diabetes1.ipynb

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    "# Predicting the prevalence of Diabetes using Google Trends online data.\n",
    "\n",
    "The main goal of this assignment is to set up a machine learning framework that predicts the prevalence of a specific chronic disease (diabetes) from digital records of online behavior (Google Trends).\n",
    "\n",
    "## Students\n",
    "\n",
    "1. Giulia Rinaldi\n",
    "1. Fernando Crema\n",
    "\n",
    "## Brief explanation of the disease\n",
    "\n",
    "According to [The Center for Disease Control and Prevention](https://www.cdc.gov/diabetes/basics/index.html) diabetes is a chronic (long-lasting) disease that affects how your body turns food into energy.\n",
    "\n",
    "There are three main types of diabetes:\n",
    "\n",
    "1. **Type 1:** Results from the pancreas's failure to produce enough insulin.\n",
    "1. **Type 2:** Begins with insulin resistance, a condition in which cells fail to respond to insulin properly.\n",
    "1. **Gestational diabetes:** Occurs when pregnant women without a previous history of diabetes develop high blood sugar levels. \n",
    "\n",
    "More than 100 million Americans are living with diabetes (30.3 million) or prediabetes (84.1 million).\n",
    "\n",
    "### Why diabetes?\n",
    "\n",
    "Multiple articles such as [Sugar, real heart disease killer](http://www.dailymail.co.uk/health/article-2574269/Sugar-not-fat-real-heart-disease-killer-We-got-wrong-diet-advice-claims-expert.html), [The Sugar Conspiracy](https://www.theguardian.com/society/2016/apr/07/the-sugar-conspiracy-robert-lustig-john-yudkin) and [is fat killing you... or is sugar?](https://www.newyorker.com/magazine/2017/04/03/is-fat-killing-you-or-is-sugar) exploit the issue of sugar consumption in modern society. Despite what normal assuptiom tell us, it seems that the major influence of what are so called \"the XXI century diseases\" is sugar. Giving the major importance of this topic seems clear that if we focus our analysis into a sugar-related disease as diabetes we would have, at least, a lot of results in the online part of our investigation.\n",
    "\n",
    "## Focus of this project using past work\n",
    "\n",
    "We can find important information to our work in several articles. From now on, some of our decisions will be taken into account this projects.\n",
    "\n",
    "[1] [Detecting influenza epidemics using search engine query data](https://www.nature.com/articles/nature07634.pdf) also found in [Link 2](https://static.googleusercontent.com/media/research.google.com/en//archive/papers/detecting-influenza-epidemics.pdf).\n",
    "\n",
    "[2] [Composite leading search index: a preprocessing method of internet search data for stock trends prediction](https://link.springer.com/article/10.1007/s10479-014-1779-z).\n",
    "\n",
    "[3] [A Preprocessing Method of Internet Search Data for Prediction Improvement: Application to Chinese Stock Market](https://www.ecb.europa.eu/events/pdf/conferences/140407/Yuan_APreprocessingMethodOfInternetSearchDataForPredictionImprovement.pdf?3f0ada5a691d67f6febf534056cdc126).\n",
    "\n",
    "[4] [Monitoring Influenza Epidemics in China with Search Query from Baidu](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0064323).\n",
    "\n",
    "### Brief explanation of past works\n",
    "\n",
    "In works [1] and [4] the focus of both projects is to detect influenza epidemics using search query data (using either Google or Baidu). In the case of [1] the importance of the report, according to the authors, is because the early detection of influenza activity, when followed by a rapid response, can reduce the impact of both seasonal and pandemic influenza. As a result, they say that a way to improve early detection is to monitor health-seeking behaviour in the form of queries to online search engines. Furthermore, in [4] they make special emphasis in the type of data they need in order to have successful results: \"The search query data for each keyword should be represented as a sequential time series with a daily, weekly or monthly resolution\". Finally, they state that studies using web search query data have achieved good results in empirical practice.\n",
    "\n",
    "Works [2] and [3] are used in work [4] to build the regression model they call **Composite Search Index** and is more or less replicated in the present work\n",
    "\n",
    "### Possible limitations of our work\n",
    "\n",
    "1. **(Sample Size):** Unfortunately, we will use data that is temporally aggregated in the **yearly** scale because the data we want to predict found in [the database of the US Center for Disease Control and Prevention](https://www.cdc.gov/brfss/brfssprevalence/index.html) and therefore we will analyze from 2004 to 2016, meaning that we have 13 data points at most.\n",
    "1. **(Epidemic vs Chronic disease):** One of the major reasons behind the usage of search query data predicting works [1] and [4] is because of the fact that influenza is an **epidemic** type of disease. This means that it can be transmitted. The normal reaction of people is to understand either symptoms, treatment, diagnosis, among others looking for information on internet. We could say that this behaviour should be the same for any other type of diseases. However, is not true as the engagement of people using the platform is directly proportional to the impact of the disease in a given time. Therefore, is easier to see the influence of an epidemic disease as the number of sick people increases rapidly given an epidemic of it. Conversely, later on we will see how the prevalence of diabetes increases over time with a predictable pattern either in Google Trend data or in the so called ground truth data.\n",
    "1. **(Google Trend data):** In projects [1] and [4] they use **absolute** values of queries (or sampling estimates) to calculate the correlation between the real value (influenza) and the this values. In our case, the Google Trend data represents a normalized estimate from 0 to 100 ( This values can reach 100 in some years of our data) that represents the \"importance\" of the query term in a given period of time. Therefore, we don't have the real value but an estimated of importance that Google doesn't explain in detail. \n",
    "\n",
    "## How this work is structured:\n",
    "\n",
    "1. Downloading data:\n",
    "    - Ground Truth Data.\n",
    "    - Google Trends data:\n",
    "        * Keyword selection.\n",
    "        * Keyword filtering.\n",
    "        * Downloading the data.\n",
    "    - Data on income and poverty.\n",
    "1. Correlation study:\n",
    "    - Correlation between the ground truth data and Google Trends data.\n",
    "    - Correlation between the ground truth data dn the income-poverty data.\n",
    "1. Prediction model:\n",
    "    - Predicting the ground truth data based on Google Trends data.\n",
    "        * Discussing keyword selection.\n",
    "        * Model selection using temporal and spatial cross-validation.\n",
    "        * Generalizability across states and years.\n",
    "    - Predicting the ground truth data based on Google Trends data and income-poverty data.\n",
    "    - Predicting the ground truth data based on Google Trends data, income-poverty data and insurance data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ground truth data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Behavioral Risk Factor Surveillance System\n",
    "\n",
    "It's possible to download the data from different sources:\n",
    "\n",
    "1. From [Diabetes Data](https://gis.cdc.gov/grasp/diabetes/DiabetesAtlas.html) we can find an interactive way with graphs and aggregations by Total, Age, Gender and Education. However, is not consistent with the data found in 2.\n",
    "1. From "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = pd.date_range(start='31/12/2004', end=\"31/12/2016\", freq=\"365D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: data/crude_prevalence/crude_prevalence_2004.csv\n",
      "Filename: data/crude_prevalence/crude_prevalence_2005.csv\n",
      "Filename: data/crude_prevalence/crude_prevalence_2006.csv\n",
      "Filename: data/crude_prevalence/crude_prevalence_2007.csv\n",
      "Filename: data/crude_prevalence/crude_prevalence_2008.csv\n",
      "Filename: data/crude_prevalence/crude_prevalence_2009.csv\n",
      "Filename: data/crude_prevalence/crude_prevalence_2010.csv\n",
      "Filename: data/crude_prevalence/crude_prevalence_2011.csv\n",
      "Filename: data/crude_prevalence/crude_prevalence_2012.csv\n",
      "Filename: data/crude_prevalence/crude_prevalence_2013.csv\n",
      "Filename: data/crude_prevalence/crude_prevalence_2014.csv\n",
      "Filename: data/crude_prevalence/crude_prevalence_2015.csv\n",
      "Filename: data/crude_prevalence/crude_prevalence_2016.csv\n"
     ]
    }
   ],
   "source": [
    "crude_prevalence = []\n",
    "interest = [\"Year\", \"LocationAbbr\", \"LocationDesc\", \"Indicator\", \"Data_Value\", \"Low_Confidence_Limit\", \"High_Confidence_Limit\", \"Sample_Size\"]\n",
    "\n",
    "for year in years:\n",
    "    print(\"Filename: {}\".format(\"data/crude_prevalence/crude_prevalence_{}.csv\".format(str(year).split('-')[0])))\n",
    "    try:\n",
    "        df = pd.read_csv(\"data/crude_prevalence/crude_prevalence_{}.csv\".format(str(year).split('-')[0]), sep=\"\\t\")[interest]\n",
    "    except KeyError:\n",
    "        df = pd.read_csv(\"data/crude_prevalence/crude_prevalence_{}.csv\".format(str(year).split('-')[0]), sep=\",\")[interest]\n",
    "    crude_prevalence.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>LocationAbbr</th>\n",
       "      <th>LocationDesc</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>Data_Value</th>\n",
       "      <th>Low_Confidence_Limit</th>\n",
       "      <th>High_Confidence_Limit</th>\n",
       "      <th>Sample_Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004</td>\n",
       "      <td>US</td>\n",
       "      <td>All States, DC and Territories (median) **</td>\n",
       "      <td>Have you ever been told by a doctor that you h...</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004</td>\n",
       "      <td>UW</td>\n",
       "      <td>All States and DC (median) **</td>\n",
       "      <td>Have you ever been told by a doctor that you h...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Have you ever been told by a doctor that you h...</td>\n",
       "      <td>8.1</td>\n",
       "      <td>7.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>Have you ever been told by a doctor that you h...</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.2</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>AZ</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>Have you ever been told by a doctor that you h...</td>\n",
       "      <td>6.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>7.7</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year LocationAbbr                                LocationDesc  \\\n",
       "0  2004           US  All States, DC and Territories (median) **   \n",
       "1  2004           UW               All States and DC (median) **   \n",
       "2  2004           AL                                     Alabama   \n",
       "3  2004           AK                                      Alaska   \n",
       "4  2004           AZ                                     Arizona   \n",
       "\n",
       "                                           Indicator  Data_Value  \\\n",
       "0  Have you ever been told by a doctor that you h...         7.1   \n",
       "1  Have you ever been told by a doctor that you h...         7.0   \n",
       "2  Have you ever been told by a doctor that you h...         8.1   \n",
       "3  Have you ever been told by a doctor that you h...         4.2   \n",
       "4  Have you ever been told by a doctor that you h...         6.6   \n",
       "\n",
       "   Low_Confidence_Limit  High_Confidence_Limit  Sample_Size  \n",
       "0                   NaN                    NaN           52  \n",
       "1                   NaN                    NaN           50  \n",
       "2                   7.2                    9.0          362  \n",
       "3                   3.2                    5.2          128  \n",
       "4                   5.5                    7.7          391  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_crude_prevalence= pd.concat(crude_prevalence)\n",
    "df_crude_prevalence.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(720, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_crude_prevalence.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = list(set(df_crude_prevalence[\"LocationDesc\"].values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing for alabama"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wRgcAZ/Lz8sBgUKiqsda79Wvf8jObjIzoFupSB8ztpZcGtx3MlJgpus/fkLT2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\nMjK0cygxGnVJtgkb72x/h3a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svvpMpmPCHWp6+mUeqOxkwJJ0M653G8bEtNGtmkVeRR7P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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2964fa320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AL_ts = pd.Series(df_crude_prevalence[df_crude_prevalence.LocationAbbr == \"AL\"][\"Data_Value\"].values,\n",
    "                  index=years)\n",
    "AL_ts = pd.DataFrame({'data_value': AL_ts,\n",
    "                      'Low Confidence Limit': df_crude_prevalence[df_crude_prevalence.LocationAbbr == \"AL\"][\"Low_Confidence_Limit\"].values,\n",
    "                      'High Confidence Limit': df_crude_prevalence[df_crude_prevalence.LocationAbbr == \"AL\"][\"High_Confidence_Limit\"].values})\n",
    "ax = AL_ts.plot(style=[':', ':', '-'], figsize=(7, 7))\n",
    "ax.lines[0].set_alpha(0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001A2969756A0>]], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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27WKlv6u1ZJzqWGofG8W+tRqsY3UslCPDPkaGcRXNAeCUOfc3dvMkSatoGAF/LfDa7mqa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a294889438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_crude_prevalence[df_crude_prevalence.LocationAbbr == \"AL\"].hist(column=[\"Sample_Size\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MoCp2G9oZEQgQfOldjzt+zAMRCIr1FtLRkDbFBygNYy+loU5G0AkEk9rU3G/s1ZrIqA34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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a296c54c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = df[\"Data_Value\"].plot().set(xlabel=\"Crema\", ylabel=\"Fernando\", title=\"Titulo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Google Trends Data\n",
    "\n",
    "### Selecting the Keywords using Text Mining\n",
    "\n",
    "According to (insert_reference) we can find the important keywords using text mining over resources that talk about diabetes. \n",
    "\n",
    "We implement a web scraping method that will work as follows:\n",
    "\n",
    "1. We will obtain **all** text content from the Wikipedia web page: [Diabetes Mellitus](https://en.wikipedia.org/wiki/Diabetes_mellitus).\n",
    "1. From the content of original page we will extract the n-grams using the package [NLTK](http://www.nltk.org/) 1-grams, 2-grams, 3-grams and 4-grams. \n",
    "1. From this n-grams we will select a group based on our research and extract the data using the [Pytrends API](https://github.com/GeneralMills/pytrends).\n",
    "\n",
    "In parallel, we will obtain all links inside the original page that are valid wikipedia links of the form: `https://en.wikipedia.org/wiki/{page}` merge all contents and repeat the steps 1, 2 and 3. Later on, as we will have the data from Pytrends we will decide based on the correlation of our results and the ground truth data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import inspect\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_soup_wikipedia(link, language=\"en\"):\n",
    "    base_url = \"https://{}.wikipedia.org/wiki/\".format(language) \n",
    "\n",
    "    # One petition per url\n",
    "    r = requests.get(base_url + link)\n",
    "\n",
    "    # Creating the soup object from Beautiful Soup\n",
    "    soup = BeautifulSoup(r.content, 'html.parser')\n",
    "    \n",
    "    return(soup)\n",
    "\n",
    "def parse_wikipedia(wiki_url, get_links=True):\n",
    "    wiki_soup = create_soup_wikipedia(link=wiki_url)\n",
    "    links, content = [], []\n",
    "\n",
    "    undesired_content = ['File', \n",
    "                        'citation', \n",
    "                        'Book', 'book',  \n",
    "                        'Help', \n",
    "                        'Template', \n",
    "                        'Wikipedia:Citation_needed', 'wikidata', 'wikipedia' \n",
    "                        'Category:CS1_maint', \n",
    "                        'Digital_object_identifier',\n",
    "                        'PubMed', '#', 'Category', 'http']\n",
    "\n",
    "    for part in wiki_soup.find_all(\"div\", {'class': 'mw-parser-output'}):\n",
    "        if get_links:\n",
    "            for link in part.find_all('a'):\n",
    "                try:\n",
    "                    if all(list(map(lambda x: x not in link['href'], undesired_content))) and '/wiki/' in link['href']:\n",
    "                        links.append(link['href'][6:])\n",
    "                except KeyError:\n",
    "                    continue\n",
    "        for paragraph in part.find_all('p'):\n",
    "            content.append(paragraph.text)\n",
    "    if get_links:\n",
    "        return content, links\n",
    "    else:\n",
    "        return content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_wikipedia_data(filename, itype):\n",
    "    if itype == \"links\":\n",
    "        links = []\n",
    "    elif itype == \"content\":\n",
    "        content = \"\"\n",
    "        \n",
    "    with open(filename, 'r') as f:\n",
    "        for line in f:\n",
    "            if itype == \"links\":\n",
    "                links.append(line.replace('\\n', ''))\n",
    "            elif itype == \"content\":\n",
    "                content += line\n",
    "                \n",
    "    return content if itype == \"content\" else links"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning data\n",
    "\n",
    "We now clean the data using the normal process:\n",
    "\n",
    "1. Transforming all upper characters to lower characters.\n",
    "1. Removing all stop words in english using the stopwords functionality of nltk.\n",
    "1. We don't do stemming as we want all words in complete manners.\n",
    "\n",
    "We give more importance to the words in the original article."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import wordpunct_tokenize\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_content(content):\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    numbers = '0123456789-–'\n",
    "    list_of_words = []\n",
    "    \n",
    "    for chunk in content:\n",
    "        for word in wordpunct_tokenize(chunk):\n",
    "            _word = word.lower()\n",
    "            if _word not in stop_words:\n",
    "                if all(list(map(lambda c: c not in string.punctuation and c not in numbers, _word))):\n",
    "                    list_of_words.append(_word)\n",
    "\n",
    "    return \" \".join(list_of_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning the principal article\n",
    "\n",
    "We first, clean the principal article as we want to give more importance to the information inside it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    diabetes_link = load_wikipedia_data(\"data/wikipedia/diabetes_links.dat\", itype=\"links\")\n",
    "    diabetes_content = load_wikipedia_data(\"data/wikipedia/diabetes_content.dat\", itype=\"content\")\n",
    "except FileNotFoundError:\n",
    "    print(\"[File Not Found] Downloading data for the first time\")\n",
    "    diabetes_content, diabetes_link = parse_wikipedia(wiki_url=\"Diabetes_mellitus\")\n",
    "    diabetes_content = clean_content(diabetes_content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning of the rest of articles\n",
    "\n",
    "To clean the rest of articles, we concatenate the cleaning of every article separated as reduces the memory used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    rest_articles_content = load_wikipedia_data(\"data/wikipedia/rest_content.dat\", itype=\"content\")\n",
    "except FileNotFoundError:\n",
    "    rest_of_content = []\n",
    "    all_links = set(diabetes_link)\n",
    "    n = len(all_links)\n",
    "    for idx, link in enumerate(all_links):\n",
    "        if idx % 20 == 0:\n",
    "            print(\"\\r Progress: {}%\".format(100*idx/n), end=\" \")\n",
    "        dirty_data = parse_wikipedia(wiki_url=link, get_links=False)\n",
    "        cleaned_data = clean_content(dirty_data)\n",
    "        rest_of_content.append(cleaned_data)\n",
    "        rest_articles_content = \" \".join(rest_of_content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving File for future use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_wikipedia_data(content, name, itype=\"content\"):\n",
    "    with open('data/wikipedia/{}.dat'.format(name), 'w') as f:\n",
    "        for c in content:\n",
    "            try:\n",
    "                f.write(c)\n",
    "                if itype == \"links\":\n",
    "                    f.write('\\n')\n",
    "            except:\n",
    "                continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_wikipedia_data(diabetes_content, \"diabetes_content\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_wikipedia_data(rest_articles_content, \"rest_content\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_wikipedia_data(diabetes_link, \"diabetes_links\", itype=\"links\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ngrams analysis for principal article\n",
    "\n",
    "To select our keywords, we will do a n-grams analysis using n = 1, 2, 3, 4. An n-gram is a term with n words that repeats commonly among the content we input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import ngrams\n",
