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prison_break_project_lab_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helper import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n",
      "<class 'list'>\n",
      "['August 19, 1971', 'Santa Martha Acatitla', 'Mexico', 'Yes', 'Joel David Kaplan Carlos Antonio Contreras Castro', \"Kaplan was a New York businessman who had been arrested for murder in 1962 in Mexico City and was incarcerated at the Santa Martha Acatitla prison in the Iztapalapa borough. Joel's sister, Judy Kaplan, arranged the means to help Kaplan escape, and on the aforementioned date, a helicopter landed in the prison yard. The guards mistakenly thought this was an official visit. In two minutes, Kaplan and his cellmate Contreras, a Venezuelan counterfeiter, were able to board the craft and were piloted away, before any shots were fired.[10] Both men were flown to Texas and then different planes flew Kaplan to California and Contreras to Guatemala.[3] The Mexican government never initiated extradition proceedings against Kaplan.[10] The escape is told in a book, The 10-Second Jailbreak: The Helicopter Escape of Joel David Kaplan.[5] It also inspired the 1975 action movie Breakout, which starred Charles Bronson and Robert Duvall.[10]\"]\n",
      "\n",
      "\n",
      "['October 31, 1973', 'Mountjoy Jail, Dublin', 'Ireland', 'Yes', \"JB O'Hagan Seamus Twomey Kevin Mallon\", 'An IRA member hijacked a helicopter and forced the pilot to land in the exercise yard of the prison\\'s D Wing at 3:40\\xa0p.m. Three prisoners who were also members of the IRA escaped aboard the helicopter. Another prisoner was quoted as saying, \"One shamefaced screw apologised to the governor and said he thought it was the new Minister for Defence (Paddy Donegan) arriving. I told him it was our Minister of Defence leaving.\" The escape became Republican lore and was immortalized by \"The Helicopter Song\", which contains the lines \"It\\'s up like a bird and over the city. There\\'s three men a\\'missing I heard the warder say\".[1]']\n",
      "\n",
      "\n",
      "['May 24, 1978', 'United States Penitentiary, Marion, Illinois', 'United States', 'No', 'Garrett Brock Trapnell Martin Joseph McNally James Kenneth Johnson', \"43-year-old Barbara Ann Oswald hijacked a Saint Louis-based charter helicopter and forced the pilot to land in the prison yard. While landing the aircraft, the pilot, Allen Barklage, who was a Vietnam War veteran, struggled with Oswald and managed to wrestle the gun away from her. Barklage then shot and killed Oswald, thwarting the escape.[11] A few months later Oswald's daughter hijacked TWA Flight 541 in an effort to free Trapnell.\"]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "url = \"https://en.wikipedia.org/wiki/List_of_helicopter_prison_escapes\"\n",
    "data = data_from_url(url)\n",
    "\n",
    "print(len(data))\n",
    "print(type(data))\n",
    "\n",
    "for row in data[0:3]:\n",
    "    print(row)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['August 19, 1971', 'Santa Martha Acatitla', 'Mexico', 'Yes', 'Joel David Kaplan Carlos Antonio Contreras Castro']\n",
      "\n",
      "\n",
      "['October 31, 1973', 'Mountjoy Jail, Dublin', 'Ireland', 'Yes', \"JB O'Hagan Seamus Twomey Kevin Mallon\"]\n",
      "\n",
      "\n",
      "['May 24, 1978', 'United States Penitentiary, Marion, Illinois', 'United States', 'No', 'Garrett Brock Trapnell Martin Joseph McNally James Kenneth Johnson']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "index = 0\n",
    "\n",
    "for row in data:\n",
    "    data[index] = row[:-1]\n",
    "    index += 1\n",
    "    \n",
    "for row in data[0:3]:\n",
    "    print(row)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1971, 'Santa Martha Acatitla', 'Mexico', 'Yes', 'Joel David Kaplan Carlos Antonio Contreras Castro']\n",
      "\n",
      "\n",
      "[1973, 'Mountjoy Jail, Dublin', 'Ireland', 'Yes', \"JB O'Hagan Seamus Twomey Kevin Mallon\"]\n",
      "\n",
      "\n",
