FinalProject_group007.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Who Loses the Most Sleep? A Demographic Analysis of Poor Sleepers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We took information that the CDC had gathered about which groups in the US get sufficient sleep (defined by the CDC as 7+ hours per night on average) from 2008–2018 and analyzed it to see what demographic trends we could discover around who was losing the most sleep. We used two metrics, slope of the regression line (our way of trying to average the loss of sleep over the 10 year period), and total decline (where we simply compared the values from 2008 and 2018). We discovered that, while some categories did not have any outlier groups in both metrics, the groups losing the most sleep were:  \n",
    "\n",
    "* Age: 55-64\n",
    "* Education: Some College\n",
    "* Household: Single Parent\n",
    "* Income: Below 100K USD\n",
    "* Insurance: Public Insurance\n",
    "* Race: Hispanic\n",
    "\n",
    "**Note:** We did have to exclude 3 racial groups from the analysis due to excess variability (from which we inferred too small a sample size), as well as excluding sexual orientation as a category altogether for similar reasons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Justin Ferrell\n",
    "- Leann Gamboa\n",
    "- Geoffrey Lizar\n",
    "- Stella Seki"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='research_question'></a>\n",
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What demographic groups in the United States have been losing the most sleep over the last decade?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='background'></a>\n",
    "\n",
    "## Background & Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many demographic groups have been critically underserved by society at large, both recently and historically; African Americans are commonly dismissed by doctors, who systematically prescribe them fewer painkillers—and at lower doses—for conditions that would otherwise merit such intervention [1]; even as of 2020, women still only make an average of 81 cents for every dollar their male colleagues make [2]; until unsettlingly recently, the law did not provide the LGBTQ+ community equal protection under the law [3]; and even breaking things down by age offers no relief, with Millennials (and now Gen Z) graduating into the worst economy the world has seen since the great depression [4]. With so many demographic groups suffering unequally in so many ways, it can be hard to know where to focus.\n",
    "\n",
    "The science of sleep is complex and involves as many as ten main structures of the brain [5]. It therefore comes as no surprise that sleep quality, quantity, and routine are affected by just about any stressor ranging from lifestyle and health factors (e.g., diet, exercise, and mental health), to personal and socio-demographic factors (e.g., financial concerns), to psychosocial and occupational factors (e.g., lack of choice or commuting) [6]. Clearly, the causes of sleep deprivation are diverse and often inscrutable [7], and with stress in America at an all-time high in 2020 [8], we think that investigating which subgroups of America have been losing the most sleep in the last decade could open the door to future analyses that explore which factors contribute the most to Americans getting worse sleep over time.\n",
    "\n",
    "**References:**\n",
    "1. **Association of American Medical Colleges:** <u>How we fail black patients in pain</u> https://www.aamc.org/news-insights/how-we-fail-black-patients-pain  \n",
    "2. **PayScale:** <u>The State of the Gender Pay Gap 2020</u> https://www.payscale.com/data/gender-pay-gap  \n",
    "3. **MetroWeekly:** <u>President Biden issues executive order protecting LGBTQ people from discrimination</u> https://www.metroweekly.com/2021/01/president-biden-issues-executive-order-protecting-lgbtq-people-from-discrimination/  \n",
    "4. **Washington Post:** <u>The unluckiest generation in U.S. history</u> https://www.washingtonpost.com/business/2020/05/27/millennial-recession-covid/  \n",
    "5. **National Institute of Neurological Disorders and Stroke:** <u>Brain Basics: Understanding Sleep</u> https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Understanding-sleep  \n",
    "6. **RAND Corporation:** <u>Why sleep matters — the economic costs of insufficient sleep</u> https://www.rand.org/pubs/research_reports/RR1791.html  \n",
    "7. **American Psychological Association:** <u>Factors that contribute to insomnia</u> https://www.apa.org/monitor/oct01/insomnia#:~:text=Extended%20time%20in%20bed%2C%20napping,or%20a%20poor%20sleeping%20surface  \n",
    "8. **American Psychological Association:** <u>Stress in America<sup>TM</sup> 2020</u> https://www.apa.org/news/press/releases/stress/2020/report#:~:text=The%20COVID%2D19%20pandemic%20has,be%20serious%20and%20long%2Dlasting  \n",
    "9. **International Journal of Health Service:** <u>Sexism and Racism in the American Health Care Industry: A Comparative Analysis</u> https://journals.sagepub.com/doi/10.2190/AK0C-M9JF-1TR1-5UYF  \n",
    "10. **Pew Research:** <u>The Changing Profile of Unmarried Parents</u> https://www.pewsocialtrends.org/2018/04/25/the-changing-profile-of-unmarried-parents/#:~:text=This%20trend%20has%20boosted%20the,as%20were%2088%25%20in%201968  \n",
    "11. **Pew Research:** <u>6 facts about economic inequality in the U.S.</u> https://www.pewresearch.org/fact-tank/2020/02/07/6-facts-about-economic-inequality-in-the-u-s/#:~:text=2Income%20inequality%20in%20the,for%20Economic%20Cooperation%20and%20Development.&text=In%202017%2C%20the%20U.S.%20had%20a%20Gini%20coefficient%20of%200.434  \n",
    "12. **National Geographic:** <u>Our nights are getting brighter, and Earth is paying the price</u> https://www.nationalgeographic.com/science/2019/04/nights-are-getting-brighter-earth-paying-the-price-light-pollution-dark-skies/#:~:text=Another%20study%20from%202017%20suggests,rural%20road%2090%20miles%20away  \n",
    "13. **Southern Medical Journal:** <u>Noise Pollution: A Modern Plague</u> https://www.medscape.com/viewarticle/554566_2  \n",
    "14. **Gallup:** <u>In U.S., More Adults identifying as LGBT</u> https://news.gallup.com/poll/201731/lgbt-identification-rises.aspx  \n",
    "15. **Frontiers in Public Health:** <u>Bigger, Brighter, Bluer-Better? Current Light-Emitting Devices - Adverse Sleep Properties and Preventative Strategies</u> https://doi.org/10.3389/fpubh.2015.00233  \n",
    "16. **National Sleep Foundation:** <u>Sleep in America Poll: Sleep and Ethnicity</u> https://www.sleepfoundation.org/wp-content/uploads/2018/10/NSF-Sleep-in-America-Poll-Summary-of-Findings-.pdf?x96585  \n",
    "17. **National Sleep Foundation:** <u>Sleep in America Poll: Sleep and Aging</u> https://www.sleepfoundation.org/wp-content/uploads/2018/10/2003SleepPollExecSumm.pdf?x96585  \n",
    "18. **NCHS Data Brief:** <u>Sleep Duration, Quality of Sleep, and Use of Sleep Medication, by Sex and Family Type, 2013-2014</u> https://www.cdc.gov/nchs/products/databriefs/db230.htm  \n",
    "19. **Centers for Disease Control and Prevention Morbidity and Mortality Weekly Report:** <u>Disproportionate Incidence of COVID-19 Infection, Hospitalizations, and Deaths Among Persons Identifying as Hispanic or Latino - Denver, Colorado March-October 2020</u> https://doi.org/10.15585/mmwr.mm6948a3  \n",
    "20. **U.S. Census Bureau:** <u>America’s Families and Living Arrangements: 2019; Table FG6</u> https://www.census.gov/data/tables/2019/demo/families/cps-2019.html  \n",
    "21. **Sleep medicine reviews:** <u>How much sleep do we need?</u> https://doi.org/10.1053/smrv.2000.0138  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the complicated and multi-dimensional nature of demographic splits, our hypothesis is similarly split into several dimensions:\n",
    "* **Anyone who the medical community critically underserves:** African Americans [1] and women in general [9] face systemic discrimination in the medical system, resulting from unconscious bias reinforced in doctors during their training. It is possible that this has led to worse sleep over time.\n",
    "* **Women:** There are several factors that contribute to our hypothesis that women have been losing more sleep than men. As mentioned, women are one demographic group that is underserved by the medical community. In addition, women are far more likely than men to be solo parents [10]\n",
    "* **Demographics with low Socioeconomic Status (SES):** It is well established that those in poverty are more sleep-deprived on average [6]. With income inequality at an all-time high (and still climbing) [11], we hypothesize that this increase will have led to an increase in sleep deprivation.\n",
    "* **Young people:** With income inequality hitting young people the worst [11], we hypothesize that similar factors will impact this demographic in an exaggerated fashion.\n",
    "* **People who live in metropolitan areas:** Though industrialization and large cities are by no means new phenomena, the rates of light [12] and noise [13] pollution have continued to rise over time. We hypothesize that these factors may have led to an increase in sleep deprivation in metropolitan areas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Dataset Name: Percent of Adults Who Get Sufficient Sleep by Year by State (NHIS_2013_%ofAdultsWhoGetSufficientSleep_byYear_byState.xlsx)  \n",
    "* Link to the dataset: https://www.healthypeople.gov/2020/data-search/Search-the-Data#objid=5261  \n",
    "* Number of observations: Undisclosed (see below); pending data request from CDC  \n",
    "\n",
    "This dataset was collected by the Center of Disease Control (CDC) through their yearly National Health Interview Survey, which collects information on the health of the civilian, noninstitutionalized population of the United States. This particular dataset includes survey information from the years 2008–2018 to calculate the percentage of the adult population that self-report getting a sufficient amount of sleep, which the CDC defines as an average of at least 7 hours of sleep per night. Demographic data is also collected for each participant, allowing the NHIS to break down the adult population into smaller, more detailed categories so the kinds of demographic analyses like the one our question is asking can be performed.  \n",
    "\n",
    "The biggest weakness in our dataset is that the total population of the participants of the survey are not included in this set, and the results are strictly defined by percentages. We don’t know how many people participated, nor do we have a count of participants per category; we will have to use variability in the data as our metric for deciding which groups may not have had statistically significant sample sizes. While this methodology is far from perfect, it is the best we can do with the existing data. Our best metric of variability is likely to be a combination of yearly standard error and year-to-year variance; too much of each is a likely indicator of an insufficient sample size.  \n",
    "\n",
    "It is also worth noting that the original spreadsheet only contains state-by-state data for 2013. We will therefore be limited to analyzing national-level data over time.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing numpy, pandas, seaborn, and matplotlib, then setting our general graphing parameters\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "params = {'figure.figsize': (17, 7),\n",
    "          'legend.fontsize': 17,\n",
    "          'figure.titlesize': 40,\n",
    "         'axes.labelsize': 20,\n",
    "         'axes.titlesize': 20,\n",
    "         'xtick.labelsize': 20,\n",
    "         'ytick.labelsize': 20}\n",
    "pylab.rcParams.update(params)\n",
    "sns.set(font_scale = 1.5, style = \"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Geography</th>\n",
       "      <th>Estimate</th>\n",
       "      <th>StdErr</th>\n",
       "      <th>LowerCI</th>\n",
       "      <th>UpperCI</th>\n",
       "      <th>Estimate.1</th>\n",
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       "      <th>PopTitle</th>\n",
       "      <th></th>\n",
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       "      <th>Total</th>\n",
       "      <td>United States</td>\n",
       "      <td>71.6</td>\n",
       "      <td>0.414</td>\n",
       "      <td>70.7</td>\n",
       "      <td>72.4</td>\n",
       "      <td>71.6</td>\n",
       "      <td>0.372</td>\n",
       "      <td>70.8</td>\n",
       "      <td>72.3</td>\n",
       "      <td>71.3</td>\n",
       "      <td>...</td>\n",
       "      <td>67.1</td>\n",
       "      <td>68.6</td>\n",
       "      <td>67.5</td>\n",
       "      <td>0.389</td>\n",
       "      <td>66.7</td>\n",
       "      <td>68.2</td>\n",
       "      <td>67.4</td>\n",
       "      <td>0.372</td>\n",
       "      <td>66.6</td>\n",
       "      <td>68.1</td>\n",
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       "      <th>Sex</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>United States</td>\n",
       "      <td>71.4</td>\n",
       "      <td>0.588</td>\n",
       "      <td>70.2</td>\n",
       "      <td>72.5</td>\n",
       "      <td>71.5</td>\n",
       "      <td>0.529</td>\n",
       "      <td>70.4</td>\n",
       "      <td>72.5</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>67.2</td>\n",
       "      <td>69.3</td>\n",
       "      <td>67.7</td>\n",
       "      <td>0.565</td>\n",
       "      <td>66.6</td>\n",
       "      <td>68.9</td>\n",
       "      <td>67.5</td>\n",
       "      <td>0.537</td>\n",
       "      <td>66.4</td>\n",
       "      <td>68.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>United States</td>\n",
       "      <td>71.7</td>\n",
       "      <td>0.519</td>\n",
       "      <td>70.7</td>\n",
       "      <td>72.8</td>\n",
       "      <td>71.7</td>\n",
       "      <td>0.497</td>\n",
       "      <td>70.7</td>\n",
       "      <td>72.6</td>\n",
       "      <td>70.6</td>\n",
       "      <td>...</td>\n",
       "      <td>66.5</td>\n",
       "      <td>68.5</td>\n",
       "      <td>67.2</td>\n",
       "      <td>0.538</td>\n",
       "      <td>66.2</td>\n",
       "      <td>68.3</td>\n",
       "      <td>67.3</td>\n",
       "      <td>0.499</td>\n",
       "      <td>66.3</td>\n",
       "      <td>68.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race/Ethnicity</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                    Geography Estimate  StdErr  LowerCI  UpperCI Estimate.1  \\\n",
       "PopTitle                                                                      \n",
       "Total           United States     71.6   0.414     70.7     72.4       71.6   \n",
       "Sex                       NaN      NaN     NaN      NaN      NaN        NaN   \n",
       "   Male         United States     71.4   0.588     70.2     72.5       71.5   \n",