    "from collections import Counter, defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_n_gram(content, n):\n",
    "    n_grams = ngrams(content.split(), n)\n",
    "    n_grams = Counter(n_grams)\n",
    "    return n_grams\n",
    "\n",
    "def by_result(result_n_gram):\n",
    "    dict_n_gram = defaultdict(list)\n",
    "    for name, quantity in result_n_gram:\n",
    "        dict_n_gram[quantity].append(\" \".join(list(name)))\n",
    "    return dict_n_gram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-grams in principal article\n",
    "\n",
    "Let's find the principal terms in the article"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "136: diabetes\n",
      "60: insulin, type\n",
      "43: blood\n",
      "35: glucose\n",
      "33: disease\n",
      "31: may\n",
      "26: mellitus\n",
      "23: dm, sugar, people, risk\n",
      "22: cases\n",
      "20: high, levels, body\n",
      "19: increased\n",
      "17: also\n",
      "16: term\n",
      "15: known\n",
      "14: diabetic\n",
      "13: include, complications, damage, due, cells, weight\n",
      "12: loss\n",
      "11: diet, medications, urine\n",
      "10: symptoms, cause, several, associated\n",
      "9: used, however\n",
      "8: death, long, cardiovascular\n"
     ]
    }
   ],
   "source": [
    "for idx, values in by_result(get_n_gram(content=diabetes_content, n=1).most_common(40)).items():\n",
    "    print(\"{}: {}\".format(idx, \", \".join(values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2-grams in principal article\n",
    "\n",
    "Let's find the 2-grams in the principal article"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26: type diabetes\n",
      "23: diabetes mellitus\n",
      "19: type dm\n",
      "18: blood sugar\n",
      "11: high blood\n",
      "7: sugar levels, cardiovascular disease, blood pressure, people diabetes, blood glucose\n",
      "6: long term, weight loss, gestational diabetes, citation needed, glucose levels, increased risk, fasting glucose\n",
      "5: low blood, onset diabetes, beta cells, mg dl, mmol l\n",
      "4: term complications, diabetes due, million deaths, known diabetic, severe cases, may also, risk diabetes, insulin secretion, physical activity, l mg\n",
      "3: increased thirst, complications include, kidney disease, damage eyes, cells body, three main, healthy diet, normal body\n"
     ]
    }
   ],
   "source": [
    "for idx, values in by_result(get_n_gram(content=diabetes_content, n=2).most_common(40)).items():\n",
    "    print(\"{}: {}\".format(idx, \", \".join(values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3-grams in principal article\n",
    "\n",
    "Let's find the 3-grams in the principal article."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7: blood sugar levels\n",
      "6: high blood sugar\n",
      "5: low blood sugar\n",
      "4: long term complications, mmol l mg, l mg dl\n",
      "3: normal body weight, diabetes type diabetes, world health organization, risks cardiovascular disease, people type diabetes\n",
      "2: hyperosmolar hyperglycemic state, chronic kidney disease, three main types, control blood pressure, type dm may, weight loss surgery, loss surgery obesity, estimated million people, million people diabetes, people diabetes worldwide, type dm several, high blood glucose, damage blood vessels, type gestational diabetes, type diabetes mellitus, diabetes mellitus characterized, diabetes mellitus cases, insulin dependent diabetes, type diabetes type, body mass index, lack physical activity, associated increased risk, blood glucose levels, forms diabetes mellitus, levels blood glucose, mg dl mmol, dl mmol l, impaired fasting glucose, glucose mmol l\n"
     ]
    }
   ],
   "source": [
    "for idx, values in by_result(get_n_gram(content=diabetes_content, n=3).most_common(40)).items():\n",
    "    print(\"{}: {}\".format(idx, \", \".join(values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4-gram in principal article\n",
    "\n",
    "Finally, we print the 4-gram in the principal article."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4: mmol l mg dl\n",
      "2: high blood sugar levels, weight loss surgery obesity, million people diabetes worldwide, type diabetes type diabetes, mg dl mmol l, glucose mmol l mg, income communities per population, term type diabetes replaced, type diabetes replaced several, diabetes replaced several former, replaced several former terms, several former terms including\n",
      "1: diabetes mellitus dm commonly, mellitus dm commonly referred, dm commonly referred diabetes, commonly referred diabetes group, referred diabetes group metabolic, diabetes group metabolic disorders, group metabolic disorders high, metabolic disorders high blood, disorders high blood sugar, blood sugar levels prolonged, sugar levels prolonged period, levels prolonged period symptoms, prolonged period symptoms high, period symptoms high blood, symptoms high blood sugar, high blood sugar include, blood sugar include frequent, sugar include frequent urination, include frequent urination increased, frequent urination increased thirst, urination increased thirst increased, increased thirst increased hunger, thirst increased hunger left, increased hunger left untreated, hunger left untreated diabetes, left untreated diabetes cause, untreated diabetes cause many\n"
     ]
    }
   ],
   "source": [
    "for idx, values in by_result(get_n_gram(content=diabetes_content, n=4).most_common(40)).items():\n",
    "    print(\"{}: {}\".format(idx, \", \".join(values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## N-gram analysis for rest of content\n",
    "\n",
    "We see that the number of characters in the rest of articles is extremly high (3 Million words) as a result, we plot the distribution of words to decide the k that we want to use.\n",
    "\n",
    "### 1-gram analysis for rest of content\n",
    "\n",
    "#### Plot of the distribution of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rl+Tah+RWOwp4PE1PBcZI2kDSUGAY8BAwAxgmaaik9clu+k+tVdxmZrb2annlsh/wKWC2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb4af52ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "one_gram = get_n_gram(content=rest_articles_content, n=1)\n",
    "\n",
    "# the histogram of the data\n",
    "n, bins, patches = plt.hist(one_gram.values(), 50, density=False, facecolor='g', alpha=0.75)\n",
    "\n",
    "\n",
    "plt.xlabel('Number of 1-gram')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of words distribution')\n",
    "plt.axis([0, 3000, 0, 3500])\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We clearly see that the 1-gram distribution follows a power law. Most of the words appear few times and few of the words retain almost all the frequencies. We must select the words that have high appearence and low frequency in the histogram.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2913: may\n",
      "2539: blood\n",
      "2245: also\n",
      "2086: insulin\n",
      "2014: diabetes\n",
      "1857: glucose\n",
      "1813: disease\n",
      "1551: cells\n",
      "1512: levels\n",
      "1412: one\n",
      "1363: used\n",
      "1288: people\n",
      "1269: risk\n",
      "1223: health\n",
      "1200: type\n",
      "1152: high\n",
      "1055: treatment\n",
      "1001: body\n",
      "994: cause\n",
      "963: many\n",
      "952: first\n",
      "944: patients\n",
      "908: symptoms\n",
      "885: include\n",
      "873: hormone\n",
      "871: however, medical\n",
      "864: often\n",
      "835: use\n",
      "833: common\n",
      "829: two\n",
      "826: low\n",
      "824: cell\n",
      "817: due\n",
      "782: normal\n",
      "781: increased\n",
      "778: found\n",
      "775: sugar\n",
      "753: known\n",
      "739: associated\n",
      "737: thyroid\n",
      "728: cases\n",
      "715: syndrome\n",
      "673: needed\n",
      "661: pressure\n",
      "659: women\n",
      "639: well\n",
      "637: years\n",
      "618: including\n",
      "608: usually\n"
     ]
    }
   ],
   "source": [
    "for idx, values in by_result(get_n_gram(content=rest_articles_content, n=1).most_common(50)).items():\n",
    "    print(\"{}: {}\".format(idx, \", \".join(values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 2-gram analysis for rest of content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "505: type diabetes\n",
      "482: blood glucose\n",
      "467: citation needed\n",
      "392: united states\n",
      "371: blood pressure\n",
      "335: blood sugar\n",
      "306: insulin resistance\n",
      "303: e g\n",
      "295: diabetes mellitus\n",
      "279: glucose levels\n",
      "245: may also\n",
      "234: cardiovascular disease\n",
      "225: long term\n",
      "223: mg dl\n",
      "219: high blood\n",
      "207: open access\n",
      "174: fatty acids\n",
      "172: beta cells\n",
      "164: mmol l\n",
      "161: heart disease\n",
      "151: risk factors\n",
      "149: world health\n",
      "139: also known, increased risk\n",
      "135: growth hormone\n",
      "128: thyroid hormone\n",
      "127: weight loss\n",
      "124: trans fats\n",
      "120: pituitary gland\n",
      "119: low blood, kidney disease\n",
      "114: sugar levels\n",
      "113: blood vessels, trans fat\n",
      "112: health organization\n",
      "107: health care\n",
      "106: nervous system\n",
      "102: glucose level\n",
      "97: l mg\n",
      "94: thyroid gland\n",
      "93: side effects\n",
      "91: caesarean section\n",
      "90: may include\n",
      "88: also used\n",
      "87: may cause\n",
      "85: people diabetes\n",
      "82: immune system, may occur\n",
      "81: high levels\n",
      "79: amino acids\n"
     ]
    }
   ],
   "source": [
    "for idx, values in by_result(get_n_gram(content=rest_articles_content, n=2).most_common(50)).items():\n",
    "    print(\"{}: {}\".format(idx, \", \".join(values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3-gram analysis for the rest of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159: blood glucose levels\n",
      "113: high blood pressure\n",
      "111: blood sugar levels\n",
      "98: world health organization\n",
      "97: l mg dl\n",
      "96: mmol l mg\n",
      "59: diabetes mellitus type\n",
      "58: blood glucose level\n",
      "54: chronic kidney disease\n",
      "53: american diabetes association\n",
      "47: type diabetes mellitus, central nervous system\n",
      "46: thyroid stimulating hormone, high blood sugar\n",
      "41: medical citation needed, multiple endocrine neoplasia\n",
      "40: risk cardiovascular disease\n",
      "38: low blood sugar, red blood cells\n",
      "37: food drug administration, coronary artery disease, people type diabetes\n",
      "36: open access journals\n",
      "35: body mass index, coronary heart disease\n",
      "32: congenital adrenal hyperplasia\n",
      "30: impaired fasting glucose, growth hormone deficiency\n",
      "29: impaired glucose tolerance\n",
      "28: stimulating hormone tsh, blood sugar level\n",
      "27: glucose tolerance test, mg dl mmol, dl mmol l\n",
      "26: follicle stimulating hormone, world health assembly, embryonic stem cells\n",
      "24: hormone replacement therapy\n",
      "22: low blood pressure, corticotropin releasing hormone, glomerular filtration rate, insulin like growth, like growth factor\n",
      "21: angiotensin ii receptor, endocrine neoplasia type, citric acid cycle\n",
      "20: low blood glucose, fasting blood glucose, drug administration fda, associated increased risk, one study found, heart attack stroke\n",
      "19: red blood cell, national institute health, waist hip ratio, centers disease control, disease control prevention, cochrane review found, patients type diabetes, risk type diabetes\n"
     ]
    }
   ],
   "source": [
    "for idx, values in by_result(get_n_gram(content=rest_articles_content, n=3).most_common(60)).items():\n",
    "    print(\"{}: {}\".format(idx, \", \".join(values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4-gram analysis for the rest of data\n",
    "\n",
    "Finally, let's see the 4-gram results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96: mmol l mg dl\n",
      "28: thyroid stimulating hormone tsh\n",
      "27: mg dl mmol l\n",
      "22: insulin like growth factor\n",
      "21: multiple endocrine neoplasia type\n",
      "19: food drug administration fda\n",
      "18: centers disease control prevention\n",
      "14: level mmol l mg, follicle stimulating hormone fsh, ° c ° f, angiotensin ii receptor antagonists, people high blood pressure, maturity onset diabetes young\n",
      "13: u food drug administration, autoimmune polyendocrine syndrome type, preventive services task force\n",
      "12: national institute health clinical, institute health clinical excellence, high blood sugar levels, less mmol l mg, short chain fatty acids\n",
      "11: insulin resistance type diabetes, body mass index bmi, like growth factor igf\n",
      "10: latent autoimmune diabetes adults, end stage kidney disease, insulin producing beta cells, range mmol l mg, l mg dl considered, onset diabetes young mody, department health human services\n",
      "9: low middle income countries, corticotropin releasing hormone crh, magnetic resonance imaging mri, united states preventive services, states preventive services task, risk coronary artery disease, end stage renal disease, risk developing type diabetes, labor force participation rate\n",
      "8: congenital adrenal hyperplasia cah, oral glucose tolerance test, type diabetes type diabetes, international statistical classification diseases, edar edar hypohidrotic ectodermal, edar hypohidrotic ectodermal dysplasia, national health nutrition examination, health nutrition examination survey, chronic obstructive pulmonary disease, calories kj per person\n"
     ]
    }
   ],
   "source": [
    "for idx, values in by_result(get_n_gram(content=rest_articles_content, n=4).most_common(50)).items():\n",
    "    print(\"{}: {}\".format(idx, \", \".join(values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading the data using the pytrend API\n",
    "\n",
    "Command to install the necessary package: `pip install pytrends` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pytrends.request import TrendReq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_list = {\"1\": [\"diabetes\", \"sugar\", \"glucose\", \"insulin\", \"diagnosis\"], \n",
    "            \"2\": [\"blood pressure\", \"fruit\", \"cholesterol\", \"diet\", \"type 1\"],\n",
    "            \"3\": [\"type 2\", \"symptoms\", \"diabetes mellitus\", \"diagnosis\", \"complications\"],\n",
    "            \"4\": [\"osteoporosis\", \"hypertension\",\"obesity\", \"fat\", \"association\"],\n",
    "            \"2-grams\": [\"blood glucose\", \"heart disease\", \"cardiovascular disease\", \"kidney disease\", \"weigth loss\"],\n",
    "            \"3-grams\": [\"blood glucose level\", \"high blood pressure\", \"high blood sugar\", \"glucose tolerance test\", \"like growth factor\"],\n",
    "            \"3-grams-2\": [\"central nervous system\", \"thyroid stimulating hormone\", \"coronary artery disease\", \"end stage renal disease\", \"risk developing diabetes\"]\n",
    "           }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_s = '-01-01 '\n",
    "d_e = '-12-31'\n",
    "pytrend = TrendReq()\n",
    "for i in range(2004,2017):\n",
    "    period = str(i) + d_s + str(i) + d_e\n",
    "    for key in key_list:\n",
    "        pytrend.build_payload(kw_list=key_list[key],\n",
    "                              geo=\"US\",\n",
    "                              cat = 630,\n",
    "                              timeframe= period)\n",
    "        if key == \"1\":\n",
    "            interest_by_region_df = pytrend.interest_by_region()\n",
    "        else:\n",
    "            interest_by_region_df = pd.concat([interest_by_region_df, pytrend.interest_by_region()], axis = 1)\n",
    "    file = str(i) + \".csv\"\n",
    "    interest_by_region_df.to_csv(\"data/pytrends/\"+file, sep = ',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transforming Google Trends data into desired format\n",
    "\n",
    "By now, we have the data separated by years in the form: `State k1 k2 ... kn.` \n",
    "\n",
    "Our goal is to create a unified matrix of data of the form: `State Year k1 k2 ... kn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "years = np.array([2015]*52)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def concatenate_trends_data():\n",
    "    trends, counter = [], 1\n",
    "    \n",
    "    for year in range(2004, 2017):\n",
    "        df = pd.read_csv('data/pytrends/{}.csv'.format(year), sep=',')\n",
    "        row, columnd = df.shape\n",
    "        df['Year'] = np.array([year]*row)\n",
    "        df.rename(columns=lambda x: \"State\" if x == 'Unnamed: 0' else x, inplace=True)\n",
    "        rest = list(sorted(x for x in set(df.columns) if x is not \"Year\" and x is not \"State\"))\n",
    "        df = df[[\"State\", \"Year\", ]+rest]\n",
    "        trends.append(df)\n",
    "        \n",
    "    return pd.concat(trends)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "keywords = concatenate_trends_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the keywords data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "keywords.to_csv('data/pytrends/keywords.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NA analysis in keywords data\n",
    "\n",
    "Before doing the correlation Analysis, we will do a NA analysis in our dataframe. According to [Google Trends Data](https://www.kaggle.com/shakos/google-trends-data-extension/data) an NA may mean:\n",
    "\n",
    "1. That the data is not available.\n",
    "1. That the data is actually 0.\n",
    "\n",
    "As a result, the first and naive approach to select a viable subset of possible keywords is to eliminate those with a lot of NA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting columns with a given threshold\n",
    "\n",
    "We now will create a function that eliminates columns given a certain threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eliminate_na_columns(df, threshold):\n",
    "    from math import ceil\n",
    "    n, c = df.shape\n",
    "    print(\"With {} threshold used, we need to have at least {} NA values\".format(threshold, ceil(threshold*n)))\n",
    "    return df.dropna(axis=1, thresh=ceil((1-threshold)*n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(663, 42)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keywords.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State                              0\n",
       "Year                               0\n",
       "american diabetes association     91\n",
       "association                        3\n",
       "blood glucose                    195\n",
       "blood glucose level              223\n",
       "blood pressure                    22\n",
       "cardiovascular disease           195\n",
       "central nervous system           541\n",
       "cholesterol                       22\n",
       "complications                     60\n",
       "coronary artery disease          541\n",
       "diabetes                           0\n",
       "diabetes mellitus                 60\n",
       "diagnosis                          0\n",
       "diagnosis.1                       60\n",
       "diet                              22\n",
       "disease control prevention        91\n",
       "end stage renal disease          541\n",
       "fat                                3\n",
       "fruit                             22\n",
       "glucose                            0\n",
       "glucose tolerance test           223\n",
       "heart disease                    195\n",
       "high blood pressure              223\n",
       "high blood sugar                 223\n",
       "hypertension                       3\n",
       "insulin                            0\n",
       "kidney disease                   195\n",
       "like growth factor               223\n",
       "obesity                            3\n",
       "osteoporosis                       3\n",
       "risk developing diabetes         541\n",
       "risk type diabetes                91\n",
       "sugar                              0\n",
       "symptoms                          60\n",
       "thyroid stimulating hormone      541\n",
       "type 1                            22\n",
       "type 2                            60\n",
       "type diabetes mellitus            91\n",
       "weigth loss                      195\n",
       "world health organization         91\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keywords.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deciding the threshold\n",
    "\n",
    "To decide the threshold, we first analyze if time is an important factor in the number of NA's as we think internet coverage determines the amount of data we may have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>american diabetes association</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>association</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blood glucose</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blood glucose level</th>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blood pressure</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardiovascular disease</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>central nervous system</th>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cholesterol</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>complications</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coronary artery disease</th>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes mellitus</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diagnosis</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diagnosis.1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diet</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disease control prevention</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end stage renal disease</th>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fat</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fruit</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glucose</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>glucose tolerance test</th>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart disease</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high blood pressure</th>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high blood sugar</th>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hypertension</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insulin</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kidney disease</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>like growth factor</th>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obesity</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>osteoporosis</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk developing diabetes</th>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>risk type diabetes</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sugar</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symptoms</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thyroid stimulating hormone</th>\n",
