从 (row,col,values) 的元组列表构造 pandas DataFrame [英] Construct pandas DataFrame from list of tuples of (row,col,values)

问题描述

我有一个像这样的元组列表

I have a list of tuples like

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

我想将它们放入一个 pandas DataFrame 中，其中行由第一列命名，列由第二列命名.处理行名的方法似乎类似于 pandas.DataFrame([x[1:] for x in data], index = [x[0] for x in data])但是如何处理列以获得 2x2 矩阵(前一组的输出是 3x4)?有没有更智能的方法来处理行标签，而不是明确地忽略它们?

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 pandas.DataFrame([x[1:] for x in data], index = [x[0] for x in data]) 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?

编辑 看来我需要 2 个数据帧 - 一个用于平均值，一个用于标准偏差，对吗?或者我可以在每个单元格"中存储一个值列表吗?

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"?

推荐答案

你可以在创建后旋转你的DataFrame:

You can pivot your DataFrame after creating:

>>> 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

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