      "[1978, 'United States Penitentiary, Marion, Illinois', 'United States', 'No', 'Garrett Brock Trapnell Martin Joseph McNally James Kenneth Johnson']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for row in data:\n",
    "    row[0] = fetch_year(row[0])\n",
    "    \n",
    "for row in data[0:3]:\n",
    "    print(row)\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data[0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attempts per Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1971, 1973, 1978, 1981, 1981, 1983, 1985, 1985, 1986, 1986, 1986, 1987, 1988, 1988, 1988, 1989, 1989, 1990, 1991, 1992, 1992, 1993, 1996, 1997, 1999, 2000, 2000, 2001, 2001, 2001, 2002, 2002, 2003, 2005, 2005, 2006, 2007, 2007, 2007, 2009, 2009, 2009, 2010, 2012, 2013, 2013, 2014, 2016, 2018, 2020]\n"
     ]
    }
   ],
   "source": [
    "years_included = []\n",
    "\n",
    "for row in data:\n",
    "    year = row[0]\n",
    "    years_included.append(year)\n",
    "\n",
    "print(years_included)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1971 2020\n"
     ]
    }
   ],
   "source": [
    "min_year = min(years_included)\n",
    "max_year = max(years_included)\n",
    "\n",
    "print(min_year, max_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "years = []\n",
    "for year in range(min_year, max_year + 1):\n",
    "    years.append(year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020]\n"
     ]
    }
   ],
   "source": [
    "print(years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1971, 0], [1972, 0], [1973, 0], [1974, 0], [1975, 0], [1976, 0], [1977, 0], [1978, 0], [1979, 0], [1980, 0], [1981, 0], [1982, 0], [1983, 0], [1984, 0], [1985, 0], [1986, 0], [1987, 0], [1988, 0], [1989, 0], [1990, 0], [1991, 0], [1992, 0], [1993, 0], [1994, 0], [1995, 0], [1996, 0], [1997, 0], [1998, 0], [1999, 0], [2000, 0], [2001, 0], [2002, 0], [2003, 0], [2004, 0], [2005, 0], [2006, 0], [2007, 0], [2008, 0], [2009, 0], [2010, 0], [2011, 0], [2012, 0], [2013, 0], [2014, 0], [2015, 0], [2016, 0], [2017, 0], [2018, 0], [2019, 0], [2020, 0]]\n"
     ]
    }
   ],
   "source": [
    "attempts_per_year = []\n",
    "\n",
    "for year in years:\n",
    "    attempts_per_year.append([year, 0])\n",
    "    \n",
    "print(attempts_per_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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, 3], [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 year_attempt in attempts_per_year:\n",
    "        year = year_attempt[0]\n",
    "        if row[0] == year:\n",
    "            year_attempt[1] += 1\n",
    "\n",
    "print(attempts_per_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "barplot(attempts_per_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Countries Frequency Table: List Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Mexico', 'Ireland', 'United States', 'France', 'Canada', 'Australia', 'Brazil', 'Italy', 'United Kingdom', 'Colombia', 'Puerto Rico', 'Chile', 'Netherlands', 'Greece', 'Belgium', 'Russia']\n"
     ]
    }
   ],
   "source": [
    "countries = []\n",
    "\n",
    "for row in data:\n",
    "    country = row[2]\n",
    "    if country not in countries:\n",
    "        countries.append(country)\n",
    "print(countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Mexico', 1],\n",
       " ['Ireland', 1],\n",
       " ['United States', 8],\n",
       " ['France', 15],\n",
       " ['Canada', 4],\n",
       " ['Australia', 2],\n",
       " ['Brazil', 2],\n",
       " ['Italy', 1],\n",
       " ['United Kingdom', 2],\n",
       " ['Colombia', 2],\n",
       " ['Puerto Rico', 1],\n",
       " ['Chile', 1],\n",
       " ['Netherlands', 1],\n",
       " ['Greece', 4],\n",
       " ['Belgium', 4],\n",
       " ['Russia', 1]]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_freq = []\n",
    "\n",
    "for country in countries:\n",
    "    country_freq.append([country, 0])\n",
    "\n",
    "for row in data:\n",
    "    for c in country_freq:\n",
    "        country = c[0]\n",
    "        if row[2] == country:\n",
