blocks|key|172182|text|您可以在创建后透视您的DataFrame：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|172183|>>>+df+=+pd.DataFrame(data)
>>>+df.pivot(index=0,+columns=1,+values=2)
#+avg+DataFrame
1++++++c1+++++c2
0+++++++++++++++
r1++avg11++avg12
r2++avg21++avg22
>>>+df.pivot(index=0,+columns=1,+values=3)
#+stdev+DataFrame
1++++++++c1+++++++c2
0+++++++++++++++++++
r1++stdev11++stdev12
r2++stdev21++stdev22|code-block|syntax|javascript|172184|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

You can pivot your DataFrame after creating:

<pre><code>&gt;&gt;&gt; df = pd.DataFrame(data)
&gt;&gt;&gt; df.pivot(index=0, columns=1, values=2)
# avg DataFrame
1 c1 c2
0 
r1 avg11 avg12
r2 avg21 avg22
&gt;&gt;&gt; df.pivot(index=0, columns=1, values=3)
# stdev DataFrame
1 c1 c2
0 
r1 stdev11 stdev12
r2 stdev21 stdev22
</code></pre>

blocks|key|170556|text|我认为最好让你的数据堆叠在一起：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|170557|df+=+pandas.DataFrame(data,+columns=['R_Number',+'C_Number',+'Avg',+'Std'])

#+Possibly+also+this+if+these+can+always+be+the+indexes:
#+df+=+df.set_index(['R_Number',+'C_Number'])|code-block|syntax|javascript|170558|那么更直观的说法是|170559|df.set_index(['R_Number',+'C_Number']).Avg.unstack(level=1)|170560|这样就隐含了你正在寻求重塑平均值或标准差的意思。然而，仅仅使用pivot，它就完全基于列约定，即您正在重塑的语义实体是什么。|offset|length|style|CODE|170561|entityMap^0|0|0|0|0|V|5|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|W|8|@$M|X|N|Y|O|P]]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|R|$]]

I submit that it is better to leave your data stacked as it is:

<pre><code>df = pandas.DataFrame(data, columns=['R_Number', 'C_Number', 'Avg', 'Std'])

# Possibly also this if these can always be the indexes:
# df = df.set_index(['R_Number', 'C_Number'])
</code></pre>

Then it's a bit more intuitive to say

<pre><code>df.set_index(['R_Number', 'C_Number']).Avg.unstack(level=1)
</code></pre>

This way it is implicit that you're seeking to reshape the averages, or the standard deviations. Whereas, just using <code>pivot</code>, it's purely based on column convention as to what semantic entity it is that you are reshaping.

blocks|key|2770356|text|这就是我在回答这个问题时所期望看到的：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2770357|#!/usr/bin/env+python

import+pandas+as+pd


df+=+pd.DataFrame([(1,+2,+3,+4),
+++++++++++++++++++(5,+6,+7,+8),
+++++++++++++++++++(9,+0,+1,+2),
+++++++++++++++++++(3,+4,+5,+6)],
++++++++++++++++++columns=list('abcd'),
++++++++++++++++++index=['India',+'France',+'England',+'Germany'])
print(df)|code-block|syntax|javascript|2770358|给出|2770359|+++++++++a++b++c++d
India++++1++2++3++4
France+++5++6++7++8
England++9++0++1++2
Germany++3++4++5++6|2770360|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

This is what I expected to see when I came to this question:

<pre><code>#!/usr/bin/env python

import pandas as pd


df = pd.DataFrame([(1, 2, 3, 4),
 (5, 6, 7, 8),
 (9, 0, 1, 2),
 (3, 4, 5, 6)],
 columns=list('abcd'),
 index=['India', 'France', 'England', 'Germany'])
print(df)
</code></pre>

gives

<pre><code> a b c d
India 1 2 3 4
France 5 6 7 8
England 9 0 1 2
Germany 3 4 5 6
</code></pre>

I have a list of tuples like

<pre><code>data = [
('r1', 'c1', avg11, stdev11),
('r1', 'c2', avg12, stdev12),
('r2', 'c1', avg21, stdev21),
('r2', 'c2', avg22, stdev22)
]
</code></pre>

and I would like to put them into a pandas DataFrame with rows named by the first column and columns named by the 2nd column. It seems the way to take care of the row names is something like <code>pandas.DataFrame([x[1:] for x in data], index = [x[0] for x in data])</code> but how do I take care of the columns to get a 2x2 matrix (the output from the previous set is 3x4)? Is there a more intelligent way of taking care of row labels as well, instead of explicitly omitting them?

EDIT It seems I will need 2 DataFrames - one for averages and one for standard deviations, is that correct? Or can I store a list of values in each "cell"?

Construct pandas DataFrame from list of tuples of (row,col,values)

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。

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我有一个元组列表，比如data = [('r1', 'c1', avg11, stdev11),('r1', 'c2', avg12, stdev12),('r2', 'c1', avg21, stdev21),('r2', 'c2', avg22, stdev22)]我想把它们放到一个pandas DataFrame...

问从(行，列，值)的元组列表构造pandas DataFrame
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