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Understanding Nested List Comprehension
List comprehensions offer a concise and elegant syntax for manipulating collections in Python. By combining nested for loops, one can construct complex data structures with ease. Here's a detailed breakdown of how nested list comprehensions work:
Consider the given list comprehension:
<code class="python">[(min([row[i] for row in rows]),max([row[i] for row in rows])) for i in range(len(rows[0]))]</code>
This code is equivalent to the following loop:
<code class="python">result = [] for i in range(len(rows[0])): innerResult = [] for row in rows: innerResult.append(row[i]) innerResult2 = [] for row in rows: innerResult2.append(row[i]) tuple = (min(innerResult), max(innerResult2)) result.append(tuple)</code>
In general, a nested list comprehension of the form:
<code class="python">[exp2([exp1 for x in xSet]) for y in ySet]</code>
can be translated to:
<code class="python">result = [] for y in ySet: innerResult = [] for x in xSet: innerResult.append(exp1) exp2Result = exp2(innerResult) result.append(exp2Result)</code>
For simpler cases, such as:
<code class="python">[exp1 for x in xSet for y in ySet] </code>
it is equivalent to:
<code class="python">result = [] for x in xSet: for y in ySet: result.append(exp1)</code>
whereas,
<code class="python">[[exp1 for x in xSet] for y in ySet]</code>
is equivalent to:
<code class="python">result = [] for y in ySet: innerResult = [] for x in xSet: innerResult.append(exp1) result.append(innerResult)</code>
It's important to note that the nesting of for loops in a single list comprehension is always from left to right. For example:
<code class="python">[item for item in row for row in matrix] </code>
This code will not work because the inner loop variable 'row' is not defined in the outer loop. The correct form is:
<code class="python">[item for row in matrix for item in row] </code>
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