       "      <td>49</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type 1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type 2</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type diabetes mellitus</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weigth loss</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>world health organization</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               2004  2005  2006  2007  2008  2009  2010  2011  \\\n",
       "State                             0     0     0     0     0     0     0     0   \n",
       "Year                              0     0     0     0     0     0     0     0   \n",
       "american diabetes association     8     8     6     7     6     7     7     6   \n",
       "association                       1     2     0     0     0     0     0     0   \n",
       "blood glucose                    15    15    15    15    15    15    15    15   \n",
       "blood glucose level              24    16    17    19    20    18    18    16   \n",
       "blood pressure                    1     1     3     1     2     3     4     0   \n",
       "cardiovascular disease           15    15    15    15    15    15    15    15   \n",
       "central nervous system           49    48    47    46    42    42    40    31   \n",
       "cholesterol                       1     1     3     1     2     3     4     0   \n",
       "complications                     5     5     1     5     4     3     6     3   \n",
       "coronary artery disease          49    48    47    46    42    42    40    31   \n",
       "diabetes                          0     0     0     0     0     0     0     0   \n",
       "diabetes mellitus                 5     5     1     5     4     3     6     3   \n",
       "diagnosis                         0     0     0     0     0     0     0     0   \n",
       "diagnosis.1                       5     5     1     5     4     3     6     3   \n",
       "diet                              1     1     3     1     2     3     4     0   \n",
       "disease control prevention        8     8     6     7     6     7     7     6   \n",
       "end stage renal disease          49    48    47    46    42    42    40    31   \n",
       "fat                               1     2     0     0     0     0     0     0   \n",
       "fruit                             1     1     3     1     2     3     4     0   \n",
       "glucose                           0     0     0     0     0     0     0     0   \n",
       "glucose tolerance test           24    16    17    19    20    18    18    16   \n",
       "heart disease                    15    15    15    15    15    15    15    15   \n",
       "high blood pressure              24    16    17    19    20    18    18    16   \n",
       "high blood sugar                 24    16    17    19    20    18    18    16   \n",
       "hypertension                      1     2     0     0     0     0     0     0   \n",
       "insulin                           0     0     0     0     0     0     0     0   \n",
       "kidney disease                   15    15    15    15    15    15    15    15   \n",
       "like growth factor               24    16    17    19    20    18    18    16   \n",
       "obesity                           1     2     0     0     0     0     0     0   \n",
       "osteoporosis                      1     2     0     0     0     0     0     0   \n",
       "risk developing diabetes         49    48    47    46    42    42    40    31   \n",
       "risk type diabetes                8     8     6     7     6     7     7     6   \n",
       "sugar                             0     0     0     0     0     0     0     0   \n",
       "symptoms                          5     5     1     5     4     3     6     3   \n",
       "thyroid stimulating hormone      49    48    47    46    42    42    40    31   \n",
       "type 1                            1     1     3     1     2     3     4     0   \n",
       "type 2                            5     5     1     5     4     3     6     3   \n",
       "type diabetes mellitus            8     8     6     7     6     7     7     6   \n",
       "weigth loss                      15    15    15    15    15    15    15    15   \n",
       "world health organization         8     8     6     7     6     7     7     6   \n",
       "\n",
       "                               2012  2013  2014  2015  2016  \n",
       "State                             0     0     0     0     0  \n",
       "Year                              0     0     0     0     0  \n",
       "american diabetes association     7     7     6     7     9  \n",
       "association                       0     0     0     0     0  \n",
       "blood glucose                    15    15    15    15    15  \n",
       "blood glucose level              15    15    15    15    15  \n",
       "blood pressure                    1     2     2     2     0  \n",
       "cardiovascular disease           15    15    15    15    15  \n",
       "central nervous system           40    40    40    33    43  \n",
       "cholesterol                       1     2     2     2     0  \n",
       "complications                     6     5     5     6     6  \n",
       "coronary artery disease          40    40    40    33    43  \n",
       "diabetes                          0     0     0     0     0  \n",
       "diabetes mellitus                 6     5     5     6     6  \n",
       "diagnosis                         0     0     0     0     0  \n",
       "diagnosis.1                       6     5     5     6     6  \n",
       "diet                              1     2     2     2     0  \n",
       "disease control prevention        7     7     6     7     9  \n",
       "end stage renal disease          40    40    40    33    43  \n",
       "fat                               0     0     0     0     0  \n",
       "fruit                             1     2     2     2     0  \n",
       "glucose                           0     0     0     0     0  \n",
       "glucose tolerance test           15    15    15    15    15  \n",
       "heart disease                    15    15    15    15    15  \n",
       "high blood pressure              15    15    15    15    15  \n",
       "high blood sugar                 15    15    15    15    15  \n",
       "hypertension                      0     0     0     0     0  \n",
       "insulin                           0     0     0     0     0  \n",
       "kidney disease                   15    15    15    15    15  \n",
       "like growth factor               15    15    15    15    15  \n",
       "obesity                           0     0     0     0     0  \n",
       "osteoporosis                      0     0     0     0     0  \n",
       "risk developing diabetes         40    40    40    33    43  \n",
       "risk type diabetes                7     7     6     7     9  \n",
       "sugar                             0     0     0     0     0  \n",
       "symptoms                          6     5     5     6     6  \n",
       "thyroid stimulating hormone      40    40    40    33    43  \n",
       "type 1                            1     2     2     2     0  \n",
       "type 2                            6     5     5     6     6  \n",
       "type diabetes mellitus            7     7     6     7     9  \n",
       "weigth loss                      15    15    15    15    15  \n",
       "world health organization         7     7     6     7     9  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NA = []\n",
    "for year in range(2004, 2017):\n",
    "    NA.append(keywords[keywords.Year == year].isnull().sum())\n",
    "NA_df = pd.concat(NA, axis=1)\n",
    "NA_df.rename(columns=lambda x: str(2004+x), inplace=True)\n",
    "NA_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot of our results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gYUmSSrCwJEklWFiSpBIsLElSCRaWJKkEC0uSVIKFJUkqwcKSJJVgYUmSSrCwJEklWFiS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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a577e5ae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NA_df.boxplot(figsize=(7,7))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that our initial hypothesis was wrong. The number of missing values is not affected by time, therefore, we can discriminate uniformly for all years. Finally, we eliminate the columns with at least 35% NA's. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With 0.35 threshold used, we need to have at least 233 NA values\n"
     ]
    }
   ],
   "source": [
    "keywords_clean = eliminate_na_columns(keywords, threshold=0.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State                       0\n",
       "Year                        0\n",
       "association                 2\n",
       "blood glucose             195\n",
       "blood glucose level       224\n",
       "blood pressure             24\n",
       "cardiovascular disease    195\n",
       "cholesterol                24\n",
       "complications              58\n",
       "diabetes                    0\n",
       "diabetes mellitus          58\n",
       "diagnosis                   0\n",
       "diagnosis.1                58\n",
       "diet                       24\n",
       "fat                         2\n",
       "fruit                      24\n",
       "glucose                     0\n",
       "glucose tolerance test    224\n",
       "heart disease             195\n",
       "high blood pressure       224\n",
       "high blood sugar          224\n",
       "hypertension                2\n",
       "insulin                     0\n",
       "kidney disease            195\n",
       "like growth factor        224\n",
       "obesity                     2\n",
       "osteoporosis                2\n",
       "sugar                       0\n",
       "symptoms                   58\n",
       "type 1                     24\n",
       "type 2                     58\n",
       "weigth loss               195\n",
       "dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keywords_clean.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing NA's to 0\n",
    "\n",
    "As we mention, NA can mean data not available or exactly 0. Therefore, we input 0 in all NA values. As a result, we will have a sparse matrix in the features space. A regression approach handling sparse input must be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "keywords_clean = keywords_clean.fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing data in columns\n",
    "\n",
    "We now analyze "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation Analysis\n",
    "\n",
    "Before doing the correlation Analysis, we need to transform our ground truth data selecting the same available information regarding spatial and temporal aggregation. By now, we have more information in the ground truth data as we have US total values + dependencies that are not states such as Puerto Rico and Guam.\n",
    "\n",
    "### Intersection of keywords and ground truth data \n",
    "\n",
    "Let's see if the State Key in our ground truth data have the same names as the keywords data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(states).intersection(set(keywords_clean[\"State\"].values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now select the rows with same States in our ground truth data, change the name of the identifier of States amnd finally just selecting the prevalence value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Year</th>\n",
       "      <th>Prevalence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2004</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2004</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2004</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2004</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>California</td>\n",
       "      <td>2004</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  Year  Prevalence\n",
       "2     Alabama  2004         8.1\n",
       "3      Alaska  2004         4.2\n",
       "4     Arizona  2004         6.6\n",
       "5    Arkansas  2004         7.1\n",
       "6  California  2004         7.1"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first change the name of the columns\n",
    "df_crude_prevalence.rename(columns=lambda x: \"State\" if x == 'LocationDesc' else x, inplace=True)\n",
    "df_crude_prevalence.rename(columns=lambda x: \"Prevalence\" if x == 'Data_Value' else x, inplace=True)\n",
    "df_crude_prevalence = df_crude_prevalence[[\"State\", \"Year\", \"Prevalence\"]]\n",
    "df_crude_prevalence = df_crude_prevalence.loc[df_crude_prevalence['State'].isin(keywords_clean[\"State\"].values)]\n",
    "df_crude_prevalence.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge ground truth data with keywords \n",
    "\n",
    "We now merge both dataframes to add the response variable **Prevalence**. To do this, we join both dataframes on the pair (state, year)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th>association</th>\n",
       "      <th>blood glucose</th>\n",
       "      <th>blood glucose level</th>\n",
       "      <th>blood pressure</th>\n",
       "      <th>cardiovascular disease</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>complications</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>...</th>\n",
       "      <th>kidney disease</th>\n",
       "      <th>like growth factor</th>\n",
       "      <th>obesity</th>\n",
       "      <th>osteoporosis</th>\n",
       "      <th>sugar</th>\n",
       "      <th>symptoms</th>\n",
       "      <th>type 1</th>\n",
       "      <th>type 2</th>\n",
       "      <th>weigth loss</th>\n",
       "      <th>Prevalence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2004</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2004</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2004</td>\n",
       "      <td>48.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2004</td>\n",
       "      <td>10.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>2004</td>\n",
       "      <td>29.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  Year  association  blood glucose  blood glucose level  \\\n",
       "0     Alabama  2004         31.0           16.0                  0.0   \n",
       "1      Alaska  2004         15.0            0.0                  0.0   \n",
       "2     Arizona  2004         48.0           35.0                  0.0   \n",
       "3    Arkansas  2004         10.0           96.0                  0.0   \n",
       "4  California  2004         29.0           47.0                 15.0   \n",
       "\n",
       "   blood pressure  cardiovascular disease  cholesterol  complications  \\\n",
       "0             0.0                     0.0          0.0            8.0   \n",
       "1             0.0                     0.0          0.0            0.0   \n",
       "2             0.0                     0.0          2.0            3.0   \n",
       "3             0.0                     0.0          0.0            0.0   \n",
       "4             0.0                     1.0          1.0            3.0   \n",
       "\n",
       "   diabetes     ...      kidney disease  like growth factor  obesity  \\\n",
       "0        59     ...                 0.0                 0.0      0.0   \n",
       "1        49     ...                 0.0                 0.0      0.0   \n",
       "2        56     ...                 0.0                 0.0      4.0   \n",
       "3        59     ...                 0.0                 0.0      0.0   \n",
       "4        57     ...                 1.0                 0.0      5.0   \n",
       "\n",
       "   osteoporosis  sugar  symptoms  type 1  type 2  weigth loss  Prevalence  \n",
       "0           0.0     10      32.0     8.0    16.0          0.0         8.1  \n",
       "1           0.0      8       0.0     0.0     0.0          0.0         4.2  \n",
       "2           0.0      5      14.0     9.0    21.0          0.0         6.6  \n",
       "3           0.0      4      48.0     0.0     0.0          0.0         7.1  \n",
       "4           0.0      5      15.0     6.0    22.0          0.0         7.1  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prevalence_keywords = pd.merge(keywords_clean, df_crude_prevalence, on=[\"State\", \"Year\"])\n",
    "df_prevalence_keywords.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation in Space\n",
    "\n",
    "We now will analyze the correlation in space. This means that we will create the correlation matrix in which every row will be a State and the columns will be the keywords. Every entry (state, keyword) will be the correlation between the state and the keyword using the function [corrcoef](https://docs.scipy.org/doc/numpy/reference/generated/numpy.corrcoef.html) from numpy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_crude_prevalence.shape\n",
    "keywords = [keyword for keyword in df_prevalence_keywords.columns if keyword is not \"State\" and keyword is not \"Year\" and keyword is not \"Prevalence\"]\n",
    "states = list(set(keywords_clean[\"State\"].values))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3167: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3168: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n"
     ]
    }
   ],
   "source": [
    "dict_correlation_state = {\n",
    "    keyword: [] for keyword in keywords    \n",
    "}\n",
    "\n",
    "dict_correlation_state[\"States\"] = states\n",
    "\n",
    "for state in states:\n",
    "    df_state = df_prevalence_keywords[(df_prevalence_keywords.State == state)]\n",
    "    for keyword in keywords:\n",
    "        dict_correlation_state[keyword].append(np.corrcoef(df_state[keyword], df_state[\"Prevalence\"])[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.822601</td>\n",
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       "      <td>0.754765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.300352</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.753936</td>\n",
       "      <td>-0.479090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>Arizona</th>\n",
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       "      <td>0.719039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.623545</td>\n",
       "      <td>-0.190179</td>\n",
       "      <td>0.032708</td>\n",
       "      <td>-0.145490</td>\n",
       "      <td>0.657732</td>\n",
       "      <td>0.490949</td>\n",
       "      <td>0.144963</td>\n",
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       "      <td>0.860131</td>\n",
       "      <td>0.468025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.736345</td>\n",
       "      <td>0.447821</td>\n",
       "      <td>0.915794</td>\n",
       "      <td>0.452533</td>\n",
       "      <td>0.829593</td>\n",
       "      <td>0.880723</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>Arkansas</th>\n",
       "      <td>0.434100</td>\n",
       "      <td>0.425867</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.405627</td>\n",
       "      <td>0.471961</td>\n",
       "      <td>0.408059</td>\n",
       "      <td>0.095869</td>\n",
       "      <td>0.607123</td>\n",
       "      <td>-0.013593</td>\n",
       "      <td>-0.030005</td>\n",
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       "      <td>0.879440</td>\n",
       "      <td>0.556073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.826077</td>\n",
       "      <td>0.143201</td>\n",
       "      <td>0.921532</td>\n",
       "      <td>-0.425577</td>\n",
       "      <td>0.638355</td>\n",
       "      <td>0.769258</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>0.287668</td>\n",
       "      <td>0.760761</td>\n",
       "      <td>0.457957</td>\n",
       "      <td>0.553659</td>\n",
       "      <td>-0.516222</td>\n",
       "      <td>0.059501</td>\n",
       "      <td>-0.268303</td>\n",
       "      <td>0.638093</td>\n",
       "      <td>-0.147969</td>\n",
       "      <td>0.703940</td>\n",
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       "      <td>0.831196</td>\n",
       "      <td>0.697520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.647474</td>\n",
       "      <td>0.337031</td>\n",
       "      <td>0.842815</td>\n",
       "      <td>0.693290</td>\n",
       "      <td>0.879822</td>\n",
       "      <td>0.819246</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>0.488439</td>\n",
       "      <td>0.763977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.336756</td>\n",
       "      <td>-0.034811</td>\n",
       "      <td>-0.064678</td>\n",
       "      <td>-0.287024</td>\n",
       "      <td>0.452394</td>\n",
       "      <td>0.008226</td>\n",
       "      <td>0.198949</td>\n",
       "      <td>...</td>\n",
       "      <td>0.763328</td>\n",
       "      <td>0.377471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.511544</td>\n",
       "      <td>-0.019589</td>\n",
       "      <td>0.796402</td>\n",
       "      <td>0.672618</td>\n",
       "      <td>0.848831</td>\n",
