practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2*7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "print(3+7)\n",
    "print(2+3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to Jupyter Notebooks!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Headings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will be a list:\n",
    "* item\n",
    "* another item\n",
    "\n",
    "1. Numbered list\n",
    "2. Another item"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "age = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42\n"
     ]
    }
   ],
   "source": [
    "print(age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_name = 'Kunal'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kunal\n"
     ]
    }
   ],
   "source": [
    "print(first_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'last_name' 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-5-a637c5453549>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'last_name' is not defined"
     ]
    }
   ],
   "source": [
    "print(last_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have to define it!\n",
    "last_name = 'Marwaha'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Marwaha\n"
     ]
    }
   ],
   "source": [
    "print(last_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We ran this cell twice!\n",
    "age = age + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n"
     ]
    }
   ],
   "source": [
    "print(age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "word = 'develop'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d\n"
     ]
    }
   ],
   "source": [
    "print(word[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e\n"
     ]
    }
   ],
   "source": [
    "print(word[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'deve'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'vel'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Challenge: How do I get \"vel\"  from develop?\n",
    "word[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 41\n",
    "y = x * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(51)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(51.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(\"Ryan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(\"Ryan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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-24-9bb4d3c3b12b>\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[0;36m51\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(51)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This turns 51 into a string! (\"51\")\n",
    "len(str(51))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# int or float\n",
    "type(\"1951 . Today was a great day\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(int(\"1951\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(3.25 + 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "builtin_function_or_method"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(print)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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-36-b06ce48a46c7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# This won't work\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;34m\"15\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m30\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": [
    "# This won't work\n",
    "\"15\" + 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(\"15\") + 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1530'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"15\" + str(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(2, 5, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(-1, 5, -10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-10"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(-1, 5, -10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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-42-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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max('b', 'a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ryan'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max('bob', 'brody', 'ryan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function round(number, ndigits=None)>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(3.712)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.7"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(3.712, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function round in module builtins:\n",
      "\n",
      "round(number, ndigits=None)\n",
      "    Round a number to a given precision in decimal digits.\n",
      "    \n",
      "    The return value is an integer if ndigits is omitted or None.  Otherwise\n",
      "    the return value has the same type as the number.  ndigits may be negative.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(\"abc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chr(97)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hi'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(\"hello\", \"hi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable     Type    Data/Info\n",
      "------------------------------\n",
      "age          int     48\n",
      "first_name   str     Kunal\n",
      "last_name    str     Marwaha\n",
      "word         str     develop\n",
      "x            int     41\n",
      "y            int     82\n"
     ]
    }
   ],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Kunal'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tab completion is also nice!\n",
    "first_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.cos(math.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this for help:\n",
    "# help(math)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math as m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import log, pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.302585092994046"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pd.read_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "oceania = pd.read_csv('data/gapminder_gdp_oceania.csv', index_col='country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0moceania\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mappend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Set the DataFrame index using existing columns.\n",
       "\n",
       "Set the DataFrame index (row labels) using one or more existing\n",
       "columns or arrays (of the correct length). The index can replace the\n",
       "existing index or expand on it.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "keys : label or array-like or list of labels/arrays\n",
       "    This parameter can be either a single column key, a single array of\n",
       "    the same length as the calling DataFrame, or a list containing an\n",
       "    arbitrary combination of column keys and arrays. Here, \"array\"\n",
       "    encompasses :class:`Series`, :class:`Index`, ``np.ndarray``, and\n",
       "    instances of :class:`~collections.abc.Iterator`.\n",
       "drop : bool, default True\n",
       "    Delete columns to be used as the new index.\n",
       "append : bool, default False\n",
       "    Whether to append columns to existing index.\n",
       "inplace : bool, default False\n",
       "    Modify the DataFrame in place (do not create a new object).\n",
       "verify_integrity : bool, default False\n",
       "    Check the new index for duplicates. Otherwise defer the check until\n",
       "    necessary. Setting to False will improve the performance of this\n",
       "    method.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "DataFrame\n",
       "    Changed row labels.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "DataFrame.reset_index : Opposite of set_index.\n",
       "DataFrame.reindex : Change to new indices or expand indices.\n",
       "DataFrame.reindex_like : Change to same indices as other DataFrame.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> df = pd.DataFrame({'month': [1, 4, 7, 10],\n",
       "...                    'year': [2012, 2014, 2013, 2014],\n",
       "...                    'sale': [55, 40, 84, 31]})\n",
       ">>> df\n",
       "   month  year  sale\n",
       "0      1  2012    55\n",
       "1      4  2014    40\n",
       "2      7  2013    84\n",
       "3     10  2014    31\n",
       "\n",
       "Set the index to become the 'month' column:\n",
       "\n",
       ">>> df.set_index('month')\n",
       "       year  sale\n",
       "month\n",
       "1      2012    55\n",
       "4      2014    40\n",
       "7      2013    84\n",
       "10     2014    31\n",
       "\n",
       "Create a MultiIndex using columns 'year' and 'month':\n",
       "\n",
       ">>> df.set_index(['year', 'month'])\n",
       "            sale\n",
       "year  month\n",
       "2012  1     55\n",
       "2014  4     40\n",
       "2013  7     84\n",
       "2014  10    31\n",
       "\n",
       "Create a MultiIndex using an Index and a column:\n",
       "\n",
       ">>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])\n",
       "         month  sale\n",
       "   year\n",
       "1  2012  1      55\n",
       "2  2014  4      40\n",
       "3  2013  7      84\n",
       "4  2014  10     31\n",
       "\n",
       "Create a MultiIndex using two Series:\n",
       "\n",
       ">>> s = pd.Series([1, 2, 3, 4])\n",
       ">>> df.set_index([s, s**2])\n",
       "      month  year  sale\n",
       "1 1       1  2012    55\n",
       "2 4       4  2014    40\n",
       "3 9       7  2013    84\n",
       "4 16     10  2014    31\n",
       "\u001b[0;31mFile:\u001b[0m      ~/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oceania.set_index?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(oceania)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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",
       "      <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",
       "      <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": [
       "             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": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oceania"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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",
      "gdpPercap_1952    2 non-null float64\n",
      "gdpPercap_1957    2 non-null float64\n",
      "gdpPercap_1962    2 non-null float64\n",
      "gdpPercap_1967    2 non-null float64\n",
      "gdpPercap_1972    2 non-null float64\n",
      "gdpPercap_1977    2 non-null float64\n",
      "gdpPercap_1982    2 non-null float64\n",
      "gdpPercap_1987    2 non-null float64\n",
      "gdpPercap_1992    2 non-null float64\n",
      "gdpPercap_1997    2 non-null float64\n",
      "gdpPercap_2002    2 non-null float64\n",
      "gdpPercap_2007    2 non-null float64\n",
      "dtypes: float64(12)\n",
      "memory usage: 208.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "oceania.info()"
   ]
  },
  {
   "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",
       "        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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\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",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\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": [
    "# summary statistics\n",
    "oceania.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(oceania)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "## HOw about for americas?\n",
    "americas = pd.read_csv('data/gapminder_gdp_americas.csv', index_col = 'country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 25 entries, Argentina to Venezuela\n",
      "Data columns (total 13 columns):\n",
      "continent         25 non-null object\n",
      "gdpPercap_1952    25 non-null float64\n",
      "gdpPercap_1957    25 non-null float64\n",
      "gdpPercap_1962    25 non-null float64\n",
      "gdpPercap_1967    25 non-null float64\n",
      "gdpPercap_1972    25 non-null float64\n",
      "gdpPercap_1977    25 non-null float64\n",
      "gdpPercap_1982    25 non-null float64\n",
      "gdpPercap_1987    25 non-null float64\n",
      "gdpPercap_1992    25 non-null float64\n",
      "gdpPercap_1997    25 non-null float64\n",
      "gdpPercap_2002    25 non-null float64\n",
      "gdpPercap_2007    25 non-null float64\n",
      "dtypes: float64(12), object(1)\n",
      "memory usage: 2.7+ KB\n"
     ]
    }
   ],
   "source": [
    "americas.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</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",
       "      <td>count</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>4079.062552</td>\n",
       "      <td>4616.043733</td>\n",
       "      <td>4901.541870</td>\n",
       "      <td>5668.253496</td>\n",
       "      <td>6491.334139</td>\n",
       "      <td>7352.007126</td>\n",
       "      <td>7506.737088</td>\n",
       "      <td>7793.400261</td>\n",
       "      <td>8044.934406</td>\n",
       "      <td>8889.300863</td>\n",
       "      <td>9287.677107</td>\n",
       "      <td>11003.031625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>3001.727522</td>\n",
       "      <td>3312.381083</td>\n",
       "      <td>3421.740569</td>\n",
       "      <td>4160.885560</td>\n",
       "      <td>4754.404329</td>\n",
       "      <td>5355.602518</td>\n",
       "      <td>5530.490471</td>\n",
       "      <td>6665.039509</td>\n",
       "      <td>7047.089191</td>\n",
       "      <td>7874.225145</td>\n",
       "      <td>8895.817785</td>\n",
       "      <td>9713.209302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1397.717137</td>\n",
       "      <td>1544.402995</td>\n",
       "      <td>1662.137359</td>\n",
       "      <td>1452.057666</td>\n",
       "      <td>1654.456946</td>\n",
       "      <td>1874.298931</td>\n",
       "      <td>2011.159549</td>\n",
       "      <td>1823.015995</td>\n",
       "      <td>1456.309517</td>\n",
       "      <td>1341.726931</td>\n",
       "      <td>1270.364932</td>\n",
       "      <td>1201.637154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2428.237769</td>\n",
       "      <td>2487.365989</td>\n",
       "      <td>2750.364446</td>\n",
       "      <td>3242.531147</td>\n",
       "      <td>4031.408271</td>\n",
       "      <td>4756.763836</td>\n",
       "      <td>4258.503604</td>\n",
       "      <td>4140.442097</td>\n",
       "      <td>4439.450840</td>\n",
       "      <td>4684.313807</td>\n",
       "      <td>4858.347495</td>\n",
       "      <td>5728.353514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3048.302900</td>\n",
       "      <td>3780.546651</td>\n",
       "      <td>4086.114078</td>\n",
       "      <td>4643.393534</td>\n",
       "      <td>5305.445256</td>\n",
       "      <td>6281.290855</td>\n",
       "      <td>6434.501797</td>\n",
       "      <td>6360.943444</td>\n",
       "      <td>6618.743050</td>\n",
       "      <td>7113.692252</td>\n",
       "      <td>6994.774861</td>\n",
       "      <td>8948.102923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>3939.978789</td>\n",
       "      <td>4756.525781</td>\n",
       "      <td>5180.755910</td>\n",
       "      <td>5788.093330</td>\n",
       "      <td>6809.406690</td>\n",
       "      <td>7674.929108</td>\n",
       "      <td>8997.897412</td>\n",
       "      <td>7807.095818</td>\n",
       "      <td>8137.004775</td>\n",
       "      <td>9767.297530</td>\n",
       "      <td>8797.640716</td>\n",
       "      <td>11977.574960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>13990.482080</td>\n",
       "      <td>14847.127120</td>\n",
       "      <td>16173.145860</td>\n",
       "      <td>19530.365570</td>\n",
       "      <td>21806.035940</td>\n",
       "      <td>24072.632130</td>\n",
       "      <td>25009.559140</td>\n",
       "      <td>29884.350410</td>\n",
       "      <td>32003.932240</td>\n",
       "      <td>35767.433030</td>\n",
       "      <td>39097.099550</td>\n",
       "      <td>42951.653090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gdpPercap_1952  gdpPercap_1957  gdpPercap_1962  gdpPercap_1967  \\\n",
       "count       25.000000       25.000000       25.000000       25.000000   \n",
       "mean      4079.062552     4616.043733     4901.541870     5668.253496   \n",
       "std       3001.727522     3312.381083     3421.740569     4160.885560   \n",
       "min       1397.717137     1544.402995     1662.137359     1452.057666   \n",
       "25%       2428.237769     2487.365989     2750.364446     3242.531147   \n",
       "50%       3048.302900     3780.546651     4086.114078     4643.393534   \n",
       "75%       3939.978789     4756.525781     5180.755910     5788.093330   \n",
       "max      13990.482080    14847.127120    16173.145860    19530.365570   \n",
       "\n",
       "       gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  gdpPercap_1987  \\\n",
       "count       25.000000       25.000000       25.000000       25.000000   \n",
