{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Lecture 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Census ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>SEX</th> <th>AGE</th> <th>CENSUS2010POP</th> <th>ESTIMATESBASE2010</th> <th>POPESTIMATE2010</th> <th>POPESTIMATE2011</th> <th>POPESTIMATE2012</th> <th>POPESTIMATE2013</th> <th>POPESTIMATE2014</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>0   </td> <td>3944153      </td> <td>3944160          </td> <td>3951330        </td> <td>3963071        </td> <td>3926665        </td> <td>3945610        </td> <td>3948350        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>1   </td> <td>3978070      </td> <td>3978090          </td> <td>3957888        </td> <td>3966510        </td> <td>3978006        </td> <td>3943077        </td> <td>3962123        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>2   </td> <td>4096929      </td> <td>4096939          </td> <td>4090862        </td> <td>3971573        </td> <td>3979952        </td> <td>3992690        </td> <td>3957772        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>3   </td> <td>4119040      </td> <td>4119051          </td> <td>4111920        </td> <td>4102501        </td> <td>3983049        </td> <td>3992425        </td> <td>4005190        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>4   </td> <td>4063170      </td> <td>4063186          </td> <td>4077552        </td> <td>4122303        </td> <td>4112638        </td> <td>3994047        </td> <td>4003448        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>5   </td> <td>4056858      </td> <td>4056872          </td> <td>4064653        </td> <td>4087713        </td> <td>4132210        </td> <td>4123408        </td> <td>4004858        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>6   </td> <td>4066381      </td> <td>4066412          </td> <td>4073013        </td> <td>4074979        </td> <td>4097780        </td> <td>4143094        </td> <td>4134352        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>7   </td> <td>4030579      </td> <td>4030594          </td> <td>4043047        </td> <td>4083240        </td> <td>4084964        </td> <td>4108615        </td> <td>4154000        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>8   </td> <td>4046486      </td> <td>4046497          </td> <td>4025604        </td> <td>4053206        </td> <td>4093213        </td> <td>4095827        </td> <td>4119524        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>9   </td> <td>4148353      </td> <td>4148369          </td> <td>4125415        </td> <td>4035769        </td> <td>4063193        </td> <td>4104133        </td> <td>4106832        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>10  </td> <td>4172541      </td> <td>4172559          </td> <td>4187063        </td> <td>4135617        </td> <td>4045840        </td> <td>4074206        </td> <td>4115254        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>11  </td> <td>4114415      </td> <td>4114443          </td> <td>4115511        </td> <td>4197472        </td> <td>4145919        </td> <td>4057128        </td> <td>4085636        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>12  </td> <td>4106243      </td> <td>4106261          </td> <td>4113280        </td> <td>4126168        </td> <td>4208013        </td> <td>4157449        </td> <td>4068790        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>13  </td> <td>4118013      </td> <td>4118055          </td> <td>4119666        </td> <td>4124244        </td> <td>4136951        </td> <td>4219776        </td> <td>4169346        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>14  </td> <td>4165982      </td> <td>4166059          </td> <td>4145614        </td> <td>4131487        </td> <td>4135749        </td> <td>4149559        </td> <td>4232480        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>15  </td> <td>4242820      </td> <td>4242946          </td> <td>4231003        </td> <td>4158977        </td> <td>4144326        </td> <td>4149781        </td> <td>4163723        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>16  </td> <td>4316139      </td> <td>4316311          </td> <td>4313252        </td> <td>4247472        </td> <td>4174855        </td> <td>4161624        </td> <td>4167234        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>17  </td> <td>4395295      </td> <td>4395475          </td> <td>4376368        </td> <td>4334957        </td> <td>4268503        </td> <td>4197758        </td> <td>4184706        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>18  </td> <td>4500855      </td> <td>4501114          </td> <td>4491005        </td> <td>4402634        </td> <td>4360345        </td> <td>4296134        </td> <td>4225590        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>19  </td> <td>4585234      </td> <td>4585665          </td> <td>4571385        </td> <td>4520683        </td> <td>4430960        </td> <td>4390087        </td> <td>4326394        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>20  </td> <td>4519129      </td> <td>4519500          </td> <td>4568470        </td> <td>4600421        </td> <td>4550552        </td> <td>4459958        </td> <td>4418887        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>21  </td> <td>4354294      </td> <td>4354571          </td> <td>4387914        </td> <td>4596145        </td> <td>4633178        </td> <td>4580949        </td> <td>4489746        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>22  </td> <td>4264642      </td> <td>4264928          </td> <td>4286987        </td> <td>4415297        </td> <td>4628678        </td> <td>4666392        </td> <td>4612322        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>23  </td> <td>4198571      </td> <td>4198852          </td> <td>4217221        </td> <td>4314751        </td> <td>4447266        </td> <td>4661403        </td> <td>4698584        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>24  </td> <td>4249363      </td> <td>4249602          </td> <td>4243589        </td> <td>4244542        </td> <td>4346226        </td> <td>4479243        </td> <td>4692635        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>25  </td> <td>4262350      </td> <td>4262575          </td> <td>4289424        </td> <td>4269956        </td> <td>4275655        </td> <td>4377385        </td> <td>4509594        