FinalProject_group065.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gFJtuqOH6Sps"
   },
   "source": [
    "# **COGS 108 - Temperature and its Relation to Exercise Duration by State**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4lr7TPft6Sp1"
   },
   "source": [
    "# **Overview**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sN8_BnS76Sp2"
   },
   "source": [
    "Our research question aimed to examine the correlation between the average duration of exercise in each state and the state's corresponding average temperature within the United States. Our team has found three exemplary datasets in hopes to find a relationship, however after representing the data geospatially and performing a series of tests on the data, we have determined that there is a weak correlation between the duration of exercise and state average temperature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cjHsdYOZ6Sp2"
   },
   "source": [
    "# **Names**\n",
    "\n",
    "- Irene Hou\n",
    "- Nick Chua\n",
    "- Sean Tran\n",
    "- Seth Kim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "329z6Fyn6Sp3"
   },
   "source": [
    "<a id='research_question'></a>\n",
    "# **Research Question**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rEKtvGFM6Sp3"
   },
   "source": [
    "In the United States during Spring 2020, did people exercise for longer durations in states with higher average temperatures?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NUkVbCOZGiw5"
   },
   "source": [
    "## **Background and Prior Work**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z985LtsFGiw5"
   },
   "source": [
    "The sedentary lifestyle in America has long plagued its citizens, starting from years before the COVID-19 pandemic. A 2014-2015 National Health Interview Survey, published by the U.S. Department of Health and Human Services indicated that on a nationwide level, only “22.9% of U.S. adults aged 18–64 met the guidelines for both aerobic and muscle-strengthening activities during LTPA [Leisure Time Physical Activity] in 2010–2015” (Blackwell & Clarke, 2018). This has long-term implications for the health of Americans, leading to higher rates of heart disease, diabetes, asthma, and even mundane but debilitating conditions such as back pain and arthritis (Mayo Foundation for Medical Education and Research, 2020). \n",
    "\n",
    "According to federal activity guidelines, adults are expected to get at least 150 minutes of moderate to vigorous exercise at least once a week (Ducharme, 2018). The National Institute of Aging claims that four types of exercise are needed in order to maintain the best health and physical ability, including strength, endurance, balance, and flexibility. Specifically, the American Fitness and Sports Association states that low-impact activities include walking, jogging, yoga, cycling, or swimming. Unfortunately, many of these activities require special equipment, a safe location, or a comfortable environment, which can be inaccessible to many Americans.\n",
    "\n",
    "Especially in light of the global COVID-19 pandemic, the sedentary lifestyle has become more   and more prominent. Working from home with little to no outdoor reprieve, especially in dense urban areas and during the cold winter months, it is unsafe and challenging for the average American to engage in traditional exercise. \n",
    "\n",
    "Interestingly, the National Health Interview Survey’s conclusion and data showed that exercise frequency varied depending on gender, occupation, and even home state. Researchers found that people working in more white-collar jobs were more likely to get more exercise, and that “high levels of unemployment and disability or poor health in a state were correlated with lower rates of meeting exercise guidelines” (Ducharme, 2018). These findings suggest that certain subgroups are disproportionately disadvantaged when it comes to access to both time and reasonable places to participate in physical activity. As a result, data presented on these topics have to be sensitive to the privilege of something many take for granted: the ability to exercise.\n",
    "\n",
    "As for our COGS108 project, we are investigating exactly what those privileges may entail. We are primarily focused on finding the link between exercise duration and outdoor temperatures. What is the relationship between how long a person exercises and the average annual state temperature of where they live? Do people in on-average colder climates exercise more than those in warmer climates? Which states are more active, and is there any relation to the temperature of that state? While our topic is not looking to find a causal relationship between temperature and exercise, the hope is that we can identify potential barriers for people when it comes to fitness. \n",
    "\n",
    "Some interesting studies that have already been done on parallel topics include a study on the impact of weather on summer and winter exercise behaviors. One particular study published in the Journal of Sport and Health Science found that adverse weather conditions such as rain, snow, or excessive heat, specifically in summer and winter months, delayed an individual’s choice to exercise (Wagner et al, 2019). The study also found that “race, age, and education exacerbate the negative effects of adverse weather conditions on the decision to exercise outdoors” (Wagner et al, 2019.) Ultimately, Wagner’s study concluded that recommending exercise required a holistic evaluation of the individual’s outdoor environment, as well as providing strategies on how to maintain exercise habits in adverse conditions. This study gives excellent insight into what we are interested in, and serves as a great foundation for what we are investigating. \n",
    "\n",
    "Finally, a study that is directly relevant to current events is the study by Brand et al. on exercise frequency and subjective well-being during COVID-19 pandemic. Published by Frontiers in Psychology, the study statistically analyzed data from 13,696 respondents in 18 countries using a cross-sectional online survey to test whether or not exercise frequency influenced overall mood during a pandemic. The conclusion indicated that lockdown restrictions did not actually decrease exercise levels, and that those who exercised the most frequently during lockdown reported the highest positive mood states (Brand et al., 2020). Additionally, people who began exercising during the pandemic while having been previously sedentary reported early positive effects only if they maintained daily exercise.\n",
    "\n",
    "Ultimately, while our question may not attain direct results related to America’s current state of affairs, we hope that our analysis on these observations will allow us to better understand what drives fitness behavior and health. Our insight could prove to be encouraging to those who are daunted by the idea of beginning their fitness journey, as we are reminded that starting can frequently be the most difficult step.\n",
    "\n",
    "\n",
    "\n",
    "References:\n",
    "\n",
    "1. Brand, R., Timme, S., & Nosrat, S. (2020). When Pandemic Hits: Exercise Frequency and Subjective Well-Being During COVID-19 Pandemic. Frontiers in psychology, 11, 570567. https://doi.org/10.3389/fpsyg.2020.570567\n",
    "\n",
    "2. Ducharme, J. (2018, June 28). Only 23% of Americans Get Enough Exercise, a New Report Says. Time. https://time.com/5324940/americans-exercise-physical-activity-guidelines/\n",
    "\n",
    "4. Mayo Foundation for Medical Education and Research. (2020, November 11). Exercise and chronic disease: Get the facts. https://www.mayoclinic.org/healthy-lifestyle/fitness/in-depth/exercise-and-chronic-disease/art-20046049?reDate=29012021\n",
    "\n",
    "5. National Institute of Aging. (n.d.). Four Types of Exercise Can Improve Your Health and Physical Ability. Retrieved January 29, 2021, from https://www.nia.nih.gov/health/four-types-exercise-can-improve-your-health-and-physical-ability\n",
    "\n",
    "6. Blackwell, D., & Clarke, T. (2018, June). State Variation in Meeting the 2008 Federal Guidelines for Both Aerobic and Muscle-strengthening Activities Through Leisure-time Physical Activity Among Adults Aged 18–64: United States, 2010–2015 (No. 112). Department of Health and Human Services.\n",
    "\n",
    "7. Wagner, A. L., Keusch, F., Yan, T., & Clarke, P. J. (2019). The impact of weather on summer and winter exercise behaviors. Journal of Sport and Health Science, 8(1), 39–45. https://doi.org/10.1016/j.jshs.2016.07.007\n",
    "\n",
    "8. Wenger, H. A. (1986). The interactions of intensity, frequency and duration of exercise training in altering cardiorespiratory fitness. PubMed. https://pubmed.ncbi.nlm.nih.gov/3529283/\n",
    "\n",
    "9. Weston, M., Taylor, K. L., Batterham, A. M., & Hopkins, W. G. (2014). Effects of low-volume high-intensity interval training (HIT) on fitness in adults: a meta-analysis of controlled and non-controlled trials. Sports medicine (Auckland, N.Z.), 44(7), 1005–1017. https://doi.org/10.1007/s40279-014-0180-z\n",
    "\n",
    "10. Wheeler, K. (2020, August 25). High-Impact vs. Low-Impact Exercises. ASFA. https://www.americansportandfitness.com/blogs/fitness-blog/high-impact-vs-low-impact-exercises\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WoJsuSI16Sp4"
   },
   "source": [
    "# **Hypothesis**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qgtauZ9C6Sp4"
   },
   "source": [
    "We expected that the higher the average temperature of the state was, the more likely an individual will exercise for longer. In the same vein, we expected a positive correlation between the average state temperature and the average exercise duration. Therefore, as the temperature rises, so does the length of time a person exercises."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_wIp2PyegAVW"
   },
   "source": [
    "# **Dataset(s)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rx6na2g-gAVW"
   },
   "source": [
    "**Dataset 1:** Temperature and Exercising Patterns Per State  \n",
    "**Link:** https://osf.io/xdkp8/  \n",
    "**Number of Observations:** 16137 observations\n",
    "\n",
    "This dataset is a massive dataset collected through a global cross-sectional survey between March and May 2020. It contains data regarding exercise that people did during the pandemic, their mental well-being, frequency of exercise, location of exercise, exercise intensity, locational data (including longitude/latitude), age, gender, mood measured on the POMS scale, and exercise session length before and after pandemic. Because there are so many extraneous observations and variables, we have pared our data and tidied it down to exercise duration as well as locational information from only within the U.S. We also excluded age and gender for now, as we believe it will not be a significant enough factor in answering our question.\n",
    "\n",
    "**Dataset 2:** Global Temperature Patterns  \n",
    "**Link:** https://www.kaggle.com/akshaychavan/average-temperature-per-country-per-year?select=GlobalLandTemperaturesByState.csv  \n",
    "**Number of Observations:** 645675 observations\n",
    "\n",
    "This dataset contains data regarding the average temperature per country per year. It contains variables on the average temperature per year, the uncertainty of the average temperature, as well as state, country, and year of each observation.\n",
    "\n",
    "**Dataset 3:** Physical Health Measures  \n",
    "**Link:** https://www.cdc.gov/nchs/data/nhsr/nhsr112.pdf\n",
    "**Number of Observations:** 52 observations\n",
    "\n",
    "This datasets contains the percentage of individuals that met the federal aerobic and muscle-strenghtening guidelines through leisure-time physical activity. It is organized by state, sex, and their working status. Every value in each column contains the percentage as well as the standard error for those percentages. We are interested mainly in the overall percentage of adults who met those guidelines by state and as such we will extract those necessary columns.\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "We will combine these datasets by observing the state location (within U.S.) of each individual and their exercise level, and seeing if there is any relation to the average temperature of that particular state. For our project, we will only be looking at the U.S. states, and removing the data from around the world, as well as any temporal data that was outside the scope of our observations (specifically, prior to 2018). The third dataset will be useful in comparison with the findings of the first and second dataset. We will be able to gauge physical capability with the mean exercise duration of each state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NHoEH3YIgAVW"
   },
   "source": [
    "# **Setup**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UpqBY5tUIfqb",
    "outputId": "0bac4d33-a33e-479a-ebdf-9d7e77abac8d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning:\n",
      "\n",
      "pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import data manipulation packages\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# Import statistical and graphing packages\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RT7zRmNTgAVX"
   },
   "source": [
    "# **Data Cleaning**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DWJfOTBNtvZD"
   },
   "source": [
    "## **Dataset 1: Temperature and Exercising Patterns Per State**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kzb4Jb3xuQLt"
   },
   "source": [
    "**Import Data**\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 796
    },
    "id": "bYjlfYnXt8Ko",
    "outputId": "7a88c4b0-d5fb-406f-844b-9c33baa866bc"
   },
   "outputs": [
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       "      <td>Never</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>Never</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>lower</td>\n",
       "      <td>Missing</td>\n",
       "      <td>High</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Never</td>\n",
       "      <td>Never</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>Never</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>Never</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>5444423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>465</td>\n",
       "      <td>5</td>\n",
       "      <td>Weights</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Walking</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Weights</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Cardio (jogging)</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2020-03-31 15:40:51</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>California</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>same</td>\n",
       "      <td>Shorter</td>\n",
       "      <td>High</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Lighter</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>5444423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>509</td>\n",
       "      <td>8</td>\n",
       "      <td>Walking</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>Walking</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>2020-03-31 17:17:27</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>9375</td>\n",
       "      <td>0</td>\n",
       "      <td>Almost every day</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>Almost every day</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>higher</td>\n",
       "      <td>Longer</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Similar</td>\n",
       "      <td>Almost every day</td>\n",
       "      <td>Almost every day</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>Almost every day</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>Almost every day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16132</th>\n",
       "      <td>16133</td>\n",
       "      <td>23202</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>5444423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>665</td>\n",
       "      <td>6</td>\n",
       "      <td>Fitness-Übungen</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Joggen</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Tischtennis</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Deutschland</td>\n",
       "      <td>Brandenburg</td>\n",
       "      <td>Brandenburg an der Havel</td>\n",
       "      <td>2020-05-06 22:21:21</td>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Brandenburg</td>\n",
       "      <td>3</td>\n",
       "      <td>875</td>\n",
       "      <td>0</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>16133</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>333333</td>\n",
       "      <td>higher</td>\n",
       "      <td>Longer</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>High</td>\n",
       "      <td>Heavier</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16133</th>\n",
       "      <td>16134</td>\n",
       "      <td>23205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>5444423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>604</td>\n",
       "      <td>9</td>\n",
       "      <td>reipas kävely</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>lihaskuntoharjoitukset (kehonpaino)</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>kuntosaliharjoittelu</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>juokseminen</td>\n",
       "      <td>Suomi</td>\n",
       "      <td>Lappi</td>\n",
       "      <td>Rovaniemi</td>\n",
       "      <td>2020-05-07 05:03:50</td>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Lappi</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>Almost every day</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>Almost every day</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>16134</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>666667</td>\n",
       "      <td>higher</td>\n",
       "      <td>Longer</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Similar</td>\n",
       "      <td>Almost every day</td>\n",
       "      <td>Almost every day</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>Almost every day</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>Almost every day</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16134</th>\n",
       "      <td>16135</td>\n",
       "      <td>23206</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>5444423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1362</td>\n",
       "      <td>4</td>\n",
       "      <td>یوگا</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>پیاده روی</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ایران</td>\n",
       "      <td>تهران</td>\n",
       "      <td>تهران</td>\n",
       "      <td>2020-05-07 06:11:00</td>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>Iran</td>\n",
       "      <td>Iran</td>\n",
       "      <td>تهران</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5625</td>\n",
       "      <td>0</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>16135</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>same</td>\n",
       "      <td>Shorter</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>Similar</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16135</th>\n",
       "      <td>16136</td>\n",
       "      <td>23207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>5444423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>744</td>\n",
       "      <td>6</td>\n",
       "      <td>Αερόβια γυμναστική στο σπίτι</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>Pilates</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eλλάδα</td>\n",
       "      <td>Κεντρική Μακεδονία</td>\n",
       "      <td>Θεσσαλονίκη</td>\n",
       "      <td>2020-05-07 06:16:29</td>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Κεντρική Μακεδονία</td>\n",
       "      <td>3</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>16136</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>same</td>\n",
       "      <td>Similar</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>High</td>\n",
       "      <td>Heavier</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>4-5 days</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16136</th>\n",
       "      <td>16137</td>\n",
       "      <td>23209</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>5444423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>2020-05-07 06:50:47</td>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>6875</td>\n",
       "      <td>0</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>Never</td>\n",
       "      <td>Never</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>16137</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>higher</td>\n",
       "      <td>Similar</td>\n",
       "      <td>Missing</td>\n",
       "      <td>Low</td>\n",
       "      <td>Missing</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>Never</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>Never</td>\n",
       "      <td>Never</td>\n",
       "      <td>2-3 days</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16137 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0   lfdn  ...  Unnamed: 58       Unnamed: 59\n",
       "0               1     47  ...     4-5 days  Almost every day\n",
       "1               2     46  ...          NaN               NaN\n",
       "2               3     44  ...          NaN               NaN\n",
       "3               4     45  ...     2-3 days               NaN\n",
       "4               5     48  ...     4-5 days  Almost every day\n",
       "...           ...    ...  ...          ...               ...\n",
       "16132       16133  23202  ...          NaN               NaN\n",
       "16133       16134  23205  ...          NaN               NaN\n",
       "16134       16135  23206  ...          NaN               NaN\n",
       "16135       16136  23207  ...          NaN               NaN\n",
       "16136       16137  23209  ...          NaN               NaN\n",
       "\n",
       "[16137 rows x 60 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the data\n",
    "data_state = pd.read_csv(\"https://raw.githubusercontent.com/sgt002/COGS108_datasets/master/data_country_mod.csv\")\n",
    "data_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WNyG8VstuiMr",
    "outputId": "9faae741-f052-4e76-9a1c-5028fe0b1461"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0              int64\n",
       "lfdn                    int64\n",
       "external_lfdn           int64\n",
       "tester                  int64\n",
       "dispcode                int64\n",
       "lastpage                int64\n",
       "quality               float64\n",
       "duration                int64\n",
       "v_86                    int64\n",
       "v_108                  object\n",
       "v_43                  float64\n",
       "v_44                  float64\n",
       "v_112                 float64\n",
       "v_103                 float64\n",
       "v_104                  object\n",
       "v_137                 float64\n",
       "v_106                 float64\n",
       "v_107                 float64\n",
       "v_140                 float64\n",
       "v_124                   int64\n",
       "v_126                  object\n",
       "v_127                 float64\n",
       "v_128                 float64\n",
       "v_129                 float64\n",
       "v_130                  object\n",
       "v_147                  object\n",
       "v_149                  object\n",
       "v_148                  object\n",
       "datetime               object\n",
       "part_date              object\n",
       "country                object\n",
       "lmmountry              object\n",
       "region_province        object\n",
       "Ex_before              object\n",
       "Ex_during              object\n",
       "ex_during              object\n",
       "ex_before              object\n",
       "ID                     object\n",
       "ex_diff                object\n",
       "ex_ratio               object\n",
       "Ex_diff                object\n",
       "shortlong              object\n",
       "intensity_bef_fact     object\n",
       "intensity_dur_fact     object\n",
       "intens_diff            object\n",
       "Ex_during_c.tr         object\n",
       "Ex_during_c.tr2        object\n",
       "Ex_before_c.tr         object\n",
       "Ex_during_c.sum        object\n",
       "Ex_before_c.sum        object\n",
       "Ex_before_c.sld        object\n",
       "Ex_during_c.sld        object\n",
       "Unnamed: 52            object\n",
       "Unnamed: 53            object\n",
       "Unnamed: 54            object\n",
       "Unnamed: 55            object\n",
       "Unnamed: 56            object\n",
       "Unnamed: 57            object\n",
       "Unnamed: 58            object\n",
       "Unnamed: 59            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the data types\n",
    "data_state.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yx_HTHIjuwC3",
    "outputId": "0e3fccbf-ced2-4faa-eb1b-f8fbd66e78e0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                0\n",
       "lfdn                      0\n",
       "external_lfdn             0\n",
       "tester                    0\n",
       "dispcode                  0\n",
       "lastpage                  0\n",
       "quality               16137\n",
       "duration                  0\n",
       "v_86                      0\n",
       "v_108                  1566\n",
       "v_43                   1131\n",
       "v_44                   1135\n",
       "v_112                  1136\n",
       "v_103                  1137\n",
       "v_104                  6695\n",
       "v_137                  6505\n",
       "v_106                  6511\n",
       "v_107                  6517\n",
       "v_140                  6521\n",
       "v_124                     0\n",
       "v_126                  1186\n",
       "v_127                   639\n",
       "v_128                   639\n",
       "v_129                   639\n",
       "v_130                  7690\n",
       "v_147                   259\n",
       "v_149                   477\n",
       "v_148                   488\n",
       "datetime                  0\n",
       "part_date                 0\n",
       "country                 315\n",
       "lmmountry               277\n",
       "region_province         484\n",
       "Ex_before               127\n",
       "Ex_during                98\n",
       "ex_during                90\n",
       "ex_before               105\n",
       "ID                        7\n",
       "ex_diff                 114\n",
       "ex_ratio                114\n",
       "Ex_diff                 115\n",
       "shortlong                15\n",
       "intensity_bef_fact       18\n",
       "intensity_dur_fact       21\n",
       "intens_diff              18\n",
       "Ex_during_c.tr          110\n",
       "Ex_during_c.tr2         103\n",
       "Ex_before_c.tr          109\n",
       "Ex_during_c.sum          89\n",
       "Ex_before_c.sum         103\n",
       "Ex_before_c.sld         107\n",
       "Ex_during_c.sld          97\n",
       "Unnamed: 52             285\n",
       "Unnamed: 53             408\n",
       "Unnamed: 54             650\n",
       "Unnamed: 55            1475\n",
       "Unnamed: 56            3309\n",
       "Unnamed: 57            6351\n",
       "Unnamed: 58           10322\n",
       "Unnamed: 59           14236\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of null values in each column\n",
    "data_state.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bNGoZH7HvASD"
   },
   "source": [
    "**Select Columns of Interest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "KR9IRYKHvHrf",
    "outputId": "f7fee203-4e2e-4881-da30-792bcbc0aba9"
   },
   "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>duration</th>\n",
       "      <th>state</th>\n",
       "      <th>datetime</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>485</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1180</td>\n",
       "      <td>CA</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>258</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>465</td>\n",
       "      <td>California</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>509</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>488</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>509</td>\n",
       "      <td>Kalifornien</td>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>462</td>\n",
       "      <td>MN</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>826</td>\n",
       "      <td>Mn</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>613</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>191 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     duration          state    datetime country\n",
       "0         485       Colorado  2020-03-31     usa\n",
       "1        1180             CA  2020-03-31     usa\n",
       "2         258       Illinois  2020-03-31     usa\n",
       "3         465    California   2020-03-31     usa\n",
       "4         509           Ohio  2020-03-31     usa\n",
       "..        ...            ...         ...     ...\n",
       "191       488        Indiana  2020-05-03     usa\n",
       "192       509   Kalifornien   2020-05-03     usa\n",
       "193       462             MN  2020-05-04     usa\n",
       "194       826             Mn  2020-05-04     usa\n",
       "195       613  Pennsylvania   2020-05-06     usa\n",
       "\n",
       "[191 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Limit observations to those in the USA\n",
    "data_state = data_state.loc[data_state['country'] == 'USA']\n",
    "\n",
    "# Select columns of interest\n",
    "data_state = data_state[['duration', 'v_149', 'datetime', 'country']]\n",
    "\n",
    "# Rename columns\n",
    "data_state.columns = ['duration', 'state', 'datetime', 'country']\n",
    "\n",
    "# Remove time observation in datime\n",
    "data_state['datetime'] = pd.to_datetime(data_state['datetime']).dt.date\n",
    "\n",
    "# Rename USA to usa\n",
    "data_state = data_state.replace('USA', 'usa')\n",
    "\n",
    "# Remove any observations with null values and reset indexing\n",
    "data_state = data_state.dropna()\n",
    "data_state.reset_index(drop=True, inplace=True)\n",
    "\n",
    "#remove any observations in the duration column that have -1 values\n",
    "data_state = data_state[data_state['duration'] != -1]\n",
    "\n",
    "data_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NK_ThGoswXjY"
   },
   "source": [
    "**Standardizing Values of Columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kC_xDmiUyBiC",
    "outputId": "137e695b-e4dd-4b01-ba0d-5c312b6b05a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Colorado', 'CA', 'Illinois', 'California ', 'Ohio', 'Virginia ',\n",
       "       'Colorado ', 'Pennsylvania', 'Indiana ', 'Texas', 'Tennessee',\n",
       "       'Wisconsin', 'PENNSYLVANIA', 'Maryland ', 'CT', 'Illinois ',\n",
       "       'pennsylvania', 'Ny', 'Florida', 'Pennsylvania ', 'Minnesota ',\n",
       "       'Iowa', 'Minnesota', 'WI', 'TN', 'New mexico ', 'Oregon', 'OHIO',\n",
       "       'California', 'Missouri ', 'New York', 'florida', 'Michigan', 'MD',\n",
       "       'New Jersey', 'District of Columbia ', 'North Carolina',\n",
       "       'Massachusetts ', 'New York ', 'New Mexico', 'Alabama',\n",
       "       'Rhode Island', 'WA', 'NY', 'Connecticut', 'TEXAS', 'NEW YORK',\n",
       "       'Massachussets', 'San Diego', 'New York State',\n",
       "       'San Francisco Bay Area', 'Washington State', 'Maryland',\n",
       "       'Massachusetts', 'SC', 'Oklahoma', 'Missouri', 'New Jersey ',\n",
       "       'Kansas', 'IL', 'Nebraska', 'MA', 'Connecticut ', '德州', 'Arizona ',\n",
       "       'OH', 'Georgia ', 'NJ', 'Kentucky', '密西根州', 'Virginia', ' Georgia',\n",
       "       '加州', 'Texas ', 'Indiana', 'Kalifornien ', 'MN', 'Mn'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_state['state'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TkWaMRekwkmZ"
   },
   "source": [
    "We are standarizing the values below, which in this project will explicitly be the state variable. The state variable is what we will be using to combine our datasets as we are investigating the relationship between exercise in certain states versus average temperatures of said state. In this case, we need to standardize the values for the datasets by changing each state variable to one standard string (lowercase and fully spelled out) so that it can be combined nicely and follow tidy data principles. In this case, Dataset 1, containing information about exercise location, had various responses in the geographic location category. Some people typed the whole pronoun, spelled incorrectly, or even reported in different languages. Therefore, we standardized these into the full name of the state by editing them to lowercase, as well as stripping the quotes surrounding each state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "9gbKw9WcwCPF"
   },
   "outputs": [],
   "source": [
    "# Standardization for data_state\n",
    "def standardize_state(string):\n",
    "\n",
    "    # Basic string pre-processing\n",
    "    string = string.lower()\n",
    "    string = string.strip()\n",
    "\n",
    "    # Fix abbreviations\n",
    "    if string == \"ca\":\n",
    "        string = \"california\"\n",
    "    if string == \"ny\":\n",
    "        string = \"new york\"\n",
    "    if string == \"wi\":\n",
    "        string = \"wisconsin\"\n",
    "    if string == \"tn\":\n",
    "        string = \"tennessee\"\n",
    "    if string == \"md\":\n",
    "        string = \"maryland\"\n",
    "    if string == \"wa\":\n",
    "        string = \"washington\"\n",
    "    if string == \"va\":\n",
    "        string = \"virginia\"\n",
    "    if string == \"sc\":\n",
    "        string = \"south carolina\"\n",
    "    if string == \"il\":\n",
    "        string = \"illinois\"\n",
    "    if string == \"ma\":\n",
    "        string = \"massachusetts\"\n",
    "    if string == \"oh\":\n",
    "        string = \"ohio\"\n",
    "    if string == \"nj\":\n",
    "        string = \"new jersey\"\n",
    "    if string == \"mn\":\n",
    "        string = \"minnesota\"\n",
    "    if string == \"ct\":\n",
    "        string = \"connecticut\"\n",
    "\n",
    "    # Fix weird inputs\n",
    "    string = string.replace(\"san diego\", \"california\")\n",
    "    string = string.replace(\"new york state\", \"new york\") \n",
    "    string = string.replace(\"san francisco bay area\", \"california\") \n",
    "    string = string.replace(\"washington state\", \"washington\") \n",
    "    string = string.replace(\"德州\", \"texas\")\n",
    "    string = string.replace(\"密西根州\", \"michigan\")\n",
    "    string = string.replace(\"加州\", \"california\")\n",
    "    string = string.replace(\"kalifornien\", \"california\")\n",
    "    string = string.replace(\"massachussets\", \"massachusetts\")\n",
    "\n",
    "    # Final strip\n",
    "    string = string.strip()\n",
    "    \n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FsvXu1r7wIpq",
    "outputId": "af62a910-eb9d-492f-8a15-3057b202a7e9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "new york                31\n",
       "california              22\n",
       "colorado                16\n",
       "texas                   13\n",
       "illinois                12\n",
       "florida                 10\n",
       "pennsylvania             8\n",
       "ohio                     7\n",
       "maryland                 7\n",
       "iowa                     6\n",
       "new jersey               6\n",
       "minnesota                6\n",
       "massachusetts            6\n",
       "michigan                 5\n",
       "connecticut              4\n",
       "north carolina           3\n",
       "indiana                  3\n",
       "wisconsin                3\n",
       "washington               2\n",
       "tennessee                2\n",
       "georgia                  2\n",
       "virginia                 2\n",
       "new mexico               2\n",
       "oregon                   2\n",
       "missouri                 2\n",
       "arizona                  1\n",
       "alabama                  1\n",
       "kansas                   1\n",
       "kentucky                 1\n",
       "oklahoma                 1\n",
       "nebraska                 1\n",
       "rhode island             1\n",
       "south carolina           1\n",
       "district of columbia     1\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply standardization function to dataset\n",
    "data_state['state'] = data_state[\"state\"].astype(str).apply(standardize_state)\n",
    "data_state['state'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FT5zfXG4_4xW"
   },
   "source": [
    "**State Codes**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w46y-ueQmYm-"
   },
   "source": [
    "As a preliminary step to creating the choropleth visuals later on during our analysis, we will add an additional columns specifying the state codes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "WEWGhxQm_7fP"
   },
   "outputs": [],
   "source": [
    "us_state_dict = {\n",
    "    'alabama': 'AL',\n",
    "    'alaska': 'AK',\n",
    "    'arizona': 'AZ',\n",
    "    'arkansas': 'AR',\n",
    "    'california': 'CA',\n",
    "    'colorado': 'CO',\n",
    "    'connecticut': 'CT',\n",
    "    'delaware': 'DE',\n",
    "    'district of columbia': 'DC',\n",
    "    'florida': 'FL',\n",
    "    'georgia': 'GA',\n",
    "    'hawaii': 'HI',\n",
    "    'idaho': 'ID',\n",
    "    'illinois': 'IL',\n",
    "    'indiana': 'IN',\n",
    "    'iowa': 'IA',\n",
    "    'kansas': 'KS',\n",
    "    'kentucky': 'KY',\n",
    "    'louisiana': 'LA',\n",
    "    'maine': 'ME',\n",
    "    'maryland': 'MD',\n",
    "    'massachusetts': 'MA',\n",
    "    'michigan': 'MI',\n",
    "    'minnesota': 'MN',\n",
    "    'mississippi': 'MS',\n",
    "    'missouri': 'MO',\n",
    "    'montana': 'MT',\n",
    "    'nebraska': 'NE',\n",
    "    'nevada': 'NV',\n",
    "    'new hampshire': 'NH',\n",
    "    'new jersey': 'NJ',\n",
    "    'new mexico': 'NM',\n",
    "    'new york': 'NY',\n",
    "    'north carolina': 'NC',\n",
    "    'north dakota': 'ND',\n",
    "    'ohio': 'OH',\n",
    "    'oklahoma': 'OK',\n",
    "    'oregon': 'OR',\n",
    "    'pennsylvania': 'PA',\n",
    "    'rhode island': 'RI',\n",
    "    'south carolina': 'SC',\n",
    "    'south dakota': 'SD',\n",
    "    'tennessee': 'TN',\n",
    "    'texas': 'TX',\n",
    "    'utah': 'UT',\n",
    "    'vermont': 'VT',\n",
    "    'virginia': 'VA',\n",
    "    'washington': 'WA',\n",
    "    'west virginia': 'WV',\n",
    "    'wisconsin': 'WI',\n",
    "    'wyoming': 'WY'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "aoGxGFfhAeqd",
    "outputId": "33f58fd5-5283-4f80-d4a0-6cce974d191f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>duration</th>\n",
       "      <th>state</th>\n",
       "      <th>datetime</th>\n",
       "      <th>country</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>485</td>\n",
       "      <td>colorado</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>usa</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1180</td>\n",
       "      <td>california</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>usa</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>258</td>\n",
       "      <td>illinois</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>usa</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>465</td>\n",
       "      <td>california</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>usa</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>509</td>\n",
       "      <td>ohio</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>usa</td>\n",
       "      <td>OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>488</td>\n",
       "      <td>indiana</td>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>usa</td>\n",
       "      <td>IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>509</td>\n",
       "      <td>california</td>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>usa</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>462</td>\n",
       "      <td>minnesota</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>usa</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>826</td>\n",
       "      <td>minnesota</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>usa</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>613</td>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>2020-05-06</td>\n",
       "      <td>usa</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>191 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     duration         state    datetime country code\n",
       "0         485      colorado  2020-03-31     usa   CO\n",
       "1        1180    california  2020-03-31     usa   CA\n",
       "2         258      illinois  2020-03-31     usa   IL\n",
       "3         465    california  2020-03-31     usa   CA\n",
       "4         509          ohio  2020-03-31     usa   OH\n",
       "..        ...           ...         ...     ...  ...\n",
       "191       488       indiana  2020-05-03     usa   IN\n",
       "192       509    california  2020-05-03     usa   CA\n",
       "193       462     minnesota  2020-05-04     usa   MN\n",
       "194       826     minnesota  2020-05-04     usa   MN\n",
       "195       613  pennsylvania  2020-05-06     usa   PA\n",
       "\n",
       "[191 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append new column holding the two letter abbreviations for each state\n",
    "data_state['code'] = data_state['state'].map(us_state_dict)\n",
    "\n",
    "data_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fvgmDj42ws7B"
   },
   "source": [
    "## **Dataset 2: Global Temperature Patterns**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "64tqlDBqxE3B"
   },
   "source": [
    "**Import Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "hot1qdBIw8DW",
    "outputId": "de97987e-bef7-485c-a703-6f0631dd2c5d"
   },
   "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>dt</th>\n",
       "      <th>AverageTemperature</th>\n",
       "      <th>AverageTemperatureUncertainty</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1855-05-01</td>\n",
       "      <td>25.544</td>\n",
       "      <td>1.171</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1855-06-01</td>\n",
       "      <td>24.228</td>\n",
       "      <td>1.103</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1855-07-01</td>\n",
       "      <td>24.371</td>\n",
       "      <td>1.044</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1855-08-01</td>\n",
       "      <td>25.427</td>\n",
       "      <td>1.073</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1855-09-01</td>\n",
       "      <td>25.675</td>\n",
       "      <td>1.014</td>\n",
       "      <td>Acre</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645670</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>21.634</td>\n",
       "      <td>0.578</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645671</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>24.679</td>\n",
       "      <td>0.596</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645672</th>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>29.272</td>\n",
       "      <td>1.340</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645673</th>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>29.202</td>\n",
       "      <td>0.869</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645674</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>645675 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                dt  AverageTemperature  ...     State Country\n",
       "0       1855-05-01              25.544  ...      Acre  Brazil\n",
       "1       1855-06-01              24.228  ...      Acre  Brazil\n",
       "2       1855-07-01              24.371  ...      Acre  Brazil\n",
       "3       1855-08-01              25.427  ...      Acre  Brazil\n",
       "4       1855-09-01              25.675  ...      Acre  Brazil\n",
       "...            ...                 ...  ...       ...     ...\n",
       "645670  2013-05-01              21.634  ...  Zhejiang   China\n",
       "645671  2013-06-01              24.679  ...  Zhejiang   China\n",
       "645672  2013-07-01              29.272  ...  Zhejiang   China\n",
       "645673  2013-08-01              29.202  ...  Zhejiang   China\n",
       "645674  2013-09-01                 NaN  ...  Zhejiang   China\n",
       "\n",
       "[645675 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import data\n",
    "data_temp = pd.read_csv(\"https://raw.githubusercontent.com/sgt002/COGS108_datasets/master/data_temperature.csv\")\n",
    "data_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GrgclMq6xJKd",
    "outputId": "0e9f40a8-e04c-4560-d0dd-13cd549a13e8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dt                                object\n",
       "AverageTemperature               float64\n",
       "AverageTemperatureUncertainty    float64\n",
       "State                             object\n",
       "Country                           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the data \n",
    "data_temp.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uA1K83huxXPv",
    "outputId": "fdbab92b-ab34-4007-8ba0-9da08b9d37e5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dt                                   0\n",
       "AverageTemperature               25648\n",
       "AverageTemperatureUncertainty    25648\n",
       "State                                0\n",
       "Country                              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of null values in each column\n",
    "data_temp.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oyztzrQRxjDr"
   },
   "source": [
    "**Select Columns of Interest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "CkYYboVwxZiu",
    "outputId": "1f87f0f2-9e54-4519-cc70-7e3a5ec230a6"
   },
   "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>datetime</th>\n",
       "      <th>average_temp</th>\n",
       "      <th>average_temp_uncertainty</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>8.274</td>\n",
       "      <td>0.092</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-02-01</td>\n",
       "      <td>11.476</td>\n",
       "      <td>0.181</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-03-01</td>\n",
       "      <td>15.244</td>\n",
       "      <td>0.136</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-04-01</td>\n",
       "      <td>16.032</td>\n",
       "      <td>0.110</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-05-01</td>\n",
       "      <td>23.743</td>\n",
       "      <td>0.113</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8408</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>10.607</td>\n",
       "      <td>0.208</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8409</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>16.267</td>\n",
       "      <td>0.276</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8410</th>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>20.222</td>\n",
       "      <td>0.133</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8411</th>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>19.621</td>\n",
       "      <td>0.217</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8412</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>15.811</td>\n",
       "      <td>1.101</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8413 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        datetime  average_temp  average_temp_uncertainty    state country\n",
       "0     2000-01-01         8.274                     0.092  Alabama     usa\n",
       "1     2000-02-01        11.476                     0.181  Alabama     usa\n",
       "2     2000-03-01        15.244                     0.136  Alabama     usa\n",
       "3     2000-04-01        16.032                     0.110  Alabama     usa\n",
       "4     2000-05-01        23.743                     0.113  Alabama     usa\n",
       "...          ...           ...                       ...      ...     ...\n",
       "8408  2013-05-01        10.607                     0.208  Wyoming     usa\n",
       "8409  2013-06-01        16.267                     0.276  Wyoming     usa\n",
