{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb84b459-5692-4372-8ae1-81fe22d15cfe",
   "metadata": {},
   "source": [
    "# Tables\n",
    "The datascience Table object is designed to analyze multiple data points with multiple charecteristics. One way to visualize this is to consider columns of data which contain values for each data point (row) and a typical table will have multiple columns reflecting the multiple charecteristics of each data point. Each column is also an array of values with each element representing the value for a given data point or row. The datascience Table has many methods which are summarized in the detailed [online reference](http://data8.org/datascience/tables.html). Datascience tables are closely related to the [pandas dataframe](https://pandas.pydata.org) which serves as a key component of a Python data science toolbox."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf4f5757-4400-4020-871f-c0acedb65055",
   "metadata": {},
   "source": [
    "#### Import requisite modules\n",
    "datascience, plotting, and plot fixes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b570ee0-306d-4fad-8ffa-e4b3b356922e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "\n",
    "# import for plotting\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.style.use('fivethirtyeight')\n",
    "# Fix for datascience plots\n",
    "import collections as collections\n",
    "import collections.abc as abc\n",
    "collections.Iterable = abc.Iterable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "296f5b49-1c3b-4cec-bdc7-39f3bcac2b64",
   "metadata": {},
   "source": [
    "### Create a first datascience table object from an array\n",
    "As an example we can create a table directly from data inserted into an array using the `with_columns` table method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3ce90b3e-a125-4e7c-b435-0c5f1e72bbe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "T=Table().with_columns('Tornados',make_array(0,0,0,1,0,0,0,1,5,1,0,0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "748210b7-beaa-4d7f-9b87-86627de1f813",
   "metadata": {},
   "source": [
    "We can create a nice month index by using the numpy arange method combined with the `with_columns` table method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6e55ed11-88ab-46af-92e5-d702dbef8c8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "T=T.with_columns('Month',np.arange(1,13,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e4654c5-3c27-4230-bb55-fce1450dbd13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Tornados</th> <th>Month</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>1    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>2    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>3    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1       </td> <td>4    </td>\n",
       "        </tr>\n",
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       "            <td>0       </td> <td>5    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>6    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>7    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1       </td> <td>8    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>5       </td> <td>9    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1       </td> <td>10   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (2 rows omitted)</p>"
      ],
      "text/plain": [
       "Tornados | Month\n",
       "0        | 1\n",
       "0        | 2\n",
       "0        | 3\n",
       "1        | 4\n",
       "0        | 5\n",
       "0        | 6\n",
       "0        | 7\n",
       "1        | 8\n",
       "5        | 9\n",
       "1        | 10\n",
       "... (2 rows omitted)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c7c77b7-3d45-47af-a0ec-6c92c7d166f8",
   "metadata": {},
   "source": [
    "We can sort using the `sort` method and using either the column label, `'Tornadoes'` in the below case, or the column index which is the first which is inxed as the value `0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "205d03a4-76e3-4343-a834-ff74345ea3f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Tornados</th> <th>Month</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>5       </td> <td>9    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1       </td> <td>4    </td>\n",
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       "            <td>1       </td> <td>8    </td>\n",
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       "        <tr>\n",
