----ch1.introduction-----.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Introduction to ACS module"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before you begin, make sure yuo have the private key for the census data API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sys\n",
    "# import os\n",
    "# Save the acs.py file in the same directory where this Rmd or ipynb file is being run.d\n",
    "# The following lines will add the current directory into the path\n",
    "# so that it can search the py file in the directory when we try to import\n",
    "# wd = os.getcwd()\n",
    "# sys.path.append(wd)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# acs will also import pandas, numpy, re, and requests modules\n",
    "import MarkeyDataTools as mdt\n",
    "\n",
    "# this reload function is just to reload the MarkeyDataTools module as I periodically edit and test the module.\n",
    "# from importlib import reload"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a test, I am creating a module that call up a dataset; and as an example I will show you how the functions in the acs.py can work out to execute the same tasks that were done in `acs_computer_broadband` in the Data/ACS folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were five tables imported separately, but using acs, you can import the variables in one table.   \n",
    "To do so, you need to set values for the following variables:   \n",
    "* year\n",
    "* tablename (in a list)\n",
    "   - For example, if you want to lookup \"B28005\"(all_ind), \"B28009A\"(white), \"B28009H\"(white_nh), \"B28009B\"(black), \"B28009I\"(hisp), set the value for source as \\[\"B28005\", \"B28009A\", \"B28009H\", \"B28009B\", \"B28009I\" \\]\n",
    "* source (the subgroup of acs datagroup)\n",
    "   - In this example, it will be acs1 as ``acs1`` is the annual data.\n",
    "   - Many counties are missing in the acs1 as this is annual; you can find others in the ``acs5``, which is conducted every 5 years.\n",
    "* state: the default value is 'KY', so you don't need to worry about it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = 2019\n",
    "group =  [\"B28005\", \"B28009A\", \"B28009H\", \"B28009B\", \"B28009I\" ]\n",
    "source = 'acs5'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part I. importing the module and calling the acs class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we alreay imported the MarkeyDataTool module by executing `import MarkeyDataTool as mdt`. Now, let's call the `acs` class   \n",
    "To call the `acs` class, you don't need anything. \n",
    "However, you need year, table and source at the very least to create a dataset using either `guide` or `insert_inputs`.     \n",
    "Let's see what it looks like to run the guide function.   \n",
    "**Please note that when you first run the acs class, it will ask for your private key**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "insert your private key for the census data :  ba1c94e9a1b43440cf3ef2bf0b7803ddc39bcf2d\n"
     ]
    }
   ],
   "source": [
    "acs = mdt.acs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "what year was the dataset created: 2019\n",
      "provide a group name to include in your dataset [when you finished, please submit \"quit\" B28005\n",
      "provide a group name to include in your dataset [when you finished, please submit \"quit\" B28009A\n",
      "provide a group name to include in your dataset [when you finished, please submit \"quit\" B28009H\n",
      "provide a group name to include in your dataset [when you finished, please submit \"quit\" B28009B\n",
      "provide a group name to include in your dataset [when you finished, please submit \"quit\" B28009I\n",
      "provide a group name to include in your dataset [when you finished, please submit \"quit\" quit\n",
      "Is the dataset \"acs5\"? [Yes/No] Yes\n",
      "What is the geological level? [State, County, County Subdivision, Tract] : Tract\n"
     ]
    }
   ],
   "source": [
    "acs.guide()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or you can simply plug those arguments into the attribute function `insert_inputs`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "acs.insert_inputs(year = 2019, table = group, source= 'acs5', region = 'Tract' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you just do this, the most important attribute you might want to take a look at is the variable table.   \n",
    "The name of the attribute is `variable_table`. This contains every variables name in the entire acs5 data.   \n",
    "Obviously, the table names are contained in the variable table, but they have extensions such as 001_E, 002_E, etc.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>label</th>\n",
       "      <th>concept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>for</td>\n",
       "      <td>Census API FIPS 'for' clause</td>\n",
       "      <td>Census API Geography Specification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>in</td>\n",
       "      <td>Census API FIPS 'in' clause</td>\n",
       "      <td>Census API Geography Specification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ucgid</td>\n",
       "      <td>Uniform Census Geography Identifier clause</td>\n",
       "      <td>Census API Geography Specification</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B24022_060E</td>\n",
       "      <td>Estimate!!Total:!!Female:!!Service occupations:!!Food preparation and serving related occupations</td>\n",
       "      <td>SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B19001B_014E</td>\n",
       "      <td>Estimate!!Total:!!$100,000 to $124,999</td>\n",
       "      <td>HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) (BLACK OR AFRICAN AMERICAN ALONE HOUSEHOLDER)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27075</th>\n",
       "      <td>B25124_022E</td>\n",
       "      <td>Estimate!!Total:!!Owner occupied:!!3-person household:!!50 or more</td>\n",
       "      <td>TENURE BY HOUSEHOLD SIZE BY UNITS IN STRUCTURE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27076</th>\n",
       "      <td>B20005I_071E</td>\n",
       "      <td>Estimate!!Total:!!Female:!!Worked full-time, year-round in the past 12 months:!!With earnings:!!$75,000 to $99,999</td>\n",
       "      <td>SEX BY WORK EXPERIENCE IN THE PAST 12 MONTHS BY EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE POPULATION 16 YEARS AND OVER (HISPANIC OR LATINO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27077</th>\n",
       "      <td>B08113_054E</td>\n",
       "      <td>Estimate!!Total:!!Worked from home:!!Speak other languages:</td>\n",
       "      <td>MEANS OF TRANSPORTATION TO WORK BY LANGUAGE SPOKEN AT HOME AND ABILITY TO SPEAK ENGLISH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27078</th>\n",
       "      <td>B06009_006E</td>\n",
       "      <td>Estimate!!Total:!!Graduate or professional degree</td>\n",
       "      <td>PLACE OF BIRTH BY EDUCATIONAL ATTAINMENT IN THE UNITED STATES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27079</th>\n",
       "      <td>B05009_012E</td>\n",
       "      <td>Estimate!!Total:!!Under 6 years:!!Living with two parents:!!One native and one foreign-born parent:!!Child is foreign born</td>\n",
       "      <td>AGE AND NATIVITY OF OWN CHILDREN UNDER 18 YEARS IN FAMILIES AND SUBFAMILIES BY NUMBER AND NATIVITY OF PARENTS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27080 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               name  \\\n",
       "0      for            \n",
       "1      in             \n",
       "2      ucgid          \n",
       "3      B24022_060E    \n",
       "4      B19001B_014E   \n",
       "...             ...   \n",
       "27075  B25124_022E    \n",
       "27076  B20005I_071E   \n",
       "27077  B08113_054E    \n",
       "27078  B06009_006E    \n",
       "27079  B05009_012E    \n",
       "\n",
       "                                                                                                                            label  \\\n",
       "0      Census API FIPS 'for' clause                                                                                                 \n",
       "1      Census API FIPS 'in' clause                                                                                                  \n",
       "2      Uniform Census Geography Identifier clause                                                                                   \n",
       "3      Estimate!!Total:!!Female:!!Service occupations:!!Food preparation and serving related occupations                            \n",
       "4      Estimate!!Total:!!$100,000 to $124,999                                                                                       \n",
       "...                                       ...                                                                                       \n",
       "27075  Estimate!!Total:!!Owner occupied:!!3-person household:!!50 or more                                                           \n",
       "27076  Estimate!!Total:!!Female:!!Worked full-time, year-round in the past 12 months:!!With earnings:!!$75,000 to $99,999           \n",
       "27077  Estimate!!Total:!!Worked from home:!!Speak other languages:                                                                  \n",
       "27078  Estimate!!Total:!!Graduate or professional degree                                                                            \n",
       "27079  Estimate!!Total:!!Under 6 years:!!Living with two parents:!!One native and one foreign-born parent:!!Child is foreign born   \n",
       "\n",
       "                                                                                                                                                                             concept  \n",
       "0      Census API Geography Specification                                                                                                                                             \n",
       "1      Census API Geography Specification                                                                                                                                             \n",
       "2      Census API Geography Specification                                                                                                                                             \n",
       "3      SEX BY OCCUPATION AND MEDIAN EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE FULL-TIME, YEAR-ROUND CIVILIAN EMPLOYED POPULATION 16 YEARS AND OVER  \n",
       "4      HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) (BLACK OR AFRICAN AMERICAN ALONE HOUSEHOLDER)                                                      \n",
       "...                                                                                                                          ...                                                      \n",
       "27075  TENURE BY HOUSEHOLD SIZE BY UNITS IN STRUCTURE                                                                                                                                 \n",
       "27076  SEX BY WORK EXPERIENCE IN THE PAST 12 MONTHS BY EARNINGS IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED DOLLARS) FOR THE POPULATION 16 YEARS AND OVER (HISPANIC OR LATINO)  \n",
       "27077  MEANS OF TRANSPORTATION TO WORK BY LANGUAGE SPOKEN AT HOME AND ABILITY TO SPEAK ENGLISH                                                                                        \n",
       "27078  PLACE OF BIRTH BY EDUCATIONAL ATTAINMENT IN THE UNITED STATES                                                                                                                  \n",
       "27079  AGE AND NATIVITY OF OWN CHILDREN UNDER 18 YEARS IN FAMILIES AND SUBFAMILIES BY NUMBER AND NATIVITY OF PARENTS                                                                  \n",
       "\n",
       "[27080 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.variable_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You don't need to check `acs_tract.variable_table` because you will get the same table as long as having the same value for the **source** argument"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part II. Pulling the acs date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the attribute function, **`gen_dataframe()`**, you will get the data you wanted.     \n",
    "This will be stored in the object `acs`, so you can use other functions to tidy and wrangle data for your needs.   \n",
    "As you see, you have the **tract** names and their FIPS numbers in the first columns.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>County</th>\n",
       "      <th>State</th>\n",
       "      <th>B28005_012E</th>\n",
       "      <th>B28005_011E</th>\n",
       "      <th>B28005_010E</th>\n",
       "      <th>B28005_016E</th>\n",
       "      <th>B28005_015E</th>\n",
       "      <th>B28005_014E</th>\n",
       "      <th>...</th>\n",
       "      <th>B28009B_005E</th>\n",
       "      <th>B28009B_001E</th>\n",
       "      <th>B28009B_002E</th>\n",
       "      <th>B28009B_006E</th>\n",
       "      <th>B28009I_002E</th>\n",
       "      <th>B28009I_001E</th>\n",
       "      <th>B28009I_004E</th>\n",
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       "      <td>21005950201</td>\n",
       "      <td>Census Tract 9502.01</td>\n",
       "      <td>Anderson County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>459</td>\n",
       "      <td>2583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>584</td>\n",
       "      <td>896</td>\n",
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       "      <td>312</td>\n",
       "      <td>44</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21005950202</td>\n",
       "      <td>Census Tract 9502.02</td>\n",
       "      <td>Anderson County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>187</td>\n",
       "      <td>2052</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>379</td>\n",
       "      <td>572</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>149</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21013961100</td>\n",
       "      <td>Census Tract 9611</td>\n",
       "      <td>Bell County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>83</td>\n",
       "      <td>1430</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>446</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21015070311</td>\n",
       "      <td>Census Tract 703.11</td>\n",
       "      <td>Boone County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>17</td>\n",
       "      <td>1464</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>323</td>\n",
       "      <td>452</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21015070312</td>\n",
       "      <td>Census Tract 703.12</td>\n",
       "      <td>Boone County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>118</td>\n",
       "      <td>1733</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>296</td>\n",
       "      <td>598</td>\n",
       "      <td>...</td>\n",
       "      <td>187</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          FIPS                 Tract            County      State B28005_012E  \\\n",
       "0  21005950201  Census Tract 9502.01   Anderson County   Kentucky  459          \n",
       "1  21005950202  Census Tract 9502.02   Anderson County   Kentucky  187          \n",
       "2  21013961100  Census Tract 9611      Bell County       Kentucky  83           \n",
       "3  21015070311  Census Tract 703.11    Boone County      Kentucky  17           \n",
       "4  21015070312  Census Tract 703.12    Boone County      Kentucky  118          \n",
       "\n",
       "  B28005_011E B28005_010E B28005_016E B28005_015E B28005_014E  ...  \\\n",
       "0  2583        0           0           584         896         ...   \n",
       "1  2052        0           15          379         572         ...   \n",
       "2  1430        0           0           212         446         ...   \n",
       "3  1464        7           15          323         452         ...   \n",
       "4  1733        0           16          296         598         ...   \n",
       "\n",
       "  B28009B_005E B28009B_001E B28009B_002E B28009B_006E B28009I_002E  \\\n",
       "0  0            356          312          44           0             \n",
       "1  0            0            0            0            149           \n",
       "2  0            0            0            0            0             \n",
       "3  0            165          165          0            35            \n",
       "4  187          240          240          0            92            \n",
       "\n",
       "  B28009I_001E B28009I_004E B28009I_003E B28009I_006E B28009I_005E  \n",
       "0  0            0            0            0            0            \n",
       "1  149          80           0            0            69           \n",
       "2  0            0            0            0            0            \n",
       "3  35           35           0            0            0            \n",
       "4  92           92           0            0            0            \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.gen_dataframe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part III. Searching the tables and variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To search a varialbe by keyword, you can use `search` attribute. If you have already used the function `gen_dataframe`, you do not need additional arguments. If you did not, you would need to give an integer for the year value.   \n",
    "Also, if you want to save the search result as a file, then the argument for `savefile`, whose default value is *False*, should now be *`True`* and you should give a `filename`. If you forgot the filename, don't worry, it will ask you once again. Please **note** that the file format should be **`csv`**. As a test, please type in **`'test.csv'`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>B17015</td>\n",
       "      <td>POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY FAMILY TYPE BY SOCIAL SECURITY INCOME BY SUPPLEMENTAL SECURITY INCOME (SSI) AND CASH PUBLIC ASSISTANCE INCOME</td>\n",
       "      <td>acs5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>B17016</td>\n",
       "      <td>POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY FAMILY TYPE BY WORK EXPERIENCE OF HOUSEHOLDER AND SPOUSE</td>\n",
       "      <td>acs5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>B17017</td>\n",
       "      <td>POVERTY STATUS IN THE PAST 12 MONTHS BY HOUSEHOLD TYPE BY AGE OF HOUSEHOLDER</td>\n",
       "      <td>acs5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>B17018</td>\n",
       "      <td>POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER</td>\n",
       "      <td>acs5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>B17012</td>\n",
       "      <td>POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY NUMBER OF RELATED CHILDREN UNDER 18 YEARS</td>\n",
       "      <td>acs5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1058</td>\n",
       "      <td>B22003</td>\n",
       "      <td>RECEIPT OF FOOD STAMPS/SNAP IN THE PAST 12 MONTHS BY POVERTY STATUS IN THE PAST 12 MONTHS FOR HOUSEHOLDS</td>\n",
       "      <td>acs5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>1066</td>\n",
       "      <td>B17020H</td>\n",
       "      <td>POVERTY STATUS IN THE PAST 12 MONTHS BY AGE (WHITE ALONE, NOT HISPANIC OR LATINO)</td>\n",
       "      <td>acs5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>1067</td>\n",
       "      <td>B17020I</td>\n",
       "      <td>POVERTY STATUS IN THE PAST 12 MONTHS BY AGE (HISPANIC OR LATINO)</td>\n",
       "      <td>acs5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>1069</td>\n",
       "      <td>B17020F</td>\n",
       "      <td>POVERTY STATUS IN THE PAST 12 MONTHS BY AGE (SOME OTHER RACE ALONE)</td>\n",
       "      <td>acs5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>1070</td>\n",
       "      <td>B17020G</td>\n",
       "      <td>POVERTY STATUS IN THE PAST 12 MONTHS BY AGE (TWO OR MORE RACES)</td>\n",
       "      <td>acs5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index     name  \\\n",
       "0    0      B17015    \n",
       "1    2      B17016    \n",
       "2    4      B17017    \n",
       "3    6      B17018    \n",
       "4    11     B17012    \n",
       "..   ..        ...    \n",
       "105  1058   B22003    \n",
       "106  1066   B17020H   \n",
       "107  1067   B17020I   \n",
       "108  1069   B17020F   \n",
       "109  1070   B17020G   \n",
       "\n",
       "                                                                                                                                                           description  \\\n",
       "0    POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY FAMILY TYPE BY SOCIAL SECURITY INCOME BY SUPPLEMENTAL SECURITY INCOME (SSI) AND CASH PUBLIC ASSISTANCE INCOME   \n",
       "1    POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY FAMILY TYPE BY WORK EXPERIENCE OF HOUSEHOLDER AND SPOUSE                                                        \n",
       "2    POVERTY STATUS IN THE PAST 12 MONTHS BY HOUSEHOLD TYPE BY AGE OF HOUSEHOLDER                                                                                        \n",
       "3    POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY EDUCATIONAL ATTAINMENT OF HOUSEHOLDER                                                         \n",
       "4    POVERTY STATUS IN THE PAST 12 MONTHS OF FAMILIES BY HOUSEHOLD TYPE BY NUMBER OF RELATED CHILDREN UNDER 18 YEARS                                                     \n",
       "..                                                                                                               ...                                                     \n",
       "105  RECEIPT OF FOOD STAMPS/SNAP IN THE PAST 12 MONTHS BY POVERTY STATUS IN THE PAST 12 MONTHS FOR HOUSEHOLDS                                                            \n",
       "106  POVERTY STATUS IN THE PAST 12 MONTHS BY AGE (WHITE ALONE, NOT HISPANIC OR LATINO)                                                                                   \n",
       "107  POVERTY STATUS IN THE PAST 12 MONTHS BY AGE (HISPANIC OR LATINO)                                                                                                    \n",
       "108  POVERTY STATUS IN THE PAST 12 MONTHS BY AGE (SOME OTHER RACE ALONE)                                                                                                 \n",
       "109  POVERTY STATUS IN THE PAST 12 MONTHS BY AGE (TWO OR MORE RACES)                                                                                                     \n",
       "\n",
       "    source  \n",
       "0    acs5   \n",
       "1    acs5   \n",
       "2    acs5   \n",
       "3    acs5   \n",
       "4    acs5   \n",
       "..    ...   \n",
       "105  acs5   \n",
       "106  acs5   \n",
       "107  acs5   \n",
       "108  acs5   \n",
       "109  acs5   \n",
       "\n",
       "[110 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.search('poverty,health')\n",
    "# acs.search('poverty,heath', savefile = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example above, comma (`,`) indicates **OR** operator. Therefore, you are searching the cases where description contains words proverty or health. I might improve the serach engine by including various seach opearators like **AND**, but for now, **OR** is the only option. However, if you know `regex`, you can also use the `search` function with the regex. Below is the example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>106</td>\n",
       "      <td>B27022</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "      <td>acs5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335</td>\n",
       "      <td>B27001</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY AGE</td>\n",
       "      <td>acs5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index    name  \\\n",
       "0  106    B27022   \n",
       "1  335    B27001   \n",
       "\n",
       "                                                                                   description  \\\n",
       "0  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25   \n",
       "1  HEALTH INSURANCE COVERAGE STATUS BY SEX BY AGE                                                \n",
       "\n",
       "  source  \n",
       "0  acs5   \n",
       "1  acs5   "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "pattern = re.compile('(?=health insurance)(?=.*sex)(?=.*age)', flags = re.I)\n",
    "acs.search(pattern)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, I searched cases where description contains words `health insurance`, `sex`, and `age`. **`flags = re.I`** lets this search engine to ignore the case of the letters. For more information on regex and its operations, please visit [regex tutorial](https://www.w3schools.com/python/python_regex.asp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's say you think you want to explore more about the group `'B27022'`. You can use the attribute function **`gen_group_varible_desc`** with the `group` argument (it is an abbreviation of generate group's variable description). The `group` argument can have one group in a string format or it can also be a list or numpy array that contains a number of it. Let's see what we get for `'B27022'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>label</th>\n",
       "      <th>concept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9906</th>\n",
       "      <td>B27022_001E</td>\n",
       "      <td>Estimate!!Total:</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9891</th>\n",
       "      <td>B27022_002E</td>\n",
       "      <td>Estimate!!Total:!!Male:</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9895</th>\n",
       "      <td>B27022_003E</td>\n",
       "      <td>Estimate!!Total:!!Male:!!Enrolled in school:</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9827</th>\n",
       "      <td>B27022_004E</td>\n",
       "      <td>Estimate!!Total:!!Male:!!Enrolled in school:!!With health insurance coverage</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9828</th>\n",
       "      <td>B27022_005E</td>\n",
       "      <td>Estimate!!Total:!!Male:!!Enrolled in school:!!No health insurance coverage</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9824</th>\n",
       "      <td>B27022_006E</td>\n",
       "      <td>Estimate!!Total:!!Male:!!Not enrolled in school:</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9825</th>\n",
       "      <td>B27022_007E</td>\n",
       "      <td>Estimate!!Total:!!Male:!!Not enrolled in school:!!With health insurance coverage</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9822</th>\n",
       "      <td>B27022_008E</td>\n",
       "      <td>Estimate!!Total:!!Male:!!Not enrolled in school:!!No health insurance coverage</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9823</th>\n",
       "      <td>B27022_009E</td>\n",
       "      <td>Estimate!!Total:!!Female:</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9835</th>\n",
       "      <td>B27022_010E</td>\n",
       "      <td>Estimate!!Total:!!Female:!!Enrolled in school:</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9836</th>\n",
