从著名数据数据可视化中我们可以学到什么 #5278

AceLeeWinnie · 2019-02-28T17:11:59Z

译文翻译完成，resolve #5248

shixi-li · 2019-03-01T01:42:48Z

@leviding 校对认领

fanyijihua · 2019-03-01T01:42:49Z

@shixi-li 好的呢 🍺

shixi-li

翻译的质量很不错。我这边提了一些小建议供商榷。

shixi-li · 2019-03-01T02:06:26Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-Data visualisation is a huge part of both Data Science and Machine Learning (ML), no doubt about it. Before creating your ML model, you need to _understand_ the data you’re going to feed it. This is usually done through an Exploratory Data Analysis (EDA); Kaggle has some great [examples](https://www.kaggle.com/kernels) of this!
+毋庸置疑，数据可视化是数据科学和机器学习（ML）的重要组成部分。在创建 ML 模型之前，你需要了解训练数据。这部分工作通常通过探索性数据分析（EDA）工具完成；对此，Kaggle 有很多很棒的[示例](https://www.kaggle.com/kernels)。


了解
=>
了解

原文斜体，按照格式规范应该做加粗处理。

shixi-li · 2019-03-01T02:35:16Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-### TLDR: Or how to make an effective visualisation
+### 太长不看版：如何进行有效的可视化


TLDR 我理解翻译为拒绝繁琐有没有可能更好一点，供商榷。

TL;DR = Too Long; Didn't Read，在这里的意思是总结。

shixi-li · 2019-03-01T02:45:32Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-Towards the beginning of the journey between _Kowno_ and _Wilna_, we see a small, almost vertical part join the larger part of the shape. This was probably some later joining reinforcements. As we move along to the right we later see that some of the army broke off and headed to _Polotrk_.
+在科夫罗和维尔纽斯段旅程开始的时候，一条细小、近乎垂直的分支连接着大部队。这很可能是后来的增援部队。当继续向右移动时，我们接着看到一些军队停止战斗，向 **polotrk** 进发。


科夫罗
=>
科夫罗

维尔纽斯
=>
维尔纽斯

还是斜体转加粗的问题。

polotrk
=>
波拉茨克

polotrk 这个单词应该是同 polotsk，也就是俄罗斯城市波拉茨克。鉴于其他英文地名已全部翻译，这个词建议也应该译出。下同。

Fengziyin1234 · 2019-03-01T05:39:12Z

校对认领

fanyijihua · 2019-03-01T05:39:13Z

@Fengziyin1234 妥妥哒 🍻

Fengziyin1234

这个文章的质量有点太差了。。我有点心疼译者

Fengziyin1234 · 2019-03-01T17:03:52Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-Right off the get go you can see that there is a clear _flow_ to the picture from left to right. This naturally matches with how writing is done in most languages, offering an intuitive guide for reading and understanding what is depicted. It looks like Napoleon’s army started in Kowno and ended up in Moscow.
+可以看到画面从左到右有一个清晰的**流动**。这天然符合绝大多数语言的书写方向习惯，在阅读和理解所描述的内容提供了直观的引导。看起来拿破仑的军队行军起始于**科夫罗**，结束于莫斯科。


科夫罗

城市都准备加粗么？？

莫斯科没有

加不加都行，但是要保持一致

Fengziyin1234 · 2019-03-01T17:05:23Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-_Positioning_ is also used as a visual queue to show the direction that the army travelled. We can see how the army started off on the left in the west and proceeded towards the north east. It wasn’t a fully direct path though.
+**位置**也用作一种视觉通道，显示了军队行进的方向。我们可以看到军队是如何从西边即左边开始向东北方向前进的。但这并不是一条完全直达的道路。


我觉得作者是不是想说 visual cue 啊？

根本没有啥 visual queue
我觉得你给改过来吧

发音相同，有可能写错了。赞同 visual cue。
建议把建议改正后的翻译一同给出。

Fengziyin1234 · 2019-03-01T17:24:26Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-Towards the beginning of the journey between _Kowno_ and _Wilna_, we see a small, almost vertical part join the larger part of the shape. This was probably some later joining reinforcements. As we move along to the right we later see that some of the army broke off and headed to _Polotrk_.
+在**科夫罗**和**维尔纽斯**段旅程开始的时候，一条细小、近乎垂直的分支连接着大部队。这很可能是后来的增援部队。当继续向右移动时，我们接着看到一些军队停止战斗，向**波拉茨克**进发。


broke off 应该是分离开吧

https://idioms.thefreedictionary.com/broken+off

有停止的意思，这里感觉是分离。

Fengziyin1234 · 2019-03-01T17:26:43Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-_Colour_ and _shape_ are also combined as with the black shape of the visualisation. It is notably smaller than the other one. It does follow the same flow and reaches the same destination, but it takes a different path. Perhaps Napoleon had a separate army division which he used to attack the flanks!
+**颜色**和**形状**结合起来，正如黑线一样。相比另一条线条来说，显得太细了。它确实遵循相同的流向并到达终点，但它采取的是不同的路径。也许拿破仑有一个单独的军队用来攻击侧翼。


