instructor_day2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abc'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'abc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'abc' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-03cfd743661f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mabc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'abc' is not defined"
     ]
    }
   ],
   "source": [
    "abc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function min>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = 22\n",
    "first_name = 'Ahmed'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ahmed is 22 years old'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'{first_name} is {age} years old'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ahmed is 22 years old\n"
     ]
    }
   ],
   "source": [
    "print(first_name, 'is', age, 'years old')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = age + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'h'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom_name = 'helium'\n",
    "atom_name[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hel'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom_name[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'m'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom_name[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'liu'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom_name[-4:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hel'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom_name[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Atom_name = 'hydrogen'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'helium'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(1.45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(first_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"int\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-380dc7406be2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfirst_name\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
     ]
    }
   ],
   "source": [
    "first_name + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AhmedABC'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_name + 'ABC'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_name = first_name + 'Walsh'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ahmed Walsh'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_name = f'{first_name} Walsh'\n",
    "full_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(full_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "object of type 'int' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-374401a99246>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()"
     ]
    }
   ],
   "source": [
    "len(age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(1 / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 // 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3.0 ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 // 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 % 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = len(full_name)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ahmed Walsh\n"
     ]
    }
   ],
   "source": [
    "x = print(full_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NoneType"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(1, 2, 3, 6, -1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'>' not supported between instances of 'str' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-50-e542df9f9a89>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: '>' not supported between instances of 'str' and 'int'"
     ]
    }
   ],
   "source": [
    "max(1, 'a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(3.712, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "round(3.712)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_string = 'Hello world!'\n",
    "len(my_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hello world!'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_string.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOL while scanning string literal (<ipython-input-60-80bd0313efb1>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-60-80bd0313efb1>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    my_string = 'Hello world!\u001b[0m\n\u001b[0m                             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
     ]
    }
   ],
   "source": [
    "my_string = 'Hello world!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pi is 3.141592653589793'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "f'pi is {math.pi}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.cos(math.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "math.cos()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math as m\n",
    "\n",
    "m.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import pi as p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "random?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>country</th>\n",
       "      <th>gdpPercap_1952</th>\n",
       "      <th>gdpPercap_1957</th>\n",
       "      <th>gdpPercap_1962</th>\n",
       "      <th>gdpPercap_1967</th>\n",
       "      <th>gdpPercap_1972</th>\n",
       "      <th>gdpPercap_1977</th>\n",
       "      <th>gdpPercap_1982</th>\n",
       "      <th>gdpPercap_1987</th>\n",
       "      <th>gdpPercap_1992</th>\n",
       "      <th>gdpPercap_1997</th>\n",
       "      <th>gdpPercap_2002</th>\n",
       "      <th>gdpPercap_2007</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>10039.59564</td>\n",
       "      <td>10949.64959</td>\n",
       "      <td>12217.22686</td>\n",
