ufo_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing the National UFO Reporting Center's Database\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"images/panda_ufo.png\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<img src=\"images/panda_ufo.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "\n",
    "mpl.rcParams['font.family'] = 'Souvenir'  # The font from the original A&D manuals!\n",
    "mpl.rcParams.update(\n",
    "    {'font.size': 12, 'lines.linewidth': 2, 'lines.markersize': 5}\n",
    "    )\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, you should _always_, whenever possible, have a quick look at the data before importing it, with a text editor, database tool, etc.:\n",
    "\n",
    "\n",
    " date_time,year,month,city,state,shape,duration,posted,url\n",
    " 8/30/18 22:00,2018,08,Morehead City,NC,Unknown,1 hour,8/31/18,http://www.nuforc.org/webreports/142/S142925.html\n",
    " 8/30/18 21:36,2018,08,Commerce,GA,Circle,5 seconds,8/31/18,http://www.nuforc.org/webreports/142/S142924.html\n",
    " 8/30/18 21:15,2018,08,Queens Village,NY,Light,15 minutes,8/31/18,http://www.nuforc.org/webreports/142/S142929.html\n",
    "\n",
    "I prefer the excellent file pager, `less`, which is perfect for examining large files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing data\n",
    "Importing with pandas is very easy. We have stored our UFO Reports data in a\n",
    "Comma Separated Variable (CSV) file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_df = pd.read_csv(\"national_ufo_reports.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a naming convention, we often put a _df at the end of a variable name to remind us that it is a DataFrame object. Recall a DataFrame is a collection of\n",
    "Series objects. For a wide format, it is helpful to think of each column as a variable and each row as an observation. It is a simple matter to interrogate\n",
    "the first few elements using the `.head()` method associated with pandas DataFrames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>shape</th>\n",
       "      <th>duration</th>\n",
       "      <th>posted</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8/30/18 22:00</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>Morehead City</td>\n",
       "      <td>NC</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1 hour</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142925.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8/30/18 21:36</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>GA</td>\n",
       "      <td>Circle</td>\n",
       "      <td>5 seconds</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142924.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8/30/18 21:15</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>Queens Village</td>\n",
       "      <td>NY</td>\n",
       "      <td>Light</td>\n",
       "      <td>15 minutes</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142929.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8/30/18 20:48</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>Independence</td>\n",
       "      <td>KS</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>3 minutes</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142928.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8/30/18 20:25</td>\n",
       "      <td>2018</td>\n",
       "      <td>8</td>\n",
       "      <td>Redding</td>\n",
       "      <td>CA</td>\n",
       "      <td>Light</td>\n",
       "      <td>5 seconds</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142926.html</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date_time  year  month            city state    shape    duration  \\\n",
       "0  8/30/18 22:00  2018      8   Morehead City    NC  Unknown      1 hour   \n",
       "1  8/30/18 21:36  2018      8        Commerce    GA   Circle   5 seconds   \n",
       "2  8/30/18 21:15  2018      8  Queens Village    NY    Light  15 minutes   \n",
       "3  8/30/18 20:48  2018      8    Independence    KS  Unknown   3 minutes   \n",
       "4  8/30/18 20:25  2018      8         Redding    CA    Light   5 seconds   \n",
       "\n",
       "    posted                                                url  \n",
       "0  8/31/18  http://www.nuforc.org/webreports/142/S142925.html  \n",
       "1  8/31/18  http://www.nuforc.org/webreports/142/S142924.html  \n",
       "2  8/31/18  http://www.nuforc.org/webreports/142/S142929.html  \n",
       "3  8/31/18  http://www.nuforc.org/webreports/142/S142928.html  \n",
       "4  8/31/18  http://www.nuforc.org/webreports/142/S142926.html  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the row indices (here, 0 - 5) and column labels ('date_time' - 'url'). We can easily call any given element. We can look at the Series from any column by directly calling it with `[]` (note the use of `.head()` here to suppress the otherwise large number of rows):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    8/30/18 22:00\n",
       "1    8/30/18 21:36\n",
       "2    8/30/18 21:15\n",
       "3    8/30/18 20:48\n",
       "4    8/30/18 20:25\n",
       "Name: date_time, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df['date_time'].head()  # Note the default for .head() is the first five rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could look at the fourth row (index of 3 because, as usual, we index from 0):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_time                                        8/30/18 20:48\n",
       "year                                                      2018\n",
       "month                                                        8\n",
       "city                                              Independence\n",
       "state                                                       KS\n",
       "shape                                                  Unknown\n",
       "duration                                             3 minutes\n",
       "posted                                                 8/31/18\n",
       "url          http://www.nuforc.org/webreports/142/S142928.html\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.loc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the head of a data file tends to have localized observations, it often pays to take a random sample of a DataFrame, to better gauge the general variability of the dataset. To take five such random samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>shape</th>\n",
       "      <th>duration</th>\n",
       "      <th>posted</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113597</th>\n",
       "      <td>6/25/69 00:00</td>\n",
       "      <td>1969</td>\n",
       "      <td>6</td>\n",
       "      <td>Moline</td>\n",
       "      <td>IL</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>15 minutes</td>\n",
       "      <td>10/25/11</td>\n",
       "      <td>http://www.nuforc.org/webreports/084/S84657.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59027</th>\n",
       "      <td>2/2/09 18:35</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>Duarte</td>\n",
       "      <td>CA</td>\n",
       "      <td>Light</td>\n",
       "      <td>1 minute</td>\n",
       "      <td>3/19/09</td>\n",
       "      <td>http://www.nuforc.org/webreports/068/S68411.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81133</th>\n",
       "      <td>8/23/04 23:00</td>\n",
       "      <td>2004</td>\n",
       "      <td>8</td>\n",
       "      <td>Caransebes (Romania)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fireball</td>\n",
       "      <td>60 sec</td>\n",
       "      <td>9/9/04</td>\n",
       "      <td>http://www.nuforc.org/webreports/039/S39107.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67325</th>\n",
       "      <td>6/7/07 00:00</td>\n",
       "      <td>2007</td>\n",
       "      <td>6</td>\n",
       "      <td>Locust Grove</td>\n",
       "      <td>GA</td>\n",
       "      <td>Cigar</td>\n",
       "      <td>2-3 minutes</td>\n",
       "      <td>11/21/10</td>\n",
       "      <td>http://www.nuforc.org/webreports/078/S78189.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63757</th>\n",
       "      <td>3/11/08 10:00</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>Upper Saddle River (Woodcliff Lake)</td>\n",
       "      <td>NJ</td>\n",
       "      <td>Fireball</td>\n",
       "      <td>4 SECONDS</td>\n",
       "      <td>3/31/08</td>\n",
       "      <td>http://www.nuforc.org/webreports/062/S62180.html</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date_time  year  month                                 city state  \\\n",
       "113597  6/25/69 00:00  1969      6                               Moline    IL   \n",
       "59027    2/2/09 18:35  2009      2                               Duarte    CA   \n",
       "81133   8/23/04 23:00  2004      8                 Caransebes (Romania)   NaN   \n",
       "67325    6/7/07 00:00  2007      6                         Locust Grove    GA   \n",
       "63757   3/11/08 10:00  2008      3  Upper Saddle River (Woodcliff Lake)    NJ   \n",
       "\n",
       "           shape     duration    posted  \\\n",
       "113597   Unknown   15 minutes  10/25/11   \n",
       "59027      Light     1 minute   3/19/09   \n",
       "81133   Fireball       60 sec    9/9/04   \n",
       "67325      Cigar  2-3 minutes  11/21/10   \n",
       "63757   Fireball    4 SECONDS   3/31/08   \n",
       "\n",
       "                                                     url  \n",
       "113597  http://www.nuforc.org/webreports/084/S84657.html  \n",
       "59027   http://www.nuforc.org/webreports/068/S68411.html  \n",
       "81133   http://www.nuforc.org/webreports/039/S39107.html  \n",
       "67325   http://www.nuforc.org/webreports/078/S78189.html  \n",
       "63757   http://www.nuforc.org/webreports/062/S62180.html  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that pandas has sensible defaults for displaying large datasets. Here, not all nine rows would fit on the screen and are thus truncated.\n",
    "\n",
    "What if you want to look at a specific range ('slice') in your DataFrame?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>posted</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 hour</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142925.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5 seconds</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142924.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15 minutes</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142929.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3 minutes</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142928.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5 seconds</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142926.html</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     duration   posted                                                url\n",
       "0      1 hour  8/31/18  http://www.nuforc.org/webreports/142/S142925.html\n",
       "1   5 seconds  8/31/18  http://www.nuforc.org/webreports/142/S142924.html\n",
       "2  15 minutes  8/31/18  http://www.nuforc.org/webreports/142/S142929.html\n",
       "3   3 minutes  8/31/18  http://www.nuforc.org/webreports/142/S142928.html\n",
       "4   5 seconds  8/31/18  http://www.nuforc.org/webreports/142/S142926.html"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.loc[0:4, 'duration':'url']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** If you look in the help documentation ( `help(pd.DataFrame.loc)` ): \"note that contrary to usual python slices, **both** the start and the stop are included!\"\n",
    "\n",
    "If you prefer to use numbers instead of labels, use `.iloc[]` instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>posted</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 hour</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142925.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5 seconds</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142924.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15 minutes</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142929.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3 minutes</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142928.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5 seconds</td>\n",
       "      <td>8/31/18</td>\n",
       "      <td>http://www.nuforc.org/webreports/142/S142926.html</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     duration   posted                                                url\n",
       "0      1 hour  8/31/18  http://www.nuforc.org/webreports/142/S142925.html\n",
       "1   5 seconds  8/31/18  http://www.nuforc.org/webreports/142/S142924.html\n",
       "2  15 minutes  8/31/18  http://www.nuforc.org/webreports/142/S142929.html\n",
       "3   3 minutes  8/31/18  http://www.nuforc.org/webreports/142/S142928.html\n",
