{"id":1034,"date":"2024-09-18T21:17:32","date_gmt":"2024-09-18T15:47:32","guid":{"rendered":"https:\/\/www.mrcoder701.com\/?p=1034"},"modified":"2024-10-03T20:00:42","modified_gmt":"2024-10-03T14:30:42","slug":"fast-python-using-automatic-memoization","status":"publish","type":"post","link":"https:\/\/www.mrcoder701.com\/2024\/09\/18\/fast-python-using-automatic-memoization\/","title":{"rendered":"Write 1000x FAST Python using Automatic Memoization one line of code)"},"content":{"rendered":"<p id=\"d449\">Have you ever written a Python function that seemed to take forever to execute? If you\u2019re a fan of elegant, efficient code, you\u2019re in for a treat. Today, I\u2019m going to show you how to make your Python functions\u00a0<strong>over 1000 times faster<\/strong>\u00a0with just\u00a0<strong>one line of code<\/strong>\u00a0using\u00a0<strong>automatic memoization<\/strong>. Let\u2019s dive in!<\/p><p id=\"d449\"><\/p><figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Python using Automatic Memoization one line of code #python #coding #programming #cache\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/tN_7DPZjM6s?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><figcaption class=\"wp-element-caption\">Write 1000x FAST Python using Automatic Memoization one line of code)<\/figcaption><\/figure><h1 class=\"wp-block-heading\" id=\"a246\">The Problem: Slow Recursive Functions<\/h1><p id=\"f431\">Imagine you have a simple recursive function to calculate Fibonacci numbers:<\/p><div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:clamp(18px, 1.125rem, 27px);font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:clamp(26px, 1.625rem, 39px);--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#282A36\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"def fib(n):\n    if n &lt;= 1:\n        return n\n    return fib(n-1) + fib(n-2)\n\nfor i in range(40):\n    print(fib(i))\" style=\"color:#F8F8F2;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki dracula\" style=\"background-color: #282A36\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #FF79C6\">def<\/span><span style=\"color: #F8F8F2\"> <\/span><span style=\"color: #50FA7B\">fib<\/span><span style=\"color: #F8F8F2\">(<\/span><span style=\"color: #FFB86C; font-style: italic\">n<\/span><span style=\"color: #F8F8F2\">):<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F8F8F2\">    <\/span><span style=\"color: #FF79C6\">if<\/span><span style=\"color: #F8F8F2\"> n <\/span><span style=\"color: #FF79C6\">&lt;=<\/span><span style=\"color: #F8F8F2\"> <\/span><span style=\"color: #BD93F9\">1<\/span><span style=\"color: #F8F8F2\">:<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F8F8F2\">        <\/span><span style=\"color: #FF79C6\">return<\/span><span style=\"color: #F8F8F2\"> n<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F8F8F2\">    <\/span><span style=\"color: #FF79C6\">return<\/span><span style=\"color: #F8F8F2\"> fib(n<\/span><span style=\"color: #FF79C6\">-<\/span><span style=\"color: #BD93F9\">1<\/span><span style=\"color: #F8F8F2\">) <\/span><span style=\"color: #FF79C6\">+<\/span><span style=\"color: #F8F8F2\"> fib(n<\/span><span style=\"color: #FF79C6\">-<\/span><span style=\"color: #BD93F9\">2<\/span><span style=\"color: #F8F8F2\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #FF79C6\">for<\/span><span style=\"color: #F8F8F2\"> i <\/span><span