NumPy를 사용하여 Python에서 숫자 조작

이 문서에서는 NumPy를 설치한 다음 NumPy 배열을 만들고 읽고 정렬하는 방법을 설명합니다.

NumPy(일명 Numerical Python)는 Python의 선형 시퀀스 및 행렬에 대한 통계 및 집합 연산을 쉽게 수행할 수 있게 해주는 라이브러리입니다. Python의 목록보다 훨씬 빠른 Python 데이터 유형에 대한 내 노트에서 소개했습니다. NumPy는 데이터 분석 및 과학 컴퓨팅에 자주 사용됩니다.

NumPy를 설치한 다음 NumPy 배열을 만들고 읽고 정렬하는 방법을 다루겠습니다. NumPy 배열은 N차원 배열의 약자인 ndarray라고도 합니다.

NumPy 설치

pip를 사용하여 NumPy 패키지를 설치하는 것은 매우 간단하며 다른 소프트웨어 패키지와 마찬가지로 설치할 수 있습니다. pip 安装 NumPy 包非常简单，可以像安装其他软件包一样进行安装：

<ol><li><code><span>pip install numpy</span></code></li></ol>

安装了 NumPy 包后，只需将其导入你的 Python 文件中：

<ol><li><code><span>import</span><span> numpy </span><span>as</span><span> np</span></code></li></ol>

将 numpy 以 np 之名导入是一个标准的惯例，但你可以不使用 np，而是使用你想要的任何其他别名。

为什么使用 NumPy? 因为它比 Python 列表要快好几个数量级

当涉及到处理大量的数值时，NumPy 比普通的 Python 列表快几个数量级。为了看看它到底有多快，我首先测量在普通 Python 列表上进行 min() 和 max() 操作的时间。

我将首先创建一个具有 999,999,999 项的 Python 列表：

<ol>
<li><code><span>>>></span><span> my_list </span><span>=</span><span> range</span><span>(</span><span>1</span><span>,</span><span> </span><span>1000000000</span><span>)</span></code></li>
<li><code><span>>>></span><span> len</span><span>(</span><span>my_list</span><span>)</span></code></li>
<li><code><span>999999999</span></code></li>
</ol>

现在我将测量在这个列表中找到最小值的时间：

<ol>
<li><code><span>>>></span><span> start </span><span>=</span><span> </span><span>time</span><span>.</span><span>time</span><span>()</span></code></li>
<li><code><span>>>></span><span> min</span><span>(</span><span>my_list</span><span>)</span></code></li>
<li><code><span>1</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>'Time elapsed in milliseconds: '</span><span> </span><span>+</span><span> str</span><span>((</span><span>time</span><span>.</span><span>time</span><span>()</span><span> </span><span>-</span><span> start</span><span>)</span><span> </span><span>*</span><span> </span><span>1000</span><span>))</span></code></li>
<li><code><span>Time</span><span> elapsed </span><span>in</span><span> milliseconds</span><span>:</span><span> </span><span>27007.00879096985</span></code></li>
</ol>

这花了大约 27,007 毫秒，也就是大约 27 秒。这是个很长的时间。现在我试着找出寻找最大值的时间：

<ol>
<li><code><span>>>></span><span> start </span><span>=</span><span> </span><span>time</span><span>.</span><span>time</span><span>()</span></code></li>
<li><code><span>>>></span><span> max</span><span>(</span><span>my_list</span><span>)</span></code></li>
<li><code><span>999999999</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>'Time elapsed in milliseconds: '</span><span> </span><span>+</span><span> str</span><span>((</span><span>time</span><span>.</span><span>time</span><span>()</span><span> </span><span>-</span><span> start</span><span>)</span><span> </span><span>*</span><span> </span><span>1000</span><span>))</span></code></li>
<li><code><span>Time</span><span> elapsed </span><span>in</span><span> milliseconds</span><span>:</span><span> </span><span>28111.071348190308</span></code></li>
</ol>