       "8410  2013-07-01        20.222                     0.133  Wyoming     usa\n",
       "8411  2013-08-01        19.621                     0.217  Wyoming     usa\n",
       "8412  2013-09-01        15.811                     1.101  Wyoming     usa\n",
       "\n",
       "[8413 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Limit observations to those in the USA\n",
    "data_temp = data_temp.loc[data_temp['Country'] == 'United States']\n",
    "\n",
    "# Rename columns\n",
    "data_temp.columns = ['datetime', 'average_temp', 'average_temp_uncertainty', 'state', 'country']\n",
    "\n",
    "# Filter out all dates that are not from the 21st century\n",
    "data_temp = data_temp.loc[data_temp['datetime'] >= '2000-01-01'] \n",
    "\n",
    "# Rename United States to usa\n",
    "data_temp = data_temp.replace('United States', 'usa')\n",
    "\n",
    "# Remove any observations with null values and reset indexing\n",
    "data_temp = data_temp.dropna()\n",
    "data_temp.reset_index(drop=True, inplace=True)\n",
    "\n",
    "data_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5kmlZH0Syogz"
   },
   "source": [
    "**Standardizing Values of Columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sxkejHlwysMo",
    "outputId": "36758665-241b-469c-c703-655e05f2d52d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California',\n",
       "       'Colorado', 'Connecticut', 'Delaware', 'District Of Columbia',\n",
       "       'Florida', 'Georgia (State)', 'Hawaii', 'Idaho', 'Illinois',\n",
       "       'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine',\n",
       "       'Maryland', 'Massachusetts', 'Michigan', 'Minnesota',\n",
       "       'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada',\n",
       "       'New Hampshire', 'New Jersey', 'New Mexico', 'New York',\n",
       "       'North Carolina', 'North Dakota', 'Ohio', 'Oklahoma', 'Oregon',\n",
       "       'Pennsylvania', 'Rhode Island', 'South Carolina', 'South Dakota',\n",
       "       'Tennessee', 'Texas', 'Utah', 'Vermont', 'Virginia', 'Washington',\n",
       "       'West Virginia', 'Wisconsin', 'Wyoming'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_temp['state'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vk2YyC6IyzlW"
   },
   "source": [
    "We again standardize the state variable for Dataset 2 below. Dataset 2 differs because it additionally contains information about the average temperature per year collected from a Kaggle set of 2018 weather observations. We standardized the state variable in Dataset 1 to simply be the full name of each state in all lowercase, so we did the same for dataset 2 in order to maintain tidy data principles. The shared variable names allow for easy combining in our analysis later in the project. For Dataset 2, we stripped the quotes surrounding each state, as well as removed some other inputs such as the specification of Georgia the U.S. state versus Georgia the country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "r7Gy440Gy3UQ"
   },
   "outputs": [],
   "source": [
    "# Standardization for data_temp\n",
    "def standardize_temp(string):\n",
    "\n",
    "    # Basic string pre-processing\n",
    "    string = string.lower()\n",
    "    string = string.strip()\n",
    "\n",
    "    # Fix weird inputs\n",
    "    string = string.replace(\"georgia (state)\", \"georgia\")\n",
    "    \n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "OxXJhiXJzB2j",
    "outputId": "02a49e83-a6ad-4bd8-9664-017ab8ea1dde"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>average_temp</th>\n",
       "      <th>average_temp_uncertainty</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>8.274</td>\n",
       "      <td>0.092</td>\n",
       "      <td>alabama</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-02-01</td>\n",
       "      <td>11.476</td>\n",
       "      <td>0.181</td>\n",
       "      <td>alabama</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-03-01</td>\n",
       "      <td>15.244</td>\n",
       "      <td>0.136</td>\n",
       "      <td>alabama</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-04-01</td>\n",
       "      <td>16.032</td>\n",
       "      <td>0.110</td>\n",
       "      <td>alabama</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-05-01</td>\n",
       "      <td>23.743</td>\n",
       "      <td>0.113</td>\n",
       "      <td>alabama</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8408</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>10.607</td>\n",
       "      <td>0.208</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8409</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>16.267</td>\n",
       "      <td>0.276</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8410</th>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>20.222</td>\n",
       "      <td>0.133</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8411</th>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>19.621</td>\n",
       "      <td>0.217</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8412</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>15.811</td>\n",
       "      <td>1.101</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8413 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        datetime  average_temp  average_temp_uncertainty    state country\n",
       "0     2000-01-01         8.274                     0.092  alabama     usa\n",
       "1     2000-02-01        11.476                     0.181  alabama     usa\n",
       "2     2000-03-01        15.244                     0.136  alabama     usa\n",
       "3     2000-04-01        16.032                     0.110  alabama     usa\n",
       "4     2000-05-01        23.743                     0.113  alabama     usa\n",
       "...          ...           ...                       ...      ...     ...\n",
       "8408  2013-05-01        10.607                     0.208  wyoming     usa\n",
       "8409  2013-06-01        16.267                     0.276  wyoming     usa\n",
       "8410  2013-07-01        20.222                     0.133  wyoming     usa\n",
       "8411  2013-08-01        19.621                     0.217  wyoming     usa\n",
       "8412  2013-09-01        15.811                     1.101  wyoming     usa\n",
       "\n",
       "[8413 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply standardization function to dataset\n",
    "data_temp['state'] = data_temp[\"state\"].apply(standardize_temp)\n",
    "data_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "id": "8gTE8J0wB8ly",
    "outputId": "28774b9b-fb25-4061-f883-5b90712561fa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>average_temp</th>\n",
       "      <th>average_temp_uncertainty</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>8.274</td>\n",
       "      <td>0.092</td>\n",
       "      <td>alabama</td>\n",
       "      <td>usa</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-02-01</td>\n",
       "      <td>11.476</td>\n",
       "      <td>0.181</td>\n",
       "      <td>alabama</td>\n",
       "      <td>usa</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-03-01</td>\n",
       "      <td>15.244</td>\n",
       "      <td>0.136</td>\n",
       "      <td>alabama</td>\n",
       "      <td>usa</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-04-01</td>\n",
       "      <td>16.032</td>\n",
       "      <td>0.110</td>\n",
       "      <td>alabama</td>\n",
       "      <td>usa</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-05-01</td>\n",
       "      <td>23.743</td>\n",
       "      <td>0.113</td>\n",
       "      <td>alabama</td>\n",
       "      <td>usa</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8408</th>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>10.607</td>\n",
       "      <td>0.208</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>usa</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8409</th>\n",
       "      <td>2013-06-01</td>\n",
       "      <td>16.267</td>\n",
       "      <td>0.276</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>usa</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8410</th>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>20.222</td>\n",
       "      <td>0.133</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>usa</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8411</th>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>19.621</td>\n",
       "      <td>0.217</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>usa</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8412</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>15.811</td>\n",
       "      <td>1.101</td>\n",
       "      <td>wyoming</td>\n",
       "      <td>usa</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8413 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        datetime  average_temp  average_temp_uncertainty    state country code\n",
       "0     2000-01-01         8.274                     0.092  alabama     usa   AL\n",
       "1     2000-02-01        11.476                     0.181  alabama     usa   AL\n",
       "2     2000-03-01        15.244                     0.136  alabama     usa   AL\n",
       "3     2000-04-01        16.032                     0.110  alabama     usa   AL\n",
       "4     2000-05-01        23.743                     0.113  alabama     usa   AL\n",
       "...          ...           ...                       ...      ...     ...  ...\n",
       "8408  2013-05-01        10.607                     0.208  wyoming     usa   WY\n",
       "8409  2013-06-01        16.267                     0.276  wyoming     usa   WY\n",
       "8410  2013-07-01        20.222                     0.133  wyoming     usa   WY\n",
       "8411  2013-08-01        19.621                     0.217  wyoming     usa   WY\n",
       "8412  2013-09-01        15.811                     1.101  wyoming     usa   WY\n",
       "\n",
       "[8413 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append new column holding the two letter abbreviations for each state\n",
    "data_temp['code'] = data_temp['state'].map(us_state_dict)\n",
    "\n",
    "data_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3H9LCTeAWIMe"
   },
   "source": [
    "## **Dataset 3: Physical Health Measures**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CiDc4qCDWIMj"
   },
   "source": [
    "**Import Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "oAMqlH5qWIMj",
    "outputId": "994aa9c3-579a-4142-b383-833dcc9dc718"
   },
   "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>Unnamed: 0</th>\n",
       "      <th>All adults aged 18–64</th>\n",
       "      <th>All men aged 18–64</th>\n",
       "      <th>All women aged 18–64</th>\n",
       "      <th>All working 1 men aged 18–64</th>\n",
       "      <th>All working women aged 18–64</th>\n",
       "      <th>All nonworking men aged 18–64</th>\n",
       "      <th>All nonworking women aged 18–64</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All states (average)</td>\n",
       "      <td>22.9 (0.20)</td>\n",
       "      <td>27.2 (0.27)</td>\n",
       "      <td>18.7 (0.23)</td>\n",
       "      <td>28.8 (0.31)</td>\n",
       "      <td>20.9 (0.28)</td>\n",
       "      <td>21.3 (0.45)</td>\n",
       "      <td>14.6 (0.31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>19.3 (1.41)</td>\n",
       "      <td>24.6 (2.05)</td>\n",
       "      <td>14.9 (1.51)</td>\n",
       "      <td>25.6 (2.44)</td>\n",
       "      <td>15.2 (1.95)</td>\n",
       "      <td>23.0 (3.37)</td>\n",
       "      <td>13.3 (2.04)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alaska . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>27.9 (2.04)</td>\n",
       "      <td>33.2 (2.70)</td>\n",
       "      <td>23.3 (2.40)</td>\n",
       "      <td>31.6 (2.93)</td>\n",
       "      <td>24.4 (2.68)</td>\n",
       "      <td>33.4 (5.59)</td>\n",
       "      <td>17.8 (4.11)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>26.3 (1.48)</td>\n",
       "      <td>29.2 (1.91)</td>\n",
       "      <td>23.0 (1.77)</td>\n",
       "      <td>31.3 (2.23)</td>\n",
       "      <td>26.7 (2.28)</td>\n",
       "      <td>22.4 (3.18)</td>\n",
       "      <td>16.9 (2.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arkansas . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>15.7 (1.32)</td>\n",
       "      <td>19.7 (1.84)</td>\n",
       "      <td>11.9 (1.45)</td>\n",
       "      <td>21.4 (2.23)</td>\n",
       "      <td>13.1 (1.82)</td>\n",
       "      <td>14.7 (2.79)</td>\n",
       "      <td>9.6 (2.05)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>California. . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>24.0 (0.53)</td>\n",
       "      <td>28.5 (0.75)</td>\n",
       "      <td>19.3 (0.58)</td>\n",
       "      <td>29.9 (0.84)</td>\n",
       "      <td>21.5 (0.72)</td>\n",
       "      <td>24.5 (1.34)</td>\n",
       "      <td>15.5 (0.78)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Colorado . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>32.5 (1.49)</td>\n",
       "      <td>33.4 (1.87)</td>\n",
       "      <td>31.5 (1.85)</td>\n",
       "      <td>34.8 (2.07)</td>\n",
       "      <td>33.6 (2.19)</td>\n",
       "      <td>26.9 (3.69)</td>\n",
       "      <td>27.1 (2.94)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Connecticut . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>24.5 (1.62)</td>\n",
       "      <td>28.0 (2.20)</td>\n",
       "      <td>21.2 (1.86)</td>\n",
       "      <td>29.0 (2.52)</td>\n",
       "      <td>24.8 (2.31)</td>\n",
       "      <td>22.6 (3.79)</td>\n",
       "      <td>13.6 (2.54)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Delaware . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>20.1 (1.81)</td>\n",
       "      <td>24.5 (2.44)</td>\n",
       "      <td>15.8 (2.04)</td>\n",
       "      <td>23.6 (2.65)</td>\n",
       "      <td>17.3 (2.39)</td>\n",
       "      <td>25.4 (5.20)</td>\n",
       "      <td>11.4 (3.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>District of Columbia. . . . . . . . . . .</td>\n",
       "      <td>30.7 (1.74)</td>\n",
       "      <td>40.3 (2.45)</td>\n",
       "      <td>22.2 (1.88)</td>\n",
       "      <td>40.5 (2.72)</td>\n",
       "      <td>24.0 (2.20)</td>\n",
       "      <td>43.0 (4.95)</td>\n",
       "      <td>18.5 (3.08)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Florida. . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>21.1 (0.93)</td>\n",
       "      <td>27.2 (1.14)</td>\n",
       "      <td>15.5 (1.04)</td>\n",
       "      <td>27.7 (1.31)</td>\n",
       "      <td>17.3 (1.24)</td>\n",
       "      <td>25.2 (2.07)</td>\n",
       "      <td>12.2 (1.25)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Georgia . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>20.2 (1.18)</td>\n",
       "      <td>27.1 (1.72)</td>\n",
       "      <td>14.2 (1.11)</td>\n",
       "      <td>29.9 (2.05)</td>\n",
       "      <td>15.2 (1.38)</td>\n",
       "      <td>21.1 (2.48)</td>\n",
       "      <td>12.8 (1.52)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Hawaii . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>24.4 (1.93)</td>\n",
       "      <td>31.2 (2.64)</td>\n",
       "      <td>18.3 (2.14)</td>\n",
       "      <td>32.8 (3.01)</td>\n",
       "      <td>19.7 (2.53)</td>\n",
       "      <td>25.6 (4.77)</td>\n",
       "      <td>14.6 (3.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Idaho . . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>31.4 (1.92)</td>\n",
       "      <td>35.2 (2.46)</td>\n",
       "      <td>27.4 (2.32)</td>\n",
       "      <td>36.5 (2.76)</td>\n",
       "      <td>27.0 (2.73)</td>\n",
       "      <td>34.8 (4.88)</td>\n",
       "      <td>28.5 (3.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Illinois . . . . . . . . . . . . . . . . . . ....</td>\n",
       "      <td>24.8 (0.81)</td>\n",
       "      <td>28.5 (1.11)</td>\n",
       "      <td>20.9 (1.00)</td>\n",
       "      <td>29.9 (1.24)</td>\n",
       "      <td>24.0 (1.06)</td>\n",
       "      <td>21.7 (1.91)</td>\n",
       "      <td>15.0 (1.90)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Indiana . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>15.1 (1.16)</td>\n",
       "      <td>20.0 (1.64)</td>\n",
       "      <td>10.1 (1.21)</td>\n",
       "      <td>21.8 (1.92)</td>\n",
       "      <td>12.5 (1.55)</td>\n",
       "      <td>11.7 (2.44)</td>\n",
       "      <td>5.1 (1.47)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Iowa . . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>22.2 (1.41)</td>\n",
       "      <td>24.2 (1.76)</td>\n",
       "      <td>19.9 (1.76)</td>\n",
       "      <td>23.6 (1.90)</td>\n",
       "      <td>20.9 (1.98)</td>\n",
       "      <td>23.7 (3.79)</td>\n",
       "      <td>16.4 (3.11)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kansas . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.2 (1.48)</td>\n",
       "      <td>24.7 (1.90)</td>\n",
       "      <td>21.7 (1.79)</td>\n",
       "      <td>26.2 (2.10)</td>\n",
       "      <td>23.8 (2.15)</td>\n",
       "      <td>16.1 (3.61)</td>\n",
       "      <td>16.3 (2.67)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kentucky . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>14.6 (1.18)</td>\n",
       "      <td>17.9 (1.66)</td>\n",
       "      <td>11.4 (1.29)</td>\n",
       "      <td>21.7 (2.21)</td>\n",
       "      <td>14.6 (1.80)</td>\n",
       "      <td>9.3 (1.89)</td>\n",
       "      <td>5.9 (1.40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Louisiana. . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>20.3 (1.41)</td>\n",
       "      <td>23.5 (1.95)</td>\n",
       "      <td>17.8 (1.61)</td>\n",
       "      <td>25.5 (2.38)</td>\n",
       "      <td>20.0 (2.06)</td>\n",
       "      <td>15.5 (2.75)</td>\n",
       "      <td>14.0 (2.22)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maine . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>24.0 (1.72)</td>\n",
       "      <td>27.3 (2.32)</td>\n",
       "      <td>20.6 (1.99)</td>\n",
       "      <td>32.4 (2.71)</td>\n",
       "      <td>23.3 (2.36)</td>\n",
       "      <td>*7.0 (2.63)</td>\n",
       "      <td>11.9 (2.81)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Maryland . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>22.9 (1.43)</td>\n",
       "      <td>29.4 (1.99)</td>\n",
       "      <td>16.2 (1.54)</td>\n",
       "      <td>29.8 (2.20)</td>\n",
       "      <td>18.3 (1.84)</td>\n",
       "      <td>26.6 (3.94)</td>\n",
       "      <td>11.3 (2.30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Massachusetts . . . . . . . . . . . . . .</td>\n",
       "      <td>29.5 (1.56)</td>\n",
       "      <td>32.9 (2.06)</td>\n",
       "      <td>26.1 (1.84)</td>\n",
       "      <td>34.1 (2.38)</td>\n",
       "      <td>28.8 (2.20)</td>\n",
       "      <td>25.1 (3.46)</td>\n",
       "      <td>18.6 (2.74)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Michigan . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.6 (0.94)</td>\n",
       "      <td>28.2 (1.36)</td>\n",
       "      <td>18.7 (1.18)</td>\n",
       "      <td>30.5 (1.63)</td>\n",
       "      <td>21.9 (1.54)</td>\n",
       "      <td>21.7 (2.27)</td>\n",
       "      <td>13.0 (1.56)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Minnesota . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>27.7 (1.40)</td>\n",
       "      <td>31.1 (1.81)</td>\n",
       "      <td>24.3 (1.68)</td>\n",
       "      <td>30.5 (1.95)</td>\n",
       "      <td>26.8 (1.89)</td>\n",
       "      <td>32.8 (4.16)</td>\n",
       "      <td>15.0 (2.75)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mississippi. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>13.5 (1.25)</td>\n",
       "      <td>17.9 (1.86)</td>\n",
       "      <td>9.7 (1.30)</td>\n",
       "      <td>19.8 (2.25)</td>\n",
       "      <td>10.5 (1.68)</td>\n",
       "      <td>13.0 (2.79)</td>\n",
       "      <td>8.9 (1.84)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Missouri. . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.6 (1.41)</td>\n",
       "      <td>29.9 (1.96)</td>\n",
       "      <td>17.9 (1.57)</td>\n",
       "      <td>31.9 (2.30)</td>\n",
       "      <td>18.2 (1.85)</td>\n",
       "      <td>20.2 (3.01)</td>\n",
       "      <td>17.5 (2.52)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Montana . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>20.2 (1.98)</td>\n",
       "      <td>20.3 (2.47)</td>\n",
       "      <td>20.3 (2.48)</td>\n",
       "      <td>24.5 (3.16)</td>\n",
       "      <td>25.1 (3.18)</td>\n",
       "      <td>10.5 (3.07)</td>\n",
       "      <td>*9.5 (2.86)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nebraska. . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.9 (1.67)</td>\n",
       "      <td>26.8 (2.22)</td>\n",
       "      <td>21.5 (1.97)</td>\n",
       "      <td>25.3 (2.43)</td>\n",
       "      <td>22.3 (2.19)</td>\n",
       "      <td>33.1 (4.70)</td>\n",
       "      <td>18.0 (3.60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Nevada . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.6 (1.55)</td>\n",
       "      <td>25.7 (2.02)</td>\n",
       "      <td>21.6 (1.85)</td>\n",
       "      <td>25.4 (2.32)</td>\n",
       "      <td>22.4 (2.26)</td>\n",
       "      <td>22.4 (3.40)</td>\n",
       "      <td>19.7 (2.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Hampshire. . . . . . . . . . . . . .</td>\n",
       "      <td>30.7 (1.92)</td>\n",
       "      <td>30.3 (2.38)</td>\n",
       "      <td>31.0 (2.43)</td>\n",
       "      <td>30.7 (2.61)</td>\n",
       "      <td>32.1 (2.73)</td>\n",
       "      <td>29.6 (5.01)</td>\n",
       "      <td>28.7 (4.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New Jersey . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>21.0 (1.19)</td>\n",
       "      <td>25.7 (1.62)</td>\n",
       "      <td>16.2 (1.33)</td>\n",
       "      <td>27.0 (1.86)</td>\n",
       "      <td>16.9 (1.59)</td>\n",
       "      <td>17.4 (2.60)</td>\n",
       "      <td>13.9 (2.02)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>New Mexico. . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.0 (1.71)</td>\n",
       "      <td>27.4 (2.37)</td>\n",
       "      <td>18.6 (1.90)</td>\n",
       "      <td>29.4 (2.86)</td>\n",
       "      <td>21.6 (2.52)</td>\n",
       "      <td>19.4 (3.50)</td>\n",
       "      <td>14.4 (2.51)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>New York . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>18.9 (0.83)</td>\n",
       "      <td>22.8 (1.15)</td>\n",
       "      <td>15.3 (0.93)</td>\n",
       "      <td>25.3 (1.31)</td>\n",
       "      <td>17.6 (1.23)</td>\n",
       "      <td>15.9 (1.78)</td>\n",
       "      <td>11.3 (1.04)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>North Carolina. . . . . . . . . . . . . . .</td>\n",
       "      <td>22.4 (0.85)</td>\n",
       "      <td>26.1 (1.44)</td>\n",
       "      <td>19.0 (0.92)</td>\n",
       "      <td>27.8 (1.81)</td>\n",
       "      <td>19.8 (1.28)</td>\n",
       "      <td>19.5 (2.40)</td>\n",
       "      <td>17.4 (1.79)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>North Dakota. . . . . . . . . . . . . . . .</td>\n",
       "      <td>20.2 (1.87)</td>\n",
       "      <td>21.7 (2.28)</td>\n",
       "      <td>18.2 (2.36)</td>\n",
       "      <td>22.1 (2.44)</td>\n",
       "      <td>20.3 (2.61)</td>\n",
       "      <td>*15.8 (5.05)</td>\n",
       "      <td>*8.8 (3.81)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Ohio . . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.9 (1.02)</td>\n",
       "      <td>29.5 (1.34)</td>\n",
       "      <td>18.7 (1.06)</td>\n",
       "      <td>31.0 (1.55)</td>\n",
       "      <td>21.4 (1.29)</td>\n",
       "      <td>24.0 (2.44)</td>\n",
       "      <td>13.2 (1.64)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Oklahoma . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>19.0 (1.41)</td>\n",
       "      <td>24.0 (1.96)</td>\n",
       "      <td>14.6 (1.55)</td>\n",
       "      <td>24.1 (2.26)</td>\n",
       "      <td>14.8 (1.89)</td>\n",
       "      <td>21.4 (3.34)</td>\n",
       "      <td>14.0 (2.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Oregon . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>25.8 (1.58)</td>\n",
       "      <td>28.6 (2.02)</td>\n",
       "      <td>22.7 (1.92)</td>\n",
       "      <td>30.9 (2.42)</td>\n",
       "      <td>26.6 (2.41)</td>\n",
       "      <td>21.6 (3.13)</td>\n",
       "      <td>15.9 (2.66)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Pennsylvania . . . . . . . . . . . . . . . .</td>\n",
       "      <td>25.6 (1.25)</td>\n",
       "      <td>29.3 (1.81)</td>\n",
       "      <td>21.8 (1.17)</td>\n",
       "      <td>31.6 (2.13)</td>\n",
       "      <td>23.4 (1.37)</td>\n",
       "      <td>19.3 (2.40)</td>\n",
       "      <td>18.3 (2.09)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Rhode Island. . . . . . . . . . . . . . . .</td>\n",
       "      <td>25.4 (1.99)</td>\n",
       "      <td>30.1 (2.75)</td>\n",
       "      <td>21.6 (2.27)</td>\n",
       "      <td>32.0 (3.28)</td>\n",
       "      <td>26.4 (2.87)</td>\n",
       "      <td>22.9 (4.29)</td>\n",
       "      <td>12.9 (2.97)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>South Carolina . . . . . . . . . . . . . .</td>\n",
       "      <td>14.8 (1.26)</td>\n",
       "      <td>20.1 (1.83)</td>\n",
       "      <td>10.0 (1.29)</td>\n",
       "      <td>21.9 (2.18)</td>\n",
       "      <td>12.0 (1.76)</td>\n",
       "      <td>12.4 (2.65)</td>\n",
       "      <td>6.9 (1.60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>South Dakota . . . . . . . . . . . . . . .</td>\n",
       "      <td>17.2 (1.69)</td>\n",
       "      <td>17.7 (2.09)</td>\n",
       "      <td>15.9 (2.09)</td>\n",
       "      <td>17.5 (2.26)</td>\n",
       "      <td>16.9 (2.37)</td>\n",
       "      <td>28.9 (5.52)</td>\n",
       "      <td>*9.8 (3.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Tennessee. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>17.1 (1.30)</td>\n",
       "      <td>20.1 (1.79)</td>\n",
       "      <td>14.1 (1.45)</td>\n",
       "      <td>22.8 (2.14)</td>\n",
       "      <td>15.0 (1.80)</td>\n",
       "      <td>10.7 (2.53)</td>\n",
       "      <td>12.8 (2.16)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Texas. . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.5 (0.70)</td>\n",
       "      <td>28.1 (0.74)</td>\n",
       "      <td>19.0 (1.02)</td>\n",
       "      <td>28.7 (0.83)</td>\n",
       "      <td>21.4 (1.38)</td>\n",
       "      <td>25.0 (1.63)</td>\n",
       "      <td>14.6 (1.13)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Utah . . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>28.2 (1.60)</td>\n",
       "      <td>29.2 (2.01)</td>\n",
       "      <td>27.1 (2.00)</td>\n",
       "      <td>30.7 (2.23)</td>\n",
       "      <td>26.5 (2.30)</td>\n",
       "      <td>22.6 (3.97)</td>\n",
       "      <td>27.4 (3.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Vermont. . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>29.5 (2.49)</td>\n",
       "      <td>35.9 (3.30)</td>\n",
       "      <td>23.4 (2.87)</td>\n",
       "      <td>40.2 (3.69)</td>\n",
       "      <td>26.8 (3.35)</td>\n",
       "      <td>21.4 (5.95)</td>\n",
       "      <td>15.2 (4.48)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Virginia . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>24.2 (1.28)</td>\n",
       "      <td>29.3 (1.75)</td>\n",
       "      <td>19.5 (1.44)</td>\n",
       "      <td>32.8 (2.02)</td>\n",
       "      <td>20.5 (1.67)</td>\n",
       "      <td>16.6 (2.76)</td>\n",
       "      <td>16.8 (2.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Washington . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>28.9 (1.34)</td>\n",
       "      <td>31.2 (1.70)</td>\n",
       "      <td>26.2 (1.64)</td>\n",
       "      <td>33.0 (1.96)</td>\n",
       "      <td>29.7 (2.04)</td>\n",
       "      <td>25.9 (2.99)</td>\n",
       "      <td>20.2 (2.35)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>West Virginia . . . . . . . . . . . . . . . .</td>\n",
       "      <td>16.8 (1.43)</td>\n",
       "      <td>19.8 (1.93)</td>\n",
       "      <td>14.0 (1.65)</td>\n",
       "      <td>21.5 (2.54)</td>\n",
       "      <td>18.7 (2.40)</td>\n",
       "      <td>14.4 (2.45)</td>\n",
       "      <td>8.4 (1.85)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Wisconsin . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.6 (1.44)</td>\n",
       "      <td>26.3 (1.89)</td>\n",
       "      <td>21.0 (1.71)</td>\n",
       "      <td>27.6 (2.11)</td>\n",
       "      <td>23.3 (1.97)</td>\n",
       "      <td>21.6 (3.68)</td>\n",
       "      <td>14.4 (2.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Wyoming . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>27.5 (1.99)</td>\n",
       "      <td>29.6 (2.43)</td>\n",
       "      <td>24.8 (2.53)</td>\n",
       "      <td>31.2 (2.66)</td>\n",
       "      <td>29.9 (3.05)</td>\n",
       "      <td>17.1 (4.58)</td>\n",
       "      <td>13.8 (3.51)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Unnamed: 0  ... All nonworking women aged 18–64\n",
       "0                                All states (average)  ...                     14.6 (0.31)\n",
       "1       Alabama . . . . . . . . . . . . . . . . . . .  ...                     13.3 (2.04)\n",
       "2    Alaska . . . . . . . . . . . . . . . . . . . . .  ...                     17.8 (4.11)\n",
       "3     Arizona . . . . . . . . . . . . . . . . . . . .  ...                     16.9 (2.37)\n",
       "4      Arkansas . . . . . . . . . . . . . . . . . . .  ...                      9.6 (2.05)\n",
       "5     California. . . . . . . . . . . . . . . . . . .  ...                     15.5 (0.78)\n",
       "6      Colorado . . . . . . . . . . . . . . . . . . .  ...                     27.1 (2.94)\n",
       "7       Connecticut . . . . . . . . . . . . . . . . .  ...                     13.6 (2.54)\n",
       "8      Delaware . . . . . . . . . . . . . . . . . . .  ...                     11.4 (3.21)\n",
       "9           District of Columbia. . . . . . . . . . .  ...                     18.5 (3.08)\n",
       "10   Florida. . . . . . . . . . . . . . . . . . . . .  ...                     12.2 (1.25)\n",
       "11    Georgia . . . . . . . . . . . . . . . . . . . .  ...                     12.8 (1.52)\n",
       "12   Hawaii . . . . . . . . . . . . . . . . . . . . .  ...                     14.6 (3.36)\n",
       "13  Idaho . . . . . . . . . . . . . . . . . . . . . .  ...                     28.5 (3.77)\n",
       "14  Illinois . . . . . . . . . . . . . . . . . . ....  ...                     15.0 (1.90)\n",
       "15    Indiana . . . . . . . . . . . . . . . . . . . .  ...                      5.1 (1.47)\n",
       "16   Iowa . . . . . . . . . . . . . . . . . . . . . .  ...                     16.4 (3.11)\n",
       "17     Kansas . . . . . . . . . . . . . . . . . . . .  ...                     16.3 (2.67)\n",
       "18     Kentucky . . . . . . . . . . . . . . . . . . .  ...                      5.9 (1.40)\n",
       "19     Louisiana. . . . . . . . . . . . . . . . . . .  ...                     14.0 (2.22)\n",
       "20    Maine . . . . . . . . . . . . . . . . . . . . .  ...                     11.9 (2.81)\n",
       "21     Maryland . . . . . . . . . . . . . . . . . . .  ...                     11.3 (2.30)\n",
       "22          Massachusetts . . . . . . . . . . . . . .  ...                     18.6 (2.74)\n",
       "23     Michigan . . . . . . . . . . . . . . . . . . .  ...                     13.0 (1.56)\n",
       "24      Minnesota . . . . . . . . . . . . . . . . . .  ...                     15.0 (2.75)\n",
       "25     Mississippi. . . . . . . . . . . . . . . . . .  ...                      8.9 (1.84)\n",
       "26    Missouri. . . . . . . . . . . . . . . . . . . .  ...                     17.5 (2.52)\n",
       "27      Montana . . . . . . . . . . . . . . . . . . .  ...                     *9.5 (2.86)\n",
       "28      Nebraska. . . . . . . . . . . . . . . . . . .  ...                     18.0 (3.60)\n",
       "29     Nevada . . . . . . . . . . . . . . . . . . . .  ...                     19.7 (2.77)\n",
       "30           New Hampshire. . . . . . . . . . . . . .  ...                     28.7 (4.36)\n",
       "31       New Jersey . . . . . . . . . . . . . . . . .  ...                     13.9 (2.02)\n",
       "32        New Mexico. . . . . . . . . . . . . . . . .  ...                     14.4 (2.51)\n",
       "33     New York . . . . . . . . . . . . . . . . . . .  ...                     11.3 (1.04)\n",
       "34        North Carolina. . . . . . . . . . . . . . .  ...                     17.4 (1.79)\n",
       "35        North Dakota. . . . . . . . . . . . . . . .  ...                     *8.8 (3.81)\n",
       "36   Ohio . . . . . . . . . . . . . . . . . . . . . .  ...                     13.2 (1.64)\n",
       "37       Oklahoma . . . . . . . . . . . . . . . . . .  ...                     14.0 (2.36)\n",
       "38     Oregon . . . . . . . . . . . . . . . . . . . .  ...                     15.9 (2.66)\n",
       "39       Pennsylvania . . . . . . . . . . . . . . . .  ...                     18.3 (2.09)\n",
       "40        Rhode Island. . . . . . . . . . . . . . . .  ...                     12.9 (2.97)\n",
       "41         South Carolina . . . . . . . . . . . . . .  ...                      6.9 (1.60)\n",
       "42         South Dakota . . . . . . . . . . . . . . .  ...                     *9.8 (3.21)\n",
       "43       Tennessee. . . . . . . . . . . . . . . . . .  ...                     12.8 (2.16)\n",
       "44   Texas. . . . . . . . . . . . . . . . . . . . . .  ...                     14.6 (1.13)\n",
       "45   Utah . . . . . . . . . . . . . . . . . . . . . .  ...                     27.4 (3.37)\n",
       "46     Vermont. . . . . . . . . . . . . . . . . . . .  ...                     15.2 (4.48)\n",
       "47   Virginia . . . . . . . . . . . . . . . . . . . .  ...                     16.8 (2.37)\n",
       "48       Washington . . . . . . . . . . . . . . . . .  ...                     20.2 (2.35)\n",
       "49      West Virginia . . . . . . . . . . . . . . . .  ...                      8.4 (1.85)\n",
       "50      Wisconsin . . . . . . . . . . . . . . . . . .  ...                     14.4 (2.77)\n",
       "51      Wyoming . . . . . . . . . . . . . . . . . . .  ...                     13.8 (3.51)\n",
       "\n",
       "[52 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import data\n",
    "data_phys = pd.read_csv(\"https://raw.githubusercontent.com/sgt002/COGS108_datasets/master/data_physical_activity_guidelines.csv\")\n",
    "data_phys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "om7AUnjLWIMl",
    "outputId": "ebbbaef5-9803-493e-e057-1c51e6fafe13"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                         object\n",
       "All adults aged 18–64              object\n",
       "All men aged 18–64                 object\n",
       "All women aged 18–64               object\n",
       "All working 1 men aged 18–64       object\n",
       "All working women aged 18–64       object\n",
       "All nonworking men aged 18–64      object\n",
       "All nonworking women aged 18–64    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the data \n",
    "data_phys.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S0AOjZ7bWIMl",
    "outputId": "9258b441-754f-45d2-a258-0c93247c9958"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                         0\n",
       "All adults aged 18–64              0\n",
       "All men aged 18–64                 0\n",
       "All women aged 18–64               0\n",
       "All working 1 men aged 18–64       0\n",
       "All working women aged 18–64       0\n",
       "All nonworking men aged 18–64      0\n",
       "All nonworking women aged 18–64    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of null values in each column\n",
    "data_phys.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wmF2q1boWIMm"
   },
   "source": [
    "**Select Columns of Interest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Mf03T7Y2WIMm",
    "outputId": "ce1fe981-9b80-4600-8f34-25a0e1048d1a"
   },
   "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>state</th>\n",
       "      <th>adults 18-64</th>\n",
       "      <th>men 18-64</th>\n",
       "      <th>women 18-64</th>\n",
       "      <th>working men 18-64</th>\n",
       "      <th>working women 18-64</th>\n",
       "      <th>nonworking men 18-64</th>\n",
       "      <th>nonworking women 18-64</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>All states (average)</td>\n",
       "      <td>22.9 (0.20)</td>\n",
       "      <td>27.2 (0.27)</td>\n",
       "      <td>18.7 (0.23)</td>\n",
       "      <td>28.8 (0.31)</td>\n",
       "      <td>20.9 (0.28)</td>\n",
       "      <td>21.3 (0.45)</td>\n",
       "      <td>14.6 (0.31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>19.3 (1.41)</td>\n",
       "      <td>24.6 (2.05)</td>\n",
       "      <td>14.9 (1.51)</td>\n",
       "      <td>25.6 (2.44)</td>\n",
       "      <td>15.2 (1.95)</td>\n",
       "      <td>23.0 (3.37)</td>\n",
       "      <td>13.3 (2.04)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alaska . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>27.9 (2.04)</td>\n",
       "      <td>33.2 (2.70)</td>\n",
       "      <td>23.3 (2.40)</td>\n",
       "      <td>31.6 (2.93)</td>\n",
       "      <td>24.4 (2.68)</td>\n",
       "      <td>33.4 (5.59)</td>\n",
       "      <td>17.8 (4.11)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>26.3 (1.48)</td>\n",
       "      <td>29.2 (1.91)</td>\n",
       "      <td>23.0 (1.77)</td>\n",
       "      <td>31.3 (2.23)</td>\n",
       "      <td>26.7 (2.28)</td>\n",
       "      <td>22.4 (3.18)</td>\n",
       "      <td>16.9 (2.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arkansas . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>15.7 (1.32)</td>\n",
       "      <td>19.7 (1.84)</td>\n",
       "      <td>11.9 (1.45)</td>\n",
       "      <td>21.4 (2.23)</td>\n",
       "      <td>13.1 (1.82)</td>\n",
       "      <td>14.7 (2.79)</td>\n",
       "      <td>9.6 (2.05)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>California. . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>24.0 (0.53)</td>\n",
       "      <td>28.5 (0.75)</td>\n",
       "      <td>19.3 (0.58)</td>\n",
       "      <td>29.9 (0.84)</td>\n",
       "      <td>21.5 (0.72)</td>\n",
       "      <td>24.5 (1.34)</td>\n",
       "      <td>15.5 (0.78)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Colorado . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>32.5 (1.49)</td>\n",
       "      <td>33.4 (1.87)</td>\n",
       "      <td>31.5 (1.85)</td>\n",
       "      <td>34.8 (2.07)</td>\n",
       "      <td>33.6 (2.19)</td>\n",
       "      <td>26.9 (3.69)</td>\n",
       "      <td>27.1 (2.94)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Connecticut . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>24.5 (1.62)</td>\n",
       "      <td>28.0 (2.20)</td>\n",
       "      <td>21.2 (1.86)</td>\n",
       "      <td>29.0 (2.52)</td>\n",
       "      <td>24.8 (2.31)</td>\n",
       "      <td>22.6 (3.79)</td>\n",
       "      <td>13.6 (2.54)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Delaware . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>20.1 (1.81)</td>\n",
       "      <td>24.5 (2.44)</td>\n",
       "      <td>15.8 (2.04)</td>\n",
       "      <td>23.6 (2.65)</td>\n",
       "      <td>17.3 (2.39)</td>\n",
       "      <td>25.4 (5.20)</td>\n",
       "      <td>11.4 (3.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>District of Columbia. . . . . . . . . . .</td>\n",
       "      <td>30.7 (1.74)</td>\n",
       "      <td>40.3 (2.45)</td>\n",
       "      <td>22.2 (1.88)</td>\n",
       "      <td>40.5 (2.72)</td>\n",
       "      <td>24.0 (2.20)</td>\n",
       "      <td>43.0 (4.95)</td>\n",
       "      <td>18.5 (3.08)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Florida. . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>21.1 (0.93)</td>\n",
       "      <td>27.2 (1.14)</td>\n",
       "      <td>15.5 (1.04)</td>\n",
       "      <td>27.7 (1.31)</td>\n",
       "      <td>17.3 (1.24)</td>\n",
       "      <td>25.2 (2.07)</td>\n",
       "      <td>12.2 (1.25)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Georgia . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>20.2 (1.18)</td>\n",
       "      <td>27.1 (1.72)</td>\n",
       "      <td>14.2 (1.11)</td>\n",
       "      <td>29.9 (2.05)</td>\n",
       "      <td>15.2 (1.38)</td>\n",
       "      <td>21.1 (2.48)</td>\n",
       "      <td>12.8 (1.52)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Hawaii . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>24.4 (1.93)</td>\n",
       "      <td>31.2 (2.64)</td>\n",
       "      <td>18.3 (2.14)</td>\n",
       "      <td>32.8 (3.01)</td>\n",
       "      <td>19.7 (2.53)</td>\n",
       "      <td>25.6 (4.77)</td>\n",
       "      <td>14.6 (3.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Idaho . . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>31.4 (1.92)</td>\n",
       "      <td>35.2 (2.46)</td>\n",
       "      <td>27.4 (2.32)</td>\n",
       "      <td>36.5 (2.76)</td>\n",
       "      <td>27.0 (2.73)</td>\n",
       "      <td>34.8 (4.88)</td>\n",
       "      <td>28.5 (3.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Illinois . . . . . . . . . . . . . . . . . . ....</td>\n",
       "      <td>24.8 (0.81)</td>\n",
       "      <td>28.5 (1.11)</td>\n",
       "      <td>20.9 (1.00)</td>\n",
       "      <td>29.9 (1.24)</td>\n",
       "      <td>24.0 (1.06)</td>\n",
       "      <td>21.7 (1.91)</td>\n",
       "      <td>15.0 (1.90)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Indiana . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>15.1 (1.16)</td>\n",
       "      <td>20.0 (1.64)</td>\n",
       "      <td>10.1 (1.21)</td>\n",
       "      <td>21.8 (1.92)</td>\n",
       "      <td>12.5 (1.55)</td>\n",
       "      <td>11.7 (2.44)</td>\n",
       "      <td>5.1 (1.47)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Iowa . . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>22.2 (1.41)</td>\n",
       "      <td>24.2 (1.76)</td>\n",
       "      <td>19.9 (1.76)</td>\n",
       "      <td>23.6 (1.90)</td>\n",
       "      <td>20.9 (1.98)</td>\n",
       "      <td>23.7 (3.79)</td>\n",
       "      <td>16.4 (3.11)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kansas . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.2 (1.48)</td>\n",
       "      <td>24.7 (1.90)</td>\n",
       "      <td>21.7 (1.79)</td>\n",
       "      <td>26.2 (2.10)</td>\n",
       "      <td>23.8 (2.15)</td>\n",
       "      <td>16.1 (3.61)</td>\n",
       "      <td>16.3 (2.67)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Kentucky . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>14.6 (1.18)</td>\n",
       "      <td>17.9 (1.66)</td>\n",
       "      <td>11.4 (1.29)</td>\n",
       "      <td>21.7 (2.21)</td>\n",
       "      <td>14.6 (1.80)</td>\n",
       "      <td>9.3 (1.89)</td>\n",
       "      <td>5.9 (1.40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Louisiana. . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>20.3 (1.41)</td>\n",
       "      <td>23.5 (1.95)</td>\n",
       "      <td>17.8 (1.61)</td>\n",
       "      <td>25.5 (2.38)</td>\n",
       "      <td>20.0 (2.06)</td>\n",
       "      <td>15.5 (2.75)</td>\n",
       "      <td>14.0 (2.22)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maine . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>24.0 (1.72)</td>\n",
       "      <td>27.3 (2.32)</td>\n",
       "      <td>20.6 (1.99)</td>\n",
       "      <td>32.4 (2.71)</td>\n",
       "      <td>23.3 (2.36)</td>\n",
       "      <td>*7.0 (2.63)</td>\n",
       "      <td>11.9 (2.81)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Maryland . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>22.9 (1.43)</td>\n",
       "      <td>29.4 (1.99)</td>\n",
       "      <td>16.2 (1.54)</td>\n",
       "      <td>29.8 (2.20)</td>\n",
       "      <td>18.3 (1.84)</td>\n",
       "      <td>26.6 (3.94)</td>\n",
       "      <td>11.3 (2.30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Massachusetts . . . . . . . . . . . . . .</td>\n",
       "      <td>29.5 (1.56)</td>\n",
       "      <td>32.9 (2.06)</td>\n",
       "      <td>26.1 (1.84)</td>\n",
       "      <td>34.1 (2.38)</td>\n",
       "      <td>28.8 (2.20)</td>\n",
       "      <td>25.1 (3.46)</td>\n",
       "      <td>18.6 (2.74)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Michigan . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.6 (0.94)</td>\n",
       "      <td>28.2 (1.36)</td>\n",
       "      <td>18.7 (1.18)</td>\n",
       "      <td>30.5 (1.63)</td>\n",
       "      <td>21.9 (1.54)</td>\n",
       "      <td>21.7 (2.27)</td>\n",
       "      <td>13.0 (1.56)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Minnesota . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>27.7 (1.40)</td>\n",
       "      <td>31.1 (1.81)</td>\n",
       "      <td>24.3 (1.68)</td>\n",
       "      <td>30.5 (1.95)</td>\n",
       "      <td>26.8 (1.89)</td>\n",
       "      <td>32.8 (4.16)</td>\n",
       "      <td>15.0 (2.75)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mississippi. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>13.5 (1.25)</td>\n",
       "      <td>17.9 (1.86)</td>\n",
       "      <td>9.7 (1.30)</td>\n",
       "      <td>19.8 (2.25)</td>\n",
       "      <td>10.5 (1.68)</td>\n",
       "      <td>13.0 (2.79)</td>\n",
       "      <td>8.9 (1.84)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Missouri. . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.6 (1.41)</td>\n",
       "      <td>29.9 (1.96)</td>\n",
       "      <td>17.9 (1.57)</td>\n",
       "      <td>31.9 (2.30)</td>\n",
       "      <td>18.2 (1.85)</td>\n",
       "      <td>20.2 (3.01)</td>\n",
       "      <td>17.5 (2.52)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Montana . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>20.2 (1.98)</td>\n",
       "      <td>20.3 (2.47)</td>\n",
       "      <td>20.3 (2.48)</td>\n",
       "      <td>24.5 (3.16)</td>\n",
       "      <td>25.1 (3.18)</td>\n",
       "      <td>10.5 (3.07)</td>\n",
       "      <td>*9.5 (2.86)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nebraska. . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.9 (1.67)</td>\n",
       "      <td>26.8 (2.22)</td>\n",
       "      <td>21.5 (1.97)</td>\n",
       "      <td>25.3 (2.43)</td>\n",
       "      <td>22.3 (2.19)</td>\n",
       "      <td>33.1 (4.70)</td>\n",
       "      <td>18.0 (3.60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Nevada . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.6 (1.55)</td>\n",
       "      <td>25.7 (2.02)</td>\n",
       "      <td>21.6 (1.85)</td>\n",
       "      <td>25.4 (2.32)</td>\n",
       "      <td>22.4 (2.26)</td>\n",
       "      <td>22.4 (3.40)</td>\n",
       "      <td>19.7 (2.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Hampshire. . . . . . . . . . . . . .</td>\n",
       "      <td>30.7 (1.92)</td>\n",
       "      <td>30.3 (2.38)</td>\n",
       "      <td>31.0 (2.43)</td>\n",
       "      <td>30.7 (2.61)</td>\n",
       "      <td>32.1 (2.73)</td>\n",
       "      <td>29.6 (5.01)</td>\n",
       "      <td>28.7 (4.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>New Jersey . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>21.0 (1.19)</td>\n",
       "      <td>25.7 (1.62)</td>\n",
       "      <td>16.2 (1.33)</td>\n",
       "      <td>27.0 (1.86)</td>\n",
       "      <td>16.9 (1.59)</td>\n",
       "      <td>17.4 (2.60)</td>\n",
       "      <td>13.9 (2.02)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>New Mexico. . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.0 (1.71)</td>\n",
       "      <td>27.4 (2.37)</td>\n",
       "      <td>18.6 (1.90)</td>\n",
       "      <td>29.4 (2.86)</td>\n",
       "      <td>21.6 (2.52)</td>\n",
       "      <td>19.4 (3.50)</td>\n",
       "      <td>14.4 (2.51)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>New York . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>18.9 (0.83)</td>\n",
       "      <td>22.8 (1.15)</td>\n",
