Python 从 (row,col,values) 的元组列表构造 pandas DataFrame
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Construct pandas DataFrame from list of tuples of (row,col,values)
提问by gt6989b
I have a list of tuples like
我有一个像这样的元组列表
data = [
('r1', 'c1', avg11, stdev11),
('r1', 'c2', avg12, stdev12),
('r2', 'c1', avg21, stdev21),
('r2', 'c2', avg22, stdev22)
]
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?
我想将它们放入一个 Pandas DataFrame 中，行由第一列命名，列由第二列命名。似乎处理行名称的方法类似于pandas.DataFrame([x[1:] for x in data], index = [x[0] for x in data])但我如何处理列以获得 2x2 矩阵（前一组的输出为 3x4）？是否有更智能的方式来处理行标签，而不是明确省略它们？
EDITIt 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"?
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？
采纳答案by Roman Pekar
You can pivot your DataFrame after creating:
您可以在创建后旋转您的 DataFrame：
>>> 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
回答by ely
I submit that it is better to leave your data stacked as it is:
我认为最好让您的数据保持原样：
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'])
Then it's a bit more intuitive to say
然后再直观一点的说
df.set_index(['R_Number', 'C_Number']).Avg.unstack(level=1)
This way it is implicit that you're seeking to reshape the averages, or the standard deviations. Whereas, just using pivot, it's purely based on column convention as to what semantic entity it is that you are reshaping.
这种方式暗示您正在寻求重塑平均值或标准偏差。而仅使用pivot，它完全基于列约定，即您正在重塑的语义实体。
回答by Martin Thoma
This is what I expected to see when I came to this question:
当我遇到这个问题时，这是我期望看到的：
#!/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)
gives
给
a b c d
India 1 2 3 4
France 5 6 7 8
England 9 0 1 2
Germany 3 4 5 6