原文这一段完全错了。
黑线是 Retreat 的方向。
我觉得这个文章好像不太行啊
@AceLeeWinnie 我觉得你可以直接把这整段改写掉，或者直接划掉，加上自己的注释。

原文确实有问题，多处缺乏对拿破仑东征的基本知识，但个人认为对数据可视化的基本科普是好的，故会改正常识错误，继续翻译。谢谢 @Fengziyin1234

原文确实有问题，多处缺乏对拿破仑东征的基本知识，但个人认为对数据可视化的基本科普是好的，故会改正常识错误，继续翻译。谢谢 @Fengziyin1234

我觉得可以～你就辛苦一下多标注一些问题，提供一些科普的知识。这个图是学可视化的入门图。还是很值得学习的！如果有需要，我也可以帮忙准备一些知识。然后文章翻译的没有什么问题，如果你要是修改好了，就@ 校对者和 leviding

Fengziyin1234 · 2019-03-01T17:27:13Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-It looks like some of the army that went to _Polotrk_ ended up joining this black coloured group midway through the trip!
+看起来一些去**波拉茨克**的军队中途加入了这个黑色的组织!


是撤退的时候相遇咯哦～
这段也是错的

Fengziyin1234 · 2019-03-01T17:29:25Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-We also see that the black group _grows_ in size as opposed to the other group, maybe because they joined in later and the weather was better (again, more on this later).
+我们也看到黑色的那一组相比另一组在人数上是逐渐增加的，可能是因为他们加入的时间较晚，天气也较好(同样，在后期更多的是这个原因)。


完全错了哦最初撤退的时间是 Oct 8，回到 kowno 的时间是12 月
然后下面的温度是在撤退的时候的温度变化。所以说莫斯科（本来应该最冷）温度最高，回到法国的时候温度反而最低。
然后返回时人口损失所以越来越细

Fengziyin1234 · 2019-03-01T17:29:39Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-We can probably say that Napoleon had 2 separate divisions to attack and that overall his army diminished in size quite greatly over time.
+可以说，拿破仑有两个独立的师可供调遣，全军规模随着时间显著缩小。


我就不多说了

Fengziyin1234

@AceLeeWinnie 你辛苦啦我个人觉得这个文章本身质量太差了。我觉得如果想发布的，请务必更正掉所有原文的错误。。。

文章翻译的还是很棒的！

Fengziyin1234 · 2019-03-01T17:32:43Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-The numbers are sparsely and intelligently placed to further emphasise just the right details without over cluttering the picture. Numbers are placed beside the shapes for the army to exactly quantify it’s size, if you wanted that extra detail. The details are there if you want them, but not in your face if you don’t!
+数字被巧妙地放置在适当的位置，进一步强化了细节，同时又不会造成混淆。如果你需要额外的细节，数字就放在代表军队的图形旁边，以便精确地量化大小。细节便于查看，但不会干扰你的视线。


sparsely 松散的？
我觉得他的意思是数字不多，但位置恰到好处。

Fengziyin1234 · 2019-03-01T17:34:44Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-The map’s scale on the right is well placed and not in anything’s way. Rivers are drawn roughly as to not be glaring and in your face. The author seems to have understood that the main purpose of drawing those rivers, in the context of depicting the march of an army, was to show their position.
+地图右边的比例尺位置很好，没有造成任何视觉阻碍。河流画得很简略，以免喧宾夺主。作者似乎明白，在描绘军队行军的背景下，画这些河流的主要目的是标记位置。


我觉得这个解释也很勉强。。
你可以看到在撤退的路上，每一次过河，军队都会骤减。所以肯定不是为了标记位置而已

Fengziyin1234 · 2019-03-01T17:34:55Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-We can see that at the 2 right-most rivers, the size of the black coloured army grew! Perhaps they resupplied at the towns there. Either way the drawing of the rivers serves the right purpose of depicting their landmarks.
+可以看到最右侧的两条河流，黑色代表的军队数量壮大了！也许是经过城镇时补充了兵力。无论如何，这些河流的流向都符合描绘其地标的正确位置。


Fengziyin1234 · 2019-03-01T17:36:25Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-We can see from left to right that over time temperatures got warmer. We now know that the attack began in the Winter and ended in the Spring!
+我们可以从左到右看到，温度随时间变化越来越暖和。而我们知道拿破仑东征开始于冬季，结束于春季。


In 1812 the French emperor raised a massive army of troops from all over Europe, the first of which entered Russia on June 24.