       "      <td>15.3 (0.93)</td>\n",
       "      <td>25.3 (1.31)</td>\n",
       "      <td>17.6 (1.23)</td>\n",
       "      <td>15.9 (1.78)</td>\n",
       "      <td>11.3 (1.04)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>North Carolina. . . . . . . . . . . . . . .</td>\n",
       "      <td>22.4 (0.85)</td>\n",
       "      <td>26.1 (1.44)</td>\n",
       "      <td>19.0 (0.92)</td>\n",
       "      <td>27.8 (1.81)</td>\n",
       "      <td>19.8 (1.28)</td>\n",
       "      <td>19.5 (2.40)</td>\n",
       "      <td>17.4 (1.79)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>North Dakota. . . . . . . . . . . . . . . .</td>\n",
       "      <td>20.2 (1.87)</td>\n",
       "      <td>21.7 (2.28)</td>\n",
       "      <td>18.2 (2.36)</td>\n",
       "      <td>22.1 (2.44)</td>\n",
       "      <td>20.3 (2.61)</td>\n",
       "      <td>*15.8 (5.05)</td>\n",
       "      <td>*8.8 (3.81)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Ohio . . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.9 (1.02)</td>\n",
       "      <td>29.5 (1.34)</td>\n",
       "      <td>18.7 (1.06)</td>\n",
       "      <td>31.0 (1.55)</td>\n",
       "      <td>21.4 (1.29)</td>\n",
       "      <td>24.0 (2.44)</td>\n",
       "      <td>13.2 (1.64)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Oklahoma . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>19.0 (1.41)</td>\n",
       "      <td>24.0 (1.96)</td>\n",
       "      <td>14.6 (1.55)</td>\n",
       "      <td>24.1 (2.26)</td>\n",
       "      <td>14.8 (1.89)</td>\n",
       "      <td>21.4 (3.34)</td>\n",
       "      <td>14.0 (2.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Oregon . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>25.8 (1.58)</td>\n",
       "      <td>28.6 (2.02)</td>\n",
       "      <td>22.7 (1.92)</td>\n",
       "      <td>30.9 (2.42)</td>\n",
       "      <td>26.6 (2.41)</td>\n",
       "      <td>21.6 (3.13)</td>\n",
       "      <td>15.9 (2.66)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Pennsylvania . . . . . . . . . . . . . . . .</td>\n",
       "      <td>25.6 (1.25)</td>\n",
       "      <td>29.3 (1.81)</td>\n",
       "      <td>21.8 (1.17)</td>\n",
       "      <td>31.6 (2.13)</td>\n",
       "      <td>23.4 (1.37)</td>\n",
       "      <td>19.3 (2.40)</td>\n",
       "      <td>18.3 (2.09)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Rhode Island. . . . . . . . . . . . . . . .</td>\n",
       "      <td>25.4 (1.99)</td>\n",
       "      <td>30.1 (2.75)</td>\n",
       "      <td>21.6 (2.27)</td>\n",
       "      <td>32.0 (3.28)</td>\n",
       "      <td>26.4 (2.87)</td>\n",
       "      <td>22.9 (4.29)</td>\n",
       "      <td>12.9 (2.97)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>South Carolina . . . . . . . . . . . . . .</td>\n",
       "      <td>14.8 (1.26)</td>\n",
       "      <td>20.1 (1.83)</td>\n",
       "      <td>10.0 (1.29)</td>\n",
       "      <td>21.9 (2.18)</td>\n",
       "      <td>12.0 (1.76)</td>\n",
       "      <td>12.4 (2.65)</td>\n",
       "      <td>6.9 (1.60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>South Dakota . . . . . . . . . . . . . . .</td>\n",
       "      <td>17.2 (1.69)</td>\n",
       "      <td>17.7 (2.09)</td>\n",
       "      <td>15.9 (2.09)</td>\n",
       "      <td>17.5 (2.26)</td>\n",
       "      <td>16.9 (2.37)</td>\n",
       "      <td>28.9 (5.52)</td>\n",
       "      <td>*9.8 (3.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Tennessee. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>17.1 (1.30)</td>\n",
       "      <td>20.1 (1.79)</td>\n",
       "      <td>14.1 (1.45)</td>\n",
       "      <td>22.8 (2.14)</td>\n",
       "      <td>15.0 (1.80)</td>\n",
       "      <td>10.7 (2.53)</td>\n",
       "      <td>12.8 (2.16)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Texas. . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.5 (0.70)</td>\n",
       "      <td>28.1 (0.74)</td>\n",
       "      <td>19.0 (1.02)</td>\n",
       "      <td>28.7 (0.83)</td>\n",
       "      <td>21.4 (1.38)</td>\n",
       "      <td>25.0 (1.63)</td>\n",
       "      <td>14.6 (1.13)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Utah . . . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>28.2 (1.60)</td>\n",
       "      <td>29.2 (2.01)</td>\n",
       "      <td>27.1 (2.00)</td>\n",
       "      <td>30.7 (2.23)</td>\n",
       "      <td>26.5 (2.30)</td>\n",
       "      <td>22.6 (3.97)</td>\n",
       "      <td>27.4 (3.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Vermont. . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>29.5 (2.49)</td>\n",
       "      <td>35.9 (3.30)</td>\n",
       "      <td>23.4 (2.87)</td>\n",
       "      <td>40.2 (3.69)</td>\n",
       "      <td>26.8 (3.35)</td>\n",
       "      <td>21.4 (5.95)</td>\n",
       "      <td>15.2 (4.48)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Virginia . . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>24.2 (1.28)</td>\n",
       "      <td>29.3 (1.75)</td>\n",
       "      <td>19.5 (1.44)</td>\n",
       "      <td>32.8 (2.02)</td>\n",
       "      <td>20.5 (1.67)</td>\n",
       "      <td>16.6 (2.76)</td>\n",
       "      <td>16.8 (2.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Washington . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>28.9 (1.34)</td>\n",
       "      <td>31.2 (1.70)</td>\n",
       "      <td>26.2 (1.64)</td>\n",
       "      <td>33.0 (1.96)</td>\n",
       "      <td>29.7 (2.04)</td>\n",
       "      <td>25.9 (2.99)</td>\n",
       "      <td>20.2 (2.35)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>West Virginia . . . . . . . . . . . . . . . .</td>\n",
       "      <td>16.8 (1.43)</td>\n",
       "      <td>19.8 (1.93)</td>\n",
       "      <td>14.0 (1.65)</td>\n",
       "      <td>21.5 (2.54)</td>\n",
       "      <td>18.7 (2.40)</td>\n",
       "      <td>14.4 (2.45)</td>\n",
       "      <td>8.4 (1.85)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Wisconsin . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>23.6 (1.44)</td>\n",
       "      <td>26.3 (1.89)</td>\n",
       "      <td>21.0 (1.71)</td>\n",
       "      <td>27.6 (2.11)</td>\n",
       "      <td>23.3 (1.97)</td>\n",
       "      <td>21.6 (3.68)</td>\n",
       "      <td>14.4 (2.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Wyoming . . . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>27.5 (1.99)</td>\n",
       "      <td>29.6 (2.43)</td>\n",
       "      <td>24.8 (2.53)</td>\n",
       "      <td>31.2 (2.66)</td>\n",
       "      <td>29.9 (3.05)</td>\n",
       "      <td>17.1 (4.58)</td>\n",
       "      <td>13.8 (3.51)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                state  ... nonworking women 18-64\n",
       "0                                All states (average)  ...            14.6 (0.31)\n",
       "1       Alabama . . . . . . . . . . . . . . . . . . .  ...            13.3 (2.04)\n",
       "2    Alaska . . . . . . . . . . . . . . . . . . . . .  ...            17.8 (4.11)\n",
       "3     Arizona . . . . . . . . . . . . . . . . . . . .  ...            16.9 (2.37)\n",
       "4      Arkansas . . . . . . . . . . . . . . . . . . .  ...             9.6 (2.05)\n",
       "5     California. . . . . . . . . . . . . . . . . . .  ...            15.5 (0.78)\n",
       "6      Colorado . . . . . . . . . . . . . . . . . . .  ...            27.1 (2.94)\n",
       "7       Connecticut . . . . . . . . . . . . . . . . .  ...            13.6 (2.54)\n",
       "8      Delaware . . . . . . . . . . . . . . . . . . .  ...            11.4 (3.21)\n",
       "9           District of Columbia. . . . . . . . . . .  ...            18.5 (3.08)\n",
       "10   Florida. . . . . . . . . . . . . . . . . . . . .  ...            12.2 (1.25)\n",
       "11    Georgia . . . . . . . . . . . . . . . . . . . .  ...            12.8 (1.52)\n",
       "12   Hawaii . . . . . . . . . . . . . . . . . . . . .  ...            14.6 (3.36)\n",
       "13  Idaho . . . . . . . . . . . . . . . . . . . . . .  ...            28.5 (3.77)\n",
       "14  Illinois . . . . . . . . . . . . . . . . . . ....  ...            15.0 (1.90)\n",
       "15    Indiana . . . . . . . . . . . . . . . . . . . .  ...             5.1 (1.47)\n",
       "16   Iowa . . . . . . . . . . . . . . . . . . . . . .  ...            16.4 (3.11)\n",
       "17     Kansas . . . . . . . . . . . . . . . . . . . .  ...            16.3 (2.67)\n",
       "18     Kentucky . . . . . . . . . . . . . . . . . . .  ...             5.9 (1.40)\n",
       "19     Louisiana. . . . . . . . . . . . . . . . . . .  ...            14.0 (2.22)\n",
       "20    Maine . . . . . . . . . . . . . . . . . . . . .  ...            11.9 (2.81)\n",
       "21     Maryland . . . . . . . . . . . . . . . . . . .  ...            11.3 (2.30)\n",
       "22          Massachusetts . . . . . . . . . . . . . .  ...            18.6 (2.74)\n",
       "23     Michigan . . . . . . . . . . . . . . . . . . .  ...            13.0 (1.56)\n",
       "24      Minnesota . . . . . . . . . . . . . . . . . .  ...            15.0 (2.75)\n",
       "25     Mississippi. . . . . . . . . . . . . . . . . .  ...             8.9 (1.84)\n",
       "26    Missouri. . . . . . . . . . . . . . . . . . . .  ...            17.5 (2.52)\n",
       "27      Montana . . . . . . . . . . . . . . . . . . .  ...            *9.5 (2.86)\n",
       "28      Nebraska. . . . . . . . . . . . . . . . . . .  ...            18.0 (3.60)\n",
       "29     Nevada . . . . . . . . . . . . . . . . . . . .  ...            19.7 (2.77)\n",
       "30           New Hampshire. . . . . . . . . . . . . .  ...            28.7 (4.36)\n",
       "31       New Jersey . . . . . . . . . . . . . . . . .  ...            13.9 (2.02)\n",
       "32        New Mexico. . . . . . . . . . . . . . . . .  ...            14.4 (2.51)\n",
       "33     New York . . . . . . . . . . . . . . . . . . .  ...            11.3 (1.04)\n",
       "34        North Carolina. . . . . . . . . . . . . . .  ...            17.4 (1.79)\n",
       "35        North Dakota. . . . . . . . . . . . . . . .  ...            *8.8 (3.81)\n",
       "36   Ohio . . . . . . . . . . . . . . . . . . . . . .  ...            13.2 (1.64)\n",
       "37       Oklahoma . . . . . . . . . . . . . . . . . .  ...            14.0 (2.36)\n",
       "38     Oregon . . . . . . . . . . . . . . . . . . . .  ...            15.9 (2.66)\n",
       "39       Pennsylvania . . . . . . . . . . . . . . . .  ...            18.3 (2.09)\n",
       "40        Rhode Island. . . . . . . . . . . . . . . .  ...            12.9 (2.97)\n",
       "41         South Carolina . . . . . . . . . . . . . .  ...             6.9 (1.60)\n",
       "42         South Dakota . . . . . . . . . . . . . . .  ...            *9.8 (3.21)\n",
       "43       Tennessee. . . . . . . . . . . . . . . . . .  ...            12.8 (2.16)\n",
       "44   Texas. . . . . . . . . . . . . . . . . . . . . .  ...            14.6 (1.13)\n",
       "45   Utah . . . . . . . . . . . . . . . . . . . . . .  ...            27.4 (3.37)\n",
       "46     Vermont. . . . . . . . . . . . . . . . . . . .  ...            15.2 (4.48)\n",
       "47   Virginia . . . . . . . . . . . . . . . . . . . .  ...            16.8 (2.37)\n",
       "48       Washington . . . . . . . . . . . . . . . . .  ...            20.2 (2.35)\n",
       "49      West Virginia . . . . . . . . . . . . . . . .  ...             8.4 (1.85)\n",
       "50      Wisconsin . . . . . . . . . . . . . . . . . .  ...            14.4 (2.77)\n",
       "51      Wyoming . . . . . . . . . . . . . . . . . . .  ...            13.8 (3.51)\n",
       "\n",
       "[52 rows x 8 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename columns\n",
    "data_phys.columns = ['state', 'adults 18-64', 'men 18-64', 'women 18-64', 'working men 18-64', 'working women 18-64', 'nonworking men 18-64', 'nonworking women 18-64']\n",
    "\n",
    "# Remove any observations with null values and reset indexing\n",
    "data_phys = data_phys.dropna()\n",
    "data_phys.reset_index(drop=True, inplace=True)\n",
    "\n",
    "data_phys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XlSjAGzUWIMn"
   },
   "source": [
    "**Standardizing Values of Columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E3sTGhG4WIMn",
    "outputId": "cd4afe9f-0a7d-4ee2-e38a-ebdf8f9146e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['All states (average)',\n",
       "       'Alabama . . . . . . . . . . . . . . . . . . .',\n",
       "       'Alaska . . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Arizona . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Arkansas . . . . . . . . . . . . . . . . . . .',\n",
       "       'California. . . . . . . . . . . . . . . . . . .',\n",
       "       'Colorado . . . . . . . . . . . . . . . . . . .',\n",
       "       'Connecticut . . . . . . . . . . . . . . . . .',\n",
       "       'Delaware . . . . . . . . . . . . . . . . . . .',\n",
       "       'District of Columbia. . . . . . . . . . .',\n",
       "       'Florida. . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Georgia . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Hawaii . . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Idaho . . . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Illinois . . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Indiana . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Iowa . . . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Kansas . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Kentucky . . . . . . . . . . . . . . . . . . .',\n",
       "       'Louisiana. . . . . . . . . . . . . . . . . . .',\n",
       "       'Maine . . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Maryland . . . . . . . . . . . . . . . . . . .',\n",
       "       'Massachusetts . . . . . . . . . . . . . .',\n",
       "       'Michigan . . . . . . . . . . . . . . . . . . .',\n",
       "       'Minnesota . . . . . . . . . . . . . . . . . .',\n",
       "       'Mississippi. . . . . . . . . . . . . . . . . .',\n",
       "       'Missouri. . . . . . . . . . . . . . . . . . . .',\n",
       "       'Montana . . . . . . . . . . . . . . . . . . .',\n",
       "       'Nebraska. . . . . . . . . . . . . . . . . . .',\n",
       "       'Nevada . . . . . . . . . . . . . . . . . . . .',\n",
       "       'New Hampshire. . . . . . . . . . . . . .',\n",
       "       'New Jersey . . . . . . . . . . . . . . . . .',\n",
       "       'New Mexico. . . . . . . . . . . . . . . . .',\n",
       "       'New York . . . . . . . . . . . . . . . . . . .',\n",
       "       'North Carolina. . . . . . . . . . . . . . .',\n",
       "       'North Dakota. . . . . . . . . . . . . . . .',\n",
       "       'Ohio . . . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Oklahoma . . . . . . . . . . . . . . . . . .',\n",
       "       'Oregon . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Pennsylvania . . . . . . . . . . . . . . . .',\n",
       "       'Rhode Island. . . . . . . . . . . . . . . .',\n",
       "       'South Carolina . . . . . . . . . . . . . .',\n",
       "       'South Dakota . . . . . . . . . . . . . . .',\n",
       "       'Tennessee. . . . . . . . . . . . . . . . . .',\n",
       "       'Texas. . . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Utah . . . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Vermont. . . . . . . . . . . . . . . . . . . .',\n",
       "       'Virginia . . . . . . . . . . . . . . . . . . . .',\n",
       "       'Washington . . . . . . . . . . . . . . . . .',\n",
       "       'West Virginia . . . . . . . . . . . . . . . .',\n",
       "       'Wisconsin . . . . . . . . . . . . . . . . . .',\n",
       "       'Wyoming . . . . . . . . . . . . . . . . . . .'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_phys['state'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NpIPKJC6WIMn"
   },
   "source": [
    "Looking at the value of the states column in Dataset 3, we find that the usage of periods and spaces for padding may pose as an issue in our analyses. To standardize these values, we want to remove the periods and strip the white spaces to adhere to the standardization methods of the other datasets. Like how we have previously cleaned our other datasets, we will also remove any capitalization from the state names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "msB5hnMyWIMo"
   },
   "outputs": [],
   "source": [
    "# Standardization for data_phys\n",
    "def standardize_phys_state(string):\n",
    "\n",
    "    # Basic string pre-processing\n",
    "    string = string.lower()\n",
    "    string = string.strip()\n",
    "\n",
    "    # Fix weird inputs\n",
    "    string = string.replace(\".\", \"\")\n",
    "\n",
    "    string = string.strip()\n",
    "    \n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "VZdyIstyWIMo",
    "outputId": "a47e45fe-3bad-4ac4-8967-b7585f0a28ca"
   },
   "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>state</th>\n",
       "      <th>adults 18-64</th>\n",
       "      <th>men 18-64</th>\n",
       "      <th>women 18-64</th>\n",
       "      <th>working men 18-64</th>\n",
       "      <th>working women 18-64</th>\n",
       "      <th>nonworking men 18-64</th>\n",
       "      <th>nonworking women 18-64</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>all states (average)</td>\n",
       "      <td>22.9 (0.20)</td>\n",
       "      <td>27.2 (0.27)</td>\n",
       "      <td>18.7 (0.23)</td>\n",
       "      <td>28.8 (0.31)</td>\n",
       "      <td>20.9 (0.28)</td>\n",
       "      <td>21.3 (0.45)</td>\n",
       "      <td>14.6 (0.31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alabama</td>\n",
       "      <td>19.3 (1.41)</td>\n",
       "      <td>24.6 (2.05)</td>\n",
       "      <td>14.9 (1.51)</td>\n",
       "      <td>25.6 (2.44)</td>\n",
       "      <td>15.2 (1.95)</td>\n",
       "      <td>23.0 (3.37)</td>\n",
       "      <td>13.3 (2.04)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alaska</td>\n",
       "      <td>27.9 (2.04)</td>\n",
       "      <td>33.2 (2.70)</td>\n",
       "      <td>23.3 (2.40)</td>\n",
       "      <td>31.6 (2.93)</td>\n",
       "      <td>24.4 (2.68)</td>\n",
       "      <td>33.4 (5.59)</td>\n",
       "      <td>17.8 (4.11)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arizona</td>\n",
       "      <td>26.3 (1.48)</td>\n",
       "      <td>29.2 (1.91)</td>\n",
       "      <td>23.0 (1.77)</td>\n",
       "      <td>31.3 (2.23)</td>\n",
       "      <td>26.7 (2.28)</td>\n",
       "      <td>22.4 (3.18)</td>\n",
       "      <td>16.9 (2.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>15.7 (1.32)</td>\n",
       "      <td>19.7 (1.84)</td>\n",
       "      <td>11.9 (1.45)</td>\n",
       "      <td>21.4 (2.23)</td>\n",
       "      <td>13.1 (1.82)</td>\n",
       "      <td>14.7 (2.79)</td>\n",
       "      <td>9.6 (2.05)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>california</td>\n",
       "      <td>24.0 (0.53)</td>\n",
       "      <td>28.5 (0.75)</td>\n",
       "      <td>19.3 (0.58)</td>\n",
       "      <td>29.9 (0.84)</td>\n",
       "      <td>21.5 (0.72)</td>\n",
       "      <td>24.5 (1.34)</td>\n",
       "      <td>15.5 (0.78)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>colorado</td>\n",
       "      <td>32.5 (1.49)</td>\n",
       "      <td>33.4 (1.87)</td>\n",
       "      <td>31.5 (1.85)</td>\n",
       "      <td>34.8 (2.07)</td>\n",
       "      <td>33.6 (2.19)</td>\n",
       "      <td>26.9 (3.69)</td>\n",
       "      <td>27.1 (2.94)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>connecticut</td>\n",
       "      <td>24.5 (1.62)</td>\n",
       "      <td>28.0 (2.20)</td>\n",
       "      <td>21.2 (1.86)</td>\n",
       "      <td>29.0 (2.52)</td>\n",
       "      <td>24.8 (2.31)</td>\n",
       "      <td>22.6 (3.79)</td>\n",
       "      <td>13.6 (2.54)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>delaware</td>\n",
       "      <td>20.1 (1.81)</td>\n",
       "      <td>24.5 (2.44)</td>\n",
       "      <td>15.8 (2.04)</td>\n",
       "      <td>23.6 (2.65)</td>\n",
       "      <td>17.3 (2.39)</td>\n",
       "      <td>25.4 (5.20)</td>\n",
       "      <td>11.4 (3.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>district of columbia</td>\n",
       "      <td>30.7 (1.74)</td>\n",
       "      <td>40.3 (2.45)</td>\n",
       "      <td>22.2 (1.88)</td>\n",
       "      <td>40.5 (2.72)</td>\n",
       "      <td>24.0 (2.20)</td>\n",
       "      <td>43.0 (4.95)</td>\n",
       "      <td>18.5 (3.08)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>florida</td>\n",
       "      <td>21.1 (0.93)</td>\n",
       "      <td>27.2 (1.14)</td>\n",
       "      <td>15.5 (1.04)</td>\n",
       "      <td>27.7 (1.31)</td>\n",
       "      <td>17.3 (1.24)</td>\n",
       "      <td>25.2 (2.07)</td>\n",
       "      <td>12.2 (1.25)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>georgia</td>\n",
       "      <td>20.2 (1.18)</td>\n",
       "      <td>27.1 (1.72)</td>\n",
       "      <td>14.2 (1.11)</td>\n",
       "      <td>29.9 (2.05)</td>\n",
       "      <td>15.2 (1.38)</td>\n",
       "      <td>21.1 (2.48)</td>\n",
       "      <td>12.8 (1.52)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hawaii</td>\n",
       "      <td>24.4 (1.93)</td>\n",
       "      <td>31.2 (2.64)</td>\n",
       "      <td>18.3 (2.14)</td>\n",
       "      <td>32.8 (3.01)</td>\n",
       "      <td>19.7 (2.53)</td>\n",
       "      <td>25.6 (4.77)</td>\n",
       "      <td>14.6 (3.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>idaho</td>\n",
       "      <td>31.4 (1.92)</td>\n",
       "      <td>35.2 (2.46)</td>\n",
       "      <td>27.4 (2.32)</td>\n",
       "      <td>36.5 (2.76)</td>\n",
       "      <td>27.0 (2.73)</td>\n",
       "      <td>34.8 (4.88)</td>\n",
       "      <td>28.5 (3.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>illinois</td>\n",
       "      <td>24.8 (0.81)</td>\n",
       "      <td>28.5 (1.11)</td>\n",
       "      <td>20.9 (1.00)</td>\n",
       "      <td>29.9 (1.24)</td>\n",
       "      <td>24.0 (1.06)</td>\n",
       "      <td>21.7 (1.91)</td>\n",
       "      <td>15.0 (1.90)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>indiana</td>\n",
       "      <td>15.1 (1.16)</td>\n",
       "      <td>20.0 (1.64)</td>\n",
       "      <td>10.1 (1.21)</td>\n",
       "      <td>21.8 (1.92)</td>\n",
       "      <td>12.5 (1.55)</td>\n",
       "      <td>11.7 (2.44)</td>\n",
       "      <td>5.1 (1.47)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>iowa</td>\n",
       "      <td>22.2 (1.41)</td>\n",
       "      <td>24.2 (1.76)</td>\n",
       "      <td>19.9 (1.76)</td>\n",
       "      <td>23.6 (1.90)</td>\n",
       "      <td>20.9 (1.98)</td>\n",
       "      <td>23.7 (3.79)</td>\n",
       "      <td>16.4 (3.11)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>kansas</td>\n",
       "      <td>23.2 (1.48)</td>\n",
       "      <td>24.7 (1.90)</td>\n",
       "      <td>21.7 (1.79)</td>\n",
       "      <td>26.2 (2.10)</td>\n",
       "      <td>23.8 (2.15)</td>\n",
       "      <td>16.1 (3.61)</td>\n",
       "      <td>16.3 (2.67)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>kentucky</td>\n",
       "      <td>14.6 (1.18)</td>\n",
       "      <td>17.9 (1.66)</td>\n",
       "      <td>11.4 (1.29)</td>\n",
       "      <td>21.7 (2.21)</td>\n",
       "      <td>14.6 (1.80)</td>\n",
       "      <td>9.3 (1.89)</td>\n",
       "      <td>5.9 (1.40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>louisiana</td>\n",
       "      <td>20.3 (1.41)</td>\n",
       "      <td>23.5 (1.95)</td>\n",
       "      <td>17.8 (1.61)</td>\n",
       "      <td>25.5 (2.38)</td>\n",
       "      <td>20.0 (2.06)</td>\n",
       "      <td>15.5 (2.75)</td>\n",
       "      <td>14.0 (2.22)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>maine</td>\n",
       "      <td>24.0 (1.72)</td>\n",
       "      <td>27.3 (2.32)</td>\n",
       "      <td>20.6 (1.99)</td>\n",
       "      <td>32.4 (2.71)</td>\n",
       "      <td>23.3 (2.36)</td>\n",
       "      <td>*7.0 (2.63)</td>\n",
       "      <td>11.9 (2.81)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>maryland</td>\n",
       "      <td>22.9 (1.43)</td>\n",
       "      <td>29.4 (1.99)</td>\n",
       "      <td>16.2 (1.54)</td>\n",
       "      <td>29.8 (2.20)</td>\n",
       "      <td>18.3 (1.84)</td>\n",
       "      <td>26.6 (3.94)</td>\n",
       "      <td>11.3 (2.30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>massachusetts</td>\n",
       "      <td>29.5 (1.56)</td>\n",
       "      <td>32.9 (2.06)</td>\n",
       "      <td>26.1 (1.84)</td>\n",
       "      <td>34.1 (2.38)</td>\n",
       "      <td>28.8 (2.20)</td>\n",
       "      <td>25.1 (3.46)</td>\n",
       "      <td>18.6 (2.74)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>michigan</td>\n",
       "      <td>23.6 (0.94)</td>\n",
       "      <td>28.2 (1.36)</td>\n",
       "      <td>18.7 (1.18)</td>\n",
       "      <td>30.5 (1.63)</td>\n",
       "      <td>21.9 (1.54)</td>\n",
       "      <td>21.7 (2.27)</td>\n",
       "      <td>13.0 (1.56)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>minnesota</td>\n",
       "      <td>27.7 (1.40)</td>\n",
       "      <td>31.1 (1.81)</td>\n",
       "      <td>24.3 (1.68)</td>\n",
       "      <td>30.5 (1.95)</td>\n",
       "      <td>26.8 (1.89)</td>\n",
       "      <td>32.8 (4.16)</td>\n",
       "      <td>15.0 (2.75)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>mississippi</td>\n",
       "      <td>13.5 (1.25)</td>\n",
       "      <td>17.9 (1.86)</td>\n",
       "      <td>9.7 (1.30)</td>\n",
       "      <td>19.8 (2.25)</td>\n",
       "      <td>10.5 (1.68)</td>\n",
       "      <td>13.0 (2.79)</td>\n",
       "      <td>8.9 (1.84)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>missouri</td>\n",
       "      <td>23.6 (1.41)</td>\n",
       "      <td>29.9 (1.96)</td>\n",
       "      <td>17.9 (1.57)</td>\n",
       "      <td>31.9 (2.30)</td>\n",
       "      <td>18.2 (1.85)</td>\n",
       "      <td>20.2 (3.01)</td>\n",
       "      <td>17.5 (2.52)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>montana</td>\n",
       "      <td>20.2 (1.98)</td>\n",
       "      <td>20.3 (2.47)</td>\n",
       "      <td>20.3 (2.48)</td>\n",
       "      <td>24.5 (3.16)</td>\n",
       "      <td>25.1 (3.18)</td>\n",
       "      <td>10.5 (3.07)</td>\n",
       "      <td>*9.5 (2.86)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>nebraska</td>\n",
       "      <td>23.9 (1.67)</td>\n",
       "      <td>26.8 (2.22)</td>\n",
       "      <td>21.5 (1.97)</td>\n",
       "      <td>25.3 (2.43)</td>\n",
       "      <td>22.3 (2.19)</td>\n",
       "      <td>33.1 (4.70)</td>\n",
       "      <td>18.0 (3.60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>nevada</td>\n",
       "      <td>23.6 (1.55)</td>\n",
       "      <td>25.7 (2.02)</td>\n",
       "      <td>21.6 (1.85)</td>\n",
       "      <td>25.4 (2.32)</td>\n",
       "      <td>22.4 (2.26)</td>\n",
       "      <td>22.4 (3.40)</td>\n",
       "      <td>19.7 (2.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>new hampshire</td>\n",
       "      <td>30.7 (1.92)</td>\n",
       "      <td>30.3 (2.38)</td>\n",
       "      <td>31.0 (2.43)</td>\n",
       "      <td>30.7 (2.61)</td>\n",
       "      <td>32.1 (2.73)</td>\n",
       "      <td>29.6 (5.01)</td>\n",
       "      <td>28.7 (4.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>new jersey</td>\n",
       "      <td>21.0 (1.19)</td>\n",
       "      <td>25.7 (1.62)</td>\n",
       "      <td>16.2 (1.33)</td>\n",
       "      <td>27.0 (1.86)</td>\n",
       "      <td>16.9 (1.59)</td>\n",
       "      <td>17.4 (2.60)</td>\n",
       "      <td>13.9 (2.02)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>new mexico</td>\n",
       "      <td>23.0 (1.71)</td>\n",
       "      <td>27.4 (2.37)</td>\n",
       "      <td>18.6 (1.90)</td>\n",
       "      <td>29.4 (2.86)</td>\n",
       "      <td>21.6 (2.52)</td>\n",
       "      <td>19.4 (3.50)</td>\n",
       "      <td>14.4 (2.51)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>new york</td>\n",
       "      <td>18.9 (0.83)</td>\n",
       "      <td>22.8 (1.15)</td>\n",
       "      <td>15.3 (0.93)</td>\n",
       "      <td>25.3 (1.31)</td>\n",
       "      <td>17.6 (1.23)</td>\n",
       "      <td>15.9 (1.78)</td>\n",
       "      <td>11.3 (1.04)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>22.4 (0.85)</td>\n",
       "      <td>26.1 (1.44)</td>\n",
       "      <td>19.0 (0.92)</td>\n",
       "      <td>27.8 (1.81)</td>\n",
       "      <td>19.8 (1.28)</td>\n",
       "      <td>19.5 (2.40)</td>\n",
       "      <td>17.4 (1.79)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>north dakota</td>\n",
       "      <td>20.2 (1.87)</td>\n",
       "      <td>21.7 (2.28)</td>\n",
       "      <td>18.2 (2.36)</td>\n",
       "      <td>22.1 (2.44)</td>\n",
       "      <td>20.3 (2.61)</td>\n",
       "      <td>*15.8 (5.05)</td>\n",
       "      <td>*8.8 (3.81)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ohio</td>\n",
       "      <td>23.9 (1.02)</td>\n",
       "      <td>29.5 (1.34)</td>\n",
       "      <td>18.7 (1.06)</td>\n",
       "      <td>31.0 (1.55)</td>\n",
       "      <td>21.4 (1.29)</td>\n",
       "      <td>24.0 (2.44)</td>\n",
       "      <td>13.2 (1.64)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>oklahoma</td>\n",
       "      <td>19.0 (1.41)</td>\n",
       "      <td>24.0 (1.96)</td>\n",
       "      <td>14.6 (1.55)</td>\n",
       "      <td>24.1 (2.26)</td>\n",
       "      <td>14.8 (1.89)</td>\n",
       "      <td>21.4 (3.34)</td>\n",
       "      <td>14.0 (2.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>oregon</td>\n",
       "      <td>25.8 (1.58)</td>\n",
       "      <td>28.6 (2.02)</td>\n",
       "      <td>22.7 (1.92)</td>\n",
       "      <td>30.9 (2.42)</td>\n",
       "      <td>26.6 (2.41)</td>\n",
       "      <td>21.6 (3.13)</td>\n",
       "      <td>15.9 (2.66)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>25.6 (1.25)</td>\n",
       "      <td>29.3 (1.81)</td>\n",
       "      <td>21.8 (1.17)</td>\n",
       "      <td>31.6 (2.13)</td>\n",
       "      <td>23.4 (1.37)</td>\n",
       "      <td>19.3 (2.40)</td>\n",
       "      <td>18.3 (2.09)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>rhode island</td>\n",
       "      <td>25.4 (1.99)</td>\n",
       "      <td>30.1 (2.75)</td>\n",
       "      <td>21.6 (2.27)</td>\n",
       "      <td>32.0 (3.28)</td>\n",
       "      <td>26.4 (2.87)</td>\n",
       "      <td>22.9 (4.29)</td>\n",
       "      <td>12.9 (2.97)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>south carolina</td>\n",
       "      <td>14.8 (1.26)</td>\n",
       "      <td>20.1 (1.83)</td>\n",
       "      <td>10.0 (1.29)</td>\n",
       "      <td>21.9 (2.18)</td>\n",
       "      <td>12.0 (1.76)</td>\n",
       "      <td>12.4 (2.65)</td>\n",
       "      <td>6.9 (1.60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>south dakota</td>\n",
       "      <td>17.2 (1.69)</td>\n",
       "      <td>17.7 (2.09)</td>\n",
       "      <td>15.9 (2.09)</td>\n",
       "      <td>17.5 (2.26)</td>\n",
       "      <td>16.9 (2.37)</td>\n",
       "      <td>28.9 (5.52)</td>\n",
       "      <td>*9.8 (3.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>tennessee</td>\n",
       "      <td>17.1 (1.30)</td>\n",
       "      <td>20.1 (1.79)</td>\n",
       "      <td>14.1 (1.45)</td>\n",
       "      <td>22.8 (2.14)</td>\n",
       "      <td>15.0 (1.80)</td>\n",
       "      <td>10.7 (2.53)</td>\n",
       "      <td>12.8 (2.16)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>texas</td>\n",
       "      <td>23.5 (0.70)</td>\n",
       "      <td>28.1 (0.74)</td>\n",
       "      <td>19.0 (1.02)</td>\n",
       "      <td>28.7 (0.83)</td>\n",
       "      <td>21.4 (1.38)</td>\n",
       "      <td>25.0 (1.63)</td>\n",
       "      <td>14.6 (1.13)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>utah</td>\n",
       "      <td>28.2 (1.60)</td>\n",
       "      <td>29.2 (2.01)</td>\n",
       "      <td>27.1 (2.00)</td>\n",
       "      <td>30.7 (2.23)</td>\n",
       "      <td>26.5 (2.30)</td>\n",
       "      <td>22.6 (3.97)</td>\n",
       "      <td>27.4 (3.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>vermont</td>\n",
       "      <td>29.5 (2.49)</td>\n",
       "      <td>35.9 (3.30)</td>\n",
       "      <td>23.4 (2.87)</td>\n",
       "      <td>40.2 (3.69)</td>\n",
       "      <td>26.8 (3.35)</td>\n",
       "      <td>21.4 (5.95)</td>\n",
       "      <td>15.2 (4.48)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>virginia</td>\n",
       "      <td>24.2 (1.28)</td>\n",
       "      <td>29.3 (1.75)</td>\n",
       "      <td>19.5 (1.44)</td>\n",
       "      <td>32.8 (2.02)</td>\n",
       "      <td>20.5 (1.67)</td>\n",
       "      <td>16.6 (2.76)</td>\n",
       "      <td>16.8 (2.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>washington</td>\n",
       "      <td>28.9 (1.34)</td>\n",
       "      <td>31.2 (1.70)</td>\n",
       "      <td>26.2 (1.64)</td>\n",
       "      <td>33.0 (1.96)</td>\n",
       "      <td>29.7 (2.04)</td>\n",
       "      <td>25.9 (2.99)</td>\n",
       "      <td>20.2 (2.35)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>west virginia</td>\n",
       "      <td>16.8 (1.43)</td>\n",
       "      <td>19.8 (1.93)</td>\n",
       "      <td>14.0 (1.65)</td>\n",
       "      <td>21.5 (2.54)</td>\n",
       "      <td>18.7 (2.40)</td>\n",
       "      <td>14.4 (2.45)</td>\n",
       "      <td>8.4 (1.85)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>wisconsin</td>\n",
       "      <td>23.6 (1.44)</td>\n",
       "      <td>26.3 (1.89)</td>\n",
       "      <td>21.0 (1.71)</td>\n",
       "      <td>27.6 (2.11)</td>\n",
       "      <td>23.3 (1.97)</td>\n",
       "      <td>21.6 (3.68)</td>\n",
       "      <td>14.4 (2.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>27.5 (1.99)</td>\n",
       "      <td>29.6 (2.43)</td>\n",
       "      <td>24.8 (2.53)</td>\n",
       "      <td>31.2 (2.66)</td>\n",
       "      <td>29.9 (3.05)</td>\n",
       "      <td>17.1 (4.58)</td>\n",
       "      <td>13.8 (3.51)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state  ... nonworking women 18-64\n",
       "0   all states (average)  ...            14.6 (0.31)\n",
       "1                alabama  ...            13.3 (2.04)\n",
       "2                 alaska  ...            17.8 (4.11)\n",
       "3                arizona  ...            16.9 (2.37)\n",
       "4               arkansas  ...             9.6 (2.05)\n",
       "5             california  ...            15.5 (0.78)\n",
       "6               colorado  ...            27.1 (2.94)\n",
       "7            connecticut  ...            13.6 (2.54)\n",
       "8               delaware  ...            11.4 (3.21)\n",
       "9   district of columbia  ...            18.5 (3.08)\n",
       "10               florida  ...            12.2 (1.25)\n",
       "11               georgia  ...            12.8 (1.52)\n",
       "12                hawaii  ...            14.6 (3.36)\n",
       "13                 idaho  ...            28.5 (3.77)\n",
       "14              illinois  ...            15.0 (1.90)\n",
       "15               indiana  ...             5.1 (1.47)\n",
       "16                  iowa  ...            16.4 (3.11)\n",
       "17                kansas  ...            16.3 (2.67)\n",
       "18              kentucky  ...             5.9 (1.40)\n",
       "19             louisiana  ...            14.0 (2.22)\n",
       "20                 maine  ...            11.9 (2.81)\n",
       "21              maryland  ...            11.3 (2.30)\n",
       "22         massachusetts  ...            18.6 (2.74)\n",
       "23              michigan  ...            13.0 (1.56)\n",
       "24             minnesota  ...            15.0 (2.75)\n",
       "25           mississippi  ...             8.9 (1.84)\n",
       "26              missouri  ...            17.5 (2.52)\n",
       "27               montana  ...            *9.5 (2.86)\n",
       "28              nebraska  ...            18.0 (3.60)\n",
       "29                nevada  ...            19.7 (2.77)\n",
       "30         new hampshire  ...            28.7 (4.36)\n",
       "31            new jersey  ...            13.9 (2.02)\n",
       "32            new mexico  ...            14.4 (2.51)\n",
       "33              new york  ...            11.3 (1.04)\n",
       "34        north carolina  ...            17.4 (1.79)\n",
       "35          north dakota  ...            *8.8 (3.81)\n",
       "36                  ohio  ...            13.2 (1.64)\n",
       "37              oklahoma  ...            14.0 (2.36)\n",
       "38                oregon  ...            15.9 (2.66)\n",
       "39          pennsylvania  ...            18.3 (2.09)\n",
       "40          rhode island  ...            12.9 (2.97)\n",
       "41        south carolina  ...             6.9 (1.60)\n",
       "42          south dakota  ...            *9.8 (3.21)\n",
       "43             tennessee  ...            12.8 (2.16)\n",
       "44                 texas  ...            14.6 (1.13)\n",
       "45                  utah  ...            27.4 (3.37)\n",
       "46               vermont  ...            15.2 (4.48)\n",
       "47              virginia  ...            16.8 (2.37)\n",
       "48            washington  ...            20.2 (2.35)\n",
       "49         west virginia  ...             8.4 (1.85)\n",
       "50             wisconsin  ...            14.4 (2.77)\n",
       "51               wyoming  ...            13.8 (3.51)\n",
       "\n",
       "[52 rows x 8 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply standardization function to dataset\n",
    "data_phys['state'] = data_phys[\"state\"].apply(standardize_phys_state)\n",
    "data_phys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N4SsCdympzNJ",
    "outputId": "37de90c1-63e8-4975-8840-0b5cc8ca183b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['22.9 (0.20)', '19.3 (1.41)', '27.9 (2.04)', '26.3 (1.48)',\n",
       "       '15.7 (1.32)', '24.0 (0.53)', '32.5 (1.49)', '24.5 (1.62)',\n",
       "       '20.1 (1.81)', '30.7 (1.74)', '21.1 (0.93)', '20.2 (1.18)',\n",
       "       '24.4 (1.93)', '31.4 (1.92)', '24.8 (0.81)', '15.1 (1.16)',\n",
       "       '22.2 (1.41)', '23.2 (1.48)', '14.6 (1.18)', '20.3 (1.41)',\n",
       "       '24.0 (1.72)', '22.9 (1.43)', '29.5 (1.56)', '23.6 (0.94)',\n",
       "       '27.7 (1.40)', '13.5 (1.25)', '23.6 (1.41)', '20.2 (1.98)',\n",
       "       '23.9 (1.67)', '23.6 (1.55)', '30.7 (1.92)', '21.0 (1.19)',\n",
       "       '23.0 (1.71)', '18.9 (0.83)', '22.4 (0.85)', '20.2 (1.87)',\n",
       "       '23.9 (1.02)', '19.0 (1.41)', '25.8 (1.58)', '25.6 (1.25)',\n",
       "       '25.4 (1.99)', '14.8 (1.26)', '17.2 (1.69)', '17.1 (1.30)',\n",
       "       '23.5 (0.70)', '28.2 (1.60)', '29.5 (2.49)', '24.2 (1.28)',\n",
       "       '28.9 (1.34)', '16.8 (1.43)', '23.6 (1.44)', '27.5 (1.99)',\n",
       "       '27.2 (0.27)', '24.6 (2.05)', '33.2 (2.70)', '29.2 (1.91)',\n",
       "       '19.7 (1.84)', '28.5 (0.75)', '33.4 (1.87)', '28.0 (2.20)',\n",
       "       '24.5 (2.44)', '40.3 (2.45)', '27.2 (1.14)', '27.1 (1.72)',\n",
       "       '31.2 (2.64)', '35.2 (2.46)', '28.5 (1.11)', '20.0 (1.64)',\n",
       "       '24.2 (1.76)', '24.7 (1.90)', '17.9 (1.66)', '23.5 (1.95)',\n",
       "       '27.3 (2.32)', '29.4 (1.99)', '32.9 (2.06)', '28.2 (1.36)',\n",
       "       '31.1 (1.81)', '17.9 (1.86)', '29.9 (1.96)', '20.3 (2.47)',\n",
       "       '26.8 (2.22)', '25.7 (2.02)', '30.3 (2.38)', '25.7 (1.62)',\n",
       "       '27.4 (2.37)', '22.8 (1.15)', '26.1 (1.44)', '21.7 (2.28)',\n",
       "       '29.5 (1.34)', '24.0 (1.96)', '28.6 (2.02)', '29.3 (1.81)',\n",
       "       '30.1 (2.75)', '20.1 (1.83)', '17.7 (2.09)', '20.1 (1.79)',\n",
       "       '28.1 (0.74)', '29.2 (2.01)', '35.9 (3.30)', '29.3 (1.75)',\n",
       "       '31.2 (1.70)', '19.8 (1.93)', '26.3 (1.89)', '29.6 (2.43)',\n",
       "       '18.7 (0.23)', '14.9 (1.51)', '23.3 (2.40)', '23.0 (1.77)',\n",
       "       '11.9 (1.45)', '19.3 (0.58)', '31.5 (1.85)', '21.2 (1.86)',\n",
       "       '15.8 (2.04)', '22.2 (1.88)', '15.5 (1.04)', '14.2 (1.11)',\n",
       "       '18.3 (2.14)', '27.4 (2.32)', '20.9 (1.00)', '10.1 (1.21)',\n",
       "       '19.9 (1.76)', '21.7 (1.79)', '11.4 (1.29)', '17.8 (1.61)',\n",
       "       '20.6 (1.99)', '16.2 (1.54)', '26.1 (1.84)', '18.7 (1.18)',\n",
       "       '24.3 (1.68)', '9.7 (1.30)', '17.9 (1.57)', '20.3 (2.48)',\n",
       "       '21.5 (1.97)', '21.6 (1.85)', '31.0 (2.43)', '16.2 (1.33)',\n",
       "       '18.6 (1.90)', '15.3 (0.93)', '19.0 (0.92)', '18.2 (2.36)',\n",
       "       '18.7 (1.06)', '14.6 (1.55)', '22.7 (1.92)', '21.8 (1.17)',\n",
       "       '21.6 (2.27)', '10.0 (1.29)', '15.9 (2.09)', '14.1 (1.45)',\n",
       "       '19.0 (1.02)', '27.1 (2.00)', '23.4 (2.87)', '19.5 (1.44)',\n",
       "       '26.2 (1.64)', '14.0 (1.65)', '21.0 (1.71)', '24.8 (2.53)',\n",
       "       '28.8 (0.31)', '25.6 (2.44)', '31.6 (2.93)', '31.3 (2.23)',\n",
       "       '21.4 (2.23)', '29.9 (0.84)', '34.8 (2.07)', '29.0 (2.52)',\n",
       "       '23.6 (2.65)', '40.5 (2.72)', '27.7 (1.31)', '29.9 (2.05)',\n",
       "       '32.8 (3.01)', '36.5 (2.76)', '29.9 (1.24)', '21.8 (1.92)',\n",
       "       '23.6 (1.90)', '26.2 (2.10)', '21.7 (2.21)', '25.5 (2.38)',\n",
       "       '32.4 (2.71)', '29.8 (2.20)', '34.1 (2.38)', '30.5 (1.63)',\n",
       "       '30.5 (1.95)', '19.8 (2.25)', '31.9 (2.30)', '24.5 (3.16)',\n",
       "       '25.3 (2.43)', '25.4 (2.32)', '30.7 (2.61)', '27.0 (1.86)',\n",
       "       '29.4 (2.86)', '25.3 (1.31)', '27.8 (1.81)', '22.1 (2.44)',\n",
       "       '31.0 (1.55)', '24.1 (2.26)', '30.9 (2.42)', '31.6 (2.13)',\n",
       "       '32.0 (3.28)', '21.9 (2.18)', '17.5 (2.26)', '22.8 (2.14)',\n",
       "       '28.7 (0.83)', '30.7 (2.23)', '40.2 (3.69)', '32.8 (2.02)',\n",
       "       '33.0 (1.96)', '21.5 (2.54)', '27.6 (2.11)', '31.2 (2.66)',\n",
       "       '20.9 (0.28)', '15.2 (1.95)', '24.4 (2.68)', '26.7 (2.28)',\n",
       "       '13.1 (1.82)', '21.5 (0.72)', '33.6 (2.19)', '24.8 (2.31)',\n",
       "       '17.3 (2.39)', '24.0 (2.20)', '17.3 (1.24)', '15.2 (1.38)',\n",
       "       '19.7 (2.53)', '27.0 (2.73)', '24.0 (1.06)', '12.5 (1.55)',\n",
       "       '20.9 (1.98)', '23.8 (2.15)', '14.6 (1.80)', '20.0 (2.06)',\n",
       "       '23.3 (2.36)', '18.3 (1.84)', '28.8 (2.20)', '21.9 (1.54)',\n",
       "       '26.8 (1.89)', '10.5 (1.68)', '18.2 (1.85)', '25.1 (3.18)',\n",
       "       '22.3 (2.19)', '22.4 (2.26)', '32.1 (2.73)', '16.9 (1.59)',\n",
       "       '21.6 (2.52)', '17.6 (1.23)', '19.8 (1.28)', '20.3 (2.61)',\n",
       "       '21.4 (1.29)', '14.8 (1.89)', '26.6 (2.41)', '23.4 (1.37)',\n",
       "       '26.4 (2.87)', '12.0 (1.76)', '16.9 (2.37)', '15.0 (1.80)',\n",
       "       '21.4 (1.38)', '26.5 (2.30)', '26.8 (3.35)', '20.5 (1.67)',\n",
       "       '29.7 (2.04)', '18.7 (2.40)', '23.3 (1.97)', '29.9 (3.05)',\n",
       "       '21.3 (0.45)', '23.0 (3.37)', '33.4 (5.59)', '22.4 (3.18)',\n",
       "       '14.7 (2.79)', '24.5 (1.34)', '26.9 (3.69)', '22.6 (3.79)',\n",
       "       '25.4 (5.20)', '43.0 (4.95)', '25.2 (2.07)', '21.1 (2.48)',\n",
       "       '25.6 (4.77)', '34.8 (4.88)', '21.7 (1.91)', '11.7 (2.44)',\n",
       "       '23.7 (3.79)', '16.1 (3.61)', '9.3 (1.89)', '15.5 (2.75)',\n",
       "       '*7.0 (2.63)', '26.6 (3.94)', '25.1 (3.46)', '21.7 (2.27)',\n",
       "       '32.8 (4.16)', '13.0 (2.79)', '20.2 (3.01)', '10.5 (3.07)',\n",
       "       '33.1 (4.70)', '22.4 (3.40)', '29.6 (5.01)', '17.4 (2.60)',\n",
       "       '19.4 (3.50)', '15.9 (1.78)', '19.5 (2.40)', '*15.8 (5.05)',\n",
       "       '24.0 (2.44)', '21.4 (3.34)', '21.6 (3.13)', '19.3 (2.40)',\n",
       "       '22.9 (4.29)', '12.4 (2.65)', '28.9 (5.52)', '10.7 (2.53)',\n",
       "       '25.0 (1.63)', '22.6 (3.97)', '21.4 (5.95)', '16.6 (2.76)',\n",
       "       '25.9 (2.99)', '14.4 (2.45)', '21.6 (3.68)', '17.1 (4.58)',\n",
       "       '14.6 (0.31)', '13.3 (2.04)', '17.8 (4.11)', '16.9 (2.37)',\n",
       "       '9.6 (2.05)', '15.5 (0.78)', '27.1 (2.94)', '13.6 (2.54)',\n",
       "       '11.4 (3.21)', '18.5 (3.08)', '12.2 (1.25)', '12.8 (1.52)',\n",
       "       '14.6 (3.36)', '28.5 (3.77)', '15.0 (1.90)', '5.1 (1.47)',\n",
       "       '16.4 (3.11)', '16.3 (2.67)', '5.9 (1.40)', '14.0 (2.22)',\n",
       "       '11.9 (2.81)', '11.3 (2.30)', '18.6 (2.74)', '13.0 (1.56)',\n",
       "       '15.0 (2.75)', '8.9 (1.84)', '17.5 (2.52)', '*9.5 (2.86)',\n",
       "       '18.0 (3.60)', '19.7 (2.77)', '28.7 (4.36)', '13.9 (2.02)',\n",
       "       '14.4 (2.51)', '11.3 (1.04)', '17.4 (1.79)', '*8.8 (3.81)',\n",
       "       '13.2 (1.64)', '14.0 (2.36)', '15.9 (2.66)', '18.3 (2.09)',\n",
       "       '12.9 (2.97)', '6.9 (1.60)', '*9.8 (3.21)', '12.8 (2.16)',\n",
       "       '14.6 (1.13)', '27.4 (3.37)', '15.2 (4.48)', '16.8 (2.37)',\n",
       "       '20.2 (2.35)', '8.4 (1.85)', '14.4 (2.77)', '13.8 (3.51)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_phys.loc[:, data_phys.columns != 'state'].values.ravel('K')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lMGwF7itpyD3"
   },
   "source": [
    "Looking at the unique values of the percentage of adults that met the physical activity guidelines, we can see that it is presented in the form '% (SE)'. To make this data more easily usable as floats, we want to convert this string into a tuple in the form [%, SE] to easily access the two values. The source summary of this dataset has also noted that some values prefaced with an asterisk '*' signifies that the estimates may be unreliable. For these values, we will set to NaN and drop them from our dataset in a future cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "skL8LN3ybd8f"
   },
   "outputs": [],
   "source": [
    "# Standardization for data_temp\n",
    "def standardize_phys_values(string):\n",
    "\n",
    "    # Check for unreliable data and set to NaN\n",
    "    if '*' in string:\n",
    "      string = np.nan\n",
    "    else:\n",
    "      # Basic string pre-processing\n",
    "      string = string.lower()\n",
    "      string = string.strip()\n",
    "\n",
    "      # Remove close parenthesis\n",
    "      string = string.replace(\")\", \"\")\n",
    "\n",
    "      # Tokenize string\n",
    "      string = [sub.split() for sub in string.split('(')]\n",
    "\n",
    "      # Construct new tuple\n",
    "      new_str = []\n",
    "      for i in string:\n",
    "          for j in i:\n",
    "              new_str.append(j)\n",
    "\n",
    "      string = new_str\n",
    "\n",
    "      string = tuple(float(s) for s in string)\n",
    "    \n",
    "    return string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "ECRLBlY0cOTA",
    "outputId": "2d335fb4-c4e7-4b7c-81b2-73b142bcae5b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>adults 18-64</th>\n",
       "      <th>men 18-64</th>\n",
       "      <th>women 18-64</th>\n",
       "      <th>working men 18-64</th>\n",
       "      <th>working women 18-64</th>\n",
       "      <th>nonworking men 18-64</th>\n",
       "      <th>nonworking women 18-64</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>all states (average)</td>\n",
       "      <td>(22.9, 0.2)</td>\n",
       "      <td>(27.2, 0.27)</td>\n",
       "      <td>(18.7, 0.23)</td>\n",
       "      <td>(28.8, 0.31)</td>\n",
       "      <td>(20.9, 0.28)</td>\n",
       "      <td>(21.3, 0.45)</td>\n",
       "      <td>(14.6, 0.31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alabama</td>\n",
       "      <td>(19.3, 1.41)</td>\n",
       "      <td>(24.6, 2.05)</td>\n",
       "      <td>(14.9, 1.51)</td>\n",
       "      <td>(25.6, 2.44)</td>\n",
       "      <td>(15.2, 1.95)</td>\n",
       "      <td>(23.0, 3.37)</td>\n",
       "      <td>(13.3, 2.04)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alaska</td>\n",
       "      <td>(27.9, 2.04)</td>\n",
       "      <td>(33.2, 2.7)</td>\n",
       "      <td>(23.3, 2.4)</td>\n",
       "      <td>(31.6, 2.93)</td>\n",
       "      <td>(24.4, 2.68)</td>\n",
       "      <td>(33.4, 5.59)</td>\n",
       "      <td>(17.8, 4.11)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arizona</td>\n",
       "      <td>(26.3, 1.48)</td>\n",
       "      <td>(29.2, 1.91)</td>\n",
       "      <td>(23.0, 1.77)</td>\n",
       "      <td>(31.3, 2.23)</td>\n",
       "      <td>(26.7, 2.28)</td>\n",
       "      <td>(22.4, 3.18)</td>\n",
       "      <td>(16.9, 2.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>(15.7, 1.32)</td>\n",