这花了大约 28,111 毫秒，也就是大约 28 秒。

现在我试试用 NumPy 找到最小值和最大值的时间：

<ol>
<li><code><span>>>></span><span> my_list </span><span>=</span><span> np</span><span>.</span><span>arange</span><span>(</span><span>1</span><span>,</span><span> </span><span>1000000000</span><span>)</span></code></li>
<li><code><span>>>></span><span> len</span><span>(</span><span>my_list</span><span>)</span></code></li>
<li><code><span>999999999</span></code></li>
<li><code><span>>>></span><span> start </span><span>=</span><span> </span><span>time</span><span>.</span><span>time</span><span>()</span></code></li>
<li><code><span>>>></span><span> my_list</span><span>.</span><span>min</span><span>()</span></code></li>
<li><code><span>1</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>'Time elapsed in milliseconds: '</span><span> </span><span>+</span><span> str</span><span>((</span><span>time</span><span>.</span><span>time</span><span>()</span><span> </span><span>-</span><span> start</span><span>)</span><span> </span><span>*</span><span> </span><span>1000</span><span>))</span></code></li>
<li><code><span>Time</span><span> elapsed </span><span>in</span><span> milliseconds</span><span>:</span><span> </span><span>1151.1778831481934</span></code></li>
<li><code><span>>>></span></code></li>
<li><code><span>>>></span><span> start </span><span>=</span><span> </span><span>time</span><span>.</span><span>time</span><span>()</span></code></li>
<li><code><span>>>></span><span> my_list</span><span>.</span><span>max</span><span>()</span></code></li>
<li><code><span>999999999</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>'Time elapsed in milliseconds: '</span><span> </span><span>+</span><span> str</span><span>((</span><span>time</span><span>.</span><span>time</span><span>()</span><span> </span><span>-</span><span> start</span><span>)</span><span> </span><span>*</span><span> </span><span>1000</span><span>))</span></code></li>
<li><code><span>Time</span><span> elapsed </span><span>in</span><span> milliseconds</span><span>:</span><span> </span><span>1114.8970127105713</span></code></li>
</ol>

找到最小值花了大约 1151 毫秒，找到最大值 1114 毫秒。这大约是 1 秒。

正如你所看到的，使用 NumPy 可以将寻找一个大约有 10 亿个值的列表的最小值和最大值的时间 从大约 28 秒减少到 1 秒。这就是 NumPy 的强大之处。

使用 Python 列表创建 ndarray

有几种方法可以在 NumPy 中创建 ndarray。

你可以通过使用元素列表来创建一个 ndarray：

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([</span><span>1</span><span>,</span><span> </span><span>2</span><span>,</span><span> </span><span>3</span><span>,</span><span> </span><span>4</span><span>,</span><span> </span><span>5</span><span>])</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[</span><span>1</span><span> </span><span>2</span><span> </span><span>3</span><span> </span><span>4</span><span> </span><span>5</span><span>]</span></code></li>
</ol>

有了上面的 ndarray 定义，我将检查几件事。首先，上面定义的变量的类型是 numpy.ndarray。这是所有 NumPy ndarray 的类型：

<ol>
<li><code><span>>>></span><span> type</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span><span>class</span><span> </span><span>'numpy.ndarray'</span><span>></span></span></code></li>
</ol>

这里要注意的另一件事是 “形状shape”。ndarray 的形状是 ndarray 的每个维度的长度。你可以看到，my_ndarray 的形状是 (5,)。这意味着 my_ndarray 包含一个有 5 个元素的维度（轴）。

<ol>
<li><code><span>>>></span><span> np</span><span>.</span><span>shape</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>(</span><span>5</span><span>,)</span></code></li>
</ol>

数组中的维数被称为它的 “秩rank”。所以上面的 ndarray 的秩是 1。

我将定义另一个 ndarray my_ndarray2 作为一个多维 ndarray。那么它的形状会是什么呢？请看下面：

<ol>
<li><code><span>>>></span><span> my_ndarray2 </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([(</span><span>1</span><span>,</span><span> </span><span>2</span><span>,</span><span> </span><span>3</span><span>),</span><span> </span><span>(</span><span>4</span><span>,</span><span> </span><span>5</span><span>,</span><span> </span><span>6</span><span>)])</span></code></li>
<li><code><span>>>></span><span> np</span><span>.</span><span>shape</span><span>(</span><span>my_ndarray2</span><span>)</span></code></li>
<li><code><span>(</span><span>2</span><span>,</span><span> </span><span>3</span><span>)</span></code></li>
</ol>

这是一个秩为 2 的 ndarray。另一个要检查的属性是 dtype，也就是数据类型。检查我们的 ndarray 的 dtype 可以得到以下结果：

<ol>
<li><code><span>>>></span><span> my_ndarray</span><span>.</span><span>dtype</span></code></li>
<li><code><span>dtype</span><span>(</span><span>'int64'</span><span>)</span></code></li>
</ol>

int64 意味着我们的 ndarray 是由 64 位整数组成的。NumPy 不能创建混合类型的 ndarray，必须只包含一种类型的元素。如果你定义了一个包含混合元素类型的 ndarray，NumPy 会自动将所有的元素类型转换为可以包含所有元素的最高元素类型。

例如，创建一个 int 和 float 的混合序列将创建一个 float64 的 ndarray：

<ol>
<li><code><span>>>></span><span> my_ndarray2 </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([</span><span>1</span><span>,</span><span> </span><span>2.0</span><span>,</span><span> </span><span>3</span><span>])</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2</span><span>)</span></code></li>
<li><code><span>[</span><span>1.</span><span> </span><span>2.</span><span> </span><span>3.</span><span>]</span></code></li>
<li><code><span>>>></span><span> my_ndarray2</span><span>.</span><span>dtype</span></code></li>
<li><code><span>dtype</span><span>(</span><span>'float64'</span><span>)</span></code></li>
</ol>

另外，将其中一个元素设置为 string 将创建 dtype 等于 <u21> 的字符串 ndarray，意味着我们的 ndarray 包含 unicode 字符串：</u21>

