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Она позволяет эффективно хранить и выполнять различные операции с многомерными данными.\r\n\r\nКаждый элемент ndarray имеет фиксированный размер и одинаковый тип данных, что обеспечивает быстрые и эффективные операции. ndarray поддерживает множество математических функций и операций, таких как умножение, сложение, вычитание, индексацию и т.д.\r\n\r\nndarray также позволяет удобно выполнить широкий спектр алгоритмов машинного обучения, анализа данных и других вычислений.\r\n\r\nПример создания ndarray:\r\n\r\n```python\r\nimport numpy as np\r\n\r\n# Создание одномерного массива\r\narr1d = np.array([1, 2, 3, 4, 5])\r\n\r\n# Создание двумерного массива\r\narr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\r\n```\r\n\r\nndarray представляет собой мощный инструмент для работы с данными и их анализа в NumPy, что делает его популярным выбором среди специалистов по анализу данных и машинному 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Она позволяет эффективно хранить и выполнять различные операции с многомерными данными.\r\n\r\nКаждый элемент ndarray имеет фиксированный размер и одинаковый тип данных, что обеспечивает быстрые и эффективные операции. ndarray поддерживает множество математических функций и операций, таких как умножение, сложение, вычитание, индексацию и т.д.\r\n\r\nndarray также позволяет удобно выполнить широкий спектр алгоритмов машинного обучения, анализа данных и других вычислений.\r\n\r\nПример создания ndarray:\r\n\r\n```python\r\nimport numpy as np\r\n\r\n# Создание одномерного массива\r\narr1d = np.array([1, 2, 3, 4, 5])\r\n\r\n# Создание двумерного массива\r\narr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\r\n```\r\n\r\nndarray представляет собой мощный инструмент для работы с данными и их анализа в NumPy, что делает его популярным выбором среди специалистов по анализу данных и машинному обучению","datePublished":"2024-07-10T11:10:31.271Z","upvoteCount":1,"author":{"@type":"Person","name":"Maksim Litvinov"},"url":"https://ru.hexlet.io/qna/data-analytics/questions/numpy-ndarray-chto-eto#answer-4036"}}}</script><div style="--container-size:var(--container-size-lg);margin-top:var(--mantine-spacing-xl);height:100%" class="m_7485cace mantine-Container-root" data-size="lg" data-strategy="block"><script type="application/ld+json">{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"position":1,"@type":"ListItem","item":{"@id":"/qna","name":"Вопросы и ответы"}},{"position":2,"@type":"ListItem","item":{"@id":"/qna/data-analytics/questions","name":"Аналитика данных"}},{"position":3,"@type":"ListItem","item":{"@id":"/qna/data-analytics/questions/numpy-ndarray-chto-eto","name":"numpy ndarray что это\r"}}]}</script><div style="margin-bottom:var(--mantine-spacing-xs)" class="m_8b3717df mantine-Breadcrumbs-root"><a style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);white-space:normal;color:inherit" class="mantine-focus-auto m_849cf0da m_f678d540 mantine-Breadcrumbs-breadcrumb m_b6d8b162 mantine-Text-root mantine-Anchor-root" data-size="sm" data-underline="hover" href="/"><div style="color:inherit" class="m_4451eb3a mantine-Center-root"><svg xmlns="http://www.w3.org/2000/svg" width="15" height="15" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="tabler-icon tabler-icon-home-link "><path d="M20.085 11.085l-8.085 -8.085l-9 9h2v7a2 2 0 0 0 2 2h4.5"></path><path d="M9 21v-6a2 2 0 0 1 2 -2h2a2 2 0 0 1 1.807 1.143"></path><path d="M20 21a1 1 0 1 0 2 0a1 1 0 1 0 -2 0"></path><path d="M20 16a1 1 0 1 0 2 0a1 1 0 1 0 -2 0"></path><path d="M15 19a1 1 0 1 0 2 0a1 1 0 1 0 -2 0"></path><path d="M21 16l-5 3l5 2"></path></svg></div></a><div class="m_3b8f2208 mantine-Breadcrumbs-separator">/</div><a style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);white-space:normal;color:inherit" class="mantine-focus-auto m_849cf0da m_f678d540 mantine-Breadcrumbs-breadcrumb m_b6d8b162 mantine-Text-root mantine-Anchor-root" data-size="sm" data-underline="hover" href="/qna">Вопросы и ответы</a><div