       "      <td>(19.7, 1.84)</td>\n",
       "      <td>(11.9, 1.45)</td>\n",
       "      <td>(21.4, 2.23)</td>\n",
       "      <td>(13.1, 1.82)</td>\n",
       "      <td>(14.7, 2.79)</td>\n",
       "      <td>(9.6, 2.05)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>california</td>\n",
       "      <td>(24.0, 0.53)</td>\n",
       "      <td>(28.5, 0.75)</td>\n",
       "      <td>(19.3, 0.58)</td>\n",
       "      <td>(29.9, 0.84)</td>\n",
       "      <td>(21.5, 0.72)</td>\n",
       "      <td>(24.5, 1.34)</td>\n",
       "      <td>(15.5, 0.78)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>colorado</td>\n",
       "      <td>(32.5, 1.49)</td>\n",
       "      <td>(33.4, 1.87)</td>\n",
       "      <td>(31.5, 1.85)</td>\n",
       "      <td>(34.8, 2.07)</td>\n",
       "      <td>(33.6, 2.19)</td>\n",
       "      <td>(26.9, 3.69)</td>\n",
       "      <td>(27.1, 2.94)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>connecticut</td>\n",
       "      <td>(24.5, 1.62)</td>\n",
       "      <td>(28.0, 2.2)</td>\n",
       "      <td>(21.2, 1.86)</td>\n",
       "      <td>(29.0, 2.52)</td>\n",
       "      <td>(24.8, 2.31)</td>\n",
       "      <td>(22.6, 3.79)</td>\n",
       "      <td>(13.6, 2.54)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>delaware</td>\n",
       "      <td>(20.1, 1.81)</td>\n",
       "      <td>(24.5, 2.44)</td>\n",
       "      <td>(15.8, 2.04)</td>\n",
       "      <td>(23.6, 2.65)</td>\n",
       "      <td>(17.3, 2.39)</td>\n",
       "      <td>(25.4, 5.2)</td>\n",
       "      <td>(11.4, 3.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>district of columbia</td>\n",
       "      <td>(30.7, 1.74)</td>\n",
       "      <td>(40.3, 2.45)</td>\n",
       "      <td>(22.2, 1.88)</td>\n",
       "      <td>(40.5, 2.72)</td>\n",
       "      <td>(24.0, 2.2)</td>\n",
       "      <td>(43.0, 4.95)</td>\n",
       "      <td>(18.5, 3.08)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>florida</td>\n",
       "      <td>(21.1, 0.93)</td>\n",
       "      <td>(27.2, 1.14)</td>\n",
       "      <td>(15.5, 1.04)</td>\n",
       "      <td>(27.7, 1.31)</td>\n",
       "      <td>(17.3, 1.24)</td>\n",
       "      <td>(25.2, 2.07)</td>\n",
       "      <td>(12.2, 1.25)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>georgia</td>\n",
       "      <td>(20.2, 1.18)</td>\n",
       "      <td>(27.1, 1.72)</td>\n",
       "      <td>(14.2, 1.11)</td>\n",
       "      <td>(29.9, 2.05)</td>\n",
       "      <td>(15.2, 1.38)</td>\n",
       "      <td>(21.1, 2.48)</td>\n",
       "      <td>(12.8, 1.52)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hawaii</td>\n",
       "      <td>(24.4, 1.93)</td>\n",
       "      <td>(31.2, 2.64)</td>\n",
       "      <td>(18.3, 2.14)</td>\n",
       "      <td>(32.8, 3.01)</td>\n",
       "      <td>(19.7, 2.53)</td>\n",
       "      <td>(25.6, 4.77)</td>\n",
       "      <td>(14.6, 3.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>idaho</td>\n",
       "      <td>(31.4, 1.92)</td>\n",
       "      <td>(35.2, 2.46)</td>\n",
       "      <td>(27.4, 2.32)</td>\n",
       "      <td>(36.5, 2.76)</td>\n",
       "      <td>(27.0, 2.73)</td>\n",
       "      <td>(34.8, 4.88)</td>\n",
       "      <td>(28.5, 3.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>illinois</td>\n",
       "      <td>(24.8, 0.81)</td>\n",
       "      <td>(28.5, 1.11)</td>\n",
       "      <td>(20.9, 1.0)</td>\n",
       "      <td>(29.9, 1.24)</td>\n",
       "      <td>(24.0, 1.06)</td>\n",
       "      <td>(21.7, 1.91)</td>\n",
       "      <td>(15.0, 1.9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>indiana</td>\n",
       "      <td>(15.1, 1.16)</td>\n",
       "      <td>(20.0, 1.64)</td>\n",
       "      <td>(10.1, 1.21)</td>\n",
       "      <td>(21.8, 1.92)</td>\n",
       "      <td>(12.5, 1.55)</td>\n",
       "      <td>(11.7, 2.44)</td>\n",
       "      <td>(5.1, 1.47)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>iowa</td>\n",
       "      <td>(22.2, 1.41)</td>\n",
       "      <td>(24.2, 1.76)</td>\n",
       "      <td>(19.9, 1.76)</td>\n",
       "      <td>(23.6, 1.9)</td>\n",
       "      <td>(20.9, 1.98)</td>\n",
       "      <td>(23.7, 3.79)</td>\n",
       "      <td>(16.4, 3.11)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>kansas</td>\n",
       "      <td>(23.2, 1.48)</td>\n",
       "      <td>(24.7, 1.9)</td>\n",
       "      <td>(21.7, 1.79)</td>\n",
       "      <td>(26.2, 2.1)</td>\n",
       "      <td>(23.8, 2.15)</td>\n",
       "      <td>(16.1, 3.61)</td>\n",
       "      <td>(16.3, 2.67)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>kentucky</td>\n",
       "      <td>(14.6, 1.18)</td>\n",
       "      <td>(17.9, 1.66)</td>\n",
       "      <td>(11.4, 1.29)</td>\n",
       "      <td>(21.7, 2.21)</td>\n",
       "      <td>(14.6, 1.8)</td>\n",
       "      <td>(9.3, 1.89)</td>\n",
       "      <td>(5.9, 1.4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>louisiana</td>\n",
       "      <td>(20.3, 1.41)</td>\n",
       "      <td>(23.5, 1.95)</td>\n",
       "      <td>(17.8, 1.61)</td>\n",
       "      <td>(25.5, 2.38)</td>\n",
       "      <td>(20.0, 2.06)</td>\n",
       "      <td>(15.5, 2.75)</td>\n",
       "      <td>(14.0, 2.22)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>maine</td>\n",
       "      <td>(24.0, 1.72)</td>\n",
       "      <td>(27.3, 2.32)</td>\n",
       "      <td>(20.6, 1.99)</td>\n",
       "      <td>(32.4, 2.71)</td>\n",
       "      <td>(23.3, 2.36)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(11.9, 2.81)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>maryland</td>\n",
       "      <td>(22.9, 1.43)</td>\n",
       "      <td>(29.4, 1.99)</td>\n",
       "      <td>(16.2, 1.54)</td>\n",
       "      <td>(29.8, 2.2)</td>\n",
       "      <td>(18.3, 1.84)</td>\n",
       "      <td>(26.6, 3.94)</td>\n",
       "      <td>(11.3, 2.3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>massachusetts</td>\n",
       "      <td>(29.5, 1.56)</td>\n",
       "      <td>(32.9, 2.06)</td>\n",
       "      <td>(26.1, 1.84)</td>\n",
       "      <td>(34.1, 2.38)</td>\n",
       "      <td>(28.8, 2.2)</td>\n",
       "      <td>(25.1, 3.46)</td>\n",
       "      <td>(18.6, 2.74)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>michigan</td>\n",
       "      <td>(23.6, 0.94)</td>\n",
       "      <td>(28.2, 1.36)</td>\n",
       "      <td>(18.7, 1.18)</td>\n",
       "      <td>(30.5, 1.63)</td>\n",
       "      <td>(21.9, 1.54)</td>\n",
       "      <td>(21.7, 2.27)</td>\n",
       "      <td>(13.0, 1.56)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>minnesota</td>\n",
       "      <td>(27.7, 1.4)</td>\n",
       "      <td>(31.1, 1.81)</td>\n",
       "      <td>(24.3, 1.68)</td>\n",
       "      <td>(30.5, 1.95)</td>\n",
       "      <td>(26.8, 1.89)</td>\n",
       "      <td>(32.8, 4.16)</td>\n",
       "      <td>(15.0, 2.75)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>mississippi</td>\n",
       "      <td>(13.5, 1.25)</td>\n",
       "      <td>(17.9, 1.86)</td>\n",
       "      <td>(9.7, 1.3)</td>\n",
       "      <td>(19.8, 2.25)</td>\n",
       "      <td>(10.5, 1.68)</td>\n",
       "      <td>(13.0, 2.79)</td>\n",
       "      <td>(8.9, 1.84)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>missouri</td>\n",
       "      <td>(23.6, 1.41)</td>\n",
       "      <td>(29.9, 1.96)</td>\n",
       "      <td>(17.9, 1.57)</td>\n",
       "      <td>(31.9, 2.3)</td>\n",
       "      <td>(18.2, 1.85)</td>\n",
       "      <td>(20.2, 3.01)</td>\n",
       "      <td>(17.5, 2.52)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>montana</td>\n",
       "      <td>(20.2, 1.98)</td>\n",
       "      <td>(20.3, 2.47)</td>\n",
       "      <td>(20.3, 2.48)</td>\n",
       "      <td>(24.5, 3.16)</td>\n",
       "      <td>(25.1, 3.18)</td>\n",
       "      <td>(10.5, 3.07)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>nebraska</td>\n",
       "      <td>(23.9, 1.67)</td>\n",
       "      <td>(26.8, 2.22)</td>\n",
       "      <td>(21.5, 1.97)</td>\n",
       "      <td>(25.3, 2.43)</td>\n",
       "      <td>(22.3, 2.19)</td>\n",
       "      <td>(33.1, 4.7)</td>\n",
       "      <td>(18.0, 3.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>nevada</td>\n",
       "      <td>(23.6, 1.55)</td>\n",
       "      <td>(25.7, 2.02)</td>\n",
       "      <td>(21.6, 1.85)</td>\n",
       "      <td>(25.4, 2.32)</td>\n",
       "      <td>(22.4, 2.26)</td>\n",
       "      <td>(22.4, 3.4)</td>\n",
       "      <td>(19.7, 2.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>new hampshire</td>\n",
       "      <td>(30.7, 1.92)</td>\n",
       "      <td>(30.3, 2.38)</td>\n",
       "      <td>(31.0, 2.43)</td>\n",
       "      <td>(30.7, 2.61)</td>\n",
       "      <td>(32.1, 2.73)</td>\n",
       "      <td>(29.6, 5.01)</td>\n",
       "      <td>(28.7, 4.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>new jersey</td>\n",
       "      <td>(21.0, 1.19)</td>\n",
       "      <td>(25.7, 1.62)</td>\n",
       "      <td>(16.2, 1.33)</td>\n",
       "      <td>(27.0, 1.86)</td>\n",
       "      <td>(16.9, 1.59)</td>\n",
       "      <td>(17.4, 2.6)</td>\n",
       "      <td>(13.9, 2.02)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>new mexico</td>\n",
       "      <td>(23.0, 1.71)</td>\n",
       "      <td>(27.4, 2.37)</td>\n",
       "      <td>(18.6, 1.9)</td>\n",
       "      <td>(29.4, 2.86)</td>\n",
       "      <td>(21.6, 2.52)</td>\n",
       "      <td>(19.4, 3.5)</td>\n",
       "      <td>(14.4, 2.51)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>new york</td>\n",
       "      <td>(18.9, 0.83)</td>\n",
       "      <td>(22.8, 1.15)</td>\n",
       "      <td>(15.3, 0.93)</td>\n",
       "      <td>(25.3, 1.31)</td>\n",
       "      <td>(17.6, 1.23)</td>\n",
       "      <td>(15.9, 1.78)</td>\n",
       "      <td>(11.3, 1.04)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>(22.4, 0.85)</td>\n",
       "      <td>(26.1, 1.44)</td>\n",
       "      <td>(19.0, 0.92)</td>\n",
       "      <td>(27.8, 1.81)</td>\n",
       "      <td>(19.8, 1.28)</td>\n",
       "      <td>(19.5, 2.4)</td>\n",
       "      <td>(17.4, 1.79)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>north dakota</td>\n",
       "      <td>(20.2, 1.87)</td>\n",
       "      <td>(21.7, 2.28)</td>\n",
       "      <td>(18.2, 2.36)</td>\n",
       "      <td>(22.1, 2.44)</td>\n",
       "      <td>(20.3, 2.61)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ohio</td>\n",
       "      <td>(23.9, 1.02)</td>\n",
       "      <td>(29.5, 1.34)</td>\n",
       "      <td>(18.7, 1.06)</td>\n",
       "      <td>(31.0, 1.55)</td>\n",
       "      <td>(21.4, 1.29)</td>\n",
       "      <td>(24.0, 2.44)</td>\n",
       "      <td>(13.2, 1.64)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>oklahoma</td>\n",
       "      <td>(19.0, 1.41)</td>\n",
       "      <td>(24.0, 1.96)</td>\n",
       "      <td>(14.6, 1.55)</td>\n",
       "      <td>(24.1, 2.26)</td>\n",
       "      <td>(14.8, 1.89)</td>\n",
       "      <td>(21.4, 3.34)</td>\n",
       "      <td>(14.0, 2.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>oregon</td>\n",
       "      <td>(25.8, 1.58)</td>\n",
       "      <td>(28.6, 2.02)</td>\n",
       "      <td>(22.7, 1.92)</td>\n",
       "      <td>(30.9, 2.42)</td>\n",
       "      <td>(26.6, 2.41)</td>\n",
       "      <td>(21.6, 3.13)</td>\n",
       "      <td>(15.9, 2.66)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>(25.6, 1.25)</td>\n",
       "      <td>(29.3, 1.81)</td>\n",
       "      <td>(21.8, 1.17)</td>\n",
       "      <td>(31.6, 2.13)</td>\n",
       "      <td>(23.4, 1.37)</td>\n",
       "      <td>(19.3, 2.4)</td>\n",
       "      <td>(18.3, 2.09)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>rhode island</td>\n",
       "      <td>(25.4, 1.99)</td>\n",
       "      <td>(30.1, 2.75)</td>\n",
       "      <td>(21.6, 2.27)</td>\n",
       "      <td>(32.0, 3.28)</td>\n",
       "      <td>(26.4, 2.87)</td>\n",
       "      <td>(22.9, 4.29)</td>\n",
       "      <td>(12.9, 2.97)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>south carolina</td>\n",
       "      <td>(14.8, 1.26)</td>\n",
       "      <td>(20.1, 1.83)</td>\n",
       "      <td>(10.0, 1.29)</td>\n",
       "      <td>(21.9, 2.18)</td>\n",
       "      <td>(12.0, 1.76)</td>\n",
       "      <td>(12.4, 2.65)</td>\n",
       "      <td>(6.9, 1.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>south dakota</td>\n",
       "      <td>(17.2, 1.69)</td>\n",
       "      <td>(17.7, 2.09)</td>\n",
       "      <td>(15.9, 2.09)</td>\n",
       "      <td>(17.5, 2.26)</td>\n",
       "      <td>(16.9, 2.37)</td>\n",
       "      <td>(28.9, 5.52)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>tennessee</td>\n",
       "      <td>(17.1, 1.3)</td>\n",
       "      <td>(20.1, 1.79)</td>\n",
       "      <td>(14.1, 1.45)</td>\n",
       "      <td>(22.8, 2.14)</td>\n",
       "      <td>(15.0, 1.8)</td>\n",
       "      <td>(10.7, 2.53)</td>\n",
       "      <td>(12.8, 2.16)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>texas</td>\n",
       "      <td>(23.5, 0.7)</td>\n",
       "      <td>(28.1, 0.74)</td>\n",
       "      <td>(19.0, 1.02)</td>\n",
       "      <td>(28.7, 0.83)</td>\n",
       "      <td>(21.4, 1.38)</td>\n",
       "      <td>(25.0, 1.63)</td>\n",
       "      <td>(14.6, 1.13)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>utah</td>\n",
       "      <td>(28.2, 1.6)</td>\n",
       "      <td>(29.2, 2.01)</td>\n",
       "      <td>(27.1, 2.0)</td>\n",
       "      <td>(30.7, 2.23)</td>\n",
       "      <td>(26.5, 2.3)</td>\n",
       "      <td>(22.6, 3.97)</td>\n",
       "      <td>(27.4, 3.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>vermont</td>\n",
       "      <td>(29.5, 2.49)</td>\n",
       "      <td>(35.9, 3.3)</td>\n",
       "      <td>(23.4, 2.87)</td>\n",
       "      <td>(40.2, 3.69)</td>\n",
       "      <td>(26.8, 3.35)</td>\n",
       "      <td>(21.4, 5.95)</td>\n",
       "      <td>(15.2, 4.48)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>virginia</td>\n",
       "      <td>(24.2, 1.28)</td>\n",
       "      <td>(29.3, 1.75)</td>\n",
       "      <td>(19.5, 1.44)</td>\n",
       "      <td>(32.8, 2.02)</td>\n",
       "      <td>(20.5, 1.67)</td>\n",
       "      <td>(16.6, 2.76)</td>\n",
       "      <td>(16.8, 2.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>washington</td>\n",
       "      <td>(28.9, 1.34)</td>\n",
       "      <td>(31.2, 1.7)</td>\n",
       "      <td>(26.2, 1.64)</td>\n",
       "      <td>(33.0, 1.96)</td>\n",
       "      <td>(29.7, 2.04)</td>\n",
       "      <td>(25.9, 2.99)</td>\n",
       "      <td>(20.2, 2.35)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>west virginia</td>\n",
       "      <td>(16.8, 1.43)</td>\n",
       "      <td>(19.8, 1.93)</td>\n",
       "      <td>(14.0, 1.65)</td>\n",
       "      <td>(21.5, 2.54)</td>\n",
       "      <td>(18.7, 2.4)</td>\n",
       "      <td>(14.4, 2.45)</td>\n",
       "      <td>(8.4, 1.85)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>wisconsin</td>\n",
       "      <td>(23.6, 1.44)</td>\n",
       "      <td>(26.3, 1.89)</td>\n",
       "      <td>(21.0, 1.71)</td>\n",
       "      <td>(27.6, 2.11)</td>\n",
       "      <td>(23.3, 1.97)</td>\n",
       "      <td>(21.6, 3.68)</td>\n",
       "      <td>(14.4, 2.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>(27.5, 1.99)</td>\n",
       "      <td>(29.6, 2.43)</td>\n",
       "      <td>(24.8, 2.53)</td>\n",
       "      <td>(31.2, 2.66)</td>\n",
       "      <td>(29.9, 3.05)</td>\n",
       "      <td>(17.1, 4.58)</td>\n",
       "      <td>(13.8, 3.51)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state  ... nonworking women 18-64\n",
       "0   all states (average)  ...           (14.6, 0.31)\n",
       "1                alabama  ...           (13.3, 2.04)\n",
       "2                 alaska  ...           (17.8, 4.11)\n",
       "3                arizona  ...           (16.9, 2.37)\n",
       "4               arkansas  ...            (9.6, 2.05)\n",
       "5             california  ...           (15.5, 0.78)\n",
       "6               colorado  ...           (27.1, 2.94)\n",
       "7            connecticut  ...           (13.6, 2.54)\n",
       "8               delaware  ...           (11.4, 3.21)\n",
       "9   district of columbia  ...           (18.5, 3.08)\n",
       "10               florida  ...           (12.2, 1.25)\n",
       "11               georgia  ...           (12.8, 1.52)\n",
       "12                hawaii  ...           (14.6, 3.36)\n",
       "13                 idaho  ...           (28.5, 3.77)\n",
       "14              illinois  ...            (15.0, 1.9)\n",
       "15               indiana  ...            (5.1, 1.47)\n",
       "16                  iowa  ...           (16.4, 3.11)\n",
       "17                kansas  ...           (16.3, 2.67)\n",
       "18              kentucky  ...             (5.9, 1.4)\n",
       "19             louisiana  ...           (14.0, 2.22)\n",
       "20                 maine  ...           (11.9, 2.81)\n",
       "21              maryland  ...            (11.3, 2.3)\n",
       "22         massachusetts  ...           (18.6, 2.74)\n",
       "23              michigan  ...           (13.0, 1.56)\n",
       "24             minnesota  ...           (15.0, 2.75)\n",
       "25           mississippi  ...            (8.9, 1.84)\n",
       "26              missouri  ...           (17.5, 2.52)\n",
       "27               montana  ...                    NaN\n",
       "28              nebraska  ...            (18.0, 3.6)\n",
       "29                nevada  ...           (19.7, 2.77)\n",
       "30         new hampshire  ...           (28.7, 4.36)\n",
       "31            new jersey  ...           (13.9, 2.02)\n",
       "32            new mexico  ...           (14.4, 2.51)\n",
       "33              new york  ...           (11.3, 1.04)\n",
       "34        north carolina  ...           (17.4, 1.79)\n",
       "35          north dakota  ...                    NaN\n",
       "36                  ohio  ...           (13.2, 1.64)\n",
       "37              oklahoma  ...           (14.0, 2.36)\n",
       "38                oregon  ...           (15.9, 2.66)\n",
       "39          pennsylvania  ...           (18.3, 2.09)\n",
       "40          rhode island  ...           (12.9, 2.97)\n",
       "41        south carolina  ...             (6.9, 1.6)\n",
       "42          south dakota  ...                    NaN\n",
       "43             tennessee  ...           (12.8, 2.16)\n",
       "44                 texas  ...           (14.6, 1.13)\n",
       "45                  utah  ...           (27.4, 3.37)\n",
       "46               vermont  ...           (15.2, 4.48)\n",
       "47              virginia  ...           (16.8, 2.37)\n",
       "48            washington  ...           (20.2, 2.35)\n",
       "49         west virginia  ...            (8.4, 1.85)\n",
       "50             wisconsin  ...           (14.4, 2.77)\n",
       "51               wyoming  ...           (13.8, 3.51)\n",
       "\n",
       "[52 rows x 8 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply standardization function to dataset\n",
    "for column in data_phys.loc[:, data_phys.columns != 'state']:\n",
    "  data_phys[column] = data_phys[column].apply(standardize_phys_values)\n",
    "\n",
    "data_phys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HS3YbsmRZ6MV",
    "outputId": "e129bef5-ff21-4009-8e7a-2b57741177ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state                     all states (average)\n",
       "adults 18-64                       (22.9, 0.2)\n",
       "men 18-64                         (27.2, 0.27)\n",
       "women 18-64                       (18.7, 0.23)\n",
       "working men 18-64                 (28.8, 0.31)\n",
       "working women 18-64               (20.9, 0.28)\n",
       "nonworking men 18-64              (21.3, 0.45)\n",
       "nonworking women 18-64            (14.6, 0.31)\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove the first row noting the average values of each state into a new dataframe for future usage\n",
    "data_phys_avg = data_phys.iloc[0]\n",
    "\n",
    "# Drop the above row from the main dataframe\n",
    "data_phys = data_phys.drop([0])\n",
    "data_phys.reset_index(drop=True, inplace=True)\n",
    "\n",
    "data_phys_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "sF8ljjoJtO8a",
    "outputId": "d6de1b6e-0a00-4c07-b88e-ac87835bd7a6"
   },
   "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>state</th>\n",
       "      <th>adults 18-64</th>\n",
       "      <th>men 18-64</th>\n",
       "      <th>women 18-64</th>\n",
       "      <th>working men 18-64</th>\n",
       "      <th>working women 18-64</th>\n",
       "      <th>nonworking men 18-64</th>\n",
       "      <th>nonworking women 18-64</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alabama</td>\n",
       "      <td>(19.3, 1.41)</td>\n",
       "      <td>(24.6, 2.05)</td>\n",
       "      <td>(14.9, 1.51)</td>\n",
       "      <td>(25.6, 2.44)</td>\n",
       "      <td>(15.2, 1.95)</td>\n",
       "      <td>(23.0, 3.37)</td>\n",
       "      <td>(13.3, 2.04)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alaska</td>\n",
       "      <td>(27.9, 2.04)</td>\n",
       "      <td>(33.2, 2.7)</td>\n",
       "      <td>(23.3, 2.4)</td>\n",
       "      <td>(31.6, 2.93)</td>\n",
       "      <td>(24.4, 2.68)</td>\n",
       "      <td>(33.4, 5.59)</td>\n",
       "      <td>(17.8, 4.11)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arizona</td>\n",
       "      <td>(26.3, 1.48)</td>\n",
       "      <td>(29.2, 1.91)</td>\n",
       "      <td>(23.0, 1.77)</td>\n",
       "      <td>(31.3, 2.23)</td>\n",
       "      <td>(26.7, 2.28)</td>\n",
       "      <td>(22.4, 3.18)</td>\n",
       "      <td>(16.9, 2.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>(15.7, 1.32)</td>\n",
       "      <td>(19.7, 1.84)</td>\n",
       "      <td>(11.9, 1.45)</td>\n",
       "      <td>(21.4, 2.23)</td>\n",
       "      <td>(13.1, 1.82)</td>\n",
       "      <td>(14.7, 2.79)</td>\n",
       "      <td>(9.6, 2.05)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>california</td>\n",
       "      <td>(24.0, 0.53)</td>\n",
       "      <td>(28.5, 0.75)</td>\n",
       "      <td>(19.3, 0.58)</td>\n",
       "      <td>(29.9, 0.84)</td>\n",
       "      <td>(21.5, 0.72)</td>\n",
       "      <td>(24.5, 1.34)</td>\n",
       "      <td>(15.5, 0.78)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>colorado</td>\n",
       "      <td>(32.5, 1.49)</td>\n",
       "      <td>(33.4, 1.87)</td>\n",
       "      <td>(31.5, 1.85)</td>\n",
       "      <td>(34.8, 2.07)</td>\n",
       "      <td>(33.6, 2.19)</td>\n",
       "      <td>(26.9, 3.69)</td>\n",
       "      <td>(27.1, 2.94)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>connecticut</td>\n",
       "      <td>(24.5, 1.62)</td>\n",
       "      <td>(28.0, 2.2)</td>\n",
       "      <td>(21.2, 1.86)</td>\n",
       "      <td>(29.0, 2.52)</td>\n",
       "      <td>(24.8, 2.31)</td>\n",
       "      <td>(22.6, 3.79)</td>\n",
       "      <td>(13.6, 2.54)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>delaware</td>\n",
       "      <td>(20.1, 1.81)</td>\n",
       "      <td>(24.5, 2.44)</td>\n",
       "      <td>(15.8, 2.04)</td>\n",
       "      <td>(23.6, 2.65)</td>\n",
       "      <td>(17.3, 2.39)</td>\n",
       "      <td>(25.4, 5.2)</td>\n",
       "      <td>(11.4, 3.21)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>district of columbia</td>\n",
       "      <td>(30.7, 1.74)</td>\n",
       "      <td>(40.3, 2.45)</td>\n",
       "      <td>(22.2, 1.88)</td>\n",
       "      <td>(40.5, 2.72)</td>\n",
       "      <td>(24.0, 2.2)</td>\n",
       "      <td>(43.0, 4.95)</td>\n",
       "      <td>(18.5, 3.08)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>florida</td>\n",
       "      <td>(21.1, 0.93)</td>\n",
       "      <td>(27.2, 1.14)</td>\n",
       "      <td>(15.5, 1.04)</td>\n",
       "      <td>(27.7, 1.31)</td>\n",
       "      <td>(17.3, 1.24)</td>\n",
       "      <td>(25.2, 2.07)</td>\n",
       "      <td>(12.2, 1.25)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>georgia</td>\n",
       "      <td>(20.2, 1.18)</td>\n",
       "      <td>(27.1, 1.72)</td>\n",
       "      <td>(14.2, 1.11)</td>\n",
       "      <td>(29.9, 2.05)</td>\n",
       "      <td>(15.2, 1.38)</td>\n",
       "      <td>(21.1, 2.48)</td>\n",
       "      <td>(12.8, 1.52)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hawaii</td>\n",
       "      <td>(24.4, 1.93)</td>\n",
       "      <td>(31.2, 2.64)</td>\n",
       "      <td>(18.3, 2.14)</td>\n",
       "      <td>(32.8, 3.01)</td>\n",
       "      <td>(19.7, 2.53)</td>\n",
       "      <td>(25.6, 4.77)</td>\n",
       "      <td>(14.6, 3.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>idaho</td>\n",
       "      <td>(31.4, 1.92)</td>\n",
       "      <td>(35.2, 2.46)</td>\n",
       "      <td>(27.4, 2.32)</td>\n",
       "      <td>(36.5, 2.76)</td>\n",
       "      <td>(27.0, 2.73)</td>\n",
       "      <td>(34.8, 4.88)</td>\n",
       "      <td>(28.5, 3.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>illinois</td>\n",
       "      <td>(24.8, 0.81)</td>\n",
       "      <td>(28.5, 1.11)</td>\n",
       "      <td>(20.9, 1.0)</td>\n",
       "      <td>(29.9, 1.24)</td>\n",
       "      <td>(24.0, 1.06)</td>\n",
       "      <td>(21.7, 1.91)</td>\n",
       "      <td>(15.0, 1.9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>indiana</td>\n",
       "      <td>(15.1, 1.16)</td>\n",
       "      <td>(20.0, 1.64)</td>\n",
       "      <td>(10.1, 1.21)</td>\n",
       "      <td>(21.8, 1.92)</td>\n",
       "      <td>(12.5, 1.55)</td>\n",
       "      <td>(11.7, 2.44)</td>\n",
       "      <td>(5.1, 1.47)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>iowa</td>\n",
       "      <td>(22.2, 1.41)</td>\n",
       "      <td>(24.2, 1.76)</td>\n",
       "      <td>(19.9, 1.76)</td>\n",
       "      <td>(23.6, 1.9)</td>\n",
       "      <td>(20.9, 1.98)</td>\n",
       "      <td>(23.7, 3.79)</td>\n",
       "      <td>(16.4, 3.11)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>kansas</td>\n",
       "      <td>(23.2, 1.48)</td>\n",
       "      <td>(24.7, 1.9)</td>\n",
       "      <td>(21.7, 1.79)</td>\n",
       "      <td>(26.2, 2.1)</td>\n",
       "      <td>(23.8, 2.15)</td>\n",
       "      <td>(16.1, 3.61)</td>\n",
       "      <td>(16.3, 2.67)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>kentucky</td>\n",
       "      <td>(14.6, 1.18)</td>\n",
       "      <td>(17.9, 1.66)</td>\n",
       "      <td>(11.4, 1.29)</td>\n",
       "      <td>(21.7, 2.21)</td>\n",
       "      <td>(14.6, 1.8)</td>\n",
       "      <td>(9.3, 1.89)</td>\n",
       "      <td>(5.9, 1.4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>louisiana</td>\n",
       "      <td>(20.3, 1.41)</td>\n",
       "      <td>(23.5, 1.95)</td>\n",
       "      <td>(17.8, 1.61)</td>\n",
       "      <td>(25.5, 2.38)</td>\n",
       "      <td>(20.0, 2.06)</td>\n",
       "      <td>(15.5, 2.75)</td>\n",
       "      <td>(14.0, 2.22)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>maryland</td>\n",
       "      <td>(22.9, 1.43)</td>\n",
       "      <td>(29.4, 1.99)</td>\n",
       "      <td>(16.2, 1.54)</td>\n",
       "      <td>(29.8, 2.2)</td>\n",
       "      <td>(18.3, 1.84)</td>\n",
       "      <td>(26.6, 3.94)</td>\n",
       "      <td>(11.3, 2.3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>massachusetts</td>\n",
       "      <td>(29.5, 1.56)</td>\n",
       "      <td>(32.9, 2.06)</td>\n",
       "      <td>(26.1, 1.84)</td>\n",
       "      <td>(34.1, 2.38)</td>\n",
       "      <td>(28.8, 2.2)</td>\n",
       "      <td>(25.1, 3.46)</td>\n",
       "      <td>(18.6, 2.74)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>michigan</td>\n",
       "      <td>(23.6, 0.94)</td>\n",
       "      <td>(28.2, 1.36)</td>\n",
       "      <td>(18.7, 1.18)</td>\n",
       "      <td>(30.5, 1.63)</td>\n",
       "      <td>(21.9, 1.54)</td>\n",
       "      <td>(21.7, 2.27)</td>\n",
       "      <td>(13.0, 1.56)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>minnesota</td>\n",
       "      <td>(27.7, 1.4)</td>\n",
       "      <td>(31.1, 1.81)</td>\n",
       "      <td>(24.3, 1.68)</td>\n",
       "      <td>(30.5, 1.95)</td>\n",
       "      <td>(26.8, 1.89)</td>\n",
       "      <td>(32.8, 4.16)</td>\n",
       "      <td>(15.0, 2.75)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>mississippi</td>\n",
       "      <td>(13.5, 1.25)</td>\n",
       "      <td>(17.9, 1.86)</td>\n",
       "      <td>(9.7, 1.3)</td>\n",
       "      <td>(19.8, 2.25)</td>\n",
       "      <td>(10.5, 1.68)</td>\n",
       "      <td>(13.0, 2.79)</td>\n",
       "      <td>(8.9, 1.84)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>missouri</td>\n",
       "      <td>(23.6, 1.41)</td>\n",
       "      <td>(29.9, 1.96)</td>\n",
       "      <td>(17.9, 1.57)</td>\n",
       "      <td>(31.9, 2.3)</td>\n",
       "      <td>(18.2, 1.85)</td>\n",
       "      <td>(20.2, 3.01)</td>\n",
       "      <td>(17.5, 2.52)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>nebraska</td>\n",
       "      <td>(23.9, 1.67)</td>\n",
       "      <td>(26.8, 2.22)</td>\n",
       "      <td>(21.5, 1.97)</td>\n",
       "      <td>(25.3, 2.43)</td>\n",
       "      <td>(22.3, 2.19)</td>\n",
       "      <td>(33.1, 4.7)</td>\n",
       "      <td>(18.0, 3.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>nevada</td>\n",
       "      <td>(23.6, 1.55)</td>\n",
       "      <td>(25.7, 2.02)</td>\n",
       "      <td>(21.6, 1.85)</td>\n",
       "      <td>(25.4, 2.32)</td>\n",
       "      <td>(22.4, 2.26)</td>\n",
       "      <td>(22.4, 3.4)</td>\n",
       "      <td>(19.7, 2.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>new hampshire</td>\n",
       "      <td>(30.7, 1.92)</td>\n",
       "      <td>(30.3, 2.38)</td>\n",
       "      <td>(31.0, 2.43)</td>\n",
       "      <td>(30.7, 2.61)</td>\n",
       "      <td>(32.1, 2.73)</td>\n",
       "      <td>(29.6, 5.01)</td>\n",
       "      <td>(28.7, 4.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>new jersey</td>\n",
       "      <td>(21.0, 1.19)</td>\n",
       "      <td>(25.7, 1.62)</td>\n",
       "      <td>(16.2, 1.33)</td>\n",
       "      <td>(27.0, 1.86)</td>\n",
       "      <td>(16.9, 1.59)</td>\n",
       "      <td>(17.4, 2.6)</td>\n",
       "      <td>(13.9, 2.02)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>new mexico</td>\n",
       "      <td>(23.0, 1.71)</td>\n",
       "      <td>(27.4, 2.37)</td>\n",
       "      <td>(18.6, 1.9)</td>\n",
       "      <td>(29.4, 2.86)</td>\n",
       "      <td>(21.6, 2.52)</td>\n",
       "      <td>(19.4, 3.5)</td>\n",
       "      <td>(14.4, 2.51)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>new york</td>\n",
       "      <td>(18.9, 0.83)</td>\n",
       "      <td>(22.8, 1.15)</td>\n",
       "      <td>(15.3, 0.93)</td>\n",
       "      <td>(25.3, 1.31)</td>\n",
       "      <td>(17.6, 1.23)</td>\n",
       "      <td>(15.9, 1.78)</td>\n",
       "      <td>(11.3, 1.04)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>(22.4, 0.85)</td>\n",
       "      <td>(26.1, 1.44)</td>\n",
       "      <td>(19.0, 0.92)</td>\n",
       "      <td>(27.8, 1.81)</td>\n",
       "      <td>(19.8, 1.28)</td>\n",
       "      <td>(19.5, 2.4)</td>\n",
       "      <td>(17.4, 1.79)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ohio</td>\n",
       "      <td>(23.9, 1.02)</td>\n",
       "      <td>(29.5, 1.34)</td>\n",
       "      <td>(18.7, 1.06)</td>\n",
       "      <td>(31.0, 1.55)</td>\n",
       "      <td>(21.4, 1.29)</td>\n",
       "      <td>(24.0, 2.44)</td>\n",
       "      <td>(13.2, 1.64)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>oklahoma</td>\n",
       "      <td>(19.0, 1.41)</td>\n",
       "      <td>(24.0, 1.96)</td>\n",
       "      <td>(14.6, 1.55)</td>\n",
       "      <td>(24.1, 2.26)</td>\n",
       "      <td>(14.8, 1.89)</td>\n",
       "      <td>(21.4, 3.34)</td>\n",
       "      <td>(14.0, 2.36)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>oregon</td>\n",
       "      <td>(25.8, 1.58)</td>\n",
       "      <td>(28.6, 2.02)</td>\n",
       "      <td>(22.7, 1.92)</td>\n",
       "      <td>(30.9, 2.42)</td>\n",
       "      <td>(26.6, 2.41)</td>\n",
       "      <td>(21.6, 3.13)</td>\n",
       "      <td>(15.9, 2.66)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>(25.6, 1.25)</td>\n",
       "      <td>(29.3, 1.81)</td>\n",
       "      <td>(21.8, 1.17)</td>\n",
       "      <td>(31.6, 2.13)</td>\n",
       "      <td>(23.4, 1.37)</td>\n",
       "      <td>(19.3, 2.4)</td>\n",
       "      <td>(18.3, 2.09)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>rhode island</td>\n",
       "      <td>(25.4, 1.99)</td>\n",
       "      <td>(30.1, 2.75)</td>\n",
       "      <td>(21.6, 2.27)</td>\n",
       "      <td>(32.0, 3.28)</td>\n",
       "      <td>(26.4, 2.87)</td>\n",
       "      <td>(22.9, 4.29)</td>\n",
       "      <td>(12.9, 2.97)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>south carolina</td>\n",
       "      <td>(14.8, 1.26)</td>\n",
       "      <td>(20.1, 1.83)</td>\n",
       "      <td>(10.0, 1.29)</td>\n",
       "      <td>(21.9, 2.18)</td>\n",
       "      <td>(12.0, 1.76)</td>\n",
       "      <td>(12.4, 2.65)</td>\n",
       "      <td>(6.9, 1.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>tennessee</td>\n",
       "      <td>(17.1, 1.3)</td>\n",
       "      <td>(20.1, 1.79)</td>\n",
       "      <td>(14.1, 1.45)</td>\n",
       "      <td>(22.8, 2.14)</td>\n",
       "      <td>(15.0, 1.8)</td>\n",
       "      <td>(10.7, 2.53)</td>\n",
       "      <td>(12.8, 2.16)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>texas</td>\n",
       "      <td>(23.5, 0.7)</td>\n",
       "      <td>(28.1, 0.74)</td>\n",
       "      <td>(19.0, 1.02)</td>\n",
       "      <td>(28.7, 0.83)</td>\n",
       "      <td>(21.4, 1.38)</td>\n",
       "      <td>(25.0, 1.63)</td>\n",
       "      <td>(14.6, 1.13)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>utah</td>\n",
       "      <td>(28.2, 1.6)</td>\n",
       "      <td>(29.2, 2.01)</td>\n",
       "      <td>(27.1, 2.0)</td>\n",
       "      <td>(30.7, 2.23)</td>\n",
       "      <td>(26.5, 2.3)</td>\n",
       "      <td>(22.6, 3.97)</td>\n",
       "      <td>(27.4, 3.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>vermont</td>\n",
       "      <td>(29.5, 2.49)</td>\n",
       "      <td>(35.9, 3.3)</td>\n",
       "      <td>(23.4, 2.87)</td>\n",
       "      <td>(40.2, 3.69)</td>\n",
       "      <td>(26.8, 3.35)</td>\n",
       "      <td>(21.4, 5.95)</td>\n",
       "      <td>(15.2, 4.48)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>virginia</td>\n",
       "      <td>(24.2, 1.28)</td>\n",
       "      <td>(29.3, 1.75)</td>\n",
       "      <td>(19.5, 1.44)</td>\n",
       "      <td>(32.8, 2.02)</td>\n",
       "      <td>(20.5, 1.67)</td>\n",
       "      <td>(16.6, 2.76)</td>\n",
       "      <td>(16.8, 2.37)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>washington</td>\n",
       "      <td>(28.9, 1.34)</td>\n",
       "      <td>(31.2, 1.7)</td>\n",
       "      <td>(26.2, 1.64)</td>\n",
       "      <td>(33.0, 1.96)</td>\n",
       "      <td>(29.7, 2.04)</td>\n",
       "      <td>(25.9, 2.99)</td>\n",
       "      <td>(20.2, 2.35)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>west virginia</td>\n",
       "      <td>(16.8, 1.43)</td>\n",
       "      <td>(19.8, 1.93)</td>\n",
       "      <td>(14.0, 1.65)</td>\n",
       "      <td>(21.5, 2.54)</td>\n",
       "      <td>(18.7, 2.4)</td>\n",
       "      <td>(14.4, 2.45)</td>\n",
       "      <td>(8.4, 1.85)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>wisconsin</td>\n",
       "      <td>(23.6, 1.44)</td>\n",
       "      <td>(26.3, 1.89)</td>\n",
       "      <td>(21.0, 1.71)</td>\n",
       "      <td>(27.6, 2.11)</td>\n",
       "      <td>(23.3, 1.97)</td>\n",
       "      <td>(21.6, 3.68)</td>\n",
       "      <td>(14.4, 2.77)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>(27.5, 1.99)</td>\n",
       "      <td>(29.6, 2.43)</td>\n",
       "      <td>(24.8, 2.53)</td>\n",
       "      <td>(31.2, 2.66)</td>\n",
       "      <td>(29.9, 3.05)</td>\n",
       "      <td>(17.1, 4.58)</td>\n",
       "      <td>(13.8, 3.51)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state  ... nonworking women 18-64\n",
       "0                alabama  ...           (13.3, 2.04)\n",
       "1                 alaska  ...           (17.8, 4.11)\n",
       "2                arizona  ...           (16.9, 2.37)\n",
       "3               arkansas  ...            (9.6, 2.05)\n",
       "4             california  ...           (15.5, 0.78)\n",
       "5               colorado  ...           (27.1, 2.94)\n",
       "6            connecticut  ...           (13.6, 2.54)\n",
       "7               delaware  ...           (11.4, 3.21)\n",
       "8   district of columbia  ...           (18.5, 3.08)\n",
       "9                florida  ...           (12.2, 1.25)\n",
       "10               georgia  ...           (12.8, 1.52)\n",
       "11                hawaii  ...           (14.6, 3.36)\n",
       "12                 idaho  ...           (28.5, 3.77)\n",
       "13              illinois  ...            (15.0, 1.9)\n",
       "14               indiana  ...            (5.1, 1.47)\n",
       "15                  iowa  ...           (16.4, 3.11)\n",
       "16                kansas  ...           (16.3, 2.67)\n",
       "17              kentucky  ...             (5.9, 1.4)\n",
       "18             louisiana  ...           (14.0, 2.22)\n",
       "19              maryland  ...            (11.3, 2.3)\n",
       "20         massachusetts  ...           (18.6, 2.74)\n",
       "21              michigan  ...           (13.0, 1.56)\n",
       "22             minnesota  ...           (15.0, 2.75)\n",
       "23           mississippi  ...            (8.9, 1.84)\n",
       "24              missouri  ...           (17.5, 2.52)\n",
       "25              nebraska  ...            (18.0, 3.6)\n",
       "26                nevada  ...           (19.7, 2.77)\n",
       "27         new hampshire  ...           (28.7, 4.36)\n",
       "28            new jersey  ...           (13.9, 2.02)\n",
       "29            new mexico  ...           (14.4, 2.51)\n",
       "30              new york  ...           (11.3, 1.04)\n",
       "31        north carolina  ...           (17.4, 1.79)\n",
       "32                  ohio  ...           (13.2, 1.64)\n",
       "33              oklahoma  ...           (14.0, 2.36)\n",
       "34                oregon  ...           (15.9, 2.66)\n",
       "35          pennsylvania  ...           (18.3, 2.09)\n",
       "36          rhode island  ...           (12.9, 2.97)\n",
       "37        south carolina  ...             (6.9, 1.6)\n",
       "38             tennessee  ...           (12.8, 2.16)\n",
       "39                 texas  ...           (14.6, 1.13)\n",
       "40                  utah  ...           (27.4, 3.37)\n",
       "41               vermont  ...           (15.2, 4.48)\n",
       "42              virginia  ...           (16.8, 2.37)\n",
       "43            washington  ...           (20.2, 2.35)\n",
       "44         west virginia  ...            (8.4, 1.85)\n",
       "45             wisconsin  ...           (14.4, 2.77)\n",
       "46               wyoming  ...           (13.8, 3.51)\n",
       "\n",
       "[47 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove any observations with null values and reset indexing\n",
    "data_phys = data_phys.dropna()\n",
    "data_phys.reset_index(drop=True, inplace=True)\n",
    "\n",
    "data_phys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Ddzopcu9WIMp",
    "outputId": "777f0e43-6460-44f2-cc8d-b9dc8bc78079"
   },
   "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>state</th>\n",
       "      <th>adults 18-64</th>\n",
       "      <th>men 18-64</th>\n",
       "      <th>women 18-64</th>\n",
       "      <th>working men 18-64</th>\n",
       "      <th>working women 18-64</th>\n",
       "      <th>nonworking men 18-64</th>\n",
       "      <th>nonworking women 18-64</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alabama</td>\n",
       "      <td>(19.3, 1.41)</td>\n",
       "      <td>(24.6, 2.05)</td>\n",
       "      <td>(14.9, 1.51)</td>\n",
       "      <td>(25.6, 2.44)</td>\n",
       "      <td>(15.2, 1.95)</td>\n",
       "      <td>(23.0, 3.37)</td>\n",
       "      <td>(13.3, 2.04)</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alaska</td>\n",
       "      <td>(27.9, 2.04)</td>\n",
       "      <td>(33.2, 2.7)</td>\n",
       "      <td>(23.3, 2.4)</td>\n",
       "      <td>(31.6, 2.93)</td>\n",
       "      <td>(24.4, 2.68)</td>\n",
       "      <td>(33.4, 5.59)</td>\n",
       "      <td>(17.8, 4.11)</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arizona</td>\n",
       "      <td>(26.3, 1.48)</td>\n",
       "      <td>(29.2, 1.91)</td>\n",
       "      <td>(23.0, 1.77)</td>\n",
       "      <td>(31.3, 2.23)</td>\n",
       "      <td>(26.7, 2.28)</td>\n",
       "      <td>(22.4, 3.18)</td>\n",
       "      <td>(16.9, 2.37)</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>(15.7, 1.32)</td>\n",
       "      <td>(19.7, 1.84)</td>\n",
       "      <td>(11.9, 1.45)</td>\n",
       "      <td>(21.4, 2.23)</td>\n",
       "      <td>(13.1, 1.82)</td>\n",
       "      <td>(14.7, 2.79)</td>\n",
       "      <td>(9.6, 2.05)</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>california</td>\n",
       "      <td>(24.0, 0.53)</td>\n",
       "      <td>(28.5, 0.75)</td>\n",
       "      <td>(19.3, 0.58)</td>\n",
       "      <td>(29.9, 0.84)</td>\n",
       "      <td>(21.5, 0.72)</td>\n",
       "      <td>(24.5, 1.34)</td>\n",
       "      <td>(15.5, 0.78)</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>colorado</td>\n",
       "      <td>(32.5, 1.49)</td>\n",
       "      <td>(33.4, 1.87)</td>\n",
       "      <td>(31.5, 1.85)</td>\n",
       "      <td>(34.8, 2.07)</td>\n",
       "      <td>(33.6, 2.19)</td>\n",
       "      <td>(26.9, 3.69)</td>\n",
       "      <td>(27.1, 2.94)</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>connecticut</td>\n",
       "      <td>(24.5, 1.62)</td>\n",
       "      <td>(28.0, 2.2)</td>\n",
       "      <td>(21.2, 1.86)</td>\n",
       "      <td>(29.0, 2.52)</td>\n",
       "      <td>(24.8, 2.31)</td>\n",
       "      <td>(22.6, 3.79)</td>\n",
       "      <td>(13.6, 2.54)</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>delaware</td>\n",
       "      <td>(20.1, 1.81)</td>\n",
       "      <td>(24.5, 2.44)</td>\n",
       "      <td>(15.8, 2.04)</td>\n",
       "      <td>(23.6, 2.65)</td>\n",
       "      <td>(17.3, 2.39)</td>\n",
       "      <td>(25.4, 5.2)</td>\n",
       "      <td>(11.4, 3.21)</td>\n",
       "      <td>DE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>district of columbia</td>\n",
       "      <td>(30.7, 1.74)</td>\n",
       "      <td>(40.3, 2.45)</td>\n",
       "      <td>(22.2, 1.88)</td>\n",
       "      <td>(40.5, 2.72)</td>\n",
       "      <td>(24.0, 2.2)</td>\n",
       "      <td>(43.0, 4.95)</td>\n",
       "      <td>(18.5, 3.08)</td>\n",
       "      <td>DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>florida</td>\n",
       "      <td>(21.1, 0.93)</td>\n",
       "      <td>(27.2, 1.14)</td>\n",
       "      <td>(15.5, 1.04)</td>\n",
       "      <td>(27.7, 1.31)</td>\n",
       "      <td>(17.3, 1.24)</td>\n",
       "      <td>(25.2, 2.07)</td>\n",
       "      <td>(12.2, 1.25)</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>georgia</td>\n",
       "      <td>(20.2, 1.18)</td>\n",
       "      <td>(27.1, 1.72)</td>\n",
       "      <td>(14.2, 1.11)</td>\n",
       "      <td>(29.9, 2.05)</td>\n",
       "      <td>(15.2, 1.38)</td>\n",
       "      <td>(21.1, 2.48)</td>\n",
       "      <td>(12.8, 1.52)</td>\n",
       "      <td>GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hawaii</td>\n",
       "      <td>(24.4, 1.93)</td>\n",
       "      <td>(31.2, 2.64)</td>\n",
       "      <td>(18.3, 2.14)</td>\n",
       "      <td>(32.8, 3.01)</td>\n",
       "      <td>(19.7, 2.53)</td>\n",
       "      <td>(25.6, 4.77)</td>\n",
       "      <td>(14.6, 3.36)</td>\n",
       "      <td>HI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>idaho</td>\n",
       "      <td>(31.4, 1.92)</td>\n",
       "      <td>(35.2, 2.46)</td>\n",
       "      <td>(27.4, 2.32)</td>\n",
       "      <td>(36.5, 2.76)</td>\n",
       "      <td>(27.0, 2.73)</td>\n",
       "      <td>(34.8, 4.88)</td>\n",
       "      <td>(28.5, 3.77)</td>\n",
       "      <td>ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>illinois</td>\n",
       "      <td>(24.8, 0.81)</td>\n",
       "      <td>(28.5, 1.11)</td>\n",
       "      <td>(20.9, 1.0)</td>\n",
       "      <td>(29.9, 1.24)</td>\n",
       "      <td>(24.0, 1.06)</td>\n",
       "      <td>(21.7, 1.91)</td>\n",
       "      <td>(15.0, 1.9)</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>indiana</td>\n",
       "      <td>(15.1, 1.16)</td>\n",
       "      <td>(20.0, 1.64)</td>\n",
       "      <td>(10.1, 1.21)</td>\n",
       "      <td>(21.8, 1.92)</td>\n",
       "      <td>(12.5, 1.55)</td>\n",
       "      <td>(11.7, 2.44)</td>\n",
       "      <td>(5.1, 1.47)</td>\n",
       "      <td>IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>iowa</td>\n",
       "      <td>(22.2, 1.41)</td>\n",
       "      <td>(24.2, 1.76)</td>\n",
       "      <td>(19.9, 1.76)</td>\n",
       "      <td>(23.6, 1.9)</td>\n",
       "      <td>(20.9, 1.98)</td>\n",
       "      <td>(23.7, 3.79)</td>\n",
       "      <td>(16.4, 3.11)</td>\n",
       "      <td>IA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>kansas</td>\n",
       "      <td>(23.2, 1.48)</td>\n",
       "      <td>(24.7, 1.9)</td>\n",
       "      <td>(21.7, 1.79)</td>\n",
       "      <td>(26.2, 2.1)</td>\n",
       "      <td>(23.8, 2.15)</td>\n",
       "      <td>(16.1, 3.61)</td>\n",
       "      <td>(16.3, 2.67)</td>\n",
       "      <td>KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>kentucky</td>\n",
       "      <td>(14.6, 1.18)</td>\n",
       "      <td>(17.9, 1.66)</td>\n",
       "      <td>(11.4, 1.29)</td>\n",
       "      <td>(21.7, 2.21)</td>\n",
       "      <td>(14.6, 1.8)</td>\n",
       "      <td>(9.3, 1.89)</td>\n",
       "      <td>(5.9, 1.4)</td>\n",
       "      <td>KY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>louisiana</td>\n",
       "      <td>(20.3, 1.41)</td>\n",
       "      <td>(23.5, 1.95)</td>\n",
       "      <td>(17.8, 1.61)</td>\n",
       "      <td>(25.5, 2.38)</td>\n",
       "      <td>(20.0, 2.06)</td>\n",
       "      <td>(15.5, 2.75)</td>\n",
       "      <td>(14.0, 2.22)</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>maryland</td>\n",
       "      <td>(22.9, 1.43)</td>\n",
       "      <td>(29.4, 1.99)</td>\n",
       "      <td>(16.2, 1.54)</td>\n",
       "      <td>(29.8, 2.2)</td>\n",
       "      <td>(18.3, 1.84)</td>\n",
       "      <td>(26.6, 3.94)</td>\n",
       "      <td>(11.3, 2.3)</td>\n",
       "      <td>MD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>massachusetts</td>\n",
       "      <td>(29.5, 1.56)</td>\n",
       "      <td>(32.9, 2.06)</td>\n",
       "      <td>(26.1, 1.84)</td>\n",
       "      <td>(34.1, 2.38)</td>\n",
       "      <td>(28.8, 2.2)</td>\n",
       "      <td>(25.1, 3.46)</td>\n",
       "      <td>(18.6, 2.74)</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>michigan</td>\n",
       "      <td>(23.6, 0.94)</td>\n",
       "      <td>(28.2, 1.36)</td>\n",
       "      <td>(18.7, 1.18)</td>\n",
       "      <td>(30.5, 1.63)</td>\n",
       "      <td>(21.9, 1.54)</td>\n",
       "      <td>(21.7, 2.27)</td>\n",
       "      <td>(13.0, 1.56)</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>minnesota</td>\n",
       "      <td>(27.7, 1.4)</td>\n",
       "      <td>(31.1, 1.81)</td>\n",
       "      <td>(24.3, 1.68)</td>\n",
       "      <td>(30.5, 1.95)</td>\n",
       "      <td>(26.8, 1.89)</td>\n",
       "      <td>(32.8, 4.16)</td>\n",
       "      <td>(15.0, 2.75)</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>mississippi</td>\n",
       "      <td>(13.5, 1.25)</td>\n",
