pandas 熊猫数据框有条件的 .mean() 取决于特定列中的值
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Panda dataframe conditional .mean() depending on values in certain column
提问by tpapz
I'm trying to create a new column which returns the mean of values from an existing column in the same df. However the mean should be computed based on a grouping in three other columns.
我正在尝试创建一个新列，该列返回同一 df 中现有列的值的平均值。然而，平均值应该基于其他三列中的分组来计算。
Out[184]:
YEAR daytype hourtype scenario option_value
0 2015 SAT of_h 0 0.134499
1 2015 SUN of_h 1 63.019250
2 2015 WD of_h 2 52.113516
3 2015 WD pk_h 3 43.126513
4 2015 SAT of_h 4 56.431392
I basically would like to have a new column 'mean' which compute the mean of "option value", when "YEAR", "daytype", and "hourtype" are similar.
当“YEAR”、“daytype”和“hourtype”相似时，我基本上想要一个新列“mean”来计算“option value”的平均值。
I tried the following approach but without success ...
我尝试了以下方法但没有成功......
In [185]: o2['premium']=o2.groupby(['YEAR', 'daytype', 'hourtype'])['option_cf'].mean()
TypeError: incompatible index of inserted column with frame index
采纳答案by Zero
Here's one way to do it
这是一种方法
In [19]: def cust_mean(grp):
....: grp['mean'] = grp['option_value'].mean()
....: return grp
....:
In [20]: o2.groupby(['YEAR', 'daytype', 'hourtype']).apply(cust_mean)
Out[20]:
YEAR daytype hourtype scenario option_value mean
0 2015 SAT of_h 0 0.134499 28.282946
1 2015 SUN of_h 1 63.019250 63.019250
2 2015 WD of_h 2 52.113516 52.113516
3 2015 WD pk_h 3 43.126513 43.126513
4 2015 SAT of_h 4 56.431392 28.282946
So, what was going wrong with your attempt?
那么，你的尝试出了什么问题？
It returns an aggregate with different shape from the original dataframe.
它返回一个与原始数据帧形状不同的聚合。
In [21]: o2.groupby(['YEAR', 'daytype', 'hourtype'])['option_value'].mean()
Out[21]:
YEAR daytype hourtype
2015 SAT of_h 28.282946
SUN of_h 63.019250
WD of_h 52.113516
pk_h 43.126513
Name: option_value, dtype: float64
Oruse transform
或使用transform
In [1461]: o2['premium'] = (o2.groupby(['YEAR', 'daytype', 'hourtype'])['option_value']
.transform('mean'))
In [1462]: o2
Out[1462]:
YEAR daytype hourtype scenario option_value premium
0 2015 SAT of_h 0 0.134499 28.282946
1 2015 SUN of_h 1 63.019250 63.019250
2 2015 WD of_h 2 52.113516 52.113516
3 2015 WD pk_h 3 43.126513 43.126513
4 2015 SAT of_h 4 56.431392 28.282946
回答by KieranPC
You can do it the way you intended by tweaking your code in the following way:
您可以通过以下方式调整代码来按照您的预期方式完成此操作：
o2 = o2.set_index(['YEAR', 'daytype', 'hourtype'])
o2['premium'] = o2.groupby(level=['YEAR', 'daytype', 'hourtype'])['option_value'].mean()
Why the original error? As explained by John Galt, the data coming out of groupby().mean() is not the same shape (length) as the original DataFrame.
为什么是原始错误？正如 John Galt 所解释的，来自 groupby().mean() 的数据与原始 DataFrame 的形状（长度）不同。
Pandas can handle this cleverly if you first start with the 'grouping columns' in the index. Then it knows how to propogate the mean data correctly.
如果您首先从索引中的“分组列”开始，Pandas 可以巧妙地处理这个问题。然后它知道如何正确地传播平均数据。
John's solution follows the same logic, because groupby naturally puts the grouping columns in the index during execution.
John 的解决方案遵循相同的逻辑，因为 groupby 在执行过程中自然会将分组列放在索引中。
