matplotlib 中的散点图 [英] scatter plot in matplotlib

问题描述

这是我的第一个 matplotlib 程序，很抱歉我的无知.

This is my first matplotlib program, so sorry for my ignorance.

我有两个字符串数组.比如说，A = ['test1','test2'] 和 B = ['test3','test4'].如果 A 和 B 元素之间存在任何关联，则其corr值将设置为 1 .

I've two arrays of string. say, A = ['test1','test2'] and B = ['test3','test4']. If any correlation exists between A and B element, their corr value will be set to 1.

        test1 | test2
test3 |   1   |   0

test4 |   0   |   1

现在，我想绘制一个散点图，其中我的 X 轴将是 A 的元素，Y 轴将是 B 的元素，如果相关值为 >1，它会被标记在散点图中.该怎么做?

Now, I want to draw a scatter diagram where my X axis will be elements of A, Y axis will be elements of B and if correlation value is 1, it'll be marked in the scattered plot. how to do that?

推荐答案

也许是这样的:

import matplotlib.pyplot
import pylab

x = [1,2,3,4]
y = [3,4,8,6]

matplotlib.pyplot.scatter(x,y)

matplotlib.pyplot.show()

让我看看我现在是否正确理解你:

Let me see if I understand you correctly now:

您有:

       test1 | test2 | test3
test3 |   1   |   0  |  1

test4 |   0   |   1  |  0

test5 |   1   |   1  |  0

现在您要在散点图中表示上述值，以1表示一个点.

Now you want to represent the above values in in a scatter plot, such that value of 1 is represented by a dot.

比方说，您的结果存储在二维列表中:

Let's say you results are stored in a 2-D list:

results = [[1, 0, 1], [0, 1, 0], [1, 1, 0]]

我们希望将它们转换为两个变量，以便能够绘制它们.

We want to transform them into two variables so we are able to plot them.

我相信这段代码会给你你想要的:

And I believe this code will give you what you are looking for:

import matplotlib
import pylab


results = [[1, 0, 1], [0, 1, 0], [1, 1, 0]]

x = []
y = []

for ind_1, sublist in enumerate(results):
    for ind_2, ele in enumerate(sublist):
        if ele == 1:
            x.append(ind_1)
            y.append(ind_2)       


matplotlib.pyplot.scatter(x,y)

matplotlib.pyplot.show()

请注意，我确实需要导入 pylab，并且您可能会使用轴标签.此外，这感觉像是一种解决方法，并且可能(可能是)一种直接的方法来做到这一点.

Notice that I do need to import pylab, and you would have play around with the axis labels. Also this feels like a work around, and there might be (probably is) a direct method to do this.

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