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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Import relevant libraries</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
<span class="o">%</span><span class="k">config</span> InlineBackend.figure_format = &#39;retina&#39; #nicer rendering of plots in retina displays

<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>

<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="kn">as</span> <span class="nn">mpl</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>

<span class="kn">import</span> <span class="nn">plotly.plotly</span> <span class="kn">as</span> <span class="nn">py</span>
<span class="kn">import</span> <span class="nn">plotly.graph_objs</span> <span class="kn">as</span> <span class="nn">go</span>

<span class="kn">import</span> <span class="nn">seaborn</span> <span class="kn">as</span> <span class="nn">sns</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set</span><span class="p">()</span>
<span class="n">pal</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">hls_palette</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">h</span><span class="o">=.</span><span class="mi">5</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">set_palette</span><span class="p">(</span><span class="n">pal</span><span class="p">)</span>

<span class="kn">import</span> <span class="nn">re</span>
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<p><h1>Analyzing the Impact of the Affordable Care Act</h1>
<br>
   <br>
</br></p>
<p>The <strong>Affordable Care Act (ACA)</strong> is the comprehensive health care reform law and its amendments addressing health insurance coverage, health care costs, and preventive care in the United States. In particular, the law established a Health Insurance Marketplace, and allowed all states to opt in to expand their Medicaid program.</p>
<p>The law was enacted in two parts, namely The Patient Protection and Affordable Care Act which was signed on March 23, 2010 by President Barack Obama and amended by the Health Care and Education Reconciliation Act of March 30, 2010. A repeal of the ACA was recently passed by the Congress.</p>
<p>This analysis aims to visualize metrics that <strong>quantify the impact of the ACA</strong> and expose some interesting findings.</p>

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<h2>The <em>Status Quo</em>: Insurance Coverage Before the ACA (1995-2010)</h2><p>According to the <a href="https://www.kaggle.com/cdc/health-care-access-coverage">source</a> of this dataset, the information about health insurance coverage between 1995 to 2010 was compiled from the <strong>US Department of Health and Human Services</strong> and the <strong>US Census Bureau</strong>. More specifically, the data seems to have originated at least in part from the <a href="https://en.wikipedia.org/wiki/American_Community_Survey">American Community Survey</a>.</p>
<p>The dataset contains a feature (a column) labeled "Category" with two distinct values: <strong>Do you have any kind of health care coverage?</strong> and <strong>Adults aged 18-64 who have any kind of health care coverage</strong>. Here we analyze the <strong>former</strong> because it's not limited by the age group. The summary statistics between both sets differ but only slightly, as expected.</p>
<p>The <strong>Figure 1</strong> below shows the trend in the percentage of the surveyed population over the years who responded yes for having any kind of insurance coverage. The data represents the mean per US Census region for each year.</p>
<p>Interestingly, the chart shows the Northeast and Midwest regions reporting coverage about 5% points higher than the West and South. However, this difference may be within the error range of the survey.</p>
<p>In general, there is a small drop of about 2% points in the reported proportion of the population covered by health insurance along the period, with the exception of the US territories which show a much more pronounced change. There was a consistent and noticeable change point in the trend in 2001 for all regions. This may have been associated with macroenomic factors, such as the economic <a href="http://www.nber.org/cycles/cyclesmain.html">recession</a> of 2001. However, a similar impact is not seen in the data for the deeper recession of 2008. It's also possible that the drop in 2001 may have been caused by a change in the survey methodology, for example.</p>
<p>The national mean of insured population along the entire period was 86%. According to the US Census, the population in 2010 was about 303 million, which amounts to about <strong>43 million people not covered by any type of health insurance</strong>.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Load the data downloaded from the referred link</span>
<span class="n">pre_ACA</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Data/HC_access_1995-2010.csv&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Process data</span>

<span class="c1">#Remove % from string values</span>
<span class="n">pre_ACA</span><span class="p">[</span><span class="s1">&#39;Yes&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pre_ACA</span><span class="o">.</span><span class="n">Yes</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s1">&#39;%&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>
<span class="n">pre_ACA</span><span class="p">[</span><span class="s1">&#39;No&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pre_ACA</span><span class="o">.</span><span class="n">No</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s1">&#39;%&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>


<span class="c1">#Get geolocation</span>
<span class="n">pre_ACA</span><span class="p">[</span><span class="s1">&#39;geolocation&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pre_ACA</span><span class="p">[</span><span class="s1">&#39;Location 1&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">))</span>


<span class="c1">#Get latitude from geolocation</span>
<span class="n">pre_ACA</span><span class="p">[</span><span class="s1">&#39;lat&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pre_ACA</span><span class="o">.</span><span class="n">geolocation</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s1">&#39;\(&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>


<span class="c1">#Get longitude from geolocation</span>
<span class="k">def</span> <span class="nf">get_long</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s1">&#39;\)|\s&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">&gt;</span><span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;&#39;</span>

<span class="n">pre_ACA</span><span class="p">[</span><span class="s1">&#39;long&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pre_ACA</span><span class="o">.</span><span class="n">geolocation</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">get_long</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>


<span class="c1">#Convert string datatypes to numeric</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;Yes&#39;</span><span class="p">,</span> <span class="s1">&#39;No&#39;</span><span class="p">,</span> <span class="s1">&#39;lat&#39;</span><span class="p">,</span> <span class="s1">&#39;long&#39;</span><span class="p">]:</span>
    <span class="n">pre_ACA</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">pre_ACA</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>


<span class="c1">#Drop redundant columns</span>
<span class="n">pre_ACA</span> <span class="o">=</span> <span class="n">pre_ACA</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;geolocation&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">pre_ACA</span> <span class="o">=</span> <span class="n">pre_ACA</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Location 1&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>


<span class="c1">#Check stats for comparison.</span>
<span class="c1">#pre_ACA[pre_ACA.Condition == &#39;Do you have any kind of health care coverage?&#39;][[&#39;Yes&#39;, &#39;No&#39;]].describe()</span>
<span class="c1">#pre_ACA[pre_ACA.Condition != &#39;Do you have any kind of health care coverage?&#39;][[&#39;Yes&#39;, &#39;No&#39;]].describe()</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Select a subset of the data</span>
<span class="n">subset</span> <span class="o">=</span> <span class="n">pre_ACA</span><span class="p">[(</span><span class="n">pre_ACA</span><span class="o">.</span><span class="n">State</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">contains</span><span class="p">(</span><span class="s1">&#39;Nationwide&#39;</span><span class="p">)</span> <span class="o">==</span> <span class="bp">False</span><span class="p">)</span>\
        <span class="o">&amp;</span> <span class="p">(</span><span class="n">pre_ACA</span><span class="o">.</span><span class="n">Condition</span> <span class="o">==</span> <span class="s1">&#39;Do you have any kind of health care coverage?&#39;</span><span class="p">)]</span> <span class="c1">#844 rows</span>