<ol>
<li><code><span>>>></span><span> my_ndarray2 </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([</span><span>1</span><span>,</span><span> </span><span>'2'</span><span>,</span><span> </span><span>3</span><span>])</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2</span><span>)</span></code></li>
<li><code><span>[</span><span>'1'</span><span> </span><span>'2'</span><span> </span><span>'3'</span><span>]</span></code></li>
<li><code><span>>>></span><span> my_ndarray2</span><span>.</span><span>dtype</span></code></li>
<li><code><span>dtype</span><span>(</span><span>'<u21><span>)</span></u21></span></code></li>
</ol>

size 属性将显示我们的 ndarray 中存在的元素总数：

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([</span><span>1</span><span>,</span><span> </span><span>2</span><span>,</span><span> </span><span>3</span><span>,</span><span> </span><span>4</span><span>,</span><span> </span><span>5</span><span>])</span></code></li>
<li><code><span>>>></span><span> my_ndarray</span><span>.</span><span>size</span></code></li>
<li><code><span>5</span></code></li>
</ol>

使用 NumPy 方法创建 ndarray

如果你不想直接使用列表来创建 ndarray，还有几种可以用来创建它的 NumPy 方法。

你可以使用 np.zeros() 来创建一个填满 0 的 ndarray。它需要一个“形状”作为参数，这是一个包含行数和列数的列表。它还可以接受一个可选的 dtype

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>zeros</span><span>([</span><span>2</span><span>,</span><span>3</span><span>],</span><span> dtype</span><span>=</span><span>int</span><span>)</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[[</span><span>0</span><span> </span><span>0</span><span> </span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>0</span><span> </span><span>0</span><span> </span><span>0</span><span>]]</span></code></li>
</ol>

NumPy 패키지를 설치한 후 Python 파일로 가져오기만 하면 됩니다. 🎜

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>ones</span><span>([</span><span>2</span><span>,</span><span>3</span><span>],</span><span> dtype</span><span>=</span><span>int</span><span>)</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[[</span><span>1</span><span> </span><span>1</span><span> </span><span>1</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>1</span><span> </span><span>1</span><span> </span><span>1</span><span>]]</span></code></li>
</ol>

🎜numpy를 np로 가져오는 것이 표준 규칙이지만 np를 사용할 수는 없지만 원하는 대로 사용할 수 있습니다. 다른 별칭 원하다. 🎜🎜NumPy를 사용하는 이유는 Python 목록보다 훨씬 빠르기 때문입니다.🎜🎜많은 수를 처리할 때 NumPy는 일반 Python 목록보다 훨씬 빠릅니다. 얼마나 빠른지 확인하기 위해 먼저 일반 Python 목록에서 min() 및 max() 작업 시간을 측정했습니다. 🎜🎜먼저 999,999,999개의 항목이 포함된 Python 목록을 만듭니다. 🎜

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>full</span><span>([</span><span>2</span><span>,</span><span>3</span><span>],</span><span> </span><span>10</span><span>,</span><span> dtype</span><span>=</span><span>int</span><span>)</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[[</span><span>10</span><span> </span><span>10</span><span> </span><span>10</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>10</span><span> </span><span>10</span><span> </span><span>10</span><span>]]</span></code></li>
</ol>

🎜이제 이 목록에서 최소값을 찾는 시간을 측정하겠습니다. 🎜

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>eye</span><span>(</span><span>3</span><span>,</span><span> dtype</span><span>=</span><span>int</span><span>)</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[[</span><span>1</span><span> </span><span>0</span><span> </span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>0</span><span> </span><span>1</span><span> </span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>0</span><span> </span><span>0</span><span> </span><span>1</span><span>]]</span></code></li>
</ol>

🎜여기에는 약 27,007밀리초, 즉 약 🎜27초🎜가 소요됩니다. 이것은 오랜 시간입니다. 이제 최대값을 찾는 시간을 찾으려고 합니다. 🎜

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>diag</span><span>([</span><span>10</span><span>,</span><span> </span><span>20</span><span>,</span><span> </span><span>30</span><span>,</span><span> </span><span>40</span><span>,</span><span> </span><span>50</span><span>])</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[[</span><span>10</span><span></span><span>0</span><span></span><span>0</span><span></span><span>0</span><span></span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span> </span><span>0</span><span> </span><span>20</span><span></span><span>0</span><span></span><span>0</span><span></span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span> </span><span>0</span><span></span><span>0</span><span> </span><span>30</span><span></span><span>0</span><span></span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span> </span><span>0</span><span></span><span>0</span><span></span><span>0</span><span> </span><span>40</span><span></span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span> </span><span>0</span><span></span><span>0</span><span></span><span>0</span><span></span><span>0</span><span> </span><span>50</span><span>]]</span></code></li>
</ol>