       "mean      6491.334139     7352.007126     7506.737088     7793.400261   \n",
       "std       4754.404329     5355.602518     5530.490471     6665.039509   \n",
       "min       1654.456946     1874.298931     2011.159549     1823.015995   \n",
       "25%       4031.408271     4756.763836     4258.503604     4140.442097   \n",
       "50%       5305.445256     6281.290855     6434.501797     6360.943444   \n",
       "75%       6809.406690     7674.929108     8997.897412     7807.095818   \n",
       "max      21806.035940    24072.632130    25009.559140    29884.350410   \n",
       "\n",
       "       gdpPercap_1992  gdpPercap_1997  gdpPercap_2002  gdpPercap_2007  \n",
       "count       25.000000       25.000000       25.000000       25.000000  \n",
       "mean      8044.934406     8889.300863     9287.677107    11003.031625  \n",
       "std       7047.089191     7874.225145     8895.817785     9713.209302  \n",
       "min       1456.309517     1341.726931     1270.364932     1201.637154  \n",
       "25%       4439.450840     4684.313807     4858.347495     5728.353514  \n",
       "50%       6618.743050     7113.692252     6994.774861     8948.102923  \n",
       "75%       8137.004775     9767.297530     8797.640716    11977.574960  \n",
       "max      32003.932240    35767.433030    39097.099550    42951.653090  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "americas.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11367.16112"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "americas.loc[\"Canada\", \"gdpPercap_1952\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent         Americas\n",
       "gdpPercap_1952     11367.2\n",
       "gdpPercap_1957       12490\n",
       "gdpPercap_1962     13462.5\n",
       "gdpPercap_1967     16076.6\n",
       "gdpPercap_1972     18970.6\n",
       "gdpPercap_1977     22090.9\n",
       "gdpPercap_1982     22898.8\n",
       "gdpPercap_1987     26626.5\n",
       "gdpPercap_1992     26342.9\n",
       "gdpPercap_1997     28954.9\n",
       "gdpPercap_2002       33329\n",
       "gdpPercap_2007     36319.2\n",
       "Name: Canada, dtype: object"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "americas.loc[\"Canada\", :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a variable called \"subset\"\n",
    "# That has all countries between Argentina and Colombia\n",
    "# and only has gdp columns from 1967 to 1997"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = americas.loc[\"Argentina\":\"Colombia\", \"gdpPercap_1967\":\"gdpPercap_1997\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "    </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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Argentina</td>\n",
       "      <td>8052.953021</td>\n",
       "      <td>9443.038526</td>\n",
       "      <td>10079.026740</td>\n",
       "      <td>8997.897412</td>\n",
       "      <td>9139.671389</td>\n",
       "      <td>9308.418710</td>\n",
       "      <td>10967.281950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bolivia</td>\n",
       "      <td>2586.886053</td>\n",
       "      <td>2980.331339</td>\n",
       "      <td>3548.097832</td>\n",
       "      <td>3156.510452</td>\n",
       "      <td>2753.691490</td>\n",
       "      <td>2961.699694</td>\n",
       "      <td>3326.143191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Brazil</td>\n",
       "      <td>3429.864357</td>\n",
       "      <td>4985.711467</td>\n",
       "      <td>6660.118654</td>\n",
       "      <td>7030.835878</td>\n",
       "      <td>7807.095818</td>\n",
       "      <td>6950.283021</td>\n",
       "      <td>7957.980824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Canada</td>\n",
       "      <td>16076.588030</td>\n",
       "      <td>18970.570860</td>\n",
       "      <td>22090.883060</td>\n",
       "      <td>22898.792140</td>\n",
       "      <td>26626.515030</td>\n",
       "      <td>26342.884260</td>\n",
       "      <td>28954.925890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Chile</td>\n",
       "      <td>5106.654313</td>\n",
       "      <td>5494.024437</td>\n",
       "      <td>4756.763836</td>\n",
       "      <td>5095.665738</td>\n",
       "      <td>5547.063754</td>\n",
       "      <td>7596.125964</td>\n",
       "      <td>10118.053180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Colombia</td>\n",
       "      <td>2678.729839</td>\n",
       "      <td>3264.660041</td>\n",
       "      <td>3815.807870</td>\n",
       "      <td>4397.575659</td>\n",
       "      <td>4903.219100</td>\n",
       "      <td>5444.648617</td>\n",
       "      <td>6117.361746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gdpPercap_1967  gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  \\\n",
       "country                                                                     \n",
       "Argentina     8052.953021     9443.038526    10079.026740     8997.897412   \n",
       "Bolivia       2586.886053     2980.331339     3548.097832     3156.510452   \n",
       "Brazil        3429.864357     4985.711467     6660.118654     7030.835878   \n",
       "Canada       16076.588030    18970.570860    22090.883060    22898.792140   \n",
       "Chile         5106.654313     5494.024437     4756.763836     5095.665738   \n",
       "Colombia      2678.729839     3264.660041     3815.807870     4397.575659   \n",
       "\n",
       "           gdpPercap_1987  gdpPercap_1992  gdpPercap_1997  \n",
       "country                                                    \n",
       "Argentina     9139.671389     9308.418710    10967.281950  \n",
       "Bolivia       2753.691490     2961.699694     3326.143191  \n",
       "Brazil        7807.095818     6950.283021     7957.980824  \n",
       "Canada       26626.515030    26342.884260    28954.925890  \n",
       "Chile         5547.063754     7596.125964    10118.053180  \n",
       "Colombia      4903.219100     5444.648617     6117.361746  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "    </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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Argentina</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bolivia</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",
       "      <td>Brazil</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",
       "      <td>Canada</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",
       "      <td>Chile</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Colombia</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gdpPercap_1967  gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  \\\n",
       "country                                                                     \n",
       "Argentina           False           False            True           False   \n",
       "Bolivia             False           False           False           False   \n",
       "Brazil              False           False           False           False   \n",
       "Canada               True            True            True            True   \n",
       "Chile               False           False           False           False   \n",
       "Colombia            False           False           False           False   \n",
       "\n",
       "           gdpPercap_1987  gdpPercap_1992  gdpPercap_1997  \n",
       "country                                                    \n",
       "Argentina           False           False            True  \n",
       "Bolivia             False           False           False  \n",
       "Brazil              False           False           False  \n",
       "Canada               True            True            True  \n",
       "Chile               False           False            True  \n",
       "Colombia            False           False           False  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset > 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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_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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>16076.58803</td>\n",
       "      <td>18970.57086</td>\n",
       "      <td>16084.954900</td>\n",
       "      <td>22898.79214</td>\n",
       "      <td>26626.51503</td>\n",
       "      <td>26342.88426</td>\n",
       "      <td>16680.087007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8493.665059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10638.799255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>16076.58803</td>\n",
       "      <td>18970.57086</td>\n",
       "      <td>10079.026740</td>\n",
       "      <td>22898.79214</td>\n",
       "      <td>26626.51503</td>\n",
       "      <td>26342.88426</td>\n",
       "      <td>10118.053180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>16076.58803</td>\n",
       "      <td>18970.57086</td>\n",
       "      <td>13081.990820</td>\n",
       "      <td>22898.79214</td>\n",
       "      <td>26626.51503</td>\n",
       "      <td>26342.88426</td>\n",
       "      <td>10542.667565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>16076.58803</td>\n",
       "      <td>18970.57086</td>\n",
       "      <td>16084.954900</td>\n",
       "      <td>22898.79214</td>\n",
       "      <td>26626.51503</td>\n",
       "      <td>26342.88426</td>\n",
       "      <td>10967.281950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>16076.58803</td>\n",
       "      <td>18970.57086</td>\n",
       "      <td>19087.918980</td>\n",
       "      <td>22898.79214</td>\n",
       "      <td>26626.51503</td>\n",
       "      <td>26342.88426</td>\n",
       "      <td>19961.103920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>16076.58803</td>\n",
       "      <td>18970.57086</td>\n",
       "      <td>22090.883060</td>\n",
       "      <td>22898.79214</td>\n",
       "      <td>26626.51503</td>\n",
       "      <td>26342.88426</td>\n",
       "      <td>28954.925890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gdpPercap_1967  gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  \\\n",
       "count         1.00000         1.00000        2.000000         1.00000   \n",
       "mean      16076.58803     18970.57086    16084.954900     22898.79214   \n",
       "std               NaN             NaN     8493.665059             NaN   \n",
       "min       16076.58803     18970.57086    10079.026740     22898.79214   \n",
       "25%       16076.58803     18970.57086    13081.990820     22898.79214   \n",
       "50%       16076.58803     18970.57086    16084.954900     22898.79214   \n",
       "75%       16076.58803     18970.57086    19087.918980     22898.79214   \n",
       "max       16076.58803     18970.57086    22090.883060     22898.79214   \n",
       "\n",
       "       gdpPercap_1987  gdpPercap_1992  gdpPercap_1997  \n",
       "count         1.00000         1.00000        3.000000  \n",
       "mean      26626.51503     26342.88426    16680.087007  \n",
       "std               NaN             NaN    10638.799255  \n",
       "min       26626.51503     26342.88426    10118.053180  \n",
       "25%       26626.51503     26342.88426    10542.667565  \n",
       "50%       26626.51503     26342.88426    10967.281950  \n",
       "75%       26626.51503     26342.88426    19961.103920  \n",
       "max       26626.51503     26342.88426    28954.925890  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset[subset > 10000].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>gdpPercap_1967</th>\n",
       "      <th>gdpPercap_1972</th>\n",
       "      <th>gdpPercap_1977</th>\n",
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       "      <th>country</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <td>Argentina</td>\n",
       "      <td>8052.953021</td>\n",
       "      <td>9443.038526</td>\n",
       "      <td>10079.026740</td>\n",
       "      <td>8997.897412</td>\n",
       "      <td>9139.671389</td>\n",
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       "      <td>10967.281950</td>\n",
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       "    <tr>\n",
       "      <td>Bolivia</td>\n",
       "      <td>2586.886053</td>\n",
       "      <td>2980.331339</td>\n",
       "      <td>3548.097832</td>\n",
       "      <td>3156.510452</td>\n",
       "      <td>2753.691490</td>\n",
       "      <td>2961.699694</td>\n",
       "      <td>3326.143191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Brazil</td>\n",
       "      <td>3429.864357</td>\n",
       "      <td>4985.711467</td>\n",
       "      <td>6660.118654</td>\n",
       "      <td>7030.835878</td>\n",
       "      <td>7807.095818</td>\n",
       "      <td>6950.283021</td>\n",
       "      <td>7957.980824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Canada</td>\n",
       "      <td>16076.588030</td>\n",
       "      <td>18970.570860</td>\n",
       "      <td>22090.883060</td>\n",
       "      <td>22898.792140</td>\n",
       "      <td>26626.515030</td>\n",
       "      <td>26342.884260</td>\n",
       "      <td>28954.925890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Chile</td>\n",
       "      <td>5106.654313</td>\n",
       "      <td>5494.024437</td>\n",
       "      <td>4756.763836</td>\n",
       "      <td>5095.665738</td>\n",
       "      <td>5547.063754</td>\n",
       "      <td>7596.125964</td>\n",
       "      <td>10118.053180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Colombia</td>\n",
       "      <td>2678.729839</td>\n",
       "      <td>3264.660041</td>\n",
       "      <td>3815.807870</td>\n",
       "      <td>4397.575659</td>\n",
       "      <td>4903.219100</td>\n",
       "      <td>5444.648617</td>\n",
       "      <td>6117.361746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           gdpPercap_1967  gdpPercap_1972  gdpPercap_1977  gdpPercap_1982  \\\n",
       "country                                                                     \n",
       "Argentina     8052.953021     9443.038526    10079.026740     8997.897412   \n",
       "Bolivia       2586.886053     2980.331339     3548.097832     3156.510452   \n",
       "Brazil        3429.864357     4985.711467     6660.118654     7030.835878   \n",
       "Canada       16076.588030    18970.570860    22090.883060    22898.792140   \n",
       "Chile         5106.654313     5494.024437     4756.763836     5095.665738   \n",
       "Colombia      2678.729839     3264.660041     3815.807870     4397.575659   \n",
       "\n",
       "           gdpPercap_1987  gdpPercap_1992  gdpPercap_1997  \n",
       "country                                                    \n",
       "Argentina     9139.671389     9308.418710    10967.281950  \n",
       "Bolivia       2753.691490     2961.699694     3326.143191  \n",
       "Brazil        7807.095818     6950.283021     7957.980824  \n",
       "Canada       26626.515030    26342.884260    28954.925890  \n",
       "Chile         5547.063754     7596.125964    10118.053180  \n",
       "Colombia      4903.219100     5444.648617     6117.361746  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>country</th>\n",
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       "      <th>Bolivia</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Canada</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>gdpPercap_1967</td>\n",
       "      <td>8052.953021</td>\n",
       "      <td>2586.886053</td>\n",
       "      <td>3429.864357</td>\n",
       "      <td>16076.58803</td>\n",
       "      <td>5106.654313</td>\n",
       "      <td>2678.729839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gdpPercap_1972</td>\n",
       "      <td>9443.038526</td>\n",
       "      <td>2980.331339</td>\n",
       "      <td>4985.711467</td>\n",
       "      <td>18970.57086</td>\n",
       "      <td>5494.024437</td>\n",
       "      <td>3264.660041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gdpPercap_1977</td>\n",
       "      <td>10079.026740</td>\n",
       "      <td>3548.097832</td>\n",
       "      <td>6660.118654</td>\n",
       "      <td>22090.88306</td>\n",
       "      <td>4756.763836</td>\n",
       "      <td>3815.807870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gdpPercap_1982</td>\n",
       "      <td>8997.897412</td>\n",
       "      <td>3156.510452</td>\n",
       "      <td>7030.835878</td>\n",
       "      <td>22898.79214</td>\n",
       "      <td>5095.665738</td>\n",
       "      <td>4397.575659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gdpPercap_1987</td>\n",
       "      <td>9139.671389</td>\n",
       "      <td>2753.691490</td>\n",
       "      <td>7807.095818</td>\n",
       "      <td>26626.51503</td>\n",
       "      <td>5547.063754</td>\n",
       "      <td>4903.219100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gdpPercap_1992</td>\n",
       "      <td>9308.418710</td>\n",
       "      <td>2961.699694</td>\n",
       "      <td>6950.283021</td>\n",
       "      <td>26342.88426</td>\n",