       "      <td>14526.12465</td>\n",
       "      <td>16788.62948</td>\n",
       "      <td>18334.19751</td>\n",
       "      <td>19477.00928</td>\n",
       "      <td>21888.88903</td>\n",
       "      <td>23424.76683</td>\n",
       "      <td>26997.93657</td>\n",
       "      <td>30687.75473</td>\n",
       "      <td>34435.36744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>10556.57566</td>\n",
       "      <td>12247.39532</td>\n",
       "      <td>13175.67800</td>\n",
       "      <td>14463.91893</td>\n",
       "      <td>16046.03728</td>\n",
       "      <td>16233.71770</td>\n",
       "      <td>17632.41040</td>\n",
       "      <td>19007.19129</td>\n",
       "      <td>18363.32494</td>\n",
       "      <td>21050.41377</td>\n",
       "      <td>23189.80135</td>\n",
       "      <td>25185.00911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  \\\n",
       "0    Australia     10039.59564     10949.64959     12217.22686   \n",
       "1  New Zealand     10556.57566     12247.39532     13175.67800   \n",
       "\n",
       "   gdpPercap_1967  gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  \\\n",
       "0     14526.12465     16788.62948     18334.19751     19477.00928   \n",
       "1     14463.91893     16046.03728     16233.71770     17632.41040   \n",
       "\n",
       "   gdpPercap_1987  gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  \\\n",
       "0     21888.88903     23424.76683     26997.93657     30687.75473   \n",
       "1     19007.19129     18363.32494     21050.41377     23189.80135   \n",
       "\n",
       "   gdpPercap_2007  \n",
       "0     34435.36744  \n",
       "1     25185.00911  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('data/gapminder_gdp_oceania.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country            object\n",
       "gdpPercap_1952    float64\n",
       "gdpPercap_1957    float64\n",
       "gdpPercap_1962    float64\n",
       "gdpPercap_1967    float64\n",
       "gdpPercap_1972    float64\n",
       "gdpPercap_1977    float64\n",
       "gdpPercap_1982    float64\n",
       "gdpPercap_1987    float64\n",
       "gdpPercap_1992    float64\n",
       "gdpPercap_1997    float64\n",
       "gdpPercap_2002    float64\n",
       "gdpPercap_2007    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'gdpPercap_1952', 'gdpPercap_1957', 'gdpPercap_1962',\n",
       "       'gdpPercap_1967', 'gdpPercap_1972', 'gdpPercap_1977', 'gdpPercap_1982',\n",
       "       'gdpPercap_1987', 'gdpPercap_1992', 'gdpPercap_1997', 'gdpPercap_2002',\n",
       "       'gdpPercap_2007'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=2, step=1)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>gdpPercap_1952</th>\n",
       "      <th>gdpPercap_1957</th>\n",
       "      <th>gdpPercap_1962</th>\n",
       "      <th>gdpPercap_1967</th>\n",
       "      <th>gdpPercap_1972</th>\n",
       "      <th>gdpPercap_1977</th>\n",
       "      <th>gdpPercap_1982</th>\n",
       "      <th>gdpPercap_1987</th>\n",
       "      <th>gdpPercap_1992</th>\n",
       "      <th>gdpPercap_1997</th>\n",
       "      <th>gdpPercap_2002</th>\n",
       "      <th>gdpPercap_2007</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>10039.59564</td>\n",
       "      <td>10949.64959</td>\n",
       "      <td>12217.22686</td>\n",
       "      <td>14526.12465</td>\n",
       "      <td>16788.62948</td>\n",
       "      <td>18334.19751</td>\n",
       "      <td>19477.00928</td>\n",
       "      <td>21888.88903</td>\n",
       "      <td>23424.76683</td>\n",
       "      <td>26997.93657</td>\n",
       "      <td>30687.75473</td>\n",
       "      <td>34435.36744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>10556.57566</td>\n",
       "      <td>12247.39532</td>\n",
       "      <td>13175.67800</td>\n",
       "      <td>14463.91893</td>\n",
       "      <td>16046.03728</td>\n",
       "      <td>16233.71770</td>\n",
       "      <td>17632.41040</td>\n",
       "      <td>19007.19129</td>\n",
       "      <td>18363.32494</td>\n",
       "      <td>21050.41377</td>\n",
       "      <td>23189.80135</td>\n",
       "      <td>25185.00911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  \\\n",
       "country                                                                       \n",
       "Australia       10039.59564     10949.64959     12217.22686     14526.12465   \n",
       "New Zealand     10556.57566     12247.39532     13175.67800     14463.91893   \n",
       "\n",
       "             gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  gdpPercap_1987  \\\n",
       "country                                                                       \n",
       "Australia       16788.62948     18334.19751     19477.00928     21888.88903   \n",
       "New Zealand     16046.03728     16233.71770     17632.41040     19007.19129   \n",
       "\n",
       "             gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  gdpPercap_2007  \n",
       "country                                                                      \n",
       "Australia       23424.76683     26997.93657     30687.75473     34435.36744  \n",
       "New Zealand     18363.32494     21050.41377     23189.80135     25185.00911  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/gapminder_gdp_oceania.csv', index_col='country')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Australia', 'New Zealand'], dtype='object', name='country')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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",
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       "    }\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>gdpPercap_1952</th>\n",