       "   Female       United States     71.7   0.519     70.7     72.8       71.7   \n",
       "Race/Ethnicity            NaN      NaN     NaN      NaN      NaN        NaN   \n",
       "\n",
       "                StdErr.1  LowerCI.1  UpperCI.1 Estimate.2  ...  LowerCI.8  \\\n",
       "PopTitle                                                   ...              \n",
       "Total              0.372       70.8       72.3       71.3  ...       67.1   \n",
       "Sex                  NaN        NaN        NaN        NaN  ...        NaN   \n",
       "   Male            0.529       70.4       72.5         72  ...       67.2   \n",
       "   Female          0.497       70.7       72.6       70.6  ...       66.5   \n",
       "Race/Ethnicity       NaN        NaN        NaN        NaN  ...        NaN   \n",
       "\n",
       "                UpperCI.8  Estimate.9  StdErr.9  LowerCI.9  UpperCI.9  \\\n",
       "PopTitle                                                                \n",
       "Total                68.6        67.5     0.389       66.7       68.2   \n",
       "Sex                   NaN         NaN       NaN        NaN        NaN   \n",
       "   Male              69.3        67.7     0.565       66.6       68.9   \n",
       "   Female            68.5        67.2     0.538       66.2       68.3   \n",
       "Race/Ethnicity        NaN         NaN       NaN        NaN        NaN   \n",
       "\n",
       "                Estimate.10  StdErr.10  LowerCI.10  UpperCI.10  \n",
       "PopTitle                                                        \n",
       "Total                  67.4      0.372        66.6        68.1  \n",
       "Sex                     NaN        NaN         NaN         NaN  \n",
       "   Male                67.5      0.537        66.4        68.5  \n",
       "   Female              67.3      0.499        66.3        68.2  \n",
       "Race/Ethnicity          NaN        NaN         NaN         NaN  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in the original (\"unclean\") csv\n",
    "unclean = pd.read_csv('Data/NHIS_2013_%ofAdultsWhoGetSufficientSleep_byYear_byState.csv', header = 1, index_col = 0)\n",
    "unclean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning 1\n",
    "Our goal is to only analyze the Estimate for each year. However, StdErr will be a good measure of variance. When the data cleaning is finished, we should end up with two dataframes: one for the estimates, and one for the StdErr. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Estimate.4</th>\n",
       "      <th>StdErr.4</th>\n",
       "      <th>...</th>\n",
       "      <th>Estimate.6</th>\n",
       "      <th>StdErr.6</th>\n",
       "      <th>Estimate.7</th>\n",
       "      <th>StdErr.7</th>\n",
       "      <th>Estimate.8</th>\n",
       "      <th>StdErr.8</th>\n",
       "      <th>Estimate.9</th>\n",
       "      <th>StdErr.9</th>\n",
       "      <th>Estimate.10</th>\n",
       "      <th>StdErr.10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PopTitle</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>71.6</td>\n",
       "      <td>0.414</td>\n",
       "      <td>71.6</td>\n",
       "      <td>0.372</td>\n",
       "      <td>71.3</td>\n",
       "      <td>0.345</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>70.8</td>\n",
       "      <td>0.334</td>\n",
       "      <td>...</td>\n",
       "      <td>69.3</td>\n",
       "      <td>0.368</td>\n",
       "      <td>68.1</td>\n",
       "      <td>0.375</td>\n",
       "      <td>67.8</td>\n",
       "      <td>0.374</td>\n",
       "      <td>67.5</td>\n",
       "      <td>0.389</td>\n",
       "      <td>67.4</td>\n",
       "      <td>0.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>71.4</td>\n",
       "      <td>0.588</td>\n",
       "      <td>71.5</td>\n",
       "      <td>0.529</td>\n",
       "      <td>72</td>\n",
       "      <td>0.494</td>\n",
       "      <td>70.7</td>\n",
       "      <td>0.483</td>\n",
       "      <td>71.2</td>\n",
       "      <td>0.479</td>\n",
       "      <td>...</td>\n",
       "      <td>69.4</td>\n",
       "      <td>0.531</td>\n",
       "      <td>68.6</td>\n",
       "      <td>0.549</td>\n",
       "      <td>68.2</td>\n",
       "      <td>0.548</td>\n",
       "      <td>67.7</td>\n",
       "      <td>0.565</td>\n",
       "      <td>67.5</td>\n",
       "      <td>0.537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>71.7</td>\n",
       "      <td>0.519</td>\n",
       "      <td>71.7</td>\n",
       "      <td>0.497</td>\n",
       "      <td>70.6</td>\n",
       "      <td>0.459</td>\n",
       "      <td>71.4</td>\n",
       "      <td>0.436</td>\n",
       "      <td>70.4</td>\n",
       "      <td>0.457</td>\n",
       "      <td>...</td>\n",
       "      <td>69.2</td>\n",
       "      <td>0.499</td>\n",
       "      <td>67.6</td>\n",
       "      <td>0.498</td>\n",
       "      <td>67.5</td>\n",
       "      <td>0.504</td>\n",
       "      <td>67.2</td>\n",
       "      <td>0.538</td>\n",
       "      <td>67.3</td>\n",
       "      <td>0.499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race/Ethnicity</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Estimate  StdErr Estimate.1  StdErr.1 Estimate.2  StdErr.2  \\\n",
       "PopTitle                                                                    \n",
       "Total              71.6   0.414       71.6     0.372       71.3     0.345   \n",
       "Sex                 NaN     NaN        NaN       NaN        NaN       NaN   \n",
       "   Male            71.4   0.588       71.5     0.529         72     0.494   \n",
       "   Female          71.7   0.519       71.7     0.497       70.6     0.459   \n",
       "Race/Ethnicity      NaN     NaN        NaN       NaN        NaN       NaN   \n",
       "\n",
       "                Estimate.3  StdErr.3  Estimate.4  StdErr.4  ...  Estimate.6  \\\n",
       "PopTitle                                                    ...               \n",
       "Total                 71.0     0.333        70.8     0.334  ...        69.3   \n",
       "Sex                    NaN       NaN         NaN       NaN  ...         NaN   \n",
       "   Male               70.7     0.483        71.2     0.479  ...        69.4   \n",
       "   Female             71.4     0.436        70.4     0.457  ...        69.2   \n",
       "Race/Ethnicity         NaN       NaN         NaN       NaN  ...         NaN   \n",
       "\n",
       "                StdErr.6  Estimate.7  StdErr.7  Estimate.8  StdErr.8  \\\n",
       "PopTitle                                                               \n",
       "Total              0.368        68.1     0.375        67.8     0.374   \n",
       "Sex                  NaN         NaN       NaN         NaN       NaN   \n",
       "   Male            0.531        68.6     0.549        68.2     0.548   \n",
       "   Female          0.499        67.6     0.498        67.5     0.504   \n",
       "Race/Ethnicity       NaN         NaN       NaN         NaN       NaN   \n",
       "\n",
       "                Estimate.9  StdErr.9  Estimate.10  StdErr.10  \n",
       "PopTitle                                                      \n",
       "Total                 67.5     0.389         67.4      0.372  \n",
       "Sex                    NaN       NaN          NaN        NaN  \n",
       "   Male               67.7     0.565         67.5      0.537  \n",
       "   Female             67.2     0.538         67.3      0.499  \n",
       "Race/Ethnicity         NaN       NaN          NaN        NaN  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a list of columns to drop, starting with 'Geography'\n",
    "to_drop = ['Geography']\n",
    "for header in unclean.columns:\n",
    "    if 'erC' in header:\n",
    "        to_drop.append(header)\n",
    "\n",
    "# Drop the columns we don't want\n",
    "unclean = unclean.drop(columns = to_drop)\n",
    "unclean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning 2\n",
    "There are also indices like 'Sex' that are general categories, not specific demographics. These do not contain data and are thus not relevant to our analysis. Because these are the only completely empty rows, we can use .dropna() to get rid of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Estimate</th>\n",
       "      <th>StdErr</th>\n",
       "      <th>Estimate.1</th>\n",
       "      <th>StdErr.1</th>\n",
       "      <th>Estimate.2</th>\n",
       "      <th>StdErr.2</th>\n",
       "      <th>Estimate.3</th>\n",
       "      <th>StdErr.3</th>\n",
       "      <th>Estimate.4</th>\n",
       "      <th>StdErr.4</th>\n",
       "      <th>...</th>\n",
       "      <th>Estimate.6</th>\n",
       "      <th>StdErr.6</th>\n",
       "      <th>Estimate.7</th>\n",
       "      <th>StdErr.7</th>\n",
       "      <th>Estimate.8</th>\n",
       "      <th>StdErr.8</th>\n",
       "      <th>Estimate.9</th>\n",
       "      <th>StdErr.9</th>\n",
       "      <th>Estimate.10</th>\n",
       "      <th>StdErr.10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PopTitle</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>71.6</td>\n",
       "      <td>0.414</td>\n",
       "      <td>71.6</td>\n",
       "      <td>0.372</td>\n",
       "      <td>71.3</td>\n",
       "      <td>0.345</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.333</td>\n",
       "      <td>70.8</td>\n",
       "      <td>0.334</td>\n",
       "      <td>...</td>\n",
       "      <td>69.3</td>\n",
       "      <td>0.368</td>\n",
       "      <td>68.1</td>\n",
       "      <td>0.375</td>\n",
       "      <td>67.8</td>\n",
       "      <td>0.374</td>\n",
       "      <td>67.5</td>\n",
       "      <td>0.389</td>\n",
       "      <td>67.4</td>\n",
       "      <td>0.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>71.4</td>\n",
       "      <td>0.588</td>\n",
       "      <td>71.5</td>\n",
       "      <td>0.529</td>\n",
       "      <td>72</td>\n",
       "      <td>0.494</td>\n",
       "      <td>70.7</td>\n",
       "      <td>0.483</td>\n",
       "      <td>71.2</td>\n",
       "      <td>0.479</td>\n",
       "      <td>...</td>\n",
       "      <td>69.4</td>\n",
       "      <td>0.531</td>\n",
       "      <td>68.6</td>\n",
       "      <td>0.549</td>\n",
       "      <td>68.2</td>\n",
       "      <td>0.548</td>\n",
       "      <td>67.7</td>\n",
       "      <td>0.565</td>\n",
       "      <td>67.5</td>\n",
       "      <td>0.537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>71.7</td>\n",
       "      <td>0.519</td>\n",
       "      <td>71.7</td>\n",
       "      <td>0.497</td>\n",
       "      <td>70.6</td>\n",
       "      <td>0.459</td>\n",
       "      <td>71.4</td>\n",
       "      <td>0.436</td>\n",
       "      <td>70.4</td>\n",
       "      <td>0.457</td>\n",
       "      <td>...</td>\n",
       "      <td>69.2</td>\n",
       "      <td>0.499</td>\n",
       "      <td>67.6</td>\n",
       "      <td>0.498</td>\n",
       "      <td>67.5</td>\n",
       "      <td>0.504</td>\n",
       "      <td>67.2</td>\n",
       "      <td>0.538</td>\n",
       "      <td>67.3</td>\n",
       "      <td>0.499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Indian or Alaska Native only</th>\n",
       "      <td>74.4</td>\n",
       "      <td>3.811</td>\n",
       "      <td>71.7</td>\n",
       "      <td>3.948</td>\n",
       "      <td>63.5</td>\n",
       "      <td>4.609</td>\n",
       "      <td>67.5</td>\n",
       "      <td>2.796</td>\n",
       "      <td>65.6</td>\n",
       "      <td>3.432</td>\n",
       "      <td>...</td>\n",
       "      <td>61.7</td>\n",
       "      <td>3.916</td>\n",
       "      <td>63.3</td>\n",
       "      <td>3.742</td>\n",
       "      <td>65.1</td>\n",
       "      <td>3.983</td>\n",
       "      <td>66.9</td>\n",
       "      <td>3.885</td>\n",
       "      <td>58.7</td>\n",
       "      <td>3.811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian only</th>\n",
       "      <td>72.1</td>\n",
       "      <td>1.587</td>\n",
       "      <td>69.9</td>\n",
       "      <td>1.520</td>\n",
       "      <td>69.3</td>\n",
       "      <td>1.383</td>\n",
       "      <td>72.0</td>\n",
       "      <td>1.211</td>\n",
       "      <td>70.7</td>\n",
       "      <td>1.252</td>\n",
       "      <td>...</td>\n",
       "      <td>69.3</td>\n",
       "      <td>1.354</td>\n",
       "      <td>68.8</td>\n",
       "      <td>1.384</td>\n",
       "      <td>67.6</td>\n",
       "      <td>1.550</td>\n",
       "      <td>66.8</td>\n",
       "      <td>1.674</td>\n",
       "      <td>67.7</td>\n",
       "      <td>1.518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Estimate  StdErr Estimate.1  \\\n",
       "PopTitle                                                               \n",
       "Total                                        71.6   0.414       71.6   \n",
       "   Male                                      71.4   0.588       71.5   \n",
       "   Female                                    71.7   0.519       71.7   \n",
       "   American Indian or Alaska Native only     74.4   3.811       71.7   \n",
       "   Asian only                                72.1   1.587       69.9   \n",
       "\n",
       "                                          StdErr.1 Estimate.2  StdErr.2  \\\n",
       "PopTitle                                                                  \n",
       "Total                                        0.372       71.3     0.345   \n",
       "   Male                                      0.529         72     0.494   \n",
       "   Female                                    0.497       70.6     0.459   \n",
       "   American Indian or Alaska Native only     3.948       63.5     4.609   \n",
       "   Asian only                                1.520       69.3     1.383   \n",
       "\n",
       "                                          Estimate.3  StdErr.3  Estimate.4  \\\n",
       "PopTitle                                                                     \n",
       "Total                                           71.0     0.333        70.8   \n",
       "   Male                                         70.7     0.483        71.2   \n",
       "   Female                                       71.4     0.436        70.4   \n",
       "   American Indian or Alaska Native only        67.5     2.796        65.6   \n",
       "   Asian only                                   72.0     1.211        70.7   \n",
       "\n",
       "                                          StdErr.4  ...  Estimate.6  StdErr.6  \\\n",
       "PopTitle                                            ...                         \n",
       "Total                                        0.334  ...        69.3     0.368   \n",
       "   Male                                      0.479  ...        69.4     0.531   \n",
