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--- python3:list_comprehensions [2019/05/07 13:49]
jguerin
+++ python3:list_comprehensions [2019/05/07 16:19] (current)
jguerin Moved the multiple loop example, switched x*2 to abs(x) in list example.
@@ Line 20: / Line 20: @@
 [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
 </code>
+===== Example Functionality =====
 ==== Multiple Loops ====
-Note that this basic syntax is easily extended to multiple loops and variables:
 <code python>
 >>> [(x**2, y**2) for x in range(3) for y in range(3)]
@@ Line 28: / Line 31: @@
 </code>
+====Conditionals====
+<code python>
+>>> [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
+[(1,3), (1,4), (2,3), (2,1), (2,4), (3,1), (3,4)]
+</code>
+====Repetitions====
+<code python>
+>>> [i for i in range(1, 5) for j in range(i)]
+[1, 2, 2, 3, 3, 3, 4, 4, 4, 4]
+</code>
+====Processing non-range() iterables====
+<code python>
+>>> vec = [-4, -2, 0, 2, 4]
+>>> [abs(x) for x in vec]
+[4, 2, 0, 2, 4]
+</code>
+====Creating a double list====
+<code python>
+>>> n = 3
+>>> [[i for i in range(j*n, j*n+n)] for j in range(n)]
+[[0, 1, 2], [3, 4, 5], [6, 7, 8]]
+</code>
+====Flattening a double list====
+<code python>
+>>> vec = [[1,2,3], [4,5,6], [7,8,9]]
+>>> [num for elem in vec for num in elem]
+[1, 2, 3, 4, 5, 6, 7, 8, 9]
+</code>
+==== Benchmarks ====
+List comprehensions are typically faster than their loop-based counterparts, often without fine-tuning. The below tests are taken directly from the above examples.
+<file python square_comprehension.py>
+n = int(10000)
+squares = [([(x**2, y**2) for x in range(n) for y in range(n)])]
+</file>
+<file python square_loop.py>
+n = int(argv[1])
+squares = []
+for i in range(n):
+    for j in range(n):
+        squares.append((i**2, j**2))
+</file>
+^ %%n%%          ^ square_comprehension.py((Python 3.5.2))          ^ square_loop.py          ^
+^ 500   | .1864s    | .2152s        |
+^ 1000  | .7104s    | .8147s        |
+^ 1500  | 1.572s    | 1.8304s       |
+^ 2000  | 2.7992    | 3.28s         |
+Note that both of the above examples require storage of (potentially) millions of tuples. Many problems will not necessarily require //storage// of this many values in memory, but instead iterating over them. In such an instances iteration using for loops or [[python3:generators|Generators]] may be advisable.

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