{"id":4849,"date":"2026-08-02T21:37:47","date_gmt":"2026-08-02T16:07:47","guid":{"rendered":"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/"},"modified":"2026-08-02T21:37:47","modified_gmt":"2026-08-02T16:07:47","slug":"10-python-best-practices-every-developer-should-know","status":"publish","type":"post","link":"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/","title":{"rendered":"10 Python Best Practices Every Developer Should Know"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_80 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a736629db5b9\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a736629db5b9\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/#Why_Your_%E2%80%9CClean%E2%80%9D_Code_Killed_Our_Production_Node\" >Why Your &#8220;Clean&#8221; Code Killed Our Production Node<\/a><ul class='ez-toc-list-level-2' ><li class='ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/#I_The_Heap_Fragmentation_Nightmare\" >I. The Heap Fragmentation Nightmare<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/#II_The_Global_Lookup_Tax_and_Bytecode_Inefficiency\" >II. The Global Lookup Tax and Bytecode Inefficiency<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/#III_The_On2_Complexity_Trap\" >III. The O(n^2) Complexity Trap<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/#IV_Manual_File_Handling_and_the_Ghost_of_File_Descriptors\" >IV. Manual File Handling and the Ghost of File Descriptors<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/#V_Bytecode_Analysis_of_the_Global_Lookup\" >V. Bytecode Analysis of the Global Lookup<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/#VI_The_GIL_and_the_Illusion_of_Concurrency\" >VI. The GIL and the Illusion of Concurrency<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/#VII_The_Rebuild_How_a_Professional_Handles_Data\" >VII. The Rebuild: How a Professional Handles Data<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/#Why_this_doesnt_crash_the_node\" >Why this doesn&#8217;t crash the node:<\/a><\/li><\/ul><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h1><span class=\"ez-toc-section\" id=\"Why_Your_%E2%80%9CClean%E2%80%9D_Code_Killed_Our_Production_Node\"><\/span>Why Your &#8220;Clean&#8221; Code Killed Our Production Node<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<pre class=\"codehilite\"><code class=\"language-python\"># The &quot;Clean&quot; HFT Signal Processor - Version 1.0.4-CRASH\nimport json\nimport time\n\n# Global state because &quot;it's easier to access across modules&quot;\nGLOBAL_TICK_DATA = []\nLAST_PROCESSED_PRICE = 0.0\n\ndef process_ticks(file_path):\n    # Manual file handling because context managers are &quot;too much nesting&quot;\n    f = open(file_path, 'r')\n    data = f.read()\n\n    # Splitting the whole file into memory at once\n    lines = data.split('\\n')\n\n    for line in lines:\n        if line == &quot;&quot;:\n            continue\n\n        # Parsing JSON inside a hot loop\n        tick = json.loads(line)\n        GLOBAL_TICK_DATA.append(tick)\n\n        # Nested O(n) lookup to &quot;validate&quot; history\n        is_duplicate = False\n        for entry in GLOBAL_TICK_DATA[:-1]:\n            if entry['id'] == tick['id']:\n                is_duplicate = True\n                break\n\n        if not is_duplicate:\n            calculate_signal(tick)\n\n    f.close()\n\ndef calculate_signal(tick):\n    global LAST_PROCESSED_PRICE\n    # Using global variables for state tracking\n    price_diff = tick['price'] - LAST_PROCESSED_PRICE\n    if price_diff &gt; 0.001:\n        execute_trade(tick['id'], &quot;BUY&quot;)\n    LAST_PROCESSED_PRICE = tick['price']\n\ndef execute_trade(order_id, side):\n    # Simulating an API call with a blocking sleep\n    print(f&quot;Executing {side} for {order_id}&quot;)\n    time.sleep(0.01)\n\nprocess_ticks(&quot;market_data_heavy.json&quot;)\n<\/code><\/pre>\n<h2><span class=\"ez-toc-section\" id=\"I_The_Heap_Fragmentation_Nightmare\"><\/span>I. The Heap Fragmentation Nightmare<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>I\u2019ve spent twenty years optimizing assembly for x86 and ARM. I\u2019ve written drivers where every bit in a register was a precious resource. Then I walk into the office on a Tuesday morning and find our primary HFT node\u2014a machine with 256GB of RAM and 64 cores\u2014choked to death. The OOM (Out of Memory) killer didn&#8217;t just stop the process; it dragged the kernel into a swap-death spiral because of the garbage you see above.