       "   Female                                    0.457  ...        69.2     0.499   \n",
       "   American Indian or Alaska Native only     3.432  ...        61.7     3.916   \n",
       "   Asian only                                1.252  ...        69.3     1.354   \n",
       "\n",
       "                                          Estimate.7  StdErr.7  Estimate.8  \\\n",
       "PopTitle                                                                     \n",
       "Total                                           68.1     0.375        67.8   \n",
       "   Male                                         68.6     0.549        68.2   \n",
       "   Female                                       67.6     0.498        67.5   \n",
       "   American Indian or Alaska Native only        63.3     3.742        65.1   \n",
       "   Asian only                                   68.8     1.384        67.6   \n",
       "\n",
       "                                          StdErr.8  Estimate.9  StdErr.9  \\\n",
       "PopTitle                                                                   \n",
       "Total                                        0.374        67.5     0.389   \n",
       "   Male                                      0.548        67.7     0.565   \n",
       "   Female                                    0.504        67.2     0.538   \n",
       "   American Indian or Alaska Native only     3.983        66.9     3.885   \n",
       "   Asian only                                1.550        66.8     1.674   \n",
       "\n",
       "                                          Estimate.10  StdErr.10  \n",
       "PopTitle                                                          \n",
       "Total                                            67.4      0.372  \n",
       "   Male                                          67.5      0.537  \n",
       "   Female                                        67.3      0.499  \n",
       "   American Indian or Alaska Native only         58.7      3.811  \n",
       "   Asian only                                    67.7      1.518  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unclean = unclean.dropna(how = 'all')\n",
    "unclean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning 3\n",
    "Before we separate our data into Estimate and StdErr values, we need to establish some dictionaries to help us with the renaming steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# New index names for our main dataframe\n",
    "est_index = {'Estimate': 2008, 'Estimate.1': 2009, 'Estimate.2': 2010, 'Estimate.3': 2011, \n",
    "            'Estimate.4': 2012, 'Estimate.5': 2013, 'Estimate.6': 2014, 'Estimate.7': 2015, \n",
    "            'Estimate.8': 2016, 'Estimate.9': 2017, 'Estimate.10': 2018}\n",
    "\n",
    "# New index names for the dataframe of standard error values\n",
    "std_err_index = {'StdErr': 2008, 'StdErr.1': 2009, 'StdErr.2': 2010, 'StdErr.3': 2011, \n",
    "            'StdErr.4': 2012, 'StdErr.5': 2013, 'StdErr.6': 2014, 'StdErr.7': 2015, \n",
    "            'StdErr.8': 2016, 'StdErr.9': 2017, 'StdErr.10': 2018}\n",
    "\n",
    "\n",
    "# The category names they used are not programming-friendly.\n",
    "# These are what will become new column names for our two dataframes\n",
    "new_cols = {'Total': 'Total', '   Male': 'Male', '   Female': 'Female', \n",
    "            '   American Indian or Alaska Native only': 'Native', '   Asian only': 'Asian', \n",
    "            '   Native Hawaiian or Other Pacific Islander only': 'Hawaiian_or_PI', \n",
    "            '   Black or African American only': 'Afr_Amer', '   White only': 'White', \n",
    "            '   2 or more races': '2_or_more', '   Hispanic or Latino': 'Hispanic', \n",
    "            '   Not Hispanic or Latino': 'Not_Hispanic', \n",
    "            '      Black or African American only, not Hispanic or Latino': 'Afr_Amer_not_Hispanic', \n",
    "            '      White only, not Hispanic or Latino': 'White_not_Hispanic', '   18-44 years': '18_to_44', \n",
    "            '      18-24 years': '18_to_24', '      25-44 years': '25_to_44', '   45-64 years': '45_to_64', \n",
    "            '      45-54 years': '45_to_54', '      55-64 years': '55_to_64', '   65 years and over': '65_Plus', \n",
    "            '      65-74 years': '65_to_74', '      75-84 years': '75_to_84', '      85 years and over': '85_Plus', \n",
    "            '   < High school': 'Below_HS', '   High school': 'HS', '   Some college': 'Some_college', \n",
    "            '   Associates degree': 'AA_AS', '   4-year college degree': 'BA_BS', \n",
    "            '   Advanced degree': 'MA_MS_Plus', '   <100': 'Below_100K', '   100-199': '100_to_199K', \n",
    "            '   200-399': '200_to_399K', '   400-599': '400_to_599K', '   600+': '600K_Plus', \n",
    "            '   Single': 'Single_Not_Parent', '   Single parent with children': 'Single_Parent', \n",
    "            '   Married couple or partners': 'Two_Adults', '   Two parent family with children': 'Two_Parent_Family', \n",
    "            '   Other': 'Other_House', '   US': 'In_US', '   Outside US': 'Outside_US', \n",
    "            '   People with disabilities': 'Disabled', '   People without disabilities': 'Not_Disabled', \n",
    "            '   Metropolitan': 'Metro', '   Non-metropolitan': 'Not_Metro', '   Insured': 'Insured', \n",
    "            '      Private': 'Private_Ins', '   Uninsured': 'Uninsured', '      Public': 'Public_Ins', \n",
    "            '   Straight': 'Straight', '      Straight, Male': 'Straight_Male', '   Gay/Lesbian': 'Gay_Les', \n",
    "            '   Bisexual': 'Bi', '      Straight, Female': 'Straight_Female', '      Gay, Male': 'Gay_Male', \n",
    "            '      Gay/Lesbian, Female': 'Gay_Female', '      Bisexual, Male': 'Bi_Male', \n",
    "            '      Bisexual, Female': 'Bi_Female', '   Married': 'Married', '   Cohabiting partner': 'Unmarried_Couple', \n",
    "            '   Divorced or separated': 'Divorced', '   Widowed': 'Widow', '   Never married': 'Never_married'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning 4\n",
    "Now it's time to separate our two sets of data (Estimate vs StdErr for each year) and rename our columns and indices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create two lists of the columns so we can separate the Estimates and StdErr\n",
    "estimate = [] # List of columns to drop for std_err_df\n",
    "std_err = [] # List of columns to drop for df\n",
    "for header in unclean.columns:\n",
    "    if 'Est' in header:\n",
    "        estimate.append(header)\n",
    "    else:\n",
    "        std_err.append(header)\n",
    "\n",
    "# Now that we have our variables defined, we can create our two dataframes.        \n",
    "df = unclean.drop(columns = std_err).rename(columns = est_index).transpose(copy = True)\n",
    "std_err_df = unclean.drop(columns = estimate).rename(columns = std_err_index).transpose(copy = True)\n",
    "\n",
    "# For the sake of conserving horizontal space, we rename the columns and index for our dataframes here\n",
    "df = df.rename(columns = new_cols).rename_axis(columns = {'PopTitle': 'Year'})\n",
    "std_err_df = std_err_df.rename(columns = new_cols).rename_axis(columns = {'PopTitle': 'Year'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data types in df:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Year\n",
       "Total               object\n",
       "Male                object\n",
       "Female              object\n",
       "Native              object\n",
       "Asian               object\n",
       "                     ...  \n",
       "Married             object\n",
       "Unmarried_Couple    object\n",
       "Divorced            object\n",
       "Widow               object\n",
       "Never_married       object\n",
       "Length: 63, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Data types in df:')\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data types in std_err_df:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Year\n",
       "Total               float64\n",
       "Male                float64\n",
       "Female              float64\n",
       "Native              float64\n",
       "Asian               float64\n",
       "                     ...   \n",
       "Married             float64\n",
       "Unmarried_Couple    float64\n",
       "Divorced            float64\n",
       "Widow               float64\n",
       "Never_married       float64\n",
       "Length: 63, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Data types in std_err_df:')\n",
    "std_err_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning 5\n",
    "We still have one problem: thanks to the original csv file having multiple ways of signaling null values, some of the values were turned into strings during the conversion. We can write a function to use with the .apply() method to convert everything to floats. As this only happened with df and not std_err_df, we only need to run this for df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data types in df after reformatting:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Year\n",
       "Total               float64\n",
       "Male                float64\n",
       "Female              float64\n",
       "Native              float64\n",
       "Asian               float64\n",
       "                     ...   \n",
       "Married             float64\n",
       "Unmarried_Couple    float64\n",
       "Divorced            float64\n",
       "Widow               float64\n",
       "Never_married       float64\n",
       "Length: 63, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def change_dtypes(value):\n",
    "    # These are the three ways the researchers indicated null values. Return np.nan for each\n",
    "    if value in ['NaN', 'DSU', '---']:\n",
    "        return np.nan\n",
    "    else:\n",
    "        return float(value)\n",
    "\n",
    "# Apply the changes to each column\n",
    "for column in df.columns:\n",
    "    df[column] = df[column].apply(change_dtypes)\n",
    "\n",
    "# Let's check again\n",
    "print('Data types in df after reformatting:')\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning 6\n",
    "Now that we've finished cleaning the data, let's take a look at our new dataframes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Year</th>\n",
       "      <th>Total</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Hawaiian_or_PI</th>\n",
       "      <th>Afr_Amer</th>\n",
       "      <th>White</th>\n",
       "      <th>2_or_more</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>...</th>\n",
       "      <th>Straight_Female</th>\n",
       "      <th>Gay_Male</th>\n",
       "      <th>Gay_Female</th>\n",
       "      <th>Bi_Male</th>\n",
       "      <th>Bi_Female</th>\n",
       "      <th>Married</th>\n",
       "      <th>Unmarried_Couple</th>\n",
       "      <th>Divorced</th>\n",
       "      <th>Widow</th>\n",
       "      <th>Never_married</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>71.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>71.7</td>\n",
       "      <td>74.4</td>\n",
       "      <td>72.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.9</td>\n",
       "      <td>72.8</td>\n",
       "      <td>62.4</td>\n",
       "      <td>74.6</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.7</td>\n",
       "      <td>68.5</td>\n",
       "      <td>62.6</td>\n",
       "      <td>72.6</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>71.6</td>\n",
       "      <td>71.5</td>\n",
       "      <td>71.7</td>\n",
       "      <td>71.7</td>\n",
       "      <td>69.9</td>\n",
       "      <td>53.6</td>\n",
       "      <td>63.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>74.2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.1</td>\n",
       "      <td>66.3</td>\n",
       "      <td>63.3</td>\n",
       "      <td>71.0</td>\n",
       "      <td>73.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>71.3</td>\n",
       "      <td>72.0</td>\n",
       "      <td>70.6</td>\n",
       "      <td>63.5</td>\n",
       "      <td>69.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>72.7</td>\n",
       "      <td>64.8</td>\n",
       "      <td>73.4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.3</td>\n",
       "      <td>69.3</td>\n",
       "      <td>63.9</td>\n",
       "      <td>71.3</td>\n",
       "      <td>73.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>71.0</td>\n",
       "      <td>70.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>67.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>54.9</td>\n",
       "      <td>62.6</td>\n",
       "      <td>72.4</td>\n",
       "      <td>65.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.9</td>\n",
       "      <td>68.0</td>\n",
       "      <td>63.1</td>\n",
       "      <td>71.9</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>70.8</td>\n",
       "      <td>71.2</td>\n",
       "      <td>70.4</td>\n",
       "      <td>65.6</td>\n",
       "      <td>70.7</td>\n",
       "      <td>44.6</td>\n",
       "      <td>64.1</td>\n",
       "      <td>72.2</td>\n",
       "      <td>58.5</td>\n",
       "      <td>73.4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.7</td>\n",
       "      <td>69.1</td>\n",
       "      <td>63.6</td>\n",
       "      <td>71.6</td>\n",
       "      <td>72.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>69.2</td>\n",
       "      <td>69.1</td>\n",
       "      <td>69.3</td>\n",
       "      <td>64.1</td>\n",
       "      <td>69.9</td>\n",
       "      <td>49.2</td>\n",
       "      <td>60.2</td>\n",
       "      <td>70.7</td>\n",
       "      <td>62.1</td>\n",
       "      <td>70.9</td>\n",
       "      <td>...</td>\n",
       "      <td>69.3</td>\n",
       "      <td>69.8</td>\n",
       "      <td>69.1</td>\n",
       "      <td>75.6</td>\n",
       "      <td>66.5</td>\n",
       "      <td>70.1</td>\n",
       "      <td>66.4</td>\n",
       "      <td>61.3</td>\n",
       "      <td>70.6</td>\n",
       "      <td>71.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>69.3</td>\n",
       "      <td>69.4</td>\n",
       "      <td>69.2</td>\n",
       "      <td>61.7</td>\n",
       "      <td>69.3</td>\n",
       "      <td>61.8</td>\n",
       "      <td>59.7</td>\n",
       "      <td>71.0</td>\n",
       "      <td>63.7</td>\n",
       "      <td>68.7</td>\n",
       "      <td>...</td>\n",
       "      <td>69.3</td>\n",
       "      <td>62.6</td>\n",
       "      <td>62.4</td>\n",
       "      <td>68.3</td>\n",
       "      <td>72.5</td>\n",
       "      <td>70.3</td>\n",
       "      <td>67.2</td>\n",
       "      <td>60.1</td>\n",
       "      <td>71.3</td>\n",
       "      <td>71.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>68.1</td>\n",
       "      <td>68.6</td>\n",
