prison-escapes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of helicopter prison escape attempts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, I analyze data from Wikipedia ([source](https://en.wikipedia.org/wiki/List_of_helicopter_prison_escapes)) examining prison escape attempts by helicopter.\n",
    "\n",
    "In this report, I tackle several questions:\n",
    "- How has the number of prison escape attempts per year evolved over time?\n",
    "- In which countries were prison escape attempts by helicopter most common?\n",
    "- What proportion of escape attempts were successful, and in which countries did the most successful attempts occur?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by importing relevant libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.core.display import display, HTML\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retrieve data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we retrieve the data from the [list of helicopter prison escapes](https://en.wikipedia.org/wiki/List_of_helicopter_prison_escapes) Wikipedia article."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://en.wikipedia.org/wiki/List_of_helicopter_prison_escapes'\n",
    "df = pd.read_html(url)[1]\n",
    "data = df.to_numpy().tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a concise preview of the data, we remove the \"Details\" in each row of the data, which contains a large amount of text about each escape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "# modify data so that defails are not present for preview\n",
    "index = 0\n",
    "for row in data:\n",
    "    data[index] = row[:5]\n",
    "    index += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we preview the first 3 rows in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['August 19, 1971', 'Santa Martha Acatitla', 'Mexico', 'Yes', 'Joel David Kaplan Carlos Antonio Contreras Castro'], ['October 31, 1973', 'Mountjoy Jail', 'Ireland', 'Yes', \"JB O'Hagan Seamus TwomeyKevin Mallon\"], ['May 24, 1978', 'United States Penitentiary, Marion', 'United States', 'No', 'Garrett Brock TrapnellMartin Joseph McNallyJames Kenneth Johnson']]\n"
     ]
    }
   ],
   "source": [
    "print(data[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How has the number of prison escapes per year evolved over time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first want to examine the number of prison escapes per year. To do so, we first need to find the range of years in the dataset. I create a function to extract the year and apply it to each date in the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to extract year from date, \n",
    "# which is provided in the data as mm dd, yyyy\n",
    "def get_year(date_string):\n",
    "    # find year in the date string\n",
    "    year = int(re.findall(\"\\d{4}\", date_string)[0])\n",
    "    return year\n",
    "\n",
    "# apply function to each row in the data\n",
    "for row in data:\n",
    "    row[0] = fetch_year(row[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can confirm that the step above worked, by again examining the first three dates in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1971\n",
      "1973\n",
      "1978\n"
     ]
    }
   ],
   "source": [
    "for row in data[:3]:\n",
    "    print(row[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we find the maximum and minimum dates in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1971 2020\n"
     ]
    }
   ],
   "source": [
    "# find minimum and maximum years in the data\n",
    "min_year = min(data, key=lambda x: x[0])[0]\n",
    "max_year = max(data, key=lambda x: x[0])[0]\n",
    "\n",
    "print(min_year, max_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to subsequently calculate the number of escape attempts by helicopter per year. So we create a new list of lists, where each list has two elements: a year, and how many attempts occurred in that year.\n",
    "\n",
    "The first step is creating the list of lists, with the year filled in and the number of attempts per year set to 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "attempts_per_year = []\n",
    "for y in range(min_year, max_year + 1):\n",
    "    attempts_per_year.append([y, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we iterate over rows in the data, and years in the calculated range, to determine the number of escape attempts per year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1971, 1], [1972, 0], [1973, 1], [1974, 0], [1975, 0], [1976, 0], [1977, 0], [1978, 1], [1979, 0], [1980, 0], [1981, 2], [1982, 0], [1983, 1], [1984, 0], [1985, 2], [1986, 3], [1987, 1], [1988, 1], [1989, 2], [1990, 1], [1991, 1], [1992, 2], [1993, 1], [1994, 0], [1995, 0], [1996, 1], [1997, 1], [1998, 0], [1999, 1], [2000, 2], [2001, 3], [2002, 2], [2003, 1], [2004, 0], [2005, 2], [2006, 1], [2007, 3], [2008, 0], [2009, 3], [2010, 1], [2011, 0], [2012, 1], [2013, 2], [2014, 1], [2015, 0], [2016, 1], [2017, 0], [2018, 1], [2019, 0], [2020, 1]]\n"
     ]
    }
   ],
   "source": [
    "for row in data:\n",
    "    for ya in attempts_per_year:\n",
    "        \n",
    "        y = ya[0] \n",
    "        \n",
    "        # if current row's year matches this year,\n",
    "        # then add to tally\n",
    "        if row[0] == y:\n",
    "            ya[1] += 1\n",
    "\n",
    "print(attempts_per_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having calculated the number of escape attempts per year, we can use a barplot to visualize the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x1300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convert attempts_per_year to dictionary for plotting\n",
    "d = {ya[0]:ya[1] for ya in attempts_per_year}\n",
    "\n",
    "# create vertical barplot\n",
    "plt.figure(figsize=(9, 13))\n",
    "axes = plt.axes()\n",
    "axes.get_xaxis().set_visible(False)\n",
    "spines = axes.spines\n",
    "spines['top'].set_visible(False)\n",
    "spines['right'].set_visible(False)\n",
    "spines['bottom'].set_visible(False)\n",
    "spines['left'].set_visible(False)\n",
    "ax = plt.barh(range(len(d)), list(d.values()), height = .5)\n",
    "plt.yticks(range(len(d)), list(d.keys()))\n",
    "plt.xticks(range(4), range(4))\n",
    "\n",
    "# show label with value for each bar\n",
    "rectangles = ax.patches\n",
    "for rectangle in rectangles:\n",
    "    x_value = rectangle.get_width()\n",
    "    y_value = rectangle.get_y() + rectangle.get_height() / 2\n",
    "    space = 5\n",
    "    ha = 'left'\n",
    "    label = \"{}\".format(x_value)\n",
    "    if x_value > 0:\n",
    "        plt.annotate(\n",
    "            label,\n",
    "            (x_value, y_value),\n",
    "            xytext=(space, 0),\n",
    "            textcoords=\"offset points\",\n",
    "            va='center',\n",
    "            ha=ha)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the barplot, we can see that the highest number of escape attempts (6) occurred in the years 2001, 2007, and 2009. There is not a clear trend in escape attempts over time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In which countries are prison escapes by helicopter most common?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will examine prison escape attempts by helicopter by country, to determine where in the world these attempts are most common. \n",