       "4   5 seconds  8/31/18  http://www.nuforc.org/webreports/142/S142926.html"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.iloc[0:5, 6:9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusingly, `.iloc[]` slices the conventional way. Also note, for both slicing methods, square brackets [] are used instead of parentheses ()."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "The following are very typical steps for Exploratory Data Analysis (EDA) of a dataset once the data are imported.\n",
    "\n",
    "What are the dimensions of the DataFrame?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(115877, 9)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_i.e._ around 116,000 rows and 9 columns\n",
    "\n",
    "We could just as easily found the number of rows by using our base Python `len()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "115877"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ufo_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What sort of data is each column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_time    object\n",
       "year          int64\n",
       "month         int64\n",
       "city         object\n",
       "state        object\n",
       "shape        object\n",
       "duration     object\n",
       "posted       object\n",
       "url          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusingly, 'object' here refers to strings as well as other possible objects.\n",
    "\n",
    "How do the numeric data vary (range, median, mean)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>115877.000000</td>\n",
       "      <td>115877.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2006.127687</td>\n",
       "      <td>6.850945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.481973</td>\n",
       "      <td>3.216381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1400.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2009.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                year          month\n",
       "count  115877.000000  115877.000000\n",
       "mean     2006.127687       6.850945\n",
       "std        11.481973       3.216381\n",
       "min      1400.000000       1.000000\n",
       "25%      2003.000000       4.000000\n",
       "50%      2009.000000       7.000000\n",
       "75%      2013.000000       9.000000\n",
       "max      2018.000000      12.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Half of all observations in this dataset (up to August 2018) were from 2009 until now. We'll investigate this later.\n",
    "\n",
    "How much data is missing?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_time       0\n",
       "year            0\n",
       "month           0\n",
       "city          227\n",
       "state        8438\n",
       "shape        3686\n",
       "duration     3896\n",
       "posted          0\n",
       "url             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the chaining of methods. This powerful concept will be used throughout the remainder of this.\n",
    "\n",
    "So none of the date-like entries are missing, as too for the URLs. The duration and shape may not\n",
    "be well defined or known, so the ~4,000 missing for each is understandable, at\n",
    "3% of the observations. The state is not defined\n",
    "for most non-US entries, and there are plenty of those. In this rare case, there is\n",
    "no need for imputation (A fancy, ten-dollar, word for 'figuring out how to handle missing\n",
    "data'; it has the same Latin roots as for input, and is similar to that\n",
    "for amputation (which is roughly 'to clean by cutting back'))!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering\n",
    "Do we need to filter by year? The dates prior to 1900 are likely to be based on heavy, retrospective, speculation, or worse: errors due to data entry!\n",
    "\n",
    "We can easily filter the 'year' field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_time    24\n",
       "year         24\n",
       "month        24\n",
       "city         24\n",
       "state        16\n",
       "shape        17\n",
       "duration     17\n",
       "posted       24\n",
       "url          24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df[ufo_df['year'] < 1900].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also the `.query()` method to acheive a similar effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_time    24\n",
       "year         24\n",
       "month        24\n",
       "city         24\n",
       "state        16\n",
       "shape        17\n",
       "duration     17\n",
       "posted       24\n",
       "url          24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.query('year<1900').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are only 24 such observations.\n",
    "\n",
    "Upon manually checking, only three of these entries are mis-entered!\n",
    "For example, one entry, http://www.nuforc.org/webreports/133/S133812.html, was dated as the year 1721, when it was obviously a replication of the time (21:30) with the yy year format (17, for 2017). This is unexpectedly nice data!\n",
    "\n",
    "Let's remove the bad dates. We can create a tiny DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "bad_dates = ufo_df[ufo_df['year'].isin([1617, 1615, 1721])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These have indices [115875,115872,115871]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([115871, 115872, 115875], dtype='int64')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_dates.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can drop these manually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_df = ufo_df.drop([115875, 115872, 115871])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...or you could have indexed from the bad_dates DataFrame (note the `.drop()` method has a convenient 'inplace' option):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_df.drop(index=bad_dates.index, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whenever you perform data wrangling and cleaning, it pays to check you performed the operation correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(115874, 9)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "OK, good. Three fewer rows, correct number of columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dates and TimeSeries\n",
    "As with most analysis frameworks, dates are a special class unto themselves.\n",
    "This is because everyone has a different way of writing them (for example, a European may write 5 March 1953 as 5/3/1953, while an American might write\n",
    "5/3/1953), and there are so many things you want to do with them. Here, we explicitly force the 'date_time' column to be a DateTime object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_df['date_time'] = pd.to_datetime(ufo_df['date_time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the output format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_time    datetime64[ns]\n",
       "year                  int64\n",
       "month                 int64\n",
       "city                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.dtypes[0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** This makes use of the default format specifications, including `dayfirst=False` and `yearfirst=False`. This is because the database follows the American convention.\n",
    "\n",
    "If we already expect a date format upon import, there is an option to specify a date-time column, so we could have done this initially, _e.g._:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_df = pd.read_csv(\"national_ufo_reports.csv\", parse_dates=[0], keep_date_col=True, infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, this particular dataset is tricky!\n",
    "The form to input an observation date only allows a two-digit year (yy). So it can't unambiguously infer what century an event happened! Thankfully the URL itself encodes the full four-digit year (yyyy) as well as the month (mm).\n",
    "The dataset we use has already parsed the 'proper' year and month from this metadata already, because we had anticipated this very issue (see the description on the Spider used to obtain this data from the NUFORC website).\n",
    "\n",
    "We could construct an accurate event date field by assuming the correct day is entered in the 'event_time' field. Then, extract only the 'day' information as\n",
    "another column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_df['day'] = [x.day for x in ufo_df['date_time']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then concatenate the year, month and day Series as a single datetime Series...\n",
    "\n",
    "...with the following caveat:\n",
    "From https://pandas-docs.github.io/pandas-docs-travis/timeseries.html:\n",
    "\"Since pandas represents timestamps in nanosecond resolution, the time span that can be represented using a 64-bit integer is limited to approximately 584 years\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1677-09-21 00:12:43.145225')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Timestamp.min"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we'll have to filter for dates after the year 1677 before we convert:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_df['event_time'] = pd.to_datetime(ufo_df[ufo_df['year'] > 1678].loc[:,['year','month','day']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df['event_time'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1762-12-11 00:00:00')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df['event_time'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-08-30 00:00:00')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df['event_time'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graphical analysis.\n",
    "Pandas has support for a wide range of graphical analysis. Effectively there are a number of wrappers to the matplotlib Python library.\n",
    "\n",
    "Let's analyze the univariate distribution of observations by month:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb21343240>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo_df['month'].hist(bins=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that the plotting functions are wrappers for matplotlib, so you can add attributes to the figures accordingly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of UFO reports by month')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ufo_df['month'].hist(bins=12)\n",
    "\n",
    "plt.xlabel(\"Month of report\")\n",
    "plt.ylabel(\"Number of reports\")\n",
    "plt.title(\"Distribution of UFO reports by month\", fontname=\"Alien Invasion\", fontsize=16)\n",
    "\n",
    "# If you want to save the figure:\n",
    "#plt.savefig(\"UFO_observations_by_month.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you could examine it interactively with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "but note that the object is 'used' up by saving or displaying."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that summer (in the Northern Hemisphere) is the time to spot UFOs.\n",
    "Who would have thought?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effect of smart-phones\n",
    "Does the introduction of so-called smart phones (I think mine is a 'dumb phone') have anything to do with the rate of sightings?\n",
    "\n",