       "      <td>67.6</td>\n",
       "      <td>63.3</td>\n",
       "      <td>68.8</td>\n",
       "      <td>66.4</td>\n",
       "      <td>58.5</td>\n",
       "      <td>69.8</td>\n",
       "      <td>58.7</td>\n",
       "      <td>68.9</td>\n",
       "      <td>...</td>\n",
       "      <td>67.8</td>\n",
       "      <td>64.4</td>\n",
       "      <td>70.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>67.3</td>\n",
       "      <td>59.7</td>\n",
       "      <td>71.6</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>67.8</td>\n",
       "      <td>68.2</td>\n",
       "      <td>67.5</td>\n",
       "      <td>65.1</td>\n",
       "      <td>67.6</td>\n",
       "      <td>60.9</td>\n",
       "      <td>59.5</td>\n",
       "      <td>69.5</td>\n",
       "      <td>57.0</td>\n",
       "      <td>68.6</td>\n",
       "      <td>...</td>\n",
       "      <td>67.4</td>\n",
       "      <td>73.3</td>\n",
       "      <td>66.5</td>\n",
       "      <td>51.9</td>\n",
       "      <td>69.4</td>\n",
       "      <td>69.0</td>\n",
       "      <td>65.6</td>\n",
       "      <td>59.2</td>\n",
       "      <td>69.9</td>\n",
       "      <td>69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>67.5</td>\n",
       "      <td>67.7</td>\n",
       "      <td>67.2</td>\n",
       "      <td>66.9</td>\n",
       "      <td>66.8</td>\n",
       "      <td>74.2</td>\n",
       "      <td>60.0</td>\n",
       "      <td>68.9</td>\n",
       "      <td>60.8</td>\n",
       "      <td>67.3</td>\n",
       "      <td>...</td>\n",
       "      <td>67.6</td>\n",
       "      <td>72.4</td>\n",
       "      <td>65.2</td>\n",
       "      <td>63.9</td>\n",
       "      <td>55.7</td>\n",
       "      <td>68.9</td>\n",
       "      <td>60.5</td>\n",
       "      <td>59.9</td>\n",
       "      <td>69.4</td>\n",
       "      <td>69.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>67.4</td>\n",
       "      <td>67.5</td>\n",
       "      <td>67.3</td>\n",
       "      <td>58.7</td>\n",
       "      <td>67.7</td>\n",
       "      <td>53.4</td>\n",
       "      <td>59.1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>60.9</td>\n",
       "      <td>68.2</td>\n",
       "      <td>...</td>\n",
       "      <td>67.5</td>\n",
       "      <td>67.8</td>\n",
       "      <td>62.5</td>\n",
       "      <td>56.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>68.6</td>\n",
       "      <td>63.5</td>\n",
       "      <td>58.6</td>\n",
       "      <td>68.7</td>\n",
       "      <td>69.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Year  Total  Male  Female  Native  Asian  Hawaiian_or_PI  Afr_Amer  White  \\\n",
       "2008   71.6  71.4    71.7    74.4   72.1             NaN      63.9   72.8   \n",
       "2009   71.6  71.5    71.7    71.7   69.9            53.6      63.5   73.0   \n",
       "2010   71.3  72.0    70.6    63.5   69.3             NaN      64.0   72.7   \n",
       "2011   71.0  70.7    71.4    67.5   72.0            54.9      62.6   72.4   \n",
       "2012   70.8  71.2    70.4    65.6   70.7            44.6      64.1   72.2   \n",
       "2013   69.2  69.1    69.3    64.1   69.9            49.2      60.2   70.7   \n",
       "2014   69.3  69.4    69.2    61.7   69.3            61.8      59.7   71.0   \n",
       "2015   68.1  68.6    67.6    63.3   68.8            66.4      58.5   69.8   \n",
       "2016   67.8  68.2    67.5    65.1   67.6            60.9      59.5   69.5   \n",
       "2017   67.5  67.7    67.2    66.9   66.8            74.2      60.0   68.9   \n",
       "2018   67.4  67.5    67.3    58.7   67.7            53.4      59.1   69.0   \n",
       "\n",
       "Year  2_or_more  Hispanic  ...  Straight_Female  Gay_Male  Gay_Female  \\\n",
       "2008       62.4      74.6  ...              NaN       NaN         NaN   \n",
       "2009       66.7      74.2  ...              NaN       NaN         NaN   \n",
       "2010       64.8      73.4  ...              NaN       NaN         NaN   \n",
       "2011       65.0      73.0  ...              NaN       NaN         NaN   \n",
       "2012       58.5      73.4  ...              NaN       NaN         NaN   \n",
       "2013       62.1      70.9  ...             69.3      69.8        69.1   \n",
       "2014       63.7      68.7  ...             69.3      62.6        62.4   \n",
       "2015       58.7      68.9  ...             67.8      64.4        70.9   \n",
       "2016       57.0      68.6  ...             67.4      73.3        66.5   \n",
       "2017       60.8      67.3  ...             67.6      72.4        65.2   \n",
       "2018       60.9      68.2  ...             67.5      67.8        62.5   \n",
       "\n",
       "Year  Bi_Male  Bi_Female  Married  Unmarried_Couple  Divorced  Widow  \\\n",
       "2008      NaN        NaN     72.7              68.5      62.6   72.6   \n",
       "2009      NaN        NaN     73.1              66.3      63.3   71.0   \n",
       "2010      NaN        NaN     72.3              69.3      63.9   71.3   \n",
       "2011      NaN        NaN     71.9              68.0      63.1   71.9   \n",
       "2012      NaN        NaN     71.7              69.1      63.6   71.6   \n",
       "2013     75.6       66.5     70.1              66.4      61.3   70.6   \n",
       "2014     68.3       72.5     70.3              67.2      60.1   71.3   \n",
       "2015     72.1       52.0     68.8              67.3      59.7   71.6   \n",
       "2016     51.9       69.4     69.0              65.6      59.2   69.9   \n",
       "2017     63.9       55.7     68.9              60.5      59.9   69.4   \n",
       "2018     56.8       58.4     68.6              63.5      58.6   68.7   \n",
       "\n",
       "Year  Never_married  \n",
       "2008           74.0  \n",
       "2009           73.9  \n",
       "2010           73.3  \n",
       "2011           74.0  \n",
       "2012           72.7  \n",
       "2013           71.8  \n",
       "2014           71.8  \n",
       "2015           70.0  \n",
       "2016           69.5  \n",
       "2017           69.8  \n",
       "2018           69.6  \n",
       "\n",
       "[11 rows x 63 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Year</th>\n",
       "      <th>Total</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Native</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Hawaiian_or_PI</th>\n",
       "      <th>Afr_Amer</th>\n",
       "      <th>White</th>\n",
       "      <th>2_or_more</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>...</th>\n",
       "      <th>Straight_Female</th>\n",
       "      <th>Gay_Male</th>\n",
       "      <th>Gay_Female</th>\n",
       "      <th>Bi_Male</th>\n",
       "      <th>Bi_Female</th>\n",
       "      <th>Married</th>\n",
       "      <th>Unmarried_Couple</th>\n",
       "      <th>Divorced</th>\n",
       "      <th>Widow</th>\n",
       "      <th>Never_married</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.414</td>\n",
       "      <td>0.588</td>\n",
       "      <td>0.519</td>\n",
       "      <td>3.811</td>\n",
       "      <td>1.587</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.119</td>\n",
       "      <td>0.458</td>\n",
       "      <td>3.810</td>\n",
       "      <td>0.987</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.562</td>\n",
       "      <td>1.587</td>\n",
       "      <td>1.003</td>\n",
       "      <td>1.128</td>\n",
       "      <td>0.826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>0.372</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0.497</td>\n",
       "      <td>3.948</td>\n",
       "      <td>1.520</td>\n",
       "      <td>9.139</td>\n",
       "      <td>1.006</td>\n",
       "      <td>0.425</td>\n",
       "      <td>3.267</td>\n",
       "      <td>0.853</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.511</td>\n",
       "      <td>1.471</td>\n",
       "      <td>0.992</td>\n",
       "      <td>1.245</td>\n",
       "      <td>0.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.345</td>\n",
       "      <td>0.494</td>\n",
       "      <td>0.459</td>\n",
       "      <td>4.609</td>\n",
       "      <td>1.383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.963</td>\n",
       "      <td>0.383</td>\n",
       "      <td>2.651</td>\n",
       "      <td>0.765</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.475</td>\n",
       "      <td>1.313</td>\n",
       "      <td>0.884</td>\n",
       "      <td>1.102</td>\n",
       "      <td>0.732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.333</td>\n",
       "      <td>0.483</td>\n",
       "      <td>0.436</td>\n",
       "      <td>2.796</td>\n",
       "      <td>1.211</td>\n",
       "      <td>9.134</td>\n",
       "      <td>0.902</td>\n",
       "      <td>0.372</td>\n",
       "      <td>2.343</td>\n",
       "      <td>0.748</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.470</td>\n",
       "      <td>1.226</td>\n",
       "      <td>0.761</td>\n",
       "      <td>1.015</td>\n",
       "      <td>0.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.334</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.457</td>\n",
       "      <td>3.432</td>\n",
       "      <td>1.252</td>\n",
       "      <td>9.324</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.369</td>\n",
       "      <td>2.568</td>\n",
       "      <td>0.716</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.471</td>\n",
       "      <td>1.292</td>\n",
       "      <td>0.842</td>\n",
       "      <td>1.074</td>\n",
       "      <td>0.683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.350</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.477</td>\n",
       "      <td>3.810</td>\n",
       "      <td>1.258</td>\n",
       "      <td>8.716</td>\n",
       "      <td>0.942</td>\n",
       "      <td>0.391</td>\n",
       "      <td>2.631</td>\n",
       "      <td>0.793</td>\n",
       "      <td>...</td>\n",
       "      <td>0.487</td>\n",
       "      <td>3.388</td>\n",
       "      <td>3.719</td>\n",
       "      <td>6.248</td>\n",
       "      <td>4.951</td>\n",
       "      <td>0.486</td>\n",
       "      <td>1.193</td>\n",
       "      <td>0.859</td>\n",
       "      <td>1.087</td>\n",
       "      <td>0.696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.368</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.499</td>\n",
       "      <td>3.916</td>\n",
       "      <td>1.354</td>\n",
       "      <td>8.308</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.411</td>\n",
       "      <td>2.633</td>\n",
       "      <td>0.841</td>\n",
       "      <td>...</td>\n",
       "      <td>0.512</td>\n",
       "      <td>4.679</td>\n",
       "      <td>4.099</td>\n",
       "      <td>6.626</td>\n",
       "      <td>4.080</td>\n",
       "      <td>0.514</td>\n",
       "      <td>1.368</td>\n",
       "      <td>0.956</td>\n",
       "      <td>1.104</td>\n",
       "      <td>0.729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.375</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.498</td>\n",
       "      <td>3.742</td>\n",
       "      <td>1.384</td>\n",
       "      <td>7.259</td>\n",
       "      <td>1.029</td>\n",
       "      <td>0.413</td>\n",
       "      <td>2.873</td>\n",
       "      <td>0.838</td>\n",
       "      <td>...</td>\n",
       "      <td>0.512</td>\n",
       "      <td>3.832</td>\n",
       "      <td>3.698</td>\n",
       "      <td>6.960</td>\n",
       "      <td>5.016</td>\n",
       "      <td>0.521</td>\n",
       "      <td>1.338</td>\n",
       "      <td>0.912</td>\n",
       "      <td>1.097</td>\n",
       "      <td>0.788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.374</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.504</td>\n",
       "      <td>3.983</td>\n",
       "      <td>1.550</td>\n",
       "      <td>7.381</td>\n",
       "      <td>1.181</td>\n",
       "      <td>0.402</td>\n",
       "      <td>2.848</td>\n",
       "      <td>1.041</td>\n",
       "      <td>...</td>\n",
       "      <td>0.517</td>\n",
       "      <td>3.702</td>\n",
       "      <td>4.234</td>\n",
       "      <td>7.801</td>\n",
       "      <td>4.025</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.469</td>\n",
       "      <td>0.949</td>\n",
       "      <td>1.158</td>\n",
       "      <td>0.845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.389</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.538</td>\n",
       "      <td>3.885</td>\n",
       "      <td>1.674</td>\n",
       "      <td>7.869</td>\n",
       "      <td>1.261</td>\n",
       "      <td>0.418</td>\n",
       "      <td>2.710</td>\n",
       "      <td>1.122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.546</td>\n",
       "      <td>3.658</td>\n",
       "      <td>4.486</td>\n",
       "      <td>6.652</td>\n",
       "      <td>4.100</td>\n",
       "      <td>0.546</td>\n",
       "      <td>1.444</td>\n",
       "      <td>0.959</td>\n",
       "      <td>1.143</td>\n",
       "      <td>0.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.372</td>\n",
       "      <td>0.537</td>\n",
       "      <td>0.499</td>\n",
       "      <td>3.811</td>\n",
       "      <td>1.518</td>\n",
       "      <td>6.019</td>\n",
       "      <td>1.237</td>\n",
       "      <td>0.413</td>\n",
       "      <td>2.815</td>\n",
       "      <td>0.971</td>\n",
       "      <td>...</td>\n",
       "      <td>0.515</td>\n",
       "      <td>3.583</td>\n",
       "      <td>4.357</td>\n",
       "      <td>6.558</td>\n",
       "      <td>4.130</td>\n",
       "      <td>0.549</td>\n",
       "      <td>1.426</td>\n",
       "      <td>0.973</td>\n",
       "      <td>1.141</td>\n",
       "      <td>0.825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Year  Total   Male  Female  Native  Asian  Hawaiian_or_PI  Afr_Amer  White  \\\n",
       "2008  0.414  0.588   0.519   3.811  1.587             NaN     1.119  0.458   \n",
       "2009  0.372  0.529   0.497   3.948  1.520           9.139     1.006  0.425   \n",
       "2010  0.345  0.494   0.459   4.609  1.383             NaN     0.963  0.383   \n",
       "2011  0.333  0.483   0.436   2.796  1.211           9.134     0.902  0.372   \n",
       "2012  0.334  0.479   0.457   3.432  1.252           9.324     0.894  0.369   \n",
       "2013  0.350  0.488   0.477   3.810  1.258           8.716     0.942  0.391   \n",
       "2014  0.368  0.531   0.499   3.916  1.354           8.308     0.982  0.411   \n",
       "2015  0.375  0.549   0.498   3.742  1.384           7.259     1.029  0.413   \n",
       "2016  0.374  0.548   0.504   3.983  1.550           7.381     1.181  0.402   \n",
       "2017  0.389  0.565   0.538   3.885  1.674           7.869     1.261  0.418   \n",
       "2018  0.372  0.537   0.499   3.811  1.518           6.019     1.237  0.413   \n",
       "\n",
       "Year  2_or_more  Hispanic  ...  Straight_Female  Gay_Male  Gay_Female  \\\n",