       "            <td>1       </td> <td>10   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>1    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>2    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>3    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>5    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>6    </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>7    </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (2 rows omitted)</p>"
      ],
      "text/plain": [
       "Tornados | Month\n",
       "5        | 9\n",
       "1        | 4\n",
       "1        | 8\n",
       "1        | 10\n",
       "0        | 1\n",
       "0        | 2\n",
       "0        | 3\n",
       "0        | 5\n",
       "0        | 6\n",
       "0        | 7\n",
       "... (2 rows omitted)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.sort(0,descending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31fec01a-31e4-49a6-b98d-873633d25764",
   "metadata": {},
   "source": [
    "We can also use a trick with the modulo operator, `%`, and then further apply the `where` method which filters the table to get rows that match the criteria which in this case is that the newly created column, `'Odd'` has the value `0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b30839e7-cb4f-43e0-b702-2c4fa6209715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Tornados</th> <th>Month</th> <th>Odd</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>2    </td> <td>0   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1       </td> <td>4    </td> <td>0   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>6    </td> <td>0   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1       </td> <td>8    </td> <td>0   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1       </td> <td>10   </td> <td>0   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0       </td> <td>12   </td> <td>0   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Tornados | Month | Odd\n",
       "0        | 2     | 0\n",
       "1        | 4     | 0\n",
       "0        | 6     | 0\n",
       "1        | 8     | 0\n",
       "1        | 10    | 0\n",
       "0        | 12    | 0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T=T.with_columns(\"Odd\",T.column(1)%2).where(\"Odd\",0)\n",
    "T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b81b1987-aad6-4494-9ab1-7d668d7901d6",
   "metadata": {},
   "source": [
    "### Census data example\n",
    "We can look at complex real world data. In this example we look at [US Census](https://www.census.gov) using the datascience table `read_table` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c46d4bd-1a53-4fcd-9193-c1d9e3f63c4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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> <th>POPESTIMATE2015</th> <th>POPESTIMATE2016</th> <th>POPESTIMATE2017</th> <th>POPESTIMATE2018</th> <th>POPESTIMATE2019</th> <th>POPESTIMATE2020</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>0   </td> <td>3944153      </td> <td>3944160          </td> <td>3951495        </td> <td>3963264        </td> <td>3926731        </td> <td>3931411        </td> <td>3954973        </td> <td>3984144        </td> <td>3963268        </td> <td>3882437        </td> <td>3826908        </td> <td>3762227        </td> <td>3735010        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>1   </td> <td>3978070      </td> <td>3978090          </td> <td>3957904        </td> <td>3966768        </td> <td>3978210        </td> <td>3943348        </td> <td>3949559        </td> <td>3973828        </td> <td>4003586        </td> <td>3981864        </td> <td>3897917        </td> <td>3842257        </td> <td>3773884        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
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       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>3   </td> <td>4119040      </td> <td>4119051          </td> <td>4111869        </td> <td>4102429        </td> <td>3983007        </td> <td>3992839        </td> <td>4007852        </td> <td>3976277        </td> <td>3984985        </td> <td>4009060        </td> <td>4035053        </td> <td>4009037        </td> <td>3921526        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>4   </td> <td>4063170      </td> <td>4063186          </td> <td>4077511        </td> <td>4122252        </td> <td>4112849        </td> <td>3994539        </td> <td>4006407        </td> <td>4022785        </td> <td>3992241        </td> <td>4000394        </td> <td>4021907        </td> <td>4045996        </td> <td>4017847        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>5   </td> <td>4056858      </td> <td>4056872          </td> <td>4064653        </td> <td>4087770        </td> <td>4132349        </td> <td>4123745        </td> <td>4007123        </td> <td>4020489        </td> <td>4038022        </td> <td>4007233        </td> <td>4012789        </td> <td>4032231        </td> <td>4054336        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>6   </td> <td>4066381      </td> <td>4066412          </td> <td>4073031        </td> <td>4075153        </td> <td>4097860        </td> <td>4142923        </td> <td>4135738        </td> <td>4020428        </td> <td>4034969        </td> <td>4052428        </td> <td>4019106        </td> <td>4022432        </td> <td>4040169        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>7   </td> <td>4030579      </td> <td>4030594          </td> <td>4043100        </td> <td>4083399        </td> <td>4085255        </td> <td>4108453        </td> <td>4154947        </td> <td>4148711        </td> <td>4034355        </td> <td>4048430        </td> <td>4063647        </td> <td>4027876        </td> <td>4029753        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>8   </td> <td>4046486      </td> <td>4046497          </td> <td>4025624        </td> <td>4053313        </td> <td>4093553        </td> <td>4096033        </td> <td>4120476        </td> <td>4167765        </td> <td>4162142        </td> <td>4047130        </td> <td>4059209        </td> <td>4071894        </td> <td>4034785        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>9   </td> <td>4148353      </td> <td>4148369          </td> <td>4125413        </td> <td>4035854        </td> <td>4063662        </td> <td>4104437        </td> <td>4107986        </td> <td>4133426        </td> <td>4181069        </td> <td>4175085        </td> <td>4058207        </td> <td>4067320        </td> <td>4078668        </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (296 rows omitted)</p>"
      ],
      "text/plain": [
       "SEX  | AGE  | CENSUS2010POP | ESTIMATESBASE2010 | POPESTIMATE2010 | POPESTIMATE2011 | POPESTIMATE2012 | POPESTIMATE2013 | POPESTIMATE2014 | POPESTIMATE2015 | POPESTIMATE2016 | POPESTIMATE2017 | POPESTIMATE2018 | POPESTIMATE2019 | POPESTIMATE2020\n",
       "0    | 0    | 3944153       | 3944160           | 3951495         | 3963264         | 3926731         | 3931411         | 3954973         | 3984144         | 3963268         | 3882437         | 3826908         | 3762227         | 3735010\n",
       "0    | 1    | 3978070       | 3978090           | 3957904         | 3966768         | 3978210         | 3943348         | 3949559         | 3973828         | 4003586         | 3981864         | 3897917         | 3842257         | 3773884\n",
       "0    | 2    | 4096929       | 4096939           | 4090799         | 3971498         | 3980139         | 3993047         | 3960015         | 3967672         | 3992657         | 4021261         | 3996742         | 3911822         | 3853025\n",
       "0    | 3    | 4119040       | 4119051           | 4111869         | 4102429         | 3983007         | 3992839         | 4007852         | 3976277         | 3984985         | 4009060         | 4035053         | 4009037         | 3921526\n",
       "0    | 4    | 4063170       | 4063186           | 4077511         | 4122252         | 4112849         | 3994539         | 4006407         | 4022785         | 3992241         | 4000394         | 4021907         | 4045996         | 4017847\n",
       "0    | 5    | 4056858       | 4056872           | 4064653         | 4087770         | 4132349         | 4123745         | 4007123         | 4020489         | 4038022         | 4007233         | 4012789         | 4032231         | 4054336\n",
       "0    | 6    | 4066381       | 4066412           | 4073031         | 4075153         | 4097860         | 4142923         | 4135738         | 4020428         | 4034969         | 4052428         | 4019106         | 4022432         | 4040169\n",
       "0    | 7    | 4030579       | 4030594           | 4043100         | 4083399         | 4085255         | 4108453         | 4154947         | 4148711         | 4034355         | 4048430         | 4063647         | 4027876         | 4029753\n",
       "0    | 8    | 4046486       | 4046497           | 4025624         | 4053313         | 4093553         | 4096033         | 4120476         | 4167765         | 4162142         | 4047130         | 4059209         | 4071894         | 4034785\n",
       "0    | 9    | 4148353       | 4148369           | 4125413         | 4035854         | 4063662         | 4104437         | 4107986         | 4133426         | 4181069         | 4175085         | 4058207         | 4067320         | 4078668\n",
       "... (296 rows omitted)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = 'http://www2.census.gov/programs-surveys/popest/datasets/2010-2020/national/asrh/nc-est2020-agesex-res.csv'\n",