🎜여기에는 약 28,111밀리초, 즉 약 🎜28초🎜가 걸렸습니다. 🎜🎜이제 NumPy를 사용하여 최소 및 최대 시간을 찾아보았습니다. 🎜

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>arange</span><span>(</span><span>1</span><span>,</span><span> </span><span>20</span><span>,</span><span> </span><span>3</span><span>)</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[</span><span> </span><span>1</span><span></span><span>4</span><span></span><span>7</span><span> </span><span>10</span><span> </span><span>13</span><span> </span><span>16</span><span> </span><span>19</span><span>]</span></code></li>
</ol>

🎜최소값을 찾는 데 약 1151ms가 걸렸고 최대값을 찾는 데 약 1114ms가 걸렸습니다. 이는 약 🎜1초🎜입니다. 🎜🎜보시다시피 NumPy를 사용하면 약 10억 개의 값 목록에서 최소값과 최대값을 찾는 시간을 약 28초에서 1초🎜로 줄일 수 있습니다. 이것이 NumPy의 힘입니다. 🎜🎜Python 목록을 사용하여 ndarray 만들기🎜🎜NumPy에서 ndarray를 만드는 방법에는 여러 가지가 있습니다. 🎜🎜요소 목록을 사용하여 ndarray를 만들 수 있습니다. 🎜

<ol><li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>arange</span><span>(</span><span>1</span><span>,</span><span> </span><span>20</span><span>,</span><span> </span><span>3</span><span>)</span></code></li></ol>

🎜위의 ndarray 정의를 통해 몇 가지 사항을 확인하겠습니다. 먼저 위에서 정의한 변수의 타입은 numpy.ndarray 입니다. 모든 NumPy ndarray의 유형은 다음과 같습니다. 🎜

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>arange</span><span>(</span><span>1</span><span>,</span><span> </span><span>20</span><span>,</span><span> </span><span>3</span><span>)</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>0</span><span>])</span></code></li>
<li><code><span>1</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>3</span><span>])</span></code></li>
<li><code><span>10</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[-</span><span>1</span><span>])</span></code></li>
<li><code><span>19</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>5</span><span>])</span></code></li>
<li><code><span>16</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>6</span><span>])</span></code></li>
<li><code><span>19</span></code></li>
</ol>

🎜여기서 주목해야 할 또 다른 사항은 "shapeshape"입니다. ndarray의 모양은 ndarray의 각 차원의 길이입니다. 보시다시피 my_ndarray의 모양은 (5,)입니다. 이는 my_ndarray에 5개의 요소가 있는 차원(축)이 포함되어 있음을 의미합니다. 🎜

<ol>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[:])</span></code></li>
<li><code><span>[</span><span> </span><span>1</span><span></span><span>4</span><span></span><span>7</span><span> </span><span>10</span><span> </span><span>13</span><span> </span><span>16</span><span> </span><span>19</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>2</span><span>:</span><span>4</span><span>])</span></code></li>
<li><code><span>[</span><span> </span><span>7</span><span> </span><span>10</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>5</span><span>:</span><span>6</span><span>])</span></code></li>
<li><code><span>[</span><span>16</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>6</span><span>:</span><span>7</span><span>])</span></code></li>
<li><code><span>[</span><span>19</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[:-</span><span>1</span><span>])</span></code></li>
<li><code><span>[</span><span> </span><span>1</span><span></span><span>4</span><span></span><span>7</span><span> </span><span>10</span><span> </span><span>13</span><span> </span><span>16</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[-</span><span>1</span><span>:])</span></code></li>
<li><code><span>[</span><span>19</span><span>]</span></code></li>
</ol>

🎜배열의 차원 수를 "순위순위"라고 합니다. 따라서 위 ndarray의 순위는 1입니다. 🎜🎜다른 ndarray my_ndarray2를 다차원 ndarray로 정의하겠습니다. 그러면 그 모양은 어떻게 될까요? 아래를 참조하세요: 🎜

<ol>
<li><code><span>>>></span><span> my_ndarray</span><span>[-</span><span>1</span><span>:]</span><span> </span><span>=</span><span> </span><span>100</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[</span><span></span><span>1</span><span> </span><span>4</span><span> </span><span>7</span><span></span><span>10</span><span></span><span>13</span><span></span><span>16</span><span> </span><span>100</span><span>]</span></code></li>
</ol>

🎜이것은 2등급의 ndarray입니다. 확인해야 할 또 다른 속성은 데이터 유형인 dtype입니다. ndarray의 dtype을 확인하면 다음과 같은 결과가 나옵니다. 🎜

<ol>
<li><code><span>>>></span><span> my_ndarray2 </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([(</span><span>1</span><span>,</span><span> </span><span>2</span><span>,</span><span> </span><span>3</span><span>),</span><span> </span><span>(</span><span>4</span><span>,</span><span> </span><span>5</span><span>,</span><span> </span><span>6</span><span>)])</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2</span><span>)</span></code></li>
<li><code><span>[[</span><span>1</span><span> </span><span>2</span><span> </span><span>3</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>4</span><span> </span><span>5</span><span> </span><span>6</span><span>]]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2</span><span>[</span><span>0</span><span>:</span><span>2</span><span>,</span><span>1</span><span>:</span><span>3</span><span>])</span></code></li>
<li><code><span>[[</span><span>2</span><span> </span><span>3</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>5</span><span> </span><span>6</span><span>]]</span></code></li>
</ol>