       "      <td>(17.9, 1.86)</td>\n",
       "      <td>(9.7, 1.3)</td>\n",
       "      <td>(19.8, 2.25)</td>\n",
       "      <td>(10.5, 1.68)</td>\n",
       "      <td>(13.0, 2.79)</td>\n",
       "      <td>(8.9, 1.84)</td>\n",
       "      <td>MS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>missouri</td>\n",
       "      <td>(23.6, 1.41)</td>\n",
       "      <td>(29.9, 1.96)</td>\n",
       "      <td>(17.9, 1.57)</td>\n",
       "      <td>(31.9, 2.3)</td>\n",
       "      <td>(18.2, 1.85)</td>\n",
       "      <td>(20.2, 3.01)</td>\n",
       "      <td>(17.5, 2.52)</td>\n",
       "      <td>MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>nebraska</td>\n",
       "      <td>(23.9, 1.67)</td>\n",
       "      <td>(26.8, 2.22)</td>\n",
       "      <td>(21.5, 1.97)</td>\n",
       "      <td>(25.3, 2.43)</td>\n",
       "      <td>(22.3, 2.19)</td>\n",
       "      <td>(33.1, 4.7)</td>\n",
       "      <td>(18.0, 3.6)</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>nevada</td>\n",
       "      <td>(23.6, 1.55)</td>\n",
       "      <td>(25.7, 2.02)</td>\n",
       "      <td>(21.6, 1.85)</td>\n",
       "      <td>(25.4, 2.32)</td>\n",
       "      <td>(22.4, 2.26)</td>\n",
       "      <td>(22.4, 3.4)</td>\n",
       "      <td>(19.7, 2.77)</td>\n",
       "      <td>NV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>new hampshire</td>\n",
       "      <td>(30.7, 1.92)</td>\n",
       "      <td>(30.3, 2.38)</td>\n",
       "      <td>(31.0, 2.43)</td>\n",
       "      <td>(30.7, 2.61)</td>\n",
       "      <td>(32.1, 2.73)</td>\n",
       "      <td>(29.6, 5.01)</td>\n",
       "      <td>(28.7, 4.36)</td>\n",
       "      <td>NH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>new jersey</td>\n",
       "      <td>(21.0, 1.19)</td>\n",
       "      <td>(25.7, 1.62)</td>\n",
       "      <td>(16.2, 1.33)</td>\n",
       "      <td>(27.0, 1.86)</td>\n",
       "      <td>(16.9, 1.59)</td>\n",
       "      <td>(17.4, 2.6)</td>\n",
       "      <td>(13.9, 2.02)</td>\n",
       "      <td>NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>new mexico</td>\n",
       "      <td>(23.0, 1.71)</td>\n",
       "      <td>(27.4, 2.37)</td>\n",
       "      <td>(18.6, 1.9)</td>\n",
       "      <td>(29.4, 2.86)</td>\n",
       "      <td>(21.6, 2.52)</td>\n",
       "      <td>(19.4, 3.5)</td>\n",
       "      <td>(14.4, 2.51)</td>\n",
       "      <td>NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>new york</td>\n",
       "      <td>(18.9, 0.83)</td>\n",
       "      <td>(22.8, 1.15)</td>\n",
       "      <td>(15.3, 0.93)</td>\n",
       "      <td>(25.3, 1.31)</td>\n",
       "      <td>(17.6, 1.23)</td>\n",
       "      <td>(15.9, 1.78)</td>\n",
       "      <td>(11.3, 1.04)</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>(22.4, 0.85)</td>\n",
       "      <td>(26.1, 1.44)</td>\n",
       "      <td>(19.0, 0.92)</td>\n",
       "      <td>(27.8, 1.81)</td>\n",
       "      <td>(19.8, 1.28)</td>\n",
       "      <td>(19.5, 2.4)</td>\n",
       "      <td>(17.4, 1.79)</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ohio</td>\n",
       "      <td>(23.9, 1.02)</td>\n",
       "      <td>(29.5, 1.34)</td>\n",
       "      <td>(18.7, 1.06)</td>\n",
       "      <td>(31.0, 1.55)</td>\n",
       "      <td>(21.4, 1.29)</td>\n",
       "      <td>(24.0, 2.44)</td>\n",
       "      <td>(13.2, 1.64)</td>\n",
       "      <td>OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>oklahoma</td>\n",
       "      <td>(19.0, 1.41)</td>\n",
       "      <td>(24.0, 1.96)</td>\n",
       "      <td>(14.6, 1.55)</td>\n",
       "      <td>(24.1, 2.26)</td>\n",
       "      <td>(14.8, 1.89)</td>\n",
       "      <td>(21.4, 3.34)</td>\n",
       "      <td>(14.0, 2.36)</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>oregon</td>\n",
       "      <td>(25.8, 1.58)</td>\n",
       "      <td>(28.6, 2.02)</td>\n",
       "      <td>(22.7, 1.92)</td>\n",
       "      <td>(30.9, 2.42)</td>\n",
       "      <td>(26.6, 2.41)</td>\n",
       "      <td>(21.6, 3.13)</td>\n",
       "      <td>(15.9, 2.66)</td>\n",
       "      <td>OR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>(25.6, 1.25)</td>\n",
       "      <td>(29.3, 1.81)</td>\n",
       "      <td>(21.8, 1.17)</td>\n",
       "      <td>(31.6, 2.13)</td>\n",
       "      <td>(23.4, 1.37)</td>\n",
       "      <td>(19.3, 2.4)</td>\n",
       "      <td>(18.3, 2.09)</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>rhode island</td>\n",
       "      <td>(25.4, 1.99)</td>\n",
       "      <td>(30.1, 2.75)</td>\n",
       "      <td>(21.6, 2.27)</td>\n",
       "      <td>(32.0, 3.28)</td>\n",
       "      <td>(26.4, 2.87)</td>\n",
       "      <td>(22.9, 4.29)</td>\n",
       "      <td>(12.9, 2.97)</td>\n",
       "      <td>RI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>south carolina</td>\n",
       "      <td>(14.8, 1.26)</td>\n",
       "      <td>(20.1, 1.83)</td>\n",
       "      <td>(10.0, 1.29)</td>\n",
       "      <td>(21.9, 2.18)</td>\n",
       "      <td>(12.0, 1.76)</td>\n",
       "      <td>(12.4, 2.65)</td>\n",
       "      <td>(6.9, 1.6)</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>tennessee</td>\n",
       "      <td>(17.1, 1.3)</td>\n",
       "      <td>(20.1, 1.79)</td>\n",
       "      <td>(14.1, 1.45)</td>\n",
       "      <td>(22.8, 2.14)</td>\n",
       "      <td>(15.0, 1.8)</td>\n",
       "      <td>(10.7, 2.53)</td>\n",
       "      <td>(12.8, 2.16)</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>texas</td>\n",
       "      <td>(23.5, 0.7)</td>\n",
       "      <td>(28.1, 0.74)</td>\n",
       "      <td>(19.0, 1.02)</td>\n",
       "      <td>(28.7, 0.83)</td>\n",
       "      <td>(21.4, 1.38)</td>\n",
       "      <td>(25.0, 1.63)</td>\n",
       "      <td>(14.6, 1.13)</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>utah</td>\n",
       "      <td>(28.2, 1.6)</td>\n",
       "      <td>(29.2, 2.01)</td>\n",
       "      <td>(27.1, 2.0)</td>\n",
       "      <td>(30.7, 2.23)</td>\n",
       "      <td>(26.5, 2.3)</td>\n",
       "      <td>(22.6, 3.97)</td>\n",
       "      <td>(27.4, 3.37)</td>\n",
       "      <td>UT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>vermont</td>\n",
       "      <td>(29.5, 2.49)</td>\n",
       "      <td>(35.9, 3.3)</td>\n",
       "      <td>(23.4, 2.87)</td>\n",
       "      <td>(40.2, 3.69)</td>\n",
       "      <td>(26.8, 3.35)</td>\n",
       "      <td>(21.4, 5.95)</td>\n",
       "      <td>(15.2, 4.48)</td>\n",
       "      <td>VT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>virginia</td>\n",
       "      <td>(24.2, 1.28)</td>\n",
       "      <td>(29.3, 1.75)</td>\n",
       "      <td>(19.5, 1.44)</td>\n",
       "      <td>(32.8, 2.02)</td>\n",
       "      <td>(20.5, 1.67)</td>\n",
       "      <td>(16.6, 2.76)</td>\n",
       "      <td>(16.8, 2.37)</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>washington</td>\n",
       "      <td>(28.9, 1.34)</td>\n",
       "      <td>(31.2, 1.7)</td>\n",
       "      <td>(26.2, 1.64)</td>\n",
       "      <td>(33.0, 1.96)</td>\n",
       "      <td>(29.7, 2.04)</td>\n",
       "      <td>(25.9, 2.99)</td>\n",
       "      <td>(20.2, 2.35)</td>\n",
       "      <td>WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>west virginia</td>\n",
       "      <td>(16.8, 1.43)</td>\n",
       "      <td>(19.8, 1.93)</td>\n",
       "      <td>(14.0, 1.65)</td>\n",
       "      <td>(21.5, 2.54)</td>\n",
       "      <td>(18.7, 2.4)</td>\n",
       "      <td>(14.4, 2.45)</td>\n",
       "      <td>(8.4, 1.85)</td>\n",
       "      <td>WV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>wisconsin</td>\n",
       "      <td>(23.6, 1.44)</td>\n",
       "      <td>(26.3, 1.89)</td>\n",
       "      <td>(21.0, 1.71)</td>\n",
       "      <td>(27.6, 2.11)</td>\n",
       "      <td>(23.3, 1.97)</td>\n",
       "      <td>(21.6, 3.68)</td>\n",
       "      <td>(14.4, 2.77)</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>(27.5, 1.99)</td>\n",
       "      <td>(29.6, 2.43)</td>\n",
       "      <td>(24.8, 2.53)</td>\n",
       "      <td>(31.2, 2.66)</td>\n",
       "      <td>(29.9, 3.05)</td>\n",
       "      <td>(17.1, 4.58)</td>\n",
       "      <td>(13.8, 3.51)</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state  adults 18-64  ... nonworking women 18-64 code\n",
       "0                alabama  (19.3, 1.41)  ...           (13.3, 2.04)   AL\n",
       "1                 alaska  (27.9, 2.04)  ...           (17.8, 4.11)   AK\n",
       "2                arizona  (26.3, 1.48)  ...           (16.9, 2.37)   AZ\n",
       "3               arkansas  (15.7, 1.32)  ...            (9.6, 2.05)   AR\n",
       "4             california  (24.0, 0.53)  ...           (15.5, 0.78)   CA\n",
       "5               colorado  (32.5, 1.49)  ...           (27.1, 2.94)   CO\n",
       "6            connecticut  (24.5, 1.62)  ...           (13.6, 2.54)   CT\n",
       "7               delaware  (20.1, 1.81)  ...           (11.4, 3.21)   DE\n",
       "8   district of columbia  (30.7, 1.74)  ...           (18.5, 3.08)   DC\n",
       "9                florida  (21.1, 0.93)  ...           (12.2, 1.25)   FL\n",
       "10               georgia  (20.2, 1.18)  ...           (12.8, 1.52)   GA\n",
       "11                hawaii  (24.4, 1.93)  ...           (14.6, 3.36)   HI\n",
       "12                 idaho  (31.4, 1.92)  ...           (28.5, 3.77)   ID\n",
       "13              illinois  (24.8, 0.81)  ...            (15.0, 1.9)   IL\n",
       "14               indiana  (15.1, 1.16)  ...            (5.1, 1.47)   IN\n",
       "15                  iowa  (22.2, 1.41)  ...           (16.4, 3.11)   IA\n",
       "16                kansas  (23.2, 1.48)  ...           (16.3, 2.67)   KS\n",
       "17              kentucky  (14.6, 1.18)  ...             (5.9, 1.4)   KY\n",
       "18             louisiana  (20.3, 1.41)  ...           (14.0, 2.22)   LA\n",
       "19              maryland  (22.9, 1.43)  ...            (11.3, 2.3)   MD\n",
       "20         massachusetts  (29.5, 1.56)  ...           (18.6, 2.74)   MA\n",
       "21              michigan  (23.6, 0.94)  ...           (13.0, 1.56)   MI\n",
       "22             minnesota   (27.7, 1.4)  ...           (15.0, 2.75)   MN\n",
       "23           mississippi  (13.5, 1.25)  ...            (8.9, 1.84)   MS\n",
       "24              missouri  (23.6, 1.41)  ...           (17.5, 2.52)   MO\n",
       "25              nebraska  (23.9, 1.67)  ...            (18.0, 3.6)   NE\n",
       "26                nevada  (23.6, 1.55)  ...           (19.7, 2.77)   NV\n",
       "27         new hampshire  (30.7, 1.92)  ...           (28.7, 4.36)   NH\n",
       "28            new jersey  (21.0, 1.19)  ...           (13.9, 2.02)   NJ\n",
       "29            new mexico  (23.0, 1.71)  ...           (14.4, 2.51)   NM\n",
       "30              new york  (18.9, 0.83)  ...           (11.3, 1.04)   NY\n",
       "31        north carolina  (22.4, 0.85)  ...           (17.4, 1.79)   NC\n",
       "32                  ohio  (23.9, 1.02)  ...           (13.2, 1.64)   OH\n",
       "33              oklahoma  (19.0, 1.41)  ...           (14.0, 2.36)   OK\n",
       "34                oregon  (25.8, 1.58)  ...           (15.9, 2.66)   OR\n",
       "35          pennsylvania  (25.6, 1.25)  ...           (18.3, 2.09)   PA\n",
       "36          rhode island  (25.4, 1.99)  ...           (12.9, 2.97)   RI\n",
       "37        south carolina  (14.8, 1.26)  ...             (6.9, 1.6)   SC\n",
       "38             tennessee   (17.1, 1.3)  ...           (12.8, 2.16)   TN\n",
       "39                 texas   (23.5, 0.7)  ...           (14.6, 1.13)   TX\n",
       "40                  utah   (28.2, 1.6)  ...           (27.4, 3.37)   UT\n",
       "41               vermont  (29.5, 2.49)  ...           (15.2, 4.48)   VT\n",
       "42              virginia  (24.2, 1.28)  ...           (16.8, 2.37)   VA\n",
       "43            washington  (28.9, 1.34)  ...           (20.2, 2.35)   WA\n",
       "44         west virginia  (16.8, 1.43)  ...            (8.4, 1.85)   WV\n",
       "45             wisconsin  (23.6, 1.44)  ...           (14.4, 2.77)   WI\n",
       "46               wyoming  (27.5, 1.99)  ...           (13.8, 3.51)   WY\n",
       "\n",
       "[47 rows x 9 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append new column holding the two letter abbreviations for each state\n",
    "data_phys['code'] = data_phys['state'].map(us_state_dict)\n",
    "\n",
    "data_phys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qjfrJ9Em4FeI"
   },
   "source": [
    "# **Data Analysis & Results (EDA)**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VpDpPfKd4FeI"
   },
   "source": [
    "**Mean Duration and Mean Temperature of Each State**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KIru7A6mxvMA"
   },
   "source": [
    "To better represent our data, we took the the duration of exercise from Dataset 1 and the average temperature readings of each state from Dataset 2 and created a new dataframe that exclusively displayed those two columns. To do this, we created a dictionary to map each state to its proper mean value. This was easily done with the prior data cleaning methods which standardize column names and row values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "0bcM60J84FeJ",
    "outputId": "5f0fc52c-5575-48f0-b6a2-73bdf7170ac3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>mean_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>colorado</td>\n",
       "      <td>537.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>california</td>\n",
       "      <td>746.863636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>illinois</td>\n",
       "      <td>588.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ohio</td>\n",
       "      <td>606.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>virginia</td>\n",
       "      <td>434.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>688.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>indiana</td>\n",
       "      <td>520.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>texas</td>\n",
       "      <td>760.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tennessee</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>wisconsin</td>\n",
       "      <td>601.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>maryland</td>\n",
       "      <td>619.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>connecticut</td>\n",
       "      <td>474.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>new york</td>\n",
       "      <td>675.838710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>florida</td>\n",
       "      <td>588.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>minnesota</td>\n",
       "      <td>659.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>iowa</td>\n",
       "      <td>504.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>new mexico</td>\n",
       "      <td>470.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>oregon</td>\n",
       "      <td>484.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>missouri</td>\n",
       "      <td>762.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>michigan</td>\n",
       "      <td>482.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>new jersey</td>\n",
       "      <td>549.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>district of columbia</td>\n",
       "      <td>496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>633.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>massachusetts</td>\n",
       "      <td>561.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>alabama</td>\n",
       "      <td>433.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>rhode island</td>\n",
       "      <td>361.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>washington</td>\n",
       "      <td>499.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>south carolina</td>\n",
       "      <td>625.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>oklahoma</td>\n",
       "      <td>858.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>kansas</td>\n",
       "      <td>350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>nebraska</td>\n",
       "      <td>1089.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>arizona</td>\n",
       "      <td>430.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>georgia</td>\n",
       "      <td>829.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>kentucky</td>\n",
       "      <td>521.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state  mean_duration\n",
       "0               colorado     537.000000\n",
       "1             california     746.863636\n",
       "2               illinois     588.333333\n",
       "3                   ohio     606.142857\n",
       "4               virginia     434.000000\n",
       "5           pennsylvania     688.000000\n",
       "6                indiana     520.666667\n",
       "7                  texas     760.923077\n",
       "8              tennessee     431.000000\n",
       "9              wisconsin     601.666667\n",
       "10              maryland     619.000000\n",
       "11           connecticut     474.500000\n",
       "12              new york     675.838710\n",
       "13               florida     588.200000\n",
       "14             minnesota     659.500000\n",
       "15                  iowa     504.666667\n",
       "16            new mexico     470.000000\n",
       "17                oregon     484.500000\n",
       "18              missouri     762.500000\n",
       "19              michigan     482.600000\n",
       "20            new jersey     549.166667\n",
       "21  district of columbia     496.000000\n",
       "22        north carolina     633.333333\n",
       "23         massachusetts     561.833333\n",
       "24               alabama     433.000000\n",
       "25          rhode island     361.000000\n",
       "26            washington     499.500000\n",
       "27        south carolina     625.000000\n",
       "28              oklahoma     858.000000\n",
       "29                kansas     350.000000\n",
       "30              nebraska    1089.000000\n",
       "31               arizona     430.000000\n",
       "32               georgia     829.500000\n",
       "33              kentucky     521.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dictionary to organize each state by its mean exercise duration\n",
    "state_mean_dur_dict = dict()\n",
    "\n",
    "# Propagate dictionary\n",
    "for x in data_state['state']:\n",
    "  state_mean_dur_dict[x] = data_state.loc[data_state['state'] == x]['duration'].mean()\n",
    "\n",
    "# Create dataframe from dictionary\n",
    "state_mean_dur = pd.DataFrame(state_mean_dur_dict.items(), columns=['state', 'mean_duration'])\n",
    "state_mean_dur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "VMpQ18LPU7MJ",
    "outputId": "b0378c36-831c-4169-f25e-e1514d685669"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>mean_temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alabama</td>\n",
       "      <td>17.982018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alaska</td>\n",
       "      <td>-3.492543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arizona</td>\n",
       "      <td>16.309824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arkansas</td>\n",
       "      <td>16.584570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>california</td>\n",
       "      <td>15.080564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>colorado</td>\n",
       "      <td>7.952455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>connecticut</td>\n",
       "      <td>10.438182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>delaware</td>\n",
       "      <td>13.131552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>district of columbia</td>\n",
       "      <td>13.077236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>florida</td>\n",
       "      <td>22.267382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>georgia</td>\n",
       "      <td>18.372406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hawaii</td>\n",
       "      <td>22.972122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>idaho</td>\n",
       "      <td>6.328309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>illinois</td>\n",
       "      <td>12.046570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>indiana</td>\n",
       "      <td>12.024376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>iowa</td>\n",
       "      <td>9.959170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>kansas</td>\n",
       "      <td>13.357152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>kentucky</td>\n",
       "      <td>13.994261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>louisiana</td>\n",
       "      <td>19.960776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>maine</td>\n",
       "      <td>5.879612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>maryland</td>\n",
       "      <td>13.285170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>massachusetts</td>\n",
       "      <td>9.095218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>michigan</td>\n",
       "      <td>7.956539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>minnesota</td>\n",
       "      <td>5.986612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>mississippi</td>\n",
       "      <td>18.469527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>missouri</td>\n",
       "      <td>13.473812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>montana</td>\n",
       "      <td>6.160152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>nebraska</td>\n",
       "      <td>10.275570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>nevada</td>\n",
       "      <td>10.665382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>new hampshire</td>\n",
       "      <td>7.124612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>new jersey</td>\n",
       "      <td>11.931370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>new mexico</td>\n",
       "      <td>12.803370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>new york</td>\n",
       "      <td>8.635958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>15.740915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>north dakota</td>\n",
       "      <td>5.599170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ohio</td>\n",
       "      <td>11.479121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>oklahoma</td>\n",
       "      <td>16.333261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>oregon</td>\n",
       "      <td>8.912115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>10.381176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>rhode island</td>\n",
       "      <td>10.396715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>south carolina</td>\n",
       "      <td>17.903491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>south dakota</td>\n",
       "      <td>8.291958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>tennessee</td>\n",
       "      <td>15.039594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>texas</td>\n",
       "      <td>19.039727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>utah</td>\n",
       "      <td>9.376764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>vermont</td>\n",
       "      <td>6.926915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>virginia</td>\n",
       "      <td>13.844752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>washington</td>\n",
       "      <td>8.457315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>west virginia</td>\n",
       "      <td>12.024073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>wisconsin</td>\n",
       "      <td>7.361267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>wyoming</td>\n",
       "      <td>6.118782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state  mean_temp\n",
       "0                alabama  17.982018\n",
       "1                 alaska  -3.492543\n",
       "2                arizona  16.309824\n",
       "3               arkansas  16.584570\n",
       "4             california  15.080564\n",
       "5               colorado   7.952455\n",
       "6            connecticut  10.438182\n",
       "7               delaware  13.131552\n",
       "8   district of columbia  13.077236\n",
       "9                florida  22.267382\n",
       "10               georgia  18.372406\n",
       "11                hawaii  22.972122\n",
       "12                 idaho   6.328309\n",
       "13              illinois  12.046570\n",
       "14               indiana  12.024376\n",
       "15                  iowa   9.959170\n",
       "16                kansas  13.357152\n",
       "17              kentucky  13.994261\n",
       "18             louisiana  19.960776\n",
       "19                 maine   5.879612\n",
       "20              maryland  13.285170\n",
       "21         massachusetts   9.095218\n",
       "22              michigan   7.956539\n",
       "23             minnesota   5.986612\n",
       "24           mississippi  18.469527\n",
       "25              missouri  13.473812\n",
       "26               montana   6.160152\n",
       "27              nebraska  10.275570\n",
       "28                nevada  10.665382\n",
       "29         new hampshire   7.124612\n",
       "30            new jersey  11.931370\n",
       "31            new mexico  12.803370\n",
       "32              new york   8.635958\n",
       "33        north carolina  15.740915\n",
       "34          north dakota   5.599170\n",
       "35                  ohio  11.479121\n",
       "36              oklahoma  16.333261\n",
       "37                oregon   8.912115\n",
       "38          pennsylvania  10.381176\n",
       "39          rhode island  10.396715\n",
       "40        south carolina  17.903491\n",
       "41          south dakota   8.291958\n",
       "42             tennessee  15.039594\n",
       "43                 texas  19.039727\n",
       "44                  utah   9.376764\n",
       "45               vermont   6.926915\n",
       "46              virginia  13.844752\n",
       "47            washington   8.457315\n",
       "48         west virginia  12.024073\n",
       "49             wisconsin   7.361267\n",
       "50               wyoming   6.118782"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dictionary to organize each state by its mean state temperature\n",
    "state_mean_temp_dict = dict()\n",
    "\n",
    "# Propagate dictionary\n",
    "for x in data_temp['state']:\n",
    "  state_mean_temp_dict[x] = data_temp.loc[data_temp['state'] == x]['average_temp'].mean()\n",
    "\n",
    "# Create dataframe from dictionary\n",
    "state_mean_temp = pd.DataFrame(state_mean_temp_dict.items(), columns=['state', 'mean_temp'])\n",
    "state_mean_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "TRaEauRwVmAc",
    "outputId": "7a3dfac7-4419-4e8c-9433-808dd8d15002"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>mean_temp</th>\n",
       "      <th>mean_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alabama</td>\n",
       "      <td>17.982018</td>\n",
       "      <td>433.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arizona</td>\n",
       "      <td>16.309824</td>\n",
       "      <td>430.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>california</td>\n",
       "      <td>15.080564</td>\n",
       "      <td>746.863636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>colorado</td>\n",
       "      <td>7.952455</td>\n",
       "      <td>537.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>connecticut</td>\n",
       "      <td>10.438182</td>\n",
       "      <td>474.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>district of columbia</td>\n",
       "      <td>13.077236</td>\n",
       "      <td>496.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>florida</td>\n",
       "      <td>22.267382</td>\n",
       "      <td>588.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>georgia</td>\n",
       "      <td>18.372406</td>\n",
       "      <td>829.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>illinois</td>\n",
       "      <td>12.046570</td>\n",
       "      <td>588.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>indiana</td>\n",
       "      <td>12.024376</td>\n",
       "      <td>520.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>iowa</td>\n",
       "      <td>9.959170</td>\n",
       "      <td>504.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>kansas</td>\n",
       "      <td>13.357152</td>\n",
       "      <td>350.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>kentucky</td>\n",
       "      <td>13.994261</td>\n",
       "      <td>521.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>maryland</td>\n",
       "      <td>13.285170</td>\n",
       "      <td>619.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>massachusetts</td>\n",
       "      <td>9.095218</td>\n",
       "      <td>561.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>michigan</td>\n",
       "      <td>7.956539</td>\n",
       "      <td>482.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>minnesota</td>\n",
       "      <td>5.986612</td>\n",
       "      <td>659.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>missouri</td>\n",
       "      <td>13.473812</td>\n",
       "      <td>762.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>nebraska</td>\n",
       "      <td>10.275570</td>\n",
       "      <td>1089.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>new jersey</td>\n",
       "      <td>11.931370</td>\n",
       "      <td>549.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>new mexico</td>\n",
       "      <td>12.803370</td>\n",
       "      <td>470.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>new york</td>\n",
       "      <td>8.635958</td>\n",
       "      <td>675.838710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>15.740915</td>\n",
       "      <td>633.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ohio</td>\n",
       "      <td>11.479121</td>\n",
       "      <td>606.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>oklahoma</td>\n",
       "      <td>16.333261</td>\n",
       "      <td>858.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>oregon</td>\n",
       "      <td>8.912115</td>\n",
       "      <td>484.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>10.381176</td>\n",
       "      <td>688.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>rhode island</td>\n",
       "      <td>10.396715</td>\n",
       "      <td>361.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>south carolina</td>\n",
       "      <td>17.903491</td>\n",
       "      <td>625.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>tennessee</td>\n",
       "      <td>15.039594</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>texas</td>\n",
       "      <td>19.039727</td>\n",
       "      <td>760.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>virginia</td>\n",
       "      <td>13.844752</td>\n",
       "      <td>434.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>washington</td>\n",
       "      <td>8.457315</td>\n",
       "      <td>499.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>wisconsin</td>\n",
       "      <td>7.361267</td>\n",
       "      <td>601.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state  mean_temp  mean_duration\n",
       "0                alabama  17.982018     433.000000\n",
       "1                arizona  16.309824     430.000000\n",
       "2             california  15.080564     746.863636\n",
       "3               colorado   7.952455     537.000000\n",
       "4            connecticut  10.438182     474.500000\n",
       "5   district of columbia  13.077236     496.000000\n",
       "6                florida  22.267382     588.200000\n",
       "7                georgia  18.372406     829.500000\n",
       "8               illinois  12.046570     588.333333\n",
       "9                indiana  12.024376     520.666667\n",
       "10                  iowa   9.959170     504.666667\n",
       "11                kansas  13.357152     350.000000\n",
       "12              kentucky  13.994261     521.000000\n",
       "13              maryland  13.285170     619.000000\n",
       "14         massachusetts   9.095218     561.833333\n",
       "15              michigan   7.956539     482.600000\n",
       "16             minnesota   5.986612     659.500000\n",
       "17              missouri  13.473812     762.500000\n",
       "18              nebraska  10.275570    1089.000000\n",
       "19            new jersey  11.931370     549.166667\n",
       "20            new mexico  12.803370     470.000000\n",
       "21              new york   8.635958     675.838710\n",
       "22        north carolina  15.740915     633.333333\n",
       "23                  ohio  11.479121     606.142857\n",
       "24              oklahoma  16.333261     858.000000\n",
       "25                oregon   8.912115     484.500000\n",
       "26          pennsylvania  10.381176     688.000000\n",
       "27          rhode island  10.396715     361.000000\n",
       "28        south carolina  17.903491     625.000000\n",
       "29             tennessee  15.039594     431.000000\n",
       "30                 texas  19.039727     760.923077\n",
       "31              virginia  13.844752     434.000000\n",
       "32            washington   8.457315     499.500000\n",
       "33             wisconsin   7.361267     601.666667"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the two datasets consisting of the means of interest\n",
    "merged_df = pd.merge(left=state_mean_temp, right=state_mean_dur, how='left', left_on='state', right_on='state')\n",
    "\n",
    "# Remove any observations with null values and reset indexing\n",
    "merged_df = merged_df.dropna()\n",
    "merged_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Ht9zZg1rFcLn",
    "outputId": "58ce490a-89a5-4d3d-cddb-89271a18848d"
   },
   "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>state</th>\n",
       "      <th>mean_temp</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alabama</td>\n",
       "      <td>17.982018</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arizona</td>\n",
       "      <td>16.309824</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>california</td>\n",
       "      <td>15.080564</td>\n",
       "      <td>746.863636</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>colorado</td>\n",
       "      <td>7.952455</td>\n",
       "      <td>537.000000</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>connecticut</td>\n",
       "      <td>10.438182</td>\n",
       "      <td>474.500000</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>district of columbia</td>\n",
       "      <td>13.077236</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>florida</td>\n",
       "      <td>22.267382</td>\n",
       "      <td>588.200000</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>georgia</td>\n",
       "      <td>18.372406</td>\n",
       "      <td>829.500000</td>\n",
       "      <td>GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>illinois</td>\n",
       "      <td>12.046570</td>\n",
       "      <td>588.333333</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>indiana</td>\n",
       "      <td>12.024376</td>\n",
       "      <td>520.666667</td>\n",
       "      <td>IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>iowa</td>\n",
       "      <td>9.959170</td>\n",
       "      <td>504.666667</td>\n",
       "      <td>IA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>kansas</td>\n",
       "      <td>13.357152</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>kentucky</td>\n",
       "      <td>13.994261</td>\n",
       "      <td>521.000000</td>\n",
       "      <td>KY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>maryland</td>\n",
       "      <td>13.285170</td>\n",
       "      <td>619.000000</td>\n",
       "      <td>MD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>massachusetts</td>\n",
       "      <td>9.095218</td>\n",
       "      <td>561.833333</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>michigan</td>\n",
       "      <td>7.956539</td>\n",
       "      <td>482.600000</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>minnesota</td>\n",
       "      <td>5.986612</td>\n",
       "      <td>659.500000</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>missouri</td>\n",
       "      <td>13.473812</td>\n",
       "      <td>762.500000</td>\n",
       "      <td>MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>nebraska</td>\n",
       "      <td>10.275570</td>\n",
       "      <td>1089.000000</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>new jersey</td>\n",
       "      <td>11.931370</td>\n",
       "      <td>549.166667</td>\n",
       "      <td>NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>new mexico</td>\n",
       "      <td>12.803370</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>new york</td>\n",
       "      <td>8.635958</td>\n",
       "      <td>675.838710</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>15.740915</td>\n",
       "      <td>633.333333</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ohio</td>\n",
       "      <td>11.479121</td>\n",
       "      <td>606.142857</td>\n",
       "      <td>OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>oklahoma</td>\n",
       "      <td>16.333261</td>\n",
       "      <td>858.000000</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>oregon</td>\n",
       "      <td>8.912115</td>\n",
       "      <td>484.500000</td>\n",
       "      <td>OR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>10.381176</td>\n",
       "      <td>688.000000</td>\n",
       "      <td>PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>rhode island</td>\n",
       "      <td>10.396715</td>\n",
       "      <td>361.000000</td>\n",
       "      <td>RI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>south carolina</td>\n",
       "      <td>17.903491</td>\n",
       "      <td>625.000000</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>tennessee</td>\n",
       "      <td>15.039594</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>texas</td>\n",
       "      <td>19.039727</td>\n",
       "      <td>760.923077</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>virginia</td>\n",
       "      <td>13.844752</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>washington</td>\n",
       "      <td>8.457315</td>\n",
       "      <td>499.500000</td>\n",
       "      <td>WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>wisconsin</td>\n",
       "      <td>7.361267</td>\n",
       "      <td>601.666667</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state  mean_temp  mean_duration code\n",
       "0                alabama  17.982018     433.000000   AL\n",
       "1                arizona  16.309824     430.000000   AZ\n",
       "2             california  15.080564     746.863636   CA\n",
       "3               colorado   7.952455     537.000000   CO\n",
       "4            connecticut  10.438182     474.500000   CT\n",
       "5   district of columbia  13.077236     496.000000   DC\n",
       "6                florida  22.267382     588.200000   FL\n",
       "7                georgia  18.372406     829.500000   GA\n",
       "8               illinois  12.046570     588.333333   IL\n",
       "9                indiana  12.024376     520.666667   IN\n",
       "10                  iowa   9.959170     504.666667   IA\n",
       "11                kansas  13.357152     350.000000   KS\n",
       "12              kentucky  13.994261     521.000000   KY\n",
       "13              maryland  13.285170     619.000000   MD\n",
       "14         massachusetts   9.095218     561.833333   MA\n",
       "15              michigan   7.956539     482.600000   MI\n",
       "16             minnesota   5.986612     659.500000   MN\n",
       "17              missouri  13.473812     762.500000   MO\n",
       "18              nebraska  10.275570    1089.000000   NE\n",
       "19            new jersey  11.931370     549.166667   NJ\n",
       "20            new mexico  12.803370     470.000000   NM\n",
       "21              new york   8.635958     675.838710   NY\n",
       "22        north carolina  15.740915     633.333333   NC\n",
       "23                  ohio  11.479121     606.142857   OH\n",
       "24              oklahoma  16.333261     858.000000   OK\n",
       "25                oregon   8.912115     484.500000   OR\n",
       "26          pennsylvania  10.381176     688.000000   PA\n",
       "27          rhode island  10.396715     361.000000   RI\n",
       "28        south carolina  17.903491     625.000000   SC\n",
       "29             tennessee  15.039594     431.000000   TN\n",
       "30                 texas  19.039727     760.923077   TX\n",
       "31              virginia  13.844752     434.000000   VA\n",
       "32            washington   8.457315     499.500000   WA\n",
       "33             wisconsin   7.361267     601.666667   WI"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append new column holding the two letter abbreviations for each state\n",
    "merged_df['code'] = merged_df['state'].map(us_state_dict)\n",
    "\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "id": "QzmGlHQKZxDd",
    "outputId": "95cf1d89-8083-4f5c-c931-37910d520c9e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Exercise Duration by State (Min)')"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set figure size\n",
    "plt.figure(figsize=(16, 8))\n",
    "\n",
    "# Plot scatterplot\n",
    "ax = sns.regplot(x = \"mean_temp\", y = \"mean_duration\", data = merged_df)\n",
    "\n",
    "# Label plots and axes\n",
    "ax.set_title('Relationship Between Average Temperature and Average Exercise Duration')\n",
    "plt.xlabel('Average Temperature by State (°C)')\n",
    "plt.ylabel('Average Exercise Duration by State (Min)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NuoXxDvM1ewg"
   },
   "source": [
    "Looking at the scatterplot generated above, we can see that there is a very weak positive correlation between average exercise duration by state and average temperature by state. We can make further observations by using an ordinary least squares model and observe the exact value of the R squared value to see how well fitted the data is with the trend line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-TVveb7z039j",
    "outputId": "0e1bae0c-a11c-430d-f2f2-b52e99ce666a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          mean_duration   R-squared:                       0.015\n",
      "Model:                            OLS   Adj. R-squared:                 -0.016\n",
      "Method:                 Least Squares   F-statistic:                    0.4908\n",
      "Date:                Mon, 15 Mar 2021   Prob (F-statistic):              0.489\n",
      "Time:                        23:34:41   Log-Likelihood:                -218.81\n",
      "No. Observations:                  34   AIC:                             441.6\n",
      "Df Residuals:                      32   BIC:                             444.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    521.5588     93.687      5.567      0.000     330.725     712.393\n",
      "mean_temp      4.9612      7.081      0.701      0.489      -9.463      19.386\n",
      "==============================================================================\n",
      "Omnibus:                       12.656   Durbin-Watson:                   1.880\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               13.244\n",
      "Skew:                           1.139   Prob(JB):                      0.00133\n",
      "Kurtosis:                       5.040   Cond. No.                         46.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# By convention, the first matrix is the \"outcome\" or \"y\" data, and the second is the \"predictor\" or \"x\" data\n",
    "# Try to predict exercise duration from temperature of state\n",
    "outcome_1, predictors_1 = patsy.dmatrices('mean_duration ~ mean_temp' ,data=merged_df)\n",
    "mod_1 = sm.OLS(outcome_1, predictors_1)\n",
    "res_1 = mod_1.fit()\n",
    "# Print out the summary results of the model fitting\n",
    "print(res_1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ADzgq0sKEVSn"
   },
   "source": [
    "Our null hypothesis is that there is no relationship between temperature and exercise duration. Since the R-squared value of 0.015 is close to 0, the model is not a good fit for our data. Since the p-value of 0.489 is higher than alpha=0.1, temperature does not significantly predict exercise duration. Since p val > alpha, then we fail to reject null hypothesis (aka accept the null hypothesis), which in this case means there is no relationship between temperature and exercise duration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4Ub5CSve2PLQ"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5H0kkonORRDP"
   },
   "source": [
    "**Mean Duration of Exercise by State and Mean Temperature by State Visualizations**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ltFeUmQ1kiJu"
   },
   "source": [
    "Below, there are two choropleth visualizations of the mean temperature per state and the mean duration of exercise per state. We want to use these visualizations to compare them geographically and see if a relationship between duration of exercise and temperature exists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "j4y3NSjgk140",
    "outputId": "e12a2a48-b2f0-45d1-a7e2-1700ab09b384"
   },
   "outputs": [
    {
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\"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Mean Duration of Exercise (Min) in the USA\"}},\n",
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     "metadata": {
      "tags": []
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   ],
   "source": [
    "#Create choropleth for mean temperature\n",
    "mean_temp_viz = go.Figure(data=go.Choropleth(\n",
    "    #takes state abbreviations and translates them into spatial locations\n",
    "    locations = merged_df['code'],\n",
    "    z = merged_df['mean_temp'].astype(float),\n",
    "    locationmode = 'USA-states',\n",
    "    colorscale = 'magma',\n",
    "    colorbar_title = '°C',\n",
    "))\n",
    "\n",
    "mean_temp_viz.update_layout(\n",
    "    title_text = 'Mean temperature (°C) in the USA',\n",
    "    geo_scope='usa'\n",
    ")\n",
    "\n",
    "#Create choropleth for mean duration \n",
    "mean_dur_viz = go.Figure(data=go.Choropleth(\n",
    "    #takes state abbreviations and translates them into spatial locations\n",
    "    locations = merged_df['code'],\n",
    "    z = merged_df['mean_duration'].astype(float),\n",
    "    locationmode = 'USA-states',\n",
    "    colorscale = 'algae',\n",
    "    colorbar_title = 'Minutes'\n",
    "))\n",
    "\n",
    "#Changing title and layout\n",
    "mean_dur_viz.update_layout(\n",
    "    title_text = 'Mean Duration of Exercise (Min) in the USA',\n",
    "    geo_scope='usa'\n",
    ")\n",
    "\n",
    "#Show plots\n",
    "mean_temp_viz.show()\n",
    "mean_dur_viz.show()\n",
    "\n",
    "# Choropleth visuals do not properly appear on Jupyter Notebook when pushed onto GitHub. Please check the Choropleth_Fig_1.pdf file for the proper image renderings.\n",
    "# Code documentation referenced from https://plotly.com/python/choropleth-maps/"
   ]
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    "id": "Q_TLtGSKwesI"
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   "source": [
    "From these two choropleths, we can compare the mean temperature and mean duration of exercise per week. States are removed from both plots for consistency, since in the dataset relating to exercise (dataset 2), there are some states with no data to analyze. Looking at the mean temperature plot, the coldest states are clustered towards the center of the top of the U.S. as expected. You can see states like Minnesota having one of the coldest average temperatures. Looking at the plot regarding the mean duration of exercise per week in the USA, it is much harder to see a clear pattern. We had hoped to be able to see some patern by comparing the two graphs, but were unable to find one from these plots. This gives evidence to the conclusion that there is possibly no relationship between the duration of exercise in each state and the temperature of each state."