       "2008      3.810     0.987  ...              NaN       NaN         NaN   \n",
       "2009      3.267     0.853  ...              NaN       NaN         NaN   \n",
       "2010      2.651     0.765  ...              NaN       NaN         NaN   \n",
       "2011      2.343     0.748  ...              NaN       NaN         NaN   \n",
       "2012      2.568     0.716  ...              NaN       NaN         NaN   \n",
       "2013      2.631     0.793  ...            0.487     3.388       3.719   \n",
       "2014      2.633     0.841  ...            0.512     4.679       4.099   \n",
       "2015      2.873     0.838  ...            0.512     3.832       3.698   \n",
       "2016      2.848     1.041  ...            0.517     3.702       4.234   \n",
       "2017      2.710     1.122  ...            0.546     3.658       4.486   \n",
       "2018      2.815     0.971  ...            0.515     3.583       4.357   \n",
       "\n",
       "Year  Bi_Male  Bi_Female  Married  Unmarried_Couple  Divorced  Widow  \\\n",
       "2008      NaN        NaN    0.562             1.587     1.003  1.128   \n",
       "2009      NaN        NaN    0.511             1.471     0.992  1.245   \n",
       "2010      NaN        NaN    0.475             1.313     0.884  1.102   \n",
       "2011      NaN        NaN    0.470             1.226     0.761  1.015   \n",
       "2012      NaN        NaN    0.471             1.292     0.842  1.074   \n",
       "2013    6.248      4.951    0.486             1.193     0.859  1.087   \n",
       "2014    6.626      4.080    0.514             1.368     0.956  1.104   \n",
       "2015    6.960      5.016    0.521             1.338     0.912  1.097   \n",
       "2016    7.801      4.025    0.500             1.469     0.949  1.158   \n",
       "2017    6.652      4.100    0.546             1.444     0.959  1.143   \n",
       "2018    6.558      4.130    0.549             1.426     0.973  1.141   \n",
       "\n",
       "Year  Never_married  \n",
       "2008          0.826  \n",
       "2009          0.789  \n",
       "2010          0.732  \n",
       "2011          0.650  \n",
       "2012          0.683  \n",
       "2013          0.696  \n",
       "2014          0.729  \n",
       "2015          0.788  \n",
       "2016          0.845  \n",
       "2017          0.815  \n",
       "2018          0.825  \n",
       "\n",
       "[11 rows x 63 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_err_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis & Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 1.0: Preliminary Analysis\n",
    "Here we preemptively identify any potential issues with our data. First, let's see what demographics are missing in each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Year</th>\n",
       "      <th>Hawaiian_or_PI</th>\n",
       "      <th>Disabled</th>\n",
       "      <th>Not_Disabled</th>\n",
       "      <th>Straight</th>\n",
       "      <th>Straight_Male</th>\n",
       "      <th>Gay_Les</th>\n",
       "      <th>Bi</th>\n",
       "      <th>Straight_Female</th>\n",
       "      <th>Gay_Male</th>\n",
       "      <th>Gay_Female</th>\n",
       "      <th>Bi_Male</th>\n",
       "      <th>Bi_Female</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>53.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>NaN</td>\n",
       "      <td>64.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>54.9</td>\n",
       "      <td>65.6</td>\n",
       "      <td>72.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>44.6</td>\n",
       "      <td>63.6</td>\n",
       "      <td>72.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>49.2</td>\n",
       "      <td>65.3</td>\n",
       "      <td>71.0</td>\n",
       "      <td>69.2</td>\n",
       "      <td>69.1</td>\n",
       "      <td>69.5</td>\n",
       "      <td>69.4</td>\n",
       "      <td>69.3</td>\n",
       "      <td>69.8</td>\n",
       "      <td>69.1</td>\n",
       "      <td>75.6</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>61.8</td>\n",
       "      <td>62.9</td>\n",
       "      <td>70.1</td>\n",
       "      <td>69.4</td>\n",
       "      <td>69.5</td>\n",
       "      <td>62.5</td>\n",
       "      <td>71.4</td>\n",
       "      <td>69.3</td>\n",
       "      <td>62.6</td>\n",
       "      <td>62.4</td>\n",
       "      <td>68.3</td>\n",
       "      <td>72.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>66.4</td>\n",
       "      <td>64.5</td>\n",
       "      <td>68.9</td>\n",
       "      <td>68.3</td>\n",
       "      <td>68.8</td>\n",
       "      <td>67.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>67.8</td>\n",
       "      <td>64.4</td>\n",
       "      <td>70.9</td>\n",
       "      <td>72.1</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>60.9</td>\n",
       "      <td>63.5</td>\n",
       "      <td>69.6</td>\n",
       "      <td>67.8</td>\n",
       "      <td>68.3</td>\n",
       "      <td>70.0</td>\n",
       "      <td>63.8</td>\n",
       "      <td>67.4</td>\n",
       "      <td>73.3</td>\n",
       "      <td>66.5</td>\n",
       "      <td>51.9</td>\n",
       "      <td>69.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>74.2</td>\n",
       "      <td>63.6</td>\n",
       "      <td>69.3</td>\n",
       "      <td>67.6</td>\n",
       "      <td>67.7</td>\n",
       "      <td>69.0</td>\n",
       "      <td>58.1</td>\n",
       "      <td>67.6</td>\n",
       "      <td>72.4</td>\n",
       "      <td>65.2</td>\n",
       "      <td>63.9</td>\n",
       "      <td>55.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>53.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.5</td>\n",
       "      <td>67.5</td>\n",
       "      <td>65.5</td>\n",
       "      <td>57.9</td>\n",
       "      <td>67.5</td>\n",
       "      <td>67.8</td>\n",
       "      <td>62.5</td>\n",
       "      <td>56.8</td>\n",
       "      <td>58.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Year  Hawaiian_or_PI  Disabled  Not_Disabled  Straight  Straight_Male  \\\n",
       "2008             NaN       NaN           NaN       NaN            NaN   \n",
       "2009            53.6       NaN           NaN       NaN            NaN   \n",
       "2010             NaN      64.9          72.1       NaN            NaN   \n",
       "2011            54.9      65.6          72.4       NaN            NaN   \n",
       "2012            44.6      63.6          72.3       NaN            NaN   \n",
       "2013            49.2      65.3          71.0      69.2           69.1   \n",
       "2014            61.8      62.9          70.1      69.4           69.5   \n",
       "2015            66.4      64.5          68.9      68.3           68.8   \n",
       "2016            60.9      63.5          69.6      67.8           68.3   \n",
       "2017            74.2      63.6          69.3      67.6           67.7   \n",
       "2018            53.4       NaN           NaN      67.5           67.5   \n",
       "\n",
       "Year  Gay_Les    Bi  Straight_Female  Gay_Male  Gay_Female  Bi_Male  Bi_Female  \n",
       "2008      NaN   NaN              NaN       NaN         NaN      NaN        NaN  \n",
       "2009      NaN   NaN              NaN       NaN         NaN      NaN        NaN  \n",
       "2010      NaN   NaN              NaN       NaN         NaN      NaN        NaN  \n",
       "2011      NaN   NaN              NaN       NaN         NaN      NaN        NaN  \n",
       "2012      NaN   NaN              NaN       NaN         NaN      NaN        NaN  \n",
       "2013     69.5  69.4             69.3      69.8        69.1     75.6       66.5  \n",
       "2014     62.5  71.4             69.3      62.6        62.4     68.3       72.5  \n",
       "2015     67.3  58.0             67.8      64.4        70.9     72.1       52.0  \n",
       "2016     70.0  63.8             67.4      73.3        66.5     51.9       69.4  \n",
       "2017     69.0  58.1             67.6      72.4        65.2     63.9       55.7  \n",
       "2018     65.5  57.9             67.5      67.8        62.5     56.8       58.4  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe that includes only demographics that are not missing information\n",
    "no_nan_vals = df.dropna(how = 'any', axis = 1)\n",
    "\n",
    "# Drop all columns from no_nan_vals, leaving only columns that are missing data\n",
    "missing_df = df.drop(labels = no_nan_vals.columns, axis = 1)\n",
    "missing_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like we're missing 2008 and 2010 data for the category of \"Native Hawaiian or Other Pacific Islander only\" (no clear reason for this is given on the site from which the data was downloaded). It also seems like disability information was not collected until 2010 (and was not collected in 2018), and that sexual orientation information was not collected until 2013. This will be good to keep in mind for our analysis. \n",
    "\n",
    "### EDA 1.0.1  \n",
    "\n",
    "Now that we have an idea of what values missing, we have to assess the reliability of data we do have. We don't have information about how many people were included in each category, so we have to find another way to narrow down which groups might have had sample sizes that make them statistically unreliable for drawing conclusions. Here, we calculate the grand mean (the mean of means) of the standard errors for each demographic category, and use this to gauge a  reasonable StdErr cutoff to determine which groups are \"too variable to be reliable\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grand Mean Standard Error =  1.3244265993266\n"
     ]
    }
   ],
   "source": [
    "# Store average of each column from 'std_err_df' as a list\n",
    "std_err_means_list = std_err_df.agg('mean').to_list()\n",
    "\n",
    "# Calculate mean value of list of averages\n",
    "mean_means = sum(std_err_means_list) / len(std_err_means_list)\n",
    "print(\"Grand Mean Standard Error = \", mean_means)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 1.0.2\n",
    "Given our pooled mean of 1.324, we determine 2 to be our reasonable cutoff. Referencing our cutoff, we identify demographic categories that are highly variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Groups with High Variability by Standard Error: \n",
      "Native              3.7948\n",
      "Hawaiian_or_PI      8.1277\n",
      "2_or_more           2.8317\n",
      "Gay_Les             2.8393\n",
      "Bi                  3.7727\n",
      "Gay_Male            3.807\n",
      "Gay_Female          4.0988\n",
      "Bi_Male             6.8075\n",
      "Bi_Female           4.3837\n"
     ]
    }
   ],
   "source": [
    "# With an aggregate mean StdErr of 1.324, I think 2 is a reasonable cutoff for \"too variable to be reliable\"\n",
    "\n",
    "# Store average of each column from 'std_err_df' as a dictionary\n",
    "std_err_means_dict = std_err_df.agg('mean').to_dict()\n",
    "\n",
    "# Initialize empty dictionary\n",
    "std_err_issues = {}\n",
    "\n",
    "# Iterate StdErr dictionary; add high variability groups to 'issues'\n",
    "print(\"Groups with High Variability by Standard Error: \")\n",
    "for item in std_err_means_dict:\n",
    "    if std_err_means_dict[item] >= 2:\n",
    "        std_err_issues[item] = std_err_means_dict[item]\n",
    "        print('%-20s' '%s' % (item, round(std_err_issues[item], 4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 1.1.0\n",
    "Standard Error isn't our only possible measure of excessive variance. Another possible indicator of small sample size would be too much year-to-year variance, or what we will call the \"Yearly Variance Magnitude\" (YVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def yearly_variance_magnitude(df):\n",
    "    # Get a list of the columns\n",
    "    columns = df.columns.tolist()\n",
    "    \n",
    "    # Dictionary for storing our outputs\n",
    "    temp_dict = {}\n",
    "    \n",
    "    # Iterate over columns to calculate the yvm for each\n",
    "    for column in columns:\n",
    "        year = 2008 # for the index\n",
    "        total_magnitude = 0 # running total of magnitude\n",
    "        divisor = 0\n",
    "        \n",
    "        # Iterate start year from 2008-2017\n",
    "        while year < 2018:\n",
    "            try:\n",
    "                # If either value is NaN, skip it and don't increase the divisor for our mean\n",
    "                int(df.loc[year][column])\n",
    "                int(df.loc[year + 1][column])\n",
    "                # Take the current year's value and subtract it from the next year's value\n",
    "                #     Then add the absolute value to total_magnitude\n",
    "                total_magnitude += abs(df.loc[year + 1][column] - df.loc[year][column])\n",
    "                divisor += 1\n",
    "                year += 1\n",
    "            except:\n",
    "                year += 1\n",
    "                \n",
    "        temp_dict[column] = np.round((total_magnitude / divisor), 3)\n",
    "    \n",
    "    # Sort the dictionary\n",
    "    variance_dict = dict(sorted(temp_dict.items(), key=lambda item: item[1]))\n",
    "    \n",
    "    # A few analysis variables\n",
    "    dict_keys = list(variance_dict.keys())\n",
    "    variance_min = variance_dict[dict_keys[0]]\n",
    "    variance_max = variance_dict[dict_keys[-1]]\n",
    "    variance_mean = np.round((sum(variance_dict.values()) / len(variance_dict)), 3)\n",
    "    variance_range = np.round((variance_max - variance_min), 3)\n",
    "    print(' Min variance: ', variance_min, '\\n', 'Max variance: ', variance_max, '\\n', \n",
    "          'Average variance: ', variance_mean, '\\n', 'Variance range: ', variance_range)\n",
    "    \n",
    "    return variance_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Statistical breakdown of YVM:\n",
      " Min variance:  0.42 \n",
      " Max variance:  12.06 \n",
      " Average variance:  1.728 \n",
      " Variance range:  11.64\n",
      "\n",
      "Straight \t\t 0.42 \t Private_Ins \t\t 0.7 \t 75_to_84 \t\t 1.38\n",
      "Total \t\t\t 0.44 \t Single_Not_Parent \t 0.71 \t Below_HS \t\t 1.39\n",
      "Straight_Female \t 0.44 \t 18_to_44 \t\t 0.77 \t Below_100K \t\t 1.41\n",
      "Metro \t\t\t 0.45 \t Widow \t\t\t 0.77 \t MA_MS_Plus \t\t 1.44\n",
      "In_US \t\t\t 0.47 \t 600K_Plus \t\t 0.78 \t Outside_US \t\t 1.44\n",
      "Straight_Male \t\t 0.48 \t Two_Adults \t\t 0.79 \t Uninsured \t\t 1.55\n",
      "Not_Hispanic \t\t 0.49 \t 45_to_54 \t\t 0.84 \t Public_Ins \t\t 1.56\n",
      "White \t\t\t 0.5 \t Two_Parent_Family \t 0.84 \t 18_to_24 \t\t 1.61\n",
      "65_Plus \t\t 0.51 \t BA_BS \t\t\t 0.88 \t AA_AS \t\t\t 1.73\n",
      "Insured \t\t 0.53 \t Not_Metro \t\t 0.9 \t Unmarried_Couple \t 2.1\n",
      "White_not_Hispanic \t 0.55 \t Divorced \t\t 0.9 \t Single_Parent \t\t 2.12\n",
      "45_to_64 \t\t 0.57 \t 65_to_74 \t\t 0.93 \t 85_Plus \t\t 2.2\n",