style=\"color: #FF79C6\">in<\/span><span style=\"color: #F8F8F2\"> <\/span><span style=\"color: #8BE9FD\">range<\/span><span style=\"color: #F8F8F2\">(<\/span><span style=\"color: #BD93F9\">40<\/span><span style=\"color: #F8F8F2\">):<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F8F8F2\">    <\/span><span style=\"color: #8BE9FD\">print<\/span><span style=\"color: #F8F8F2\">(fib(i))<\/span><\/span><\/code><\/pre><\/div><p><\/p><p><\/p><p id=\"6c55\">At first glance, this code is clean and straightforward. However, it\u2019s painfully slow for larger values of&nbsp;<code>n<\/code>. Running this snippet can take&nbsp;<strong>around 52 seconds<\/strong>! Why? Because the&nbsp;<code>fib()<\/code>&nbsp;function recalculates the same values multiple times, leading to an exponential increase in computation time.<\/p><hr class=\"wp-block-separator has-alpha-channel-opacity\"\/><h1 class=\"wp-block-heading\" id=\"7f68\">The Magic Solution: Automatic Memoization with&nbsp;<code>functools.cache<\/code><\/h1><p id=\"cf18\">Enter the hero of our story:&nbsp;<strong>memoization<\/strong>. Memoization is a technique that&nbsp;<strong>stores the results of expensive function calls<\/strong>&nbsp;and returns the cached result when the same inputs occur again. In Python, the&nbsp;<code>functools<\/code>&nbsp;library provides decorators to make memoization effortless.<\/p><h1 class=\"wp-block-heading\" id=\"e58e\">Step 1: Using&nbsp;<code>cache<\/code>&nbsp;for Automatic Memoization<\/h1><p id=\"0f6c\">Here\u2019s how you can transform the slow Fibonacci function into a blazing-fast version with just one line of code:<\/p><div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:clamp(18px, 1.125rem, 27px);font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:clamp(26px, 1.625rem, 39px);--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#282A36\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"from functools import cache\n\n@cache\ndef fib(n):\n    if n &lt;= 1:\n        return n\n    return fib(n-1) + fib(n-2)\n\nfor i in range(40):\n    print(fib(i))\" style=\"color:#F8F8F2;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki dracula\" style=\"background-color: #282A36\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #FF79C6\">from<\/span><span style=\"color: #F8F8F2\"> functools <\/span><span style=\"color: #FF79C6\">import<\/span><span style=\"color: #F8F8F2\"> cache<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #50FA7B\">@cache<\/span><\/span>\n<span class=\"line\"><span style=\"color: #FF79C6\">def<\/span><span style=\"color: #F8F8F2\"> <\/span><span style=\"color: #50FA7B\">fib<\/span><span style=\"color: #F8F8F2\">(<\/span><span style=\"color: #FFB86C; font-style: italic\">n<\/span><span style=\"color: #F8F8F2\">):<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F8F8F2\">    <\/span><span style=\"color: #FF79C6\">if<\/span><span style=\"color: #F8F8F2\"> n <\/span><span style=\"color: #FF79C6\">&lt;=<\/span><span style=\"color: #F8F8F2\"> <\/span><span style=\"color: #BD93F9\">1<\/span><span style=\"color: #F8F8F2\">:<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F8F8F2\">        <\/span><span style=\"color: #FF79C6\">return<\/span><span style=\"color: #F8F8F2\"> n<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F8F8F2\">    <\/span><span style=\"color: #FF79C6\">return<\/span><span style=\"color: #F8F8F2\"> fib(n<\/span><span