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>26  </td> <td>4152305      </td> <td>4152593          </td> <td>4160810        </td> <td>4313805        </td> <td>4299059        </td> <td>4305217        </td> <td>4406472        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>27  </td> <td>4248869      </td> <td>4249120          </td> <td>4237010        </td> <td>4183474        </td> <td>4340038        </td> <td>4326704        </td> <td>4332545        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>28  </td> <td>4215249      </td> <td>4215497          </td> <td>4247540        </td> <td>4258146        </td> <td>4207330        </td> <td>4364812        </td> <td>4351851        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>29  </td> <td>4223076      </td> <td>4223293          </td> <td>4210285        </td> <td>4266588        </td> <td>4280142        </td> <td>4230048        </td> <td>4387476        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (276 rows omitted)</p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full = Table.read_table('nc-est2014-agesex-res.csv')\n",
    "full.show(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>SEX</th> <th>AGE</th> <th>POPESTIMATE2010</th> <th>POPESTIMATE2014</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>0   </td> <td>3951330        </td> <td>3948350        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>1   </td> <td>3957888        </td> <td>3962123        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>2   </td> <td>4090862        </td> <td>3957772        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>3   </td> <td>4111920        </td> <td>4005190        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>4   </td> <td>4077552        </td> <td>4003448        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>5   </td> <td>4064653        </td> <td>4004858        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>6   </td> <td>4073013        </td> <td>4134352        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>7   </td> <td>4043047        </td> <td>4154000        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>8   </td> <td>4025604        </td> <td>4119524        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>9   </td> <td>4125415        </td> <td>4106832        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (296 rows omitted)</p>"
      ],
      "text/plain": [
       "SEX  | AGE  | POPESTIMATE2010 | POPESTIMATE2014\n",
       "0    | 0    | 3951330         | 3948350\n",
       "0    | 1    | 3957888         | 3962123\n",
       "0    | 2    | 4090862         | 3957772\n",
       "0    | 3    | 4111920         | 4005190\n",
       "0    | 4    | 4077552         | 4003448\n",
       "0    | 5    | 4064653         | 4004858\n",
       "0    | 6    | 4073013         | 4134352\n",
       "0    | 7    | 4043047         | 4154000\n",
       "0    | 8    | 4025604         | 4119524\n",
       "0    | 9    | 4125415         | 4106832\n",
       "... (296 rows omitted)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partial = full.select('SEX', 'AGE', 'POPESTIMATE2010', 'POPESTIMATE2014')\n",
    "partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>SEX</th> <th>AGE</th> <th>2010</th> <th>2014</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>0   </td> <td>3951330</td> <td>3948350</td>\n",
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       "</table>\n",
       "<p>... (296 rows omitted)</p>"
      ],
      "text/plain": [
       "SEX  | AGE  | 2010    | 2014\n",
       "0    | 0    | 3951330 | 3948350\n",
       "0    | 1    | 3957888 | 3962123\n",
       "0    | 2    | 4090862 | 3957772\n",
       "0    | 3    | 4111920 | 4005190\n",
       "0    | 4    | 4077552 | 4003448\n",
       "0    | 5    | 4064653 | 4004858\n",
       "0    | 6    | 4073013 | 4134352\n",
       "0    | 7    | 4043047 | 4154000\n",
       "0    | 8    | 4025604 | 4119524\n",
       "0    | 9    | 4125415 | 4106832\n",
       "... (296 rows omitted)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple = partial.relabeled('POPESTIMATE2010', '2010').relabeled('POPESTIMATE2014', '2014')\n",
    "simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>SEX</th> <th>AGE</th> <th>2010</th> <th>2014</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2   </td> <td>999 </td> <td>157257573</td> <td>161920569</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1   </td> <td>999 </td> <td>152089484</td> <td>156936487</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>999 </td> <td>309347057</td> <td>318857056</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2   </td> <td>100 </td> <td>45058    </td> <td>58468    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1   </td> <td>100 </td> <td>9351     </td> <td>13729    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>100 </td> <td>54409    </td> <td>72197    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2   </td> <td>99  </td> <td>26074    </td> <td>32791    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1   </td> <td>99  </td> <td>6104     </td> <td>9037     </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>99  </td> <td>32178    </td> <td>41828    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2   </td> <td>98  </td> <td>37533    </td> <td>46536    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (296 rows omitted)</p>"
      ],
      "text/plain": [
       "SEX  | AGE  | 2010      | 2014\n",
       "2    | 999  | 157257573 | 161920569\n",
       "1    | 999  | 152089484 | 156936487\n",
       "0    | 999  | 309347057 | 318857056\n",
       "2    | 100  | 45058     | 58468\n",
       "1    | 100  | 9351      | 13729\n",
       "0    | 100  | 54409     | 72197\n",
       "2    | 99   | 26074     | 32791\n",
       "1    | 99   | 6104      | 9037\n",
       "0    | 99   | 32178     | 41828\n",
       "2    | 98   | 37533     | 46536\n",
       "... (296 rows omitted)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple.sort('AGE', descending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <thead>\n",
       "        <tr>\n",
       "            <th>SEX</th> <th>AGE</th> <th>2010</th> <th>2014</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>0   </td> <td>3951330</td> <td>3948350</td>\n",
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       "            <td>0   </td> <td>6   </td> <td>4073013</td> <td>4134352</td>\n",
