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diff --git a/content/labs/Lab_7/IM939_Lab7-Part3.html b/content/labs/Lab_7/IM939_Lab7-Part3.html
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--- a/content/labs/Lab_7/IM939_Lab7-Part3.html
+++ b/content/labs/Lab_7/IM939_Lab7-Part3.html
@@ -606,7 +606,7 @@ <h2 data-number="34.3" class="anchored" data-anchor-id="reading-the-dataset"><sp
 <p>Let’s have a look at our dataset</p>
 <div class="cell" data-execution_count="2">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>df_profession.tail()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="31">
+<div class="cell-output cell-output-display" data-execution_count="2">
 <div>
 
 
@@ -664,7 +664,7 @@ <h2 data-number="34.3" class="anchored" data-anchor-id="reading-the-dataset"><sp
 </div>
 <div class="cell" data-execution_count="3">
 <div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>df_profession_category.tail()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="32">
+<div class="cell-output cell-output-display" data-execution_count="3">
 <div>
 
 
@@ -722,7 +722,7 @@ <h2 data-number="34.3" class="anchored" data-anchor-id="reading-the-dataset"><sp
 </div>
 <div class="cell" data-execution_count="4">
 <div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>df_age</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="33">
+<div class="cell-output cell-output-display" data-execution_count="4">
 <div>
 
 
@@ -786,7 +786,7 @@ <h2 data-number="34.3" class="anchored" data-anchor-id="reading-the-dataset"><sp
 </div>
 <div class="cell" data-execution_count="5">
 <div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>df_geography.tail()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="34">
+<div class="cell-output cell-output-display" data-execution_count="5">
 <div>
 
 
@@ -847,7 +847,7 @@ <h2 data-number="34.3" class="anchored" data-anchor-id="reading-the-dataset"><sp
 <div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>indices_to_drop <span class="op">=</span> df_profession[df_profession[<span class="st">'Code'</span>] <span class="op">&lt;</span> <span class="dv">10</span>].index</span>
 <span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>df_profession.drop(indices_to_drop, inplace<span class="op">=</span><span class="va">True</span>)</span>
 <span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>df_profession</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="35">
+<div class="cell-output cell-output-display" data-execution_count="6">
 <div>
 
 
@@ -1117,7 +1117,7 @@ <h3 data-number="34.3.1" class="anchored" data-anchor-id="missing-values"><span
 <span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>df_profession.isna().<span class="bu">sum</span>()</span>
 <span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>df_profession_category.isna().<span class="bu">sum</span>()</span>
 <span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>df_age.isna().<span class="bu">sum</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="38">
+<div class="cell-output cell-output-display" data-execution_count="9">
 <pre><code>age_group    0
 GPGmedian    0
 GPGmean      0
@@ -1128,7 +1128,7 @@ <h3 data-number="34.3.1" class="anchored" data-anchor-id="missing-values"><span
 <div class="cell" data-execution_count="10">
 <div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's plot the mean and median Gender Pay Gap (GPG)</span></span>
 <span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>df_profession.boxplot(column<span class="op">=</span>[<span class="st">'GPGmedian'</span>, <span class="st">'GPGmean'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="39">
+<div class="cell-output cell-output-display" data-execution_count="10">
 <pre><code>&lt;Axes: &gt;</code></pre>
 </div>
 <div class="cell-output cell-output-display">
@@ -1139,7 +1139,7 @@ <h3 data-number="34.3.1" class="anchored" data-anchor-id="missing-values"><span
 <div class="cell" data-execution_count="11">
 <div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's look at the distribution of the values in the columns</span></span>
 <span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>df_profession.describe()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="40">
+<div class="cell-output cell-output-display" data-execution_count="11">
 <div>
 
 
@@ -1210,7 +1210,7 @@ <h3 data-number="34.3.1" class="anchored" data-anchor-id="missing-values"><span
 <div class="cell" data-execution_count="12">
 <div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's try to visualise what's going on with a histogram - what type of skew do you notice?</span></span>
 <span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>df_profession[[<span class="st">'GPGmedian'</span>]].plot(kind<span class="op">=</span><span class="st">'hist'</span>, ec<span class="op">=</span><span class="st">'black'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="41">