style=\"color: #FF79C6\">-<\/span><span style=\"color: #BD93F9\">1<\/span><span style=\"color: #F8F8F2\">) <\/span><span style=\"color: #FF79C6\">+<\/span><span style=\"color: #F8F8F2\"> fib(n<\/span><span style=\"color: #FF79C6\">-<\/span><span style=\"color: #BD93F9\">2<\/span><span style=\"color: #F8F8F2\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #FF79C6\">for<\/span><span style=\"color: #F8F8F2\"> i <\/span><span style=\"color: #FF79C6\">in<\/span><span style=\"color: #F8F8F2\"> <\/span><span style=\"color: #8BE9FD\">range<\/span><span style=\"color: #F8F8F2\">(<\/span><span style=\"color: #BD93F9\">40<\/span><span style=\"color: #F8F8F2\">):<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F8F8F2\">    <\/span><span style=\"color: #8BE9FD\">print<\/span><span style=\"color: #F8F8F2\">(fib(i))<\/span><\/span><\/code><\/pre><\/div><p><\/p><p id=\"d06e\"><strong>Boom!<\/strong>&nbsp;The execution time drops from 52 seconds to an astonishing&nbsp;<strong>0.0136 seconds<\/strong>! How? The&nbsp;<code>@cache<\/code>&nbsp;decorator&nbsp;<strong>remembers<\/strong>&nbsp;the results of previous function calls, eliminating redundant computations and skyrocketing your code\u2019s performance.<\/p><h1 class=\"wp-block-heading\" id=\"f4b1\">Understanding the&nbsp;<code>cache<\/code>&nbsp;Decorator<\/h1><p id=\"aa8a\">The&nbsp;<code>cache<\/code>&nbsp;decorator works by storing the return values of the function in a cache (a special kind of dictionary). When you call the function with the same arguments, Python fetches the result directly from the cache instead of recalculating it. This simple addition not only speeds up your code but also keeps it clean and readable.<\/p><h1 class=\"wp-block-heading\" id=\"2eaa\">Optimizing Memory with&nbsp;<code>lru_cache<\/code><\/h1><p id=\"7e7a\">While&nbsp;<code>@cache<\/code>&nbsp;is fantastic for speed, it can consume a lot of memory if you\u2019re caching a vast number of function calls. This is where the&nbsp;<strong>Least Recently Used (LRU) cache<\/strong>&nbsp;comes into play.<\/p><hr class=\"wp-block-separator has-alpha-channel-opacity\"\/><p><\/p><h1 class=\"wp-block-heading\" id=\"4368\">Step 2: Implementing&nbsp;<code>lru_cache<\/code>&nbsp;for Better Memory Management<\/h1><p id=\"96ef\">By using\u00a0<code>lru_cache<\/code>, you can limit the number of cached results, ensuring your program doesn&#8217;t use excessive memory:<\/p><div class=\"wp-block-kevinbatdorf-code-block-pro\" data-code-block-pro-font-family=\"Code-Pro-JetBrains-Mono\" style=\"font-size:clamp(18px, 1.125rem, 27px);font-family:Code-Pro-JetBrains-Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,monospace;line-height:clamp(26px, 1.625rem, 39px);--cbp-tab-width:2;tab-size:var(--cbp-tab-width, 2)\"><span style=\"display:block;padding:16px 0 0 16px;margin-bottom:-1px;width:100%;text-align:left;background-color:#282A36\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"54\" height=\"14\" viewBox=\"0 0 54 14\"><g fill=\"none\" fill-rule=\"evenodd\" transform=\"translate(1 1)\"><circle cx=\"6\" cy=\"6\" r=\"6\" fill=\"#FF5F56\" stroke=\"#E0443E\" stroke-width=\".5\"><\/circle><circle cx=\"26\" cy=\"6\" r=\"6\" fill=\"#FFBD2E\" stroke=\"#DEA123\" stroke-width=\".5\"><\/circle><circle cx=\"46\" cy=\"6\" r=\"6\" fill=\"#27C93F\" stroke=\"#1AAB29\" stroke-width=\".5\"><\/circle><\/g><\/svg><\/span><span role=\"button\" tabindex=\"0\" data-code=\"from