blocks|key|2366304|text|更新：|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|2366305|是否可以对行/列进行排序，以便最高计数的行位于顶部？在这种情况下，顺序是b，a，c。|blockquote|2366306|你可以这样做(在这里)：|2366307|In+[80]:+x+=+df.pivot_table(index='customer1',columns='customer2',aggfunc='size',fill_value=0)

In+[81]:+idx+=+x.max(axis=1).sort_values(ascending=0).index

In+[82]:+idx
Out[82]:+Index(['b',+'a',+'c'],+dtype='object',+name='customer1')

In+[87]:+sns.heatmap(x[idx].reindex(idx),+annot=True)
Out[87]:+<matplotlib.axes._subplots.AxesSubplot+at+0x9ee3f98>|code-block|syntax|javascript|2366308|​|2366309|📷|atomic|2366310|2366311|旧答案：|2366312|您可以从热图()模块中使用seaborn方法：|CODE|2366313|In+[42]:+import+seaborn+as+sns

In+[43]:+df
Out[43]:
++customer1+customer2
0+++++++++a+++++++++b
1+++++++++a+++++++++c
2+++++++++a+++++++++c
3+++++++++b+++++++++a
4+++++++++b+++++++++c
5+++++++++b+++++++++c
6+++++++++c+++++++++c
7+++++++++a+++++++++a
8+++++++++b+++++++++c
9+++++++++b+++++++++c

In+[44]:+x+=+df.pivot_table(index='customer1',columns='customer2',aggfunc='size',fill_value=0)

In+[45]:+x
Out[45]:
customer2++a++b++c
customer1
a++++++++++1++1++2
b++++++++++1++0++4
c++++++++++0++0++1

In+[46]:+sns.heatmap(x)
Out[46]:+<matplotlib.axes._subplots.AxesSubplot+at+0xb150b70>|2366314|2366315|2366316|2366317|或附加注释：|2366318|In+[48]:+sns.heatmap(x,+annot=True)
Out[48]:+<matplotlib.axes._subplots.AxesSubplot+at+0xc596d68>|2366319|2366320|2366321|2366322|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/05f211cdb0289bbdf2741897baba3311.png|imageAlt|1|LINK|MUTABLE|url|https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html|2|https://developer.qcloudimg.com/http-save/yehe-900000/71844e07eaea0ea594c13ff80e49ab28.png|3|https://developer.qcloudimg.com/http-save/yehe-900000/403d4709a1882b2c48fd099632b0cd05.png^0|0|3|0|0|0|0|0|0|1|0|0|0|0|4|0|D|7|4|4|1|0|0|0|0|1|2|0|0|0|0|0|0|1|3|0|0^^$0|@$1|2|3|4|5|6|7|1U|8|@$9|1V|A|1W|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1X|8|@]|D|@]|E|$]]|$1|I|3|J|5|6|7|1Y|8|@]|D|@]|E|$]]|$1|K|3|L|5|M|7|1Z|8|@]|D|@]|E|$N|O]]|$1|P|3|Q|5|6|7|20|8|@]|D|@]|E|$]]|$1|R|3|S|5|T|7|21|8|@]|D|@$9|22|A|23|1|24]]|E|$]]|$1|U|3|Q|5|6|7|25|8|@]|D|@]|E|$]]|$1|V|3|W|5|6|7|26|8|@$9|27|A|28|B|C]]|D|@]|E|$]]|$1|X|3|Y|5|6|7|29|8|@$9|2A|A|2B|B|Z]]|D|@$9|2C|A|2D|1|2E]]|E|$]]|$1|10|3|11|5|M|7|2F|8|@]|D|@]|E|$N|O]]|$1|12|3|Q|5|6|7|2G|8|@]|D|@]|E|$]]|$1|13|3|S|5|T|7|2H|8|@]|D|@$9|2I|A|2J|1|2K]]|E|$]]|$1|14|3|Q|5|6|7|2L|8|@]|D|@]|E|$]]|$1|15|3|16|5|6|7|2M|8|@]|D|@]|E|$]]|$1|17|3|18|5|M|7|2N|8|@]|D|@]|E|$N|O]]|$1|19|3|Q|5|6|7|2O|8|@]|D|@]|E|$]]|$1|1A|3|S|5|T|7|2P|8|@]|D|@$9|2Q|A|2R|1|2S]]|E|$]]|$1|1B|3|Q|5|6|7|2T|8|@]|D|@]|E|$]]|$1|1C|3|-4|5|6|7|2U|8|@]|D|@]|E|$]]]|1D|$1E|$5|1F|1G|1H|E|$1I|1J|1K|-4]]|1L|$5|1M|1G|1N|E|$1O|1P]]|1Q|$5|1F|1G|1H|E|$1I|1R|1K|-4]]|1S|$5|1F|1G|1H|E|$1I|1T|1K|-4]]]]

UPDATE: 

<blockquote>
 Is it possible to sort the rows/columns so the highest count rows are
 at the top ? In this case the order would be b,a,c
</blockquote>

IIUC you can do it this way (where ):

<pre><code>In [80]: x = df.pivot_table(index='customer1',columns='customer2',aggfunc='size',fill_value=0)

In [81]: idx = x.max(axis=1).sort_values(ascending=0).index

In [82]: idx
Out[82]: Index(['b', 'a', 'c'], dtype='object', name='customer1')