       "      <td>0.687339</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>0.584777</td>\n",
       "      <td>0.438127</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.084013</td>\n",
       "      <td>-0.200226</td>\n",
       "      <td>-0.048677</td>\n",
       "      <td>-0.288569</td>\n",
       "      <td>0.688396</td>\n",
       "      <td>0.337517</td>\n",
       "      <td>0.443634</td>\n",
       "      <td>...</td>\n",
       "      <td>0.620769</td>\n",
       "      <td>0.231064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.566026</td>\n",
       "      <td>0.098486</td>\n",
       "      <td>0.925510</td>\n",
       "      <td>0.213120</td>\n",
       "      <td>0.751747</td>\n",
       "      <td>0.728912</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>0.379108</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.146414</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.405593</td>\n",
       "      <td>-0.222425</td>\n",
       "      <td>0.782084</td>\n",
       "      <td>-0.136936</td>\n",
       "      <td>0.183656</td>\n",
       "      <td>...</td>\n",
       "      <td>0.691315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.415233</td>\n",
       "      <td>0.177539</td>\n",
       "      <td>0.686528</td>\n",
       "      <td>-0.184493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>District of Columbia</th>\n",
       "      <td>-0.206131</td>\n",
       "      <td>0.175814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.353372</td>\n",
       "      <td>-0.716400</td>\n",
       "      <td>-0.051893</td>\n",
       "      <td>0.248221</td>\n",
       "      <td>0.177846</td>\n",
       "      <td>0.088918</td>\n",
       "      <td>0.257573</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059816</td>\n",
       "      <td>0.169360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.284149</td>\n",
       "      <td>0.096274</td>\n",
       "      <td>0.259938</td>\n",
       "      <td>0.339505</td>\n",
       "      <td>0.348241</td>\n",
       "      <td>0.229860</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>0.361131</td>\n",
       "      <td>0.800430</td>\n",
       "      <td>-0.184453</td>\n",
       "      <td>0.388033</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>-0.214782</td>\n",
       "      <td>-0.132109</td>\n",
       "      <td>0.701894</td>\n",
       "      <td>0.520903</td>\n",
       "      <td>0.135973</td>\n",
       "      <td>...</td>\n",
       "      <td>0.924610</td>\n",
       "      <td>0.323434</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.675603</td>\n",
       "      <td>0.469262</td>\n",
       "      <td>0.899702</td>\n",
       "      <td>0.497410</td>\n",
       "      <td>0.786687</td>\n",
       "      <td>0.805177</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>-0.046480</td>\n",
       "      <td>0.133165</td>\n",
       "      <td>0.309467</td>\n",
       "      <td>0.464075</td>\n",
       "      <td>0.140374</td>\n",
       "      <td>0.247244</td>\n",
       "      <td>-0.712889</td>\n",
       "      <td>0.650117</td>\n",
       "      <td>-0.504069</td>\n",
       "      <td>0.316169</td>\n",
       "      <td>...</td>\n",
       "      <td>0.720520</td>\n",
       "      <td>-0.393698</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.727797</td>\n",
       "      <td>-0.470820</td>\n",
       "      <td>0.869571</td>\n",
       "      <td>0.363871</td>\n",
       "      <td>0.797002</td>\n",
       "      <td>0.851473</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>0.123430</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.235049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.347448</td>\n",
       "      <td>-0.042640</td>\n",
       "      <td>0.511900</td>\n",
       "      <td>-0.483989</td>\n",
       "      <td>-0.271104</td>\n",
       "      <td>...</td>\n",
       "      <td>0.450289</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.458914</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.631224</td>\n",
       "      <td>0.004196</td>\n",
       "      <td>0.090484</td>\n",
       "      <td>0.410954</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>-0.214203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.062110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.237506</td>\n",
       "      <td>0.343475</td>\n",
       "      <td>0.642450</td>\n",
       "      <td>-0.303832</td>\n",
       "      <td>-0.114263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.844287</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.119071</td>\n",
       "      <td>0.517894</td>\n",
       "      <td>0.722450</td>\n",
       "      <td>0.480592</td>\n",
       "      <td>0.316480</td>\n",
       "      <td>0.270412</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>0.013350</td>\n",
       "      <td>0.509219</td>\n",
       "      <td>-0.071070</td>\n",
       "      <td>0.669638</td>\n",
       "      <td>-0.252821</td>\n",
       "      <td>0.150837</td>\n",
       "      <td>0.280215</td>\n",
       "      <td>0.470505</td>\n",
       "      <td>-0.151111</td>\n",
       "      <td>0.374345</td>\n",
       "      <td>...</td>\n",
       "      <td>0.790266</td>\n",
       "      <td>0.122955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.708582</td>\n",
       "      <td>0.362105</td>\n",
       "      <td>0.868081</td>\n",
       "      <td>0.417055</td>\n",
       "      <td>0.726859</td>\n",
       "      <td>0.788345</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>0.228728</td>\n",
       "      <td>0.441691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.662245</td>\n",
       "      <td>0.349259</td>\n",
       "      <td>-0.208045</td>\n",
       "      <td>-0.466148</td>\n",
       "      <td>0.380249</td>\n",
       "      <td>-0.273110</td>\n",
       "      <td>0.606164</td>\n",
       "      <td>...</td>\n",
       "      <td>0.688349</td>\n",
       "      <td>-0.475087</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.514060</td>\n",
       "      <td>-0.005127</td>\n",
       "      <td>0.909107</td>\n",
       "      <td>0.393375</td>\n",
       "      <td>0.787013</td>\n",
       "      <td>0.741794</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>0.360802</td>\n",
       "      <td>0.169938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.104670</td>\n",
       "      <td>0.195950</td>\n",
       "      <td>0.047239</td>\n",
       "      <td>0.072554</td>\n",
       "      <td>0.346983</td>\n",
       "      <td>0.054286</td>\n",
       "      <td>0.104597</td>\n",
       "      <td>...</td>\n",
       "      <td>0.718310</td>\n",
       "      <td>-0.147453</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.529848</td>\n",
       "      <td>0.215169</td>\n",
       "      <td>0.891130</td>\n",
       "      <td>0.013606</td>\n",
       "      <td>0.610687</td>\n",
       "      <td>0.344335</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>0.070868</td>\n",
       "      <td>0.367990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.038414</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.708063</td>\n",
       "      <td>0.328342</td>\n",
       "      <td>0.421622</td>\n",
       "      <td>0.176636</td>\n",
       "      <td>-0.234254</td>\n",
       "      <td>...</td>\n",
       "      <td>0.706458</td>\n",
       "      <td>0.038322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.537678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.866163</td>\n",
       "      <td>0.530485</td>\n",
       "      <td>0.779840</td>\n",
       "      <td>0.487528</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>0.147147</td>\n",
       "      <td>0.736690</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.435178</td>\n",
       "      <td>0.316836</td>\n",
       "      <td>-0.014897</td>\n",
       "      <td>-0.146212</td>\n",
       "      <td>0.459726</td>\n",
       "      <td>0.055492</td>\n",
       "      <td>0.003575</td>\n",
       "      <td>...</td>\n",
       "      <td>0.915004</td>\n",
       "      <td>0.516014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.764983</td>\n",
       "      <td>0.383919</td>\n",
       "      <td>0.830297</td>\n",
       "      <td>0.551921</td>\n",
       "      <td>0.672264</td>\n",
       "      <td>0.733322</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>0.536247</td>\n",
       "      <td>0.530635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.206959</td>\n",
       "      <td>0.284856</td>\n",
       "      <td>0.117058</td>\n",
       "      <td>0.192163</td>\n",
       "      <td>0.456341</td>\n",
       "      <td>0.158980</td>\n",
       "      <td>-0.037216</td>\n",
       "      <td>...</td>\n",
       "      <td>0.825782</td>\n",
       "      <td>-0.075288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.030456</td>\n",
       "      <td>0.105310</td>\n",
       "      <td>0.773331</td>\n",
       "      <td>0.648987</td>\n",
       "      <td>0.791758</td>\n",
       "      <td>0.728344</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>0.246827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.373188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.452270</td>\n",
       "      <td>-0.097961</td>\n",
       "      <td>0.616357</td>\n",
       "      <td>0.610168</td>\n",
       "      <td>0.405187</td>\n",
       "      <td>...</td>\n",
       "      <td>0.835655</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.762564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.875649</td>\n",
       "      <td>-0.160253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.354233</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>0.237767</td>\n",
       "      <td>0.617277</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.462656</td>\n",
       "      <td>-0.221332</td>\n",
       "      <td>-0.601715</td>\n",
       "      <td>-0.234203</td>\n",
       "      <td>0.678187</td>\n",
       "      <td>0.257404</td>\n",
       "      <td>0.729272</td>\n",
       "      <td>...</td>\n",
       "      <td>0.739997</td>\n",
       "      <td>-0.352074</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.760194</td>\n",
       "      <td>0.346533</td>\n",
       "      <td>0.882529</td>\n",
       "      <td>0.502784</td>\n",
       "      <td>0.749201</td>\n",
       "      <td>0.944493</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>0.527850</td>\n",
       "      <td>0.438097</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.229391</td>\n",
       "      <td>-0.523434</td>\n",
       "      <td>0.211023</td>\n",
       "      <td>0.051314</td>\n",
       "      <td>0.752579</td>\n",
       "      <td>-0.411348</td>\n",
       "      <td>0.472507</td>\n",
       "      <td>...</td>\n",
       "      <td>0.906224</td>\n",
       "      <td>0.337338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.892858</td>\n",
       "      <td>-0.092429</td>\n",
       "      <td>0.924856</td>\n",
       "      <td>0.554960</td>\n",
       "      <td>0.850611</td>\n",
       "      <td>0.820775</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>-0.072670</td>\n",
       "      <td>0.475059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.226478</td>\n",
       "      <td>-0.037083</td>\n",
       "      <td>0.075283</td>\n",
       "      <td>-0.455004</td>\n",
       "      <td>0.615161</td>\n",
       "      <td>0.145106</td>\n",
       "      <td>0.582298</td>\n",
       "      <td>...</td>\n",
       "      <td>0.783020</td>\n",
       "      <td>-0.411151</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.733229</td>\n",
       "      <td>0.046756</td>\n",
       "      <td>0.933410</td>\n",
       "      <td>0.542315</td>\n",
       "      <td>0.799002</td>\n",
       "      <td>0.625747</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>0.135992</td>\n",
       "      <td>0.293320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.580183</td>\n",
       "      <td>-0.500016</td>\n",
       "      <td>-0.004721</td>\n",
       "      <td>-0.126696</td>\n",
       "      <td>0.666267</td>\n",
       "      <td>0.071625</td>\n",
       "      <td>0.575665</td>\n",
       "      <td>...</td>\n",
       "      <td>0.872023</td>\n",
       "      <td>-0.326352</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.640090</td>\n",
       "      <td>0.587398</td>\n",
       "      <td>0.942189</td>\n",
       "      <td>0.289723</td>\n",
       "      <td>0.658597</td>\n",
       "      <td>0.805108</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>0.206473</td>\n",
       "      <td>0.714785</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.478851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.240133</td>\n",
       "      <td>0.438911</td>\n",
       "      <td>0.562535</td>\n",
       "      <td>-0.184438</td>\n",
       "      <td>0.065261</td>\n",
       "      <td>...</td>\n",
       "      <td>0.918659</td>\n",
       "      <td>-0.103724</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.507601</td>\n",
       "      <td>0.166616</td>\n",
       "      <td>0.881201</td>\n",
       "      <td>0.020948</td>\n",
       "      <td>0.731785</td>\n",
       "      <td>0.676691</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>0.104867</td>\n",
       "      <td>0.382966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.131451</td>\n",
       "      <td>-0.038170</td>\n",
       "      <td>-0.196926</td>\n",
       "      <td>-0.296527</td>\n",
       "      <td>0.353150</td>\n",
       "      <td>-0.348835</td>\n",
       "      <td>0.473493</td>\n",
       "      <td>...</td>\n",
       "      <td>0.761676</td>\n",
       "      <td>-0.182969</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.708809</td>\n",
       "      <td>0.436858</td>\n",
       "      <td>0.924653</td>\n",
       "      <td>0.469884</td>\n",
       "      <td>0.819984</td>\n",
       "      <td>0.692431</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>0.421165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.281663</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.197306</td>\n",
       "      <td>0.085552</td>\n",
       "      <td>0.438676</td>\n",
       "      <td>-0.154744</td>\n",
       "      <td>-0.216206</td>\n",
       "      <td>...</td>\n",
       "      <td>0.762519</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.504846</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.869937</td>\n",
       "      <td>0.510662</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.605925</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>-0.086209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.137059</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.454604</td>\n",
       "      <td>-0.272927</td>\n",
       "      <td>-0.220252</td>\n",
       "      <td>-0.346531</td>\n",
       "      <td>0.647482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.340050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.186909</td>\n",
       "      <td>0.421251</td>\n",
       "      <td>0.652426</td>\n",
       "      <td>0.233788</td>\n",
       "      <td>0.721415</td>\n",
       "      <td>0.122700</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>0.419075</td>\n",
       "      <td>0.524147</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.319825</td>\n",
       "      <td>0.545179</td>\n",
       "      <td>0.180760</td>\n",
       "      <td>-0.236697</td>\n",
       "      <td>0.531822</td>\n",
       "      <td>-0.271813</td>\n",
       "      <td>0.174293</td>\n",
       "      <td>...</td>\n",
       "      <td>0.774117</td>\n",
       "      <td>0.096900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.785791</td>\n",
       "      <td>0.043219</td>\n",
       "      <td>0.892724</td>\n",
       "      <td>0.205076</td>\n",
       "      <td>0.569343</td>\n",
       "      <td>0.715211</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>0.060157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.072039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.033975</td>\n",
       "      <td>-0.161289</td>\n",
       "      <td>0.543972</td>\n",
       "      <td>0.037516</td>\n",
       "      <td>0.294786</td>\n",
       "      <td>...</td>\n",
       "      <td>0.560029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.317958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.867074</td>\n",
       "      <td>0.032118</td>\n",
       "      <td>0.740871</td>\n",
       "      <td>0.536874</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>0.299636</td>\n",
       "      <td>0.594201</td>\n",
       "      <td>0.643521</td>\n",
       "      <td>0.346768</td>\n",
       "      <td>-0.012413</td>\n",
       "      <td>0.696345</td>\n",
       "      <td>-0.220897</td>\n",
       "      <td>0.630910</td>\n",
       "      <td>0.674291</td>\n",
       "      <td>0.466907</td>\n",
       "      <td>...</td>\n",
       "      <td>0.756688</td>\n",
       "      <td>-0.488601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.644091</td>\n",
       "      <td>0.445315</td>\n",
       "      <td>0.732861</td>\n",
       "      <td>0.589961</td>\n",
       "      <td>0.893780</td>\n",
       "      <td>0.827371</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>0.028100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.339575</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.027049</td>\n",
       "      <td>-0.355842</td>\n",
       "      <td>0.119250</td>\n",
       "      <td>-0.485476</td>\n",
       "      <td>0.529962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.633678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.119854</td>\n",
       "      <td>0.382419</td>\n",
       "      <td>0.846603</td>\n",
       "      <td>0.026774</td>\n",
       "      <td>0.495039</td>\n",
       "      <td>0.264897</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>0.104006</td>\n",
       "      <td>0.709284</td>\n",
       "      <td>0.284725</td>\n",
       "      <td>0.568809</td>\n",
       "      <td>-0.190796</td>\n",
       "      <td>-0.148583</td>\n",
       "      <td>-0.425864</td>\n",
       "      <td>0.652932</td>\n",
       "      <td>-0.322077</td>\n",
       "      <td>0.542449</td>\n",
       "      <td>...</td>\n",
       "      <td>0.847362</td>\n",
       "      <td>0.752394</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.796568</td>\n",
       "      <td>0.404488</td>\n",
       "      <td>0.869555</td>\n",
       "      <td>0.591775</td>\n",
       "      <td>0.707022</td>\n",
       "      <td>0.839425</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>0.109399</td>\n",
       "      <td>0.654955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.720444</td>\n",
       "      <td>0.066027</td>\n",
       "      <td>-0.279713</td>\n",
       "      <td>-0.302085</td>\n",
       "      <td>0.455477</td>\n",
       "      <td>-0.380551</td>\n",
       "      <td>0.356218</td>\n",
       "      <td>...</td>\n",
       "      <td>0.827075</td>\n",
       "      <td>-0.106504</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.555003</td>\n",
       "      <td>-0.076035</td>\n",
       "      <td>0.924561</td>\n",
       "      <td>0.282344</td>\n",
       "      <td>0.700295</td>\n",
       "      <td>0.814384</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>-0.104555</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.257894</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.316281</td>\n",
       "      <td>0.130594</td>\n",
       "      <td>0.299040</td>\n",
       "      <td>0.083452</td>\n",
       "      <td>0.149189</td>\n",
       "      <td>...</td>\n",
       "      <td>0.676087</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.267684</td>\n",
       "      <td>0.156582</td>\n",
       "      <td>0.727994</td>\n",
       "      <td>0.099510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>0.423725</td>\n",
       "      <td>0.817716</td>\n",
       "      <td>-0.383491</td>\n",
       "      <td>0.709156</td>\n",
       "      <td>-0.364708</td>\n",
       "      <td>0.556019</td>\n",
       "      <td>-0.175982</td>\n",
       "      <td>0.718780</td>\n",
       "      <td>0.009825</td>\n",
       "      <td>0.435342</td>\n",
       "      <td>...</td>\n",
       "      <td>0.780073</td>\n",
       "      <td>0.319283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.530347</td>\n",
       "      <td>0.116706</td>\n",
       "      <td>0.776989</td>\n",
       "      <td>0.525274</td>\n",
       "      <td>0.752016</td>\n",
       "      <td>0.795550</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>-0.112217</td>\n",
       "      <td>0.342898</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.450806</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.351791</td>\n",
       "      <td>0.345210</td>\n",
       "      <td>0.609209</td>\n",
       "      <td>0.178276</td>\n",
       "      <td>0.047789</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919920</td>\n",
       "      <td>0.324932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.174510</td>\n",
       "      <td>0.351791</td>\n",
       "      <td>0.934374</td>\n",
       "      <td>0.074788</td>\n",
       "      <td>0.832877</td>\n",
       "      <td>0.780543</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>-0.137425</td>\n",
       "      <td>0.215492</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.015879</td>\n",
       "      <td>0.201687</td>\n",
       "      <td>-0.114055</td>\n",
       "      <td>-0.191582</td>\n",
       "      <td>0.165405</td>\n",
       "      <td>0.149859</td>\n",
       "      <td>-0.378457</td>\n",
       "      <td>...</td>\n",
       "      <td>0.454128</td>\n",
       "      <td>0.272083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.373354</td>\n",
       "      <td>-0.108438</td>\n",
       "      <td>0.723777</td>\n",
       "      <td>0.195008</td>\n",
       "      <td>0.588420</td>\n",
       "      <td>0.156737</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>0.471304</td>\n",
       "      <td>0.328526</td>\n",
       "      <td>0.326070</td>\n",
       "      <td>0.121224</td>\n",
       "      <td>-0.397436</td>\n",
       "      <td>-0.009158</td>\n",
       "      <td>0.099841</td>\n",
       "      <td>0.696042</td>\n",
       "      <td>-0.431545</td>\n",
       "      <td>0.577151</td>\n",
       "      <td>...</td>\n",
       "      <td>0.869672</td>\n",
       "      <td>-0.385744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.911186</td>\n",
       "      <td>0.364678</td>\n",
       "      <td>0.938219</td>\n",
       "      <td>0.353174</td>\n",
       "      <td>0.742208</td>\n",
       "      <td>0.815874</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>0.511162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.489112</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.365190</td>\n",
       "      <td>-0.011098</td>\n",
       "      <td>0.703676</td>\n",
       "      <td>-0.174501</td>\n",
       "      <td>0.449345</td>\n",
       "      <td>...</td>\n",
       "      <td>0.616736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.764356</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.842898</td>\n",
       "      <td>0.260776</td>\n",
       "      <td>0.799855</td>\n",
       "      <td>0.381839</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>0.231210</td>\n",
       "      <td>0.665730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.408578</td>\n",
       "      <td>-0.063747</td>\n",
       "      <td>-0.150356</td>\n",
       "      <td>-0.054719</td>\n",
       "      <td>0.640532</td>\n",
       "      <td>0.199307</td>\n",
       "      <td>0.713433</td>\n",
       "      <td>...</td>\n",
       "      <td>0.778289</td>\n",
       "      <td>0.570238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.691713</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.870717</td>\n",
       "      <td>0.173825</td>\n",
       "      <td>0.769824</td>\n",
       "      <td>0.756967</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>-0.293139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.065033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.103604</td>\n",
       "      <td>-0.092914</td>\n",
       "      <td>0.381572</td>\n",
       "      <td>-0.395637</td>\n",
       "      <td>0.029620</td>\n",
       "      <td>...</td>\n",
       "      <td>0.681554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.243212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.744599</td>\n",
       "      <td>-0.267339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>0.034856</td>\n",
       "      <td>0.599291</td>\n",
       "      <td>0.212809</td>\n",
       "      <td>0.572561</td>\n",
       "      <td>-0.302367</td>\n",
       "      <td>0.028997</td>\n",
       "      <td>-0.611388</td>\n",
       "      <td>0.475329</td>\n",
       "      <td>0.273653</td>\n",
       "      <td>0.466212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.657097</td>\n",
       "      <td>0.616065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.586910</td>\n",
       "      <td>0.372553</td>\n",
       "      <td>0.813169</td>\n",
       "      <td>0.361907</td>\n",
       "      <td>0.785924</td>\n",
       "      <td>0.646792</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>-0.261957</td>\n",
       "      <td>0.813703</td>\n",
       "      <td>0.450311</td>\n",
       "      <td>0.694803</td>\n",
       "      <td>-0.342863</td>\n",
       "      <td>0.170243</td>\n",
       "      <td>-0.338738</td>\n",
       "      <td>0.522324</td>\n",
       "      <td>-0.291485</td>\n",
       "      <td>0.226726</td>\n",
       "      <td>...</td>\n",
       "      <td>0.826999</td>\n",
       "      <td>-0.877403</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.652517</td>\n",
       "      <td>0.327905</td>\n",
       "      <td>0.871691</td>\n",
       "      <td>0.172527</td>\n",
       "      <td>0.885451</td>\n",
       "      <td>0.856268</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>-0.103190</td>\n",
       "      <td>0.155300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.541511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.154780</td>\n",
       "      <td>-0.187016</td>\n",
       "      <td>0.490570</td>\n",
       "      <td>-0.056975</td>\n",
       "      <td>0.677931</td>\n",
       "      <td>...</td>\n",
       "      <td>0.654188</td>\n",
       "      <td>0.482907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.523956</td>\n",
       "      <td>0.532567</td>\n",
       "      <td>0.845798</td>\n",
       "      <td>0.275795</td>\n",
       "      <td>0.801470</td>\n",
       "      <td>0.543326</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>-0.095837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.101721</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.168700</td>\n",
       "      <td>0.281023</td>\n",
       "      <td>-0.382686</td>\n",
       "      <td>0.335941</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.550163</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.799571</td>\n",
       "      <td>-0.064934</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>0.666342</td>\n",
       "      <td>0.325987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.557120</td>\n",
       "      <td>0.328536</td>\n",
       "      <td>0.070637</td>\n",
       "      <td>-0.255854</td>\n",
       "      <td>0.525083</td>\n",
       "      <td>-0.514923</td>\n",
       "      <td>0.603991</td>\n",
       "      <td>...</td>\n",
       "      <td>0.740488</td>\n",
       "      <td>-0.081709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.647557</td>\n",
       "      <td>0.328536</td>\n",
       "      <td>0.877523</td>\n",
       "      <td>0.277733</td>\n",
       "      <td>0.756217</td>\n",
       "      <td>0.674328</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>-0.043472</td>\n",
       "      <td>0.469229</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.493423</td>\n",
       "      <td>-0.355768</td>\n",
       "      <td>-0.350215</td>\n",
       "      <td>-0.374339</td>\n",
       "      <td>0.680298</td>\n",
       "      <td>0.232814</td>\n",
       "      <td>0.178843</td>\n",
       "      <td>...</td>\n",
       "      <td>0.926755</td>\n",
       "      <td>0.654178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.586210</td>\n",
       "      <td>0.237732</td>\n",
       "      <td>0.889646</td>\n",
       "      <td>0.537109</td>\n",
       "      <td>0.765652</td>\n",
       "      <td>0.870637</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>0.071376</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.201985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.104894</td>\n",
       "      <td>0.074039</td>\n",
       "      <td>0.429469</td>\n",
       "      <td>-0.082358</td>\n",
       "      <td>0.602862</td>\n",
       "      <td>...</td>\n",
       "      <td>0.766269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.565459</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.798095</td>\n",
       "      <td>-0.091998</td>\n",
       "      <td>0.667543</td>\n",
       "      <td>0.440226</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>0.521120</td>\n",
       "      <td>0.708878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.106753</td>\n",
       "      <td>0.247196</td>\n",
       "      <td>-0.078960</td>\n",
       "      <td>-0.406835</td>\n",
       "      <td>0.728562</td>\n",
       "      <td>-0.242698</td>\n",
       "      <td>0.350567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.874227</td>\n",
       "      <td>-0.111121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.851214</td>\n",
       "      <td>0.555713</td>\n",
       "      <td>0.911947</td>\n",
       "      <td>0.341156</td>\n",
       "      <td>0.674567</td>\n",
       "      <td>0.622016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>-0.319879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.536306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.599422</td>\n",
       "      <td>-0.177048</td>\n",
       "      <td>0.090295</td>\n",
       "      <td>0.040169</td>\n",
       "      <td>0.132869</td>\n",
       "      <td>...</td>\n",
       "      <td>0.768578</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.601438</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.720759</td>\n",
       "      <td>-0.182921</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      association  blood glucose  blood glucose level  \\\n",
       "States                                                                  \n",
       "Alabama                  0.193325       0.556536                  NaN   \n",
       "Alaska                   0.029357            NaN                  NaN   \n",
       "Arizona                  0.129777       0.719039                  NaN   \n",
       "Arkansas                 0.434100       0.425867                  NaN   \n",
       "California               0.287668       0.760761             0.457957   \n",
       "Colorado                 0.488439       0.763977                  NaN   \n",
       "Connecticut              0.584777       0.438127                  NaN   \n",
       "Delaware                 0.379108            NaN                  NaN   \n",
       "District of Columbia    -0.206131       0.175814                  NaN   \n",
       "Florida                  0.361131       0.800430            -0.184453   \n",
       "Georgia                 -0.046480       0.133165             0.309467   \n",
       "Hawaii                   0.123430            NaN                  NaN   \n",
       "Idaho                   -0.214203            NaN                  NaN   \n",
       "Illinois                 0.013350       0.509219            -0.071070   \n",
       "Indiana                  0.228728       0.441691                  NaN   \n",
       "Iowa                     0.360802       0.169938                  NaN   \n",
       "Kansas                   0.070868       0.367990                  NaN   \n",
       "Kentucky                 0.147147       0.736690                  NaN   \n",
       "Louisiana                0.536247       0.530635                  NaN   \n",
       "Maine                    0.246827            NaN                  NaN   \n",
       "Maryland                 0.237767       0.617277                  NaN   \n",
       "Massachusetts            0.527850       0.438097                  NaN   \n",
       "Michigan                -0.072670       0.475059                  NaN   \n",
       "Minnesota                0.135992       0.293320                  NaN   \n",
       "Mississippi              0.206473       0.714785                  NaN   \n",
       "Missouri                 0.104867       0.382966                  NaN   \n",
       "Montana                  0.421165            NaN                  NaN   \n",
       "Nebraska                -0.086209            NaN                  NaN   \n",
       "Nevada                   0.419075       0.524147                  NaN   \n",
       "New Hampshire            0.060157            NaN                  NaN   \n",
       "New Jersey               0.299636       0.594201             0.643521   \n",
       "New Mexico               0.028100            NaN                  NaN   \n",
       "New York                 0.104006       0.709284             0.284725   \n",
       "North Carolina           0.109399       0.654955                  NaN   \n",
       "North Dakota            -0.104555            NaN                  NaN   \n",
       "Ohio                     0.423725       0.817716            -0.383491   \n",
       "Oklahoma                -0.112217       0.342898                  NaN   \n",
       "Oregon                  -0.137425       0.215492                  NaN   \n",
       "Pennsylvania             0.471304       0.328526             0.326070   \n",
       "Rhode Island             0.511162            NaN                  NaN   \n",
       "South Carolina           0.231210       0.665730                  NaN   \n",
       "South Dakota            -0.293139            NaN                  NaN   \n",
       "Tennessee                0.034856       0.599291             0.212809   \n",
       "Texas                   -0.261957       0.813703             0.450311   \n",
       "Utah                    -0.103190       0.155300                  NaN   \n",
       "Vermont                 -0.095837            NaN                  NaN   \n",
       "Virginia                 0.666342       0.325987                  NaN   \n",
       "Washington              -0.043472       0.469229                  NaN   \n",
       "West Virginia            0.071376            NaN                  NaN   \n",
       "Wisconsin                0.521120       0.708878                  NaN   \n",
       "Wyoming                 -0.319879            NaN                  NaN   \n",
       "\n",
       "                      blood pressure  cardiovascular disease  cholesterol  \\\n",
       "States                                                                      \n",
       "Alabama                     0.445255                     NaN     0.427529   \n",
       "Alaska                      0.374873                     NaN     0.318714   \n",
       "Arizona                     0.623545               -0.190179     0.032708   \n",
       "Arkansas                    0.405627                0.471961     0.408059   \n",
       "California                  0.553659               -0.516222     0.059501   \n",
       "Colorado                    0.336756               -0.034811    -0.064678   \n",
       "Connecticut                 0.084013               -0.200226    -0.048677   \n",
       "Delaware                    0.146414                     NaN     0.405593   \n",
       "District of Columbia        0.353372               -0.716400    -0.051893   \n",
       "Florida                     0.388033                0.062798    -0.214782   \n",
       "Georgia                     0.464075                0.140374     0.247244   \n",
       "Hawaii                      0.235049                     NaN     0.347448   \n",
       "Idaho                       0.062110                     NaN    -0.237506   \n",
       "Illinois                    0.669638               -0.252821     0.150837   \n",
       "Indiana                     0.662245                0.349259    -0.208045   \n",
       "Iowa                       -0.104670                0.195950     0.047239   \n",
       "Kansas                     -0.038414                     NaN     0.708063   \n",
       "Kentucky                   -0.435178                0.316836    -0.014897   \n",
       "Louisiana                   0.206959                0.284856     0.117058   \n",
       "Maine                       0.373188                     NaN     0.452270   \n",
       "Maryland                    0.462656               -0.221332    -0.601715   \n",
       "Massachusetts               0.229391               -0.523434     0.211023   \n",
       "Michigan                    0.226478               -0.037083     0.075283   \n",
       "Minnesota                   0.580183               -0.500016    -0.004721   \n",
       "Mississippi                 0.478851                     NaN     0.240133   \n",
       "Missouri                    0.131451               -0.038170    -0.196926   \n",
       "Montana                     0.281663                     NaN     0.197306   \n",
       "Nebraska                   -0.137059                     NaN    -0.454604   \n",
       "Nevada                      0.319825                0.545179     0.180760   \n",
       "New Hampshire               0.072039                     NaN     0.033975   \n",
       "New Jersey                  0.346768               -0.012413     0.696345   \n",
       "New Mexico                  0.339575                     NaN    -0.027049   \n",
       "New York                    0.568809               -0.190796    -0.148583   \n",
       "North Carolina              0.720444                0.066027    -0.279713   \n",
       "North Dakota               -0.257894                     NaN    -0.316281   \n",
       "Ohio                        0.709156               -0.364708     0.556019   \n",
       "Oklahoma                    0.450806                     NaN     0.351791   \n",
       "Oregon                      0.015879                0.201687    -0.114055   \n",
       "Pennsylvania                0.121224               -0.397436    -0.009158   \n",
       "Rhode Island                0.489112                     NaN     0.365190   \n",
       "South Carolina              0.408578               -0.063747    -0.150356   \n",
       "South Dakota               -0.065033                     NaN    -0.103604   \n",
       "Tennessee                   0.572561               -0.302367     0.028997   \n",
       "Texas                       0.694803               -0.342863     0.170243   \n",
       "Utah                        0.541511                     NaN    -0.154780   \n",
       "Vermont                     0.101721                     NaN          NaN   \n",
       "Virginia                    0.557120                0.328536     0.070637   \n",
       "Washington                  0.493423               -0.355768    -0.350215   \n",
       "West Virginia              -0.201985                     NaN    -0.104894   \n",
       "Wisconsin                   0.106753                0.247196    -0.078960   \n",
       "Wyoming                     0.536306                     NaN     0.599422   \n",
       "\n",
       "                      complications  diabetes  diabetes mellitus  diagnosis  \\\n",
       "States                                                                        \n",
       "Alabama                   -0.218737  0.684322           0.413846   0.423561   \n",
       "Alaska                    -0.490708  0.822601                NaN  -0.130080   \n",
       "Arizona                   -0.145490  0.657732           0.490949   0.144963   \n",
       "Arkansas                   0.095869  0.607123          -0.013593  -0.030005   \n",
       "California                -0.268303  0.638093          -0.147969   0.703940   \n",
       "Colorado                  -0.287024  0.452394           0.008226   0.198949   \n",
       "Connecticut               -0.288569  0.688396           0.337517   0.443634   \n",
       "Delaware                  -0.222425  0.782084          -0.136936   0.183656   \n",
       "District of Columbia       0.248221  0.177846           0.088918   0.257573   \n",
       "Florida                   -0.132109  0.701894           0.520903   0.135973   \n",
       "Georgia                   -0.712889  0.650117          -0.504069   0.316169   \n",
       "Hawaii                    -0.042640  0.511900          -0.483989  -0.271104   \n",
       "Idaho                      0.343475  0.642450          -0.303832  -0.114263   \n",
       "Illinois                   0.280215  0.470505          -0.151111   0.374345   \n",
       "Indiana                   -0.466148  0.380249          -0.273110   0.606164   \n",
       "Iowa                       0.072554  0.346983           0.054286   0.104597   \n",
       "Kansas                     0.328342  0.421622           0.176636  -0.234254   \n",
       "Kentucky                  -0.146212  0.459726           0.055492   0.003575   \n",
       "Louisiana                  0.192163  0.456341           0.158980  -0.037216   \n",
       "Maine                     -0.097961  0.616357           0.610168   0.405187   \n",
       "Maryland                  -0.234203  0.678187           0.257404   0.729272   \n",
       "Massachusetts              0.051314  0.752579          -0.411348   0.472507   \n",
       "Michigan                  -0.455004  0.615161           0.145106   0.582298   \n",
       "Minnesota                 -0.126696  0.666267           0.071625   0.575665   \n",
       "Mississippi                0.438911  0.562535          -0.184438   0.065261   \n",
       "Missouri                  -0.296527  0.353150          -0.348835   0.473493   \n",
       "Montana                    0.085552  0.438676          -0.154744  -0.216206   \n",
       "Nebraska                  -0.272927 -0.220252          -0.346531   0.647482   \n",
       "Nevada                    -0.236697  0.531822          -0.271813   0.174293   \n",
       "New Hampshire             -0.161289  0.543972           0.037516   0.294786   \n",
       "New Jersey                -0.220897  0.630910           0.674291   0.466907   \n",
       "New Mexico                -0.355842  0.119250          -0.485476   0.529962   \n",
       "New York                  -0.425864  0.652932          -0.322077   0.542449   \n",
       "North Carolina            -0.302085  0.455477          -0.380551   0.356218   \n",
       "North Dakota               0.130594  0.299040           0.083452   0.149189   \n",
       "Ohio                      -0.175982  0.718780           0.009825   0.435342   \n",
       "Oklahoma                   0.345210  0.609209           0.178276   0.047789   \n",
       "Oregon                    -0.191582  0.165405           0.149859  -0.378457   \n",
       "Pennsylvania               0.099841  0.696042          -0.431545   0.577151   \n",
       "Rhode Island              -0.011098  0.703676          -0.174501   0.449345   \n",
       "South Carolina            -0.054719  0.640532           0.199307   0.713433   \n",
       "South Dakota              -0.092914  0.381572          -0.395637   0.029620   \n",
       "Tennessee                 -0.611388  0.475329           0.273653   0.466212   \n",
       "Texas                     -0.338738  0.522324          -0.291485   0.226726   \n",
       "Utah                      -0.187016  0.490570          -0.056975   0.677931   \n",
       "Vermont                   -0.168700  0.281023          -0.382686   0.335941   \n",
       "Virginia                  -0.255854  0.525083          -0.514923   0.603991   \n",
       "Washington                -0.374339  0.680298           0.232814   0.178843   \n",
       "West Virginia              0.074039  0.429469          -0.082358   0.602862   \n",
       "Wisconsin                 -0.406835  0.728562          -0.242698   0.350567   \n",
