Discount markdown parser testing

TablesDocsPartial-discountoutput.html

<h3><code>Tables.columns</code> usage</h3>

<p><pre><code class=", now let's take a look at a case utlizing [`Tables.columns`](@ref).">
The following code is taken from the [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl/blob/master/src/other/tables.jl)
Tables.jl implementation:
</code></pre>
getvector(x::AbstractVector) = x
getvector(x) = collect(x)</p>

<h1>note that copycols is ignored in this definition (Tables.CopiedColumns implies copies have already been made)</h1>

<p>fromcolumns(x::Tables.CopiedColumns, names; copycols::Bool=true) =
    DataFrame(AbstractVector[getvector(Tables.getcolumn(x, nm) for nm in names],
              Index(names),
              copycols=false)
[&hellip;]
<pre><code>

So here we have a generic `DataFrame` constructor that takes a single, untyped argument,
</code></pre>
It then passes the <a href="@ref"><code>Tables.AbstractColumns</code></a>-compatible object to an internal function <code>fromcolumns</code>,
which dispatches on a special kind of <code>Tables.AbstractColumns</code> object called a <a href="@ref"><code>Tables.CopiedColumns</code></a>,</p>

TablesDocsPartial-githuboutput.html

<h3 dir="auto"><code>Tables.columns</code> usage</h3>
<p dir="auto">Ok, now let's take a look at a case utlizing <a href="@ref"><code>Tables.columns</code></a>.<br>
The following code is taken from the <a href="https://github.com/JuliaData/DataFrames.jl/blob/master/src/other/tables.jl">DataFrames.jl</a><br>
Tables.jl implementation:</p>
<div class="highlight highlight-source-julia position-relative overflow-auto"><pre><span class="pl-en">getvector</span>(x<span class="pl-k">::</span><span class="pl-c1">AbstractVector</span>) <span class="pl-k">=</span> x
<span class="pl-en">getvector</span>(x) <span class="pl-k">=</span> <span class="pl-c1">collect</span>(x)

<span class="pl-c"><span class="pl-c">#</span> note that copycols is ignored in this definition (Tables.CopiedColumns implies copies have already been made)</span>
<span class="pl-en">fromcolumns</span>(x<span class="pl-k">::</span><span class="pl-c1">Tables.CopiedColumns</span>, names; copycols<span class="pl-k">::</span><span class="pl-c1">Bool</span><span class="pl-k">=</span><span class="pl-c1">true</span>) <span class="pl-k">=</span>
    <span class="pl-c1">DataFrame</span>(AbstractVector[<span class="pl-c1">getvector</span>(Tables<span class="pl-k">.</span><span class="pl-c1">getcolumn</span>(x, nm) <span class="pl-k">for</span> nm <span class="pl-k">in</span> names],
              <span class="pl-c1">Index</span>(names),
              copycols<span class="pl-k">=</span><span class="pl-c1">false</span>)
[<span class="pl-k">...</span>]</pre><div class="zeroclipboard-container position-absolute right-0 top-0">
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getvector(x) = collect(x)

# note that copycols is ignored in this definition (Tables.CopiedColumns implies copies have already been made)
fromcolumns(x::Tables.CopiedColumns, names; copycols::Bool=true) =
    DataFrame(AbstractVector[getvector(Tables.getcolumn(x, nm) for nm in names],
              Index(names),
              copycols=false)
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<p dir="auto">So here we have a generic <code>DataFrame</code> constructor that takes a single, untyped argument,<br>
calls <a href="@ref"><code>Tables.columns</code></a> on it, then <a href="@ref"><code>Tables.columnnames</code></a> to get the column names.<br>
It then passes the <a href="@ref"><code>Tables.AbstractColumns</code></a>-compatible object to an internal function <code>fromcolumns</code>,<br>
which dispatches on a special kind of <code>Tables.AbstractColumns</code> object called a <a href="@ref"><code>Tables.CopiedColumns</code></a>,</p>