQOb60pp6mX3xYT1nBmZmeYWaGZFZnZrQd43szs7tDzK8xsaqvnNprZx2a2zMwWhbNO6XkXTM2jur6JV1eVel2KiIh0QkHpXqDnFgO0NmtsgN01DSzbsrvHr92TwhbOzMwH3AucCYwDLjWzcW1OOxMYFXq7DrivzfOznHOTnXPTw1WneGPmsEzy+qXw3BKt2hQRiSaFPbyNRmsnjM7BF2fML+jd952Fc+RsJlDknFvvnKsHngbOa3POecBjLugDIMPMtE9FDIiLMy6YMoh3i3ZQUrHP63JERKSDCssqye2bREZqYo9fOz0lgWlD+/X6Vk7hDGeDgC2tHm8NHevoOQ6YZ2aLzey6g13EzK4zs0Vmtqi8vHcn6d7mgql5OAcvLFU7JxGRaFFYWsno3J4fNWsxa0yAT0r2UlpR61kN4RbOcHag9bVt7+A71DnHOeemEpz6vNHMTjjQRZxzDzjnpjvnpufkaCVlNBmWnca0of14dsnWmOmXJiISzRqbmlm7vcqTKc0WJ48NALCgF3cLCGc42woMbvU4D2g7RHLQc5xzLe+3A88TnCaVXubCqXkUba/i420VXpciIiLt2LizhvrG5h7fRqO10bl+BmWk9OqpzXCGs4XAKDMbbmaJwCXA7DbnzAauCK3aPBqocM6VmFmamfUBMLM04DRgZRhrFY+cPXEAifFxPLtYe56JiEQ6LxcDtDAzThqTw7tFO6hrbPKsjnAKWzhzzjUC3wFeBVYDzzjnVpnZDWZ2Q+i0ucB6oAh4EPh26Hgu8K6ZLQc+Al52zv0rXLWKd9JTEjh1XC6zlxdT39jsdTkiInIIhaV7iTPID/g9rePksQFq6pv4qJd2mokP54s75+YSDGCtj93f6mMH3HiAz1sPTApnbRI5Lpqax8srSphfuJ3Tj+zvdTkiInIQBaWVDMtOIznB52kdx47MJik+jvkF5b2yc09Yw5lIR3xhVDbZ/iSeW7JV4UxEJIIVllVy5EDv7jdrkZLo45iRWfx94WZK9+5j+tBMpg/rx7gBfYn3RX/zI4Uz8Vy8L44vTx7Io+9vZHd1Pf3Sen7vHBERObSa+kY276rhgil5XpcCwM/OGccf31jLwo27mftxsNtMaqKPyYMzmD60H9OHZTJlSAZ9khM8rrTzFM4kIlwwNY+/vLuBOSuKueKYYV6XIyIibawtq8I5GNPf2/vNWozM8fOHS6YAUFKxj0Ubd7N4024WbdrFPfOLaHYQZzCmf19mDOvHtFBgG5SR4nHl7VM4k4gwbmBfjhjQlyc/3MwlM4aQGB/9w9IiIr1Jy0pNL7fROJgB6SmcOymFcycNBKCqrpFlm/ewaNMuFm3czbOLt/LY+5sAGJiezLRhmfsD29j+ffHFHWjbVe8onEnEuHHWSL7z5FJufXYFd148CbPI+mEREYllBaWVJCfEMSQz1etS2uVPiuf4UdkcPyobCG6eW1BayaKNu1i0aTcLN+xizvLi/edOGZKx/761yYMzSEvyNh4pnEnEOGfiQDaUV3Pna2von57Mf5wx1uuSREQkpLBsL6Nz+0TcKFNHxPviGD8onfGD0rnquOE459i2Zx+LN+1m4cbg6Nof3liDc+CLM8YN6Ms/bjjGs1WpCmcSUb5zcj7FFbX8acE6BqQnc7nuPxMRiQiFpZXMGhPwuoxuYWbk9Uslr18q500OtvSu2NfA0s3B+9a27d7n6XYhCmcSUcyM/3fekZRX1vLz2asI9E3W9hoiIh7bUVXHjqp6xnjYGSDc0lMSOGlMgJMiIIDqrmuJOPG+OP546VQm5WVw01NLWbypd+4ALSISLdbsb9sUeYsBeiOFM4lIKYk+HrpyOgPSk/nGo4tYV17ldUkiIjGrIBTORkfINhq9ncKZRKwsfxKPXjOT+Djjyr9+xPbKWq9LEhGJSYWllWSmJZLjT/K6lJigcCYRbWhWGg9dOYOdVfVc/fBCquoavS5JRCTmFJRVMia3j7Y46iEKZxLxJg3O4E+XTaWgtJJvP7GEhqZmr0sSEYkZzc2OtWWVvXoxQKRROJOoMGtsgF+fP4G315Rz67Mf45zzuiQRkZiwZXcNNfVNjFU46zHaSkOixsUzBlNcsY8/vL6WgRnJ/PC0MV6XJCLS6xXsb9ukcNZTFM4kqnzvi6Morajlj28W0T89mcuOGup1SSIivVpLT83RuQpnPUXhTKKKmfHLL4+nbG8tP3thJYE+yZw6LtfrskREeq3CskqGZKZ63m8yluieM4k68b447r1sKhMGpfPdp5awZPNur0sSEem1CksrNWrWwxTOJCqlJsbz0FUzyO2bzDcfXcR6bVIrItLt6hqb2LCjWosBepjCmUStbH8Sj149E4ArH/6I8so6jysSEeldirZX0dTstBigh3UonJnZUDM7JfRxipnpuyQRYVh2Gg9dOZ3yyjqueWQh1dqkVkSk2xTu76mpX/s9qd1wZmbXAv8E/hw6lAe8EMaaRDplypB+3Pu1qawqruDGJ7VJrYhIdyksrSTRF8ew7DSvS4kpHRk5uxE4DtgL4JxbCwTCWZRIZ33xiFx++eUJLCgs5z+f1ya1IiLdoaC0kpEBPwk+3QXVkzqyLrbOOVff0k/LzOIB/eaTiPO1o4ZQWrGPu98sYkB6CjefOtrrkkREotqaskqOHpHldRkxpyPh7C0zuw1IMbNTgW8Dc8JblkjX3HzqaEoqarnrjbX0T0/m0plDvC5JRCQqVdQ0UFJRq200PNCRccpbgXLgY+B6YK5z7j/DWpVIF5kZv7pgAieOzuGnL6zkzYIyr0sSEYlKhWVaDOCVjoSz7zrnHnTOfcU5d5Fz7kEz+17YKxPpogRfHH+6bCpHDOjDjU8sZfmWPV6XJCISdQpL9wLqqemFjoSzKw9w7KpurkOkW6UlxfPXq2aQ3SeRax5ZyMYd1V6XJCISVQpKK+mTHM+A9GSvS4k5Bw1nZnapmc0BhpvZ7FZv84GdPVeiSNcE+iTz6NUzaXaOax9bpBWcIiKdUFhaydj+fWhZECg951ALAt4DSoBs4M5WxyuBFeEsSqS7jMjx8+2T8vnfuavZWV1Ptj/J65JERCKec47CskrOmzzQ61Ji0kHDmXNuE7AJOKbnyhHpfqNy/UCwDYnCmYhI+4oraqmsbWRM/75elxKTOtIh4GgzW2hmVWZWb2ZNZra3J4oT6Q75gU/DmYiItG+N2jZ5qiMLAu4BLgXWAinAN4E/hrMoke40MD2FlASfwpmISAcVhMLZ6IDCmRc6sgktzrkiM/M555qAh83svTDXJdJt4uKMkYE01pUrnImIdERh6V4GpCeTnprgdSkxqSPhrMbMEoFlZvZbgosE1AFVokp+jp8PN+zyugwRkahQUFqp/c081JFpzctD530HqAYGAxeEsyiR7pYf8FNSUUtVXaPXpYiIRLSGpmbWlVcpnHmo3XDmnNvknKt1zu11zv038P+AS8Jfmkj3aVkUsF5TmyIih7RhRzUNTU6LATx0qE1oB5vZA2b2kpl908xSzexOoBAI9FyJIodPKzZFRDqmZTHAmFxto+GVQ42cPQYUE1yZeSTwATAQmOic61BvTTM7w8wKzazIzG49wPNmZneHnl9hZlPbPO8zs6Vm9lKHvyKRAxialUZ8nCmcichnOOf4yzvrefyDTdQ3NntdTkRYU1qJL7SQSrxxqAUBmc65X4Q+ftXMyoAZzrm6jrywmfmAe4FTga3AQjOb7Zz7pNVpZwKjQm9HAfeF3rf4HrAaUHyXw5Lgi2NoVqrCmYh8xp/fXs9vXikA4P4F6/j+KaM4f8og4n0duSW7dyoorWREdhpJ8T6vS4lZh/zbZ2b9zCzTzDKBUiC11eP2zASKnHPrnXP1wNPAeW3OOQ94zAV9AGSY2YDQtfOAs4G/dPJrEjmg/ICfIt1zJiIhL60o5jevFHDOxAE8es1MsvyJ3PLPFZz2h7d5eUUJzc2x2Y+3sGwvo3W/macONXKWDiwGWnc8XRJ674AR7bz2IGBLq8db+eyo2MHOGURwu44/AP8B6G+IdIv8gJ/XV2+nvrGZxPjY/V+xiMCijbv4wTPLmT60H3d8ZRLJCT5OGJXNq6vKuHNeITc+uYRxA/pyy+ljOGlMTsw0/66qa2TLrn1cPG2w16XEtEP11hx2mK99oL/Jbf8bcsBzzOwcYLtzbrGZnXTIi5hdB1wHMGTIkC6UKbEiP+CnqdmxaWc1o3KV+UVi1YYd1Vz72CIGZaTw4BXTSU4ITt+ZGWeM78+p43KZvXwbv39tLVc/spBpQ/txy+ljOHpElseVh9+astBiAI2ceSqcwwdbCe6J1iKP4AKDjpxzHPAlM9tIcDr0ZDP724Eu4px7wDk33Tk3PScnp7tql14oPyf4j43uOxOJXbuq67n64Y8wMx6+agb90hI/d44vzjh/Sh5v/PBE/vf88WzbvY9LHviAyx/6kOVb9vR80T2ocH9PTd3q7aVwhrOFwCgzGx7qMHAJMLvNObOBK0KrNo8GKpxzJc65nzjn8kKjd5cAbzrnvh7GWiUGtKw8UjgTiU21DU1c+9giiitqefCK6QzLPvRqxARfHJcdNZQFt5zET88+glXFeznv3n9z3WOL9oeY3qawtJLURB95/VK8LiWmhS2cOecaCXYVeJXgistnnHOrzOwGM7shdNpcYD1QBDwIfDtc9YikJsYzKCNFiwJEYlBzs+OH/1jO4k27+cNXJzNtaL8Of25ygo9vfmEEb//HLH5w6mjeX7eTM+56m+8/vZSNO6rDWHXPKyjdy+jcPsTFxcY9dpGq3d6aZva4c+7y9o4diHNuLsEA1vrY/a0+dsCN7bzGAmBBe9cS6YgROWkaOROJQb99tZCXV5Rw21ljOWvCgC69hj8pnpu+OIorjhnK/W+t55H3NjBnRQkXT8/juyePYmBGdI82OecoLK3k9CP7e11KzOvIyNmRrR+E9i+bFp5yRMIrP+BnXXlVzC6RF4lFT3y4ifvfWsfXjx7CtV9ob6OB9mWkJnLrmWN5+5ZZfP2oIfxz8VZOumMB/zPnE3ZUdWgr0IhUXlXH7poGRmvBlOcO1b7pJ2ZWCUw0s72ht0pgO/Bij1Uo0o3yA35qG5rZtmef16WISA+YX7Cdn72wkpPHBvjFuUd265YYgb7J/Pd543nzhydx3qSBPPLeBk747XzueLWQin0N3XadnvLpYgCFM68dNJw5537tnOsD3O6c6xt66+Ocy3LO/aQHaxTpNvk5oR6buu9MpNdbua2CG59cwhED+vLHS6eEbdf/wZmp3P6VSbz2gxM5eWyAe+YX8YX/e5N75xdRU98YlmuGQ0s40zYa3uvI39SXzCwNwMy+bma/M7OhYa5LJCxaGqCv031nIr1a8Z59fOPRhWSkJPDXq2aQltTuLdaHbWSOn3u+NpWXbzqeGcMyuf3VQr724IfUNTaF/drdYeW2CrL9SWT5k7wuJeZ1JJzdB9SY2SSCO/ZvItgUXSTqZPmT6JeaoEUBIr1YZW0D1zyykJq6Jv569Qxy+yb36PWPHJjOQ1fN4K5LJrNsyx7+30uftP9JHnt7TTmzlxdzyhEBr0sROhbOGkOrKs8D7nLO3YVaKkkUa1kUICK9T0NTM99+YglF26u47+vTPN1M9bzJg7j2C8P52webeW7JVs/qaM+WXTXc9PRSRgX68PNzx3ldjtCxcFZpZj8BLgdeDq3WTAhvWSLhkx/wa+RMwqKwtJImrQT2jHOOn72wknfW7uBXF0zg+FHZXpfEj88Yy8zhmdz2/MesLtnrdTmfU9vQxA1/W0xzs+PPl08jNTH807/Svo6Es68CdcA1zrlSgo3Jbw9rVSJhNDLHz+6aBnZG8ZJ3iTzzC7Zz+h/ejooprN7qTwvW8fTCLdx0cj4XT4+Mxt3xvjju+doU+iYncMPfFkfUKk7nHLc99zGflOzlrkumtNsxQXpOu+EsFMieBVruENwBPB/OokTCqWVRgEbPpLs0NTt+80oBcQaPvLeRd9aWe11SzHlx2TZuf7WQ86cM4uZTR3tdzmcE+iTzp8umsm33Pn74zPKI2Wfx8Q828dzSbXz/i6OZNVb3mkWSdsOZmV0L/BP4c+jQIOCFMNYkElb7w5nuO5Nu8uySrRSWVXL7RZPID/j50T+Ws6em3uuyYsaH63dyyz9WcNTwTH5z4YRu3cusu0wflsltZx3B66vLuO+tdV6Xw8KNu/ifOZ9wyhEBvntyvtflSBsdmda8ETgO2AvgnFsLKGJL1BqYnkJKgk8jZ9It9tU38bt5a5g0OIMLpg7iD1+dzM6qev7z+ZUE11JJOK0rr+K6xxczODOFBy6fTlK8z+uSDurq44ZxzsQB3DmvkH8X7fCsjrK9tXz7iSUMzkzld1+drD6aEagj4azOObf/v4BmFg/oXxyJWnFxxsiAemxK93j4vQ2U7q3ltjPHYmaMH5TOD04bzcsfl/D80m1el9er7aiq4+qHF5LgMx65eibpqZG9Vs3M+L8LJzIyx893n1pKsQedSuobg6tZq+sauf/r0+ibHNl/ZrGqI+HsLTO7DUgxs1OBfwBzwluWSHjl5/i1Ea0ctl3V9dw3fx2nHBHgqBFZ+49ff8JIZg7L5L9eXMXW3TUeVth71TY08c1HF7G9spa/XDmDwZmpXpfUIWlJ8dz39WnUNTTx7SeW9PgGtb98+RMWb9rNby+aqE4AEawj4ezHQDnwMXA9MBf4aTiLEgm3/ICf4opaquuip7WKRJ573iyiur6RH58x9jPHfXHGnRdPwgE/eGa5ttfoZs3Nju8/vYzlW/dw1yVTmDw4w+uSOiU/4Of2r0xi2ZY9/PKl1T123X8u3spj72/iuhNGcM7EgT12Xem8Q4YzM4sDPnbOPeic+4pz7qLQx/qXRqLa/jZOWhQgXbR5Zw2Pf7CRi6cPZlTu50cgBmem8osvHclHG3bx4DvrPaiw9/r1K6v516pSfnb2OE4/sr/X5XTJWRMGcO0XhvP4B5t4fmn4N6hdua2C257/mGNHZvEfp48J+/Xk8BwynDnnmoHlZjakh+oR6RHaTkMO1x3zCvHF2SG3bbhw6iDOHN+fO+cVsqq4oger670ee38jD76zgauOHcY1xw/3upzD0rJB7U+eC+8Gtbuq67n+8cVkpyWGtQG8dJ+OfIcGAKvM7A0zm93yFu7CRMJpaFYa8XGmcCZdsmLrHmYvL+Ybxw8/ZN9GM+NX50+gX2oiN/99GbUN0dEAO1K9/kkZv5i9ilOOyOVn50R/m6HWG9R+K0wb1DY2NXPTU0spr6rj/sunqal5lOhIOPtv4Bzgf4A7W72JRK0EXxxDs1IVzqTTnHP8em4BmWmJXH/iyHbP75eWyO1fmcSasip++6/CHqiwd/p30Q6+89QSxg9K5+5LJ+PrJds/BPokc+9lU9m6ex8/+kf3b1B7x7w1vFu0g19+eTwT8zK69bUlfDpyz9m9zrm32r71UH0iYTMyx6+NaKXTFqwp5/31O/nuyfkd3obgxNE5XHnMUP767w28u9a7/a2i1btrd3DNIwsZlpXGw1fN6HX9H2cMy+QnZx3Ba5+Ucf/b3bdB7Ssfl3D/W+u47KghEdPOSjpG95xJzMoP+Nm0s4b6xmavS5Eo0dTs+L9XChiSmcplRw3t1OfeeuYRjMxJU/eATnp7TTnfeHQhw7PTePLao3vttNw1oQ1q73i1ezaoXVtWyY/+sZwpQzL4+bnRPwUca3TPmcSs/ICfpmbHpp3VXpciUeK5JVspKK3kltPHkBjfuZuqUxJ93HXJFHZU1fHTF9Q9oCPeWlPONx9bxMgcP09dezSZaYlelxQ2LRvUjsjxc9NTSymp6PoGtXtrG7j+8cWkJMZz32XTIrprghyY7jmTmKXtNKQzahua+N1ra5iUl87ZEwZ06TXGD0rn5lNH89KKEl5cVtzNFfYuCwq3c+1jixgV8PPEN4+iXy8OZi3SkuK5/+vTqA1tUNuVUf3mZscPn1nO5l01/OmyqfRPP/iCFYlc7YazA9xr1ghcHP7SRMJrZI6205COe/jfGympqOXWM484rF6EN5w4kulD+/GzF1eqe8BBzC/YznWPLY6pYNaiZYPapZv38MuXP+n05/9pQRGvfVLGf559BDOHZ4ahQukJHRqXN7PJZvZbM9sI/BLouS2NRcIkLSmegenJCmfSrt3V9fxpQREnjw1wzMis9j/hEHxxxu+/Onn/CIe6B3zWmwVlXP/4Ysb078MT3zyKjNTYCWYtzpowgG8eP5zH3t/EC53ozzq/cDt3vraGL08eyFXHDgtfgRJ2Bw1nZjbazH5uZquBe4AtgDnnZjnn7umxCkXCaGRAKzalfffML6K67vNtmrpqcGYq//WlI/lwwy7+ou4B+73+STCYjR3Qh799IzaDWYsfnzmWmcMyufW5FRSUtr9B7aad1XzvqaWM7d+XX18wEbPesdVIrDrUyFkB8EXgXOfc8c65PwLaQVF6lfyAn3Xbq7t9byHpPbbsquHx9zdx0bS8bm0U/ZVpeZx+ZC53zCvkk+Lw7Q4fLV77pIxvPbGYcQP68vg3jiI9tWPblPRWCaENavskJ3DD44vZW3vwDWr31Tdx/eOLMTP+/PVppCRqAUC0O1Q4uxAoBeab2YNm9kVAUVx6lfyAn30NTRQfxsoo6d3umFeIGYds09QVZsavL5hIRmoi3//70pjuHjBvVSnffmIx4wam89g3jiI9JbaDWYtA32Tu/dpUtuzex4+eWX7AFb7OOW59bgWFZZXcdclkhmSlelCpdLeDhjPn3PPOua8CY4EFwM1ArpndZ2an9VB9ImGVr0UBcggrt1Xw4rJgm6YB6Snd/vqZaYn89qKJrCmr4vZXY7N7wL9WlvLtJ5Zw5MB0Hv/GTAWzNmYOz+QnZ45l3idl3P/W56fAH/73Rl5cVsyPThvDSWMCHlQo4dCR1ZrVzrknnHPnAHnAMuDWcBcm0hPUAF0OxjnHr19ZTb/UBG44qf02TV01a0yAy48eykPvbuiWzUejyb9WlvCdJ5cwIS+dx74xs8MdF2LNN44fztkTB3D7qwW81+rvyAfrd/K/c1dz2rhcvtWBVmISPTq1i6Jzbpdz7s/OuZPDVZBIT8ryJ9EvNUF7ncnnvL12B/8u2sl3Tx4V9tBw21lHMCInjR8+s5yKmu5vfh2JXvm4hBufXMrEvHQeu0bB7FBaNqgdnp3Gd0Mb1JZW1PKdJ5cwNDOVOy+edFjbu0jk6dwW1yK9UH7Ar5Ez+YymZsev565mcGYKlx0d/u51KYk+/vDVycHuAS+uDPv1vPbyihK+89RSJg/O4LFvHEUfBbN2+ZPi+fPln25Q+60nFrOvvokHrpimP79eSOFMYp7CmbT1wtJtoTZNY3us9c3EvAy+f8oo5iwv5sVlHd/bKtq8tKKYm55eypTBGTx6zUz8Sb2riXk45Qf68NuLghvULt28hzsvnkR+oPtWEEvk0E+FxLyROX521zSws6qu1zZVlo6rbWjiznmFTBiUzjldbNPUVTecOJL5heX89IWVTB+WyaCM7l+E4KXZy4u5+e/LmDakH3+9eoaCWRecPXEA5ZXj8PniOGN8z/79lJ6jkTOJeVoUIK09+t5Giitq+clZY3v8Pp54Xxy/vzjYPeBHzyzvVfvvvbhsG99/einThvbjYQWzw3LVccO5/OihXpchYaRwJjFvfzjTooCYt6emnnvnF3HSmByOHZntSQ1DslL5r3OP5P31O3no3Q2e1NDdXly2jZv/vowZwzJ5+KoZpCmYiRySwpnEvIHpKaQk+DRyJtw7v4jKukZuPbN72jR11Vem53HauFxuf7WQ1SXR3T3g+aVbufnvy5g5PJOHr1YwE+kIhTOJeXFxxoicNIWzGLdlVw2PvreJC6fmMbZ/X09rCXYPmEDflARu/vuyqO0e8OzirfzgmeUcPSKLh6+aSWqigplIRyiciRCc2lxfXu11GeKh3722BjP4QTe3aeqqLH8St180kYLSSu6cF33dA/65eCs/+udyjh2ZxUNXzlC/R5FO0H9jRAi2cXpxWTHVdY2adolBK7dV8MKybVx/wkgGRtAKyVljA3z96CE8+M4Glm+pIDnRR3J8HCmJPpLjfaQk+khKiCMlwUdygi/0Po7k0OPWx1JaHWt5HO8L/v/cOUdTs6Oxue375uD7ppZjzTSGHh/qvLXbK7n91UKOG5nNg1dMVzAT6aSw/hYyszOAuwAf8Bfn3G/aPG+h588CaoCrnHNLzCwZeBtICtX4T+fcf4WzVoltLYsC1pdXMyEv3eNqpKf9378KSE9J4FthbNPUVf951jgaGh2bdlWzd18D2xuaqG1oYl9DE7UNzexraKK+sblLrx0fZziCm+52ty+MCgaz5AQFM5HOCls4MzMfcC9wKrAVWGhms51zn7Q67UxgVOjtKOC+0Ps64GTnXJWZJQDvmtkrzrkPwlWvxLZPV2xWKpzFmLfXlPPO2h389OwjIrLpdkqij/+7aOIhz2ludtQ2fhrWahua2FffRF1jE/vqm6ltaKK2MXistrGZ2vpPA16cGb44Iz7O8PlC7+PiQu/t0/c+I771cd9BzouLIyHeGBXog08thUS6JJwjZzOBIufcegAzexo4D2gdzs4DHnPOOeADM8swswHOuRKg5e7shNBb79nwRyLO0Kw0fHGmRQExprnZ8ZtXCsjrl8Llx0TvvlFxcUZqYjypiV5XIiLdIZwLAgYBW1o93ho61qFzzMxnZsuA7cBrzrkPw1eqxLrE+DiGZqUqnMWYF5dv45OSvdxy+pgea9MkItKecIazA41ntx39Oug5zrkm59xkIA+YaWbjD3gRs+vMbJGZLSovLz+ceiXG5eeox2YsqW1o4o5X1zB+UF/OnTjQ63JERPYLZzjbCgxu9TgPKO7sOc65PcAC4IwDXcQ594BzbrpzbnpOTs5hliyxLD/gZ9POGhqaunZztUSXx9/fxLY9+/jJmUf0eJsmEZFDCWc4WwiMMrPhZpYIXALMbnPObOAKCzoaqHDOlZhZjpllAJhZCnAKUBDGWkXID/hpbHZs2qn9znq7ipoG7plfxAmjczgu35s2TSIiBxO2BQHOuUYz+w7wKsGtNP7qnFtlZjeEnr8fmEtwG40igltpXB369AHAo6EVn3HAM865l8JVqwh8tgF6fqCPx9VIOP1pQRF7axu49Qxv2zSJiBxIWPc5c87NJRjAWh+7v9XHDrjxAJ+3ApgSztpE2hqZ82k4k97BOcf2yjrWl1ezYUc1G3dWs768mrfXlHP+lEGMG+htmyYRkQPRVugiIWlJ8QxMT1Y4i0IVNQ2s31HFhh3BELZ+RzUbQx/X1H/alzIxPo7hWWmcMb6/583NRUQORuFMpJWRAT9F5QpnkWhffRMbd1Z/GsDKq9mwo4qNO2vYVV2//zxfnJHXL4Xh2WnMHJ7JiOw0hmWnMTw7jYHpKbr5X0QinsKZSCv5AT9Pf7SF5manX+Ie27yzhofeXc/a7cERsZKK2s88379vMsOzg6Ngw7OC4Wt4ThqD+6WSGB/OtU4iIuGlcCbSSn7Az76GJoor9pHXL9XrcmJSU7Pjkfc2cserhTgcRwzoyzEjsxiRncbwbD/DslMZlpWmBvUi0mvpXzeRVvJbLQpQOOt5a8oq+Y9/rmDZlj2cPDbAL788noEZKV6XJSLSoxTORFppvZ3GSWMCHlcTO+obm7lvwTrumb8Wf1I8d10ymS9NGoiZppZFJPYonIm0kpmWSEZqAuvKtRFtT1m+ZQ8/fnYFBaWVfGnSQP7r3HFk+ZO8LktExDMKZyKtmBn5OX7WaTuNsNtX38TvX1/DX95ZT6BPMn+5YjqnjMv1uiwREc8pnIm0kR/wM++TMq/L6NXeX7eTW59bwaadNVw6cwg/OWssfZMTvC5LRCQiKJyJtJEf8PP0wi3sqq4nMy3R63J6lb21Dfx6bgFPfbSZoVmpPHntURw7Ur0tRURaUzgTaWNkq0UBM4dnelxN7/H6J2X89IWVbK+s5boTRnDzKaNJSfR5XZaISMRROBNpo/V2Ggpnh29nVR3/PecTZi8vZmz/Pvz58mlMGpzhdVkiIhFL4UykjUEZKaQk+NRj8zA555i9vJhfzF5FVV0jN58ymm+dNFK794uItEPhTKSNuDhjRE6aemwehuI9+/jpCyt5s2A7kwdn8NuLJjI6t4/XZYmIRAWFM5EDyA/4WbRxt9dlRJ3mZseTH23mN68U0NTs+Nk547jq2GH41KdURKTDFM5EDiA/x8+Ly4qprmtUD8cO2rCjmlufXcGHG3ZxXH4Wvz5/IkOy1AJLRKSz9FtH5ABa2jitL69mQl66x9VEtqZmx1/eWc/vXltDYnwcv71wIl+ZnqfWSyIiXaRwJnIA+3tsllcqnB3Ctj37+P7TS1m4cTenjsvll18eT27fZK/LEhGJagpnIgcwNCsNX5xpxeYhvPJxCT9+dgVNzY7fXTyJ86cM0miZiEg3UDgTOYDE+DiGZqUqnB3Avvom/uelVTz10RYm5aVz1yVTGJad5nVZIiK9hsKZyEHk5/gVztr4pHgvNz29lHXlVdxw4kh+cOpo7VsmItLNFM5EDiI/4OfNgu00NDWT4IvtAOKc45H3NvLruQWkpybw+DVHcfwo9cQUEQkHhTORg8gP+GlsdmzaWbN/gUAs2llVxy3/XMGbBds5eWyA2y+aSJY/yeuyRER6LYUzkYPIb9UAPVbD2btrd/CDZ5axZ18Dvzh3HFceO0w3/YuIhJnCmchBjAw1QF8Xg22c6hubufO1Qh54ez0jstN45OqZjBvY1+uyRERigsKZyEGkJcUzID055hYFbNpZzU1PLWX51gounTmEn58zjpREn9dliYjEDIUzkUPID8TWis3nlmzlZy+sxBdn3HfZVM6cMMDrkkREYo7CmcghjMzx88yiLTQ3O+J6cfPuytoGfvbCSl5YVszMYZn8/pLJDMpI8bosEZGYpHAmcgj5AT819U2U7K3ttWFl6ebdfO/pZWzdXcPNp4zmxlkjiY/xrUNERLykcCZyCK1XbPa2cNbc7Lj/7XX8bt4acvsm8/frj2HGsEyvyxIRiXkKZyKH0DqcnTg6x+Nquk/Z3lp+8Mwy/l20k7MnDOBX508gPTXB67JERASFM5FDykpLJCM1oVctCnj9kzJu+edyahua+c0FE/jqjMHau0xEJIIonIkcgpmRn+NnXS8IZ/WNzfxq7moeeW8j4wb05e5Lp8Ts5roiIpFM4UykHfkBP/M+KfO6jMP2pwVFPPLeRq45bjg/PnMMSfHau0xEJBJpSZZIO/IDfnZV17Orut7rUrpsV3U9f3lnA2cc2Z+fnztOwUxEJIIpnIm0Y2SrRQHR6r4FRdTUN/LD00Z7XYqIiLRD4UykHfk50R3OSir28ej7mzh/Sh6jcvt4XY6IiLRD4UykHYMyUkhJ8EVtOLv7jSKcc3z/lFFelyIiIh0Q1nBmZmeYWaGZFZnZrQd43szs7tDzK8xsauj4YDObb2arzWyVmX0vnHWKHEpcnDEiJ4115dEXzjbuqOaZRVv42swhDM5M9bocERHpgLCFMzPzAfcCZwLjgEvNbFyb084ERoXergPuCx1vBH7onDsCOBq48QCfK9JjorUB+u9fX0OiL44bT873uhQREemgcI6czQSKnHPrnXP1wNPAeW3OOQ94zAV9AGSY2QDnXIlzbgmAc64SWA0MCmOtIoeUn+Nn25591NQ3el1Kh60u2cvs5cVcfdwwAn2SvS5HREQ6KJzhbBCwpdXjrXw+YLV7jpkNA6YAH3Z/iSId07JZ6/ryao8r6bg75xXSJyme608Y6XUpIiLSCeEMZwfqB+M6c46Z+YFnge875/Ye8CJm15nZIjNbVF5e3uViRQ4lP8q201i8aRevr97O9SeOVM9MEZEoE85wthUY3OpxHlDc0XPMLIFgMHvCOffcwS7inHvAOTfdOTc9J6f3NKaWyDI0Kw1fnEVFOHPO8dt/FZLtT+Lq44Z5XY6IiHRSOMPZQmCUmQ03s0TgEmB2m3NmA1eEVm0eDVQ450os2IX5IWC1c+53YaxRpEMS4+MYmpkaFeHsnbU7+HDDLr57cj6pierQJiISbcL2L7dzrtHMvgO8CviAvzrnVpnZDaHn7wfmAmcBRUANcHXo048DLgc+NrNloWO3OefmhqtekfaMDPgpivDtNJxz3P5qIYMyUrhk5uD2P0FERCJOWP9bHQpTc9scu7/Vxw648QCf9y4Hvh9NxDP5AT/zC7bT0NRMgi8y92/+18pSPt5WwR1fmaT+mSIiUSoyf8OIRKD8HD+NzY5NO2u8LuWAmpodd8wrJD/g5/wp2nlGRCRaKZyJdFCkr9h8fuk21pVX86PTRuOL08CziEi0UjgT6aCRoXAWiW2c6hqb+P1ra5iYl87pR/b3uhwRETkMCmciHeRPimdAenJEjpw9/dEWtu3Zxy2njyG42FlERKKVwplIJ0Rij82a+kb++GYRR4/I5Pj8bK/LERGRw6RwJtIJI3P8rCuvorm5bbML7zz8743sqKrjltPHatRMRKQXUDgT6YT8gJ+a+iZK9tZ6XQoAFTUN/PmtdZxyRIBpQ/t5XY6IiHQDhTORTmhZsbkuQqY2//z2OirrGvnhaWO8LkVERLqJwplIJ0TSdhrbK2t5+N8b+dKkgRwxoK/X5YiISDdROBPphKy0RDJSEyKijdO9bxbR0NTMzaeM9roUERHpRgpnIp1gZuTneL9ic8uuGp78aDMXzxjMsOw0T2sREZHupXAm0kn5Ab/n95z94fW1xJlx08mjPK1DRES6n8KZSCflB/zsrK5nd3W9J9dfW1bJ80u3cuWxw+ifnuxJDSIiEj4KZyKd1NLGyav7zu6ct4bUxHhuOHGkJ9cXEZHwUjgT6aT8HO9WbC7fsod/rSrl2i+MIDMtscevLyIi4adwJtJJgzJSSE6I8ySc3TGvkMy0RL7xheE9fm0REekZCmcinRQXZ4zI7vkVm++t28E7a3fw7ZNG4k+K79Fri4hIz1E4E+mCnm6A7pzj9lcLGZCezNePHtpj1xURkZ6ncCbSBfkBP9v27KOmvrFHrvfG6u0s3byHm744iuQEX49cU0REvKFwJtIFLW2c1pdXh/1azc2OO+YVMjw7jYum5YX9eiIi4i2FM5Eu6Mkem3NWFFNQWsnNp44mwacfWRGR3k7/0ot0wbCsNHxxFvZw1tDUzO9eW8MRA/pyzoQBYb2WiIhEBoUzkS5IjI9jaGZq2MPZM4u2sGlnDbecPpq4OAvrtUREJDIonIl00ciAn3Vh7BJQ29DE3W+sZfrQfswaEwjbdUREJLJosySRLhoV8PPaJ2Wc+ru3GJ3bh1G5fsbk9mFUbh+GZaUSf5j3hz32/kbK9tZx9yVTMNOomYhIrFA4E+miK48dhhkUllbx8bYK5q4swbngc4m+OEbkpDE6tw+jc/2Myu3D6Nw+DMlMxdeB6cnK2gb+tGAdJ47O4agRWWH+SkREJJIonIl0UW7fZG45fez+x/vqmyjaXsWaskrWbK9kbVkVizftZvby4v3nJMXHkR/w7x9pGx0Ihra8fimfuafswXc2sKemgR+dNqZHvyYREfGewplIN0lJ9DEhL50JeemfOV5d18jaUGhbW1bJmrIqPli/k+eXbvv0cxN8jMr1MyrQh/yAn4feWc9ZE/p/7rVERKT3UzgTCbO0pHgmD85g8uCMzxzfW9vA2rKq/YFt7fZK3i0q59klW0nwGT84VaNmIiKxSOFMxCN9kxOYNrQf04b2+8zxipoGahoaGZCe4lFlIiLiJYUzkQiTnppAOglelyEiIh7RPmciIiIiEUThTERERCSCKJyJiIiIRBCFMxEREZEIonAmIiIiEkEUzkREREQiiMKZiIiISARROBMRERGJIApnIiIiIhFE4UxEREQkgphzzusauo2ZlQObwnyZbGBHmK8h3U/ft+ik71v00vcuOun71rOGOudy2h7sVeGsJ5jZIufcdK/rkM7R9y066fsWvfS9i076vkUGTWuKiIiIRBCFMxEREZEIonDWeQ94XYB0ib5v0Unft+il71100vctAuieMxEREZEIopEzERERkQiicNZBZnaGmRWaWZGZ3ep1PdJxZrbRzD42s2VmtsjreuTAzOyvZrbdzFa2OpZpZq+Z2drQ+35e1igHdpDv3S/MbFvo526ZmZ3lZY3yWWY22Mzmm9lqM1tlZt8LHdfPXARQOOsAM/MB9wJnAuOAS81snLdVSSfNcs5N1hLxiPYIcEabY7cCbzjnRgFvhB5L5HmEz3/vAH4f+rmb7Jyb28M1yaE1Aj90zh0BHA3cGPq9pp+5CKBw1jEzgSLn3HrnXD3wNHCexzWJ9CrOubeBXW0Onwc8Gvr4UeDLPVmTdMxBvncSwZxzJc65JaGPK4HVwCD0MxcRFM46ZhCwpdXjraFjEh0cMM/MFpvZdV4XI52S65wrgeAvEyDgcT3SOd8xsxWhaU9Nj0UoMxsGTAE+RD9zEUHhrGPsAMe0zDV6HOecm0pwWvpGMzvB64JEYsB9wEhgMlAC3OlpNXJAZuYHngW+75zb63U9EqRw1jFbgcGtHucBxR7VIp3knCsOvd8OPE9wmlqiQ5mZDQAIvd/ucT3SQc65Mudck3OuGXgQ/dxFHDNLIBjMnnDOPRc6rJ+5CKBw1jELgVFmNtzMEoFLgNke1yQdYGZpZtan5WPgNGDloT9LIshs4MrQx1cCL3pYi3RCyy/4kPPRz11EMTMDHgJWO+d+1+op/cxFAG1C20GhZeB/AHzAX51z/+ttRdIRZjaC4GgZQDzwpL53kcnMngJOArKBMuC/gBeAZ4AhwGbgK8453XgeYQ7yvTuJ4JSmAzYC17fcyyTeM7PjgXeAj4Hm0OHbCN53pp85jymciYiIiEQQTWuKiIiIRBCFMxEREZEIonAmIiIiEkEUzkREREQiiMKZiIiISARROBORmGRmVW0eX2Vm93hVj4hIC4UzEZFuZGY+r2sQkeimcCYi0oaZDTWzN0JNu98wsyGh44+Y2UWtzqsKvT/JzOab2ZMEN/UUEemyeK8LEBHxSIqZLWv1OJNP27LdAzzmnHvUzK4B7ga+3M7rzQTGO+c2dHehIhJbFM5EJFbtc85NbnlgZlcB00MPjwEuCH38OPDbDrzeRwpmItIdNK0pItK+lj53jYT+3Qw1jk5sdU51TxclIr2TwpmIyOe9B1wS+vgy4N3QxxuBaaGPzwMSerYsEYkFCmciIp93E3C1ma0ALge+Fzr+IHCimX0EHIVGy0QkDMw51/5ZIiIiItIjNHImIiIiEkEUzkREREQiiMKZiIiISARROBMRERGJIApnIiIiIhFE4UxEREQkgiiciYiIiEQQhTMRERGRCPL/AfIOo4K0HVKoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
    "\n",
    "# Create a line plot of 'hourly_arrest_rate'\n",
    "hourly_arrest_rate.plot()\n",
    "\n",
    "# Add the xlabel, ylabel, and title\n",
    "plt.xlabel('Hour')\n",
    "plt.ylabel('Arrest Rate')\n",
    "plt.title('Arrest Rate by Time of Day')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting drug-related stops\n",
    "In a small portion of traffic stops, drugs are found in the vehicle during a search. You'll assess whether these drug-related stops are becoming more common over time.\n",
    "\n",
    "The Boolean column drugs_related_stop indicates whether drugs were found during a given stop. You'll calculate the annual drug rate by resampling this column, and then you'll use a line plot to visualize how the rate has changed over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop_datetime\n",
      "2005-12-31    0.006501\n",
      "2006-12-31    0.007258\n",
      "2007-12-31    0.007970\n",
      "2008-12-31    0.007505\n",
      "2009-12-31    0.009889\n",
      "2010-12-31    0.010081\n",
      "2011-12-31    0.009731\n",
      "2012-12-31    0.009921\n",
      "2013-12-31    0.013094\n",
      "2014-12-31    0.013826\n",
      "2015-12-31    0.012266\n",
      "Freq: A-DEC, Name: drugs_related_stop, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the annual rate of drug-related stops\n",
    "print(ri.drugs_related_stop.resample('A').mean())\n",
    "\n",
    "# Save the annual rate of drug-related stops\n",
    "annual_drug_rate = ri.drugs_related_stop.resample('A').mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
    "\n",
    "# Create a line plot of 'annual_drug_rate'\n",
    "annual_drug_rate.plot()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing drug and search rates\n",
    "\n",
    "The rate of drug-related stops increased significantly between 2005 and 2015. You might hypothesize that the rate of vehicle searches was also increasing, which would have led to an increase in drug-related stops even if more drivers were not carrying drugs.\n",
    "\n",
    "You can test this hypothesis by calculating the annual search rate, and then plotting it against the annual drug rate. If the hypothesis is true, then you'll see both rates increasing over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>drugs_related_stop</th>\n",
       "      <th>search_conducted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-12-31</th>\n",
       "      <td>0.006501</td>\n",
       "      <td>0.049167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-12-31</th>\n",
       "      <td>0.007258</td>\n",
       "      <td>0.041758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>0.007970</td>\n",
       "      <td>0.044056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-12-31</th>\n",
       "      <td>0.007505</td>\n",
       "      <td>0.040310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>0.009889</td>\n",
       "      <td>0.049861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               drugs_related_stop  search_conducted\n",
       "stop_datetime                                      \n",
       "2005-12-31               0.006501          0.049167\n",
       "2006-12-31               0.007258          0.041758\n",
       "2007-12-31               0.007970          0.044056\n",
       "2008-12-31               0.007505          0.040310\n",