       "Wyoming                   -0.177048  0.090295           0.040169   0.132869   \n",
       "\n",
       "                         ...        insulin  kidney disease  \\\n",
       "States                   ...                                  \n",
       "Alabama                  ...       0.781648        0.094676   \n",
       "Alaska                   ...       0.754765             NaN   \n",
       "Arizona                  ...       0.860131        0.468025   \n",
       "Arkansas                 ...       0.879440        0.556073   \n",
       "California               ...       0.831196        0.697520   \n",
       "Colorado                 ...       0.763328        0.377471   \n",
       "Connecticut              ...       0.620769        0.231064   \n",
       "Delaware                 ...       0.691315             NaN   \n",
       "District of Columbia     ...       0.059816        0.169360   \n",
       "Florida                  ...       0.924610        0.323434   \n",
       "Georgia                  ...       0.720520       -0.393698   \n",
       "Hawaii                   ...       0.450289             NaN   \n",
       "Idaho                    ...       0.844287             NaN   \n",
       "Illinois                 ...       0.790266        0.122955   \n",
       "Indiana                  ...       0.688349       -0.475087   \n",
       "Iowa                     ...       0.718310       -0.147453   \n",
       "Kansas                   ...       0.706458        0.038322   \n",
       "Kentucky                 ...       0.915004        0.516014   \n",
       "Louisiana                ...       0.825782       -0.075288   \n",
       "Maine                    ...       0.835655             NaN   \n",
       "Maryland                 ...       0.739997       -0.352074   \n",
       "Massachusetts            ...       0.906224        0.337338   \n",
       "Michigan                 ...       0.783020       -0.411151   \n",
       "Minnesota                ...       0.872023       -0.326352   \n",
       "Mississippi              ...       0.918659       -0.103724   \n",
       "Missouri                 ...       0.761676       -0.182969   \n",
       "Montana                  ...       0.762519             NaN   \n",
       "Nebraska                 ...       0.340050             NaN   \n",
       "Nevada                   ...       0.774117        0.096900   \n",
       "New Hampshire            ...       0.560029             NaN   \n",
       "New Jersey               ...       0.756688       -0.488601   \n",
       "New Mexico               ...       0.633678             NaN   \n",
       "New York                 ...       0.847362        0.752394   \n",
       "North Carolina           ...       0.827075       -0.106504   \n",
       "North Dakota             ...       0.676087             NaN   \n",
       "Ohio                     ...       0.780073        0.319283   \n",
       "Oklahoma                 ...       0.919920        0.324932   \n",
       "Oregon                   ...       0.454128        0.272083   \n",
       "Pennsylvania             ...       0.869672       -0.385744   \n",
       "Rhode Island             ...       0.616736             NaN   \n",
       "South Carolina           ...       0.778289        0.570238   \n",
       "South Dakota             ...       0.681554             NaN   \n",
       "Tennessee                ...       0.657097        0.616065   \n",
       "Texas                    ...       0.826999       -0.877403   \n",
       "Utah                     ...       0.654188        0.482907   \n",
       "Vermont                  ...       0.363500             NaN   \n",
       "Virginia                 ...       0.740488       -0.081709   \n",
       "Washington               ...       0.926755        0.654178   \n",
       "West Virginia            ...       0.766269             NaN   \n",
       "Wisconsin                ...       0.874227       -0.111121   \n",
       "Wyoming                  ...       0.768578             NaN   \n",
       "\n",
       "                      like growth factor   obesity  osteoporosis     sugar  \\\n",
       "States                                                                       \n",
       "Alabama                              NaN  0.502187      0.171871  0.754422   \n",
       "Alaska                               NaN -0.300352           NaN  0.753936   \n",
       "Arizona                              NaN  0.736345      0.447821  0.915794   \n",
       "Arkansas                             NaN  0.826077      0.143201  0.921532   \n",
       "California                           NaN  0.647474      0.337031  0.842815   \n",
       "Colorado                             NaN  0.511544     -0.019589  0.796402   \n",
       "Connecticut                          NaN  0.566026      0.098486  0.925510   \n",
       "Delaware                             NaN  0.415233      0.177539  0.686528   \n",
       "District of Columbia                 NaN -0.284149      0.096274  0.259938   \n",
       "Florida                              NaN  0.675603      0.469262  0.899702   \n",
       "Georgia                              NaN  0.727797     -0.470820  0.869571   \n",
       "Hawaii                               NaN  0.458914           NaN  0.631224   \n",
       "Idaho                                NaN -0.119071      0.517894  0.722450   \n",
       "Illinois                             NaN  0.708582      0.362105  0.868081   \n",
       "Indiana                              NaN  0.514060     -0.005127  0.909107   \n",
       "Iowa                                 NaN  0.529848      0.215169  0.891130   \n",
       "Kansas                               NaN  0.537678           NaN  0.866163   \n",
       "Kentucky                             NaN  0.764983      0.383919  0.830297   \n",
       "Louisiana                            NaN  0.030456      0.105310  0.773331   \n",
       "Maine                                NaN  0.762564           NaN  0.875649   \n",
       "Maryland                             NaN  0.760194      0.346533  0.882529   \n",
       "Massachusetts                        NaN  0.892858     -0.092429  0.924856   \n",
       "Michigan                             NaN  0.733229      0.046756  0.933410   \n",
       "Minnesota                            NaN  0.640090      0.587398  0.942189   \n",
       "Mississippi                          NaN  0.507601      0.166616  0.881201   \n",
       "Missouri                             NaN  0.708809      0.436858  0.924653   \n",
       "Montana                              NaN  0.504846           NaN  0.869937   \n",
       "Nebraska                             NaN  0.186909      0.421251  0.652426   \n",
       "Nevada                               NaN  0.785791      0.043219  0.892724   \n",
       "New Hampshire                        NaN  0.317958           NaN  0.867074   \n",
       "New Jersey                           NaN  0.644091      0.445315  0.732861   \n",
       "New Mexico                           NaN  0.119854      0.382419  0.846603   \n",
       "New York                             NaN  0.796568      0.404488  0.869555   \n",
       "North Carolina                       NaN  0.555003     -0.076035  0.924561   \n",
       "North Dakota                         NaN  0.267684      0.156582  0.727994   \n",
       "Ohio                                 NaN  0.530347      0.116706  0.776989   \n",
       "Oklahoma                             NaN  0.174510      0.351791  0.934374   \n",
       "Oregon                               NaN  0.373354     -0.108438  0.723777   \n",
       "Pennsylvania                         NaN  0.911186      0.364678  0.938219   \n",
       "Rhode Island                         NaN  0.764356           NaN  0.842898   \n",
       "South Carolina                       NaN  0.691713           NaN  0.870717   \n",
       "South Dakota                         NaN -0.243212           NaN  0.744599   \n",
       "Tennessee                            NaN  0.586910      0.372553  0.813169   \n",
       "Texas                                NaN  0.652517      0.327905  0.871691   \n",
       "Utah                                 NaN  0.523956      0.532567  0.845798   \n",
       "Vermont                              NaN  0.550163           NaN  0.799571   \n",
       "Virginia                             NaN  0.647557      0.328536  0.877523   \n",
       "Washington                           NaN  0.586210      0.237732  0.889646   \n",
       "West Virginia                        NaN  0.565459           NaN  0.798095   \n",
       "Wisconsin                            NaN  0.851214      0.555713  0.911947   \n",
       "Wyoming                              NaN  0.601438           NaN  0.720759   \n",
       "\n",
       "                      symptoms    type 1    type 2  weigth loss  \n",
       "States                                                           \n",
       "Alabama               0.340627  0.733156  0.821001          NaN  \n",
       "Alaska               -0.479090       NaN       NaN          NaN  \n",
       "Arizona               0.452533  0.829593  0.880723          NaN  \n",
       "Arkansas             -0.425577  0.638355  0.769258          NaN  \n",
       "California            0.693290  0.879822  0.819246          NaN  \n",
       "Colorado              0.672618  0.848831  0.687339          NaN  \n",
       "Connecticut           0.213120  0.751747  0.728912          NaN  \n",
       "Delaware             -0.184493       NaN       NaN          NaN  \n",
       "District of Columbia  0.339505  0.348241  0.229860          NaN  \n",
       "Florida               0.497410  0.786687  0.805177          NaN  \n",
       "Georgia               0.363871  0.797002  0.851473          NaN  \n",
       "Hawaii                0.004196  0.090484  0.410954          NaN  \n",
       "Idaho                 0.480592  0.316480  0.270412          NaN  \n",
       "Illinois              0.417055  0.726859  0.788345          NaN  \n",
       "Indiana               0.393375  0.787013  0.741794          NaN  \n",
       "Iowa                  0.013606  0.610687  0.344335          NaN  \n",
       "Kansas                0.530485  0.779840  0.487528          NaN  \n",
       "Kentucky              0.551921  0.672264  0.733322          NaN  \n",
       "Louisiana             0.648987  0.791758  0.728344          NaN  \n",
       "Maine                -0.160253       NaN  0.354233          NaN  \n",
       "Maryland              0.502784  0.749201  0.944493          NaN  \n",
       "Massachusetts         0.554960  0.850611  0.820775          NaN  \n",
       "Michigan              0.542315  0.799002  0.625747          NaN  \n",
       "Minnesota             0.289723  0.658597  0.805108          NaN  \n",
       "Mississippi           0.020948  0.731785  0.676691          NaN  \n",
       "Missouri              0.469884  0.819984  0.692431          NaN  \n",
       "Montana               0.510662       NaN  0.605925          NaN  \n",
       "Nebraska              0.233788  0.721415  0.122700          NaN  \n",
       "Nevada                0.205076  0.569343  0.715211          NaN  \n",
       "New Hampshire         0.032118  0.740871  0.536874          NaN  \n",
       "New Jersey            0.589961  0.893780  0.827371          NaN  \n",
       "New Mexico            0.026774  0.495039  0.264897          NaN  \n",
       "New York              0.591775  0.707022  0.839425          NaN  \n",
       "North Carolina        0.282344  0.700295  0.814384          NaN  \n",
       "North Dakota          0.099510       NaN       NaN          NaN  \n",
       "Ohio                  0.525274  0.752016  0.795550          NaN  \n",
       "Oklahoma              0.074788  0.832877  0.780543          NaN  \n",
       "Oregon                0.195008  0.588420  0.156737          NaN  \n",
       "Pennsylvania          0.353174  0.742208  0.815874          NaN  \n",
       "Rhode Island          0.260776  0.799855  0.381839          NaN  \n",
       "South Carolina        0.173825  0.769824  0.756967          NaN  \n",
       "South Dakota         -0.267339       NaN       NaN          NaN  \n",
       "Tennessee             0.361907  0.785924  0.646792          NaN  \n",
       "Texas                 0.172527  0.885451  0.856268          NaN  \n",
       "Utah                  0.275795  0.801470  0.543326          NaN  \n",
       "Vermont              -0.064934       NaN       NaN          NaN  \n",
       "Virginia              0.277733  0.756217  0.674328          NaN  \n",
       "Washington            0.537109  0.765652  0.870637          NaN  \n",
       "West Virginia        -0.091998  0.667543  0.440226          NaN  \n",
       "Wisconsin             0.341156  0.674567  0.622016          NaN  \n",
       "Wyoming              -0.182921       NaN       NaN          NaN  \n",
       "\n",
       "[51 rows x 30 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_correlation_state = pd.DataFrame.from_dict(dict_correlation_state).set_index(\"States\").sort_index()\n",
    "df_correlation_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boxplot representation of the correlations\n",
    "\n",
    "We now plot the boxplot of the correlations. It's important to see that some values inside the correlation matrix are NaN. This refers to the fact that some vectors when we slice over the state will be full of zeros. The boxplot function of pandas takes into account this. As a result, we only visualize the values in which the correlation is different than NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lkg43s20KRjKdKUnufrWkmyW9WtJ9kp6S9GfhtkfN7COSfhCe6lJ3rzcoDgAAAACgQYkm\niu6emWC7S3pPjW3XSro2jnIBAAAAQC/r9KanAAAAAIA2I1EEAAAAAESQKAIAAAAAIkgUAQAAAAAR\niQ5mAwBAtzCzutuD8dcAAEgHahQBAGhA+STEi1bd9KyJiQEASBNqFAEgpagBAwBU4n9D66T9saRG\nEQBSqrLGq7IWDADQe2gd0Tpp/z9LoggAAAAAiKDpKQAAQBdLe/M3AMmgRhEAAKCLpb35G4BkkCgC\nAAAAACJoegoAAACEaMoLBKhRBAAAAEKMCgoESBQBAAAAABE0PQUAAGghmi6ik5x4ya3avmtPze2L\nV2+oun7e7Jm6+6Iz4ipWbHj/tQ6JIgAAVfTalyu0TuUX0cWrN+j+Na9JqDToddt37an5+hseHtay\nZcuqbqv1Gdfpyt9/vPemhkQRAIAqeu3LFQAA5eijCAAAAACIIFEEAAAAAETQ9BQAAABIqbl9q3X8\nutW1d1hX6zhJon9fLyNRBAAAAFLqydE19LdGU2h6CgAAAACIIFEEAAAAAETQ9BQA0BJMcgwAQHqQ\nKAIAWoJJxtGp0vgjxomX3Krtu/bU3F6rf9m82TN190VnxFUsAClCoggAAFItjT9ibN+1hwFKAMSK\nPooAAAAAgAgSRQAAAABABE1PAdSVxr49AAAAqI9EsQPwRRydrPz1l4Z+PQAAAM3qpYGkSBQ7QBo7\n2QMAAABp00sDSZEoAgBQxdy+1Tp+3eraO6yrdZwk8WMfAKC7kSgCAFDFk6NreuZXYwDoVr3UFLTd\nSBQBAAAAdKVeagrabiSKAJAivfTLKgOBAQAQHxJFAEiRXvpllRF5AQCIz7SkCwAAAAAA6CzUKAIA\nAAApVrfVyMbaXRLQ2xJNFM3sTEmXS5ou6bPuvqZi+yclDYaLB0h6rrsfHG7bJ+mecNsv3P117Sk1\nAADodGnvr8v0LWhUvWb5NNtHPYklimY2XdJVkk6XtE3SD8zsRne/d2wfd//rsv2HJL247BS73P1F\n7SovAADoHmnvr8v0LQDilmQfxVMk3efuP3P3pyWtl3ROnf0zkoptKRkAAAAA9LAkm54eKemBsuVt\nkk6ttqOZLZK0RNLtZav3N7PNkvZKWuPuX69x7PmSzpek+fPna3h4eNIF3bFjR1PHTUU747Xz+tr9\nWBKv9dJ8fWmJV+ucE8VrZ1nikObHMi2vzXKDg4N1t5dKpSnH6JTnr93njPv62vHc1dPu90Pa3nuV\nuv36ptIMe3h4TlMxO+WzJfbXi7sncpP0ZgX9EseW/1TSFTX2XVW5TdKC8O/zJN0v6fkTxVy6dKk3\no1QqNXVcsxatuqmt8dp5fe1+LInXWml+baYlXr3nqF68OJ7bdr5e2l3+dj+WaXht1pP254/ra612\nvj7TfG3u6bi+dr8XOum91+zjKWmzN5CvJdn0dJuko8uWj5L0YI1936aKZqfu/mD492eShhXtvwgA\nAAAAaFKSTU9/IOkYM1si6ZcKksFzK3cysxdKOkTSd8rWHSLpKXffbWaHS3qFpI+1pdQAgHFpH1kS\nAIBelVii6O57zey9km5RMD3Gte7+IzO7VEF16I3hrhlJ68Nq0jF9kq4xs98rGJBnjZeNlgoAjTCz\nutujHzuoJu0jSwIAUK6XpqZJdB5Fd79Z0s0V6y6sWL64ynHflnR8rIUDkHqViSDzSQEAgHp6aWqa\nRBNF9AZqbQB0q7r/2DfWblYLoLvUa0ZPE3r0KhJFxK48EaTGBohXLzWJiVu9zyo+y4B0qdWMPm01\nRMBkkCgmhAEg0oMaU3SSXmoSg9biswwAUI5EMSEMAJEe1JgCSAM+ywAA5ZKcRxEAAAAA0IGoUQQA\nAAAQC5q1dy9qFAEAAADEwt3Hb4tW3RRZJknsbNQoAgAAdCGmbwEQJxJFAACAKWr3aOZM3wIgbiSK\nADoG/RgAdCtGMweSQ+16PEgUq+DLKpCMyvcWv4oDAIB6qF2PD4liFXxZRS9rd/MpAAAAdB4SRQAR\nNJ8CAAAA02MAAAAAACKoUexB9MEE0Cpz+1br+HWra++wrtZxkkSTfgAAOhWJYg+iDyaAVnlydA1N\nlQEASCGangIAAAAAIqhRBAAAAHpEZRckWxvdThckjKFGEQAAAOgR7j5+K5VKkWWSRJQjUQQAAAAA\nRJAoAgAAAAAi6KMIAClTd0TRjdW3zZs9M6bSAACAbkSiCAApUm+qG6bCAQAAjaLpKQAAAAAgghpF\nAGiTyiHJKzHa3MROvORWbd+1p+q2Wk1u582eqbsvOiPOYqEDze1brePXra69w7pax0kSNe8AQKII\nAG1SmQjSFHTytu/aU/UxGx4e1rJly6oeU7fPJlLrydE1Nd9fvF7QSfgREZ2KpqcAADTAzMZvW9ee\nHVme6IseANRSPofholU3Ma8hOgaJIgAADWCSagBAL6HpKQAAHYjmaACAJJEoAgDQgcoTQfqzAvGq\nO/gRAx+hR5EoAgAAoKfVGvyIgY/Qy+ijCAAAAACIIFEEAAAAAETQ9BSpwwAQU8Mk1QAAACBRROow\nAMTUMEk1AAAASBQBAAAAoEF1fxzfWH3bvNkzYypNfEgUAQBT0iv/MAEAqNdSLW0t2UgUAQBN66V/\nmEA99O8GkDaJJopmdqakyyVNl/RZd19TsX2FpI9L+mW46kp3/2y47TxJHwrXf9Tda3wEdyb+oQAA\nkB707waQNoklimY2XdJVkk6XtE3SD8zsRne/t2LXG9z9vRXHHirpIkknS3JJd4THPtaGordE2v+h\nnHjJrdq+a0/VbbWuYd7smbr7ojPiLBYAAACABiRZo3iKpPvc/WeSZGbrJZ0jqTJRrOZPJN3m7o+G\nx94m6UxJxZjKiknavmtP1UQ4DUkwAAAAkHZJJopHSnqgbHmbpFOr7PdGM/tjSf8t6a/d/YEaxx4Z\nV0EBpEe92m6JGm8AAAAp2USx2qzolTOh/7ukorvvNrN3K+i5d1qDxwZBzM6XdL4kzZ8/X8PDw00V\nttnjmjnnjh076sZrZ1lafc60XFunxJvo8WxWml+b23ft0XVnzqm6bceOHTrwwAOrbluxcWdXXF8v\nxOuEz5a43nv1EK9154zr9ZLmz85OitfO/31pf+7ieCzfs2mndtb+PbbmD7JzZkpXLa/+/3kqeC9M\ngbsncpP0Mkm3lC1fIOmCOvtPl7Q9vJ+RdE3ZtmskZSaKuXTpUm/GolU3NXVcs+cslUodU5ZWnzMN\n19ZJ8eo9ns3q1demezquL+3xOuWzJY73Xj1peO7aHa/d73U+W9qnnf/70v7cpfF7hILKo5q3OHXL\n8ydpszeQr02LLwWd0A8kHWNmS8xsP0lvk3Rj+Q5mdkTZ4uskjYb3b5F0hpkdYmaHSDojXAcAAACg\nR5UnOqVSqVrlExqUWNNTd99rZu9VkOBNl3Stu//IzC5VkOXeKOkvzOx1kvZKelTSivDYR83sIwqS\nTUm61MOBbQAAAIDJqjmo3sbafdeBNEt0HkV3v1nSzRXrLiy7f4GCJqnVjr1W0rWxFhAAgDLFYlH5\nfF6jo6Pq6+tTLpdTJpNJulgApqjWlGWLV2+ouQ1Iu0QTRQAAukWxWFQul1OhUNC+ffs0ffp0ZbNZ\nSSJZBACkDokiAAANyOfzKhQKGhwcHJ8TtlAoaGhoiESxAUxNE7+xGu+t946q/yZqvAFMDYkiAAAN\nGB0d1cDAQGTdwMCARkdHaxyBctt37anZhG8s8a6mZr+xBtQ9NmX9zsprvFfc/ISuePVB1HgDmBIS\nRQAAGtDX16eRkRENDg6OrxsZGVFfX1+CpUIt9fqVpa3fmdkz00ufdtppwd9/CJbPPfdcEkUATUly\negwAALpGLpdTNptVqVTS3r17VSqVlM1mlcvlki4aepy7a9q0aXr66acjUwI8/fTTmjaNr3oAmkON\nIgAADRirlRkaGhof9TSfz1Nbg45AjTeAViNRBLpMeROjaphMFohPJpNRJpOp26cOSMJYjffYqLxj\nNd75fD7pogHoUiSKQJcpTwTj6mfTSwNAAEAaUOMNoNVIFAFE9NIAEACQJtR4A2glejgDAAAAACKo\nUewRTHQMAAAAoFEkij0iiYmOASSr2sBHtvaZ+wx8BLQW/bsBpAmJIgCkVGUiSL8lID707waQNiSK\nAAAAqIsuLEDvIVEEAABAXXRhAXoPiSIAAADQJtTOoluQKAIAAABtQu0sugWJIgAAPYhajfQpFovK\n5/MaHR1VX1+fcrmcMplM0sUC0KVIFAEA6EHUaqRLsVhULpdToVDQvn37NH36dGWzWUkiWQTQFBLF\nEL+sAkDnm9u3WsevW11947pax0gSUxMg3fL5vAqFggYHB8cT/UKhoKGhIRJFAE0hUQzxyyoAdL4n\nR9dU/azmcxq9bnR0VAMDA5F1AwMDGh0dTahEQHJoht0aJIoJqvvlZWPtGkwAAIByfX19GhkZ0eDg\n4Pi6kZER9fX1JVgqoP1oht06JIoJqVV7KQUJZL3t3YDmYQAAtE8ul9Nb3/pWzZkzR1u3btWiRYu0\nc+dOXX755UkXDWgrmmG3DokiYkHzMABpRHMmdAMzS7oIQGJoht06JIoAADSA5kzoZPl8XjfccEOk\nFqVUKlGLgp5DM+zWmZZ0AQAA6AblzZlmzJihwcFBFQoF5fP5pIsGUIsChHK5nLLZrEqlkvbu3atS\nqaRsNqtcLpd00boONYoAekrd/rMSfWhRE1/E0cmoRQECmUxG3/72t3XWWWdp9+7dmjVrllauXEnN\nehNIFAH0lFr9ZyX60KI+voijk43Voow1jR6rRaHGu/Pwg2W8isWiNmzYoG984xuRbgIvf/nLSRYn\niUQRAIAG8EUcnWzsC/DQ0ND4YEv5fJ4vxh2IHyzjxainrUOiCABAA/gijk6XyWSUyWTqJhtA2tFN\noHVIFAEAaFCcX8RPvORWbd+1p+b2WrUJ82bP1N0XndHSsgBAt6KbQOuQKAIA0AG279pDczQAmCK6\nCbQOiSIAAACAVKCbQOuQKPYIRtgCAABAL6C/bmuQKPYIRtgCAABA3KicSA8SRQAAGlQsFpXP58eb\nM+VyOZozAUAZKifSg0QRAIAGFItF5XK58QESxiZxlkSyCABInWlJFwAAgG5QPonzjBkzNDg4qEKh\nwEh6ALpOsVhUf3+/li9frv7+fhWLxaSLhA6UaI2imZ0p6XJJ0yV91t3XVGx/n6R3Sdor6beS/pe7\nbw237ZN0T7jrL9z9dW0rOACg5zCJM4A0oHUEGpVYjaKZTZd0laSzJB0rKWNmx1bsdqekk939BElf\nlfSxsm273P1F4Y0kEQAQq7FJnMsxiTOAbkPrCDQqyRrFUyTd5+4/kyQzWy/pHEn3ju3g7qWy/b8r\n6R1tLSEAoGFm9ux1a5+57+5tLE3rMYnz1DASItAZaB2BRiWZKB4p6YGy5W2STq2zf1bSN8qW9zez\nzQqapa5x96+3vogAMDUnXnKrtu/aU3N7rVHe5s2eqbsvOiOuYsWiMhFM2/xVTOI8NYyECHSGsdYR\ng4OD4+toHYFqkkwUn/3Ts1T152Yze4ekkyW9smz1Qnd/0MyeJ+l2M7vH3X9a5djzJZ0vSfPnz9fw\n8HDNAtXatmPHjqaOm4p2njOu66t2XFoeS+J1d7x2vhe279qj686cUzPegQceWHXbio07W37tE11f\nq8UVL8nPliOOOEJXXnll5Llr5TV2wud0muI1U5a4pPW910i8bv8sS9tj+YY3vEFvf/vb9f73v19L\nlizRJz/5SX384x9XNptt2bX26mtFSsd7fZy7J3KT9DJJt5QtXyDpgir7vUrSqKTn1jnXdZLeNFHM\npUuXei2LVt1Uc1upVGrquGa1+5xxXF+t49LwWJ5w8S2+aNVNk76dcPEtLS9LHNeX9nid8l6IK149\n9eLFIY54nfLZ0s5rmyhet7w2O+m9kIbPsnrS8Pqsp52fZWn4P1TN9ddf78cdd5xPmzbNjzvuOL/+\n+utbdu5OuL5G4sWhW97rkjZ7A/lakjWKP5B0jJktkfRLSW+TdG75Dmb2YknXSDrT3X9Ttv4QSU+5\n+24zO1zSKxQd6AaIzfZde2g+BQAAulYmk1Emk0ldFwG0VmKJorvvNbP3SrpFwfQY17r7j8zsUgVZ\n7o2SPi7pQElfCQdJGJsGo0/SNWb2ewUjt65x93urBgIAAAAATEqi8yi6+82Sbq5Yd2HZ/VfVOO7b\nko6Pt3QAkA7FYlH5fH58AJZ8Q7kXAAAgAElEQVRcLscALAAAoK5EE0UAQLyYWBkAADRjWtIFAADE\nh4mVAQBAM0gUASDFmFgZAAA0g0QRAFJsbGLlckysDAAAJkKiCAAplsvllM1mVSqVtHfvXpVKJWWz\nWeVyuaSLBgAAOhiD2aDrnXjJrdq+a0/N7bXmL5w3e6buvuiMuIoFdISxAWuGhobGRz3N5/MMZAMA\nAOoiUUTX275rj+5f85qq2+pNJFsrgQQAAAB6HYkiAKQY02MAAIBm0EcRAFKM6TEAAEAzqFEEgBRj\negwAmBwziy6vjW539zaWBkhOQzWKZjbLzM41sw+a2YVjt7gLBwCYGqbHAIDJcffxW6lUiiyTJKKX\nNNr09N8knSNpr6SdZTcAQAdjegwAANCMRpueHuXuZ8ZaEgA9ielN4sX0GAAAoBmNJorfNrPj3f2e\nWEsDoOcwvUn8MpmMMplM3ccTAACgXKOJ4oCkFWb2c0m7JZkkd/cTYisZAAAAACARjSaKZ8VaCgAA\nAABAx2hoMBt33yrpYEmvDW8Hh+sAAAA6mplFblvXnh1ZBioVi0X19/dr+fLl6u/vV7FYTLpIQNs1\nVKNoZn8paaWkr4Wrvmhm/+zuV8RWsh5S7Z9U+Zw9DMUMAEDzKv+P0l8X9RSLReVyORUKBe3bt0/T\np09XNpuVJAYCQ09ptOlpVtKp7r5TksxsraTvSEpNoji3b7WOX7e69g7rah0nSdUH4mgU/8AApEmx\nWFQ+nx8fZTWXy/HlCkDXyOfzKhQKGhwcHP9OVigUNDQ0xGcZekqjiaJJ2le2vC9clxpPjq5h5EUA\nmCJ+iQfQ7UZHRzUwMBBZNzAwoNHR0YRKhE6V9laBjSaKn5P0PTP713D59ZIK8RQJQCep/BAs/wCU\nuvNDsO4PPBtrz9vYrdpZw8cv8QC6XV9fn0ZGRjQ4ODi+bmRkRH19fQmWCp0o7a0CG0oU3f0yMxtW\nME2GSfozd78zzoKh+9X8Mp7CL+JpVv4hmIYPwFotB6TgNVtvezdqdw0fv8QD6ZRkF512y+Vyymaz\n45+bpVJJ2WxW+Xw+6aIBbVU3UTSzg9z9CTM7VNL94W1s26Hu/mi8xUO3qvVlO41fxHsBfc66V7tr\n+PglHkinXuqiM/bZODQ0NP5/L5/P83+vi/C9pTUmqlG8XtLZku6QVF63auHy82IqF4AOQZ+z7tbu\nGj5+iQeQBplMRplMJraWNL3WBaKd+N7SOnUTRXc/O/y7pD3FAdBp6HPW3dpdw9eOX+Jp1g6gm/Va\nF4h243tL6zQ6j+Imd18+0ToA6UOfs+6WRA1fnL/E06wdAFAP31taZ6I+ivtLOkDS4WZ2iJ6ZEuMg\nSQtiLhuADkCfs+5GXxsAQC/he0vrTFSj+OeS/kpBUniHnkkUn5B0VYzlAtAh6HPW/eLuawMAQKfg\ne0vrTNRH8XJJl5vZkLtf0aYyAegg1EgBQHdgpEeA7y2t1Og8ileYWb+kYyXtX7b+83EVDEDnoEYK\nQCsw0mN8GOkReAbfW1qj0cFsLpK0TEGieLOksySNSCJRBGJ24iW3avuuPTW31/riNW/2TN190Rlx\nFQsAJoWRHuPFSI8AWq2hRFHSmySdKOlOd/8zM5sv6bPxFQvAmO279vTMJMcA2mdu32odv2517R3W\n1TpOkkjqOg0jPQJotUYTxV3u/nsz22tmB0n6jaTnxVguAECL0G8J1Tw5uoYfoVKEkR7RSWhmng6N\nJoqbzexgSZ9RMPrpDknfj61UAICWoN8S0BsY6bG1+IGteTQzT49GB7P5P+Hdq81so6SD3P2H8RUL\nANAK9FsCegMjPbYOP7ABgWn1NprZSZU3SYdKmhHeBwB0MPotAb0jk8loy5Yt2rRpk7Zs2UJS06Ty\nH9hmzJihwcFBFQoFamfRcyaqUfxEnW0u6bQWlgXoCgwAgW5CvyUAmBx+YAMCdRNFdx+stx3oRQwA\ngW5CvyUAmBx+YAMCjc6jeICk90la6O7nm9kxkl7o7jfFWjoAwJTQbwloPzN79rq1z9x39zaWBpPF\nD2xAoNFRTz+nYLTTl4fL2yR9RdKUEkUzO1PS5ZKmS/qsu6+p2D5L0uclLZX0iKS3uvv94bYLJGUl\n7ZP0F+5+y1TKAgBplclklMlk6tZ4A2idykSQ91534Qc2IFB3MJsyz3f3j0naI0nuvkvSs38umwQz\nmy7pKklnSTpWUsbMjq3YLSvpMXd/gaRPSlobHnuspLdJOk7SmZL+KTwfAABAVUNDQ9p///01ODio\n/fffX0NDQ0kXCR2KgYGAxmsUnzaz2QoGsJGZPV/S7inGPkXSfe7+s/Cc6yWdI+nesn3OkXRxeP+r\nkq60oD3HOZLWu/tuST83s/vC831nimUCAAApNDQ0pKuvvlpr167Vscceq3vvvVerVq2SJF1xxRUJ\nlw4AOk+jieJFkjZKOtrMviTpFZJWTDH2kZIeKFveJunUWvu4+14z2y7psHD9dyuOPbJaEDM7X9L5\nkjR//nwNDw/XLFCtbTt27GjquGZNFK9ZdQdT2Vh925yZrb++OK6t3c9d2uPVEtdrs1Our93nTMvz\n1ynx0nBtvfrZEtc5y11zzTVauXKlTjrpJO3YsUMnnXSSVq5cqWuuuUZvfOMbY42dlvdep7xe2vl4\ntvu5k9LxWVZPmq8vdfHcve5NQRPToxUkaK+RdLakwyc6roHzvllBv8Sx5T+VdEXFPj+SdFTZ8k/D\nclwl6R1l6wuS3jhRzKVLl3oti1bdVHNbqVRq6rhm1YsXhziuoZ2x2v3cpT1ePXG8Njvp+tLw+qyn\n3Z8t7YzXzs8x93iurf+6/qZvzeik12Y7nj9JvnPnTnd/5vp27tzpwVeheKXhvddJr5d2Pp5p/k7m\nzvX1ajxJm72BfG3CGkV3dzP7ursvldTK8f23KUhAxxwl6cEa+2wzsxmS5kl6tMFjAQDoGky9E69Z\ns2bp6quv1vve977xdVdffbVmzZqVYKkAoHM1OpjNd83sJS2O/QNJx5jZEjPbT8HgNDdW7HOjpPPC\n+2+SdHuYBd8o6W1mNsvMlkg6RtL3W1w+AEiFYrGo/v5+LV++XP39/SoWi0kXCWi7lStXatWqVbrs\nssv0u9/9TpdddplWrVqllStXJl00AOhIjfZRHJT052a2VdJOBc1R3d1PaDawB30O3yvpFgXTY1zr\n7j8ys0sVVIfeqKBJ6RfCwWoeVZBMKtzvywoGvtkr6T3uvq/ZsgBAWhWLReVyufH5wKZPn65sNitJ\njOKHnjI2YM0HP/hB7d69W7NmzdK73/1uBrIBgBoaTRTPiiO4u98s6eaKdReW3f+dgr6M1Y7NS2Lm\nUwAdbW7fah2/bnXtHdbVOk4KuoVPTT6fV6FQ0ODg4HjzxUKhoKGhIRJF9JwrrrhCV1xxBfMaAkAD\nJkwUzWyapA3u3t+G8gBAqiTd72x0dFQDAwORdQMDAxodHW3J+QEAQDo1MpjN783sbjNb6O6/aEeh\ngMlIusYGU8PzF6++vj6NjIx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U2WfQzO4qu/3OzF4fbrvOzH5etu1F7b8KAAAAAEinpGoU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\nhp/vX2EM8ARxPXyLpLdk/Ky3morWr7ijvo30XgHvl/voa21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z6rXNN+hgEbhP+ZDCGK/O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\nBOMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a5792ce160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_correlation_state.boxplot(figsize=(15, 7))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Correlation of ground truth data against States\")\n",
