（翻译文档）快速入门教程

Author: lisniuse

docs/.vuepress/sidebar/user_zh.js

@@ -24,7 +24,7 @@ module.exports = function () {
  ['/user/basics/', '目录'],
  ['/user/basics/types', '数据类型'],
  ['/user/basics/creation', '创建数组'],
- ['/user/basics/io', '输入输出'],
+ ['/user/basics/io', 'NumPy与输入输出'],
  ['/user/basics/indexing', '索引'],
  ['/user/basics/broadcasting', '广播'],
  ['/user/basics/byteswapping', '字节交换'],

docs/README.md

@@ -7,7 +7,7 @@ meta:
  content: 这是NumPy官方的中文文档，NumPy是用Python进行科学计算的基础软件包。
 heroImage: /logo.svg
 actionText: 快速开始 →
-actionLink: /docs/
+actionLink: /user/
 footer: 署名-非商业性使用-相同方式共享 3.0 中国大陆 (CC BY-NC-SA 3.0 CN) | Copyright © 2019-present Zhi Bing
 ---
 

docs/user/basics/README.md

@@ -1,11 +1,11 @@
-# NumPy basics
+# NumPy 基础知识
 
-- [Data types](/user/basics/types.html)
-- [Array creation](/user/basics/creation.html)
-- [I/O with NumPy](/user/basics/io.html)
-- [Indexing](/user/basics/indexing.html)
-- [Broadcasting](/user/basics/broadcasting.html)
-- [Byte-swapping](/user/basics/byteswapping.html)
-- [Structured arrays](/user/basics/rec.html)
-- [Writing custom array containers](/user/basics/dispatch.html)
-- [Subclassing ndarray](/user/basics/subclassing.html)
+- [数据类型](/user/basics/types.html)
+- [创建数组](/user/basics/creation.html)
+- [NumPy与输入输出](/user/basics/io.html)
+- [索引](/user/basics/indexing.html)
+- [广播](/user/basics/broadcasting.html)
+- [字节交换](/user/basics/byteswapping.html)
+- [结构化数组](/user/basics/rec.html)
+- [编写自定义数组容器](/user/basics/dispatch.html)
+- [子类化数组](/user/basics/subclassing.html)

docs/user/basics/creation.md

@@ -1,147 +1,105 @@
-# Array creation
+# 创建数组
 
-::: tip See also
+::: tip 另见
 
-[Array creation routines](https://numpy.org/devdocs/reference/routines.array-creation.html#routines-array-creation)
+[数组创建相关API](/reference/routines.array-creation.html#routines-array-creation)
 
 :::
 
-## Introduction
+## 简介
 
-There are 5 general mechanisms for creating arrays:
+创建数组有5种常规机制：
 
-1. Conversion from other Python structures (e.g., lists, tuples)
-1. Intrinsic numpy array creation objects (e.g., arange, ones, zeros,
-etc.)
-1. Reading arrays from disk, either from standard or custom formats
-1. Creating arrays from raw bytes through the use of strings or buffers
-1. Use of special library functions (e.g., random)
+1. 从其他Python结构（例如，列表，元组）转换
+1. numpy原生数组的创建（例如，arange、ones、zeros等）
+1. 从磁盘读取数组，无论是标准格式还是自定义格式
+1. 通过使用字符串或缓冲区从原始字节创建数组
+1. 使用特殊库函数（例如，random）
 
-This section will not cover means of replicating, joining, or otherwise
-expanding or mutating existing arrays. Nor will it cover creating object
-arrays or structured arrays. Both of those are covered in their own sections.
+本节不包括复制，连接或以其他方式扩展或改变现有数组的方法。它也不会涵盖创建对象数组或结构化数组。这些都包含在他们自己的章节中。
 
-## Converting Python array_like Objects to NumPy Arrays
+## 将Python array_like对象转换为Numpy数组
 
-In general, numerical data arranged in an array-like structure in Python can
-be converted to arrays through the use of the array() function. The most
-obvious examples are lists and tuples. See the documentation for array() for
-details for its use. Some objects may support the array-protocol and allow
-conversion to arrays this way. A simple way to find out if the object can be
-converted to a numpy array using array() is simply to try it interactively and
-see if it works! (The Python Way).
+通常，在Python中排列成array-like结构的数值数据可以通过使用array()函数转换为数组。最明显的例子是列表和元组。有关其使用的详细信息，请参阅array()的文档。一些对象可能支持数组协议并允许以这种方式转换为数组。找出对象是否可以使用array()转换为一个数组numpy 数组的简单方法很简单，只要交互式试一下，看看它是否工作！（Python方式）。
 
-Examples:
+例子：
 
-``` python
+```python
 >>> x = np.array([2,3,1,0])
 >>> x = np.array([2, 3, 1, 0])
 >>> x = np.array([[1,2.0],[0,0],(1+1j,3.)]) # note mix of tuple and lists,
  and types
 >>> x = np.array([[ 1.+0.j, 2.+0.j], [ 0.+0.j, 0.+0.j], [ 1.+1.j, 3.+0.j]])
 ```
 
-## Intrinsic NumPy Array Creation
+## Numpy原生数组的创建
 
-NumPy has built-in functions for creating arrays from scratch:
+Numpy内置了从头开始创建数组的函数：
 
-zeros(shape) will create an array filled with 0 values with the specified
-shape. The default dtype is float64.
+zeros(shape)将创建一个用指定形状用0填充的数组。默认的dtype是float64。
 
-``` python
->>> np.zeros((2, 3))
-array([[ 0., 0., 0.], [ 0., 0., 0.]])
+```python
+>>> np.zeros((2, 3)) array([[ 0., 0., 0.], [ 0., 0., 0.]])
 ```
 