    "We will have to aggregate over year and count the number of occurences. We do this aggregation with the `.groupby()` operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Increase in UFO reports over time')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAEaCAYAAACxTkgOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XecpFWV8PFfVXV1de7pntCTYGaAg4QhSlRUgiAiKrjuKiiKrriKGMFXWbOwy6qgu2ZXV8WIrIIBdRcXAQOjLDjIEgbOwAwTmBy6p1Pl9497K0xPVXdVd3VX6PP9fIbqelLdulT3qXvvee4NpNNpjDHGmFoQrHYBjDHGmAwLSsYYY2qGBSVjjDE1w4KSMcaYmmFByRhjTM2woGSMMaZmNFW7AGZqVPXjwMeA9wKf85vfBHzLP/4J+AxwIbAWOAJYCqz3x14E/BfwZ+A4YBvwVX9NgE8AdwP3+OeXA9/2P6eAS/y5meu9B/hX//PHReQTeWVdCfyff3qliHwlb99PgVfmvc6ZQBI4B1ie95r51z9TRO4dUx/f8mV8SEROUNUPAdf73S8Skd+p6t3++j8VkYsZQ1XXZV5TRN6kqsvy3t/l/rFgeYAA8GugBfg34HzgOcDjwKkism/Ma2XL4jdd5B9XA6cC/555P/7f5QAiEhjn3D/47RcAPwJacZ+PfwEiwB24ur5rgtf+OvBGCn+2zgLmAbcAIeDj/h/AN0TkijHvsxn3OTsL6PdluQEYAl4OPAls8of/k4h82J/3BHA4cJeIvDjv/82/+vIuB+4RkbNUNXN/y3t8Web4518SkasYwx9/M3A87rN/s7/echFZPua1Puev+SZgB7BEROJ51/qTr7NVuN+JMynxM2v2Zy2lxpf5RQE4DLi0wDGLcL+U5QoCLy3j+OW4oAcu2OQr9Pohf85UHAU86n/+2HgHVsiZuIA01pHAoWVc5wRg8STLcAbui8ff4ALSWBcCXRV47aNx/4/GOqzAtsW4gDRWO/BKEdkMrPHbzgZQ1SW4gATwixLKU8zLpnBuxiHkvpjMxwV8AFT1CNzvGcDvgBdV4PVmLQtK9e+LuD94m/O23e+33e6fnw/8j//5w4zfQv5Y3jWP9D/nW4Nrba2hsM1+/xH556pqBNdiucdvOktVi33+PuGvvxbYN2bf7XnXv3+c95HxGuAn/uezVfWMEs4pR7Hy7AP+mfIC4VX+XzHX5r1WOeeuwbUCxitLofOvxX0G8t3ut42t+1/jWlxr2f+zWMi1uBbgs2O23+UfT1bVDnxw8u4ocJ1zcHXxhgL7TgG+M0E5jvRlyfcGDvzClPEc4Gn/8xvztl/uH4dxQXbs57rcz+ysZkGpzonIThFZA8TzNg+LyBoR6ffPX4Dr0gH3zfNCIFHkktsy1/T/do7ZPyoiTwCjZRb1bFxrSP3zXly3yVhHAN3AUeLcVubrjBXCtVDW+ucfA/ZO8ZqlSInIdnItwwn51sKmCQ8s/9yoiOwZryyFzheRLf6zlb+t338uhsdc4mLgTf7/2esnKO4WEdnN/p9ZyAWlJuCF5ILSYyLy1ATX3I+IKLB7gmPWiMiWMds2jPNaryLXYrtQVef5L1aZ9/szf4yZAgtKs8dZwO/9z28DfjDJ6xzv++IzAeWBMfuX4L6Zr2H/b94XFrhW/jfSHf5xIe4b98Oqeqmqju0eujjv+qeUWObXAj/2P78YNwZWKZMpT0Gqeju58Z0tHNiSuCHvtcY7dzpeu5D8LywfBDao6r+oat8ki3E3bpwSXEA6zf9crOvuLlxdHNAiUtU9uLEcOPAzOlktuC9zaSCMGzs6D/eZB1dvhbo9K/YZmQ0sKM0eLwK+4H8+AjewXKy1VI6OEo/LBKUOYIP/+cV5+5+PG0R+wj9fCXwfeN1UC4hrLe3Ke90zcX8Aa1mIwmNTpViH+wM5ra8tIl8CTgJuw/2h7gQ+APxwMi8qInuBv/inZ5P7LExlPAlK/4yW4hxyX+4uxyWMgKvzQmNppkwWlGaX3wD/63++lMm1lsaOKY0d31gvIgH/7xMAqnoscLDfv5NcQDjDZ2UhInER+TYuMeH9edcLjLn+t/OuX04WUxz4lP/5JbhMsEqYbHkKyR/XWYDr4swSkTdlXqvIuZlxi+eKSKySr12MiDwoIn+DS3rIdJcVKl+pMmOfxwEP4z4vqwodKCIrfH0USqDIH1MqNAY3WccDf/Q/n0juPf+IvOSHMSr5GWl4lhJe51R1Hi41N5y3uc1nBI39tpzGJRv8DPfH/3sc2Frqy7sm7N9FA35MSVWLjSmFVfU5mXNFZBf7d91dD/xTppzA6cC9+WnYuBZSMd2qutD/vEFERsY5dqz/wCV6LMK1liYKTIeqaheursoVVNUFQMldWSKyWVWLjin5993tj30if58fEyomoqo945VlotcuUp6P4dKktwInAwOUFswWqWov+39mM+7CdQVmvjD/SkRSBY5DVQ/x1xgWkY35+0REVXXcMaUp+DXwbtzntxvXCv+hL3chU/nMzjrWUqp/V+HugVmSt+0Uv+2Ae3BwXSEP+58v5cBurE/kXfNxxs8GK6TQmNLY8aRo3s9jM50ux3XlFTPp/nkRiQI3+qcvxd33U8iAf3wBrj7+OW/f4AQvk/SPncA/+vPH7pusomNKEzgCly32iYkOnKSFwHVlHH8D8FYKj7/8kdznYy4ua7CYomNK02wfrssS4NW4MdoNxQ+3MaVyWFCaZUQkTa6lshLX5bANd8NkxflWV+Yejht9F8Z1uDEtyI0r3YdryeXbTW6MoVK+Rq719/+KHHMthTPZHqBwanK+71M4SeB2cvdLzZTb2P8LQMavcTewTtVfKJyF+T8Ftm0iNxaTbwj4eeaJb0Xc55+eJSK/nWohp0kmWPZRfiaqGUfAFvkzxhhTK6ylZIwxpmZYUDLGGFMzLCgZY4ypGRaUjDHG1IxZe5/S448/HsDNpDww0bHGGGP20wVsOvLIIyueKTdrgxIuII13b4ExxpjiDgY2TnhUmWZzUBoAOOtt72VoxG4ziLS1Ex0eqnYxaoLVRY7VRY7VhdPe2sLdX/0cTFMv02wOSgAMjYwyOGyzfsQDIaJWD4DVRT6rixyri5lhiQ7GGGNqhgUlY4wxNcOCkjHGmJphQckYY0zNsKBkjDGmZlhQMsaYBtbStoCW9pLXmqw6C0rGGNOwAhxx6rs48tR3MbVV6meOBSVjjGlQkbZ5hJs7aAq309TcXu3ilMSCkjHGNKjWjoXZn5vCHVUsSeksKBljTIPKD0rhZgtKxhhjqqi1Y1H2Z+u+M8YYU1X7dd9ZS8kYY0zVBIK0tC/IPrXuO2OMMVXT0jaPYDC3EIS1lIwxxlRNpusunU4BELbsO2OMMdWSSXIYHtgMWKKDMcaYKmptdy2lfXvWAtZ9Z4wxpopafPfdwG4XlOol0WFGl0NX1T7gc0AHsAs4Cfi2iNykql3AN4GVwFYgCVwhIk/7c8PAF4DzgTVAL/B2EXkw7/rXAZcDDwHLgatF5M4ZeXPGGFNFwVCEVDIKQCAQpKV9PgCDe54GoCncjpv/Ll2lEpZmpltKPwTaReQVIvIm4N3Ajap6OvAZ4CXAOcC5QB9we9651wBXABeLyPnAHuCXqtoKoKqXAB8GrhKRlwP3AT9V1aUz89aMMaY6lhx2ASecdT0dc1YAEGmbTzDYxOjwTpKJERLxEQLBEKFwa5VLOrGZDkonAYm852H/PAlcBtwhIptFJI5rNR2rqmeragi4ErhfRFb7c7+GC1yX+OfvArYAP8/b3wq8dRrfjzHGVF179zKCoTBL5UIgl3k3MrgVgERsEKiPDLyZDkr/CrxKVX+jqv8PuBF4NdCJCyAP5B27yj+egeuKW1psv+/aOwV4UEQybdPVQNSfb4wxDSsUbgGgs/dQOnsPy2bejY4JSvWQ7DCjY0rAJ3HB5SxcsNgMdAGZXMWBvGN3+MfFuBbRePvn4wJsdr+IpFV1p99fVKStnXggVPYbaUSR9tr/wM4Uq4scq4ucWq2LcLgt+/PSw19GIj4EQDy+h0h7B8nUKACtXfOIx7dP6bUirS1TOn8iMxaUVDUC/BewCTgMmAf8O/Ad4BX+sJ68U7r94zZyAajY/p240bv8/ZljnhqvXNHhIaLDIyW/j0YVae8gOjRY7WLUBKuLHKuLnFqui2AoAkAqGaejewXJhEt42LdrPdGhQWIje92BqaYpv4dwOjml8ycyk913lwJnAteKSFJEtuEy5QBeCcSAE/OOP94/rgLWAduL7ReRGC7j7gRVDQCo6qG4LL9VGGNMAws1udbLtmfu9c8jpNMpRoZcqygRcy2neriBdiaDUqZr7di8bWfiEh2+gcvMu0BV+3xgeTPwBHCniCSArwAnq+pKf+5bgN3A9/3zzwMLcSnjmf0xXMKDMcY0pEAgRDDUTDqVZMu6/yERHwYgOryLdCoOQDyT6GBjSjki8hNVvRq4SVXfhkuYnwO8TET+pKpP4u49uhN3D1MT8CoRSflL3IBLeLhNVZ8AVgAXicg+v/9m3D1OX1bV1cAxwCUism5m3qExxsy8TJp3MjFKMjHK1vX3sFQuYHjf5uwxluhQhIh8FvhskX27yY0tFdofJdfdV2h/Gncv0zVTK6UxxtSPTNddIuHGxreuu4tUMsbeHY9kj4nHfVCqg5Twmc6+M8YYU0GhpkxLyQWldDrJtmfu2e+YRB1139ncd8YYU8cyLaVkYrToMbnuO0t0MMYYM42aMi2lePGgFM9m31lLyRhjzDTKtZSK32+ZTsVJJqIEg03Z42uVBSVjjKlj+dl346mXDDwLSsYYU8dKaSlB/WTgWVAyxpg6lsu+K62lFK7xZAcLSsYYU8cyQSkRH7+llKiTZAcLSsYYU8cyy1ZM2H1nY0rGGGOmWyn3KUH93EBrQckYY+pYU5ljSpboYIwxZtqUm30XjlhQMsYYM01CJczoANZSMsYYMwNKbilZooMxxpjpFAg2EQyFSaUSpPyCfsXYfUrGGGOmVamZdwCpZIxUMkYw1Eww1DzdRZu0ktdTUtUWoFdEnlXVdtxy5c+IyM+nrXTGGGOKGruW0kTisSEirc00NXcQG9k9nUWbtHJaSt8Dfuh//i7wb8DtqvrRipfKGGPMhJoyLaUJkhwy6uFepXKC0jnAlaq6CHglcD1wFvCW6SiYMcaY8ZXbUkrUwaSs5QSlNSLyKPBS//zzIvI7YFPli2WMMWYiuSmGSmspxaOZe5U6p61MU1XymBLQrqo3Ay8G/iQiO1X1HOCQ6SmaMcaY8ZQ6Q3hGbHQPAM0tPdNWpqkqp6X0DuA8oAv4uN/278B3KlwmY4wxJcjOEF5i9102KLXWblAqp6UUAhYDARFJ+W2vAmIVL5UxxpgJZVPCJ1i2IiM64oJSpEFaSneJSDovICEif8W1lowxxsywUpdCz6j7lpKqngi8DAgAAVX9iP85IwwcMX3FM8YYU0ypUwxl7D+mFADS01SyyZuo++5E4ANAG670nyhwTKrANmOMMdOsqYwZHcDN6hCPDRJu7iDc3Ek8NjCdxZuUcYOSiHwD+Iaqngf8F+6+pLGGp6NgxhhjxlfufUoAsZE9hJs7aG7tqb+glCEid6rqe3DTDN0+zWUyxhhTgnLmvsuIje6hvfsgmlt6GOp/ZrqKNmnlJDrcBFw7XQUxxhhTnmxKeInZdwBRP64UqdFkh3KC0p+BTlXd72ZZfwOtMcaYGZbLviuv+w5q9wbacu5T+jzwLWCNqj4EDOLuXToZlwhhjDFmBk2u+87NDl6raeHlBKXv44IQwEl522svp9AYYxpcIBgmGGwilUqQTiVKPi+WvYG2d7qKNiXlBKWbgBERyaaFq2oA+GDFS2WMMWZc5c7mkBHN3kA7p+JlqoSSg5KIfBBAVc/A3TD7iIj8CbhhmspmjDGmiKYyJ2PNSMQGSSVjNIXbCYYipJLR6SjepJWz8mwIuBW4CH8rsKreBVwsIkPTVD5jjDEF5JatKK+lBBAd3Utr+wKaW3oYHdpa6aJNSTndd1cDFwBfAR4FDgOuAD4DXFnqRVQ1CLwV+DvgceAbIrJaVcPAF4DzgTVAL/B2EXkw79zrgMuBh4DlwNUicmfe/itx3YkPAYcD14vI98p4j8YYUxcy3XelzhCeLzayh9b2BURaay8olZMS/hbgPBG5SkS+IiJXAy/CtZxKoqotwM9wM0NcJCLvEJHVfvc1uCB3sYicD+wBfqmqrf7cS4APA1eJyMuB+4CfqupSv/95wBeBm0TkFcDNwM2qmp+UYYwxDaHctZTyZTPwajAtvJyglBaR3+dv8AFlcxnX+BRwKPAPwBJV7YVs1+CVwP15QeprQB9wiX/+LmAL8PO8/a24VhfAO4E48E3//Ot55xljTEPJBqX4ZILSXgAirbWXgVdOUAqq6un5G1R1JTC/lJNVtQsXeFpw8+jdCOxU1ZtwXXFLgQfyTlnlH8/wXXunAA+KSCYFfTUQBc7IHAc8KSL7AERkJ7A2b78xxjSMcmcIzxcdybSUai8Dr5wxpW8B96jqLcATwBLgMuDHJZ7/fP96q4A3iEhSVb8LvA/Y7Y/Jnx1wh39cjAt8wfz9IpJW1Z1+P7hW1YYxr7mD/e+pMsaYhlDuWkr5cusq1V5LqZyg9ClgJS4QZdyDCyqlyHRe3iciSf/zrcDrybXY8js4u/3jNmAn7ibdsR2g3cBT/ucdRfZvG69QkbZ24oHQeIfMGpH2jmoXoWZYXeRYXeTUUl1EWjsBCARTkyhX1F+jt+xzI60tZb5Wecq5TykJXKqqnwKOBDQ/M64E6/zjYXnbInn7tuPWb8o43j+uEpGYn9roBFUN+FbSoUAHuW6+PwMXqmqniOxT1Xb/Wj8br1DR4SGiw+U3fxtNpL2D6NBgtYtRE6wucqwucmquLtLuz3d0uL/scsWGR0inU4QjXcSGh0mnS18WL5xOTnzQFJQzpoSqHgX8Pa51c4WqnlDG6Q/gUsnfoKqL/LbX4brufoFLNT/Zj1OBy/bbjZveCNzcewtxKeOZ/TFcwkNmf9iXDf/YgsvIM8aYhjLZGR0A0ukk8egAgUCQcKR74hNmUMlBSVUvwiUXXIW7X+mtwKqxyQ/FiEgcyKRy362qv8eNB71MRPpxM0N8F7hNVX8BHINLG9/nL3EzbqqjL6vqbcCrgUtEZJ2//j24tPIPqOqPcSvmXikifyj1PRpjTL2Yyn1KkMvAq7WJWcsZU/okcDdwHbAeOAT4OO7m2ZIy3HwAeXmRfVHcjbHFzk3jgs414xxzEy5wGWNMQ5vKfUrgMvA65iwn0tJDDXVKlhWUDgZO9sEDYLOqvozcWJExxpgZMpm1lPJlM/Bq7AbacsaU+nFp4GPPzy5dkbkZ1hhjzPSazFpK+bKL/dVYWng5LaVbcfcpfQnYigtG84B2Vb0Ml1TwYWBZxUtpjDFmP7kZHSbXUsrcQNvSNq9iZaqEcoLS23ErzGaWqkjjZgsH+Lb/2Rb8M8aYaRZqaiUYDJFMxkhPMkV7eGATAG1dB1FLf77LCUrP4qYHKnZvUhB4/5RLZIwxZlytHQsBGB0ad26AccVjA0RH9hBp7aGlfcGUrlVJ5QSllwIbfGp3QX7aIGOMMdOotcPd6jmyb8uUrjPU/wyR1h46upfVZVAaAr6mqhtw6eFNuNkSLhGRvQAiUvpC8cYYYyaltdMHpcGpB6XehcfTPmcZO5+9vxJFm7Jysu++jbuP6CPA/xORGJAAPlv5YhljjCkm01IanmJQGtz7DADt3bWTn1ZOUDoOOAG4Frd2Ebgphy6udKGMMcYU11ah7rvhgY2kU0naOhYRDIYrUbQpKycoxUTkryLyaeApVV0O7CrzGsYYY8bRFG7P3oNUSDjSRVNzO4n4MPFo/5ReK5WKMzy4hUAw5LPwqq+cgLJVVa/0P9+Bm+HhQ+TWQjLGGDMFwVCElWdcy5GnvpdAkSV1KpXkkDHU77rwOubURhdeOUHpE8AXVPV+XJfdj4GP4SZKNcYYM0VdvYcRbu6gtaOP3kXPLXhMbjzp2Yq85lC/Wxu1VsaVSg5KIvIr3HIQBwGnAl24ZSOun56iGWPM7NI19/Dsz4sOeTG5+Qly2jrdYtsjg1sr8pq1luxQztIVJwBPicgiYAHQISJXWhq4McZURiYoJZMxWtsX0NN37AHHZG6cHdlXmZbS6NA2kolRIq09hJu7KnLNqSin++6P+AX1RGSnBSNjjKmccHMXrR2LSCaibHryDgAWH3LemKMCuaBUoZYSpHNdeDUwrlROUHoGSKnqfqFUVQ8M5cYYY8rSNVcA2LfnKXZsuo/YaD9tXUvonndk9phI21yCoWZiI3smvWRFIYOZZIca6MIrZ0aHdwO3A+tU9S5gEAgBL8PNFm6MMWaSOn3X3cCuJ0mnEmxdfzcHH3ERiw45l/6djwPQ1uHGk6Z60+xYQ9lxpYMret3JKCco3eGPb8UtRZ5RG1PLGmNMHevu9UFp95MA7Nh0H4sPOZfOnkOYM/9o9u54tGLTC42VaSm5oFTdGcPL6b77MfBN3DLoK/y/Q4FvTEO5jDFm1oi0zae5tYd4bDB7/1EqGePZp+8EYPnRr6Ep3F7xe5QyErF9JOJDhJpaaAq3VfTa5Sq5pSQil6pqUERSY3b9Q4XLZIwxs0pXXtddfitl2zO/o2fBsXT2Hsqyo15dsTnvCkkmotnZJBLxoYpfv1RlTRFUICAZY4yZomxQ8l13OWnWPfIDkokovQtPoLWjj3QqOS3LTGSWVQ82RSp+7XLYvHXGGFNVAbp6XeadayntLzqyi41P/DT7fHR4J+lU5e/IyQSlUMiCkjHGNJxI61wOOfaybJdbMW1dS2kKtzE6vJPYSOGpRHdsWsXeHY8BlbtpdqxUMgow7mSwM8GCkjHGTIOlh7+cuYuey2HHv4nAOMtCdPYcAsC+3WvHvd66R37A1mfuzSY/VFoyUeNBSVXfoao9ec/PnJESGWNMnYu0zs1OEdTSvoClh7206LEtbQuAiVtAidggG9fcXvF08IzsmFINd9/9K7A87/ldhQ5S1bMrWSBjjKl3fcvPJBAIsm/P06TTKfqWn1l0wtOW9vkAjA7vmMkiHiCZ7b6rblAaLyX8UeDXqjrsnwdU9ekCx80HOiteMmOMqUOhcBvzlpwKwPpHb2XekpNZtOIcVqy8lEdXfeaAJIWWdtdSGh3aPuNlzZfKJDrUavcdcAHwG2CU3C2+gQL/ypkVwhhjGtqCg55PKNTM3h2PMTq0lc1rf83I4DZaO/pYcuj5+x0bDDXT3DKHVCpBdHRPlUrsZMeUqtx9VzSgiMizwGWZ56o6KiIrxh6nqq+ZprIZY0xdCQSb6Dv4hQBsXf9bANKpBOsfvYUjT3038w86nU16R/b4SJvruosO74R0dW8Dzd2nVN2WUjmtnFYAVT0DOAJ4RET+JCI/mpaSGWNMnZm76CTCkU6G+jful003uHcd8dgg4eYOmlt6iPlWUYsPSqND1R1PgtoZUyonJTygqj8B7sWtq/RHVb1TVdunp2jGGFNf5h90OgBbn7n7gH3DA5sBd19SRi7JobrjSZA3plTD2XdjXYMbZ/oKcBUuO+804DPTUC5jjKkrgWATbZ1LSadT7N3+yAH7h/dtAqCtc0l2WyYdvJZaSvXUffcW4DwR+X1mg6p+D/glcGWlC2aMMfWkrXMxwWCIkcEtpJKxA/bnWkp5QamGWkrZaYbqqPsunR+QAERkNbC5skUyxpj609Z1EABD/RsL7h/e54NSZ373XQ21lLLZd7WbEn7Asap6ev4GVV2Ju0/JGGNmtfZMUBrYVHD/6NB2kokokdYeQuE2msLtNIXbSMRHSMT2zWRRC0olaiPRoZzuu28B96jqLcATwBJcyviPp6NgxhhTTzJBaXhgQ5Ej0owMbqFjznLaOpdkb6KNVnkmh4xksv5Swj8FrCTv3iXgHuB95bygqvbhAtnRItLrt4WBLwDnA2uAXuDtIvJg3nnXAZcDD+GmP7paRO7M238l8EG//3DgehH5XjllM8aYyQgEw7R2LCSdTmXHjgoZHthMx5zltHctJRFzC+lVeyaHjFQyRjqdIhRqhkCwavdNldx9JyJJEbkUOAG4FDhZRM4Wkb2lXkNV5wJ3A6cAPXm7rgGuAC4WkfOBPcAvVTVzb9QlwIeBq0Tk5cB9wE9Vdanf/zzgi8BNIvIK4GbgZlU9qdSyGWPMZLV1LiYQDDEyuJVUKl70uPwMvOx4Uo20lKA2ZnUoe+kKEfmriNyS34ophao2Af8JPA6sytsewmXv3e8TJ8DdB9UHXOKfvwvYAvw8b38r8Fb//J1AHPimf/71vPOMMWZa5bruCo8nZWT2t3UtIdJeOzfOZqRq4AbamVxP6f3AUbgWUb7lwFLggbxtmaB1hu/aOwV4UEQyi9evBqLAGZnjgCdFZB+AiOwE1ubtN8aYadPWnUlyKDae5AwPbiWdStLS3pe9X6kW0sEzcstXVG9caUYmU1XVecA/Au8Skd2qmr+7zz8O5G3LfHVYjMvuC+bvF5G0qu70+zPXGPtp2AFM2H0XaWsnHgiV+E4aW6S9o9pFqBlWFzlWFznF6qJjjluWIh7bNWF9RUd20tLeR0vbPADSDNdMHafTruuxtXMOaQYLHhNpnd6ANVMzfF+JCxLfKbAvE4Dyx5i6/eM2YCduhvL8/Zljnsq7RqH92yYqWHR4iOjwyESHNbxIewfRocIfwtnG6iLH6iKnWF0Eg2Fa2haQTiXp37523DElgMH+DbS0u+/isdF+RgYKL4FeDfGYW6koGU8V/f8eTientQwld9+p6qJMYsEkLMLdZPt9n1J+lL/mLcBrge3AiXnHH+8fV4lIDJdRd4KqBvx5hwId5Lr5/gwcpqqdfn87cFjefmOMmRatnUsIBIKMDG2bMCAB+2Xn1VKSA+Tdq1Qn3XePARuBY8t9ERF5e/5zVb0HeJGIvNY/DwEfUdWVIvIIbkqj3cD3/Smfx90ndT7wa78/hkt4yOy/GHg9bm6+1wMtuIw8Y4yZNu3d48/kMFYmAw9qJx08oxaWrygn0WEYGPABJEtVFxc5viCfvv0c/3PmHqcbgO8Ct6k+q6gHAAAgAElEQVTqL4BjgIsyiQu4FO+bgC+r6m3Aq4FLRGQdgIjcg0sr/4Cq/hj4AHCliPyhnLIZY0y5cpl3pQalZ7M/18qNsxm1sHxFOS2l1+EmX31MVX8GDAIh4I24DLqSiMh9uO68/G1R3I2xxc5J44LONeMccxMucBljzIzJznlXYlBKxoeJjuwm0tpbcy2lVA3cp1ROUPo57t4gYf/gkC58uDHGNLZgKEJrRx/pVHK/FtBEtq2/h56+4xjIWwiwFuRmCq+PMaUHgX3AZ8kFoiBuah9jjJlVAoEQhx73BgKBIIP960mXkOSQsW3D79i24XfTWLrJyc1/Vx8tpZcAYREZymxQ1RYReUnli2WMMbUswIpjXsec+UcTjw2y7pEfVrtAFVELy1eUk+hwEvB/qnqPqvaqagTYpKpHTVPZjDGmJi076tXMXXQiycQoTz74NUaHJrwlsi7UwvIV5QSlrwBtwOnAv/jkhC/jZvc2xphZYd7S01hw0PNJJWPoX75ectZdPch031VzTKmcoNQFHAScA7zaT7D6T8Bzp6NgxhhTi+YvOQ2AZx7/Cfv2PDXB0fUl031XzTGlcoJSK4C/9+e/cWsWteDSwo0xpuE1t8yhY85ykskYu7eunviEOpPNvquTMaVVwH/4qXz+iJsW6DvA09NRMGOMqTU9fW4GtP4dj5JKxqpcmsqrhaUrysm+uxa3uN4luBm75/jtl1a6UMYYU4t6F7qgtHvrQ1UuyfTIZt/Vw5iSiKwBngf8BLfg3r3ApSLyo2kqmzHG1IxwxHfdJaL073y82sWZFrn1lJqrVoaylq4QkTWq+gHcOkbrRWTL9BTLGGNqS/e8owHo3/lYQ3bdOWmSiSihpgjBUHNV3mfJQckvF3EruWUlUNWv4hbum94FNowxpsrmzFsJNG7XXUYq6YJSKNRS20EJd0/SEuDTwHpgBW4JiZ3AxypeMmOMqRHNLb20dR7kuu52PFbt4kyrZGKUcKSLUFML8djAxCdUWDlB6TjgeBHZmtmgqv+OSw+3oGSMaVi9C48DoH/HYyUt5FfPqn2vUjkp4bsYs7y4iDwNNPb/IWPMrNbevZy+g18IwO5tjXdv0ljVXlOpaEtJVfuA/FJtAT6nqp/1z+cDV+FuoDXGmIYSCIRYcthLWbjibAKBIMODm9nb4F13AKnsDbQ1FpSATezfkgoAZwPvHHPcOypdKGOMqaZAIMSRp76b9u6DSadTbHn6f9i55fekU4lqF23aVXtNpfGC0t24hIbNRfaPAn8SkXsrXShjjKmmzt7DaO8+mNhoP0/99VsM7l1PpL2j2sWaEZnuu2ANBqU3A5v9UuRFqeo5InJXZYtljDHV09I+H4C9Ox5lcO/66hZmhiWrvCR60aAkIpvyn/v7lD6Cmyk84DdHgJOB6t3+a4wxFdbStgCA0aHtVS7JzKv2/HflpIR/FzitwPZxW1LGGFNvWtp9UBqefUGp2mNK5aSEH4NLdAiLSFBEgrjMuw9NS8mMMaZKskFpFraUcvPf1X5QGgTW5E8pJCIx4EsVL5UxxlRJMBgm0tpLKpUkNrK72sWZcckqL4leTvfdF4D/VtWv45auABfUPggcUemCGWNMNUR8kkN0eCfpdKrKpZl59TSmtAXXhfdvedsC2JiSMaaBtLS5oDQbx5Og+qvPlhOUPgXcCfwK6PfbMi0lY4xpCLN5PAnyxpTqoKXUDLx07H1LqvqTyhbJGGOqJxeUdlS5JNWRm/uu9hMd1gBXq2p4zPYPVLA8xhhTVbO9pZSqo0SH7bguvE+p6th9lhZujGkI2RtnZ/mYUrVSwssJSqfjAtOavG1B4JSKlsgYY6qkqbmTpnArifgwidhgtYtTFel0klQqQTDYRCDYNOOT0JYTlC7GzYW3Ln+jqr6mskUyxpjqyMx5N1u77jKSiVGCzR2EQhESNRyUOoEXqOoL8rYFgSuBH1W0VMYYUwWzPckhI5WIQnMHoaYWEvGhGX3tcoLSL3D3JWUmY7X7k4wxDaV1lo8nZSSTmbTwmR9XKico/QYXmDJhMwxchluB1hhj6t5sz7zLqObyFSUHJRF56dhtqnoLsK7A4cYYU3csKDnVnP+unPuUCkkDeytREGOMqaZAIESkdS7pdIrR4Z3VLk5Vpaq4fEXJLSVVvRWYl7cpDKwEtpVxjR7gs8Bzga2AADeKyJdUtQv4pr/mViAJXCEiT/tzw7hJYc/HpaX3Am8XkQfzrn8dcDnwELAcuFpE7iy1fMaY2SvSOpdAMER0ZDfpVLzaxamq3JhSbbeUzgfOzPt3CrAeuKKMa/wIN6P4qSJyHnAL8EVVPRr4DPAS4BzgXKAPuD3v3Gv8a10sIucDe4BfqmorgKpeAnwYuEpEXg7cB/xUVZeWUT5jzCxl6eA5Q/0b2bPtr8RGZ74jrJxEh6uAH4lIdDIvpKohXMC5U0RG/Oa1/vEoXNLEz0Rksz/+m8BNqno2cC8u9fx+EVntz/ka8BPgElwL6124mcx/nrf/rf7fRydTZmPM7GHjSTk7Nt3Hjk33VeW1i7aUVPXG/Oci8p3JBiR/fhJ4JXCzv34L8F5ciycItAIP5J2yyj+egeuKW1psv+/aOwV4MG/C2NVA1J9vjDHjaus6CICRwa1VLsnsNl5L6X2q+t4SrjEkIl2lvJiI3AHZ8aEfAgcBLwMO9ocM5B2euXttMa4rb7z983GBLbtfRNKqutPvLyrS1k48ECql+A0v0t5R7SLUDKuLnNlSF11zDwcgOrql6HueLXUxnkjr9CY/jBeUNuG6xcbeJLsQeDMu0aEf+Eg5L6iqbcCPcWNLLxCRh1X1XL+7J+/Qbv+4jVwAKrZ/py9n/v7MMU+NV57o8BDR4ZHxDpkVIu0dRIdm51xfY1ld5MyWumhpX0i4uYNYtJ+BnYXvcpktdTGRcDo5rdcfLyhdISL/nb9BVd+AG7sJA98BrhGRknMnVTWCWySwBZfskAk2q4EYcGLe4cf7x1W4e6G2F9svIjFVfQg4QVUDvpV0KNBBrpvPGGMK6pp7GAD7dh2wAoKZYUXHlPIDkqrOU9WfAd8C9gHnicjl5QQk7wu4rrhzRWSHqjar6teBs3DdeReoap+qBnCtsSdwiREJ4CvAyaq60l/rLcBu4Pv++edxrbjz8/bHcAkPxhhTVFevADCwe+0ER5rpNmH2nc9++y6wCNc6eqeI7Cv3hVT1RFxKdxzY4tdkCuPm0vs4cBfu3qM7gV2+bK8SkZS/xA24hIfbVPUJYAVwUV5Zbsbd4/RlVV0NHANcMnZWc2OM2V+Azl7fUtptLaVqGzcoqeq7gRtx8929VkRunewLichfyE3mWswrxjk/irsxttj+NO5epmsmUz5jzOzU1rmYpnA70ZHdREd2Vbs4s954KeFfwc2+8BBwfLGApKrfmqayGWPMtOvMdt1ZK6kWjNdS+gf/+FzgqQJLoOd7U8VKZIwxMygznmRJDrVhvKC0B/gqLlmgmCDwtoqWyBhjZkogSGfvoYC1lGrFeEHprSLyk4kuoKoPTHSMMcbUovaugwg1tTA6tJ14tL/axTGMnxI+YUDyx/2icsUxxpjKCgRCtHYsLLivy8aTas5U11MyxpiatuiQc1n5/A/S03fcAftyqeB2f1KtsKBkjGloHT0rAJi3+OT9tgdDETp7DiWdTllLqYZYUDLGNLTWNrckRde8IwiGcovW9SxYSTAUZt+ep0nEbE67WmFByRjTsIKhZppb3TzNwWAT3fOOzO7rXXgCALu3ri54rqkOC0rGmIbV0jZ/v+c9fccCEGpqpWveEaTTKfZs+2s1imaKsKBkjGlYmdVkhwY2ATBn/lEEgk3MWXAMwWAT+3avta67GlPOcujGGFNXMkFpYOcaANq7ltLVe7h13dUwC0rGmIaV6b4bHd5BcluM9q6lzF96Gl1zDyedSrJn28NVLqEZy7rvjDENK9NSGh3azt7tLgD19B1LMBhiYLeSiA9Vs3imAAtKxpiGlR+URga3Mjq0PbvPuu5qkwUlY0xDCjd3EWpqIREbyraIMt11qVSSPdv/r5rFM0VYUDLGNKT8VlLGri0Pkkol2LPtIZLx4WoVzYzDEh2MMQ0pG5SGd2S3jQxu4eF7P0kiYQGpVllQMsY0pEItJYB4bKAaxTElsu47Y0xDyqaDjwlKprZZUDLGNKRMS2nEglJdsaBkjGk4gUCISGsv6XSKaN6Ykql9FpSMMQ0n0jaPQDBEdGQ36XSy2sUxZbCgZIxpOLkkB2sl1RsLSsaYhpOb887Gk+qNBSVjTMMplg5uap8FJWNMw7GgVL8sKBljGkpHzyG0dS4BLCjVI5vRwRjTMOYtPY1lR/4twWCI3VsfIh7tr3aRTJksKBnTgJpbemjvXsbwvk1Eh3cWPCbSOpe+ZS+ie96RbN/we7Zt+D2QntmCVkgwFGGpvIy+ZS8EYOv6u9n4xM+rXCozGRaUjGkQgWCYngUrmbfkVLrmHk4g4Hrno8O76N/1BNGRXaRTSdLpFJ29h9Kz4JjsMQcf+SrmLFjJukd+QGx0bzXfRllC4Tb6Dn4hfcteQFO4nVQqwfpHb2XXs/dXu2hmkiwoGVPHmlt6mTP/KLrnH0lnrxAKNQOQSsbZt3ctbZ1LiLTNZUHb8w44N5VKsPPZ/2VwzzqWysvomns4Rz/vA25Zh2SMdDJOMjnI1nW/n6YbUAOEmlpoCrcRCrfS1NxBuLnT/YtkHrtoau4gOryLof5nGOrfQJo0Hd3LaO9eRtfcwwk1RQDYt+dpNj7xM4b6n5mGspqZYkHJmGkToLmlm3Ck2z9PEwgEae1YTHv3Mtq7DyIeHWDz2l+X/Ye0tWMhiw95CT0Lj8u2dgAG+59h5+b72b3lLyQTI0CAtq6ldPUKTc3tBAIhAoEg8dg+dm76c3bG7L07HmH5Ua+hp+8Y5i89fb/XmrvodJ55/D/Zt3ttmWVcRNfc5xAIBBge3MLIvi2k00nmzD+angXH0DX3cII+iE6krXMxPX3HFNy3d8fjbHn6Nwzufbqs8pnaZEHJmAoKhiIsOeyldM0VIm3zsy2XojoX0z3vCHZteZBN+ksSsUECgRAEAgUXoWvrXMKiQ86ld+HxAH7Buofp3/EY/TvXFFiWIc3wwEaGBzaOW4xEbJC1D/0HXXMPJ9I6j2AoTDDUzLwlp9Da0ccRJ1/F7i2riUX7CUe6aI50k0oliI3uITa6h0R8hFComVBTC+FIF11zD6e5Zc6E9ZWIj5BMjJCID5GIDZOI7SMe20c8Nkg8OkA8OkAiPkxL+wI6ug+mvXsZAEP9Gxjqf4bBveuJjuya8HVM/bCgZEyFtHcv45BjL6OlbV52Wzw6QHR0j8sfCECAAKND2xns38DwwEa65x/FwmVnMnfRc5m76Ln7XS86vIvd2x5iz9aHCIXbWLjibLrnPgdwwWjHxvvYsu6uimaYDex6Engy+3zP9vvpWXAKiw85l95FJ5R1rdhoP/0715BKRmntXExb5yKCoQgDu55k7/b/Y8/2R0jE9pV0reGBjeze8mBZr2/qU0MFJVXtAr4JrAS2AkngChGxdr2ZFqGmViKtc5mzYCWLDzmXQDDE8MBmnlnzE0b2PUsyMTru+YN717Fj430sPfxCehYcQxpIp5IEAgEibXNZtOIcFq04J3t8MhFlx6ZVbF1/94ykO6fTSbY8/Rt2b/kLcxefRCoZIxYdIB7tJxgM09zSQ3PLHELhVlKJKMnEKInECIN71zOyb3OBKwao1ww/MzMaKigBnwFeAhwBbAdWA7cDx1WzUGbqgsEwqVT8gO2hphaCweaiq4k2t/a6QfGugwiF2xkd2srI4FZGh7aTSiUIBAJAkKbmdppb5hBp6SHc2sFw/1aiIzuJje6lOdJNpH0BLW3zaW7toTnS7bqwWnpoCrfu93pb19/NpifvKCsxIDa6h6cf/u6YrQE65qygd+Hx9PQdSzqdZvvGP7Bj431+rGhmRUd28exT/12BK1lAMuNrmKCkqnOAy4Cfichmv+2bwE2qeraI/LaqBZxlAsEwHXOW09l7GB3dy4hF+xncs47BveuIDu8kGGom6Mcgmlu6aY7MIdzSTTqVJJkYIZkYpam5nY45h9A5ZwXNrT0k4sOMDu8gOryLpnAbrR0Ls+MWyUTUBZGRvYTCLdnMrVBTy7S+T/e6u4gO72L7ht8zsPvJiU8qSZrBvU8zuPdpNqy5rULXNKb2NUxQAp4LtAIP5G1b5R/PAAoGpUOPeyPDUf+tNvslLvdtbv/vdemCPxb+9hdwPRXuP7n/BgCCBIMhAsEmAsEQpF03STqdJJ1KAWnS6TR+B2n/uP/rBCAQIOAfIeC/9ee6R7LXIOAO8aXIPaZJp1Ok0ymCoRDpFASCQZ+h5a9JgEAwRCAYdoPfwSaCwXCu7Nlr+Xrw1wsEwwSDof1qZP6SUwvUU2nSqSRN4TY6upfR4Qe7AVLJGMlkjHBzB22dS7LTy2TEY4PZVOJ4bJDW9j5aOxa6WaQDAV+/kIwPExvdQ3R0D8FggFC4k0jrXJpb5hCPDjA6tJ3Roe1ER3YTj/YTi/YTH+0nER+a9HsyxhyokYJSn3/M78fJLKayuNhJ3XOPoPnAXiEzRel0ipHBZxnsX8fQwDOEI920dx5Me9cyf5NjjFQyTioZ9dlWA8RjAwQIEGxqJRSKkErFGR7YwNC+DUSHdxAKtxFpmUtzay/J+AjR4e3EonuBNMFQC5HWXsLN3SSToyRigyTigwd0dQ3srtx7DDUHCDV3VO6CNSrS3vjvsVRWFxBpnd7eh0YKSpkA1JO3LXODyLZiJ6196NsMjcbyvvDnvvkH9jsy71lg/z3jymvhZP+bTpNOJUmlEtmxh0Ag5FokgSD7tXp8xhaBAnPnplO+FeWum85vTWVaUfktp7xj3b8ggUCAQCBIuKWN2MhgtrWWTqdy56USpFJxF0RSCf/cPY5tvQUCQVfWdKrgGNDU7GO46P/KQUYGCk+nU65IewfRocGKXKveWV3kWF044WleybeRgtJqIAacmLfteP+46sDDnYHdTzA4PPMDx7WmUr9wtvS0MWYqGmbpChHZCfwQuEBV+1Q1ALwZeAK4s6qFM8YYU5JGaikBvA/oxQWhXbj39yoRSVW1VMYYY0rSUEFJRHYDr6h2OYwxxkxOw3TfGWOMqX8WlIwxxtQMC0rGGGNqRkONKU1G+zTfCFYvIq0t037/Qb2wusixusixunCm+2/mbA5KXQB3f/Vz1S6HMcbUoy6g4lPVz+agtAk4mP2nJTLGGDOxLtzf0IoLuKlpjDHGmOqzRAdjjDE1w4KSMcaYmmFByRhjTM1oiEQHVe3ALYH+UuADwDki8me/rwf4PG6hv8zaBu8SkVV+fxD4R+ASYB1u6fRfAO8RkZg/5jrgcuAhYDlwtYjU3CSvU6mHvGs0AV8E/h44WUQeytt3JfBBXD0cDlwvIt+b1jc1SRWqi3P8uVuB20Tkp3n7ZkVd+M/DJ4DnAYpbTHM18E4RGfHH1MXvR4Z/z5/FvZetgAA3isiXVLUL+Caw0u9LAleIyNP+3DDwBeB8YA1urs23i8iDedevm/qYYl20AP8MnAds8Md9W0Q+6vdPWFeFNEpL6VTgbOAioB23Am3G94AXAqeKyMnAX4HfqupRfv8HgauBC0XkQuAtwNv9I6p6CfBh4CoReTlwH/BTVV067e+qfFOpB/zM6t8F3oT7wjInb9/zcMHqJhF5BXAzcLOqnjSt72jyploXHwY+B1wpIm8YE5BmU138I+4P7IUi8lbgxcCrgfdA3f1+ZPwIF6RPFZHzgFuAL6rq0cBngJcA5wDn4hYPvT3v3GuAK4CLReR8YA/wS1Vthbqsj6nUxeeBVwIvEpELgH8CPqKqF/j949ZVMQ0RlETkLhH5NK6Fk6Wqy4ELgFtFZLvf/CmgBR90cFE+hptVHGCtfzzaP74L2AL83D//Gu6X+q2VfRdTN8V6APeN+FTgywUu/04gjvvmBPB1//iuihS+wqZSF6p6EfAx4FVAsMAflFlTF8BJQMr/A/dlJUhu4cy6+f0AUNUQ7o/s3kxLj9zv/FHAZcAdIrJZRDL/j49V1bP9uVcC94vIan/O13B/rC/xz+umPqZSF/6Y84ARXLDJP/foEuuqoIYISuOY7x9DedvW+ccj/eN7gY/imqbgvhkC3O6bn6cAD4pIJnd+NRDFdXfUiwnrQVWfj3vvl1H4hrgzgCdFZB9k169aS33VA5T2mbgWd//af+IC9VpVvdt3g8Hsqosv4f6QrFbVa3CB7UYR+WY9/n6ISBL37f5myHZBvRf3hzWICyAP5J2S6dI9A9cVt7TY/nqrjynWBcAbcL0JmWGQD+K+4N/BBHU1XrkaPSg9BuzGLfwX9tte4B9DACKyWkS+JiJDqvp+XNfVe0Tkf3C/tEHybrD1H7adwOIZeg+VMGE9ADcB3xeRPxa5Rh8H3mi8g/qqB5igLnzgOQnXh/5iEbkEeAdwJvB+f9ysqAsAEflvXMuwGzcedTgQ8cfX5e+HiNwhIrf49/BD4CDckjeZ34X8/7c7/ONi3P/38fbXXX1MoS4Qkd+JyH/49eo+D7wIeJ2IPM7EdVVUQwclERnCffPvA/6oqt/FffMFtyJtlqp+HPg4cImI/JvfvBNIAz1jLt1Nrvui5k1UD6r6QuBk4LpxLrODOq8HKOkz0YX7vXhMRDJdurf6x1P942ypC1T1BuAq4BjcN98v4FrUN1DHvx+q2gb8DJfY9AIR+QO5P5r576fbP24rYX9d1sck6yJzbkhV/wN4DfASEfmx31XS+YU0dFACEJFfAQtwv3zvw1X+MK5lAGR/8d4OnCkiP8o7N4bLoDnBJwGgqocCHeSaonVhgno4DDfAfb2q3oIbyAb4qKpmumn+DBymqp0Aqtruz6ureoAJ62IrMIp7bxnN/jEzrcqsqAtVPQTXJfMpEdkpIlER+RjwMHBGvf5+qGoE+BUuG+xUEXnY71qN6346Me/w4/3jKlzX5vZi++uxPqZQF/mJUS8AThORu/OOHbeuxitTowWl4JhHAEQkLiJP4JqXHwL+TkTWA6jqpbhMoheLyP/6be9U1U/50z8PLMSlNYIbAI7hBu1qVVn1ICLfFJETReS1IvJaIPNt55Mi8mY/yPl5IAy83u97PW5A/IvT/WamqNy6SAHfAY5X1fP84ZfhvgF/xz+fFXWBG8SOA8dmjvXJEctw421Qn78fX8C1Ds8VkR2q2qyqXwfOwnVhXaCqff6P7ptxrcY7RSQBfAU4WVVX+mu9BdcF+n3/vN7qY1J14c+9FpeNeZaIPKWqQVX9Z1V9d4l1VVBDzH2nqotxv0x/g6vg/8NF/2uBFbjKPBwYBK4TkXX+vCZcNO/G/QJCboDvNSJyq/+f8Rl/7dW4bowPiMhtM/PuSjfZehhzjYNwH8bnAz8A/kFEBv2+q3HjCw/gvgF9RkS+Ms1va1KmUhe+5XMDcCHwJK4VdL2IfDvvmNlSF+cD1+OSX/bgAtJ3gC+KSLqefj8AVPVE4EFcsI35zWEggKuLEeDbuPe5C5dC/yYRecyfH8EFmOfh/kCvwN1783u/v27qYyp1oap95HoOov4xBESA00XkzxPVVTENEZSMMcY0hkbrvjPGGFPHLCgZY4ypGRaUjDHG1AwLSsYYY2qGBSVjjDE1oyGWrjBmLH+X+p241PY9wAUi8qe8/R/FzV4wArxSRH5TlYIW4Gea/houVfd4EdlY5SIZM2OspWQakogM49YP2o378rV2zCGZmTu+WGMBqQs36/gPcTN4bxr/jNqhqgtU9UOq2jzx0cYUZkHJNCw/i/cHgU7gxjG7r8PdOH3DTJdrAkfiblLcIiJ/zpttuh6cibvRdlGVy2HqmAUl0+i+AfwReKOfnQBVfRXwt8A7RGTPeCdXwbgLoNUqv37Oy/3TQDXLYuqbzehgGp6qHo6bcHYHbk6y3wG/EpE35B1zGm5p5yNxa2ttAN7ip1R5O26y0r3APOBx4EMislpVT8etH/Nb3ESTnwZuF5G/LVKWZf6YM/zrrAU+ISL3qurrcCvBZmwUkYMLXEOAu3CzLd+ImwrmERE52U/t8mHc1EGDuFmavywiH/fv40bgL7hW4gtw08Lci1thd1MJZZzn3+tc3Iq0/wns83V7wkRlN2Yi1lIyDU9EnsQt5HgQbq66Xbg1kgBQ1RcB9/h9S3BT+J9CbgXeJ4G3iVsu/Dh/nXv8HHkpIIFbLO2fgLvJzQW2H1Vd4l9jOS74rcCtafRbVb1YRL6PmwgTXBAo9kc9gJvJ/CTgW8AfcAEI4Ke4NcFeLCLPwc1f+DFVPRO3QN82XLAZ9O/jBbil0m9V1cBEZcSNzw3h1sT5GW5W7N24hJKbfRlWWEAyk2VBycwWnwXW4LrHXp9ZNdb7FK4VdK2fJfzFuMklN4JbThwYUdVP45IQIrh1l44TkT8D/4WbyPLTInKuiLyewt6Pa2l9SEQG/MqfV/vXvqnIOQfwQfaH/ul/+tc8y3dPng/8i4g87pfWOB0XNLf6ltAGf961fimKh4Fb/HHPnaiMIrIV+Hd/jf8VkTNF5DjJLadtzJRYUDKzgv/jmllcbOzs6MfgWg7vVtVf4lpInwXeBqCq38B1+Q0CHyG36F8myyzTB55ZzqGYzBLja/LKFQWeAlb4rrdSFXrNY/zj4ar6Gf86aVyraQ3725L383r/eEiJZSz1/RpTNgtKxrjxlWW4P8TvAxaIyNVA0o9H/T1unOiTvpVS7PdmsMj2DPWPS7IbVIO4rrK1/o9/ufJfc7t/nIdbE+sYETkNuM8nImSNyepb4R83l1nGid6vMWWzoGRmk9CYx4yv4sZKLsJ1b7X5hc7+iru5NgXM94uYLcKtlQO5LLPM49jrjvUlf73rVDXi1975JDCf8ZeiL6TQa96BCyznAgER2e3HyzYDf5d/sl83C1U9DtbdmmAAAAEMSURBVLgEt67OqhLLONH7DZf5XozJsqBkZgW/KN9p/unYBdc+Dbwbd5/NDtwf8QW41Tg3AlfgWhMbcEkFz/jzrvN/1F/mn39ovDKIyOPAS3DjUY/hsvheDbxRRL7jWyTv9odfqKrdRd7LIuCNY19TRHbhEiXuA36tqntxyQcfFZEfjrnMHaq6Dpfg8T1cF1+qhDK24FZqBniPT/bIyLScfqGqPxivLowpxlLCjZlFVPUe4EUiYvcSmZpkLSVjZpcAZJf1NqbmWFAyZpZQ1RcDp/qn/1rNshhTjHXfGWOMqRnWUjLGGFMzLCgZY4ypGRaUjDHG1AwLSsYYY2qGBSVjjDE1w4KSMcaYmvH/AaDU0rYty+OOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo_df.groupby('year')['posted'].count().reset_index().plot(x='year', y='posted', xlim=[1920.0, 2020.0], legend=False)\n",
    "plt.xlabel(\"Year of report\")\n",
    "plt.ylabel(\"Number of reports\")\n",
    "plt.title(\"Increase in UFO reports over time\", fontname=\"Covert Ops\", fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The smart phone was introduced around the middle of 2007 (more or less)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Effect of technology on reports by month')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo_df['smartphone_epoch'] = ['pre-smartphone' if x < 2007 else 'post-smartphone' for x in ufo_df['year']]\n",
    "\n",
    "ufo_df[ufo_df['smartphone_epoch'] == 'post-smartphone']['month'].plot.hist(bins=12, color='blue', alpha=0.5, label=\"post-smartphone\", legend=True)\n",
    "ufo_df[ufo_df['smartphone_epoch'] == 'pre-smartphone']['month'].plot.hist(bins=12, color='red', alpha=0.5, label=\"pre-smartphone\", legend=True)\n",
    "\n",
    "plt.xlabel(\"Month of report\")\n",
    "plt.ylabel(\"Number of reports\")\n",
    "plt.title(\"Effect of technology on reports by month\", fontname=\"Alien Invasion\", fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the plot is really a matplotlib object, you could also have obtained\n",
    "the legend by calling it directly, _i.e._: `plt.legend()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look at the univariate statistics of the UFO shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Reported UFO shape')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo_df.groupby('shape')['posted'].count().sort_values(ascending=False).head(20).plot.bar()\n",
    "plt.xlabel(\"UFO shape\")\n",
    "plt.ylabel(\"Number of observations\")\n",
    "plt.title(\"Reported UFO shape\", fontname=\"Covert Ops\", fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So by far the majority of shapes were 'lights'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What cities saw the most UFOs?\n",
    "\n",
    "### Further data cleaning\n",
    "Often in the EDA phase, it becomes clear that further cleaning is required. This\n",
    "is the case here!\n",
    "\n",
    "Recall we noted that there were 227 cities missing. This hasn't been a problem until now, because we didn't care about the city names. There are quite a few tasks required to clean up the city names. \n",
    "\n",
    "For example, there are many entries that attempt to clarify or condition with commentary in parentheses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ra/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84925</th>\n",
       "      <td>2003-12-03 16:45:00</td>\n",
       "      <td>2003</td>\n",
       "      <td>12</td>\n",
       "      <td>Vernon (Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107704</th>\n",
       "      <td>1991-05-05 20:30:00</td>\n",
       "      <td>1991</td>\n",
       "      <td>5</td>\n",
       "      <td>Grants (southeast of)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75830</th>\n",
       "      <td>2005-07-27 08:00:00</td>\n",
       "      <td>2005</td>\n",
       "      <td>7</td>\n",
       "      <td>Columbia (near)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92873</th>\n",
       "      <td>2001-04-24 08:00:00</td>\n",
       "      <td>2001</td>\n",
       "      <td>4</td>\n",
       "      <td>London (Ontario) (Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58222</th>\n",
       "      <td>2009-04-10 00:20:00</td>\n",
       "      <td>2009</td>\n",
       "      <td>4</td>\n",
       "      <td>Tabusintac (Canada)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date_time  year  month                       city\n",
       "84925  2003-12-03 16:45:00  2003     12            Vernon (Canada)\n",
       "107704 1991-05-05 20:30:00  1991      5      Grants (southeast of)\n",
       "75830  2005-07-27 08:00:00  2005      7            Columbia (near)\n",
       "92873  2001-04-24 08:00:00  2001      4  London (Ontario) (Canada)\n",
       "58222  2009-04-10 00:20:00  2009      4        Tabusintac (Canada)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.dropna()[ufo_df['city'].dropna().str.contains(\"\\)$\")].loc[:, 'date_time':'city'].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Note the use of vectorized string methods and string matching using regular expressions -- the `\\)$` means \"look for a `)` at the very end of the string\")\n",
    "\n",
    "There are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ra/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "8673"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.dropna()[ufo_df['city'].dropna().str.contains(\"\\)$\")].count()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8,673 of these observations!\n",
    "\n",
    "(Note the double use of .dropna() to prevent data misalignment (this is enforced by pandas, so you'll get an error if data threatens to become misaligned)).\n",
    "\n",
    "Including this mysterious gem:\n",
    "\n",
    "`((town name temporarily deleted)), OK` \n",
    "\n",