<span class="c1">#Drop redundant columns</span>
<span class="n">subset</span> <span class="o">=</span> <span class="n">subset</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Category&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">subset</span> <span class="o">=</span> <span class="n">subset</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;Condition&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>


<span class="c1">#Load file with state codes</span>
<span class="n">state_codes</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Data/state_codes.csv&#39;</span><span class="p">)</span>
<span class="n">state_codes</span> <span class="o">=</span> <span class="n">state_codes</span><span class="p">[[</span><span class="s1">&#39;code&#39;</span><span class="p">,</span><span class="s1">&#39;state&#39;</span><span class="p">]]</span>
<span class="n">state_codes</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;code&#39;</span><span class="p">,</span> <span class="s1">&#39;State&#39;</span><span class="p">]</span>


<span class="c1">#Merge state code into dataset</span>
<span class="n">subset</span><span class="o">=</span> <span class="n">subset</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">state_codes</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s1">&#39;State&#39;</span><span class="p">)</span>
</pre></div>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Define lists with US Census regions</span>

<span class="n">Northeast</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Connecticut&#39;</span><span class="p">,</span> <span class="s1">&#39;Maine&#39;</span><span class="p">,</span> <span class="s1">&#39;Massachusetts&#39;</span><span class="p">,</span> <span class="s1">&#39;New Hampshire&#39;</span><span class="p">,</span> <span class="s1">&#39;Rhode Island&#39;</span><span class="p">,</span> <span class="s1">&#39;Vermont&#39;</span><span class="p">,</span> <span class="s1">&#39;Delaware&#39;</span><span class="p">,</span> 
             <span class="s1">&#39;New Jersey&#39;</span><span class="p">,</span> <span class="s1">&#39;New York&#39;</span><span class="p">,</span> <span class="s1">&#39;Pennsylvania&#39;</span><span class="p">]</span>

<span class="n">Midwest</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Illinois&#39;</span><span class="p">,</span> <span class="s1">&#39;Indiana&#39;</span><span class="p">,</span> <span class="s1">&#39;Michigan&#39;</span><span class="p">,</span> <span class="s1">&#39;Ohio&#39;</span><span class="p">,</span> <span class="s1">&#39;Wisconsin&#39;</span><span class="p">,</span> <span class="s1">&#39;Iowa&#39;</span><span class="p">,</span> <span class="s1">&#39;Kansas&#39;</span><span class="p">,</span> <span class="s1">&#39;Minnesota&#39;</span><span class="p">,</span> <span class="s1">&#39;Missouri&#39;</span><span class="p">,</span> 
           <span class="s1">&#39;Nebraska&#39;</span><span class="p">,</span> <span class="s1">&#39;North Dakota&#39;</span><span class="p">,</span> <span class="s1">&#39;South Dakota&#39;</span><span class="p">]</span>

<span class="n">South</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Florida&#39;</span><span class="p">,</span> <span class="s1">&#39;Georgia&#39;</span><span class="p">,</span> <span class="s1">&#39;Maryland&#39;</span><span class="p">,</span> <span class="s1">&#39;North Carolina&#39;</span><span class="p">,</span> <span class="s1">&#39;South Carolina&#39;</span><span class="p">,</span> <span class="s1">&#39;Virginia&#39;</span><span class="p">,</span> <span class="s1">&#39;District of Columbia&#39;</span><span class="p">,</span>
         <span class="s1">&#39;West Virginia&#39;</span><span class="p">,</span> <span class="s1">&#39;Alabama&#39;</span><span class="p">,</span> <span class="s1">&#39;Kentucky&#39;</span><span class="p">,</span> <span class="s1">&#39;Mississippi&#39;</span><span class="p">,</span> <span class="s1">&#39;Tennessee&#39;</span><span class="p">,</span> <span class="s1">&#39;Arkansas&#39;</span><span class="p">,</span> <span class="s1">&#39;Louisiana&#39;</span><span class="p">,</span> 
         <span class="s1">&#39;Oklahoma&#39;</span><span class="p">,</span> <span class="s1">&#39;Texas&#39;</span><span class="p">]</span>

<span class="n">West</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Arizona&#39;</span><span class="p">,</span> <span class="s1">&#39;Colorado&#39;</span><span class="p">,</span> <span class="s1">&#39;Idaho&#39;</span><span class="p">,</span> <span class="s1">&#39;Montana&#39;</span><span class="p">,</span> <span class="s1">&#39;Nevada&#39;</span><span class="p">,</span> <span class="s1">&#39;New Mexico&#39;</span><span class="p">,</span> <span class="s1">&#39;Utah&#39;</span><span class="p">,</span> <span class="s1">&#39;Wyoming&#39;</span><span class="p">,</span> <span class="s1">&#39;Alaska&#39;</span><span class="p">,</span> 
        <span class="s1">&#39;California&#39;</span><span class="p">,</span> <span class="s1">&#39;Hawaii&#39;</span><span class="p">,</span> <span class="s1">&#39;Oregon&#39;</span><span class="p">,</span> <span class="s1">&#39;Washington&#39;</span><span class="p">]</span>

<span class="n">Territories</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Puerto Rico&#39;</span><span class="p">,</span> <span class="s1">&#39;Guam&#39;</span><span class="p">,</span> <span class="s1">&#39;Virgin Islands&#39;</span><span class="p">]</span>


<span class="c1">#Create series with grouped data per region</span>
<span class="n">northeast</span> <span class="o">=</span> <span class="n">subset</span><span class="p">[</span><span class="n">subset</span><span class="o">.</span><span class="n">State</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">Northeast</span><span class="p">)]</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;Year&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">Yes</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">midwest</span> <span class="o">=</span> <span class="n">subset</span><span class="p">[</span><span class="n">subset</span><span class="o">.</span><span class="n">State</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">Midwest</span><span class="p">)]</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;Year&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">Yes</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">south</span> <span class="o">=</span> <span class="n">subset</span><span class="p">[</span><span class="n">subset</span><span class="o">.</span><span class="n">State</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">South</span><span class="p">)]</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;Year&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">Yes</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">west</span> <span class="o">=</span> <span class="n">subset</span><span class="p">[</span><span class="n">subset</span><span class="o">.</span><span class="n">State</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">West</span><span class="p">)]</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;Year&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">Yes</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">territories</span> <span class="o">=</span> <span class="n">subset</span><span class="p">[</span><span class="n">subset</span><span class="o">.</span><span class="n">State</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">Territories</span><span class="p">)]</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">&#39;Year&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">Yes</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
</pre></div>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Create plot with percentage of people who have answered to have some form of insurance (mean per US census region)</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>