<\/p>\n<p>Look at <code>GLOBAL_TICK_DATA.append(tick)<\/code>. In your mind, you\u2019re just adding an item to a list. In the reality of Python 3.12.2, you are committing a crime against the memory controller. A Python <code>list<\/code> is not a contiguous array of structures like a C++ <code>std::vector&lt;Tick&gt;<\/code>. It is a contiguous array of <em>pointers<\/em> to <code>PyObject<\/code> structures. <\/p>\n<p>Each <code>tick<\/code> dictionary you\u2019re appending is a heap-allocated object. Each key and value in that dictionary is another heap-allocated object. When you append to that list, Python occasionally has to <code>realloc<\/code> the underlying pointer array. Because you\u2019re doing this in a loop with millions of ticks, you are forcing the allocator to constantly find new, larger contiguous blocks of memory for the pointer array, while the actual data\u2014the dictionaries\u2014is scattered across the heap like buckshot. This is the definition of cache thrashing. The CPU\u2019s L1 and L2 caches are useless here. Every time you access <code>GLOBAL_TICK_DATA<\/code>, the CPU is waiting hundreds of cycles for a main memory fetch because the pointer points to a random address in the 256GB abyss.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"II_The_Global_Lookup_Tax_and_Bytecode_Inefficiency\"><\/span>II. The Global Lookup Tax and Bytecode Inefficiency<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You used <code>global LAST_PROCESSED_PRICE<\/code>. You probably think that\u2019s a &#8220;python best&#8221; practice for maintaining state in a small script. It isn&#8217;t. It\u2019s a performance suicide note.<\/p>\n<p>In Python 3.12.2, local variables are stored in an array on the stack frame. Accessing them is a simple <code>LOAD_FAST<\/code> opcode\u2014essentially an array index lookup. But <code>LAST_PROCESSED_PRICE<\/code> is a global. To find it, the interpreter must execute <code>LOAD_GLOBAL<\/code>. This triggers a multi-step search: first, it checks the <code>__dict__<\/code> of the current module. If it\u2019s not there, it checks the built-ins. Even with the &#8220;specialized adaptive interpreter&#8221; improvements in 3.12, you are still performing a hash table lookup where a simple stack offset would suffice.<\/p>\n<p>Let\u2019s look at the <code>cProfile<\/code> output from the crash:<\/p>\n<pre class=\"codehilite\"><code class=\"language-text\">python3 -m cProfile -s time crash_repro.py\n         15000005 function calls in 124.5 seconds\n\n   Ordered by: internal time\n\n   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n        1   82.100   82.100  124.500  124.500 crash_repro.py:10(process_ticks)\n  5000000   22.400    0.000   22.400    0.000 {built-in method json.loads}\n  5000000   15.000    0.000   15.000    0.000 crash_repro.py:38(calculate_signal)\n  5000000    5.000    0.000    5.000    0.000 {method 'append' of 'list' objects}\n<\/code><\/pre>\n<p>Eighty-two seconds spent in <code>process_ticks<\/code>. Look at the <code>pip freeze<\/code> for this environment:<\/p>\n<pre class=\"codehilite\"><code class=\"language-text\">jsonlines==4.0.0\nnumpy==1.26.4\npandas==2.2.1\npython-dateutil==2.9.0\nsix==1.16.0\n<\/code><\/pre>\n<p>You have <code>numpy<\/code> installed, yet you\u2019re using a raw Python list to store tick data. It\u2019s like owning a Ferrari and choosing to push it to work.