    "http://www.nuforc.org/webreports/013/S13977.html\n",
    "\n",
    "Now _this_ is the kind of thing we came here for!\n",
    "\n",
    "First we'll get rid of them no-good city NaNs: `.dropna()` has some helpful parameters to specify rows/columns and in-place filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_df.dropna(subset=['city'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps we could split the city strings by \" (\" and retain the 0th element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_time           24\n",
       "year                24\n",
       "month               24\n",
       "city                24\n",
       "state               24\n",
       "shape               24\n",
       "duration            23\n",
       "posted              24\n",
       "url                 24\n",
       "day                 24\n",
       "event_time          24\n",
       "smartphone_epoch    24\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df[ufo_df['city'] == 'New York'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ufo_df['city'] = ufo_df['city'].str.split(sep=\" (\")[0]` \n",
    "Unfortunately pandas' `str.split` method does not have the rich support of the base\n",
    "`str.split()` method.\n",
    "\n",
    "While we are at it, there are a few entries that have non-standard capitalization (such as 'AmArillo'), we we enforce this with .title():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_df['city'] = [x.title().split(sep=\" (\")[0] for x in ufo_df['city']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are still 20 cities with parentheses, and at least one with '{'s. I was hoping it wouldn't come to this...\n",
    "\n",
    "...but we're going to have to take a brief dip into the world of regular expressions (regex):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove anything following a [, ( or {:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_df['city'] = [re.split(\"\\s*[\\(\\{]\", x.title())[0] for x in ufo_df['city']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check nothing went awry (as sometimes occurs with regex's!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ra/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>shape</th>\n",
       "      <th>duration</th>\n",
       "      <th>posted</th>\n",
       "      <th>url</th>\n",
       "      <th>day</th>\n",
       "      <th>event_time</th>\n",
       "      <th>smartphone_epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25404</th>\n",
       "      <td>2014-04-07 21:05:00</td>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>Iowa City Hwy 218 North, I-380 North, Cedar Ra...</td>\n",
       "      <td>IA</td>\n",
       "      <td>Other</td>\n",
       "      <td>50 minutes</td>\n",
       "      <td>4/11/14</td>\n",
       "      <td>http://www.nuforc.org/webreports/108/S108434.html</td>\n",
       "      <td>7</td>\n",
       "      <td>2014-04-07</td>\n",
       "      <td>post-smartphone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                date_time  year  month  \\\n",
       "25404 2014-04-07 21:05:00  2014      4   \n",
       "\n",
       "                                                    city state  shape  \\\n",
       "25404  Iowa City Hwy 218 North, I-380 North, Cedar Ra...    IA  Other   \n",
       "\n",
       "         duration   posted                                                url  \\\n",
       "25404  50 minutes  4/11/14  http://www.nuforc.org/webreports/108/S108434.html   \n",
       "\n",
       "       day event_time smartphone_epoch  \n",
       "25404    7 2014-04-07  post-smartphone  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.dropna()[ufo_df['city'].dropna().str.contains(\"\\)$|\\(\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are only two entries with (these types of) entry errors left! We can manually impute them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New York City', 'New York', 'New York Mills', 'West New York',\n",
       "       'New York State Thruway / Catskill', 'New York City, Manhattan',\n",
       "       'New York City/Far Rockaway', 'New York City/Staten Island',\n",
       "       'New York City/Philadelphia', 'New York/Philadelphia',\n",
       "       'New York City/Central Park', 'New York/San Francisco',\n",
       "       'Central New York', 'East New York', 'New York Worlds Fair'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df.loc[115268, 'city'] = 'Aliquippa'  # The largest 'city' in Beaver County, PA\n",
    "ufo_df.loc[25404, 'city'] = 'Cedar Rapids'\n",
    "\n",
    "ufo_df[ufo_df['city'].str.contains(\"New York\")]['city'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are plenty of locations that are between main population centers, often separated by a '/' or a '&'. Most cases, the larger settlement is on the left of the slash or ampersand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_df['city'] = [re.sub(\"\\s*/.*\", \"\", x.title()) for x in ufo_df['city']]\n",
    "ufo_df['city'] = [re.sub(\"\\s*&.*\", \"\", x.title()) for x in ufo_df['city']]\n",
    "ufo_df['city'] = [re.sub(\"^[Bb]etween\", \",\", x.title()) for x in ufo_df['city']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check. Check. **CHECK.** _Always_ check!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New York City', 'New York', 'New York Mills', 'West New York',\n",
       "       'New York State Thruway', 'New York City, Manhattan',\n",
       "       'Central New York', 'East New York', 'New York Worlds Fair'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df[ufo_df['city'].str.contains(\"New York\")]['city'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good. Mostly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TEST EFFECT OF NOT INCLUDING ALL THE VARIANTS OF NEW YORK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "uniq_ny = ufo_df[ufo_df['city'].str.contains(\"New York\")]['city'].unique()\n",
    "for uniq in uniq_ny:\n",
    "    ufo_df[ufo_df['city'] == uniq]['city'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "731\n",
    "29\n",
    "4\n",
    "6\n",
    "1\n",
    "1\n",
    "1\n",
    "1\n",
    "1\n",
    "\n",
    "SO NOT VERY MUCH (29/731 = 4%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create function based on this, taking in city name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getUniqueSimilarCities(df, city_name):\n",
    "    \"\"\"docstring for getUniqueSimilarCities\"\"\"\n",
    "    uniq_list = df[df['city'].str.contains(city_name)]['city'].unique()\n",
    "    name_count = []\n",
    "    for uniq in uniq_list:\n",
    "        name_count.append((uniq, df[df['city'] == uniq]['city'].count()))\n",
    "    return name_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getUniqueSimilarCities(df, city_name):\n",
    "    \"\"\"docstring for getUniqueSimilarCities\"\"\"\n",
    "    uniq_list = df[df['city'].str.contains(city_name)]['city'].unique()\n",
    "    count_df = pd.DataFrame(uniq_list, columns=['city'])\n",
    "    name_count = []\n",
    "    for uniq in uniq_list:\n",
    "        name_count.append(df[df['city'] == uniq]['city'].count())\n",
    "    count_df['count'] = name_count\n",
    "    return count_df.sort_values('count', ascending=False)\n",
    "\n",
    "\n",
    "counter = 0\n",
    "for Idx in city_pop['city'].head(100).tail(50):\n",
    "    Z = getUniqueSimilarCities(ufo_df, Idx)\n",
    "    fraction_remainder = Z['count'][1:].sum()/Z['count'].sum()\n",
    "    fraction_secondary = 1 - Z['count'][0:2].sum()/Z['count'].sum()\n",
    "    if fraction_remainder > 0.05:\n",
    "        counter += 1\n",
    "        print(\n",
    "            \"{0} City: {1} Fraction of similar city names {2:.2%};\\\n",
    "            fraction of primary and secondary only {3:.2%}\".format(\n",
    "                counter, Z['city'][0], fraction_remainder, fraction_secondary)\n",
    "        )\n",
    "\n",
    "#getUniqueSimilarCities(ufo_df, \"Chicago\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ra/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:189: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "def purgeDirectionalModifiers(df, city_name):\n",
    "    \"\"\"Strip off directional prefixes North, South etc. from city names in DataFrame df\"\"\"\n",
    "    direction_list = [\"North\", \"N.\", \"East\", \"E.\", \"South\", \"S.\", \"West\", \"W.\"]\n",
    "    direction_regex = re.compile(' |'.join(direction_list) + ' ')\n",
    "    match_idx = df['city'].str.contains(city_name).index\n",
    "    df['city'].loc[match_idx] = [re.sub(direction_regex, \"\", x) for x in df['city'].loc[match_idx]]\n",
    "\n",
    "\n",
    "def purgeCitySuffix(df, city_name):\n",
    "    \"\"\"Strip specifiers for city, county etc.\"\"\"\n",
    "    suffix_list = [\"City\", \"County\", \"Area\", \"Bay\", \"Airport\", \"D.C.\", \"Dc\", \",\"]\n",
    "    suffix_regex = re.compile(' ' + '$| '.join(suffix_list) + \"$\")\n",
    "    match_idx = df['city'].str.contains(city_name).index\n",
    "    df['city'].loc[match_idx] = [re.sub(suffix_regex, \"\", x) for x in df['city'].loc[match_idx]]\n",
    "\n",
    "\n",
    "cities_to_clean = [\"Sacramento\", \"Seattle\", \"Milwaukee\", \"Baltimore\",\n",
    "                   \"Las Vegas\", \"Boston\", \"San Francisco\", \"Washington\",\n",
    "                   \"Chicago\", \"Los Angeles\", \"New York\"]\n",
    "\n",
    "for city in cities_to_clean:\n",
    "    purgeDirectionalModifiers(ufo_df, city)\n",
    "    purgeCitySuffix(ufo_df, city)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many cities are there in this data set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18366"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ufo_df['city'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The US cities aren't uniquely specified; we need to add their US state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'UFO reports by city')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo_df['city'] = ufo_df['city'] + ', ' + ufo_df['state']\n",
    "\n",
    "ufo_df.groupby('city')['posted'].count().sort_values(ascending=False).head(10).plot.barh()\n",
    "plt.gca().invert_yaxis()\n",
    "plt.ylabel(\"US city\")\n",
    "plt.xlabel(\"Number of reports\")\n",
    "plt.title(\"UFO reports by city\", fontname=\"Alien Invasion\", fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about the observations per capita? We'll need more data.\n",
    "\n",
    "The US census bureau for the 770 most populous cities: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml\n",
    "\n",
    "However, there are far too many columns for our needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_pop = pd.read_csv(\"PEP_2017_PEPANNRSIP.US12A_with_ann.csv\",\n",
    "                       encoding='latin-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the non-extraneous census-specific column labels and drop the rest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_pop = city_pop.rename(index=str, columns={'GC_RANK.rank-label': 'rank', 'GC_RANK.display-label.1': 'city_state', 'respop72017': 'pop'})\n",
    "city_pop = city_pop[['rank', 'city_state', 'pop']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we could have used the `.drop()` method as above.\n",
    "\n",
    "Further cleaning:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_pop['city_state'] = city_pop['city_state'].str.replace(' \\(balance\\)', '')  # This term means something to the Census Bureau..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are still a few (eight) cities with hyphens or official county designations or other weirdnesses\n",
    "\n",
    "Let's impute them manually, otherwise we'd miss out on some major cities!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_pop.loc['23', 'city_state'] = \"Nashville city, Tennessee\"\n",
    "city_pop.loc['59', 'city_state'] = \"Lexington city, Kentucky\"\n",
    "city_pop.loc['88', 'city_state'] = \"Winston city, North Carolina\"\n",
    "city_pop.loc['121', 'city_state'] = \"Augusta city, Georgia\"\n",
    "city_pop.loc['171', 'city_state'] = \"Macon County, Georgia\"\n",
    "city_pop.loc['219', 'city_state'] = \"Athens County, Georgia\"\n",
    "city_pop.loc['28', 'city_state'] = \"Louisville city, Kentucky\"\n",
    "city_pop.loc['55', 'city_state'] = \"Honolulu city, Hawaii\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The convention is largely \"X city, Y\" where X is the name of the city and Y is the full name of the state. So we _could have_ split by the \" city, \" delimiter.\n",
    "Unfortunately there are towns, cities, villages and counties in the mix. This means we have to dip briefly into the murky waters of regular expressions (regex):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_delimiter = ' [Cc]ity, '\n",
    "city_delimiter += '| town, '  # Note the pipe operator '|', used as logical OR\n",
    "city_delimiter += '| [Cc]ounty, '\n",
    "city_delimiter += '| village, '\n",