Napoleon led his army out of Moscow on October 19,

显然，开始于夏季，10也到了莫斯科，12 月返回

Fengziyin1234 · 2019-03-01T17:37:11Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-### TLDR: Or how to make an effective visualisation
+### 总结：如何进行有效的可视化


TLDR

也可以翻译作长话短说

Fengziyin1234 · 2019-03-01T17:38:07Z

TODO1/the-most-famous-data-visualisation-ever-and-what-we-can-learn-from-it.md


-(5) Don’t over clutter the picture, only show what’s necessary. Positioning is very important.
+(5) 不要过多地填满整个画面，只展示必要信息。布局尤其重要。



用了一个很不恰当的例子，总结没啥问题。。。

AceLeeWinnie · 2019-03-04T17:33:35Z

@Fengziyin1234 @shixi-li 已修改事实错误，请两位译者再看下是否可理解。

Fengziyin1234 · 2019-03-04T18:08:34Z

@Fengziyin1234 @shixi-li 已修改事实错误，请两位译者再看下是否可理解。

我觉得没啥问题了，我只是觉得这都是你的知识，其实你在多改一改，都可以发出去单独说这是自己的文章了呢哈哈哈

然后我觉得 reference 可以看这个 https://zh.wikipedia.org/wiki/%E4%BF%84%E6%B3%95%E6%88%98%E4%BA%89

我只是对于这些个文章的平台有些担忧。我觉得对于我来说，wiki百科更加准确。不过你自己定吧～

感谢你去修改文章哦～我觉得这个比翻译要麻烦很多。

Fengziyin1234 · 2019-03-04T18:14:27Z

最后一个点最低温度竟低达 -30°R (-37.5°C)。这个你做了research嘛我不知道 R是什么标志啊！这个数据靠谱不～

AceLeeWinnie · 2019-03-05T01:29:09Z

@Fengziyin1234 没问题的话 approve 一下~

leviding · 2019-03-05T06:55:25Z

@AceLeeWinnie 可以，辛苦了，本文不仅仅是翻译了，可以算作你的一篇文章。

leviding · 2019-03-05T07:07:33Z

@AceLeeWinnie 已经 merge 啦~ 快快麻溜发布到掘金然后给我发下链接，方便及时添加积分哟。

掘金翻译计划有自己的知乎专栏，你也可以投稿哈，推荐使用一个好用的插件。
专栏地址：https://zhuanlan.zhihu.com/juejinfanyi

从著名数据数据可视化中我们可以学到什么

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从著名数据数据可视化中我们可以学到什么 #5248

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从著名数据数据可视化中我们可以学到什么 #5278

从著名数据数据可视化中我们可以学到什么 #5278

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leviding commented Mar 5, 2019


		Data visualisation is a huge part of both Data Science and Machine Learning (ML), no doubt about it. Before creating your ML model, you need to _understand_ the data you’re going to feed it. This is usually done through an Exploratory Data Analysis (EDA); Kaggle has some great [examples](https://www.kaggle.com/kernels) of this!
		毋庸置疑，数据可视化是数据科学和机器学习（ML）的重要组成部分。在创建 ML 模型之前，你需要了解训练数据。这部分工作通常通过探索性数据分析（EDA）工具完成；对此，Kaggle 有很多很棒的[示例](https://www.kaggle.com/kernels)。


		### TLDR: Or how to make an effective visualisation
		### 太长不看版：如何进行有效的可视化


		Towards the beginning of the journey between _Kowno_ and _Wilna_, we see a small, almost vertical part join the larger part of the shape. This was probably some later joining reinforcements. As we move along to the right we later see that some of the army broke off and headed to _Polotrk_.
		在科夫罗和维尔纽斯段旅程开始的时候，一条细小、近乎垂直的分支连接着大部队。这很可能是后来的增援部队。当继续向右移动时，我们接着看到一些军队停止战斗，向 polotrk 进发。


		Right off the get go you can see that there is a clear _flow_ to the picture from left to right. This naturally matches with how writing is done in most languages, offering an intuitive guide for reading and understanding what is depicted. It looks like Napoleon’s army started in Kowno and ended up in Moscow.
		可以看到画面从左到右有一个清晰的流动。这天然符合绝大多数语言的书写方向习惯，在阅读和理解所描述的内容提供了直观的引导。看起来拿破仑的军队行军起始于科夫罗，结束于莫斯科。