<span class="n">northeast</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s1">&#39;Northeast&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">)</span>
<span class="n">midwest</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s1">&#39;Midwest&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;D&#39;</span><span class="p">)</span>
<span class="n">south</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s1">&#39;South&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;o&#39;</span><span class="p">)</span>
<span class="n">west</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s1">&#39;West&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;D&#39;</span><span class="p">)</span>
<span class="n">territories</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">label</span><span class="o">=</span><span class="s1">&#39;Territories&#39;</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">&#39;s&#39;</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">set_facecolor</span><span class="p">(</span><span class="s1">&#39;#F9F9F9&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;#E4E4E4&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="mi">75</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Figure 1: Average Percentage of Insured People per Census Region (before ACA)&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;#232323&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">labelsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper right&#39;</span><span class="p">);</span>
</pre></div>

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<h2>The Insurance Landscape in the Years After the ACA (2010-2015)</h2><p>The dataset about health insurance coverage from 2010 is not continuous but it contains more detailed features related to estimates of the uninsured population and a breakdown by source of coverage for each state. Details on the source of the data can be found <a href="https://www.kaggle.com/hhs/health-insurance">here</a>.</p>
<p>Let's take a closer look at the uninsured population data, then at the sources of insurance coverage, and finally dive into Medicare and Medicaid data.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Load the data downloaded from the referred link</span>
<span class="n">post_ACA</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Data/HC_access_afterACA.csv&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Process the data</span>

<span class="c1">#Remove % from string values</span>
<span class="n">cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Uninsured Rate (2010)&#39;</span><span class="p">,</span> <span class="s1">&#39;Uninsured Rate (2015)&#39;</span><span class="p">,</span><span class="s1">&#39;Uninsured Rate Change (2010-2015)&#39;</span><span class="p">]</span> <span class="c1">#% values</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">cols</span><span class="p">:</span>
    <span class="n">post_ACA</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">post_ACA</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">re</span><span class="o">.</span><span class="n">sub</span><span class="p">(</span><span class="s1">&#39;%&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">x</span><span class="p">))</span>


<span class="c1">#Remove $ from string values</span>
<span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;Average Monthly Tax Credit (2016)&#39;</span><span class="p">]</span> <span class="o">=</span> \
<span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;Average Monthly Tax Credit (2016)&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>


<span class="c1">#Convert string datatypes to numeric</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">cols</span><span class="p">:</span>
    <span class="n">post_ACA</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">post_ACA</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
    
<span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;Average Monthly Tax Credit (2016)&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;Average Monthly Tax Credit (2016)&#39;</span><span class="p">])</span>


<span class="c1">#Remove trailing whitespace from state names</span>
<span class="n">post_ACA</span><span class="o">.</span><span class="n">State</span> <span class="o">=</span> <span class="n">post_ACA</span><span class="o">.</span><span class="n">State</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">rstrip</span><span class="p">())</span>


<span class="c1">#Merge state code into dataset</span>
<span class="n">post_ACA</span> <span class="o">=</span> <span class="n">post_ACA</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">state_codes</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s1">&#39;State&#39;</span><span class="p">)</span>


<span class="c1">#Create column with the total insured population per state. Assign code for United States.</span>
<span class="n">post_ACA</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">51</span><span class="p">:,(</span><span class="s1">&#39;code&#39;</span><span class="p">)]</span> <span class="o">=</span> <span class="s1">&#39;US&#39;</span>
<span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;insured_pop&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">post_ACA</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,(</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">13</span><span class="p">)]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>


<span class="c1">#Calculate the proportion of enrollment change per state between 2013 and 2016 from the country&#39;s </span>
<span class="c1">#total new enrollments for the period.</span>
<span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;medicaid_delta&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;Medicaid Enrollment Change (2013-2016)&#39;</span><span class="p">]</span><span class="o">/</span>\
<span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;Medicaid Enrollment Change (2013-2016)&#39;</span><span class="p">][</span><span class="mi">51</span><span class="p">])</span> <span class="o">*</span><span class="mi">100</span>


<span class="c1">#post_ACA.head()</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#EXPLORING DATA ABOUT THE UNINSURED POPULATION</span>

<span class="c1">#States with the largest proportion of uninsured population before ACA (2010)</span>
<span class="c1">#post_ACA.ix[:,:4].sort_values(by=&#39;Uninsured Rate (2010)&#39;, ascending = False)[:19]</span>

<span class="c1">#Texas still has the highest rate of uninsured population in the country</span>
<span class="c1">#post_ACA[&#39;Uninsured Rate (2015)&#39;].max() #17%</span>

<span class="c1">#All states have shown a drop in uninsured population. Massachusetts had the lowest change in the country...</span>
<span class="c1">#and also the lowest rate of uninsured population before and after ACA. Will California follow with SB 562?</span>
<span class="c1">#post_ACA[post_ACA[&#39;Uninsured Rate (2015)&#39;] &lt; 3]</span>