       "      <th>gdpPercap_1957</th>\n",
       "      <th>gdpPercap_1962</th>\n",
       "      <th>gdpPercap_1967</th>\n",
       "      <th>gdpPercap_1972</th>\n",
       "      <th>gdpPercap_1977</th>\n",
       "      <th>gdpPercap_1982</th>\n",
       "      <th>gdpPercap_1987</th>\n",
       "      <th>gdpPercap_1992</th>\n",
       "      <th>gdpPercap_1997</th>\n",
       "      <th>gdpPercap_2002</th>\n",
       "      <th>gdpPercap_2007</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10298.085650</td>\n",
       "      <td>11598.522455</td>\n",
       "      <td>12696.452430</td>\n",
       "      <td>14495.021790</td>\n",
       "      <td>16417.33338</td>\n",
       "      <td>17283.957605</td>\n",
       "      <td>18554.709840</td>\n",
       "      <td>20448.040160</td>\n",
       "      <td>20894.045885</td>\n",
       "      <td>24024.175170</td>\n",
       "      <td>26938.778040</td>\n",
       "      <td>29810.188275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>365.560078</td>\n",
       "      <td>917.644806</td>\n",
       "      <td>677.727301</td>\n",
       "      <td>43.986086</td>\n",
       "      <td>525.09198</td>\n",
       "      <td>1485.263517</td>\n",
       "      <td>1304.328377</td>\n",
       "      <td>2037.668013</td>\n",
       "      <td>3578.979883</td>\n",
       "      <td>4205.533703</td>\n",
       "      <td>5301.853680</td>\n",
       "      <td>6540.991104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10039.595640</td>\n",
       "      <td>10949.649590</td>\n",
       "      <td>12217.226860</td>\n",
       "      <td>14463.918930</td>\n",
       "      <td>16046.03728</td>\n",
       "      <td>16233.717700</td>\n",
       "      <td>17632.410400</td>\n",
       "      <td>19007.191290</td>\n",
       "      <td>18363.324940</td>\n",
       "      <td>21050.413770</td>\n",
       "      <td>23189.801350</td>\n",
       "      <td>25185.009110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10168.840645</td>\n",
       "      <td>11274.086022</td>\n",
       "      <td>12456.839645</td>\n",
       "      <td>14479.470360</td>\n",
       "      <td>16231.68533</td>\n",
       "      <td>16758.837652</td>\n",
       "      <td>18093.560120</td>\n",
       "      <td>19727.615725</td>\n",
       "      <td>19628.685413</td>\n",
       "      <td>22537.294470</td>\n",
       "      <td>25064.289695</td>\n",
       "      <td>27497.598692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10298.085650</td>\n",
       "      <td>11598.522455</td>\n",
       "      <td>12696.452430</td>\n",
       "      <td>14495.021790</td>\n",
       "      <td>16417.33338</td>\n",
       "      <td>17283.957605</td>\n",
       "      <td>18554.709840</td>\n",
       "      <td>20448.040160</td>\n",
       "      <td>20894.045885</td>\n",
       "      <td>24024.175170</td>\n",
       "      <td>26938.778040</td>\n",
       "      <td>29810.188275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10427.330655</td>\n",
       "      <td>11922.958888</td>\n",
       "      <td>12936.065215</td>\n",
       "      <td>14510.573220</td>\n",
       "      <td>16602.98143</td>\n",
       "      <td>17809.077557</td>\n",
       "      <td>19015.859560</td>\n",
       "      <td>21168.464595</td>\n",
       "      <td>22159.406358</td>\n",
       "      <td>25511.055870</td>\n",
       "      <td>28813.266385</td>\n",
       "      <td>32122.777857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10556.575660</td>\n",
       "      <td>12247.395320</td>\n",
       "      <td>13175.678000</td>\n",
       "      <td>14526.124650</td>\n",
       "      <td>16788.62948</td>\n",
       "      <td>18334.197510</td>\n",
       "      <td>19477.009280</td>\n",
       "      <td>21888.889030</td>\n",
       "      <td>23424.766830</td>\n",
       "      <td>26997.936570</td>\n",
       "      <td>30687.754730</td>\n",
       "      <td>34435.367440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  \\\n",
       "count        2.000000        2.000000        2.000000        2.000000   \n",
       "mean     10298.085650    11598.522455    12696.452430    14495.021790   \n",
       "std        365.560078      917.644806      677.727301       43.986086   \n",
       "min      10039.595640    10949.649590    12217.226860    14463.918930   \n",
       "25%      10168.840645    11274.086022    12456.839645    14479.470360   \n",
       "50%      10298.085650    11598.522455    12696.452430    14495.021790   \n",
       "75%      10427.330655    11922.958888    12936.065215    14510.573220   \n",
       "max      10556.575660    12247.395320    13175.678000    14526.124650   \n",
       "\n",
       "       gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  gdpPercap_1987  \\\n",
       "count         2.00000        2.000000        2.000000        2.000000   \n",
       "mean      16417.33338    17283.957605    18554.709840    20448.040160   \n",
       "std         525.09198     1485.263517     1304.328377     2037.668013   \n",
       "min       16046.03728    16233.717700    17632.410400    19007.191290   \n",
       "25%       16231.68533    16758.837652    18093.560120    19727.615725   \n",
       "50%       16417.33338    17283.957605    18554.709840    20448.040160   \n",
       "75%       16602.98143    17809.077557    19015.859560    21168.464595   \n",
       "max       16788.62948    18334.197510    19477.009280    21888.889030   \n",
       "\n",
       "       gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  gdpPercap_2007  \n",
       "count        2.000000        2.000000        2.000000        2.000000  \n",
       "mean     20894.045885    24024.175170    26938.778040    29810.188275  \n",
