EX101x - 8.3 - Graph solution DelftX Data Analysis to the MAX English Chinese (China)

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Now that we have a problem in the form of a graph,

现在，我们面临一个图形问题，

we can also start to use different tools. So we don't need

而我们也可以开始使用不同的工具了, 所以不再需要

generic tools anymore. A spreadsheet or Python are like a Swiss Army knife.

通用的工具。电子表格或 Python就好比一把瑞士军刀。

They can do anything you want, you can use them for budgeting or planning,

它们可以做任何你想要做的事，你可以借助它们做预算和规划，

or for doing a Bacon-like problem. But now we can use a very specialized tool

或解决类似Bacon数的问题。但现在我们可以使用一款非常专业的图形工具，

for graphs, very much

like the tongs you use to eat snails. They're

近似于你吃蜗牛时所使用的钳子。它们

very, very useful if you're eating snails, but they're pretty useless for eating

仅在你吃蜗牛时非常有用，但在其他方面，例如

anything else, like eating pasta or eating rice,

吃意大利面或米饭时却毫无用武之地，

they're a very specialized tools. And this is also what the graph tool is about,

也就是说他们是一款非常专门化的工具。而图形工具也是如此，

and the graph tool that we are going to use, is called Neo4j.

我们要使用的图形工具，被称为Neo4j。

And this is a graph database, the word already says it,

它是一个图形数据库，其名称已经透露了该工具的功能，

it's really used to store and analyse graphs.

它确实是用来存储和分析图表的。

So if you download Neo4J, this is a freely available tool, you can just

这款免费提供的工具非常容易获得，如果你下载了Neo4j，

download it for many different platforms.

它可以被应用于很多不同的平台。

And if you do that and if you start it, this is what you will see.

如果你下载并启动它，这将是你看到的。

Just this little box, what do we do?

就是这个小小的操作界面，我们要拿它做什么呢？

So the first thing that we have to do is select a folder

我们首先要做就是从我们的文件系统里选择一个文件夹，

on our filesystem, where we're going to save the database, just an empty folder,

而我们要用它来保存数据库，

您只需选择一个这样普通的空白文件夹，

you just select a folder, and you hit Start. And this is what you get: the green box

然后按下开始，将会得到一个

with a link. And if you click it, you'll be taken to your web browser, just a

含有链接的绿色操作窗口。如果你点击它，你将会被带到你的网页浏览器（就是

normal browser, like Firefox or Chrome,

一般的浏览器，如火狐或Chrome），

will start (your normal browser) and you'll see this. That's it, this is Neo4j.

它将启动（您的浏览器），之后你便会看到Neo4j。

So it is not immediately clear

一开始你并不能立即明确在此接下来要进行的事情。

what you have to do here. First let's start with getting our Bacon data

首先，让我们将Bacon数据

into the database. So we go back to our spreadsheet

导入数据库。所以我们回到电子表格

and we have to save our spreadsheet as CSV file,

并且必须将电子表格保存为CSV文件，

a Comma Separated Value file. This is a format that Neo4j and many other tools

即“逗号分隔值”文件。这是一种 Neo4j和许多其它工具

can easily read. Maybe you've done this before, you just click 'Save As' and you

可以轻松读取的格式。也许之前你已经做过这样的步骤，即点击“另存为”，然后选择你

select a location where you want to save your

要保存CSV文件的路径，

CSV file, you enter a file name and then, from the drop-down list,

输入一个文件名，然后从“另存为”的下拉列表中

the 'Save As' one, you select CSV. There

选择CSV。这就是逗号分隔值文件啦。

we go, comma separated value. We have a dot too many,

我们发现文件包含太多点符，

we remove the dot and we click 'Save'.

删除这些点符，然后点击“保存”。

And now we save only this worksheet, [we] can

而现在我们只保存了这个工作表，[我们] 可以

save as a CSV. Excel warns us, he says:

将其保存为CSV格式。但 Excel警告我们：

I can only save one worksheet, you lose pivot tables.

只能保存一个工作表，你将失去所有数据透视表。

We know that, we are okay with it, we want to save it as a CSV file.

我们知道这点，但没有关系，我们想要将其保存为CSV文件。

So what we have now if we open the CSV file,

所以如果我们现在打开该CSV 文件,

is this. This is how the CSV file looks like if you open it with notepad

便会得到这个。这是用记事本或其他任何文本编辑器打开

or any other text editor. You have a first-line that

CSV文件看起来的样子。在该例子中你的第一行，

is the first column of the spreadsheet, in this case movie,

也就是该电子表格的第一列是电影

and then you get one line for all of the lines in the spreadsheet.