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "9coGarYJRuWN"
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   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SW0UiyZfyY95"
   },
   "source": [
    "**Ratio between Mean Exercise Duration per State / Temperature per State**\n",
    "\n",
    "We chose to turn mean duration per state and mean annual temperatures per state into a ratio in order to better and more accurately see the differences of temperature increases on exercise duration. A ratio helps us determine the slope relationship between exercise duration change for every 1°C temperature rise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
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     "height": 1000
    },
    "id": "9hkol-vEnUdN",
    "outputId": "65a278e4-76c1-4f9a-d47b-cb7388525f2d"
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>mean_temp</th>\n",
       "      <th>mean_duration</th>\n",
       "      <th>code</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alabama</td>\n",
       "      <td>17.982018</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>AL</td>\n",
       "      <td>24.079611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arizona</td>\n",
       "      <td>16.309824</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>AZ</td>\n",
       "      <td>26.364478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>california</td>\n",
       "      <td>15.080564</td>\n",
       "      <td>746.863636</td>\n",
       "      <td>CA</td>\n",
       "      <td>49.524915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>colorado</td>\n",
       "      <td>7.952455</td>\n",
       "      <td>537.000000</td>\n",
       "      <td>CO</td>\n",
       "      <td>67.526321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>connecticut</td>\n",
       "      <td>10.438182</td>\n",
       "      <td>474.500000</td>\n",
       "      <td>CT</td>\n",
       "      <td>45.458108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>district of columbia</td>\n",
       "      <td>13.077236</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>DC</td>\n",
       "      <td>37.928503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>florida</td>\n",
       "      <td>22.267382</td>\n",
       "      <td>588.200000</td>\n",
       "      <td>FL</td>\n",
       "      <td>26.415319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>georgia</td>\n",
       "      <td>18.372406</td>\n",
       "      <td>829.500000</td>\n",
       "      <td>GA</td>\n",
       "      <td>45.149231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>illinois</td>\n",
       "      <td>12.046570</td>\n",
       "      <td>588.333333</td>\n",
       "      <td>IL</td>\n",
       "      <td>48.838246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>indiana</td>\n",
       "      <td>12.024376</td>\n",
       "      <td>520.666667</td>\n",
       "      <td>IN</td>\n",
       "      <td>43.300931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>iowa</td>\n",
       "      <td>9.959170</td>\n",
       "      <td>504.666667</td>\n",
       "      <td>IA</td>\n",
       "      <td>50.673568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>kansas</td>\n",
       "      <td>13.357152</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>KS</td>\n",
       "      <td>26.203192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>kentucky</td>\n",
       "      <td>13.994261</td>\n",
       "      <td>521.000000</td>\n",
       "      <td>KY</td>\n",
       "      <td>37.229548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>maryland</td>\n",
       "      <td>13.285170</td>\n",
       "      <td>619.000000</td>\n",
       "      <td>MD</td>\n",
       "      <td>46.593308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>massachusetts</td>\n",
       "      <td>9.095218</td>\n",
       "      <td>561.833333</td>\n",
       "      <td>MA</td>\n",
       "      <td>61.772387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>michigan</td>\n",
       "      <td>7.956539</td>\n",
       "      <td>482.600000</td>\n",
       "      <td>MI</td>\n",
       "      <td>60.654510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>minnesota</td>\n",
       "      <td>5.986612</td>\n",
       "      <td>659.500000</td>\n",
       "      <td>MN</td>\n",
       "      <td>110.162474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>missouri</td>\n",
       "      <td>13.473812</td>\n",
       "      <td>762.500000</td>\n",
       "      <td>MO</td>\n",
       "      <td>56.591260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>nebraska</td>\n",
       "      <td>10.275570</td>\n",
       "      <td>1089.000000</td>\n",
       "      <td>NE</td>\n",
       "      <td>105.979525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>new jersey</td>\n",
       "      <td>11.931370</td>\n",
       "      <td>549.166667</td>\n",
       "      <td>NJ</td>\n",
       "      <td>46.027127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>new mexico</td>\n",
       "      <td>12.803370</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>NM</td>\n",
       "      <td>36.709086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>new york</td>\n",
       "      <td>8.635958</td>\n",
       "      <td>675.838710</td>\n",
       "      <td>NY</td>\n",
       "      <td>78.258688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>15.740915</td>\n",
       "      <td>633.333333</td>\n",
       "      <td>NC</td>\n",
       "      <td>40.234848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ohio</td>\n",
       "      <td>11.479121</td>\n",
       "      <td>606.142857</td>\n",
       "      <td>OH</td>\n",
       "      <td>52.803943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>oklahoma</td>\n",
       "      <td>16.333261</td>\n",
       "      <td>858.000000</td>\n",
       "      <td>OK</td>\n",
       "      <td>52.530846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>oregon</td>\n",
       "      <td>8.912115</td>\n",
       "      <td>484.500000</td>\n",
       "      <td>OR</td>\n",
       "      <td>54.364199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>pennsylvania</td>\n",
       "      <td>10.381176</td>\n",
       "      <td>688.000000</td>\n",
       "      <td>PA</td>\n",
       "      <td>66.273803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>rhode island</td>\n",
       "      <td>10.396715</td>\n",
       "      <td>361.000000</td>\n",
       "      <td>RI</td>\n",
       "      <td>34.722506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>south carolina</td>\n",
       "      <td>17.903491</td>\n",
       "      <td>625.000000</td>\n",
       "      <td>SC</td>\n",
       "      <td>34.909393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>tennessee</td>\n",
       "      <td>15.039594</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>TN</td>\n",
       "      <td>28.657689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>texas</td>\n",
       "      <td>19.039727</td>\n",
       "      <td>760.923077</td>\n",
       "      <td>TX</td>\n",
       "      <td>39.965020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>virginia</td>\n",
       "      <td>13.844752</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>VA</td>\n",
       "      <td>31.347619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>washington</td>\n",
       "      <td>8.457315</td>\n",
       "      <td>499.500000</td>\n",
       "      <td>WA</td>\n",
       "      <td>59.061297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>wisconsin</td>\n",
       "      <td>7.361267</td>\n",
       "      <td>601.666667</td>\n",
       "      <td>WI</td>\n",
       "      <td>81.734122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state  mean_temp  mean_duration code       ratio\n",
       "0                alabama  17.982018     433.000000   AL   24.079611\n",
       "1                arizona  16.309824     430.000000   AZ   26.364478\n",
       "2             california  15.080564     746.863636   CA   49.524915\n",
       "3               colorado   7.952455     537.000000   CO   67.526321\n",
       "4            connecticut  10.438182     474.500000   CT   45.458108\n",
       "5   district of columbia  13.077236     496.000000   DC   37.928503\n",
       "6                florida  22.267382     588.200000   FL   26.415319\n",
       "7                georgia  18.372406     829.500000   GA   45.149231\n",
       "8               illinois  12.046570     588.333333   IL   48.838246\n",
       "9                indiana  12.024376     520.666667   IN   43.300931\n",
       "10                  iowa   9.959170     504.666667   IA   50.673568\n",
       "11                kansas  13.357152     350.000000   KS   26.203192\n",
       "12              kentucky  13.994261     521.000000   KY   37.229548\n",
       "13              maryland  13.285170     619.000000   MD   46.593308\n",
       "14         massachusetts   9.095218     561.833333   MA   61.772387\n",
       "15              michigan   7.956539     482.600000   MI   60.654510\n",
       "16             minnesota   5.986612     659.500000   MN  110.162474\n",
       "17              missouri  13.473812     762.500000   MO   56.591260\n",
       "18              nebraska  10.275570    1089.000000   NE  105.979525\n",
       "19            new jersey  11.931370     549.166667   NJ   46.027127\n",
       "20            new mexico  12.803370     470.000000   NM   36.709086\n",
       "21              new york   8.635958     675.838710   NY   78.258688\n",
       "22        north carolina  15.740915     633.333333   NC   40.234848\n",
       "23                  ohio  11.479121     606.142857   OH   52.803943\n",
       "24              oklahoma  16.333261     858.000000   OK   52.530846\n",
       "25                oregon   8.912115     484.500000   OR   54.364199\n",
       "26          pennsylvania  10.381176     688.000000   PA   66.273803\n",
       "27          rhode island  10.396715     361.000000   RI   34.722506\n",
       "28        south carolina  17.903491     625.000000   SC   34.909393\n",
       "29             tennessee  15.039594     431.000000   TN   28.657689\n",
       "30                 texas  19.039727     760.923077   TX   39.965020\n",
       "31              virginia  13.844752     434.000000   VA   31.347619\n",
       "32            washington   8.457315     499.500000   WA   59.061297\n",
       "33             wisconsin   7.361267     601.666667   WI   81.734122"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df['ratio'] = merged_df['mean_duration'] / merged_df['mean_temp']\n",
    "# for every one degree increase in temperature, the average duration of the workout increases by the ratio variable amount\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8N8AwigIylYd"
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   "source": [
    "The following turns our ratio data into a choropleth visualization, which allows us to geographically see the differences in ratio for each state."
   ]
  },
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     "metadata": {
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     "output_type": "display_data"
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   ],
   "source": [
    "ratio_vis = go.Figure(data=go.Choropleth(\n",
    "    # takes the state abbreviations and translates them into spatial locations\n",
    "    locations = merged_df['code'], \n",
    "    # Prepares the data to be same data type and color codes\n",
    "    z = merged_df['ratio'].astype(float),\n",
    "    # matches our state locations to the data for each state\n",
    "    locationmode = 'USA-states',\n",
    "    colorscale = 'Reds',\n",
    "    colorbar_title = \"Minute / 1°C\",\n",
    "))\n",
    "\n",
    "# sets up our visualization and layout\n",
    "ratio_vis.update_layout(\n",
    "    title_text = 'Exercise Duration (Min) per Temperature (°C) in the U.S.A.',\n",
    "    # limits our map scope to USA\n",
    "    geo_scope='usa', \n",
    ")\n",
    "\n",
    "ratio_vis.show()\n",
    "\n",
    "# Choropleth visuals do not properly appear on Jupyter Notebook when pushed onto GitHub. Please check the Choropleth_Fig_2.pdf file for the proper image renderings.\n",
    "# Code documentation referenced from https://plotly.com/python/choropleth-maps/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4FuGpJ_fyvi1"
   },
   "source": [
    "This choropleth map gives us a visualization of each state's status regarding exercise duration and outdoor temperature in 2020. You can see from above that states that have darker reds (Minnesota at a ratio of 110.2 duration/temp and Nebraska at a ratio of 106 duration/temp, for example) represent the highest amount of exercise per 1°C increase. While no concrete conclusions can be drawn from this visual, we still can note the anomalies of darker colored Northern (and colder) states versus lighter (and warmer) colored Southern states (Florida at a ratio of 24.1 duration/temp and Alabama at a ratio of 26.4 duration/temp, for example). Obviously, many other factors influence these findings, which will be explored further in our data analysis below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qoGEijGSB87D"
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "No8bZltqCK-c"
   },
   "source": [
    "**Ratio between Percent Adults Physically Fit per State / Temperature per State**"
   ]
  },
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   "execution_count": 41,
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    "id": "yzlr-jyICBnA",
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       "      <th>state</th>\n",
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       "    <tr>\n",
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       "      <td>pennsylvania</td>\n",
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       "      <td>2.466002</td>\n",
       "      <td>PA</td>\n",
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       "      <th>6</th>\n",
       "      <td>indiana</td>\n",
       "      <td>12.024376</td>\n",
       "      <td>(15.1, 1.16)</td>\n",
       "      <td>1.255782</td>\n",
       "      <td>IN</td>\n",
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       "    <tr>\n",
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       "      <td>texas</td>\n",
       "      <td>19.039727</td>\n",
       "      <td>(23.5, 0.7)</td>\n",
       "      <td>1.234261</td>\n",
       "      <td>TX</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tennessee</td>\n",
       "      <td>15.039594</td>\n",
       "      <td>(17.1, 1.3)</td>\n",
       "      <td>1.136999</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>wisconsin</td>\n",
       "      <td>7.361267</td>\n",
       "      <td>(23.6, 1.44)</td>\n",
       "      <td>3.205970</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>maryland</td>\n",
       "      <td>13.285170</td>\n",
       "      <td>(22.9, 1.43)</td>\n",
       "      <td>1.723727</td>\n",
       "      <td>MD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>connecticut</td>\n",
       "      <td>10.438182</td>\n",
       "      <td>(24.5, 1.62)</td>\n",
       "      <td>2.347152</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>new york</td>\n",
       "      <td>8.635958</td>\n",
       "      <td>(18.9, 0.83)</td>\n",
       "      <td>2.188524</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>florida</td>\n",
       "      <td>22.267382</td>\n",
       "      <td>(21.1, 0.93)</td>\n",
       "      <td>0.947574</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>minnesota</td>\n",
       "      <td>5.986612</td>\n",
       "      <td>(27.7, 1.4)</td>\n",
       "      <td>4.626991</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>iowa</td>\n",
       "      <td>9.959170</td>\n",
       "      <td>(22.2, 1.41)</td>\n",
       "      <td>2.229101</td>\n",
       "      <td>IA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>new mexico</td>\n",
       "      <td>12.803370</td>\n",
       "      <td>(23.0, 1.71)</td>\n",
       "      <td>1.796402</td>\n",
       "      <td>NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>oregon</td>\n",
       "      <td>8.912115</td>\n",
       "      <td>(25.8, 1.58)</td>\n",
       "      <td>2.894936</td>\n",
       "      <td>OR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>missouri</td>\n",
       "      <td>13.473812</td>\n",
       "      <td>(23.6, 1.41)</td>\n",
       "      <td>1.751546</td>\n",
       "      <td>MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>michigan</td>\n",
       "      <td>7.956539</td>\n",
       "      <td>(23.6, 0.94)</td>\n",
       "      <td>2.966114</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>new jersey</td>\n",
       "      <td>11.931370</td>\n",
       "      <td>(21.0, 1.19)</td>\n",
       "      <td>1.760066</td>\n",
       "      <td>NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>district of columbia</td>\n",
       "      <td>13.077236</td>\n",
       "      <td>(30.7, 1.74)</td>\n",
       "      <td>2.347591</td>\n",
       "      <td>DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>north carolina</td>\n",
       "      <td>15.740915</td>\n",
       "      <td>(22.4, 0.85)</td>\n",
       "      <td>1.423043</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>massachusetts</td>\n",
       "      <td>9.095218</td>\n",
       "      <td>(29.5, 1.56)</td>\n",
       "      <td>3.243463</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>alabama</td>\n",
       "      <td>17.982018</td>\n",
       "      <td>(19.3, 1.41)</td>\n",
       "      <td>1.073294</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>rhode island</td>\n",
       "      <td>10.396715</td>\n",
       "      <td>(25.4, 1.99)</td>\n",
       "      <td>2.443079</td>\n",
       "      <td>RI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>washington</td>\n",
       "      <td>8.457315</td>\n",
       "      <td>(28.9, 1.34)</td>\n",
       "      <td>3.417160</td>\n",
       "      <td>WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>south carolina</td>\n",
       "      <td>17.903491</td>\n",
       "      <td>(14.8, 1.26)</td>\n",
       "      <td>0.826654</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>oklahoma</td>\n",
       "      <td>16.333261</td>\n",
       "      <td>(19.0, 1.41)</td>\n",
       "      <td>1.163270</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>kansas</td>\n",
       "      <td>13.357152</td>\n",
       "      <td>(23.2, 1.48)</td>\n",
       "      <td>1.736897</td>\n",
       "      <td>KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>nebraska</td>\n",
       "      <td>10.275570</td>\n",
       "      <td>(23.9, 1.67)</td>\n",
       "      <td>2.325905</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>arizona</td>\n",
       "      <td>16.309824</td>\n",
       "      <td>(26.3, 1.48)</td>\n",
       "      <td>1.612525</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>georgia</td>\n",
       "      <td>18.372406</td>\n",
       "      <td>(20.2, 1.18)</td>\n",
       "      <td>1.099475</td>\n",
       "      <td>GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>kentucky</td>\n",
       "      <td>13.994261</td>\n",
       "      <td>(14.6, 1.18)</td>\n",
       "      <td>1.043285</td>\n",
       "      <td>KY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state  mean_temp  percent_pass     ratio code\n",
       "0               colorado   7.952455  (32.5, 1.49)  4.086789   CO\n",
       "1             california  15.080564  (24.0, 0.53)  1.591452   CA\n",
       "2               illinois  12.046570  (24.8, 0.81)  2.058677   IL\n",
       "3                   ohio  11.479121  (23.9, 1.02)  2.082041   OH\n",
       "4               virginia  13.844752  (24.2, 1.28)  1.747955   VA\n",
       "5           pennsylvania  10.381176  (25.6, 1.25)  2.466002   PA\n",
       "6                indiana  12.024376  (15.1, 1.16)  1.255782   IN\n",
       "7                  texas  19.039727   (23.5, 0.7)  1.234261   TX\n",
       "8              tennessee  15.039594   (17.1, 1.3)  1.136999   TN\n",
       "9              wisconsin   7.361267  (23.6, 1.44)  3.205970   WI\n",
       "10              maryland  13.285170  (22.9, 1.43)  1.723727   MD\n",
       "11           connecticut  10.438182  (24.5, 1.62)  2.347152   CT\n",
       "12              new york   8.635958  (18.9, 0.83)  2.188524   NY\n",
       "13               florida  22.267382  (21.1, 0.93)  0.947574   FL\n",
       "14             minnesota   5.986612   (27.7, 1.4)  4.626991   MN\n",
       "15                  iowa   9.959170  (22.2, 1.41)  2.229101   IA\n",
       "16            new mexico  12.803370  (23.0, 1.71)  1.796402   NM\n",
       "17                oregon   8.912115  (25.8, 1.58)  2.894936   OR\n",
       "18              missouri  13.473812  (23.6, 1.41)  1.751546   MO\n",
       "19              michigan   7.956539  (23.6, 0.94)  2.966114   MI\n",
       "20            new jersey  11.931370  (21.0, 1.19)  1.760066   NJ\n",
       "21  district of columbia  13.077236  (30.7, 1.74)  2.347591   DC\n",
       "22        north carolina  15.740915  (22.4, 0.85)  1.423043   NC\n",
       "23         massachusetts   9.095218  (29.5, 1.56)  3.243463   MA\n",
       "24               alabama  17.982018  (19.3, 1.41)  1.073294   AL\n",
       "25          rhode island  10.396715  (25.4, 1.99)  2.443079   RI\n",
       "26            washington   8.457315  (28.9, 1.34)  3.417160   WA\n",
       "27        south carolina  17.903491  (14.8, 1.26)  0.826654   SC\n",
       "28              oklahoma  16.333261  (19.0, 1.41)  1.163270   OK\n",
       "29                kansas  13.357152  (23.2, 1.48)  1.736897   KS\n",
       "30              nebraska  10.275570  (23.9, 1.67)  2.325905   NE\n",
       "31               arizona  16.309824  (26.3, 1.48)  1.612525   AZ\n",
       "32               georgia  18.372406  (20.2, 1.18)  1.099475   GA\n",
       "33              kentucky  13.994261  (14.6, 1.18)  1.043285   KY"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the two datasets consisting of percent of adults who met federal guidelines and mean temperature of each state\n",
    "merged_df2 = pd.merge(left=state_mean_temp, right=data_phys[[\"state\", \"adults 18-64\"]], how='left', left_on='state', right_on='state')\n",
    "\n",
    "# Align rows with previous obsercations\n",
    "merged_df2 = pd.merge(left=state_mean_dur, right=merged_df2, how='left', left_on='state', right_on='state')\n",
    "merged_df2 = merged_df2.drop(['mean_duration'], axis = 1)\n",
    "merged_df2.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Remove any observations with null values and reset indexing\n",
    "merged_df2 = merged_df2.dropna()\n",
    "merged_df2.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Create new column containing the ratio of the existing\n",
    "merged_df2['ratio'] = merged_df2['adults 18-64'].str[0] / merged_df2['mean_temp']\n",
    "\n",
    "# Append new column holding the two letter abbreviations for each state\n",
    "merged_df2['code'] = merged_df2['state'].map(us_state_dict)\n",
    "\n",
    "# Rename columns\n",
    "merged_df2.columns = ['state', 'mean_temp', 'percent_pass', 'ratio', 'code']\n",
    "\n",
    "merged_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "id": "NF5S999_M5FO",