       "      <td>7596.125964</td>\n",
       "      <td>5444.648617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gdpPercap_1997</td>\n",
       "      <td>10967.281950</td>\n",
       "      <td>3326.143191</td>\n",
       "      <td>7957.980824</td>\n",
       "      <td>28954.92589</td>\n",
       "      <td>10118.053180</td>\n",
       "      <td>6117.361746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "country            Argentina      Bolivia       Brazil       Canada  \\\n",
       "gdpPercap_1967   8052.953021  2586.886053  3429.864357  16076.58803   \n",
       "gdpPercap_1972   9443.038526  2980.331339  4985.711467  18970.57086   \n",
       "gdpPercap_1977  10079.026740  3548.097832  6660.118654  22090.88306   \n",
       "gdpPercap_1982   8997.897412  3156.510452  7030.835878  22898.79214   \n",
       "gdpPercap_1987   9139.671389  2753.691490  7807.095818  26626.51503   \n",
       "gdpPercap_1992   9308.418710  2961.699694  6950.283021  26342.88426   \n",
       "gdpPercap_1997  10967.281950  3326.143191  7957.980824  28954.92589   \n",
       "\n",
       "country                Chile     Colombia  \n",
       "gdpPercap_1967   5106.654313  2678.729839  \n",
       "gdpPercap_1972   5494.024437  3264.660041  \n",
       "gdpPercap_1977   4756.763836  3815.807870  \n",
       "gdpPercap_1982   5095.665738  4397.575659  \n",
       "gdpPercap_1987   5547.063754  4903.219100  \n",
       "gdpPercap_1992   7596.125964  5444.648617  \n",
       "gdpPercap_1997  10118.053180  6117.361746  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Argentina</th>\n",
       "      <th>Bolivia</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Chile</th>\n",
       "      <th>Colombia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>9426.898250</td>\n",
       "      <td>3044.765722</td>\n",
       "      <td>6403.127146</td>\n",
       "      <td>23137.308467</td>\n",
       "      <td>6244.907317</td>\n",
       "      <td>4374.571839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>909.818471</td>\n",
       "      <td>329.249939</td>\n",
       "      <td>1632.865541</td>\n",
       "      <td>4558.185360</td>\n",
       "      <td>1945.456612</td>\n",
       "      <td>1216.715492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>8052.953021</td>\n",
       "      <td>2586.886053</td>\n",
       "      <td>3429.864357</td>\n",
       "      <td>16076.588030</td>\n",
       "      <td>4756.763836</td>\n",
       "      <td>2678.729839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>9068.784401</td>\n",
       "      <td>2857.695592</td>\n",
       "      <td>5822.915060</td>\n",
       "      <td>20530.726960</td>\n",
       "      <td>5101.160026</td>\n",
       "      <td>3540.233955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>9308.418710</td>\n",
       "      <td>2980.331339</td>\n",
       "      <td>6950.283021</td>\n",
       "      <td>22898.792140</td>\n",
       "      <td>5494.024437</td>\n",
       "      <td>4397.575659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>9761.032633</td>\n",
       "      <td>3241.326821</td>\n",
       "      <td>7418.965848</td>\n",
       "      <td>26484.699645</td>\n",
       "      <td>6571.594859</td>\n",
       "      <td>5173.933859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>10967.281950</td>\n",
       "      <td>3548.097832</td>\n",
       "      <td>7957.980824</td>\n",
       "      <td>28954.925890</td>\n",
       "      <td>10118.053180</td>\n",
       "      <td>6117.361746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "country     Argentina      Bolivia       Brazil        Canada         Chile  \\\n",
       "count        7.000000     7.000000     7.000000      7.000000      7.000000   \n",
       "mean      9426.898250  3044.765722  6403.127146  23137.308467   6244.907317   \n",
       "std        909.818471   329.249939  1632.865541   4558.185360   1945.456612   \n",
       "min       8052.953021  2586.886053  3429.864357  16076.588030   4756.763836   \n",
       "25%       9068.784401  2857.695592  5822.915060  20530.726960   5101.160026   \n",
       "50%       9308.418710  2980.331339  6950.283021  22898.792140   5494.024437   \n",
       "75%       9761.032633  3241.326821  7418.965848  26484.699645   6571.594859   \n",
       "max      10967.281950  3548.097832  7957.980824  28954.925890  10118.053180   \n",
       "\n",
       "country     Colombia  \n",
       "count       7.000000  \n",
       "mean     4374.571839  \n",
       "std      1216.715492  \n",
       "min      2678.729839  \n",
       "25%      3540.233955  \n",
       "50%      4397.575659  \n",
       "75%      5173.933859  \n",
       "max      6117.361746  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset.T.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No axis named Canada for object type <class 'pandas.core.frame.DataFrame'>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-105-87c318c86e63>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mamericas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Canada\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, axis)\u001b[0m\n\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m             \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m         \u001b[0mnew_self\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnew_self\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_axis_number\u001b[0;34m(cls, axis)\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"No axis named {0} for object type {1}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: No axis named Canada for object type <class 'pandas.core.frame.DataFrame'>"
     ]
    }
   ],
   "source": [
    "# This isn't working\n",
    "# xamericas.loc(\"Canada\")"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "\n",
    "    GDP per capita for all countries in 1982.\n",
    "    GDP per capita for Denmark for all years.\n",
    "    GDP per capita for all countries for years after 1985.\n",
    "    GDP per capita for each country in 2007 as a multiple of GDP per capita for that country in 1952.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Argentina               8997.897412\n",
       "Bolivia                 3156.510452\n",
       "Brazil                  7030.835878\n",
       "Canada                 22898.792140\n",
       "Chile                   5095.665738\n",
       "Colombia                4397.575659\n",
       "Costa Rica              5262.734751\n",
       "Cuba                    7316.918107\n",
       "Dominican Republic      2861.092386\n",
       "Ecuador                 7213.791267\n",
       "El Salvador             4098.344175\n",
       "Guatemala               4820.494790\n",
       "Haiti                   2011.159549\n",
       "Honduras                3121.760794\n",
       "Jamaica                 6068.051350\n",
       "Mexico                  9611.147541\n",
       "Nicaragua               3470.338156\n",
       "Panama                  7009.601598\n",
       "Paraguay                4258.503604\n",
       "Peru                    6434.501797\n",
       "Puerto Rico            10330.989150\n",
       "Trinidad and Tobago     9119.528607\n",
       "United States          25009.559140\n",
       "Uruguay                 6920.223051\n",
       "Venezuela              11152.410110\n",
       "Name: gdpPercap_1982, dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# americas.info() gives you more information\n",
    "americas.loc[:  ,    \"gdpPercap_1982\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent         Americas\n",
       "gdpPercap_1952     3758.52\n",
       "gdpPercap_1957     4245.26\n",
       "gdpPercap_1962     4957.04\n",
       "gdpPercap_1967     5788.09\n",
       "gdpPercap_1972     5937.83\n",
       "gdpPercap_1977     6281.29\n",
       "gdpPercap_1982      6434.5\n",
       "gdpPercap_1987     6360.94\n",
       "gdpPercap_1992     4446.38\n",
       "gdpPercap_1997     5838.35\n",
       "gdpPercap_2002     5909.02\n",
       "gdpPercap_2007     7408.91\n",
       "Name: Peru, dtype: object"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "americas.loc[\"Peru\", :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Argentina</td>\n",
       "      <td>9139.671389</td>\n",
       "      <td>9308.418710</td>\n",
       "      <td>10967.281950</td>\n",
       "      <td>8797.640716</td>\n",
       "      <td>12779.379640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Bolivia</td>\n",
       "      <td>2753.691490</td>\n",
       "      <td>2961.699694</td>\n",
       "      <td>3326.143191</td>\n",
       "      <td>3413.262690</td>\n",
       "      <td>3822.137084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Brazil</td>\n",
       "      <td>7807.095818</td>\n",
       "      <td>6950.283021</td>\n",
       "      <td>7957.980824</td>\n",
       "      <td>8131.212843</td>\n",
       "      <td>9065.800825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Canada</td>\n",
       "      <td>26626.515030</td>\n",
       "      <td>26342.884260</td>\n",
       "      <td>28954.925890</td>\n",
       "      <td>33328.965070</td>\n",
       "      <td>36319.235010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Chile</td>\n",
       "      <td>5547.063754</td>\n",
       "      <td>7596.125964</td>\n",
       "      <td>10118.053180</td>\n",
       "      <td>10778.783850</td>\n",
       "      <td>13171.638850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Colombia</td>\n",
       "      <td>4903.219100</td>\n",
       "      <td>5444.648617</td>\n",
       "      <td>6117.361746</td>\n",
       "      <td>5755.259962</td>\n",
       "      <td>7006.580419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>5629.915318</td>\n",
       "      <td>6160.416317</td>\n",
       "      <td>6677.045314</td>\n",
       "      <td>7723.447195</td>\n",
       "      <td>9645.061420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cuba</td>\n",
       "      <td>7532.924763</td>\n",
       "      <td>5592.843963</td>\n",
       "      <td>5431.990415</td>\n",
       "      <td>6340.646683</td>\n",
       "      <td>8948.102923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>2899.842175</td>\n",
       "      <td>3044.214214</td>\n",
       "      <td>3614.101285</td>\n",
       "      <td>4563.808154</td>\n",
       "      <td>6025.374752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Ecuador</td>\n",
       "      <td>6481.776993</td>\n",
       "      <td>7103.702595</td>\n",
       "      <td>7429.455877</td>\n",
       "      <td>5773.044512</td>\n",
       "      <td>6873.262326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>El Salvador</td>\n",
       "      <td>4140.442097</td>\n",
       "      <td>4444.231700</td>\n",
       "      <td>5154.825496</td>\n",
       "      <td>5351.568666</td>\n",
       "      <td>5728.353514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Guatemala</td>\n",
       "      <td>4246.485974</td>\n",
       "      <td>4439.450840</td>\n",
       "      <td>4684.313807</td>\n",
       "      <td>4858.347495</td>\n",
       "      <td>5186.050003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Haiti</td>\n",
       "      <td>1823.015995</td>\n",
       "      <td>1456.309517</td>\n",
       "      <td>1341.726931</td>\n",
       "      <td>1270.364932</td>\n",
       "      <td>1201.637154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Honduras</td>\n",
       "      <td>3023.096699</td>\n",
       "      <td>3081.694603</td>\n",
       "      <td>3160.454906</td>\n",
       "      <td>3099.728660</td>\n",
       "      <td>3548.330846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Jamaica</td>\n",
       "      <td>6351.237495</td>\n",
       "      <td>7404.923685</td>\n",
       "      <td>7121.924704</td>\n",
       "      <td>6994.774861</td>\n",
       "      <td>7320.880262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Mexico</td>\n",
       "      <td>8688.156003</td>\n",
       "      <td>9472.384295</td>\n",
       "      <td>9767.297530</td>\n",
       "      <td>10742.440530</td>\n",
       "      <td>11977.574960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>2955.984375</td>\n",
       "      <td>2170.151724</td>\n",
       "      <td>2253.023004</td>\n",
       "      <td>2474.548819</td>\n",
       "      <td>2749.320965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Panama</td>\n",
       "      <td>7034.779161</td>\n",
       "      <td>6618.743050</td>\n",
       "      <td>7113.692252</td>\n",
       "      <td>7356.031934</td>\n",
       "      <td>9809.185636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Paraguay</td>\n",
       "      <td>3998.875695</td>\n",
       "      <td>4196.411078</td>\n",
       "      <td>4247.400261</td>\n",
       "      <td>3783.674243</td>\n",
       "      <td>4172.838464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Peru</td>\n",
       "      <td>6360.943444</td>\n",
       "      <td>4446.380924</td>\n",
       "      <td>5838.347657</td>\n",
       "      <td>5909.020073</td>\n",
       "      <td>7408.905561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>12281.341910</td>\n",
       "      <td>14641.587110</td>\n",
       "      <td>16999.433300</td>\n",
       "      <td>18855.606180</td>\n",
       "      <td>19328.709010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>7388.597823</td>\n",
       "      <td>7370.990932</td>\n",
       "      <td>8792.573126</td>\n",
       "      <td>11460.600230</td>\n",
       "      <td>18008.509240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>United States</td>\n",
       "      <td>29884.350410</td>\n",
       "      <td>32003.932240</td>\n",
       "      <td>35767.433030</td>\n",
       "      <td>39097.099550</td>\n",
       "      <td>42951.653090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Uruguay</td>\n",
       "      <td>7452.398969</td>\n",
       "      <td>8137.004775</td>\n",
       "      <td>9230.240708</td>\n",
       "      <td>7727.002004</td>\n",
       "      <td>10611.462990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Venezuela</td>\n",
       "      <td>9883.584648</td>\n",
       "      <td>10733.926310</td>\n",
       "      <td>10165.495180</td>\n",
       "      <td>8605.047831</td>\n",
       "      <td>11415.805690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     gdpPercap_1987  gdpPercap_1992  gdpPercap_1997  \\\n",
       "country                                                               \n",
       "Argentina               9139.671389     9308.418710    10967.281950   \n",
       "Bolivia                 2753.691490     2961.699694     3326.143191   \n",
       "Brazil                  7807.095818     6950.283021     7957.980824   \n",
       "Canada                 26626.515030    26342.884260    28954.925890   \n",
       "Chile                   5547.063754     7596.125964    10118.053180   \n",
       "Colombia                4903.219100     5444.648617     6117.361746   \n",
       "Costa Rica              5629.915318     6160.416317     6677.045314   \n",
       "Cuba                    7532.924763     5592.843963     5431.990415   \n",
       "Dominican Republic      2899.842175     3044.214214     3614.101285   \n",
       "Ecuador                 6481.776993     7103.702595     7429.455877   \n",
       "El Salvador             4140.442097     4444.231700     5154.825496   \n",
       "Guatemala               4246.485974     4439.450840     4684.313807   \n",
       "Haiti                   1823.015995     1456.309517     1341.726931   \n",
       "Honduras                3023.096699     3081.694603     3160.454906   \n",
       "Jamaica                 6351.237495     7404.923685     7121.924704   \n",
       "Mexico                  8688.156003     9472.384295     9767.297530   \n",
       "Nicaragua               2955.984375     2170.151724     2253.023004   \n",
       "Panama                  7034.779161     6618.743050     7113.692252   \n",
       "Paraguay                3998.875695     4196.411078     4247.400261   \n",
       "Peru                    6360.943444     4446.380924     5838.347657   \n",
       "Puerto Rico            12281.341910    14641.587110    16999.433300   \n",
       "Trinidad and Tobago     7388.597823     7370.990932     8792.573126   \n",
       "United States          29884.350410    32003.932240    35767.433030   \n",
       "Uruguay                 7452.398969     8137.004775     9230.240708   \n",
       "Venezuela               9883.584648    10733.926310    10165.495180   \n",
       "\n",
       "                     gdpPercap_2002  gdpPercap_2007  \n",
       "country                                              \n",
       "Argentina               8797.640716    12779.379640  \n",
       "Bolivia                 3413.262690     3822.137084  \n",
       "Brazil                  8131.212843     9065.800825  \n",
       "Canada                 33328.965070    36319.235010  \n",
       "Chile                  10778.783850    13171.638850  \n",