       "std       3578.979883     4205.533703     5301.853680     6540.991104  \n",
       "min      18363.324940    21050.413770    23189.801350    25185.009110  \n",
       "25%      19628.685413    22537.294470    25064.289695    27497.598692  \n",
       "50%      20894.045885    24024.175170    26938.778040    29810.188275  \n",
       "75%      22159.406358    25511.055870    28813.266385    32122.777857  \n",
       "max      23424.766830    26997.936570    30687.754730    34435.367440  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2 entries, Australia to New Zealand\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   gdpPercap_1952  2 non-null      float64\n",
      " 1   gdpPercap_1957  2 non-null      float64\n",
      " 2   gdpPercap_1962  2 non-null      float64\n",
      " 3   gdpPercap_1967  2 non-null      float64\n",
      " 4   gdpPercap_1972  2 non-null      float64\n",
      " 5   gdpPercap_1977  2 non-null      float64\n",
      " 6   gdpPercap_1982  2 non-null      float64\n",
      " 7   gdpPercap_1987  2 non-null      float64\n",
      " 8   gdpPercap_1992  2 non-null      float64\n",
      " 9   gdpPercap_1997  2 non-null      float64\n",
      " 10  gdpPercap_2002  2 non-null      float64\n",
      " 11  gdpPercap_2007  2 non-null      float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 208.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>country</th>\n",
       "      <th>Australia</th>\n",
       "      <th>New Zealand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gdpPercap_1952</th>\n",
       "      <td>10039.59564</td>\n",
       "      <td>10556.57566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpPercap_1957</th>\n",
       "      <td>10949.64959</td>\n",
       "      <td>12247.39532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpPercap_1962</th>\n",
       "      <td>12217.22686</td>\n",
       "      <td>13175.67800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpPercap_1967</th>\n",
       "      <td>14526.12465</td>\n",
       "      <td>14463.91893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpPercap_1972</th>\n",
       "      <td>16788.62948</td>\n",
       "      <td>16046.03728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpPercap_1977</th>\n",
       "      <td>18334.19751</td>\n",
       "      <td>16233.71770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpPercap_1982</th>\n",
       "      <td>19477.00928</td>\n",
       "      <td>17632.41040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpPercap_1987</th>\n",
       "      <td>21888.88903</td>\n",
       "      <td>19007.19129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpPercap_1992</th>\n",
       "      <td>23424.76683</td>\n",
       "      <td>18363.32494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpPercap_1997</th>\n",
       "      <td>26997.93657</td>\n",
       "      <td>21050.41377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpPercap_2002</th>\n",
       "      <td>30687.75473</td>\n",
       "      <td>23189.80135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdpPercap_2007</th>\n",
       "      <td>34435.36744</td>\n",
       "      <td>25185.00911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "country           Australia  New Zealand\n",
       "gdpPercap_1952  10039.59564  10556.57566\n",
       "gdpPercap_1957  10949.64959  12247.39532\n",
       "gdpPercap_1962  12217.22686  13175.67800\n",
       "gdpPercap_1967  14526.12465  14463.91893\n",
       "gdpPercap_1972  16788.62948  16046.03728\n",
       "gdpPercap_1977  18334.19751  16233.71770\n",
       "gdpPercap_1982  19477.00928  17632.41040\n",
       "gdpPercap_1987  21888.88903  19007.19129\n",
       "gdpPercap_1992  23424.76683  18363.32494\n",
       "gdpPercap_1997  26997.93657  21050.41377\n",
       "gdpPercap_2002  30687.75473  23189.80135\n",
       "gdpPercap_2007  34435.36744  25185.00911"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 30 entries, Albania to United Kingdom\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   gdpPercap_1952  30 non-null     float64\n",
      " 1   gdpPercap_1957  30 non-null     float64\n",
      " 2   gdpPercap_1962  30 non-null     float64\n",
      " 3   gdpPercap_1967  30 non-null     float64\n",
      " 4   gdpPercap_1972  30 non-null     float64\n",
      " 5   gdpPercap_1977  30 non-null     float64\n",
      " 6   gdpPercap_1982  30 non-null     float64\n",
      " 7   gdpPercap_1987  30 non-null     float64\n",
      " 8   gdpPercap_1992  30 non-null     float64\n",
      " 9   gdpPercap_1997  30 non-null     float64\n",
      " 10  gdpPercap_2002  30 non-null     float64\n",
      " 11  gdpPercap_2007  30 non-null     float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 3.0+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('data/gapminder_gdp_europe.csv', index_col='country')\n",
    "df = data.copy()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Albania                    5937.029526\n",
       "Austria                   36126.492700\n",
       "Belgium                   33692.605080\n",
       "Bosnia and Herzegovina     7446.298803\n",
       "Bulgaria                  10680.792820\n",
       "Croatia                   14619.222720\n",
       "Czech Republic            22833.308510\n",
       "Denmark                   35278.418740\n",
       "Finland                   33207.084400\n",
       "France                    30470.016700\n",
       "Germany                   32170.374420\n",
       "Greece                    27538.411880\n",
       "Hungary                   18008.944440\n",
       "Iceland                   36180.789190\n",