       "2009-12-31               0.009889          0.049861"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate and save the annual search rate\n",
    "annual_search_rate = ri.search_conducted.resample('A').mean()\n",
    "\n",
    "# Concatenate 'annual_drug_rate' and 'annual_search_rate'\n",
    "annual = pd.concat([annual_drug_rate, annual_search_rate], axis='columns')\n",
    "\n",
    "annual.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
    "\n",
    "# Create subplots from 'annual'\n",
    "annual.plot(subplots=True)\n",
    "\n",
    "# Display the subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tallying violations by district\n",
    "The state of Rhode Island is broken into six police districts, also known as zones. How do the zones compare in terms of what violations are caught by police?\n",
    "\n",
    "You'll create a frequency table to determine how many violations of each type took place in each of the six zones. Then, you'll filter the table to focus on the \"K\" zones, which you'll examine further in the next exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>violation</th>\n",
       "      <th>Equipment</th>\n",
       "      <th>Moving violation</th>\n",
       "      <th>Other</th>\n",
       "      <th>Registration/plates</th>\n",
       "      <th>Seat belt</th>\n",
       "      <th>Speeding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>district</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>Zone K1</th>\n",
       "      <td>672</td>\n",
       "      <td>1254</td>\n",
       "      <td>290</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>5960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone K2</th>\n",
       "      <td>2061</td>\n",
       "      <td>2962</td>\n",
       "      <td>942</td>\n",
       "      <td>768</td>\n",
       "      <td>481</td>\n",
       "      <td>10448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone K3</th>\n",
       "      <td>2302</td>\n",
       "      <td>2898</td>\n",
       "      <td>705</td>\n",
       "      <td>695</td>\n",
       "      <td>638</td>\n",
       "      <td>12322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone X1</th>\n",
       "      <td>296</td>\n",
       "      <td>671</td>\n",
       "      <td>143</td>\n",
       "      <td>38</td>\n",
       "      <td>74</td>\n",
       "      <td>1119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone X3</th>\n",
       "      <td>2049</td>\n",
       "      <td>3086</td>\n",
       "      <td>769</td>\n",
       "      <td>671</td>\n",
       "      <td>820</td>\n",
       "      <td>8779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone X4</th>\n",
       "      <td>3541</td>\n",
       "      <td>5353</td>\n",
       "      <td>1560</td>\n",
       "      <td>1411</td>\n",
       "      <td>843</td>\n",
       "      <td>9795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "violation  Equipment  Moving violation  Other  Registration/plates  Seat belt  \\\n",
       "district                                                                        \n",
       "Zone K1          672              1254    290                  120          0   \n",
       "Zone K2         2061              2962    942                  768        481   \n",
       "Zone K3         2302              2898    705                  695        638   \n",
       "Zone X1          296               671    143                   38         74   \n",
       "Zone X3         2049              3086    769                  671        820   \n",
       "Zone X4         3541              5353   1560                 1411        843   \n",
       "\n",
       "violation  Speeding  \n",
       "district             \n",
       "Zone K1        5960  \n",
       "Zone K2       10448  \n",
       "Zone K3       12322  \n",
       "Zone X1        1119  \n",
       "Zone X3        8779  \n",
       "Zone X4        9795  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a frequency table of districts and violations and save the frequency table as 'all_zones'\n",
    "all_zones = pd.crosstab(ri.district, ri.violation)\n",
    "\n",
    "all_zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>violation</th>\n",
       "      <th>Equipment</th>\n",
       "      <th>Moving violation</th>\n",
       "      <th>Other</th>\n",
       "      <th>Registration/plates</th>\n",
       "      <th>Seat belt</th>\n",
       "      <th>Speeding</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>district</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>Zone K1</th>\n",
       "      <td>672</td>\n",
       "      <td>1254</td>\n",
       "      <td>290</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>5960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone K2</th>\n",
       "      <td>2061</td>\n",
       "      <td>2962</td>\n",
       "      <td>942</td>\n",
       "      <td>768</td>\n",
       "      <td>481</td>\n",
       "      <td>10448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zone K3</th>\n",
       "      <td>2302</td>\n",
       "      <td>2898</td>\n",
       "      <td>705</td>\n",
       "      <td>695</td>\n",
       "      <td>638</td>\n",
       "      <td>12322</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "violation  Equipment  Moving violation  Other  Registration/plates  Seat belt  \\\n",
       "district                                                                        \n",
       "Zone K1          672              1254    290                  120          0   \n",
       "Zone K2         2061              2962    942                  768        481   \n",
       "Zone K3         2302              2898    705                  695        638   \n",
       "\n",
       "violation  Speeding  \n",
       "district             \n",
       "Zone K1        5960  \n",
       "Zone K2       10448  \n",
       "Zone K3       12322  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select rows 'Zone K1' through 'Zone K3' and save the smaller table as 'k_zones'\n",
    "k_zones = all_zones.loc['Zone K1':'Zone K3']\n",
    "\n",
    "k_zones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting violations by district\n",
    "Now that you've created a frequency table focused on the \"K\" zones, you'll visualize the data to help you compare what violations are being caught in each zone.\n",
    "\n",
    "First you'll create a bar plot, which is an appropriate plot type since you're comparing categorical data. Then you'll create a stacked bar plot in order to get a slightly different look at the data. Which plot do you find to be more insightful?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10, 5)\n",
    "\n",
    "# Create a bar plot of 'k_zones'\n",
    "k_zones.plot(kind='bar')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1a9eyZs0aHnroIY4++uiNtlm5ciV77703u+66K1OmTKkcyWrTpg2rVq2q9nOrnvPChQt58803OeSQQ7b5/CRJaswcGdtBOnbsyFVXXbVZ++jRo7ngggvIz89n99135+67765cV1RURL9+/ZgwYcKn+qzLLruM4cOHk5+fT+/evcnPz6dt282/SdqsWTNOPPFEJkyYsNHnVjj00EM5//zzKSwsBOCiiy6id+/eG21zzjnn8NWvfpW+fftSUFBA165dAdhrr7048sgjycvL4/jjj+fyyy/fqL5LLrmEnj170rx5cyZMmLDRiJgkSU1ZbOslrvqmb9++adNvDC5YsIBu3bplVFF2ysrKWL9+PS1btmTJkiUMHjyYhQsXsttuu2VdWiaaaj+QtPPdVFS7e0iq9r5T/GjWJdRKRMxKKfWtbp0jY43A2rVrGTRoEOvXryelxNixY5tsEJMkqaExjDUCbdq02ey+YpIkqWFwAr8kSVKGDGOSJEkZMoxJkiRlyDAmSZKUIcPYDrBs2TJOPvlkunTpwkEHHcRVV13Fxx9/TGlpKY899ljldlUfAi5JkpqmRv1typ5399z6Rp/C3OHV39m+qpQSp512GpdeeimPPPIIZWVljBgxgmuvvZYePXpQUlKy2fMqa6usrIxmzZrVybEkSVI2HBmrY0899RQtW7bkggsuAMrven/zzTfzm9/8hquvvpri4mIKCgoqHwo+f/58Bg4cyIEHHsitt95aeZz/9//+H4WFhRQUFHDxxRdTVlYGQOvWrbn++us57LDDmDZt2s4/QUmSVKcMY3Xs5Zdfpk+fPhu17bHHHnTq1Ikf/OAHFBUVUVpaWvlsyFdeeYW//OUvzJgxgx/96EesX7+eBQsWUFxczHPPPUdpaSnNmjWrfLbjmjVryMvLY/r06Rx11FE7/fwkSVLdatSXKbOQUiIitrn9K1/5Ci1atKBFixbsvffevPPOO/ztb39j1qxZ9OvXD4APP/yQvffeGygfaTv99NN37ElIkqSdxjBWx3r06MEDDzywUdu//vUv3nrrrWrnd1V9YHazZs3YsGEDKSWGDx/Of/3Xf222fcuWLZ0nJklSI2IYq2ODBw9m1KhR3HPPPZx33nmUlZXxne98h/PPP5999tmH6dOnb9MxTj75ZL71rW+x99578/7777Nq1SoOOOCAnXAGUt3wAco7X0N9gLLU1G11zlhE3BUR/4yIeVXaiiOiNPdaGhGlufZOEfFhlXXjquzTJyLmRsTiiLg1ctfsIqJF7niLI2J6RHSq+9PceSKChx56iD/84Q906dKFgw8+mJYtW/KTn/yEQYMGMX/+/I0m8Fene/fu/Od//ifHHXcc+fn5HHvssSxfvnwnnoUkSdpZtmVkbALwK+CeioaUUlHFckTcBKyssv2SlFJBNccZC4wAXgAeA4YCjwMXAv+bUvpiRAwD/hsoqmb/T21bbkWxI+y///788Y9/3Ky9RYsWzJw5c4v7zZtXmXcpKiqqnORf1erVq+umSEmSVC9sdWQspfQ08H5163KjW2cC99V0jIjYF9gjpTQtpZQoD3an5FafDNydW54EDI7qZrpLkiQ1Qtt7a4ujgXdSSouqtHWOiBcj4u8RcXSurQOwrMo2y3JtFeveAkgpbaB8lG2v6j4sIkZERElElKxYsWI7S5ckScre9oaxs9h4VGw58IWUUm/g28DvI2IPoLqRrpT7WdO6jRtTuiOl1Del1Ld9+/bbUbYkSVL9UOtvU0ZEc+A0oPIOpymlj4CPcsuzImIJcDDlI2Edq+zeEXg7t7wM2B9YljtmW7ZwWVSSJKmx2Z6RsS8Dr6SUKi8/RkT7iGiWWz4Q6AK8llJaDqyKiP65+WDnAY/kdpsMDM8tnwE8lZtXJkmS1Ohty60t7gOmAYdExLKIuDC3ahibT9wfAMyJiJcon4x/SUqpYpTrUuA3wGJgCeXfpAQYD+wVEYspv7Q5ajvOR5IkqUHZlm9TnpVS2jeltGtKqWNKaXyu/fyU0rhNtn0gpdQjpdQrpXRoSumPVdaVpJTyUkoHpZSuqBj9SimtSyl9LaX0xZRSYUrptbo+yZ2pWbNmFBQUkJeXx1e/+lU++OCDWh3n7bff5owzztji+g8++IDbb7+9Vsf+yU9+stH7I444olbHqXDfffdxww03bHH90qVLycvLq/EYS5cu5fe///121SFJUkPUqO/Av6Brtzo9XrdXFmx1m1atWlFaWgrA8OHDue2227j22ms/9Wftt99+TJo0aYvrK8LYZZddttm6srKyGh+Z9JOf/ITvf//7le+ff/75T11fVX/+85+58sort+sYFWHs7LPP3q7jSJLU0GzvtylVg8MPP5x//OMfACxZsoShQ4fSp08fjj76aF555ZXK9v79+9OvXz+uv/56WrduDWw8mvTyyy9TWFhIQUEB+fn5LFq0iFGjRrFkyRIKCgoYOXIkU6dOZdCgQZx99tn07NkTgFNOOYU+ffrQo0cP7rjjDgBGjRrFhx9+SEFBAeeccw5A5WemlBg5ciR5eXn07Nmz8ikBU6dOZeDAgZxxxhl07dqVc845h4ppfSklSktLOfTQQxk9ejTnnnsuxxxzDF26dOHOO+/c7HeydOlSjj76aA499FAOPfTQyiA4atQonnnmGQoKCrj55pspKytj5MiR9OvXj/z8fH79618DsHz5cgYMGFA5+vjMM8/U8Z+aJEk7V6MeGctSWVkZf/vb37jwwvIpdiNGjGDcuHF06dKF6dOnc9lll/HUU09x1VVXcdVVV3HWWWcxbty4ao81btw4rrrqKs455xw+/vhjysrKGDNmDPPmzaschZs6dSozZsxg3rx5dO7cGYC77rqLz33uc3z44Yf069eP008/nTFjxvCrX/2qcr+qHnzwQUpLS3nppZd499136devHwMGDADgxRdf5OWXX2a//fbjyCOP5LnnnuOoo47ixRdfpFevXlTcp3fOnDm88MILrFmzht69e/OVr3xlo8/Ye++9efLJJ2nZsiWLFi3irLPOoqSkhDFjxnDjjTfy6KPlz9a74447aNu2LTNnzuSjjz7iyCOP5LjjjuPBBx9kyJAhXHvttZSVlbF27drt/rOSJClLhrE6VjHqtHTpUvr06cOxxx7L6tWref755/na175Wud1HH30EwLRp03j44YcBOPvss/nud7+72TEPP/xwbrjhBpYtW8Zpp51Gly5dqv3swsLCyiAGcOutt/LQQw8B8NZbb7Fo0SL22qva++kC8Oyzz3LWWWfRrFkz9tlnH770pS8xc+ZM9thjDwoLC+nYsfzuJBXnd9RRR/HnP/+Z448/vvIYJ598Mq1ataJVq1YMGjSIGTNmUFBQULl+/fr1XHHFFZSWltKsWTMWLlxYbS1PPPEEc+bMqbxUu3LlShYtWkS/fv34+te/zvr16znllFM2OrYkSQ2RlynrWMWcsTfeeIOPP/6Y2267jU8++YTPfvazlJaWVr4WLNj6/LMKZ599NpMnT6ZVq1YMGTKEp556qtrtPvOZz1QuT506lb/+9a9MmzaNl156id69e7Nu3boaP6emO4q0aNGicrlZs2Zs2LABKA9Nxx13XOW6TZ9kten7m2++mX322YeXXnqJkpISPv744y3W8stf/rLy9/X6669z3HHHMWDAAJ5++mk6dOjAueeeyz333FPt/pIkNRSGsR2kbdu23Hrrrdx44420atWKzp0784c//AEoDxovvfQSAP379+eBBx4AYOLEidUe67XXXuPAAw/kyiuv5KSTTmLOnDm0adOGVatWbfHzV65cyZ577snuu+/OK6+8wgsvvFC5btddd2X9+vWb7TNgwACKi4spKytjxYoVPP300xQWFtb4GRs2bNhotO2RRx5h3bp1vPfee0ydOpV+/fptts++++7LLrvswu9+9zvKysoANjufIUOGMHbs2Mo6Fy5cyJo1a3jjjTfYe++9+cY3vsGFF17I7Nmzt1ifJEkNgWFsB+rduze9evVi4sSJ3HvvvYwfP55evXrRo0cPHnmk/J63t9xyCz//+c8pLCxk+fLltG3bdrPjFBcXk5eXR0FBAa+88grnnXcee+21F0ceeSR5eXmMHDlys32GDh3Khg0byM/P57rrrqN///6V60aMGEF+fn7lBP4Kp556Kvn5+fTq1YtjjjmGn/70p3z+85/f4vk9+eSTfPnLX96orbCwkK985Sv079+f6667jv3222+j9Zdddhl33303/fv3Z+HChZWjefn5+TRv3pxevXpx8803c9FFF9G9e3cOPfRQ8vLyuPjii9mwYQNTp06loKCA3r1788ADD3DVVVdt5U9BkqT6LRrqze779u2bSkpKNmpbsGAB3brV7e0sdrS1a9fSqlUrIoKJEydy3333VQa1+u6iiy7ioosuqgx6o0ePpnXr1tXOe9uZGmI/aIxuKjox6xKanO8UP5p1CU2O/Xzna6j9PCJmpZT6VrfOCfwZmzVrFldccQUpJT772c9y1113ZV3SNvvNb36TdQmSJDV4hrGMHX300ZXzxxq60aNHZ12CJEkNjnPGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxurYDTfcQI8ePcjPz6egoIDp06fX6jgPP/ww8+fPr3bd+eefX/mYoG1R9aHjpaWlPPbYY7WqSZIk1b1G/W3K2y6p/rFBtXX5uGNqXD9t2jQeffRRZs+eTYsWLXj33Xe3+LifrXn44Yc58cQT6d69e63235LS0lJKSko44YQT6vS4kiSpdhwZq0PLly+nXbt2lc9xbNeuXeUd6GfNmsWXvvQl+vTpw5AhQ1i+fDkAd955J/369aNXr16cfvrprF27lueff57JkyczcuRICgoKWLJkyWaf9de//pWjjz6agw8+mEcfLb8BXllZGSNHjqRfv37k5+fz61//eqN9Pv74Y66//nqKi4spKCiguLh4R/46JEnSNjCM1aHjjjuOt956i4MPPpjLLruMv//97wCsX7+ef//3f2fSpEnMmjWLr3/961x77bUAnHbaacycOZOXXnqJbt26MX78eI444ghOOukkfvazn1FaWspBBx202WctXbqUv//97/zpT3/ikksuYd26dYwfP562bdsyc+ZMZs6cyZ133snrr79euc9uu+3Gj3/8Y4qKiigtLaWoqGjn/GIkSdIWNerLlDtb69atmTVrFs888wxTpkyhqKiIMWPG0LdvX+bNm8exxx4LlI9g7bvvvgDMmzePH/zgB3zwwQesXr2aIUOGbNNnnXnmmeyyyy506dKFAw88kFdeeYUnnniCOXPmVM4nW7lyJYsWLeLggw/eMScsSZK2m2GsjjVr1oyBAwcycOBAevbsyd13302fPn3o0aMH06ZN22z7888/n4cffphevXoxYcIEpk6duk2fExGbvU8p8ctf/nKzQLd06dLano4kSdrBvExZh1599VUWLVpU+b60tJQDDjiAQw45hBUrVlSGsfXr1/Pyyy8DsGrVKvbdd1/Wr1/PvffeW7lvmzZtWLVq1RY/6w9/+AOffPIJS5Ys4bXXXuOQQw5hyJAhjB07lvXr1wOwcOFC1qxZs9F+WzuuJEnauQxjdWj16tUMHz6c7t27k5+fz/z58xk9ejS77bYbkyZN4pprrqFXr14UFBTw/PPPA/Af//EfHHbYYRx77LF07dq18ljDhg3jZz/7Gb179652Av8hhxzCl770JY4//njGjRtHy5Ytueiii+jevTuHHnooeXl5XHzxxWzYsGGj/QYNGsT8+fOdwC9JUj0RKaWsa6iVvn37ppKSko3aFixYQLdu3TKqSPWF/aB+uKnoxKxLaHK+U/xo1iU0Ofbzna+h9vOImJVS6lvdOkfGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYWwHuOGGG+jRowf5+fkUFBQwffr0HfZZAwcOpOIWHyeccAIffPDBDvssSZJU9xr145Dq+v4v23Jvk2nTpvHoo48ye/ZsWrRowbvvvsvHH39cp3VsyWOPPbZTPkeSJNUdR8bq2PLly2nXrh0tWrQAoF27duy333506tSJa665hsLCQgoLC1m8eDEAK1as4PTTT6dfv37069eP5557DoA1a9bw9a9/nX79+tG7d28eeeQRAD788EOGDRtGfn4+RUVFfPjhh5Wf3alTJ959912WLl1Kt27d+MY3vkGPHj047rjjKrebOXMm+fn5HH744YwcOZK8vLyd+euRJEmbMIzVseOOO4633nqLgw8+mMsuu4y///3vlev22GMPZsyYwRVXXME3v/lNAK666iq+9a1vMXPmTB544AEuuugioPxS5zHHHMPMmTOZMmUKI0eOZM2aNYwdO5bdd9+dOXPmcO211zJr1qxq61i0aBGXX345L7/8Mp/97Gd54IEHALjgggsYN24c06ZNo1mzZjv2lyFJkraqUV+mzELr1q2ZNWsWzzzzDFOmTKGoqIgxY8YAcNZZZ1X+/Na3vgXAX//6V+bPn1+5/7/+9S9WrVrFE088weTJk7nxxhsBWLduHW+++SZPP/00V155JQD5+fnk5+dXW0fnzp0pKCgAoE+fPixdupQPPviAVatWccQRRwBw9tln8+ijDfOxEpIkNRaGsR2gWbNmDBw4kIEDB9KzZ0/uvvtuACKicpuK5U8++YRp06bRqlWrjY6RUuKBBx7gkEMO2ez4VY+zJRWXSSvq+fDDD2mozyGVJKkx8zJlHXv11VdZtGhR5fvS0lIOOOAAAIqLiyt/Hn744UD5Zc1f/epXG20PMGTIEH75y19WBqgXX3wRgAEDBnDvvfcCMG/ePObMmbPNte255560adOGF154AYCJEyfW5hQlSVIdMozVsdWrVzN8+HC6d+9Ofn4+8+fPZ/To0QB89NFHHHbYYfziF7/g5ptvBuDWW2+lpKSE/Px8unfvzrhx4wC47rrrWL9+Pfn5+eTl5XHdddcBcOmll7J69Wry8/P56U9/SmFh4aeqb/z48YwYMYLDDz+clBJt27atu5OXJEmfWjTUS1d9+/ZNFffXqrBgwQK6deuWUUU169SpEyUlJbRr1y7TOlavXk3r1q0BGDNmDMuXL+cXv/hFpjXVtfrcD5qSur61jLZuW26/o7plP9/5Gmo/j4hZKaW+1a1zzlgT86c//Yn/+q//YsOGDRxwwAFMmDAh65IkSWrSDGM7ydKlS7MuAYCioiKKioqyLkOSJOU4Z0ySJClDjS6MNdQ5cKob/vlLkhqaRhXGWrZsyXvvvec/yE1USon33nuPli1bZl2KJEnbbKtzxiLiLuBE4J8ppbxc22jgG8CK3GbfTyk9llv3PeBCoAy4MqX0l1x7H2AC0Ap4DLgqpZQiogVwD9AHeA8oSiktrc3JdOzYkWXLlrFixYqtb6xGqWXLlnTs2DHrMiRJ2mbbMoF/AvArygNTVTenlG6s2hAR3YFhQA9gP+CvEXFwSqkMGAuMAF6gPIwNBR6nPLj9b0rpixExDPhvoFYzzHfddVc6d+5cm10lSZIysdXLlCmlp4H3t/F4JwMTU0ofpZReBxYDhRGxL7BHSmlaKr+GeA9wSpV97s4tTwIGx7Y870eSJKkR2J45Y1dExJyIuCsi9sy1dQDeqrLNslxbh9zypu0b7ZNS2gCsBPbajrokSZIajNqGsbHAQUABsBy4Kdde3YhWqqG9pn02ExEjIqIkIkqcFyZJkhqDWoWxlNI7KaWylNInwJ1AxQMSlwH7V9m0I/B2rr1jNe0b7RMRzYG2bOGyaErpjpRS35RS3/bt29emdEmSpHqlVmEsNweswqnAvNzyZGBYRLSIiM5AF2BGSmk5sCoi+ufmg50HPFJln+G55TOAp5L3ppAkSU3Ettza4j5gINAuIpYBPwQGRkQB5ZcTlwIXA6SUXo6I+4H5wAbg8tw3KQEu5f9ubfF47gUwHvhdRCymfERsWB2clyRJUoOw1TCWUjqrmubxNWx/A3BDNe0lQF417euAr22tDkmSpMaoUd2BX5IkqaExjEmSJGXIMCZJkpQhw5gkSVKGDGOSJEkZMoxJkiRlyDAmSZKUIcOYJElShgxjkiRJGTKMSZIkZcgwJkmSlCHDmCRJUoYMY5IkSRkyjEmSJGXIMCZJkpQhw5gkSVKGDGOSJEkZMoxJkiRlyDAmSZKUIcOYJElShgxjkiRJGTKMSZIkZcgwJkmSlCHDmCRJUoYMY5IkSRkyjEmSJGXIMCZJkpQhw5gkSVKGDGOSJEkZMoxJkiRlyDAmSZKUIcOYJElShgxjkiRJGTKMSZIkZcgwJkmSlCHDmCRJUoYMY5IkSRkyjEmSJGXIMCZJkpQhw5gkSVKGDGOSJEkZMoxJkiRlyDAmSZKUIcOYJElShrYaxiLiroj4Z0TMq9L2s4h4JSLmRMRDEfHZXHuniPgwIkpzr3FV9ukTEXMjYnFE3BoRkWtvERHFufbpEdGp7k9TkiSpftqWkbEJwNBN2p4E8lJK+cBC4HtV1i1JKRXkXpdUaR8LjAC65F4Vx7wQ+N+U0heBm4H//tRnIUmS1EBtNYyllJ4G3t+k7YmU0obc2xeAjjUdIyL2BfZIKU1LKSXgHuCU3OqTgbtzy5OAwRWjZpIkSY1dXcwZ+zrweJX3nSPixYj4e0QcnWvrACyrss2yXFvFurcAcgFvJbBXHdQlSZJU7zXfnp0j4lpgA3Bvrmk58IWU0nsR0Qd4OCJ6ANWNdKWKw9SwbtPPG0H5pU6+8IUvbE/pkiRJ9UKtR8YiYjhwInBO7tIjKaWPUkrv5ZZnAUuAgykfCat6KbMj8HZueRmwf+6YzYG2bHJZtEJK6Y6UUt+UUt/27dvXtnRJkqR6o1ZhLCKGAtcAJ6WU1lZpbx8RzXLLB1I+Uf+1lNJyYFVE9M/NBzsPeCS322RgeG75DOCpinAnSZLU2G31MmVE3AcMBNpFxDLgh5R/e7IF8GRurv0LuW9ODgB+HBEbgDLgkpRSxSjXpZR/M7MV5XPMKuaZjQd+FxGLKR8RG1YnZyZJktQAbDWMpZTOqqZ5/Ba2fQB4YAvrSoC8atrXAV/bWh2SJEmNkXfglyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIytNUwFhF3RcQ/I2JelbbPRcSTEbEo93PPKuu+FxGLI+LViBhSpb1PRMzNrbs1IiLX3iIiinPt0yOiUx2foyRJUr21LSNjE4Chm7SNAv6WUuoC/C33nojoDgwDeuT2uT0imuX2GQuMALrkXhXHvBD435TSF4Gbgf+u7clIkiQ1NFsNYymlp4H3N2k+Gbg7t3w3cEqV9okppY9SSq8Di4HCiNgX2COlNC2llIB7Ntmn4liTgMEVo2aSJEmNXW3njO2TUloOkPu5d669A/BWle2W5do65JY3bd9on5TSBmAlsFct65IkSWpQ6noCf3UjWqmG9pr22fzgESMioiQiSlasWFHLEiVJkuqP2oaxd3KXHsn9/GeufRmwf5XtOgJv59o7VtO+0T4R0Rxoy+aXRQFIKd2RUuqbUurbvn37WpYuSZJUf9Q2jE0GhueWhwOPVGkflvuGZGfKJ+rPyF3KXBUR/XPzwc7bZJ+KY50BPJWbVyZJktToNd/aBhFxHzAQaBcRy4AfAmOA+yPiQuBN4GsAKaWXI+J+YD6wAbg8pVSWO9SllH8zsxXweO4FMB74XUQspnxEbFidnJkkSVIDsNUwllI6awurBm9h+xuAG6ppLwHyqmlfRy7MSZIkNTXegV+SJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDBnGJEmSMtQ86wKaopuKTsy6hCbnO8WPZl2CJEnVcmRMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKUK3DWEQcEhGlVV7/iohvRsToiPhHlfYTquzzvYhYHBGvRsSQKu19ImJubt2tERHbe2KSJEkNQa3DWErp1ZRSQUqpAOgDrAUeyq2+uWJdSukxgIjoDgwDegBDgdsjollu+7HACKBL7jW0tnVJkiQ1JHV1mXIwsCSl9EYN25wMTEwpfZRSeh1YDBRGxL7AHimlaSmlBNwDnFJHdUmSJNVrdRXGhgH3VXl/RUTMiYi7ImLPXFsH4K0q2yzLtXXILW/aLkmS1OhtdxiLiN2Ak4A/5JrGAgcBBcBy4KaKTavZPdXQXt1njYiIkogoWbFixfaULUmSVC/UxcjY8cDslNI7ACmld1JKZSmlT4A7gcLcdsuA/avs1xF4O9fesZr2zaSU7kgp9U0p9W3fvn0dlC5JkpStughjZ1HlEmVuDliFU4F5ueXJwLCIaBERnSmfqD8jpbQcWBUR/XPfojwPeKQO6pIkSar3mm/PzhGxO3AscHGV5p9GRAHllxqXVqxLKb0cEfcD84ENwOUppbLcPpcCE4BWwOO5lyRJUqO3XWEspbQW2GuTtnNr2P4G4IZq2kuAvO2pRZIkqSHarjAmSVJT1nLPb2ddghoBw5ikHcJ/pCRp2/hsSkmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDHkHfkk7xDFTL8+6hCZoQdYFSKoFR8YkSZIyZBiTJEnKkJcpJUmqJS/HZ6HxXY53ZEySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQDwqXtEOc+T3/etnZ5mZdgKRa8W9LSZJqyf/p2Pka4/90eJlSkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDDXPugBJkhqqua+/mXUJagS2a2QsIpZGxNyIKI2Iklzb5yLiyYhYlPu5Z5XtvxcRiyPi1YgYUqW9T+44iyPi1oiI7alLkiSpoaiLy5SDUkoFKaW+ufejgL+llLoAf8u9JyK6A8OAHsBQ4PaIaJbbZywwAuiSew2tg7okSZLqvR0xZ+xk4O7c8t3AKVXaJ6aUPkopvQ4sBgojYl9gj5TStJRSAu6pso8kSVKjtr1zxhLwREQk4NcppTuAfVJKywFSSssjYu/cth2AF6rsuyzXtj63vGm7pAbMuTSStG22N4wdmVJ6Oxe4noyIV2rYtrp5YKmG9s0PEDGC8suZfOELX/i0tUqSJNU723WZMqX0du7nP4GHgELgndylR3I//5nbfBmwf5XdOwJv59o7VtNe3efdkVLqm1Lq2759++0pXZIkqV6odRiLiM9ERJuKZeA4YB4wGRie22w48EhueTIwLCJaRERnyifqz8hd0lwVEf1z36I8r8o+kiRJjdr2XKbcB3godxeK5sDvU0p/joiZwP0RcSHwJvA1gJTSyxFxPzAf2ABcnlIqyx3rUmAC0Ap4PPeSJElq9GodxlJKrwG9qml/Dxi8hX1uAG6opr0EyKttLZIkSQ2Vj0OSJEnKkGFMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJU6zAWEftHxJSIWBARL0fEVbn20RHxj4gozb1OqLLP9yJicUS8GhFDqrT3iYi5uXW3RkRs32lJkiQ1DM23Y98NwHdSSrMjog0wKyKezK27OaV0Y9WNI6I7MAzoAewH/DUiDk4plQFjgRHAC8BjwFDg8e2oTZIkqUGo9chYSml5Sml2bnkVsADoUMMuJwMTU0ofpZReBxYDhRGxL7BHSmlaSikB9wCn1LYuSZKkhqRO5oxFRCegNzA913RFRMyJiLsiYs9cWwfgrSq7Lcu1dcgtb9pe3eeMiIiSiChZsWJFXZQuSZKUqe0OYxHRGngA+GZK6V+UX3I8CCgAlgM3VWxaze6phvbNG1O6I6XUN6XUt3379ttbuiRJUua2K4xFxK6UB7F7U0oPAqSU3kkplaWUPgHuBApzmy8D9q+ye0fg7Vx7x2raJUmSGr3t+TZlAOOBBSmln1dp37fKZqcC83LLk4FhEdEiIjoDXYAZKaXlwKqI6J875nnAI7WtS5IkqSHZnm9THgmcC8yNiNJc2/eBsyKigPJLjUuBiwFSSi9HxP3AfMq/iXl57puUAJcCE4BWlH+LslF/k/KEl5ZkXYIkSaonah3GUkrPUv18r8dq2OcG4IZq2kuAvNrWIklSFjqt+33WJTQ5S7MuYAfwDvySJEkZ2p7LlKqlM7/nr31nm5t1AZIkbYEjY5IkSRkyjEmSJGXI62WSdggnNu98S7MuQFKtODImSZKUIcOYJElShrxMmYG5r7+ZdQmSJKmecGRMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ82zLqAp6rTu91mX0OQszboASZK2wJExSZKkDBnGJEmSMmQYkyRJypBhTJIkKUOGMUmSpAwZxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyVG/CWEQMjYhXI2JxRIzKuh5JkqSdoV6EsYhoBtwGHA90B86KiO7ZViVJkrTj1YswBhQCi1NKr6WUPgYmAidnXJMkSdIO1zzrAnI6AG9Veb8MOGzTjSJiBDAi93Z1RLy6E2rT/2kHvJt1EbUR/511BWpA7OdqCuznO98BW1pRX8JYVNOWNmtI6Q7gjh1fjqoTESUppb5Z1yHtSPZzNQX28/qlvlymXAbsX+V9R+DtjGqRJEnaaepLGJsJdImIzhGxGzAMmJxxTZIkSTtcvbhMmVLaEBFXAH8BmgF3pZRezrgsbc5LxGoK7OdqCuzn9UiktNnULEmSJO0k9eUypSRJUpNkGJMkScqQYUySJClDhjF9KhFxQdY1SHUlIrpGxOCIaL1J+9CsapLqWkQURkS/3HL3iPh2RJyQdV36P07g16cSEW+mlL6QdR3S9oqIK4HLgQVAAXBVSumR3LrZKaVDMyxPqhMR8UPKn/vcHHiS8qfbTAW+DPwlpXRDdtWpgmFMm4mIOVtaBRycUmqxM+uRdoSImAscnlJaHRGdgEnA71JKv4iIF1NKvbOtUNp+uX5eALQA/gfomFL6V0S0AqanlPKzrE/l6sV9xlTv7AMMAf53k/YAnt/55Ug7RLOU0mqAlNLSiBgITIqIA6j+EW1SQ7QhpVQGrI2IJSmlfwGklD6MiE8yrk05zhlTdR4FWqeU3tjktZTy4W2pMfifiCioeJMLZidS/gDlnlkVJdWxjyNi99xyn4rGiGgLGMbqCS9T6lOJiN1SSh9nXYe0vSKiI+WjBv9TzbojU0rPZVCWVKciokVK6aNq2tsBn08pzcugLG3CkTFtJiKu20J7W+CJnVyOtKNcsIUgtgfgpGY1FldvoX098KudWYi2zDCm6hwdERv9YxQRnweeBqZkU5JU57bUz58BnsqmJKnO1fT3uf28njCMqTonAb0i4ucAEdEFeBa4PaX0o0wrk+pOTf38x5lWJtUd+3kD4JwxVSsidgUmUj6UfTjwzZTSQ9lWJdUt+7maAvt5/WcY02Yi4tu5xV0pn2/wDOVD2gCklH6eRV1SXbKfqymwnzcM3mdM1WlTZfnWatqkxsB+rqbAft4AODImSZKUISfwS5IkZcgwJkmSlCHDmCRJUoYMY9qiiNgnIsZHxOO5990j4sKs65Lqkv1cTYH9vH4zjKkmE4C/APvl3i8EvplVMdIOMgH7uRq/CdjP6y3DmGrSLqV0P/AJQEppA1CWbUlSnbOfqymwn9djhjHVZE1E7AUkgIjoD6zMtiSpztnP1RTYz+sxb/qqmnwbmAwcFBHPAe2BM7ItSapz9nM1BfbzesybvqpGEdEcOAQI4NWU0vqMS5LqnP1cTYH9vP4yjKlGEXEE0Ikqo6gppXsyK0jaAeznagrs5/WXlym1RRHxO+AgoJT/m+iZAP/jVaNhP1dTYD+v3xwZ0xZFxAKge7KTqBGzn6spsJ/Xb36bUjWZB3w+6yKkHcx+rqbAfl6PeZlSNWkHzI+IGcBHFY0ppZOyK0mqc/ZzNQX283rMMKaajM66AGknGJ11AdJOMDrrArRlzhlTjSJiH6Bf7u2MlNI/s6xH2hHs52oK7Of1l3PGtEURcSYwA/gacCYwPSK8SaAaFfu5mgL7ef3myJi2KCJeAo6t+L+niGgP/DWl1CvbyqS6Yz9XU2A/r98cGVNNdtlkGPs97DNqfOznagrs5/WYE/hVkz9HxF+A+3Lvi4DHM6xH2hHs52oK7Of1mJcpVaOIOA04ivJnmT2dUnoo45KkOmc/V1NgP6+/DGPaTEQMSSn9ZQvrvpZS+sPOrkmqa/ZzNQX284bB68WqzmMRMSUiOlSz7ns7vRppx7CfqymwnzcAhjFVZw7we+CFiPjaJusig3qkHcF+rqbAft4AGMZUnZRSuhMYDFwdEb+NiN0r1mVYl1SX7OdqCuznDYBhTFuUUloIHA68A7wYEYdlXJJU5+znagrs5/WbE/i1mYh4MaXUe5O2gcBdQPuUUpss6pLqkv1cTYH9vGHwPmOqzo82bUgpTY2IPsDFGdQj7Qj2czUF9vMGwJExSZKkDDlnTJIkKUOGMUmSpAw5Z0xSoxURo4HVwB6UP/7lr1vY7hRgYUpp/hbWXwKsTSnds4X1nYAjUkq/r4OyJTUxhjFJjV5K6fqtbHIK8CiwWRiLiOYppXFb2b8TcDblN9eUpE/FCfySGpWIuBY4D3gLWAHMAvKAR1NKkyJiDHASsAF4AniQ8iC2Mvc6HRgPPA8cCUwG2gCrU0o3RsQXgXFAe6AM+BpwL9ANeB24O6V08845W0mNgSNjkhqN3Nf1hwG9Kf/7bTblYaxi/eeAU4GuKaUUEZ9NKX0QEZPJhbXcdgCfTSl9Kfd+dJWPuRcYk1J6KCJaUj73dhTw3ZTSiTv6HCU1Pk7gl9SYHA08lFJam1L6F+WjWlX9C1gH/CYiTgPW1nCs4k0bIqIN0CGl9BBASmldSqmmY0jSVhnGJDU2W5x7kVLaABQCD1A+T+zPNRxnTTVtPlhZUp0zjElqTJ4GTo2IVrlRrK9WXRkRrYG2KaXHgG8CBblVqyifF1aj3Gjbsty3L4mIFrmHLm/T/pJUHcOYpEYjpTSb8suLpZSPfj2zySZtgEcjYg7wd+BbufaJwMiIeDEiDtrKx5wLXJk7xvPA54E5wIaIeCkivlXj3pK0Cb9NKUmSlCFHxiRJkjJkGJMkScqQYUySJClDhjFJkqQMGcYkSZIyZBiTJEnKkGFMkiQpQ4YxSZKkDP1/vRdJTAEmFxUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10, 8)\n",