<span class="c1">#States with the largest decreases in uninsured population after ACA (2015)</span>
<span class="n">uninsured_rate</span> <span class="o">=</span> <span class="n">post_ACA</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">]]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">&#39;Uninsured Rate Change (2010-2015)&#39;</span><span class="p">,</span> <span class="n">ascending</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)[:</span><span class="mi">17</span><span class="p">]</span>
<span class="c1">#uninsured_rate</span>
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<h3>The Effect of the ACA in the Proportion of Uninsured Population</h3><p>The mean uninsured population rate in the United States in 2010, according to this data source, <strong>was 15.5%</strong>. This is within the range of the number from the previous dataset (about 15% for 2010). This number is reoprted to have dropped to <strong>9.4% in 2015</strong>.</p>
<p>In addition, the data shows:
<br>
</br></p>
<li><strong>16 million</strong> more people insured by Medicaid between 2013 and 2016</li>
<li><strong>11 million</strong> people insured by the Healthcare Marketplace in 2016</li>
<li>an increment of <strong>19 million</strong> in insured individuals between 2010 and 2015.</li><p>Most of the increase in the insured population was <strong>driven by the expansion of Medicaid and the Marketplace</strong> rather than Employer and Medicare, which will be shown later in this analysis.</p>
<p>The <strong>top 5 states with the highest percentage of uninsured population in 2010</strong> (before the ACA), were Texas, Nevada, Florida, Arkansas and Georgia. Except for Nevada, all these states remain among the top 5 in terms of uninsured population, albeit at lower levels. Leading the list, <strong>Texas</strong> had 23.7% uninsured individuals in 2010, and it still had the <strong>highest</strong> uninsured rate in 2015 (17.1%).</p>
<p><strong>All states have seen a drop in the uninsured rate</strong> after the implementation of the ACA. <strong>Massachusetts</strong> had the <strong>lowest</strong> drop in the uninsured rate (-1.6% points) because the state already had he lowest rate (only 4.4%) before the new law. Could California, still with 8.6% uninsured population in 2015 follow if <a href="https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201720180SB562">SB562</a> is approved?</p>
<p>The <strong>Figure 2</strong> below interactively displays information about the states that have seen a decrease in uninsured population at or above the <strong>national average of 6.1%</strong> between 2010 and 2015.</p>
<p>The political representation of the states shown in the plot is approximately even between the typically "red" and "blue + swing" groups. Except for <strong>Texas, Idaho and South Carolina</strong>, all the other states have opted to expand their Medicaid programs. Therefore, the drop in uninsured rates for these 3 states was driven mostly by people signing up into the Health Insurance Marketplace. This is demonstrated in the next session, where we analyze health insurance by source of coverage.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Equivalent interactive version</span>
<span class="c1">#(0,120,171) - big sky blue; (64, 224, 208) - turquoise blue</span>

<span class="n">trace2010</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Bar</span><span class="p">(</span>
    <span class="n">x</span><span class="o">=</span><span class="n">uninsured_rate</span><span class="p">[</span><span class="s1">&#39;code&#39;</span><span class="p">],</span>
    <span class="n">y</span><span class="o">=</span><span class="n">uninsured_rate</span><span class="p">[</span><span class="s1">&#39;Uninsured Rate (2010)&#39;</span><span class="p">],</span>
    <span class="n">name</span><span class="o">=</span><span class="s1">&#39;2010 - Uninsured Rate&#39;</span><span class="p">,</span>
    <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
        <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(64, 224, 208)&#39;</span>
    <span class="p">)</span>
<span class="p">)</span>
<span class="n">trace2015</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Bar</span><span class="p">(</span>
    <span class="n">x</span><span class="o">=</span><span class="n">uninsured_rate</span><span class="p">[</span><span class="s1">&#39;code&#39;</span><span class="p">],</span>
    <span class="n">y</span><span class="o">=</span><span class="n">uninsured_rate</span><span class="p">[</span><span class="s1">&#39;Uninsured Rate (2015)&#39;</span><span class="p">],</span>
    <span class="n">name</span><span class="o">=</span><span class="s1">&#39;2015 - Uninsured Rate&#39;</span><span class="p">,</span>
    <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
        <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(0, 120, 171)&#39;</span>
    <span class="p">)</span>
<span class="p">)</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">trace2010</span><span class="p">,</span> <span class="n">trace2015</span><span class="p">]</span>
<span class="n">layout</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Layout</span><span class="p">(</span>
    <span class="n">title</span><span class="o">=</span><span class="s1">&#39;Figure 2: States Showing the Largest Changes in Uninsured Population&#39;</span><span class="p">,</span>
    <span class="n">xaxis</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
        <span class="n">tickfont</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
            <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
            <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(107, 107, 107)&#39;</span>
        <span class="p">)</span>
    <span class="p">),</span>
    <span class="n">yaxis</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
        <span class="n">title</span><span class="o">=</span><span class="s1">&#39;Uninsured Population (%)&#39;</span><span class="p">,</span>
        <span class="n">titlefont</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
            <span class="n">size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
            <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(107, 107, 107)&#39;</span>
        <span class="p">),</span>
        <span class="n">tickfont</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
            <span class="n">size</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span>
            <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(107, 107, 107)&#39;</span>
        <span class="p">)</span>
    <span class="p">),</span>
    <span class="n">legend</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
        <span class="n">bgcolor</span><span class="o">=</span><span class="s1">&#39;rgba(255, 255, 255, 0)&#39;</span><span class="p">,</span>
        <span class="n">bordercolor</span><span class="o">=</span><span class="s1">&#39;rgba(255, 255, 255, 0)&#39;</span>
    <span class="p">),</span>
    <span class="n">barmode</span><span class="o">=</span><span class="s1">&#39;group&#39;</span><span class="p">,</span>
    <span class="n">bargap</span><span class="o">=</span><span class="mf">0.15</span><span class="p">,</span>
    <span class="n">bargroupgap</span><span class="o">=</span><span class="mf">0.1</span>
<span class="p">)</span>

<span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">)</span>
<span class="n">py</span><span class="o">.</span><span class="n">iplot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;uninsured-pop-drop&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#EXPLORING DATA ABOUT SOURCES OF INSURANCE</span>

<span class="c1">#List dataset columns with insured population breakdown by source of insurance</span>
<span class="n">cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">u&#39;code&#39;</span><span class="p">,</span> <span class="s1">u&#39;Employer Health Insurance Coverage (2015)&#39;</span><span class="p">,</span>
       <span class="s1">u&#39;Marketplace Health Insurance Coverage (2016)&#39;</span><span class="p">,</span>
       <span class="s1">u&#39;Medicaid Enrollment (2016)&#39;</span><span class="p">,</span> <span class="s1">u&#39;Medicare Enrollment (2016)&#39;</span><span class="p">,</span>
       <span class="s1">u&#39;insured_pop&#39;</span><span class="p">]</span>

<span class="c1">#Select data with enrollment per source of insurance. </span>
<span class="n">insurance_type</span> <span class="o">=</span> <span class="n">post_ACA</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,(</span><span class="n">cols</span><span class="p">)]</span>

<span class="c1">#Calculate total insured population and share of government coverage for each state.</span>
<span class="n">insurance_type</span><span class="p">[</span><span class="s1">&#39;single_payer&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">insurance_type</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,(</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">)]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="n">insurance_type</span><span class="o">.</span><span class="n">ix</span><span class="p">[:,(</span><span class="s1">&#39;insured_pop&#39;</span><span class="p">)]</span>
<span class="n">insurance_type</span><span class="p">[</span><span class="s1">&#39;market&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span> <span class="n">insurance_type</span><span class="o">.</span><span class="n">single_payer</span>

