{"id":8148,"date":"2021-01-24T19:21:16","date_gmt":"2021-01-24T13:51:16","guid":{"rendered":"http:\/\/www.pythonpool.com\/?p=8148"},"modified":"2026-07-13T12:31:39","modified_gmt":"2026-07-13T07:01:39","slug":"python-dynamic-array","status":"publish","type":"post","link":"https:\/\/www.pythonpool.com\/python-dynamic-array\/","title":{"rendered":"Dynamic Arrays in Python: Lists, Capacity, and Practical Choices"},"content":{"rendered":"<p><strong>Quick answer:<\/strong> Python lists provide a practical dynamic array for general objects: append and extend grow the sequence as needed. NumPy arrays are better for homogeneous numerical operations but have fixed shape semantics and different resizing costs.<\/p>\n<figure class=\"pythonpool-article-visual\"><img src=\"https:\/\/www.pythonpool.com\/wp-content\/uploads\/2026\/07\/python-dynamic-array-b110.png\" alt=\"Python Pool infographic comparing a Python list that grows with append and a fixed-shape NumPy array that resizes by creating a new array\" width=\"1536\" height=\"1054\" loading=\"lazy\" decoding=\"async\"><figcaption>Python lists provide amortized dynamic growth for general objects, while NumPy arrays are fixed-shape numerical containers whose resizing has different costs.<\/figcaption><\/figure>\n<p>A Python dynamic array is the idea behind the built-in <code>list<\/code>: you can append, index, replace, and remove items without declaring the final size first. Python handles the memory growth for you, so most code should use a normal list instead of writing a custom container.<\/p>\n<p>Still, implementing a small dynamic array is useful because it explains why <code>append()<\/code> is usually fast, why indexes can raise errors, and why copying sometimes happens behind the scenes. This guide uses Python lists for everyday code and <code>ctypes<\/code> for a learning implementation.<\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #990303;color:#990303\" 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: #990303;color:#990303\" 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><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 eztoc-toggle-hide-by-default' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#Quick_Answer\" >Quick Answer<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#How_Dynamic_Arrays_Work\" >How Dynamic Arrays Work<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#Implement_a_Dynamic_Array_with_ctypes\" >Implement a Dynamic Array with ctypes<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#Use_the_Custom_Dynamic_Array\" >Use the Custom Dynamic Array<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#List_arrayarray_and_deque\" >List, array.array, and deque<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#Memory_and_Resizing_Notes\" >Memory and Resizing Notes<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#Common_Mistakes\" >Common Mistakes<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#Use_Lists_For_General_Growth\" >Use Lists For General Growth<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#Understand_Amortized_Growth\" >Understand Amortized Growth<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#Choose_Preallocation_Carefully\" >Choose Preallocation Carefully<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#Compare_NumPy_Arrays\" >Compare NumPy Arrays<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#Avoid_Unnecessary_Copies\" >Avoid Unnecessary Copies<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#Test_Growth_And_Types\" >Test Growth And Types<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.pythonpool.com\/python-dynamic-array\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Quick_Answer\"><\/span>Quick Answer<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Use a Python <code>list<\/code> when you need a dynamic array. The official Python tutorial covers common <a href=\"https:\/\/docs.python.org\/3\/tutorial\/datastructures.html#more-on-lists\">list methods<\/a> such as <code>append()<\/code>, <code>pop()<\/code>, and indexing.<\/p>\n<div class=\"pythonpool-code-scroll\" style=\"max-width:105%;overflow-x:auto;-webkit-overflow-scrolling:touch;\">\n<pre><code class=\"language-python\">values = []\n\nvalues.append(10)\nvalues.append(20)\nvalues.append(30)\n\nprint(values[1])\nprint(values.pop())\nprint(values)\n<\/code><\/pre>\n<\/div>\n<p>The list starts empty, grows as values are appended, and shrinks when the last item is popped. For a deeper look at removal behavior, see PythonPool&#8217;s <a href=\"https:\/\/www.pythonpool.com\/python-list-pop\/\">Python list pop()<\/a> guide.