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       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>9   </td> <td>4125415</td> <td>4106832</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (293 rows omitted)</p>"
      ],
      "text/plain": [
       "SEX  | AGE  | 2010    | 2014\n",
       "0    | 0    | 3951330 | 3948350\n",
       "0    | 1    | 3957888 | 3962123\n",
       "0    | 2    | 4090862 | 3957772\n",
       "0    | 3    | 4111920 | 4005190\n",
       "0    | 4    | 4077552 | 4003448\n",
       "0    | 5    | 4064653 | 4004858\n",
       "0    | 6    | 4073013 | 4134352\n",
       "0    | 7    | 4043047 | 4154000\n",
       "0    | 8    | 4025604 | 4119524\n",
       "0    | 9    | 4125415 | 4106832\n",
       "... (293 rows omitted)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_999 = simple.where('AGE', are.below(999))\n",
    "no_999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "everyone = no_999.where('SEX', 0).drop('SEX')\n",
    "males = no_999.where('SEX', 1).drop('SEX')\n",
    "females = no_999.where('SEX', 2).drop('SEX')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>AGE</th> <th>2010</th> <th>2014</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>1932910</td> <td>1930493</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1   </td> <td>1937556</td> <td>1938870</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2   </td> <td>2002177</td> <td>1935270</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3   </td> <td>2010648</td> <td>1956572</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4   </td> <td>1993240</td> <td>1959950</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>5   </td> <td>1988080</td> <td>1961391</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>6   </td> <td>1993603</td> <td>2024024</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7   </td> <td>1979908</td> <td>2031760</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>8   </td> <td>1971142</td> <td>2014402</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>9   </td> <td>2018378</td> <td>2009560</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (91 rows omitted)</p>"
      ],
      "text/plain": [
       "AGE  | 2010    | 2014\n",
       "0    | 1932910 | 1930493\n",
       "1    | 1937556 | 1938870\n",
       "2    | 2002177 | 1935270\n",
       "3    | 2010648 | 1956572\n",
       "4    | 1993240 | 1959950\n",
       "5    | 1988080 | 1961391\n",
       "6    | 1993603 | 2024024\n",
       "7    | 1979908 | 2031760\n",
       "8    | 1971142 | 2014402\n",
       "9    | 2018378 | 2009560\n",
       "... (91 rows omitted)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "females"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>AGE</th> <th>2010</th> <th>2014</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>54  </td> <td>2196798</td> <td>2331961</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>53  </td> <td>2268457</td> <td>2316131</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>24  </td> <td>2082209</td> <td>2301237</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>23  </td> <td>2066126</td> <td>2298701</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>51  </td> <td>2289164</td> <td>2283261</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>52  </td> <td>2283408</td> <td>2282051</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>50  </td> <td>2355349</td> <td>2279824</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>55  </td> <td>2183762</td> <td>2263367</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>56  </td> <td>2108681</td> <td>2254749</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>22  </td> <td>2098759</td> <td>2244480</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (91 rows omitted)</p>"
      ],
      "text/plain": [
       "AGE  | 2010    | 2014\n",
       "54   | 2196798 | 2331961\n",
       "53   | 2268457 | 2316131\n",
       "24   | 2082209 | 2301237\n",
       "23   | 2066126 | 2298701\n",
       "51   | 2289164 | 2283261\n",
       "52   | 2283408 | 2282051\n",
       "50   | 2355349 | 2279824\n",
       "55   | 2183762 | 2263367\n",
       "56   | 2108681 | 2254749\n",
       "22   | 2098759 | 2244480\n",
       "... (91 rows omitted)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "females.sort('2014', descending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>AGE</th> <th>2010</th> <th>2014</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>23  </td> <td>2151095</td> <td>2399883</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>24  </td> <td>2161380</td> <td>2391398</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>22  </td> <td>2188228</td> <td>2367842</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>21  </td> <td>2241095</td> <td>2310734</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>25  </td> <td>2177171</td> <td>2295836</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>20  </td> <td>2331846</td> <td>2269570</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>54  </td> <td>2091677</td> <td>2242828</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>26  </td> <td>2102375</td> <td>2240026</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>19  </td> <td>2334906</td> <td>2220790</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>53  </td> <td>2170965</td> <td>2219617</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (91 rows omitted)</p>"
      ],
      "text/plain": [
       "AGE  | 2010    | 2014\n",
       "23   | 2151095 | 2399883\n",
       "24   | 2161380 | 2391398\n",
       "22   | 2188228 | 2367842\n",
       "21   | 2241095 | 2310734\n",
       "25   | 2177171 | 2295836\n",
       "20   | 2331846 | 2269570\n",
       "54   | 2091677 | 2242828\n",
       "26   | 2102375 | 2240026\n",
       "19   | 2334906 | 2220790\n",
       "53   | 2170965 | 2219617\n",
       "... (91 rows omitted)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "males.sort('2014', descending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_2014 = Table().with_columns(\n",
    "    'Age', males['AGE'],\n",
    "    'Males', males['2014'],\n",