<span class="c1">#Breakdown for each source of insurance.</span>
<span class="n">insurance_type</span><span class="p">[</span><span class="s1">&#39;prop_employer&#39;</span><span class="p">]</span> <span class="o">=</span> \
<span class="n">insurance_type</span><span class="p">[</span><span class="s1">&#39;Employer Health Insurance Coverage (2015)&#39;</span><span class="p">]</span><span class="o">/</span><span class="n">insurance_type</span><span class="o">.</span><span class="n">insured_pop</span>

<span class="n">insurance_type</span><span class="p">[</span><span class="s1">&#39;prop_marketplace&#39;</span><span class="p">]</span> <span class="o">=</span> \
<span class="n">insurance_type</span><span class="p">[</span><span class="s1">&#39;Marketplace Health Insurance Coverage (2016)&#39;</span><span class="p">]</span><span class="o">/</span><span class="n">insurance_type</span><span class="o">.</span><span class="n">insured_pop</span>

<span class="n">insurance_type</span><span class="p">[</span><span class="s1">&#39;prop_medicaid&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">insurance_type</span><span class="p">[</span><span class="s1">&#39;Medicaid Enrollment (2016)&#39;</span><span class="p">]</span><span class="o">/</span><span class="n">insurance_type</span><span class="o">.</span><span class="n">insured_pop</span>
<span class="n">insurance_type</span><span class="p">[</span><span class="s1">&#39;prop_medicare&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">insurance_type</span><span class="p">[</span><span class="s1">&#39;Medicare Enrollment (2016)&#39;</span><span class="p">]</span><span class="o">/</span><span class="n">insurance_type</span><span class="o">.</span><span class="n">insured_pop</span>


<span class="c1">#insurance_type.tail()</span>

<span class="c1">#Print stats for US:</span>
<span class="c1">#insurance_type.iloc[:-1].single_payer.describe() #mean = ~ 41%</span>

<span class="c1">#States where the government already covers more than 50% of the insured population</span>
<span class="c1">#insurance_type[insurance_type.single_payer &gt;= 0.50] #NM AND AR</span>

<span class="c1">#State where the government represents the lowest propertion of insurance source</span>
<span class="c1">#insurance_type[insurance_type.single_payer &lt; 0.25] #UT</span>
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<h3>The State of Health Insurance by Source of Coverage in 2016</h3><p>By the end of 2016, there were 4 main <strong>sources of insurance coverage:</strong>
<br>
</br></p>
<li><strong>Employer:</strong> health insurance provided through employment and paid mostly by the employer</li>
<li><strong>Medicare:</strong> health insurance provided by the government, mainly for individuals aged 65 or older</li>
<li><strong>Medicaid:</strong> health insurance provided by the government for very low income individuals</li>
<li><strong>Marketplace:</strong> health insurance provided through the marketplace paid by the insured individual</li><p>While not all numbers before 2016 are available in the dataset, the <strong>baseline</strong> (national average) for the breakdown of health insurance by source is:
<br>
</br></p>
<li>Employer: 55%</li>
<li>Medicaid: 23.5%</li>
<li>Medicare: 18%</li>
<li>Marketplace: 3.5%</li><p>The baseline indicates that <strong>the government already covers about 41% of all insured individuals in 2016</strong>. The bubble chart in the <strong>Figure 3</strong> below shows the percentage of insured individuals covered by government-provided (single payer) health insurance in each state.</p>
<p>Most states hover around the baseline, but there 3 interesting <em>"anomalies"</em>:
<br>
</br></p>
<li><strong>New Mexico and Arkansas</strong> have more than <strong>50%</strong> of insured individuals covered by Medicare or Medicaid</li>
<li><strong>Utah</strong> has only about <strong>25%</strong> of insured individuals covered by Medicare or Medicaid</li>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Scale size of insured population to be used as bubble dimension</span>

<span class="c1">#color= [&#39;hsl(&#39;+str(h)+&#39;,50%&#39;+&#39;,50%)&#39; for h in np.linspace(0, 360, N)]</span>
<span class="n">size</span> <span class="o">=</span> <span class="n">insurance_type</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">insured_pop</span><span class="o">/</span><span class="mf">500000.</span> <span class="c1">#disregard last row which is data for the US</span>
<span class="c1">#size.min(), size.max()</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Bubble chart: proportion of single payer per state (similar to Fig3a but showing all states). Nationwide mean: 41%</span>
<span class="c1">#The bubble size is proportional to the total insured population in each state.</span>

<span class="n">trace</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Scatter</span> <span class="p">(</span><span class="n">x</span> <span class="o">=</span> <span class="n">insurance_type</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">code</span><span class="p">,</span> 
                    <span class="n">y</span> <span class="o">=</span> <span class="n">insurance_type</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">single_payer</span><span class="p">,</span>
                    <span class="n">text</span> <span class="o">=</span> <span class="s1">&#39;Proportion of Medicare + Medicaid&#39;</span><span class="p">,</span>
                    <span class="n">marker</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">size</span> <span class="o">=</span> <span class="n">size</span><span class="p">,</span> 
                                  <span class="n">color</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">insurance_type</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> 
                                  <span class="n">opacity</span><span class="o">=</span> <span class="mf">0.7</span><span class="p">,</span> 
                                  <span class="n">colorscale</span><span class="o">=</span><span class="s1">&#39;Viridis&#39;</span><span class="p">),</span> 
                    <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;markers&#39;</span><span class="p">)</span>


<span class="n">layout</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
    <span class="n">title</span> <span class="o">=</span> <span class="s1">&#39;Figure 3: Proportion of Insured Population Covered by the Government&lt;br&gt;(bubble size is proportional to population)&#39;</span><span class="p">,</span>
    <span class="n">xaxis</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">tickangle</span><span class="o">=-</span><span class="mi">90</span><span class="p">)</span>
<span class="p">)</span>