    "\n",
    "# Create a stacked bar plot of 'k_zones'\n",
    "k_zones.plot(kind='bar', stacked=True)\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting stop durations to numbers\n",
    "In the traffic stops dataset, the stop_duration column tells you approximately how long the driver was detained by the officer. Unfortunately, the durations are stored as strings, such as '0-15 Min'. How can you make this data easier to analyze?\n",
    "\n",
    "You'll convert the stop durations to integers. Because the precise durations are not available, you'll have to estimate the numbers using reasonable values:\n",
    "\n",
    "Convert '0-15 Min' to 8   \n",
    "Convert '16-30 Min' to 23   \n",
    "Convert '30+ Min' to 45   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['0-15 Min' '16-30 Min' '30+ Min']\n",
      "[ 8 23 45]\n"
     ]
    }
   ],
   "source": [
    "# Print the unique values in 'stop_duration'\n",
    "print(ri.stop_duration.unique())\n",
    "\n",
    "# Create a dictionary that maps strings to integers\n",
    "mapping = {'0-15 Min':8, '16-30 Min':23, '30+ Min':45}\n",
    "\n",
    "# Convert the 'stop_duration' strings to integers using the 'mapping'\n",
    "ri['stop_minutes'] = ri.stop_duration.map(mapping)\n",
    "\n",
    "# Print the unique values in 'stop_minutes'\n",
    "print(ri.stop_minutes.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting stop length\n",
    "If you were stopped for a particular violation, how long might you expect to be detained?\n",
    "\n",
    "You'll visualize the average length of time drivers are stopped for each type of violation. Rather than using the violation column in this exercise, you'll use violation_raw since it contains more detailed descriptions of the violations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "violation_raw\n",
      "APB                                 17.967033\n",
      "Call for Service                    22.124371\n",
      "Equipment/Inspection Violation      11.445655\n",
      "Motorist Assist/Courtesy            17.741463\n",
      "Other Traffic Violation             13.844490\n",
      "Registration Violation              13.736970\n",
      "Seatbelt Violation                   9.662815\n",
      "Special Detail/Directed Patrol      15.123632\n",
      "Speeding                            10.581562\n",
      "Suspicious Person                   14.910714\n",
      "Violation of City/Town Ordinance    13.254144\n",
      "Warrant                             24.055556\n",
      "Name: stop_minutes, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10, 8)\n",
    "\n",
    "# Calculate the mean 'stop_minutes' for each value in 'violation_raw'\n",
    "print(ri.groupby('violation_raw').stop_minutes.mean())\n",
    "\n",
    "# Save the resulting Series as 'stop_length'\n",
    "stop_length = ri.groupby('violation_raw').stop_minutes.mean()\n",
    "\n",
    "# Sort 'stop_length' by its values and create a horizontal bar plot\n",
    "stop_length.sort_values().plot(kind='barh')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The most Nobel of Prizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>category</th>\n",
       "      <th>prize</th>\n",
       "      <th>motivation</th>\n",
       "      <th>prize_share</th>\n",
       "      <th>laureate_id</th>\n",
       "      <th>laureate_type</th>\n",
       "      <th>full_name</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>birth_city</th>\n",
       "      <th>birth_country</th>\n",
       "      <th>sex</th>\n",
       "      <th>organization_name</th>\n",
       "      <th>organization_city</th>\n",
       "      <th>organization_country</th>\n",
       "      <th>death_date</th>\n",
       "      <th>death_city</th>\n",
       "      <th>death_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1901</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>The Nobel Prize in Chemistry 1901</td>\n",
       "      <td>\"in recognition of the extraordinary services ...</td>\n",
       "      <td>1/1</td>\n",
       "      <td>160</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Jacobus Henricus van 't Hoff</td>\n",
       "      <td>1852-08-30</td>\n",
       "      <td>Rotterdam</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Male</td>\n",
       "      <td>Berlin University</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1911-03-01</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1901</td>\n",
       "      <td>Literature</td>\n",
       "      <td>The Nobel Prize in Literature 1901</td>\n",
       "      <td>\"in special recognition of his poetic composit...</td>\n",
       "      <td>1/1</td>\n",
       "      <td>569</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Sully Prudhomme</td>\n",
       "      <td>1839-03-16</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1907-09-07</td>\n",
       "      <td>Châtenay</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1901</td>\n",
       "      <td>Medicine</td>\n",
       "      <td>The Nobel Prize in Physiology or Medicine 1901</td>\n",
       "      <td>\"for his work on serum therapy, especially its...</td>\n",
       "      <td>1/1</td>\n",
       "      <td>293</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Emil Adolf von Behring</td>\n",
       "      <td>1854-03-15</td>\n",
       "      <td>Hansdorf (Lawice)</td>\n",
       "      <td>Prussia (Poland)</td>\n",
       "      <td>Male</td>\n",
       "      <td>Marburg University</td>\n",
       "      <td>Marburg</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1917-03-31</td>\n",
       "      <td>Marburg</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1901</td>\n",
       "      <td>Peace</td>\n",
       "      <td>The Nobel Peace Prize 1901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/2</td>\n",
       "      <td>462</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Jean Henry Dunant</td>\n",
       "      <td>1828-05-08</td>\n",
       "      <td>Geneva</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1910-10-30</td>\n",
       "      <td>Heiden</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1901</td>\n",
       "      <td>Peace</td>\n",
       "      <td>The Nobel Peace Prize 1901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/2</td>\n",
       "      <td>463</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Frédéric Passy</td>\n",
       "      <td>1822-05-20</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1912-06-12</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1901</td>\n",
       "      <td>Physics</td>\n",
       "      <td>The Nobel Prize in Physics 1901</td>\n",
       "      <td>\"in recognition of the extraordinary services ...</td>\n",
       "      <td>1/1</td>\n",
       "      <td>1</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Wilhelm Conrad Röntgen</td>\n",
       "      <td>1845-03-27</td>\n",
       "      <td>Lennep (Remscheid)</td>\n",
       "      <td>Prussia (Germany)</td>\n",
       "      <td>Male</td>\n",
       "      <td>Munich University</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "      <td>1923-02-10</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    category                                           prize  \\\n",
       "0  1901   Chemistry               The Nobel Prize in Chemistry 1901   \n",
       "1  1901  Literature              The Nobel Prize in Literature 1901   \n",
       "2  1901    Medicine  The Nobel Prize in Physiology or Medicine 1901   \n",
       "3  1901       Peace                      The Nobel Peace Prize 1901   \n",
       "4  1901       Peace                      The Nobel Peace Prize 1901   \n",
       "5  1901     Physics                 The Nobel Prize in Physics 1901   \n",
       "\n",
       "                                          motivation prize_share  laureate_id  \\\n",
       "0  \"in recognition of the extraordinary services ...         1/1          160   \n",
       "1  \"in special recognition of his poetic composit...         1/1          569   \n",
       "2  \"for his work on serum therapy, especially its...         1/1          293   \n",
       "3                                                NaN         1/2          462   \n",
       "4                                                NaN         1/2          463   \n",
       "5  \"in recognition of the extraordinary services ...         1/1            1   \n",
       "\n",
       "  laureate_type                     full_name  birth_date          birth_city  \\\n",
       "0    Individual  Jacobus Henricus van 't Hoff  1852-08-30           Rotterdam   \n",
       "1    Individual               Sully Prudhomme  1839-03-16               Paris   \n",
       "2    Individual        Emil Adolf von Behring  1854-03-15   Hansdorf (Lawice)   \n",
       "3    Individual             Jean Henry Dunant  1828-05-08              Geneva   \n",
       "4    Individual                Frédéric Passy  1822-05-20               Paris   \n",
       "5    Individual        Wilhelm Conrad Röntgen  1845-03-27  Lennep (Remscheid)   \n",