    "    'Females', females['2014']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Age</th> <th>Males</th> <th>Females</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>2017857</td> <td>1930493</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1   </td> <td>2023253</td> <td>1938870</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2   </td> <td>2022502</td> <td>1935270</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3   </td> <td>2048618</td> <td>1956572</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4   </td> <td>2043498</td> <td>1959950</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>5   </td> <td>2043467</td> <td>1961391</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>6   </td> <td>2110328</td> <td>2024024</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7   </td> <td>2122240</td> <td>2031760</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>8   </td> <td>2105122</td> <td>2014402</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>9   </td> <td>2097272</td> <td>2009560</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (91 rows omitted)</p>"
      ],
      "text/plain": [
       "Age  | Males   | Females\n",
       "0    | 2017857 | 1930493\n",
       "1    | 2023253 | 1938870\n",
       "2    | 2022502 | 1935270\n",
       "3    | 2048618 | 1956572\n",
       "4    | 2043498 | 1959950\n",
       "5    | 2043467 | 1961391\n",
       "6    | 2110328 | 2024024\n",
       "7    | 2122240 | 2031760\n",
       "8    | 2105122 | 2014402\n",
       "9    | 2097272 | 2009560\n",
       "... (91 rows omitted)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "percent_females = 100*pop_2014['Females']/(pop_2014.column('Males') + pop_2014.column('Females'))\n",
    "counts_and_percents = pop_2014.with_column('Percent Female', percent_females)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Age</th> <th>Males</th> <th>Females</th> <th>Percent Female</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>2017857</td> <td>1930493</td> <td>48.8937       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1   </td> <td>2023253</td> <td>1938870</td> <td>48.9351       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2   </td> <td>2022502</td> <td>1935270</td> <td>48.898        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3   </td> <td>2048618</td> <td>1956572</td> <td>48.8509       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4   </td> <td>2043498</td> <td>1959950</td> <td>48.9565       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>5   </td> <td>2043467</td> <td>1961391</td> <td>48.9753       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>6   </td> <td>2110328</td> <td>2024024</td> <td>48.9563       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7   </td> <td>2122240</td> <td>2031760</td> <td>48.9109       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>8   </td> <td>2105122</td> <td>2014402</td> <td>48.8989       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>9   </td> <td>2097272</td> <td>2009560</td> <td>48.9321       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (91 rows omitted)</p>"
      ],
      "text/plain": [
       "Age  | Males   | Females | Percent Female\n",
       "0    | 2017857 | 1930493 | 48.8937\n",
       "1    | 2023253 | 1938870 | 48.9351\n",
       "2    | 2022502 | 1935270 | 48.898\n",
       "3    | 2048618 | 1956572 | 48.8509\n",
       "4    | 2043498 | 1959950 | 48.9565\n",
       "5    | 2043467 | 1961391 | 48.9753\n",
       "6    | 2110328 | 2024024 | 48.9563\n",
       "7    | 2122240 | 2031760 | 48.9109\n",
       "8    | 2105122 | 2014402 | 48.8989\n",
       "9    | 2097272 | 2009560 | 48.9321\n",
       "... (91 rows omitted)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_and_percents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line Graphs ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts_and_percents.plot('Age', 'Percent Female')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Age</th> <th>Males</th> <th>Females</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>2017857</td> <td>1930493</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1   </td> <td>2023253</td> <td>1938870</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2   </td> <td>2022502</td> <td>1935270</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3   </td> <td>2048618</td> <td>1956572</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4   </td> <td>2043498</td> <td>1959950</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>5   </td> <td>2043467</td> <td>1961391</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>6   </td> <td>2110328</td> <td>2024024</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7   </td> <td>2122240</td> <td>2031760</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>8   </td> <td>2105122</td> <td>2014402</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>9   </td> <td>2097272</td> <td>2009560</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (91 rows omitted)</p>"
      ],
      "text/plain": [
       "Age  | Males   | Females\n",
       "0    | 2017857 | 1930493\n",
       "1    | 2023253 | 1938870\n",
       "2    | 2022502 | 1935270\n",
       "3    | 2048618 | 1956572\n",
       "4    | 2043498 | 1959950\n",
       "5    | 2043467 | 1961391\n",
       "6    | 2110328 | 2024024\n",
       "7    | 2122240 | 2031760\n",
       "8    | 2105122 | 2014402\n",
       "9    | 2097272 | 2009560\n",
       "... (91 rows omitted)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop_2014.plot('Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Age</th> <th>Males</th> <th>Females</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>65  </td> <td>1607688</td> <td>1776761</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>66  </td> <td>1589127</td> <td>1758649</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>67  </td> <td>1653257</td> <td>1832245</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>68  </td> <td>1211480</td> <td>1361047</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>69  </td> <td>1187554</td> <td>1347458</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>70  </td> <td>1149247</td> <td>1317238</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>71  </td> <td>1169356</td> <td>1350392</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>72  </td> <td>1010896</td> <td>1183338</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>73  </td> <td>913008 </td> <td>1089183</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>74  </td> <td>857432 </td> <td>1032934</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Age  | Males   | Females\n",
       "65   | 1607688 | 1776761\n",
       "66   | 1589127 | 1758649\n",