functools import lru_cache\n\n@lru_cache(maxsize=5)\ndef fib(n):\n    if n &lt;= 1:\n        return n\n    return fib(n-1) + fib(n-2)\n\nfor i in range(40):\n    print(fib(i))\" style=\"color:#F8F8F2;display:none\" aria-label=\"Copy\" class=\"code-block-pro-copy-button\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:24px;height:24px\" fill=\"none\" viewBox=\"0 0 24 24\" stroke=\"currentColor\" stroke-width=\"2\"><path class=\"with-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2m-6 9l2 2 4-4\"><\/path><path class=\"without-check\" stroke-linecap=\"round\" stroke-linejoin=\"round\" d=\"M9 5H7a2 2 0 00-2 2v12a2 2 0 002 2h10a2 2 0 002-2V7a2 2 0 00-2-2h-2M9 5a2 2 0 002 2h2a2 2 0 002-2M9 5a2 2 0 012-2h2a2 2 0 012 2\"><\/path><\/svg><\/span><pre class=\"shiki dracula\" style=\"background-color: #282A36\" tabindex=\"0\"><code><span class=\"line\"><span style=\"color: #FF79C6\">from<\/span><span style=\"color: #F8F8F2\"> functools <\/span><span style=\"color: #FF79C6\">import<\/span><span style=\"color: #F8F8F2\"> lru_cache<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #50FA7B\">@lru_cache<\/span><span style=\"color: #F8F8F2\">(<\/span><span style=\"color: #FFB86C; font-style: italic\">maxsize<\/span><span style=\"color: #FF79C6\">=<\/span><span style=\"color: #BD93F9\">5<\/span><span style=\"color: #F8F8F2\">)<\/span><\/span>\n<span class=\"line\"><span style=\"color: #FF79C6\">def<\/span><span style=\"color: #F8F8F2\"> <\/span><span style=\"color: #50FA7B\">fib<\/span><span style=\"color: #F8F8F2\">(<\/span><span style=\"color: #FFB86C; font-style: italic\">n<\/span><span style=\"color: #F8F8F2\">):<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F8F8F2\">    <\/span><span style=\"color: #FF79C6\">if<\/span><span style=\"color: #F8F8F2\"> n <\/span><span style=\"color: #FF79C6\">&lt;=<\/span><span style=\"color: #F8F8F2\"> <\/span><span style=\"color: #BD93F9\">1<\/span><span style=\"color: #F8F8F2\">:<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F8F8F2\">        <\/span><span style=\"color: #FF79C6\">return<\/span><span style=\"color: #F8F8F2\"> n<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F8F8F2\">    <\/span><span style=\"color: #FF79C6\">return<\/span><span style=\"color: #F8F8F2\"> fib(n<\/span><span style=\"color: #FF79C6\">-<\/span><span style=\"color: #BD93F9\">1<\/span><span style=\"color: #F8F8F2\">) <\/span><span style=\"color: #FF79C6\">+<\/span><span style=\"color: #F8F8F2\"> fib(n<\/span><span style=\"color: #FF79C6\">-<\/span><span style=\"color: #BD93F9\">2<\/span><span style=\"color: #F8F8F2\">)<\/span><\/span>\n<span class=\"line\"><\/span>\n<span class=\"line\"><span style=\"color: #FF79C6\">for<\/span><span style=\"color: #F8F8F2\"> i <\/span><span style=\"color: #FF79C6\">in<\/span><span style=\"color: #F8F8F2\"> <\/span><span style=\"color: #8BE9FD\">range<\/span><span style=\"color: #F8F8F2\">(<\/span><span style=\"color: #BD93F9\">40<\/span><span style=\"color: #F8F8F2\">):<\/span><\/span>\n<span class=\"line\"><span style=\"color: #F8F8F2\">    <\/span><span style=\"color: #8BE9FD\">print<\/span><span style=\"color: #F8F8F2\">(fib(i))<\/span><\/span><\/code><\/pre><\/div><p><\/p><p id=\"80ff\"><strong>What\u2019s Happening Here?<\/strong><\/p><ul class=\"wp-block-list\"><li><code>@lru_cache(maxsize=5)<\/code>\u00a0tells Python to cache only the\u00a0<strong>last 5<\/strong>\u00a0function calls.