       "      <td>B27022_011E</td>\n",
       "      <td>Estimate!!Total:!!Female:!!Enrolled in school:!!With health insurance coverage</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9833</th>\n",
       "      <td>B27022_012E</td>\n",
       "      <td>Estimate!!Total:!!Female:!!Enrolled in school:!!No health insurance coverage</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9834</th>\n",
       "      <td>B27022_013E</td>\n",
       "      <td>Estimate!!Total:!!Female:!!Not enrolled in school:</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9829</th>\n",
       "      <td>B27022_014E</td>\n",
       "      <td>Estimate!!Total:!!Female:!!Not enrolled in school:!!With health insurance coverage</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9830</th>\n",
       "      <td>B27022_015E</td>\n",
       "      <td>Estimate!!Total:!!Female:!!Not enrolled in school:!!No health insurance coverage</td>\n",
       "      <td>HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name  \\\n",
       "9906  B27022_001E   \n",
       "9891  B27022_002E   \n",
       "9895  B27022_003E   \n",
       "9827  B27022_004E   \n",
       "9828  B27022_005E   \n",
       "9824  B27022_006E   \n",
       "9825  B27022_007E   \n",
       "9822  B27022_008E   \n",
       "9823  B27022_009E   \n",
       "9835  B27022_010E   \n",
       "9836  B27022_011E   \n",
       "9833  B27022_012E   \n",
       "9834  B27022_013E   \n",
       "9829  B27022_014E   \n",
       "9830  B27022_015E   \n",
       "\n",
       "                                                                                   label  \\\n",
       "9906  Estimate!!Total:                                                                     \n",
       "9891  Estimate!!Total:!!Male:                                                              \n",
       "9895  Estimate!!Total:!!Male:!!Enrolled in school:                                         \n",
       "9827  Estimate!!Total:!!Male:!!Enrolled in school:!!With health insurance coverage         \n",
       "9828  Estimate!!Total:!!Male:!!Enrolled in school:!!No health insurance coverage           \n",
       "9824  Estimate!!Total:!!Male:!!Not enrolled in school:                                     \n",
       "9825  Estimate!!Total:!!Male:!!Not enrolled in school:!!With health insurance coverage     \n",
       "9822  Estimate!!Total:!!Male:!!Not enrolled in school:!!No health insurance coverage       \n",
       "9823  Estimate!!Total:!!Female:                                                            \n",
       "9835  Estimate!!Total:!!Female:!!Enrolled in school:                                       \n",
       "9836  Estimate!!Total:!!Female:!!Enrolled in school:!!With health insurance coverage       \n",
       "9833  Estimate!!Total:!!Female:!!Enrolled in school:!!No health insurance coverage         \n",
       "9834  Estimate!!Total:!!Female:!!Not enrolled in school:                                   \n",
       "9829  Estimate!!Total:!!Female:!!Not enrolled in school:!!With health insurance coverage   \n",
       "9830  Estimate!!Total:!!Female:!!Not enrolled in school:!!No health insurance coverage     \n",
       "\n",
       "                                                                                          concept  \n",
       "9906  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9891  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9895  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9827  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9828  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9824  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9825  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9822  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9823  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9835  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9836  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9833  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9834  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9829  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  \n",
       "9830  HEALTH INSURANCE COVERAGE STATUS BY SEX BY ENROLLMENT STATUS FOR YOUNG ADULTS AGED 19 TO 25  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.gen_group_variable_desc('B27022')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What if I want to see the description of the variables in the groups `\"B28005\" and \"B28009A\"` together? The group argument would be \\['B28005', 'B28009A' \\]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>label</th>\n",
       "      <th>concept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2468</th>\n",
       "      <td>B28005_001E</td>\n",
       "      <td>Estimate!!Total:</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2461</th>\n",
       "      <td>B28005_002E</td>\n",
       "      <td>Estimate!!Total:!!Under 18 years:</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2457</th>\n",
       "      <td>B28005_003E</td>\n",
       "      <td>Estimate!!Total:!!Under 18 years:!!Has a computer:</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2452</th>\n",
       "      <td>B28005_004E</td>\n",
       "      <td>Estimate!!Total:!!Under 18 years:!!Has a computer:!!With dial-up Internet subscription alone</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2486</th>\n",
       "      <td>B28005_005E</td>\n",
       "      <td>Estimate!!Total:!!Under 18 years:!!Has a computer:!!With a broadband Internet subscription</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2483</th>\n",
       "      <td>B28005_006E</td>\n",
       "      <td>Estimate!!Total:!!Under 18 years:!!Has a computer:!!Without an Internet subscription</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2477</th>\n",
       "      <td>B28005_007E</td>\n",
       "      <td>Estimate!!Total:!!Under 18 years:!!No computer</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2479</th>\n",
       "      <td>B28005_008E</td>\n",
       "      <td>Estimate!!Total:!!18 to 64 years:</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2475</th>\n",
       "      <td>B28005_009E</td>\n",
       "      <td>Estimate!!Total:!!18 to 64 years:!!Has a computer:</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2298</th>\n",
       "      <td>B28005_010E</td>\n",
       "      <td>Estimate!!Total:!!18 to 64 years:!!Has a computer:!!With dial-up Internet subscription alone</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2294</th>\n",
       "      <td>B28005_011E</td>\n",
       "      <td>Estimate!!Total:!!18 to 64 years:!!Has a computer:!!With a broadband Internet subscription</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2289</th>\n",
       "      <td>B28005_012E</td>\n",
       "      <td>Estimate!!Total:!!18 to 64 years:!!Has a computer:!!Without an Internet subscription</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>B28005_013E</td>\n",
       "      <td>Estimate!!Total:!!18 to 64 years:!!No computer</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2368</th>\n",
       "      <td>B28005_014E</td>\n",
       "      <td>Estimate!!Total:!!65 years and over:</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2366</th>\n",
       "      <td>B28005_015E</td>\n",
       "      <td>Estimate!!Total:!!65 years and over:!!Has a computer:</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2364</th>\n",
       "      <td>B28005_016E</td>\n",
       "      <td>Estimate!!Total:!!65 years and over:!!Has a computer:!!With dial-up Internet subscription alone</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2378</th>\n",
       "      <td>B28005_017E</td>\n",
       "      <td>Estimate!!Total:!!65 years and over:!!Has a computer:!!With a broadband Internet subscription</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2376</th>\n",
       "      <td>B28005_018E</td>\n",
       "      <td>Estimate!!Total:!!65 years and over:!!Has a computer:!!Without an Internet subscription</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2377</th>\n",
       "      <td>B28005_019E</td>\n",
       "      <td>Estimate!!Total:!!65 years and over:!!No computer</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6000</th>\n",
       "      <td>B28009A_001E</td>\n",
       "      <td>Estimate!!Total:</td>\n",
       "      <td>PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (WHITE ALONE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6177</th>\n",
       "      <td>B28009A_002E</td>\n",
       "      <td>Estimate!!Total:!!Has a computer:</td>\n",
       "      <td>PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (WHITE ALONE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181</th>\n",
       "      <td>B28009A_003E</td>\n",
       "      <td>Estimate!!Total:!!Has a computer:!!With dial-up Internet subscription alone</td>\n",
       "      <td>PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (WHITE ALONE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6190</th>\n",
       "      <td>B28009A_004E</td>\n",
       "      <td>Estimate!!Total:!!Has a computer:!!With a broadband Internet subscription</td>\n",
       "      <td>PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (WHITE ALONE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6186</th>\n",
       "      <td>B28009A_005E</td>\n",
       "      <td>Estimate!!Total:!!Has a computer:!!Without an Internet subscription</td>\n",
       "      <td>PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (WHITE ALONE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6191</th>\n",
       "      <td>B28009A_006E</td>\n",
       "      <td>Estimate!!Total:!!No Computer</td>\n",
       "      <td>PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (WHITE ALONE)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name  \\\n",
       "2468  B28005_001E    \n",
       "2461  B28005_002E    \n",
       "2457  B28005_003E    \n",
       "2452  B28005_004E    \n",
       "2486  B28005_005E    \n",
       "2483  B28005_006E    \n",
       "2477  B28005_007E    \n",
       "2479  B28005_008E    \n",
       "2475  B28005_009E    \n",
       "2298  B28005_010E    \n",
       "2294  B28005_011E    \n",
       "2289  B28005_012E    \n",
       "2374  B28005_013E    \n",
       "2368  B28005_014E    \n",
       "2366  B28005_015E    \n",
       "2364  B28005_016E    \n",
       "2378  B28005_017E    \n",
       "2376  B28005_018E    \n",
       "2377  B28005_019E    \n",
       "6000  B28009A_001E   \n",
       "6177  B28009A_002E   \n",
       "6181  B28009A_003E   \n",
       "6190  B28009A_004E   \n",
       "6186  B28009A_005E   \n",
       "6191  B28009A_006E   \n",
       "\n",
       "                                                                                                label  \\\n",
       "2468  Estimate!!Total:                                                                                  \n",
       "2461  Estimate!!Total:!!Under 18 years:                                                                 \n",
       "2457  Estimate!!Total:!!Under 18 years:!!Has a computer:                                                \n",
       "2452  Estimate!!Total:!!Under 18 years:!!Has a computer:!!With dial-up Internet subscription alone      \n",
       "2486  Estimate!!Total:!!Under 18 years:!!Has a computer:!!With a broadband Internet subscription        \n",
       "2483  Estimate!!Total:!!Under 18 years:!!Has a computer:!!Without an Internet subscription              \n",
       "2477  Estimate!!Total:!!Under 18 years:!!No computer                                                    \n",
       "2479  Estimate!!Total:!!18 to 64 years:                                                                 \n",
       "2475  Estimate!!Total:!!18 to 64 years:!!Has a computer:                                                \n",
       "2298  Estimate!!Total:!!18 to 64 years:!!Has a computer:!!With dial-up Internet subscription alone      \n",
       "2294  Estimate!!Total:!!18 to 64 years:!!Has a computer:!!With a broadband Internet subscription        \n",
       "2289  Estimate!!Total:!!18 to 64 years:!!Has a computer:!!Without an Internet subscription              \n",
       "2374  Estimate!!Total:!!18 to 64 years:!!No computer                                                    \n",
       "2368  Estimate!!Total:!!65 years and over:                                                              \n",
       "2366  Estimate!!Total:!!65 years and over:!!Has a computer:                                             \n",
       "2364  Estimate!!Total:!!65 years and over:!!Has a computer:!!With dial-up Internet subscription alone   \n",
       "2378  Estimate!!Total:!!65 years and over:!!Has a computer:!!With a broadband Internet subscription     \n",
       "2376  Estimate!!Total:!!65 years and over:!!Has a computer:!!Without an Internet subscription           \n",
       "2377  Estimate!!Total:!!65 years and over:!!No computer                                                 \n",
       "6000  Estimate!!Total:                                                                                  \n",
       "6177  Estimate!!Total:!!Has a computer:                                                                 \n",
       "6181  Estimate!!Total:!!Has a computer:!!With dial-up Internet subscription alone                       \n",
       "6190  Estimate!!Total:!!Has a computer:!!With a broadband Internet subscription                         \n",
       "6186  Estimate!!Total:!!Has a computer:!!Without an Internet subscription                               \n",
       "6191  Estimate!!Total:!!No Computer                                                                     \n",
       "\n",
       "                                                                                  concept  \n",
       "2468  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2461  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2457  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2452  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2486  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2483  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2477  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2479  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2475  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2298  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2294  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2289  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2374  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2368  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2366  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2364  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2378  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2376  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "2377  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD        \n",
       "6000  PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (WHITE ALONE)  \n",
       "6177  PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (WHITE ALONE)  \n",
       "6181  PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (WHITE ALONE)  \n",
       "6190  PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (WHITE ALONE)  \n",
       "6186  PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (WHITE ALONE)  \n",
       "6191  PRESENCE OF A COMPUTER AND TYPE OF INTERNET SUBSCRIPTION IN HOUSEHOLD (WHITE ALONE)  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.gen_group_variable_desc(['B28005','B28009A'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, the latest search result is stored as attribute `'group_variable_desc'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>label</th>\n",
       "      <th>concept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2468</th>\n",
       "      <td>B28005_001E</td>\n",
       "      <td>Estimate!!Total:</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2461</th>\n",
       "      <td>B28005_002E</td>\n",
       "      <td>Estimate!!Total:!!Under 18 years:</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2457</th>\n",
       "      <td>B28005_003E</td>\n",
       "      <td>Estimate!!Total:!!Under 18 years:!!Has a computer:</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2452</th>\n",
       "      <td>B28005_004E</td>\n",
       "      <td>Estimate!!Total:!!Under 18 years:!!Has a computer:!!With dial-up Internet subscription alone</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2486</th>\n",
       "      <td>B28005_005E</td>\n",
       "      <td>Estimate!!Total:!!Under 18 years:!!Has a computer:!!With a broadband Internet subscription</td>\n",
       "      <td>AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name  \\\n",
       "2468  B28005_001E   \n",
       "2461  B28005_002E   \n",
       "2457  B28005_003E   \n",
       "2452  B28005_004E   \n",
       "2486  B28005_005E   \n",
       "\n",
       "                                                                                             label  \\\n",
       "2468  Estimate!!Total:                                                                               \n",
       "2461  Estimate!!Total:!!Under 18 years:                                                              \n",
       "2457  Estimate!!Total:!!Under 18 years:!!Has a computer:                                             \n",
       "2452  Estimate!!Total:!!Under 18 years:!!Has a computer:!!With dial-up Internet subscription alone   \n",
       "2486  Estimate!!Total:!!Under 18 years:!!Has a computer:!!With a broadband Internet subscription     \n",
       "\n",
       "                                                                            concept  \n",
       "2468  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD  \n",
       "2461  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD  \n",
       "2457  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD  \n",
       "2452  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD  \n",
       "2486  AGE BY PRESENCE OF A COMPUTER AND TYPES OF INTERNET SUBSCRIPTION IN HOUSEHOLD  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.group_variable_desc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part IV. Renaming and aggregating variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might want to rename the group **`B28005`** with the name **`computer_internet`** as variables in the group indicate estimates of the population having a computer or an internet subscription by age group. In addition, since **`B28009A`** is technically the same group for the white population alone, so it would be appropriate to rename the group as **`computer_internet_white`**.   \n",
    "To do so, you use the **`rename_group`** attribute function and the **`sub`** argument in the function requires a dictionary in the following format:   \n",
    "* { 'new name1' : 'original group name1', 'new name2': 'original group name2'}\n",
    "\n",
    "\n",
    "Hence, in our example, the sub argument would be {'computer_internet' : 'B28005', 'computer_internet_white': 'B28009A'}. Obviously, you may not need to rename them as long as you are comfortable with the original group name. Don't worry about the case of the letters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If you want to permanently replace the group names, set the inplace argument, whose default value is *`False`*, as *`True`*.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we have to discuss `aggregate` attribute function after this, we will not touch the inplace argment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_dict = {'computer_internet' : 'B28005', 'computer_internet_white': 'b28009A'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>County</th>\n",
       "      <th>State</th>\n",
       "      <th>computer_internet_012E</th>\n",
       "      <th>computer_internet_011E</th>\n",
       "      <th>computer_internet_010E</th>\n",
       "      <th>computer_internet_016E</th>\n",
       "      <th>computer_internet_015E</th>\n",
       "      <th>computer_internet_014E</th>\n",
       "      <th>...</th>\n",
       "      <th>B28009B_005E</th>\n",
       "      <th>B28009B_001E</th>\n",
       "      <th>B28009B_002E</th>\n",
       "      <th>B28009B_006E</th>\n",
       "      <th>B28009I_002E</th>\n",
       "      <th>B28009I_001E</th>\n",
       "      <th>B28009I_004E</th>\n",
       "      <th>B28009I_003E</th>\n",
       "      <th>B28009I_006E</th>\n",
       "      <th>B28009I_005E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>129</td>\n",
       "      <td>804</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>204</td>\n",
       "      <td>349</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>219</td>\n",
       "      <td>960</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>367</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>15</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21001970300</td>\n",
       "      <td>Census Tract 9703</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>65</td>\n",
       "      <td>1572</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>409</td>\n",
       "      <td>623</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>125</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21001970401</td>\n",
       "      <td>Census Tract 9704.01</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>250</td>\n",
       "      <td>1662</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>599</td>\n",
       "      <td>906</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>290</td>\n",
       "      <td>194</td>\n",
       "      <td>96</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21001970402</td>\n",
       "      <td>Census Tract 9704.02</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>219</td>\n",
       "      <td>1355</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>477</td>\n",
       "      <td>570</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>21239050106</td>\n",
       "      <td>Census Tract 501.06</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>65</td>\n",
       "      <td>1849</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>803</td>\n",
       "      <td>882</td>\n",
       "      <td>...</td>\n",
       "      <td>102</td>\n",
       "      <td>175</td>\n",
       "      <td>121</td>\n",
       "      <td>54</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>21239050107</td>\n",
       "      <td>Census Tract 501.07</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>153</td>\n",
       "      <td>2304</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>490</td>\n",
       "      <td>604</td>\n",
       "      <td>...</td>\n",
       "      <td>49</td>\n",
       "      <td>101</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>367</td>\n",
       "      <td>367</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>21239050200</td>\n",
       "      <td>Census Tract 502</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>126</td>\n",
       "      <td>1919</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>802</td>\n",
       "      <td>963</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>108</td>\n",
       "      <td>972</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>327</td>\n",
       "      <td>347</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>62</td>\n",
       "      <td>1185</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>323</td>\n",
       "      <td>403</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>178</td>\n",
       "      <td>237</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1115 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             FIPS                 Tract            County      State  \\\n",
       "0     21001970100  Census Tract 9701      Adair County      Kentucky   \n",
       "1     21001970200  Census Tract 9702      Adair County      Kentucky   \n",
       "2     21001970300  Census Tract 9703      Adair County      Kentucky   \n",
       "3     21001970401  Census Tract 9704.01   Adair County      Kentucky   \n",
       "4     21001970402  Census Tract 9704.02   Adair County      Kentucky   \n",
       "...           ...                   ...            ...           ...   \n",
       "1110  21239050106  Census Tract 501.06    Woodford County   Kentucky   \n",
       "1111  21239050107  Census Tract 501.07    Woodford County   Kentucky   \n",
       "1112  21239050200  Census Tract 502       Woodford County   Kentucky   \n",
       "1113  21239050300  Census Tract 503       Woodford County   Kentucky   \n",
       "1114  21239050400  Census Tract 504       Woodford County   Kentucky   \n",
       "\n",
       "     computer_internet_012E computer_internet_011E computer_internet_010E  \\\n",
       "0     129                    804                    34                      \n",
       "1     219                    960                    0                       \n",
       "2     65                     1572                   0                       \n",
       "3     250                    1662                   38                      \n",
       "4     219                    1355                   0                       \n",
       "...   ...                     ...                  ..                       \n",
       "1110  65                     1849                   0                       \n",
       "1111  153                    2304                   24                      \n",
       "1112  126                    1919                   9                       \n",
       "1113  108                    972                    0                       \n",
       "1114  62                     1185                   0                       \n",
       "\n",
       "     computer_internet_016E computer_internet_015E computer_internet_014E  \\\n",
       "0     3                      204                    349                     \n",
       "1     0                      212                    367                     \n",
       "2     0                      409                    623                     \n",
       "3     0                      599                    906                     \n",
       "4     22                     477                    570                     \n",
       "...   ..                     ...                    ...                     \n",
       "1110  23                     803                    882                     \n",
       "1111  7                      490                    604                     \n",
       "1112  0                      802                    963                     \n",
       "1113  0                      327                    347                     \n",
       "1114  20                     323                    403                     \n",
       "\n",
       "      ... B28009B_005E B28009B_001E B28009B_002E B28009B_006E B28009I_002E  \\\n",
       "0     ...  0            0            0            0            28            \n",
       "1     ...  0            87           15           72           0             \n",
       "2     ...  0            0            0            0            73            \n",
       "3     ...  0            290          194          96           80            \n",
       "4     ...  0            25           25           0            60            \n",