      "400_to_599K \t\t 0.57 \t Hispanic \t\t 0.94 \t 2_or_more \t\t 2.87\n",
      "Married \t\t 0.57 \t 100_to_199K \t\t 0.96 \t Native \t\t 3.41\n",
      "200_to_399K \t\t 0.61 \t Some_college \t\t 0.98 \t Gay_Les \t\t 3.8\n",
      "Female \t\t\t 0.62 \t 55_to_64 \t\t 1.12 \t Gay_Male \t\t 4.68\n",
      "Never_married \t\t 0.64 \t Asian \t\t\t 1.16 \t Gay_Female \t\t 4.72\n",
      "HS \t\t\t 0.65 \t Afr_Amer \t\t 1.18 \t Bi \t\t\t 5.42\n",
      "Male \t\t\t 0.67 \t Afr_Amer_not_Hispanic \t 1.24 \t Bi_Male \t\t 10.08\n",
      "Other_House \t\t 0.67 \t Disabled \t\t 1.357 \t Hawaiian_or_PI \t 10.243\n",
      "Not_Disabled \t\t 0.686 \t 25_to_44 \t\t 1.37 \t Bi_Female \t\t 12.06\n"
     ]
    }
   ],
   "source": [
    "# Analysis time!\n",
    "print(' Statistical breakdown of YVM:')\n",
    "variance_dict = yearly_variance_magnitude(df)\n",
    "\n",
    "# Code to print them out neatly\n",
    "print('')\n",
    "keys = list(variance_dict.keys())\n",
    "keys_small = keys[:21]\n",
    "keys_medium = keys[21:42]\n",
    "keys_large = keys[42:]\n",
    "for a, b, c in zip(keys_small, keys_medium, keys_large):\n",
    "    tab_a = '\\t'\n",
    "    tab_b = '\\t'\n",
    "    tab_c = '\\t'\n",
    "    if len(a) < 7:\n",
    "        tab_a = '\\t\\t\\t'\n",
    "    elif len(a) < 14:\n",
    "        tab_a = '\\t\\t'\n",
    "    if len(b) < 6:\n",
    "        tab_b = '\\t\\t\\t'\n",
    "    elif len(b) < 14:\n",
    "        tab_b = '\\t\\t'\n",
    "    if len(c) < 6:\n",
    "        tab_c = '\\t\\t\\t'\n",
    "    elif len(c) < 14:\n",
    "        tab_c = '\\t\\t'\n",
    "    print(a, tab_a, variance_dict[a], '\\t', b, tab_b, variance_dict[b], '\\t', c, tab_c, variance_dict[c])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 1.1.1\n",
    "We have some clear outliers here. Let's see if they're the same ones as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Groups from previous metric of variance: \n",
      "2_or_more \tStdErr:  2.832 \t YVM:  2.87\n",
      "Native \t\tStdErr:  3.795 \t YVM:  3.41\n",
      "Gay_Les \tStdErr:  2.839 \t YVM:  3.8\n",
      "Gay_Male \tStdErr:  3.807 \t YVM:  4.68\n",
      "Gay_Female \tStdErr:  4.099 \t YVM:  4.72\n",
      "Bi \t\tStdErr:  3.773 \t YVM:  5.42\n",
      "Bi_Male \tStdErr:  6.808 \t YVM:  10.08\n",
      "Hawaiian_or_PI \tStdErr:  8.128 \t YVM:  10.243\n",
      "Bi_Female \tStdErr:  4.384 \t YVM:  12.06\n",
      "\n",
      "Potential new issues: \n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print('Groups from previous metric of variance: ')\n",
    "new_issues = []\n",
    "watch_these = []\n",
    "for group in variance_dict:\n",
    "    if len(group) < 7:\n",
    "        tab = '\\t\\t'\n",
    "    else:\n",
    "        tab = '\\t'\n",
    "    if group in std_err_issues:\n",
    "        watch_these.append(group)\n",
    "        print(group,  tab + 'StdErr: ', np.round(std_err_means_dict[group], 3), \n",
    "              '\\t YVM: ', variance_dict[group])\n",
    "    # Lowest potential outlier YVM in std_err_issues\n",
    "    elif variance_dict[group] >= 2.87:\n",
    "        new_issues.append(group)\n",
    "        watch_these.append(group)\n",
    "print('\\nPotential new issues: ')\n",
    "for group in new_issues:\n",
    "    print(group, tab + ' StdErr: ', np.round(std_err_means_dict[group], 3), \n",
    "          ' YVM: ', variance_dict[group])\n",
    "if len(new_issues) == 0:\n",
    "    print('None')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 1.1.2  \n",
    "\n",
    "With both of these metrics largely agreeing about which groups could be potentially underrepresented in the data (and thus be unreliable in our analysis), we can be relatively certain that these groups are too variable to be included. The only demographics in the \"Sexual Orientation\" category that appear to have adequate representation are Straight, Straight_Male, and Straight_Female. We will have to make a decision about whether to include these after checking our visualization of this category.  \n",
    "\n",
    "Let's take a look at the YVM stats if we drop the variable groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Statistical breakdown of YVM:\n",
      " Min variance:  0.42 \n",
      " Max variance:  2.2 \n",
      " Average variance:  0.955 \n",
      " Variance range:  1.78\n",
      "\n",
      "Straight \t\t 0.42 \t Male \t\t\t 0.67 \t 55_to_64 \t\t 1.12\n",
      "Total \t\t\t 0.44 \t Other_House \t\t 0.67 \t Asian \t\t\t 1.16\n",
      "Straight_Female \t 0.44 \t Not_Disabled \t\t 0.686 \t Afr_Amer \t\t 1.18\n",
      "Metro \t\t\t 0.45 \t Private_Ins \t\t 0.7 \t Afr_Amer_not_Hispanic \t 1.24\n",
      "In_US \t\t\t 0.47 \t Single_Not_Parent \t 0.71 \t Disabled \t\t 1.357\n",
      "Straight_Male \t\t 0.48 \t 18_to_44 \t\t 0.77 \t 25_to_44 \t\t 1.37\n",
      "Not_Hispanic \t\t 0.49 \t Widow \t\t\t 0.77 \t 75_to_84 \t\t 1.38\n",
      "White \t\t\t 0.5 \t 600K_Plus \t\t 0.78 \t Below_HS \t\t 1.39\n",
      "65_Plus \t\t 0.51 \t Two_Adults \t\t 0.79 \t Below_100K \t\t 1.41\n",
      "Insured \t\t 0.53 \t 45_to_54 \t\t 0.84 \t MA_MS_Plus \t\t 1.44\n",
      "White_not_Hispanic \t 0.55 \t Two_Parent_Family \t 0.84 \t Outside_US \t\t 1.44\n",
      "45_to_64 \t\t 0.57 \t BA_BS \t\t\t 0.88 \t Uninsured \t\t 1.55\n",
      "400_to_599K \t\t 0.57 \t Not_Metro \t\t 0.9 \t Public_Ins \t\t 1.56\n",
      "Married \t\t 0.57 \t Divorced \t\t 0.9 \t 18_to_24 \t\t 1.61\n",
      "200_to_399K \t\t 0.61 \t 65_to_74 \t\t 0.93 \t AA_AS \t\t\t 1.73\n",
      "Female \t\t\t 0.62 \t Hispanic \t\t 0.94 \t Unmarried_Couple \t 2.1\n",
      "Never_married \t\t 0.64 \t 100_to_199K \t\t 0.96 \t Single_Parent \t\t 2.12\n",
      "HS \t\t\t 0.65 \t Some_college \t\t 0.98 \t 85_Plus \t\t 2.2\n"
     ]
    }
   ],
   "source": [
    "# Create a dictionary of the YVM stats after dropping the \"problematic\" groups\n",
    "print(' Statistical breakdown of YVM:')\n",
    "temp_df = df.drop(watch_these, axis = 1)\n",
    "yvm_dict = yearly_variance_magnitude(temp_df)\n",
    "\n",
    "# Code to print them out neatly\n",
    "print('')\n",
    "keys = list(yvm_dict.keys())\n",
    "keys_small = keys[:18]\n",
    "keys_medium = keys[18:36]\n",
    "keys_large = keys[36:]\n",
    "for a, b, c in zip(keys_small, keys_medium, keys_large):\n",
    "    tab_a = '\\t'\n",
    "    tab_b = '\\t'\n",
    "    tab_c = '\\t'\n",
    "    if len(a) < 7:\n",
    "        tab_a = '\\t\\t\\t'\n",
    "    elif len(a) < 14:\n",
    "        tab_a = '\\t\\t'\n",
    "    if len(b) < 6:\n",
    "        tab_b = '\\t\\t\\t'\n",
    "    elif len(b) < 14:\n",
    "        tab_b = '\\t\\t'\n",
    "    if len(c) < 6:\n",
    "        tab_c = '\\t\\t\\t'\n",
    "    elif len(c) < 14:\n",
    "        tab_c = '\\t\\t'\n",
    "    print(a, tab_a, yvm_dict[a], '\\t', b, tab_b, yvm_dict[b], '\\t', c, tab_c, yvm_dict[c])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 2: Visualizations  \n",
    "\n",
    "Now we should take a look at the both the overall trend, and examine how the groups we identified as potential issues look compared to the rest of the groups in their respective categories. Dependent on the results, we may consider dropping the Native/Hawaiian, Pacific Islander, or 2 or more race groups, as well as sexual orientation as a category entirely. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, section our two potentially variable categories for ease of graphing\n",
    "race = pd.DataFrame(data = {'Native': df['Native'], \n",
    "                            'Asian': df['Asian'], \n",
    "                            'Hawaiian_or_PI': df['Hawaiian_or_PI'], \n",
    "                            'Afr_Amer': df['Afr_Amer'], \n",
    "                            'White': df['White'], '2_or_more': df['2_or_more'], \n",
    "                            'Hispanic': df['Hispanic'], \n",
    "                            'Not_Hispanic': df['Not_Hispanic'], \n",
    "                            'Afr_Amer_not_Hispanic': df['Afr_Amer_not_Hispanic'], \n",
    "                            'White_not_Hispanic': df['White_not_Hispanic']})\n",
    "\n",
    "orientation = pd.DataFrame(data = {'Straight': df['Straight'], \n",
    "                                   'Straight_Male': df['Straight_Male'], \n",
    "                                   'Straight_Female': df['Straight_Female'], \n",
    "                                   'Gay_Les': df['Gay_Les'], \n",
    "                                   'Gay_Male': df['Gay_Male'], \n",
    "                                   'Gay_Female': df['Gay_Female'], \n",
    "                                   'Bi': df['Bi'], \n",
    "                                   'Bi_Male': df['Bi_Male'], \n",
    "                                   'Bi_Female': df['Bi_Female']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 2.1: Total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage of Adequate Sleepers')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lineplot of Total Trend\n",
    "trend_pl = sns.lineplot(data = df['Total'])\n",
    "trend_pl.set_title('Total Trend', fontsize = 30)\n",
    "trend_pl.set_xlabel('Year')\n",
    "trend_pl.set_ylabel('Percentage of Adequate Sleepers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 2.2: Race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage of Adequate Sleepers')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lineplot of race breakdown (all columns)\n",
    "race_linepl_1 = sns.lineplot(data = race, dashes = False)\n",
    "\n",
    "# format title and legend\n",
    "race_linepl_1.set_title('Race Over Time', fontsize = 30)\n",
    "race_linepl_1.legend(loc = 4, prop = {'size': 14}, ncol = 2)\n",
    "race_linepl_1.set_xlabel('Year')\n",
    "race_linepl_1.set_ylabel('Percentage of Adequate Sleepers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 2.2.1: Race Re-Plot\n",
    "The three potential watch groups we identified were the Native/Hawaiian, Pacific Islander, or 2 or more race groups. We can confirm visually here that said groups each vary wildly. In the following, we regenerate the race lineplot without the three unreliable race breakdowns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage of Adequate Sleepers')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove 'Native', 'Hawaiian or PI', and '2 or more' columns\n",
    "race = race.drop(['Native', 'Hawaiian_or_PI', '2_or_more'], axis = 1)\n",
    "\n",
    "# lineplot of race breakdowns (dropped columns)\n",
    "race_linepl_2 = sns.lineplot(data = race, dashes = False)\n",
    "\n",
    "# format title and legend\n",
    "race_linepl_2.set_title('Race Over Time, Narrowed', fontsize = 30)\n",
    "race_linepl_2.legend(loc = 3, prop = {'size': 15}, ncol = 2)\n",
    "race_linepl_2.set_xlabel('Year')\n",
    "race_linepl_2.set_ylabel('Percentage of Adequate Sleepers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 2.2.2: Race Conclusions\n",
    "With those three groups removed, the trends are overall easier to observe. Although it is unclear if the decline is any more dramatic than in other groups, the fact that African Americans (Hispanic or otherwise) are shifted so far down our y-axis is potentially something to keep in mind."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 2.3: Sexual Orientation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage of Adequate Sleepers')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lineplot of sexual orientation (all columns)\n",
    "sex_orient_linepl_1 = sns.lineplot(data = orientation, dashes = False)\n",
    "\n",
    "# format title and legend\n",
    "sex_orient_linepl_1.set_title('Sexual Orientation', fontsize = 30)\n",
    "sex_orient_linepl_1.legend(loc = 3, prop = {'size': 14}, ncol = 2)\n",
    "sex_orient_linepl_1.set_xlabel('Year')\n",
    "sex_orient_linepl_1.set_ylabel('Percentage of Adequate Sleepers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 2.3.1: Orientation Re-plot\n",
    "These data are incredibly variable when compared to the narrowed race data. Because the gender distinctions are sub-categories within the overall category of sexual orientation, we can attempt to salvage the category by plotting just the main three: Straight, Gay/Lesbian, and Bisexual."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage of Adequate Sleepers')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove columns with large variance\n",
    "orientation = orientation.drop(['Straight_Male', \n",
    "                                'Straight_Female', \n",
    "                                'Gay_Male', \n",
    "                                'Gay_Female', \n",
    "                                'Bi_Male', \n",
    "                                'Bi_Female'], axis = 1)\n",
    "\n",
    "# lineplot of sexual orientation (dropped columns)\n",
    "sex_orient_linepl_2 = sns.lineplot(data = orientation, dashes = False)\n",
    "\n",
    "# format title and legend\n",
    "sex_orient_linepl_2.set_title('Sexual Orientation', fontsize = 30)\n",
    "sex_orient_linepl_2.legend(loc = 3, prop = {'size': 17})\n",
    "sex_orient_linepl_2.set_xlabel('Year')\n",
    "sex_orient_linepl_2.set_ylabel('Percentage of Adequate Sleepers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA 2.13.2: Orientation Conclusions\n",
    "\n",
    "As predicted, these demographic groups are too variable to be reliable in our analysis. With only ~3.5-4% of the population identifying as LGBT [14], we can safely assume that this variability is due to a too-small sample size.  \n",
    "\n",
    "Given that we only have data from 6 of the 11 years that our data span generally, and that only the Straight, Straight_Male, and Straight_Female demographic groups have reliable data even during these years, we are forced to discard sexual orientation as a category for examination. \n",
    "\n",
    "**References:**  \n",
    "14\\. **Gallup:** <u>In U.S., More Adults identifying as LGBT</u> https://news.gallup.com/poll/201731/lgbt-identification-rises.aspx  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA Conclusion\n",
    "Plotting the rest of the major categories reveals a worrying trend: none of the demographic groups (among those we did not reject for excessive variability) showed an overall increase in sufficient sleep over the years. There are many studies that explore the causes behind this trend of worsening sleep [6][7][15], but what we are interested in identifying demographic patterns in our categories.  \n",
    "\n",
    "With nearly every group losing sleep, the question we've asked indicates that we should be looking for the greatest loss over time.  \n",
    "\n",
    "**References:**  \n",
    "6\\. **RAND Corporation:** <u>Why sleep matters — the economic costs of insufficient sleep</u> https://www.rand.org/pubs/research_reports/RR1791.html  \n",
    "7\\. **American Psychological Association:** <u>Factors that contribute to insomnia</u> https://www.apa.org/monitor/oct01/insomnia#:~:text=Extended%20time%20in%20bed%2C%20napping,or%20a%20poor%20sleeping%20surface  \n",