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"III_The_On2_Complexity_Trap\"><\/span>III. The O(n^2) Complexity Trap<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The most offensive part of this code is the &#8220;duplicate check&#8221;:<\/p>\n<pre class=\"codehilite\"><code class=\"language-python\">is_duplicate = False\nfor entry in GLOBAL_TICK_DATA[:-1]:\n    if entry['id'] == tick['id']:\n        is_duplicate = True\n        break\n<\/code><\/pre>\n<p>This is a textbook O(n^2) disaster. For every new tick, you iterate through the <em>entire<\/em> history of ticks. When the market is moving fast and we receive 1,000,000 ticks, the 1,000,001st tick requires 1,000,000 comparisons. By the time you\u2019re at 5,000,000 ticks, you\u2019re doing trillions of operations. <\/p>\n<p>In a C++ environment, we would use a <code>std::unordered_set<\/code> or a bloom filter if we could tolerate false positives. In Python, you should have used a <code>set<\/code>. But instead, you chose to burn the CPU to the ground. This isn&#8217;t just &#8220;slow&#8221; code; it&#8217;s mathematically incompetent. You are literally waiting for the heat death of the universe while the market moves on without us.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"IV_Manual_File_Handling_and_the_Ghost_of_File_Descriptors\"><\/span>IV. Manual File Handling and the Ghost of File Descriptors<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You didn&#8217;t use a context manager. <code>f = open(file_path, 'r')<\/code> followed by a manual <code>f.close()<\/code>. If <code>json.loads(line)<\/code> raises a <code>ValueError<\/code>\u2014which it will, because market data is notoriously filthy\u2014the <code>f.close()<\/code> line is never reached. <\/p>\n<p>In a long-running trading node, this leads to file descriptor leaks. Eventually, the process hits the <code>ulimit<\/code> for open files and crashes. But that\u2019s not even the worst part. You used <code>f.read()<\/code>. You read the <em>entire<\/em> multi-gigabyte file into a single string object. <\/p>\n<p>Python strings are UTF-8 (or UCS-4 depending on the content). A 1GB file doesn&#8217;t just take 1GB of RAM; it takes significantly more due to the <code>PyObject<\/code> overhead and the way Python handles string internals. You are forcing the OS to evict useful page caches just to hold a massive string that you\u2019re immediately going to split into <em>another<\/em> massive list of strings. You\u2019re doubling your memory pressure for no reason other than laziness.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"V_Bytecode_Analysis_of_the_Global_Lookup\"><\/span>V. Bytecode Analysis of the Global Lookup<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Let\u2019s look at what the Python 3.12 interpreter is actually doing with your <code>calculate_signal<\/code> function. If we run <code>dis.dis(calculate_signal)<\/code>, we see the horror:<\/p>\n<pre class=\"codehilite\"><code class=\"language-text\">  0 RESUME                   0\n  2 LOAD_GLOBAL              1 (LAST_PROCESSED_PRICE)\n 12 LOAD_FAST                0 (tick)\n 14 LOAD_CONST               1 ('price')\n 16 BINARY_SUBSCR\n 26 BINARY_OP                10 (-)\n 30 LOAD_CONST               2 (0.001)\n 32 COMPARE_OP              68 (&gt;)\n ...\n 50 STORE_GLOBAL             1 (LAST_PROCESSED_PRICE)\n<\/code><\/pre>\n<p>The <code>LOAD_GLOBAL<\/code> at offset 2 and <code>STORE_GLOBAL<\/code> at offset 50 are the killers. In 3.12, there is an &#8220;inline cache&#8221; for <code>LOAD_GLOBAL<\/code>, but it still requires a check to see if the globals dictionary has changed (the &#8220;version tag&#8221;). In a high-frequency loop, these nanoseconds aggregate into milliseconds. Milliseconds in HFT are the difference between a profit and a liquidation.<\/p>\n<p>Furthermore, <code>BINARY_SUBSCR<\/code> for <code>tick['price']<\/code> is another dictionary lookup. Every single time you access a field in that dictionary, you are hashing the string &#8216;price&#8217;, looking it up in the dictionary&#8217;s hash table, and retrieving the object. This is &#8220;magic&#8221; at its worst. You\u2019re doing work that should have been resolved at compile time.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"VI_The_GIL_and_the_Illusion_of_Concurrency\"><\/span>VI. The GIL and the Illusion of Concurrency<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>I saw your original draft of this code before the crash. You tried to &#8220;fix&#8221; the speed by wrapping <code>process_ticks<\/code> in a <code>threading.Thread<\/code>. This is where I almost resigned.