🎜int64는 ndarray가 64비트 정수로 구성되어 있음을 의미합니다. NumPy는 한 가지 유형의 요소만 포함해야 하는 혼합 유형의 ndarray를 생성할 수 없습니다. 혼합된 요소 유형을 포함하는 ndarray를 정의하면 NumPy는 모든 요소 유형을 모든 요소를 ​​포함할 수 있는 가장 높은 요소 유형으로 자동 변환합니다. 🎜🎜예를 들어 int와 float의 혼합 시퀀스를 생성하면 float64의 ndarray가 생성됩니다. 🎜

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([</span><span>1</span><span>,</span><span> </span><span>2</span><span>,</span><span> </span><span>3</span><span>,</span><span> </span><span>4</span><span>,</span><span> </span><span>5</span><span>,</span><span> </span><span>6</span><span>,</span><span> </span><span>7</span><span>,</span><span> </span><span>8</span><span>,</span><span> </span><span>9</span><span>,</span><span> </span><span>10</span><span>])</span></code></li>
<li><code><span>>>></span><span> my_ndarray2 </span><span>=</span><span> my_ndarray</span><span>[</span><span>my_ndarray </span><span>></span><span> </span><span>5</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2</span><span>)</span></code></li>
<li><code><span>[</span><span> </span><span>6</span><span></span><span>7</span><span></span><span>8</span><span></span><span>9</span><span> </span><span>10</span><span>]</span></code></li>
</ol>

🎜또한 다음 중 하나를 설정합니다. 요소 string의 경우 <u21>와 동일한 <code>dtype의 문자열 ndarray가 생성됩니다. 즉, ndarray에는 유니코드 문자열이 포함됩니다: 🎜

<ol>
<li><code><span>>>></span><span> my_ndarray2 </span><span>=</span><span> my_ndarray</span><span>[</span><span>my_ndarray </span><span>%</span><span> </span><span>2</span><span> </span><span>==</span><span> </span><span>0</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2</span><span>)</span></code></li>
<li><code><span>[</span><span> </span><span>2</span><span></span><span>4</span><span></span><span>6</span><span></span><span>8</span><span> </span><span>10</span><span>]</span></code></li>
</ol>

🎜 size 속성은 ndarray에 있는 총 요소 수를 표시합니다. 🎜

<ol>
<li><code><span>>>></span><span> my_ndarray2 </span><span>=</span><span> my_ndarray</span><span>[</span><span>my_ndarray </span><span>%</span><span> </span><span>2</span><span> </span><span>==</span><span> </span><span>1</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2</span><span>)</span></code></li>
<li><code><span>[</span><span>1</span><span> </span><span>3</span><span> </span><span>5</span><span> </span><span>7</span><span> </span><span>9</span><span>]</span></code></li>
</ol>

🎜NumPy 메서드를 사용하여 ndarray 만들기🎜🎜ndarray를 만들기 위해 목록을 직접 사용하고 싶지 않다면 여러 NumPy가 있습니다. 생성하는 데 사용할 수 있는 방법입니다. 🎜🎜 np.zeros()를 사용하여 0으로 채워진 ndarray를 만들 수 있습니다. 행과 열의 수를 포함하는 목록인 "모양"을 인수로 사용합니다. 또한 ndarray의 데이터 유형인 선택적 dtype 매개변수를 허용할 수도 있습니다. 🎜

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>zeros</span><span>([</span><span>2</span><span>,</span><span>3</span><span>],</span><span> dtype</span><span>=</span><span>int</span><span>)</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[[</span><span>0</span><span> </span><span>0</span><span> </span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>0</span><span> </span><span>0</span><span> </span><span>0</span><span>]]</span></code></li>
</ol>

你可以使用 np. ones() 来创建一个填满 1 的 ndarray：

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>ones</span><span>([</span><span>2</span><span>,</span><span>3</span><span>],</span><span> dtype</span><span>=</span><span>int</span><span>)</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[[</span><span>1</span><span> </span><span>1</span><span> </span><span>1</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>1</span><span> </span><span>1</span><span> </span><span>1</span><span>]]</span></code></li>
</ol>

你可以使用 np.full() 来给 ndarray 填充一个特定的值：

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>full</span><span>([</span><span>2</span><span>,</span><span>3</span><span>],</span><span> </span><span>10</span><span>,</span><span> dtype</span><span>=</span><span>int</span><span>)</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[[</span><span>10</span><span> </span><span>10</span><span> </span><span>10</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>10</span><span> </span><span>10</span><span> </span><span>10</span><span>]]</span></code></li>
</ol>

