Pandas Dataframe 分组和标准差
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Pandas Dataframe grouping and standard deviation
提问by H. Lewis
Given a pandas dataframe in the following format:
给定以下格式的 Pandas 数据框：
GroupNo | at1 | at2 | at3 | at4
1 | 0.02|1.12 | 1.88 | 3.2
1 | 6.11|1.13 | 0.88 | 5.2
4 | 2.02|1.16 | 2.88 | 0.2
3 | 0.20|0.12 | 1.48 | 1.25
2 | 0.02|1.12 | 1.88 | 1.4
3 | 3.02|1.12 | 1.98 | 2.2
3 | 0.40|0.18 | 1.48 | 1.25
How could I find the mean standard deviation per group?
我怎样才能找到每组的平均标准偏差？
For example, group by groupNo, find a standard deviation of the attributes in that group number, find a mean of them standard deviations
例如，group by groupNo，找到该组号中属性的标准差，找到它们的均值标准差
Any help would be great, H
任何帮助都会很棒，H
回答by jezrael
I think you need GroupBy.stdwith DataFrame.mean:
我认为你需要GroupBy.std有DataFrame.mean：
print (df.groupby('GroupNo').std())
at1 at2 at3 at4
GroupNo
1 4.306280 0.007071 0.707107 1.414214
2 NaN NaN NaN NaN
3 1.573573 0.560833 0.288675 0.548483
4 NaN NaN NaN NaN
print (df.groupby('GroupNo').std().mean(axis=1))
GroupNo
1 1.608668
2 NaN
3 0.742891
4 NaN
dtype: float64
Or maybe need:
或者可能需要：
print (df.groupby('GroupNo').mean().std(axis=1))
GroupNo
1 1.453848
2 0.788480
3 0.535371
4 1.149420
dtype: float64