       "\n",
       "       birth_country   sex   organization_name organization_city  \\\n",
       "0        Netherlands  Male   Berlin University            Berlin   \n",
       "1             France  Male                 NaN               NaN   \n",
       "2   Prussia (Poland)  Male  Marburg University           Marburg   \n",
       "3        Switzerland  Male                 NaN               NaN   \n",
       "4             France  Male                 NaN               NaN   \n",
       "5  Prussia (Germany)  Male   Munich University            Munich   \n",
       "\n",
       "  organization_country  death_date death_city death_country  \n",
       "0              Germany  1911-03-01     Berlin       Germany  \n",
       "1                  NaN  1907-09-07   Châtenay        France  \n",
       "2              Germany  1917-03-31    Marburg       Germany  \n",
       "3                  NaN  1910-10-30     Heiden   Switzerland  \n",
       "4                  NaN  1912-06-12      Paris        France  \n",
       "5              Germany  1923-02-10     Munich       Germany  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading in the Nobel Prize data\n",
    "nobel = pd.read_csv('data/nobel.csv')\n",
    "\n",
    "# Taking a look at the first several winners\n",
    "nobel.head(n=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So, who gets the Nobel Prize?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "911"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the number of (possibly shared) Nobel Prizes handed\n",
    "# out between 1901 and 2016\n",
    "display(len(nobel))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      836\n",
       "Female     49\n",
       "Name: sex, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the number of prizes won by male and female recipients.\n",
    "display(nobel['sex'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States of America    259\n",
       "United Kingdom               85\n",
       "Germany                      61\n",
       "France                       51\n",
       "Sweden                       29\n",
       "Japan                        24\n",
       "Netherlands                  18\n",
       "Canada                       18\n",
       "Italy                        17\n",
       "Russia                       17\n",
       "Name: birth_country, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the number of prizes won by the top 10 nationalities.\n",
    "nobel['birth_country'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### USA dominance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>decade</th>\n",
       "      <th>usa_born_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1900</td>\n",
       "      <td>0.017544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1910</td>\n",
       "      <td>0.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1920</td>\n",
       "      <td>0.074074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1930</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1940</td>\n",
       "      <td>0.302326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1950</td>\n",
       "      <td>0.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1960</td>\n",
       "      <td>0.265823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1970</td>\n",
       "      <td>0.317308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1980</td>\n",
       "      <td>0.319588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1990</td>\n",
       "      <td>0.403846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000</td>\n",
       "      <td>0.422764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.292683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    decade  usa_born_winner\n",
       "0     1900         0.017544\n",
       "1     1910         0.075000\n",
       "2     1920         0.074074\n",
       "3     1930         0.250000\n",
       "4     1940         0.302326\n",
       "5     1950         0.291667\n",
       "6     1960         0.265823\n",
       "7     1970         0.317308\n",
       "8     1980         0.319588\n",
       "9     1990         0.403846\n",
       "10    2000         0.422764\n",
       "11    2010         0.292683"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the proportion of USA born winners per decade\n",
    "nobel['usa_born_winner'] = nobel['birth_country'] == 'United States of America'\n",
    "nobel['decade'] = (np.floor(nobel['year'] / 10) * 10).astype(int)\n",
    "prop_usa_winners = nobel.groupby('decade', as_index=False)['usa_born_winner'].mean()\n",
    "\n",
    "# Display the proportions of USA born winners per decade\n",
    "prop_usa_winners"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### USA dominance, visualized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting the plotting theme\n",
    "sns.set()\n",
    "\n",
    "# and setting the size of all plots.\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [11, 7]\n",
    "\n",
    "# Plotting USA born winners\n",
    "ax = sns.lineplot(x='decade', y='usa_born_winner', data=prop_usa_winners)\n",
    "\n",
    "# Adding %-formatting to the y-axis\n",
    "from matplotlib.ticker import PercentFormatter\n",
    "ax.yaxis.set_major_formatter(PercentFormatter(1.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the gender of a typical Nobel Prize winner?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating the proportion of female laureates per decade\n",
    "nobel['female_winner'] = nobel['sex'] == 'Female'\n",
    "prop_female_winners = nobel.groupby(['decade', 'category'], as_index=False)['female_winner'].mean()\n",
    "\n",
    "# Plotting female winners with % winners on the y-axis\n",
    "ax = sns.lineplot(x='decade', y='female_winner', hue='category', data=prop_female_winners)\n",
    "ax.yaxis.set_major_formatter(PercentFormatter(1.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The first woman to win the Nobel Prize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>category</th>\n",
       "      <th>prize</th>\n",
       "      <th>motivation</th>\n",
       "      <th>prize_share</th>\n",
       "      <th>laureate_id</th>\n",
       "      <th>laureate_type</th>\n",
       "      <th>full_name</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>birth_city</th>\n",
       "      <th>...</th>\n",
       "      <th>sex</th>\n",
       "      <th>organization_name</th>\n",
       "      <th>organization_city</th>\n",
       "      <th>organization_country</th>\n",
       "      <th>death_date</th>\n",
       "      <th>death_city</th>\n",
       "      <th>death_country</th>\n",
       "      <th>usa_born_winner</th>\n",
       "      <th>decade</th>\n",
       "      <th>female_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1903</td>\n",
       "      <td>Physics</td>\n",
       "      <td>The Nobel Prize in Physics 1903</td>\n",
       "      <td>\"in recognition of the extraordinary services ...</td>\n",
       "      <td>1/4</td>\n",
       "      <td>6</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Marie Curie, née Sklodowska</td>\n",
       "      <td>1867-11-07</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1934-07-04</td>\n",
       "      <td>Sallanches</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>1900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    year category                            prize  \\\n",
       "19  1903  Physics  The Nobel Prize in Physics 1903   \n",
       "\n",
       "                                           motivation prize_share  \\\n",
       "19  \"in recognition of the extraordinary services ...         1/4   \n",
       "\n",
       "    laureate_id laureate_type                    full_name  birth_date  \\\n",
       "19            6    Individual  Marie Curie, née Sklodowska  1867-11-07   \n",
       "\n",
       "   birth_city  ...     sex organization_name organization_city  \\\n",
       "19     Warsaw  ...  Female               NaN               NaN   \n",
       "\n",
       "   organization_country  death_date  death_city death_country usa_born_winner  \\\n",
       "19                  NaN  1934-07-04  Sallanches        France           False   \n",
       "\n",
       "    decade  female_winner  \n",
       "19    1900           True  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Picking out the first woman to win a Nobel Prize\n",
    "nobel[nobel.sex == 'Female'].nsmallest(1, 'year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repeat laureates"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>category</th>\n",
       "      <th>prize</th>\n",
       "      <th>motivation</th>\n",
       "      <th>prize_share</th>\n",
       "      <th>laureate_id</th>\n",
       "      <th>laureate_type</th>\n",
       "      <th>full_name</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>birth_city</th>\n",
       "      <th>...</th>\n",
       "      <th>sex</th>\n",
       "      <th>organization_name</th>\n",
       "      <th>organization_city</th>\n",
       "      <th>organization_country</th>\n",
       "      <th>death_date</th>\n",
       "      <th>death_city</th>\n",
       "      <th>death_country</th>\n",
       "      <th>usa_born_winner</th>\n",
       "      <th>decade</th>\n",
       "      <th>female_winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1903</td>\n",
       "      <td>Physics</td>\n",
       "      <td>The Nobel Prize in Physics 1903</td>\n",
       "      <td>\"in recognition of the extraordinary services ...</td>\n",
       "      <td>1/4</td>\n",
       "      <td>6</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Marie Curie, née Sklodowska</td>\n",
       "      <td>1867-11-07</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1934-07-04</td>\n",
       "      <td>Sallanches</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>1900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1911</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>The Nobel Prize in Chemistry 1911</td>\n",
       "      <td>\"in recognition of her services to the advance...</td>\n",
       "      <td>1/1</td>\n",
       "      <td>6</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Marie Curie, née Sklodowska</td>\n",