    "city_delimiter += '| municipality, '"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can split the cities and states according to this delimiter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_pop['city'] = [re.split(city_delimiter, x)[0] for x in city_pop['city_state']]\n",
    "city_pop['state'] = [re.split(city_delimiter, x)[-1] for x in city_pop['city_state']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fun aside: how many cities have the same name in the US?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Springfield    5\n",
       "Lakewood       4\n",
       "Albany         3\n",
       "Bloomington    3\n",
       "Charleston     2\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_pop.groupby('city').count().sort_values('state', ascending=False)['state'].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are _five_ Springfields!\n",
    "\n",
    "There are 36 major cities in the US with duplicated names. We'll need to retain the state information to disambiguate. This is why people have to specify the US state. You don't want to get on the plane to Portland, only to realize you're heading to the complete opposite coast!\n",
    "\n",
    "Sanity check to make sure we imported fine and the data looks like it make sense:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>rank</th>\n",
       "      <th>city_state</th>\n",
       "      <th>pop</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>163</td>\n",
       "      <td>Palmdale city, California</td>\n",
       "      <td>157519</td>\n",
       "      <td>Palmdale</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>257</td>\n",
       "      <td>Westminster city, Colorado</td>\n",
       "      <td>112812</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>Colorado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>718</td>\n",
       "      <td>Lenexa city, Kansas</td>\n",
       "      <td>53553</td>\n",
       "      <td>Lenexa</td>\n",
       "      <td>Kansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>746</td>\n",
       "      <td>Olympia city, Washington</td>\n",
       "      <td>51609</td>\n",
       "      <td>Olympia</td>\n",
       "      <td>Washington</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>199</td>\n",
       "      <td>Roseville city, California</td>\n",
       "      <td>135329</td>\n",
       "      <td>Roseville</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     rank                  city_state     pop         city       state\n",
       "162   163   Palmdale city, California  157519     Palmdale  California\n",
       "256   257  Westminster city, Colorado  112812  Westminster    Colorado\n",
       "717   718         Lenexa city, Kansas   53553       Lenexa      Kansas\n",
       "745   746    Olympia city, Washington   51609      Olympia  Washington\n",
       "198   199  Roseville city, California  135329    Roseville  California"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_pop.sample(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I created a text file with all the states' names and abbreviations, `state_abbrev.txt`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_ref = pd.read_csv(\"state_abbrev.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's add the abbreviation references to the `city_pop` DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_pop = city_pop.merge(state_ref, on='state', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare to be merged!\n",
    "**NOTE:** by doing this, we are automatically resigning ourselves to analyzing US-only data; the following transformations make no sense if there is no associated US state.\n",
    "\n",
    "How much data will this discard?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07189118610945378"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_df['state'].isnull().sum()/len(ufo_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So 7.2% of the entries have no defined state. By inspection, most of these are from outside of the US. This could be a follow-up investigation.\n",
    "\n",
    "Also..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47929"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_pop['pop'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...cities with populations of at less than 47,929 will be omitted.\n",
    "\n",
    "Combine the city and state into one column to make merge syntax easier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_pop['city_abbrev'] = city_pop['city'] + ', ' + city_pop['abbreviation']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ugh. Now we have too many columns. We only wanted two!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_pop = city_pop[['city_abbrev', 'pop']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the ten most populous cities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Ten most populous US cities')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdgAAAEaCAYAAACy+UYmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3XeclNX1x/HPspClKWis0SiWo278KSp2DWhsscUSC3bFGiNGYzcWjA07ViyxYGJLbBhLMKKxoWLBgln0a4tdMQrSVtllf3+cO2EYZ2dnYWd3dve8X699zc7T7p3ryplbnudUNDQ0EEIIIYSW1aWtKxBCCCF0RBFgQwghhBKIABtCCCGUQATYEEIIoQQiwIYQQgglEAE2hBBCKIGubV2BENoDSb8DRuRsPiZtOzC9vyVnO8BmwJfAncCawBNm9gtJg4B/pWPWMrPXcspbHrgJ+AXwqpmtnbWvC/AR8JN03ZOAJYExwCLA5cAvgVWBF1MdDgcuASqAYekH4DTgNuB94KxU9z7p9wOBD8xsszx1+yC9PTDrcwMcYWbXSfoLsE/a9htgJFAHHAA8W8T5mfsHzzKzYZI2A55I2zYHBqXPMMXMFsmp3wrAk8BPgdFAd2Ab4D1gE+AI4EwAM6tI5/wrXfNJM9tM0qtA/1S3zYB+wEz8v5Vyyrs5q61WSNveT+fcgrfpa8Dy+H/zfulnslfBpuZc7yRgeE7bAfzHzPplXXtE+nxPAMcCl6XjDgJuBkalz/lB2r4zsFam3dL5w9K+fsCuNPI3YmbnEpoterAhlN5uwN3p980l/byIcwYBT6Xf15LUP2vf1nhwBf/Hc0Vgezy45loPWAUP7hV59q9cRF2aY3B63SfPvq7AHkWevyAG4cE114rAhkVeo3+ebT2Z2+7N0RcPrrkWT/tybTsfZbSE1vob6TQiwIZQnFuB1YD9s7aNT9vuyzn2vrR9tXRMF7w3+Vbaf2YR5XXB//Gdnt4fkLXvwPT6Id7zyDYNOK+JMm7Fe1Lv4L3rT4Bq4Kqc47Zg3s+bzzT8c45tZP/5eC/7xfk8f0GdQnHt3dQ13kk/sxa4RnBu1vXqChw3Fm+76vSzRZ5jxqd9uX+D1Xi9C7mKuX+nn+Tsy/0bCfMhhohDKIKZfQN8I2mprM0zzewtAEn5T5xrMD7s9wf8H8p7iyh2d/wfzv2AfSSdCPQGdkr7HwAOAa7LOmeOmX0p6YsC190R2M7MsoPFpNzPYWbvFlHHejN7S9K0RvZ/aWZTJM2cz/MX1GcsWFDcEBhlZtZC9VkZeKfI6003syn4cG5eZjYTmCSpb872zH/PfD3nYuT7GwnNFD3YEFreLnjAmgSsn7ZV4r3N99P7nZg7p9iYZZjbe1gCHzocjM8pNuC9n+5F1mly1u+XA/+RdIqkhYs8vzF903zpzm10fqlk2ms14AlJ4yRtP5/XqgW+Tb8vm673nKRfSco3JNtajmLu3+kylO5vpNOKABtC69mOuYtXtsYXJTVlZeZdEJTpvT6JL74pipmdiPecM0Oxi+NDybkLtxbUuDY+v6X0wxcnfZzebwQ8KGlgcy9kZl/gc8LH44vTwHvGo4HlFrimLaQV/0Y6jQiwIbS8W8ysIv08mbW9Al9VmvlHezOa7sVuz9wFUjsAE9Pvz+ArQotmZo+b2ZbMu9BnQXtQmTnUzM/prXx+SZjZTDO7HFgJ781lzFd7mdm3ZnYJvtDqoRao4gIzs7Oy/k7/k7aV4m+k04o52BBaXp+sudoPs3eY2feSLmLurTQnNnGtrvhCl+OBHwGzgW+AR/BbbHJ1kbQEftvO/2TdSvI6fptLS6nPzEM3Yok0P9hzPs9fUVJXfIVrXpJ+DCyW3n6Ws3tpoNEhTkmrAF+Rs+o46zahs/FbbIrRV1I/fOh+oaxrZd/WdADzDsUW0ju1XeZvaXaR8+JFyWm3d4EbKM3fSKcVPdgQWl6+OdhsNzB3bnUz5q4ubsy7eI8VfGh5CH5PZj4LAafi97HmsyYwtInyWtIp+Lzxes08L7N6ej/8NqfsXmTugqjsucRdcvadzw/bIvv8K4BL8Z5lvmufTtPDuJn51b74orTjgB+nbdNzjm3sv0s+W+BtV5N+Wnqlde4cbEZr/410WBFgQ2hlaWXmJentdsAaRZw2Kr0OwG+dyPUwc/+hz/YKHsDH473fbPU0PUQ9v+7Is202c4e7m3IKMCPP9tFm9nIT5z4JfJpn+/vA8/iDNV7Is38GcEH6/dk8+z8k/5ehy4GX8mz/Cr8V5ivm9mCzvUb+W2DG4IvYWltr/410eBWRcD2EEEJoedGDDSGEEEogAmwIIYRQAhFgQwghhBKIABtCCCGUQNwHGwCoqampwB/jlm8lagghhMYtDHxcXV09z6rhCLAhY1lyHooQQgihaMsx91GYQATYMNe3AJsfcSwzZtW2dV3KWlXPXnw3M98tmiFbtFNxop2KU67t1KtHd5649jLIM/oXATbMY8asWqbPjAxVhcyuqOS7aKMmRTsVJ9qpOO2xnWKRUwghhFACEWBDCCGEEogh4jCP/oOG8f2cqrauRtmqr6vlzefPYZ0thlPZtdhc551PtFNxop2KU+p2enHMMS1+TYgebAghhFASZd2DlTQY+BNQC2xrZi+m7ZsCf8YzTmxjZmrhcq8GjgQmAJub2dS0fSCeh/ObtL1Z5Uo6GrgIONDM8mUbKXTulsBf8HRke5rZg2n76nh2kGXx5Nw3AqsDZ5rZH7POvwr4LfBXM9uzOWWHEEJovrLuwZrZncDLwCLAg5JWTtufwVNSPdXSwTU5Ck9rtRRQkbX9Q6AbcNJ8lvsAnjS72WOwZvYYnsaqO3CXpA3T9jeB+4GJZvYCsDeecmrpnEt8hid6PmI+6h1CCKGZyjrAJg3ASDzB9MOSFil1gWbWAByEB/arACRV4L3pMWZ2W6nr0IgG4G/Av4G/S+qXe4CZvQ6cBxwuaSsASavhSbiPNLNvWq+6IYTQeZX1EHGWr4Bf40mQ/yxpx9wD0vDroXgC45WAk4C10+sIMztW0rPAxsCm6bhXgQlmtmnu9cxskqTTgIslPQz0A1YF1skqsy+eoHkjfBj7v8AJZjZR0tnAycAVwCHAG8C+WeeuALwLfAIMNLP3i2yL2tQWLwP3SNo4zzHnAjsCoyStD9wF/NnMmkx2XV9XS/2cyBHcmPq62nleQ37RTsWJdipOe22n9hJgMbNXJP0OuA44LnufpFPx+cUBZva5pAfwuc5t8AD7cjp0HLAh8LKZ1Ur6HHioQLGXAtsC1+O9/c3MbHIqswvwd3xOdCMzmyHpUmCcpP7A08BpwP74nGgFUJl17T2BN/G53K+a2RYfStoXnw++BP8Ckr1/tqS98TnkN4Ea4Ohirq0JVzI9nuTUpEkvXdzWVWgXop2KE+1UnFK1U1Wv3vN/bo/GVzW3mwALYGbXS9oCHwKdCLyedh2J99KmpSC8DXA28DYwFdgMXyC0MSlQSpoArADcW6C8BknH4L3P4WY2Pmv3qnhPeKiZZZ7fdT5wLLC/mZ0lCeAuM/sYIGtI9xigP77YqVnBNatuYyRdDJwAjAdm5ex/S9I1+JeRA8zs+6Kuu/bQuE2ngPq6Wia9dDGrrXt83FZRQLRTcaKdilPqdnpl7MnzfW63hvpG97WrAJscjgfKtZkbYPsAOwB9AeE92YkAkh4EdpV0ObAePjR8GPAU8IKZvdVEedPT69Sc7ZkolB24MguiZmdtyxdAP0/lj5Q00cxeznNMMf4AbAmsjy/6ytVY3RtV2bU7lRFgm1TZtXv8g1iEaKfiRDsVp721U3tY5JS9ihczm8IPV8I+jC9IOtfMzge6SXpIUjd8eHcRfDj1buBC4Ff4/OglC1Cv1/Ce7X6pHPBh6pnA7Y3UP/P7nXgvdgJwq6QfFVlmblvMxhdjNf4VKoQQQpso6wAr6Vh8AdGv06IgAMzsIXxIODNZeDg+l/qUpJeBW4CrzGy2mT2V9lUAJwJXA5OAt4B7iqjGmen1oOxVu2ml8c54D/Y5SWPx+dptzOyDNEcKMFhSn/T7Iel1iJnNwXu3PwOulbSGpNq0oCpfW+yJL27aXNKArHq8Blyc1RaZ41cG9klvzyric4YQQmhBFQ0NsWK0XKQVwRfmW9VcajU1NX2AKesf8JvIptOEql69+W7G9KYP7OSinYoT7VSccm2n3j17MH7USIC+1dXV80zHlXUPtjNJq5LPwhdnhRBCaOfa4yKnDikNGW/V1vUIIYTQMqIHG0IIIZRABNgQQgihBCLAhhBCCCUQATaEEEIogQiwIYQQQglEgA0hhBBKIAJsCCGEUAIRYEMIIYQSiAdNhHn0HzQs0tUVUF9Xy5vPn8M6WwxvV1k9WlumnULozFotwEo6GE+WPhPYysxeKHF5FXjS88/MbPdSlpXK2xO4ATjPzIaXsJyV8UTug4BPgBXxrD6XmNmjOcfenvZvYmaRcSeEEFpRqw0Rm9mNwMfAuFIH12Qwnjd2N0k7tkJ59wALASXr1kjaCk+T1wD0T0kBqvFMQf+QtFTWsRsBewIb4AnpQwghtKIOOQebeq+nARfgvbzhaVvJmFldKa8vaVE8j+wzZnaQmX2byp0K7A88D8zJOuUM4C/Ai8AZknqVsn4hhBDmVVZzsJKqgUvxBOm9geeAk8zsa0k/x3O5vgOsC5xjZtc3cqkdgH54QvX/AhcBOwH3p3IuAo7C88SuB1QBAnY3s89T7+8GoEe6ThdgNrAS8DUeuAcB09IxB5jZ6zmfZQngMjzf6xx8aHwvYAqekL07sBzwNrBrEQF6L2BR4IrcHWb2Od5bz5S9JrA1sCbew/0bcCgwookyqK+rpX5OpDBsTH1d7TyvIb9onxDKKMBKWhqfM73XzLaV1BN4FHhM0gbAlcALZnZoer9CgcsdD9xoZl9Jug7vzR5NCrDAX9Mx4EGzJ/AhcKSkK4BHgDFmtqekfYBRwOZm9pGkMXjAHADU4QH3eLwXmfks3YDH8V7lAKAvHugPS8dvDiwGdAN+T3EjCWum13eKOPY44CEze1NSTTpnqKTLU6L4RmnClUyfFf84NmXSSxe3dRXahapevdu6Cu1CtFNxyrGdqno0PitYNgEW2Bn4MamHZmYzJV2OB8ONgZeBIZKWB/4B3JjvIpL6AwOBOkmZgDoZ2FzSSmb2bnoPHoTqgG8lTQGWBbYE+gCZ3vHtwDXAIZK+wHuGO+EB8o9AJT4Um20QsDqwN/5FYEQq8168F9odX5j0EHCLmX1fRPtMS6+LA281dlDqOQ8GXsn6/DPxAL05HvgbZWsPjVXEBdTX1TLppYtZbd3jYxVxAZl2KscE2eWmXBOJl5tybaduDY2vHy2nAJv5Vz072GTmTWeb2cGSbgG2A07Ch0zXy3OdIcBEM9sis0GS4UOxBwKnZx2b25vrwtw2mQ1gZg2SGvCg2yftOx6oASYAp6Uh2myZ44YDHwD3AfuZ2ZRUn5WAX+GBcKikAWb2Wp7Pku2feM90H+CZ3J2SlgM+TfunA4MygVvSwsAXwEE0EWAru3anMgJskyq7do8AG0IoqC0WOVVmv5HUR9IxeO9uJh4gM8OshwKvAy9LuhH4xMxOwYeLlyJHOmcvYEz2djMTvthnn7Sp0IKnZ4D6zLGSdsMD5r3AROAjvN2ONbOrga0kXZJ1fhfgWeBbPNAdDdyM94BPkHQkHvyuwgNsJd4bLsjMxuBD1UdIOl1S11S/KknDgHF473h/4InsXnFaEPV3YJc09B5CCKHEWi3AStoZWBLYVNJ9ku6WdDc+3HmsmX2IDxPvIOkxPEjNBLY3s+/wHtjjku7Fh14PyFPMyfgQ6paS/jdYL2kZYAlgBUlnAkekXQdJ6iJpB+An+NDytHT9LSRNBG4C3gdeTvXYFvgOqJH0Bh7QrpW0U7rmr4FaYHtg+fT5/g1sCNyKB+k/SHoUD3qXmNlYSdtKqpN0TYFmPCiVtxUwUdK/8C8OS+ILv7YB1gLWS5858/l7AasAvfCFYiGEEEqsoqEhVoxmSz3DHYEHzKw+vX8I6Gdmq5a47L2B7cxs31KWk09NTU0fYMqhN30Rc7AFZJ5QtPqGp8UQcQGZdirHObNyU65zi+WmXNupd88ejB81EqBvdXX11Ox95TQHWy76keZ4Uw91Gfy2oVNLWWiaJx3K3N51m3jtyWFMnzmrLatQ9qp69eaVsSe3dTXKXjmu+AyhNUWAzWFm7+DDua1d7rfARq1dbgghhNLokE9yCiGEENpaBNgQQgihBCLAhhBCCCUQATaEEEIogQiwIYQQQglEgA0hhBBKIAJsCCGEUAIRYEMIIYQSiAAbQgghlEA8ySnMo/+gYfEs4gIyz9hdZ4vhJX8W8Ytjjinp9UMIpdWpAqyklYHT8ITonwAr4onPLwGeAx4B1jezH7VZJedDeo7xHXiu3H/gWYXuAVYCLjSzk9qweiGE0Cl1miFiSVsBr+FJ1vub2aZANZ4b9h94KrcngG6NnP8zSTNS7tWykp5j/Lf09q6UvP0v6f1lbVOrEELo3DpFD1bSosCdwDNmdlBmu5lNlbQ/nkx9Dp5ovTE1eM7VD0tZ1xBCCB1DpwiwwF7AosAVuTvM7HNgYwBJpNfngB7pnJPM7A5gCHAecBvwe0ndgD8CWwAL4wnh9wEMuApP0r4MPgQ9NAXz1YCbgaXTz4/wHvUmwARgGLADMCVt/4OZPdWiLdGE+rpa6udEjuDG1NfVzvMaQgiN6SwBds30+k6Rxw8xsxpJ5wO3SPo73ss9Hw+6ABfjidnXwgPlcGBJPFBuZ2b/SXOj/wbOl3Q0Psf7KbAynhLvaWBvM3tO0m3A5vjw9eR0/FhJm5rZCwv06ZtBE65k+qwIHk2Z9NLFJS+jI+RT7QifoTVEOxWnHNupqkfjix07S4Cdll4XB95q6mAzq0m/voUHz8XM7ANJMwEkVeGJ0a9I858Ah6R9vYFj0pxvDT7HW40PL/cDzjGzOuCZlND98BTABwOXmdnkdL0r8R7zEUCrBVhbe2isIi6gvq6WSS9dzGrrHl/yVcTtPal7Va/efDdjeltXo+xFOxWnXNupW0PjM4udJcD+EzgOH8J9JnenpOXwnmWxuuOBty7PvuvxnugaZvaVpH/hQTbT1rOzjm0A+gBV6Zjv81xvdp5tJVPZtTuVEWCbVNm1e8kDbAihfesUq4jNbAwwCjhC0umSuoL3RNOq4HHMHfot5npT8dt69pS0mKQKSSdK2hNYFfgkBddF8N4r+NDxt8Deqez1gDWAe83sa+AhYHdJC6XjDwR6AjctwEcHqMx+I2nPNBccQgihhDpLDxbgIGAscCiwj6TP8aD6LD58WwfsBCDpYDO7MfMeOFjSi/jCpEGSlgZ2A67BFzF9hQ8nXwt8CVwnaRx+y8/XwP8BqwO7AJdJmoQPV3+T6gS+iOo6YJykj4GfALua2fOpTuPwgNzfzN7L/mCS+gD7p7eHSHoC2DW9/6ukz/De8vLAesAvgEnz2Y4hhBCK0GkCrJk1AH9OP41ZO+ecXXL2546d7pznGk8Aq+S7uKTtgE3NbJqkCuAG4DFJfdLc6675zkt12VjSW+T0SNO+qXjQzLZm7nEhhBBaT6cJsG0tDRefAByVbgNaHO+RHp8WPTV1/qHAc2amUtbztSeHMX3mrFIW0e5V9erd7hcghRBKLwJsKzGzb/DFT/N7/g14jzeEEEI70CkWOYUQQgitLQJsCCGEUAIRYEMIIYQSiAAbQgghlEAE2BBCCKEEIsCGEEIIJRABNoQQQiiBCLAhhBBCCcSDJsI8+g8aFunqCqivq+XN589hnS2GF5VN58Uxx7RCrUII5Sh6sCGEEEIJdIgerKTd8If4VwC/MrNH0/aFgAeBgcAYYHczm9bohcqcpA2A54FaPHtPf6AemAX0Bj4DTsez+tQBq5vZe5IWBe4AtgZOM7Nz26D6IYTQqXSIHqyZ3Q18AXyeCa5p+zTg5vT2zlIE15QLdoqk9Vv62nksCjwMrGBma6VtNwJ9gf2A/wJ3AlfgSeF/mo6ZBiwB3BrBNYQQWkeH6MG2sU2BPnhy9JIys0eAR3I2N5hZPfCX9IOkM4AdgdslrQucmOo3tNR1DCGE4DpdgJXUHRgG7ABMwROR/8HMnpJ0Hh6M1jezVyQdBVyGJ1d/ELgeMGAh4FvgcuCedOknJI03sw0kbQRcmLZnep1nAL2Ap/Ah3Yr0