       "Colombia                5755.259962     7006.580419  \n",
       "Costa Rica              7723.447195     9645.061420  \n",
       "Cuba                    6340.646683     8948.102923  \n",
       "Dominican Republic      4563.808154     6025.374752  \n",
       "Ecuador                 5773.044512     6873.262326  \n",
       "El Salvador             5351.568666     5728.353514  \n",
       "Guatemala               4858.347495     5186.050003  \n",
       "Haiti                   1270.364932     1201.637154  \n",
       "Honduras                3099.728660     3548.330846  \n",
       "Jamaica                 6994.774861     7320.880262  \n",
       "Mexico                 10742.440530    11977.574960  \n",
       "Nicaragua               2474.548819     2749.320965  \n",
       "Panama                  7356.031934     9809.185636  \n",
       "Paraguay                3783.674243     4172.838464  \n",
       "Peru                    5909.020073     7408.905561  \n",
       "Puerto Rico            18855.606180    19328.709010  \n",
       "Trinidad and Tobago    11460.600230    18008.509240  \n",
       "United States          39097.099550    42951.653090  \n",
       "Uruguay                 7727.002004    10611.462990  \n",
       "Venezuela               8605.047831    11415.805690  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# americas.info() gives you more information\n",
    "americas.loc[:  ,    \"gdpPercap_1985\":]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 - 1 + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Argentina              2.161851\n",
       "Bolivia                1.427595\n",
       "Brazil                 4.298739\n",
       "Canada                 3.195102\n",
       "Chile                  3.343074\n",
       "Colombia               3.267819\n",
       "Costa Rica             3.671499\n",
       "Cuba                   1.601726\n",
       "Dominican Republic     4.310868\n",
       "Ecuador                1.951461\n",
       "El Salvador            1.879194\n",
       "Guatemala              2.135726\n",
       "Haiti                  0.652933\n",
       "Honduras               1.616606\n",
       "Jamaica                2.525721\n",
       "Mexico                 3.443687\n",
       "Nicaragua              0.883355\n",
       "Panama                 3.954710\n",
       "Paraguay               2.137387\n",
       "Peru                   1.971228\n",
       "Puerto Rico            6.271564\n",
       "Trinidad and Tobago    5.956629\n",
       "United States          3.070062\n",
       "Uruguay                1.856200\n",
       "Venezuela              1.484539\n",
       "dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "americas.loc[:  ,    \"gdpPercap_2007\"] / americas.loc[:  ,    \"gdpPercap_1952\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# It's called \"matplotlib\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Position (mi)')"
      ]
     },
     "execution_count": 141,
     "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": [
    "time = [0, 1, 2, 3]\n",
    "position = [0, 100, 200, 300]\n",
    "\n",
    "plt.style.use('ggplot') # or use 'fivethirtyeight', or try out others on your own!\n",
    "plt.plot(time, position)\n",
    "plt.xlabel(\"Time (hr)\")\n",
    "plt.ylabel(\"Position (mi)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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",
       "      <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",
       "      <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": [
       "             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": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oceania"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Extract year from last 4 characters of each column name\n",
    "years = data.columns.str.strip('gdpPercap_')\n",
    "# Convert year values to integers, saving results back to dataframe\n",
    "data.columns = years.astype(int)\n",
    "\n",
    "data.loc['Australia'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gdpPercap_1952', 'gdpPercap_1957', 'gdpPercap_1962', 'gdpPercap_1967',\n",
       "       'gdpPercap_1972', 'gdpPercap_1977', 'gdpPercap_1982', 'gdpPercap_1987',\n",
       "       'gdpPercap_1992', 'gdpPercap_1997', 'gdpPercap_2002', 'gdpPercap_2007'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oceania.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1952', '1957', '1962', '1967', '1972', '1977', '1982', '1987', '1992',\n",
       "       '1997', '2002', '2007'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = oceania.columns.str.strip('gdpPercap_')\n",
    "years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, 2002,\n",
       "            2007],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['gdpPercap_1952', 'gdpPercap_1957', 'gdpPercap_1962', 'gdpPercap_1967',\n",
       "       'gdpPercap_1972', 'gdpPercap_1977', 'gdpPercap_1982', 'gdpPercap_1987',\n",
       "       'gdpPercap_1992', 'gdpPercap_1997', 'gdpPercap_2002', 'gdpPercap_2007'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oceania.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "oceania.columns = years.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, 2002,\n",
       "            2007],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oceania.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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",
       "      <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",
       "      <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": [
       "                    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": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oceania"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fadc8462bd0>"
      ]
     },
     "execution_count": 156,
     "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": [
    "oceania.loc[\"Australia\",:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fadc3d584d0>"
      ]
     },
     "execution_count": 161,
     "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": [
    "oceania.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Year')"
      ]
     },
     "execution_count": 165,
     "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": [
    "oceania.T.plot(kind='bar')\n",
    "plt.ylabel(\"GDP Per Capita\")\n",
    "plt.xlabel(\"Year\")\n",
    "# give it a y label \"GDP Per Capita\"\n",
    "# give it an x label \"year\"\n",
    "# Bonus: Can you make a bar plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fadbd780e50>"
      ]
     },
     "execution_count": 172,
     "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": [
    "oceania.T.plot.scatter(x = 'Australia', y = 'New Zealand')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AmCroI2U333yz2rVrJ0kaM2aM4uLi9Msvv2jSpEm2FQcAViPLAJgq6CNlJ5544oGfExISNG7cOFsKAgA7kWUATNVgU7Z69eqgBklPT7ekGACwA1kGOyQlJembz7fpnZdf0MCLh8vj8Vi+j43vrtOH76zW6EsutHxsowWOvorR44epwaYs2G8jEWQATEaWwQ6dO3fWsuf+pvETJ+mDf6zUjbPu1/GtrXmwfW3NPj07Z7bWvfqSCvP/3Kw+m6d1P03rl65X8hnJtu1j/bPrdVr302wbP1wNNmV33nnngZ8ff/xxnXPOOTrllFNsLwoArESW/Vvd/v2uHt9Ep512mt58/TXde9/9unXYYI2/b4569o/s4fZfbd2seVMnq1vnU7R61UolJiZaVK075N6Vq/MuOE/d0rup++Dulo+/ZdUWbX5ls1a/GdwR9MYU9DVl9fX1uu+++5SQkKC0tDQNHDjQ0g/KRx99pCVLlqi+vl7nnXceDwYGYAu7s0wyM8/Szh2krNy7VbXnJ00adqViYqx9yt7aTz7UuAfzdP6QwZaO6wbHHnus7sm9W+cPGaxbsqaoz5A/6KqsGTqmxbEhjVNfX6+XlyzUikfna9Ydd2jEiBG2nBI1XZs2bTTvwXkaO2msst7J0vHtjrds7D079+i5yc9p0fxFat26tWXjWiXob1+OGTNGCxcu1FVXXaUvv/xSt9xyi3Jzc7VmzZqwnvv1W/X19Xr00Uc1Y8YMzZkzRyUlJfo2jEfwAMDR2Jllkrl5NmLkCL1YXKzn1/+f+ky4QaVbNlky7q7du3XDA7nKuG+Wbpk+TQsXLbJkXDdKS0vTWytXqub/leuOjEtC3n7e1En68M2X9drf/66RI0c2y4bsP/r376+MKzO0bPKygx6FFolAIKDnJj2nq668SmeffbYlY1otpD+VoqKidNZZZ+mss87SN998o6KiIj388MNavHixUlNTNWLEiLD+4ty2bZvat29/4FtR/fv313vvvafkZPvOJ8NZ3iHX6v4VW2zfB3A4dmWZZHaede3aVX9btkzLX3xRQ2feqssGnKP7b5yg1seHfiSivr5ej732smY8ukDDLhumf6xZo5YtW9pQtbt89tln2rRpk04fcK7WfrpT//xsV9Dbtut9iTb83zR9+OGH6tixo41VusPU7Km66NKLVPJoiQbcNCDi8UoWl6j++3rl/MXcx1aF1JT9+uuv+te//qW1a9dq+/bt6tu3r2688Ua1bdtWr7zyiu677z7l5+eHXERVVZWSkpIO/DspKUllZWWHrOf1Nu1nGTb19/dbFSuf1Ny5s23dx+VTn5TX+4St+2iu3P5ZtSvLJHfk2aTJk3X16NGaMX26uo8Zqaem3anBvYN/9ueu3bt16axbtT8uVm+sWqnf//73NlbrDvv27dMds2ZpyeNP6I935alP+vmSpIGntgthlO46t4dXs6dP1pp31uqRBQ8beYqtMS1ftlx9U/uqS2oXdege/g2ed2zZoVV/XqXSdaXq1KmTdQVaLOimrKCgQB9//LG6d++uIUOGqE+fPoqNjT3w+rXXXqvrr78+rCIOd2jycIdtKyrsfYD14XiHXKs+pT81yn6ceH9NHXNqD0d+Fy1qYuzMMsnsPPut/fv3y5ecrOp9NSre21K3hZRzUdrTub9Oj/pOMTExjr8Xp23dulUTJk1Sq/bJ+vPylWqV1Dbssbqe3lOzn39df/3zPereo4cemjNHAwZEfpTIrVq2bKkZ02Zo3th5ylyVqZhjQr8Wsra6Vs+OfVa3T79d8fHxjn9eG8qyoN+d3+/XjTfeeMSuPSoqSovCvJYgKSlJlZWVB/5dWVmpNm2s+VpxpCpWPqny2yfbvh/P1Ccl3W/7foDmzs4sk8zOs//4+OOPddvUaWoVG6d1cxep+8mhfxN1z2kjdOfji5R+7rmaOm2aRl11laKigr5MuUkIBAJatHixHioq0qgpt+m8K66y5DqwY449TjfOuk+9Bg3RxMz/1bBLh2rG9Ok65phjLKjafa4adZVW/WOVXr37VQ29d2jI279696vq3qm7RmWMsqE6awX9GzR06NCjHkYN9wPTpUsX7dixQ99//73q6uq0bt069e7dO6yxAKAhdmaZZHae/fTTT7p9xgzdcO21yhp6hd4uXBBWQyZJxx8Xr8IJt2jVA3P1wlNP67Khl2rz5s0WV2y2Tz75RPMXPKJ7l76iwVdebfmF+b8fOEh/Ll6pd/6vVCtWrLB0bDfxeDwqfKBQG1/aqK1vbw1p261vb9WmFZtUkFfgii9OWPud6DBFR0drzJgxuvfee1VfX69BgwbppJNOcroswFW8Q67VXxef3ij7wZGZmmdr167VlMz/1SVnD9Dmx5YqMaGVJeP27NpN64oWafGrL2nUiJEafe01unXqVEvGNl1NTY1O9CWrfcdOtu0joU2SOp36O9XU1Ni2Dzdo2bKlTjjhBH3+VWe9F1LOnS7vefGu+RKKEU2ZJPXq1Uu9evVyugzAtSpWPqmpf7P/d+gWTrUflYl59rdnntH0kaOVeXmG5WNHRUVp7MWX6eJ+A9Qp49Jm05Sh8RQ+WCglSf9zXYw8ng1Bb1dfX69HR7ymOQ/VamqO+Z/L5nUBAAA0Y21b2ftNPrvHR/P07rvv6qlnntKIeaHfTDcqKkoj5o3Qk08/qdLSUpsqtA5NGQAAMNKPP/6oiZkTdcVDVyjhxISwxmjVvpWueOgKTcicoB9//NHiCq1FUwYAAIwTCAQ09bap6nZBN/W4oEdEY/W4oIf8Q/yafvt0i6qzB00ZAAAwzvMvPK8PP/1QF919kSXjXXz3xVq/ab2ef+F5S8azA00ZAAAwyvbt23Xn3XfqqkVXKe7YOEvGjDsuTqMWjdKsP83S9u3bLRnTasZ8+xIAYJ+oqCjNe+l5vVK6LrTtPFGqD9QHtW59fb2iosy/FxTMVltbq/GTx2vQlEHyneazdOzk05M16JZBGj95vFa8uEIxMWa1QWZVY6CTfD55zu3TKPsBALtMve02vfvuuyFv949//EODBg0Kev2RE24OeR/Abz009yHVJdQpbVyaLeOnjU/TZ299pgeLHlROllkPJ6cpO4p/hfEVWp/Pp/LychuqAYDwJCcnKzk5OeTtMjMzNW/ePBsqAg7vlddf0UVzLrLtsV1RUVE6f8b5emXKK8Y1ZVxTBgAAjBLOg8dNGj9cZlYFAIDhEhMTtW3TRo04rWNI23kkBUJYv37/fl0z9H9C2gfciaYMjvAmJ+vyU7227wMA7NKlSxd99tlWBQKhtFjS4sWLddNNN4W0TWxsbEjrw51oyuCI98K44Jhr9QCYJpxv791zzz0aP368DdXA7bimDAAAwAA0ZQAAAAagKQMAADAATRkAAIABaMoAAAAMQFMGAABgAMdvifHUU09p/fr1iomJ0YknnqgJEyYoPj7e6bIAIGTkGYBIOH6k7IwzzlBBQYHy8/PVoUMHLV++3OmSACAs5BmASDjelPXs2VPR0dGSpG7duqmqqsrhigAgPOQZgEg43pT91urVq3XmmWc6XQYARIw8AxCqRrmmLDc3V7t37z5keUZGhvr06SNJevHFFxUdHa2BAwcecRyv195nJVrJTbW6CfNqhub8/0CewQrM6ZHFRDfO5e4x0THG/T80yjufOXNmg6+//fbbWr9+vWbNmiWPx3PE9SoqKqwuzTZuqtVNmFczOPH/YEp4kmewAnN6ZHX76xptP6ZlmeOnLz/66CO99NJLmjZtmo455hinywGAsJFnACLh+C0xHn30UdXV1Sk3N1eS5Pf7NXbsWIerAoDQkWcAIuF4UzZ37lynSwAAS5BngDXWLVmnhBMTbBv/p//3k21jR8LxpgyANTokd9Atibc0yn4AwC6zps/S+g/WSzWhbffB+g/U66xewa3cRjpr+lmhF2czmjKgiXj/3fdD3sbn86m8vNyGagAgPOnp6UpPTw95O5/Pp2effdaGihqP4xf6AwAAgKYMAADACDRlAAAABqApAwAAMAAX+kegoKBAhYWFh33N5/MddnlWVpays7PtLAsAQtJQlkmHzzOyDLCeJxAIBJwuIlhueSyF1+t1Ta1uwjcFreemOTXlMUtWcUtGkGfWc9PvnZu4ZV6NfswSAAAAaMoAAACMQFMGAABgAJoyAAAAA9CUAQAAGICmDAAAwAA0ZQAAAAagKQMAADAATRkAAIABaMoAAAAMQFMGAABgAGOashUrVmjEiBH66aefnC4FACJCngEIhxFN2a5du7Rhwwa1bdvW6VIAICLkGYBwGdGUPfHEE7r66qvl8XicLgUAIkKeAQiX403Z+++/r8TERHXq1MnpUgAgIuQZgEjENMZOcnNztXv37kOWZ2RkaPny5brjjjuCGsfr9Vpdmm3cVKtJ7rrrLv3pT3864us+n++QZXfeeafuuusuG6tq2vishoY8Q7AayjOyzB5u/6x6AoFAwKmdf/3117r77rt1zDHHSJIqKyvVpk0b3X///WrduvUh61dUVDR2iWHxer2uqdVNmFfr+Xw+lZeXO11GUEwPW/IMwWJO7eGWPGsoyxrlSNmRdOzYUYsXLz7w74kTJ+r+++9XQkKCg1UBQOjIMwCRcvyaMgAAADh8pOy/zZ8/3+kSAMAS5BmAUHGkDAAAwAA0ZQAAAAagKQMAADAATRkAAIABaMoAAAAMQFMGAABgAJoyAAAAA9CUwXjFxcVKT09XdHS00tPTVVxc7HRJABAysgxHQ1MGoxUXFysvL0+5ubmqrq5Wbm6u8vLyCDMArkKWIRg0ZTBaUVGR8vPzlZqaqtjYWKWmpio/P19FRUVOlwYAQSPLEAyaMhitrKxMKSkpBy1LSUlRWVmZQxUBQOjIMgSDpgxG8/v9Ki0tPWhZaWmp/H6/QxUBQOjIMgSDpgxGy8zMVE5OjkpKSlRbW6uSkhLl5OQoMzPT6dIAIGhkGYIR43QBQEOGDRsmSZo5c6YyMjLk9/s1bdq0A8sBwA3IMgTDEwgEAk4XEayKigqnSwiK1+t1Ta1uwrxaz+fzqby83OkyguL1ep0uwVJu+Szze2c95tQebsmzhrKM05cAAAAGoCkDAAAwAE0ZAACAAYy40P+1117T66+/rujoaPXq1UujR492uiQACAt5BiBcjjdlGzdu1Pvvv6/8/HzFxsbqxx9/dLokAAgLeQYgEo6fvnzzzTd16aWXKjY2VpLUqlUrhysCgPCQZwAi4fiRsh07dujTTz/V0qVLFRsbq2uuuUZdu3Z1uiwACBl5BiASjXKfstzcXO3evfuQ5RkZGVq6dKl69OihG264QZ9//rnmzJmjefPmyePx2F0W0Ox5PB656FaFRiDPADM1hTxrlCNlM2fOPOJrb775pvr27SuPx6OuXbsqKipKe/bsUUJCwiHruuVme9wY0B7Mqz3cMqem3DyWPEOkmFP7uGFejb55bJ8+fbRx40ZJ/57Muro6HX/88Q5XBQChI88ARMLxa8rS09P18MMPKzs7WzExMZo4cSKH+gG4EnkGIBKON2UxMTHKzMx0ugwYrLi4WEVFRSorK5Pf71dmZiYP8YWRyDM0hCzD0Th++hJoSHFxsfLy8pSbm6vq6mrl5uYqLy9PxcXFTpcGAEEjyxAMmjIYraioSPn5+UpNTVVsbKxSU1OVn5+voqIip0sDgKCRZQiG46cvgYaUlZUpJSXloGUpKSkqKytzqCJ3KigoUGFh4WFf8/l8hyzLyspSdna23WUBzQZZZo2Gskxyf57RlMFofr9fpaWlSk1NPbCstLRUfr/fwarcJzs7+7ChxFfzgcZBllnjSFkmNY084/QljJaZmamcnByVlJSotrZWJSUlysnJ4WJqAK5CliEYHCmD0f7zzaSZM2cqIyNDfr9f06ZN4xtLAFyFLEMwGuUxS1Zxy2HJpnAI1UTMq/XcNKem3NHfKm6ad7fU6hbMqT3cMq9G39EfAAAANGUAAABGoCkDAAAwAE0ZAACAAWjKAAAADOCqb18CAAA0VRwpAwAAMABNGQAAgAFoygAAAAzAY5ZscO+996qsrEynnnqqpk+f7nQ5TcJXX32lRYsWae/evYqKitLw4cPVv39/p8tytZ07dyo/P1/19fXav3+/LrzwQnVbc0oAAAXTSURBVJ1//vlOlwXDkGfWIsvs0VTyjKbMBkOHDtW+ffu0atUqp0tpMuLi4jRp0iR16NBBVVVVmj59unr27Kn4+HinS3OtNm3a6J577lFsbKyqq6uVnZ2t3r17KzEx0enSYBDyzFpkmT2aSp5x+jIC27ZtU05OjmpqalRdXa2srCx9/fXXOv3003Xsscc6XZ5rHW5e6+rq1KFDB0lSYmKiWrVqpZ9++snhSt3jcHNaUVGh2NhYSVJtba3q6+sdrhJOIs+sR5bZoynnGUfKItC1a1f17t1bS5cuVU1NjQYOHKiOHTs6XZbrHW1et23bprq6Op144okOVukuR5rTXbt2afbs2fruu+80evRo1/1VCeuQZ9Yjy+zRlPOMI2URuuKKK7RhwwZ98cUXuvTSS50up8k40rz+8MMPmjt3rsaPH6+oKD6+oTjcnLZt21b5+fkqKirSmjVrtHv3boerhJPIM+uRZfZoqnnGJyFCP//8s6qrq7V3717V1NQ4XU6Tcbh5/fXXXzV79mxlZGSoW7duDlfoPg19VhMTE3XSSSfp008/dag6mIA8sx5ZZo+mmmc0ZRFauHChRo4cqYEDB+rpp592upwm47/nta6uTvn5+UpLS9PZZ5/tdHmu9N9zWllZeSDMfv75Z23dulVer9fhKuEk8sx6ZJk9mmqecU1ZBNasWaPo6GgNGDBA9fX1uuOOO7Rx40Y999xzKi8vV3V1tcaNG6dx48bpzDPPdLpc1zjcvJaUlGjLli3as2eP3n77bUnSxIkT1alTJ0drdYvDzek333yjv/71r/J4PAoEArrkkku4hqgZI8+sR5bZoynnGc++BAAAMACnLwEAAAxAUwYAAGAAmjIAAAAD0JQBAAAYgKYMAADAADRlcNRdd92lt956K6h1J06cqE8++SSs/USyLQAcDVkGK9CUAQAAGICmDAAAwADc0R+2++6773Tbbbdp5syZ6ty5s6qqqnTrrbcqKyvrkPUWLlyo7du3y+PxqGfPnrrxxhsVHx9/YJ3PP/9cS5Ys0e7du9WnTx/ddNNNiouLkyStX79eS5cu1c6dO5WcnKw//vGPOvnkkxv1vQJousgy2I0jZbBd+/btdfXVV2vu3Lnat2+fFixYoHPOOUc9evQ4ZN3LLrtMCxcu1Jw5c1RZWally5Yd9Po///lP3X777Zo7d6527NihF198UZL0xRdfaMGCBRo7dqwee+wxDR48WA888IBqa2sb5T0CaPrIMtiNpgyNYvDgwWrfvr1mzJihH374QaNGjTpknfbt2+uMM85QbGysEhISdNFFF2nz5s0HrXPBBReobdu2atmypS677DKVlJRIkt566y0NHjxYfr9fUVFROvfccxUTE6OysrJGeX8AmgeyDHbi9CUazXnnnacHHnhAY8eOVWxs7CGv//jjj1qyZIm2bNmi6upq1dfXq2XLlget07Zt2wM/t2vXTlVVVZKkXbt2ac2aNXr99dcPvF5XV3fgdQCwClkGu9CUoVFUV1friSeeUHp6upYtW6Z+/fodElLPPPOMJCk/P1/HH3+8SktL9dhjjx20zq5duw76OTExUZKUlJSk4cOHa/jw4Ta/EwDNGVkGO3H6Eo1iyZIlOuWUUzRu3Dj16tVLf/nLXw5ZZ+/evWrRooXi4+NVVVWll19++ZB13njjDVVWVurnn3/W8uXLdfbZZ0v691+uK1euVFlZmQKBgKqrq/XBBx9o7969tr83AM0HWQY7caQMtnvvvff00UcfqaCgQJJ03XXX6dZbb9XatWsPWu/KK6/UvHnzdN1116l9+/ZKS0vT3//+94PWGTBggO655x798MMP6t27ty6//HJJUpcuXXTzzTfrscce044dOxQXF6dTTz1V3bt3b5w3CaDJI8tgN08gEAg4XQQAAEBzx+lLAAAAA9CUAQAAGICmDAAAwAA0ZQAAAAagKQMAADAATRkAAIABaMoAAAAMQFMGAABgAJoyAAAAA/x/zUfEA1tk/K0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "=================================\n",
    "Box plots with custom fill colors\n",
    "=================================\n",
    "\n",
    "This plot illustrates how to create two types of box plots\n",
    "(rectangular and notched), and how to fill them with custom\n",
    "colors by accessing the properties of the artists of the\n",
    "box plots. Additionally, the ``labels`` parameter is used to\n",
    "provide x-tick labels for each sample.\n",
    "\n",
    "A good general reference on boxplots and their history can be found\n",
    "here: http://vita.had.co.nz/papers/boxplots.pdf\n",
    "\"\"\"\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Random test data\n",
    "np.random.seed(123)\n",
    "all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))\n",
    "\n",
    "# rectangular box plot\n",
    "bplot1 = axes[0].boxplot(all_data,\n",
    "                         vert=True,   # vertical box aligmnent\n",
    "                         patch_artist=True)   # fill with color\n",
    "\n",
    "# notch shape box plot\n",
    "bplot2 = axes[1].boxplot(all_data,\n",
    "                         notch=True,  # notch shape\n",
    "                         vert=True,   # vertical box aligmnent\n",
    "                         patch_artist=True)   # fill with color\n",
    "\n",
    "# fill with colors\n",
    "colors = ['pink', 'lightblue', 'lightgreen']\n",
    "for bplot in (bplot1, bplot2):\n",
    "    for patch, color in zip(bplot['boxes'], colors):\n",
    "        patch.set_facecolor(color)\n",
    "\n",
    "# adding horizontal grid lines\n",
    "for ax in axes:\n",
    "    ax.yaxis.grid(True)\n",
    "    ax.set_xticks([y+1 for y in range(len(all_data))], )\n",
    "    ax.set_xlabel('xlabel')\n",
    "    ax.set_ylabel('ylabel')\n",
    "\n",
    "# add x-tick labels\n",
    "plt.setp(axes, xticks=[y+1 for y in range(len(all_data))],\n",
    "         xticklabels=['x1', 'x2', 'x3', 'x4'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What do you notice about the asia dataset?\n",
    "Try plotting something of interest to you.\n",