然后你在电子表格里得到代表所有行数的一行值。

And columns are separated, of course, with a comma,

当然，列是以逗号分隔开的，

because it's a comma separated value format. So we have this data.

因为它是一种“逗号分隔值”格式。所以，我们有了这个数据。

Now we have to get this data into Neo4J.

现在我们需要把这个数据导入Neo4j。

So let's go back to Neo4j, get our CSV file,

因此，让我们回到Neo4j，找到我们放在这里的CSV 文件

there it is, and what we cannot do is just select the data and put it in, we cannot just

我们不能只是选择并且载入数据，

copy-paste. We have to use a special command called LOAD CSV.

复制粘贴是不可行的。我们必须使用一种特殊的指令，叫做加载CSV(LOAD CSV)。

So we type that into Neo4J, LOAD CSV

因此，我们将该指令输入Neo4j中

from the location where I have just

从我刚才保存该CSV文件的路径

saved the CSV file

and then for every line in the CSV file

然后针对该CSV文件中的每一行

we're going to perform a certain operation. So here's the location of the

我们均要进行一项特定的操作。那么这里是

该CSV文件的位置，

and we're going to open it as a row

我们将其作为一行来打开，

and this means that we're going to iterate over the spreadsheet,

这意味着我们要对该电子表格和

over the CSV file, and every line is called

CSV文件进行迭代，并且每一排

row. And this is very much like the for-loop in Python where we have an

被称为行。这很像 Python 中的for-loop，在那里有一个

iterator 'i' that first has the value 1 and then 2 and then 3,

迭代程序“i”，首先产生为1 的值，然后接着是2和3，

this is also what happens to the row. So what are we going to do

这也是发生在行中的现象。所以我们应该

for each of the rows? We're going to create something that is a movie,

针对每一行做些什么呢？我们将要创建的内容是电影，

we will create a movie and the name of the movie

我们将创建一个电影，电影的名称

is 'row.movie'. That's the first column, in this case the only column of our

为“row.movie”。这是第一列，在这种情况下，它也是我们

CSV file. So we can run this

CSV文件的唯一列。因此，我们可以通过

by hitting the Play button and then Neo4J says: okay,

点击播放按钮来运行该文件了，然后 Neo4j 提示：好了，

I have created four nodes. We don't really know yet

我创建了四个节点。尚未得知

what has happened, but something has happened, because we have created four nodes.

发生了什么，但确有事情发生了，因为我们已经创建了四个节点。

So what we can do now, is get the nodes

所以，我们现在能做的，就是把这些节点拎出来。

out. And I'm not going to explain the entire syntax of this language,

我不打算解释这种语言的全部语法，

this language is called Cypher by the way, I'm just going to show you

但顺便提一下，这种语言被称为Cypher，我只是要

a few things that you need to know for this example, but we'll put links online

将该例子里你所需要知道的几点展示给你，但我们会把链接放到网上

to background material, where you can learn all about Cypher.

背景材料中，在那里你可以学到关于Cypher的所有东西。

So how are we going to get the nodes out? There is a special command called MATCH

那么我们如何把这些节点拎出来？有一个特殊的指令称为MATCH

and that command means looking for all the nodes. So in this case we say:

而这指令将能寻找所有的节点。所以在该例子里，我们说：

we match just a node 'n', any type of node,

我们只匹配一个节点'N'，它可以是任何类型的节点，

and return that to us. And now we see the four

然后将其返回给我们。现在我们看到四个

nodes that we have entered into the

我们已经输入

数据库的节点，

we see they are movies and they have the labels that were in the CSV file.

我们看到他们是电影，并且他们有包含在CSV文件里的标签。

We can change the layout a little bit, we can say that the labels

我们可以改变一点点布局，我们可以说该标签

need to be the name of the movie and we can play with the size of the nodes.

必须是电影的名称，并且我们可以操控节点的大小。

So know it is easier to see that we indeed have the 4 movies

所以很容易地看出我们的确有4部电影

in our Neo4j database that were in our CSV file.

存在于我们的CSV文件里的Neo4j 数据库中。

They're right there, but what you can also see is that

它们就在数据库里，但你同时也可以看到

this is a tool that's made for graphs.