       "Ireland                   40675.996350\n",
       "Italy                     28569.719700\n",
       "Montenegro                 9253.896111\n",
       "Netherlands               36797.933320\n",
       "Norway                    49357.190170\n",
       "Poland                    15389.924680\n",
       "Portugal                  20509.647770\n",
       "Romania                   10808.475610\n",
       "Serbia                     9786.534714\n",
       "Slovak Republic           18678.314350\n",
       "Slovenia                  25768.257590\n",
       "Spain                     28821.063700\n",
       "Sweden                    33859.748350\n",
       "Switzerland               37506.419070\n",
       "Turkey                     8458.276384\n",
       "United Kingdom            33203.261280\n",
       "Name: gdpPercap_2007, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gdpPercap_2007']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1601.056136"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Albania', 'gdpPercap_1952']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Albania    1601.056136\n",
       "Austria    6137.076492\n",
       "Belgium    8343.105127\n",
       "Name: gdpPercap_1952, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Albania':'Belgium', 'gdpPercap_1952']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Albania    1601.056136\n",
       "Austria    6137.076492\n",
       "Belgium    8343.105127\n",
       "Name: gdpPercap_1952, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:3, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdpPercap_1962</th>\n",
       "      <th>gdpPercap_1967</th>\n",
       "      <th>gdpPercap_1972</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>8243.582340</td>\n",
       "      <td>10022.401310</td>\n",
       "      <td>12269.273780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montenegro</th>\n",
       "      <td>4649.593785</td>\n",
       "      <td>5907.850937</td>\n",
       "      <td>7778.414017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>12790.849560</td>\n",
       "      <td>15363.251360</td>\n",
       "      <td>18794.745670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>13450.401510</td>\n",
       "      <td>16361.876470</td>\n",
       "      <td>18965.055510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>5338.752143</td>\n",
       "      <td>6557.152776</td>\n",
       "      <td>8006.506993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gdpPercap_1962  gdpPercap_1967  gdpPercap_1972\n",
       "country                                                    \n",
       "Italy           8243.582340    10022.401310    12269.273780\n",
       "Montenegro      4649.593785     5907.850937     7778.414017\n",
       "Netherlands    12790.849560    15363.251360    18794.745670\n",
       "Norway         13450.401510    16361.876470    18965.055510\n",
       "Poland          5338.752143     6557.152776     8006.506993"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc['Italy':'Poland', 'gdpPercap_1962':'gdpPercap_1972']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = df > 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdpPercap_1962</th>\n",
       "      <th>gdpPercap_1967</th>\n",
       "      <th>gdpPercap_1972</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10022.40131</td>\n",
       "      <td>12269.27378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montenegro</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>12790.84956</td>\n",
       "      <td>15363.25136</td>\n",
       "      <td>18794.74567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>13450.40151</td>\n",
       "      <td>16361.87647</td>\n",
       "      <td>18965.05551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gdpPercap_1962  gdpPercap_1967  gdpPercap_1972\n",
       "country                                                    \n",
       "Italy                   NaN     10022.40131     12269.27378\n",
       "Montenegro              NaN             NaN             NaN\n",
       "Netherlands     12790.84956     15363.25136     18794.74567\n",
       "Norway          13450.40151     16361.87647     18965.05551\n",
       "Poland                  NaN             NaN             NaN"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gdpPercap_1962</th>\n",
       "      <th>gdpPercap_1967</th>\n",
       "      <th>gdpPercap_1972</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13120.625535</td>\n",
       "      <td>13915.843047</td>\n",
       "      <td>16676.358320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>466.373656</td>\n",
       "      <td>3408.589070</td>\n",
       "      <td>3817.597015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12790.849560</td>\n",
       "      <td>10022.401310</td>\n",
       "      <td>12269.273780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12955.737547</td>\n",
       "      <td>12692.826335</td>\n",
       "      <td>15532.009725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13120.625535</td>\n",
       "      <td>15363.251360</td>\n",
       "      <td>18794.745670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13285.513523</td>\n",
       "      <td>15862.563915</td>\n",
       "      <td>18879.900590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13450.401510</td>\n",
       "      <td>16361.876470</td>\n",
       "      <td>18965.055510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gdpPercap_1962  gdpPercap_1967  gdpPercap_1972\n",
       "count        2.000000        3.000000        3.000000\n",
       "mean     13120.625535    13915.843047    16676.358320\n",
       "std        466.373656     3408.589070     3817.597015\n",