       "...   ... ..            ..           ..          ..            ..            \n",
       "1110  ...  102          175          121          54           35            \n",
       "1111  ...  49           101          85           16           367           \n",
       "1112  ...  0            13           13           0            58            \n",
       "1113  ...  0            37           14           23           9             \n",
       "1114  ...  0            70           40           30           178           \n",
       "\n",
       "     B28009I_001E B28009I_004E B28009I_003E B28009I_006E B28009I_005E  \n",
       "0     28           28           0            0            0            \n",
       "1     0            0            0            0            0            \n",
       "2     125          73           0            52           0            \n",
       "3     80           80           0            0            0            \n",
       "4     60           60           0            0            0            \n",
       "...   ..           ..          ..           ..           ..            \n",
       "1110  35           35           0            0            0            \n",
       "1111  367          195          0            0            172          \n",
       "1112  58           58           0            0            0            \n",
       "1113  22           9            0            13           0            \n",
       "1114  237          164          0            59           14           \n",
       "\n",
       "[1115 rows x 47 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.rename_group(sub_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check how the column name of the **`acs_data`** has transformed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FIPS', 'Tract', 'County', 'State', 'computer_internet_012E',\n",
       "       'computer_internet_011E', 'computer_internet_010E',\n",
       "       'computer_internet_016E', 'computer_internet_015E',\n",
       "       'computer_internet_014E', 'computer_internet_013E',\n",
       "       'computer_internet_018E', 'computer_internet_019E',\n",
       "       'computer_internet_017E', 'computer_internet_004E',\n",
       "       'computer_internet_003E', 'computer_internet_002E',\n",
       "       'computer_internet_001E', 'computer_internet_009E',\n",
       "       'computer_internet_007E', 'computer_internet_008E',\n",
       "       'computer_internet_006E', 'computer_internet_005E', 'B28009A_001E',\n",
       "       'B28009A_002E', 'B28009A_003E', 'B28009A_005E', 'B28009A_004E',\n",
       "       'B28009A_006E', 'B28009H_001E', 'B28009H_002E', 'B28009H_003E',\n",
       "       'B28009H_004E', 'B28009H_005E', 'B28009H_006E', 'B28009B_003E',\n",
       "       'B28009B_004E', 'B28009B_005E', 'B28009B_001E', 'B28009B_002E',\n",
       "       'B28009B_006E', 'B28009I_002E', 'B28009I_001E', 'B28009I_004E',\n",
       "       'B28009I_003E', 'B28009I_006E', 'B28009I_005E'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.rename_group(sub_dict).columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's discuss how to aggregate the columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As an example, we will sum up the values in the B28005_002E, B28005_003E, B28005_004E, B28005_005E, B28005_006E, and B28005_007E columns to create the 'Under 18 Years' column. Similar to `rename_group` attribute function, you need to provide a dictionary, for the `variables_dictionary` argument, that is constructed in the following way:   \n",
    "\n",
    "`{'group name' : {'new variable name 1' : [ list of suffices ] , 'new variable name 2' : [list of suffices] } }`\n",
    "\n",
    "Then, in our example, it will be `{'B28005' : {'Under 18 Years' : [ '002E', '002E', ... , '007E' ] } }`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "arg = {'B28005' : {'Under 18 Years' : ['002E', '003E','004E', '005E','006E','007E'] } }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>Under 18 Years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>1106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21001970300</td>\n",
       "      <td>Census Tract 9703</td>\n",
       "      <td>1842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21001970401</td>\n",
       "      <td>Census Tract 9704.01</td>\n",
       "      <td>3075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21001970402</td>\n",
       "      <td>Census Tract 9704.02</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>21239050106</td>\n",
       "      <td>Census Tract 501.06</td>\n",
       "      <td>1491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>21239050107</td>\n",
       "      <td>Census Tract 501.07</td>\n",
       "      <td>3441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>21239050200</td>\n",
       "      <td>Census Tract 502</td>\n",
       "      <td>1690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>1068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>1487.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1115 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             FIPS                 Tract  Under 18 Years\n",
       "0     21001970100  Census Tract 9701     708.0         \n",
       "1     21001970200  Census Tract 9702     1106.0        \n",
       "2     21001970300  Census Tract 9703     1842.0        \n",
       "3     21001970401  Census Tract 9704.01  3075.0        \n",
       "4     21001970402  Census Tract 9704.02  2015.0        \n",
       "...           ...                   ...     ...        \n",
       "1110  21239050106  Census Tract 501.06   1491.0        \n",
       "1111  21239050107  Census Tract 501.07   3441.0        \n",
       "1112  21239050200  Census Tract 502      1690.0        \n",
       "1113  21239050300  Census Tract 503      1068.0        \n",
       "1114  21239050400  Census Tract 504      1487.0        \n",
       "\n",
       "[1115 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.aggregate(arg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you set the inplace argument as `True`, then it will not only return the output dataframe, but also create the new columns in the `acs.acs_data` object. Another difference from the previous example is that it will contain all the pre-existing variables and the new variables will be placed at the end of the dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function can handle more than one group and more than one new variables. Let's take a look at the following example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "arg = {'B28005' : {'Under 18 Years' : ['002E', '003E','004E', '005E','006E','007E'], '18 to 64 Years' : ['008E', '009E','010E', '011E','012E','013E'] } ,\n",
    "      'B28009A' : {'Computer in the home (white)' : ['003E', '004E','005E'], 'No Computer in the home (white)' : ['006E'] } }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>Under 18 Years</th>\n",
       "      <th>18 to 64 Years</th>\n",
       "      <th>Computer in the home (white)</th>\n",
       "      <th>No Computer in the home (white)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>708.0</td>\n",
       "      <td>9270.0</td>\n",
       "      <td>4378.0</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>6879.0</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21001970300</td>\n",
       "      <td>Census Tract 9703</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>4905.0</td>\n",
       "      <td>2336.0</td>\n",
       "      <td>431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21001970401</td>\n",
       "      <td>Census Tract 9704.01</td>\n",
       "      <td>3075.0</td>\n",
       "      <td>4696.0</td>\n",
       "      <td>2172.0</td>\n",
       "      <td>229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21001970402</td>\n",
       "      <td>Census Tract 9704.02</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>5553.0</td>\n",
       "      <td>2308.0</td>\n",
       "      <td>302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>21239050106</td>\n",
       "      <td>Census Tract 501.06</td>\n",
       "      <td>1491.0</td>\n",
       "      <td>4580.0</td>\n",
       "      <td>2277.0</td>\n",
       "      <td>454.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>21239050107</td>\n",
       "      <td>Census Tract 501.07</td>\n",
       "      <td>3441.0</td>\n",
       "      <td>6188.0</td>\n",
       "      <td>2685.0</td>\n",
       "      <td>654.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>21239050200</td>\n",
       "      <td>Census Tract 502</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>1186.0</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>1068.0</td>\n",
       "      <td>6572.0</td>\n",
       "      <td>3193.0</td>\n",
       "      <td>479.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>1487.0</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>866.0</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1115 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             FIPS                 Tract  Under 18 Years  18 to 64 Years  \\\n",
       "0     21001970100  Census Tract 9701     708.0           9270.0           \n",
       "1     21001970200  Census Tract 9702     1106.0          6879.0           \n",
       "2     21001970300  Census Tract 9703     1842.0          4905.0           \n",
       "3     21001970401  Census Tract 9704.01  3075.0          4696.0           \n",
       "4     21001970402  Census Tract 9704.02  2015.0          5553.0           \n",
       "...           ...                   ...     ...             ...           \n",
       "1110  21239050106  Census Tract 501.06   1491.0          4580.0           \n",
       "1111  21239050107  Census Tract 501.07   3441.0          6188.0           \n",
       "1112  21239050200  Census Tract 502      1690.0          2398.0           \n",
       "1113  21239050300  Census Tract 503      1068.0          6572.0           \n",
       "1114  21239050400  Census Tract 504      1487.0          1635.0           \n",
       "\n",
       "      Computer in the home (white)  No Computer in the home (white)  \n",
       "0     4378.0                        340.0                            \n",
       "1     3496.0                        274.0                            \n",
       "2     2336.0                        431.0                            \n",
       "3     2172.0                        229.0                            \n",
       "4     2308.0                        302.0                            \n",
       "...      ...                          ...                            \n",
       "1110  2277.0                        454.0                            \n",
       "1111  2685.0                        654.0                            \n",
       "1112  1186.0                        130.0                            \n",
       "1113  3193.0                        479.0                            \n",
       "1114  866.0                         168.0                            \n",
       "\n",
       "[1115 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.aggregate(arg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to do the same with the suffix indices is to use **`iaggregate`** attribute function. Everything is the same, but the list of suffices is not just a list of integers. In our example, it would be `{'B28005' : {'Under 18 Years' :  np.arange(2, 8) } }`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>Under 18 Years_B28005</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>1106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21001970300</td>\n",
       "      <td>Census Tract 9703</td>\n",
       "      <td>1842.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21001970401</td>\n",
       "      <td>Census Tract 9704.01</td>\n",
       "      <td>3075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21001970402</td>\n",
       "      <td>Census Tract 9704.02</td>\n",
       "      <td>2015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>21239050106</td>\n",
       "      <td>Census Tract 501.06</td>\n",
       "      <td>1491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>21239050107</td>\n",
       "      <td>Census Tract 501.07</td>\n",
       "      <td>3441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>21239050200</td>\n",
       "      <td>Census Tract 502</td>\n",
       "      <td>1690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>1068.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>1487.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1115 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             FIPS                 Tract  Under 18 Years_B28005\n",
       "0     21001970100  Census Tract 9701     708.0                \n",
       "1     21001970200  Census Tract 9702     1106.0               \n",
       "2     21001970300  Census Tract 9703     1842.0               \n",
       "3     21001970401  Census Tract 9704.01  3075.0               \n",
       "4     21001970402  Census Tract 9704.02  2015.0               \n",
       "...           ...                   ...     ...               \n",
       "1110  21239050106  Census Tract 501.06   1491.0               \n",
       "1111  21239050107  Census Tract 501.07   3441.0               \n",
       "1112  21239050200  Census Tract 502      1690.0               \n",
       "1113  21239050300  Census Tract 503      1068.0               \n",
       "1114  21239050400  Census Tract 504      1487.0               \n",
       "\n",
       "[1115 rows x 3 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg = {'B28005' : {'Under 18 Years' :  np.arange(2, 8) } }\n",
    "acs.iaggregate(arg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the previous example, you can do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "arg = {'B28005' : {'Under 18 Years' :np.arange(2, 8), '18 to 64 Years' : np.arange(8, 14) } ,\n",
    "      'B28009A' : {'Computer in the home (white)' : np.arange(3, 6), 'No Computer in the home (white)' : [6] } }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>Under 18 Years_B28005</th>\n",
       "      <th>18 to 64 Years_B28005</th>\n",
       "      <th>Computer in the home (white)_B28009A</th>\n",
       "      <th>No Computer in the home (white)_B28009A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>708.0</td>\n",
       "      <td>3007.0</td>\n",
       "      <td>1381.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>3629.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21001970300</td>\n",
       "      <td>Census Tract 9703</td>\n",
       "      <td>1842.0</td>\n",
       "      <td>5647.0</td>\n",
       "      <td>2610.0</td>\n",
       "      <td>630.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21001970401</td>\n",
       "      <td>Census Tract 9704.01</td>\n",
       "      <td>3075.0</td>\n",
       "      <td>6522.0</td>\n",
       "      <td>3240.0</td>\n",
       "      <td>649.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21001970402</td>\n",
       "      <td>Census Tract 9704.02</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>5222.0</td>\n",
       "      <td>2532.0</td>\n",
       "      <td>452.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>21239050106</td>\n",
       "      <td>Census Tract 501.06</td>\n",
       "      <td>1491.0</td>\n",
       "      <td>5804.0</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>21239050107</td>\n",
       "      <td>Census Tract 501.07</td>\n",
       "      <td>3441.0</td>\n",
       "      <td>7577.0</td>\n",
       "      <td>3848.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>21239050200</td>\n",
       "      <td>Census Tract 502</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>6320.0</td>\n",
       "      <td>3298.0</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>1068.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>1733.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>1487.0</td>\n",
       "      <td>3879.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>180.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1115 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             FIPS                 Tract  Under 18 Years_B28005  \\\n",
       "0     21001970100  Census Tract 9701     708.0                   \n",
       "1     21001970200  Census Tract 9702     1106.0                  \n",
       "2     21001970300  Census Tract 9703     1842.0                  \n",
       "3     21001970401  Census Tract 9704.01  3075.0                  \n",
       "4     21001970402  Census Tract 9704.02  2015.0                  \n",
       "...           ...                   ...     ...                  \n",
       "1110  21239050106  Census Tract 501.06   1491.0                  \n",
       "1111  21239050107  Census Tract 501.07   3441.0                  \n",
       "1112  21239050200  Census Tract 502      1690.0                  \n",
       "1113  21239050300  Census Tract 503      1068.0                  \n",
       "1114  21239050400  Census Tract 504      1487.0                  \n",
       "\n",
       "      18 to 64 Years_B28005  Computer in the home (white)_B28009A  \\\n",
       "0     3007.0                 1381.0                                 \n",
       "1     3629.0                 1690.0                                 \n",
       "2     5647.0                 2610.0                                 \n",
       "3     6522.0                 3240.0                                 \n",
       "4     5222.0                 2532.0                                 \n",
       "...      ...                    ...                                 \n",
       "1110  5804.0                 3054.0                                 \n",
       "1111  7577.0                 3848.0                                 \n",
       "1112  6320.0                 3298.0                                 \n",
       "1113  3450.0                 1733.0                                 \n",
       "1114  3879.0                 1800.0                                 \n",
       "\n",
       "      No Computer in the home (white)_B28009A  \n",
       "0     204.0                                    \n",
       "1     142.0                                    \n",
       "2     630.0                                    \n",
       "3     649.0                                    \n",
       "4     452.0                                    \n",
       "...     ...                                    \n",
       "1110  56.0                                     \n",
       "1111  165.0                                    \n",
       "1112  248.0                                    \n",
       "1113  89.0                                     \n",
       "1114  180.0                                    \n",
       "\n",
       "[1115 rows x 6 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.iaggregate(arg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4-1. Slicing Tables given group names and variable index number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we move on to talk about creating a variable with a subset of variables, it is essential to discuss how to slice the acs data with given group name and their variable index numbers. As an example, you are interested in the values of 'B28005_001E', 'B28005_003E', 'B28005_005E', 'B28009A_001E', 'B28009A_001E',and 'B28009A_001E'. Simply, you can find the values by running the following line:   \n",
    "`acs.loc[:, ['B28005_001E', 'B28005_003E', 'B28005_005E', 'B28009A_001E', 'B28009A_001E', 'B28009A_001E'] ]`\n",
    "\n",
    "However, there is another function you might need to consider: `group_isel`. It takes the list of groupname and the variable index (or suffix) numbers.\n",
    "In our case, the group names are 'B28005' and 'B28009A'. The index numbers are 1, 3, and 5 for both. Let's see what you will get when you run the following code:   \n",
    "\n",
    "\n",
    "`acs.group_isel( ['B28005','B28009A'], [[1,3,5], [1,3,5]] )`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>B28005_001E</th>\n",
       "      <th>B28005_003E</th>\n",
       "      <th>B28005_005E</th>\n",
       "      <th>B28009A_001E</th>\n",
       "      <th>B28009A_003E</th>\n",
       "      <th>B28009A_005E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>1607</td>\n",
       "      <td>232</td>\n",
       "      <td>221</td>\n",
       "      <td>1585</td>\n",
       "      <td>48</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>1965</td>\n",
       "      <td>360</td>\n",
       "      <td>299</td>\n",
       "      <td>1832</td>\n",
       "      <td>0</td>\n",
       "      <td>287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21001970300</td>\n",
       "      <td>Census Tract 9703</td>\n",
       "      <td>3258</td>\n",
       "      <td>582</td>\n",
       "      <td>582</td>\n",
       "      <td>3240</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21001970401</td>\n",
       "      <td>Census Tract 9704.01</td>\n",
       "      <td>4251</td>\n",
       "      <td>957</td>\n",
       "      <td>745</td>\n",
       "      <td>3889</td>\n",
       "      <td>38</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21001970402</td>\n",
       "      <td>Census Tract 9704.02</td>\n",
       "      <td>3102</td>\n",
       "      <td>599</td>\n",
       "      <td>529</td>\n",
       "      <td>2984</td>\n",
       "      <td>22</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>21239050106</td>\n",
       "      <td>Census Tract 501.06</td>\n",
       "      <td>3324</td>\n",
       "      <td>497</td>\n",
       "      <td>424</td>\n",
       "      <td>3110</td>\n",
       "      <td>23</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>21239050107</td>\n",
       "      <td>Census Tract 501.07</td>\n",
       "      <td>4299</td>\n",
       "      <td>1147</td>\n",
       "      <td>1054</td>\n",
       "      <td>4013</td>\n",
       "      <td>31</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>21239050200</td>\n",
       "      <td>Census Tract 502</td>\n",
       "      <td>3662</td>\n",
       "      <td>558</td>\n",
       "      <td>468</td>\n",
       "      <td>3546</td>\n",
       "      <td>9</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>1888</td>\n",
       "      <td>356</td>\n",
       "      <td>272</td>\n",
       "      <td>1822</td>\n",
       "      <td>0</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>2235</td>\n",
       "      <td>455</td>\n",
       "      <td>455</td>\n",
       "      <td>1980</td>\n",
       "      <td>20</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1115 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             FIPS                 Tract B28005_001E B28005_003E B28005_005E  \\\n",
       "0     21001970100  Census Tract 9701     1607        232         221          \n",
       "1     21001970200  Census Tract 9702     1965        360         299          \n",
       "2     21001970300  Census Tract 9703     3258        582         582          \n",
       "3     21001970401  Census Tract 9704.01  4251        957         745          \n",
       "4     21001970402  Census Tract 9704.02  3102        599         529          \n",
       "...           ...                   ...   ...        ...         ...          \n",
       "1110  21239050106  Census Tract 501.06   3324        497         424          \n",
       "1111  21239050107  Census Tract 501.07   4299        1147        1054         \n",
       "1112  21239050200  Census Tract 502      3662        558         468          \n",
       "1113  21239050300  Census Tract 503      1888        356         272          \n",
       "1114  21239050400  Census Tract 504      2235        455         455          \n",
       "\n",
       "     B28009A_001E B28009A_003E B28009A_005E  \n",
       "0     1585         48           148          \n",
       "1     1832         0            287          \n",
       "2     3240         0            91           \n",
       "3     3889         38           517          \n",
       "4     2984         22           394          \n",
       "...    ...         ..           ...          \n",
       "1110  3110         23           106          \n",
       "1111  4013         31           240          \n",
       "1112  3546         9            254          \n",
       "1113  1822         0            197          \n",
       "1114  1980         20           110          \n",
       "\n",
       "[1115 rows x 8 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.group_isel( ['B28005','B28009A'], \n",
    "                     [[1,3,5], [1,3,5]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It has another argument, `stack`, and its default value is `False`. When you set this `True`, what happens is they stack the dataframe in a way that the `Variable` column contain the column names in the original data frame and `Values` column contain the corresponding values. Let's see what you actually get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>Variable</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>B28005_001E</td>\n",
       "      <td>1607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>B28005_003E</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>B28005_005E</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>B28009A_001E</td>\n",
       "      <td>1585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>B28009A_003E</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6685</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>B28005_003E</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6686</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>B28005_005E</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6687</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>B28009A_001E</td>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6688</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>B28009A_003E</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6689</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>B28009A_005E</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6690 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             FIPS              Tract      Variable Values\n",
       "0     21001970100  Census Tract 9701  B28005_001E   1607 \n",
       "1     21001970100  Census Tract 9701  B28005_003E   232  \n",
       "2     21001970100  Census Tract 9701  B28005_005E   221  \n",
       "3     21001970100  Census Tract 9701  B28009A_001E  1585 \n",
       "4     21001970100  Census Tract 9701  B28009A_003E  48   \n",
       "...           ...                ...           ...  ..   \n",
       "6685  21239050400  Census Tract 504   B28005_003E   455  \n",
       "6686  21239050400  Census Tract 504   B28005_005E   455  \n",
       "6687  21239050400  Census Tract 504   B28009A_001E  1980 \n",
       "6688  21239050400  Census Tract 504   B28009A_003E  20   \n",
       "6689  21239050400  Census Tract 504   B28009A_005E  110  \n",
       "\n",
       "[6690 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.group_isel( ['B28005','B28009A'], \n",
    "                     [[1,3,5], [1,3,5]], stack = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4-2. Subgrouping and aggregate by the subgroups (`acs_computer_broadband` example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is basically an extension of the **`isel`** with the stack argument **`True`**. Let's see how it was done in R below."