    "Ryan's suggestion: North Korea, South Korea's gdp over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "asia = pd.read_csv('data/gapminder_gdp_asia.csv', index_col='country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting columns to years\n",
    "asia.columns = asia.columns.str.strip('gdpPercap_').astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fadbddb7c90>"
      ]
     },
     "execution_count": 189,
     "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": [
    "asia.describe().loc['mean'].plot()\n",
    "asia.describe().loc['min'].plot()\n",
    "asia.describe().loc['max'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fadbda8f450>"
      ]
     },
     "execution_count": 181,
     "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": [
    "asia.loc['Afghanistan'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "pressures = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pressures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "pressures.append(23.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[23.4]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pressures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "pressures.append(17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pressures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.4"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pressures[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\"Cecil\", \"Kunal\", \"Ryan\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Kunal', 'Ryan']"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ryan'"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "del names[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Cecil', 'Kunal']"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class list in module builtins:\n",
      "\n",
      "class list(object)\n",
      " |  list(iterable=(), /)\n",
      " |  \n",
      " |  Built-in mutable sequence.\n",
      " |  \n",
      " |  If no argument is given, the constructor creates a new empty list.\n",
      " |  The argument must be an iterable if specified.\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __add__(self, value, /)\n",
      " |      Return self+value.\n",
      " |  \n",
      " |  __contains__(self, key, /)\n",
      " |      Return key in self.\n",
      " |  \n",
      " |  __delitem__(self, key, /)\n",
      " |      Delete self[key].\n",
      " |  \n",
      " |  __eq__(self, value, /)\n",
      " |      Return self==value.\n",
      " |  \n",
      " |  __ge__(self, value, /)\n",
      " |      Return self>=value.\n",
      " |  \n",
      " |  __getattribute__(self, name, /)\n",
      " |      Return getattr(self, name).\n",
      " |  \n",
      " |  __getitem__(...)\n",
      " |      x.__getitem__(y) <==> x[y]\n",
      " |  \n",
      " |  __gt__(self, value, /)\n",
      " |      Return self>value.\n",
      " |  \n",
      " |  __iadd__(self, value, /)\n",
      " |      Implement self+=value.\n",
      " |  \n",
      " |  __imul__(self, value, /)\n",
      " |      Implement self*=value.\n",
      " |  \n",
      " |  __init__(self, /, *args, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __iter__(self, /)\n",
      " |      Implement iter(self).\n",
      " |  \n",
      " |  __le__(self, value, /)\n",
      " |      Return self<=value.\n",
      " |  \n",
      " |  __len__(self, /)\n",
      " |      Return len(self).\n",
      " |  \n",
      " |  __lt__(self, value, /)\n",
      " |      Return self<value.\n",
      " |  \n",
      " |  __mul__(self, value, /)\n",
      " |      Return self*value.\n",
      " |  \n",
      " |  __ne__(self, value, /)\n",
      " |      Return self!=value.\n",
      " |  \n",
      " |  __repr__(self, /)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __reversed__(self, /)\n",
      " |      Return a reverse iterator over the list.\n",
      " |  \n",
      " |  __rmul__(self, value, /)\n",
      " |      Return value*self.\n",
      " |  \n",
      " |  __setitem__(self, key, value, /)\n",
      " |      Set self[key] to value.\n",
      " |  \n",
      " |  __sizeof__(self, /)\n",
      " |      Return the size of the list in memory, in bytes.\n",
      " |  \n",
      " |  append(self, object, /)\n",
      " |      Append object to the end of the list.\n",
      " |  \n",
      " |  clear(self, /)\n",
      " |      Remove all items from list.\n",
      " |  \n",
      " |  copy(self, /)\n",
      " |      Return a shallow copy of the list.\n",
      " |  \n",
      " |  count(self, value, /)\n",
      " |      Return number of occurrences of value.\n",
      " |  \n",
      " |  extend(self, iterable, /)\n",
      " |      Extend list by appending elements from the iterable.\n",
      " |  \n",
      " |  index(self, value, start=0, stop=9223372036854775807, /)\n",
      " |      Return first index of value.\n",
      " |      \n",
      " |      Raises ValueError if the value is not present.\n",
      " |  \n",
      " |  insert(self, index, object, /)\n",
      " |      Insert object before index.\n",
      " |  \n",
      " |  pop(self, index=-1, /)\n",
      " |      Remove and return item at index (default last).\n",
      " |      \n",
      " |      Raises IndexError if list is empty or index is out of range.\n",
      " |  \n",
      " |  remove(self, value, /)\n",
      " |      Remove first occurrence of value.\n",
      " |      \n",
      " |      Raises ValueError if the value is not present.\n",
      " |  \n",
      " |  reverse(self, /)\n",
      " |      Reverse *IN PLACE*.\n",
      " |  \n",
      " |  sort(self, /, *, key=None, reverse=False)\n",
      " |      Stable sort *IN PLACE*.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  __new__(*args, **kwargs) from builtins.type\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __hash__ = None\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "word = \"helium\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "charlist = list(word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['h', 'e', 'l', 'i', 'u', 'm']"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charlist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'h-e-l-i-u-m'"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"-\".join(charlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'he'"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ium'"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'heium'"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[:2] + word[-3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# For loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "Woohoo\n",
      "3\n",
      "Woohoo\n",
      "5\n",
      "Woohoo\n",
      "7\n",
      "Woohoo\n"
     ]
    }
   ],
   "source": [
    "for number in [2,3,5, 7]:\n",
    "    print(number)\n",
    "    print(\"Woohoo\")\n",
    "    # You can have more lines here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Challenge: print the first 5 square numbers (like 1x1, 2x2, 3x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 4\n",
      "3 6\n",
      "5 10\n",
      "7 14\n"
     ]
    }
   ],
   "source": [
    "# Hint:\n",
    "for number in [2,3,5, 7]:\n",
    "    print(number, 2*number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(1, 11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "4\n",
      "9\n",
      "16\n",
      "25\n"
     ]
    }
   ],
   "source": [
    "for number in [1,2,3,4,5]:\n",
    "    print(number*number)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1\n",
      "2 4\n",
      "3 9\n",
      "4 16\n",
      "5 25\n",
      "6 36\n",
      "7 49\n",
      "8 64\n",
      "9 81\n",
      "10 100\n"
     ]
    }
   ],
   "source": [
    "for number in list(range(1, 11)):\n",
    "    print(number, number*number) # or print(number**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Challenge #2: Sum up the first 5 square numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hint: before your for loop, try storing the total in a variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1 1\n",
      "2 4 5\n",
      "3 9 14\n",
      "4 16 30\n",
      "5 25 55\n",
      "55\n"
     ]
    }
   ],
   "source": [
    "total = 0\n",
    "for x in list(range(1, 6)):\n",
    "    total = total + x**2\n",
    "    print(x, x**2, total)\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GLOB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['practice.ipynb', 'simple.txt', 'data']"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.glob(\"*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['simple.txt']"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glob.glob(\"*.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/gapminder_gdp_europe.csv',\n",
       " 'data/gapminder_gdp_oceania.csv',\n",
       " 'data/gapminder_all.csv',\n",
       " 'data/gapminder_gdp_africa.csv',\n",
       " 'data/gapminder_gdp_americas.csv',\n",
       " 'data/gapminder_gdp_asia.csv']"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Challenge: Search for the gapminder csvs\n",
    "glob.glob(\"data/gapminder*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Similar to !ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge\n",
    "Get all the gapminder datasets (except the gapminder all dataset)\n",
    "\n",
    "For each one, I want you to print out the filename and the minimum gdpPer capita in 1952.\n",
    "\n",
    "Bonus: Try making one plot per dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = glob.glob(\"data/gapminder_gdp*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['data/gapminder_gdp_europe.csv',\n",
       " 'data/gapminder_gdp_oceania.csv',\n",
       " 'data/gapminder_gdp_africa.csv',\n",
       " 'data/gapminder_gdp_americas.csv',\n",
       " 'data/gapminder_gdp_asia.csv']"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = datasets[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Albania                    1601.056136\n",
       "Austria                    6137.076492\n",
       "Belgium                    8343.105127\n",
       "Bosnia and Herzegovina      973.533195\n",
       "Bulgaria                   2444.286648\n",
       "Croatia                    3119.236520\n",
       "Czech Republic             6876.140250\n",
       "Denmark                    9692.385245\n",
       "Finland                    6424.519071\n",
       "France                     7029.809327\n",
       "Germany                    7144.114393\n",
       "Greece                     3530.690067\n",
       "Hungary                    5263.673816\n",
       "Iceland                    7267.688428\n",
       "Ireland                    5210.280328\n",
       "Italy                      4931.404155\n",
       "Montenegro                 2647.585601\n",
       "Netherlands                8941.571858\n",
       "Norway                    10095.421720\n",
       "Poland                     4029.329699\n",
       "Portugal                   3068.319867\n",
       "Romania                    3144.613186\n",
       "Serbia                     3581.459448\n",
       "Slovak Republic            5074.659104\n",
       "Slovenia                   4215.041741\n",
       "Spain                      3834.034742\n",
       "Sweden                     8527.844662\n",
       "Switzerland               14734.232750\n",
       "Turkey                     1969.100980\n",
       "United Kingdom             9979.508487\n",
       "Name: gdpPercap_1952, dtype: float64"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(test, index_col='country').loc[:, 'gdpPercap_1952']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "973.5331947999999"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "331.0"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().loc['min', 'gdpPercap_1952']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/gapminder_gdp_europe.csv 973.5331947999999 973.5331947999999 True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/gapminder_gdp_oceania.csv 10039.595640000001 10039.595640000001 True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/gapminder_gdp_africa.csv 298.8462121 298.8462121 True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/gapminder_gdp_americas.csv 1397.7171369999999 1397.7171369999999 True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/gapminder_gdp_asia.csv 331.0 331.0 True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for filename in datasets:\n",
    "    data = pd.read_csv(filename, index_col='country')\n",
    "    minimum = data.gdpPercap_1952.min() # you can use it instead of .loc[]\n",
    "    minimum_alt = data.describe().loc['min', 'gdpPercap_1952']\n",
    "    print(filename, minimum, minimum_alt, minimum == minimum_alt)\n",
    "    \n",
    "    data.gdpPercap_1952.plot(kind='bar')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is markdown!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is not part of the function\n"
     ]
    }
   ],
   "source": [
    "def greet(name):\n",
    "    # There can be more stuff in here\n",
    "    return \"Hello \" + name\n",
    "\n",
    "print(\"This is not part of the function\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(greet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.greet(name)>"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "greet() missing 1 required positional argument: 'name'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-316-db845682bfe7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgreet\u001b[0m\u001b[0;34m(\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: greet() missing 1 required positional argument: 'name'"
     ]
    }
   ],
   "source": [
    "greet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello Ryan'"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greet(\"Ryan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello Cecil'"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greet(\"Cecil\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello World'"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "greet(\"World\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a function that takes a number and returns the 24 times the number "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "def times_24(the_input):\n",
    "   # print(the_input)\n",
    "    return the_input*24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times_24(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = times_24(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple():\n",
    "    return \"How do you do\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'How do you do'"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Challenge: Make a mult function that takes a, b, and c and multiplies them together.\n",
    "## Make c default to 1.\n",
    "## Hint: get help on the round function and look at the signature\n",
    "## (or search online \"default parameters in a function python\")\n",
    "## Bonus: Can you test that it works? Use \"assert\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function round in module builtins:\n",
      "\n",
      "round(number, ndigits=None)\n",
      "    Round a number to a given precision in decimal digits.\n",
      "    \n",
      "    The return value is an integer if ndigits is omitted or None.  Otherwise\n",
      "    the return value has the same type as the number.  ndigits may be negative.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"HELP\"\n",
    "def mult(a, b, c=1):\n",
    "    hidden_variable = \"hello\"\n",
    "    \"\"\"Multiplies a, b, and c together.\n",
    "        You can specify 2 or 3 arguments.\"\"\"\n",
    "#     print(a, b, c)\n",
    "#     c = 1\n",
    "#     print(a, b, c)\n",
    "    return a*b*c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert 3 == 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "This should be equal",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-388-716be9ca7621>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"This should be equal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m: This should be equal"
     ]
    }
   ],
   "source": [
    "assert 2 == 3, \"This should be equal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bool"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Interesting fact about data types\n",
    "1 == True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert 6 == mult(1, 2, 3), \"mult should work properly\"\n",
    "# Practice writing a few more assert statements. Look at the special cases!\n",
    "assert 0 == mult(0, 0), \"0*0 should equal 0\"\n",
    "assert 30 == mult(5, 6), \"tests two-argument function\"\n",
    "assert -96 == mult(-8, 6, 2)\n",
    "assert mult(6, 2) == mult(2, 6), \"commutative\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "def echo(a):\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Kunal'"
      ]
     },
     "execution_count": 417,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "echo(\"Kunal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable      Type                          Data/Info\n",
      "-----------------------------------------------------\n",
      "age           int                           48\n",