class="m_3b8f2208 mantine-Breadcrumbs-separator">/</div><a style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);white-space:normal;color:inherit" class="mantine-focus-auto m_849cf0da m_f678d540 mantine-Breadcrumbs-breadcrumb m_b6d8b162 mantine-Text-root mantine-Anchor-root" data-size="sm" data-underline="hover" href="/qna/data-analytics/questions">Аналитика данных</a><div class="m_3b8f2208 mantine-Breadcrumbs-separator">/</div><p style="--text-fz:var(--mantine-font-size-sm);--text-lh:var(--mantine-line-height-sm);white-space:normal;color:var(--mantine-color-dimmed)" class="mantine-focus-auto m_f678d540 mantine-Breadcrumbs-breadcrumb m_b6d8b162 mantine-Text-root" data-size="sm">numpy ndarray что это
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class="m_347db0ec mantine-Badge-root" data-variant="default" data-size="sm"><span class="m_5add502a mantine-Badge-label">data-аналитика</span></div></a></div><div style="--group-gap:var(--mantine-spacing-md);--group-align:center;--group-justify:flex-start;--group-wrap:wrap;margin-bottom:var(--mantine-spacing-xl);font-size:var(--mantine-font-size-sm)" class="m_4081bf90 mantine-Group-root"><div style="--group-gap:calc(0.25rem * var(--mantine-scale));--group-align:center;--group-justify:flex-start;--group-wrap:wrap;margin-inline-start:auto" class="m_4081bf90 mantine-Group-root"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="tabler-icon tabler-icon-calendar "><path d="M4 7a2 2 0 0 1 2 -2h12a2 2 0 0 1 2 2v12a2 2 0 0 1 -2 2h-12a2 2 0 0 1 -2 -2v-12"></path><path d="M16 3v4"></path><path d="M8 3v4"></path><path d="M4 11h16"></path><path d="M11 15h1"></path><path d="M12 15v3"></path></svg><p class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-inherit="true">3 года назад</p></div><div style="--group-gap:calc(0.25rem * var(--mantine-scale));--group-align:center;--group-justify:flex-start;--group-wrap:wrap" class="m_4081bf90 mantine-Group-root"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="tabler-icon tabler-icon-user "><path d="M8 7a4 4 0 1 0 8 0a4 4 0 0 0 -8 0"></path><path d="M6 21v-2a4 4 0 0 1 4 -4h4a4 4 0 0 1 4 4v2"></path></svg><p class="mantine-focus-auto m_b6d8b162 mantine-Text-root" data-inherit="true">Arthur Cheremisin</p></div></div><div role="link" tabindex="0" style="cursor:pointer"><button style="display:block;width:100%" class="mantine-focus-auto m_87cf2631 mantine-UnstyledButton-root" type="button" aria-label="Присоединяйтесь к нашему Telegram-сообществу"><div 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style="--title-fw:var(--mantine-h2-font-weight);--title-lh:var(--mantine-h2-line-height);--title-fz:var(--mantine-h2-font-size);margin-block:var(--mantine-spacing-xl)" class="m_8a5d1357 mantine-Title-root" data-order="2">Ответы</h2><div style="margin-bottom:var(--mantine-spacing-xl);padding:var(--mantine-spacing-lg)" class="m_e615b15f mantine-Card-root m_1b7284a3 mantine-Paper-root" data-with-border="true" id="answer-4036"><div style="--group-gap:calc(1.125rem * var(--mantine-scale));--group-align:stretch;--group-justify:flex-start;--group-wrap:nowrap" class="m_4081bf90 mantine-Group-root"><div style="--stack-gap:var(--mantine-spacing-md);--stack-align:stretch;--stack-justify:flex-start;font-size:var(--mantine-font-size-h1);font-weight:lighter;text-align:center" class="m_6d731127 mantine-Stack-root">1<a style="color:inherit" class="mantine-focus-auto m_849cf0da m_b6d8b162 mantine-Text-root mantine-Anchor-root" data-underline="hover" href="/qna/data-analytics/questions/numpy-ndarray-chto-eto/answers/4036/vote"><div style="--ti-bg:transparent;--ti-color:var(--mantine-color-indigo-light-color);--ti-bd:calc(0.0625rem * var(--mantine-scale)) solid transparent;color:inherit" class="m_7341320d mantine-ThemeIcon-root" data-variant="transparent"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="tabler-icon tabler-icon-thumb-up "><path d="M7 11v8a1 1 0 0 1 -1 1h-2a1 1 0 0 1 -1 -1v-7a1 1 0 0 1 1 -1h3a4 4 0 0 0 4 -4v-1a2 2 0 0 1 4 0v5h3a2 2 0 0 1 2 2l-1 5a2 3 0 0 1 -2 2h-7a3 3 0 0 1 -3 -3"></path></svg></div></a><div style="--ti-bg:transparent;--ti-color:var(--mantine-color-indigo-light-color);--ti-bd:calc(0.0625rem * var(--mantine-scale)) solid transparent;color:inherit" class="m_7341320d mantine-ThemeIcon-root" data-variant="transparent"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="tabler-icon tabler-icon-check "><path d="M5 12l5 5l10 -10"></path></svg></div></div><div style="--stack-gap:var(--mantine-spacing-md);--stack-align:stretch;--stack-justify:flex-start;width:100%;min-width:0rem" class="m_6d731127 mantine-Stack-root"><div style="margin-bottom:auto" class="m_d08caa0 mantine-Typography-root"><p>numpy ndarray (или numpy N-dimensional array) - это основная структура данных в библиотеке NumPy, которая представляет собой многомерный массив элементов одного типа. Она позволяет эффективно хранить и выполнять различные операции с многомерными данными.</p>
<p>Каждый элемент ndarray имеет фиксированный размер и одинаковый тип данных, что обеспечивает быстрые и эффективные операции. ndarray поддерживает множество математических функций и операций, таких как умножение, сложение, вычитание, индексацию и т.д.</p>
<p>ndarray также позволяет удобно выполнить широкий спектр алгоритмов машинного обучения, анализа данных и других вычислений.</p>
<p>Пример создания ndarray:</p>
<div style="margin-bottom:var(--mantine-spacing-lg)" class="m_e597c321 mantine-CodeHighlight-codeHighlight" dir="ltr"><div class="m_be7e9c9c mantine-CodeHighlight-controls"><button style="--ai-bg:transparent;--ai-hover:transparent;--ai-color:inherit;--ai-bd:none" class="mantine-focus-auto mantine-active m_d498bab7 mantine-CodeHighlight-control m_8d3f4000 mantine-ActionIcon-root m_87cf2631 mantine-UnstyledButton-root" data-variant="none" type="button" aria-label="Copy code"><span class="m_8d3afb97 mantine-ActionIcon-icon"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round"><path stroke="none" d="M0 0h24v24H0z" fill="none"></path><path d="M8 8m0 2a2 2 0 0 1 2 -2h8a2 2 0 0 1 2 2v8a2 2 0 0 1 -2 2h-8a2 2 0 0 1 -2 -2z"></path><path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2"></path></svg></span></button></div><div style="--scrollarea-scrollbar-size:calc(0.25rem * var(--mantine-scale));--sa-corner-width:0px;--sa-corner-height:0px" class="m_f744fd40 mantine-CodeHighlight-scrollarea m_d57069b5 mantine-ScrollArea-root" dir="ltr"><div style="overflow-x:hidden;overflow-y:hidden;overscroll-behavior-inline:none" class="m_c0783ff9 mantine-ScrollArea-viewport" data-scrollbars="xy"><div class="m_b1336c6 mantine-ScrollArea-content"><pre class="m_2c47c4fd mantine-CodeHighlight-pre" style="padding:0"><code class="m_5caae6d3 mantine-CodeHighlight-code">import numpy as np
# Создание одномерного массива
arr1d = np.array([1, 2, 3, 4, 5])
# Создание двумерного массива
arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])</code></pre></div></div></div><button class="mantine-focus-auto m_c9378bc2 mantine-CodeHighlight-showCodeButton m_87cf2631 mantine-UnstyledButton-root" data-hidden="true" type="button">Expand code</button></div>
<p>ndarray представляет собой мощный инструмент для работы с данными и их анализа в NumPy, что делает его популярным выбором среди специалистов по анализу данных и машинному обучению</p></div><div class="m_3eebeb36 mantine-Divider-root" data-orientation="horizontal" role="separator"></div><div style="--group-gap:var(--mantine-spacing-md);--group-align:center;--group-justify:flex-start;--group-wrap:wrap;font-size:var(--mantine-font-size-sm)" class="m_4081bf90 mantine-Group-root"><div style="--group-gap:var(--mantine-spacing-xs);--group-align:center;--group-justify:flex-start;--group-wrap:wrap;margin-inline-start:auto" class="m_4081bf90 mantine-Group-root"><svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.2" stroke-linecap="round" stroke-linejoin="round" class="tabler-icon tabler-icon-calendar "><path d="M4 7a2 2 0 0 1 2 -2h12a2 2 0 0 1 2 2v12a2 2 0 0 1 -2 2h-12a2 2 0 0 1 -2 -2v-12"></path><path d="M16 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