   ]
  },
  {
   "attachments": {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:3b5f896d-6cd9-4ea5-9098-3c931b391738.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And below is the result of the transformation."
   ]
  },
  {
   "attachments": {
    "ef0735d9-5c79-445f-9b2f-ec6770ac49bf.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:ef0735d9-5c79-445f-9b2f-ec6770ac49bf.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In short, the lines executed created the three variables, **`age`, `comp`, and `status`** based on the subgrouping."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do the same within the acs module, we can utilize the **`gen_subgroups`** attribute function. This takes two main arguments with one optional argument. The main arguments are **`new_variables`** and **`groups`**. The datatype for an input for new_variables is a dictionary with the keys equal to the new variable names and with the values of another dictionary, which contains values of the new variable and the \"suffix numbers\" that defines this values. Then, the input for the **`new_variables`** in our case will be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>County</th>\n",
       "      <th>age</th>\n",
       "      <th>comp</th>\n",
       "      <th>status</th>\n",
       "      <th>B28005</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Under 18 Years</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Under 18 Years</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Under 18 Years</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>18 to 64 Years</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>804.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>18 to 64 Years</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10030</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>18 to 64 Years</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10031</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>18 to 64 Years</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10032</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>65 Years and Over</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10033</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>65 Years and Over</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10034</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>65 Years and Over</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10035 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              FIPS              Tract            County                age  \\\n",
       "0      21001970100  Census Tract 9701   Adair County     Under 18 Years      \n",
       "1      21001970100  Census Tract 9701   Adair County     Under 18 Years      \n",
       "2      21001970100  Census Tract 9701   Adair County     Under 18 Years      \n",
       "3      21001970100  Census Tract 9701   Adair County     18 to 64 Years      \n",
       "4      21001970100  Census Tract 9701   Adair County     18 to 64 Years      \n",
       "...            ...                ...            ...                ...      \n",
       "10030  21239050400  Census Tract 504    Woodford County  18 to 64 Years      \n",
       "10031  21239050400  Census Tract 504    Woodford County  18 to 64 Years      \n",
       "10032  21239050400  Census Tract 504    Woodford County  65 Years and Over   \n",
       "10033  21239050400  Census Tract 504    Woodford County  65 Years and Over   \n",
       "10034  21239050400  Census Tract 504    Woodford County  65 Years and Over   \n",
       "\n",
       "                          comp               status  B28005  \n",
       "0      Computer in the Home     Broadband Access     221.0   \n",
       "1      Computer in the Home     No Broadband Access  11.0    \n",
       "2      No Computer in the Home  No Broadband Access  6.0     \n",
       "3      Computer in the Home     Broadband Access     804.0   \n",
       "4      Computer in the Home     No Broadband Access  163.0   \n",
       "...                     ...                     ...    ...   \n",
       "10030  Computer in the Home     No Broadband Access  62.0    \n",
       "10031  No Computer in the Home  No Broadband Access  69.0    \n",
       "10032  Computer in the Home     Broadband Access     255.0   \n",
       "10033  Computer in the Home     No Broadband Access  68.0    \n",
       "10034  No Computer in the Home  No Broadband Access  80.0    \n",
       "\n",
       "[10035 rows x 7 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg1 = {'age': {'Under 18 Years': np.arange(2, 8)    , '18 to 64 Years':  np.arange(8, 14)   , '65 Years and Over':  np.arange(14, 20)   }   , \n",
    "        'comp':  {'Computer in the Home': np.concatenate([np.arange(4, 7), np.arange(10, 13), np.arange(16, 19)]), 'No Computer in the Home': np.array([7,13,19])     }   ,\n",
    "        'status':  {'Broadband Access': np.array([5, 11, 17])       , 'No Broadband Access': np.array([4,6,7,10,12, 13, 16, 18,19])   } }\n",
    "acs.gen_subgroups(arg1, groups =['B28005'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's do another example. Since other groups 'B28009A', 'B28009B' do not contain information about age, we only can create two new variables; **`comp`** and **`status`**. Since the suffix numbers are not matching between B28005 and the other, we should use gen_subgroups twice and **merge** the two tables by the **`comp`** and **`status`**. Below is the R code for B28009A"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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wdBwBiI1xnJALBAYIlCMBA5kPl8SBvllLkQSPCLKtNBuH+m2lJ+AHCIDAfghAbOynr7boKcTGFnsFPu2MwGuLjZ11Frvr18ngzI099h18BgEQWJ8AxMb6zI9UI8TGkXoTbXkSAYiNJ4FHtSAAAiAAAisQgNhYAfKBq4DYOHDnomkgAAIgAAIgAAIg8CgBiI1HCb52eYiN1+5/tB4EQAAEQAAEQAAEBglAbAziQWKHAMRGBxCSQWBNAnbXpnF196Zx+fS0aDttOTvO/oy5/v6X+8f7sxt3MvqM9R7NFJ0ZknG8uZ+//dJ99ZH2MPbXX/zz96O1etH23D5+7b749hfHBOn6m+/dddEaYRwE9kMAYmM/fbVFTyE2ttgr8OllCSwjNhROdVihevrgJZ1fcebg992Hz8kWC4uf3Kf0xLm7xMYE+7quvV1nW/u2BeGnD8TacA0Cw4sN567//NJNEhu3X9xX33zpvoh/KoH2H9+rdBE2GnIQOcHGd3/oNLqeli5tsVbuvx+uX4sNx22tMLi/cpQEgV0TgNjYdfc93XmIjad3ARwAgUcJ9EY2lP0FxAYFv36kgraVlR2eSCDYgFj5MeFyafsTXBnMykJRnR0ymNkmstBI2wQ3RSePaNS5ksCQAJ8CZx2scyAdhYQXFZKXXbn9nh3syGIl/MrvXf3dfffN1+5nOfyRxYm6N2LHBfGS6vCBfvRpcnoAFspFO5Zj875Xv3MsMGQ0iOrJ2t80jAQQeAkCEBsv0c2LNRJiYzG0MHwUArUD4+hXWjoF+yLBlxySF8+b0L9MixigMnKwnirLtuT5yZ1qAeso+3q6lK5f9cSA2PABrvjRKK9MyWVNDHz6IKLD5/JbzYbRj/d5GuXgX+s/fHZ5Ph9Uj7EvvrQ+pQ/1mSByIKAvo9l5BqPTi74JDIt+zOvQJ8wz+6xC/65kj5w/I+SeKWjZr/bU4N4v9zadg3wtLkh8qF/+OV3d0zgGTUXSwbvOPzbdBPwsor69Y4pTz7/Wi4PnIAACTABiAy/CIwQgNh6hh7KvQcD86syNpgBTR4K3G8/19kB8UJmCSQkyRWAYoaIpkt0iSKXIbIz9JBAkuNamo9/6ZPGQwf6S3ixfGKQH+TQnEgz/+O2vak6ftyE2aCpWnIblA2tvZ4r9erUiMmKXFaLrRojjf5xf9cMYHsxQlYnG+MK8E9k75YVFel98Saoze1ablpZX0ryzYiMTApVStWlYXOYbGj0hoZFGUai4tc8mSbDoNRBGOLDgkWctMZAJFBqdIMHjRynSqEmlAeZRzz+THbcgAAKGAMSGAYLbSQQgNibhQubXJFAGg9ezCIcaEZvfB5ox0K0VkWctsSHp/DnCfhFMBwPV59aec66aL3OifkMBcRQMtSwkHBpiw5ST0Y7MStd+ljvelGJhuE+scPBibKjPnbNlYuV0UfDUzPV1KlX4PDCFKpWqX4lQkDUZg9OQ7KiGmOTnsq5Dj3IMiI0gFprBfhQTZpqT84ImW6RN9YeRkqo98bPyWc3famelPB6BwKsTgNh49TfgsfZDbDzGD6VfhQAFi/KrNf0qLdex/T5gTDs+6V+lhwPbaIIudD1ZwkT7RXAbjFWfe/+07/46jZRkrjRvwjqNsAicdksqRzgeERtj7NedKwJ3V/aJFxQyjaw2nU31QdH/w2KjsB2m2/mRi7bYyEY2HhUbMopArxktII+BfmJWDcop2Yw8cD61hqNajoL5sSMb7IIIjOCbKk/Jek2K9ycXPKkV5VXPv7IEnoAACGgCEBuaBq6nEoDYmEoM+V+UAAWn/pdtChzzUYoQhMaHNngsA9smxKrYmG6/+St7VWxYf5veDSSkxeG1NRap4L1iY6z9VJO+KsQGT2M6xX70YiCJqyY/Nmr7w9c0WKbKPXnIZeP7Q899HdmjGadRuTBykI1wmOA+eVcbuTAjEUaMcAumrNnQlYXrbCoX25dRlfSZ+V+xER/1/IsZcQECIFAjALFRo4JnYwlAbIwlhXwvT4ACwrfLxZ2LNQ9WTPj79Ku0TR9AWRUbtnzdflpDYtNVfY2gl4Pdol2qXOdSr9NYQmyMt193lMWGah/fq9EJK0by9Ju7XtWCDh6AOuVrdqjaat+JPwN9QlmyNRxhlET5663MuEA8BO9x3QPfD4wU8JQjnW7XbdTEh17X0UsXTuHT1EcjE4Ww4Dz5onRjRd1OrF+VzC6DSE3ftSwVNyBwWAIQG4ft2lUaBrGxCmZUcggCIdBIIkK1ioN4mYJzdle555+mrVhQ5exlK2AVezz9pmb/zV0uZxenQmU/iatK2E76BV+l+DUHcTctaks9ny5D17yDlF5vMfM0qmn2rXf+3ouJszvH9tm2+T4Sfuer3FO+MJIRy9amWDEJtdtYjZ/YlPdET7UTwSFp1r/QrjunUvlpT2lEgNduyE5RVDUfYmfS1bQrbp3JE4VKcG3qORq2vJ/aJT5YYaPvpUIveAoRIsnFpxccsm7F1l9krz2A2KhRwbMXIACx8QKdvGATITYWhAvTIAAC2yBgRy624dV9XvDC+ZnOMLnPg42VakyxElEhi8rn8tqPcOYjXXPZhh0Q2CoBiI2t9sw+/ILY2Ec/wUsQAIEHCBxJbDAGHuGQwxQfAIOi0wjwyEZj1GmaJeQGgV0RgNjYVXdtzlmIjc11CRwCARCYm8DhxMbcgGAPBEAABAYIQGwMwEFSlwDERhcRMoAACIAACIAACIDA6xKA2Hjdvp+j5RAbc1CEDRAAARAAARAAARA4KAGIjYN27ErNgthYCTSqAQEQAIHNECjWfOitYcOuTWq3qjn81gfr+d2vxm5bO672vdt3eitfs/XvOAKdXIP26fybs3und5XLzPXSs8wL3ejtlqfuRLaQS7s0ex+7h8VGb3vtXbI0Tg9+x0zeF7uF2HixDkdzQQAEXptAfTcrLTbCCeNWbGS7PpVCwW6fa7ek1WIgC6xn6o6+/XFb3/p2NNon2wHroGIm/zMmjcBMbw9s+cpBjXEXrm/0OSfOjbFffzdSA3vpKecSV1ps5O/rErUd1ybExmJ9q/9eaHyHF6t744YhNjbeQXAPBEAABGYjMHBOBwWycvYEBdxZMGv+4WwF5NFP/kfXnI1Bz0TAkD0J3GOhBy8G7ZvglNuT2htrDs+/+KYUGxysL+l/xoQCwtyH7ET12gnw/CwvE9tFFx37kpcFRXOEw7leutiZ/5P6ML1T+n2VurQYY9E19zsmFd35mQniO208q9iokQ3+/qQ+ynwdSssy3nlTsb8675HfsTtbuOtiEBu77j44DwIgAAJCoDw0MD/b8cETyCXQ5up88C7iRDxInxQsN4KOlGm9Kw5E8kCcA5Famz7+4r4ygf56jrZqqvAkcZUF06VAaVkbfk5Tpn5wv/7dytVLb5Vb8rl/H3MeJLBuTp+IkosR/X4Gvh+/dyRSSGh7QZ0EqYg9bUO//0Vgq985Ft9yYKX6zPqP+JAfKV0L/mhf28re3w5fXc6OesnBnuRPli8xGhYbud9pdC2VZ7HLfyfkeXUbqQWab9GfQ00cIzY4T+Ibf/yQ9v/zF/fzt5ROf1cEP00f3e3fkO8vkAax8QKdjCaCAAgcnUAQGrm6yBvNJ7v/5D7lT0fcmVGBUIL+0bWBghgrA3lJec5nDNR09RRUqUAi+qyDRJ3/mdccAOZiyQdv+pkO4lSQN9lvL0r/8dtfjZK99EaxJR/X+Jj6incgC04DOwreQ7AdBUcI6CXIjALD1Fm3r/vHeQGj3rncRe9D/E5l/oWyJEREYFD9cp0b6tzVfyhg/8m+8o/bHOoYFhuhSuNz5ginUSCv3k3DkOuL9df/3sls6ptK3XmfeL6x/0TYhPb59pNvQbhGwZH8fcg/7esLXkNsvGCno8kgAAIHI3A9u9Opc9jcwBSqYRr1f/R1IOLLyz/SecAybHud1DzoCHXqQIcDlRAY6ut13OvXon2V3JXgSpJ8wJyCpPh85EVvqlQvfWQ1s2Ur30VruvYO62cUiAZexDUEvPzehGC0rCMPXot3rPIeFXm0m0Ufa/96QkUb6l17uzrophI13/SzecSGeSezdzjn2fKp2Tq2pUYtZIRIxEvBN1/HxG1Vfe35qPcijHRobppP0y8kMAGIDbwIIAACILBzArfLmzu9XbIpI0WTFhAb8VdYWxn/w24CC5tnxftqUEA+ciBigq9KkLiiq/WqaoFSx08Kjpv9U68lPu2JiV56NBQvwm5WtOPVe33yPY2SDE3ZigYGL0ohYLOXgaxzvt/96IAKKonrE8QGv6MSIKtP6cPqO2ybOerevO+hTM2+fra42OiJhV7buHz+d472X19HU+rvKU4fEhuP+hcrfc0LiI3X7He0GgRA4EgExoxs3D2NKvzqGf4h9tjqAUtC6tMlUErPn3TFgUJlSgu1qRVEVOa1P8n7MJc/D6R49EJ+ta041g/AK4X4UW+aVC+9tEvi5Mc/5XkuPNrTtSR//5MDRRmZqGavvY/62R1iI7w38kt3Ecy23rlWn9UEpWpLYV+lTbusf3dr9vWzxcVGZeRgUruYd/4d0f770b787wD9jPMOiY1H/ZvUmONlhtg4Xp+iRSAAAi9HIKzZGBzd8EFiCvomQDL/kPM/zEOLqDlwSotrR9d0u7i308mdhtaejDamM+rAkuaM0CLwhn+VIFFbGr4e0w/DFlqpuXjwv9Q3xdy9/Lny3gLwXnqrBUs+9/1b3UUsVJsFnvQse6dHig31znN/KOGQ2/f9U/hD/aLK5ESG+zS3n5ecdreg2OCAPA/4o28Z7/DUPLNMY9kxF8YWFcmZeb4iDildf6c476DYCPmb/TfGSa7VnenvuMG/q8fa2k8+iI399BU8BQEQAIEBAuVuVG8XvRePc+7uqVQSnMmcaPMLofzDraZ/6H/UB5zOkxYTG1SNBKS+DU3/OGgp25c72rpbTmxQjRwcBcaF0GC/pX8aQqrltnremyLVS1emVr/UfPyOSHk/5uk6KB4pNv75vdotKred7yT1vbtW36P8HSzESPj1PO3mlKbC5YHzI2iXFBshwK/9PVARA7ng823K+2jC+q+K/YKZ+Y7oBfZjxAZ5eLd/scuW/TsiVrOxC4iNjXUI3AEBEACBJQlwsPj+nl2plvQq2ab1J4VISsnbv6IpbbOPzKzT7N670Utfx8vn1MJBZjaV8Dl+PKvWUdOonuXc3urd8d8R96KG2LiXHMqBAAiAwF4J8AiHnke/kYbwyEZnV62NuFp3g361fHN2QKmed0tPwzqK5mF+vfQttWUZXyA2WlshL8P7uFb3+nfEYz0CsfEYP5QGARAAARAAARA4OAGIDYiNg7/iizYPYmNRvDAOAiAAAiAAAiAAAvsmgGlU++6/Z3sPsfHsHkD9IAACIAACIAACILBhAhAbG+6cHbgGsbGDToKLr0OAD2frnQRd4PC7W7TXpIbdL2i7Pf7zxDnxfNbDBtcKFEw3/qBYc+F3mPE7FIUdb154Mes9vad3ruFrtc3pPfZQBgSORABi40i9uX5bIDbWZ44aQaBJYBmxoaobc/ibyj7uMiwepdOB9QLT2iFyd4mNCfbHObzNXLLta0cQ1ncE0mIj3z9+VGPtlpC1QDuc3SDbchZbr3a3lg0iKGyLWW49m6eX9ke1ZCBTbt/Wr8WGPuxrwCCSQOBlCEBsvExXL9JQiI1FsMIoCKxJoDeyoXxZQGxQ8OsPiqND435wv/5N9ZFAmGd71aXtKzoPXbJQvPegJhYaaRejpugcOCeDFrBKAE2Bsw7W/S/16QwGEgySlxt9+91d1ZEcvBg2O7xKnUNABYo97XOxUx6a92h66JoginTbxnVar37nWGDIaBDVk7V/XC3IBQJHJQCxcdSeXaddEBvrcEYtOyZwPddONL65y1sKDh0H8TJNiT71VCURA1RG8qiyTj9vnCw6yr6eLqXrV/AHxIYPcMW/RnllSi5rYuDTBxEdPtfttx/cOxr54D95Gs4FkR4AACAASURBVOXgX+s/fHZ5Pi9WxtgXX1qf0of8GaaT5dPONDvPYHR60TeBYSE88jr0WRLMPqvQvxPZI3f/CeDZr/YEiUcp7KFkip5NL8QFiQ9VntPVvZzeq4N3nX9sugn4WUR9+7UrD0NTvtcue/7VyuAZCIBAJACxEVHg4g4CEBt3QEORFyNgfnXm1lOAqSPB282lH4Z9UJmCSQkyRWAYoaJxkt0iSKXIbIz9JBAkuNamo9+ZEPI57C/pzfKFQXqQT3MiwfCP31rbJFLehtjIpmH5wNrbmWK/6qATkRG7rBBdN0Ic/+P8qh/G8GCGqkw0xhfmncjeKS8s0vviS1Kd2bPatLS8kuadFRt8L0KgUqq2zSeX4REREhr5yIi1zyZJsASx0Ev3IyEVsZIJFBqdoFOf/ShFNjJTaYN+1K1fZ8Y1CIBAQQBio0CCBxMIQGxMgIWsr0qgDAavZxEONSY2vw80Y6BbKyLPWmJD0vlzhP0imA4Gqs+tPef8SE0SL1n1QzcUEOt1G0XeAbFhysloR2aiaz/LHW9KsTDcJ1Y4eDE21OfO2TKxcroouGvm+jqVKnwemEKVStWvRCi011uocnZUQ5L4uUzFoqBfEpxrBvNBLPTSXVjvkaZHeUGTjWBQ/UEgVe0ld4qrav5WO4vSeAACIACxgXfgEQIQG4/QQ9nXIaBFAP0qXfyC7QNGv9uTn0aTfpUeDmwziLqeLGGi/SK4Dcaqz71/2vf7dq0iIfGT+xQWgdOUqXKE4xGxMcZ+Bi3eFIG7K/vECwqZRlabzqb6oOj/YbFR2A5Tufw70hYb6R1yzj0qNtSUJB65yEYNPKpqUE5JZhoS5/smCY5qOQrmx45scPUiMEjQfO+uqjwl6zUp5ZoR73/r/z3/WuXwHARAwBOA2MCb8AgBiI1H6KHsCxGg4NT/sk2BYz5KEYLQ+NAGj2Vg2wRXFRvT7Td/Za+KDetv07uBhLQ4vLbGIhW8V2yMtZ9q0leF2OBpTKfYj14MpJGcJj82avvD1zRYpso9echl4/tDz30d2aMZp1E55wP7NJIQ1nEoQZK8q41c1BZcV6ZByVQtI1a4hR+/jiMVui65zqZycXkZVUmfmf9SsPZ5R/01M3gGAq9KAGLjVXt+nnZDbMzDEVZegAAFhG+XizsXax6smPD36Vdpmz4Aqyo2bPm6/bSGxKar+hpBLwe7RbtUuc6lXqexhNgYb7/uKIsN1T6+V6MTVozk6Td31Vs18ayoyqYB1b4Tfwb6hLJkazjCKIny11uZcYF4CN7juge+TyMV4nX85ClHOt2u26iJD72uo5cea/IXpj4amSiEBefJBY6xom4n1q9KZpdBpKbvWpaKGxA4LAGIjcN27SoNg9hYBTMqOQSBEGgkEaFaxUG8TME5u6vc80/TViyocvayFbCKPTmDQe6j/Td3uZzDoX2VQFjq4XLpF3x5TJ9ecEgb6LOeT5fhcrTTlF5vMfM0Kt6harR9652/92Li7M5h+lLZNt9HMpXsfJV7YhBGMmLZ2hQrJqF2G6vxE5uJcfYuSSArfVxryp1Tqfy0pzQiwGs3ZNSB+p5GGcL5F/HTjHLYPFGoRD99QC/lp6b7qV3ioxU2+l4qrIzOSFL1s+dftVD+UPooG3LKs+AOBI5IAGLjiL26XpsgNtZjjZpAAASeRMCOXDzJjVmq5YXzM51hMotDzzbSmGIlokcWlc/lph/hVKvj5zIMOyCwYQIQGxvunB24BrGxg06CiyAAAo8ROJLYYBI8wiGHKT7GBqUnEOCRjXEjfhOsIisIbJ4AxMbmu2jTDkJsbLp74BwIgMAcBA4nNuaAAhsgAAIgMJIAxMZIUMhWJQCxUcWChyAAAiAAAiAAAiAAAkQAYgPvwSMEIDYeoYeyIAACIAACIAACIHBwAhAbB+/ghZsHsbEwYJgHARAAgc0RKNZ86K1hw65NareqOfzn3az0IX/6UMGwg1l5CGSouZc+h4MdG4P+d8oieUUCdlvnlarW78dKVa5azV9/fXY/f/tl2IK68nfEBr6jqwJBZZMIQGxMwoXMIAACILBvAvXdrLTY8Kd1F7s4Zbs+Nc634LMvZPtaCUw8rywYq56R4c8Refcf/3L1vZ566cv2S9//ZeuH9ZEEIDZGgpqWLRcbjb8j3HO/o9NahNxrEoDYWJM26gIBEACBZxIYOKeDzrmQszEosM4O0eMALp11wYG3HpmgNpmD+IpmUrqMlpA9c46Hz++DleYIRwhm2ulFrfM9GPS/dubH7+67ahvvdcnXEbf0/SYXc/danbNcdur7nIY3YmuofZkYXcDf0n7+A8ECVWYmaRrV4N8RMXfvOxwz4uKFCEBsvFBno6kgAAJHJlAeGpifPeeDgB//nM6AAx0RClzcBzoiTpybMfDh6Rg/uU8tN3vprXKLPhchoEd8rNiQPGHkZ4oQCb/WZwKQDmO86TGgAfsklL79xf38T6qbRKPvry+iYPRlv/tD2zBt+SaJUUJJgaf448VnGtGKgih7Z8jhX9xX6vDI9P6kX8q1LbE/put0Od/GvJQIhTyfbmOeX9/lZVQ7Vfs4D/Upi27JkwS6t6f5Jn66rtZ1tB8zlN8562fiG76vHwN/8jv4mTNu+zdpzcYmv6MRHC6eQABi4wnQUSUIgAAIzEsgCI1cXeRV3B0AlEENGdbBpnMUpHztfv5DBZNTgunM08/ux/dDZ4j00jNjK9349n/17Zdp9IaYRAYlwzJ4bLsqgXI7R8d+CCwp+GRbSnD4gFSCzBR853X69BS82v73nuVljLdBaEQb7JOpT43WEJ88EDb2mrfhXdQ6LLyvLIKiQCiZNU2GhKH2xUA/9nkSUL64ZxjbxDysGGl7UL4vxn/D038nRSD6vCQ4byL4RHBEf4f9myQ23Ba/o222SFmeAMTG8oxRAwiAAAgsS+B6dqdT57C5gSlUw86ZoCZkzgIvCWBi4BKCmxjYDdeQp/amYfTSc2vr3FGg9r27ZgG1Ehu1wLL2rOqsDwJjkF7LU7Oln1EgGvomBfG+j7TYyOrIgtfSB+r/GDgHn7J3wvhZBsu5zaGyxlTnluyWQXzNfu3ZkPGh/GX7nMueZTyplvr3qlU/21KjQjJ6JH1Q8y09s30dRJ56L/xIRxJ/1r9pYmOL39EWWTxfgwDExhqUUQcIgAAILEjgdnlzp7dLY2F1qHgBsSGBjkyPaQerUxrfC1R66WVdt99+cO/en/0fvQCdmHz4XBaY/IQCXB+opQBTiY0i0ExTijJm1XrzoLyapWdfBZXkn+83G4DKr+Chhsxm6QMFsrH/Q5EU3JZeclolWJb2D5UtrQ092abY6ImFoRZRWnqvJKcWK/pa0sPICotM29el2Oj5B7GRuOJqOgGIjenMUAIEQAAEtkVgzMjG3dOoQqCTjVLo4IVQlMEo/1IaRzqm4OpNweilm7qo3UpgZMLj/cDaEGNm+JbaL78KS+D3S5pGpUcZxFDtmaRln96ejExkSXJTs6Wf3SE2fPApbSr7d6rYKINlcd5/Hl1slCMHeft7dyU/ec/8fLEav/RMf1/Vu6rei55/08TGxO9or/FI3z0BiI3ddyEaAAIgAAJhzcbg6IYfEbhngbgfuUhTU/JA1NPnwCaKizwQmtQ/PVHUS59U2VyZVQBHJjnQ/zJOXbJTUjjLx69VescPsZcJPl2m5J0FpyqopOetkY24W1gQj2nkIhcbvv/LkQ1+3vIxtEFGMrT3dJ0CY5sy9Z58Te+qlK7Zrz2T/LXPofZxWnz/fen8mWeYmNZqaD/LbVE+0+fZSBSl6z4bITaKPs99mSQ2hr6jt4t7O53caWh9WV417g5AAGLjAJ2IJoAACICAc+VuVG8Xs0r27qlUKoDmqTDyi3fOnYM3mSrTCjrzIuauN0Wql27MrXZLgV3OpAwOffAnc+0HRyqqfpvyxDljbNJ14DtSbHz3z+9d9C+zHUa3Qt9SwEx9XQbOAz5Qm0Q0yTuidriaGvhXEfFD8mEZsSEBfGSkGJf9XZv6ZPioBfHt9viU0r4RG4SXBGyFrQgTL/TIh/CuqvfC19L2b7zY6HxHITZ6XX3IdIiNQ3YrGgUCIAACdQL1Q/3qedd96oOUrR7qty6LtWvzQWZr1GFtb1Df9giMExu977BvF60xK34I2V6T4dGMBCA2ZoQJUyAAAiCwCwI8wjG0vezKreBpF2fXPKyvl76yu8erDmLjeH06b4u6YmPsd5RHNjo7583rOqxtgADExgY6AS6AAAiAAAiAwPMIQGw8j/0+au6KjX00A14+iQDExpPAo1oQAAEQAAEQAAEQ2AMBiI099NJ2fYTY2G7fwDMQAAEQAAEQAAEQeDoBiI2nd8GuHYDY2HX3wXkQAAEicD2vu5WiP6vhB/fr3+APAiAAAscnALFx/D5esoUQG0vShW0QAIFVCKwnNsbttrJKo1EJCIAACKxEAGJjJdAHrQZi46Adi2ZNJMA7ZLw5eyzBeCv+jAOcUzSe2Jw51xIbPKKhTqOesw2wBQIgAAJbJQCxsdWe2YdfEBv76Cd4+SAB2tf7RKeWyp940nJ5EJrPkwuPdvmbu7wpu2L/dHJJeJRChILjbJ/x6zn5xjbW3xqw3Uaep9Txr+SY2h86Tw5zCoyK9EYfs1/VzFSn5yRiI2+DYThQ/6jy7rP78T2mTjW6CY9BAAQOTABi48Cdu0LTIDZWgIwqnkxgzKhFJ8/tpk5iDkFrHv+WgiK1ukzLxUaZnsquc+WD9FxgZTXfbi4R8P5qsSTBelZG31hmLK6MGND59TXlDeLQ+xnKkc3wnOsnERM7xYvA6GOn/m558ofOpsCohu4ZXIMACLwIAYiNF+nohZoJsbEQWJjdEgEfHKdAtOIbB6MDwXZWpCYOas+kUJmWi40wOhJHW6TcuE+/WPns3r0v//z45xgbJjDvFinz98QKp2ftK5k0q+W+kRGMN/f2FkaNSIQEcVETO/pZr36dV/ywzzCFSsjgEwRA4NUIQGy8Wo/P216IjXl5wtqWCaipSvEHcPG3KzZ8cBynYWXTpMjIUPBcplEgG391z3zwU7KKNMmzyGfpX1lNEERqmljpo8qTCYuwW5QqKxyLfigrDmxJCF7dmexez8yOBISUt8KAzOhnfD1Qv84rLthnEBtCBp8gAAKvRgBi49V6fN72QmzMyxPW9kCARYcZxRgUGz4YT8F1LTivPRMYZRoFssme5Auf7Ite82HSze3yIxtBREhk78qRjdwlm9+5cmQhLzF85+2dLxd3phX8xOft4i7n1IdWGJA9/axXv84rvhTPMI1K0OATBEDgxQhAbLxYh8/cXIiNmYHC3AYJXK/uqt2qCgsSBCl41dk5uNVpVTEQAuKsIrGSiw0OfE9KbNyu7poWRHRGScTmvJ/iU9QTmfncfxnFSWLp5q55A7JAn01VmWWVDN5w4B9Hk4g1TadKaz4KYWDEhu/DtoDrlmfvsEB8sJOQCAIgcFgCEBuH7dpVGgaxsQpmVPJUAmr61ND0HQm4a3mytLeLu4bdrVLAzdFttmOTTtPl6TkFtzE9BOJSL33GtBXBaR+9LymYdxnDs7vKPauTMJKhpymZaVTcjEo76+KmbLT3LfnD4kDVMUosDNQ/qrxzDlOpyr7BExAAgeMTgNg4fh8v2UKIjSXpwjYIgECXAAf6Wqhk143Rpq7VpTLgUL+lyMIuCIDAdglAbGy3b/bgGcTGHnoJPoIACGyKgF8ngzM3NtUpcAYEQGAxAhAbi6F9CcMQGy/RzWgkCIAACIAACIAACNxHAGLjPm4o5QlAbOBNAAEQAAEQAAEQAAEQaBKA2GiiQcIIAhAbIyAhCwiAAAiAAAiAAAi8KgGIjVft+XnaDbExD0dYAYFZCNhdl8YZtVvT2lI+Pe12lXZ1sjkXv//7X+4f789u3Mnmi3uz3wrozI+M4839/O2X7quPtIeyv/7in7/vt31P8Pz28Wv3xbe/OCZI1998n2+Z/QSfUCUIbIUAxMZWemKffkBs7LPf4PVBCSwjNhQs3rJ2brFB50+cOfh99+FzqoyFxU/uU3ri3F1iY4J9XdferrOtedt99OkDsTZcg8DwYsO56z+/dJPExu0X99U3X7ov4p9KoP3H9ypdhI2GHEROsPHdHzqNrqelS1uslfvvh+vXYsNxWysM7q8cJUFg1wQgNnbdfU93HmLj6V0AB0DgUQK9kQ1lfwGxQcGvH6mgbWFlhyYSCDYgVn5MuFza/gRXBrOyUFRnfwxmtoksNNI2v03RySMada4kMCTAp8BZB+scSEch4UWF5GVXbr9nB0uyWAm/8ntXf3ffffO1+1kOn2Rxou6N2HFBvKQ6fKAffZqcHoCFctGO5di879XvHAsMGQ2ierL2Nw0jAQReggDExkt082KNhNhYDC0MH4VA7cA3+pWWTrG+SPAlh9zFMyL0L9MiBvThd6os2zqlAwFrAeso+3q6lK5f9cSA2PABrvjRKK9MyWVNDHz6IKLD5/JbxYbRj/d5GuXgX+s/fPaH5skoSRArY+yLL61P6UN9pkd+oKBm5xmMTi/6JjAs+jGvQx/cyOyzCv27kj1y/oyPe6agZb/aE6TeL/c2nYN8LS5IfKhf/jld3dM4Bk1F0sG7zj823QT8LKK+vWOKU8+/1ouD5yAAAkwAYgMvwiMEIDYeoYeyr0HA/OrMjaYAU0eCtxvP9fZAfFCZgkkJMkVgGKGiKZLdIkilyGyM/SQQJLjWpqPfp5RP0u0v6c3yUiD7zKc5kbD4x29/ZTnSDeVtiA0SGXEalg+svZ0p9lNN+kpERuyyQnTdCHH8j/OrfhjDgxmqMtEYX5h3InunvLBI74svSXVmz2rT0vJKmndWbGRCoFKqNg2Ly3xDoyckNNIoChW39tkkCRa9BsIIBxY88qwlBjKBQqMTJHj8KEUaNak0wDzq+Wey4xYEQMAQgNgwQHA7iQDExiRcyPyaBMpg8HoW4VAjYvP7QDMGurUi8qwlNiSdP0fYL4LpYKD63NpzzlXzZU7UbyggjoKhlmVAbJhyMtqRWenaz3LHm1IsDPeJFQ5ejA31uXO2TKycLgqemrm+TqUKnwemUKVS9SsRCrImY3Aakh3VEJP8XNZ16FGOAbERxEIz2I9iwkxzcl7QZIu0qf4wUlK1J35WPqv5W+2slMcjEHh1AhAbr/4GPNZ+iI3H+KH0qxCgYFF+taZfpeU6tt8HjGnHJ/2r9HBgG03Qha4nS5hovwhug7Hqc++f9t1flyMgmUvFDQmJn9ynsAicdksqRzgeERtj7BdO8YMicHdln3hBIdPITqm/o0nVB0X/D4uNwnaYbudHLtpiIxvZeFRsyCgCvWa0gDwG+rGB9REKSjYjD168JMHRDObHjmywCyIwgm8kBozPcTSD/Un1pxbUr3r+1UvhKQiAgBCA2BAS+LyHAMTGPdRQ5gUJUHDqf9mmwDEfpQhBaHxog8cysG0CrIqN6fabv7JXxYb1t+ndQEJaHF5bY5EK3is2xtpPNemrQmzwNKZT7EcvBpK4avJjo7Y/fE2DZarck4dcNr4/9NzXkT2acRqVCyMH2QiHCe6Td7WRCzMSYcQIt2DKmg1dWbjOpnKxfRlVSZ+Z/xUb8VHPv5gRFyAAAjUCEBs1Kng2lgDExlhSyPfyBCggfLtc3LlY82DFhL9Pv0rb9AGUVbFhy9ftpzUkNl3V1wh6Odgt2qXKdS71Oo0lxMZ4+3VHWWyo9vG9Gp2wYiRPv7nrVS3o4AGoU75mh6qt9p34M9AnlCVbwxFGSZS/3sqMC8RD8D56pICnHOmRBLtuoyY+9LqOXrpwCp+mPhqZKIQF58kXpRsr6nZi/apkdhlEavquZam4AYHDEoDYOGzXrtIwiI1VMKOSQxAIgUYSEapVHMTLFJyzu8o9/zRtxYIqZy9bAavY4+k3Nftv7nI5px2tsp/EVSVsJ/2Cr1L8moO4mxa1pZ5Pl6Fr3mlKr7eYeRrVNPvWO3/vxcTZnWP7bNt8H8lUsvNV7ilfGMmIZWtTrJiEu7zJO1DjJzZTnuxdkkBW+rjWlDunUvlpT2lEgNduyE5R1Id8iJ1JV1OYuHUmTxQq0U8f0Mu6kKnpfmqX+GCFjb6XCr3gKUSIJBefPf+KAuUD6aPW96ssgScgcAgCEBuH6ManNQJi42noUTEIgMBaBOzIxVr1LlEPL5yf6QyTJfxb3WZjipWIHllUPpdffoQzH+mayzbsgMBWCUBsbLVn9uEXxMY++gleggAIPEDgSGKDMfAIhxym+AAYFJ1GgEc27KjYNBPIDQJ7JACxscde247PEBvb6Qt4AgIgsBCBw4mNhTjBLAiAAAjUCEBs1Kjg2VgCEBtjSSEfCIAACIAACIAACLwgAYiNF+z0GZsMsTEjTJgCARAAARAAARAAgaMRgNg4Wo+u2x6IjXV5ozYQAAEQeD6BYs2H3ho27Nqkdquaw2F9sJ7f/UptWxt2MCsPgQw199LncLBjY9D/TtmnJGfnptititfwiOqs7SK2Rt3PqGP579AzWiV1QmwICXzeQwBi4x5qKAMCIAACOyVQ381KB0rhhHErNrJdn5RQMBwKIRHSdbDuqmdk+HNE3v3Hv1x9r6deunFk5tu+/zNX+Kg5iI1HCU4sP+I7NNHilrJDbGypN/bnC8TG/voMHoMACIDAfQQGzumgcy7kbAwKrLPzK1hopF+pW4LCRUFSESMU/IqAoXzmHA/fIC8omiMcrpd+H5ZRpQb99yMHcbvdb750X1TbN6qmeTLtVGz481Yq7888VBa1MvgdWrTm5Y1DbCzP+Mg1QGwcuXfRNhAAgRciUB4amJ895wP1H/+cjoTFhQgFLu5/xRVx4i2GZx9/cV9980CwyFOmfnKfWm720lvlFn1upynlv3I7J+n+uRclSbx514xgsWKFxAOJmPjHMja2P36vBI/Ur+sw5QftS3ldx5dJPAa2XoR6H7/6+Msd06ionq/dV99+mYtd6bsoZoWDYTgxPX9/NRtvf1q6OHnMT4iNY/brWq2C2FiLNOoBARAAgcUIBKGRq4u8truDdBs4e7P0K64e/YiChAM+E8jmnnTuPrsf3w+dIdJL75hfJFmCcTHug+af43wwCWQlOCamck1lSsbMUwuO201NL/P2NH8eEYiCMIiCWF7qT/2S5ycXhuxL+TT65UexVBtYrCT7XniodEEz9Ek2qA30GX0PBYKQ0G3OTI1MjwLC+FvwyIw3phaaPEe+hdg4cu8u3zaIjeUZowYQAAEQWJbA9exOp85hcwNTqIadKwNhyp8FZ1pg6Othw43U3lSpXnrD7KKPUzDuRx5S0O2r9ekx0LW+MDMTmNeexXK2T8i+KZ8F7JX6TbAdTfNFzb4SGpwnt0nvQy4EKj7llZg7qlPakNumjIX4sqU/fl0KFJWnLJ/X0RNHvXRV1SEvITYO2a2rNQpiYzXUqAgEQAAEliFwu7y509tF/fJdqWcBseGDSx+YxkB6g2Lj9tsP7t37s/+jF6ATkw+fK7CmPsoDV1m7koJvk27N1wJ/5qgDfM85TaNSwX1NmEwWGwP24zQw7bhuky+b2kv5KF3Egy7XuKY2qNGMTMxacVsxYfPbLJwep6DJNCzNl0ooBsqXZKuXnnIe7Qpi42g9um57IDbW5Y3aQAAEQGB+AmNGNu6eRhV+VY5TdMh9H3SxwAhBsQ6C5ToKkEkt7k2T6qWbyqjdSmBkwuP9wNoQY2b4VgfePqf/JVxGOMr0zF5NLGTPQpAb+8Dfx+A+yxssd8RG/kt/x/4KYqMuBoTfEiMbWQ+YG8vDJIsoif1h0493D7FxvD5ds0UQG2vSRl0gAAIgsAiBsGZjcHTj/gXidn5+HkibBnHgm4JEk9q/7YmiXnq/hgVylGKCg+f463iZnjthxEMxbciW9/dRbNg1H9wHekcsnz/uBhbEQyrfs2/Tyfv8WVW8jB7ZIFv2nTFMpE2tAH9kel0A39z1j7jAhrsmHynppee9OXTHo5CnkxtaXjVU/llpEBvPIn+MeiE2jtGPaAUIgMDLEyh3o3q75AGUu3sqFUW/tMuUTD+xgaGCz/kG0lXW8rK3HqOXXlpc54kPvGVEx3/qKUR5YF73ydiIQiXk5qlWir/cS/Ct+4fKFiMbX7ufaYcq6UMpJ86IPU7/3l3lnvPV/LfPwmgAl6e2U7pmIBWVnyxUrD8iuLQI0W0M7cjEw93p2vfAOOPfSy/b1HoCsdEig+dHJgCxceTeRdtAAARAwBCoH+pnMj3l1guJrR7q9xQkqPSABG7u8vbm7O8AW28oRja23kPb9g9iY9v9A+9AAARAYH4CPMIxtL3s/FUOWuSpUWfXPMyvlz5oHIkgsB0CPLKxtzlUzjmIje28Q3v0BGJjj70Gn0EABEAABEAABEBgJQIQGyuBPmg1EBsH7Vg0CwRAAARAAARAAATmIACxMQfF17UBsfG6fY+WgwAIgAAIgAAIgECXAMRGFxEyDBCA2BiAgyQQWJuA36mkcxJ04ZTfhag9DdjuUjTVflHh/Q/C3Psf/7zfBEo653eV0msu9DahYeecyu4+YNcmoLduHdzat20CKSBwWAIQG4ft2lUaBrGxCmZUAgLjCCwjNlTdYw5/U9nHXfpD1viEZn0ac+08hLvExgT74xzeZq7bxb2dTu7Ef9qCsL6blBYbzuVnBIxobrFlaGXrWtkKNWw5ms5oEPv59qDZlqScZVp6aV/qufdzuH4tNhy3tcLg3qpRDgR2TgBiY+cd+GT3ITae3AGoHgQeJ9Ab2VA1LCA2KPj1IxW0dekP7te/qT4SCPOczry0fUXnoUsWioOH6g2YZ6GRtsNsis6BczJIYEiAT4GzDtb9L/VyRoP/lLzs1e13d1VHcuQH0lEOc2YCixN9hkIuduRMjlTHo+mBXRBFum0DVFVSr37nWGDIaBDVk52zoEzhEgRekADExgt2+oxNhtiYiU8/ZgAAIABJREFUESZMHZPA9Xxyp2KOktkrnYN4+VWaPvUv0yIGqIzkSYGlc/r5yZ1qAeso+3q6lK5f9cuA2PABrvjXKK9MyWVNDHz6IKLD57r99oPjkY/3Z/fufZ5GOfjX+g+fXZ7Pi5Ux9sWX1qf0IX+G0YO8SzU7z2B0etE3gWHRj3kd+sA9Zp9V6N+J7JHz51DcMwUt+9WeIPV+ubfphbgg8aF++ed0dS8HsungXecfm24CfhZR337tvjC2Wv0en/f8ixlxAQIgUCMAsVGjgmdjCUBsjCWFfK9LwPzqzCAowNSR4O3m0g/DPqhMwaQEmSIwjFDRZMluEaRSZDbGfhIIElxr09HvTAj5HPaX9Gb5wiA9yKc5kWBonpfAeRtig4RInIalT4qeYr/qoBOREbusEF03Qhz/4/yqH8bwYIaqTDTGF+adyN4pLyzS++JLUp3Zs9q0tLyS5p0VG3wvQqBSqjYNi8t8Q6Mn9uRo56x9NkmCJYiFXrofCamIlUxU0OgEjab4UYo0alJpgHnUrd/kxy0IgEBOAGIj54G7aQQgNqbxQu6XJFAGg9ezCIcaEJvfB5ox0K0VkWctsSHp/DnCfhFMBwPV59aec66aL3OifkMBcRQMtSwkHBpiw5ST0Y7MStd+ljvelGJhuE+scPBibKjPnbNlYuV0UfDUzPV1KlX4PDCFKpWqX4lQ+KK53kKVs6MaksTPZSqWnkI1IDaCWGgG+1FMmGlOPG2L6lIChOoPAqlqT/ysfFbzt9pZKY9HIPDqBCA2Xv0NeKz9EBuP8UPpVyFAwaL8ak2/Sst1bL8PGP3iXj+NJv0qPRzYRhN0oevJEibaL4LbYKz63Punfe8tUs5cizdhnUZYBE7TpsoRjkfExhj70ZnsogjcXdknXlDINLLadDbVB0X/D4uNwnaYyuXfkbbYSO+Q7EB13zoYG2zzyIUO5AMtmy9CNNOQON83SXBUy1EwP3ZkgyvyIyZeEH3vrqo8Jes1KX4kJNUf/Wxc9PxrFMNjEACBQABiA6/CIwQgNh6hh7IvRICCU//LNgWO+ShFCELjQxs8loFtE1xVbEy33/yVvSo2rL9N7wYS0uLw2hqLVPBesTHWfqpJXxVig6cxnWI/ejGQpqE1+bFR2x++psEyVe7JQy4b3x967uvIHs04jcov+P4yW0TO6zjMGgnxsAzWzUiEESPcgo9fx5GI5jSpsVO52L6MqqTP0QvFe/5JQ/EJAiBQJQCxUcWChyMJQGyMBIVsIEAB4dvl4s7FmgcrJvx9+lXapg+wrIoNW75uP60hsemqvkbQy8Fu0S5VrnOp12ksITbG2687ymJDtY/v1eiEFSN5+s1d9VZNPABV2TSg2nfiz0CfUJZsDUcYJVH+eiszLhAPwXtc98D3AyMFPOVIp9t1GzXxkXbHIvH087dK3Nj6BZN8mvpI7BTCgvOoaVZStvo5sf6qDemnSt+38uM5CByEAMTGQTrySc2A2HgSeFS7QwLh1/AkIlQbOIiXKThnd5V7/mnaigVVzl62AlaxJ2cwyH20/+Yul3M4o2EgGOJy6Rd8Xb0XHNIG+qzn02XomneQ0ustZp5GNc2+9c7fezFxducwfalsm+8jmUp2vso9MQgjGbFsbYoVk1C7jdX4ic3EOHuXwvs1OIXtznUbPDIR1mvIug1Z/8Ce0yiETTejHNZGFCoRuQ/oxc7UdD+1S0YtrLDR91KhFzyFCJHk4rPnX1GgfCB9lA05ldnwBASORgBi42g9um57IDbW5Y3aQAAEnkDAjlw8wYXZquSF8zOdYTKbU880FEZJROQUnwNTte5x249wqq3L7jGCMiCwMwIQGzvrsI25C7GxsQ6BOyAAAvMTOJLYYDo8wiGHKc7PCxYbBHhkY9yIX8MCHoPALglAbOyy2zbjNMTGZroCjoAACCxF4HBiYylQsAsCIAACFQIQGxUoeDSaAMTGaFTICAIgAAIgAAIgAAKvRwBi4/X6fM4WQ2zMSRO2QAAEQAAEQAAEQOBgBCA2DtahKzcHYmNl4KgOBEAABJ5OoFjzobeGDbs2zb2wmna80of8VQ4VfISLPgvE75w1dlvccbUubZ/POREmZuvfcR52cumtggv7dP7N2b3Tu8pl5nrpWeaFbvR2y1N3IlvIpaea9d9T2fVNv59LuFWKjbz+Jercmk3NuPw7Zgvfka0RS/5AbCQWuAIBEACBwxOo72alxYY/rVtvjctQsl2fykDe/+MrW9eqMzUCUf0PdRZYz0S8bz+IqLDFrwRpsfqsfdSOfLvdvv1o6b4LLQbYl7x+Mqq3B65u+WvboAXjCPv1dyM1p5eeci5xpcVG/r4uUdv2bebBfvZ+LuA8xIZzGWP9fVK8n/sdUY5s7BJiY2MdAndAAARAYDECA+d0UCArATj9o5oFsyb45X905Vf4mrPFL+fOscCQ4JfsmXM8amYmPaM6m/ZNcBqCcmkv19PzadD+JE/rmbP6KbDOBR0LDWmfq/yyX2uTrqljX7JysNQc4XCuly525v+kPkwCTL+vVBfzMe/U9Z8p/6P+sP3sLJr5bN/n207FRnhPs79f7gOwfqmRfwc87zuyPpKxNUJsjCWFfCAAAiCwaQLloYH52XMPnkAeA12CkAc6JRYKhp8djCmvOMDJg3cWTLpNWTCuym7issKTAh8VXOdi5BGnaTrID+7Xv1s2eumtcss+FzGgBaQVG5LHn8Uy7f20fLNfuekbIdMEqV9ElOj3S/JI2jdJ3Hsy/jsVy35Tjg76EcEwevjPX9zP3yYbUv9VHdCZB/TD9qX8TfuvvsM8sqHTTP1je5fq+epbOkQ0/z5K+byPvkw/IIQMk9LV94OKcxsVf/39GZMuPvY/t/kd6fu9XA6IjeXYwjIIgAAIrEQgCI1cXeR188nuP7lP+dMRdz5IyQMX/0uyfSbG+B91E2hJ2jM+YyClKzfBugu/uPpgrx4I6eKrXnOQZ3zKBJSIPz/i8VgbvCj9x29/NZrYS28UW/gxBaE2iNVio3gHmN94wWHFBtf3MR3uGANZee+pz+Sa2l70oe+rJI5u7pbMhcBY9bn1l+0ZsaEFis3vhu1H/1WArtv815//t/tKiQ8RPsn/MR1M7ykxl/c1L8P1NUQI5RyVHv03f28VPPK6/fd//PtgSpvbbX5HjJOr3kJsrIoblYEACIDAAgSuZ3c6dQ6bG5hCNeyR+Uc7ZNaBiH/k83GgG//BH7a8VmoRaFLFRfCXvPGBlwr0UtJzrmq+ZsGTiIzkM/fPnf3QmwbSS38GJGrvd3+EdzAE+Uls1N7h2rO25z7QTWuSbJBdfceUufL7EoJnLUhUfvt+sv0sr/df/Cjr77TPvFNleTVa45z7/P/8H7l46o5u6saEa6oztKGsz4ovW356el5H+I5kDHUdvXSdt3+9xe9I3+vlckBsLMcWlkEABEBgFQK3y5s7vV2c+mG0rHcBsdEa2fCB0ly/EpZNmfokDzpCaQp8msF4HshNrW/2/CYwZPssNkRc+EAp649MjEzzqBco9dLL2sJOPbTj1Xt98j39Ajw0Zau01HrixQbNlfkl/gKfxEYtUPV9LMFvy648z8VCKKven+o7JoVDYJ71j/xSr2zIaEGaSiX9WxMm+Tta1u/Tszr5PUqCSU9lKsvnYuP//b/slKa8/tjUgYvYR5RH9RMXsffWzqh03bZwrfmSTcVAhFpWVS89y9y+mf4dads6QgrExhF6EW0AARB4bQJjRjbunkYVgo7sF8FeoFEJdJ7ZQxyopMCNXOHgKmuTdnBj/vOCcCPeKCiKgVTF30qbdQvb170pIL300jIFXj/+Kc9z4dGeriX5x33qQFYC55/jAvEKn4YAaNWWiw0Jls00ptgfpZWivIgNeQc5yFV9zPfpnS3L+zZJwCxtTj84GIHVsV+Wn1ls8PtYioEkhoy/BcJH041By8MkP/aDyfTviK3+aPcQG0frUbQHBEDgBQmENRuDoxv+H8AU9E3AZH5V5MBkYG61/HoogdDomm4X93Y6udPQ2pPRxnRGE2yGwKfln2+fCvy0qcHrMf0waKCZmAebPvBKgVoIDFWf5PmbZisJvcWtvfSKyRUeUXtTf/r+1tsXF8G0ead7LhY8TbBa2LcGjXhwYUcx8dmW9+9gEhv+O5Xu2R+1yNznT++sLd+zb9PJff3sr//v/8wWddv6bXPtPdnS7yunGybeZmqDtTEqvSX4/vjdXbVB2/+9dF22ez30HVnu74iuW0/MALHxRPioGgRAAATmIxD+EaNgPfx5u6TfObmeu6dSpV9yW4uPJfiQKSASRE1q32Jig7yQANT/ulr4x4GP/PLaDniG27NsIKEZF4GbBIey204r6BpuQHdr261ODyE2WZ/aYFJGEoSPXuzcYULJmr2847oPdGDeMsd5Yv3GX/N+fvXxd95tSgsm7YOsT5E2s+2wQ5T3z77D+ftv7df8189oN6qh+ltt9s9JHFt/KKUUzboObod5j+9Oz77flb8DeunDDcxSh78jy/4dkTmyoRuIjQ11BlwBARAAgaUJ8D+E7+/ZlWppz7x9Wn9SiKR1qp6nFprSNvvIzDyu9az03o1ees8+0vdLoDzUb79tWdLzUd+RHf8dcS87iI17yaEcCIAACOyVAI9w6Hn0G2kIj2x0dtXaiKt1N+hXyzdnB5Tqebf0NKyjaB7m10vfUlvgyxIEIDZ6VMd+R/b6d0Sv/cPpEBvDfJAKAiAAAiAAAiAAAi9NAGLjpbv/4cZDbDyMEAZAAARAAARAAARA4LgEIDaO27drtAxiYw3KqAMEQAAEQAAEQAAEdkoAYmOnHbcRtyE2NtIRcAMEHiKgtxDk69rOH60aevuXp3J+N5W0/WJKefRK+1DuUDJq0d2jLqA8CIAACIBAlQDERhULHo4kALExEhSygcCmCWixUdnycdh3HegP51xHbJgzEcSluxc1h4V7dHqxXgA7+pC7R8tLA/AJAiAAAvskALGxz37bitcQG1vpCfgBAo8QIIER9yMn8SCjDz5wl73YsypIoPDptePFRlbe3PD+53Iarknr35KfaTSGbFV9vuOcCBoV8QfZ0aF2P7hf/yZvSECM2/710fL9tiMHCIAACGybAMTGtvtn695BbGy9h+Df4wR4O03ajjIcpsMHnuXba9Le/nIQGn9WTmK+ntNhaZzH7KWf28jtP96I+y20AncapfCHUonYyA998kKE6jXPo6jxPvnRDjkMTX1a4cEjLim9KiZGNJOC/3/89teInD5LTSx8+iCiwzlHIxz/8S9njr+L9rvlY05cgAAIgMAxCUBsHLNf12oVxMZapFHP8wjIqcQnEQAkOuTau3W7qVAz5NdawguNvIxukBcaKZ3zawM688rXSVQE0RBEQBIhXmzQaa1RALSmYtFoiBEb0pzBkY0gNKJ9Pe1LDIz8vP32g3s3IA5KM/k0KCpfiBWeUnVu2B1RvqwUT0AABEDgMAQgNg7TlU9pCMTGU7Cj0lUJxJGNsbX6EZCkFey9tXNzl7dTfuoxnRBqBI0tJfccPNN6gsofP/1Hct75aaZLebGgpy3JyIa2X3vmnLtTbPDoRyZSGva1C63rO6ZSRVMkKvS6jZgQLsK6kGxth87TK6/z4hoEQAAEDkIAYuMgHfmkZkBsPAk8ql2RwCixoadY+elSUWx0y5dl/ZSsNNKxYmvLqmQ9B4uOX8LaCAr2ZV1HLfCvPbtfbPCoxzdpChWNomQjKaXX7Sd3i42wTkNGMd63p2NFAZgJk/Hl284jBQRAAAT2RwBiY399tiWPITa21BvwZRkCI8XC20WmUtmRDHtv3ayMbNgsA/cxsF1qZMN5YfHzx695mhRPn/poF5SrKVTs67xioxzZGADSSWJek6ZRkcG0OLy2BiNW2RzZGFk+GsIFCIAACByHAMTGcfryGS2B2HgGddS5LoGe2LDpfH9ycWTDOefXbNAi87rrds1GPdeznsoC77DbE6+X+DLsREU+1YRF7dnwyAYLCrsoXJps12zI8zs+py4QpypIoMg6jarYkNGOhojplr+jHSgCAiAAAnshALGxl57app8QG9vsF3g1JwErJiq2s52k3i7uGnanSqMdIjjUjlRmx6rMRmXHq0q1qz3iaUyyZiIE/n4nKnKhJixqz4bFhtiRKVLFQvJQb0zXC9LHkrhjChWPhOjpUCIsZBoV3TdEBrnVLT/Wd+QDARAAgZ0SgNjYacdtxG2IjY10BNwAARDoEAhTnGZZNN+pCskgAAIgAAKJAMRGYoGr6QQgNqYzQwkQAIGVCdDUp3cjD+Fb2TVUBwIgAAKHJwCxcfguXrSBEBuL4oVxEAABEAABEAABENg3AYiNffffs72H2Hh2D6B+EAABEAABEAABENgwAYiNDXfODlyD2NhBJ8FFEAABEAABEAABEHgWAYiNZ5E/Rr0rig178FllG9HKlqOE2W87mnYB0luSPqMbeHea9z+4X/+eWrtnoHc4mmphlfxbW4gbdg+SrUsLBr30ogAegAAIgAAIgAAIjCUAsTGWFPLVCKwmNnhbULNVaOFQQ2ykfL3D1VLOZa7oYK+ze9fYJpNFSCPN+7N9sbHdhbjD7P2hbe2+GXofvHikBci5gBx9noMcBPf+7PROScWWqUNOIA0EQAAEQAAENkoAYmOjHbMTt1YTGzw68fCQRFtszGN/uNd6YqKXPmx9A6l3nGGwrtdecDRHOPiU6HM8vG2Ub/qMBWp/OI+B+rJdj7b82f0ouyQ1bOncuAYBEAABEACBvRGA2Nhbj23L38XFRnnQWZgOpUY58jyV6VWRWSk28rJpqtXJCpswanLiw9Yqp0Ofr07bKqc6UVCZ//Id3QoXzV+yr2cn9dKndc25m7u8Kd9PJ1fWb2tL9+w38czq0RzH2PeBvP5lPtVAV559sx2Gr+bP/p0voY1ndxVb8g5w2Td3uV7cW+ifk6TlTjjHU6Z+cp/sc7nvpUs++awJBHqmDoFLJ05LIf1ZExuf3Y+DI1y6PK5BAARAAARAYNsEIDa23T9b925xsSEAxo08UECrg2QpLZ+l2Igp55PTAa485087PYuDcgp6/X+yJkQCfAqO5TraoV+9OwFkU2xEIz7ot2IjioWYb9oFl6cgXQXoOe+bu92STZ8/tZ9TBoP0IDSs49Fk2S+6fl8f9auIHhEcoa9FqET/fb6iD7g+Cu7z6UrRjVHpeW664357L9OolHhQWf30ska92TQqEm3DolSZxSUIgAAIgAAIbJ4AxMbmu2jTDr6E2CiD+Tw41oFxq7c4IO2IDaem4dTt1MWGCyMSzVi+biw+Ldvn/ChNDN5jVn9hxBY/5IC5MWJQy69N1tLVM/YvNI5Y+0vqAy02cpFZa5OvcoGpVKotnz4MCQVft13boYqzcGmPDumcuAYBEAABEACBfRCA2NhHP23Vy5cQGywmZHqO+pTg/uliI7wdyU8z6tB5e2qBefGMg389VcvUMSA2ClvGn2o61+cFBKfvQGyQoKR1GjKK0T6x2o+uFKIjiM18pMTAwi0IgAAIgAAI7IwAxMbOOmxj7r6E2KgGw6ojxogNHrXojWwom/XLxsiGycz+tEYlTF66rbUve6YCfy6uRh2iuaFpVLX8sSDvTexOJyNeVJl7xEabwfzTqLgpMipVW8MR2zowshHLqWlY8Vk0gAsQAAEQAAEQ2B0BiI3dddmmHD6M2NABbUHYrtkwGUaJDUdB5NAUG2O0elsXG9errB7xhTKhEOzws+ri8r7YsPa8LSMOwk5O9SlAYc1GUwDl09LIZc2U6+uObKhF8UP9NSSKqOJeeuCZfzQEgggQ5+JoR52PXqfRsJVXiDsQAAEQAAEQ2A0BiI3ddNUmHT2M2Ch2S7KBsSxCvnMaFfVeb92Gn37TWPfA3d8QG7S4XflVjBLI6MWdYsPudvV2uYadofJ1Ejx6I9u4Fq9rEBzKz2wBt+Urc9TE967YeHPn81vkkNmOviyxXqMUkXYaFd3XRQY55n3S6ZhGFTsMFyAAAiAAAgcgALFxgE58YhNWExtPbOOMVYdpNA9Pp5rRpZlN9QXTzBWSORYqRvgU1fTY99ILg3gAAiAAAiAAAiAwggDExghIyNIkALHRRNNO8L9cPzqlqm3/6Sm8WHzo1/yZPeyJDZ4aNXBYXy99ZndhDgRAAARAAAReiQDExiv19vxthdiYnyksTiXQExtT7SE/CIAACIAACIDAbAQgNmZD+ZKGIDZestvRaBAAARAAARAAARAYRwBiYxwn5KoTgNioc8FTEAABEAABEAABEAAB2grlr7/AAQTuJgCxcQe6w6/ZuIPJQ0Ww5uIhfCgMAiAAAiAAAksSgNhYku7xbUNsTOpj7Hg0CdekzPezbW01Sztr0Yngvf8eLd+zj3QQAAEQAAEQ2DMBiI09997zfYfYmNAHHJRWtr2VA/fyszJOTo6aqKenrV5r6faciTF5hpvSPiejZjs/68OfD5LaZw4EtGdshLM4Yhtq6QInc9oLjjECIRajURHpE3UIH/XVKDuPlo+O4AIEQAAEQAAEjkkAYuOY/bpWq1YRG3KatA5qYyAqLTUBqY5Fa+XzYLgMpFN5Sntzl8uZD4yjesWPlEfOekiH62Vp7GN5+Fty/c2d7CGCkqg/r+SDCdTl0DtdPrCIPlA5na5tjrkWttGgL3S73fiCeTTtB6Ghynp+ZTvkYEWVNVR0cW/UB746arE/VLBWJ0+pGjoY0TS4Jhbo2YfPMePgoXwjykdDuAABEAABEACBFyQAsfGCnT5jk9cTG6eTE4FBwapcc1tqwbUKylls0K/lMYr1wWqycXMhbmZznD8GskGIUFkO9r0fHDCLvU79bJR+NZdf0E0HDAfrKvNYseG8z+Ke97sW3CvbA5ci1lpZBv1nn7VQICvGv2i48Zz5Ghu1Z2yHRN20Mz7yaVBUvhQr/rDCut0x5WMTcQECIAACIAACL0YAYuPFOnzm5q4nNmLkXLagDHbzoLUWLNeeieXcHtkKgS4FuEGEcJ7gU56frOT105PWFCpOu8w7ssH+iM/cKD2NyQTtzgfn796fXfGHxZFvSxJmbDD7X9n+lFxPs2JP8pfcOKUqLBp53R1TqaR659ynD0OHLYZ1Ie/beYbLq4pwCQIgAAIgAAIvQgBi40U6eqFmbkJssHAI8/zTuoC05qEmLOwzH6CnaVBp2hEFtcNio1c/se+KDet/TVwNjWxk5YdGMXyQfpI2dV8ME9Rz4B84abGV1Z/Y70lsUB/ROg0ZxXhXGeHwuESg5aJjfPkudGQAARAAARAAgcMQgNg4TFc+pSGbEBv1gDbxsMKCUvQzLzRSgJ7b64uNPH+qN7tadRpVVnP1htrvRyskcG6NbIRRESN+uM1abMRpZ6a6HUyjYo9lcXhtDUZs0sDIxqjy0RAuQAAEQAAEQOBlCEBsvExXL9LQTYgNF35tN/FwbLAWFvJQP9PXlM73MXjui41e/b5OCurzX8LFl1FixTs2boG4GA6ft8vFXbNnZrQiS6vcBL56KtVosSGLuVXntNvb8KuYRuXzpTU4ymcSC80RCZUvu6S+Ces0GmJDRjt+/DMrGG765Wul8AwEQAAEQAAEXoEAxMYr9PJybdyG2KD26ek9YUqPxLdWTFD2/FkIXmM5uafRDroenkbFeAfqF/ytqVTt4NuX5HQzTUlPg7qnvBYO4t/wpzBRU830yEbhX5pKFXePkjxRyIUaK+xoOlz0sZIe0zKn71mvUYpAERYyjYru6yKDKu+Xz1zEDQiAAAiAAAi8GAGIjRfr8Jmbu4rYmNnnJ5oL03Aau1I90bEDVA22B+hENAEEQAAEQOCABCA2DtipKzYJYuMO2DzC0ZhSdYc5FOGpU+NO+wYsEAABEAABEACBdQlAbKzL+2i1QWwcrUfRHhAAARAAARAAARCYkQDExowwX9AUxMYLdjqaDAIgAAIgAAIgAAJjCUBsjCWFfDUCEBs1KngGAiAAAiAAAiAAAiDABCA28CI8QmAXYoN3npKdkE56l6RHmn5/2e2u2fA7TtV3emq3d+puWFPtO6zJaMNHCgiAAAiAAAhsnADExsY7aOPu7UJsJIY+mJYtcdPzta62vmPSMmIj0fUHBE4WG2zgfnZe3NGhhfk5J7SlLZ0Y3vvv0fI9+0gHARAAARAAgSMTgNg4cu8u3zaIjcA4P7ejDp6D1gNue9sb2Ug0HhEbZMULjjECIdbZOKSP+mKUnUfLR0dwAQIgAAIgAAKvSQBi4zX7fa5WryQ2ygPlitEJc/Bbkc4tHhjZ6JU36XKoXv3AvZMrT7cuD38b1wkNn8mfeDieD+LpIDz5o0cPohi4nmN65p9+Xp1mNs7+9fIW7ev6UzuHxUY23S22LZXmK55SFU77NknV25pYoGcfPsfsg4f2jSgfDeECBEAABEAABECgIACxUSDBgwkEVhEb3VGDIASiwODgmU7/tv8NBO46yLblg/16AO3r6Pr450/u3Z2jGlEsqOZQfbG97uZut5ToBVBqfxREUoDaJ9epGB3D7i5v2q4kjrMf+TCvcOq6mODPtthgflFgtPP5E7uHTvTOKvS1/vaDe/deplGR6CvFipwaXjspPJ9GVS9f1oonIAACIAACIAACRABiA+/BIwRWERs+WK4Fr971MhhviApXf94rX6aXyHpi46EpVEXwfnXnGJiXvjgjlsb4H0g2xIapo2u/JRZaz8t+aft8x1Qq5f6nD/m6DZUUp2nZtR06z3B5nRPXIAACIAACIAACRABiA+/BIwRWERveQR+o8jQhE2hzoK+mEMlUovLH+zKoJdu98j0hEW2UFUa2g2KDpwbRL+/6T/7rO/kgIwcUiMt1rIAFQJpGdTqZkQ3DLJbLLjzjajMm2W+JisbzMHIk/RY/qz7fLzZknYaMYryrjHB4HDR6ISMhCdD48qkMrkAABEAABEDg1QlAbLz6G/BY+1cUG+KyP4HPAAAVy0lEQVRoEB0qIm7/Ci5l5LMuNnrle+lkvStIHphGxd5TsM/BN7U/CYmYdlIjP92RB+FhPz1bhdZnYHtT7Nc5yzStQig1Rpysd/7eC4HadKd6/vCU+NM6jdoajFgw7Hhldq3i5FHloyFcgAAIgAAIgAAIBAIQG3gVHiGwgti4uetVLUioBfbhl/EiSC5a1giCe+Xll/eBCliQDKT7tQZDU3gKZ80DEgJv7nIp11tYMcT3M45sjLKvxI6tPzXEi5lSbASxVh3JSKX5ikeB8lEfk6Nyq9ZZNMSGjHbURUy/fKVSPAIBEAABEAABEMA0KrwDDxJYRWzQouU4tYamS9WCUhEEajpVGfs3xAZB6JXvpYdf56OfFR8Hp1KN6AgfxKsRhljGB/FS99vlymsvsh2zKv7E4vGiMbIRFo4P2j9fQp3UVzUfqZK22KDUYjpb4fM9U6hIKOQiT4SFTKOi+7rIIK/65SM+XIAACIAACIAACBQEMLJRIMGDCQRWEBsTvNl81jBN585dqTbfvEUdBLtF8cI4CIAACIAACCxEAGJjIbAvYhZi446O5hEO82v7HWZep0hYQD/qEL7XoYKWggAIgAAIgMAuCEBs7KKbNuskxMZmuwaOgQAIgAAIgAAIgMDzCUBsPL8P9uwBxMaeew++gwAIgAAIgAAIgMDCBCA2FgZ8cPMQGwfvYDQPBEAABEAABEAABB4hALHxCD2UhdgY8Q50z+AYYSPLQmc+vB/aQSnLjRsQAIGDE8A6sIN3MJoHAjsnALGx8w58svsQGyM6YE6x4bdtnXrOxAgnkQUEQGDfBO7eSEEOszy7d9lOeeW2z3VAj5avW8VTEACB4xCA2DhOXz6jJRAbI6jPJjZ4RKMUGv78DXMWyenk5JyReno6C6OWbg/eG5NnBIrtZuFzVBKT7ToKz55F4NHv8aPlR7WbBUd+rkyvHI2KyE5v6cwZEhDj7Dxavucf0kEABPZPAGJj/334zBasJjayYPd8dmd1cJz8I67z2GCZ88QD//KgUspHkNdzdnCgpGsbEsjHMgMXg+WpLnXadzRTBL/+18Pa4XPc7uIAvGgpXTTqKsqHAwxjGw2PZHDeK82XDxCMDlQOXSzSdJ+awwkjS3+oYzqckE6mz59JWjqYsHUQ5PABhdPISB3epvdBt4dPPMwPtqy8M/r9ZxvmncjTz+46zclu7ty+PXzTcM58o7Q3d7nQd+Hk6LsrtqSbB79DXc/GZDD+dcX6yZ3EOTb/WHlur2bC76zuI/1u+B8W7N9x0kqeUvXhs9x2P2tigZ6lv2tohKP8kUMM98tLTnyCAAi8KgGIjVft+XnavY7YMEGyD0RSMCZBqvzjS+lyTc2s/0NuyuvAwQTXYj9mMf70UA6XrwetElxF2/yLZf0f/KJ9sZC5aPhdlveB073tNbWOuvWMdHClixl/5LRxcTCKCSnjmUpyOh0+9bmzLAobYqvy/lCSLZ+y33Elgar4R/7LdTB3uzmSRv4/n794x5UAl5zyyX2sBErxfknGOz+9feNztFW+4/k7F9pPHcZcleAInTj8HYoV3X0xhsdwnpu7pQ5ynFeLB/vOGk9zHiKu0/ehSDfls9vGCGiWJ7sx06CofCFWSHCc3buq6BhTPqsQNyAAAi9GAGLjxTp85uauIjbKf+QpOEmBTZmuW1kGOs7lz4ryFPCoQKFID7+Gx2BWV1e57pYvAte8fWxyIIAYHYgU9XhnbXm+V3yFV/UX90p7pz/ywWaTZ81v/awQCr5/o70iXYK59A55QaLus0aU/lGf6mA/yz75prQ/bCJ/f6V/2v7Y/HOLpYp93YAuf/W+U97w3eP3UIuN2KFkfCoz7VB5Xb7zZZ7ye1zmkSf2O0XPh8oX+ZlZEhsiwjIEUpn9vGMqVTLx2f2YrdtIKXwV1oXkazt0nk55nRXXIAACL0MAYuNlunqRhq4gNmqBjApOOv+I14MSb1OmQRRBAAWya4oNI350kBV7rSc24hSxsHajFpXoAD0aDr/cZ+VVkKPy+Usf5KVpRkWG6Q9qwaiyUgRilMZtCeKgKO/7NyIo0qeKDTO6YQNB5et9l2MC5/DOqn5K4qJXXvrMrusZ6ucpLenUX3vvmKGsK6Lyqi+fIDZ8axVj9f0XEsXfE5IQPvk9Vf2j/w6hLEPli3e88Y6xDa5joO/uFhuyTkNGMc6VEY7QWP77yC4on1DesMMtCIDAsQlAbBy7f5du3Q7Ehg8gUmBGSPJnRRDQExtZoNRHXNjn8uZX9FinCry06VWnUemK69fUppxpPd+4p3cEqzqALXj6/p1TbHjR6vts3rYToU77w/sq4ti+v+W9pZ6/7zb18fuO/aJ/rNhT7zzlHSM22KaIlcdbkFvw7Um8fWrxPVaFvNBIAqAQDzOJDamSfakIIk4f+GFCytc+ZXF4bQ1GzD8wsjGqfDSECxAAgVciALHxSr09f1tXEBvyy7v8Qx4CAfkltPOPODW5+IffBD9ZEBGCGP2rJKfb+e6tf+grjMeVDwHn+WwWnopBPy86LdqU55X2paT8SgfoKqXgo9Lo8na5mMXE9eDYB1z3BYCekRFg0Y+yvrLPUllvS/lh+pvNFs9UwBvrzS98nbQ5gbyLeXpcGxJVjklv3pbty7PadH+vxV6PvU9v+B0r83b1ux+TOhfD9ZdihPPH7xDVG/qP+qUlNhR37otYPjnn+z69Cyll6Ormrle14KLxdwr73Ohb/26kJfc1/4bKlzxoFCr11/WabFNL8vx520gslGsu8jzFHQmUsE6jLjZktKO+bsx1yxc14gEIgMALEYDYeKHOXqCpq4gN+eXWrxmgIFIFJ43AwLbVByEyjcQEIyIwaHoCBTBxlMFb8YEEBZlSPgUBtp7aPZV/u1y65TmAkKCrZqjxi+VQ4EFmvF3xXT4Tg3vK60BXXJV6GvGYZGt+5n1kdjPSfUT9YCrRZc9XH9zGLIWwYCjuzbAW/9N7ZlwNPtTazjnFx1ixKd+89UH+YDEWitJ3Z3eVe1XI+q+DVaq6ly4jLPeIjb79IGTkO5QJBfV9JoYtscG70CkGFZ7+PUjvdiVL5ZH8gCG2zbsXS/TakMr7v6PoXv9dMbb82V35XUpl9fvt38+UFt2ji3umUNHfK9k6DREWMo2K7hsig+rsls88xA0IgMALEoDYeMFOn7HJK4kN4zH/Qzw1oDA2JtzyP/QqqNNFyyAgBRxT1zVwMJgFYbomf73tQ/0oaFuvX0o6Cz8Z8d5RHzbFyMLuzWKeREzjXZ/F/p1Ghr6DuUkK6BuBeJ7xeHdhipOcmXG8BqJFIAACeyUAsbHXntuG308RGxx4dILyOfGMD3QeqJUDWTX1Z8gUj3Cc1T74Q5nXS2OxtMFAdR4C4dfvofaZX6PnqXdNK2qEYc1qR9Q19jtI+XYt9kawqGXhqVPvxx3CVyuPZyAAAiCwJAGIjSXpHt/2OmJDpozIFIyVf7kcG+jc1d1BZNDUiFcMku5itmohNcVmRYG7ahN3UNmi38EdtB8uggAIgMCeCUBs7Ln3nu/7OmLj+e2EByAAAiAAAiAAAiAAAncQgNi4AxqKRAIQGxEFLkAABEAABEAABEAABCwBiA1LBPdTCEBsTKE1ZV3GFLs279PWdPjddjAdzHbI3Pd+G+R8B6G566jb8ztabW8Bdm/jhF56vbV4CgIgAAIgMAcBiI05KL6uDYiNKX2/gth4blAFsTHldbgr75N3HNqq2GCWPZHdS292iN0KNmTkvhjYEjbae7R8NIQLEAABENglAYiNXXbbZpyG2NhMV4T97of2w3/AVw4yn7lAmoXagbfVHdU3fkRjma1NvVAc2mxrlIsPZJplETgLigEB0Euv+E8C3h+mSfxlx6fO2RPKzqPllSlcggAIgMAuCUBs7LLbNuP0SmLDB0Jy2Bp96qCoCFJo96oYGFPZN3e5nJ3s+OR/nRUbvXRiPa5+sSv1SC/p5/WzN9SOR2HHrXIqUu5Dmd4+Ydz7MVR+oP5iJ7Bwjkjky6cqMlvpH903ciDj+XJxb3IYX7Cp25Az0oeq5X5LHTWO/B7IjmXaP+mIBT/b/tcOEPS8I6fwvl7OxJYElfSHma5EgXJ2+FreoNwHU7boR0mXuvT5MP46+udMHst2jP/N+muHDQZfkgO+oWrnNnoPbLLQoOB+SJD10sWOfNbEwqcPIjrCQXoD/dItLxXhEwRAAAQOSgBi46Adu1KzVhEbhZgwjSvSQ/Bz43whWKXIJAQ8FORyYMbRSi+djNzczRvzFikoVAGXBLkSPJNtuc5dpbrKX+fZF2UvL0N33sdos/Yr/+CUjuHy/fpDQDjoI/lpgmhuSAhUqawEi9IXyt5NAw75smCy1mYFivsg2vN1Rl4q3yKXHd9ckW44hffSY8kFh2YwFCRzH6otofPvhO9/bavkMCZPEJaRc7Ayxv/bzaWvkHkfo5nyZPjop30nuE4RTDEXX/CZEwPBfy89t0Z3+TQoKl+ImTC9rS4GR5QvK8UTEAABEDgMAYiNw3TlUxqyitjwgVQZpEuL88DKBkQU2ISyFLCEQIltcvTVS5da0qcNzov6U1ZzperSKSpY04/jdRFYVYLpoekhvfK9+vnH+bdMYEXfsgsTRHOafkbtDwEi1WmD1miL8plfrouAPWaOYkwH07aPdG57TUH8u/e1P+rXa1sou/f+Nk/eLnzXTPL3lfz2IsnkcUNTqHzeTFxlfe7T27ypMRXmWRvDTa3f1LO2/9pYxV/G0BYbZX8O+Dv0XSA3eunaVXtNouLDZ/s03bPts3vXytMrnyzhCgRAAAQOQwBi4zBd+ZSGrCI2fMtCwETTZEyQWgT7KvjxQdTjYoODHZmiY3wo6m92BQVIHdHEdeS/2BZ1Bz+y4HIggBpVXoK9Sv3UnDLYqzXS95EO+vPRDmp/S2z44DFNk5ogNsKv3rosX5v3pObxrM84wLdTkBiee8v63XBS7ytxni42SnaeRf4e+REW71/27jAEbyPvuwod5WtMVc/a/qvvb+39lfev4QB/x0I53c/V7APfBfa5lx4bZi/COg0ZxXjfnq7lT/S2omN8eVsz7kEABEBgzwQgNvbce8/3fUWxIY0NQYuKMopgXwU/c4gNH6ynwM0G3kX94mrxSQFdW2xIdranA2VqjwTpksl+cgDUWBg7pryyV9S/uNjwgW4KgCuBbzE6oBwe+6u8LqKuHx/ZUMboknmrfi58v0dsONeeRuXtJX7GH3sbxJn6CpHT5WiSLSdt0++meVYXG/Y7W/d36Htkv3M11+RZb5pUL13s5J9pcTj1Q7lgPORujmyMLJ9XijsQAAEQOAQBiI1DdOPTGrGC2Li56zXN9qaW2qAku5dfuWNApAJ8SgvPOXjhaKuX3qgv2i/T272h6lKZrteruquNIvhAcDiYHFogPly+X38IoFWbM4fjjQmi+bl+Rn5URjZsMN4MhlUAH+v0FzWBZLIsd3u9uqwHq+1JvrOvepqYEsf1YD24ToFsYy0Cv8/C1rb0dnX5V8i/D7nY0P1kDah75Wt8qp7V/bf11d/H9J2MltNF9Z1IyfqqLcp8rl66tiXXep1GVWzIaEerf9Q6j2p5qQifIAACIHBAAhAbB+zUFZu0iti4vIXdaWQahQ16QyASp86o4GeOkQ351Vemb5yvPliS0YZM7AzCp3Ip6JSsEnyKfbEr6f5T6kwsCvExOD2kXX5c/T4YHfaxFrDqZ+RDRWzIyInq3+vlLe4eJhx8QJ3anwfLQfSJDfq074kYmvuT3jddrxYSoS7N+HzVTHIhVw/WxeGhdRu1XZ0Ca/39CH4W7w5VYdrRzGO5qu9b0//M9tld5T7rRPOO2noq7ciKUxsGvwMj0gW1+uSREL0GQ4SFTKOi+4bIIDPd8qouXIIACIDAEQlAbByxV9dr0wpiY73GHKEm+tX03UJnbRyBz+7bEALdYjek3TdshgaEKUx+ilPFXi+9UgSPQAAEQAAEHicAsfE4w1e2ALGxxd5HULXFXpnRJz/C0ZpSNWNFuzHVE9m99N00FI6CAAiAwA4JQGzssNM25DLExoY6A66AAAiAAAiAAAiAwNYIQGxsrUf25Q/Exr76C96CAAiAAAiAAAiAwKoEIDZWxX24yiA2DtelaBAIgAAIgAAIgAAIzEcAYmM+lq9oCWJjiV43ay54N5v3Y0+zXsIh2AQBEAABEAABEACB+whAbNzHDaU8AYiNmd+E5kJW7EI0M2mYAwEQAAEQAAEQWIMAxMYalI9bB8TGnH3LIxqNU8CpHhYcGOGYEzlsgQAIgAAIgAAILEsAYmNZvke3vorYKA7NU4eIEWBJzw99CweahR7I0s7n7HC9Wnl7oJk+lO1kDuaT8rGzG/5pG8VhZG7oBPBouTwgLCXhCgRAAARAAARAAAQ2RwBiY3NdsiuHtiM26GTkGMH7E5qjYODTipP48MIjneQtIkDyp1OQfV9wfn2aMZ9kbMrHuvMTocmC2I9ZjD9cC49aDIxqyGvRG/2QfPgEARAAARAAARAAgQ0QgNjYQCfs2IXtiI0YyXuaerRBX4fU6shGvR+McOFM+bPCfmNkI9m/uvPp5DKXx4oITKVKGHEFAiAAAiAAAiCweQIQG5vvok07uAOxkQsDT5OC/YGRiQx5RRg4b1NGUiA2MmC4AQEQAAEQAAEQAIFIAGIjosDFHQReQGzUxEr+bLLY4GlYZmQD06jueP1QBARAAARAAARAYOsEIDa23kPb9m99sREC9ZNaQ1EE+7JOIsxT8ms0ZM1GGJUYPbLh3KQ1Gy3/TlJ/WMOh/PddjAXi237V4R0IgAAIgAAIgMA9BCA27qGGMkJgFbHhJICnReAUpHfXRKQdqryjIjBO7sRrJaZMo/IWWNBQ/fwnTcHi1FH+0Q5YUj4JDwHJn711G1ivkeHCDQiAAAiAAAiAwPYJQGxsv4+27OE6YmNuAiwOjGCYuw5lrzbyopKzSxzql+HADQiAAAiAAAiAwM4JQGzsvAOf7P4uxQYH/8U0puVIThEb7AWPcJzdj396n26//eDevcdhfsv1ECyDAAiAAAiAAAgsRQBiYymyr2F3H2KDz7WQKUz02ZjGtFCfTRYbC/kBsyAAAiAAAiAAAiCwNgGIjbWJH6u+fYiNYzFHa0AABEAABEAABEBgNwQgNnbTVZt0FGJjk90Cp0AABEAABEAABEBgGwQgNrbRD3v1AmJjrz0Hv0EABEAABEAABEBgBQIQGytAPnAVEBsH7lw0DQRAAARAAARAAAQeJQCx8SjB1y4PsfHa/Y/WgwAIgAAIgAAIgMAgAYiNQTxI7BCA2OgAQjIIgAAIgAAIgAAIvDIBiI1X7v3H2w6x8ThDWAABEAABEAABEACBwxKA2Dhs167SMIiNVTCjEhAAARAAARAAARDYJwGIjX3221a8htjYSk/ADxAAARAAARAAARDYIAGIjQ12yo5c+v8B2OEslB7xb8YAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>County</th>\n",
       "      <th>comp</th>\n",
       "      <th>status</th>\n",
       "      <th>B28009A</th>\n",
       "      <th>B28009B</th>\n",
       "      <th>B28009H</th>\n",
       "      <th>B28009I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>287.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3340</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3341</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>89.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1657.0</td>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3343</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3344</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>180.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3345 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             FIPS              Tract            County  \\\n",
       "0     21001970100  Census Tract 9701   Adair County      \n",
       "1     21001970100  Census Tract 9701   Adair County      \n",
       "2     21001970100  Census Tract 9701   Adair County      \n",
       "3     21001970200  Census Tract 9702   Adair County      \n",
       "4     21001970200  Census Tract 9702   Adair County      \n",
       "...           ...                ...            ...      \n",
       "3340  21239050300  Census Tract 503    Woodford County   \n",
       "3341  21239050300  Census Tract 503    Woodford County   \n",
       "3342  21239050400  Census Tract 504    Woodford County   \n",
       "3343  21239050400  Census Tract 504    Woodford County   \n",
       "3344  21239050400  Census Tract 504    Woodford County   \n",
       "\n",
       "                         comp               status  B28009A  B28009B  B28009H  \\\n",
       "0     Computer in the Home     Broadband Access     1185.0   0.0      1179.0    \n",
       "1     Computer in the Home     No Broadband Access  196.0    0.0      196.0     \n",
       "2     No Computer in the Home  No Broadband Access  204.0    0.0      204.0     \n",
       "3     Computer in the Home     Broadband Access     1403.0   15.0     1403.0    \n",
       "4     Computer in the Home     No Broadband Access  287.0    0.0      287.0     \n",
       "...                    ...                     ...    ...    ...        ...     \n",
       "3340  Computer in the Home     No Broadband Access  197.0    0.0      197.0     \n",
       "3341  No Computer in the Home  No Broadband Access  89.0     23.0     89.0      \n",
       "3342  Computer in the Home     Broadband Access     1670.0   40.0     1657.0    \n",
       "3343  Computer in the Home     No Broadband Access  130.0    0.0      116.0     \n",
       "3344  No Computer in the Home  No Broadband Access  180.0    30.0     121.0     \n",
       "\n",
       "      B28009I  \n",
       "0     28.0     \n",
       "1     0.0      \n",
       "2     0.0      \n",
       "3     0.0      \n",
       "4     0.0      \n",
       "...   ...      \n",
       "3340  0.0      \n",
       "3341  13.0     \n",
       "3342  164.0    \n",
       "3343  14.0     \n",
       "3344  59.0     \n",
       "\n",
       "[3345 rows x 9 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg1 = {'comp':  {'Computer in the Home': np.arange(3, 6), 'No Computer in the Home': np.array([6])},\n",
    "        'status':  {'Broadband Access': np.array([4]) , 'No Broadband Access': np.array([3,5,6])}}\n",
    "table1 = acs.gen_subgroups(arg1, ['B28009A','B28009B','B28009H','B28009I'])\n",
    "table1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>County</th>\n",
       "      <th>comp</th>\n",
       "      <th>status</th>\n",
       "      <th>B28005</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>1207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>1464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3340</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3341</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>1895.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3343</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3344</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3345 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             FIPS              Tract            County  \\\n",