    "15\\. **Frontiers in Public Health:** <u>Bigger, Brighter, Bluer-Better? Current Light-Emitting Devices - Adverse Sleep Properties and Preventative Strategies</u> https://www.frontiersin.org/articles/10.3389/fpubh.2015.00233/full  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA Epilogue: Removing Excess Variability\n",
    "\n",
    "Unfortunately, the groups in the following cell did not make the cut for data we believe is sufficiently reliable, and must be dropped from the full analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11, 51)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop problematic groups\n",
    "to_drop = ['Straight', 'Straight_Male', 'Straight_Female', 'Gay_Les', 'Gay_Male', 'Gay_Female', \n",
    "           'Bi', 'Bi_Male', 'Bi_Female', 'Native', 'Hawaiian_or_PI', '2_or_more']\n",
    "df = df.drop(columns = to_drop)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 1: Statistics\n",
    "\n",
    "To start, we should build a dataframe to hold our statistics conclusions with the demographic groups as the index, rather than the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set our categories, create a dataframe to store our metrics for analysis\n",
    "demo_type = ['total', 'gender', 'gender',  'race', 'race', 'race', 'race', 'race', \n",
    "             'race', 'race', 'age_general', 'age_specific', 'age_specific', 'age_general', \n",
    "             'age_specific', 'age_specific', 'age_general', 'age_specific', 'age_specific', \n",
    "             'age_specific', 'education', 'education', 'education', 'education', 'education', \n",
    "             'education', 'income', 'income', 'income', 'income', 'income', 'household', \n",
    "             'household', 'household', 'household', 'household', 'birth_country', \n",
    "             'birth_country', 'disability', 'disability', 'location', 'location', \n",
    "             'insurance', 'insurance', 'insurance', 'insurance', 'marital_status', \n",
    "             'marital_status', 'marital_status', 'marital_status', 'marital_status']\n",
    "demo_list = df.columns.tolist()\n",
    "analysis_df = pd.DataFrame(index = demo_list, data = {'demo_type': demo_type, 'demo': demo_list})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 1.1: Slope of the Regression\n",
    "\n",
    "There are a couple of ways we can judge which groups are losing the most sleep. First, we can calculate a line of best fit for the data for each group, and compare the slopes of those lines. This is essentially asking, \"if this trend continues as it has on average, who can we expect will be worse off later?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>demo_type</th>\n",
       "      <th>demo</th>\n",
       "      <th>slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>total</td>\n",
       "      <td>Total</td>\n",
       "      <td>-0.502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>gender</td>\n",
       "      <td>Male</td>\n",
       "      <td>-0.474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>gender</td>\n",
       "      <td>Female</td>\n",
       "      <td>-0.528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>race</td>\n",
       "      <td>Asian</td>\n",
       "      <td>-0.430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afr_Amer</th>\n",
       "      <td>race</td>\n",
       "      <td>Afr_Amer</td>\n",
       "      <td>-0.583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         demo_type      demo  slope\n",
       "Total        total     Total -0.502\n",
       "Male        gender      Male -0.474\n",
       "Female      gender    Female -0.528\n",
       "Asian         race     Asian -0.430\n",
       "Afr_Amer      race  Afr_Amer -0.583"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the line of best fit to match the line graphs from above\n",
    "def best_fit_slope(column, df = df):\n",
    "    x = []\n",
    "    y = []\n",
    "    \n",
    "    for year, item in zip(df.index, df[column]):\n",
    "        # Filter out nan\n",
    "        if item > 0:\n",
    "            x.append(year - 2008)\n",
    "            y.append(item)\n",
    "            \n",
    "    slope, intercept = np.polyfit(x, y, 1)\n",
    "\n",
    "    return np.round(slope, 3)\n",
    "\n",
    "# Add in a new column to analysis_df to hold the slope data\n",
    "analysis_df = analysis_df.assign(slope = analysis_df['demo'])\n",
    "analysis_df['slope'] = analysis_df['slope'].apply(best_fit_slope)\n",
    "\n",
    "# Visualize our data\n",
    "analysis_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 1.2: Total Decline\n",
    "\n",
    "The other, rather simple way to try to answer this question is to simply take the difference of the observed values in 2008 and 2018. For the sake of consistently having negative values to more easily compare this metric to the slope from before, we will subtract 2008 from 2018."
   ]
  },
  {
   "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",
       "    }\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>demo_type</th>\n",
       "      <th>demo</th>\n",
       "      <th>slope</th>\n",
       "      <th>decline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>total</td>\n",
       "      <td>Total</td>\n",
       "      <td>-0.502</td>\n",
       "      <td>-4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>gender</td>\n",
       "      <td>Male</td>\n",
       "      <td>-0.474</td>\n",
       "      <td>-3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>gender</td>\n",
       "      <td>Female</td>\n",
       "      <td>-0.528</td>\n",
       "      <td>-4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>race</td>\n",
       "      <td>Asian</td>\n",
       "      <td>-0.430</td>\n",
       "      <td>-4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afr_Amer</th>\n",
       "      <td>race</td>\n",
       "      <td>Afr_Amer</td>\n",
       "      <td>-0.583</td>\n",
       "      <td>-4.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         demo_type      demo  slope  decline\n",
       "Total        total     Total -0.502     -4.2\n",
       "Male        gender      Male -0.474     -3.9\n",
       "Female      gender    Female -0.528     -4.4\n",
       "Asian         race     Asian -0.430     -4.4\n",
       "Afr_Amer      race  Afr_Amer -0.583     -4.8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total decline: Where each group started subtracted from where they ended\n",
    "def total_decline(column, df = df):\n",
    "    \n",
    "    data = list(filter(lambda item: item > 0, df[column].tolist()))\n",
    "    \n",
    "    return data[-1] - data[0]\n",
    "\n",
    "# Add in a new column to analysis_df to hold the decline data\n",
    "analysis_df = analysis_df.assign(decline = analysis_df['demo'])\n",
    "analysis_df['decline'] = analysis_df['decline'].apply(total_decline)\n",
    "\n",
    "# Visualize our data\n",
    "analysis_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 1.3: Comparison\n",
    "\n",
    "Now that we have our two metrics, we should take a look at some of the details."
   ]
  },
  {
   "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>slope</th>\n",
       "      <th>decline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.522824</td>\n",
       "      <td>-4.417647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.135659</td>\n",
       "      <td>1.301953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.918000</td>\n",
       "      <td>-7.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.584000</td>\n",
       "      <td>-4.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.518000</td>\n",
       "      <td>-4.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-0.458500</td>\n",
       "      <td>-3.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-0.228000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           slope    decline\n",
       "count  51.000000  51.000000\n",
       "mean   -0.522824  -4.417647\n",
       "std     0.135659   1.301953\n",
       "min    -0.918000  -7.200000\n",
       "25%    -0.584000  -4.950000\n",
       "50%    -0.518000  -4.400000\n",
       "75%    -0.458500  -3.900000\n",
       "max    -0.228000   0.100000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we can take a look at a statistical breakdown of our metrics\n",
    "analysis_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                decline   R-squared:                       0.748\n",
      "Model:                            OLS   Adj. R-squared:                  0.743\n",
      "Method:                 Least Squares   F-statistic:                     145.2\n",
      "Date:                Fri, 12 Mar 2021   Prob (F-statistic):           2.89e-16\n",
      "Time:                        09:07:16   Log-Likelihood:                -50.198\n",
      "No. Observations:                  51   AIC:                             104.4\n",
      "Df Residuals:                      49   BIC:                             108.3\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.0788      0.372     -0.212      0.833      -0.826       0.668\n",
      "slope          8.2988      0.689     12.051      0.000       6.915       9.683\n",
      "==============================================================================\n",
      "Omnibus:                        6.643   Durbin-Watson:                   1.720\n",
      "Prob(Omnibus):                  0.036   Jarque-Bera (JB):               10.835\n",
      "Skew:                           0.095   Prob(JB):                      0.00444\n",
      "Kurtosis:                       5.250   Cond. No.                         9.51\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# We should check to see how connected our two metrics are, with slope predicting decline\n",
    "outcome, predictors = patsy.dmatrices('decline ~ slope', analysis_df)\n",
    "mod = sm.OLS(outcome, predictors)\n",
    "res = mod.fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 1.3.1: Comparison Conclusions\n",
    "\n",
    "With an R-squared value of .748, we can be sure that our two metrics are at least somewhat strongly related, as we hoped. However, they are not perfectly aligned. Our best results will come from incorporating both of these values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 25% value (based on median) for Slope is -0.584\n",
    "# Let's create a dataframe that drops everything above that value\n",
    "slope_df = analysis_df.sort_values(by = 'slope', ascending = False)\n",
    "drop_list = []\n",
    "for index, row in slope_df.iterrows():\n",
    "    if row['slope'] > -0.584:\n",
    "        drop_list.append(index)\n",
    "        \n",
    "slope_df = slope_df.drop(drop_list, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 25% value for Decline (based on median) is -4.95\n",
    "# Let's create a dataframe that drops everything above that value\n",
    "decline_df = analysis_df.sort_values(by = 'decline', ascending = False)\n",
    "\n",
    "drop_list = []\n",
    "for index, row in analysis_df.iterrows():\n",
    "    if row['decline'] > -4.95:\n",
    "        drop_list.append(index)\n",
    "        \n",
    "decline_df = analysis_df.sort_values(by = 'decline', ascending = False).drop(drop_list, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>demo_type</th>\n",
       "      <th>demo</th>\n",
       "      <th>slope</th>\n",
       "      <th>decline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45_to_64</th>\n",
       "      <td>age_general</td>\n",
       "      <td>45_to_64</td>\n",
       "      <td>-0.585</td>\n",
       "      <td>-5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55_to_64</th>\n",
       "      <td>age_specific</td>\n",
       "      <td>55_to_64</td>\n",
       "      <td>-0.714</td>\n",
       "      <td>-6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25_to_44</th>\n",
       "      <td>age_specific</td>\n",
       "      <td>25_to_44</td>\n",
       "      <td>-0.642</td>\n",
       "      <td>-5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Some_college</th>\n",
       "      <td>education</td>\n",
       "      <td>Some_college</td>\n",
       "      <td>-0.632</td>\n",
       "      <td>-6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA_MS_Plus</th>\n",
       "      <td>education</td>\n",
       "      <td>MA_MS_Plus</td>\n",
       "      <td>-0.614</td>\n",
       "      <td>-5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single_Parent</th>\n",
       "      <td>household</td>\n",
       "      <td>Single_Parent</td>\n",
       "      <td>-0.918</td>\n",
       "      <td>-7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Two_Parent_Family</th>\n",
       "      <td>household</td>\n",
       "      <td>Two_Parent_Family</td>\n",
       "      <td>-0.717</td>\n",
       "      <td>-5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Below_100K</th>\n",
       "      <td>income</td>\n",
       "      <td>Below_100K</td>\n",
       "      <td>-0.735</td>\n",
       "      <td>-6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100_to_199K</th>\n",
       "      <td>income</td>\n",
       "      <td>100_to_199K</td>\n",
       "      <td>-0.764</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public_Ins</th>\n",
       "      <td>insurance</td>\n",
       "      <td>Public_Ins</td>\n",
       "      <td>-0.651</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>race</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>-0.790</td>\n",
       "      <td>-6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>total</td>\n",
       "      <td>Total</td>\n",
       "      <td>-0.502</td>\n",
       "      <td>-4.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      demo_type               demo  slope  decline\n",
       "45_to_64            age_general           45_to_64 -0.585     -5.3\n",
       "55_to_64           age_specific           55_to_64 -0.714     -6.6\n",
       "25_to_44           age_specific           25_to_44 -0.642     -5.3\n",
       "Some_college          education       Some_college -0.632     -6.6\n",
       "MA_MS_Plus            education         MA_MS_Plus -0.614     -5.6\n",
       "Single_Parent         household      Single_Parent -0.918     -7.2\n",
       "Two_Parent_Family     household  Two_Parent_Family -0.717     -5.8\n",
       "Below_100K               income         Below_100K -0.735     -6.7\n",
       "100_to_199K              income        100_to_199K -0.764     -6.0\n",
       "Public_Ins            insurance         Public_Ins -0.651     -5.0\n",
       "Hispanic                   race           Hispanic -0.790     -6.4\n",
       "Total                     total              Total -0.502     -4.2"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the values in the worst 25% of both slope and decline\n",