这是一个制作图形的工具。

If I just say: give me everything that is in the database

如果我只是说：给我数据库里的所有东西，

the neutral, the normal form is not a list

那么这个中立、正常的格式不是

as you would get in a normal database. No, it

一个你能够从正常数据库里面得到的列表。

is our little dots,

它是我们的那些小点符，

the nodes in our database. And this shows us that we're really working with the graph

即我们数据库里的节点。这说明了我们的确正在此使用图形

database here, something that's

数据库，而它是用来

optimized for thinking about nodes. Now what we

优化节点的工具。现在我们

also need to do in addition to the

还需要做的是

movies is get the actors in.

把演员也放进去。

And we can again use the LOAD CSV command for that

我们也可以再次使用LOAD CSV 指令

and now we're not creating a movie, we are creating an actor. And we do that

而现在我们不再创建电影，我们要创建的是演员。并且我们

really by saying "create an actor", we're not going to add a label

真的是以“创建一个演员”的形式来做这件事，我们不会增加一个命名为

"this is a type actor", no, we really say "create an actor".

“这是一个演员类型”的标签，不是这样，我们真的是说“创建一个演员”。

And this sounds a little bit cryptic maybe, but let me show you how that looks like,

这听起来也许有点含义模糊的意味，但还是让我为你展示它看起来如何，

if I show you all the nodes.

我把所有的节点展示给你看你就一目了然了。

Initially, all of them look great and you see that

起初，这一切看起来都不错，你可以看到

indeed some are an actor and some are a movie.

确实有些是演员，有些是电影。

But what we can do, is again we can play with the layout. We can say:

但是我们能做的是，再次尝试改变布局。我们可以这么说：

we want movies to be green and then you see

我们要把电影改成绿色的，然后你看

all the movie nodes are now green, and we can do the same for actors.

所有电影的节点就变成绿色了，并且我们也能在演员部分做同样的改变。

Let's say we want actors to be pink, and now you can see that we really have

比方说，我们希望演员是粉红色的，而现在你可以看到是

two different types of nodes in our database,

我们的数据库里有两种不同类型的节点，

movies and actors.

它们分别是电影和演员。

So of course, the next step

所以，下一步

that we need to perform, now we have the movies and the actors in our database,

我们需要执行的是：将我们数据库中现有电影和演员

is make the connection. So again, we have saved

做一种连接。所以，我们再一次

the third worksheet of our spreadsheet as a CSV file, where we now have two

以CSV文件的格式对我们第三个电子表格的工作表进行保存，如今在该文件里我们有两个

One that's called 'fullname' and one that is called 'movie'.

一个叫做“全名”，而另一个叫做“电影”。

And again we're using the MATCH command now

并且我们还要再一次使用MATCH 指令

First, we'll load the CSV file, we know this by now, as a row from the CSV file,

首先，我们将要加载CSV文件，作为来自该文件中的一行，现在我们已经掌握了这一步，

but we're going to match each column, the first item

但我们接下来要对每一列进行匹配，第一项

with an actor and second item with a movie and this is how that looks like.

与演员匹配，第二项与电影匹配，这就是它看起来的样子。

So we say MATCH...

因此我们说MATCH ...

Yes, we say MATCH...

是的，我们说MATCH ...

There it is, we say MATCH, we are going to match

在这里，我们说MATCH，也就是指我们要将

an 'a' is an actor and what are we going to match it with?

命名为'A'的演员进行匹配，那么我们要用什么与它匹配呢？

Well, the name, the full name of the actor, has to be equal

嗯，是名字，是演员的全名，它必须等同于

to the name that is in the row of our CSV file.

我们 CSV文件里该行中的名字。

So that's also called full name, so in the first case the actor that we're going to

所以，这也叫全名，那么在第一种情况下，我们要用来

make a connection for first is Kevin Bacon

做首个连接的演员是凯文· 培根，

with the movie Crazy Stupid Love. So this first match line

连接对象是电影《疯狂盲目的爱情》。因此，这第一个匹配行

is going to search for the actor in the first line and the second match that I'm

将要搜索第一行的演员，而现在我要做的第二个匹配

is saying: I'm matching movie and the name of the movie has to be equal to the

是这样的：我正在对电影进行匹配，而电影的名称必须等同于

我CSV文件中的电影列，

in my CSV file, the second column, after the comma.

即逗号后的第二列。

So what this first two things do is find an actor

那么，这前面两样东西要做的就是找到演员

and find a movie. So if I have those too what I need to do

和电影。所以，如果有了这些之后，我需要做的就是

is make a connection between them and I do that with a CREATE.