+<div class="cell-output cell-output-display" data-execution_count="12">
 <pre><code>&lt;Axes: ylabel='Frequency'&gt;</code></pre>
 </div>
 <div class="cell-output cell-output-display">
@@ -1230,26 +1230,26 @@ <h3 data-number="34.3.1" class="anchored" data-anchor-id="missing-values"><span
 <span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a>    width<span class="op">=</span><span class="dv">600</span>,</span>
 <span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a>    height<span class="op">=</span><span class="dv">400</span></span>
 <span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="42">
+<div class="cell-output cell-output-display" data-execution_count="13">
 
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@@ -1315,26 +1315,26 @@ <h3 data-number="34.3.1" class="anchored" data-anchor-id="missing-values"><span
 <span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a>    width<span class="op">=</span><span class="dv">600</span>,</span>
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 <span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="43">
+<div class="cell-output cell-output-display" data-execution_count="14">
 
 <style>
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+<div id="altair-viz-f5e0278b95bd4a44bb3740c451b693ab"></div>
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@@ -1397,7 +1397,7 @@ <h3 data-number="34.3.1" class="anchored" data-anchor-id="missing-values"><span
 <span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's create the bar plot</span></span>
 <span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a>df_professions_sorted.plot.bar(x<span class="op">=</span><span class="st">'Description'</span>, y<span class="op">=</span><span class="st">'GPGmedian'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="44">
+<div class="cell-output cell-output-display" data-execution_count="15">
 <pre><code>&lt;Axes: xlabel='Description'&gt;</code></pre>
 </div>
 <div class="cell-output cell-output-display">
@@ -1407,7 +1407,7 @@ <h3 data-number="34.3.1" class="anchored" data-anchor-id="missing-values"><span
 <p>Let’s look at age-based differences next:</p>
 <div class="cell" data-execution_count="16">
 <div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>df_age.sort_values(<span class="st">'age_group'</span>, ascending<span class="op">=</span><span class="va">True</span>).plot.bar(x <span class="op">=</span> <span class="st">'age_group'</span>, y <span class="op">=</span> <span class="st">'GPGmedian'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="45">
+<div class="cell-output cell-output-display" data-execution_count="16">
 <pre><code>&lt;Axes: xlabel='age_group'&gt;</code></pre>
 </div>
 <div class="cell-output cell-output-display">
@@ -1425,9 +1425,21 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 <ul>
 <li>If you download the zip file for the latest <a href="https://www.ons.gov.uk/file?uri=/employmentandlabourmarket/peopleinwork/earningsandworkinghours/datasets/annualsurveyofhoursandearningsashegenderpaygaptables/2023provisional/genderpaygap2023provisional.zip">2023 Pay Gap ONS statistics</a>, you will notice that they have color-coded their cells based on the certainty of estimates. On the one hand, this is very good practice - being transparent about the quality of the data. On the other hand, it tells us that we need to be careful about what insights we can draw from the data.</li>
 <li>If you look at the statistics for one year, you can get a glimpse of what’s happening across various categories (geography, age, profession, etc.) in terms of GPG but it’s a cross-sectional view. But you can collate a longitudinal view should you wish to. E.g., by downloading the zip folders across the desired years and collating the information for desired categories for multiple years. But remember that this will be a ‘simplified approach’ to a longitudinal view and will have limitations. Also, recollect one of the figures Cagatay showed in the earlier lectures - it’s common to spend a lot of time at the start of your Data Science project just collating the necessary information. If you fancy, you can write a script to automate the data collation process!</li>
-<li>We have geography information as area codes from the ONS source, but wouldn’t it be nice if we are able to visualise GPG by Geography on a map of England (with <a href="https://www.gov.uk/government/publications/levelling-up-the-united-kingdom">Levelling Up agenda</a> and all). That’s the data hunt I went on. And found this:<a href="https://martinjc.github.io/UK-GeoJSON/">UK GEO-Json</a>. Now let’s see what visualisation we can create with it.</li>