<span class="n">data</span><span class="o">=</span><span class="p">[</span><span class="n">trace</span><span class="p">]</span>
<span class="n">fig</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">)</span>
<span class="n">py</span><span class="o">.</span><span class="n">iplot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;proportion-single-payer&#39;</span><span class="p">)</span>
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<p>The <strong>Figure 4</strong> below goes more in detail and shows the breakdown by source of coverage for the <strong>top 10 states</strong> in terms of total number of insured individuals. There are some noteworthy <strong>pattterns</strong>:
<br>
</br></p>
<li><strong>Texas, Georgia and Illinois</strong> have above average insurance coverage rates provided by <strong>Employers</strong></li>
<li><strong>Texas, Florida, North Carolina and Georgia</strong> have above average rates of insurance coverage provided by the <strong>Marketplace</strong></li>
<li><strong>California, New York</strong> and to a lesser extent <strong>Ohio</strong> have above average insurance coverage rates provided by <strong>Medicaid</strong></li>
<li><strong>Florida</strong>, unsurprisingly, has well above average rate of insurance coverage rates provided by <strong>Medicare</strong></li>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Select top 10 states by insured population</span>
<span class="n">top10_pop</span> <span class="o">=</span> <span class="n">insurance_type</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">&#39;insured_pop&#39;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">11</span><span class="p">)</span>
<span class="c1">#top10_pop</span>
<span class="n">top10</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;CA&#39;</span><span class="p">,</span> <span class="s1">&#39;TX&#39;</span><span class="p">,</span> <span class="s1">&#39;NY&#39;</span><span class="p">,</span> <span class="s1">&#39;FL&#39;</span><span class="p">,</span> <span class="s1">&#39;PA&#39;</span><span class="p">,</span> <span class="s1">&#39;IL&#39;</span><span class="p">,</span> <span class="s1">&#39;OH&#39;</span><span class="p">,</span> <span class="s1">&#39;MI&#39;</span><span class="p">,</span> <span class="s1">&#39;NC&#39;</span><span class="p">,</span> <span class="s1">&#39;GA&#39;</span><span class="p">]</span> <span class="c1">#top 10 states labels</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Create plot. Note: there is a daily limit to the number of Plotly&#39;s API calls. An error will be raised beyond limit.</span>
<span class="c1">#Color palette source: http://colrd.com/palette/27656/ </span>
<span class="c1">#Baseline: #55% employer, 23.5% medicaid, 3.5% marketplace, 18% medicare</span>

<span class="n">data</span> <span class="o">=</span> <span class="p">[</span>
    <span class="n">go</span><span class="o">.</span><span class="n">Bar</span><span class="p">(</span>
        <span class="n">x</span><span class="o">=</span><span class="n">top10</span><span class="p">,</span>
        <span class="n">y</span><span class="o">=</span><span class="n">top10_pop</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span><span class="mi">1</span><span class="p">],</span>
        <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;Employer&#39;</span><span class="p">,</span>
        <span class="n">text</span> <span class="o">=</span> <span class="n">top10_pop</span><span class="o">.</span><span class="n">prop_employer</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;{:,.0f}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;%&#39;</span><span class="p">,</span>
        <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
        <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(161,173,167)&#39;</span>
        <span class="p">),</span>
    <span class="p">),</span>
    <span class="n">go</span><span class="o">.</span><span class="n">Bar</span><span class="p">(</span>
        <span class="n">x</span><span class="o">=</span><span class="n">top10</span><span class="p">,</span>
        <span class="n">y</span><span class="o">=</span><span class="n">top10_pop</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span><span class="mi">3</span><span class="p">],</span>
        <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;Medicaid&#39;</span><span class="p">,</span>
        <span class="n">text</span> <span class="o">=</span> <span class="n">top10_pop</span><span class="o">.</span><span class="n">prop_medicaid</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;{:,.0f}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;%&#39;</span><span class="p">,</span>
        <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
        <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(62,152,195)&#39;</span>
        <span class="p">),</span>
    <span class="p">),</span>
    <span class="n">go</span><span class="o">.</span><span class="n">Bar</span><span class="p">(</span>
        <span class="n">x</span><span class="o">=</span><span class="n">top10</span><span class="p">,</span>
        <span class="n">y</span><span class="o">=</span><span class="n">top10_pop</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span><span class="mi">4</span><span class="p">],</span>
        <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;Medicare&#39;</span><span class="p">,</span>
        <span class="n">text</span> <span class="o">=</span> <span class="n">top10_pop</span><span class="o">.</span><span class="n">prop_medicare</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;{:,.0f}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;%&#39;</span><span class="p">,</span>
        <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
        <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(13,96,136)&#39;</span>
        <span class="p">),</span>
    <span class="p">),</span>
    <span class="n">go</span><span class="o">.</span><span class="n">Bar</span><span class="p">(</span>
        <span class="n">x</span><span class="o">=</span><span class="n">top10</span><span class="p">,</span>
        <span class="n">y</span><span class="o">=</span><span class="n">top10_pop</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:,</span><span class="mi">2</span><span class="p">],</span>
        <span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;Marketplace&#39;</span><span class="p">,</span>
        <span class="n">text</span> <span class="o">=</span> <span class="n">top10_pop</span><span class="o">.</span><span class="n">prop_marketplace</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;{:,.1f}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;%&#39;</span><span class="p">,</span>
        <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
        <span class="n">color</span><span class="o">=</span><span class="s1">&#39;rgb(108,195,9)&#39;</span>
        <span class="p">),</span>
    <span class="p">)</span>

<span class="p">]</span>

<span class="n">layout</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Layout</span><span class="p">(</span>
    <span class="n">barmode</span><span class="o">=</span><span class="s1">&#39;stack&#39;</span><span class="p">,</span>
    <span class="n">title</span><span class="o">=</span><span class="s1">&#39;Figure 4: Source of Coverage Breakdown for the Top 10 States by Insured Population&#39;</span>
<span class="p">)</span>

<span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">)</span>
<span class="n">py</span><span class="o">.</span><span class="n">iplot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;insurance-source-breakdown&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#EXPLORING DATA ABOUT MEDICARE</span>

<span class="c1">#Select Medicare data</span>
<span class="n">medicare</span> <span class="o">=</span> <span class="n">insurance_type</span><span class="p">[[</span><span class="s1">&#39;code&#39;</span><span class="p">,</span> <span class="s1">&#39;Medicare Enrollment (2016)&#39;</span><span class="p">,</span> <span class="s1">&#39;insured_pop&#39;</span><span class="p">]]</span>\
<span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s1">&#39;Medicare Enrollment (2016)&#39;</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:,:]</span>


<span class="c1">#Get top 10 states by number of Medicare enrollees</span>
<span class="n">medicare_top10</span> <span class="o">=</span> <span class="n">medicare</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>


<span class="c1">#Estimated total population 65 years or older per state in 2015 (source: US Census website)</span>
<span class="n">pop_65</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">([</span><span class="mi">5188754</span><span class="p">,</span> <span class="mi">3942468</span><span class="p">,</span> <span class="mi">3225168</span><span class="p">,</span> <span class="mi">2964315</span><span class="p">,</span> <span class="mi">2179788</span><span class="p">,</span> <span class="mi">1842952</span><span class="p">,</span> <span class="mi">1830277</span><span class="p">,</span> <span class="mi">1570671</span><span class="p">,</span> <span class="mi">1516824</span><span class="p">,</span> <span class="mi">1304924</span><span class="p">],</span> 
                  <span class="n">index</span><span class="o">=</span> <span class="n">medicare_top10</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>