-ones(shape) will create an array filled with 1 values. It is identical to
-zeros in all other respects.
+ones(shape)将创建一个用1个值填充的数组。它在所有其他方面与zeros相同。
 
-arange() will create arrays with regularly incrementing values. Check the
-docstring for complete information on the various ways it can be used. A few
-examples will be given here:
+arange()将创建具有有规律递增值的数组。检查文档字符串以获取有关可以使用的各种方式的完整信息。这里给出几个例子：
 
-``` python
+```python
 >>> np.arange(10)
 array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
->>> np.arange(2, 10, dtype=float)
+>>> np.arange(2, 10, dtype=np.float)
 array([ 2., 3., 4., 5., 6., 7., 8., 9.])
 >>> np.arange(2, 3, 0.1)
 array([ 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])
 ```
 
-Note that there are some subtleties regarding the last usage that the user
-should be aware of that are described in the arange docstring.
+请注意，关于用户应该注意的最后用法在arange文档字符串中有一些细微的描述。
 
-linspace() will create arrays with a specified number of elements, and
-spaced equally between the specified beginning and end values. For
-example:
+linspace() 将创建具有指定数量元素的数组，并在指定的开始值和结束值之间平均间隔。例如：
 
-``` python
+```python
 >>> np.linspace(1., 4., 6)
 array([ 1. , 1.6, 2.2, 2.8, 3.4, 4. ])
 ```
 
-The advantage of this creation function is that one can guarantee the
-number of elements and the starting and end point, which arange()
-generally will not do for arbitrary start, stop, and step values.
+这个创建函数的优点是可以保证元素的数量以及开始和结束点，对于任意的开始，停止和步骤值，arange()通常不会这样做。
 
-indices() will create a set of arrays (stacked as a one-higher dimensioned
-array), one per dimension with each representing variation in that dimension.
-An example illustrates much better than a verbal description:
+indices() 将创建一组数组（堆积为一个更高维的数组），每个维度一个，每个维度表示该维度中的变化。一个例子说明比口头描述要好得多：
 
-``` python
+```python
 >>> np.indices((3,3))
 array([[[0, 0, 0], [1, 1, 1], [2, 2, 2]], [[0, 1, 2], [0, 1, 2], [0, 1, 2]]])
 ```
 
-This is particularly useful for evaluating functions of multiple dimensions on
-a regular grid.
+这对于评估常规网格上多个维度的功能特别有用。
 
-## Reading Arrays From Disk
+## 从磁盘读取数组
 
-This is presumably the most common case of large array creation. The details,
-of course, depend greatly on the format of data on disk and so this section
-can only give general pointers on how to handle various formats.
+这大概是大阵列创建的最常见情况。当然，细节很大程度上取决于磁盘上的数据格式，所以本节只能给出如何处理各种格式的一般指示。
 
-### Standard Binary Formats
+### 标准二进制格式
 
-Various fields have standard formats for array data. The following lists the
-ones with known python libraries to read them and return numpy arrays (there
-may be others for which it is possible to read and convert to numpy arrays so
-check the last section as well)
+各种字段都有阵列数据的标准格式。下面列出了那些已知的Python库来读取它们并返回numpy数组（可能有其他可能读取并转换为numpy数组的其他数据，因此请检查最后一节）
 
-``` python
+```
 HDF5: h5py
 FITS: Astropy
 ```
 
-Examples of formats that cannot be read directly but for which it is not hard to
-convert are those formats supported by libraries like PIL (able to read and
-write many image formats such as jpg, png, etc).
+无法直接读取但不易转换的格式示例是像PIL这样的库支持的格式（能够读取和写入许多图像格式，如jpg，png等）。
 
-### Common ASCII Formats
+### 常见ASCII格式
 
-Comma Separated Value files (CSV) are widely used (and an export and import
-option for programs like Excel). There are a number of ways of reading these
-files in Python. There are CSV functions in Python and functions in pylab
-(part of matplotlib).
+逗号分隔值文件（CSV）被广泛使用（以及Excel等程序的导出和导入选项）。有很多方法可以在Python中阅读这些文件。python中有CSV函数和pylab函数（matplotlib的一部分）。
 
-More generic ascii files can be read using the io package in scipy.
+更多通用的ascii文件可以在scipy中使用io软件包读取。
 
-### Custom Binary Formats
+### 自定义二进制格式
 
-There are a variety of approaches one can use. If the file has a relatively
-simple format then one can write a simple I/O library and use the numpy
-fromfile() function and .tofile() method to read and write numpy arrays
-directly (mind your byteorder though!) If a good C or C++ library exists that
-read the data, one can wrap that library with a variety of techniques though
-that certainly is much more work and requires significantly more advanced
-knowledge to interface with C or C++.
+有各种各样的方法可以使用。如果文件具有相对简单的格式，那么可以编写一个简单的 I/O 库，并使用 numpy fromfile() 函数和 .tofile() 方法直接读取和写入numpy数组（尽管介意你的字节序）！如果存在一个读取数据的良好 C 或 C++ 库，可以使用各种技术来封装该库，但这肯定要做得更多，并且需要更多的高级知识才能与C或C++ 接口。
 
-### Use of Special Libraries
+### 使用特殊库
 
-There are libraries that can be used to generate arrays for special purposes
-and it isn’t possible to enumerate all of them. The most common uses are use
-of the many array generation functions in random that can generate arrays of
-random values, and some utility functions to generate special matrices (e.g.
-diagonal).
+有些库可用于生成特殊用途的数组，且无法列出所有的这些库。最常见的用途是随机使用许多数组生成函数，这些函数可以生成随机值数组，以及一些实用函数来生成特殊矩阵（例如对角线）。