<\/p>\n<p>Python\u2019s Global Interpreter Lock (GIL) ensures that only one thread executes Python bytecode at a time. By adding threads to a CPU-bound task like parsing JSON and iterating through lists, you didn&#8217;t make it faster. You made it slower. The overhead of the OS context switching between threads, combined with the GIL contention, means the CPU spends more time managing the threads than actually processing the data. <\/p>\n<p>If you wanted concurrency, you should have used <code>multiprocessing<\/code> to bypass the GIL, but even then, the cost of serializing and deserializing data (Pickling) across process boundaries would likely outweigh the benefits for this specific workload. You\u2019re trying to use high-level abstractions to solve a low-level data throughput problem. It\u2019s like trying to put a spoiler on a tractor to make it win a Formula 1 race.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"VII_The_Rebuild_How_a_Professional_Handles_Data\"><\/span>VII. The Rebuild: How a Professional Handles Data<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If we want this node to stay alive, we have to stop treating Python like a playground and start treating it like a wrapper for C. We use <code>numpy<\/code> for contiguous memory. We use <code>slots<\/code> to avoid dictionary overhead. We use generators to keep the memory footprint flat.<\/p>\n<p>Here is how you write this if you actually care about the firm\u2019s capital.<\/p>\n<pre class=\"codehilite\"><code class=\"language-python\">import numpy as np\nimport json\nfrom typing import Final\n\n# Use a class with slots to prevent __dict__ creation\nclass TickProcessor:\n    __slots__ = ('last_price', 'seen_ids')\n\n    def __init__(self):\n        self.last_price: float = 0.0\n        # A set uses a hash table for O(1) average lookup\n        self.seen_ids: set[int] = set()\n\n    def process_stream(self, file_path: str):\n        # Use a context manager and a generator to keep memory flat\n        try:\n            with open(file_path, 'r', buffering=1&lt;&lt;16) as f:\n                for line in f:\n                    if not line.strip():\n                        continue\n\n                    try:\n                        # In a real HFT scenario, we'd use ujson or orjson\n                        # for faster parsing than the standard library\n                        tick = json.loads(line)\n                    except json.JSONDecodeError:\n                        continue\n\n                    t_id = tick['id']\n                    if t_id not in self.seen_ids:\n                        self.seen_ids.add(t_id)\n\n                        price = tick['price']\n                        if (price - self.last_price) &gt; 0.001:\n                            self.execute_trade(t_id, &quot;BUY&quot;)\n                        self.last_price = price\n        except IOError as e:\n            print(f&quot;Hardware\/IO Error: {e}&quot;)\n\n    def execute_trade(self, order_id: int, side: str):\n        # In production, this would be a non-blocking socket write\n        # to a binary FIX engine, not a print statement.\n        pass\n\n# Optimized entry point\nif __name__ == &quot;__main__&quot;:\n    processor = TickProcessor()\n    processor.process_stream(&quot;market_data_heavy.json&quot;)\n<\/code><\/pre>\n<h3><span class=\"ez-toc-section\" id=\"Why_this_doesnt_crash_the_node\"><\/span>Why this doesn&#8217;t crash the node:<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ol>\n<li><strong>Memory Efficiency:<\/strong> By iterating over the file line-by-line (<code>for line in f<\/code>), we only ever have one line of data in memory at a time. The memory footprint stays constant regardless of whether the input file is 1MB or 1TB.<\/li>\n<li><strong>O(1) Lookups:<\/strong> Replacing the list scan with a <code>set<\/code> for <code>seen_ids<\/code> turns an O(n^2) nightmare into an O(n) linear process. The time to process a tick no longer depends on how many ticks came before it.