<story><title>Matlab–Python–Julia Cheatsheet</title><url>https://cheatsheets.quantecon.org/</url></story><parent_chain></parent_chain><comment><author>eigenspace</author><text>The second example in the first section is a bit misleading. They say that to create a column matrix, ie. an (n, 1) matrix, the syntax is&lt;p&gt;&lt;pre&gt;&lt;code&gt;    [1 2 3]&amp;#x27;
&lt;/code&gt;&lt;/pre&gt;
but that will give you the (lazy) hermitian adjoint of the column vector, not a column matrix.&lt;p&gt;&lt;pre&gt;&lt;code&gt;    julia&amp;gt; [1 2 3]&amp;#x27; isa Matrix
    false
&lt;/code&gt;&lt;/pre&gt;
Instead, if you really need a true (n, 1) matrix you should 1) not use adjoint because that will also take complex conjugates (unless that&amp;#x27;s what you wanted) and 2) use collect to turn the lazy transpose (or adjoint) into a true matrix&lt;p&gt;&lt;pre&gt;&lt;code&gt;    julia&amp;gt; collect(transpose([1 2 3]))
    3×1 Array{Int64,2}:
     1
     2
     3
&lt;/code&gt;&lt;/pre&gt;
However, you almost never need to do this sort of thing and you should be fine using either transpose on a row vector or just using a real column vector.&lt;p&gt;Also, near the end they talk about closures but don&amp;#x27;t actually show any closures unless one is to assume the code snippets they&amp;#x27;re showing are actually inside a function body themselves.</text></comment>
<story><title>Matlab–Python–Julia Cheatsheet</title><url>https://cheatsheets.quantecon.org/</url></story><parent_chain></parent_chain><comment><author>msaharia</author><text>So, looks like Julia would be an easier transition for a lot of academic scientists. What am I missing? I mostly use R and Python. Can anyone tell me briefly why I should use Julia over Python?</text></comment>