In [87]: sns.heatmap(x[idx].reindex(idx), annot=True)
Out[87]: &lt;matplotlib.axes._subplots.AxesSubplot at 0x9ee3f98&gt;
</code></pre>

<a href="https://i.stack.imgur.com/sIVen.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/sIVen.png" alt="enter image description here"></a>

OLD answer:

you can use <a href="https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html" rel="nofollow noreferrer">heatmap()</a> method from <code>seaborn</code> module:

<pre><code>In [42]: import seaborn as sns

In [43]: df
Out[43]:
 customer1 customer2
0 a b
1 a c
2 a c
3 b a
4 b c
5 b c
6 c c
7 a a
8 b c
9 b c

In [44]: x = df.pivot_table(index='customer1',columns='customer2',aggfunc='size',fill_value=0)

In [45]: x
Out[45]:
customer2 a b c
customer1
a 1 1 2
b 1 0 4
c 0 0 1

In [46]: sns.heatmap(x)
Out[46]: &lt;matplotlib.axes._subplots.AxesSubplot at 0xb150b70&gt;
</code></pre>

<a href="https://i.stack.imgur.com/YMVgk.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/YMVgk.png" alt="enter image description here"></a>

or with annotations:

<pre><code>In [48]: sns.heatmap(x, annot=True)
Out[48]: &lt;matplotlib.axes._subplots.AxesSubplot at 0xc596d68&gt;
</code></pre>

<a href="https://i.stack.imgur.com/dN2YV.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/dN2YV.png" alt="enter image description here"></a>

blocks|key|3116179|text|正如@MaxU所提到的，seaborn.heatmap应该能工作。看来您可以使用Pandas+DataFrame作为输入。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3116180|seaborn.heatmap(data,+vmin=None,+vmax=None,+cmap=None,+center=None,+robust=False,+annot=None,+fmt='.2g',+annot_kws=None,+linewidths=0,+linecolor='white',+cbar=True,+cbar_kws=None,+cbar_ax=None,+square=False,+ax=None,+xticklabels=True,+yticklabels=True,+mask=None,+**kwargs)|3116181|https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html#seaborn.heatmap|3116182|entityMap|0|LINK|mutability|MUTABLE|url^0|C|F|0|0|7L|0|0|2L|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|T|8|@$9|U|A|V|B|C]]|D|@]|E|$]]|$1|H|3|I|5|6|7|W|8|@]|D|@$9|X|A|Y|1|Z]]|E|$]]|$1|J|3|-4|5|6|7|10|8|@]|D|@]|E|$]]]|K|$L|$5|M|N|O|E|$P|I]]]]

As @MaxU mentioned, <code>seaborn.heatmap</code> should work. It appears that you can use the Pandas DataFrame as the input.

<code>seaborn.heatmap(data, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt='.2g', annot_kws=None, linewidths=0, linecolor='white', cbar=True, cbar_kws=None, cbar_ax=None, square=False, ax=None, xticklabels=True, yticklabels=True, mask=None, **kwargs)</code>

<a href="https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html#seaborn.heatmap" rel="nofollow">https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.heatmap.html#seaborn.heatmap</a>

I have a dataframe df with two columns <code>customer1</code> and <code>customer2</code> which are string valued. I would like to make a square graphical representation of the count number for each pair from those two columns. 

I can do

<pre><code>df[['customer1', 'customer2']].value_counts()
</code></pre>

which will give me the counts. But how can I make something that looks a little like:

<a href="https://i.stack.imgur.com/GZ6yI.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/GZ6yI.png" alt="enter image description here"></a> 

from the result?

I can't provide my real dataset but here is a toy example with three labels in csv.

<pre><code>customer1,customer2
a,b
a,c
a,c
b,a
b,c
b,c
c,c
a,a
b,c
b,c
</code></pre>

How to draw a graphical count table in pandas

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

文章

问答

视频

学习中心

腾讯云实验室

直播

竞赛

腾讯云代码分析专区

腾讯iOA零信任安全管理系统专区

腾讯云架构师技术同盟交流圈

腾讯云数据库专区

腾讯云顾问专区

腾讯云原生专区

腾讯混元专区

腾讯云TCE专区

腾讯云Lighthouse专区

腾讯云HAI专区

腾讯云Edgeone专区

腾讯云存储专区

腾讯云智能专区

腾讯轻联专区 

腾讯云开发专区

TAPD专区

腾讯轻量云游戏服专区

腾讯云最具价值专家

腾讯云架构师技术同盟

腾讯云创作之星

腾讯云开发者先锋

腾讯云代码助手

云原生构建

TAPD 敏捷项目管理

Cloud Studio

SDK中心

API中心

命令行工具

涵盖代码开发、场景应用、自动测试全流程，助你从零构建专属AI助手

一站式MCP教程库，解锁AI应用新玩法

我有一个dataframe，它有两个列customer1和customer2，它们都是字符串值。我想对这两列中的每对计数进行方形的图形表示。我能做到df[['customer1', 'customer2']].value_counts()这会让我数清楚。但是我怎么能做一些看起来有点像：​从结果来看？我不能提供我真正的数...

问如何绘制大熊猫图形计数表
EN

Stack Overflow用户

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问如何绘制大熊猫图形计数表EN