问题:Seaborn地块未显示

我确定我忘记了一些非常简单的内容,但是我无法获得某些与Seaborn合作的计划。

如果我做:

import seaborn as sns

然后,我照常使用matplotlib创建的任何图都将获得Seaborn样式(背景为灰色网格)。

但是,如果我尝试执行以下示例之一,例如:

In [1]: import seaborn as sns

In [2]: sns.set()

In [3]: df = sns.load_dataset('iris')

In [4]: sns.pairplot(df, hue='species', size=2.5)
Out[4]: <seaborn.axisgrid.PairGrid at 0x3e59150>

pairplot函数返回一个PairGrid对象,但该图未显示。

我有些困惑,因为matplotlib似乎可以正常运行,并且Seaborn样式已应用于其他matplotlib图,但是Seaborn函数似乎没有任何作用。有人知道可能是什么问题吗?

I’m sure I’m forgetting something very simple, but I cannot get certain plots to work with Seaborn.

If I do:

import seaborn as sns

Then any plots that I create as usual with matplotlib get the Seaborn styling (with the grey grid in the background).

However, if I try to do one of the examples, such as:

In [1]: import seaborn as sns

In [2]: sns.set()

In [3]: df = sns.load_dataset('iris')

In [4]: sns.pairplot(df, hue='species', size=2.5)
Out[4]: <seaborn.axisgrid.PairGrid at 0x3e59150>

The pairplot function returns a PairGrid object, but the plot doesn’t show up.

I’m a little confused because matplotlib seems to be functioning properly, and the Seaborn styles are applied to other matplotlib plots, but the Seaborn functions don’t seem to do anything. Does anybody have any idea what might be the problem?


回答 0

使用seaborn创建的图需要像普通的matplotlib图一样显示。可以使用

plt.show()

来自matplotlib的功能。

最初,我发布了使用seaborn(sns.plt.show())中已导入的matplotlib对象的解决方案,但是这被认为是不好的做法。因此,只需直接导入matplotlib.pyplot模块并使用

import matplotlib.pyplot as plt
plt.show()

如果使用IPython笔记本,则可以调用内联后端以消除在每次绘制后调用show的必要性。各自的魔力是

%matplotlib inline

Plots created using seaborn need to be displayed like ordinary matplotlib plots. This can be done using the

plt.show()

function from matplotlib.

Originally I posted the solution to use the already imported matplotlib object from seaborn (sns.plt.show()) however this is considered to be a bad practice. Therefore, simply directly import the matplotlib.pyplot module and show your plots with

import matplotlib.pyplot as plt
plt.show()

If the IPython notebook is used the inline backend can be invoked to remove the necessity of calling show after each plot. The respective magic is

%matplotlib inline

回答 1

我经常问这个问题,而且总是花些时间才能找到要搜索的内容:

import seaborn as sns
import matplotlib.pyplot as plt

plt.show()  # <--- This is what you are looking for

请注意:在Python 2中,您也可以使用sns.plt.show(),但在Python 3中则不能。

完整的例子

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""Visualize C_0.99 for all languages except the 10 with most characters."""

import seaborn as sns
import matplotlib.pyplot as plt

l = [41, 44, 46, 46, 47, 47, 48, 48, 49, 51, 52, 53, 53, 53, 53, 55, 55, 55,
     55, 56, 56, 56, 56, 56, 56, 57, 57, 57, 57, 57, 57, 57, 57, 58, 58, 58,
     58, 59, 59, 59, 59, 59, 59, 59, 59, 60, 60, 60, 60, 60, 60, 60, 60, 61,
     61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 62, 62, 62, 62, 62, 62, 62, 62,
     62, 63, 63, 63, 63, 63, 63, 63, 63, 63, 64, 64, 64, 64, 64, 64, 64, 65,
     65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 66, 66, 66, 66, 66,
     67, 67, 67, 67, 67, 67, 67, 67, 68, 68, 68, 68, 68, 69, 69, 69, 70, 70,
     70, 70, 71, 71, 71, 71, 71, 72, 72, 72, 72, 73, 73, 73, 73, 73, 73, 73,
     74, 74, 74, 74, 74, 75, 75, 75, 76, 77, 77, 78, 78, 79, 79, 79, 79, 80,
     80, 80, 80, 81, 81, 81, 81, 83, 84, 84, 85, 86, 86, 86, 86, 87, 87, 87,
     87, 87, 88, 90, 90, 90, 90, 90, 90, 91, 91, 91, 91, 91, 91, 91, 91, 92,
     92, 93, 93, 93, 94, 95, 95, 96, 98, 98, 99, 100, 102, 104, 105, 107, 108,
     109, 110, 110, 113, 113, 115, 116, 118, 119, 121]

sns.distplot(l, kde=True, rug=False)

plt.show()

在此处输入图片说明

I come to this question quite regularly and it always takes me a while to find what I search:

import seaborn as sns
import matplotlib.pyplot as plt

plt.show()  # <--- This is what you are looking for

Please note: In Python 2, you can also use sns.plt.show(), but not in Python 3.

