使用Pandas groupby计算许多坡度 [英] Using Pandas groupby to calculate many slopes
问题描述
DataFrame(MultiIndex)格式的一些说明性数据:
Some illustrative data in a DataFrame (MultiIndex) format:
|entity| year |value|
+------+------+-----+
| a | 1999 | 2 |
| | 2004 | 5 |
| b | 2003 | 3 |
| | 2007 | 2 |
| | 2014 | 7 |
|entity| year |value|
+------+------+-----+
| a | 1999 | 2 |
| | 2004 | 5 |
| b | 2003 | 3 |
| | 2007 | 2 |
| | 2014 | 7 |
在上述示例中，我想使用scipy.stats.linregress为每个实体a和b计算斜率.我在 split-apply-combine建议之后尝试在第一列上使用groupby ，但似乎有问题，因为它期望一个值Series(a和b)，而我需要对右边的两列进行操作.
I would like to calculate the slope using scipy.stats.linregress for each entity a and b in the above example. I tried using groupby on the first column, following the split-apply-combine advice, but it seems problematic since it's expecting one Series of values (a and b), whereas I need to operate on the two columns on the right.
这可以很容易地在R中通过plyr完成，不确定如何在熊猫中进行.
This is easily done in R via plyr, not sure how to approach it in pandas.
推荐答案
可以使用apply函数将函数应用于groupby.在这种情况下，传递的函数为linregress.请参见以下内容:
A function can be applied to a groupby with the apply function. The passed function in this case linregress. Please see below:
In [4]: x = pd.DataFrame({'entity':['a','a','b','b','b'],
'year':[1999,2004,2003,2007,2014],
'value':[2,5,3,2,7]})
In [5]: x
Out[5]:
entity value year
0 a 2 1999
1 a 5 2004
2 b 3 2003
3 b 2 2007
4 b 7 2014
In [6]: from scipy.stats import linregress
In [7]: x.groupby('entity').apply(lambda v: linregress(v.year, v.value)[0])
Out[7]:
entity
a 0.600000
b 0.403226
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