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_Dynamic_Arrays_Work\"><\/span>How Dynamic Arrays Work<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A dynamic array stores items in a contiguous block of slots. It tracks two values: the current length and the larger capacity. Length is the number of real items. Capacity is the number of allocated slots available before another resize is needed.<\/p>\n<p>When the array is full and another item is appended, the container allocates a larger block, copies the existing items into that block, writes the new item, and updates its capacity. This occasional resize is more expensive than a normal append, but because it does not happen on every append, appending to a list is usually treated as amortized constant time. The Python wiki&#8217;s <a href=\"https:\/\/wiki.python.org\/moin\/TimeComplexity\">time complexity<\/a> page documents the common list operation costs.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Implement_a_Dynamic_Array_with_ctypes\"><\/span>Implement a Dynamic Array with ctypes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The following example builds a small educational dynamic array. It is not meant to replace <code>list<\/code>; it shows the resize logic in plain Python. The official <a href=\"https:\/\/docs.python.org\/3\/library\/ctypes.html\">ctypes<\/a> documentation explains the library used here to allocate a low-level array of Python object references.<\/p>\n<div class=\"pythonpool-code-scroll\" style=\"max-width:105%;overflow-x:auto;-webkit-overflow-scrolling:touch;\">\n<pre><code class=\"language-python\">import ctypes\n\n\nclass DynamicArray:\n    def __init__(self):\n        self.length = 0\n        self.capacity = 1\n        self.array = self._make_array(self.capacity)\n\n    def __len__(self):\n        return self.length\n\n    def __getitem__(self, index):\n        if not 0 &lt;= index &lt; self.length:\n            raise IndexError(\"index out of range\")\n        return self.array[index]\n\n    def append(self, value):\n        if self.length == self.capacity:\n            self._resize(2 * self.capacity)\n        self.array[self.length] = value\n        self.length += 1\n\n    def pop(self):\n        if self.length == 0:\n            raise IndexError(\"pop from empty dynamic array\")\n        value = self.array[self.length - 1]\n        self.array[self.length - 1] = None\n        self.length -= 1\n        return value\n\n    def _resize(self, new_capacity):\n        new_array = self._make_array(new_capacity)\n        for index in range(self.length):\n            new_array[index] = self.array[index]\n        self.array = new_array\n        self.capacity = new_capacity\n\n    @staticmethod\n    def _make_array(capacity):\n        return (capacity * ctypes.py_object)()\n<\/code><\/pre>\n<\/div>\n<p>The important part is <code>append()<\/code>. If the current length equals the current capacity, the array doubles its capacity before writing the new value. Doubling is a common teaching strategy because it avoids resizing on every append.<\/p>\n<p><!-- Python Pool visual layout repair 2026-07-13 --><\/p>\n<figure class=\"pythonpool-article-visual pythonpool-supporting-visual\"><img src=\"https:\/\/www.pythonpool.com\/wp-content\/uploads\/2026\/07\/dynamic-array-list-b190.png\" alt=\"Python Pool infographic showing a dynamic array list, indexed values, capacity, and append\" width=\"1536\" height=\"1054\" loading=\"lazy\" decoding=\"async\"><figcaption>Python lists provide dynamic sequence storage with indexed access.<\/figcaption><\/figure>\n<h2><span class=\"ez-toc-section\" id=\"Use_the_Custom_Dynamic_Array\"><\/span>Use the Custom Dynamic Array<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>After the class is defined, the custom container can append values, read by index, report its length, and remove the final item.<\/p>\n<div class=\"pythonpool-code-scroll\" style=\"max-width:105%;overflow-x:auto;-webkit-overflow-scrolling:touch;\">\n<pre><code class=\"language-python\">numbers = DynamicArray()\n\nfor value in [10, 20, 30, 40]:\n    numbers.append(value)\n\nprint(len(numbers))\nprint(numbers[2])\nprint(numbers.pop())\n<\/code><\/pre>\n<\/div>\n<p>This example prints the length, the item at index <code>2<\/code>, and the removed last value. If you request an index outside the current length, the class raises <code>IndexError<\/code>. PythonPool&#8217;s <a href=\"https:\/\/www.pythonpool.com\/python-list-index-out-of-range\/\">list index out of range<\/a> article covers the same boundary problem for normal lists.