Complete Example

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""Visualize C_0.99 for all languages except the 10 with most characters."""

import seaborn as sns
import matplotlib.pyplot as plt

l = [41, 44, 46, 46, 47, 47, 48, 48, 49, 51, 52, 53, 53, 53, 53, 55, 55, 55,
     55, 56, 56, 56, 56, 56, 56, 57, 57, 57, 57, 57, 57, 57, 57, 58, 58, 58,
     58, 59, 59, 59, 59, 59, 59, 59, 59, 60, 60, 60, 60, 60, 60, 60, 60, 61,
     61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 62, 62, 62, 62, 62, 62, 62, 62,
     62, 63, 63, 63, 63, 63, 63, 63, 63, 63, 64, 64, 64, 64, 64, 64, 64, 65,
     65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 66, 66, 66, 66, 66,
     67, 67, 67, 67, 67, 67, 67, 67, 68, 68, 68, 68, 68, 69, 69, 69, 70, 70,
     70, 70, 71, 71, 71, 71, 71, 72, 72, 72, 72, 73, 73, 73, 73, 73, 73, 73,
     74, 74, 74, 74, 74, 75, 75, 75, 76, 77, 77, 78, 78, 79, 79, 79, 79, 80,
     80, 80, 80, 81, 81, 81, 81, 83, 84, 84, 85, 86, 86, 86, 86, 87, 87, 87,
     87, 87, 88, 90, 90, 90, 90, 90, 90, 91, 91, 91, 91, 91, 91, 91, 91, 92,
     92, 93, 93, 93, 94, 95, 95, 96, 98, 98, 99, 100, 102, 104, 105, 107, 108,
     109, 110, 110, 113, 113, 115, 116, 118, 119, 121]

sns.distplot(l, kde=True, rug=False)

plt.show()

Gives

enter image description here


回答 2

为了避免混淆(评论中似乎有一些)。假设您使用Jupyter:

%matplotlib inline>显示的曲线INSIDE笔记本

sns.plt.show()> 在笔记本的外侧显示图

%matplotlib inline从某种意义上讲,即使绘图被调用,绘图也会显示笔记本中,它将覆盖sns.plt.show()sns.plt.show()

是的,很容易将行包含到您的配置中:

在IPython Notebook中内联自动运行%matplotlib

但是在实际代码中将其与导入保持在一起似乎是一个更好的约定。

To avoid confusion (as there seems to be some in the comments). Assuming you are on Jupyter:

%matplotlib inline > displays the plots INSIDE the notebook

sns.plt.show() > displays the plots OUTSIDE of the notebook

%matplotlib inline will OVERRIDE sns.plt.show() in the sense that plots will be shown IN the notebook even when sns.plt.show() is called.

And yes, it is easy to include the line in to your config:

Automatically run %matplotlib inline in IPython Notebook

But it seems a better convention to keep it together with imports in the actual code.


回答 3

这对我有用

import matplotlib.pyplot as plt
import seaborn as sns
.
.
.
plt.show(sns)

This worked for me

import matplotlib.pyplot as plt
import seaborn as sns
.
.
.
plt.show(sns)

回答 4

我的建议是给

plt.figure()并给出一些sns图。例如

sns.distplot(data)

尽管看起来它不会显示任何图,但是当您最大化图形时,您将能够看到该图。

My advice is just to give a

plt.figure() and give some sns plot. For example

sns.distplot(data).

Though it will look it doesnt show any plot, When you maximise the figure, you will be able to see the plot.


回答 5

如果您在IPython控制台(不能使用%matplotlib inline)而不是Jupyter笔记本中进行绘制,并且不想plt.show()重复运行,则可以使用以下命令启动IPython控制台ipython --pylab

$ ipython --pylab     
Python 3.6.6 |Anaconda custom (64-bit)| (default, Jun 28 2018, 17:14:51) 
Type 'copyright', 'credits' or 'license' for more information
IPython 7.0.1 -- An enhanced Interactive Python. Type '?' for help.
Using matplotlib backend: Qt5Agg

In [1]: import seaborn as sns

In [2]: tips = sns.load_dataset("tips")

In [3]: sns.relplot(x="total_bill", y="tip", data=tips) # you can see the plot now

If you plot in IPython console (where you can’t use %matplotlib inline) instead of Jupyter notebook, and don’t want to run plt.show() repeatedly, you can start IPython console with ipython --pylab:

$ ipython --pylab     
Python 3.6.6 |Anaconda custom (64-bit)| (default, Jun 28 2018, 17:14:51) 
Type 'copyright', 'credits' or 'license' for more information
IPython 7.0.1 -- An enhanced Interactive Python. Type '?' for help.
Using matplotlib backend: Qt5Agg

In [1]: import seaborn as sns

In [2]: tips = sns.load_dataset("tips")

In [3]: sns.relplot(x="total_bill", y="tip", data=tips) # you can see the plot now

回答 6

从您的代码片段的风格可以看出,我想您使用的是IPython而不是Jupyter Notebook。

在GitHub上的此期中,IPython的一名成员在2016年明确指出,图表的显示仅在“仅在Jupyter内核中有效”时才起作用。因此, %matplotlib inline将无法正常工作。

我只是遇到了同样的问题,建议您使用Jupyter Notebook进行可视化。

To tell from the style of your code snippet, I suppose you were using IPython rather than Jupyter Notebook.

In this issue on GitHub, it was made clear by a member of IPython in 2016 that the display of charts would only work when “only work when it’s a Jupyter kernel”. Thus, the %matplotlib inline would not work.

I was just having the same issue and suggest you use Jupyter Notebook for the visualization.


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