+<li>We have geography information as area codes from the ONS source, but wouldn’t it be nice if we are able to visualise GPG by Geography on a map of England (with <a href="https://www.gov.uk/government/publications/levelling-up-the-united-kingdom">Levelling Up agenda</a> and all). That’s the data hunt I went on. And the ONS’ Geodata portal provides datasets from the different administrative boundaries, so I downloaded this one:<a href="https://geoportal.statistics.gov.uk/datasets/ons::counties-and-unitary-authorities-may-2023-boundaries-uk-buc/about">Counties and Unitary Authorities (May 2023) Boundaries UK BUC</a>. Now let’s see what visualisation we can create with it.</li>
 </ul>
-<p><span style="color: blue;">Takeaway:</span> Collating data from multiple sources is a significant, valuable and legitimate part of the Data Science project journey</p>
+<div class="callout callout-style-default callout-tip callout-titled">
+<div class="callout-header d-flex align-content-center">
+<div class="callout-icon-container">
+<i class="callout-icon"></i>
+</div>
+<div class="callout-title-container flex-fill">
+Takeaway
+</div>
+</div>
+<div class="callout-body-container callout-body">
+<p>Collating data from multiple sources is a significant, valuable and legitimate part of the Data Science project journey</p>
+</div>
+</div>
 <div class="cell" data-execution_count="17">
 <div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Getting the geospatial polygons for England</span></span>
 <span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> geopandas <span class="im">as</span> gpd </span>
@@ -1435,7 +1447,7 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 <span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a>geo_states_england <span class="op">=</span> gpd.read_file(<span class="st">'data/Counties_and_Unitary_Authorities_May_2023_UK_BUC_-7406349609691062173.gpkg'</span>)</span>
 <span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a>geo_states_england.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="46">
+<div class="cell-output cell-output-display" data-execution_count="17">
 <div>
 
 
@@ -1536,7 +1548,7 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 <div class="cell" data-execution_count="20">
 <div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Let's check again</span></span>
 <span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>geo_states_england.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="49">
+<div class="cell-output cell-output-display" data-execution_count="20">
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@@ -1607,26 +1619,26 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 <span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a>    height<span class="op">=</span><span class="dv">300</span></span>
 <span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a>)</span>
 <span id="cb28-8"><a href="#cb28-8" aria-hidden="true" tabindex="-1"></a>pre_GPG_England</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="50">
+<div class="cell-output cell-output-display" data-execution_count="21">
 
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@@ -1676,7 +1688,7 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 </script>
 </div>
 </div>
-<p>What’s that?!</p>
+<p>Wait, what’s that?! That’s not what we were expecting!</p>
 <div class="callout callout-style-default callout-warning callout-titled">
 <div class="callout-header d-flex align-content-center">
 <div class="callout-icon-container">
@@ -1687,9 +1699,9 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 </div>
 </div>
 <div class="callout-body-container callout-body">
-<p>Because the Earth is round, and maps are flat, geospatial data needs to be “projected”. There are many types of projecting geospatial data, and all of them come with some tradeoff in terms of distorting area and/or distance (in other words, none of them are perfect). You can read more here.</p>
-<p>Now, the geospatial dataset that we are using for this notebook was downloaded from and uses a Coordinate Reference System (CRS) known as EPSG:27700 - OSGB36 / British National Grid. Regretfully, Altair works with a different CRS: WGS 84 (also known as epsg:4326), and this is creating the conflict.</p>
-<p>We have two options: either reproject our data using geopandas, or according to <a href="https://altair-viz.github.io/user_guide/data.html#projections">Altair documentation</a> try using the project configuration `(type: ‘identity’, reflectY’: True)``. It draws the geometries without applying a projection.</p>
+<p>Because the Earth is round, and maps are flat, geospatial data needs to be “projected”. There are many types of projecting geospatial data, and all of them come with some tradeoff in terms of distorting area and/or distance (in other words, none of them are perfect). You can read more <a href="https://en.wikipedia.org/wiki/Map_projection">here</a>.</p>
+<p>Now, the geospatial dataset that we are using for this notebook was downloaded from the <a href="https://geoportal.statistics.gov.uk/datasets/ons::counties-and-unitary-authorities-may-2023-boundaries-uk-buc/about">Office for National Statistics’ Geoportal</a> and uses a Coordinate Reference System (CRS) known as <code>EPSG:27700 - OSGB36 / British National Grid</code>. Regretfully, Altair works with a different CRS: <code>WGS 84</code> (also known as <code>epsg:4326</code>), and this is creating the conflict.</p>
