feat: range_vs_repeat - itertools.repeat() is faster than range() for looping a fixed number of times when you don't need the loop variable

Author: DahlitzFlorian

ebook/chapters/third_party_chapters/timing.tex

@@ -39,3 +39,19 @@ \subsection{f-strings VS str}
 
 \textbf{Note:} Even though f-strings are faster in this situation, keep in mind, that the builtin \lstinline{str} function is preferred to be used.
 Clean code is usually more important than efficiency.
+
+
+\subsection{range VS repeat}
+
+\lstinline{itertools.repeat} is faster than \lstinline{range} for looping a fixed number of times when you don't need the loop variable.
+An example is given below.
+
+\lstinputlisting[caption=range\_vs\_repeat.py]{../third_party/range_vs_repeat.py}
+
+As you can see, \lstinline{itertools.repeat} is slightly faster.
+
+\begin{lstlisting}[caption=Output of range\_vs\_repeat.py]
+$ python range_vs_repeat.py
+"Range" spend time: 0.01480103
+"Repeat" spend time: 0.01320004
+\end{lstlisting}

ebook/python-snippets.epub

@@ -33,6 +33,7 @@ A collection of useful snippets using third party packages.
 | parse_complex_excel_sheets | Parse complex Excel Sheets using `pandas` and `xlrd` |
 | port_scanner_nmap | Port Scanner using nmap |
 | pytest_rename_class_backwards_compatibility | Rename a critical class and test for backwards compatibility |
+| range_vs_repeat | `itertools.repeat()` is faster than `range()` for looping a fixed number of times when you don't need the loop variable |
 | reduce_pandas_df_memory | Reduce pandas df memory usage by converting `object` to `category` |
 | refactoring_code | Refactoring Python code using rope |
 | resize_images | Resize all images in the current directory using OpenCV |

ebook/python-snippets.mobi

@@ -0,0 +1,10 @@
+import itertools
+import random
+
+from boxx import timeit
+
+with timeit(name="Range"):
+    min(random.random() for i in range(100_000))
+
+with timeit(name="Repeat"):
+    min(random.random() for _ in itertools.repeat(None, 100_000))