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Zahlen in Python mit NumPy manipulieren
- WBOYnach vorne
- 2023-04-15 17:55:031471Durchsuche
In diesem Artikel wird die Installation von NumPy und das anschließende Erstellen, Lesen und Sortieren von NumPy-Arrays erläutert.
NumPy (auch bekannt als Numerical Python) ist eine Bibliothek, die es einfach macht, statistische Operationen und Mengenoperationen an linearen Sequenzen und Matrizen in Python durchzuführen. Ich habe es in meinen Notizen zu Python-Datentypen vorgestellt, die um Größenordnungen schneller sind als die Listen von Python. NumPy wird häufig in der Datenanalyse und im wissenschaftlichen Rechnen verwendet.
Ich behandle die Installation von NumPy und das anschließende Erstellen, Lesen und Sortieren von NumPy-Arrays. NumPy-Arrays werden auch Ndarrays genannt, kurz für N-dimensionale Arrays.
NumPy installieren
Die Installation des NumPy-Pakets mit pip ist sehr einfach und kann wie jedes andere Softwarepaket installiert werden: 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”。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”。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>Nach der Installation des NumPy-Pakets importieren Sie es einfach in Ihre Python-Datei: 🎜
<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>🎜Es ist eine Standardkonvention,
numpy als np zu importieren, aber Sie können np nicht verwenden, sondern einen beliebigen anderen Alias verwenden wollen. 🎜🎜Warum NumPy verwenden? Weil es um Größenordnungen schneller ist als Python-Listen. 🎜🎜Wenn es um die Verarbeitung großer Zahlen geht, ist NumPy um Größenordnungen schneller als normale Python-Listen. Um zu sehen, wie schnell es ist, habe ich zunächst die Zeit für min()- und max()-Operationen auf einer normalen Python-Liste gemessen. 🎜🎜Ich werde zunächst eine Python-Liste mit 999.999.999 Elementen erstellen: 🎜<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>🎜Jetzt messe ich die Zeit, um den Mindestwert in dieser Liste zu finden: 🎜
<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>🎜Das hat etwa 27.007 Millisekunden gedauert, also etwa 🎜27 Sekunden🎜. Das ist eine lange Zeit. Jetzt versuche ich, die Zeit zu finden, um den Maximalwert zu finden: 🎜
<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>🎜Das hat etwa 28.111 Millisekunden gedauert, also etwa 🎜28 Sekunden🎜. 🎜🎜Jetzt habe ich versucht, mit NumPy die Mindest- und Höchstzeiten zu ermitteln: 🎜
<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>🎜Es dauerte ungefähr 1151 ms, um die Mindest- und 1114 ms zu ermitteln, um die Höchstzeit zu ermitteln. Das ist ungefähr 🎜1 Sekunde🎜. 🎜🎜Wie Sie sehen, kann die Verwendung von NumPy die Zeit zum Ermitteln des minimalen und maximalen Werts einer Liste von etwa 1 Milliarde Werten 🎜 von etwa 28 Sekunden auf 1 Sekunde 🎜 verkürzen. Das ist die Stärke von NumPy. 🎜🎜Ndarrays mit Python-Listen erstellen🎜🎜Es gibt mehrere Möglichkeiten, ndarrays in NumPy zu erstellen. 🎜🎜Sie können ein Ndarray erstellen, indem Sie eine Liste von Elementen verwenden: 🎜
<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>🎜Mit der obigen Ndarray-Definition werde ich ein paar Dinge überprüfen. Zunächst einmal ist der Typ der oben definierten Variablen
numpy.ndarray. Hier sind die Typen aller NumPy-Darrays: 🎜<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>🎜Eine weitere zu beachtende Sache ist „shape“. Die Form eines Ndarray ist die Länge jeder Dimension des Ndarray. Wie Sie sehen können, ist die Form von
my_ndarray (5,). Das bedeutet, dass my_ndarray eine Dimension (Achse) mit 5 Elementen enthält. 🎜<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>🎜Die Anzahl der Dimensionen in einem Array wird als „rank“ bezeichnet. Der Rang des obigen Ndarray ist also 1. 🎜🎜Ich werde ein weiteres Ndarray
my_ndarray2 als mehrdimensionales Ndarray definieren. Welche Form wird es also haben? Siehe unten: 🎜<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>🎜Dies ist ein Ndarray von Rang 2. Eine weitere zu überprüfende Eigenschaft ist
dtype, der Datentyp. Die Überprüfung des dtype unseres ndarray liefert das folgende Ergebnis: 🎜<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>🎜
int64 bedeutet, dass unser ndarray aus 64-Bit-Ganzzahlen besteht. NumPy kann keine Ndarrays mit gemischten Typen erstellen, die Elemente nur eines Typs enthalten dürfen. Wenn Sie ein ndarray definieren, das gemischte Elementtypen enthält, konvertiert NumPy automatisch alle Elementtypen in den höchsten Elementtyp, der alle Elemente enthalten kann. 🎜🎜Wenn Sie beispielsweise eine gemischte Sequenz aus int und float erstellen, wird ein Ndarray von float64 erstellt: 🎜<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>🎜Legen Sie außerdem eines davon fest Elemente Für
string wird ein String-ndarray von dtype gleich <u21> erstellt, was bedeutet, dass unser ndarray Unicode-Strings enthält: 🎜<pre class="brush:php;toolbar:false"><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></pre>🎜<code> Das size-Attribut zeigt die Gesamtzahl der in unserem ndarray vorhandenen Elemente an: 🎜<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>🎜Erstellen eines ndarray mit NumPy-Methoden🎜🎜Wenn Sie eine Liste nicht direkt zum Erstellen eines ndarray verwenden möchten, gibt es mehrere NumPy Methoden, mit denen Sie es erstellen können. 🎜🎜Sie können
np.zeros() verwenden, um ein mit Nullen gefülltes Ndarray zu erstellen. Als Argument wird eine „Form“ verwendet, bei der es sich um eine Liste handelt, die die Anzahl der Zeilen und Spalten enthält. Es kann auch einen optionalen dtype-Parameter akzeptieren, der der Datentyp von ndarray ist: 🎜<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”：
<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。
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