What is the difference between condensed and redundant distance matrices?
<p>New to python and programming in general:</p>

<p>The documentation to squareform states the following:</p>

<blockquote>
  <p>Converts a vector-form distance vector to a square-form distance
  matrix, and vice-versa.</p>
</blockquote>

<p>Converts a 1D array into a squared matrix?</p>

<p>Where the paramenter X:</p>

<blockquote>
  <p>Either a condensed or redundant distance matrix.</p>
</blockquote>

<p>and returns: </p>

<blockquote>
  <p>If a condensed distance matrix is passed, a redundant one is returned,
  or if a redundant one is passed, a condensed distance matrix is
  returned.</p>
</blockquote>

<ol>
<li>what is the difference between condensed and redundant matrices?</li>
<li>what is the relationship between condensed/redundant matrix and vector/square form in which it takes?</li>
</ol>

<hr>

<p>The return of pdist papers to return condensed distance matrix:</p>

<blockquote>
  <p>Returns a condensed distance matrix Y. For each i and j (where i is
  less than j is less than n), the metric dist(u=X[i], v=X[j]) is
  computed and stored in entry ij.</p>
</blockquote>

<p>Am I right in thinking that in each element Y stores the distance between a particular point and an other point? An example with 3 observations would mean a condensed matrix with 9 elements? </p>