<\/li>\n\n<li>This\u00a0<strong>balances speed and memory usage<\/strong>, making your program more efficient without hogging resources.<\/li><\/ul><hr class=\"wp-block-separator has-alpha-channel-opacity\"\/><h1 class=\"wp-block-heading\" id=\"aff3\">Why This Matters<\/h1><p id=\"2c54\">Automatic memoization transforms the way you write and optimize Python code. Here are a few reasons why you should embrace this technique:<\/p><ol class=\"wp-block-list\"><li><strong>Speed Boost:<\/strong>\u00a0Achieve up to 1000x faster execution with minimal code changes.<\/li>\n\n<li><strong>Simplicity:<\/strong>\u00a0With decorators like\u00a0<code>@cache<\/code>\u00a0and\u00a0<code>@lru_cache<\/code>, memoization is as easy as adding a single line.<\/li>\n\n<li><strong>Memory Efficiency:<\/strong>\u00a0Control cache size with\u00a0<code>lru_cache<\/code>\u00a0to prevent excessive memory usage.<\/li>\n\n<li><strong>Readability:<\/strong>\u00a0Keep your code clean and maintainable without intricate caching logic.<\/li><\/ol><h1 class=\"wp-block-heading\" id=\"a40c\">Final Thoughts<\/h1><p id=\"5aa2\">Memoization is a powerful tool in a Python developer\u2019s arsenal. Whether you\u2019re working on recursive functions, dynamic programming, or any computation-heavy tasks, decorators like&nbsp;<code>@cache<\/code>&nbsp;and&nbsp;<code>@lru_cache<\/code>&nbsp;can make your code not only faster but also more elegant.<\/p><p id=\"5488\">So next time you find your Python code lagging, remember the magic of memoization. A single line of code could be the key to unlocking incredible performance improvements. Happy coding! <\/p><h1 class=\"wp-block-heading\" id=\"c02b\">Quick Recap<\/h1><ul class=\"wp-block-list\"><li><strong>Problem:<\/strong>\u00a0Slow recursive functions due to redundant calculations.<\/li>\n\n<li><strong>Solution:<\/strong>\u00a0Use\u00a0<code>@cache<\/code>\u00a0from\u00a0<code>functools<\/code>\u00a0to automatically memoize function results.<\/li>\n\n<li><strong>Optimization:<\/strong>\u00a0Implement\u00a0<code>@lru_cache(maxsize=5)<\/code>\u00a0to balance speed and memory usage.<\/li><\/ul><p id=\"ba31\">Embrace memoization and watch your Python code transform from sluggish to lightning-fast!<\/p><p><\/p><hr class=\"wp-block-separator has-alpha-channel-opacity\"\/><h1 class=\"wp-block-heading\" id=\"88b0\">A Note From the Author<\/h1><p id=\"bf18\">Thank you so much for taking the time to read the story. If you found my article helpful and interesting, please share your thoughts in the comment section, and don\u2019t forget to share and clap <\/p><p id=\"406f\">If you have enjoyed this post or any of my other work, I greatly appreciate donations from those who might be so inclined to support my writing. If you\u2019d like to leave me a \u201ctip,\u201d I have an account set up&nbsp;<a href=\"https:\/\/buymeacoffee.com\/gajanan0707\" rel=\"noreferrer noopener\" target=\"_blank\">HERE<\/a>. Thank you!<\/p><figure class=\"wp-block-image is-resized\"><a href=\"https:\/\/medium.com\/r?url=https%3A%2F%2Fbuymeacoffee.com%2Fgajanan0707\"><img decoding=\"async\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:510\/0*-28l8qlBJUAi6fq0.png\" alt=\"\" style=\"width:844px;height:auto\"\/><\/a><figcaption class=\"wp-element-caption\"><a href=\"https:\/\/medium.com\/r?url=https%3A%2F%2Fbuymeacoffee.com%2Fgajanan0707\">https:\/\/buymeacoffee.com\/gajanan0707<\/a><\/figcaption><\/figure><p id=\"1944\"><strong>Happy Coding! <\/strong><\/p><p id=\"e15f\"><strong>If you\u2019re a fan of\u00a0<em>Demon Slayer<\/em>\u00a0and want to bring a piece of the action home, check out these amazing toys on Amazon! Whether you\u2019re collecting or gifting, these figures capture the essence of your favorite characters perfectly.