    "\n",
    "To do so, we work with the pandas dataframe version of the data (loaded above), which has a column `Country`. We can first examine which countries are included in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Mexico' 'Ireland' 'United States' 'France' 'Canada' 'Australia' 'Brazil'\n",
      " 'Italy' 'United Kingdom' 'Puerto Rico' 'Chile' 'Netherlands' 'Greece'\n",
      " 'Belgium' 'Russia']\n"
     ]
    }
   ],
   "source": [
    "print(df['Country'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we count the number of occurrences of each country in the data, using the pandas `value_counts()` function. This returns a series, with country as the index and value indicating the number of occurrences. I create a dataframe from the indices and values and display them nicely:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Number of Occurrences</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>France</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>United States</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Belgium</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Greece</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Canada</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Brazil</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Australia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Chile</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mexico</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Russia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Italy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Ireland</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get series with value counts\n",
    "countries_frequency = df['Country'].value_counts()\n",
    "\n",
    "# create dataframe from indices and values\n",
    "d = pd.DataFrame({'Country': countries_frequency.index,\n",
    "            'Number of Occurrences': countries_frequency.values})\n",
    "\n",
    "# show the table as HTML\n",
    "display(HTML(d.to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table above, we can see that France has had the most attempted prison escapes by helicopter, with 15 total attempts. This is followed by the United States, which had 8 escape attempts documented in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What proportion of prison escape attempts were successful? Which countries had more successful than unsuccessful attempts?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will examine how many of the total escape attempts were successful.\n",
    "\n",
    "To first find the proportion of successful attempts, we calculate the proportion of successful attempts to total attempts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7083333333333334\n"
     ]
    }
   ],
   "source": [
    "prop_success = len(df[df['Succeeded'] == 'Yes'])/len(df['Succeeded'])\n",
    "print(prop_success)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can see that the majority (~70.8%) of escape attempts by helicopter were successful.\n",
    "\n",
    "Next, we examine attempt success by country. Specifically, we calculate the proportion of successful attempts by country. We return to the numpy array form of the data we used earlier in this report, and we start by finding the total number of successful and unsuccessful attempts per country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Australia', 2, 1, 1], ['Netherlands', 1, 0, 1], ['Ireland', 1, 1, 0], ['Italy', 1, 1, 0], ['Russia', 1, 1, 0], ['Canada', 4, 3, 1], ['Greece', 4, 2, 2], ['Belgium', 4, 2, 2], ['Mexico', 1, 1, 0], ['Brazil', 2, 2, 0], ['United Kingdom', 2, 1, 1], ['Puerto Rico', 1, 1, 0], ['France', 15, 11, 4], ['United States', 8, 6, 2], ['Chile', 1, 1, 0]]\n"
     ]
    }
   ],
   "source": [
    "# list all countries in the data\n",
    "countries = []\n",
    "for row in data:\n",
    "    countries.append(row[2])\n",
    "\n",
    "# iterate over unique countries, creating a list of lists\n",
    "# to store total attempts, # successes and # failures for each country\n",
    "n_success_by_country = []\n",
    "for name in set(countries):\n",
    "    n_success_by_country.append([name, 0, 0, 0])\n",
    "    \n",
    "# iterate over rows in data and countries,\n",
    "# counting successes and failures for each \n",
    "for row in data:\n",
    "    for sf in n_success_by_country:\n",
    "        \n",
    "        country = sf[0] \n",
    "        \n",
    "        # if current row's country matches this country,\n",
    "        # then add to tally\n",
    "        if row[2] == country:\n",
    "            \n",
    "            # add to total attempts tally\n",
    "            sf[1] += 1\n",
    "            \n",
    "            # if success, add to success tally\n",
    "            if row[3] == 'Yes':\n",
    "                sf[2] += 1\n",
    "            \n",
    "            # if failure, add to failure tally\n",
    "            if row[3] == 'No':\n",
    "                sf[3] += 1\n",
    "\n",
    "print(n_success_by_country)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have counted total attempts, number of successes, and number of failures by country, we can compute the proportion of successful attempts per country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sf in n_success_by_country:\n",
    "    sf.append(sf[2]/sf[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can display the results in a nicely-formatted table, sorting the elements in order of (a) decreasing total attempts and (b) decreasing proportion of successful escape attempts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th># attempts</th>\n",
       "      <th># successful</th>\n",
       "      <th># failed</th>\n",
       "      <th>Proportion success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>France</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>United States</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Canada</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Greece</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Belgium</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Brazil</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Australia</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Ireland</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Italy</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Russia</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mexico</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Chile</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = pd.DataFrame(n_success_by_country,\n",
    "            columns=['Country', '# attempts', '# successful', '# failed', 'Proportion success'])\n",
    "d.sort_values(by=['# attempts','Proportion success'], \n",
    "              inplace=True,\n",
    "              ascending=False)\n",
    "\n",
    "# show the table as HTML\n",
    "display(HTML(d.to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table above, we can see that countries with a high total number of attempts also had high proportions of successful attempts."
   ]
  }
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