       "67   | 1653257 | 1832245\n",
       "68   | 1211480 | 1361047\n",
       "69   | 1187554 | 1347458\n",
       "70   | 1149247 | 1317238\n",
       "71   | 1169356 | 1350392\n",
       "72   | 1010896 | 1183338\n",
       "73   | 913008  | 1089183\n",
       "74   | 857432  | 1032934"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_2014.where('Age', are.between(65, 75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1947, 1946, 1945, 1944, 1943, 1942])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2014 - np.arange(67, 73)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>AGE</th> <th>2010</th> <th>2014</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>3951330</td> <td>3948350</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1   </td> <td>3957888</td> <td>3962123</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2   </td> <td>4090862</td> <td>3957772</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3   </td> <td>4111920</td> <td>4005190</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4   </td> <td>4077552</td> <td>4003448</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>5   </td> <td>4064653</td> <td>4004858</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>6   </td> <td>4073013</td> <td>4134352</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7   </td> <td>4043047</td> <td>4154000</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>8   </td> <td>4025604</td> <td>4119524</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>9   </td> <td>4125415</td> <td>4106832</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (91 rows omitted)</p>"
      ],
      "text/plain": [
       "AGE  | 2010    | 2014\n",
       "0    | 3951330 | 3948350\n",
       "1    | 3957888 | 3962123\n",
       "2    | 4090862 | 3957772\n",
       "3    | 4111920 | 4005190\n",
       "4    | 4077552 | 4003448\n",
       "5    | 4064653 | 4004858\n",
       "6    | 4073013 | 4134352\n",
       "7    | 4043047 | 4154000\n",
       "8    | 4025604 | 4119524\n",
       "9    | 4125415 | 4106832\n",
       "... (91 rows omitted)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "everyone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "everyone = everyone.with_columns(\n",
    "    'Change', everyone.column('2014') - everyone.column('2010')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>AGE</th> <th>2010</th> <th>2014</th> <th>Change</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>67  </td> <td>2693709</td> <td>3485502</td> <td>791793</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>64  </td> <td>2706063</td> <td>3488136</td> <td>782073</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>66  </td> <td>2621346</td> <td>3347776</td> <td>726430</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>65  </td> <td>2678532</td> <td>3384449</td> <td>705917</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>71  </td> <td>1953614</td> <td>2519748</td> <td>566134</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>34  </td> <td>3822188</td> <td>4362895</td> <td>540707</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>23  </td> <td>4217221</td> <td>4698584</td> <td>481363</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>59  </td> <td>3694276</td> <td>4155417</td> <td>461141</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>24  </td> <td>4243589</td> <td>4692635</td> <td>449046</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>70  </td> <td>2062581</td> <td>2466485</td> <td>403904</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (91 rows omitted)</p>"
      ],
      "text/plain": [
       "AGE  | 2010    | 2014    | Change\n",
       "67   | 2693709 | 3485502 | 791793\n",
       "64   | 2706063 | 3488136 | 782073\n",
       "66   | 2621346 | 3347776 | 726430\n",
       "65   | 2678532 | 3384449 | 705917\n",
       "71   | 1953614 | 2519748 | 566134\n",
       "34   | 3822188 | 4362895 | 540707\n",
       "23   | 4217221 | 4698584 | 481363\n",
       "59   | 3694276 | 4155417 | 461141\n",
       "24   | 4243589 | 4692635 | 449046\n",
       "70   | 2062581 | 2466485 | 403904\n",
       "... (91 rows omitted)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "everyone.sort('Change', descending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>AGE</th> <th>2010</th> <th>2014</th> <th>Change</th> <th>Growth Rate</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>100 </td> <td>54409  </td> <td>72197  </td> <td>17788 </td> <td>0.0732777  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>93  </td> <td>219063 </td> <td>286333 </td> <td>67270 </td> <td>0.069241   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>99  </td> <td>32178  </td> <td>41828  </td> <td>9650  </td> <td>0.0677683  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>67  </td> <td>2693709</td> <td>3485502</td> <td>791793</td> <td>0.0665437  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>71  </td> <td>1953614</td> <td>2519748</td> <td>566134</td> <td>0.0656868  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>64  </td> <td>2706063</td> <td>3488136</td> <td>782073</td> <td>0.0655256  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>94  </td> <td>170775 </td> <td>218655 </td> <td>47880 </td> <td>0.0637358  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>98  </td> <td>47037  </td> <td>60185  </td> <td>13148 </td> <td>0.0635604  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>66  </td> <td>2621346</td> <td>3347776</td> <td>726430</td> <td>0.0630606  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>65  </td> <td>2678532</td> <td>3384449</td> <td>705917</td> <td>0.0602244  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (91 rows omitted)</p>"
      ],
      "text/plain": [
       "AGE  | 2010    | 2014    | Change | Growth Rate\n",
       "100  | 54409   | 72197   | 17788  | 0.0732777\n",
       "93   | 219063  | 286333  | 67270  | 0.069241\n",
       "99   | 32178   | 41828   | 9650   | 0.0677683\n",
       "67   | 2693709 | 3485502 | 791793 | 0.0665437\n",
       "71   | 1953614 | 2519748 | 566134 | 0.0656868\n",
       "64   | 2706063 | 3488136 | 782073 | 0.0655256\n",
       "94   | 170775  | 218655  | 47880  | 0.0637358\n",
       "98   | 47037   | 60185   | 13148  | 0.0635604\n",
       "66   | 2621346 | 3347776 | 726430 | 0.0630606\n",
       "65   | 2678532 | 3384449 | 705917 | 0.0602244\n",