       "0     21001970100  Census Tract 9701   Adair County      \n",
       "1     21001970100  Census Tract 9701   Adair County      \n",
       "2     21001970100  Census Tract 9701   Adair County      \n",
       "3     21001970200  Census Tract 9702   Adair County      \n",
       "4     21001970200  Census Tract 9702   Adair County      \n",
       "...           ...                ...            ...      \n",
       "3340  21239050300  Census Tract 503    Woodford County   \n",
       "3341  21239050300  Census Tract 503    Woodford County   \n",
       "3342  21239050400  Census Tract 504    Woodford County   \n",
       "3343  21239050400  Census Tract 504    Woodford County   \n",
       "3344  21239050400  Census Tract 504    Woodford County   \n",
       "\n",
       "                         comp               status  B28005  \n",
       "0     Computer in the Home     Broadband Access     1207.0  \n",
       "1     Computer in the Home     No Broadband Access  196.0   \n",
       "2     No Computer in the Home  No Broadband Access  204.0   \n",
       "3     Computer in the Home     Broadband Access     1464.0  \n",
       "4     Computer in the Home     No Broadband Access  287.0   \n",
       "...                    ...                     ...    ...   \n",
       "3340  Computer in the Home     No Broadband Access  197.0   \n",
       "3341  No Computer in the Home  No Broadband Access  125.0   \n",
       "3342  Computer in the Home     Broadband Access     1895.0  \n",
       "3343  Computer in the Home     No Broadband Access  130.0   \n",
       "3344  No Computer in the Home  No Broadband Access  210.0   \n",
       "\n",
       "[3345 rows x 6 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg1 = {'comp':  {'Computer in the Home': np.concatenate([np.arange(4, 7), np.arange(10, 13), np.arange(16, 19)]), 'No Computer in the Home': np.array([7,13,19])     }   ,\n",
    "        'status':  {'Broadband Access': np.array([5, 11, 17])       , 'No Broadband Access': np.array([4,6,7,10,12, 13, 16, 18,19])   } }\n",
    "table2 = acs.gen_subgroups(arg1, groups =['B28005'])\n",
    "table2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>County</th>\n",
       "      <th>comp</th>\n",
       "      <th>status</th>\n",
       "      <th>B28005</th>\n",
       "      <th>B28009A</th>\n",
       "      <th>B28009B</th>\n",
       "      <th>B28009H</th>\n",
       "      <th>B28009I</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>1207.0</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>196.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>204.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>287.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3340</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>197.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3341</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>125.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1657.0</td>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3343</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>130.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3344</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>210.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3345 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             FIPS              Tract            County  \\\n",
       "0     21001970100  Census Tract 9701   Adair County      \n",
       "1     21001970100  Census Tract 9701   Adair County      \n",
       "2     21001970100  Census Tract 9701   Adair County      \n",
       "3     21001970200  Census Tract 9702   Adair County      \n",
       "4     21001970200  Census Tract 9702   Adair County      \n",
       "...           ...                ...            ...      \n",
       "3340  21239050300  Census Tract 503    Woodford County   \n",
       "3341  21239050300  Census Tract 503    Woodford County   \n",
       "3342  21239050400  Census Tract 504    Woodford County   \n",
       "3343  21239050400  Census Tract 504    Woodford County   \n",
       "3344  21239050400  Census Tract 504    Woodford County   \n",
       "\n",
       "                         comp               status  B28005  B28009A  B28009B  \\\n",
       "0     Computer in the Home     Broadband Access     1207.0  1185.0   0.0       \n",
       "1     Computer in the Home     No Broadband Access  196.0   196.0    0.0       \n",
       "2     No Computer in the Home  No Broadband Access  204.0   204.0    0.0       \n",
       "3     Computer in the Home     Broadband Access     1464.0  1403.0   15.0      \n",
       "4     Computer in the Home     No Broadband Access  287.0   287.0    0.0       \n",
       "...                    ...                     ...    ...     ...    ...       \n",
       "3340  Computer in the Home     No Broadband Access  197.0   197.0    0.0       \n",
       "3341  No Computer in the Home  No Broadband Access  125.0   89.0     23.0      \n",
       "3342  Computer in the Home     Broadband Access     1895.0  1670.0   40.0      \n",
       "3343  Computer in the Home     No Broadband Access  130.0   130.0    0.0       \n",
       "3344  No Computer in the Home  No Broadband Access  210.0   180.0    30.0      \n",
       "\n",
       "      B28009H  B28009I  \n",
       "0     1179.0   28.0     \n",
       "1     196.0    0.0      \n",
       "2     204.0    0.0      \n",
       "3     1403.0   0.0      \n",
       "4     287.0    0.0      \n",
       "...     ...    ...      \n",
       "3340  197.0    0.0      \n",
       "3341  89.0     13.0     \n",
       "3342  1657.0   164.0    \n",
       "3343  116.0    14.0     \n",
       "3344  121.0    59.0     \n",
       "\n",
       "[3345 rows x 10 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2.merge(table1, on = list(table2.columns[:5]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do the same, you can also utilize the attribute function **`merge_gen_subgroups`** which takes a list of the argument dictionaries and a list of group names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Tract</th>\n",
       "      <th>County</th>\n",
       "      <th>comp</th>\n",
       "      <th>status</th>\n",
       "      <th>B28009A</th>\n",
       "      <th>B28009B</th>\n",
       "      <th>B28009H</th>\n",
       "      <th>B28009I</th>\n",
       "      <th>B28005</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1179.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>196.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21001970100</td>\n",
       "      <td>Census Tract 9701</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21001970200</td>\n",
       "      <td>Census Tract 9702</td>\n",
       "      <td>Adair County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>287.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3340</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3341</th>\n",
       "      <td>21239050300</td>\n",
       "      <td>Census Tract 503</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>89.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>Broadband Access</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1657.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1895.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3343</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3344</th>\n",
       "      <td>21239050400</td>\n",
       "      <td>Census Tract 504</td>\n",
       "      <td>Woodford County</td>\n",
       "      <td>No Computer in the Home</td>\n",
       "      <td>No Broadband Access</td>\n",
       "      <td>180.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3345 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             FIPS              Tract            County  \\\n",
       "0     21001970100  Census Tract 9701   Adair County      \n",
       "1     21001970100  Census Tract 9701   Adair County      \n",
       "2     21001970100  Census Tract 9701   Adair County      \n",
       "3     21001970200  Census Tract 9702   Adair County      \n",
       "4     21001970200  Census Tract 9702   Adair County      \n",
       "...           ...                ...            ...      \n",
       "3340  21239050300  Census Tract 503    Woodford County   \n",
       "3341  21239050300  Census Tract 503    Woodford County   \n",
       "3342  21239050400  Census Tract 504    Woodford County   \n",
       "3343  21239050400  Census Tract 504    Woodford County   \n",
       "3344  21239050400  Census Tract 504    Woodford County   \n",
       "\n",
       "                         comp               status  B28009A  B28009B  B28009H  \\\n",
       "0     Computer in the Home     Broadband Access     1185.0   0.0      1179.0    \n",
       "1     Computer in the Home     No Broadband Access  196.0    0.0      196.0     \n",
       "2     No Computer in the Home  No Broadband Access  204.0    0.0      204.0     \n",
       "3     Computer in the Home     Broadband Access     1403.0   15.0     1403.0    \n",
       "4     Computer in the Home     No Broadband Access  287.0    0.0      287.0     \n",
       "...                    ...                     ...    ...    ...        ...     \n",
       "3340  Computer in the Home     No Broadband Access  197.0    0.0      197.0     \n",
       "3341  No Computer in the Home  No Broadband Access  89.0     23.0     89.0      \n",
       "3342  Computer in the Home     Broadband Access     1670.0   40.0     1657.0    \n",
       "3343  Computer in the Home     No Broadband Access  130.0    0.0      116.0     \n",
       "3344  No Computer in the Home  No Broadband Access  180.0    30.0     121.0     \n",
       "\n",
       "      B28009I  B28005  \n",
       "0     28.0     1207.0  \n",
       "1     0.0      196.0   \n",
       "2     0.0      204.0   \n",
       "3     0.0      1464.0  \n",
       "4     0.0      287.0   \n",
       "...   ...        ...   \n",
       "3340  0.0      197.0   \n",
       "3341  13.0     125.0   \n",
       "3342  164.0    1895.0  \n",
       "3343  14.0     130.0   \n",
       "3344  59.0     210.0   \n",
       "\n",
       "[3345 rows x 10 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arg1 = {'comp':  {'Computer in the Home': np.arange(3, 6), 'No Computer in the Home': np.array([6])},\n",
    "        'status':  {'Broadband Access': np.array([4]) , 'No Broadband Access': np.array([3,5,6])}}\n",
    "\n",
    "arg2 = {'comp':  {'Computer in the Home': np.concatenate([np.arange(4, 7), np.arange(10, 13), np.arange(16, 19)]), 'No Computer in the Home': np.array([7,13,19])     }   ,\n",
    "        'status':  {'Broadband Access': np.array([5, 11, 17])       , 'No Broadband Access': np.array([4,6,7,10,12, 13, 16, 18,19])   } }\n",
    "\n",
    "\n",
    "group1 = ['B28009A','B28009B','B28009H','B28009I']\n",
    "group2 = ['B28005']\n",
    "\n",
    "acs.merge_gen_subgroups([arg1, arg2], [group1, group2])"
   ]
  }
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