    "            c[1] += 1\n",
    "            \n",
    "country_freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Countries Frequency Table: Dictionary Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Mexico': 1, 'Ireland': 1, 'United States': 8, 'France': 15, 'Canada': 4, 'Australia': 2, 'Brazil': 2, 'Italy': 1, 'United Kingdom': 2, 'Colombia': 2, 'Puerto Rico': 1, 'Chile': 1, 'Netherlands': 1, 'Greece': 4, 'Belgium': 4, 'Russia': 1}\n"
     ]
    }
   ],
   "source": [
    "countries = {}\n",
    "\n",
    "for row in data:\n",
    "    country = row[2]\n",
    "    if country not in countries:\n",
    "        countries[country] = 1\n",
    "    else:\n",
    "        countries[country] += 1\n",
    "        \n",
    "print(countries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Countries Frequency Table: pandas Method"
   ]
  },
  {
   "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>Prison Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>Success</th>\n",
       "      <th>Escapee(s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1971</td>\n",
       "      <td>Santa Martha Acatitla</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Joel David Kaplan Carlos Antonio Contreras Castro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1973</td>\n",
       "      <td>Mountjoy Jail, Dublin</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>Yes</td>\n",
       "      <td>JB O'Hagan Seamus Twomey Kevin Mallon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1978</td>\n",
       "      <td>United States Penitentiary, Marion, Illinois</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Garrett Brock Trapnell Martin Joseph McNally J...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1981</td>\n",
       "      <td>Fleury-Mérogis, Essonne, Ile de France</td>\n",
       "      <td>France</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Gérard Dupré Daniel Beaumont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1981</td>\n",
       "      <td>Orsainville Prison, Quebec City</td>\n",
       "      <td>Canada</td>\n",
       "      <td>No</td>\n",
       "      <td>Marina Paquet (hijacker) Giles Arseneault (pri...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year                                   Prison Name        Country Success  \\\n",
       "0  1971                         Santa Martha Acatitla         Mexico     Yes   \n",
       "1  1973                         Mountjoy Jail, Dublin        Ireland     Yes   \n",
       "2  1978  United States Penitentiary, Marion, Illinois  United States      No   \n",
       "3  1981        Fleury-Mérogis, Essonne, Ile de France         France     Yes   \n",
       "4  1981               Orsainville Prison, Quebec City         Canada      No   \n",
       "\n",
       "                                          Escapee(s)  \n",
       "0  Joel David Kaplan Carlos Antonio Contreras Castro  \n",
       "1              JB O'Hagan Seamus Twomey Kevin Mallon  \n",
       "2  Garrett Brock Trapnell Martin Joseph McNally J...  \n",
       "3                       Gérard Dupré Daniel Beaumont  \n",
       "4  Marina Paquet (hijacker) Giles Arseneault (pri...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(data, columns=[\"Year\", \"Prison Name\", \"Country\", \"Success\", \"Escapee(s)\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country\n",
       "France            15\n",
       "United States      8\n",
       "Canada             4\n",
       "Belgium            4\n",
       "Greece             4\n",
       "United Kingdom     2\n",
       "Brazil             2\n",
       "Australia          2\n",
       "Colombia           2\n",
       "Italy              1\n",
       "Ireland            1\n",
       "Mexico             1\n",
       "Chile              1\n",
       "Puerto Rico        1\n",
       "Netherlands        1\n",
       "Russia             1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Country\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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