       "... (91 rows omitted)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "everyone.with_columns(\n",
    "    'Growth Rate', (everyone.column('2014')/everyone.column('2010')) ** (1/4) - 1\n",
    ").sort('Growth Rate', descending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "actors = Table.read_table('actors.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Actor</th> <th>Total Gross</th> <th>Number of Movies</th> <th>Average per Movie</th> <th>#1 Movie</th> <th>Gross</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Harrison Ford     </td> <td>4871.7     </td> <td>41              </td> <td>118.8            </td> <td>Star Wars: The Force Awakens</td> <td>936.7</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Samuel L. Jackson </td> <td>4772.8     </td> <td>69              </td> <td>69.2             </td> <td>The Avengers                </td> <td>623.4</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Morgan Freeman    </td> <td>4468.3     </td> <td>61              </td> <td>73.3             </td> <td>The Dark Knight             </td> <td>534.9</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Tom Hanks         </td> <td>4340.8     </td> <td>44              </td> <td>98.7             </td> <td>Toy Story 3                 </td> <td>415  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Robert Downey, Jr.</td> <td>3947.3     </td> <td>53              </td> <td>74.5             </td> <td>The Avengers                </td> <td>623.4</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Eddie Murphy      </td> <td>3810.4     </td> <td>38              </td> <td>100.3            </td> <td>Shrek 2                     </td> <td>441.2</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Tom Cruise        </td> <td>3587.2     </td> <td>36              </td> <td>99.6             </td> <td>War of the Worlds           </td> <td>234.3</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Johnny Depp       </td> <td>3368.6     </td> <td>45              </td> <td>74.9             </td> <td>Dead Man's Chest            </td> <td>423.3</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Michael Caine     </td> <td>3351.5     </td> <td>58              </td> <td>57.8             </td> <td>The Dark Knight             </td> <td>534.9</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Scarlett Johansson</td> <td>3341.2     </td> <td>37              </td> <td>90.3             </td> <td>The Avengers                </td> <td>623.4</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (40 rows omitted)</p>"
      ],
      "text/plain": [
       "Actor              | Total Gross | Number of Movies | Average per Movie | #1 Movie                     | Gross\n",
       "Harrison Ford      | 4871.7      | 41               | 118.8             | Star Wars: The Force Awakens | 936.7\n",
       "Samuel L. Jackson  | 4772.8      | 69               | 69.2              | The Avengers                 | 623.4\n",
       "Morgan Freeman     | 4468.3      | 61               | 73.3              | The Dark Knight              | 534.9\n",
       "Tom Hanks          | 4340.8      | 44               | 98.7              | Toy Story 3                  | 415\n",
       "Robert Downey, Jr. | 3947.3      | 53               | 74.5              | The Avengers                 | 623.4\n",
       "Eddie Murphy       | 3810.4      | 38               | 100.3             | Shrek 2                      | 441.2\n",
       "Tom Cruise         | 3587.2      | 36               | 99.6              | War of the Worlds            | 234.3\n",
       "Johnny Depp        | 3368.6      | 45               | 74.9              | Dead Man's Chest             | 423.3\n",
       "Michael Caine      | 3351.5      | 58               | 57.8              | The Dark Knight              | 534.9\n",
       "Scarlett Johansson | 3341.2      | 37               | 90.3              | The Avengers                 | 623.4\n",
       "... (40 rows omitted)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "actors.scatter('Number of Movies', 'Total Gross')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R0RGLFy/WcalERFQXgqZJLlu2DCdOnMCGDRuQmpoKpfKvZ4QMGTIEx44d01mBRESkGUE9+F27dmHRokWYMGECSktL1fbZ29vj7t27OimOiIg0J6gHn5OTg65du1a5r6ysDMXFxVotioiI6k9QwNvb2+PSpUtV7rt8+TIcHR21WhQREdWfoID39/fHunXrkJiYqOqtSyQS/PDDD1i/fj3ee+89nRZJRER1J2gMPigoCFevXsX06dPx4YcfAgCGDx+OwsJCjBs3DtOnT9dpkUREVHeCAl4qleKLL77AlClTcOzYMWRlZcHS0hLe3t7o37+/rmskIiIN1OlGJy8vL3h5eemqFiIi0qI6BfwPP/yAS5cu4cGDB2jXrh369euH1157TVe1ERFRPQgK+MePH2PSpEk4deoUDAwMIJPJkJubC6VSif79+2PLli2wsLDQda1ERFQHgmbRhIaG4scff8Tnn3+Ohw8f4tatW3j48CHi4uJw5coVhIaG6rpOIiKqI0E9+O+++w7h4eGYMGGCapuhoSF8fX3x+PFjrFixQmcFEhGRZgT14A0MDNClS5cq9zk5OcHAQNDbEBFRAxKUzCNGjEBycnKV+3bt2gUfHx+tFkVERPUnaIhm2LBhWLBgAXx9fTF69GhYW1sjMzMT3377La5fv45Vq1bh5MmTquNff/11nRVMRETCCAr4SZMmAQDS09Nx5MiRSvvff/99AFCtFZ+Tk6PFEomISBOCAn7fvn26roOIiLRMUMBzOQIioqaH01+IiESKAU9EJFIMeCIikWLAExGJFAOeiEik6hTwZWVluHbtGk6fPo0nT57oqiYiItICwQG/ceNGdO3aFf3798eoUaOQmpoKAHjnnXcQFxenswKJiEgzggJ+y5YtCAsLg4+PD7788ksolUrVvldeeQV79+7VWYFERKQZQQEfExOD2bNnIzo6GiNHjlTb17VrV9y8eVMnxRERkeYEBfydO3cwePDgKve1atUKeXl5Wi2KiIjqT1DAW1lZ4e7du1Xuu3nzJtq1a6fVooiIqP4EBfywYcOwevVq3L59W7VNIpEgOzsb69ev53rwRESNkKCAX7RoEYyNjfHKK69g9OjRkEgkCAsLQ79+/SCVSjFv3jxd10lERHUkKOAtLS2RkpKCuXPnoqSkBJ07d0ZJSQmmTp2Kw4cPw9zcXNd1EhFRHQlaLhgAzMzMMG/ePPbWiYiaCC5VQEQkUoJ68G+99Va1+wwMDNCmTRu4u7sjICAA1tbWWiuOiIg0JyjglUolbt26hYcPH8Le3l710O07d+7AxsYGcrkcR44cwfr163HgwAG4uLjoum4iIqqFoCGaWbNmwdjYGCdOnMCVK1dw+PBhXLlyBSkpKTA2NkZoaCguX76Mtm3bYtmyZbqumYiIBBAU8MuXL0dYWBh69eqltt3d3R2hoaFYsWIFOnTogP/93//F2bNndVIoERHVjaCAv3XrFqysrKrc17ZtW6SlpQEAOnfujKdPn1b7PmvXrsWgQYPQsWNHdOnSBX5+frh27ZraMUqlEitXroSLiwtsbGzg4+OD69evqx1TVFSEkJAQODg4oH379vD390d6erqQj0JE1GwICng7Ozts3bq1yn1fffUV7OzsAADZ2dmwtLSs9n1Onz6NyZMn4/vvv8fevXvRokULjBkzBo8fP1YdEx0djZiYGERGRuL48eOQy+UYO3Ys8vPzVcfMnz8f+/btw+bNm3Hw4EHk5+fDz88PpaWlgj40EVFzIOgi67x58zBt2jR4eXlh1KhRkMvlePToEfbu3Yvr169j06ZNAICTJ0/Cw8Oj2vdJTk5We71hwwbY2dnh/PnzGD58OJRKJWJjYzFnzhyMHj0aABAbGwsnJyckJSUhMDAQeXl52LZtG2JiYjBo0CDV+7i5ueHEiRPw9vbWqCGIiMRGUMCPHz8eVlZWWLlyJdauXYvnz5/D0NAQvXv3xu7duzFw4EAAwIoVKyCVSgWfvKCgAGVlZZDJZABerFqZkZGhtnJly5Yt4eXlhQsXLiAwMBBXrlzB8+fP1Y6xtbWFs7MzLly4wIAnIvqT4DtZBw0ahEGDBqGsrAzZ2dmwsrKCgYH6CI+JiUmdTh4WFgY3Nzf069cPAJCRkQEAkMvlasfJ5XI8ePAAAJCZmQmpVFrpmoBcLkdmZma15yp/AlVzxjbQHNtOc2w7zQlpOycnp2r3CQ74cgYGBpUCWBMLFizA+fPn8d1331Xq9UskErXXSqWy0raKajumpkZoDlJTU5t9G2iKbac5tp3mtNF2ggO+uLgYR44cwc2bN1FYWKi2TyKR1GmNmvnz5yM5ORn79u1Dp06dVNsVCgWAF710