    "plt.ylabel(\"Correlation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Which one to select\n",
    "\n",
    "According to [4], we should select the keywords with a maximum correlation of at least 0.4. Using this approach, would mean that we select all the keywords. As a result, we will be more restrictive in our approach and select the keywords with an average correlation of at least $\\alpha$ with $\\alpha$ being a desired parameter by the analyst. In our case, we will select 0.4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_keywords_by_alpha(df, alpha=0.2):\n",
    "    keywords_passed = []\n",
    "    for keyword, correlation in df.mean(axis=0, skipna=True).items():\n",
    "        if correlation >= alpha:\n",
    "            keywords_passed.append(keyword)\n",
    "    return keywords_passed\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blood glucose\n",
      "blood glucose level\n",
      "blood pressure\n",
      "diabetes\n",
      "diagnosis\n",
      "diet\n",
      "fat\n",
      "fruit\n",
      "glucose\n",
      "high blood pressure\n",
      "high blood sugar\n",
      "hypertension\n",
      "insulin\n",
      "obesity\n",
      "osteoporosis\n",
      "sugar\n",
      "symptoms\n",
      "type 1\n",
      "type 2\n"
     ]
    }
   ],
   "source": [
    "keywords_state_passed = []\n",
    "keywords_state_passed = filter_keywords_by_alpha(df=df_correlation_state)\n",
    "print(\"\\n\".join(keywords_state_passed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation in time\n",
    "\n",
    "We now will analyze the correlation in time. This means that we will create the correlation matrix in which every row will be a Year and the columns will be the keywords. Every entry (year, keyword) will be the correlation between the year and the keyword using the function [corrcoef](https://docs.scipy.org/doc/numpy/reference/generated/numpy.corrcoef.html) from numpy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_prevalence_keywords' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-9751933890b3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0myears\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_prevalence_keywords\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Year\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'df_prevalence_keywords' is not defined"
     ]
    }
   ],
   "source": [
    "years = list(set(df_prevalence_keywords[\"Year\"].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3167: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[:, None]\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py:3168: RuntimeWarning: invalid value encountered in true_divide\n",
      "  c /= stddev[None, :]\n"
     ]
    }
   ],
   "source": [
    "dict_correlation_years = {\n",
    "    keyword: [] for keyword in keywords    \n",
    "}\n",
    "\n",
    "dict_correlation_years[\"Year\"] = years\n",
    "\n",
    "for year in years:\n",
    "    df_year = df_prevalence_keywords[(df_prevalence_keywords.Year == year)]\n",
    "    for keyword in keywords:\n",
    "        dict_correlation_years[keyword].append(np.corrcoef(df_year[keyword], df_year[\"Prevalence\"])[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>association</th>\n",
       "      <th>blood glucose</th>\n",
       "      <th>blood glucose level</th>\n",
       "      <th>blood pressure</th>\n",
       "      <th>cardiovascular disease</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>complications</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>diabetes mellitus</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>...</th>\n",
       "      <th>insulin</th>\n",
       "      <th>kidney disease</th>\n",
       "      <th>like growth factor</th>\n",
       "      <th>obesity</th>\n",
       "      <th>osteoporosis</th>\n",
       "      <th>sugar</th>\n",
       "      <th>symptoms</th>\n",
       "      <th>type 1</th>\n",
       "      <th>type 2</th>\n",
       "      <th>weigth loss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>-0.193041</td>\n",
       "      <td>0.123598</td>\n",
       "      <td>0.100892</td>\n",
       "      <td>0.064474</td>\n",
       "      <td>0.021309</td>\n",
       "      <td>-0.055870</td>\n",
       "      <td>0.095851</td>\n",
       "      <td>0.015846</td>\n",
       "      <td>-0.059638</td>\n",
       "      <td>-0.024404</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063277</td>\n",
       "      <td>0.081398</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.060086</td>\n",
       "      <td>0.160195</td>\n",
       "      <td>0.147830</td>\n",
       "      <td>0.173657</td>\n",
       "      <td>0.184658</td>\n",
       "      <td>0.324630</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>0.127342</td>\n",
       "      <td>0.241516</td>\n",
       "      <td>0.211754</td>\n",
       "      <td>0.300984</td>\n",
       "      <td>0.106044</td>\n",
       "      <td>0.130867</td>\n",
       "      <td>-0.183228</td>\n",
       "      <td>0.237074</td>\n",
       "      <td>0.331187</td>\n",
       "      <td>-0.029018</td>\n",
       "      <td>...</td>\n",
       "      <td>0.243083</td>\n",
       "      <td>0.247427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.152289</td>\n",
       "      <td>0.114494</td>\n",
       "      <td>0.407484</td>\n",
       "      <td>0.111094</td>\n",
       "      <td>0.256981</td>\n",
       "      <td>0.232120</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>0.172905</td>\n",
       "      <td>0.175485</td>\n",
       "      <td>0.204306</td>\n",
       "      <td>0.185550</td>\n",
       "      <td>-0.211977</td>\n",
       "      <td>0.224511</td>\n",
       "      <td>-0.150394</td>\n",
       "      <td>-0.034656</td>\n",
       "      <td>0.377453</td>\n",
       "      <td>-0.126880</td>\n",
       "      <td>...</td>\n",
       "      <td>0.134339</td>\n",
       "      <td>-0.000455</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.017962</td>\n",
       "      <td>-0.120540</td>\n",
       "      <td>0.559140</td>\n",
       "      <td>-0.048274</td>\n",
       "      <td>0.115512</td>\n",
       "      <td>0.137262</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>0.284226</td>\n",
       "      <td>0.336020</td>\n",
       "      <td>0.440353</td>\n",
       "      <td>-0.050305</td>\n",
       "      <td>-0.098773</td>\n",
       "      <td>-0.005630</td>\n",
       "      <td>0.122531</td>\n",
       "      <td>0.139394</td>\n",
       "      <td>0.154639</td>\n",
       "      <td>0.123991</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.066584</td>\n",
       "      <td>0.304834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.070487</td>\n",
       "      <td>-0.064788</td>\n",
       "      <td>0.508847</td>\n",
       "      <td>0.457688</td>\n",
       "      <td>0.191829</td>\n",
       "      <td>0.274081</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>0.104401</td>\n",
       "      <td>0.426639</td>\n",
       "      <td>0.290444</td>\n",
       "      <td>0.057093</td>\n",
       "      <td>-0.196492</td>\n",
       "      <td>0.112974</td>\n",
       "      <td>0.256682</td>\n",
       "      <td>0.134722</td>\n",
       "      <td>0.175521</td>\n",
       "      <td>-0.112109</td>\n",
       "      <td>...</td>\n",
       "      <td>0.231391</td>\n",
       "      <td>0.212722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.103039</td>\n",
       "      <td>0.106078</td>\n",
       "      <td>0.306665</td>\n",
       "      <td>0.080509</td>\n",
       "      <td>0.368129</td>\n",
       "      <td>0.255769</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>0.089768</td>\n",
       "      <td>0.330873</td>\n",
       "      <td>0.223292</td>\n",
       "      <td>0.018368</td>\n",
       "      <td>-0.150599</td>\n",
       "      <td>-0.033486</td>\n",
       "      <td>0.091902</td>\n",
       "      <td>0.149782</td>\n",
       "      <td>0.179463</td>\n",
       "      <td>-0.052011</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214284</td>\n",
       "      <td>0.190580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.262170</td>\n",
       "      <td>-0.048507</td>\n",
       "      <td>0.225829</td>\n",
       "      <td>0.204494</td>\n",
       "      <td>0.333467</td>\n",
       "      <td>0.468616</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>-0.073196</td>\n",
       "      <td>0.312786</td>\n",
       "      <td>0.297954</td>\n",
       "      <td>0.154817</td>\n",
       "      <td>-0.015963</td>\n",
       "      <td>0.168047</td>\n",
       "      <td>0.160915</td>\n",
       "      <td>0.205662</td>\n",
       "      <td>0.504088</td>\n",
       "      <td>-0.051002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.174847</td>\n",
       "      <td>0.324056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.079482</td>\n",
       "      <td>-0.062182</td>\n",
       "      <td>0.197590</td>\n",
       "      <td>0.459549</td>\n",
       "      <td>0.278351</td>\n",
       "      <td>0.498644</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0.103663</td>\n",
       "      <td>0.351342</td>\n",
       "      <td>0.126079</td>\n",
       "      <td>0.231067</td>\n",
       "      <td>-0.169899</td>\n",
       "      <td>0.092125</td>\n",
       "      <td>0.136444</td>\n",
       "      <td>0.221796</td>\n",
       "      <td>0.401556</td>\n",
       "      <td>0.028233</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213345</td>\n",
       "      <td>0.420645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.126834</td>\n",
       "      <td>-0.016497</td>\n",
       "      <td>0.515724</td>\n",
       "      <td>0.257488</td>\n",
       "      <td>0.240060</td>\n",
       "      <td>0.377524</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.190752</td>\n",
       "      <td>0.368728</td>\n",
       "      <td>0.219414</td>\n",
       "      <td>0.321772</td>\n",
       "      <td>0.278334</td>\n",
       "      <td>0.120625</td>\n",
       "      <td>0.431040</td>\n",
       "      <td>0.120344</td>\n",
       "      <td>0.399806</td>\n",
       "      <td>0.139680</td>\n",
       "      <td>...</td>\n",
       "      <td>0.281219</td>\n",
       "      <td>0.298376</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.121615</td>\n",
       "      <td>0.092376</td>\n",
       "      <td>0.465507</td>\n",
       "      <td>0.441139</td>\n",
       "      <td>0.439196</td>\n",
       "      <td>0.354134</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>-0.055467</td>\n",
       "      <td>0.300925</td>\n",
       "      <td>0.202717</td>\n",
       "      <td>0.198816</td>\n",
       "      <td>-0.213305</td>\n",
       "      <td>0.051583</td>\n",
       "      <td>0.470525</td>\n",
       "      <td>0.218923</td>\n",
       "      <td>0.224665</td>\n",
       "      <td>0.366286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.177408</td>\n",
       "      <td>0.197256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.089973</td>\n",
       "      <td>0.084480</td>\n",
       "      <td>0.540399</td>\n",
       "      <td>0.292988</td>\n",
       "      <td>0.248027</td>\n",
       "      <td>0.412942</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.183287</td>\n",
       "      <td>0.367713</td>\n",
       "      <td>0.248303</td>\n",
       "      <td>0.382383</td>\n",
       "      <td>0.224990</td>\n",
       "      <td>0.104641</td>\n",
       "      <td>0.231738</td>\n",
       "      <td>0.246873</td>\n",
       "      <td>0.248232</td>\n",
       "      <td>0.243242</td>\n",
       "      <td>...</td>\n",
       "      <td>0.426637</td>\n",
       "      <td>0.345728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.230370</td>\n",
       "      <td>-0.100157</td>\n",
       "      <td>0.630971</td>\n",
       "      <td>0.408304</td>\n",
       "      <td>0.332442</td>\n",
       "      <td>0.432869</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.203932</td>\n",
       "      <td>0.325937</td>\n",
       "      <td>0.156701</td>\n",
       "      <td>0.379397</td>\n",
       "      <td>0.100795</td>\n",
       "      <td>0.364186</td>\n",
       "      <td>0.165321</td>\n",
       "      <td>0.148438</td>\n",
       "      <td>0.335843</td>\n",
       "      <td>0.041388</td>\n",
       "      <td>...</td>\n",
       "      <td>0.237064</td>\n",
       "      <td>0.128285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.017482</td>\n",
       "      <td>0.070271</td>\n",
       "      <td>0.543486</td>\n",
       "      <td>0.300653</td>\n",
       "      <td>0.235784</td>\n",
       "      <td>0.247375</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>-0.126341</td>\n",
       "      <td>0.360459</td>\n",
       "      <td>0.134700</td>\n",
       "      <td>0.332158</td>\n",
       "      <td>0.255729</td>\n",
       "      <td>0.085223</td>\n",
       "      <td>0.435936</td>\n",
       "      <td>0.225444</td>\n",
       "      <td>0.486662</td>\n",
       "      <td>0.241629</td>\n",
       "      <td>...</td>\n",
       "      <td>0.357618</td>\n",
       "      <td>0.194157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.091682</td>\n",
       "      <td>-0.125849</td>\n",
       "      <td>0.732054</td>\n",
       "      <td>0.454938</td>\n",
       "      <td>0.439692</td>\n",
       "      <td>0.500121</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      association  blood glucose  blood glucose level  blood pressure  \\\n",
       "Year                                                                    \n",
       "2004    -0.193041       0.123598             0.100892        0.064474   \n",
       "2005     0.127342       0.241516             0.211754        0.300984   \n",
       "2006     0.172905       0.175485             0.204306        0.185550   \n",
       "2007     0.284226       0.336020             0.440353       -0.050305   \n",
       "2008     0.104401       0.426639             0.290444        0.057093   \n",
       "2009     0.089768       0.330873             0.223292        0.018368   \n",
       "2010    -0.073196       0.312786             0.297954        0.154817   \n",
       "2011     0.103663       0.351342             0.126079        0.231067   \n",
       "2012     0.190752       0.368728             0.219414        0.321772   \n",
       "2013    -0.055467       0.300925             0.202717        0.198816   \n",
       "2014     0.183287       0.367713             0.248303        0.382383   \n",
       "2015     0.203932       0.325937             0.156701        0.379397   \n",
       "2016    -0.126341       0.360459             0.134700        0.332158   \n",
       "\n",
       "      cardiovascular disease  cholesterol  complications  diabetes  \\\n",
       "Year                                                                 \n",
       "2004                0.021309    -0.055870       0.095851  0.015846   \n",
       "2005                0.106044     0.130867      -0.183228  0.237074   \n",
       "2006               -0.211977     0.224511      -0.150394 -0.034656   \n",
       "2007               -0.098773    -0.005630       0.122531  0.139394   \n",
       "2008               -0.196492     0.112974       0.256682  0.134722   \n",
       "2009               -0.150599    -0.033486       0.091902  0.149782   \n",
       "2010               -0.015963     0.168047       0.160915  0.205662   \n",
       "2011               -0.169899     0.092125       0.136444  0.221796   \n",
       "2012                0.278334     0.120625       0.431040  0.120344   \n",
       "2013               -0.213305     0.051583       0.470525  0.218923   \n",
       "2014                0.224990     0.104641       0.231738  0.246873   \n",
       "2015                0.100795     0.364186       0.165321  0.148438   \n",
       "2016                0.255729     0.085223       0.435936  0.225444   \n",
       "\n",
       "      diabetes mellitus  diagnosis     ...        insulin  kidney disease  \\\n",
       "Year                                   ...                                  \n",
       "2004          -0.059638  -0.024404     ...       0.063277        0.081398   \n",
       "2005           0.331187  -0.029018     ...       0.243083        0.247427   \n",
       "2006           0.377453  -0.126880     ...       0.134339       -0.000455   \n",
       "2007           0.154639   0.123991     ...      -0.066584        0.304834   \n",
       "2008           0.175521  -0.112109     ...       0.231391        0.212722   \n",
       "2009           0.179463  -0.052011     ...       0.214284        0.190580   \n",
       "2010           0.504088  -0.051002     ...       0.174847        0.324056   \n",
       "2011           0.401556   0.028233     ...       0.213345        0.420645   \n",
       "2012           0.399806   0.139680     ...       0.281219        0.298376   \n",
       "2013           0.224665   0.366286     ...       0.177408        0.197256   \n",
       "2014           0.248232   0.243242     ...       0.426637        0.345728   \n",
       "2015           0.335843   0.041388     ...       0.237064        0.128285   \n",
       "2016           0.486662   0.241629     ...       0.357618        0.194157   \n",
       "\n",
       "      like growth factor   obesity  osteoporosis     sugar  symptoms  \\\n",
       "Year                                                                   \n",
       "2004                 NaN -0.060086      0.160195  0.147830  0.173657   \n",
       "2005                 NaN  0.152289      0.114494  0.407484  0.111094   \n",
       "2006                 NaN  0.017962     -0.120540  0.559140 -0.048274   \n",
       "2007                 NaN  0.070487     -0.064788  0.508847  0.457688   \n",
       "2008                 NaN -0.103039      0.106078  0.306665  0.080509   \n",
       "2009                 NaN -0.262170     -0.048507  0.225829  0.204494   \n",
       "2010                 NaN -0.079482     -0.062182  0.197590  0.459549   \n",
       "2011                 NaN -0.126834     -0.016497  0.515724  0.257488   \n",
       "2012                 NaN -0.121615      0.092376  0.465507  0.441139   \n",
       "2013                 NaN  0.089973      0.084480  0.540399  0.292988   \n",
       "2014                 NaN  0.230370     -0.100157  0.630971  0.408304   \n",
       "2015                 NaN  0.017482      0.070271  0.543486  0.300653   \n",
       "2016                 NaN  0.091682     -0.125849  0.732054  0.454938   \n",
       "\n",
       "        type 1    type 2  weigth loss  \n",
       "Year                                   \n",
       "2004  0.184658  0.324630          NaN  \n",
       "2005  0.256981  0.232120          NaN  \n",
       "2006  0.115512  0.137262          NaN  \n",
       "2007  0.191829  0.274081          NaN  \n",
       "2008  0.368129  0.255769          NaN  \n",
       "2009  0.333467  0.468616          NaN  \n",
       "2010  0.278351  0.498644          NaN  \n",
       "2011  0.240060  0.377524          NaN  \n",
       "2012  0.439196  0.354134          NaN  \n",
       "2013  0.248027  0.412942          NaN  \n",
       "2014  0.332442  0.432869          NaN  \n",
       "2015  0.235784  0.247375          NaN  \n",
       "2016  0.439692  0.500121          NaN  \n",
       "\n",
       "[13 rows x 30 columns]"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_correlation_years = pd.DataFrame.from_dict(dict_correlation_years).set_index(\"Year\").sort_index()\n",
    "df_correlation_years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boxplot representation of the correlations\n",
    "\n",
    "We now plot the boxplot of the correlations. It's important to see that some values inside the correlation matrix are NaN. This refers to the fact that some vectors when we slice over the state will be full of zeros. The boxplot function of pandas takes into account this. As a result, we only visualize the values in which the correlation is different than NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Iz0TEzj7rPCUiPhkRn4iIt4+6jJIkSZI0qbaOK3BEbAFeAzwKuBa4LCIuyMxP9qxzIvAi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a578d90a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_correlation_years.boxplot(figsize=(15, 7))\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Correlation of ground truth data against Years\")\n",