       "      <td>1867-11-07</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>Sorbonne University</td>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>1934-07-04</td>\n",
       "      <td>Sallanches</td>\n",
       "      <td>France</td>\n",
       "      <td>False</td>\n",
       "      <td>1910</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1917</td>\n",
       "      <td>Peace</td>\n",
       "      <td>The Nobel Peace Prize 1917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/1</td>\n",
       "      <td>482</td>\n",
       "      <td>Organization</td>\n",
       "      <td>Comité international de la Croix Rouge (Intern...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1910</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>1944</td>\n",
       "      <td>Peace</td>\n",
       "      <td>The Nobel Peace Prize 1944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/1</td>\n",
       "      <td>482</td>\n",
       "      <td>Organization</td>\n",
       "      <td>Comité international de la Croix Rouge (Intern...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1940</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>1954</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>The Nobel Prize in Chemistry 1954</td>\n",
       "      <td>\"for his research into the nature of the chemi...</td>\n",
       "      <td>1/1</td>\n",
       "      <td>217</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Linus Carl Pauling</td>\n",
       "      <td>1901-02-28</td>\n",
       "      <td>Portland, OR</td>\n",
       "      <td>...</td>\n",
       "      <td>Male</td>\n",
       "      <td>California Institute of Technology (Caltech)</td>\n",
       "      <td>Pasadena, CA</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>1994-08-19</td>\n",
       "      <td>Big Sur, CA</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>True</td>\n",
       "      <td>1950</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>1954</td>\n",
       "      <td>Peace</td>\n",
       "      <td>The Nobel Peace Prize 1954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/1</td>\n",
       "      <td>515</td>\n",
       "      <td>Organization</td>\n",
       "      <td>Office of the United Nations High Commissioner...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1950</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>1956</td>\n",
       "      <td>Physics</td>\n",
       "      <td>The Nobel Prize in Physics 1956</td>\n",
       "      <td>\"for their researches on semiconductors and th...</td>\n",
       "      <td>1/3</td>\n",
       "      <td>66</td>\n",
       "      <td>Individual</td>\n",
       "      <td>John Bardeen</td>\n",
       "      <td>1908-05-23</td>\n",
       "      <td>Madison, WI</td>\n",
       "      <td>...</td>\n",
       "      <td>Male</td>\n",
       "      <td>University of Illinois</td>\n",
       "      <td>Urbana, IL</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>1991-01-30</td>\n",
       "      <td>Boston, MA</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>True</td>\n",
       "      <td>1950</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>1958</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>The Nobel Prize in Chemistry 1958</td>\n",
       "      <td>\"for his work on the structure of proteins, es...</td>\n",
       "      <td>1/1</td>\n",
       "      <td>222</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Frederick Sanger</td>\n",
       "      <td>1918-08-13</td>\n",
       "      <td>Rendcombe</td>\n",
       "      <td>...</td>\n",
       "      <td>Male</td>\n",
       "      <td>University of Cambridge</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2013-11-19</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>False</td>\n",
       "      <td>1950</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>1962</td>\n",
       "      <td>Peace</td>\n",
       "      <td>The Nobel Peace Prize 1962</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/1</td>\n",
       "      <td>217</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Linus Carl Pauling</td>\n",
       "      <td>1901-02-28</td>\n",
       "      <td>Portland, OR</td>\n",
       "      <td>...</td>\n",
       "      <td>Male</td>\n",
       "      <td>California Institute of Technology (Caltech)</td>\n",
       "      <td>Pasadena, CA</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>1994-08-19</td>\n",
       "      <td>Big Sur, CA</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>True</td>\n",
       "      <td>1960</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>1963</td>\n",
       "      <td>Peace</td>\n",
       "      <td>The Nobel Peace Prize 1963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/2</td>\n",
       "      <td>482</td>\n",
       "      <td>Organization</td>\n",
       "      <td>Comité international de la Croix Rouge (Intern...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1960</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>1972</td>\n",
       "      <td>Physics</td>\n",
       "      <td>The Nobel Prize in Physics 1972</td>\n",
       "      <td>\"for their jointly developed theory of superco...</td>\n",
       "      <td>1/3</td>\n",
       "      <td>66</td>\n",
       "      <td>Individual</td>\n",
       "      <td>John Bardeen</td>\n",
       "      <td>1908-05-23</td>\n",
       "      <td>Madison, WI</td>\n",
       "      <td>...</td>\n",
       "      <td>Male</td>\n",
       "      <td>University of Illinois</td>\n",
       "      <td>Urbana, IL</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>1991-01-30</td>\n",
       "      <td>Boston, MA</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>True</td>\n",
       "      <td>1970</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>1980</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>The Nobel Prize in Chemistry 1980</td>\n",
       "      <td>\"for their contributions concerning the determ...</td>\n",
       "      <td>1/4</td>\n",
       "      <td>222</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Frederick Sanger</td>\n",
       "      <td>1918-08-13</td>\n",
       "      <td>Rendcombe</td>\n",
       "      <td>...</td>\n",
       "      <td>Male</td>\n",
       "      <td>MRC Laboratory of Molecular Biology</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2013-11-19</td>\n",
       "      <td>Cambridge</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>False</td>\n",
       "      <td>1980</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>1981</td>\n",
       "      <td>Peace</td>\n",
       "      <td>The Nobel Peace Prize 1981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/1</td>\n",
       "      <td>515</td>\n",
       "      <td>Organization</td>\n",
       "      <td>Office of the United Nations High Commissioner...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1980</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year   category                              prize  \\\n",
       "19   1903    Physics    The Nobel Prize in Physics 1903   \n",
       "62   1911  Chemistry  The Nobel Prize in Chemistry 1911   \n",
       "89   1917      Peace         The Nobel Peace Prize 1917   \n",
       "215  1944      Peace         The Nobel Peace Prize 1944   \n",
       "278  1954  Chemistry  The Nobel Prize in Chemistry 1954   \n",
       "283  1954      Peace         The Nobel Peace Prize 1954   \n",
       "298  1956    Physics    The Nobel Prize in Physics 1956   \n",
       "306  1958  Chemistry  The Nobel Prize in Chemistry 1958   \n",
       "340  1962      Peace         The Nobel Peace Prize 1962   \n",
       "348  1963      Peace         The Nobel Peace Prize 1963   \n",
       "424  1972    Physics    The Nobel Prize in Physics 1972   \n",
       "505  1980  Chemistry  The Nobel Prize in Chemistry 1980   \n",
       "523  1981      Peace         The Nobel Peace Prize 1981   \n",
       "\n",
       "                                            motivation prize_share  \\\n",
       "19   \"in recognition of the extraordinary services ...         1/4   \n",
       "62   \"in recognition of her services to the advance...         1/1   \n",
       "89                                                 NaN         1/1   \n",
       "215                                                NaN         1/1   \n",
       "278  \"for his research into the nature of the chemi...         1/1   \n",
       "283                                                NaN         1/1   \n",
       "298  \"for their researches on semiconductors and th...         1/3   \n",
       "306  \"for his work on the structure of proteins, es...         1/1   \n",
       "340                                                NaN         1/1   \n",
       "348                                                NaN         1/2   \n",
       "424  \"for their jointly developed theory of superco...         1/3   \n",
       "505  \"for their contributions concerning the determ...         1/4   \n",
       "523                                                NaN         1/1   \n",
       "\n",
       "     laureate_id laureate_type  \\\n",
       "19             6    Individual   \n",
       "62             6    Individual   \n",
       "89           482  Organization   \n",
       "215          482  Organization   \n",
       "278          217    Individual   \n",
       "283          515  Organization   \n",
       "298           66    Individual   \n",
       "306          222    Individual   \n",
       "340          217    Individual   \n",
       "348          482  Organization   \n",
       "424           66    Individual   \n",
       "505          222    Individual   \n",
       "523          515  Organization   \n",
       "\n",
       "                                             full_name  birth_date  \\\n",
       "19                         Marie Curie, née Sklodowska  1867-11-07   \n",
       "62                         Marie Curie, née Sklodowska  1867-11-07   \n",
       "89   Comité international de la Croix Rouge (Intern...         NaN   \n",
       "215  Comité international de la Croix Rouge (Intern...         NaN   \n",
       "278                                 Linus Carl Pauling  1901-02-28   \n",
       "283  Office of the United Nations High Commissioner...         NaN   \n",
       "298                                       John Bardeen  1908-05-23   \n",