+<p>We have two options: either reproject our data using geopandas, or according to <a href="https://altair-viz.github.io/user_guide/data.html#projections">Altair documentation</a> try using the project configuration <code>(type: 'identity', reflectY': True)</code>. It draws the geometries without applying a projection.</p>
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 <span id="cb29-9"><a href="#cb29-9" aria-hidden="true" tabindex="-1"></a>    reflectY<span class="op">=</span><span class="va">True</span></span>
 <span id="cb29-10"><a href="#cb29-10" aria-hidden="true" tabindex="-1"></a>)</span>
 <span id="cb29-11"><a href="#cb29-11" aria-hidden="true" tabindex="-1"></a>pre_GPG_England</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="51">
+<div class="cell-output cell-output-display" data-execution_count="22">
 
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 <div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>geo_states_england</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="53">
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 <div class="sourceCode cell-code" id="cb33"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>df_geography</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="54">
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@@ -2010,7 +2022,7 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
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 <div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Check the merged data</span></span>
 <span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a>geo_states_england.head(<span class="dv">10</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="56">
+<div class="cell-output cell-output-display" data-execution_count="27">
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@@ -2169,26 +2181,26 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 <span id="cb36-10"><a href="#cb36-10" aria-hidden="true" tabindex="-1"></a>    reflectY<span class="op">=</span><span class="va">True</span></span>
 <span id="cb36-11"><a href="#cb36-11" aria-hidden="true" tabindex="-1"></a>)</span>
 <span id="cb36-12"><a href="#cb36-12" aria-hidden="true" tabindex="-1"></a>post_GPG_England</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display" data-execution_count="57">
+<div class="cell-output cell-output-display" data-execution_count="28">
 
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-  #altair-viz-c4696faff28f441c95f70c33ba4f92c1.vega-embed {
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+    if (outputDiv.id !== "altair-viz-cce9e561ed014ab8ac968a147ec533d2") {
+      outputDiv = document.getElementById("altair-viz-cce9e561ed014ab8ac968a147ec533d2");
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     const paths = {
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@@ -2242,26 +2254,26 @@ <h4 data-number="34.4.0.1" class="anchored" data-anchor-id="but-first"><span cla
 <div class="sourceCode cell-code" id="cb37"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a><span class="co"># side by side view</span></span>
 <span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a>GPG_England <span class="op">=</span> pre_GPG_England <span class="op">|</span> post_GPG_England</span>
 <span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a>GPG_England</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<div class="cell-output cell-output-display column-page" data-execution_count="58">
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diff --git a/search.json b/search.json
index bf38c93..f9ce41f 100644
--- a/search.json
+++ b/search.json
@@ -1019,7 +1019,7 @@
     "href": "content/labs/Lab_7/IM939_Lab7-Part3.html#geography",
     "title": "34  Lab: Gender gaps",
     "section": "34.4 Geography",
-    "text": "34.4 Geography\n\n34.4.0.1 But first:\nSince moving on from our Iris and Wine datasets, the real-world datasets rarely come prepared (ready to use).\n\nIf you download the zip file for the latest 2023 Pay Gap ONS statistics, you will notice that they have color-coded their cells based on the certainty of estimates. On the one hand, this is very good practice - being transparent about the quality of the data. On the other hand, it tells us that we need to be careful about what insights we can draw from the data.\nIf you look at the statistics for one year, you can get a glimpse of what’s happening across various categories (geography, age, profession, etc.) in terms of GPG but it’s a cross-sectional view. But you can collate a longitudinal view should you wish to. E.g., by downloading the zip folders across the desired years and collating the information for desired categories for multiple years. But remember that this will be a ‘simplified approach’ to a longitudinal view and will have limitations. Also, recollect one of the figures Cagatay showed in the earlier lectures - it’s common to spend a lot of time at the start of your Data Science project just collating the necessary information. If you fancy, you can write a script to automate the data collation process!