What is the translation of " NUMPY ARRAY " in Chinese?

• numpy数组
• 一个numpy数组

Examples of using Numpy array in English and their translations into Chinese

That means NumPy array can be any dimension.
这意味着NumPy数组可以是任何维度的。

Converting a Torch Tensor to a NumPy Array.
转换一个TorchTensor为NumPy数组.

Eval returns a numpy array with the same contents as the tensor.
Eval会返回一个与张量内容相同的Numpy数组。

Let's say that I have the following numpy array:.
比如我有个numpyarray如下：.

Learn to use two-dimensional NumPy arrays and Pandas DataFrames.
学习如何使用二维NumPy数组和PandasDataFrames.

How to get indices of N maximum values in a NumPy array?
如何在numpy数组中获取N个最大值的索引？?

Learn to use two-dimensional NumPy arrays and Pandas DataFrames.
你将学习如何使用二维NumPy数组和PandasDataFrames。

Data: Sequence of observations as a list or 2D NumPy array.
Data:作为一个列表或2DNumPy阵列的观察序列。

As you can see in the above code a NumPy array is actually called an ndarray.
如你所看，在上边的代码中NumPy的数组其实被称为ndarray。

Q39How to get indices of N maximum values in a NumPy array?
Python-如何在NumPy数组中获得N个最大值的索引？?

Then, we created a simple NumPy array of 5 integers and then we printed it.
然后，我们创建了一个包含5个整数的简单NumPy数组，然后我们将其打印出来。

Suppose that the input volume is a numpy array X. Then:.
假设输入存储在一个numpyarrayX里，那么：.

Now suppose, we want to create a NumPy array of length 5 but with all elements as 0, can we do it?
现在假设，我们要创建一个长度为5的NumPy数组，但所有元素都为0，我们可以这样做吗？是的。?

To compute the cdf at a number of points, we can pass a list or a numpy array….
为了计算在一个点上的cdf，我们可以传递一个列表或一个numpy数组。

First, we can map the image into a NumPy array of its pixel values:.
首先，我们可以将图像映射到它的像素值的NumPy数组中：.

The most important data structure that NumPy provides is a powerful object called a NumPy array.
NumPy提供的最重要的数据结构是一个称为NumPy数组的强大对象。

We can create a 2D NumPy array from our list of X patterns, then reshape it into the required 3D format.
我们可以从我们的X模式列表创建一个2DNumPy数组，然后将其重塑为所需的3D格式。

Eval() computes the current value of W and returns it as a NumPy array(it's the same thing as doing sess. run(W)).
Eval()用于计算当前的W值，并以NumPy数组返回（这与sess.run(W)所做的事情一样）。

The Torch Tensor and NumPy array will share their underlying memory locations, and changing one will change the other.
TorchTensor和NumPy数组将会共享它们的实际的内存位置,改变一个另一个也会跟着改变.

This accepts any sequence-like object(including other arrays) and produces a new NumPy array containing the passed data.
它接受任何数组对象（包括其它数组），产生一个包含所传递的数据的新NumPy数组。

The main difference is that the NumPy array also contains some additional properties, like dimension, shape, and type.
主要区别在于NumPy数组还包含一些其他属性，如尺寸，形状和类型。

This accepts any sequence-like object(including other arrays) and produces a new NumPy array containing the passed data.
它接受一切序列型的对象（包括其他数组），然后产生一个新的含有传入数据的NumPy数组。

Since data2 was a list of lists, the NumPy array arr2 has two dimensions with shape inferred from the data.
因为data2是列表的列表，NumPy数组arr2的两个维度的shape是从data2引入的。

NumPy array indexing is a rich topic, as there are many ways you may want to select a subset of your data or individual elements.
NumPy数组的索引是一个内容丰富的主题，因为选取数据子集或单个元素的方式有很多。

To build a 1-d tensor, we will use a NumPy array, which we will construct by passing a built-in Python list.
为了构建一维张量，我们将使用一个NumPy数组，我们将通过传递一个内置的Python列表来构造这个数组。

NumPy array indexing is a rich topic, as there are many ways you may want to select a subset of your data or individual elements.
NumPy的索引是一个内容丰富的主题，因为有许多方法可以使你在你的数据中选取一个子集或单个元素。

This flexibility has allowed the NumPy array dialect and NumPy ndarray class to become the de-facto language of multi-dimensional data interchange used in Python.
这种灵活性使NumPy数组方言和NumPyndarray类成为在Python中使用的多维数据交换的首选对象。

A NumPy array can be easily converted into a TensorFlow tensor with the auxiliary function convert_to_tensor, which helps developers convert Python objects to tensor objects.
NumPy数组可以很容易地用辅助函数convert_to_tensor转换成TensorFlow张量，这可以帮助开发人员将Python对象转换成张量对象。

SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy.
SciPy构建在NumPy数组对象之上，是Stack的一部分，其中包括Matplotlib、Pandas和SymPy等工具。

Whenever you see“array,”“NumPy array,” or“ndarray” in the text, with few exceptions they all refer to the same thing: the ndarray object.
每当你在正文中看见“array”,“NumPyarray”,or“ndarray”，除了很少的列外之外，它们都指的是同一个东西：ndarray对象。