fjHgOjM7taU+o5nNkLQnMA54Af8CsIGZfdvUufV1tdTPaWipqnQ49XW187yGEEJjOlqAXVzSnTnbVsh5fyOeNq6/mU2WdDQwVtKmwD+BU4CF07EPA1cCi+A9wiHAsvhw9LHAM+laTwCbm9m/JK2R3p9tZuem+c/xwGpmtqOkJ4AjgV3M7H5J+wOjJP3JzN5rqYYwswmSLsQD+0vAx8WcpwlXMn1WBI+mTHrp4qKOq+rVu8Q1KW+d/fMXK9qpOOXYTlU9Gr+boKMF2MlmNjh7g6QDgfXT772AwcBlZjY5HXIlcB5wBDAq53pzsn5/C5iO9wgfBG4zsy8l/SznnH2BKnweFDP7WtJ1wIWSlgcy5Y5Orx+m12WBFguwkpYGDgduA/YC7pa0Q1PJ3W3toXGbTgH1dbVMeuliVlv3+KJu0+nMidmrevXmuxnT27oaZS/aqTjl2k7dGuob3dfRAmxTqvCh2e/z7JuNDxfnZWY1klYEfgXsCjwlaUc86OaW0ZCul1GRVUbmerlltdiCM0ld8MD6jJntK2kCcDFwLnBSoXMru3anMgJskyq7di8qwIYQOq8OsYq4WGb2NfAQsHu6hQfgQHwB0E3Ap2nbSul1k8y5kvYA9jWzG4FdgJnAklmXzwTR2/EAe2A6rzewP/CImX2adVwpnQCsg/fKMbNLgL8Bx0tapxXKDyGETq9DBFhJ+wFLA0tI2jJrexdgn/T2kBRUhwBvAOMkPQIcA+xqZs+bmfAh49MkjQHOwYeJBwMTgf0kPQM8CdwL3IrfkwpwnqTTzWw8fsvMcen6TwMvA/tIWgz4darbwem8w9LrLmn7OEnTUm+50GfOPCJoC0mrZm3/HXA23kvvm3XK+/h/79FpEVYIIYQSqmhoiBWj5UbSW8AOKeC3ipqamj7AlPUP+A3TZ85qrWLbpXKdCyo30U7FiXYqTrm2U++ePRg/aiRA3+rq6qnZ+zpED7YjkXQo8FxrBtcQQggtr7Mtcip7ZnYDcENb1yOEEMKCKboHK6lHKSsSQgghdCTN6cFOlvQc8CIwAXjSzL4sTbVCCCGE9q05c7A/wh/7dwRwF/CJpAdjRWoIIYTwQ80JsD83swFmtiiwOnAqsC7wjKRbJPUpSQ1DCCGEdqjoAGtmL2T9XmNmF+E5VivwJxs9m567G0IIIXR6zVnktFN6XVbSvumBC/sCrwI/wzPEjChJLUMIIYR2pjlDxPdJ+h74D/5Q/I2Bf+FPQfoYzxCze4vXMIQQQmiHmnsf7L/TzyTgfjN7PWvfF/zwwfchhBBCp9ScAPuUmW2Wu1HSXmZ2h5l9CCzeYjULbaL/oGGdJl3di2OOafqgEEKYT81aRdzI9qEtUZEQQgihIynYg5W0K/BbfKVwhaTHcw7phqdFKxupzrfjeVm/wXOw9gCex+eJewAPA5PMbKu2qmdLSE/XegO4z8xOyNl3Gp5VZw6ehm8OsBzwCfDTPPloQwghtKCmerDf4unY+uM5TjfL+dmEeROLtzkzuxf4HPgKWNLMlgR+CWwA3I+nnfsA/3LQaiSNkjQ1Kw9tSzgGWBH4vaS1cvb1Ba4FljCznwKbA3XAtRFcQwih9Ar2YM3sMeAxSUsCn5pZe8q+M8PMZgOY2ThJD+C3Fa0I1LdBfY4DhpnZtJa4mKSe6ZonAcOA84FtM/vN7PisY7sAfwJeBy5oifJDCCEUVtQiJzP7QlJ1vn2SBpnZky1brZL4MT5MmsnXt5qk1/Gh5BnAEDN7FUDSEOBoYAreE7zGzK5P+5YALsPv/Z0DzAT2wh+2cTEwGu8tdwEm4wnfvwZuwpOj/wS4E9gSD3YXAO+k66xtZl8V+XkOZu69x8sDv5W0VuYz5DgFGAAMyHzpaEx9XS31c6KDG0IIC6qpOdgT8GHW44GzUk821wCgbB+TKKkCOAjYGrjCzL6SBB5oB6bXZ4FrgI1TPtbrgEFm9rSkdfGnVC0EXAE8js/nDsCD73+Bw4ArgcuBZYFV8eHYicDJwIHAM3gPcyF8yPohfJh9PD78vlWxwVVSJXAscJmZzZZ0Cf6M6KOBITnHbgGcBQw2s3eavPaEK5k+q7aYarR7Vb16t8m5nUm0U3GinYpTju1U1aN7o/ua6sHuD1QDxwNb4Q/7z1Wu3Z1FJP0JGIQHu8PM7Kas/Z+Z2RQASW/jwQ7gEOAVM3sawMxekvQYcBTwGv4c5r2BFfDe42TgXjObnAL3o2Y2K133M2BZM5sj6dNMwWZWL2kw8AJwDx5cJzXjs+2AL1jaStLAtO0LYE9Jv8sMQ0taCrgN/2JxdzEXtrWHdprbdF4Ze/J8nVfVqzffzYhbvpsS7VScaKfilGs7dWtofMaxqQA7BPhp+n0E8LqZPZB9gKQLF6h2pTMdH8o9w8w+bergLFX4cG22CnwxV6anPhxfKHUfsF8mUCe5Xzgam7euACrxhWS7AY81o45DgEfMbMfMBklbAY8CewA3pl7uncC7wInFXriya3cqO0mADSGEUiq4aMnMXkyrcjGzc4CXs/dL2sTMiv7Hu5XNNrM3mxlcAf4MDJC0JoCkn+G92xvwoeRv8eB9NHAzcIikE9JQdFHSoqM78IC4FXCApKKeeiBpcXyoeUzOrsfx1dP7pven4Y+z3MfM6iR1kfRLSS9J6ldsXUMIIcyf5jzsfzjwVM7mv6Y5y7IhaWtgKWCx9Hvu/vWANYA1JK0naTE8EC0taVu81zsCuEfSg8BfgfOAS83sc2B7fFHRW/hjIzcEbsXnQAF+LamvpA2BNYH/k2TMnRsdDNwNbAO8iSew/wC4VNLvJC0uaYqkfzfyEYfjtxjtlBPUV8dz9g6SdDA+R9sNeF9SA75y+hFgbeDLYtoyhBDC/KtoaChuCjXNId5qZidnbdsEGGU1MM1QAAAgAElEQVRmK5eofp1SWmk83syWba0ya2pq+gBT1j/gN0yfOau1im2XynUuqNxEOxUn2qk45dpOvXv2YPyokQB9q6urp2bva859rTOyg2syDlhsAesXfmg4cEZbVyKEEML8a06AbUj3h2Y7kcig0+LMbP+cFc8hhBDameZk0zkNuDM94/Zt/DaRVfEnCIUQQgghS9E9WDP7K37/ZxX+0IYVgJH4QwxCCCGEkKVZCdfN7E5JdwFLAJPNbE5pqhVCCCG0b80KsAApE8sXJahLCCGE0GG0p+w4IYQQQrsRATaEEEIogQiwIYQQQgk0ew5WUg9gPeAjM3u/5asUQgghtH+NBlhJXYGdgUlmNjFt2xRPr7ZYev9nPFF5rCbuIPoPGtbh09W9OKaovAohhLBACvVg/4A/ru83wMSU/uwOPLheC0zDc6eeAFxQbIGSdgVux++n/QZPA9cDT2J+ZPr9YTywb9XEtQbiAf8eMzui0LE5541IZa1gZp8UOO4SPGvOz83s+QLHdUn1+BWwvJl9XGxdmiulBzwhvf0UT+A+C3gAONbMpmcduwaec3ZPM/t7qeoUQgjhhwrNwe4FnG9m16f3GwHLACPM7LfpucTbAAc1p8CU/u5z4CtgSTNbEvglsAFwPzARzy7TLd/5kg6W9J2kLczsKeA7oPGU8vn9I10/bxlZHsG/hBS8furBP4i35w++tEhaRNLXkm5pZj3zuSa9jjazZfActcPwLzu5X3Quxut+jaSeLVB2CCGEIhUKsMviD53P2AxPJn5dZoOZvYz3nubHDDObna4zDu+BrQ6siKdWa8ztwFrAE+l93XyWX4zmXLvROpvZN8D6QEuOTU5J124ws5HAx3gPGgBJGwBbpDKXBYa2YNkhhBCaUGiIeEr2cCOwA/CFmb2dc1xtC9Xlx8AcIJPuZzVJr+NDyTPwud5X8V7ZEGBPPCj/T5oTXgvvAf8UOMvM7kt5U08CDscfkrFcznkr43lglwB6pfP3zjnmGOBCvKe6ClCJ956PTF8QMu5Ow+lLAheb2aWSdgSuACYAu6ZrHYnng10FuCMltJ8vkrrhQ8UfZm0+ER86vyLl7D1W0ggz+67QterraqmfU1wKwxBCCI0rFGA/kXQ0cBWwK94Duyv7AEnr4/+wz7cU/A7Cn298hZl9JQk80A5Mr8/iQ6Mb4/PARwKL5rnc2fjq5lmSfg/8TdJyeJLzc4FtzOwxSdvjgRJJiwDPANeY2R8lrQO8DOwGvJd17SuAk1Md1gQmp7qMTgnVM043s0ckHQ5cK+khvLc9NavOfwP+kj7rAOAlSW+n5z03t/16AJfg/x2GpW0r4wvU1kuHXQSMwr+U3FrwehOuZPqslvrOVJ6qevUui2t0BtFOxYl2Kk45tlNVj8ZnEAsF2OHA3XivrRveu7wys1PS7sAIPADPj0Uk/QkYhA/FHpaTou0zM5uSynobH6IGHwptzPLAjZLmADPxXqbh88mvmdlj6bgZWef8Gu9tjpDUP32e94BHgf8lkjezOZJqgXFm9mWq16140No463o16fWt9Lq0mb0laUrWMT2AyyX1yzq+usDnymddSXcCv8B7wpuZ2dNp32+Bb4Ez0peVSrw9DqGJAGtrD+3wq4hfGZub1rh5yjXxc7mJdipOtFNxyrWdujU0PqPZaIBNQ6u7AUfhw7eXZYZC05DkncBjwKXzWa/p+LDsGWb26Xxe438krY6vPh5mZudK2gxfPFWBfzlobK64T3q9EV+VeyVwf+oFr5zn+O/zbKtsRj0r8R7tm8AGZlYvad9Uz+Z4B++xH5H5IpKu3w3YDzjHzC7J2n4ucKqklczs3cYuWtm1O5UdPMCGEEJrKHQfbB8zuw+4L3efmc2WZGb2Xp5TizXbzN5cgPOzdcEXR3UFXkvbNsna/3fgBElLmtkXwMJZ+/6J96A/BI7Hg+U5kt4EPsq6/jzS0PaB+K1GT+NDssXoha/GvisF102aOqERUxppv+3wL0RjcrbfBpwK7EukGAwhhJIrNET8rKQL+GHPqg6ff2z0vtBCJG0NLAXUSdrazB7N2b8esEbW7+/jQ7BLS9oWH1IGGJyC4NL4atmz8XnGayQdl1Xvg/FFUYsC/5T0KrBl2neAmZ2Veurn4sGpK/Ak/sXi7HTcIcDjWeWuiy9wmpHKng7sn1Xe6cBOmfeSvsPnbefgC6yOBU6UtDZ+G83XwM6SrsCD/InAfmZ2R54mPCC9rpu+5Cir7box91adnfFbnjLWSa9HSXrIzF7Kc+0QQggtpKKhIf+K0TSPWchMfEHPZS1eqzIl6QPgKTPbv6ljF7Cc64HxZvanUpaTraampg8w5dCbvujwc7AL+iSncp0LKjfRTsWJdipOubZT7549GD9qJEDf6urqqdn7CvVgX8UXAOUG2i7ASsARwMWSXjOzx3NP7mgkDcJ73lvl9hxbuJy18N7u70tx/aa89uQwps+c31ubQwghZBQKsANz7oPN9j7wmKTL8Pm8Dh9gzexJmv/EqPkp51Vgw1KXE0IIobQKrSIupi9+GTCp5aoTQgghdAwLmg92Bv6kpRBCCCFkWdAA+2t8BWwIIYQQshS6D/ZsMzs9z/Zu+HN+98EfHfhQ6aoXQgghtE8F88FKOrXA/go85dwpLVulEEIIof0rFGDr8Of+5j5oogF/zu3TwPBCCctDCCGEzqpQgD3azK5ttZqEEEIIHUiji5wiuIYQQgjzb0FXEYcQQgghj0JDxKET6j9oWId9FvGCPoM4hBCao9UCbMpAcz7+cIrF8Ow2V5vZ1SUqrwJfiPWZme1eijJam6QT8Ww5/8XT5K2MJ13oln7uAGqBg/C0fRulvLb9gXuAJYA9zOwfbVD9EELoVFpliFjSj4GxwAtmtrOZbQr8AX/UYqkMxtPc7SZpx+acKGkdSTNaohKSDpb0naQtWuByPYDL8Xyyh6Zth+BfVobjQfYQ4EXgJ3juWfBV30sCv4vgGkIIraO1erAD8STnn2Y2mNl9khYrRWGp93oa3tvbDxgu6UEzy5+b74e2A3q2UHVuB8YBby3ohczsf4nSpf8l82lIz40+JWvfwXiQvV3SznjP9kEzu3lB6xBCCKE4rRVgX8SHNC9Nw5UPA+PMbDKApDWAm9MxCwOfAUcBX+KZepbCg/PSeG/t78AQM2ssZ+0OQD/gEnw49SI8Afr9qbyL0vUfAtbDn6csYHc8Dd+Z6bgG4EIzO0nSEOBoYArQF7jGzK6XtAnwIJ6AflU8vV8VcKyZ3Q1cjCd83xN4QNIyqV4GVAJvA8eb2Yfz0a55mdkbko4BRgI1+OMsDy7m3Pq6WurnFPs9JIQQQmNaJcCa2ccpsB4NbAYcCHST9AiwL/AuMNjM3kmPYnweuN7MtpM0GrgQuMrMRkgaCDwJ3ErjafKOB240s68kXYf3Zo8mBVjgr+kY8Ny2PYEPgSPN7IzUOzzTzCoAJB0KXAcMMrOn03zys5IWAkYAHwEbAWuZ2QeSbgIuBe7Ge49HAotK6gU8kT7vBvhDO+5I11rDzKbMVwPnYWbXStoLHz142MxmFnOeJlzJ9Fm1LVWNslLVq3dZXqsji3YqTrRTccqxnap6NJ7FtNUWOZnZR8AJAJJ643OIF+HDuEcCe0vaA3gH6I33VAEmp9fR6TXT01s2XzkpkA8E6iRlAupkYHNJK5nZu1nXfMjM6oBvJU1p7Jr4vOYrZvZ0+iwvSXoMOMrMLpH0NfCNmX2QVcfMtT7Ous5AvOf6+1Rupjc9Hu9hj2qk/GaTtCUwAA/yR0j61MzObuo8W3toh11F/MrYk1vkOlW9evPdjGKyOXZu0U7FiXYqTrm2U7eG+kb3tUqAlbQTgJmNTq/TgcskDcaHVf8IHAdUm9l7km7Be7rZcsctG1ugNQSYaGb/W1QkyfCh2AOB7AQGxV6zCl+tm60CmF3gWrmPmMxcB+D7PMfNpoVIWhL4C56M4RrgPuAsSS+b2cOFzq3s2p3KDhpgQwihNbXWgyYWwRfcDMhskLQKsDpwJx5kvwH+I6kK73ll5AtUeaXh5b2AMdnbzUz4PPA+zblmWiwF8GdggKQ10/af4V8AbmhmHf8JfAEcJKkiXf83wOe0bFaimwGZ2VVpnno/4D3gujRMHUIIocRaJcCa2S34MPBVkkZLehCfBz3JzEYCw/Ag8yLwDJ5oYKnU8z0oXSazSOew9LpLnqJOBhYHtkzD0ACkhUVLACtIOhNfyAQe6LpI2gG/rWVguqVoVtp/v6Rf47cTjQDuyar7efiirY2A/sCaktaTtHimbpJ2ySprsJnNwFcor4Tfo/s0sDywpZlNlbS4pCmS/l2oPSV1AYamt0Mk9cjadxewLdBdUmXaPBNv32WBx9NnDCGEUEIVDQ2xYrScSPoJMN7MGpsPLomampo+wJRDb/qiw87BttSTnMp1LqjcRDsVJ9qpOOXaTr179mD8qJEAfaurq6dm74tHJZaf4cAZbVX4a08OY/rMWU0fGEIIoaAIsGXGzPZv6zqEEEJYcJFNJ4QQQiiBCLAhhBBCCUSADSGEEEogAmwIIYRQAhFgQwghhBKIABtCCCGUQATYEEIIoQQiwIYQQgglEA+aCPPoP2hYh3lUYks9GjGEEOZH9GBDCCGEEijbHqykdYHzgRnAYngC9qvN7OoWLKMvnit1M2ACnuZuVWBF4F7gQjOrbanyWoKkHwHHAPsD3+HJ6euBW4ER2fWV9Ev8c2xqZq+0QXVDCKHTKssAm9KpjQWuNLPT0rZdgLuAFguwZjZF0pN4gD3BzMamsv4PD7aDJG0NLAy8CzxgZge2VPnNJakPnlO2D7C3mb2ctv8GT6zeAFyQtnUBLgW643lg1zezSJ0UQgitpFyHiAfiQe3TzAYzuw/vyZZKfVZZE4HfAb8ADjWzb4D18Z5jW7ocT1K/fSa4AqSculcA/806dndgGeA0YF1gj1asZwghdHpl2YPFE69/gyc07w88DIwzs8kAktYAbk7HLAx8BhwFfAk8DiyFB+el8aHlvwNDzGxOM+owGvgeGCzpEzyATQB2TXU4Gjg0lbkSnjz+rpSA/QagB9AP/xIzOx0zDe9hbgTU4gHxhBTQC0rD2XsDo83sndz9Zva7nE0nAtfiyeKPAU7BRwAKqq+rpX5OdHRDCGFBlWWANbOPU2A9Gh++PRDoJukRYF98uHawmb0jqRvwPHC9mW0naTRwIXCVmY2QNBB4Ep+jfLwZdZgt6Wu8F/gEMBUP1kg6FfgtMMDMPpf0AHCRpLHAI8AYM9tT0j7AKGBz4JNUj4WAjcxshqRLgXGS+pvZ+01UqRroBvwguOaStDne093BzGZJugo4S9JAM3uq4LkTrmT6rLKadp5vVb16t8trdyTRTsWJdipOObZTVY/uje4rywALYGYfAScASOqN9xYvwnuARwJ7S9oDDzi9ScEPmJxeR6fXD9Prss0pX9JCwJLAJDObLmlK1u4j8d7gNEm/A7YBzga2xOdHr0/H3Y7PjR4CfA1sCgw1sxlp//nAsfiCpbOaqNK09Lp4EdU/Gu8dj5QEPg87O9WjYIC1tYd2mNt0Xhl7ckmuW9WrN9/NmF6Sa3ck0U7FiXYqTrm2U7eG+kb3lWWAlbQTgJmNTq/TgcskDcZX+f4ROA6oNrP3JN2C93Sz5Y5zNne++XCgArgtz74+wA5AX0B4T3aipH3T/tmp3g2SGtLxmaj1fdZ1KrKPb8K/8V7wjpIWMrNp2TvTAqiu+OfcAe/h35O1/zbg15J+m3tutsqu3ansIAE2hBDaUrkucloEuF3SgMwGSavgw5534kH2G+A/kqqAAVnnVjB/KrPK2hU4B1+xe1OeYx9OdTzXzM7Hh68fAl7AF0vtk66zGx5c7wVeA94A9kvD2uDDzDPxnm5Baf744HS9hyQtl1XfzfAAvB0+T1sJPJpziduAnqQ55BBCCKVVlj1YM7sl9fyukvQlHjCWxRcSjZT0DPBnfDFUPVAHLJV6vgelyxwMnA4clt7vAtySXY6kFfDVtgA3ShoHrIW3ywXA+WY2Jy1cWhOYk27hORwYATwl6fN0/MlmJkl7A+dJmggsB7wPvJx6szvjC6CekzQVn4/dxsw+kFSZjq0CVkq99tx2GZO+dJwGPJquUYHPD++HB/A30radUxtlrJ1ez5T0VBFzviGEEBZARUNDrBhtKZK6Ajvi98vWp/cPAf3MbNUizv8Rvvp5+ax52lZRU1PTB5iy/gG/YfrMWa1ZdLtTrnNB5SbaqTjRTsUp13bq3bMH40eNBOhbXV09NXtfWfZg27F+wEnAXpLewFcgLwKcWuT5Z+Crn1s1uIYQQmh5EWBbULo/dcMFOP+0FqxOCCGENlSui5xCCCGEdi0CbAghhFACEWBDCCGEEogAG0IIIZRABNgQQgihBCLAhhBCCCUQATaEEEIogQiwIYQQQgnEgybCPPoPGtbu09W9OOaYtq5CCCFEDzaEEEIohU7fg5W0KfAInrT9Vjwx+vpALXCRmf2jifNXA8YANcABeIq7H5nZaiWo64l4lp//4un6VsbT3XVLP3ekeh+Ep8fbyMxmSeoP3AMsAezR1GcKIYSw4Dp9D9bMnsED1sdmdoCZHWtmmwAPAg9LGtLE+ZOAj4DuZvYF8DrQvUTV7QFcjicRODRtOwRYFBiOB9lD8DR+PwF6pWO+BZYEfhfBNYQQWken78Fmqc9+Y2aXSdoCuELSaDP7b4Fz6xr5vUWZ2VmZ3yVlfm1IuWNPydp3MB5kb085aO8AHjSzm0tVtxBCCPOKAFvY34DtgR0lzQL+iA8FrwyMBU4ws+8bO1nSMcCRwJvAKsAdZnZO2ncusDXwHd4z3TNl41lgZvZGKntkqu/XeAL6JtXX1VI/J3IEhxDCgooAW9jn6XUZ4HrgKTP7TNIywDvAJ8CFBc7/G/AXM/tK0gDgJUlvA2/jOWI3NbNnJR0BVLZkxc3sWkl7AQOBh81sZjHnacKVTJ9V25JVaXVVvXp3iDI6gmin4kQ7Facc26mqR+MzghFgC7P0+jE+X32upHXwXuFsoLqJ83sAl0vql84hnfME8AUwWtKDwN1m9lZLVlzSlsAA4G7gCEmfmtnZTZ1naw9t97fpvDL25JJev6pXb76bMb2kZXQE0U7FiXYqTrm2U7eG+kb3RYBthKQu+EKiacDf8UVPvYAN08rcD4CKAudX4oH0TWADM6uXtC9QYWaTJa0C7JB+HpA01MyubqG6Lwn8BTgZuAa4DzhL0stm9nChcyu7dqeynQfYEEIoB51+FXGW/w3RSuoN3ASsARxlZl8DqwJKwbUfvko3W5ec33vhQ8sTU3DdJOv6mwMnAHea2d7Au8BSLfhZbk51vcrM5gD7Ae8B10nqVfjUEEIILaHTB9jUq1waWFbS45LuBwSsAOxgZremQ48E1pf0L+Au4DNgUOqJ/h+wVppn/TkeWDcBjgX2kjQWn6v9GtgZn4MdiM/JPo7P514saXFJUyT9u4k6dwGGprdDJPXI2ncXsC3QPfWiwe+V/RxYFnhc0o/np61CCCEUr6KhIVaMlhNJPwHGm9myrVluTU1NH2DK+gf8hukzZ7Vm0e1Ouc4FlZtop+JEOxWnXNupd88ejB81EqBvdXX11Ox9nb4HW4aGA2e0dSVCCCEsmFjkVGbMbP+2rkMIIYQFFz3YEEIIoQQiwIYQQgglEAE2hBBCKIEIsCGEEEIJRIANIYQQSiACbAghhFACEWBDCCGEEogAG0IIIZRAPGgizKP/oGFll67uxTHHtHUVQgih2dptgJW0LnA+MANYDFgUuLqlUr7lKW9z4E5gCeBgM7upFOUUUY+lgD8A2+E5ZZcDPgCuMrM7c449DxgM9Dezaa1c1RBC6NTa5RBxygYzFnjBzHY2s03xoHNZqco0sydISdPbMLj2B94AVgQ2NrONgZXwVHR3SNog69h+wHFAP2BYa9c1hBA6u3YZYPFUbwsDn2Y2mNl9eE+2Q5LUDbgbTzu3i5l9AWBm3wGHA48B32WdchLwfDrnKEk/bd0ahxBC59Zeh4hfBL4BLk29uoeBcWY2GUDSGnjS8W/wQPwZcBTwJfA4ntz8UzwP7KLA34EhKTl50STtiGe+yQxT3wZcmBKs/xYPfJ8DPwUOMrPnJW0NnI3naF0BuMvMTiqiuK2BlfEE8N9n7zCzWcBWWfVaAjgQ2BWYDOyO92abnMysr6ulfk6kMAwhhAXVLgOsmX2cAuvRwGZ4MOkm6RFgX+BdYLCZvZN6fs8D15vZdpJG48nPrzKzEZIGAk8Ct+LBtygpUI4G9jOz29KQ7MvATySdCVwB7J/27QU0SPo58CCwtZn9S9LvgUskXWdm7zVR5Jrp9Z0iqvdb4G0zeyTV9Qk8MftpZlYwoaImXMn0WbVFFNF6qnr1busq/EA51qkcRTsVJ9qpOOXYTlU9uje6r10GWAAz+wg4AUBSb+BQ4CLgAuBIYG9Je+ABqTfeUwXv0YEHR4AP02tzE5wfDHxtZrel+nwg6U7gMLy3+A5wXerlPoD3um8FJgJPS9oBOB74B/CfIsrLLFJavNBBkn4EHAF8Lun+tLkHsBCwB1Bw/tjWHlp2q4hfGXtyW1dhHuWa+LncRDsVJ9qpOOXaTt0a6hvd1y4DrKSdAMxsdHqdDlwmaTCwKvBHPMhVm9l7km7Be7rZcsdBfzAfncr5CzDKzI5KmzPznFXA9zmnVAD16WdNfKXvtsAtwNpAH8CAP+HD1vub2WNFfuyxwBxgn1Sn3LouDUwBfgn8GFgzM08rqQsexA+iiQBb2bU7lWUWYEMIoT1qr4ucFgFulzQgs0HSKsDq+K00q+Lzr/+RVAUMyDq3ohnlbA08ZWZHpeusDExI+/4MLC3pl6n8pfA5z5uBn+HD0I+Y2WHAU/i878N4b/p+MzsVmCDprjRnXJCZ1eBzt7+UdI2knqncSkm/AV7HVxfvD7yaCa7p3DnAHcAmaSg7hBBCibXLAGtmt+DDwFdJGi3pQeCvwElmNhK/LeVzfFj2GaAOWCr1SA9Klzk4vR6WXnfJU9QIYHFJfwX+hc/tDkl1uAfvJV+dyn8UD+7HAe/jt8e8IukhoBdwCnAtcFqq9yv43PBLZvaGpDUk1Up6uMDnHgbsSAr0kp4CXgE2ATbCh49/Bawoac2c09fGv1z8SVJlY2WEEEJoGRUNDbFitFxI2hhfhbxpa5ddU1PTB5hy6E1flN0cbLk9yalc54LKTbRTcaKdilOu7dS7Zw/GjxoJ0Le6unpq9r52OQfbEaV50rPwYeA289qTw5g+c1ZbViGEEDqECLBlIs2TbtXkgSGEENqFdjkHG0IIIZS7CLAhhBBCCcQQcZhHrwJPJQmuqkf3gjeXBxftVJxop+KUazsV+jczAmzIWBjgiWtLlpAohBA6soWBWEUc8voYzy37bVtXJIQQ2pmF8X9D5xH3wYYQQgglEIucQgghhBKIABtCCCGUQATYEEIIoQRikVMnIWlhPFXd/+GJEOqBQxtL9C7pSOBk4FVgFeAcM/tBmryOZj7a6XA8wf1H6ZzngMPNbGq+4zuK5rZT1nlnAqcCu5vZAyWvaBubn3aStA7+2NQZwKNmVjDFZEcwH//fHQPsBvwbz6L2OXCEmU3Od3xbiR5s53ERsA2wBf5IxiWB+/IdmJIOXAVcYma/AkYBoySt20p1bUvNaae98XY62My2T+ftAfyhdarapopupwxJF+DB9UfAoqWuYJloVjtJOgC4FzjTzAZ3huCaNOf/u/35//buPVbOoozj+LdFrhWhUMEiEg7wA5RyFSl4A6rBGyLQQiNaxapokYtQkHtAbRAl2CaIFUilEkUQYgSsAk25SAliaQqaCvIjUEFAAQWxRC4V/eOZhWXZPWe38d097Hk+ycl73nfn3Z3Mec/OO/POzAPfAg4t4UAnATsCs7qT1fZlBTsC2N4QmAb8UtIjkl4k7hZ3sj2pySlHA7U0ABeX7TGVZ7aHVqOc9iPutB8v+yuI0Ig7dCG7PbMa5YTt6cAXgbO7l9Pe6rScyg3svHLO07YHbHcSv/p1aTWup92J0Jsvlv1RRG/s35qk7amsYEeGdwLrAnfWHbu9bJuFxnsvcJ+kfwFIehK4v0XaftJpOc0CjgNq4YdOBNYkWiD9rKNysr0V0dI/Cvhz5bkbPjq9nk4kbtDmENfWMuAu25tVmclhoNNymge8QMTbPhVYUH56