\nWe have geography information as area codes from the ONS source, but wouldn’t it be nice if we are able to visualise GPG by Geography on a map of England (with Levelling Up agenda and all). That’s the data hunt I went on. And found this:UK GEO-Json. Now let’s see what visualisation we can create with it.\n\nTakeaway: Collating data from multiple sources is a significant, valuable and legitimate part of the Data Science project journey\n\n# Getting the geospatial polygons for England\nimport geopandas as gpd \nimport altair as alt\n\ngeo_states_england = gpd.read_file('data/Counties_and_Unitary_Authorities_May_2023_UK_BUC_-7406349609691062173.gpkg')\ngeo_states_england.head()\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nCTYUA23NMW\nBNG_E\nBNG_N\nLONG\nLAT\nGlobalID\ngeometry\n\n\n\n\n0\nE06000001\nHartlepool\n\n447160\n531474\n-1.27018\n54.676102\n{224B1BB0-27FA-4B44-AD01-F22525CE232E}\nMULTIPOLYGON (((448973.593 536745.277, 448290....\n\n\n1\nE06000002\nMiddlesbrough\n\n451141\n516887\n-1.21099\n54.544701\n{8A06DF87-1F09-4A1C-9D6E-A32D40A0B159}\nMULTIPOLYGON (((451894.299 521145.303, 448410....\n\n\n2\nE06000003\nRedcar and Cleveland\n\n464361\n519597\n-1.00608\n54.567501\n{4A930CE8-4656-4A98-880E-8110EE3D8501}\nMULTIPOLYGON (((478232.568 518788.831, 478074....\n\n\n3\nE06000004\nStockton-on-Tees\n\n444940\n518183\n-1.30664\n54.556900\n{304224A1-E808-4BF2-8F3E-AC43B0368BE8}\nMULTIPOLYGON (((452243.536 526335.188, 451148....\n\n\n4\nE06000005\nDarlington\n\n428029\n515648\n-1.56835\n54.535301\n{F7BBD06A-7E09-4832-90D0-F6CA591D4A1D}\nMULTIPOLYGON (((436388.002 522354.197, 435529....\n\n\n\n\n\n\n\n\nprint(geo_states_england.columns)\n\nIndex(['CTYUA23CD', 'CTYUA23NM', 'CTYUA23NMW', 'BNG_E', 'BNG_N', 'LONG', 'LAT',\n       'GlobalID', 'geometry'],\n      dtype='object')\n\n\n\n# Let's drop the columns we don't need\ngeo_states_england = geo_states_england.drop(['CTYUA23NMW', 'BNG_E', 'BNG_N', 'GlobalID'], axis=1)\n\n\n# Let's check again\ngeo_states_england.head()\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nLONG\nLAT\ngeometry\n\n\n\n\n0\nE06000001\nHartlepool\n-1.27018\n54.676102\nMULTIPOLYGON (((448973.593 536745.277, 448290....\n\n\n1\nE06000002\nMiddlesbrough\n-1.21099\n54.544701\nMULTIPOLYGON (((451894.299 521145.303, 448410....\n\n\n2\nE06000003\nRedcar and Cleveland\n-1.00608\n54.567501\nMULTIPOLYGON (((478232.568 518788.831, 478074....\n\n\n3\nE06000004\nStockton-on-Tees\n-1.30664\n54.556900\nMULTIPOLYGON (((452243.536 526335.188, 451148....\n\n\n4\nE06000005\nDarlington\n-1.56835\n54.535301\nMULTIPOLYGON (((436388.002 522354.197, 435529....\n\n\n\n\n\n\n\n\n# Let's create a map of England\npre_GPG_England = alt.Chart(geo_states_england, title='Map of England').mark_geoshape().encode(\n    tooltip=['CTYUA23NM']\n).properties(\n    width=500,\n    height=300\n)\npre_GPG_England\n\n\n\n\n\n\n\nWhat’s that?!\n\n\n\n\n\n\nMap projections\n\n\n\nBecause the Earth is round, and maps are flat, geospatial data needs to be “projected”. There are many types of projecting geospatial data, and all of them come with some tradeoff in terms of distorting area and/or distance (in other words, none of them are perfect). You can read more here.\nNow, the geospatial dataset that we are using for this notebook was downloaded from and uses a Coordinate Reference System (CRS) known as EPSG:27700 - OSGB36 / British National Grid. Regretfully, Altair works with a different CRS: WGS 84 (also known as epsg:4326), and this is creating the conflict.\nWe have two options: either reproject our data using geopandas, or according to Altair documentation try using the project configuration `(type: ‘identity’, reflectY’: True)``. It draws the geometries without applying a projection.\n\n\n\n# Let's create a map of England\npre_GPG_England = alt.Chart(geo_states_england, title='Map of England').mark_geoshape().encode(\n    tooltip=['CTYUA23NM']\n).properties(\n    width=500,\n    height=300\n).project(\n    type='identity',\n    reflectY=True\n)\npre_GPG_England\n\n\n\n\n\n\n\n\ndf_geography.info()\n\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nRangeIndex: 390 entries, 0 to 389\nData columns (total 4 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   Description  390 non-null    object \n 1   Code         390 non-null    object \n 2   GPGmedian    386 non-null    float64\n 3   GPGmean      387 non-null    float64\ndtypes: float64(2), object(2)\nmemory usage: 12.3+ KB\n\n\n\ngeo_states_england\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nLONG\nLAT\ngeometry\n\n\n\n\n0\nE06000001\nHartlepool\n-1.27018\n54.676102\nMULTIPOLYGON (((448973.593 536745.277, 448290....