你可以使用 np.eye() 来创建一个单位矩阵 / ndarray，这是一个沿主对角线都是 1 的正方形矩阵。正方形矩阵是一个行数和列数相同的矩阵：

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>eye</span><span>(</span><span>3</span><span>,</span><span> dtype</span><span>=</span><span>int</span><span>)</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[[</span><span>1</span><span> </span><span>0</span><span> </span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>0</span><span> </span><span>1</span><span> </span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>0</span><span> </span><span>0</span><span> </span><span>1</span><span>]]</span></code></li>
</ol>

你可以使用 np.diag() 来创建一个沿对角线有指定数值的矩阵，而在矩阵的其他部分为 0：

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>diag</span><span>([</span><span>10</span><span>,</span><span> </span><span>20</span><span>,</span><span> </span><span>30</span><span>,</span><span> </span><span>40</span><span>,</span><span> </span><span>50</span><span>])</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[[</span><span>10</span><span></span><span>0</span><span></span><span>0</span><span></span><span>0</span><span></span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span> </span><span>0</span><span> </span><span>20</span><span></span><span>0</span><span></span><span>0</span><span></span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span> </span><span>0</span><span></span><span>0</span><span> </span><span>30</span><span></span><span>0</span><span></span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span> </span><span>0</span><span></span><span>0</span><span></span><span>0</span><span> </span><span>40</span><span></span><span>0</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span> </span><span>0</span><span></span><span>0</span><span></span><span>0</span><span></span><span>0</span><span> </span><span>50</span><span>]]</span></code></li>
</ol>

你可以使用 np.range() 来创建一个具有特定数值范围的 ndarray。它是通过指定一个整数的开始和结束（不包括）范围以及一个步长来创建的：

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>arange</span><span>(</span><span>1</span><span>,</span><span> </span><span>20</span><span>,</span><span> </span><span>3</span><span>)</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[</span><span> </span><span>1</span><span></span><span>4</span><span></span><span>7</span><span> </span><span>10</span><span> </span><span>13</span><span> </span><span>16</span><span> </span><span>19</span><span>]</span></code></li>
</ol>

读取 ndarray

ndarray 的值可以使用索引、分片或布尔索引来读取。

使用索引读取 ndarray 的值

在索引中，你可以使用 ndarray 的元素的整数索引来读取数值，就像你读取 Python 列表一样。就像 Python 列表一样，索引从 0 开始。

例如，在定义如下的 ndarray 中：

<ol><li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>arange</span><span>(</span><span>1</span><span>,</span><span> </span><span>20</span><span>,</span><span> </span><span>3</span><span>)</span></code></li></ol>

第四个值将是 my_ndarray[3]，即 10。最后一个值是 my_ndarray[-1]，即 19：

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>arange</span><span>(</span><span>1</span><span>,</span><span> </span><span>20</span><span>,</span><span> </span><span>3</span><span>)</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>0</span><span>])</span></code></li>
<li><code><span>1</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>3</span><span>])</span></code></li>
<li><code><span>10</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[-</span><span>1</span><span>])</span></code></li>
<li><code><span>19</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>5</span><span>])</span></code></li>
<li><code><span>16</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>6</span><span>])</span></code></li>
<li><code><span>19</span></code></li>
</ol>

使用分片读取 ndarray

你也可以使用分片来读取 ndarray 的块。分片的工作方式是用冒号（:）操作符指定一个开始索引和一个结束索引。然后，Python 将获取该开始和结束索引之间的 ndarray 片断：

<ol>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[:])</span></code></li>
<li><code><span>[</span><span> </span><span>1</span><span></span><span>4</span><span></span><span>7</span><span> </span><span>10</span><span> </span><span>13</span><span> </span><span>16</span><span> </span><span>19</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>2</span><span>:</span><span>4</span><span>])</span></code></li>
<li><code><span>[</span><span> </span><span>7</span><span> </span><span>10</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>5</span><span>:</span><span>6</span><span>])</span></code></li>
<li><code><span>[</span><span>16</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[</span><span>6</span><span>:</span><span>7</span><span>])</span></code></li>
<li><code><span>[</span><span>19</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[:-</span><span>1</span><span>])</span></code></li>
<li><code><span>[</span><span> </span><span>1</span><span></span><span>4</span><span></span><span>7</span><span> </span><span>10</span><span> </span><span>13</span><span> </span><span>16</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>[-</span><span>1</span><span>:])</span></code></li>
<li><code><span>[</span><span>19</span><span>]</span></code></li>
</ol>

分片创建了一个 ndarray 的引用（或视图）。这意味着，修改分片中的值也会改变原始 ndarray 的值。

比如说：

<ol>
<li><code><span>>>></span><span> my_ndarray</span><span>[-</span><span>1</span><span>:]</span><span> </span><span>=</span><span> </span><span>100</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[</span><span></span><span>1</span><span> </span><span>4</span><span> </span><span>7</span><span></span><span>10</span><span></span><span>13</span><span></span><span>16</span><span> </span><span>100</span><span>]</span></code></li>
</ol>