       "min      12790.849560    10022.401310    12269.273780\n",
       "25%      12955.737547    12692.826335    15532.009725\n",
       "50%      13120.625535    15363.251360    18794.745670\n",
       "75%      13285.513523    15862.563915    18879.900590\n",
       "max      13450.401510    16361.876470    18965.055510"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[mask]\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.describe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>gdpPercap_1952</th>\n",
       "      <th>gdpPercap_1957</th>\n",
       "      <th>gdpPercap_1962</th>\n",
       "      <th>gdpPercap_1967</th>\n",
       "      <th>gdpPercap_1972</th>\n",
       "      <th>gdpPercap_1977</th>\n",
       "      <th>gdpPercap_1982</th>\n",
       "      <th>gdpPercap_1987</th>\n",
       "      <th>gdpPercap_1992</th>\n",
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       "      <th>gdpPercap_2007</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Croatia</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hungary</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ireland</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montenegro</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Romania</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Serbia</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovak Republic</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  \\\n",
       "country                                                                  \n",
       "Albania                          False           False           False   \n",
       "Austria                           True            True            True   \n",
       "Belgium                           True            True            True   \n",
       "Bosnia and Herzegovina           False           False           False   \n",
       "Bulgaria                         False           False           False   \n",
       "Croatia                          False           False           False   \n",
       "Czech Republic                    True            True            True   \n",
       "Denmark                           True            True            True   \n",
       "Finland                           True            True            True   \n",
       "France                            True            True            True   \n",
       "Germany                           True            True            True   \n",
       "Greece                           False           False           False   \n",
       "Hungary                          False           False           False   \n",
       "Iceland                           True            True            True   \n",
       "Ireland                          False           False           False   \n",
       "Italy                            False           False           False   \n",
       "Montenegro                       False           False           False   \n",
       "Netherlands                       True            True            True   \n",
       "Norway                            True            True            True   \n",
       "Poland                           False           False           False   \n",
       "Portugal                         False           False           False   \n",
       "Romania                          False           False           False   \n",
       "Serbia                           False           False           False   \n",
       "Slovak Republic                  False           False           False   \n",
       "Slovenia                         False           False           False   \n",
       "Spain                            False           False           False   \n",
       "Sweden                            True            True            True   \n",
       "Switzerland                       True            True            True   \n",
       "Turkey                           False           False           False   \n",
       "United Kingdom                    True            True            True   \n",
       "\n",
       "                        gdpPercap_1967  gdpPercap_1972  gdpPercap_1977  \\\n",
       "country                                                                  \n",
       "Albania                          False           False           False   \n",
       "Austria                           True            True            True   \n",
       "Belgium                           True            True            True   \n",
       "Bosnia and Herzegovina           False           False           False   \n",
       "Bulgaria                         False           False           False   \n",
       "Croatia                          False           False           False   \n",
       "Czech Republic                    True            True            True   \n",
       "Denmark                           True            True            True   \n",
       "Finland                           True            True            True   \n",
       "France                            True            True            True   \n",
       "Germany                           True            True            True   \n",
       "Greece                           False            True           False   \n",
       "Hungary                          False           False           False   \n",
       "Iceland                           True            True            True   \n",
       "Ireland                          False           False           False   \n",
       "Italy                            False           False           False   \n",