\n\n\n1\nE06000002\nMiddlesbrough\n-1.21099\n54.544701\nMULTIPOLYGON (((451894.299 521145.303, 448410....\n\n\n2\nE06000003\nRedcar and Cleveland\n-1.00608\n54.567501\nMULTIPOLYGON (((478232.568 518788.831, 478074....\n\n\n3\nE06000004\nStockton-on-Tees\n-1.30664\n54.556900\nMULTIPOLYGON (((452243.536 526335.188, 451148....\n\n\n4\nE06000005\nDarlington\n-1.56835\n54.535301\nMULTIPOLYGON (((436388.002 522354.197, 435529....\n\n\n...\n...\n...\n...\n...\n...\n\n\n213\nW06000020\nTorfaen\n-3.05101\n51.698399\nMULTIPOLYGON (((333723.000 192653.903, 330700....\n\n\n214\nW06000021\nMonmouthshire\n-2.90280\n51.778301\nMULTIPOLYGON (((329597.402 229251.797, 326793....\n\n\n215\nW06000022\nNewport\n-2.89769\n51.582298\nMULTIPOLYGON (((343091.833 184213.309, 342279....\n\n\n216\nW06000023\nPowys\n-3.43531\n52.348598\nMULTIPOLYGON (((322891.550 333139.949, 321104....\n\n\n217\nW06000024\nMerthyr Tydfil\n-3.36425\n51.748600\nMULTIPOLYGON (((308057.304 211036.201, 306294....\n\n\n\n\n218 rows × 5 columns\n\n\n\n\ndf_geography\n\n\n\n\n\n\n\n\nDescription\nCode\nGPGmedian\nGPGmean\n\n\n\n\n0\nDarlington UA\nE06000005\n5.4\n13.3\n\n\n1\nHartlepool UA\nE06000001\n6.2\n8.9\n\n\n2\nMiddlesbrough UA\nE06000002\n14.5\n15.6\n\n\n3\nRedcar and Cleveland UA\nE06000003\n12.8\n12.3\n\n\n4\nStockton-on-Tees UA\nE06000004\n17.1\n16.9\n\n\n...\n...\n...\n...\n...\n\n\n385\nSouth Lanarkshire\nS12000029\n6.1\n7.5\n\n\n386\nStirling\nS12000030\n7.4\n21.9\n\n\n387\nWest Dunbartonshire\nS12000039\n17.5\n12.8\n\n\n388\nWest Lothian\nS12000040\n8.3\n9.6\n\n\n389\nNorthern Ireland\nN92000002\n8.1\n9.6\n\n\n\n\n390 rows × 4 columns\n\n\n\n\n# Add the data\ngeo_states_england = geo_states_england.merge(df_geography, left_on = 'CTYUA23CD', right_on = 'Code')\n\n\n# Check the merged data\ngeo_states_england.head(10)\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nLONG\nLAT\ngeometry\nDescription\nCode\nGPGmedian\nGPGmean\n\n\n\n\n0\nE06000001\nHartlepool\n-1.27018\n54.676102\nMULTIPOLYGON (((448973.593 536745.277, 448290....\nHartlepool UA\nE06000001\n6.2\n8.9\n\n\n1\nE06000002\nMiddlesbrough\n-1.21099\n54.544701\nMULTIPOLYGON (((451894.299 521145.303, 448410....\nMiddlesbrough UA\nE06000002\n14.5\n15.6\n\n\n2\nE06000003\nRedcar and Cleveland\n-1.00608\n54.567501\nMULTIPOLYGON (((478232.568 518788.831, 478074....\nRedcar and Cleveland UA\nE06000003\n12.8\n12.3\n\n\n3\nE06000004\nStockton-on-Tees\n-1.30664\n54.556900\nMULTIPOLYGON (((452243.536 526335.188, 451148....\nStockton-on-Tees UA\nE06000004\n17.1\n16.9\n\n\n4\nE06000005\nDarlington\n-1.56835\n54.535301\nMULTIPOLYGON (((436388.002 522354.197, 435529....\nDarlington UA\nE06000005\n5.4\n13.3\n\n\n5\nE06000006\nHalton\n-2.68853\n53.334202\nMULTIPOLYGON (((358131.901 385425.802, 355191....\nHalton UA\nE06000006\n3.4\n4.6\n\n\n6\nE06000007\nWarrington\n-2.56167\n53.391602\nMULTIPOLYGON (((367582.201 396058.199, 367158....\nWarrington UA\nE06000007\n12.8\n14.6\n\n\n7\nE06000008\nBlackburn with Darwen\n-2.46360\n53.700802\nMULTIPOLYGON (((372966.498 423266.501, 371465....\nBlackburn with Darwen UA\nE06000008\n22.3\n15.3\n\n\n8\nE06000009\nBlackpool\n-3.02199\n53.821602\nMULTIPOLYGON (((333572.799 437130.702, 333041....\nBlackpool UA\nE06000009\n4.4\n3.5\n\n\n9\nE06000010\nKingston upon Hull, City of\n-0.30382\n53.769199\nMULTIPOLYGON (((515429.592 427689.472, 516047....\nKingston upon Hull UA\nE06000010\n16.1\n7.9\n\n\n\n\n\n\n\n\n# Let's plot the GPG by geography now\npost_GPG_England = alt.Chart(geo_states_england, title='GPG by region - England').mark_geoshape().encode(\n    color='GPGmedian',\n    tooltip=['Description', 'GPGmedian']\n).properties(\n    width=500,\n    height=300\n).project(\n    type='identity',\n    reflectY=True\n)\npost_GPG_England\n\n\n\n\n\n\n\n\n# side by side view\nGPG_England = pre_GPG_England | post_GPG_England\nGPG_England\n\n\n\n\n\n\n\nNOTE: There seems to be a blip in the rendering of the post_GPG_England map. Do you think a cleaner dataset will help overcome this issue?\nHow do the results in this workbook compare to the visualisation we saw during the lecture, for example, for the UK in Information is Beautiful But remember the earnings across the two might be for different years - do remember to check the metadata!"