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"List_arrayarray_and_deque\"><\/span>List, array.array, and deque<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A Python list is the right default for mixed objects, repeated appends, random access, and general scripting. The standard library also has other sequence containers for narrower jobs. The <a href=\"https:\/\/docs.python.org\/3\/library\/array.html\">array<\/a> module stores compact C-style numeric values, while <a href=\"https:\/\/docs.python.org\/3\/library\/collections.html#collections.deque\">collections.deque<\/a> is optimized for appending and popping from both ends.<\/p>\n<div class=\"pythonpool-code-scroll\" style=\"max-width:105%;overflow-x:auto;-webkit-overflow-scrolling:touch;\">\n<pre><code class=\"language-python\">from array import array\nfrom collections import deque\n\nregular_list = [1, 2, 3]\npacked_numbers = array(\"i\", [1, 2, 3])\nqueue = deque([1, 2, 3])\n\nregular_list.append(4)\npacked_numbers.append(4)\nqueue.appendleft(0)\n<\/code><\/pre>\n<\/div>\n<p>Choose the container by access pattern. Use a list for index-heavy work, an array for compact numeric storage, and a deque for queue-like operations. If you are copying or nesting lists, PythonPool&#8217;s <a href=\"https:\/\/www.pythonpool.com\/python-copy-list\/\">copy list<\/a> and <a href=\"https:\/\/www.pythonpool.com\/python-2d-list\/\">2D list<\/a> guides are useful follow-ups.<\/p>\n<figure class=\"pythonpool-article-visual pythonpool-supporting-visual\"><img src=\"https:\/\/www.pythonpool.com\/wp-content\/uploads\/2026\/07\/dynamic-array-growth-b190.png\" alt=\"Python Pool infographic mapping append operations through allocation, growth, and amortized cost\" width=\"1536\" height=\"1054\" loading=\"lazy\" decoding=\"async\"><figcaption>Growth strategies reduce the average cost of repeated appends.<\/figcaption><\/figure>\n<h2><span class=\"ez-toc-section\" id=\"Memory_and_Resizing_Notes\"><\/span>Memory and Resizing Notes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Dynamic arrays trade some unused capacity for fast growth. A list may reserve more slots than it currently needs so the next few appends can happen without immediately allocating again. That is normal. The exact growth strategy is an implementation detail, so code should not depend on a specific capacity formula. The Python C API documentation for <a href=\"https:\/\/docs.python.org\/3\/c-api\/list.html\">list objects<\/a> is the official low-level reference.<\/p>\n<p>Memory problems usually come from keeping too many large objects alive, not from the dynamic array idea itself. If a program fails while allocating a huge list, PythonPool has related guides for <a href=\"https:\/\/www.pythonpool.com\/python-memory-error\/\">Python MemoryError<\/a> and <a href=\"https:\/\/www.pythonpool.com\/solved-oserror-errno-12-cannot-allocate-memory\/\">OSError errno 12<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Common_Mistakes\"><\/span>Common Mistakes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li>Writing a custom dynamic array when a normal Python list is enough.<\/li>\n<li>Confusing length with capacity. Length is the number of real items.<\/li>\n<li>Assuming every append allocates a new array.<\/li>\n<li>Using an old index after popping, sorting, or copying a list.<\/li>\n<li>Depending on CPython-specific resize details in portable code.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Use_Lists_For_General_Growth\"><\/span>Use Lists For General Growth<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A list accepts objects of different types and supports append, extend, indexing, slicing, and iteration. Build the collection with the interface rather than depending on its internal capacity.<\/p>\n<figure class=\"pythonpool-article-visual pythonpool-supporting-visual\"><img src=\"https:\/\/www.pythonpool.com\/wp-content\/uploads\/2026\/07\/dynamic-array-choices-b190.png\" alt=\"Python Pool infographic comparing list, array, deque, NumPy array, and workload\" width=\"1536\" height=\"1054\" loading=\"lazy\" decoding=\"async\"><figcaption>Choose a sequence type based on element types, access patterns, and workload.<\/figcaption><\/figure>\n<h2><span class=\"ez-toc-section\" id=\"Understand_Amortized_Growth\"><\/span>Understand Amortized Growth<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Implementations generally over-allocate so repeated append is efficient on average, but individual growth steps can copy references. Batch extend when the incoming size is already known.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Choose_Preallocation_Carefully\"><\/span>Choose Preallocation Carefully<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A list of placeholders can reserve a logical length, but it is not the same as reserving capacity and can create accidental sentinel values. Fill by index only when the shape is part of the design.