\u00a0<\/strong><a href=\"https:\/\/amzn.to\/3Zg8F6J\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Grab yours here!<\/strong><\/a><strong>\u00a0<\/strong><\/p><figure class=\"wp-block-image is-resized\"><a href=\"https:\/\/amzn.to\/3Zg8F6J\"><img decoding=\"async\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:457\/0*m3yTtlugmDIHLGpn.png\" alt=\"\" style=\"width:842px;height:auto\"\/><\/a><figcaption class=\"wp-element-caption\"><a href=\"https:\/\/amzn.to\/3Zg8F6J\" rel=\"noreferrer noopener\" target=\"_blank\">https:\/\/amzn.to\/3Zg8F6J<\/a><\/figcaption><\/figure><figure class=\"wp-block-image is-resized\"><a href=\"https:\/\/amzn.to\/3Zg8F6J\"><img decoding=\"async\" src=\"https:\/\/miro.medium.com\/v2\/resize:fit:457\/0*oeudgVcwMV_AXQPb.png\" alt=\"\" style=\"width:864px;height:auto\"\/><\/a><figcaption class=\"wp-element-caption\"><a href=\"https:\/\/amzn.to\/3Zg8F6J\" rel=\"noreferrer noopener\" target=\"_blank\">https:\/\/amzn.to\/3Zg8F6J<\/a><\/figcaption><\/figure><p id=\"a38e\"> Let\u2019s Get in Touch! <\/p><ul class=\"wp-block-list\"><li><strong>GitHub:<\/strong>\u00a0<a href=\"https:\/\/github.com\/gajanan0707\" target=\"_blank\" rel=\"noreferrer noopener\">@gajanan0707<\/a><\/li>\n\n<li><strong>LinkedIn:<\/strong>\u00a0<a href=\"https:\/\/www.linkedin.com\/in\/gajanan-patil-0124b7145\/\" target=\"_blank\" rel=\"noreferrer noopener\">Gajanan Rajput<\/a><\/li>\n\n<li><strong>Website:<\/strong>\u00a0<a href=\"https:\/\/mrcoder701.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">mrcoder701.com<\/a><\/li>\n\n<li><strong>YouTube:<\/strong>\u00a0<a href=\"https:\/\/www.youtube.com\/@mrcoder701\" target=\"_blank\" rel=\"noreferrer noopener\">mrcoder701<\/a><\/li>\n\n<li><strong>Instagram:<\/strong>\u00a0<a href=\"https:\/\/instagram.com\/mr_coder_701\" target=\"_blank\" rel=\"noreferrer noopener\">mr_coder_701<\/a><\/li>\n\n<li><strong>Medium<\/strong>:\u00a0<a href=\"https:\/\/medium.com\/@rajputgajanan50\">Gajanan Rajput<\/a><\/li><\/ul><p><a href=\"https:\/\/medium.com\/tag\/python?source=post_page-----16bcc94c210a--------------------------------\"><\/a><\/p>","protected":false},"excerpt":{"rendered":"<p>Have you ever written a Python function that seemed to take forever to execute? If you&rsquo;re a fan of elegant, efficient code, you&rsquo;re in for a treat. Today, I&rsquo;m going&hellip;<\/p>\n","protected":false},"author":2,"featured_media":1035,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[172],"class_list":["post-1034","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","tag-coding"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Write 1000x FAST Python using Automatic Memoization one line of code) - &#x1f31f;Code with MrCoder7&#xfe0f;&#x20e3;0&#xfe0f;&#x20e3;1&#xfe0f;&#x20e3;<\/title>\n<meta name=\"description\" content=\"Python cache means implementing caching at a software level using the methods provided by the language. Python cache stores the data in the main memory (RAM) of the computer. 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