在它们之间创建一种连接，我可以利用CREATE来完成。

Just as I said 'create an actor' and 'create a movie',

正如我所说的“创建一个演员”和 “创建一个电影”，

I'm now creating a link from 'a', the actor,

我现在从“A”（演员）创建一个链接，

to 'm', the movie, and I'm giving that connection

到“M”（电影），并且我给该连接

a name. I'm naming it 'acted', so

命名为“acted”，所以就形成了

'a acted in m', Kevin Bacon acted in

“A acted in M”，即凯文·培根演过

Crazy Stupid Love. And if I execute this CREATE,

《疯狂盲目的爱情》。如果我执行此 CREATE，

then, again, Neo4J says: okay, I have created a bunch of relations.

Neo4j则会再次提示：OK，我已经创建了一堆关联。

So let's have a look at how it actually is visualised.

因此，让我们来看看它实际上的直观形象是怎样的。

If I just use the same

如果我依旧只是使用

'show me all nodes', then I get a graph

“给我展示所有的节点”，那么我会立即得到一个图形。

instantaneously. So if I just put my data in and I

所以，如果我只是载入我的数据，然后

对Neo4j下指令：

give me the data, this is what I get. It's immediately visualized as a graph

给我数据，这就是我所得到的。它立刻以图形的形式将其形象化，

and you can see, of course we noticed by now, Kevin Bacon has played in Crazy

并且你可以自然看出，凯文·培根与朱丽安·摩尔

Stupid Love with

一同演出了《疯狂盲目的爱情》

Julianne Moore. It's all right there and very much looks like

。这一切都在图里，而且看起来非常像

the picture that we had in the previous video that we were drawing on the

我们在之前的视频中于

电子表格上绘制的图，

we get a graph immediately. So we're almost there, we have the right representation

我们立即就得到一个图行。所以，我们，

现在的呈现方式是正确的，

but now, of course, we still want to perform the calculation

不过我们当然还是想要执行

of the Bacon number. So let me show you how that goes, if you use Neo4j.

Bacon数的计算。因此，让我为你展示如何使用Neo4j进行这一步。

So what we want to do, is calculate

我们想要做的就是

a path through the graph, a number of steps like we did

通过该图形计算出一个路径，即类似于我们处理

Delft, Rotterdam, Groningen, now we want to

代尔夫特、鹿特丹、格罗宁根时的步数，而现在我们要计算的是

have Kevin Bacon

从凯文·培根

to Julianne Moore. So we're matching again, we're looking into database, but now

到朱丽安·摩尔的步数。因此，我们再次进行匹配，我们开始调查数据库，但现在

we're not looking for one node, we're looking for

我们要寻找的并非一个节点，我们寻找的是

a path through the nodes from an actor

一条路径，它通过来自一名

with the name Kevin Bacon to another actor

叫做凯文·培根的演员的节点到另一位

with no specific name. We don't know the name and

没有特定名称的演员的节点。我们不知道该名称以及

how does the path have to look like? Well, it has to be two hops,

该路径应该是什么样子的？但它必须是两个分离度，

an actor, a movie, another actor. And what do

一位演员，一部电影，另一位演员。而

we want to return?

我们想要得到的结果是什么？

If we have found such a path from Kevin Bacon to another actor,

如果我们发现了从凯文· 培根到另一个演员这样的路径，

we would like to have the name of the other actor and if we execute this,

那么我们就想拥有那个演员的名称，如果我们执行匹配，

indeed we get all the actors that have a Bacon number of 2,

我们实际上就得到所有Bacon数为2的演员，

that are one-hop away or in this visualization two hops away

也就是说他们距离凯文·培根一个分离度，或在

from Kevin Bacon. We can do the same thing

该视觉化图形里他们之间距离2个分离度。我们可以

for everyone who has Bacon number 3, because then we need four hops.

针对每一位Bacon数为3的演员来进行同样的操作，因为那样的话，我们需要四个分离度。

It works. It's that easy to do it with Neo4j.

这方法有用。使用 Neo4j来执行这方法很简单。

The only problem that we have here, is that Scarlett Johansson

我们这里遇到的唯一的问题是，斯嘉丽·约翰逊

occurs twice, because there are two ways that we can reach her from Kevin Bacon.

出现了两次，因为我们有两种方式可以从凯文·培根接触到她。

And we can solve this

with a very similar construct that we have used all the way back in the pivot

用一个非常类似的概念来解决这个问题，我们已经

在老早之前的数据透视表的视频里使用过这种方法，

the DISTINCT. And if we add that, we get only two

它就是DISTINCT。如果我们加上对这个的使用，我们就只得到两个

actors with a Bacon number of 3. So you see it's just

Bacon数为3的演员。所以，你看到的它只是

one-line and then we get everyone with Bacon number 2,

单行，随后我们得到Bacon数为2的所有人，

everyone with Bacon

得到Bacon数为

number 3, which is already pretty amazing, but we can do better than that.