<span class="c1">#Load total population for top 10 states (source: US Census website, downloaded as pop_65+_perState.csv)</span>
<span class="n">popTop10</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Data/pop_2016.csv&#39;</span><span class="p">)</span>
<span class="n">popTop10</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">medicare_top10</span><span class="o">.</span><span class="n">index</span>
<span class="c1">#popTop10.head()</span>


<span class="c1">#Add columns with % of insured population covered by Medicare, population 65 and over, and total population.</span>
<span class="n">medicare_top10</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="s1">&#39;total_pop&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">popTop10</span><span class="o">.</span><span class="n">pop_2016</span>
<span class="n">medicare_top10</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="s1">&#39;pop_over_65&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pop_65</span>
<span class="n">medicare_top10</span><span class="p">[</span><span class="s1">&#39;%Medicare&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">medicare_top10</span><span class="p">[</span><span class="s1">&#39;Medicare Enrollment (2016)&#39;</span><span class="p">]</span><span class="o">/</span><span class="n">medicare_top10</span><span class="o">.</span><span class="n">total_pop</span>
<span class="n">medicare_top10</span><span class="o">.</span><span class="n">loc</span><span class="p">[:,</span><span class="s1">&#39;perc_over_65&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">medicare_top10</span><span class="o">.</span><span class="n">pop_over_65</span><span class="o">/</span><span class="n">medicare_top10</span><span class="o">.</span><span class="n">total_pop</span>


<span class="c1">#The top 10 states represent 51% of all Medicare enrollees</span>
<span class="n">US_medicare_total</span> <span class="o">=</span> <span class="n">insurance_type</span><span class="p">[</span><span class="s1">&#39;Medicare Enrollment (2016)&#39;</span><span class="p">][</span><span class="mi">51</span><span class="p">]</span>
<span class="c1">#medicare_top10[&#39;Medicare Enrollment (2016)&#39;].sum()*1./US_medicare_total</span>


<span class="c1">#States with the largest numbers of individuals insured by Medicare are also the states with the largest </span>
<span class="c1">#population aged 65 or older. The top 10 states represent 51% of all Medicare enrollees.</span>
<span class="c1">#medicare_top10</span>
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<p>Let's now look at the <strong>Medicare</strong> numbers. As expected, the <strong>top 10 Medicare markets</strong> by the number of enrollees in 2016 are in the states with the <strong>largest populations</strong> in the country, although the percentage of individuals aged 65 or older varies from 11.6% (Texas) to 19.1% (Florida) in this group, according to data from the US Census.</p>
<p>The <strong>Figure 5</strong> below provides an interactive illustration of the number of Medicare customers in each of the selected states. Together, <strong>the top 10 states represented 51% of all Medicare enrollees in 2016</strong>. The proportion of the population on Medicare exceeds that of the population aged 65 or older (some people are eligible to receive the benefit earlier), indicating that pretty much everybody in this age group takes advantage of the benefit. The marketplace insurance premium cost for this age group can be at least <a href="https://www.cms.gov/cciio/resources/data-resources/marketplace-puf.html">3 times</a> that of a 21 years old individual.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Create text to show when hovering over map</span>
<span class="n">medicare_top10</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">medicare_top10</span><span class="p">[</span><span class="s1">&#39;code&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;&lt;br&gt;&#39;</span> <span class="o">+</span>\
<span class="s1">&#39;Total Population over 65: &#39;</span> <span class="o">+</span> <span class="n">medicare_top10</span><span class="p">[</span><span class="s1">&#39;perc_over_65&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;{:,.1f}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;%&#39;</span><span class="o">+</span><span class="s1">&#39;&lt;br&gt;&#39;</span> <span class="o">+</span>\
<span class="s1">&#39;Total Population on Medicare: &#39;</span><span class="o">+</span> <span class="n">medicare_top10</span><span class="p">[</span><span class="s1">&#39;%Medicare&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;{:,.1f}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;%&#39;</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Visualize Top 10 Medicare markets (by number of enrollees), by number of population 65 years or older.</span>

<span class="n">trace0</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Bar</span><span class="p">(</span>
    <span class="n">x</span><span class="o">=</span><span class="n">medicare_top10</span><span class="o">.</span><span class="n">code</span><span class="p">,</span>
    <span class="n">y</span><span class="o">=</span><span class="n">medicare_top10</span><span class="p">[</span><span class="s1">&#39;Medicare Enrollment (2016)&#39;</span><span class="p">],</span>
    <span class="n">text</span><span class="o">=</span><span class="n">medicare_top10</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">],</span>
    <span class="n">marker</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span>
        <span class="n">color</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">medicare_top10</span><span class="o">.</span><span class="n">code</span><span class="p">)),</span>
        <span class="n">colorscale</span><span class="o">=</span><span class="s1">&#39;Viridis&#39;</span>
    <span class="p">),</span>
    <span class="n">opacity</span><span class="o">=</span><span class="mf">0.7</span>
<span class="p">)</span>

<span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">trace0</span><span class="p">]</span>
<span class="n">layout</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Layout</span><span class="p">(</span>
    <span class="n">title</span><span class="o">=</span><span class="s1">&#39;Figure 5: Top 10 Medicare Markets by Number of Enrollees (2016)&#39;</span><span class="p">,</span>
<span class="p">)</span>

<span class="n">fig</span> <span class="o">=</span> <span class="n">go</span><span class="o">.</span><span class="n">Figure</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">)</span>
<span class="n">py</span><span class="o">.</span><span class="n">iplot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;top10-medicare-markets&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#EXPLORING DATA ABOUT MEDICAID</span>

<span class="c1">#Check states with the largest proportion (&gt; 2.5%) of additional Medicaid enrollees between 2013 and 2016.</span>
<span class="c1">#post_ACA[post_ACA[&#39;medicaid_delta&#39;] &gt;2.5][[&#39;medicaid_delta&#39;, &#39;code&#39;, &#39;State Medicaid Expansion (2016)&#39;]].\</span>
<span class="c1">#sort_values(by=&#39;medicaid_delta&#39;, ascending=False)[1:]</span>