对于秩超过 1 的 ndarray 的分片，可以使用 [行开始索引:行结束索引, 列开始索引:列结束索引] 语法：

<ol>
<li><code><span>>>></span><span> my_ndarray2 </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([(</span><span>1</span><span>,</span><span> </span><span>2</span><span>,</span><span> </span><span>3</span><span>),</span><span> </span><span>(</span><span>4</span><span>,</span><span> </span><span>5</span><span>,</span><span> </span><span>6</span><span>)])</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2</span><span>)</span></code></li>
<li><code><span>[[</span><span>1</span><span> </span><span>2</span><span> </span><span>3</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>4</span><span> </span><span>5</span><span> </span><span>6</span><span>]]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2</span><span>[</span><span>0</span><span>:</span><span>2</span><span>,</span><span>1</span><span>:</span><span>3</span><span>])</span></code></li>
<li><code><span>[[</span><span>2</span><span> </span><span>3</span><span>]</span></code></li>
<li><code><span> </span><span>[</span><span>5</span><span> </span><span>6</span><span>]]</span></code></li>
</ol>

使用布尔索引读取 ndarray 的方法

读取 ndarray 的另一种方法是使用布尔索引。在这种方法中，你在方括号内指定一个过滤条件，然后返回符合该条件的 ndarray 的一个部分。

例如，为了获得一个 ndarray 中所有大于 5 的值，你可以指定布尔索引操作 my_ndarray[my_ndarray > 5]。这个操作将返回一个包含所有大于 5 的值的 ndarray：

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([</span><span>1</span><span>,</span><span> </span><span>2</span><span>,</span><span> </span><span>3</span><span>,</span><span> </span><span>4</span><span>,</span><span> </span><span>5</span><span>,</span><span> </span><span>6</span><span>,</span><span> </span><span>7</span><span>,</span><span> </span><span>8</span><span>,</span><span> </span><span>9</span><span>,</span><span> </span><span>10</span><span>])</span></code></li>
<li><code><span>>>></span><span> my_ndarray2 </span><span>=</span><span> my_ndarray</span><span>[</span><span>my_ndarray </span><span>></span><span> </span><span>5</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2</span><span>)</span></code></li>
<li><code><span>[</span><span> </span><span>6</span><span></span><span>7</span><span></span><span>8</span><span></span><span>9</span><span> </span><span>10</span><span>]</span></code></li>
</ol>

例如，为了获得一个 ndarray 中的所有偶数值，你可以使用如下的布尔索引操作：

<ol>
<li><code><span>>>></span><span> my_ndarray2 </span><span>=</span><span> my_ndarray</span><span>[</span><span>my_ndarray </span><span>%</span><span> </span><span>2</span><span> </span><span>==</span><span> </span><span>0</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2</span><span>)</span></code></li>
<li><code><span>[</span><span> </span><span>2</span><span></span><span>4</span><span></span><span>6</span><span></span><span>8</span><span> </span><span>10</span><span>]</span></code></li>
</ol>

而要得到所有的奇数值，你可以用这个方法：

<ol>
<li><code><span>>>></span><span> my_ndarray2 </span><span>=</span><span> my_ndarray</span><span>[</span><span>my_ndarray </span><span>%</span><span> </span><span>2</span><span> </span><span>==</span><span> </span><span>1</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2</span><span>)</span></code></li>
<li><code><span>[</span><span>1</span><span> </span><span>3</span><span> </span><span>5</span><span> </span><span>7</span><span> </span><span>9</span><span>]</span></code></li>
</ol>

ndarray 的矢量和标量算术

NumPy 的 ndarray 允许进行矢量和标量算术操作。在矢量算术中，在两个 ndarray 之间进行一个元素的算术操作。在标量算术中，算术运算是在一个 ndarray 和一个常数标量值之间进行的。

如下的两个 ndarray：

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([</span><span>1</span><span>,</span><span> </span><span>2</span><span>,</span><span> </span><span>3</span><span>,</span><span> </span><span>4</span><span>,</span><span> </span><span>5</span><span>])</span></code></li>
<li><code><span>>>></span><span> my_ndarray2 </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([</span><span>6</span><span>,</span><span> </span><span>7</span><span>,</span><span> </span><span>8</span><span>,</span><span> </span><span>9</span><span>,</span><span> </span><span>10</span><span>])</span></code></li>
</ol>

如果你将上述两个 ndarray 相加，就会产生一个两个 ndarray 的元素相加的新的 ndarray。例如，产生的 ndarray 的第一个元素将是原始 ndarray 的第一个元素相加的结果，以此类推：

<ol>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2 </span><span>+</span><span> my_ndarray</span><span>)</span></code></li>
<li><code><span>[</span><span> </span><span>7</span><span></span><span>9</span><span> </span><span>11</span><span> </span><span>13</span><span> </span><span>15</span><span>]</span></code></li>
</ol>