+    "text": "34.4 Geography\n\n34.4.0.1 But first:\nSince moving on from our Iris and Wine datasets, the real-world datasets rarely come prepared (ready to use).\n\nIf you download the zip file for the latest 2023 Pay Gap ONS statistics, you will notice that they have color-coded their cells based on the certainty of estimates. On the one hand, this is very good practice - being transparent about the quality of the data. On the other hand, it tells us that we need to be careful about what insights we can draw from the data.\nIf you look at the statistics for one year, you can get a glimpse of what’s happening across various categories (geography, age, profession, etc.) in terms of GPG but it’s a cross-sectional view. But you can collate a longitudinal view should you wish to. E.g., by downloading the zip folders across the desired years and collating the information for desired categories for multiple years. But remember that this will be a ‘simplified approach’ to a longitudinal view and will have limitations. Also, recollect one of the figures Cagatay showed in the earlier lectures - it’s common to spend a lot of time at the start of your Data Science project just collating the necessary information. If you fancy, you can write a script to automate the data collation process!\nWe have geography information as area codes from the ONS source, but wouldn’t it be nice if we are able to visualise GPG by Geography on a map of England (with Levelling Up agenda and all). That’s the data hunt I went on. And the ONS’ Geodata portal provides datasets from the different administrative boundaries, so I downloaded this one:Counties and Unitary Authorities (May 2023) Boundaries UK BUC. Now let’s see what visualisation we can create with it.\n\n\n\n\n\n\n\nTakeaway\n\n\n\nCollating data from multiple sources is a significant, valuable and legitimate part of the Data Science project journey\n\n\n\n# Getting the geospatial polygons for England\nimport geopandas as gpd \nimport altair as alt\n\ngeo_states_england = gpd.read_file('data/Counties_and_Unitary_Authorities_May_2023_UK_BUC_-7406349609691062173.gpkg')\ngeo_states_england.head()\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nCTYUA23NMW\nBNG_E\nBNG_N\nLONG\nLAT\nGlobalID\ngeometry\n\n\n\n\n0\nE06000001\nHartlepool\n\n447160\n531474\n-1.27018\n54.676102\n{224B1BB0-27FA-4B44-AD01-F22525CE232E}\nMULTIPOLYGON (((448973.593 536745.277, 448290....\n\n\n1\nE06000002\nMiddlesbrough\n\n451141\n516887\n-1.21099\n54.544701\n{8A06DF87-1F09-4A1C-9D6E-A32D40A0B159}\nMULTIPOLYGON (((451894.299 521145.303, 448410....\n\n\n2\nE06000003\nRedcar and Cleveland\n\n464361\n519597\n-1.00608\n54.567501\n{4A930CE8-4656-4A98-880E-8110EE3D8501}\nMULTIPOLYGON (((478232.568 518788.831, 478074....\n\n\n3\nE06000004\nStockton-on-Tees\n\n444940\n518183\n-1.30664\n54.556900\n{304224A1-E808-4BF2-8F3E-AC43B0368BE8}\nMULTIPOLYGON (((452243.536 526335.188, 451148....\n\n\n4\nE06000005\nDarlington\n\n428029\n515648\n-1.56835\n54.535301\n{F7BBD06A-7E09-4832-90D0-F6CA591D4A1D}\nMULTIPOLYGON (((436388.002 522354.197, 435529....\n\n\n\n\n\n\n\n\nprint(geo_states_england.columns)\n\nIndex(['CTYUA23CD', 'CTYUA23NM', 'CTYUA23NMW', 'BNG_E', 'BNG_N', 'LONG', 'LAT',\n       'GlobalID', 'geometry'],\n      dtype='object')\n\n\n\n# Let's drop the columns we don't need\ngeo_states_england = geo_states_england.drop(['CTYUA23NMW', 'BNG_E', 'BNG_N', 'GlobalID'], axis=1)\n\n\n# Let's check again\ngeo_states_england.head()\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nLONG\nLAT\ngeometry\n\n\n\n\n0\nE06000001\nHartlepool\n-1.27018\n54.676102\nMULTIPOLYGON (((448973.593 536745.277, 448290....\n\n\n1\nE06000002\nMiddlesbrough\n-1.21099\n54.544701\nMULTIPOLYGON (((451894.299 521145.303, 448410....\n\n\n2\nE06000003\nRedcar and Cleveland\n-1.00608\n54.567501\nMULTIPOLYGON (((478232.568 518788.831, 478074....\n\n\n3\nE06000004\nStockton-on-Tees\n-1.30664\n54.556900\nMULTIPOLYGON (((452243.536 526335.188, 451148....\n\n\n4\nE06000005\nDarlington\n-1.56835\n54.535301\nMULTIPOLYGON (((436388.002 522354.197, 435529....\n\n\n\n\n\n\n\n\n# Let's create a map of England\npre_GPG_England = alt.Chart(geo_states_england, title='Map of England').mark_geoshape().encode(\n    tooltip=['CTYUA23NM']\n).properties(\n    width=500,\n    height=300\n)\npre_GPG_England\n\n\n\n\n\n\n\nWait, what’s that?! That’s not what we were expecting!\n\n\n\n\n\n\nMap projections\n\n\n\nBecause the Earth is round, and maps are flat, geospatial data needs to be “projected”. There are many types of projecting geospatial data, and all of them come with some tradeoff in terms of distorting area and/or distance (in other words, none of them are perfect). You can read more here.\nNow, the geospatial dataset that we are using for this notebook was downloaded from the Office for National Statistics’ Geoportal and uses a Coordinate Reference System (CRS) known as EPSG:27700 - OSGB36 / British National Grid. Regretfully, Altair works with a different CRS: WGS 84 (also known as epsg:4326), and this is creating the conflict.\nWe have two options: either reproject our data using geopandas, or according to Altair documentation try using the project configuration (type: 'identity', reflectY': True). It draws the geometries without applying a projection.