       "306                                   Frederick Sanger  1918-08-13   \n",
       "340                                 Linus Carl Pauling  1901-02-28   \n",
       "348  Comité international de la Croix Rouge (Intern...         NaN   \n",
       "424                                       John Bardeen  1908-05-23   \n",
       "505                                   Frederick Sanger  1918-08-13   \n",
       "523  Office of the United Nations High Commissioner...         NaN   \n",
       "\n",
       "       birth_city  ...     sex                             organization_name  \\\n",
       "19         Warsaw  ...  Female                                           NaN   \n",
       "62         Warsaw  ...  Female                           Sorbonne University   \n",
       "89            NaN  ...     NaN                                           NaN   \n",
       "215           NaN  ...     NaN                                           NaN   \n",
       "278  Portland, OR  ...    Male  California Institute of Technology (Caltech)   \n",
       "283           NaN  ...     NaN                                           NaN   \n",
       "298   Madison, WI  ...    Male                        University of Illinois   \n",
       "306     Rendcombe  ...    Male                       University of Cambridge   \n",
       "340  Portland, OR  ...    Male  California Institute of Technology (Caltech)   \n",
       "348           NaN  ...     NaN                                           NaN   \n",
       "424   Madison, WI  ...    Male                        University of Illinois   \n",
       "505     Rendcombe  ...    Male           MRC Laboratory of Molecular Biology   \n",
       "523           NaN  ...     NaN                                           NaN   \n",
       "\n",
       "    organization_city      organization_country  death_date   death_city  \\\n",
       "19                NaN                       NaN  1934-07-04   Sallanches   \n",
       "62              Paris                    France  1934-07-04   Sallanches   \n",
       "89                NaN                       NaN         NaN          NaN   \n",
       "215               NaN                       NaN         NaN          NaN   \n",
       "278      Pasadena, CA  United States of America  1994-08-19  Big Sur, CA   \n",
       "283               NaN                       NaN         NaN          NaN   \n",
       "298        Urbana, IL  United States of America  1991-01-30   Boston, MA   \n",
       "306         Cambridge            United Kingdom  2013-11-19    Cambridge   \n",
       "340      Pasadena, CA  United States of America  1994-08-19  Big Sur, CA   \n",
       "348               NaN                       NaN         NaN          NaN   \n",
       "424        Urbana, IL  United States of America  1991-01-30   Boston, MA   \n",
       "505         Cambridge            United Kingdom  2013-11-19    Cambridge   \n",
       "523               NaN                       NaN         NaN          NaN   \n",
       "\n",
       "                death_country usa_born_winner  decade  female_winner  \n",
       "19                     France           False    1900           True  \n",
       "62                     France           False    1910           True  \n",
       "89                        NaN           False    1910          False  \n",
       "215                       NaN           False    1940          False  \n",
       "278  United States of America            True    1950          False  \n",
       "283                       NaN           False    1950          False  \n",
       "298  United States of America            True    1950          False  \n",
       "306            United Kingdom           False    1950          False  \n",
       "340  United States of America            True    1960          False  \n",
       "348                       NaN           False    1960          False  \n",
       "424  United States of America            True    1970          False  \n",
       "505            United Kingdom           False    1980          False  \n",
       "523                       NaN           False    1980          False  \n",
       "\n",
       "[13 rows x 21 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting the laureates that have received 2 or more prizes.\n",
    "nobel.groupby('full_name').filter(lambda group: len(group) >= 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How old are you when you get the prize?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fe0090ff7f0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Converting birth_date from String to datetime\n",
    "nobel['birth_date'] = pd.to_datetime(nobel['birth_date'])\n",
    "\n",
    "# Calculating the age of Nobel Prize winners\n",
    "nobel['age'] = nobel['year'] - nobel['birth_date'].dt.year\n",
    "\n",
    "# Plotting the age of Nobel Prize winners\n",
    "sns.lmplot(x='year', y='age', data=nobel, lowess=True, \n",
    "           aspect=2, line_kws={'color' : 'black'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age differences between prize categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fdff93a2850>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x2160 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Same plot as above, but separate plots for each type of Nobel Prize\n",
    "sns.lmplot(x='year', y='age', row='category', data=nobel, lowess=True, \n",
    "           aspect=2, line_kws={'color' : 'black'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oldest and youngest winners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>category</th>\n",
       "      <th>prize</th>\n",
       "      <th>motivation</th>\n",
       "      <th>prize_share</th>\n",
       "      <th>laureate_id</th>\n",
       "      <th>laureate_type</th>\n",
       "      <th>full_name</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>birth_city</th>\n",
       "      <th>...</th>\n",
       "      <th>organization_name</th>\n",
       "      <th>organization_city</th>\n",
       "      <th>organization_country</th>\n",
       "      <th>death_date</th>\n",
       "      <th>death_city</th>\n",
       "      <th>death_country</th>\n",
       "      <th>usa_born_winner</th>\n",
       "      <th>decade</th>\n",
       "      <th>female_winner</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>2007</td>\n",
       "      <td>Economics</td>\n",
       "      <td>The Sveriges Riksbank Prize in Economic Scienc...</td>\n",
       "      <td>\"for having laid the foundations of mechanism ...</td>\n",
       "      <td>1/3</td>\n",
       "      <td>820</td>\n",
       "      <td>Individual</td>\n",
       "      <td>Leonid Hurwicz</td>\n",
       "      <td>1917-08-21</td>\n",
       "      <td>Moscow</td>\n",
       "      <td>...</td>\n",
       "      <td>University of Minnesota</td>\n",
       "      <td>Minneapolis, MN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>2008-06-24</td>\n",
       "      <td>Minneapolis, MN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>False</td>\n",
       "      <td>2000</td>\n",
       "      <td>False</td>\n",
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       "     year   category                                              prize  \\\n",
       "793  2007  Economics  The Sveriges Riksbank Prize in Economic Scienc...   \n",
       "\n",
       "                                            motivation prize_share  \\\n",
       "793  \"for having laid the foundations of mechanism ...         1/3   \n",
       "\n",
       "     laureate_id laureate_type       full_name birth_date birth_city  ...  \\\n",
       "793          820    Individual  Leonid Hurwicz 1917-08-21     Moscow  ...   \n",
       "\n",
       "           organization_name organization_city      organization_country  \\\n",
       "793  University of Minnesota   Minneapolis, MN  United States of America   \n",
       "\n",
       "     death_date       death_city             death_country usa_born_winner  \\\n",
       "793  2008-06-24  Minneapolis, MN  United States of America           False   \n",
       "\n",
       "    decade  female_winner   age  \n",
       "793   2000          False  90.0  \n",
       "\n",
       "[1 rows x 22 columns]"
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       "      <th>year</th>\n",
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       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>2014</td>\n",
       "      <td>Peace</td>\n",
       "      <td>The Nobel Peace Prize 2014</td>\n",
       "      <td>\"for their struggle against the suppression of...</td>\n",
       "      <td>1/2</td>\n",
       "      <td>914</td>\n",
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       "      <td>Malala Yousafzai</td>\n",
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       "      <td>Mingora</td>\n",
       "      <td>...</td>\n",
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       "     year category                       prize  \\\n",
       "885  2014    Peace  The Nobel Peace Prize 2014   \n",
       "\n",
       "                                            motivation prize_share  \\\n",
       "885  \"for their struggle against the suppression of...         1/2   \n",
       "\n",
       "     laureate_id laureate_type         full_name birth_date birth_city  ...  \\\n",
       "885          914    Individual  Malala Yousafzai 1997-07-12    Mingora  ...   \n",
       "\n",
       "    organization_name organization_city organization_country death_date  \\\n",
       "885               NaN               NaN                  NaN        NaN   \n",
       "\n",
       "    death_city death_country usa_born_winner decade  female_winner   age  \n",
       "885        NaN           NaN           False   2010           True  17.0  \n",
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       "[1 rows x 22 columns]"
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     },
     "execution_count": 74,
     "metadata": {},
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   ],
   "source": [
    "# The oldest winner of a Nobel Prize as of 2016\n",
    "display(nobel.nlargest(1, 'age'))\n",
    "\n",
    "# The youngest winner of a Nobel Prize as of 2016\n",
    "nobel.nsmallest(1, 'age')"
   ]
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