W1tb1fasrCzVl4q1tTVKS0uRnZ2Ntm3bqh3j5eUluAYiIrETFPAPHjzAsGHDcPfuXUgkEtUzWf/eYxYa8KGhoUhOTsb+/fvRtWtXtX329vZQKBRISUlBnz59AACFhYU4d+4cli5dCuDF3HtDQ0OkpKRgwoQJAID09HTcuHEDnp6egmogImoOBAV8eHg42rZti4MHD6JHjx44duwYrKyssH37duzevbvS7JjqBAcHIyEhAdu3b4dMJlONuZuamqJ169aQSCSYMWMG1qxZAycnJzg6OiIqKgqmpqYYP348AMDc3BwBAQEIDw+HXC6HhYUFFi5cCFdXV9XFXiIiEhjw586dw7Jly1SP5jMwMIC9vT0WLlyIsrIyhIaGYufOnbW+T/l0yvIpkOVCQ0Mxf/58AEBQUBCePXuGkJAQ5ObmwsPDA8nJyTAzM1MdHxERAalUisDAQBQWFmLAgAGIi4ur0wweIiKxExTwOTk5sLGxgYGBAUxNTZGbm6vaN2DAAGzcuFHQyf7+e9WRSCSYP3++KvCrYmJigk8//RSffvqpoPMSETVHgu5kbd++PbKzswEAnTp1wvHjx1X7Ll++DGNjY91UR0REGhPUg3/ttddw5swZjBw5EoGBgQgODsbPP/8MQ0NDHDt2DIGBgbquk4iI6khQwC9atEi1XszkyZNRUlKC3bt3Izs7G0FBQXyMHxFRIyQo4Fu0aIGOHTuqXk+fPh3Tp0/XWVFERFR/tY7Bl5SUwMHBQW3cnYiIGr9aA75FixawtrbmFEQioiZG0CwaX1/fateDJyKixknQGLydnR2SkpIwaNAgjBgxAgqFotLCXgEBATopkIiINCMo4IODgwEA9+/fx5UrVyrtl0gkDHgiokZGUMD/9NNPuq6DiIi0TPAQDRERNS11euDH1atXcfbsWeTk5CAwMBAKhQJpaWmQy+Vqi4EREZH+CQr4oqIiTJs2Dfv27VM9OWnYsGFQKBQIDw+Ho6MjFi9erONSiYioLgRNk1y2bBlOnDiBDRs2IDU1VfXADwAYMmQIjh07prMCSV1G1mMELYlFwEerEbQkFplZta/QSUTNk6CA37VrFxYtWoQJEybAwsJCbZ+9vT3u3r2rk+KosoiYeNzPzEFxcQnuZ+YgYn3t6/ATUfMkKOBzcnIqPV6vXFlZGYqLi7VaFFUvJy8fBn/eg2AgkSA7N1/PFRFRYyUo4O3t7XHp0qUq912+fBmOjo5aLYqqZ2luhrI/h8jKlEpYmvPiNhFVTVDA+/v7Y926dUhMTFT11iUSCX744QesX78e7733nk6LpL8snDURHRSWMDJqgfbWllg4a6K+SyKiRkrQLJqgoCBcvXoV06dPx4cffggAGD58OAoLCzFu3DguHdyAlFDib9e4iYiqJSjgpVIpvvjiC0yZMgXHjx/Ho0ePYGlpCW9vb/Tv31/XNdLflF9kNZBIVBdZ14XP0HdZRNQI1elGJy8vL3h5eemqFhLgfmY2rqfeRfHzEhgZtgDA7jwRVU3QGPyAAQOwfv16ZGZm6roeqsXtexl4VlQMpVKJZ0XFuHMvQ98lEVEjJSjgra2t8cknn8DV1RUTJkzArl27UFhYqOvaqAqdbW3Q0sQIBgYStDQ2QidbG32XRESNlKAhmqSkJDx69AjffPMNEhMTMWXKFJiZmWHUqFHw9fXFgAEDdF0n/amdtSWUeDEHvkypRDtrS32XRESNlKAePADI5XLMnDkTJ06cwPnz5zFlyhScPHkSY8aMQY8ePXRZI/0Np0kSkVB1ushaztnZGfPmzYOLiwuWLFmC+/fva7suqoZ1WxlnzRCRIHUO+JMnTyIhIQH79+9HQUEBPDw8MHfuXF3URkRE9SAo4K9du4bExEQkJSXh/v376NixI6ZPnw5/f3906dJF1zUSEZEGBAX8q6++ijZt2mDMmDHw8/PjXHgioiZAUMB/+eWXGD58OIyNjXVdDxERaYmgWTRjxoypNtxPnz6NWbNmabUoIiKqP41m0aSlpWHnzp1ISEjA77//jlatWiEmJkbbtRERUT0IDvi8vDzs3r0b8fHxuHjxIgCgR48emDt3LsaNG6ezAomISDM1BnxZWRmOHj2K+Ph4HDp0CIWFhWjXrh2mTJmCTZs2YeXKlXj11VcbqlYiIqqDagN+0aJF+Oabb/Do0SOYmJhg5MiRmDhxIgYOHIg//vgDGzdubMg6iYiojqoN+JiYGEgkEgwZMgSxsbGwtPxrzRPJn88EJfHLyHqMiJh45OTlw9LcDAtnTYR1W5m+yyIiAaqdRfPee++hdevWOHz4MPr27YuQkBBcvny5IWujRqD8ASPFxSWqB4wQUdNQbcD/85//xG+//YbPP/8c7u7u+OKLLzBkyBD069cP0dHR7MU3Ezl5+TD48/9rA4kE2bn5eq6IiISq8SKriYkJJkyYgAkTJuDhw4eIj49HQkICPvvsMwDAkiVLMHnyZIwePRomJiYNUjDpVsUhGRMjIxQWPVctT2xpbqbvEolIIMHLBdvY2GDOnDk4d+4cjh07hilTpuDWrVv44IMP4OzsrMsaqQFVHJIxMACXJyZqojS60alPnz7o06cPIiIicOjQIcTHx2u7LtKTikMyTwuLsSFijp6rIiJNCO7BV8XQ0BCjRo3C119/ra16SM8szc1QpnzxIG8OyRA1bfUKeBIfPjGKSDw0GqKhpqu2ee18YlT1eE8ANTXswTcznNeuObYdNTUM+GaG89o1x7ajpoYB38zwIqrm2HbU1DDgmxleRNUc246aGl5kbWZ4EVVzbDtqatiDJyISKfbgSQ2nAhKJB3vwpIZTAYnEgwFPajgVkEg8Gjzgz5w5A39/f3Tr1g0ymQw7duxQ269UKrFy5Uq4uLjAxsYGPj4+uH79utoxRUVFCAkJgYODA9q3bw9/f3+kp6c35McQrdqmAmZkPUbQklgEfLQaQUtikZmVq48yiUiABg/4J0+eoHv37li1ahVatmxZaX90dDRiYmIQGRmJ48ePQy6XY+zYscjP/6snOX/+fOzbtw+bN2/GwYMHkZ+fDz8/P5SWljbkRxGl2qYCLoragqNn/o0LP/6Ko2f+jUVrvtLaufnlQaRdktzcXKW+Tt6hQwesXr0a7777LoAXvXcXFxdMnToVwcHBAIBnz57ByckJy5YtQ2BgIPLy8uDo6IiYmBj4+voCAO7duwc3NzckJSXB29tbXx+nUUtNTYWTk1O936fn8A/w9GkhJBIJlEoljI0N8carfbRyUTZoSeyLNej/fLhIB4VlvaYl1nbBWOgFZW21XXPEttOcNtquUY3B37lzBxkZGRg8eLBqW8uWLeHl5YULFy4AAK5cuYLnz5+rHWNrawtnZ2fVMaRLSqD8cY0SCXL/eKK1i7LaHv+v7YKxri8o8y8S0rdGNU0yIyMDACCXy9W2y+VyPHjwAACQmZkJqVQKKyurSsdkZmZW+96pqalarrbp0UYbdLG1xvkrv6GkrAxSAwOYtjTGs6dPVfvv/F6k8XmkKENBQYHqrwO5ZZt61Xz33gM8L/lr2K5ibbXt/ztN6lgRuwuPcv6ARCJBTk4uQiPisGDGuDq/T1PH//Y0J6TtaurlN6qAL1fxgd5KpbLWh3zXdkxz/zNRW38qy9tawdKiDUpKStFCKoWRkSFatmqlGlZpb22p8XlWL/gAEet3IjtXO3Pw7WzbqQ35VKyttv3lNG27UhigdevWqtclMGh2/w45RKM5bbRdowp4hUIB4EUv3dbWVrU9KytL1au3trZGaWkpsrMfhfH2AAAU60lEQVSz0bZtW7VjvLy8GrbgZuhZUTF6dO2seq1UKtFeYakK5Wn+wxG0JFajMXltLwWwcNbESl8YddlfX5bmZmpfIFycjBpaowp4e3t7KBQKpKSkoE+fPgCAwsJCnDt3DkuXLgUAuLu7w9DQECkpKZgwYQIAID09HTdu3ICnp6feam8uKoZWe2v1C6F/v1BaPq6tr/VbavvC0PXaMrr+AiGqTYMHfEFBAdLS0gAAZWVluHfvHv7zn//AwsICHTt2xIwZM7BmzRo4OTnB0dERUVFRMDU1xfjx4wEA5ubmCAgIQHh4OORyOSwsLLBw4UK4urpi4MCBDf1xmp3aQqu2C6X6XAqhoc/NxclI3xo84H/88Ue89dZbqtcrV67EypUrMXHiRMTGxiIoKAjPnj1DSEgIcnNz4eHhgeTkZJiZ/fXnbUREBKRSKQIDA1FYWIgBAwYgLi4OUqm0oT9Os1NbaNU2LFE+c0UbPfy6BrY2z03UFOh1Hjw1nIa62JWZlVvjhdKAj1ajuLhE9drIqAW2rZ2n0bnqOm++4rnLlGVob23FefA6xLbTnOguslLTV98efl3Udd58xXPfvpcBQMIePYlWo7rRicRPm09FMjEywtXf/osff7mJq7/9FyZGRnU6d2dbGy6sRqLGHjw1qLpceKxtjP1vN9QCSsCglu5KxXNXHOLhNEYSG/bgqdGqbSmB8jn57t0d0cO5M54WFtfp/fmMVRI79uCp0aptjL2+4/kVe/Tla8fwaVYkFgx4arRqC/CKc/Ir3kU7beJwfL7zEKdRUrPFgKdGq7abqmoaU7+fmYPAkDWw//NC6v3MHCxa+xVaGhtXG/h8mhWJDQOeGq263glaMaAf5xXg6bM7KH5eAiPDFihTlsG1a+dqe+i6XjumOd3FS40DL7KSaFR83GBR8XM8KyqGUqnEs6Ji5OU/rbGHXt+LrrWt/67PB5rr+2HqXBtfPxjwJBoVA7qPqyNamhjBwECClsZGkJmZ1vi8WSWUUNbjvu7aQlSfQ0D6Hn7S9xdMc8WAJ9GoGNCtTVtCqXyxTQkl+vRwrLGHXt8QEjLrp6YvGF3S57kB/X/BNFccgyfRqDgL5tbtdAB/3QjVqqVxjWP69Q2hus76ach59/peuljfa+M312sQXGysmWgOiz5VXEzsP9fT0LObg+p1bQubTQ1bh7P//gXPn5fC0FCK3t0dYWXRBnfvPYCdbbtaQ6G2hdbqqymGVPm/O123TW20/UD3hsDFxoj+pmIvsXXrlihTKgX3GisufXAt9Q66dOqA5yWlVc66qSpwdRkaTXmevr7Xxm+uQ0QcgyfRqHiRdcunwXWaFVNx6YNnhcU1hkJDXzhsriGlDfq+BqEv7MGTaFTVS6xLr7G6vwCAqkOhoQO3Yn0mRkZcWkEgfV+D0Bf24In+VPEvgLULpuL2vYf4NS0dt+89xPSJI9SOb+heYcX6JBJw6qFA5V/+29bOQ/QnM5rNFyEvsjYTzeEiq7aVX5h79vQpWrZqVenCXMULh9P8h+PzeOFr39SXNp+OpSv8d6c5XmQl0qHahmBqWwuntoug9Z0Vo8+ph01xRk9zxCEaomrUdQimrmPy9b1IO81/OG7fe4j/XE+rcghJl3hnatPAgCeqRvmYt2ELqaBZOLr+Qqjo8/hD6GRrg57dHNDJ1gafxx+s0+/XB2f0NA0coiGqRvkQjNCx0LrO1KjvEIs+Q7a22suHcITeJEa6wR48kZbUdaZGfVev1Ofc7tpqLx/C+ftNYtTw2IMn0pP63t2pz7ndtdXOIZzGgQFP1ETp+/b/mpQP4QDN687RxoZDNESkdXW9QM0HgugGe/BEpHV1vUDdlBdSa8zYgyciveOYvW4w4IlI75rrao+6xoAnIr2r75RRqhrH4IlI7xrzjCBd0+W6PuzBExHpkS7X9WHAExHpkS4vMDPgiYj0SJcXmBnwRER6pMsLzLzISkSkR7q8wMwePBGRSDHgiYhEigFPRCRSDHgiIpFiwBMRiRQDnohIpBjwREQixYAnIhIpSW5urlLfRRARkfaxB09EJFIMeCIikWLAExGJFAOeiEikGPBERCLFgBeJtWvXYtCgQejYsSO6dOkCPz8/XLt2Te0YpVKJlStXwsXFBTY2NvDx8cH169f1VHHjtWbNGshkMoSEhKi2se2q9/DhQ3zwwQfo0qULFAoFPD09cfr0adV+tl3VSktLsXz5cvTs2RMKhQI9e/bE8uXLUVJSojqmvm3HgBeJ06dPY/Lkyfj++++xd+9etGjRAmPGjMHjx49Vx0RHRyMmJgaRkZE4fvw45HI5xo4di/x87T0irKm7dOkStmzZAldXV7XtbLuq5ebmYujQoVAqlUhMTMSFCxewevVqyOVy1TFsu6qtW7cOmzZtQmRkJC5evIhVq1Zh48aNWLt2reqY+rYd58GLVEFBAezs7LBjxw4MHz4cSqUSLi4umDp1KoKDgwEAz549g5OTE5YtW4bAwEA9V6x/eXl5eP311xEdHY3Vq1eje/fu+PTTT9l2NVi6dCnOnDmD77//vsr9bLvq+fn5wcLCAnFxcaptH3zwAR4/foyEhASttB178CJVUFCAsrIyyGQyAMCdO3eQkZGBwYMHq45p2bIlvLy8cOHCBX2V2ajMmTMHo0ePxuuvv662nW1XvQMHDsDDwwOBgYFwdHRE//798fnnn0P55zNG2XbVe/nll3H69Gn89ttvAIBff/0Vp06dwpAhQwBop+34yD6RCgsLg5ubG/r16wcAyMjIAAC1P53LXz948KDB62tstmzZgrS0NGzYsKHSPrZd9W7fvo3Nmzdj5syZmDNnDn7++WeEhoYCAKZNm8a2q8GcOXNQUFAAT09PSKVSlJSUIDg4GFOmTAGgnX93DHgRWrBgAc6fP4/vvvsOUqlUbZ9EIlF7rVQqK21rblJTU7F06VIcOnQIRkZG1R7HtqusrKwMvXv3xieffAIA6NWrF9LS0rBp0yZMmzZNdRzbrrLk5GTEx8dj06ZNcHFxwc8//4ywsDDY2dnh/fffVx1Xn7bjEI3IzJ8/H7t27cLevXvRqVMn1XaFQgEAyMzMVDs+KyurUg+hubl48SKys7PxyiuvwMrKClZWVjhz5gw2bdoEKysrWFpaAmDbVUWhUMDZ2VltW9euXXHv3j3VfoBtV5Xw8HDMnj0b48aNg6urK/z9/TFr1ix89tlnALTTdgx4EQkNDUVSUhL27t2Lrl27qu2zt7eHQqFASkqKalthYSHOnTsHT0/Phi61UfHx8cHZs2dx6tQp1U/v3r0xbtw4nDp1Co6Ojmy7arz88su4efOm2rabN2+iY8eOAPjvriZPnz6t9Be2VCpFWVkZAO20nTQsLGyx1iomvQkODkZ8fDy++uor2Nra4smTJ3jy5AkAwMjICBKJBKWlpfjss8/g6OiI0tJSLFy4EBkZGVi3bh2MjY31/An0x8TEBHK5XO3nm2++gZ2dHd599122XQ1sbW0RGRkJAwMD2NjY4OTJk1i+fDnmzp0LDw8Ptl0Nbty4gYSEBDg6OsLQ0BCnTp3CsmXL8Pbbb8Pb21srbcdpkiJRPlumotDQUMyfPx/Ai7G7VatW4auvvkJubi48PDwQFRWF7t27N2SpTYKPj49qmiTAtqvJ999/j6VLl+LmzZuwtbXF1KlTMX36dNU4Mduuavn5+VixYgX279+PrKwsKBQKjBs3DvPmzYOJiQmA+rcdA56ISKQ4Bk9EJFIMeCIikWLAExGJFAOeiEikGPBERCLFgCciEikGPNXZjh07IJPJYGdnh9zcXLV9JSUlkMlkWLlyZYPXtXLlSshkMrUHJjRGZWVlCAsLg7OzMywsLPDOO+9Ue6ybmxtkMplqAaqKfHx8IJPJMGzYMF2VCzc3N8yYMUNn70+6w8XGSGN//PEH1q1bh8WLF+u7lCZlz549iIuLw/Lly9GvXz/VWjfVMTMzw4EDB5Cfnw8zMzPV9rt37+Ls2bNq23Rh+/btaNOmjU7PQbrBHjxpbPDgwfj8889Vy5o2B0VFRfV+jxs3bgAAZs6ciX79+sHR0bHG4wcOHAhDQ0Ps3btXbXtCQgLs7Ozg5uZW75pq0qtXL3Tu3Fmn5yDdYMCTxsqfMhMVFVXjceVDJxXNmDFDLZzu3LkDmUyGL774AkuWLEHXrl1ha2uLadOm4enTp0hLS8Pbb7+NDh06oHfv3vj666+rPN+NGzcwcuRItGvXDs7OzlixYoVqAady2dnZ+Oijj9CtWzdYW1vjpZdewldffaV2TPlQ1JkzZzBp0iTY2dnB29u7xs969OhRDBkyBDY2NrCzs8M777yD1NRU1X43NzesWrUKAGBpaQmZTIYdO3bU+J4tW7bEW2+9hYSEBLXtCQkJ8PPzq3Lp2IcPH2L69OlwcHCAtbU1vLy81H7/8uXLkMlkOHToUKXf/eijj9ClSxc8f/5cVXPFIZrbt29j6tSp6NKlC6ytrdG/f3/s27dP7ZibN2/i3XffVS3W1qNHD0yaNKnRD6GJCQOeNGZjY4OpU6diy5YtuHv3rtbed+3atXjw4AFiY2Mxf/58JCcnY+7cuXjvvffw5ptvYvv27XB1dcWsWbOqfADxu+++i4EDB2LHjh0YP348Pv30U0RGRqr2//HHHxg6dCgOHz6MsLAwJCYmYtiwYfjoo4+qfODHtGnTYG9vj61bt9Y4HHX06FH4+vrC1NQUX3zxBdasWYPr169j2LBhuH//PoAXwx3lY+5HjhzBkSNHMHTo0FrbxN/fH6dPn0Z6ejqAF8+OvXnzJvz9/Ssd++TJE/j4+ODo0aMIDw/Hjh074OrqiunTp6u+xDw8PODk5FTpS6O4uBi7d+/G22+/DUNDwypruXfvHt544w1cvXoVERER2LlzJ3r16oX3338fBw8eVB3n5+eHBw8eYM2aNdi1axc++eQTGBsbV/qyJd3hGDzVy5w5c/Dll18iMjISMTExWnnPzp07q55T6e3tjXPnziEhIQEbNmyAn58fAKB37944dOgQ9uzZg27duqn9/qRJkzB37lwAL4aR8vPzERMTgxkzZkAmkyEuLg6///47zp49iy5dugB4MQySl5eHyMhITJ48GS1a/PWfxqhRo7B06dJa616+fDk6deqEpKQk1e+/9NJL6Nu3L/7v//4PERER6NWrF9q3b6/aJ1T//v3RoUMHJCYmYu7cuYiPj4enpyccHBwqHbtjxw7cunUL+/btw2uvvQYAGDJkCDIzM7F8+XIEBARAKpXCz88PUVFRyMvLg7m5OQDg8OHDePz4cZVfHOVWrVoFpVKJAwcOqK4feHt7Iz09HRERERgxYgSys7Nx69YtfP311xgxYoTqdydMmCD4M1P9sQdP9WJhYYHZs2cjPj5ebSiiPsqfSVmufG37vw+PyGQyyOVyVY/278aOHav2ety4cSgoKFD19o8dOwYPDw/Y29ujpKRE9ePt7Y2cnBz8+uuvar8/cuTIWmt+8uQJfvrpJ7z99ttqXw6dOnWCp6cnzpw5U+t71EQikcDPzw8JCQkoLi5GcnJytSF89uxZtG/fXhXu5Xx9fZGVlaX6fL6+vigqKsKePXtUxyQkJMDJyQkeHh7V1nLs2DEMGTIEbdq0qdR+V69exR9//AFLS0t06tQJS5YswZYtW3Dr1q16fX7SDAOe6m3mzJmwsLBARESEVt6v4nh9+VBBVduruuhZ1TMsAaieY/no0SOcPXsWbdu2VfuZNGkSACAnJ0ft921sbGqtOTc3F0qlUvUUnr9TKBR4/Phxre9RG39/f/z666+IjIzE06dPK32RlXv8+HG1dZTvBwA7Ozt4eXkhPj5e9RkOHz6s+iupOo8ePUJ8fHyl9vv4448BvGg/iUSCb7/9Fu7u7liyZAk8PDzQq1cvbN68WePPT3XHIRqqt9atW2Pu3LlYtGgRPvzww0r7y9e2Li4uVnvmacUg1ZZHjx7B1NRU7TUAtGvXDsCLi5tyuVx1sbOiirNahDz/UiaTQSKRVDmjKCMjo9apkEI4Ojqib9+++Oyzz/DWW29V+wwACwuLSk9ZKq8DgFotfn5+CAoKwt27d3H8+HEUFxfXOoxiaWmJV155BXPmzKlyf3k7d+rUCRs2bIBSqcTPP/+MjRs34v/9v/8HOzu7Sn+lkW6wB09aMWXKFLRv3x7Lly+vtK/88W3Xrl1TbcvNzcXFixd1Usvu3bvVXu/atQutW7dWjdV7e3vjt99+g62tLXr37l3pR5N55aampnB3d8eePXtQWlqq2n737l1cvHgRr776av0+1J8+/PBDDBs2DFOnTq32mFdffRXp6ek4f/682vakpCTI5XK1Z6iOGTMGJiYm+Oabb5CQkAAvLy/Y29vXWIO3tzd++eUXuLi4VNl+FZ80JJFI0LNnT6xYsQIAqrwwTrrBHjxphbGxMebNm4egoKBK+9544w20adMGQUFBmD9/PoqKivCPf/xDrZetTVu2bEFZWRn69OmDY8eOYevWrQgLC1P1eGfOnIndu3dj+PDhmDlzJhwdHfH06VOkpqbi7Nmz2Llzp0bnXbhwIXx9feHn54fJkyfjyZMnWLlyJdq0aYPZs2dr5bONGjUKo0aNqvGYd955B3FxcQgICMDHH3+M9u3bIzExESkpKVi3bp3ac0DbtGmD4cOHY9OmTXj48CGio6NrrWHBggXw9vbGiBEjMHXqVNUdzdevX8ft27cRExODq1evIiwsDG+//TYcHBxQWlqKr7/+Gi1atMCAAQPq3Q4kDAOetObdd9/FP/7xj0oX1GQyGRISErBgwQIEBgaiffv2mDdvHk6cOIHTp09rvY6vv/4a8+bNw6effoo2bdogODgY8+bNU+03NzfH4cOHERkZiXXr1uHBgwcwNzeHk5MT3nrrLY3P+8YbbyAxMRGRkZEIDAyEkZERXn31VSxdulQ1bNEQTE1NceDAAYSHh2Px4sUoKCiAo6Oj2iykv/Pz80NycjJMTEwwevToWt+/Y8eOSElJwapVq7Bs2TJkZWXB0tIS3bp1w8SJEwG8GO+3tbVFTEwM7t+/D2NjY3Tv3h0JCQlwd3fX+memqvGRfUREIsUxeCIikWLAExGJFAOeiEikGPBERCLFgCciEikGPBGRSDHgiYhEigFPRCRSDHgiIpH6/3eN9NsGIVCGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "actors.scatter('Number of Movies', 'Average per Movie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Actor</th> <th>Total Gross</th> <th>Number of Movies</th> <th>Average per Movie</th> <th>#1 Movie</th> <th>Gross</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Anthony Daniels </td> <td>3162.9     </td> <td>7               </td> <td>451.8            </td> <td>Star Wars: The Force Awakens       </td> <td>936.7</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Orlando Bloom   </td> <td>2815.8     </td> <td>17              </td> <td>165.6            </td> <td>Dead Man's Chest                   </td> <td>423.3</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Emma Watson     </td> <td>2681.9     </td> <td>17              </td> <td>157.8            </td> <td>Harry Potter / Deathly Hallows (P2)</td> <td>381  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Daniel Radcliffe</td> <td>2634.4     </td> <td>17              </td> <td>155              </td> <td>Harry Potter / Deathly Hallows (P2)</td> <td>381  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Actor            | Total Gross | Number of Movies | Average per Movie | #1 Movie                            | Gross\n",
       "Anthony Daniels  | 3162.9      | 7                | 451.8             | Star Wars: The Force Awakens        | 936.7\n",
       "Orlando Bloom    | 2815.8      | 17               | 165.6             | Dead Man's Chest                    | 423.3\n",
       "Emma Watson      | 2681.9      | 17               | 157.8             | Harry Potter / Deathly Hallows (P2) | 381\n",
       "Daniel Radcliffe | 2634.4      | 17               | 155               | Harry Potter / Deathly Hallows (P2) | 381"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actors.where('Average per Movie', are.above(150))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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}