\n\n\n\n# Let's create a map of England\npre_GPG_England = alt.Chart(geo_states_england, title='Map of England').mark_geoshape().encode(\n    tooltip=['CTYUA23NM']\n).properties(\n    width=500,\n    height=300\n).project(\n    type='identity',\n    reflectY=True\n)\npre_GPG_England\n\n\n\n\n\n\n\n\ndf_geography.info()\n\n&lt;class 'pandas.core.frame.DataFrame'&gt;\nRangeIndex: 390 entries, 0 to 389\nData columns (total 4 columns):\n #   Column       Non-Null Count  Dtype  \n---  ------       --------------  -----  \n 0   Description  390 non-null    object \n 1   Code         390 non-null    object \n 2   GPGmedian    386 non-null    float64\n 3   GPGmean      387 non-null    float64\ndtypes: float64(2), object(2)\nmemory usage: 12.3+ KB\n\n\n\ngeo_states_england\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nLONG\nLAT\ngeometry\n\n\n\n\n0\nE06000001\nHartlepool\n-1.27018\n54.676102\nMULTIPOLYGON (((448973.593 536745.277, 448290....\n\n\n1\nE06000002\nMiddlesbrough\n-1.21099\n54.544701\nMULTIPOLYGON (((451894.299 521145.303, 448410....\n\n\n2\nE06000003\nRedcar and Cleveland\n-1.00608\n54.567501\nMULTIPOLYGON (((478232.568 518788.831, 478074....\n\n\n3\nE06000004\nStockton-on-Tees\n-1.30664\n54.556900\nMULTIPOLYGON (((452243.536 526335.188, 451148....\n\n\n4\nE06000005\nDarlington\n-1.56835\n54.535301\nMULTIPOLYGON (((436388.002 522354.197, 435529....\n\n\n...\n...\n...\n...\n...\n...\n\n\n213\nW06000020\nTorfaen\n-3.05101\n51.698399\nMULTIPOLYGON (((333723.000 192653.903, 330700....\n\n\n214\nW06000021\nMonmouthshire\n-2.90280\n51.778301\nMULTIPOLYGON (((329597.402 229251.797, 326793....\n\n\n215\nW06000022\nNewport\n-2.89769\n51.582298\nMULTIPOLYGON (((343091.833 184213.309, 342279....\n\n\n216\nW06000023\nPowys\n-3.43531\n52.348598\nMULTIPOLYGON (((322891.550 333139.949, 321104....\n\n\n217\nW06000024\nMerthyr Tydfil\n-3.36425\n51.748600\nMULTIPOLYGON (((308057.304 211036.201, 306294....\n\n\n\n\n218 rows × 5 columns\n\n\n\n\ndf_geography\n\n\n\n\n\n\n\n\nDescription\nCode\nGPGmedian\nGPGmean\n\n\n\n\n0\nDarlington UA\nE06000005\n5.4\n13.3\n\n\n1\nHartlepool UA\nE06000001\n6.2\n8.9\n\n\n2\nMiddlesbrough UA\nE06000002\n14.5\n15.6\n\n\n3\nRedcar and Cleveland UA\nE06000003\n12.8\n12.3\n\n\n4\nStockton-on-Tees UA\nE06000004\n17.1\n16.9\n\n\n...\n...\n...\n...\n...\n\n\n385\nSouth Lanarkshire\nS12000029\n6.1\n7.5\n\n\n386\nStirling\nS12000030\n7.4\n21.9\n\n\n387\nWest Dunbartonshire\nS12000039\n17.5\n12.8\n\n\n388\nWest Lothian\nS12000040\n8.3\n9.6\n\n\n389\nNorthern Ireland\nN92000002\n8.1\n9.6\n\n\n\n\n390 rows × 4 columns\n\n\n\n\n# Add the data\ngeo_states_england = geo_states_england.merge(df_geography, left_on = 'CTYUA23CD', right_on = 'Code')\n\n\n# Check the merged data\ngeo_states_england.head(10)\n\n\n\n\n\n\n\n\nCTYUA23CD\nCTYUA23NM\nLONG\nLAT\ngeometry\nDescription\nCode\nGPGmedian\nGPGmean\n\n\n\n\n0\nE06000001\nHartlepool\n-1.27018\n54.676102\nMULTIPOLYGON (((448973.593 536745.277, 448290....\nHartlepool UA\nE06000001\n6.2\n8.9\n\n\n1\nE06000002\nMiddlesbrough\n-1.21099\n54.544701\nMULTIPOLYGON (((451894.299 521145.303, 448410....\nMiddlesbrough UA\nE06000002\n14.5\n15.6\n\n\n2\nE06000003\nRedcar and Cleveland\n-1.00608\n54.567501\nMULTIPOLYGON (((478232.568 518788.831, 478074....\nRedcar and Cleveland UA\nE06000003\n12.8\n12.3\n\n\n3\nE06000004\nStockton-on-Tees\n-1.30664\n54.556900\nMULTIPOLYGON (((452243.536 526335.188, 451148....\nStockton-on-Tees UA\nE06000004\n17.1\n16.9\n\n\n4\nE06000005\nDarlington\n-1.56835\n54.535301\nMULTIPOLYGON (((436388.002 522354.197, 435529....\nDarlington UA\nE06000005\n5.4\n13.3\n\n\n5\nE06000006\nHalton\n-2.68853\n53.334202\nMULTIPOLYGON (((358131.901 385425.802, 355191....\nHalton UA\nE06000006\n3.4\n4.6\n\n\n6\nE06000007\nWarrington\n-2.56167\n53.391602\nMULTIPOLYGON (((367582.201 396058.199, 367158....\nWarrington UA\nE06000007\n12.8\n14.6\n\n\n7\nE06000008\nBlackburn with Darwen\n-2.46360\n53.700802\nMULTIPOLYGON (((372966.498 423266.501, 371465....\nBlackburn with Darwen UA\nE06000008\n22.3\n15.3\n\n\n8\nE06000009\nBlackpool\n-3.02199\n53.821602\nMULTIPOLYGON (((333572.799 437130.702, 333041....\nBlackpool UA\nE06000009\n4.4\n3.5\n\n\n9\nE06000010\nKingston upon Hull, City of\n-0.30382\n53.769199\nMULTIPOLYGON (((515429.592 427689.472, 516047....\nKingston upon Hull UA\nE06000010\n16.1\n7.9\n\n\n\n\n\n\n\n\n# Let's plot the GPG by geography now\npost_GPG_England = alt.Chart(geo_states_england, title='GPG by region - England').mark_geoshape().encode(\n    color='GPGmedian',\n    tooltip=['Description', 'GPGmedian']\n).properties(\n    width=500,\n    height=300\n).project(\n    type='identity',\n    reflectY=True\n)\npost_GPG_England\n\n\n\n\n\n\n\n\n# side by side view\nGPG_England = pre_GPG_England | post_GPG_England\nGPG_England\n\n\n\n\n\n\n\nNOTE: There seems to be a blip in the rendering of the post_GPG_England map. Do you think a cleaner dataset will help overcome this issue?\nHow do the results in this workbook compare to the visualisation we saw during the lecture, for example, for the UK in Information is Beautiful But remember the earnings across the two might be for different years - do remember to check the metadata!"
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