    "# A local copy can be accessed here in case census.gov moves the file:\n",
    "# data = path_data + 'nc-est2015-agesex-res.csv'\n",
    "\n",
    "full_census_table = Table.read_table(data)\n",
    "full_census_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b7082ced-593c-42f2-8a85-bdf28490f5ac",
   "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>POPESTIMATE2020</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
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       "            <td>0   </td> <td>0   </td> <td>3951495        </td> <td>3735010        </td>\n",
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       "            <td>0   </td> <td>4   </td> <td>4077511        </td> <td>4017847        </td>\n",
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       "            <td>0   </td> <td>7   </td> <td>4043100        </td> <td>4029753        </td>\n",
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       "        </tr>\n",
       "    </tbody>\n",
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       "<p>... (296 rows omitted)</p>"
      ],
      "text/plain": [
       "SEX  | AGE  | POPESTIMATE2010 | POPESTIMATE2020\n",
       "0    | 0    | 3951495         | 3735010\n",
       "0    | 1    | 3957904         | 3773884\n",
       "0    | 2    | 4090799         | 3853025\n",
       "0    | 3    | 4111869         | 3921526\n",
       "0    | 4    | 4077511         | 4017847\n",
       "0    | 5    | 4064653         | 4054336\n",
       "0    | 6    | 4073031         | 4040169\n",
       "0    | 7    | 4043100         | 4029753\n",
       "0    | 8    | 4025624         | 4034785\n",
       "0    | 9    | 4125413         | 4078668\n",
       "... (296 rows omitted)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "partial_census_table = full_census_table.select('SEX', 'AGE', 'POPESTIMATE2010', 'POPESTIMATE2020')\n",
    "partial_census_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd86218a-7368-486d-b65b-fa247514a328",
   "metadata": {},
   "source": [
    "We can extract a column as an array using the `column()` method and even multiply by 3 in this case to get a new array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "587dd336-4bf0-4b72-9522-714d15ba2344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    3,    6,    9,   12,   15,   18,   21,   24,   27,   30,\n",
       "         33,   36,   39,   42,   45,   48,   51,   54,   57,   60,   63,\n",
       "         66,   69,   72,   75,   78,   81,   84,   87,   90,   93,   96,\n",
       "         99,  102,  105,  108,  111,  114,  117,  120,  123,  126,  129,\n",
       "        132,  135,  138,  141,  144,  147,  150,  153,  156,  159,  162,\n",
       "        165,  168,  171,  174,  177,  180,  183,  186,  189,  192,  195,\n",
       "        198,  201,  204,  207,  210,  213,  216,  219,  222,  225,  228,\n",
       "        231,  234,  237,  240,  243,  246,  249,  252,  255,  258,  261,\n",
       "        264,  267,  270,  273,  276,  279,  282,  285,  288,  291,  294,\n",
       "        297,  300, 2997,    0,    3,    6,    9,   12,   15,   18,   21,\n",
       "         24,   27,   30,   33,   36,   39,   42,   45,   48,   51,   54,\n",
       "         57,   60,   63,   66,   69,   72,   75,   78,   81,   84,   87,\n",
       "         90,   93,   96,   99,  102,  105,  108,  111,  114,  117,  120,\n",
       "        123,  126,  129,  132,  135,  138,  141,  144,  147,  150,  153,\n",
       "        156,  159,  162,  165,  168,  171,  174,  177,  180,  183,  186,\n",
       "        189,  192,  195,  198,  201,  204,  207,  210,  213,  216,  219,\n",
       "        222,  225,  228,  231,  234,  237,  240,  243,  246,  249,  252,\n",
       "        255,  258,  261,  264,  267,  270,  273,  276,  279,  282,  285,\n",
       "        288,  291,  294,  297,  300, 2997,    0,    3,    6,    9,   12,\n",
       "         15,   18,   21,   24,   27,   30,   33,   36,   39,   42,   45,\n",
       "         48,   51,   54,   57,   60,   63,   66,   69,   72,   75,   78,\n",
       "         81,   84,   87,   90,   93,   96,   99,  102,  105,  108,  111,\n",
       "        114,  117,  120,  123,  126,  129,  132,  135,  138,  141,  144,\n",
       "        147,  150,  153,  156,  159,  162,  165,  168,  171,  174,  177,\n",
       "        180,  183,  186,  189,  192,  195,  198,  201,  204,  207,  210,\n",
       "        213,  216,  219,  222,  225,  228,  231,  234,  237,  240,  243,\n",
       "        246,  249,  252,  255,  258,  261,  264,  267,  270,  273,  276,\n",