3的所有人已经是相当惊人了，但我们可以做得更好。

We can calculate

我们可以只用一行Cypher代码计算出

all Bacon number of everyone with just one line

所有人的Bacon数。

of Cypher code. So what we are going to do, is we're going to say

所以，我们即将做的事情就是：我们要说

give me the shortest path and the shortest path

给我最短的路径，然而最短的路径

is not a function that I have defined, this is just a built-in function in

不是我已经定义的函数，这仅仅是一个存在于

Cypher. So we're going to say:

Cypher的内置功能。因此，我们会说：

give me the shortest path from the node that matches Kevin Bacon, we've seen that in a

给我从与凯文·培根匹配的节点处出发的最短路径，我们已经在

previous queries as well,

先前的问题中看到过，

and what I want is only the star, so not two steps or four steps or

而我想要的只是该明星，所以不是两个步数、四个步数或

100 steps, any number of steps. We don't care, that's what the little star

100个步数，不是任何数目的步数。我们并不在乎，这就是小星这标志所

and what we want to have in return is, again, the name of the actor,

而再一次申明：我们要得到的结果是该演员的名字，

but we also want, because we have named this path

但是我们已经把该路径命名为‘p',

'p', the length of the path. TADA!

所以我们也希望得到这条路径的长度。看吧！

And now indeed we get: Kevin Bacon has 0, because the path

现在我们的确得到：凯文·培根的Bacon数是0，因为

from him to him is zero and people with Bacon number 2

从他到他自己的路径是零，而 Bacon数为2的人

have two hops and Bacon number 3

有两个分离度，以及Bacon数为3的

have four hops. So we're almost there, we only want

有四个分离度。现在我们几乎要完成了，我们只希望

Kevin Bacon to have 1 and all the

凯文·培根的Bacon数是1，并且所有

two steps as one step. So if we divide it by two and add one,

两个步数视为一个步数。所以，如果我们把它除以两再加上一，

we have all Bacon numbers for everyone,

我们就有所有大家的Bacon数了，

doesn't matter how long the path is, in

在Cypher只有一行的情况下，

only one line of Cypher. Isn't that cool? Remember that last week, we needed

路径的长度并不重要。是不是很酷呢？记得上周，我们需要

like 25 array formulas, for all of that actors and we still needed

25组数组公式，对于所有的演员，我们仍然需要

20, 25 lines of Python and here we can do it in just one line of Cypher.

Python中的20、25行，但是我们仅用Cypher中短短的一行就能做到这一点。

So don't get me wrong, this is not a Neo4j

所以，不要误会我的意思，这并不是 Neo4j

advertisement, I'm not saying that Neo4j is better than Python or is

广告，我并不是说Neo4j 比Python或

better than Excel,

For some problems that are really graphy,

对于一些较为图形化的问题而言，

Neo4j is a very good solution, but

Neo4j确实是一个很好的解决方案，但是

like the thongs for a snail, it's very specific.

就像吃蜗牛时使用的钳子一样，它只能被运用在特定的情况下。

You can't use it for planning or budgeting. It's for one specific tool only.

你不能用它来进行规划或预算。这只是一款特定的工具。

So what I hope you take away from this week and previous week's videos,

所以我希望大家从这周和前一周的视频中学到的是，

is that sometimes you shouldn't run

有时你不应该立即选择

to your spreadsheet immediately. Of course in

你所惯用的电子表格。当然在

the first six weeks

我向你演示了

everything there is to know about spreadsheets,and you can do many many things

一切关于学习电子表格的方法，你可以

用它们做很多事情。

But sometimes it's also very good strategy just to

但有时不从电子表格出发来思考

step away from the spreadsheet. Don't go to your computer,

也是非常棒的策略。先别奔向您的计算机，

first think about a problem, go to the white board and sometimes you can use a tool

先思考一个问题，请去白板，有时你可以使用一款非常适合你的工具，

that is very suited for small part of your problem better.

它能让你更好得处理你问题中的某一小部分。

So if you ever need to calculate a Bacon number

所以，如果你需要计算 Bacon数

now you know that Neo4J, or another graph database, might be a better solution

比起电子表格，现在你已经知道Neo4j，或其他图形数据库。它们可能是一个

than a spreadsheet.

更好的解决方案。

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