    "both = []\n",
    "for slope in slope_df.index.tolist():\n",
    "    if slope in decline_df.index.tolist():\n",
    "        both.append(slope)\n",
    "\n",
    "# Create new dataframes with just the values in this list (and 'Total' for comparison)\n",
    "worst_sleepers_analysis = analysis_df.drop(labels = [i for i in analysis_df.index if \n",
    "                                                     (i not in both and i != 'Total')], axis = 0)\n",
    "worst_sleepers_df = df.drop(labels = [i for i in analysis_df.index if \n",
    "                                      (i not in both and i != 'Total')], axis = 1)\n",
    "\n",
    "# Display the data, grouped by demographic category, then sorted by decline\n",
    "worst_sleepers_analysis.sort_values(by = ['demo_type', 'decline'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 1.4: Deciding on a Primary Metric\n",
    "\n",
    "Now that we have incorporated both metrics into deciding which groups are generally worst off, we have answered our original question. The groups in `worst_sleepers_df` are the ones who have lost the most sleep over time. However, we notice from the `demo_type` column in `worst_sleepers_analysis` that some categories have more than one group—and that the slope and decline do not necessarily agree for each demographic. If we want to truly answer our question, we should find a single \"winner\" for each category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'education': 'Decline',\n",
       " 'age_specific': 'Decline',\n",
       " 'income': 'Decline',\n",
       " 'household': 'Slope'}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This function is to help decide which metric to use to calculate \"winners\"\n",
    "def slope_or_decline(df = worst_sleepers_analysis, stats_df = analysis_df):\n",
    "    # Calculate the standard deviation of all slopes and declines\n",
    "    slope_std = stats_df['slope'].agg('std')\n",
    "    decline_std = stats_df['decline'].agg('std')\n",
    "    \n",
    "    # These are the demographic groups we need to check\n",
    "    demo_check_list = ['education', 'age_specific', 'income', 'household']\n",
    "    out_dict = {}\n",
    "    for demo in demo_check_list:\n",
    "        # Create a slice of df for only the current demographic group\n",
    "        temp_df = df[df['demo_type'] == demo]\n",
    "        \n",
    "        # Get lists of the slopes and declines, each with 2 values\n",
    "        slopes = temp_df['slope'].tolist()\n",
    "        declines = temp_df['decline'].tolist()\n",
    "        \n",
    "        # Divide the difference of the slopes and declines by the appropriate std values\n",
    "        slope_dif = (abs(slopes[0] - slopes[1]) / slope_std)\n",
    "        decline_dif = (abs(declines[0] - declines[1]) / decline_std)\n",
    "        \n",
    "        # Add our decision to the dictionary\n",
    "        if slope_dif == decline_dif:\n",
    "            out_dict[demo] = 'Hung Jury'\n",
    "        elif slope_dif > decline_dif:\n",
    "            out_dict[demo] = 'Slope'\n",
    "        else:\n",
    "            out_dict[demo] = 'Decline'\n",
    "            \n",
    "            \n",
    "    return(out_dict)\n",
    "\n",
    "slope_or_decline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 1.5: Deciding the \"Winners\"\n",
    "\n",
    "As it happens, the same demographic \"wins\" by either metric in the `household` category, so going by this, it seems that Decline should be our primary metric for concluding which demographic gets the title of \"worst\" in each category. It is also worth noting that the \"worst\" narrow age group falls within the range of the \"worst\" broad age group, and has both a larger decline and a steeper slope, so the broad age group has been omitted in favor of the narrow one.\n",
    "\n",
    "Going by this, the worst sleepers in each demographic category are:\n",
    "\n",
    "* Age: 55-64\n",
    "* Education: Some College\n",
    "* Household: Single Parent\n",
    "* Income: Below 100K USD\n",
    "* Insurance: Public Insurance\n",
    "* Race: Hispanic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of the \"losing\" demographics; drop from both dataframes\n",
    "to_drop = ['45_to_64', '25_to_44', 'MA_MS_Plus', '100_to_199K', 'Two_Parent_Family']\n",
    "worst_sleepers_analysis = worst_sleepers_analysis.drop(labels = to_drop, axis = 0).sort_values(by = 'demo_type')\n",
    "worst_sleepers_df = worst_sleepers_df.drop(columns = to_drop)\n",
    "\n",
    "# # While we're at it, we need to start caring about colors. Let's create our color pallete\n",
    "colors = {'55_to_64': 'purple', \n",
    "          'Some_college': 'blue', \n",
    "          'Single_Parent': 'green', \n",
    "          'Below_100K': 'yellow', \n",
    "          'Public_Ins': 'orange', \n",
    "          'Hispanic': 'red', \n",
    "          'Total': 'black'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 2: Visualizations\n",
    "\n",
    "Now that we have our \"winners\" for each category, we should visualize the data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 2.1: Line Plot\n",
    "\n",
    "The obvious first step is to look at how the trends actually move over time. For comparison, we have included the Total category in black."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Percentage of Adequate Sleepers')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw the line plot\n",
    "worst_pl_1 = sns.lineplot(data = worst_sleepers_df, dashes = False, palette = colors)\n",
    "\n",
    "# format title and legend\n",
    "worst_pl_1.set_title('Worst Sleepers Over Time', fontsize = 30)\n",
    "worst_pl_1.legend(loc = 3, prop = {'size': 14}, ncol = 2)\n",
    "worst_pl_1.set_xlabel('Year')\n",
    "worst_pl_1.set_ylabel('Percentage of Adequate Sleepers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 2.1.1: Line Plot Conclusions\n",
    "\n",
    "Overall, most of the groups do appear to be getting less sleep in general, but that is not our question. We want to know which of our groups are losing the most sleep. It's easy to nod and put on a smart look and say \"oh, yes, the slopes do look steeper,\" but this plot, while definitely illustrative of the differing amounts of sleep, does not allow us to easily draw that conclusion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 2.2: Bar Plots\n",
    "\n",
    "A bar chart is a much easier visualization for comparing slope and decline between groups. The colors remain the same here for comparison. The bars have been left pointing down to help drive home the fact that these are negative values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Slope')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw the bar plot for slope\n",
    "worst_bar_slope = sns.barplot(data = worst_sleepers_analysis.sort_values(by = 'slope'), \n",
    "                              x = 'demo', y = 'slope', palette = colors)\n",
    "\n",
    "# format title and legend\n",
    "worst_bar_slope.set_title('Worst Sleepers by Slope (Predicted Sleep Loss Over Time)', fontsize = 30)\n",
    "worst_bar_slope.set_xlabel('Demographic Group')\n",
    "worst_bar_slope.set_ylabel('Slope')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Decline')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw the bar plot for decline\n",
    "worst_bar_decline = sns.barplot(data = worst_sleepers_analysis.sort_values(by = 'decline'), \n",
    "                                x = 'demo', y = 'decline', palette = colors)\n",
    "\n",
    "# format title and legend\n",
    "worst_bar_decline.set_title('Worst Sleepers by Total Decline 2008–2018 ', fontsize = 30)\n",
    "worst_bar_decline.set_xlabel('Demographic Group')\n",
    "worst_bar_decline.set_ylabel('Decline')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis 2.2.1: Bar Plots Conclusion\n",
    "\n",
    "We can draw a couple of conclusions from these bar plots. First, each of our selected demographic groups have far larger slopes and declines than the Total trend, doing a far better job of illustrating our main takeaway: these groups are declining faster than the average. Second, while the bars are clearly not ordered the same in each graph, we do have an overall winner in both categories: Single Parents. This group appears to be declining faster than any other group, and—returning to our line plot—they also appear to be getting the least sleep overall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sleep is inherently a personal and private matter, making it a highly difficult subject to collect data on. To protect the privacy of those whose data we use, we only used publicly available and anonymous data sets. Participants were all ages 18 or older. We can also confirm that this data was collected in an ethical fashion by the CDC. While we cannot be sure that they did not accidentally sample in a biased way, we have no reason to suspect that a branch of the government dedicated to the health of the nation had any cause to deliberately bias their data.  \n",
    "\n",
    "Additionally, we have done our best to avoid making assumptions or generalizations about any demographic groups. Instead, we have sought to find correlations that could shed light onto stressors and the resulting effects they have on the amount of sleep Americans get each night. We also acknowledge that any trends we find in our analysis are far from the only consequences that systemic discrimination has on any demographic group.  \n",
    "\n",
    "Concerning privacy, the CDC had already removed not only identifying information, but any information on the level of the individual by the time it was released to the public, so privacy was not a factor we were forced to consider further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Individually, each demographic we identified as having the largest declines in sleep quality can be confirmed by current evidence. In a comparison among Asian, Black/African American, Hispanic, and White race groups, Hispanics report the highest percentage of poor sleep, reported as inability to get enough sleep due to their weekday routine [16]. Meanwhile, in an analysis of adults aged 55–84, 55–64-year-olds were 26 percent more likely to report their sleep as fair to poor and report the highest concentration of sleep problems, at 71% (compared with 64–65%) [17]. Finally, single parents, our category with the most stark decline, are more likely to experience lower sleep quality (42.6 percent) compared with adults in two-parent households (32.7 percent), and adults living without children (31.0 percent) [18]. \n",
    "\n",
    "However, it is important we note that our categories are not mutually exclusive; a person can fall into several of the demographic categories with the highest decline in sleep quality. Indeed, there are established relationships between several of our demographics: individuals with public insurance (e.g., Medicaid) by definition earn less than $100k per year; lesser education (e.g., \"Some College\") is a predictor of lower income; and single parents not only lack a second breadwinner, but must pay the costs of raising a child, further increasing cost of living, and therefore raising their respective poverty threshold. Furthermore, ethnic minorities, such as Hispanics, suffer vast prejudice in the healthcare sector, aptly demonstrated by one Colorado study reporting Hispanics making up 62.1 percent of hospitalizations and 51.2 percent of deaths from the SARS-CoV-2 pandemic [19]. Perhaps the only demographic in our conclusions with no intrinsic relationship to our other categories is age. \n",
    "\n",
    "In the present study, we rejected the null hypothesis for two of our five predictions: the medically underserved and low socioeconomic status (SES). However, we failed to reject the null for the remaining three: young people, women, and city-dwellers. The importance of our findings lies largely in future directions; we hope that those doing further research into sleep quality can take our findings into account, placing a special focus on the groups we identified. \n",
    "\n",
    "There are a few limitations in the present analysis, however. Firstly, the Center for Disease Control's definition of optimal sleep quality as sleep duration is problematic at best. While sleep quality is indeed contingent on sleep duration, sufficient duration varies from person to person, and such a simplistic metric overlooks factors such as sleep quality, sleep disruption, and biological/psychological constraints on sleep [21]. Secondly, the nature of the CDC’s data as released to the public (percentages and standard error, not individual data points) meant that our conclusions were limited to broad, sweeping judgments. Without data on the level of individuals, a hearty asterisk must be placed on any conclusions we draw, as we cannot conclusively state the interactions that were previously mentioned are, in fact, behind the patterns we see.  \n",
    "\n",
    "\n",
    "**References:**  \n",
    "16\\. **National Sleep Foundation:** <u>Sleep in America Poll: Sleep and Ethnicity</u> https://www.sleepfoundation.org/wp-content/uploads/2018/10/NSF-Sleep-in-America-Poll-Summary-of-Findings-.pdf?x96585  \n",
    "17\\. **National Sleep Foundation:** <u>Sleep in America Poll: Sleep and Aging</u> https://www.sleepfoundation.org/wp-content/uploads/2018/10/2003SleepPollExecSumm.pdf?x96585  \n",
    "18\\. **NCHS Data Brief:** <u>Sleep Duration, Quality of Sleep, and Use of Sleep Medication, by Sex and Family Type, 2013-2014</u> https://www.cdc.gov/nchs/products/databriefs/db230.htm  \n",
    "19\\. **Centers for Disease Control and Prevention Morbidity and Mortality Weekly Report:** <u>Disproportionate Incidence of COVID-19 Infection, Hospitalizations, and Deaths Among Persons Identifying as Hispanic or Latino - Denver, Colorado March-October 2020</u> https://doi.org/10.15585/mmwr.mm6948a3  \n",
    "20\\. **U.S. Census Bureau:** <u>America’s Families and Living Arrangements: 2019; Table FG6</u> https://www.census.gov/data/tables/2019/demo/families/cps-2019.html  \n",
    "21\\. **Sleep medicine reviews:** <u>How much sleep do we need?</u> https://doi.org/10.1053/smrv.2000.0138  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* *Background Research, Reference Gathering, and Dataset Discovery: Justin*  \n",
    "* *Data Wrangling and Analysis (Coding): Justin and Geoff*  \n",
    "* *Data Wrangling and Analysis (Process): Everyone*  \n",
    "* *Data Visualization (Coding): Justin and Geoff*  \n",
    "* *Data Visualization (Process): Everyone*  \n",
    "* *Discussion Writing: Justin and Geoff*  \n",
    "* *Ethics Discussion: Leann and Stella*  \n",
    "* *Video Script: Everyone*  \n",
    "* *Video Visuals: Geoff and Stella*  \n",
    "* *Video Voiceover: Everyone*  \n",
    "* *Video Editing: Leann and Stella*  "
   ]
  }
 ],
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