这里，7 是 1 和 6 的和，这是我相加的 ndarray 中的前两个元素。同样，15 是 5 和10 之和，是最后一个元素。

请看以下算术运算：

<ol>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2 </span><span>-</span><span> my_ndarray</span><span>)</span></code></li>
<li><code><span>[</span><span>5</span><span> </span><span>5</span><span> </span><span>5</span><span> </span><span>5</span><span> </span><span>5</span><span>]</span></code></li>
<li><code><span>>>></span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2 </span><span>*</span><span> my_ndarray</span><span>)</span></code></li>
<li><code><span>[</span><span> </span><span>6</span><span> </span><span>14</span><span> </span><span>24</span><span> </span><span>36</span><span> </span><span>50</span><span>]</span></code></li>
<li><code><span>>>></span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray2 </span><span>/</span><span> my_ndarray</span><span>)</span></code></li>
<li><code><span>[</span><span>6.</span><span> </span><span>3.5</span><span></span><span>2.66666667</span><span> </span><span>2.25</span><span> </span><span>2.</span><span></span><span>]</span></code></li>
</ol>

在 ndarray 中加一个标量值也有类似的效果，标量值被添加到 ndarray 的所有元素中。这被称为“广播broadcasting”：

<ol>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray </span><span>+</span><span> </span><span>10</span><span>)</span></code></li>
<li><code><span>[</span><span>11</span><span> </span><span>12</span><span> </span><span>13</span><span> </span><span>14</span><span> </span><span>15</span><span>]</span></code></li>
<li><code><span>>>></span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray </span><span>-</span><span> </span><span>10</span><span>)</span></code></li>
<li><code><span>[-</span><span>9</span><span> </span><span>-</span><span>8</span><span> </span><span>-</span><span>7</span><span> </span><span>-</span><span>6</span><span> </span><span>-</span><span>5</span><span>]</span></code></li>
<li><code><span>>>></span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray </span><span>*</span><span> </span><span>10</span><span>)</span></code></li>
<li><code><span>[</span><span>10</span><span> </span><span>20</span><span> </span><span>30</span><span> </span><span>40</span><span> </span><span>50</span><span>]</span></code></li>
<li><code><span>>>></span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray </span><span>/</span><span> </span><span>10</span><span>)</span></code></li>
<li><code><span>[</span><span>0.1</span><span> </span><span>0.2</span><span> </span><span>0.3</span><span> </span><span>0.4</span><span> </span><span>0.5</span><span>]</span></code></li>
</ol>

ndarray 的排序

有两种方法可以对 ndarray 进行原地或非原地排序。原地排序会对原始 ndarray 进行排序和修改，而非原地排序会返回排序后的 ndarray，但不会修改原始 ndarray。我将尝试这两个例子：

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([</span><span>3</span><span>,</span><span> </span><span>1</span><span>,</span><span> </span><span>2</span><span>,</span><span> </span><span>5</span><span>,</span><span> </span><span>4</span><span>])</span></code></li>
<li><code><span>>>></span><span> my_ndarray</span><span>.</span><span>sort</span><span>()</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[</span><span>1</span><span> </span><span>2</span><span> </span><span>3</span><span> </span><span>4</span><span> </span><span>5</span><span>]</span></code></li>
</ol>

正如你所看到的，sort() 方法对 ndarray 进行原地排序，并修改了原数组。

还有一个方法叫 np.sort()，它对数组进行非原地排序：

<ol>
<li><code><span>>>></span><span> my_ndarray </span><span>=</span><span> np</span><span>.</span><span>array</span><span>([</span><span>3</span><span>,</span><span> </span><span>1</span><span>,</span><span> </span><span>2</span><span>,</span><span> </span><span>5</span><span>,</span><span> </span><span>4</span><span>])</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>np</span><span>.</span><span>sort</span><span>(</span><span>my_ndarray</span><span>))</span></code></li>
<li><code><span>[</span><span>1</span><span> </span><span>2</span><span> </span><span>3</span><span> </span><span>4</span><span> </span><span>5</span><span>]</span></code></li>
<li><code><span>>>></span><span> </span><span>print</span><span>(</span><span>my_ndarray</span><span>)</span></code></li>
<li><code><span>[</span><span>3</span><span> </span><span>1</span><span> </span><span>2</span><span> </span><span>5</span><span> </span><span>4</span><span>]</span></code></li>
</ol>

正如你所看到的，np.sort() 方法返回一个已排序的 ndarray，但没有修改它。

总结

我已经介绍了很多关于 NumPy 和 ndarray 的内容。我谈到了创建 ndarray，读取它们的不同方法，基本的向量和标量算术，以及排序。NumPy 还有很多东西可以探索，包括像 union() 和 intersection()这样的集合操作，像 min() 和 max() 这样的统计操作，等等。

我希望我上面演示的例子是有用的。祝你在探索 NumPy 时愉快。 

 

위 내용은 NumPy를 사용하여 Python에서 숫자 조작의 상세 내용입니다. 자세한 내용은 PHP 중국어 웹사이트의 기타 관련 기사를 참조하세요!