<span class="c1">#These 3 states actually had a decrease in the number of enrollments in Medicaid.</span>
<span class="c1">#post_ACA[post_ACA[&#39;Medicaid Enrollment Change (2013-2016)&#39;] &lt; 0] #WY, OK, NE</span>
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<p>Finally, let's take a closer look at <strong>changes in Medicaid enrollment between 2013 and 2016</strong>. The colors in the interactive map below, <strong>Figure 6</strong>, vary from light yellow to dark blue as the proportion of increase in Medicaid enrollment grows in relation to the nation total. The dark grey colors represent either negative growth (decrease in number of enrollees), or no data available for comparison (which was the case for Maine and Connecticut). By hovering over the map, it's also possible to check whether a state has opted to expand Medicaid.</p>
<p>On the map, <strong>California stands out</strong> because it rpresents the largest proportion of additional Medicaid enrollees between 2013 and 2016: <strong>for every 4 new enrollees, 1 was from California</strong>. This is expected in part because it's also the most populous state. <strong>New York, Washington, Ohio, Kentucky and Colorado follow with the largest proportions of new enrollees</strong> after California. In Washington, Kentucky and Colorado, most of the drop in uninsured population rate was driven by the Medicaid expansion, which offers coverage to adults whose income fall below 133% the <a href="https://www.medicaid.gov/affordable-care-act/eligibility/index.html">Federal Poverty Level</a>.</p>
<p>Three states have actually shown a <strong>decrease in number of Medicaid recipients: Wyoming, Nebraska and Oklahoma</strong>. The health insurance source breakdown for Oklahoma is similar to the national average, whereas in Wyoming and Nebraska, employers and the marketplace make up for the lower Medicaid rates.</p>

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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Create a color scale</span>
<span class="c1">#palette source: http://colrd.com/palette/19057/</span>
<span class="n">scl3</span> <span class="o">=</span> <span class="p">[[</span><span class="mf">0.0</span><span class="p">,</span> <span class="s1">&#39;rgb(255,255,217)&#39;</span><span class="p">],[</span><span class="mf">0.25</span><span class="p">,</span> <span class="s1">&#39;rgb(237,248,177)&#39;</span><span class="p">],[</span><span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="s1">&#39;rgb(127,205,187)&#39;</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="s1">&#39;rgb(12,44,132)&#39;</span><span class="p">]]</span>

<span class="c1">#Create text to show when hovering over map</span>
<span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;State&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;&lt;br&gt;&#39;</span> <span class="o">+</span>\
<span class="s1">&#39;Expanded Medicaid: &#39;</span> <span class="o">+</span> <span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;State Medicaid Expansion (2016)&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;&lt;br&gt;&#39;</span> <span class="o">+</span>\
<span class="s1">&#39;Medicaid Enrollment in 2016: &#39;</span><span class="o">+</span> <span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;Medicaid Enrollment (2016)&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;{:,.0f}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;&lt;br&gt;&#39;</span><span class="o">+</span>\
<span class="s1">&#39;Enrollment Change from 2013: &#39;</span><span class="o">+</span> <span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;Medicaid Enrollment Change (2013-2016)&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;{:,.0f}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>

<span class="c1">#Apply log to the color scale so that there is more contrast</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">post_ACA</span><span class="o">.</span><span class="n">medicaid_delta</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="c1">#Create the choropleth using Plotly Python API.</span>

<span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="nb">dict</span><span class="p">(</span>
        <span class="nb">type</span><span class="o">=</span><span class="s1">&#39;choropleth&#39;</span><span class="p">,</span>
        <span class="n">colorscale</span> <span class="o">=</span> <span class="n">scl3</span><span class="p">,</span>
        <span class="n">autocolorscale</span> <span class="o">=</span> <span class="bp">False</span><span class="p">,</span>
    <span class="n">showscale</span> <span class="o">=</span> <span class="bp">False</span><span class="p">,</span>
        <span class="n">locations</span> <span class="o">=</span> <span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;code&#39;</span><span class="p">],</span>
        <span class="n">z</span> <span class="o">=</span> <span class="n">z</span><span class="p">,</span>
        <span class="n">locationmode</span> <span class="o">=</span> <span class="s1">&#39;USA-states&#39;</span><span class="p">,</span>
    <span class="n">hoverinfo</span> <span class="o">=</span> <span class="s1">&#39;text&#39;</span><span class="p">,</span>
        <span class="n">text</span> <span class="o">=</span> <span class="n">post_ACA</span><span class="p">[</span><span class="s1">&#39;text&#39;</span><span class="p">],</span>
        <span class="n">marker</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
            <span class="n">line</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
                <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;rgb(255,255,255)&#39;</span><span class="p">,</span>
                <span class="n">width</span> <span class="o">=</span> <span class="mf">1.5</span>
            <span class="p">)),</span>
        <span class="c1">#colorbar = dict(</span>
        <span class="c1">#    title = &quot;% of New Medicaid Enrollees from 2013&quot;)</span>
        <span class="p">)]</span>

<span class="n">layout</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
        <span class="n">title</span> <span class="o">=</span> <span class="s1">&#39;Figure 6: Change in Medicaid Enrollment by State between 2013 and 2016&lt;br&gt;(hover for actual numbers)&#39;</span><span class="p">,</span>
        <span class="n">geo</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
            <span class="n">scope</span><span class="o">=</span><span class="s1">&#39;usa&#39;</span><span class="p">,</span>
            <span class="n">projection</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s1">&#39;albers usa&#39;</span><span class="p">),</span>
            <span class="n">showlakes</span> <span class="o">=</span> <span class="bp">True</span><span class="p">,</span>
            <span class="n">lakecolor</span> <span class="o">=</span> <span class="s1">&#39;rgb(255, 255, 255)&#39;</span><span class="p">),</span>
             <span class="p">)</span>
    
<span class="n">fig</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">)</span>
<span class="n">py</span><span class="o">.</span><span class="n">iplot</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="s1">&#39;medicaid-cloropleth-USA&#39;</span><span class="p">)</span>
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<h3>Takeaways</h3><p>This report provides useful information to a wide range of interested parties, from a curious citizen to a company evaluating how health insurance can affect its business in different states. Through this analysis, we could see that <strong>the characteristics of health insurance coverage may vary significantly among states</strong>. The underlying causes for such variations can now be analyzed in depth, depending on the interest of the reader. The data suggests that factors like <strong>demographics, economic situation and politics</strong> have variable levels of impact on the state level.</p>
<p>Undoubtedly, <strong>the Affordable Healthcare Act has significantly affected the health insurance business</strong> in the country in general. The evidence shows that many newly eligible individuals have signed up for Medicaid where it was extended, and many have also purchased insurance through the marketplace, whether due to the mandate or to take advantage of tax credits if elegible. Together, these initiatives have <strong>resulted on a major drop in the uninsured population</strong> from over 40 million (15%) in 2010 to an estimated 30 million people (9.4%) in 2016, despite the population growth in the period.</p>

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