       "        279,  282,  285,  288,  291,  294,  297,  300, 2997])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*partial_census_table.column(\"AGE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4dcc5207-4254-4c77-b035-699a9577963f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>GENDER</th> <th>AGE</th> <th>2010</th> <th>2020</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0     </td> <td>0   </td> <td>3951495</td> <td>3735010</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0     </td> <td>1   </td> <td>3957904</td> <td>3773884</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0     </td> <td>2   </td> <td>4090799</td> <td>3853025</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0     </td> <td>3   </td> <td>4111869</td> <td>3921526</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0     </td> <td>4   </td> <td>4077511</td> <td>4017847</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0     </td> <td>5   </td> <td>4064653</td> <td>4054336</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0     </td> <td>6   </td> <td>4073031</td> <td>4040169</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0     </td> <td>7   </td> <td>4043100</td> <td>4029753</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0     </td> <td>8   </td> <td>4025624</td> <td>4034785</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>0     </td> <td>9   </td> <td>4125413</td> <td>4078668</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (296 rows omitted)</p>"
      ],
      "text/plain": [
       "GENDER | AGE  | 2010    | 2020\n",
       "0      | 0    | 3951495 | 3735010\n",
       "0      | 1    | 3957904 | 3773884\n",
       "0      | 2    | 4090799 | 3853025\n",
       "0      | 3    | 4111869 | 3921526\n",
       "0      | 4    | 4077511 | 4017847\n",
       "0      | 5    | 4064653 | 4054336\n",
       "0      | 6    | 4073031 | 4040169\n",
       "0      | 7    | 4043100 | 4029753\n",
       "0      | 8    | 4025624 | 4034785\n",
       "0      | 9    | 4125413 | 4078668\n",
       "... (296 rows omitted)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_pop = partial_census_table.relabeled('POPESTIMATE2010', '2010').relabeled('POPESTIMATE2020', '2020').relabeled('SEX','GENDER')\n",
    "us_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0c463bc2-3e1c-4755-bfca-6ffe604cefd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datascience.tables.Table"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(us_pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5af64f24-76ab-49ef-8bd1-82261306e7b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>GENDER</th> <th>AGE</th> <th>2010</th> <th>2020</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0     </td> <td>80  </td> <td>1319720</td> <td>1526957</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1     </td> <td>80  </td> <td>549205 </td> <td>663256 </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2     </td> <td>80  </td> <td>770515 </td> <td>863701 </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "GENDER | AGE  | 2010    | 2020\n",
       "0      | 80   | 1319720 | 1526957\n",
       "1      | 80   | 549205  | 663256\n",
       "2      | 80   | 770515  | 863701"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_pop.where('AGE',80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fb08c304-2f6d-4bce-99ff-b219828b70d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>GENDER</th> <th>AGE</th> <th>2010</th> <th>2020</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0     </td> <td>70  </td> <td>2062464</td> <td>3192029</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1     </td> <td>70  </td> <td>954009 </td> <td>1485086</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2     </td> <td>70  </td> <td>1108455</td> <td>1706943</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "GENDER | AGE  | 2010    | 2020\n",
       "0      | 70   | 2062464 | 3192029\n",
       "1      | 70   | 954009  | 1485086\n",
       "2      | 70   | 1108455 | 1706943"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_pop.where('AGE',70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d1da58b-c6dc-4a19-a506-4a4f1e762a9b",
   "metadata": {},
   "source": [
    "Plot the whole population with including both genders (coded as GENDER = 0) and compare 2010 and 2020 US population as a function of age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fce2a9a6-118d-4dea-89bb-7fc4ac75970d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "us_pop.where('GENDER',0).select('AGE','2010','2020').where('AGE',are.below(100)).plot('AGE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86d5a03a-42b7-4178-b484-1b2febe8ab23",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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