		_Positioning_ is also used as a visual queue to show the direction that the army travelled. We can see how the army started off on the left in the west and proceeded towards the north east. It wasn’t a fully direct path though.
		位置也用作一种视觉通道，显示了军队行进的方向。我们可以看到军队是如何从西边即左边开始向东北方向前进的。但这并不是一条完全直达的道路。


		Towards the beginning of the journey between _Kowno_ and _Wilna_, we see a small, almost vertical part join the larger part of the shape. This was probably some later joining reinforcements. As we move along to the right we later see that some of the army broke off and headed to _Polotrk_.
		在科夫罗和维尔纽斯段旅程开始的时候，一条细小、近乎垂直的分支连接着大部队。这很可能是后来的增援部队。当继续向右移动时，我们接着看到一些军队停止战斗，向波拉茨克进发。


		_Colour_ and _shape_ are also combined as with the black shape of the visualisation. It is notably smaller than the other one. It does follow the same flow and reaches the same destination, but it takes a different path. Perhaps Napoleon had a separate army division which he used to attack the flanks!
		颜色和形状结合起来，正如黑线一样。相比另一条线条来说，显得太细了。它确实遵循相同的流向并到达终点，但它采取的是不同的路径。也许拿破仑有一个单独的军队用来攻击侧翼。


		It looks like some of the army that went to _Polotrk_ ended up joining this black coloured group midway through the trip!
		看起来一些去波拉茨克的军队中途加入了这个黑色的组织!


		We also see that the black group _grows_ in size as opposed to the other group, maybe because they joined in later and the weather was better (again, more on this later).
		我们也看到黑色的那一组相比另一组在人数上是逐渐增加的，可能是因为他们加入的时间较晚，天气也较好(同样，在后期更多的是这个原因)。


		We can probably say that Napoleon had 2 separate divisions to attack and that overall his army diminished in size quite greatly over time.
		可以说，拿破仑有两个独立的师可供调遣，全军规模随着时间显著缩小。


		The numbers are sparsely and intelligently placed to further emphasise just the right details without over cluttering the picture. Numbers are placed beside the shapes for the army to exactly quantify it’s size, if you wanted that extra detail. The details are there if you want them, but not in your face if you don’t!
		数字被巧妙地放置在适当的位置，进一步强化了细节，同时又不会造成混淆。如果你需要额外的细节，数字就放在代表军队的图形旁边，以便精确地量化大小。细节便于查看，但不会干扰你的视线。


		The map’s scale on the right is well placed and not in anything’s way. Rivers are drawn roughly as to not be glaring and in your face. The author seems to have understood that the main purpose of drawing those rivers, in the context of depicting the march of an army, was to show their position.
		地图右边的比例尺位置很好，没有造成任何视觉阻碍。河流画得很简略，以免喧宾夺主。作者似乎明白，在描绘军队行军的背景下，画这些河流的主要目的是标记位置。


		We can see that at the 2 right-most rivers, the size of the black coloured army grew! Perhaps they resupplied at the towns there. Either way the drawing of the rivers serves the right purpose of depicting their landmarks.
		可以看到最右侧的两条河流，黑色代表的军队数量壮大了！也许是经过城镇时补充了兵力。无论如何，这些河流的流向都符合描绘其地标的正确位置。


		We can see from left to right that over time temperatures got warmer. We now know that the attack began in the Winter and ended in the Spring!
		我们可以从左到右看到，温度随时间变化越来越暖和。而我们知道拿破仑东征开始于冬季，结束于春季。


		### TLDR: Or how to make an effective visualisation
		### 总结：如何进行有效的可视化


		(5) Don’t over clutter the picture, only show what’s necessary. Positioning is very important.
		(5) 不要过多地填满整个画面，只展示必要信息。布局尤其重要。

从著名数据数据可视化中我们可以学到什么 #5278

从著名数据数据可视化中我们可以学到什么 #5278

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AceLeeWinnie commented Feb 28, 2019

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Fengziyin1234 commented Mar 1, 2019

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Fengziyin1234 Mar 4, 2019 • edited Loading

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AceLeeWinnie commented Mar 4, 2019

Fengziyin1234 commented Mar 4, 2019

Fengziyin1234 commented Mar 4, 2019

AceLeeWinnie commented Mar 5, 2019

leviding commented Mar 5, 2019

leviding commented Mar 5, 2019

Fengziyin1234 Mar 4, 2019 •

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