      "all_data      list                          n=3\n",
      "americas      DataFrame                                         conti<...>.047831    11415.805690  \n",
      "asia          DataFrame                                              <...>34.820827   2280.769906  \n",
      "ax            AxesSubplot                   AxesSubplot(0.554545,0.07;0.395455x0.81)\n",
      "axes          ndarray                       2: 2 elems, type `object`, 16 bytes\n",
      "bplot         dict                          n=6\n",
      "bplot1        dict                          n=6\n",
      "bplot2        dict                          n=6\n",
      "cat           int                           7\n",
      "charlist      list                          n=6\n",
      "color         str                           lightgreen\n",
      "colors        list                          n=3\n",
      "csv           str                           data/gapminder_gdp_asia.csv\n",
      "data          DataFrame                                         gdpPe<...>.820827     2280.769906  \n",
      "datasets      list                          n=5\n",
      "echo          function                      <function echo at 0x7fadb9e60320>\n",
      "fig           Figure                        Figure(432x288)\n",
      "filename      str                           data/gapminder_gdp_asia.csv\n",
      "first_name    str                           Kunal\n",
      "glob          module                        <module 'glob' from '/hom<...>3/lib/python3.7/glob.py'>\n",
      "greet         function                      <function greet at 0x7fadb9e294d0>\n",
      "last_name     str                           Marwaha\n",
      "log           builtin_function_or_method    <built-in function log>\n",
      "m             module                        <module 'math' from '/hom<...>37m-x86_64-linux-gnu.so'>\n",
      "math          module                        <module 'math' from '/hom<...>37m-x86_64-linux-gnu.so'>\n",
      "minimum       float                         331.0\n",
      "minimum_alt   float64                       331.0\n",
      "mult          function                      <function mult at 0x7fadb9e29b90>\n",
      "names         list                          n=2\n",
      "np            module                        <module 'numpy' from '/ho<...>kages/numpy/__init__.py'>\n",
      "number        int                           10\n",
      "oceania       DataFrame                                         1952 <...>3189.80135  25185.00911  \n",
      "output        int                           48\n",
      "patch         PathPatch                     PathPatch12((2.85, -2.36199) ...)\n",
      "pd            module                        <module 'pandas' from '/h<...>ages/pandas/__init__.py'>\n",
      "pi            float                         3.141592653589793\n",
      "plt           module                        <module 'matplotlib.pyplo<...>es/matplotlib/pyplot.py'>\n",
      "position      list                          n=4\n",
      "pressures     list                          n=2\n",
      "simple        function                      <function simple at 0x7fadb9e299e0>\n",
      "subset        DataFrame                                gdpPercap_1967<...>.648617     6117.361746  \n",
      "test          str                           data/gapminder_gdp_europe.csv\n",
      "time          list                          n=4\n",
      "times_24      function                      <function times_24 at 0x7fadb9e29950>\n",
      "total         int                           55\n",
      "word          str                           helium\n",
      "x             int                           5\n",
      "y             int                           82\n",
      "years         Index                         Index(['1952', '1957', '1<...>],\\n      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# \"Scope\" or \"variable scope\"\n",
    "# made within functions, stays within functions\n",
    "%whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Other ways to catch errors: Try/Except blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult(1, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-96"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult(-2, 6, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult(2, 6, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult(2, 6, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mult(5, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function mult in module __main__:\n",
      "\n",
      "mult(a, b, c=1)\n",
      "    Multiplies a, b, and c together.\n",
      "    You can specify 2 or 3 arguments.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(mult)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final challenge of day 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Challenge: Make a function that takes a filename\n",
    "# and prints out the minimum gdp per capita in 1952\n",
    "# and plots out the gdp per capita in 1952 by country.\n",
    "\n",
    "## Bonus: Can you make another function that takes a filename\n",
    "## and plots the mean gdp per capita over time?\n",
    "\n",
    "## Super bonus: Plot the gdp per capita in 1952 for all regions of the world.\n",
    "## Also plot the mean gdp per capita over time for all regions of the world.\n",
    "\n",
    "## Superduper bonus: Only make the plots if the mean gdppercapita in 1952 was over 10K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gapminder_plot(filename):\n",
    "    data = pd.read_csv(filename, index_col='country')\n",
    "    minimum = data.gdpPercap_1952.min() # you can use it instead of .loc[]\n",
    "    minimum_alt = data.describe().loc['min', 'gdpPercap_1952']\n",
    "    print(filename, minimum, minimum_alt, minimum == minimum_alt)\n",
    "    \n",
    "    data.gdpPercap_1952.plot(kind='bar')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_mean_gdp(filename):\n",
    "    data = pd.read_csv(filename, index_col='country')\n",
    "    data.describe().loc['mean'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/gapminder_gdp_asia.csv 331.0 331.0 True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gapminder_plot('data/gapminder_gdp_asia.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mean_gdp('data/gapminder_gdp_americas.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/gapminder_gdp_europe.csv 973.5331947999999 973.5331947999999 True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pM2+vAPWeP1NK1Tm+PjExMec8tjWQfHiSnLiTfLhrCfmo2LaZ4qfnQnmZW3tg54vp+JeFBMV0adTXawk5qU+Di1tNTQ1paWkMHjyYgQMHAhAeHo7NZsNsNmOz2Wjfvj3gPPI6fvynG+MVFxdjNpuJiIhg9+7drnar1UqvXr2wWCwUFxe7jY+IiKgzjsOHDzf0W/EbMTExko8zSE7cST7ctYR8OLLeR7+1DLTDvaNnPxzT53CMAGjE78FXcnKhBbZBk7Jaa55//nliY2MZN26cqz0pKYmcnBwAcnJyGDBggKs9NzcXrTV79+4lLCwMs9lMQkICO3fuxG63Y7fb2blzJwkJCZjNZkJDQ9m7dy9aa3Jzc0lKSmpIyEII0aLo2locbzyPfvNFj8KmhvwaY+ZfUG1NzRSd72rQkduePXvIzc2la9euPPTQQwBMmDCB1NRU0tPTycrKomPHjsyePRuAxMRE8vPzmTlzJsHBwcyYMQMAk8nEjTfeyNy5cwG46aabMJmcP6y7776bJUuWUF1dTUJCAomJiQ0JWQghWgxdXobjhadh9w73DqVQ46egRlwvl0bVQ+nzObHlw3zh8NlX+Mp0gi+RnLiTfLjzxXzoY0ec92Ar/N69IyQU454HUf0GNOnr+0pOLnRaUnYoEUIIH6O/2Y1jyeNgL3HviIjEuP9RVOduzRNYCyLFTQghfIhjcxb6tWehpsa9o1u8c4/I9ubmCayFkeImhBA+QDsc6LWvo//1jkefGjAYNWkmKjikGSJrmaS4CSFEM9OlJTheexZ2bPHoU9dNQF13qywcOU9S3IQQoplordGf56DfXuZ5fi0wyLmV1sCU5gmuhZPiJoQQzUAXH8Px+nOwa5tnZ7twjPv+B3Xp5d4PzE9IcRNCCC/SDgc650P0P1+FqgrPAV3jMGb8CWXp5P3g/IgUNyGE8BJdeAjHq/+AfV95dgYGOc+tjfodKlA+mhtKMiiEEE1M19SgP/on+oO3PZf4A3TvhXHXH1DRnb0fnJ+S4iaEEE1IH/wGxyv/gEMHPTvbhKJuvAs1ZHSrv/9aY5PiJoQQTUBXVaHffR39ybueO/kD9B2Acfu9qIhI7wfXCkhxE0KIRqa/2onjtQw4dsSzs1046tZ7nBdmy7VrTUaKmxBCNBJdZke/8zJ607/r7FdXD0ONn4pq197LkbU+UtyEEKIR6O2bcbz5Apy0eXZGRGJMnIHqfaX3A2ulpLgJIUQD6BNWHG88X+fWWSiFGjYW9bs7UG3CvB9cKybFTQghLoDWGv3pJ+h3lkNFmeeAi7pg3HW/7DLSTKS4CSHEedJFh3G8mgF7dnl2BgSgfnMTasx4VFCQ94MTgBQ3IYQ4Z7q2Fv3vd9Hvvg7V1Z4DusVj3PkHVOdLvB6bcNeg4rZkyRLy8/MJDw8nLS0NgPT0dNetycvLywkLC2PBggUUFRUxa9Ys1y3De/TowbRp0wDYv38/GRkZVFdXk5iYyOTJk1FKYbfbSU9P59ixY0RGRjJr1ixMJlNDQhZCiAuivz/gvBj7v/s8O4NDUKl3oEaMQxkB3g9OeGhQcRs6dCijR48mIyPD1TZr1izXv1999VXCwn46iRodHc2CBQs8nmfp0qVMnz6dHj168MQTT1BQUEBiYiKZmZn06dOH1NRUMjMzyczM5I477mhIyEIIcV70qWr0+2+j162G2lrPAT37YUy8DxUZ7f3gRL0atN9Lr1696j2S0lrz2WefMWjQoLM+h81mo6Kigvj4eJRSDBkyhLy8PADy8vJISXHeyyglJcXVLoQQ3qD3fonjr3903h37zMIWZkJN+iPGrL9KYfNBTXbO7auvviI8PJyLLrrI1VZUVMTDDz9MaGgot956Kz179sRqtWKxWFxjLBYLVqsVgJMnT2I2mwEwm82UlJxxMz8hhGgCuqIcvfoVdPaHdfarKwehJkxDhZu9HJk4V01W3DZt2uR21GY2m1myZAnt2rVj//79LFiwgLS0NLTWjfJ6p8/lCSfJhyfJiTvJh7vT+ajY+im2jCdwHD/qMSbAEon593MIvWaol6NrHi35d6RJilttbS1bt27lySefdLUFBQUR9OOy2Li4OKKioigsLMRisVBcXOwaV1xcTEREBADh4eHYbDbMZjM2m4327evfsub0Ihbh/IWUfLiTnLhr7nzoqir09k/hyCEIDoGQUGgTCiFtUG1C3b6mTSi0aQPBbZps5/yYmBh+2PMV+q2l6K25dY5RQ36NvvEubGEmbK3gd6m5f0d+HseFaJLitmvXLmJiYtymG0tKSjCZTBiGwdGjRyksLCQqKgqTyURoaCh79+6lR48e5ObmMnr0aACSkpLIyckhNTWVnJwcBgwY0BThCiG8RJ+qRud+jP7wnbq3qQLOOpdzuti5/T/sx4J4Zp+zQKozxtLmp69VYBBaa8qy/oXjhQVgL/V8zU4XOZf3X9anUXIgvKNBxW3hwoXs3r2b0tJS7r33XsaPH8/w4cM9piQBdu/ezcqVKwkICMAwDO655x7XYpS7776bJUuWUF1dTUJCAomJiQCkpqaSnp5OVlYWHTt2ZPbs2Q0JVwjRTHTNKfSn/3YuzLAdv/Anqqp0/nfm85/ttc/2fIGBEBSCta4dRgwD9atU1PUTUMEh5xupaGZKN9ZJr2bmC4fPvsJXphN8ieTEnbfyoWtr0Z9lod9/G4qLmvz1Gk2Xbhh3zURdfGlzR9JsfOU941PTkkKI1k07atFbc9HvvQVFhXUPCg5BDRoBoW2dR2OV5VBZia6qgMpKqKyAqoof/18J1VVNH3hgEOq6W1GjfocKlI/Hlkx+ekKIRqMdDvT2Teh333QuFqlLYBBq6BjUb25AtT/3pfTaUesselWehU///OvKyh//7Rynf14gf/7/yoqf7pBtGHB5X4wJ01DRnRshE6K5SXETQjSY1hp2bMHx7hvww3/rHhQQiBoyCjXmZlQHS91jzkIZARDW1vnfmX3n/Ww/xnyqGqoquahzF46clOto/YkUNyHEBdNaw3+24Xj3dfhuf92DAgJQySNQY29BWSK9G+BZKKWclyEEh2C0NYEUN78ixU0Icd601rC7AMfa1+HA3roHKQN1zTDUuFtkeyrhdVLchBDnRe/ZhSPzddi3u+4BSqEGDHEuzIiO9W5wQvxIipsQ4pzofbtxrH0Dvv5P/YOuTMa47jZUbFfvBSZEHaS4CSHOSh/Y65x+/HJH/YMSBmJcfxuqSzfvBSbEWUhxE0LUSX+337n6cefW+gf1vhLjt7ehLunhtbiEOBdS3IQQbvQP/3UWtfzP6h/Usx/Gb29HXXq59wIT4jxIcRNCAKCPHEK/+yZ626dQ36588VdgXH876rLe3g1OiPMkxU2IVk4XFaLffwu9JeenHTvOFHcZxm9vh579nNeHCeHjpLgJ0Urp4iL0+2+jN68HRz1F7eLuzqLWu78UNdGiSHETopXR1uNY17yCY91aqK2pe1DnSzB+exv0GyhFTbRIUtyEaCX0f79FZ72P3ppLWc2pugdd1AXj+gnQP7nJ7nothDdIcRPCj+maGvSOz9Dr34Nvv65/YKcY5005B1zr3KBYiBZOipsQfkiX2NC569A5H8EJa/0DO0ahxt2KunooKkCKmvAfUtyE8CP6wDfOqcdtG6GmnvNpQEBULI5Rv0Ulj5Sbcgq/JL/VQrRwuuYUevtm59RjfTv0n9azH8bwcVz06+spPHrUOwEK0QwaVNyWLFlCfn4+4eHhpKWlAbBy5UrWr19P+/btAZgwYQL9+/cHYM2aNWRlZWEYBpMnTyYhIQGAgoICli9fjsPhYMSIEaSmpgJQVFTEwoULsdvtdOvWjfvvv59A+StTCAD0CSs69yN07jo4aat/YEgb561nho1FxTg3NJYpSOHvGlQphg4dyujRo8nIyHBrHzt2LNdff71b26FDh9i8eTPPPPMMNpuNefPmsWjRIgBeeuklHn30USwWC3PnziUpKYnOnTuzYsUKxo4dy6BBg3jxxRfJyspi1KhRDQlZiBZNaw3796CzPkBv31T/Un6AyGjU8LHOG4WGmbwXpBA+oEHFrVevXhQVFZ3T2Ly8PJKTkwkKCqJTp05ER0ezb98+AKKjo4mKigIgOTmZvLw8YmNj+fLLL/njH/8IOAvpO++8I8VNtEr61Cn0tk+dU4//3Xf2wVckYgwfB72vlOX8otVqkjm+devWkZubS1xcHHfeeScmkwmr1UqPHj/tHB4REYHV6lzFZbFYXO0Wi4VvvvmG0tJSwsLCCPhx+uTn44VoLbStGJ3zoXPqsfRk/QNDQlHJw51HatGdvRegED6q0YvbqFGjuOmmmwB4++23efXVV5kxY4ZzOqUOdbVfyI4IMTEx5/0Yfyb58NRScqK1pvqrnZS+9zYVm7KgtrbesYExXTFdN562I8dhnOfUY0vJh7dIPjy15Jw0enHr0KGD698jRozgqaeeApxHZMXFxa4+q9VKREQEgFt7cXExZrOZdu3aUV5eTm1tLQEBAW7j63L48OHG/lZarJiYGMnHGVpCTvSpavTWjeis9+C7/Wcf3CcJY/hYHL0SKTUMSk+UwImSc36tlpAPb5J8ePKVnFxogW304maz2TCbzQBs3bqVLl26AJCUlMTixYsZN24cNpuNwsJCunfvjtaawsJCioqKiIiIYPPmzcycOROlFFdccQVbtmxh0KBBZGdnk5SU1NjhCtHstPUYOvtD9MaPwX6WAhUa5lwcMmwsKqrl/kUthDc0qLgtXLiQ3bt3U1payr333sv48eP58ssvOXjwIEopIiMjmTZtGgBdunThmmuuYfbs2RiGwdSpUzF+PNk9ZcoU5s+fj8PhYNiwYa6CePvtt7Nw4ULeeustunXrxvDhwxv47QrhG7TW8M2XOLLehx1b6t+VHyC6M2r4ONQ1Q1FtwrwXpBAtmNL1nQxrYXzh8NlX+Mp0gi/xlZzo6ir05znorA/g0IH6ByoFfQdgDB8LPRMafWd+X8mHr5B8ePKVnPjMtKQQwpMusaE/Xov+9BMoK61/YGhb1LUjnVOPkdHeC1AIPyPFTYgmpE+dQq9/F/3BSqisqH/gRV2cU49XD0W1CfVegEL4KSluQjQBrTXs3Ipj5Utw7Ejdg5QB/a5yTj1e3lduCipEI5LiJkQj0z98h2PlMthdUPeAMBNq8K9QQ8egOkZ5NzghWgkpbkI0El1Wil77Bjrnw7pXP4a1RV13K2rwaFRIiPcDFKIVkeImRAPp2lrn7vxr36h7sYgyUCmjUdffhmrX3vsBCtEKSXETogH0VztxvLUUDn9X94DL+mDceg+q8yVejUuI1k6KmxAXQBcV4nhnORRsqXtAxyiMm6dA4tWyUESIZiDFTYjzoCvL0R+8g/73Wqip415qIW1QY8ejRl6PCgr2foBCCECKmxDnRDsc6M82oNe8Wu9dr9U1w1E3TER1sNTZL4TwHiluQvwCve8r53m1+m4SGneZ87xat3jvBiaEqJcUNyHqoa3H0f98Bb01p+4BHSJQN05CXTVE7ngthI+R4ibEGXR1FfrjNegP/wnVVZ4DAoNQv/4davSNslWWED5KipsQP9Jaw/ZNOFb9LxQX1T3oymSMmybLziJC+DgpbkIA+rtvnefVvtld94DO3Zzn1S7r7d3AhBAXRIqbaNV0yQl05grnrWjqurWhqT3qd3egrv0VygjwfoBCiAsixU20SrrmFDrrA/T7b0FFueeAgADUsHGo625BhZm8H6AQokGkuIlWR+/ahuPtl+DoD3UP6H0lxvipqIs6ezcwIUSjaVBxW7JkCfn5+YSHh5OWlgbAa6+9xvbt2wkMDCQqKooZM2bQtm1bioqKmDVrluuW4T169GDatGkA7N+/n4yMDKqrq0lMTGTy5MkopbDb7aSnp3Ps2DEiIyOZNWsWJpP8FS0ujC485LwVzRf5dQ+IjnUWtT5J3g1MCNHoGlTchg4dyujRo8nIyHC19e3bl9tuu42AgABWrFjBmjVruOOOOwCIjo5mwYIFHs+zdOlSpk+fTo8ePXjiiScoKCggMTGRzMxM+vTpQ2pqKpmZmWRmZrqeS4hzpcvs2F5Mw/H+Sqit9RwQ+uOtaIaNQQUGeT9AIUSja9CVp7169eOCntMAAB4lSURBVPI4kurXrx8BAc4T7/Hx8Vit1rM+h81mo6Kigvj4eJRSDBkyhLy8PADy8vJISUkBICUlxdUuxLnQWuPYtB7Ho/diX/umZ2FTCjXk1xh/ew7jV7+VwiaEH2nSc25ZWVkkJye7vi4qKuLhhx8mNDSUW2+9lZ49e2K1WrFYftqLz2KxuAriyZMnMZvNAJjNZkpKSpoyXOFHdGU5+rXn6t9dJL43xi13o7rGeTcwIYRXNFlxW716NQEBAQwePBhwFqclS5bQrl079u/fz4IFC0hLS3NeONsITp/LE06tOR/V+/dS/OQj1PzgeY+1gE4X0WHqHwkdNKLV34qmNf+O1EXy4akl56RJilt2djbbt2/nsccec32ABAUFERTknPaJi4sjKiqKwsJCLBYLxcXFrscWFxcTEREBQHh4ODabDbPZjM1mo337+u9ifPjw4ab4VlqkmJiYVpkPrTV64zr0m0uh5pRbnwoJgdE3oUelciI4hBOFhc0UpW9orb8j9ZF8ePKVnFxogW303V4LCgpYu3Ytc+bMISQkxNVeUlKCw+EA4OjRoxQWFhIVFYXZbCY0NJS9e/eitSY3N5ekJOdqtaSkJHJynNNKOTk5DBgwoLHDFX5CV5Sjl/4d/doSj8JG7MVELXodY9wtqOCQup9ACOFXlG7AvODChQvZvXs3paWlhIeHM378eNasWUNNTY1rocnpJf9btmxh5cqVBAQEYBgGN998s6uIffvttyxZsoTq6moSEhKYMmUKSilKS0tJT0/n+PHjdOzYkdmzZ9d7KYAv/IXhK3zlLy5v0d99i+OFp6HI82hMDR6FuuUeYrt1a1U5+SWt7Xfkl0g+PPlKTi70yK1Bxc2X+MIPwVf4yi9lU9Nao3M+RL/9kufRWkgoauIMjIHO1batJSfnSvLhTvLhyVdycqHFTXYoES2SLi9Dv5aB3vapZ2fnSzCmP4yKlh1GhGitpLiJFkf/91scLzwFx4549Kkho1G3TJVza0K0clLcRIuhtUZv+AD9zstQU+PeGRKKuvM+jKuGNE9wQgifIsVNtAi6vAzHK/+A/M2enZ27Ydw7BxXVcq/JEUI0Liluwufpg984V0MeP+rRp4b+BjV+KioouBkiE0L4KiluwmdprdFZ76PfWQ61Z0xDtglF3Xk/xoBrmyc4IYRPk+ImfJIut+P438WwY4tnZ9dLMaY/hOok05BCiLpJcRM+Rx/Y65yGLC7y6FPDxqJunoIKkh38hRD1k+ImfIbWGr3+XfSqVzynIUPDMO66H3XloOYJTgjRokhxEz5Bl5U6pyELPvfsvLi786LsyGjvByaEaJGkuIlmp7/9GseLC8B6zKNPjbgOdeMkmYYUQpwXKW6i2Wit0Z+sRa9+xfMu2aFtMSbNRPW/pnmCE0K0aFLcRLPQZaU4li+CnVs9Oy/pgTHtIZmGFEJcMCluwuuc05BPg/W4R58aeT3qxrtQgTINKYS4cFLchNdohwP9SSZ6zWue05BhbTEm/xGVcHXzBCeE8CtS3IRX6NISHMsXwq5tnp3d4p2rIS2dvB+YEMIvSXETTU5/sxvH0r+DrY5pyFGpqN9NlGlIIUSjkuImmox2ONDrVqMzV4DD4d7Ztp1zGrLfVc0TnBDCrzWouC1ZsoT8/HzCw8NJS0sDwG63k56ezrFjx4iMjGTWrFmYTCa01ixfvpwdO3YQEhLCjBkziIuLAyA7O5vVq1cDcMMNNzB06FAA9u/fT0ZGBtXV1SQmJjJ58mSUUg0JWXiJLj2J4+V0+CLfs/PSyzHueQhlifR+YEKIVsFoyIOHDh3Kn/70J7e2zMxM+vTpw+LFi+nTpw+ZmZkA7NixgyNHjrB48WKmTZvGsmXLAGcxXLVqFY8//jiPP/44q1atwm63A7B06VKmT5/O4sWLOXLkCAUFBQ0JV3iBdtTi2Pgxjv97f52FTf36BowHH5fCJoRoUg0qbr169cJkMrm15eXlkZKSAkBKSgp5eXkAbNu2jSFDhqCUIj4+nrKyMmw2GwUFBfTt2xeTyYTJZKJv374UFBRgs9moqKggPj4epRRDhgxxPZfwTXrPLhx/m41+9VkoOeHeaWqHMfMxjJsmoQJlNlwI0bQa/VPm5MmTmM1mAMxmMyUlJQBYrVY6duzoGmexWLBarVitViwWi6s9IiKizvbT44Xv0UWFOFYtr/v2NADde2Lc8yAqQo7WhBDe4bU/obXWHm31nT9TStU5/mxiYuTeXj/njXw4yuyUvPUSpe++CTU1Hv0qOIR2N99F+/FTfOJoTX5H3Ek+3Ek+PLXknDT6J054eDg2mw2z2YzNZqN9+/aA88jr+PGfloIXFxdjNpuJiIhg9+7drnar1UqvXr2wWCwUFxe7jY+IiKj3dQ8fPtzY30qLFRMT06T50LW16I0fo999A0pP1jlGXZWCuuFOyiyRlBV53pfN25o6Jy2N5MOd5MOTr+TkQgtsg8651SUpKYmcnBwAcnJyGDBggKs9NzcXrTV79+4lLCwMs9lMQkICO3fuxG63Y7fb2blzJwkJCZjNZkJDQ9m7dy9aa3Jzc0lKSmrscMV50rsLcMx7AP36c3UXtm7xGI88jXHP/5FFI0KIZtOgI7eFCxeye/duSktLuffeexk/fjypqamkp6eTlZVFx44dmT17NgCJiYnk5+czc+ZMgoODmTFjBgAmk4kbb7yRuXPnAnDTTTe5FqncfffdLFmyhOrqahISEkhMTGxIuKIB9JEfnOfV6troGMDc0bkn5IDBKKPR/2YSQojzovT5ntzyUb5w+OwrGnM6QZfZ0e+/hd7wged+kADBIajRN6JG/Q4VEtIor9kUfGWKxVdIPtxJPjz5Sk4udFqy+c/yC5+ka2rQuR+h330TykrrHKOuGYb63Z0os6XOfiGEaC5S3IQH/cV2HCtfhsLv6x5w6eUYt9yD6tbDu4EJIcQ5kuImXPTh73C883LdW2YBWDqhbpyEShok26AJIXyaFDeBtpeg330TnfOh5wbHACGhqDE3OW8kGuy759WEEOI0KW6tmK45hc7+F/q9t6C8zHOAUqjkEajUO1Ad6r/GUAghfI0Ut1ZIaw3/2eacgjz6Q92D4q/AGH836uJLvRucEEI0AilurYw+dBDHypfgq511D+gYhXHzZEi8Rs6rCSFaLClurYQuPYle+zo692PQdZxXaxOKGncLavh1qCC5K7YQomWT4ubn9KlT6Kz30R+8DRXlngOUgRr8K9Rvb0O1N3s/QCGEaAJS3PyU1hoKPneeVzt2pO5Bl/fFuGUqqnM37wYnhBBNTIqbH6r+dg+OjCdhz666B3S6COPmKdDvKjmvJoTwS1Lc/IguPIRe90+Obs6CurYMDW2Luu5W1LAxqEA5ryaE8F9S3Fo4rTXs2YXj40zYta3uQYaBGjIadf1tqHbtvRugEEI0AyluLZSuqUFv+xT9SSZ8t7/+gb0SMcZPRcV29V5wQgjRzKS4tTC63O68C/b698F2vP6B0bEY46dC7yvlvJoQotWR4tZC6ONH0evfQ2/8BKoq6h/YLR7LLZOxXXI5KiDAewEKIYQPkeLm4/T+PeiPM9H5n9V98TWAUtBvIMaoVOjek7DYWE74wE0GhRCiuUhx80HaUQsFW3F8kgn7vqp/YHAwKnmkc7f+qAu7W60QQvgjKW4+RFdVojevR//7XSgqrH9guBk1bCwqZTTKJKsfhRDiTE1S3A4fPkx6errr66KiIsaPH09ZWRnr16+nfXvnB/KECRPo378/AGvWrCErKwvDMJg8eTIJCQkAFBQUsHz5chwOByNGjCA1NbUpQm5W+oQVveEDdM5HUFZa/8DYi1G/+i3qqhTZ/1EIIc6iSYpbTEwMCxYsAMDhcDB9+nSuuuoqNmzYwNixY7n++uvdxh86dIjNmzfzzDPPYLPZmDdvHosWLQLgpZde4tFHH8VisTB37lySkpLo3LlzU4TtdfrQQfQna9Fbc6Cmpv6BvRKd59N6JcjKRyGEOAdNPi25a9cuoqOjiYyMrHdMXl4eycnJBAUF0alTJ6Kjo9m3bx8A0dHRREVFAZCcnExeXl6LLm5aa/hyh/N82u6C+gcGBKIGpjiP1Dpf4rX4hBDCHzR5cdu0aRODBg1yfb1u3Tpyc3OJi4vjzjvvxGQyYbVa6dGjh2tMREQEVqsVAIvF4mq3WCx88803TR1yk9CnTqG35qA/WQs//Lf+gWEm1NDfOM+pyd2vhRDigjRpcaupqWH79u3cdtttAIwaNYqbbroJgLfffptXX32VGTNmOI9m6lBXe33TcjExvrlasLbkBGX/+iel769E24rrHRd4UWdMqbfRduR1GG1CG/y6vpqP5iQ5cSf5cCf58NSSc9KkxW3Hjh1069aNDh06ALj+DzBixAieeuopwHlEVlz80we/1WolIsJ51PLz9uLiYszmuu85dtjHruvSRYfRn7yL3rweqqvqH9i9F8aoVBz9BlBqBFBqtQG2Br12TEyMz+WjuUlO3Ek+3Ek+PPlKTi60wDZpcTtzStJms7mK09atW+nSpQsASUlJLF68mHHjxmGz2SgsLKR79+5orSksLKSoqIiIiAg2b97MzJkzmzLkBtFaw76vnJsY7/y87p35wXmD0CuTUaNSUd3ivRukEEK0Ak1W3KqqqvjPf/7DtGnTXG0rVqzg4MGDKKWIjIx09XXp0oVrrrmG2bNnYxgGU6dOxTAMAKZMmcL8+fNxOBwMGzbMVRB9ia6pQe/Y4tzE+MDe+geGhDrvej3iOlTHKO8FKIQQrYzS9Z3wamGa4/BZnyhG565D534MJ631DzR3RI0Yhxo8ChVmavK4fGU6wZdITtxJPtxJPjz5Sk58clrSH2mtYe8XODZ8ADu2gKOe/R4Bul7qnHq8chAqUFIthBDeIp+450hXlKO3bEBv+BcUfn/2wX0HOC+6ju8tF10LIUQzkOL2C/QP36Gz/4X+bMPZbzUTEoq6Zihq+HWoi1ruReZCCOEPpLjVQdfUQMEWHBv+BXu/OPvgi7qgho1BXT0MFRrmnQCFEEKclRS3nznnBSKGgUq8BjVsjEw9CiGED2r1xe28FoiER6CGjEIN+TWqg6X+cUIIIZpVqy1u57VAJL43xrAxkHC1rHoUQogWoNV9Up/3ApGhY1CxF3svQCGEEA3WKoqbLBARQojWxa+L2/ksECHxaoxhY2WBiBBC+AG/K24XtEBk8K9RZlkgIoQQ/sJvitv5LhBRQ8egEmWBiBBC+CO/+WR3PDRZFogIIYQA/Ki41VvYZIGIEEK0Ov5T3H5OFogIIUSr5l/FLdzsXBwyRBaICCFEa+Y3xU1Ne1gWiAghhAD8qLgZA65t7hCEEEL4iCYrbvfddx9t2rTBMAwCAgJ48sknsdvtpKenc+zYMSIjI5k1axYmkwmtNcuXL2fHjh2EhIQwY8YM4uLiAMjOzmb16tUA3HDDDQwdOrSpQhZCCOEnmvTI7S9/+Qvt27d3fZ2ZmUmfPn1ITU0lMzOTzMxM7rjjDnbs2MGRI0dYvHgx33zzDcuWLePxxx/HbrezatUqnnzySQAeeeQRkpKSMJlMTRm2EEKIFs7w5ovl5eWRkpICQEpKCnl5eQBs27aNIUOGoJQiPj6esrIybDYbBQUF9O3bF5PJhMlkom/fvhQUFHgzZCGEEC1Qkx65zZ8/H4Bf/epXjBw5kpMnT2I2mwEwm82UlJQAYLVa6dixo+txFosFq9WK1WrFYvlp1WNERARW61n2iBRCCCFowuI2b948IiIiOHnyJH/729+IiYmpd6zW2qOtvmvT6ms/2/O3RpIPT5ITd5IPd5IPTy05J01W3CIiIgAIDw9nwIAB7Nu3j/DwcGw2G2azGZvN5jofZ7FYOH78uOuxxcXFmM1mIiIi2L17t6vdarXSq1evOl/v8OHDTfWttDgxMTGSjzNITtxJPtxJPjz5Sk4utMA2yTm3yspKKioqXP/+z3/+Q9euXUlKSiInJweAnJwcBgwYAEBSUhK5ublordm7dy9hYWGYzWYSEhLYuXMndrsdu93Ozp07SUhIaIqQhRBC+BGl65oTbKCjR4/y97//HYDa2lquvfZabrjhBkpLS0lPT+f48eN07NiR2bNnuy4FeOmll9i5cyfBwcHMmDGDSy+9FICsrCzWrFkDOC8FGDZsWGOHK4QQws80SXETQgghmpNXLwUQQgghvEGKmxBCCL8jxU0IIYTfaZRLAYqKinjqqadIS0s767hbbrmFrl274nA4iI2N5b777iMkJKQxQmiwN998k9zcXOx2O6+99pqr/dixYzz33HOUlJRgMpm4//77XReWn/5+ADp27MicOXMAePrppykoKOCiiy7i0ksvZdq0aQTWcbcCf8vH8ePHef755ykuLgZg7ty5dOrUqVXm44svvuCVV15xjTt8+DB//OMfueqqq1i8eDF79uzBZrNx7bXX1psP8K+cAKxYsYL8/Hy01vTp04fJkydTXV3NE088wd69e4mOjubKK6/k9ttvr/M1/TEfO3bsAODGG28kOTkZaPmfIVVVVTzzzDMcPXoUwzDcfqanTp3i2WefZf/+/bRr144HHniATp06AbBmzRqysrIwDIPJkyeTkJDA8ePHycjI4MSJEyilGDlyJGPGjPnlIHQjOHr0qJ49e/Yvjrvjjjtc/160aJF+7733zvk1amtrLyi2c7Vnzx5ttVrdYtRa67S0NL1hwwattda7du3SixcvdvWdOfa09evX61mzZmmHw6HT09P1unXr6hznb/n4y1/+onfu3Km11rqiokJXVlZqrVtvPk4rLS3VkyZNcuVj+/bt+siRI3rWrFlnzYfW/pWTr7/+Wj/66KO6trZW19bW6j/96U/6iy++0JWVlXrjxo169uzZ+tSpU/rPf/6zzs/Pr/M1/Skf27dv13/96191TU2Nrqio0HPmzNFlZWVa65b/nqmsrNS7du3SWmuPn+lHH32kX3jhBa211p9++ql+5plntNZaf//99/rBBx/U1dXV+ujRo/oPf/iDrq2t1VarVX/77bdaa63Ly8v1zJkz9ffff/+LMZzTkduqVav49NNPsVgstGvXjri4OHr37s1zzz1HcHAwl19+uWtsdnY2W7du5dSpUxQVFXHttddy8803ezzn5ZdfznfffQdAbm4uH374ITU1NfTo0YO7774bwzCYOHEi48aNY+fOndx5550EBgbyv//7v1RVVREYGMhjjz1GaWkpzz77LFVVVQBMmTKFyy67jC+//JKVK1diMpk4fPgwPXv2dD1vXeLj4+tsP3ToEHfddRcAV1xxBQsWLHDlo7q6moULF9aZD6WUazeV999/n7y8PL/OR3Z2Nna7naysLA4ePNiq8wHu75ny8nIiIyP54YcfPHLSvXt38vPzKSgo8Pv3TFZWFna7nYULF3LJJZdQXl7Oiy++iMlkcn2GBAYGEhQUxBtvvMG//vUvv87HRx99RGhoKP/4xz+Ii4sjIiKCBx98ELPZ3OLfMyEhIfTu3dv1M+3WrZtrRmfbtm2u+K+++mpefvlltNbk5eWRnJxMUFAQnTp1Ijo6mn379hEfH+/atjE0NJTY2FisViudO3euM9+n/WJx+/bbb/n88895+umnqa2tZc6cOcTFxbFkyRKmTJlCr1693A7BAfbt20daWhohISHMnTuX/v37u65bA+e1bwUFBSQkJHDo0CE2b97MvHnzCAwMZNmyZWzcuJGUlBSqqqro0qULt9xyCzU1NTzwwAM88MADdO/enfLycoKDgwkPD+fRRx8lODiYwsJCFi1a5LqLwL59+3jmmWeIjIxk/vz5bN26lauvvvqXvmU3F198MZ9//jljxoxh69atVFRU8Nlnn/H0008zceJEtm7dyt69e/noo4/4wx/+4JaPmpoa9u7dS2VlJfPnz/frfEyYMIHs7Gy2b9/OV199xdq1a5k1axa9e/dudfnYtWuX23vmnnvuITk52eM9o7Vm48aN9OvXj+zsbL9/zyxcuJAVK1awbt06tm3bRlhYGLNnz3Z7z5SVlXHw4EGUUjz22GN+nY97772XNWvW8O233xIbG8uOHTsYMWIEd999t1+9Z8rKyti+fbtrKvHnewYHBAQQFhZGaWkpVquVHj16uB5X117CRUVFHDhwgO7du/9i3n+xuH399dcMGDCA4OBgAK688kqqqqooKytzbYU1ZMgQt936+/btS7t27QC46qqr+Prrr7n00kuprq7moYceAqBnz54MHz6cf//73xw4cIC5c+cCUF1d7dqWyzAMV+IOHz6M2Wx2fVNhYWGAc273pZde4uDBgxiGQWFhoSuO7t27ExUVBcCgQYP4+uuvz/sXc+LEibz88stkZ2fTs2dPwsLC6N+/P8HBwTz33HO89957OBwO1q1b59py7HQ+li1bRkxMDB06dPD7fAQEBLB3716Sk5OJiIjgvffeo6ioqNXlIyIign379rneMzabDa01FouFXbt2ub1nsrKySElJISYmplW8Z6xWK0ePHmXUqFGEhYXx3nvvuR4zZMgQduzYwaJFi+jbty8BAQF+n4+kpCS+++47PvjgA3JycggMDHRtNeUv75na2loWLVrEb37zG9fjdD17CdfV/nOVlZWkpaUxadIkV6xn84vFra4XDAkJqXcD47qcHhscHOyatvn586ekpHDbbbd5PC4oKMh1yFvfN/7+++8THh7OggUL0FrXeyL6Qp2eKgBncnNycggKCnL1gXNvzKCgIA4ePEh0dDQAJ0+epKSkhOTkZLf9McE/8xEREUG3bt0ICwsjNDSUkJAQ9u/fz/Dhw4HWk4/PP/+coKAgTp06BcBnn31GdHQ0oaGhbu+Zjz/+mNraWu68805yc3M9ntefcnL6d2Tr1q306NGDsrIy2rZtS3BwMN98842r4FutVnr37k1cXBxffPGF23P6Yz4A185NZrOZd955x/X5Af7xnnnhhReIjo5m7NixrjaLxUJxcTEWi4Xa2lrKy8sxmUyu9tOsVqvrM7ampoa0tDQGDx7MwIEDz+m1f/FSgMsvv5zt27dTXV1NZWUl+fn5gLPKf/311wBs3LjR7TG7du3CbrdTXV1NXl4el112Wb3P36dPH7Zs2cLJkycBsNvtHDt2zGNcbGwsNpuNffv2AVBRUeFKjNlsxjAMcnNzcTgcrsfs27ePoqIiHA4Hn332mdu5wXNVUlLies41a9YwcOBAtm/fjs1mo7S0lPz8fKqqqqitraW6uhpwroCqrKzkgQcewDCMVpGPrl27UlpayrZt2wBwOByu1V2tKR/Dhg1ze89s3LiR8vJy4Kf3zPr169m2bRsdO3Z0fdD4c05O/4506NCBL774gvz8fBwOB7W1ta4P2KVLl6K1ZtKkSa0mH5WVlRw7doz8/HxsNhsOh8O10tEf3jNvvfUW5eXlrp/paVdeeSXZ2dkAbNmyhSuuuAKlFElJSWzevNl17rmwsJDu3bujteb5558nNjaWcePG/XLCf3RO22+tXLmSTZs2ERkZSfv27enVqxdxcXGuk+P9+vXj888/Jy0tjezsbNcH/pEjR9xOfk6cONHj/BzA5s2bWbNmDVprAgICmDp1KvHx8R7j9+3bx/Lly6muriY4OJg///nP2Gw217mKK664gg8//JDXXnuNL7/8klWrVtG+fXu+++67XzwZvGLFCj799FPXXQuGDx/O+PHj2bJlC2+88QZKKXr27MnUqVNZs2YNGzZswG63ExAQQEhICMOHD2fbtm2uv0YDAgLo3Lkzdrud0NBQIiMj/TofmzZtok2bNhw9epSQkBC6devG8ePHCQkJaXX5CAoKYuXKleTm5mK1Whk4cCBXXHGF6z3z3//+l7Zt21JdXU1MTAydOnUiICDA798zmzZtoqqqioqKCteCg++//x7DMDhw4ACBgYHExsZit9sJDw8nPDzcr/NxeqxhGLRr145bb72VtWvX+sVnSHFxMb///e+JjY11/ZE7evRoRowYQXV1Nc8++ywHDhzAZDLxwAMPuKYsV69ezYYNGzAMg0mTJpGYmMjXX3/NY489RteuXV1HrBMmTKB///51/hxczmVZZ0VFhWt555w5c1zLMuuyYcMGvWzZsnN52ib1xRdf6CeeeKJJnlvy4U7y4Uly4k7y4U7y0fTO6VKAF154gUOHDnHq1ClSUlKIi4s7l4f5LcmHO8mHJ8mJO8mHO8lH02t1dwX405/+5DrZf9r999/v2mmktZF8uJN8eJKcuJN8uPPVfLS64iaEEML/ycbJQggh/I4UNyGEEH5HipsQQgi/I8VNCCGE35HiJoQQwu/8f/BQKagyw4HNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/gapminder_gdp_oceania.csv 10039.595640000001 10039.595640000001 True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/gapminder_gdp_africa.csv 298.8462121 298.8462121 True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/gapminder_gdp_americas.csv 1397.7171369999999 1397.7171369999999 True\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/gapminder_gdp_asia.csv 331.0 331.0 True\n"
     ]
    },
    {
     "data": {
      "image/png": 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ICGrUqEFUVBQAUVFR1KhRg6VLl/L444/zxRdfAJCYmEhMTAyLFi1i+vTprFu3DpPJhMlkYt26dUybNo3Fixdz8OBBEhMT77W6QgghKoF7Cl5paWkcPXqUvn37AmA2mzl9+jRdunQBoHfv3hw+fBiAI0eO0Lt3bwC6dOnCqVOnMJvNHD58mK5du1KlShXq1atHgwYNiI+PJz4+ngYNGlC/fn3c3Nzo2rWrci4hhBB/b/cUvD777DOGDh2KwWAA4Pbt23h4eODq6gqAt7c3RqMRAKPRiI+PDwCurq54eHhw+/Ztm+PWryl+3MfHRzmXEEKIv7cyj3n9/vvv1K5dG39/f06fPl1qebPZbHfMYDCoHi+pvBo/Pz9Nx++1rF7PXVHqUZ7n1ks9rt7De+jlM1aGc1eUepTnufVajzIHr7Nnz3LkyBGOHTtGXl4ed+7c4bPPPiMnJ4fCwkJcXV0xGo14e3sDRS2ntLQ0fHx8KCwsJCcnB09PT+W4hfVrrI+npaXh5eWlWpekpCS7Y35+fqrH1Wgpq9dzV5R6lOe59VoPa6W9Tq+fUY/nrij1KM9zV/R6lBTQytxtOGTIEFatWsXy5csZP348QUFBvPHGG7Rr145Dhw4BsHfvXsLCwgDo1KkTe/fuBeDQoUO0a9cOg8FAWFgYMTEx5Ofnk5KSwvXr12nRogUBAQFcv36dlJQUCgoKiImJUc4lhBDi7+2eU+WLe+GFF1iyZAlfffUVzZs3Jzw8HIDw8HCWLVvGuHHj8PT0ZPz48QA0btyYhx56iIkTJ+Li4sJLL72Ei0tRTB01ahSzZs3CZDLRp08fGjdufL+rK4QQQofuS/Bq164d7dq1A6B+/frMmTPHroy7uzsTJ05Uff2AAQMYMGCA3fGOHTvSsWPH+1FFIYQQlYissCGEEEJ3JHgJIYTQHQleQgghdEeClxBCCN2R4CWEEEJ3JHgJIYTQHQleQgghdEeClxBCCN2R4CWEEEJ3JHgJIYTQHQleQgghdEeClxBCCN2R4CWEEEJ3JHgJIYTQHQleQgghdEeClxBCCN2R4CWEEEJ3JHgJIYTQHQleQgghdEeClxBCCN2R4CWEEEJ3JHgJIYTQHQleQgghdEeClxBCCN2R4CWEEEJ3JHgJIYTQHQleQgghdEeClxBCCN2R4CWEEEJ3JHgJIYTQHQleQgghdEeClxBCCN1xK+sLU1NTWb58ORkZGRgMBh5++GH69etHVlYWixcv5ubNm9StW5cJEybg6emJ2WwmMjKSY8eOUbVqVcaOHYu/vz8Ae/fuZcuWLQAMGDCA3r17A5CQkMDy5cvJy8sjNDSUkSNHYjAY7v1TCyGE0LUyt7xcXV0ZNmwYixcvZtasWezcuZPExES2bdtGcHAwERERBAcHs23bNgCOHTtGcnIyERERvPLKK6xduxaArKwsNm/ezOzZs5k9ezabN28mKysLgDVr1jBmzBgiIiJITk4mNjb2PnxkIYQQelfm4OXl5aW0nKpXr07Dhg0xGo0cPnyYXr16AdCrVy8OHz4MwJEjR+jZsycGg4HAwECys7NJT08nNjaW9u3b4+npiaenJ+3btyc2Npb09HTu3LlDYGAgBoOBnj17KucSQgjx93ZfxrxSUlK4ePEiLVq04NatW3h5eQFFAS4zMxMAo9GIr6+v8hofHx+MRiNGoxEfHx/luLe3t+pxS3khhBCizGNeFrm5uSxcuJARI0bg4eHhsJzZbLY75mj8ymAwqJZ3xM/PT9Pxey2r13NXlHqU57n1Uo+r9/AeevmMleHcFaUe5XluvdbjnoJXQUEBCxcupEePHjz44IMA1K5dm/T0dLy8vEhPT6dWrVpAUcspNTVVeW1aWhpeXl54e3sTFxenHDcajbRt2xYfHx/S0tJsynt7e6vWIykpye6Yn5+f6nE1Wsrq9dwVpR7leW691sNaaa/T62fU47krSj3K89wVvR4lBbQydxuazWZWrVpFw4YNeeKJJ5TjYWFh7Nu3D4B9+/bRuXNn5Xh0dDRms5lz587h4eGBl5cXISEhHD9+nKysLLKysjh+/DghISF4eXlRvXp1zp07h9lsJjo6mrCwsLJWVwghRCVS5pbX2bNniY6OpkmTJkyaNAmA559/nv79+7N48WKioqLw9fVl4sSJAISGhnL06FHeeOMN3N3dGTt2LACenp4MHDiQqVOnAjBo0CA8PT0BGD16NCtWrCAvL4+QkBBCQ0Pv6cMKIYSoHMocvFq3bs2mTZtU/23GjBl2xwwGA6NHj1YtHx4eTnh4uN3xgIAAFi5cWNYqCiGEqKRkhQ0hhBC6I8FLCCGE7kjwEkIIoTsSvIQQQuiOBC8hhBC6I8FLCCGE7kjwEkIIoTsSvIQQQuiOBC8hhBC6I8FLCCGE7kjwEkIIoTsSvIQQQuiOBC8hhBC6I8FLCCGE7kjwEkIIoTtl3s+rMih8+X8AuGp1zHXNd39NZYQQQjhNWl5CCCF0R4KXEEII3ZHgJYQQQnckeAkhhNAdCV5CCCF0R4KXEEII3flbp8oLIURxMoVGH6TlJYQQQnckeAkhhNAdCV5CCCF0R8a8hKjgLGMwIOMwQlhI8BJ/CrUbsKObr9yshRClkW5DIYQQuiMtL52pKK0SqYcQ4q8kLS8hhBC6Iy0vIYQoI2n5/3UqfPCKjY0lMjISk8lE37596d+//19dJSEqNC3JMRWFBAF7evwe/0wVOniZTCbWrVvH22+/jY+PD1OnTiUsLIxGjRr91VUTOqXlJik31MqhonyP5VmPv8NnLK5CB6/4+HgaNGhA/fr1AejatSuHDx92GLz+DhdHefo7fMbypMc18fT6nUurpOy0fud/xn21LOet0MHLaDTi4+Oj/L+Pjw/nz5//C2vknPtxcdyvH6Ieb6ii4tJyPek1MAp9MJjNZvNfXQlHfvnlF44fP86rr74KQHR0NPHx8YwaNeovrpkQQoi/UoVOlffx8SEtLU35/7S0NLy8vP7CGgkhhKgIKnTwCggI4Pr166SkpFBQUEBMTAxhYWF/dbWEEEL8xSp0tyHA0aNH+c9//oPJZKJPnz4MGDDgr66SEEKIv1iFD15CCCFEcRU621DYO3r0KFevXiU/P185NmjQoL+wRn8fCQkJ+Pv7O1X2999/p1OnTuVcI3E/5eXlERUVRWJiInl5ecrxsWPH3pfzX7lyhcTERJvfbq9eve7LuUuTkJBQ4r87e11XJJUqeJ09e5abN29SWFioHPuzLo7iyuNCXb16NXl5eZw+fZrw8HAOHTpEixYt7rWqNpwNjpmZmezevdvu7+3oh/7pp5+W+L6jRo1i+/btJZZ54oknbP7/zJkzfP3116SmplJYWIjZbMZgMLBs2TK71/7666988cUX3Lp1C0Ap+5///KfE97S2a9cuJfO1NBcuXCgxeG3YsIGhQ4eWeuzP4My1ajKZmDVrFu+8886fXT1V+fn5/Prrr6SkpGAymZTjatfqmTNnaNasGdWqVSM6OpqLFy/Sr18/6tata1Nu2bJl+Pn5cfz4cQYOHMiBAwdo2LChwzokJSWxdu1abt26xcKFC7l8+TJHjhxh4MCBdmW//vpr4uLiSExMJDQ0lGPHjtG6desS7wnO3kOSk5PZvn273W/x7bffVv57/fr1QFGATkhIoGnTppjNZq5cuUKLFi344IMPHNYDICcnx+bv7OnpaVcmMzOTbdu2ce3aNZvgP3PmzBLPbbF27VpGjx7tVFmoRMFr6dKl3Lhxg2bNmuHi8n95KI4uDq03My3ltVyoWr7wc+fO8dFHH/Hvf/+bZ555hieffJKPPvrI4d9E68WkJTjOnz+f1q1bExwcbPP3diQ/P5/ExES6du0KwKFDh2jevDnNmjVTyty5c6fU81hbtWoVw4cPx9/fv9Q6bNiwgcmTJ9/T6izOBi6AwYMHl/jvJ0+etDsWGxurGry0BGkouk4iIyNJTEykoKAAk8lEtWrV7uladXFxwd3dnZycHDw8PEr8bC+++CIGg8HueEm/Ga2/x/nz5+Ph4YG/vz9VqlQpsT5r165lwYIFXLp0ie+++47w8HCWLVvGe++9Z1MuOTmZiRMncuTIEXr37k337t2ZNWuWw/N+8sknDBs2jNWrVwPQtGlTIiIiVIPXoUOHWLBgAZMnT2bs2LFkZGSwatUqh+fWcg9ZuHAh4eHh9OjRw+HvwPKbX7JkCWPGjKFJkyZAUYD8/vvvHdbjp59+YtOmTbi7uyvHHF17ERERdO3alWPHjvHyyy+zd+9eatWq5fDcxfXu3dvpslCJgldCQgKLFi1S/dGo0Xoz01Jey4Wq5Qu3XEBVq1bFaDRSs2ZNUlJSHNZD68WkJTjevXtXUyshOTmZmTNn4uZWdMn985//ZNasWYwYMUIp88wzzzh9PgAPDw9CQ0OdKlunTh3NgevXX3/lzJkzGAwGWrduzQMPPKBaLi8vj127dnHmzBkAWrduzSOPPGLzg7fYtWsXO3fuJCUlhX//+9/K8Tt37tCqVSvV82sJ0lDUyh0/fjyLFi1i7ty57Nu3j+TkZNWyWq7VKlWq8Oabb9K+fXuqVq2qHC8+7/Lzzz8vtY7Faf09Go1Gpk+f7lRZV1dXDAYDR44coV+/foSHh7Nv3z7VcgA1atTgypUr1KlTh5s3bzo8b15ent3DnaPvx93dHRcXF1xcXMjJyaF27dol/na1fC8uLi489thjDs9l7dq1a0rgAmjSpAmXLl1yWP77779n4cKFTgWh27dvEx4ezo4dO2jbti1t27YttdV19+5dDAYD7u7umnuRKk3waty4MRkZGU7PA9N6M9NSXsuFquUL79ixI9nZ2Tz55JNMnjwZg8FAeHi4w3povZi0BMdOnTpx9OhROnbs6PB81oxGI7m5uUp3Q25uLkajUbWss90x7dq1Y/369Tz44INKUAT1/nt/f38WL15M586dbZ7UH3zwQdU6rF27luTkZLp16wYUPYGeOHFCtVtj2bJlVK9enUcffRSAgwcPsmzZMiZOnGhXtnv37oSEhPDll1/ywgsvKMerV6+u2hUD2oK0RYMGDTCZTLi4uNCnTx+bLiRrWq7Vjh07Ov19W7t165ZN15evr69dGa2/x8DAQK5cuWJzI3akWrVqbN26lejoaN5//31MJhMFBQV25R5++GGysrJ49tlnmT9/Prm5uSW2oGvWrElycrLywHzo0CGH95+AgACys7Pp27cvU6ZMoVq1aiXerLV8L2FhYezevZsHHnjA5neg1kJu2LAhq1atokePHhgMBqKjo0vsGq1fv77Ng0pJLO/t5eXF0aNH8fLycvgbT0hIYNWqVWRmZmI2m6lTpw6vvvoqzZs3d+q9oBIFr9u3bzNx4kRatGhh8wVOnjxZtbyzN7Nff/1VU3nQdqFq+cIt/fldunShU6dO5Ofnl9iFo+XcoC047tixg61bt+Lm5oabm1up3Tz9+/fnrbfeol27dgDExcU5bGk52x0THx8P2A9GqwXoO3fuULVqVU6cOGFz3FHwiouLY+HChcqNqVevXjYtJWvXr19nwYIFyv8HBQUxadIk1bIA9erVUw2CWVlZqgFMS5CGooePgoICmjVrxoYNG6hTpw53795VLavlWtXarXPkyBE+//xz0tPTqVWrFqmpqTRs2JBFixbZldX6cHHmzBn27t1LvXr1qFKlinL9qfUUTJgwgQMHDvDaa69Rp04dUlNT+Z//+R+7cn379gWgbdu2Drtkrb300kusXr2aa9euMWbMGOrVq8cbb7yhWtbyfT/yyCOEhIRw584dmjZt6vDcWr6XqKgoAL755hub4ytXrrQrO3bsWHbt2sWOHTsAaNOmDY888ojDegwZMoS3336bli1b2lx7aqscDRgwgJycHIYNG0ZkZCQ5OTkMHz5c9bwrV65k+PDhBAUFAXD69GlWrFhh8zsqTaUJXlq7nJy9mf3+++/Kfzt789NyoWr5wkFbUorWc2sJjlq7hvr06UNoaKiyNuULL7xAnTp1VMs62x3j7EAwaM8Y8/PzIzU1VRnUT0tLc/iU36xZM86dO0dgYCAA58+fd9gFGBERwZQpU5SHA+uZKo7GErQEaYDXX38dk8nEqFGj+OGHH0hLS+PNN99ULavlWr1+/UYF08wAACAASURBVDpffvmlXRKBoxv9xo0bmTVrFh988AHz58/n1KlTHDx4ULWs1oeLadOmqR5XU6dOHR588EGuX78OFLWYrLuAo6Oj6dmzp8OEoeKJQlCUwHLhwgXeeecdcnNzMZvNVK9e3WEdNm7cyLPPPgsUPbyYTCYiIiLuS7BTC1KOuLu788QTT6h+JjWrV68mKCiIJk2alDok07RpUzw8PGjSpIlybWZkZKiWrVq1qhK4oOgBrVq1ak5+iiKVJni1bdtWU3lnb2ZabnrXrl2jYcOGqmmpjtKsLRlp1l+4I1qTUpw996lTpwgKClJamcU5uoFkZWWRnJxskwxS0vfg5uaGl5cXeXl5JCUlkZSUpFpeS3eMs9mRWtOgb9++zYQJE5QgeuHCBQIDA5k3bx5g26KPj48nOjpa6Q6ztDDefPNNu9bAlClTAFi+fLnq+6rREqQBJeC6u7s7fKgry7W6YsUKBg8ezH/+8x+mTZvGnj17SqyHq6srNWvWxGw2YzKZCAoK4osvvlAtq/XhwvIZi3dJqtm9ezc///wzWVlZLF26FKPRyJo1a5gxYwaA0irVkjDk4uLCzp076dq1q1M33dTUVLZu3crTTz9Nfn4+ixYtUu0iK8v3Ynld8YeK7t2725Urnvxj4egBxNXVtcQHXmuvv/46Xbp04bXXXlO6GufMmaP8Zqy1aNGCtWvXKnWMiYmhXbt2XL58GaDEVqlFpQlezmZYffvttzz11FMOU7cdLfq7YcMGBgwYgLu7O7Nnz+by5csMHz6cnj17KmW+//57Xn31VSUttTi1m5CWlHNnk1K0fsa4uDiCgoJsWpnW1ILXzz//zI4dOzAajTYtD0c3Wi3l1bpjxo0bZ1dOS3ak1jRoy1OyM7S0Aqw5G/xzcnL4+uuv+eOPP5QygwYNsmsVL1q0iIkTJypBszjrIFqWazUvL4/g4GDMZjN169Zl8ODBzJgxw+G4UI0aNcjNzaV169ZERERQu3ZtJSlC7dxaHi60dEnu3LmTOXPmKN/TP/7xDyWrEYqSh0B7701wcDDfffedXQBT6/odO3YsERERbN26ldOnTxMSEqLa+tm+fTtjxozR9L188803nDhxgmvXrtGhQweOHz9O69atVYOX1uSfdu3asXv3bjp16mTTnav2GZs0aUKbNm2YMWMGEyZMoEGDBjhaA8MSnL/88kub43FxcRgMBrtMUDWVJnipZVhZugmsWW5YWiflHT9+nKFDh/Lbb7/h7e3NxIkTee+992yClyWVWsuTspaUc2eTUrR+RsvNR8vT744dO5gzZw7Tp09n5syZXLt2jU2bNt2X8vXr13eqO0ZLdqTWNOi2bdty8+ZNrl+/Tvv27cnLy6OwsFC1LnXr1uXMmTNcv36dPn36kJmZSW5uLvXq1XN4fi3BfMWKFTRp0oQJEyYARd1cK1assBuDGzlyJPB/rbuSlOVadXd3x2Qy8Y9//IP//d//xdvb2yYIFDdp0iTc3d0ZMWIE+/fvJycnx+GEeq0PF1q6JKtUqWIzXlNYWKga3G/cuEFkZCTnz5/HYDAQGBjI8OHDlf0Ei7O0PHfu3KkcK971a92C6tevH6tXr6ZVq1a0bdtWtSU1ZswYTCYTzz77LK1bt3b4+a3FxMQomYnjxo0jPT1dGS8uTmvyz4EDBwDYunWrw89offxf//oXTZs2Zd68ebzwwgsOH7Tff/99p+vgSKUJXuBchpVlYV+tg8+WVtHRo0fp3r276pOHo243C7UWjJaUc2eTUsr6GXfs2EHv3r2pXr06n3zyCRcvXmTIkCF06NDBrqy7u7uSnZifn0/Dhg1JSkpyeG6t5Z3pDtSSHak1Dbp4V1NaWppNV5O1r7/+mgsXLijBq6CggKVLl5Y48VNLML9x44ZNoHrmmWdUE0IsDzU1a9ZUstUs3bMhISE2ZctyrQ4fPpy8vDxGjhzJxo0bOXXqFP/v//0/h+eoVq0aGRkZxMfH4+npSUhICDVr1lQtq/XhQkuXZNu2bdmyZQt5eXmcOHGCnTt3qk4gj4iI4F//+pfytz148CAff/wxs2fPVj2vM12/xVtQnp6eXLt2TTmu9vDg4uLC+vXrS/z81qwzE+/cuUOdOnUc/g60Jv9o6d62tLJat27NjBkzWLJkCdeuXVMtm5mZyVdffYXRaGTKlCkkJiYSHx+v6Z5VaYKXlgwr0D6Bt1OnTowfPx53d3dGjx5NZmam3eRIS7fbrVu3OHfunJJZd/r0adq1a6d6Q9CScu5st8bcuXNL7Fp0lIG5Z88e+vXrR2xsLLdu3eK1115j5cqVqsHL29ub7OxsOnfuzIcffkiNGjXw9vZ2+J5ayjvbHaiWHWnJGCtOaxp0aV1N1n777Tfmz5+v/F29vb1LHT/REszd3d05c+aM8iR+5swZ1TlkFjNnzuT9998nOzubDz74AH9/f2JiYmySAxx1EVuoXauW76BatWpOtdJ//vlnNm/eTFBQEGazmcjISAYOHKiawar14cLSJdmmTZtSuySHDBlCVFQUTZo04aeffiI0NFT1OjGbzTY9KT179rRpVRVXUFDArl27lO7cdu3a8fDDD9sEBa3jlRYdOnTg0KFDPPjgg6UOEzRv3pzs7Gz69OnDlClT8PDwcJhyrjX5B5xf6WPq1KnKf3t5eTFjxgzOnj2res7ly5fTo0cPvv32W6Co4bF48eK/Z/DSkmEF2ifwvvDCCzz11FN4eHgoqw289dZbNmUsP+i5c+eyaNEi5Uk4PT2ddevWqZ5XS8q5s0kpljTgX3/9lYyMDHr06AEUPUkWXxLHmuXJ6dixY/Tp04dmzZo57LO2PJ0OHjyYuLg4cnJy7J7uy1re2e5ALdmRWtOgne1qgqJEFIPBoPx7bm5uqefXEsxffvllli9fTk5ODmazGU9Pz1KDR9WqVYmKiuLRRx/lqaeecnitanHhwgW2bNliN9jvqKv2u+++Y/78+Upr6/bt27z99tuqwUvrw4WlS3L48OFKl6SjhzsXFxcefvhhHn74YdV/z8rKAoqCz7Zt2+jatSsGg4GYmJgSu9jWrl1LQUEB//rXv4Ci7ty1a9eqrsSiZTkrKBr7unv3rnKvKem+8MorrwDw6KOPEhISQk5OjsOWlNZgqmWlD8uUnOI9Jmr3rczMTLp378533xXtqu3m5ubUGJy1ShO8Dh8+TL9+/WwyrHbs2EG/fv1Uy2udwHv37l127txJamoqY8aMIT09naSkJNXuh5s3b9qMS9WuXVt1/A2cSzl/5513+OCDD+yW3XF0QVsulo0bN9oMfIaFhZX4Gf39/fnwww9JSUlhyJAh3LlzR/WGbTKZmDRpEgsXLrR5P0e0lre0aEvrDnRmZQut6yVaONvVBPDQQw+xevVqsrOz2b17N3v27Clx8jhoC+bNmjVjwYIF5OTkAOqTT62ZzWbOnTvHgQMHlBupdbCx5mwyCBQ98A0bNsyptGko2kzWeoywevXqqhOUQfvDxebNm5XudsvTuqO1IUtbXqv4tIWffvpJea3BYHAYYC5cuOD0/D4ty1mBtqkoK1asoE2bNrRp04YGDRqUWl7L4t5aVvrQkkBVtWpVsrKylOsoPj6+xKkGaipN8Nq3b59doNq7d6/D4KV1Au+KFSvw9/fn3LlzQNEPc9GiRao3tLZt2zJr1ixldQZLGqi1klJiwbYP2jJ2onVuVWZmJjdu3FAGnFNSUsjMzHRY/tVXX+XSpUvKrPrbt2+rPqG7uLjQtGlTUlNTHd6M7qV8p06dnOoOdGZlC0v3XVJSEhcuXFDGA3///XfatGnjsA7OdjVBUUv3xIkTVK9enaSkJJ599lnat29f4mfcuHEjbdq0ITAwsNRgrvWpfcSIEWzdupXOnTvTuHFjbty4YXf9WTibDAJQq1YtTZvBent7M23aNMLCwpTlmQICApQHCusHh+zsbPbt22eXdeso+1fL2pClZdhpGdex5uLiQnJyshIwbty44bD1oGU5Kyhar7BPnz6EhISU2iLp1q0bZ86cITo6mtTUVPz9/WnTpo3yu7CmdXFvLSt9aEmgGjZsGPPmzePGjRvMnDkTo9GouiJNSXQfvA4cOMCBAwdISUmxmU9w584dh4PDoH0C740bN5gwYYKS0VTSmMNLL73Eb7/9RlxcHFDUJVJ8XbyypMRCUSsmIyPD5ibmKCAMHz6cd999VwleN2/e5OWXX3ZYb4PBQGJiIkePHmXQoEHcvXvX4Rya9PR0JXnEevkYR+NpzpY3mUwEBwdTo0aNUrsDnVnZwtIK//DDD5k3b57ydPfMM8+oplVbqHU1WY87Fde+fXslYJlMJvbv369016qpW7cuBw4cIDIykmrVqilPzp07d7Yrq/Wp3dPT0+bvWr9+fYdBwNlkEChqJa5atYqgoCCnVsGoX7++TaaeJfCpjQfOmTOHli1bltqqs6wNWbzeJa0NqSXDTstuEEOHDuW9996jfv36mM1mUlNTee2111TLalnOCorS9/fu3UtkZCRdunShd+/eDrMvO3ToQHBwMBcvXuTkyZPs3LmThIQE1eCldXFvLSt9aEmgCggIYObMmSQmJgLQqFEjm256Z+g+eLVq1QovLy9u377Nk08+qRyvVq2aw4luJpOJ69ev06lTJ6cmB0NRSy0vL0/5YSUnJ5f4xw4MDMTFxQWDwaD6ZY8ZMwbQ1gf9448/snnzZmrXrq3Uw9GSOAAhISFEREQoGT8NGzYs8ea3du1aDAYDp0+fZtCgQVSrVo1169YxZ84cu7Ja58Q4W97FxYXPP/9cybSqUqWKwzprWdkiNTXV5vtyc3NTTQgwmUzExMRgNBoJCQmhSZMm/P7772zdupW8vDzmz5+vlM3JyWHnzp0YjUbCwsJo3749O3fu5LvvvqNZs2YlBq/w8HDCw8PJyMggJiaG77//nt27d6u2rrU+ta9Zs4aCggIla69GjRoOy2pJBtmzZw9JSUkUFBTYtAYcBS/Ld56bm1vqRN78/HynJsOWZW1IZzPstG5bEhwcTEREBElJSZjN5hJ/X1qWs4L/exjKycnhwIEDfPjhh/j4+NC3b1969Ohh8zlmzZpFTk4OAQEBtG7dmlmzZjkcP9W6uLeWlT60JFBB0W/QsqvEqVOn+O677zTNmaw0Oynn5uaqpgc7CjDvvfeepsBx4sQJvvnmGxITE+nQoQNnz55l7Nixqt0xMTExbNiwQekO+uOPPxg2bBhdunRRPbezSz6NGzeO2bNnl9iiLOu5oagVNG/ePN566y3lJj1p0iRN643dD5s2baJJkyalZlpNmDCBpKQku5UtLMkT1jeGLVu28Msvv9C5c2cMBgO//fYbXbt25emnn7Y55/Lly0lLS6NFixacP3+eunXrcu7cOYYMGWLXep4/fz41atQgMDCQkydPkp2dTUFBASNHjrTZ6kXNqlWrSExMpHbt2rRp04bWrVvTvHlz1Yy5Tz75hMcee8zpp3Yo6ibdu3cvv/zyCy1atKB3796qWaOXLl1STQZRq/+bb76pjFs649y5c6xcuZLc3FxWrlzJpUuX2L17t+q6jtu3b6datWpOTYaFoodHHx8fqlSpwunTp7l8+TK9evVSDdSOJrwW//2/+eabyvjOggULlPEdR/Pmio+5tmnThn/+85+qwd9R5mRJCVS3b99m//79REdH4+XlRY8ePThz5gxXrlzh3XffVcp9+umnXLx4kWrVqtG6dWvatGlDy5YtVQPp5s2beeyxxzh58iTr1q1T1i997rnnHNbDaDTa3UOc6epW6zGJi4tjzZo1pKen07lzZ55++mmWL19OXl4eTz/9tLJlkjN03/KycCY92FpgYCDr1q2ja9euNt1YjrJ02rdvT/PmzTl//jxms5kRI0Y4zE7cunUrc+bMoXbt2kDR2NMHH3ygGry0LPnk6+tb6mB9Wc8NRenKJpNJCRiZmZl2wUPrXk1l2dvJ2UwrLU9pAwYMICQkRLnRjB07VjWdOCEhgQULFuDi4kJeXh4vvfQSS5cuVV2H8caNG8rNvG/fvrz00kusWLHCqYHn27dvYzKZqFGjBp6entSsWdNhqrfWp3YoWpvxueeew9/fn8jISC5duoTZbOb555+3aSlpSQZp2bIliYmJTq/+/tlnnzF9+nTlQahZs2ZKYkhxbm5ubNiwwanJsFA0JjR37lySk5NZtWoVnTp1IiIiwiZd28LZh1St25Zo2U1Ay3JWUJTBee3aNXr27MmUKVOU669r1652wdTSJZyTk8O+fftYtmwZt27dUp33pnVx7w0bNvDLL7/QqFEjm94e6+CVk5PDrVu3+Mc//gHAL7/8okw/6tChg81v57PPPmPkyJEEBgYSGxvL1KlTeeaZZ5xea9FapQleUHp6sDVL4kXxiaHFL/TiCRWWLyI1NVUZHC3OZDIpgQuKnh6tx6iKn7+0JZ8sA9z16tXj3XffpWPHjjZPVY6+eK17nD322GMsWLCAW7du8d///pdDhw7ZPZFpTRopy95Ozr7G0Q2h+BigdbZjaauOWKfsuru74+fn53ABYetWvYuLC/Xq1XM6Y8oyrpSYmMjx48d57733MJlMqplcWpefunz5Mnv27OHYsWMEBwczefJk/P39MRqNvP322zbBSy0b0zK+Vrz1dfbsWfbt26cpiBb/LhwlH/zwww9EREQ4vXmhi4sLrq6u/Prrr/Tr14/HHnvM7veudcFdrduWaNlNwNnlrOLj4/Hx8eHRRx8lKCiIvXv3snr1anx9fRk8eDCenp7MnTvX5jWW1t+FCxeoU6cOPXv2tEtGKsukdCjK4l6yZEmJww3r16+nVatWSvD68ssvCQ0NJS8vj7Nnzyqp/BaWseEuXbqwYcMGHn/88RLr5kilCV5a0oPB+aexsmyfHRISYpdt6GjA2JklnywD3L6+vvj6+lJQUKC6H1FZzm2tR48e+Pv7K5lckyZNuqedh8tKbdv2xx9/3O5G6OwNQUu247Vr15REALPZrCQGqN2oL126pIzTmM1m8vLyGD58eKnbw0BRtuMff/zBH3/8QXZ2NkFBQQ6TQbQ+tX/66af07duXIUOG2HRheXt72z2MXLhwgYSEBCVr9ujRowQEBPDTTz/RpUsXnnrqKaWs1iDq4+PD2bNnMRgMFBQUsGPHDodJB40aNXJ63ygo6iU4cOAA0dHRSnJK8d+7lgV3zWYz/fv3p0aNGk5vW6JlzNXZ5azWrFnDO++8Q1BQEHFxcfz3v/9l5MiRXLp0iU8++UR17mp2djaPPPKI3co71soyKR2Kkm4KCwtLDF4XLlywCVDVq1dXWoPvvPOOTdmcnByOHDnisG5aslkrTfDSkh4Mzqcfl2X77GHDhim78JrNZtVsQ8sqGLm5uaUu+fTUU09x584dm9YcFG03UFKTX+seZ1C0kkT16tWVv4mz6e33k9q27UuXLrUbu9Cyvp2z2Y6LFy92up4bN27U8KlsHTt2jLZt29KvX78SVyYBbYvQguMxHsBmBQkomqA7b948JaFi8ODBLFy4kPfee4/JkyfbBK+6deuqZrs68vLLL/PZZ59hNBp59dVXad++PS+99JJqWRcXF2W/t9L2jYL/25fq6aefpl69eqSkpNglyGhZcNdgMLBgwQIlY7mkdSkttOwm4OxyViaTSRnni4mJoW/fvnTp0oUuXbrYteosiTCWSdJ5eXk2qwVZ3xvKMikdinofJk2aRHBwsMPvpfgE/tdff135b0t3tEWrVq345ZdfVP/fYDD8PYOXZaKxRUnpwaA9/Vjr9tmtWrUqMdswLCyMW7du2TXv4+Li7G5mkZGRhISE2D0dnTx5kjNnzjhMf9eaEWidzeji4uJUt1B50LJtu7Pr2zn7tyhpAP1+UktacERLkAZt+24Vz8J0dXUlNTUVd3d3u9+F1mzXWrVqORxzLq5Dhw4EBQVhMBiUsc6SNGrUyOb3Xa9ePfr3769a1tkFd1u2bEl8fLzT29FraYk6u5yVyWSisLAQV1dXTp06ZdOiKf7AsGjRIqZNm+ZwJSHrfb7KsmcZFN2nigeU4sMQLi4uZGRkKN3rlvuk0Wi0K6u2O0RZ6T54ffbZZ4wYMcLhen6OWhla04+1bJ/tzJpuR44c4fnnn7frlqhatSpff/21TdkzZ84oqfXWevToYTPAXZzWPc527NjBkiVLNGUzlgfLtu379+9XxoLUukm1bLnRtm1bMjIyuHDhAlC0Tl/xluyfyTqRxdINrLaFD2gL0qBt361u3boxffp0m8nb3bp1Izc3167L2NnrY/PmzSX+u3XvRmFhIf/973/Zs2cPvr6+mM1m0tLS6N27N88//7zda7Vs+2Lh7IK7p0+fZvfu3dStW5eqVauW+vDm7JgrOL+cVbdu3Xj33XeVxZUtD7fJycl2vSyW4OnMZpRl2bMMilpOxRd6sOzCbPHkk08yb948hg0bpiRBXbx4kfXr16vuWH2/6D54WbpBtP6RtE4a1LJ9tjNrut28eVO1Pz0gIKDEBUmLU5vpUJYMP9CezVheLNu2v/rqqyVu2/7WW29RpUoVRo4cSXR0NHfu3HHYwio+feHTTz8tcfpCeSuelPLbb78pi6YWpyVIg7Z9twYNGkRoaChnz57FbDbz8ssvExAQAGDXanL2+lAbu7p79y5RUVHcvn3bJnitX7+e3NxcJXMPim6Y69evZ8OGDYwYMcLmPFq2fbFwdsFdrWN6Wrpzree5WZazsiz7Zm3AgAEEBQWRkZFB+/btld+xyWRSPruanJwckpOTbYKo9fhbWfcsc2blop49e1KrVi02btzI1atXMRgMNGrUiMGDB2vafkUr3QcvS/aYs60MyxNbYWGhpvRjLdtnO7Omm3XfdHHF/61WrVqq3Rnx8fGq2VllyfAD7dmM5aVOnTo27+nr62uT3q8WnC1BfPPmzTRo0IDnnnuO4OBg5d+1TF/4KzzwwAPKCtvFadkXC7Tvu9W8eXO8vLxKHed09vqwXizgzp077Nixgz179tC1a1ebf4OiBJGPP/7Y5vv08PDg5ZdfZvz48XbBy5J8ZJlIbG3Xrl2qD5SOFty1LMhrGWOy7jLOzc3l8OHDHDhwQDX9HrR35xaXmpqqetySAGLNz8/P4XmioqLYvn07RqORJk2acOHCBVq2bGkzF8zC2U0/Ha1clJubq9ryDgkJKXFh7vKg++BlUdrimxZantisOTOOYOlPdrSmm7WAgAB2795tt9J1VFSUXTr3sGHDWLx4Mb169VL+LSEhgX379jF+/PgyfR41WrMZ7zdnW4wlBWeTycSVK1dYunSpzYRaLdMXHNm0aZNq68XZXbytWacum81mpTtTjZZ9sUDbvltaxjm1XB9ZWVls376d/fv306tXL+bNm6c64dh6NX5rlvFiR7755huqVKlCUFAQANu2bSMuLk41eMXExAC2C+5C0Yoh1veIgoICjh49yoEDBzh+/DgPPvig0mJRo7U7t7z88MMPzJ07l+nTp/P+++9z9epVvvnmG9Wyzm76WZaVi7SKj4+3S5hT2/3ZkUoTvJzd3rr4gLyz6cfOjCNY+pMdrelmbcSIEXz00UccOHBACUgXLlygoKDALquoRYsWzJ49m507d7J3716gKA1+9uzZ93XcxtKlYFlNvrQlfe63srYYrbm4uNCsWTO7dd2KT184ePCg5i4NR3PE1HbxTk5OLvFc1unBljlijuYlatkXC7Ttu6VlnNPZLqf169fz22+/0bdvXxYuXFjiddSwYUP27dtnN3E+Ojq6xNbGW2+9xbx58xg6dCixsbFcu3bN4d+vtIV3T5w4oQSsdu3a0bNnTy5cuFDq386ZJAxH86ssUyvuB+u94QoKCmjcuLHDveGc3fSzbt261K1bl1mzZtntKJ6Xl6d5Bfjili9fTmJios0CCgaD4e8ZvLRub601/diZcQQt/cl16tThww8/5NSpU1y9ehUoWhvM8iRZXO3atUvc3+h+uHLlCsuWLVO6U2rWrMnrr79O48aNy/V9y0PxJ2bL9AXL2M4///lPu+kLpSkpjdeZXbwB0tLS8PHxUb0xHjlyRPU9nN0XqyybkGoZ58zMzOTbb7+163IqPmdy+/btuLm5sWXLFpuEIrUx19GjR/PRRx+xZ88em4e4vLw8hxN+oagr/a233lJW03GUwAGlL+M0a9YsWrduzQcffKCkyH/22Wel/j3UkjCKd+eWNL/K0RY7zrJkJdapU4fs7Gw6derErFmzqFGjhsOH2vLcUVyL8+fPs2jRIs17eFmrNMFL6/bWWvurtYwjaNm0LygoyGHA+rOtXr2aF198UanP6dOn+eSTT/jwww//4pqVnXVXpHVyy88//0yVKlVUx8egaPuYH3/80W5NN7UAoGUX7/fff5/p06fbzSPas2cPW7ZsUQ1ezu6LVZbMLi3jnJYNXI8ePVriBq5a5r95e3sze/Zs5SHObDYTGhpq931YFO9aLigo4MaNGxw6dMhhMlJpyzjNnTuXgwcPKsGrW7duTnUpqyVhFFfW+VXOmDZtGvPmzVOuyeeee46TJ0+Sk5PjcGf28thRvCx75jVq1Ijbt2/fU89RpQleWre31tpfrWUcQeumfRXF3bt3bQJpu3btHN6E9aIs42MACxYsoE+fPnTq1KnUp0Mtu3gPHz6cDz/8kKlTpyrL6WzdupUDBw7YDbBrGUMF7VMjQNs4ltYNXLVw9iGuLF3LpS3j1Lx5c5o3b87QoUM5c+YMBw8epKCggNmzZ/PAAw843IFZbYzWMnf0xRdftJtHdr+pZRo7CvoW5bGjeFn2zLtz5w4TJkywW0BYbR85RypN8NL6I3J20qCFlnEErZv2VRT16tVj8+bNSlrx/v37/7RJu38FR+NjUPSjdbSRaXF169YlLy+PjIyMUruOLS2c2bNnM2nSJKKiorhw4QLvvfeeXUKDljFUa1omKWsZ59S6gWt5y8rKIjk52aYLUy2Aa1nGIWtH5gAAH0lJREFUqXXr1rRu3ZqRI0dy4sQJYmJiHAavJ554Ai8vL7p3747ZbCYmJoaMjAz8/PxYuXKlarbf/ZSZmVliq0etxaN1Y1NndhQvy555jiaUa1FptkQBbdtbW7ZQMZvNSn91jx497AauyzKOcPLkSQ4ePOj0pn3OUquLh4cHAQEBPPzww6WuSlCarKwsNm3apIwLtWnThmeeecbhthSV2YEDB7h+/TodOnQotRv6yJEjrF+/noKCApYvX86lS5fYuHFjictwnTlzhgULFhAYGMiECROc+u6c2RcLiuYPWZKLJk+erCQXqXUPaRnntDxJp6amKhu4PvPMM3/Jg9rPP//Mjh07MBqNNsFJ7SFWy9Y5WkybNs1uovP06dOZNWvWn7KV0CuvvMIjjzyi2gID9TH4WbNmKa1D6x6F4lMYLEwmE1FRUZw4cQKz2UyHDh3o27ev6j1x/PjxLFiwQLnn5efnM2nSJJYsWaJ67rS0NK5fv05QUBB5eXlKlq6zKk3LS+v21tZ/pI4dO1KzZk3VL6Qs4whaN+1zVv369cnMzLRZ8Ld27dokJSXxySef3PPSK56eniUuqfV3cuXKFaKjozl16pTNd6h2c/z666+ZM2eO8qTdrFkzh4Pglq4ms9lMfn4+p06d4uWXXy5xArmWfbFA2yRlLeOcrVq1wsPDw+kNXMvTjh07mDNnDtOnT2fmzJlcu3bNbocIC62Tj51lmTNmmSt46NAhuzKnTp0iKCjIYdbhvdwTvLy8Spzvp0brykJqO4o70rNnT6ZNm2azZ17xtTQtoqKi2LlzJzk5OSxdupTU1FTWrVtnt5BvSSpN8HJ2e+tz587x5Zdf4unpycCBA1m2bBmZmZmYzWZef/11u4l2ZRlHuHz5sqZN+5x16dIlm0VXw8LCmDlzJu+9957qHkLOKkvrsrL77bffWLZsmVNbk7u6ujqdsVeWMRst+2KBtuQiLeOc06ZNo1mzZvTu3ZvQ0NC/dDzXOj08Pz+fhg0b2qWH5+Tk4OHh4TCt+157FN544w0iIyNZt24dULQ24rhx45R94KBordKgoCCHWYf3ErzK0mnm7MpCZVmGy9k986BofqF1Ioifnx8ZGRmaPkulCV7Obm/96aef8vzzz5OTk8P777/P1KlTCQwM5Nq1a3z88ccOZ4mrfYmW5vfAgQNtuhu1btrnrMzMTJvVD1JTU8nMzARw6ibriKV1+euvv5KRkaGszn3w4MFKPeZVkqZNm5Kdne1UNlTjxo05cOAAJpOJ69ev8+OPP6quknAvnN0XC7QlF2kZ5/z44485efIkUVFRREZG8tBDD9G7d+8S52OVF29vb7Kzs+ncuTMffvghNWrUsFvQOiIigilTpihb0lvf7K0nJ1uCnKXrtDhHQa5+/foOFz2wbG8zePBgTCYTISEhmnYJdoaWdHWtKwtVr16dM2fOKH87ZzVr1ow6deqUulqLu7u7zT1L64IBUImCV8eOHcnOzubJJ59U/uBqkzgLCwuV7dA3bdqk3GQcLbJrERoaiouLizKJ7uDBg5jNZjw8PFi+fLnNRVyWTfucMWzYMN555x0aNGiA2WwmJSWF0aNHk5ub63B3ZGdYWpcbN25Ubdn9Hd26dYvx48c7tZ3MqFGj2LJlC1WqVCEiIoIOHTowYMCA+1YXLftigbbkotdee41NmzaxcOFCZZzT0WsMBgPt27enffv2nDp1iqVLl7Jr1y6aNm3KCy+8cN8Ddkks2YKDBw8mLi6OnJwcuwdPy2+ytEnKzga54jIzM9m9e7fddIrifz8XFxd27tx534OXlpaj0WhUWu7OaNq0KevXrycjI4OHHnqI7t27221OWpyW1VpatWrFt99+q3SdqyWClKbSBC9nt7e2fmItPkhe0hPG2bNnbRbRbNKkibKwZvG06PLqY+/YsSMRERFcu3YNKGpqWz5DWXcjtZaZmcmNGzeUzLaUlBSlZfd34+yEcJPJxKZNmxg2bJjqKuj3g9q+WGrjXdZr0KlRC7xaxjlv377N/v37iY6Opnbt2owaNYqwsDAuXbrEokWLSg0S90NeXh4//fQTycnJNGnShPDwcIdd+8WnzRRnSb5xNsgVN3/+fFq3bk1wcHCp0ymCg4P57rvv6Nq1q814+5+VDFWvXj1NvSj9+vWjX79+3Lx5k4MHD7JixQry8/Pp1q0bXbt2VW1ta1mtZejQoezevRs/Pz927NhBSEiIU+Nq1ipN8IKiAFP8Kah4i8Sy+631zreAMoDuSG5uLufPn6dly5ZA0byy3NxcALsUe60732qRkJCgfMbLly8D9p+xrIYPH867776rBK+bN2863Cussmvbtq3Nsjh3795V7dpwcXEp9SZ5r9T2xfrhhx/sHljOnTuHr68v3bp1K3VPqrIEurfffpsePXowadIkfHx8lOMBAQElrgF4Py1fvhxXV1fatGnDsWPHSExMdLjaumUXdEcsvQrOBrni7t69y9ChQ52oNUrGp/Vq9iW16u63W7duaU6rh6J7Wf/+/enfvz8XL15k5cqVfP3116oT0bWs1uLi4sIjjzzicGcOZ1SaVPmlS5dy48YNm7WywPFOrFrFx8crGV9Q1Cf86quv0qhRI44ePWrTJaB16SlnlfdnhKLBb0vLrmHDhk5t1FkZFV8W5/r16w6Xxfn888+5fv06Dz30kM12IPeaXVqS1157zW4fJ5PJpKzTd+XKFTp27Ei3bt1U095feumlEgOdWmvG0g30V3rzzTeVZKjCwkJllYl7Yekqz8vLIyEhgaZNm2I2m7ly5QotWrSw27bE4quvviIwMNDhahYVSVnS6qFoBZPY2FhiYmI4efIkbdu2pVu3bqpLq61cuZKkpCSnVmuJjY1l48aNdo2NyMhIpz9TpWl5JSQksGjRonL7cbVo0YKFCxeSk5OD2WymRo0ayr8V78u+160SHCnvz2h5j/Jq2emJM8viWGRlZVGzZk1OnTplc7w8g5caFxcXZWuK/Px8Dh48yLvvvsugQYN47LHHbMquWbNGCXQHDhwoMdCVpZVWXorv+lwSZ9PULS2wJUuWMGbMGCUT78qVK3z//fcOz79jxw62bt2Km5sbbm5uqtMd4uPj8fX1VXYZ3rdvH7/++iu+vr4MHjz4T+s21JpWb7k2jh07RkBAAN26deOVV14pcR6WltVa1q1bx4QJE2jSpEmZ1zesNMGrcePGZGRkKPv93G9aZqaX11YJ5f0ZHbXs/o7By5llcSzKc/06rfLz8zl69CgHDx7k5s2bPPbYY6pBVEug09IdWd4s3f6ATde/WuDQmqZ+7do1mxTyJk2acOnSJYd1cWbaw5o1a5S5S3FxcXz55ZeMHDmSS5cu8cknnzhcRux+09rBtnXrVrp168aLL77odIDVsjC5j4+P3X1GK90HL8scpdzcXCZOnOhUdlhZzJ8/X0mNL60rTevSU866fft2uX7GP6NlpxfOLItjkZaWxqeffqpkBLZq1YqRI0fajAuVRUn7m6ltp7Fs2TKuXr1KaGgogwYNKnUuj7OBTksrrbxpWfTXknTj7MNFw4YNWbVqFT169MBgMBAdHa2a1Xnt2jUaNmzocKzMeozMZDIpN/+YmBj69u1Lly5d6NKlS4mr5t9vWleBL0uWsbO7DkBRwsa8efNo166dzb3M2SXZoBIEr7KsgFEWzsxMT05OJiMjw26rhNTU1PsyLqV1C2+tyrtlpydDhgwhKiqKJk2a8NNPPxEaGqosalrcihUr6N69uzJRfP/+/axYsULTagFqtE5o3r9/P1WrVlXmmlmotUq0BDotrbSKyNlek7Fjx7Jr1y527NgBFG2dopZQsH37dsb8//buPKip8+sD+DdhSYphE3DptFSLBmxdEIWKKIqIra22DlqtbamK1qLoVKxKqVp/4gIolbGoY1UobnUqdRktWjvuBlBLES0qoCgjKCogiwghCcn7B5P7EkFNSC7JDecz45jcQJ4HR+7Js53zzTcv3BDS/GatVCqZ0iW5ubmYPXu2xmvtpT2mJ7WtOgA0HVOysLDAs2fP2vxhmfPBqy0ZMNpCm5PpKSkpmDp1aotSCYWFhdi/f3+bqzirvfPOO6iqqmKq7vbq1cugxSjZHtlxCZ/Ph4+PD3x8fF74C6hWU1ODgIAA5vnIkSORlpbGdhdb0GVUokugA7QfpZkibWdNrK2tMW7cuBfuvFMLDAxEVVUVE6TOnj2LS5cuMam4mvPz88P//vc/2Nrawtramsmy/vDhQ6135nGFLlUHqqur9d5ow/ngpcZ2eYK8vLxXnkwvKytrtUS2m5vbSwu+aSsjIwN79uxhAnZycjJCQkKY3Gr6YntkxwUqlQqpqak4ceIEVCoVVCoV+Hw+xo4d+8IFbzs7O5w/f545wC6RSLQ662JMugQ6XacjTY22+fzCw8NbHQU8v539+XWsffv2vXAdKzg4GH379kVVVRX69+/PvL9SqXzhFn+u0qXqQL9+/ZgNNW1ur83faWLYLk+gzcHjl5X1NkTJ70OHDiEmJoYZbdXU1GDVqlUGC17tNYo1ZWlpacjPz0dMTAxTMPLRo0fYsWMH/vzzz1Y/lc+ZMwdJSUnYuXMneDwexGKxSW3i0JeuozRTo20+v9jYWOaxXC5HZmZmqymjdF3Hai3ziDFSarEtODgYdXV1CAkJYaoOqDfXPO/UqVM4evRoizRRHXKrfE5OjkZ5gtGjR2Pp0qWYNGmSRinyttLm4LGbmxtOnjzZ4qT46dOnX3jQURdKpVJjmlAkEhlk3vxlGwO4cHMypPPnz2PZsmUaU4Vdu3bF/PnzsXr1ao3gVVFRAScnJzg7O7eYWs3Kymo1pxsX6TJKMyW65vN7frT80UcfYfny5ZgyZYrGdVNZxzI16g1N2lQdUCcz1ofZBC9tyhPoQ5uDx9OnT0d8fDwkEgkTrAoLC6FQKAyys8jT0xNr1qzRKIkycOBAvd+3LZnOzVVjY2Ora1x2dnYahykBIDo6GkuXLmVGaGpnzpzBwYMHOVmQ1JzousbcfPegSqVCYWEhk5SguY60jsUWPp+P9PR0PHr0CMHBwaioqEB1dbVOH/LNJnhpU55AH9ocPHZwcMDq1auRm5uL4uJiAE35CPWZ120uJCQEly5dQl5eHlQqFUaPHg1vb2+DvDdp8rLs/M+/Nm3aNKxevRpRUVHo3r07gKapXYlEwnoVXfJq9vb2LfIgvuzISvPdg3w+Hy4uLoiIiGjxdR1pHYstSUlJaGxsxM2bNxEcHAyBQIDt27cjJiZG6/fgfPDas2cPvvzyS9y5c+eV5Qn0ocvB4759+xosYAFNu5lGjhwJoOlgpXqXl0KhwMaNG7FgwQKDtdXRNT8E21xruS/VaXDWrl2LxYsX4/Tp0ygsLMTKlSs7ZPVpU6NLHkRAt7NNHWUdSxdyubzFbs7a2tpWfxcKCgoQFxeHJUuWAGhaAnlVVo7ncT54XblyBZ999hkOHz4MX19f1tph6+CxNo4fPw6FQqGxliaVSrF+/XqzWVcxFbqu7/Tr1w/h4eFYuXIlxGIxli9f3qJaATGOkpISJg/iqFGjtNp0lZ2djeLiYo0PKrpWK+6o4uPjsXjxYmaGorKyErGxsa1uibewsIBSqWRGrU+fPtX5vBfng5enpydmzpyJhoYGJk2MmiE3Gzx/8Liurq7d/lMvX74ca9asgUwmw4cffoiamhrExMSgb9+++OKLL9qlD6Ql9UYX9agsNzcXX3/9dYfc6GKKdMmDCADbtm2DTCbD9evXMWrUKFy8eNHo6bC4xNvbGxs2bMCiRYtQXl6OdevWISQkpNWvff/99/HTTz+hpqYG+/fvR2Zmps73U7PJKr9u3TpmCAo0ncuSSCSt1j3SV01NDWxtbds1jVJdXR1iYmLg4eGBrKwsBAUF6ZRKhZCOZsqUKUzCAHVKLYFA8MIPF4sWLUJ8fDzzt1QqRXx8PJYtW2aM7nPSX3/9hZycHJSVlWH27Nlwd3fXeD0mJgYzZ85Ely5dUFxcjP/++w8qlQr9+vXT+fwg50deakuWLEFRUREkEgkyMzPRpUsXg2QAKCgowG+//QaRSISJEydi06ZNqKmpgUqlwrx581pUb2WDOit2YGAgdu3ahb59+8LJyYm5zpVMB4S0J12ngNXTvQKBAE+ePIGtrS0eP37MRtfMSvM6YSqVChUVFejRowdu3bqFW7duaRwvGTFiBNasWYMRI0bg448/1is/JueD14MHD5CRkYH09HSIRCIMHToUKpXKYOXrk5OTMXXqVNTV1SE6OhpRUVEQi8W4f/8+Nm7c2C7Bq3lWbPX26+bXKHgRoj8vLy88e/YM48ePR2RkJHg83gvzWZL/V19fr/FcXevr+etAU/koLy8v/PHHH4iKisLw4cM1Msu/KjVXc5wPXhEREfDw8EBkZCS6desGAAbNK9fY2IgBAwYAaEomqd5l1Fq2abaYU7YGrigoKMCvv/6KkpISKBQKKJVKCIVCWscyY+o1lyFDhmDQoEGQy+V0bksLuqaVs7S0hFAohFwuh1Qq7biJeb/77jukp6dj5cqVGDBgAPz8/HSuXfMyzT8VPL+LjEqHmK/k5GQsWLAAGzZsQGxsLM6dO4eHDx8au1uEBaZSMJLrHjx4gKNHj7aojtx8FiwnJwc7d+7E4MGDERcXp1F5XFecD17qzN9SqRT//PMP0tLSUF1dje3bt8PHx4cZNbWV+txP88J3QOvnfoh56datG5RKJfh8PgICAmjh3kyZSsFIrktISEBQUBACAwNfWGTy4MGDWLhwoUFqwXE+eKkJhUIMHz4cw4cPR21tLTIzM3H48GG9gxdX87oR/QgEAigUCvTo0QN79uyBg4MDGhoajN0twgJTKRjJdXw+v9X6Z81FR0cbrD2zCV7NiUQiBAUFISgoyNhdMSj17sLmbGxs4OrqatC6XgSYN28elEolQkNDkZaWhoqKCvoEbqYo0a5hDBo0CCdOnICPj49Gpg22pl3NMniZq9OnT6OgoADvvvsugKYpjt69e6O0tBSTJk2Cv7+/kXtoPlxcXCCTyVBVVUV1zswcJdo1jHPnzgEAjhw5wlzj8Xgt6qEZitkcUu4IYmNjERYWxiwsV1VVYceOHQgLC8OKFSuYVDhEf1lZWdi9ezcUCgU2b96MoqIi/P777x2yqnRHUFBQwCTaVR9sfvDgAaRSqUHKGRHDo5EXh5SVlTGBC2jKml1aWgqRSNRueRY7itTUVMTExDDZ4Xv06GGQatjENFGiXcO4d+8eSkpKNDazjRgxgpW2KHhxSJ8+fRAbG8vULLt06RL69OkDqVSKTp06Gbl35sXCwoKmjAjRQWpqKm7cuIGSkhIMHDgQV65cgYeHBwUvAsycOZOp5wU0faJ57733wOPxDJZRhDR58803IZFIoFQqUVpaiuPHj7f66ZwQ0uTixYtYv349IiMjMXfuXFRVVWHr1q2stUfBi0N4PB6zhZewKzQ0FAcPHoSVlRV+/vlnDBgwAMHBwcbuFiEmy9raGnw+H3w+H3V1dbC3t2c1NyQFLw65dOkS9u7di+rqagCg0hssUSqV2L9/P0JCQjB16lRjd4cQTnBzc8OzZ88QGBiI77//HkKhkNWSMhS8OGTPnj2IjIzEG2+8YeyumDU+n487d+4YuxuEcIq6/NSYMWPg6emJ+vp6vPXWW6y1R8GLQxwcHChwtZOePXsiLi4Ovr6+GvnXKIM/IS01NjaCz+eDx+OhvLwcd+7cQdeuXVltk4IXh7z99ttISEiAt7e3xgl2uqEaXm1tLWxtbZGbm6txnf6tCdF08uRJ7N27F0KhEBMnTsTRo0fRs2dP3L17FwEBAZgwYQIr7VLw4pD6+noIBAJcu3ZN4zrdUA2PytAQop1jx44hMTERUqkUERER2Lx5M+zs7NDQ0ICoqCgKXoRuqO2poqICycnJyM/PB4/Hg7u7O2bMmAEnJydjd40Qk2JpaQmRSASRSIRu3brBzs4OQFNya0tL9kIMBS8OoRtq+9myZQuGDRuGhQsXAgAuXLiALVu2MKUzCCFNZDIZ7t69C5VKBYVCwTwGwGrZKApeHEI31PZTU1ODgIAA5vnIkSMNWqGbEHPh6OiIXbt2AWjaVKZ+rH7OFgpeHEI31PZjZ2eH8+fPY9iwYQAAiUQCW1tbI/eKENNjrOw+FLw4hG6o7WfOnDlISkrCzp07wePxIBaLac2REBNCJVE4pLy8HElJSSgoKGBuqDNmzICLi4uxu2Y2KioqXriGmJWVhcGDB7dzjwghraHgRUgz3377LZYuXYouXbpoXD9z5gwOHjyIxMREI/WMENIcBS8OSE5OfunroaGh7dQT85ednY2UlBRERUWhe/fuAIBDhw5BIpHghx9+oJ2dhLxAdHQ0fvzxx1deMxRa8+KA5pVcU1NTqSw9i7y8vGBlZYW1a9di8eLFOH36NAoLC7Fy5UqIRCJjd48QkyOTySCTyfD06VPU1tYy1+vq6lBZWclauzTy4pglS5Zg3bp1xu6G2cvLy8P69eshFosREREBa2trY3eJEJN07NgxpKWlobKyEp07d2bOeNnY2CAwMBAffPABK+1S8OKYyMhIxMXFGbsbZuurr74Cj8eDSqWCXC6HpaUl+Hw+lZ8h5BWOHz+OsWPHtlt7NG1ISDPND1gSQrTn4OCA+vp6vPbaazhw4ADu3r2L4OBgjWUPQ6LgxQHq0QAANDQ0YNq0aQCoGCUhxHQcOHAAvr6+yMvLw9WrVzF+/Hjs2LEDa9euZaU9Cl4cQKMBQoip4/P5AJp27I4ZMwbe3t5ITU1lrz3W3pkQQkiH0blzZ2zbtg2ZmZkYOHAg5HI52NxSQRs2CCGE6K2hoQE5OTlwdXVF9+7dUVlZiXv37mHAgAGstEcjL0IIIXoTCASwt7dHXl4eAMDCwoI56M8GCl6EEEL0lpqaisOHD+Pw4cMAAIVCwWo6NQpehBBC9Hb58mVERkZCIBAAaFoDq6+vZ609Cl6EEEL0ZmlpCR6PxxzrkUql7LbH6rsTQgjpEHx9fbFt2zY8e/YMJ0+exJkzZxAYGMhae7TbkBBCiEFcu3YNV69ehUqlgqenJ/r3789aWxS8CCGEtFlaWhrc3d3Rs2dPWFhYtFu7NG1ICCGkzSoqKpCSkoL79+/jrbfeglgshoeHB8RiMatlhGjkRQghRG8KhQKFhYXIz89HQUEBbt26BRsbGyQkJLDSHo28CCGE6E0mk6G+vh51dXWor6+Ho6MjXF1dWWuPRl6EEELa7JdffkFJSQmEQiF69+7N/GG78jid8yKEENJm5eXlkMvlcHBwQOfOneHk5IROnTqx3i6NvAghhOhFpVKhuLgYBQUFyM/PR3FxMUQiEcRiMSZPnsxKm7TmRQghRC88Hg+urq7o1KkTbGxsYGNjg+zsbNy+fZu14EUjL0IIIW127NgxZsRlYWEBd3d3iMViuLu7w9XVlSlSaWg08iKEENJmZWVlGDJkCKZNmwZHR8d2a5dGXoQQQjiHdhsSQgjhHApehBBCOIeCFyGEEM6h4EWImQgPD8e1a9eM3Q1C2gUFL0I6iMbGRmN3gRCDod2GhLCkvLwcKSkpuHnzJlQqFfz8/DBjxgwcOnQIp06dgkwmg6enJ0JDQ2FjY4Pr168jMTERW7duZd4jPDwc33zzDfr374/9+/ejpKQE1tbWuHz5MpydnREeHg43NzckJiZCIpHA0tISfD4fkyZNgq+vL+bNm4ewsDCkpqaiS5cuEAqF8PT0xNixY5k2Fi1ahMmTJ8PHx8cY/0yEtAmNvAhhgVKpRFxcHJydnbF582Zs3boVfn5+OHv2LM6ePYsVK1Zg06ZNkEqlSEpK0vp9//33XwwdOhQpKSkYPHgwkpOTAQDz58+Hs7MzIiMjsXv3bnzyySfM99y4cQMJCQlYunQpRowYgQsXLjCvFRUV4cmTJ/Dy8jLcD09IO6DgRQgLbt++jSdPniAkJARCoRDW1tbw8PCARCLBuHHj0LVrVwiFQnz++efIyMjQekrPw8MDXl5e4PP58Pf3R1FR0Su/59NPP2X64O3tjdLSUpSWlgIAzp8/j6FDh8LSkvIVEG6h4EUIC8rLy+Hi4tKiLHplZSVcXFyY587OzmhsbER1dbVW72tvb888tra2hlwuf2Xgc3JyYh5bWVnB19cXFy5cgFKpRHp6Ovz9/bVqmxBTQsGLEBY4OzujvLy8RWBxdHREWVkZ87y8vBwWFhawt7eHQCBAQ0MD85pSqURNTY3efeHxeBrPR44ciQsXLiA3NxcCgQBisVjvNghpbxS8CGFBr1694OjoiL1790IqlUImkyEvLw9+fn5IS0vD48ePIZVKsW/fPvj6+sLCwgKvv/465HI5srOzoVAocODAAcjlcq3bdHBwwOPHj1/5dWKxGHw+H7t27aJRF+EsCl6EsIDP5yMyMhIPHz7E3LlzMWfOHGRkZCAgIAD+/v5YsWIFwsPDYWVlhdDQUACAjY0NZs2aha1btyIsLAwCgUBjyu9VJkyYgAMHDmD69Ok4cuTIS7/W398f9+7dw/Dhw/X6OQkxFtoqT0gHdO7cOZw8eRKrVq0ydlcIaRMaeRHSwTQ0NODvv//G6NGjjd0VQtqMghchHUhOTg5mzZoFe3t7DBs2zNjdIaTNaNqQEEII59DIixBCCOdQ8CKEEMI5FLwIIYRwDgUvQgghnEPBixBCCOf8H01NI3osu/UsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for filename in datasets:\n",
    "    gapminder_plot(filename)\n",
    "    plt.show()\n",
    "    plot_mean_gdp(filename)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End of Day 1"
   ]
  },
  {
   "cell_type": "code",
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