GomsmaxgR4ZNy7Z+EYnas4pm/7yb8toFJ55okbafdFROku6XNFfS47anAmcBsyXNqzabPdfp9XQ2sETSZZXmavjptJz2AZ4Cpkg6DJgM7ER0h/azTv/v7gY+TdzMzgD2JFqwwyuQNVnBjhS1i3Vs3bENyrZZt8oTDWlr6YddF8z/WaflBIDtzwOXAjMlHV9R3oaTtsvJ9gBwCDFoZ6Tp9HoaC/xF0oMAkhYB/wAmVpbD4aGjcrI9A7gA2AvYEjiBePwwv7IcrqasYEeGZUSXym51x3Yp29tfm5w7gG1srw9gewywTYu0/aTTcsL2l4kuvYMkzak2e8NGJ+W0LTHS8wjblxMtDoAZtn9se1ylOe2tTq+nB4Etba9Rd2wtmizB12faLifb6wHfBS6U9KCk/0iaS3QRD7tHWLlU4ghhez5wMCBiUM5iYGPgHUTXyvjakHjb+wA3AUdKmlumovwAeJ+kxd3Pffd0WE57AwuB/SXdUI4dAhwgaVr3c989nZRTw3mHA5cAn5M0vzu57Z0Or6dTiO70L0m6yPaBxEjaaf0+Ra7dcrK9JtGqXShpajl3I+L57QJJR/cg+y1lC3bkOB64GbgBWEQ8szhY0kvAt4F7bG8JIOlmotvlJNtXAScRlW1fV65F2+UEnF9e/7ntlbZXAj8D/tDtTPdAJ+UEvDxa9KCyO3UEDN6BzsrpXOI5/im2rwNmAyf3e+VatFVOZYTxh4HxthfbvhK4jvFJ3gsAAAVUSURBVJhKOLMnOR9EtmBTSimlCmQLNqWUUqpAVrAppZRSBbKCTSmllCqQFWxKKaVUgYymk1JKaUSw/UZge+AjxOyID0i6Y4hz9iDm4zY2SP8LDEhqufxnVrAppabK8o8/BNZreOlRYirFzG6EB7P9TWIKxo8kzRgqfUqDmEisfXwgMIZYA3ko6wL/BHaTtALA9tnAnoNVrpBdxCmlFiRdASwpuwcTqwptS8xXnAZcY7sb3yHXEl9y7XwZYnsd26fb3rjsj7a9wPatZaGCNEJJWiTpO8Q11a7fEZXpCgDbGwBHAhcOdWK2YFNK7VhWJvm7xCzdl1hkfWdiqbsqPT50klfZiYiscitwC7Eo/LuI0ILZqEhN2d6GWNxjE6J1uwI4TNIzwH11SY8ilnYcNP4x5MWWUuqQpFXAw2V308HSdluJn/qJsjsKQNLzkjaRNF7S873LXRqubI8llmdcImki8BngY8CUhnTrAccCl0h6Yaj3zRZsSqkjtt9MLMb+b2CJ7cnAycBGRDfuNcBpkv5e1tO9hFg/9jZg/5JmKXCUpOW25wJfAI6T9L2yhOItwNtoEq6sfN5ZRIt0DPAYcJakG4GfAJ8sSW+y/QKwH3AeMIFY0/Yp228AvgZMJwarjAIuBs6TtMr2bODocmx/4C3ASuCnwDHlJiP1j8nEzeIc2zsT8YsfIJZurHcEMI64LoaULdiUUttsTyQCHIwmvmymAFcBV0namhhEMgm4szyrWgzcC2wHbEEs5j6hbK+1vS5wNXGzPw5A0qPAXcQi729qko1HgFOBXYEdgOeA620PlDiqtdB4+0pamxiU9VJ5v1oYtEtLusMliahovw5cWV5fCqxBtGSml3PnEtGAauspp/5Ruy7mEX/v84EJkl6OZGR7LWKw3SJJ97fzplnBppTacZHtB4gBTvcBu5dF6M8BHiIWqkfSw8BpRJzOYyU9CfypvMeZkp6R9BARnWmA6IZ7rsnnPdsqI5J+S4R2m0XEAN2EqKD3apHeRMg8AGzvQrRyr6gFsJD0G+LL9UDb7yduDAB+JWlhWXS+1prZrlXe0uvG6IbtQmAVcS1/lbjROtN2fVSszwKb08bgpsYPSSmlwcwhukrHSjpU0t22xwMbAi4VUM3yst2+4T0eq/t9Rdlu1WlGbJ8B/B5Ynwjv9v3y0lptvsXby/behuPN8t2sos9Ha69TtjezfQHR+wLRJXwOEQFrCvAh4mbsHqJHpX4g0wxi7MHV7X5eXigppXb8sTZNoc6TwNPAWxuOb122yxuO14fuGijbR4gRmRCjfWny+8tsjwHOAJZKOqYce/dQmW98m7JtN9+pT5THD18pP42uZpDKU9JurV5rJVuwKaXBjGr1Qpm2MwfY3vZ0ANvjiCkyf+W1A0E2t72G7S2IeYQPA78gKrxVxLQfbG8NfLCcM5pXvqfWAV4sPxvaXrs856114zXmtWklLelOylxe2zuWz9yVaNXcKOk24vlro1o+WpZJSvWygk0pNWX7IGCPsntumQLTaBbRojzd9jKiq+1RYG9JjfNXLyeend5NzFF9j6Rny2pQJwA72F5eXn+mnHM8URkDfJQYvTyVGEG8oqR9urw+0/ZGxGhfiOfG19veG/h4OXZi2U4mBmctsL0U+DVwGbHCD0TXM8ABtvcoC1R8oxz7lO1mg69SepUMuJ5SqpTt+cQAkYEm3cwp9a1swaaUqlZr+a7d01yk1GVZwaaUKmN7Aq+srDSnl3lJqduyizillFKqQLZgU0oppQpkBZtSSilVICvYlFJKqQJZwaaUUkoVyAo2pZRSqkBWsCmllFIF/gfwgK8lwS866gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "city_pop.sort_values('pop', ascending=False).head(10).plot.barh(x='city_abbrev', legend=False)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel('Population')\n",
    "plt.ylabel('US city')\n",
    "plt.title(\"Ten most populous US cities\", fontname=\"Covert Ops\", fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge the city_pop references into the ufo_df DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_merged = ufo_df.merge(city_pop, left_on='city', right_on='city_abbrev', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get rid of the resulting city `NaNs`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "ufo_merged.dropna(subset=['city'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the cities with the most recorded UFO sightings are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb1c189f28>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo_merged.groupby('city')['posted'].count().sort_values(ascending=False).head(25).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, what are the cities with highest recorded UFO sightings per capita?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = ufo_merged.groupby('city')['posted'].count()\n",
    "A = pd.DataFrame(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>posted</th>\n",
       "      <th>pop</th>\n",
       "      <th>obs_per_1000</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tinley Park, IL</th>\n",
       "      <td>158</td>\n",
       "      <td>56668.0</td>\n",
       "      <td>2.788170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarasota, FL</th>\n",
       "      <td>111</td>\n",
       "      <td>56994.0</td>\n",
       "      <td>1.947573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Olympia, WA</th>\n",
       "      <td>96</td>\n",
       "      <td>51609.0</td>\n",
       "      <td>1.860141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bellingham, WA</th>\n",
       "      <td>121</td>\n",
       "      <td>89045.0</td>\n",
       "      <td>1.358863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santa Fe, NM</th>\n",
       "      <td>109</td>\n",
       "      <td>83776.0</td>\n",
       "      <td>1.301089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pensacola, FL</th>\n",
       "      <td>68</td>\n",
       "      <td>52590.0</td>\n",
       "      <td>1.293021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Everett, WA</th>\n",
       "      <td>141</td>\n",
       "      <td>110079.0</td>\n",
       "      <td>1.280898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auburn, WA</th>\n",
       "      <td>100</td>\n",
       "      <td>80776.0</td>\n",
       "      <td>1.237991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orlando, FL</th>\n",
       "      <td>337</td>\n",
       "      <td>280257.0</td>\n",
       "      <td>1.202468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yakima, WA</th>\n",
       "      <td>111</td>\n",
       "      <td>93667.0</td>\n",
       "      <td>1.185049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ocala, FL</th>\n",
       "      <td>66</td>\n",
       "      <td>59110.0</td>\n",
       "      <td>1.116562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vancouver, WA</th>\n",
       "      <td>193</td>\n",
       "      <td>175673.0</td>\n",
       "      <td>1.098632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bradenton, FL</th>\n",
       "      <td>62</td>\n",
       "      <td>56508.0</td>\n",
       "      <td>1.097190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santa Cruz, CA</th>\n",
       "      <td>68</td>\n",
       "      <td>65021.0</td>\n",
       "      <td>1.045816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle, WA</th>\n",
       "      <td>736</td>\n",
       "      <td>724745.0</td>\n",
       "      <td>1.015530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marietta, GA</th>\n",
       "      <td>61</td>\n",
       "      <td>61048.0</td>\n",
       "      <td>0.999214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Redmond, WA</th>\n",
       "      <td>63</td>\n",
       "      <td>64291.0</td>\n",
       "      <td>0.979919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Santa Barbara, CA</th>\n",
       "      <td>90</td>\n",
       "      <td>92101.0</td>\n",
       "      <td>0.977188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Redding, CA</th>\n",
       "      <td>88</td>\n",
       "      <td>91794.0</td>\n",
       "      <td>0.958668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burbank, CA</th>\n",
       "      <td>100</td>\n",
       "      <td>104834.0</td>\n",
       "      <td>0.953889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albany, OR</th>\n",
       "      <td>51</td>\n",
       "      <td>53503.0</td>\n",
       "      <td>0.953218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grand Junction, CO</th>\n",
       "      <td>59</td>\n",
       "      <td>62475.0</td>\n",
       "      <td>0.944378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missoula, MT</th>\n",
       "      <td>69</td>\n",
       "      <td>73340.0</td>\n",
       "      <td>0.940824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milford, CT</th>\n",
       "      <td>49</td>\n",
       "      <td>52970.0</td>\n",
       "      <td>0.925052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Las Vegas, NV</th>\n",
       "      <td>592</td>\n",
       "      <td>641676.0</td>\n",
       "      <td>0.922584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    posted       pop  obs_per_1000\n",
       "city                                              \n",
       "Tinley Park, IL        158   56668.0      2.788170\n",
       "Sarasota, FL           111   56994.0      1.947573\n",
       "Olympia, WA             96   51609.0      1.860141\n",
       "Bellingham, WA         121   89045.0      1.358863\n",
       "Santa Fe, NM           109   83776.0      1.301089\n",
       "Pensacola, FL           68   52590.0      1.293021\n",
       "Everett, WA            141  110079.0      1.280898\n",
       "Auburn, WA             100   80776.0      1.237991\n",
       "Orlando, FL            337  280257.0      1.202468\n",
       "Yakima, WA             111   93667.0      1.185049\n",
       "Ocala, FL               66   59110.0      1.116562\n",
       "Vancouver, WA          193  175673.0      1.098632\n",
       "Bradenton, FL           62   56508.0      1.097190\n",
       "Santa Cruz, CA          68   65021.0      1.045816\n",
       "Seattle, WA            736  724745.0      1.015530\n",
       "Marietta, GA            61   61048.0      0.999214\n",
       "Redmond, WA             63   64291.0      0.979919\n",
       "Santa Barbara, CA       90   92101.0      0.977188\n",
       "Redding, CA             88   91794.0      0.958668\n",
       "Burbank, CA            100  104834.0      0.953889\n",
       "Albany, OR              51   53503.0      0.953218\n",
       "Grand Junction, CO      59   62475.0      0.944378\n",
       "Missoula, MT            69   73340.0      0.940824\n",
       "Milford, CT             49   52970.0      0.925052\n",
       "Las Vegas, NV          592  641676.0      0.922584"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = A.join(city_pop.set_index('city_abbrev'), how='left')\n",
    "\n",
    "A.dropna(subset=['pop'], inplace=True)\n",
    "A['obs_per_1000'] = 1000*A['posted']/A['pop']\n",
    "\n",
    "A.sort_values('obs_per_1000', ascending=False).head(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! Who would have thought that Tinley Park, Illinois would have the most UFO reports per capita? The second, Sarasota, FL, isn't even close!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Cities with highest UFO reports per capita')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A.sort_values('obs_per_1000', ascending=False)['obs_per_1000'].head(10).plot.barh(legend=False)\n",
    "plt.gca().invert_yaxis()\n",
    "plt.ylabel(\"City\")\n",
    "plt.xlabel(\"Reports per 1,000 residents\")\n",
    "plt.title(\"Cities with highest UFO reports per capita\", fontname=\"Covert Ops\", fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What are the states with the most observations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "CA    13235\n",
       "FL     6312\n",
       "WA     5767\n",
       "TX     4833\n",
       "NY     4580\n",
       "AZ     3909\n",
       "PA     3784\n",
       "IL     3521\n",
       "OH     3470\n",
       "MI     2922\n",
       "Name: posted, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo_merged.groupby('state')['posted'].count().sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case studies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Phoenix Lights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcb1bd205f8>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo_merged[(ufo_merged['year'] > 1910) & (ufo_merged['city']=='Phoenix, AZ')].groupby('year')['posted'].count().plot(x='year', y='posted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fcb1c0149b0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo_merged[ufo_merged['city'] == 'Phoenix, AZ'].hist('year', bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Roswell, NM in 1947"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fcb1bf63208>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo_merged[ufo_merged['city'] == 'Roswell, NM'].hist('year', bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chigago O'Hare lights in 2006"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fcb1c096d68>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo_merged[ufo_merged['city'] == 'Chicago, IL'].hist('year', bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aurora, TX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fcb1cd04cc0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ufo_merged[ufo_merged['city'] == 'Aurora, TX'].hist('year', bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ha! Just a single observation, from 1897. You didn't expect a UFO sighting from back then! The erport is quite an interesting read: http://www.nuforc.org/webreports/088/S88780.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Other notes:** over 720 labels of 'HOAX' in the summary pages. This would make an interesting follow-up study."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}