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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
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‎ebook/chapters/third_party_chapters/timing.tex

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

‎ebook/python-snippets.epub

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‎ebook/python-snippets.mobi

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‎ebook/python-snippets.pdf

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‎third_party/README.md

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

‎third_party/range_vs_repeat.py

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

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