    "outputId": "82ac383d-c4a7-4169-9096-d0b065f1f0ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Temperature by State (°C)')"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T/a71R9/Xsvjr+7Kkl0e18K9UuGGxs9pG3L3f3d/j7qdH+7pb0jcrviNjLDT/vt5CF4KBaF+13oepXLuMe12q+F5PYNy5sIZa32+gbghsgenpVbjrGQ8yutz9w7FlvGL5idKO9Ep6ScW22j306Z2MTzL/UUmrbPzgVSdImsq2pdCkyiTdbaHv0S9j0yVpp8LFTnzbcVmFWpayWoNGjHst7p539w+4+5MUmo++THUeLMZCX77PKlxcHhMFJb/RoX6ok72/8fJmFWpA36LqgW2tz3nK+5lgu+srttvh7j9TxecTXRytmuK6Yy8ttv5zFAKdV0paEr1fB1TRb7feogvOrynUil+i2Pvr7rvc/U3ufrxCs8lPWawvXWSPpBGFJvOVXi3pDxVq945SqBWRpvaafizpQqs9ONxBxb4DFvraTdTsr/Lz6lRoBlo2k3PFRHoVamyXxra5KLqwnur7K4VAz6KmjtX8k6S/iW4MPkWhe8ODCkH1sdHrTSnUsE/U//AyhWDp4eh89PVo/VdH83dqfNPt+HvZplCr+FFJy6Nj93s6gmPX3Qfd/Z3uvlqhlv5/m9nzy7OnsakvSXqrwuA82Yp5vZL+u+Kz7nb3t0Tz/0OhW8Uqdz9KYTyAen4PeyV9sGL/ne5e2VVhOip/K8pNr6dyXMff18m+s9U+g8l+iyrXme53bUkUuB72+qJ1fq7Q5WPc+asWd9+jcLwer1DTW+11/X00/SnuvkjhHBk/Dqq9rsmuXeJ+rdDUfDI/UhiDo2uS5Z6oib/fQN0Q2ALTU74D+6Lo7nG7hQEoVk6w/HckHWdm7zCzNjPrsUOpKj4t6YNRkCUzOzbq+zgVuyWdVOPC+pcKP+jvsjDQzrkKfaEqa8oOY6Hf5CsVmhefEft7u6RXRxehX5P012a2JHrtb6/YzN3RskkLg109d5LXMhb8m9l5ZvaUKBAYUGgSVZe+jDFdCj/8j0X7fL2iwcFiZVppsQFqJvFehaZ6D1aZV+tzfkzhtR1JztVPK3wG5cFTjjKzcuqm70o63cJgJSmFpqArprhuNT0KNWaPSUqZ2ZUK/RVnw7UKTWovUOzC0MxeEfve9St8nuOOk6jFwhckfdzMjo+Ox2dGwU6PQmC3V+HC9++nUaaPKzQd/JKFQVzMQgqbM2LL3KfQQuAPLLQ4+BtJE+Vq/U9JLzOzZ0fH3FUa//s8k3NFVVHN0Y2SPmYhXUciei3PjfYx6fsbbSencHF72HfczF6g0MzzO9Gk30l6XnTctelQrfTZkh5098NafkQB8/MVbnCVz0VPk/QPOnTD62uSrjCzx1kYQOfdsU1kon09JqlgYfCseL/HKbMwqNITohtFBxRq8srvybjzWC3u/juF96vawE7fkXSqhcHE0tHfWXaon2yPpH3uPmJhQK5XV9nGTHxW0pujmmEzs67oGJ5K7d1E/q+ZdUaf++sllQcim+5xPdl3ttpnMJ3foiMpkyR9wMwy0Q3AlynceCm7VuGm4FMUWvdUZWb/YGZPNrNU9F6/RaEbwV5V/53oUegudSD6jvxVxSYr34vpXrt8T5O/V9Kh0Zy/YWHQs4SZHWNhQK2XRq+tXaFf7k1T2B4wIwS2wDS4e6/CHeP3KvzY9Cr8oFT9LnlIa/EChaByl0LfqvOi2Vcr3Hm/0cwGFQaPmGp+xvIP514zu7PKfnPRPl+iUGv1KYX+a1unsO0LFZqTXRvV2uzyMJDPFxT6Pr1YoTnhQwoXqjfq8DvRV0T7Lzej+2aN/X1I0t9YaB71lwoB2H8qBLX3KozMOKU73VPl7vdI+pjC3fTdChcdt8YWuVmhr9MuM9szhe096u4T5b2d8HOOams+KOnW6PWfM43XcIPCxf31Fpqi/Ubh8y7f8X+FQp+tvQqDrdw6lXUn8EOFAdDuU/jcR1TR3LNR3P1WhYu6OysCn7Mk/dLMhhTe3yu8eu7av1QYqOp2hSbX/6Dwfb1W4bU8otBc9hfTKNMeSecovA8/Vehbe7fCxeZbomUOKNTKfS7ax0GFfs7VtrdF0tsUauN2KgSS8WVncq6o5VKFwO+eaJ//qUPNCqf6/kqhCfMl8QnRzYN/VDgXlL1dIXD4kaS3unsxmv6aaHo1l0i6291vrDgf/T9JTzWzJysEYzcq1DLdpXBRXpBUjM7Bf64Q/PYrBIIbar4rEzslKvuQwrnjU+5+SzSv8jxWk7v/1EOXgsrpgwqB90UKNX+7FI7Z8k2Rt0q6KjoOrtShwbTqwt03KwzG9c8K79cDOjR41pH672g7P5b0UXe/MZo+3eN6su/s5xW6Luw3s/JvznR+i46kTLsU3qdHFZrIv7nid/YGRc2bq9TOx3VGy+5XGMPiRIWbeRP9TnxAYaC0Awo3MiuD5nHH43SvXRTe65daNIL8RNx9VKEGfatC4Dqg0K97qQ619Hq5Qh/yw453oN7MfSYt4QAAaCwzu1nSf7j755pdFlRnIcXZn7n7XdNcb5lC4PN0H9/XcSZleYnCgE8n1mN7ODJmdpLCzc+0T54zfd4ys98qdP34UbPLMh1m9veS+tz9EzPczi8VBiv8TX1KBkyMwBYAMGeZ2VkKNQGrotosYJyoVuk8hVrb5Qp9an/h7u9oasEWOAJbycz+l0KN+6lep/RgACaWanYBAACoxsz+XaFp/BUEtajBFJpmflWhG8V3FZrpAk1jZhsV0sxdQlALzA5qbAEAAAAALY3BowAAAAAALY3AFgAAAADQ0uZVH9ulS5f6SSed1OxiAAAAAADq7I477tjj7sdWmzevAtuTTjpJmzdvbnYxAAAAAAB1ZmYPTTSPpsgAAAAAgJZGYAsAAAAAaGkEtgAAAACAlkZgCwAAAABoaQS2AAAAAICWRmALAAAAAGhpBLYAAAAAgJZGYAsAAAAAaGkNC2zNbJWZ3WJm95jZFjO7Ipr+j2a21cx+bWY3mNniCdZ/0Mz+x8zuNrPNjSonAAAAAKC1NbLGtiDpne7+JEnnSHqbmT1J0k2SnuzuT5V0n6S/rrGN89z9DHdf28ByAgAAAABaWMMCW3ff6e53Ro8HJd0r6XHufqO7F6LFfiFpZaPKAAAAAACY/2alj62ZnSTp6ZJ+WTHrDZK+P8FqLulGM7vDzC5vXOkAAAAAAK0s1egdmFm3pG9Ieoe7D8Sm/x+F5srXTbDqs939ETNbJukmM9vq7puqbP9ySZdL0gknnFD38gMAAAAA5raG1tiaWVohqL3O3f8rNv11kl4m6TXu7tXWdfdHov99km6QdPYEy33G3de6+9pjjz22zq8AAAAAADDXNXJUZJP0eUn3uvvHY9NfLOldki5w9+wE63aZWU/5saQXSvpNo8oKAAAAAGhdjWyK/CxJl0j6HzO7O5r2Xkn/T1KbQvNiSfqFu7/ZzI6X9Dl3f6mk5ZJuiOanJP2Hu/+ggWUFgHlh49Y+XbNpu3r7s1q1pFPr163WuWuWNbtYAAAADWUTtARuSWvXrvXNm0l5C2Bh2ri1T1du2KJ00tSRTmo4X1S+6LrqgtMJbgEAQMszszsmSgU7K6MiAwAa75pN25VOmjozKZmF/+mk6ZpN25tdNAAAgIYisAWAeaK3P6uOdHLctI50Ujv6qw5nAAAAMG8Q2ALAPLFqSaeG88Vx04bzRa1c0tmkEgEAAMwOAlsAmCfWr1utfNGVzRXkHv7ni67161Y3u2gAAAANRWALAPPEuWuW6aoLTteynnYdGM5rWU87A0cBAIAFoZHpfgAAs+zcNcsIZAEAwIJDjS0AAAAAoKUR2AIAAAAAWhqBLQAAAACgpRHYAgAAAABaGoEtAAAAAKClEdgCAAAAAFoagS0AAAAAoKUR2AIAAAAAWhqBLQAAAACgpRHYAgAAAABaGoEtAAAAAKClEdgCAAAAAFoagS0AAAAAoKUR2AIAAAAAWhqBLQAAAACgpRHYAgAAAABaGoEtAAAAAKClEdgCAAAAAFoagS0AAAAAoKUR2AIAAAAAWhqBLQAAAACgpRHYAgAAAABaGoEtAAAAAKClEdgCAAAAAFoagS0AAAAAoKUR2AIAAAAAWhqBLQAAAACgpRHYAgAAAABaGoEtAAAAAKClEdgCAAAAAFoagS0AAAAAoKUR2AIAAAAAWhqBLQAAAACgpRHYAgAAAABaGoEtAAAAAKClEdgCAAAAAFoagS0AAAAAoKUR2AIAAAAAWhqBLQAAAACgpRHYAgAAAABaGoEtAAAAAKClEdgCAAAAAFoagS0AAAAAoKUR2AIAAAAAWhqBLQAAAACgpRHYAgAAAABaWsMCWzNbZWa3mNk9ZrbFzK6Iph9tZjeZ2f3R/yUTrH9ZtMz9ZnZZo8oJAAAAAGhtjayxLUh6p7s/SdI5kt5mZk+S9B5JP3b3UyT9OHo+jpkdLel9kp4h6WxJ75soAAYAAAAALGwNC2zdfae73xk9HpR0r6THSfpDSf8eLfbvki6ssvqLJN3k7vvcvV/STZJe3KiyAgAAAABa16z0sTWzkyQ9XdIvJS13953RrF2SlldZ5XGSemPPd0TTqm37cjPbbGabH3vssbqVGQAAAADQGhoe2JpZt6RvSHqHuw/E57m7S/KZbN/dP+Pua9197bHHHjuTTQEAAAAAWlBDA1szSysEtde5+39Fk3eb2XHR/OMk9VVZ9RFJq2LPV0bTAAAAAAAYp5GjIpukz0u6190/Hpu1QVJ5lOPLJH2ryuo/lPRCM1sSDRr1wmgaAAAAAADjNLLG9lmSLpH0PDO7O/p7qaQPS3qBmd0v6fzoucxsrZl9TpLcfZ+kv5V0e/R3VTQNAAAAAIBxLHRznR/Wrl3rmzdvbnYxAAAAAAB1ZmZ3uPvaavNmZVRkAAAAAAAahcAWAAAAANDSCGwBAAAAAC2NwBYAAAAA0NIIbAEAAAAALY3AFgAAAADQ0ghsAQAAAAAtbV4FtsWSq1AsNbsYAAAAAIBZlGp2AeqpUHI9vC+rzkxK3e0pdWWSMrNmFwsAAAAA0EDzKrAty+YKyuYKSiZM3W0hyG1LJZtdLAAAAABAA8zLwLasWHIdGM7rwHBebemkuttS6mlLKZGgFhcAAAAA5ot5HdjGjeaLGs0Xte9gTl2ZpHra0+rIUIsLAAAAAK1uwQS2Ze6uodGChkYLSicTY02V08l5NY4WAAAAACwYCy6wjcsXS+rP5tSfzakjqsVlwCkAAAAAaC0LOrCNG84VNZwrKmGm7vaUuttSak/TVBkAAAAA5joC2woldw0M5zUwnFcmlVBPW1rd7SklGXAKANBEG7f26ZpN29Xbn9WqJZ1av261zl2zrNnFAgBgTqBjaQ25Qkl7D47q4X1Z9Q2MKJsrNLtIAIAFaOPWPl25YYv6Bke0uCOtvsERXblhizZu7Wt20QAAmBMIbKegPODUrgMjenhvVvsO5pQrlJpdLADAAnHNpu1KJ02dmZTMwv900nTNpu3NLhoAAHMCTZGnqVAqaX82p/3Z3Fhu3O42mioDABqntz+rxR3pcdM60knt6M82qUQAAMwtBLYzEM+N25kJQW4noyoDAOps1ZJO9Q2OqDNz6Gd7OF/UyiWdTSwVAABzB02R68DddXC0oN0DI3p4X1aPDY5qJF9sdrEAAPPE+nWrlS+6srmC3MP/fNG1ft3qZhcNAIA5gRrbOiuWXIMjeQ2O5JVKJNTZliR1EABgRs5ds0xXKfS13dGf1UpGRQYAYBwC2wYqlEoaGC5pYDivZCIM9hGC3ATNlQEA03LummUEsgAATIDAdpbEa3KTCVNH1Ce3I02fXAAAAACYCQLbJiiWXEMjBQ2NFJQwU2cmqc62lLoYeAoAAAAApo3AtslKUY7codFClJswqc5MUl2ZlBKkEAIAAACASRHYziHl0ZUPjha0x3LqSCfV1UaQCwAAAAC1ENjOUeV0DtlcCHK7Mkl1t6fG5TAEAAAAABDYtgSPNVdOJRLqakuqpz2tTIo0xAAAAABAYNtiCqWSDgyXdGA4r0wqoZ72tLrbUkrSVBkAAADAAkVg28JyhZL2Do1q38GcOqP0QZ2MrAwAAABggSGwnQfig04lE6buthRNlYE62Li1T9ds2q7e/qxWLenU+nWrde6aZc0uFgAAACoQ+cwzxZLrwHBeO/qzemT/sA4M57eGVNIAACAASURBVFUsebOLBbScjVv7dOWGLeobHNHijrT6Bkd05YYt2ri1r9lFAwAAQAUC23lsNF/U3qFRPbwvq76BEWVzhWYXCWgZ12zarnTS1JlJRTmmU0onTdds2t7sogEAAKACTZEXgMpRlbvbU+ppTymd5L4GMJHe/qwWd6THTetIJ7WjP9ukEgEAAGAiRDYLTKFU0v5sTr37snp0/7AGRvIq0VQZOMyqJZ0azhfHTRvOF7VySWeTSgQAAICJENguYCP5ovYMRk2VB0c0UnERDyxk69etVr7oyuYKcg//80XX+nWrm100AAAAVKApMlRy19BIQUMjoalyZ1tSXZmUOjLJZhcNaJpz1yzTVQp9bXf0Z7WSUZEBAADmLAJbjFMolTQwXNLAcF7JRBgwp6stqY40+XGx8Jy7ZhmBLAAAQAsgsMWEiiXX4EhegyN5JczGanI7MwS5AAAAAOYOAltMSby5csJMnZmkOttS6kwnlUgQ5GJyG7f26ZpN29Xbn9UqmvUCAACgjghsMW2lWPogM1NHOjlWm5skyEUVG7f26coNW5ROmhZ3pNU3OKIrN2zRVRLBLQAAAGZsQQW2t23fp+tv79XOgWEdt6hDF521SmevPrrZxWpp5dFis7mC9mhU7ekQ4Ha1JZUiTy4i12zarnQy9NmWpM5MStlcQdds2k5gCwAAgBlbMJHHbdv36eqb79feg6Na1J7S3oOjuvrm+3Xb9n3NLtq8MpIvau/BkEJo54FhDZInF5J6+7PqSI8fZbsjndSO/myTSgQAAID5ZMEEttff3qtUIjSbNYX/qYTp+tt7m120eWs4V9Rjg6N6aF9WfQMjyuYKzS4SmmTVkk4NV+RJHs4XtXJJZ5NKBAAAgPlkwQS2OweG1Z4e/3Lb0wntGhhuUokWDo/65O46MKKH92a1d2hUo4Xi5Cti3li/brXyxdBsvdx8PV90rV+3utlFAwAAwDywYALb4xZ1aCRfGjdtJF/SikUdTSrRwlQolXRgOK9H+oe1oz+rA8N5FWmqPO+du2aZrrrgdC3radeB4byW9bTrqgtOp38tAAAA6mLBDB510VmrdPXN92s4X1R7OqGRfEmFkuuis1Y1u2gLVq5Q0t6hUe07mFNXJqme9rQ6MsnJV0RLOnfNMgJZAAAANMSCCWzPXn20rtApuv72Xu0aGNYKRkWeMzyWPiidTKi7LaXu9pTSjKoMAAAAYAoWTGArheCWQHZuyxdL6s/m1J/NqSOqxe3KJGVGflwAAAAA1S2owBatZThX1HCuqGTC1NWWUk97Sm0pmioDAAAAGK9hga2ZfUHSyyT1ufuTo2lflXRatMhiSfvd/Ywq6z4oaVBSUVLB3dc2qpyY+4ol18BwXgPDeWVSCfW0p9XTllIiQS0uAAAAgMbW2H5R0j9LurY8wd1fVX5sZh+TdKDG+ue5+56GlQ4tiQGnAAAAAFRqWGDr7pvM7KRq8yx0mHylpOc1av+Y36oNONXTnlKKAacAAACABadZUcBzJO129/snmO+SbjSzO8zs8lksF1pQecCph/dltevAiIZGC3InNy4AAACwUDRr8KiLJX2lxvxnu/sjZrZM0k1mttXdN1VbMAp8L5ek41eSk3ahy+YKyuYKSiYsqsVNK5OiFhcAAACYz2b9it/MUpL+WNJXJ1rG3R+J/vdJukHS2TWW/Yy7r3X3tUcfs7TexUWLKpZcB4bz2tGf1SP7hzUwklepRC0uAAAAMB81oyrrfElb3X1HtZlm1mVmPeXHkl4o6TezWD7MM6P5ovYMjurhfVn1DY5oJF9sdpEAAAAA1FHDAlsz+4qkn0s6zcx2mNkbo1kXqaIZspkdb2bfi54ul/RTM/uVpNskfdfdf9CocmLhKLlraKSgR/cPq3dfVvuzORWKpWYXCwAAAMAM1exja2bPlPRahcGejpM0rFB7+l1JX3b3CdP1uPvFE0x/XZVpj0p6afR4u6SnTa34wJHJF0vadzCn/mxenZmkuttS6swkFQbsBgAAANBKJgxszez7kh6V9C1JH5TUJ6ld0qmSzpP0LTP7uLtvmI2CAo3g7jo4WtDB0YISZupsS6orQ5ALAAAAtJJaNbaXuPueimlDku6M/j5mZozWhHmj3FR5aCQKcjNJdbal1JlOKpEgyAUAAADmqlqB7WIzO83db41PNLNnSdrl7r+tEvgC80LJXUOjBQ2NFmRm6kgn1dWWVGcmpSRBLgAAADCn1Bo86hOSBqpMH4jmAQuCuyubK+ixaGTlnQeGdWA4z8BTAAAAwBxRq8Z2ubv/T+VEd/8fMzupYSUC5jB313CuqOFcUXsltaWT6s6k1NmWVDrZjOxZAAAAAGo2Ra4xr6PeBQFa0Wi+qNF8UXsPSplUIgw81ZZUWyrZ7KIBAAAAC0atKqbNZvamyolm9qeS7mhckYDWlCuU1J/N6ZH+kCd3z9CohnNFuXuziwYAAADMa7VqbN8h6QYze40OBbJrJWUk/VGjCwa0snyxpPxwSQPD+bERljsyDD4FAAAANMKEga2775b0+2Z2nqQnR5O/6+43z0rJgHkiPsKyNKr2dFKdmaTa0+EPAAAAwMzUqrGVJLn7LZJumYWyAAvCSL6okXxRkpRM2FiA25FOKpNiACoAAABguiYMbM3sFZJeI8klXe/uX521UgELRLHkOjha0MHRgiQpnUyEIDcTAl2aLQMAAACTq1Vj+25JZ0ePb5dEYAs0WL5YUr5Y0uBIXlIYabkjCnTbU0klCHQBAACAw9QKbL8s6dro8ddnoSwAKuQKJeUKJR0YzsvM1BYLdNtSCZkR6AIAAAC1Bo/6hJl1STJ3H5rFMgGowt3H+uf2ZyUzU3s6HugyEBUAAAAWplp9bM3dD9ZaOVqGJJ1AE7i7hnNFDeeK0kEGogIAAMDCVasp8i1m9g1J33L3h8sTzSwj6dmSLlMYLfmLDS0hgCmpHIgqlUioPRPV6KaTSiUJdAEAADA/1QpsXyzpDZK+YmYnS9ovqV1SUtKNkj7h7nc1vogAjkShVNLQSElDI4dGXC6PttyRZiAqAAAAzB+1+tiOSPqUpE+ZWVrSUknD7r5/tgoHoH7yxZLywyUNRANRlUdc7mQgKgAAALS4WjW2Y9w9L2lng8sCYJa4u0bzRY3mi9ofDURVrsltzyQYiAoAAAAtZUqBLYD5zd2VzRWUzRXGBqIKQW5SnfTPBQAAwBxHYAvgMMWSa2i0oKHRQ/1zOzNJ3fFgv/7tZw+qtz+rVUs6tX7dap27ZlmTSwsAqLRxa5+u2bSd8zWABWNK1TBmdqKZnR897jCznsYWC8Bcki+WdNOW3Xrft7fokf1ZdWeS2jUwrCs3bNHGrX3NLh4AIGbj1j5duWGL+gZHtLgjrb7BEc7XAOa9SQNbM3uTpP+UdE00aaWkbzayUEfq4X1ZffLmB3TjPbv18N6sSqTYBerm+tt7lUqY2lNJlVxKJxKSXJ+8+QEN54oipTUAzA3XbNqudNLUmUnJLPxPJ03XbNre7KIBQMNMpSny2ySdLemXkuTu95vZnGzLMpIv6oa7Hhl73plJ6tTl3TpteY9OWxH+VixqZ/RX4AjsHBjWovbxp4y2VEKP7M9q54Hh0C83k1RXJkU6IQBoot7+rBZ3pMdN60gntaM/26QSAUDjTSWwHXX3XDkYNLOUpDlZNXN0V0a/d8Jibds9pKHRgrK5ou7uPaC7ew+MLbOoPaU1K3p06ooenba8R2tW9OiY7rYmlhpoDcct6tDeg6PqSB8aMXkkX9KKRR2Son65IwUNjRQOjbKcSaorw+BTADCbVi3pVN/giDozhy7zhvNFrVzS2cRSAUBjTSWw/W8ze6+kDjN7gaS3Svp2Y4t1ZJZ2t+kfX/E0ubse2T+sbbuGtG33gLbtGtT9u4c0UihpYKSg2x7s120P9sfWy4Qa3XLN7vIeLaq40wksdBedtUpX33y/hvNFtacTGsmXVCi5Ljpr1WHLxkdZ3ispk0qEmtxMUu1pUgkBQCOtX7daV27YomyuoI50UsP5ovJF1/p1q5tdNABoGJusX5yZJSS9UdILJZmkH7r7Z2ehbNP2lDPO9G/dtKnqvGLJ9dDeg9q2e0jbdg1q265B/faxIRVK1V//8YvbxzVhPnVZjzoyXJBjYbtt+z5df3uvdg0Ma8WiDl101iqdvfroaW0jnUyoqy2l7raUMilqcgGgEcqjIu/oz2oloyIDmCfM7A53X1t13hQC2yvc/erJps0FtQLbanKFkn6356C2RoHufbsH9eDeg6oW65qkE47pDM2YoybMjz+2mwtzYAba0kl1R0Fukj65AAAAqGGmge2d7n5mxbS73P3pdSxjXUw3sK1mOF/UA7uHtG13CHa37R7Ujv7hqssmE6bVS7vGmi+vWdGjE4/ppD8hME3lPrnd7Sl1ZZIM8AYAAIDD1ApsJ+xja2YXS3q1pJPNbENsVo+kffUt4tzRkU7qKSuP0lNWHjU2bWikoPt2D2prVKu7ddeg+gZHVSy57u8b0v19Q/qOdkoKfQmfcGz3WBPmNct7tPLoDiW4UAcmFO+TmzBTZ1tSi9rT9McFAADAlNQaPOpnknZKWirpY7Hpg5J+3chCzTXd7SmdeeISnXnikrFp+w7mdF+sVnfbrkH1Z/PKFUq6Z+eA7tk5MLbs+LRDi7RmRY+WL2qjVgqoouSHRldOJxOhqXJ7SmlaQgAAAGACkzZFbiX1aIp8pNxdjw2OauvuQd23qxzwhrRD1RzVkdZpy0PN7qmkHQIm1ZEJ/XG7Mily5AIAACxAM+1je46kT0p6oqSMpKSkg+6+qN4FnalmBrbV1Eo7VM3S7sy4kZhJOwQcrtxUuactzUjlAAAAC8gR9bGN+WdJF0n6uqS1ki6VdGr9ijd/mZlWLunUyiWdev4TwxD7xZLr4X3ZsZGYt+0e1PbHhpQvuvYM5bRnaK9u/e3esW0cd1T7uJGYT1nePS7hOrDQxJsqJxM2ljqI/rgAAAAL11RqbDe7+1oz+7W7PzWaNm9HRW6GctqhsZGYd5F2CJiuVCKhrrakughyF7xy/s7e/qxWkb8TAIB5Y6ZNkTdJOl/S5yTtUhhQ6nXu/rR6F3SmWjWwrWYkX9QDfUPjanYnSjuUSphOJu0QMCadTKirLaWutqTaUgS5C8nGrX26csMWpZMhhdRwvqh80XXVBacT3AIA0OJmGtieKGm3Qv/av5B0lKR/cfff1rugMzWfAttqJko7VE057dCaFT06lbRDWMDKIyt3taVo2bAAXPyZX6hvcGRcl41srqBlPe36yuXnNLFkAABgpmbax/ZCd79a0oikD0QbvELS1fUrIqaCtEPA9OWLJfVnc+rP5pRJHQpySR80P/X2Z7W4YtC9jnRSO/qzTSoRAACYDVMJbC/T4UHs66pMQxMc3ZXROauP0Tmrj5E0Pu3Qtl1R6qEo7VA2V9TdvQd0d++BsfXjaYdOW9GjNSsW6eiuTLNeDtBQuUJJ+wo57TuYU1s6qe5MSp1tSYLceWTVks7DamyH80WtXNLZxFIBAIBGmzCwNbOLJb1a0slmtiE2a5GkfY0uGI6MmWnZonYtW9SudaccK6l22qEDw3nd9mC/bnuwf2wbS7szUZAbBqgi7RDmo9F8UaP5ovYeDANPtacTas8k1Z5K0mS5ha1ft1pXbtiibK4wro/t+nWrm100AADQQBP2sY361p4s6UOS3hObNSjp1+5eaHzxpme+97Gtp3jaoft2DWprLO1QNccvbj+UY3c5aYcwvyUTpvZ0MvpLMABViymPiryjP6uVjIoMAMC8MaPBo2IbOUbSOkkPu/sddSxf3RDYzsyRpB0qB7ukHcJ8ljAbC3Lb00m1pRL0TQcAAJhlRxTYmtl3JL3H3X9jZsdJulPSZkmPl/QZd/9Eowp8pAhs6286aYeSUdqhNVGt7mkrenQSaYcwD5mZMqmE2lOJsZrdZIJAFwAAoJGONLDd4u6nR4/fK2mNu19qZj2SbnX3pzasxEeIwHZ2xNMOlWt3STuEhS6TSqgjnVRnJqX2NDW6AAAA9Xak6X7yscfPl/RZSXL3QTMr1bF8aDG10g6Va3bv2z1x2qGuTFKnLO+JRmMm7RDmh1yhpFw0IJuZqT2dUGc6pfYMfXQBAAAarVZg22tmb5e0Q9KZkn4gSWbWIYkhcjFOtbRDfYOj4/rrbts9qIOjRR3MFXV3737d3bt/bP3KtEOnLe/RMd1tzXo5wIy4u4ZzRQ3nilJ51OVMQp2ZlDrTSSVotgwAAFBXtQLbN0q6StL5kl7l7uUo5BxJ/9bogqG1mZmWL2rX8ljaoZK7Ht0/rG27Qs3ufbunkHaoPBLzCtIOoXUVSiUNjZQ0NFIYV5tLDl0AAID6mPKoyK2APratJ552qFyrWyvt0HFHtYfBqUg7hHkinUyoqy2lzkwYhAoAAADV1SXdTysgsJ0fymmHyrW6pB3CQpFMmDoySZosAwAAVEFgi5Y3nC/qgd1Dh/rsTpJ2aPXSrrFa3TUrenTS0i7SsaClmJnaUgl1ZVLqyCS5WQMAABa8GQW2ZnaMu+9tSMnqjMB2YRkcyeu+3UNjgW6ttENtqYQeH6UdKge8pB1CK0knE1FtblId6SSjiKMhNm7t0zWbtqu3P6tVSzq1ft1qnbtmWbOLBQCApJkHtvdLulthwKjv+xSreM3sC5JeJqnP3Z8cTXu/pDdJeixa7L3u/r0q675Y0tWSkpI+5+4fnso+CWwXhtu279P1t/dq58CwjlvUoYvOWqWzVx8taXzaoXIz5v5svup2SDuEVlWuzW1PhyCXvLmoh41b+3Tlhi1KJ00d6aSG80Xli66rLjid4BYAMCfMNLA1hZGR3yDpLElfk/RFd79vkvXWSRqSdG1FYDvk7h+tsV5S0n2SXqCQauh2SRe7+z01CyoC24Xgtu37dPXN9yuVCCPLjuRLKpRcVzzvlLHgNq5W2qFq4mmHTo2aMZN2CHNdOdANQS6BLo7MxZ/5hfoGR8YNyJfNFbSsp11fufycJpYMAICgVmA76XCyUQ3tTZJuMrPzJH1Z0lvN7FeS3uPuP59gvU1mdtIRlPdsSQ+4+/ao8NdL+kNJkwa2mP+uv71XqUSoTZA0Vqtw/e29VQPb2mmHhrRt94C27SLtEFqbu2skX9RIPtywIaUQjkRvf1aLK85tHemkdvRnm1QiAACmbtLA1syOkfRaSZdI2i3p7ZI2SDpD0tclnTzNff6ZmV0qabOkd7p7f8X8x0nqjT3fIekZ09wH5qmdA8Na1D7+sG1PJ7RroPpAUtUkzLRySadWLunU858YmtfVSju0ZyinPUN7detvD3U1H5d2aEWPTl3Wo44MqVowN7i7hnNFDeeK2ntQyqQSYaRlUgqhhlVLOg+rsR3OF7VySWcTSwUAwNRMJQHozyV9SdKF7r4jNn2zmX16mvv7V0l/K8mj/x9TaOJ8xMzsckmXS9LxK1fNZFNoAcct6tDeg6NjNbaSNJIvacWijhltN5kwnby0Sycv7dJLnrxC0qG0Q/FmzOW0QzsPjGjngRHdsi10Fy+nHVoT1eieRtohzCG5Qkm5Qk77s1IqEQah6mpjECqMt37dal25YYuyucK4Prbr161udtEAAJjUlPrYurub2SKFlsmDU954aIr8nXIf26nMM7NnSnq/u78oev7XCjv+0GT7o4/t/DfdPrb1NpIv6oG+oXE1uxOlHUpFwTJphzBXlZssd6ST6sgk1ZaiNnehK4+KvKM/q5WMigwAmGNmOnjUWoURkXsUKqb2S3qDu98xhR2fpFjwambHufvO6PFfSHqGu19UsU5KYfCo50t6RGHwqFe7+5bJ9kdguzCUR0XeNTCsFRWjIjfD0EhhbCTmydIOZVIJPYG0Q5ijklH/9Y4opVCKvrkAAGAOmWlg+2tJb3P3n0TPny3pU+7+1EnW+4qkcyUtVeib+77o+RkKTZEflLTe3Xea2fEKaX1eGq37UkmfUEj38wV3/+BUXiiBLeaKctqheI7dydIOrYmNxEzaIcwF6WRCnZmkutpS9M0FAABNN9PA9i53f3rFtDvd/cw6lrEuCGwxV7m7Hhsc1dYoyL1v16C27R7S0Gih6vLxtEPlml3SDqGZ0smEutpS6m5L0XccAAA0xREFtmZWDlwvldQh6SsKNa2vkjTi7v+7AWWdEQJbtJJDaYcO1eqW0w5Vs7Q7o9NWHKrZJe0QmiWTSqi7LaWuthSphAAAwKw50sD2lhrbdHd/Xj0KV08Etmh1h6Ud2jWo3z42pEKp+vf0+MXt43LsknYIs60tnVRXhsGnAABA482oKXIrIbDFfFROO1QOdu/bfSjtUKV42qFyf13SDmG2lAefao8Gn6I2FwAA1BOBLTDPDOeLemD30KEcuzXSDiUTptUVaYdOPKaTEW/RcOlkYmyE5Y50UglSXQEAgBkgsAUWgHjaofL/qaQdOnVFj9aQdggNZmZqS4VRlmm2DAAAjkStwDY124UB0Bjd7SmdeeISnXnikrFpE6UdyhVKumfngO7ZOTC2bDntUBiNeRFph1BX7q6RfFEj+aJ0UEolQm1uZ4baXAAAMHNTSffTKemdkk5w9zeZ2SmSTnP378xGAaeDGlugtpmmHVqzYpGO7srMcqkx35mZ2tOJ0D83+gMAAKg00zy2X5V0h6RL3f3JUaD7M3c/o/5FnRkCW2D63F2PkHYIcwiDUAEAgGpm2hT58e7+KjO7WJLcPWu0TQTmDTPTyiWdWrmkU89/4nJJIe3QQ3sPatvuocPSDu0ZymnPA3t16wN7x7ZRmXbolGXd6szQ0wFHplhyDY0WxloSpJMJtaeTYwNRJWm2DAAAKkzlyjNnZh2SXJLM7PGSqo9IA2BeSCZMq4/t1upju/WSJ6+QVDvt0KP7R/To/hHdsu0xSaQdQn3liyXliyUNjuQljQ9021MJRvgGAABTCmzfJ+kHklaZ2XWSniXpdY0sFIC5J5NKjNXIlk2UdsglPbQ3q4f2ZvXDLbslHZ526LQVPTqJtEM4AtUC3bZYH12aLgMAsPDU7GNrZglJfyLpx5LOUaiI+YW775md4k0PfWyB5juStENjg1ORdgh1kEok1NmWVFcmpfZ0gpG9AQCYJ2Y6eNTmiVaeawhsgbmpP5sb66sbTztUTWcmqVOXd0e1uot02opurVjUTnCCI5JMWJRWKKVO0goBANDSZhrYfljSHklflXSwPN3d99WzkPVAYAu0hsq0Q+WA9+BoserylWmHTlveo2O622a51Gh1ZmG05c62pDrTSZrBAwDQYmYa2P6uymR399X1KFw9EdgCravkrkejtEPlZsxTSTs0NhozaYcwTeW+ue3ppNpTSQY3AwBgjptRYNtKCGyB+aVW2qFqKtMOnbqsRx2Z5CyXGq0qmbCxILctnVBbiv65AADMJTOtsb202nR3v7YOZaur7sed6uf/9b/porNW6ezVRze7OAAaIJ526L6oKXM57VAl0g5hJsxMbakQ4LalSS0EAECzzTSw/WTsabuk50u6093/pH5FrI/FJ6zxM//8X5Uvuq582ZP0rFOWqlBylUo+9j9XLClfdM2nmmpgoZso7VA1yYTp5KVdWkPaIRyBVCIxVpvblkqqLZVgQCoAAGZJXZsim9liSde7+4vrUbh6WnzCGn/OX31O2VxBy3ra9ZXLz5lw2XIexHyhHOyGv+IETRwBtJZ42qFywDtZ2qE1K3p0KmmHMA1mpkwq5NANeXRpvgwAQKPUCmxTR7C9g5JOnlmRGqsjndSO/mzNZdLJhNLJhJQZP71UcuVLIcAtlFyFoqtQfl4M06jtBea+7vaUzjxxic48ccnYtH0Hc+Ny7JbTDuUKJd2zc0D37BwYW7Yrk9Qpy3ui0ZgXac2KHi1f1EbQgnHcXaP5okbzRe1XCHTb04koyE3STxcAgFkyaWBrZt+WVI7kEpKeJOnrjSzUTA3ni1q5pPOI1k0kTG2J2oPNhKC3NBbohqC3NBYIF53gF5iLju7K6JzVx+ic1cdICkFJ3+DouPy65bRDB3NF3d27X3f37h9bn7RDmIy7azhX1HAupK5KWDQgVXp+NV3euLVP12zart7+rFYt6dT6dat17pplzS4WAGABm0of2+fGnhYkPeTuOxpaqiO0+IQ1/ntXfFr5ouuqC05v6o9sIerLmy+VlC+EoDcX/SfoBeauyrRD23YN6oE+0g6hfjJR/9xysNtqg5lt3NqnKzdsUToZ8gIP54tz4ncXADD/zXTwqH9w93dPNm0u6Fl5mr/syn+f83eO88VQ25srlg7V9Ea1vvTxBeae6aYdOu6o9jA4VRTonrK8W52ZI+n5gYUgmbCx2ty2KNhNzuFa3Ys/8wv1DY6MO6anMrYFAAAzNdPA9k53P7Ni2q/d/al1LGNdrF271jdv3tzsYsxIeQTnQinU+FY2cy6UqtcaAZhd8bRD26I+u5OlHSrX6pJ2CJNJJw+NvJyJUg7NlSbMz/6Hm7W4Iz2u77C768BwXj959/OaWDIAwHx3RINHmdlbJL1V0moz+3VsVo+kW+tbRJQlEqZMwpRR9Qtedz80sFUs6C2WfGxUZ2p9gcbLpBJjfW3LJko75JIe2pvVQ3uzuvGe3ZIOTzu0ZkWPTlraNadr6jB7yiP1D40Wxqalk4dqdMv5dZsxMNWqJZ2H1djOZGwLAADqYcIaWzM7StISSR+S9J7YrEF33zcLZZu2+VBjWw/ufqi2NxrROf68UHSV6OcLzIrBkbzu3z007bRD5WbMpB3CRMqphtpS42t3G40+tgCAZqlLHlszWyapvfzc3R+uT/Hqh8B26spNnovl9EbRQFeFIk2egUabKO1QNeW0Q2tW9OjU6D9phzCReH/dcrqhRjRhLo+KvKM/q5WMigwAmCUz7WP7ckkfl3S8pD5JJ0q6191Pr3dBZ4rAtn7Ktb7jBrqKAt98kaAXqKdaaYeqIe0QatjxFgAAIABJREFUpiOdjILc9KGaXQAAWtFMA9tfSXqepB+5+9PN7DxJr3X3N9a/qDNDYDs74kFvvlhSrpzaqFCiiTNQJ/G0Q+Vg9/7dpB3CzCXMxvXVbU/P7VGYAQAom2lgu9nd10YB7tPdvWRmv3L3pzWisDNBYNt85UGsygNZxUdzZmArYGZmlHZoRY9OXdajjgy1dfPJbdv36frbe7VzYFjHLerQRWet0tmrj572duKjMJdrdmnuDgCYa2Ya2P5I0oUKg0gtVWiOfJa7/369CzpTBLZzX3xgK/r2AjNXmXZo2+5BPTRJ2qE1sZpd0g61rtu279PVN9+vVMLUnk5oJB9Gyb/i/7N353Fy3Wed7z/PqaWrepNasiU5kWRbiWWFxMTxNmbICMWBsEwwmZlAHOCGZZg4XG4WGBjCZMYMznAnIdwwYTKAMiGQAGMHhgAmLElmbKEQSOQsdhI5WoxsI9uS2pYlq7trP+e5f5xT1dWt6k3dXev3/Xrp1VWnlv6VTkndT/1+v+d72zWXVNw2MzMyKWvs021XYyoREZHFrLawHQGKQAD8ELAB+AN3P7vWA10tFba9z33uLG8tKXzDxjFnuQ3PRAZVPXboyJkpjjXFDrWSbood2q3YoZ7yMx9/mLMzZfKZ2Vn4YjVk88gQ73/D2i+qmr+EeSgdkE6p2BURkfa5pBzb5IEp4JPu/iogAj66DuMTaajPEsS/p7VeMlnP7q03tlLhKzJXPpPiuu0buG77hsaxqVKVY/UlzE2xQ7XIOT45zfHJaeAUAEPpgBcpdmjdrNXy4VMXiozn5v4Yz2UCTl9o/SHGakXuFCshxcpsU7N0EJDLzC5hzqbWpwuziIjIUhYtbN09NLPIzDa4+/PtGpTIYtKpgHQKcpmlC996hm999leFrwyqsVyGG6+c4MYrJxrHFoodKtciHjl1gUdOXWjctx47FHdjHh/42KFLLU6blw+P59KcnSnzgfuP83ZWvnz4ivH8RTO2pWrEtvH8il/PpapFEdPliOlyrXEsO2+vrrowi4hIOyxnKfKfAa8APgPM1I+7+9vWd2grp6XIshz1wjde8hzNKXwVZSSDzN15ZqrMkaTIPXZ6iiPLjB2qL2MehNih1extXcvlw+u5x3YtBWZJsTubraslzCIicikueSly4hPJH5G+UJ/xbaVVfm/9shpbSb8zM7aM59gynmPvNZcDi8cOPV+scujxcxx6/FzjOeqxQ/U9u/0YO3TvgydJB9YoTvOZFMVqyL0PnlyyoFzL5cO37NrE27mGex88yekLRbatYlnzeorcKVVDStWQ54tVIF7C3DyjO5TWEmYREVmdJQtbd/+omeWBne5+tA1jEukYMyObtpbdP6OoqYNzMtvbvORZUUbSjwIztk8Ms31imFe/ZCsQxw7943OFeAlzMqt74plpqqHz7HSFZx89y+cene0v+IKNudl83T6IHVpNcbrWy4dv2bWp6wrZ5ahFEbVyxEx59lgmFVzUnGpQl7qLiMjKLVnYmtn3Ar8KZIGrzex64G53v329ByfSTYLAGApSDC3wr6Ze+Db29db3+Eazl0X6QSrppHz1ZSN898u2ARfHDh07M8XjSezQ0+dLPH2+xANHnwF6P3ZoNcXpHTfv4AP3H6dYDecsH77j5h3rOeSeUM9Anyber2tNS5gVOSQiIktZzh7bLwG3AQfc/RXJsa+7+8vaML4V0R5b6WbNGb6VWkQ5DClXI+3rlb7VHDtUL3aXih2qd2Hu5tih1e5trTee6ublw92qeb9uVsWuiHSJA0cm2X/wBCfPFdgxMcyde3exb8+WTg+rL602x/bz7n6rmX2lqbD9qrt/8zqMdVVU2EoviqJ4L2+5qmJX+t9UqcrxM9PxzG5T7FAr2XTAi5PYod3bxtjTRbFDKk67R3OxO5Q0p8qoOZWItMmBI5Pcdd9hMilr9Fyohs7dt79Uxe06WG3zqMNm9oNAysyuAd4G/N1aDlBkkAWBkQtSSXxR3GSnXuxWk67N1TCiUlPBK71vLJfhhisnuGEZsUOVFrFDw9kUu7eOJkuYOxc71Kt7W/tRc3MqkuZUqcAaHZjVnEpE1tP+gyfIpIzhbFxWDWfTFCo19h88ocK2zZZT2L4VeBdQBu4BPgW8ez0HJTLo5ha7s9zrBa9TrUWNPb21ZIlzpIxe6UGbRrLcumszt+7aDLSOHTp6Zprpco1CJeShk8/z0MnZaPUN+UyyhHk06cg8zqaRbKdejnSBMHJmyrU5zama83WzKTWnEpG1cfJcgY3zuv/nMymePFfo0IgG13K6IheAd5nZe+OrPrX+wxKRVswsmX0AWsSFNu/jDd0Jw9mGVrUovq7YIul2rWKH3J2nFosdeuw5Dj32XOM5Lh8dYve20UaDqt19GDskK1OpxStfpkrxdTMjkzIVuyKyKjsmhpmcKjVmbCHuMbF9YriDoxpMy9ljezPwEWAsOfQ88OPu/qV1HtuKaY+tyNLckyI3mhtb1HxdHZylF4SR88TZGY6emY4L3tNT/MMz09QWeP/2W+yQrL16sZtNxft0M+mATMrIBFrKLCKtaY9te622edRXgZ9y988m118J/IaaR4n0LxW/0qsqtYgTz05z9PT0RbFD8/V67JC0VzoIyKSNdJDM7maCi7aLiMhgqndFfvJcge3qiryuVlvYNrohNx37srvfsIZjXBMqbEXap7n4rYZNy52brqv4lW5Qjx2qL2E+ukjsUCowdl1i7FC9U/KpC0WuUKfkgRBY3KQqn0mp0BURaYPVFrb/FcgTN45y4A1ACfh9AHf/8pqOdhVU2Ip0l3rxW0v29s7O+M5eV/ErnTBdqs3pxHxkmbFD9WXM2yfmxg6tNttW+sP8Qjeb0hJmEZG1tNrC9oFFbnZ3v201g1tLKmxFeo+KX+kW9dih5gZV5wrVlvcdyaa4JpnRvXbbGH/44EmmStWLmodsHhni/W94ebtegnShdBCQThnpZK9uJh2QDoxMKljWSgAREZm1qhxbd3/V2g9JRCRWb9YSr+BrvYyvVfFbTS7XkpxfkdVaKnaoXvDOlENmKiEPnTzPQyfPNx6fMhjKpMilgyRD1Th9ofWSZxkctSiiFgEtPiMJzMim40ZVcRyRZnlFRC7VcmZsNwJvAq6iqRB297et68gugWZsRQZTPeaoFiUZv+FswauiV9ZS5M7TSezQkaTYPT45TbnW+n2WSRm3XL2JPdviyKFrFTsky1AvdOtfc+mAdEpNzUREVrsU+e+AzwNfAxo/ud39o2s5yLWgwlZE5nN3KmGcX1kveuPLKnhlbYSR8xcPn+J3/v6xxsqChQpduDh26Joto3OWMIu0kkkF5LOpxh5eLWMWkUG02sK2Kzsgt6LCVkSWq17wVsPZOKP6LG8YOdES/zeKzFfvinz6QpEtYzn2XnMZqVSwotih3cm+XcUOyVKyyZL3fCYudlXoisggWG1h+9PANPBJoNEy0t2fW+JxHwFeC0y6+8uSY+8DvheoAP8A/Ji7n2/x2MeBKSAEagsNfj4VtiKyVubEGIVOtWk/by1ylvq/U2S+lcQOpQPj6stG5hS7y40dksE0f/lyNhWQSRlmes+ISP9YbWH7U8AvA+eJ434g7oa8a4nH7SUuiD/WVNi+Brjf3Wtm9t7kiX6+xWMfB25y92cXHdw8KmxFpF1qyWxvveCtN7Kqd3VW4SvLsZLYoaF0wIu3jM5Zxjw/dkikWb05X73QrX/Vfl0R6VWr6ooM/FvgxSstMt39oJldNe/Yp5uufh54/UqeU0SkW6RTAekU5Bfo5DxnxjeJLKqFEdXkqyKMBGA0l+aGKye44cqJxrF67FC92K3HDpVrEYefvsDhpy807tscO1Sf2d06PqRZOgGSLRc1pzJvz3cqiRvKpmeLXXVjFpFet5zC9lGgsA7f+8eBjy9wmwOfNjMH9rv7hxZ6EjN7M/BmgJ07d675IEVELkUqMFJB66IXIIri2d5GdFGyxFmNraRV7NDkVHlOvu5isUMb8hmu3TramNW9dusYm0eHOvVypAvF+eAhpWo453gmFVxU8Go5s4j0iuUsRf4T4KXAA8zdY7tk3E8yY/vJ+lLkpuPvAm4C/qW3GICZvdDdnzKzLcBngLe6+8Glvp+WIotIP2ju5FypRY3LmuWVuvmxQ8fOTHH8zDSlBboxXzaabRS59a+KHZLlaCxnnlfwajmziHTCapci/2nyZ60G86PETaVe3aqoBXD3p5Kvk0lhfQuwZGErItIPzIyhdIqh9NwZ3zCZ0W0Uu2FEtRapg/MACszYPjHM9olhXv2SrUD8/nji7AxHz0zHs7qnp/iHZ6apRc6z0xWeffQsn3v0bOM5FDskyzFnOXPT9u/AbE6zqqG0ljOLSGctOWMLYGZ5YKe7H13Rk8+bsTWz7wLeD3ybuz+zwGNGgMDdp5LLnwHudve/Xur7acZWRAZRvZGVCl6Zr1KLeOzZmTn7dRU7JOspHdS7MxuZxnLmQB29RWRNrLYr8vcCvwpk3f1qM7ueuNC8fYnH3QPsAy4DzgC/CPwCMATUPzL+vLu/xcxeAHzY3b/HzHYBf5Lcngb+p7v/8jJepwpbEZEmtTC6aElzNVTH5kG3ktihVGDsumyksXxZsUNyqVKBkU727NaL3Yz28IrICq22sP0ScBtwwN1fkRz7+vx9s91Aha2IyOLq+3cbM7xJsyo1rBpsU6Uqx89Mc6SpQdVisUMvunyUPdsUOyRrI5MKSKcsnu1NLqvoFZFWVrvHturuz8/7j0W/AYmI9KDZ/bvE62cSUeSNZcz1YlcNqwbHWC6zaOxQfc/u+WIcO/TIqQs8ckqxQ7I24g/XAMKLbksHAZl0XPRmUrOzvplA+3lFZK4FC1sz+5fu/gngsJn9IJAys2uAtwF/164BiojI+gsCIxekyGXmNqxq3r9bDsOk6NVy5kGwYOzQmdlCV7FDst5qUUStAq2K3vry5nRg8eXACJKvqcBImal7s8gAWXApspl92d1vMLNh4F3Aa4h7S3wKeLe7l9o3zOXRUmQRkfXnHmfvNu/fLVVDNasaQO2OHTp04jnuffAkpy4UuWI8zx037+CWXZvW6uVIn0o1Ct+gcTnVXAAnl7XCQKT7XdIe23phu64jW2PrWdgeODLJ/oMnOHmuwI6JYe7cu4t9e7asy/cSEelF5VpIqRpRrsZfa5F2rQyixWKHWrliQ27Oft3dW8bIZ1MX3e/Qief4wP3HSQdGLhMk7zHn7bddo+JW1kTzsudsKr5cb3IlIt3hUgvbAvDoQk/q7t+8NsNbO+tV2B44Msld9x0mkzLymRTFakg1dO6+/aUqbkVEFlAN45ncUjWiXAu1hHmANccO1ZcwP7GM2KH6zO6LLh/lnX/8Nc7OlMk3LZcvVkM2jwzx/je8vH0vRgaOWTyj25zbm02+ikh7XWrzqMeA712fIfWW/QdPkElZI7h+OJumUKmx/+AJFbYi0jHdvpKkPtMxlouvuzvl2myDqnJNebuDIpsOGjOydfXYoSPJnt1jSeyQA0+cLfDE2QKfOnwGgHTSJGg4m6JSi8il46Iilwk4faF1VJHIWqlvv5jfPd7M4viidMBQKkUmbUlXZxW8Ip2wWGFbcfcn2jaSLnbyXIGN8/YA5TMpnjxX6NCIRGTQNa8k2ZjPMDlV4q77DnM3dFVx28zMyGUublBVqc3L261pGfMgyGdSXLd9A9dt39A4NlWqcqy+hLkpdqi+jPlCqcaFUg2IZ3YzqYDxfJrPPHKGa7eOsX2TYoekfdydSi1urjdNrXE8FdjszG59djelLs4i622xwvZzbRtFl9sxMczkVKkxYwvxJ83bJ4Y7OCoRGWT9tJKk/stfc/xQGM3m7JbDsNGZWUuZ+9tYLsONV05wY4vYof/zjUn+9tFnqYYRkYMDlTDi2ekK/+WvjgCzsUNxN+ZxxQ5JR4SRE0YhpercTs7pYLbYzSRZvSp4RdbOgoWtu/8/7RxIN7tz7y7uuu8whUptzh7bO/fu6vTQRGRA9ftKklRg5LOppInQ7OucP7sb519qdrefNccOHTrxHPcc+keefr7IyFCaqzaPcKFUXXbsUD1jV7FD0glxdFFEoTL3eL1pVX37RjY1m9krIsu32IytJPbt2cLdxDMkT54rsL0L97KJyGAZ1JUkrWZ3oyiOHirXlzKH2rvbr27ZtallB+TFYoeeL1Y59Pg5Dj1+rnH/euzQnqTYXWnskMhaqmf1FhfI6g1sNpaocdmMIJi9vfk+IoNqwa7IvUg5tiIyKNStfWnVeTO75ZpmdwdJI3bo9BRHzkxx7PT0orFDL9iYm83XXSR2SKTbLbcYbv4q0isuKe5n3hP8U+AqmmZ43f1jazXAtaLCVkQGSb0rslaSLF+9M3M1jKglXU6rkVMLI8IFCh7pH5VaxIlnpzl6enrJ2KHAYOem4bjQbYodUsSL9BszIzCWXQzHx1QMS2esqrA1s98DXgQ8BI01Eu7ub1vTUa4BFbYiInKposipRk0Fb+iUqqFmeftcc+zQsaTYffJc6wihdGBcfdlIo9jds22Mqy4b0YyXDBwVw9Ipqy1svwF8k/fAmmUVtiIistaiyCnVQsrVqPFV+3f721SpyvEz0xyZFzvUSjYd8OLLR9mTLGFW7JBIa/ViuNVSaBXDslyLFbbLaR71dWAbcGpNRyUiItIDgiCOVRrOzh4r10LKtYhSNS50NavbX8ZyGW64coIbWsQONWfsnitUqdQiHjl1gUdOXWjcV7FDIhdzd0JnRds+zOYWvSmz+JiKYWlhOTO2DwDXA4eAxseV7n77+g5t5TRjKyIinRBG8bLlUjWklDSs6oGFTrIK7s4zU2WO1IvdpOCdKV/c2RaS2KFtY43ooT3bxtk0km15XxG5dM3FcDqYjU7KpKxxXR8y9a7VLkX+tlbH3f1v1mBsa0qFrYiIdIMo8saMbqkWUqp2X6F76MRz3PvgSU5dKHLFeJ47bt7RMkpHlm82dmiao2cucPT0bOxQK5ePDrF7W7KMeWscPaTYIZH1ZWakAyOdSrKDg4BUKj4WmKnw7XKr7orcK1TYiohIN6p3Yy5WQorVeBlzJ3/+HjrxHB+4/zjpwMhlAkrViFrkvP22a1TcrrFG7NCZ6cbMrmKHRLpbfWlzOhV/zQRBoxDOpgItd+6g1c7Y3gr8N+AlQBZIATPuPr7WA10tFbYiItIL6g2p6oVuZYEZvfXyMx9/mLMzZfKZ2YKpWA3ZPDLE+9/w8raOZRBVahGPPTsTN6daInbIgJ2bhxuzuoodEum8dBCQScdLm7Op+HImFZBJ6d/leltt86gPAncAfwTcBLwJ2L12wxMRERkssw2p4h/D7d6je+pCkfHc3F8BcpmA0xdax9zI2sqmg8aMbN1CsUMOPHG2wBNnC3zq8BkgbpazS7FDIh1TiyJqFZhNQo3Vlzln03GRm05ZXPimAv37bIPlFLa4+6NmlnL3EPgdM/sK8AvrOzQREZHBkAqMkaE0I0Pxj+X13qN7xXj+ohnbUjVi23h+zb6HrEw+k+K67Ru4bvuGxrGpUpVj9SXMTbFDYeQcn5zm+OQ0n0xCKy6KHdo2xvYJxQ6JtJO7U02y0OcLLF7anA7iIjcdWGNvb3xdxe9qLaewLZhZFnjIzH6FOPZH8+wiIiLrJAiMfDbV2FtZ36NbCSOqja9OLbq0Jcx33LyDD9x/nGI1nLPH9o6bd6zly5BVGstluPHKCW5cZexQc8auYodEOiNyp1JzKiz8/3bzjG82FcRf01rivFzL2WN7JXCGeH/tTwMbgN9w90fXf3groz22IiIySKLIqYRzC95KLVpWTmS9K/LpC0W2qStyz3J3JqfKjSJ3qdihjfkMu7eNsWfrWNKRWbFDIt0uMGsUufWid1CXN6+6K7KZ5YGd7n50rQe3llTYioiIQDWMKNciykkH5kotIuqjFARZ3Gzs0BRHTk9x7Ixih0T6USqYbVqVTc12bu7nyKLVdkX+XuBXgay7X21m1wN3u/vtaz/U1VFhKyIi0lqlFlGuhY3YoVZ7wKR/NWKHTk81oocUOyTSn+pLmlPNfyze05uy2WPpoPcK4NUWtl8CbgMOuPsrkmNfc/fr1nykq6TCVkREZHmqYUShMhs51E+59rI8lxo7tDvZt6vYIZHel5rXvCqTmr2eTnVf8bvauJ+quz8/7wXpp5+IiEgPy6QCNuQDNuQzuDulakShUqOg2dyBsVjs0NGmBlULxQ6lA+Pqy0bmFLuKHRLpLWHkhNHiTa3SQUAqZWQCI52aLXjTQXcte15OYXvYzH4QSJnZNcDbgL9b32GJiIhIu5jNdmHeDNTCiFKyR7cdubrSPVrFDk2Xahw7E+/XbY4dqjXFDpHEDg2lA168ZXTOMmbFDon0tloUUYugvMDtqaTgbS58M00zwO0qfJezFHkYeBfwGuKVKJ8C3u3upfUf3spoKbKIiMjaq8cNlauz+3Q1qzvY6rFD9eZU9dihVhQ7JDLY6suaU4ER2Oye3yCIbwsCGnt/l/p/YdVdkXuFClsREZH2qM/qFishpaqWLw+6RuxQUyfmxWKHNuQzSZE7yrXbxhQ7JCIAjcI3SJpbzS+Ex/PZlRe2ZnbfYt9UXZFFRESkLoycYjVUoSsNkTtPnSvOLmM+PcXxyWnKC8QOXTaaTYrceM/utYodEpF5XrRl7JIK22eAk8A9wBeIlyE3uPvfrPE4V02FrYiISHeoF7rlakgljJcxK0tXFDskIqtxqYVtCvgO4I3ANwN/Adzj7ofXa6CrpcJWRESke9WzdOOv8Z9+2hIll2Z+7NCxM1M8vkDsUGCwc9NwY6/utYodEhkol1TYzrmT2RBxgfs+4Jfc/YNrO8S1ocJWRESkt9SbUdUbU1UWWKYqg2Wh2KFWmmOH6gWvYodE+tMlF7ZJQfvPiYvaq4D7gI+4+1PrMM5VU2ErIiLS26Io6cCcFLylaki4wDJVGSxTpSrHz0xfFDvUimKHRPrTYoXtgjm2ZvYx4GXAXxLP0n59ncYnIiIiAkAQzGbq1lXDOEu3UouoJJfVnGrwjOUy3HDlBDdcOdE41hw7dDT5c75YpVyLOPz0BQ4/faFx3+bYod3JV8UOifSPxfbYRsBMcrX5Tga4u4+v89hWTDO2IiIigyGKPG5KNa/g1Z7dwdYcO9S8jHnR2KEkcqi+jHnz6FCbRy0iy3VJM7burl34IiIi0pWCwMgFKXKZuR1yNbs72MyMreM5to7n2Lv7ciCOHXr6fHFOxu7xM9OUahHPF6scevwchx4/13iOeuxQYxmzYodEesKymkf1Cs3YioiIyHzu8b7dajjbjVmzu4OtETuURA4tFTt0xYbcbHMqxQ6JdMyquyL3ChW2IiIishzus0uZ6x2Zq6Gr2B1g82OHjp6Z4okFYocM2Ll5OC52FTsk0jaXtBRZREREpF+ZGUPpFEPpFOTiY/WZ3XI1olgNKVVDIhW6AyObDhozsnX12KEjZ2Yzdp88V8SBJ84WeOJsgU8dPgModkik0zRjKyIiIm1z4Mgk+w+e4OS5Ajsmhrlz7y727dnS6WEtqFwLKVXj2CFFDwnEsUPH6kuYFTsk0lZaiiwiIiIdd+DIJHfdd5hMyshnUhSr8fLfu29/aVcXt83F+As35nnTrVdy89WbGvt26w6deI57HzzJqQtFrhjPc8fNO7hl16YOjlzapTl26FhS7J4rVFveV7FDIpdOha2IiIh03Bs/9Hkmp0oMZ2d3QhUqNbaM5bjnzbd2cGQLW6oYr4URpVrE/Y+c4T1/fYRUYOQyAaVqRC1y3n7bNSpuB1AjdujMbL6uYodEVk97bEVERKTjTp4rsHFebEo+k+LJc4UOjWhp+w+eIJOyRjE+nE1TqNTYf/AE+/ZsIZ0KGE0F3PPgSfLZFPlMCndIBwGFSo17v3hShe0AmhM7dI1ih0TaQYWtiIjIAOrEXtcdE8MXzdgWqyHbJ4bX9fuuxnKL8fr9zAwzCDDGchmenSrxgo15ipWQYjWkrJihgRWYsX1imO0Tw7z6JVuBptih01ON6KF67NCz0xWeffQsn3v0bOM5XrAxN2e/rmKHRGapsBURERkwzctrN+YzTE6VuOu+w9wN61rc3rl3F3fdd5hCpTZnWe+de3et2/dcreUW4wvdb8emEXKZFLlMigniJaqlpOtysRoqT3fApQJj1+Wj7Lp8lO++Lj5WqUWceHaao6enL4odevp8iafPl3jg6DMABAY7Nw3PmdlV7JAMKhW2IiIiA2ap5bXrZd+eLdydfP8nzxXY3gNdkZdbjC/3fmYWL1lOZtnqhW6pGlJKOjCr0B1s2XTAnm3j7Nk23ji2UOxQ5PD42QKPLxA7VG9OpdghGQRqHiUiIjJgXvne+xvLZuvcneeLVT7787d1cGTdqb5se6lifLn3W8z8LN1yTRFD0tpKY4dedPnonIzd7ZsUOyS9R12RRUREpKEXuxMPskotolQLKSczu80RQyLNLiV2KO7GPK7YIekJ6oosIiIiDb2413WQZdNBvGcyF1+PonhWt5Q0oypVQ6I+mqiQS7dpJMutuzZz667NQFPsUNOsbj12aKYS8tDJ8zx08nzj8fNjh/ZsG2fTSLZTL0dkRdZ1xtbMPgK8Fph095clxzYBHweuAh4HfsDdz7V47I8A/yG5+p/d/aNLfT/N2IqIiCzPWiyble5RTvbnlqthkqGrWV1pLXLnqXPFxszu0dNTHJ+cplxr/Z65fHSI3duSZcxJg6qxnGKHpDM6thTZzPYC08DHmgrbXwGec/f3mNk7gQl3//l5j9sEfBG4CXDgS8CNrQrgZipsRURERKAWRpSS2dxS0n1ZZCHNsUNHzkxx7PR0I3aolXrs0J5tY+xW7JC0UUf32JrZVcAnmwrbo8A+dz9lZlcAB9z92nmPeWNynzuT6/uT+92z2PdSYSsi0r86kbsq0i/Qrd/5AAAgAElEQVTCyClWQwqVGsWKGlLJ0haLHZpPsUPSLt22x3aru59KLp8Gtra4zwuBk03Xn0yOiYjIAOpU7qpIv0gFxuhQmtGh+Fe/UjWkUIkLXc3mSitLxQ4dS4rdpWKH6sWuYodkvXW0eZS7u5mt6iNDM3sz8GaAnTt3rsm4RESku3Qqd1WkX+UyKXKZFJtGstTCiEI1pFiJ/6gRlSwkn0lx3fYNXLd9Q+PYVKnK8TPT8X7dptihWuQcn5zm+OQ0nySe08qmA16s2CFZJ50obM+Y2RVNS5EnW9znKWBf0/XtwIFWT+buHwI+BPFS5LUdqoiIdIOT5wpszM9tVpLPpHjyXKFDIxLpH+lUwHgqYDxpCFSuhZQqccRQqaply7K4sVyGG66c4IYrJxrH6rFDzd2YzxWqVGoRj5y6wCOnLjTuW48dahS728bYOqbYIVm5ThS29wE/Arwn+fpnLe7zKeD/NbP6v5DXAL/QnuGJiEi32TExfFHuarEasn1iuIOjEulPQ+kUQ+kUG4gL3UotolgN1XFZlq1V7NAzU2WO1IvdJWKHNuYz7N42xp6tY0lHZsUOydLWtbA1s3uIZ14vM7MngV8kLmj/0Mz+NfAE8APJfW8C3uLuP+Huz5nZu4EHk6e6292fW8+xiohI91LuqkjnNHJ087MzuoVyyIz258oymRlbxnNsGc+x95rLgTh26OnzxbgT87zYofPFKocee45Dj83++j8/dmj31jHG84odklnr3hW5ndQVWUSkfyl3VaT7VMOIQjmkUK1Rqkb00++V0n7NsUNHz8TdmJcTO1RfwqzYof7X0bifdlJhKyIiItIZYeQUKjUKakIla2glsUMG7Nw83JjVVexQ/+m2uB8RERER6TOpwBjLZRjLZXB3CpV4ubJyc2U1VhI75MATZws80SJ2aM+2ePmyYof6lwpbEREREVlTZsbIUJqRJDe3mGTmzpRDNZ+SVbvU2CGS2KGhdMCLt4zOWca8fUKxQ71Oha2IiIiIrKt8NkU+m2LzaNx8aqYcMlOuUQ1V5MraWCx26MjpqUb80LlClXIt4vDTFzj89CKxQ1vH2Dqu2KFeoj22IiIiItIR5VrIdEkzudIe7s7kVHlOvm49dqiVeuzQtVtHuXbbmGKHuoCaR4mIiIhIVytVQ6bLNWbKNe3JlbaJ3HnqXLExs9scO9TKZaPZpMiN9+xeq9ihtlJhKyIiIiI9o1iJi9xCRUWutF9z7FDcoGpasUNdQoWtiIiIiPSkUjWMOyxrT650UKUW8dizM41Z3WNnpnhcsUNtp7gfEREREelJuUyKXCbFppEs1TCikHRYLlUj+mmCRrpbNh00ZmTrLjV2qN6cSrFDa0sztiIiIiLSc6LIKVRDCuUahUpI1Ee/00rvWih2qJVG7FDTzK5ihxanGVsRERER6StBYIwOpRkdSuPuFJPmU8VKqH250jELxQ7N6cR8eorzRcUOrTXN2IqIiIhI33B3StVIzaeka600dmhDPpMUuYodUvMoERERERlIpWrceKpYDaksEOEi0mmRO0+fL8admFcYO3Tt1jh6aBBih1TYioiIiMjAq4URhWpIqRJqX+46OnTiOe598CSnLhS5YjzPHTfv4JZdmzo9rJ7THDt09Mw0R09PLSt2qL6M+Zo+jB1SYSsiIiIi0sTdKdfiLsvFaki52noZqKzMoRPP8YH7j5MOjFwmoFSNqEXO22+7RsXtGpgfO3T0zBRPLBA7FBjs3DQ8pzlVr8cOqXmUiIiIiEgTM2tECUE8mzujKKFVu/fBk6QDI5/8veYzKYrVkHsfPKnCdg0sFTtUz9h98lyRyOHxswUeH5DYIRW2IiIiIjLw0qmADfmADfmMooRW4dSFIuO5uSVGLhNw+kKxQyPqf/lMiuu2b+C67Rsax6ZKVY4ly5ebY4dqkXN8cprjk9P8+VdPAU2xQ8msbq/GDqmwFRERERFp0ipKaKYcUqyE1CI1oFrMFeN5zs6UGzO2AKVqxLbxfAdHNXjGchluvHKCG+fFDh07EzenOnZmiiOnlhc7tDv52u2xQypsRURERNrowJFJ9h88wclzBXZMDHPn3l3s27Ol08OSBZgZw9k0w9n41+ZyLS5wC5WQkvblXuSOm3fwgfuPU6yGc/bY3nHzjk4PbeBtGsly667N3LprM7B47NBMJeShk+d56OT5xuM35DONyKH6MubNo0OdejkXUfMoERERkTY5cGSSu+47TCZljb2H1dC5+/aXqrjtQWEUz+YWKjWKlVCZuYl6V+TTF4psU1fkntIqdujRyWlKS8QONZYxr3PskLoii4iIiHSBN37o80xOlRqzfwCFSo0tYznuefOtHRyZrIVSNZ7NnanUlJkrfaM5dujImSmOnZ5eVuxQfWZ39xrGDqkrsoiIiEgXOHmuwMZ5sxn5TIonzxU6NCJZS/UuyxMjWaphHCWkLsvS61KBsevyUXZdPsp3X3cFEMcOnXh2ujGze+zMdCN26OnzJZ4+X+KBo88A7YsdUmErIiIi0iY7JoYvmrEtVkO2Twx3cFSyHjLqsix9LJsO2LNtnD3bxvm+5FixEvLoZOdih1TYioiIiLTJnXt3cdd9hylUanP22N65d1enhybrqFWX5elyjUJZRa70j3y2s7FDKmxFpCV17RQRWXv79mzhbmD/wRM8ea7Adv3/OnCauyz7qFOoJEVuJdRyZek7S8UOHU3+LCd26NptY4t+LzWPEpGLqGuniIhIe0WRM1OpxXm5VRW5MjgWix2a74n3vlbNo0Rk+fYfPEEmZY09YMPZNIVKjf0HT6iwFRERWQdBYIzlMozlMoSNIjeOERLpZ2bG1vEcW8dz7N19ORDHDj11rjhnZvf45PSiz6PCVkQuoq6dIiIinZMKjPFchvFchloYMVMOma7UKFdV5MpgCMzYsWmYHZuGefVLtgJx7NDuX174MSpsReQi6topIiLSHdKpgA3DARuGM1TDiJlyjalSjWqonFwZLEt1TFZhKyIXUddOERGR7pNJBWwczrJxOEu5FsYzuaUatah1kXvoxHPc++BJTl0ocsV4njtu3sEtuza1edQi7bG2qbgi0hf27dnC3be/lC1jOZ4vVtkyllPjKBERkS4ylE6xaSTLzs3DvGBjnrFcZk4syqETz/GB+49zdqbMeC7N2ZkyH7j/OIdOPNfBUYusH83YikhL+/ZsUSErIiLSA3KZFLlMistGs434oHu/eJJ0EKcbAI0VWPc+eFKzttKXVNiKiIiIiPQBM2NkKM3IUJpnpkqM5zJETiM6KJcJOH2h2OFRiqwPLUUWEREREekzOzeNUAkjsumAbDoglTLKtYht4/lOD01kXaiwFRERERHpM3fu3UU1dAqVGgCVWgQYP/WqF7EhnyEdqAyQ/qKlyCIiIiIifWbfni3cDew/eIInzxXYPjHMnXt3NfpnbB6FYiVkqlylUA6JkuXKIr1Kha2IiIiISB9aqhFkPpsin03ho06xGscHFSo1wkhFrvQeFbYiIiIiIgPMzBjOphnOpoEhStWQmXKNmXK4YEauSLdRYSsiIiIiIg31+KDNo1Cuzc7kxvt0RbqTClsREREREWlpKJ1iKJ1i00iWWhhRqIYUyiHFatiIERLpBipsRURERERkSelUwHgqYDyXwT3el1uohBQrIdVQs7nSWSpsRUREetSBI5PsP3iCk+cK7JjX8VREZD3N3ZcbxwkVKjWmy1qyLGvv0InnuPfBk2Quv+q6he6jwlZERKQHHTgyyV33HSaTMjbmM0xOlbjrvsPcDSpuRaTtsumAbDrLxuEslVrETDkucjWTK6t16MRzfOD+46QDA49qC91PycwiIiI9aP/BE2RS8YxJfeYkkzL2HzzR6aGJyIDLpgMmRrLs2DTM9olhJoazZFIqO+TS3PvgSdKBkc+kFr2fZmxFRER60MlzBTbmM3OO5TMpnjxX6NCIREQuVp/JnRjJNjosz2gmV1bg1IUi47mly1Z9dCIiItKDdkwMU6yGc44VqyHbJ4Y7NCIRkcXVuyvv2DTMCzbm2ZDPkA5UjsjirhjPU6ou/UGI3kkiIiI96M69u6iGTqFSwz3+Wg2dO/fu6vTQRESWFOfkDrFzc1zkjuczpALr9LCkC91x8w5qkV/0Ye58KmxFRER60L49W7j79peyZSzH88UqW8Zy3H37S9U4SkR6Ti6T4rLRIa7cPMIVG+IiV3type6WXZt4+23XsHlkCCxYcE2y9VOw8k033eRf/OIXOz0MERERERFZpWoYNXJyi9WQfqpb5NK8aMvYl9z9pla3qXmUiIiIiIh0nUwqYEM+YEM+g7tTqsZZuYVKqOZTchEVtiIiIiIi0tXMjHw2RT6bYjPJbG45ZLpSo7zE3ksZDG1fvG5m15rZQ01/LpjZO+bdZ5+ZPd90n7vaPU4REREREelOmVTAhuEML9yYZ+emYTaPDJFbIudU+lvbZ2zd/ShwPYCZpYCngD9pcdfPuvtr2zk2ERERERHpLelUwIbhuNCthREzlZBCpUaxopncQdLppcivBv7B3Z/o8DhERERERKTHpZv25UZJREyxGjeg0r7c/tbpwvYO4J4FbvsWM3sYeBr4WXc/3L5hiYiIiIhILwsCY2QozchQXPJUw6hR5JaqIWGkLsv9pGOFrZllgduBX2hx85eBK9192sy+B/hT4JoFnufNwJsBdu7cuU6jFRER6T4Hjkyy/+AJTp4rsGNimDv37lKOrYjIAjKpgEwqYDyXAaBUjQvcQiWkXIsUJ9TjOpl8/N3Al939zPwb3P2Cu08nl/8SyJjZZa2exN0/5O43uftNl19++fqOWEREpEscODLJXfcdZnKqxMZ8hsmpEnfdd5gDRyY7PTQRkZ6Qy6TYOJzlBRvzXLlpmK3jOcbzGTKpTpZIcqk6edbeyALLkM1sm5lZcvkW4nGebePYREREutr+gyfIpIzhbBqz+GsmZew/eKLTQxMR6Tn1ZcuXjQ6xY9MwOzYNc9nYEKNDaYK4LJEu15GlyGY2AnwHcGfTsbcAuPtvAa8HftLMakARuMO1NkBERKTh5LkCG/OZOcfymRRPnit0aEQiIv2jedmyuzNdrjFVqlFSZm7X6khh6+4zwOZ5x36r6fIHgQ+2e1wiIiK9YsfEMJNTJYazsz/Ki9WQ7RPDHRyViEj/MTPGchnGchkqtYipUpXpck3Np7qMFpCLiIj0oDv37qIaOoVKDff4azV07ty7q9NDExHpW9l0wObRIXZuGmbLeI58NtXpIUmi03E/IiIicgn27dnC3cR7bZ88V2C7uiKLiLSNmTE6lGZ0KE01jJgq1Zgu1ahFysrtFBW2IiIiPWrfni0qZEVEOiyTCtg0kmViOEOhEnKhVKVY0V7cdlNhKyIiIiIiskpmcWflkaZZ3KlSVXtx20SFrYiIiIiIyBrSLG77qbAVERERERFZB82zuLUwYqYcMl2pUVZs0JpTYSsiIiIiIrLO0qmADcMBG4YzVMOImXKN6XKNSk0Np9aCClsREREREZE2yqQCNg5n2TicVZG7RlTYioiIiIiIdEhzkVupRUyXa8yUa1RDFbkrocJWRERERESkC2TTAZvSWTaNZClVw0aRq87KS1NhKyIiIiIi0mVymRS5TIrNI1mK1ZDpUo2ZSoi7itxWVNiKiIiIiIh0KTNjOJtmOJsmipyZSrwfV/FBc6mwFRERERER6QFBYIzlMozl4s7K06UaU6UatUj7cVXYioiIiIiI9JhMKmBiJMvESJZiJWSqVB3opcoqbEVERERERHpYPpsin00RRc5UEh1Urg7WUmUVtiIiIiIiIn0gCIwN+Qwb8hlK1ZCpUtxVORqAWVwVtiIiIiIiIn2muavydKXGhWKVSq1/9+KqsBUREREREelTQWCM5zKM5+JZ3AulKjPl/tuLq8JWRERERERkANRncaOReC/uVKl/ZnFV2IqIiIiIiAyQftyLq8JWRERERERkQPXLXlwVtiIiIiIiIgNu/l7cXpvFVWErIiIiIiIiDfVZ3MtGsxSrITPlkEKlRhh1b5GrwlZEREREREQuYmYMZ9MMZ9PAEKVqyEy5xkw5pBZ113JlFbYiIiIiIiKypMZ+3FEo1+KZ3JlyjWrY+SJXha2IiIiIiIisyFA6xVA6xaaRLOVayHSpszO5KmxFRERERETkkg2lUwyNxjO5xUrIdLn9jadU2IqIiIiIiMiayGdT5LNx46lCUuQWKiG+zkWuClsRERERGQgHjkyy/+AJTp4rsGNimDv37mLfni2dHpZIXzIzRobSjAyliSJnphIvVS5W16fIDdb8GUVEREREusyBI5Pcdd9hJqdKbMxnmJwqcdd9hzlwZLLTQxPpe0FgjOUybNuQ48pNw1w+NsRwNo2Zrd33WLNnEhERERHpUvsPniCTssYv08PZNJmUsf/giU4PTWSgNBe5OzcNc9kaFblaiiwiIiIife/kuQIb85k5x/KZFE+eK3RoRCKSCozxXIbxXIawsVy5RqkarXi5sgpbEREREel7OyaGmZwqMZyd/fW3WA3ZPjHcwVGJSN1CRW6xEi7r8VqKLCIiIiJ97869u6iGTqFSwz3+Wg2dO/fu6vTQRGSeepF7xYY8V24e4bKxIfLZ1KKPUWErIiIiIn1v354t3H37S9kyluP5YpUtYznuvv2l6oos0uWai9zFaCmyiIiIiAyEfXu2qJAV6VOasRUREREREZGepsJWREREREREepoKWxEREREREelpKmxFRERERESkp6mwFRERERERkZ6mwlZERERERER6mgpbERERERER6WkqbEVERERERKSnqbAVERERERGRnqbCVkRERERERHqaClsRERERERHpaSpsRUREREREpKepsBUREREREZGepsJWREREREREepoKWxEREREREelpKmxFRERERESkp6mwFRERERERkZ6mwlZERERERER6mrl7p8ewZszsGeCJTo+jy1wGPNvpQUiDzkd30fnoLjof3UPnorvofHQXnY/uovPRPdpxLq5098tb3dBXha1czMy+6O43dXocEtP56C46H91F56N76Fx0F52P7qLz0V10PrpHp8+FliKLiIiIiIhIT1NhKyIiIiIiIj1NhW3/+1CnByBz6Hx0F52P7qLz0T10LrqLzkd30fnoLjof3aOj50J7bEVERERERKSnacZWREREREREepoK2z5iZh8xs0kz+/q84281syNmdtjMfqVT4xs0rc6HmV1vZp83s4fM7ItmdksnxzgozGyHmT1gZo8k/w7enhzfZGafMbPjydeJTo91ECxyPt6X/F/1VTP7EzPb2OmxDoKFzkfT7f/WzNzMLuvUGAfJYudDP8/ba5H/q/SzvAPMLGdmh8zs4eR8/FJy/Goz+4KZPWpmHzezbKfHOggWOR9/YGZHzezrye/CmbaNSUuR+4eZ7QWmgY+5+8uSY68C3gX8c3cvm9kWd5/s5DgHxQLn49PAr7n7X5nZ9wD/zt33dXCYA8HMrgCucPcvm9kY8CXgdcCPAs+5+3vM7J3AhLv/fAeHOhAWOR/bgfvdvWZm7wXQ+Vh/C50Pd3/EzHYAHwb2ADe6u7Ii19ki/z62op/nbbXIufiv6Gd525mZASPuPp0US38LvB34GeAT7n6vmf0W8LC7/2YnxzoIFjkfm4C/Su72P4GD7TofmrHtI+5+EHhu3uGfBN7j7uXkPvoh2CYLnA8HxpPLG4Cn2zqoAeXup9z9y8nlKeAbwAuB7wM+mtzto8S/sMg6W+h8uPun3b2W3O3zxIWurLNF/n0A/Brw74j/75I2WOR86Od5my1yLvSzvAM8Np1czSR/HLgN+F/Jcf0sb5OFzoe7/2VymwOHaOPPchW2/W838M+SJRp/Y2Y3d3pAA+4dwPvM7CTwq8AvdHg8A8fMrgJeAXwB2Orup5KbThPPiEgbzTsfzX6c2U98pU2az4eZfR/wlLs/3NFBDbB5/z7087yD5p0L/SzvEDNLmdlDwCTwGeAfgPNNH4o+yewHc7LO5p8Pd/9C020Z4P8C/rpd41Fh2//SxEsCbgV+DvjDZOmAdMZPAj/t7juAnwZ+u8PjGShmNgr8MfAOd7/QfFvyyaJmpdpoofNhZu8CasAfdGpsg6j5fBD//f974K6ODmqAtfj3oZ/nHdLiXOhneYe4e+ju1xPPAt5CvE1COmT++TCzlzXd/BvEy5A/267xqLDtf08S7ztwdz8ERIAagHTOjwCfSC7/EfF/ytIGySeHfwz8gbvXz8GZZA9VfS+Vlva1yQLnAzP7UeC1wA+5mkC0TYvz8SLgauBhM3uc+JeWL5vZts6NcnAs8O9DP887YIFzoZ/lHebu54EHgG8BNppZOrlpO/BUxwY2oJrOx3cBmNkvApcT739uGxW2/e9PgVcBmNluIAuo+UfnPA18W3L5NuB4B8cyMJJZjd8GvuHu72+66T7iX1BIvv5Zu8c2iBY6H2b2XcT7OW9390KnxjdoWp0Pd/+au29x96vc/SriouoGdz/dwaEOhEX+v9LP8zZb5FzoZ3kHmNnllnTLN7M88B3E+54fAF6f3E0/y9tkgfNxxMx+AvhO4I3uHrV1TPpAvH+Y2T3APuJPcM8Avwj8HvAR4HqgAvysu9/fqTEOkgXOx1HgA8RLykrA/+3uX+rUGAeFmb0S+CzwNeJZDoiXWX4B+ENgJ/AE8APuPr/hl6yxRc7HrwNDwNnk2Ofd/S3tH+FgWeh8uPtfNt3nceAmdUVef4v8+/jf6Od5Wy1yLi6gn+VtZ2bfTNwcKkU8OfeH7n63me0C7iVeqv8V4IfrTdZk/SxyPmrEv1NNJXf9hLvf3ZYxqbAVERERERGRXqalyCIiIiIiItLTVNiKiIiIiIhIT1NhKyIiIiIiIj1Nha2IiIiIiIj0NBW2IiIiIiIi0tNU2IqIdIiZhWb2kJl93cz+yMyGOzCGfWb2T1f4mCEz+9/J2N/Q4va0mT1jZu9Z5Dl+1Mw+uMT3ucrMvp5cvt7MvmeZ49ucjO0hMzttZk81Xd9df87lMrPXmdk3LXDbfzIzN7MXNx17R3LspiWe9x0LnXMzO2BmR5vG/fpW91vgsY+b2WXLvf8Sz/W7rb53cvyxZGxfNrNvaRr3oq97Gd/zBWb2v9ZyvMltP2NmR8zsa2b2sJm938wySzzfW8zsTS2ON96bizy2+f17k5n9+kpey0ol76c3JZffa2ZfNbOPNd3+w2b2jqbr15nZ767nmERE2kmFrYhI5xTd/Xp3fxlxLuWyMlvNLL2GY9gHrKiwBV4BkIz94y1u/w7gGPD9ZmarG17D9cCyClt3P5uM7Xrgt4Bfa7peuYTv/TqgZWGb+BpwR9P17wcOL+N53wEs9mHGD9XH7e6XVOgth5mlLvGhP5f8nb4T2L9W43H3p9192YX8cpjZW4DXALe6+3XAzcAkkF9iLL/l7h9b7D7L4e5fdPe3rfZ5FpL8n/DjwP80sw3ADe7+zUAlKWDzwI8B/71pTF8DtpvZzvUal4hIO6mwFRHpDp8FXmxmI2b2ETM7ZGZfMbPvg8YM531mdj/wf8xs1Mx+J5l9+qqZ/avkfq8xs79PZtH+yMxGk+OPm9kvJce/ZmZ7zOwq4mL6p5OZt3/WPCAz22Rmf5o8/+fN7JvNbAvw+8DNyWNe1OK1vBH4APCPwLc0Pd+PmdkxMzsEfGvT8TmzbGY2PW8cWeBu4A31WWIz+7am2cyvmNnYCv6uU2b2P8zssJl9OvmlHzP7N2b2YDKb98dmNmzxbPbtwPsWeb1/CtTP04uA54Fnm8Z/0Tkxs7cBLwAeMLMHljNoM7s8GdeDyZ9vTY5vTl7HYTP7MGBNj/nh5L30kJntrxexZjZtZv+fmT0MfIuZ3ZU859fN7EMr/EDiIPDipuvfn3zPY/X3lJkdNLPrm8b1t2b28lbncd5MZ8rMfjUZ11fN7K3J8ZWO913AT7r7eQB3r7j7e9z9Qv3vo2lsr7dkJtPiGfmfTS7fmLw3HgZ+qun+KTN7XzKer5rZnfO/ucUrIz7Z9JwfsXh2+0TyXqjf76Lzlfz53eS1fs3MfrrF67sN+LK714AIyCR/J8NAFfhZ4L+5e3Xe4/6cuR/KiIj0LBW2IiIdZvFsy3cTz/y9C7jf3W8BXkVcUI0kd70BeL27fxvwH4Hn3f26ZGbmfouXn/4H4Nvd/Qbgi8DPNH2rZ5Pjvwn8rLs/ztwZzc/OG9ovAV9Jnv/fAx9z90ngJ4DPJo/5h3mvJQd8O/EvzPcQF7mY2RXJ830r8EoWnwGdw90rwF3Ax5tmiX8W+KlkxvCfAcXlPh9wDfDf3f2lwHngXyXHP+HuN7v7y4FvAP/a3f8OuI9kdnL+601cAE6a2cuIi4TGLPZC58Tdfx14GniVu79qgXH+QVPRt5n4w4Jfc/ebkzF/OLnfLwJ/m7yePwF2Jt/7JcAbgG9N/p5C4IeSx4wAX3D3l7v73wIfTF77y4hnMV+7vL9KAL6X+L1bl07ev+9Ixgbw28CPJuPaDeTc/WGWPo9vBq4Crk/eh3+QHF/2eM1sHBh198dW8Jpa+R3grcn7o9m/Jv63eDPxTPC/MbOrl3iuPcB3ArcAv2hmmUXO1/XAC939Zcls8++0eL5vBb4E4O5TwF8CXwFOEX/Q8k/c/U9bPO6LxH/vIiI9T4WtiEjn5M3sIeJfLv+R+Jf/1wDvTI4fAHIkhQrwGXd/Lrn87cxdVngOuJW4YPxc8vgfAa5s+n6fSL5+ibhYWMorgd9Lnv9+YHNSJCzmtcAD7l4E/hh4XTJL+E+AA+7+TFKotlrCvBKfA96fzHZtTGaqlusxd38oudz8d/EyM/usmX2NuKB46Qqe817iovZ1xMVl3VLnZDHNS5HPEp/zDybPcx8wbvGM/F7iWXTc/S+Ac8njXw3cCDyYPObVwK7ktpD4/NS9ysy+kLz221jea39f8rxvJi7u6qtadA8AAAS2SURBVFq9z/4IeK3Fe1p/HPjd5PhS5/Hbgf31403v/0sZLwBm9p3JhwWP2zL3l5vZxmR8B5NDv9d082uANyV/F18ANhN/eLKYv3D3srs/S7wkeisLn68TwC4z+29m9l3EH6TMdwXwTP2Ku/9K8r75t8C7gbvM7CfM7A/N7D80PW6SeOWAiEjPW8t9WiIisjLFZGamIVk++K/c/ei84/8EmFni+Yy4+H3jAreXk68h6/f//xuBV5rZ48n1zcSFx2JqJB+0mlkAZJf6Ju7+HjP7C+J9t58zs+909yPLHGO56XLI7D7L3wVe5+4Pm9mPEu8/Xq5PAu8DvujuF5pWxi51TlYiIN4jWmo+uMgqXAM+6u6/0OK2kruHyeNzwG8AN7n7STP7T8QfqCzl5xbY+3vR+8zdC2b2GeIl2z9AXMC1PI9A6eKnbHpRKxxvcj6mzexqd3/M3T8FfCpZGlx/r3nTQ5bz2ucMiXgm91PzxnnVIo+Z/x5Ms8j5MrOXE8/wvoX47+/H592l2GrcZvaK5HmPAv/F3b/T4i0M17j78eQxK1ntICLStTRjKyLSXT4FvLW+ZzD5xbSVzzB3n98E8HngWy3p0Gvxft3dS3y/KWCh/amfJVm6amb7iJcyt5otqo9hnHhZ4053v8rdr0rG+Ebimaxvs3g/aIa4wVLd4ySFDvF+1ladaueM08xe5O5fc/f3Ag8SL+1crTHgVDK+H2o6vtjfERAXbsDPA78876bFzsmSzzvPp4G31q/Y7J7Vg8APJse+G5hIjv8f4PUW74uu75luNVtcL4ieTWaA17RxU5MPA78OPJisMFjOefwMcGeyXB8z23SJ4/0vwG8mM6/1D5CaC8EzZvaS5IOVfzH/wcne3PNm9srkUPP741PATybvGyzuvD0y/zmWoeX5SpazB+7+x8TL2m9o8dhvMHefc927ibctZIB6k7CI2aZlu4EVdQkXEelWKmxFRLrLu4l/Cf2qmR1Orrfyn4GJpKHMw8R7NZ8h3sd4j5l9Ffh7li74/hz4F9aieRTwn4Abk+d6D/Ey2sX8C+L9wc2zUX9GvAfzueT5/p54+ek3mu7zP4iL3oeJm021mpl+APgmm40Yekfy2r9K3Bznr5YY23L8R+IC/HNA8+zvvcDPWdzcqFXzKADc/V53//K8Y4udkw8Bf23LbB4FvA24yeIGRY8w20X7l4C9yfvlXxIva8fdHyEuhD6dfO/PEC9ZnT/u88Tn4OvERdqDyxzPirj7l4iX0TbvEV3qPH6Y+PV8NXl//OAljvc3iQvHLyTf63PEe1C/ktz+TuJZ978j3pfayo8B/z1ZJtw8Tf5h4BHgyxY3vdrPJayIWOR8vRA4kHzf3wdazcD/FfGS9AYzex3xCoKnk7+zh5Kl2/X9zRDv4/+LlY5VRKQbmbsvfS8RERGRVTCzFxDvG9/j7lGHh9N3zOxPgH+XLDFezv2HgL8BXrnCPeoiIl1JM7YiIiKyrszsTcSz4e9SUbtu3kmLGflF7ATeqaJWRPqFZmxFRERERESkp2nGVkRERERE5P9vvw5IAAAAAAT9f92OQF/ImtgCAACwJrYAAACsiS0AAABrYgsAAMCa2AIAALAW3VM6Hg+FirEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set figure size\n",
    "plt.figure(figsize=(16, 8))\n",
    "\n",
    "# Plot scatterplot\n",
    "ax = sns.regplot(y = merged_df2.percent_pass.str[0], x = \"mean_temp\", data = merged_df2)\n",
    "\n",
    "# Label plots and axes\n",
    "ax.set_title('Mean Temperature by State (°C) Against Percent of Adults That Met Federal Physical Guidelines (%)')\n",
    "plt.xlabel('Mean Temperature by State (°C)')\n",
    "plt.ylabel('Percent of Adults That Met Federal Physical Guidelines (%)')"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "From this scatterplot measuring the relationship between the percent of adults that meet federal phyical guidelines against the mean temperature by state (°C), it is difficult to discern whether or not there is any strong correlation.The spread of the points is smaller in the middle and larger at the ends so it is not very homoscedastic. We can identify what appears to be a slight negative correlation, in which the lower the mean temperature of the state is, the higher the percent of adults that meet federal physical guidelines. \n",
    "However, in order to get a better picture of whether or not this pattern is true and significant, we need to perform an OLS regression on the above data."
   ]
  },
  {
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     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:     percent_pass_first   R-squared:                       0.221\n",
      "Model:                            OLS   Adj. R-squared:                  0.197\n",
      "Method:                 Least Squares   F-statistic:                     9.071\n",
      "Date:                Mon, 15 Mar 2021   Prob (F-statistic):            0.00504\n",
      "Time:                        23:34:43   Log-Likelihood:                -92.462\n",
      "No. Observations:                  34   AIC:                             188.9\n",
      "Df Residuals:                      32   BIC:                             192.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     29.7209      2.279     13.041      0.000      25.079      34.363\n",
      "mean_temp     -0.5188      0.172     -3.012      0.005      -0.870      -0.168\n",
      "==============================================================================\n",
      "Omnibus:                        1.199   Durbin-Watson:                   2.049\n",
      "Prob(Omnibus):                  0.549   Jarque-Bera (JB):                0.583\n",
      "Skew:                          -0.311   Prob(JB):                        0.747\n",
      "Kurtosis:                       3.153   Cond. No.                         46.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# By convention, the first matrix is the \"outcome\" or \"y\" data, and the second is the \"predictor\" or \"x\" data\n",
    "# Try to predict exercise duration from temperature of state\n",
    "percent_pass_first = merged_df2.percent_pass.str[0]\n",
    "outcome_2, predictors_2 = patsy.dmatrices('percent_pass_first ~ mean_temp' ,data=merged_df2)\n",
    "mod_2 = sm.OLS(outcome_2, predictors_2)\n",
    "res_2 = mod_2.fit()\n",
    "# Print out the summary results of the model fitting\n",
    "print(res_2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xb6te0Nf8N9q"
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   "source": [
    "The results of our OLS regression on the relationship between the percent of adults that meet federal phyical guidelines against the mean temperature by state (°C) indicate an R-squared value of 0.221. An R-squared value of 0.221 could be considered meaningful for real world data, as it indicates that about 22% percent variation of one variable is completely explained by the other. This R-squared value is represented by how tightly the data fits the trendline in the above scatterplot. Additionally, if we set our significance level alpha to less than or equal to 0.005,  we can see that the found p-value of 0.005 could be considered significant. The probability of obtaining an effect this extreme with our data is 0.5%. As a result of this OLS regression, we recognize that there is a significant negative correlation between average state temperature and amount of adults in that state who meet federal physical exercise guidelines. As our data analysis findings do not draw a particularly risky conclusion, the possibility of a false positive is not as big of a concern. Therefore, while other factors and variables could have influenced these results, the risks are low enough for us to overlook it and conclude that the lower the state average temperature is, the more likely an adult in that state meets federal guidelines for exercise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qYrCGjhAGixH"
   },
   "source": [
    "# **Ethics & Privacy**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oDmeR83PGixH"
   },
   "source": [
    "Since the dataset we are planning to explore is open access and open data, the public dataset has its information already on the internet through previous surveys. The question that we are trying to answer is mainly taking provided data and turning it into something more meaningful--therefore there is little-to-no privacy concern. The data is therefore valid for this intended use, which is to examine the relationship between fitness and frequency of fitness. However, some ethical concerns include being aware of how the information and relationships that we find may influence current practices and human behavior. We also want to be careful and aware of possible ageism bias, classism, and gender bias in the case that the data itself has a skew toward a certain demographic. We should be transparent with the results of the data and clearly explain the results in the context of the data in case the results may disadvantage certain subgroups. As mentioned before, previous findings had suggested that exercise and certain physical activities are strongly associated with privileges that many of us may not be aware of, including geographic location, living conditions, gender, poverty level, and occupation. Ultimately, data presented on these topics have to be sensitive to the privilege of something many take for granted: the ability to exercise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Nk44QL3v6Sp9"
   },
   "source": [
    "# **Conclusion & Discussion**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hdWE9rSo6Sp9"
   },
   "source": [
    "Throughout this project, our team has been interested in the average amount of exercise of any American individual. We came across many datasets that recorded observations of how long a person exercises for, what type of exercise they perform, and how often they exercise per week, all categorized by state and sex. Our initial question and exploratory interest was on whether or not the type of exercise affected exercise duration. Over time, we chose to investigate a variation of that original research question, which we thought would be more compelling: does the average state temperature have any correlation to exercise duration? \n",
    "\n",
    "We hypothesized that the higher the average annual temperature of the state was, the more likely an individual from that state will exercise for longer. We came up with this hypothesis because we thought that warmer temperatures meant that it was more likely for individuals to spend time being active outside. But we could only guess as to the reasons why, as our data analysis would not reveal any causal relationships, only correlations. \n",
    "\n",
    "Ultimately, our analysis found a weak positive correlation between average temperature and the average exercise duration. Our team decided to explore the statistical results and determined that with a R-squared value of 0.015 , there is little to no correlation between average annual temperature and average exercise duration by state. For our analyses of the percentage of adults from that state that met the federal physical guidelines and the mean temperature of each state, there was a slight negative correlation. The lower the temperatures were, the higher the percentage of adults that met the federal physical guidelines. In our tests, we determined that there was little to no significant difference in the effects of temperature on the duration of exercise. We concluded that our results yielded different results from what we initially hypothesized; however, there were a few confounding variables and factors to consider. \n",
    "\n",
    "Our primary confounding variable was geographic location and its impact on both temperatures and population. By diverging our data into states, we would have to consider the overall population of each state. The spread of individuals deriving from each individual state differed significantly. Where some states had 5-7 responses, other states had 1-2. For the states with 1-2 responses, the average duration of exercise may prove to be more volatile and unrepresentative of the average duration of exercise for the state. Additionally, temperature observations were not from 2020, but from 2018. This could have been an inaccurate factor in our data analysis as our exercise dataset was from 2020. In the end, we decided that temperatures would not be so drastically different over the course of two years as to significantly impact our results; nevertheless, it is still important to acknowledge its potential effects or non-effects. Lastly, we need to consider the type of exercise as well as an individual’s potential access to a gym or home gym. Individuals with a home gym or home exercise routines would be generally unaffected by outdoor temperature. Each of those variables were not accounted for in our statistical analysis.\n",
    "\n",
    "In conclusion, the average annual temperature of a state does not have a significant effect on the average duration of exercise in that state. Surprisingly, we did find that temperature had a greater effect on the state percentage of adults who met the federal physical guidelines. This meant that states with lower mean temperatures yielded higher percentages of adults that met the federal physical guidelines--colder states alluded to more active populations. While these findings are not significant enough to suggest anything major, it is important to acknowledge the impact of exercise availability to all populations. Populations in denser and more ecologically extreme climates are indeed affected, if only slightly, and this is something that should be addressed and considered when doctors, media, and fitness professionals recommend physical activity. As said before, exercise is often a privilege that certain subgroups and regions are more predisposed to, and the beginning of a healthier America starts with a holistic evaluation of all the different factors that are affecting today’s health crisis.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hYV7RQB26Sp9"
   },
   "source": [
    "# **Team Contributions**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Jqw2NHR66Sp9"
   },
   "source": [
    "|     Contribution     |               Names               |\n",
    "|---|----------------------------------------------|\n",
    "| Overview | Sean Tran|\n",
    "| Background and Prior Work  | Irene Hou|\n",
    "| Dataset(s)  | Irene Hou, Nick Chua, Sean Tran|\n",
    "| Setup  | Sean Tran |\n",
    "| Data Cleaning | Nick Chu, Sean Tran |\n",
    "| Data Analysis & Results (EDA) | Irene Hou, Nick Chua, Sean Tran, Seth Kim|\n",
    "| Ethics & Privacy  | Irene Hou |\n",
    "| Conclusion & Discussion | Irene Hou, Sean Tran|\n",
    "| Script | Irene Hou, Sean Tran |\n",
    "| Presentation Slides | Irene Hou, Sean Tran |\n",
    "| Presentation | Nick Chua, Seth Kim |"
   ]
  }
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