       "Montenegro                       False           False           False   \n",
       "Netherlands                       True            True            True   \n",
       "Norway                            True            True            True   \n",
       "Poland                           False           False           False   \n",
       "Portugal                         False           False           False   \n",
       "Romania                          False           False           False   \n",
       "Serbia                           False           False           False   \n",
       "Slovak Republic                  False           False           False   \n",
       "Slovenia                         False           False            True   \n",
       "Spain                            False           False           False   \n",
       "Sweden                            True            True            True   \n",
       "Switzerland                       True            True            True   \n",
       "Turkey                           False           False           False   \n",
       "United Kingdom                    True            True            True   \n",
       "\n",
       "                        gdpPercap_1982  gdpPercap_1987  gdpPercap_1992  \\\n",
       "country                                                                  \n",
       "Albania                          False           False           False   \n",
       "Austria                           True            True            True   \n",
       "Belgium                           True            True            True   \n",
       "Bosnia and Herzegovina           False           False           False   \n",
       "Bulgaria                         False           False           False   \n",
       "Croatia                          False           False           False   \n",
       "Czech Republic                   False           False           False   \n",
       "Denmark                           True            True            True   \n",
       "Finland                           True            True            True   \n",
       "France                            True            True            True   \n",
       "Germany                           True            True            True   \n",
       "Greece                           False           False            True   \n",
       "Hungary                          False           False           False   \n",
       "Iceland                           True            True            True   \n",
       "Ireland                          False           False            True   \n",
       "Italy                             True            True            True   \n",
       "Montenegro                       False           False           False   \n",
       "Netherlands                       True            True            True   \n",
       "Norway                            True            True            True   \n",
       "Poland                           False           False           False   \n",
       "Portugal                         False           False           False   \n",
       "Romania                          False           False           False   \n",
       "Serbia                           False           False           False   \n",
       "Slovak Republic                  False           False           False   \n",
       "Slovenia                          True            True           False   \n",
       "Spain                            False           False            True   \n",
       "Sweden                            True            True            True   \n",
       "Switzerland                       True            True            True   \n",
       "Turkey                           False           False           False   \n",
       "United Kingdom                    True            True            True   \n",
       "\n",
       "                        gdpPercap_1997  gdpPercap_2002  gdpPercap_2007  \n",
       "country                                                                 \n",
       "Albania                          False           False           False  \n",
       "Austria                           True            True            True  \n",
       "Belgium                           True            True            True  \n",
       "Bosnia and Herzegovina           False           False           False  \n",
       "Bulgaria                         False           False           False  \n",
       "Croatia                          False           False           False  \n",
       "Czech Republic                   False           False           False  \n",
       "Denmark                           True            True            True  \n",
       "Finland                           True            True            True  \n",
       "France                            True            True            True  \n",
       "Germany                           True            True            True  \n",
       "Greece                           False            True            True  \n",
       "Hungary                          False           False           False  \n",
       "Iceland                           True            True            True  \n",
       "Ireland                           True            True            True  \n",
       "Italy                             True            True            True  \n",
       "Montenegro                       False           False           False  \n",
       "Netherlands                       True            True            True  \n",
       "Norway                            True            True            True  \n",