    "plt.ylabel(\"Correlation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking Stability over years\n",
    "\n",
    "We now check the stability of the correlation among years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OQBEAX8N/Q+qoQ0REdG9uM/LyfhG1fsdc+7OUss7z6iuvq407Pb+asbGx1Ol0f3wuLy8X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\nH/XDKLv8CZqbj4DP98LYMVlnGIZJvt3z5eTklMfHx3fbBZwg7lZOTo4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KbOpaL5M22LqM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fEURR5OtHqjnk9vHxhEGYGz6Cfy+SWuW/9jKiCMHKTrx7W/CXdIAgoraGMJiPofevQmY7IsUf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VfP1Q8Xk1l08euhcFigpN7CmrJWNpS34/AGSVRHs2hkGktGpzM8/TwlRgCdTZHW9vKPIgp/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a8AohIQOTGY9fQa2traW+vr7/vBETE0NGRgYTJkxArz+zIrXD7eX20nr2WOzckxzNI/8B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CCgyCwNNhvtzl5470GnIOhA7zNeU9iKLI8uomnjtdjVIq4ZVIf6Z4XrutkfnQxyi2LeEj6xSS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5Lp52zN6iVDrloq9kdEW7OeDW+cpFWrZ+LSIPYc2kp5eTljx45l1KhR3Q4WTQaBhNU5nEms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\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cnfi+UzQuzdgM5wyCVSDvWCWp2wooy1Xh6CIjcWAwibeE4ORqyzZQG9UsP7acNelrkIql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\nOwsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a578c4d9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_correlation_years.plot(figsize=(20, 7))\n",
    "plt.xticks(years, rotation=90)\n",
    "plt.title(\"Correlation of ground truth data against years by topic\")\n",
    "plt.ylabel(\"Correlation\")\n",
    "plt.legend(loc=\"center right\", bbox_to_anchor=(1.15,0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's hard to measure the stability in this case as all the correlations by year are almost the same. We analyze the standard deviation for every year and all are between 0.13 and 0.23 with 2016 being the year with the most dispersion.\n",
    "\n",
    "Finally we can say that the correlation by years have less impact that aggregating by state. This makes sense as Google itself certifies that the data is independent between states. As a result, a geographical aggregation should work better than a yearly based agregation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "2004    0.135297\n",
       "2005    0.154205\n",
       "2006    0.194505\n",
       "2007    0.183319\n",
       "2008    0.166444\n",
       "2009    0.187038\n",
       "2010    0.176056\n",
       "2011    0.166785\n",
       "2012    0.144288\n",
       "2013    0.166965\n",
       "2014    0.170101\n",
       "2015    0.147065\n",
       "2016    0.235182\n",
       "dtype: float64"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_correlation_years.std(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which keyword to select\n",
    "\n",
    "Similar to our spatial aggregation analysis we will now select the keywords that passes a threshold $\\alpha$. In this particular case, the $\\alpha$ should be smaller because the correlation values are smaller."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blood glucose\n",
      "blood glucose level\n",
      "blood pressure\n",
      "complications\n",
      "diabetes\n",
      "diabetes mellitus\n",
      "diagnosis.1\n",
      "diet\n",
      "glucose\n",
      "high blood pressure\n",
      "high blood sugar\n",
      "hypertension\n",
      "insulin\n",
      "kidney disease\n",
      "sugar\n",
      "symptoms\n",
      "type 1\n",
      "type 2\n"
     ]
    }
   ],
   "source": [
    "keywords_year_passed = []\n",
    "keywords_year_passed = filter_keywords_by_alpha(df=df_correlation_years, alpha=0.15)\n",
    "print(\"\\n\".join(keywords_year_passed))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Poverty Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/data_poverty/poverty.txt\") as f:\n",
    "    content = f.readlines()\n",
    "    \n",
    "dict_poverty = {\"State\": [], \"Year\":[], \"Percent_Poverty\": [] }\n",
    "for line in content:\n",
    "    try:\n",
    "        year = int(line)\n",
    "    except:\n",
    "        lst = line.split(\" \")\n",
    "        name = lst[0:len(lst)-5]\n",
    "        l = lst[len(lst)-5:]\n",
    "        if name[0] != \"STATE\":\n",
    "            dict_poverty[\"State\"].append(\" \".join(name).strip('\\n'))\n",
    "            x = l[3].split(\",\")\n",
    "            try:\n",
    "                n = x[0] + \".\" + x[1]\n",
    "            except:\n",
    "                if len(lst[3]) == 3:\n",
    "                    n = l[3][0] + l[3][1] + \".\" + l[3][2]\n",
    "                else:\n",
    "                    n = l[3]\n",
    "            dict_poverty[\"Percent_Poverty\"].append(float(n))\n",
    "            dict_poverty[\"Year\"].append(year)\n",
    "            \n",
    "df_poverty = pd.DataFrame(data = dict_poverty, columns=[\"State\", \"Year\", \"Percent_Poverty\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Year</th>\n",
       "      <th>Percent_Poverty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2016</td>\n",
       "      <td>16.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2016</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2016</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2016</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>2016</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>2016</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>2016</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>2016</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>2016</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>2016</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>2016</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Hawaii</td>\n",
       "      <td>2016</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>2016</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>2016</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>2016</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>2016</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>2016</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>2016</td>\n",
       "      <td>15.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>2016</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Maine</td>\n",
       "      <td>2016</td>\n",
       "      <td>12.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>2016</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>2016</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>2016</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>2016</td>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>2016</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Montana</td>\n",
       "      <td>2016</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>2016</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2016</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>2016</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>2004</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>634</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>2004</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>2004</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>2004</td>\n",
       "      <td>18.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>2004</td>\n",
       "      <td>12.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>Montana</td>\n",
       "      <td>2004</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>2004</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>2004</td>\n",
       "      <td>10.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>2004</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>2004</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>2004</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>New York</td>\n",
       "      <td>2004</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>2004</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>2004</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>2004</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>2004</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>2004</td>\n",
       "      <td>11.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>2004</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>2004</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>2004</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>653</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>2004</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>2004</td>\n",
       "      <td>15.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>Texas</td>\n",
       "      <td>2004</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>Utah</td>\n",
       "      <td>2004</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>2004</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>2004</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>Washington</td>\n",
       "      <td>2004</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>2004</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>2004</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>2004</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>663 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    State  Year  Percent_Poverty\n",
       "0                 Alabama  2016             16.2\n",
       "1                  Alaska  2016             12.6\n",
       "2                 Arizona  2016             16.1\n",
       "3                Arkansas  2016             16.0\n",
       "4              California  2016             13.9\n",
       "5                Colorado  2016              8.5\n",
       "6             Connecticut  2016              9.8\n",
       "7                Delaware  2016             11.6\n",
       "8    District of Columbia  2016             16.3\n",
       "9                 Florida  2016             13.0\n",
       "10                Georgia  2016             15.4\n",
       "11                 Hawaii  2016              9.3\n",
       "12                  Idaho  2016             11.1\n",
       "13               Illinois  2016             12.1\n",
       "14                Indiana  2016             11.8\n",
       "15                   Iowa  2016              9.8\n",
       "16                 Kansas  2016             11.2\n",
       "17               Kentucky  2016             15.2\n",
       "18              Louisiana  2016             20.2\n",
       "19                  Maine  2016             12.7\n",
       "20               Maryland  2016              7.1\n",
       "21          Massachusetts  2016              9.6\n",
       "22               Michigan  2016             11.1\n",
       "23              Minnesota  2016              8.7\n",
       "24            Mississippi  2016             21.1\n",
       "25               Missouri  2016             13.0\n",
       "26                Montana  2016             11.7\n",
       "27               Nebraska  2016              9.6\n",
       "28                 Nevada  2016             10.1\n",
       "29          New Hampshire  2016              6.4\n",
       "..                    ...   ...              ...\n",
       "633         Massachusetts  2004              9.3\n",
       "634              Michigan  2004             13.3\n",
       "635             Minnesota  2004              7.0\n",
       "636           Mississippi  2004             18.7\n",
       "637              Missouri  2004             12.2\n",
       "638               Montana  2004             14.2\n",
       "639              Nebraska  2004              9.5\n",
       "640                Nevada  2004             10.9\n",
       "641         New Hampshire  2004              5.5\n",
       "642            New Jersey  2004              8.0\n",
       "643            New Mexico  2004             16.5\n",
       "644              New York  2004             15.0\n",
       "645        North Carolina  2004             14.6\n",
       "646          North Dakota  2004              9.7\n",
       "647                  Ohio  2004             11.6\n",
       "648              Oklahoma  2004             10.8\n",
       "649                Oregon  2004             11.8\n",
       "650          Pennsylvania  2004             11.4\n",
       "651          Rhode Island  2004             11.5\n",
       "652        South Carolina  2004             14.9\n",
       "653          South Dakota  2004             13.5\n",
       "654             Tennessee  2004             15.9\n",
       "655                 Texas  2004             16.5\n",
       "656                  Utah  2004             10.1\n",
       "657               Vermont  2004              7.8\n",
       "658              Virginia  2004              9.4\n",
       "659            Washington  2004             11.4\n",
       "660         West Virginia  2004             14.2\n",
       "661             Wisconsin  2004             12.4\n",
       "662               Wyoming  2004             10.0\n",
       "\n",
       "[663 rows x 3 columns]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_poverty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Income Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_income = []\n",
    "interest = [\"Year\", \"LocationAbbr\", \"LocationDesc\", \"Indicator\", \"Data_Value\", \"Low_Confidence_Limit\", \"High_Confidence_Limit\", \"Sample_Size\"]\n",
    "\n",
    "for year in range(2004, 2017):\n",
    "    file = \"data/data_income/{}.csv\".format(str(year))\n",
    "    df = pd.read_csv(file, sep=\";\")[interest]\n",
    "    dict_income.append(df)\n",
    "df = pd.concat(dict_income).dropna(axis=0, how='all')\n",
    "df.Year = list(map(int, df_income.Year))\n",
    "\n",
    "dic_data = {\"Year\" : [], \"State\": [], \"Income<15k\": [], \"Income<25k\": [],\"Income<35k\": [],\"Income<50k\": [],\"Income>50k\": []}\n",
    "for idx, row in df.iterrows():\n",
    "    if idx%5 == 0:\n",
    "        dic_data[\"Year\"].append(row[0])\n",
    "        dic_data[\"State\"].append(row[2])\n",
    "        dic_data[\"Income<15k\"].append(row[4])\n",
    "    elif idx%5 == 1:\n",
    "        dic_data[\"Income<25k\"].append(row[4])\n",
    "    elif idx%5 == 2:\n",
    "        dic_data[\"Income<35k\"].append(row[4])\n",
    "    elif idx%5 == 3:\n",
    "        dic_data[\"Income<50k\"].append(row[4])\n",
    "    else:\n",
    "        dic_data[\"Income>50k\"].append(row[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th>Income&lt;15k</th>\n",
       "      <th>Income&lt;25k</th>\n",
       "      <th>Income&lt;35k</th>\n",
       "      <th>Income&lt;50k</th>\n",
       "      <th>Income&gt;50k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2004</td>\n",
       "      <td>15.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2004</td>\n",
       "      <td>10.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2004</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.8</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2004</td>\n",
       "      <td>11.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>6</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>2004</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  Year Income<15k Income<25k Income<35k Income<50k Income>50k\n",
       "0     Alabama  2004       15.3        9.3        6.3        5.9        5.2\n",
       "1      Alaska  2004       10.2        4.5        2.3        4.3        3.4\n",
       "2     Arizona  2004        7.9        8.8        7.5        6.3        3.7\n",
       "3    Arkansas  2004       11.6        8.7        7.6          6        4.3\n",
       "4  California  2004       11.5        5.5        7.5        6.4        5.2"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income = pd.DataFrame.from_dict(dic_data)\n",
    "#clean_income = df_income[[\"LocationDesc\", \"Year\", \"Data_Value\"]]\n",
    "df_income = df_income[[\"State\", \"Year\", \"Income<15k\", \"Income<25k\",\"Income<35k\",\"Income<50k\",\"Income>50k\"]]\n",
    "df_income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Health Insurence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/health_insurence/2013_2016.txt\") as f:\n",
    "    content = f.readlines()\n",
    "\n",
    "insurence = {}\n",
    "\n",
    "for y in range(2004, 2017):\n",
    "    insurence[y] = {\"State\": [], \"Year\":[], \"Percent_Health\": []}\n",
    "\n",
    "c = 0\n",
    "for line in content:\n",
    "    if c==3:\n",
    "        lst = line.split(\" \")\n",
    "        name = lst[0: len(lst) - 8]\n",
    "        percent = lst[len(lst) - 8 :]\n",
    "        for i in range(2013, 2017):\n",
    "            insurence[i][\"State\"].append(\" \".join(name).strip('\\n'))\n",
    "            insurence[i][\"Year\"].append(i)\n",
    "        percent = lst[len(lst) - 8 :]\n",
    "        for idx, l in enumerate(percent[0::2]):\n",
    "            x = l.split(\",\")\n",
    "            try:\n",
    "                n = x[0] + \".\" + x[1]\n",
    "            except:\n",
    "                if len(l) == 3:\n",
    "                    n = l[0] + l[1] + \".\" + l[2]\n",
    "                else:\n",
    "                    n = l\n",
    "            insurence[2012 +idx + 1][\"Percent_Health\"].append(100.0 - float(n))\n",
    "    else:\n",
    "        c += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/health_insurence/2012_1999.txt\") as f:\n",
    "    content = f.readlines()\n",
    "\n",
    "c = 0\n",
    "for line in content:\n",
    "    if c == 0:\n",
    "        c = 1\n",
    "    else:\n",
    "        lst = line.split(\" \")\n",
    "        if len(lst) < 5:\n",
    "            for i in range(2004,2013):\n",
    "                insurence[i][\"State\"].append(\" \".join(lst).strip('\\n'))\n",
    "        else:\n",
    "            l = lst[4]\n",
    "            x = l.split(',')\n",
    "            year = int(lst[0])\n",
    "            if year > 2003:\n",
    "                try:\n",
    "                    n = x[0] + \".\" + x[1]\n",
    "                except:\n",
    "                    if len(l) == 3:\n",
    "                        n = l[0] + l[1] + \".\" + l[2]\n",
    "                    else:\n",
    "                        n = l\n",
    "                insurence[year][\"Percent_Health\"].append(100 - float(n))\n",
    "                insurence[year][\"Year\"].append(year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Year</th>\n",
       "      <th>Percent_Health</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2004</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2004</td>\n",
       "      <td>84.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2004</td>\n",
       "      <td>83.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2004</td>\n",
       "      <td>84.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>2004</td>\n",
       "      <td>82.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  Year  Percent_Health\n",
       "0     Alabama  2004            88.0\n",
       "1      Alaska  2004            84.7\n",
       "2     Arizona  2004            83.8\n",
       "3    Arkansas  2004            84.1\n",
       "4  California  2004            82.5"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flag = 0\n",
    "for i in insurence:\n",
    "    if flag == 0:\n",
    "        flag = 1\n",
    "        df_insurence = pd.DataFrame.from_dict(insurence[i])\n",
    "    else:\n",
    "        df = pd.DataFrame.from_dict(insurence[i])\n",
    "        df_insurence = pd.concat([df_insurence, df])\n",
    "df_insurence = df_insurence[[\"State\", \"Year\", \"Percent_Health\"]]\n",
    "df_insurence.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>Year</th>\n",
       "      <th>Income&lt;15k</th>\n",
       "      <th>Income&lt;25k</th>\n",
       "      <th>Income&lt;35k</th>\n",
       "      <th>Income&lt;50k</th>\n",
       "      <th>Income&gt;50k</th>\n",
       "      <th>Percent_Poverty</th>\n",
       "      <th>Percent_Health</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2004</td>\n",
       "      <td>15.3</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>16.9</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2004</td>\n",
       "      <td>10.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>84.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2004</td>\n",
       "      <td>7.9</td>\n",
       "      <td>8.8</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>14.4</td>\n",
       "      <td>83.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2004</td>\n",
       "      <td>11.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>7.6</td>\n",
       "      <td>6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>15.1</td>\n",
       "      <td>84.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>2004</td>\n",
       "      <td>11.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>6.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>13.2</td>\n",
       "      <td>82.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  Year Income<15k Income<25k Income<35k Income<50k Income>50k  \\\n",
       "0     Alabama  2004       15.3        9.3        6.3        5.9        5.2   \n",
       "1      Alaska  2004       10.2        4.5        2.3        4.3        3.4   \n",
       "2     Arizona  2004        7.9        8.8        7.5        6.3        3.7   \n",
       "3    Arkansas  2004       11.6        8.7        7.6          6        4.3   \n",
       "4  California  2004       11.5        5.5        7.5        6.4        5.2   \n",
       "\n",
       "   Percent_Poverty  Percent_Health  \n",
       "0             16.9            88.0  \n",
       "1              9.1            84.7  \n",
       "2             14.4            83.8  \n",
       "3             15.1            84.1  \n",
       "4             13.2            82.5  "
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_income_poverty = pd.merge(df_income, df_poverty, on=[\"State\", \"Year\"])\n",
    "df_income_poverty_health = pd.merge(df_income_poverty, df_insurence, on = [\"State\", \"Year\"])\n",
    "df_income_poverty_health.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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