<\/p>\n<figure class=\"pythonpool-article-visual pythonpool-supporting-visual\"><img src=\"https:\/\/www.pythonpool.com\/wp-content\/uploads\/2026\/07\/dynamic-array-check-b190.png\" alt=\"Python Pool infographic testing indexing, resizing, memory, mutation, and validation\" width=\"1536\" height=\"1054\" loading=\"lazy\" decoding=\"async\"><figcaption>Check indexing, resize behavior, memory expectations, mutation, and invariants.<\/figcaption><\/figure>\n<h2><span class=\"ez-toc-section\" id=\"Compare_NumPy_Arrays\"><\/span>Compare NumPy Arrays<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>NumPy arrays store homogeneous values and support vectorized numerical operations. Resizing can allocate and copy, so collect data in a list first when the final length is unknown and then convert once.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Avoid_Unnecessary_Copies\"><\/span>Avoid Unnecessary Copies<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Slicing a list creates a new list, while views and copies have different rules in numerical libraries. Track ownership and mutation when large collections move across function boundaries.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Test_Growth_And_Types\"><\/span>Test Growth And Types<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Test empty and large collections, append versus extend, insertion, deletion, mixed objects, conversion to NumPy, memory-sensitive batches, and the expected shape or type contract.<\/p>\n<p>The <a href=\"https:\/\/docs.python.org\/3\/tutorial\/datastructures.html\">official Python sequence documentation<\/a> covers list operations. Related Python Pool references include <a href=\"2\">NumPy arrays<\/a> and <a href=\"https:\/\/www.pythonpool.com\/python-testing-framework\/\">tests<\/a>.<\/p>\n<p>For related collection choices, compare <a href=\"1\">list growth<\/a>, <a href=\"2\">NumPy arrays<\/a>, and <a href=\"https:\/\/www.pythonpool.com\/python-testing-framework\/\">shape tests<\/a> before selecting a dynamic structure.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span>Frequently Asked Questions<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>What is Python&#8217;s dynamic array?<\/h3>\n<p>A Python list behaves like a dynamically growing sequence and can append or extend as items arrive.<\/p>\n<h3>Are Python lists implemented as arrays?<\/h3>\n<p>CPython lists use a resizable array internally, but programs should rely on the list interface rather than implementation details.<\/p>\n<h3>Should I use a list or NumPy array?<\/h3>\n<p>Use a list for general Python objects and changing length; use NumPy for homogeneous numerical operations and known array semantics.<\/p>\n<h3>Why can repeated resizing be slow?<\/h3>\n<p>Growing or copying storage has a cost, so choose append, extend, preallocation, or a batch-building strategy that matches the workload.<\/p>\n<p><script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What is Python's dynamic array?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"A Python list behaves like a dynamically growing sequence and can append or extend as items arrive.\"}},{\"@type\":\"Question\",\"name\":\"Are Python lists implemented as arrays?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"CPython lists use a resizable array internally, but programs should rely on the list interface rather than implementation details.\"}},{\"@type\":\"Question\",\"name\":\"Should I use a list or NumPy array?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Use a list for general Python objects and changing length; use NumPy for homogeneous numerical operations and known array semantics.\"}},{\"@type\":\"Question\",\"name\":\"Why can repeated resizing be slow?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Growing or copying storage has a cost, so choose append, extend, preallocation, or a batch-building strategy that matches the workload.\"}}]}<\/script><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Build dynamic arrays in Python with lists, append and extend, capacity tradeoffs, NumPy arrays, resizing costs, and clear type choices.<\/p>\n","protected":false},"author":12,"featured_media":32366,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[15],"tags":[3403,3401,3404,3406,3400,3402,3405,3399],"class_list":["post-8148","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tutorials","tag-class-dynamic-array-python","tag-create-dynamic-array-python","tag-dynamic-array-python","tag-dynamic-array-python-3-5","tag-initialize-an-dynamic-array-python","tag-python-dynamic-2d-array","tag-python-dynamic-array-generator","tag-what-is-a-dynamic-array-in-python","infinite-scroll-item"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.1 (Yoast SEO v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ 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