       "Poland                           False           False           False  \n",
       "Portugal                         False           False           False  \n",
       "Romania                          False           False           False  \n",
       "Serbia                           False           False           False  \n",
       "Slovak Republic                  False           False           False  \n",
       "Slovenia                         False           False            True  \n",
       "Spain                             True            True            True  \n",
       "Sweden                            True            True            True  \n",
       "Switzerland                       True            True            True  \n",
       "Turkey                           False           False           False  \n",
       "United Kingdom                    True            True            True  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_higher = data > data.mean()\n",
    "mask_higher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "wealth_score = mask_higher.aggregate('sum', axis=1) / len(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.groupby(wealth_score).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('output/wealth_sum.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = [0, 1, 2, 3]\n",
    "position = [0, 100, 200, 300]\n",
    "\n",
    "plt.plot(time, position)\n",
    "plt.xlabel('Time (hr)')\n",
    "plt.ylabel('Position (km)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 2 entries, Australia to New Zealand\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   gdpPercap_1952  2 non-null      float64\n",
      " 1   gdpPercap_1957  2 non-null      float64\n",
      " 2   gdpPercap_1962  2 non-null      float64\n",
      " 3   gdpPercap_1967  2 non-null      float64\n",
      " 4   gdpPercap_1972  2 non-null      float64\n",
      " 5   gdpPercap_1977  2 non-null      float64\n",
      " 6   gdpPercap_1982  2 non-null      float64\n",
      " 7   gdpPercap_1987  2 non-null      float64\n",
      " 8   gdpPercap_1992  2 non-null      float64\n",
      " 9   gdpPercap_1997  2 non-null      float64\n",
      " 10  gdpPercap_2002  2 non-null      float64\n",
      " 11  gdpPercap_2007  2 non-null      float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 208.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('data/gapminder_gdp_oceania.csv', index_col='country')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = data.columns.str.strip('gdpPercap_')\n",
    "data.columns = years.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>1952</th>\n",
       "      <th>1957</th>\n",
       "      <th>1962</th>\n",
       "      <th>1967</th>\n",
       "      <th>1972</th>\n",
       "      <th>1977</th>\n",
       "      <th>1982</th>\n",
       "      <th>1987</th>\n",
       "      <th>1992</th>\n",
       "      <th>1997</th>\n",
       "      <th>2002</th>\n",
       "      <th>2007</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>10039.59564</td>\n",
       "      <td>10949.64959</td>\n",
       "      <td>12217.22686</td>\n",
       "      <td>14526.12465</td>\n",
       "      <td>16788.62948</td>\n",
       "      <td>18334.19751</td>\n",
       "      <td>19477.00928</td>\n",
       "      <td>21888.88903</td>\n",
       "      <td>23424.76683</td>\n",
       "      <td>26997.93657</td>\n",
       "      <td>30687.75473</td>\n",
       "      <td>34435.36744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>10556.57566</td>\n",
       "      <td>12247.39532</td>\n",
       "      <td>13175.67800</td>\n",
       "      <td>14463.91893</td>\n",
       "      <td>16046.03728</td>\n",
       "      <td>16233.71770</td>\n",
       "      <td>17632.41040</td>\n",
       "      <td>19007.19129</td>\n",
       "      <td>18363.32494</td>\n",
       "      <td>21050.41377</td>\n",
       "      <td>23189.80135</td>\n",
       "      <td>25185.00911</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    1952         1957         1962         1967         1972  \\\n",
       "country                                                                        \n",
       "Australia    10039.59564  10949.64959  12217.22686  14526.12465  16788.62948   \n",
       "New Zealand  10556.57566  12247.39532  13175.67800  14463.91893  16046.03728   \n",
       "\n",
       "                    1977         1982         1987         1992         1997  \\\n",
       "country                                                                        \n",
       "Australia    18334.19751  19477.00928  21888.88903  23424.76683  26997.93657   \n",
       "New Zealand  16233.71770  17632.41040  19007.19129  18363.32494  21050.41377   \n",
       "\n",
       "                    2002         2007  \n",
       "country                                \n",
       "Australia    30687.75473  34435.36744  \n",
       "New Zealand  23189.80135  25185.00911  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.loc['Australia', :].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'GDP per capita')"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.T.plot()\n",
    "plt.ylabel('GDP per capita')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 122,
     "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": [
    "plt.style.use('ggplot')\n",
    "data.T.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_bulk_density(mass, volume):\n",
    "    '''Return dry bulk density = powder mass / powder volume.'''\n",
    "    assert volume > 0\n",
    "    return mass / volume"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "calc_bulk_density?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}