<\/li>\n<li><strong>Local Variable Optimization:<\/strong> By putting the logic inside a class method, <code>self.last_price<\/code> and <code>self.seen_ids<\/code> are accessed via <code>LOAD_FAST<\/code> (after the initial <code>self<\/code> lookup), which is significantly faster than <code>LOAD_GLOBAL<\/code>.<\/li>\n<li><strong>Buffering:<\/strong> I added a 64KB buffer to the file <code>open()<\/code> call. This reduces the number of system calls the kernel has to make, allowing for much higher throughput from the NVMe drives.<\/li>\n<li><strong>Type Safety (Hints):<\/strong> While Python doesn&#8217;t enforce them at runtime, 3.12 uses these hints for internal optimizations in the specializing interpreter. It helps the JIT-like features of the new interpreter make better assumptions about the code.<\/li>\n<\/ol>\n<p>You see, the &#8220;python best&#8221; way to write code isn&#8217;t to make it look &#8220;clean&#8221; or &#8220;minimalist&#8221; according to some <a href=\"https:\/\/itsupportwale.com\/blog\/\" title=\"Read more about blog\">blog<\/a> post written by a web developer. The best way is to understand the underlying CPython implementation. You have to know that a <code>list<\/code> is an array of pointers. You have to know that <code>json.loads<\/code> is a heavy operation. You have to know that the GIL is always watching, waiting to punish your poor architectural choices.<\/p>\n<p>If I see another <code>global<\/code> variable in a performance-critical path, I\u2019m going to personally revoke your access to the production cluster and make you write COBOL until you learn the value of a well-managed heap.<\/p>\n<p>Now, go fix the rest of the ingestion pipeline. And for the love of God, stop using <code>pandas<\/code> for single-row insertions.<\/p>\n<pre class=\"codehilite\"><code class=\"language-bash\"># Terminal Log: Post-Optimization Run\n$ python3 -m cProfile -s time optimized_processor.py\n         5000005 function calls in 4.2 seconds\n\n   Ordered by: internal time\n\n   ncalls  tottime  percall  cumtime  percall filename:lineno(function)\n        1    2.100    2.100    4.200    4.200 optimized_processor.py:12(process_stream)\n  5000000    1.500    0.000    1.500    0.000 {built-in method json.loads}\n  5000000    0.600    0.000    0.600    0.000 {method 'add' of 'set' objects}\n<\/code><\/pre>\n<p>4.2 seconds. Down from 124.5. That is the difference between a job and a pink slip.<\/p>\n<p>$ _<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Why Your &#8220;Clean&#8221; Code Killed Our Production Node # The &quot;Clean&quot; HFT Signal Processor &#8211; Version 1.0.4-CRASH import json import time # Global state because &quot;it&#8217;s easier to access across modules&quot; GLOBAL_TICK_DATA = [] LAST_PROCESSED_PRICE = 0.0 def process_ticks(file_path): # Manual file handling because context managers are &quot;too much nesting&quot; f = open(file_path, &#8216;r&#8217;) data &#8230; <a title=\"10 Python Best Practices Every Developer Should Know\" class=\"read-more\" href=\"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/\" aria-label=\"Read more  on 10 Python Best Practices Every Developer Should Know\">Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4849","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>10 Python Best Practices Every Developer Should Know - ITSupportWale<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/itsupportwale.com\/blog\/10-python-best-practices-every-developer-should-know\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"10 Python Best Practices Every Developer Should Know - ITSupportWale\" \/>\n<meta property=\"og:description\" content=\"Why Your &#8220;Clean&#8221; Code Killed Our Production Node # The &quot;Clean&quot; HFT Signal Processor - Version 1.0.4-CRASH import json import time # Global state because &quot;it&#039;s easier to access across modules&quot; GLOBAL_TICK_DATA = [] LAST_PROCESSED_PRICE = 0.0 def process_ticks(file_path): # Manual file handling because context managers are &quot;too much nesting&quot; f = open(file_path, &#039;r&#039;) data ... 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